diff --git a/third_party/ALIKE/LICENSE b/third_party/ALIKE/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..4ee705bf59834a4b0195b1b0e499ee950469668e --- /dev/null +++ b/third_party/ALIKE/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2022, Zhao Xiaoming +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/third_party/ALIKE/README.md b/third_party/ALIKE/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8f40f15c56f6c54b14bb438e47096737a440fe89 --- /dev/null +++ b/third_party/ALIKE/README.md @@ -0,0 +1,131 @@ +# News + +- The [ALIKED](https://github.com/Shiaoming/ALIKED) is released. +- The [ALIKE training code](https://github.com/Shiaoming/ALIKE/raw/main/assets/ALIKE_code.zip) is released. + +# ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction + +ALIKE applies a differentiable keypoint detection module to detect accurate sub-pixel keypoints. The network can run at 95 frames per second for 640 x 480 images on NVIDIA Titan X (Pascal) GPU and achieve equivalent performance with the state-of-the-arts. ALIKE benefits real-time applications in resource-limited platforms/devices. Technical details are described in [this paper](https://arxiv.org/pdf/2112.02906.pdf). + +> ``` +> Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter C. Y. Chen, Zhengguo Li, "ALIKE: Accurate and Lightweight Keypoint +> Detection and Descriptor Extraction," IEEE Transactions on Multimedia, 2022. +> ``` + +![](./assets/alike.png) + + +If you use ALIKE in an academic work, please cite: + +``` +@article{Zhao2023ALIKED, + title = {ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation}, + url = {https://arxiv.org/pdf/2304.03608.pdf}, + doi = {10.1109/TIM.2023.3271000}, + journal = {IEEE Transactions on Instrumentation & Measurement}, + author = {Zhao, Xiaoming and Wu, Xingming and Chen, Weihai and Chen, Peter C. Y. and Xu, Qingsong and Li, Zhengguo}, + year = {2023}, + volume = {72}, + pages = {1-16}, +} + +@article{Zhao2022ALIKE, + title = {ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction}, + url = {http://arxiv.org/abs/2112.02906}, + doi = {10.1109/TMM.2022.3155927}, + journal = {IEEE Transactions on Multimedia}, + author = {Zhao, Xiaoming and Wu, Xingming and Miao, Jinyu and Chen, Weihai and Chen, Peter C. Y. and Li, Zhengguo}, + month = march, + year = {2022}, +} +``` + + + +## 1. Prerequisites + +The required packages are listed in the `requirements.txt` : + +```shell +pip install -r requirements.txt +``` + + + +## 2. Models + +The off-the-shelf weights of four variant ALIKE models are provided in `models/` . + + + +## 3. Run demo + +```shell +$ python demo.py -h +usage: demo.py [-h] [--model {alike-t,alike-s,alike-n,alike-l}] + [--device DEVICE] [--top_k TOP_K] [--scores_th SCORES_TH] + [--n_limit N_LIMIT] [--no_display] [--no_sub_pixel] + input + +ALike Demo. + +positional arguments: + input Image directory or movie file or "camera0" (for + webcam0). + +optional arguments: + -h, --help show this help message and exit + --model {alike-t,alike-s,alike-n,alike-l} + The model configuration + --device DEVICE Running device (default: cuda). + --top_k TOP_K Detect top K keypoints. -1 for threshold based mode, + >0 for top K mode. (default: -1) + --scores_th SCORES_TH + Detector score threshold (default: 0.2). + --n_limit N_LIMIT Maximum number of keypoints to be detected (default: + 5000). + --no_display Do not display images to screen. Useful if running + remotely (default: False). + --no_sub_pixel Do not detect sub-pixel keypoints (default: False). +``` + + + +## 4. Examples + +### KITTI example +```shell +python demo.py assets/kitti +``` +![](./assets/kitti.gif) + +### TUM example +```shell +python demo.py assets/tum +``` +![](./assets/tum.gif) + +## 5. Efficiency and performance + +| Models | Parameters | GFLOPs(640x480) | MHA@3 on Hpatches | mAA(10°) on [IMW2020-test](https://www.cs.ubc.ca/research/image-matching-challenge/2021/leaderboard) (Stereo) | +|:---:|:---:|:---:|:-----------------:|:-------------------------------------------------------------------------------------------------------------:| +| D2-Net(MS) | 7653KB | 889.40 | 38.33% | 12.27% | +| LF-Net(MS) | 2642KB | 24.37 | 57.78% | 23.44% | +| SuperPoint | 1301KB | 26.11 | 70.19% | 28.97% | +| R2D2(MS) | 484KB | 464.55 | 71.48% | 39.02% | +| ASLFeat(MS) | 823KB | 77.58 | 73.52% | 33.65% | +| DISK | 1092KB | 98.97 | 70.56% | 51.22% | +| ALike-N | 318KB | 7.909 | 75.74% | 47.18% | +| ALike-L | 653KB | 19.685 | 76.85% | 49.58% | + +### Evaluation on Hpatches + +- Download [hpatches-sequences-release](https://hpatches.github.io/) and put it into `hseq/hpatches-sequences-release`. +- Remove the unreliable sequences as D2-Net. +- Run the following command to evaluate the performance: + ```shell + python hseq/eval.py + ``` + + +For more details, please refer to the [paper](https://arxiv.org/abs/2112.02906). diff --git a/third_party/ALIKE/alike.py b/third_party/ALIKE/alike.py new file mode 100644 index 0000000000000000000000000000000000000000..303616d52581efce0ae0eb86af70f5ea8984909d --- /dev/null +++ b/third_party/ALIKE/alike.py @@ -0,0 +1,143 @@ +import logging +import os +import cv2 +import torch +from copy import deepcopy +import torch.nn.functional as F +from torchvision.transforms import ToTensor +import math + +from alnet import ALNet +from soft_detect import DKD +import time + +configs = { + 'alike-t': {'c1': 8, 'c2': 16, 'c3': 32, 'c4': 64, 'dim': 64, 'single_head': True, 'radius': 2, + 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-t.pth')}, + 'alike-s': {'c1': 8, 'c2': 16, 'c3': 48, 'c4': 96, 'dim': 96, 'single_head': True, 'radius': 2, + 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-s.pth')}, + 'alike-n': {'c1': 16, 'c2': 32, 'c3': 64, 'c4': 128, 'dim': 128, 'single_head': True, 'radius': 2, + 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-n.pth')}, + 'alike-l': {'c1': 32, 'c2': 64, 'c3': 128, 'c4': 128, 'dim': 128, 'single_head': False, 'radius': 2, + 'model_path': os.path.join(os.path.split(__file__)[0], 'models', 'alike-l.pth')}, +} + + +class ALike(ALNet): + def __init__(self, + # ================================== feature encoder + c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128, + single_head: bool = False, + # ================================== detect parameters + radius: int = 2, + top_k: int = 500, scores_th: float = 0.5, + n_limit: int = 5000, + device: str = 'cpu', + model_path: str = '' + ): + super().__init__(c1, c2, c3, c4, dim, single_head) + self.radius = radius + self.top_k = top_k + self.n_limit = n_limit + self.scores_th = scores_th + self.dkd = DKD(radius=self.radius, top_k=self.top_k, + scores_th=self.scores_th, n_limit=self.n_limit) + self.device = device + + if model_path != '': + state_dict = torch.load(model_path, self.device) + self.load_state_dict(state_dict) + self.to(self.device) + self.eval() + logging.info(f'Loaded model parameters from {model_path}') + logging.info( + f"Number of model parameters: {sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e3}KB") + + def extract_dense_map(self, image, ret_dict=False): + # ==================================================== + # check image size, should be integer multiples of 2^5 + # if it is not a integer multiples of 2^5, padding zeros + device = image.device + b, c, h, w = image.shape + h_ = math.ceil(h / 32) * 32 if h % 32 != 0 else h + w_ = math.ceil(w / 32) * 32 if w % 32 != 0 else w + if h_ != h: + h_padding = torch.zeros(b, c, h_ - h, w, device=device) + image = torch.cat([image, h_padding], dim=2) + if w_ != w: + w_padding = torch.zeros(b, c, h_, w_ - w, device=device) + image = torch.cat([image, w_padding], dim=3) + # ==================================================== + + scores_map, descriptor_map = super().forward(image) + + # ==================================================== + if h_ != h or w_ != w: + descriptor_map = descriptor_map[:, :, :h, :w] + scores_map = scores_map[:, :, :h, :w] # Bx1xHxW + # ==================================================== + + # BxCxHxW + descriptor_map = torch.nn.functional.normalize(descriptor_map, p=2, dim=1) + + if ret_dict: + return {'descriptor_map': descriptor_map, 'scores_map': scores_map, } + else: + return descriptor_map, scores_map + + def forward(self, img, image_size_max=99999, sort=False, sub_pixel=False): + """ + :param img: np.array HxWx3, RGB + :param image_size_max: maximum image size, otherwise, the image will be resized + :param sort: sort keypoints by scores + :param sub_pixel: whether to use sub-pixel accuracy + :return: a dictionary with 'keypoints', 'descriptors', 'scores', and 'time' + """ + H, W, three = img.shape + assert three == 3, "input image shape should be [HxWx3]" + + # ==================== image size constraint + image = deepcopy(img) + max_hw = max(H, W) + if max_hw > image_size_max: + ratio = float(image_size_max / max_hw) + image = cv2.resize(image, dsize=None, fx=ratio, fy=ratio) + + # ==================== convert image to tensor + image = torch.from_numpy(image).to(self.device).to(torch.float32).permute(2, 0, 1)[None] / 255.0 + + # ==================== extract keypoints + start = time.time() + + with torch.no_grad(): + descriptor_map, scores_map = self.extract_dense_map(image) + keypoints, descriptors, scores, _ = self.dkd(scores_map, descriptor_map, + sub_pixel=sub_pixel) + keypoints, descriptors, scores = keypoints[0], descriptors[0], scores[0] + keypoints = (keypoints + 1) / 2 * keypoints.new_tensor([[W - 1, H - 1]]) + + if sort: + indices = torch.argsort(scores, descending=True) + keypoints = keypoints[indices] + descriptors = descriptors[indices] + scores = scores[indices] + + end = time.time() + + return {'keypoints': keypoints.cpu().numpy(), + 'descriptors': descriptors.cpu().numpy(), + 'scores': scores.cpu().numpy(), + 'scores_map': scores_map.cpu().numpy(), + 'time': end - start, } + + +if __name__ == '__main__': + import numpy as np + from thop import profile + + net = ALike(c1=32, c2=64, c3=128, c4=128, dim=128, single_head=False) + + image = np.random.random((640, 480, 3)).astype(np.float32) + flops, params = profile(net, inputs=(image, 9999, False), verbose=False) + print('{:<30} {:<8} GFLops'.format('Computational complexity: ', flops / 1e9)) + print('{:<30} {:<8} KB'.format('Number of parameters: ', params / 1e3)) diff --git a/third_party/ALIKE/alnet.py b/third_party/ALIKE/alnet.py new file mode 100644 index 0000000000000000000000000000000000000000..53127063233660c7b96aa15e89aa4a8a1a340dd1 --- /dev/null +++ b/third_party/ALIKE/alnet.py @@ -0,0 +1,164 @@ +import torch +from torch import nn +from torchvision.models import resnet +from typing import Optional, Callable + + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels, + gate: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None): + super().__init__() + if gate is None: + self.gate = nn.ReLU(inplace=True) + else: + self.gate = gate + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self.conv1 = resnet.conv3x3(in_channels, out_channels) + self.bn1 = norm_layer(out_channels) + self.conv2 = resnet.conv3x3(out_channels, out_channels) + self.bn2 = norm_layer(out_channels) + + def forward(self, x): + x = self.gate(self.bn1(self.conv1(x))) # B x in_channels x H x W + x = self.gate(self.bn2(self.conv2(x))) # B x out_channels x H x W + return x + + +# copied from torchvision\models\resnet.py#27->BasicBlock +class ResBlock(nn.Module): + expansion: int = 1 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + gate: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None + ) -> None: + super(ResBlock, self).__init__() + if gate is None: + self.gate = nn.ReLU(inplace=True) + else: + self.gate = gate + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('ResBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in ResBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = resnet.conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.conv2 = resnet.conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x: torch.Tensor) -> torch.Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.gate(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.gate(out) + + return out + + +class ALNet(nn.Module): + def __init__(self, c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128, + single_head: bool = True, + ): + super().__init__() + + self.gate = nn.ReLU(inplace=True) + + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.pool4 = nn.MaxPool2d(kernel_size=4, stride=4) + + self.block1 = ConvBlock(3, c1, self.gate, nn.BatchNorm2d) + + self.block2 = ResBlock(inplanes=c1, planes=c2, stride=1, + downsample=nn.Conv2d(c1, c2, 1), + gate=self.gate, + norm_layer=nn.BatchNorm2d) + self.block3 = ResBlock(inplanes=c2, planes=c3, stride=1, + downsample=nn.Conv2d(c2, c3, 1), + gate=self.gate, + norm_layer=nn.BatchNorm2d) + self.block4 = ResBlock(inplanes=c3, planes=c4, stride=1, + downsample=nn.Conv2d(c3, c4, 1), + gate=self.gate, + norm_layer=nn.BatchNorm2d) + + # ================================== feature aggregation + self.conv1 = resnet.conv1x1(c1, dim // 4) + self.conv2 = resnet.conv1x1(c2, dim // 4) + self.conv3 = resnet.conv1x1(c3, dim // 4) + self.conv4 = resnet.conv1x1(dim, dim // 4) + self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) + self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True) + self.upsample8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True) + self.upsample32 = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=True) + + # ================================== detector and descriptor head + self.single_head = single_head + if not self.single_head: + self.convhead1 = resnet.conv1x1(dim, dim) + self.convhead2 = resnet.conv1x1(dim, dim + 1) + + def forward(self, image): + # ================================== feature encoder + x1 = self.block1(image) # B x c1 x H x W + x2 = self.pool2(x1) + x2 = self.block2(x2) # B x c2 x H/2 x W/2 + x3 = self.pool4(x2) + x3 = self.block3(x3) # B x c3 x H/8 x W/8 + x4 = self.pool4(x3) + x4 = self.block4(x4) # B x dim x H/32 x W/32 + + # ================================== feature aggregation + x1 = self.gate(self.conv1(x1)) # B x dim//4 x H x W + x2 = self.gate(self.conv2(x2)) # B x dim//4 x H//2 x W//2 + x3 = self.gate(self.conv3(x3)) # B x dim//4 x H//8 x W//8 + x4 = self.gate(self.conv4(x4)) # B x dim//4 x H//32 x W//32 + x2_up = self.upsample2(x2) # B x dim//4 x H x W + x3_up = self.upsample8(x3) # B x dim//4 x H x W + x4_up = self.upsample32(x4) # B x dim//4 x H x W + x1234 = torch.cat([x1, x2_up, x3_up, x4_up], dim=1) + + # ================================== detector and descriptor head + if not self.single_head: + x1234 = self.gate(self.convhead1(x1234)) + x = self.convhead2(x1234) # B x dim+1 x H x W + + descriptor_map = x[:, :-1, :, :] + scores_map = torch.sigmoid(x[:, -1, :, :]).unsqueeze(1) + + return scores_map, descriptor_map + + +if __name__ == '__main__': + from thop 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copy +import os +import cv2 +import glob +import logging +import argparse +import numpy as np +from tqdm import tqdm +from alike import ALike, configs + + +class ImageLoader(object): + def __init__(self, filepath: str): + self.N = 3000 + if filepath.startswith('camera'): + camera = int(filepath[6:]) + self.cap = cv2.VideoCapture(camera) + if not self.cap.isOpened(): + raise IOError(f"Can't open camera {camera}!") + logging.info(f'Opened camera {camera}') + self.mode = 'camera' + elif os.path.exists(filepath): + if os.path.isfile(filepath): + self.cap = cv2.VideoCapture(filepath) + if not self.cap.isOpened(): + raise IOError(f"Can't open video {filepath}!") + rate = self.cap.get(cv2.CAP_PROP_FPS) + self.N = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1 + duration = self.N / rate + logging.info(f'Opened video {filepath}') + logging.info(f'Frames: {self.N}, FPS: {rate}, Duration: {duration}s') + self.mode = 'video' + else: + self.images = glob.glob(os.path.join(filepath, '*.png')) + \ + glob.glob(os.path.join(filepath, '*.jpg')) + \ + glob.glob(os.path.join(filepath, '*.ppm')) + self.images.sort() + self.N = len(self.images) + logging.info(f'Loading {self.N} images') + self.mode = 'images' + else: + raise IOError('Error filepath (camerax/path of images/path of videos): ', filepath) + + def __getitem__(self, item): + if self.mode == 'camera' or self.mode == 'video': + if item > self.N: + return None + ret, img = self.cap.read() + if not ret: + raise "Can't read image from camera" + if self.mode == 'video': + self.cap.set(cv2.CAP_PROP_POS_FRAMES, item) + elif self.mode == 'images': + filename = self.images[item] + img = cv2.imread(filename) + if img is None: + raise Exception('Error reading image %s' % filename) + return img + + def __len__(self): + return self.N + + +class SimpleTracker(object): + def __init__(self): + self.pts_prev = None + self.desc_prev = None + + def update(self, img, pts, desc): + N_matches = 0 + if self.pts_prev is None: + self.pts_prev = pts + self.desc_prev = desc + + out = copy.deepcopy(img) + for pt1 in pts: + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + cv2.circle(out, p1, 1, (0, 0, 255), -1, lineType=16) + else: + matches = self.mnn_mather(self.desc_prev, desc) + mpts1, mpts2 = self.pts_prev[matches[:, 0]], pts[matches[:, 1]] + N_matches = len(matches) + + out = copy.deepcopy(img) + for pt1, pt2 in zip(mpts1, mpts2): + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + cv2.line(out, p1, p2, (0, 255, 0), lineType=16) + cv2.circle(out, p2, 1, (0, 0, 255), -1, lineType=16) + + self.pts_prev = pts + self.desc_prev = desc + + return out, N_matches + + def mnn_mather(self, desc1, desc2): + sim = desc1 @ desc2.transpose() + sim[sim < 0.9] = 0 + nn12 = np.argmax(sim, axis=1) + nn21 = np.argmax(sim, axis=0) + ids1 = np.arange(0, sim.shape[0]) + mask = (ids1 == nn21[nn12]) + matches = np.stack([ids1[mask], nn12[mask]]) + return matches.transpose() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='ALike Demo.') + parser.add_argument('input', type=str, default='', + help='Image directory or movie file or "camera0" (for webcam0).') + parser.add_argument('--model', choices=['alike-t', 'alike-s', 'alike-n', 'alike-l'], default="alike-t", + help="The model configuration") + parser.add_argument('--device', type=str, default='cuda', help="Running device (default: cuda).") + parser.add_argument('--top_k', type=int, default=-1, + help='Detect top K keypoints. -1 for threshold based mode, >0 for top K mode. (default: -1)') + parser.add_argument('--scores_th', type=float, default=0.2, + help='Detector score threshold (default: 0.2).') + parser.add_argument('--n_limit', type=int, default=5000, + help='Maximum number of keypoints to be detected (default: 5000).') + parser.add_argument('--no_display', action='store_true', + help='Do not display images to screen. Useful if running remotely (default: False).') + parser.add_argument('--no_sub_pixel', action='store_true', + help='Do not detect sub-pixel keypoints (default: False).') + args = parser.parse_args() + + logging.basicConfig(level=logging.INFO) + + image_loader = ImageLoader(args.input) + model = ALike(**configs[args.model], + device=args.device, + top_k=args.top_k, + scores_th=args.scores_th, + n_limit=args.n_limit) + tracker = SimpleTracker() + + if not args.no_display: + logging.info("Press 'q' to stop!") + cv2.namedWindow(args.model) + + runtime = [] + progress_bar = tqdm(image_loader) + for img in progress_bar: + if img is None: + break + + img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + pred = model(img_rgb, sub_pixel=not args.no_sub_pixel) + kpts = pred['keypoints'] + desc = pred['descriptors'] + runtime.append(pred['time']) + + out, N_matches = tracker.update(img, kpts, desc) + + ave_fps = (1. / np.stack(runtime)).mean() + status = f"Fps:{ave_fps:.1f}, Keypoints/Matches: {len(kpts)}/{N_matches}" + progress_bar.set_description(status) + + if not args.no_display: + cv2.setWindowTitle(args.model, args.model + ': ' + status) + cv2.imshow(args.model, out) + if cv2.waitKey(1) == ord('q'): + break + + logging.info('Finished!') + if not args.no_display: + logging.info('Press any key to exit!') + cv2.waitKey() diff --git a/third_party/ALIKE/hseq/cache/alike-l-ms.npy b/third_party/ALIKE/hseq/cache/alike-l-ms.npy new file mode 100644 index 0000000000000000000000000000000000000000..bd988fb065ecd4a900178a3cb974bbbf56de0dc0 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/alike-l-ms.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1350ab826afdd9b7542a556e2fda9ad9f94388a875c8edb7874e4bcdfebc63ca +size 13124 diff --git a/third_party/ALIKE/hseq/cache/alike-l.npy b/third_party/ALIKE/hseq/cache/alike-l.npy new file mode 100644 index 0000000000000000000000000000000000000000..7c63bbec1588af102721df60d0ab8043586036d1 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/alike-l.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:999daff1155f3d4736bb7374fb2058f520b0cb4c75b5d7d87fc1e7025a7d2a7d +size 13124 diff --git a/third_party/ALIKE/hseq/cache/alike-n-ms.npy b/third_party/ALIKE/hseq/cache/alike-n-ms.npy new file mode 100644 index 0000000000000000000000000000000000000000..02e2d32258dcaed882ca7a28e7dd47c97c4bb65a --- /dev/null +++ b/third_party/ALIKE/hseq/cache/alike-n-ms.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e5967048eddb61e423bf2ea05a2a626e18d8a716b6a0ad42471059aec0b934c +size 13124 diff --git a/third_party/ALIKE/hseq/cache/alike-n.npy b/third_party/ALIKE/hseq/cache/alike-n.npy new file mode 100644 index 0000000000000000000000000000000000000000..3ec339ab8cd7a629d752576e8b275cba215614da --- /dev/null +++ b/third_party/ALIKE/hseq/cache/alike-n.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e2eba5ff96b25d0a100b6c7273549de91586e6069dcb5320a20edbb24ea462e +size 13124 diff --git a/third_party/ALIKE/hseq/cache/aslfeat.npy b/third_party/ALIKE/hseq/cache/aslfeat.npy new file mode 100644 index 0000000000000000000000000000000000000000..24fb50ccae5d7fa86fb6d4224beb983e54160895 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/aslfeat.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce06fd1b6265e09ed3b26768b68f624e2d556358ab98addd8ebdb7a5a076abe8 +size 15352 diff --git a/third_party/ALIKE/hseq/cache/d2.npy b/third_party/ALIKE/hseq/cache/d2.npy new file mode 100644 index 0000000000000000000000000000000000000000..741588a2e42c40fd8a3f7c097d56898ef66c5ceb --- /dev/null +++ b/third_party/ALIKE/hseq/cache/d2.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:976d81c6b51a98f89eac60c6d25990130c1df571ef6536280f4b00577eab56f0 +size 15352 diff --git a/third_party/ALIKE/hseq/cache/disk.npy b/third_party/ALIKE/hseq/cache/disk.npy new file mode 100644 index 0000000000000000000000000000000000000000..27871bccf7a206df33b94f25db28259b2b7cd456 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/disk.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df2d9e0dfd0baa19f2af12f4604368ca65a1643159e7e3438e25efc41ab15357 +size 15352 diff --git a/third_party/ALIKE/hseq/cache/lfnet.npy b/third_party/ALIKE/hseq/cache/lfnet.npy new file mode 100644 index 0000000000000000000000000000000000000000..2b3fc3514b2c85a856aae46f5f75bcf6cc6e2afd --- /dev/null +++ b/third_party/ALIKE/hseq/cache/lfnet.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:417327dee726cffccc6dfbc9b0e6b3c06b277ea8878ccf87b87475d1cd6e65ca +size 15352 diff --git a/third_party/ALIKE/hseq/cache/r2d2.npy b/third_party/ALIKE/hseq/cache/r2d2.npy new file mode 100644 index 0000000000000000000000000000000000000000..247b6e2952cf7a2a2e86479c4b888eb55f63cdd2 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/r2d2.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1375a21adcc932db2c9e210e52f633c1903cca6d37066391eb9d645ff87d0120 +size 15352 diff --git a/third_party/ALIKE/hseq/cache/superpoint.npy b/third_party/ALIKE/hseq/cache/superpoint.npy new file mode 100644 index 0000000000000000000000000000000000000000..b2d1ec429e6ffd960bc8a35128d6926683ba5162 --- /dev/null +++ b/third_party/ALIKE/hseq/cache/superpoint.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e4d4a4ca79518af47467e9ddd69fe159c9305a580dadc4fdab6ffde6f8b48c2 +size 15352 diff --git a/third_party/ALIKE/hseq/eval.py b/third_party/ALIKE/hseq/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..abca625044013a0cd34a518223c32d3ec8abb8a3 --- /dev/null +++ b/third_party/ALIKE/hseq/eval.py @@ -0,0 +1,162 @@ +import cv2 +import os +from tqdm import tqdm +import torch +import numpy as np +from extract import extract_method + +use_cuda = torch.cuda.is_available() +device = torch.device('cuda' if use_cuda else 'cpu') + +methods = ['d2', 'lfnet', 'superpoint', 'r2d2', 'aslfeat', 'disk', + 'alike-n', 'alike-l', 'alike-n-ms', 'alike-l-ms'] +names = ['D2-Net(MS)', 'LF-Net(MS)', 'SuperPoint', 'R2D2(MS)', 'ASLFeat(MS)', 'DISK', + 'ALike-N', 'ALike-L', 'ALike-N(MS)', 'ALike-L(MS)'] + +top_k = None +n_i = 52 +n_v = 56 +cache_dir = 'hseq/cache' +dataset_path = 'hseq/hpatches-sequences-release' + + +def generate_read_function(method, extension='ppm'): + def read_function(seq_name, im_idx): + aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method))) + if top_k is None: + return aux['keypoints'], aux['descriptors'] + else: + assert ('scores' in aux) + ids = np.argsort(aux['scores'])[-top_k:] + return aux['keypoints'][ids, :], aux['descriptors'][ids, :] + + return read_function + + +def mnn_matcher(descriptors_a, descriptors_b): + device = descriptors_a.device + sim = descriptors_a @ descriptors_b.t() + nn12 = torch.max(sim, dim=1)[1] + nn21 = torch.max(sim, dim=0)[1] + ids1 = torch.arange(0, sim.shape[0], device=device) + mask = (ids1 == nn21[nn12]) + matches = torch.stack([ids1[mask], nn12[mask]]) + return matches.t().data.cpu().numpy() + + +def homo_trans(coord, H): + kpt_num = coord.shape[0] + homo_coord = np.concatenate((coord, np.ones((kpt_num, 1))), axis=-1) + proj_coord = np.matmul(H, homo_coord.T).T + proj_coord = proj_coord / proj_coord[:, 2][..., None] + proj_coord = proj_coord[:, 0:2] + return proj_coord + + +def benchmark_features(read_feats): + lim = [1, 5] + rng = np.arange(lim[0], lim[1] + 1) + + seq_names = sorted(os.listdir(dataset_path)) + + n_feats = [] + n_matches = [] + seq_type = [] + i_err = {thr: 0 for thr in rng} + v_err = {thr: 0 for thr in rng} + + i_err_homo = {thr: 0 for thr in rng} + v_err_homo = {thr: 0 for thr in rng} + + for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)): + keypoints_a, descriptors_a = read_feats(seq_name, 1) + n_feats.append(keypoints_a.shape[0]) + + # =========== compute homography + ref_img = cv2.imread(os.path.join(dataset_path, seq_name, '1.ppm')) + ref_img_shape = ref_img.shape + + for im_idx in range(2, 7): + keypoints_b, descriptors_b = read_feats(seq_name, im_idx) + n_feats.append(keypoints_b.shape[0]) + + matches = mnn_matcher( + torch.from_numpy(descriptors_a).to(device=device), + torch.from_numpy(descriptors_b).to(device=device) + ) + + homography = np.loadtxt(os.path.join(dataset_path, seq_name, "H_1_" + str(im_idx))) + + pos_a = keypoints_a[matches[:, 0], : 2] + pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1) + pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h))) + pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2:] + + pos_b = keypoints_b[matches[:, 1], : 2] + + dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1)) + + n_matches.append(matches.shape[0]) + seq_type.append(seq_name[0]) + + if dist.shape[0] == 0: + dist = np.array([float("inf")]) + + for thr in rng: + if seq_name[0] == 'i': + i_err[thr] += np.mean(dist <= thr) + else: + v_err[thr] += np.mean(dist <= thr) + + # =========== compute homography + gt_homo = homography + pred_homo, _ = cv2.findHomography(keypoints_a[matches[:, 0], : 2], keypoints_b[matches[:, 1], : 2], + cv2.RANSAC) + if pred_homo is None: + homo_dist = np.array([float("inf")]) + else: + corners = np.array([[0, 0], + [ref_img_shape[1] - 1, 0], + [0, ref_img_shape[0] - 1], + [ref_img_shape[1] - 1, ref_img_shape[0] - 1]]) + real_warped_corners = homo_trans(corners, gt_homo) + warped_corners = homo_trans(corners, pred_homo) + homo_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1)) + + for thr in rng: + if seq_name[0] == 'i': + i_err_homo[thr] += np.mean(homo_dist <= thr) + else: + v_err_homo[thr] += np.mean(homo_dist <= thr) + + seq_type = np.array(seq_type) + n_feats = np.array(n_feats) + n_matches = np.array(n_matches) + + return i_err, v_err, i_err_homo, v_err_homo, [seq_type, n_feats, n_matches] + + +if __name__ == '__main__': + errors = {} + for method in methods: + output_file = os.path.join(cache_dir, method + '.npy') + read_function = generate_read_function(method) + if os.path.exists(output_file): + errors[method] = np.load(output_file, allow_pickle=True) + else: + extract_method(method) + errors[method] = benchmark_features(read_function) + np.save(output_file, errors[method]) + + for name, method in zip(names, methods): + i_err, v_err, i_err_hom, v_err_hom, _ = errors[method] + + print(f"====={name}=====") + print(f"MMA@1 MMA@2 MMA@3 MHA@1 MHA@2 MHA@3: ", end='') + for thr in range(1, 4): + err = (i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) + print(f"{err * 100:.2f}%", end=' ') + for thr in range(1, 4): + err_hom = (i_err_hom[thr] + v_err_hom[thr]) / ((n_i + n_v) * 5) + print(f"{err_hom * 100:.2f}%", end=' ') + print('') diff --git a/third_party/ALIKE/hseq/extract.py b/third_party/ALIKE/hseq/extract.py new file mode 100644 index 0000000000000000000000000000000000000000..1342e40dd2d0e1d1986e90f995c95b17972ec4e1 --- /dev/null +++ b/third_party/ALIKE/hseq/extract.py @@ -0,0 +1,159 @@ +import os +import sys +import cv2 +from pathlib import Path +import numpy as np +import torch +import torch.utils.data as data +from tqdm import tqdm +from copy import deepcopy +from torchvision.transforms import ToTensor + +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) +from alike import ALike, configs + +dataset_root = 'hseq/hpatches-sequences-release' +use_cuda = torch.cuda.is_available() +device = 'cuda' if use_cuda else 'cpu' +methods = ['alike-n', 'alike-l', 'alike-n-ms', 'alike-l-ms'] + + +class HPatchesDataset(data.Dataset): + def __init__(self, root: str = dataset_root, alteration: str = 'all'): + """ + Args: + root: dataset root path + alteration: # 'all', 'i' for illumination or 'v' for viewpoint + """ + assert (Path(root).exists()), f"Dataset root path {root} dose not exist!" + self.root = root + + # get all image file name + self.image0_list = [] + self.image1_list = [] + self.homographies = [] + folders = [x for x in Path(self.root).iterdir() if x.is_dir()] + self.seqs = [] + for folder in folders: + if alteration == 'i' and folder.stem[0] != 'i': + continue + if alteration == 'v' and folder.stem[0] != 'v': + continue + + self.seqs.append(folder) + + self.len = len(self.seqs) + assert (self.len > 0), f'Can not find PatchDataset in path {self.root}' + + def __getitem__(self, item): + folder = self.seqs[item] + + imgs = [] + homos = [] + for i in range(1, 7): + img = cv2.imread(str(folder / f'{i}.ppm'), cv2.IMREAD_COLOR) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # HxWxC + imgs.append(img) + + if i != 1: + homo = np.loadtxt(str(folder / f'H_1_{i}')).astype('float32') + homos.append(homo) + + return imgs, homos, folder.stem + + def __len__(self): + return self.len + + def name(self): + return self.__class__ + + +def extract_multiscale(model, img, scale_f=2 ** 0.5, + min_scale=1., max_scale=1., + min_size=0., max_size=99999., + image_size_max=99999, + n_k=0, sort=False): + H_, W_, three = img.shape + assert three == 3, "input image shape should be [HxWx3]" + + old_bm = torch.backends.cudnn.benchmark + torch.backends.cudnn.benchmark = False # speedup + + # ==================== image size constraint + image = deepcopy(img) + max_hw = max(H_, W_) + if max_hw > image_size_max: + ratio = float(image_size_max / max_hw) + image = cv2.resize(image, dsize=None, fx=ratio, fy=ratio) + + # ==================== convert image to tensor + H, W, three = image.shape + image = ToTensor()(image).unsqueeze(0) + image = image.to(device) + + s = 1.0 # current scale factor + keypoints, descriptors, scores, scores_maps, descriptor_maps = [], [], [], [], [] + while s + 0.001 >= max(min_scale, min_size / max(H, W)): + if s - 0.001 <= min(max_scale, max_size / max(H, W)): + nh, nw = image.shape[2:] + + # extract descriptors + with torch.no_grad(): + descriptor_map, scores_map = model.extract_dense_map(image) + keypoints_, descriptors_, scores_, _ = model.dkd(scores_map, descriptor_map) + + keypoints.append(keypoints_[0]) + descriptors.append(descriptors_[0]) + scores.append(scores_[0]) + + s /= scale_f + + # down-scale the image for next iteration + nh, nw = round(H * s), round(W * s) + image = torch.nn.functional.interpolate(image, (nh, nw), mode='bilinear', align_corners=False) + + # restore value + torch.backends.cudnn.benchmark = old_bm + + keypoints = torch.cat(keypoints) + descriptors = torch.cat(descriptors) + scores = torch.cat(scores) + keypoints = (keypoints + 1) / 2 * keypoints.new_tensor([[W_ - 1, H_ - 1]]) + + if sort or 0 < n_k < len(keypoints): + indices = torch.argsort(scores, descending=True) + keypoints = keypoints[indices] + descriptors = descriptors[indices] + scores = scores[indices] + + if 0 < n_k < len(keypoints): + keypoints = keypoints[0:n_k] + descriptors = descriptors[0:n_k] + scores = scores[0:n_k] + + return {'keypoints': keypoints, 'descriptors': descriptors, 'scores': scores} + + +def extract_method(m): + hpatches = HPatchesDataset(root=dataset_root, alteration='all') + model = m[:7] + min_scale = 0.3 if m[8:] == 'ms' else 1.0 + + model = ALike(**configs[model], device=device, top_k=0, scores_th=0.2, n_limit=5000) + + progbar = tqdm(hpatches, desc='Extracting for {}'.format(m)) + for imgs, homos, seq_name in progbar: + for i in range(1, 7): + img = imgs[i - 1] + pred = extract_multiscale(model, img, min_scale=min_scale, max_scale=1, sort=False, n_k=5000) + kpts, descs, scores = pred['keypoints'], pred['descriptors'], pred['scores'] + + with open(os.path.join(dataset_root, seq_name, f'{i}.ppm.{m}'), 'wb') as f: + np.savez(f, keypoints=kpts.cpu().numpy(), + scores=scores.cpu().numpy(), + descriptors=descs.cpu().numpy()) + + +if __name__ == '__main__': + for method in methods: + extract_method(method) diff --git a/third_party/ALIKE/matlab/createfigure.m b/third_party/ALIKE/matlab/createfigure.m new file mode 100644 index 0000000000000000000000000000000000000000..038090c7e570aeaed25bd4dfaffb71134d707082 --- /dev/null +++ b/third_party/ALIKE/matlab/createfigure.m @@ -0,0 +1,75 @@ +function createfigure(X1, YMatrix1, Y1, l1, l2, l3) +%CREATEFIGURE(X1, YMatrix1, Y1) +% X1: vector of x data +% YMATRIX1: matrix of y data +% Y1: vector of y data + +% Auto-generated by MATLAB on 29-Oct-2021 15:42:14 + +% Create figure +figure1 = figure; + +% Create axes +axes1 = axes('Parent',figure1); +hold(axes1,'on'); + +% Create multiple lines using matrix input to plot +plot1 = plot(X1,YMatrix1,'Parent',axes1,'LineWidth',1); +set(plot1(1),'LineStyle','-.','Color',[1 0 0]); +set(plot1(2),'Color',[0 1 0]); +set(plot1(3),'LineStyle','--',... + 'Color',[0.87058824300766 0.490196079015732 0]); + +% Uncomment the following line to preserve the X-limits of the axes +% xlim(axes1,[-1.1 1.1]); +% Uncomment the following line to preserve the Y-limits of the axes +ylim(axes1,[0 2.2]); +box(axes1,'on'); +hold(axes1,'off'); +% Set the remaining axes properties +set(axes1,'XColor',[0 0 0],'YColor',[0 0 0],'YTick',[0 0.5 1 1.5 2 2.5]); +% Create axes +axes2 = axes('Parent',figure1); +hold(axes2,'on'); +colororder([0.494 0.184 0.556;0.466 0.674 0.188;0.301 0.745 0.933;0.635 0.078 0.184;0 0.447 0.741;0.85 0.325 0.098;0.929 0.694 0.125]); + +% Create plot +plot(X1,Y1,'Parent',axes2,'LineWidth',1,'LineStyle',':','Color',[0 0 1]); + +% Uncomment the following line to preserve the X-limits of the axes +% xlim(axes2,[-1.1 1.1]); +% Uncomment the following line to preserve the Y-limits of the axes +ylim(axes2,[0 1.6]); +hold(axes2,'off'); +% Set the remaining axes properties +set(axes2,'Color','none','HitTest','off','XColor',[0 0 0],'YAxisLocation',... + 'right','YColor',[0 0 0],'YTick',[0 0.5 1 1.5]); +% Create textbox +annotation(figure1,'textbox',... + [0.255427607968038,0.605539475745798,0.304947448327989,0.235148519909872],... + 'Color',[0.8 0 0],... + 'String',{sprintf('peak loss=%.4f',l1)},... + 'EdgeColor','none'); + +% Create textbox +annotation(figure1,'textbox',... + [0.631790371410027,0.083530640355914,0.178879315581032,0.235148519909871],... + 'Color',[0 0 1],... + 'String',{'keypoint'},... + 'EdgeColor','none'); + +% Create textbox +annotation(figure1,'textbox',... + [0.59663112557549,0.640686239621974,0.318247136419826,0.22093023731067],... + 'Color',[0 0.498039215803146 0],... + 'String',{sprintf('peak loss=%.4f',l2)},... + 'EdgeColor','none'); + +% Create textbox +annotation(figure1,'textbox',... + [0.595423071596731,0.415858983920567,0.318247136419826,0.235148519909871],... + 'Color',[0.87058824300766 0.490196079015732 0],... + 'String',{sprintf('peak loss=%.4f',l3)},... + 'FitBoxToText','off',... + 'EdgeColor','none'); + diff --git a/third_party/ALIKE/matlab/peakloss_rect.m b/third_party/ALIKE/matlab/peakloss_rect.m new file mode 100644 index 0000000000000000000000000000000000000000..fa0d811c126aec1d6f6868352d89be69ea351577 --- /dev/null +++ b/third_party/ALIKE/matlab/peakloss_rect.m @@ -0,0 +1,19 @@ +clear; +close all; + +x = -1:0.01:1; + +p0 = 0.5; +p1 = -0.5; + +d = abs(x - p0); + +c0 = 2 .* (x>=-0.75 & x <= -0.25); +c1 = 2 .* (x>=0.25 & x <= 0.75); +c2 = 1.25 .* (x>=0.1 & x <= 0.9); + +peak_loss0 = sum(d.*c0) / length(x) +peak_loss1 = sum(d.*c1) / length(x) +peak_loss2 = sum(d.*c2) / length(x) + +createfigure(x, [c0;c1;c2], d, peak_loss0,peak_loss1, peak_loss2); \ No newline at end of file diff --git a/third_party/ALIKE/models/alike-l.pth b/third_party/ALIKE/models/alike-l.pth new file mode 100644 index 0000000000000000000000000000000000000000..525f6dd5128d95650096d860e371cbd558203ffa --- /dev/null +++ b/third_party/ALIKE/models/alike-l.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bed5fbbf352ab1c3e92e2241881f8b84edce949984fa23bc7f2517eab93938a0 +size 2639857 diff --git a/third_party/ALIKE/models/alike-n.pth b/third_party/ALIKE/models/alike-n.pth new file mode 100644 index 0000000000000000000000000000000000000000..a8e366e28e6fcc52ad14bc2c9b6bfaba15a436d2 --- /dev/null +++ b/third_party/ALIKE/models/alike-n.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bd4789272eec779be280f8fc1007608ff604241440a0a3377c1559199412ee3 +size 1338420 diff --git a/third_party/ALIKE/models/alike-s.pth b/third_party/ALIKE/models/alike-s.pth new file mode 100644 index 0000000000000000000000000000000000000000..9bdcec17286fbebe42c4e31e0f024ad5187a5493 --- /dev/null +++ b/third_party/ALIKE/models/alike-s.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9c0789ff0a09f576cc24afe4924d3233471499d1ce3b0248d650c8794e99a94 +size 724468 diff --git a/third_party/ALIKE/models/alike-t.pth b/third_party/ALIKE/models/alike-t.pth new file mode 100644 index 0000000000000000000000000000000000000000..428d75400279f96a70e60d87739cb018d7d2130b --- /dev/null +++ b/third_party/ALIKE/models/alike-t.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0840329a6b88518d914b03af2be956f5607055a389ba17441db02bb94f7d12e +size 350644 diff --git a/third_party/ALIKE/requirements.txt b/third_party/ALIKE/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..14ca745ea1572bda6b2bd7c4eb88bb026b566781 --- /dev/null +++ b/third_party/ALIKE/requirements.txt @@ -0,0 +1,6 @@ +opencv-python~=4.5.1.48 +numpy~=1.19.5 +tqdm~=4.60.0 +torch~=1.8.0 +torchvision~=0.9.0 +thop~=0.0.31-2005241907 \ No newline at end of file diff --git a/third_party/ALIKE/soft_detect.py b/third_party/ALIKE/soft_detect.py new file mode 100644 index 0000000000000000000000000000000000000000..2d23cd13b8a7db9b0398fdc1b235564222d30c90 --- /dev/null +++ b/third_party/ALIKE/soft_detect.py @@ -0,0 +1,194 @@ +import torch +from torch import nn +import torch.nn.functional as F + + +# coordinates system +# ------------------------------> [ x: range=-1.0~1.0; w: range=0~W ] +# | ----------------------------- +# | | | +# | | | +# | | | +# | | image | +# | | | +# | | | +# | | | +# | |---------------------------| +# v +# [ y: range=-1.0~1.0; h: range=0~H ] + +def simple_nms(scores, nms_radius: int): + """ Fast Non-maximum suppression to remove nearby points """ + assert (nms_radius >= 0) + + def max_pool(x): + return torch.nn.functional.max_pool2d( + x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius) + + zeros = torch.zeros_like(scores) + max_mask = scores == max_pool(scores) + + for _ in range(2): + supp_mask = max_pool(max_mask.float()) > 0 + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == max_pool(supp_scores) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +def sample_descriptor(descriptor_map, kpts, bilinear_interp=False): + """ + :param descriptor_map: BxCxHxW + :param kpts: list, len=B, each is Nx2 (keypoints) [h,w] + :param bilinear_interp: bool, whether to use bilinear interpolation + :return: descriptors: list, len=B, each is NxD + """ + batch_size, channel, height, width = descriptor_map.shape + + descriptors = [] + for index in range(batch_size): + kptsi = kpts[index] # Nx2,(x,y) + + if bilinear_interp: + descriptors_ = torch.nn.functional.grid_sample(descriptor_map[index].unsqueeze(0), kptsi.view(1, 1, -1, 2), + mode='bilinear', align_corners=True)[0, :, 0, :] # CxN + else: + kptsi = (kptsi + 1) / 2 * kptsi.new_tensor([[width - 1, height - 1]]) + kptsi = kptsi.long() + descriptors_ = descriptor_map[index, :, kptsi[:, 1], kptsi[:, 0]] # CxN + + descriptors_ = torch.nn.functional.normalize(descriptors_, p=2, dim=0) + descriptors.append(descriptors_.t()) + + return descriptors + + +class DKD(nn.Module): + def __init__(self, radius=2, top_k=0, scores_th=0.2, n_limit=20000): + """ + Args: + radius: soft detection radius, kernel size is (2 * radius + 1) + top_k: top_k > 0: return top k keypoints + scores_th: top_k <= 0 threshold mode: scores_th > 0: return keypoints with scores>scores_th + else: return keypoints with scores > scores.mean() + n_limit: max number of keypoint in threshold mode + """ + super().__init__() + self.radius = radius + self.top_k = top_k + self.scores_th = scores_th + self.n_limit = n_limit + self.kernel_size = 2 * self.radius + 1 + self.temperature = 0.1 # tuned temperature + self.unfold = nn.Unfold(kernel_size=self.kernel_size, padding=self.radius) + + # local xy grid + x = torch.linspace(-self.radius, self.radius, self.kernel_size) + # (kernel_size*kernel_size) x 2 : (w,h) + self.hw_grid = torch.stack(torch.meshgrid([x, x])).view(2, -1).t()[:, [1, 0]] + + def detect_keypoints(self, scores_map, sub_pixel=True): + b, c, h, w = scores_map.shape + scores_nograd = scores_map.detach() + # nms_scores = simple_nms(scores_nograd, self.radius) + nms_scores = simple_nms(scores_nograd, 2) + + # remove border + nms_scores[:, :, :self.radius + 1, :] = 0 + nms_scores[:, :, :, :self.radius + 1] = 0 + nms_scores[:, :, h - self.radius:, :] = 0 + nms_scores[:, :, :, w - self.radius:] = 0 + + # detect keypoints without grad + if self.top_k > 0: + topk = torch.topk(nms_scores.view(b, -1), self.top_k) + indices_keypoints = topk.indices # B x top_k + else: + if self.scores_th > 0: + masks = nms_scores > self.scores_th + if masks.sum() == 0: + th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th + masks = nms_scores > th.reshape(b, 1, 1, 1) + else: + th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th + masks = nms_scores > th.reshape(b, 1, 1, 1) + masks = masks.reshape(b, -1) + + indices_keypoints = [] # list, B x (any size) + scores_view = scores_nograd.reshape(b, -1) + for mask, scores in zip(masks, scores_view): + indices = mask.nonzero(as_tuple=False)[:, 0] + if len(indices) > self.n_limit: + kpts_sc = scores[indices] + sort_idx = kpts_sc.sort(descending=True)[1] + sel_idx = sort_idx[:self.n_limit] + indices = indices[sel_idx] + indices_keypoints.append(indices) + + keypoints = [] + scoredispersitys = [] + kptscores = [] + if sub_pixel: + # detect soft keypoints with grad backpropagation + patches = self.unfold(scores_map) # B x (kernel**2) x (H*W) + self.hw_grid = self.hw_grid.to(patches) # to device + for b_idx in range(b): + patch = patches[b_idx].t() # (H*W) x (kernel**2) + indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M + patch_scores = patch[indices_kpt] # M x (kernel**2) + + # max is detached to prevent undesired backprop loops in the graph + max_v = patch_scores.max(dim=1).values.detach()[:, None] + x_exp = ((patch_scores - max_v) / self.temperature).exp() # M * (kernel**2), in [0, 1] + + # \frac{ \sum{(i,j) \times \exp(x/T)} }{ \sum{\exp(x/T)} } + xy_residual = x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None] # Soft-argmax, Mx2 + + hw_grid_dist2 = torch.norm((self.hw_grid[None, :, :] - xy_residual[:, None, :]) / self.radius, + dim=-1) ** 2 + scoredispersity = (x_exp * hw_grid_dist2).sum(dim=1) / x_exp.sum(dim=1) + + # compute result keypoints + keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2 + keypoints_xy = keypoints_xy_nms + xy_residual + keypoints_xy = keypoints_xy / keypoints_xy.new_tensor( + [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1) + + kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0), + keypoints_xy.view(1, 1, -1, 2), + mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN + + keypoints.append(keypoints_xy) + scoredispersitys.append(scoredispersity) + kptscores.append(kptscore) + else: + for b_idx in range(b): + indices_kpt = indices_keypoints[b_idx] # one dimension vector, say its size is M + keypoints_xy_nms = torch.stack([indices_kpt % w, indices_kpt // w], dim=1) # Mx2 + keypoints_xy = keypoints_xy_nms / keypoints_xy_nms.new_tensor( + [w - 1, h - 1]) * 2 - 1 # (w,h) -> (-1~1,-1~1) + kptscore = torch.nn.functional.grid_sample(scores_map[b_idx].unsqueeze(0), + keypoints_xy.view(1, 1, -1, 2), + mode='bilinear', align_corners=True)[0, 0, 0, :] # CxN + keypoints.append(keypoints_xy) + scoredispersitys.append(None) + kptscores.append(kptscore) + + return keypoints, scoredispersitys, kptscores + + def forward(self, scores_map, descriptor_map, sub_pixel=False): + """ + :param scores_map: Bx1xHxW + :param descriptor_map: BxCxHxW + :param sub_pixel: whether to use sub-pixel keypoint detection + :return: kpts: list[Nx2,...]; kptscores: list[N,....] normalised position: -1.0 ~ 1.0 + """ + keypoints, scoredispersitys, kptscores = self.detect_keypoints(scores_map, + sub_pixel) + + descriptors = sample_descriptor(descriptor_map, keypoints, sub_pixel) + + # keypoints: B M 2 + # descriptors: B M D + # scoredispersitys: + return keypoints, descriptors, kptscores, scoredispersitys diff --git a/third_party/ASpanFormer/.github/workflows/sync.yml b/third_party/ASpanFormer/.github/workflows/sync.yml new file mode 100644 index 0000000000000000000000000000000000000000..42e762d5299095226503f3a8cebfeef440ef68d7 --- /dev/null +++ b/third_party/ASpanFormer/.github/workflows/sync.yml @@ -0,0 +1,39 @@ +name: Upstream Sync + +permissions: + contents: write + +on: + schedule: + - cron: "0 0 * * *" # every day + workflow_dispatch: + +jobs: + sync_latest_from_upstream: + name: Sync latest commits from upstream repo + runs-on: ubuntu-latest + if: ${{ github.event.repository.fork }} + + steps: + # Step 1: run a standard checkout action + - name: Checkout target repo + uses: actions/checkout@v3 + + # Step 2: run the sync action + - name: Sync upstream changes + id: sync + uses: aormsby/Fork-Sync-With-Upstream-action@v3.4 + with: + upstream_sync_repo: apple/ml-aspanformer + upstream_sync_branch: main + target_sync_branch: main + target_repo_token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, no need to set + + # Set test_mode true to run tests instead of the true action!! + test_mode: false + + - name: Sync check + if: failure() + run: | + echo "::error::Due to insufficient permissions, synchronization failed (as expected). Please go to the repository homepage and manually perform [Sync fork]." + exit 1 diff --git a/third_party/ASpanFormer/.gitignore b/third_party/ASpanFormer/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a4b668777112a4fbc96b1763c8da4ad91c9bcac9 --- /dev/null +++ b/third_party/ASpanFormer/.gitignore @@ -0,0 +1,32 @@ +.vscode/ +__pycache__/ +*.pyc +*.DS_Store +*.swp +*.pth +tmp.* +*/.ipynb_checkpoints/* + +logs/ +# weights/ +dump/ +demo/*.mp4 +demo/demo_images/ +src/loftr/utils/superglue.py +demo/utils.py + +demo/*.jpg +demo/*.png + +notebooks/QccDayNight.ipynb +notebooks/westlake.ipynb +assets/westlake +assets/qcc_pairs.txt +configs/.petrel* +tools/draw_QccDayNights.py + +scripts/slurm/ +scripts/sbatch_submit.sh +src/utils/client.py + +scannet_indices/ diff --git a/third_party/ASpanFormer/CODE_OF_CONDUCT.md b/third_party/ASpanFormer/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..c991377a60951acbcd7f586ebcf0184840e30e55 --- /dev/null +++ b/third_party/ASpanFormer/CODE_OF_CONDUCT.md @@ -0,0 +1,71 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the open source team at [opensource-conduct@group.apple.com](mailto:opensource-conduct@group.apple.com). All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 1.4, +available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html](https://www.contributor-covenant.org/version/1/4/code-of-conduct.html) \ No newline at end of file diff --git a/third_party/ASpanFormer/CONTRIBUTING.md b/third_party/ASpanFormer/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..03d1703dce5cbd70896fcb8abc0fbdc664751320 --- /dev/null +++ b/third_party/ASpanFormer/CONTRIBUTING.md @@ -0,0 +1,7 @@ +# Contribution Guide + +Thanks for your interest in contributing. This project was released to accompany a research paper for purposes of reproducability, and beyond its publication there are limited plans for future development of the repository. + +## Before you get started + +We ask that all community members read and observe our [Code of Conduct](CODE_OF_CONDUCT.md). \ No newline at end of file diff --git a/third_party/ASpanFormer/LICENSE b/third_party/ASpanFormer/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e20657c86559c67eb94e9b9269ba802de8cc9189 --- /dev/null +++ b/third_party/ASpanFormer/LICENSE @@ -0,0 +1,9 @@ +Copyright (C) 2021, 2022 Apple Inc. All Rights Reserved. + +IMPORTANT: This Apple software is supplied to you by Apple Inc. ("Apple") in consideration of your agreement to the following terms, and your use, installation, modification or redistribution of this Apple software constitutes acceptance of these terms. If you do not agree with these terms, please do not use, install, modify or redistribute this Apple software. + +In consideration of your agreement to abide by the following terms, and subject to these terms, Apple grants you a personal, non-commercial, non-exclusive license, under Apple's copyrights in this original Apple software (the "Apple Software"), to use, reproduce, modify and redistribute the Apple Software, with or without modifications, in source and/or binary forms for non-commercial purposes only; provided that if you redistribute the Apple Software in its entirety and without modifications, you must retain this notice and the following text and disclaimers in all such redistributions of the Apple Software. Neither the name, trademarks, service marks or logos of Apple Inc. may be used to endorse or promote products derived from the Apple Software without specific prior written permission from Apple. Except as expressly stated in this notice, no other rights or licenses, express or implied, are granted by Apple herein, including but not limited to any patent rights that may be infringed by your derivative works or by other works in which the Apple Software may be incorporated. + +The Apple Software is provided by Apple on an "AS IS" basis. APPLE MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE APPLE SOFTWARE OR ITS USE AND OPERATION ALONE OR IN COMBINATION WITH YOUR PRODUCTS. + +IN NO EVENT SHALL APPLE BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) ARISING IN ANY WAY OUT OF THE USE, REPRODUCTION, MODIFICATION AND/OR DISTRIBUTION OF THE APPLE SOFTWARE, HOWEVER CAUSED AND WHETHER UNDER THEORY OF CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY OR OTHERWISE, EVEN IF APPLE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/third_party/ASpanFormer/README.md b/third_party/ASpanFormer/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e1b788606b6acf4a1b5e0e40d07789ac8ea8ea5b --- /dev/null +++ b/third_party/ASpanFormer/README.md @@ -0,0 +1,98 @@ +# Submodule used in [hloc](https://github.com/Vincentqyw/Hierarchical-Localization) toolbox + +# ASpanFormer Implementation + +![Framework](assets/teaser.png) + +This is a PyTorch implementation of ASpanFormer for ECCV'22 [paper](https://arxiv.org/abs/2208.14201), “ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer”, and can be used to reproduce the results in the paper. + +This work focuses on detector-free image matching. We propose a hierarchical attention framework for cross-view feature update, which adaptively adjusts attention span based on region-wise matchability. + +This repo contains training, evaluation and basic demo scripts used in our paper. + +A large part of the code base is borrowed from the [LoFTR Repository](https://github.com/zju3dv/LoFTR) under its own separate license, terms and conditions. The authors of this software are not responsible for the contents of third-party websites. + +## Installation +```bash +conda env create -f environment.yaml +conda activate ASpanFormer +``` + +## Get started +Download model weights from [here](https://drive.google.com/file/d/1eavM9dTkw9nbc-JqlVVfGPU5UvTTfc6k/view?usp=share_link) + +Extract weights by +```bash +tar -xvf weights_aspanformer.tar +``` + +A demo to match one image pair is provided. To get a quick start, + +```bash +cd demo +python demo.py +``` + + +## Data Preparation +Please follow the [training doc](docs/TRAINING.md) for data organization + + + +## Evaluation + + +### 1. ScanNet Evaluation +```bash +cd scripts/reproduce_test +bash indoor.sh +``` +Similar results as below should be obtained, +```bash +'auc@10': 0.46640095171012563, +'auc@20': 0.6407042320049785, +'auc@5': 0.26241231577189295, +'prec@5e-04': 0.8827665604024288, +'prec_flow@2e-03': 0.810938751342228 +``` + +### 2. MegaDepth Evaluation + ```bash +cd scripts/reproduce_test +bash outdoor.sh +``` +Similar results as below should be obtained, +```bash +'auc@10': 0.7184113573584142, +'auc@20': 0.8333835724453831, +'auc@5': 0.5567622479156181, +'prec@5e-04': 0.9901741341790503, +'prec_flow@2e-03': 0.7188964321862907 +``` + + +## Training + +### 1. ScanNet Training +```bash +cd scripts/reproduce_train +bash indoor.sh +``` + +### 2. 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--git a/third_party/ASpanFormer/assets/scannet_test_1500/test.npz b/third_party/ASpanFormer/assets/scannet_test_1500/test.npz new file mode 100644 index 0000000000000000000000000000000000000000..d2011c2913a9ae1311d18b08c089bd999ba3ad30 --- /dev/null +++ b/third_party/ASpanFormer/assets/scannet_test_1500/test.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b982b9c1f762e7d31af552ecc1ccf1a6add013197f74ec69c84a6deaa6f580ad +size 71687 diff --git a/third_party/ASpanFormer/assets/teaser.pdf b/third_party/ASpanFormer/assets/teaser.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9e826ee0d43982068c60528017f93481e0c7cd1e --- /dev/null +++ b/third_party/ASpanFormer/assets/teaser.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfb83d72b2ff7929cb99a820620562205237147aaf5952acd9152185926c6b81 +size 2671548 diff --git a/third_party/ASpanFormer/assets/teaser.png b/third_party/ASpanFormer/assets/teaser.png new file mode 100644 index 0000000000000000000000000000000000000000..c7adcde5f6f35b2e274303dba763bab5d78f43b7 --- /dev/null +++ b/third_party/ASpanFormer/assets/teaser.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7eea1427c6c092f5db0720b39f55cb15584e8b7aea11b28244f2e7f8da1d0967 +size 6957484 diff --git a/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py b/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py new file mode 100644 index 0000000000000000000000000000000000000000..fc2b44807696ec280672c8f40650fd04fa4d8a36 --- /dev/null +++ b/third_party/ASpanFormer/configs/aspan/indoor/aspan_test.py @@ -0,0 +1,10 @@ +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent / '../../../')) +from src.config.default import _CN as cfg + +cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.ASPAN.MATCH_COARSE.BORDER_RM = 0 +cfg.ASPAN.COARSE.COARSEST_LEVEL= [15,20] +cfg.ASPAN.COARSE.TRAIN_RES = [480,640] diff --git a/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py b/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..886d10d8f55533c8021bcca8395b5a2897fb8734 --- /dev/null +++ b/third_party/ASpanFormer/configs/aspan/indoor/aspan_train.py @@ -0,0 +1,11 @@ +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent / '../../../')) +from src.config.default import _CN as cfg + +cfg.ASPAN.COARSE.COARSEST_LEVEL= [15,20] +cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.ASPAN.MATCH_COARSE.SPARSE_SPVS = False +cfg.ASPAN.MATCH_COARSE.BORDER_RM = 0 +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29] diff --git a/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f0b9c04cbf3f466e413b345272afe7d7fe4274ea --- /dev/null +++ b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_test.py @@ -0,0 +1,21 @@ +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent / '../../../')) +from src.config.default import _CN as cfg + +cfg.ASPAN.COARSE.COARSEST_LEVEL= [36,36] +cfg.ASPAN.COARSE.TRAIN_RES = [832,832] +cfg.ASPAN.COARSE.TEST_RES = [1152,1152] +cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.ASPAN.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1202080b234562d8cc65d924d7cccf0336b9f7c0 --- /dev/null +++ b/third_party/ASpanFormer/configs/aspan/outdoor/aspan_train.py @@ -0,0 +1,20 @@ +import sys +from pathlib import Path +sys.path.append(str(Path(__file__).parent / '../../../')) +from src.config.default import _CN as cfg + +cfg.ASPAN.COARSE.COARSEST_LEVEL= [26,26] +cfg.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' +cfg.ASPAN.MATCH_COARSE.SPARSE_SPVS = False + +cfg.TRAINER.CANONICAL_LR = 8e-3 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.ASPAN.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/third_party/ASpanFormer/configs/data/__init__.py b/third_party/ASpanFormer/configs/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/ASpanFormer/configs/data/base.py b/third_party/ASpanFormer/configs/data/base.py new file mode 100644 index 0000000000000000000000000000000000000000..03aab160fa4137ccc04380f94854a56fbb549074 --- /dev/null +++ b/third_party/ASpanFormer/configs/data/base.py @@ -0,0 +1,35 @@ +""" +The data config will be the last one merged into the main config. +Setups in data configs will override all existed setups! +""" + +from yacs.config import CfgNode as CN +_CN = CN() +_CN.DATASET = CN() +_CN.TRAINER = CN() + +# training data config +_CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None +_CN.DATASET.TRAIN_NPZ_ROOT = None +_CN.DATASET.TRAIN_LIST_PATH = None +_CN.DATASET.TRAIN_INTRINSIC_PATH = None +# validation set config +_CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None +_CN.DATASET.VAL_NPZ_ROOT = None +_CN.DATASET.VAL_LIST_PATH = None +_CN.DATASET.VAL_INTRINSIC_PATH = None + +# testing data config +_CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None +_CN.DATASET.TEST_NPZ_ROOT = None +_CN.DATASET.TEST_LIST_PATH = None +_CN.DATASET.TEST_INTRINSIC_PATH = None + +# dataset config +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +cfg = _CN diff --git a/third_party/ASpanFormer/configs/data/debug/.gitignore b/third_party/ASpanFormer/configs/data/debug/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/ASpanFormer/configs/data/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/ASpanFormer/configs/data/megadepth_test_1500.py b/third_party/ASpanFormer/configs/data/megadepth_test_1500.py new file mode 100644 index 0000000000000000000000000000000000000000..9616432f52a693ed84f3f12b9b85470b23410eee --- /dev/null +++ b/third_party/ASpanFormer/configs/data/megadepth_test_1500.py @@ -0,0 +1,13 @@ +from configs.data.base import cfg + +TEST_BASE_PATH = "assets/megadepth_test_1500_scene_info" + +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" +cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/megadepth_test_1500.txt" + +cfg.DATASET.MGDPT_IMG_RESIZE = 1152 +cfg.DATASET.MGDPT_IMG_PAD=True +cfg.DATASET.MGDPT_DF =8 +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 \ No newline at end of file diff --git a/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py b/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py new file mode 100644 index 0000000000000000000000000000000000000000..8f9b01fdaed254e10b3d55980499b88a00060f04 --- /dev/null +++ b/third_party/ASpanFormer/configs/data/megadepth_trainval_832.py @@ -0,0 +1,22 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TRAIN_DATA_ROOT = "data/megadepth/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_info_0.1_0.7" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/trainvaltest_list/train_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0 + +TEST_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500" +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +# 368 scenes in total for MegaDepth +# (with difficulty balanced (further split each scene to 3 sub-scenes)) +cfg.TRAINER.N_SAMPLES_PER_SUBSET = 100 + +cfg.DATASET.MGDPT_IMG_RESIZE = 832 # for training on 32GB meme GPUs diff --git a/third_party/ASpanFormer/configs/data/scannet_test_1500.py b/third_party/ASpanFormer/configs/data/scannet_test_1500.py new file mode 100644 index 0000000000000000000000000000000000000000..60e560fa01d73345200aaca10961449fdf3e9fbe --- /dev/null +++ b/third_party/ASpanFormer/configs/data/scannet_test_1500.py @@ -0,0 +1,11 @@ +from configs.data.base import cfg + +TEST_BASE_PATH = "assets/scannet_test_1500" + +cfg.DATASET.TEST_DATA_SOURCE = "ScanNet" +cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test" +cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" +cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt" +cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz" + +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 diff --git a/third_party/ASpanFormer/configs/data/scannet_trainval.py b/third_party/ASpanFormer/configs/data/scannet_trainval.py new file mode 100644 index 0000000000000000000000000000000000000000..c38d6440e2b4ec349e5f168909c7f8c367408813 --- /dev/null +++ b/third_party/ASpanFormer/configs/data/scannet_trainval.py @@ -0,0 +1,17 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/scannet/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "ScanNet" +cfg.DATASET.TRAIN_DATA_ROOT = "data/scannet/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_data/train" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/scene_data/train_list/scannet_all.txt" +cfg.DATASET.TRAIN_INTRINSIC_PATH = f"{TRAIN_BASE_PATH}/intrinsics.npz" + +TEST_BASE_PATH = "assets/scannet_test_1500" +cfg.DATASET.TEST_DATA_SOURCE = "ScanNet" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = TEST_BASE_PATH +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt" +cfg.DATASET.VAL_INTRINSIC_PATH = cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val diff --git a/third_party/ASpanFormer/data/megadepth/index/.gitignore b/third_party/ASpanFormer/data/megadepth/index/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/ASpanFormer/data/megadepth/index/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/ASpanFormer/data/megadepth/test/.gitignore b/third_party/ASpanFormer/data/megadepth/test/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/ASpanFormer/data/megadepth/test/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/ASpanFormer/data/megadepth/train/.gitignore b/third_party/ASpanFormer/data/megadepth/train/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/ASpanFormer/data/megadepth/train/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/ASpanFormer/data/scannet/index/.gitignore b/third_party/ASpanFormer/data/scannet/index/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/ASpanFormer/data/scannet/index/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/ASpanFormer/data/scannet/test/.gitignore b/third_party/ASpanFormer/data/scannet/test/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/ASpanFormer/data/scannet/test/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/ASpanFormer/data/scannet/train/.gitignore b/third_party/ASpanFormer/data/scannet/train/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/ASpanFormer/data/scannet/train/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/ASpanFormer/demo/demo.py b/third_party/ASpanFormer/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..f3d95b10dc3166c18ad8493be7a3d36a25d8fc3b --- /dev/null +++ b/third_party/ASpanFormer/demo/demo.py @@ -0,0 +1,63 @@ +import os +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + +from src.ASpanFormer.aspanformer import ASpanFormer +from src.config.default import get_cfg_defaults +from src.utils.misc import lower_config +import demo_utils + +import cv2 +import torch +import numpy as np + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('--config_path', type=str, default='../configs/aspan/outdoor/aspan_test.py', + help='path for config file.') +parser.add_argument('--img0_path', type=str, default='../assets/phototourism_sample_images/piazza_san_marco_06795901_3725050516.jpg', + help='path for image0.') +parser.add_argument('--img1_path', type=str, default='../assets/phototourism_sample_images/piazza_san_marco_15148634_5228701572.jpg', + help='path for image1.') +parser.add_argument('--weights_path', type=str, default='../weights/outdoor.ckpt', + help='path for model weights.') +parser.add_argument('--long_dim0', type=int, default=1024, + help='resize for longest dim of image0.') +parser.add_argument('--long_dim1', type=int, default=1024, + help='resize for longest dim of image1.') + +args = parser.parse_args() + + +if __name__=='__main__': + config = get_cfg_defaults() + config.merge_from_file(args.config_path) + _config = lower_config(config) + matcher = ASpanFormer(config=_config['aspan']) + state_dict = torch.load(args.weights_path, map_location='cpu')['state_dict'] + matcher.load_state_dict(state_dict,strict=False) + matcher.cuda(),matcher.eval() + + img0,img1=cv2.imread(args.img0_path),cv2.imread(args.img1_path) + img0_g,img1_g=cv2.imread(args.img0_path,0),cv2.imread(args.img1_path,0) + img0,img1=demo_utils.resize(img0,args.long_dim0),demo_utils.resize(img1,args.long_dim1) + img0_g,img1_g=demo_utils.resize(img0_g,args.long_dim0),demo_utils.resize(img1_g,args.long_dim1) + data={'image0':torch.from_numpy(img0_g/255.)[None,None].cuda().float(), + 'image1':torch.from_numpy(img1_g/255.)[None,None].cuda().float()} + with torch.no_grad(): + matcher(data,online_resize=True) + corr0,corr1=data['mkpts0_f'].cpu().numpy(),data['mkpts1_f'].cpu().numpy() + + F_hat,mask_F=cv2.findFundamentalMat(corr0,corr1,method=cv2.FM_RANSAC,ransacReprojThreshold=1) + if mask_F is not None: + mask_F=mask_F[:,0].astype(bool) + else: + mask_F=np.zeros_like(corr0[:,0]).astype(bool) + + #visualize match + display=demo_utils.draw_match(img0,img1,corr0,corr1) + display_ransac=demo_utils.draw_match(img0,img1,corr0[mask_F],corr1[mask_F]) + cv2.imwrite('match.png',display) + cv2.imwrite('match_ransac.png',display_ransac) + print(len(corr1),len(corr1[mask_F])) \ No newline at end of file diff --git a/third_party/ASpanFormer/demo/demo_utils.py b/third_party/ASpanFormer/demo/demo_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a104e25d3f5ee8b7efb6cc5fa0dc27378e22c83f --- /dev/null +++ b/third_party/ASpanFormer/demo/demo_utils.py @@ -0,0 +1,44 @@ +import cv2 +import numpy as np + +def resize(image,long_dim): + h,w=image.shape[0],image.shape[1] + image=cv2.resize(image,(int(w*long_dim/max(h,w)),int(h*long_dim/max(h,w)))) + return image + +def draw_points(img,points,color=(0,255,0),radius=3): + dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])] + for i in range(points.shape[0]): + cv2.circle(img, dp[i],radius=radius,color=color) + return img + + +def draw_match(img1, img2, corr1, corr2,inlier=[True],color=None,radius1=1,radius2=1,resize=None): + if resize is not None: + scale1,scale2=[img1.shape[1]/resize[0],img1.shape[0]/resize[1]],[img2.shape[1]/resize[0],img2.shape[0]/resize[1]] + img1,img2=cv2.resize(img1, resize, interpolation=cv2.INTER_AREA),cv2.resize(img2, resize, interpolation=cv2.INTER_AREA) + corr1,corr2=corr1/np.asarray(scale1)[np.newaxis],corr2/np.asarray(scale2)[np.newaxis] + corr1_key = [cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])] + corr2_key = [cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])] + + assert len(corr1) == len(corr2) + + draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))] + if color is None: + color = [(0, 255, 0) if cur_inlier else (0,0,255) for cur_inlier in inlier] + if len(color)==1: + display = cv2.drawMatches(img1, corr1_key, img2, corr2_key, draw_matches, None, + matchColor=color[0], + singlePointColor=color[0], + flags=4 + ) + else: + height,width=max(img1.shape[0],img2.shape[0]),img1.shape[1]+img2.shape[1] + display=np.zeros([height,width,3],np.uint8) + display[:img1.shape[0],:img1.shape[1]]=img1 + display[:img2.shape[0],img1.shape[1]:]=img2 + for i in range(len(corr1)): + left_x,left_y,right_x,right_y=int(corr1[i][0]),int(corr1[i][1]),int(corr2[i][0]+img1.shape[1]),int(corr2[i][1]) + cur_color=(int(color[i][0]),int(color[i][1]),int(color[i][2])) + cv2.line(display, (left_x,left_y), (right_x,right_y),cur_color,1,lineType=cv2.LINE_AA) + return display \ No newline at end of file diff --git a/third_party/ASpanFormer/docs/TRAINING.md b/third_party/ASpanFormer/docs/TRAINING.md new file mode 100644 index 0000000000000000000000000000000000000000..99238b612d961a5a6aa29885bad23808c7aa6e07 --- /dev/null +++ b/third_party/ASpanFormer/docs/TRAINING.md @@ -0,0 +1,72 @@ + +# Traininig ASpanFormer + +## Dataset setup +Generally, two parts of data are needed for training ASpanFormer, the original dataset, i.e., ScanNet and MegaDepth, and the offline generated dataset indices. The dataset indices store scenes, image pairs, and other metadata within each dataset used for training/validation/testing. For the MegaDepth dataset, the relative poses between images used for training are directly cached in the indexing files. However, the relative poses of ScanNet image pairs are not stored due to the enormous resulting file size. + +### Download datasets +#### MegaDepth +We use depth maps provided in the [original MegaDepth dataset](https://www.cs.cornell.edu/projects/megadepth/) as well as undistorted images, corresponding camera intrinsics and extrinsics preprocessed by [D2-Net](https://github.com/mihaidusmanu/d2-net#downloading-and-preprocessing-the-megadepth-dataset). You can download them separately from the following links. +- [MegaDepth undistorted images and processed depths](https://www.cs.cornell.edu/projects/megadepth/dataset/Megadepth_v1/MegaDepth_v1.tar.gz) + - Note that we only use depth maps. + - Path of the download data will be referreed to as `/path/to/megadepth` +- [D2-Net preprocessed images](https://drive.google.com/drive/folders/1hxpOsqOZefdrba_BqnW490XpNX_LgXPB) + - Images are undistorted manually in D2-Net since the undistorted images from MegaDepth do not come with corresponding intrinsics. + - Path of the download data will be referreed to as `/path/to/megadepth_d2net` + +#### ScanNet +Please set up the ScanNet dataset following [the official guide](https://github.com/ScanNet/ScanNet#scannet-data) +> NOTE: We use the [python exported data](https://github.com/ScanNet/ScanNet/tree/master/SensReader/python), +instead of the [c++ exported one](https://github.com/ScanNet/ScanNet/tree/master/SensReader/c%2B%2B). + +### Download the dataset indices + +You can download the required dataset indices from the [following link](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf). +After downloading, unzip the required files. +```shell +unzip downloaded-file.zip + +# extract dataset indices +tar xf train-data/megadepth_indices.tar +tar xf train-data/scannet_indices.tar + +# extract testing data (optional) +tar xf testdata/megadepth_test_1500.tar +tar xf testdata/scannet_test_1500.tar +``` + +### Build the dataset symlinks + +We symlink the datasets to the `data` directory under the main ASpanFormer project directory. + +```shell +# scannet +# -- # train and test dataset +ln -s /path/to/scannet_train/* /path/to/ASpanFormer/data/scannet/train +ln -s /path/to/scannet_test/* /path/to/ASpanFormer/data/scannet/test +# -- # dataset indices +ln -s /path/to/scannet_indices/* /path/to/ASpanFormer/data/scannet/index + +# megadepth +# -- # train and test dataset (train and test share the same dataset) +ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/train +ln -sv /path/to/megadepth/phoenix /path/to/megadepth_d2net/Undistorted_SfM /path/to/ASpanFormer/data/megadepth/test +# -- # dataset indices +ln -s /path/to/megadepth_indices/* /path/to/ASpanFormer/data/megadepth/index +``` + + +## Training +We provide training scripts of ScanNet and MegaDepth. The results in the ASpanFormer paper can be reproduced with 8 v100 GPUs. For a different setup, we scale the learning rate and its warm-up linearly, but the final evaluation results might vary due to the different batch size & learning rate used. Thus the reproduction of results in our paper is not guaranteed. + + +### Training on ScanNet +``` shell +scripts/reproduce_train/indoor.sh +``` + + +### Training on MegaDepth +``` shell +scripts/reproduce_train/outdoor.sh +``` \ No newline at end of file diff --git a/third_party/ASpanFormer/environment.yaml b/third_party/ASpanFormer/environment.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5c52328762e971c94b447198869ec0036771bf76 --- /dev/null +++ b/third_party/ASpanFormer/environment.yaml @@ -0,0 +1,12 @@ +name: ASpanFormer +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - python=3.8 + - cudatoolkit=10.2 + - pytorch=1.8.1 + - pip + - pip: + - -r requirements.txt diff --git a/third_party/ASpanFormer/requirements.txt b/third_party/ASpanFormer/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..815830f7bd8115b858bf5e49e85aed4f62d3f3b0 --- /dev/null +++ b/third_party/ASpanFormer/requirements.txt @@ -0,0 +1,18 @@ +#opencv_python==4.4.0.46 +albumentations==0.5.1 --no-binary=imgaug,albumentations +ray>=1.0.1 +einops==0.3.0 +kornia==0.4.1 +loguru==0.5.3 +yacs>=0.1.8 +tqdm +autopep8 +pylint +ipython +jupyterlab +matplotlib +h5py +pytorch-lightning==1.3.5 +loguru +joblib>=1.0.1 +torchmetrics==0.4 \ No newline at end of file diff --git a/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh b/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..41e5c76a146fb84a2296f7fc63e6da881c0c8e03 --- /dev/null +++ b/third_party/ASpanFormer/scripts/reproduce_test/indoor.sh @@ -0,0 +1,31 @@ +#!/bin/bash -l +# a indoor_ds model with the pos_enc impl bug fixed. + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/scannet_test_1500.py" +main_cfg_path="configs/aspan/indoor/aspan_test.py" +ckpt_path='weights/indoor.ckpt' +dump_dir="dump/indoor_dump" +profiler_name="inference" +n_nodes=1 # mannually keep this the same with --nodes +n_gpus_per_node=-1 +torch_num_workers=4 +batch_size=1 # per gpu + +python -u ./test.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --ckpt_path=${ckpt_path} \ + --dump_dir=${dump_dir} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers}\ + --profiler_name=${profiler_name} \ + --benchmark \ + --mode integrated + \ No newline at end of file diff --git a/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh b/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..817fe50b47f52dfa3f9b2d664f415527a7a9ea6d --- /dev/null +++ b/third_party/ASpanFormer/scripts/reproduce_test/outdoor.sh @@ -0,0 +1,30 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/megadepth_test_1500.py" +main_cfg_path="configs/aspan/outdoor/aspan_test.py" +ckpt_path="weights/outdoor.ckpt" +dump_dir="dump/outdoor_dump" +profiler_name="inference" +n_nodes=1 # mannually keep this the same with --nodes +n_gpus_per_node=-1 +torch_num_workers=4 +batch_size=1 # per gpu + +python -u ./test.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --ckpt_path=${ckpt_path} \ + --dump_dir=${dump_dir} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers}\ + --profiler_name=${profiler_name} \ + --benchmark \ + --mode integrated + \ No newline at end of file diff --git a/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh b/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..705723bf14a6e6fbe949df64bbc3a68a9159e659 --- /dev/null +++ b/third_party/ASpanFormer/scripts/reproduce_train/indoor.sh @@ -0,0 +1,34 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/scannet_trainval.py" +main_cfg_path="configs/aspan/indoor/aspan_train.py" + +n_nodes=1 +n_gpus_per_node=8 +torch_num_workers=36 +batch_size=3 +pin_memory=true +exp_name="indoor-ds-bs-aspan-bs=$(($n_gpus_per_node * $batch_size))" + +CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=100 \ + --flush_logs_every_n_steps=100 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 \ + --parallel_load_data \ + --mode integrated \ No newline at end of file diff --git a/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh b/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..c447e8feaa5c7ef7ff74da3b622151c7018447a6 --- /dev/null +++ b/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh @@ -0,0 +1,34 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +TRAIN_IMG_SIZE=832 +data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py" +main_cfg_path="configs/aspan/outdoor/aspan_train.py" + +n_nodes=1 +n_gpus_per_node=8 +torch_num_workers=8 +batch_size=1 +pin_memory=true +exp_name="outdoor-ds-aspan-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=100 \ + --flush_logs_every_n_steps=100 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=30 \ + --mode integrated diff --git a/third_party/ASpanFormer/src/ASpanFormer/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3bfd5a901e83c7e8d3b439f21afa20ac8237635e --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/__init__.py @@ -0,0 +1,2 @@ +from .aspanformer import LocalFeatureTransformer_Flow +from .utils.cvpr_ds_config import default_cfg diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dff6704976cbe9e916c6de6af9e3b755dfbd20bf --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/__init__.py @@ -0,0 +1,3 @@ +from .transformer import LocalFeatureTransformer_Flow +from .loftr import LocalFeatureTransformer +from .fine_preprocess import FinePreprocess diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..632dd22077806d2b53f66a09d0567925a30d1523 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py @@ -0,0 +1,198 @@ +import torch +from torch.nn import Module +import torch.nn as nn +from itertools import product +from torch.nn import functional as F + +class layernorm2d(nn.Module): + + def __init__(self,dim) : + super().__init__() + self.dim=dim + self.affine=nn.parameter.Parameter(torch.ones(dim), requires_grad=True) + self.bias=nn.parameter.Parameter(torch.zeros(dim), requires_grad=True) + + def forward(self,x): + #x: B*C*H*W + mean,std=x.mean(dim=1,keepdim=True),x.std(dim=1,keepdim=True) + return self.affine[None,:,None,None]*(x-mean)/(std+1e-6)+self.bias[None,:,None,None] + + +class HierachicalAttention(Module): + def __init__(self,d_model,nhead,nsample,radius_scale,nlevel=3): + super().__init__() + self.d_model=d_model + self.nhead=nhead + self.nsample=nsample + self.nlevel=nlevel + self.radius_scale=radius_scale + self.merge_head = nn.Sequential( + nn.Conv1d(d_model*3, d_model, kernel_size=1,bias=False), + nn.ReLU(True), + nn.Conv1d(d_model, d_model, kernel_size=1,bias=False), + ) + self.fullattention=FullAttention(d_model,nhead) + self.temp=nn.parameter.Parameter(torch.tensor(1.),requires_grad=True) + sample_offset=torch.tensor([[pos[0]-nsample[1]/2+0.5, pos[1]-nsample[1]/2+0.5] for pos in product(range(nsample[1]), range(nsample[1]))]) #r^2*2 + self.sample_offset=nn.parameter.Parameter(sample_offset,requires_grad=False) + + def forward(self,query,key,value,flow,size_q,size_kv,mask0=None, mask1=None,ds0=[4,4],ds1=[4,4]): + """ + Args: + q,k,v (torch.Tensor): [B, C, L] + mask (torch.Tensor): [B, L] + flow (torch.Tensor): [B, H, W, 4] + Return: + all_message (torch.Tensor): [B, C, H, W] + """ + + variance=flow[:,:,:,2:] + offset=flow[:,:,:,:2] #B*H*W*2 + bs=query.shape[0] + h0,w0=size_q[0],size_q[1] + h1,w1=size_kv[0],size_kv[1] + variance=torch.exp(0.5*variance)*self.radius_scale #b*h*w*2(pixel scale) + span_scale=torch.clamp((variance*2/self.nsample[1]),min=1) #b*h*w*2 + + sub_sample0,sub_sample1=[ds0,2,1],[ds1,2,1] + q_list=[F.avg_pool2d(query.view(bs,-1,h0,w0),kernel_size=sub_size,stride=sub_size) for sub_size in sub_sample0] + k_list=[F.avg_pool2d(key.view(bs,-1,h1,w1),kernel_size=sub_size,stride=sub_size) for sub_size in sub_sample1] + v_list=[F.avg_pool2d(value.view(bs,-1,h1,w1),kernel_size=sub_size,stride=sub_size) for sub_size in sub_sample1] #n_level + + offset_list=[F.avg_pool2d(offset.permute(0,3,1,2),kernel_size=sub_size*self.nsample[0],stride=sub_size*self.nsample[0]).permute(0,2,3,1)/sub_size for sub_size in sub_sample0[1:]] #n_level-1 + span_list=[F.avg_pool2d(span_scale.permute(0,3,1,2),kernel_size=sub_size*self.nsample[0],stride=sub_size*self.nsample[0]).permute(0,2,3,1) for sub_size in sub_sample0[1:]] #n_level-1 + + if mask0 is not None: + mask0,mask1=mask0.view(bs,1,h0,w0),mask1.view(bs,1,h1,w1) + mask0_list=[-F.max_pool2d(-mask0,kernel_size=sub_size,stride=sub_size) for sub_size in sub_sample0] + mask1_list=[-F.max_pool2d(-mask1,kernel_size=sub_size,stride=sub_size) for sub_size in sub_sample1] + else: + mask0_list=mask1_list=[None,None,None] + + message_list=[] + #full attention at coarse scale + mask0_flatten=mask0_list[0].view(bs,-1) if mask0 is not None else None + mask1_flatten=mask1_list[0].view(bs,-1) if mask1 is not None else None + message_list.append(self.fullattention(q_list[0],k_list[0],v_list[0],mask0_flatten,mask1_flatten,self.temp).view(bs,self.d_model,h0//ds0[0],w0//ds0[1])) + + for index in range(1,self.nlevel): + q,k,v=q_list[index],k_list[index],v_list[index] + mask0,mask1=mask0_list[index],mask1_list[index] + s,o=span_list[index-1],offset_list[index-1] #B*h*w(*2) + q,k,v,sample_pixel,mask_sample=self.partition_token(q,k,v,o,s,mask0) #B*Head*D*G*N(G*N=H*W for q) + message_list.append(self.group_attention(q,k,v,1,mask_sample).view(bs,self.d_model,h0//sub_sample0[index],w0//sub_sample0[index])) + #fuse + all_message=torch.cat([F.upsample(message_list[idx],scale_factor=sub_sample0[idx],mode='nearest') \ + for idx in range(self.nlevel)],dim=1).view(bs,-1,h0*w0) #b*3d*H*W + + all_message=self.merge_head(all_message).view(bs,-1,h0,w0) #b*d*H*W + return all_message + + def partition_token(self,q,k,v,offset,span_scale,maskv): + #q,k,v: B*C*H*W + #o: B*H/2*W/2*2 + #span_scale:B*H*W + bs=q.shape[0] + h,w=q.shape[2],q.shape[3] + hk,wk=k.shape[2],k.shape[3] + offset=offset.view(bs,-1,2) + span_scale=span_scale.view(bs,-1,1,2) + #B*G*2 + offset_sample=self.sample_offset[None,None]*span_scale + sample_pixel=offset[:,:,None]+offset_sample#B*G*r^2*2 + sample_norm=sample_pixel/torch.tensor([wk/2,hk/2]).cuda()[None,None,None]-1 + + q = q.view(bs, -1 , h // self.nsample[0], self.nsample[0], w // self.nsample[0], self.nsample[0]).\ + permute(0, 1, 2, 4, 3, 5).contiguous().view(bs, self.nhead,self.d_model//self.nhead, -1,self.nsample[0]**2)#B*head*D*G*N(G*N=H*W for q) + #sample token + k=F.grid_sample(k, grid=sample_norm).view(bs, self.nhead,self.d_model//self.nhead,-1, self.nsample[1]**2) #B*head*D*G*r^2 + v=F.grid_sample(v, grid=sample_norm).view(bs, self.nhead,self.d_model//self.nhead,-1, self.nsample[1]**2) #B*head*D*G*r^2 + #import pdb;pdb.set_trace() + if maskv is not None: + mask_sample=F.grid_sample(maskv.view(bs,-1,h,w).float(),grid=sample_norm,mode='nearest')==1 #B*1*G*r^2 + else: + mask_sample=None + return q,k,v,sample_pixel,mask_sample + + + def group_attention(self,query,key,value,temp,mask_sample=None): + #q,k,v: B*Head*D*G*N(G*N=H*W for q) + bs=query.shape[0] + #import pdb;pdb.set_trace() + QK = torch.einsum("bhdgn,bhdgm->bhgnm", query, key) + if mask_sample is not None: + num_head,number_n=QK.shape[1],QK.shape[3] + QK.masked_fill_(~(mask_sample[:,:,:,None]).expand(-1,num_head,-1,number_n,-1).bool(), float(-1e8)) + # Compute the attention and the weighted average + softmax_temp = temp / query.size(2)**.5 # sqrt(D) + A = torch.softmax(softmax_temp * QK, dim=-1) + queried_values = torch.einsum("bhgnm,bhdgm->bhdgn", A, value).contiguous().view(bs,self.d_model,-1) + return queried_values + + + +class FullAttention(Module): + def __init__(self,d_model,nhead): + super().__init__() + self.d_model=d_model + self.nhead=nhead + + def forward(self, q, k,v , mask0=None, mask1=None, temp=1): + """ Multi-head scaled dot-product attention, a.k.a full attention. + Args: + q,k,v: [N, D, L] + mask: [N, L] + Returns: + msg: [N,L] + """ + bs=q.shape[0] + q,k,v=q.view(bs,self.nhead,self.d_model//self.nhead,-1),k.view(bs,self.nhead,self.d_model//self.nhead,-1),v.view(bs,self.nhead,self.d_model//self.nhead,-1) + # Compute the unnormalized attention and apply the masks + QK = torch.einsum("nhdl,nhds->nhls", q, k) + if mask0 is not None: + QK.masked_fill_(~(mask0[:,None, :, None] * mask1[:, None, None]).bool(), float(-1e8)) + # Compute the attention and the weighted average + softmax_temp = temp / q.size(2)**.5 # sqrt(D) + A = torch.softmax(softmax_temp * QK, dim=-1) + queried_values = torch.einsum("nhls,nhds->nhdl", A, v).contiguous().view(bs,self.d_model,-1) + return queried_values + + + +def elu_feature_map(x): + return F.elu(x) + 1 + +class LinearAttention(Module): + def __init__(self, eps=1e-6): + super().__init__() + self.feature_map = elu_feature_map + self.eps = eps + + def forward(self, queries, keys, values, q_mask=None, kv_mask=None): + """ Multi-Head linear attention proposed in "Transformers are RNNs" + Args: + queries: [N, L, H, D] + keys: [N, S, H, D] + values: [N, S, H, D] + q_mask: [N, L] + kv_mask: [N, S] + Returns: + queried_values: (N, L, H, D) + """ + Q = self.feature_map(queries) + K = self.feature_map(keys) + + # set padded position to zero + if q_mask is not None: + Q = Q * q_mask[:, :, None, None] + if kv_mask is not None: + K = K * kv_mask[:, :, None, None] + values = values * kv_mask[:, :, None, None] + + v_length = values.size(1) + values = values / v_length # prevent fp16 overflow + KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V + Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps) + queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length + + return queried_values.contiguous() \ No newline at end of file diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..5bb8eefd362240a9901a335f0e6e07770ff04567 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/fine_preprocess.py @@ -0,0 +1,59 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops.einops import rearrange, repeat + + +class FinePreprocess(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.cat_c_feat = config['fine_concat_coarse_feat'] + self.W = self.config['fine_window_size'] + + d_model_c = self.config['coarse']['d_model'] + d_model_f = self.config['fine']['d_model'] + self.d_model_f = d_model_f + if self.cat_c_feat: + self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True) + self.merge_feat = nn.Linear(2*d_model_f, d_model_f, bias=True) + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu") + + def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data): + W = self.W + stride = data['hw0_f'][0] // data['hw0_c'][0] + + data.update({'W': W}) + if data['b_ids'].shape[0] == 0: + feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) + feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) + return feat0, feat1 + + # 1. unfold(crop) all local windows + feat_f0_unfold = F.unfold(feat_f0, kernel_size=(W, W), stride=stride, padding=W//2) + feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2) + feat_f1_unfold = F.unfold(feat_f1, kernel_size=(W, W), stride=stride, padding=W//2) + feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2) + + # 2. select only the predicted matches + feat_f0_unfold = feat_f0_unfold[data['b_ids'], data['i_ids']] # [n, ww, cf] + feat_f1_unfold = feat_f1_unfold[data['b_ids'], data['j_ids']] + + # option: use coarse-level loftr feature as context: concat and linear + if self.cat_c_feat: + feat_c_win = self.down_proj(torch.cat([feat_c0[data['b_ids'], data['i_ids']], + feat_c1[data['b_ids'], data['j_ids']]], 0)) # [2n, c] + feat_cf_win = self.merge_feat(torch.cat([ + torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf] + repeat(feat_c_win, 'n c -> n ww c', ww=W**2), # [2n, ww, cf] + ], -1)) + feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0) + + return feat_f0_unfold, feat_f1_unfold diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py new file mode 100644 index 0000000000000000000000000000000000000000..7dcebaa7beee978b9b8abcec8bb1bd2cc6b60870 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/loftr.py @@ -0,0 +1,112 @@ +import copy +import torch +import torch.nn as nn +from .attention import LinearAttention + +class LoFTREncoderLayer(nn.Module): + def __init__(self, + d_model, + nhead, + attention='linear'): + super(LoFTREncoderLayer, self).__init__() + + self.dim = d_model // nhead + self.nhead = nhead + + # multi-head attention + self.q_proj = nn.Linear(d_model, d_model, bias=False) + self.k_proj = nn.Linear(d_model, d_model, bias=False) + self.v_proj = nn.Linear(d_model, d_model, bias=False) + self.attention = LinearAttention() + self.merge = nn.Linear(d_model, d_model, bias=False) + + # feed-forward network + self.mlp = nn.Sequential( + nn.Linear(d_model*2, d_model*2, bias=False), + nn.ReLU(True), + nn.Linear(d_model*2, d_model, bias=False), + ) + + # norm and dropout + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + + def forward(self, x, source, x_mask=None, source_mask=None, type=None, index=0): + """ + Args: + x (torch.Tensor): [N, L, C] + source (torch.Tensor): [N, S, C] + x_mask (torch.Tensor): [N, L] (optional) + source_mask (torch.Tensor): [N, S] (optional) + """ + bs = x.size(0) + query, key, value = x, source, source + + # multi-head attention + query = self.q_proj(query).view( + bs, -1, self.nhead, self.dim) # [N, L, (H, D)] + key = self.k_proj(key).view(bs, -1, self.nhead, + self.dim) # [N, S, (H, D)] + value = self.v_proj(value).view(bs, -1, self.nhead, self.dim) + + message = self.attention( + query, key, value, q_mask=x_mask, kv_mask=source_mask) # [N, L, (H, D)] + message = self.merge(message.view( + bs, -1, self.nhead*self.dim)) # [N, L, C] + message = self.norm1(message) + + # feed-forward network + message = self.mlp(torch.cat([x, message], dim=2)) + message = self.norm2(message) + + return x + message + + +class LocalFeatureTransformer(nn.Module): + """A Local Feature Transformer (LoFTR) module.""" + + def __init__(self, config): + super(LocalFeatureTransformer, self).__init__() + + self.config = config + self.d_model = config['d_model'] + self.nhead = config['nhead'] + self.layer_names = config['layer_names'] + encoder_layer = LoFTREncoderLayer( + config['d_model'], config['nhead'], config['attention']) + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]) + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, feat0, feat1, mask0=None, mask1=None): + """ + Args: + feat0 (torch.Tensor): [N, L, C] + feat1 (torch.Tensor): [N, S, C] + mask0 (torch.Tensor): [N, L] (optional) + mask1 (torch.Tensor): [N, S] (optional) + """ + + assert self.d_model == feat0.size( + 2), "the feature number of src and transformer must be equal" + + index = 0 + for layer, name in zip(self.layers, self.layer_names): + if name == 'self': + feat0 = layer(feat0, feat0, mask0, mask0, + type='self', index=index) + feat1 = layer(feat1, feat1, mask1, mask1) + elif name == 'cross': + feat0 = layer(feat0, feat1, mask0, mask1) + feat1 = layer(feat1, feat0, mask1, mask0, + type='cross', index=index) + index += 1 + else: + raise KeyError + return feat0, feat1 + diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..c398f770833bf2066cda60a7ff546ec29640d433 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py @@ -0,0 +1,244 @@ +import copy +import torch +import torch.nn as nn +import torch.nn.functional as F +from .attention import FullAttention, HierachicalAttention ,layernorm2d + + +class messageLayer_ini(nn.Module): + + def __init__(self, d_model, d_flow,d_value, nhead): + super().__init__() + super(messageLayer_ini, self).__init__() + + self.d_model = d_model + self.d_flow = d_flow + self.d_value=d_value + self.nhead = nhead + self.attention = FullAttention(d_model,nhead) + + self.q_proj = nn.Conv1d(d_model, d_model, kernel_size=1,bias=False) + self.k_proj = nn.Conv1d(d_model, d_model, kernel_size=1,bias=False) + self.v_proj = nn.Conv1d(d_value, d_model, kernel_size=1,bias=False) + self.merge_head=nn.Conv1d(d_model,d_model,kernel_size=1,bias=False) + + self.merge_f= self.merge_f = nn.Sequential( + nn.Conv2d(d_model*2, d_model*2, kernel_size=1, bias=False), + nn.ReLU(True), + nn.Conv2d(d_model*2, d_model, kernel_size=1, bias=False), + ) + + self.norm1 = layernorm2d(d_model) + self.norm2 = layernorm2d(d_model) + + + def forward(self, x0, x1,pos0,pos1,mask0=None,mask1=None): + #x1,x2: b*d*L + x0,x1=self.update(x0,x1,pos1,mask0,mask1),\ + self.update(x1,x0,pos0,mask1,mask0) + return x0,x1 + + + def update(self,f0,f1,pos1,mask0,mask1): + """ + Args: + f0: [N, D, H, W] + f1: [N, D, H, W] + Returns: + f0_new: (N, d, h, w) + """ + bs,h,w=f0.shape[0],f0.shape[2],f0.shape[3] + + f0_flatten,f1_flatten=f0.view(bs,self.d_model,-1),f1.view(bs,self.d_model,-1) + pos1_flatten=pos1.view(bs,self.d_value-self.d_model,-1) + f1_flatten_v=torch.cat([f1_flatten,pos1_flatten],dim=1) + + queries,keys=self.q_proj(f0_flatten),self.k_proj(f1_flatten) + values=self.v_proj(f1_flatten_v).view(bs,self.nhead,self.d_model//self.nhead,-1) + + queried_values=self.attention(queries,keys,values,mask0,mask1) + msg=self.merge_head(queried_values).view(bs,-1,h,w) + msg=self.norm2(self.merge_f(torch.cat([f0,self.norm1(msg)],dim=1))) + return f0+msg + + + +class messageLayer_gla(nn.Module): + + def __init__(self,d_model,d_flow,d_value, + nhead,radius_scale,nsample,update_flow=True): + super().__init__() + self.d_model = d_model + self.d_flow=d_flow + self.d_value=d_value + self.nhead = nhead + self.radius_scale=radius_scale + self.update_flow=update_flow + self.flow_decoder=nn.Sequential( + nn.Conv1d(d_flow, d_flow//2, kernel_size=1, bias=False), + nn.ReLU(True), + nn.Conv1d(d_flow//2, 4, kernel_size=1, bias=False)) + self.attention=HierachicalAttention(d_model,nhead,nsample,radius_scale) + + self.q_proj = nn.Conv1d(d_model, d_model, kernel_size=1,bias=False) + self.k_proj = nn.Conv1d(d_model, d_model, kernel_size=1,bias=False) + self.v_proj = nn.Conv1d(d_value, d_model, kernel_size=1,bias=False) + + d_extra=d_flow if update_flow else 0 + self.merge_f=nn.Sequential( + nn.Conv2d(d_model*2+d_extra, d_model+d_flow, kernel_size=1, bias=False), + nn.ReLU(True), + nn.Conv2d(d_model+d_flow, d_model+d_extra, kernel_size=3,padding=1, bias=False), + ) + self.norm1 = layernorm2d(d_model) + self.norm2 = layernorm2d(d_model+d_extra) + + def forward(self, x0, x1, flow_feature0,flow_feature1,pos0,pos1,mask0=None,mask1=None,ds0=[4,4],ds1=[4,4]): + """ + Args: + x0 (torch.Tensor): [B, C, H, W] + x1 (torch.Tensor): [B, C, H, W] + flow_feature0 (torch.Tensor): [B, C', H, W] + flow_feature1 (torch.Tensor): [B, C', H, W] + """ + flow0,flow1=self.decode_flow(flow_feature0,flow_feature1.shape[2:]),self.decode_flow(flow_feature1,flow_feature0.shape[2:]) + x0_new,flow_feature0_new=self.update(x0,x1,flow0.detach(),flow_feature0,pos1,mask0,mask1,ds0,ds1) + x1_new,flow_feature1_new=self.update(x1,x0,flow1.detach(),flow_feature1,pos0,mask1,mask0,ds1,ds0) + return x0_new,x1_new,flow_feature0_new,flow_feature1_new,flow0,flow1 + + def update(self,x0,x1,flow0,flow_feature0,pos1,mask0,mask1,ds0,ds1): + bs=x0.shape[0] + queries,keys=self.q_proj(x0.view(bs,self.d_model,-1)),self.k_proj(x1.view(bs,self.d_model,-1)) + x1_pos=torch.cat([x1,pos1],dim=1) + values=self.v_proj(x1_pos.view(bs,self.d_value,-1)) + msg=self.attention(queries,keys,values,flow0,x0.shape[2:],x1.shape[2:],mask0,mask1,ds0,ds1) + + if self.update_flow: + update_feature=torch.cat([x0,flow_feature0],dim=1) + else: + update_feature=x0 + msg=self.norm2(self.merge_f(torch.cat([update_feature,self.norm1(msg)],dim=1))) + update_feature=update_feature+msg + + x0_new,flow_feature0_new=update_feature[:,:self.d_model],update_feature[:,self.d_model:] + return x0_new,flow_feature0_new + + def decode_flow(self,flow_feature,kshape): + bs,h,w=flow_feature.shape[0],flow_feature.shape[2],flow_feature.shape[3] + scale_factor=torch.tensor([kshape[1],kshape[0]]).cuda()[None,None,None] + flow=self.flow_decoder(flow_feature.view(bs,-1,h*w)).permute(0,2,1).view(bs,h,w,4) + flow_coordinates=torch.sigmoid(flow[:,:,:,:2])*scale_factor + flow_var=flow[:,:,:,2:] + flow=torch.cat([flow_coordinates,flow_var],dim=-1) #B*H*W*4 + return flow + + +class flow_initializer(nn.Module): + + def __init__(self, dim, dim_flow, nhead, layer_num): + super().__init__() + self.layer_num= layer_num + self.dim = dim + self.dim_flow = dim_flow + + encoder_layer = messageLayer_ini( + dim ,dim_flow,dim+dim_flow , nhead) + self.layers_coarse = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(layer_num)]) + self.decoupler = nn.Conv2d( + self.dim, self.dim+self.dim_flow, kernel_size=1) + self.up_merge = nn.Conv2d(2*dim, dim, kernel_size=1) + + def forward(self, feat0, feat1,pos0,pos1,mask0=None,mask1=None,ds0=[4,4],ds1=[4,4]): + # feat0: [B, C, H0, W0] + # feat1: [B, C, H1, W1] + # use low-res MHA to initialize flow feature + bs = feat0.size(0) + h0,w0,h1,w1=feat0.shape[2],feat0.shape[3],feat1.shape[2],feat1.shape[3] + + # coarse level + sub_feat0, sub_feat1 = F.avg_pool2d(feat0, ds0, stride=ds0), \ + F.avg_pool2d(feat1, ds1, stride=ds1) + + sub_pos0,sub_pos1=F.avg_pool2d(pos0, ds0, stride=ds0), \ + F.avg_pool2d(pos1, ds1, stride=ds1) + + if mask0 is not None: + mask0,mask1=-F.max_pool2d(-mask0.view(bs,1,h0,w0),ds0,stride=ds0).view(bs,-1),\ + -F.max_pool2d(-mask1.view(bs,1,h1,w1),ds1,stride=ds1).view(bs,-1) + + for layer in self.layers_coarse: + sub_feat0, sub_feat1 = layer(sub_feat0, sub_feat1,sub_pos0,sub_pos1,mask0,mask1) + # decouple flow and visual features + decoupled_feature0, decoupled_feature1 = self.decoupler(sub_feat0),self.decoupler(sub_feat1) + + sub_feat0, sub_flow_feature0 = decoupled_feature0[:,:self.dim], decoupled_feature0[:, self.dim:] + sub_feat1, sub_flow_feature1 = decoupled_feature1[:,:self.dim], decoupled_feature1[:, self.dim:] + update_feat0, flow_feature0 = F.upsample(sub_feat0, scale_factor=ds0, mode='bilinear'),\ + F.upsample(sub_flow_feature0, scale_factor=ds0, mode='bilinear') + update_feat1, flow_feature1 = F.upsample(sub_feat1, scale_factor=ds1, mode='bilinear'),\ + F.upsample(sub_flow_feature1, scale_factor=ds1, mode='bilinear') + + feat0 = feat0+self.up_merge(torch.cat([feat0, update_feat0], dim=1)) + feat1 = feat1+self.up_merge(torch.cat([feat1, update_feat1], dim=1)) + + return feat0,feat1,flow_feature0,flow_feature1 #b*c*h*w + + +class LocalFeatureTransformer_Flow(nn.Module): + """A Local Feature Transformer (LoFTR) module.""" + + def __init__(self, config): + super(LocalFeatureTransformer_Flow, self).__init__() + + self.config = config + self.d_model = config['d_model'] + self.nhead = config['nhead'] + + self.pos_transform=nn.Conv2d(config['d_model'],config['d_flow'],kernel_size=1,bias=False) + self.ini_layer = flow_initializer(self.d_model, config['d_flow'], config['nhead'],config['ini_layer_num']) + + encoder_layer = messageLayer_gla( + config['d_model'], config['d_flow'], config['d_flow']+config['d_model'], config['nhead'],config['radius_scale'],config['nsample']) + encoder_layer_last=messageLayer_gla( + config['d_model'], config['d_flow'], config['d_flow']+config['d_model'], config['nhead'],config['radius_scale'],config['nsample'],update_flow=False) + self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(config['layer_num']-1)]+[encoder_layer_last]) + self._reset_parameters() + + def _reset_parameters(self): + for name,p in self.named_parameters(): + if 'temp' in name or 'sample_offset' in name: + continue + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, feat0, feat1,pos0,pos1,mask0=None,mask1=None,ds0=[4,4],ds1=[4,4]): + """ + Args: + feat0 (torch.Tensor): [N, C, H, W] + feat1 (torch.Tensor): [N, C, H, W] + pos1,pos2: [N, C, H, W] + Outputs: + feat0: [N,-1,C] + feat1: [N,-1,C] + flow_list: [L,N,H,W,4]*1(2) + """ + bs = feat0.size(0) + + pos0,pos1=self.pos_transform(pos0),self.pos_transform(pos1) + pos0,pos1=pos0.expand(bs,-1,-1,-1),pos1.expand(bs,-1,-1,-1) + assert self.d_model == feat0.size( + 1), "the feature number of src and transformer must be equal" + + flow_list=[[],[]]# [px,py,sx,sy] + if mask0 is not None: + mask0,mask1=mask0[:,None].float(),mask1[:,None].float() + feat0,feat1, flow_feature0, flow_feature1 = self.ini_layer(feat0, feat1,pos0,pos1,mask0,mask1,ds0,ds1) + for layer in self.layers: + feat0,feat1,flow_feature0,flow_feature1,flow0,flow1=layer(feat0,feat1,flow_feature0,flow_feature1,pos0,pos1,mask0,mask1,ds0,ds1) + flow_list[0].append(flow0) + flow_list[1].append(flow1) + flow_list[0]=torch.stack(flow_list[0],dim=0) + flow_list[1]=torch.stack(flow_list[1],dim=0) + feat0, feat1 = feat0.permute(0, 2, 3, 1).view(bs, -1, self.d_model), feat1.permute(0, 2, 3, 1).view(bs, -1, self.d_model) + return feat0, feat1, flow_list \ No newline at end of file diff --git a/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py b/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py new file mode 100644 index 0000000000000000000000000000000000000000..01b797a420cf5ccea5b53fee3ceda8b5e157573f --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/aspanformer.py @@ -0,0 +1,133 @@ +import torch +import torch.nn as nn +from torchvision import transforms +from einops.einops import rearrange + +from .backbone import build_backbone +from .utils.position_encoding import PositionEncodingSine +from .aspan_module import LocalFeatureTransformer_Flow, LocalFeatureTransformer, FinePreprocess +from .utils.coarse_matching import CoarseMatching +from .utils.fine_matching import FineMatching + + +class ASpanFormer(nn.Module): + def __init__(self, config): + super().__init__() + # Misc + self.config = config + + # Modules + self.backbone = build_backbone(config) + self.pos_encoding = PositionEncodingSine( + config['coarse']['d_model'],pre_scaling=[config['coarse']['train_res'],config['coarse']['test_res']]) + self.loftr_coarse = LocalFeatureTransformer_Flow(config['coarse']) + self.coarse_matching = CoarseMatching(config['match_coarse']) + self.fine_preprocess = FinePreprocess(config) + self.loftr_fine = LocalFeatureTransformer(config["fine"]) + self.fine_matching = FineMatching() + self.coarsest_level=config['coarse']['coarsest_level'] + + def forward(self, data, online_resize=False): + """ + Update: + data (dict): { + 'image0': (torch.Tensor): (N, 1, H, W) + 'image1': (torch.Tensor): (N, 1, H, W) + 'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position + 'mask1'(optional) : (torch.Tensor): (N, H, W) + } + """ + if online_resize: + assert data['image0'].shape[0]==1 and data['image1'].shape[1]==1 + self.resize_input(data,self.config['coarse']['train_res']) + else: + data['pos_scale0'],data['pos_scale1']=None,None + + # 1. Local Feature CNN + data.update({ + 'bs': data['image0'].size(0), + 'hw0_i': data['image0'].shape[2:], 'hw1_i': data['image1'].shape[2:] + }) + + if data['hw0_i'] == data['hw1_i']: # faster & better BN convergence + feats_c, feats_f = self.backbone( + torch.cat([data['image0'], data['image1']], dim=0)) + (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split( + data['bs']), feats_f.split(data['bs']) + else: # handle different input shapes + (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone( + data['image0']), self.backbone(data['image1']) + + data.update({ + 'hw0_c': feat_c0.shape[2:], 'hw1_c': feat_c1.shape[2:], + 'hw0_f': feat_f0.shape[2:], 'hw1_f': feat_f1.shape[2:] + }) + + # 2. coarse-level loftr module + # add featmap with positional encoding, then flatten it to sequence [N, HW, C] + [feat_c0, pos_encoding0], [feat_c1, pos_encoding1] = self.pos_encoding(feat_c0,data['pos_scale0']), self.pos_encoding(feat_c1,data['pos_scale1']) + feat_c0 = rearrange(feat_c0, 'n c h w -> n c h w ') + feat_c1 = rearrange(feat_c1, 'n c h w -> n c h w ') + + #TODO:adjust ds + ds0=[int(data['hw0_c'][0]/self.coarsest_level[0]),int(data['hw0_c'][1]/self.coarsest_level[1])] + ds1=[int(data['hw1_c'][0]/self.coarsest_level[0]),int(data['hw1_c'][1]/self.coarsest_level[1])] + if online_resize: + ds0,ds1=[4,4],[4,4] + + mask_c0 = mask_c1 = None # mask is useful in training + if 'mask0' in data: + mask_c0, mask_c1 = data['mask0'].flatten( + -2), data['mask1'].flatten(-2) + feat_c0, feat_c1, flow_list = self.loftr_coarse( + feat_c0, feat_c1,pos_encoding0,pos_encoding1,mask_c0,mask_c1,ds0,ds1) + + # 3. match coarse-level and register predicted offset + self.coarse_matching(feat_c0, feat_c1, flow_list,data, + mask_c0=mask_c0, mask_c1=mask_c1) + + # 4. fine-level refinement + feat_f0_unfold, feat_f1_unfold = self.fine_preprocess( + feat_f0, feat_f1, feat_c0, feat_c1, data) + if feat_f0_unfold.size(0) != 0: # at least one coarse level predicted + feat_f0_unfold, feat_f1_unfold = self.loftr_fine( + feat_f0_unfold, feat_f1_unfold) + + # 5. match fine-level + self.fine_matching(feat_f0_unfold, feat_f1_unfold, data) + + # 6. resize match coordinates back to input resolution + if online_resize: + data['mkpts0_f']*=data['online_resize_scale0'] + data['mkpts1_f']*=data['online_resize_scale1'] + + def load_state_dict(self, state_dict, *args, **kwargs): + for k in list(state_dict.keys()): + if k.startswith('matcher.'): + if 'sample_offset' in k: + state_dict.pop(k) + else: + state_dict[k.replace('matcher.', '', 1)] = state_dict.pop(k) + return super().load_state_dict(state_dict, *args, **kwargs) + + def resize_input(self,data,train_res,df=32): + h0,w0,h1,w1=data['image0'].shape[2],data['image0'].shape[3],data['image1'].shape[2],data['image1'].shape[3] + data['image0'],data['image1']=self.resize_df(data['image0'],df),self.resize_df(data['image1'],df) + + if len(train_res)==1: + train_res_h=train_res_w=train_res + else: + train_res_h,train_res_w=train_res[0],train_res[1] + data['pos_scale0'],data['pos_scale1']=[train_res_h/data['image0'].shape[2],train_res_w/data['image0'].shape[3]],\ + [train_res_h/data['image1'].shape[2],train_res_w/data['image1'].shape[3]] + data['online_resize_scale0'],data['online_resize_scale1']=torch.tensor([w0/data['image0'].shape[3],h0/data['image0'].shape[2]])[None].cuda(),\ + torch.tensor([w1/data['image1'].shape[3],h1/data['image1'].shape[2]])[None].cuda() + + def resize_df(self,image,df=32): + h,w=image.shape[2],image.shape[3] + h_new,w_new=h//df*df,w//df*df + if h!=h_new or w!=w_new: + img_new=transforms.Resize([h_new,w_new]).forward(image) + else: + img_new=image + return img_new diff --git a/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py b/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e731b3f53ab367c89ef0ea8e1cbffb0d990775 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/backbone/__init__.py @@ -0,0 +1,11 @@ +from .resnet_fpn import ResNetFPN_8_2, ResNetFPN_16_4 + + +def build_backbone(config): + if config['backbone_type'] == 'ResNetFPN': + if config['resolution'] == (8, 2): + return ResNetFPN_8_2(config['resnetfpn']) + elif config['resolution'] == (16, 4): + return ResNetFPN_16_4(config['resnetfpn']) + else: + raise ValueError(f"LOFTR.BACKBONE_TYPE {config['backbone_type']} not supported.") diff --git a/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py b/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..985e5b3f273a51e51447a8025ca3aadbe46752eb --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/backbone/resnet_fpn.py @@ -0,0 +1,199 @@ +import torch.nn as nn +import torch.nn.functional as F + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution without padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_planes, planes, stride=1): + super().__init__() + self.conv1 = conv3x3(in_planes, planes, stride) + self.conv2 = conv3x3(planes, planes) + self.bn1 = nn.BatchNorm2d(planes) + self.bn2 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + + if stride == 1: + self.downsample = None + else: + self.downsample = nn.Sequential( + conv1x1(in_planes, planes, stride=stride), + nn.BatchNorm2d(planes) + ) + + def forward(self, x): + y = x + y = self.relu(self.bn1(self.conv1(y))) + y = self.bn2(self.conv2(y)) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x+y) + + +class ResNetFPN_8_2(nn.Module): + """ + ResNet+FPN, output resolution are 1/8 and 1/2. + Each block has 2 layers. + """ + + def __init__(self, config): + super().__init__() + # Config + block = BasicBlock + initial_dim = config['initial_dim'] + block_dims = config['block_dims'] + + # Class Variable + self.in_planes = initial_dim + + # Networks + self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(initial_dim) + self.relu = nn.ReLU(inplace=True) + + self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 + self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 + self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 + + # 3. FPN upsample + self.layer3_outconv = conv1x1(block_dims[2], block_dims[2]) + self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) + self.layer2_outconv2 = nn.Sequential( + conv3x3(block_dims[2], block_dims[2]), + nn.BatchNorm2d(block_dims[2]), + nn.LeakyReLU(), + conv3x3(block_dims[2], block_dims[1]), + ) + self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) + self.layer1_outconv2 = nn.Sequential( + conv3x3(block_dims[1], block_dims[1]), + nn.BatchNorm2d(block_dims[1]), + nn.LeakyReLU(), + conv3x3(block_dims[1], block_dims[0]), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, dim, stride=1): + layer1 = block(self.in_planes, dim, stride=stride) + layer2 = block(dim, dim, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + # ResNet Backbone + x0 = self.relu(self.bn1(self.conv1(x))) + x1 = self.layer1(x0) # 1/2 + x2 = self.layer2(x1) # 1/4 + x3 = self.layer3(x2) # 1/8 + + # FPN + x3_out = self.layer3_outconv(x3) + + x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) + x2_out = self.layer2_outconv(x2) + x2_out = self.layer2_outconv2(x2_out+x3_out_2x) + + x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True) + x1_out = self.layer1_outconv(x1) + x1_out = self.layer1_outconv2(x1_out+x2_out_2x) + + return [x3_out, x1_out] + + +class ResNetFPN_16_4(nn.Module): + """ + ResNet+FPN, output resolution are 1/16 and 1/4. + Each block has 2 layers. + """ + + def __init__(self, config): + super().__init__() + # Config + block = BasicBlock + initial_dim = config['initial_dim'] + block_dims = config['block_dims'] + + # Class Variable + self.in_planes = initial_dim + + # Networks + self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(initial_dim) + self.relu = nn.ReLU(inplace=True) + + self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 + self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 + self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 + self.layer4 = self._make_layer(block, block_dims[3], stride=2) # 1/16 + + # 3. FPN upsample + self.layer4_outconv = conv1x1(block_dims[3], block_dims[3]) + self.layer3_outconv = conv1x1(block_dims[2], block_dims[3]) + self.layer3_outconv2 = nn.Sequential( + conv3x3(block_dims[3], block_dims[3]), + nn.BatchNorm2d(block_dims[3]), + nn.LeakyReLU(), + conv3x3(block_dims[3], block_dims[2]), + ) + + self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) + self.layer2_outconv2 = nn.Sequential( + conv3x3(block_dims[2], block_dims[2]), + nn.BatchNorm2d(block_dims[2]), + nn.LeakyReLU(), + conv3x3(block_dims[2], block_dims[1]), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, dim, stride=1): + layer1 = block(self.in_planes, dim, stride=stride) + layer2 = block(dim, dim, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + # ResNet Backbone + x0 = self.relu(self.bn1(self.conv1(x))) + x1 = self.layer1(x0) # 1/2 + x2 = self.layer2(x1) # 1/4 + x3 = self.layer3(x2) # 1/8 + x4 = self.layer4(x3) # 1/16 + + # FPN + x4_out = self.layer4_outconv(x4) + + x4_out_2x = F.interpolate(x4_out, scale_factor=2., mode='bilinear', align_corners=True) + x3_out = self.layer3_outconv(x3) + x3_out = self.layer3_outconv2(x3_out+x4_out_2x) + + x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) + x2_out = self.layer2_outconv(x2) + x2_out = self.layer2_outconv2(x2_out+x3_out_2x) + + return [x4_out, x2_out] diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py b/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..953ee55a09144a4ce0099e709f3a992d021aa0ab --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/coarse_matching.py @@ -0,0 +1,331 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops.einops import rearrange + +from time import time + +INF = 1e9 + +def mask_border(m, b: int, v): + """ Mask borders with value + Args: + m (torch.Tensor): [N, H0, W0, H1, W1] + b (int) + v (m.dtype) + """ + if b <= 0: + return + + m[:, :b] = v + m[:, :, :b] = v + m[:, :, :, :b] = v + m[:, :, :, :, :b] = v + m[:, -b:] = v + m[:, :, -b:] = v + m[:, :, :, -b:] = v + m[:, :, :, :, -b:] = v + + +def mask_border_with_padding(m, bd, v, p_m0, p_m1): + if bd <= 0: + return + + m[:, :bd] = v + m[:, :, :bd] = v + m[:, :, :, :bd] = v + m[:, :, :, :, :bd] = v + + h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int() + h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int() + for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)): + m[b_idx, h0 - bd:] = v + m[b_idx, :, w0 - bd:] = v + m[b_idx, :, :, h1 - bd:] = v + m[b_idx, :, :, :, w1 - bd:] = v + + +def compute_max_candidates(p_m0, p_m1): + """Compute the max candidates of all pairs within a batch + + Args: + p_m0, p_m1 (torch.Tensor): padded masks + """ + h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0] + h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0] + max_cand = torch.sum( + torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0]) + return max_cand + + +class CoarseMatching(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + # general config + self.thr = config['thr'] + self.border_rm = config['border_rm'] + # -- # for trainig fine-level LoFTR + self.train_coarse_percent = config['train_coarse_percent'] + self.train_pad_num_gt_min = config['train_pad_num_gt_min'] + + # we provide 2 options for differentiable matching + self.match_type = config['match_type'] + if self.match_type == 'dual_softmax': + self.temperature=nn.parameter.Parameter(torch.tensor(10.), requires_grad=True) + elif self.match_type == 'sinkhorn': + try: + from .superglue import log_optimal_transport + except ImportError: + raise ImportError("download superglue.py first!") + self.log_optimal_transport = log_optimal_transport + self.bin_score = nn.Parameter( + torch.tensor(config['skh_init_bin_score'], requires_grad=True)) + self.skh_iters = config['skh_iters'] + self.skh_prefilter = config['skh_prefilter'] + else: + raise NotImplementedError() + + def forward(self, feat_c0, feat_c1, flow_list, data, mask_c0=None, mask_c1=None): + """ + Args: + feat0 (torch.Tensor): [N, L, C] + feat1 (torch.Tensor): [N, S, C] + offset: [layer, B, H, W, 4] (*2) + data (dict) + mask_c0 (torch.Tensor): [N, L] (optional) + mask_c1 (torch.Tensor): [N, S] (optional) + Update: + data (dict): { + 'b_ids' (torch.Tensor): [M'], + 'i_ids' (torch.Tensor): [M'], + 'j_ids' (torch.Tensor): [M'], + 'gt_mask' (torch.Tensor): [M'], + 'mkpts0_c' (torch.Tensor): [M, 2], + 'mkpts1_c' (torch.Tensor): [M, 2], + 'mconf' (torch.Tensor): [M]} + NOTE: M' != M during training. + """ + N, L, S, C = feat_c0.size(0), feat_c0.size(1), feat_c1.size(1), feat_c0.size(2) + # normalize + feat_c0, feat_c1 = map(lambda feat: feat / feat.shape[-1]**.5, + [feat_c0, feat_c1]) + + if self.match_type == 'dual_softmax': + sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, + feat_c1) * self.temperature + if mask_c0 is not None: + sim_matrix.masked_fill_( + ~(mask_c0[..., None] * mask_c1[:, None]).bool(), + -INF) + conf_matrix = F.softmax(sim_matrix, 1) * F.softmax(sim_matrix, 2) + + elif self.match_type == 'sinkhorn': + # sinkhorn, dustbin included + sim_matrix = torch.einsum("nlc,nsc->nls", feat_c0, feat_c1) + if mask_c0 is not None: + sim_matrix[:, :L, :S].masked_fill_( + ~(mask_c0[..., None] * mask_c1[:, None]).bool(), + -INF) + + # build uniform prior & use sinkhorn + log_assign_matrix = self.log_optimal_transport( + sim_matrix, self.bin_score, self.skh_iters) + assign_matrix = log_assign_matrix.exp() + conf_matrix = assign_matrix[:, :-1, :-1] + + # filter prediction with dustbin score (only in evaluation mode) + if not self.training and self.skh_prefilter: + filter0 = (assign_matrix.max(dim=2)[1] == S)[:, :-1] # [N, L] + filter1 = (assign_matrix.max(dim=1)[1] == L)[:, :-1] # [N, S] + conf_matrix[filter0[..., None].repeat(1, 1, S)] = 0 + conf_matrix[filter1[:, None].repeat(1, L, 1)] = 0 + + if self.config['sparse_spvs']: + data.update({'conf_matrix_with_bin': assign_matrix.clone()}) + + data.update({'conf_matrix': conf_matrix}) + # predict coarse matches from conf_matrix + data.update(**self.get_coarse_match(conf_matrix, data)) + + #update predicted offset + if flow_list[0].shape[2]==flow_list[1].shape[2] and flow_list[0].shape[3]==flow_list[1].shape[3]: + flow_list=torch.stack(flow_list,dim=0) + data.update({'predict_flow':flow_list}) #[2*L*B*H*W*4] + self.get_offset_match(flow_list,data,mask_c0,mask_c1) + + @torch.no_grad() + def get_coarse_match(self, conf_matrix, data): + """ + Args: + conf_matrix (torch.Tensor): [N, L, S] + data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c'] + Returns: + coarse_matches (dict): { + 'b_ids' (torch.Tensor): [M'], + 'i_ids' (torch.Tensor): [M'], + 'j_ids' (torch.Tensor): [M'], + 'gt_mask' (torch.Tensor): [M'], + 'm_bids' (torch.Tensor): [M], + 'mkpts0_c' (torch.Tensor): [M, 2], + 'mkpts1_c' (torch.Tensor): [M, 2], + 'mconf' (torch.Tensor): [M]} + """ + axes_lengths = { + 'h0c': data['hw0_c'][0], + 'w0c': data['hw0_c'][1], + 'h1c': data['hw1_c'][0], + 'w1c': data['hw1_c'][1] + } + _device = conf_matrix.device + # 1. confidence thresholding + mask = conf_matrix > self.thr + mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', + **axes_lengths) + if 'mask0' not in data: + mask_border(mask, self.border_rm, False) + else: + mask_border_with_padding(mask, self.border_rm, False, + data['mask0'], data['mask1']) + mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', + **axes_lengths) + + # 2. mutual nearest + mask = mask \ + * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ + * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) + + # 3. find all valid coarse matches + # this only works when at most one `True` in each row + mask_v, all_j_ids = mask.max(dim=2) + b_ids, i_ids = torch.where(mask_v) + j_ids = all_j_ids[b_ids, i_ids] + mconf = conf_matrix[b_ids, i_ids, j_ids] + + # 4. Random sampling of training samples for fine-level LoFTR + # (optional) pad samples with gt coarse-level matches + if self.training: + # NOTE: + # The sampling is performed across all pairs in a batch without manually balancing + # #samples for fine-level increases w.r.t. batch_size + if 'mask0' not in data: + num_candidates_max = mask.size(0) * max( + mask.size(1), mask.size(2)) + else: + num_candidates_max = compute_max_candidates( + data['mask0'], data['mask1']) + num_matches_train = int(num_candidates_max * + self.train_coarse_percent) + num_matches_pred = len(b_ids) + assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches" + + # pred_indices is to select from prediction + if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min: + pred_indices = torch.arange(num_matches_pred, device=_device) + else: + pred_indices = torch.randint( + num_matches_pred, + (num_matches_train - self.train_pad_num_gt_min, ), + device=_device) + + # gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200) + gt_pad_indices = torch.randint( + len(data['spv_b_ids']), + (max(num_matches_train - num_matches_pred, + self.train_pad_num_gt_min), ), + device=_device) + mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero + + b_ids, i_ids, j_ids, mconf = map( + lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], + dim=0), + *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']], + [j_ids, data['spv_j_ids']], [mconf, mconf_gt])) + + # These matches select patches that feed into fine-level network + coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids} + + # 4. Update with matches in original image resolution + scale = data['hw0_i'][0] / data['hw0_c'][0] + scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale + scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale + mkpts0_c = torch.stack( + [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], + dim=1) * scale0 + mkpts1_c = torch.stack( + [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], + dim=1) * scale1 + + # These matches is the current prediction (for visualization) + coarse_matches.update({ + 'gt_mask': mconf == 0, + 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches + 'mkpts0_c': mkpts0_c[mconf != 0], + 'mkpts1_c': mkpts1_c[mconf != 0], + 'mconf': mconf[mconf != 0] + }) + + return coarse_matches + + @torch.no_grad() + def get_offset_match(self, flow_list, data,mask1,mask2): + """ + Args: + offset (torch.Tensor): [L, B, H, W, 2] + data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c'] + Returns: + coarse_matches (dict): { + 'm_bids' (torch.Tensor): [M], + 'mkpts0_c' (torch.Tensor): [M, 2], + 'mkpts1_c' (torch.Tensor): [M, 2], + 'mconf' (torch.Tensor): [M]} + """ + offset1=flow_list[0] + bs,layer_num=offset1.shape[1],offset1.shape[0] + + #left side + offset1=offset1.view(layer_num,bs,-1,4) + conf1=offset1[:,:,:,2:].mean(dim=-1) + if mask1 is not None: + conf1.masked_fill_(~mask1.bool()[None].expand(layer_num,-1,-1),100) + offset1=offset1[:,:,:,:2] + self.get_offset_match_work(offset1,conf1,data,'left') + + #rihgt side + if len(flow_list)==2: + offset2=flow_list[1].view(layer_num,bs,-1,4) + conf2=offset2[:,:,:,2:].mean(dim=-1) + if mask2 is not None: + conf2.masked_fill_(~mask2.bool()[None].expand(layer_num,-1,-1),100) + offset2=offset2[:,:,:,:2] + self.get_offset_match_work(offset2,conf2,data,'right') + + + @torch.no_grad() + def get_offset_match_work(self, offset,conf, data,side): + bs,layer_num=offset.shape[1],offset.shape[0] + # 1. confidence thresholding + mask_conf= conf<2 + for index in range(bs): + mask_conf[:,index,0]=True #safe guard in case that no match survives + # 3. find offset matches + scale = data['hw0_i'][0] / data['hw0_c'][0] + l_ids,b_ids,i_ids = torch.where(mask_conf) + j_coor=offset[l_ids,b_ids,i_ids,:2] *scale#[N,2] + i_coor=torch.stack([i_ids%data['hw0_c'][1],i_ids//data['hw0_c'][1]],dim=1)*scale + #i_coor=torch.as_tensor([[index%data['hw0_c'][1],index//data['hw0_c'][1]] for index in i_ids]).cuda().float()*scale #[N,2] + # These matches is the current prediction (for visualization) + data.update({ + 'offset_bids_'+side: b_ids, # mconf == 0 => gt matches + 'offset_lids_'+side: l_ids, + 'conf'+side: conf[mask_conf] + }) + + if side=='right': + data.update({'offset_kpts0_f_'+side: j_coor.detach(), + 'offset_kpts1_f_'+side: i_coor}) + else: + data.update({'offset_kpts0_f_'+side: i_coor, + 'offset_kpts1_f_'+side: j_coor.detach()}) + + diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py b/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py new file mode 100644 index 0000000000000000000000000000000000000000..fdc57e84936c805cb387b6239ca4a5ff6154e22e --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/cvpr_ds_config.py @@ -0,0 +1,50 @@ +from yacs.config import CfgNode as CN + + +def lower_config(yacs_cfg): + if not isinstance(yacs_cfg, CN): + return yacs_cfg + return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} + + +_CN = CN() +_CN.BACKBONE_TYPE = 'ResNetFPN' +_CN.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)] +_CN.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd +_CN.FINE_CONCAT_COARSE_FEAT = True + +# 1. LoFTR-backbone (local feature CNN) config +_CN.RESNETFPN = CN() +_CN.RESNETFPN.INITIAL_DIM = 128 +_CN.RESNETFPN.BLOCK_DIMS = [128, 196, 256] # s1, s2, s3 + +# 2. LoFTR-coarse module config +_CN.COARSE = CN() +_CN.COARSE.D_MODEL = 256 +_CN.COARSE.D_FFN = 256 +_CN.COARSE.NHEAD = 8 +_CN.COARSE.LAYER_NAMES = ['self', 'cross'] * 4 +_CN.COARSE.ATTENTION = 'linear' # options: ['linear', 'full'] +_CN.COARSE.TEMP_BUG_FIX = False + +# 3. Coarse-Matching config +_CN.MATCH_COARSE = CN() +_CN.MATCH_COARSE.THR = 0.1 +_CN.MATCH_COARSE.BORDER_RM = 2 +_CN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' # options: ['dual_softmax, 'sinkhorn'] +_CN.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1 +_CN.MATCH_COARSE.SKH_ITERS = 3 +_CN.MATCH_COARSE.SKH_INIT_BIN_SCORE = 1.0 +_CN.MATCH_COARSE.SKH_PREFILTER = True +_CN.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.4 # training tricks: save GPU memory +_CN.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock + +# 4. LoFTR-fine module config +_CN.FINE = CN() +_CN.FINE.D_MODEL = 128 +_CN.FINE.D_FFN = 128 +_CN.FINE.NHEAD = 8 +_CN.FINE.LAYER_NAMES = ['self', 'cross'] * 1 +_CN.FINE.ATTENTION = 'linear' + +default_cfg = lower_config(_CN) diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py b/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..6e77aded52e1eb5c01e22c2738104f3b09d6922a --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/fine_matching.py @@ -0,0 +1,74 @@ +import math +import torch +import torch.nn as nn + +from kornia.geometry.subpix import dsnt +from kornia.utils.grid import create_meshgrid + + +class FineMatching(nn.Module): + """FineMatching with s2d paradigm""" + + def __init__(self): + super().__init__() + + def forward(self, feat_f0, feat_f1, data): + """ + Args: + feat0 (torch.Tensor): [M, WW, C] + feat1 (torch.Tensor): [M, WW, C] + data (dict) + Update: + data (dict):{ + 'expec_f' (torch.Tensor): [M, 3], + 'mkpts0_f' (torch.Tensor): [M, 2], + 'mkpts1_f' (torch.Tensor): [M, 2]} + """ + M, WW, C = feat_f0.shape + W = int(math.sqrt(WW)) + scale = data['hw0_i'][0] / data['hw0_f'][0] + self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale + + # corner case: if no coarse matches found + if M == 0: + assert self.training == False, "M is always >0, when training, see coarse_matching.py" + # logger.warning('No matches found in coarse-level.') + data.update({ + 'expec_f': torch.empty(0, 3, device=feat_f0.device), + 'mkpts0_f': data['mkpts0_c'], + 'mkpts1_f': data['mkpts1_c'], + }) + return + + feat_f0_picked = feat_f0_picked = feat_f0[:, WW//2, :] + sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1) + softmax_temp = 1. / C**.5 + heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1).view(-1, W, W) + + # compute coordinates from heatmap + coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2] + grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2] + + # compute std over + var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords_normalized**2 # [M, 2] + std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability + + # for fine-level supervision + data.update({'expec_f': torch.cat([coords_normalized, std.unsqueeze(1)], -1)}) + + # compute absolute kpt coords + self.get_fine_match(coords_normalized, data) + + @torch.no_grad() + def get_fine_match(self, coords_normed, data): + W, WW, C, scale = self.W, self.WW, self.C, self.scale + + # mkpts0_f and mkpts1_f + mkpts0_f = data['mkpts0_c'] + scale1 = scale * data['scale1'][data['b_ids']] if 'scale0' in data else scale + mkpts1_f = data['mkpts1_c'] + (coords_normed * (W // 2) * scale1)[:len(data['mconf'])] + + data.update({ + "mkpts0_f": mkpts0_f, + "mkpts1_f": mkpts1_f + }) diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py b/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..f95cdb65b48324c4f4ceb20231b1bed992b41116 --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/geometry.py @@ -0,0 +1,54 @@ +import torch + + +@torch.no_grad() +def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1): + """ Warp kpts0 from I0 to I1 with depth, K and Rt + Also check covisibility and depth consistency. + Depth is consistent if relative error < 0.2 (hard-coded). + + Args: + kpts0 (torch.Tensor): [N, L, 2] - , + depth0 (torch.Tensor): [N, H, W], + depth1 (torch.Tensor): [N, H, W], + T_0to1 (torch.Tensor): [N, 3, 4], + K0 (torch.Tensor): [N, 3, 3], + K1 (torch.Tensor): [N, 3, 3], + Returns: + calculable_mask (torch.Tensor): [N, L] + warped_keypoints0 (torch.Tensor): [N, L, 2] + """ + kpts0_long = kpts0.round().long() + + # Sample depth, get calculable_mask on depth != 0 + kpts0_depth = torch.stack( + [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0 + ) # (N, L) + nonzero_mask = kpts0_depth != 0 + + # Unproject + kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3) + kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) + + # Rigid Transform + w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L) + w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] + + # Project + w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) + w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth + + # Covisible Check + h, w = depth1.shape[1:3] + covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \ + (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1) + w_kpts0_long = w_kpts0.long() + w_kpts0_long[~covisible_mask, :] = 0 + + w_kpts0_depth = torch.stack( + [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0 + ) # (N, L) + consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 + valid_mask = nonzero_mask * covisible_mask * consistent_mask + + return valid_mask, w_kpts0 diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py b/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py new file mode 100644 index 0000000000000000000000000000000000000000..07d384ae18370acb99ef00a788f628c967249ace --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/position_encoding.py @@ -0,0 +1,61 @@ +import math +import torch +from torch import nn + + +class PositionEncodingSine(nn.Module): + """ + This is a sinusoidal position encoding that generalized to 2-dimensional images + """ + + def __init__(self, d_model, max_shape=(256, 256),pre_scaling=None): + """ + Args: + max_shape (tuple): for 1/8 featmap, the max length of 256 corresponds to 2048 pixels + temp_bug_fix (bool): As noted in this [issue](https://github.com/zju3dv/LoFTR/issues/41), + the original implementation of LoFTR includes a bug in the pos-enc impl, which has little impact + on the final performance. For now, we keep both impls for backward compatability. + We will remove the buggy impl after re-training all variants of our released models. + """ + super().__init__() + self.d_model=d_model + self.max_shape=max_shape + self.pre_scaling=pre_scaling + + pe = torch.zeros((d_model, *max_shape)) + y_position = torch.ones(max_shape).cumsum(0).float().unsqueeze(0) + x_position = torch.ones(max_shape).cumsum(1).float().unsqueeze(0) + + if pre_scaling[0] is not None and pre_scaling[1] is not None: + train_res,test_res=pre_scaling[0],pre_scaling[1] + x_position,y_position=x_position*train_res[1]/test_res[1],y_position*train_res[0]/test_res[0] + + div_term = torch.exp(torch.arange(0, d_model//2, 2).float() * (-math.log(10000.0) / (d_model//2))) + div_term = div_term[:, None, None] # [C//4, 1, 1] + pe[0::4, :, :] = torch.sin(x_position * div_term) + pe[1::4, :, :] = torch.cos(x_position * div_term) + pe[2::4, :, :] = torch.sin(y_position * div_term) + pe[3::4, :, :] = torch.cos(y_position * div_term) + + self.register_buffer('pe', pe.unsqueeze(0), persistent=False) # [1, C, H, W] + + def forward(self, x,scaling=None): + """ + Args: + x: [N, C, H, W] + """ + if scaling is None: #onliner scaling overwrites pre_scaling + return x + self.pe[:, :, :x.size(2), :x.size(3)],self.pe[:, :, :x.size(2), :x.size(3)] + else: + pe = torch.zeros((self.d_model, *self.max_shape)) + y_position = torch.ones(self.max_shape).cumsum(0).float().unsqueeze(0)*scaling[0] + x_position = torch.ones(self.max_shape).cumsum(1).float().unsqueeze(0)*scaling[1] + + div_term = torch.exp(torch.arange(0, self.d_model//2, 2).float() * (-math.log(10000.0) / (self.d_model//2))) + div_term = div_term[:, None, None] # [C//4, 1, 1] + pe[0::4, :, :] = torch.sin(x_position * div_term) + pe[1::4, :, :] = torch.cos(x_position * div_term) + pe[2::4, :, :] = torch.sin(y_position * div_term) + pe[3::4, :, :] = torch.cos(y_position * div_term) + pe=pe.unsqueeze(0).to(x.device) + return x + pe[:, :, :x.size(2), :x.size(3)],pe[:, :, :x.size(2), :x.size(3)] \ No newline at end of file diff --git a/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py b/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py new file mode 100644 index 0000000000000000000000000000000000000000..5cef3a7968413136f6dc9f52b6a1ec87192b006b --- /dev/null +++ b/third_party/ASpanFormer/src/ASpanFormer/utils/supervision.py @@ -0,0 +1,151 @@ +from math import log +from loguru import logger + +import torch +from einops import repeat +from kornia.utils import create_meshgrid + +from .geometry import warp_kpts + +############## ↓ Coarse-Level supervision ↓ ############## + + +@torch.no_grad() +def mask_pts_at_padded_regions(grid_pt, mask): + """For megadepth dataset, zero-padding exists in images""" + mask = repeat(mask, 'n h w -> n (h w) c', c=2) + grid_pt[~mask.bool()] = 0 + return grid_pt + + +@torch.no_grad() +def spvs_coarse(data, config): + """ + Update: + data (dict): { + "conf_matrix_gt": [N, hw0, hw1], + 'spv_b_ids': [M] + 'spv_i_ids': [M] + 'spv_j_ids': [M] + 'spv_w_pt0_i': [N, hw0, 2], in original image resolution + 'spv_pt1_i': [N, hw1, 2], in original image resolution + } + + NOTE: + - for scannet dataset, there're 3 kinds of resolution {i, c, f} + - for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f} + """ + # 1. misc + device = data['image0'].device + N, _, H0, W0 = data['image0'].shape + _, _, H1, W1 = data['image1'].shape + scale = config['ASPAN']['RESOLUTION'][0] + scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale + scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale + h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1]) + + # 2. warp grids + # create kpts in meshgrid and resize them to image resolution + grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2] + grid_pt0_i = scale0 * grid_pt0_c + grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1) + grid_pt1_i = scale1 * grid_pt1_c + + # mask padded region to (0, 0), so no need to manually mask conf_matrix_gt + if 'mask0' in data: + grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0']) + grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1']) + + # warp kpts bi-directionally and resize them to coarse-level resolution + # (no depth consistency check, since it leads to worse results experimentally) + # (unhandled edge case: points with 0-depth will be warped to the left-up corner) + _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1']) + _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0']) + w_pt0_c = w_pt0_i / scale1 + w_pt1_c = w_pt1_i / scale0 + + # 3. check if mutual nearest neighbor + w_pt0_c_round = w_pt0_c[:, :, :].round().long() + nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1 + w_pt1_c_round = w_pt1_c[:, :, :].round().long() + nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0 + + # corner case: out of boundary + def out_bound_mask(pt, w, h): + return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h) + nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0 + nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0 + + loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0) + correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1) + correct_0to1[:, 0] = False # ignore the top-left corner + + # 4. construct a gt conf_matrix + conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device) + b_ids, i_ids = torch.where(correct_0to1 != 0) + j_ids = nearest_index1[b_ids, i_ids] + + conf_matrix_gt[b_ids, i_ids, j_ids] = 1 + data.update({'conf_matrix_gt': conf_matrix_gt}) + + # 5. save coarse matches(gt) for training fine level + if len(b_ids) == 0: + logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}") + # this won't affect fine-level loss calculation + b_ids = torch.tensor([0], device=device) + i_ids = torch.tensor([0], device=device) + j_ids = torch.tensor([0], device=device) + + data.update({ + 'spv_b_ids': b_ids, + 'spv_i_ids': i_ids, + 'spv_j_ids': j_ids + }) + + # 6. save intermediate results (for fast fine-level computation) + data.update({ + 'spv_w_pt0_i': w_pt0_i, + 'spv_pt1_i': grid_pt1_i + }) + + +def compute_supervision_coarse(data, config): + assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!" + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_coarse(data, config) + else: + raise ValueError(f'Unknown data source: {data_source}') + + +############## ↓ Fine-Level supervision ↓ ############## + +@torch.no_grad() +def spvs_fine(data, config): + """ + Update: + data (dict):{ + "expec_f_gt": [M, 2]} + """ + # 1. misc + # w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i') + w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i'] + scale = config['ASPAN']['RESOLUTION'][1] + radius = config['ASPAN']['FINE_WINDOW_SIZE'] // 2 + + # 2. get coarse prediction + b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids'] + + # 3. compute gt + scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale + # `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later + expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2] + data.update({"expec_f_gt": expec_f_gt}) + + +def compute_supervision_fine(data, config): + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_fine(data, config) + else: + raise NotImplementedError diff --git a/third_party/ASpanFormer/src/__init__.py b/third_party/ASpanFormer/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/ASpanFormer/src/config/default.py b/third_party/ASpanFormer/src/config/default.py new file mode 100644 index 0000000000000000000000000000000000000000..40abd51c3f28ea6dee3c4e9fcee6efac5c080a2f --- /dev/null +++ b/third_party/ASpanFormer/src/config/default.py @@ -0,0 +1,180 @@ +from yacs.config import CfgNode as CN +_CN = CN() + +############## ↓ ASPAN Pipeline ↓ ############## +_CN.ASPAN = CN() +_CN.ASPAN.BACKBONE_TYPE = 'ResNetFPN' +_CN.ASPAN.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)] +_CN.ASPAN.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd +_CN.ASPAN.FINE_CONCAT_COARSE_FEAT = True + +# 1. ASPAN-backbone (local feature CNN) config +_CN.ASPAN.RESNETFPN = CN() +_CN.ASPAN.RESNETFPN.INITIAL_DIM = 128 +_CN.ASPAN.RESNETFPN.BLOCK_DIMS = [128, 196, 256] # s1, s2, s3 + +# 2. ASPAN-coarse module config +_CN.ASPAN.COARSE = CN() +_CN.ASPAN.COARSE.D_MODEL = 256 +_CN.ASPAN.COARSE.D_FFN = 256 +_CN.ASPAN.COARSE.D_FLOW= 128 +_CN.ASPAN.COARSE.NHEAD = 8 +_CN.ASPAN.COARSE.NLEVEL= 3 +_CN.ASPAN.COARSE.INI_LAYER_NUM = 2 +_CN.ASPAN.COARSE.LAYER_NUM = 4 +_CN.ASPAN.COARSE.NSAMPLE = [2,8] +_CN.ASPAN.COARSE.RADIUS_SCALE= 5 +_CN.ASPAN.COARSE.COARSEST_LEVEL= [26,26] +_CN.ASPAN.COARSE.TRAIN_RES = None +_CN.ASPAN.COARSE.TEST_RES = None + +# 3. Coarse-Matching config +_CN.ASPAN.MATCH_COARSE = CN() +_CN.ASPAN.MATCH_COARSE.THR = 0.2 +_CN.ASPAN.MATCH_COARSE.BORDER_RM = 2 +_CN.ASPAN.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' # options: ['dual_softmax, 'sinkhorn'] +_CN.ASPAN.MATCH_COARSE.SKH_ITERS = 3 +_CN.ASPAN.MATCH_COARSE.SKH_INIT_BIN_SCORE = 1.0 +_CN.ASPAN.MATCH_COARSE.SKH_PREFILTER = False +_CN.ASPAN.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory +_CN.ASPAN.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock +_CN.ASPAN.MATCH_COARSE.SPARSE_SPVS = True +_CN.ASPAN.MATCH_COARSE.LEARNABLE_DS_TEMP = True + +# 4. ASPAN-fine module config +_CN.ASPAN.FINE = CN() +_CN.ASPAN.FINE.D_MODEL = 128 +_CN.ASPAN.FINE.D_FFN = 128 +_CN.ASPAN.FINE.NHEAD = 8 +_CN.ASPAN.FINE.LAYER_NAMES = ['self', 'cross'] * 1 +_CN.ASPAN.FINE.ATTENTION = 'linear' + +# 5. ASPAN Losses +# -- # coarse-level +_CN.ASPAN.LOSS = CN() +_CN.ASPAN.LOSS.COARSE_TYPE = 'focal' # ['focal', 'cross_entropy'] +_CN.ASPAN.LOSS.COARSE_WEIGHT = 1.0 +# _CN.ASPAN.LOSS.SPARSE_SPVS = False +# -- - -- # focal loss (coarse) +_CN.ASPAN.LOSS.FOCAL_ALPHA = 0.25 +_CN.ASPAN.LOSS.FOCAL_GAMMA = 2.0 +_CN.ASPAN.LOSS.POS_WEIGHT = 1.0 +_CN.ASPAN.LOSS.NEG_WEIGHT = 1.0 +# _CN.ASPAN.LOSS.DUAL_SOFTMAX = False # whether coarse-level use dual-softmax or not. +# use `_CN.ASPAN.MATCH_COARSE.MATCH_TYPE` + +# -- # fine-level +_CN.ASPAN.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2'] +_CN.ASPAN.LOSS.FINE_WEIGHT = 1.0 +_CN.ASPAN.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window) + +# -- # flow-sloss +_CN.ASPAN.LOSS.FLOW_WEIGHT = 0.1 + + +############## Dataset ############## +_CN.DATASET = CN() +# 1. data config +# training and validating +_CN.DATASET.TRAINVAL_DATA_SOURCE = None # options: ['ScanNet', 'MegaDepth'] +_CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.TRAIN_NPZ_ROOT = None +_CN.DATASET.TRAIN_LIST_PATH = None +_CN.DATASET.TRAIN_INTRINSIC_PATH = None +_CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.VAL_NPZ_ROOT = None +_CN.DATASET.VAL_LIST_PATH = None # None if val data from all scenes are bundled into a single npz file +_CN.DATASET.VAL_INTRINSIC_PATH = None +# testing +_CN.DATASET.TEST_DATA_SOURCE = None +_CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.TEST_NPZ_ROOT = None +_CN.DATASET.TEST_LIST_PATH = None # None if test data from all scenes are bundled into a single npz file +_CN.DATASET.TEST_INTRINSIC_PATH = None + +# 2. dataset config +# general options +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 # discard data with overlap_score < min_overlap_score +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 +_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile'] + +# MegaDepth options +_CN.DATASET.MGDPT_IMG_RESIZE = 640 # resize the longer side, zero-pad bottom-right to square. +_CN.DATASET.MGDPT_IMG_PAD = True # pad img to square with size = MGDPT_IMG_RESIZE +_CN.DATASET.MGDPT_DEPTH_PAD = True # pad depthmap to square with size = 2000 +_CN.DATASET.MGDPT_DF = 8 + +############## Trainer ############## +_CN.TRAINER = CN() +_CN.TRAINER.WORLD_SIZE = 1 +_CN.TRAINER.CANONICAL_BS = 64 +_CN.TRAINER.CANONICAL_LR = 6e-3 +_CN.TRAINER.SCALING = None # this will be calculated automatically +_CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning + +# optimizer +_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw] +_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime +_CN.TRAINER.ADAM_DECAY = 0. # ADAM: for adam +_CN.TRAINER.ADAMW_DECAY = 0.1 + +# step-based warm-up +_CN.TRAINER.WARMUP_TYPE = 'linear' # [linear, constant] +_CN.TRAINER.WARMUP_RATIO = 0. +_CN.TRAINER.WARMUP_STEP = 4800 + +# learning rate scheduler +_CN.TRAINER.SCHEDULER = 'MultiStepLR' # [MultiStepLR, CosineAnnealing, ExponentialLR] +_CN.TRAINER.SCHEDULER_INTERVAL = 'epoch' # [epoch, step] +_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR +_CN.TRAINER.MSLR_GAMMA = 0.5 +_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing +_CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' interval + +# plotting related +_CN.TRAINER.ENABLE_PLOTTING = True +_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting +_CN.TRAINER.PLOT_MODE = 'evaluation' # ['evaluation', 'confidence'] +_CN.TRAINER.PLOT_MATCHES_ALPHA = 'dynamic' + +# geometric metrics and pose solver +_CN.TRAINER.EPI_ERR_THR = 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue) +_CN.TRAINER.POSE_GEO_MODEL = 'E' # ['E', 'F', 'H'] +_CN.TRAINER.POSE_ESTIMATION_METHOD = 'RANSAC' # [RANSAC, DEGENSAC, MAGSAC] +_CN.TRAINER.RANSAC_PIXEL_THR = 0.5 +_CN.TRAINER.RANSAC_CONF = 0.99999 +_CN.TRAINER.RANSAC_MAX_ITERS = 10000 +_CN.TRAINER.USE_MAGSACPP = False + +# data sampler for train_dataloader +_CN.TRAINER.DATA_SAMPLER = 'scene_balance' # options: ['scene_balance', 'random', 'normal'] +# 'scene_balance' config +_CN.TRAINER.N_SAMPLES_PER_SUBSET = 200 +_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = True # whether sample each scene with replacement or not +_CN.TRAINER.SB_SUBSET_SHUFFLE = True # after sampling from scenes, whether shuffle within the epoch or not +_CN.TRAINER.SB_REPEAT = 1 # repeat N times for training the sampled data +# 'random' config +_CN.TRAINER.RDM_REPLACEMENT = True +_CN.TRAINER.RDM_NUM_SAMPLES = None + +# gradient clipping +_CN.TRAINER.GRADIENT_CLIPPING = 0.5 + +# reproducibility +# This seed affects the data sampling. With the same seed, the data sampling is promised +# to be the same. When resume training from a checkpoint, it's better to use a different +# seed, otherwise the sampled data will be exactly the same as before resuming, which will +# cause less unique data items sampled during the entire training. +# Use of different seed values might affect the final training result, since not all data items +# are used during training on ScanNet. (60M pairs of images sampled during traing from 230M pairs in total.) +_CN.TRAINER.SEED = 66 + + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return _CN.clone() diff --git a/third_party/ASpanFormer/src/datasets/__init__.py b/third_party/ASpanFormer/src/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1860e3ae060a26e4625925861cecdc355f2b08b7 --- /dev/null +++ b/third_party/ASpanFormer/src/datasets/__init__.py @@ -0,0 +1,3 @@ +from .scannet import ScanNetDataset +from .megadepth import MegaDepthDataset + diff --git a/third_party/ASpanFormer/src/datasets/megadepth.py b/third_party/ASpanFormer/src/datasets/megadepth.py new file mode 100644 index 0000000000000000000000000000000000000000..a70ac715a3f807e37bc5b87ae9446ddd2aa4fc86 --- /dev/null +++ b/third_party/ASpanFormer/src/datasets/megadepth.py @@ -0,0 +1,127 @@ +import os.path as osp +import numpy as np +import torch +import torch.nn.functional as F +from torch.utils.data import Dataset +from loguru import logger + +from src.utils.dataset import read_megadepth_gray, read_megadepth_depth + + +class MegaDepthDataset(Dataset): + def __init__(self, + root_dir, + npz_path, + mode='train', + min_overlap_score=0.4, + img_resize=None, + df=None, + img_padding=False, + depth_padding=False, + augment_fn=None, + **kwargs): + """ + Manage one scene(npz_path) of MegaDepth dataset. + + Args: + root_dir (str): megadepth root directory that has `phoenix`. + npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. + mode (str): options are ['train', 'val', 'test'] + min_overlap_score (float): how much a pair should have in common. In range of [0, 1]. Set to 0 when testing. + img_resize (int, optional): the longer edge of resized images. None for no resize. 640 is recommended. + This is useful during training with batches and testing with memory intensive algorithms. + df (int, optional): image size division factor. NOTE: this will change the final image size after img_resize. + img_padding (bool): If set to 'True', zero-pad the image to squared size. This is useful during training. + depth_padding (bool): If set to 'True', zero-pad depthmap to (2000, 2000). This is useful during training. + augment_fn (callable, optional): augments images with pre-defined visual effects. + """ + super().__init__() + self.root_dir = root_dir + self.mode = mode + self.scene_id = npz_path.split('.')[0] + + # prepare scene_info and pair_info + if mode == 'test' and min_overlap_score != 0: + logger.warning("You are using `min_overlap_score`!=0 in test mode. Set to 0.") + min_overlap_score = 0 + self.scene_info = np.load(npz_path, allow_pickle=True) + self.pair_infos = self.scene_info['pair_infos'].copy() + del self.scene_info['pair_infos'] + self.pair_infos = [pair_info for pair_info in self.pair_infos if pair_info[1] > min_overlap_score] + + # parameters for image resizing, padding and depthmap padding + if mode == 'train': + assert img_resize is not None and img_padding and depth_padding + self.img_resize = img_resize + self.df = df + self.img_padding = img_padding + self.depth_max_size = 2000 if depth_padding else None # the upperbound of depthmaps size in megadepth. + + # for training LoFTR + self.augment_fn = augment_fn if mode == 'train' else None + self.coarse_scale = getattr(kwargs, 'coarse_scale', 0.125) + + def __len__(self): + return len(self.pair_infos) + + def __getitem__(self, idx): + (idx0, idx1), overlap_score, central_matches = self.pair_infos[idx] + + # read grayscale image and mask. (1, h, w) and (h, w) + img_name0 = osp.join(self.root_dir, self.scene_info['image_paths'][idx0]) + img_name1 = osp.join(self.root_dir, self.scene_info['image_paths'][idx1]) + + # TODO: Support augmentation & handle seeds for each worker correctly. + image0, mask0, scale0 = read_megadepth_gray( + img_name0, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + image1, mask1, scale1 = read_megadepth_gray( + img_name1, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + + # read depth. shape: (h, w) + if self.mode in ['train', 'val']: + depth0 = read_megadepth_depth( + osp.join(self.root_dir, self.scene_info['depth_paths'][idx0]), pad_to=self.depth_max_size) + depth1 = read_megadepth_depth( + osp.join(self.root_dir, self.scene_info['depth_paths'][idx1]), pad_to=self.depth_max_size) + else: + depth0 = depth1 = torch.tensor([]) + + # read intrinsics of original size + K_0 = torch.tensor(self.scene_info['intrinsics'][idx0].copy(), dtype=torch.float).reshape(3, 3) + K_1 = torch.tensor(self.scene_info['intrinsics'][idx1].copy(), dtype=torch.float).reshape(3, 3) + + # read and compute relative poses + T0 = self.scene_info['poses'][idx0] + T1 = self.scene_info['poses'][idx1] + T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[:4, :4] # (4, 4) + T_1to0 = T_0to1.inverse() + + data = { + 'image0': image0, # (1, h, w) + 'depth0': depth0, # (h, w) + 'image1': image1, + 'depth1': depth1, + 'T_0to1': T_0to1, # (4, 4) + 'T_1to0': T_1to0, + 'K0': K_0, # (3, 3) + 'K1': K_1, + 'scale0': scale0, # [scale_w, scale_h] + 'scale1': scale1, + 'dataset_name': 'MegaDepth', + 'scene_id': self.scene_id, + 'pair_id': idx, + 'pair_names': (self.scene_info['image_paths'][idx0], self.scene_info['image_paths'][idx1]), + } + + # for LoFTR training + if mask0 is not None: # img_padding is True + if self.coarse_scale: + [ts_mask_0, ts_mask_1] = F.interpolate(torch.stack([mask0, mask1], dim=0)[None].float(), + scale_factor=self.coarse_scale, + mode='nearest', + recompute_scale_factor=False)[0].bool() + data.update({'mask0': ts_mask_0, 'mask1': ts_mask_1}) + + return data diff --git a/third_party/ASpanFormer/src/datasets/sampler.py b/third_party/ASpanFormer/src/datasets/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..81b6f435645632a013476f9a665a0861ab7fcb61 --- /dev/null +++ b/third_party/ASpanFormer/src/datasets/sampler.py @@ -0,0 +1,77 @@ +import torch +from torch.utils.data import Sampler, ConcatDataset + + +class RandomConcatSampler(Sampler): + """ Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset + in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be done with replacement. + However, it is impossible to sample data without replacement between epochs, unless bulding a stateful sampler lived along the entire training phase. + + For current implementation, the randomness of sampling is ensured no matter the sampler is recreated across epochs or not and call `torch.manual_seed()` or not. + Args: + shuffle (bool): shuffle the random sampled indices across all sub-datsets. + repeat (int): repeatedly use the sampled indices multiple times for training. + [arXiv:1902.05509, arXiv:1901.09335] + NOTE: Don't re-initialize the sampler between epochs (will lead to repeated samples) + NOTE: This sampler behaves differently with DistributedSampler. + It assume the dataset is splitted across ranks instead of replicated. + TODO: Add a `set_epoch()` method to fullfill sampling without replacement across epochs. + ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/e9846dd758cfb1500eb9dba2d86f6912eb487587/pytorch_lightning/trainer/training_loop.py#L373 + """ + def __init__(self, + data_source: ConcatDataset, + n_samples_per_subset: int, + subset_replacement: bool=True, + shuffle: bool=True, + repeat: int=1, + seed: int=None): + if not isinstance(data_source, ConcatDataset): + raise TypeError("data_source should be torch.utils.data.ConcatDataset") + + self.data_source = data_source + self.n_subset = len(self.data_source.datasets) + self.n_samples_per_subset = n_samples_per_subset + self.n_samples = self.n_subset * self.n_samples_per_subset * repeat + self.subset_replacement = subset_replacement + self.repeat = repeat + self.shuffle = shuffle + self.generator = torch.manual_seed(seed) + assert self.repeat >= 1 + + def __len__(self): + return self.n_samples + + def __iter__(self): + indices = [] + # sample from each sub-dataset + for d_idx in range(self.n_subset): + low = 0 if d_idx==0 else self.data_source.cumulative_sizes[d_idx-1] + high = self.data_source.cumulative_sizes[d_idx] + if self.subset_replacement: + rand_tensor = torch.randint(low, high, (self.n_samples_per_subset, ), + generator=self.generator, dtype=torch.int64) + else: # sample without replacement + len_subset = len(self.data_source.datasets[d_idx]) + rand_tensor = torch.randperm(len_subset, generator=self.generator) + low + if len_subset >= self.n_samples_per_subset: + rand_tensor = rand_tensor[:self.n_samples_per_subset] + else: # padding with replacement + rand_tensor_replacement = torch.randint(low, high, (self.n_samples_per_subset - len_subset, ), + generator=self.generator, dtype=torch.int64) + rand_tensor = torch.cat([rand_tensor, rand_tensor_replacement]) + indices.append(rand_tensor) + indices = torch.cat(indices) + if self.shuffle: # shuffle the sampled dataset (from multiple subsets) + rand_tensor = torch.randperm(len(indices), generator=self.generator) + indices = indices[rand_tensor] + + # repeat the sampled indices (can be used for RepeatAugmentation or pure RepeatSampling) + if self.repeat > 1: + repeat_indices = [indices.clone() for _ in range(self.repeat - 1)] + if self.shuffle: + _choice = lambda x: x[torch.randperm(len(x), generator=self.generator)] + repeat_indices = map(_choice, repeat_indices) + indices = torch.cat([indices, *repeat_indices], 0) + + assert indices.shape[0] == self.n_samples + return iter(indices.tolist()) diff --git a/third_party/ASpanFormer/src/datasets/scannet.py b/third_party/ASpanFormer/src/datasets/scannet.py new file mode 100644 index 0000000000000000000000000000000000000000..3520d34c0f08a784ddbf923846a7cb2a847b1787 --- /dev/null +++ b/third_party/ASpanFormer/src/datasets/scannet.py @@ -0,0 +1,113 @@ +from os import path as osp +from typing import Dict +from unicodedata import name + +import numpy as np +import torch +import torch.utils as utils +from numpy.linalg import inv +from src.utils.dataset import ( + read_scannet_gray, + read_scannet_depth, + read_scannet_pose, + read_scannet_intrinsic +) + + +class ScanNetDataset(utils.data.Dataset): + def __init__(self, + root_dir, + npz_path, + intrinsic_path, + mode='train', + min_overlap_score=0.4, + augment_fn=None, + pose_dir=None, + **kwargs): + """Manage one scene of ScanNet Dataset. + Args: + root_dir (str): ScanNet root directory that contains scene folders. + npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. + intrinsic_path (str): path to depth-camera intrinsic file. + mode (str): options are ['train', 'val', 'test']. + augment_fn (callable, optional): augments images with pre-defined visual effects. + pose_dir (str): ScanNet root directory that contains all poses. + (we use a separate (optional) pose_dir since we store images and poses separately.) + """ + super().__init__() + self.root_dir = root_dir + self.pose_dir = pose_dir if pose_dir is not None else root_dir + self.mode = mode + + # prepare data_names, intrinsics and extrinsics(T) + with np.load(npz_path) as data: + self.data_names = data['name'] + if 'score' in data.keys() and mode not in ['val' or 'test']: + kept_mask = data['score'] > min_overlap_score + self.data_names = self.data_names[kept_mask] + self.intrinsics = dict(np.load(intrinsic_path)) + + # for training LoFTR + self.augment_fn = augment_fn if mode == 'train' else None + + def __len__(self): + return len(self.data_names) + + def _read_abs_pose(self, scene_name, name): + pth = osp.join(self.pose_dir, + scene_name, + 'pose', f'{name}.txt') + return read_scannet_pose(pth) + + def _compute_rel_pose(self, scene_name, name0, name1): + pose0 = self._read_abs_pose(scene_name, name0) + pose1 = self._read_abs_pose(scene_name, name1) + + return np.matmul(pose1, inv(pose0)) # (4, 4) + + def __getitem__(self, idx): + data_name = self.data_names[idx] + scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name + scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}' + + # read the grayscale image which will be resized to (1, 480, 640) + img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg') + img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg') + # TODO: Support augmentation & handle seeds for each worker correctly. + image0 = read_scannet_gray(img_name0, resize=(640, 480), augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + image1 = read_scannet_gray(img_name1, resize=(640, 480), augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + + # read the depthmap which is stored as (480, 640) + if self.mode in ['train', 'val']: + depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png')) + depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png')) + else: + depth0 = depth1 = torch.tensor([]) + + # read the intrinsic of depthmap + K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3) + + # read and compute relative poses + T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1), + dtype=torch.float32) + T_1to0 = T_0to1.inverse() + + data = { + 'image0': image0, # (1, h, w) + 'depth0': depth0, # (h, w) + 'image1': image1, + 'depth1': depth1, + 'T_0to1': T_0to1, # (4, 4) + 'T_1to0': T_1to0, + 'K0': K_0, # (3, 3) + 'K1': K_1, + 'dataset_name': 'ScanNet', + 'scene_id': scene_name, + 'pair_id': idx, + 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'), + osp.join(scene_name, 'color', f'{stem_name_1}.jpg')) + } + + return data diff --git a/third_party/ASpanFormer/src/lightning/data.py b/third_party/ASpanFormer/src/lightning/data.py new file mode 100644 index 0000000000000000000000000000000000000000..73db514b8924d647814e6c5def919c23393d3ccf --- /dev/null +++ b/third_party/ASpanFormer/src/lightning/data.py @@ -0,0 +1,326 @@ +import os +import math +from collections import abc +from loguru import logger +from torch.utils.data.dataset import Dataset +from tqdm import tqdm +from os import path as osp +from pathlib import Path +from joblib import Parallel, delayed + +import pytorch_lightning as pl +from torch import distributed as dist +from torch.utils.data import ( + Dataset, + DataLoader, + ConcatDataset, + DistributedSampler, + RandomSampler, + dataloader +) + +from src.utils.augment import build_augmentor +from src.utils.dataloader import get_local_split +from src.utils.misc import tqdm_joblib +from src.utils import comm +from src.datasets.megadepth import MegaDepthDataset +from src.datasets.scannet import ScanNetDataset +from src.datasets.sampler import RandomConcatSampler + + +class MultiSceneDataModule(pl.LightningDataModule): + """ + For distributed training, each training process is assgined + only a part of the training scenes to reduce memory overhead. + """ + def __init__(self, args, config): + super().__init__() + + # 1. data config + # Train and Val should from the same data source + self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE + self.test_data_source = config.DATASET.TEST_DATA_SOURCE + # training and validating + self.train_data_root = config.DATASET.TRAIN_DATA_ROOT + self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional) + self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT + self.train_list_path = config.DATASET.TRAIN_LIST_PATH + self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH + self.val_data_root = config.DATASET.VAL_DATA_ROOT + self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional) + self.val_npz_root = config.DATASET.VAL_NPZ_ROOT + self.val_list_path = config.DATASET.VAL_LIST_PATH + self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH + # testing + self.test_data_root = config.DATASET.TEST_DATA_ROOT + self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional) + self.test_npz_root = config.DATASET.TEST_NPZ_ROOT + self.test_list_path = config.DATASET.TEST_LIST_PATH + self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH + + # 2. dataset config + # general options + self.min_overlap_score_test = config.DATASET.MIN_OVERLAP_SCORE_TEST # 0.4, omit data with overlap_score < min_overlap_score + self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN + self.augment_fn = build_augmentor(config.DATASET.AUGMENTATION_TYPE) # None, options: [None, 'dark', 'mobile'] + + # MegaDepth options + self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840 + self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True + self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True + self.mgdpt_df = config.DATASET.MGDPT_DF # 8 + self.coarse_scale = 1 / config.ASPAN.RESOLUTION[0] # 0.125. for training loftr. + + # 3.loader parameters + self.train_loader_params = { + 'batch_size': args.batch_size, + 'num_workers': args.num_workers, + 'pin_memory': getattr(args, 'pin_memory', True) + } + self.val_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': args.num_workers, + 'pin_memory': getattr(args, 'pin_memory', True) + } + self.test_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': args.num_workers, + 'pin_memory': True + } + + # 4. sampler + self.data_sampler = config.TRAINER.DATA_SAMPLER + self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET + self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT + self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE + self.repeat = config.TRAINER.SB_REPEAT + + # (optional) RandomSampler for debugging + + # misc configurations + self.parallel_load_data = getattr(args, 'parallel_load_data', False) + self.seed = config.TRAINER.SEED # 66 + + def setup(self, stage=None): + """ + Setup train / val / test dataset. This method will be called by PL automatically. + Args: + stage (str): 'fit' in training phase, and 'test' in testing phase. + """ + + assert stage in ['fit', 'test'], "stage must be either fit or test" + + try: + self.world_size = dist.get_world_size() + self.rank = dist.get_rank() + logger.info(f"[rank:{self.rank}] world_size: {self.world_size}") + except AssertionError as ae: + self.world_size = 1 + self.rank = 0 + logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)") + + if stage == 'fit': + self.train_dataset = self._setup_dataset( + self.train_data_root, + self.train_npz_root, + self.train_list_path, + self.train_intrinsic_path, + mode='train', + min_overlap_score=self.min_overlap_score_train, + pose_dir=self.train_pose_root) + # setup multiple (optional) validation subsets + if isinstance(self.val_list_path, (list, tuple)): + self.val_dataset = [] + if not isinstance(self.val_npz_root, (list, tuple)): + self.val_npz_root = [self.val_npz_root for _ in range(len(self.val_list_path))] + for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root): + self.val_dataset.append(self._setup_dataset( + self.val_data_root, + npz_root, + npz_list, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root)) + else: + self.val_dataset = self._setup_dataset( + self.val_data_root, + self.val_npz_root, + self.val_list_path, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root) + logger.info(f'[rank:{self.rank}] Train & Val Dataset loaded!') + else: # stage == 'test + self.test_dataset = self._setup_dataset( + self.test_data_root, + self.test_npz_root, + self.test_list_path, + self.test_intrinsic_path, + mode='test', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.test_pose_root) + logger.info(f'[rank:{self.rank}]: Test Dataset loaded!') + + def _setup_dataset(self, + data_root, + split_npz_root, + scene_list_path, + intri_path, + mode='train', + min_overlap_score=0., + pose_dir=None): + """ Setup train / val / test set""" + with open(scene_list_path, 'r') as f: + npz_names = [name.split()[0] for name in f.readlines()] + + if mode == 'train': + local_npz_names = get_local_split(npz_names, self.world_size, self.rank, self.seed) + else: + local_npz_names = npz_names + logger.info(f'[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.') + + dataset_builder = self._build_concat_dataset_parallel \ + if self.parallel_load_data \ + else self._build_concat_dataset + return dataset_builder(data_root, local_npz_names, split_npz_root, intri_path, + mode=mode, min_overlap_score=min_overlap_score, pose_dir=pose_dir) + + def _build_concat_dataset( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None + ): + datasets = [] + augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source + if data_source=='GL3D' and mode=='val': + data_source='MegaDepth' + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + if str(data_source).lower() == 'gl3d': + npz_names = [f'{n}.txt' for n in npz_names] + #npz_names=npz_names[:8] + for npz_name in tqdm(npz_names, + desc=f'[rank:{self.rank}] loading {mode} datasets', + disable=int(self.rank) != 0): + # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time. + npz_path = osp.join(npz_dir, npz_name) + if data_source == 'ScanNet': + datasets.append( + ScanNetDataset(data_root, + npz_path, + intrinsic_path, + mode=mode, + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir)) + elif data_source == 'MegaDepth': + datasets.append( + MegaDepthDataset(data_root, + npz_path, + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + augment_fn=augment_fn, + coarse_scale=self.coarse_scale)) + else: + raise NotImplementedError() + return ConcatDataset(datasets) + + def _build_concat_dataset_parallel( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None, + ): + augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + #npz_names=npz_names[:8] + with tqdm_joblib(tqdm(desc=f'[rank:{self.rank}] loading {mode} datasets', + total=len(npz_names), disable=int(self.rank) != 0)): + if data_source == 'ScanNet': + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + ScanNetDataset, + data_root, + osp.join(npz_dir, x), + intrinsic_path, + mode=mode, + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir))(name) + for name in npz_names) + elif data_source == 'MegaDepth': + # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers. + raise NotImplementedError() + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + MegaDepthDataset, + data_root, + osp.join(npz_dir, x), + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + augment_fn=augment_fn, + coarse_scale=self.coarse_scale))(name) + for name in npz_names) + else: + raise ValueError(f'Unknown dataset: {data_source}') + return ConcatDataset(datasets) + + def train_dataloader(self): + """ Build training dataloader for ScanNet / MegaDepth. """ + assert self.data_sampler in ['scene_balance'] + logger.info(f'[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!).') + if self.data_sampler == 'scene_balance': + sampler = RandomConcatSampler(self.train_dataset, + self.n_samples_per_subset, + self.subset_replacement, + self.shuffle, self.repeat, self.seed) + else: + sampler = None + dataloader = DataLoader(self.train_dataset, sampler=sampler, **self.train_loader_params) + return dataloader + + def val_dataloader(self): + """ Build validation dataloader for ScanNet / MegaDepth. """ + logger.info(f'[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init.') + if not isinstance(self.val_dataset, abc.Sequence): + sampler = DistributedSampler(self.val_dataset, shuffle=False) + return DataLoader(self.val_dataset, sampler=sampler, **self.val_loader_params) + else: + dataloaders = [] + for dataset in self.val_dataset: + sampler = DistributedSampler(dataset, shuffle=False) + dataloaders.append(DataLoader(dataset, sampler=sampler, **self.val_loader_params)) + return dataloaders + + def test_dataloader(self, *args, **kwargs): + logger.info(f'[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init.') + sampler = DistributedSampler(self.test_dataset, shuffle=False) + return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params) + + +def _build_dataset(dataset: Dataset, *args, **kwargs): + return dataset(*args, **kwargs) diff --git a/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py b/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ee20cbec4628b73c08358ebf1e1906fb2c0ac13c --- /dev/null +++ b/third_party/ASpanFormer/src/lightning/lightning_aspanformer.py @@ -0,0 +1,276 @@ + +from collections import defaultdict +import pprint +from loguru import logger +from pathlib import Path + +import torch +import numpy as np +import pytorch_lightning as pl +from matplotlib import pyplot as plt + +from src.ASpanFormer.aspanformer import ASpanFormer +from src.ASpanFormer.utils.supervision import compute_supervision_coarse, compute_supervision_fine +from src.losses.aspan_loss import ASpanLoss +from src.optimizers import build_optimizer, build_scheduler +from src.utils.metrics import ( + compute_symmetrical_epipolar_errors,compute_symmetrical_epipolar_errors_offset_bidirectional, + compute_pose_errors, + aggregate_metrics +) +from src.utils.plotting import make_matching_figures,make_matching_figures_offset +from src.utils.comm import gather, all_gather +from src.utils.misc import lower_config, flattenList +from src.utils.profiler import PassThroughProfiler + + +class PL_ASpanFormer(pl.LightningModule): + def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None): + """ + TODO: + - use the new version of PL logging API. + """ + super().__init__() + # Misc + self.config = config # full config + _config = lower_config(self.config) + self.loftr_cfg = lower_config(_config['aspan']) + self.profiler = profiler or PassThroughProfiler() + self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) + + # Matcher: LoFTR + self.matcher = ASpanFormer(config=_config['aspan']) + self.loss = ASpanLoss(_config) + + # Pretrained weights + print(pretrained_ckpt) + if pretrained_ckpt: + print('load') + state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict'] + msg=self.matcher.load_state_dict(state_dict, strict=False) + print(msg) + logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint") + + # Testing + self.dump_dir = dump_dir + + def configure_optimizers(self): + # FIXME: The scheduler did not work properly when `--resume_from_checkpoint` + optimizer = build_optimizer(self, self.config) + scheduler = build_scheduler(self.config, optimizer) + return [optimizer], [scheduler] + + def optimizer_step( + self, epoch, batch_idx, optimizer, optimizer_idx, + optimizer_closure, on_tpu, using_native_amp, using_lbfgs): + # learning rate warm up + warmup_step = self.config.TRAINER.WARMUP_STEP + if self.trainer.global_step < warmup_step: + if self.config.TRAINER.WARMUP_TYPE == 'linear': + base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR + lr = base_lr + \ + (self.trainer.global_step / self.config.TRAINER.WARMUP_STEP) * \ + abs(self.config.TRAINER.TRUE_LR - base_lr) + for pg in optimizer.param_groups: + pg['lr'] = lr + elif self.config.TRAINER.WARMUP_TYPE == 'constant': + pass + else: + raise ValueError(f'Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}') + + # update params + optimizer.step(closure=optimizer_closure) + optimizer.zero_grad() + + def _trainval_inference(self, batch): + with self.profiler.profile("Compute coarse supervision"): + compute_supervision_coarse(batch, self.config) + + with self.profiler.profile("LoFTR"): + self.matcher(batch) + + with self.profiler.profile("Compute fine supervision"): + compute_supervision_fine(batch, self.config) + + with self.profiler.profile("Compute losses"): + self.loss(batch) + + def _compute_metrics(self, batch): + with self.profiler.profile("Copmute metrics"): + compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match + compute_symmetrical_epipolar_errors_offset_bidirectional(batch) # compute epi_errs for offset match + compute_pose_errors(batch, self.config) # compute R_errs, t_errs, pose_errs for each pair + + rel_pair_names = list(zip(*batch['pair_names'])) + bs = batch['image0'].size(0) + metrics = { + # to filter duplicate pairs caused by DistributedSampler + 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], + 'epi_errs': [batch['epi_errs'][batch['m_bids'] == b].cpu().numpy() for b in range(bs)], + 'epi_errs_offset': [batch['epi_errs_offset_left'][batch['offset_bids_left'] == b].cpu().numpy() for b in range(bs)], #only consider left side + 'R_errs': batch['R_errs'], + 't_errs': batch['t_errs'], + 'inliers': batch['inliers']} + ret_dict = {'metrics': metrics} + return ret_dict, rel_pair_names + + + def training_step(self, batch, batch_idx): + self._trainval_inference(batch) + + # logging + if self.trainer.global_rank == 0 and self.global_step % self.trainer.log_every_n_steps == 0: + # scalars + for k, v in batch['loss_scalars'].items(): + if not k.startswith('loss_flow') and not k.startswith('conf_'): + self.logger.experiment.add_scalar(f'train/{k}', v, self.global_step) + + #log offset_loss and conf for each layer and level + layer_num=self.loftr_cfg['coarse']['layer_num'] + for layer_index in range(layer_num): + log_title='layer_'+str(layer_index) + self.logger.experiment.add_scalar(log_title+'/offset_loss', batch['loss_scalars']['loss_flow_'+str(layer_index)], self.global_step) + self.logger.experiment.add_scalar(log_title+'/conf_', batch['loss_scalars']['conf_'+str(layer_index)],self.global_step) + + # net-params + if self.config.ASPAN.MATCH_COARSE.MATCH_TYPE == 'sinkhorn': + self.logger.experiment.add_scalar( + f'skh_bin_score', self.matcher.coarse_matching.bin_score.clone().detach().cpu().data, self.global_step) + + # figures + if self.config.TRAINER.ENABLE_PLOTTING: + compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match + figures = make_matching_figures(batch, self.config, self.config.TRAINER.PLOT_MODE) + for k, v in figures.items(): + self.logger.experiment.add_figure(f'train_match/{k}', v, self.global_step) + + #plot offset + if self.global_step%200==0: + compute_symmetrical_epipolar_errors_offset_bidirectional(batch) + figures_left = make_matching_figures_offset(batch, self.config, self.config.TRAINER.PLOT_MODE,side='_left') + figures_right = make_matching_figures_offset(batch, self.config, self.config.TRAINER.PLOT_MODE,side='_right') + for k, v in figures_left.items(): + self.logger.experiment.add_figure(f'train_offset/{k}'+'_left', v, self.global_step) + figures = make_matching_figures_offset(batch, self.config, self.config.TRAINER.PLOT_MODE,side='_right') + for k, v in figures_right.items(): + self.logger.experiment.add_figure(f'train_offset/{k}'+'_right', v, self.global_step) + + return {'loss': batch['loss']} + + def training_epoch_end(self, outputs): + avg_loss = torch.stack([x['loss'] for x in outputs]).mean() + if self.trainer.global_rank == 0: + self.logger.experiment.add_scalar( + 'train/avg_loss_on_epoch', avg_loss, + global_step=self.current_epoch) + + def validation_step(self, batch, batch_idx): + self._trainval_inference(batch) + + ret_dict, _ = self._compute_metrics(batch) #this func also compute the epi_errors + + val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1) + figures = {self.config.TRAINER.PLOT_MODE: []} + figures_offset = {self.config.TRAINER.PLOT_MODE: []} + if batch_idx % val_plot_interval == 0: + figures = make_matching_figures(batch, self.config, mode=self.config.TRAINER.PLOT_MODE) + figures_offset=make_matching_figures_offset(batch, self.config, self.config.TRAINER.PLOT_MODE,'_left') + return { + **ret_dict, + 'loss_scalars': batch['loss_scalars'], + 'figures': figures, + 'figures_offset_left':figures_offset + } + + def validation_epoch_end(self, outputs): + # handle multiple validation sets + multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs + multi_val_metrics = defaultdict(list) + + for valset_idx, outputs in enumerate(multi_outputs): + # since pl performs sanity_check at the very begining of the training + cur_epoch = self.trainer.current_epoch + if not self.trainer.resume_from_checkpoint and self.trainer.running_sanity_check: + cur_epoch = -1 + + # 1. loss_scalars: dict of list, on cpu + _loss_scalars = [o['loss_scalars'] for o in outputs] + loss_scalars = {k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars])) for k in _loss_scalars[0]} + + # 2. val metrics: dict of list, numpy + _metrics = [o['metrics'] for o in outputs] + metrics = {k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} + # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0 + val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) + for thr in [5, 10, 20]: + multi_val_metrics[f'auc@{thr}'].append(val_metrics_4tb[f'auc@{thr}']) + + # 3. figures + _figures = [o['figures'] for o in outputs] + figures = {k: flattenList(gather(flattenList([_me[k] for _me in _figures]))) for k in _figures[0]} + + # tensorboard records only on rank 0 + if self.trainer.global_rank == 0: + for k, v in loss_scalars.items(): + mean_v = torch.stack(v).mean() + self.logger.experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch) + + for k, v in val_metrics_4tb.items(): + self.logger.experiment.add_scalar(f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch) + + for k, v in figures.items(): + if self.trainer.global_rank == 0: + for plot_idx, fig in enumerate(v): + self.logger.experiment.add_figure( + f'val_match_{valset_idx}/{k}/pair-{plot_idx}', fig, cur_epoch, close=True) + plt.close('all') + + for thr in [5, 10, 20]: + # log on all ranks for ModelCheckpoint callback to work properly + self.log(f'auc@{thr}', torch.tensor(np.mean(multi_val_metrics[f'auc@{thr}']))) # ckpt monitors on this + + def test_step(self, batch, batch_idx): + with self.profiler.profile("LoFTR"): + self.matcher(batch) + + ret_dict, rel_pair_names = self._compute_metrics(batch) + + with self.profiler.profile("dump_results"): + if self.dump_dir is not None: + # dump results for further analysis + keys_to_save = {'mkpts0_f', 'mkpts1_f', 'mconf', 'epi_errs'} + pair_names = list(zip(*batch['pair_names'])) + bs = batch['image0'].shape[0] + dumps = [] + for b_id in range(bs): + item = {} + mask = batch['m_bids'] == b_id + item['pair_names'] = pair_names[b_id] + item['identifier'] = '#'.join(rel_pair_names[b_id]) + for key in keys_to_save: + item[key] = batch[key][mask].cpu().numpy() + for key in ['R_errs', 't_errs', 'inliers']: + item[key] = batch[key][b_id] + dumps.append(item) + ret_dict['dumps'] = dumps + + return ret_dict + + def test_epoch_end(self, outputs): + # metrics: dict of list, numpy + _metrics = [o['metrics'] for o in outputs] + metrics = {k: flattenList(gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} + + # [{key: [{...}, *#bs]}, *#batch] + if self.dump_dir is not None: + Path(self.dump_dir).mkdir(parents=True, exist_ok=True) + _dumps = flattenList([o['dumps'] for o in outputs]) # [{...}, #bs*#batch] + dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch] + logger.info(f'Prediction and evaluation results will be saved to: {self.dump_dir}') + + if self.trainer.global_rank == 0: + print(self.profiler.summary()) + val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) + logger.info('\n' + pprint.pformat(val_metrics_4tb)) + if self.dump_dir is not None: + np.save(Path(self.dump_dir) / 'LoFTR_pred_eval', dumps) diff --git a/third_party/ASpanFormer/src/losses/aspan_loss.py b/third_party/ASpanFormer/src/losses/aspan_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0cca52b36fc997415937969f26caba8c41ac2b8e --- /dev/null +++ b/third_party/ASpanFormer/src/losses/aspan_loss.py @@ -0,0 +1,231 @@ +from loguru import logger + +import torch +import torch.nn as nn + +class ASpanLoss(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config # config under the global namespace + self.loss_config = config['aspan']['loss'] + self.match_type = self.config['aspan']['match_coarse']['match_type'] + self.sparse_spvs = self.config['aspan']['match_coarse']['sparse_spvs'] + self.flow_weight=self.config['aspan']['loss']['flow_weight'] + + # coarse-level + self.correct_thr = self.loss_config['fine_correct_thr'] + self.c_pos_w = self.loss_config['pos_weight'] + self.c_neg_w = self.loss_config['neg_weight'] + # fine-level + self.fine_type = self.loss_config['fine_type'] + + def compute_flow_loss(self,coarse_corr_gt,flow_list,h0,w0,h1,w1): + #coarse_corr_gt:[[batch_indices],[left_indices],[right_indices]] + #flow_list: [L,B,H,W,4] + loss1=self.flow_loss_worker(flow_list[0],coarse_corr_gt[0],coarse_corr_gt[1],coarse_corr_gt[2],w1) + loss2=self.flow_loss_worker(flow_list[1],coarse_corr_gt[0],coarse_corr_gt[2],coarse_corr_gt[1],w0) + total_loss=(loss1+loss2)/2 + return total_loss + + def flow_loss_worker(self,flow,batch_indicies,self_indicies,cross_indicies,w): + bs,layer_num=flow.shape[1],flow.shape[0] + flow=flow.view(layer_num,bs,-1,4) + gt_flow=torch.stack([cross_indicies%w,cross_indicies//w],dim=1) + + total_loss_list=[] + for layer_index in range(layer_num): + cur_flow_list=flow[layer_index] + spv_flow=cur_flow_list[batch_indicies,self_indicies][:,:2] + spv_conf=cur_flow_list[batch_indicies,self_indicies][:,2:]#[#coarse,2] + l2_flow_dis=((gt_flow-spv_flow)**2) #[#coarse,2] + total_loss=(spv_conf+torch.exp(-spv_conf)*l2_flow_dis) #[#coarse,2] + total_loss_list.append(total_loss.mean()) + total_loss=torch.stack(total_loss_list,dim=-1)*self.flow_weight + return total_loss + + def compute_coarse_loss(self, conf, conf_gt, weight=None): + """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt. + Args: + conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1) + conf_gt (torch.Tensor): (N, HW0, HW1) + weight (torch.Tensor): (N, HW0, HW1) + """ + pos_mask, neg_mask = conf_gt == 1, conf_gt == 0 + c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w + # corner case: no gt coarse-level match at all + if not pos_mask.any(): # assign a wrong gt + pos_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_pos_w = 0. + if not neg_mask.any(): + neg_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_neg_w = 0. + + if self.loss_config['coarse_type'] == 'cross_entropy': + assert not self.sparse_spvs, 'Sparse Supervision for cross-entropy not implemented!' + conf = torch.clamp(conf, 1e-6, 1-1e-6) + loss_pos = - torch.log(conf[pos_mask]) + loss_neg = - torch.log(1 - conf[neg_mask]) + if weight is not None: + loss_pos = loss_pos * weight[pos_mask] + loss_neg = loss_neg * weight[neg_mask] + return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() + elif self.loss_config['coarse_type'] == 'focal': + conf = torch.clamp(conf, 1e-6, 1-1e-6) + alpha = self.loss_config['focal_alpha'] + gamma = self.loss_config['focal_gamma'] + + if self.sparse_spvs: + pos_conf = conf[:, :-1, :-1][pos_mask] \ + if self.match_type == 'sinkhorn' \ + else conf[pos_mask] + loss_pos = - alpha * torch.pow(1 - pos_conf, gamma) * pos_conf.log() + # calculate losses for negative samples + if self.match_type == 'sinkhorn': + neg0, neg1 = conf_gt.sum(-1) == 0, conf_gt.sum(1) == 0 + neg_conf = torch.cat([conf[:, :-1, -1][neg0], conf[:, -1, :-1][neg1]], 0) + loss_neg = - alpha * torch.pow(1 - neg_conf, gamma) * neg_conf.log() + else: + # These is no dustbin for dual_softmax, so we left unmatchable patches without supervision. + # we could also add 'pseudo negtive-samples' + pass + # handle loss weights + if weight is not None: + # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out, + # but only through manually setting corresponding regions in sim_matrix to '-inf'. + loss_pos = loss_pos * weight[pos_mask] + if self.match_type == 'sinkhorn': + neg_w0 = (weight.sum(-1) != 0)[neg0] + neg_w1 = (weight.sum(1) != 0)[neg1] + neg_mask = torch.cat([neg_w0, neg_w1], 0) + loss_neg = loss_neg[neg_mask] + + loss = c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() \ + if self.match_type == 'sinkhorn' \ + else c_pos_w * loss_pos.mean() + return loss + # positive and negative elements occupy similar propotions. => more balanced loss weights needed + else: # dense supervision (in the case of match_type=='sinkhorn', the dustbin is not supervised.) + loss_pos = - alpha * torch.pow(1 - conf[pos_mask], gamma) * (conf[pos_mask]).log() + loss_neg = - alpha * torch.pow(conf[neg_mask], gamma) * (1 - conf[neg_mask]).log() + if weight is not None: + loss_pos = loss_pos * weight[pos_mask] + loss_neg = loss_neg * weight[neg_mask] + return c_pos_w * loss_pos.mean() + c_neg_w * loss_neg.mean() + # each negative element occupy a smaller propotion than positive elements. => higher negative loss weight needed + else: + raise ValueError('Unknown coarse loss: {type}'.format(type=self.loss_config['coarse_type'])) + + def compute_fine_loss(self, expec_f, expec_f_gt): + if self.fine_type == 'l2_with_std': + return self._compute_fine_loss_l2_std(expec_f, expec_f_gt) + elif self.fine_type == 'l2': + return self._compute_fine_loss_l2(expec_f, expec_f_gt) + else: + raise NotImplementedError() + + def _compute_fine_loss_l2(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 2] + expec_f_gt (torch.Tensor): [M, 2] + """ + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + if correct_mask.sum() == 0: + if self.training: # this seldomly happen when training, since we pad prediction with gt + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + else: + return None + flow_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1) + return flow_l2.mean() + + def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 3] + expec_f_gt (torch.Tensor): [M, 2] + """ + # correct_mask tells you which pair to compute fine-loss + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + + # use std as weight that measures uncertainty + std = expec_f[:, 2] + inverse_std = 1. / torch.clamp(std, min=1e-10) + weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std + + # corner case: no correct coarse match found + if not correct_mask.any(): + if self.training: # this seldomly happen during training, since we pad prediction with gt + # sometimes there is not coarse-level gt at all. + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + weight[0] = 0. + else: + return None + + # l2 loss with std + flow_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1) + loss = (flow_l2 * weight[correct_mask]).mean() + + return loss + + @torch.no_grad() + def compute_c_weight(self, data): + """ compute element-wise weights for computing coarse-level loss. """ + if 'mask0' in data: + c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float() + else: + c_weight = None + return c_weight + + def forward(self, data): + """ + Update: + data (dict): update{ + 'loss': [1] the reduced loss across a batch, + 'loss_scalars' (dict): loss scalars for tensorboard_record + } + """ + loss_scalars = {} + # 0. compute element-wise loss weight + c_weight = self.compute_c_weight(data) + + # 1. coarse-level loss + loss_c = self.compute_coarse_loss( + data['conf_matrix_with_bin'] if self.sparse_spvs and self.match_type == 'sinkhorn' \ + else data['conf_matrix'], + data['conf_matrix_gt'], + weight=c_weight) + loss = loss_c * self.loss_config['coarse_weight'] + loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()}) + + # 2. fine-level loss + loss_f = self.compute_fine_loss(data['expec_f'], data['expec_f_gt']) + if loss_f is not None: + loss += loss_f * self.loss_config['fine_weight'] + loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()}) + else: + assert self.training is False + loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound + + # 3. flow loss + coarse_corr=[data['spv_b_ids'],data['spv_i_ids'],data['spv_j_ids']] + loss_flow = self.compute_flow_loss(coarse_corr,data['predict_flow'],\ + data['hw0_c'][0],data['hw0_c'][1],data['hw1_c'][0],data['hw1_c'][1]) + loss_flow=loss_flow*self.flow_weight + for index,loss_off in enumerate(loss_flow): + loss_scalars.update({'loss_flow_'+str(index): loss_off.clone().detach().cpu()}) # 1 is the upper bound + conf=data['predict_flow'][0][:,:,:,:,2:] + layer_num=conf.shape[0] + for layer_index in range(layer_num): + loss_scalars.update({'conf_'+str(layer_index): conf[layer_index].mean().clone().detach().cpu()}) # 1 is the upper bound + + + loss+=loss_flow.sum() + #print((loss_c * self.loss_config['coarse_weight']).data,loss_flow.data) + loss_scalars.update({'loss': loss.clone().detach().cpu()}) + data.update({"loss": loss, "loss_scalars": loss_scalars}) diff --git a/third_party/ASpanFormer/src/optimizers/__init__.py b/third_party/ASpanFormer/src/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e1db2285352586c250912bdd2c4ae5029620ab5f --- /dev/null +++ b/third_party/ASpanFormer/src/optimizers/__init__.py @@ -0,0 +1,42 @@ +import torch +from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR + + +def build_optimizer(model, config): + name = config.TRAINER.OPTIMIZER + lr = config.TRAINER.TRUE_LR + + if name == "adam": + return torch.optim.Adam(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY) + elif name == "adamw": + return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY) + else: + raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!") + + +def build_scheduler(config, optimizer): + """ + Returns: + scheduler (dict):{ + 'scheduler': lr_scheduler, + 'interval': 'step', # or 'epoch' + 'monitor': 'val_f1', (optional) + 'frequency': x, (optional) + } + """ + scheduler = {'interval': config.TRAINER.SCHEDULER_INTERVAL} + name = config.TRAINER.SCHEDULER + + if name == 'MultiStepLR': + scheduler.update( + {'scheduler': MultiStepLR(optimizer, config.TRAINER.MSLR_MILESTONES, gamma=config.TRAINER.MSLR_GAMMA)}) + elif name == 'CosineAnnealing': + scheduler.update( + {'scheduler': CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}) + elif name == 'ExponentialLR': + scheduler.update( + {'scheduler': ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}) + else: + raise NotImplementedError() + + return scheduler diff --git a/third_party/ASpanFormer/src/utils/augment.py b/third_party/ASpanFormer/src/utils/augment.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c5d3e11b6fe083aaeff7555bb7ce3a4bfb755d --- /dev/null +++ b/third_party/ASpanFormer/src/utils/augment.py @@ -0,0 +1,55 @@ +import albumentations as A + + +class DarkAug(object): + """ + Extreme dark augmentation aiming at Aachen Day-Night + """ + + def __init__(self) -> None: + self.augmentor = A.Compose([ + A.RandomBrightnessContrast(p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)), + A.Blur(p=0.1, blur_limit=(3, 9)), + A.MotionBlur(p=0.2, blur_limit=(3, 25)), + A.RandomGamma(p=0.1, gamma_limit=(15, 65)), + A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)) + ], p=0.75) + + def __call__(self, x): + return self.augmentor(image=x)['image'] + + +class MobileAug(object): + """ + Random augmentations aiming at images of mobile/handhold devices. + """ + + def __init__(self): + self.augmentor = A.Compose([ + A.MotionBlur(p=0.25), + A.ColorJitter(p=0.5), + A.RandomRain(p=0.1), # random occlusion + A.RandomSunFlare(p=0.1), + A.JpegCompression(p=0.25), + A.ISONoise(p=0.25) + ], p=1.0) + + def __call__(self, x): + return self.augmentor(image=x)['image'] + + +def build_augmentor(method=None, **kwargs): + if method is not None: + raise NotImplementedError('Using of augmentation functions are not supported yet!') + if method == 'dark': + return DarkAug() + elif method == 'mobile': + return MobileAug() + elif method is None: + return None + else: + raise ValueError(f'Invalid augmentation method: {method}') + + +if __name__ == '__main__': + augmentor = build_augmentor('FDA') diff --git a/third_party/ASpanFormer/src/utils/comm.py b/third_party/ASpanFormer/src/utils/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..26ec9517cc47e224430106d8ae9aa99a3fe49167 --- /dev/null +++ b/third_party/ASpanFormer/src/utils/comm.py @@ -0,0 +1,265 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +[Copied from detectron2] +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import functools +import logging +import numpy as np +import pickle +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert _LOCAL_PROCESS_GROUP is not None + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def _serialize_to_tensor(data, group): + backend = dist.get_backend(group) + assert backend in ["gloo", "nccl"] + device = torch.device("cpu" if backend == "gloo" else "cuda") + + buffer = pickle.dumps(data) + if len(buffer) > 1024 ** 3: + logger = logging.getLogger(__name__) + logger.warning( + "Rank {} trying to all-gather {:.2f} GB of data on device {}".format( + get_rank(), len(buffer) / (1024 ** 3), device + ) + ) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to(device=device) + return tensor + + +def _pad_to_largest_tensor(tensor, group): + """ + Returns: + list[int]: size of the tensor, on each rank + Tensor: padded tensor that has the max size + """ + world_size = dist.get_world_size(group=group) + assert ( + world_size >= 1 + ), "comm.gather/all_gather must be called from ranks within the given group!" + local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device) + size_list = [ + torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size) + ] + dist.all_gather(size_list, local_size, group=group) + + size_list = [int(size.item()) for size in size_list] + + max_size = max(size_list) + + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + if local_size != max_size: + padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device) + tensor = torch.cat((tensor, padding), dim=0) + return size_list, tensor + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group) == 1: + return [data] + + tensor = _serialize_to_tensor(data, group) + + size_list, tensor = _pad_to_largest_tensor(tensor, group) + max_size = max(size_list) + + # receiving Tensor from all ranks + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.all_gather(tensor_list, tensor, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group=group) == 1: + return [data] + rank = dist.get_rank(group=group) + + tensor = _serialize_to_tensor(data, group) + size_list, tensor = _pad_to_largest_tensor(tensor, group) + + # receiving Tensor from all ranks + if rank == dst: + max_size = max(size_list) + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.gather(tensor, tensor_list, dst=dst, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + return data_list + else: + dist.gather(tensor, [], dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2 ** 31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/third_party/ASpanFormer/src/utils/dataloader.py b/third_party/ASpanFormer/src/utils/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6da37b880a290c2bb3ebb028d0c8dab592acc5c1 --- /dev/null +++ b/third_party/ASpanFormer/src/utils/dataloader.py @@ -0,0 +1,23 @@ +import numpy as np + + +# --- PL-DATAMODULE --- + +def get_local_split(items: list, world_size: int, rank: int, seed: int): + """ The local rank only loads a split of the dataset. """ + n_items = len(items) + items_permute = np.random.RandomState(seed).permutation(items) + if n_items % world_size == 0: + padded_items = items_permute + else: + padding = np.random.RandomState(seed).choice( + items, + world_size - (n_items % world_size), + replace=True) + padded_items = np.concatenate([items_permute, padding]) + assert len(padded_items) % world_size == 0, \ + f'len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}' + n_per_rank = len(padded_items) // world_size + local_items = padded_items[n_per_rank * rank: n_per_rank * (rank+1)] + + return local_items diff --git a/third_party/ASpanFormer/src/utils/dataset.py b/third_party/ASpanFormer/src/utils/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..209bf554acc20e33ea89eb9e7024ba68d0b3a30b --- /dev/null +++ b/third_party/ASpanFormer/src/utils/dataset.py @@ -0,0 +1,222 @@ +import io +import cv2 +import numpy as np +import h5py +import torch +from numpy.linalg import inv +import re + + +try: + # for internel use only + from .client import MEGADEPTH_CLIENT, SCANNET_CLIENT +except Exception: + MEGADEPTH_CLIENT = SCANNET_CLIENT = None + +# --- DATA IO --- + +def load_array_from_s3( + path, client, cv_type, + use_h5py=False, +): + byte_str = client.Get(path) + try: + if not use_h5py: + raw_array = np.fromstring(byte_str, np.uint8) + data = cv2.imdecode(raw_array, cv_type) + else: + f = io.BytesIO(byte_str) + data = np.array(h5py.File(f, 'r')['/depth']) + except Exception as ex: + print(f"==> Data loading failure: {path}") + raise ex + + assert data is not None + return data + + +def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT): + cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \ + else cv2.IMREAD_COLOR + if str(path).startswith('s3://'): + image = load_array_from_s3(str(path), client, cv_type) + else: + image = cv2.imread(str(path), cv_type) + + if augment_fn is not None: + image = cv2.imread(str(path), cv2.IMREAD_COLOR) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image = augment_fn(image) + image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + return image # (h, w) + + +def get_resized_wh(w, h, resize=None): + if resize is not None: # resize the longer edge + scale = resize / max(h, w) + w_new, h_new = int(round(w*scale)), int(round(h*scale)) + else: + w_new, h_new = w, h + return w_new, h_new + + +def get_divisible_wh(w, h, df=None): + if df is not None: + w_new, h_new = map(lambda x: int(x // df * df), [w, h]) + else: + w_new, h_new = w, h + return w_new, h_new + + +def pad_bottom_right(inp, pad_size, ret_mask=False): + assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}" + mask = None + if inp.ndim == 2: + padded = np.zeros((pad_size, pad_size), dtype=inp.dtype) + padded[:inp.shape[0], :inp.shape[1]] = inp + if ret_mask: + mask = np.zeros((pad_size, pad_size), dtype=bool) + mask[:inp.shape[0], :inp.shape[1]] = True + elif inp.ndim == 3: + padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype) + padded[:, :inp.shape[1], :inp.shape[2]] = inp + if ret_mask: + mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool) + mask[:, :inp.shape[1], :inp.shape[2]] = True + else: + raise NotImplementedError() + return padded, mask + + +# --- MEGADEPTH --- + +def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None): + """ + Args: + resize (int, optional): the longer edge of resized images. None for no resize. + padding (bool): If set to 'True', zero-pad resized images to squared size. + augment_fn (callable, optional): augments images with pre-defined visual effects + Returns: + image (torch.tensor): (1, h, w) + mask (torch.tensor): (h, w) + scale (torch.tensor): [w/w_new, h/h_new] + """ + # read image + image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) + + # resize image + w, h = image.shape[1], image.shape[0] + w_new, h_new = get_resized_wh(w, h, resize) + w_new, h_new = get_divisible_wh(w_new, h_new, df) + + image = cv2.resize(image, (w_new, h_new)) + scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float) + + if padding: # padding + pad_to = max(h_new, w_new) + image, mask = pad_bottom_right(image, pad_to, ret_mask=True) + else: + mask = None + + image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized + if mask is not None: + mask = torch.from_numpy(mask) + + return image, mask, scale + + +def read_megadepth_depth(path, pad_to=None): + if str(path).startswith('s3://'): + depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True) + else: + depth = np.array(h5py.File(path, 'r')['depth']) + if pad_to is not None: + depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False) + depth = torch.from_numpy(depth).float() # (h, w) + return depth + + +# --- ScanNet --- + +def read_scannet_gray(path, resize=(640, 480), augment_fn=None): + """ + Args: + resize (tuple): align image to depthmap, in (w, h). + augment_fn (callable, optional): augments images with pre-defined visual effects + Returns: + image (torch.tensor): (1, h, w) + mask (torch.tensor): (h, w) + scale (torch.tensor): [w/w_new, h/h_new] + """ + # read and resize image + image = imread_gray(path, augment_fn) + image = cv2.resize(image, resize) + + # (h, w) -> (1, h, w) and normalized + image = torch.from_numpy(image).float()[None] / 255 + return image + + +def read_scannet_depth(path): + if str(path).startswith('s3://'): + depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED) + else: + depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) + depth = depth / 1000 + depth = torch.from_numpy(depth).float() # (h, w) + return depth + + +def read_scannet_pose(path): + """ Read ScanNet's Camera2World pose and transform it to World2Camera. + + Returns: + pose_w2c (np.ndarray): (4, 4) + """ + cam2world = np.loadtxt(path, delimiter=' ') + world2cam = inv(cam2world) + return world2cam + + +def read_scannet_intrinsic(path): + """ Read ScanNet's intrinsic matrix and return the 3x3 matrix. + """ + intrinsic = np.loadtxt(path, delimiter=' ') + return intrinsic[:-1, :-1] + + +def read_gl3d_gray(path,resize): + img=cv2.resize(cv2.imread(path,cv2.IMREAD_GRAYSCALE),(int(resize),int(resize))) + img = torch.from_numpy(img).float()[None] / 255 # (h, w) -> (1, h, w) and normalized + return img + +def read_gl3d_depth(file_path): + with open(file_path, 'rb') as fin: + color = None + width = None + height = None + scale = None + data_type = None + header = str(fin.readline().decode('UTF-8')).rstrip() + if header == 'PF': + color = True + elif header == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + dim_match = re.match(r'^(\d+)\s(\d+)\s$', fin.readline().decode('UTF-8')) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + scale = float((fin.readline().decode('UTF-8')).rstrip()) + if scale < 0: # little-endian + data_type = ' best_num_inliers: + ret = (R, t[:, 0], mask.ravel() > 0) + best_num_inliers = n + + return ret + + +def compute_pose_errors(data, config): + """ + Update: + data (dict):{ + "R_errs" List[float]: [N] + "t_errs" List[float]: [N] + "inliers" List[np.ndarray]: [N] + } + """ + pixel_thr = config.TRAINER.RANSAC_PIXEL_THR # 0.5 + conf = config.TRAINER.RANSAC_CONF # 0.99999 + data.update({'R_errs': [], 't_errs': [], 'inliers': []}) + + m_bids = data['m_bids'].cpu().numpy() + pts0 = data['mkpts0_f'].cpu().numpy() + pts1 = data['mkpts1_f'].cpu().numpy() + K0 = data['K0'].cpu().numpy() + K1 = data['K1'].cpu().numpy() + T_0to1 = data['T_0to1'].cpu().numpy() + + for bs in range(K0.shape[0]): + mask = m_bids == bs + ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) + + if ret is None: + data['R_errs'].append(np.inf) + data['t_errs'].append(np.inf) + data['inliers'].append(np.array([]).astype(np.bool)) + else: + R, t, inliers = ret + t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) + data['R_errs'].append(R_err) + data['t_errs'].append(t_err) + data['inliers'].append(inliers) + + +# --- METRIC AGGREGATION --- + +def error_auc(errors, thresholds): + """ + Args: + errors (list): [N,] + thresholds (list) + """ + errors = [0] + sorted(list(errors)) + recall = list(np.linspace(0, 1, len(errors))) + + aucs = [] + thresholds = [5, 10, 20] + for thr in thresholds: + last_index = np.searchsorted(errors, thr) + y = recall[:last_index] + [recall[last_index-1]] + x = errors[:last_index] + [thr] + aucs.append(np.trapz(y, x) / thr) + + return {f'auc@{t}': auc for t, auc in zip(thresholds, aucs)} + + +def epidist_prec(errors, thresholds, ret_dict=False,offset=False): + precs = [] + for thr in thresholds: + prec_ = [] + for errs in errors: + correct_mask = errs < thr + prec_.append(np.mean(correct_mask) if len(correct_mask) > 0 else 0) + precs.append(np.mean(prec_) if len(prec_) > 0 else 0) + if ret_dict: + return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)} if not offset else {f'prec_flow@{t:.0e}': prec for t, prec in zip(thresholds, precs)} + else: + return precs + + +def aggregate_metrics(metrics, epi_err_thr=5e-4): + """ Aggregate metrics for the whole dataset: + (This method should be called once per dataset) + 1. AUC of the pose error (angular) at the threshold [5, 10, 20] + 2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth) + """ + # filter duplicates + unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers'])) + unq_ids = list(unq_ids.values()) + logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...') + + # pose auc + angular_thresholds = [5, 10, 20] + pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids] + aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20) + + # matching precision + dist_thresholds = [epi_err_thr] + precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr) + + #offset precision + try: + precs_offset = epidist_prec(np.array(metrics['epi_errs_offset'], dtype=object)[unq_ids], [2e-3], True,offset=True) + return {**aucs, **precs,**precs_offset} + except: + return {**aucs, **precs} diff --git a/third_party/ASpanFormer/src/utils/misc.py b/third_party/ASpanFormer/src/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..25e4433f5ffa41adc4c0435cfe2b5696e43b58b3 --- /dev/null +++ b/third_party/ASpanFormer/src/utils/misc.py @@ -0,0 +1,139 @@ +import os +import contextlib +import joblib +from typing import Union +from loguru import _Logger, logger +from itertools import chain + +import torch +from yacs.config import CfgNode as CN +from pytorch_lightning.utilities import rank_zero_only +import cv2 +import numpy as np + +def lower_config(yacs_cfg): + if not isinstance(yacs_cfg, CN): + return yacs_cfg + return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} + + +def upper_config(dict_cfg): + if not isinstance(dict_cfg, dict): + return dict_cfg + return {k.upper(): upper_config(v) for k, v in dict_cfg.items()} + + +def log_on(condition, message, level): + if condition: + assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL'] + logger.log(level, message) + + +def get_rank_zero_only_logger(logger: _Logger): + if rank_zero_only.rank == 0: + return logger + else: + for _level in logger._core.levels.keys(): + level = _level.lower() + setattr(logger, level, + lambda x: None) + logger._log = lambda x: None + return logger + + +def setup_gpus(gpus: Union[str, int]) -> int: + """ A temporary fix for pytorch-lighting 1.3.x """ + gpus = str(gpus) + gpu_ids = [] + + if ',' not in gpus: + n_gpus = int(gpus) + return n_gpus if n_gpus != -1 else torch.cuda.device_count() + else: + gpu_ids = [i.strip() for i in gpus.split(',') if i != ''] + + # setup environment variables + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + if visible_devices is None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids) + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}') + else: + logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.') + return len(gpu_ids) + + +def flattenList(x): + return list(chain(*x)) + + +@contextlib.contextmanager +def tqdm_joblib(tqdm_object): + """Context manager to patch joblib to report into tqdm progress bar given as argument + + Usage: + with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: + Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) + + When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing) + ret_vals = Parallel(n_jobs=args.world_size)( + delayed(lambda x: _compute_cov_score(pid, *x))(param) + for param in tqdm(combinations(image_ids, 2), + desc=f'Computing cov_score of [{pid}]', + total=len(image_ids)*(len(image_ids)-1)/2)) + Src: https://stackoverflow.com/a/58936697 + """ + class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def __call__(self, *args, **kwargs): + tqdm_object.update(n=self.batch_size) + return super().__call__(*args, **kwargs) + + old_batch_callback = joblib.parallel.BatchCompletionCallBack + joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback + try: + yield tqdm_object + finally: + joblib.parallel.BatchCompletionCallBack = old_batch_callback + tqdm_object.close() + + +def draw_points(img,points,color=(0,255,0),radius=3): + dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])] + for i in range(points.shape[0]): + cv2.circle(img, dp[i],radius=radius,color=color) + return img + + +def draw_match(img1, img2, corr1, corr2,inlier=[True],color=None,radius1=1,radius2=1,resize=None): + if resize is not None: + scale1,scale2=[img1.shape[1]/resize[0],img1.shape[0]/resize[1]],[img2.shape[1]/resize[0],img2.shape[0]/resize[1]] + img1,img2=cv2.resize(img1, resize, interpolation=cv2.INTER_AREA),cv2.resize(img2, resize, interpolation=cv2.INTER_AREA) + corr1,corr2=corr1/np.asarray(scale1)[np.newaxis],corr2/np.asarray(scale2)[np.newaxis] + corr1_key = [cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])] + corr2_key = [cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])] + + assert len(corr1) == len(corr2) + + draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))] + if color is None: + color = [(0, 255, 0) if cur_inlier else (0,0,255) for cur_inlier in inlier] + if len(color)==1: + display = cv2.drawMatches(img1, corr1_key, img2, corr2_key, draw_matches, None, + matchColor=color[0], + singlePointColor=color[0], + flags=4 + ) + else: + height,width=max(img1.shape[0],img2.shape[0]),img1.shape[1]+img2.shape[1] + display=np.zeros([height,width,3],np.uint8) + display[:img1.shape[0],:img1.shape[1]]=img1 + display[:img2.shape[0],img1.shape[1]:]=img2 + for i in range(len(corr1)): + left_x,left_y,right_x,right_y=int(corr1[i][0]),int(corr1[i][1]),int(corr2[i][0]+img1.shape[1]),int(corr2[i][1]) + cur_color=(int(color[i][0]),int(color[i][1]),int(color[i][2])) + cv2.line(display, (left_x,left_y), (right_x,right_y),cur_color,1,lineType=cv2.LINE_AA) + return display diff --git a/third_party/ASpanFormer/src/utils/plotting.py b/third_party/ASpanFormer/src/utils/plotting.py new file mode 100644 index 0000000000000000000000000000000000000000..8696880237b6ad9fe48d3c1fc44ed13b691a6c4d --- /dev/null +++ b/third_party/ASpanFormer/src/utils/plotting.py @@ -0,0 +1,219 @@ +import bisect +import numpy as np +import matplotlib.pyplot as plt +import matplotlib +from copy import deepcopy + +def _compute_conf_thresh(data): + dataset_name = data['dataset_name'][0].lower() + if dataset_name == 'scannet': + thr = 5e-4 + elif dataset_name == 'megadepth' or dataset_name=='gl3d': + thr = 1e-4 + else: + raise ValueError(f'Unknown dataset: {dataset_name}') + return thr + + +# --- VISUALIZATION --- # + +def make_matching_figure( + img0, img1, mkpts0, mkpts1, color, + kpts0=None, kpts1=None, text=[], dpi=75, path=None): + # draw image pair + assert mkpts0.shape[0] == mkpts1.shape[0], f'mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}' + fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi) + axes[0].imshow(img0, cmap='gray') + axes[1].imshow(img1, cmap='gray') + for i in range(2): # clear all frames + axes[i].get_yaxis().set_ticks([]) + axes[i].get_xaxis().set_ticks([]) + for spine in axes[i].spines.values(): + spine.set_visible(False) + plt.tight_layout(pad=1) + + if kpts0 is not None: + assert kpts1 is not None + axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c='w', s=2) + axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c='w', s=2) + + # draw matches + if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0: + fig.canvas.draw() + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0)) + fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1)) + fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]), + (fkpts0[i, 1], fkpts1[i, 1]), + transform=fig.transFigure, c=color[i], linewidth=1) + for i in range(len(mkpts0))] + + axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color, s=4) + axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color, s=4) + + # put txts + txt_color = 'k' if img0[:100, :200].mean() > 200 else 'w' + fig.text( + 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes, + fontsize=15, va='top', ha='left', color=txt_color) + + # save or return figure + if path: + plt.savefig(str(path), bbox_inches='tight', pad_inches=0) + plt.close() + else: + return fig + + +def _make_evaluation_figure(data, b_id, alpha='dynamic'): + b_mask = data['m_bids'] == b_id + conf_thr = _compute_conf_thresh(data) + + img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + kpts0 = data['mkpts0_f'][b_mask].cpu().numpy() + kpts1 = data['mkpts1_f'][b_mask].cpu().numpy() + + # for megadepth, we visualize matches on the resized image + if 'scale0' in data: + kpts0 = kpts0 / data['scale0'][b_id].cpu().numpy()[[1, 0]] + kpts1 = kpts1 / data['scale1'][b_id].cpu().numpy()[[1, 0]] + epi_errs = data['epi_errs'][b_mask].cpu().numpy() + correct_mask = epi_errs < conf_thr + precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0 + n_correct = np.sum(correct_mask) + n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu()) + recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches) + # recall might be larger than 1, since the calculation of conf_matrix_gt + # uses groundtruth depths and camera poses, but epipolar distance is used here. + + # matching info + if alpha == 'dynamic': + alpha = dynamic_alpha(len(correct_mask)) + color = error_colormap(epi_errs, conf_thr, alpha=alpha) + + text = [ + f'#Matches {len(kpts0)}', + f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}', + f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}' + ] + + # make the figure + figure = make_matching_figure(img0, img1, kpts0, kpts1, + color, text=text) + return figure + +def _make_evaluation_figure_offset(data, b_id, alpha='dynamic',side=''): + layer_num=data['predict_flow'][0].shape[0] + + b_mask = data['offset_bids'+side] == b_id + conf_thr = 2e-3 #hardcode for scannet(coarse level) + img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + + figure_list=[] + #draw offset matches in different layers + for layer_index in range(layer_num): + l_mask=data['offset_lids'+side]==layer_index + mask=l_mask&b_mask + kpts0 = data['offset_kpts0_f'+side][mask].cpu().numpy() + kpts1 = data['offset_kpts1_f'+side][mask].cpu().numpy() + + epi_errs = data['epi_errs_offset'+side][mask].cpu().numpy() + correct_mask = epi_errs < conf_thr + + precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0 + n_correct = np.sum(correct_mask) + n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu()) + recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches) + # recall might be larger than 1, since the calculation of conf_matrix_gt + # uses groundtruth depths and camera poses, but epipolar distance is used here. + + # matching info + if alpha == 'dynamic': + alpha = dynamic_alpha(len(correct_mask)) + color = error_colormap(epi_errs, conf_thr, alpha=alpha) + + text = [ + f'#Matches {len(kpts0)}', + f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}', + f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}' + ] + + # make the figure + #import pdb;pdb.set_trace() + figure = make_matching_figure(deepcopy(img0), deepcopy(img1) , kpts0, kpts1, + color, text=text) + figure_list.append(figure) + return figure + +def _make_confidence_figure(data, b_id): + # TODO: Implement confidence figure + raise NotImplementedError() + + +def make_matching_figures(data, config, mode='evaluation'): + """ Make matching figures for a batch. + + Args: + data (Dict): a batch updated by PL_LoFTR. + config (Dict): matcher config + Returns: + figures (Dict[str, List[plt.figure]] + """ + assert mode in ['evaluation', 'confidence'] # 'confidence' + figures = {mode: []} + for b_id in range(data['image0'].size(0)): + if mode == 'evaluation': + fig = _make_evaluation_figure( + data, b_id, + alpha=config.TRAINER.PLOT_MATCHES_ALPHA) + elif mode == 'confidence': + fig = _make_confidence_figure(data, b_id) + else: + raise ValueError(f'Unknown plot mode: {mode}') + figures[mode].append(fig) + return figures + +def make_matching_figures_offset(data, config, mode='evaluation',side=''): + """ Make matching figures for a batch. + + Args: + data (Dict): a batch updated by PL_LoFTR. + config (Dict): matcher config + Returns: + figures (Dict[str, List[plt.figure]] + """ + assert mode in ['evaluation', 'confidence'] # 'confidence' + figures = {mode: []} + for b_id in range(data['image0'].size(0)): + if mode == 'evaluation': + fig = _make_evaluation_figure_offset( + data, b_id, + alpha=config.TRAINER.PLOT_MATCHES_ALPHA,side=side) + elif mode == 'confidence': + fig = _make_evaluation_figure_offset(data, b_id) + else: + raise ValueError(f'Unknown plot mode: {mode}') + figures[mode].append(fig) + return figures + +def dynamic_alpha(n_matches, + milestones=[0, 300, 1000, 2000], + alphas=[1.0, 0.8, 0.4, 0.2]): + if n_matches == 0: + return 1.0 + ranges = list(zip(alphas, alphas[1:] + [None])) + loc = bisect.bisect_right(milestones, n_matches) - 1 + _range = ranges[loc] + if _range[1] is None: + return _range[0] + return _range[1] + (milestones[loc + 1] - n_matches) / ( + milestones[loc + 1] - milestones[loc]) * (_range[0] - _range[1]) + + +def error_colormap(err, thr, alpha=1.0): + assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}" + x = 1 - np.clip(err / (thr * 2), 0, 1) + return np.clip( + np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)*alpha], -1), 0, 1) diff --git a/third_party/ASpanFormer/src/utils/profiler.py b/third_party/ASpanFormer/src/utils/profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..6d21ed79fb506ef09c75483355402c48a195aaa9 --- /dev/null +++ b/third_party/ASpanFormer/src/utils/profiler.py @@ -0,0 +1,39 @@ +import torch +from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler +from contextlib import contextmanager +from pytorch_lightning.utilities import rank_zero_only + + +class InferenceProfiler(SimpleProfiler): + """ + This profiler records duration of actions with cuda.synchronize() + Use this in test time. + """ + + def __init__(self): + super().__init__() + self.start = rank_zero_only(self.start) + self.stop = rank_zero_only(self.stop) + self.summary = rank_zero_only(self.summary) + + @contextmanager + def profile(self, action_name: str) -> None: + try: + torch.cuda.synchronize() + self.start(action_name) + yield action_name + finally: + torch.cuda.synchronize() + self.stop(action_name) + + +def build_profiler(name): + if name == 'inference': + return InferenceProfiler() + elif name == 'pytorch': + from pytorch_lightning.profiler import PyTorchProfiler + return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100) + elif name is None: + return PassThroughProfiler() + else: + raise ValueError(f'Invalid profiler: {name}') diff --git a/third_party/ASpanFormer/test.py b/third_party/ASpanFormer/test.py new file mode 100644 index 0000000000000000000000000000000000000000..541ce84662ab4888c6fece30403c5c9983118637 --- /dev/null +++ b/third_party/ASpanFormer/test.py @@ -0,0 +1,69 @@ +import pytorch_lightning as pl +import argparse +import pprint +from loguru import logger as loguru_logger + +from src.config.default import get_cfg_defaults +from src.utils.profiler import build_profiler + +from src.lightning.data import MultiSceneDataModule +from src.lightning.lightning_aspanformer import PL_ASpanFormer +import torch + +def parse_args(): + # init a costum parser which will be added into pl.Trainer parser + # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + 'data_cfg_path', type=str, help='data config path') + parser.add_argument( + 'main_cfg_path', type=str, help='main config path') + parser.add_argument( + '--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint') + parser.add_argument( + '--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir") + parser.add_argument( + '--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset') + parser.add_argument( + '--batch_size', type=int, default=1, help='batch_size per gpu') + parser.add_argument( + '--num_workers', type=int, default=2) + parser.add_argument( + '--thr', type=float, default=None, help='modify the coarse-level matching threshold.') + parser.add_argument( + '--mode', type=str, default='vanilla', help='modify the coarse-level matching threshold.') + parser = pl.Trainer.add_argparse_args(parser) + return parser.parse_args() + + +if __name__ == '__main__': + # parse arguments + args = parse_args() + pprint.pprint(vars(args)) + + # init default-cfg and merge it with the main- and data-cfg + config = get_cfg_defaults() + config.merge_from_file(args.main_cfg_path) + config.merge_from_file(args.data_cfg_path) + pl.seed_everything(config.TRAINER.SEED) # reproducibility + + # tune when testing + if args.thr is not None: + config.ASPAN.MATCH_COARSE.THR = args.thr + + loguru_logger.info(f"Args and config initialized!") + + # lightning module + profiler = build_profiler(args.profiler_name) + model = PL_ASpanFormer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir) + loguru_logger.info(f"ASpanFormer-lightning initialized!") + + # lightning data + data_module = MultiSceneDataModule(args, config) + loguru_logger.info(f"DataModule initialized!") + + # lightning trainer + trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False) + + loguru_logger.info(f"Start testing!") + trainer.test(model, datamodule=data_module, verbose=False) diff --git a/third_party/ASpanFormer/tools/SensorData.py b/third_party/ASpanFormer/tools/SensorData.py new file mode 100644 index 0000000000000000000000000000000000000000..a3ec2644bf8b3b988ef0f36851cd3317c00511b2 --- /dev/null +++ b/third_party/ASpanFormer/tools/SensorData.py @@ -0,0 +1,125 @@ + +import os, struct +import numpy as np +import zlib +import imageio +import cv2 +import png + +COMPRESSION_TYPE_COLOR = {-1:'unknown', 0:'raw', 1:'png', 2:'jpeg'} +COMPRESSION_TYPE_DEPTH = {-1:'unknown', 0:'raw_ushort', 1:'zlib_ushort', 2:'occi_ushort'} + +class RGBDFrame(): + + def load(self, file_handle): + self.camera_to_world = np.asarray(struct.unpack('f'*16, file_handle.read(16*4)), dtype=np.float32).reshape(4, 4) + self.timestamp_color = struct.unpack('Q', file_handle.read(8))[0] + self.timestamp_depth = struct.unpack('Q', file_handle.read(8))[0] + self.color_size_bytes = struct.unpack('Q', file_handle.read(8))[0] + self.depth_size_bytes = struct.unpack('Q', file_handle.read(8))[0] + self.color_data = ''.join(struct.unpack('c'*self.color_size_bytes, file_handle.read(self.color_size_bytes))) + self.depth_data = ''.join(struct.unpack('c'*self.depth_size_bytes, file_handle.read(self.depth_size_bytes))) + + + def decompress_depth(self, compression_type): + if compression_type == 'zlib_ushort': + return self.decompress_depth_zlib() + else: + raise + + + def decompress_depth_zlib(self): + return zlib.decompress(self.depth_data) + + + def decompress_color(self, compression_type): + if compression_type == 'jpeg': + return self.decompress_color_jpeg() + else: + raise + + + def decompress_color_jpeg(self): + return imageio.imread(self.color_data) + + +class SensorData: + + def __init__(self, filename): + self.version = 4 + self.load(filename) + + + def load(self, filename): + with open(filename, 'rb') as f: + version = struct.unpack('I', f.read(4))[0] + assert self.version == version + strlen = struct.unpack('Q', f.read(8))[0] + self.sensor_name = ''.join(struct.unpack('c'*strlen, f.read(strlen))) + self.intrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4) + self.extrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4) + self.intrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4) + self.extrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4) + self.color_compression_type = COMPRESSION_TYPE_COLOR[struct.unpack('i', f.read(4))[0]] + self.depth_compression_type = COMPRESSION_TYPE_DEPTH[struct.unpack('i', f.read(4))[0]] + self.color_width = struct.unpack('I', f.read(4))[0] + self.color_height = struct.unpack('I', f.read(4))[0] + self.depth_width = struct.unpack('I', f.read(4))[0] + self.depth_height = struct.unpack('I', f.read(4))[0] + self.depth_shift = struct.unpack('f', f.read(4))[0] + num_frames = struct.unpack('Q', f.read(8))[0] + self.frames = [] + for i in range(num_frames): + frame = RGBDFrame() + frame.load(f) + self.frames.append(frame) + + + def export_depth_images(self, output_path, image_size=None, frame_skip=1): + if not os.path.exists(output_path): + os.makedirs(output_path) + print 'exporting', len(self.frames)//frame_skip, ' depth frames to', output_path + for f in range(0, len(self.frames), frame_skip): + depth_data = self.frames[f].decompress_depth(self.depth_compression_type) + depth = np.fromstring(depth_data, dtype=np.uint16).reshape(self.depth_height, self.depth_width) + if image_size is not None: + depth = cv2.resize(depth, (image_size[1], image_size[0]), interpolation=cv2.INTER_NEAREST) + #imageio.imwrite(os.path.join(output_path, str(f) + '.png'), depth) + with open(os.path.join(output_path, str(f) + '.png'), 'wb') as f: # write 16-bit + writer = png.Writer(width=depth.shape[1], height=depth.shape[0], bitdepth=16) + depth = depth.reshape(-1, depth.shape[1]).tolist() + writer.write(f, depth) + + def export_color_images(self, output_path, image_size=None, frame_skip=1): + if not os.path.exists(output_path): + os.makedirs(output_path) + print 'exporting', len(self.frames)//frame_skip, 'color frames to', output_path + for f in range(0, len(self.frames), frame_skip): + color = self.frames[f].decompress_color(self.color_compression_type) + if image_size is not None: + color = cv2.resize(color, (image_size[1], image_size[0]), interpolation=cv2.INTER_NEAREST) + imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color) + + + def save_mat_to_file(self, matrix, filename): + with open(filename, 'w') as f: + for line in matrix: + np.savetxt(f, line[np.newaxis], fmt='%f') + + + def export_poses(self, output_path, frame_skip=1): + if not os.path.exists(output_path): + os.makedirs(output_path) + print 'exporting', len(self.frames)//frame_skip, 'camera poses to', output_path + for f in range(0, len(self.frames), frame_skip): + self.save_mat_to_file(self.frames[f].camera_to_world, os.path.join(output_path, str(f) + '.txt')) + + + def export_intrinsics(self, output_path): + if not os.path.exists(output_path): + os.makedirs(output_path) + print 'exporting camera intrinsics to', output_path + self.save_mat_to_file(self.intrinsic_color, os.path.join(output_path, 'intrinsic_color.txt')) + self.save_mat_to_file(self.extrinsic_color, os.path.join(output_path, 'extrinsic_color.txt')) + self.save_mat_to_file(self.intrinsic_depth, os.path.join(output_path, 'intrinsic_depth.txt')) + self.save_mat_to_file(self.extrinsic_depth, os.path.join(output_path, 'extrinsic_depth.txt')) \ No newline at end of file diff --git a/third_party/ASpanFormer/tools/extract.py b/third_party/ASpanFormer/tools/extract.py new file mode 100644 index 0000000000000000000000000000000000000000..12f55e2f94120d5765f124f8eec867f1d82e0aa7 --- /dev/null +++ b/third_party/ASpanFormer/tools/extract.py @@ -0,0 +1,47 @@ +import os +import glob +from re import split +from tqdm import tqdm +from multiprocessing import Pool +from functools import partial + +scannet_dir='/root/data/ScanNet-v2-1.0.0/data/raw' +dump_dir='/root/data/scannet_dump' +num_process=32 + +def extract(seq,scannet_dir,split,dump_dir): + assert split=='train' or split=='test' + if not os.path.exists(os.path.join(dump_dir,split,seq)): + os.mkdir(os.path.join(dump_dir,split,seq)) + cmd='python reader.py --filename '+os.path.join(scannet_dir,'scans' if split=='train' else 'scans_test',seq,seq+'.sens')+' --output_path '+os.path.join(dump_dir,split,seq)+\ + ' --export_depth_images --export_color_images --export_poses --export_intrinsics' + os.system(cmd) + +if __name__=='__main__': + if not os.path.exists(dump_dir): + os.mkdir(dump_dir) + os.mkdir(os.path.join(dump_dir,'train')) + os.mkdir(os.path.join(dump_dir,'test')) + + train_seq_list=[seq.split('/')[-1] for seq in glob.glob(os.path.join(scannet_dir,'scans','scene*'))] + test_seq_list=[seq.split('/')[-1] for seq in glob.glob(os.path.join(scannet_dir,'scans_test','scene*'))] + + extract_train=partial(extract,scannet_dir=scannet_dir,split='train',dump_dir=dump_dir) + extract_test=partial(extract,scannet_dir=scannet_dir,split='test',dump_dir=dump_dir) + + num_train_iter=len(train_seq_list)//num_process if len(train_seq_list)%num_process==0 else len(train_seq_list)//num_process+1 + num_test_iter=len(test_seq_list)//num_process if len(test_seq_list)%num_process==0 else len(test_seq_list)//num_process+1 + + pool = Pool(num_process) + for index in tqdm(range(num_train_iter)): + seq_list=train_seq_list[index*num_process:min((index+1)*num_process,len(train_seq_list))] + pool.map(extract_train,seq_list) + pool.close() + pool.join() + + pool = Pool(num_process) + for index in tqdm(range(num_test_iter)): + seq_list=test_seq_list[index*num_process:min((index+1)*num_process,len(test_seq_list))] + pool.map(extract_test,seq_list) + pool.close() + pool.join() \ No newline at end of file diff --git a/third_party/ASpanFormer/tools/preprocess_scene.py b/third_party/ASpanFormer/tools/preprocess_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..d20c0d070243519d67bbd25668ff5eb1657474be --- /dev/null +++ b/third_party/ASpanFormer/tools/preprocess_scene.py @@ -0,0 +1,242 @@ +import argparse + +import imagesize + +import numpy as np + +import os + +parser = argparse.ArgumentParser(description='MegaDepth preprocessing script') + +parser.add_argument( + '--base_path', type=str, required=True, + help='path to MegaDepth' +) +parser.add_argument( + '--scene_id', type=str, required=True, + help='scene ID' +) + +parser.add_argument( + '--output_path', type=str, required=True, + help='path to the output directory' +) + +args = parser.parse_args() + +base_path = args.base_path +# Remove the trailing / if need be. +if base_path[-1] in ['/', '\\']: + base_path = base_path[: - 1] +scene_id = args.scene_id + +base_depth_path = os.path.join( + base_path, 'phoenix/S6/zl548/MegaDepth_v1' +) +base_undistorted_sfm_path = os.path.join( + base_path, 'Undistorted_SfM' +) + +undistorted_sparse_path = os.path.join( + base_undistorted_sfm_path, scene_id, 'sparse-txt' +) +if not os.path.exists(undistorted_sparse_path): + exit() + +depths_path = os.path.join( + base_depth_path, scene_id, 'dense0', 'depths' +) +if not os.path.exists(depths_path): + exit() + +images_path = os.path.join( + base_undistorted_sfm_path, scene_id, 'images' +) +if not os.path.exists(images_path): + exit() + +# Process cameras.txt +with open(os.path.join(undistorted_sparse_path, 'cameras.txt'), 'r') as f: + raw = f.readlines()[3 :] # skip the header + +camera_intrinsics = {} +for camera in raw: + camera = camera.split(' ') + camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2 :]] + +# Process points3D.txt +with open(os.path.join(undistorted_sparse_path, 'points3D.txt'), 'r') as f: + raw = f.readlines()[3 :] # skip the header + +points3D = {} +for point3D in raw: + point3D = point3D.split(' ') + points3D[int(point3D[0])] = np.array([ + float(point3D[1]), float(point3D[2]), float(point3D[3]) + ]) + +# Process images.txt +with open(os.path.join(undistorted_sparse_path, 'images.txt'), 'r') as f: + raw = f.readlines()[4 :] # skip the header + +image_id_to_idx = {} +image_names = [] +raw_pose = [] +camera = [] +points3D_id_to_2D = [] +n_points3D = [] +for idx, (image, points) in enumerate(zip(raw[:: 2], raw[1 :: 2])): + image = image.split(' ') + points = points.split(' ') + + image_id_to_idx[int(image[0])] = idx + + image_name = image[-1].strip('\n') + image_names.append(image_name) + + raw_pose.append([float(elem) for elem in image[1 : -2]]) + camera.append(int(image[-2])) + current_points3D_id_to_2D = {} + for x, y, point3D_id in zip(points[:: 3], points[1 :: 3], points[2 :: 3]): + if int(point3D_id) == -1: + continue + current_points3D_id_to_2D[int(point3D_id)] = [float(x), float(y)] + points3D_id_to_2D.append(current_points3D_id_to_2D) + n_points3D.append(len(current_points3D_id_to_2D)) +n_images = len(image_names) + +# Image and depthmaps paths +image_paths = [] +depth_paths = [] +for image_name in image_names: + image_path = os.path.join(images_path, image_name) + + # Path to the depth file + depth_path = os.path.join( + depths_path, '%s.h5' % os.path.splitext(image_name)[0] + ) + + if os.path.exists(depth_path): + # Check if depth map or background / foreground mask + file_size = os.stat(depth_path).st_size + # Rough estimate - 75KB might work as well + if file_size < 100 * 1024: + depth_paths.append(None) + image_paths.append(None) + else: + depth_paths.append(depth_path[len(base_path) + 1 :]) + image_paths.append(image_path[len(base_path) + 1 :]) + else: + depth_paths.append(None) + image_paths.append(None) + +# Camera configuration +intrinsics = [] +poses = [] +principal_axis = [] +points3D_id_to_ndepth = [] +for idx, image_name in enumerate(image_names): + if image_paths[idx] is None: + intrinsics.append(None) + poses.append(None) + principal_axis.append([0, 0, 0]) + points3D_id_to_ndepth.append({}) + continue + image_intrinsics = camera_intrinsics[camera[idx]] + K = np.zeros([3, 3]) + K[0, 0] = image_intrinsics[2] + K[0, 2] = image_intrinsics[4] + K[1, 1] = image_intrinsics[3] + K[1, 2] = image_intrinsics[5] + K[2, 2] = 1 + intrinsics.append(K) + + image_pose = raw_pose[idx] + qvec = image_pose[: 4] + qvec = qvec / np.linalg.norm(qvec) + w, x, y, z = qvec + R = np.array([ + [ + 1 - 2 * y * y - 2 * z * z, + 2 * x * y - 2 * z * w, + 2 * x * z + 2 * y * w + ], + [ + 2 * x * y + 2 * z * w, + 1 - 2 * x * x - 2 * z * z, + 2 * y * z - 2 * x * w + ], + [ + 2 * x * z - 2 * y * w, + 2 * y * z + 2 * x * w, + 1 - 2 * x * x - 2 * y * y + ] + ]) + principal_axis.append(R[2, :]) + t = image_pose[4 : 7] + # World-to-Camera pose + current_pose = np.zeros([4, 4]) + current_pose[: 3, : 3] = R + current_pose[: 3, 3] = t + current_pose[3, 3] = 1 + # Camera-to-World pose + # pose = np.zeros([4, 4]) + # pose[: 3, : 3] = np.transpose(R) + # pose[: 3, 3] = -np.matmul(np.transpose(R), t) + # pose[3, 3] = 1 + poses.append(current_pose) + + current_points3D_id_to_ndepth = {} + for point3D_id in points3D_id_to_2D[idx].keys(): + p3d = points3D[point3D_id] + current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (.5 * (K[0, 0] + K[1, 1])) + points3D_id_to_ndepth.append(current_points3D_id_to_ndepth) +principal_axis = np.array(principal_axis) +angles = np.rad2deg(np.arccos( + np.clip( + np.dot(principal_axis, np.transpose(principal_axis)), + -1, 1 + ) +)) + +# Compute overlap score +overlap_matrix = np.full([n_images, n_images], -1.) +scale_ratio_matrix = np.full([n_images, n_images], -1.) +for idx1 in range(n_images): + if image_paths[idx1] is None or depth_paths[idx1] is None: + continue + for idx2 in range(idx1 + 1, n_images): + if image_paths[idx2] is None or depth_paths[idx2] is None: + continue + matches = ( + points3D_id_to_2D[idx1].keys() & + points3D_id_to_2D[idx2].keys() + ) + min_num_points3D = min( + len(points3D_id_to_2D[idx1]), len(points3D_id_to_2D[idx2]) + ) + overlap_matrix[idx1, idx2] = len(matches) / len(points3D_id_to_2D[idx1]) # min_num_points3D + overlap_matrix[idx2, idx1] = len(matches) / len(points3D_id_to_2D[idx2]) # min_num_points3D + if len(matches) == 0: + continue + points3D_id_to_ndepth1 = points3D_id_to_ndepth[idx1] + points3D_id_to_ndepth2 = points3D_id_to_ndepth[idx2] + nd1 = np.array([points3D_id_to_ndepth1[match] for match in matches]) + nd2 = np.array([points3D_id_to_ndepth2[match] for match in matches]) + min_scale_ratio = np.min(np.maximum(nd1 / nd2, nd2 / nd1)) + scale_ratio_matrix[idx1, idx2] = min_scale_ratio + scale_ratio_matrix[idx2, idx1] = min_scale_ratio + +np.savez( + os.path.join(args.output_path, '%s.npz' % scene_id), + image_paths=image_paths, + depth_paths=depth_paths, + intrinsics=intrinsics, + poses=poses, + overlap_matrix=overlap_matrix, + scale_ratio_matrix=scale_ratio_matrix, + angles=angles, + n_points3D=n_points3D, + points3D_id_to_2D=points3D_id_to_2D, + points3D_id_to_ndepth=points3D_id_to_ndepth +) \ No newline at end of file diff --git a/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh b/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh new file mode 100644 index 0000000000000000000000000000000000000000..c983ee464bb36439d68f52d60f981414e2c6e84b --- /dev/null +++ b/third_party/ASpanFormer/tools/preprocess_undistorted_megadepth.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash + +if [[ $# != 2 ]]; then + echo 'Usage: bash preprocess_megadepth.sh /path/to/megadepth /output/path' + exit +fi + +export dataset_path=$1 +export output_path=$2 + +mkdir $output_path +echo 0 +ls $dataset_path/Undistorted_SfM | xargs -P 8 -I % sh -c 'echo %; python preprocess_scene.py --base_path $dataset_path --scene_id % --output_path $output_path' \ No newline at end of file diff --git a/third_party/ASpanFormer/tools/reader.py b/third_party/ASpanFormer/tools/reader.py new file mode 100644 index 0000000000000000000000000000000000000000..f419fbaa8a099fcfede1cea51fcf95a2c1589160 --- /dev/null +++ b/third_party/ASpanFormer/tools/reader.py @@ -0,0 +1,39 @@ +import argparse +import os, sys + +from SensorData import SensorData + +# params +parser = argparse.ArgumentParser() +# data paths +parser.add_argument('--filename', required=True, help='path to sens file to read') +parser.add_argument('--output_path', required=True, help='path to output folder') +parser.add_argument('--export_depth_images', dest='export_depth_images', action='store_true') +parser.add_argument('--export_color_images', dest='export_color_images', action='store_true') +parser.add_argument('--export_poses', dest='export_poses', action='store_true') +parser.add_argument('--export_intrinsics', dest='export_intrinsics', action='store_true') +parser.set_defaults(export_depth_images=False, export_color_images=False, export_poses=False, export_intrinsics=False) + +opt = parser.parse_args() +print(opt) + + +def main(): + if not os.path.exists(opt.output_path): + os.makedirs(opt.output_path) + # load the data + sys.stdout.write('loading %s...' % opt.filename) + sd = SensorData(opt.filename) + sys.stdout.write('loaded!\n') + if opt.export_depth_images: + sd.export_depth_images(os.path.join(opt.output_path, 'depth')) + if opt.export_color_images: + sd.export_color_images(os.path.join(opt.output_path, 'color')) + if opt.export_poses: + sd.export_poses(os.path.join(opt.output_path, 'pose')) + if opt.export_intrinsics: + sd.export_intrinsics(os.path.join(opt.output_path, 'intrinsic')) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/third_party/ASpanFormer/tools/undistort_mega.py b/third_party/ASpanFormer/tools/undistort_mega.py new file mode 100644 index 0000000000000000000000000000000000000000..68798ff30e6afa37a0f98571ecfd3f05751868c8 --- /dev/null +++ b/third_party/ASpanFormer/tools/undistort_mega.py @@ -0,0 +1,69 @@ +import argparse + +import imagesize + +import os + +import subprocess + +parser = argparse.ArgumentParser(description='MegaDepth Undistortion') + +parser.add_argument( + '--colmap_path', type=str,default='/usr/bin/', + help='path to colmap executable' +) +parser.add_argument( + '--base_path', type=str,default='/root/MegaDepth', + help='path to MegaDepth' +) + +args = parser.parse_args() + +sfm_path = os.path.join( + args.base_path, 'MegaDepth_v1_SfM' +) +base_depth_path = os.path.join( + args.base_path, 'phoenix/S6/zl548/MegaDepth_v1' +) +output_path = os.path.join( + args.base_path, 'Undistorted_SfM' +) + +os.mkdir(output_path) + +for scene_name in os.listdir(base_depth_path): + current_output_path = os.path.join(output_path, scene_name) + os.mkdir(current_output_path) + + image_path = os.path.join( + base_depth_path, scene_name, 'dense0', 'imgs' + ) + if not os.path.exists(image_path): + continue + + # Find the maximum image size in scene. + max_image_size = 0 + for image_name in os.listdir(image_path): + max_image_size = max( + max_image_size, + max(imagesize.get(os.path.join(image_path, image_name))) + ) + + # Undistort the images and update the reconstruction. + subprocess.call([ + os.path.join(args.colmap_path, 'colmap'), 'image_undistorter', + '--image_path', os.path.join(sfm_path, scene_name, 'images'), + '--input_path', os.path.join(sfm_path, scene_name, 'sparse', 'manhattan', '0'), + '--output_path', current_output_path, + '--max_image_size', str(max_image_size) + ]) + + # Transform the reconstruction to raw text format. + sparse_txt_path = os.path.join(current_output_path, 'sparse-txt') + os.mkdir(sparse_txt_path) + subprocess.call([ + os.path.join(args.colmap_path, 'colmap'), 'model_converter', + '--input_path', os.path.join(current_output_path, 'sparse'), + '--output_path', sparse_txt_path, + '--output_type', 'TXT' + ]) \ No newline at end of file diff --git a/third_party/ASpanFormer/train.py b/third_party/ASpanFormer/train.py new file mode 100644 index 0000000000000000000000000000000000000000..21f644763711481e84863ed5d861ec57d95f2d5c --- /dev/null +++ b/third_party/ASpanFormer/train.py @@ -0,0 +1,134 @@ +import math +import argparse +import pprint +from distutils.util import strtobool +from pathlib import Path +from loguru import logger as loguru_logger + +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only +from pytorch_lightning.loggers import TensorBoardLogger +from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor +from pytorch_lightning.plugins import DDPPlugin + +from src.config.default import get_cfg_defaults +from src.utils.misc import get_rank_zero_only_logger, setup_gpus +from src.utils.profiler import build_profiler +from src.lightning.data import MultiSceneDataModule +from src.lightning.lightning_aspanformer import PL_ASpanFormer + +loguru_logger = get_rank_zero_only_logger(loguru_logger) + + +def parse_args(): + def str2bool(v): + return v.lower() in ("true", "1") + # init a costum parser which will be added into pl.Trainer parser + # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + 'data_cfg_path', type=str, help='data config path') + parser.add_argument( + 'main_cfg_path', type=str, help='main config path') + parser.add_argument( + '--exp_name', type=str, default='default_exp_name') + parser.add_argument( + '--batch_size', type=int, default=4, help='batch_size per gpu') + parser.add_argument( + '--num_workers', type=int, default=4) + parser.add_argument( + '--pin_memory', type=lambda x: bool(strtobool(x)), + nargs='?', default=True, help='whether loading data to pinned memory or not') + parser.add_argument( + '--ckpt_path', type=str, default=None, + help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer') + parser.add_argument( + '--disable_ckpt', action='store_true', + help='disable checkpoint saving (useful for debugging).') + parser.add_argument( + '--profiler_name', type=str, default=None, + help='options: [inference, pytorch], or leave it unset') + parser.add_argument( + '--parallel_load_data', action='store_true', + help='load datasets in with multiple processes.') + parser.add_argument( + '--mode', type=str, default='vanilla', + help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer') + parser.add_argument( + '--ini', type=str2bool, default=False, + help='pretrained checkpoint path, helpful for using a pre-trained coarse-only ASpanFormer') + + parser = pl.Trainer.add_argparse_args(parser) + return parser.parse_args() + + +def main(): + # parse arguments + args = parse_args() + rank_zero_only(pprint.pprint)(vars(args)) + + # init default-cfg and merge it with the main- and data-cfg + config = get_cfg_defaults() + config.merge_from_file(args.main_cfg_path) + config.merge_from_file(args.data_cfg_path) + pl.seed_everything(config.TRAINER.SEED) # reproducibility + # TODO: Use different seeds for each dataloader workers + # This is needed for data augmentation + + # scale lr and warmup-step automatically + args.gpus = _n_gpus = setup_gpus(args.gpus) + config.TRAINER.WORLD_SIZE = _n_gpus * args.num_nodes + config.TRAINER.TRUE_BATCH_SIZE = config.TRAINER.WORLD_SIZE * args.batch_size + _scaling = config.TRAINER.TRUE_BATCH_SIZE / config.TRAINER.CANONICAL_BS + config.TRAINER.SCALING = _scaling + config.TRAINER.TRUE_LR = config.TRAINER.CANONICAL_LR * _scaling + config.TRAINER.WARMUP_STEP = math.floor( + config.TRAINER.WARMUP_STEP / _scaling) + + # lightning module + profiler = build_profiler(args.profiler_name) + model = PL_ASpanFormer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler) + loguru_logger.info(f"ASpanFormer LightningModule initialized!") + + # lightning data + data_module = MultiSceneDataModule(args, config) + loguru_logger.info(f"ASpanFormer DataModule initialized!") + + # TensorBoard Logger + logger = TensorBoardLogger( + save_dir='logs/tb_logs', name=args.exp_name, default_hp_metric=False) + ckpt_dir = Path(logger.log_dir) / 'checkpoints' + + # Callbacks + # TODO: update ModelCheckpoint to monitor multiple metrics + ckpt_callback = ModelCheckpoint(monitor='auc@10', verbose=True, save_top_k=5, mode='max', + save_last=True, + dirpath=str(ckpt_dir), + filename='{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}') + lr_monitor = LearningRateMonitor(logging_interval='step') + callbacks = [lr_monitor] + if not args.disable_ckpt: + callbacks.append(ckpt_callback) + + # Lightning Trainer + trainer = pl.Trainer.from_argparse_args( + args, + plugins=DDPPlugin(find_unused_parameters=False, + num_nodes=args.num_nodes, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0), + gradient_clip_val=config.TRAINER.GRADIENT_CLIPPING, + callbacks=callbacks, + logger=logger, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0, + replace_sampler_ddp=False, # use custom sampler + reload_dataloaders_every_epoch=False, # avoid repeated samples! + weights_summary='full', + profiler=profiler) + loguru_logger.info(f"Trainer initialized!") + loguru_logger.info(f"Start training!") + trainer.fit(model, datamodule=data_module) + + +if __name__ == '__main__': + main() diff --git a/third_party/ASpanFormer/weights/indoor.ckpt b/third_party/ASpanFormer/weights/indoor.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..2796eb17fc76406378825926bdbc5c90341b4cca --- /dev/null +++ b/third_party/ASpanFormer/weights/indoor.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ac787a487f20601e0f3b99784e8bcabc8bccdb115ae701f2c908b2d672dad12 +size 63125007 diff --git a/third_party/ASpanFormer/weights/outdoor.ckpt b/third_party/ASpanFormer/weights/outdoor.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..0d67dec4cf395b967fd47de83181d1f6340ac005 --- /dev/null +++ b/third_party/ASpanFormer/weights/outdoor.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b1041cad1f484b85b86df8aadc3e7b0ea3060ba2a412550b2f1fc58f501689d1 +size 63125007 diff --git a/third_party/ASpanFormer/weights_aspanformer.tar b/third_party/ASpanFormer/weights_aspanformer.tar new file mode 100644 index 0000000000000000000000000000000000000000..97fa67f373567a85e545a7e78c00078005017022 --- /dev/null +++ b/third_party/ASpanFormer/weights_aspanformer.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fdcef2d135408bacab31f70fe569bbf9f972a14bf8b4e7983a4862a7e54358a4 +size 126259200 diff --git a/third_party/DarkFeat/.gitignore b/third_party/DarkFeat/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a79937ab52bdb8bca803c5ad0ded48961dcafa4a --- /dev/null +++ b/third_party/DarkFeat/.gitignore @@ -0,0 +1,5 @@ +**/__pycache__/ +test +runs +figures +*.log \ No newline at end of file diff --git a/third_party/DarkFeat/README.md b/third_party/DarkFeat/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2b94dce50a61b358d7f05c1942fde15cb2874b73 --- /dev/null +++ b/third_party/DarkFeat/README.md @@ -0,0 +1,95 @@ +# DarkFeat + +DarkFeat: Noise-Robust Feature Detector and Descriptor for Extremely Low-Light RAW Images (AAAI2023 Oral) + +darkfeat demo + +### Installation + +```shell +git clone git@github.com:THU-LYJ-Lab/DarkFeat.git +cd DarkFeat +pip install -r requirements.txt +``` + +[Pytorch](https://pytorch.org/) installation is machine dependent, please install the correct version for your machine. + +### Demo + +```shell +python ./demo_darkfeat.py \ + --input /path/to/your/sequence \ + --output_dir ./output \ + --resize 960 640 \ + --model_path /path/to/pretrained/weights +``` + +Sample raw image sequences and pretrained weights can be downloaded from [here](https://drive.google.com/drive/folders/1zkUCsBVEmQcPZPhsEUymA5GIvAzi12hD?usp=sharing). + +Note that different pytorch and cuda versions may cause different model output results, and the output matches may differ from those shown in the gif. The results are tested in python 3.6, PyTorch 1.10.2 and cuda 10.2. + +### Evaluation + +1. Download [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark) Dataset. + +2. Preprocessing the data in MID dataset, you can choose whether to enable histogram equalization or not: + + ```shell + python raw_preprocess.py --dataset_dir /path/to/MID/dataset + ``` + +3. Extract the keypoints and descriptors, followed by a nearest neighborhood matching: + + ```shell + python export_features.py \ + --model_path /path/to/pretrained/weights \ + --dataset_dir /path/to/MID/dataset + ``` + +4. Estimate the pose through corresponding keypoint pairs: + + ```shell + python pose_estimation.py --dataset_dir /path/to/MID/dataset + ``` + +5. Finally collect the results of pose estimation errors: + + ``` + python read_error.py + ``` + +### Training from scratch + +We use [GL3D](https://github.com/lzx551402/GL3D) as our source training-use matching dataset. Please follow the [instructions](https://github.com/lzx551402/GL3D) to download and unzip all the data (including GL3D group and tourism group). + +Then using the preprocessing code provided by ASLFeat to generate matching informations: + +```shell +git clone https://github.com/lzx551402/tfmatch +# please edit the GL3D path in the shell script before executing. +cd tfmatch +sh train_aslfeat_base.sh +``` + +To launch the training, configure your training hyperparameters inside `./configs` and then run: + +```shell +# stage1 +python run.py --stage 1 --config ./configs/config_stage1.yaml \ + --dataset_dir /path/to/your/GL3D/dataset \ + --job_name YOUR_JOB_NAME +# stage2 +python run.py --stage 2 --config ./configs/config_stage1.yaml \ + --dataset_dir /path/to/your/GL3D/dataset \ + --job_name YOUR_JOB_NAME \ + --start_cnt 160000 +# stage3 +python run.py --stage 3 --config ./configs/config.yaml \ + --dataset_dir /path/to/your/GL3D/dataset \ + --job_name YOUR_JOB_NAME \ + --start_cnt 220000 +``` + +### Acknowledgements + +This project could not be possible without the open-source works from [ASLFeat](https://github.com/lzx551402/ASLFeat), [R2D2](https://github.com/naver/r2d2), [MID](https://github.com/Wenzhengchina/Matching-in-the-Dark), [GL3D](https://github.com/lzx551402/GL3D), [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork). We sincerely thank them all. \ No newline at end of file diff --git a/third_party/DarkFeat/checkpoints/DarkFeat.pth b/third_party/DarkFeat/checkpoints/DarkFeat.pth new file mode 100644 index 0000000000000000000000000000000000000000..2b28a0fc38779abea7a41cfaa830cae31c4f2791 --- /dev/null +++ b/third_party/DarkFeat/checkpoints/DarkFeat.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f9c832df932465a24c9849b65df04d9f33f04df3510fd8becf6bf73b28f77b2 +size 2934451 diff --git a/third_party/DarkFeat/configs/config.yaml b/third_party/DarkFeat/configs/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7ffead73fc3eac520aa7aa4bf3811c5069a4c149 --- /dev/null +++ b/third_party/DarkFeat/configs/config.yaml @@ -0,0 +1,24 @@ +training: + optimizer: 'SGD' + lr: 0.01 + momentum: 0.9 + weight_decay: 0.0001 + lr_gamma: 0.1 + lr_step: 200000 +network: + input_type: 'raw-demosaic' + noise: true + noise_maxstep: 1 + model: 'Quad_L2Net' + loss_type: 'HARD_CONTRASTIVE' + photaug: true + resize: 480 + use_corr_n: 512 + det: + corr_weight: true + safe_radius: 12 + kpt_n: 512 + score_thld: -1 + edge_thld: 10 + nms_size: 3 + eof_size: 5 \ No newline at end of file diff --git a/third_party/DarkFeat/configs/config_stage1.yaml b/third_party/DarkFeat/configs/config_stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f94e1da377bf8f507d6fa6db394b1016227d0e25 --- /dev/null +++ b/third_party/DarkFeat/configs/config_stage1.yaml @@ -0,0 +1,24 @@ +training: + optimizer: 'SGD' + lr: 0.1 + momentum: 0.9 + weight_decay: 0.0001 + lr_gamma: 0.1 + lr_step: 200000 +network: + input_type: 'raw-demosaic' + noise: true + noise_maxstep: 1 + model: 'Quad_L2Net' + loss_type: 'HARD_CONTRASTIVE' + photaug: true + resize: 480 + use_corr_n: 512 + det: + corr_weight: true + safe_radius: 12 + kpt_n: 512 + score_thld: -1 + edge_thld: 10 + nms_size: 3 + eof_size: 5 \ No newline at end of file diff --git a/third_party/DarkFeat/darkfeat.py b/third_party/DarkFeat/darkfeat.py new file mode 100644 index 0000000000000000000000000000000000000000..e78ad2604aafb759a6241365ac93fd1ef38f76f3 --- /dev/null +++ b/third_party/DarkFeat/darkfeat.py @@ -0,0 +1,359 @@ +import torch +from torch import nn +from torch.nn.parameter import Parameter +import torchvision.transforms as tvf +import torch.nn.functional as F +import numpy as np + + +def gather_nd(params, indices): + orig_shape = list(indices.shape) + num_samples = np.prod(orig_shape[:-1]) + m = orig_shape[-1] + n = len(params.shape) + + if m <= n: + out_shape = orig_shape[:-1] + list(params.shape)[m:] + else: + raise ValueError( + f'the last dimension of indices must less or equal to the rank of params. Got indices:{indices.shape}, params:{params.shape}. {m} > {n}' + ) + + indices = indices.reshape((num_samples, m)).transpose(0, 1).tolist() + output = params[indices] # (num_samples, ...) + return output.reshape(out_shape).contiguous() + + +# input: pos [kpt_n, 2]; inputs [H, W, 128] / [H, W] +# output: [kpt_n, 128] / [kpt_n] +def interpolate(pos, inputs, nd=True): + h = inputs.shape[0] + w = inputs.shape[1] + + i = pos[:, 0] + j = pos[:, 1] + + i_top_left = torch.clamp(torch.floor(i).int(), 0, h - 1) + j_top_left = torch.clamp(torch.floor(j).int(), 0, w - 1) + + i_top_right = torch.clamp(torch.floor(i).int(), 0, h - 1) + j_top_right = torch.clamp(torch.ceil(j).int(), 0, w - 1) + + i_bottom_left = torch.clamp(torch.ceil(i).int(), 0, h - 1) + j_bottom_left = torch.clamp(torch.floor(j).int(), 0, w - 1) + + i_bottom_right = torch.clamp(torch.ceil(i).int(), 0, h - 1) + j_bottom_right = torch.clamp(torch.ceil(j).int(), 0, w - 1) + + dist_i_top_left = i - i_top_left.float() + dist_j_top_left = j - j_top_left.float() + w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left) + w_top_right = (1 - dist_i_top_left) * dist_j_top_left + w_bottom_left = dist_i_top_left * (1 - dist_j_top_left) + w_bottom_right = dist_i_top_left * dist_j_top_left + + if nd: + w_top_left = w_top_left[..., None] + w_top_right = w_top_right[..., None] + w_bottom_left = w_bottom_left[..., None] + w_bottom_right = w_bottom_right[..., None] + + interpolated_val = ( + w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) + + w_top_right * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) + + w_bottom_left * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1)) + + w_bottom_right * + gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1)) + ) + + return interpolated_val + + +def edge_mask(inputs, n_channel, dilation=1, edge_thld=5): + b, c, h, w = inputs.size() + device = inputs.device + + dii_filter = torch.tensor( + [[0, 1., 0], [0, -2., 0], [0, 1., 0]] + ).view(1, 1, 3, 3) + dij_filter = 0.25 * torch.tensor( + [[1., 0, -1.], [0, 0., 0], [-1., 0, 1.]] + ).view(1, 1, 3, 3) + djj_filter = torch.tensor( + [[0, 0, 0], [1., -2., 1.], [0, 0, 0]] + ).view(1, 1, 3, 3) + + dii = F.conv2d( + inputs.view(-1, 1, h, w), dii_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + dij = F.conv2d( + inputs.view(-1, 1, h, w), dij_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + djj = F.conv2d( + inputs.view(-1, 1, h, w), djj_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + + det = dii * djj - dij * dij + tr = dii + djj + del dii, dij, djj + + threshold = (edge_thld + 1) ** 2 / edge_thld + is_not_edge = torch.min(tr * tr / det <= threshold, det > 0) + + return is_not_edge + + +# input: score_map [batch_size, 1, H, W] +# output: indices [2, k, 2], scores [2, k] +def extract_kpts(score_map, k=256, score_thld=0, edge_thld=0, nms_size=3, eof_size=5): + h = score_map.shape[2] + w = score_map.shape[3] + + mask = score_map > score_thld + if nms_size > 0: + nms_mask = F.max_pool2d(score_map, kernel_size=nms_size, stride=1, padding=nms_size//2) + nms_mask = torch.eq(score_map, nms_mask) + mask = torch.logical_and(nms_mask, mask) + if eof_size > 0: + eof_mask = torch.ones((1, 1, h - 2 * eof_size, w - 2 * eof_size), dtype=torch.float32, device=score_map.device) + eof_mask = F.pad(eof_mask, [eof_size] * 4, value=0) + eof_mask = eof_mask.bool() + mask = torch.logical_and(eof_mask, mask) + if edge_thld > 0: + non_edge_mask = edge_mask(score_map, 1, dilation=3, edge_thld=edge_thld) + mask = torch.logical_and(non_edge_mask, mask) + + bs = score_map.shape[0] + if bs is None: + indices = torch.nonzero(mask)[0] + scores = gather_nd(score_map, indices)[0] + sample = torch.sort(scores, descending=True)[1][0:k] + indices = indices[sample].unsqueeze(0) + scores = scores[sample].unsqueeze(0) + else: + indices = [] + scores = [] + for i in range(bs): + tmp_mask = mask[i][0] + tmp_score_map = score_map[i][0] + tmp_indices = torch.nonzero(tmp_mask) + tmp_scores = gather_nd(tmp_score_map, tmp_indices) + tmp_sample = torch.sort(tmp_scores, descending=True)[1][0:k] + tmp_indices = tmp_indices[tmp_sample] + tmp_scores = tmp_scores[tmp_sample] + indices.append(tmp_indices) + scores.append(tmp_scores) + try: + indices = torch.stack(indices, dim=0) + scores = torch.stack(scores, dim=0) + except: + min_num = np.min([len(i) for i in indices]) + indices = torch.stack([i[:min_num] for i in indices], dim=0) + scores = torch.stack([i[:min_num] for i in scores], dim=0) + return indices, scores + + +# input: [batch_size, C, H, W] +# output: [batch_size, C, H, W], [batch_size, C, H, W] +def peakiness_score(inputs, moving_instance_max, ksize=3, dilation=1): + inputs = inputs / moving_instance_max + + batch_size, C, H, W = inputs.shape + + pad_size = ksize // 2 + (dilation - 1) + kernel = torch.ones([C, 1, ksize, ksize], device=inputs.device) / (ksize * ksize) + + pad_inputs = F.pad(inputs, [pad_size] * 4, mode='reflect') + + avg_spatial_inputs = F.conv2d( + pad_inputs, + kernel, + stride=1, + dilation=dilation, + padding=0, + groups=C + ) + avg_channel_inputs = torch.mean(inputs, axis=1, keepdim=True) # channel dimension is 1 + # print(avg_spatial_inputs.shape) + + alpha = F.softplus(inputs - avg_spatial_inputs) + beta = F.softplus(inputs - avg_channel_inputs) + + return alpha, beta + + +class DarkFeat(nn.Module): + default_config = { + 'model_path': '', + 'input_type': 'raw-demosaic', + 'kpt_n': 5000, + 'kpt_refinement': True, + 'score_thld': 0.5, + 'edge_thld': 10, + 'multi_scale': False, + 'multi_level': True, + 'nms_size': 3, + 'eof_size': 5, + 'need_norm': True, + 'use_peakiness': True + } + + def __init__(self, model_path='', inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False): + super(DarkFeat, self).__init__() + inchan = 3 if self.default_config['input_type'] == 'rgb' or self.default_config['input_type'] == 'raw-demosaic' else 1 + self.config = {**self.default_config} + + self.inchan = inchan + self.curchan = inchan + self.dilated = dilated + self.dilation = dilation + self.bn = bn + self.bn_affine = bn_affine + self.config['model_path'] = model_path + + dim = 128 + mchan = 4 + + self.conv0 = self._add_conv( 8*mchan) + self.conv1 = self._add_conv( 8*mchan, bn=False) + self.bn1 = self._make_bn(8*mchan) + self.conv2 = self._add_conv( 16*mchan, stride=2) + self.conv3 = self._add_conv( 16*mchan, bn=False) + self.bn3 = self._make_bn(16*mchan) + self.conv4 = self._add_conv( 32*mchan, stride=2) + self.conv5 = self._add_conv( 32*mchan) + # replace last 8x8 convolution with 3 3x3 convolutions + self.conv6_0 = self._add_conv( 32*mchan) + self.conv6_1 = self._add_conv( 32*mchan) + self.conv6_2 = self._add_conv(dim, bn=False, relu=False) + self.out_dim = dim + + self.moving_avg_params = nn.ParameterList([ + Parameter(torch.tensor(1.), requires_grad=False), + Parameter(torch.tensor(1.), requires_grad=False), + Parameter(torch.tensor(1.), requires_grad=False) + ]) + self.clf = nn.Conv2d(128, 2, kernel_size=1) + + state_dict = torch.load(self.config["model_path"]) + new_state_dict = {} + + for key in state_dict: + if 'running_mean' not in key and 'running_var' not in key and 'num_batches_tracked' not in key: + new_state_dict[key] = state_dict[key] + + self.load_state_dict(new_state_dict) + print('Loaded DarkFeat model') + + def _make_bn(self, outd): + return nn.BatchNorm2d(outd, affine=self.bn_affine, track_running_stats=False) + + def _add_conv(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True, k_pool = 1, pool_type='max', bias=False): + d = self.dilation * dilation + conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride, bias=bias) + + ops = nn.ModuleList([]) + + ops.append( nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params) ) + if bn and self.bn: ops.append( self._make_bn(outd) ) + if relu: ops.append( nn.ReLU(inplace=True) ) + self.curchan = outd + + if k_pool > 1: + if pool_type == 'avg': + ops.append(torch.nn.AvgPool2d(kernel_size=k_pool)) + elif pool_type == 'max': + ops.append(torch.nn.MaxPool2d(kernel_size=k_pool)) + else: + print(f"Error, unknown pooling type {pool_type}...") + + return nn.Sequential(*ops) + + def forward(self, input): + """ Compute keypoints, scores, descriptors for image """ + data = input['image'] + H, W = data.shape[2:] + + if self.config['input_type'] == 'rgb': + # 3-channel rgb + RGB_mean = [0.485, 0.456, 0.406] + RGB_std = [0.229, 0.224, 0.225] + norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) + data = norm_RGB(data) + + elif self.config['input_type'] == 'gray': + # 1-channel + data = torch.mean(data, dim=1, keepdim=True) + norm_gray0 = tvf.Normalize(mean=data.mean(), std=data.std()) + data = norm_gray0(data) + + elif self.config['input_type'] == 'raw': + # 4-channel + pass + elif self.config['input_type'] == 'raw-demosaic': + # 3-channel + pass + else: + raise NotImplementedError() + + # x: [N, C, H, W] + x0 = self.conv0(data) + x1 = self.conv1(x0) + x1_bn = self.bn1(x1) + x2 = self.conv2(x1_bn) + x3 = self.conv3(x2) + x3_bn = self.bn3(x3) + x4 = self.conv4(x3_bn) + x5 = self.conv5(x4) + x6_0 = self.conv6_0(x5) + x6_1 = self.conv6_1(x6_0) + x6_2 = self.conv6_2(x6_1) + + comb_weights = torch.tensor([1., 2., 3.], device=data.device) + comb_weights /= torch.sum(comb_weights) + ksize = [3, 2, 1] + det_score_maps = [] + + for idx, xx in enumerate([x1, x3, x6_2]): + alpha, beta = peakiness_score(xx, self.moving_avg_params[idx].detach(), ksize=3, dilation=ksize[idx]) + score_vol = alpha * beta + det_score_map = torch.max(score_vol, dim=1, keepdim=True)[0] + det_score_map = F.interpolate(det_score_map, size=data.shape[2:], mode='bilinear', align_corners=True) + det_score_map = comb_weights[idx] * det_score_map + det_score_maps.append(det_score_map) + + det_score_map = torch.sum(torch.stack(det_score_maps, dim=0), dim=0) + + desc = x6_2 + score_map = det_score_map + conf = F.softmax(self.clf((desc)**2), dim=1)[:,1:2] + score_map = score_map * F.interpolate(conf, size=score_map.shape[2:], mode='bilinear', align_corners=True) + + kpt_inds, kpt_score = extract_kpts( + score_map, + k=self.config['kpt_n'], + score_thld=self.config['score_thld'], + nms_size=self.config['nms_size'], + eof_size=self.config['eof_size'], + edge_thld=self.config['edge_thld'] + ) + + descs = F.normalize( + interpolate(kpt_inds.squeeze(0) / 4, desc.squeeze(0).permute(1, 2, 0)), + p=2, + dim=-1 + ).detach().cpu().numpy(), + kpts = np.squeeze(torch.stack([kpt_inds[:, :, 1], kpt_inds[:, :, 0]], dim=-1).cpu(), axis=0) \ + * np.array([W / data.shape[3], H / data.shape[2]], dtype=np.float32) + scores = np.squeeze(kpt_score.detach().cpu().numpy(), axis=0) + + idxs = np.negative(scores).argsort()[0:self.config['kpt_n']] + descs = descs[0][idxs] + kpts = kpts[idxs] + scores = scores[idxs] + + return { + 'keypoints': kpts, + 'scores': torch.from_numpy(scores), + 'descriptors': torch.from_numpy(descs.T), + } diff --git a/third_party/DarkFeat/datasets/InvISP/LICENSE b/third_party/DarkFeat/datasets/InvISP/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0c7a7ab19788c339529ee9c85d301a582c3c8010 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Yazhou XING + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/third_party/DarkFeat/datasets/InvISP/README.md b/third_party/DarkFeat/datasets/InvISP/README.md new file mode 100644 index 0000000000000000000000000000000000000000..654d33dae8e00fcd61b6f38f8e2763ae87dfefa4 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/README.md @@ -0,0 +1,117 @@ +# Invertible Image Signal Processing + + +![Python 3.6](https://img.shields.io/badge/Python-3.6-green.svg?style=plastic) +![pytorch 1.4.0](https://img.shields.io/badge/PyTorch-1.4.0-green.svg?style=plastic) + +**This repository includes official codes for "[Invertible Image Signal Processing (CVPR2021)](https://arxiv.org/abs/2103.15061)".** + +![](./figures/teaser.png) +**Figure:** *Our framework* + +Unprocessed RAW data is a highly valuable image format for image editing and computer vision. However, since the file size of RAW data is huge, most users can only get access to processed and compressed sRGB images. To bridge this gap, we design an Invertible Image Signal Processing (InvISP) pipeline, which not only enables rendering visually appealing sRGB images but also allows recovering nearly perfect RAW data. Due to our framework's inherent reversibility, we can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images, without any memory overhead. We also integrate a differentiable JPEG compression simulator that empowers our framework to reconstruct RAW data from JPEG images. Extensive quantitative and qualitative experiments on two DSLR demonstrate that our method obtains much higher quality in both rendered sRGB images and reconstructed RAW data than alternative methods. + +> **Invertible Image Signal Processing**
+> Yazhou Xing*, Zian Qian*, Qifeng Chen (* indicates joint first authors)
+> HKUST
+ +[[Paper](https://arxiv.org/abs/2103.15061)] +[[Project Page](https://yzxing87.github.io/InvISP/index.html)] +[[Technical Video (Coming soon)](https://yzxing87.github.io/TBA)] + +![](./figures/result_01.png) +**Figure:** *Our results* + + +## Known issue (10/2021) +There exists some errors in the bilinear demosaicing implementation of the python library ``colour_demosaicing``. You can fix it through add the 'constant' parameter in convolve method in [this file](https://colour-demosaicing.readthedocs.io/en/latest/_modules/colour_demosaicing/bayer/demosaicing/bilinear.html#demosaicing_CFA_Bayer_bilinear) of your package. Otherwise the demosaicing results will be out of its original range and the trained results will face some incorrect color issues. + +## Installation +Clone this repo. +```bash +git clone https://github.com/yzxing87/Invertible-ISP.git +cd Invertible-ISP/ +``` + +We have tested our code on Ubuntu 18.04 LTS with PyTorch 1.4.0, CUDA 10.1 and cudnn7.6.5. Please install dependencies by +```bash +conda env create -f environment.yml +``` + +## Preparing datasets +We use [MIT-Adobe FiveK Dataset](https://data.csail.mit.edu/graphics/fivek/) for training and evaluation. To reproduce our results, you need to first download the NIKON D700 and Canon EOS 5D subsets from their website. The images (DNG) can be downloaded by +```bash +cd data/ +bash data_preprocess.sh +``` +The downloading may take a while. After downloading, we need to prepare the bilinearly demosaiced RAW and white balance parameters as network input, and ground truth sRGB (in JPEG format) as supervision. +```bash +python data_preprocess.py --camera="NIKON_D700" +python data_preprocess.py --camera="Canon_EOS_5D" +``` +The dataset will be organized into +| Path | Size | Files | Format | Description +| :--- | :--: | ----: | :----: | :---------- +| data | 585 GB | 1 | | Main folder +| ├  Canon_EOS_5D | 448 GB | 1 | | Canon sub-folder +| ├  NIKON_D700 | 137 GB | 1 | | NIKON sub-folder +|     ├  DNG | 2.9 GB | 487 | DNG | In-the-wild RAW. +|     ├  RAW | 133 GB | 487 | NPZ | Preprocessed RAW. +|     ├  RGB | 752 MB | 487 | JPG | Ground-truth RGB. +| ├  NIKON_D700_train.txt | 1 KB | 1 | TXT | Training data split. +| ├  NIKON_D700_test.txt | 5 KB | 1 | TXT | Test data split. + +## Training networks +We specify the training arguments into `train.sh`. Simply run +```bash +cd ../ +bash train.sh +``` +The checkpoints will be saved into `./exps/{exp_name}/checkpoint/`. + +## Test and evaluation +### Use your trained model +To reconstruct the RAW from JPEG RGB, we need to first save the rendered RGB into disk then do test to recover RAW. +Original RAW images are too huge to be directly tested on one 2080 Ti GPU. We provide two ways to test the model. + +1. Subsampling the RAW for visualization purpose: + ```bash + python test_rgb.py --task=EXPERIMENT_NAME \ + --data_path="./data/" \ + --gamma \ + --camera=CAMERA_NAME \ + --out_path=OUTPUT_PATH \ + --ckpt=CKPT_PATH + ``` + After finish, run + ```bash + python test_raw.py --task=EXPERIMENT_NAME \ + --data_path="./data/" \ + --gamma \ + --camera=CAMERA_NAME \ + --out_path=OUTPUT_PATH \ + --ckpt=CKPT_PATH + ``` +2. Spliting the RAW data into patches, for quantitatively evaluation purpose. Turn on the `--split_to_patch` argument. See `test.sh.` The PSNR and SSIM metrics can be obtained by + ```bash + python cal_metrics.py --path=PATH_TO_SAVED_PATCHES + ``` +### Use our pretrained weights +We also provide our trained model for a reference. The checkpoints are placed in `pretrained/` folder. Specify the correct PATH in `test.sh`, then you can get similar results as our paper. Please note that in the context of ISP, one trained model can only be applied for a specific camera. This is due to the camera-dependent proprietary raw color space and photo-finishing steps. + + +## Citation + +``` +@inproceedings{xing21invertible, + title = {Invertible Image Signal Processing}, + author = {Xing, Yazhou and Qian, Zian and Chen, Qifeng}, + booktitle = {CVPR}, + year = {2021} +} +``` +## Acknowledgement +Part of the codes benefit from [DiffJPEG](https://github.com/mlomnitz/DiffJPEG) and [Invertible-Image-Rescaling](https://github.com/pkuxmq/Invertible-Image-Rescaling). + +## Contact +Feel free to contact me if there is any question. (Yazhou Xing, yzxing87@gmail.com) diff --git a/third_party/DarkFeat/datasets/InvISP/__init__.py b/third_party/DarkFeat/datasets/InvISP/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/datasets/InvISP/cal_metrics.py b/third_party/DarkFeat/datasets/InvISP/cal_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..cc3e501664487de4c08ab8c89328dd266fba2868 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/cal_metrics.py @@ -0,0 +1,114 @@ +import cv2 +import numpy as np +import math +# from skimage.metrics import structural_similarity as ssim +from skimage.measure import compare_ssim +from scipy.misc import imread +from glob import glob + +import argparse + +parser = argparse.ArgumentParser(description="evaluation codes") + +parser.add_argument("--path", type=str, help="Path to evaluate images.") + +args = parser.parse_args() + +def psnr(img1, img2): + mse = np.mean( (img1/255. - img2/255.) ** 2 ) + if mse < 1.0e-10: + return 100 + PIXEL_MAX = 1 + return 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) + +def psnr_raw(img1, img2): + mse = np.mean( (img1 - img2) ** 2 ) + if mse < 1.0e-10: + return 100 + PIXEL_MAX = 1 + return 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) + + +def my_ssim(img1, img2): + return compare_ssim(img1, img2, data_range=img1.max() - img1.min(), multichannel=True) + + +def quan_eval(path, suffix="jpg"): + # path: /disk2/yazhou/projects/IISP/exps/test_final_unet_globalEDV2/ + # ours + gt_imgs = sorted(glob(path+"tar*.%s"%suffix)) + pred_imgs = sorted(glob(path+"pred*.%s"%suffix)) + + # with open(split_path + "test_gt.txt", 'r') as f_gt, open(split_path+"test_rgb.txt","r") as f_rgb: + # gt_imgs = [line.rstrip() for line in f_gt.readlines()] + # pred_imgs = [line.rstrip() for line in f_rgb.readlines()] + + assert len(gt_imgs) == len(pred_imgs) + + psnr_avg = 0. + ssim_avg = 0. + for i in range(len(gt_imgs)): + gt = imread(gt_imgs[i]) + pred = imread(pred_imgs[i]) + psnr_temp = psnr(gt, pred) + psnr_avg += psnr_temp + ssim_temp = my_ssim(gt, pred) + ssim_avg += ssim_temp + + print("psnr: ", psnr_temp) + print("ssim: ", ssim_temp) + + psnr_avg /= float(len(gt_imgs)) + ssim_avg /= float(len(gt_imgs)) + + print("psnr_avg: ", psnr_avg) + print("ssim_avg: ", ssim_avg) + + return psnr_avg, ssim_avg + +def mse(gt, pred): + return np.mean((gt-pred)**2) + +def mse_raw(path, suffix="npy"): + gt_imgs = sorted(glob(path+"raw_tar*.%s"%suffix)) + pred_imgs = sorted(glob(path+"raw_pred*.%s"%suffix)) + + # with open(split_path + "test_gt.txt", 'r') as f_gt, open(split_path+"test_rgb.txt","r") as f_rgb: + # gt_imgs = [line.rstrip() for line in f_gt.readlines()] + # pred_imgs = [line.rstrip() for line in f_rgb.readlines()] + + assert len(gt_imgs) == len(pred_imgs) + + mse_avg = 0. + psnr_avg = 0. + for i in range(len(gt_imgs)): + gt = np.load(gt_imgs[i]) + pred = np.load(pred_imgs[i]) + mse_temp = mse(gt, pred) + mse_avg += mse_temp + psnr_temp = psnr_raw(gt, pred) + psnr_avg += psnr_temp + + print("mse: ", mse_temp) + print("psnr: ", psnr_temp) + + mse_avg /= float(len(gt_imgs)) + psnr_avg /= float(len(gt_imgs)) + + print("mse_avg: ", mse_avg) + print("psnr_avg: ", psnr_avg) + + return mse_avg, psnr_avg + +test_full = False + +# if test_full: +# psnr_avg, ssim_avg = quan_eval(ROOT_PATH+"%s/vis_%s_full/"%(args.task, args.ckpt), "jpeg") +# mse_avg, psnr_avg_raw = mse_raw(ROOT_PATH+"%s/vis_%s_full/"%(args.task, args.ckpt)) +# else: +psnr_avg, ssim_avg = quan_eval(args.path, "jpg") +mse_avg, psnr_avg_raw = mse_raw(args.path) + +print("pnsr: {}, ssim: {}, mse: {}, psnr raw: {}".format(psnr_avg, ssim_avg, mse_avg, psnr_avg_raw)) + + diff --git a/third_party/DarkFeat/datasets/InvISP/config/config.py b/third_party/DarkFeat/datasets/InvISP/config/config.py new file mode 100644 index 0000000000000000000000000000000000000000..dc42182ecf7464cc85ed5c77b7aeb9ee4e3ecd74 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/config/config.py @@ -0,0 +1,21 @@ +import argparse + +BATCH_SIZE = 1 + +DATA_PATH = "./data/" + + + +def get_arguments(): + parser = argparse.ArgumentParser(description="training codes") + + parser.add_argument("--task", type=str, help="Name of this training") + parser.add_argument("--data_path", type=str, default=DATA_PATH, help="Dataset root path.") + parser.add_argument("--batch_size", type=int, default=BATCH_SIZE, help="Batch size for training. ") + parser.add_argument("--debug_mode", dest='debug_mode', action='store_true', help="If debug mode, load less data.") + parser.add_argument("--gamma", dest='gamma', action='store_true', help="Use gamma compression for raw data.") + parser.add_argument("--camera", type=str, default="NIKON_D700", choices=["NIKON_D700", "Canon_EOS_5D"], help="Choose which camera to use. ") + parser.add_argument("--rgb_weight", type=float, default=1, help="Weight for rgb loss. ") + + + return parser diff --git a/third_party/DarkFeat/datasets/InvISP/data/Canon_EOS_5D.txt b/third_party/DarkFeat/datasets/InvISP/data/Canon_EOS_5D.txt new file mode 100644 index 0000000000000000000000000000000000000000..b2a01137c15059c99e7ad26301c7ffdafdcbe72d --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/data/Canon_EOS_5D.txt @@ -0,0 +1,777 @@ +https://data.csail.mit.edu/graphics/fivek/img/dng/a3674-jmac_MG_0392.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a1902-_MG_7217.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a0023-07-06-02-at-15h06m48-s_MG_1489.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a0282-20060619_125715__MG_9197.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a2314-20080426_111248__MG_9227.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a2113-20070619_135552__MG_8411.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a3057-dvf_002.dng +https://data.csail.mit.edu/graphics/fivek/img/dng/a0121-jmac_MG_7813.dng 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+a2035-_DGW6313 +a3885-_DGW6320 +a1234-_DGW6333 +a0312-_DSC5579 +a4610-_DGW0346 +a3441-dgw_064 +a4391-_DGW0277 +a1769-_DGW6405 +a1652-dgw_004 +a3657-_DSC5954 +a1977-_DGW6239 +a1880-_DGW6418 +a2984-_DGW6399 +a1418-dgw_066 +a1583-dgw_079 +a4914-_DGW0237 diff --git a/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.py b/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..62271771a17a4863b730136d49f2a23aed0e49b2 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.py @@ -0,0 +1,56 @@ +import rawpy +import numpy as np +import glob, os +import colour_demosaicing +import imageio +import argparse +from PIL import Image as PILImage +import scipy.io as scio + +parser = argparse.ArgumentParser(description="data preprocess") + +parser.add_argument("--camera", type=str, default="NIKON_D700", help="Camera Name") +parser.add_argument("--Bayer_Pattern", type=str, default="RGGB", help="Bayer Pattern of RAW") +parser.add_argument("--JPEG_Quality", type=int, default=90, help="Jpeg Quality of the ground truth.") + +args = parser.parse_args() +camera_name = args.camera +Bayer_Pattern = args.Bayer_Pattern +JPEG_Quality = args.JPEG_Quality + +dng_path = sorted(glob.glob('/mnt/nvme2n1/hyz/data/' + camera_name + '/DNG/*.cr2')) +rgb_target_path = '/mnt/nvme2n1/hyz/data/'+ camera_name + '/RGB/' +raw_input_path = '/mnt/nvme2n1/hyz/data/' + camera_name + '/RAW/' +if not os.path.isdir(rgb_target_path): + os.mkdir(rgb_target_path) +if not os.path.isdir(raw_input_path): + os.mkdir(raw_input_path) + +def flip(raw_img, flip): + if flip == 3: + raw_img = np.rot90(raw_img, k=2) + elif flip == 5: + raw_img = np.rot90(raw_img, k=1) + elif flip == 6: + raw_img = np.rot90(raw_img, k=3) + else: + pass + return raw_img + + + +for path in dng_path: + print("Start Processing %s" % os.path.basename(path)) + raw = rawpy.imread(path) + file_name = path.split('/')[-1].split('.')[0] + im = raw.postprocess(use_camera_wb=True,no_auto_bright=True) + flip_val = raw.sizes.flip + cwb = raw.camera_whitebalance + raw_img = raw.raw_image_visible + if camera_name == 'Canon_EOS_5D': + raw_img = np.maximum(raw_img - 127.0, 0) + de_raw = colour_demosaicing.demosaicing_CFA_Bayer_bilinear(raw_img, Bayer_Pattern) + de_raw = flip(de_raw, flip_val) + rgb_img = PILImage.fromarray(im).save(rgb_target_path + file_name + '.jpg', quality = JPEG_Quality, subsampling = 1) + np.savez(raw_input_path + file_name + '.npz', raw=de_raw, wb=cwb) + diff --git a/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.sh b/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.sh new file mode 100644 index 0000000000000000000000000000000000000000..17dae1fa90b6b3a21fc1fb91b0c63eb6f54ffeba --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/data/data_preprocess.sh @@ -0,0 +1,14 @@ +!/bin/bash +dir_nikon="./NIKON_D700/DNG/" +dir_canon="./Canon_EOS_5D/DNG/" +if [ ! -d "$dir_nikon" ];then +mkdir $dir_nikon +fi +if [ ! -d "$dir_canon" ];then +mkdir $dir_canon +fi +wget -P./NIKON_D700/DNG -i NIKON_D700.txt +wget -P./Canon_EOS_5D/DNG -i Canon_EOS_5D.txt +python data_preprocess.py +python data_preprocess.py --camera="Canon_EOS_5D" + diff --git a/third_party/DarkFeat/datasets/InvISP/dataset/FiveK_dataset.py b/third_party/DarkFeat/datasets/InvISP/dataset/FiveK_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..4c71bd3b4162bd21761983deef6b94fa46a364f6 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/dataset/FiveK_dataset.py @@ -0,0 +1,132 @@ +from __future__ import print_function, division +import os, random, time +import torch +import numpy as np +from torch.utils.data import Dataset +from torchvision import transforms, utils +import rawpy +from glob import glob +from PIL import Image as PILImage +import numbers +from scipy.misc import imread +from .base_dataset import BaseDataset + + +class FiveKDatasetTrain(BaseDataset): + def __init__(self, opt): + super().__init__(opt=opt) + self.patch_size = 256 + input_RAWs_WBs, target_RGBs = self.load(is_train=True) + assert len(input_RAWs_WBs) == len(target_RGBs) + self.data = {'input_RAWs_WBs':input_RAWs_WBs, 'target_RGBs':target_RGBs} + + def random_flip(self, input_raw, target_rgb): + idx = np.random.randint(2) + input_raw = np.flip(input_raw,axis=idx).copy() + target_rgb = np.flip(target_rgb,axis=idx).copy() + + return input_raw, target_rgb + + def random_rotate(self, input_raw, target_rgb): + idx = np.random.randint(4) + input_raw = np.rot90(input_raw,k=idx) + target_rgb = np.rot90(target_rgb,k=idx) + + return input_raw, target_rgb + + def random_crop(self, patch_size, input_raw, target_rgb,flow=False,demos=False): + H, W, _ = input_raw.shape + rnd_h = random.randint(0, max(0, H - patch_size)) + rnd_w = random.randint(0, max(0, W - patch_size)) + + patch_input_raw = input_raw[rnd_h:rnd_h + patch_size, rnd_w:rnd_w + patch_size, :] + if flow or demos: + patch_target_rgb = target_rgb[rnd_h:rnd_h + patch_size, rnd_w:rnd_w + patch_size, :] + else: + patch_target_rgb = target_rgb[rnd_h*2:rnd_h*2 + patch_size*2, rnd_w*2:rnd_w*2 + patch_size*2, :] + + return patch_input_raw, patch_target_rgb + + def aug(self, patch_size, input_raw, target_rgb, flow=False, demos=False): + input_raw, target_rgb = self.random_crop(patch_size, input_raw,target_rgb,flow=flow, demos=demos) + input_raw, target_rgb = self.random_rotate(input_raw,target_rgb) + input_raw, target_rgb = self.random_flip(input_raw,target_rgb) + + return input_raw, target_rgb + + def __len__(self): + return len(self.data['input_RAWs_WBs']) + + def __getitem__(self, idx): + input_raw_wb_path = self.data['input_RAWs_WBs'][idx] + target_rgb_path = self.data['target_RGBs'][idx] + + target_rgb_img = imread(target_rgb_path) + input_raw_wb = np.load(input_raw_wb_path) + input_raw_img = input_raw_wb['raw'] + wb = input_raw_wb['wb'] + wb = wb / wb.max() + input_raw_img = input_raw_img * wb[:-1] + + self.patch_size = 256 + input_raw_img, target_rgb_img = self.aug(self.patch_size, input_raw_img, target_rgb_img, flow=True, demos=True) + + if self.gamma: + norm_value = np.power(4095, 1/2.2) if self.camera_name=='Canon_EOS_5D' else np.power(16383, 1/2.2) + input_raw_img = np.power(input_raw_img, 1/2.2) + else: + norm_value = 4095 if self.camera_name=='Canon_EOS_5D' else 16383 + + target_rgb_img = self.norm_img(target_rgb_img, max_value=255) + input_raw_img = self.norm_img(input_raw_img, max_value=norm_value) + target_raw_img = input_raw_img.copy() + + input_raw_img = self.np2tensor(input_raw_img).float() + target_rgb_img = self.np2tensor(target_rgb_img).float() + target_raw_img = self.np2tensor(target_raw_img).float() + + sample = {'input_raw':input_raw_img, 'target_rgb':target_rgb_img, 'target_raw':target_raw_img, + 'file_name':input_raw_wb_path.split("/")[-1].split(".")[0]} + return sample + +class FiveKDatasetTest(BaseDataset): + def __init__(self, opt): + super().__init__(opt=opt) + self.patch_size = 256 + + input_RAWs_WBs, target_RGBs = self.load(is_train=False) + assert len(input_RAWs_WBs) == len(target_RGBs) + self.data = {'input_RAWs_WBs':input_RAWs_WBs, 'target_RGBs':target_RGBs} + + def __len__(self): + return len(self.data['input_RAWs_WBs']) + + def __getitem__(self, idx): + input_raw_wb_path = self.data['input_RAWs_WBs'][idx] + target_rgb_path = self.data['target_RGBs'][idx] + + target_rgb_img = imread(target_rgb_path) + input_raw_wb = np.load(input_raw_wb_path) + input_raw_img = input_raw_wb['raw'] + wb = input_raw_wb['wb'] + wb = wb / wb.max() + input_raw_img = input_raw_img * wb[:-1] + + if self.gamma: + norm_value = np.power(4095, 1/2.2) if self.camera_name=='Canon_EOS_5D' else np.power(16383, 1/2.2) + input_raw_img = np.power(input_raw_img, 1/2.2) + else: + norm_value = 4095 if self.camera_name=='Canon_EOS_5D' else 16383 + + target_rgb_img = self.norm_img(target_rgb_img, max_value=255) + input_raw_img = self.norm_img(input_raw_img, max_value=norm_value) + target_raw_img = input_raw_img.copy() + + input_raw_img = self.np2tensor(input_raw_img).float() + target_rgb_img = self.np2tensor(target_rgb_img).float() + target_raw_img = self.np2tensor(target_raw_img).float() + + sample = {'input_raw':input_raw_img, 'target_rgb':target_rgb_img, 'target_raw':target_raw_img, + 'file_name':input_raw_wb_path.split("/")[-1].split(".")[0]} + return sample + diff --git a/third_party/DarkFeat/datasets/InvISP/dataset/__init__.py b/third_party/DarkFeat/datasets/InvISP/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py b/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..34c5de9f75dbfb5323c2cdad532cb0a42c09df22 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/dataset/base_dataset.py @@ -0,0 +1,84 @@ +from __future__ import print_function, division +import numpy as np +from torch.utils.data import Dataset +import torch + +class BaseDataset(Dataset): + def __init__(self, opt): + self.crop_size = 512 + self.debug_mode = opt.debug_mode + self.data_path = opt.data_path # dataset path. e.g., ./data/ + self.camera_name = opt.camera + self.gamma = opt.gamma + + def norm_img(self, img, max_value): + img = img / float(max_value) + return img + + def pack_raw(self, raw): + # pack Bayer image to 4 channels + im = np.expand_dims(raw, axis=2) + H, W = raw.shape[0], raw.shape[1] + # RGBG + out = np.concatenate((im[0:H:2, 0:W:2, :], + im[0:H:2, 1:W:2, :], + im[1:H:2, 1:W:2, :], + im[1:H:2, 0:W:2, :]), axis=2) + return out + + def np2tensor(self, array): + return torch.Tensor(array).permute(2,0,1) + + def center_crop(self, img, crop_size=None): + H = img.shape[0] + W = img.shape[1] + + if crop_size is not None: + th, tw = crop_size[0], crop_size[1] + else: + th, tw = self.crop_size, self.crop_size + x1_img = int(round((W - tw) / 2.)) + y1_img = int(round((H - th) / 2.)) + if img.ndim == 3: + input_patch = img[y1_img:y1_img + th, x1_img:x1_img + tw, :] + else: + input_patch = img[y1_img:y1_img + th, x1_img:x1_img + tw] + + return input_patch + + def load(self, is_train=True): + # ./data + # ./data/NIKON D700/RAW, ./data/NIKON D700/RGB + # ./data/Canon EOS 5D/RAW, ./data/Canon EOS 5D/RGB + # ./data/NIKON D700_train.txt, ./data/NIKON D700_test.txt + # ./data/NIKON D700_train.txt: a0016, ... + input_RAWs_WBs = [] + target_RGBs = [] + + data_path = self.data_path # ./data/ + if is_train: + txt_path = data_path + self.camera_name + "_train.txt" + else: + txt_path = data_path + self.camera_name + "_test.txt" + + with open(txt_path, "r") as f_read: + # valid_camera_list = [os.path.basename(line.strip()).split('.')[0] for line in f_read.readlines()] + valid_camera_list = [line.strip() for line in f_read.readlines()] + + if self.debug_mode: + valid_camera_list = valid_camera_list[:10] + + for i,name in enumerate(valid_camera_list): + full_name = data_path + self.camera_name + input_RAWs_WBs.append(full_name + "/RAW/" + name + ".npz") + target_RGBs.append(full_name + "/RGB/" + name + ".jpg") + + return input_RAWs_WBs, target_RGBs + + + def __len__(self): + return 0 + + def __getitem__(self, idx): + + return None diff --git a/third_party/DarkFeat/datasets/InvISP/environment.yml b/third_party/DarkFeat/datasets/InvISP/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..20a58415354b80fb01f72fbbeb8d55edee6067ce --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/environment.yml @@ -0,0 +1,56 @@ +name: invertible-isp +channels: + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _pytorch_select=0.2=gpu_0 + - blas=1.0=mkl + - ca-certificates=2021.1.19=h06a4308_1 + - certifi=2020.12.5=py36h06a4308_0 + - cffi=1.14.5=py36h261ae71_0 + - cudatoolkit=10.1.243=h6bb024c_0 + - cudnn=7.6.5=cuda10.1_0 + - freetype=2.10.4=h5ab3b9f_0 + - intel-openmp=2020.2=254 + - jpeg=9b=h024ee3a_2 + - lcms2=2.11=h396b838_0 + - ld_impl_linux-64=2.33.1=h53a641e_7 + - libffi=3.3=he6710b0_2 + - libgcc-ng=9.1.0=hdf63c60_0 + - libpng=1.6.37=hbc83047_0 + - libstdcxx-ng=9.1.0=hdf63c60_0 + - libtiff=4.1.0=h2733197_1 + - lz4-c=1.9.3=h2531618_0 + - mkl=2020.2=256 + - mkl-service=2.3.0=py36he8ac12f_0 + - mkl_fft=1.3.0=py36h54f3939_0 + - mkl_random=1.1.1=py36h0573a6f_0 + - ncurses=6.2=he6710b0_1 + - ninja=1.10.2=py36hff7bd54_0 + - numpy=1.19.2=py36h54aff64_0 + - numpy-base=1.19.2=py36hfa32c7d_0 + - olefile=0.46=py36_0 + - openssl=1.1.1k=h27cfd23_0 + - pillow=8.2.0=py36he98fc37_0 + - pip=21.0.1=py36h06a4308_0 + - pycparser=2.20=py_2 + - python=3.6.13=hdb3f193_0 + - pytorch=1.4.0=cuda101py36h02f0884_0 + - readline=8.1=h27cfd23_0 + - setuptools=52.0.0=py36h06a4308_0 + - six=1.15.0=py36h06a4308_0 + - sqlite=3.35.3=hdfb4753_0 + - tk=8.6.10=hbc83047_0 + - torchvision=0.2.1=py36_0 + - wheel=0.36.2=pyhd3eb1b0_0 + - xz=5.2.5=h7b6447c_0 + - zlib=1.2.11=h7b6447c_3 + - zstd=1.4.9=haebb681_0 + - pip: + - colour-demosaicing==0.1.6 + - colour-science==0.3.16 + - imageio==2.9.0 + - rawpy==0.16.0 + - scipy==1.2.0 + - tqdm==4.59.0 + diff --git a/third_party/DarkFeat/datasets/InvISP/model/__init__.py b/third_party/DarkFeat/datasets/InvISP/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/datasets/InvISP/model/loss.py b/third_party/DarkFeat/datasets/InvISP/model/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..abe8b599d5402c367bb7c84b7e370964d8273518 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/model/loss.py @@ -0,0 +1,15 @@ +import torch.nn.functional as F +import torch + + +def l1_loss(output, target_rgb, target_raw, weight=1.): + raw_loss = F.l1_loss(output['reconstruct_raw'], target_raw) + rgb_loss = F.l1_loss(output['reconstruct_rgb'], target_rgb) + total_loss = raw_loss + weight * rgb_loss + return total_loss, raw_loss, rgb_loss + +def l2_loss(output, target_rgb, target_raw, weight=1.): + raw_loss = F.mse_loss(output['reconstruct_raw'], target_raw) + rgb_loss = F.mse_loss(output['reconstruct_rgb'], target_rgb) + total_loss = raw_loss + weight * rgb_loss + return total_loss, raw_loss, rgb_loss \ No newline at end of file diff --git a/third_party/DarkFeat/datasets/InvISP/model/model.py b/third_party/DarkFeat/datasets/InvISP/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..9dd0e33cee8ebb26d621ece84622bd2611b33a60 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/model/model.py @@ -0,0 +1,179 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import torch.nn.init as init + +from .modules import InvertibleConv1x1 + + +def initialize_weights(net_l, scale=1): + if not isinstance(net_l, list): + net_l = [net_l] + for net in net_l: + for m in net.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, a=0, mode='fan_in') + m.weight.data *= scale # for residual block + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, a=0, mode='fan_in') + m.weight.data *= scale + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + init.constant_(m.weight, 1) + init.constant_(m.bias.data, 0.0) + + +def initialize_weights_xavier(net_l, scale=1): + if not isinstance(net_l, list): + net_l = [net_l] + for net in net_l: + for m in net.modules(): + if isinstance(m, nn.Conv2d): + init.xavier_normal_(m.weight) + m.weight.data *= scale # for residual block + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + init.xavier_normal_(m.weight) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + init.constant_(m.weight, 1) + init.constant_(m.bias.data, 0.0) + + +class DenseBlock(nn.Module): + def __init__(self, channel_in, channel_out, init='xavier', gc=32, bias=True): + super(DenseBlock, self).__init__() + self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias) + self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias) + self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias) + self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias) + self.conv5 = nn.Conv2d(channel_in + 4 * gc, channel_out, 3, 1, 1, bias=bias) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + if init == 'xavier': + initialize_weights_xavier([self.conv1, self.conv2, self.conv3, self.conv4], 0.1) + else: + initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4], 0.1) + initialize_weights(self.conv5, 0) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + + return x5 + +def subnet(net_structure, init='xavier'): + def constructor(channel_in, channel_out): + if net_structure == 'DBNet': + if init == 'xavier': + return DenseBlock(channel_in, channel_out, init) + else: + return DenseBlock(channel_in, channel_out) + # return UNetBlock(channel_in, channel_out) + else: + return None + + return constructor + + +class InvBlock(nn.Module): + def __init__(self, subnet_constructor, channel_num, channel_split_num, clamp=0.8): + super(InvBlock, self).__init__() + # channel_num: 3 + # channel_split_num: 1 + + self.split_len1 = channel_split_num # 1 + self.split_len2 = channel_num - channel_split_num # 2 + + self.clamp = clamp + + self.F = subnet_constructor(self.split_len2, self.split_len1) + self.G = subnet_constructor(self.split_len1, self.split_len2) + self.H = subnet_constructor(self.split_len1, self.split_len2) + + in_channels = 3 + self.invconv = InvertibleConv1x1(in_channels, LU_decomposed=True) + self.flow_permutation = lambda z, logdet, rev: self.invconv(z, logdet, rev) + + def forward(self, x, rev=False): + if not rev: + # invert1x1conv + x, logdet = self.flow_permutation(x, logdet=0, rev=False) + + # split to 1 channel and 2 channel. + x1, x2 = (x.narrow(1, 0, self.split_len1), x.narrow(1, self.split_len1, self.split_len2)) + + y1 = x1 + self.F(x2) # 1 channel + self.s = self.clamp * (torch.sigmoid(self.H(y1)) * 2 - 1) + y2 = x2.mul(torch.exp(self.s)) + self.G(y1) # 2 channel + out = torch.cat((y1, y2), 1) + else: + # split. + x1, x2 = (x.narrow(1, 0, self.split_len1), x.narrow(1, self.split_len1, self.split_len2)) + self.s = self.clamp * (torch.sigmoid(self.H(x1)) * 2 - 1) + y2 = (x2 - self.G(x1)).div(torch.exp(self.s)) + y1 = x1 - self.F(y2) + + x = torch.cat((y1, y2), 1) + + # inv permutation + out, logdet = self.flow_permutation(x, logdet=0, rev=True) + + return out + +class InvISPNet(nn.Module): + def __init__(self, channel_in=3, channel_out=3, subnet_constructor=subnet('DBNet'), block_num=8): + super(InvISPNet, self).__init__() + operations = [] + + current_channel = channel_in + channel_num = channel_in + channel_split_num = 1 + + for j in range(block_num): + b = InvBlock(subnet_constructor, channel_num, channel_split_num) # one block is one flow step. + operations.append(b) + + self.operations = nn.ModuleList(operations) + + self.initialize() + + def initialize(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + init.xavier_normal_(m.weight) + m.weight.data *= 1. # for residual block + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + init.xavier_normal_(m.weight) + m.weight.data *= 1. + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + init.constant_(m.weight, 1) + init.constant_(m.bias.data, 0.0) + + def forward(self, x, rev=False): + out = x # x: [N,3,H,W] + + if not rev: + for op in self.operations: + out = op.forward(out, rev) + else: + for op in reversed(self.operations): + out = op.forward(out, rev) + + return out + diff --git a/third_party/DarkFeat/datasets/InvISP/model/modules.py b/third_party/DarkFeat/datasets/InvISP/model/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..88244c0b211860d97be78ba4f60f4743228171a7 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/model/modules.py @@ -0,0 +1,387 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .utils import split_feature, compute_same_pad + + +def gaussian_p(mean, logs, x): + """ + lnL = -1/2 * { ln|Var| + ((X - Mu)^T)(Var^-1)(X - Mu) + kln(2*PI) } + k = 1 (Independent) + Var = logs ** 2 + """ + c = math.log(2 * math.pi) + return -0.5 * (logs * 2.0 + ((x - mean) ** 2) / torch.exp(logs * 2.0) + c) + + +def gaussian_likelihood(mean, logs, x): + p = gaussian_p(mean, logs, x) + return torch.sum(p, dim=[1, 2, 3]) + + +def gaussian_sample(mean, logs, temperature=1): + # Sample from Gaussian with temperature + z = torch.normal(mean, torch.exp(logs) * temperature) + + return z + + +def squeeze2d(input, factor): + if factor == 1: + return input + + B, C, H, W = input.size() + + assert H % factor == 0 and W % factor == 0, "H or W modulo factor is not 0" + + x = input.view(B, C, H // factor, factor, W // factor, factor) + x = x.permute(0, 1, 3, 5, 2, 4).contiguous() + x = x.view(B, C * factor * factor, H // factor, W // factor) + + return x + + +def unsqueeze2d(input, factor): + if factor == 1: + return input + + factor2 = factor ** 2 + + B, C, H, W = input.size() + + assert C % (factor2) == 0, "C module factor squared is not 0" + + x = input.view(B, C // factor2, factor, factor, H, W) + x = x.permute(0, 1, 4, 2, 5, 3).contiguous() + x = x.view(B, C // (factor2), H * factor, W * factor) + + return x + + +class _ActNorm(nn.Module): + """ + Activation Normalization + Initialize the bias and scale with a given minibatch, + so that the output per-channel have zero mean and unit variance for that. + + After initialization, `bias` and `logs` will be trained as parameters. + """ + + def __init__(self, num_features, scale=1.0): + super().__init__() + # register mean and scale + size = [1, num_features, 1, 1] + self.bias = nn.Parameter(torch.zeros(*size)) + self.logs = nn.Parameter(torch.zeros(*size)) + self.num_features = num_features + self.scale = scale + self.inited = False + + def initialize_parameters(self, input): + if not self.training: + raise ValueError("In Eval mode, but ActNorm not inited") + + with torch.no_grad(): + bias = -torch.mean(input.clone(), dim=[0, 2, 3], keepdim=True) + vars = torch.mean((input.clone() + bias) ** 2, dim=[0, 2, 3], keepdim=True) + logs = torch.log(self.scale / (torch.sqrt(vars) + 1e-6)) + + self.bias.data.copy_(bias.data) + self.logs.data.copy_(logs.data) + + self.inited = True + + def _center(self, input, reverse=False): + if reverse: + return input - self.bias + else: + return input + self.bias + + def _scale(self, input, logdet=None, reverse=False): + + if reverse: + input = input * torch.exp(-self.logs) + else: + input = input * torch.exp(self.logs) + + if logdet is not None: + """ + logs is log_std of `mean of channels` + so we need to multiply by number of pixels + """ + b, c, h, w = input.shape + + dlogdet = torch.sum(self.logs) * h * w + + if reverse: + dlogdet *= -1 + + logdet = logdet + dlogdet + + return input, logdet + + def forward(self, input, logdet=None, reverse=False): + self._check_input_dim(input) + + if not self.inited: + self.initialize_parameters(input) + + if reverse: + input, logdet = self._scale(input, logdet, reverse) + input = self._center(input, reverse) + else: + input = self._center(input, reverse) + input, logdet = self._scale(input, logdet, reverse) + + return input, logdet + + +class ActNorm2d(_ActNorm): + def __init__(self, num_features, scale=1.0): + super().__init__(num_features, scale) + + def _check_input_dim(self, input): + assert len(input.size()) == 4 + assert input.size(1) == self.num_features, ( + "[ActNorm]: input should be in shape as `BCHW`," + " channels should be {} rather than {}".format( + self.num_features, input.size() + ) + ) + + +class LinearZeros(nn.Module): + def __init__(self, in_channels, out_channels, logscale_factor=3): + super().__init__() + + self.linear = nn.Linear(in_channels, out_channels) + self.linear.weight.data.zero_() + self.linear.bias.data.zero_() + + self.logscale_factor = logscale_factor + + self.logs = nn.Parameter(torch.zeros(out_channels)) + + def forward(self, input): + output = self.linear(input) + return output * torch.exp(self.logs * self.logscale_factor) + + +class Conv2d(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding="same", + do_actnorm=True, + weight_std=0.05, + ): + super().__init__() + + if padding == "same": + padding = compute_same_pad(kernel_size, stride) + elif padding == "valid": + padding = 0 + + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding, + bias=(not do_actnorm), + ) + + # init weight with std + self.conv.weight.data.normal_(mean=0.0, std=weight_std) + + if not do_actnorm: + self.conv.bias.data.zero_() + else: + self.actnorm = ActNorm2d(out_channels) + + self.do_actnorm = do_actnorm + + def forward(self, input): + x = self.conv(input) + if self.do_actnorm: + x, _ = self.actnorm(x) + return x + + +class Conv2dZeros(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size=(3, 3), + stride=(1, 1), + padding="same", + logscale_factor=3, + ): + super().__init__() + + if padding == "same": + padding = compute_same_pad(kernel_size, stride) + elif padding == "valid": + padding = 0 + + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding) + + self.conv.weight.data.zero_() + self.conv.bias.data.zero_() + + self.logscale_factor = logscale_factor + self.logs = nn.Parameter(torch.zeros(out_channels, 1, 1)) + + def forward(self, input): + output = self.conv(input) + return output * torch.exp(self.logs * self.logscale_factor) + + +class Permute2d(nn.Module): + def __init__(self, num_channels, shuffle): + super().__init__() + self.num_channels = num_channels + self.indices = torch.arange(self.num_channels - 1, -1, -1, dtype=torch.long) + self.indices_inverse = torch.zeros((self.num_channels), dtype=torch.long) + + for i in range(self.num_channels): + self.indices_inverse[self.indices[i]] = i + + if shuffle: + self.reset_indices() + + def reset_indices(self): + shuffle_idx = torch.randperm(self.indices.shape[0]) + self.indices = self.indices[shuffle_idx] + + for i in range(self.num_channels): + self.indices_inverse[self.indices[i]] = i + + def forward(self, input, reverse=False): + assert len(input.size()) == 4 + + if not reverse: + input = input[:, self.indices, :, :] + return input + else: + return input[:, self.indices_inverse, :, :] + + +class Split2d(nn.Module): + def __init__(self, num_channels): + super().__init__() + self.conv = Conv2dZeros(num_channels // 2, num_channels) + + def split2d_prior(self, z): + h = self.conv(z) + return split_feature(h, "cross") + + def forward(self, input, logdet=0.0, reverse=False, temperature=None): + if reverse: + z1 = input + mean, logs = self.split2d_prior(z1) + z2 = gaussian_sample(mean, logs, temperature) + z = torch.cat((z1, z2), dim=1) + return z, logdet + else: + z1, z2 = split_feature(input, "split") + mean, logs = self.split2d_prior(z1) + logdet = gaussian_likelihood(mean, logs, z2) + logdet + return z1, logdet + + +class SqueezeLayer(nn.Module): + def __init__(self, factor): + super().__init__() + self.factor = factor + + def forward(self, input, logdet=None, reverse=False): + if reverse: + output = unsqueeze2d(input, self.factor) + else: + output = squeeze2d(input, self.factor) + + return output, logdet + + +class InvertibleConv1x1(nn.Module): + def __init__(self, num_channels, LU_decomposed): + super().__init__() + w_shape = [num_channels, num_channels] + w_init = torch.linalg.qr(torch.randn(*w_shape))[0] + + if not LU_decomposed: + self.weight = nn.Parameter(torch.Tensor(w_init)) + else: + p, lower, upper = torch.lu_unpack(*torch.lu(w_init)) + s = torch.diag(upper) + sign_s = torch.sign(s) + log_s = torch.log(torch.abs(s)) + upper = torch.triu(upper, 1) + l_mask = torch.tril(torch.ones(w_shape), -1) + eye = torch.eye(*w_shape) + + self.register_buffer("p", p) + self.register_buffer("sign_s", sign_s) + self.lower = nn.Parameter(lower) + self.log_s = nn.Parameter(log_s) + self.upper = nn.Parameter(upper) + self.l_mask = l_mask + self.eye = eye + + self.w_shape = w_shape + self.LU_decomposed = LU_decomposed + + def get_weight(self, input, reverse): + b, c, h, w = input.shape + + if not self.LU_decomposed: + dlogdet = torch.slogdet(self.weight)[1] * h * w + if reverse: + weight = torch.inverse(self.weight) + else: + weight = self.weight + else: + self.l_mask = self.l_mask.to(input.device) + self.eye = self.eye.to(input.device) + + lower = self.lower * self.l_mask + self.eye + + u = self.upper * self.l_mask.transpose(0, 1).contiguous() + u += torch.diag(self.sign_s * torch.exp(self.log_s)) + + dlogdet = torch.sum(self.log_s) * h * w + + if reverse: + u_inv = torch.inverse(u) + l_inv = torch.inverse(lower) + p_inv = torch.inverse(self.p) + + weight = torch.matmul(u_inv, torch.matmul(l_inv, p_inv)) + else: + weight = torch.matmul(self.p, torch.matmul(lower, u)) + + return weight.view(self.w_shape[0], self.w_shape[1], 1, 1), dlogdet + + def forward(self, input, logdet=None, reverse=False): + """ + log-det = log|abs(|W|)| * pixels + """ + weight, dlogdet = self.get_weight(input, reverse) + + if not reverse: + z = F.conv2d(input, weight) + if logdet is not None: + logdet = logdet + dlogdet + return z, logdet + else: + z = F.conv2d(input, weight) + if logdet is not None: + logdet = logdet - dlogdet + return z, logdet diff --git a/third_party/DarkFeat/datasets/InvISP/model/utils.py b/third_party/DarkFeat/datasets/InvISP/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d1bef31afd7d61d4c942ffd895c818b90571b4b7 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/model/utils.py @@ -0,0 +1,52 @@ +import math +import torch + + +def compute_same_pad(kernel_size, stride): + if isinstance(kernel_size, int): + kernel_size = [kernel_size] + + if isinstance(stride, int): + stride = [stride] + + assert len(stride) == len( + kernel_size + ), "Pass kernel size and stride both as int, or both as equal length iterable" + + return [((k - 1) * s + 1) // 2 for k, s in zip(kernel_size, stride)] + + +def uniform_binning_correction(x, n_bits=8): + """Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0). + + Args: + x: 4-D Tensor of shape (NCHW) + n_bits: optional. + Returns: + x: x ~ U(x, x + 1.0 / 256) + objective: Equivalent to -q(x)*log(q(x)). + """ + b, c, h, w = x.size() + n_bins = 2 ** n_bits + chw = c * h * w + x += torch.zeros_like(x).uniform_(0, 1.0 / n_bins) + + objective = -math.log(n_bins) * chw * torch.ones(b, device=x.device) + return x, objective + + +def split_feature(tensor, type="split"): + """ + type = ["split", "cross"] + """ + C = tensor.size(1) + if type == "split": + # return tensor[:, : C // 2, ...], tensor[:, C // 2 :, ...] + return tensor[:, :1, ...], tensor[:,1:, ...] + elif type == "cross": + # return tensor[:, 0::2, ...], tensor[:, 1::2, ...] + return tensor[:, 0::2, ...], tensor[:, 1::2, ...] + + + + diff --git a/third_party/DarkFeat/datasets/InvISP/pretrained/canon.pth b/third_party/DarkFeat/datasets/InvISP/pretrained/canon.pth new file mode 100644 index 0000000000000000000000000000000000000000..b7a126d418459dba22fcb60b9906104fb59d8296 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/pretrained/canon.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e620bd152f0f8a1db5266ed1219fe3c608c478d543f899495ef2a6b16261fa1b +size 5750545 diff --git a/third_party/DarkFeat/datasets/InvISP/test.sh b/third_party/DarkFeat/datasets/InvISP/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..dc71a15aef80302525ed8cba5a8e29f1e28db05d --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/test.sh @@ -0,0 +1,15 @@ +# python test_rgb.py --task=pretrained \ +# --data_path="./data/" \ +# --gamma \ +# --camera="Canon_EOS_5D" \ +# --out_path="./exps/" \ +# --ckpt="./pretrained/canon.pth" \ +# # --split_to_patch + +python test_raw.py --task=pretrained \ + --data_path="./data/" \ + --gamma \ + --camera="Canon_EOS_5D" \ + --out_path="./exps/" \ + --ckpt="./pretrained/canon.pth" \ + --split_to_patch diff --git a/third_party/DarkFeat/datasets/InvISP/test_raw.py b/third_party/DarkFeat/datasets/InvISP/test_raw.py new file mode 100644 index 0000000000000000000000000000000000000000..37610f8268e4586864e0275236c5bb1932f894df --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/test_raw.py @@ -0,0 +1,118 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +import torch +import numpy as np +import os, time, random +import argparse +from torch.utils.data import Dataset, DataLoader +from PIL import Image as PILImage +from glob import glob +from tqdm import tqdm + +from model.model import InvISPNet +from dataset.FiveK_dataset import FiveKDatasetTest +from config.config import get_arguments + +from utils.JPEG import DiffJPEG +from utils.commons import denorm, preprocess_test_patch + + +os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp') +os.environ['CUDA_VISIBLE_DEVICES'] = str(np.argmax([int(x.split()[2]) for x in open('tmp', 'r').readlines()])) +# os.environ['CUDA_VISIBLE_DEVICES'] = '7' +os.system('rm tmp') + +DiffJPEG = DiffJPEG(differentiable=True, quality=90).cuda() + +parser = get_arguments() +parser.add_argument("--ckpt", type=str, help="Checkpoint path.") +parser.add_argument("--out_path", type=str, default="./exps/", help="Path to save checkpoint. ") +parser.add_argument("--split_to_patch", dest='split_to_patch', action='store_true', help="Test on patch. ") +args = parser.parse_args() +print("Parsed arguments: {}".format(args)) + + +ckpt_name = args.ckpt.split("/")[-1].split(".")[0] +if args.split_to_patch: + os.makedirs(args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name), exist_ok=True) + out_path = args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name) +else: + os.makedirs(args.out_path+"%s/results_%s/"%(args.task, ckpt_name), exist_ok=True) + out_path = args.out_path+"%s/results_%s/"%(args.task, ckpt_name) + + +def main(args): + # ======================================define the model============================================ + net = InvISPNet(channel_in=3, channel_out=3, block_num=8) + device = torch.device("cuda:0") + + net.to(device) + net.eval() + # load the pretrained weight if there exists one + if os.path.isfile(args.ckpt): + net.load_state_dict(torch.load(args.ckpt), strict=False) + print("[INFO] Loaded checkpoint: {}".format(args.ckpt)) + + print("[INFO] Start data load and preprocessing") + RAWDataset = FiveKDatasetTest(opt=args) + dataloader = DataLoader(RAWDataset, batch_size=1, shuffle=False, num_workers=0, drop_last=True) + + input_RGBs = sorted(glob(out_path+"pred*jpg")) + input_RGBs_names = [path.split("/")[-1].split(".")[0][5:] for path in input_RGBs] + + print("[INFO] Start test...") + for i_batch, sample_batched in enumerate(tqdm(dataloader)): + step_time = time.time() + + input, target_rgb, target_raw = sample_batched['input_raw'].to(device), sample_batched['target_rgb'].to(device), \ + sample_batched['target_raw'].to(device) + file_name = sample_batched['file_name'][0] + + if args.split_to_patch: + input_list, target_rgb_list, target_raw_list = preprocess_test_patch(input, target_rgb, target_raw) + else: + # remove [:,:,::2,::2] if you have enough GPU memory to test the full resolution + input_list, target_rgb_list, target_raw_list = [input[:,:,::2,::2]], [target_rgb[:,:,::2,::2]], [target_raw[:,:,::2,::2]] + + for i_patch in range(len(input_list)): + file_name_patch = file_name + "_%05d"%i_patch + idx = input_RGBs_names.index(file_name_patch) + input_RGB_path = input_RGBs[idx] + input_RGB = torch.from_numpy(np.array(PILImage.open(input_RGB_path))/255.0).unsqueeze(0).permute(0,3,1,2).float().to(device) + + target_raw_patch = target_raw_list[i_patch] + + with torch.no_grad(): + reconstruct_raw = net(input_RGB, rev=True) + + pred_raw = reconstruct_raw.detach().permute(0,2,3,1) + pred_raw = torch.clamp(pred_raw, 0, 1) + + target_raw_patch = target_raw_patch.permute(0,2,3,1) + pred_raw = denorm(pred_raw, 255) + target_raw_patch = denorm(target_raw_patch, 255) + + pred_raw = pred_raw.cpu().numpy() + target_raw_patch = target_raw_patch.cpu().numpy().astype(np.float32) + + raw_pred = PILImage.fromarray(np.uint8(pred_raw[0,:,:,0])) + raw_tar_pred = PILImage.fromarray(np.hstack((np.uint8(target_raw_patch[0,:,:,0]), np.uint8(pred_raw[0,:,:,0])))) + + raw_tar = PILImage.fromarray(np.uint8(target_raw_patch[0,:,:,0])) + + raw_pred.save(out_path+"raw_pred_%s_%05d.jpg"%(file_name, i_patch)) + raw_tar.save(out_path+"raw_tar_%s_%05d.jpg"%(file_name, i_patch)) + raw_tar_pred.save(out_path+"raw_gt_pred_%s_%05d.jpg"%(file_name, i_patch)) + + np.save(out_path+"raw_pred_%s_%05d.npy"%(file_name, i_patch), pred_raw[0,:,:,:]/255.0) + np.save(out_path+"raw_tar_%s_%05d.npy"%(file_name, i_patch), target_raw_patch[0,:,:,:]/255.0) + + del reconstruct_raw + + +if __name__ == '__main__': + + torch.set_num_threads(4) + main(args) + diff --git a/third_party/DarkFeat/datasets/InvISP/test_rgb.py b/third_party/DarkFeat/datasets/InvISP/test_rgb.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e054b899d9142609e3f90f4a12d367a45aeac0 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/test_rgb.py @@ -0,0 +1,105 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +import torch +import numpy as np +import os, time, random +import argparse +from torch.utils.data import Dataset, DataLoader +from PIL import Image as PILImage + +from model.model import InvISPNet +from dataset.FiveK_dataset import FiveKDatasetTest +from config.config import get_arguments + +from utils.JPEG import DiffJPEG +from utils.commons import denorm, preprocess_test_patch +from tqdm import tqdm + +os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp') +os.environ['CUDA_VISIBLE_DEVICES'] = str(np.argmax([int(x.split()[2]) for x in open('tmp', 'r').readlines()])) +# os.environ['CUDA_VISIBLE_DEVICES'] = '7' +os.system('rm tmp') + +DiffJPEG = DiffJPEG(differentiable=True, quality=90).cuda() + +parser = get_arguments() +parser.add_argument("--ckpt", type=str, help="Checkpoint path.") +parser.add_argument("--out_path", type=str, default="./exps/", help="Path to save results. ") +parser.add_argument("--split_to_patch", dest='split_to_patch', action='store_true', help="Test on patch. ") +args = parser.parse_args() +print("Parsed arguments: {}".format(args)) + + +ckpt_name = args.ckpt.split("/")[-1].split(".")[0] +if args.split_to_patch: + os.makedirs(args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name), exist_ok=True) + out_path = args.out_path+"%s/results_metric_%s/"%(args.task, ckpt_name) +else: + os.makedirs(args.out_path+"%s/results_%s/"%(args.task, ckpt_name), exist_ok=True) + out_path = args.out_path+"%s/results_%s/"%(args.task, ckpt_name) + + +def main(args): + # ======================================define the model============================================ + net = InvISPNet(channel_in=3, channel_out=3, block_num=8) + device = torch.device("cuda:0") + + net.to(device) + net.eval() + # load the pretrained weight if there exists one + if os.path.isfile(args.ckpt): + net.load_state_dict(torch.load(args.ckpt), strict=False) + print("[INFO] Loaded checkpoint: {}".format(args.ckpt)) + + print("[INFO] Start data load and preprocessing") + RAWDataset = FiveKDatasetTest(opt=args) + dataloader = DataLoader(RAWDataset, batch_size=1, shuffle=False, num_workers=0, drop_last=True) + + print("[INFO] Start test...") + for i_batch, sample_batched in enumerate(tqdm(dataloader)): + step_time = time.time() + + input, target_rgb, target_raw = sample_batched['input_raw'].to(device), sample_batched['target_rgb'].to(device), \ + sample_batched['target_raw'].to(device) + file_name = sample_batched['file_name'][0] + + if args.split_to_patch: + input_list, target_rgb_list, target_raw_list = preprocess_test_patch(input, target_rgb, target_raw) + else: + # remove [:,:,::2,::2] if you have enough GPU memory to test the full resolution + input_list, target_rgb_list, target_raw_list = [input[:,:,::2,::2]], [target_rgb[:,:,::2,::2]], [target_raw[:,:,::2,::2]] + + for i_patch in range(len(input_list)): + input_patch = input_list[i_patch] + target_rgb_patch = target_rgb_list[i_patch] + target_raw_patch = target_raw_list[i_patch] + + with torch.no_grad(): + reconstruct_rgb = net(input_patch) + reconstruct_rgb = torch.clamp(reconstruct_rgb, 0, 1) + + pred_rgb = reconstruct_rgb.detach().permute(0,2,3,1) + target_rgb_patch = target_rgb_patch.permute(0,2,3,1) + + pred_rgb = denorm(pred_rgb, 255) + target_rgb_patch = denorm(target_rgb_patch, 255) + pred_rgb = pred_rgb.cpu().numpy() + target_rgb_patch = target_rgb_patch.cpu().numpy().astype(np.float32) + + # print(type(pred_rgb)) + pred = PILImage.fromarray(np.uint8(pred_rgb[0,:,:,:])) + tar_pred = PILImage.fromarray(np.hstack((np.uint8(target_rgb_patch[0,:,:,:]), np.uint8(pred_rgb[0,:,:,:])))) + + tar = PILImage.fromarray(np.uint8(target_rgb_patch[0,:,:,:])) + + pred.save(out_path+"pred_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) + tar.save(out_path+"tar_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) + tar_pred.save(out_path+"gt_pred_%s_%05d.jpg"%(file_name, i_patch), quality=90, subsampling=1) + + del reconstruct_rgb + +if __name__ == '__main__': + torch.set_num_threads(4) + main(args) + diff --git a/third_party/DarkFeat/datasets/InvISP/train.py b/third_party/DarkFeat/datasets/InvISP/train.py new file mode 100644 index 0000000000000000000000000000000000000000..16186cb38d825ac1299e5c4164799d35bfa79907 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/train.py @@ -0,0 +1,98 @@ +import numpy as np +import os, time, random +import argparse +import json + +import torch.nn.functional as F +import torch +from torch.utils.data import Dataset, DataLoader +from torch.optim import lr_scheduler + +from model.model import InvISPNet +from dataset.FiveK_dataset import FiveKDatasetTrain +from config.config import get_arguments + +from utils.JPEG import DiffJPEG + +os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp') +os.environ['CUDA_VISIBLE_DEVICES'] = str(np.argmax([int(x.split()[2]) for x in open('tmp', 'r').readlines()])) +# os.environ['CUDA_VISIBLE_DEVICES'] = "1" +os.system('rm tmp') + +DiffJPEG = DiffJPEG(differentiable=True, quality=90).cuda() + +parser = get_arguments() +parser.add_argument("--out_path", type=str, default="./exps/", help="Path to save checkpoint. ") +parser.add_argument("--resume", dest='resume', action='store_true', help="Resume training. ") +parser.add_argument("--loss", type=str, default="L1", choices=["L1", "L2"], help="Choose which loss function to use. ") +parser.add_argument("--lr", type=float, default=0.0001, help="Learning rate") +parser.add_argument("--aug", dest='aug', action='store_true', help="Use data augmentation.") +args = parser.parse_args() +print("Parsed arguments: {}".format(args)) + +os.makedirs(args.out_path, exist_ok=True) +os.makedirs(args.out_path+"%s"%args.task, exist_ok=True) +os.makedirs(args.out_path+"%s/checkpoint"%args.task, exist_ok=True) + +with open(args.out_path+"%s/commandline_args.yaml"%args.task , 'w') as f: + json.dump(args.__dict__, f, indent=2) + +def main(args): + # ======================================define the model====================================== + net = InvISPNet(channel_in=3, channel_out=3, block_num=8) + net.cuda() + # load the pretrained weight if there exists one + if args.resume: + net.load_state_dict(torch.load(args.out_path+"%s/checkpoint/latest.pth"%args.task)) + print("[INFO] loaded " + args.out_path+"%s/checkpoint/latest.pth"%args.task) + + optimizer = torch.optim.Adam(net.parameters(), lr=args.lr) + scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[50, 80], gamma=0.5) + + print("[INFO] Start data loading and preprocessing") + RAWDataset = FiveKDatasetTrain(opt=args) + dataloader = DataLoader(RAWDataset, batch_size=args.batch_size, shuffle=True, num_workers=0, drop_last=True) + + print("[INFO] Start to train") + step = 0 + for epoch in range(0, 300): + epoch_time = time.time() + + for i_batch, sample_batched in enumerate(dataloader): + step_time = time.time() + + input, target_rgb, target_raw = sample_batched['input_raw'].cuda(), sample_batched['target_rgb'].cuda(), \ + sample_batched['target_raw'].cuda() + + reconstruct_rgb = net(input) + reconstruct_rgb = torch.clamp(reconstruct_rgb, 0, 1) + rgb_loss = F.l1_loss(reconstruct_rgb, target_rgb) + reconstruct_rgb = DiffJPEG(reconstruct_rgb) + reconstruct_raw = net(reconstruct_rgb, rev=True) + raw_loss = F.l1_loss(reconstruct_raw, target_raw) + + loss = args.rgb_weight * rgb_loss + raw_loss + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + print("task: %s Epoch: %d Step: %d || loss: %.5f raw_loss: %.5f rgb_loss: %.5f || lr: %f time: %f"%( + args.task, epoch, step, loss.detach().cpu().numpy(), raw_loss.detach().cpu().numpy(), + rgb_loss.detach().cpu().numpy(), optimizer.param_groups[0]['lr'], time.time()-step_time + )) + step += 1 + + torch.save(net.state_dict(), args.out_path+"%s/checkpoint/latest.pth"%args.task) + if (epoch+1) % 10 == 0: + # os.makedirs(args.out_path+"%s/checkpoint/%04d"%(args.task,epoch), exist_ok=True) + torch.save(net.state_dict(), args.out_path+"%s/checkpoint/%04d.pth"%(args.task,epoch)) + print("[INFO] Successfully saved "+args.out_path+"%s/checkpoint/%04d.pth"%(args.task,epoch)) + scheduler.step() + + print("[INFO] Epoch time: ", time.time()-epoch_time, "task: ", args.task) + +if __name__ == '__main__': + + torch.set_num_threads(4) + main(args) diff --git a/third_party/DarkFeat/datasets/InvISP/train.sh b/third_party/DarkFeat/datasets/InvISP/train.sh new file mode 100644 index 0000000000000000000000000000000000000000..c94626d01d4adb7b6a453b6f09fa2c9f6479f90d --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/train.sh @@ -0,0 +1,16 @@ +# python train.py --task=debug \ +# --data_path="./data/" \ +# --gamma \ +# --aug \ +# --camera="NIKON_D700" \ +# --out_path="./exps/" \ +# # --debug_mode + +python train.py --task=debug2 \ + --data_path="./data/" \ + --gamma \ + --aug \ + --camera="Canon_EOS_5D" \ + --out_path="./exps/" \ + --debug_mode + diff --git a/third_party/DarkFeat/datasets/InvISP/utils/JPEG.py b/third_party/DarkFeat/datasets/InvISP/utils/JPEG.py new file mode 100644 index 0000000000000000000000000000000000000000..8997ee98a41668b4737a9b2acc2341032f173bd3 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/utils/JPEG.py @@ -0,0 +1,43 @@ + + +import torch +import torch.nn as nn + +from .JPEG_utils import diff_round, quality_to_factor, Quantization +from .compression import compress_jpeg +from .decompression import decompress_jpeg + + +class DiffJPEG(nn.Module): + def __init__(self, differentiable=True, quality=75): + ''' Initialize the DiffJPEG layer + Inputs: + height(int): Original image height + width(int): Original image width + differentiable(bool): If true uses custom differentiable + rounding function, if false uses standrard torch.round + quality(float): Quality factor for jpeg compression scheme. + ''' + super(DiffJPEG, self).__init__() + if differentiable: + rounding = diff_round + # rounding = Quantization() + else: + rounding = torch.round + factor = quality_to_factor(quality) + self.compress = compress_jpeg(rounding=rounding, factor=factor) + # self.decompress = decompress_jpeg(height, width, rounding=rounding, + # factor=factor) + self.decompress = decompress_jpeg(rounding=rounding, factor=factor) + + def forward(self, x): + ''' + ''' + org_height = x.shape[2] + org_width = x.shape[3] + y, cb, cr = self.compress(x) + + recovered = self.decompress(y, cb, cr, org_height, org_width) + return recovered + + diff --git a/third_party/DarkFeat/datasets/InvISP/utils/JPEG_utils.py b/third_party/DarkFeat/datasets/InvISP/utils/JPEG_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e2ebd9bdc184e869ade58eea1c6763baa1d9fc91 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/utils/JPEG_utils.py @@ -0,0 +1,75 @@ +# Standard libraries +import numpy as np +# PyTorch +import torch +import torch.nn as nn +import math + +y_table = np.array( + [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, + 55], [14, 13, 16, 24, 40, 57, 69, 56], + [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, + 77], [24, 35, 55, 64, 81, 104, 113, 92], + [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], + dtype=np.float32).T + +y_table = nn.Parameter(torch.from_numpy(y_table)) +# +c_table = np.empty((8, 8), dtype=np.float32) +c_table.fill(99) +c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], + [24, 26, 56, 99], [47, 66, 99, 99]]).T +c_table = nn.Parameter(torch.from_numpy(c_table)) + + +def diff_round_back(x): + """ Differentiable rounding function + Input: + x(tensor) + Output: + x(tensor) + """ + return torch.round(x) + (x - torch.round(x))**3 + + + +def diff_round(input_tensor): + test = 0 + for n in range(1, 10): + test += math.pow(-1, n+1) / n * torch.sin(2 * math.pi * n * input_tensor) + final_tensor = input_tensor - 1 / math.pi * test + return final_tensor + + +class Quant(torch.autograd.Function): + + @staticmethod + def forward(ctx, input): + input = torch.clamp(input, 0, 1) + output = (input * 255.).round() / 255. + return output + + @staticmethod + def backward(ctx, grad_output): + return grad_output + +class Quantization(nn.Module): + def __init__(self): + super(Quantization, self).__init__() + + def forward(self, input): + return Quant.apply(input) + + +def quality_to_factor(quality): + """ Calculate factor corresponding to quality + Input: + quality(float): Quality for jpeg compression + Output: + factor(float): Compression factor + """ + if quality < 50: + quality = 5000. / quality + else: + quality = 200. - quality*2 + return quality / 100. \ No newline at end of file diff --git a/third_party/DarkFeat/datasets/InvISP/utils/__init__.py b/third_party/DarkFeat/datasets/InvISP/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/datasets/InvISP/utils/commons.py b/third_party/DarkFeat/datasets/InvISP/utils/commons.py new file mode 100644 index 0000000000000000000000000000000000000000..e594e0597bac601edc2015d9cae670799f981495 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/utils/commons.py @@ -0,0 +1,23 @@ +import numpy as np + + +def denorm(img, max_value): + img = img * float(max_value) + return img + +def preprocess_test_patch(input_image, target_image, gt_image): + input_patch_list = [] + target_patch_list = [] + gt_patch_list = [] + H = input_image.shape[2] + W = input_image.shape[3] + for i in range(3): + for j in range(3): + input_patch = input_image[:,:,int(i * H / 3):int((i+1) * H / 3),int(j * W / 3):int((j+1) * W / 3)] + target_patch = target_image[:,:,int(i * H / 3):int((i+1) * H / 3),int(j * W / 3):int((j+1) * W / 3)] + gt_patch = gt_image[:,:,int(i * H / 3):int((i+1) * H / 3),int(j * W / 3):int((j+1) * W / 3)] + input_patch_list.append(input_patch) + target_patch_list.append(target_patch) + gt_patch_list.append(gt_patch) + + return input_patch_list, target_patch_list, gt_patch_list diff --git a/third_party/DarkFeat/datasets/InvISP/utils/compression.py b/third_party/DarkFeat/datasets/InvISP/utils/compression.py new file mode 100644 index 0000000000000000000000000000000000000000..3ae22f8839517bfd7e3c774528943e8fff59dce7 --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/utils/compression.py @@ -0,0 +1,185 @@ +# Standard libraries +import itertools +import numpy as np +# PyTorch +import torch +import torch.nn as nn +# Local +from . import JPEG_utils + + +class rgb_to_ycbcr_jpeg(nn.Module): + """ Converts RGB image to YCbCr + Input: + image(tensor): batch x 3 x height x width + Outpput: + result(tensor): batch x height x width x 3 + """ + def __init__(self): + super(rgb_to_ycbcr_jpeg, self).__init__() + matrix = np.array( + [[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], + [0.5, -0.418688, -0.081312]], dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) + # + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + image = image.permute(0, 2, 3, 1) + result = torch.tensordot(image, self.matrix, dims=1) + self.shift + # result = torch.from_numpy(result) + result.view(image.shape) + return result + + + +class chroma_subsampling(nn.Module): + """ Chroma subsampling on CbCv channels + Input: + image(tensor): batch x height x width x 3 + Output: + y(tensor): batch x height x width + cb(tensor): batch x height/2 x width/2 + cr(tensor): batch x height/2 x width/2 + """ + def __init__(self): + super(chroma_subsampling, self).__init__() + + def forward(self, image): + image_2 = image.permute(0, 3, 1, 2).clone() + avg_pool = nn.AvgPool2d(kernel_size=2, stride=(2, 2), + count_include_pad=False) + cb = avg_pool(image_2[:, 1, :, :].unsqueeze(1)) + cr = avg_pool(image_2[:, 2, :, :].unsqueeze(1)) + cb = cb.permute(0, 2, 3, 1) + cr = cr.permute(0, 2, 3, 1) + return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) + + +class block_splitting(nn.Module): + """ Splitting image into patches + Input: + image(tensor): batch x height x width + Output: + patch(tensor): batch x h*w/64 x h x w + """ + def __init__(self): + super(block_splitting, self).__init__() + self.k = 8 + + def forward(self, image): + height, width = image.shape[1:3] + # print(height, width) + batch_size = image.shape[0] + # print(image.shape) + image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) + return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) + + +class dct_8x8(nn.Module): + """ Discrete Cosine Transformation + Input: + image(tensor): batch x height x width + Output: + dcp(tensor): batch x height x width + """ + def __init__(self): + super(dct_8x8, self).__init__() + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos( + (2 * y + 1) * v * np.pi / 16) + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + # + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float() ) + + def forward(self, image): + image = image - 128 + result = self.scale * torch.tensordot(image, self.tensor, dims=2) + result.view(image.shape) + return result + + +class y_quantize(nn.Module): + """ JPEG Quantization for Y channel + Input: + image(tensor): batch x height x width + rounding(function): rounding function to use + factor(float): Degree of compression + Output: + image(tensor): batch x height x width + """ + def __init__(self, rounding, factor=1): + super(y_quantize, self).__init__() + self.rounding = rounding + self.factor = factor + self.y_table = JPEG_utils.y_table + + def forward(self, image): + image = image.float() / (self.y_table * self.factor) + image = self.rounding(image) + return image + + +class c_quantize(nn.Module): + """ JPEG Quantization for CrCb channels + Input: + image(tensor): batch x height x width + rounding(function): rounding function to use + factor(float): Degree of compression + Output: + image(tensor): batch x height x width + """ + def __init__(self, rounding, factor=1): + super(c_quantize, self).__init__() + self.rounding = rounding + self.factor = factor + self.c_table = JPEG_utils.c_table + + def forward(self, image): + image = image.float() / (self.c_table * self.factor) + image = self.rounding(image) + return image + + +class compress_jpeg(nn.Module): + """ Full JPEG compression algortihm + Input: + imgs(tensor): batch x 3 x height x width + rounding(function): rounding function to use + factor(float): Compression factor + Ouput: + compressed(dict(tensor)): batch x h*w/64 x 8 x 8 + """ + def __init__(self, rounding=torch.round, factor=1): + super(compress_jpeg, self).__init__() + self.l1 = nn.Sequential( + rgb_to_ycbcr_jpeg(), + # comment this line if no subsampling + chroma_subsampling() + ) + self.l2 = nn.Sequential( + block_splitting(), + dct_8x8() + ) + self.c_quantize = c_quantize(rounding=rounding, factor=factor) + self.y_quantize = y_quantize(rounding=rounding, factor=factor) + + def forward(self, image): + y, cb, cr = self.l1(image*255) # modify + + # y, cb, cr = result[:,:,:,0], result[:,:,:,1], result[:,:,:,2] + components = {'y': y, 'cb': cb, 'cr': cr} + for k in components.keys(): + comp = self.l2(components[k]) + # print(comp.shape) + if k in ('cb', 'cr'): + comp = self.c_quantize(comp) + else: + comp = self.y_quantize(comp) + + components[k] = comp + + return components['y'], components['cb'], components['cr'] \ No newline at end of file diff --git a/third_party/DarkFeat/datasets/InvISP/utils/decompression.py b/third_party/DarkFeat/datasets/InvISP/utils/decompression.py new file mode 100644 index 0000000000000000000000000000000000000000..b73ff96d5f6818e1d0464b9c4133f559a3b23fba --- /dev/null +++ b/third_party/DarkFeat/datasets/InvISP/utils/decompression.py @@ -0,0 +1,190 @@ +# Standard libraries +import itertools +import numpy as np +# PyTorch +import torch +import torch.nn as nn +# Local +from . import JPEG_utils as utils + + +class y_dequantize(nn.Module): + """ Dequantize Y channel + Inputs: + image(tensor): batch x height x width + factor(float): compression factor + Outputs: + image(tensor): batch x height x width + """ + def __init__(self, factor=1): + super(y_dequantize, self).__init__() + self.y_table = utils.y_table + self.factor = factor + + def forward(self, image): + return image * (self.y_table * self.factor) + + +class c_dequantize(nn.Module): + """ Dequantize CbCr channel + Inputs: + image(tensor): batch x height x width + factor(float): compression factor + Outputs: + image(tensor): batch x height x width + """ + def __init__(self, factor=1): + super(c_dequantize, self).__init__() + self.factor = factor + self.c_table = utils.c_table + + def forward(self, image): + return image * (self.c_table * self.factor) + + +class idct_8x8(nn.Module): + """ Inverse discrete Cosine Transformation + Input: + dcp(tensor): batch x height x width + Output: + image(tensor): batch x height x width + """ + def __init__(self): + super(idct_8x8, self).__init__() + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos( + (2 * v + 1) * y * np.pi / 16) + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + + def forward(self, image): + + image = image * self.alpha + result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 + result.view(image.shape) + return result + + +class block_merging(nn.Module): + """ Merge pathces into image + Inputs: + patches(tensor) batch x height*width/64, height x width + height(int) + width(int) + Output: + image(tensor): batch x height x width + """ + def __init__(self): + super(block_merging, self).__init__() + + def forward(self, patches, height, width): + k = 8 + batch_size = patches.shape[0] + # print(patches.shape) # (1,1024,8,8) + image_reshaped = patches.view(batch_size, height//k, width//k, k, k) + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) + return image_transposed.contiguous().view(batch_size, height, width) + + +class chroma_upsampling(nn.Module): + """ Upsample chroma layers + Input: + y(tensor): y channel image + cb(tensor): cb channel + cr(tensor): cr channel + Ouput: + image(tensor): batch x height x width x 3 + """ + def __init__(self): + super(chroma_upsampling, self).__init__() + + def forward(self, y, cb, cr): + def repeat(x, k=2): + height, width = x.shape[1:3] + x = x.unsqueeze(-1) + x = x.repeat(1, 1, k, k) + x = x.view(-1, height * k, width * k) + return x + + cb = repeat(cb) + cr = repeat(cr) + + return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) + + +class ycbcr_to_rgb_jpeg(nn.Module): + """ Converts YCbCr image to RGB JPEG + Input: + image(tensor): batch x height x width x 3 + Outpput: + result(tensor): batch x 3 x height x width + """ + def __init__(self): + super(ycbcr_to_rgb_jpeg, self).__init__() + + matrix = np.array( + [[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], + dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + result = torch.tensordot(image + self.shift, self.matrix, dims=1) + #result = torch.from_numpy(result) + result.view(image.shape) + return result.permute(0, 3, 1, 2) + + +class decompress_jpeg(nn.Module): + """ Full JPEG decompression algortihm + Input: + compressed(dict(tensor)): batch x h*w/64 x 8 x 8 + rounding(function): rounding function to use + factor(float): Compression factor + Ouput: + image(tensor): batch x 3 x height x width + """ + # def __init__(self, height, width, rounding=torch.round, factor=1): + def __init__(self, rounding=torch.round, factor=1): + super(decompress_jpeg, self).__init__() + self.c_dequantize = c_dequantize(factor=factor) + self.y_dequantize = y_dequantize(factor=factor) + self.idct = idct_8x8() + self.merging = block_merging() + # comment this line if no subsampling + self.chroma = chroma_upsampling() + self.colors = ycbcr_to_rgb_jpeg() + + # self.height, self.width = height, width + + def forward(self, y, cb, cr, height, width): + components = {'y': y, 'cb': cb, 'cr': cr} + # height = y.shape[0] + # width = y.shape[1] + self.height = height + self.width = width + for k in components.keys(): + if k in ('cb', 'cr'): + comp = self.c_dequantize(components[k]) + # comment this line if no subsampling + height, width = int(self.height/2), int(self.width/2) + # height, width = int(self.height), int(self.width) + + else: + comp = self.y_dequantize(components[k]) + # comment this line if no subsampling + height, width = self.height, self.width + comp = self.idct(comp) + components[k] = self.merging(comp, height, width) + # + # comment this line if no subsampling + image = self.chroma(components['y'], components['cb'], components['cr']) + # image = torch.cat([components['y'].unsqueeze(3), components['cb'].unsqueeze(3), components['cr'].unsqueeze(3)], dim=3) + image = self.colors(image) + + image = torch.min(255*torch.ones_like(image), + torch.max(torch.zeros_like(image), image)) + return image/255 + diff --git a/third_party/DarkFeat/datasets/__init__.py b/third_party/DarkFeat/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/datasets/gl3d/io.py b/third_party/DarkFeat/datasets/gl3d/io.py new file mode 100644 index 0000000000000000000000000000000000000000..9e5b4b0459d6814ef6af17a0a322b59202037d4f --- /dev/null +++ b/third_party/DarkFeat/datasets/gl3d/io.py @@ -0,0 +1,76 @@ +import os +import re +import cv2 +import numpy as np + +from ..utils.common import Notify + +def read_list(list_path): + """Read list.""" + if list_path is None or not os.path.exists(list_path): + print(Notify.FAIL, 'Not exist', list_path, Notify.ENDC) + exit(-1) + content = open(list_path).read().splitlines() + return content + + +def load_pfm(pfm_path): + with open(pfm_path, 'rb') as fin: + color = None + width = None + height = None + scale = None + data_type = None + header = str(fin.readline().decode('UTF-8')).rstrip() + + if header == 'PF': + color = True + elif header == 'Pf': + color = False + else: + raise Exception('Not a PFM file.') + + dim_match = re.match(r'^(\d+)\s(\d+)\s$', + fin.readline().decode('UTF-8')) + if dim_match: + width, height = map(int, dim_match.groups()) + else: + raise Exception('Malformed PFM header.') + scale = float((fin.readline().decode('UTF-8')).rstrip()) + if scale < 0: # little-endian + data_type = ' 0: + img = cv2.resize( + img, (config['resize'], config['resize'])) + return img + + +def _parse_depth(depth_paths, idx, config): + depth = load_pfm(depth_paths[idx]) + + if config['resize'] > 0: + target_size = config['resize'] + if config['input_type'] == 'raw': + depth = cv2.resize(depth, (int(target_size/2), int(target_size/2))) + else: + depth = cv2.resize(depth, (target_size, target_size)) + return depth + + +def _parse_kpts(kpts_paths, idx, config): + kpts = np.load(kpts_paths[idx])['pts'] + # output: [N, 2] (W first H last) + return kpts diff --git a/third_party/DarkFeat/datasets/gl3d_dataset.py b/third_party/DarkFeat/datasets/gl3d_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db3d2db646ae7fce81424f5f72cdff7e6e34ba60 --- /dev/null +++ b/third_party/DarkFeat/datasets/gl3d_dataset.py @@ -0,0 +1,127 @@ +import os +import numpy as np +import torch +from torch.utils.data import Dataset +from random import shuffle, seed + +from .gl3d.io import read_list, _parse_img, _parse_depth, _parse_kpts +from .utils.common import Notify +from .utils.photaug import photaug + + +class GL3DDataset(Dataset): + def __init__(self, dataset_dir, config, data_split, is_training): + self.dataset_dir = dataset_dir + self.config = config + self.is_training = is_training + self.data_split = data_split + + self.match_set_list, self.global_img_list, \ + self.global_depth_list = self.prepare_match_sets() + + pass + + + def __len__(self): + return len(self.match_set_list) + + + def __getitem__(self, idx): + match_set_path = self.match_set_list[idx] + decoded = np.fromfile(match_set_path, dtype=np.float32) + + idx0, idx1 = int(decoded[0]), int(decoded[1]) + inlier_num = int(decoded[2]) + ori_img_size0 = np.reshape(decoded[3:5], (2,)) + ori_img_size1 = np.reshape(decoded[5:7], (2,)) + K0 = np.reshape(decoded[7:16], (3, 3)) + K1 = np.reshape(decoded[16:25], (3, 3)) + rel_pose = np.reshape(decoded[34:46], (3, 4)) + + # parse images. + img0 = _parse_img(self.global_img_list, idx0, self.config) + img1 = _parse_img(self.global_img_list, idx1, self.config) + # parse depths + depth0 = _parse_depth(self.global_depth_list, idx0, self.config) + depth1 = _parse_depth(self.global_depth_list, idx1, self.config) + + # photometric augmentation + img0 = photaug(img0) + img1 = photaug(img1) + + return { + 'img0': img0 / 255., + 'img1': img1 / 255., + 'depth0': depth0, + 'depth1': depth1, + 'ori_img_size0': ori_img_size0, + 'ori_img_size1': ori_img_size1, + 'K0': K0, + 'K1': K1, + 'rel_pose': rel_pose, + 'inlier_num': inlier_num + } + + + def points_to_2D(self, pnts, H, W): + labels = np.zeros((H, W)) + pnts = pnts.astype(int) + labels[pnts[:, 1], pnts[:, 0]] = 1 + return labels + + + def prepare_match_sets(self, q_diff_thld=3, rot_diff_thld=60): + """Get match sets. + Args: + is_training: Use training imageset or testing imageset. + data_split: Data split name. + Returns: + match_set_list: List of match sets path. + global_img_list: List of global image path. + global_context_feat_list: + """ + # get necessary lists. + gl3d_list_folder = os.path.join(self.dataset_dir, 'list', self.data_split) + global_info = read_list(os.path.join( + gl3d_list_folder, 'image_index_offset.txt')) + global_img_list = [os.path.join(self.dataset_dir, i) for i in read_list( + os.path.join(gl3d_list_folder, 'image_list.txt'))] + global_depth_list = [os.path.join(self.dataset_dir, i) for i in read_list( + os.path.join(gl3d_list_folder, 'depth_list.txt'))] + + imageset_list_name = 'imageset_train.txt' if self.is_training else 'imageset_test.txt' + match_set_list = self.get_match_set_list(os.path.join( + gl3d_list_folder, imageset_list_name), q_diff_thld, rot_diff_thld) + return match_set_list, global_img_list, global_depth_list + + + def get_match_set_list(self, imageset_list_path, q_diff_thld, rot_diff_thld): + """Get the path list of match sets. + Args: + imageset_list_path: Path to imageset list. + q_diff_thld: Threshold of image pair sampling regarding camera orientation. + Returns: + match_set_list: List of match set path. + """ + imageset_list = [os.path.join(self.dataset_dir, 'data', i) + for i in read_list(imageset_list_path)] + print(Notify.INFO, 'Use # imageset', len(imageset_list), Notify.ENDC) + match_set_list = [] + # discard image pairs whose image simiarity is beyond the threshold. + for i in imageset_list: + match_set_folder = os.path.join(i, 'match_sets') + if os.path.exists(match_set_folder): + match_set_files = os.listdir(match_set_folder) + for val in match_set_files: + name, ext = os.path.splitext(val) + if ext == '.match_set': + splits = name.split('_') + q_diff = int(splits[2]) + rot_diff = int(splits[3]) + if q_diff >= q_diff_thld and rot_diff <= rot_diff_thld: + match_set_list.append( + os.path.join(match_set_folder, val)) + + print(Notify.INFO, 'Get # match sets', len(match_set_list), Notify.ENDC) + return match_set_list + diff --git a/third_party/DarkFeat/datasets/noise.py b/third_party/DarkFeat/datasets/noise.py new file mode 100644 index 0000000000000000000000000000000000000000..aa68c98183186e9e9185e78e1a3e7335ac8d5bb1 --- /dev/null +++ b/third_party/DarkFeat/datasets/noise.py @@ -0,0 +1,82 @@ +import numpy as np +import random +from scipy.stats import tukeylambda + +camera_params = { + 'Kmin': 0.2181895124454343, + 'Kmax': 3.0, + 'G_shape': np.array([0.15714286, 0.14285714, 0.08571429, 0.08571429, 0.2 , + 0.2 , 0.1 , 0.08571429, 0.05714286, 0.07142857, + 0.02857143, 0.02857143, 0.01428571, 0.02857143, 0.08571429, + 0.07142857, 0.11428571, 0.11428571]), + 'Profile-1': { + 'R_scale': { + 'slope': 0.4712797750747537, + 'bias': -0.8078958947116487, + 'sigma': 0.2436176299944695 + }, + 'g_scale': { + 'slope': 0.6771267783987617, + 'bias': 1.5121876510805845, + 'sigma': 0.24641096601611254 + }, + 'G_scale': { + 'slope': 0.6558756156508007, + 'bias': 1.09268679594838, + 'sigma': 0.28604721742277756 + } + }, + 'black_level': 2048, + 'max_value': 16383 +} + + +# photon shot noise +def addPStarNoise(img, K): + return np.random.poisson(img / K).astype(np.float32) * K + + +# read noise +# tukey lambda distribution +def addGStarNoise(img, K, G_shape, G_scale_param): + # sample a shape parameter [lambda] from histogram of samples + a, b = np.histogram(G_shape, bins=10, range=(-0.25, 0.25)) + a, b = np.array(a), np.array(b) + a = a / a.sum() + + rand_num = random.uniform(0, 1) + idx = np.sum(np.cumsum(a) < rand_num) + lam = random.uniform(b[idx], b[idx+1]) + + # calculate scale parameter [G_scale] + log_K = np.log(K) + log_G_scale = np.random.standard_normal() * G_scale_param['sigma'] * 1 +\ + G_scale_param['slope'] * log_K + G_scale_param['bias'] + G_scale = np.exp(log_G_scale) + # print(f'G_scale: {G_scale}') + + return img + tukeylambda.rvs(lam, scale=G_scale, size=img.shape).astype(np.float32) + + +# row noise +# uniform distribution for each row +def addRowNoise(img, K, R_scale_param): + # calculate scale parameter [R_scale] + log_K = np.log(K) + log_R_scale = np.random.standard_normal() * R_scale_param['sigma'] * 1 +\ + R_scale_param['slope'] * log_K + R_scale_param['bias'] + R_scale = np.exp(log_R_scale) + # print(f'R_scale: {R_scale}') + + row_noise = np.random.randn(img.shape[0], 1).astype(np.float32) * R_scale + return img + np.tile(row_noise, (1, img.shape[1])) + + +# quantization noise +# uniform distribution +def addQuantNoise(img, q): + return img + np.random.uniform(low=-0.5*q, high=0.5*q, size=img.shape) + + +def sampleK(Kmin, Kmax): + return np.exp(np.random.uniform(low=np.log(Kmin), high=np.log(Kmax))) diff --git a/third_party/DarkFeat/datasets/noise_simulator.py b/third_party/DarkFeat/datasets/noise_simulator.py new file mode 100644 index 0000000000000000000000000000000000000000..17e21d3b3443aaa3585ae8460709f60b05835a84 --- /dev/null +++ b/third_party/DarkFeat/datasets/noise_simulator.py @@ -0,0 +1,244 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable +import torch +import numpy as np +import os, time, random +import argparse +from torch.utils.data import Dataset, DataLoader +from PIL import Image as PILImage +from glob import glob +from tqdm import tqdm +import rawpy +import colour_demosaicing + +from .InvISP.model.model import InvISPNet +from .utils.common import Notify +from datasets.noise import camera_params, addGStarNoise, addPStarNoise, addQuantNoise, addRowNoise, sampleK + + +class NoiseSimulator: + def __init__(self, device, ckpt_path='./datasets/InvISP/pretrained/canon.pth'): + self.device = device + + # load Invertible ISP Network + self.net = InvISPNet(channel_in=3, channel_out=3, block_num=8).to(self.device).eval() + self.net.load_state_dict(torch.load(ckpt_path), strict=False) + print(Notify.INFO, "Loaded ISPNet checkpoint: {}".format(ckpt_path), Notify.ENDC) + + # white balance parameters + self.wb = np.array([2020.0, 1024.0, 1458.0, 1024.0]) + + # use Canon EOS 5D4 noise parameters provided by ELD + self.camera_params = camera_params + + # random specify exposure time ratio from 50 to 150 + self.ratio_min = 50 + self.ratio_max = 150 + pass + + # inverse demosaic + # input: [H, W, 3] + # output: [H, W] + def invDemosaic(self, img): + img_R = img[::2, ::2, 0] + img_G1 = img[::2, 1::2, 1] + img_G2 = img[1::2, ::2, 1] + img_B = img[1::2, 1::2, 2] + raw_img = np.ones(img.shape[:2]) + raw_img[::2, ::2] = img_R + raw_img[::2, 1::2] = img_G1 + raw_img[1::2, ::2] = img_G2 + raw_img[1::2, 1::2] = img_B + return raw_img + + # demosaic - nearest ver + # input: [H, W] + # output: [H, W, 3] + def demosaicNearest(self, img): + raw = np.ones((img.shape[0], img.shape[1], 3)) + raw[::2, ::2, 0] = img[::2, ::2] + raw[::2, 1::2, 0] = img[::2, ::2] + raw[1::2, ::2, 0] = img[::2, ::2] + raw[1::2, 1::2, 0] = img[::2, ::2] + raw[::2, ::2, 2] = img[1::2, 1::2] + raw[::2, 1::2, 2] = img[1::2, 1::2] + raw[1::2, ::2, 2] = img[1::2, 1::2] + raw[1::2, 1::2, 2] = img[1::2, 1::2] + raw[::2, ::2, 1] = img[::2, 1::2] + raw[::2, 1::2, 1] = img[::2, 1::2] + raw[1::2, ::2, 1] = img[1::2, ::2] + raw[1::2, 1::2, 1] = img[1::2, ::2] + return raw + + # demosaic + # input: [H, W] + # output: [H, W, 3] + def demosaic(self, img): + return colour_demosaicing.demosaicing_CFA_Bayer_bilinear(img, 'RGGB') + + # load rgb image + def path2rgb(self, path): + return torch.from_numpy(np.array(PILImage.open(path))/255.0) + + # InvISP + # input: rgb image [H, W, 3] + # output: raw image [H, W] + def rgb2raw(self, rgb, batched=False): + # 1. rgb -> invnet + if not batched: + rgb = rgb.unsqueeze(0) + + rgb = rgb.permute(0,3,1,2).float().to(self.device) + with torch.no_grad(): + reconstruct_raw = self.net(rgb, rev=True) + + pred_raw = reconstruct_raw.detach().permute(0,2,3,1) + pred_raw = torch.clamp(pred_raw, 0, 1) + + if not batched: + pred_raw = pred_raw[0, ...] + + pred_raw = pred_raw.cpu().numpy() + + # 2. -> inv gamma + norm_value = np.power(16383, 1/2.2) + pred_raw *= norm_value + pred_raw = np.power(pred_raw, 2.2) + + # 3. -> inv white balance + wb = self.wb / self.wb.max() + pred_raw = pred_raw / wb[:-1] + + # 4. -> add black level + pred_raw += self.camera_params['black_level'] + + # 5. -> inv demosaic + if not batched: + pred_raw = self.invDemosaic(pred_raw) + else: + preds = [] + for i in range(pred_raw.shape[0]): + preds.append(self.invDemosaic(pred_raw[i])) + pred_raw = np.stack(preds, axis=0) + + return pred_raw + + + def raw2noisyRaw(self, raw, ratio_dec=1, batched=False): + if not batched: + ratio = (random.uniform(self.ratio_min, self.ratio_max) - 1) * ratio_dec + 1 + raw = raw.copy() / ratio + + K = sampleK(self.camera_params['Kmin'], self.camera_params['Kmax']) + q = 1 / (self.camera_params['max_value'] - self.camera_params['black_level']) + + raw = addPStarNoise(raw, K) + raw = addGStarNoise(raw, K, self.camera_params['G_shape'], self.camera_params['Profile-1']['G_scale']) + raw = addRowNoise(raw, K, self.camera_params['Profile-1']['R_scale']) + raw = addQuantNoise(raw, q) + raw *= ratio + return raw + + else: + raw = raw.copy() + for i in range(raw.shape[0]): + ratio = random.uniform(self.ratio_min, self.ratio_max) + raw[i] /= ratio + + K = sampleK(self.camera_params['Kmin'], self.camera_params['Kmax']) + q = 1 / (self.camera_params['max_value'] - self.camera_params['black_level']) + + raw[i] = addPStarNoise(raw[i], K) + raw[i] = addGStarNoise(raw[i], K, self.camera_params['G_shape'], self.camera_params['Profile-1']['G_scale']) + raw[i] = addRowNoise(raw[i], K, self.camera_params['Profile-1']['R_scale']) + raw[i] = addQuantNoise(raw[i], q) + raw[i] *= ratio + return raw + + def raw2rgb(self, raw, batched=False): + # 1. -> demosaic + if not batched: + raw = self.demosaic(raw) + else: + raws = [] + for i in range(raw.shape[0]): + raws.append(self.demosaic(raw[i])) + raw = np.stack(raws, axis=0) + + # 2. -> substract black level + raw -= self.camera_params['black_level'] + raw = np.clip(raw, 0, self.camera_params['max_value'] - self.camera_params['black_level']) + + # 3. -> white balance + wb = self.wb / self.wb.max() + raw = raw * wb[:-1] + + # 4. -> gamma + norm_value = np.power(16383, 1/2.2) + raw = np.power(raw, 1/2.2) + raw /= norm_value + + # 5. -> ispnet + if not batched: + input_raw_img = torch.Tensor(raw).permute(2,0,1).float().to(self.device)[np.newaxis, ...] + else: + input_raw_img = torch.Tensor(raw).permute(0,3,1,2).float().to(self.device) + + with torch.no_grad(): + reconstruct_rgb = self.net(input_raw_img) + reconstruct_rgb = torch.clamp(reconstruct_rgb, 0, 1) + + pred_rgb = reconstruct_rgb.detach().permute(0,2,3,1) + + if not batched: + pred_rgb = pred_rgb[0, ...] + pred_rgb = pred_rgb.cpu().numpy() + + return pred_rgb + + + def raw2packedRaw(self, raw, batched=False): + # 1. -> substract black level + raw -= self.camera_params['black_level'] + raw = np.clip(raw, 0, self.camera_params['max_value'] - self.camera_params['black_level']) + raw /= self.camera_params['max_value'] + + # 2. pack + if not batched: + im = np.expand_dims(raw, axis=2) + img_shape = im.shape + H = img_shape[0] + W = img_shape[1] + + out = np.concatenate((im[0:H:2, 0:W:2, :], + im[0:H:2, 1:W:2, :], + im[1:H:2, 1:W:2, :], + im[1:H:2, 0:W:2, :]), axis=2) + else: + im = np.expand_dims(raw, axis=3) + img_shape = im.shape + H = img_shape[1] + W = img_shape[2] + + out = np.concatenate((im[:, 0:H:2, 0:W:2, :], + im[:, 0:H:2, 1:W:2, :], + im[:, 1:H:2, 1:W:2, :], + im[:, 1:H:2, 0:W:2, :]), axis=3) + return out + + def raw2demosaicRaw(self, raw, batched=False): + # 1. -> demosaic + if not batched: + raw = self.demosaic(raw) + else: + raws = [] + for i in range(raw.shape[0]): + raws.append(self.demosaic(raw[i])) + raw = np.stack(raws, axis=0) + + # 2. -> substract black level + raw -= self.camera_params['black_level'] + raw = np.clip(raw, 0, self.camera_params['max_value'] - self.camera_params['black_level']) + raw /= self.camera_params['max_value'] + return raw diff --git a/third_party/DarkFeat/datasets/sample.dat b/third_party/DarkFeat/datasets/sample.dat new file mode 100644 index 0000000000000000000000000000000000000000..3edfb76db709167bd289493ddc3a4d1169703662 Binary files /dev/null and b/third_party/DarkFeat/datasets/sample.dat differ diff --git a/third_party/DarkFeat/datasets/utils/common.py b/third_party/DarkFeat/datasets/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..6433408a39e53fcedb634901268754ed1ba971b3 --- /dev/null +++ b/third_party/DarkFeat/datasets/utils/common.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python +""" +Copyright 2017, Zixin Luo, HKUST. +Commonly used functions +""" + +from __future__ import print_function + +import os +from datetime import datetime + + +class ClassProperty(property): + """For dynamically obtaining system time""" + + def __get__(self, cls, owner): + return classmethod(self.fget).__get__(None, owner)() + + +class Notify(object): + """Colorful printing prefix. + A quick example: + print(Notify.INFO, YOUR TEXT, Notify.ENDC) + """ + + def __init__(self): + pass + + @ClassProperty + def HEADER(cls): + return str(datetime.now()) + ': \033[95m' + + @ClassProperty + def INFO(cls): + return str(datetime.now()) + ': \033[92mI' + + @ClassProperty + def OKBLUE(cls): + return str(datetime.now()) + ': \033[94m' + + @ClassProperty + def WARNING(cls): + return str(datetime.now()) + ': \033[93mW' + + @ClassProperty + def FAIL(cls): + return str(datetime.now()) + ': \033[91mF' + + @ClassProperty + def BOLD(cls): + return str(datetime.now()) + ': \033[1mB' + + @ClassProperty + def UNDERLINE(cls): + return str(datetime.now()) + ': \033[4mU' + ENDC = '\033[0m' + + diff --git a/third_party/DarkFeat/datasets/utils/photaug.py b/third_party/DarkFeat/datasets/utils/photaug.py new file mode 100644 index 0000000000000000000000000000000000000000..41f2278c720355470f00a881a1516cf1b71d2c4a --- /dev/null +++ b/third_party/DarkFeat/datasets/utils/photaug.py @@ -0,0 +1,50 @@ +import cv2 +import numpy as np +import random + + +def random_brightness_np(image, max_abs_change=50): + delta = random.uniform(-max_abs_change, max_abs_change) + return np.clip(image + delta, 0, 255) + +def random_contrast_np(image, strength_range=[0.3, 1.5]): + delta = random.uniform(*strength_range) + mean = image.mean() + return np.clip((image - mean) * delta + mean, 0, 255) + +def motion_blur_np(img, max_kernel_size=3): + # Either vertial, hozirontal or diagonal blur + mode = np.random.choice(['h', 'v', 'diag_down', 'diag_up']) + ksize = np.random.randint( + 0, (max_kernel_size+1)/2)*2 + 1 # make sure is odd + center = int((ksize-1)/2) + kernel = np.zeros((ksize, ksize)) + if mode == 'h': + kernel[center, :] = 1. + elif mode == 'v': + kernel[:, center] = 1. + elif mode == 'diag_down': + kernel = np.eye(ksize) + elif mode == 'diag_up': + kernel = np.flip(np.eye(ksize), 0) + var = ksize * ksize / 16. + grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1) + gaussian = np.exp(-(np.square(grid-center) + + np.square(grid.T-center))/(2.*var)) + kernel *= gaussian + kernel /= np.sum(kernel) + img = cv2.filter2D(img, -1, kernel) + return np.clip(img, 0, 255) + +def additive_gaussian_noise(image, stddev_range=[5, 95]): + stddev = random.uniform(*stddev_range) + noise = np.random.normal(size=image.shape, scale=stddev) + noisy_image = np.clip(image + noise, 0, 255) + return noisy_image + +def photaug(img): + img = random_brightness_np(img) + img = random_contrast_np(img) + # img = additive_gaussian_noise(img) + img = motion_blur_np(img) + return img diff --git a/third_party/DarkFeat/demo_darkfeat.py b/third_party/DarkFeat/demo_darkfeat.py new file mode 100644 index 0000000000000000000000000000000000000000..ca50ae5b892e7a90e75da7197c33bc0c06e699bf --- /dev/null +++ b/third_party/DarkFeat/demo_darkfeat.py @@ -0,0 +1,124 @@ +from pathlib import Path +import argparse +import cv2 +import matplotlib.cm as cm +import torch +import numpy as np +from utils.nnmatching import NNMatching +from utils.misc import (AverageTimer, VideoStreamer, make_matching_plot_fast, frame2tensor) + +torch.set_grad_enabled(False) + + +def compute_essential(matched_kp1, matched_kp2, K): + pts1 = cv2.undistortPoints(matched_kp1,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + pts2 = cv2.undistortPoints(matched_kp2,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + K_1 = np.eye(3) + # Estimate the homography between the matches using RANSAC + ransac_model, ransac_inliers = cv2.findEssentialMat(pts1, pts2, K_1, method=cv2.RANSAC, prob=0.999, threshold=0.001, maxIters=10000) + if ransac_inliers is None or ransac_model.shape != (3,3): + ransac_inliers = np.array([]) + ransac_model = None + return ransac_model, ransac_inliers, pts1, pts2 + + +sizer = (960, 640) +focallength_x = 4.504986436499113e+03/(6744/sizer[0]) +focallength_y = 4.513311442889859e+03/(4502/sizer[1]) +K = np.eye(3) +K[0,0] = focallength_x +K[1,1] = focallength_y +K[0,2] = 3.363322177533149e+03/(6744/sizer[0])# * 0.5 +K[1,2] = 2.291824660547715e+03/(4502/sizer[1])# * 0.5 + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='DarkFeat demo', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + '--input', type=str, + help='path to an image directory') + parser.add_argument( + '--output_dir', type=str, default=None, + help='Directory where to write output frames (If None, no output)') + + parser.add_argument( + '--image_glob', type=str, nargs='+', default=['*.ARW'], + help='Glob if a directory of images is specified') + parser.add_argument( + '--resize', type=int, nargs='+', default=[640, 480], + help='Resize the input image before running inference. If two numbers, ' + 'resize to the exact dimensions, if one number, resize the max ' + 'dimension, if -1, do not resize') + parser.add_argument( + '--force_cpu', action='store_true', + help='Force pytorch to run in CPU mode.') + parser.add_argument('--model_path', type=str, + help='Path to the pretrained model') + + opt = parser.parse_args() + print(opt) + + assert len(opt.resize) == 2 + print('Will resize to {}x{} (WxH)'.format(opt.resize[0], opt.resize[1])) + + device = 'cuda' if torch.cuda.is_available() and not opt.force_cpu else 'cpu' + print('Running inference on device \"{}\"'.format(device)) + matching = NNMatching(opt.model_path).eval().to(device) + keys = ['keypoints', 'scores', 'descriptors'] + + vs = VideoStreamer(opt.input, opt.resize, opt.image_glob) + frame, ret = vs.next_frame() + assert ret, 'Error when reading the first frame (try different --input?)' + + frame_tensor = frame2tensor(frame, device) + last_data = matching.darkfeat({'image': frame_tensor}) + last_data = {k+'0': [last_data[k]] for k in keys} + last_data['image0'] = frame_tensor + last_frame = frame + last_image_id = 0 + + if opt.output_dir is not None: + print('==> Will write outputs to {}'.format(opt.output_dir)) + Path(opt.output_dir).mkdir(exist_ok=True) + + timer = AverageTimer() + + while True: + frame, ret = vs.next_frame() + if not ret: + print('Finished demo_darkfeat.py') + break + timer.update('data') + stem0, stem1 = last_image_id, vs.i - 1 + + frame_tensor = frame2tensor(frame, device) + pred = matching({**last_data, 'image1': frame_tensor}) + kpts0 = last_data['keypoints0'][0].cpu().numpy() + kpts1 = pred['keypoints1'][0].cpu().numpy() + matches = pred['matches0'][0].cpu().numpy() + confidence = pred['matching_scores0'][0].cpu().numpy() + timer.update('forward') + + valid = matches > -1 + mkpts0 = kpts0[valid] + mkpts1 = kpts1[matches[valid]] + + E, inliers, pts1, pts2 = compute_essential(mkpts0, mkpts1, K) + color = cm.jet(np.clip(confidence[valid][inliers[:, 0].astype('bool')] * 2 - 1, -1, 1)) + + text = [ + 'DarkFeat', + 'Matches: {}'.format(inliers.sum()) + ] + + out = make_matching_plot_fast( + last_frame, frame, mkpts0[inliers[:, 0].astype('bool')], mkpts1[inliers[:, 0].astype('bool')], color, text, + path=None, small_text=' ') + + if opt.output_dir is not None: + stem = 'matches_{:06}_{:06}'.format(stem0, stem1) + out_file = str(Path(opt.output_dir, stem + '.png')) + print('Writing image to {}'.format(out_file)) + cv2.imwrite(out_file, out) diff --git a/third_party/DarkFeat/export_features.py b/third_party/DarkFeat/export_features.py new file mode 100644 index 0000000000000000000000000000000000000000..c7caea5e57890948728f84cbb7e68e59d455e171 --- /dev/null +++ b/third_party/DarkFeat/export_features.py @@ -0,0 +1,128 @@ +import argparse +import glob +import math +import subprocess +import numpy as np +import os +import tqdm +import torch +import torch.nn as nn +import cv2 +from darkfeat import DarkFeat +from utils import matching + +def darkfeat_pre(img, cuda): + H, W = img.shape[0], img.shape[1] + inp = img.copy() + inp = inp.transpose(2, 0, 1) + inp = torch.from_numpy(inp) + inp = torch.autograd.Variable(inp).view(1, 3, H, W) + if cuda: + inp = inp.cuda() + return inp + +if __name__ == '__main__': + # Parse command line arguments. + parser = argparse.ArgumentParser() + parser.add_argument('--H', type=int, default=int(640)) + parser.add_argument('--W', type=int, default=int(960)) + parser.add_argument('--histeq', action='store_true') + parser.add_argument('--model_path', type=str) + parser.add_argument('--dataset_dir', type=str, default='/data/hyz/MID/') + opt = parser.parse_args() + + sizer = (opt.W, opt.H) + focallength_x = 4.504986436499113e+03/(6744/sizer[0]) + focallength_y = 4.513311442889859e+03/(4502/sizer[1]) + K = np.eye(3) + K[0,0] = focallength_x + K[1,1] = focallength_y + K[0,2] = 3.363322177533149e+03/(6744/sizer[0])# * 0.5 + K[1,2] = 2.291824660547715e+03/(4502/sizer[1])# * 0.5 + Kinv = np.linalg.inv(K) + Kinvt = np.transpose(Kinv) + + cuda = True + if cuda: + darkfeat = DarkFeat(opt.model_path).cuda().eval() + + for scene in ['Indoor', 'Outdoor']: + base_save = './result/' + scene + '/' + dir_base = opt.dataset_dir + '/' + scene + '/' + pair_list = sorted(os.listdir(dir_base)) + + for pair in tqdm.tqdm(pair_list): + opention = 1 + if scene == 'Outdoor': + pass + else: + if int(pair[4::]) <= 17: + opention = 0 + else: + pass + name=[] + files = sorted(os.listdir(dir_base+pair)) + for file_ in files: + if file_.endswith('.cr2'): + name.append(file_[0:9]) + ISO = ['00100', '00200', '00400', '00800', '01600', '03200', '06400', '12800'] + if opention == 1: + Shutter_speed = ['0.005','0.01','0.025','0.05','0.17','0.5'] + else: + Shutter_speed = ['0.01','0.02','0.05','0.1','0.3','1'] + + E_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'E_estimated.npy') + F_GT = np.dot(np.dot(Kinvt,E_GT),Kinv) + R_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'R_GT.npy') + t_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'T_GT.npy') + + id0, id1 = sorted([ int(i.split('/')[-1]) for i in glob.glob(f'{dir_base+pair}/?????') ]) + + cnt = 0 + + for iso in ISO: + for ex in Shutter_speed: + dark_name1 = name[0] + iso+'_'+ex+'_'+scene+'.npy' + dark_name2 = name[1] + iso+'_'+ex+'_'+scene+'.npy' + + if not opt.histeq: + dst_T1_None = f'{dir_base}{pair}/{id0:05d}-npy-nohisteq/{dark_name1}' + dst_T2_None = f'{dir_base}{pair}/{id1:05d}-npy-nohisteq/{dark_name2}' + + img1_orig_None = np.load(dst_T1_None) + img2_orig_None = np.load(dst_T2_None) + + dir_save = base_save + pair + '/None/' + + img_input1 = darkfeat_pre(img1_orig_None.astype('float32')/255.0, cuda) + img_input2 = darkfeat_pre(img2_orig_None.astype('float32')/255.0, cuda) + + else: + dst_T1_histeq = f'{dir_base}{pair}/{id0:05d}-npy/{dark_name1}' + dst_T2_histeq = f'{dir_base}{pair}/{id1:05d}-npy/{dark_name2}' + + img1_orig_histeq = np.load(dst_T1_histeq) + img2_orig_histeq = np.load(dst_T2_histeq) + + dir_save = base_save + pair + '/HistEQ/' + + img_input1 = darkfeat_pre(img1_orig_histeq.astype('float32')/255.0, cuda) + img_input2 = darkfeat_pre(img2_orig_histeq.astype('float32')/255.0, cuda) + + result1 = darkfeat({'image': img_input1}) + result2 = darkfeat({'image': img_input2}) + + mkpts0, mkpts1, _ = matching.match_descriptors( + cv2.KeyPoint_convert(result1['keypoints'].detach().cpu().float().numpy()), result1['descriptors'].detach().cpu().numpy(), + cv2.KeyPoint_convert(result2['keypoints'].detach().cpu().float().numpy()), result2['descriptors'].detach().cpu().numpy(), + ORB=False + ) + + POINT_1_dir = dir_save+f'DarkFeat/POINT_1/' + POINT_2_dir = dir_save+f'DarkFeat/POINT_2/' + + subprocess.check_output(['mkdir', '-p', POINT_1_dir]) + subprocess.check_output(['mkdir', '-p', POINT_2_dir]) + np.save(POINT_1_dir+dark_name1[0:-3]+'npy',mkpts0) + np.save(POINT_2_dir+dark_name2[0:-3]+'npy',mkpts1) + diff --git a/third_party/DarkFeat/fig/fig.gif b/third_party/DarkFeat/fig/fig.gif new file mode 100644 index 0000000000000000000000000000000000000000..e82c0007c93e18e05cf67e767e0bfe861eafb680 --- /dev/null +++ b/third_party/DarkFeat/fig/fig.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:526d2b455e852b323e6864b2e24b57cdf2482dd0f63dca1139c898e6b1b0f126 +size 15429829 diff --git a/third_party/DarkFeat/nets/__init__.py b/third_party/DarkFeat/nets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/nets/geom.py b/third_party/DarkFeat/nets/geom.py new file mode 100644 index 0000000000000000000000000000000000000000..043ca6e8f5917c56defd6aa17c1ff236a431f8c0 --- /dev/null +++ b/third_party/DarkFeat/nets/geom.py @@ -0,0 +1,323 @@ +import time +import numpy as np +import torch +import torch.nn.functional as F + + +def rnd_sample(inputs, n_sample): + cur_size = inputs[0].shape[0] + rnd_idx = torch.randperm(cur_size)[0:n_sample] + outputs = [i[rnd_idx] for i in inputs] + return outputs + + +def _grid_positions(h, w, bs): + x_rng = torch.arange(0, w.int()) + y_rng = torch.arange(0, h.int()) + xv, yv = torch.meshgrid(x_rng, y_rng, indexing='xy') + return torch.reshape( + torch.stack((yv, xv), axis=-1), + (1, -1, 2) + ).repeat(bs, 1, 1).float() + + +def getK(ori_img_size, cur_feat_size, K): + # WARNING: cur_feat_size's order is [h, w] + r = ori_img_size / cur_feat_size[[1, 0]] + r_K0 = torch.stack([K[:, 0] / r[:, 0][..., None], K[:, 1] / + r[:, 1][..., None], K[:, 2]], axis=1) + return r_K0 + + +def gather_nd(params, indices): + """ The same as tf.gather_nd but batched gather is not supported yet. + indices is an k-dimensional integer tensor, best thought of as a (k-1)-dimensional tensor of indices into params, where each element defines a slice of params: + + output[\\(i_0, ..., i_{k-2}\\)] = params[indices[\\(i_0, ..., i_{k-2}\\)]] + + Args: + params (Tensor): "n" dimensions. shape: [x_0, x_1, x_2, ..., x_{n-1}] + indices (Tensor): "k" dimensions. shape: [y_0,y_2,...,y_{k-2}, m]. m <= n. + + Returns: gathered Tensor. + shape [y_0,y_2,...y_{k-2}] + params.shape[m:] + + """ + orig_shape = list(indices.shape) + num_samples = np.prod(orig_shape[:-1]) + m = orig_shape[-1] + n = len(params.shape) + + if m <= n: + out_shape = orig_shape[:-1] + list(params.shape)[m:] + else: + raise ValueError( + f'the last dimension of indices must less or equal to the rank of params. Got indices:{indices.shape}, params:{params.shape}. {m} > {n}' + ) + + indices = indices.reshape((num_samples, m)).transpose(0, 1).tolist() + output = params[indices] # (num_samples, ...) + return output.reshape(out_shape).contiguous() + +# input: pos [kpt_n, 2]; inputs [H, W, 128] / [H, W] +# output: [kpt_n, 128] / [kpt_n] +def interpolate(pos, inputs, nd=True): + h = inputs.shape[0] + w = inputs.shape[1] + + i = pos[:, 0] + j = pos[:, 1] + + i_top_left = torch.clamp(torch.floor(i).int(), 0, h - 1) + j_top_left = torch.clamp(torch.floor(j).int(), 0, w - 1) + + i_top_right = torch.clamp(torch.floor(i).int(), 0, h - 1) + j_top_right = torch.clamp(torch.ceil(j).int(), 0, w - 1) + + i_bottom_left = torch.clamp(torch.ceil(i).int(), 0, h - 1) + j_bottom_left = torch.clamp(torch.floor(j).int(), 0, w - 1) + + i_bottom_right = torch.clamp(torch.ceil(i).int(), 0, h - 1) + j_bottom_right = torch.clamp(torch.ceil(j).int(), 0, w - 1) + + dist_i_top_left = i - i_top_left.float() + dist_j_top_left = j - j_top_left.float() + w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left) + w_top_right = (1 - dist_i_top_left) * dist_j_top_left + w_bottom_left = dist_i_top_left * (1 - dist_j_top_left) + w_bottom_right = dist_i_top_left * dist_j_top_left + + if nd: + w_top_left = w_top_left[..., None] + w_top_right = w_top_right[..., None] + w_bottom_left = w_bottom_left[..., None] + w_bottom_right = w_bottom_right[..., None] + + interpolated_val = ( + w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) + + w_top_right * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) + + w_bottom_left * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1)) + + w_bottom_right * + gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1)) + ) + + return interpolated_val + + +def validate_and_interpolate(pos, inputs, validate_corner=True, validate_val=None, nd=False): + if nd: + h, w, c = inputs.shape + else: + h, w = inputs.shape + ids = torch.arange(0, pos.shape[0]) + + i = pos[:, 0] + j = pos[:, 1] + + i_top_left = torch.floor(i).int() + j_top_left = torch.floor(j).int() + + i_top_right = torch.floor(i).int() + j_top_right = torch.ceil(j).int() + + i_bottom_left = torch.ceil(i).int() + j_bottom_left = torch.floor(j).int() + + i_bottom_right = torch.ceil(i).int() + j_bottom_right = torch.ceil(j).int() + + if validate_corner: + # Valid corner + valid_top_left = torch.logical_and(i_top_left >= 0, j_top_left >= 0) + valid_top_right = torch.logical_and(i_top_right >= 0, j_top_right < w) + valid_bottom_left = torch.logical_and(i_bottom_left < h, j_bottom_left >= 0) + valid_bottom_right = torch.logical_and(i_bottom_right < h, j_bottom_right < w) + + valid_corner = torch.logical_and( + torch.logical_and(valid_top_left, valid_top_right), + torch.logical_and(valid_bottom_left, valid_bottom_right) + ) + + i_top_left = i_top_left[valid_corner] + j_top_left = j_top_left[valid_corner] + + i_top_right = i_top_right[valid_corner] + j_top_right = j_top_right[valid_corner] + + i_bottom_left = i_bottom_left[valid_corner] + j_bottom_left = j_bottom_left[valid_corner] + + i_bottom_right = i_bottom_right[valid_corner] + j_bottom_right = j_bottom_right[valid_corner] + + ids = ids[valid_corner] + + if validate_val is not None: + # Valid depth + valid_depth = torch.logical_and( + torch.logical_and( + gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) > 0, + gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) > 0 + ), + torch.logical_and( + gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1)) > 0, + gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1)) > 0 + ) + ) + + i_top_left = i_top_left[valid_depth] + j_top_left = j_top_left[valid_depth] + + i_top_right = i_top_right[valid_depth] + j_top_right = j_top_right[valid_depth] + + i_bottom_left = i_bottom_left[valid_depth] + j_bottom_left = j_bottom_left[valid_depth] + + i_bottom_right = i_bottom_right[valid_depth] + j_bottom_right = j_bottom_right[valid_depth] + + ids = ids[valid_depth] + + # Interpolation + i = i[ids] + j = j[ids] + dist_i_top_left = i - i_top_left.float() + dist_j_top_left = j - j_top_left.float() + w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left) + w_top_right = (1 - dist_i_top_left) * dist_j_top_left + w_bottom_left = dist_i_top_left * (1 - dist_j_top_left) + w_bottom_right = dist_i_top_left * dist_j_top_left + + if nd: + w_top_left = w_top_left[..., None] + w_top_right = w_top_right[..., None] + w_bottom_left = w_bottom_left[..., None] + w_bottom_right = w_bottom_right[..., None] + + interpolated_val = ( + w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) + + w_top_right * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) + + w_bottom_left * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1)) + + w_bottom_right * gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1)) + ) + + pos = torch.stack([i, j], axis=1) + return [interpolated_val, pos, ids] + + +# pos0: [2, 230400, 2] +# depth0: [2, 480, 480] +def getWarp(pos0, rel_pose, depth0, K0, depth1, K1, bs): + def swap_axis(data): + return torch.stack([data[:, 1], data[:, 0]], axis=-1) + + all_pos0 = [] + all_pos1 = [] + all_ids = [] + for i in range(bs): + z0, new_pos0, ids = validate_and_interpolate(pos0[i], depth0[i], validate_val=0) + + uv0_homo = torch.cat([swap_axis(new_pos0), torch.ones((new_pos0.shape[0], 1)).to(new_pos0.device)], axis=-1) + xy0_homo = torch.matmul(torch.linalg.inv(K0[i]), uv0_homo.t()) + xyz0_homo = torch.cat([torch.unsqueeze(z0, 0) * xy0_homo, + torch.ones((1, new_pos0.shape[0])).to(z0.device)], axis=0) + + xyz1 = torch.matmul(rel_pose[i], xyz0_homo) + xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0) + uv1 = torch.matmul(K1[i], xy1_homo).t()[:, 0:2] + + new_pos1 = swap_axis(uv1) + annotated_depth, new_pos1, new_ids = validate_and_interpolate( + new_pos1, depth1[i], validate_val=0) + + ids = ids[new_ids] + new_pos0 = new_pos0[new_ids] + estimated_depth = xyz1.t()[new_ids][:, -1] + + inlier_mask = torch.abs(estimated_depth - annotated_depth) < 0.05 + + all_ids.append(ids[inlier_mask]) + all_pos0.append(new_pos0[inlier_mask]) + all_pos1.append(new_pos1[inlier_mask]) + # all_pos0 & all_pose1: [inlier_num, 2] * batch_size + return all_pos0, all_pos1, all_ids + + +# pos0: [2, 230400, 2] +# depth0: [2, 480, 480] +def getWarpNoValidate(pos0, rel_pose, depth0, K0, depth1, K1, bs): + def swap_axis(data): + return torch.stack([data[:, 1], data[:, 0]], axis=-1) + + all_pos0 = [] + all_pos1 = [] + all_ids = [] + for i in range(bs): + z0, new_pos0, ids = validate_and_interpolate(pos0[i], depth0[i], validate_val=0) + + uv0_homo = torch.cat([swap_axis(new_pos0), torch.ones((new_pos0.shape[0], 1)).to(new_pos0.device)], axis=-1) + xy0_homo = torch.matmul(torch.linalg.inv(K0[i]), uv0_homo.t()) + xyz0_homo = torch.cat([torch.unsqueeze(z0, 0) * xy0_homo, + torch.ones((1, new_pos0.shape[0])).to(z0.device)], axis=0) + + xyz1 = torch.matmul(rel_pose[i], xyz0_homo) + xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0) + uv1 = torch.matmul(K1[i], xy1_homo).t()[:, 0:2] + + new_pos1 = swap_axis(uv1) + _, new_pos1, new_ids = validate_and_interpolate( + new_pos1, depth1[i], validate_val=0) + + ids = ids[new_ids] + new_pos0 = new_pos0[new_ids] + + all_ids.append(ids) + all_pos0.append(new_pos0) + all_pos1.append(new_pos1) + # all_pos0 & all_pose1: [inlier_num, 2] * batch_size + return all_pos0, all_pos1, all_ids + + +# pos0: [2, 230400, 2] +# depth0: [2, 480, 480] +def getWarpNoValidate2(pos0, rel_pose, depth0, K0, depth1, K1): + def swap_axis(data): + return torch.stack([data[:, 1], data[:, 0]], axis=-1) + + z0 = interpolate(pos0, depth0, nd=False) + + uv0_homo = torch.cat([swap_axis(pos0), torch.ones((pos0.shape[0], 1)).to(pos0.device)], axis=-1) + xy0_homo = torch.matmul(torch.linalg.inv(K0), uv0_homo.t()) + xyz0_homo = torch.cat([torch.unsqueeze(z0, 0) * xy0_homo, + torch.ones((1, pos0.shape[0])).to(z0.device)], axis=0) + + xyz1 = torch.matmul(rel_pose, xyz0_homo) + xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0) + uv1 = torch.matmul(K1, xy1_homo).t()[:, 0:2] + + new_pos1 = swap_axis(uv1) + + return new_pos1 + + + +def get_dist_mat(feat1, feat2, dist_type): + eps = 1e-6 + cos_dist_mat = torch.matmul(feat1, feat2.t()) + if dist_type == 'cosine_dist': + dist_mat = torch.clamp(cos_dist_mat, -1, 1) + elif dist_type == 'euclidean_dist': + dist_mat = torch.sqrt(torch.clamp(2 - 2 * cos_dist_mat, min=eps)) + elif dist_type == 'euclidean_dist_no_norm': + norm1 = torch.sum(feat1 * feat1, axis=-1, keepdims=True) + norm2 = torch.sum(feat2 * feat2, axis=-1, keepdims=True) + dist_mat = torch.sqrt( + torch.clamp( + norm1 - 2 * cos_dist_mat + norm2.t(), + min=0. + ) + eps + ) + else: + raise NotImplementedError() + return dist_mat diff --git a/third_party/DarkFeat/nets/l2net.py b/third_party/DarkFeat/nets/l2net.py new file mode 100644 index 0000000000000000000000000000000000000000..e1ddfe8919bd4d5fe75215d253525123e1402952 --- /dev/null +++ b/third_party/DarkFeat/nets/l2net.py @@ -0,0 +1,116 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parameter import Parameter + +from .score import peakiness_score + + +class BaseNet(nn.Module): + """ Helper class to construct a fully-convolutional network that + extract a l2-normalized patch descriptor. + """ + def __init__(self, inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False): + super(BaseNet, self).__init__() + self.inchan = inchan + self.curchan = inchan + self.dilated = dilated + self.dilation = dilation + self.bn = bn + self.bn_affine = bn_affine + + def _make_bn(self, outd): + return nn.BatchNorm2d(outd, affine=self.bn_affine) + + def _add_conv(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True, k_pool = 1, pool_type='max', bias=False): + # as in the original implementation, dilation is applied at the end of layer, so it will have impact only from next layer + d = self.dilation * dilation + # if self.dilated: + # conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=1) + # self.dilation *= stride + # else: + # conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride) + conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride, bias=bias) + + ops = nn.ModuleList([]) + + ops.append( nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params) ) + if bn and self.bn: ops.append( self._make_bn(outd) ) + if relu: ops.append( nn.ReLU(inplace=True) ) + self.curchan = outd + + if k_pool > 1: + if pool_type == 'avg': + ops.append(torch.nn.AvgPool2d(kernel_size=k_pool)) + elif pool_type == 'max': + ops.append(torch.nn.MaxPool2d(kernel_size=k_pool)) + else: + print(f"Error, unknown pooling type {pool_type}...") + + return nn.Sequential(*ops) + + +class Quad_L2Net(BaseNet): + """ Same than L2_Net, but replace the final 8x8 conv by 3 successive 2x2 convs. + """ + def __init__(self, dim=128, mchan=4, relu22=False, **kw): + BaseNet.__init__(self, **kw) + self.conv0 = self._add_conv( 8*mchan) + self.conv1 = self._add_conv( 8*mchan, bn=False) + self.bn1 = self._make_bn(8*mchan) + self.conv2 = self._add_conv( 16*mchan, stride=2) + self.conv3 = self._add_conv( 16*mchan, bn=False) + self.bn3 = self._make_bn(16*mchan) + self.conv4 = self._add_conv( 32*mchan, stride=2) + self.conv5 = self._add_conv( 32*mchan) + # replace last 8x8 convolution with 3 3x3 convolutions + self.conv6_0 = self._add_conv( 32*mchan) + self.conv6_1 = self._add_conv( 32*mchan) + self.conv6_2 = self._add_conv(dim, bn=False, relu=False) + self.out_dim = dim + + self.moving_avg_params = nn.ParameterList([ + Parameter(torch.tensor(1.), requires_grad=False), + Parameter(torch.tensor(1.), requires_grad=False), + Parameter(torch.tensor(1.), requires_grad=False) + ]) + + def forward(self, x): + # x: [N, C, H, W] + x0 = self.conv0(x) + x1 = self.conv1(x0) + x1_bn = self.bn1(x1) + x2 = self.conv2(x1_bn) + x3 = self.conv3(x2) + x3_bn = self.bn3(x3) + x4 = self.conv4(x3_bn) + x5 = self.conv5(x4) + x6_0 = self.conv6_0(x5) + x6_1 = self.conv6_1(x6_0) + x6_2 = self.conv6_2(x6_1) + + # calculate score map + comb_weights = torch.tensor([1., 2., 3.], device=x.device) + comb_weights /= torch.sum(comb_weights) + ksize = [3, 2, 1] + det_score_maps = [] + + for idx, xx in enumerate([x1, x3, x6_2]): + if self.training: + instance_max = torch.max(xx) + self.moving_avg_params[idx].data = self.moving_avg_params[idx] * 0.99 + instance_max.detach() * 0.01 + else: + pass + + alpha, beta = peakiness_score(xx, self.moving_avg_params[idx].detach(), ksize=3, dilation=ksize[idx]) + + score_vol = alpha * beta + det_score_map = torch.max(score_vol, dim=1, keepdim=True)[0] + det_score_map = F.interpolate(det_score_map, size=x.shape[2:], mode='bilinear', align_corners=True) + det_score_map = comb_weights[idx] * det_score_map + det_score_maps.append(det_score_map) + + det_score_map = torch.sum(torch.stack(det_score_maps, dim=0), dim=0) + # print([param.data for param in self.moving_avg_params]) + + return x6_2, det_score_map, x1, x3 diff --git a/third_party/DarkFeat/nets/loss.py b/third_party/DarkFeat/nets/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0dd42b4214d021137ddfe72771ccad0264d2321f --- /dev/null +++ b/third_party/DarkFeat/nets/loss.py @@ -0,0 +1,260 @@ +import torch +import torch.nn.functional as F + +from .geom import rnd_sample, interpolate, get_dist_mat + + +def make_detector_loss(pos0, pos1, dense_feat_map0, dense_feat_map1, + score_map0, score_map1, batch_size, num_corr, loss_type, config): + joint_loss = 0. + accuracy = 0. + all_valid_pos0 = [] + all_valid_pos1 = [] + all_valid_match = [] + for i in range(batch_size): + # random sample + valid_pos0, valid_pos1 = rnd_sample([pos0[i], pos1[i]], num_corr) + valid_num = valid_pos0.shape[0] + + valid_feat0 = interpolate(valid_pos0 / 4, dense_feat_map0[i]) + valid_feat1 = interpolate(valid_pos1 / 4, dense_feat_map1[i]) + + valid_feat0 = F.normalize(valid_feat0, p=2, dim=-1) + valid_feat1 = F.normalize(valid_feat1, p=2, dim=-1) + + valid_score0 = interpolate(valid_pos0, torch.squeeze(score_map0[i], dim=-1), nd=False) + valid_score1 = interpolate(valid_pos1, torch.squeeze(score_map1[i], dim=-1), nd=False) + + if config['network']['det']['corr_weight']: + corr_weight = valid_score0 * valid_score1 + else: + corr_weight = None + + safe_radius = config['network']['det']['safe_radius'] + if safe_radius > 0: + radius_mask_row = get_dist_mat( + valid_pos1, valid_pos1, "euclidean_dist_no_norm") + radius_mask_row = torch.le(radius_mask_row, safe_radius) + radius_mask_col = get_dist_mat( + valid_pos0, valid_pos0, "euclidean_dist_no_norm") + radius_mask_col = torch.le(radius_mask_col, safe_radius) + radius_mask_row = radius_mask_row.float() - torch.eye(valid_num, device=radius_mask_row.device) + radius_mask_col = radius_mask_col.float() - torch.eye(valid_num, device=radius_mask_col.device) + else: + radius_mask_row = None + radius_mask_col = None + + if valid_num < 32: + si_loss, si_accuracy, matched_mask = 0., 1., torch.zeros((1, valid_num)).bool() + else: + si_loss, si_accuracy, matched_mask = make_structured_loss( + torch.unsqueeze(valid_feat0, 0), torch.unsqueeze(valid_feat1, 0), + loss_type=loss_type, + radius_mask_row=radius_mask_row, radius_mask_col=radius_mask_col, + corr_weight=torch.unsqueeze(corr_weight, 0) if corr_weight is not None else None + ) + + joint_loss += si_loss / batch_size + accuracy += si_accuracy / batch_size + all_valid_match.append(torch.squeeze(matched_mask, dim=0)) + all_valid_pos0.append(valid_pos0) + all_valid_pos1.append(valid_pos1) + + return joint_loss, accuracy + + +def make_structured_loss(feat_anc, feat_pos, + loss_type='RATIO', inlier_mask=None, + radius_mask_row=None, radius_mask_col=None, + corr_weight=None, dist_mat=None): + """ + Structured loss construction. + Args: + feat_anc, feat_pos: Feature matrix. + loss_type: Loss type. + inlier_mask: + Returns: + + """ + batch_size = feat_anc.shape[0] + num_corr = feat_anc.shape[1] + if inlier_mask is None: + inlier_mask = torch.ones((batch_size, num_corr), device=feat_anc.device).bool() + inlier_num = torch.count_nonzero(inlier_mask.float(), dim=-1) + + if loss_type == 'L2NET' or loss_type == 'CIRCLE': + dist_type = 'cosine_dist' + elif loss_type.find('HARD') >= 0: + dist_type = 'euclidean_dist' + else: + raise NotImplementedError() + + if dist_mat is None: + dist_mat = get_dist_mat(feat_anc.squeeze(0), feat_pos.squeeze(0), dist_type).unsqueeze(0) + pos_vec = dist_mat[0].diag().unsqueeze(0) + + if loss_type.find('HARD') >= 0: + neg_margin = 1 + dist_mat_without_min_on_diag = dist_mat + \ + 10 * torch.unsqueeze(torch.eye(num_corr, device=dist_mat.device), dim=0) + mask = torch.le(dist_mat_without_min_on_diag, 0.008).float() + dist_mat_without_min_on_diag += mask*10 + + if radius_mask_row is not None: + hard_neg_dist_row = dist_mat_without_min_on_diag + 10 * radius_mask_row + else: + hard_neg_dist_row = dist_mat_without_min_on_diag + if radius_mask_col is not None: + hard_neg_dist_col = dist_mat_without_min_on_diag + 10 * radius_mask_col + else: + hard_neg_dist_col = dist_mat_without_min_on_diag + + hard_neg_dist_row = torch.min(hard_neg_dist_row, dim=-1)[0] + hard_neg_dist_col = torch.min(hard_neg_dist_col, dim=-2)[0] + + if loss_type == 'HARD_TRIPLET': + loss_row = torch.clamp(neg_margin + pos_vec - hard_neg_dist_row, min=0) + loss_col = torch.clamp(neg_margin + pos_vec - hard_neg_dist_col, min=0) + elif loss_type == 'HARD_CONTRASTIVE': + pos_margin = 0.2 + pos_loss = torch.clamp(pos_vec - pos_margin, min=0) + loss_row = pos_loss + torch.clamp(neg_margin - hard_neg_dist_row, min=0) + loss_col = pos_loss + torch.clamp(neg_margin - hard_neg_dist_col, min=0) + else: + raise NotImplementedError() + + elif loss_type == 'CIRCLE': + log_scale = 512 + m = 0.1 + neg_mask_row = torch.unsqueeze(torch.eye(num_corr, device=feat_anc.device), 0) + if radius_mask_row is not None: + neg_mask_row += radius_mask_row + neg_mask_col = torch.unsqueeze(torch.eye(num_corr, device=feat_anc.device), 0) + if radius_mask_col is not None: + neg_mask_col += radius_mask_col + + pos_margin = 1 - m + neg_margin = m + pos_optimal = 1 + m + neg_optimal = -m + + neg_mat_row = dist_mat - 128 * neg_mask_row + neg_mat_col = dist_mat - 128 * neg_mask_col + + lse_positive = torch.logsumexp(-log_scale * (pos_vec[..., None] - pos_margin) * \ + torch.clamp(pos_optimal - pos_vec[..., None], min=0).detach(), dim=-1) + + lse_negative_row = torch.logsumexp(log_scale * (neg_mat_row - neg_margin) * \ + torch.clamp(neg_mat_row - neg_optimal, min=0).detach(), dim=-1) + + lse_negative_col = torch.logsumexp(log_scale * (neg_mat_col - neg_margin) * \ + torch.clamp(neg_mat_col - neg_optimal, min=0).detach(), dim=-2) + + loss_row = F.softplus(lse_positive + lse_negative_row) / log_scale + loss_col = F.softplus(lse_positive + lse_negative_col) / log_scale + + else: + raise NotImplementedError() + + if dist_type == 'cosine_dist': + err_row = dist_mat - torch.unsqueeze(pos_vec, -1) + err_col = dist_mat - torch.unsqueeze(pos_vec, -2) + elif dist_type == 'euclidean_dist' or dist_type == 'euclidean_dist_no_norm': + err_row = torch.unsqueeze(pos_vec, -1) - dist_mat + err_col = torch.unsqueeze(pos_vec, -2) - dist_mat + else: + raise NotImplementedError() + if radius_mask_row is not None: + err_row = err_row - 10 * radius_mask_row + if radius_mask_col is not None: + err_col = err_col - 10 * radius_mask_col + err_row = torch.sum(torch.clamp(err_row, min=0), dim=-1) + err_col = torch.sum(torch.clamp(err_col, min=0), dim=-2) + + loss = 0 + accuracy = 0 + + tot_loss = (loss_row + loss_col) / 2 + if corr_weight is not None: + tot_loss = tot_loss * corr_weight + + for i in range(batch_size): + if corr_weight is not None: + loss += torch.sum(tot_loss[i][inlier_mask[i]]) / \ + (torch.sum(corr_weight[i][inlier_mask[i]]) + 1e-6) + else: + loss += torch.mean(tot_loss[i][inlier_mask[i]]) + cnt_err_row = torch.count_nonzero(err_row[i][inlier_mask[i]]).float() + cnt_err_col = torch.count_nonzero(err_col[i][inlier_mask[i]]).float() + tot_err = cnt_err_row + cnt_err_col + if inlier_num[i] != 0: + accuracy += 1. - tot_err / inlier_num[i] / batch_size / 2. + else: + accuracy += 1. + + matched_mask = torch.logical_and(torch.eq(err_row, 0), torch.eq(err_col, 0)) + matched_mask = torch.logical_and(matched_mask, inlier_mask) + + loss /= batch_size + accuracy /= batch_size + + return loss, accuracy, matched_mask + + +# for the neighborhood areas of keypoints extracted from normal image, the score from noise_score_map should be close +# for the rest, the noise image's score should less than normal image +# input: score_map [batch_size, H, W, 1]; indices [2, k, 2] +# output: loss [scalar] +def make_noise_score_map_loss(score_map, noise_score_map, indices, batch_size, thld=0.): + H, W = score_map.shape[1:3] + loss = 0 + for i in range(batch_size): + kpts_coords = indices[i].T # (2, num_kpts) + mask = torch.zeros([H, W], device=score_map.device) + mask[kpts_coords.cpu().numpy()] = 1 + + # using 3x3 kernel to put kpts' neightborhood area into the mask + kernel = torch.ones([1, 1, 3, 3], device=score_map.device) + mask = F.conv2d(mask.unsqueeze(0).unsqueeze(0), kernel, padding=1)[0, 0] > 0 + + loss1 = torch.sum(torch.abs(score_map[i] - noise_score_map[i]).squeeze() * mask) / torch.sum(mask) + loss2 = torch.sum(torch.clamp(noise_score_map[i] - score_map[i] - thld, min=0).squeeze() * torch.logical_not(mask)) / (H * W - torch.sum(mask)) + + loss += loss1 + loss += loss2 + + if i == 0: + first_mask = mask + + return loss, first_mask + + +def make_noise_score_map_loss_labelmap(score_map, noise_score_map, labelmap, batch_size, thld=0.): + H, W = score_map.shape[1:3] + loss = 0 + for i in range(batch_size): + # using 3x3 kernel to put kpts' neightborhood area into the mask + kernel = torch.ones([1, 1, 3, 3], device=score_map.device) + mask = F.conv2d(labelmap[i].unsqueeze(0).to(score_map.device).float(), kernel, padding=1)[0, 0] > 0 + + loss1 = torch.sum(torch.abs(score_map[i] - noise_score_map[i]).squeeze() * mask) / torch.sum(mask) + loss2 = torch.sum(torch.clamp(noise_score_map[i] - score_map[i] - thld, min=0).squeeze() * torch.logical_not(mask)) / (H * W - torch.sum(mask)) + + loss += loss1 + loss += loss2 + + if i == 0: + first_mask = mask + + return loss, first_mask + + +def make_score_map_peakiness_loss(score_map, scores, batch_size): + H, W = score_map.shape[1:3] + loss = 0 + + for i in range(batch_size): + loss += torch.mean(scores[i]) - torch.mean(score_map[i]) + + loss /= batch_size + return 1 - loss diff --git a/third_party/DarkFeat/nets/multi_sampler.py b/third_party/DarkFeat/nets/multi_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..dc400fb2afeb50575cd81d3c01b605bea6db1121 --- /dev/null +++ b/third_party/DarkFeat/nets/multi_sampler.py @@ -0,0 +1,172 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from .geom import rnd_sample, interpolate + +class MultiSampler (nn.Module): + """ Similar to NghSampler, but doesnt warp the 2nd image. + Distance to GT => 0 ... pos_d ... neg_d ... ngh + Pixel label => + + + + + + 0 0 - - - - - - - + + Subsample on query side: if > 0, regular grid + < 0, random points + In both cases, the number of query points is = W*H/subq**2 + """ + def __init__(self, ngh, subq=1, subd=1, pos_d=0, neg_d=2, border=None, + maxpool_pos=True, subd_neg=0): + nn.Module.__init__(self) + assert 0 <= pos_d < neg_d <= (ngh if ngh else 99) + self.ngh = ngh + self.pos_d = pos_d + self.neg_d = neg_d + assert subd <= ngh or ngh == 0 + assert subq != 0 + self.sub_q = subq + self.sub_d = subd + self.sub_d_neg = subd_neg + if border is None: border = ngh + assert border >= ngh, 'border has to be larger than ngh' + self.border = border + self.maxpool_pos = maxpool_pos + self.precompute_offsets() + + def precompute_offsets(self): + pos_d2 = self.pos_d**2 + neg_d2 = self.neg_d**2 + rad2 = self.ngh**2 + rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple + pos = [] + neg = [] + for j in range(-rad, rad+1, self.sub_d): + for i in range(-rad, rad+1, self.sub_d): + d2 = i*i + j*j + if d2 <= pos_d2: + pos.append( (i,j) ) + elif neg_d2 <= d2 <= rad2: + neg.append( (i,j) ) + + self.register_buffer('pos_offsets', torch.LongTensor(pos).view(-1,2).t()) + self.register_buffer('neg_offsets', torch.LongTensor(neg).view(-1,2).t()) + + + def forward(self, feat0, feat1, noise_feat0, noise_feat1, conf0, conf1, noise_conf0, noise_conf1, pos0, pos1, B, H, W, N=2500): + pscores_ls, nscores_ls, distractors_ls = [], [], [] + valid_feat0_ls = [] + noise_pscores_ls, noise_nscores_ls, noise_distractors_ls = [], [], [] + valid_noise_feat0_ls = [] + valid_pos1_ls, valid_pos2_ls = [], [] + qconf_ls = [] + noise_qconf_ls = [] + mask_ls = [] + + for i in range(B): + tmp_mask = (pos0[i][:, 1] >= self.border) * (pos0[i][:, 1] < W-self.border) \ + * (pos0[i][:, 0] >= self.border) * (pos0[i][:, 0] < H-self.border) + + selected_pos0 = pos0[i][tmp_mask] + selected_pos1 = pos1[i][tmp_mask] + valid_pos0, valid_pos1 = rnd_sample([selected_pos0, selected_pos1], N) + + # sample features from first image + valid_feat0 = interpolate(valid_pos0 / 4, feat0[i]) # [N, 128] + valid_feat0 = F.normalize(valid_feat0, p=2, dim=-1) # [N, 128] + qconf = interpolate(valid_pos0 / 4, conf0[i]) + + valid_noise_feat0 = interpolate(valid_pos0 / 4, noise_feat0[i]) # [N, 128] + valid_noise_feat0 = F.normalize(valid_noise_feat0, p=2, dim=-1) # [N, 128] + noise_qconf = interpolate(valid_pos0 / 4, noise_conf0[i]) + + # sample GT from second image + mask = (valid_pos1[:, 1] >= 0) * (valid_pos1[:, 1] < W) \ + * (valid_pos1[:, 0] >= 0) * (valid_pos1[:, 0] < H) + + def clamp(xy): + xy = xy + torch.clamp(xy[0], 0, H-1, out=xy[0]) + torch.clamp(xy[1], 0, W-1, out=xy[1]) + return xy + + # compute positive scores + valid_pos1p = clamp(valid_pos1.t()[:,None,:] + self.pos_offsets[:,:,None].to(valid_pos1.device)) # [2, 29, N] + valid_pos1p = valid_pos1p.permute(1, 2, 0).reshape(-1, 2) # [29, N, 2] -> [29*N, 2] + valid_feat1p = interpolate(valid_pos1p / 4, feat1[i]).reshape(self.pos_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_feat1p = F.normalize(valid_feat1p, p=2, dim=-1) # [29, N, 128] + valid_noise_feat1p = interpolate(valid_pos1p / 4, feat1[i]).reshape(self.pos_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_noise_feat1p = F.normalize(valid_noise_feat1p, p=2, dim=-1) # [29, N, 128] + + pscores = (valid_feat0[None,:,:] * valid_feat1p).sum(dim=-1).t() # [N, 29] + pscores, pos = pscores.max(dim=1, keepdim=True) + sel = clamp(valid_pos1.t() + self.pos_offsets[:,pos.view(-1)].to(valid_pos1.device)) + qconf = (qconf + interpolate(sel.t() / 4, conf1[i]))/2 + noise_pscores = (valid_noise_feat0[None,:,:] * valid_noise_feat1p).sum(dim=-1).t() # [N, 29] + noise_pscores, noise_pos = noise_pscores.max(dim=1, keepdim=True) + noise_sel = clamp(valid_pos1.t() + self.pos_offsets[:,noise_pos.view(-1)].to(valid_pos1.device)) + noise_qconf = (noise_qconf + interpolate(noise_sel.t() / 4, noise_conf1[i]))/2 + + # compute negative scores + valid_pos1n = clamp(valid_pos1.t()[:,None,:] + self.neg_offsets[:,:,None].to(valid_pos1.device)) # [2, 29, N] + valid_pos1n = valid_pos1n.permute(1, 2, 0).reshape(-1, 2) # [29, N, 2] -> [29*N, 2] + valid_feat1n = interpolate(valid_pos1n / 4, feat1[i]).reshape(self.neg_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_feat1n = F.normalize(valid_feat1n, p=2, dim=-1) # [29, N, 128] + nscores = (valid_feat0[None,:,:] * valid_feat1n).sum(dim=-1).t() # [N, 29] + valid_noise_feat1n = interpolate(valid_pos1n / 4, noise_feat1[i]).reshape(self.neg_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_noise_feat1n = F.normalize(valid_noise_feat1n, p=2, dim=-1) # [29, N, 128] + noise_nscores = (valid_noise_feat0[None,:,:] * valid_noise_feat1n).sum(dim=-1).t() # [N, 29] + + if self.sub_d_neg: + valid_pos2 = rnd_sample([selected_pos1], N)[0] + distractors = interpolate(valid_pos2 / 4, feat1[i]) + distractors = F.normalize(distractors, p=2, dim=-1) + noise_distractors = interpolate(valid_pos2 / 4, noise_feat1[i]) + noise_distractors = F.normalize(noise_distractors, p=2, dim=-1) + + pscores_ls.append(pscores) + nscores_ls.append(nscores) + distractors_ls.append(distractors) + valid_feat0_ls.append(valid_feat0) + noise_pscores_ls.append(noise_pscores) + noise_nscores_ls.append(noise_nscores) + noise_distractors_ls.append(noise_distractors) + valid_noise_feat0_ls.append(valid_noise_feat0) + valid_pos1_ls.append(valid_pos1) + valid_pos2_ls.append(valid_pos2) + qconf_ls.append(qconf) + noise_qconf_ls.append(noise_qconf) + mask_ls.append(mask) + + N = np.min([len(i) for i in qconf_ls]) + + # merge batches + qconf = torch.stack([i[:N] for i in qconf_ls], dim=0).squeeze(-1) + mask = torch.stack([i[:N] for i in mask_ls], dim=0) + pscores = torch.cat([i[:N] for i in pscores_ls], dim=0) + nscores = torch.cat([i[:N] for i in nscores_ls], dim=0) + distractors = torch.cat([i[:N] for i in distractors_ls], dim=0) + valid_feat0 = torch.cat([i[:N] for i in valid_feat0_ls], dim=0) + valid_pos1 = torch.cat([i[:N] for i in valid_pos1_ls], dim=0) + valid_pos2 = torch.cat([i[:N] for i in valid_pos2_ls], dim=0) + + noise_qconf = torch.stack([i[:N] for i in noise_qconf_ls], dim=0).squeeze(-1) + noise_pscores = torch.cat([i[:N] for i in noise_pscores_ls], dim=0) + noise_nscores = torch.cat([i[:N] for i in noise_nscores_ls], dim=0) + noise_distractors = torch.cat([i[:N] for i in noise_distractors_ls], dim=0) + valid_noise_feat0 = torch.cat([i[:N] for i in valid_noise_feat0_ls], dim=0) + + # remove scores that corresponds to positives or nulls + dscores = torch.matmul(valid_feat0, distractors.t()) + noise_dscores = torch.matmul(valid_noise_feat0, noise_distractors.t()) + + dis2 = (valid_pos2[:, 1] - valid_pos1[:, 1][:,None])**2 + (valid_pos2[:, 0] - valid_pos1[:, 0][:,None])**2 + b = torch.arange(B, device=dscores.device)[:,None].expand(B, N).reshape(-1) + dis2 += (b != b[:,None]).long() * self.neg_d**2 + dscores[dis2 < self.neg_d**2] = 0 + noise_dscores[dis2 < self.neg_d**2] = 0 + scores = torch.cat((pscores, nscores, dscores), dim=1) + noise_scores = torch.cat((noise_pscores, noise_nscores, noise_dscores), dim=1) + + gt = scores.new_zeros(scores.shape, dtype=torch.uint8) + gt[:, :pscores.shape[1]] = 1 + + return scores, noise_scores, gt, mask, qconf, noise_qconf diff --git a/third_party/DarkFeat/nets/noise_reliability_loss.py b/third_party/DarkFeat/nets/noise_reliability_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..9efddae149653c225ee7f2c1eb5fed5f92cef15c --- /dev/null +++ b/third_party/DarkFeat/nets/noise_reliability_loss.py @@ -0,0 +1,40 @@ +import torch +import torch.nn as nn +from .reliability_loss import APLoss + + +class MultiPixelAPLoss (nn.Module): + """ Computes the pixel-wise AP loss: + Given two images and ground-truth optical flow, computes the AP per pixel. + + feat1: (B, C, H, W) pixel-wise features extracted from img1 + feat2: (B, C, H, W) pixel-wise features extracted from img2 + aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2 + """ + def __init__(self, sampler, nq=20): + nn.Module.__init__(self) + self.aploss = APLoss(nq, min=0, max=1, euc=False) + self.sampler = sampler + self.base = 0.25 + self.dec_base = 0.20 + + def loss_from_ap(self, ap, rel, noise_ap, noise_rel): + dec_ap = torch.clamp(ap - noise_ap, min=0, max=1) + return (1 - ap*noise_rel - (1-noise_rel)*self.base), (1. - dec_ap*(1-noise_rel) - noise_rel*self.dec_base) + + def forward(self, feat0, feat1, noise_feat0, noise_feat1, conf0, conf1, noise_conf0, noise_conf1, pos0, pos1, B, H, W, N=1500): + # subsample things + scores, noise_scores, gt, msk, qconf, noise_qconf = self.sampler(feat0, feat1, noise_feat0, noise_feat1, \ + conf0, conf1, noise_conf0, noise_conf1, pos0, pos1, B, H, W, N=1500) + + # compute pixel-wise AP + n = qconf.numel() + if n == 0: return 0, 0 + scores, noise_scores, gt = scores.view(n,-1), noise_scores, gt.view(n,-1) + ap = self.aploss(scores, gt).view(msk.shape) + noise_ap = self.aploss(noise_scores, gt).view(msk.shape) + + pixel_loss = self.loss_from_ap(ap, qconf, noise_ap, noise_qconf) + + loss = pixel_loss[0][msk].mean(), pixel_loss[1][msk].mean() + return loss \ No newline at end of file diff --git a/third_party/DarkFeat/nets/reliability_loss.py b/third_party/DarkFeat/nets/reliability_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..527f9886a2d4785680bac52ff2fa20033b8d8920 --- /dev/null +++ b/third_party/DarkFeat/nets/reliability_loss.py @@ -0,0 +1,105 @@ +import torch +import torch.nn as nn +import numpy as np + + +class APLoss (nn.Module): + """ differentiable AP loss, through quantization. + + Input: (N, M) values in [min, max] + label: (N, M) values in {0, 1} + + Returns: list of query AP (for each n in {1..N}) + Note: typically, you want to minimize 1 - mean(AP) + """ + def __init__(self, nq=25, min=0, max=1, euc=False): + nn.Module.__init__(self) + assert isinstance(nq, int) and 2 <= nq <= 100 + self.nq = nq + self.min = min + self.max = max + self.euc = euc + gap = max - min + assert gap > 0 + + # init quantizer = non-learnable (fixed) convolution + self.quantizer = q = nn.Conv1d(1, 2*nq, kernel_size=1, bias=True) + a = (nq-1) / gap + #1st half = lines passing to (min+x,1) and (min+x+1/a,0) with x = {nq-1..0}*gap/(nq-1) + q.weight.data[:nq] = -a + q.bias.data[:nq] = torch.from_numpy(a*min + np.arange(nq, 0, -1)) # b = 1 + a*(min+x) + #2nd half = lines passing to (min+x,1) and (min+x-1/a,0) with x = {nq-1..0}*gap/(nq-1) + q.weight.data[nq:] = a + q.bias.data[nq:] = torch.from_numpy(np.arange(2-nq, 2, 1) - a*min) # b = 1 - a*(min+x) + # first and last one are special: just horizontal straight line + q.weight.data[0] = q.weight.data[-1] = 0 + q.bias.data[0] = q.bias.data[-1] = 1 + + def compute_AP(self, x, label): + N, M = x.shape + # print(x.shape, label.shape) + if self.euc: # euclidean distance in same range than similarities + x = 1 - torch.sqrt(2.001 - 2*x) + + # quantize all predictions + q = self.quantizer(x.unsqueeze(1)) + q = torch.min(q[:,:self.nq], q[:,self.nq:]).clamp(min=0) # N x Q x M [1600, 20, 1681] + + nbs = q.sum(dim=-1) # number of samples N x Q = c + rec = (q * label.view(N,1,M).float()).sum(dim=-1) # nb of correct samples = c+ N x Q + prec = rec.cumsum(dim=-1) / (1e-16 + nbs.cumsum(dim=-1)) # precision + rec /= rec.sum(dim=-1).unsqueeze(1) # norm in [0,1] + + ap = (prec * rec).sum(dim=-1) # per-image AP + return ap + + def forward(self, x, label): + assert x.shape == label.shape # N x M + return self.compute_AP(x, label) + + +class PixelAPLoss (nn.Module): + """ Computes the pixel-wise AP loss: + Given two images and ground-truth optical flow, computes the AP per pixel. + + feat1: (B, C, H, W) pixel-wise features extracted from img1 + feat2: (B, C, H, W) pixel-wise features extracted from img2 + aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2 + """ + def __init__(self, sampler, nq=20): + nn.Module.__init__(self) + self.aploss = APLoss(nq, min=0, max=1, euc=False) + self.name = 'pixAP' + self.sampler = sampler + + def loss_from_ap(self, ap, rel): + return 1 - ap + + def forward(self, feat0, feat1, conf0, conf1, pos0, pos1, B, H, W, N=1200): + # subsample things + scores, gt, msk, qconf = self.sampler(feat0, feat1, conf0, conf1, pos0, pos1, B, H, W, N=1200) + + # compute pixel-wise AP + n = qconf.numel() + if n == 0: return 0 + scores, gt = scores.view(n,-1), gt.view(n,-1) + ap = self.aploss(scores, gt).view(msk.shape) + + pixel_loss = self.loss_from_ap(ap, qconf) + + loss = pixel_loss[msk].mean() + return loss + + +class ReliabilityLoss (PixelAPLoss): + """ same than PixelAPLoss, but also train a pixel-wise confidence + that this pixel is going to have a good AP. + """ + def __init__(self, sampler, base=0.5, **kw): + PixelAPLoss.__init__(self, sampler, **kw) + assert 0 <= base < 1 + self.base = base + + def loss_from_ap(self, ap, rel): + return 1 - ap*rel - (1-rel)*self.base + diff --git a/third_party/DarkFeat/nets/sampler.py b/third_party/DarkFeat/nets/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..b732a3671872d5675be9826f76b0818d3b99d466 --- /dev/null +++ b/third_party/DarkFeat/nets/sampler.py @@ -0,0 +1,160 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from .geom import rnd_sample, interpolate + +class NghSampler2 (nn.Module): + """ Similar to NghSampler, but doesnt warp the 2nd image. + Distance to GT => 0 ... pos_d ... neg_d ... ngh + Pixel label => + + + + + + 0 0 - - - - - - - + + Subsample on query side: if > 0, regular grid + < 0, random points + In both cases, the number of query points is = W*H/subq**2 + """ + def __init__(self, ngh, subq=1, subd=1, pos_d=0, neg_d=2, border=None, + maxpool_pos=True, subd_neg=0): + nn.Module.__init__(self) + assert 0 <= pos_d < neg_d <= (ngh if ngh else 99) + self.ngh = ngh + self.pos_d = pos_d + self.neg_d = neg_d + assert subd <= ngh or ngh == 0 + assert subq != 0 + self.sub_q = subq + self.sub_d = subd + self.sub_d_neg = subd_neg + if border is None: border = ngh + assert border >= ngh, 'border has to be larger than ngh' + self.border = border + self.maxpool_pos = maxpool_pos + self.precompute_offsets() + + def precompute_offsets(self): + pos_d2 = self.pos_d**2 + neg_d2 = self.neg_d**2 + rad2 = self.ngh**2 + rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple + pos = [] + neg = [] + for j in range(-rad, rad+1, self.sub_d): + for i in range(-rad, rad+1, self.sub_d): + d2 = i*i + j*j + if d2 <= pos_d2: + pos.append( (i,j) ) + elif neg_d2 <= d2 <= rad2: + neg.append( (i,j) ) + + self.register_buffer('pos_offsets', torch.LongTensor(pos).view(-1,2).t()) + self.register_buffer('neg_offsets', torch.LongTensor(neg).view(-1,2).t()) + + def gen_grid(self, step, B, H, W, dev): + b1 = torch.arange(B, device=dev) + if step > 0: + # regular grid + x1 = torch.arange(self.border, W-self.border, step, device=dev) + y1 = torch.arange(self.border, H-self.border, step, device=dev) + H1, W1 = len(y1), len(x1) + x1 = x1[None,None,:].expand(B,H1,W1).reshape(-1) + y1 = y1[None,:,None].expand(B,H1,W1).reshape(-1) + b1 = b1[:,None,None].expand(B,H1,W1).reshape(-1) + shape = (B, H1, W1) + else: + # randomly spread + n = (H - 2*self.border) * (W - 2*self.border) // step**2 + x1 = torch.randint(self.border, W-self.border, (n,), device=dev) + y1 = torch.randint(self.border, H-self.border, (n,), device=dev) + x1 = x1[None,:].expand(B,n).reshape(-1) + y1 = y1[None,:].expand(B,n).reshape(-1) + b1 = b1[:,None].expand(B,n).reshape(-1) + shape = (B, n) + return b1, y1, x1, shape + + def forward(self, feat0, feat1, conf0, conf1, pos0, pos1, B, H, W, N=2500): + pscores_ls, nscores_ls, distractors_ls = [], [], [] + valid_feat0_ls = [] + valid_pos1_ls, valid_pos2_ls = [], [] + qconf_ls = [] + mask_ls = [] + + for i in range(B): + # positions in the first image + tmp_mask = (pos0[i][:, 1] >= self.border) * (pos0[i][:, 1] < W-self.border) \ + * (pos0[i][:, 0] >= self.border) * (pos0[i][:, 0] < H-self.border) + + selected_pos0 = pos0[i][tmp_mask] + selected_pos1 = pos1[i][tmp_mask] + valid_pos0, valid_pos1 = rnd_sample([selected_pos0, selected_pos1], N) + + # sample features from first image + valid_feat0 = interpolate(valid_pos0 / 4, feat0[i]) # [N, 128] + valid_feat0 = F.normalize(valid_feat0, p=2, dim=-1) # [N, 128] + qconf = interpolate(valid_pos0 / 4, conf0[i]) + + # sample GT from second image + mask = (valid_pos1[:, 1] >= 0) * (valid_pos1[:, 1] < W) \ + * (valid_pos1[:, 0] >= 0) * (valid_pos1[:, 0] < H) + + def clamp(xy): + xy = xy + torch.clamp(xy[0], 0, H-1, out=xy[0]) + torch.clamp(xy[1], 0, W-1, out=xy[1]) + return xy + + # compute positive scores + valid_pos1p = clamp(valid_pos1.t()[:,None,:] + self.pos_offsets[:,:,None].to(valid_pos1.device)) # [2, 29, N] + valid_pos1p = valid_pos1p.permute(1, 2, 0).reshape(-1, 2) # [29, N, 2] -> [29*N, 2] + valid_feat1p = interpolate(valid_pos1p / 4, feat1[i]).reshape(self.pos_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_feat1p = F.normalize(valid_feat1p, p=2, dim=-1) # [29, N, 128] + + pscores = (valid_feat0[None,:,:] * valid_feat1p).sum(dim=-1).t() # [N, 29] + pscores, pos = pscores.max(dim=1, keepdim=True) + sel = clamp(valid_pos1.t() + self.pos_offsets[:,pos.view(-1)].to(valid_pos1.device)) + qconf = (qconf + interpolate(sel.t() / 4, conf1[i]))/2 + + # compute negative scores + valid_pos1n = clamp(valid_pos1.t()[:,None,:] + self.neg_offsets[:,:,None].to(valid_pos1.device)) # [2, 29, N] + valid_pos1n = valid_pos1n.permute(1, 2, 0).reshape(-1, 2) # [29, N, 2] -> [29*N, 2] + valid_feat1n = interpolate(valid_pos1n / 4, feat1[i]).reshape(self.neg_offsets.shape[-1], -1, 128) # [29, N, 128] + valid_feat1n = F.normalize(valid_feat1n, p=2, dim=-1) # [29, N, 128] + nscores = (valid_feat0[None,:,:] * valid_feat1n).sum(dim=-1).t() # [N, 29] + + if self.sub_d_neg: + valid_pos2 = rnd_sample([selected_pos1], N)[0] + distractors = interpolate(valid_pos2 / 4, feat1[i]) + distractors = F.normalize(distractors, p=2, dim=-1) + + pscores_ls.append(pscores) + nscores_ls.append(nscores) + distractors_ls.append(distractors) + valid_feat0_ls.append(valid_feat0) + valid_pos1_ls.append(valid_pos1) + valid_pos2_ls.append(valid_pos2) + qconf_ls.append(qconf) + mask_ls.append(mask) + + N = np.min([len(i) for i in qconf_ls]) + + # merge batches + qconf = torch.stack([i[:N] for i in qconf_ls], dim=0).squeeze(-1) + mask = torch.stack([i[:N] for i in mask_ls], dim=0) + pscores = torch.cat([i[:N] for i in pscores_ls], dim=0) + nscores = torch.cat([i[:N] for i in nscores_ls], dim=0) + distractors = torch.cat([i[:N] for i in distractors_ls], dim=0) + valid_feat0 = torch.cat([i[:N] for i in valid_feat0_ls], dim=0) + valid_pos1 = torch.cat([i[:N] for i in valid_pos1_ls], dim=0) + valid_pos2 = torch.cat([i[:N] for i in valid_pos2_ls], dim=0) + + dscores = torch.matmul(valid_feat0, distractors.t()) + dis2 = (valid_pos2[:, 1] - valid_pos1[:, 1][:,None])**2 + (valid_pos2[:, 0] - valid_pos1[:, 0][:,None])**2 + b = torch.arange(B, device=dscores.device)[:,None].expand(B, N).reshape(-1) + dis2 += (b != b[:,None]).long() * self.neg_d**2 + dscores[dis2 < self.neg_d**2] = 0 + scores = torch.cat((pscores, nscores, dscores), dim=1) + + gt = scores.new_zeros(scores.shape, dtype=torch.uint8) + gt[:, :pscores.shape[1]] = 1 + + return scores, gt, mask, qconf diff --git a/third_party/DarkFeat/nets/score.py b/third_party/DarkFeat/nets/score.py new file mode 100644 index 0000000000000000000000000000000000000000..a78cf1c893bc338c12803697d55e121a75171f2c --- /dev/null +++ b/third_party/DarkFeat/nets/score.py @@ -0,0 +1,116 @@ +import torch +import torch.nn.functional as F +import numpy as np + +from .geom import gather_nd + +# input: [batch_size, C, H, W] +# output: [batch_size, C, H, W], [batch_size, C, H, W] +def peakiness_score(inputs, moving_instance_max, ksize=3, dilation=1): + inputs = inputs / moving_instance_max + + batch_size, C, H, W = inputs.shape + + pad_size = ksize // 2 + (dilation - 1) + kernel = torch.ones([C, 1, ksize, ksize], device=inputs.device) / (ksize * ksize) + + pad_inputs = F.pad(inputs, [pad_size] * 4, mode='reflect') + + avg_spatial_inputs = F.conv2d( + pad_inputs, + kernel, + stride=1, + dilation=dilation, + padding=0, + groups=C + ) + avg_channel_inputs = torch.mean(inputs, axis=1, keepdim=True) # channel dimension is 1 + + alpha = F.softplus(inputs - avg_spatial_inputs) + beta = F.softplus(inputs - avg_channel_inputs) + + return alpha, beta + + +# input: score_map [batch_size, 1, H, W] +# output: indices [2, k, 2], scores [2, k] +def extract_kpts(score_map, k=256, score_thld=0, edge_thld=0, nms_size=3, eof_size=5): + h = score_map.shape[2] + w = score_map.shape[3] + + mask = score_map > score_thld + if nms_size > 0: + nms_mask = F.max_pool2d(score_map, kernel_size=nms_size, stride=1, padding=nms_size//2) + nms_mask = torch.eq(score_map, nms_mask) + mask = torch.logical_and(nms_mask, mask) + if eof_size > 0: + eof_mask = torch.ones((1, 1, h - 2 * eof_size, w - 2 * eof_size), dtype=torch.float32, device=score_map.device) + eof_mask = F.pad(eof_mask, [eof_size] * 4, value=0) + eof_mask = eof_mask.bool() + mask = torch.logical_and(eof_mask, mask) + if edge_thld > 0: + non_edge_mask = edge_mask(score_map, 1, dilation=3, edge_thld=edge_thld) + mask = torch.logical_and(non_edge_mask, mask) + + bs = score_map.shape[0] + if bs is None: + indices = torch.nonzero(mask)[0] + scores = gather_nd(score_map, indices)[0] + sample = torch.sort(scores, descending=True)[1][0:k] + indices = indices[sample].unsqueeze(0) + scores = scores[sample].unsqueeze(0) + else: + indices = [] + scores = [] + for i in range(bs): + tmp_mask = mask[i][0] + tmp_score_map = score_map[i][0] + tmp_indices = torch.nonzero(tmp_mask) + tmp_scores = gather_nd(tmp_score_map, tmp_indices) + tmp_sample = torch.sort(tmp_scores, descending=True)[1][0:k] + tmp_indices = tmp_indices[tmp_sample] + tmp_scores = tmp_scores[tmp_sample] + indices.append(tmp_indices) + scores.append(tmp_scores) + try: + indices = torch.stack(indices, dim=0) + scores = torch.stack(scores, dim=0) + except: + min_num = np.min([len(i) for i in indices]) + indices = torch.stack([i[:min_num] for i in indices], dim=0) + scores = torch.stack([i[:min_num] for i in scores], dim=0) + return indices, scores + + +def edge_mask(inputs, n_channel, dilation=1, edge_thld=5): + b, c, h, w = inputs.size() + device = inputs.device + + dii_filter = torch.tensor( + [[0, 1., 0], [0, -2., 0], [0, 1., 0]] + ).view(1, 1, 3, 3) + dij_filter = 0.25 * torch.tensor( + [[1., 0, -1.], [0, 0., 0], [-1., 0, 1.]] + ).view(1, 1, 3, 3) + djj_filter = torch.tensor( + [[0, 0, 0], [1., -2., 1.], [0, 0, 0]] + ).view(1, 1, 3, 3) + + dii = F.conv2d( + inputs.view(-1, 1, h, w), dii_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + dij = F.conv2d( + inputs.view(-1, 1, h, w), dij_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + djj = F.conv2d( + inputs.view(-1, 1, h, w), djj_filter.to(device), padding=dilation, dilation=dilation + ).view(b, c, h, w) + + det = dii * djj - dij * dij + tr = dii + djj + del dii, dij, djj + + threshold = (edge_thld + 1) ** 2 / edge_thld + is_not_edge = torch.min(tr * tr / det <= threshold, det > 0) + + return is_not_edge diff --git a/third_party/DarkFeat/pose_estimation.py b/third_party/DarkFeat/pose_estimation.py new file mode 100644 index 0000000000000000000000000000000000000000..c87877191e7e31c3bc0a362d7d481dfd5d4b5757 --- /dev/null +++ b/third_party/DarkFeat/pose_estimation.py @@ -0,0 +1,137 @@ +import argparse +import cv2 +import numpy as np +import os +import math +import subprocess +from tqdm import tqdm + + +def compute_essential(matched_kp1, matched_kp2, K): + pts1 = cv2.undistortPoints(matched_kp1,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + pts2 = cv2.undistortPoints(matched_kp2,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + K_1 = np.eye(3) + # Estimate the homography between the matches using RANSAC + ransac_model, ransac_inliers = cv2.findEssentialMat(pts1, pts2, K_1, method=cv2.RANSAC, prob=0.999, threshold=0.001, maxIters=10000) + if ransac_inliers is None or ransac_model.shape != (3,3): + ransac_inliers = np.array([]) + ransac_model = None + return ransac_model, ransac_inliers, pts1, pts2 + + +def compute_error(R_GT,t_GT,E,pts1_norm, pts2_norm, inliers): + """Compute the angular error between two rotation matrices and two translation vectors. + Keyword arguments: + R -- 2D numpy array containing an estimated rotation + gt_R -- 2D numpy array containing the corresponding ground truth rotation + t -- 2D numpy array containing an estimated translation as column + gt_t -- 2D numpy array containing the corresponding ground truth translation + """ + + inliers = inliers.ravel() + R = np.eye(3) + t = np.zeros((3,1)) + sst = True + try: + _, R, t, _ = cv2.recoverPose(E, pts1_norm, pts2_norm, np.eye(3), inliers) + except: + sst = False + # calculate angle between provided rotations + # + if sst: + dR = np.matmul(R, np.transpose(R_GT)) + dR = cv2.Rodrigues(dR)[0] + dR = np.linalg.norm(dR) * 180 / math.pi + + # calculate angle between provided translations + dT = float(np.dot(t_GT.T, t)) + dT /= float(np.linalg.norm(t_GT)) + + if dT > 1 or dT < -1: + print("Domain warning! dT:",dT) + dT = max(-1,min(1,dT)) + dT = math.acos(dT) * 180 / math.pi + dT = np.minimum(dT, 180 - dT) # ambiguity of E estimation + else: + dR, dT = 180.0, 180.0 + return dR, dT + + +def pose_evaluation(result_base_dir, dark_name1, dark_name2, enhancer, K, R_GT, t_GT): + try: + m_kp1 = np.load(result_base_dir+enhancer+'/DarkFeat/POINT_1/'+dark_name1) + m_kp2 = np.load(result_base_dir+enhancer+'/DarkFeat/POINT_2/'+dark_name2) + except: + return 180.0, 180.0 + try: + E, inliers, pts1, pts2 = compute_essential(m_kp1, m_kp2, K) + except: + E, inliers, pts1, pts2 = np.zeros((3, 3)), np.array([]), None, None + dR, dT = compute_error(R_GT, t_GT, E, pts1, pts2, inliers) + return dR, dT + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--histeq', action='store_true') + parser.add_argument('--dataset_dir', type=str, default='/data/hyz/MID/') + opt = parser.parse_args() + + sizer = (960, 640) + focallength_x = 4.504986436499113e+03/(6744/sizer[0]) + focallength_y = 4.513311442889859e+03/(4502/sizer[1]) + K = np.eye(3) + K[0,0] = focallength_x + K[1,1] = focallength_y + K[0,2] = 3.363322177533149e+03/(6744/sizer[0]) + K[1,2] = 2.291824660547715e+03/(4502/sizer[1]) + Kinv = np.linalg.inv(K) + Kinvt = np.transpose(Kinv) + + PE_MT = np.zeros((6, 8)) + + enhancer = 'None' if not opt.histeq else 'HistEQ' + + for scene in ['Indoor', 'Outdoor']: + dir_base = opt.dataset_dir + '/' + scene + '/' + base_save = 'result_errors/' + scene + '/' + pair_list = sorted(os.listdir(dir_base)) + + os.makedirs(base_save, exist_ok=True) + + for pair in tqdm(pair_list): + opention = 1 + if scene == 'Outdoor': + pass + else: + if int(pair[4::]) <= 17: + opention = 0 + else: + pass + name = [] + files = sorted(os.listdir(dir_base+pair)) + for file_ in files: + if file_.endswith('.cr2'): + name.append(file_[0:9]) + ISO = ['00100', '00200', '00400', '00800', '01600', '03200', '06400', '12800'] + if opention == 1: + Shutter_speed = ['0.005','0.01','0.025','0.05','0.17','0.5'] + else: + Shutter_speed = ['0.01','0.02','0.05','0.1','0.3','1'] + + E_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'E_estimated.npy') + F_GT = np.dot(np.dot(Kinvt,E_GT),Kinv) + R_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'R_GT.npy') + t_GT = np.load(dir_base+pair+'/GT_Correspondence/'+'T_GT.npy') + result_base_dir ='result/' +scene+'/'+pair+'/' + for iso in ISO: + for ex in Shutter_speed: + dark_name1 = name[0]+iso+'_'+ex+'_'+scene+'.npy' + dark_name2 = name[1]+iso+'_'+ex+'_'+scene+'.npy' + + dr, dt = pose_evaluation(result_base_dir,dark_name1,dark_name2,enhancer,K,R_GT,t_GT) + PE_MT[Shutter_speed.index(ex),ISO.index(iso)] = max(dr, dt) + + subprocess.check_output(['mkdir', '-p', base_save + pair + f'/{enhancer}/']) + np.save(base_save + pair + f'/{enhancer}/Pose_error_DarkFeat.npy', PE_MT) + \ No newline at end of file diff --git a/third_party/DarkFeat/raw_preprocess.py b/third_party/DarkFeat/raw_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..226155a84e97f15782d3650f4ef6b3fa1880e07b --- /dev/null +++ b/third_party/DarkFeat/raw_preprocess.py @@ -0,0 +1,62 @@ +import glob +import rawpy +import cv2 +import os +import numpy as np +import colour_demosaicing +from tqdm import tqdm + + +def process_raw(args, path, w_new, h_new): + raw = rawpy.imread(str(path)).raw_image_visible + if '_00200_' in str(path) or '_00100_' in str(path): + raw = np.clip(raw.astype('float32') - 512, 0, 65535) + else: + raw = np.clip(raw.astype('float32') - 2048, 0, 65535) + img = colour_demosaicing.demosaicing_CFA_Bayer_bilinear(raw, 'RGGB').astype('float32') + img = np.clip(img, 0, 16383) + + # HistEQ start + if args.histeq: + img2 = np.zeros_like(img) + for i in range(3): + hist,bins = np.histogram(img[..., i].flatten(),16384,[0,16384]) + cdf = hist.cumsum() + cdf_normalized = cdf * float(hist.max()) / cdf.max() + cdf_m = np.ma.masked_equal(cdf,0) + cdf_m = (cdf_m - cdf_m.min())*16383/(cdf_m.max()-cdf_m.min()) + cdf = np.ma.filled(cdf_m,0).astype('uint16') + img2[..., i] = cdf[img[..., i].astype('int16')] + img[..., i] = img2[..., i].astype('float32') + # HistEQ end + + m = img.mean() + d = np.abs(img - img.mean()).mean() + img = (img - m + 2*d) / 4/d * 255 + image = np.clip(img, 0, 255) + + image = cv2.resize(image.astype('float32'), (w_new, h_new), interpolation=cv2.INTER_AREA) + + if args.histeq: + path=str(path) + os.makedirs('/'.join(path.split('/')[:-2]+[path.split('/')[-2]+'-npy']), exist_ok=True) + np.save('/'.join(path.split('/')[:-2]+[path.split('/')[-2]+'-npy']+[path.split('/')[-1].replace('cr2','npy')]), image) + else: + path=str(path) + os.makedirs('/'.join(path.split('/')[:-2]+[path.split('/')[-2]+'-npy-nohisteq']), exist_ok=True) + np.save('/'.join(path.split('/')[:-2]+[path.split('/')[-2]+'-npy-nohisteq']+[path.split('/')[-1].replace('cr2','npy')]), image) + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser() + parser.add_argument('--H', type=int, default=int(640)) + parser.add_argument('--W', type=int, default=int(960)) + parser.add_argument('--histeq', action='store_true') + parser.add_argument('--dataset_dir', type=str, default='/data/hyz/MID/') + args = parser.parse_args() + + path_ls = glob.glob(args.dataset_dir + '/*/pair*/?????/*') + for path in tqdm(path_ls): + process_raw(args, path, args.W, args.H) + diff --git a/third_party/DarkFeat/read_error.py b/third_party/DarkFeat/read_error.py new file mode 100644 index 0000000000000000000000000000000000000000..406b92dbd3877a11e51aebc3a705cd8d8d17e173 --- /dev/null +++ b/third_party/DarkFeat/read_error.py @@ -0,0 +1,56 @@ +import os +import numpy as np +import subprocess + +# def ratio(losses, thresholds=[1,2,3,4,5,6,7,8,9,10]): +def ratio(losses, thresholds=[5,10]): + return [ + '{:.3f}'.format(np.mean(losses < threshold)) + for threshold in thresholds + ] + +if __name__ == '__main__': + scene = 'Indoor' + dir_base = 'result_errors/Indoor/' + save_pt = 'resultfinal_errors/Indoor/' + + subprocess.check_output(['mkdir', '-p', save_pt]) + + with open(save_pt +'ratio_methods_'+scene+'.txt','w') as f: + f.write('5deg 10deg'+'\n') + pair_list = os.listdir(dir_base) + enhancer = os.listdir(dir_base+'/pair9/') + for method in enhancer: + pose_error_list = sorted(os.listdir(dir_base+'/pair9/'+method)) + for pose_error in pose_error_list: + error_array = np.expand_dims(np.zeros((6, 8)),axis=2) + for pair in pair_list: + try: + error = np.expand_dims(np.load(dir_base+'/'+pair+'/'+method+'/'+pose_error),axis=2) + except: + print('error in', dir_base+'/'+pair+'/'+method+'/'+pose_error) + continue + error_array = np.concatenate((error_array,error),axis=2) + ratio_result = ratio(error_array[:,:,1::].flatten()) + f.write(method + '_' + pose_error[11:-4] +' '+' '.join([str(i) for i in ratio_result])+"\n") + + + scene = 'Outdoor' + dir_base = 'result_errors/Outdoor/' + save_pt = 'resultfinal_errors/Outdoor/' + + subprocess.check_output(['mkdir', '-p', save_pt]) + + with open(save_pt +'ratio_methods_'+scene+'.txt','w') as f: + f.write('5deg 10deg'+'\n') + pair_list = os.listdir(dir_base) + enhancer = os.listdir(dir_base+'/pair9/') + for method in enhancer: + pose_error_list = sorted(os.listdir(dir_base+'/pair9/'+method)) + for pose_error in pose_error_list: + error_array = np.expand_dims(np.zeros((6, 8)),axis=2) + for pair in pair_list: + error = np.expand_dims(np.load(dir_base+'/'+pair+'/'+method+'/'+pose_error),axis=2) + error_array = np.concatenate((error_array,error),axis=2) + ratio_result = ratio(error_array[:,:,1::].flatten()) + f.write(method + '_' + pose_error[11:-4] +' '+' '.join([str(i) for i in ratio_result])+"\n") diff --git a/third_party/DarkFeat/requirements.txt b/third_party/DarkFeat/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..579c30a3063ffe54e9d0eca07ecc10dc0154d6b9 --- /dev/null +++ b/third_party/DarkFeat/requirements.txt @@ -0,0 +1,7 @@ +colour_demosaicing +opencv-python +pyyaml +rawpy +tensorboardX +tqdm +matplotlib diff --git a/third_party/DarkFeat/run.py b/third_party/DarkFeat/run.py new file mode 100644 index 0000000000000000000000000000000000000000..0e4c87053d2970fc927d8991aa0dab208f3c4917 --- /dev/null +++ b/third_party/DarkFeat/run.py @@ -0,0 +1,48 @@ +import cv2 +import yaml +import argparse +import os +from torch.utils.data import DataLoader + +from datasets.gl3d_dataset import GL3DDataset +from trainer import Trainer +from trainer_single_norel import SingleTrainerNoRel +from trainer_single import SingleTrainer + + +if __name__ == '__main__': + # add argument parser + parser = argparse.ArgumentParser() + parser.add_argument('--config', type=str, default='./configs/config.yaml') + parser.add_argument('--dataset_dir', type=str, default='/mnt/nvme2n1/hyz/data/GL3D') + parser.add_argument('--data_split', type=str, default='comb') + parser.add_argument('--is_training', type=bool, default=True) + parser.add_argument('--job_name', type=str, default='') + parser.add_argument('--gpu', type=str, default='0') + parser.add_argument('--start_cnt', type=int, default=0) + parser.add_argument('--stage', type=int, default=1) + args = parser.parse_args() + + # load global config + with open(args.config, 'r') as f: + config = yaml.load(f, Loader=yaml.FullLoader) + + # setup dataloader + dataset = GL3DDataset(args.dataset_dir, config['network'], args.data_split, is_training=args.is_training) + data_loader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4) + + os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu + + + if args.stage == 1: + trainer = SingleTrainerNoRel(config, f'cuda:0', data_loader, args.job_name, args.start_cnt) + elif args.stage == 2: + trainer = SingleTrainer(config, f'cuda:0', data_loader, args.job_name, args.start_cnt) + elif args.stage == 3: + trainer = Trainer(config, f'cuda:0', data_loader, args.job_name, args.start_cnt) + else: + raise NotImplementedError() + + trainer.train() + + \ No newline at end of file diff --git a/third_party/DarkFeat/trainer.py b/third_party/DarkFeat/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..e6ff2af9608e934b6899058d756bb2ab7d0fee2d --- /dev/null +++ b/third_party/DarkFeat/trainer.py @@ -0,0 +1,348 @@ +import os +import cv2 +import time +import yaml +import torch +import datetime +from tensorboardX import SummaryWriter +import torchvision.transforms as tvf +import torch.nn as nn +import torch.nn.functional as F + +from nets.geom import getK, getWarp, _grid_positions, getWarpNoValidate +from nets.loss import make_detector_loss, make_noise_score_map_loss +from nets.score import extract_kpts +from nets.multi_sampler import MultiSampler +from nets.noise_reliability_loss import MultiPixelAPLoss +from datasets.noise_simulator import NoiseSimulator +from nets.l2net import Quad_L2Net + + +class Trainer: + def __init__(self, config, device, loader, job_name, start_cnt): + self.config = config + self.device = device + self.loader = loader + + # tensorboard writer construction + os.makedirs('./runs/', exist_ok=True) + if job_name != '': + self.log_dir = f'runs/{job_name}' + else: + self.log_dir = f'runs/{datetime.datetime.now().strftime("%m-%d-%H%M%S")}' + + self.writer = SummaryWriter(self.log_dir) + with open(f'{self.log_dir}/config.yaml', 'w') as f: + yaml.dump(config, f) + + if config['network']['input_type'] == 'gray': + self.model = eval(f'{config["network"]["model"]}(inchan=1)').to(device) + elif config['network']['input_type'] == 'rgb' or config['network']['input_type'] == 'raw-demosaic': + self.model = eval(f'{config["network"]["model"]}(inchan=3)').to(device) + elif config['network']['input_type'] == 'raw': + self.model = eval(f'{config["network"]["model"]}(inchan=4)').to(device) + else: + raise NotImplementedError() + + # noise maker + self.noise_maker = NoiseSimulator(device) + + # reliability map conv + self.model.clf = nn.Conv2d(128, 2, kernel_size=1).cuda() + + # load model + self.cnt = 0 + if start_cnt != 0: + self.model.load_state_dict(torch.load(f'{self.log_dir}/model_{start_cnt:06d}.pth', map_location=device)) + self.cnt = start_cnt + 1 + + # sampler + sampler = MultiSampler(ngh=7, subq=-8, subd=1, pos_d=3, neg_d=5, border=16, + subd_neg=-8,maxpool_pos=True).to(device) + self.reliability_relitive_loss = MultiPixelAPLoss(sampler, nq=20).to(device) + + + # optimizer and scheduler + if self.config['training']['optimizer'] == 'SGD': + self.optimizer = torch.optim.SGD( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + momentum=self.config['training']['momentum'], + weight_decay=self.config['training']['weight_decay'], + ) + elif self.config['training']['optimizer'] == 'Adam': + self.optimizer = torch.optim.Adam( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + weight_decay=self.config['training']['weight_decay'] + ) + else: + raise NotImplementedError() + + self.lr_scheduler = torch.optim.lr_scheduler.StepLR( + self.optimizer, + step_size=self.config['training']['lr_step'], + gamma=self.config['training']['lr_gamma'], + last_epoch=start_cnt + ) + for param_tensor in self.model.state_dict(): + print(param_tensor, "\t", self.model.state_dict()[param_tensor].size()) + + + def save(self, iter_num): + torch.save(self.model.state_dict(), f'{self.log_dir}/model_{iter_num:06d}.pth') + + def load(self, path): + self.model.load_state_dict(torch.load(path)) + + def train(self): + self.model.train() + + for epoch in range(2): + for batch_idx, inputs in enumerate(self.loader): + self.optimizer.zero_grad() + t = time.time() + + # preprocess and add noise + img0_ori, noise_img0_ori = self.preprocess_noise_pair(inputs['img0'], self.cnt) + img1_ori, noise_img1_ori = self.preprocess_noise_pair(inputs['img1'], self.cnt) + + img0 = img0_ori.permute(0, 3, 1, 2).float().to(self.device) + img1 = img1_ori.permute(0, 3, 1, 2).float().to(self.device) + noise_img0 = noise_img0_ori.permute(0, 3, 1, 2).float().to(self.device) + noise_img1 = noise_img1_ori.permute(0, 3, 1, 2).float().to(self.device) + + if self.config['network']['input_type'] == 'rgb': + # 3-channel rgb + RGB_mean = [0.485, 0.456, 0.406] + RGB_std = [0.229, 0.224, 0.225] + norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) + img0 = norm_RGB(img0) + img1 = norm_RGB(img1) + noise_img0 = norm_RGB(noise_img0) + noise_img1 = norm_RGB(noise_img1) + + elif self.config['network']['input_type'] == 'gray': + # 1-channel + img0 = torch.mean(img0, dim=1, keepdim=True) + img1 = torch.mean(img1, dim=1, keepdim=True) + noise_img0 = torch.mean(noise_img0, dim=1, keepdim=True) + noise_img1 = torch.mean(noise_img1, dim=1, keepdim=True) + norm_gray0 = tvf.Normalize(mean=img0.mean(), std=img0.std()) + norm_gray1 = tvf.Normalize(mean=img1.mean(), std=img1.std()) + img0 = norm_gray0(img0) + img1 = norm_gray1(img1) + noise_img0 = norm_gray0(noise_img0) + noise_img1 = norm_gray1(noise_img1) + + elif self.config['network']['input_type'] == 'raw': + # 4-channel + pass + + elif self.config['network']['input_type'] == 'raw-demosaic': + # 3-channel + pass + + else: + raise NotImplementedError() + + desc0, score_map0, _, _ = self.model(img0) + desc1, score_map1, _, _ = self.model(img1) + + conf0 = F.softmax(self.model.clf(torch.abs(desc0)**2.0), dim=1)[:,1:2] + conf1 = F.softmax(self.model.clf(torch.abs(desc1)**2.0), dim=1)[:,1:2] + + noise_desc0, noise_score_map0, noise_at0, noise_att0 = self.model(noise_img0) + noise_desc1, noise_score_map1, noise_at1, noise_att1 = self.model(noise_img1) + + noise_conf0 = F.softmax(self.model.clf(torch.abs(noise_desc0)**2.0), dim=1)[:,1:2] + noise_conf1 = F.softmax(self.model.clf(torch.abs(noise_desc1)**2.0), dim=1)[:,1:2] + + cur_feat_size0 = torch.tensor(score_map0.shape[2:]) + cur_feat_size1 = torch.tensor(score_map1.shape[2:]) + + desc0 = desc0.permute(0, 2, 3, 1) + desc1 = desc1.permute(0, 2, 3, 1) + score_map0 = score_map0.permute(0, 2, 3, 1) + score_map1 = score_map1.permute(0, 2, 3, 1) + noise_desc0 = noise_desc0.permute(0, 2, 3, 1) + noise_desc1 = noise_desc1.permute(0, 2, 3, 1) + noise_score_map0 = noise_score_map0.permute(0, 2, 3, 1) + noise_score_map1 = noise_score_map1.permute(0, 2, 3, 1) + conf0 = conf0.permute(0, 2, 3, 1) + conf1 = conf1.permute(0, 2, 3, 1) + noise_conf0 = noise_conf0.permute(0, 2, 3, 1) + noise_conf1 = noise_conf1.permute(0, 2, 3, 1) + + r_K0 = getK(inputs['ori_img_size0'], cur_feat_size0, inputs['K0']).to(self.device) + r_K1 = getK(inputs['ori_img_size1'], cur_feat_size1, inputs['K1']).to(self.device) + + pos0 = _grid_positions( + cur_feat_size0[0], cur_feat_size0[1], img0.shape[0]).to(self.device) + + pos0_for_rel, pos1_for_rel, _ = getWarpNoValidate( + pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), + r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) + + pos0, pos1, _ = getWarp( + pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), + r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) + + reliab_loss_relative = self.reliability_relitive_loss(desc0, desc1, noise_desc0, noise_desc1, conf0, conf1, noise_conf0, noise_conf1, pos0_for_rel, pos1_for_rel, img0.shape[0], img0.shape[2], img0.shape[3]) + + det_structured_loss, det_accuracy = make_detector_loss( + pos0, pos1, desc0, desc1, + score_map0, score_map1, img0.shape[0], + self.config['network']['use_corr_n'], + self.config['network']['loss_type'], + self.config + ) + + det_structured_loss_noise, det_accuracy_noise = make_detector_loss( + pos0, pos1, noise_desc0, noise_desc1, + noise_score_map0, noise_score_map1, img0.shape[0], + self.config['network']['use_corr_n'], + self.config['network']['loss_type'], + self.config + ) + + indices0, scores0 = extract_kpts( + score_map0.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + indices1, scores1 = extract_kpts( + score_map1.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + + noise_score_loss0, mask0 = make_noise_score_map_loss(score_map0, noise_score_map0, indices0, img0.shape[0], thld=0.1) + noise_score_loss1, mask1 = make_noise_score_map_loss(score_map1, noise_score_map1, indices1, img1.shape[0], thld=0.1) + + total_loss = det_structured_loss + det_structured_loss_noise + total_loss += noise_score_loss0 / 2. * 1. + total_loss += noise_score_loss1 / 2. * 1. + total_loss += reliab_loss_relative[0] / 2. * 0.5 + total_loss += reliab_loss_relative[1] / 2. * 0.5 + + self.writer.add_scalar("acc/normal_acc", det_accuracy, self.cnt) + self.writer.add_scalar("acc/noise_acc", det_accuracy_noise, self.cnt) + self.writer.add_scalar("loss/total_loss", total_loss, self.cnt) + self.writer.add_scalar("loss/noise_score_loss", (noise_score_loss0 + noise_score_loss1) / 2., self.cnt) + self.writer.add_scalar("loss/det_loss_normal", det_structured_loss, self.cnt) + self.writer.add_scalar("loss/det_loss_noise", det_structured_loss_noise, self.cnt) + print('iter={},\tloss={:.4f},\tacc={:.4f},\t{:.4f}s/iter'.format(self.cnt, total_loss, det_accuracy, time.time()-t)) + # print(f'normal_loss: {det_structured_loss}, noise_loss: {det_structured_loss_noise}, reliab_loss: {reliab_loss_relative[0]}, {reliab_loss_relative[1]}') + + if det_structured_loss != 0: + total_loss.backward() + self.optimizer.step() + self.lr_scheduler.step() + + if self.cnt % 100 == 0: + noise_indices0, noise_scores0 = extract_kpts( + noise_score_map0.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + noise_indices1, noise_scores1 = extract_kpts( + noise_score_map1.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + if self.config['network']['input_type'] == 'raw': + kpt_img0 = self.showKeyPoints(img0_ori[0][..., :3] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0][..., :3] * 255., indices1[0]) + noise_kpt_img0 = self.showKeyPoints(noise_img0_ori[0][..., :3] * 255., noise_indices0[0]) + noise_kpt_img1 = self.showKeyPoints(noise_img1_ori[0][..., :3] * 255., noise_indices1[0]) + else: + kpt_img0 = self.showKeyPoints(img0_ori[0] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0] * 255., indices1[0]) + noise_kpt_img0 = self.showKeyPoints(noise_img0_ori[0] * 255., noise_indices0[0]) + noise_kpt_img1 = self.showKeyPoints(noise_img1_ori[0] * 255., noise_indices1[0]) + + self.writer.add_image('img0/kpts', kpt_img0, self.cnt, dataformats='HWC') + self.writer.add_image('img1/kpts', kpt_img1, self.cnt, dataformats='HWC') + self.writer.add_image('img0/noise_kpts', noise_kpt_img0, self.cnt, dataformats='HWC') + self.writer.add_image('img1/noise_kpts', noise_kpt_img1, self.cnt, dataformats='HWC') + self.writer.add_image('img0/score_map', score_map0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/score_map', score_map1[0], self.cnt, dataformats='HWC') + self.writer.add_image('img0/noise_score_map', noise_score_map0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/noise_score_map', noise_score_map1[0], self.cnt, dataformats='HWC') + self.writer.add_image('img0/kpt_mask', mask0.unsqueeze(2), self.cnt, dataformats='HWC') + self.writer.add_image('img1/kpt_mask', mask1.unsqueeze(2), self.cnt, dataformats='HWC') + self.writer.add_image('img0/conf', conf0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/conf', conf1[0], self.cnt, dataformats='HWC') + self.writer.add_image('img0/noise_conf', noise_conf0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/noise_conf', noise_conf1[0], self.cnt, dataformats='HWC') + + if self.cnt % 5000 == 0: + self.save(self.cnt) + + self.cnt += 1 + + + def showKeyPoints(self, img, indices): + key_points = cv2.KeyPoint_convert(indices.cpu().float().numpy()[:, ::-1]) + img = img.numpy().astype('uint8') + img = cv2.drawKeypoints(img, key_points, None, color=(0, 255, 0)) + return img + + + def preprocess(self, img, iter_idx): + if not self.config['network']['noise'] and 'raw' not in self.config['network']['input_type']: + return img + + raw = self.noise_maker.rgb2raw(img, batched=True) + + if self.config['network']['noise']: + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)) + + rgb = self.noise_maker.raw2rgb(raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return torch.tensor(rgb) + + raise NotImplementedError() + + + def preprocess_noise_pair(self, img, iter_idx): + assert self.config['network']['noise'] + + raw = self.noise_maker.rgb2raw(img, batched=True) + + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + noise_raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2packedRaw(noise_raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)) + + noise_rgb = self.noise_maker.raw2rgb(noise_raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return img, torch.tensor(noise_rgb) + + raise NotImplementedError() diff --git a/third_party/DarkFeat/trainer_single.py b/third_party/DarkFeat/trainer_single.py new file mode 100644 index 0000000000000000000000000000000000000000..65566e7e27cfd605eba000d308b6d3610f29e746 --- /dev/null +++ b/third_party/DarkFeat/trainer_single.py @@ -0,0 +1,294 @@ +import os +import cv2 +import time +import yaml +import torch +import datetime +from tensorboardX import SummaryWriter +import torchvision.transforms as tvf +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from nets.geom import getK, getWarp, _grid_positions, getWarpNoValidate +from nets.loss import make_detector_loss +from nets.score import extract_kpts +from nets.sampler import NghSampler2 +from nets.reliability_loss import ReliabilityLoss +from datasets.noise_simulator import NoiseSimulator +from nets.l2net import Quad_L2Net + + +class SingleTrainer: + def __init__(self, config, device, loader, job_name, start_cnt): + self.config = config + self.device = device + self.loader = loader + + # tensorboard writer construction + os.makedirs('./runs/', exist_ok=True) + if job_name != '': + self.log_dir = f'runs/{job_name}' + else: + self.log_dir = f'runs/{datetime.datetime.now().strftime("%m-%d-%H%M%S")}' + + self.writer = SummaryWriter(self.log_dir) + with open(f'{self.log_dir}/config.yaml', 'w') as f: + yaml.dump(config, f) + + if config['network']['input_type'] == 'gray' or config['network']['input_type'] == 'raw-gray': + self.model = eval(f'{config["network"]["model"]}(inchan=1)').to(device) + elif config['network']['input_type'] == 'rgb' or config['network']['input_type'] == 'raw-demosaic': + self.model = eval(f'{config["network"]["model"]}(inchan=3)').to(device) + elif config['network']['input_type'] == 'raw': + self.model = eval(f'{config["network"]["model"]}(inchan=4)').to(device) + else: + raise NotImplementedError() + + # noise maker + self.noise_maker = NoiseSimulator(device) + + # load model + self.cnt = 0 + if start_cnt != 0: + self.model.load_state_dict(torch.load(f'{self.log_dir}/model_{start_cnt:06d}.pth')) + self.cnt = start_cnt + 1 + + # sampler + sampler = NghSampler2(ngh=7, subq=-8, subd=1, pos_d=3, neg_d=5, border=16, + subd_neg=-8,maxpool_pos=True).to(device) + self.reliability_loss = ReliabilityLoss(sampler, base=0.3, nq=20).to(device) + # reliability map conv + self.model.clf = nn.Conv2d(128, 2, kernel_size=1).cuda() + + # optimizer and scheduler + if self.config['training']['optimizer'] == 'SGD': + self.optimizer = torch.optim.SGD( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + momentum=self.config['training']['momentum'], + weight_decay=self.config['training']['weight_decay'], + ) + elif self.config['training']['optimizer'] == 'Adam': + self.optimizer = torch.optim.Adam( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + weight_decay=self.config['training']['weight_decay'] + ) + else: + raise NotImplementedError() + + self.lr_scheduler = torch.optim.lr_scheduler.StepLR( + self.optimizer, + step_size=self.config['training']['lr_step'], + gamma=self.config['training']['lr_gamma'], + last_epoch=start_cnt + ) + for param_tensor in self.model.state_dict(): + print(param_tensor, "\t", self.model.state_dict()[param_tensor].size()) + + + def save(self, iter_num): + torch.save(self.model.state_dict(), f'{self.log_dir}/model_{iter_num:06d}.pth') + + def load(self, path): + self.model.load_state_dict(torch.load(path)) + + def train(self): + self.model.train() + + for epoch in range(2): + for batch_idx, inputs in enumerate(self.loader): + self.optimizer.zero_grad() + t = time.time() + + # preprocess and add noise + img0_ori, noise_img0_ori = self.preprocess_noise_pair(inputs['img0'], self.cnt) + img1_ori, noise_img1_ori = self.preprocess_noise_pair(inputs['img1'], self.cnt) + + img0 = img0_ori.permute(0, 3, 1, 2).float().to(self.device) + img1 = img1_ori.permute(0, 3, 1, 2).float().to(self.device) + + if self.config['network']['input_type'] == 'rgb': + # 3-channel rgb + RGB_mean = [0.485, 0.456, 0.406] + RGB_std = [0.229, 0.224, 0.225] + norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) + img0 = norm_RGB(img0) + img1 = norm_RGB(img1) + noise_img0 = norm_RGB(noise_img0) + noise_img1 = norm_RGB(noise_img1) + + elif self.config['network']['input_type'] == 'gray': + # 1-channel + img0 = torch.mean(img0, dim=1, keepdim=True) + img1 = torch.mean(img1, dim=1, keepdim=True) + noise_img0 = torch.mean(noise_img0, dim=1, keepdim=True) + noise_img1 = torch.mean(noise_img1, dim=1, keepdim=True) + norm_gray0 = tvf.Normalize(mean=img0.mean(), std=img0.std()) + norm_gray1 = tvf.Normalize(mean=img1.mean(), std=img1.std()) + img0 = norm_gray0(img0) + img1 = norm_gray1(img1) + noise_img0 = norm_gray0(noise_img0) + noise_img1 = norm_gray1(noise_img1) + + elif self.config['network']['input_type'] == 'raw': + # 4-channel + pass + + elif self.config['network']['input_type'] == 'raw-demosaic': + # 3-channel + pass + + else: + raise NotImplementedError() + + desc0, score_map0, _, _ = self.model(img0) + desc1, score_map1, _, _ = self.model(img1) + + cur_feat_size0 = torch.tensor(score_map0.shape[2:]) + cur_feat_size1 = torch.tensor(score_map1.shape[2:]) + + conf0 = F.softmax(self.model.clf(torch.abs(desc0)**2.0), dim=1)[:,1:2] + conf1 = F.softmax(self.model.clf(torch.abs(desc1)**2.0), dim=1)[:,1:2] + + desc0 = desc0.permute(0, 2, 3, 1) + desc1 = desc1.permute(0, 2, 3, 1) + score_map0 = score_map0.permute(0, 2, 3, 1) + score_map1 = score_map1.permute(0, 2, 3, 1) + conf0 = conf0.permute(0, 2, 3, 1) + conf1 = conf1.permute(0, 2, 3, 1) + + r_K0 = getK(inputs['ori_img_size0'], cur_feat_size0, inputs['K0']).to(self.device) + r_K1 = getK(inputs['ori_img_size1'], cur_feat_size1, inputs['K1']).to(self.device) + + pos0 = _grid_positions( + cur_feat_size0[0], cur_feat_size0[1], img0.shape[0]).to(self.device) + + pos0_for_rel, pos1_for_rel, _ = getWarpNoValidate( + pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), + r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) + + pos0, pos1, _ = getWarp( + pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), + r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) + + reliab_loss = self.reliability_loss(desc0, desc1, conf0, conf1, pos0_for_rel, pos1_for_rel, img0.shape[0], img0.shape[2], img0.shape[3]) + + det_structured_loss, det_accuracy = make_detector_loss( + pos0, pos1, desc0, desc1, + score_map0, score_map1, img0.shape[0], + self.config['network']['use_corr_n'], + self.config['network']['loss_type'], + self.config + ) + + total_loss = det_structured_loss + self.writer.add_scalar("loss/det_loss_normal", det_structured_loss, self.cnt) + + total_loss += reliab_loss + + self.writer.add_scalar("acc/normal_acc", det_accuracy, self.cnt) + self.writer.add_scalar("loss/total_loss", total_loss, self.cnt) + self.writer.add_scalar("loss/reliab_loss", reliab_loss, self.cnt) + print('iter={},\tloss={:.4f},\tacc={:.4f},\t{:.4f}s/iter'.format(self.cnt, total_loss, det_accuracy, time.time()-t)) + + if det_structured_loss != 0: + total_loss.backward() + self.optimizer.step() + self.lr_scheduler.step() + + if self.cnt % 100 == 0: + indices0, scores0 = extract_kpts( + score_map0.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + indices1, scores1 = extract_kpts( + score_map1.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + + if self.config['network']['input_type'] == 'raw': + kpt_img0 = self.showKeyPoints(img0_ori[0][..., :3] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0][..., :3] * 255., indices1[0]) + else: + kpt_img0 = self.showKeyPoints(img0_ori[0] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0] * 255., indices1[0]) + + self.writer.add_image('img0/kpts', kpt_img0, self.cnt, dataformats='HWC') + self.writer.add_image('img1/kpts', kpt_img1, self.cnt, dataformats='HWC') + self.writer.add_image('img0/score_map', score_map0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/score_map', score_map1[0], self.cnt, dataformats='HWC') + self.writer.add_image('img0/conf', conf0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/conf', conf1[0], self.cnt, dataformats='HWC') + + if self.cnt % 10000 == 0: + self.save(self.cnt) + + self.cnt += 1 + + + def showKeyPoints(self, img, indices): + key_points = cv2.KeyPoint_convert(indices.cpu().float().numpy()[:, ::-1]) + img = img.numpy().astype('uint8') + img = cv2.drawKeypoints(img, key_points, None, color=(0, 255, 0)) + return img + + + def preprocess(self, img, iter_idx): + if not self.config['network']['noise'] and 'raw' not in self.config['network']['input_type']: + return img + + raw = self.noise_maker.rgb2raw(img, batched=True) + + if self.config['network']['noise']: + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)) + + rgb = self.noise_maker.raw2rgb(raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return torch.tensor(rgb) + + raise NotImplementedError() + + + def preprocess_noise_pair(self, img, iter_idx): + assert self.config['network']['noise'] + + raw = self.noise_maker.rgb2raw(img, batched=True) + + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + noise_raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2packedRaw(noise_raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-gray': + factor = torch.tensor([0.299, 0.587, 0.114]).double() + return torch.matmul(torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), factor).unsqueeze(-1), \ + torch.matmul(torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)), factor).unsqueeze(-1) + + noise_rgb = self.noise_maker.raw2rgb(noise_raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return img, torch.tensor(noise_rgb) + + raise NotImplementedError() diff --git a/third_party/DarkFeat/trainer_single_norel.py b/third_party/DarkFeat/trainer_single_norel.py new file mode 100644 index 0000000000000000000000000000000000000000..a572e9c599adc30e5753e11e668d121cd378672a --- /dev/null +++ b/third_party/DarkFeat/trainer_single_norel.py @@ -0,0 +1,265 @@ +import os +import cv2 +import time +import yaml +import torch +import datetime +from tensorboardX import SummaryWriter +import torchvision.transforms as tvf +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + +from nets.l2net import Quad_L2Net +from nets.geom import getK, getWarp, _grid_positions +from nets.loss import make_detector_loss +from nets.score import extract_kpts +from datasets.noise_simulator import NoiseSimulator +from nets.l2net import Quad_L2Net + + +class SingleTrainerNoRel: + def __init__(self, config, device, loader, job_name, start_cnt): + self.config = config + self.device = device + self.loader = loader + + # tensorboard writer construction + os.makedirs('./runs/', exist_ok=True) + if job_name != '': + self.log_dir = f'runs/{job_name}' + else: + self.log_dir = f'runs/{datetime.datetime.now().strftime("%m-%d-%H%M%S")}' + + self.writer = SummaryWriter(self.log_dir) + with open(f'{self.log_dir}/config.yaml', 'w') as f: + yaml.dump(config, f) + + if config['network']['input_type'] == 'gray' or config['network']['input_type'] == 'raw-gray': + self.model = eval(f'{config["network"]["model"]}(inchan=1)').to(device) + elif config['network']['input_type'] == 'rgb' or config['network']['input_type'] == 'raw-demosaic': + self.model = eval(f'{config["network"]["model"]}(inchan=3)').to(device) + elif config['network']['input_type'] == 'raw': + self.model = eval(f'{config["network"]["model"]}(inchan=4)').to(device) + else: + raise NotImplementedError() + + # noise maker + self.noise_maker = NoiseSimulator(device) + + # load model + self.cnt = 0 + if start_cnt != 0: + self.model.load_state_dict(torch.load(f'{self.log_dir}/model_{start_cnt:06d}.pth')) + self.cnt = start_cnt + 1 + + # optimizer and scheduler + if self.config['training']['optimizer'] == 'SGD': + self.optimizer = torch.optim.SGD( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + momentum=self.config['training']['momentum'], + weight_decay=self.config['training']['weight_decay'], + ) + elif self.config['training']['optimizer'] == 'Adam': + self.optimizer = torch.optim.Adam( + [{'params': self.model.parameters(), 'initial_lr': self.config['training']['lr']}], + lr=self.config['training']['lr'], + weight_decay=self.config['training']['weight_decay'] + ) + else: + raise NotImplementedError() + + self.lr_scheduler = torch.optim.lr_scheduler.StepLR( + self.optimizer, + step_size=self.config['training']['lr_step'], + gamma=self.config['training']['lr_gamma'], + last_epoch=start_cnt + ) + for param_tensor in self.model.state_dict(): + print(param_tensor, "\t", self.model.state_dict()[param_tensor].size()) + + + def save(self, iter_num): + torch.save(self.model.state_dict(), f'{self.log_dir}/model_{iter_num:06d}.pth') + + def load(self, path): + self.model.load_state_dict(torch.load(path)) + + def train(self): + self.model.train() + + for epoch in range(2): + for batch_idx, inputs in enumerate(self.loader): + self.optimizer.zero_grad() + t = time.time() + + # preprocess and add noise + img0_ori, noise_img0_ori = self.preprocess_noise_pair(inputs['img0'], self.cnt) + img1_ori, noise_img1_ori = self.preprocess_noise_pair(inputs['img1'], self.cnt) + + img0 = img0_ori.permute(0, 3, 1, 2).float().to(self.device) + img1 = img1_ori.permute(0, 3, 1, 2).float().to(self.device) + + if self.config['network']['input_type'] == 'rgb': + # 3-channel rgb + RGB_mean = [0.485, 0.456, 0.406] + RGB_std = [0.229, 0.224, 0.225] + norm_RGB = tvf.Normalize(mean=RGB_mean, std=RGB_std) + img0 = norm_RGB(img0) + img1 = norm_RGB(img1) + noise_img0 = norm_RGB(noise_img0) + noise_img1 = norm_RGB(noise_img1) + + elif self.config['network']['input_type'] == 'gray': + # 1-channel + img0 = torch.mean(img0, dim=1, keepdim=True) + img1 = torch.mean(img1, dim=1, keepdim=True) + noise_img0 = torch.mean(noise_img0, dim=1, keepdim=True) + noise_img1 = torch.mean(noise_img1, dim=1, keepdim=True) + norm_gray0 = tvf.Normalize(mean=img0.mean(), std=img0.std()) + norm_gray1 = tvf.Normalize(mean=img1.mean(), std=img1.std()) + img0 = norm_gray0(img0) + img1 = norm_gray1(img1) + noise_img0 = norm_gray0(noise_img0) + noise_img1 = norm_gray1(noise_img1) + + elif self.config['network']['input_type'] == 'raw': + # 4-channel + pass + + elif self.config['network']['input_type'] == 'raw-demosaic': + # 3-channel + pass + + else: + raise NotImplementedError() + + desc0, score_map0, _, _ = self.model(img0) + desc1, score_map1, _, _ = self.model(img1) + + cur_feat_size0 = torch.tensor(score_map0.shape[2:]) + cur_feat_size1 = torch.tensor(score_map1.shape[2:]) + + desc0 = desc0.permute(0, 2, 3, 1) + desc1 = desc1.permute(0, 2, 3, 1) + score_map0 = score_map0.permute(0, 2, 3, 1) + score_map1 = score_map1.permute(0, 2, 3, 1) + + r_K0 = getK(inputs['ori_img_size0'], cur_feat_size0, inputs['K0']).to(self.device) + r_K1 = getK(inputs['ori_img_size1'], cur_feat_size1, inputs['K1']).to(self.device) + + pos0 = _grid_positions( + cur_feat_size0[0], cur_feat_size0[1], img0.shape[0]).to(self.device) + + pos0, pos1, _ = getWarp( + pos0, inputs['rel_pose'].to(self.device), inputs['depth0'].to(self.device), + r_K0, inputs['depth1'].to(self.device), r_K1, img0.shape[0]) + + det_structured_loss, det_accuracy = make_detector_loss( + pos0, pos1, desc0, desc1, + score_map0, score_map1, img0.shape[0], + self.config['network']['use_corr_n'], + self.config['network']['loss_type'], + self.config + ) + + total_loss = det_structured_loss + + self.writer.add_scalar("acc/normal_acc", det_accuracy, self.cnt) + self.writer.add_scalar("loss/total_loss", total_loss, self.cnt) + self.writer.add_scalar("loss/det_loss_normal", det_structured_loss, self.cnt) + print('iter={},\tloss={:.4f},\tacc={:.4f},\t{:.4f}s/iter'.format(self.cnt, total_loss, det_accuracy, time.time()-t)) + + if det_structured_loss != 0: + total_loss.backward() + self.optimizer.step() + self.lr_scheduler.step() + + if self.cnt % 100 == 0: + indices0, scores0 = extract_kpts( + score_map0.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + indices1, scores1 = extract_kpts( + score_map1.permute(0, 3, 1, 2), + k=self.config['network']['det']['kpt_n'], + score_thld=self.config['network']['det']['score_thld'], + nms_size=self.config['network']['det']['nms_size'], + eof_size=self.config['network']['det']['eof_size'], + edge_thld=self.config['network']['det']['edge_thld'] + ) + + if self.config['network']['input_type'] == 'raw': + kpt_img0 = self.showKeyPoints(img0_ori[0][..., :3] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0][..., :3] * 255., indices1[0]) + else: + kpt_img0 = self.showKeyPoints(img0_ori[0] * 255., indices0[0]) + kpt_img1 = self.showKeyPoints(img1_ori[0] * 255., indices1[0]) + + self.writer.add_image('img0/kpts', kpt_img0, self.cnt, dataformats='HWC') + self.writer.add_image('img1/kpts', kpt_img1, self.cnt, dataformats='HWC') + self.writer.add_image('img0/score_map', score_map0[0], self.cnt, dataformats='HWC') + self.writer.add_image('img1/score_map', score_map1[0], self.cnt, dataformats='HWC') + + if self.cnt % 10000 == 0: + self.save(self.cnt) + + self.cnt += 1 + + + def showKeyPoints(self, img, indices): + key_points = cv2.KeyPoint_convert(indices.cpu().float().numpy()[:, ::-1]) + img = img.numpy().astype('uint8') + img = cv2.drawKeypoints(img, key_points, None, color=(0, 255, 0)) + return img + + + def preprocess(self, img, iter_idx): + if not self.config['network']['noise'] and 'raw' not in self.config['network']['input_type']: + return img + + raw = self.noise_maker.rgb2raw(img, batched=True) + + if self.config['network']['noise']: + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)) + + rgb = self.noise_maker.raw2rgb(raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return torch.tensor(rgb) + + raise NotImplementedError() + + + def preprocess_noise_pair(self, img, iter_idx): + assert self.config['network']['noise'] + + raw = self.noise_maker.rgb2raw(img, batched=True) + + ratio_dec = min(self.config['network']['noise_maxstep'], iter_idx) / self.config['network']['noise_maxstep'] + noise_raw = self.noise_maker.raw2noisyRaw(raw, ratio_dec=ratio_dec, batched=True) + + if self.config['network']['input_type'] == 'raw': + return torch.tensor(self.noise_maker.raw2packedRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2packedRaw(noise_raw, batched=True)) + + if self.config['network']['input_type'] == 'raw-demosaic': + return torch.tensor(self.noise_maker.raw2demosaicRaw(raw, batched=True)), \ + torch.tensor(self.noise_maker.raw2demosaicRaw(noise_raw, batched=True)) + + noise_rgb = self.noise_maker.raw2rgb(noise_raw, batched=True) + if self.config['network']['input_type'] == 'rgb' or self.config['network']['input_type'] == 'gray': + return img, torch.tensor(noise_rgb) + + raise NotImplementedError() diff --git a/third_party/DarkFeat/utils/__init__.py b/third_party/DarkFeat/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/DarkFeat/utils/matching.py b/third_party/DarkFeat/utils/matching.py new file mode 100644 index 0000000000000000000000000000000000000000..ca091f418bb4dc4d278611e5126a930aa51e7f3f --- /dev/null +++ b/third_party/DarkFeat/utils/matching.py @@ -0,0 +1,128 @@ +import math +import numpy as np +import cv2 + +def extract_ORB_keypoints_and_descriptors(img): + # gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + detector = cv2.ORB_create(nfeatures=1000) + kp, desc = detector.detectAndCompute(img, None) + return kp, desc + +def match_descriptors_NG(kp1, desc1, kp2, desc2): + bf = cv2.BFMatcher() + try: + matches = bf.knnMatch(desc1, desc2,k=2) + except: + matches = [] + good_matches=[] + image1_kp = [] + image2_kp = [] + ratios = [] + try: + for (m1,m2) in matches: + if m1.distance < 0.8 * m2.distance: + good_matches.append(m1) + image2_kp.append(kp2[m1.trainIdx].pt) + image1_kp.append(kp1[m1.queryIdx].pt) + ratios.append(m1.distance / m2.distance) + except: + pass + image1_kp = np.array([image1_kp]) + image2_kp = np.array([image2_kp]) + ratios = np.array([ratios]) + ratios = np.expand_dims(ratios, 2) + return image1_kp, image2_kp, good_matches, ratios + +def match_descriptors(kp1, desc1, kp2, desc2, ORB): + if ORB: + bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) + try: + matches = bf.match(desc1,desc2) + matches = sorted(matches, key = lambda x:x.distance) + except: + matches = [] + good_matches=[] + image1_kp = [] + image2_kp = [] + count = 0 + try: + for m in matches: + count+=1 + if count < 1000: + good_matches.append(m) + image2_kp.append(kp2[m.trainIdx].pt) + image1_kp.append(kp1[m.queryIdx].pt) + except: + pass + else: + # Match the keypoints with the warped_keypoints with nearest neighbor search + bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True) + try: + matches = bf.match(desc1.transpose(1,0), desc2.transpose(1,0)) + matches = sorted(matches, key = lambda x:x.distance) + except: + matches = [] + good_matches=[] + image1_kp = [] + image2_kp = [] + try: + for m in matches: + good_matches.append(m) + image2_kp.append(kp2[m.trainIdx].pt) + image1_kp.append(kp1[m.queryIdx].pt) + except: + pass + + image1_kp = np.array([image1_kp]) + image2_kp = np.array([image2_kp]) + return image1_kp, image2_kp, good_matches + + +def compute_essential(matched_kp1, matched_kp2, K): + pts1 = cv2.undistortPoints(matched_kp1,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + pts2 = cv2.undistortPoints(matched_kp2,cameraMatrix=K, distCoeffs = (-0.117918271740560,0.075246403574314,0,0)) + K_1 = np.eye(3) + # Estimate the homography between the matches using RANSAC + ransac_model, ransac_inliers = cv2.findEssentialMat(pts1, pts2, K_1, method=cv2.FM_RANSAC, prob=0.999, threshold=0.001) + if ransac_inliers is None or ransac_model.shape != (3,3): + ransac_inliers = np.array([]) + ransac_model = None + return ransac_model, ransac_inliers, pts1, pts2 + + +def compute_error(R_GT,t_GT,E,pts1_norm, pts2_norm, inliers): + """Compute the angular error between two rotation matrices and two translation vectors. + Keyword arguments: + R -- 2D numpy array containing an estimated rotation + gt_R -- 2D numpy array containing the corresponding ground truth rotation + t -- 2D numpy array containing an estimated translation as column + gt_t -- 2D numpy array containing the corresponding ground truth translation + """ + + inliers = inliers.ravel() + R = np.eye(3) + t = np.zeros((3,1)) + sst = True + try: + cv2.recoverPose(E, pts1_norm, pts2_norm, np.eye(3), R, t, inliers) + except: + sst = False + # calculate angle between provided rotations + # + if sst: + dR = np.matmul(R, np.transpose(R_GT)) + dR = cv2.Rodrigues(dR)[0] + dR = np.linalg.norm(dR) * 180 / math.pi + + # calculate angle between provided translations + dT = float(np.dot(t_GT.T, t)) + dT /= float(np.linalg.norm(t_GT)) + + if dT > 1 or dT < -1: + print("Domain warning! dT:",dT) + dT = max(-1,min(1,dT)) + dT = math.acos(dT) * 180 / math.pi + dT = np.minimum(dT, 180 - dT) # ambiguity of E estimation + else: + dR,dT = 180.0, 180.0 + return dR, dT diff --git a/third_party/DarkFeat/utils/misc.py b/third_party/DarkFeat/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..1df6fdec97121486dbb94e0b32a2f66c85c48f7d --- /dev/null +++ b/third_party/DarkFeat/utils/misc.py @@ -0,0 +1,158 @@ +from pathlib import Path +import time +from collections import OrderedDict +import numpy as np +import cv2 +import rawpy +import torch +import colour_demosaicing + + +class AverageTimer: + """ Class to help manage printing simple timing of code execution. """ + + def __init__(self, smoothing=0.3, newline=False): + self.smoothing = smoothing + self.newline = newline + self.times = OrderedDict() + self.will_print = OrderedDict() + self.reset() + + def reset(self): + now = time.time() + self.start = now + self.last_time = now + for name in self.will_print: + self.will_print[name] = False + + def update(self, name='default'): + now = time.time() + dt = now - self.last_time + if name in self.times: + dt = self.smoothing * dt + (1 - self.smoothing) * self.times[name] + self.times[name] = dt + self.will_print[name] = True + self.last_time = now + + def print(self, text='Timer'): + total = 0. + print('[{}]'.format(text), end=' ') + for key in self.times: + val = self.times[key] + if self.will_print[key]: + print('%s=%.3f' % (key, val), end=' ') + total += val + print('total=%.3f sec {%.1f FPS}' % (total, 1./total), end=' ') + if self.newline: + print(flush=True) + else: + print(end='\r', flush=True) + self.reset() + + +class VideoStreamer: + def __init__(self, basedir, resize, image_glob): + self.listing = [] + self.resize = resize + self.i = 0 + if Path(basedir).is_dir(): + print('==> Processing image directory input: {}'.format(basedir)) + self.listing = list(Path(basedir).glob(image_glob[0])) + for j in range(1, len(image_glob)): + image_path = list(Path(basedir).glob(image_glob[j])) + self.listing = self.listing + image_path + self.listing.sort() + if len(self.listing) == 0: + raise IOError('No images found (maybe bad \'image_glob\' ?)') + self.max_length = len(self.listing) + else: + raise ValueError('VideoStreamer input \"{}\" not recognized.'.format(basedir)) + + def load_image(self, impath): + raw = rawpy.imread(str(impath)).raw_image_visible + raw = np.clip(raw.astype('float32') - 512, 0, 65535) + img = colour_demosaicing.demosaicing_CFA_Bayer_bilinear(raw, 'RGGB').astype('float32') + img = np.clip(img, 0, 16383) + + m = img.mean() + d = np.abs(img - img.mean()).mean() + img = (img - m + 2*d) / 4/d * 255 + image = np.clip(img, 0, 255) + + w_new, h_new = self.resize[0], self.resize[1] + + im = cv2.resize(image.astype('float32'), (w_new, h_new), interpolation=cv2.INTER_AREA) + return im + + def next_frame(self): + if self.i == self.max_length: + return (None, False) + image_file = str(self.listing[self.i]) + image = self.load_image(image_file) + self.i = self.i + 1 + return (image, True) + + +def frame2tensor(frame, device): + if len(frame.shape) == 2: + return torch.from_numpy(frame/255.).float()[None, None].to(device) + else: + return torch.from_numpy(frame/255.).float().permute(2, 0, 1)[None].to(device) + + +def make_matching_plot_fast(image0, image1, mkpts0, mkpts1, + color, text, path=None, margin=10, + opencv_display=False, opencv_title='', + small_text=[]): + H0, W0 = image0.shape[:2] + H1, W1 = image1.shape[:2] + H, W = max(H0, H1), W0 + W1 + margin + + out = 255*np.ones((H, W, 3), np.uint8) + out[:H0, :W0, :] = image0 + out[:H1, W0+margin:, :] = image1 + + # Scale factor for consistent visualization across scales. + sc = min(H / 640., 2.0) + + # Big text. + Ht = int(30 * sc) # text height + txt_color_fg = (255, 255, 255) + txt_color_bg = (0, 0, 0) + + for i, t in enumerate(text): + cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX, + 1.0*sc, txt_color_bg, 2, cv2.LINE_AA) + cv2.putText(out, t, (int(8*sc), Ht*(i+1)), cv2.FONT_HERSHEY_DUPLEX, + 1.0*sc, txt_color_fg, 1, cv2.LINE_AA) + + out_backup = out.copy() + + mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int) + color = (np.array(color[:, :3])*255).astype(int)[:, ::-1] + for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, color): + c = c.tolist() + cv2.line(out, (x0, y0), (x1 + margin + W0, y1), + color=c, thickness=1, lineType=cv2.LINE_AA) + # display line end-points as circles + cv2.circle(out, (x0, y0), 2, c, -1, lineType=cv2.LINE_AA) + cv2.circle(out, (x1 + margin + W0, y1), 2, c, -1, + lineType=cv2.LINE_AA) + + # Small text. + Ht = int(18 * sc) # text height + for i, t in enumerate(reversed(small_text)): + cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX, + 0.5*sc, txt_color_bg, 2, cv2.LINE_AA) + cv2.putText(out, t, (int(8*sc), int(H-Ht*(i+.6))), cv2.FONT_HERSHEY_DUPLEX, + 0.5*sc, txt_color_fg, 1, cv2.LINE_AA) + + if path is not None: + cv2.imwrite(str(path), out) + + if opencv_display: + cv2.imshow(opencv_title, out) + cv2.waitKey(1) + + return out / 2 + out_backup / 2 + diff --git a/third_party/DarkFeat/utils/nn.py b/third_party/DarkFeat/utils/nn.py new file mode 100644 index 0000000000000000000000000000000000000000..8a80631d6e12d848cceee3b636baf49deaa7647a --- /dev/null +++ b/third_party/DarkFeat/utils/nn.py @@ -0,0 +1,50 @@ +import torch +from torch import nn + + +class NN2(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, data): + desc1, desc2 = data['descriptors0'].cuda(), data['descriptors1'].cuda() + kpts1, kpts2 = data['keypoints0'].cuda(), data['keypoints1'].cuda() + + # torch.cuda.synchronize() + # t = time.time() + + if kpts1.shape[1] <= 1 or kpts2.shape[1] <= 1: # no keypoints + shape0, shape1 = kpts1.shape[:-1], kpts2.shape[:-1] + return { + 'matches0': kpts1.new_full(shape0, -1, dtype=torch.int), + 'matches1': kpts2.new_full(shape1, -1, dtype=torch.int), + 'matching_scores0': kpts1.new_zeros(shape0), + 'matching_scores1': kpts2.new_zeros(shape1), + } + + sim = torch.matmul(desc1.squeeze().T, desc2.squeeze()) + ids1 = torch.arange(0, sim.shape[0], device=desc1.device) + nn12 = torch.argmax(sim, dim=1) + + nn21 = torch.argmax(sim, dim=0) + mask = torch.eq(ids1, nn21[nn12]) + matches = torch.stack([torch.masked_select(ids1, mask), torch.masked_select(nn12, mask)]) + # matches = torch.stack([ids1, nn12]) + indices0 = torch.ones((1, desc1.shape[-1]), dtype=int) * -1 + mscores0 = torch.ones((1, desc1.shape[-1]), dtype=float) * -1 + + # torch.cuda.synchronize() + # print(time.time() - t) + + matches_0 = matches[0].cpu().int().numpy() + matches_1 = matches[1].cpu().int() + for i in range(matches.shape[-1]): + indices0[0, matches_0[i]] = matches_1[i].int() + mscores0[0, matches_0[i]] = sim[matches_0[i], matches_1[i]] + + return { + 'matches0': indices0, # use -1 for invalid match + 'matches1': indices0, # use -1 for invalid match + 'matching_scores0': mscores0, + 'matching_scores1': mscores0, + } diff --git a/third_party/DarkFeat/utils/nnmatching.py b/third_party/DarkFeat/utils/nnmatching.py new file mode 100644 index 0000000000000000000000000000000000000000..7be6f98c050fc2e416ef48e25ca0f293106c1082 --- /dev/null +++ b/third_party/DarkFeat/utils/nnmatching.py @@ -0,0 +1,41 @@ +import torch + +from .nn import NN2 +from darkfeat import DarkFeat + +class NNMatching(torch.nn.Module): + def __init__(self, model_path=''): + super().__init__() + self.nn = NN2().eval() + self.darkfeat = DarkFeat(model_path).eval() + + def forward(self, data): + """ Run DarkFeat and nearest neighborhood matching + Args: + data: dictionary with minimal keys: ['image0', 'image1'] + """ + pred = {} + + # Extract DarkFeat (keypoints, scores, descriptors) + if 'keypoints0' not in data: + pred0 = self.darkfeat({'image': data['image0']}) + # print({k+'0': v[0].shape for k, v in pred0.items()}) + pred = {**pred, **{k+'0': [v] for k, v in pred0.items()}} + if 'keypoints1' not in data: + pred1 = self.darkfeat({'image': data['image1']}) + pred = {**pred, **{k+'1': [v] for k, v in pred1.items()}} + + + # Batch all features + # We should either have i) one image per batch, or + # ii) the same number of local features for all images in the batch. + data = {**data, **pred} + + for k in data: + if isinstance(data[k], (list, tuple)): + data[k] = torch.stack(data[k]) + + # Perform the matching + pred = {**pred, **self.nn(data)} + + return pred diff --git a/third_party/GlueStick/.gitignore b/third_party/GlueStick/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c246e14ed9611a54be01334d4c2e734dca731e4b --- /dev/null +++ b/third_party/GlueStick/.gitignore @@ -0,0 +1,132 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ +.idea/* +*events.out.tfevents.* +/outputs \ No newline at end of file diff --git a/third_party/GlueStick/LICENSE b/third_party/GlueStick/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..866f33543245c285b350696b00be76bc278ca4a7 --- /dev/null +++ b/third_party/GlueStick/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Computer Vision and Geometry Lab + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/third_party/GlueStick/README.md b/third_party/GlueStick/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3145f02d47f4c60dd7d9a7d04e10f87b8f55dad7 --- /dev/null +++ b/third_party/GlueStick/README.md @@ -0,0 +1,48 @@ +# GlueStick +[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cvg/GlueStick/blob/main/gluestick_matching_demo.ipynb) [![arXiv](https://img.shields.io/badge/arXiv-2304.02008-b31b1b.svg?style=flat)](https://arxiv.org/abs/2304.02008) [![Project Page](https://badgen.net/badge/color/project/green?icon=awesome&label)](https://iago-suarez.com/gluestick) + +Joint deep matcher for points and lines 🖼️💥🖼️ + +![Visualization of point and line matches](resources/demo_seq1.gif) + +This repository contains the official implementation of +[GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://arxiv.org/abs/2304.02008). + +## Install 🛠️ + +To install the software in Ubuntu 22.04 follow these instructions: +```bash +sudo apt-get install build-essential cmake libopencv-dev libopencv-contrib-dev +git clone --recursive https://github.com/cvg/GlueStick.git +cd GlueStick +# Create and activate a virtual environment +python -m venv venv +source venv/bin/activate +pip install -r requirements.txt +pip install -e . +``` + +## Running GlueStick 🏃 +Download the weights of the model: +``` +wget https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar -P resources/weights +``` + +You can execute the inference with it with: +``` +python -m gluestick.run -img1 resources/img1.jpg -img2 resources/img2.jpg +``` + +## Training 🏋️ +We want to provide you with high-quality and flexible code for training. Stay tuned, we will release it soon! + +## Citation 📝 +If you use this code in your project, please consider citing the following paper: +```bibtex +@article{pautrat_suarez_2023_gluestick, + title={{GlueStick}: Robust Image Matching by Sticking Points and Lines Together}, + author={Pautrat, R{\'e}mi* and Su{\'a}rez, Iago* and Yu, Yifan and Pollefeys, Marc and Larsson, Viktor}, + journal={ArXiv}, + year={2023} +} +``` diff --git a/third_party/GlueStick/gluestick/__init__.py b/third_party/GlueStick/gluestick/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d3051821ecfb2e18f4b9b4dfb50f35064106eb57 --- /dev/null +++ b/third_party/GlueStick/gluestick/__init__.py @@ -0,0 +1,53 @@ +import collections.abc as collections +from pathlib import Path + +import torch + +GLUESTICK_ROOT = Path(__file__).parent.parent + + +def get_class(mod_name, base_path, BaseClass): + """Get the class object which inherits from BaseClass and is defined in + the module named mod_name, child of base_path. + """ + import inspect + mod_path = '{}.{}'.format(base_path, mod_name) + mod = __import__(mod_path, fromlist=['']) + classes = inspect.getmembers(mod, inspect.isclass) + # Filter classes defined in the module + classes = [c for c in classes if c[1].__module__ == mod_path] + # Filter classes inherited from BaseModel + classes = [c for c in classes if issubclass(c[1], BaseClass)] + assert len(classes) == 1, classes + return classes[0][1] + + +def get_model(name): + from .models.base_model import BaseModel + return get_class('models.' + name, __name__, BaseModel) + + +def numpy_image_to_torch(image): + """Normalize the image tensor and reorder the dimensions.""" + if image.ndim == 3: + image = image.transpose((2, 0, 1)) # HxWxC to CxHxW + elif image.ndim == 2: + image = image[None] # add channel axis + else: + raise ValueError(f'Not an image: {image.shape}') + return torch.from_numpy(image / 255.).float() + + +def map_tensor(input_, func): + if isinstance(input_, (str, bytes)): + return input_ + elif isinstance(input_, collections.Mapping): + return {k: map_tensor(sample, func) for k, sample in input_.items()} + elif isinstance(input_, collections.Sequence): + return [map_tensor(sample, func) for sample in input_] + else: + return func(input_) + + +def batch_to_np(batch): + return map_tensor(batch, lambda t: t.detach().cpu().numpy()[0]) diff --git a/third_party/GlueStick/gluestick/drawing.py b/third_party/GlueStick/gluestick/drawing.py new file mode 100644 index 0000000000000000000000000000000000000000..8e6d24b6bfedc93449142647410057d978d733ef --- /dev/null +++ b/third_party/GlueStick/gluestick/drawing.py @@ -0,0 +1,166 @@ +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + + +def plot_images(imgs, titles=None, cmaps='gray', dpi=100, pad=.5, + adaptive=True): + """Plot a set of images horizontally. + Args: + imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W). + titles: a list of strings, as titles for each image. + cmaps: colormaps for monochrome images. + adaptive: whether the figure size should fit the image aspect ratios. + """ + n = len(imgs) + if not isinstance(cmaps, (list, tuple)): + cmaps = [cmaps] * n + + if adaptive: + ratios = [i.shape[1] / i.shape[0] for i in imgs] # W / H + else: + ratios = [4 / 3] * n + figsize = [sum(ratios) * 4.5, 4.5] + fig, ax = plt.subplots( + 1, n, figsize=figsize, dpi=dpi, gridspec_kw={'width_ratios': ratios}) + if n == 1: + ax = [ax] + for i in range(n): + ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i])) + ax[i].get_yaxis().set_ticks([]) + ax[i].get_xaxis().set_ticks([]) + ax[i].set_axis_off() + for spine in ax[i].spines.values(): # remove frame + spine.set_visible(False) + if titles: + ax[i].set_title(titles[i]) + fig.tight_layout(pad=pad) + return ax + + +def plot_keypoints(kpts, colors='lime', ps=4, alpha=1): + """Plot keypoints for existing images. + Args: + kpts: list of ndarrays of size (N, 2). + colors: string, or list of list of tuples (one for each keypoints). + ps: size of the keypoints as float. + """ + if not isinstance(colors, list): + colors = [colors] * len(kpts) + axes = plt.gcf().axes + for a, k, c in zip(axes, kpts, colors): + a.scatter(k[:, 0], k[:, 1], c=c, s=ps, alpha=alpha, linewidths=0) + + +def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.): + """Plot matches for a pair of existing images. + Args: + kpts0, kpts1: corresponding keypoints of size (N, 2). + color: color of each match, string or RGB tuple. Random if not given. + lw: width of the lines. + ps: size of the end points (no endpoint if ps=0) + indices: indices of the images to draw the matches on. + a: alpha opacity of the match lines. + """ + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + ax0, ax1 = ax[indices[0]], ax[indices[1]] + fig.canvas.draw() + + assert len(kpts0) == len(kpts1) + if color is None: + color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist() + elif len(color) > 0 and not isinstance(color[0], (tuple, list)): + color = [color] * len(kpts0) + + if lw > 0: + # transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(ax0.transData.transform(kpts0)) + fkpts1 = transFigure.transform(ax1.transData.transform(kpts1)) + fig.lines += [matplotlib.lines.Line2D( + (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), + zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw, + alpha=a) + for i in range(len(kpts0))] + + # freeze the axes to prevent the transform to change + ax0.autoscale(enable=False) + ax1.autoscale(enable=False) + + if ps > 0: + ax0.scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps) + ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps) + + +def plot_lines(lines, line_colors='orange', point_colors='cyan', + ps=4, lw=2, alpha=1., indices=(0, 1)): + """ Plot lines and endpoints for existing images. + Args: + lines: list of ndarrays of size (N, 2, 2). + colors: string, or list of list of tuples (one for each keypoints). + ps: size of the keypoints as float pixels. + lw: line width as float pixels. + alpha: transparency of the points and lines. + indices: indices of the images to draw the matches on. + """ + if not isinstance(line_colors, list): + line_colors = [line_colors] * len(lines) + if not isinstance(point_colors, list): + point_colors = [point_colors] * len(lines) + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines and junctions + for a, l, lc, pc in zip(axes, lines, line_colors, point_colors): + for i in range(len(l)): + line = matplotlib.lines.Line2D((l[i, 0, 0], l[i, 1, 0]), + (l[i, 0, 1], l[i, 1, 1]), + zorder=1, c=lc, linewidth=lw, + alpha=alpha) + a.add_line(line) + pts = l.reshape(-1, 2) + a.scatter(pts[:, 0], pts[:, 1], + c=pc, s=ps, linewidths=0, zorder=2, alpha=alpha) + + +def plot_color_line_matches(lines, correct_matches=None, + lw=2, indices=(0, 1)): + """Plot line matches for existing images with multiple colors. + Args: + lines: list of ndarrays of size (N, 2, 2). + correct_matches: bool array of size (N,) indicating correct matches. + lw: line width as float pixels. + indices: indices of the images to draw the matches on. + """ + n_lines = len(lines[0]) + colors = sns.color_palette('husl', n_colors=n_lines) + np.random.shuffle(colors) + alphas = np.ones(n_lines) + # If correct_matches is not None, display wrong matches with a low alpha + if correct_matches is not None: + alphas[~np.array(correct_matches)] = 0.2 + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines + for a, l in zip(axes, lines): + # Transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + endpoint0 = transFigure.transform(a.transData.transform(l[:, 0])) + endpoint1 = transFigure.transform(a.transData.transform(l[:, 1])) + fig.lines += [matplotlib.lines.Line2D( + (endpoint0[i, 0], endpoint1[i, 0]), + (endpoint0[i, 1], endpoint1[i, 1]), + zorder=1, transform=fig.transFigure, c=colors[i], + alpha=alphas[i], linewidth=lw) for i in range(n_lines)] diff --git a/third_party/GlueStick/gluestick/geometry.py b/third_party/GlueStick/gluestick/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..97853c4807d319eb9ea0377db7385e9a72fb400b --- /dev/null +++ b/third_party/GlueStick/gluestick/geometry.py @@ -0,0 +1,175 @@ +from typing import Tuple + +import numpy as np +import torch + + +def to_homogeneous(points): + """Convert N-dimensional points to homogeneous coordinates. + Args: + points: torch.Tensor or numpy.ndarray with size (..., N). + Returns: + A torch.Tensor or numpy.ndarray with size (..., N+1). + """ + if isinstance(points, torch.Tensor): + pad = points.new_ones(points.shape[:-1] + (1,)) + return torch.cat([points, pad], dim=-1) + elif isinstance(points, np.ndarray): + pad = np.ones((points.shape[:-1] + (1,)), dtype=points.dtype) + return np.concatenate([points, pad], axis=-1) + else: + raise ValueError + + +def from_homogeneous(points, eps=0.): + """Remove the homogeneous dimension of N-dimensional points. + Args: + points: torch.Tensor or numpy.ndarray with size (..., N+1). + Returns: + A torch.Tensor or numpy ndarray with size (..., N). + """ + return points[..., :-1] / (points[..., -1:] + eps) + + +def skew_symmetric(v): + """Create a skew-symmetric matrix from a (batched) vector of size (..., 3). + """ + z = torch.zeros_like(v[..., 0]) + M = torch.stack([ + z, -v[..., 2], v[..., 1], + v[..., 2], z, -v[..., 0], + -v[..., 1], v[..., 0], z, + ], dim=-1).reshape(v.shape[:-1] + (3, 3)) + return M + + +def T_to_E(T): + """Convert batched poses (..., 4, 4) to batched essential matrices.""" + return skew_symmetric(T[..., :3, 3]) @ T[..., :3, :3] + + +def warp_points_torch(points, H, inverse=True): + """ + Warp a list of points with the INVERSE of the given homography. + The inverse is used to be coherent with tf.contrib.image.transform + Arguments: + points: batched list of N points, shape (B, N, 2). + homography: batched or not (shapes (B, 8) and (8,) respectively). + Returns: a Tensor of shape (B, N, 2) containing the new coordinates of the warped points. + """ + # H = np.expand_dims(homography, axis=0) if len(homography.shape) == 1 else homography + + # Get the points to the homogeneous format + points = to_homogeneous(points) + + # Apply the homography + out_shape = tuple(list(H.shape[:-1]) + [3, 3]) + H_mat = torch.cat([H, torch.ones_like(H[..., :1])], axis=-1).reshape(out_shape) + if inverse: + H_mat = torch.inverse(H_mat) + warped_points = torch.einsum('...nj,...ji->...ni', points, H_mat.transpose(-2, -1)) + + warped_points = from_homogeneous(warped_points, eps=1e-5) + + return warped_points + + +def seg_equation(segs): + # calculate list of start, end and midpoints points from both lists + start_points, end_points = to_homogeneous(segs[..., 0, :]), to_homogeneous(segs[..., 1, :]) + # Compute the line equations as ax + by + c = 0 , where x^2 + y^2 = 1 + lines = torch.cross(start_points, end_points, dim=-1) + lines_norm = (torch.sqrt(lines[..., 0] ** 2 + lines[..., 1] ** 2)[..., None]) + assert torch.all(lines_norm > 0), 'Error: trying to compute the equation of a line with a single point' + lines = lines / lines_norm + return lines + + +def is_inside_img(pts: torch.Tensor, img_shape: Tuple[int, int]): + h, w = img_shape + return (pts >= 0).all(dim=-1) & (pts[..., 0] < w) & (pts[..., 1] < h) & (~torch.isinf(pts).any(dim=-1)) + + +def shrink_segs_to_img(segs: torch.Tensor, img_shape: Tuple[int, int]) -> torch.Tensor: + """ + Shrink an array of segments to fit inside the image. + :param segs: The tensor of segments with shape (N, 2, 2) + :param img_shape: The image shape in format (H, W) + """ + EPS = 1e-4 + device = segs.device + w, h = img_shape[1], img_shape[0] + # Project the segments to the reference image + segs = segs.clone() + eqs = seg_equation(segs) + x0, y0 = torch.tensor([1., 0, 0.], device=device), torch.tensor([0., 1, 0], device=device) + x0 = x0.repeat(eqs.shape[:-1] + (1,)) + y0 = y0.repeat(eqs.shape[:-1] + (1,)) + pt_x0s = torch.cross(eqs, x0, dim=-1) + pt_x0s = pt_x0s[..., :-1] / pt_x0s[..., None, -1] + pt_x0s_valid = is_inside_img(pt_x0s, img_shape) + pt_y0s = torch.cross(eqs, y0, dim=-1) + pt_y0s = pt_y0s[..., :-1] / pt_y0s[..., None, -1] + pt_y0s_valid = is_inside_img(pt_y0s, img_shape) + + xW, yH = torch.tensor([1., 0, EPS - w], device=device), torch.tensor([0., 1, EPS - h], device=device) + xW = xW.repeat(eqs.shape[:-1] + (1,)) + yH = yH.repeat(eqs.shape[:-1] + (1,)) + pt_xWs = torch.cross(eqs, xW, dim=-1) + pt_xWs = pt_xWs[..., :-1] / pt_xWs[..., None, -1] + pt_xWs_valid = is_inside_img(pt_xWs, img_shape) + pt_yHs = torch.cross(eqs, yH, dim=-1) + pt_yHs = pt_yHs[..., :-1] / pt_yHs[..., None, -1] + pt_yHs_valid = is_inside_img(pt_yHs, img_shape) + + # If the X coordinate of the first endpoint is out + mask = (segs[..., 0, 0] < 0) & pt_x0s_valid + segs[mask, 0, :] = pt_x0s[mask] + mask = (segs[..., 0, 0] > (w - 1)) & pt_xWs_valid + segs[mask, 0, :] = pt_xWs[mask] + # If the X coordinate of the second endpoint is out + mask = (segs[..., 1, 0] < 0) & pt_x0s_valid + segs[mask, 1, :] = pt_x0s[mask] + mask = (segs[:, 1, 0] > (w - 1)) & pt_xWs_valid + segs[mask, 1, :] = pt_xWs[mask] + # If the Y coordinate of the first endpoint is out + mask = (segs[..., 0, 1] < 0) & pt_y0s_valid + segs[mask, 0, :] = pt_y0s[mask] + mask = (segs[..., 0, 1] > (h - 1)) & pt_yHs_valid + segs[mask, 0, :] = pt_yHs[mask] + # If the Y coordinate of the second endpoint is out + mask = (segs[..., 1, 1] < 0) & pt_y0s_valid + segs[mask, 1, :] = pt_y0s[mask] + mask = (segs[..., 1, 1] > (h - 1)) & pt_yHs_valid + segs[mask, 1, :] = pt_yHs[mask] + + assert torch.all(segs >= 0) and torch.all(segs[..., 0] < w) and torch.all(segs[..., 1] < h) + return segs + + +def warp_lines_torch(lines, H, inverse=True, dst_shape: Tuple[int, int] = None) -> Tuple[torch.Tensor, torch.Tensor]: + """ + :param lines: A tensor of shape (B, N, 2, 2) where B is the batch size, N the number of lines. + :param H: The homography used to convert the lines. batched or not (shapes (B, 8) and (8,) respectively). + :param inverse: Whether to apply H or the inverse of H + :param dst_shape:If provided, lines are trimmed to be inside the image + """ + device = lines.device + batch_size, n = lines.shape[:2] + lines = warp_points_torch(lines.reshape(batch_size, -1, 2), H, inverse).reshape(lines.shape) + + if dst_shape is None: + return lines, torch.ones(lines.shape[:-2], dtype=torch.bool, device=device) + + out_img = torch.any((lines < 0) | (lines >= torch.tensor(dst_shape[::-1], device=device)), -1) + valid = ~out_img.all(-1) + any_out_of_img = out_img.any(-1) + lines_to_trim = valid & any_out_of_img + + for b in range(batch_size): + lines_to_trim_mask_b = lines_to_trim[b] + lines_to_trim_b = lines[b][lines_to_trim_mask_b] + corrected_lines = shrink_segs_to_img(lines_to_trim_b, dst_shape) + lines[b][lines_to_trim_mask_b] = corrected_lines + + return lines, valid diff --git a/third_party/GlueStick/gluestick/models/__init__.py b/third_party/GlueStick/gluestick/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/GlueStick/gluestick/models/base_model.py b/third_party/GlueStick/gluestick/models/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..30ca991655a28ca88074b42312c33b360f655fab --- /dev/null +++ b/third_party/GlueStick/gluestick/models/base_model.py @@ -0,0 +1,126 @@ +""" +Base class for trainable models. +""" + +from abc import ABCMeta, abstractmethod +import omegaconf +from omegaconf import OmegaConf +from torch import nn +from copy import copy + + +class MetaModel(ABCMeta): + def __prepare__(name, bases, **kwds): + total_conf = OmegaConf.create() + for base in bases: + for key in ('base_default_conf', 'default_conf'): + update = getattr(base, key, {}) + if isinstance(update, dict): + update = OmegaConf.create(update) + total_conf = OmegaConf.merge(total_conf, update) + return dict(base_default_conf=total_conf) + + +class BaseModel(nn.Module, metaclass=MetaModel): + """ + What the child model is expect to declare: + default_conf: dictionary of the default configuration of the model. + It recursively updates the default_conf of all parent classes, and + it is updated by the user-provided configuration passed to __init__. + Configurations can be nested. + + required_data_keys: list of expected keys in the input data dictionary. + + strict_conf (optional): boolean. If false, BaseModel does not raise + an error when the user provides an unknown configuration entry. + + _init(self, conf): initialization method, where conf is the final + configuration object (also accessible with `self.conf`). Accessing + unknown configuration entries will raise an error. + + _forward(self, data): method that returns a dictionary of batched + prediction tensors based on a dictionary of batched input data tensors. + + loss(self, pred, data): method that returns a dictionary of losses, + computed from model predictions and input data. Each loss is a batch + of scalars, i.e. a torch.Tensor of shape (B,). + The total loss to be optimized has the key `'total'`. + + metrics(self, pred, data): method that returns a dictionary of metrics, + each as a batch of scalars. + """ + default_conf = { + 'name': None, + 'trainable': True, # if false: do not optimize this model parameters + 'freeze_batch_normalization': False, # use test-time statistics + } + required_data_keys = [] + strict_conf = True + + def __init__(self, conf): + """Perform some logic and call the _init method of the child model.""" + super().__init__() + default_conf = OmegaConf.merge( + self.base_default_conf, OmegaConf.create(self.default_conf)) + if self.strict_conf: + OmegaConf.set_struct(default_conf, True) + + # fixme: backward compatibility + if 'pad' in conf and 'pad' not in default_conf: # backward compat. + with omegaconf.read_write(conf): + with omegaconf.open_dict(conf): + conf['interpolation'] = {'pad': conf.pop('pad')} + + if isinstance(conf, dict): + conf = OmegaConf.create(conf) + self.conf = conf = OmegaConf.merge(default_conf, conf) + OmegaConf.set_readonly(conf, True) + OmegaConf.set_struct(conf, True) + self.required_data_keys = copy(self.required_data_keys) + self._init(conf) + + if not conf.trainable: + for p in self.parameters(): + p.requires_grad = False + + def train(self, mode=True): + super().train(mode) + + def freeze_bn(module): + if isinstance(module, nn.modules.batchnorm._BatchNorm): + module.eval() + if self.conf.freeze_batch_normalization: + self.apply(freeze_bn) + + return self + + def forward(self, data): + """Check the data and call the _forward method of the child model.""" + def recursive_key_check(expected, given): + for key in expected: + assert key in given, f'Missing key {key} in data' + if isinstance(expected, dict): + recursive_key_check(expected[key], given[key]) + + recursive_key_check(self.required_data_keys, data) + return self._forward(data) + + @abstractmethod + def _init(self, conf): + """To be implemented by the child class.""" + raise NotImplementedError + + @abstractmethod + def _forward(self, data): + """To be implemented by the child class.""" + raise NotImplementedError + + @abstractmethod + def loss(self, pred, data): + """To be implemented by the child class.""" + raise NotImplementedError + + @abstractmethod + def metrics(self, pred, data): + """To be implemented by the child class.""" + raise NotImplementedError diff --git a/third_party/GlueStick/gluestick/models/gluestick.py b/third_party/GlueStick/gluestick/models/gluestick.py new file mode 100644 index 0000000000000000000000000000000000000000..c2a6c477eebecc2c43feea007f99c2115aa7c216 --- /dev/null +++ b/third_party/GlueStick/gluestick/models/gluestick.py @@ -0,0 +1,558 @@ +import warnings +from copy import deepcopy + +warnings.filterwarnings("ignore", category=UserWarning) +import torch +import torch.utils.checkpoint +from torch import nn +from .base_model import BaseModel + +ETH_EPS = 1e-8 + + +class GlueStick(BaseModel): + default_conf = { + 'input_dim': 256, + 'descriptor_dim': 256, + 'bottleneck_dim': None, + 'weights': None, + 'keypoint_encoder': [32, 64, 128, 256], + 'GNN_layers': ['self', 'cross'] * 9, + 'num_line_iterations': 1, + 'line_attention': False, + 'filter_threshold': 0.2, + 'checkpointed': False, + 'skip_init': False, + 'inter_supervision': None, + 'loss': { + 'nll_weight': 1., + 'nll_balancing': 0.5, + 'reward_weight': 0., + 'bottleneck_l2_weight': 0., + 'dense_nll_weight': 0., + 'inter_supervision': [0.3, 0.6], + }, + } + required_data_keys = [ + 'keypoints0', 'keypoints1', + 'descriptors0', 'descriptors1', + 'keypoint_scores0', 'keypoint_scores1'] + + DEFAULT_LOSS_CONF = {'nll_weight': 1., 'nll_balancing': 0.5, 'reward_weight': 0., 'bottleneck_l2_weight': 0.} + + def _init(self, conf): + if conf.bottleneck_dim is not None: + self.bottleneck_down = nn.Conv1d( + conf.input_dim, conf.bottleneck_dim, kernel_size=1) + self.bottleneck_up = nn.Conv1d( + conf.bottleneck_dim, conf.input_dim, kernel_size=1) + nn.init.constant_(self.bottleneck_down.bias, 0.0) + nn.init.constant_(self.bottleneck_up.bias, 0.0) + + if conf.input_dim != conf.descriptor_dim: + self.input_proj = nn.Conv1d( + conf.input_dim, conf.descriptor_dim, kernel_size=1) + nn.init.constant_(self.input_proj.bias, 0.0) + + self.kenc = KeypointEncoder(conf.descriptor_dim, + conf.keypoint_encoder) + self.lenc = EndPtEncoder(conf.descriptor_dim, conf.keypoint_encoder) + self.gnn = AttentionalGNN(conf.descriptor_dim, conf.GNN_layers, + checkpointed=conf.checkpointed, + inter_supervision=conf.inter_supervision, + num_line_iterations=conf.num_line_iterations, + line_attention=conf.line_attention) + self.final_proj = nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim, + kernel_size=1) + nn.init.constant_(self.final_proj.bias, 0.0) + nn.init.orthogonal_(self.final_proj.weight, gain=1) + self.final_line_proj = nn.Conv1d( + conf.descriptor_dim, conf.descriptor_dim, kernel_size=1) + nn.init.constant_(self.final_line_proj.bias, 0.0) + nn.init.orthogonal_(self.final_line_proj.weight, gain=1) + if conf.inter_supervision is not None: + self.inter_line_proj = nn.ModuleList( + [nn.Conv1d(conf.descriptor_dim, conf.descriptor_dim, kernel_size=1) + for _ in conf.inter_supervision]) + self.layer2idx = {} + for i, l in enumerate(conf.inter_supervision): + nn.init.constant_(self.inter_line_proj[i].bias, 0.0) + nn.init.orthogonal_(self.inter_line_proj[i].weight, gain=1) + self.layer2idx[l] = i + + bin_score = torch.nn.Parameter(torch.tensor(1.)) + self.register_parameter('bin_score', bin_score) + line_bin_score = torch.nn.Parameter(torch.tensor(1.)) + self.register_parameter('line_bin_score', line_bin_score) + + if conf.weights: + assert isinstance(conf.weights, str) + state_dict = torch.load(conf.weights, map_location='cpu') + if 'model' in state_dict: + state_dict = {k.replace('matcher.', ''): v for k, v in state_dict['model'].items() if 'matcher.' in k} + state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()} + self.load_state_dict(state_dict) + + def _forward(self, data): + device = data['keypoints0'].device + b_size = len(data['keypoints0']) + image_size0 = (data['image_size0'] if 'image_size0' in data + else data['image0'].shape) + image_size1 = (data['image_size1'] if 'image_size1' in data + else data['image1'].shape) + + pred = {} + desc0, desc1 = data['descriptors0'], data['descriptors1'] + kpts0, kpts1 = data['keypoints0'], data['keypoints1'] + + n_kpts0, n_kpts1 = kpts0.shape[1], kpts1.shape[1] + n_lines0, n_lines1 = data['lines0'].shape[1], data['lines1'].shape[1] + if n_kpts0 == 0 or n_kpts1 == 0: + # No detected keypoints nor lines + pred['log_assignment'] = torch.zeros( + b_size, n_kpts0, n_kpts1, dtype=torch.float, device=device) + pred['matches0'] = torch.full( + (b_size, n_kpts0), -1, device=device, dtype=torch.int64) + pred['matches1'] = torch.full( + (b_size, n_kpts1), -1, device=device, dtype=torch.int64) + pred['match_scores0'] = torch.zeros( + (b_size, n_kpts0), device=device, dtype=torch.float32) + pred['match_scores1'] = torch.zeros( + (b_size, n_kpts1), device=device, dtype=torch.float32) + pred['line_log_assignment'] = torch.zeros(b_size, n_lines0, n_lines1, + dtype=torch.float, device=device) + pred['line_matches0'] = torch.full((b_size, n_lines0), -1, + device=device, dtype=torch.int64) + pred['line_matches1'] = torch.full((b_size, n_lines1), -1, + device=device, dtype=torch.int64) + pred['line_match_scores0'] = torch.zeros( + (b_size, n_lines0), device=device, dtype=torch.float32) + pred['line_match_scores1'] = torch.zeros( + (b_size, n_kpts1), device=device, dtype=torch.float32) + return pred + + lines0 = data['lines0'].flatten(1, 2) + lines1 = data['lines1'].flatten(1, 2) + lines_junc_idx0 = data['lines_junc_idx0'].flatten(1, 2) # [b_size, num_lines * 2] + lines_junc_idx1 = data['lines_junc_idx1'].flatten(1, 2) + + if self.conf.bottleneck_dim is not None: + pred['down_descriptors0'] = desc0 = self.bottleneck_down(desc0) + pred['down_descriptors1'] = desc1 = self.bottleneck_down(desc1) + desc0 = self.bottleneck_up(desc0) + desc1 = self.bottleneck_up(desc1) + desc0 = nn.functional.normalize(desc0, p=2, dim=1) + desc1 = nn.functional.normalize(desc1, p=2, dim=1) + pred['bottleneck_descriptors0'] = desc0 + pred['bottleneck_descriptors1'] = desc1 + if self.conf.loss.nll_weight == 0: + desc0 = desc0.detach() + desc1 = desc1.detach() + + if self.conf.input_dim != self.conf.descriptor_dim: + desc0 = self.input_proj(desc0) + desc1 = self.input_proj(desc1) + + kpts0 = normalize_keypoints(kpts0, image_size0) + kpts1 = normalize_keypoints(kpts1, image_size1) + + assert torch.all(kpts0 >= -1) and torch.all(kpts0 <= 1) + assert torch.all(kpts1 >= -1) and torch.all(kpts1 <= 1) + desc0 = desc0 + self.kenc(kpts0, data['keypoint_scores0']) + desc1 = desc1 + self.kenc(kpts1, data['keypoint_scores1']) + + if n_lines0 != 0 and n_lines1 != 0: + # Pre-compute the line encodings + lines0 = normalize_keypoints(lines0, image_size0).reshape( + b_size, n_lines0, 2, 2) + lines1 = normalize_keypoints(lines1, image_size1).reshape( + b_size, n_lines1, 2, 2) + line_enc0 = self.lenc(lines0, data['line_scores0']) + line_enc1 = self.lenc(lines1, data['line_scores1']) + else: + line_enc0 = torch.zeros( + b_size, self.conf.descriptor_dim, n_lines0 * 2, + dtype=torch.float, device=device) + line_enc1 = torch.zeros( + b_size, self.conf.descriptor_dim, n_lines1 * 2, + dtype=torch.float, device=device) + + desc0, desc1 = self.gnn(desc0, desc1, line_enc0, line_enc1, + lines_junc_idx0, lines_junc_idx1) + + # Match all points (KP and line junctions) + mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1) + + kp_scores = torch.einsum('bdn,bdm->bnm', mdesc0, mdesc1) + kp_scores = kp_scores / self.conf.descriptor_dim ** .5 + kp_scores = log_double_softmax(kp_scores, self.bin_score) + m0, m1, mscores0, mscores1 = self._get_matches(kp_scores) + pred['log_assignment'] = kp_scores + pred['matches0'] = m0 + pred['matches1'] = m1 + pred['match_scores0'] = mscores0 + pred['match_scores1'] = mscores1 + + # Match the lines + if n_lines0 > 0 and n_lines1 > 0: + (line_scores, m0_lines, m1_lines, mscores0_lines, + mscores1_lines, raw_line_scores) = self._get_line_matches( + desc0[:, :, :2 * n_lines0], desc1[:, :, :2 * n_lines1], + lines_junc_idx0, lines_junc_idx1, self.final_line_proj) + if self.conf.inter_supervision: + for l in self.conf.inter_supervision: + (line_scores_i, m0_lines_i, m1_lines_i, mscores0_lines_i, + mscores1_lines_i) = self._get_line_matches( + self.gnn.inter_layers[l][0][:, :, :2 * n_lines0], + self.gnn.inter_layers[l][1][:, :, :2 * n_lines1], + lines_junc_idx0, lines_junc_idx1, + self.inter_line_proj[self.layer2idx[l]]) + pred[f'line_{l}_log_assignment'] = line_scores_i + pred[f'line_{l}_matches0'] = m0_lines_i + pred[f'line_{l}_matches1'] = m1_lines_i + pred[f'line_{l}_match_scores0'] = mscores0_lines_i + pred[f'line_{l}_match_scores1'] = mscores1_lines_i + else: + line_scores = torch.zeros(b_size, n_lines0, n_lines1, + dtype=torch.float, device=device) + m0_lines = torch.full((b_size, n_lines0), -1, + device=device, dtype=torch.int64) + m1_lines = torch.full((b_size, n_lines1), -1, + device=device, dtype=torch.int64) + mscores0_lines = torch.zeros( + (b_size, n_lines0), device=device, dtype=torch.float32) + mscores1_lines = torch.zeros( + (b_size, n_lines1), device=device, dtype=torch.float32) + raw_line_scores = torch.zeros(b_size, n_lines0, n_lines1, + dtype=torch.float, device=device) + pred['line_log_assignment'] = line_scores + pred['line_matches0'] = m0_lines + pred['line_matches1'] = m1_lines + pred['line_match_scores0'] = mscores0_lines + pred['line_match_scores1'] = mscores1_lines + pred['raw_line_scores'] = raw_line_scores + + return pred + + def _get_matches(self, scores_mat): + max0 = scores_mat[:, :-1, :-1].max(2) + max1 = scores_mat[:, :-1, :-1].max(1) + m0, m1 = max0.indices, max1.indices + mutual0 = arange_like(m0, 1)[None] == m1.gather(1, m0) + mutual1 = arange_like(m1, 1)[None] == m0.gather(1, m1) + zero = scores_mat.new_tensor(0) + mscores0 = torch.where(mutual0, max0.values.exp(), zero) + mscores1 = torch.where(mutual1, mscores0.gather(1, m1), zero) + valid0 = mutual0 & (mscores0 > self.conf.filter_threshold) + valid1 = mutual1 & valid0.gather(1, m1) + m0 = torch.where(valid0, m0, m0.new_tensor(-1)) + m1 = torch.where(valid1, m1, m1.new_tensor(-1)) + return m0, m1, mscores0, mscores1 + + def _get_line_matches(self, ldesc0, ldesc1, lines_junc_idx0, + lines_junc_idx1, final_proj): + mldesc0 = final_proj(ldesc0) + mldesc1 = final_proj(ldesc1) + + line_scores = torch.einsum('bdn,bdm->bnm', mldesc0, mldesc1) + line_scores = line_scores / self.conf.descriptor_dim ** .5 + + # Get the line representation from the junction descriptors + n2_lines0 = lines_junc_idx0.shape[1] + n2_lines1 = lines_junc_idx1.shape[1] + line_scores = torch.gather( + line_scores, dim=2, + index=lines_junc_idx1[:, None, :].repeat(1, line_scores.shape[1], 1)) + line_scores = torch.gather( + line_scores, dim=1, + index=lines_junc_idx0[:, :, None].repeat(1, 1, n2_lines1)) + line_scores = line_scores.reshape((-1, n2_lines0 // 2, 2, + n2_lines1 // 2, 2)) + + # Match either in one direction or the other + raw_line_scores = 0.5 * torch.maximum( + line_scores[:, :, 0, :, 0] + line_scores[:, :, 1, :, 1], + line_scores[:, :, 0, :, 1] + line_scores[:, :, 1, :, 0]) + line_scores = log_double_softmax(raw_line_scores, self.line_bin_score) + m0_lines, m1_lines, mscores0_lines, mscores1_lines = self._get_matches( + line_scores) + return (line_scores, m0_lines, m1_lines, mscores0_lines, + mscores1_lines, raw_line_scores) + + def loss(self, pred, data): + raise NotImplementedError() + + def metrics(self, pred, data): + raise NotImplementedError() + + +def MLP(channels, do_bn=True): + n = len(channels) + layers = [] + for i in range(1, n): + layers.append( + nn.Conv1d(channels[i - 1], channels[i], kernel_size=1, bias=True)) + if i < (n - 1): + if do_bn: + layers.append(nn.BatchNorm1d(channels[i])) + layers.append(nn.ReLU()) + return nn.Sequential(*layers) + + +def normalize_keypoints(kpts, shape_or_size): + if isinstance(shape_or_size, (tuple, list)): + # it's a shape + h, w = shape_or_size[-2:] + size = kpts.new_tensor([[w, h]]) + else: + # it's a size + assert isinstance(shape_or_size, torch.Tensor) + size = shape_or_size.to(kpts) + c = size / 2 + f = size.max(1, keepdim=True).values * 0.7 # somehow we used 0.7 for SG + return (kpts - c[:, None, :]) / f[:, None, :] + + +class KeypointEncoder(nn.Module): + def __init__(self, feature_dim, layers): + super().__init__() + self.encoder = MLP([3] + list(layers) + [feature_dim], do_bn=True) + nn.init.constant_(self.encoder[-1].bias, 0.0) + + def forward(self, kpts, scores): + inputs = [kpts.transpose(1, 2), scores.unsqueeze(1)] + return self.encoder(torch.cat(inputs, dim=1)) + + +class EndPtEncoder(nn.Module): + def __init__(self, feature_dim, layers): + super().__init__() + self.encoder = MLP([5] + list(layers) + [feature_dim], do_bn=True) + nn.init.constant_(self.encoder[-1].bias, 0.0) + + def forward(self, endpoints, scores): + # endpoints should be [B, N, 2, 2] + # output is [B, feature_dim, N * 2] + b_size, n_pts, _, _ = endpoints.shape + assert tuple(endpoints.shape[-2:]) == (2, 2) + endpt_offset = (endpoints[:, :, 1] - endpoints[:, :, 0]).unsqueeze(2) + endpt_offset = torch.cat([endpt_offset, -endpt_offset], dim=2) + endpt_offset = endpt_offset.reshape(b_size, 2 * n_pts, 2).transpose(1, 2) + inputs = [endpoints.flatten(1, 2).transpose(1, 2), + endpt_offset, scores.repeat(1, 2).unsqueeze(1)] + return self.encoder(torch.cat(inputs, dim=1)) + + +@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32) +def attention(query, key, value): + dim = query.shape[1] + scores = torch.einsum('bdhn,bdhm->bhnm', query, key) / dim ** .5 + prob = torch.nn.functional.softmax(scores, dim=-1) + return torch.einsum('bhnm,bdhm->bdhn', prob, value), prob + + +class MultiHeadedAttention(nn.Module): + def __init__(self, h, d_model): + super().__init__() + assert d_model % h == 0 + self.dim = d_model // h + self.h = h + self.merge = nn.Conv1d(d_model, d_model, kernel_size=1) + self.proj = nn.ModuleList([deepcopy(self.merge) for _ in range(3)]) + # self.prob = [] + + def forward(self, query, key, value): + b = query.size(0) + query, key, value = [l(x).view(b, self.dim, self.h, -1) + for l, x in zip(self.proj, (query, key, value))] + x, prob = attention(query, key, value) + # self.prob.append(prob.mean(dim=1)) + return self.merge(x.contiguous().view(b, self.dim * self.h, -1)) + + +class AttentionalPropagation(nn.Module): + def __init__(self, num_dim, num_heads, skip_init=False): + super().__init__() + self.attn = MultiHeadedAttention(num_heads, num_dim) + self.mlp = MLP([num_dim * 2, num_dim * 2, num_dim], do_bn=True) + nn.init.constant_(self.mlp[-1].bias, 0.0) + if skip_init: + self.register_parameter('scaling', nn.Parameter(torch.tensor(0.))) + else: + self.scaling = 1. + + def forward(self, x, source): + message = self.attn(x, source, source) + return self.mlp(torch.cat([x, message], dim=1)) * self.scaling + + +class GNNLayer(nn.Module): + def __init__(self, feature_dim, layer_type, skip_init): + super().__init__() + assert layer_type in ['cross', 'self'] + self.type = layer_type + self.update = AttentionalPropagation(feature_dim, 4, skip_init) + + def forward(self, desc0, desc1): + if self.type == 'cross': + src0, src1 = desc1, desc0 + elif self.type == 'self': + src0, src1 = desc0, desc1 + else: + raise ValueError("Unknown layer type: " + self.type) + # self.update.attn.prob = [] + delta0, delta1 = self.update(desc0, src0), self.update(desc1, src1) + desc0, desc1 = (desc0 + delta0), (desc1 + delta1) + return desc0, desc1 + + +class LineLayer(nn.Module): + def __init__(self, feature_dim, line_attention=False): + super().__init__() + self.dim = feature_dim + self.mlp = MLP([self.dim * 3, self.dim * 2, self.dim], do_bn=True) + self.line_attention = line_attention + if line_attention: + self.proj_node = nn.Conv1d(self.dim, self.dim, kernel_size=1) + self.proj_neigh = nn.Conv1d(2 * self.dim, self.dim, kernel_size=1) + + def get_endpoint_update(self, ldesc, line_enc, lines_junc_idx): + # ldesc is [bs, D, n_junc], line_enc [bs, D, n_lines * 2] + # and lines_junc_idx [bs, n_lines * 2] + # Create one message per line endpoint + b_size = lines_junc_idx.shape[0] + line_desc = torch.gather( + ldesc, 2, lines_junc_idx[:, None].repeat(1, self.dim, 1)) + message = torch.cat([ + line_desc, + line_desc.reshape(b_size, self.dim, -1, 2).flip([-1]).flatten(2, 3).clone(), + line_enc], dim=1) + return self.mlp(message) # [b_size, D, n_lines * 2] + + def get_endpoint_attention(self, ldesc, line_enc, lines_junc_idx): + # ldesc is [bs, D, n_junc], line_enc [bs, D, n_lines * 2] + # and lines_junc_idx [bs, n_lines * 2] + b_size = lines_junc_idx.shape[0] + expanded_lines_junc_idx = lines_junc_idx[:, None].repeat(1, self.dim, 1) + + # Query: desc of the current node + query = self.proj_node(ldesc) # [b_size, D, n_junc] + query = torch.gather(query, 2, expanded_lines_junc_idx) + # query is [b_size, D, n_lines * 2] + + # Key: combination of neighboring desc and line encodings + line_desc = torch.gather(ldesc, 2, expanded_lines_junc_idx) + key = self.proj_neigh(torch.cat([ + line_desc.reshape(b_size, self.dim, -1, 2).flip([-1]).flatten(2, 3).clone(), + line_enc], dim=1)) # [b_size, D, n_lines * 2] + + # Compute the attention weights with a custom softmax per junction + prob = (query * key).sum(dim=1) / self.dim ** .5 # [b_size, n_lines * 2] + prob = torch.exp(prob - prob.max()) + denom = torch.zeros_like(ldesc[:, 0]).scatter_reduce_( + dim=1, index=lines_junc_idx, + src=prob, reduce='sum', include_self=False) # [b_size, n_junc] + denom = torch.gather(denom, 1, lines_junc_idx) # [b_size, n_lines * 2] + prob = prob / (denom + ETH_EPS) + return prob # [b_size, n_lines * 2] + + def forward(self, ldesc0, ldesc1, line_enc0, line_enc1, lines_junc_idx0, + lines_junc_idx1): + # Gather the endpoint updates + lupdate0 = self.get_endpoint_update(ldesc0, line_enc0, lines_junc_idx0) + lupdate1 = self.get_endpoint_update(ldesc1, line_enc1, lines_junc_idx1) + + update0, update1 = torch.zeros_like(ldesc0), torch.zeros_like(ldesc1) + dim = ldesc0.shape[1] + if self.line_attention: + # Compute an attention for each neighbor and do a weighted average + prob0 = self.get_endpoint_attention(ldesc0, line_enc0, + lines_junc_idx0) + lupdate0 = lupdate0 * prob0[:, None] + update0 = update0.scatter_reduce_( + dim=2, index=lines_junc_idx0[:, None].repeat(1, dim, 1), + src=lupdate0, reduce='sum', include_self=False) + prob1 = self.get_endpoint_attention(ldesc1, line_enc1, + lines_junc_idx1) + lupdate1 = lupdate1 * prob1[:, None] + update1 = update1.scatter_reduce_( + dim=2, index=lines_junc_idx1[:, None].repeat(1, dim, 1), + src=lupdate1, reduce='sum', include_self=False) + else: + # Average the updates for each junction (requires torch > 1.12) + update0 = update0.scatter_reduce_( + dim=2, index=lines_junc_idx0[:, None].repeat(1, dim, 1), + src=lupdate0, reduce='mean', include_self=False) + update1 = update1.scatter_reduce_( + dim=2, index=lines_junc_idx1[:, None].repeat(1, dim, 1), + src=lupdate1, reduce='mean', include_self=False) + + # Update + ldesc0 = ldesc0 + update0 + ldesc1 = ldesc1 + update1 + + return ldesc0, ldesc1 + + +class AttentionalGNN(nn.Module): + def __init__(self, feature_dim, layer_types, checkpointed=False, + skip=False, inter_supervision=None, num_line_iterations=1, + line_attention=False): + super().__init__() + self.checkpointed = checkpointed + self.inter_supervision = inter_supervision + self.num_line_iterations = num_line_iterations + self.inter_layers = {} + self.layers = nn.ModuleList([ + GNNLayer(feature_dim, layer_type, skip) + for layer_type in layer_types]) + self.line_layers = nn.ModuleList( + [LineLayer(feature_dim, line_attention) + for _ in range(len(layer_types) // 2)]) + + def forward(self, desc0, desc1, line_enc0, line_enc1, + lines_junc_idx0, lines_junc_idx1): + for i, layer in enumerate(self.layers): + if self.checkpointed: + desc0, desc1 = torch.utils.checkpoint.checkpoint( + layer, desc0, desc1, preserve_rng_state=False) + else: + desc0, desc1 = layer(desc0, desc1) + if (layer.type == 'self' and lines_junc_idx0.shape[1] > 0 + and lines_junc_idx1.shape[1] > 0): + # Add line self attention layers after every self layer + for _ in range(self.num_line_iterations): + if self.checkpointed: + desc0, desc1 = torch.utils.checkpoint.checkpoint( + self.line_layers[i // 2], desc0, desc1, line_enc0, + line_enc1, lines_junc_idx0, lines_junc_idx1, + preserve_rng_state=False) + else: + desc0, desc1 = self.line_layers[i // 2]( + desc0, desc1, line_enc0, line_enc1, + lines_junc_idx0, lines_junc_idx1) + + # Optionally store the line descriptor at intermediate layers + if (self.inter_supervision is not None + and (i // 2) in self.inter_supervision + and layer.type == 'cross'): + self.inter_layers[i // 2] = (desc0.clone(), desc1.clone()) + return desc0, desc1 + + +def log_double_softmax(scores, bin_score): + b, m, n = scores.shape + bin_ = bin_score[None, None, None] + scores0 = torch.cat([scores, bin_.expand(b, m, 1)], 2) + scores1 = torch.cat([scores, bin_.expand(b, 1, n)], 1) + scores0 = torch.nn.functional.log_softmax(scores0, 2) + scores1 = torch.nn.functional.log_softmax(scores1, 1) + scores = scores.new_full((b, m + 1, n + 1), 0) + scores[:, :m, :n] = (scores0[:, :, :n] + scores1[:, :m, :]) / 2 + scores[:, :-1, -1] = scores0[:, :, -1] + scores[:, -1, :-1] = scores1[:, -1, :] + return scores + + +def arange_like(x, dim): + return x.new_ones(x.shape[dim]).cumsum(0) - 1 # traceable in 1.1 diff --git a/third_party/GlueStick/gluestick/models/superpoint.py b/third_party/GlueStick/gluestick/models/superpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..0e0948a90cf5c858ddd14cc498231479fa10d6e3 --- /dev/null +++ b/third_party/GlueStick/gluestick/models/superpoint.py @@ -0,0 +1,224 @@ +""" +Inference model of SuperPoint, a feature detector and descriptor. + +Described in: + SuperPoint: Self-Supervised Interest Point Detection and Description, + Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich, CVPRW 2018. + +Original code: github.com/MagicLeapResearch/SuperPointPretrainedNetwork +""" + +import torch +from torch import nn + +from .. import GLUESTICK_ROOT +from ..models.base_model import BaseModel + + +def simple_nms(scores, radius): + """Perform non maximum suppression on the heatmap using max-pooling. + This method does not suppress contiguous points that have the same score. + Args: + scores: the score heatmap of size `(B, H, W)`. + size: an interger scalar, the radius of the NMS window. + """ + + def max_pool(x): + return torch.nn.functional.max_pool2d( + x, kernel_size=radius * 2 + 1, stride=1, padding=radius) + + zeros = torch.zeros_like(scores) + max_mask = scores == max_pool(scores) + for _ in range(2): + supp_mask = max_pool(max_mask.float()) > 0 + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == max_pool(supp_scores) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +def remove_borders(keypoints, scores, b, h, w): + mask_h = (keypoints[:, 0] >= b) & (keypoints[:, 0] < (h - b)) + mask_w = (keypoints[:, 1] >= b) & (keypoints[:, 1] < (w - b)) + mask = mask_h & mask_w + return keypoints[mask], scores[mask] + + +def top_k_keypoints(keypoints, scores, k): + if k >= len(keypoints): + return keypoints, scores + scores, indices = torch.topk(scores, k, dim=0, sorted=True) + return keypoints[indices], scores + + +def sample_descriptors(keypoints, descriptors, s): + b, c, h, w = descriptors.shape + keypoints = keypoints - s / 2 + 0.5 + keypoints /= torch.tensor([(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)], + ).to(keypoints)[None] + keypoints = keypoints * 2 - 1 # normalize to (-1, 1) + args = {'align_corners': True} if torch.__version__ >= '1.3' else {} + descriptors = torch.nn.functional.grid_sample( + descriptors, keypoints.view(b, 1, -1, 2), mode='bilinear', **args) + descriptors = torch.nn.functional.normalize( + descriptors.reshape(b, c, -1), p=2, dim=1) + return descriptors + + +class SuperPoint(BaseModel): + default_conf = { + 'has_detector': True, + 'has_descriptor': True, + 'descriptor_dim': 256, + + # Inference + 'return_all': False, + 'sparse_outputs': True, + 'nms_radius': 4, + 'detection_threshold': 0.005, + 'max_num_keypoints': -1, + 'force_num_keypoints': False, + 'remove_borders': 4, + } + required_data_keys = ['image'] + + def _init(self, conf): + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256 + + self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1) + self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1) + self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1) + self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1) + self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1) + self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1) + self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1) + self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1) + + if conf.has_detector: + self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0) + + if conf.has_descriptor: + self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convDb = nn.Conv2d( + c5, conf.descriptor_dim, kernel_size=1, stride=1, padding=0) + + path = GLUESTICK_ROOT / 'resources' / 'weights' / 'superpoint_v1.pth' + self.load_state_dict(torch.load(str(path)), strict=False) + + def _forward(self, data): + image = data['image'] + if image.shape[1] == 3: # RGB + scale = image.new_tensor([0.299, 0.587, 0.114]).view(1, 3, 1, 1) + image = (image * scale).sum(1, keepdim=True) + + # Shared Encoder + x = self.relu(self.conv1a(image)) + x = self.relu(self.conv1b(x)) + x = self.pool(x) + x = self.relu(self.conv2a(x)) + x = self.relu(self.conv2b(x)) + x = self.pool(x) + x = self.relu(self.conv3a(x)) + x = self.relu(self.conv3b(x)) + x = self.pool(x) + x = self.relu(self.conv4a(x)) + x = self.relu(self.conv4b(x)) + + pred = {} + if self.conf.has_detector and self.conf.max_num_keypoints != 0: + # Compute the dense keypoint scores + cPa = self.relu(self.convPa(x)) + scores = self.convPb(cPa) + scores = torch.nn.functional.softmax(scores, 1)[:, :-1] + b, c, h, w = scores.shape + scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8) + scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8) + pred['keypoint_scores'] = dense_scores = scores + if self.conf.has_descriptor: + # Compute the dense descriptors + cDa = self.relu(self.convDa(x)) + all_desc = self.convDb(cDa) + all_desc = torch.nn.functional.normalize(all_desc, p=2, dim=1) + pred['descriptors'] = all_desc + + if self.conf.max_num_keypoints == 0: # Predict dense descriptors only + b_size = len(image) + device = image.device + return { + 'keypoints': torch.empty(b_size, 0, 2, device=device), + 'keypoint_scores': torch.empty(b_size, 0, device=device), + 'descriptors': torch.empty(b_size, self.conf.descriptor_dim, 0, device=device), + 'all_descriptors': all_desc + } + + if self.conf.sparse_outputs: + assert self.conf.has_detector and self.conf.has_descriptor + + scores = simple_nms(scores, self.conf.nms_radius) + + # Extract keypoints + keypoints = [ + torch.nonzero(s > self.conf.detection_threshold) + for s in scores] + scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] + + # Discard keypoints near the image borders + keypoints, scores = list(zip(*[ + remove_borders(k, s, self.conf.remove_borders, h * 8, w * 8) + for k, s in zip(keypoints, scores)])) + + # Keep the k keypoints with highest score + if self.conf.max_num_keypoints > 0: + keypoints, scores = list(zip(*[ + top_k_keypoints(k, s, self.conf.max_num_keypoints) + for k, s in zip(keypoints, scores)])) + + # Convert (h, w) to (x, y) + keypoints = [torch.flip(k, [1]).float() for k in keypoints] + + if self.conf.force_num_keypoints: + _, _, h, w = data['image'].shape + assert self.conf.max_num_keypoints > 0 + scores = list(scores) + for i in range(len(keypoints)): + k, s = keypoints[i], scores[i] + missing = self.conf.max_num_keypoints - len(k) + if missing > 0: + new_k = torch.rand(missing, 2).to(k) + new_k = new_k * k.new_tensor([[w - 1, h - 1]]) + new_s = torch.zeros(missing).to(s) + keypoints[i] = torch.cat([k, new_k], 0) + scores[i] = torch.cat([s, new_s], 0) + + # Extract descriptors + desc = [sample_descriptors(k[None], d[None], 8)[0] + for k, d in zip(keypoints, all_desc)] + + if (len(keypoints) == 1) or self.conf.force_num_keypoints: + keypoints = torch.stack(keypoints, 0) + scores = torch.stack(scores, 0) + desc = torch.stack(desc, 0) + + pred = { + 'keypoints': keypoints, + 'keypoint_scores': scores, + 'descriptors': desc, + } + + if self.conf.return_all: + pred['all_descriptors'] = all_desc + pred['dense_score'] = dense_scores + else: + del all_desc + torch.cuda.empty_cache() + + return pred + + def loss(self, pred, data): + raise NotImplementedError + + def metrics(self, pred, data): + raise NotImplementedError diff --git a/third_party/GlueStick/gluestick/models/two_view_pipeline.py b/third_party/GlueStick/gluestick/models/two_view_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..e0e21c1f62e2bd4ad573ebb87ea5635742b5032e --- /dev/null +++ b/third_party/GlueStick/gluestick/models/two_view_pipeline.py @@ -0,0 +1,176 @@ +""" +A two-view sparse feature matching pipeline. + +This model contains sub-models for each step: + feature extraction, feature matching, outlier filtering, pose estimation. +Each step is optional, and the features or matches can be provided as input. +Default: SuperPoint with nearest neighbor matching. + +Convention for the matches: m0[i] is the index of the keypoint in image 1 +that corresponds to the keypoint i in image 0. m0[i] = -1 if i is unmatched. +""" + +import numpy as np +import torch + +from .. import get_model +from .base_model import BaseModel + + +def keep_quadrant_kp_subset(keypoints, scores, descs, h, w): + """Keep only keypoints in one of the four quadrant of the image.""" + h2, w2 = h // 2, w // 2 + w_x = np.random.choice([0, w2]) + w_y = np.random.choice([0, h2]) + valid_mask = ((keypoints[..., 0] >= w_x) + & (keypoints[..., 0] < w_x + w2) + & (keypoints[..., 1] >= w_y) + & (keypoints[..., 1] < w_y + h2)) + keypoints = keypoints[valid_mask][None] + scores = scores[valid_mask][None] + descs = descs.permute(0, 2, 1)[valid_mask].t()[None] + return keypoints, scores, descs + + +def keep_random_kp_subset(keypoints, scores, descs, num_selected): + """Keep a random subset of keypoints.""" + num_kp = keypoints.shape[1] + selected_kp = torch.randperm(num_kp)[:num_selected] + keypoints = keypoints[:, selected_kp] + scores = scores[:, selected_kp] + descs = descs[:, :, selected_kp] + return keypoints, scores, descs + + +def keep_best_kp_subset(keypoints, scores, descs, num_selected): + """Keep the top num_selected best keypoints.""" + sorted_indices = torch.sort(scores, dim=1)[1] + selected_kp = sorted_indices[:, -num_selected:] + keypoints = torch.gather(keypoints, 1, + selected_kp[:, :, None].repeat(1, 1, 2)) + scores = torch.gather(scores, 1, selected_kp) + descs = torch.gather(descs, 2, + selected_kp[:, None].repeat(1, descs.shape[1], 1)) + return keypoints, scores, descs + + +class TwoViewPipeline(BaseModel): + default_conf = { + 'extractor': { + 'name': 'superpoint', + 'trainable': False, + }, + 'use_lines': False, + 'use_points': True, + 'randomize_num_kp': False, + 'detector': {'name': None}, + 'descriptor': {'name': None}, + 'matcher': {'name': 'nearest_neighbor_matcher'}, + 'filter': {'name': None}, + 'solver': {'name': None}, + 'ground_truth': { + 'from_pose_depth': False, + 'from_homography': False, + 'th_positive': 3, + 'th_negative': 5, + 'reward_positive': 1, + 'reward_negative': -0.25, + 'is_likelihood_soft': True, + 'p_random_occluders': 0, + 'n_line_sampled_pts': 50, + 'line_perp_dist_th': 5, + 'overlap_th': 0.2, + 'min_visibility_th': 0.5 + }, + } + required_data_keys = ['image0', 'image1'] + strict_conf = False # need to pass new confs to children models + components = [ + 'extractor', 'detector', 'descriptor', 'matcher', 'filter', 'solver'] + + def _init(self, conf): + if conf.extractor.name: + self.extractor = get_model(conf.extractor.name)(conf.extractor) + else: + if self.conf.detector.name: + self.detector = get_model(conf.detector.name)(conf.detector) + else: + self.required_data_keys += ['keypoints0', 'keypoints1'] + if self.conf.descriptor.name: + self.descriptor = get_model(conf.descriptor.name)( + conf.descriptor) + else: + self.required_data_keys += ['descriptors0', 'descriptors1'] + + if conf.matcher.name: + self.matcher = get_model(conf.matcher.name)(conf.matcher) + else: + self.required_data_keys += ['matches0'] + + if conf.filter.name: + self.filter = get_model(conf.filter.name)(conf.filter) + + if conf.solver.name: + self.solver = get_model(conf.solver.name)(conf.solver) + + def _forward(self, data): + + def process_siamese(data, i): + data_i = {k[:-1]: v for k, v in data.items() if k[-1] == i} + if self.conf.extractor.name: + pred_i = self.extractor(data_i) + else: + pred_i = {} + if self.conf.detector.name: + pred_i = self.detector(data_i) + else: + for k in ['keypoints', 'keypoint_scores', 'descriptors', + 'lines', 'line_scores', 'line_descriptors', + 'valid_lines']: + if k in data_i: + pred_i[k] = data_i[k] + if self.conf.descriptor.name: + pred_i = { + **pred_i, **self.descriptor({**data_i, **pred_i})} + return pred_i + + pred0 = process_siamese(data, '0') + pred1 = process_siamese(data, '1') + + pred = {**{k + '0': v for k, v in pred0.items()}, + **{k + '1': v for k, v in pred1.items()}} + + if self.conf.matcher.name: + pred = {**pred, **self.matcher({**data, **pred})} + + if self.conf.filter.name: + pred = {**pred, **self.filter({**data, **pred})} + + if self.conf.solver.name: + pred = {**pred, **self.solver({**data, **pred})} + + return pred + + def loss(self, pred, data): + losses = {} + total = 0 + for k in self.components: + if self.conf[k].name: + try: + losses_ = getattr(self, k).loss(pred, {**pred, **data}) + except NotImplementedError: + continue + losses = {**losses, **losses_} + total = losses_['total'] + total + return {**losses, 'total': total} + + def metrics(self, pred, data): + metrics = {} + for k in self.components: + if self.conf[k].name: + try: + metrics_ = getattr(self, k).metrics(pred, {**pred, **data}) + except NotImplementedError: + continue + metrics = {**metrics, **metrics_} + return metrics diff --git a/third_party/GlueStick/gluestick/models/wireframe.py b/third_party/GlueStick/gluestick/models/wireframe.py new file mode 100644 index 0000000000000000000000000000000000000000..0e3dd9873c6fdb4edcb4c75a103673ee2cb3b3fa --- /dev/null +++ b/third_party/GlueStick/gluestick/models/wireframe.py @@ -0,0 +1,274 @@ +import numpy as np +import torch +from pytlsd import lsd +from sklearn.cluster import DBSCAN + +from .base_model import BaseModel +from .superpoint import SuperPoint, sample_descriptors +from ..geometry import warp_lines_torch + + +def lines_to_wireframe(lines, line_scores, all_descs, conf): + """ Given a set of lines, their score and dense descriptors, + merge close-by endpoints and compute a wireframe defined by + its junctions and connectivity. + Returns: + junctions: list of [num_junc, 2] tensors listing all wireframe junctions + junc_scores: list of [num_junc] tensors with the junction score + junc_descs: list of [dim, num_junc] tensors with the junction descriptors + connectivity: list of [num_junc, num_junc] bool arrays with True when 2 junctions are connected + new_lines: the new set of [b_size, num_lines, 2, 2] lines + lines_junc_idx: a [b_size, num_lines, 2] tensor with the indices of the junctions of each endpoint + num_true_junctions: a list of the number of valid junctions for each image in the batch, + i.e. before filling with random ones + """ + b_size, _, _, _ = all_descs.shape + device = lines.device + endpoints = lines.reshape(b_size, -1, 2) + + (junctions, junc_scores, junc_descs, connectivity, new_lines, + lines_junc_idx, num_true_junctions) = [], [], [], [], [], [], [] + for bs in range(b_size): + # Cluster the junctions that are close-by + db = DBSCAN(eps=conf.nms_radius, min_samples=1).fit( + endpoints[bs].cpu().numpy()) + clusters = db.labels_ + n_clusters = len(set(clusters)) + num_true_junctions.append(n_clusters) + + # Compute the average junction and score for each cluster + clusters = torch.tensor(clusters, dtype=torch.long, + device=device) + new_junc = torch.zeros(n_clusters, 2, dtype=torch.float, + device=device) + new_junc.scatter_reduce_(0, clusters[:, None].repeat(1, 2), + endpoints[bs], reduce='mean', + include_self=False) + junctions.append(new_junc) + new_scores = torch.zeros(n_clusters, dtype=torch.float, device=device) + new_scores.scatter_reduce_( + 0, clusters, torch.repeat_interleave(line_scores[bs], 2), + reduce='mean', include_self=False) + junc_scores.append(new_scores) + + # Compute the new lines + new_lines.append(junctions[-1][clusters].reshape(-1, 2, 2)) + lines_junc_idx.append(clusters.reshape(-1, 2)) + + # Compute the junction connectivity + junc_connect = torch.eye(n_clusters, dtype=torch.bool, + device=device) + pairs = clusters.reshape(-1, 2) # these pairs are connected by a line + junc_connect[pairs[:, 0], pairs[:, 1]] = True + junc_connect[pairs[:, 1], pairs[:, 0]] = True + connectivity.append(junc_connect) + + # Interpolate the new junction descriptors + junc_descs.append(sample_descriptors( + junctions[-1][None], all_descs[bs:(bs + 1)], 8)[0]) + + new_lines = torch.stack(new_lines, dim=0) + lines_junc_idx = torch.stack(lines_junc_idx, dim=0) + return (junctions, junc_scores, junc_descs, connectivity, + new_lines, lines_junc_idx, num_true_junctions) + + +class SPWireframeDescriptor(BaseModel): + default_conf = { + 'sp_params': { + 'has_detector': True, + 'has_descriptor': True, + 'descriptor_dim': 256, + 'trainable': False, + + # Inference + 'return_all': True, + 'sparse_outputs': True, + 'nms_radius': 4, + 'detection_threshold': 0.005, + 'max_num_keypoints': 1000, + 'force_num_keypoints': True, + 'remove_borders': 4, + }, + 'wireframe_params': { + 'merge_points': True, + 'merge_line_endpoints': True, + 'nms_radius': 3, + 'max_n_junctions': 500, + }, + 'max_n_lines': 250, + 'min_length': 15, + } + required_data_keys = ['image'] + + def _init(self, conf): + self.conf = conf + self.sp = SuperPoint(conf.sp_params) + + def detect_lsd_lines(self, x, max_n_lines=None): + if max_n_lines is None: + max_n_lines = self.conf.max_n_lines + lines, scores, valid_lines = [], [], [] + for b in range(len(x)): + # For each image on batch + img = (x[b].squeeze().cpu().numpy() * 255).astype(np.uint8) + if max_n_lines is None: + b_segs = lsd(img) + else: + for s in [0.3, 0.4, 0.5, 0.7, 0.8, 1.0]: + b_segs = lsd(img, scale=s) + if len(b_segs) >= max_n_lines: + break + + segs_length = np.linalg.norm(b_segs[:, 2:4] - b_segs[:, 0:2], axis=1) + # Remove short lines + b_segs = b_segs[segs_length >= self.conf.min_length] + segs_length = segs_length[segs_length >= self.conf.min_length] + b_scores = b_segs[:, -1] * np.sqrt(segs_length) + # Take the most relevant segments with + indices = np.argsort(-b_scores) + if max_n_lines is not None: + indices = indices[:max_n_lines] + lines.append(torch.from_numpy(b_segs[indices, :4].reshape(-1, 2, 2))) + scores.append(torch.from_numpy(b_scores[indices])) + valid_lines.append(torch.ones_like(scores[-1], dtype=torch.bool)) + + lines = torch.stack(lines).to(x) + scores = torch.stack(scores).to(x) + valid_lines = torch.stack(valid_lines).to(x.device) + return lines, scores, valid_lines + + def _forward(self, data): + b_size, _, h, w = data['image'].shape + device = data['image'].device + + if not self.conf.sp_params.force_num_keypoints: + assert b_size == 1, "Only batch size of 1 accepted for non padded inputs" + + # Line detection + if 'lines' not in data or 'line_scores' not in data: + if 'original_img' in data: + # Detect more lines, because when projecting them to the image most of them will be discarded + lines, line_scores, valid_lines = self.detect_lsd_lines( + data['original_img'], self.conf.max_n_lines * 3) + # Apply the same transformation that is applied in homography_adaptation + lines, valid_lines2 = warp_lines_torch(lines, data['H'], False, data['image'].shape[-2:]) + valid_lines = valid_lines & valid_lines2 + lines[~valid_lines] = -1 + line_scores[~valid_lines] = 0 + # Re-sort the line segments to pick the ones that are inside the image and have bigger score + sorted_scores, sorting_indices = torch.sort(line_scores, dim=-1, descending=True) + line_scores = sorted_scores[:, :self.conf.max_n_lines] + sorting_indices = sorting_indices[:, :self.conf.max_n_lines] + lines = torch.take_along_dim(lines, sorting_indices[..., None, None], 1) + valid_lines = torch.take_along_dim(valid_lines, sorting_indices, 1) + else: + lines, line_scores, valid_lines = self.detect_lsd_lines(data['image']) + + else: + lines, line_scores, valid_lines = data['lines'], data['line_scores'], data['valid_lines'] + if line_scores.shape[-1] != 0: + line_scores /= (line_scores.new_tensor(1e-8) + line_scores.max(dim=1).values[:, None]) + + # SuperPoint prediction + pred = self.sp(data) + + # Remove keypoints that are too close to line endpoints + if self.conf.wireframe_params.merge_points: + kp = pred['keypoints'] + line_endpts = lines.reshape(b_size, -1, 2) + dist_pt_lines = torch.norm( + kp[:, :, None] - line_endpts[:, None], dim=-1) + # For each keypoint, mark it as valid or to remove + pts_to_remove = torch.any( + dist_pt_lines < self.conf.sp_params.nms_radius, dim=2) + # Simply remove them (we assume batch_size = 1 here) + assert len(kp) == 1 + pred['keypoints'] = pred['keypoints'][0][~pts_to_remove[0]][None] + pred['keypoint_scores'] = pred['keypoint_scores'][0][~pts_to_remove[0]][None] + pred['descriptors'] = pred['descriptors'][0].T[~pts_to_remove[0]].T[None] + + # Connect the lines together to form a wireframe + orig_lines = lines.clone() + if self.conf.wireframe_params.merge_line_endpoints and len(lines[0]) > 0: + # Merge first close-by endpoints to connect lines + (line_points, line_pts_scores, line_descs, line_association, + lines, lines_junc_idx, num_true_junctions) = lines_to_wireframe( + lines, line_scores, pred['all_descriptors'], + conf=self.conf.wireframe_params) + + # Add the keypoints to the junctions and fill the rest with random keypoints + (all_points, all_scores, all_descs, + pl_associativity) = [], [], [], [] + for bs in range(b_size): + all_points.append(torch.cat( + [line_points[bs], pred['keypoints'][bs]], dim=0)) + all_scores.append(torch.cat( + [line_pts_scores[bs], pred['keypoint_scores'][bs]], dim=0)) + all_descs.append(torch.cat( + [line_descs[bs], pred['descriptors'][bs]], dim=1)) + + associativity = torch.eye(len(all_points[-1]), dtype=torch.bool, device=device) + associativity[:num_true_junctions[bs], :num_true_junctions[bs]] = \ + line_association[bs][:num_true_junctions[bs], :num_true_junctions[bs]] + pl_associativity.append(associativity) + + all_points = torch.stack(all_points, dim=0) + all_scores = torch.stack(all_scores, dim=0) + all_descs = torch.stack(all_descs, dim=0) + pl_associativity = torch.stack(pl_associativity, dim=0) + else: + # Lines are independent + all_points = torch.cat([lines.reshape(b_size, -1, 2), + pred['keypoints']], dim=1) + n_pts = all_points.shape[1] + num_lines = lines.shape[1] + num_true_junctions = [num_lines * 2] * b_size + all_scores = torch.cat([ + torch.repeat_interleave(line_scores, 2, dim=1), + pred['keypoint_scores']], dim=1) + pred['line_descriptors'] = self.endpoints_pooling( + lines, pred['all_descriptors'], (h, w)) + all_descs = torch.cat([ + pred['line_descriptors'].reshape(b_size, self.conf.sp_params.descriptor_dim, -1), + pred['descriptors']], dim=2) + pl_associativity = torch.eye( + n_pts, dtype=torch.bool, + device=device)[None].repeat(b_size, 1, 1) + lines_junc_idx = torch.arange( + num_lines * 2, device=device).reshape(1, -1, 2).repeat(b_size, 1, 1) + + del pred['all_descriptors'] # Remove dense descriptors to save memory + torch.cuda.empty_cache() + + return {'keypoints': all_points, + 'keypoint_scores': all_scores, + 'descriptors': all_descs, + 'pl_associativity': pl_associativity, + 'num_junctions': torch.tensor(num_true_junctions), + 'lines': lines, + 'orig_lines': orig_lines, + 'lines_junc_idx': lines_junc_idx, + 'line_scores': line_scores, + 'valid_lines': valid_lines} + + @staticmethod + def endpoints_pooling(segs, all_descriptors, img_shape): + assert segs.ndim == 4 and segs.shape[-2:] == (2, 2) + filter_shape = all_descriptors.shape[-2:] + scale_x = filter_shape[1] / img_shape[1] + scale_y = filter_shape[0] / img_shape[0] + + scaled_segs = torch.round(segs * torch.tensor([scale_x, scale_y]).to(segs)).long() + scaled_segs[..., 0] = torch.clip(scaled_segs[..., 0], 0, filter_shape[1] - 1) + scaled_segs[..., 1] = torch.clip(scaled_segs[..., 1], 0, filter_shape[0] - 1) + line_descriptors = [all_descriptors[None, b, ..., torch.squeeze(b_segs[..., 1]), torch.squeeze(b_segs[..., 0])] + for b, b_segs in enumerate(scaled_segs)] + line_descriptors = torch.cat(line_descriptors) + return line_descriptors # Shape (1, 256, 308, 2) + + def loss(self, pred, data): + raise NotImplementedError + + def metrics(self, pred, data): + return {} diff --git a/third_party/GlueStick/gluestick/run.py b/third_party/GlueStick/gluestick/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6baa88834f0b4dfde769ebe6c671e4ec49d4ed10 --- /dev/null +++ b/third_party/GlueStick/gluestick/run.py @@ -0,0 +1,107 @@ +import argparse +import os +from os.path import join + +import cv2 +import torch +from matplotlib import pyplot as plt + +from gluestick import batch_to_np, numpy_image_to_torch, GLUESTICK_ROOT +from .drawing import plot_images, plot_lines, plot_color_line_matches, plot_keypoints, plot_matches +from .models.two_view_pipeline import TwoViewPipeline + + +def main(): + # Parse input parameters + parser = argparse.ArgumentParser( + prog='GlueStick Demo', + description='Demo app to show the point and line matches obtained by GlueStick') + parser.add_argument('-img1', default=join('resources' + os.path.sep + 'img1.jpg')) + parser.add_argument('-img2', default=join('resources' + os.path.sep + 'img2.jpg')) + parser.add_argument('--max_pts', type=int, default=1000) + parser.add_argument('--max_lines', type=int, default=300) + parser.add_argument('--skip-imshow', default=False, action='store_true') + args = parser.parse_args() + + # Evaluation config + conf = { + 'name': 'two_view_pipeline', + 'use_lines': True, + 'extractor': { + 'name': 'wireframe', + 'sp_params': { + 'force_num_keypoints': False, + 'max_num_keypoints': args.max_pts, + }, + 'wireframe_params': { + 'merge_points': True, + 'merge_line_endpoints': True, + }, + 'max_n_lines': args.max_lines, + }, + 'matcher': { + 'name': 'gluestick', + 'weights': str(GLUESTICK_ROOT / 'resources' / 'weights' / 'checkpoint_GlueStick_MD.tar'), + 'trainable': False, + }, + 'ground_truth': { + 'from_pose_depth': False, + } + } + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + + pipeline_model = TwoViewPipeline(conf).to(device).eval() + + gray0 = cv2.imread(args.img1, 0) + gray1 = cv2.imread(args.img2, 0) + + torch_gray0, torch_gray1 = numpy_image_to_torch(gray0), numpy_image_to_torch(gray1) + torch_gray0, torch_gray1 = torch_gray0.to(device)[None], torch_gray1.to(device)[None] + x = {'image0': torch_gray0, 'image1': torch_gray1} + pred = pipeline_model(x) + + pred = batch_to_np(pred) + kp0, kp1 = pred["keypoints0"], pred["keypoints1"] + m0 = pred["matches0"] + + line_seg0, line_seg1 = pred["lines0"], pred["lines1"] + line_matches = pred["line_matches0"] + + valid_matches = m0 != -1 + match_indices = m0[valid_matches] + matched_kps0 = kp0[valid_matches] + matched_kps1 = kp1[match_indices] + + valid_matches = line_matches != -1 + match_indices = line_matches[valid_matches] + matched_lines0 = line_seg0[valid_matches] + matched_lines1 = line_seg1[match_indices] + + # Plot the matches + img0, img1 = cv2.cvtColor(gray0, cv2.COLOR_GRAY2BGR), cv2.cvtColor(gray1, cv2.COLOR_GRAY2BGR) + plot_images([img0, img1], ['Image 1 - detected lines', 'Image 2 - detected lines'], dpi=200, pad=2.0) + plot_lines([line_seg0, line_seg1], ps=4, lw=2) + plt.gcf().canvas.manager.set_window_title('Detected Lines') + plt.savefig('detected_lines.png') + + plot_images([img0, img1], ['Image 1 - detected points', 'Image 2 - detected points'], dpi=200, pad=2.0) + plot_keypoints([kp0, kp1], colors='c') + plt.gcf().canvas.manager.set_window_title('Detected Points') + plt.savefig('detected_points.png') + + plot_images([img0, img1], ['Image 1 - line matches', 'Image 2 - line matches'], dpi=200, pad=2.0) + plot_color_line_matches([matched_lines0, matched_lines1], lw=2) + plt.gcf().canvas.manager.set_window_title('Line Matches') + plt.savefig('line_matches.png') + + plot_images([img0, img1], ['Image 1 - point matches', 'Image 2 - point matches'], dpi=200, pad=2.0) + plot_matches(matched_kps0, matched_kps1, 'green', lw=1, ps=0) + plt.gcf().canvas.manager.set_window_title('Point Matches') + plt.savefig('detected_points.png') + if not args.skip_imshow: + plt.show() + + +if __name__ == '__main__': + main() diff --git a/third_party/GlueStick/gluestick_matching_demo.ipynb b/third_party/GlueStick/gluestick_matching_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6c02358f7e4d1b6a388c426eb19e3849e1c167b6 --- /dev/null +++ b/third_party/GlueStick/gluestick_matching_demo.ipynb @@ -0,0 +1,1132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "is_executing": true + }, + "id": "_BY4CluidpCw" + }, + "source": [ + "# GlueStick Image Matching Demo 🖼️💥🖼️\n", + "\n", + "\n", + "In this python notebook we show how to obtain point and line matches using GlueStick. GlueStick is a unified pipeline that uses a single GNN to process both types of features and predicts coherent point and line matched that help each other in the matching process.\n", + "\n", + "![](https://iago-suarez.com/gluestick/static/images/method_overview2.svg)\n", + "\n", + "If you use this python notebook please cite our work:\n", + "\n", + "> Pautrat, R.* and Suárez, I.* and Yu, Y. and Pollefeys, M. and Larsson, V. (2023). \"GlueStick: Robust Image Matching by Sticking Points and Lines Together\". ArXiv preprint." + ] + }, + { + "cell_type": "code", + "source": [ + "# Download the repository\n", + "!git clone https://github.com/cvg/GlueStick.git\n", + "%cd GlueStick" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CVBUeKT4dqBu", + "outputId": "db7a0e29-d4b5-4609-d65b-4e0f50a3a1e9" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'GlueStick'...\n", + "remote: Enumerating objects: 33, done.\u001b[K\n", + "remote: Counting objects: 100% (33/33), done.\u001b[K\n", + "remote: Compressing objects: 100% (31/31), done.\u001b[K\n", + "remote: Total 33 (delta 3), reused 24 (delta 0), pack-reused 0\u001b[K\n", + "Unpacking objects: 100% (33/33), 30.89 MiB | 8.17 MiB/s, done.\n", + "/content/GlueStick\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Install requirements\n", + "!pip install -r requirements.txt" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "v-5DsNXreiGn", + "outputId": "e0007926-eebc-4ab1-faf7-2fdce2bf08f0" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting git+https://github.com/iago-suarez/pytlsd.git@d518527 (from -r requirements.txt (line 12))\n", + " Cloning https://github.com/iago-suarez/pytlsd.git (to revision d518527) to /tmp/pip-req-build-u60qtkws\n", + " Running command git clone --filter=blob:none --quiet https://github.com/iago-suarez/pytlsd.git /tmp/pip-req-build-u60qtkws\n", + "\u001b[33m WARNING: Did not find branch or tag 'd518527', assuming revision or ref.\u001b[0m\u001b[33m\n", + "\u001b[0m Running command git checkout -q d518527\n", + " Resolved https://github.com/iago-suarez/pytlsd.git to commit d518527\n", + " Running command git submodule update --init --recursive -q\n", 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"Successfully built antlr4-python3-runtime pytlsd\n", + "Installing collected packages: pytlsd, antlr4-python3-runtime, omegaconf\n", + "Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.2.3 pytlsd-0.0.3\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "pydevd_plugins" + ] + } + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Download the pre-trained model" + ], + "metadata": { + "id": "7McenwHtfGLE" + } + }, + { + "cell_type": "code", + "source": [ + "!wget https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar -P resources/weights" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jmdiMOTFfBNN", + "outputId": "5041123a-52a0-453a-bebc-54bda11d4e51" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-04-04 23:22:22-- https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar\n", + "Resolving github.com (github.com)... 140.82.114.3\n", + "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/622867606/b6e2035f-ead7-4d20-93f4-855c5396a8b2?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20230404%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230404T232223Z&X-Amz-Expires=300&X-Amz-Signature=d7d6b2730dd0af6674207751cbb9655a3590b05d35fccf115fb9ae48905ff13a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=622867606&response-content-disposition=attachment%3B%20filename%3Dcheckpoint_GlueStick_MD.tar&response-content-type=application%2Foctet-stream [following]\n", + "--2023-04-04 23:22:23-- 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"\n", + "checkpoint_GlueStic 100%[===================>] 107.37M 57.6MB/s in 1.9s \n", + "\n", + "2023-04-04 23:22:25 (57.6 MB/s) - ‘resources/weights/checkpoint_GlueStick_MD.tar’ saved [112588421/112588421]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "udUG35j0dpC0" + }, + "outputs": [], + "source": [ + "from os.path import join\n", + "\n", + "import cv2\n", + "import torch\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from gluestick import batch_to_np, numpy_image_to_torch, GLUESTICK_ROOT\n", + "from gluestick.drawing import plot_images, plot_lines, plot_color_line_matches, plot_keypoints, plot_matches\n", + "from gluestick.models.two_view_pipeline import TwoViewPipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0GkvjCpvdpC2" + }, + "source": [ + "Define the configuration and model that we are going to use in our demo:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lxWDkN5XdpC2", + "outputId": "3026899d-721c-4163-c1d0-81aea226b40a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TwoViewPipeline(\n", + " (extractor): SPWireframeDescriptor(\n", + " (sp): SuperPoint(\n", + " (relu): ReLU(inplace=True)\n", + " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv1a): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv1b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv2a): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv2b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv3a): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv3b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv4a): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (conv4b): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (convPa): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (convPb): Conv2d(256, 65, kernel_size=(1, 1), stride=(1, 1))\n", + " (convDa): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (convDb): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " )\n", + " (matcher): GlueStick(\n", + " (kenc): KeypointEncoder(\n", + " (encoder): Sequential(\n", + " (0): Conv1d(3, 32, kernel_size=(1,), stride=(1,))\n", + " (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n", + " (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n", + " (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (8): ReLU()\n", + " (9): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n", + " (10): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (11): ReLU()\n", + " (12): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " (lenc): EndPtEncoder(\n", + " (encoder): Sequential(\n", + " (0): Conv1d(5, 32, kernel_size=(1,), stride=(1,))\n", + " (1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n", + " (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU()\n", + " (6): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n", + " (7): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (8): ReLU()\n", + " (9): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n", + " (10): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (11): ReLU()\n", + " (12): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " (gnn): AttentionalGNN(\n", + " (layers): ModuleList(\n", + " (0-17): 18 x GNNLayer(\n", + " (update): AttentionalPropagation(\n", + " (attn): MultiHeadedAttention(\n", + " (merge): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " (proj): ModuleList(\n", + " (0-2): 3 x Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " (mlp): Sequential(\n", + " (0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))\n", + " (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv1d(512, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (line_layers): ModuleList(\n", + " (0-8): 9 x LineLayer(\n", + " (mlp): Sequential(\n", + " (0): Conv1d(768, 512, kernel_size=(1,), stride=(1,))\n", + " (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " (3): Conv1d(512, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (final_proj): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " (final_line_proj): Conv1d(256, 256, kernel_size=(1,), stride=(1,))\n", + " )\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "MAX_N_POINTS, MAX_N_LINES = 1000, 300\n", + "\n", + "# Evaluation config\n", + "conf = {\n", + " 'name': 'two_view_pipeline',\n", + " 'use_lines': True,\n", + " 'extractor': {\n", + " 'name': 'wireframe',\n", + " 'sp_params': {\n", + " 'force_num_keypoints': False,\n", + " 'max_num_keypoints': MAX_N_POINTS,\n", + " },\n", + " 'wireframe_params': {\n", + " 'merge_points': True,\n", + " 'merge_line_endpoints': True,\n", + " },\n", + " 'max_n_lines': MAX_N_LINES,\n", + " },\n", + " 'matcher': {\n", + " 'name': 'gluestick',\n", + " 'weights': str(GLUESTICK_ROOT / 'resources' / 'weights' / 'checkpoint_GlueStick_MD.tar'),\n", + " 'trainable': False,\n", + " },\n", + " 'ground_truth': {\n", + " 'from_pose_depth': False,\n", + " }\n", + "}\n", + "\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "pipeline_model = TwoViewPipeline(conf).to(device).eval()\n", + "pipeline_model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 163 + }, + "id": "SYTcXss9dpC5", + "outputId": "78b7b6ec-d760-4025-a35c-cec0a4d7dd0c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Choose the FIRST image from your computer (Recommended resolution: 640x640)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving img1.jpg to img1 (1).jpg\n", + "Choose the SECOND image from your computer\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving img2.jpg to img2 (1).jpg\n" + ] + } + ], + "source": [ + "# Load input images \n", + "import sys\n", + "\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "if not IN_COLAB:\n", + " # We are running a notebook in Jupyter\n", + " img_path0 = join('resources', 'img1.jpg')\n", + " img_path1 = join('resources', 'img2.jpg')\n", + "else:\n", + " # We are running in Colab: Load from user's disk using Colab tools\n", + " from google.colab import files\n", + " print('Choose the FIRST image from your computer (Recommended resolution: 640x640)')\n", + " uploaded_files = files.upload()\n", + " img_path0 = list(uploaded_files.keys())[0]\n", + " print('Choose the SECOND image from your computer')\n", + " uploaded_files = files.upload()\n", + " img_path1 = list(uploaded_files.keys())[0]" + ] + }, + { + "cell_type": "code", + "source": [ + "img = cv2.imread(img_path0, cv2.IMREAD_GRAYSCALE)\n", + "\n", + "gray0 = cv2.imread(img_path0, 0)\n", + "gray1 = cv2.imread(img_path1, 0)\n", + "\n", + "# Plot them using matplotlib\n", + "f, axarr = plt.subplots(1, 2)\n", + "axarr[0].imshow(gray0, cmap='gray')\n", + "axarr[1].imshow(gray1, cmap='gray')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 386 + }, + "id": "h8cWFvtih1c-", + "outputId": "ea02228c-8227-4cdf-d1bd-b9ddbf3af11d" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 8 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "pKtIXPqxdpC6" + }, + "outputs": [], + "source": [ + "# Convert images into torch and execute GlueStick💥\n", + "\n", + "torch_gray0, torch_gray1 = numpy_image_to_torch(gray0), numpy_image_to_torch(gray1)\n", + "torch_gray0, torch_gray1 = torch_gray0.to(device)[None], torch_gray1.to(device)[None]\n", + "x = {'image0': torch_gray0, 'image1': torch_gray1}\n", + "pred = pipeline_model(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upsEtgjudpC6", + "outputId": "fbac085e-0d07-4436-d845-0da145045984" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Detected Keypoints: 1560 img1, 1558 img2\n", + "Detected Lines: 300 img1, 300 img2\n", + "\n", + "Matched 443 points and 108 lines\n" + ] + } + ], + "source": [ + "print(f\"Detected Keypoints: {pred['keypoints0'].shape[1]} img1, {pred['keypoints1'].shape[1]} img2\")\n", + "print(f\"Detected Lines: {pred['lines0'].shape[1]} img1, {pred['lines1'].shape[1]} img2\\n\")\n", + "print(f\"Matched {(pred['matches0'] >= 0).sum()} points and {(pred['line_matches0'] >= 0).sum()} lines\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eV29wX9MdpC7" + }, + "source": [ + "Show some matches" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Qy314eoPdpC7" + }, + "outputs": [], + "source": [ + "pred = batch_to_np(pred)\n", + "kp0, kp1 = pred[\"keypoints0\"], pred[\"keypoints1\"]\n", + "m0 = pred[\"matches0\"]\n", + "\n", + "line_seg0, line_seg1 = pred[\"lines0\"], pred[\"lines1\"]\n", + "line_matches = pred[\"line_matches0\"]\n", + "\n", + "valid_matches = m0 != -1\n", + "match_indices = m0[valid_matches]\n", + "matched_kps0 = kp0[valid_matches]\n", + "matched_kps1 = kp1[match_indices]\n", + "\n", + "valid_matches = line_matches != -1\n", + "match_indices = line_matches[valid_matches]\n", + "matched_lines0 = line_seg0[valid_matches]\n", + "matched_lines1 = line_seg1[match_indices]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ACHNz8PTdpC8" + }, + "source": [ + "## Detected Lines" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "GDsSua4RdpC8", + "outputId": "31ef0700-e884-439e-e026-fc9a16c8cbdc" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "img0, img1 = cv2.cvtColor(gray0, cv2.COLOR_GRAY2BGR), cv2.cvtColor(gray1, cv2.COLOR_GRAY2BGR)\n", + "plot_images([img0, img1], ['Image 1 - detected lines', 'Image 2 - detected lines'], pad=0.5)\n", + "plot_lines([line_seg0, line_seg1], ps=3, lw=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RCF0V9PrdpC9" + }, + "source": [ + "## Detected Points " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "aoqEF86ydpC9", + "outputId": "5b8b68f6-ca14-4f6f-939a-9e98a85c9768" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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7ZP0cWQQRRBBBBDb8e6wbPv7xj+Ov//qv8cd//Md43/veVzAvUd0Q1Q1R3RBBBBE4iE40XGK466678Od//ud429vehuuuuw779+/HfffdN2+1Gpj76aPPfOYzeOMb34j3ve99WLp0Ke677z4kEgkAP18Zjsfj+PKXv4w77rgDmzdvxtve9ja0tLTgwoULePjhh1FZWYnvfe97Xpq+973vhb/HPTs7i3379uEjH/kIAOCVr3wltm3bdkl4f9e73oXPfOYzePOb34xnn30WS5cuxde+9rUFP4W1GJ527NgBAPiTP/kTvOENb0BJSQle8YpXYPXq1fjIRz6C//f//X9x5swZvPrVr0ZFRQVOnz6N7373u/id3/kdfOhDH0I8HsfnP/95vOIVr8D27dvxtre9DUuXLsWRI0dw8OBB3H///fPGee9734uXvvSlKCoqwhve8Abs3LkT7373u/FXf/VXeO655/CSl7wEJSUlOH78OL71rW/hk5/8JF73utehvr4eH/rQh/BXf/VXuOuuu3DnnXdiz549+OEPf7jg98h9sGbNGtxwww343d/9XUxPT+Pee+/FkiVL8Ed/9Edhm49//OO44447cO211+Id73hH+DNVVVVV+PCHP/xCVBjC9u3bUVRUhI997GMYHh5GWVkZbrnlFnzjG9/A5z73ObzmNa/B6tWrMTo6ii996UuorKy8ZMVKBBFEEMGvE/x7qxu++93v4o/+6I+wdu1abNy4EV//+tfn3b/99tvDn018oRDVDVHdEEEEEfwKw7/dD1z8+wPrZ6rS6fSCtjt37gw2b9684PrKlSuDl7/85eH3qamp4IMf/GCwdOnSIJlMBtdff33w+OOPBzt37gx27tw5r++pU6eCl7/85UEymQzq6+uDD37wg8F3vvOdAEDwxBNPzGu7Z8+e4LWvfW2wZMmSoKysLFi5cmVw9913Bz/60Y/y8ul+ekv7+8pXvpK3/2Lg7NmzwStf+coglUoFdXV1wfve977wJ534J48K5ekv/uIvgpaWliAejy/42aXvfOc7wQ033BCk0+kgnU4HGzZsCN7znvcER48enYfjZz/7WXD77bcHFRUVQTqdDrZt2xZ8+tOfDu9nMpngD/7gD4L6+vogFost+MmqL37xi8GOHTuCZDIZVFRUBFu3bg3+6I/+KOjo6AjbZLPZ4J577gl1f/PNNwcHDhwIVq5cWfDPVH384x8P/uZv/iZYvnx5UFZWFtx4443B3r17F7R/6KGHguuvvz5IJpNBZWVl8IpXvCI4dOjQvDbWz1RJe3Wg2eeXvvSlYNWqVUFRUVGov927dwdvfOMbgxUrVgRlZWVBQ0NDcNdddwXPPPOMl78IIogggl8F+HWoG/7sz/7MrBm0XP5CIaoborohgggi+NWEWBAU8CaXCP7N4N5778UHPvABnD9/ft7PFUXwqw1nzpxBW1sbPv7xj+NDH/rQL5ucCCKIIIII/oNAVDf8ekJUN0QQQQT/3iF6R8MvESYnJ+d9n5qawhe+8AWsXbs2KhYiiCCCCCKIIIJ5ENUNEUQQQQQR/EeB6B0Nv0R47WtfixUrVmD79u0YHh7G17/+dRw5csT8SaQIIogggggiiODXF6K6IYIIIogggv8oEC00/BLhpS99Kb785S/jvvvuQzabxaZNm/DNb34T/+k//adfNmkRRBBBBBFEEMG/M4jqhggiiCCCCP6jQPSOhggiiCCCCCKIIIIIIogggggiiOCSQfSOhggiiCCCCCKIIIIIIogggggiiOCSQbTQEEEEEUQQQQQRRBBBBBFEEEEEEVwyKPgdDc899xwAIB6fW5uIxWLz7svrQRAgFouFbfh/oZ81fIyb2zDku+Y+yydICsXj+kna8gHTGgRB+BePx0MccrxcLucd3wduLNlOG8Ndk/zkcrkFtLtrmtw0HcixmFbZnnm02jJ9ltxZnxYdFs1aH25ryVbSacndos/Xju1Ga7uYcR0sRvZsJ7KNtA2NFg0vf5b+4HCxHVpjaf3lNcdrIfclvOY1r1kgsxcKN9988wI6nP9rMTAej4c8u1ih6VPKw8Vkd53B6d2KX9yfdSrHYF/k60wjf2a80s95bI6huVxuHq3SXmSskLSxXbEMtPjmdMA+rvHCfMbjcWSzWQBAUVERcrncPPlzfJE5zt0rKipSbZfHsuhl0OKUFXOsmGbJjnnTxrPsx8p1WuyzaGP5aXKx7mv5QNONxZfM45wvfXwwSNlo40p8DofTt7MVyYPDyb7k/EeL1a5NNpudx7+UnyaXbDY7z16Zf8vmLB/32Rz7v9QBt3G8uPbsy8ybJTfWpdOR1L3GD19zfWWs476anbl+mp4lH0wny4jzqMs1jFP6jMYL0yr1wSBlJXG4zywz1q1Fl8OpxVXZV8Onycz5kfMpSa/Wh+M48+fTB+Nm/3I2LPE63bu8wv4o/cKKNVqMcf4hdSJtRcPJNuPokzzLfiwnF1/Y/uVnzb40vvLlKkmDbO/44PjtcElfk/FFyoVrVTku60/qzfI5CVpuc3UEx28tRkt6Oeaxn8RiMRQXF8+rU1jO7vM3v/lNlWcJBS80aIlCXreucX+rmNCSCwtDG0dTvEU39/Hxp7Xn5KMlBet7vrE0mfE4bBAWndr4Gh+ynWZ43M8KKPn4lDTzfS25FSI3DZ+PJksWGj/aNdfPF7wcXYXIQ+OFbd/Hk+++NQ5ft+yLeWS/5OLCosn3XRvHkp1lr742FnBxoBU1WnFyqcEK8g44KclE6PhgetnfpIzleK6dVgxKHNqiIuuLkzOPJxOgLwZwUcR8azGfCzp3jXmRnzmGM92uDdumlLOjz0q+Gi1yDK2o0mK8No4sFrjIZv+Vk8N8vmX5NPPB4zCw/LkIl/dkf1/+9uVEKx9x/tJ8je3O4oELSM7BUl8OpP9KnflyiOZbPp40WuSYUp7aRpAmY6bLqjek3LX7RUVFC2Qv8UketIUCOYa2iMljMw2y+Jc6ZFvw5UzOFZYtMu9abtVsyl23JglaPOI6SS6aWIsGMkbwogbbr7RTS1aa7THvchzJp7MNiz+nMzf55DzEcnKTNSk3qR9f7NL8mm1L82kJnPO0WKPpjn1A8u78gf2UFy4ymcw8niRNFo1sw1xnyHEcbg2nphP2DeaNbYLp5ZgleZOTab4HLFzUYL9mmphPyYuUmcPNvLjcqvk5+zePJ2mWfGgy9OU+y745NxaSH7XYwosMLFtg/qaODxa90MABipnTGGbiNCPgaywsHy6tr09BljK165bha7Qw/9oYFjBeec2naEBfoc03hsWbhkPjWSsuZJJyeCxn0XgpVM5awMzHrwRfUC5E5j67kLbEyUS29V3XxvXhknxotuyDfLZl8SevWe199x3NFl4uGnx64TF4UqglD1mcabIrNIA+X7AKZOZPFgIyKWgTJNcemJ/ENLzumrbjKu9bcV3es05c+fyI7dl9jsfjC3burZ0QbWwuHtkf5WeOKVZM0/KKtsov7zM+ny3m8yFpo1yoyglkvnwreeWi1fmM5jvaRFzy5ORu0cE+qtm8lV+crKX9sz3IPlwvcF+teLdqBVkgF5KHWT6uL8tB2ot2mqwQGfI4bB+ON6krbRKl+RZPNBwOrdhnOcjx5YIa+5x1IoJ5sSZdWvzkopon4JYe5Nh8ckPqw1cXWXlfs1Uu6B2fPB7T5k4wMC/ad8kj+5bTJS9MWLq1fFfTG8dKtjeJQ/vMkyPmkf3H0azFSaZBi4Ochxi3hkdbMJA0aDFUi/GyvcMlT09YeVXyXUhssnKg1tbxJ3ljXWi+aMVYjTY5rk8HMv9oduj+rNOtTJsGHG99/zkXsizz5XSWpYyrEg+wcE6nja3FCs13tHvsZ5z/tUVZB76TORYsaqHBJ3Q2XC2IW0nUGofbWAnAAi3AMo0OtB11jX5O2GxYWjCzEpUmA80A87XlezLB+RKkLwBqvEg+NRq4SGEdMa2WPPL10/iQ3zlQSZw+Gnx8M1iBz0ezRY+VTCQebec1H018PV+SYZqscTQ6LboLoYtpcGDtart7WuJ37bnI1HZ1fLajJa1LCZofuf+yoHS08jE75kWLzdpCDctRS0buu9x50uxUs3ctGcr4IMeUOLTVd1/M1uIunxTQaNPk4LNDl3SlTbCsWQ7sq+6eVTz6fJ2vSzuQhTUD27DPt+V3WfRouzIa71JWml6krfpolNdY11zo+GxD062lQ9neyu3MN/eT4zC/Mg5pk06JyxrHyhOWrCza+b5V4Gp2JxcZfDbPMtF2jTXw+YS2O+7jT4vdrBO2LceDXOS06hCfjLUcItvkiycMbNOWH2t9Ldla9GltOV6x/Vj5RKtdpWx4Mi5xu/xn2SrrI1+ssU4H+vQnr0uaNZ7dd1488522kidttLFlG+ZPy6mMn+shWRPJ6xb47Ew+WqHFfDkej810WwuJLHuWnesL+Bd+WNeSnyAI5i3cyTFYFtrpB01mGp2WfC3dMZ383Y1t1UxsQ1aMYBt2sUm75+O7ECh4oUFOXN1/NgB5XSsE+Jrsy/gAW1C+wKzRqhmxHF9ro7W37ufDz/SzEXFbjT8tscs+Wj/LeS2cFnAg5X4+/iUOHj+fMfsSvebUmp1YiVTTiUar5cQa/cxbviSutbNk6aODeWY8lp41PPK69l/e1xIg08y8uDZasaeB0y0Xq5qNSDvl4sC14UDK/sOxgxPRpQJX0DLtbmxHKxciWgzNZwuyPSc/niCwX0t6gJ8nd75vJU753+cP0s58OuNEKvuyrhxNvjjCdFk+KnmxCiOHi21Vi5uSbm2hTMpb8qwVL/liKtsV08GLC1Ju2uSY9WjR5Isv8o990rcDLWnRFkJ88mB958tdcsIj7Z554KJSFsDWiSHNFrUJtQMt5rHPaf7K7bkd02ONYfm4xpvkneO39EVpa1LnUq9yccPiXYsLlm9a+Vb6KcswX56TbXz0aP2ZFykruWvNY0latQVMLV/Lz3zP6UI+isG4mHZfHGSQ/Lvv2mKEXJDT9OwWWyXv+XyA44SG1zoFp/m9lIk2nkYPy8Lx6v473jR58D0rR0lf1TYgnCykvVh5m8fgOpDl62zH9XfX3GaFjPWcD53OtYUZGRss/5N8FLLBqo3ny1VSF44nlgvbhWsv7/EpAcZvjSu/86KKlDc/RqTRoPVlWixbksDyzgfP+9EJvma1kYRbwmPD1a5pfVm5chytyHKgJVofnb7+hQTaQhKARo91csJKlhpOzXms8bRAKa+zPph37bNv/HyQTw8SrKPumt7kdQ6e+cbzBSRNN4UkH3Zs3/i+otpXEGm6yld4+uwKyH/KgldcrYLAx2u+YsbydSs2cPHmk/cLsd18oI3Lx7yZN/mnFSAsXy0RWLam8cqTXCseS/laeBxwkePGlrhdsSvvywKCfVaOqT1SIndAND+3Cg4rhslCX+pJk7dVCMvvEh/Tavmtlg802WsxIF++km0tmTHPTJOFj3XGC1dyHO00gLzOPHKssmSixQmNJ4lbix3ymo9n/iyvyevWqQB5zxpD6smyDV/esXKjJjtfPeNwykUGrY2G2zcBsopaLQ7JPsyP5j8aHm0MLUb46JHysehkGXEs0/oCWGB3Pr+WeUVOdvi/xrevTpC05FuIs2jgsbRj8JpOtVMy1gKFxg/HCgma/0kcHMtYD9a4klaJm+2R8Vs8yQUpHsfCzTRo9q/RKGOT9gJSXz6W+Y1xSho1O9N27bXxWBeWf/L7QSSfWszifOSLT3xdi8XyOvPMPq35SD5fk7y4a5oM3TUrr2i+aeWzQqDghQaNIQm+Y5YasVbg9QVPKwFobax+mtB8OC2lM6+yvy+YWPclPi0oae21MSRYOz6FGIkvMLI8tWAmadAcRjtGaxVCGk0+g7eCPvfRkpjGm68Y8dmAT04+Oiywkq7Wzgp2HNx50mfxZtFnycTyVebFxzvTx21dwuPAy4HV2jHWxvUVDJcSNJvio8nyvnY8kOWh7T7LxzBkO8YhdcfXNFlp7SVvbEOa/mVilcVIIbYj7/nsTtvN0kDKnI/IOjv02VEs9vPHW/LtlGi+qeUGJyO588X0Wnbs+nPhJP1eO/nHE1ZeJHI488UstgfNpmRbzealHbMsJH382Tf5lzS5/5YMLf+QPMgx+Ei8jLeyr8ShTcS1olz7LPFbC3FaTmWdMH0sI01mbkyLJi1vsqwcyBe5sZx9vi/5ZR7YxzRb0HyZ+ZP2rvHIwD7DL5Jz9sljyz9+fIN55Wsaz86X3c6tJi/3WdMjy4TtjenW+kkblz6hyVvGHJan/JUeh0+rHy0flqDJjvnTTjfkw6H5gc/XmHeWtRxH6lPDqdmulAn389HtQOYHy87ddXmSQbaVu+xazrNwa/KRfsX2y4tOWuyw4gbHNM0O2b94Q0iC9O18p22cTTtcnN/kuFoMkjKSpxlcGy1GWTYoZcbxSNus0hYvfLCoEw3a8R0mnI2AcWiMstIZt+YwllNatHMbbSfQFywYlw+snVwtUGu42anyBRQeg3FpBlqIAfJ9H73cTjNQSatP31YysOjTgoKPfnYoplujrRCHypesmRZtAiD58PFjyaHQ9rKtnFjlw89gyYlp4TGs5Mp4eXWZP2v0aXrmMVnfErRn/35R4MbmHWzLpuWquyYzS0bSJ2US0fTAsrV8gPEXEj8lPsm39l3zUwbur9mTo8MtuDC/LAvrJAfLhAsx952TvkWPVgxYhZL7LnOD1Kfsz2Nq+rV2T9hfZQzniXQhu/msDz4Wru3QyP6SP1eYFRUVzdtdcqD5hJXXuB/Lnu3QOtbLvFo+rNmbRovvsREtP7jPjj7WEeNi2Wg1lYWDZa5NRqzTSiwDTY7uM+clCTJ+yYVZSY+V/+RYsj7RcrHVR9oHy9LJ332XtiDlwD9HyLuP3I/xMX3aAhzL2ZenNRmxvWu2yDqWPDK/vvvaNUmL83upM01GVp6QdEsc2pFw3tW2bMOiWdqulLMVq7UFX9mP8bI+mCa2Fxm7NP/32ZSPR/lfttVOmlh+78NtxT3XxvJDHkezYS2uOD44/7M8nX1Y9YgcV4vlzDfL2edT2nUr3zPv7LeWT0g8nB+0xQVfrJXwvN7RwEkzXyKxkpxFrObQEo/GHNOkXdPo9CnMEqJ1XQsG8r9lSBrvvjEYp0+WfM2Stc/hrb4+Ofsc0dKH1UfTrSUjxsW7ixZei1e+lk/e7Nxaf/dZ06HPiS1cGm2WTWuQTz5MF4NvbPZXTo4aD3Jsh9dKqID9DCZg/yKCbKf5oBz7FwFyl8ayITepCoJg3guYLB070HaqLb5ckpEFj48m/i5144sZ7r/2yIpFl0x0Gm5OmJJ3pidfAcoyZbtjPrRdW+ZRG4tp5GIk36kF+V0rUriPw6nds4o4x4OVG9iuLDlbtMv78s+Sr6Q33y4z27UmH8mXa6NN1CQ9LCMpX27DhT0vXMr28qSM1Ku0f6aLdS11wHTI8SVImbHutFNvTKOUjbQZbfHJ0jmPq9mHloO0a1Ye0fh2/y08PHFnsHjjPKWdFtTo45Ns2mNT7EOctzQ7lKcpNLt3+DRZ+exL2r1bRNAWXqQ8HT5pkxotDngXXvN76d8cNyx5c3vZx/Hh+2lWic+3Y2z5CtPEcVrzQ9le40PToRaz89VC7r98B4bVjm3TJ68gCNSfiPThdmCdEJBy0+5reCVuLf/li2/aGJqcNF/0xQHpA1yDsQ65ZuDxLN+WbWTM104ESr60E0waf/ngeb2jQSZozcB8ziVxaG2t5A0sfAbLN4bEpRVUGj+MW8MJ5HcU/qzRyo7ic0JLnlow4bFkGy0ZW8FIo5/Bl7B8jqXh9jkvJ26tjQayrbZLI2Wv3dP41XTGdFp8yOuWvHz2XOg4WvDkwkG2Y540Gcn2vmRXiG9yzLBosiaZvoKTQQZRS4Ya//nwvlDQdMgTEf6JSt6xs+KK9EvXTivKLL1pJ5E0f3bX5T2rAJH6Zr1qR1UljxIn+7EW43k81qcW9zQ63T25IKQtakkeLJ93OuCTQ5ovsb4Zj/ZZ4uCJopw4aTttckytMNHGlLvb8oVfLBNpW1bc0OQt77McpF6lPWmnYXw8yjGseoQLQYlPA9azttjBxbNrL+Xl/jR9Ofp8C4QyVrA+OK5YMnGg7XaxriRNGg6GfLnRyiOW3VoxjeOGbKvlb/YhCXIRRYs9hdiVr9Zw7QuJa5YsLZlZ8d1nyxoNzBdPkBxOPqnkbJ5/PpTxyeuabjW+2bfZx/LVI3J895knYJre8uHnGorjqCZ3rY9mr9ZJPu2EmG9cPqVUqH1xW+u6dgKF22u42V99Y2n+5wNLxpZtSznyNWtSr/Go2ZBsL8GKESwTlg/bveYbbDfS3mU/LV5oj+EUAot6R4MUAAchXxDg61rg0oKGY8ZShPyvJR5fomJ+NNw+GUhcWoDRHJcNg6/x9cU4j5UI8tHOPPBOEvOnFSAWrZrhav8l3T48hfDPIO2CnW4xeJ4vDYvhxaLN0qnlWxKHtovGspfttXZaH/Yb/mzZuOUzvnZsL5ZfyDbaLp4lMx6fi45fJMhfn3D0aDFVfuZJhEWjZgOcEHmXwFfcOND6yHsaDfK70w/Tz7tdTB/jZ1mxzKwdDo0uJ1PtUQ4A6guwHPA9Xwy2Cg3XXu6wa3gsm5B4HH2aH0kaJH98XdqB9py51A/j1+jTbJqPKbs+1gkl1jHbq7UTzXzJ/ryApAHHPC7M+D8XY5K+bDZr+pv87uSiyVyTjUWz5euyneOHx5N60uKS9FMHvppLo4np0HSp2b+0BXlfxhVN55o8pY+xnVmPQVm+Jf9rR+WtSb6VP5keK68xfSxHjiksD4sWyZN1Uk7yJnUmceZ79EHybNHBi6UsL7kg7OOJ9aXVNeyLkk/m36pp+LsDPg3HfsX5gOUPYMG7D/jEkaZrnhQzz9KffRtz7r92lJ51yf2kXDT9W/LUTlQxbxzbNDnL//LkihxP4ncLwQ58JzKlbLV7sr7R6nLO2a6tuyZ/8lzqTfNHd1/jTYvHcsHbh0Pi8dVXDM/7Vyc4Gco2/Jn7+fpYgcH9txjTcEp8wMJAnw984zGNcoxCAo4WWOVYbIi+Io5p1RxZ61PIUSOJh3XPY1uJnfFa+rSSgYbH4snqX2gSsHjnYMrttXaA/7kmK8Hm41sL4hb4go+vv+W/DqdMVpq+fbKX1yy78fm/ZueSRsbjS375+C40kC4WNL6ZTp9sNHvT+lr8WgsCHC9lX6aTEzd/tvwBsH8BgsfhIiufXVkx0GeDGg4th/CE2PGhFf/57EajS/Js+YIEJxO5S8i+yLrTFpI1P5a2ZB3jZD61ExvynmYnzKOcTFi2qJ30YJlI3q24IdvI7/KapFsuSvDpAZ+upFzli/5keysXSBzsC9rukkYHL9xJnNpCC9Mh/5gf3z03tgM+os7xyjodyfxw7HF9JU2ZTEZdONH8SvqFlKW793x1zbRaC5IaXVY8tmTN9wrRmZzEWCdnJA3aLjr7l6Sba1jWL9s8L3bxArnEwTxx3ODJmG/jgfXoyxWF6oXjrEYr49D6WbbGetBOf/o2BNj33KOZciGI7ZFt29GbzWbn2Tbj0GIdx1cr9sm+Vh5kGXG85nuMR5v0y1Nmlj2wHjUaff5u1TISn7XBIU8Tcn/NRiXNzkdc3cAntTSw9FLofPp5LTTwAIU4g7ymtWOjLASvLyBYPLDBaU7AY3Cw0JJDIeMzWP19RapGv9beR6PveTprHHnf115+ZvlafBWqd21MDgRaovf1t+7J7z4n5oTA/PiCrEanVWTKdr6Aa8lyMTZr0Vyon2u+YtEjx7T0KO1cW3W1Fsy04koD3tWVuBazMLkY0PStjfV84g3bFctcnoaQq+sOuBDw2YcVU6VfyLHyJSiZPAG7uJV0+GQhk6jlb5Iv6UPuv7ZYyLK1fEXi5iLD8gVfvuPilW2cdWvh48UDXx7S4grzZt1nmbvCNF8RzDKTOtF25mR8sPKOphMXT7QFGNcGWPime22HTd7TftNcykridnQ44MUIxmMdm5b8WnnO8t1C/cFqzzuoGv1ybH5/gAaazxZaQ7AdaLKRv3bB/i7xyBigxS0rP2v+wzhYntJHeAxNtoX4JY8vF5e0vM6xgemWdFjxw6KH7YhzjZQL5718spd0WCcn8uVQS3eSDqbRWrzlMXiBUqOFYzbnOs4F2kRd49ta2Nf0Z/kC39fyKuO2/E/bOLDo8OVhK7by2Fpck/Gf7U2Lk5p+ZT9Lxhbv+YDH0OjyLYSwfJheK8+x7TA+n84tWNRCAwtRY8YyZM1xtXbcJ9942hg+3BourWCz2koetCDgc5xC6eSV3Hzy0o6aOtDGYh2wA2kJxTe+a2sVaYUau+bM+WyAk6iPRpYHt+cJlxXs3We5s+Qb08e7ZTvPB49lV9zv+V73yYbbaZNXXyL02YiV4K32Gk6rSNJoXcyRsOcLWjB39PFnLpQkP7lcLiyYefLmEpHGn5v08xiaX/FOE8tG9vXpka9LHq3vnOTlWDzxs47+sTxjsfk7+z7eNT58ucLd13bjJK0Sp5aDNPolb9ozrHJ85stNXuTOleVTEq88ds07H/Iz26WkQ8qMizmWo8+2LDq1uGTlDOtFZ5qOLHn7JguWHlkekjbOvVo7qybQYr+8Zu3qS31xoS1pYRlK3jmOFKIPjhG+uC1/JYbxsgwlPdZOI9sNP3vPvMvHppztcCyU+Cx+NN2w/vidALK9JkNNFpr++PETjXafTbKMNT4dWKc9NNvguK7htfKj7Ou+WycBZB9t59qKQxpo/uNrp/Ho8/V8NPhyuBYntX4WyJMg8r8Wz7Q6g3OCBF7M8y0YabWOu67Jik+u8GdLdla+kbg1O3H9uV0hjzRJ2qwcIduxfTt+WYZcR1i0sYw1m2QZukf9mOZ8MZnhef+8JTPD1zSGtXbyu3M+NlY2ch+NlkDy8abx5OurCZ/7WbT4DEwL+lafQgKkZUTcRnNMjT9fss8nc1+y8tGmJQ9Jo0+ePhp8BYHWRgY3Sbt17NI3Ht8vxDYsfrQAn4+mfImykOKIi1EZbK3AJ3FqY1rjWvbI47nPssjK598cjGUc+UWBVXxIkPetSTTvuAHzEyEXBYD+XL6Ugaa3fPGDv7POuLCSE1eJX363imNuL+1co1uTuSxcJF2868Rj+Oh1nx29+QoybbHAZxda0Sf7uV1QKVfNl6W+mX8HLAcpQ59c5DhMI7DwBZcsP54wMG3a86ma/hm0POKusX3JSZ9boNHijtwRl3z4aHRyl4uDckLLOUnyla/Qs2KWVktJPwQw75SJle/ldd+7ECybkW3y5VrNN9x/HptjiuRP0ut0rfksxxOOXZYupR4kvdrpFwn5JiZWjmMdaXTL/ixrXiRjffPiBLeV9TkvYvlOqTGt7jvzZ8lYi4uarfG48hSdFRs0fUoblPrkSaYW/2KxhcfS5TX2Ae7rk51Gs5WreBGQ/1v5iOWq+bPsz4+taPnG4tWyBcmTlkc1OUra2F58cZLlyv2Zd86LMu9yvNDyixzTkpVWb7gxNDq5n2ZjWu6wdCPvcwyQPPn8T8Lz+nlLSazmGBpz8r72WY7hPlvKtq7lM2ZJt48XKyAVCpbxa85uOZiGJ984mgx8hiZBS1CM10puluwLcW7N+H26Z/6t75rD+gKgRqdFg3XsS6NBtitE7/lsz2cTVoD1+VCh9uVLgO67Nh5PkC0/YLwONNm5pOiuW7vY2pFG36RVG+eFxgIL+OctNf/0xVrtM+PQ9K4V6fJ5vyAI5j1zmc/+2Z6tQtZKnlpc0cbVrmv2wKDZjza25IEXzzTcheRBDeQE3ipALH/N58/WdW1ywrxqBYW7zgWHttAq+1l+o9HoszErp0h74lij6ZXb86TTsgnJq7vvxmG5yXu+I7Sc5yQOptOyKc5R1hF46Rvs6778x7LQ+kperJzH9PBna1z33TpGzs8tS5A0cm7SxmA8nBdcO+ukiQ80vcrr1maANQafhNLa+/xL2pymX0B/BEbKToujfNqJ/ZDHsmxAk592OsXh107iyBjFcvPJguUoebVivNWGZcBjyzY8LsvegdXfl1M5N2r24/NdK86yzDg3aHgtfpl3a2NEnuCQi4Uy9mm+L/H7Fs4lHtapo4X9Wcsx8jvrjq/JR+Q0O5J+5auhWX++vCFrTp9epby0TSwrR1mw6Hc0MOGaYjmgcMCSRieZ0/BYdGj3C2Gak43Wz3K+fPh89FrXrcDC9/mzA5ajZUAW+Nr5ipd8AUjDVSg9vuInnz54vEJ0yfapyTCf/jU6JR7e7bd41UCbrGs0aIWDpS8tQbEstLHkONp3zR44eViJVdLB/GkytWhhufh8yJcQFuNHzwdcEOexLV345Cj788RY2o9W3LprvAsj8bL9yKTv6OFk79pxLpBj++zG0ebAenZdK8KkjF0f15+LE/eZi0Nth479XYuDWoHCyd2SC4NV0Fk0aTLI92iXJU/Ox5rsHU5nO774rfFsPYag2TmfUtJihixEeUcv3yKPtHXehdbkwXyxTbvx5TtHrPzi2jNYeVaTE9uH7Mu25x4HkDrgCQnTwXK3ZKSBpk+JR8qUedZ4YplofsnXtDzMfGmTV9+4Ug6abUjQ6NXinmZr7r+2aGjR4vix8ob0AykDSZfl84xDtueX0zKfkj4rF2u8Sfo5pjnZWItTWp7nz1LuMmcwTVaM476+2kHaqOY37AdabHWftZyrnWrkiaOWU5g/K86ybLUYL8f0xQbG5z5rjyhIX9AWNRif9AHtZBHw85MYWn5hv2Y6pTw1Onz+am2Q8XepJ5nTpEzYhnks+V0bh/tYOUfL2YXAohYaNMOy/vM1K7CyAVrC1ujJ58iAnqjZaTUHzkeXFmjYAK1AmQ+0IGkFTAu3Rq+vYJH48gVJ7u+jg/Gys7OsfYar8cdysYoEDY+W5PLpzUqMFv3smDyGZTeF+IGVPC0+LN58Y2j489l6Pt/Skpk1vu84plWsyOsaLe6/nHgwLTJJ/SJAWwzQ4qv8LifFhfgMFz2SZ/ZJRwP3s4pMHl/iZ9zS/vl4M/NXVFQUHuPmpCqf2dZs3fIZ1jMfy9b6SLo4l/gSOydjtk/tkRGWLctZi/2+UxY8mbZwsQyYFqkDNzGVk1LJH++WWXFP0l5UVKTuNso+2iKrFdckj2xn+fKCJg/L77T++eoMKWNrd5z75nt0Rf7nxRXN9ritlA1/5z7SF7TcK33e2YuUCfsit3efeWJn+Va+RUBLtr4dUKcby1YsuUgZsK5ZBvF4PJyIy0mN40dbwJX4ecGY6ZQ8aXFG2gfLx6p7OG4yfxwHrHqHcwrLhHVo0cc5UPLGumScmt9oevKNKe+xHrTY4cZxoMUKeU/6MYMbT8qZH7nimCOvOzlJWrX84trJU1/5dCT5ZD+QspBtNVtyn10dIOn36ZJlKNs5HqV/y0eE+DEZ7bSLFcMkP3IcbsOxWIsXDFLXrp92ysdXp0l6nL9xHeTa8mlOd49zqM+GNVjUK9UlIax0ZtxK2vKa1g/Q33TO7X0BSXMwLVFadFpJLJ9QrbGZT402q00heID8E2AejxOGbKvJx+JX668Frkwmg927d2NsbGxef5Y1H2ctVOaWs2pBaDG8FKIz7X6h43Efq3+h8uAxOQhp+i8EL+uJ6c1nJ9KnC/URq7jTko/PvzV70uyMd3cKkcsLAY5zXPRw4nU0avExFvv5rzXINlL//Nm1ZXDtpN40P+Okw4Uvt5GJi3mQE1jeBXDjWgstlm7dmFKObE/apMuabPA17bNVtOXLPbJAkTxoNMr7km72JXdMkotubZLCu/+Sbv6v/TG9Wh+WuyZHB5oOGL9W4Ev5sd1rhaGc2Mp3MThb1Yox911OInz5QNJgXWf5S1+34pL071wuN++XCiTvmj5kGy3eWO2lH2uxgHFL4L7MI/uBJXftv+WXLD/Zj/Uv+1v0F9pWk4XzR3mN7dnnRxpOjg/s/xyXXBuO5dzXXZe+I8fRdq6ZH7YPjhNSFr78wvxYduf+3AKmxiOwMDZYC79avGa65ClATUeOHo6VcpGVJ+DMk/vM8VmLBcyTFs9ZLvliupSbFsstXBofHJN9CypajmA5aI/IaDZp0SBPM8ixtDin9We8zsf5pCRvmlh6Yx9kHmV81OIaX9NyjrXYKOljuWpjObsuBAo+0eCQawGNv2uf8xHkC3zWWFZi9OHW7mn9eedd6+e7xwFBK/Y5gVpjaEbBNOajRwtULD/+bvFj0ckBxsHs7Cy6urqwa9cunDx5Ei9+8YtRX1+/oL+jQcMr72lj+OjUdlAZj9aP2/G4mqNq35k/1ruPLktXVjuLfh5Hwy/p0mzD4tOSh6ZXbQKgteUYotmk1d4X2LWYIb/L5J8vBrwQsHzGmoBZ8dV9591+yR8nFvYH+dknI5n0uLCSdqTtpDOvlvyZLu2a9TZ6l+itoo/1KXmy/Eouemg+w7KQ97mA5JMU7G9yHE1usVhM3Q1lGcoCR15jP+GCi3d1tKO2Er+mSycPeXya+bbyhLbQxvmKwfIL7Qgsy5xp4EccLH618bSxNPo0W2d6rJifLz8wPk1mUmfSTjSZSjuz6Oc+VuxmnPnwyHbSLjT/0eRpyUTyzuNpoOmcc2U+HNqEDVj4PgSHQ9LvTnlZoC0U+2og9jGtvZS95MvyNfYV+d9n79pJJUsv0p+l/HnXXZuUMX2SHj6l4bNjHl+TM8vcN6mT8ZJlI2nlHCTpt2oUX87i61as0MbMVzswsN37aOXY4P6zfNhutTrFsm0r50r9au+nYj9l3O6/PF2ixXJNXoxXm9dJP2D7cH002TuZav4s479cpHYnSbR4yXgLhUvy85bchq9puBwUEvCta77xNYO2cPqMR0vUWmC1EieDzyk1Gn3Gna8Is3DyZ02vGt/ady2IsiMcOnQIP/jBDzAwMIDq6mqMjY0tWGiwCiff+FoBoMlVKwpcHz4O5uNdK2wsmqwdNStpu6LCfbeCEvOpySufPDlA+Y5i5bNTn33KaxrP3EbTk5ZEtPE13fB4mpwtXrTAfakh3zN6jib5meMP2yYnUWDhyw2lrC07yWfz2k6CL2FKHrgdF5wa75qONXtyRblW4HGS1iZRsi/ToY3PSdh9lgWwZaPymtQR77BxYSlBi4tOBpr9uwKB6dDii7xmFTgSpJy0xV15TTtGK+01Xwzy0cZ5zY0nJ2z5YjGPYxWuGj3WPYnHPSLEuUGzaW3ByDoNlY8HtkWnF2vy78Zi/Vg5UDstI+mQtsEy0fKIZkvaWCwPzhe8WMF51IqXTL8lS+aR4zDzo8VBmYu1CbT8L+1N1jCSHs2mJJ/MD8tFm/BofeV1X23EMpfA48iFfk1mmr9puZKvs4wsOqxTDhpPro1lBxZ+zYb40Tqnc4lTO+3FdGl0yzjIdOc7YSbbsvzZ5nz+rfmENS7bVKExlttadTXTrY3DGxfao0saDy4Pa/J1eCx+rJMUEqcDRwf7i7tn+Yk8/aGN55NnEATqSR4fLPpEg/bf91le0xzP19Y3vhY8ADu4yvGtgi/fGA6X1S5fstd4LYRvSwY+/nx9ZT85rhWYfd/z0VpSUoJt27YhHo/j0KFDuOuuu1BeXq6urGs6kYHJ0omUAdPvo9VKelYwYp1bAcZnW1wEuM8afxbtXHhqhRi38dGt2YWvGPDJKp/vSjqYZ2tVVsPNvFj0avRYSYL5zOevLxSkHKQO3XdtNx1YuAsj6XbXtSTKBYKkw9de0unuy77awg2Pp9k795HtnAw0WphGSZNrox2PZVwuQcvvmlxYBix791lO1lifFj9yUYN143vsg3cc3DXXT4IvbrE8WY4aaHFP6k7yZvVn35Vja7gkjbKfldN4wYlPwTj6nG1oR9o1fheTY3wxSfJttSlk0q71lQWmdVpHkysDP6ts5UN+Nt6BZo+avny5Q/qpvObzPx7HihUSv6V3nw/kyyGWnqXdWTHRxf9YLDZvt1HyKr9btEo7t2Iux0IeS6sjGJ+Wy5h/N8kt5NEbLVfxmNrip6V/67vGt8Y70xQEwYKfq3RttOtMI/MpfUuLw1wXaI92WfFd4rPec8RyBfRNKNlOixuWnXFbq7+seSQ9MiYybgafLbn7PI6WXyU9mk3niyc+vyg0z2gyYJq1WMR2pcnFXWM8vKjMC13u82JO/D6vn7csJGhpQrMCBrezPvsEpvXjazz50hSkBVUfbo0f/qw5Pn+2xtCM2eLPMiyLVgk8WWVHdOCjl2UKIEwuGzZsQEVFBSoqKubhKVS+3EcreixH9IEWbOU9a8x8tBYyHqD/hEw+mWv0aDS7vr6kq43hK3Dz8ZVvLJnIND452Fv2xrJiv+aj4IX4p/tv0XcpQYuF8p605UIShgP5m87SF9gvGbevUPPtgDPNFp98TWufT6e+OMf0StzSDvLlGJ983H3+9QvLtniilu84subP7rr22IecHGsnCFjm2hhaHAXm53v37gItf/IYWoz0+bs1plaYuv8c4+UCpWa3mp0wfW5MeYzUKgyt+CqLMuZP8zVJr/b8uYXHtXc+rj2iwzJzP1nr2jtc2m6fBMtHLPtiGfl8xAFPuDV7lDJ091m/1skHKfd8euA446DQesWKpywLXwyXsmCblHxadFgx3+FkHch86fBzvNJwsO2xfjS+8p3i0+xejsdxkOsmlp2WMxxvcjHfyn2WDt3YGq0cN/hkm/RBxqHplu1Fsy0NeKPCl/80/thWNFq1SahGJ/u05F3LE5I/bZGY2/C4lmw0OjX+WXasA+2xHykXLbZaeV3Kmj+zTGRNIe1I+m4+2cjvljwcTqlnXrTPZ38SnvfPW2pO6YAN0gqmVhCwBKU5y2KgkGSRL7nIdppjWXLRVgfz4bTuWYHPR6trazmZr6jT6PA5q7zuDPb8+fMYHBxEU1MTUqmUyq+VBCV9LF8tgFl6K0R+fN/CpyVgaxzfeNaRNQ46mm1YgUPjxeHh4K4FP1kwaM+RWnxbPORLlpZ9WnxK3NyGedQSgqVfeY37XmrQZKL9BxZOhLRJp7znkgK31WxZ0mDFVy5sfMWDb6JlLfy4/9pjHfliHdNl3bNyiZarNLvU5KH14UmvpV/tJIUscrRcwTZj+bQ24dImlPlyn+yvnTbiWMGx3OGUNqjtHrI9MY9Mn5Z/ZEHENHG8kf00nplefumVHM99l4WYLDR9k0LLHy0/k5/lIpPUu/suaWGetLGsOMxtWMdsu5o9azan8SnvWznC6YRlyWPLa9qJFe3Enzaudl1eY3u2Yqgcx7rHOpCf+cg0x1SNNs1u8o3vZMOLtRIvsPAnFC070PjLFyecfWu2KXFZpzaZVvYP91mTsyU3TWZWbrL8WvKSr87xxWYZ37gfn3TTxtHysMPDj/wxzZru+DvrrVDZyAV8tictljBOzXd4zEJ0qY3LvHIO4H7WKS7OMUy3xq9soz324qPD8h0JXE86/9M2FqQvaXKxYFGPTviOrVjXNCeW99noCjkaw33ldzbeFwqWUVvOw30senwGb9HOstRwsIHxkRtfAGTIV0jINhrfrt3Q0BAOHjyIWCyGdDqNDRs2mLRwcaElMY1f6zvLzge+Nqx3rTjS2ltyXgydnFA0eWj0+fBa9irv50uKPnzW+JY+rYApaZHjaHbJfQD9ZI4V6H3H6C81aCdZHEjflbt9jnarCOIiSuNZfpf/taKFY7ZWwGinHXxFgaNPW8TREmIs9vO3G7N9sx7ZBq0FGZ8PFlI8acDFGfNvxTrZn+XuewSC8UvgSai0FYt/X06ReGWe1mxA833JD0+82WYBzPtZM41PTYbsF5oM5DiWLKSfSBvittoRXy1+sTwlfVIvWhxgniQ/WqHny0P5TodpY0k5ugUXiVPS4qsPND36YjvzwnFBXvPVIby4oI2r9bHsDVhYlOeLC5JubSeSfdTd1073WHFL82MpEyuHS/wStPdW+XBrvDJ+yaPsI2McL9RZY1t0B0Ew7yW0Wt7SdK7JRtq7b5FV6++TiaZvS46avzjarDGkzzKv8nEAB9qpB41HSRPXDoxLO10XBPMfX+MagesIOR7jkjikbnk8a6FXs13m33qcJl/8sGyUcXHu8PErdST1y+OyPLQ8qp2wzBerLf6smKLBoh+d0EBTiNY/X7t8wdpi0gpK2n/GV6iw8gW6fPc0w/Q5smXQgD15csahJV2NznxgBXMrKMn/cqyJiQmMj48DAMbGxhYUZBIvg1YAAvaOoCU3Tc++IMBykP+tICHb5wvSVkDh8a2dKO27b2wf3z6+pIzd93zPXhfqb5Jf+aZfab8++qTNM38yYWiTBaaL6WG+fxEgd0s5Lki6XFtZoDDvlk2yPvL5DffXfFzS4fM7Lq4Yj/yv8aP9d6C93Znl4u5bpzokPdpxVz7OzsCTBT5qLHe2Ld+SEzmmJ99kUpOr+7OehZb9rMJCnorhN977ChMHvJDE8teKSi3OS3ocX9YJAVlYWr5hxXn2BcalTS41fbMeNN/k8TVfc9flDp8cU7aX9Fk0sg+xLCweXE6ydq9ZD5o++bo1CfLJwhqH42MQBCgpKSno0Te2LQm8iOauab7HeVLS4uPHkrnrr+Uf65EXGX+0hXqOhZJ+lkksNv8dA4xH4923e8vy4s880WVaJP1abWHFa9+mAecuvs66ZxlZNYEVZ1mGVi5mWWl+q9mLNr7kkeODj34pO+5n6ZZla01gOS4yv3zqzrJPi1+LH01nVuzkHCPb8NiyrSYjjUcthlj0+GKGJQu5OcWbF5pdSJxa7mXQFm8Kgef1MkjtuxWMrPsWTnldC0DW2FqxZ/238FjGqBm91ldLHsyHJhMen51DM2ItSFr3fNfz8VCI/C0nBIC+vj48/fTTmJ6eRjweR29vL0ZHR1FTU6MGZEsuhTiZJX++lo+HQpKJbJcvyOSjkfFpNimvWYEvHzCPkn6tv2UrWnHGvFny04pLbXdQw2kFYV59zvfZ0pflj78IcHy7z5ru+agnJwufPpkHV4hpzzvKz06W/B4Cpkt+5sJUjs0FBx/t5GSXz45lLPXFa9neV8AC+k8h8sRfixXMC+vFXS8klspY6H5jnY8vS3lpxZyvIPXlINYtH5+VxYnro016tBeNsQ1osraKIdapvG8t6rh7kga2ZdnH2bJrw++jkH+8cOTwsF2wT7EeNP3zIomlU17EYZvTYhwv9LAutMVsxiPH5bgtQZucyZ1UbaHEqgOYPwtyuRymp6cXxBM5hpbrrJgpbVs7uaPpVC58Wr6i8S31Kr9L3DLvskzlWBwbNP550Yj17PBYOVKLudYpIdnG6s9+pLW14jyD5JkfNZHg+HN5y8mM45cvF2l+Ln1F8qBtqlly0BaXrbzq/EnaqVUf5KNdxnEZj6Q8pbwkHexTFq9W3tFkxPi5v7X4LBfJZZyzThP57M6Xh1w7a7FZ8in7cz6NxWLmCT62Ke4vY4aM5cwf8yHH0PKOJnNJh7U46IPn/Y4G6xoLy8JhjWEZreUwEnyOZRmBFsQ0fJbBy++Wc2v85DNKi0erL9+3EoTGn8WXhosDqxZ4JK6xsTEMDAyEyfiHxcW4p6cH743H8aaqqnlJVvLM3yXI71ZSZVzWf2sMpsOyXQ4IFg8+fFrisWQuv3Nbn26tdj7emG6rkLB402xHJtJCFix8NFoJzCqaLFxc6Fp0XWpgfchk4b5zHNV0rD1iIe/7+ms7YO4ey1faAdOo7SBpsUE7meFwW3aVy+VQXFysJkUpL/eZ9S95smTIfbRJGCdxi2Y5RiH8aUWAFmdlYSN14drJiYYFckLOcYflIO9rRZ0cS5tsuAm5Fhcs/mQ7eZ8LaMmPWxzQZC/bsy6Bhc/7u5Mg3I7jFj/DKnFwDJFy0nKXbG/5qsTNJx20RwS0WC1lofm2BPZvpkObPElcXOhLe3B0aLuBWtx11zh2yAnF9PQ0ZmdnkUgkQvnISZiGn+2QbZH9wzrlpMUYli3vLsoxGZgWbZFH0m/FICdjtjltcqTlXD7NouF3ffPVvZzjONYxDq2P7MefNbrcZ44bvNjpeGXdsc6susHKofIe+x7bmXV6wFcDcD8tNkraNbm4vtImeJFbyylazJITZvYhn29rtse2ywvJmv1wbpay5ngt44ilY8tHeFwr7kleuL8cq5D4wfnX8WTRzfFDy7lubM7rPCb/gpMco1Ao+AyEVMzz+dPwaQRzn3w4tXYM0iB8AtICpUYLf/Y5Uj55WrQXwrNGc75rnOwKoVOjm2XKNLvA8GAyic9ecw0Orl6N8vJy7Fq2DB2xGL4wMBC2476WYzIdTIPm+BI0Z1qMfWpBgHFZfX3Bh/9yuRyOHz+O3t7eeXRrfSwo1C4kPT6eHBRSdMj+XLBZ8uA+3FbzDZl8ZBsuruTOpSZv1162s/j/RQAXLFoBXMgqPCdqyYf77xKULLpkXz5hIGngRMeJSyukWJ+sR7lzrtGrje3GkLi4UOL+2vgWzZI2CVZilzzwpE87sSFlzzRLHBIPg/QrHw/us3xEhyfSjEvy5vrFYrF5L6JiOTFeh9v1kXKQMuBJBtuSlI0mE60wd/9ZFzzJYbnJezK2uOu5XC5c1LBikMTBcpLg5GnlAdax9FNt0YJ5krHMsiFfbpM+J+1C7mj5agieJLC82QZ9u75sf1Lm09PTGB0dxcTEBKampjA5OYlMJqPKnPHydyuHsf9yPpFtJU7Hp7Md30kUKWs5lhUb3X13+onzmQQ5Nts110w8jqTR4WI5WXWJhlPjSYLmz8yTtVjB1zR5sf1YtGtxVZONNrbmG/xdsyErHnHs5BcCajTJcWVMtXji8a3YwN8lfmeLFo+aTjjOu2taHeJA809ZH3AsteShteG2ji5NvpyvnG6YZl/8YHpYD9zOopHjjWXL0l5l3SuB9WI9CrMYKHihwRdMrIDC1/ieFqQ1PNbkhnH6/pgXK8ho7X3g48eSQSHgKw4ALEgultNYgZev+XRo4c1XBPf29uJzvb3oKS3Fw3V1GBsbw0uGhtACYG02i2tOnMDfDQ2pfFuy9MlYsxMtSGkOo+H12bAv+Gm0sa1ZEAQBxsfHcebMGTz99NOYmpoyx9LkrwVQy7cAezWVcco+Gl6fTRRCR764oCU/GR/kGCxrn65ksM3nL5cSZILiiYS7z0mbaZYJzucfPtloYzHIBGYVXbL44UJOk6nvOhcasdjCX6TQ/Njd4x07q2hzE2E3njw+L/n2yVXDz/xriylSp8yHxK89p8/9HfhetsVFjuONFweYDgaNF+sEhRUDJZ5C4jwXtlLGXEBLW9Emg5pseCKs9XPXuQDW/EEr4jVeZHtg/rP92gIGL57wffYZHofftyHvSZ6y2ey8FwIyjZpM+b8mZ/c5Ho+juPjnh2m1OMWyljxLfygvL0cqlUI6nUZVVRXKysrCfvwSWSsHu7G0BWpfjtPymtSFFqfZVjjGyf4SH4/LC778x7ph3UmaGDgfSBxSptqihRa/ZZxhHUtZS375pJQGWkyUOJj2QkHTKU+itT7cX8tBjF/2ky9b5TbMkxYftMVcbVzOg5ocmT4t57M/sq356muNL8eDtgPPC56Wztn2WC4sC85Ji/EnKU9Zx0n5W3wyaP2dHJk/q54BsOA0F/MoaZfxWjutwPhlXGZZ5INFPTqhBVYWgmSOhW21t8aTzFgBUeLR2mqJXX7WAq5WOFg4NJqkgbIhWjwwHxr/zJ8WrCxYLA+SHqufJsvx8XGcP38ePT096OjowLWVlfjZ0qW47sIFjI+PY+XevXgLgC/ceCP6Ewl8rq8Pb66uXiArHoPp0GizApukj+1TG69Qu/DJTrsmx5K7F27Mr4+M4ItDQ7jy5Em8aGAANTU15vPv2jiyuLb8xpKZFZAkfbKPL4D58Ft8FGqf+RKfHIt9xjoZwvQW4k8vFOLx+IJkIMeWtHMBqtHvS+r5nhHm/vK7L5ZrPsRJ1+fPnEyd/Wq+Z00a5f9sNjtvB16L43IslrdGn68Yt2iU9Gl5gOUvkzrLkHmXMrN8l/lmPXAe13yT7ULaoiYv7qPZr/vTHkuRY2h+6dtFl3LX6HL0xOPxcALto127L7/LhRXtlI2md9mfX9SpHeVmWhz/WnxwbTn+W7HNyqmcFzVd+eKMHFMeuXdHqnlybNkY25E2nryWSCTChQv3MkirlpJ+JmXONMj4pcmJd9qlbCy5SDq02MknGqzaT/NpB5ps88U0bSyOaxyPrGPUkn6OM1YekLar6UzSwfJlHlle0m/YR9jOHQ7HnwT+znxK0L5zzNdo1fpK+jUZ+yZ9PKaW77RrFr/ahF0DiU97lECLn5rvyxhi5QmrbuI2uVxuwUK8FWt9PiJ1Ke3Eik2ST5kXuFaQvq/5AfPCtuBiURAE8xasJF/ysSnO0Rbdmtx9tm/Bon91wudU2mervXafA6rlxFpisoqufILId98K2oUqyufY3NeSicarNbbspyUzXx9tbKs9G2MsFkNnZyd27dqF/v5+TE1NYXp6Gg1jY3hLRQVSqRSS6TSCYG4x4voLF/BYSwvuDIJwgmCBzw58+rb414oH2dcKas8HuLiweACAL4+MoD2Xw3RLCy47dw6Tk5MLfsNdw8OQL+m475aP5bMvyz61PrFYDPeNjOAr4+N4UyKBt9bWqsURoD+PaIEVR1xy48DNyZblwXzlk/ELBQ2/lXRdQrMWSrSkaBUmXFzx9XwJ12rDCyBaf19856TMj3c4P9COH7u+vJug/Xf8skwYL7eR47mE7XSixXb3XbNDppF1wvFHyyHaro+Un+TBKkZlG0mjRiePwTLx0Svfe2DxJnFK3tzigBUTrAmwFWMcf9oRcKZLw+mLl6wDQP8FAt7ddTxok1bZh4GLTG0saVNyl117PEfGGPYdqQttfG6Xz2544URO3K04Iv1N6qKsrAzLli1DJpNBR0cHZmdnVf4kHkmzfMmdz9ckDs1G3DVefGKfYBxaDuV4JWUm9eXAOh3l7vFPVjJoNi3H5Pyjted4pslfjseLbdIPZLyQ8uTn8x1+tifWj5RVJpNZ8MJdzok+HWknG6S8fXWMZu9WPNGuyXu8YJZvN93Cqe1+a/WA7CPvade02sGKcZacLD4YtAV6Kx5Le5ZjMQ88luWX0t5dLJGnTTT7kzFI4pH3JVixX8Za91hfIXRr47KPuXtyUVzGQW0ByQfP+2WQViKRzGn9HINMuDUOg884+T7jtujXjMcHFs0+XrRrnMx9wGP4gmq+sSyeuMDzFQoM2WwWBw4cwMDAACYnJzE1NYVz586hu7sb6XQaa9euRSqVQi4392K3NYcOYcOxY1iyZAl6GhvR1NQ0L9FoQQzQE42DxRxts+xIKy4tm/TJO989jZ53VVbiSyMjuG1kBLOzsxi4+B4L1i3bgmZHVh+tfz5+NZyaHVh+9JXxcXQWFeF/j42hbf9+rFq1CsuXL5/XjhO4z1ZZlj575SLBAs0OfPb0QkErbBxwQaetYFs4ufCVY1nXuYjR9C3plTbgFgF4XPZX9zJHtjGXzLhQk37PBZVW/Em5aXy56zxZkjhl4vbFBxmnfMmbZc5v7XbXC80B+Z7Nl6dkLDuQuiukiJO8aLKQdsFt2L61mMRHpLmocvYh+ZB9ucjkdhK345l1my+WyTa+kxVyIiwLOG1M2UbKhsflnKzFaC4KuQjkuCrlpfmTJhN3T/sFDyu2yO9BEMw71sv2mi+/M62Or3g8js2bN+OOO+5Af38//vEf/xF9fX2qfuR4knYt37E9Mr/ar/JoMYl9RvKm+aiML1YbXryU+LRTchourrGYRs49vt1gyb/lG5J32YZlHYvFFpw6Yp1zvpc+wj4lbU2LX+6/1Lukj2lj/Unf4T7WGJI+2UbLvZasna61ukfLYxqdkmffYrQWJ+XYmp9K4LjFemI/0tqxzllu7jvXHrywrNUUPK4FcpFCy3NBEKC4uFiNjZrvanFa07m0ZddO41vjxy18yI1cSS/zzvFQ5mOtztMWQTR4wT9v6SuO2DC0AknDuxgafNelIrTPFi35nJy/s3Pw2IXwYyUoK+FzkHP3Ch2PcWvJ1uJXo6uzsxNHjhzB2NgYZmdnAQDV1dUYHx9HZWUlSkpKkM1mw4WG4eFhtLe3o/faa/E/enrw/uJivLOhYR4v7IhWUB8bG8PQ0BDq6+tRWlqqyrCQIkreY+crJHnmw8kg6ZqYmMBV3d24sawMk2VlOFBTg9nZWZUexm+NqwUiK3FbvFtjWQlB4hkeHkZvby+u7unBI01NuPr8ebS3t2NwcBCjo6NYt26decxLS4qa/KwFBonT8mXGJUErSi8lyARvxQ3L9jgZWD4i+eL4pOlMiyfyHk+iNNvn+MmFmktM2nFxLtQK8TlOwlqRIycLXIhb9itpZ1os3WgyZJlofMg2vNOiydeSDdNs+b/8r43nOzLO40rcmtyk/Hkc/inJWGzhL764xRltkqrFJx6f5VBIXvHxzYtFPvvT5C7lK2nVikUGLU5L+pz8tN0yeTJE2in/13KmxMs5gunRaHLfeYHPsnUei+UkJ5vFxcXYtGkT4vE4pqenkclkFuhZ41nKVPM9qSdNr4Be32oxJV/usWoFKU/ttBaPx/rIZ0/cRsv5Vu534Ft447Zajrf6LFZ+lm4djbI/P8fu2mp5x4o1vppC2rDGv9Qd/8SmpFeLU8yXZcuW3KRvcOzh8eU9C3hxm3G66zxJlvIIgmDBiWaOKYXkMM4fvlxp1b1aHy0OuXYyd3FOcDRLG9JqBCuuO944pvIYLj9KWTjgdzy5NpwntZ+D5ZzG8XoxG3GLPtGgMWu1y9eGP3ObQgJlobTnC15awJb9mCdOuFaRIvtpwdBqq33PN441lkW3a6fJSV6TDqPhyuVyOHHiBI4ePYpcLofa2lokEglUVFRg8+bNSCQSAIDi4mLEYrHwaGMikcDJHTswkkjgC4ODeGdDwwLZa/ywHk+cOIFnnnkGGzduxNVXX22uVrKMLBlo/ayAbY3hAx5/dHQUTz75JHp7exGLze3+/jidxhMrVuDsuXP40Lp16gq1htO6p+lOKyaYXytZanzI69PT03jsscfQ29uLVdPTaN2/H6WlpUBxMUZHR3Hq1CmsXLkSyWTStHmtqLXk6PNRnw/4ZPWLPNEgx5I08I408yD54Em0TB68gq/pPp/NSl/TTjtZSVnzXb4nr2m8Shnx7iEnbmDhrrUbm3+j2uFkerV46TtKzji04k/Lc1rsdsUmF8TsgxY+LoD45Ij7802wHe3aiQgt1/BjIwyF+CwXbZIGvs4yyPfuGi3X8ziMW/bnya7sxz7mrlv2q+VY107SKScY3NfSv6Zv91/ypU3qrbjOE64gsF9yJiGfvl0bjXYfX1qx7NqkUinE43FMTU1hamoKVVVVmJ2dxdjYmJcud50Lf8t+mHcpU44vPttzfGn5SupeW4CybEAbV6Nd/mn1K+Ng+llOUn4aTZYfsB+y3TGvHAeYH+1UjCUjd1/7GVufnjWZaJNHiYNjAudJHlceU2e5W/RZNssykHikrNnuGb8lExcTOIZb+vfh1l54zHmN+1qnWDTQbJjrGM2G2Xd5LLZ/rb5h2TIf1n1N9rIfL64wfdp3AOojVL44Lu3D5VxtQSofLOpEg0UYByurnXXPp2SrjdVHu8/9LR6YF8uxpQP4DF1LjNb4vmTItGk8asHI4tNKDpoOOdExXUEwtxp59dVXY3h4GD/84Q+RyWTCkwXFxcUoLS3F9PT0vH7Lly9HU1MTps6dw9Ntbbgjl1MDl8Y30zc1NYXZ2VmMjo6qMtRo1/jlMaxdUs2WrNVn2V8bL5fL4cyZM7hw4QJmZ2eRyWSQzWbx+M03YzCVwj9MTOA1J09i7dq183almB5Ndxb/7C++dryrqxVgGt6pqSn09fVhdHQ0/I3zqakpxONxVFdXo6SkBKOjo0gmk+rYGl2FxADN3i19WjLTYsAvAizfcuDbUQfm/5Sf1pcToXzMgeXF8UP2kccBXXteydeKZwecfC2QfuT+c5zlQkWOaT1iIq+xDfNiDffh46murxYbePIqC1KtiHLfrSOTGjg9Mf2aXzKdUl7aIpo1vrQNyZOUjxxX/ud47jvJwj7HiydybPksrORXLtq4+Mo6kGNau5Waft1YcmIjx/PJUsudml9IuthOtNqDbczlY8YhwcofjDdfTWfVfQ6HPHmg3Zc0sm8wXumn8kj05ZdfjuLiYszMzKC4uBiNjY2oqKjAwYMHzZM0TJ8WtwrxSV4Y4nE0+fjqM99xZjdBkDrSjlJb+VuzAc1GtfipLf5Zk2/GL7/7TmtpPsh4NZ1Zi6qFyJ4/a3T79O/sUOYP/slaliODpFu7ZumNaWd5+PKtbCN1otmKa8OTSl4QcZ+thX3p276TosxfISfFOO9IeWo1n/vPeVP+1/yaZaOdaLLsWrZhfCwfxmctEstYqeHSfIrfuSBxsR9pecn10zYkfPC83tEghcDfLQZ9uLTr+RSk4dCckIuXQiAf7kLvaUZl4df6sCx9wdwyYB+9Fm4rOLMTyP+pVAo7d+5EY2MjHnvsMUxOTmJmZgbDw8Pzfnu8pqYmnGjG43Fc09ODzSdP4tDq1bimshLvqa/HW2tr543pSzax2NxPXI2Pj2NmZmaBvq1g7XD5dKIlBk1Grq0mHylDbSI3MzODEydO4MSJE+jt7UV1dTWWL1+OGy5cwK6WFuw4dQpPDw+js7MTW7ZsQW1t7QJ9FZLk89kn45P/mW9NtiyPoqIilJSUoL+/H/F4HE1NTUin0xgcHMTIyAjq6+tRWVnpLQq07xpt+Wi2joJzXyvY/yJA8zm2Sy2Wye88GecjcdJXpJx5AUMrmF171nEhNsKFgnXEk3FKPcmdY8s2tSJV80PWqTYB03xU8qDZD/MgCwNHfy43/5l5bVxJt+RJTqgte5c8Sv6k7OQkRbZlvLwIoN2TY3N81vxYtmOb0uoEy+alrBztmjylLUmeGazaRKObr0l8Vo5whawb35e3NfvWxmK6GScvmDn74XE4p8pxNH/XZKTpnTc65KKhu6bZMuOT/uPaau83KS4uxooVK8KTkkNDQzh+/DiGh4cXyNTpw70EkOnX9Oe7psVGzaa5H7exaJHXOKZbC+ZWruI8KvGyT8pYx7YiP0u/sl4QymNqu9PW4rDsx/ik/F3Ok/bn82k5ttSDNeFmHcv/clwtz1knK6x4xH7JtDBofirjI8cDqVfJH59+lPc0etnv2W4s8PHli48yJjAurb+24SrvWRuaHDN893jxRdLMumK75EVvLW9KXCwbKybLOlCTqWa/rH9Nplou9dGhwaJPNGjJSbvvu2Y5rtWvUGY0XJoyOTksFne+oFEIbRzsfe3kfx9O67vW1+qnJViLJoaqqio0NTVh6dKlePrpp3Hs2DEMDAyECb64uBglJSVYunQpNm/ejJqaGoyMjODcuXN44pZbMJzL4QuDg+FCg6Sf6S0qKkI2m8XU1BRGR0fDgnpiYgJVVVXz6LQKh3wyKaTYWIz9aAm+r68Pzz77LM6dO4dMJoPW1laUl5fjhsFBvAJAZX09TgwP4/Tp0wiCADfccMO859mskxSaPmVisHi3gq1VDFkySKVSuPrqq9HQ0ICpqanQBqqqqjA9PY3W1lakL/4KieYXWqHjk7kv8eSLPxauXyQEQTDvBVscT7XEID+zLrmIABZOgiwZOTxcQAALX/6m9ZULHlqRwDSwDnkXxuHSCi93jY9bctLUCglJP8tE8uJkJ08QaUWCNrFyMnP4LBloE2X2LbnIwPZg6UN+lrLQbFqLq5Z8uNDjnyzUgGly8nRjaMfmpWyZJq3QlH4g7ULSLXXD9Gn2zHr1xSQrj2jX2abZ75gvSQ8Xgywbbcdb4tEmtFZu0CaE0r80GjWZSruTtEt+tJqM45K7zickcrkcuru7ccUVV6CsrAytra0YGhrCE088MY8+jVatsHYgTw365CXplfe0yYEFPNG2ahXN7nhMC6wa0pKzHJNxSDtinbr/vChk7aCybKw4qe08S5otO3Njy7xk2YEld189pE28+IQFy0+zOfYpzcctm9LuaeM52rQ4qD3fb50Ay5fTedNCi6s+H9fswtKbo5ntinUg7VXTD8vEtdN+CU/LRRpdclyZxyW/si+fGrJkIWWufddkqclR5jOJQ/ob49bGKBSe1zsafPc1o7fa5gMreLIRFQqWwvIZeiFjWo7O9y1HWgzNviSZr28hdPiSKtPA/5cvX46enh585zvfwfDwMBKJBBoaGlBZWYlMJoO+vj60t7djcnISW7ZsQTabRUNDAy4/dgy7t2/HcCaD/zM4iLfU1Jh8fHVgAJ/t6cHOnh6sPngQFy5cQCKRwNDQEH70ox/huuuuQ2Nj4zy6CrFJyw6swlIrkizbsGzn1KlT6OnpQV9fH170ohdh2bJl4YKM+3WGdDqNJ598MtyNYZo0OiRoyTSf/ViBTOujtZ2dnUVfXx9OnjyJ4uJiJJNJBEGAnp4eTExMYGxsDBUVFVi1alXeokzSbSVw+Tlf0PXpsVC/fKGgTdCsJMiJWeKQBbuDfInUkre14yTH1WjRihKtcNL6ueJHHvdm4ILSvVTWKnq071zYOZrlEXzm28cHFzXunjWh88UJ9k3WgaUvC58saKwFF8bBPDpeeFde293QJtCSJuuEjXUslkGLW7K/bMM4ufDV7JR3nDjWaydSJH4+7cF+IeljvmOx2IKFKeaZ/2s7dlZfzhUa31KGbCeavn01Hvsbxw05iefx3HfL3h3N8Xg8zIVHjx7FZZddhkwmg9raWtTW1mLNmjU4e/ZsGFvcn1bTaAsn7LdSDg5Y/vzMP8vCkrmkQbaXJ6KkDjVZM61WTmTflnHC0km+moL5lH7m+HCP3sm44KuntPjIfLuxeHFb3mf+NP4lfuskgwaSP6lXzXZkjJB5hWOH5n9Mu4yhWp3AcpKy0Pydx+ExOe6zPbFeNR1w7tTotWTtO6EodW8teFsxl8dlvfHPynPece3kiTGfnTpeWNbuu+XbVnv2E81uuG7SaOGYpS18sv1pPpoPLtlCg2VQmpFYgVG7zn1k+0IZ1YTCRsQ0aPT68GljFkKPZiDWOD65cX+LP1+RoOGw+Hf/mZfe3l6MjY2hvr4e2WwWra2tqKioCN8GXVNTg1OnTmF0dBQdHR2ora1FfX09Wvfvx/5Nm9Afj+NvBwbCUw2a3j7X14f2IMC/lJXh7gsXkMvNvYBydnYWHR0dOHjwIGpra8PHM6zEYdmopQuWn5XoObBptuLaDg4OIh6PY8mSJVixYgXi8TgymQyeamrCxwHcvHs3in/wA8zOziKdTiOTyaCsrEzFxcDjWjan8Wj5XL7k4Hjfv38/vve97yGXy6GhoQHT09MoLS1FTU0NUqkUfpRO42/jcfzn0VG8ubp6AZ0yochxfbafz1+0uOKSFO+qF1JsvFDgoM0Tbn5LOxcMXIRyErdkp/GYL4ZoNMgigOXMyUnyxrrVTkLI/6wvxi9pzhffWEa82y8XIaxYrMnc+ulK6zMXSlwISTpkccA0ycLHkh3zr8UyTUaSVy0XyLEZJx/d1WKLRY9mV9JP2I75GKvkOxaLLXivjVx80PTMctP8huUlx5O+qy0e+vyG6WcatAk/97MWFjU+NBuQOLkQ1YBjj8YD24qmd63ukJ9Pbt6ME5dfjhf39uLKzk4Ac79s1dfXhyVLlqC8vBwPJBJ4aOdO3DY4iFvHxjA6Ooqenh50dnaG73DSFg0sn5A08MRQox3wP0aZD9iOtUU86zi9ZTNMq5YnrdrFisXAz9/5Y/Hr41vK0i3S8Hiar0kaeDLI95lfLX679toig2WrMndIHNp4mk/zwrYV76z8wbWlzGPWyScJlt+5ew4Px0LWjWwvP3PMzRf/OY4xbo5X8qQB78BLXfLiD8dhLX6yLiQdGu18ykqTu7NTyxak7LWaQ7MJ9guOu9p42mIY65D9wtEv5S/x+U40SnhB72hgA7T6aYUKB/N8SSYfbWxAEnxB36JnMfwvBjQ8ksZ8Y2sGX0ixUgj9hejRhy+TyeDQoUP4P4ODeOJtb8PWI0fQc//9OHv2bLgL6RYWstkskskkqqurwwnorf39eHLlSlxWVoarjx3D79XV4c10siEIArxyagpfnZhAy+OP4+zZs2htbUVRUREqKysxNTWF3t5eDAwMoKmpSeXBF0Al8E6tJgvNfnzFB48Vj8eRSqWwdOlSpNNpTE1NYXJyEj+pr8dAURF+UF6O15WW4uTmzXjossswPT6Ot5aVmUHc0qvlb1Zg1WTm81HZP5fLobe3N3wfh7vuTjYkk0kcv/xyDCST+Pr0NN5Mcsvni75g7AOfr2uJnWm5lODwy4TOCUk7Bs1JU9vZl/1dGzmplS9s0oosX6yWdLjPLtFrxyWZD9e30ATFSdAX53hR0RevZF8tjjuZ8dFdlqfDLW2f5acldI12Oa5WJEhdS7lruC1ftuKDL57J69oRUC3faoWq9FMrfzHN8ruV47UTAZqtsb589QXbs8Qh5ej6y4JTtuF+LGcpG9dee+RF41v7DuhH1KX8GSyb1PjVFmS18S2duhgn+7qFGc1eJA3Htm/HaEUFnkqn8ZLpacRiMdTV1YW/apXJZPDD8nL0xuP4QXk5fruyEs3NzfjG6Ci+NzqKTQcOoF48VsHxjuOL9GmrFpD9nE8y+GTOi3GsL6bFF/80YBuX/fg0h7R37ZSF5Zcar5xHNB41mWn2Lfv7JoIa7w7kQjDHBi1PMO+MV4v58rp1citfjNZqG402KQ8HnKc0W9V0YdVd1skpSxZarpE8WLGWcUta2D+0xxms3MExxjp5xjpnmfjo1+Sp5Zl8Mcbh4IVFazFLAtc/1kmoIFj4aCHjkLz4Ft8seViw6N9vk0ZgOXi+vpqyGK+vUOK2DjSn5HGsRK2NY+HR2mn3fKA5ttbP4oMLGuanEDq5/9TUFIaGhhYc53J48+lvaGgI/f39eGrlSgyVl+OZtjb09vZiYmIC2WwW2WwWQ0NDaG9vDx+ZqKysBABUVFTg6u5u/N+aGjw9OoozmQw+PzCwwNay2Sy2nTmDrR/7GLLf/S7OnDmD4eHh8OWTq1atQmVlJY4fP46ZmZl5DsPyZHvisazrEvIVbpa8HBQXF6O5uRmtra0AgNnZWWSzWbzo9GlUj49jy6FDqKurw5GtW9GXSOCr4+PzxtV8RdOZ/C7psIqQfAmBg7CEeDyOzZs3o6GhAfF4HCUlJYjFYkgkEuF7GbYfO4bmXA5vKy9XZe2jR+NJ05HUPduAL5ZY9FxK4MkPT7ylXOXKsQv2VnHpaM7lcvOeh+ek68a13pLNCU7GGwDhwqFWpFgJ3H3XdpK5naZ7Tpr5xnegTci1+KklTv45KHds28lKPveqxQaWudZGs11pD/KUi8+XZV/ZTt7noknKke1AjsEFnxUznK1abRi/vC9tt1BfZF/3tdd2+mUM5PrBZ1PaOGwPThdyEUnTH+uSdyctnqUsmR/2FUebxpNWQMrYIdtqurSOBFtxVavBNB/gca6/cAHL43G8fGICmUwG6XQazc3NqK+vR39/P3bt2oXVu3ejdmICWw4dwkMPPYSenh7cNzuL7pIS7N+0ST1qr8lOAvuTRq+7rvm5lZ8lbvZXrY9Gu3Z024fP2YFGj7NVGeOsGG6No51Ok7mG+Wfc2mTKsg1tF9n11XiUtQDnIgnSf7julXgkfRxDrEmYhtfK5RaPjM/FHEkbxzaWj+SBFzQZj7bhIcflWAEsrCu0+KzlCY6JXL/lq3sWu4FhnSjQ6HPjazWVBK2+0eyIc682DtsT14HMj2vj/Ff6s+PHWlxz16TvO19ym1MOz2LheZ9oYAJZSVo7LXhoRa5vPM2RNVyF0C37MB2WY2tgBZR8QcTC40tSlrw0WgrhweGZnJzEj370I4yNjeGGG27AsmXLTLyOt6/09+PT3d14Z2UlfrelBbHY3KmG6y5cwKONjah95BEkk0mUlpairKwMpaWlmJ2dDZ/RB+Z+dWFqagrJZBJPNDTgr7u60Dw6inhNDX53yZIFushms+jp6UFjYyMymQzGxsaQTCZD53DvhNi3bx8GBwexdOlSdXfNJw+WvSbLfLr1FQ6Sp3Q6jSVLloST8ZKSEiSTSdw+NYVtjzyC7u5u1GzahNsGB/FERQXeWVW1QMdaoQhAXf31gWVPPAb7Oo8dj8fR0tKCa6+9Ft3d3ejr60M8HkdpaWnI5wdWr8bqpUvVVXOWrxZb8vkB82PJSKNfBuPFxIHFgoXbip/aBITl7kDKVf6kkQO3S8c4ZGKVq+u8K8O77ky3nNi4sbQFTPdZm9TIwl2O7ejneKk9pyp54iOnrF8urtxOK9PFPGh5UDtp4uNd0idxWY9NaLisWOOuWUWnz86lL0hZWLmKefZ91gp8KUuteGeZyUm89AEtTllyknTIR3w0PrV4yLTL9hw3eQIk9WpN2C3eNbpke6sQLYRWOZbcIXOfrZNUUjbPpwZiXO5zSUkJXltcjI+Wl+PQuXOYLS9HKpXCiRMnsH79epw6dQo//OEPgd5ebH/oIZSVlSGzcSOOHj2KN+3YgS8ND+PWoSFUVldjcHBwwQIi266W+5k/jWaJy+dvsr27p51EkTFNOy2h6dHigScukg7N/62YzPah0aH5LePTbFw7ncRtrDgi7Y5zm5v4WvGBdcZ2bNFgxXeNZ3lf4tL44pjgwMU5+diIlpe0mGLRwH2YV9lG5gMLtDpL1hKynYXHF1ek7K3a3lcHSJvVHnFmfJr++bMWjy1epK1pC0kyrzFPWg3l+Mpms/Nyl5afCskj2n1A/0lTfrzXgkX/6oQkhr9bwrUSm4bLGo8Tl+aUFl2F0M54tM/adw2/tsO1mKSbrwD0BTnmw323eHD/R0dHMTAwEL600eJV0vbZ3l6czWTw6a4uXNfXh6qqKszMzGBzZyeWPv00Ojo6ENTWory8HFVVVSgtLUUmkwl/8jKVSmF6ejo81vajJUswUFSEbCqFB2tq0CQem3AwODiIgYEBrFmzBtlsFn19faitrUUmk8H4+Dj27NmDhoYGlJWVhYsZVmLSgpEEPsZk6UGTOfe3+qRSKcRiMUxPT6OiogKJRAKzs7OYnJwMFyFSqRTuzGTwl83NKCkpmceHZle+ICu/a3z7igeNV6vwKCkpwdNNTfhyOo2bkklc3dODeDyOn9XW4sdLluBd1dVYJ4oKjW55jWnXAqgWOK2ChgsvGfi1CesvArSE7D5rBYi0J1mAyKLUtecTCVJm2oRbfteSHhddVmxgOrmt5h9af77mK5T46KrkkfvINpI2bWFJFnSar7k+WrHCO3taweY+866a7MMLcZa9y37sj1L/LDcuvOW4bkKpnQrSaOXr7EPSt/IVc77YrMUAjV/ZRxZJkheHRx411SbRWt7OF/+lvhifxgfrV9YSvJOl+SifpJC8aPgl/3IsSZe0bxlnpPz4cQqOweyLvl1EeV3iSyaTGB8fxwMPPICOjg5s27YNxcVz5evw8DDS6TSy2Wz4M8rDw8M4ceIEOjs7sWN8HG/q6kI2m0VFQwNGRkbm8cWy8dGu5TrWK+vUgS928n3fDi7HVikrKw9q9GtxworJPI4GWi7TbM+BXDiXfLHfMB/5ZK/lVU1OVozhvOmrdziHSj40+lg+DniC6P5rC5PamCwXlqekF/D/BKL1ngfLx2Oxn78UUcYIyy7ZJyzfY7BilbQjK/dxTGO7YHp84/tkp9HPcVSzX0kfxxytDtBodNfcfMo6ye3Gsk6tavq37KTQzcxFnWiwAozW1oFPcb6+FqNaENOuMY5CaPcZmBV8+L77ucL169dj5cqVphP56LEMSeuvBX+fEWo8xGIxlJaWhj8ZmUgkFhTeWuB8T309PtXVhcvOnsWTfX0oLi4OH5Oorq5GOp3G+Pj4vBMNAJBMJlFbW4uioiL09vairKwMAwMDuOL4cexZtw4vHR1Fsq1NnbD09fUhFouhsrISuVwOra2tKC4uxujoaGj8Z86cQTKZxPr16xfox5KPr9AtVBe+sazg4xZfgDnbeaKhAbs3bcItfX1Yf/QoampqUFxcHL65WcPBwVWj07IXLYH7CgqrvyzgHY6vjI/jfBDggZoaXHbuHHK5HO5fswZ9RUX46sQEfruy0owPvkTN9Gj/HQ7mifvl01WhMW+x4OjhxQMugHlH35eYuciQkw+pF/7JSteOeZU0aS87ZFvgGCnfNm7F7ULsjfvIJGoV4kyjHE/zIy4CpLxkH46JTIuWK3y5k4sIq6jloskqVHwFAutci0lucUErfDVaeGzJsyU7ayHKl7t9MUeLFbKP9iiJXFiQutZ2EC1/47HkfT4BoC06WzSz/zNI2br/VhGr0azFREmvr8YJgiA8Pssxw1qgta6xHUlaZKyKxeZOKnZ3d+Pw4cOoq6tDLpcL3+/kfjb5Na95DUpKSjA1NYWenh6cP38evb29eOCBB9Df349YbO6dDu5xzUJzi/zsZG35veSB+zFYOdjZi+XbWnspQylb3iyxcoVmh/K0kKY7viYXoNgWtDho6d/RLfFIP9be6WBNqqz8xDRp+URbGGV++fQT10FSnpa9abFTo1G7JmUnx9NON7K8WebymhV7Lf3xqSctR8lFEpY7j6v5j1ZDyHgl+dIWaKVstBwm2zN/fM+Nx6dFNd+w7JxjivWohhubazCZL3i+ptkrx2rJE7fn051a/HCnPguBRT1swYnL+vO1Z6YK+azRwM4o/3NRthj+tCTN9FhBOwgC7N+/H0eOHMGRI0fUXTWND41fDghWf1/hIr87g9HG/My5c7jx7Fk8Wl2N4eFhPP744xgYGPDSm8vl8LYlS/DEmjW4ZXwcU1NT6OzsRCw2VxSUlpaiqqoK5eXliMfjmJycDOlNp9PI5XLhokQqlUIymcSLurrwwT17sO3sWfzFc8/hioMH8dWLBQIw97OWb47HcXjNGuRyOaRSqfDnIN2x/Pr6eqTTaUxMTCCRSKj2aslHk41mzxYeTYd8jz+nUikEQYATJ06gpKQEz7S1YaSiAj9tasL4xfcxuMdNJiYmVNzuPwcaa1wNLNqtBCjbWPjfXlGB5lwO150/j9HRUYyNjeG68+fRODuLt17kWxvPkrFGp7umTSasPoXq1BrvUoAbj498S/45YXMyZD7j8fi8o6fumrQN3hmVY0q6XF+ZkFjOrq8sKmXi4RMWPDbvlMrn/7TCxuFgO7T05tq5seTz81rRlQ+0XKfh4CLLKupZ1yzbfLlWa8+0sN44b/n8nPMF+ybTLnnQYo/MyZqfazRwoaedrmG5M01aW02HWizQ/lt/DpyNSd92villVlxcvMCXLN7c2FzcabFZm1S4764vP4er8SzHZHtiv+R4JfXHdsG4NR/iOFBXV4frr78emzdvRnFxMaqqqpDJZLBkyRLkcjlMTU2hqakpPPGXSqVQX18fLjxMTU0tkJcVR1h+Gs2SPs491mTMl3e0E1BWHuPYKPvxwjSPpQHXEowzn/8zfi0OMH2+BTRNbtKeNN+QsZ3llc+2LL3I9loe1mSkjS/7aHFPxgmmRcpCjsmPMkr5ci6xJu2xmP6rQSwL/sz3JX1ygsq+LxcmNV1q93z51YdLo9/JzYqtFn4H2k4/vyNFsxF+1JX5isViC05O+sZmebi+/HiQ5E3jWdqRpQvXn0/SsG3lgxf0Mki+zp8twXCbQsbkMawxLRqtZMqGaAVjDYdGU1FREUpKSjA7O1sQT0yDj8d8fGifNZ7c/Ww2i5MnT+JTnZ3oiMXwk/p6FBcX49y5c3jssccwMTGhyuOr/f3Ycfgw/uhnP8Pp06fnBZXx8XHMzMygu7sbMzMzyGazGBwcRHFxMcbGxsK2k5OTOHLkSFhw1dTUoKSkBBMTExgaGsIPysvRHgT4/MWFhlhs7mctLwB4fPlyDAwMhC+TLCkpQVFRESYnJ5HJZMLTDC0X3xshIV+hoek3nz1psvfhl3praGhAMplEZWUlqqurcdmxY0gNDuLqc+dC3oIgwMTEBDo7OwvyKU3/Pr58/Gp85EuuLii9MZ3GD+vq8NriYqRSKVRUVOCGgQH8XRDgtyoqTB9gHrRxmUetYLR0xHiYfven7fpeKtCKU5mYJXASlyvYjmZeUJB42R7dNe3nwYIgmJc8ZeHg+nPClfbBRZCjV1tBl4siViKUOK0xtR0uSbuUuZMT/yybNoFy4JOvpSfN1hin+3O8ab5tFUfawoQmS8vWWZY8ceIFI9eOdaIdi5d6l7KTL4NjPWo+yp+ljCSNPllb8UyOzf4n+2py5mKQYwX3S6VS2LRpE2pra1W9y/FYHxLcmFx0WgU208j+KL9zfw3kWL646j5LPrXjwO665fsSfyKRwMzMDDZu3Iibb74ZNTU18yZLpaWlqKioQElJSfio5tjYGKamplBRUYG6ujqk02kkk8kF8Yh1Z8lV8++ysrJwQ4P9xrXh2GL5nZYPeXxfPtf4YN2xTTB9PFGSMuLFA76mxVDr0R+NL9eeJ9EsD20xk+Xq8LoXFWqy98nY2adm87zYyTGOX3op+2gy0GxOo499RsOn2THHUR4rn074uxUDOEZqGyJanvPZC8vd3dNiumYLTv+ajfryhqYbtkFJt3s5tpZv5IsYfTFA/nd9NDlI/UtblfK1aiqNJ0lTEAQLaiOHV4vX1hgMz+vRCc0B8gVKrXCwxuE+Pmak0bDANDr4vmxjBR5upyX2yclJDA8PIx6fe8t+PB7HZ9vb8eWREbyvqQnvaGjwKl7iZTo0mRQCPt6DIMDJkydx//33Y3tNDXavWYObe3pQfHFSODw8jK6uLrSJRxgcfLa3F+25HP6ptBRrf/pTZDKZMNm6xYTR0dHwvQzV1dXhz0/G43HMzMyEuxLuVwhGRkbQ29uLqqoq1NXV4cV9ffjZ0qV4d2NjSPPv1dXhc319uGN6GtPT04jH53Zcx8bGMDk5if7+fgBzk3f36w2SZ0u2+fTCgckn73xtuMCvqqrC+vXrUVpaivHxcVze04PWffuwYcMGIJkMg44WrPKB5Y9cuGvFjhWINTlq/u/adHR04NSpU8hkMiEPQ0NDZnuNN5/8fTQVIhcG5rVQX1ssrFixAn19fZiamlqQGNhGOJnwLjXTzosQEiePI4s6Loa0F/04e+FEycd9HVgFheTR2Z97szHL3eHnfhKnxqcvl/AzpLxLrLVx4/BxWodb7kqw70m5cVyy8o9WkMn2vmc5NV2zvzvI5XLhYy5awSZxMc38qAU/asA+rS3uuOJF++lDrajVduG4cJL2rNUH0vbyFU6+nVJfneFi9xVXXIGtW7fi4YcfRn9/vyobLsC1zxwfLLuR1zWb0iaWMgf4jspL+bh2Um/ys6TVB1JPvIDk+o+Pj+PYsWNoaGhAaWlp+Ohke3s7jh49iv7+fgwPDyMWi6GsrAwzMzPo6+tDJpMJf1bZbZxoedHZCzD/BWfWiQtH6/r167Fs2TL87Gc/C08garvJls7YfuTPz/ECKsdeBicvbUzNtyVuLc5oE53i4mL1BbkcH91ntjPuJ3mX3x0eK6cwPvk4IOcO99+qLZgeSQPj0fpqPAH6u5As+9P835IR86LFAXfMXuYESYvPrplfjq/aYzGunXyUgGWr5Uf+LuWgLeiw7tnmJY2W3GR+t351y33nHMI0S79lWTFodYG8py26yrjEetY2pGQtpsVsKSO2MxmztLzkxuSYUggs6mWQlgALGUxzhnz9rISgJXerWNCCUiFjW8Ui4wiCAGNjY3j00UfR0dERPk/f3d2Ne2tq0Ftaik91d+OdjY3zcOcrbgqhm/m3+nD/WCyG4eFh/OxnP8PevXuxZcUK3DYxgXg8jqmLbaanp3Hu3Dm0tLQgmUzOo/M9DQ34TE8Pru3qwvj4eMhzLDY3cc7lcmhoaAAw99vWQRDg1KlTaG5uRvnFnzMsKipCIpHAxMRE+NhDPB5He3s7ampqcHV3N96QSuH6LVtCut9aW4u31NRgamoKf793Lw4ePIgnGxrw8ObNaPrpT/Hi8nKUl5eHpyhmZmZM+VoyZdlrgZFxWnZYCMTjcaxevRrd3d3IZDKoqKjA7OxsqIN4PI7p6WmkUinU1NSoY0matGBptbeSLIPE6SveeUwXyJ1NlJaWIhaLhTYgcXOhw7gs/7PuFQqWj78QnIXAzp07cfDgQVy4cAHA3K+vDA0NLZjcc1LgRSGZaHkSoT1LDMxPUDIh8UKGFe80PbkEx4UN24m87+65d8NoOGXRyzhZd7ww4IotjWeWMz/CIosMCZr9M22OHylPLSfJgop/0YHlL+Xg2kh5se3yc6OSfgamT1t8Ytm7zw6fXGCQ+uGdc23RyI0jn/FmWUg5WLvAkh+5a29NMngcyTvjt4oyKTO2y1hsbtK7YsUKlJSUYHp6egEODb9WUHIfnoDJxzW4P9uD441pYN25a3z0VytmJU2a/bKs2UY0WiTtvb29GBkZwapVq3DDDTegt7cXP/3pT7F792709fVhcnISs7OziMfj4aJZWVkZ6uvrw0V89y4k36SFYwjbBS/yLl++HMuXL8eKFStw7NixeWMwr06+ml1Jvch7HMulrTn5Sx+UvGj6kfddfOQJDi9EuZhWW1uLdevWYf/+/RgdHV0Q23nyWmh9IduyHGTcYRth//bVwBoe951jnNSZzGtarOFrnBOknHkBzerHfLM9aDi0/OKAT5b5+ln2yqcIePfe9fGdSGA9cM5iHWvxVNLFiyFWveDuc+7QFqE4zmuf2ZeYfpaZu+7qAq7HJE6WucPNOZPt0fV1tsoxleUr/7vP0tY5NzudS9lxbvbBohcaFguLCTYStIBl0eFLavna+JIOj6U54cjICHbt2oWTJ0+Gq72xWAxTU1O4sbMTu1pa8J+XLlUDgcStOZxFu9WG6bUC78zMDJ555hmMj4+jpqYGuVwupDubzWJqagqzs7NIJpMYGRkJFxocrrfX1eG3Kirwd7t2oe/ir0Zks1lkMhnEYjFMTEwgmUwiFpubLE9MTKC5uTksToqLizE7O4tcLofBwUEMDw+jpqYGQ0NDGBgYwPj4OBKJBMrLyxe81TQWm3sD9Utf+lIMDAzgX5Yvx2Q6je6bbsLyffsQBAGmp6fDhQspEylzn7wtvWv3fbry6U3id7szbrKRy+XCn/10L7uqqKhAbW3tPDycBLUk5KNH41mzTR/9mjwdxONx1NXVhcdYH6urw0/q6/FbpaVYlcuhpKRkXgGl+Z+lG5/cfb6l9eGk4Wx5ZmYG09PTurBeIFRXV4cvJWtoaMDZs2cxPj4e2oCckMkEZC0GyJ0EWfhpyUAWC1z8yMTqrvMkXJNvIXahFTWlpaUoLy/HzMwMJicnQx7kUUSJR0u0/J3pY35l0eWzG1ksutioFayykHN+rMnI8q0gCObtZMoFEq1fIZN/7scTfCkXBhlXeJIpeWY9S1lJ39JkZdHBRRbvnsl+7nOhCwq+wohjKPMk+7OfSF+UOOLxOJLJJMrLy8NFV1m4WUUly5RPCWi7bFoB7Iur/J0XWOT43I/5ZL0XFRUhmUwuiJ+a73D9Je85/O5E3w033ICKigqMjY1h165dePLJJ8NfkKitrUVZWRlGR0cxOjqKyclJDA4OIgiC8LHMJUuWLHgvhhyXF2PcAmA8Hld/HjGZTKKurg4DAwM4efKk6vearC15Sr5ZVppeZfy2dMyTB2vRWcpBi9XxeBzLli3D8uXLceLECYyPjy/w5ULszOefllx8NZoWfyRo8pGg6V2rHbR7Gk9sw1JGPJFkHq38xfxyHOATdozLxQ+O2Zwz5NjagiUwfxGax9TihlwQ5XwggeO8tENNB5p8ZK7SFgJ8srTacvzTYhjL1vHJ+PlRHB5P80mpW+2UBstNnuzRYqnkS+YMbs+LjfF4PIwlmp3lg+f185aFQL5gu5iAs5jxrALPGruQokdLTADwpe5ufPzsWVwzPo5rLh4LT6fTmJ2dxfDwMC5vb8fV3d3YHgRAS4vJn28MeV9LAoXIhHF+8vRpfKa0FFtTKRSfOoWNGzeGPxXlVt16e3vDn6JsampaYLTDw8M4f/48crkcKisrMTAwgOnpaSSTSUxPT6P84u9cx2JzvxCRzWaRTqfn0TQ9PY0LFy5gZGQEU1NTKCoqQjqdxtTUFCYnJ8Nfv9CCmXt79GVHj+Lp1las3r0bM7kc6urqMDs7i1QqFf7KhSUbLYgtFjS9+GzKASejmZkZZDIZxONxpFKpsJ1758eaNWvCF11JHFoAWSydPvAlUasoyeVyGBkZwdmzZ3H27FmMjo5iZmYGD61fj/6SEnx1fBydzz2HnzY14V3V1fjN8vIFeLUiw/osxwb0Nwe7CaBbwJmcnMTExATGx8fD/yMjI+H32dlZ7G9rw74NGzCSV0qLh3Q6jVQqhUOHDqG1tRWrV69GV1cXJicnFxSVmp3KHUyeNGt2yJMSDbTk6yuCrRjGux2yr/tcUlKCVCqFlpYWXHbZZejq6grf2eLe08J8s73zBN8C3ulzOPloLu82cNyxihWJn5OxvO7+O1xy8uKbDLh+jg5ZsPBJDddPFjuON0ezJU/5WdvJ03yTr1knEiQe2UYuaGgxRS7ASLol77y7bxVQUqYa75p+2a58Rb37HI/HUVFREf7yUk1NDU6fPo2RkZHwfUKZTEZ9NIkXZiTNrCdZUDL9zDfri0HLBZp8rMmK+19cXIytW7dizZo1ePzxx3HhwoVQv1zH8DgabUVFRSgtLcXWrVuxYsUKDAwMhI+cZTIZNDY2YsuWLbjuuuswPT2NkydP4sCBAzh9+jQuXLgQvi/K1TBjY2NIp9MLfEXjRTu6L2WdyWRw4sQJdHR0oKenJ9wIcH2l3DS98X1ND+zj7pprK+ONlJ82kbDoYh3zNWDul8Lc6ZDm5mb09fXNo4XB4k+Tu6+9u2fFIB/tTJ/sK0+DSJ1qjztIXOz/PL7My/wOJPk4g0W7Npm07jt6ZDtNRloN52jV4oYWxxm/Jg/e2OP8wSdeGC/zqtV62Wx2nlzZNiQfhY5hyUADzRetGpnHl/ZmxUKm3xcb3DVevGE7kKfPOB4tttZm3fjqLgmLekcDD8rMM2FWUtOcX8Nt0aAZkRZ8tDG0Nr5+Go2uz2d7etAZj2NXczM2HDuGiYkJtLS0hD9ZGARzx/f27NmDNWvWoL6+XuXTN7bP6PPJxWr79elpDKZSeGbVKryyoQFLly5FcXExJicnUVxcjMbGRsTjcTzb0oJvTEzgg319eNuSJfNwZDIZjI+P49SpUygqKsL4+DgqKirCXeyKigokk0kkEonQwdyKfzweD9/dMDw8jEwmg7KysvARjLKyMmQyGfWFmk52JSUlaGpqwnUnTmD5nj1IpVLIVFXh9OnTOLFpE/asWYOJ8XG8Q0zaNTn5nMiSaSH6k9fz6cdNMN1uSyKRwPj4OIaHh1FXV4eGhgY0indVSBoW448Wz4V+tpKrvHffyAi+MDSEa86dww0DA4jFYqiurgYA3Nbfjx/V1eG6zk48uGwZ+gB8eXgYrystRSKRCHHyQgHzJk/QzM7OhgsH7v/4+Hi4YDA5OYnJyUnMzMxgdnYWmUwmTIDunRHFxcXzdlDdaZgDmzZhtLxc1dkLhbGxMfT19aGvrw8PPPAAmpub0dvbi9nZWZSWli7YCYjFFv6UkvMly66144Nc9HHC410qxmMVbrIPj8MJrqSkBMuXL8fmzZsBIFwU3LlzJwYHB3HkyBGMjo6Gjw7JcbkYZFq0AlErRJhe4OeTWuaTH0Hx4ZJ6YbplIVZcXIySkhIkk0k0NDQgHo/j9OnT4XPknMj5WVJnDyxf3uVkHfvyH1+XYzI+BuuECJ8icXgBzDu5wTKSRbR2ukeLSVIGUnasa6uIljJy7bSdHSkz1kksNrewfuutt6KxsTHMaUuXLsWWLVswPj6Op5qa8FBtLVY/+yxWHTgwj1Zfvrd8zFcka8Ut33OftbEYtIV/iTudTuPKK69ERUUFDhw4YE6ENLlL/I6u0tJSXHnllWhubkZtbS0ymQx6e3vR1taGhoYGrF+/HtPT03jqqacwMTGBmZkZlJSU4JprrsGuJUtwcNMm1Dz0EFacOYPR0VFMTU3N2/Bge3LAp7gkrcXFxUin06isrERtbS1SqRQGBwcxOzuL6YsnPDk3Wv7H362JrNaeJwmsP8tH5GfeZdVqXndCZfny5YjF5t6XUVZWFr5fiGnjxTKOP5r/aXLX5GLFL+ZZji1tS9Ip5Sf9nXH6fE3+ZzloO9isK82HWJYSWD+aL/KEXsqFNwE02+E4z3K07Jpp5F19zcdYJmzTbDMyz7AefHLRxuZxJe1WX80OmBfGp+mCH39i+riWsOxSO5Gk2ZHklXM00wxgXgzT5JPPjxkW/TJITSiSYC3wWLh8RijbSnx8nR3AGkvDZ13jQsai671Ll+JTnZ14e10dypYsQSqVQnFxMXK5uedDy8rK0N3dje7ubqxduxa33XZb3uOd+Wjz8VZI/1gsht9vbMTHTp/GFV1d2LRpE/YsX46f1Nfjpu5u3DAwgKKiIqRSKTzT1obuIMCnu7vDhQY3ZjKZREVFBcrKysJ3CLhJmps4TE1NhT89yUbs3kNQU1MTFp4uec3MzGDP8uX4YiaDD3Z343eamtQA7OTtXizpTjA8tXIlBkpK8IXBQbzz4ks4tWChychXcGkBLJ8+tAAqoaenB8eOHQsXGdx/d+w0k8lg+fLl6ukMLeBavqmBVvQwzxxYNZ919zKZDL40PIzzQYCf1Nfjqq6ucDEgm81idWcnmi8uCGwaGsLedevQevQoHq+pwfXXX7/gkYWpqSlMTExgbGwMY2NjGB8fD1/8mclkkMlkMD09jdnZ2fAkQDqdDh/JcLbheHN25k41ODt0/uqO67tktvLJJ3HyiitU2b1Q6O3tRW9vL4C5hab+/n7MzMyESYiDuLVybBUNQRDMO8qrHeXTbMW3Q+76uGtsG7IwlQlOTvxKS0uxYsUK3HzzzSgvL8fQ0BC6urpQWVmJiooKHDlyBD09PZiZmTELQ0ebLOi043+crC1eJI1SNhKP1sf9aTsK2hFDR2cqlUJrayva2tpQWVmJTCaDiYmJcLLiFqmtY5NWTJE8uEUp+YJNbbdcswWOC3yPixdJn5VrXbuysjKsX79+biHvwIFw4U8e+ZQ6kzu2ViEl/1vx2smTF080nTmdSn6B+adV5J8cu6qqCtddd134s4vpdDpcRK6rq8PmzZvxKQDDAI5t3462/fvn0cu70xr/TJ8DbbdMytDhl/pjHKxvS5YsG9enrKwM2Ww2/OUtZ4dW7aiNde6yy3B42zas37sXOy5cwBVXXIHS0lJMT09j3bp1mJycRGtrK3bv3o329nY888wzOHPmDIqLi5HJZDA6Ooqamhoc/S//BbNLlmD4JS/B+Cc+gcnJyfDF1My39plrQGe/7nRFLBZDV1cXhoaGsGnTJjyYTOJnS5di3XPPYc3hwwtsM588eYGd7UvKTMPn0x1DIXWwyxmlpaWoqalBMpnEsWPHcOHChdCunF61Y9Xszw7kJIZjJYOLmZwPtd1x1hfnI9lO5gxtfMs/tBpOblJYtaWvBmWZaQv78p7Ep9koxwqJQ74/iOOM/K/lO6uOtO5pfq7Vj1rOkG1cfeY2hrQYrI2t8cI1q6Yry6Ys3riPtihg2YX8rNV92mSe7VEuxGt0sX58tZtVJ8VisXknchYLBS80WAWKxoCWULgN4/QFSnftbzs68MmODrx36VL83rJlC4qKSwXaiiDTCwDvqK/HO+rrcezYMXx3eHjeSxOrqqoAICykTpw4gRe/+MXeQsdyRDZ2KyjnS+LOad9xcRL4+NgY4qkUftLQgP5EAj9tasJNQ0OIx+OorKzE28rL8Q8Afv/iix0db7HY3M5FY2Mj+i/+/GRJSQkGBwdRVVUVvnPAJXQ30ZQB2QV4d8zUvR26qKgI5eXl2LNmDQYTCfzNuXNYffAgHluyBP+cSOD36urw9ro6BMHcT2SOjY1hYGAANTU14aMat/T14cnWVvzexXaa/Hhn2CdXKT+tvRYoNdxc9AbB3C9/uGdJZ2dnw3sAUFNTE749u6WlRT3GyXrREjsnO402XzHiK0SlbA8dOoRVp05heO1abDl+HOfPn0dRUVG42BSLzf3UWzKZxE1DQ7jxyScxMTGBv52cxH85fhxbDh3CqgMHwpd6xeNzx+jdBHVmZgapVAqVlZXhwlYQBOF7QGKxWDhJm56eDhMrgNC23OKDsxXesXTHmsfHx3HZ2bO4vL0deM1rdIN4AbB+/frw5EU2m533oji2TbYdLS5xGy25OnB+xxMYiZPHl8kMWJgAeYfAyVUWX06njY2NaGhoQCwWw+DgIPbv34/y8nLceOONyGaz6O/vx9TUFCorK+e9o0XyzXRau0eaPPIdC7X8xZKPVXTw/3g8jkQigSuuuAJXX301hoeHcfz4cdTV1QGYi5MbN27EqVOnMDIyMo8Oq2iSdMoJJeuBC1HXj+XkvlvxgO2J5SrtV/JfWlqK0tJSbNmyBdu2bUNvby96enpQUlKCzs5OTE5OqjTJcdk3tJ167RSEVkQxbsk3v8Fe+gjbjhy7uLgYV199NVpaWnDq1CkkEgnU1dWF7yByLyl+TXk5vjY9jW3Hjy84pcS60O75gHnWikzJg3Y6yBpPm3xIOysqKkJNTQ2WLFmCoaEhDA0NLXjHEuOSYzlZ7t+4EaOpFI5t345tF08itLa2hnmhoqICU1NTOHz4MC5cuICysrJwAcG99HFychI1Dz6I7C23oPmxx+bF9qmpKSQSCczOzoZ5gSeJmt/H43G0tbVh7dq1GBkZwd69ezEyMoINGzZg7dq1+GhREUYSCRy57DKsOXzYW59Km5RxX4vXLB/pe+wb2g64VhNoOpX0On02NTVh8+bN4U+Hzs7OYmBgANXV1aGPccyxPku5Srq1hVoJcvGfT9zJ9prPsg74u/QHSQNPaBlkvHG2xQtFWnuWgdae+bBwsPwsmbPstPeeybjJeLRYYsVpjT4r3jIN2tyF47Gj0ZIbx3LLrjifWnRYNsb4fN+lLfIiG/Np4dPqbQd8+krywLbtZClPaUqepO1JW9AWERcDi350whFjJT1fwOA2hRIcBHO7c//z/HlcAPDXZ8/i2ovH52pqahaFk4NUPh60vg7i8bmfanzssccwMTGBsrIyDA8PI5fLYXR0FMXFxUgkEmhoaEBra6v6vCnjtwxKcwAtoGqGyBCPx7Fy5Uo888wzGBoawnXnz+MnDQ24aXAQ2WwWPy4vx0+bmvDeykrsvrig43YnXLJ3z+p1dXWF72aYmJjAxMQEamtrw0lFSUlJ+O4BeVTdHQ93z1wBCH8CcWpqCuv27sXRbdtwZXs7jvb34xtXX42emRl8vq8P76ivRyw296LJ0dFRzM7OIp1OY2BgAA0NDXjZ7Cw+tmxZuNjDycRXXMnrWvC2Jh8+m2GdfqW/H5/q6sJVp09j1cGDmJmZmffSKRkA4vE4nnnmGWzcuDGcXDOd7jOPxe0Kbc/gZDY7OxseD3WnDSYmJjA8PIzvxWL4YUUFtmUyeMuDD86dMrh4OsG9f8I9kyz9YHx8HEd+53cwVlGBg5s2YePx4wAQ7uwWFxeH/QFgcnIS2WwWw8PD4fO7ZWVlSCaT4a+XBEEQvvPD/QVBEE7mE4kEstksenp6UF9fjwOrVuGZtja0PfMMXn5xtf/kyZOora1FU1OTKZcXAjMzM2hpacH27duxf/9+nD17NjzRASA8wcJFmFYUAfNfhORAiw++giRfXNdiJxd/sr27z9e7u7sxMDCAsrIydHV1YXh4GL29vVixYkX4GJFbKEqn0+Fb5FkeMiHK71bxIHfhJI0y0cuCS9Kv9WHZaIUZ09nU1IQXvehFyGQy6OrqQmNjI1pbW9Hd3R3a5tjY2Dx5y2OjWkFg6dbqo02sJL38XysuueCSxarEF4/Hw13gqqoqrFu3DgDCz+vWrcP999+Ps2fPhjTJRSPtaHe+765ekLIppMh07XiCYT2qwzI8uWkTftLWhjclEth28R1FnZ2dYWyPx+MYGBjAK8vLcUc8jqfGxnAoT90iZZ2v8NRqAu7H8pLyYJB9tQUReS+RSGDZsmXYsWMHgLln+letWoVz585haGgIMzMzZmHP/7cdPYpTV16JV8diSFVUoK+vD/X19RgeHsbExAQaGxvR3t4+dzJu0yY0NjZiZGQE/f396OjowPT0NM6ePYu6xx9H/RNPhC+mXnLx1KmbMGcymXlxlnlmPy4pKcG6detQXV2NPXv2oKKiAitXrkRjYyP6+vqwbWgIT65YgXX79s3zCS0maKeLLP1r1zS98ikDK55rNsK0xGIx1NXV4VWvehWqq6vR0dGBCxcuYOnSpaivr0fvtdfikYYGrH3uOaw+eFC1GSlX3/hMg8a/dk+zW4mXNxHkZxlT8y1eajRqsVfjUct/Pplb+UOjrRCZaXRLOTFtHFdZphK/dfpBjiXl6/MDzmea/K0xJK9SBnLhzXoUQANNtlqus2jxydf6LGsU2VfzYSuGS/nwfZlPGa9lS1pdIa/zZx8s+mWQvuDFRiSva8FRttWId8x/6swZfLKzE0uHhzGZTOKKc+fw495eHF67Fo+1tOC/LF8eTj6twMN4Nfr4XiG8f7a9Hf9z9WrsLC9HU28vqqqqMDo6Gga64eFhpFKp8LiohcuSjwWFHIO1HCsIAhQXF2NgYABnz57Fpqkp4F/+BVffcAOKSkvxo9pa9BcV4YvDw/i95cvxpe5u/M25c3jN7Cy2njmDkpIStLS0oLy8HPX19ejv70d1dTVKS0vDF+sVFRWFzysC81fEcrkcpqam5hV07mcp3Y5meu9evGTvXqxZswZBIoGbe3rw1MqV+N2Lu3/ZbDb8ac2SkhIkEgl0dHQgk8mgpqYG6XR6njw0Ocn7+Z6NzAdsZ2xjEr40PIzu4mL8rKUFm0+dCo+butMv09PT4fF+d/y5r68PK1eu9I6tFaPsf+56LpcLH2twY7k3ds/MzIQLCCMjI/MeX3D93IsV3VG8h976VoyVl+PZ1aux7uhRpNNpJJPJBSv98jEFV3wuf/xxnL3qKqzauzd8/jOZTIbv7ZALMG7Bw4F7TjSZTCKTyaCjowONjY1IpVIYHR1FZ2cntm3bhmdbWvCz5mZsPnAAt09N4Yn6ejzx4hfjsmPHcHD1aoyUl+PMVVeh//vfR1FREW655Ra0t7fj7Nmzz8se8sHRo0fR09OD8fFxJJNJJJNJjI2NhYtzDtxinNSn1Lm0AQ20WCefkZdFGaC//Vkbm0EmP21nTUJfXx/6+/vR3NyMLVu2IB6PY/fu3Xj00UcxODiIwcHB8HEr7aSCo4ePvvpiqqRNFprMj3aEXysquK+Vv/h7RUUF0uk0Dh48iCCYO/nW3d2NvXv34uzZszh58iSCIAh/kUTil4sgvqLG+pyv6NfiBLeVRa8cg4sveZJiyZIl4QS0tLQUk5OTOH/+PI4cOYKqqiq0t7djZmYmfMxJAhfZko58cVubPLIMtUKZHy/QYjsf3wWAg1u2YCwex1cnJvA/MefjBw8exMHVq9Fx/fV4dVUVtg4MhI9IuRjoFkfz1QBsy+5P+q7k3aprpK61HUyrZpK7vA5ObdmC45dfjldOTeGuFSvCF7lWVlZi1apVmJycRDqdxoULF8JfoOCi3eFzi8KbT57EH6xcieXLl+PclVeGdhGLxbB3717ceOONqKurww033IBEIoFYLIZVq1ahr68PDQ0N2L9/P9atW4fOzk6UlJRgZGQEiUQCmzZtQklJCdrb20N58XtZWPbSzqurq7F8+XL09vbikUcewfT0NHK5HJqamtDW1oabKitR9H//79zLnC++G0qzIR6L6zT+btWk+U4CcGzS6kGNLpdvt23bhqamJvT19YUnK1tbW3H33XfjrUVFGABwaMsWrDl0KMQrfzJzMWBNjHz8WLKx6mKf/DXQbN53YoTH0OYWWj0m/dl912Kaxquv9ve10/Rv5WwtH3AM1nK1LzdqPMmYpdmkVuPyGFoOcv0lXt6hZ57Yb316tOyHY7CUsYzfPrsuxL65RtBOqspx3YKkxOdygcQv6SvUrixY9EJDPsSsNAdaQLQCKLf7bE8PuoqKMFVWhnf86EcoKyvDRBDgXysrMZDJ4JOdneGz+D4aCynMfbRpCePLw8PoTyTwaGMjruzoQHl5OSorK5FIJDA6OoqJiQnU19ejubnZiytfELGCsO+zBbHY3DsAxsfHUV5ejnh87me4XJK47vx5PNXairddPC3yub4+dBYV4e+np1Hf1YXZ2Vk888wzaGxsRFlZWfjTUm7iGovFwp1mWdi5P7eD764HQRAeZx8ZGUFvby+Ob9yIJ2+9FTt7enBVVxe2nzuHP7v8cjTV1SGbzWLfvn2YmZnB6a1b8fgdd6B4cBCbLv4cpPaSMQ7kLCdrddzJ3pKzpS+rwIvFYvi9JUvw6Z4eLNu3D2fPng135t1LCNPpdPiTf52dnRgYGMCjjz6Km2++Gc3Nzaodygm8O3XgXoQo32/gPk9MTGBkZCRcaHBHSmdmZlBaWop0Oo3p6WkMDw+jqqoqfHdCRUVFWPjJX3FofOQRzF53HZY+9lhYPE9OToa8uZMrjk93dDWVSmHHhQtY/7Wvzb1Yq64O+1pbsWftWlx56hRisRieam3F1sOHsXN4GDMzMzh8+DC2bt2KQ2vW4KdNTVizezdeOjMT2lp1dTWSySRqampQW1uLjo4OPHbVVRirrMTxK67A+m9+E09ffTXGystxcPNmbNi3D6d27MB/SiSA0lIsWbIEN954I3p6evDoo4+afvRC4KmnngrfRC8LbfmYCSdvX6KTEw62dbmjwEmPi0ItkVg7wFoCljhkgs9ms+FL1JqamsJHaEpKSrB+/frw5XG9vb2hHbqdSEmvHFuLj1qytgoGxmX5v2yjxVWtUNHoC4IA/f39OHXqFEZHR5FOp5HNZrFr1y709vYim81idHQUFRUVyOVy4ftauBiT43IRIB9R02Kg1Iu168/FDxccjFfGUB7PnX5zC6jZbBYXLlxAb28vzp8/j+bm5vARK3cSTovJ8qinPPkgi1BNZ1L2lv5ZR6xLbmPZ+7rnnsPxyy/HjcPDePa557B06VKUl5fj7FVXob+4GP8Sj2PF8DDGx8fR2NiIDRs24IknnkBtbe28XwXRagPegeUFFln0806utB3NJ+Q9F3OkHfsmIoe3bsVYOo2fVFbitwYHcfz48XBRqaKiAvX19Thx4kR4Ms2i78SmTThxxRV40enT2HLqVPj+p7a2Npw6dQrZbBa1tbXhBkY6ncb69euRyWTCEwrul6rcpkXJb/wGnlu7FssefxzBD3+Iffv2IZVK4bLLLsPxjRvxSHU11u3di1XipZXWAmQ8Hg9PQBw+fBhnz56dG+/uu3Holluw+eBBrNy3L3wMzsUuOdmQNsWTEPkuAp6k8yQhnw1r+pXXZFv3J09QAnOnN1pbWzEyMoL29vawX0dHB8bGxnBDbS0erK7G+ouPiGg2ok2iHPDirfa4FfuZFqd5MVHKU76fxpogaf7GcvQtmnN716eQOCJlYd2TtEo+5QYEsPBlwdxP4rUWnLWFRN9E2Jqgsg040E4jsr26e9IfnIy0mlqLk7IdzzMkzVwbSZzao1TWBgrnR5Yr0894GRwezS7cIiDrQKt3tHiTLx6wTN2mFJ9KseKPBYtaaLASrLzvCy4OrEmalsQB4FUzM/i7qSm0Pvssent7UVZWhqKiIrzo9Gns27AB773400X5HEQbnw2f7/sCEAC8v6UFHz9zBld1dyObzYar56WlpaisrEQ8Hsf69evD9zf4CtV8dHAbNi5NnlpfYM7xamtrEYvNHV+XE9ibhofx6vZ2XNnYiCAI8N7GRnziwgVsu/gSoHQ6jde97nUoLS3F3//93yOdTiMIgvAUwejoKIaGhsKFhpqaGpSVlYWLDM5h3K8EBMHckeFUKoWhoSGcPXsWPb/925iuqcGTiQSuvViEj4+PIx6Po7e3FydPnkRFRQV2r16NkbIyPFpWhhsvPvoxPj4ePs9vyVoWqnzPkns+mWo2KPtOTU3h3Llz2DYxgY/29OBnp08j2dCwwIld8eReyjk6OoqDBw/i4YoK7Bsfx90AbhULB+7EgTty7t5T4F506N5L4E4HOD3LRwsmJiZQVFSEsrIyBEGAiYmJUD/uZIGbMJSVlSGRSIS7rrlcDkt7etD5+c/PTSCbm7Fy5cp5xYsV0N2CQ3d3N9LpNJqbm7F33TqMXtRtLBYLFwS2fv/7iMfjuO2223D8+HH8rLkZY5WVuHDDDTjxxS+ivr4eb3rTm3D8+HGMj4+jtrYWN9xwAx588EHs7O7GnspKvD6ZRO3atbgbwP1FRXhFSQlWFhdjZ3U1EokEftTWhqqqKlRXV4cvOf1FQCaTQUtLSzipdnpzp0Dcr3BoyVxLVFpR6UDqnO1Xe6ZbJjotOcl2WgKSn2W8SaVSuPbaa8NYMz09jYGBAYyMjOD8+fOIx+ceLRgbG8Pg4GC4MMXFONPLoBWkXIxIumQxYuFy9yVuHkMD2RcARkZGMDIygtra2jAvlJeX4/HHH0fXxUXcTCazoKjTihhrHKsw5LZSLpJ3beLuy+WaXcnxZmdn0dvbi4MHD6KiogL9/f0YHh7G1NQU9u3bh8nJyXChm+mXepPFj5xo8zPiXCjKSbqkTdqqdk2rSdw1bUK45tAhvKizE3fddRd+cjG23X777SivqMA3czn8Ri6HlpYWjI2Nob6+HsePH593so/H0mTK+nLf5QReti2kuHWFPfu21V5e33LoEE6/6EW4dWgI/9/3voebb74ZNTU1mJiYQHFxMerr6/HUU0+Fj1zKAlniPrJtG8bSaRzctAlvTKfDTYtUKoUgmHvsrba2Nlz0npycxMjICIqKirBkyRIMDg6is7MzPCk1OjqKI1u3oieXw9QVV+Adw8Po7+9He3s7enp68MjOnRgsKcFhsSMvF3CcjKXvJRIJTE5O4szFU51TU1Pov/VWDJeX4+SVV+LmixtL8kSrFkMl7kLqN9lH04EPGA8vKjtblry6OFxeXo4HHngABw4cQH9/P5LJZPjenOqiInzr7rvxFIDjZWXzHv/Unuf2TXAs3phHbSLE/1l38pr87+67mqe0tBRjY2Nh7OW4puUSa1FKAycDbbFP3md5yOtyXD49oMmGY5kmV6uecDi12KfJNN+Y0u+tmpr5l7h8i6RWrpe1DPOm8SvHlTS7djKPcF6W3zW5afmCZaWdApL0MMjxZX9+xxC3tWoBjWeWq29TygeL/tUJDTRDtpKcdl8LtlIBH1i9Gq3f/S6mSkqQuuwyFBUVYWBgALO7d+M3y8vxumuuKZhpn2Np95lnNqp3NTbi+v5+PNDRge8VF+PY1q24pb8ft4yNobS0FLfddhtWrFix4BlHjU/NUCWNhRR/lqx5PPcm4bGxMQwNDSGdTocv3nNvgF928f0M72howJWdnXioowNlF5/FXL58Ob7//e/j6NGjWLt2LeLxuedP3WLCzMwMJicnUS8eaZmZmQmfP3bHRysqKsKfpXIT8aKiIlzX3o596TRu7OxELDa3GPK9730P9fX1mJ2dRU9PD4IgwHYAT7e2YuuJEzhz5gxKS0tx9uxZVFZW4qabbjKPY1m698nVCtj5ZO9W13t6evCd73wnfDxhZmYGNTU1qKysDB+XcBPMdDodTkA7OjoQi8Xw06YmjAH48sgIRv7lX5BIJMJFABeMZdDJZrOYnJycV0TJXZPh4WFUVlaGJyiqq6vnBS5XNLlfcnCPNaRSKTzV2Ig969bhqjNnsOnECRw/fhwVFRVoaWkJfwFE23Hkws0tbLziFa9AZ2cnjh8/jq2VlTh++eVYfegQ4vE4Tu7YgdeWlQElJdi0aRPOX3EF/nn7drQNDWEwFsObq6tRfc01aG1tRXNzM44ePRqOnU6nUVtbi5tHR/GRizuHD5SU4OXZLP5LfT3Onz+PAyUl4SKMW2RwC2rupzkvNWzduhX79u1DIpFAPB4PT4C4x4rcYqpWpMlJlrRJp18rHstJicPFBaFV8FrFg2yjXXP0lpSU4MSmTbi/rQ3bjx3DHbOzWLZsGYaHh3Hw4EGcOXMGPT09AOaSa11dHcov/rRovjH5u2/Xj4tDKTPr5IeUnxVLNPvm76lUCtu2bcPq1atRVVWFXC6H7u5uDA4Oora2FuvWrcPAwAAAzHtUSPq2VgiwLXBuZR90NsBy0nK25JkXDLUCUcrDtRkbG8P58+fR09ODnTt3ory8HD09PTh8+DCefPLJ8HEsy744LmuLltK2WR6x2M9fwpVvssZ8ckHJiwLS5oqKitDW1ob29nZMT0+jvr4e69evx3oAv33xBbOTy5bh6NGjKC4uxpkzZ8IXRZaVlXltWn5nPQXBz1+YJnmRRar1CJJmU5ouNVnF43G8IghQf/o0nnrqKZw8fRpvfvObw4XyoaEhFBcXh3VEV1cXRkZGFiwMAcDG/ftx+kUvwt2lpSgvL0dHRwdWrlyJVCqF8vJyDFz8NazW1lZ0dXWhqakJyWQSHR0dqKmpQX9/f5h3Tp06hXQ6jTclEvh0Vxe2HjkSLphv27YNa9euRaqoCP9fNovNZ86gpqYGQ0NDCybKLKuWlhYcPXoUs7OzqK6uRi6Xw8tyOTwQj+OVuVz40sSnly7F/Q0NWL93L9r27/fGC63OkzlYm0BJm5Pg8wstrmv25f6ampqwa9cufOtb30IqlcLNN9+M9evX4+mnn0ZXVxdGR0fR3d2NqqoqTE9Ph6ccfTGUgWMV26pVG1v9Jb8WyJxaXFyM6upqvOhFL0JZWRl27dqF8fHx8F1U1phS3lyba+3y1emsz3w+L3XKE1iWp2vjcoBrI32eJ/FazeHyI+OR43B/lp+kxao7OO9Im+dcJ2nh/KbRxt/5ETmHU/MjCZY++CSHtajEPDqwNlE4Jrn7/Os5ckytTvLVEhI0O5My4sWYQuB5nWgopI2vLQcHrahz14aHh7Fr167wJ5N+lErh8RUrcGNnJy6PxcK38fscvVAeuFgspG8Q/HzneO/GjZhIJvGT+nrcMjY2t/JbXR3+5KUF+WRm0VMIaEkOQPjIg9vJrqiowPT0NM6dO4d169ahu7sbQ0NDqK+vB4BwEuRe1nj+/Hk8/PDDGBsbQ3FxMbLZbLgrNTs7i4qKinA3WDqmw1FcXIzp6ekw8Hd3d2Nfayuefve7cfmJE7htchK37t4999NvMzMYHR0NnykvLi4OJyHX9vbiKrcTePGt0yUlJdi7dy927NiB2tpaAPOfR7eez7Jkx0HI6Vy+32Bqaio8XeB2W+Rpgyfq6/FUWxvW1NSg7vDhkJ61a9eG9iGPgbrJWTwex5IlSzA1NYXmXbvQccMN2LB/P0ZGRjA8PBwG71js54+ruPciuFMR7jlWFyRyuVz4iEsqlUImk0EqlcLU1BT6+vpQU1ODhosnLU6ePIn169eHpw6am5uRSCSwd/16jFVWzr2T4cgRLF++HCtWrAh5kMHa8cqTFmnbLS0tuOyyy/Doo4/iN9evx+7aWvztjh24sbMTX25pwfT0NL63dCmWLl2K/zE7i56SEuQqK/HoxUemfpxIIJlMLgjupaWlWLp0KXK5HJLJZFgkVlZWIhabW+xwk323ODM2Nhb+n5qael5+lw/q6urQ3NyM4eFhDF48iVNaWhoel3f0S//l5K0VFFyksc1rxay2IwzMf1GWpjPXRiZud02CG+PApk0YKy3F/g0bcE86je7ubhQVFaG/vz98U73zpaKiIpRcXADSCiJHh0yaVh7Rkq67L/9LufAYXJBYC5h8ikcu/i1duhTr1q3D7t27sXbtWuRyObS3t+PgwYNob29HEAThr6G4OOn+SzkGQRAuRHDBw4Ww1CfzKu9x8aTJiu3EmpRJHC7WNzQ04LLLLkNzczMymQyqq6vDF0MGQRD+FLIGUvZOBqwTzR4lv5JPPhbLMuKCTOpVK6odFBUVoaqqCufOncPLXvYyVFVVhe9fcI+zlZSUoLm5Gfv27QtzL/uL068GPDYvishCk3epNTxaHHHXtb58bePGjYjFYli5ciWOHTuG8fHx8Kc9U6kUZmZmwpe8Xnnlldi7dy/6+/sX2Nvaw4fxkqkpXHvttWhatSo82TU9PY0VK1aEO+bJZBKDg4Phr1sEwdwJQHe67tChQ5icnERDQwPW9PTg2c9/HoODg+hubsb27dtxxRVXoKKiAsuHh7H+wgVkYjEUb9qEJ5980jzZ5PitrKzEiRMnwscHr7rqKnxg9Wr8dm8vzvf0oHnZMjQ0NOB/ZrMYKCvDwU2bwscytHqXfdEq/BnynW6zgNtaE7Pi4mJMTk7igQcewNTUFO6++268/OUvD38Z5uGHH0ZxcTEeeeQRbNmyJXzEYmRkBAAWnMhiu+KYzDLQ5MF4NH7kf0vmbjGppKQEbW1tuOqqq1BTU4Px8XFs374dDyaT+EE6jXV792LtxZ8o5RxsxQ6uFWU7ebrF4sMXy/kFvhwLuY/To9skmp6eDh9hdb/M4uSk7bRzznT/5eRWO9EhTwgybzJuu3taLJcnjWUcc+PJOkOLx7LmkcC1UyF2yXlB+851iaTD4ZZjsoy02kv7LOsP2dfJRotbhfiMlde0uk7+XLHvFBLDot/RoEG+ALfY9u7+l7q78fGzZ7HswgVc3d2NsrIyPL5lCwZTKfykvh4rnnsufJO87GsFa+2eL+hZ/VghIyMjiMfj2HL4ME5ecQVuHRpCLBYLj5gzHk40Fg1cMFn9LbBk4QpA9xvu6XQ63Bl3b2/e19qKvzpzBh8KAvzOxd0DVzD9oLgY/09nJ5qXLcNVF19c9/jjj2PdunUoLy8Pd4fdyw0nJydRUVERFlWTk5PhpNj9YsTk5CSebmvDcHk59m3YgFuffRaDg4Ohcff19QFA+HOaTr7FxcXh5No9HnDhwgU8t2IFPnf4MF4xOYlr+/rwYDKJx1pa8IFly/C2JUvCya97CaM7qukmOu4nE0dHR8MFA3fywj2iIFe9XdBzCyduYQyYOzr89KpVGCkvx5GtW/HSvXuRy+VwdP16fPbaa3H12bO4qqsrlJl794F7n4GbeG05dQo3DAzMyXL58nnH//auWIEnVq7E5oMHcWVHBw6sWoWnW1ux49QplJaW4rFly3DFiRO4aWgID1dUYO+6deH7L44ePYpEIhH+1FdRUREGBwcxNTWFK6+8Eh0dHRgZGcG2bdtQXl6OI0eO4OqmJhzctAmrDx/G1NQUXvayl4VHV4H5v4JQVFSEn9XW4qdLl+KGjg5c09OzoJgvKSkJT1ek02l8dWIC3cXFeLSxMVwsccXl2ysq8MWhIdx28YTF7OwsysvLkU6nEYvFQruLxWKYnJwMC9uBgYG54679/Th//jyWLFmCw4cPo6+vDydPnkQ6ncaRI0eQTqfR2tqKw4cPo6ury+tjzxdWXHxxmtvlk7/IkUgk1JcSOt/VdqKlzLWJA0/G3DVOGHw0TkvEHJdkW0ez1m7dc8/hzFVX4Y3JJHK5HNLpNEZGRrBx40acPn069OV4PB6+YySdTs9bTJC0WZNmB3LSpRUXkn4Lh+Sdv1vFARdC7vqzLS34ckkJXtnais2lpRgdHcXRo0fDxUr3HhW36OjeWcB0Mo3axFgrBNgWtKJKK26kL1uFi7Ub4yZklZWV4U8+TkxMoKOjA3v27EFvb29o0yxfeQqBx9SKOAZJv6ORc66UG/sa/5d8cdwHgJObN+OBNWvwG62tqK2qCmOTewQul8vh3Llz6OjowBNPPIHOzk7U1dXNe/mrLEa1YpWLe98ESCuefbUXy1Lzf8Y9ODiIffv24ejRowAQPupUVlaGJUuWYHR0FE8//TQeffTR8Kc/BwcHwxf6uolGUVERent70dvbi5UrV4aPlQ0PD6O8vDw8qVBbW4vq6mq0t7dj6dKlGB4eRmlpKSoqKvD4449jbGwMq1atwoYNG8J8WlRUhPXr1+P6669HEAQYHBzEwMBAuFAvH1/RNiGCIMCxDRvwg5YWvCSdxp3ZLNrb23H33XejpKQkPAnq+Fm/bx+m1q3D9hMnzJOsUp9Sz1ptqOlUftfsIR8wXslzIpHA+Pg4RkZGsHr1arzkJS9BMpnE5ORkuPlRWVmJNWvWIJPJYMOGDRgeHsaxY8fCU62cW6ycIBcrJS2+yZG1+C3lo8UMN87xjRtx4oor8FslJaipqEAQzG1eNTc34yEAffE4ZjZtChcapGxl7NRo1q5retL0JfMTLxJLW5Hy03zWta2qqsJtt92G6upq9F58Uf3AwACefPJJ9PX1hQtC2mkv912TtXWSQ9IiJ/uaPjW5ateYHqljt6HleM1kMvM2JOV7KzS8LGvWoxsrl8vNWwTXFhYkSBza6Uo+FShPZUhaZJ3mPssXg2t1iKUbK+5zvNAWj6Rdsr4LhUU/OsGJR5ssSyLZ8LifJJqF8amuLpwPAkxu346bH30UQRDg2vPn8eTKlbihrw8VFRX4bHs7fn/PHry/uRnvamzMSzcDC84XgC2eBwcHUVJSglsHBvC6AwfC3dLy8vJwoUFL0prcLOVx8pOfrSTF/dxYXV1dOH36NHp6ehbs6DY0NGBkZARPt7aiOx7Hx06fxqtisfBYfS6XwyONjehPJJC7/nq84bnnMDExge7ubjQ1Nc07wTE7OxsWWwBCebiTD0EQhEVWb28vmh59FLGdO/Gis2eRyWRQVVUVvrRydHQ03OVw74SoqamZt2voTlukUik8u3o1xhIJfHtmBvEnn8T9r3oVRmIx/D/nz2P3s89iR0dH+LLE6elpjI2NhXTGYrF5kx6HG/j5m7GBn6/eTkxMIJvNYmpqKgwE2Ys/k+gCx6rychzfvh0rn3lmjtemJpy/7jqMpdN4qrUV1/X1hQsXMkDV19cjkUjg9OnTSKfTGB0dxcDAANatW4e9K1bgp83NuK69Hbvb2jCcSmHf+vXYOTyM3atXY7S8HHvWrkUMwFh5OfatX4+2b38b+177WoxVVuLxi++1eNWrXoVnn30WZ8+exbXXXov169fjgQcewIYNG3Dddddh165dGBoawl133YXvZjL4weWX47fLyvC5FSvwo+PHMbV9O7Zs2YIzZ87gsbo67F2/Hjd1dWHHhQuhrf900yYMJJPY1dyMa3t71We9EokEqqqqUF5ejnckEvjb/n7cOjKCIJhb4HKLQncnEtjc348LFy5guK0No6Oj+G42iydmZvD2sTFUBwGeXroUT65cibuHh5E4cwaH167Ffx8fx639/Sg/fx4AsH79+vDkThAE4WJLZWVl+IZx94bySw3x+NzP/pWUlIT25v6sCYScgAALV9idfXJhq+3+uskjF4M8GXP/5YkgSRdPhNxnBxJH2/79eFFnJzbu2IHxlSvR1NSEWGxuYai9vX3ez+c6X3c79xIc/Vrhwzs83I+LIG6nFSJaEec+a9+1XBkEAZ5qbcVoMokfpVL43YsLt84GOzs7wwnX+Pg4Tm3Zgmdvvx1r9+xB2/79asHCxYZWtPIkhos+yZcPOP9wASxtyl0vKirCyc2bcf+yZbjq9Gm8q6UlPPG2f/9+TE5Ooq+vDxMTEyHO8vLyELfc1dfo03KhA+10jcOhFXYsI6sWYDuXstu/YQNGi4vxT/E4fufiy04HBgYQBAEaGxvD0znuF32CIAhrBVmQy91PObZWi3A94kAuVPKCk7a7ahXDstjlcd2i/qlTp1BUVITMnXfi7SUlePv0NN6USKCkpASlpaXhhH7//v247bbbFtDqbNv9DLj7NYfHHnsMzc3N4QbIwMAAnnjiCaxbtw6JRAJ9fX3he4T6+vpw4cKF8GTmwMAA1q5dixUrVqC9vR2XX345Jicnw93rWGxugSEej4d5x21saPZycNMmjCYSeKi2Fi/p7MStt96Ktra2cIEwHp97ZPTMmTPYcOwYluzaNffT3uLFvtJ+tNgk/0s5azvZEh/bi2a37hpPkrR2ZWVl6O/vR3l5OXbu3ImKiorwlOXU1BQqKyuxY8cOrF69Gs888wyOHz+Ow4cPhz9Zrp3+YXtmG3RttImOJiOJ28o3Uo5SNoc2b8ZwaSm+HYvhTRcXo2ZnZ9He3o4XJ5O4v7ISrc89t6AvnxiUOYTpcO3ddc3XOG/JXKfpSeLhMZj38vJy3H777Vi1alW4gVRWVoa6ujrMzs7ikUceCWtU7fSU5veOTskr2yjLR9MlL2BwG65DNNqWLl2KNWvWoLe3F9XV1di4cSMGBwfx0EMPYXx8fEEuYnq0Gofp4X6OJq4x2P589QTbkrsufcHlTrcZG4/Hw0fGL1y4EOpNjsm1mPs1OKdfpptrKAbNp+Q9mUMLgYIXGiylWcHA+i6v5wsQ71+6FPd2dOClF9/im8vlcMvYGF5y9OjcMdsgwJ41a9A5M4NPdXWFCw0WDRw4uK0WuDQ8so87Yu2eMZ2ZmcHs7CwSiQSqq6sXvJhDM8RCx2fZMU6tOHLw2XPn8MnOTrxsdBTrjx4NCyA3nntngzu58I6KCnxxeBgbDh7E8WwW27ZtQ9nFl/5cd/48HmtpwXUXLgCYe7ylrKwMg4ODc88/plLhS83ccVJ3hNQFvOnp6XlBanh4GJU/+Qlun5zE7Ows/nXFChy97DJcceIEtoyOoq6uDrlcDl1dXVi5cmV4isEd0cxkMuFELQgCtD71FE5feSVWX9w1W/HEEzh4yy3IFhXhBxUVaOnqQhAE4ZvQ3aJDWVkZAIQ/8egSrJNtWVlZ+PIg990Vi8D81Ur3aw65XA7Fhw+j/vHH5wq0qiqsWLECV589i+fWrsV1HR3z3hbvJlzuTf3JZDL8OdGZmRn09vZieHgYT6xciYnycjxdXIw1zzyD7BVX4O7iYvT39+Oqs2fx1KZNmC0rQ0tnJ5KpFF6fTKKkrg6vDwL8MJvFFe3tYRC7+eabsX//ftx6660oKipCS0sLVq1ahcbGRlRVVSEen3t56Dc6O9GfTOI7AN5fXIyqqiqkUqlwgrR782aMptPY1dKC6y+eJACAm7q65k40XHznhjw+5mTX39+PgYEBXLhwAXe1taH4ySeRyWTQ29yMrq4unD9/Hrnc3DtE9uzZE/4qx/T0NB5pbMRwaSm+PjODP4jF8NiyZRgoLcU/x2J4T10dnmptRV88jh/X1eHtdXVYs2YNqqqqsHXrVpw8eRJtbW2IxeaeTXWnb0pKSsKXuF5qGBwcnOcPbtU8CIJ5OwxysuDuyWQh/2u7Qy7ByDjBxxVl0pWFhIwnVuHjQEvkTF8ul8Pg4CCefvppJBIJrFmzJpx4rly5EufPnw+PSLtfoZHAE0OtcHDxVhYFTpb5co7EywsFfArE3eddFhmTORave+45nL36arx0ZgaVjY2Ix+MYHBzEiRMnkEwmw5fIVVZW4vAtt2CqpgYnrrgCqw8eXKAnS8YacBGi5SKr+NPkxMWV9osJru3BTZswWlaGfRs3YtXFSd7s7Cza2tpw/vx59PX1he9/ObFpE7puugnr9uzB6kOH5umOf0mI9cWFkyyerXwp77OPFFIn8Pd1e/fi+OWX45apKZQuXYp4PI6xi+9qki9DPH/+PCYmJpBKpUKfl7tvVhGu0cOnNXhCy3KydMq8a/bONMXjc0fQt27diu3bt+P9S5agIwjw1fFxvCoWCxfO3ebCxK234q82b8aK0dHwBYzSVpYsWRLuUvb19eHBBx/Ea1/72nCR5vTp02hvb8fw8HD4yNXatWtRVFSEyclJ3HzzzTh69CiWLVuGbDaL8vJyXHPNNfMe4xi7+EhrbW0tSktLceDAAVRUVKixxPl9LBbDhv37cWz7dtx58fRRfX09ysrKMDw8PLegkMlgaGgIRUVFaGpqCk9uSru1Jh1aDafFfu5r6cWyU0cD+wLbwPLly5FKpZBIJHDllVeGCwzDw8Ooq6vDlVdeifLyckxPT6O6uhp79uzByMhIuMnEkzBt4qjlEl6w1eIbX2ddsb3L+ObgsmPHcOKKK/DG4mL09fUhFothYGAAx48fx7bKSpSfOoWuri7ELr6fiU+daI8ASL1I37TiDfPE/sz6cnTwZFfLVUVFRWhubsayZcvgTgcWFxdjamrq/6ftv8Mjzc77QPRXOUegCjnnDHSj0XnyDGc4JIdBDBqKVLCCg7TSatfX97m7vrt7vXf32tKVLMtay+HKVjJpiyIlciRyOKEndO5GNxqNHAsFoICqQuUcv/tH1Xv61Jmvenps+jwPHgBVXzjnPe95c2BtwMUoBvEZ/J7xMK436tEPOWVWTu6Qo1fiWukztVqNixcvsqKver0efr8fbrcb7e3tODg4YLXg+L2Xi6543NpE2UeksbwxQ4SV+Hz+mXKfifunVqtx6dIltLe3I5FIoKWlBZFIBFeuXIHf7/8InefPMw8/kZeIZ73e+Dh+J/KTjxufKKLhk1zzJEKdnPAEPNqcX2xqwi81NyOfz+NH8TiOjo6g1+shSRLMZjPm5ubg7u7GHwSD+PWWliea4+MIc725P24Ui0WWvkECBYXw8OE29RBMnNeTzKPeGuQIGI1/FQ7jUKHAd1Uq/P1quKEkSaxtYSKRwPs2G5ZGR3HO68U3dnfxq9VQuD+023FDpcKk1YqpvT1Me72YqXp6C0ol8xCQsYWQkCypFAqs1+tZmgJdS8YIvV6Pjo4ONDQ0oFgsYnN6GlGjEXd6etB4/Tq7l8JQtVotMy6QJxAAayvVcf8+JnZ3K+07W1qgWl2teE7Pn0fP/fuspRoRLqPRCKvVKpsjXyqVaghzJBJh+W/zra2429eHs9UojFtdXbhwcACjwYArLhfGlpfxUjXNIhKJYHR0lBGTUwcHeDaRqNShyGah1+uZ0YOvtUAeXvIQXb58GYuLi3guGMRdiwV9RiMWz5zB37VaYbZY8Htf/CK+VCphQ6uFR6FApKkJt3t7EQqF8COHAz/V1IQvhkK4HwhAaTCwUH2Cq0ajYWkKKpWKtWnT6/X4eZMJ/+rkBD/rcDB40I/Vaq20RtVo8FSVGNJaL4RCOBcMVnCyCldaH1lf19fXcXR0hJtuN27F45hwOHC+2mWGoljsdjuMRiP6+/sRCATQ1taGcrmM5xcXccNoxDer6ROXfD7c7OjA181mOBwOvBAO41prKz5brQsSjUaRSqXg9XqRTqexvb0Nm82GTNXQ5ff74fF4WC7jT3qEw2H2w79DFP7Ez0XBQ1QuxFEvt5P+piEKvXIGBznrtSigiMyOfvPMPpFIYG9vD2fPnoVarWZ4bTAYkM1modFo2Gf1DAuiJ0eO18gJn3JeMzmmKUmPjDp0jZwALQqScpEH9H334iKmvV60trVBOTQEh8OBjo4OdubJ0KBSqTCwsICd2VkM3Lv3WKVCxAkx9FIU/kg4Ej1o9QRNEZ4i/hBuifCn+4cePMD26dP4UjWKzeFwsKK3oVCItT00Go04unQJabsdGzMz6FlaqtkLcYj7JtfKTvQYyp0r3jNDcBHxQPxbhBXd27e8jJHNTXR2diI3MFDD36jbUiwWw0k1GpM8xWJOc729Ft9NaxaVnsfdw+8Z/z7+/SKcxbUTzMh4Mjs7i8HBQfxKoYA/SibxulqNa9euYXZ2lrWbVKvVuNvbi4RGg8zkJIbW12uipLZHR/Hm8DA+d3KCn29owMHBAWKxGH7wgx9genoaPp+PFYsNhULQ6/U4OjrC5cuXYTAYMDAwgGg0iu7ubjRWW2CXy2X09/cz50k6ncadO3dYtNrW1hZu3LiBsbExWRznz8rA6iomdnfx/PPP46HHg/X1dfT09CAcDmNnZ4fhssPhwOjoKALnzuEHkoTBBw/Qv7Iii7Mi/eX3nt9H/tzK3S8qFeJ3j6NxoudaqVSyekuNjY1wu93I5/NMUW1ra0NrayuOjo5w7do12O12RKNR1npUxF2RBou0EqgfySCeMxG/6ymqRPfEv2k8E4/jf5EkBA4OsHhwgP7+fqTTaZjNZjx8+BDBYJDJkTzc+TmLuPI4RVW8T4S/uPeP4yH8/yJNp8+0Wi1Onz4NnU6HbDaLQqGAVCqFhw8foqurC4FAAOFwGCaTicle4jp4GUBuTeJ8RA83DzOam1yKDA9XcR3iGaD12u121hI4FoshnU6z+lyUzrO3t4d8Pl/znsfJVvx8+fE43BT3mNYot9eioU3kR/x+qtVq9Pb2YmJighlFiUeazWacnJzIGqN53JGbr4i7co4pfu9EWIlrFu973PjExSCfVAF/0uueZLJ/HA7j/2xsxKV8Hi+k07jqcODdwUH8w+5u/P3WVvz99vYnUtSfFDAfR5xplMtl/NHJCX6rvx+Xj49xMRRCNpuFQlHpkkCF9Mgz97hniZ+JCFzv+idZm0KhwK+3tOCf7e6i48EDeL1eJqAoFAqmaC4ODSFmNOJqayts3/oWq4Z9xe1GTK3G7e5uTHo8NQKCQqGAw+FgXgugUjiSL2TGw5PSG3jhuVAoYHh4uKZo4DmvFzc6OtBz/z5WVlbQ1dUFp9MJt9vN3lssFlnPbN6TuTU6iusvvoiBhQVM7e2hUCggHo9j1ufDc2+9BQBQu1xY7O7Gvb4+TG9uQqVU4m5vL2Z3d3E5EkEgEEAkEsHQ0BCuOp240dGBZwMBnAsGcXJyglKpBKvVinv9/UhYLLjT04NyuYyM1Yp5nQ5SuYykxYKd2Vls/fEfI5VK4eWXX0Y0GmVCJ/CowArVZQDAWknygirBU6/Xo6GhAS6XC19Qq/F7vb04v72NsMGAb5fLUCQSONHp8ANJwq/Z7fiXwSBeSqeh0WhgsVjQ0NAAm82GQCDA4KhWq1EoFJiRJZfLsS4YsVgM29vbuGKx4B/u7OCbej1+8/59DA8PI6hUYmVlBXdaWvCPjo9xxuXCzOYmzlUZtYiv4v+8VV6hUKC3txfHx8f4dns7fJKEQn8/Pl81YKhUKjQ1NcHlcjHDFKXghEIhzB0f49PFIqanp/GBWo2LoRA+K0no6urCYjKJ87kcXq9WL/dptUilUtjb20M2m8V8ayt+z2rFs8Egxqr92N9///2PKAE/yUGGJyoeShEUdF75syvCT44BiIow8MirW4+xioIF/7x6DFVUqMV38oxWFDroHrVaDYvFgnw+j3A4jFwuxyKDeNohx+zEdYgwUChqc07lQgz5IacgE+xEI4ecoCc+Q4wWEUc6nUYgEECiGqVlMBhYjRwqfqnRaGBfXcXA6mrl+cI7+bWKZ0oUJsR5yHk55ObPKwn1FGv+Pt4jz+NM7/IyxnZ2YO/uRuSppxgNLxaLCIfDmJ+fZ88eXFjA1qlTGLh/X3ZPxDnLCUv1vpcb/LPleL4onPH31RPyJUmqeO/TaSiVSqytrWF5eRlf+cpXWKof1SVyOp01NWDk8F18hwhzcV71lE1RKeO/l4OJqOzxcObhnU6nYbFYYDKZ8PVSCZcjETy4excHx8cYGxuDVqtl+D26tITl8XEMLS5+ZD4r4+NI6PX4y0IBr1frIymVSmi1Wty8eZPNheoEEI+0WCwAwPix1Wpl66X2xEajEfv7+0gkEvB6vbh79y4cDgf29/dRLBbh9XrhcDg+AlPR21ssFuHz+dDT08M6ahwcHOCNN95AsVjE5cuXodfrMTg4iP8hFkNUqcTqxAQzNIj7LDfE/XmS/RINzuLeydH/x72fikdTnQyK8qQaSkCFTuzt7cFoNLLIFl6mq3cexXXKKX/1eI7cXMXvP04OVigqKTO7u7tYW1vDyy+/jN7eXpYC29LSggcPHrA6XY97t2hklYM9P+gc88YPkWbKrYeHpRglIe77+uAgViYmYDeZ8OmjI0SjUWi1Wni9XhYRenh4yIy9crgj8hB+LuK6nkQneZxRm/5/HAx5HqRWqzE5OQmFQsGU71wuB71ez7rWEV0Ih8MMZuKz+HfzOFhvP+XmyQ/xOfy1cpEncrBQKBSsM86pU6dYdBeldCuVlRbhbW1tLIVCnBOfCsN/x6+f199E/vZxMtKTfC43fmLFIOsJPnLXyY16mwAAv3t4CJ9CgbedTswdH+OHnZ04USjw+34/foWLZHjc4f2kQ+5efhPK5TIePnyI347HEdBq8UFTEy6Fw8yKqFQq4ff7kUqlPhICzD+v3udygsHjBj1LFPwBIJlM4lI4jMFcDu9GIpDcbpbvTgUP1Wo1Tm1vY763Fx03byIejyOdTqO/vx9ndnZwp6cHUxsbLPSQhABSTqempmCz2Viee7lcKQJJodBUqA94VL+AOjVEo1HYbDYkk0k4HA4UCgVMeb2Y3t/H+vo69o1GpNNp6HQ61sIKAMut44VihUKB+d5epIxG7M7O4qlolHlIzWYz5tvasDA4iKeOj7HQ1YW42YwHQ0NQKpVIGI24PzCACzduoFAooLOzE36/H7dPnULGZsNtkwnPJBIYGxtDT08P3n//fcxsbWFzehoT+/vI5/PYmJ7G89XuHWuTk/hllwvK4WE0NTVhcHAQ77//fo0Sw6eXiHUrFAoFSzGhbg4ajQZ6vR4ulwsWiwUajQbf0OnwR8kkfsXlggLAP/f58DWtFj9js2Hm4ACharVo8uJTqk+pVMJVpxO/k0rh4uEhho+O8PseD/5CqcSUJOHTy8tQKpU4Pj7G+319iEgS/jSbxevRKMLhMBNEbnZ0IKxQINvSgsFqa8lyuVzjEaaoFp6w8dcAQCqVglarxSvJJH5ss2F2f58VbgwGg8hms9je3obD4cBhtf7D7u4uq5HxA4UC/1M4jPM2G/KFAm62teGpgwPMpFK43dyM6yoVziSTmAyHsdDRgTsaDc7odLjb1ISoXo+3HQ4MVPGVzjpfqO0nOdxuN6s7QsVk69FRnnGJ4aWPEywJn3iv1eNoiahsyTFNud/8XD4uWoD+pmKY4XAY8XgckUiEdaER1yjHG8T18AJTPeWw3nf8+sV7xHfKKabi9TxOi88juptMJmGxWFhBLr1eD5PJVJN/Kcfw5QQdepfomZLjLyIMRMWUN/yJedX8foj7Iu6RJD2KDshms9jZ2UFPTw+GhoZY7qnFYmGRaSqVCraVFQysrVWep5QPTZbjjSJ+0/ck3FJXpHoeOn6NvFArd874dfL7xOMDGb9jsRji8Tg2NjaQy+UQj8dZq2KF4lFqweNw+3FKppzALgrSIvzEPROfJ34uXivC5/j4GJlMBslkEmtra4w+x+NxpFIpVrsIAAbW1tCztMSMFjyuDT98iLWJCYytraEwNQWj0QiXywWn04lMJoOLFy+ipaUFV65cwcnJCXQ6HQYGBtizKcKQ2lRSVyiVSsVkCr1ej76+PoRCIXR3dyOTySCVSn0k9ZGnPzzeAYDX68UXvvAF3L59G7FYDGtrawgGgzh//jy+8pWvsNamU+vrSLa2YmRpiZ0lOXzj4Su33/X2Uk4+lFMa+D0Tvaji+uhzSpMgT79Go0EikYDRaIQkSaz7hFarRSaT+Ui3mHqyq5y3+3H0Q45viDCpt4Z69E+hULB0tbNnz6K5uZlFo5BHXKFQ4Dv5PN5vasLo0hKGqoVO661HHHSNmMsvdzbFdfNng57Br18uAoJf59LoKOImE/44k4Hj7t1Ke92hIbS0tGBmZgZHR0dYXl6Gw+GoMR7y9E3OgMKvQYTn44acvCEa8Pjr5KJc+L+phXowGGTOMq/Xi7GxMXi9Xty4cYO10+WNtPQOUZbhU8/k5i7u28fxV/6s8/DhI2P4e3nepVQq8eyzz2J0dBQAmPErVy0dQA7ajo4OhMNhJJNJdi8PP/79It3+OEMVb0QW919OjnwS3RT4rzQ0iBOSI4pyROFxz+Lvo89+vbUV/8fWFs5Wc9nP7u3hXn//E6dLyB0afk6P+1689p/v7OD3jo7wTCCA3qUlTDQ3I9fbi4mtLXi9Xkawstksenp6avpji8TySeZebw31hARSLkjZKxaLODo6wp/92Z+xYkd2u515hAuFAita1RYKYWpvD36/H9snJ6z2wZTXi/HdXRwcHGDe48Hly5dZXjkA5HI5mEwmHBwcMAW4WCxCp9OxGgWkRFL0BAkBgUAAOzs7yOfzsNvtcLlc7NCR8O10OqHRaBCPx6HVall9BBLSKaeJYHDx8BDX2towd3DACKrVaoVer8efj40hazLhtl6PqfV1rE1M4KVsFnt7e1geHcXrOh2i0Sj6+/vxyiuv4K/+6q9wyefDotWKC34/yuUybDYbC+f8zNAQllpb8TvlMl5JJPCtkREsLCzgTiqFP2hpgclkwl+YTGhpaWFzpDkDYIYaIqZ0DRkW7nd04Hp7O87s7mKu6v3K5/MIBAIolUrI5XKY2N3F/3h4iBcaGxGPx5G9dg3rQ0O4oFBg5uAAZ/1+9PX14cGDB/D5fPiD/X28abXiTEsLrjc1IQTgnYYG9BUK+K5ajWOFAvnubnw9FEJLtaXki9Eobtls+JqyUqtheHgYDocDnZ2deDmRwFWzGTPV+gsKxaP6FDzzEivmSpLEit1Q6LgkSZjY3cVzLhe2Dw5wo6UFv5vP46lsFjMAfqTV4n+NRHBGr4cE4LZej1MHBxg7PsaViQnENRq829gIKBSIGwy40tiIgdVVXG9vR8RoxN2eHpwPBnGvvx+xqmHpKZ8PV1tbcdHng8lkQqFQwO3mZtzs6MBZr/eJz+gnGQ8fPoSvSs+oiJacIiAnbH0cnaXP6IePPuAFl3rCtJznkmdG9YY4D17h5oWUYrEIj8eDhoYGFAoFxGIxlnudz+dZ2pDItHkYiO/kGaWccCcKZ7xX6XEeb/Ed/BrkhA8Asmvm7ycFisLoI5EIm7cYyljPWy+3F6JARNeKgo0oXIhCqxwvrAdjcR6icMbP4/j4mBXeJf7AFwaup4Q9Du/4efHv5r/nnyEquPUGv1+012KUC/8ZDw/qkEPKWWNjI2zVLhTJZJLxLlLY+HmKcBRHPYFYHCKuiA4IOYH4k55vSZJYMcZUKoWjoyNmmPX5fNDpdDg4OGAeb5EWqdVq2O12pFIpTHo8OH14WDHC9fVBoVCwtnxmsxlWqxUOhwOXL1/G7du30dHRgZmZGSaHkFMjGo3WKBhqtRrhcJhF7Q0ODmJsbIx5BoFKdNnW1hbbGx5uIpxTqRRisRhGR0dZVBytI5PJwGazAQBa797FZyg6qw5OirCn7z5OThaVU36+cgq7nOFT7h307kAgwFK6yAtuNpvxu9vbeM/txn/X1ISpXA6Hh4c1jh5+f+WGnPxKzod6dF38TBwiPZODl3hvQ0MDXnrpJRwfH+NHP/oRTCYThoeHUSwWsb6+jps3b+Ltz38eGYsFy2NjGFpfr1kj/7teSha/5nrfy/F2WhN9JvJ8OdrLw2psZQXLo6O4GI3CarViYGCAFVdVq9VwuVx49dVX4fV6sb6+ztL1RB1ODm5yeowcTZDjUyLMRGOe+HyRJ9GPSqWC1+vFxsYG8vk8rFYrmpub4Xa7sbe3B6VSiWA13VbujPDr5OVw8X3iZ3L8kt+vepGT/PnjnyXyyJX+fiyNjkJvNKK/GklMrc/X19dhs9mwubnJuvJQ2lq9aDd+H+UiSx9Xu4GXY+Tw4nH0qd74xF0n+PFxBFHu/ye5R7zm6xYLpOVlBINBFAwGPJtI4J93dqKrtVXW+/Nxh/pJ5s/fx1/3b6NRHKtUeNNqxS+XSpj1+XDq4KCSZ5vLIZVKMaX58PAQd+7cwXPPPVeTQiB3AOi3HJKTIEZeaKqrkE6nWUcG+p1MJpFMJpFKpXDV4cCtri6MLi2h7fAQ8XgcDocDLperZo0UNi9JEguNo4J4QAXhdnd38eDBA1b8UqvVIpfLAQBr0xaLxTA4OMiKAlEYNK2hXC4zBZRC7TQaDRobG3FycgKlUsmUjY6ODtxpbsb12VkMLCxgcG0NCoWCPUev18NoNNZ4bcmjN+f348zxcUVZqQoi5Cm9fHSE+d5efFahwEWXC6/MzOD39/bwQ4sFv9HSgp91OPCdvj5cvnwZHR0dcDgc+JJGg992u/H9mzdRLJdZ/jh55f5VKFTBCZsNv6VSweVyobW1lQkdVqsVTqezJueTiJxSqawRvogIUFTI9fZ2hA0G3Onuxlm/HzcaG/HbySTGczmYolH8xt4eJhIJDB0d4fj4mLWmesfpRLBcRrq1FRdOTmA2m9HX14ejoyP8Z6cTxwCutbfjqeNj3O3txWekStXvX7TZ8EeJBM6GQnC5XEgkEkgkEjiVTuO/7+vDysoK/NX2XuSlm0mn8fMNDbjj9yNYbf/JEyZSGongUSqISqViRbTu37/PcP9eezvuWK04ZbXiXnMzojpdxRCyvIzr1dodt7q7oVAoEDUasTAwgMuRCC4fHeGGSoW5arTD7e5unN7dRblcxpndXdzp7sasx4NMJoPpzU3c6+vDKY8HAx4Peh4+RKlUwhEqws+HMzNIGI241tr6sTTiv2SMjo4ikUjgoEo76jGveoKKaPHnhbx6jFyOgYh0U47RiJ+LggSvgMkxY5HZUfvUUqnEaFEoFILf7/8IfRS9EnyEDL92/nu5tYoKO9ES/h5+TSJsRAGKF44fJ2TK8cxkMolYLIZCNU0nFArh6OgIyWSShSmL8BOFHXFPach5DcUIER4G/HPkvFXiHPj9FWEjJ4QCj7w5g4ODCIfDCIVCTIiia0SDTD2F65MI7Pw+11OCROFTvJdXoMT18nSbruU75wDA2NgYAoEAi2DicY4MsSKs5dYi4iV/Bvjr68kSNETFrJ53URSW6dk83tO5isfjyOVyWFtbw2uvvQaPxwOgwvfS6TRWV1dZqDb/DJfLhcuXL2NjYwMtLS04OjqCz+eDVqtFf38/S21wOp0s+rK3txeL3d34Q6USJaUSr1VhSJGkVNeFOkBJUsUYEgwG0dTUhEwmg87OTgAVOt/f3w+v1wuFQsFaNyoUCiZriUOSKsaGs2fPYnV1ldU2GqjW5NDr9axAHfFBOVjWw3HxM/EM1sOXerRIxGO5syLeQ5F25XIZN2/eREdHBy5evIh3YzFEDQb8+3Qav69SsVQ3MVybxzP+M/qccLcenRXXKn4n95lo2BTXTdetDw3hjZkZHJyc4KeMRjz99NOszTJF11qtVnTeuoX9c+cwJhgZxDnJGUnk1iQn18vR6nr3iM+ux4tHNjcxs79fKSQ4NoaHDx8iEonAYDCgp6cHyWQSBwcH+KNgEKvPPIPJtTWMbG6y54l1NujZYiRFvf3j912UMx4XUSNez6+VXy/N/86dO/jmN7+J2dlZpl9cunQJHR0diMfj8Hg8ODo6kpVz+HXV+4435vD/031yirfI/0SZRE6eoesfjowgbjbjT9JpDF+/zgqdt7e34+WXXwZQcehms1lmnOOdHSL8RPouF2UhZ3CvJyfUW2M92Uccn6jrxJNs1uOuqzepekSThlarhdPpZAifTCYRj8efdOp1x+MElHqA/Y22Nvwzjwd91c4NlFOr0WhYwbpyuQy/3w+Xy4Uf//jHrHVbJpOBy+XCiy++yEJGKRc+k8mwfG36O5VKsVD3VCr1kQIuFLVACEPEnnJ6bs3MIGoy4eHoKBquX4fJZILBYIBarWaMVJIqnTLomXq9Hk6nk6U1AJWQ+/X19Zp8S51Oh0QiAQA4Pj7GyckJ2tvba9p5UjQDFRmUJImFAdEBoJSBfD4Pp9OJhz09WJ+cxCWfD3c6OxE1GrE5PY2+5eUayyCtm+ozUIgmf4j4KAeC0eVIBP97YyN2d3cBqxVarRZ/nMngRKfDHyUS+DsuF8xmMywWC5RKJdxuN8M9ei8pySRU/n2dDr+zv48vSRVj0NraGiKRCPx+P0KhEI6Pj/G20YgPW1ow3dqKqarltVgssrB5ES9pnWf39ioFJg8PodVq8V5TE06UShQGBqBWq3FcLqPQ349P5XIYGBhAsVhER0cHflqtxl+r1Xg2m0V3dzfMZjNSqRT0ej2+DOBbxSLOHR3hYjiMv9fWhsXlZaTzeZw+PERfNIrj42PsJBKIx+PQaDS46XbjdxMJXFKrIbW24ndsNjx/dISJKtxv3ryJWCwGk8lU0y6VzhDtnZjLTV09qJ2PWq3GfF8fogYDFgYHcengADfa23Hx+Bg2m63SvaK5GWc8HkiSVEnp2dzE/v4+bJub+JT0yDD3cjWk81iphNvvx2t370KhUCCiVqMrGETnwkKlPzinjAAVT/zo0hJWxsYwsrwMfO5z/+VEps4wm81oaWnBXrWIKK/AEZ6JzJ4X9usp9wBq8B6oLTxUT7iTU8jF6+ToIi+E8UNUSkXBqa2tjbVqPTw8hN/vZ55POg9y3QzqKb5y76b1APK1CXhclBMM5YReMfRQTgGVmxsPt3w+j0gkwqKcTk5OoNfrGb2Vi0ARh5xwL+6nuG/inojzFtdeDyYiv6Yh5znh789kMqwQcDqdxvHxMYuyk6s3IPcO8RpRcJcT2uVwpN7zRVjw662Hb/x9Wq0WjY2NLErJbrezrj3kbSdDNUU0yOGbnAGAf1c9eUpUtEQ6In5PQ4yokvMU8/SRrlcqlayo5blz59Dc3Izj42OMjo6iubkZKpUKsVhMNo0rm82iXC6joaEBTU1N2NraQl9fHzsHXV1dSCQScLvdaGlpYTTvL1UqHEgS/l08js9ZrTWOGOI1JG8UCgVoNBqoqoqxgSt+3NXVBbvdjs3NTYyNjUGn07EC1uvr6zg5OWF7RLDk6QkZNpqamnDu3Dmk02ns7u5iZWWF1XCQwyERL3ncFfdJ3GdRwXsc/ZJTMsT95WkMXX90dIRYLAaLxYIvfelLzCM+5/HgweAgfqmhAU5FxbgTr7afFs+InALEh4/TXOTwXFRq5fBd7jzLKXn8UCqVWB4bQ0KvxxWdDr9eTT1VKCrpVZTmHIvFMFAteC7Xnr4ez+OHuDY5fsjv5+PosdyQgw399PX1QZIk5gmfmJiAXq9HJBKpyHU7O1g5dw5pux1Lo6MY3dp67DvE8/9x65abt/gcORotKsMiHtN5jsVi+NKXvoTh4WEcVetQNDQ0QK1Ww2w24z8mk3j3zBmMLi/XrE2Otymqsl+9LkWPo7M8jQVqaagoJ9STg+j35NoaFoeHMb2zg3BVD3z++efR3t7Oigl/5jOfQSKRQDQaxR+Hw3jL4cDk2hqGNzc/Ql94uPHvkpPx5NZJdJTqRNQbj8MFfvxXd534uBeJSPVx//ODjAr/wuPBH7a24hmNBs8mEljp68OXczn85tERfrHa0vJJnvcko55QAlSA/3dbW/ELjY14MxTCh/v7NRucz+dhMplQLBZxePo0/rq3F+MrK8jcu8cU142NDdy4cQNWq5Up+4SguVwOSqWyxvJPiJFKpbC0tISBgQHW5pA6XFBXBLHK6oDJhLWJCQwtLjJFymq1spQKghPvRVcoKp4Gs9mMzc1NZLNZHB0dwWw2w2AwwOl0soJ8mUwGS0tL0Gg06O/vZ50JKG+SN4BQNINOp0OhUGC1BxoaGnBycoKWlhb09PTgg9lZpEwm3NbpcKGqZM7s7ECSJPhmZ7ExPY10MIhzwSAAMK94uVxmRgy+xRLtG79WjUYDm83G4PQzWi3+bSaDX2lqQiKRYEUAj46O4PV6kUgkYLVamRKkUqmwurqKg4MDWCwWvDQ4iNTVqzAajQi1tsLj8SAajSJYnWMqlcKVxkYElUpkOzsxVS2ixNdiIILFE65yuYy542OcCwQqTFqtxrPBIK63t+NSKASn04m/0Wpx4egI2WwWgUAA+/v7yGQyGN3awqc6OvBwYwNHOh3W19extbWFUqmE4Y0N/J9aLYKhEK43NuK3AJyrFir7HbUa5zIZzMTjeMdkwi2TCWdNJtxubEREo8E7DQ2AJCGq1+NNmw2dsRgzmpCBi49eoQgcwmv+LN3v6MDd6WnMeTxQKpW43d2NOY8Hp7e3caenB9NbW+jd2UHf8jIkSYIfQNvBAT5VVU50Oh26HjyoPFOlYm3K6P2iEEdwJnyniBLab1I8C4UC+paX0fPw4UeMQD+pkcvlYLPZ0Nvbi/39fdaOiSfqorBfT4ATx+MEIfqMxzFRyRCFXLloAP46OU+FeD3Nkxg6Fcptb2+H2WxGMBhk6TXE3EThTpy73LoILnzEh3iN+Bx+fnICodzfNMRQVrlrxP8pjD4WiyEQCLAQZMJHmoecAio+TxTeRIGDn4OIL6Jgwgv6j0snoTXze8rXXxGVDvp/a2uLtZWlkHg5I4OoDPF/i/Pl8VPujND5F+8XhU45OiEHSzkFgOctVNyTeDLVCygWizg8PGQFlnO5HParnZs+yRDXLzcP8SzI4baIt3K8p977RU+Y1WpFa2srXC4XMpkMIpEIo/WRSIS1oaN3EUzJsTIxMQF/NS1xvZoPr9Vq4XK5MDY2BrPZzM5HMpnET9ts+Ha5jJ+rOjDy+TyTaWgUi0Umh8TjcYRCIbS2tjKjQyaTgcViQSqVYqmk7e3tsNvtODk5gUqlwu3bt5HJZBicCI/UajVL/ykUCnA4HLBYLNjd3UWpVEIqlWKylBg2Xe98imeFxyl+X/gh0hjxGlGhF/efP+90HeWFHx8fo62tDRaLhbUrfzaRwP9drcaAw4GYUsmMOfxz6Nk8nohnip+buJ7HGRbk6LT4LLnP+e/HV1awPDaGZ7NZ3N/awpkzZ5gsRl5kAKzttNwekYNLbh/EIUdfxe94+PPXPu4MitfT77XBQXxveBiv5XL4lWoKEqUXSZLEanz1zc9jZ3YWE2trNTAToyPpuXLvEufwOL1LjjbREHFEzoFC8zKZTLhw4QJKpRLeeustaLVa9Pb2IpfLIRaL4d69e/jxxASSZjMWh4cxtr39EfwT8ZAibh8nA4hnV4RJPWOCHBzoOl4+GdncxNj2NgwGA1xjY+jq6oLZbMbOzg7i8Tgr4p5Op7G/v48ft7UhbrHgwfAwhjc3a2Ap9y7xMxH+wEflTT4KX+7Z/O+PG//VNRrkBBq56z7pNf/C48G/OD7G034/fmSx4ESvx1sOBy6cnOC7Gg2CuRx+5/AQv9jU9EQEqN77xGv48HZiJBSyQnnk8Xgcf6NS4a9fegnnDw4wsbsLk8lUo9wsDA4iaTJhcXgYzbdvI51OA6goGJSbTEII/y5e4QHAlFuj0QibzcaIBV+kjt5JxZeASojRyOYmRjY3cXBwgJxCAbfbzYRcEurpeRQVQLk/2mplfirqRDmSAwMDMBqNUKlUeNjTg/nnn8fpnR1Mejzw+XxobW2tUSj5yAmeYBBTo4JYTqcTRqORdZuY9Xgwe3KCs34/YrEY5v1+rHzpS8jZ7biu12PO7wdQWyWamLskSTVeYhpkbMnlclhfX4fJZMLIyAh6l5bwaz4fnrPbcXN1FV6vFw8ePEBvby/C4TCuNTTgf8tmMdfYiLPVAjRkWc1kMsxr1dDQgIaGBgwMDMDr9aK/v59FanxDr8e3y2VMVEP5+T6/pVIJ821tmO/txczWFia8XgYf8sQQHpz1+/FiJoNwOAzDyQmea23FtteL6y4XfjuRwNP5PGYB/K1ajetKJc5W21D+k2IRcwYDxnw+XDGbcbetDXOpFG673YhqtXi/eo6iBgOut7VhZn8fd7q7ETUacbu7G2f39nC7uxsXfT5IAK63teH84WFNjnIymaxJlymXy9geG8Pq+Dgm19dZjQk6d/O9vYiZzbjV2QkoFIgbjbje1oYvf//76HrwoHJeOOWL9pb6z9PeE37xUT4k3PMtV/P5PPL5PBQKBZxOJ/PE8WeRnkd4S4VHf9KDcKapqQnXr1/Hzs4OUqlUzTVyggdP63hBjr9ejibXEwxIeOaFw3rMSFTM5ARCupZfA71Dq9XCbrejpaWF1V2RpEq6VkNDQ00nFDlhUVQ6+XfJCTK8QiwydpqXOOTgyd8jCr/17hWv4eGdSCQQCARw+vRpaDQa7O3tscg2saaPnHBKa6kn1PCGNHE99XCDziu/blHgo7/FsFIelvx14vdOpxOhUIgZcU9OTth8+bXJzVeMbqm3FvEZfHSMnNImClFyuC1GZ4lKmwhLg8HAlF+dToempiZWAHRychLBYBDb29tQq9Us/VAOduJ+iPsgGuMeFwVBQ1TK+GeLBkMRn0VBm66Lx+OMHofDYXi9XgwMDAAAVlZWWLcC8ozTu3U6Hbq6uqBSqViFf/pscXERWq0WDQ0NyOfz8Hq96Onpgc1mw5d1Onw2m4WzGt5Phm4K96d3pNNp1uWqVCpBp9PB7XbDbrfD7/fDaDRieXkZf/EXfwGTyYSvfe1rsFgsMJvNyGazUKvVrJAorZ8q2iuVlWLf2WwWzz//PADg4OCA1YQQcYbHy3r0UqTvNEQ6RmdVpIMi/oj0Wdw7UVmi60qlEvb29lh1f1LGRkZGYDabmVGRNwzL4Y+4jnoKtTg3EVfrRXvIwU/8XqQnQ+vrGN3aqrRHdbtZxAtQMZi53W5WQJS/n58fyZH8fok0W+58yp0lOVovR9v4++rtGwA8HB5G3GjE32q1mLt/H319fejo6AAAVpT+4OAAHcfHmPZ62dofR8/4+dejLbz8wK+P3zveQMnD5nHODrqWnuF0OrG3t4euri6cP38eZrOZpWJSyvrE6ioWBgYwWVXA6XlyRnwRR+R4Lr9+OX7Az5OXKWhN9WAn4oNCocDY2Biam5vh9/vh9/vhcDjQ1dUFSZIQCoWwu7uL7e1ttO3tQbpwocZQVE/24PGXN7rLXVvvf3Gtn3R8YkNDPebzJPcB9S2Z/OTL5TLrNPGm1YrnQyG829CA50IhSJKEZwIBXHG58Omqkk7KgJzATUSZlHlS6PP5PHLVNkqZTIblaPE1Duh7EgboOeVyGd99+WWmlJ0+PGREl5jd2b09XG9vx+jKCpqqShwdrmQyCbVaXVP0zGQysXQKImaUK0fhhy0tLdgZH8d/7uhgbRiXlpbQ3t6OhoYGLC4uwul0oqOjAzs7O2hra4Pdbsfu7i5GR0fR2NjImCcAxkzJ0MB7Ne53dODa6dNo/uAD9C0vo6GhASaTCVarFUCFGa1UCyuumUx4iUu14IsAUhszCiOlyAtS+IrFIvR6PfL5PBKJBCbTaTTfvg29Xo+c3c4Eb51Oh7GVFezMzuLpkxNGIHkFUalUsp7PtPe8l5CuKZVKlXofVaOK0+mEz+eD1WpFR0dH5SC3taGjowPNzc34k+5uHAH4oKUFZwMBqNVqtLW1ob29HcPDw/jjSAS/PzKCLxaLOBePs/aXVN24VCphdGsL/0yvx72dHUSrOEYt7VQqFe729iJuseBefz+G1tcZnlJFdp1Oh5suV0Xp93oxHY3ibaMRdw0GzBoMuOt2I6rT4W2nE/0rK/hgfBxRvR7X29uhABCuGgxOHRzg3sAAogYD5nt7cblaOPPswQEkScKtri6c29/HfFsbsioVjLkc5jwejO/uYnxnB0ClGGX34mJFAKwaF2gPzGYzI2gqlQqrExNIWK14ODKCuePjGuI+s7WF+b4+jC4vV9qrDg9j+OHDmr3iGTfwqL5DKBRiXiW+ZglFUfDnXvTSUX0PyufljX2kINDPf6uIBvJ66nQ6LHZ14d3ZWfTeuYPW+XlmMCMYiEoTDTmhkf8Rh8hQ+SGnkIshgfwc5MJyxXfRb6PRiK6uLjQ0NGBkZAQOhwP5fJ7VlcnlcqxS9NraGrLZbE1/b56Rys1fVLbqCREEMxGG/DNEwZx/P992Uw6G9eZF9yqVSthsNrjdbrxnteJ/DoXw0xoNhpqbWXg9GctEIUXu+eK8eWFNFOr5Oct9JsKPH+J8HjcHEX4k2CgUChwcHGBqaoqFue7t7TFaKXpDRfjJPb/e+8XBr1c0NvD48jgPopyAKIcrFF1H0YV/LUn4dwMD+LlEAoMGAxoaGuDz+TA5OYlwOIxwOMxolQjbjxPy5QxuvAAvp+iJAifB+5MoOiL+7O3t4fTp0yiXy6zW0qlTp1Aul7G0tISGhga2Pj7li+SJTCYDrVYLo9GIu3fv4ujoCOFwGGfOnGFdeSRJwvz8PKamppBOp2sUbUmSmLfPaDQypxC1GZUkCc3NzSzigdrira2t4YMPPoDP54NGo8F7772HiYkJtlbK3edh39raiubmZkiShEgkArPZjOnpaaTTaayvr2NqaorBSC6qjvZBVFT4/RFlYZ728fSeX389GifigpxBUI4mHhwcIB6Pw+12I51Os+gFShem9Fm598sp2eK8+P+fVDnnny8qVyI8RL7Iw6FcrhQPDQaDCAaD6OjoYJEqFosFbrcb0WiUyTT8/skpw3JrfhJeRfshd6Z4uAD4CFzEqDP6fnxlpZIOsb2NWD4Ps9nMDPpmsxnNzc0sopcv6F6Pp/HzEes3yO0bvxY5g7TcfoqyhBzdo+t8Ph+eeeaZmmgktVoNg8GAk5MTBAIBtK+tofn27Ur9p495t5xMJMdvRDwS6b4c7vHfi/eI30mShI3hYfxlZydeikbx93p6YDKZmCwYrXbPs1ZTxYa2t3ExFKrATqDrPOzkeCU/T7k589fWk7vqyQL1xic2NMgRi3qC2cfdy39eLpcRjUYRj8cRj8dx2eertLPc38fQ4SH6qkrAbqmEYLGIosOBw2AQ/+u9e/h2qYSfVqnwSrWSMxkJqNZBLpdjkQn5fB6SJNV47onp0lxoPlTVP5VKsb66ZKQYXV7G2sQE5vb2UC5X0g7y+TwjyOeDQYzv7FTyE1GxJpJyfXDqFH7Q34+ZzU3MHR9je3sbLS0tsNvtWF5eRk9PDxobG7G2toampia0tLTA5/Ohv78ff9nfj4Rej7s9PRiqthJ0OBw4OjpCS0sLXnnlFfzW+jrWf/VX8Q2dDsU33oDpp38a3+npwdm9PZypFkghxUqn0+FWUxM+bG7GRZ8Psz4fyuUybrS3I2k0IvDMM0xBpLZdlPZwxuOpeLmPj1EoFJBOp9kPUAl/pFoG2WwW6XSaFYKknLijoyM4HA5YrVa2D0qlEgcHB9jf30d/fz+r3D0Wj+PVW7egVCpRqBpLJOlR6BN1sygWi8yYIipLarUaDocDHR0daGtrY8Wc2tvbYbVa4fF4YDKZYDQasb6+jlKphJcTCfzYbsfc8TFTTO/du4dCoYDt7W38K7cbQZ0OfylJeHFxEbFYDDddLvxWKISL6TRmCgV8r1TCDYcDZ3p7Mby5iXw+zyJTlEolpjc2cH9gAOOrq6yNDeEgVcO+1d2NqMmE211dOH14iHt9fYgajbg3MMBqGVwOBGC32/HU8TGutbbiQrXf7vWODoytruLw8BB98/PIT0yg4949KB88wCWO2DxVPbdv/ezPIq3TwRqPo295GQkuVFAknmQUoQ4S/BhdWsLK+DimqrDkuwmM7+xgoLperVaLwbW1yrms1nig5/NDoagU7SLrLu+55RU6uarDZFSTJAnRaotO3hJPzJvuPTh1Cntnz8oTwv/KcXh4yCJ/rre3I67VYufMGbhv3QIARpdo/qKiKDJjOWZWj/mJ19H/ooAsCse8UCmGT4uDP2+tra347Gc/C+BRBBLRBoJ/S0sLOjo64PF4kEwmaxRUOWGWn5P4vyjwyTFLUQEQFU1xbXJMXBziPtH9e1NT2JyZwQvhMD7b0ICWlhZ8Lh7HfrGIPy0U8C/tdpw9exbfK5XwflMTBu7fZ+lCIoOXe76YOsB/Lu43Lxjyn/NRdPzncrhQb71ysKPzSp5iMnST0BsOh1muPimCcngqJ+yJ34tr4vFGTqiSE5bkDGiiYiMndBENylWr8Q8NDUGlUuFPs1n41Wp8q1jEdyYmYDQaMT4+jkKhgJaWFsTj8ZoK4jTkcJ3/rp5iWQ9+cnMWlVi5vROHeCaASuQAFX8OBAI1yjhF95FBmBwEJHvRoNSFpqYmzM/PI5/PY2FhAbOzs/D5fLh+/ToTtk+fPs3au3V2dkKSpBoHiiRJiMVi7P9YLAaFQoH29nZmSDYYDFheXsbKygo720ajkRVxFM8/RRZOTk6yVI5EIoGhoSE0NzfD4/EgGAyyMHyRZsnRTTlBXqTV9faSD2sW90401vH4yz9XTmGk/++1teE7qRR+ORLBVw0GKBQKBrtgMIi2tjaWpspX7udHPcVcTj8gw5GcofhxZ0OEgZwhhR+i3EK6ABmnJEmC3W5HrJoSSs+tRw/oO35v6TteMefPr3h/vfMn0rPH0R/6Pby5ieHNTajVavj0ehweHqKpqYnNQaVSMcOfSO/r7V+999a7T1ynuGaaCx8pJD5LfBfBL5fLYWdnB0tLS+jo6EBvby+r7aVSqXBwcACVSsUcXiLd59fFyzT8O+XkG3FeIhx4PsnvqWjcFXGX/+xuby/iWi3ec7vxc9V6Rk1NTVCr1bBarSy1JxgMslp9/Hrk9k2OfojwldsnOZoiPq+eYV5ufCJDQz1gyyF+vQNOgxZDilsmk8Ht27cxPz+PcrmMAQCD1QUWBCJzr78fcZMJb5dKeDcSQcxsxr9OJCB98EFNUTcSXkjBIW+nQqGo8YKLIVCEMCqVioWF0RqoG8NIKISX5+dZ2DUxWxIaiDFRaI9SqUQ8HofZbMaPRkeRNBhwv78f54NB1nYmEAjA4XCgr68Pq6ur6O7uxvPPP4+rV6/ieG4OPx4aQnsqhXyhgM9Vw4ReffVVLHZ34zsHB/gVpxMvDw/jV+12JLRa/FCjwS+1tuKvx8YQKpdxs6MDkx5PTd5gqVTCh83NCFfD1k8fHgIAzh8c4FpbG2Y9HjQ2NrI+96T85vN59B4eYtLjgcViQTyTQWtra02XiWQyCVU1d75cLsPr9bLaCAaDAYtdXbj/4osYW1rCRHVeZrOZKY9UkX9iYoJV8qaICiKUPCOg4prlcpmFifEVx6mTw8bGBvx+P4rFIkwmEx4+fIhyuYy7d+/i4cOHzEjwTCCAU/k8wuEw8tU2bDfdblx3uXDh8BAzqRT+NBpFzOGAAUDDyQn+jt2O03o95t1uxLRaXHG50LmwgKunTiFmNOJWVxf6VlZYi06FQsEiHnqXliqFOjl8o8iIWCyGmc1NLAwM4PTuLrLZLGa2tnC3txcTGxvo2NpC58ICyuUytkslWJeW8KmqVz6fz+MZiqZRKtF+7x67FlW48O8rFArovXsXnrk5TG9tQaFQ1ESJEMz5lBi5MO1SqYSB1VUMrq1Bq9WioNWye+h7KhBH4a98OhEfoihJFYvv+tQUeu7cga1QgNvtBgDWNpXmIrbV5CN2KJKB1kv1PBQKBctfpFDeWxcuIF3Ft5/08Pl8MBgMkCQJn9Lr8SOrFae8XhS4KuoqlQqbIyNYnZjAyMOHGFhdBYAahYz3StLg6TTvseBpsJziSPfKCUIiw5ej+cQ49Xo97HY7g29TUxM0Gg3z9hI95J9JgofdbmfRTWJ4sBh1UU9pJAYsxxx5HOXn/7i1EXx5wa8enPjvaaxPTSFmNOKm2Yz/2W4HAHxDp8Of5XL4hl6P9mpv8P9nKoUEgI2pKfQtL39kzXL7K65fTtmm/+WiEuoJgzxM6AzKDV4I4ddOyp/b7UZnZycmJibgdDqhVFY6C5XLZfT398PhcGB/fx/b29ts/nJeeTnFgadF/NrE6+SGKGTyQmK9++UEcx5mY2NjrJ3yyckJJEnCVxQKfFuhwFeqMgfhdTqdhs1mQ3NzM/artZ7kZCZxT+oZj+QUSbnzKwdLOWGaP2Pi+vlzS4UtyVi0NTqKv1AokAiH8VWDAZ/61KcQCASQTCZx5coVRKNRpgjo9XpmfFAoFGhpacG5c+cQDAbh8XgQi8WQTCaxtLTECi9TTSe3281SGICKAkI1HEjO440NTU1NUCqVaGhogFJZKWBJKXIGgwFTU1P46le/yiKKstksjEYjFAoFM/qOj4+jt7cXhUIB/ymTwZ+89BK+VqW/ZrMZp0+fhtPpRHd3NwKBACKRCONt4h6KvFLuc3E/iXbyZ0Ruf+RwmOcT4lmR4wOLIyOIAfj/xeP4uWrXD0p5mp2dBQC0trZWZKNqvbDHKaOiQUAOHnL4zM9PzkAhvotfE4+nvBxB71KpVGhsbGRRzeFwGDs7O6ylp8h3+D0RnyV+Rr/l9pSfd721yu1fPR4gF+0gSRJzjJbLZeYwVavV6OnpQSwWq0nb4nkfz8t5eNJvucKJcvvxcbSbhx1/j7h3ogyzu7sLAOjs7GR0igyNNpuNdXsTjVOiEUqOp8kZl0TnGQ1ePqnHh+VkEDk5Q6FQYKpaEPJ0IIDtYhFTU1NQq9XMKUMOVLVaXVOklMdTXrf7OBlPbm94nOT3gj7jDSr1ni83/ouKQfIToUGKA68sUBRAKpVCJpNBMplEOp1mUQupVAoKhYJ9Tq30SPgn7yIfJg8AA/fvY3V8HAMPH0Kj0WBlfBxDDx/iuJoDToIs30ZPkiTmQSRAFYtFLPf14W5PD+b29jC0vs6uj8VicDgc0Gg02NzcZO2pPB4PRkdHmSea31B6JzEXPgRaoVAwJebpQABvO50Yr7bOef311zE6Ooq//Mu/xNNPP42uri784Ac/wDPPPAOXy4VgMIj/NDCAE0mCBODvXrmCF154ASvNzTh16hT+u91dnOj1+PNCAf8PtRqfzWRwxWTCP3C7YXO58Ct2O37v6AizXi+bA488F30+XG9rw4VqW0ClUom542OM7+xU9qGavkFIfnBwgOuNjfC88grO7e9j7vgYklRpI2UwGACAFVii8CzyYPH5kg9HRxHT6zHf34+OhQXWrcDpdMJgMLBcSTIe+P1+JBKJirGpvR33BwYwtryMofV1xugAwO1210QFKBSVIph3WlpwtbUVz5+cYDKfx/cB/KNYDBesVhRLJfwfSiWmDQbcb2lBjE9DmJxERKfDhy0tgCQhotfjw+ZmDK6t4Xp7O9I6HRzpNAIOB2ImExYGBnBxfx83Ozpw9uAAVqsVZ/f2cLOzE9bjY/zJSy9han0dE7u7DN5kcJMkiSmSJFDQuYpEIigUiwiFQjg4OIDr8BCfunYNAJCudvLgixSRB4IXokmQ4usZ0JnN5XIsOsFy5Qpe29urGIk4XKFBRgCesObzeezPzGBjehqDCwvoevCArY86gojGKlI+j+fmsH/uHDpu3kTb/DxbM6+0rH35y0jb7dg5fRpP378PrVYLrVYLs9nMoimogwpFMAGPujAUCgXmcSIDIa2X6BlfwLJ3fh6bMzNPSiI/0bBarYzWfL5cxmdjMTz0+/HjVIoVGGtra8OPTp9G0mDAzunT+GKVLlJeLN/ulmeO9DfBgRd2aM95xktRXvTDK9ZiYU2egfK4AKBiFKnW4/gcKjSAjDrL1fQYKuomChlKpRK5XA59fX3o7u6G1+vFStUgJxc9IQqvJNTxDFD00vP3Ah/1hj1OQJITzHkjNf88EmJJETu/v4+V8XF8s8p/VCoVvm6x4Bs2Ww08f06S8C8DAfQuLLDnKxSPcoLlBDBRCOeFW5q7qFTXg4f4tyhsiDASBX9eiVEqK4URP//5z8NSre4OVIxkWq2WdWlqa2vD2NgY/vW//tcs4k/01ooKWT3Fgp8XLwyLSjjBT4RJPcWHvpObE62VCgwODQ2hUCggGAxCkiQ8pVbj6eoeBgSFwuVy4SFXcFbEI3EPRGWDhzndz89PVNz458gJx6KBoZ6SRL9VKhVGR0fhcrlwcHAApVKJ/6xQ4BDAv4lG8Vy1DhF1enA6nUgkEmhvb8fc3BwCgQBLdbRYLCgWi+ju7sanP/1pvP3223jhhRdwcHCAcDhciXK9fBlms5mdCYo8SCQSrBsWACZjWCwWlkLR0tICjUaDdDrNIkgvXbrEahG88MILcDgcrIikSqVibSrPnz/PUh339/fhdDrxx8UiQno9fqBU4ldTKWg0Gjz99NPQaDR47rnnsLm5iffee6/m7IpCe709l9sTgjePu/XOwccZiPjPRGWHnv9cMIi7Viv+jtXK4HnV4cB3Ghvxd1Qq/IxCgZGREcRiMdaGVE5h5pVWnjY9TgmVGyLcaIhRBPVoufgZ6SskzxuNRmZ0MpvNiMViH4Gh3DkRaa24Fv5sirRabl6irCbijqj0yvEe+lulUqGpqQmFQgHhcBixWAyJRAK7VUcVf9ZF/iLuYT1FVJzL4/CyHmzkruF5uvg8kgsDgQBLfwUqcl57ezvcbnfdek+P4188fZXbb37IGZ/keIN4v4iX/PrGdnYwvrsLpVIJX0MDOjs7WftvOkMmk4lFa/ByCP8e8YeHJf9uOZiLeCS+Q+4MPsl4YkNDMBhk6QhUFFFsycj/kNBAgi6/IGrnKEkSqxqcyWQQjUaxNjiI4LPPYs7jwaTHw4RxMgxIkoRJjwedCwtobGyEyWTCM9evVxQlmw0GgwHFagqFTqdjBYFMJhP8fj+sViuWe3txs6sLFw4OcKerC1GTCXd6etBUDVtubW2F3+9nNRfC4TB0Ol0lXO+55/Bn09M46/HgUiRS4w3mN44YNQnVADDf1oYbHR045/XiF997D6+++io2NjYwNzcHtVqN5uZm9Pb2smgIi8XCLOs/bzbj/woGcenoiBVJam5uhtlsxq84nfjdgwP8emcnkskkLoXD+KcXL8Lv9+PNeBxfUKuhe/iwktLA7Qkh+cTuLkartQTIqACAeRoop1Gn08Hv92N5eRmbv/EbyFituFntokBh6F1dXax4JkUQALVKL3nzz+/v41prK6a8XlZEkr4zGo0wm81Ip9NMucpkMkypud/fj4TViuWxMYxVvWHEEMPhMJqbm2sqP0ciEbw/N4eYwYC3HA70TbWr1QABAABJREFUr6zgw+ZmRPR6XGtrgwQgajTiweAgLuzv41ZnJy74fNDr9bjo81XqGOztoVQq4U5PD05Xi3md8Xhwt7cX5/f3kc/lcLevD6d2djDs8aBveRmFQgH+Uglth4f49NWreONrX0PSZsPi8DB6l5bY+SqVHrXnpAgf+pwMD+tTU8jYbFidmEA2k8Hu3Bw6b95Ew7VrrHo/ERRJqkQAbExPo+n999Fw7RpTrumZvJGBEYSqUk6dRShNBgDDGwBMQafzT2dzdXwcGZsNaxMTcHz4YQ2B45UQMsZRGs3hxYvI2O04unwZF6rewEKhwO5Xq9UobW1haWQEYxsbaGxshEJRiUIg4xbNjwwYNEeedlB0E8GAaAvPcCkCQqPRQPkxAtB/6aAuC+RtCIfDWK7iSywWw9raGkKhECabm7EyPo5Xs1l0dnYyeqJUKpmBhAwCdE54Wkvf0VmkNZJ1XJIeFU4lfAPwkXQknrnwz6FRLBbx5tAQEno91icn4To6Yh4H0QAA1Ea00VwsFgucTidyuRwWFhaQzWZhs9ngdDqxNjCAu319OL29jam9PbaXomDE43+90FM5hit6CUXPsRyz5q8lnCKGbDab0dnZCYPBAK1Khb9X7ZTiVTwyQvCCvUKhwAtKJWyrq9g4PISqqpSJgjIvCIjMnz7nBVQ5gUhO4OLfwf8W4STOhZ8//a3X61k+aWNjI9t/wivCO974QF2YJEli9Q3kBCn+/XJKm5wAX8+TJQqhcusRhS/+2aJCl8vlEAqFmNGTzo6cQF0ulxGLxRAOh2u6rIge64/bF17heZxBSU4ZEoVnOeWsnpJKaWg6nY51m1CpVPiSTofvKJX4YrGIHFfsOZlMolwuQ6/Xo6urC6lUCvPz86zWkUajQSaTYQaJn/qpn0JHRwc+/PBDNDY2AgCGhoYQjUaZN49op1KpZDJaIBBgRi6qteV0OpHJZFiEwsLCAiKRCC5evAhLNZqI6KNCoUAqlYK/Wmya5LJ8Ps8cTul0Gj+lVuPbkoQvSxJ7pyRJjIebzWaMjY2hUCjA4/HUPcuiwsOfFcJF8XrxHNdTaOopVXKKrnjf6xYL/pdqHj/VQvq+Xo8AgD/N5fC5au2Knp4e1pmMP7f1jB3iWZZTwsQ5i3OVw0mRxosKkZzyJ0kSTk5OWHp1MplkUQA0VzmFtN7z+TkQL34cPZXTGQDUpJjK7Y94PU8L+WvK5TIikQgLsydjPkUai/MXlU5+iDxQpGn1It/k1i/SHX4t9LeIP7Q+ej5FSgOPdItUKoV0Os32T4RRPV7Hj3rz5+fwceuQu4fezeOUeLYJruVyGeFwGLlcjjl6qK4dRXLF4/EaWUecX729FI0RIi7zn/FnVVyX3FofN57Y0DB+/z7m9vZYaL2I2Hw0AylGfNV3EmCpkjsxHYpeIEJ/dPkyMlYrFgYG8EI6zYDJ50BSqNZKfz8WBgdx4eAAp6o5/WNjYygWi5VeuAMDiMVirEhQIpFAqVTC7e5uJK1WzOt0uODx4P3mZoxtbkKlUmFqagp/kcth+Zd/GV8sFqF69138wi/8Am40NuL72SxgNiNaLldqE4TDDAbkZSQhQfSoSJKEGx0dCBsMuNXZiZn9fXR0dCCZTMJmsyGbzcLlcsFiseB3trbw+42NmHvjDZw/OcHG8DDeT6fxlN+P2eNjXG1rw78Lh3G5UMClRAKjW1v4BwsLeLW7G7du3cLOzg42NzexubkJj8fDFBiaKx8uDjzKz6VDSsYT2tt0Og2TyYRCoYD79+9Dr9fjzO4uHur1mN3bg9VqZQX2JKlSOZoKWyaTSWawEUPix7JZTO3tQaFQIFHFB54A8oSXiC8dxOGHD7E6MYGxlRW2Brpnpb8ffz0zg+mNDYxtb7O2o3MeD+Z7e3HR54NOp8OFw0Pc6OjApaOjSh2D9nbMeTzoX19Hx/37yOfzWM/lYFpdxQucsvypq1ehUqngBdDg8eBT164xAfnFq1dRLpexWxWC+FSSQqGAjhs34JmbQ9utW/D5fOwckXBOHnkq4kOFIJVKJQobG1geHcXk9jYeXryIjMUC36VL6K7WhaB2cUS0tk+dQtbhwOHFi9D88Ic1CoqhmncpEkW9Xo9yuZJu4PP5mMGQzjgA1m+cBGO+vsbww4fYOnUKk1tb6O7urklbIEJKaUxk+FAoFJhYXcXK2Bimq+eQPqcIiFwuh5Y7d9C5sABJkpCp0hi6lp7PK5lkyFAqlTU90uk34RTfDpX+B4C/nZlBwmR6UhL5iUZLSwsAsEgWrVaLxcVFZtihYrC98/N4oSokR6q5iJL0qC4Jf1aBR11qaC2ED7Rm2m/y1gGoeR7hRzabhdPprMC6akgShRzac1rDa/k83lCr8alq95vFxUVGd/g0ND5KTa1W17Slo7otsVgMwWAQ6XQayWQSd19+GVGTCQ+Hh/Flru0Yb7yiH6IX+Xwe2WyWnQu6lt5H6W68YUEuBYhnyqK3mxcmydBww+XC3zideC4Uws9YrR8R8glPSXigs6RUVkK7zWYz4vE4isUi3G43VgcGMN/bi9mdHUxWjSz0PlEg5M8BUCsM8VFIZGgUlR1RICNc5M+knJeJx53e3l5YrVYYDAZ4PJ6a/aFBdJH4wdjYGEuZWVpaqkkPormIuM5/xwu/4hzrKe38vOsZdETjGH3Hf07GFYvFgv39ffT09NQIgOJziZ699957TDYh2kt8ljrQyCkb4jPFNYsRCySPyAnaIqzqKS/iO8kBpFarsbGxwXjUtEqF6ep1XjyiE2T0LRQK2NjYgMlkQjKZhM/nQ6Davrmnp4cZGkwmEzv/CsWj+lCNjY0wGAw1RSQpalKhUKCxsRFutxsbGxusLpbNZoPJZILBYEA2m8X8/DwsFgsLIyc6oVar4fF42DypVkyhUGD8cnt7G319ffiMUolXJQlKSWLpbnz0mEajwcDAAPb392sUHx7XRGVN3GfxLMrtjdx3/O96n9MQlWn6bbVaWZoLjS+Vy/iOQoGfkiS2pq6uLkZzy+Uyi1aWO0805BQjEQb1lBxxXfw9YqQe/w65dyoUj6KpOzo6YDQa4fP5WEtT0ahM7xDfK/e/aMzm3ynKuHLKnxz9lns/Pz85g9LKygpcLhcz6JOT0ufz1dwrDh6GPKzFiBSal5zxh1eo+XWJHnj+GaLuJMd3CQ5UrJXuSSQS2N7eZs5JHlb8PERHCQ87EX/EyMF6506MDOGvldsnHsflBsms5LSnjABKYRdpOT8XOdzi91TOCCHyM3E/xcgdwmM5fJQbT2xoiJhMuNHejta7d9nk+WgDUihIqaKweRKGlEolO8AqlYoRsVwuh0QiAYfDgWQyie7bt+F/6ik8V7VQ84opbY7NZoPD4cDfTkwgZTDgarmM9nv34HK54HA4kM1mce7cObyl1+PDM2fwFQDLh4e4/fzzeF2tRkcggDWLBR2pFOb8fnQ+eIDBwUHM/vzPY2xsDP/vu3cR1WrxjlqN3+zrw8WLF/E/bm8jbDSiUalEa7GIsWohOvIWAmDEl1fC+DCo89Vw+ktHR1Aqldjb20MgEEAmk8HS0hK8Xi+i0Sj+VSiEE50O19rbMby5iTcMBkRKJaTcbszs7+N6RweiajXestsRCoVYD9lCoYDGxkbo9Xo0NzejXK7URWhvb8fOzk4NPGm+hCjkndbpdMhmsyznnfdGHh8fo1gsor29HV2RCF5+8IB54iknkgpMkuK9sbGBQqGAsbExFllCe0lwIsEqkUjAarWyg01GqoXOTiw/9RTGV1dZOsfQ+joG19YqBqqqMEDwXzpzBjmDATdHRuD48ENIUqX4X3siga4HDyqGgFIJ1vV1vFzdp0wmgxeqfx/m8zXeYb7OA82NjwwAwCIxSGDR6XSwWCzscJIHqOvkBJfffLPyvMFBBgc6P6R8EM7QKBaLmNjdxfDGBvvu4cgIxlZW4HA44PV6EQqFmGJQLBbRfv069s6eRevNm2hra/uIQsV7VvnffFVpMjoQgaEIBF6J5Ql3SzCIp69cqeBFFYbZbJbtDSmlvOcAAOx+P164excmkwnpKkwJ/pSyo1AoajrMkILM17ug80Z0huDLf8ZHuvDMiz+z5XIZl6uRLP8tBuWrkwA+Pz/PDEBkcNNoNCiVSiyViPCAN+CSIk+wIobA/y96OilNRqlUsla1hDOE9wBYVBfPgHmjhMgwz6rVmJMquaHx6rza2tqYAZIGCRL8maLn6fV67O/vw+PxAKh4K46Pj9F16xZUFy7gtWIRzc3NbD9pT+l/MrSUy5U86/39fRYpQdcQbUkkEqzbAw1eKBIFJT5yhOBI55/Wp1Kp8E8NBgSVSrzndmNmfh7Ao0ghuoZP6aMzpVAoWLvcYDCI3d1d5HI5zH/qU4iazXg4MoKvW63sWfkqneKNF7QnFK1DOE3zJfxOVVN0KL2IN7jRZ7Q/vJGAzi8vMFNtohsuF95vasIzgQB+1uGoKbRMMCUDBz2fRnt7O9RqNf7qr/4KCoUC6RdewLW2Njx1dIQz1ZRI3ojIGylEowTRZ34PRaMKDTKgiWdEFDrrCdgAsNLfj6XRUbyWzWJoYwPxeBwul4vRSzpPlO6k1WpxfHyMWCyGSCSCUCgEjUYDt9uNoaEhhEIhxgP494jv57+XUzL480n4y8NcFHR5wVhOaLbb7WhoaGDfU42FeDyO/v7+mvtpn+l/jUYDu92OW7duwev1oq2tDZFIBG+99Rby+Tz0ej1ef/111n0rlUohmUzi8PAQwWAQSuWj9rik/FssFmaYb2pqYjhN+N/Y2IhMJoOWlhaWrrOxsYFAIICbN2/i9OnTaG1tZR5RKtBMtSB0Oh0T7BsbG9k5Pa4Whebpj6hIEs5Eo1GGn7wzhIa4Z/QsOeVGTvmT87rLKb9yCqV4PT8PSv0xVFMyy+Uy5nI5XKjy/lA1RRioRJK2trYy44z4TDklVFy/nCIq3k8w5fk1/5kIC349ckOSJBwfH7NUXqVSie7ubvh8PgSDQVZPRW6e/G8RvmI0HH9/vf2ptyf8/OWUYxFOImyo84rZbEZDQwNyuRyOqgXhxTMuN2e5OcnhnlyUg9x+y8GIV2x5I4yo5PO0WK1WY21tDRaLBSMjI5AkiUVXxWIxnJycfMSAKsJRfK4c3HkHmbhP4pmSwzueBosGDjkY02c0J7/fz+5JJpPMcMmvR8SxenxK3Ae5feefQfPl5bV6OPNx44kNDfZUinmv6UVkSEilUqxFIU2OOgZIUqVVzsDAAMLhMEqlElpaWhCNRmE0GqFUKhEKhaDX65FKpXA5EkHf0hKr3MsfdBJUSHm4cHCA95uacNrjwcTEBL70pS/hD30+/HkyiX/U24s7u7uIqlR4W6tFUqtFUKPBX6lUSDudKCkU2K96K51OJxwOB9xuN7RaLT6XzeJdsxm/aLWiqakJdrsdP2sw4N8nk/i/tbeje3ERy4kElHp9TfFHXugUN79cLuOs34/LkUhFSFSrsbKygt3dXfT392NtbQ1erxf/dHUVCQD6QgGtV6/C7/djYm0NC4ODuFwNtx5fWcHDkRF8OpVCV1cXgsEgBgYG0NTUBK/Xy0INt7a2oFarWXEl8g4v9fZia2YGM1tbGNveZvOLxWKsOjhZQCk0naxrAwMDaG9vrxT3q3Ya4EMZaf/JE5nNZqHRaNizAbA2gsCjAiYkwHq9XobY1Bpz8aWXkLbZsDQygoHVVQZbUnh5YxTlm1eBz7pQJBIJFtbKC5E8saT9IwWM8smp9RYftg48qndAxQh5oY6UAYI5zYnWSf/zQjIpiCSY8p6ocrlc47kZXFtD9+IiyuUyzBYLwpcu4f7cHDpu3kTH/ftQq9XoW1nB2M5OZW7V9oE0N1oX/24iqGQ8o5xWEsIoYklUxHjCqFAoWOcV6iZB3xP8yDBA76cijBQJQ3AnuPHdLEQjAXm0aPDMgV8b/znVouDhTThJEVO5XA47w8PIVBXUn/QgDxnhCBkLaZ3FYhFGoxFtbW3MY3pycgKLxQKHw8Fq4FABJADMYEddMyiiiPKXCfd4PLTZbDU1cPg95Wkw4StvwBDpM+FJqVRCKBRirZgofY3woFQq4UdaLf7GaMSrqRSeTSTYmU4mk6zlIa9suW/exGelSkVwTzX6jWfcogBKhhij0YhcLoeTkxP2rGKxiHQ6zWrKUMoWrYNfHx9BIgpEfC0Bgk2xWMRFmw1XGhtxKRCA2WyGzWaDWq1mtIAvUMzDsVAoIB6PY3t7G2traygWiwgEAmi7fh2ap5/GV5RKtLa2MmMgL7TR+gmvRaGIPzNkTOBTi4guEr0kZZ3wkCKyqGZOPp9nFbEJJv9fux0htRoftrRg5vZtJJNJloJF55qUb5oPeWcoNW5vbw8HBwfY/MIXkKqmtX29KnPwKV/0Py9wiUY1wk25qAT6jI9I4RVBXrjmI0d4Oke//6q1FRGNBm/qdJit1gXo6+tDJpNhHXJsNhs75w0NDVhbW2NpptTi2efzIZlMolAooL29HQMDA1jo7MQHzc14JhDAxVCIGSrk1kZzJxynPSP+SOdPVEz5M88/g4djuVyG1WpFS0tLJWLHZMJnMxmWxhSLxeB0OpkcpFAoWGtJoNIqkiKVCKey2SxCoRDUajXGx8dhqspj5XIZoVAI0WgUCoUCOzs7rEgsyZp05ikCjwz0FCnX1NSEcvlRUehsNstSK4gmkfxKtDeXy6G1tRU+nw9tbW1oampi0ZDkrXc4HPD5fGhpaamRRXkeSHh6cHCAu3fvMgWIOpNR/r+ckkq/eQ8p7YVolOXPOG+gEJUNOaVbVNp4ZUihqEQSkpGM0mL4NEPeqKvVamGxWODz+ZBKpT5itJNTrMXPeHwWcZrukTOai88XYcrDhL+WCtVOTk6iqamphh7Ozc0hmUxieXkZwWAQqVSK0UTeaCqncImf8/sk5x3nr3+c0i8OXrGn++Rw5vDwEF1dXejt7WX8tK2tDcFgkMk+Ijzp3npzEN/LX8NHEohwEJ8r9wxyvvCwE+HC44darUYgEGCpVycnJwgGgzU0Xm7IKfv8s3l48o4Mfv/k9Lx67xD/F2kvr8wDYNHjg4ODrGuITqeD0+lENBqtMZzXezY/xOt4OsDDV+QPooz/JEYMufHEhoZf+fDDSmXWSAQdHR04ODiA2+2GXq/H1tYWNBoNstksyyEJh8NYWlpiIW9XnU7cPX0az4VC8KVSmH/qKQwDWJUk/LRSidmjI0QuX8b3Wltx+egIlyORjwCERzwAmDs+xsz+Pp577jkcHBygsbERf+7x4Fitxr8MBPBlScL3VSr8WnMz1tbW8JbJhJ83mXCYTOL75TLOVkPm+fDqQCCAM0dH+N9On8aNGzfw5+k0/uH8PC4eHuIfx+M4bbHgb+7frzkMpBASclNEAB8CRJtCCq1er8fw8DAL98lmswCAf59KIWW3w5bLofn2bbinp9EeDGJqbw/t7e3ISBJmfT68kE5Do9Hg+9//Pvx+PzKZDL797W+zPLM33niD1dG4du0as/wXi0WsT00habHg/sAAK0hIME4mk8y4QK1CVSoVFjo7cfPUKZze2YFtd5cpY2azmQmRklRbTM9gMKC/v58JOpFqnjIROPLSkuBMYeMajQbrQ0NYn5xE1+3b6L93D9unT6Pv3j0Eq106CBey2Swr/EgHoee997B//jx67tyBQlEJSaXuH3yOMB9JwAtv5XKl/RXNk/ea8gIcL9xQGDbtMx+hQM/hlWTCOZ6QkrLDK/P0PeHnYnc37vX14fTODtrm5xGLxaDT6XBw4QIyNhuOLl/GxaqHTKF45KEnRkl7QeGivODOEyHqTcynNxB8xBQEXsEhT7xOp2OKBS+c0HvImEC4ZzQaawxHfBoWeVrpM7VazQp2AWD7zytC2WyWGT5zuRzS6TTbK15h4ZUUihQg2Hu/+lUUqh68/xaDCDi9l4wParUagXPncPP0aTSm03i1rQ2ZTAY3btxAV1cXenp6kMvlsL6+Dr/fj9dee42dr/feew/PPfccGhoakEwm8f777+PMmTNobm5GqVTCH/p8+KCpCV9VKvFsIoG/UanwVyoVvqZS4dUqXty7dw+Dg4Nwu90ol8vY399nwrIkSSwKq7W1FUAlDSMWi6G5uZmFJHu9XvT09MDlctX07CZ4v5lOww/gLbsdf6eqjBaLRUSjUfzwhz+EUqlkCrrBYEB3dze6u7tZxBPtGc/4KPKFjCl82g4vePD1O6g1Fg3R880LK+RJJZpAZ4z38pfLZXymVMLze3tQKpUwORys+j3RBTrnRPfovBGeRiIRxONxJtgY334bL3g8aJ2cxD3UejjpzBFd4b2sdN4Jp0RDA8GMIlpo/aIxkXgd8QSaO3mNaFzOZnGlsRFn9/exsbEBl8uF5ubmj9A1oh10noknPHjwAF6vFwDQ8O67kJ57DufCYRSqOcY8nyXjI28soP3i10NVuwkWxWIRNputxkgkKiO0PsIFMsbwNAx4pPz8TKmEb0sSXkoksLOzg97eXuY4oTmQZ5iMqUTPeMVMqVQyQznlUr9/+jTCOh2utbbim9WOJZTiSClBvJJGdK1cLjNj1ejoKFMO6Hq+Lgt/hsh4zsObcCKRSEClUuGHFgv8SiX+1mTCOYOBpT7odDpYqxE3hUIBTqcTer2eGVCj0SiLZBPlJ7fbzYwohJNkCHU6nejq6mJpJeVymXWNID6ayWQYnyAcJa80nXOSBRwOB8bHx+F2u6FUKlktqGw2y7zY+WpXL6vVCqfTiXg8jpWVFfT39zPjGeGDtVowkQy2Go0GqVQKBwcHODw8ZAbf9Asv4E5PDyZWVjC8uVlDc0TFg4ZoPKB9FBUoUQngw5pFuZlXIqiCPXXloFRJpbJSeO7WrVsYHBysoYNEawhf8vk8gsEgjqtFwfm58d5g8bzx/4vGE1H55OFBv3kaJWdM4a8Tf9xuN15++WU0NzfXRFfa7XaUy2U4nU7YbDYkEgncvXsXR0dHdR2J/Lz4IRpU6s1LNDTVM1SI75NT+Hg9iSJTqQA2UHGqPvfcc/D7/djd3cXh4SGrBUBnsd6Qoxc8f30SePBGCvEeOZ7LP0t8drFYxNbWFp555hnY7XbkcjkEg0F4vd4aGZJ4Dk8nP05B5uUFXkYU58SPemez3rtEZV+MeKByAtQtRK1Ws6KefPt20bgoZ8ii54t8rt7cRJwT94CXqZ5kPLGhgSx+kiThVlMT7pw9i8vHxxXB9OJFvBSL4fj4GN8dGsLl42M022xYHRjA/vnz+I3WVvxfJyeIK5VYbGxEMplEWKvFbQBFAD/SavFyoYBbJhOCSiU+aGrCpXCYAVu0hvFIYDAYYDQaWVupL5XL+I5SiX/gcqF1bw+/duoU0okEjAcH+EcjI7h58yYWSyUUm5oAgIXcb29vo6GhAalUCuvr6xgdHUUul8P7TU2IlMt42+HAdDU8nardk1DDE3RSoCg/n7dUkdJULpfxntWK35YknHW7MbC8DIfDAZVKhZnNTaxOTODs4SF6x8ZYISIShtVqNYsqyWQyWFtbg0qlQjqdxuHhIcxmM6tRQQISHV4SbCfX1rA8Oorpzc2aua8NDmJ1YgID9+5hvNpCJhAIoFAo4ObzzyNtNuNmRwfw/e8zIxMVCiKGQgoDMTESwklAJwJATC2ZTDLFkwSaQqGA5S98Adlqh4Fzf/AHcN24UYFjlXASbEl4J9hoNBpM5/NQVgsRYmioZl9ICOXzzskLQrCgNBKaLwBWQ4Gup8PHpx9QGxqah1JZCYUnAsLjDB9uSZ5j8iryHijCHfpZGBxE1GDAwsAA+ldWWOht3/w8tmZm0L+wgEQiUSMYE2EgHOKFSsINmjO9h3CcBHlesOLrqvDWXoIJGdlIoSdlhTcG0RoJdyjFgod/NptloZhEcMnAQ8Vk6bm8UlSPgfOGJlL4eE8QAGZ0kiQJuoUFeM6ceVIS+YmGyIzo3ST4HV68iKzJhLd0Onzu8BAbGxvY3d1FKBRCW1sbAoEAHjx4gGg0iqGhIWi1Wty7dw97e3uYn5/H2NgYPB4PDg4OGH0Mh8N4t7kZUb0eP1Cp8HdbW/Frfj/CKhX+slDAZ8qVIkQLCwuw2+1ob29HLBbD5uYmTCYThoeHUSwWsby8jEAggKGhIcb87ty5g29+85usuK/f78fly5eZB57fI0mS8HWNBt8qlfBNvR4GLt2BD38mw2U6ncbY2BirwPz2229jenoaLpcLhUIBt2/fRnd3N5qamhAKhfD+++9jdnYWU1NTKBQKODg4QCQSwVCVFoTDYYRCIQwODkKtVuN7pRJ+oNfjC8UiXspmEQ6HcXJywnLtC4UC/H5/xdBbzZlMp9NobW2FQlHx3MZiMbjdbua13NnZYe33RkdH0dHRUaP4k3fbaDQyAYBo1MHBATQaDavTYjKZ2H5QuDrREh6P5LzbvKAlSY86caRSKZTLZXYGeOWczgvRP95wQbSP6BvRkEKhgBczGZxfXUU8Hkeomt5IQi6dRfKS8mkaxBsoXUar1aJtfh69S0s4++KLiDqdNXMh/s8rO/Q3T9t4wwpP6ygEnhfC5J5ByjPRGhLe6ZmEsw3ZLH6hihdt3d3o6upifC2dTsNgMLBUJdqnYrFSsNpkMjH4mM1mFvUJAIeHh+i5exfKuTk8F48jVK1XwJ8P0etG+7S7u4sPP/wQarUa58+fZ3ROVGj5dfCCMs+jCB65XA43btzAmXIZ19raMHd0hL29PUZfKN2SnBq035SaSN3HtNU2xwCYAezDDz/ExYsXa6Kr1Go1TCYTJiYm8Morr7D0BzJckWOC6D7VbUin0+ya5uZmtnfZbBb9/f342te+xhwkxJdbWlpgsViwu7uLzc1NFAoFlvZ1cnLC4Lazs4Oenh709/czRwKfbprL5ZjDyuPxMP50fHyMxZYWZEwmLI6M4JVisSZylMdRGrwhlfaVx3lalxhlxvPBbDZbIwfw+H2vvR33+vrwXCiEp6uGfh5Wfr+ftaAl4xHNie8elMvl4PV6WZ0onp+Jig7hv8ifiebw+Mb/Lcoy4n3i+kQDHC+fKJVKTExMwOFwsP/pWjJglkolVtvD7XazVqWkvPKRUDQHfjzO8CAaRuTu441BIkzknklwoaFUKtHe3o6nnnqKtfAEKnhjMpnQ1dWF5uZm/Hkigf9ULmO02r2NfyYfQSK+R1yLnBFFjDKUW4OoxPLvEo1W/H1EQ7PZLCt2qdPpMDQ0BLPZjJOTExwfHyMUCjHDphzseJ4s4uTjDAviPop8V9xvfojwFJ0Z9F0gEIDD4cDw8DCL2Ghvb0cikWAOM5LrRWONHPzo+fwcxXvEs0VnWE5e/STjiQ0NX//613H79m288soruLCzg7hKhWWnE+l0GiGtFh8ajcg3NCCk0eDD5mb8rNGI3TNnENPr8R9SKXwVwHeVSvxaczNWVlbwjtmMUZUK99Jp/HprK3TJJH7OZMK/jUZxuVrxV/S00A9tBuUHvvfee9DpdOjr60PTrVv4J83NmDCb8cb6OgwGA/L5PDweD46OjnBycoK3ensR1WjwQXMzepaWoFKpoNfr0d7ejkKhgL29PeY1/Ew0iqt2O54OhzEwMICDgwPWM/p2czNudXWxVoVk3aUWJFSHgvcyGQwGqNVq3OrsRFinw/X2dsz6fMz6+HQshmevX6+s1WKpKBRdXbg7M4PZ7W2MHx3VKEcEI6Wy0huawgqpMGUymWQeNRIeJnZ3MbW3BwAoVOG71NuLu7OzkFQqbMzMYGhjgyGfTqdD9+3b2JubQ9+9eyzyga8STjDR6XSMGPPKJK/AEsPU6XQwm80wm83MKMLqAezt4YFOh8ndXUxMTDAlnPeui4IqhfTyig3PhEngpvtJYCMBkhQ9MSyZUiNIUecJEOEn4RAPZ2JgFO1B7Tv5Q03r4L2bfKFEMtIUi0XkcjkMqlRYHhuD7eQE3/7MZ9B79y56l5bQv7KCvuXlSmRAtfsE7R3wiPnynio+h5v/IeGLnw89jyeKlAdO+0x7QEoM7Uc2m0UqlWK1GkhZo3XxBgde8CXGDqBmj8iIpVQqGT6RAErzpj3khXBe4KBn0n7y6RQ1VnoZYeAnMXhizhu06P0XDw/xvtuN/sVFFDs6WBiyJEnw+Xws9Fij0WBrawtnz55FJBKBVqtFqBpivb+/D0mq5NseHR0hEolgMhbD2uQkfrYq5H6hUMB/iMcxvr+PA4MBt2/fRiaTwdbWFux2O/b29lh9l9HRUcRiMWxsbCBR9d5ms1k8fPgQ0WgUu7u7MJvNuH//PsxmM5qamqDT6VhkCR/R8EW1Gl+t0kLCTwBMSbBYLFCpKoVGg8EgbDYb9Ho9vF4v8xp3d3fj5OSEFbbq7OxkXo7+/n6m+ESjURwdHeHcuXNM6FepVBgcHIQkSXgzEMCJUokfAvilpiak02ksLy/jqaeegl6vRzAYxLVr13D69GkYqh7cH/7wh5iZmYFer0cgEMD169cxMTEBs9mMUCiEd999F6+++ipsNhuampoYDpICFYlEWCcfwnvCQ6vVyopcUfekubk5uFwubG5uYmNjA5/+9KcBAD6fD7u7u5iZmYFGo8Hh4SGi0Simp6ehVFbqAEUiEebVPql2dNFoNIjH4zg8PERzczM6OzsrBt7lZbjdblYHY3d3F42NjWhsbESxWITH40F7eztLpzg4OEBLSwtrD7dXDaVPJBJoaGhAV1fXR1JMaI95QS8ajWK7msZHRpbJyUm0trbCYrGwSunUWYci7UgWoNB4nmaR14c3SlAHJZ4mEI+Q885RtB513AqHw2hoaKhJPaO0srt37yIWi6GlpQWRSITJAyqVCi6XixmvKcqTaA45EgqFQk0BYLVaDdW77+LLx8eYnJxEWatl8gNPP3heRrS0tbUV3d3dODo6QjKZZEWZea8WL1PxlfYJVrQXxEcp1eP5VApf9PtRBlDs6EA8HofdbmeOhXw+D4PBwOgVGXapdTmlLFBKYyQSgUqlgs/nQ2dnJ1uXUllJGaOUjevXr2NkZAQ+n495os1mM5MBFAoFqwNG7dLb29sZH/L7/QgGgxgfH4fP56tZJ8kV6XQau7u7WF1dxeuvv87wolSqtMt0u90s4pNX+ukZxG8VCgWi0WiNkcr13nvwP/UUGq5fx529PVy+fJnJmbQHZGwn/kY8m/gWb2TicZY3YhHuEg/lnVS8J/JPXS5E1Wpc1WrxC1UZhYpmptNpbG5u4tSpU6ywK/FoMjgQ/z06OsJ6tcU436I28eyzuNvTg4s+H+aqdVYINrwMxfMGnifTOabBR43ROkn2pfXyuCPyVXq+yWSCUqnE8vJyTeSnqJzRHMxmM6anp1EuV+r+rKys1ETRErx5g4doROHnwMsfomJHg0/jomtFxVV8Px8tYLFY8PTTT6OlpYWdSaJZ9AyTyYQ3ikWclEp4MDSEiWrdOdHQIyr4/Hfi5/z1fMSquA7eCCRngKJr5aIbSI8YHh7GqVOnoFara7oYtre3o7m5GQMDA/iPySS+q1ZjbGkJo9vbNeuj5/GR53KKOT8/ufXwv/l18zjJr4XHA3G9olxItIZgMzo6yiKsQqEQjqvOftIx5PCE3x9x/XL4JRpe6u2TuDePG09saDAYDGhubobb7cbn19bwQ4sFv+J0YicaxbsWC75YKiEtSfhPuRzOHRxApVLhab8fd00m/FpTEzoWFvAPJichlUqwBYP4J2fO4Pr16/hGoYBTHR347sICfP39KFdz6kQLLf9DACuXy2hsbERDQwMymQwcDgecTic6OjrQ2NgIl8uFsbGxSlvKZBK9vb3wer34CoC/yudx4fgYanWlrRqF5a2trUGr1WJ1dbUSKheLYWZ/H9FoFO9U83xp4293dSFmNmNhYAADq6uQJImF37lcLlitVgBgYflXHQ4sDA7ijMeD0zs7uNPTgzmvl+UMErEgpYo84vf6+hA1GnG3txenDg9rvEkAGHOVpEdtpEhJ5XOwSGAghsZbpxdefhmSSgVFqYTWq1cRDodr8pO7FxfRNj9fQeoqUyUrvlqtZgXf+JoOhJy88Ge1WpknAHiU65xOp1mF6Xw+j86FBXQvLlaEVrUaiUSCHT7eO0ef8YeFvicmLR4kXommwXvq6V7yvpHARUYQwktewSaGzlspSdAkQSgejzOiQB5NiqwgYYiUcB73gUcpGyqVCt1aLTb+8T9GzmzG6rlz2D59mnXYoNB1ERYiEePnKRI+Mg6QdzqVSrHwOvLEUcoKCRcUis7jHJ8DSISJ9zzw1lvy7BHjp+9JuALAlM9CoVCzf+Th5Y16vJGJiCcJ9DzRpPeRcYLwQKFQYG9uDulquPJPetDcCD9of2m+509O0HTrFpRKJYxDQ8wop1KpWN4xKUF8txdSUJxOJywWC0tbINiNP3yIv9fWhrFqKsXz6TQC3/oWWlpa0Pzii1AqlaygrkKhYGHmKpUK+/v7rNe8VqvF3t4e+vr6kEwmYTabsbKygomJCZycnKCtrQ02m+0jtJvgTvSbhFc6W3zNG5VKBWfVmL24uIimpibMz8/D7/fjgw8+gM1mY0V0fT4fS9nTaDQ4Pj7G2toaIpEIHjx4gEKhgPX1dZTLZWxtbaG3t5fN53O5HP40l8PT+TweJpO4du0aC5W2WCy4Ve0Qs76+Dq1Wi4WFBfh8PiwvL6OhoQELCwvw+/1YWlpCd3c3FhYWAADDw8PMEEYGUFJIqOgdL5ySl4ZPoWlra2OdiZLJJFZWVhCuRvspFAocVLstXbhwAfl8Htvb24jFYpibm0OpVILX68XBwQEmJibY9W1tbRgdHUW5XMZ3vvMdHBwcYGZmBplMBj6fDxqNBmNjY0in01hZWcH4+Di6u7uRSqWwsrKCxsZGdHZ2IhQK4erVq/jsZz+LtrY25PN5rKys4PXXX0drayvDRZGW8UI9r1jYbDaWgpZIJJin2Wg04sMPP4TD4cBLL72EcrmMO3fuoLe3F11dXQCA7373u7hw4QJaWlqQy+Xw5ptvYnZ2lj3zzp07OHfuHEwmE7LZLFZXV3Hp0iUolUrcuXMHqVQKly5dAgBm8BgfH4dGo8FitRYORRcuLi5ieHgYNpsN+XweV69eRVdXFzo6OuByuVgHJj6djgxHxKP5NAGgogRSGgXf6lGlUmFjYwPHx8eYmJhAX18f85hbrVYWwUitu+1VepVIJNDY2Mi8skTr+IgWnU7HIhAJP3mvtkajqeFLpVKJGefGxsZq0nEymQxL/aH6RgaDgRkXeBrc0NCAQqGAUCjE+t2r1Wq89dZbmJmZQTabxcnJCTvLm5ub+P73v4/l5WUYDAZYLBZW7JGM2OVypZVfOByG3W6HyWRCW1sbTk5O0NjYiFQqhXfeeQcnJyfIZrPweDzo6+tjMhrxfNofauuYy+XQ19cHhUKB3t5euN3umloSJPPRnHgaR4od4bvygw9ge/99qNVq+A0G7O/vMzmIZDrCe6p9RUYagiE9r1wuMxrCRxwRXeCdHTz94dPEvilJ+FaxiNcAZiigTh2RSIQZbimdgNKj0+k0qxNkNBpht9vZfvJnfKW9HQmDAbe6uvBTXAQW0QQ+SomnBfw6QqEQiwqliDcqGk50kpRMmp8YqVgqlXCnuRnvNjbi+XAYE4kEEokEOjs7a2REGrySXywWGe8sl8tYXFyE3W7Hg64uXG1pweXjY8xVi4OKESm87MHrM/xvuk78jDea8LRTxFVx0HdtbW0oFovY3d2tUYZJbiYc+YpCgT8rl/F0IoGhoSGUSpWaez6fr0ZJpiEaU4inifvGXy8XgUI//P1ySrsIS5r32bNnMTU1xZyU9D2dHeKzfwsgUCohPziIiWoHJFEW4Z/Pz4f2hMcHca1y+83vn9w+1TNAEaxUqkrrzqGhIQwODtak1/3nbBbfMpvxBb0eX2hpwebICL6rVmNqfR0Dq6s1sq34HlEPEOclfl9v32ldcvtUbzyxoSGVSqGp6u25cHKCfzw1hd3dXXQnk/h/zc7ijTfewBWzGRInXE57vfhULoczZjP+9uFDhgSbm5vY3d3F9vY2lEolRkZGkEwm8YbRiJBej/ebmnAuEACAjwgpvNBKuXeUY0bFaEqlEu7evYtUKoXFxUUcHh4ikUjge9/7HiKRCNyZDH6z6hmJVr3YyWQSP/rRj6BUVmosvPPOO1AqlYhVU0KIkJCXrFQq4dT2diXnbm3tIxtIfVBJeVnu68PV6WmUlUrc6+vDf3/vHp66f79yvUZTk+ZAih55h/sUCuQnJtD/4AEOqkYc3rsei8UYU+G7L/BGBkrlIKQmpCGDwODCAjanpzG0uIjxYBDazk7mtSIljBCOvMx8OCgVeaI9JjiSl6ZUqhSISyQSrNUoFQOlto7JZJJ5/al+AxEpEthpncAj5swTLloTKf6854ru5Q0UhFe8gksKPYCaDgl82D4ZCgqFAhKJBMubJaMBnyrCMxE+soLgyUcXkBeHYEgChm92FvtnzsB15QpU77yDxitXEHnxRUgGA+IWCxaHhjDt9TKFlfdEkcGDPBG0JkmSWA9pEijpe/5awiF+jjwcVapKXQZSgGk9tD7eeMYTLGIGvJWVD9Gms8TvN4CaqBgizCSU85EMJPjxggcJ0rQfZASk6AyCvSRJmN3dxf2BgSclkZ940N7THtD6CZcJj/nILoriIQ8TAEZniIaQ4YJCi/mQTzLmEO6TYks580qlkuGeyWRCKBRi55QMumREI88uFQI2GAxoaGhAc3Mz7HY7WwcZqHiBkubM7yNQ24KRzoLT6UQul0NTUxPzjJJCTCHQqVQKTqcTCoWCFY5TKpWsMC55Obu6upjBgGj6S9ksQt/6FqbOnEHT6CgTLr1eL5566ilmSNna2mKFwii6oqurC/F4HGazGbu7u2hvb0cgEIDdbmf0hs+BBSo0jVIgCPcJpkRTCZdNJhNSqRSuXr0KhUIBj8cDm82G3WptnZWVFSgUCqYobG1twWKxYGdnB/F4nClny8vLKJfLWF1dRU9PD4M7tbG9du0a9vf3sbe3B5VKhdXVVSwtLSEYDMLv92N9fR0bGxsIh8PweDxQq9V48OABstks1tbWEAqFsLCwwOppkCdN9MDSmabzx/NMOndmsxl2ux1HR0fY2dlBOp1m6YzJZBJ7e3vY3NyEwWCA2+3G1tYW9vb20NvbC6fTCY/Hg729PQwODkKn08Hr9SKRSKCtrQ1qtRrRaBTvvvsuo/PBYJAVHiyXy9jc3ITZbMbQ0BDi8Tg2NjagUqlY+8X19XW0tbWht7eXKVif+cxn0NnZWcNfSTkxGo0sAoOcFEClQCJP9yjMXa1WM8MEtb5cX19nNQdOnTqFd955B8899xxGRkaQzWbx7W9/G+fPn8fw8DBUKhX+zb/5N3jttdfQ0tKCUqmE7373u3jllVfQ3t6OZDKJ733ve/jyl7+MhoYGBINB/Mmf/Am+9rWvwel0IpFI4Mc//jE++9nPVoqvejz48MMP8eqrr8JsNrO6K+RN5vkX7SvxEirWSAZMirjMZDKIx+M1kYR3WlrwqUAAn8nlYPN60draisXFRXg8Hmg0Gpw9exYqlQoNDQ0MVny9h3K5zCIkgUeFlq1Waw1u//mf/znsdjvee+89vPrqq0xmJHwymUxMlsnlcojH4yyU3u12s0KTRLNoDhQtwzuK6LpSqcQi7mi9lL5FqbAAWOE3opd0Rkjepd8kd/Dv4Y05dB3JbzztIb7YolbjN6s0N1KNxKFoBovFgkKhAIfDUXH2VAv2Er2n55AMTu8ivlYul9F+/TpOnnsOrySTNWvnzzw5DUTDCL3LaDTCbDaza6n+UrFYhNPpRDKZRDweZ0XMA4EATCYTC6cnGfqK242gRoP3XC58sSrP3r59GwaD4SPRDHx0BQ/XXC6HBw8eIBQK4dbp00gYDLja2orPVmkptUPloy152Zn4HPEmktFo8M4R+p+fG5+uQfxadMZ+6HDgnYYGfDaTgfvwkKWH0BwIr4jfPC9JeEGhQMluR75qZNrd3cXO+DhudXZWnKKHhzWRI6IsS4P/jPg+b2jhnaMky/GyEM8b6HlEG+lzpbKSEjI8PIxCoYDj42MoFAp2NkRn1utqNf60VMJTiURNKqRYc4PfA56G89/xOgbJtCIP46/l58GvUfyb/1+pVMJut+PChQtoa2tjUVIkr/8FgCOVCj/QaPBLVit+EI/juFxGvqcHw1wUuty6xLmJUUTi4Peb/hejHZ7U2PDEhob3338fDocDqVQKa2traGtrw+rqKiKRCHp7e7G7u4u/OX8eMYMBN9rb0Vn16hiNRjQ1NcHhcKC9vR3lchkDAwPo7e3F/v4+mpubYTab4Xa78TM6Hf40k8FTVcWeP1w8EeMtuNRvnMLdisUirl+/zojfvXv3WFspsgwBFcWY3kF5kfQdL5jTYSdBniICyuUyhtbX0VqtKiyhNtelWCyyUEmVSoV7/f0oK5VQlMtIKRT4oUaDkc1Nhqw80aA+sLShHffvo+XOHWSzWUSrRIz3FvNhnEqlkikGROz49oS894zPVe1PJHDh7bcrSFW12AOPKijTPPkQTRIkFApFTdcF8nRQUUClslKPgZgB1TsgBYfmSEqMQqGoMSLw9QN4IswzJZ4A8hENNFeKwCCBgIw5ZAzhFXJSyqltJ4UDiqGntFYiiDRPwhdinOQNICMKj78kCJBQmsvlGIPiD/MHly4hZzTC99JLGHjnHViuXIFSocDxK69Ak0rB9d57uHXrFsMj2m/CLVFwEQ00NGdaEz8H+p72hRcsaW2aqrGMN0yQQENzob8JpwgPKd+TvDO88YXOI9UmoXt4Is8blESjDu0Jf47p/TwjJBil02mWSxy12dg8f9KDx1fyFhKNI7wgQYs8jwQjq9XKBDSlUskqoGu1WuYJpbMNPDJeUDQIX7WdDBy8wMcLV+TxVKvVzFhJNCCRSDDBGAAToAgn+MgSwj8Kv6azRDjGRzaJQh55ZWl+RNuJrkjSo+Kl4XCYdZGggnzZat0FMqbxxfhoHgQvEpyJwUuSxLx8NpsNZrOZFaKj+gOUxmKz2WCz2dDY2FizNh5f6Szw+eW88ZYXNGn/NRoNXC4XGhsb4aum2RWLRbhcLhaqrVZXuo/whRepwCoJYg0NDUzwJVwjg5Xb7UY+n2eFPo1GIwwGAxwOB0KhECYnJ6FQKGC1Wtn/VDAvFouhp6cHGo0GLS0tzKNEuPI4AYzOAPEYoo8WiwWhUAgnJyeIRqMwGAwsDdDr9UKhUODw8BBGoxEPHz6EwWDAwcEBFAoFFhYWoFQqsbGxgVAohO3tbVb4j96pVCpZR4Dd3V3YbDZcv34doVAI+/v7aGtrw40bNxAOhxEIBGAwGPDgwQOsVVsqHx8fw+l04sGDB9DpdCwii5wWZCzlDUp6vZ7RHN6ACoDxajKqd3Z2Mv5kNpvR399fSVMNhXDt2jVoNBpEIhFsbW1heXmZGVCCwSC2trZQrNYAIGMlwUelUmFlZQWZTAZHR0cAgIWFBahUKqbQLywssLbZSqUSOzs7MJlMmJqaYjSHnBkET9HzTrV16Lzq9Xqm1PGyF/EZg8GAnYsXkVSr8QODAS/6/SgWiyziwel04vTp08zISp1slMpKGqfVaq1J20ylUgiFQujp6WEptufPn2fps2fOnIFer2f7PTo6Cp1Oh9bWVgwPD2N1dRWxWAwNDQ04rka+UscQs9nMIr6I7hEMyDBMDheaI9EZon02mw0HBwf43ve+h66uLszMzMDpdCJcbeve0tICSap0b1Or1WhoaECpVMJfFgq40tGBV9NpfF6pxP7+Ptrb22t4Jb2XHB4kD/JeVopaIKNzMplEJpPB1NQUM1JTO1M+mpT2lKIYyVhP9B54xIOt770H140byLa2wjM9zeqo8bIAnXueRtCg1Bqz2czkMuBRTSu/349cLgefz8d4BMnn4XCY0dxSqYRzXi+utbbi3NER1n0+SJLEOouQIZ/nOfxaSPa7efMm64o2cP8+vOfO4dPV4ukqlYp1f6JOM1SEnIxPkiQhk8mw78hQTzIfT5tEzzov4/IOJLqG9v9/1+kQVCrxQ5UKn0MlOouX54ifE32me4k2UXH6W6+9hojRiAeDg/hGdV2E47xOwCuiIl3j58/vN33HRwyIUbD8fZIk4XpjI95zu/G0349Xm5pQKpWwtrbGIqRF4xx9dkmScL5UgtJsRrm3F6VSqRKdODSEm52dmN3ZYWnkouGEV6T57/hIlHpGChqiIUaEDT8I74aGhljqIp1N0jm/olDgP0kSvopKJPvXlEp8q1zGs9WIfZJ7o9EoYrHYR/CHfy8v6/Fz4vcTkO/EUW8N9cYTGxrIw9XR0YGGhgaWW5bL5dDR0YHe3l58sVDA9zMZzHm9MBqNcDqduNvaiv9pexvnbDaMhUJYW1tDPB7HW2+9VWljVfVMJBIJLC8vo9TTg2Q1PIoPWSEgURg3HWrKxROtaFSMibzldHB5DwspeaQA8hZhIsZ8OD8dEj5EjjxjfDg3HRbeekzF+go6HbJGI+4PDED35pvs3bRWyqXilXESqsnTTxZlej4ZDchYQvMlmJGHkw4I/zd5o+ndvCGHt17xkQR8lABPEHmFhg4VH6pHlXypbSK9mwh1KpWqIV684ksEkqzofBtCiobg/yeDAu+hp70guBBB4w0E7GBUBXISIvnQfJo3KbmZTIbtETEMXlnjGTSv2BKe8PmxpPzxeKpQKFDmiBfh5vHTT6NgNkMbDML0zjvI4VFBQYIbEQWqYUDCIF3DG/BIMRAJLllTeSGBJ1qEgyKeiKkkPEHjcahcLrO8SV7BpTojpOCaTKaaWgq8t588F1Qkh9bB1/agufJ4wAtoxJyIbmxMT/83S53gGaKYxkN4JUkSxsfHmTfMaDSyIk6bm5ssJWJwcBA+n49VmCalkKIz2tvb4Xa74fF4YDAYWEQInRGVSsUEW1L4rVYrLBYLq2eg1WrhcDhYNwTyLPKeGVJ0ic4SXvNKicViYWdJTumibiFkaEkkEuxsUrE1WhcJaX6/H2azmRUGpvND9SF4IylFRJCxAXiU58t7YAkXiL7TWeFbzFIxRxKSyIjDnw2FQsEMlrxR4y+yWfyFUonXSyX8lFbLcJIPsafnEP0TeRcZiQifKDWIaGcqlWL0iNJuCG68UUulqqSoEA6pVCqWgkX0grzEJLBSPSAyoHZ1dcHj8TAazis2orDGG194wzF/Bkj5PHXqFObn55myYbFY2DsVCgWmpqbg9/uZ8Ds+Po79/X1Wl2hkZIQpiqKgbrVa0djYyPbQ5XLBaDQiHA4jlUpheHgYkUiEtVvt6elBOBwGtSgkxYkUaF6IJOGe1ktFP8W1i4It4Z1Go0FDQwNOTk6g1WpZi1pKZzAajTg8PERjYyP8fj9MJhNOTk6Qy+VYMVH+jDkcDuzs7ECtVmN1dRUmkwl7e3twOBzs9xsKBTbMZnSo1ThlMmFxcRFdXV3Y29tDV1cXmze/Tl4GoB+gtr4WT/tJmeFlHq1Wi76+PswplXhTqcTnAfgyGdy8eZMp/i6XCx6Phxn64vE4i3YiI4ZarWZtKiVJQmc1KlOhUKChoYEZqxoaGjA2Nob+/n4Eg0Hcvn2bFXcEgNnZWVZri1Ii9vb2EI/H8cwzz9QopLzzgDeWUbQVRbKYTCYmcxwfH0OprESOJRIJ3Lx5E7lcDqdPn0YwGITD4cD09DRyuRzefvttDA8Po7OzEycnJ3gjm0VCrcaPbTb8kl6Pt956C11dXXC5XIjFYnj77bfx6quvQqfTYWtrCysrK/jGN74BrVaLpaUlBAIBXLx4EYVCATdu3IDT6cTAwAA6OjrQ2dkJt9vN8JGPIiX85ZVMXlElg6VarWaGDeLbsVgMq6ur6O/vR1tbG4tQoJQHl8uFZDLJeAM5BU0mEw4PD9HW1sZoAvEGkr+o053JZILVaq2R63kePwXg57JZhCUJfqUSY2NjbP+IP/HP5OXWcrmMQCCAhYUF9u6h9XX8UtVIkameV5LJSe5IpVKsCCPvxKSoWOKlfPtnnj7yCiLBmXCPFD6aJ13zrN2Ot51OvBCPY2VzE93d3TW1aog38+eS+AJFch8fH6P9+nUoL13Cl1Uq1pGEzjxvDOfnKTdH3tnE60OigkufyRktJEnCb1ssCKlUuNHejq/HYrh//z7cbjei0SjrQEZ0k2gx4QBPd0ulStHhW88+i6jRiIfDw/i5agtzMQJDNO4QP+P5FclRPO0TneQ8b+ONffxzrzc24oPmZnwqGkV3Ne3F6/VCkiQW3S1JEp4plXC5+twThQJPKRS4LEkoaTQoVx35RP/IeMQ7bngeK0Y1iBEp4rx5HVs0OnzceGJDg0ajQWNjIx4+fAhJkrC2tsZCUN98881Kv9aDA/zDqhfJ2NgItVqNbxWLCGu1eM/txtS1a4wAra2tsUlSpMG9vj5EDQbc6+/HS9WiOLR4InCUB0geLhLseA8yL1hSTjn90EaQwkGHjAAPoKZTAG+FpO/oc4o84AkMKc60MWTZPnVwgFmfD2uDg1gcHsbk5iYmJyc/cjDIm04CokKhYC2aaE40d97Ky8+DPHLkbSdYSJKEnfFxrIyPY2ptDaPb2zVRDeTtI0WUlFMysPBhbzyR4q2Q9F7eQ8grgwQbsraR4aBUKjGjEUWCAKjxiFHqgmhR44VVYnr8GiisnuBMDIU/9CQ0EyPjFRES6HniQoSEN9rwAiQdQl5R4Vuy0fN4ZZMUEYIrz3ym7t3D8ugo2m/cgNlsruSJ3ryJw0uX0L+wgO7ubnYfKUF01kgAEKMR+IgPOmt8OB+tnwQpHjdIyCBY6fV6tp9kYODD5WnwFm0Rf+iH9/aWSo86hJBiSQYkEe/pDPHGMlJY6YeINgDsTkxgfWoKww8eoL2axkRrUigU6L93D1unTtWliT+pQftOcCHYkiAWiURYYVmNRoNkMgmfz8dgTH3g8/k8NBoNHA4HDg8PkayGrLrd7kqERjQKu93OBEjgUeX2iYkJ9g6DwYCBgQEmdBmNRlZgKRqNwmw2Q61Wo6enB0dHRyxUdXx8nNVhIQGdcIpn0nzkCuEH7TedbzrLra2tCAaD6OjoYF77lpYWXLp0CW63G06nEy6XCwMDA2hqasLJyQmMRiNaWlpYAT6bzQar1YqxsbEaIwZPVzQaDcv3NxgMyGaz6OnpgUKhYP9TlwmKlGhoaGDfJxIJuN1upmQSjebpDB+l8x2VCoeShG+VSvgyZ5yliA4adPZ4I4Eo1BG9EaNIeCGVIjJo7TQnXriiIQoPFJ1Bn1MhNKJVPI3nlU7eAM8boHkjiihM88Iab5yha4hfAI+8oWRMEr2jPK0WeQXBj85MqVRihaNJeCdBjYyyVChPNEKnUimG6/RMMi7p9XpW4I9Scmh/REMD4QYZrakAKhmJLBYLPv/5z+ODDz6A3++HXq/H4OAgdnd3mSec6jJQ8Ud+vXa7HSMjI9jd3UU0GoXFYkFDQwNLZVydmEBEr0dxdhanDw9hMpnQ2NjIjE7iHhH+EK7T3Elm4HkO4Z1er2fFQCkKTq/Xo7m5Gb85Oor/oVjEwcEBftjVhXw+j5/5mZ+B0+nEnTt3UCgUMDg4iFwuxwwvZDilKAtJkuDxeJBMJjE5Ocn2gDze165dQ2dnJ0ujsdvtcLvdKBQKrEuM2+3G7OwsVldXYbPZoFAocP/+fXR3d9dExZDhkQyx/LnjZT9egbTb7Szyymg0oq2tDXq9HvF4HF6vlxVrXVlZwc7ODqLRKBYXF6HRaLC2toYJpxOeM2fwTbMZWk2lI00gEIBarcb9+/fh9/tZcdy1tTXm1AmFQtja2qqhM8ViEe3t7RgdHcXAwACjWzzNoX0jJY2iTsk5RQ4TkotTqRSrnRCNRhleU02bS5cuYXp6GuFwGMViERcvXgQA3Lp1C6dOnWI0fHNzEy+++CKLLE0mk+jq6kIikcDt27fx6U9/mnUXODo6wuzsLKsxY7PZ0N/fD0mScP36dUxOTjKYLy8v49SpU2hubmYGVFHZIjmYP58Er3w+j+bmZpw/fx5ut5vxRzL0UmSbWq1GOp1mRgc6KyR78go4ycVEz3lnC13LG79pj+gskmOlWCzipVwOF6pdoizVrkokO5GMxMtJRE8VCgXm5+exs7ODcrmMhmvX8Jlypc3nQ6WyRhbkf/POOp4n8eeA5k18indEAR9tQ83LsXTNa/k8/lqrxWtV+qzVarG4uMjSNXmdjOZFvIjnGYuLi9jd3UWnwQDVhQv4KaUSTqeTybTkpOSNtLxCTjAXcYZgIvJOnscR3eB5NP3/O1VDyrsuF74UDKJUqtRxKpcrUVDEG3j5kM4m8ScagUAAu7u7leYIVWMYOSr4c033E8/kHa30fP6HPiN9m4/m/rjxxIaGeDyOK1euMEZx9epVlpLAe3GpGCH9PrW1hbt9fTi9u8vCzWjzgUftjfL5PM54PLjV1YUze3sIh8O46XbjXn8/Znd2WP45ebjutrZWiiNubmKyWumaBwgV1eORg1eseC8VKfI8cElA4hkFHU4ycFD+mlJZ6cWcyWQYg+HDvMgTUSwWMevzYXhjo2Lw0OuZ8YMnJPRMADUVmmlTqRc3z8DoQJZKJWbFf9DZibt9fZjd2cHA6ipKpRLWJycRN5mwMj6OV7jwdRLsaA68Ykyw4a+hg0ae5HQ6zQgtzbVYLLLwT0pREK2aPPLSEL3jdJgMBgPzfJNwQYoxf+Ap3JgXyvmDzSumRARoTiSs0H7Q5yToE5Eg/OULRPKt4uh+svQXi0VmFBMjBgj/CS/JMkt1Olb6+7E+MYGuGzfgvnMHB+fOwXfxIjpu3sSlP/zDChFRPUrTII8v4T3hB+EICUB09njhibdm0jqIEdE+kEJDBUVp3XwniXK5zARswhu+cCQJKLzCBzwStvenp+GZm0Pv/DzKpRJ2z5xBT7XDBhFWfv8JZ/n50+AttbROpVKJ9akpJG02rE9Po29lhTEWEpqGNzbQ8/Ah8IUvPAmJ/ESDt2bTnhPOlMuV0DelUolAIIDJyUksLi4y7/3o6CjrdiBJlfxZChvOZrPY3t5GR0cHY7Ber5d5qnghBQDz/h8fH6NYLLJCdeFwGG63G/F4nJ3zk5MT9k4SJAOBAFPW6JxLksSKUNJ+8xZy0UgkGrSIThA8crkcuru7EQ6HWfQFCZaSJMHtdjOjQCgUQn9/P8vnjkQisNvtaG1tRUNDA1ZXV1mhQtqHdDoNnU6HtrY27O7uMnrd19eHQCDAPMojIyMIh8MsumR8fBzpdJoV/5ucnGRRBISjdI5o3QSDn1ap8K1SCa+rHxXUA2ork9M5MxqN6OnpYe9Sq9Xo7u5mHSnUajXa29vZ3HQ6HZqbm5kCQt+TQEuGH4K/2WyG0+lkec1kmKGid7xSnUgk4HQ6YTQa2f8Oh4MZm0iY4Wk6/eaFXjqXvDeLcID3dFMHJ6L5pNTRtbRefp68gZbn37QfZDguFovMUwuAKaW0D/R+mitvuKB18gIoPZ9wlwzt9B1FI/DGNZ4P8Oun33xeu2g8otof9Ayiq2Q8JF5LRgK+JTBQKRhJ8o4kSTi9vY21qSn0b2yw55FiRGeUaDmdVd5ITQ4CXlEi2kBRDFTMke6jNCdKWyNvr8vlgs/ng8lkQnNzMwqFAqLRaA3O0NwpNSKfz7PaT+SEoagSSuUIh8N47bXX0NjYyJTm8fFx1jWFBkUbJJNJVjjz9OnTNUoG78AhOY5kBl5hp/0ivHS73UzppHSN1tZW2Gw2xONxRKNRWK1WJlvncjk0NjaiUCjgzNER/qlWC3PVgWE2m3F4eAir1YrDw0PodDosLi6y80410ailLwDcuXMHfr+ftVclmYGPQqHzQIP2PZPJwG63o1QqMU8yyQQNDQ2swKXBYEBbWxsODw8BAG63GwaDAffv32eh4GToooK2LS0tyGQyWF9fZzDr6OiAz+djdVN2dnZweHiI4+Nj2O123L9/n9UoS6fTWF1dxcjICPL5PPb29vDgwQN0d3ezCJ6GhgbMzc2xeZP3lzf083tMZ5KMWmTgGh8fBwC88cYbePbZZ2G327G5uYmHDx/iC1/4AlQqFW7cuIGOjg6Mj48jHo/j7bffxgsvvACDwYD19XWoVCoMVGtALS4uorOzk9UfuXfvHubm5qBQKLC7u4tcLoeenh6USiXcuXMHU1NTMJlMCIfD8Hq96Ovrg0qlwr1795DJZDA+Po5CoYCenp6atAzaV16Zp7X/zd/8DVQqFRwOBwYHB9m5UCgUTH8hJZPoA9FdkgfFSALe8St61+k3L58RTSWZk35fLpVwukpf9qoFyrVaLVpbW5mRgKfPdN4oqiIej8Pv92N/fx+ZTAadCwv4WrX+x67g9JCTJ2lehCP8ugg/eB7PO1J45x1/L09DXlWp8IbRiM9U68UolUrWKYyciHQOeWcd/abPj4+PceXKFXZOqWYP7RPBn2gn0XVe7+N1dOJ1pPuSUf9toxH/GcCXJQnfxMePJzY0xGIxSNKjHskEIGImcsqhQqHAxXAYl6uFlhRVpKUJFwoF3G1txa2uLpza2sLU5ibGqhWf04UC5nt7ETebMd/bi0mPhyFCKpXC3Z4exC0WzPf1oXd5+SPMhw4UDzhC3FAoVBFKL17E1swMxlZWMO31skgAXkgiQY5XnklB4r3xVKSG8jL5EHRe0aN38JtKyioJliTE8OGX/CGn3Fu5AjySJMFisUChUOA/DA4iqtfjXl8fpr1eZLNZnPV6cbu7G+cPDpinmEL/KX+QDgAxEz4FgfaNN37QWkTLGi+gqFQqZiSg/3kixbfdIeGKjEUUgiemmZAizh9cIjqiZZS+Jzjx19F9JFSJCikJfOLaiEBStAzhF8GuXC4jkUjUCH+k2NFZorWTgk6w5YXxpc99Dnm7HXtnz6L93j0cXb6MrNOJw4sXWUsiwgtiiERMSNGhddBaab9oD0nAo0ECeblcZoYkOk+84YDOCRm+6DPewk2RMbTfRLR4AxnvMSuXy7h27hzSdjs8Z85UBDmHA96zZzG+swPgkVJKRFzOU8pH9BAsSAAslUrom5/H9qlTGFxcrNlvUsTWBgawNjX1pCTyEw1esaJzT95/ogctLS1MEKH6FD09PczTGIlE4HK5YLFYmFCt1WqZV5PSAOx2O/PoUSV3XqCi0HHKGS2Xy6z4Ihk6E4kE9Ho9S1+gudpsNuzs7DAjhdPpxNbWFoBHQhsfWsgrY7RfvCJFwisvwJbLZWxsbKC3txfxeJx5uACwivNUqJGMLevr68z4e3x8jGQyib6+PtYViAadWYpa43tUR6NR1hqwWCwiEokwxaNcLjOeSApcLBZDNptFJpNh4cGEf7TnBIMvAPi88lGNF55HER4SvXQ4HCynW6VSsY4inmoVbep0QHtLRfcoB9vpdMJqtWJjY6MmhFuprEQ8UVg87SlV2SeaQZFeFKU2MDDAjBwqlQqjo6MoFAqw2+3MMEW8kjck0TN5bxfBRfSMKJVK9PX1wW63swiazs5OViiSIlAMBgOcTidOTk4wPDwMnU4Hm82GRCLB1kGGCt7wodPp0Nvby2psSJKE5uZmxONx6HQ6VtSU0jSVSiXr4qJSqdDc3MwUpXA4XCN4KhQKVlCQWtDyxpdCoQCTycTSfPizwtNTggv9TZFjVPSOCh/abDZEo1FmAKNzTnOi55MhhsK6yUhDYdNnAwH8f8xm/MfDQySqRhoybPGwI/yh55IBRavVfqQ9I9EKWhPvvOGjU8lIWSqV4Pf7GT85OjqC0+lENBpljh1SPgh38vk8jo+Psb29DafTiZ6eHtZmllcY6R10fgGwFKtEIsGiZEnuAsAcaqdOnYLL5WL4SbImnyJGZzmfz+Pk5KQmdZBgRQYGPn1RpVJhcnISiUQCe3t7zEtLZ5VSB8goRsXhiIdTRAhF5AwNDaGxsRHb29sAgMHBQfj9fhwfH0Or1eLs2bPY3NzE4uIinE5nDS3m50rnkGQl2j8K9edTKCjaTKfTIRQKsUgZeqbFYmHdQPr6+rC1tcVSFKjYbiAQgNFoRDweR0tLC1KpFOx2O0tnymazLE1oe3sbXV1dODw8RLFY6bqyvLyMTCaDvb09NDc3s5ptS0tLiMfjePDgAc5U5Ql+vTwv5gf/OfFWqknk8Xjg8Xiwu7uL1tZWZvRIp9PweDwIh8PY2tqCw+FAIBDA2toaNjc3MTo6CqvVivX1dUxPT8NutzMcoEif3d1dxs8ymQyWq23L+/v7EQ6Hsbm5iYsXL8JgMGBlZQV+vx/nzp1DOp3G9vY2Xn/9dbS3tzP+SbIpb8DmZVuK2nK73bBYLMhkMjhz5gw6OzuxsLCA5uZmuFwuZtSYmpqCJEnY2NiAwWBAa2sr8vk8lpeXMTMzwwzQ29vbGBsbg9FoxM7ODhoaGlj0TjgcZkaCUCjEoksKhQLrFEPRm6Q0E46Qw45aRPf29rIzSYNPM8zn8+jq6sL+/j7K5TJaW1sxNjaGwcHBGr2Vp9/0DDoLPC8jBx3hSLFYZCmVNHjPP11H8+EdtySTPpXP4+lqVGqwei5sNhsrMCwXYcFHKZDj586dOwgGg8xgvrGxweRv3rhNhiM+DRmojZIkeJKxmtatUqnw552dCGg0+I/FIn4XHz+e2NBAB4JXoqj1HeVGk5JFCydGxltG+DCeTCaDa2fOIGkyYb6vD5MeTw2ijK+uYnlsDKeqxgfeojSxtlZJQVhfrxHWiAEoFAqs9Pdj69QpuK9cgfPaNQbYyOXLiH3qU4DJhJzBgJXxcczs77PcMEIeMRSTlD8+/IcEGPI8ERMh7w4AxgyIgRFyUage7+mhvwkZCJZ8eCIp4gQnMhbQNVTcciCfR25kBO3z87h//34l7Pz2bXTm8/AUi9ioGhB4QYgP0REt8SQk0NwMBgNTjPg6BnQ/7/3nq6nzCiEvsPDeIlJY+UgOMuLQfEmwoTBMijrg8/0JvoQ/fOEmnsjygh5vvaeDRgI0hc9RnQgyDlCYHBEPPiyM9o+MVuL5IAMKPZ8PuU4mk2j+4AMcX76M9tu3YTKZMLiwgK1Tp+AKh/HOz/88pjc2MFotAEbEhwxDZBzijW80aL4kDPKeP15AJS81zZdSi4gO0P5LkoTV/n5sTE9j4P599CwtfcTQQ++ls0P4xQuOGo0GI0tLWJuYwOjyMkqlElYnJzG0uMiEV+BRqoxoaCK4k5JKhJLoF2sBeXSEp69cqQjS1bB4WlOpVMLGzAySFsuTkshPNPgQeYvFgra2NuaBttlsrJVwW1sb0uk0ent7EYvFMDMzU4HPyAhyuRympqaYUEodIiYmJpDJZDA2NoaTkxPMzs5CpapUzee9pMQMR0ZGMDExgUgkgo6ODmSzWRbSSkLE+Pg4HA4HmpqacHx8zCIEqF2d3W5Hd3c38xKTQZWPTKD9FZkZ/z3RUvrRarWsoJbVakWpVGJGr8bGRmagpCgvtVqNo6MjmM1mhgN86zmNRoP9/X1cvHiR4SUp3dlsltXEISMheToLhQJisRh7FrXg6+joYJFcwWAQTU1NzBhNuExr5ddNZ4toKa+EAo/CSkmgoqKIuVwOkUgEqVSK0aBgMMhq1NC8YrEY644Tj8eZEYQi7Uh5LJUqOeNUMLNcruQjd3R0MA+oJFWiZoLBIMrlMkL/f9r+M7iy7EoXxL57rrcwCe8THokEkEAiTXlfLLLIYpFsuibZ/fq9N6N5GiliQqEJxcQLhRT6oZB5MRHSD0kxejOvPbub3hWryOoyrCyTld4hkUgg4b2/DuY6/bj32/jOyexmPQ37RCAAXHPO3msv+621197cRDgcxvb2NgqFAtbX1xGLxQyAo8EVvw/AgKjq0HG9FeDWQCAej2NjY8PYDjY8dLmKjSl3dnbMiRGxWMyASgRel5eXjV6gfstkMiY7ySxwOBxGIBDAYun4aIIAm5ub8Hq9qKurMw57MBhEe3u7Od6ZwC5pQ30JwARkpANfHxoaQjwexyeffPLIyh7gqCqzuroa6+vraGpqMnv/vV4venp6TC+shYUFdHR0GP2xvLxsZIi07unpKeq90jnsPT098Pl8puHf4OAg3O7idqXZ2Vn09fUhEAigqqrKBNUEQSnLzBhyrsyM0x7Qtvl8PjQ2NmJmZsbIF2WeQP3Ozg7i8bjpsZHP57G+vo7p6WnU19ejvb3dHAnNXh17e3vmZDA26KQNm52dxcLCArq7u02Gm4EOqzVoJ1mezLW0LAu3jh/Hu9XVeG1/H398/Lg5MUQBONKF32Hyjb4R9YsmggqF4skqDCBo+2OxGCzLMlty+f1UKmV6iaRSKSMn/H4ymbRlJJPJJOrq6szzaN91KyN9PT11QcFFlVsmE1ntxO0RpAHBqkKhgPLyctNfjVtW1G976qmnkMlk8ODBA0SjUXR0dGBhYQHJZBL19fVoaWnB2NgYent7UV5ebnR2TU0Nurq6MD8/j0wmg4aGBrS2tmJsbAzZbBadnZ2mMrS2thZdXV2YmJhAbW0t2tvb0dbWZsBqBUIVuCLArbEGcLTlgcGw3+9HRUUFZmZmUF1djb29PTQ2Nhowj2sYi8WwtLRkGoqWlZVhcXHR6OPq6mqbT3/v3j309PTg7t27ODw8xMTEBHZ3d7G6ugqXy4Xm5mbcvn0bh4eHuH79OjweD27evIlsNmv6cWxubpogXZOsWvlEudU5ulzFbT11dXUIhUKIRqOYnJzEpUuX8Pzzz5tqkbW1NXN05o0bN3Dq1Cl4vd7iSRUPHmB0dBQulwt37txBdXU1Wlpa4PV68eDBA6NXJyYmsL6+joGBAWQyGXz44Yc4efKkaXa8vLyMjo4O+Hw+jI2NGd1xcHCAK1eu4LXXXkMsFoPH4zFH5eqcFMRmhbfH40FFRYXh2ZGREUQiEYyPj6O1tdXYr5mZGfT19cHtdpveNeXl5djb28P09DSOl/TA+vq60cUEeJqamkzvnEwmY6pM1tfXjb3J5/NYWFhAXV0dAoGAsenl5eVmS6zb7Ta6XXuPKFDALWoKPmQyGVNh2Nvbi97eXlOdS7Bat63Rr2HVCnWCs88cwWDGZF6vF18vFPCTQgFf+6ddXNv1mYGGjY0NALA1tlLHgIqRIAIXXcun+TngKJPXc/Mm7g8PY/j+fVupWy6XQ9/9+zg1N1dEUlz2hozd4+Nov30bLpcLh4JEquKfHh3FQWUlVp99FlUff2z2Z8987nM4qKqCN5VCZHcXPbduIR6PY2ZwEDd7enDizh2cKGXl6PBp4M8gFThC+GlQtb8CF4aZQKJCnAcNHenCIJbbD8gINK4sOSOwwP3qGjwrqgcAxwFYbjd2pdxcu5GTCekgqJOvTKyBgc6dY9dydr7Hi+/rVgxtwEMAhk4MA3M9S5worLOqgbzGIINrpsadR09qRQYBDK6R80cz85yLVphw3YPBoAlyaMiZfVLQhUGMM6PLdScARd4a6+zE3ZMnUX/hAho+/BD5fB5zzz6LQj6Pxt/9DsPvvosr/81/g71IBJ+2tcH1i1/Y6EejqcqJa660otPBH1XS/K7H47FVu6gyonKiPE+OjCBVVoap06dxYmrKjIPgiVYL6VYR0rlQKFYstd64gc6xMYMU95SOmEOJV/l8OgZa4aCAkYJVfAaBSQaijzK6lmVh+P59XP8XOt5SS7ZpsNjMcHh4GB0dHaYiwev1orGx0dY5PxgMGmNER6ijo8PIdllZGY4fP47KykqT0eXxo6SDZVmoq6tDbW0tQqGQCarpqOZyOfT29gKAKcHr7e3F1tYWent7EYlE0N7ejmQyiba2NlRWVhpAIxAI2KoDnA4A6czfBMC6urowNjZmsm90sEZHR5HJZHDq1Cncv3/fONLd3d1IJpN4/vnn4Xa70d3djVwuh3PnzpmAMJvNoqGhAdXV1SajoX15stks+vv7TQ8K7t8myNPY2Ij9/X0DKjDobGtrQygUMoAHs0jRaNToOrWTmvWmndQKPI7FCdbSflRXV2NhYQHBYBDBYNBUrXDrBnUjm4LGYjEjD8Fg0GSMtMkb14UgOQBTkcH7RaNR1NTUmMqGvb091NfXG/2yvb1tHD7Vb7rGnD/ff1RWUfWN1+s1fUjIP8vLyzh9+rRxLBcWFjA4OGhOeZiamjLHkbKMlGumIDerUZaWlkwjYVa0xONxHBwcYHl52TS2TCaTWFxcNLyWyWSwsLCAhoYGbG9v2+ZKO0M7pxVXOs9QKGQy5HzPSQva96lSomV7e9tkpoHi1oe1tTUslbrop1Ip089Fq0gU6NGjPAuFYud+HsvHwJ4VhJZlYb20X5jgGe2GrvPOzg4SiYSNDuob8BoYGEAymcT7779vZIA8ls1mceXKFczPz2NnZwd7e3uoqalBX18f6urq0N7ebnhBe3T4fD4sLi5ia2vLAJIHBwcm051MJk15fCKRwFNPPYWuri5TgeH1erGzs4NsNmu2TXV2diISieBiSwsSXi/eDoXw70vH6Cr/krfpVyqAFg6Hcfz4cbMVQkFV2iRuNyoUitspCoWC+V+BmYaGBgSDQRw/fhx37tyx6QcGG9zmk06nbVVNh4eHxiYQRCFw9uDBA6N7NMHCNeMa0X/TBAF92VQqZasACYVCxl8DYKp71Rfh0b+slKWej8fjJuiJRqPGb+BneXEtmZQjgEnwQ08/4lzoj1VVVZktMTwFDThqhK56mf4EcFSG7/f7UVNTY3S+z+dDXV2daXJuWRZqa2tNNVksFkNXVxfu3r2LaDSKvr4+5HLFkw/0mNRIJGLAk9u3byOXy6GhocFs+QiFQhgaGsL8/Dz29/fNiSDsqdDd3Y2FhQWMjo6aShgFj1TXOsFdvs/EJ09RWl5ehsfjwf37941+ZdLtwYMH2N3dxebmJnZ3d3GnVFW+u7uL9fV1XL58Gd/73vcMH4XDYUxPTyMUCuHq1atmm83ExARu3LiByspK+P1+vPvuu0b/xuNxfPTRR8ZeX7p0CTs7O6ivrzdgq8vlslVNqW/pTGhEIhG0tLSgqanJgECTk5MGgJ2amsLS0hJ6e3uRSCRw7do1vPjiiwiHw1heXsbS0hKGShWu8/PzGBgYQF1dHVwuF5aWlowPtbu7i2QyaWzV2toaTpw4gerqaqysrGB1dRVdXV3w+Xy4fv268dUSiQTu37+P73znO6ipqTFAoCZeVfYB+3YIAhPb29s4e/Ysqqqq8ODBAzQ2NqKhoQEHBwf44IMPcP78eXNM8/7+PlpbWw1wdPz4cQMmzs/Po6urC2VlZVhfX0cqlUJTU1MxOXXrFr7S24ugnFD4z12fGWi43NCAm729OHnnDvpKQThRQS4sf9Mo3e/rw4PRUQyMjaFvctLmzHPxO8fGMDQ7WyRYSUlwKwKZhhksZm00WFfnhkETkcrmTz7B/GOPoeP6dXMGu2VZyN26hTvnzqEAoPfmTYwuL8N77BjeOHkSiVAIE0NDeLmUldDgmopIs6MafCmTczsCS+ZJL3VsOH6W26rTqc/VzC8dJip7r9drK7/SqgsNCmnQnYEenSlFc/k+Dac+kwqYQanuI1angWPgd6n0FMzg57QCgevPsXEfKmnNBkNUMJrdJj9qAEca0yhrbwcaZworjYpz3Qk86FYBrpMe5UbHh+tNEIXz4udoGHUvmzPDNz44iGQshvnHHkPtxYvYeO45HFRVYfGJJ9B26xa8Xi+OX7qE2fPn0XfnDhoaGgyirwEN+YD8w/kpwk2e1iwnL64bA3UGNZQzGjLydt/t27g7MICemzdtgA2rCnR/rrNRFsdDx0TBEPIc+Y9z0XE6ATHnxWfR2XNeKhuFQgH9U1Pon5oCvvSlhz77P/eiXGqQTd3w4YcfYm9vD319fbbjHgGYY7vIN8x2EOxkhptrGQgEsLq6ajK8PI6QDlVra6sBwizLMqdPMHvr9XqNY05nc2ZgAP/BsvDtw0N83e83mU+CIsys0skHjnpDkBeUzqpveJb91tYW2trazP5aBgV9fX2oqKgwVTWdnZ2wLAutpeZxjY2NpkElANNnoq2tDW63Gy0tLRgaGjIOJlCs1mtpaTHlo21tbeYoy1AoZPb8VlRUIJfLmaaS9fX1cLlc6O/vNw4QQRdmadkoUAE5yoUT0CNQMj4+bo6xdLlcePzxx9HU1GRKR7u7u1FeXo7Ozk5sbGyY7TTt7e3Y2NhAR0cHKioq0NnZibW1NfO/x+MxJd2q6x9//HFzwkM8Hsf58+fh8RRPbEgmk8Yx6e/vx+3bt3Hu3DlEIhEMDQ3h8uXLJovV2dlpGkSrjlF+JS84bU00GkVdXZ3pyM/7hcNhtLa2YnNzE+3t7YjFYjh+/DhSqZRtTaempkyH9YaGBqytrRkAjU4peY0ZWAYakUgEuVzxFAo2SyWYyXJvBoEM1gqFYh8SAoYMcAg8EyxXcIVrTV5nY0jgqF8J/wZgMqher9cAb1VVVQgGg6bKhcEVdQODhUdVSCwtLaGrq8usBQMT2uG5uTm0tbUZP2t2dtZsk9FeEYDd92PVkIKJTjkHYErPlQ70X7xeLy5duoSFhQVUVlaiUCigtrbWBKDMevv9fpw9e9ZUEwJAW1sbOjo6zFYe6qGqqiqT0FlaWkJPTw8++ugjc3LNiRMnkMvlsLy8bHpCcLvF4OAgvrW7i7/Z38eXStsrmExIJBLY2toytOUPExik0ZkzZ+ByufDOO+/YZB0Aksmk4R1WZ6yvrwMAWlpaDO1cLpc5opNbtLhnmv4fAc7a2loDAnM7xeHhodGn3ILFrXYEZZxl05Rb9aloCwiCcO6kCY/Bpf/Ftcpkis0+mRUOh8PmGGBunaipqcHi4iJ8Ph+i0ShisZjZ4qLbNtxuN6qrq02foFAoZPpasCcEexl5PMVGn9vb2wY4JShDQJ9NVmk7ydeaOFPwjFWApD/9xd3dXXRL40X20mF1CdeR//MoXN4PgOlnRLvmdrtNFTD1h/p09GVdLpfRZz6fz9hD+kz0PRVIok/L3xwDe8tVVFSgsbHRgHc1NTXo7e3F9PQ09vb2TGURffHq6mpzKlAgEEA4HEZTU5MJSrPZ4lG7BwcHqK+vN8dzt7a2YmlpyfSTYu+fXC6HaDSKg4MD1NXVwe/3o6WlBbdu3TL9qVSeVN+pr0Qepd6nvO3t7SGRSODWrVtIp9NIp9PY2trCWKlP1/r6Ou7cuYPFxUXMz88jl8vhwoULpjpheXkZFy5cwOjoqHluLBbD1NQUIpEI7t27Z7a1LC4uYmxszOjsCxcuIB6Po6qqCul0Gh9++CHOnz+PmZkZfPrpp0YetHKb8YHqD15cV/pOzc3NplJmfHzcVPVsbGzg+vXrWFtbw/b2Nvb393HhwgX09fVhfX0d8/PzuHXrlomTP/roIwPI5PPF7beMze7du4e5uTl0dXXZAMB/7vrMQMPNnh7Eo1HcOnECW9vbmD5zBlXvvIPou+/a9quos774jW8gW1GB2/39aL5+3RBMnS1mFyjYDCYUwWEGVrNCmqWhEDOLRIXYFY/D/fbbRSXV32+Uas3yMiazWSRiMdwbGsILpSOIzs/N4ZPmZoxOT9sAD5bKssqAwS8DXQoyDQ0VNxmBwkDlRKWtSo6MReZyZlm5LcBZZUCHRjNn/JuKSZFjZ3aFz2I5OWlNJco10TVQsIUBtoIRqgA0q0GaxeNxA3A4exNwrOQBVh3Q6dbn8odOodfrtZUxcmzsRFsoHHVLdTpMDLac41UQhfQEjo4poqK2LMtWdkk6M/DnejFD6QRwNEi2LAujU1O43NGBuo8/hsvlQuNHH2HpySfRfuWK4aXOu3ePqgZKpVE0HnQIFHRQhFuzmaQxDTTXmbwWiURs+/fJm86qDZfLhfrdXTz5/vtFoLCiwvCuKkTKp/Ix7wHYq4j4fafx0Mv5v/I4LwUyVI8oqMTPUH6UF/7QFzuAFwrFPYTMqlmWZZqUVVdX24Ixzo1j1202wBGQ5Mye874EdvRehULBOHxcD+DobHj2FKGDcnh4iH/w+bAE4G8yGbxcAih4cpBmr5h5V9rq85X+GoxZVrH0lOXQ7e3tWF5eBgD80rLwo/p6fHN7G6+V7lFXV2ec9MPDQ3R0dGBrawtra2tmvzOPKeZxn6x0Itji9XpNyXJV6cSk3d1dExDmcjljAxgcMbPKYJSgN4/cIzhJPcZ1oN7TLD6rUliWu7a2hs7OTnOaBjOgXV1dpjIjFovhxIkT5phFHjvNTFlVVRW6urpML4NIJIK+vj7jvP/g4AD/6fnn4YpG0eZymQ70rOjo7OxEKpVCc3MzXC4Xurq6kM/nzXFnPT09SKVSqKurg8/nQ09PD3Z3d+Hz+bC6uopUKgXgKLukNsQJQLjdbpw7dw5vvPEGjh07hoqKCuNUnjx5Ejs7O+jp6UEgEMDg4CC2t7cxODhoAs90Oo2BgQEAwPnz53Hx4kWzv/X06dOYnp62AZqtra1oampCNptFb28vMpmM2Vcej8dNRQeBKZaYDg8PY3t7GydPnoTP50NfX5/Z8rS1tYWNjQ1ThUcn++eFAn7s8eD1TAafF4CJx3IrH/A3ZTIYDOLVV1/FX//1X6OnpwfBYNBUBnR3dyMajeLEiRPY29tDd3e3qXRiIKy6gYBUb28v7ty5g/b2doRCIfT09JhsltfrxfHjxzE2NoaGhgZDg7W1NTM++nvOqk6Xy4Wf5HL4m0wG3/X58DVpSMzPMVPNoJk+WyKRwPLyMoLBIGKxGDo7O3HmzBnU19djc3PTBNu7u7vGd9rd3TXyRj2YyWSwvLxsqrdSqRQqKyvR29sLn8+HX/ziF6iqqjLJGWb3GSCxKiCbzeKbwSAGZ2aK1Rp9fTawiJl78jJw1KNB9XRDQ4Otoo/yz0CSFb8LCwtGx6ytrWF3d9cEcqzgYqXO3t4eysrKDN03NzdRXl5uthixuoQ+1vb2Nra3t01l7ObmJjY2NpAqHSNfWVlpy27ThnC9V1dXbdU5AExjd37n/PnzaGlpwc9+9jMDxlRUVJimwkzoRCIRc/Q8twKRJ1paWky2Ph6Pm60TQBEw9ng8qKqqgt/vN31l6urqDHihAVc+n0dbWxsWFhZMNVZzc7PJrhPkU95UwFf9bACmSsztdputga2trVhbWzMNa6urqw1gUlNTY4AvNo5nbwvOhaAM/etAIIBoNIqmpiZMTEyYvjdMmLndbkMb8gD5iME8Az/emzacvMqED/0Hzp+yQICLYyI4wrgFgK3iiQk00mpvbw+xWAzV1dUGkAJgQBGtnCYtdfuN0uHg4ADBYBB1dXVoamrC8ePH0dbWZktOaLzBuIt8Sd9bxwAUgZH6+npjw5lwLC8vN3y5urpqqmaam5sNUMbtej09PQ/xXCgUMttIaWN2d3dNnxEenwzAJEQaGhpMbytujVVfTGNinRv1L3takZ7sDZRMJrG8vIxkMom1tTVUV1djbm4O6XQa29vbBlDd2tpCV1cXlpeXzbb7TCaD27dvY2RkxDyLfXwODg4wMTGBbDaLzc3NRybsHnV9ZqCh99YtjJ08ia7r1zF29iwOjh3DxnPPoeHyZSOcNDwMRIPXrmH23Dk0X7xolItmk8l8/J8OFwMx4KhcnUxFAaFTR0GlEDH419J5/R6VSc/167g7OIjYygr+n2fPovHDD1H3N3+D4+k01nI5/KLkEGjAReDDGVRzvgQKtKyb72kjJAZSvDTYoeByPhqkaYaESoLKgPNkqbmCFlQEVHyKkpFWDDDN2pWCZpZKadZdt2ywGoM8oKXSpDcdSVVOCljwPSoZostsBKlzoGHTNeE6cFysJKCyJY1Y0qd8Sp5zKinSiNndeDxuAzdIa46Dx54BRwAFgQbOkc/muBhcETzR5w/OzmL/4AA3Hn8cuVwOzVevouvuXXi9XsyMjuLBmTPovn4dPaUOzarwtYEX1+VmWxvGBwdNdZFmV8mDWkWge3LpDAJHnYa1rNIZkCsYoc49n6O/1bmmDKjzpsCNrokTnNBnq9PgzBjqczSr6rwvv/MvdalssDqLgQezW9vb26bxkQKPuj1Es1H8nAa4rKyi88TvZDIZxGIxExBXVFQY4EhB30LhaO8wUOSL72UyxWDC78exEgBHnue2pcXFRePoArDpPaex1Lnp0YBra2umizgrFH6SyWCpUMAPXC78kQBWnDuNrfIH11u3bOm2HWdmx7KOjoWl/Hs8HmxvbxvQmVkQ6v10Oo1kMmn0AOW9rKzMrLkTcFE6OOfBY+2oF7nNzufzGaCWNpSl74eHh2Y//cbGhsnSs6ElUKweYyn3X/r9WPP58Jd7e3ixBLI0NjaaJpdA0SHa3t42929qasLm5qZxaNva2kzAZ1kWent7TS8L7V1AG6PyRZvCtWcmMxKJmOP24vE4crmc2SbECh8eXUigmpn5QqFYIjsyMmLkhMEIn1NWVob+/n6z9p2dnUZn8XQR2kblJ+5TZ2VFJlPsnM/eBqlUytbPBihWZ/04FMKK242fAHii5JzxefRZFIhxuYr78tlvZWBgAC+99BI6OzsRCATQ2dmJ9fV1NDY2GrAjkUiYzHZvb6+pXKEMHDt2DM3Nzebz8XgcDQ0N5rSU5eVlAxgNDw+bLURerxcDAwO4XPLz8vk84vE4AJiqpXv37pn5/HljI1a9XvxFOo0zJT4inUmPyspK0/+IemN5eRn5/NEJKC+99BLKysrg8/lMWTWDKYKGbH5M8I/6jpU/5P9jx44ZG/b666+bIJGyW1FRgcPD4rG8XAue2MAsJjN4TOoooOAEsHn9OJvFnzc2ovLUKdRevGiCPQIVPFEjkUgYUJYBFunApq4M0rU6tVAoVmGura1hdHTUJJKmp6fxxBNPIJfLGT0VDodxeHiItbU1G700Sah2l7qZlQ/OSltNVnEtuIVgbW3N0M7tdmNrawvz8/MoKyvD3NycCdoSiQQ2NjawsLBgGmiyQqFQKJjtMgTeNjY2sLq6aiqG19fXTQXU+vo61tbWsL+/j5qaGhwcHJitVwQ+0+k0NjY2DBDLfetcF2fVmfox6lvrMcYMRAkilJWVoaKiwgTnbW1t8Hg85hQgngJBYFpjIlagsGlibW0tAoEAjh07ZuSto6PDVOCEw2E0NDRgdXXVVBISkGfVtwKX9Is1yZLP540fS31KWa2vr8f9+/dNoo99BCyreASsz+fD3t6ekaGdnR0DTAPFqh02cGU/lVgsBr/fj2QyiXQ6jerqang8Huzs7CAYDJoeKqlUymz5JOBPkEJ9HvKkJpY0SFe/k5VpwBHw4XYXm3Cytws/T1+oULBv17Ysy+h8+joAzLZTntxEYJ89zbSxbjQaNSAct0TW19ejv78f58+ff2hOTASqv0p7pGvJebe0tGBkZMTYl6amJpSXl6OqqgoHBwc4ceIEHjx4gOXlZWMX7t+/j729PdTW1iIWi2F0dBTNzc3Gn6T+qa2tRWVlJba3t8322s9yfWagYW9vD/mS4uq+fh0PTp9G3717OHHihC0YoAD6fD4MHB4i9957ReXU0QGgaGj0yCh1+OjssYyXxp6OJwNoZoPo0JGpKLCa1QWOjowiI93p6MD9zk4M3rmD23192Csrw9z58/C88QYsq1iWxP2twFFDETIfnb5HBVlEDKlE9EhEztlppBSkIfNqRQHnSDo7Mwn8Gzgq11fUknPg2dpkXGaxmalTI8L7cU+uZVlGEfDi+cGalSPqykCFY9OKlOstLUXQ6to102eD46ECJp0UGACOTjFgQMatJ2OlJoQnbt/GyQcPbEqG9CTPKK95PB6Md3fjenc3Tk1MoLd0tBefRWGlEde11uCEPK8CT4Oua6gBLflU108R2jsnTiAdi2Hl6adxutS0zOPx4L1z55CKxTB1+jQeW1+3KRuWhWqlQTabxb2hISSiUdzu78ep+fmHgAEaXAUGuKZ0Jkh7RcIVMOB9yEtOJegM4nXu/Lz+5n15cSzOsfNzHIO+pxka/aw6v7w314xBlBPE+ENeGpTT4aGesywL29vbmJubw4XKSrwVi+GVRAIvl7JIykOkN0E58jkDZd0io3RQ/clyUjpTGvTQeQKOsnRP5XJ4xrLgPjjApjQnYxn3zs4OlpaWbBU1uu1IAV9dDyLmDNgAmDLuWCwGl8uF77pc+JvDQ/yxbPXhPZwArtKHjpSCCZwTeUcbuFEW2N2f9kCDDAKUbHpGutE5Wl1dtc2Pl/Kerg0rVAgaMsiuq6szn9XMqGZU6Vhy7zmfSd2n4C6/881CAf9QKODrgKk8KRQKJttRW1trMj60ZU49pdubXC6X2QanukTXgzzoBBb5/WQyiU8//RQ7Ozt4/vnnTV+JQqHYg0B1Jp3e3d1dszdVdQCBIa4Vy5ipF2ZnZ43tLRQKWFtbM9/lcYm6HtQLbAZGYDASiWB3d9ds8eQP1+XL+/v4sceDVyULTOdbfRfLOjoijts2zpw5g83NTYyMjBiH/Kf5PP6uuxvfdLnw5c1NWJaFO+3t+D+6XPjG5iZet4ondqgdPXHiBICiH/fDgwN8f2AA397fxx+VaHHixAkkk0nj4wwPDxsA++cA/mZwEBupFL5Rcrq5tgR6qMOeXV/H25WVeM2xNZAXM3/kB34vk8kgEokgGo2aBmgqb1tbWybpABT9EtpDt7tY7bm3t4c333wTTz75pGmQWF1dbfbkW1YxA+7xeGxAAnB0JDbBsf39fUSjURNksOF5KpXC2NiYSWhwPX8TCBg9/XzpaPf/qakJa14vkufOofrjjwEcAcKHh4coLy/H008/jRs3bqC5uRmFQgFnz57F8vIyvF4v6uvrzTGFBPF0ayZQ9PfYcK+/vx/ZbBZdXV0IBAI4deoUxsfHTWVcb28vVlZWDAjY3Nxs05nq95HOGnjqc536m/aENCPQxuCmrq7OgHBssMeTjKqqqjA/P4/V1VWcPXvW2BFuJ+A4PB6POQFmbm4O3d3d8Pv9WF1dxfLysgnmtre3Da+5XC5MTk7ihRdeQC6Xw+7urpkDba0mJjQJ59xC6vf7zYkJ/MlkMpicnMTQ0BB2d3eRy+UwMzNjQJqdnR1sbm5ibW0NQLFypaqqCpubm1hZWTHVdW63GysrK6ivrzdbH1OplAGJ+TqPeCYAdnBwgIqKCiQSCWM7lTcYh3COyvP0FVhJTHoRCCGoxS2CDQ0NpkKooqLCAGFutxttbW1YXl42J1dw2wTpyyqSUCiE7u5u3LlzB4FAAI2NjSgvLzcV3cePH8fOzo4BZ2pra031THt7u4lZ6KtpolIvBfSpi8PhsG377bFjx7C5uYlkMonW1lYEAgEkS7JbWVlpQHxuWVteXjZgydLSkpGDbLbYAHt1dRVtbW0IBAIGDOXWt3g8bk5l4clZCqTwSG3qPF7UPc6EDH0FTbjv7u6aPidMePD71BFsMMy4inqR9prgDwEgTQhls/aG9vv7+0aX/77rMwMNc+fPIx2JYPnJJ/Hdt97C8++/XxxIVZUNYWFjDjo22sCRTh8Zhxl6Dpx/0whrAMR9lFTuiswRmVYm4mIo45Hpb/T0YDcUwp3+fgzcuYMrQ0MohEJIPPccqj/+GHsvvYSJxx5D28WLqL982Za95YKosWUgTSSLn+FzGfxrFYSCIpyrdk8FYBiCc2YwzmeyjG5vb8+ABXTK6ATTqSN9tCqCPxyns6SJTgfpms/nca+nB3cHBtB74wa6790zwSifmc8fdTblc50KYHxwEMloFFMjIzizvGwMlW67IY3I5KSPgh2aef3l6dNIRiKYOHUKz5W6n/M9NZbAUcBKoOFGdzd2IxHc6O7GiclJ875miRW40qBa10v50pnJ0WCDfONElvVvADg5Noabvb3ovXXLHPHmcrkwWDpx5dT9+w9V+/C7CrR4PB4M37+Pa11dGLl/3+asK601gOdFPlNwizRxggXK83o/ZzZEZVGDf93+w3ErEKl73ZU/nGMFgNvt7bjR3Y3h+/dxYmrK3IPlnzp+j8djwE8G3cqv/xIXDSXXQsvrDw4OMD09jYGBAbwZjWLN68Wb0Sg+VzKwToeQwS372pCv6QQ8ypnkGlC21eFn0M9ycWZr9LtOYCedTuOTmhq8XVmJl/f38bhkiEhDBR2cjdWYYeJr6XQaBwcHpox0ZWUFwWAQp9xuDJXWcbG0huyDQJqSRhwf9SaDdAV6ucacFw0+DSozU9zawuw2gxWPx2PKF8mrHo/HlC3SgVJ+4kk5pImuA8fPbMXOzo4NLFEAkHZBnWPVo+yPQceGTjt5biSbxenS99dLDkN5eTnq6+vNWOl4KrjBLI+Cibyv2+3G/Py8yf47waCDgwNznJ3ug87ni3tnKyoqkEqlTGa6qqrKJru6VqQfs2YKZPDSjC0DPOV3AiPU1eRF6oRcLmdOoiIQ5QT/+cM96gR9SLPn9/cxsL6O8vJyw5e67k7+YeaLpbikNWXpH/b2sORy4R8AfKU09h+4XFh0ufD3hQK+WBoD50+/gJn6v8vnseRy4fu5HF4pBeT/GA7jx243vpVK4SuldYnH43C5XPibbBYLAP48lcKXXS7jXHK9yd8ejwfP7O7iuUSiuG7iHHPduA+c/LH+2GO4+swzaProI7Teu4dYLIbz58/bPsOtSuSTQqFYZURfy7KKWbc7d+7g4sWLqK6uhtfrxebmJnp6emw6V/mVDbZZ6qzJGfpOdNi5veDw8NBssdIqjTejUaz7fHgzGsWzpQrQ5zc28I+VlWgrnRTANdna2oLP58Po6CgqKiowNDRkeLuxsRGBQMAAmNXV1canDgQCaGpqsiUTWO0BAK2trTg4OEB1dTUsy0J3d7c5GcXlcmF4eNh0/w8Ggzhz5ozZnkE9oRUbtCmTk5O2kyXe8vvxy2AQr6bTeNWhk0hf7VFTVVWFZ555Bg8ePDB9V86ePYu5uTlTVUd9XCgUTA8c3WbFpsZ1dXXo6uoy1SzcCra9vY2Wlhb09PQYsLWlpcWUrnu9Xtzv7cV/Hwzif5FM4hslmnDczP7TH9BqD+quaDSKRCJh9F9DQwPm5uZMsF9TU4PZ2Vkkk0l0dnYiGAyao1ojkQjy+TxWVlYMEMTthkAxg86eCDy+dGFhwcxvaWkJnZ2dZrxTU1M4c+YMstniSXk8IpJVHVrVSv6gDnEmvOin19TUmCqYubk5Uy6fzWaxsbGBtbU1WJaFpaUlU+kci8WwurpqekWRvzc2NlBXV4djx47ZAO50Oo3Dw0OUlZWZ+CWbzdoa64ZCIVRUVJgG6pWVlUYXJBIJW7UMK5dpizQBrH4O7RibJLOSjcBPIBDA8ePHTePNpqYmLC4umuqV7u5uAwgcO3bM5qfSB2PlQkNDg+mx1N7ejpmZGQPAdXd348aNG7AsC83NzeYI2ny+2G+KPhybQBYKxQo9rgP1DeVCtzD5fD4DxPH+CwsLAGCOR7127ZqhN5O62WwWtbW1pjkyj1BWX0nj7OrqanNaBpMgv+/6zEADg56eW7cM4qSZcy4siUCDrJkSOkzcXqCBt5Nx6NzQcLPpjDouirY4A2waV5bianaiqVDA/sgIqi9cwOEHH6DQ0YHD6mqsPfssou++i+nRURxWVGDm7Fm03LgBAKZsTbd8GCKWFoALzPEQbKEgq1Hm4lHYdKx0Fhkkc7E1+OX/pIeCNurIONeJAQSdUWeAyHVwooT83L1Sk8KJU6fQNzlp29elxpfj16COa3Li9m3c6e/HyVLzFXUAOFaOTbM/vLdmy/jds7Oz+LS1FWdKaLLyB9dA+VSDtdEHD3C5vR2jpUoINZgKAqiTAhwFi3RanVtb+BnSVunoBKN0bPxc7/37aLx6tRiASQOckw8eoHdiohislBqP6foxgNX1G5qdxcD0dHE8rkf3K+CltCFA5nyd/yt/cN2dWSydtwJSGqjwf10f57Fd5GfNOpMfOB7KyK3eXuyGw7jR04MnSlkAvTfXi2AonQsN6sY6O3Gzpwf/V/zhL60KyeVyiMfjNlpQZ7ySSOCtsjJ8oZSpcGZeGMwzm05DRb4jaKC0dvIdx6DAjWaxFYTU7Re8CKT+prwca2433orF8Eyp3JHzJGik33PKImWC+imXy2GixOcsDf5tMIhfRyL4QjKJF+V8cDoynKsT3GVgryAh6Uje4bpwjKlUCouLi4hEIgaIoqPJS4Hnw8NDvB0K4UJ9PZ7zevFCycapHKrsc+0IIOjfdMi455xyAxQB6EQiYbq36xzi8bjRnwSq+R55W2nG7zPTt7m5aV5TYILrzrUk770bieDtykp8JZPBV0s0p11SQJDBIG0dM5RcB2YrfT6fafY2OzuLpaUlfHjsGN4Ih/Gl/X28VKquU4CelXqkD0urdRud9v4hz3q9Xhw7dsyMlfvICfJwnQOBgAED2NWffEp9RD2igCjXm9s9FOBSOeI2As6JftD29rY5Qo7Vg/l8Ht/xevG3pa1LodKzv72/j+/ncviGy4VUKQuXzWaxvr4Ov99vgi2/34/v5vP4m0wG3/F6UVYC936RSmEln8ePLQt/WtoqRd75s1QKf5FO41+Fw0ilUiY4VR5hNR8DYNXFXGOdO/2dxccfR6ayEvOPPYZTc3M4duwYcrkc5kqnjTE4YHChACsbbZO/Dg8PTWn66uqq6WWi+o00tiwLN2/exN27d9Hc3IzW1lbU1taaZ5aVlSGZTJq+J+QH3SbKcRQKBTy3sYH3qqvxSiJh/IHnEgk8ubWFT+7dw0xp7AwYKisrTbNClbtMJmPKylVnb25uGh3GvdQul8tkoXmkeWVlpTly3uPx4JOaGvy3a2v4Y48Hr1sWysrKDNDhcrnMdiUmcdQ/zWQymJ+fN3LP9fxVOIzV0u9XSkfcUrfU1tZibGzMZFjz+Tz6+/tNc1pWO9TV1RnZ9HqLDYcJaLOZKwFzl8tl+mj4fD4DRFiWZRrA+v1+VFVVoampCUtLS7CsYo+j0dFRI/u/jkaxlM/jf0wk8PVStQSbAdIWsaJI5Zd8c/LkSZOpZ3b+scceMzwwODiIQCCArq4u0ztmZmYGkUgEPT09cLvdaGpqKuq48+fxHwMB/OnBAb5e6k3AU2h4HGtjYyOi0Si6u7uxtLRkqua4xYQnWkxOTiISieAtvx9/8dhjyGcy+F5JX+hx9BoYa5N6zo9H9bISgj1aFhcXMTIygnA4jKWlJcTjcTQ1NcHv95stRQRD79+/jyeeeAIHBwdYWVnB8ePHDQ25ZY/brRcXF1FbW2uqQJqbm43eHh8fR11dnTlFp7W1FXt7e5iZmTHgodNfdoKFzoQf/Rk2l2Xy4ODgwBwfHYlEEI/HDaBRXV2NjY0NbG1tmSqFra0tTE9Pmwos2rt8vlh5lc1mje7JlBrJ8iQSl8tldDGBJp5uxcbdDQ0NJtkSDAYNEKZ+GBPHupZut9v03SkUCqisrERtba05spz9IypKvdPq6+uNHaqvr8fk5CSqqqoAFCuRCO7wCofD2N/fR1tbG5aWlhCLxWxJuX/u+sxAg8fjgVVaKHY29nq9towNjQo/T8WoDgADMy2LU6PEz9Jh5r57BRDYh4GNyghw8EeDXA2mqbh9MzM48eabRgAaLlzA8pNPoub994sK6513sPXii+gfHzeNWyhI6phrsECnkEZ0f3//oaCeSpWC4UTYfT6fzbEnfTVw4H4qBknAUaWIZjcZNNFpYfbIGcRrtsGZKSKtuZ5+vx8nx8ZwZWgIBx4Pxjo70Tk2BpfLhcm+PowPDaH98mV03LljGijq/n6u86m5OQzOzBTnIGAR10ODQQVLNGh3Ou8jCwsYmJ4ufk9OM+D7nJ8GubxOLy1hZHGxCMSUgC59ltJKwSSlP42TNsPj97neihCqktT/nevBAFLBFUXinQEknWZmT7TiwjlvZ5CpDiLvx+fqXPn+jZYWXO3qwvDEBAZnZ23z5Bw4Dv6vF5+pa0lniwZD76mVESp3pJvK/Yk7d3Crrw/dt26Zxot8Jr9P3UG68T3e40Z3NxKxGP4lrs3NTdOLhqerKM3J8y/t7eFzJRm0hAcUuee65XI5fPTRR7Z+IjpfriX5k84mgWE6yF6v1zhtCtCQLsoTHI/P58Mr8bgpH1ZZ5mepCxXU0vXUyiXyGdeIlSi/jkax6vHgjWgUL5ayIQwcODYexayllVot5XK5bB29OQ8NSrj1hHaOckZaUNcqTXO5HH5XW4sNnw/vHDuGF0uOif4AMHRW2QCOZJV2rlAo4MqVK+jv70d5ebn5LPfEUqZ0KwpLvcnTOl7Ske9z/ryv2mKOzQk2a5YPAH5bUYF1nw8/syx8tUQ/Zs5Jbzq5tOcK5Or8SUM2xFtdXUVdXR1+GQxizePBz/x+nFtdtTmXXCutWmPAy+ZtwFEygN/hPLidwuPxYHNzE/v7+ygrK0NlZaVtbEyAsOSZ96YO43wIRJD2BNiYtVVg3cmTnD+P2EylUvjZz36GpaUlnDlzBm+HQvi534/PJ5P4P5e6+s+U+Hc4k8EwisHhasm5pV/AgIo64ZTXi5ESX8+VsoyvVFTgF8EgvrC3h+nNTQN4uN1uPAngWY8H2c1NHAg4x0v7r7jdbqOTOUfKkcqAZRVLb6Nvvon4K68g+tvfIhQKobm5GTMzM4jH42hubkZZWZkB90gr0pMVRWya19bWhsbGRrhcLlRUVODg4MCUsZNPCFoAMCenuN1uzM3NGZCOYD0bTwYCAczNzZn+AJR5tXXPxuN4qXQ6R8EB3GcyGQPkkTfmTp3Cv4tE8F2/H1+Vil4GsbSDpLXKrALzCtDzdeo9l8uFH7vdpnrlS/IZBhH0FVXWWdXHvmoKfrndbnz54AA/9XrxuVIFBKtseRw55YuVF5WVlaZ3gPasIhjB77tcLiQSCaPzNzY24Ha78Ybbjb+LxfCNzU18UXT15uam0aeFQsE0lS8vLzdb72jXstksvhcI4C/39/EnJbu7v79vOwpW7ZvqQj7P5/Ohvr7eFuOwrwJQ7P3Co1E9Ho9pWEnQg2Ch2+3Gr0IhLObz+KuDA3y7BGKz6S9PWuBY+vv7cezYMdPH4/HHHzdAy4kTJ8ye/79PpbDu8+HP02l8r6zMrN3W1pbZ/kz+4DZLBZ0LhQKeeOIJBINBM97h4WGsrq4iFArh+PHjAIC6ujoEg0EMDg5icXHRHLOcSqVM5n5wcNDwBvUb+fr48eOGN44dO4b29nasra0hFAqZBpuZTAY1NTVobW01x86+6fPhL0+dQuLgAN/yHJ2Qpv4w+Vj9bE3CslGoZVkmjn3w4AGOHz8Ot9uNtbU1rK+vo6qqyvSfYd+EfD6Pu3fvYmZwEP9DYyP+JJ3Gf1k6lYF+B4+K3NrawsrKCtzu4vaEO3fuYGBgABsbG0gmk5iamkJzczNSqRRmZ2fNyTCTk5NoaGgw4LPGfuonUAfpNiW/34/19XVsbm6aI7a3trYwNTWFjo4Os31nc3MToVAI29vbCIVCWF9fx+7urumbwqMva2pq0FU63p3+8sbGhvGv2GiSR6D/c9dnBhpu9/VhNxjEnf5+vFBCYpgVIJMy0OLiK1DAXgDaSJDlsXSsNOOlvzVQ120WWgVBdJdlfU4HQBFZDWZzuRyqJifRPT5eLBsqbRnIBQK4PjyMdDqN9tu3bX0iODcCA7pPiApfj7mi0aBDTmbRrC9ppoZkrLMT94aG0HfrFk5MTpp50mkhjShMagB5Tz7HKZRUOs6g2BkYEBknSnj89m3c7OtDsqwMYydPou7TTxEIBDA+NIRkWRkejI7ixOSkGRORPj5Dg3I12lqtou/pFhmuqzod6qzebGvDp21tODszg5GFhYfmSoFVh1SDe81qOnlH6ecEQpyvOUEEPpM8fOv4cVxub8f5uTkzTip7OjnO/e3ManJc5F3NFun/fF95X8eriljnqEG8vq8BxlhnJ652dODA48FeIIAbPT04v75uC2S4JmqsSSPSmDzGLI+zzJnfo45QRJ5BAxtWqvOezWbRtLGB47duFWUlGrX1TKFjQmdEs1zUWfl8HjXb20hFo79PNf7/dSlQ9HF1Nd75sz/DsXfeQc0nn8DrLTYRVF3Hi7RRvlZQZ21tDSsrK2aeqpcIUvJ75BkFPfP5vNnryswGdanyKPlUQYfnk0l8rtTZfK9Uvso1VSCOKL2uF3mN2WgFKRjUud1uvJpK4Y1wGF+QY3mVT93uYjf6ixcvmmew/4RuveFnaZdisZjZk0peYeCjWxRUp+jF/5/b2MB7NTV4oVRFoxV4dHYYZD1qPcnT5BHNzPw2GMQbkQi+mE7jxVI/AJVbwF6RQQBCdZECtsqLqrcKhWI1hFYVcoxq7/P5PF7e3cVbsRi+lM0CpfXmmvIeTkDHuS1S9QTHvrW1hc3NTXR2duKLe3v4lWXhC6mUCRL52UKhYPhJA3+lp9objoHvqVyRJyyr2LlfdftvAgH8IhjESx4P+ktBGpvkMeMEwGyL4TO4xYbbO1KlnibMRNXW1pomZQoOE2Bm8AUAP/f7seLx4M1oFF/MH1Vkkc9pQ7lG6itodpjyor0vHs/l8FKpYWaqtLVJfSvaIQaSDJjJb1pJ6fP5bEHybwIB/Doaxed2d/GC9KGxLAuVFy6g6qOP4Ha7kervRz5fPO6RJy9wC6fyKtedNoCvccyUGY5Bq0Op+xnQ5/PFLTukF++puo+f0wQE/RvVBep7aXUqcJQ44zrcHRjAvsuF/5RM4omDA/w2GMSP3W58w+XCF0pzoS5UW+kECQCYrRHcV89Sfb/fj+/u7uJvDg/xJ8EgyqS/FvWR6sG5uTlzT91qplVRLpcLrxwe4pkSQADZU+52u02FBem5NDqKf9/Sgu/4fPiagFfkf9ok0pRyQF4rFAr4+4MDLLpc+IHLhdc9HpsuoRyzKiElOoLyzu1tLwN4xbLgLYFrHo/HbDNR34Tfpb50+ohvvfUW7t+/j76+PkMXAhYej8c0s6QMLy4u2kDlZDKJr+Tz+AGAr+bz2NjYwE9yOfzI78e31tfx9RLIwHkBxWwyGy9SL7BSgz2Uvufz4a8PD/G90rGi5O21tTWjd7ShIv1B2lC3u3i8MmlG0IOJ3vLycjNnr9eL9vZ204PA6/VieHjYAPgtLS2Ix+M2meMRifX19QaQyGQyGBgYMA2Iq6qq8OSTTxpZGxwcREtLC2KxGP5+e7vYaHZvD9+ORIys0YdUu6b+EXB0vPbJkyeRTqdNg2seS+p2u9FXOlmGPUTY14DHa8bjcVRWVuL/ks9jJxzGj10u/Nv80dYCHpXc3t5uesiUl5ejsbERhUKxwoBHM4dCIVRWVpqqjVAoZPiU+pt+riYFNBlGv5WyzB/2CikvL0ckEsHKygqGhoZQVlaGXK7YZHt4eBjhcBjr6+vo7Ow0p7ksLCygu7sbFyor8V5FBf71wYHZZkQfkqdwzczM4Nlnn8VnuT4z0FC9tYXt+nocK3V6pWHVox7ZMIeZfDqi6kypQWfDMRKKQsTXaMyoMFgJod9R46xBqho/4KiLp1Yi0OCrgxIIBHDj2WeRKZ2zPjUygtYbN4yA0viqk62OlDI6n68ZSg2m+TkdH+eVzRYb+CXLyjA+OIgTk5Om3HC8uxvjg4MYvn8fw/Pz5plkPs30sGGV7kXVAJZODQ2xopvqaAQCAYOy9d26hTv9/ei8ds18tufGDdw/dQo9t27Z0G8NNIGjJk6kizO4dTb6VOeXCkv5ibQrFAq4dPw4dsNhXDp+HKfm5mwOvjrczsw+nX46Dww26ZSrcXRWF/A9Bo6aOeP6apBeKBRwpaMDO6EQLra24lRpjx/lhWtPut1ub8fd4WEMhUI4MTlpkx/yg2VZxZ4EPT04NTGBAcdRbuQr0l1BG/KpovrqKPBiAOB2u3Gtqws7oRBCBwcoT6UwMjVljtTi+hDNZoacgIE28dJASGmrgRfBOafM65Yd8hv5RwNKyqV+n/yglVgMrAkSFgoFbFZXI+8Iyv5QF/fmBQIBfPD880hFInC//DKeK3UgD4VCuHfvnnHEVI9ms1kDBihIkM1m0dfXh7a2NlNGzCCaZ5iTNn6/35Rgbm1toaqqColEAtXV1ab7MJ031SkK7AFHzid5VmWfYyK9SWc6mfy+lrWzEozOC4+6Ij++kErhhRLIoLysesTtdmNyctI01GPDI+o38rrH4zGl0AS7NFgYHBw0epN8x/mSn8hTHMNziQReKvXMyZSyyppV14wSHT6VRy2l51wIBr0RDmPV48EvQyE8U8qQapDO+f/HtTXc6u1F/YULqPnkk4fAJufa8Rkc3/KZM1h4/HG8KttTdJzUJy6XC8/G4zg1N4fW1lZAKhgVCHXOmfNREIrrp6ATfz+fTJqtOIelaie9yAezs7OGl3jKhDMbxPHzOdr01O0ulm3PzMxgdnbW8J3b7cYPH38c2243fhkKoa60H5rP4txyueL577qVgs5+WSnDyDVi5ra2thatra1wu90GUGAJs1YHXLlyBd3V1djr7sbg3Bw+fPDAdDpnkM1qCO57p/6kDtnb20M6nTaNDwmuUidYlmXe571p/7mO3BPMXiW/Ky/He9XVeGZtDY+XMl3qa1mWhV+eOoUNjwdvhMPon5oy4AoAW4DAvgq6FZH7ohWoZoUAS4Tdbrfp00Ieoq3heDgX59Ytyyr2eGGAzHHQrjoTDuRvAtz0Hdg7Snk+GAzajowm36fTaVS98w7Wn3sOTXfuYCISwfdPnsSGx4O/zWTQ8+CBzd+hXNJecT0ISDIQTCQShga0Z32pFP4f0Wgx4BXgjLLHnhns8s85Uk45X+ow1QVamcR1IO2p96dOn8a+242/3NvDCyU6U5fTf6Ft0+PiVa6/6PXiZ34/Xs/lkChVRDi3MdNP4Jg0sKe+oj5gZcHBwQEePHjw0BYz3i8cDhs5Uj3v9/uxs7ODsrIy44fw3qrLSA9nxUg+n8fXC8UmvHt7e9g/PMQPfb5i5Uk2i6+IP6n8S9+JupHb0PjeKx4PXvV4kD88xPb2trG3FRUVtuaAXDO1W07g991330VHRweCwaDZiqTblzKZDBKJhNkCoNVZ3MqWzWbNCTU/B/B3wSC+tbODbwSDpv8NK9F43Ci3GmSzWdPrjQ2Bv+vz4a9LW75I4729PWxvb9uAYfVfldcJItTU1Jjkw9DQkAGGamtrTcDv9Rabq7LiLRqN4rHHHsPe3h7+1O3GX+zv43ultSC/1dTUIJvNorGxEceOHTPx6tmzZw3oWFNTgyeffNLYgZGREQNO+/1+Ux2ga8PPanWYJgYIpvLEEs7T6/ViZGTEgEw1NTUYGRkxgMPAwADW19dNkmVoaMjomw/q67EC4K8ODvCtUvI+WtIjVVVV5tQmZw++f+r6zEDDA68XecvCbDCITz/91DAclbGWbBNh0yCBKGU2mzUZKipFzaxTUQBHCo6KjdUSBCScisHtdj+EZmqXVb5OJaBKQYX32MYG0uXlsDIZtFy8iEQigcSzz2Ly9GmcuH0bvRMTRgGpEnY6QHc6OnCrtxd9pe8we8bMGS9Fkfm/1+vF6akpXO/qwqnSHiwyHE8QuNnTg7MrKwCOjCbnoackOAEGzWJSweiWEw0eqNy0B0bPvXtou3mz6ISUxnVichKjS0vFzz3i2RQMjoH8wXXTDCsAm6LgZxWMUcfU5XLhWnMz9t1uBA8OMDo1Zcv26TPVaDCw5G/lJ83caUaP46EBoXJUhFlRRwI+fGY+n0fPjRsY6+/HifFxs0eTBld5ye12Y3xgAIlYDDd6enCy1MjJ6/XieksLrnR0YGRyEsPz87jV14d4OIxbfX14pnTcnAYHqqjUweQaFwoFE+RxrpodVSXeHQhg7ORJnLh92xyvuSt0VgfJsiyzj4sOL+egAYfOnXzA1/Vvy7KMjGuFE9eQ/Mx1UEeZY9JqCCegpkb38fl5fNLa+ih1+D/74hFchUIBow8e4EpHB87OzyMajRrHX7srq9PLgIK8yL3vbILF8sC9vT3k83mzlYJZAwY63Ovv8/mwsbEBn8+H5eVlk4Xl6Q/MlHFdlMfZPIivKQBAR4CNqR61TmysRqNVKBTQ1dWF1tZWmx6aLW3NUdnQMWjwc3h4iHPnzqFQKJh9rXQI6eDQeU+lUuZsbJZLhsNhHBwcoLKy0vAJ7RYDID3alveOx+NmrmwqyfXhnAlQa1WFU8+53cV+AV5v8Yx5oKjvXkkk8OtoFC/t7Jj9ogpwk8fHTp7EQUUFVp56Cr0TE7aqCrfbbZphsTSZ8k1ZuHjuHBKRCH7r8+HZ2Vlb8OS0cdQppJPbXWwGWVFRYcr3CTjy/tQ37JukWUmuJS8GUQruqs0mvT6tq8PPWlsRe/NNVF64gHA4bPa4alNlOq/7+/um6kfX5E//9E9NxqajowMfHjuG35aXo/fwEA+yWXwxl8Pjjz9ueEcbNNN3YWCs26E0I86g0+v1GqArFAqZps4ej8f0hVDA+1teL/7Vxgbc0ShyJ08aWvIYWe1jpfT2+/2mczlPHODzqSeAo61GtH0EvdjjinY6EAiY8tl3u7qwHQrh3aoqdNy5Y9aSYzg4OMDA3bu42tWFgclJLC8vGyCFWVoCHLlczvQP4POXlpaM/BIQTKfTZs60ZdqThdWyBBoVJJ+bm7OVjhNg4A/5hCdVBINBbGxs2MBx8i8Ak6Vl6fj+/j7i8bjRRR9XV+Pdb30LPSdOoOPOHQMYZLe3kf/hD4vjO34cTy0v44P6ejxXOl0jl8uZ7LxWlaiOpU3QrV17pe0bGvR7vV5jG9TG0ufTZJ7qIuoV8kkmk8GvvV78uqICr8TjGF5ff2iPNmWYRwJXv/su1p99FrXXr+NBQ4NtPdS/oHwokEH57spm8b8t3X+8YN9yzDF6PB7U19fb7CV5hn4GK6uj0aih4c7OjuErzpu04eUM+pqbm/Gb3/wGqVSquK0jn8c3XC78kc9n00lca/Iq9aduseYzv5LN4ideL75WAui4VmpD2IuCvqcmPdXPU9uSTCaxsbFhy/ZrApaAIHUsZWh6ehptbW2orq62nVikPlJZWZn5W20M15ZgXT6fx/c9HixZFv42l8PnS/0PnPNggpqAJnUp1/Qxy8JTJfszu71t4sDy8nIDOvJS/lHQmvw+XdpmTXBNG7wqOK3VKvRrX3W58KVSlp9bgf42mcRfhkL4k1IFAP1b3oc85vF4bEebUt/wCgaDxp7S10skEoiXGsySzgp6akyTzWYNaJPP582WG9qYtrY2pNNp5PN5VFVVoby83KwBm6fmcjn8SSCAvz48xHekYoJHd/p8PtNoVoHbf+76zEDDidu3cX94GF23b5vzbSlMRNQ5eWd2QjMIzFpx8hqQarChzqfL5TKMCMBkuigwJLYKGJ+jZYU08npKA3sr0Cju7+9jvbISsCx4EwlUfPAB0tksJoaHkS4rw73BQbNPVMcPHB2HSCMwdvIkEuEwxgcHzfnZDCK0uRNppM6UZVk4s7yMU3Nzxf8FyTw9NYVrnZ0YnZ42jMxL14AKS0vLlUGdBkWDNDoWBIX0GRyrZsTVgOk9FZDiM0kHBT54T1VmStsbra24fPw4zs7MYHhhwfY5v9+P611d2A8EULm3h2ficRRKjdIU0WTAoI5QOp02fMVxK120KkcrE6icSWsGvcBRRoqKlLLBOZ1bXcX50n4plJBmBoW5XA7j3d240dOD4YkJ9Ny8ibGTJ9F5/Tputrbidn8/hu7dw832duxGIrja0YH+qSkMjo/jRnc3Tt67Z/aScpxOVF//p9yoMtZAToN50nx0aQnn2GW4osJWvUS5UKeIylEzwwr8UGaVV5wZAo5NkXgF+RTQ0uBHeVerLOh4aICsaH0+n0dvMon+Bw+Az38ef+hrcHDQlOaesSx4cznkGxqQq601JefObWnMolKXcn+ec/sZu0UrEErHUwE4ouzBYNAYH9ImFArh4OAA6XTaBM2kDfkIgK3XAdeIOleBS36H96EzrY4Sx9rQ0GDWi6XDu7u78PmK53bncjlbzxzLssxxURxTfX29cc7cbrdxtAkCUBexG7jH4zFZETqGPIddHQZmW5UGwFEllrMTPnmPTiVpqdktAMYpy+VyKCsrw5kzZ8xYFxYWkM1mUe9y4V+V+HVB9I3L5TL6w+fzYTSdxo2eHpxeXMTp06cN33MeLOUk6E1dnEgkimXim5u44Pfj7NwclldWTBk5gxYF5vlszofNtDwejzkqjE4WM2x0gMkv2iCyvr7edM4msMPgic/lvJmld7lceLuhAXuBAKzPfx7nStk30jkajRrAnN9h1pp7+S3LsoE6/N5vysqwVnK2/vuSznbJMWmBQMCWyaYj5vSJNPDns8lPbnfxCNn9/X0DCDAI1wozHofG5zErS1kKh8MmMFUbxmMCWVFCx1ZtILOyBOy4/UF7T/G+Wo0W93rxo3wer7ndeOaZZwwfkKaFQgFP5/PI7u0hV1cHT1OTeTb5nbKiwB1Q7JSuFWoMQilrbKbG/ymrerQvfUH6QAyCI5GI0SvcekB9wyDhblcXrnV14cz0NM4sLyObzSJRqjgDitUYW1tbxjbmcjlTBVZTUwO32433amqwEwphYmgIZ6USmHMkyNu+vo62mzfhcrmwWKKN9kgAYLOR5H82kWMArc2AaXsJ+FFP07ejnaQOVLCFa6j8nclk8LOhIWy63fiHSAQ/7+zE06ureLIEjtCn7+/vN/bk+NISXN//PjweDzZlm4RWCjj9Ms5RkwWck1YecI0LhYKJB5y8pXqKc2eWnQ3etXqF8q/+sgbx1LHJZBKXLl3CXw0PY9VbbM7KuMDZB2djYwO5XO6hxvgaQ5wuFHC29PwH+aMtDVrVzTFzbuQJ6qI3vV781O/Hd7xefLVkHyjbOjfdgqNb2Dl2oNgMcHZ2Fi0tLTYdxiQPACMHzqQQ/Rr1M/+oUMCPAHzDshAobUcgbbnmHAN9Fc7PqT958XnsLaCAIcEDv99vq6JxuYoJ69XVVUQiEdTW1tpiVepE2m76KvS9yAcKSGQyGfy5x4NFAH+eTuO5UhLH6cvyu9ongpcmAkhTVk2Fw2HTo8npqysPaYyRzWYxOTmJ8vJyeDwec+wpx62APn1Ffj8ej+O5TAbP5PPwZrOIS4JBQVaXy2Xs+O+7/rOaQbqso+0FOjkVcCoLEpEBPIkei8UMMseggAtHBa8ZM8CulFlmziwdt2rQWeIC0HFRI0nDr8/Q4Ijj6rh6FdNnzuD41atmf17vzZt4MDqKM6XjT7SERZkIOEJAz87MFMvj79839KEQken1+RpYq8CoknC5XBiancXw/HzxddfDGR4aTQqmE6XT4EWrGTTzpuvOddTAjXSkMuXaMxC639eH+6dOoXZnB2sVFRi+fx8DMzM2PuHYiGgquKTrn81mcaW9HduhEC62tWFwdtZWgZFMJtE/NoabPT3oHx/HzMyMLbh0IrGqvNTAcj8x+VIDbN0rpcaCdFOedwYZfAbXgfOlctZ9d263Gz/s68NuKISbfX342k9+gpbr11EoFPCbP/kTJKNRXO/qQn/p5I4TY2PY399Hz717OPngQXHtXfYjX8k72vxHgQbyBempPKBro9/nOnIepBfnqN+n/Kpx1PXVS19TvmYgzfuSx51yROdJ11/v62xcpkg+50YaKKjxh77+YnsbF+rr8eTyMs6VjsBS4Jb6ylmiT+PDcase5XzJSwwI+L9ulSJ9CNqyJJMGhzqemUFmMrnOejntAKtmNCDl5+ioMIjRyiDyHQPNQuFojyrXWKuDGLzSCeAYFGxSYI2Oko5fsy6krWbhGbgQzaejmc1mTVkleU8z47o/m3LBMWgGg7qToJfqFeVBt9ttHHLSj1kh2i6CQM8lEnjqk0+KQUYpe0KHktltLZ3Xffculwsji4voL53AU5C5kLbkUY6TmWKOg92zCRKoHiJ4wIytlt66XMWGYezKDRS3GCWTSUNbTUrQ8cvn8zhlWbjc3o4zi4vo6uqybenk8WDk9cPDQ1ujPwAmoFpZWTEBnsfjwROLi7jQ2IgXd3awublpgntWfzmzgxwbM2TUm2ozVPcTVGTAT7ArFouZ9VYnmDLqdhf3w+/t7ZkTa1i9oyAD11dtHXUcZZ60oq7g1gpmxDXbrcCeZVkY8flwsrQOG9LTgzbRCWyrT8e1oFxz7E55oczQRlN+GbhRfxAc0YSUbsdVXRONRk31QWVlpdGJ1Ckulws/ikaxaVm43t2Nf1dqMsmGkuRhjpd6g74u9dx3SkDMV9xuvPjiiyY41ipKzQoqYKWAjupIDWCoQ/P54rGACkSrT831zWaztswxP8P3tAKQeolrnc1mcXpqCpeOH8eBx4PNYBDvVVej5949pNNpU8Whp31ov53NzU2sra3Zgi2126ycqa6ufii5Rf6n7CgfZzIZc2KDJg4YkPNvzkv9OOpg8qb6Q04wgONhYujSpUv4fFcX3ozF8JqAeGpH8/k8YrGYja+pC6kfuI7UEeqX8DMKqKl94P+ZTAY/DoWw4nLhbzMZvFLSKxqYO+mpvo1T3jweD27evImKigpbtYBW1yjgvl06fYTJW86JuqrV5cL/riQjV1xH28kDgYABSBWo16BZk0jkAdVD29vbpqcHv/trjwc/8fvx2sEBvo6j7SGkR319PbZLVRHU9WpneR/qWdWhav8IXn/78BB/Vyjg2243coeHqKystCUCVPep3VYAkJ8lD1DmkskkFhcXYVlHVRUah1Fv6PP29/cxOzuL2tpaU42q31Me/KeSdbynbl1z+uBOnvqnrs8MNNwfGUEyEsHkyAie2t5+CAHUCavjpfsyFb3KZIrH+KhDpt/RzJkiuXSuVVkoUci8oVDIfIagwERvL8ZOnkTP9etouXHDtpeeTgsAdI+Pm5JwVyyGyRMnMD08jHNTUzi1sICCKC6lgRLf5SqehNA/NVV0gkuGRxlEjYYqTxUwBTKo+PR/VTZahqoKVhWpOv66LvxbA2HNzpARvV4v7vf14UZ3Nwbv3cPA9DS8JYRSM3VvjY4iHgohGYkgb1m42deHp0tlOWRW3ndvb8/wEA2lGgUA6PX7cevECfTcuoX19fWHAqbOsTEMTE8X7ykABuej9Kax5z34Nx1vrqMTRVeUW+mp4AgAmyLUMig1gJyrEUTPUdOhJxYX8WFTE85MTxtj4fP5cHJsDHf6+3FqYgIn5+cxUjoj13IAf1rBonyq/KSGjw6c0sRJA36OcySvKTCj/KiONefpLIPWChcNqDhmvq8yTINAB1plSseihpHP0IoUGme+zjJnOlzOtf5DXxdbWrDl9eKTlhZ8WdZOy10pT9SPzL6o/lC5deojltfTGWP2k/JFMIyBnAIEvB8DMpfLZWu0q2AkAJvhYsCuPM33WIngPC2E43a73eYYKMoI+UkDVoLOCoCp48z11UBcdaHqW80cKZigWSTuVWV2g045n0+Z4/c12NP1Ig8rsKtOhcoc3+P9Vd8AR+AgA2PaEf6mLeTa6x5fjpf6gOvANXZuA+NcNYuowc/Ozg78fr8JdhlAk1/V/jPYSafTZqsQeZ9ry+BH50Nd6rwPAPRnMvj22hoQCuGgo8M4gKxkyWQyZg3z+bypHCBPEVgDYHRBVVUVnr97F6/OzxfpVdr3yqqNeDxuei+ozgGOgAuuE3+TT8m75DU2R+O4Y7GYmX8uZz/ejPt5ubWGIJhmTLlutAcKnmmGnLJI4N6yLNPImnpQATs+n/zA897JK04/JpvNmqoiyh6fpSCdU8+qU692gKAZP0PdQT9Fs7d8nrPxOF9LJBKYn59HXV2dqSBhZUChUMCzlZX4bXk5RqanMVYqn3a5XLZsvOoN6hXys2VZOOf3Y6jU12JZklPU7c6AWEEC+rkM3ghgcG3dbrdpJFooFGxl11wfBiYqR1qdxEBC6UY/mvY6l8sZsPpcaZ1/kc/jJ/k8vmxZePzxx226VfsscL5qx8lPDBx53OF7772HxcVFG7DEH/Vnqdsob83NzTh58qSNf9T+K+DLi/TRoF39YcC+PVblif5xKpXClxIJfIlyL4Ejx81nptNpbJaqmrXRN9fFCS5zbcgL6oNpQkgTgV9Mp/GLYBBfc7ngkjFQHkkX0p73oy7RqkiXy4WJiQn09PRgsq8PvwyH8dr+Pj4npwlRLlkZQb6n7006U8Zpe7S6LplM2hrkEmRyxjzkGW6bohzEYrGH/HHLsvBTnw/LloWf+f34mgD41MM+X/EY5Xw+j7q6OqOXSVvn1hnKCytElFaFQgHtHg/+29K87pb8HNqacDhsqi0ps2oveB/GDfTDfmlZ+IdCAS/t7OAZORGH89VEOvmKPJzNZhEIBGwVLWoPlLbqtyvgp/1nlHfIK4oB/L7rMwMNtdvbSIRCqN3aMkEiGYwT1aZ2JCCZmgqCzEeUTEEG7plkPwIn4kihJJHoJPC3BivqsFEYxvr7kYjFcOWJJ+CyLHTdvYtCoYB7PT24NzSErqtX0TU+DsuyMN7djcmREXRfu4ap4WGkYjFc7ujA6aUlw6xO5lUBVOPB+fFS9FCVAe+jv9UpV1ROGYeGhIyjdGYGzxksqmFXIw7AKMJrzc34uKkJo9PTOL24aD7/o95e7IbDuDAygssDA3jy/n2cmpszpZ75fB5D4+O41t2N6s1NrFVWov/ePSwvLxtB4lg5D9KHTozToD+2sYGz775bFJrS3jBV0vwugxNmVFSp6z5zPk8DHFXuAB4KBDTrzfUj/5OeagSYXdF1Iv2pPGkslD9OTk/j5PQ0LtbW4q9efhkD4+Pon5rC6NISRpeWiuMsdfvVgJPz4P+qRJRH+TrHoz8KEujcSEN+R+VWsyNcU/I/6WpZR70VGJxpsKrOpvK+yglf0yoF1S/qFPPeVKpqAJ1ZIo6FzjjnfKe9HVe7uvB/xx/++qZl4YeFAr4KoKGhwRZkKl2cYOCjgCuekqGOC+WZa6JdzElrGhKi9eR3pRXfYzm1BgCUPV0bBtHMjtJZU8NE54Z8o3PSNXPOg89ShJ6yRH3g8XiMHtNgX/mUNFKnhzzFjAq/z2CVzaM4BpZZMyPLjAydZ+UrygTXlYER9Tr1jtpNBQpJV9KR9yR9mVnX3ijkecs6yj4rQMF5qhwfHh6aYJWZbN6PvEAgTG2zZVkmaaCgIuevwQHXnbzIQIDOizpBHAPLhTVoYRZZ+ZfywZMKeA/2aSCA4XK5TOMu6mk6aZZV7Bfw22AQ/3vLwvO1tfhqKaCnTITDYVRXV2Nzc9Nkw5324FHrp/9zuwnpwypJNlsk4MCffD5vGrpxXbluBBSZaSePEpygg89gQIEG8ht1CNeIPhUdatUNDGrpx9ExV1+LAURZWZkBLriumhTinPkadQQBCup0BSUpJ6p7SFuCRHxN5Yr6kr6Bx+NBY2MjqqurTV8G0vHw8BBf9Xjw5d1dbAHwVlcbsIp0pjxqeTt9EI5NZZH6hPpJnXsds86Pa09fm/4EbSrnxzWkbFJPUt9qcETdxfXnlgfylIKKBBy1CWGhUEB/Po+hkuyuluZIX0u3u1Cfa+BJ+/JeNIpfx2L43O4uukt+d0tLi9kKon4MeUB9W465p6cHhULByI5m3UlXBbvVD9Jtn1xTDeb4W9eR66WgPd97lC/v8/mQTqfxgx/8wGwbV3+F9+d9qb+4Hvf7+jA9Ooqv5fP4YomulDfSI5vN4oVUCs8lEsWjeUv2iOPVhIsGmBwfr0ymeNIcbcnm5iZ+GQphxe3GLwIBfFG+S31MPbCzs2PzrfVzKsd8j3LA9dIEMm0H11+TFlqxpCC0Br2vHx7iJz4fXtvfR0ESFOrPrq+vY21tDVcaG/Ejy8LrmQy+gIervklvVueQZloFqnGI+t7pdNpsDaQ8a38n5UXqA/Z9+H5TE1a9XrwVi+HF0ikpfJ7qGM6NvjZ5KpPJ4Nq1a6a6Qu29AmecYzqdNuAFdTJ5mIkO+i7q57/44ov4fddnBhrmw2HkLQsL0SjmS2X7FGoyERFU3U9Jo6YBgCLcmlVUY68OFQmjjR1VoZERFRHSIIJjGhgfxyejo8i73Zg4dQrnSuWdkyUg4cGZMzizsoJCoYCp06eRKivD5OnTOHn7NsYHBjA6M2MLCMk4gL0syRlY0nHT0nJllHw+j2vNzbjc3o7RBw8wVGp8RqPAe9IIqyLV7IKOTcEZMphm7/mbBkFLiPn7wtmz2A2F8ElzM5quXjXv9d26hU/OnEHB7cae34+LLS1ovXHDlgnvvX8f/Q8eGKHzer2wyspsfMA5UVFwDTXYJa0oRI9ypEl7VsrQ+GtVAxUZcIQEKh/xfhpIaXZbnTLSgcpSlRKVpKLRwFEZJ19T0EEDfPLVwcEBrnV2Ih4O405/Px5bX7fR11mWpZkaBdkUfFKAgM/h3GggHmUsnbLn/AyNrc6H/KXPUsdKM5SaIc3n84an1ZHU7zsz5U5nQp0BPUaQY9JqqXQ6je3tbRtQR7749LnnsP8vdLzld2MxfM9lr2AC7N32KbOckzpA6mzSEGpgQGeYfEBHhkAD94lrwEYn1glIUndpN3inE+cMSpkBIL01qOa4tTSc89YAg3TRY9DUcXFWvTH4VmebQQszZyqDNOrKW6yEUz5lszcN7oj8Ox0pdZj4bNUX3Iahjhf1FIM5zaIxkGADP/K2bvOgg00+oW5Q54U2mTLCdXfqWK4x19DlOsqucQ3oBKudZUUA9a3qcOUnJz00K8T3OE8FcjRrRj+DgSXHoHqOOjCTydi23+gWOAUkOW8CMz9sbsaqy4XfVlTgmwcH+Fk+jx9ZFr6eyeAriYQJbFixqQ4bn+10fsnjtHNOgJl6VgEJBRkVOFe7p7yifocG7goC0kHmawrq8D4KKjqz0/l83lRsEHxSO8Mg1uPxIBqNmqN6Nejls6kXNGnAz7H6SQEhDci4pvytY1Xd96gA3+PxmNNouP+Z28RIQwZgNTU1NnmifDG4VxBFbTllgXqW39HGz+QfvqcgLvmcdoz6T+223pM8BcDm91LvqE/D3luqszVJoX6ZgkgAbPQnPQkakk8eJWNcP97njZoarHk8+FUohM2PP0ahUDwCsKqqysgSeZVgLu0X9SWrMe6xAtnlsjX41P4pH1dV4Z3GRjy9soLzS0tG1xNU0youBmwKzHN9WSUVjUaRTCYN7Ug/lT3Sh+Oi/HNM3EIUDAbN8aKWZRkZ8nq9GB8cRCoUws+zWby8s2Nsvfr1XBfVp6Q7+9g4/QzaZvKIBvn8icfj+NL+Pn4ZDOLLJfrwWarXgsEgJiYmcO3aNcP/vB/vT1qoT84YpqKiAqdOnbKtgd6f+pWva6BMHqNfHwgE8GIuhxdL/Z0yJV/IqYsDgQDW1tbwM7cbSy4Xfur14lV5hlYmaV8nBRU0rqJuozwxCc55qu3k+NXP4b1om7+0v49fud14tcSDGh+QlqrfeV/VkdeuXcPw8DBqa2ttcuusRKBu4NpxnFrZqT4C//+s139WM8h7Q0M4MTaGaDRqJsVBKgpEwo53d+NOfz/6bt1Cz717NiMI2Ju2TPX3497QEAbGxnBiaso4hHe7unCrrw+nJibQd/++MRY0VlrWQQdOnW7dCzc0OwvLsnCrtxcjU1Nm/Cfu3MHdkycxNDFhhLf/9m3cHRjAybExdN+/j6HZ2eKeQhEAPpdE52KRNhokMBDk39wHyc9c7ezEbiiEa52deHpn56EAVwNBOmOkhTrsCr6Q+XR8VIYqzDTE/JvjGik1nRx+8MCWYTlZAhAuDwwAAE4/eGCOE9NAjYoSsAfX6nCRoal8nEGQ8gyfrw6LjluRWhoOFVDt0UEakW81C6VjpYPNsTkzStqLwSkLGoDRWVBFrQF5LpczvEwUdKDU4HGwdGKJOlIKynGcmi3QQJAKTx1UNZzqjPG3BgqFwlFnf+f4Kb9KZ9KBPEZ6O8EGBSEZYPIYVTrxXE9mSLkPT4EJGmzlcwISOl+OixdpVVtbaxQ8HYZ8Po+D8XHc6e/Hv8TFru3kYeUZjpEOFctjua50/NjMSrcIqG6g8VUUmnxAmmj1gMoXy+4ZpNIhAGBz2HWtgaOKJPI6P6OVIjzDWtdP5Vf3lrKslg3L9FJDrQ6I6gzVg3t7e0aWNVNG50DlRN/jfZ02jzyttFAnWINXjoUN1pRPKUekEdeM68CsPPUUK7bohDOj7XT+6SyyuaXqS6fuZeDMoEmz2Jw/nQ5t9Eca8RncnqBrAhxV+VEu8/k8otGomYOulcrz7u4uYrGYoRWzcxyTOpCki66/ziGfz9uep8GQ7sH1eDx4PZPBL9xufK0kq39VVYUdrxd/kUrh4Fe/sgE8WkGh60950+wk6a32jYCPjolroo4tA0euHatcqA+0+aSCPHyOPl/tFGnjDDacwTv5l7xJfa1rx3kTFEqlUojH46aahOAb6cGeBwxUXS6Xzd6SFymf5CPaPOor0oi/1cfR4El1FOfAwJSyzuokjsOpn5VveDQcedyZNaQt1fFTN/h8PlNtpIGT00emTea9dN1In2w2a4JqBYx0fbnGDKC5ps5eQAQwuYYMrKlX+MO15hjoI2rfCPWJycO0V6/t7+NnhQI6rl/Hzs6O0feZTAaNjY2IxWJm3dSXoi/CsvlMpnjUIufKZrTkJTYSfrunBzvBIN6trkbL9esGPFY9r/67ExBmRtvj8aCurg65XA4PHjyw2XBWZatfRT3RWjrBivdg9RO/w2oZ6gOCdo8vLOCq349zS0uY2thANpu1Be/q1x4cHJiTnrgWBMqd/hurnXd3d/G78nL8tqICL2xu4uzWFvb29kxviSe3tvB8SYftlb7Pygyn7qAcK6DrcrlQVlaGpaUlG29rRVp9fb2ZN4FPBcwVBNNtyqQ9q0vU5+H7rPxT/UZ7EY/H8bVcDj+yLHwla6+spF4hjX9pFbdidF+/jq67d83YtFqN31M/gvNoa2tDbW2tkR/neLTqAgBe3t/Hl10uHBweIim+AteRsqy6mvxFedWqJKc9Vpvj8XgQDAaxvLxsTv6hbeJYmXDQOMIJWPxTl6ug0cE/c/2bf/NvHgqMNGjNZDK4192N+8PD6L15E9337uEX3/wmErEYYvE4/vjXvzbGkwuhSM//9Pzz2I1EUJ5K4d++955573946inslF7/L95/H/l83hyFpHvcARgHloxK5atKnCWUiuQfHh4aR5b3pWOtBpvC6lTiGjwrXVwuFy7W1uJiayseX1jA6NKSLYgj4xQKBVxpaMCl9naM3L+Pk6WjV2g0lPmcDqUiTsykUTGr40KmczKkZVkmm6GXsyJAs4dkOq4hjaGi9lrKC8Cg9wqC8DO8N5tDUXEoeMO5O9F0vk7UkZcqCRp/OnwaRHM8zBAqT3INlP7OoJo01T3UdO5yuRwu1dfjYmsrRiYncXpx0dDo4OAAyWQSW1tb5vu7u7uorq5GKBQy5XUMurgvTZ1PVcRcT64R/1d+Jq/ROdfP8b76OX2OOr7KB6ulRoY07JpFU+XGNWYGgjzENd7Z2THd9RXV1fGQRyKRiJE9XWMqPhppp1LUwIh/j3V24np3dxFQHB83uoxGfm9vD7/5zW/wh776/7v/DjNnz6LjyhW03rhhWzsF1hjIcF68crmc7QhByhzXXIE7pbk6/Apker1ekzVzu92oqKhAJBIxjkqhUDD7JymjztJcPgt4ODvI9fD7/YjFYgCOMi26vpZl2fbLE3TjkWROQEGNp1ZDOAEHADbdT94mP5AOzjHremjAxtepFxTQZTBF/an9TxQsUTAOOJJjDRT4Oep24Og4U5a4awUH76nzZCBKPa5yqjxAHajz45x0jFplRxvFDBy7vytwpPfj81wulynRBey9VMiPQDFo4BFkGjw6syykm2bzFLxR+jqBbdKaQaDToZ2fn8f/4coVjA8Oovv6dXh//WsTqFFHO8ek8qvZTY6NwSc/r/aaY+J6874cE/mK31W7o1VivJ8G4rynAhIK1ug8VHaUz52Zb+c4dB11zTT4cmb9HmVrtHGrAkTKtxyHOt8q+woQKkDh9XqNXqHcp0vlyZRRgkQaxHBc1KM8ppOX6iPVFQSiqIOUFs7gSdeG66JrSrtI3eI8yYLBF+2o2ndNJhA40gBCfRkmCA4PD03vFs5RdRl5Ris7mBgh73Ct+Zs8cOXKFfzgBz9AOp2G1+vFytmzWHj8cQzevYtXMhl0d3ejrKzMyKUCTc5xKN8VCkcnUbDB6pteL34ZCuGL6TReka1ACvgqeMxtQaQRaUGZyeWOqtoImhN883q9BlBJJpPmPuQ1t9ttmthTVzPYA4q9C+gjsqpN5RE4aoaqdorroU2TqRcU+FZQHQD+X489hu1wGBWpFL73m98YXqI9dVZfqW6hTdja2kI2mzUAslaweDwe7OzsGFBPK3lIMwW8SA/qdN3eQXCO4yD/cjz8IZ+xYkVtdqFQwNTUFGZmZvC1r33NVh1KvaF8kcvl8L8qK8Oazwf/xgZO/Yf/YNNx5HOlLfnR6/WisrISr776KkKhkK0inXpJY0KundtdPGkok8mYIzad1bz6DPpY5OnDw0O8//77aGhoQHd3t21rCsdM3ZDLFbdOfPLJJ7hz547hOZU1+pqq1zweD37729/i912fuaKBl89XPGKMZUdUmtlsFlOnTyMZi2FyZARPbm9jdGoKV7u6cHp62rZ/UJUaCXV2ZgaXjh/H6IMHNgKOTE0Vz5ifnbUZFuAoMHU6alwQCpUqa0W9NQOh973b2Ylrw8NwuVx45sEDnC41qNHgVPtRUAEo49H5+2hkBPFQCB/U16Pu009tRpeftSwLHTs7aLt1qwiGiCPEknU1pGRCOnxqUMjATiRKBZL3U0ZToVIwgwKvxlYdEd1ewEsNFHBU1q9ZTTX+5AkqJpfrqPyN66Pj5Lh5b3W0eD+OgUAAP6egCYNhBTrUWCtgxO85M8c8zoooPo/jY0bww//qv8JeLIabPT14udSpfHt72xwLw2fTqclkMtjY2EChUMDWE0/galcXOq5cwdDOji3YpFKmQuN8XC6XOQr0sYUFDM/PGxnQ72uQRLoS+VTAjcZOwSilL8/mpVEg39NpJz/oyTCaIaKC39/fR2VlpcmIaiUS6U96O7M0zkBC90nrPVwu+8kLXq8XN7q7sRuJ4GZPD8ref998h8G7Gtc/5DU1MoKDigpMnDqF0NtvG2PGizLDsfNSh3lxcdEYRr5Ho67BiwKaqscUpOF91VlTmdaqCDVyvJy6l387QQQNJBQ05D251qqjNAhx2g4NFmlodeuSzjOfPypVdTpK/K2VQZyr8iovlQHVt9TZOmZ+jzJBp5xj12BMeVaDSV0jgj+6BdG55kojVkTQWS4UCrZ9wqSp6ko+n+MiTVXfa/BCedPMoJaekl4MlPkdPpv2mrLJOTsr9JzromCM6rZ/ypHTdeH3NFtKm6aAS3l5OQZnZtA7MYFCoYDl0kke1KlOHUNeUbBJARfSR200x805K985/ROlqV4KAGkgS1rzx8knvKf6RryPypYz0NBx67hUbvnj1BkKUvB/lQX1KZw+Cemoz1LdoJ9x8oWTN/Q19SOVRlpNAMAW6FO++SytrFOe1rXX7a6P8oWUj9QeqQ7k9+iHKyhBevL3o7ZIqc2nblAQRn0fBZWd4JIG6+ytRjvL95SebrcbPwfwI8vCtywL4eVl2/bG+ccew35lJW729qLiP/5H3L9/HzU1NaipqTEAta4VfZZH2adwOAyPx2MApDOZDEZLOj5ZCtjUD1bfkryh/Y/oS3NdGf84e3X4/X5zfCr1SSZz1ICT/g23FAJHDYfZQJZVGdT1BBwAmF4hfIZWHJHf+QxNFDh9bLVhX0il8FuvFy+lUujv77eBj6wuUPrQFpCHAJjqHo/HY3r2cEyZTAbhcNjWTwc4OmWFJ+coEEZdqKcWKnBGe8r4QauWKC9MPvB7DMjJO7W1tSawdvZRIK9xrc/U1eHDhgacmJrCyJkzZu3Ji2wmy4bITK4eHh6iqqoKGxsbxj9Q4MQJJKlufsvvx1uxGJ5IJvHU9rax22oP0um0SUZplShpzioX2mPV0wqIkD4VFRXGz+B4qqur0draitnZWZsv/VkrGj4z0MAsojOoI9PmcjkM3buHm319GJmagsfjweDsLAZLAAEvpzEiY52am8Pw/HyRMcUYjCwsYGB6uiiA8l0qdf6vR6kQQVblqsTks4nQqHOdy+Vw+8QJ7JUY7UJDA+ovXbIha6pg1VmkE83nu1wuDE9O4np3N85MT6OsrAzAoxvfqaOpYwRgMzj6LKeDwrE8ykmnQKvxo0FnCZwG0vpDhadIm9NRV+dYlTL/1wCfSkRpz0BVQQ+eDU+FoY6n1+s12zWYUYvH48aZraurszmTmumhEOqPHrFGBcagQI0nDag6JWrcnA5c9XvvYe3ZZ9Fw5Qq2S8jk9vY2tre30dLSgpqaGqP8KisrUSgUsLGxgWg0it92diIRjeLe0BBarl83c6Uscl21TNHr9eLi008jHg7jw4YGNF65YhsXL814qWzomvCezJyr7HF99vb2THmz7u1Tpxiw9wxR3uBazM/Pm+oIZ9DrdrvR3t5u0F2WSTqDaOU1zQLyN1+739eHe0NDGJ6YwLnZWXx6/DhOTkygtrbWVnGi3bP/0FfHlSuYOXcO7deuoaqqykZz5Seur9MZJp96xGFSXaS04T2dgb8GCE6kmvfnuA4PD237cHVtHwX2qG3QZ+jf6uRzrk7dowGa6kENmniRfx8FMKnt0mDkUfypn3XOiWNyglwqR2oHyJOaJQTs52Xruuu4NBhx9qPgnDTQ4vrzcgIxzsw7x8Dvqv7XeTuDc6euUMeR76kt0ted9OR96ZBzXIVCARO9vbjR04PRGzcwUKry07Wl/VewVZ+vmTzSjjxI2ml1FQBT1cNM96W6OnzQ0IDuri4cv3UL+XyxMer6+fO4d+YMuq9fR+fYmKGDZudob5lhZvWJBi+6Zi6Xy9ZUVBMIHD/XzilbjwJZSFOlL2VGAxA9HYPP0KwbgVsF3zUY0L4o+j1nprVQOCoj59x1Hhy7glAKpujzyQs6DidNnFlgBZT0+xwjx6v05PdUlhgYKx9xfM4qQ/6t+kTBAufFZ6muJz0oMwpc8jV9Hl9TUIm8SLml36c6lc+n/Oi9db2U53Qt+Trvp1VTpI/f78fP/uiPsBuN4v8bj+PZkl/DezV99BEWH38cTR9/jFwuh83NTezu7mJrawvt7e1mmyPnR9lXQIXzZrCtdoU8T1lQ0AeACRz5WR5TSxuoPJpMJk11NXnBsoo9Pxjckdbcbub008mPu7u7Zoskn83vMnBX+0OQ4lE+CrewcBuK81kaB5BvntrexnOJRJG3SttTWUmWTCYRCoVs2zBZJal22OVymSqLVCplepEAMA2LWRnDbShKO6ct1YBZZU17nqgM6GeoPxmzUDdpUM/kl9op8oDqWt7/xXQaL9y/j4NCAYWGBrNWrFqxLAsVpZOJ+LplWTh27Bjy+Tzm5uYM6KH6gM8Mh8PmiFiu/6+OH8e234/3qqvRceeO4T32bCLISVtH+XO73bjc0ICPXnsNo9PTCJWa+ZPO1HO8D2OIQCCA/v5+wz969HWhUEB9fb1JPKuf//uuzww0KNpNAmu5SS6Xw+DsLM6srBQfLoGnXuqQkWiK5PJZNB6PmgjRJ6fTSMEEjrLtTmNCw6UKU0tCXC4XRqen8XFvLwqFAk5NTODByZO41tmJwfFxnCwd5+g0RqpsydCFQgFnV1bw2Pp6UYBLJXZkBH7H6cDyNVWiZHoadFUcuk9Jy2SZlSat6VRoEEim43d0/yLHqU3gNLOhzgwvzSZTYHWtlV4aPKgRvHv3rjm/V4WOR4pZVrHLeTAYRDQaNdUyDIbHxsYwXiqDJwD1qKBK6aqvkX+cQYgGaxrcqgyo0+T3+9F++zbab99GWVkZEqVjtHjcjdfrRTKZNKd1ZDIZhEIhs42n58YNjA8Oou/OHfOcfL54ZrGW23GNSPMTpealI6XeGep8UyZ1nlwjKhBVPMeOHTPntbN7Lp3lvb09pNNpNDQ02I7sUYTdKXukkwaQHF8kEjEZAS0Vz+VyxnlnAyZWfXBftj5Dy6VVTrn2t19/HQdlZbjZ14f/9cWLOLe2hrW1NWQse4WN8sAf+mppbcWK34/jx4/jtJTWK7BAOpHeyoOAPYOpTp0Cgwq8kIcI1GkTPW2KyHk7x0N9wHXT+zFAI3+pTOulwQN5njTnGlEOnePhpd93OtgKaJA2Kq8KNvC7zkyg83nq6KvtUz5xPltBHh0zP6u9A5yBFv/WbIl+V3UWv09eUOfRGUyp3ncGP/qaBrYa9GpQ5Zyn066T59T2OGWRn+fWEu394XK5cOXzn8deNIqPm5qQ/+lPbevM8dDWKDikJZ9O4NxpYwniqD3QsX7yX//X2AsEcL2rC+5f/cpsx5w5dw6pWAyz587h63KShsfjMfvvWbrNZ7jdblvgQnula0+HnnJHPch5ahCvMqKAiQLz1NmaxebfegoF19aZeXW73cZGqV6gfXhUEoQVQ85qFP4P2DvNa5BKvtJKP6Bog1OplCkt1/5LnDfXj/flGHV8zDo6gTnyPcdHv0Gr2vj5VCplAjzaKw2aAJgO7irTHAvLpxU01flz7VUvUgcxoOaaO4FRro3qaade4W+nP8M58nkKyvEiTVVuOE7SXkEK1ZsMkJo+/hiZ0VE0XbqEnZ0dm85vunoVLdevF59bStZxzqR3U1OT4XP1+bVJLXnHsiyzvUEDLNWrOk7SnvzP0/FIWwIoBAfoL3GcbJCaz+eNjWDPBO1zodVlpI9muWmfyUOszuB68TfnqPJNP1DtvtqHfP7o+GY9rpk85tQp5PVUKgUAJsGnYDsBWMuyDECjtM1mswiHw2a9FQBR/1R1rwKJOkddN9KYc1PaMJAmn7C3IKvtOF5+XkEKBaU1LqLe5OczmWKz4a2tLbNlRO1nJpNBZWUl4vE4GhoaTF8R8ipPknG73aZCkSCgy+XC64eH+LXPh1cODzE8PGz0ovrz2m+FMZ5lWfj/1Ndj2+PBzZ4e/El5uc33cNJSfUhWyRAoYiKX43Panc9y/WdtnVD06J9yIjX4dDYN4/40DTydBlMDEzI/GZ6CqM4MleDt9nZc7+rC6PQ0hufnbYvJ+19tasLl9nacL1VP5HI53O/rw6etrTgzM4PBmRm43W4MTE8jc3iI6z09yGQyuN3Xh91S9/8XS4tKw8bFokOjCkmVuzKGBkWqKBVxdwYI/DwNoNJRjabul1cQgetAYVInn2Nk0Kadm3UeHBsDXL6v5YKq/DTwU6XJLQZ8jdsy+DcADAwMwOfzYX19HZubm0Z5U+laVrEEiaX91dXVpkyOAbw6AbouFEink6RglzrNTkPtDLrIx4rscp48P5fgwXoJdDo8PMR0KUvH9VoYGcH8+fPovHYNNZ98grZ0GpUXLqCiogJ5txvb29sIh8Oora012zw0MKJ81K+t4cn337dt4VClrcGIU3E70U2WUe3u7iKVStnK4r1eL0KhkNkK4kQ4nU4HFb4aFVYoEGDRTI+CgSxL1JJArt2j9rxqUKWGJ5vNIn/7NqZGR3H6wQObE6jZXH3+v8R1vasLWx4PLh8/jn9TXW3LNjgDaQVSFDxVvlXwk9VBbBKoJZeaDVfdqrqHNORYtFSXOpk0A/BQQKMyoLqH68f9mXTC+DcbfipQqg4H9Rb5U3WHrjPHT8dAg34t/Ve9QJoBRw3BnEE/aaj6QMEdDaIVOHIGIbwndYg6LU7wRIMN5+vkc83oA0fVOAr8cE008HJWaKndUV6kDdKL3+d+WTarVB2j8+TlHJOCJeRvBkodV69ienQUHdevm7JpBUyUlnp/8hODeQU4nECEyofTYXe5XGj55BPMnT+Ppo8/Np8pFArov3MH94aG8KX9fTQ0NJixhcNhE5xQR/K5LpfLOJVer9eU5ypv+Hw+pFIpWyDPwJlrQweVfKd0ZNXEo4An2gENyFXGyWuaiaQe0VMNSEMnL/NvJjNIQ9pG2iJuuctkMsZHo47neOiP0IarDJJHVE/y+F0d06NkTn1SztWZjCGN3W63eU+TLQyk2NPEsizjn2hFHBuk6hUIBFBeXm6ez3E6QQJWfihg5hwLK2Moe+zdkk6nbbxFmqovx+Bbgz0N6tR/57ooPQmqqU/LvjwM7khz0jkej+Pg4ADVH3+M2osXizoED/e1oNzwxJLa2lo8+eSTpgebyu6j/DnSk9tvmblWO0bZUjlQkEKDPg3CCWoVCsUTMrg2vD9BJNqnfD5vthDQZ89kMuZEFT2SU5uKalygvExZ1rmqPuM4mYDSxpyq29Sv4PrxIu97vV5TiUDghRl52hf1m5iQ4vj0swAMSPio4JQ0U/+C/ECwg7LFSgUFXvhZ2mfKo9o9ygjpznHw+6zGVR+Muo7zoc+iflYymTSxk26tUZ8tmUya99X/YHKY9CXvU9af2t/Hs/F4kTYuexKFPg4v8ibX77XSaSFfKvVYUWBV/1Z+Ik85QUO1Nx6PB7+0LPzY48GXS2v7+67P7ElTEWuQzIXS0m1FhKkA+Bki9Moc3K+iDpU2EbQsq1gC0tSExxcW8Nj6Oi7W1uLDxkbU7+5iKRpFfTyO8dpa5C0Ll9vbMVg6htLpsH/a2ordUAgfNjai6epVFAoFfPz444iHw/ikuRn1ly4Zxr7+3HNIxGIY6+/Hc6uruNDQgKdWVmyZTgqrZlAVjXIaLXVEOTcAD1UQ8DsUFl0DZ0WDoo4UeFUAdFS4RmRooGgM0+m0CRK9Xi/KyspsilePn2IpFpUohVWPXqJx1dIsdSadvxU15BWJRDA3N4eWlhZsb29jb28Pa2traGlpQUVFhS37wucdHh5id3cXS0tL8Pv9KC8vt5XOJRIJW/M5jk2dfY6D9HI6oWr41eAVCgWD4LrdbiyePo25c+fQ+OGHCFy+DI+n2AgnHo/b5h6NRhGLxYxBvPzUU9gvK8PU6dNouHwZ8yUwjMZr56mn8OHJk2j66CO0XL9uQ4MBoL29HdXV1cjn80iUjmCjonMi+QxS1OCwAoYOKZ03GhHd5811p9PFbvoEgZSPFWRTACCbzWJhYcHwpDqyj+IZ8hi3cszMzNh4jI5Xd3c3amtrcePGDWxsbDzU76H15k08U9oD6S6VBwJ2sIHj1X35f8jre4EA/i6Xw3fDYbSWSuuchlENoTrX6tzp2Bmw0ojQICodtWzPsiyjv2hg+Hw+Q7M16tip0wo87Pw7K0I4bmdgT11Jg81KH5VRrf6iLqKe5d5DBV8VEM/n87ajMZ3gK2nDxl+8l4JmBEbpMDuDZ93Cx8/Q2eOeWLWFCvZyjqoveX8FjNXxB+wZCK02IN8woKPNVb7gWCm7qu/VNnPd1M5p5jyfzyOZTCKfz6OsrMy8RhrpOAkicS60QcwOkf9CoRDi8TgymQw6d3fxykcfFfnpxAl4vV6z35oXA1qOmxlrdeCdQIOOUflY7TWDgdbdXRz8/OdF3mluNgDsTCnwd7lceC8Ww5uxGF7b38fnSo6jOv1Kc5VxrjXHz+BQ/Qu1Q3R2uW1QbZfTtjJhoPqMfEkgQO2o6g/KHNeaPMI5cCz8vDMI1ZJ8zZYy+8fPavUkQRrNUFJ/UTZ1jKSTPk9tulZfqu6jDGiVlso4s4Gkrd6He+Wd/h3HrokY2kelQz6fNyeUaIDr5AcNXFVWqZs4d+oDygMD2lzu6Fg98rLqJPpvHINz69Gj7I/yKvULv089QYBKtypdqqvDB/X1GLx3D74330QikTDAmepErgH5KhQKYf2xx/B2Tw+eXVtD2a1bBlDh+rBMn+tOP4W0oa9I3mOFEXUNAR0G/Zwn7RPpQn50Vg05fXMF/TQ7rO+73cXG3hqQEpxRHa66Q+UagG3d+FmnDlBAhWtF2vEzCkY6n8H5E0jme8Fg0Oh9jk8rSjRxwXVKpVK2TDiBBwV+1A9VX5XjV3BJZR84CppJW/KXzon6lbEKZVT5gXZS15zPJ3BFIE7BetIyGo2acdGPJtCyv79vO3Jct9ZR19DXpozxc9ls1iRnyMsE8zgfjouNabPZLJ7Z38eTW1vFzwmNFVhU2089Ql+HY9PkM32sH7S2Ys2y8GO3G/9v/P7rMwMNqlzVQeRgWO5MAVIUiUygzmYul8PlhgZ80txsqgkoDFSqQFGoLpw+jd1QCB/U1aHh8mW8PzCAeDCIHZ8Pebcbu8Eg8pYFK5/HwN27SKVS5jmaFR2enMS1UtUDj8w6OzuLy+3tGJ2ZQXmpvORmaysyfj8Ce3vou30bvfE4BmdmisbIYdgVjXe5jrKrOmc16BQSZ0aBjEUmp9CoUWUgyMCMr/H+n1RX43JHB/pu3ULvxIQJUNPpNNbX122NO5lp1xLG8fFxbG9vG8FTB45jU0XPcanDpg4V6e5soqbf43dJDxrsmZkZVFRUoLa2FrFYDPF4HNlsFuvr6wgEAgYcKSsrM0bOXypFv3X8OCZHRtB++TJcAKZGRxGen0e8oQENH3yAhitXbMEL+VZBBFXeqoA5dgUn6LzNDg1hfHAQGa8XmUgEC48/js67d+H3+015FQCUl5ejvLwclZWVKCsrM/uyzs/P46Jlof36ddOUJZPJYHd3Fy6XC3cHB7FXXo6Vp5/GU6XtE+o40cATWR4fHzc0ZuZGnQfd56YBrmVZiEajaGxstM3Z6RipoaRiamhosAENyhvOwDiTySAajRqFT5qq0dbn86KTARxlW7gmhUIBkUgE4XDYKH4i67w/S5tJm09ra3FheBgnbt/GwPS0jb+dWbE/1LW0tITDigqsbG1hQrY76TzVqGoATToyw6C87Mzq0GGgg64ZVhoereTgZ2mMqbeo1zkOlRW1CcxoEP3XLIQG3Ao0cD7MxBH0VD3ipIlm+NQx1MCBn6fDojrMSV8nsKJ6TxF+/s3LqQsU+OIc1Yl1yoM6yaQrx6bywDnplgsGLORt1anqKNEZdTpr1JtOujK4o14h/QgoalDCtdWyVAXeNfOpjpJW2TDoICjCbLA6P+RzDZY4Jx2Hguy5nL2nkmYFNeuv68/f3MamY+a9A4EAft3Whi2fDz8ugWtJtxt/m8lg5+//3sZ3Giho5R9pQtoqX5FnlM4E0jgm5/cVJKAck3c1YNXPKlCg81eeJC00qKYcOwEfrhd1itKe8yA/015rBRO3m9BHSSaThrdYeq4BG3mY2U2CL6QBZUDpovMmPbSxKm2F2jflE1ZiUMY0SZbP541dIs+QJmpzGDyQR8gT9A/ZA0Jlht9zgoEcB3U014lzIw9wfPysHifKZxEoCAQCJpFEcFB5krKldkMb/2kQ9lFTEza9Xlxqa0PHyorxV/WkHN6H60Lf93pTEw6CQbwRiWDrz//cxmdO20g66/+qu/g+56O0BWDjKdWH5CeVB8oMZYFy4PF4DJ+q387gjsEhjwsl/XmkpTYzVP9Mba0C3JRr3ofj00oTtWscK+/ttEvA0fYTvk6e4v/UY7TvwNEpJ5w/5ZE8SyBHn+lMYFBeyUPKx5o4UH2nQf+jYiy+xwoqggsEAThmzoF870wEcKz0cxUAymQyJjZh9a9ufeO8UqmU8Uc5XuoW8id9Hgb2HD8TaKqbmZxUQJn+IMfurHRU+VQwh/OhbBEQIe34Q75xuVzm+OcvSfLjn7s+M9CwePo0Pm5qwvn5eZxZXjYOjxP5SSQSAI4ykFR0nLCioB+OjharCVpa0H77tk1hUEm43W4DEIxMTSEcDuP0gwe41tmJ2p0drFZUoG5nByvl5RiZnMTwygqsUpkln0mGPbO8jMfW14uMVHKkntjcxFPb28Vxls6NvdHbi/1AAOWpFM6triJYWWmMhTIz50w0XBFQNQhOdFNRM1UQVLo0fnTatBmT7udngzY6TXf//b/HQVkZJoeHcXZlBdvb28b4VVZWmnGzWU06nTZGfGNjw8yJwbdeChKpM6sKQZW//uZ7qrDI7FouzTn6/X6Ew2Gk02ns7OyY5y0vLwM4OtaHhpEoZi5X3AM4dfo00uXlmD5zBgCQLi/HXiyGgtuN1WefRSgcxvSZM+i9eRPtt28bJF2VE8evSKzyE2kxeeIE7g0N4eTYGCb7+5GOxeBNpRDc2kLLxYvY29szWT86QzyqLxAIIBaLGVo/sbmJkYUFbG5uYr7E/5ppPX7pEmbPncPg+LhxjDhOyooaA2audF04H/5QjlXZ5/PFTuua1VXjymdxbckLPJaTr+vYCJ7x4vvhcNiUyKqDpM+429WFH/f14enlZdNc1u/3o7Gx0QaWkE7MildUVCAcDhulrDypTsZHzc2Ih8MY6+/H0Oysuaca4D/09VOfDymvFz+wLOz/9Kc2/lKHBjiqtNBghOvs1DEEE9TB4loDsPVo4bqpDlfHWR1hdag0WNc1o04ir6sjRqCCfMvP8zkMypl1UgdHA26O51E6lvPU8fP5jzKovCf1OmnOsWgwomPRzBC/Q/7WwF7thdJEdYzqGq69OsHKF/yf31OnkPxBWSb9nECIzgfAQ/LP/+lU8Yd7STUozefzZl11644TjKFTrckD0tJZbsp10kCEc2CApvfSpIYTIGYWyAm+0fl/FKhEmSAvulwu2+lJ5Is/drvxD4UCeqamsLe3h3tDQzi7tISOjg4bXzJx4vF4TCKDTiD1tQY7XAfaNPIBx8qsGv0O8oIGkyozvLeCBQp+8b4EmBQkIl3pc6i/QvunICf/ZwaOsqHl0GyKx/f5XQVDGcySBxjYqFyrPlde43g0UHTac9KZc1ZfU/WM6iClsdoFDeqcFXl8lupk1am61UNtYzZ7dOqJAsl8TUEdjpfAGOXrUSdb6LqSvzgfgj5cI+UR1R+auONY8/m8SfhwXD8H8A+FAvryebiyWcQuXDBrqTaJfKO+JX3l+t/9DitPP43Gjz6yBTm8nL4oGww6+VzXUH0k9V/187yctlF1qPIc39P5kVfpkzh9SwXCSQu1r0pn5Tf1uxQ04Pc4Xuopp0w4bZfyo/IzQQ/eg//zfVY6KLhPWwoUfXSPx2NK9vUeXCO12zoGJ60UWFLwkONngE2+0cST+ofqKzh5m2Pie6QF6cbPavU+vxcOh22Vgxo78X6pVMpUqDt5Xf13/k+e4TMYA3LOTDYTBGBbAud2av2O87lOXnb63xyHgjL83r/zevG/dMjBP3d9ZqDhg/p6xEMhXKivR+OVK8b51cY0uh9eA3M1Cuo4jE5P42pHB0anp03wSCIqMnpudRXn19aK9wkG8cTmJp7Y3LQpctf8fJEJKyttBCODaJCkTEumUjTt3OwsLra24uzMDMY6O3G5vR3nZmfROTaGZDJpmvlx76L+VFdXG8VPtF2ZQVFAzWRwwbSZohpvVaqkk5NJGi5cwMrTT+PE5CRcLpcBFNi8hUJJgWEp497enlEcPBlDm5XwCEcyJumoJWcqxOqMOh05VdY8ds3j8WBpdBRz586h6eOP0Tk2hmw2i1kJ+g4ODhCJRHDs2DHbazs7O2bbxOHhISorK9F19SomR0bQe/MmXJaFsZMnEZ6fR6KhAc2ffIIHjz2GvYoK3B8exqm5ObPfknSkkSaqSN7lGtPxzmQyuPP1r2O/rAx3T57EqYkJXOnoQPulS+gYGys6hKXqAtKdjl1FRQWi0ehDWQqCJe3t7SarkMsVt60MTE/jzPJyUa7c9pJh5zpYloWOjg5MTU0hlUrZgga3241Tp04hGo3iww8/NE1+eB+Px4OKigqjWFQxOUElDXwoZ7rGvCcv3o8XeVIdWA1i8/k8rnd1YTcYxAf19RiamzMGjw2SuD4MdGm4SXPNMNDh5rMB4PzcHD5qasLJu3dtjozqhz/09cLmJj4KBPBsPI7z58/bwEPSQWlFI+R0jkkj0kAdEB4xRedP9QbXhTpI15H6hzpeDYw6Tyobqsuo6xgQKm/yHpyrBgrKA7ovm0EVHUF1APl93VKmjj95gTZKnU3SRbOcpLc6G0ojXRs6c+rQ6XfpVFH/q8OnetTZ70f5U51Uzb6QRhwLaUSecQYoClqpw+FcF8o/6cHx0F7wfd5HQRoCNJqtdQYq1HEa5AMwmS/STAEQfpZVeOQf0oT+gjbELRQKprqGDi75Wvv9qK3i+JSmuVzO6Hzy3Fs+H37q8eDbbjfKEgm8UypX5ft6D9LfsixTkko9qfxL2eN4NKOlsp5Op22ggMqt+lYEtnw+nwHzdf0VkHNWeCmf00dQIJPfc4JU1MlMklA2WJmgzqvTAaaTzDFb1tH+Zc5P9ZdWZ6pO1Myq+iMaaHAe3HqpVV2q/1UXAkdHKWoVkeoGJoLU79Rm1OQhypDH48He3p6xv9QFlA/yiuoKDWo1sOQYCeTo5zg21Sn0P1XvauDKsRMkUf1GnmYZPfUSgx2Px4M/f+IJ7EQiSKXT+NpPfoJbt27hAPZG6KrvVV74jKZr19B26xai0ShWnn8e9wYHMTA+joHpaaMHue5cP630pZ9G/aGAs/oNTMoQYKHMaXCltljBB/XxKXsAbLJJ3Um6aqzEdVJfTmnA53DdNK7hvFgBo5U3/JzyhjN24KWv62vOS59N/esMThUMUlusMY3qIZ23+n5O3az3UZ7R6m4nSKTxIZ/BjL8z4aZggvImfUfeh34UdRa3k7tcLtvWHuod6iHKpdoD0lT1qvoBTMyQj7nGpEskErHpHm4PIo9TRypNNWbk38643BlPcE0VGNW1+izXZwYazk5P43J7O0amp015uzpKHLiTkUlYTpgD9/l8eCGVwtOlMnZUVj6EwD3qnhRuzTgpg1EA+Dfvwe/oPZzMTUHtHh83wfp/fPZZ7JaOCvS9+Sai0SimSsd31tbWwuVymaP95ubmcPXqVcN4ahxViQJHgqdOhypeBRU0OAIePjqJV92nn2JgehrNzc2IRKNmHOl0Gpubm0aBer1ecwYvS4XKy8ttIMfiyAjmHnsMkfl5JBob0fjRR2i6etW2NuroqqLRbJMyMRmZhjQQCGDx9GncGxzEoWw3ODE5ib29PaysrAAAampqcOzYMZSXl9sqANgde3193ZwXm8/nUXfpEpqvXzcK8ckLF2wdwEPhMO6fOoVTk5PG6Zyfn7eVfKmyJO9q9o5Z8t6bNzF1+jROT09jYH4e9ZcuFWkqyL0aZiqAnZ0dVJZ4XrOB+/v7GB8fRzqdRn19vQF+tF+HZu20akjXgvTlmqpjS2ebSolbNyg3NLwA8GldHT6oq8PA+LhplqoyROVLZ1h5nc9So0e6KoChVU4aZPHvkdIRsU+trhr+IXqu4+H31YBwDTS41BL0QqGA0aUldNy5U8zYuu1NzHjfP/T1XCKBl6emivd3HTWq1B/N9NIxZVBNY0EjQx1G/uTnMpmMzeDoOus6qfOvcs33yMO8t4JKTv2v2UqOUXU/QTwNoJwyxwwbHTnV4RpI0yFhrwZ+n/NTumhGknMnbZzlp+Qlfo4Bp9MOOcFf0ksBt0dVotB54BgYWGkAo/RTu8f78DWWQtMxJn2dAYSOleMir6keIQ0pJ5Qz7RGhwRntucfjsTnY6iyq80Z75ty64ByngiLkWwKvmqGn48a50w6QP9Q5VUcMOApW9NnUF7rGCkb88PXXEQ8E8D8mEvjm4iKuvvoq4qEQrnd3448jEUMvZ68RnYsTwCO/ch0YPFCva+UGZdkJ2GrgSZ7nvbjWdMqdOkAdX62Oof3QLbEayDFQU7nTMeRyOVRUVJjXCaKonPE9rZrSZm0cp+op7cugeoGfpZ7nfXXc5C9taKeBJGXdeeKH1+u1BViqQ6inyU86Dq6D+qbUTZFIBGVlZeYzmjVWEJNy4gwE1O9S28jPkwakF7cmsUKJPEOepT13uVxIJBK24/ZIL4L35BG3u3iaCo80TKfTGLh7F9e6ulD1u9/hxo0bRi/o+MhXTtl3Bp+VlZX45OxZxH0+TI2M4H/T2WnWTxuJK1/zGaxyoCxqQKmgDwBzxDnHxrmSF2ivVA8CMGA4baHqNoJPh4eHhpakhfrf2ruO+tupJ7WCiXQif5AWHIP6Z/ys+laqZ0kvXRsFqshnHCt53WlPFOTk/06/z2kX9fuUWX5PfUL6teoz6lw0ZiJt9T3eh99X/an/O3UB56N+ivKn096TXgpQKfCgY9UxkTbqwytQxPFptaqCh/yMbnFlgkF98UeBO8CR38gqCY0jdG0pK5fq6/FhQwOeXF7GT4eG8Puuzww0+Hw+WG43QqGQ2Yuii6GE56UToVJSZleGpPHQzysjOB06dV44Bi6w/k0C8buarVPETBUMg7BEIoHu69dxd2AAw/fvo6mpCR6PB9XV1aYRXjabRTwex/379xGLxRAKhWyOOQN9Itscy6MEnXNVhaivk1GpKLPZYsOhzSeewOozz6Dxww9xrBSkb25umjFMlypGuEcsm81ie3vbZB739vZM4ByJRBAIBHDpySexV1aG/bIyFNxuLD/1FHru3TNImmVZtrNwOSYFIh61Vvwche/94WGkStsNQqXtBuxOzH1VDAbYNEdLkHjPvb09VFZW2gyLKmm3222czIHpaQxMT6O8vBwoZT/YOZyOhwYHNTU1iEQimJycNM2s6LhFNjbwx6urxaM43Uf9K7QagOvg9/txeHiIxcVF+Hw+M2bOg5/hfmdWxwCwHRmpIBV55G5XF8b6+/H44iKG5+eLwl1SHprNogIi0qr7uNRx4Zgu1NdjKxjE9e5uc5oLP6syro6+gmoaRKjRIk+HQiFTccH7qd4AgIGZGTy+uYlIJIJDCUgfJT/8LueqTjPXVZ1PNRbOQItj/Je4Pqqqwns1NXhhcxNP7+wY502DWjYOKxSO9ufSUSEPqKPslC8Gg2ogeP1TBhewBwqPCrTUMKru57owsOFcNABRp4F8o0aepfTayBU4KgVVI8s58kf7Daiz5XIdHbkHHGX9tL+H08Fw0oryrrZFAz/9rjoJys+kF9eINC8UCjZgQLO4vIdm6rT8WCsAFOBWx4hyqZkNPlOdQ5UnXrlczjjB2teA9FCZ11JOtV0Ejfg/HeiCyDL1vDrFOia+z872CqBqoMUASO+tfoUGbhyr+gzq3Kud1jXf2drC73w+vLC7i76+PrywuYkLfj+e39hATU2NaXCtDjVw1PiSgYQ2/1IZKhQKpnkxs1qUZdKC9lr9KvKds2Ep+23w81xzBazJO4FAwFbu7PTRlL+1qonz4me1D4HqVN2yotlB0onvl5WVoby83OakU3YpA7q2mm3XSgP+/yj6UK44Zq0s4proNlnOhc8mD7JJsjOI0pORtBqIgbuCJNTRzgy66hjKuOoWXRc+m/PhGmlV1P7+vg2IIy86s5wEb7XikjqJcqpAWjgcRiAQOAKsZ2Zw7KOPMD8/b7PBCiYpaOasuFFblk6n8W23Gz92ufCdUAhtlZW2/jxazaV2h3NQf1PtAoFd+pP8LPmK+oc8ptlklTkF8/meygjXuqyszKxDoVAwlcLZbNY0QuXYNeGg8sR1I03Z6JiVNCrnjGUoD4+q5uTnM5mM7Ths4OiITSYsnLZCEzE6b9oI+llqE3RtnP6WVsSRvxQg4efVh+Rc+L+Cfaq7eDmBWrUTOhdn3KV0JT+RJ5zfccbDnL/SW8emY9XxarynYAFpSlop/ZWWToDVOW/VV04fhs93fpa88Y9/9mdIBYN4uwQi/77rMwMNn7a1YSsQwCfNzXhya8swMh0W5+CU8ZwBMxWCOiTOkhtO3ok68hnAERjBsagh5XOVQAouuFwuXG5owKetrXhsYQGnFxcN46yurpqA9nMHB3j83XdxcHCAtVJDxUwmY6oYKODHjh0zRouL7PV6jWFcPnMGS08+iYYLF1B/6ZJRrkTDlWa8lAmciCIdP5/Ph4kXXsBBqUngyQcPsLi4aAQ1lUqhUCigqqqqGFiXrt3dXWxubmJ9fd3QUA1m++XLmBwZQdnyMnZqa9Hy6adGyRGIUYPpBIKI1lIgVLlks1kEg0G0tbVhaGIC17q6cPzSJXTdvVtUFI6TCwjghEIhm5C6S8BXQ0ODEUY6SeSXeDyOQqFgy9LyHpotrqysxPr6OiZLlSyku9frRV1dHYLBIA4PD03XZHXiGcyr46LIIwEYOjJ1dXXGwO3s7BhlQWPV3t4OoFjFoICQAjlOg3y7rw/xSAQfNzdjZGHBKBV1KNXZ5n00QFQe5PyeWl7G7+rrcWpqyqYP1Oi4XMWtIKyK0UBC9YQqLf4mWKd7WHl/NZjkm3w+jyuNjfiosREn7941zRspH2os9vb2bPs2qTO8Xi/Ky8sfUv4aDFGZqwH4Q17v19Zi3evFu1VV+JJDdsgzDCbUgaFs7ezs2DJtpAGvfD5v+JKGjuvPoJzvcx1Y3mlZFt6NRvFudTVe2NzEs/G4Dfj1er14NxrFO1VV+EIigadKPOz1Fo9lei8Ww9uVlXh5ZwevlPiWp9swc8atcj6fD+/FYnirrAxfSCTwvByhyvm8HQrh7YoKfC4ex0ulTvTkn09qavDrSAQv7+7iya0tG62onw8PD1FWVvZQ5sHn8+Efw2G8VVaGV0r3Jj9YloV/DIXwRiSCVxIJPJdI2CppGITo88h3tInqVFMX8n91eN+NRvFGJIIvJJN4WY6143q95ffj54EAXk2lzPvqfFHW1cFSh5L/6xnfWtKtOtsZYKo8KmirTbf4WTq12l9B+Yz3p7+gwQJwJJvUy48KwDOZjAnk+b+CAPQr1P7zM6QRdV82e9RLQPWD+iZarUPaPJ9M4oVSVWMhEMArh4d4+cEDsxXOWanAZyuPkAd4OW1/IBBAPB43PWaCwaBJLDCA0PXjj/KY0o3j1y0ZmpnlmuoWFud9FaDI54vH9iWTSSSTSXMSEO+rwKkCnpyb8i7/zuWKp0MxAPT5fIhGo4anPB4Ptra2TGALHHVB/8dQCD8PBvHC5iaeKYHS4XAYvw0E8FOfD69nMviiAM3OQEXBJHW2mYV+1Hh5ilUkEnkIuCF9fmlZ+Lnfj9czGXxZ/IpCoYBfuFz4sceDr2YyeEUqhCivCgKTn9WPoQ5RuSeYpVWSTtlz2m4FNbSKgoG30oPvMcilzL3p8+FXoRA+n0xicGYGn376KZaWlmxAN30g8rLqTqc/Ql1LfTA8P49nYjG8HQrhS/v7+Fouhy+UZFUbECt4TT+QdpKvM+FDHtWqNs6fOkRpB9iPPuZv7TOmMsjvcutRNBo1/jJjIOCo8oigE2VUdZAml6if6P8rSKL6U2Wda6/8rQCeE6jWbDl5gbqBfpaCeZwrX9P+Kkov8jIvBeAUzHHyAcfC+5MevJwgohM0UB+UNOBaqJ1QOaCNUFtLGeAz6JvzHrrdSEEZjtE5HwWTSGuOT4FdTeDpmGkPFPzQWFrXUMehY9AxOT+nFSyU+0KhgJN37mDs5En0jY0BX/kKft/1mYGGMzMzuNjSgnMLC7ZjAslkSgRVTpyIOjJkegocF4j/60Q1G6EGVJFoNbj6OQU9uMhkDpfLhU9aWrATCuGD+nq4f/UrGxgSiUSQzxe7hSaTSezs7GB3d9cw2Pb2NsLhMBKJBCorKxEKhUyXXiLGVIIHBwdYefppHFRWYu2ZZxCNRvHg9Gl037iBjjt3HjJuii4rMgrAIP5Lo6OYPnMGQ/fuoWJlBeloFOG5OQNs7O7uIhgMoqqqCuFwGPX19bauzNxfVFVVZWipBqv99m3UffqpyX4XCgVYJYNKRyQQCGB9fR07Ozu2LJtmVNkpm8LL+bGBy9DsLFpv3CjSrrQvO58/KuWj86H3VV7b2dnB1NQUampq0NjYaLpya1ZDkTtemlGk0fb5fDh27JiNlwhmMIukWSbLskzHYLfbjYu1tfj0scdwcmwMg6WjF9Vh1mxaJpPB+vo6IqVSW2YbtHKBdKDDQDlQp5gyODA+jtsnTuCxxUVboELedzrvTkBBHV2V5zMrKxheWMD29rZRMhpMEVDa3t5GMpm0Zc+orHXcVIQ329rwaVsbOq5cQefYGGKlBq5cE+oRZwANAB81NZkqixOTk8ZhVlBCHQHSlzLlVLzOYF5l0ck3f6jr1VQKvwqH8flS81wFELV5oDNg4TjVAdbgmQaKGVPNhANHhjeVSsHv95sGYvqMXC6H34XDWPd48H5tLb4bi9mMvMfjwfuBANbcbvy2shL/6tgxG5D4f3K7sWFZeL+mBv+2dF+nkef9AODtQgGrloXfVlTgX1dX28aZy+XwbjaLNcvCO1VV+LfCD9lsFm+73Vh3ufDOsWP4ZqliS9eYTW+DwaBtLyd58bfBYHEe5eX4Rml7FWXvTb8fq243flNWhq9KtoUZsM3NTdNwkHaQAZrTNrKkn845f7tcLrwZCGDV7cZbsRi+UdK1pBkA/LI0jjfCYXxBdDSPzSorK7M51QRys9ksfmlZeLuyEq+mUnipZLcpJ5SVnZ0deDweo6e1/8E7kQjeiETw+Xgcr4gtVhrqmeX5fPE40d/4/fhFMIgvpFL4Yt5e3aEg4q8sC29Eo/hiOo2XSqAgHTeV41wuZzp3s+cQ98RyPAS134lEirQqATOqE3w+H37t8eCNaPQhmrwdDOJXkQg+t7uLZ+Nx29Hbb/p8xTPJ9/fxipT+M2j7pWXhV6EQvg7gKw5HVwM50oFOtdpE8h4ddA0WGQhFo1FTAab3Jf15P01e6G91StVP48lIWjmlPKgBtFZI7e7uGjljxaTaLAV46P8RINLgiTqQYwOOwLhUKmWOIdQjJgnWW5aFX9TVYc3jwT9WVuKFVMpkZ38ciWDFsvBTrxevlBogqq6nb6U6VKvdnIE2r0KhgPLycpt9o6zz/0gkgjcODrAC4BceD/6ktOWTz/r54SFWAPzM7ca3Sg3lFIijz6cJEfqUBA51bJlMxvRNUBuv/hB79mjFDflUgx6tAFX/2Wkj3e7i1oI3QiGsWBbejMXQtLWFpaUlAw4puEPQmTzJNaY/qVt71J698847CIfD+P6rr2I3EMBfpFLY+/nPbdUaelGmOV8Gf84AjP4pABOIK7ioW1l0DqSRs6EnacOKPMoGAMRiMdsWD86fv7mlRXkzFovZGiyq/dLqOs6R/ijtv+ppPfLRqZfU5yV/a0acYAorYDSuctpzyjdtCP1jDd6d+oXfUb+Hr+kaqz1wymOhUDCxF++v9oEAiAIZuhbqZxI4cQIDTj8+mUwaXcMxcJs26acAjNq0fL54DL3GxVrxSNlhvMQ5UL4pr4/qQ6J6iGPTyiKnbtaxafUJwTeuRz6fxwcVFZiqrcXLa2s46/1sx79/ZqDhqe1tPL2zUwz+XUdlQ8rIJIQ6esoIzk7SVFzKrCQMYHeKE4kEgsEgEomErYkYlWttbe1DwSUZR8tctaxleGICV7u6cPLuXdTW1potAZlMBqurq6YRktfrNQ4dnRzuiy0UCpguleEfHBzg2LFjZmsJBd/lcqH+gw+w9OSTaLp4EZOPPYZ0eXmxYeHEhBEIZXqlg5ZPUcE8OH0aqbIy3D5xovi+2410Swt8Ph8ODg5QUVFhmJDVAFoiygxkOBxGLBYzQqjKhLR1BmqxWMx8/9ixY6itrUUikbChXvl8Hi0tLYjFYrh69ao5PYJC0tzcbNvL6gyG+frh4SHi8TjipayqOvMAUFlZaXiDgvOo4IzfsSwLt44fx63eXoxOT2NodtYYOp/PZwJeddz0/GWerMC56j6oqx0dSEYiGDt50rbNgIbE5XKZhpJ1dXXmZAdVUrqPb2dnB5ubm6iurraBUZyHznVgehqjS0vFqhXLwo3WVlzt6ECX14veiQkjGwBwr6cH7w0N4enlZVPOp9eDkyfx7tAQzs3NmeolGgHyKXmLYMPkiROYOXsWT6+s4OzKykO8TLkkPa60t2MnGMT9U6fQPzVlM6B0hlXB6xgfX1jAx01NGJmdRUVFhbkvA2zKOyt0yL/kP85H9ZT+rYbqUU7mH+J6IZXCk1tbRWfEbW+q4/F4TJMhddqdqLIaFA0gGGw6wRUngEanVgMP6skv7e/jV6EQvprLPeRAJxIJvJpO49eRCL4OoICjICGfz+O1/X38PBAovlewZ8r4fOoBAPjq4SF+7HLhm9bDDbcKhQK+ms3ix243vinAC8Glr+Vy+KHLhS+XbAJg7z1BfUE+AGAz7F/JZPBTlwtfkX4+NLCvZzL4KYCvSokqHSfLssyxu+ok8NkEPPW8eGY+dXyWZeHrhQJ+kM/jmyX9wovf+0Y+j7/LZvGN0n0pI+Fw2JzPzbnRkWUTqX8EsOrx4M1oFF8VgFR5KBAIoKyszIAfSoc3s9kiCFRZiX/t4FMGf5rx4O9fZ7NYdbnwm/Jy/JnbXqGowd5vDw+xCuCtWAx/Ul5ueHe/VLmhgQ/nblmWCWypL7WU9M1wGKuloOfbJXvNsblcLvza48Gqx2OeSdn6jcuFNZcL/1hZiW+VaMqxvgFgxbLwq3AY3ypVzZEXDw4O8IbHg2WXCz8qFMD8juotlUHKHwNJ53pTz2pgoXxNn+JR4MWj/CjVZUp76hJ+RvexOwMI1cnqW6k/wHtqcKlBke5N18yn6mf6HuRTt/voVCk2OyMATz3u8XjwhWQSv45G8VLpKGjK6Bf39vCrcBhf3NszwQ/HT7BEG+aqjXrUVh515DUQ47w1AAOAb3s8+NtsFl/P28ugAeBbbje+n83i67DrQ83CAzBZa2egRR2ogD7Hqfqer3HrE8v0Ndih3tE+MfRbyDs6X5UNj8eDb1kW/qFQwB/h6GhjrVxh4MQAXNfP7/ejtbUVZWVlNv9OYwW3u9hH7POJBN72evHM9jb6+vrMSTi69YJ8yX4R9NGoq1jRRz5i/4V0Om1kkXzFOdLX0eppPsct+o1rozEI70uaEnggfzuz5hqM8nO0ZdT9Ci7QH9PPKd15KQipYKJzHvyeglUEZVRGNHOv49BncLuzNr9kxYeCoFpho6CCAptKZ77nTOJpJQwvpx7jODSRxfGr3Kjt4fc5Dn6GFViUUU3eEAzQZKeOO58/SrDz85Qb0t5sRYI9RuR3dVuKrqlWJqnfq/Kh8yCPFwrFnj5MplJH8rO5XA7/t2AQG5aFj5qa8F1JEP5z12cGGi5UVuKT5macn5vD6aUlM1glEAdCBIygAH8sq9gJn4RUY0kGVMPMZjW8EokEAoGAOXlAlWuhULAZYAA2RlSnhQavb3ISuXweN/r6sN3cjMVIBB1Xr6L1xg0kEgnTQDEcDqOyshLV1dVm3+D+/j6Wl5dtaHg4HEYqlbKVLxJZHJyZwam5ObjdbsRiMdw/dQojDx4gHA5jdnbWOIjOfVRqrPmax+NBdGkJe2VlqNvZQWhyEhOnTqHn5k0bGhsKhcy2Aw2+GTCHw2H4/X7EYjHTrEYNvqKRj1L8mslbX1+37Rfid30+HyoqKkx5G8cWCASM88592U6giIqtpqbG9L7Y2Ngwz6fBa2lpMcbCGZwqj/Lv2319iEejuNrZafoZ0MlSZ83pUDiNPWBvXDo6PY1PW1sxNDFhlMq97m5c7+lB/507aL99G/v7+0ZxqnLQQJLKhH0clpeXkUwmEYvFzNGY/Jw6QWoULh8/ju1gEIcnTqDjzh0bH93q60MiEMDv6urwRDJpZIW/b584gVQ4jIutrXhya+shR/dRge6D0VEko1F85PHg/NqaLcDUdaBSf2p5Gb+rq0P/xIRZZx0HaUMZVj0wurSEJzY3i93MvfaO8mqk6CgpIqu8qwqejiz/V/r+S1xqWNWgc4w0MrplTOnjpBV/qzHjaxoYqL5Vh1jvmc1m8Xm3G18tZdYscWp4z1dzOXxXnAHlny9ks/i6w3Hie5wb71MoFPCFbBYvptOorKy0AdWc12sAXojHi0eouY72fBcKBXwJwOdKe0jZgFV1gOo9dbz53quHh/hyJoNs7qiXBMGXV0rl1urIq445PDw0JyHoujoztI/q0K1O1RcBPFfK1ucBc7IRafAVtxvn19ZQXl6OnKwr6aOOL/UVg/8vJhL4VTiM10t2heuuwQppovvd+fu1RAK/DIfxLYfOUdBAgzPy9VezWfzY48G3PZ6H+Frp/y23G3+byeBbEsDRxjEg0Iw4bYaWzzr56csHB/iJ14uv5vPIiq7ifb+4t4c3IhF8U/SlZVn4ejaLv8vn8c0SqOF2u41T/7X9ffywUMA33W74pbkq6fkdAN/PZvG1QgEuy155pjRS/qa/RNlQgBOAoQEdXB2rU771deVxdZZVV6jt4FicQIPOQflGdTLXxKmnGEg4q2f4N9dVS6dTqZSRsWAwaHqpkAZer9ecRc9jlEm/F9NpvG5ZiB8cIC+A5OcODvClQmk/vPfoRAdu7yDfKvDCNXcCJmrvSAe1NWrnSLfXLQuveTyIx+PIlwJvXl9xu/GFEl/n3PbeVqQV/SD+T75zNqZ1rhsv5TOtFOK8XS4XotGoeS7BASdNqM+Ul/V5X3G78cVSlcpl62ibgdom8ovyHa9QKISp/n58UF+Pl3d28OTWlrF/DKI9Hg+eTybxbGk7bL5UWcsxapKKfKSBNkFfgrIu11FlGYNE0obVTOqXOCtfyOf6PfWVGQc5kxqUR5U3DXrVx9bAkj6KbmsgMOc85UnXTe/H+/Bv5TMFAiifrMRTkI5ry/szUUiac460ByofzvGpHlNfQeevIAfHRFrz+dQ15F3t7+D0sfi3VmE/aoucgiYcl9frtVWGcE7UR/n8URU+ATfyrwKVajvYJJ601hN4NMFHG6D20MlXwNEWbKUjQXgFkZS/gKOeM6zaVhCJ98rlcvizgwP81eEhvhsIoLGkO37f9ZmBhg8bGrAbCuFCYyNab940ROVvp4PPDrTcX8iJc28xA3NlTg28/H6/Wai9vT3s7OwgHA6bpigsvZvo7cXtvj7U7e5iMRLBwPg4+u7ftzlEJJhmEDieG93d2I1GkQiHkbcs3B8exsjCAo4dO4a6ujrDwH6/HxUVFcaxZKkpy2a4P52ltAx++D6PcXS5XBicmcHzySSuNjXhH157De2XL6NzbMzGvPyb5Vbb29tmztlsFvGGBuQtC6sVFXj57l10jo0VGaeEenq9XiQSCSQSCdTX16OqqsqGSCcSCUyWTl0gkqyBldt9tMdSg1k1bMAR8s/sC5UnM1But9vsl9SyYlaPMHOlKKsq31wuh/HubkycOoVTExMYXVqyZUCoeNl1nQCGBj9UUJzH0MQEbnR348zsrE3xEPUmT3MMpFssFjOGShUHacFtIF6vFyjN5UZvLxLRKO709+PU3JypiHGiqaS5OqHj3d346PnncfLOHVRcuGCUoWZFVYnzJ5/P49zcHC61taHl2jUkEgmTQQCA3ps3MT06iieXl00DSw1aem/exIPTp3Fubs7Ii9MIaODPe06OjODJlZWHjK1epOu5tTUMLyxgZWUFOXk+ac7nPMopUWdPP6uBDL/zqB9VtjRaaog4Dn3mH/riWqujr3JFh051gfNHDaLTodSu7OpYEshTx1nlm064Zqw0u8Ex+qUMWOnNbKQ6v+o4/yiTKVY7FAr4susoe8GmUwyQNXikDqaj7HTu+VkFGXgxcEin07bjdHkxc6VyrM6Qc97ObWa6n1Lll3RQ3tXAhHNTh/Xw8NDoHy0rV4fyUZkfrrWC1KT55w4O8GWuodd+jrk65pyrZo8KhQK+kMvhWwDc1lFlkgL4fI0XafH5bBbfKJXS63g4b87hdcvCi5kMAm77Niven309NEDVoPpRgdNT29t4KRAo6g+KpKcAAQAASURBVGuhNen88v4+vh0Oww17BvHFvT08k8k8tH3O5XLh5f19fMHjQcBtL6XPZosnOXzdsvAainuZWdSrsq200bXjRdnTrTter9e2d1rnyHup36U6zykDSnddc11P8raurwZw5HEtsadDr9UwOiZNHjDwom4jLejXJBIJlJWVma1Aeh4958uqIJULyiV9EScwzbnT/6PzT3pS53B+HBf9NwC2hAtpqnpYwR4tQ+bFKrVHnfqhsqT7xJ3rpbqIfpPSXAMtfkeBTt0WQJ3BddKgUWmnYKLT79C1VllkUKhBp/I26arBVk9PD/6uqQnrbjf+8dgxfL4EMoZCIVugpQkC9h8rFI4quKjruRWDvMEtElxPgga8p+p8rXhT+jOopG/PdWUMo/cgf7JUX3uxka78n7QiYOC0I85mlKQFx8i14T1Jc64n/9fq2Uf5jE4wgOPg2Cnr5DH1IZx6h/PSoJ7rrryi9CC/83/16WibtO8L5ZCyw35MCnByDRlEU7aoA5zbI/gd9b90XE4AVX0wBTWcfgIv3lt1Dueo29a0MTj5Sf1ePkvBGKW1+m7UiVr5QN2g66dbVPSemmgFgJMAPhcO4z+FQlhPp3EGv//6z+rRcLm9HWfn5lBWVmbLCjpRVDqqkUgEBwcHSCaTRpAY2HICTkSfk2VZSC5X3GdVUVGBK42NmDlzBn23bmFgZgaWZeF6dzfioRB2AgHkLQs3enpw/NYtmyPFBacgc4Hz+TxCAwNI9PaiLplEEsDJu3dtJXMbGxtmoQF7KTCzTjwGyOfzmd8ej8ccuRaJRGzlaLzX5ePHsRsOY3J4GKfm5jBZ2m9OmoTDYbS1tWF1dRUbGxtIlPZzezwetF68iOWnnsLZEh34OlFgIrLMjmmJPwBUV1ejubnZNLBSwVVmczr9pCmZmYKne445Fj1XliVqpKGeZ8u9nU4FyPFMDA0hWVaGOydO4Jnd3YcEjkc2raysmPmqkxSJRGxBa//UFAZnZopGDMCt48dxvbsbJ8fG0HPv3kPOIR1dDcqcPUU4d93Tl8vlMDQ+jus9PRiZnDSlv3S2iLiTrjonALjY2op4KITb/f145do1W4CkTrBTkRUKxSMbn9rexq1794AS4MPvDUxP4zvRKPb29nDVUQoGAD337pn90iiVgSn91FHjGHru3cNziQRqampQcNvL0JzOMfnJ6bhpEEadwGfr8/kenT11lpUmNAZ8jip2rWBRo6cBLHXZv8RFeVDHQmnFAFKDUwUi+LoaLDolehqMZq01M/aoufIe1MG6hvytKL+WBDrXW9cJODKwP/P7sWJZ+GE+jy+KgWf1Dnvj8FKwgrqNa6mXZvWdQaI6iGqc1clggEFglNvmnI4b58QAU9dFAw2tonHSwJld5ucODg4QCARs52Or40JQROfGhpcEoDkeroMTRHAGCcyg8LhQ0sbp4DhBMGayVU/ybwWMnEEtv69/U39r6TmdLI6Xjhu39wUCARNocH506DWjoyW6OnZ1xAhWp9NpW2M/pScr/jhGDZZ1vHt7ew/ZWifoRno5ZYp0V/6gM6xBkfKN016rg0wHUQF+p1/kDK44XuUB6g8Fuvg+TxpwyjznwLlpAM9KBQaECuIVCsVqBW5noGxyfXWtqTfI2xqcKC2dQAzlyUkL3oeBnRPopZ5xOvdOHaX6mnPQIE39KpVJtX3q/HPt+QyVJ64tX9PKEdKcfoUGyVwj7XXm1FOcN/lLddWj7DH/Z0ZW9YzLVaxipb/JCuH9/X0Eg0G0tLTgjz0e/H2hgG+VqljJJ0pvXpy7+qH5fDGbzMakqs+oO9knR30enRODVQXv0+m08aETiYStik1lnAlBv99vAO10Om14mbZYQW313bWngsfjwVt+P34RCOClnR08tb2NfD5vqwRmpbLf70c8HjfxB2MdPV2JNHL6mgxCtSmu6kjGFFpJoLbg/0fbfwZHlp1ZguBxCXeHu0NrBIBAAAioCEQEQqdgMsmkyiQzk5lMMpOyyK4W0zvzY9Zsxmx6xmzadmZs1mxtbW2ne3d6q7qKTS0zqWVRpIzI0FoCCGgtHXCHcn9vf7ifi/NugFVZZuQzgwFw8d693/3E+cT9Lnme/KsBA8oe50d5VZ5RnUjMo3pK5U5lTRMSxCLcSqdyoDbYxpi27dLgpdJCZUF1MvUa1572R7cGqQ2hLeK96OtplYpWh9iYVseu+FfxG9eer1Gf2YEXxbxq83UtqCdUT1KmfvjYY1gKBvHDQAB/g3/6es+Bho47d9B26xZ8Ph+WRCFxAiRGOBzG7bY2XOnowCOTk6g+exaZTMYs4traGlKpFAA8lPFRcEuDvr6+jnQ6jeLiYoydPIl0Mon7hw/j0aUl+Hw+HHvwAFfa21G/uorRWAw1b76JiYkJj+PL+zL4QeWVy+VwobERjt+PjWAQz3/ve3mFLHvE2PCR2z24SOvr6xgaGsLCwgKqq6tRVVVlmF4VHPe+kl4KNo8PD+NsczN67tyB3+83jiiZlo0C2YOCSiMQCKD8wQM8WzDas1Z0Sk8ooHIdHx/3OHbb29ue85vtCKcynGbReFQZL+690j1yFAq9tzYY5Pz5m4qCQni1uRnXOjvRdf062m7dQteNG7h/6BBOjI15hFXXgw4CaRYK5U/cmJubM+8pSFO+uNTWhpVYDFfa21F99qy5L5U/hV87vCo4sIGs/hwcGYEL4GpXF0LBoAmQBQI7R9Np+Z8qxhMjIzjT2IiDd+96Smf5HNtJ1vWlPOml41UHRAMovBQscox8RiaTMcDBzqADO8pXx8lLeUnHqfPQsfJvzWJRSXMvGRWkDaDtTv8a1VUjovqHdOAz9ESdP+dF46Ogj/RRMKByoUZGM+qAt1s+K8l40gM7UrNrNLBTbqigMZvNGnCi49IADelhB73UYQcePiqTa/6i4+BVnw8vON4sOPmKINAGM8DONjTaG16ctx0U1VJXHhnLsXC++l3qKgIvDYIrfxJcqROroGV7e9uzjU8DSbv9Jr04FjuAz3WmHeR3FThwLhqs3E2GlL+57lr2SXniWPh8m7bcL28HeDgONtPie6qbCIToJNIJUtlXB21jY8M4+cp7dmM18mtZWZnp6aNrYNs18jXHq+vP55DnuT1AdaQCV46B97adLHtrmDrydom58jwrWeissMoBQH6rjQBofk/32WvggQETjkMBqgZceJFndqs+0AAo6WJjB52HrhNtH09woiNE/BMOhz174kkHDcJwu6XqF86B+Il4hbhUg3dKA81gU36V522dQrnfLWCj4+GlNOV3OF7baVK5JN+qTOlz2PNM702+sgMovIcGX9SG6Gd302nk8d30B7/HMRCjakB7/tQpLH3oQ/jE+jqecV0kk0nE43Gsrq7C78+Xjz/v9+OThbXZdl2P/lanlWukiTvFNeQZTWxxHEwEafDCdtbsgCf7dhHzaPBDZQWAOR6WPK2OJqs4NGikv3X7sOu6+PXWFmYB/KGyEn9VUeGpvstkMsamEYMRc/MIUw1SadCW62sHN5RW6+vrxmnXYCf5xsZZts2iTBFnEK+Rh4mrVVfy3roe6pvotgNtJmsH7Ehr3bLPcWmC3MZVXFP9vG0vOD47oagndjDRSz+McqJjJJ/p2JWf1KfgPPk5PUXJ1inUGcqjuiVY6U2sYcuq2lMNHCmdPrq6it8Gg/hQOo33cr3nQAMfolEa/s/GZRzY+Q99CGvFxXinsRGfCoVQVlZmFndzc9MIHMEYlS0XXh1Cdrv3+/35zvpdXTg8MGAE4vDYGJ4sdCFeWFjA0tISAnv2YGlpyYzPcfLdW/ft24c7d+5gZWXFKN29589j/NQpnBgd9YA3EjedTiOdTptMBZ1Cvz/fRIvnB3PhlQG1VFdBJZsRnhgdxRd/+9t8RCwYNNUQBA8EN7wvG43QKJNe3LbAdaIyYoalpKQEyWTSo6gZ8dzY2MDy8jKAfAWFBknSBSbiuHlf3kMdC7t3AB0bZswqKiqMgKkx4lqr43OjqwtrySTuHTqEk7OzKJ+expNvvWVOu1AgTUXK8ZNeV5qacHbPHnRev46ygQHzLHXkKdyH79/Htf370Xrt2kONL7kGFEo9Io4Ahgp1N2PtOA6udXYiFY/j4r59ODQ29pCDoQ4BL7/fj8NjY2i/fTu/FoUAj/15vY8qEa4DQcQ/5uDb4II8rM/QaDjlQO+pa287gja9lQ8U0KlOsR0mW/ml02ksLS2Z79PY8l7hcBjT09MmyBkMBg2QphGorKz0GHsFcQSgf6lLzzLXy6aT0pd0JY0zmYzHGebfNOasnAF2wILruoanSUs9opb3t/cAkrfVcCtgZSBOA6322vv9fnwkm8VHUNgaIvqI/MBLARF5cmZmBj6fz9OMlXShHaL+5HsEJLq2fBZfZ5aczyIwUmDht8ZKe0C+VJuhAQACezXavI8+izRXJ0kdDtJiN0eRYIJOEDOyfJ+Op5ZlE0wS8Oj9SFM6cyqrHOfm5qbn5BPlG1YuquOpsm5njHkPVkKqLPKYR35Gq0jUwQXgkSXNLJMWGiSmAw/ABM8JElXmSHPd183nZbP5Uz2+u7aGz/jzWyfKyso8c+L41LkAvH1g9HOcB+fHY5U1WKEyos8iX2iTZX0GdZ8NqHWrFHUI37P1sGbLbJ2s9NPvqI5OJBLmGey7pID5V6EQfhqL4bnSUjxVyC5Tz4RCIfymqAjfLynBM+vreKoQQKUdDgbzx1QTZwLA7+Nx/CgUwvOhkDmxRZ055Qn+zSbCqn/UmVV55/sa7Mlms1hZWTF4Sh0IOnx28FKTDHYlBb9jBxAcxzHBWbWTKm90doGd7R/8LtdOj/mz+Ys6kBjIDqLZP5yHbmEJBAKYeuwxbMbj+H08jr8W3BeJRJDJZEyFmD6busB+Hv/WNdQgJG0B7T6xqCZFbTnkutkYzrZ/pJ3SSuXQnoMmkXbDVXpxbbiOnwkE8K1sFi84DgIF7K3ft30DDSxQDjc2Nh4aJyusuTYcu+KMWCz2ULJW7arypuI1flYbXtI+sfGnOta2jdNKBFt/c7zqGNPp1q2ePl++ynxlZcUkXcjHm5ub+F1xMX5bVobnt7fxtATwOG7FNdR3nGs4HMYbJSX4eTyOp9fW8MHCgQCBQACvl5TgF/E4Pri4iA8XAj1/iMfxD+Xl+HAqhY8WAjL2No4/JhL4RSKBj66u4gOFPjXkUc5ffzMZRD2vWILrorJr+2q6FU+30+jzyEt2MIc2vigcht/i93/ses+BhuvXr5sF50P9fj/GDx/G8IkTaL982fQJ6L11C/f6+nBqfNwYHxJBy3hqamo8R2OpYSSRCLxcN1/yzoaKPhmLKoRYLGYUDaO4juOgvLwc0WjUGLpUKoVAIIDOe/fwnD9fBTAuRprggH0Z4vG4yY5zrNXV1SgpKTGl+VQCZCIdH+cFANf278dyodlex507CIVCBgyyLIplbqpYFNjq/icFXXc6OnC9qwt9d+5g361b5n4ExmREdfZpGHUN6KAxUKLA1e/fOZ6UhoL34zzJI3w2hYOXGix1uFzXxcG7d82pEBqkUWFQsHChrg5n9uzBkcFB9BeOd3y3qQnLxcW41duLvpERjxByfOSZAw8e4IlUCvfv30dODLwGjuzv8DMa6WTkUA2Pz+dD3927uN7VhePDw+a7lB+9F+ekQJD3sKO2qnj4Wbu80u/3m54QXHPXdT1gnp2YSXuCLTXqys+uu1MGqoZC5Zbry7XVS+mnUXJdf+VT3VfLn+XlZSwuLmJtbQ3RaBTxeNwcmcVMGTvxc/sOj/vb3t42Rzuq8rSzlVTQ71WR/nOvuro6M0c6M6SbjuNP8S17wigP8HM2KLIdcc5ds6x0QmlYNKio0Xg6ZeQv6gbNaqid4Jjs+yh/qf7X1zh/Ohncr6uARwE1gQcvvq9ABdgJutDgarMu0kD7HtDZtsfG72g1iuoyzTLw0jO3bbBF0E+Ax7FwfTgutQm8FCipDGWzOyc4KPAHds5OX1tb82R6SBMeY61g33F2zmzXTDDf00CU6mt7/zJ5nn/TJjNLp+PlumoWiPdjkEhlgaXP9nxVh2im3C4PVtvNcWqfKXX6vgNg0ufDNzY30TsxYWyeOuka6FVdo3adx8clClva+H3q493KrdUW8jUNcPEefF2zmpwf6WGXLPO3Oi3kP11D0kO3EqnsEX+Q/j/z+/FaMIjns1l81O83SRJiiZ+GQpj0+fDjcBjP+f0PORA/DATM6R8fd3eaQvr9fvw8EMD3ALwE4NlC08gfuy4mfT78KBzGZ2QLoR4jDezYuEAggJ/6fPgegM/4fHhOZEITYKQneVJlx+/3mz4fuj6KixT8k/9JI+214vfvNJXjxTWjHtNqW9Ul6qT9BMD3XBefcl18wsIn5H3V48pjNu7gpXNSfB+LxdDb22tOZdvY2MDkW29h7emn8Wk5wpy00GaiqjNU7yo91DG1HVH9DDE1eVFpqAFWyjP9Ba4Z76f3Jv1VDu3XSUMdm+pIvq5JTV03fuYTAD6U9fYL2s2+6xhVb6nTTyyjeFb1hPYd47M0sEQa6TiUJvq3YlmlJ/GCJsy0ihLw9iJQPQXsZODV5nIdNTDIwGlNTQ0qKys9VQWO4+Df+/2Y9fnws2AQnxW96jiOpycIX7d75PwGwCyA35SV4SvV1ea7/2Muh1kAf6yqwl8V5v6HYBCzgXzfkb8q0El7tQDAb4JBzPryJzR9UuwPAzO6tZzfd5x8xbttn3XdbfunwRPd3kc9ksvlTDKXup4BO/KN4+RPqfllIoGZYBC//HM3gzxw4AAmCk4cszyRSATnH38cmVgM9w8dQtWZM3AcBy3XruGjhQzLggAGMhK/q0dj2Q4MPwvAc7SYXq7r4vKePbi0bx9Ojo2haXraABHucyUzsqqCTglLnJKFM+K19EgViOu6uLVvH4aOHsXRBw/QXwATPp/PdAvlVhCWJuucVAA5h6NDQ7jU1objIyMAdiLN2sgwm816KgyKioqQSCRMORirKEgnMi5PVLi6fz86CiX3/FGDoMIViUQQjUZN6aLuQSLNlaE1os6gAwM3VKK6VcFn8YAe2WcbXCDfVLF/YiLf+BM7kWF+T+fg9/txtqkJS7EYLra2mvU5OTaGM42NOHD3rkdpqLOgJaVceztqq3xgK3xVqATCu0WIDzx4gJOzs3nHxdlp5kOAonJhAwV9jjqhdqBBM8K8x9bWFtLptImmcw1ut7fje+3tODY8jNDiopFp0oSZtJKSEpypqsJb9fU4dO8eDjx44FlPAi01QKQHLw2mqBLkfWz+UrknTRhVJl34vp6cQp2k2VIaHz6bDQcZ/OIJMhwnx6Frb1cc/LmuyclJj/GyHVIb2Ok4bP6yA1Y6dka+GY3nZ37q8+E1nw8vuvlTDyiDukWG4ELlWJsVAjsBGb/f/5CRVkfFdvgU6NhroM4V5/uLYBDfLS7GS8h3clcdncvlUFpa6nGI1PhqqSyv3WwSZSYSieBHjoNv53J4ORDAc/6dDuau6+K1XA7fLSvDy4EAXrCyxqSvDSK5xuR5Bb6cr64bbYDKC3sb0Vnl514vKcH3XBef9vvxnMVLCsopA7Q3dBoJxrX6iWur2R1uh9DeO1w3ypx9opTShdVt5AetlKAMK2AMhUL4RSCAb1dUmIoBncs7lZV4LRTKO5a+ncwvT1JSnUK75jiOCb6yMouv02bZwI2l0KpLuHYvAfjGxgY+ur5ugvK7OXpcKw18KZ8wwZHJZExvJ60OYNZay8l1PLre/K1BN35Gtz2qrtaxqLNHXEE5Z0CBmEwBrXZjp7NMjMT7/6C6GjN+P77v86FPTi8jvZ9MJvGb0lJ8IJXCeCpl7s2xfaC0FL8uKcH7FhYwvrSESCRiePFbdXWYCQbxre1tHJ6ehuM4+EA8jl8nk3hqdRXDhWA06aOZTF2bb9XVYSYUwte3tnBwasrIjeIndYx2w0v6GVteqWuU13lvDQhRH7ORrd5LA4mKQzgmnRcAfM/vx0QhgMLMqvIk+Y/j1ky12mMGNjgW6rUfuy5+4PPhxXAYz+3Zg4aGBrOmw8PDGPnP/xmfbGnBoUOH4FqBSNW/yq/6mjrR5Bn9jOob4kV1dkkL1Ql0pNUu2UEVG49poEjvp+vIJJxtc1SnqMzqPG29qXJOmu0WqLCDD5QZDbQwSUBZ1Gfwe5QBBp34fX0OP6P2gc9TrE+5oe+heIVzYqWd0tvGYDpOXWNbB/L5/Kzifv39aQDfdRx8MpeDY2EJ+3m6PtRvn3FdfCeXw0u+nURjIBDAy66Lb+Vy+LTfj3BBd38ql8ufVuTzwV+4j55m4rr54B+DgKWF45ZzuRzS6TTi8fhDxxzTR9wtOGfzrS3DrH5RXmQVUC6XMz6nrjN9L+XDT2az+FEggE+/R3z8ngMN8XgcCwsLDynn901P4x/Ky1H79tvGuHCBNRKkBCCxlHFUsangMttAoeZFJjvf0oLlaBRnGhvReOmS2ZuskTuWPnKRAW+mjL+pfFV5AMBAfz/WkklcamvD0clJw1ihUMg0L1tdXcXS0hISiQQCgQBu7tuHa/v3o/f2bRwaHfU4AwdHRnBqfh65XA7Lsg2Fyp60YhZFy2k5djt7zXn1Dw3hYqFpZ2lpqWdfmO30Ad6zcNXpY9CBAFTXhMzG1yjUjEwHC9tANPDA9eAz7eyArqvyiypB8odd0n5qfBzvNDbiyNCQUQrHpqbQPTCAtbU1+KzgkUbJKVAERQoogZ0qEp/PZ4JVCtp52ZlaBRSqvHcL9HAN3yovx1t1dXh0agqnC01I9VKjxXmoolXlOzc3h1QqhampKQA7Cj0cDuNKRwcykQjerKvDY4WsJsGDRuMdx8GZPXuwEo3iSns7ugcGHjKEauDsNdMxq+zb7+1GM67zboZnY2MD6+vrpppG+zXQuOt6UH5YKs69gnp/NeDqAP4lm0HaYEWzWuos0iFTGtpOOz+n4EuzA3QSKM8/aGzEjN+P72xvo29szONI6zOof3S/vgYwtMEuaafNkKgbVFcowOSljreCEv58s7YWM6EQvrW9jf5C0E75Rx04AA8BewbaSCN1cu1ATTAYxDfKyjAVCODrm5s4Vdjvz/e/VVqK6UAA39jawiMLC2YMyqMEAgTnyluaSeN31YFRe2jrSHUYOe7vFLLA385m8dQu3bh/EQjghwA+mcufBgHA02jU3n/LeXJ9yDvqSOpYlC9tXlbaaiBTAanqROWlYDCI77guJgB8x3HwDODJvL8aDGISwHddF09bAStWLqpjwBOr1Jax2oOfVfDOe9kNxlzXxY8cB98PBPCSz4f/bWwsL2fJJH4bieDHkQieyWTwoUI2iDylPK76hhiHMpFIJIzd5msM4Oj47aCuAm/AGyjiHLWSQwGlOmy6NurE0PnQU5/I0/zREm6fz2d0BMf2ab8f33ccfGJ722Qb1T5+xufDy9vbCMTjQDxuqlMYbP44gI+vrCBQVATU1nqcoE9ms/iRz4dnCtt6HMfB07kcPpFK5TGMnBj2p/RuIBDAc9vb+LHPh2cKwSPSDthJCqmdUBujVWDkJ7VhGtCnjNm05ppTb9hJBFsu1Rbw4jrxc0/E4/hNaSmeWFrC4OIigJ3+WryH9tliebeti/k9Potr8629ezEbDuNb2SwOFI4Md9389qx79+4hEAhgeXnZ41TuFiBggG03rKBBNHVq9X0NUuozlM/Vz6DcqF0gj6icqvPGS+VF8ZDyEwDPGvNz6rTpuNTO6Od1fvq+YmLlH9UvXEO7OlNxkt5bZUGfZc+Z/KB60U7M6Brws3YiwLZ9lC2dB/+3aapYTeel3+Fr/P2M6+KjucJW22DQI8u7rbXqN7/fj2d9PnzMdeE6O1tsg8EgngsE8NTGhmfLycddF58QvcD7Kcb6RDaLj0pQR+lNf1vxg87F9id0jZRvbH9Lg79qH7RqhDxDPuP3Q6EQPr69jRdcF3557j92/bN6NNDgarT6+OIinB//OJ/V9+1kvWjEdfH0XtoMRIlhg1ElukaRaSyODQ/nKxoKzQ6j0ag5B1QNBJUjDXssFkM0GjWGkRFrGrRAYKcEsufmTdw5eBBHBgfNc9mUiWOKxWKIxWJm7jd7erBaXIwbXV1mu4e9L0qDK+l02vR0sB3UXC5nmrsw+6WCqBGrQ6Oj6B0aQnFxMdyCM0W68rcaLn1GLpfD5T17cLapyWSwyXSkhypeBhf4tyogGmR77ZQf9DuqKCgoalA5fq3M4HiOT0/j0OhovtS3EKyxgypquPR//QyzBvyfwMl1XVO1wegfwUUymTT9MmhkVNlq5Jh0V+Wvr79dX4/FaBRv1dfj9Py8AYNKCzXC9vrbUWOWpBHorq6uYmtrCy3nzmHs1Cn03LqFhYUFj+zpuH0+Hx6bmsKbtbU4eP++J6rN+aqS4ph0DZWeupZKYzV4tmGinKiyZZ8X191paBQSXmcggaWT1DNFRUXIZDKeConNzU2cra7GWwcOoO/uXRx48MAALYKov8Slhsfn2zkuUpW6TV8NnuzWcMsOapFvmFVQffMpAK86Dp53XdTW1j5k+HWduJ72+gUCAc/pQBwnaa5jI48rgONvOrf8/G564+Pr6/iZz4ePFYLICjIZEFIjrVtBNLBJumhjLbU3nMvT6TR+FovhmUIgS7tKP5PJ4GexGD5eCFxxjHQCHWenGoHry/kTlOhY6QSTHtQj6pQrANMeDD6fD08lk/hlIoEPLC9jOpMx9+H8v9vcjFm/H9/LZtFb6BGjzgwD8bqlSvWzllWSj1Tf6fN0Xe0eAVx/0ssGhxyTzvF9JSX4bWkp3r+ygsFUyjjgPp8PT5aV4TelpfhgKoWRwtY/roEG1tVmKY9xbRRj6BYF/S7HSB76Vnk5ZoJBfDubxf9S4NeWlhb8Yn0d046DX5eU4L9qbDT3VcBGWpBv7RJxdYKYddUqFB2r8oXKLrBzYo7aS14KWtXRoY3Vpo98Fj9vZ9I4do6T/EKdqwGqTyEf8MoWFSFn3Ufpr2vAOVFva9Cea+n3+/F0LodnHAdOnuAesM7ncFykjfIw7/nhzU2zV9ov/Kp6Tu0saaKv21uIfl1UhJ8UFeFjhZNSfhaL4bmtLVOJo44kx6N4wQ5MUwfyczouHRPH/nnXxWdzOSCZxFYBo2qwSe0kq/7USedlf2drawuZTAafzOXwY8fBs9ms2TaytbWF8+fPY2FhAS0tLSgpKTHbrtTR4nhV9ys/82/S1MZO/IzqUx2z9l5QHGjrB3XiiIkUJyqv8ztanaU8xq3KqtPsfj+8L38rL+q1m1Oo+tl2PlWH6PiJj3YLlvBv/T5pp3ytwRx+h9+zcZ1dYaC2UWVbgxx6b11HHTNf1yCNzpWvs/pK10vvA+Chhqq8vwbw+D19jets84kdwNK563dIL+VNO3liB/ls+2oHBfi6zlPttOJ3HS+/S/vCpDx5TLcv83O6hu/les+BBtd1TWks/yc4UrDFIAKdfp0UP8umfdzDxUlwEag42W2Z0VUqQnXk+icm8OjiIra3t/HWvn241tmJnlu30H77tlm4m/v24XcHD+LU+Dha5+eN8eO9bIZQgBcKhXDgwQOcmJnJf6fgeKqiPl9bi3ebm1G7soKZsjLUraxgMxhEZGMDh+7f9yhTFVg+k4BXHQwGM4Ad40u6aqDFVgIqmAoANNJuO/e6pu82N2M5FsPl9nYcLpzyYCtDVfL8WzuIk3nJE/a+MN0yoEKg66qOrxpR5UddP824qtDxt86ba8cM1/b2NqampjA3N+c5yov7ozY2NrCysoL5+XlT9qTNhsrKyjxKWpWx0przs+lEejw2PY03amvx2NSUx5njd3aLMCs9VOlvbW2ZE1t8vnzwj4a6+uxZvH91FXNzcxgsyBwNY3l5ObLZLNbX11FWVoYTMzPoGxnB4uIiHHmmglSdJy9VkhyfKlL9jM236pipUqbSZO8Fbp0g3RkU4HzplLAB0dLSEqanp1FTU2MyXtlsFu80NmIlFsPV/fvRV9jORDlLvMc9aP/c662yMnwzm8Vng0F8UsqZ6XwoTUgXyo7SVQ0deYkX+UV7MpD+n/T58EkAbjAIR4C3yooGNtVJ4/PpjOgYqTPJ0/y+gjRbz/FSG6KAyHVdfHhrCx8pBBOChXJp5TcdK7+jFQ1qpPk+P8PxUKeGw2F8JhTCS44DXyyGXFGRx2l7we/HJ7e24A+F4CsYZeVtZgZIbwU6tp5Tg65gU+0m10IdRt4jEAjgZQCvuC6cRALhAsjXOb3suvi+4+B5AA0NDZ7gswJ51ckcpwJoPRtcnXqOUcdP+VHnjnQiD2swhs8ljUm/LwUC+Fw2C18yCSSTcN2dZrUdrosvuy5QXo5cYVuJ2lWbjspPdNTUxtDZUp5kwENtn9/vx0vZLH7ounjJ50NLSwtSqRSKiorwV34/vprJ4EuSdCCf2YCRGEedld2AJtcjFAqZLZOkJeel/MXvE8Qqr6hM2rKrwTc9eUHHr/ZNZUkBK3lE76UYwnakbZDMsRITsaLK3iutc1Hb4Lo7zVG5PUadAmDn5BOVFZYVU8ZsnGb3xNEKAH7exiyc088yGUy6Ln5Z6Oc14/fjZ8EgvmBlmZnA0jXhM0kD8qkGmjXQqjpC9azKA+kL7AQPlLZ64pKtn6j3uXc8FArh6VwO2XQaPy70wfhgJoOhoSGMjIygsrIS/f39aGxsRKxwXLZmasPhcH6rWjaLl4NBPCs8oPqA2605P8V0Oj8Nzqos8DXVe+q72E6i8qX+aNCAOk7ll46aNk4lze3ghuosDTSoreLWHH6e+NVu+us4O6cl6dip0+h4c75KP152YJ68FQjsNNT8U8693oO6h8Eu1cOkk1Z52QEQ3lN1ENeFc1F9TvuozrzaTfse5C8N/CstbRoq3yvv6Nj5XdJL/9b+U8pLGtCx8R5/7xZ8/Md08W58ZfuBuwVDWFnH96ljtUJNbdhuQbE/db3nQAOVtgrbaF8fftXbi/rlZTRfvQoAGD10CBdPnMBj09PoGRz0MBIN69XmZtzo6kKfZM1t55SESqVScF3XOBRkYiUOiXW5vT1/TGFHB+rOnzeNJq93dWEtFsOZxkbsvX7dNLnaLepHpasKhAukWWVd3HN792I5FkMqFoPj92MlEoHj96NkdRWHRkcRlDJXZTIuKJ/HPVQ08poJUJCqQQMyk2bwdB8dLxpePo/35Rj4jBMjIzjb1IQjg4NGqMjoCgTIE1wrVoawlJogi8pPQcKttrZ8s9CxMXStrHh4iopEX7OVvG1E9NLvK0iwHVwCENKRZX0MJFCp8Kggv99vwCQVZDabNad5kEe007+tUJR2KlNU6qfm5nBqbi5P64B3KwYvm19tsMnfq6urpkmn4ziefZUKKkkDGhIG85TuwMPZBCpae3uDrcB2e0/nos3rbGCnPK/3oy6IRCJIp9MIBvNNVMPhMJaXl838NzY2kEgksL29jdXVVczMzJhGOuqQnhgZyTcTvX/fw0faAO7PfX3bcTDuuvi24+CLhWCGGg91OhT4Km0YgbaNDv9XIGkbUAUbtqzYxo+v2fpDHU3eS7NvpK8aPj5HM6S6tU2Nl23seSm4VKCTy+U8+x+BfMZC11C/pzbAdjj19Aren3paA2AKXjlOVnnYx6vxh/ex37MDMsBOVpqASR0Hu4GX2mg6Tq7r4iXHwactB1fXS+2JnW3SABQdC7sSgXaT/6+trRndor1rNKCs91E6Ki004GHTyg5g8d67ASnNkmrwXZ0y1aXKX+QdDYK4rouPbG/jmYLe+qnPh++WlODFuTm8Eo/jo6EQnM1NrEgjaw08c10JhLXaSIGlrftUjhXEkwbqEJIfbJlhbwV1HDmezc1No0ftUmyOSR0sYCcLxjXbLflgO4Vs4Km9DThXDZpSflhRpPaIz1WgTSxoJyx0S6hdEajfI/6irNMW2gFW8jWfrQHSPxVg/0JREb6xtYWXAwFkczl8z3XxhWgUUQl4EfORVnwGx6hVVRpc8fl8ZgsQ5wzsVHKow2gHjzXwoPrF3qKjfMR1o36hbvp1Oo0p18VPQiF8paoKr7/+OjY2NjA6Oor5+Xk0NjaiqakJJSUlKC4uNvcuKirC1ysqMB0M4msbGzi6uLhrwIDybcssaUWMoliCz+Ac7W0zfI+VRRoEoJ5VnMS1UNrzHnwWaaq6SO2D+h16RLBtU8gXGxsbJsC4W+UK5+k4jqc6yg5uagBKgyxq2207TbpTDikb5DW+p1Wi/K1/k4dUlij7mpnX56qtUnukQTwbjyoGIu4rLy8391F7Cez4UKq7OD5NjOjvbDaLxcVFU8Wuz85ms5ifn0dVVZUHQ9gJDbUvOg/yxW6NvtX+kSb6Ov+2/R59TYP7WuVAumivmd0CUXo/Bq7ey/WeAw0ATOd2DuzN7m4sRyLYPHoUnffuwXVdDPX3Yy0exzsNDei6f/8hRxgArnd2IpVI4GZPDz5WmJgqAzLA6yUleLOuDt03buDg8PBDxLYdrUP37+N6Vxc6bt40RjGbzaLz6lUMHz+OE+PjhgHZeFG7GlPo1NGiYQmHw4gVorW6ZSKXy+H48DDONjWhbnkZs+XlaFhbw1hxMQ7eu2fGrGMnE9u9J+j8qqFWZcltCrothPdQIKUMYhthjQIq8AHyjHl4bAzdAwN5wC3KlTRWIHCjtRWv9vWh7dIltFy/bmhLZTY8PIz5+XnMzc2Zvbe5XA7XX3gBmeJinGlqQvfgoMeB5TjU2KlBoUFU4K2Cp8pJAbEaBBVQ3iOX2zkukJFbOguhUMhsbQHg6ZLLwIIC7j/lnCrtCSx1THo8H+dIoL+b08X7XGtpwaW2NpweH8epuTkAO8evVlZWGiNVVVVl1pBBhdbWVlMSTcCbSqXMd0gfLUVWcKsA2gbj9ut/Smlxfir/qvx0zvzc1tYWVldXDY9oiTHPnE8kEp5mmG1tbWbddI97/8SEOUaUp9nYa/bnvl4OBPAtx8ErwaA5Rpa0U0Ova66gnGtigwJbLyoQ4T3sdQB2MpA2sLPXkpc6spoVUifSlmnbmdP3GU3ns3Y7sk3lXF/j55gdVlAej8c9pdNKXw22qHOpQMo25upcMPis8shxcE72M3QtSGOui2aR+D63AekYeOleed5D9c9uQFRpqOumutMGpsoLwMMVZmo7Xdf1NEXkvbi+qsPtZ5BOSjM7I65Oo4JzvqZyo6BJeUXBqNJFZYava88apSnxTDgcxqvz85jM5fBqMIh/WegDoPZJHX4F3CpPCqht/lSn0s54Kh3U3nOsKmd0nrWsX9cJgCfwzdfVoVQ7rHaaelm/o5lCjltlXAGw/q9z13Xg9zlm2k9WrnEs/A57+WgzNQXySkc6dMzgUb40qMo1Z0JBcYvqO/6vjuqTrouPRSLY2NhALpfDx4JBBDc2sFDYjsmxaMaeARafz2cwievu9GchX2tps9/v91RlEB/q8Y4cu80/xJl02vlcdfoVF/OZdG5fCYXwrWwWXyocybq2tmb4PJvNYnx8HBMTE6itrUVvby+qqqrMFubntrfz/TXkyFINKAAw4+I8VBa4nqpbyAu8KOOKU0hjlW2tSADyFYERWbtgMIhIJOIJwvK3najhfckPxCIMZpO3VF5IV8oZnVrOjXPQzzOIojgJ2NGXdtUPeVNpqEEXfl5xEmWH8+c4NRCjfGVvu7AdZdXz1Ln02cgDtCWUA1273bCkrgntGfGu6n5dL+pF+z4q01w/BlRSqRTW19c9DfoBIJ1OY3Z21pyGpnqKNLePcObzVHfw2ZwP56pbAzVpZGMh2y+iHtDAoR3Y4jiY+NExKj9zTnai6R+73nOgYW1tzSPk29vbODU2hnebm9F48aIZcNeNGxg4cgSnJyY8ylYF/uDdu7h94ADePzvrycDYQPadhgasxGK41dOD/okJ856t1Dn5Aw8e4H0rKxgdHcWG9AjoGRrC50tLMT8/jyV3p0KBP7oodhSSoFfBig2yjoyPo/32bYTDYSQSCfh8PqTTaWQyGWAXp9jOigE7p0iogGu5O7NJ/J4+XyPOdhSKHaIJeu19WhcbGnB2zx4cGx7GydlZw5R2BYMCLUbnbvb0IB2P40ZPD8reeguO45hjMR3HMQaex6eRfk3vvovpxx/HqULgRyO6uhZUDKQJ143jsMfFH6WH8pMKhSqb1dVV4+yxCoAGSM8kZ7ZU50IFHwgEPOWYKsS24NsZPfIGq0B0XuR39h1Rvuc9L7W1IZVI4N3mZpyen4fPlz/+MBaLoaSkBLlczkTOCQDIY2VlZQiHw0in01hdXUUikTDbEnh/yi9lYDeeU16xlehuykgNpCow0kNBqH6HJ6Swq7xmNTVTbAev1LGhDGUyGbxbU4N3Ghtx4PZtdN2/b/iRwHI3XvpzXC8XF+MViy8AbwWIHUCwMwQaYScN+B4Bsw0o1fhrYJK8rPTS9ecYdF31NZvWupZ8PuemJd1+v9/00qBez+XyPWnoONgZNZ/Pm5nkmtmd4Tk+WyeQVvZnOG778+qQqnHXzJY+UxuTqhFX/av6x15XXfutrS0DQmx9YQeN7FMkNNtnr58GE+x15T3tIIkCZltfK8/p8VmUNc2kquPNseg6Km3UkbL5jnPdLTAHePdv7+bc6nzs8biui+9kMvhqOo0vxmL4jBytSj54LZvFV1dWcCAYBHw+vJjLYWVlxRNoI3+rA8KL9woEAqaBtZ4OpLrMpr/aD9XLah80kMB15hrYNObz7GepzVIcQ2eb92ZJPOnHe2iQ4zuZDL65tYVXgkF8unAaBZ+hz9dgBQDTS4vjt3mOelpplsvlTENr7X/C8WiwRfmG/KE9prSR7G56Wb9r30f1HBtj8n2t6iI/0OHUag/dNkW9SOyqtkOdPupY8ohdGcW/1W5SXtkDiXTkejKgQHujlS8A8LFcDh91XfjW1zGXSmFubs6MTxMJU1NTWFxcRHV1NXp6etDa2prfhubLV5VE/H5zxKvaeN02pDrA5/N5ZIy017kSw9FG2HTW9eB4KXtaWUN66HYc0l6rYFjVRt4mv9lyyjXXAAK3vfL5lIN0Om3sBwMfmUzmIXvE7ebEplrloFgOgPHpSHvKpPoRHBfHT96zHWfVJyoPate0marS2cYk1DF2E1yOZ7ekod04MRQKoba21qyd0pyXBnhUf9Dv47N/5Dj4ruvilWAQnwwGUVNT8xCNfD4f4vE46uvrPXOy9QtlmY6967pmKy8/q/PQcau82rLPz9g+BPUm5UFtsNoBYjDdMsXxqq2hLvvnXO850KAKkkz3yMICDo+N4c6dO6ZpTvfAAD5WmNCqz5vp4KAPPHiAZwrM50qgQCfuOA5Ojo7mnYA7dzzONS8FQzZBuBiMQKqDoQZVweBuGSU+R8fG5wE7e2ZssK3vM4qpr3OOVOypVMpTqpfNZvGHRAJv19ej/coV1D94YJg+Go2ioqLCjE0dcTKuOnIctyozriF7MlzYuxcnZmY8jLqbYVUj1XH5Mu4dOoS6t9/G+vq6ccoVwPJ+ukduz+XL+ATyQr4uikb5S4VJs6DKRwrQVbh0bXkvfs8GBQqmKioqzKkhzI7S0EQiERw8eNBk/wFgdnbWBJbs8SnQUCeGCtTn83lKkZWPyRt6P50X15F/H75/H1fa23FifNwjR9vb21haWjLgFwWas3IjFAphZWXFHDsXCoWwWOhIzWCD0kqDG6SvnQHdTXZsmbL/1yCRzQv25ff7PWen7+YE73bxmZQPRm7faWjAUmG7VeuNG+bz6lD+pS7lR52D8jjHToOo4IvfsQ2/7WzYxlcBmYIlvS/gjWIrD/A9fscev47N5huVV5ULjkXpvdsz1Fjysg2s3kPBHcfE+1AvKJBnxlQB6m7rZNsiPoffJ43t8fN/21bQ+dEMFEGcrpHa091ki3PjM2zdqPL7pyqS+Fl9Bp+rjqraZNVNunVF78X776Y3yLfqzHMuSiPlH5/P58l0kfdU9xKAKlBTGdEgBG0O3/v7tTWMuS6+mk7j42KXuO3sq+vrGHMcrKdS+H/MzAAAFsrLTQ+qTKEx58DAAMLhMPbs2WOC77pven5+Hr/73e/Q09OD9vZ2D2+QN7WKUQMousXBdlzU5vIeuh1TnVEGiPR4bmI+OhLqLL2azeL7AD6yuoqnC049myLz/mxgXVpaCp/Ph69vb2MCwNc2N/FkOo1wOIyZmRnMzs6ipaXFyB0rFNQu0HGjLrR1AnlOj+MkP2o2X/mDcyF/KT9rqTrHRH5bW1tDSUmJoS2DIIovNQjAZ7ISMhgMYm1tzVNNy3nQFtNGadUCsSJ5gT+qz1VvqVyqHqL+VAeRY+b4iOW0iamN5TUoxkvtseM4nipc2irK7urqKoaHh3H8+HH8dHsbU46Dn0Qi+EJpqSd4S17l/dU2q97l/DTBaeNgW4/qlmPA24yUdFKa6pg0saHrzEvtm2Jp9U20OkX1Hb/LIIE6mGzcm81mEZeTVDSwTF7g5/V4VDuopHNj8EkDATaOZbCA26S5bRqACT6SL6lDbD2rvhpf122g9Nt41K/tu2kQTX1Gm37U14rlmJSgnOsYgJ3+ISo736ytxUwwiK+tr6NnctKT7P5joRnzR1MpPCUHIGjSQIMZnDv/14AX9ZXyFump+ku3+di2WOdjB7g14KD8SJkktqacKr/yO69ms/h2JILPRKN4PvjeQgj/rB4NGi3mIi0vL5tIHhWKAj0VTo2A2dlZEkcJdWRiAm23b+cNtuxL0+oCCiwXwefzmUgUn8NoEYVAG0EpuNF72EqJ4+dvdQhZpsuFUwMG7OyVU/DDn5mZGSwuLmJtbc1z5nQ2m8WZujqk4nHc6ulB2ZtvehafjGcDYxuQ6Tpw7bQU6dToKN7ZswfHC1tT+Bnb+VBlzDWrPX8eNefOYWNjA9uua6KrjLhS0KkodBw6fjWA+jk1kArOVXjtrK1+XhWKloCp0ndd15xVW1paapQ4QRL5hY0HfT6fiSwnEglPM0KuvSpSBfH257QcUYMjtvNgBy10nR3HQffAAHoGB1FeXg5/IdIYj8dNJUt1dbWRYT16h2uhoE6fSSPENbna3IyL+/bh8L176Bka8jiq6sTYzvlujpnylq4vX+d3buzdi6udnTg1Oor+wtGyr5eU4FxLCz6wsOA5BnS3Z9jO7fb2NlZWVpBKpbC9vY2Oq1dx9fBhbAWDuNPRgc579zxOy1/qWlpa8uiQ3bLc5B81CnxfP08Z4ue1UouXDbpUN+iztFM2n6u8qPpSwRgA4yDo3mDlZ83w7LZW6kDyc7y/fo9gyg42aLWWZkVIXw1oqMwpaFD9rXzDS8euYJ/0INBRHrbnp/fR/5Xe9jqpXVLa6mV32KYjsJs8ci1JIx0jn8fP2QEJBSbkN71UpvU9fpe6TwESeUOPvVbaqY7QteF9VFb0soNXtl3UeWsW2+/340vZLP7L+jq+XFyMYul5xGd9CcD/b3kZTywsmKqz9fV187zNzU20tbVhYGAAV69eRWVlJU6dOoX9+/cbR7y8vBwNDQ241tyMbzY14dTAAE7Ozpp1o31XB5nVjpqJVnox0KUl4Xx9Nx7y+fJBrs3CsZC8nzZUpJ3l6z9sbMR8OIyfbG+j/q23UFFRgVgsZgIZNTU1GBsbw6VLl9DU1ISOjg68UF2NH/j96HBdfNHvx0eWlnBgYgL37t1DeXm56Ymjji0r8dQJ0PGpHeX62lVHOk/SQ22e6k4+V/mU78/MzGBiYgJLS0uoqqoyJ06xfNpxHKTTaXwnk8HvKirwEoDPxGJmLdLpNLa3t7G4uIgzZ87gscceQ21trVlnBnKIOzRomcvlPAkC6kBW3ulWM/KzOn32PLVyV3mMssggRzgcxtTUFLLZLMrLy812LdvhI7ZwHAevbm/jez4fOh5/HEfGx3G/sxPvNDZiz9mzaLx0CUVFRWhra0NnZycqKytRVFSEz/t8+PrmJj5f2M5sy7LqOhunc778jtorfld1q2bwFb+TN+zgLG2IbsVSvc/vqS5X+6X8q44dx6FBITr6HBOfpdUnDIJlMhmjNzWBoNuUde4cC/me96Rc2fShb6U0VAdY7xUtbJehb8EkGunC8cRiMcNrpAewc0wq6ccACYNBSmfd2mL7hOpP8v48/ZBzJL8qX2mVJe+liYjnt/NH3j5bCO6oH/nreBwzgQB+lUzio7tUjKrNoD+kuFkruxS7qB7TbQqabOEY2XuMdl99DvUHNbhAnGb7kTYmUbvvOA6+Vl2NmVAIX9/expGJCZw+fRr/1PWeAw0krEZTyFTMtgUCAQx0d+NHXV04NT6OztVVwwgqaJrt4YQAmGiuljDxUuVGwimIpeOcyWSQyWRMaV82m8VAdze+0dGB3mwWzTMzZtHu7d+P7x88iJOjo2hdWPAATQXTKoQkth3h031vyjgqbCoInEcoFHroGELeq/vGDdzo7sa+y5eRy+Ueym6RnhrAIH0B795ZjaJRkHK5HI5MTKDz/v38HKXEUBWtDbjVkafhKykp8ZTNJhIJI7x79uwxioPN+2wQoAEINZZ8jYrecRycqarCW3V1eN/sLE7Oznp4yQYc2uTHdmxd18Wlxkace+QRdBX6gExPTxtFQiXFdWK2X0vUtEmQ7USpgtcsHi91cPQ7nLvKmAICpT8/q/zD4JoCdj5feUUNifImP8dSKr5/ad8+rMTjuNTejo67dx/SD6qgdC1sp9I2Bko/XpSZq4V+LmeamtBw6RICgQDePnUKq9EofldRYbb7KKCyjRufubW1heXlZaysrGB1dRWu66Lt1i3c6unBakkJbvX0mP4kmhH7S1zfSafxk0gEH0uncXB4GOFwGE1NTUammO1SRb+b06TgUZ07G1jqvdRY8zsElxo44Dj4LAV/uVy+TFyDr3QMVCfRcAPe5pK8h8oi1yudTqOystLIrV2mx/VmVkfpoEEJNdA6J8qSAmu1K7ZzrrZLqx94fy3t1/4Mts6nTrLtmB1MIfiNxWKGF9Ux4pjUYfxTwSgFZPwsL66Pzptj52tKD50LS7htZ01tk+0EKF1VTskXxBYqyxq8Vd2uvKzPsddRwTq/S50AeE/aUFzi9+ePS/x4JAJfwYEkfTn3j8diaB4dRTKZxOLaGu7fv49EIoG9e/eioqICRUVFSCQSZrtXLBZDU1OTGWNlZSW2t7cxPz+Pa11dWCkuxuvV1cBPfmLGxrkrSNb10waGwA6YprxRhpUGtpOm60V6sExcAbDy/ccrK/EzX/6c+FgsZk4XaGxsRCKRgOu6KC0txcLCAmZmZpBMJvFoMIgPxWL4V7EYpn0+/KakBOnqavxi7168c/Ei9l6/buSLmFKdo0AgYBwY1QXU1UwwcesSTxvSzKIGXWwZVPlRG5XNZvPJlO1tnDp1ChUVFQgE8qcYpVIpUxWYTqfR0tKCXyWTWA6H8Y21NXwgnUZpaanBiOwLdr6uDl+PxXD82jUcGR/3NIHUNedYyAvEuTwRS8dNXasOp+u6nuCAOliqw4gbKCOO45ijoJeXl5FKpbC2toZ4PG6wXFFRkTn2+8aNGxg+cADvNDYiEwphGUCwvx//y/Hj+KzjYBnA9tGjqL9wAR0dHXj00UeRTCYRCOS3DT0TCOBjwSCQzSKTyz2kM9TR1ECD4hm+r0EB0lG3cnHNqbs1EKC8ZQeoSU/ypjpxyo+72Wj+DXgDJaqHaLvJswzyqX6jjOv9/X6/J0CrFTacN2VIAweaiNOABuUpEol4HFzek7xCPahNifl56me7akF9KbWJmtTimtPua38/3aas/ijlS2nq8+3049Pkn8o/X7P7OOla0RZ8orDNJGsF0p/f3sZrAJ4tBPzURuvnFIvpfKhjFKvrWLkuug4aYMnlch5ZV39JbarKkuJE2u3dKjkUw3CtXsjl8FoggOez+Wb47+V6z4EGRro5qLsdHfiHzk50bm+jZmICmUwm74y0tWE1GsU7DQ3ovHfP42woOLAz2QSUBEa2U8RF528aUILTlZUV3L17FyUlJQ85qBeefx5rkQgutraipNAJNxAI4HpXF1ZjMZxtasLeGzc8JWhUMOqkquJTcKlOMy/bKbdfo6LjSQbayIx79Q+NjqL99m2sra1hxfV2xuX9bKc8l8sZZlcAyOfaY9Lx2kpRDTG/owGLZDKJcDhsQBWQ73HACOfy8rLZigAAq6urprxSDQXHrwyuThC/77r5SPzb9fVYiETwRk2Nyf4oUOZn1amxlSX/vtDailRxMW719uLg8DAqKio85arFxcUAvCXUFNArTU34TVsbToyM4GihnEqzfyrc6ugoMLD5QuWBoIH7D3dz3JWOVNa82NlZ5618okqXcpTL5TA9Pf1Q+Z3P58ORwUFc3LcPvbdvm3lpdtg2nrw00KZrawevbJ4GgL67d3G1owNd16+b7/XcvImb3d3ounsXEwUQu7m5ia2tLc9eYW6PoBFeW1vD2toaNjY2DAj1+Xzou3cP1/bvR+/t22Y+rruTMftLXD8rLsYUgF8mEqiZmcHCwgLefPNNs681Ho+bqhQFhaxQqamp8YBPrqM6Cn9KHlSfqY4g3YPBICYmJpBOp00TKn2PIKC4uBi1tbXm3rrH03YgbfClTizv6/P5sLy8jMuXL6O2thY+n88DEu0jdHlMMctHVZeo3uClPKfNlzgW1fmUleXlZU/5PrADRmm0qbNtG8d72s69z5dv/GqDVt5zaWkJExMT2Lt3r7E1XHvNPJLmrB5TXe/375Sg67h0HmrTVD4J2LLZrLHrKsNqXwk26fQo6Faa829dJ/t93nthYQHFxcWoKBzVSdxhB0Bor7keGtTicxTU81mauVH7psCTjrZWzXF8P3YcfDObxfsHBxF/91309/fj3LlzqKysNIHhRCKBXC6H1dVVtLW1oba21mybWFlZQSKRMJnHV199FVXJJDZPn0bt228jlUqZoB3tKueh68fXSUeljTpP1A/Ks8orqufJZxxbNpvF6uqqB8QHAgH0Xb6Mv85mUVtbi3NDQ8Y+NjQ0IJFImF5NR48eRSKRQGlpKZaWlhAMBvHJcBivhcN4KpXCD5JJpJNJ3D98GPHf/x4AzHZEzeypruNa2FWxGohnwFLtNd8jjlJ8QLqQZqlUygQNSWduK8lms4jFYvj1r38Nv9+PQ4cOoaWlBVtbWygrK8OTU1P4bTaLZ7NZLC4vY3l5GfX19SgvLzcO663eXqSKi/FmXR2Cv/iFB7Pxeeo0qKyoDmRQRuVLM5XK1zp/lX/VfdSbxBP9/f2oqKhAe3u7aezt9/tRXl5ujlK8f/8+7t27h9898gjSfj9KATT6fPireByVsRi+srGB/zAzg9pz5+Dz+XDw4EGzd16dZpVFxScq3zovfkdPPVF9psEXTa5p0FV1K3WGjU1UN5JPKBsa7GCAQAPt5E2erqXVAoovNNlGZ59VItRJpA35QgMFtCOcA+lkl+wrVrNttep/vahHKWu7VSaq3eP/6izz0vGQtnxdqwGJMfg8fofrqUkL0k51oW2zSC+uAcenfM81Vzulc7f1KfH5lx0HX7b4V/lEZZP8wB5wfL4mPmzsspsd4zx4fzuhwmonDUTyt25BU55WXrexk9LAV7hnKBxGKOvtt/SnrvccaEilUibK5jgOLnd0YK2oCJl9+3Dw1VcNgTuvXcNQfz9Ojo8/lCVT0KnMpszI1/hdbUBpOysa2SHw1fJZPqfj6lXcP3wY+y5dMp1jAaD75k0MHD6M46Ojhjl2c3TVUeRrDHIoaFYG00Xj/bioFAo6R7wfM1gATCkSDU5VVRVCoRBWV1dNJ1ZVPsDOMVCch4J7BR5/KmrFcaqS1Tnzs1opwP1ZDCLQoUulUqbCgWviuvntFSUlJWaNtSRMBUgFk8qFa/2+2Vm8Xl2Nx2dmPGPTufA+tpOuzk8wGMTJ0VG829yM42NjqKio8ABS0kmVJy/HcXC5vR3LsRjONTfj4PDwroEGRks5JlVAdnCF8+WzaDj0fRtYkGabm5tYWFjwgMzq6mqUl5d7otcciypb8gq3FbAZkQZW4vE4Dg4Po3tgAOvr68iJMVfHRWlkBw9U8fM1rq/SR+WlZ3AQ++/ezY+3sP69Q0Poun8fa2trGHEcAwBnZ2c95XeskGJGCYDJBDG44rouugcGcHB4OA8CJatDPfKXuN43PY1/KC/HiYkJzM7OIpPJmN9zp05h5vHH8dHVVXwEO9kH0k+rvpR2KtdqJG3DYcu9/p/NZrGysoKRkRFsbW2hurrabA/y+XwmO+66LkpKSkwFA59JficY4HgBb4MtBfY+n8/okYmJCSwsLGBwcBArKyuYPXkS048/jsP37uFQofKKcwoGg6irq0N1dbWRFwVNCrIViAIwwWbVH6rvHcfB2toa3n77bWxsbKC4uNgEEzgXysbRo0fR3Nzs4V/SwwaedIZ07yp1Pd9bXFzE4OAgLl68aPQqeZg6lyAuFouhtbXVk93kpTp8NydVnU4dOz83Pz+Pi4VGzzy9Q4Exwcrx48dRWVm5qx3k/LXahPO2gTnpPzAwgDNnzpi+RfF43Oh/rXY8cOCACXTZukXXwF4T/s3vaTbP5/NhfX0dFy9eNBl0zocO61e7urAQieAPVVX4H/v6TH+CpqYmAPnKzPX1dSNDW1tbmJubQ1lZmaFTJpPB+vo60uk0rl69iupwGBXvvJMfo+jRVCrlkVmCSwbdtMJNwb2NsdSm2TRRvcGAHj/HEvpE4QheOm3JZBLxeBxzc3Po7Ow02KW8vBxLS0tIp9NIJpOorKxEIpFAOBxGSUkJamtr8VmfD5/OZvHTs2dRtbmJrUceQfXrr3u6ndvN23TvNddQj2PWwIRiLNKJPKuBO85fsQftBrcpKPbb2trC22+/jdLSUmxvb6Ours68V1JSgnA4jEwmg+cDAXxkdjZfwbm5iebmZkSjUaysrCCTyeAHP/gB6uvrsXX8OOoL+oW6ROXVDsipM0IZUh6n7HHNmM1XftA1Vz25trZmjn3ns9fW1pBIJJBKpfDcc8/h3LlzmJ2dRVVVFY4fP46qqipsbm6iq6srT6flZbxdXIwPLi7i+UAA1T4ffMXFOD49jc/95je4desWHJ8PS0tLWFpa8siV4gBmrBkMVrpwPTQ4xkCDOshakahBC9Vv5Afel8/aDf/5fD5j63QcGtxQfWo/RxORfKaNg2zcaPMv56AYjnPXigY9HYX8SX2rTjt5Qf+n36WOu/peHK/yD7fo6prRNigWd92dU1X4LNKBuoZyzIokxZiqrzUhZB+7rHhJnWxdE7XftiypnlGdydeVH1U+VQcrjlX8Yc9Pec3GIzp+G8ORP7QHg/p36huqzqT/o3yttln9YJUfvfdPV1Yw6Th4LRDAV6qqHqLFbtd7DjTMFY7NYzOaPWfOYOL0aVT88Y/Y2NgwTNN39y6eLQDSFRFULhYnaUeflIE5IRsI6wKqgQBgMi/cXx8I5PcyZTIZtN++jWNTU1hbW8OU7LPqun8fnwCwvr6OFXEw+XzNfijxFbTaAsuLi833bAeTQllaWorKykoDVtbX180ep3Sh7I4MxuMPY4V9fwxEMOthZ684JgqUMpjSk2Oj8HHuNrPpuvj9fk9pYjweN05IcXExtra2TJmdDfj5/IsNDTh78iQO3r2Lg8PDHhCohla/7zgOTs3N4cTMTP5+wEP8Yc+ba2VHcH0+H/pGRnBweDi/zqKY7eiqOmNUigfv3MGV9nb03LmDtysqcKO7Gwdu3zZOq9JQ+YE0U0DE0icNaNmKzl5b8p7fn68g0UzF5uYmZmdnsbm5iWQyCcdxzJnCXDeWe7Kihkd4ciuMGlAbpAMPZyZ3C5rYMqH00OARXyOgsuVK9UQ4HMattjZcbmtD7+3b2Hfzpskm6fqRVgTHdNDV2SF9aZh0n7sa2z/39cXycnw5EMCYz4dXZ2bMUWwAMHbqFDaSSfzcddHwxhvo7OxEW1ubqXAAYBrIqhEhndSptMEN/+Zn1dmn0clms2hvb0dPT48JVOmaq4M4NTVlDNXGxoYJ7pC/NHtIfar3IqjhXuwbN27k9fHKCnK5HMZPncJGSQlu9vTgXxaCCgQ25B32U6GOUx1OnaDZIzoTynuaNQXyTsWDBw9QVVWFlpYW7Nu3zxPg0b48AHDnzh1T/kybwbkxyEWHgjwO7PRNYQDcdV3cuHEDy8vLmC/0H4lGo2hsbERbW5tx3HYDfXbDYXW+uO7U5QqsVM8DMGt369YtNDc3o62tDfX19R6HnnxGPhgYGPC8zm1+dBpsB5+/tVGf3+83Dvr8/Hw+eHr6NIaPH8fz2SyeDYU8DYdVJytA52ukJwEvx6COJflZddX4+Dj8fj/279+Purq6h7DAZ3M5/NB18YntbSSTScO7i4uL2NrawsDAgKEdZWJzcxMDAwMoKipCa2urKUVnIAGAcfQIyB1nZ8uG0o4/u5VQK36y7RZ1mmbpbHnkeBTgz506hbn3vx/PZDJ4fzoNv9+PqqoqU72RSqXgOA4GBwdNP4etrS0kk0kMDg4iGAyioaEB7e3tWF1dNdU8ExMTaBofR/HvfgcAyBXkL5VKeXQ+ZYUBNq49e0Kpk6qX7WzZMsOxql0lL84X+O7k6ChOzs4iEAigrKwMpaWl2NzcxOjoKIqLixGLxUz2cLiwBW5ychJbW1vIZDLo6+vz6IulpSWMjIygcXoaZW++mZfHYNCcBKV2XQN7un76GcVXmi22M/mcN/mY92Six3aUgsEgKioq0NjYCNfNBx0dx0FXVxe2t7fR0NCApaUlZLNZVFZWYmxsDJ8MBvFsoY9S6Z495pSNN998E9euXUMulzNBp7W1Nfw2GsWPi4rw4vY2ni+sn90ID9jpPcP5qiyTb21MpI7/bvpadeHS0pIHC2vA4B/DMPoM1bsMCKt+oS7ScWswVrGf8ryeqEL+1OMpbT0OPBzQ3w2TqU+iSWDiJL9/J6Glz9B1cBzH00icelBlUpMdXEt1ehXn286+bhXfzTfQrWHqbNs4U3/rGuqa2XOkQ666UDGSYlWOS2nMgKb6iuQJ8qLqao5B8YJiWU06+gr+NddL+V3HaMuI+kG7YUA+j/Kn9kbp9koohG9ub+Nz4bCnWu4fu95zoCESiZhSKQDYc/kyTs7O4vLly9jO7px9SrClzK7MQ+dKBd4OKJAwKgwkMiesC+g4+f1kjOSyPJ9CoISvrKyEz5fveEvB0MYr6oRr9oiXjoFzsyNYFAJ+nmPkM1zXxZWmJlx44gl0XruGxokJrK6uGrC4tbVl9gRyITW6yI7H+kylC18PBoMme6uAWpUf50PDZF86dj6TGUpmOWwjr4pfn63vAcC55masFBfj2v79ODQ66qGrgmW+zvlzHso/qmhVMdlbWm60tuJSWxsO3L6N/Xfvmu8lEomHALwKmgou16f99m3suXwZPp8PP3rxRawmErjR3Y2+kRGP0rPno/soHcfB+vq6KdUsKSnx7MdVRbubotCxaqVDUVERUqkU3q6owL2+PnTduIHKK1dw/fp14zCtr6+bbCnHRaNBmitNdd+jLdOqtDguOzinY+aaKW+QbxlEOHD7NnofPHjIgXBdF5fb27ESj+NGdzcOj409ZHTtQJE6HlSoGhWnPGjEV2Xpz32NFvidmc7h4WFzDGz1H/+I2fe9D3Vnz+L69esYHh5GU1MT9u3bh1OnThngNTk56QEDpI/Kuq4XL1t36DqTj44dO5bfe764iHfeeQfJZNLTMZ/GiA1RHSdfWaI6gfRVB1bXUMEakM/eRiIR44z4fD7Uvvkmph9/HNXnzuHHQ0PYs2cPmpubUV9fj3379pm5jY6OPuRo2YEEAmh11jS4ovovl8shmUzi1KlTSCaTWFhYwNzcnMlCMJtAWjY3N3vAPnmQz2fmjTKvmR/aKtfNZ+Xu3r1rHG2fz4f5+XmcOXMGd+/eRWtrKw4ePIiysjLU1taiqKgIm5ubGBsbM+una6xAWZ0W1aM6Ds49m82iuroa/f39CAQCGB8fN4EDgiHavUAgYKpK6CwooNSjBJUPeR8NLM7NzWF6etrIwq3eXmQiEXx3dRVtV6/i0KFDKC4uNt2x19bWzOkOnKOtb5T/bdywW6CluLgY08eP499tb+PTfj+e83srGj8L4LMA1lwX80VFGB0dRUVFBUZGRoxczs/P4/jx4wgGg0in06YBLem9traG1dVVnD9/HpFIxJPB5/qoE8zxE6zaDpDOX/mbc+ZFrEO54/rxmaxeUn07fvo01ouL8ZtQCJ8QHlteXobP58PExAT8fj+Gh4fxu1gM8x/4AD64sIATs7NIJpNYW1vD3bt3UV9fj2w2i4WFBQQCATx48MCAdcoOg4a2vrDxGXWXYkSuuWZxKYt2UJxgnRWlNna4f+gQMvE4Lra2mjmXl5cjmUziwYMHSCaT5pjGwcFBrK2tIRQKGb7d2NjA+Pg4jh49imw2i5GREQSDQYyNjSEYDCKVSpktfGxWx3XiGurWGV1DxXC23rd1vR0kpk5RHeW6LuZPn8boyZN4dHISjy4uIhgMoqSkBPv378fy8rIZTyKRwMjICG7evOmpGE2lUrh37x5KSkqwb98+81oul8Pc3By2t7eN//CTn/wEFRUV+NGLLyIVjeIb6+s4trjo6b9B/lNnXrG8Yg3d5qmO0/z8vHHIFLcyEFBWVoaioiKsr68/1AiWtkO3IGqPBr6m1QU/9fnw8+JiPJdO4yOFJCAxFfmOwR8bWypf6vqprlZ9TkeWz+BFnrafs7m5aQJ81Il0elVfasWYYgStAAXyQb/t7W1kMhmEw2Gzduqg8ztaRaE9n2wconhQ/SiVC3XuyctacaG6QMeg+kOdYw0yq12wfU31OblWig//lD5WfKO6S4MZtKvZbNYE2vQZand5aUU9n8Nn8F72PGwfTHG1ztWmB6steb+PuC6eDgQQkrX4p673HGhYXV01DhkJrEclhQrZhng8/hDBdaFu7tuH73Z348m5OZwulHrbDrLtEAMP70EBds52dt18+U55eTlqampMAIFNIRkVrCqUeWxtbaG4uBglJSUPgQ41brwYANiNkezXlNFt8Mr3fT5fvpdFPI47Bw7g4PAwYrGYqVCIRqNYX19HcXGxKX1jIOBqczN+uX8/HpmcROe9ewC8ZVHqUHEsWnatilqZ1A7+6H5ozpMAWRWxZgF5LxUsVY7suEzaVMzPYyUaRfXSkifqC+wYU5ZP7RbI4OcUFJEv+B17P9nl9nak4nFc7+xE79CQARwrKyvIZrOIRCJmX78GC7RkdW1tzTRr43aRo0NDuNTWhsP373vW2o6Mks5aFshGYbq/dH193QAQypoGthzH8QQEVGGR7qWlpRg8cgRriQRu9/biE7dvo6am5iGnH8hnTXfjcTVyClxVsdpOvcqGOj4anFAlat/rcns7VpNJXO/uRuf9+x5ngHM9cv8+Lre3o7+wP9gGnK67k83SQJgqXC275dgIZgDgWksLrrS34+/x57+Gh4exsLCA1dVVAHnnhidhVLz9NqrPns1nNQtbPfbv34+mpiaPfhoaGkJra6unt4zKyZ/KzJCG+ll+vqioCNXV1QiHw/jB5ib+zudD9fY2DtTX43cVFfjExgaekWASAExOTprGkArWVM+obKpOcV0Xr5eU4A+VlTg2O4umdNo0ofL5fCh94w1UnTmDoqIirBYXmx4bOvZUKoXh4WHs2bMHwMNdlHWsGphUvlFe5DgPHDiAX4XD+GY6jSPj42i9cQN9fX1GvtjpmXReXFz0PM8OwFIP6xgIgOhgpdNprK2tme1o2sRMZaWxsdF0bHccB3Nzc7setatrb2c3bflTfR+LxXDkyBH82HXxX1IpNN68icaGBrxRW4uPrK7iQwXng7Y/Go0aXlaHUIGbPledXA2ozszMYHNzE0tLSwiHw6h76y1MPvII9ly8iIuDg1hcXMSpU6fQ1tYGAJifn8f6+roHMBKAcr01q71bhZLqpUgkgp6eHvzfslmMOg7+dnUV+6enPfqRc/L7830URkZGsL29jaqqKty5c8ezB7alpSXfY6kQaNBM0blz5zA5OWnAJfvLKD+pzaBOj0ajpmqA8kSaqo23s1KKBXar+IrH4yYLrRU/7Zcv48GxY3glkUB5ebmxUwMDA7h9+zZCoRA2NjbyxxWeOIHNSARv1NXhxaIiLC8vY3FxESsrK8axXlxcxLnCaVXRaNQEJylTvMg3xJlcK/IV5dR22tROqkNqO3iu63qaJqt+aDl/HvPvfz8+Ewqht7fX3C+TyWBsbAyBQADr6+vY2toygdHe3l6z7WK20KR4dnYW1dXVprLl7NmznmykBno1MEbdoPpbaWLLr763Gw6yeUKfl81mMXDkCDLFxXi3uRkvFho9hkIhU62xtLQEx8lXLo2MjOB3xcWYOH0aj05Oom90FNlsFrOzs5icnERrayvGx8exvLyMay0t+NbHPoaGkhLUnjtnSuz7+/uRjETwquPghcLWYMUYDB6os6T8rXOi46+O2+LiIm7cuIHS0lKUlZUZe0S+p4w1NjYiHo+bIDf1EelJ55iZc1032kvq8F/kcpgNhfDtTAbuL39pEkpcz6qqKtMYXXEheVyD4hyn4hfyMqvKgXwl9/DwsEfPR6NRj05pampCKpXyVLLodhV1vKmLSXP7b9d1sbS0ZLZAMDjJdeD97e29xFYcg+J2lVENnCjuVpzGXmSv5XL4YSCA7nQad1wXrwSD+GjW25yZtGbSmb4haa8BSgaN6O9pcJKfVezC32rnbYdffRP1i+gDaACDgR97C5UmJTRxqnqMeo+8RvlWf1m3SnHsmhzRe6ofAcAzJn7WTuL+Y9d7DjSwRJCZl8mjR/H24cNoqahAV2HvLBnQcRxcqKvD2eZmHBkYwIEHD4zAXNu/H8vRKP5YXY3ThZMedIK81JFkEMNuHENlca62Fm/396Pz2jVUjo5icXHROICLi4vGKaPCcF0X9zo7MdTfj0enptBeCHhQYeh47EVRYKmCqk4yP2Nn8HTsJ0dHca65GScmJ03jM95XDYQ+y3EcXOvsxHJxMc40NqLjzh0PSLMNFp9lOxk2w6jjQeNmOyHXWlpwcd8+HLh9G10FZzoSiaCsrMysi4J7KksAJliSSqWQSqVMFHymrAyO34/ZQpMkpZ3STP9WYadw2XPRz+q8fD4fjty/j0vt7ThUONucQZW1tTWcq6nB3b4+7L92DWVvvmm2sfh8+f15i48+iqH+fnTfvIkj4+OeMvzeoSF0Dwzk17wA3JQnlK8V5KnR18+wIejq6qpRgqo01PjaToKu+5GBAVxsa8OhwUFjcBV4ajBGaWoHaXRsNOY2z9j8xff0f/1R4Eswmc1m0X3jBm739uLQ/fsmcKk8HA6HcWJ2FicL27lQkHWuuc5f6atgUteC68Q1I5C5un8/VgrNQP/cV2VlpeeYzXQ6/ZBTUVRUhJaWFvT29pp90JxjNpvN96gYGcG5c+eMfqX+UL7gVgM16Po3DXAoFEJTU5NpbPb1rS1MBwJY7e/HbCKB6UAAP4vF8JIYU7/fj3g8juVC0zO+Tt2ljbq4LjZYfr26GvPhMN6qq8NHfv970zWe92KVWmNjI5588kk0NTWZzIjj5LehLSwsYH19HWtrax5HX8Ea70k6Uf7sksFgMIi9e/eipKQE30ylMOY4SNfWor/QoJGfJR+Ttiwhp5NG/lZ9pDKszgQDC3fu3DHl61rCyH3xx48fR19fn3HugTwon52dRSqVMqX2uzn0CgRVruloEhiHw2E0NDQgFArh64uLGAew3NWF0WQSs34/fldRgX8p+2+5XXFsbAy5XL5vRLhQVqmOj+oDlWXuC2Y1g8pB3fnzaLpyBfF4HN39/Th9+jSSyaTh79nZ2Yca19KOk/+1UZp+lvZJy1JbWloQjUbxpUwGf7+2hrZ799B98qQn06TzmJubQ0tLCyKRiHGWotEoIpEIent7TbNSZoPJr9oUWQMjXCcCTto7/lRVVaGzsxPb29sYGRnxnCihfMxABzPJmh3k2Gn/ioqKzNaGUCiEb6+t4ZdFRei6fh3td+6gd2gIH0in8cQTT2Bkft6jo2pqarBv3z7cunULsVgMe8+fR+rDH8ZXkknsLSkx20N4lOfi4iImJiYwPz9vAje0bX5/ftuE4q6lxx7D8lNP4fDAAPZcvowF63QwDWqpU8r1pI1Wh434hE0qr+/di3ebm9H87rtovXEDgUAA3QMD+PypU1hbW8NwIdMYjUYRCASwvLyMzc1NtLa24sKFC8aZ2Lt3r6lUiEajiMfjZjvf4OAgrly5YnpsRQrHpTJpQb5afPRRLD31FPaeP4+68+c99jQouEKDEmrT1ZlRGaADp8dwqm7cd+EC5t7/frzo86G+vt48KxAI4N69ezh37hzq6+vNaU33Dx1CuqgIZ5ua8KlIBIuLi4jH48YJ5BHg33EcrJeXY+zkSZS8/rrBgoODg3isthafbWoy2z65hUEdSztYZM+XW8800OC6+eP+nn32WRMYs508/r24uIg7d+6YLTGAt2G34hv2FGNwhraTMnconcabdXU4ODCArq4u7NmzB/F43ONL2DxqB0+AndNi1LFUR5wO8fr6Ou4WqnLZHFqDabxPJpNBTU2NSQaQBhoI0e0p/Ax9C/pNHNtbb71lgrvqJAeDQdTW1np6lwDek5g0yMM5cpysFlQcA+SDKXNzcx6dFg6H8e3ubswFApjO5ZDz5Y9HfdLacsKKFFYuqd1X+8s1YoBhZmbGJDo1OUD+00SsfU8NAtrfU91m+5r6t46NfMLABTEjX9MqXPUFeC8ma3VrtfIw+xAphtcG08qPOs73um0C+GcEGrhHlUfevHHkCFYTCYydOoX3r65ic3PTAAyfz4dzLS1YKS7G5fZ2HBkfB5AX4CODg7i6fz/eJ6cF7OYE8z3u8bUj10qwcy0tWIvFcLevD8empgyDFhcXmz4BdGS4CL/q78dScTHerq9Hy7Vr5j0S9WpzM67t34/TExM4NjXlGZ8tkLq4FCAAHgOqc9rc3MSR8XEcm5rKf6aw0FxUftZ2ljY2NnDwzh1c7ehA7717uNjQgCvvfz/67txBz9CQGQMVkgIOgn4yp63EVdjsaDoAXCwcbXi9qwsHCuXsjOZHo1Gz3z8ajZqyKm7/KC8vx/r6OpaXl1FUVGROBjn24AEu7duHwwMDnigcDaKWUGokXsdOZte5cA01akl+OTA8jJ6hofw8sSPApaWlGDhyJF9lcvAgnr9/3zj7XL/vHD+OtUQCd/v68Ggh22QrCKU/11ozF3aZtlaSqCJUntFAEw0Ij/fR0nk+U6PCLdevo/Hy5XwWTOSM47jd3o5bvb3ou3vXyCn52t5CYe/nVaOg99XLDrRQhs9WV+PNujqcHBnBqfl5IzvZbBa1m5v4wLvv5o1DwUnW/XhKXw2YkJ7ATud8+2hEDYa5ruvZP86yYRqbYw8e4ELBsfxzXyUlJZiamkI6nTbZIvI+AUM4HMZQTw9+cfAgXnIcfMaiM4MNLFElDdR5sx0V1aGUs2g0img0imQyifr6esNznw2F8Herq2i4fBknTp7ET4qK8Hw2i5ysI2leUVHxULBMn0VdqBVouVx+29rHNzbwWjaLxgsXMDMz49l7TScxGAzi0KFDaGhoMOtNHmeFw8WLF40josEW0pP3UhClWyqYAYrFYqipqUEoFMJnC/sRO4eGPGXM9jYiOqnj4+MGuGlwi1kSDXaRj30+H34ZCuHVxkY0rawgfucOMpmMx+6Fw2F0d3ejs7PT6EO9Njc3ce3aNZNJ1UCNOhQ2yLADDMXFxRjo7sa11VV8MRrFF2Mx/N3qKmqvXcPJkyfxo3AYnxGbx2fE43E0Nzdjbm7ONG+0daPyjAKbXxcV4SeRCLrSaZSMjZnsG8ddVFSEgwcP4vjx46bKj/fb3t7GgwcPcO3aNc/eaOoqPdVD+ZGlp9zmwdM7+vr64PP58JlYDB93XXx9eBj+06cNP6mdAvLbB5ubmxGLxXD16lXjKB85cgTJZPIh+8QG0MzuAw930WczXg2Ocd3m5+dx4cIFBIP5E5EmJyc9lVvkSW775Jqrk8SxFBcXm3l/+MMfNk7sH+fnkfb7MfHII/ifC1tnfh0O41+7Lj64uYlnC3OPx+NoaGjA4uIiqqqqcPToUfSsr+NfFyqL6HCzyePS0hIGCsF4bt2jLNPea8AFABY+8AFsVVTgWjCI5qtX0dTUhHQ6bebL9aUDq6XstIN0Whj8cl0Xe/bswcmTJ1FUVIRXNzeRdl1MPfooXi7Q5FpLC/5NIoFHV1dxunBaRiQSQTgcxvve9z7TE6y5uRnpdBoHDhwwASXKbnl5OWKxGAYGBkyVKitstcqGawsAC08+ia2KCkw++ijev7qK2dlZM18Ghaif+b/aQPI8bQDXu7KyEl1dXYjFYkin0/jFL37hSU48urSEZ7a2MDY2hruF8bH5LenV3d2Nd999F7lcDt03b2L6scfwhUQC+xIJxONxJBIJcxLZxsYGfv3rX6Ny/35snj6Nyj/8wejMXC6HPyQS+GYyiU8tLeGLyDuGCwsLnmagmrDTpIf6Akwi0paQB+rq6lBXV4etrS384Q9/8Ogk6mU6rAcPHsTGxoZnm4ViZF6KwTgOymc2m8WR3/0OG9//PkKhEN6Ix1FfX48jR46gvr4elZWVCIfDWFhYwMrKyq6BYOV7tUnq0GtixHVdHDt2zFRqsyJDMaXfn9+W9+DBA2QyGbPNUfGcz5dvgJvL5UxVNWmlQRbHyfdwmZiYQEtLC9rb21FVVWVwMsevDjJtE/+njtFAvVZsAzvbPzj2+4XEZmVlpSfg8dj0NN6orcXejQ2Mx+N4JRxGtLD1XIPIGmjkvDWxZWOZ13I5/H0ohN5bt8yWbtX/ra2tqKio8Mguv68JONX76p8o75BWk5OTmJ2dNb0u1Lf0+fIn/O3fvx+LhW1NDHoSG/Nz6j/Q38tkMh4fiOvLU2TYTF37H7EXoFagkAY/8/vxaiCAF3I5fML3Z65omJ2dNaBkdXUVrRcuYODwYbRevWr23N3t6MAPDx/OO+fDwzjX0oKjQ0PmHq7r4vDYGD5SYCK+poulkUqWbSlosYMNfr8fT8zM4M36erw/lUJ7e/tDUW1eus/kidlZvF5bi7pUCl976inTkJD3v7p/P1KJBM42NeH49DQAb5mNgkidnzKaKiwAuL53Ly62tqL75k3sv3sX0WgUpaWlnrPn1RlQALK5uYmVlRXsmZjA3uvXEQwG8f1nn0UqkcC1zk4cGB5+aL+MKkkqJzVq6hxrtkaVFGnYPziIS/v2oX9oyFMZkslksLS0ZM4DZ4M6OhfxeBxnKisx9MQTOHjnDvonJkzkuOv+ffQODeXphZ0Gflq2c6mxEedaWvDY1BQeKTgRHKM6Cpy7HXDgXLguquC5pnRGjg4N4XxrK/qHhx/qog7A0ODI4KDHsGujOQ3S5HI53O3owO2DB3FqdBS9Dx546M7va/RRwTkVyaXGRlzp6MChe/dMkEf39NrRUioODYAxe6Ty4DgOfnr4MNZiMdw+cAAfLlQr2eOz6WnPlQbKHr/Nazr3t+rqsBiN4syePegrnPpCB9/er6xzZJ8AKj5dWwIvlsoRXNKhINjm+AlMeA86uLlcDhfr63GpuRkHbt4Enn4af+6LwTlmx+gYRKNR1NfXmxND/kNvL2b8fnzfcfApCSawbJq0AWAaCjIzoPxRWlqKQ4cOIRAI4Pr161hbW0MsFkN5ebkBCkVFRUgmkwYMfDoUQvPVq/jj3bt45vhxPLO+nnfS4W3QCuQNMbvuc811j6Y2bFLDvL29jX+xuYmqH/8Y58+fR6rQEJM8TP04e/Ik/teuLrycy+HFgkHkmmcyGSP/0WjUbJlLJpNm/qRvR0cHuru7MTIygmvXrnl4lvYtkUiguroaAPDpaBQvhsP49vAwivbsMfpG5V/Bb2NjowG/Wm5I54K8q42u/H4/fpnJYMF1kTl4EF0/+5mR2UgkglgshtmTJ/E3R49iLRjEswIEaStJYyYDYrGYccq5VvF4HCdPnkQgEDBBCQXNnMP5lhbMOg6+tr6On5SU4NHtbXz1zh08fewYXtolw8j1ZqNZOhukEy8+g7wL5OXuv02lMOs4WNu7F/0/+pFxACjPMydO4P88cQKZaBTPbm+bbLACWvY54OkGfDZ1aFtbG/z+fB8BlufzPQYz9u/fbxxjXqStJgyAHX0WjUZRWVmJUCiE2tpaA+L2799vvkvc4TgOiouLEQ6Hsby8jLGxMXNvvXjigQbWVe+Sb6i/tre3zTYarVrI5XIoKytDLpdveKe8q/xXXl5uGji7rosXHAdfS6fxcjKJsrIy+Hw+/GBrC1Oui9+WleGzBR2jDe+Ki4sxODiI2tpaA3I1oLm6umqaX//+9783+8gdxzHl9KzsUAe86g9/wOIHPoCGd9/F7OzsQ+uqWVK/3497nZ0YLFS37rt5E4DXaaNe6enpQWlpKbLZLD4H4P+7sIBP+/147LHHEAqF8H9ks5h0HLxRU4MPZjKmiSGP1F1eXsbc3Bxqa2sxPz+Prq4uj71jUIlrf+/ePSPr5B8GSDXpUPmHP2Dpgx9E7bvvmr4OXGdmNHl/9jfh68pLxM2hUAixWAzPPvssSktLja2+efMmTp8+be7D5JDr5k8TouPhuq452WZhYQElJSXo7u7GR0pL8bE9e0zFDKt1kskkMpkMVldXUVRUhIq330bpG2/k16eQHQ4EArhx5AjWIxF8Y2UFke9+F+Xl5aaygWtLTKN2jJXNqndVHoPBIJqbm5FMJo0sRqNRHD582Ogt6m7HcUwD9pGREdOQk5faONuPsPl0amoKFy5cME61Jiz0+9xmQ4eOvKy4lHbcrlSiXPG94uJi0yz+3XffNXKtdpnPra+vR0NDg6mQ5Xt2okt9AtvO+Xz5XkgrKyu4ceMGhoaGUFVVhUceecTgFco6t9Epr+q6atUIAFOZRfxC+7KysoJTp06hoqLCE5wAgMcCAfy7QiJpc3MTQ0NDGHYc1NbWmnXgs1hdR3qzMgLIV+uzAicQCOBrpaWYLyrCtf378eXKStTU1BhZ8Pny21xs513ppbqPvKm6W/nI7/djfn4eExMTJmDNSgxgJ4FYXl6OXC6Huro6zM7OmlYGmgxUvMFLAw7Etgw8Ly0t4e7du4YPNAhXW1uLvXv3enAG3/9BZSWm/X58z3Fwcnb2oWfudr3nQEN7e7sZuN/vx0EAgVu3EIhE4C/sW/15dzcWo1G809CAr/zxj+geGMgTt3APAiwVBHWyuGgkMAmoDg2Z5UZrK650dODk6CiOPniA4zMzeePpeMty1VDz/svLy2iemMArly7h6x/+MFbicVzbvx9HxsfN8w7fv4+rHR04VWh0xDEpqNTXyGw0DHZm2ufL92VYKS7Grd5eHBodNccJ0kik02mzTxLId6GmwaDyJohzXReH79/HlfZ29BWMmJYp/2MOLH+rQlFFyvkok/UODZkjHLOOt3s3A0LxeNw0BePaOo6DN0+cQDqZxK2eHvRPTDyktDlOzoGRWyDfMHIxGsVb9fV4rACWdA50IKlQ1PhoFkfntdvfjuOgZ2gIe2/cyEd4LcF0XRfthRMOotEoHJ/vIZ6lE6G0vX3wIFKJBN5tafEEGrg+yuf8rUEsv9+Pa/v35/tKdHXh2NSUKR3WaCM/b0f+1RHRigcqsyMDA7iwbx8ODQxgpiBDNAgEftwTTCVJ0EblxnkoiNIgHOdKWgYCATw+PY3fNDRgMxTCjdZWnCxkYrk1Sys0WHXAfa40HHyODWpzuZypJGG2g8+nDmKjVdKCYI2fvfDYY1hLJHC1owN/iYtzI3/S0enr60NlZaU5AvYFx8GPHAcvWnJL+WDQTq+5uTlPBJpbI55//nk4joPZ2VmzFjxmDMhXYLW2tnp67+RyObOtQnlUq1v4WV1rO7qugSs74s9Aq4LM+vp6tLa2or29HSUlJfivy8ow7ffju66L56ytPvyegrbV1VWTOSIdIpEIDh06hM7OTmxsbGBxcRHLy8sPVcTU1dWZfafkRcqCNnSkLLFagdm00tJSQwvSRZtI8vPaPfyL0Sj+w+wsyt94wwB/Nrzs6OjAf9/QgPlAAD90HDxdCFqofHBd/P78yQ2rq6seABsIBFBZWYlXXnkF0WgU165dw+DgoAFkXLdAIIBHDx3CxX378FkJ/AHw/K2AUQPEbCyr1R8KojVzx/X6UiyGv11ZQfztt829kskkGhoacOLECfwPe/ZgIRjE91wXT0s5qq5ZMBjE9PQ0ZmdnPYE2Bqs+//nP5x3Re/dw//59TzCBvEHHSwP8pC/BvTrXfIb21xkbG/OcbqO6DMjr5xs3bpgtncon+ix1AmwHSSu7gsEgZmZmTNUA1yUYzDfz++IXv4iFhQX83d/9nQf889lVVVX4/Oc/b+YKAM/5/XB/8xs8/+lPG/q8EgziW9ksXo5EUF8I5NDRp07p7u5GW1ubkQUboJaVlZmTj3j8NYMd9jYW0qXu/PmdZEQBWGcyGWxsbKCsrMyMj8GimccfRzoUwlB/Pw6PjXkC4HSyP/axj6GlpcXI8UuRCILvvovDhw8jVJgbK5lejkbR2dlpsqB0dOvq6hCLxRCPx1FeXm5K5NUW07liEIh09/l8nv4uGkSqfOcdtN++nQ/ICQ5iAkGzsg8OHMDA4cN4fGYG1dL7gZhuoKsL17q68Bm/H+Xl5eb5DLjU1taaQA/tH/kwFouZ8mnyeDqdxp49e/LY8/Bhg9M4HgYGQ6EQFhYWsLCw4Nlfrnze8NZbGD99GrXvvIN79+4ZWi0uLpogLWVRq9G0glMTKHy9vLwcfX19Bp+wmlcrW9QmAMDCwgL8fr/neHOVB7VztDnku2w2i8nJSbxeOJ6VTmxrayseffRR07CX4wCAsULVlgYQWC2jtoV2l/NT3VRRUYHq6mpj51lRRt9BE2sMWE9MTBhsQD6hHaGO0UCL2hQ6/ZcvXzaB/Fwuh6amJlOlxXVZWVkxWxjVZiifKxYg/9ABZjVDJBJBcXGx4d0LFy54+oipXdne3kZFRQXq6+tN/zStVtS1JN5TfETd7Pf78dlMBn+zvIyGc+fw88FBtLa24ujRowaX0cZuSmJO+Yn0pF6yAxKKNxmkfeaZZxCLxTzbIJS/iY3v3r2L7e1tlJWVGf2r60TbSLmkL0psS7y8sbGBoaEhFBcXo7m5GXv37vXImMo08RXv8YrPh++6Ll4KBlFTU4P3cr3nQEN5ebkn4qXRKAreY1NTeKO2FidGRz3ZGjKgCvqfuvQZSkCNxhunPRbDOw0NaL1xA47jmOaOfA6/z+0Xrusah537iXtv3cL1zk4cHhz0PLd3aAj9ExP5ZkFWYxN1FNWwKLPxcxyDz1fYL79vH44ODZkurZf37MH1zk60X7mC6lu3sL6+br5XVFRkmhWxlIVRN5/Ph96hIfSyYiSws++YzyTD614aew3twAPXSasjOFe7bItrQUXqOI6nHJT3PnT/Pq50dODgnTse8GEDKGDHUQbyx19uBIMo3trC41NTnmfqReFlUEZLzbgfUjv/6vdIA82I2wZJeZxjI9DjemsmiYLu9/tx5P59XNm/HycLWXvlD3srgka5+Voul8PBu3dxdf9+w6N6ZCD/J48oENYSes6H9KNTdGp+Ho8tL2N7exvrBcOmivBKUxMutraif2gInYWAls375As6GfoZe50oQ6fm5/HHmhosxWJ4vboa9RcuwHEcE1Swv6sOqjo++hydI5/FsekJNFwbBTC2XBy4fRuX+/qQlUZkf87Ldg4XHnkEP3vqKRT7fGiVPdsfy2ZN88WNAh2i0aiH9vbFrD6dYtIiEomYo4j5XdthUydhc3PTnM5DnlE6qyypwSMNSWvVm7zUGKoxCwaDmDlxAnefegp1oRDqCrR40XHwqs+HTwEeoEAZofzzdS2Xp8Hk1gDHcUzJ++LioodnlC84Vx5FzEuzitSzAIwc2kF0/k062Bknn8+HlyIRpH7zG1y8eBGBcBg1NTX49Kc/bZzmT7kuflgIOHGsdkBP9w3vRgMN8nd2duLChQueCh/O/bGlJfzfa2uRyWSMnNvzoY5SR8kGcHYVmZ2hIx98JhbD4bEx/B+XLmHT50MsFsPTTz+Nuro6FBUV4SWfDz8E8Cl4ARWfz2dEo1Gsra2ZbVMcA4ErbQPLpElH8r1dWcB56rqRnuokcF7Ly8tIJpOerVAK1FkGPDU1Zbr26zM5XtJPnTM6Svy7vr7eVBO67k4vH36XQR/ahFQqZYJqGsjgUdkq21xjBi9c18UzjoOPAYgWeEj18cbGBt5++21sbm6aih6Ok/NjVv/MmTNmexNpaScIVI8wyEdQHY1GkUqlsLq6apwZ0j8Wi+HTfj9+HAig+c4d3Lp166FAbCKRMH0TKNu6zYTz/2QwaJoiAvAE7oLB/HGU586dw/j4OJqbm03wUOUhGAyatZ6bmzP6SrHZn7q4hnQQ7WoPv9+P2y+9hHQkgnebm7H/a18zSSvqlVsf+hA2IxH8PBTCv4U3KaZOmuqoyspKT3M/1Rs1NTVwHAejo6MeOeTacMvE2toa6uvrUV5ebjKvXFPyRs25cyh/++08fyUSJmBJPqDTz/Emk0mcOHECKysruHfv3kNBRlYCHjx40ONYakUI5897cs6sXmNwzuZF8q/6MrlcDj8B8F3XReXduyiztiUnk0lUVFSYOfP1cDiMBw8eYHh42NNwMBAIYKSvD4P9/dh/9SpaCwkv3TrIQNPV5mbcb27GVwB8smAfmAm39SKQD3itrKxgZWUF5eXlhpd4Xw2oqz1xHAe/Ky7Gr5NJPDE7i81f/crIbjgcRmtrKx5//HET8CDfbW9vY3p6GisrK2ZrD+2v9tNQnUz6MCkWDAZRXV2NhoYGI8Pl5eWor683zyFeZJAxEolgenoa8/PznsoB4gsG1ahz1F7RTmxvbyM3OIjjN24AADbicdTV1XkCRa7rYmVlxdh7rh/f1wpntYPAjl/E9QkXbH08HsfCwgJuFhKZKlcqP7W1tYhGo6bfBnUN56RY2MbQXF/HcRCPx7G+vo6JiQlzikxPTw86OzuRTCbhuq5ptEx6U0ZP+Xw4hTzeWLCe8aeu9xxo4KWOIge9tbWF5eVlNA4P46VCCWcWu3fpJOhTJcvPcTH4HAXR6gy6rou+u3dxpaMDBwrO69ramlk49goIBoOmGzLL5JaWlsypAq7romdwEAcePIDf78eW490Lr5kKzsF20HlxXFRCnKNWFxwaHTXPcpF3Vm/19mK1uBgDR47gSOH8bt3DR6XHMWjUmrTj8wlGdGzqQCmdKegKsHXu6gDrPTQyaN9P10sd8447d9Bx506eNnIsC5+rBkZB6rmWFmTCYVRsbODk3Bx8gYAHuNhARYMmAMwxoOrAq7FRR4UK1w4U2PR0XdeAEy3d1EwMvxMIBPDI4iJOvvVWfl3FybAdD70/6UtjUFRUBB8A19lpwkh+oxNNJcrxcYxUsFwvYMchIZiwHSbOa2NjA+8++ihSsRjONjWh8p13HgKRdORJKwXJpK9tpHmdHB3FW/X16Ll1y9yXoJ98cae9HZcOHQIAHLt+3bNFSMEEn609HLSiY7tQcq3OjuM4uNrcjPMtLei9dQv7pbz1wIMHuNnTg1ShI/qf++IebNJt8pFHsB6N4jXHwYcLMq5rpoCKJbOcN4NLGnXWverBYND0qiE/2I0BgXxgk010XdfFvXv3sLW1hYaGBvNZ8rteqhtUVxFc8G9bf3D8LDGmHhg/dQrrRUX4geviqcK6fgLAx2U/Jauo6DhqYJkBQwXQBCPxeBwAUFZWhoaGBrO3n7zPgK4GLx88eGAqO8jPagP4fd3vac9Xf1O2VdcA3p4/Y4cP49+WlOCFXA4f3tzE0z4fninwglu49/r6OuLxuCcLQsCvckbAqnswKysrjaypfiOPcezr6+vmSD7b+eW99TkKekgnjo9rpPyhuIDvTR49in/f3o4Xcjk86/OZtQ8EAnD9fhOMDwaDnu2Q3FJBPtJ5ky59fX24Xth6yHWifNjZ0vHxcU9G2XYalP+DwSA+8IEP4MKFC3BdFw0NDZ6tWYFAAP8Qi+GHR46gv6QEJYV+OAzmcF2UJhqUUp6qrKxEf38/JicncenSJc9c1NFlIzO1q9SRDEAkk0kzH67ZwsKCJ0DIsfA+fI+O0r59+/D4449jZGQEe/fuNeMgf7GcmtvZdP+92lc7gKeX3+/Hnj17UFZWhoWFBcOj5JtwOIy6ujp8paoK/3Ushv/PxASuFJwwlUPqI92uoGuqjjeDB/zhd9h3qqurC8XFxcYWkkaUlc3NTdOklZVjtgzp+KgTSQPyJHlDcZnrumg5dw5rH/sYPuPz4d0CDld8UPvGG0h9+MP4N/v2wS/YlQ7f1taW2etNnc6AC/ED55TL5TA2NmaOY7X5g2s+ODiIS5cu4d69e8ZBJLZQ54vrrYE88snc3BzGx8c9NOrt7cXjjz+OsbEx/OY3v8Hc3JyHR6LRKL74xS+iubkZKysrBs+R3rpdijpJx1NRUYGKigpPUNUOBNm89NryMmYcB6n+fpw4c8Y43/F4HD09PR4MqfqYPEfe4VgfHD2KdEkJxk6dwiMLC4Z+lDtWub3W348p18XX1tfxyUTCvK8l8rourusimUzCcfJVoaWlpR6e1YoGO9H420gEM4EAfpFIoG9+3tx7/PBhXHjf+xApKsInLac+GAzi9u3bGBkZ8fgRlCGtYtJgivZxKikpQVdXl8E65FvqaPKP9tVaXFzE2toaSkpKDC4lfRmsVMyv+JtVsWfPnsXk5CQcx0FlZSWOHDmCgwcPwu9/ePvx0NCQqbDV5AvnQplQbKt2KpFIoLW11chIOBxGV1eXqYjkdxnQCIVC5tjc6elpTyBJbQTvx9cdx8EfEgn8vqICT87Po290FAMDA5icnASQ3zpy+vRpVFdXmy1Qfr/fU8lH/lBfya5q/ceu9xxo2NjYMJmtbDZrmhltbm6aCDMnvrGx4QE1ZIprLS242dODJ2ZncXp+3nN/HTDLYTWLxHvzft0DA+geGMgLP/IlxOfr6nC9sxPN586h8s03DdOGw2FMHz+OoRMn0HvrFg4OD3ueSUeVSt5mDB2j7aRyTFwEChAXQhWWRg15j/7BwXyVw/AwSktL4TiOOdfXdo7JxOq0qaHWPgV8Lu+htAN2ms6ocNhOAplbFb46GQq4GbxhWbqCCIIOjpHjo7ATsNBZzuVy2NjYwJGBAVxpb8cjU1PmPc7DBgWkP/e7U6B1HXW8u70OeI9V0ntT4GmIGGBj+ZQNSHO5HG63t+NKRwdOjIyYPh8KGO252A4I6XW2qQmr8TgutLZiz5UrDzl1WjquEVvSV6OoBBk+n8+TJbEBj+M4GOzpwVY4jMjGBvru3kUymfTIIoMBCpaVp3Qe6mDyOYfHxlD5zjt58FfgKzUkfr8fN7q7sVFQzDd7e/ExK6ikvKzgQfleDYwCcsdxcLG1FUvRKK51dWF/IVPCnwO3b+NaZyf+Epc2QgsGg9hz9iyWn3oKzxbWTcGlOszaRJEypMBeAZZG7VkGSyfdDhYAO9USfGY6nUYymURtba0xhHbWUWVZg228n8qWrRNUb9KYZrNZNJ87h6WnnsILIhfKczSiHCez03aWWQOYNLoMpBB82GBanTvK/dzcHA4cOIC6ujpsbm56nD91mBRMkz6qbzg20sYeq4LuWz09SPl8+GEggKclQ8f7um7+aD5gxzHmvckDOh4CNQ2OaqBLHX727CBADwaD6Orqeii7AuzsBVc+1cwv/7fHspsjyfeGjx9HJhDAaz4fni6skfIWA4aqr9Sx1Yy9ZssAmKpHXRe101zHXC6H5eVlHDhwwMxb7Z+uLXksm83iSlMTvu/z4VOTk3hybc3YpNnZWXyvvR2L4TDerq/HRwvOPnGTHcDQgIE+O5FI4PHHH0c8HsfMzIzHDpL/OZ/i4mJTtVBaWmp60FAfMPiiMp3NZvETAL9+9lkU+/143uI5DRyrzLW3t5sS54WCk5TJZLC2toa1tTWsrq4inU6b8VGeOX6VPRtzAUBFRQUOHTqE6elpM4fdgDXnw4AzeU91g+IAv9+PqakpI9cqg+QFZhiZACK9Ozs7sba2hpaWFriuaypldJ8+kx1sBqn313VVvMbxdnV1IZvNmu04KnfBYBBVFy/iP/zVX2FlZQVXCk2TOceioiKUXLiAZ30+fOnf/TtTueXz+UyzPLX9diKA650Te3TkyBETUNXGfLRhgUC+grS5uRkDAwOmYabSWuVffycSCdTV1WF+ft7DG1x/HgtJudfgKh1QDdTqD3Gg0pvz4n1YLWDPX/lQq1B8Ph8+Hw7jv2xsoOT8eTQ2NuLUqVOor68341THnc+l3M3MzGB2dtbwanFxMdqvXMHcE0/gC6WlmFlfN+/zfpSz/6a2Fv9hZgZ/FY97dP76+rqHZraslZWVmWpIxeFqW3V9stksPre9jW/ncqgrNMyPxWJoaWnB9x9/HIvRKL7vuviEVBIDMOsRCARME07eN5lMoqenBxMTE5iZmfFUkjEA2tfXh46ODg9vUu7UF9JAkeu65lnZbNZUIpFfWAXMddTG+MTyb731FsYLAWD6MdXV1UYOtre3jS7w+/24du0a0um0x/76fD6M9PXhfk8P9l+5gr2Fygja30gkYuzrk08+aZIXuVy+N0U6nfZUIKs92NraMv3wqqqqPHbO1psMjNAffb2iAnPBIH5fWYnY736H8UKbgIaGBhw/fhwNDQ0PBaUjkQjOnDmD+cJJQ4pDNfDywgsv4J+63nOgYWBgwBgzdhMe7O7Guc5OdN+4gZ7FRXNGKRlBASIAXOvsRCoWwxs1NTg6OelRQJp1th1sTp6TVOdJme5GdzdWEwmMnjyJJwpHhJHJz504gXQigdsHDqB/YsIwD8epwEmjbQqYCKrIOKqM1CmgkFKh8X0FFBSSAw8e4ODwcL50Xb6rDKPjI2Pp37w3qzjICKo4yIQKFJRxbMeMQk3QSuPJ72o2jMqeNOU9Cb4IbjWaqaVIOj5mn7e2tnBodBSPLS3ln2M55/qblwJO13Xxbk0Nru7fb47ktEvEdN5URnaUXYG1riGVogq4HQS60t6OVCKB83v34mghSrobYLV/20EI9gs5NDiIRCJh6Ew+udPRgSuFrP/xW7fQNzLieZaCYd0qoOXDfI1/BwIBvHrwIDYiEZSm0/lGqcGHT50AdiKqyqe7OVkqr7vJj64dP9Nz6xYuHzqEgN+Pxwunv6ixoQHQAB6wE6yk/Cp/kxaRSASPTE7irfp6HLx/3/PdbDaLlmvX0Hz1KvCJT+DPfdXV1Zl9vY7jYM/ly/jvOjvzvRkKNNDslI6bvFpUVOTZC0k9SRBFgBgOh5FIJIwR5ppxDRSwxWIx82yuCYEa4D2qihf1EHlIGy/Z+pWf5XuM/tNwBgIB7Ll8Gf++v9/wOsdhOyKs2LEr31S/6jO1ozN5x85EaImqNt4C4OFNPgvYCXDpUYO2Q8px7EYH/ubJHa7rovf2bQwfO4YXxEG3gatun2KATsGT2kyuD3W22jcFSaSTXuw7wTXlGNXWKxBRvczP7uZk8H/SllnWlnPnsPTBD+KTljNKGqhzreusIMled9U3u/UbIa1It2w2a/hA9ZGuq+qLdDqN4eFh/KC8HJM+H14LBvEvWluxvb2NtbU1RKNRfGxtDT93XbRdvw4AmDt1Crd7e1H1xz+i5t13AQDTx49j+vHHUf/WW6g7f97QkA0bT5w4gf7+fgwODpr3OHeVDQ2mEBTyM/YcFLO4rot/KCvDfCiE77gunhO9rPqTvLC9vW2OGCU4pww5jmO2OIyOjpoKkYqKCtNnQddX6UvnPBQKobGxER/5yEeQy+UwIT2ebPyovMU1t+VQAxSUJQBobGw0PR94MTirZdhqe9PpNJqbm+E4jsnAb2xsmGN2v7+5ibcbGtAxP4+GAo1sbKdyx/HGYjF0dnaip6cHt27d8vC4jRvIrxosow0nblN5A4CpqSnjDO52P/veiuEvNjTge66Lz01P47lAAJlMxmxHTqfT+EUwiD/09qIpnUbwF7/wrJMdCFBaNjc3o7q6GhcvXvTQg+NmRR5tFKu2QqH8Mb/V1dUmGKE8tLa2Znr16LN5KXZTmbADwkoD3uOlaBQfB/C/37mDmrY29PT0PJQU1CA/A3uapWfJfklJCXqHhvBvPvxhlJWU4D8lElhZWTHfZ+P4pqYmHKuowJNra0jIcaK0heoP8cfW/YrNqCsUE+scuIXob0dHsVJfjyeffBJ1dXUIh0L4oZtvHAsJBAAwfsjs7CxmZmbMvSorK3H48GE8++yz+OUvf4k333zTBB8ps1VVVXj55ZfNkdk23tSEnlaLcZ7JZBIJ2YqjvKf9K9SB531Jt0Qigb1792Kkrw//U0MDPuU4eFZ41nEck/DRZB17Spx59FGsBgIYPHoU/QXcTx0cDAbR2dmJk4Ujk9WOcKsz56eYln+zeTd1I+2WLePEvMSQn8vmj5rtKmzdI73W1tZMwkIryihra2tr2NjY8ARhgXzAvqioCCsrK3gv13sONAwPDxsgBeTP/z3T1IR0IoFbvb3on5gwURkVaio8Zu+vdnTgsUJUmgRUA+H37/QCUIbX3xqEUADRe/MmbvT04EjBaVAwdOj+fVxqa0Pf3buexVXwxnurs6xRP3WEdEEA4GpzMy63t+P48DD2371rGrmoA8Z7aJmuMi6FJxaLmWAHmYgKyc4+2IGH3XoRaKbTdvT0c/yfAAvwZoPVCbefoVFyjg2AqUzRsn5VEOqQ3Gprw/WuLvQPDuLw2Jgnq6vPVCXJcfN/He+V9nasFk7laL992+NQKBB3HMccPcWz6TV6zs+QD5id0/fvtLfjamcnjty/j4MjI7jW0oLtQjXAsQcPzOeojEkDrTrgGunnHMcxwahAIAAEd8rluR63enqwURj3ja4uPFU4HYDz1Ooa7Q0Qi8U860Dwzr8fn57GW3V16B8eNvRSutvRetsZsD+ncmQHH/h9e7277t9H38gImpub4bou1tJpkzGjXNAh5PNpcLVEV9cRyCvK1tZWnJiZwbGpKczMzGC9MF69J8Hwn/saGhpCMBhERUUFlpeXzfw1C8/15W8GCra3t00kXcvuJ48exdSjj+LQ/fvounvXgBCWXH5rdRX/ZzaLvkcewcHhYU9AMRQKoaysDG+UluK/mp3FK6EQ2nw7Ze+ZTAbr6+smoq/8kM1msbCwYHQot3fR+FLObP7hOm9tbaGyshKlpaXmvGxWyuieTvIp15XzpuxSl4weOoTx06fRPziI3qEhI/PxeNzswc1msygrK8O+ffs8OjQUCqG+vt7MkfpKt6coAFAb9jO/H69lMnhhbQ2fwMP9cJT/bBnI5fKNtYqLi7G8vIzugQH8T319eT4W/cYrGAwaPqfeJa+MHzmC4WPH0H3jBtpu3zYAp7KyEsFg0PQwaGxsNCCCNIpEIiYzzXtzPe1SXm534TxTheC+zk/1ggZ2KK+c++bmJvbs2YNr166h5do1/K8nTuS3Lhb6//DSijPKKe85fPAgBo8cQcfVq2grOGjhcBjl5eXmWYFAAFVVVR59HAwGjfOiwSPqWPZ44Fg5H8re8vKy2b7zlWQSX81k8MVIBOvr6+bYuJWVFXzY70fP4CBu3bsHFBXhXl8f1ktLMfu+96H56lX4fD7MP/kkNktLMfnoo6g+exY+X/4YyiNHjuD48eOoq6sztFTnmachcN3C4TCqq6tNuS2PsNU580QTXTMA+MjqKn6dTOIzhaoACC9TH/DZ6+vrmJ+fR2Njo3EG/f78STA88ry0tBTl5eWmtJnBHmYV6XiRL4E8Nuro6EB/fz/a2tpQUlKCwcJpTxynJlUYJKSNVxCudlqBOXUtv2vbJsoqj+lW+zwyMoLS0lLU1dWZrQTc4725uYmFhQVcDIeRCodx9+BB7L1+3QQvNThC2hK7VVVVYf/+/XjsscdMRYD9WdKPY1SnVgMtDDbr94C8PWhqavIcX0makMd5b9JpcXERq6ur+F4wiAmfD992HHypogLxeByZTMb0Z/hjdTWWYzHkjh5F929/uytO53i5fs3NzXjkkUfw4MGDh6qAuX7ESazIVB4IhUI4duwY4oUMv+Kd5eVl01/OpoMGNDh/G9tybWzMyfeoY+hw2vSkrKZSKdMzxd6uAgCdnZ1oaGgwgdHa2lpcqK/H9a4uvJjL4flCoKG0tNQTHKIeXFhYQDKZND6HHXDhj53sUsyndFF6+P1+s0WhpaUFjuPgg+vr+GCB/k7BRvMkGSYn9HQ613XR19eHU6dOIZfLPbQm7LHQ0tKCkpISs51Sx2MH8WmXNcBM2upaU+8rvUk7xSbxeBxdXV04fvw4Kisr8eVwGJM+H77vung66z39kBVTk5OTRo+wKezLgQBeDQbRMTSE4UIDffJ7UVERurq60NTUhAcPHpigDO0KbSNprzLMOZeWliIejxu7rLzI7+i8AOAzjoNX/H78YGoKd4uLUVVVZY7pLC8v91R8aMAoEomYYDFxVmlpKf7Vv/pXSCQS+Lu/+zu8l+s9BxpaW1tNBoULz8BB3717nn2R6lxrYKB3aAhPpFJGSdqCrgwOeBsxaYZNjb8HrEkGhEqXhD42NYXDhRI0X+HYMwU7NCrZbBY39u7FhdZWnB4fx6n5eU9ESSObvAKBAC61tWE5FsO55ma0375tjLo6Aho18/v9nnJhv99vhE2DABQiBTlq8Pndiw0NOL93L04MD+PIxIR5pkYoSQ/eXwWP89jc3MQ7lZW40dWF7ps30Ts0ZJwHRuS5BsrUXAt1xJVfFGCqM637rm50dWE1mcSV9nZT8aIKkpft7GpASY1kz61buNnTg5qlJbz2wgs4fP8+egYHPQqY99IGLBybKixeqpj5EwgE8IPubqSKi3G5owN9o6O40t6O9UgEJaurODQ6Ckd4XB17HYsqfs5B5YBzZzabzz9w5w4uFc5/f2RiwgA2PVmBSoRVDLYTbq9NPB7Hsa0tHJ+exvLyMpat6h86vxyTyih5a7e1U5nV++lvXVd+ht/1+/2Ynp42Dqm+rkpX39Mx8t7avVh1i/5oZ/o/9zU/P28CJlyXmZkZNDY2Gv5ghRjXiYEIACYAohUG46dOYb20FBdbWxH4+c+Ry+X7ChDg/E0qhXEAqbo6VJ054wmyBgL55nGv9vZiAsA3t7bwPxRAM/UejSIve/15nKRNf+UJ/a7qsCtXrnj4JZ1OIxQKmeAqx8CtcOpgKoANBAIYPn4c68kkLrS2ounKFYRCIVRXV6OystJsnSDY5RgbGhpMQ61LjY34f05P43NFRXgG3qy3vUWK3w8Gg3g1EMCU348f+nx4bhdboWPU4Kjj5PtUXC9kujlXzpGXZqioX6mbtQ/CUH8/0iUluH3wIE7OzaGxsRHFxcUoKSkxoDiXyyEejyMej6O2ttY42kVFRXijtBT/emYGn49E8IRkOMmL+qPBVx4zykoUzk2TCarLNLs7OTmJxcVFM1/qItUDADxbMjXQ5vP5MHDkCNKlpRg8cgQnC0chRqNR0/yPwbo9e/ZgfX0dkUgEFRUViEajeKeyEp/JZvGVdBrPS1UQx82+T1o5AgA/yuXwzUgEL0WjeCUex75QCM8X5s9eB4lEwhw5msvlsLa2hmAwiJbz5zFw5Agaz5wxvLzv4kXcO3QItW+8YXilvb0dBw8eNHv27d4PC488golHHsHx4WH0jYwYfVBTU2P4rLW11TRCZUYsGo3iYn09Pjg1hS8nEvhkAUi/f3UVT+dy5oQBystPfT58Y30dz29v45kCr/08EMAPS0vxZdfFC4WqR64HHVD+Tf21uroK193pgcVgx9TUlOHtsrIyHDt2DG1tbSgrKzP40u/3Y7K/Hw+OHzdYh7JZWVmJb6fT+Nr8PB7v6cGBVMo4p6TDzX378AWfD3+9toZPR6MP4VUbc2xubmJ8fNzglGAwiJ/6fHg1GsW/jsfxucKRjOQXYqy1tTV8NBzGT6NRtN24YWSO9CgvL4fjOFhcXDTf2bdvHz70oQ+hpaXFU95Onq+qqvJkFuPxuLFlLS0tnsorBj9qa2vNethOMO9DvcG/ZwtH1tGxy+VyWFxcRFNTE/46EMDfp9P4SqFCjvcLhUIoLy/Hh5aW8CvHQdvVq54yfcU3rCzr7u5Gc3Mzent7UV5ejomJCdPoTp/PwBXHq5UYrLbq7u7edW6RSASVlZUenU0dqok/6hE70KGYwg7IADCVBoqH+HnlB/YGCoVChu+Jy2hbGxoajON8+PBh/O/b21iOxfAbvx//TeGUAfoDWk3MIJjqpt0CB+pYK6bl/5Q9ypq+HovFsLKy4gnYEV/SLwgG8800Q6GQZ8sE16K0tBRVVVXw+XyePkgMelZVVeGZZ54x2zdVz29tbWFxcRGJQs8syrUmADTQqGtl43fyuY0bEokEYrGYOUnhhVwOPwwE8GmfzwS3GDyNRqMmsKWJl2AwiEcXF/F/PXECX339dZwt9Hehvu3p6UF3d7fHdyCvKC4hLzFAQTrS9+Ua2LJlV7ZxrnytpKQETzzxBNra2kxFNj9H/UbakNdSqRRyuZw5BZENxYuLi3c99Wy36z0HGmzA4LquOfXAdV1AJmo7Iwo61bHUSfIiYcLhMCaPHsWF1lacHB01jRIJXNWxYC+H1w4dQioWw+WODhyfmTHKgwzLBmGaAaViYQYxGAziwr59WC4uxtmmJpycm/MAKmUKjQAfe/AAF1pbcWJ0FDf37csfjTk+jiPj42bhNEhAR49/U2FoAxmCCQXkdsaT9z1z4gTWiotxtrkZB0dGDG0VsGo2QAVRy7gcx8GlgwexEY3iyuHDODw25pk3mwWp8qKA836qiFWQdb9UMBjEjdZW3OrpweGBAfQODZnTKY4OD3sEhwKoa67RO/7mPKmcOu/dQ8/gIH7w3HNYicdxub0dvUNDptO27YRqKbLyJYWeNCfdAZhyuFNjY3iroQGHC1uMDg8M5Cto7t0zvK0KUfmBc1Fjr0ZLAw7q1POzHXfu4MTMjDkzfXpuDktLSx5+Ib+TlzgezdTx88FgEAcOHDCNYTY3N5FOp83aU7loZFm3FCkvKD2VDqpoaVx1iwPHZoMhIN9Eh+fRky+4PiqX/L46u47jeKoUzlZV4c36enRdv44DheNH1QnVjN+f86Jcct/01tYW3nzzTTQ0NKCvrw979uwxwIJrrQFK8jqNTi6Xw56zZzF28iQa3nnHZOLVEfvrZBL/cW4Oe65dMxlYAhT+ftnvx/cAvCwgU7fbKK0pC+zNwi7bpJ/SXflc9abr5o/xymQymJmZQS6XL0P/1a9+hY6ODhw+fNgDIHYLHFG3cUx7z53D8PHjaHz3XczMzCAYDCKVSiGVSuHJJ5+E6+a79N+7dw9zc3MAgKWlJWMPXmtqwlwuh69vbOBpoQ3Hr+BCg5Mvui5eBfDleBw1hS0oNuhRvazvp9NprK6umqZx4+Pj+MUvfoG+vj7s3bvXIy9cT8qFzR9tly/j/qFDqH/3Xdy9exdDQ0MoKipCc3MzTp8+Db/fj5mZGdwrND8dKPQ6Itj6VX09Jn0+fG19HU9gR2/pc1V3EJCUlZUhHo8bEKl2wwa4HDuQ1yNcC58v3zvm97//PQ4ePIjm5mYjM7vxkgZK2wvzbjx/3mSLKioqzLYhZm2Wl5exsLBg+M7v9+MnjY1YyuXwd6ureK6wr17npv2P1D58F8B0MIgf+f34NyUlnko1rgv5k4A5HA5ja2sLtefOofytt/I2q3C/6rNnUfTrXxvZpOPFvd2UaZWfkUJg7UxjI4p+/WsEg0HjlHL829vbmJubQzqdRmVlJZLJJNbW1vCztjbMZ7P4z4V5c83oEHKefr8f39ncxJTfj58UFeGvCo7Ej1ZXMZHL4b9sbOClaNR8n1iLsksAvLGx4amicV3XVAxodjIcDqOystKAYbX7w0ePYr2sDOd9PlS/+y5qampQUVGBYDCIr2YyGM3lkE4k8JHZWcRiMVPBEgwG8U5jIxZcF3+3uopXxFFQ+dHXvpFK4XsVFXg5EMCnCvbvJ+vrmCrc4+XiYg8PKth/OpfDgVu38Ob588hI1VUgEDDNMVWmjh8/bqpv9KhyzehTtwaDQdxqa8NHFxbw5XgciUQC8Xjc4JiamhosLS2Z/eBqV208q/j7O5kMvuk4eD6bxScKGeqfBwL4fnk5vuLz4aVwGJ+S6lt1dqLRKD44N4fk669jcnISS7I1U32AmePHMfPEE9iTy+FIITsL7PSHKS4uxp49e0xQ2O/3o7a2Fn+/sIC/WV5G19GjaC5g+HA4jNkTJ/ClYBAv53L470Wv0CZsbGwYebHnq7qZZfyKV/TzGoRS/fvEE09gYmLiocCw+kFaaUB7ySA615vBIQbaHrt6Fe80NOCLlZWe4IjieNo/4iHyLufPSxMW9tY4Xhq8UQxPfaLHZpPPNAHZ0NBgeJz9YTQRqSfqsREm+TkQCOCpp54yslpU6DnCZ/1gawvfaGzEZ/1+PO3sVPFo9p/4luOyq3yUZopNuGZlZWUYGxszn386l8PH3Z3qIaUJg0dcE+q8mpoaZDIZBAIBlJeX50/Q6uvDU0tLeLGsDB0dHeZ+rOQib6ysrJjjfxUncbycg66lVi7YOFiDXZwT761H19In49+0UaFQCMXFxZ7Th4C8v72ysoKWlhZzYss/db3nQIOeMWsbW/62hVidJo26qUOgl+2sXmxtNQ5/38iIJ4vF0jC/32+U6SMTE3insRGPTE6a6D0XiRUQAB5yttSgbm9vo2ZpCaloFA2F/c8UFmVYZQIAODI+jlNzc9je3sZ/PHky34uithblb73lYRoqejKAZgB4b41oqUH2+/2e85gJ5oPBIKoXF5FOJBDZ3sbfvu99ODk2hmOFvTj8vj5XgTrLkm3D4/P5TBbGcRyTsWQUneMgbXSflq6/bcg5lu8fOIBUPI4r7e3oKZQ5dw8M5DNicg+9r4Jtjd5pEzBefN7h+/dxub0dh+/f92SHNzc3sbGx8RBfk2f1bwJIbaTnui4qKioQCoXQPzmJhkuX8pUGgQAOPHiAnsHBvBEuHBejtFEHWNdFLyoVVTa2IVNgpDxJB8qOcGrwRnma89EAHJ+hziOws5dMZXg32dc56MXv2BkApTe/f6utDVf378epsTE8WSi7ZTk+Aw36o/ewA5maRSdd325owHIshutdXegZHHzos3+prRMM8AEwZe1PPvkkqqurUVxc7NlHrnqBF4OrDDSEw2HUX7iAuvPn83pMgljMcL2SSKDj7l387N49OAUjzEAZfz8fieALhcZl18SBt4EN+ZD312AA52evL19XWSAdqGO4rj09PSbYonzFZ5IWut4ENs3XrqH52jVPkGp9fR2pQoYTyBv5TCZjeHO9sN0oGo3i/fPzeLe5GZ8vKgJkO9ifysxzfh93HHw6FEKisH9TeZL0UqCv+op0oB4tLS1Fa2trvmcHvNkotV10XtWGtd26hbrz5/NzC+yUa5NXFLRq1QDX9UXHwY/DYXwhEoFTaJIXiUQ8VYSqUxSwaVmz/Tldv93sBO0Qs5DMiNr3UJ3F7weDQbTeuIHGS5eQyWSw5ds5k1316r1790wjrI2NDaRSKfj9fnTfuIHREyfw5ULGTPnU7/eb0xnUdvp8PrwSDOLbuRy+VCgtVn2p2wNUBuLxuKd5Nt+bO3kSg/39qH79ddQXzow/cOAAuru7H6okUsex7q23MPnoo2g4c8aUt05NTWF2dtbQdnR01BxvyX4Cfr8f3ckkRk6cwJcLzh7npXaD4/5CJIKvb27iS7EYYoUS5b/2+fC3q6v4klQKqV2ibiAN1CnnfbndgN8pLS1FZWUlFhYWUFNTY+bA77ReuICBI0dQXzgqc3Fx0QQ6X+7vx3f9fhx68ACzs7NwHAeDhSpGn8+HhlwORU8+ib8udHbXoL5iI/Lda8Egpl0XP/D5TPO9LxcCGv8ikTD3VYeKzpzKv9pwBu/pKIfDYcTjcRNQUqzI7/AoOsqZ3+/H5Y9/HBnHwVfTaXx0fh7DhSbn5eXlOHbsGG7evGmSDzbtKeuK9fx+P77rupgOBvGzcBj/MplEOBzGawsLmMjl8NVMBp+1ekRRjxB/BQIBk5Fm80sN0gLA6MmTWC8uxi8dB18Q/tdkQyqVQlFRkak4Kioqwv9rZQUjuRzSe/fi5du3EY/HUVRUhJ+1tGAe+Sq8/851PTaevgeDW7QRijN+5vfjB9ksPjI1hef8O0Fk9Tc0oKI87jj5ZsF37txBd3e3wSR6sYqUMsvTnerr641fVVNTY4KhfOazPh+em5pCX3W1p58WbZWt/zSYSZmhPeFcg8Gg56hWlSvV1yr729vb6OzsRFlZmUnSqoxns1msrKygurra9HgiRtMtSTx1xufzmeoBVi7W19ejt7fX0DQY3GmQHAgE8F3HwXQwiO+4Lp63tmRQdhVzcv3UHinNbHkA8oGGqULjedJG/SIAxh5zq6nq+wMHDuDJJ580AauGhgbca2zM92krLcX/VlPj8X253gwQsSpI7ZWN6xV/2H6RbRdV1vk7mUxidnbWY6sUf5F/OT7tjVJUVIT19XVsb2+bxAxPyPinrvccaNDMmG00qBzUEbcZNhgM4lJDA87s2YP3z8/j0cVFc+/dso7XWlqwGQohtrWFk6Ojniid3+83UXCOaWtrC3tmZvDihQvI5XJ44HgbaWiXVX6PgJVKl8SeTCbh+P2YKBgSKmpdRHVGdaFzuRyqFxexVlyMmqUls9+VNNRFBbx9CmhEVPCVzpqFv9TYiMvt7TgyMIC+kREsVFfDDQQwV1ICx+/Hu01NpnyUFwVQlSQVH0vxt7a2cPzWLVzt6MDRoSGjsFmips6aOgu288B7qxEnL3CbwpH793Fh3z4cvHvXA7pIJ36OtLAj8XaEUfdsqmPcOzSEA4U+CTl3J4K9tbWF8fFxU75u8yEBus5RQX4sFjNdaTWAoaVONo+rQuCac0670VWfp/fSgAN5lPcLh8Nobm42mWilkToHpLUqL9KeoHptbQ2zs7OeYwr1+47ImS0LetnKU+env5XWPp8P1zs7sZZM4nwwiCcL+1wVxNkBKNJTI92kNe9NegPAIxMTeLOuDp23bnnorY7vX+LSDvCMjjc1NRn+0fEr73CNaLSp18j/up46/+LiYvgKzhe3Z5GOzJBxX6XqNpblawmfrqM6FApAbF6wDSGdagJSnUdpaelDlQz8ob5We6ARfo6Lulj1MrCTRYhGoya7Sr7g2B5fXsb/fvo01tbWdm1IxXnZdKbOYONcBnD0M7xUZlgOSbkDgNraWnR1dZn3yZdatcX7aG8c5W2bdva+df6vfBIOh/F8IIB/W1ODra0tjBVOhyJ41HFrFoRypTKksqfvq33gszl3v9+PyspKPProo56AoB1sUPuovEb50fkqf5aVlXkyetQnfSMj+H9/8IMoSSQe4gm1bRwz+fUToRBeiEQQLzhXvLRSUqu2WEZrd4j3+/0YP30ameJiTD/2GJqvXkUymcTjjz9ustw233BujZcuYc/ly/n/BVQyiM7xch4K5vtGRvD3zz9vTktQuVa+DwQCeDEcxmcK24tI75fjcbwcjz/UN4VjLCr02CBvqU7g3xUVFabJYyCQLxs/efKkp+eE/uy7eRNVZ87k6Sc09vv9+FxJCf5tSQm+/fbb+KPfexqH67pouXYN//ORIzjS2mq2hXHOwI7eJw99MRbD1zY28LlCtSMAvBSJ4AulpR67z3krVtP/laaT/f0YPXUKNa+/jqYrV1BWVoZnn30WNTU1uwbpQqGQJ6jO1/deuICVD30IX04ksFjIAJPmrFrZ3Nw0mXOthlVcp8/7YjSKr29u4nOF7Ro+nw9/VVyMv89k8OVCxYQmyVQGOU9tHqzyQ3k4NjyMm7EYXggGkQU89oUNYefm5jA3N2dkpqGhAX914gS+msmge3gYt2/fBpDPjLeFwwgeO4anC5lkDU4rLiSf2Jj0h8EgJn0+/KakBP+XkhKPHlP8yaAl+ZR88/vf/x4TExM4f/489u3bZ0rvlS/U6WVl6MLCgmnGV11dbRIblE8GQVOpFCKRCDY3N82pBUVFRZ5T+VCgI8doVxJxvg8KOLhYAqPqi9j61ufz4ZehEF4NBtG1uIgvuC6ihcol4qxMJoN0Om3sHqsc2RsqkUiYNcxkMgiFQigqKsLBgwexurqKSCSCffv2GXzBDDrn4LouPldUhK9mMvgUdqrFbCebQZD19XVTGag+lfqkts3w+Xyora01PaH4nso3dRzHmUwmTeUF/29ubsbNmzcBAG1tbXj0rbdwrqgIX6mu9jybQTmuN9dMg9m2LVUcT5lRPrT1qr2OPl++ISer+Hipn0C/hu9zCwyxIflrYWEBfr/f49/+Y9d7DjRoGQuBbjAYNNkUglJlXo06ZbNZvHnsGJYjEfyhqsocb2k7LIz2vt3QgEw4jEQqhdpz5zBtgTRgJ/ihzpTf7zflPBr0sB0SABjo6sLtgwfRd/cueoeGzCIfvHMHN7u7cXpqyrOPlEqWfyvDclyu62KuogJOIID5wp5gzot76jT6rYpaHT47WKPRQ8dxcHX//nxPgPZ29I2M4PC9e7jU3o76VAozZWV4ZHLSU1rOuakzazMyrwMPHqC/YPhzQlcq6enpaU/5o95THVX7b54/vXfvXgBA74MH2Hfrlmf9bAWg62ZXLCjtGMklKFJgxUuBLteRPExDp7RWJWQ7tlpKy2fvZoD1u/w8P6f7oZR/NZiiDpytgChnjpM/+zwhGRa7GamOh5etxBQs2o4tQZ/Kqo7FzlypY2U7WEoXACZTqg4U3+u+eRM3u7txTErabGfFdmjIK5q54PYONawAcHRqCvvv3cPi4iL8fyKD/5e4NMvk9/vNdh7SSJ0/jonyRx1JUEaa2XsiqbODwaABFgxwMIrO+6rjyXsoHbSnhy1H5A/babVlGvBWbuk62g6U67omAEgHhev4q3AYP11by/dQKMyVx+gpnex7Z7NZLC8vY2trC6urq1heXjYOn+pEAjiCKDs4q/PgPMlvfL4GjHVNVIdQX3LbF20Ws/rKH7Yet4OaqttV1+hYNVDN5/K7qk907JQhn89nynt5T4I9OgPa90Tvy8/bAF95XXEA6axjArynSij/aUBY78lggL7H+6iuVlnkb86P++PVOaO86G/9HmlI54BlqCwLtwPQvP/piQm8UVODqrffRiAQQFtbGxKJhKGpBpN1zWw7yTVh+TKrlew9uIrpbFlU/KZroHyvvMH3yJuqTzTJoEc/cv5VVVWYnJyE3+83R/2RTrSXtg2mnKg82TJmZ22BnY74ynPkGa636oNPFRXhlULjNbWltk5jAJP0pUyo3eQYxk6fxnpZGaYfewwt166hrKwMLS0tHtlW3U/Z4prxXnuvX8d/+9RTaCkpwd8UAkB8Bkudl5eXPc/3+/0PyanKzKejUXy+pMQTCP5McTE+V2iiq442n8fxsWqZfKfZYH4vGo3is1VVqA0EwBGQf7n2eryuru/nSkrwV5WV+C9/+AP+UNC3wWAQTVeu4MuVlaYXg52EUR2jzjnH9SkAPwDwxYKDpfiLdtR1XVPJyXWgrWI1x9jYGJLJpNm2xGdooIv3Y/l5KBQywYbDhw+bagTa6s3NTayurhpe+MHWFl69cwdfSSTwvJUE0WSABluUnyorK035POV7Y2PD6AeVVfZD+lkqhelcDqnmZrT+4hdob2/Hnj17HvLxuB0zl8uZ0vzJyUnU1NSgoaHBbLEgPqWjmkgkzFG/QL5ZKTPlvP/zgQCOLi8jEolgQxxyDQry8zyRh9l46ncbZ6isEedeuXIFPT09KC5sieJ37CPBg8EgysvLUVNTg8rKStzp6MB/7O3FeiqFo4WxhUIhPOf348XJSRyuqTG9eRhs0jmrrSMmIS+rHeUcudWU/lUgEDDypzrK1o9AvnKD+FGDxupPUI/FYjHE43GUl5ebyoZcLme2I1Hm/qnrPQca7t27ZwamQgTsZAnJpApoKGh+vx/HHjzA5bY2PFEoaeOlCoxEPXz/Pi63taG60MiP3fzVAaPyV4CtQJh/U0nr+36/H786cgSpaBRX9+9H98CAGW/P4CBOzc0Zx00NlS48fwhouFB99+7h6v79OFbo1s+xUcGzGkPfowJTR0+fS2c4Ho8jEongyMAAzu/di77CsY1dAwPYe+MGqqqqzHmtPgFBanBVEStgoLFlBJZjs50Kny/fbdWO2FP5KkBRPrGdDBoKFWg1hnxPv8/v2uCHa2N/3s4oXmlqwo3ubpwcHUX3wADi8ThKS0sNz+r3OX+lFQGGbr85V1uLN2prceDWLRwuHOGlDi9prspNZUOv3Rw0BTAKCFV2tESMipEG0aabLbcK/OyxkOcUjJI/la7Ky7zswIC+ru/r67rNwufzYf/du2i/fRu1tbVwCnuI/1SAT4G3XnRGbEBuj0P5Ryud/hIX58r14H5UXROOXQEDZZAVDTr3yaNH8eDYMey/ehW1584Z/cR986StHoPLORYVFeFGayv+0/IyPl9UhKcK+ol6S+Wc9FP67GYX1Hgpz+lvOrDkQQZdlAYa1AOA14JBTDoOvrG5iY8Vns9sjmZQ1YnjeqZSKc8xthwHnbGA3I9zptOkZ9mTz+iwU9Y1YMkx2zJgO7ecq76me1R3ywSrDOj2KtJct5KRn7lvnmOwdSjHog4L348X9oHvNi8AnsCmrYNVznWcqh/UsVG+UpnVcTOYrMFJvmYDbL3fn9peZweGOFbb4aE94WvqcGlAiGuQyWTMvThWVkxQvvicI+PjKH3jDYyMjMAJBnHkyBFP0Eztofbfseei9FfdRrrTltiBYfIgZcPuS6Gl439qnfk6eZJyaX+eNo14iM7qkSNHTDWT2ha1nTb9lX8pQ1rC/qcCoPY8uB76HdWvdnBMeZOfoV5XDMTmtfzOvosXMXz8OKrefhslJSXo7+/33EPHoTKuvM73WK3ARqXqPAUC+b4pvB9pp8/QuegcSR/lbcqbHhvNeTJIrjqQDrU6ujwWj/Sm7JHm9vqoDQHyeponCyhtstmsKWtXGVZ+U/+C+hoAnnEcfCoaRaJQQauYTdeXsqAYHdjRfR/84AcNvTWRpUFT/aEd2t7extTUFBYXF814iUunpqawurqKYDDfNPV7AMa2tvC3Kyv4ZOE4ZJ8vH8Dhfv/dcBXHy6a/1EFKfw0yqp7/cnEx/tPyMkrefRepVAorKyuor6/3BL/8fr9pEkhdl81mMT09jenpady+fRs1NTXo6ekxgY7Z2Vlcv37dnABWX1+PdDqNWCyG2tpaD4/YVT2cF3lag4o1NTWmGlp1DuVS9RXv/4OtLXxtYwO1gQBmfv97dHd3o6Ojw8OT5A3ii0gkgrW1NUxMTODqhz6EzUgE33EcPFIILgYCASwuLppth6xq+XkggL8fHsa/Li/Hh2Q+OiY+194iQnuwXAi6lJSUmM8qxlU9ocE+AGYtKioqPJiHcsKKJJ/PZ7axJZNJwxPUAY7j/Pl7NLDjMZlPlYcyMv+3s4KO4+DY1BQ+JKUivJTA/N07NIR9N2/ih4VGfpfa202gIZFIoKRQ4mQLFAVbo1gAPMrfcfJlrY9NT+MPlZXouXXLBEnUuNAYcozqsNmOmRq97oEB9AwO5s9aFQeRCz4/P4/5+XljyFUAqKiBfGd5Mh2j7V1dXQgEAui6fx9NV67kO9P7ds/2246UMrGul5bmKu1UoDlGn+//T9t/B0eW3Wmi2HdvGgBp4G0VbMFVFcrbru4mu8lu2mk2yXYkm+SQY3afdnaliKfdJ20otKEXsSGF3guFpJBCoVg3M+RwhhyaZpNDjqVn+y5vgYIpAFUF7zMTaZB5r/7I/A6+PJXFqdHj3AgEgDT3nvM7P/P9zPmdYtOTlpaWsqaKpI8tGLYRZ4Q/m81iY2PDZGxsYMTvchwPc/htcKgg2Qa5AHDtwAFsxWJ4r6fHBJe0TItrrDylYNx2aF3XxdudnUhEIrg+MoIjMzNlmUIF9npPrrMNWOzgiX5ex6Dz1yg0X6fSUb7m6xoItOlMvlc+tytOWJ3C7+rY1UGx11XXV3nCBkAK8PVeBPo6Dl5qzFXe+FvHq6CN7xNEMePGfaa6br/NSytgAJgGRnTsFbQ6jmOaUvr+buWJHWziaQMTx4/jZGk/LyPvf1dVhW8uLOCZ5mYc7Ow031NA9LOmJiwWCvh6JoMPZrMGCD1s2wRQHnCwAw2qA/SyeUD3n3qeZ/Z32rLA77wC4LVAAF+uqsJO6aQD8kIul8PssWOYPXsWB2/cwOMrK+b5tbW12NjYQH2p7PnYsWNIJpPY3t5GPB435Z43+vvx/1xYwO9WV+NJsTEKxhm80Lmq46brYtsKnYv+r0E2bnXhpVkxvb/qa4LWeydOFHlgaqrsFII9e/YYmtfV1WFoaKgsI+L7PkaHhvCFfB6/v7WFF0vAjoEW27EEysvFSaNK+lzljE446UkeIK9pKafS1NYvtGW+X6x+GT9wAFOnTuHk5CSOlbZbOo5jMoyFQgHRaBT9/f1mHK5bzKTG43GTSdW1szPc9lpSPnUszARyzSg7HA/tuq4veVdtMst4NQhiP9/3fSycOYP7jz+Oo7dv49DUlOHPhoYG85mOjg4jq7pWzFSpjVc8oDaBP7r1RO0h7UIoFEI6nUYmkylmIOV4SNJR+Yjd7H2/eDoGe3NwfVT3a9CFfTNIY1aMAkVZ57FtqkvY8V9tjq4b7bFettOp+NeuNrSxDh2SdDpt7tVz5QqOzc5iamoKjUNDGB4eLuM51WeO45hMP8fN9WF2EYApqff93f3xbLJIe5fL5R6oOq4UnFe7zHHZtFB8rfqfAU11Vvidpccew9izz6LVcfCcyA0vVgfYwRU+Z2dnB5FIpKyRHdehqakJtaVeWLpWDMKo7mFgy56f8hufqbqfPG7bNcqZVhMoLXWe1H2qzwuFgjniWS/al/b2drS1tSEcDuMPEgl8LZPBvyglXfTimtlV2BrE52k7Om/SS/mC3y0UCvhMIICDq6v4Txcu4ORHPoLu7m5DF9rucDiMVCplTpqpqqpCY2Mj7t69a+iey+XQ0tJi6FNfX4+dnR3TPymRSGB7e7ssSKDroThU58a1+JHj4Fu+j98Lh/GFUmNNXVulkY2vv5HL4T6AzaNH8YH19bKjgBXLahKR2wkAoPUXv8DaM8/gK52dqCmdRMPkxcLCAlZXV9HY2IhgMIj/urGBu76P/7S+jo+VKt6p42tqakwlp2Jw1YHcxk5MaPt0D8PPvMfGxgauXbuGffv24ejRo2XHwvKH86beWV5exurqKqanp8tw86M2S3/kQMPw8LARLO2uSqXPSSQSCayurj5gGAlYdHGVmPytzUYcx8GRsTFcHR42jfw0qp5MJs1RSeqkaDSeGSdGfoFiaU5vby/OLCyg9Z13itFmidgq8OI9OWbOhcpTuwPbxpAGjo4bF72qqgrb29smwkUFaztg9nm76mjZEVplJlWEOgdVJvac+LoaTv6+0d+Py4ODODs7i5P37xsQxL1XCix1/fTeCgwBlJ2ZTT7QslRb2dnRfNLSdmrUMCk45HiOjo3h2v79eKx0ign5g8rnam8vLvb343ip94UCKQ0a6PieuH8fv2xvx5HR0TK+/k0lWxRozezYa6M0s40g15efI5/YFQE2mFVQaRsVlSEFH0pH5SNgdwuAroMN1u3xaPBPo7DBYPCBLB3HSoeK/KUVQKwusUGaTX8aQMqgBqX0nvl83pThKz1/m5cGDDg27Tlj604F3HydGQQa4N7338fUqVNof/tts0+wuroabW1t+FUuh3u+jx+EwwhPTJQ5yawCeHp5GW93deFLoRAKqVQZQFfeVGBi60j7szZfVNI9dLw4V1unAOW8/pzv4180NWFnZweLGxvwfd80yAoGg5g4cQLbpVNm4j//OYBiFUdnZyfOnDmDQCCAlZUVjI6OmiZ0S0tLZv/83z72GBYLxU76j3vlpYS2TlPHWoMQttzxO5pxUPmwZc92Mm1Z0sCMVsc5joNbhw8jFYvhna4u1P/qV+jt7UV1dbVp5uu6xQ7XLFdn9igYDOLNPXuw4nn441QKL5UcJZ0Tx6cAXNfZrmbTz7iuW+ZIqD7R+WvvEDsgqs6B6iDHcXD72DGkYjG819uL1nfeQSBQPJZNg9lsZMXSUe51vtTVhf/H8jL+IB7Hi7If33EcczSl7ZSpHtP1JH7QrQXM7OtRiEpHfqYSILbtns7F933MnD2LdF0drg4PY3hszFTC/CwWw3+cnMTv1tSgzrovafxeezv+P7Oz+HJ1NT4l9yY/0/7aupVzV15nsJRyGA6HTaWWOle2zCQSCcNjXFvFa3ZJNwDcO3ECd8+dw4nxcRydnTV4hA1PW1tb0dXVVRYYcRwHV3p68C8jEfzR1hZeKm0voG1RB+1hmE8xmOIZjpM2jEeI6ok9qr81qKEYQx1FygCd997eXrMlgs7dP1RX41/PzuKZ4WEcLGFh8nt/f7/BkByv2mvyoVZ86JztgAPpz8+TFuQ38hWz5lxTvnf33Dmkq6rwfd/HC6U58LQE2jEGZ5j84TO5NQOA6VHArSk1NTWoK50Uo0kePp86hTqVWxV4fDQ/z2ReJZyjmF8dfNd1y46tjcViZTqNcq4VWzU1NWZLta656vtoNIqGhgajo7mGn49G8a+7u5HP57G2toZgMIhsNou/C4fxrVAIL+3s4JOWv0G9WigUTJCBVyXfQW0UHWYmmpmIUaeX/9uJE9te5HI5s+XadV1zDLLjOEin01hYWDCnMqgNJN24zdm2Cby+5Xm4B+BrpaalXAPbB+Vv6mrHcfB7AP50ext7b93C0aNH0dHRAd/3TYsA+5mOsxtocJziaUGDt27h4//xP2IplUImk0EkEsGVnh68PjiIVCiEF0s9c/5FfT3+OJHAHzU3A9KrS+ejdsG2//QXqWv0svG32mH+H4lE0NfXh8HBQcPvXDvqBOIdVr9yCyB92bW1NWQyGUSjUTzK9ciBBkZZaARoJGgAmBFQo8v3VaBsQmi5KADTFIvfGZmcNFkZTwjN+6+urmJxcbHsfspYlQwFm+7wsyw71+hYJYDHZ/JSQVJloMpZhZwlZ45TPGYkGo0axaoGWw0P76G0zufzWF5eRm1tbVk2yx4Xx2HPjc/Q53A9+VqZcR4exlYshgv79uHU3FwZGOG9eCn9+ff1fftwZXgYR0ZHceLePfMdu7yX41Nh0+wKgDIwpjyoc1PArvd0XRdHpqdxdnERNTU12Bb+5vsXBwawEY3i4sAAjpSid/Yc+Qwa0DOLi2gvdft35Fl2xFkBFu+pJaJ2uaaup37HjlpyvTV4QQdeK3L4GSqLSrSzo+F6X45f+YROmIJqlRvb4dLX9HNqkDkeLYfj59mxWXuE8L68N8GTyhDw4BFApD1PeVADa6/db/vSzKHrFkvlSWOtTlJwx7+pH1Mlg8b9jN2XL2PP+fNFWpTK9aibPpFI4EeRCPaPjiKVShkjS2MVDofx5Noa/scTJ5DNZjFbOmrQcYr7qnVfrwaaHcfBXweD+G4igd/zPHP0mfLvw3iB/LexsVHGd3a5qf5NWinfEDxSJvsvXMDkiRPY+9ZbZux0xjc3N5FKpXDjxo2yBoykezabxUc3N/GrtjZ8JRJBXrKtastUf9qOAi+1D/zfBl+8D51G1Te2LOo9KHMADIBLp9OoqqrCvvffx8SJE2h74w2srq6iUCigpqYG0WgUc3NzxVLPK1ewsrJS5uiFQiGMNDRg6uRJ/F5pj6oG6tlEqpJskWfVCeN47UC3OjCUVZ2rHYihTKsTpjK0uLiI7e1tDF26hNvHj6Pv/HnjqO7ZswexWAxLS0vY3Nw0+4gzmYxxkHzfxy9aW7GWLx7z+GKp87faJq2AIB/YOv5heln5lOvM37quPImAr3GuSgvVGwyy7jt/HtOnT6Pv0iWsra0hFAqhubkZf1dXh+WdHfy3nR38O9dFKpVCMplEQ0OD0a0/a2rCaj6Pr21v41OlKi47oK0BV66n4gjSgY4HnTrqa4JbyiLXkvewqwJ8f3c/vK4BEzuFQgF3Tp3CdjyO9/v60H3lCtrb25HP55FOp7G9vY21tTVzCkNXV5fRV2/s2YNVAP95YwMvt7WVPZsYTZuk2nbYpoE6Mxp4UvulJ63p98lX1Fv6PH5fHe67d+8avqBT/KsPfhAL+Tx+WF2NZ8bHDZ5tbGzExMQEurq6kMvlynq+aLJKMZiOwQ4uKEbhWmvwVfE1sFtRqXzU/fbb2PjYx/CK++D2LNqjS52dePff/BscHx/H04mEOTK5pqYG30ql8M2tLXyisxMnTpxAfX09wuEw3m5pwR9UVeHVdBr/m1KGXWXSxr5q7+3Ltl223tXP2Fh5e3u7jH7Ku7yn53n4++pq/Pz3fx9NP/sZRiYnTdWFlssDxeob9ipRHaoJUert77ouFgB833XxPMoTSkDRRqyvr6OlpcXINOVXe5Rx7fgs0qi+vt40UGbA0XEcpFIpM0dui9GEkPKG53kmUBEKhUxVelNTk5kvS/EpN9TT9Bc4Vg3Gka6vuLtHc9sJR47D3i5Gmr8aDuOL8Tj+3/fvY6d0EgjfV38mk8mYYBJ1G09XSSQSWFxchOMUq2Zqa2vxt7W12AgE8F3Pwx+VtnT8nuviKw0NiMfjZgu62hsGdxTz8bJ1TSW8rfKrNOKco9EoamtrDa2pi3SteAWDxUaQWgntOMXg+/b2tqma+seuRw40XLp06QGni4xUXV2No0ePmlIudcJUQGyFq4TQz+j3bMIpAfnslpaWsvLCStEhBUAs60okEmVRGR2XOoV6Hx2TMog6apptUrDObqoAjDF23eKxL1qqo/PVe5PxGTFndI7CpHup7bIz21HV5+g9NKjBsR8dG8OVoSGcm583xpHgk8ETALjW14fLQ0M4dvs2Dt+5Y2h+dXgYW/E4ru7fb5pMamaFQEbHTDrzWeqQK1DRdVAB1D3bKoi8bKeB9zg+Po6L/f04Vup9Ucno2GvO/7WsX8fEi0aazr7uOePnbDCrl+3k2KBEKx+o7JWX6bywu7jyqt5Xm08FAgEjL2q8tKSYEVYdp00vdZz5nOrq6rIjDDVTQplVkL+5uYl0Om06pFfaY6xrq/SjPPIzBEt79+5FNBpFpNRMS42zgv7f9mWvL7eDKS+o4mc5LGVFs31sLEUDHAwGzVGO2WwWkUgEz6fT+JTv43tXrmB9c7OM/0OhkAlaatCI4yLf8jWlEQD8petiDsWMwCtSfs7P6voD5foegCnn5ZqwP4HaDhsA8j4ch5bd9l27hj3nzyOVSiGP3coZbfI4PDyM93kUqBy9XF9fj48mEvgPR48im81iskSLTCaDVCpVlgHjGpBPqZM1CMPP8iL/2/Sx6cR7cp10mxXnqzJLPen7PrqvXEHtL39Z1IGl6o1wOIx79+5hdnYWmUzGVNX5/m7WJhwOY+DmTfxfHnsMzbGY0Q+q41XPql3RElqVSf1tZ4zVedYTEnzfN4CWc1Xdb9syfqf32jX0XrtWPLa4ROvx8XGsrq5iZGQEc3NzuH37Nu7evYtAoLhNanV1FcFgEIdu3sTUyZP4w9paAA+Wm9q/NftoZ6EKhYLhRXUwXNctCzDYADCTyRhZUJvI9QVgSlU1UNpz5Qq6Ll0qAtzSs5aWlnB0bAw3Dh7Es5ubuF1qeAsACwsLRpccamjAxPHj+HKpHF3Hpdl62i0FuDp2vqbAmM8g3zMwog4Mf7OyyN5Wws8yg0dHpf/CBUyeOoWe997D2toakslk8YjfPXuQSCRw7949ZDIZFAoFjI6OGpk6WFuL0cOH8bJUo+pcNQig82Jwl30HONdKNlqxAEvVbaxGfud8qIM4FqUPsSPpT/7IZDI4OzuL8319ODE7i+3SqQvkmXQ6jWQyidnZWTQ1NZVtx+Ja6JgVZ+npQ7z0aHV1HpnYoA5iozs7gNhx/jz+7dAQ9u3bZ2SAlUWuW9zC9F5fH9KRCC4BiP+X/4KGhgZTtfBXBw7gvuPgtVAIz0xMoLa2FvF4HH/d24s1AN/IZvEvSo30FCsB5afmsTRfMZoGJColGVRmbV1IZ4u4yvM8s4VAL67vD6uqkKypQf6pp3Di3j1TSUda8PmhUAjRaNQkFDxvtxq6trbWYH7HcfBqKISvZzJ4yZJBYs1kMlmWgCItuObEUprMUPln9RcrPYn9yau2TP8kEsGPv/AFNLe1YXhszFSXaRVnU1MT2tracPr0aUxOTqK5udmMSTGOrgnxqfp51I8vBIP4gpxUwzkq3rAdcxtbxONxpNPpsr5+nBP5nd8lZmA1JYMnDKo6TnGb59dTKXyuVLmjeoP3tLEgMQQ/Wynox7kp39r+Ki8bh9TV1WF5edmshe1D+v7uUdgATCUkZbpQKDaj3NjYQEdHxwOyUul65EADiUNgqgEDNkOhMaKB4efJyEokFQY7KgqUNwBTx873/bJISjAYLGuIYUeG1ejxPlSY7MirwEjBC99TAeRlj12ddIJkG6CR0egE8/P2eb22ItMMhoItBZ+qJFUobEDD+2pAwXbUbSdtZHISh+/cQVtbGzKl1yqtGSsfrgwP49jsrHnW8YkJXBwYwKGbNx8ApjRQNvBU2vNvfV0z9fZ6cWx05vk53ps0Y4aE/OY4Dvpv3MC+69fhOA7SAhL13jSuqrD4TM5Zf9uBDipn3XKkTgS/y3XU6LVmSnhvzQ6GQsXzfVmyqvyqvMr/9T3loZWVFTQ0NJjoZ6VAH+mmTpzOm/dWnlO9wH2lWlpqK3aCFTY/ZFCAsqX0tw2s0tR2fmikuGVBZYH8+H5HB97r6cH/Eb/9S3VNMLh7KoSuO9dJnQqCM9d1jeNLnUwwSrpwvbPZLJaWllBTU2NOWmBFCAATZNCSddKTxxtpEEL5OhAI4BUA33McfMUqy7Tnqzyra5GTLJTjOKbjs+3oAbsnNZBOOh7la+4LV0cnn8+bao61tTVDR+oK1y1WS9SWnE1g1w4xI6j77ZW/7LFWsiUKgHnZssd7qJPG5/G30oOypxVBXHPei3NjkC4ajWK71NtCnVp1oMk/PAJQbbzteKvMAOXVdaoL7IC38gOfo/yvjpd9D7XLBHZcR63YIt2WlpYM2FV9QVr1XbuGf3/wIA7U1honENhNCOja6FpzrVT/69pyrpTzhwFcz/NMkIEg1uYH5T3bKdLAK7N+ZxcX8UednVheWcF1i5d4z4MTE/j3Bw+irXR0tW0n9flMLui62uBW50x9onac/MXPqJ72/WKVljqnWoKt4++/eRPt779f/F4pwMpKDjasU8wAFPXMoakpfNpxcPr06bJAptJUx6aBLMVgiof5XcVCxGl6rCHnwCNwfd83Okqr90g7Bu62t7exurpqTikoFAom8Hz0wgW8EAzinYsXcXV1FQDMe8FgEIuLi5ieni7bNkh6MjCgAX7lL63iIU10rThfjpWVE+l02ryv9/V932x15hrrFr5gMIjH793DT5ua0Pn228hms5ifnzcO3QcWFvCr9nY8tbyMtbU1rK2tIRwOozcSAc6cwfHZWVxYXkZdXR26urqM7qPjqNuu1VZQ3irhZHVM7ddIt5qaGjiOY9bSpqmNp18B8CfJJJreeANra2umSpNBJ/bz2NzcxL179xCPx9HQ0GDGyKoBytLOzg4+H4ng9Py8aUbJMbMHQiAQMD0sOF++r5hbfTbbd4lGo0gmk2W+i64deadQKOC1YBBb4TB2nnwSJ+/fR3NzM2KxGDKZTFlQcHBwEK2trSawtba2hkgkUlY1z0p53XZHnlJbxdcVY+rr9lqoTiKf19bWYm1tzdBM56S87rrFnhB/88UvomvfPhy/exd3Dh/Glx0HX/Y8PF/iqy/X1WHg5k0cO3bMyIjaASbnORY74KPjVgykOJ76Rt9XXaaf8X0fdXV1uH//ftkzNBCnPiATjRxrvHT8cyBQbKyttvk3XY8caOjs7DSDsqOzDCQkk0ksLy+bCgEVSgomL11wNbZUcLajYIM62yG2FaJGiyiUjuPgSnc3rgwP48m5OXTMzpY5GgRqzKjZ91QDqUrXdmKA3aZ7wO6xM7ZDzM+wy7o6jbZxVcOtz7ajr5WisnTWlGHpWHFdbEDMe1MIbEBMJUWnxHEcHBkdxZXhYRy6davsPODBW7fQVaqI8UudcvXeqvQ1M67j1zEBlbPNnMfFvXvxxmOP4dDNmzhcOjfYjl4HAgETKefa2uChkoCr8uX+3kKhUNaRXdeISvxSVxcuDQzg8fv3cW552awH76eOBn87TjGKqHurGeSjQSMdc7kckskkFhYWzPxs4KB0sh0ZGzxXAo826OI6ECDrfJRvlSb8+93WVvyqvR2n79zBifv3zet2dFYBbi6XM82gqPBVZ/B5HF+lqhXlYT1ZheNmtN7zPLx56hSSj7gH7Z96adSeDq4NbPVSnqLcbW5uGoDDe6m+ZTVDPB7Hzs6O2SoAwDTIYmkujQifwQyKXeHAsWh58KcAfLW+3oANXsonCjpVNvgsyjODG3qcpn3xHoVCwTjUW1tbxoDbgUD+TZlvaWkx2wVoZO1yYtKfvED+4GdtkMI11TmTBpw//9e/qZc0KA2Ud723HQF1Brn+rPLRruM6Bz6PDXy3trbKZEWDDVqtxAATaa42UG2VVpTw2bYuUBujvKRzUp6zqx8J9Eh33/fNlg7OgePWklc+t7W1FXfu3ClbS9rhTCZTFniqpMvswIG9hnbwSR1tjt1xioE0DfTxYgUJx6w8xt+2POnZ7iov3E6zs7ODzs5OXL58ucyOKw2TySRaWlrKnCrbvitPKq2Vz+0gjG5B4PF52mNAKy4pR5pJ40UniPflZ7TSig7X6uqqKQseGxszY1N7W1VVha2tLZNB5Wds3Eha2dtc+Hn+VvvIH60MYIVgpTlxDdRR0HVm5Q0xsWaEKW90BBggYxY7l8thY2PDHOkLwFRkEBNUwrQcg66nygDpzvXQSi7+3djYiHv37pX1WfB935zCwrFoVWcwGMTTW1tY/ZM/QTqdxqq3u60wGAziE8vLeCEYxIXLl7FQsk/5fB57L1zAh5NJdHV1mdJ2rh0TkXZ/Edpd7Z9GujwsWamywfXmGpP/SBt1vJmMoX34bDAI/PSneO+995Dzi4FO6p69e/ciHo+jpqYGCwsL2NrawqVLl8zJE67r4vyePfhRTQ1eKBTw74aGzHMYwKIfA6AsmMxqM64bA1Za9avztH2feDxujlRkEk+/w6C353l42ffx9VQKjW+8gbm5OQBFncQjQOkvzszMoLu7G6lUChsbG4jFYpibm0N9fX3ZSWrKB+QXDSLa66Rr/I/hKZXh+vp6jJf6AerrlXjgL/J5bMXjmDhxAk1vvomLn/kM0o6Db+bz+HhJPrj9Y2trC3v27CnjNz6D/KKypb/1uZy72hw7ia9rYvuHDLZTH6gNok+g+Ok72Sz+09GjOOC6OLy1hUgkYmRYe0P9Y9cjBxr0xAktEVdg6zi7ZUl831ainLw6NTpYghvbGGqUSqsntre3EY1GjZCr48txKwC6PDyMjWgUb3Z24jPvvPMAoKBzxzJk3lfL7OwF1P00jFqpE6FMb2dP1cnW+doOKBU/AaEyHhlD1wgoP+7RNpR6f9d1cbm7G293dWHkxg3sv327oiHk9/ySctTqlUAggL5r19Bz5Qo8z0NWxqL3YFRdaaZAmvNTw0x6Kegl4Od7XGvXdfFOdze2IhHcOnwYH04my/iT67q8vGyMdTabLesKzXFr6a6ulQY5GJltamp6oDpES18v9vdjIxrF211dOLe8XGbMdF00smwrDvIW56oBOc/zTMMoBXm8p1bBcE58zx4Dz25eW1t7IMikytH3i41Cr588iWO3b+N3KigdyrnNQ7/eswdr1dV4s7MTLe+881Clrg4gZVCDCxyHXgrUOFd1Qng/lgPSSeJ3uY2pZXUVqUfcg/ZPvTgf1YM0NLZTahtU8gCzYsFg8eirpbNncefMGZyZnsbBa9ewubmJRCKB7u5utLS0YGtrC4ODg/C8YpagqanJ7LsPBoNoaWkx40qn0w8coUlZVp2lMmHrG9IeeLCKQXUSgTT/1yBapUwTv68ymslkTDO4+dOnMXXqFHrefdcEONnVu6WlBZubm8jlcmhqanpgCw4DMADKtqIAD2aSbb2q97HHXCngwP/pQHCdKde2EVe54xjoCO3s7JSV6qrc0iZHo1HE43Esl/pvaHm0NkdlIyvaUwWoOgf7NerffyzLofKqGIB2VvuBKFDU11T+acM0uMjX6HS1t7dje3u7bIuGOnO0JVxXBeMcs2bVNCDASx11xSB6abWj8pUdgGPQh8BUnXzSnY6RZqPo4NKhrK+vx/r6upmPjpP8ofPk+0pjWx8pfrMxhdLD7qfEQB/PYNf7kCfUEVW7T/7Sz1MnaUkyALOnmHNQunAbIHu68Dm2/eEzFH+qk2P/2LzMYJlmt+1AMGWffKW4R2nCrKfvFwOFLGvPZrNYXFxEKBRCb28vfv3rX5ujzclrAJBKpbC4uIiuri4zN6WlvZ6kq/KLrdcBmOC/ZmHpyGgiQx1z4ix9XWXODsAzs+37xSq0/v5+7Nmzx+gKylI+n0d9fb3RX0o7PfVDg0HKq5Q5dSTtMep8lBZ0gBnwVB9J6aY8yzVKpVJwXdc022tsbEQikcDW1pbZLlRTU4P29na0trYiGo3if9zYKG4hCQbxb8XOskEmsIvryFMaaFDe4Praa6vvce7xeLx49K63WzGsgblUKmWSQV8KhdD4xhv4yfnzyOZymJ6eNp/t6+vD/Pw8FhcXkUgkcPPmTVNtl0wmMTc3Z2QtGo2it7fX0I1BYcqU6mfqq2+n0/im5+H3YzG89Bu28ilvc40bGhrK+Ief54/a/d+tqcH/d2UFsV/+EltbW2j4yU9Q8/zzeCkSweLiIlpbW/Hflpfx9QMH8Hw6jT+8fx/r6+uIRqOoqakxW5uow5RXKOMqG7YfbY+vEu/awRTfL/ZoIOaybbj9+a+n01gOh5EeGEDva6/h7t27WF9fBwAMDQ3h8ccff4BnKl2PHGgAdpUfAa5Gk23DAZSXPGoVhBJJgZNtmNVBIOEUyBQKBdMAiwaE37MdfT77xMSEOUGBZW2cG8Edv6OvKZjmWLTqwnGKHZzfPHkS+69exeCtW2VRejISv0elpNEoOuKVnLt8vnhOMPdkE5xWck5sZrMdWTtS7TgO3unuxkYkgusHDuDwnTtlx3NxjbntpLW1FbW1tQ/0o7AVsJbxEgQvLi6irq4OjY2Nhr5q1AkubEChpfk2EOAY+fe5e/fwdmcnzt27V7Z1Qg0+9ysSqKlx1aYnnN/DgkzMGjGDwM9d7urCxYEBHJ+YwKm5OZyamsKFgQGcu3evjB8JCm2jRz55p6UFl4aGcOjmTQyPjZm1oEHRMmH+zQoZ0kd52FZCGvDiZxhcU8NF/nccB6ODg7h28CAO3byJ6wcOIFFXhyv79+OTpZMObNBWKer5wfl5/O2ePciHw5g5cgQHJyfLlB55WgM26kipY2OvkQY21JlQ+vLoNQYtKYe6JWO9tRX+I5aG/VMv1U92gJIXnRGCVgYBkqXgGfmAfQ0mT53CVjSKy4OD+Fg2i9bWVqysrODgwYPY2dnBvn370NraikAggMbGRqNr1tfXkUgkzHFT5AnuL2a2hTpH14a01mCJroWul62bKHsauNP1JojkOmsTKsoOm/45jmOCrXdOn8Z2fT1WPvQh/LuhISQSCYTDYdM0c319HT09Pejr6zMlktWl47A2NzfR1tZmdJtW06gN4Hy0eZzyeSWgojJuX+nS3mLKIIEon6kBzL+vqsIPNjfxuzU1eEEALe3Z/KlTmDl7Fk/Oz+PMwoLJPvGYx+3tbZw9exZnzpwxwJAl2ltbWybYQrn6WTSKX7guPpvP43ekCk4rzezgdqU1t0GSOvRcR669/s+x/F1VFV4Ph/Evt7fxkuh1BcPbzz6La/v34+zMDD6azWJ9fR2xWAzV1dVYXl5GT08PWltby5zDuro6zMzMIB6PGz2q2UFdV9orzdTSkbWBIbAb/KZdoczp1gjlEQ2EqpPKYIIGUpgJjUQimDt1ClOnTuH0nTsmkM2fbDaLvXv3oqWlBbW1tdgudTpvbW3FQok/bOeS41AbwqCbgl7V2Yq1uH4q75pQ0Huo7dYtNHy+BviCwWLn+bq6uuJxiUeP4tjt2zhXCp4xoHj79m309vaWYdJCoYDGxsYHAtSUMV0HXRvVa7Zs28EJXWtWldj2SWWEvGZXqxKn8Pk1NTWIxWJoaWkxvRg8z0N3dzdisRhSqRRaW1vR1NSE8QMH8H/atw/HPQ89V66YezuOY4IyWv1lB9eUF3VNeZE3uT+dFQO0GZQRTRbx+9y6RvkhX+lnqaOIQRkQra6uhud5aG9vNw34aLs9z8OePXuMA0u6cK4aKFB+42XjIvt1xU62zIbDYYTDYbP1gTqEl257pOxrs1QNqDU0NCCbzaK+vh7Nzc1GL7EZYyKRwOcDAfxZNosXnPItHLYOpv7i1gPiBS3Vt/0AxU1cJ36GCV3tyaH+x9bWVtn2EWJsrbhkICqZTCKZTCKTyZj+UqlUCul0Gh/+8IfheR7W19fR2tpqxqSVdYqTdO0CgQD+Ip/HXd/HnySTeEWODLVtkQZb+Fptba0JShF7kb4/BPDdQgF/mE7jczU1eDUWQ/Nbb+F7ly9jy/PQ9Oab+GAggA99/OOYWVnB7du38e1YDJuxGH7kuvhkqbKDW4FXVlawvLyMgwcPmjEoRlBbQJxHW6A8ZfOtvmbbWsdxzCkpal/s53JdvxqN4r9ubqLr+nUkk0mzjcl1XUxPT+POnTuP1KfhkQMN7PStmQQFjKokqaBUMWuAgN+xCaROked5ZZkNzVbod7lo9uJQAWkWFAAGb93Ck2tryOfzmLT2y3ieh9HBQYwdPYon5uZwZmHBMBufD6DMeVUg+HZXFzYjEdwcGUHv1atGGXMuVDI1NTVlTSEVpNuNTlT4g8EgMpmMaThCB4TKRJ190ojOim0oOXb+fW52Fm91deGxe/fMmdtqoAuFginDsxt+KehSEEHjwQwxFRj/5zqR9qrg7YCD8piCIAUqXIdjs7Pou3YNnufhXolXFRxf6enBlaNHcWZ6Gifv3y8TSBo+rqseOah8QiDKhpPK557n4fxTTyERj+PiwABOz8/j6OwsTi8sFNe7ZOx1/jofNQJX9u9HMh7HjYMHMTw2ZkoxyR9aYkV+IJBUEKzAVjMrHIMNpig/2tSMtL55+DAS0ShuHTqEY6OjuDw0hOMTEw+AN+UznZ/v+zi9sICft7ZiIxrF1QMHcNI6zUS/r/qBF40KHUxbl/BS46v3Y5BBP2M/78TkJC709+Of41InnfKpDreCQs6dBjuVSpk1pdxHIhGcuXMHl4eH8YLnobe31/Q6iMfjmJ+fx8bGhsnoBwIBc6RjPB5Hc3OzcUI0qNFW6pTM8VH2+cOMt+pqjr8SYLPnzuCB2hI2vFOwqnxcqbRfG5AdHRvDxIkTeDUUwr59+7C0tGS2Sa2srKC1tRU7OzsmuFJVVWUqP1y32AWbVSMcG4958n3fGH0AZfOuFMC1nSb7UqBI+8JAqE1L8uf3gkHMeR6+nsnghVjM0J/bt6bPnDEn5/zr0hF/gUCxedzy8jLa29vNNgugqJ9rampMJlibdhUKBfxDUxOWXRevh8P4fPDBPgPkg99UffIwflDZJi04FmIGPu8HgQDmHAd/kkzihfp6I/e0oVVVVfjV8DDWampweWgI/85xsLa2BsfZ7WvS1taGra0t7N2715xs0NzcjOHhYaPHtQcV5ZDzIN/bSRDbxnDcui2TF50MZu9tJ4d4gfzHi04X9YTjOIjH4wgGg7hz6hQ2olHcPHQI//tSAoYNTEOhEI4ePYo7d+5gz549Rg66u7tx4MABo09o65WfVVY1aKsJF9Wt+j3Fi5QTPVKQn1VHV7evqN1SOvGEs8kTJ7AViWDixAn8h0LBOC2bm5vo6+tDd3c3stmsSWokpbz+fqkpNYOMmlxQG6lBgYfZNk20ALtBZM/zEIvFTJM/W/6JXe390eoQu26xEimXy6GzsxPnzp2D7/vo6OjA5OQkmpqakE6nMTQ0hD179qC2thb/25YWLAaDuDI0hP++vx/37t17wOZznAwaaHBe9Ts/r7xObOd5xaaHdXV1Zv6BQAB1dXVlNoP3Xjh9Gheefhp538enSscU/sh18X3Pw+8C+Eyp+jgcDqO5uRkDAwMm409bVyitc09Pj5nH7LFj+MbICBaXlvD4+noZj9Fn0KSY7VPYgVENbNlVGeRZlXlWNPDoReLRSklJ0o62iliE2HVtbQ3t7e2mj05NTQ1yuRwSiQQKhQK2trbwVDiMofn5YjB6aMjQ3a4e1MpzdVq1bF6xmX7Xft33d7cKq8Ou+Kq5udlgZlZZ8n3abFYuAMXeUHV1dchkMmhoaMDy8jKOHTuGtbU11NfXY2VlxRy9zPHa1XK2TPm+jy+Gw/iLnR38QWnLMV+3ZbXSd7mtQPUX+eK7gQDuA/iTVMo0vSbGop6bmprCwsIClpeX0dvbi2dWV/G38TgOj44i2N2Njo4OtJVOu3n33XfLsATpqIlyu6KOtLTnZFe9cg3tOXqeZ3jPdV2z/UX1giZSPh+J4Mm1NfzFxAQyJZyRTqcRDocxMzOD8fHxR6pqeORAAyOzWspG4WHkkEzGzIoqVypR2zBrYAAA3m1rw1snTuDA9es4OjNT9n0S3lYMmUyxRaECHTKlLUwqjLW1tYbovNfokSPYjMXwdmcnTs/PP2BQgN2sBudPJjgzPY33enrw2Pw8BgcHzRhIm5WVFWxubsLziqWqbE6mRk1LOzlmXo5TzELE43HTPE4zMLrHk5/XuetcVKE4joMzi4vIFwp4t7cXruPg+L17DwhpOBxGW1vbA3uU7UixHRSgEs3ni91vt7a2zOeYhR8dHMTNQ4dwYnIS4XAY7/f14cm5OZwtHV2qPKNrr3PL5/O40tODd7q7sf/qVQyNjpZlQjiGq8PDSNTW4t3ubjS9+abhi9v792P0yBEMXb5sOuVyvRVIqwOvR5RyXIVCAR1bW0jF42grlRnp2MkvapQ4BhvUHB8fx6WBARwdHzfKOhQK4UZ/vzkd49DUVFlG1Qb7dmZBFVQlPuPaaNZD6X1qchLn9+3D8YkJDE9OYmh0tOgYtbWVPdeWeTVMvu/j9J07eLenB0dLW3X0GWr0bUdF9ZAaBDsgws/bJY9qdHV8NqA+ODGBA+PjwHPP4bd90WDTIGswk1F0G9CSLjTkjGzTAXlqawsfu3YN0WgUC75vGhtR7zQ0NKC3txcrKyvm3Ora2lpsbGwYcMh1oD7h/l/SRAEon23TV9ffNnSqxzUrq4ZSe9vYjgyzMuR3rdBiVdvZxUV8YWwMXV1dSIfDiEQiWF1dNQ0xE4kEqqurTVXW7OwsqqqqUF9fbyoJ1C5pQEXnwN92VpCXDd4qXeRxgn7SmLTVsZD2r/g+XgsGTfNNZpHonJ+ensalgQE8DyBTyngnk0ksLi4iHo9jY2MD9fX1Jut69+5dNDU1obW1taxZou8Xq9E+sraGX7W343OS9VDdoMEye26V/rdtEOdIWhO4q43xfR8v+X6x6Wip0S3lV537J+fn8U53Nz6+vY1cdTVqamowMzOD7e1tJJNJtLe3m4zp8vKyaaiaz+fR3d1dZs/JX8lk0vS2IK+pzrYvDZBTnjlXBn1IB8UyxE/KSxqAU1qRb7jN84m5ObzX04NP5nLIlJ61vb1tKnZ2dnZMr590Oo3V1VU0NDQgkUigra2tDIRXCjQoblO9qQ6H6lflWW5f5TMIrFXGyEsMvvDe6gzzNTY2fGpxEW93deEz+TwCpUDi+vo6UqkUstksqqurESudnhIIBHDv3j3TLK++vr7MYQJ2+7HY66OJDa1C07Er/tJ5aQm0Bs10zrbOtx3ZaDSKdDptGqDzuM7h4WFsbGyYz7S3t2NlZQWfyeXwo2AQz+dyiNfXY3h42BxzW4mfOC7bhqqs6nqSBvl8HvF43LzPhoFMdAQCgbKs973HH0emthavFQr4ZCnA8t2aGsz5Pr6eyeAzkQhqamrQ3NyMcDiMM2fOoLa2Fs3NzaYf0draGoLBIJ555hlT6fDv9+7FWiCAt6JRPLu9bRphKx8rHqX94Xo9LECql2IX2xln4CCVSpXZAsVO+sOsfiQSQUtLC44dO4bt7W3U19ebtWASKxaLIRQKYWVlBR0dHQiFQqbRKXsbUTZIb/IbsbWeWGHPVde8Eh7kuu/s7KCmpgb5fN4cxck+MxoY57h5YgYdaSas0uk0lpaW0NPTY6oekskk6urqsLi4iLa2NqysrCCXy2FtbQ1TU1Po7u4ukysdn64Dx/tidTU+L72pbDtk+496RSIRI5PkL8rkywC+C+ArJUzEICDloaqqCul0Gs3NzQgGg1hbW8OXmptx4MIFbG5uYu+5cybguby8jNbWVly6dAl79+5Fc3NzRVyu9NfEGn9zTfjdSpjD9gGJFagj9VI7xaA2ExkMdlEn8kjtR7keOdDAKJRGvG2QocLNwfFzrrtbHqmGi5cJNHR3YzMSwdjRo/hoiZHtYEE+n8fa2ho8z0NHR8cDoJyEp8AqeACKBri9vR09PT1wXdeUS+/s7ODx+/fxbnc3HiudWawLZDuBHDeZ4+T9+zh5/34xM2Jl+AAgFouZ4+L4HVXGdsTOfk4+n8fW1pY5LYNg6EZ/Py4PDeHkxAROyPGRwIP7MXXtFNwVCgW819ODjWgUv96zB/W//jUAGAXhuq6Jvmt5uS38HJNGwDkOoJg90wBUMBjEjf5+vP/YY/ADAVweGoLrONiIRvHW3r04fveucWqU5+wqB17vdnebzM6JUiRf6eo4Dk5PT+NCfz9O3b2Lrq4uc7+/OXEC2/E4pk+fxjOpVBnttSJAwYI65EqPpYYGeK6L5cbGsvsQVCjv2BkhdaJHJifRd+1aUZ5kL+qF/n5sxmK4NDiIQ1NTZWOz/9YMmA2abMOiIEezaTrvkakpHJycLEbH87uNjvj9SkBF78NxHJ2ZQc+VK8VKDPfB40ptB9sGabbBJz0VUHFtKwXheA8NWNhj1e/8c1wEznSUyCO2M6tbrCKRCGKxGD74wQ+a8dXV1aGjowOpVAqpVMrsUW5oaMD8/LzZBpNIJOA4jtkiwEZFlF1WTZEuKv8KNjl2XprFtI2dvkadw4g9A9OqY9V5ZwBGn2E7YjU1Nejo6IDrFkuMe3p60NbWhlu3bpnjjzUz1NzcjPX1dTMGbhm5ceMGWltbUS8d+Dlm/mbwRYG6Op7260on1cU2UNKmkxrYYPmrfv9TAP67lhazxYBgh6DgrO/j37kuZmdnMZtOmz3NTU1NmJ+fR1NTE1KpFBKJBGpra9Hd3W0CLi0tLSbYwDX/cCqFfyWBBZsX6GgBMIGph/F6pUAMsKvzNDivfBMIBPA8gFfCYTRGo8bGh0IhNDY2wnGKJaFPh0L40sYGFhYWsFBbi8bGRnR3d2NhYcEEinZ2dpBKpVBXV4f6+nrMz88jGo1idXUVbW1tZXqcAWZtEKq2T2lBfiIP8HtcG52PZht5T66nyow6qAqQg8EgamtrsbOzg+rqapxOJvHVRAJLS0uYBdDY2IhoNIrNzU2sra2hubkZiUTCbJXZs2cPZmdnjczoyUIatFZHRoO+Nn/THpNvPa9Y6s5ACCuOdF3V8aLuIb8rXlN9TgcmGo1iZHUVr8bjWFhYwGgpoxqPx3H//n3s7Oygo6PDNCePRqPo6enB9PQ0gsGgKVHXgJmW/SuWJI5Vm6J00GoBrg1fY48AO+PIz+bz+bK+FKofVBYCgYDpozI2NoZo6bg8OqHc7+04Dh5fXcVHS32nZktJndbWVoMz9RkqazZO4pgV3yv/07nS9eMxuhosJL32vvUWFj7wAXy6xBeO4+AVx8H3HAdflqaQPOmIWwnz+TwaGxtRKFXq8PSCnZ0dNDQ04IVCAd/1PLwcDuPgwYMmmceLAUKtYOSzbGykNszGAmrD9D0G/La3t01VA3UB5Ua3aLqua06SaG1tRV1dHc6ePWuqZNkjKZPJYN++fUgkEibTfvnyZRw6dAjZbNZUktAu6XHutLHaL257exs/iUTwo1QK/10ohJdLulr52saDpAODCfy/vr7e9Ofgdm76BZR7BvpaW1vR2dlpAgs8Bcv3fezfvx+O46Curs4EAUOhENbX102/MdWzGvy0MbNiaXuNbJ2lc9M1JT9TT+r1nOfhs6EQ6uUUmUgkYraUhMNh3D1+HH8Uj+PZnR18sXSaVyQSwcbGhjklxfd9w6OFQgGLi4vGF1VsRd5RntXqBT0KVXWS6lgbq/F9PYLTxi38PGnOogEGVri+hULBbEf9x65HDjSw3J0GgAEG3y92kq2urkZ9fT0aGhoecJDfbW3FW52d+ODCAs6WGMw22r7v453WVmSCQURzOTy9tGQik7ahJsMxk2c7a0pkZUgSjsqLgsJSmYudnbjQ2Ylzs7M4cf8+8ihvZsZ7qlKyQe+bTU24PDSEw7duYWh01Cgbfi4Wi5UBV2A3s1oJeFGRsLO453mmjJfrcXFgAMl4HJcGB3G2VA6tVRdAeYmiPoN08X0fJ6em8ObgIHbCYdw7cQIjk5Pmc3SIKjnGEDqRrnZpKcfAedOJcV0XY0eOwA8E4HoeTk5OIhAI4HxfH07PzJjxsbyTyoqKW7u1e56H06WqksOljtOkK+nhui6OTE/jWAlk+c5u1Pvk1BQu9vfj1NRUWTaCitoWZn2uOuW+75uy+xMlGnIedrSS/KQAlevGcavzyXU8dvs2Lg8O4vjERNn8dFxce3XMNTKsAIm8famrC5cGB3F8fBxHpqfLAlbK8+RZrrd+jjTQNed7/NHgoa3sNQCia6AyQuebhtuWU70vx64ypHpEx6eXrXx/m5eul86XfM25cxy6/WlkZAT19fVlHbttcKQgUrf/kFaaIeQ8yXPkN6CcRwCYoJKW7+m6qJPGZ/I11Xtcr6WlJZNx4fMoI3b1GnlAdXp7e7tpSsQ5M3jb3t5uovfUNbwoR6SblilyjKQJs8wqrzo3G9joGlR6jfdXEJAqBTZ5H2YMFPDT5vJv3n9kZASdnZ1lYMRxHBNEVZk4ePBg2XrqOul9+T0GntT+qmyr/bODLTYwt7P/GhjkM5SHVB/y88pD4XAYhw4dwoc+9KGyyhrHcXDgwIGy7/eXtkBpYICOV09PD3zfNyd3aMAvn88bUGbTi7LFKhvyi+oz2nHyEXnNthe+75tAvn5f6av82djYiNOnTyMWi5WtcWNjo/leIFAsY1dZ4fNc10Vzaf9yPl88AtA+YlfLdzku1Q+2/aeesj9LepGHlBcYSCT/sK+BBjj5/erqagwNDeHMmTNG/4fDYTNHrltfX5+hWV1dXZnM8rNMMOk4tN8R6aSyonhG9YbjOAZzcd6s4NA9zSqHyh/s/6I8SX1fXV1tjsZrbGxEfX292UrBgEYsFkNNTY1x4nivlpYWwwsaRCE9tWGmyhnnYQcYVI/bdkt76WjDasUce86fR9+1a3jqD/4AflMTfN/HcwC+UAqaUL/V1taa+ebzeUxMTCAYDKKtrQ25XA63b98228TS6TROhUI4hWIydKK62lToKNagvlA8rI6bbVcq6UiVV5svqqurzekeetqK/T0+m84b+X96ehptbW0mGZDJZBAMFk9mYJNj3/dNU8/GxkZzLDUvBvi0JwX5g3z6w3AYc76P/7q1hc8GHzytQPGpJg+IuzkvVmRw7anDWX1EmaqtrUVbWxt6enrQ1dVl6Mztm319fYhGoygUCti7dy8cx8HevXtRKBS3anMuOkY69eRX9QF83y9LNKtdfxje5FhJw1AohFQq9cD2RX5GEzA8FW5gYABdXV34v46MYDkQwN83NOCTKysoFApobm5GKBQyR5lGo1HU19djbm4OsVisLKBBHUrZtGVOx62nvamO0zEr3cirruuabbI2RlGfg69VVVUhFouZE33ox+qW13/seuRAg+2kArvlk/b+M3VsdnZ28MbevVivqcGvOjpwbmWlDEhyMq7r4o2ODmyHw3A9D6lUCsvLy4b4epRGoVAwe255aSkUDTsNei6XMwvHCB+PNeQieJ6H9554AolYDH83NATHcXB6fr4symRfNj0KhQIuDQ4iEY/jrZMnkc/ncWB83DBBNps12UONdpIWLDMiszGK5HmeaY4zcfAgbo6M4OTkJI7OzsJxHJyanMSFgQGcmp4uA0ksSbb39KgiUYfi2Owszu/bh81YDFf378eZhYUyGvGzekSVrZzUodb52bzE367r4tj4OC4PDuLo7ds4Vuo4e2purkgHaZqjJdUcsw3qj9+9i5HJSSSTSZMlp1FUHiaP8O9AIICRyUkcGB+H7/tIlpxS0ot7Djm3QKDY6+HiwABOTk6aoAzndWB8HIO3bhW3yEhWQh0pBVhq1Pk6+UDLiDn+kclJHJqaKipU7JZzK29SQXGeo0NDuDQ4iMO3bmHg5k0zD808XBocxGYsVlyPmZmy4AVpoTS3s12VQJqCZR0fn287IPpZNXb8n6WzlO9MJlO2p56GiNkF3p/RWCpyPe3AdpBUNv45Li3p0wAeUB7cpPEmDX3fN8qefEMAz60U2qVcnVM7OKrBLA1EMdOn+3ft5k8KiKPRaJlhqgSw+D3lAR0P580z4G0nlZ9lZo+v0cHmHMgvoVDIyCz5W/U4P6vGm/8rPQkcuWbMuuqJHHRQuG6qC+3f+p4+m0Zf5ZVAgvelPlJZDAaD6O3tLeMbBR+q8zWAoLTVi7RUWc5kMqZMVmVEwRzl0pZx/W3bH10LddJouzSTr5lmfj8ajaKhocHQQ/dJ/yadokFj3pPjp87kUbCsftBECz9PnmSPCw2I2HP2PM8cYauf0bWgLlJsoNt1SBtmkjTTrjLjum7ZvZTO9powo2472HbwXDHVw0Ct3pvJBn5X52cHxLl2WslkO7h0bg4cOFBGD5u3SUvVfXrqmNJB7ROwW73C79JZo57Vedp6gzynvKrOnu34Us8w08x5243eenp6zHdo45hF5Odra2sfuhZKK+oPyimxsNoDjoNzruR8q60kvwHlp2O4rlsWNCc92cyYVUjs0UPad3d348iRI/C83b5m2oOpu7vbOOEcJx0+BtQLhUJZQs9ec/6v1XK2TtLP2f8rpuH8WXFA/a24V5110j4Wi2FgYADV1dXYv38/NjY2sLq6ilAohIaGBszNzSGbzaKmpgabm5tmvI8//rjRx5R/DVzaAcBoNFoWRHvR8/Ca6+KrEqzSHgHKL/q/53mmuaOeOKf+lQZTqRcZnIhGo7h79y46OjoMLySTSYTDYaTTaXPaw8bGBpaWltDV1WX6cnDdmFjmVnm1Z4rziYE4bxuT2n/r/3ymvYUd2G2CqtUOPL2rqakJe/bswcu+j9c9Dy9b8s+tEcSfwWAQ+/fvx+TkJFpbW8vklkFLrWLlRX1E3GHbGnvNKq2r53mora01VSXkG/2+VoiwajCTyWBkZMT0zWA17KNcjxxoUACihq1QKGB1ddXsFaOg6WdGSqXsHyw5rhpB1WjVE3Nz+FFfHzzXxdudndh74UIZOOW9WaZ0v9TITw0Xlafu41WDYzcuU0Zt29hAorYWnuvina4u0yhQhVcX/v2ODrzR0YEz09M4t7ICADgxMYFfHzsGLxDA6JEjOCfBEru6QK98Pm8CC7YSp5LwfR83Dx3CVql64cjMDAqFAvaPj6Pn6lU0NzfDKwUB+HmtllAlyXVQY+p5Ho7fvo3LQ0M4VXJibR7gWJVp9f78H3iwkY4NCvj5Q1NTGJmcLNJaGpXod1Ux2I66/pBf6CBoFN++nw3IyRs6T2ayAoGAMWQ0wJcGBrAZi+Fifz8OTkyU8Yb9m/NXPmKTKFX0ChYVCBUKhTKlq+/pGmjgisG3UCiEWGmbxUY0iqv79+OxpaWy/fHkg+Pj48XTMiYny4CFTTfOQYMn9jqprCsg43t2RFSdaQXWHJtW8mxvb5v1IKhh9jGXyyGdTpt9uARkqVQKW1tbxmCx6RxLHpWnbN7/bV9aFVZbW2ucZxv8aFUC91wDMOMnyKSBos6lTPM9XUvySqFQMEefMev/QwDfcRw829qKtUAAf1JXhxfyeTxbAsNaQq60ZeWJ8nclQ6fjcV3XlNrzNZ7rzc8pgOAzXNc1e7HJQ9xOQv4k4OA49UeBImmSz+exUtLhvu/jB76P7zgOPtzejidL+4Jtp4Jj1Kys6lcbbNr6UOmiJa4EZ7wn52wffcftH9xnyfPJ9b42KNZx2TZwZWXFNIl8LZ/HdxwHH6+rw/DCQpmDqetlBzT4XJVh22HU71dyErk1hfcHYAAP7eHGxobhB2buNcCtDpHNhxwvgVw2m8X8/LxxjH7kuviLYBDPtrVhfyk4qWup1QuKNez5KL3UidPghh2gs9dKKwUI6NfW1gzPMzNKWimPKu8pzyqO41F63HfN79vZO5Vfnateqqtt/tAAoNoEGyQzwMN15+9EImEcE/K62hPVN0pHjkVtFE9XYZ8srdxQueE9aUMVs1C/8JkK1ElfdewrrTHHxmoDpafSgc/gGFTP6nYIpYUtc9TRruuabQc2XuPa6oljem/FS7p+qhNJT55kQ3ukeld7dNl4p6OjoyzpwmfyosPLNeD7itF1nZQ31cbTlnF9VTYeVo2gdNWLdsumh30p7t67dy+Gh4eN/eAa8X4DAwMPYFfOmePkc/SYW00a2IFZ3/fxacfBy4EA2uNxc3IHn2nP1eZZ4k9iLwZi+Tf1HNeSFWehUAhtbW3Y3NxEJpNBVVWV6cXgOMX+IuybRB5aX183ASjKCtcsJduaednVn3aQSf+2Mbl+xvd9xONxsw2Jc+ecVI583zcJb98vVqSNTE3hTMlmbJeOia+rq0Mul0NNTY05aYM9ORTDco11zUlPuwKMifffhFP1PrZtIs0r4W+Vdeqburo606todXXV9AuptBaVrkcONIyPj5cZMTXqwWDQ7KtiJolEyOVyGJmcxCdKBM1jlxnsRT81N4dkMon3e3sxcvMmpg8fxqXBQZy6cwcnSscC2oaMBKWy0Sw0sBu5VIWnQJvE9TyvuJ/eceB6xRJ8jUzqQtHxe2PPHqxHInivt9cEGg5OTCCfz+PagQOmyR0vPTvbVoQaeaMjy71PdBhd18XR0VFc3b/fOIK8bOFRh8l2DG1wx/t4noeRqSkcKW1ZKMj3aZjshliFQgHX+vpwZWgIh0qZct/fddrtLLs24PF93xhZx3EwNjSEG4cO4dTUFM4tL5uSKF0DFXLO2/6bAJC0VudWszIM7HBt2OxEs8Get9tEJx6Pm8672WwWxycmcGlgACdZWeA/GMgB8FDDVSnoRTqpkuOYyf/K9zaQp7FhKWN9fT1c18XGxgZGbtzAtf37cWh0FFtbW4jH46ipqTFnE3vebiVGOByGFyjfv8n5KDhTZ7CSgf9Nxp/zoZGwwQdfC4VCxpAz88vxJhIJE/FPp9Nmb242m8XFixfNmd7BYLHpYltbm9EV7Ggdj8eRLRkFytrDwPRv61JgpFl8BTRaSr2wsGAyj9RpbD7E6g4FUdQh5EXtVUNHfnt7G7du3TJjicVi+E5rKxYCAfx9fT28eBwrrovXgkE8I7xtGzDS086qVgIvNq9z/fk+o/12t37KpeM4psGlBv7Y+EmPK6YtUNpowFflbGFhAePj46irq0M0GsV3amuxEAjg583N+Kx8X/W3yqGCEdth1AoBlXH9DMEabSifoVtm9LtLS0tYXFw0FR7V1dWmy7fSns+0HRHNJPp+MWgxPj6OXC6HxsZGfLuxEfOui7+ORvFEqZu4rYtV5tPpdNnedF1j0qdS4MFxHCNzHBvBps1DBNPLy8vGbjAwQxul31Hbq+Pl61ynhYUFzJcaP9fW1uIv6uowHwjg7+rr8XtWZQRpyt+qz1Te7LErryqIt/EUx62ZbpWbxcVF44Rms1k0NTUBQFmVj+pndRgpU3xOOp3G/Py8CcrSXpOf1J6qrlf7pnNR/q7UX8T3fYMNlT9UbzGgyrJlxykem5dMJs19qcvt0n7eV2XADjIVCgXcu3cPiUTC9MtS3aO6gvMnb5K/+CzlZZ2TJiooX5WqL3mxUoHX64UCvl0o4FOZDD6ayRjHQOesATi9n+It0nR7e9s0wtXnZkr3pk5U5922RbazpjTgTzAYxNbWFkKh3WNcbZvvurtHV/LZ+XweiUSiTHbYV8MOxClP6nPV8d7e3ja8R0ecz+Y6Es9W4hNbT1XCAbYt021H2pfFXgterIBSXrVxoq0rOQd7vHyvqqoK6+vrxkn+IYDvh8P4TC6H58SHoTwzEafPsmVSnU7yz8bGhjkimHieOFbvUV9fj6eeeqoswE4cGwgEzBYnm7ZcS8Xw/Iw2Ntf561iJfzW4q+tlf1dfJw5ipl7lWcdKPRgKhXD27FmTMKJMcavP+vq64XVWxvX19Rm6NTU1mQCO0qiSrdRgv91vhWPS9eOYdW46R906YeN0tZOOU+zJkU6n0dnZiWQyifX1dTiOg0bpQfebrkcONPT19RkhUsIDu2XAatg0QqvKUJldJ8WFO3znDrouXYLrunjthRewGYvh/b4+HJmefqhjzf81Y6BKUKPR/OF31Ik7MjaGa/v347G7d4vVAs5uwx7bqQKA9o0NbFZXo6Pk6JCxD05M7PY3KAmKXaamR6gwQKKgQEvugN1o5sjUFA7duVO8p7O795z3sWlEWmhEn+/ZdPA8z5TIqoCrIuRYuJ41NTW4duAANiMR3BgZwbHZWayurhrDbTsmqhRUAbiui+sHD2IrFsOFffvwWKmshwqVToQGiew1tcGdBhUIwAhSXLfY3JJduauqqlBXV2doyflyHmy2xIx4NBrFqbk5s30hWwILdmaLc+YctcqGsvQwI6LzBMpPSbCdJwYk+BzuRd3a2oLjlPZ6zs5ieGys2Jirutoc/VZbW2ua+6gjr5FUPoPP8f3dUky+Xklp2xFz/RzXRJWcOmWO45h+HAzSxeNxpFIpLCws4O7du9je3kZDQwP27NmDhoYGjI6Olh1d1tfXh0wmg7m5OSwsLGBubs6UuXGr0urqKqqrqxGPx7G1tWVk4WGZid/GpdVNGvAirTh+GlYeO0lnlyWEXCtWOCSTSVP5sG/fPpOtnJqaguM4WFxcxMmTJ7GxsYHp6WlTGTYwMIB4PI4X8nl8H8CnS+vyA8/DC1IKzPVRo2WXB5NXKQfko0pAW7u1O45Tlj3VACH/ZxUOUH46BbBb4l9TU4NQKIRbt24hGAxi7969qK2tNRH5W7duIZlMoqmpCXv37sXKygoWFxdNt+tAIIDnAgH8KBrFp3M5VFdXlxl2XScFRTY9ADzA/wpWVHa1HLSSQ6eAlNuFWP1BHafH7TJ4NVPa/kReoOO0sLCAe/fu4fTp01hfX8fq6irW19extbWF7u5uvFAo4DUAny0UcLGzE/8hGMQrjoPn/QePO+Q2Bw1G6pzVVisGUEygJdd0zvQ7/LwG3ygfDEwwuEpnbK10jHV3dzfW19dNw8fFxUXEYjE0NTVha2sLy8vLmJiYwODgIKqrq/E5AN/2fbzolAfmdQ05r4f96Hop6CN9NMCs/MLve15xX73eUwPH1CHEEvl83lS2sCFsVVUVmpqakEgkEI1GsbGxgYmJCfT19RmnkI0w2a+BvMdnUm4p+7lczpRcU64Vt6ge4320EkkvXWPen7xNp951XVO5wZ4KdB5p3xnUoK1rb283vJ5MJrGwsICOjg40NDSYs+s3Njawd+/esvWkQ2CPT2224lx1zlQeyNPkR+or8oNtS9fW1sp4/duOgznHwQ+rq/E7JZr4vm8cdFbYUObJTwzOJZNJZLNZ1NbWIpFIYHl5Gaurq6ivr0csFjPj4Aksun1Q58/PqdNjOzca2OC2Mv5tJ0V4n0QiUUZfVqZls1nzHZ48ofxo4yJ+1nYyZ2ZmygK/Ws2gNt12ysiHyhN833bG+Bm+x0ADm/MqTfhM8iN7D/B7bIJoO+r2ODlX8h7XmZVOvl9++snrsRgWAgH8oKoKn5XtKRoIs/UTn1dJ3wWDQXNqE79PHew4zgPbWYnvOGbiXcUKnJOtN1UeSBvaWLuCUv0y8oAdELfpqJcGSBkwuH37dhlfKfZhFbDvF7dCHDhw4AG54HP27t1bFpgGdiuTAJjG0xqI0sSF0sWeGz+nwWvl14ddnlesCqLcc31Ul5GP+Iy+vj6MjIyYALT2rXqU65EDDVxkOxqtRoavP0zxqmCqkFf62/M8HB0bw5XhYZwoVVNc7e3Fhf7+Yn+C0tGXykx26RDvaS+MOvvquA6PjeHk/fvFbs6B3dMKdE4qGAt1dfBcF/OlPXPMuqnDSDowwkrHlQ1Q9PM04ABMeQ1Qfn6sHQWlAKpAciyVKjkqraGuse6N5fpqsIBMxgxaIpHAwMWLuHX4MEbGxhAMBtHT04N0Om0MBy9+lyDRFsxDN2/i5uHDODE5ab6nn+M8CmJ8da+eOkMsV2QgxnGKZ46HQiGzpz8QCKC2tha5XA6Li4uYn5/HxsYG4vE46urqsLGxgc3NTdTW1qKlpQXNzc2mCcrm5qbpgJ1KpUz2SWl+a3AQY0eO4OTUFI7fvWvWTXlOaaMCT7mx5YXPUNBCnuLWIO67pcPBLBhLq1dKTWpCoRCamppMNp/ONgBsbGyYMn11eNUZZORYjTovBRj6P7+nfMiLDhtBNY/NpezmcjlMTExgeXnZlLPG43E0NjbC933Ml86WpjIMBHaPNCO/RiIR7OzsYHp6GqFQCO3t7WhsbDQZWQJ5ZkwfVZH+Uy8NCtrNf5RuBNYEMfr9TCaD1z0P3w+F8GImgy+iWGJ89epVA0AmJycRj8dNQCYUCpkM7szMDDY3N3Hw4EHTlfxjnoePlaofAoEAfodBpUJ5TxMFDKwY4TrbAYVKOl4DDdqvggCdn1H+Uv5QfmIpPbOl29vbRq7fe+89DA4OorGxERMTE6irq8PS0pKJxKdSKVMd0NjYaLqbfziVwkdKYClrbcNTfUowxcsGr7auU5ooMFIaUE/y/78NhfDDcBifzuXw8ZJTScda92snEgnwnOuamhqEw2EsLS1hdHQU2WwWd+/excrKCtra2kw2ZnJyEmtra5gt9fs5cuQIAOCT+Tw+uLGBqqoq/KtYDPOui2/7Pj5eCi6qHmMGk1k8e4152TqB46bdVvtIPiOIUtBKBw7YDeBz62EymTTO59TUFGZnZ3H69GmMjo4apzWVSqG9vd3Me2VlBUNDQ2hsbEQul8OHdnbw0RJ4zlo4h/Pl/Hd2dkwwj3PURoAqJ3YAQnWm0op2XI8Os0/2URBKPk4mk6iqqkIikcDk5CSampqwtLSEubk5E6QIBoPY2Ngwn4mYUloEAAEAAElEQVRGoxgZGSkD2xqg1zVSfFFpvDomlQ/SjLrUrkrkpfrExl5qO/P5vDlFhgGVnZ0drK6uYmxsDIcPH8a9e/ewsbGBPXv2IJPJmCDyxMQEstksTp8+jVAoZLZY8rkaRFFd7Di7WyA0gKLOsPI3ZUFtHXWI7fhwKwHp+pLn4buui5d83wQGCoXiiQs8OYQN/pj8qKurw+zsLDo7O7GysoKNjQ309fVhbW0N8/Pz5mhjxY1cC5VPO7ig2E/nx/3v3B5RKBSwtbWFe/fuYWVlxZyIoDiY36MTqs6d/mZTViYAWC1NjMXMOtdveXkZ8XgchULBBIx5whv1EANpXBN7q4rSQPWzzeu2H8D3mQRj9QTnrk7q6uoq0um00VXEqclksmxbqP18BoOoI8kL09PThn+ZiOHzAeC5dBp/VVODFwoP9oVR2dSAnY3ldJ6BQHH7KpNQ7Iej98tms9jc3MTExIRZEzaE1N4jD7vs99hgk/LGsahzbffrUJrZvmalYLjqUY6XOku3hVAOtB8Fe2gxYWRXFOlVaYu053llJw1xDuxDwgou23fVMbOix8aOlfiW60t7xeSrbmOmftMKfFZTKT7Xefxj1yMHGjhxZXpOhj9U1JygKhg7YqLCpBF6ff/ozAyOlvoQ7DgOLpQaFV7o7zd7+oGiAFzftw8X+vtxdHQUXZcvP9AQRAnP59FIaKZQG6xwTrzUkHqehzMzM3inuxtn7941Y1dGIA2oUNhAhAaD46GSUgBWU1NjznRNJpOG5rwXBU2DFWokKNQaZLEZo9J4uQ4KMnzfN1lXOnfpdNo4sPs3NzE8NlYEE7EY1tfXzSkk2WwWyWTS3J/Ma/d5uDU4iOsHD+LM5CQO3bkDr+QsUljpkBBE03BzvOqM2JE213WN8lA+Xl5exo0bN7C5uWnOg3Ycx+wly+Vy5hSVpaUl3Lx5E3V1ddi7dy/27t2LUKh4pnFNTQ3q6+vNnmmO58bICFLxOC729+NkKcvO6LrKggZQFMArWOM8KEtcH2bwCORSqZQJwCQSCczMzGBxcdE42zT0DC7cu3fPZHxTqRQymQwikYjpbs3+BQzWKEjR9eOYbUeTv21nU50slTHf900ncXVG5+fnsbS0BM/zzPF7DIxpH436+npkMhkEAoGyM4LD4bA535h8nc/nsbi4iEQigaamJmP8mWF41P1n/0svBRiaIVQZ5PYR1bue5+F7rosFx8H3AgE8ff8+5ubmcPfuXSQSCdNc6tatW2ZPXjqdxoULF+B5xUaKw8PDiMfjRk9pRkH1HwNofF1BqcodLzVymm3QSw0fPx8KhQwvU29Rblgebus1GxzncjmMj4/j5s2bWF9fx4ULF9DZ2YnNzU3MzMygv78fjuNgamoK5/fsweWRETw/MIDHBJBRR2Ql6EJwoU6IBmLtoLR92Y6aAgt9ZiQSMTQIlYIM84EAflhVhedLNOK55rpdgWAsm83iLxIJvBYM4tDSEnru3cOlS5dQU1OD+fl55PN59Pb24nXPwy8iETyxsYGXhobQ1NSEQCCAzc1NNDc3m0aLn3McfMf38TKKdknL8rn+GhywwZ3qBQWupIlmVD3PM41/1W7R3ukxoPqbYO072SxeD4XwsaUldE1MIJlMYnx83JR7Dg8Pw/d9fH1jA2/u3YuP7NmDV/btM7bWdqDpFHBtVTa0utO2vboHnt9TMGc7nPq/BhK00ba9lZD3YQf2dDqNjY2NMl0JFB1ZOtCO42BmZgbhcBiHDx82wZVK/X9Idw186Zh17jpX1e2UEf1NedLghdJdtygoX2vQgviBQca7d+/i9u3bpkIxmUxipdR4PBQK4cKFC8jlcmhra8PRo0dNwoFOvDpLfA4D7HZgX+214hDFb7yCwaDR2/Y68x6sXOFcnwfwiZLeyZdwbCAQMMc6bm9vY2NjA8lkEpcvX0Y6nUZXV5cJLtHWX79+HdlsFj09PeYIW/Kn4k2dG7C7NbJS8kB1Mm0Gx84jGx3HKWuerff2/WImXteYF2WMyYBEIoFUKoWNjQ1Eo1GsrKxgaWnJbH/kSWTr6+toampCJpMxwdbW1tay9dStDGorNGurQSzV4bbzWwlLE9snk0nT3I/yyecxCECnzfeLVSps7BiPxxGLxcw2IfIuaby0tGQSQYuLi7h79y7a29vNNkrf98tK2Z9JpfDhZLLYGLNUzaA0oRypLlH66Bypk+LxeNk2Fw0GA0XfhgHSjY0NIyP0tXiSoB1M4aUNj1ltySpFbUis49P76Q/nqHxbaT11HJ7nmW0Cim30eb7vY3l5uayPSigUQnNzcxmvq42i3BHL8F6uWzyBg2uu2Ib6R/1kDTbo3xpYsddQbS5tNm3G9va2wX8qg6QffQJWw+gWTdte/qbrkQMNjY2NpmyRe6BIDFXE7AhPBc7F1iy5MpkSxFbwvCcJeHx8HJeGhswRiHw9Go3i/L592IhEcHl4GGcWF03DEnXUNFKtxOdYFdhznPb+LzWgx+/exdDoaLELvFte6kqmCYfDZg84HSCeKU0lw33y6XTaZF2pmOj4MItOoKUlUGrs+FwAZU6cKgzOnYpWu/UqaKVypvKtra1FPp83DkxNTY0x7M3NzaYkkY7g1tYWgsEg6urqTNafGSiOh8+/duAAkrW1uLBvH47OzOBiZyfe7+vDY7OzOHHvXhngVMWg96Kh4BFZrFhgCWE4HEY4HDbVC+l02ihPlhVWl7YUxONxZDIZc8QSOyWz1H5jYwPt7e3Yu3evCWQ1NjZibW3NrP/Q5cuYOnkSJ6SxJoEShZl/61YVBiNoyBl1JE/SOLB0OpPJYG1tDYFAAJFIBFtbW5idncXS0pLht0KhgGQyaeQqEomgUChgc3MT165dQ21trTmHPJVKmeg190pymwnnxt8adLRlx75U8fGzzAJwTRlkYDAlmUyaEn8qOI4tEAhgbW0NiUQC29vbJoDFigaW1zHT19DQYJwAnlfNMr/5+Xn09PQY5VlXV2dA4D/HpbRgaTCwW8FgO6S6x5v60fM8PLe9jR9Ho3jRK5bDDQwMYGBgoMyZ4/ow+8cMgRporofqBs3AaQm1AkQ1oKojdY62063P5ZYBvsZtDwoKedGhVVBuBz+ocxobG/Hkk08a/aAgi93ROzs78SdNTVgJBvG3VVX4XIkf6HhwHbTXA1/X56qNeViQodJrGpzRYDiBC0HMp3M5/KCqCp8rZSIUTKk9I62qq6vxIwDLjoOxY8fwvztwwMz/0KFDhobvOQ423GJT239tHd+o9vqzwSBedHf3pOq4K1WcKQ/o3DUwpAEI6jzVAXbptf6vz+L/hUIB29vbeK2qCouBAP6hsRFfe+qpsiCIZqT+Z9/HhuPgzUgE/0JKSB+29gr0qIM1+KCYws4u0T7bTfZUl6oTy2drUMsGjXw+s+wMstbX16OjowNHjhwxQSOttlD9rHPR8dpYTass7aAQL8oAKwUZ8GIAyAbf/I5iKuIwgm79HLEkn6Hzb2pqQiQSQW9vb9kZ75R72gJedPw1AMofzp3YkThLEzecR6VqUcWHjlPcC724uFgWUOVa8n86NbwPt4jY21eJVYhJY7EYPvjBD5qGdMeOHTMOHZtdRqPR36gzFGdzTFpdZGN0PpsYRbc80Ha1t7eb4BVprfaBDWfJC0xkqJ/AeWSzWayvrxvMdv/+fSQSCYPJWltb8UZjI34WjeLD6TRebGhAQ0ODoZv+MFCstOU68bdtv5RmlRw3Xqy6YIUBbSgv8i/lzXYWs9ksvrG1hb+qqcEnEgn0XbuGxsZGBAIBLC4umiq0i52deD0cxmMLC/h4yfFTHaL8Tl5itabiTMX6tg21Lw1IxeNxLC8vl9l9fp9joY5gFTcDY+TZuro6BINBrK2tmb+ZLJqZmSk2tfc8zMzMYGtrC62trWU01a2mti+na636Q22HBgOAB3uoEe9qck119M7OjmlkyfvzCGRWCus2EsUp3IbNRvDZbBYrKytIp9OmAodrQZun+kv1JH0d9jKp5FMr/6vtdF3XbDnTE1r4Pfqb9gknLS0txudW3vjHrkcONCSTSbO/nSWKDDbYoFbBqiprNcA2WCBR1GnRhlCu6+Lw9DQOT08XP1tyllzXRSKRwOGbN3H1wAEcu33bZGWBIrjWLLP9bC0HvjkwgBsjI3hsdhan5+fNHCoFSCoZKY3y0Vkh7ch8KysrmJubw+rqKrLZLFy3uJ++urra0DObzSISiaC7u9tEbwGY5iIMotglNWq4Ffzq+uh8bGXJOWrQorq62hyRo92uWXaaz+fR0tJiDODS0lKx7P7ZZzF1+jROjI9j+PZtcxbr1taWKZ1TBXDg2jWMHj6MU7Oz8DwP7/f1YSMaxXs9PTi7uGiy9vxN4cvlcriwdy/e6+3F2ZkZHJudNQEaRoi5bzmTyeDOnTvG4aIBphCz/wKDajwbmcaYZ96ycocKore31xjGeDyOtbU1FAoF9F69iseWlop7P0tKWcvkKwFSRg4Dgd1mYFoy6ziOARCM+jOYsLGxgTt37iCRSBijQOVOIK5BDEaJWf3Dvdqu66Krq6ssQEPQwIAZQYYaXyps20lWR8MGmnx/bHgYtw4fxsmJCbSsrJho6vj4uAnGkYe4Btf6+nBpYAB733oLjW+8YQwbHWk+kxUaDGAAMAY4lUoZA7GwsIDW1lazRgqyf9sXDf3yuXP4P+/fj8/m83iu5EA8LNNDYKDVBc+m03ieAEmCAXoROLOZpAaH+D+BCHUSqwfsYKXq+0rbn9Q5rARadEwEIRpoGhsexmvRKJ7PZPCx0lgIRDUbwvvb2SUaRUbsKxlCAuNCoYCPbW7i72pr8RnPgye6W++pOp5rp4FpVlvZW0D0u0ofDfaSxuS3+dOnceXppxENBvHxkn58emsLz5Zsb8bZDTwpCObFe3/GdfFd18Vnvd1MHdeNc/ys6+L7wSA+U3Kq1HmmLuJcuD56HztYoPPW3+qk81LeIO0cx8HS2bP471ta8Irv4+Pi8JBGTHZo6aaCsY+m0/hpYyNe9P0yMKYy43keXvL9Yh8GGY8G7klX3Q9MnuHfdtWgYgOCek268D2tSFN9qSXdPA2HAQeui2bD7MABnXwNBmoAh/bWBp7U3fa62k4zg/fUUeQ1Bb929k+3s2qJ8MNk0/f9soypjRPIRxoQ5Of0xBpgt9qITrB+j5cdxFf8RkcXQFlja3WauZa2g8okDe+tQUr9rO/7xvHgM+nAKV/YNCJ+4alD5BnSlFiJPKqyqJhDnSXKIk8G0O0uD3N2GBQmHRgk2NjYMDJg42XiXNKC2ElpxfGyerWqqgqdnZ04depUmWPoui7+J8/Dquvirc5O/BHKqwi4lhwH7ZsGlZQOvCphY33PtnvkO+om6g1iJNtf4Jy4PlVVVfih62IOwA+qqvBvS8GQ7e3tslMNXgsGsRIK4f3eXvyb0hj1NA3iQiZcyAMcG/UR/36Yc8rf+j4ANDQ0IJVKmYA9AKOT+Ry+zoaXpFcymcRfbm/jx9vbeG57G3svXEBrayuqq6uxvLxstjtub2+bhr+dnZ2GD1X3kec0AEmeIa8SNyqvP2yuuka1tbXwvGKPHPJmJdxB3qE/sr6+bugdiURQV1eHtbU1rK2toaurC6urq2hsbMSdO3ews7NjTrdgvyjqVgbhdG76TNXjDCZxXLpunJvadP1MTU0Ntre3zdGbXGsNtgLlfUaSyaTRV6xueJTrkVE0wT2VBLsdM+pBRuAef43G2hFyFWI1uGpo1YAoQGJJNEvfKDD9N29i78WLReFtajKZSDa92NraMpHjSiDQ931c3b8fiWgU73Z34+T9+wAejIbZgQa9CMJu79+Pm4cO4djt2+bYQ+6LJiPt2bMH9fX1pgMyKwTa29tNl+DR0VEsLS2hp6fHVDWEQiHU1tZifX29DOyoEaMRIH0V6OpFw6tKUrPlNGJ0BqqqqpBKpXD79m2sra0hGo2a/ZHr6+sGMFZVVeHSkSPIxON4r68PQ2NjyOVySCaTqK6uRmtrK7a3t02Fg+d56L16FcNjY+jo6IAXCOCx2Vm819ODx2ZnzXg1AEKBCIVCON/Xh81oFO/19uJYKfhRVVVlKknYjXdubg6BQMBktpk5y2azphIhHA4jFosBKJaBkce0HwDlgKXoU1NT6OnpQSQSQXV1NWpqakwE1+Zz8ogCO/K8HbCiYdzZ2cHUoUMYP3YMZ2dmcKJUTkZlkUwmMTMzY5oMUVYJFurr641sMhgSCARMh30aJoKwXC6H2dlZbG5uoqGhocyQMNMDwFRKcC0qOZYqX5Vkjj83Dx1CsrYW7/X14eDkpOE10p2AhCd1FAoFXBkaQiIex/0nnsAHS3NnkInyqlkS6hgGqwjUuNeQQNjzvGKlR18fZs+erSg7/0svOsOTjz+O7XAYr7sunllfB4Ay3WlnUpTGNAbUqdr/hHzEdaeMElwQeGlmhTqEDouCBN5PQSCwK5PqoKtB08s2dkAxCKsBtosDA1hzXfywqgrPlgJj3MKilQTKOwoEtNu07eBz3KRLPp/Hua0tfKi0dWYH5VUdKrOqVymvGpDRfec6Jv1f6aZjo8w5joP7TzyBdDyO7/k+Po5dJ4PVNbSt6mirE8/7fzSTwZmNjWLPCUuv8HMfdxx8siTzeQksaOadz+Y41HFTJ5yguRKQU2fW5mddS9d1sfDUU8gEg/ie55keGQTGtC+FQqGsYz7XK5fL4ezGBj5T4pNMiR9UJshHH/I8HFtfL5ZYB4Nla8U1Z7aWDf3Ip7oP26aX8om97lw7fU1lgnxEOmlQmMBTwa3NS/w8+d91XVNubOsNpT91OvWjDUgZGGdQmt9Vva/AW2ljB6Jon9hLQ7dQ8DP2Hn7eRx0N3/fLysqZtbW3ZPI+fFYmkzFjV3lWPqKeIc2Zpa6urjb8xC1eOmelAWnIPgJ8fiUdwCCa7pFXGipGpu7S5uHK17yy2axx+PSZxHfMvmtlrNpIpQX/5nu2c2bTj32i6NTaztLW1hZSqZSpfOWlAWcNYlE3ajae3/M8D887Dl4Ph/EiAK+Cs8zfdnBL8RbtQSaTMfjvYbysQQPSgXNl1SfvS8xLZ1n7GlCO+azPOQ6+7ft4ORjEyZMnAeyeSMLvvBoM4rueh5dlHORDBmJZ4q7ZdCZTXNc128RUNm0fweZR6nz23nBd18iDNqUGKuMX4q4fR6OYd128Hg7j/1BXZ5oh1tfXo7a21ti5uro6dHZ2mm3lGvhWh5vrZiddNZhkr5d92TaLWxdZJcukBW2IPk/XxnEcc7T6N7a28MONDZyZnsbAzZvo7OzEwsICGhoaTBWC67pljXhp3+xtiHwOA0q23FMPq31Qfa+6VG0St8DT7tjBeJV3tWfsjQMUA08DAwMV6arXIwca/uEf/gFVVVVoaWlBd3e3Wcz6+nrk83lzzBydI11UBfqVFpYEIONQqSiAIwPQMWQ0NZ1OY3x8HI2NjWhubsbq6ipu3ryJWCyGvXv3moaKDQ0N2NnZMWV5lYDwyI0bGDtyBGdKjSaVkWxlr8oT2G3OFolEMHbkCLaiUVwZHkbv1au4e/cufL94PqueqUwQ09bWhkAggHQ6bZoP8tzZYDCIhYUFrK+vo6ury+xfa2xsxPLysqEry5b4P5WLlj9XcvB0PgQUFH4eG8igzvj4uKkGYBSsvr7ebJeh4+77PrrffRezZ89iz1tv4datW4jFYmhpaTFHENbU1Jj12t7eNk4ueeDEvXs4t7xcBAIW06vy832/2CujqwsnpqYMaGFTt3A4jLW1NSwsLJg9zazS0PK8fD6P+fl5c+wjARAFkWWp2oiISujWwAB+fuIETk9N4eDkpAEznCsdEo6ZYMjuV2EDbwLdfD6PsSNHkKqrwzs9Pei7ft04quwczi03dKz1eFH+JghlhQbXT42fAiLyvvZd4fFJbNbI+2gAUWVLnS99jQCP6zVw8SJunDmDnXAYV3p6cG552QQZ7MaedDZGbt7EtQMHcPDaNeNMs0cFv6Nj5/O0jJb85vt+Wdf+YDCImTNnkH7E43v+qdezzz6LhoYG9CSTeLOuDs/ncgZo0rCp80qdqJlEz/PwZlMTflhVhRcKBXxS9puqwxIIBPDjQAA/7Ogwn1PHU3vVOI6Dv3Ic/FV1NV4sFPBJq+pGHUcGfrQJ0sP4gN+zHdhMJoPa2lqcPXsWNTU1GMrl8HfV1XjJcUygk1ktRs9tR1WN7IU9e/D9cBgveR6e83bPm7aBIef+N8Eg/qqmBp8PBMwxlraDFhRH1AafVVVV+JHr4jt1dfhKIIAvVbBtevE+/IyC2HPnzqEzm8UvCgV8qaYGdaVtLrR7XAOtHNKsrq7R9/N5fCsex+ddF88JOGHQVB0w/h0KhfDazg7+MhrFl6qq8JLwoQI40lwDBCxZVzm3waq9BkqnaDSKI0eOYE8igfcaG/GFkuwTYPK7r+Xz+K7j4BXHwacFhDFY2tzcjGg0WgZuyWvEEzwql/qclwaZOC82h+VYFZ+oXNqOvzqclDVbx6rTwnWtrq5Gc3MznnnmGbS0tJQF/OiU8Nkci9JdneQf+D6+09JSPC0E5ZUaTNr8wPfx3UgErwD4jFRvKY9Sl7S3t5tqOk1qaKUVbQnHwrV2XbesCpN05ntVVVWIRCI4c+YM9u3bV9aZHYDhWZvvGGz423AY3w8G8UKhgE8JluQ9OMa/q6rC6+EwPruzg+ex22uD9FT5VodFnRs6ljZ41ww55xgOh00QgUCe9GejwzNnzpj3gd3TL1TfkP+Jk3T7gQZENVjAddMkgOpf2kF1TlzXNQ6fzrlSgJD3Y+CNyQBiDc6XvKY0ZiJG6a2OsX6eTpDqcQYePc/D4zs7+EDJCc5J9Z/aOPIDdQGfp7zBZ3FMdnWbjaFVVoipGIhi7yh9TYMl5K9CYbf30Kc9D88DyBd2T1ewA/2fBPCx0vs7rmvooqewaNKI/KDHsjNBxDkqj+mcuKbqqDIxQx5UWnIcxNiUA459Z2cHv5NKFU9zyuexd+9eg1mbS0co8zt8JnlY9YjqELWh9qW6WtdSbbDOm7Ti+Cnnam8pKw+r3qUO/7HnYcFx8F5vL36/1Buso6Oj7BAA+96cB4NFvK/+qCxrxYw9Dl1Htbn6Wn19PRYWFgzfaDBe5Zljo5zTB0qlUlhcXKxIe/t65EBDf3+/abqzurqKvr4+NDU1IZVKmSYwAEykVZmg0sLaSlqdTFUQZDwaMQotO9yyW/7m5qYBZGyKdH3fPtw7dw6nSkdCqmOVTCbLSsB2dnYwPDaGD5WaNGokTIMM/LyOlUGCUCiEhoYGnJyawoV9+7Dv4kXcvXvXZLnpHIZCIXOkT1tbm6EHG21tbm4iFothz549RkGk02ncu3fPnHzAZ21sbBhDpt2T1VDaES5mZUh7KsNoNIrq6mqTURgdGsKlwUG0/uIXiL7xBmKxGBoaGkymXEv4WVZP5/DEvXs4fvdu0dku7QF6u6UFc088gaO3b+PEvXtGUZFpVbHYAR0AZQaL/OW6Lo7OzKDv2rWiUfd9rK6uYmVlBd3d3aZcvr293dDA83aPDuP9eNIEAwCO45hoMnmPgQNGr7kXfOr0aSRjMVwcHMR2Oo2rH/4wWn/xCzT8+tfo6ekxQstnq8CrUlUh5/vcotD51luYPnMGfZcvmwwijcb2M8/gjYEBHB4dxYl798r2lJN2BOIEBpwjL41cUqHQ8HP7UTAYxP2TJzF65AgGLl7EgfHxB4yhOvVcI3tenJtmSvtv3MDtY8ew3dCA83192PrGNwxQUX6m8XZdF8716+jN55HyfVyUudjgQDNDqpdYfsrPskqFOqz55z/H8oc+9I+pxv+/rv3796O5uRmHXRe/W4oO+5ZhY9ZM9SMvExSorsac4+D7joNPCNhQHRwMBvHjQACLjoMfBIP4XKkUU8EnMxMA8FdVVZhzXXzPcfCc6Dw6BCp7GrTib11j2+FWfvA8z2zn2L9/P1pbW3HK9/H5fLGbuS+6S7ezcK0YOCTwcRwHP6iqwrzj4HsAnkfRKNK55ve1h8ePnOJRct/yiqdtaGCWNGTmTR1Czg8AvpvP4z6Ab+zs4EsV1tqWAf5PmmkV2b/auxf/6xKQol5XmSoUCibTQt3B0nrydaFQwDfDYcy5Lr5ZKODDpYwTQT8zWnwGZT+Xy+FbwSDuOw6+nsngE6X7cq04dg0UqjNGQMvPkUY2b9hBBmZ4Ojo68KmuLvwb30fA8wBn90hTzvc7pfX6NoBPy/3V6aZOodPAsar+V0dKwbLKBIO0fIY6xuRJBdO6rgqUgd1yYrvRoQJx3/eNHW9sbERDQwOSyaSZg45Pn8cxUg4CgWKvnu9UVRVp5fv4hFSicR7ZbBbfqanBvOviLwsFPFPaskBeU8eRMsSx2JfKB2mvyQfKleolzsV1XXNKSmtrK5qamsoqSrSaRgM75H/HcfD9Uqb0ewCeFR2uch8KhfB6NIo5x8FrgQA+XEqM6ZrQrlPHMOhL+0nesB1Om0f4P+VH5ZUX8dOBAwcMfYj1FLOpjuA9yDfb29smyaSOMteXfb+4Dgxm8766Zrz4DF0r1V263oqVNflGPclG2dTfVVVVePnll9Hf328SIHYAoFLQmnbQriZSPEBbrsFbe8waeNZ768/D1vY3Xa7rorW1FR0dHaiqqjJbthiA1S0kWjVEh9a+lz1+1Te6JqqDKRfaNFbpyOQY/RVuqVXbwKy5ZuyVZxn0d13XVCPSZykUCqirqzPJB3XMOYaP5XL4aMnO7pTwHGlBncPkm1YbKf1/COB1AC/k8/i4VLiRv/ksyquNQ9W3pMzyIi3YHoAJS9JadZ8GwFVH5fN5vBgKFU+Owe4WEn6GOpW00wAqda36yjp+/Z96wQ6YcB627OrleR7i8TgmS4cq2IE5O7hO2aZPxGTmb33rxM7ODmpra+G6runums/n0dHRge3tbXMeLLcosOSPSqhSJJDE0cilRlnJsIlEwjizq6urmJ2dxfb2dply8LximVImk0E0GkUkEsHl06eRqa/Hu7296L12DXV1dcaoNDY2mn4HNCoKNnRh1VlX4ecCEKRHIhHkcjkcm51F4xtvFI/5qa8vc8oJxILBIO7fv49sNmvO4M3ndxtwRCKRYpNJxzH701OpFBKJhGnKyGAJ93KqgiXDUJEwG2IzXjAYxJXubrzb24u+99/H0ZkZ45yzNN1/6il8amHBGG46GFxblsqxeaDjOGhsbDRZI87/3aefRioWw82REURqanB+3z60//rXaHv3XWxsbBS3TXheGbDRMatAEwzoGtEQJRIJzJ86hbefeQYnJiYwNDpq5kseY+CABlKj6VS4DLg4zm5Wh89W4d77xhvYOXMGrW+9hSuPPYZMYyMWPvABdLz/fllZrQIPG6BWkg0qtkwmg+a330bNT36CYDCIN0rZViqwS08/Xdym0tuL9a99rSzqybI9NTzKJ+QjXlQy3N9HZZZIJJDNZjHzxS8i39CAyRMncKB07Kw6wXYgRe9L+dEgnVYX9L3/PiZPnEDTz38OAFh78kksPfUUet9/H3vOnzclrKSpKkdVqjbA4KV0mTl6FONnzmDw8mUM3LxpZI/AqaqqCs6tWxi4eRN48UX8tq8bN27g5ZdffmDMzHQpX6tuJGCm/vmy4+DPd3bwxVAI9SXj7nm75au8Xt3ZwV/6Pl4NhRAvRaSVJ1h67HkeXnUc/CWAL4ZCqJUTXtSpUWBuO5Wciw2YKvF6Pp9HKpXCnTt3MDw8XCbXAMrAGJ/FbIA2haJ8fjmbxZ/ncni1uhoR7OoJghcAhr6e5xU/X6JfXSlQrQ3D6GRQ96l9IFj4/VwOf7q9jd8v6T6bDjbvK7CnDPi+j5mZGbPViRc/yzEwk6c2iVkkjiuXy+GruRz+LJfD70Yi6CyVZlKWtQIkHA4b8BAOh/F729v4WjqNV4NBNJQ6nCtgtTNMDNhpxQTf4+9Kzont9Hueh7t37+LZZ58ty2rxexzD7+Vy+LNsFl+prkZTSf9xHLYeoNOt4JA8TKdcT3KhzVSnmAECtTUA8O1MBn+cSOD343F8pa6uTNbsrBO/r3bArkShI8H1XVtbw+HDh/HNVAp/vrODL4XDeMkKOtGhYHBF9XA4HMbv7uzgz3M5fLGqCk2lfcdcK+qV39/extczGXw1GkVP6YhbzYwHAgF8P5/Hn2Wz+HJ1NV4tNb2rdOm8+T9lj/paqx84b13fxcVFPP7440bemDlV+jOgpbz0lUwG38jl8Llg8Yhi3lM76weDQXzR9/Etz8MXq6qwp4S7uM6kndpojtf3fdMk2b5s3GUHCShnaq9ICwbxeSqW3kcdPuVNYDehxx4SpAPnSj1FB5NYgWvBRJPiWn7WDhbaOl7XTZ9Raf6UH8oiUKyKZGCMvwGYtaLMalWzbntmhYPv+yaQ4jiOaThO3c1MNvfqs5KJSUHKAvFfIBAw/Sk0qKN8x0sdVccpJgiGhoZw7Ngxc6w4tztzvWjL6EQz+ahrpc9gg0kGU2zcqViLtqGS7VW+sINCdvBLcYHaeI6NTUebm5vx40AAr4dC+HQuhw+XThIhXenwKx150Q5pYMBOjgC7jca1egwAXncc3HeKJ209V8IsuuYql5rwUpujl96bv+nL/TgQwOvhMD6dzZpEDvWXyoIG2DzPwyfzeXyydP+C55VhOOIZXW9W5DKQpk1aSR893pxJH2I22z9VWdW11d/swUYdQn1DTKG2lXJEXtLPP8r1yIEGKvxgMIjOzk7EYjGk02ksLy+jtbUVmUzGlIJxDwezJ2qAbPClkX9mV3gGLoUhFothfn4e169fN93nGxsbjfKmwDLIQEO0/+pVjJ84gf5Ll7C8vIyLnZ2YOXsWp6amMHDzpgFluVwOiUTCHImjETUt11XBIVAj4d/v6MC1/ftx6s4dHJ2ZQSwWMyUmjIoDMH0JXLfYnRdAmYB0dnaWKTYudiQSMQ0XL3d3453ubnS+9RbiP/+5YU7SlSDEzoAClaOl5/ftQyIWw82zZzF16hSOj4/jyPQ0joyO4sr+/Tg2MWG2U6jzS8OiEXbSTQEL1783EsHt48ex9/338dapU9iOxZA+fRqRn/zEMK0tHByj7/smIq48xHmpsgqFQpj/wAeQravD211d2P7Wt1BXV2ccNDac4ZpqFN1WjCq8VDI0kCz76r95E91XrmBzc7N47OdTT6H5F78wTj7vyx8F15WcL/2sGmxmKNiIhUeIHrh2DbePHcOed981RzhyLdirQp0DpV0ymTQ9EDhHNRiuWzxGymSt338f9594AoOXLwPYBZMaHVaFZjtc+j95n885VJJLACj09OCnTz+NdGMj7j/+OOrr63F1/34MX7mC/vHxsmwMeVAdGZ2r6h9+5+0nnkC6thZ3Tp/GE6urZs40+gR4iUTiAX78bVwbGxtYXV1FVVUVfhwI4LuOg5d8H58q0VK3fBAEsceGguJPhsN4vpTFSJecUQ2A0lH+RCCA5wIBBHzfbMf5K8fBtwoFvBoM4mOlyplCoYCPBgL4dE0NXN9HVrruKyBSZ9BuwPawwILN54yQs8JsdXUV/1BTg7/0fbzs+/hMiaf0tB7HcUzfFN2GxsDkM4UCnsrngXQaG5LFYQBBZdvzPHwkm8VzVVXw8nkkZD8+v6MZRDrl5KFgMIjv5XL4ZqGAr9TU4PPS/Znz1L956d+UkUKheA795OQk9u7d+wBQ9zwPrxcK+Jbn4TM7O4ZPAJSNt1Ao7nE9DeBs6f1ZyUxT13FriFawVVdX4xnXxblSlclKqfEr15c6hc/9kVtsOPlqIICXS7ZfEwu2HOrfNvAsFApIJBJYXFwsnprhuvg2gFcAfKLkFAWDQTzteXgmEEB+YwMLm5sAdk+kUMDEZ9hBKwbhWCmmFRAKFilntClAeZXWf3Zd3Hcc/OeNDXwsm0VLS0vZnGynQf9W0EhnmnTZ2dnB1tYWxsfHsXfvXnytrg7zgQD+NJXC2dLpBZQx3b/Nak/eMxwO41Amg/+pJKfj/m6DT7V5hwsF/M+ldb1S4jc9tQEA/qSxEfOuiz/d3sZH0mnU1tYanW0nYahHVSeTv8PhsMENNihmg+H5+XncvXvXJCxYScC/tcKG96mursY5z8PpUnXdsjjzus3EdV08XV2NZ4NB+Ok0lks0Y7NjdW4p3/yfpeC0DTZP22utQQLiPc1aOs5uz435+Xm0tbU9gDMU+Cs/aaWibq+zM7RaAapVgWojdVy8N3VdJSfNDo7pswGYDD63IBL76ponk0lzDCbX1/PKj60mr1Im6JgqLs7n84hEIqYaxvd9bG5uljmdvl+s+mDSkn4KdTrxJGWdcs/qPhvrV8KnpNvq6mqZI0mfRoOZ6pzRz9CjZzWAyOoBBhqU5xhkUdvLnmLBYBDRaBS50nZMrTrVah1WwmjSSwMa5GOV73g8jn379iEej+OHnod5AD+qqcHv1tcbbMx7aXWFJp1t3iOvqk9IumnFIkr66QslO/iFYBBVrlt2WoMGxVk5zj59tj0iX2sgmu+NjIxg3759+L95HuZ9Hz+KRPCVUr8IXQf1e/T7ih34nbpSQNqukrOTbg9z4D3Pw185TnHrIIBXSnxPOdcEhh08sjGH6xYPIeBRrKzCUbyjOI/yphUP9jN/0/XIgYbV1VUEg0G0tbWZIy7C4bDp3M6u9SzrJANpyeBvcqjUMWX0Mh6Pm6YcOzs76OjoKGsIx0g078EzZmnszi4t4cmf/rSoSPv78caZM9iMxXBh3z4cmpoyxzFOHTqEuSefxMHr19FXUjCaebIFxGbUnZ0d3BgZwXZdHX5dV4etrS30z80ZBps+cgRjR46g/+JF9F69WtYcSKsECLwZYdVIOa9CoYA3z5xBOh7H1MmTGPzxj1FdXW3GqFFIjcyrUeDYqcxPT0/j7c5OZIJBJOvq8E53N7a+8Q3UXbuGU3/7t3BdFzdyufKy9dL62MEBCk86nS7L/rmui47z57Hvxg3z2cmTJ9H6y1+Wbf0gTewoOhUCHSflIQU0BAV73nwT808+iT1vv43a2lqsPvEEJk+exMiNG9hz/jxqa2vLlIEaU9JZI3qqGO4cOoTxEycwdPkyjt+9C8dxsL6+jlwuh+GxMRybnS0qCznlgMKua6qRWs2I8X3yNx2CeDxeVlLmOA6am5vRubqKEz/+Mba2thDau9d8nwEfnqut8+QzGaTTbAvXg5mhtbU1s7Wl9/59nH3ttSKflbb1cG72HHjZ66jKlxVJnKtWjRy+dQujR47gzPQ0zh86hFQshskTJ/D4ykpZ9JvjtjPFSkcFO8FgEGdLzUaPjo8b+dPmOKpo/zmu2dlZ/PKXv8TIyAj+sqsLi4EA/nJnB8fv3y/LBHIeuueSc6CBJJDR7VBcX4IGLd8kzf6svh4LwSC+lsng0Px8Ga/TWQHK9zpS/qi3eAqRAnoN0v0mcKa8fPPmTVRVVeHPP/ABLIZC+GY+jzMrK2YNGbDj92js6ByoXGkJMfUgs3gKQAmM6LCpo6FA19YNpEUgEMDX6uqwEAjgj1MpvGztS+VnOS7lK71vPp83wPanP/0pPvCBD6C2ttYAZn7/z6NRLAQC+J7r4kk5XosBFKU//yYfkUcoC6Sh2h6eVkP6cb2ZJdTgQSgUwndrajDnOPim5+GzUmlhB2n1N/BgabMGUH/2s5/h6NGj+GZHBxaCQXyzUMC5Ugd7zcRqENu2GXwOy+7VuQNg+jZxDFr1wO9rJYM6hUDRnr7iFLdvvBrc7c+gNkPnT17SzD7fJ73J13xtenoaly9fxsdPnMDfxGL4RKmakWPVkmnVp67rllUAKF+obKq+1+Nu+Rk9feb5dBo/qK7Gp7JZbGPXmfzW9ja+tr2NP6yrw1cbG8tsC++ljgdlkXKm8sH1W1lZwdWrV3H48GGDBclXrEDhvDlWOld06jVYrmvDZ3LOOxLAon7RgAKDm3a/Iy0r1zW0s6mKwYhb9d58/+rVq6YxM7+rgQVNHJDHyI+FQsHMUfEFMSTHpJUsGsSws8DcThiJRNDQ0FB2P1723Ll+Gsgi7QCYbar8/K1bt9DX12f6uymGIF+q/mFwV3WV2hQ9SYEB1EAggHg8brLEeqQ8A5K6Jro2zc3NZTS2Lx2D4ltWRf4kEsGfZTL4UlUVPumX9/5QHUb9ohWnDH76/m5fBSYsueWNTiGTrLxPMBg01R32lmbqg9dK1UlfqavDl6uqTNN89cWoS+z/SbPl5WXEYjF8KZ3GNwsFfF4CepQVbVzM4DcxnmIXriv1DQBzoiFPf1BsHw6H8Yrv4/MlHJKWyhw7sWlXSOg8+betq7kOPMXt5Xgc3wbwckkXsSlsJpMxVfBsiql4S3UUZVR1HuWF4+Va60kgduAQAF7b3sac7+N7joMvlgIWyWTSVEEqxreDC7bPVFVVhYMHD6KlpcW8rvqE4wR2Kyp1PDZtf9P1yIGGxx57rAyIqUFWZslms1guNfFjs7hKmVs76EDlXygUcHv/fowfP46zMzMYmZxEoVBAe3u7mSQZyvM80/1Ss+1KCBK+UCig7/x5jB87hr3vv48bN26YDOHcE08g29yMiePH8cn3338gAg2Ul36RsYHdvZwHrl3D+XPn4AcCxa0BP/kJWltb4fs+xo4eRaq+HrePHUPzW2+VLb4yuhoBPoPzisVihtb7LlzA1MmTaPzZz5DJZFBXV1cR4Fbab6ZKmp8/fvcumt58E+93dODe449jz5tvmhMWGC0kCNUx0jlTIEHwvrm5WeYc8IrH43Acp9iJ9eLFIhN2dyOfz5tuvzYgtWlkGzo+W6Pqw2NjODI9XRSapiacP3UKiXgcY0eO4NjsrFk3VbAaWNDn8m/S7WdnziAVi2Hq1Cl8XLqCs8cGs1S20tbSJ2A3U01gz3FohFVLv5nJ5j0DgYA5GpXNtBTEEuhpxJ/fp2KmAVMHgwEDvsbnsmSdjp1G6dWh4GWXmdpG21ZUDKhwnIO3buHwnTtFJZzP49LAAA6NjpYZC9JZaa2ODEGLKk5WZ3meh2wmY0o52QxS+w48qiL9p17b29u4ceMGurq68EI+j9ddF5/O500DRPIG10CbVFLHEfxyrmw0xLGzqonGXwGs4zh4Lp3GjyMRPJdOG7rzIg1UB9JQMiDEklzqZJtWNkjRNeG6MLCazWZx+/ZtvPjEE3jd9zEcCODftrXhxUIBn5AgNefEORJMaKCDY7SdewXbzCaxr4zeq0ZK8vkadZlG9F3Xxcu+j+95Hj5vBXF4KYCxHW7+T5krFAqYm5tDJpNBf3+/sWek1ysAvuv7eNl1ja6kTKr+4uuaCVLQxQwuaUheocOmiQJuUdFANr/3BcBsx+E+cJVp2/ZUArIMtJAPxsbG8MQTT+Bzrouv+T4ygQDebmnB89jtup3P582WRw2mM0jIS7NNDJaRz3Xu5CvXdfGTmhr8KBrFi4UCfqek+/QZxD+vhEL4QiBQJoMaxNH116yzBliVNuqck79nZ2fxBx/5CP4wEIBXXY1sLGYcM5ToQVyiOouOFNecOkSzUL7vm+pQ22bwu+SDVx0HX3QcOCVeoFP2p9vbmC0U8N+2tvDVUtNcO+PFOVM2yWucO2ml1YL379/Hhz70IeRyOTQ0NGCzVLmi68376lYuOoyOs9tniTJEbEBHztYDdNh4lDf5kwFXDXAo/lXswKozXgr6md3lGAAYJ+/O4cP4m64ufM5x8EmxqXTcuV5bW1vm6HPdYqal2BqM5vO4Pde2x3S0NdDAoAuDHnopvuQYKW8MCCg2Ix01YB4IBLC2toaJiQkcOXKkDPfYTqLyhQZNFSszuGDrEzrXdOo9zzPVxNq9n0EKOsnBYBANDQ1lgUmdk85Dg3vEFefPn8cff+hDmHdd/EkyiZOl7ZiKaRXv2UFZxXp8P12yz6y4VLqwxF+3GagTq41ifd/Hf6uvx0IggP+ytYVPl8atPp0mVhiYV/tVXV2Nra0tLC8v4ynPw0erqpBNpbAe2N2OF4lEsLGxAc/zzDbvzc1Nkwi2g6OxUg83DUqS/lwv6lXtp6I4mfclXbgmuv1ada3yMO+n60EM8MV4HK9a/p7v+4jFYoZ3WD3CJJ3necbPoQzaVUwM/LNhv/pK1Fdcb9oEz/PwFd/Hn5W2usVKMso1VFmybXAlm+y6xT4bHR0dZTxlyy/vR3ujeudRr0cONNy+fdtk2cnsZGgSghE2lnNomSUVuy1YnDQZamdnp+iY19biV+3tWPqv/xXxeNyAKR6LqGVONJa2s5hIJMqyOPVLS3jq/HkAQCKbNYqn6ac/xeqzz+LQxEQZUwC7homGUgGDKqvDd+5ga2sLM2fOoPfCBdTV1SEejyMWi+HY7du4PDSEAzdvYs+ePQ8oVv4mrUgjOqx0OtgjIzA9jaHR0SITDg4aRuO4KpW8KajiMzQIEwqFMHDzJkYmJ4tjam5GU1OT6bVgn5lKelB58N5uKdJI51cdGzqtzEwwykfjUAkEqrNAWugY7HnRqLuuW5atPzkxgcvDwzgzPV0GlhT0cvz237Zz8NjMDN7t7cXx8XEDbtVAcGxqMElvOulUrPyf9+B72kWYsqUGjWNMpVLY2dnBlZ4eXB4awomJCRybnTX3KRQKptRcgR3HpMqFipzKj9ufWNZKwEU62FkXG8goP6tCr1TqRX7iuNmXJRKJIBqNYt/16+h4/33s7OxgQ84LV6DHZ9rgXoEJ6XlxYADJujrcPHwYh+7cMUCElThsNFgpWPbbuHZ2dkym5lO+Dzefx2ulrQEft04BAWC2slDnaSAxEAiUbSXgmlBfc20UeIdCIXwuEMDnAXhVVSgEg/grxzF7Lj9acjo1k60/BOBaQv2PXbYsaZUGAeFnXBefCwTwqlcsy3wNwGdKcsqSUG6ro4OuToQdAFa+12ZYXAMG0RRM/k0ohO8FAnixUMAHNzYQjUbLHEpgl7dfKBTwXC6HqPSLsOesv3mpztHMu+oItTOhUAgfy2ZxpnT6EKQzPD9DG0AeB8r7DvB5duadF/WMznFra8sc+abfCQQC+Kzj4AVxmm2bXgncKQ/ob64l1+jTjoPvArgP4Nu+j+dKtOCpDASZum7UHXydjp8d7GHQzbYbjuPgf0gmseD7+KtwGB9aWsLm5qY5/UEzwhp013uQL6m/1VlkNtOWF5v/eH/qXI69ptTElbQiGFbnm7pPZYz0UBDv+75xPlXHeZ6H72Sz+GY+jy8Fg3hFzqHntgeO86uRiKlo4MVsGD+jNNJAB/+3P6OBCOqs5uZmgyV0Ppwf6U5wTKzAMWill2IlG198fWMDP3IcfM738VkZq81jatN5T76nVT2kK50GrU5SW3f3sceQrqnBDwG85Jf3HOI86LQpP/F13dqkeoOZcK0O4BUMBvG65+HbAL7kuni5ZFs430pJNR2XOqWkCWWNWF7pwvtwDdfW1gxeYoC/pqYGNTU1Rk7U9qgt0walrJZQ2VH8Tn3wt+EwvgPgRc8rO5VEs+4AyuyZbfd4P31d519VVYWJiQl86rHH8KOaGny2UDBroDwdDAaNc62Omz6TtpxONu0U6UsswHuStsS1GlxVm/updBo/qqnBC5I01pO6NABK28l70G5sbm7ivffeQ2dnp+l3Q/sTCASwtLRkdNTq6ipc1zX9M9bW1gx/EvM3Njaa96njXLd4MgkxKMekVS1q2/g66REOh1FTU4O6urqybW2VHHDF+7xHVVUVLly4gMHBwbJqF11PBgqA8op9AKbKnDoqEAiY6hKuJRPz9BODwaBpBkt/i7JE3Pak5+GDrotgPo+FUoVbVVUVmpqaHgig6KU8oLLCQJYG5Zkw1S1iKr9KK/s5D7seOdDAkiTXdY0wkjELheJe71wuVyzfDhWPvuPeUQ6OE6xEAAUog5cu4faxY2h/6y1zogVLQ9bW1tDc3FyWredeeRWqG/39uHnoEAYuXjT9GACYktTFxUUTvOidm4P3p3+Knp4e5EuRKB0fmbpS6QgVeDAYxMGJCRycmCgyfV0dGhoaEIvFcG55GUdnZop0a2srWyA1wnxNBYhKVjPlVWIUaNx0rAok1VnlvYHdaCAdVwqCbl/QLRl0LEhjKrNKARLSg/fRsQHA9b4+nH/mGQxduYL9t29XdDgVDNjroHuZaLBtIdIx5fN59N+8ieHbt3Gjvx9//OEP4+jYGI7OzBhh4/dU6Shw1at7dRUt77wDx3GwJeVLmUwG4XDYZBttPrfHqw2UNBOhfGBngxyn2E2azVCptC4NDiIRj+P8vn3ovXrV3JcySb5hF35mAei0KUjOZrOoq6szn02n0yagwjXV4IhmH8h3ldbOBpWks23YyV81NTVGhmhk1AjbIKCSo6NOGJ99qasL+aoqVKXTOHzrltnHGAwGTSNU8nCl9f9tXFxzOj2vVVWZ86U/Kfv92JuF4K8S8KGO4FqoQVBArSBC14l0+tHGBuY8Dz+srkbV3/0d+vv70d/fbwCegnV1XHlPXQMb/FX6LHtFULcqiHgFxWz5i54HT8bL3iS6tnrfSk4c+Y7GWksYKwVRfrCygjnPww/CYUQvXEBHRweOHDlS0VhTf6oDZDvTetlAhw6cBgxUdxME1tbWmq0qPHWI3+f9OBaVJ8qe7QwB5eXzfC5lmTSamJjAlStX8JnPfKZMFn5TAMG+VD71uepQcwz8yefzeNEt9oB4SUAumypX4iflO8o7K27082oPbV79qufha5kM/rC2FvNXrmB9fR1NTU0PzJnBAZ2zzkEdO02C2M4+eaSSDaLdp7wxSE/bS/2u1TZ8nh3kVVts08Fu2PedXA4LwSD+olDASyX5sRuVAcCrsRi+VFtrnEOupy2HjuMYzKhjsOVQEx90kthAW+2TBpnoBFCXqmOs97b5VWnEz3w/mcSyW+wN8rxUQdgBCY6FukT1FwMitO+s+uKeeDrIqr+63nkHy08/jc/F4whgd/sG58j52U6oPS7laX5X+YL6jTbnO2truO/7+IudnbJGo3ye0s4ODqneVPsQDodNsIU0Vj1E2eAx6eSH2tpac+qXBi517hrg4Tw1QKnj1LXa2dnBa6kU5jwP3wPwWdFZxPC8r/oRlXSM8gvnTjpUV1cjlUrhxXQany3Jg+/sZtf/OhjE90MhvBoI4IUSlrexn64BAyCUy0wmY3BbQ0ND2Zg4jh+5Lr7rOPhsPo9P+bvVF5zDpzIZfDKRKAuuZDKZsl4lGlCljSY92MxyenoanZ2dZfxE/0F9DOoM+/QNOuZaGRCPx8uq+ChbihH5owE/rvuPXRffCwbxiu/js1Zlnsq+/q80J919v1ixMDc3h0OHDpleEYrVKfukBzE69TP1pdI5WWqYqT8M1HA7H9dYgwypVMpgDdVfdjWKVrKoHalkk3mPZDKJO3fumC03ihWUHjp3xf6FQgEnT56seH+9HjnQ0NvbW6Z4KcgsAeGxcBxoMBg0RlAdmUqLrQA2GAxi/+3bODgxAd/3kSud3czGiXymZujsY+qCwSB+cPw4tmMxzJw9i6e3tsyC01lPJBLmmEYabm1cA+ABhWc7fOoQArtZXW2ywuwGs6O28dKFtA2fAkMKLctMORc1CnQ4gAc7bldaAwYK1ABpucz29jby+Tyu9fXh2rlzOHb7No7MzJgx80gbgiaCgImDBzHx5JM4VlpHZsWBUqSwvx+J2lrcOnwY7e+9Z7ZnUOnz+dr8SQ2VGng7qsnoKlCMMvL4vLq6OkQiEVw7cACJaBRX9+/HydJ+eA3C2IpHgaMCSgLJSCTyAE8o2LL3k/u+b6L3nIuCJzvrQX6gbGj0vLa2Fo2NjQiFQjh95w7O79uHk3fumL2P5AMG6dgNmRkEAGWyBADn9+zBhf5+HL51C8dmZ40xoAG2S/O47mrYlYa2I0e50MyL8iGNMhU4gyK+75vSbt7DBviqUyopXCrn8319yFRXI7a5ieGxMQRL0WQ+i8/mc/65LgaKfN/HZ3M5fL9UTaA8xwox0lL343OeyvN2EIA0ppGwacd1CQaD+HJ1Nf4sk8GnczlMT0+joaEBg4ODZSWK9hrbTvXDHGs72My50HAB5Q2UPh0I4HcKBXjW/VQ/MyNgG1Q7SKlAXEsvdeyqA3+3pgZfT6fx5epqLK6slFVt2ZkRzk0zU/b7lRxwrp3qMB0PdSbBOx2MaDRa5rAmEokyueA9FGDpa5Q3/Z+foQ5loNTzilsTN6X8V+0Kx6BAXXVfJSdaeY66g9lPtae+7+OT+Tx+h7YXu3qQ99/c3MTy8nJZ0kGDAOpo8Nl2FYg6jlVVVXippQVfLPXv+YFkktQR5j3tuXDsGqDmd2mj7YArx0IaUtao+4ih7K1B6XQad+/eNd+3HWCuA50nBfiqw4kj6uvrDZ9/IRDAf15fx6dDIewIZmHFkD5L9Tzf0+cxkMGsu+5H57gp17wvgyjEY/wJBAJYXl7G6uqqSYhoUE7pqk4jt8lRvxDrsUyejs7Ty8v4eUsLXimN1a78oa3WTvmVdJ5iFB0bHRbaS26n6Lx4ER/LZvHpl18u0yO8fzKZNDZS6aI8aesnpb/yLYMhvu/ji8Eg/lsyiVdLgXzlWdpjnR/vo8k1rotuteNec27npONFDESnmbaKnw1ZvKa4WwNiHAtQfgwtx0c6KX/8HoD/srGBjyeT8EpNa3V+Nl/b79u4kJfyL5sOZjIZc4oI1zBYCjLMOQ6+4zj4w8bGB4KBnK/qbMWmOid7DPzs6ysrmCsU8FoggI+VmmCqHlZ7k06nTXCBwXv6c4p5mWXn+tbU1BibwDmyoT712JWeHnzPdfE5p3hENu2XYkblG8XL1Ne8L+eo+BGAqbylrft+Loc5AN9zXXw5FjO8CjzYEFFlVeWHn6kq9a9goFNtm26t5ziJGVnVo8k3Tc7V19eXVfTyu+Rt+0hdjiUmPEu+J0ZVf7MSj1bCH8o72WwWHR0dZb4kAIPPuF6Udw32KK77TdcjBxqam5vLwIE6VTs7O4jH4yYCo8b3YQJtK2gSg5F7EpNgnAqazhKwCzy09I/jOz4+jouDgzh9547JBJCIJCCNAYGTGhR7UdXpo/FQh5efIwP4vm/Kgah8qHTVSbANFt/Xe7EJieu6uL5vHy4PDWHw0iUcvnOnbKxkBo6PjGNHADXypg6IAv1CoXjiRnV1NW6MjCARi+HqgQM4s7hYRqNkMmkaB+bz+WLH7GPHkKytxbX9+/HY0hJ83zcOQXV1Nc5MT+Pdnh4MXr1qSpEIUHQNuJ6kLwVLFQMvVabqDKli9n0fJycn8ebQEHZCIdwcGMDxu3cfuA/XRp9RKUBEAKPOMnlVhVv5X9fYBsQUap0j+VMFnLShAdjZ2UHftWvov3GjmMETnqWC59qz1IsyoyCsUCjgYn8/tuJxXN2/H0dnZsqeTWPE+dh/q8HQNXyYs04a6Odu79+PGyMjOD4+joFSg0DtrBsOh02zJl1nm85qtHVdAeCppSX8OhDAwLVrZXSdOHgQV/fvx/Hbt9F1+fIDAYzf5sUxch0+vrODj5UCtTkrIMA1z2QyeP/99xGPx8t0pDozCm70In8yYKU6IRKJoKOjA5+PRPC5mhrcuXMH/ymXM/uSgV2QqUG1QCCAb2cy+It8Hl+NRPBKSS9XAma246uBW3XUWQpLftR7ETTxu+Pj45gpnfDDz5HXNfgB7AYfOBedE8cUDofR39+Pl+vr8UoJcP3fS/LCkxk4djuqbwdS9LL1lS0/BCe8OHcCPwaTFJzw8zdu3EAymTR04Xc14ERdT7BjAzb+HQgE0NbWhoaGBvO67nGmnOq6UG9X0l02wLH/p57V+ev68n6+7xvwqfdOJpO4ceMGtra24DhOWf8YvZ8GOjTAqbLwblsb3ursxL/M5/F52R/NrLTeQx1fvqa8xoQAfyvdSGvdv877MdNLOeM2Njug7vvFJMD169exsbGBYCl7p5k3G6jyniy7pt55p7UVb3d14fdDIbxQwnW/Uygg8dd/jY989KMolKraqGc4JzqrHJPqYnWKfH/3SFzeh0Bd6UE+zefzBje5rmu2F3I9VldXcenSJXNkoZ4WEAqFcL6jA7/u6MDj9+7hzOKicXy5lnzu+x0deK+nB3+A4nYF3/fx1OYmHltawsmTJ81aKtCmkwGU9zyxE2g6R8UPvAf/5/r7vm+OiwfKK0Mdx8GdO3fMCUXEF4qVbP62ZUh1Rk9Pj/n8C6EQWq5fx+OPP162Xiqf+reNp2y9Rvypjf946ZwAGNyiMk08vby8jIWFBaPvqV9ZQc1AFO+lySk7MMFtN5+rqcHJ+/exvrGBQglz2jrGxg+2f2K/rnoc2D02WddZfZ5XAHzXcfB7JXtFv0Cfob91PThnDTqSnqrHfj8axX9aX8dzUg6vzTYdxykL8lA27SAAE2GUb1a+8jS9paUlk5TRk2G4zt9zXdz1fXzbdfFVKetXO6k2RGW8UlBA/S7qorGxMTQ1NRk98nnXxbd8H6+KDuSlNltxneJ6js91dyul2EdF9T5xBrdmaKCI96KPRZm2KxA1eK3rxwbspMHGxobpI8UxqE+tiUqlm02/SpjM94sHKFC3aKDN94vNSAuFgpFDDVg+aoCB1yMHGhRw2FF7TpQKgQsyvn8//vbkSXxgYQFnSw4qF812vkgozVioA7eysmKMkBqfdDptyk40s75eMoQ7Oztmn48uUK50ikIulzNAVaOTmsWrtHg2o3Kc2peApWDcc0OnmjQAUEYvVWAaAOH8HMfB9QMHkIrHMXHihNmLr6WTui4KULXyQIELP6dgwd4Hd3RsDO+NjCAXCuFKTw+Ozc4iFAqhubkZDQ0NZcITjUZx+s4dXBwcND0MNIvmOA6GxsbQ8s47RcMi+59UGdiZJApjJYWlfystOQ/P281kDty8iff7+rAVj+P9vj4cmpoqUxIEj3ZQQwNNl7u7cX7fPhy8fh1HpqfNeyxx1y7e5BXytgaiVHhVFmxeU8cC2N0qxLUNBoO4NTCA6yMjeGpxEU+urZUpHZUp/tbAmvLOU0tL+IXv49CtW2WAor6+3jxTwbpW09gRTgVrSkt+Vz/D7984eBCJ2lpcGR7GgfFxpErd1l3Xxa3BQdw+dgzPrq1h3/Xrpp+C3iMSiaC9vR3pdBrTpbVRJev7Pg5ubeH0/DwuX76MlJQwXn76aSRiMVwaHET7+++Xjf23famTQWeiEtBQnZTP5zE6OopCoXgUYjC4ezQov6eAkPO15UNBx/iBA7g6PIx/6Xl4yd/dmkMDw14SCgqo54LBIP48FMI938fX0ml8vtTDxXY47d/kGZUDAMaho5MGlOtHBdeuW+wfMDExgdnZWQAwe3x1m4wGbhWc6nP5ubvHj+Om7+Or2Sw+V3KwGDgnjxCoaeZCA8aVgKiuuf03ZVGdJZaT8jUNGDKTy3VOp9O4evUqbt++jerq6rLTdFQX8vN8Tx1EOi5aMUG5Yb8d6hnyKe0dnVzdg650Vb1jAx0FbwyG0LEn/9vgSnVSoVDA+vo6Lly4gFAohP7+frNVwg6qaACedFRZ+VV7O5ZdF3+aSuHzpe7uAEy2T3lGg6+V9rFWV1cbPWk7gWqvaQsUNJLniVFY1afOO99fX1/HjRs3UF9fbyqP+BzaUqBYsqsBeFYCOY6DN/fuxXp1Nb5ZKOBFqzJEfxSTca4azFL8oliCYyY/KWDWSxMf+nz9HPlteXkZs7Oz6O3tRVNTk7lvJpPBL44dw0ZNDX69Zw/6b94sC1iqY/rGiRNYCwbxZ5kMXik5iNRrGqTXcfB95WWVY86Z4+daMQjE72kPDX6WDg3nSD7g5y9cuIB0Om3ssF0dq+NUPEccFAgEEIvFzBYwXlpdoU6fjdG1QkNxivIrtzCQ3lqlYGMNJiXVieE9M5kM3nzzTXO0Luep2WuuJ/UPn8FARCAQQGdnJ3p7e8u2Fm9tbaGxsdEE3fQevA91hO2sKS/aAQrHcYxjZjcE5fV8IIDPV1ejvmQnd3Z2cOXKlbLtT6orGQQgDwC7jZhVB0WjUbS1tSEQCOBzkQhOLywgkUrBL1VYqK0jP5LvFJvx2UyIKtZzXbcsq87qJvqFHBODF68Gg/im5+GrkYg5dlNlm/yi2yMoL/yM9tKh7VK7mEqlTFDcdV182nVNbxWbN20srrhb5Zjv0Z5tb2+bIIs9Fu3d4Di7lXOKsZQuipN1PJR56hflm/n5+QcCMGo3dVuL8qYGBTRwoZfnFfu+zMzMVKzaI09zO3Eln8u+58Ouf1KgQRcKAGKl8pRUKmWOJFFDOnr0KJLRKN7YswePLS2Ze9mT11I/NeacBJs95nI5jA0P4+ahQzg9NYVDpWoFMimbJWazWfzy9OliZnZ4GOeWl+F5XlmJKffBabaXEXvNBHG8JK7NsBpwIFjjvZLJJFzXxZWeHlw7cACHb93CkenpMjpwrnZ2BNh1dn3fN/t4D1y/jhsjIzhw/XrFyLJmuzUzpu8rk6hDz3tRmHmPwVu3cHlwEFu1tbjY348n19YeMHJqKJ7a2sIHz58vMm59Pc7v2YO3u7pw6OZNHL971yiphoYGXO3txeWhIey/ehWPLS2VCagqH/5tR/T4o6Ca63R7/37cPnoUx8fHMTI1Zdb1yOgobh06hCcXF8t6FJBObJK1vr5ellHiOl8ZGsJWTQ1ujIyYPg+k25WeHlwcGMD+K1cwMjX1AOi2f5TP1RhrCSUDeZMjIxg9fBhHRkdxcm7OdBv2PA/XDh5EIh7HW8EghkZHkUwmDS14/+bmZnR0dGB6ehrr6+vmWVzD+vp6nPN91P/qV0ilUlgpgQbX3d0iwmi47kuzgw520MSWGeV/giYGDg/duoVrBw7g6MQEHMcxcwSAW4cOIRmL4ZfBIFoSCTNHm7/b29tRKBTKupUraM3lcma7DoMVnudh5MYN3Dp8GCM3bpjxU+/9c1zs+q0OhmsZStLJ931zzC8zPlVVVRgZGUF7e3uZbnJd9wGDpQaQvwOBAL4zOIilYBB/ur2NF0uGnSX7egKIGkiVwy+Fw6aiQcdrr7POg//zxCCOxz4mU/UjjbvyFbOLGxsbCIVCOHPmDPr7+8u2VXHc2lQPKA8Uky/+h/Z2zPs+vp5O45WSs6j2QIM2CuApI7xsEF6JHjoP8reOxwYJCtYJ6Hl/AsJQKIRXXnnFbCNUO8pnaaZKx0XAX19fXxaoZpCcAFzBlgZ/bEdCA2TKE/psmz8AmEAK9YkGNwKBgCktVbkpFIqnMj3zzDNoaGgwz7ITHwTPbKKoa7ZaKOAbuRy+Utqrmk6nsbm5iebm5gfGyzUmP3B8pLFWVShtaKeUDhyHOoakCQPkkUjE2AGus8pia2srnn/+eeM0ke58hvbX4ji4fnnfxzcLBfxu6ci8YDCIra0t40AoBmJATGmgc7f5XrcJcLyVAg3q5NiJLN8vZvsdZ7cZJml89uxZHD9+vDy4XSjgz3M5fD4Ww3Of/rShha5fIBCAA+DPd3bwJUlIJRKJBxxMxXisBlFnl2ugOpwyp7qBussOlnLsrHxVJ4x6i5gyGAzihRdeKLuHOrwcbyV9Q4wZDAYfOCLWXgf9vr1OinttPUL+0BM/7Ay8Ym3lCcU6rutidXUVAHDo0KEH6M3vc0wMBmuQJRAoVmdxvny2OmxcL5vfVMfbelh5zdYJdMRpG3ScDNLYgbObN29idHQUjY2NJmD7m/wMriWDpcFgEEePHkVfX59Z16qqKqyvr8NxHFNtQDvBSmWtjOH7vDSwQ96lbggEAujq6sLNmzdNph3YPZ0IKNqSzwQC+FKpSezKygreYWJR5qjNmFU3cO4MMpGvDhw4YPqGqZ0gr9lywIsVTRqIpR3RwLuNy4PBoGkGbFdy6sVnTZS2+2t1iI6L668/xDUcB7dEkw4MJqiPRj2iPElZUz1s81ElGY5Go0in02aclD+do+qUnZ0dbG1tlflbj3I9cqBBj9NSYVQni/8zqnng2jVMnDiBc/fuPWCwbCFWR473UCaiQrl+8CASsRjO79uHo7OzppSHFxfr3N27eK+nB2dL+8y5Xz8QCJhIaSgUwsTBg7g5MoKDN26gZWmpLBugIFuVmCrYShFzGgYy+NXhYSTicdw6fBi/IxlSBfxkhkrKTA1/37VrODgxgWg0io3S/fkdjXKxpIbMTiHnpdkVLfEBimC0qqrKlHW5roujY2O4dfgwHrt/3wBbNSbNzc1IJBLYKJ15rrzxXm8vNmtqcHX/ftN7AygqsivDw0jW1mL0yBGc+fu/f8ABV3rrNhXNuqkzoh2Pbx06hGQ8jkuDgxi4dcvQYeDmTXzK97G8vIyphYUyPvd9H52dnaivrzflYbougUAAj929izf37sXAlSum1JXX5cFBJGtrMXb0KA5OTppgjRoLdaaoXDXQpApPy57HjhxBsq4OV/fvx+HpaWQyGaRSKXieh8FLlzBx4gQ+sLJSVuXDOXHNKcO2YubrGshhZpsVP2wIqT8qI/p/JSVkO04a7eU6HyhtmaiurkagVJ5G3hy5cQOjR4/ig6urDxgUPk8NuSpeBfhU7Lr9wHEcHJmexqm5OaysrMApyZHKxW/zUsOqgF2dPN8vls4xS8DGTARWwWAQBw8exIEDBypmDu2rEoD6suvi246DL5f098bGBkZHRxEMBk0FFgGgrhOzPi9XVeErpfPWbeClVyXwTuDDcVEGtI+CXjw2l6BMS2jD4TC6urrQ19dXZlw1a6XZInVe+awvuS7+0vfxlZJD89Zbbxn+t0sTVU9XcpyU7/Q1pQVfo6FXh0ADJQrkHccxDZGpD/md6upqNDQ0lB1VqnJJPtI14XvaB4I0/X9NT+MbR47gI+vrZYFP4Dd3nVY+UF7T95Uedo8apaXKLXVSNBo191D9of1m+J72WtHghz3+z9XU4IvSCHpsbAzz8/M4ePBg2edU3ykY4zhVftVx42sqQ2q3lGcUPKoTQDzBLJ7ukScdeX8N0gB4YKxch5dCxdNnXHf3eNDLly+bxm46JgXqus42hrPtF+2dbnOgbCjWUhpqubctBwrCFbz7vo8XXRcfSadRU+Jjmx/5+VfCYbxUcuJ9v5jpHh8fx9NPP20wHefC30xq6fraDjfpy0CLzp8yqBUfvAfXmmvGJrB6tGwgEEBjY2NZNruSTlF5UVnguHnRMfI8ryzIofNQ/tV7Kv7ic4kftLKB3fT5PcoAt33WlZxRBtGbm5sNb8diMTz22GMP2HAdj+/v9m7i6xpUIc09z8PGxoYJHNP+aqW2jZPs4LHqS5s3GBSijrLpo98lnTiGWCyGF1544YEgIXGaBnl1jXmP1tZWM0465XSkdW3S6bSxh2z+x0CMBgw1A08+5v8A0NHRYZ7B8fFZ5FvdBuS6Lm7cuGEq7mz+1PW1g4KsiKmvrzeBHF0r5QUbo6i/Rr4k3RioIM0qyU4sFjO2WXGtroni2fv37+PGjRvGzuo68VJ9zPVn0OjC3r14u6sLfxSJ4DPubu8Tnhqn+lVPL9KApdroSraZF8cWiUTMtnaVfeUH1b87OzsYGxtDKpUyeujMmTMPfQ6vRw40cDIqaABMVI1NZtigw/d97L99G5+goyXE4ESV+Krs+F4wGDQNVhyn6ESenJjA9YMH8eTysimb4/0IPnO5HB5bWsK55WW4rovqhgZsb28bRtne3kZVVRXi8Th+eOgQErW1uHnoEM7+9KdlAI+XZqy4AFoJQMNIhtY90Pl8HkNXrmDixAk8vbqK+yVHnd/zPA+Dg4NwXRdjY2NlSs91XQwMDJjMmr5uM4ACZ5ZvMQOg54/r+NUIkX4qyJxXPp/HoTt38GI4jImJCdwvlbKTF3jkWCKRwMrKShnocBwH50qO+dC1a2VOku/7GL58GaNHj2Lk+nUzDzWQwINOig1UFGzQiQSAoStXMH78OI6Oj5cJvZbIKbDj+3aZkO30PLa0hJHJSSwtLSEk22EA4OCNG7hRClyRngpyyYOci/6tDobKBec4cvMmboyMYPDKFdOxls17jkxP49zyMnp6ejANlIFMnQfpoIZZx0X68KxuKm8qNy0d5drYoIav26DQVl5KCzW+fC6PruXrh6en8cTaGvbu3YtRK7hhG1+bT5S+/JuZAWYMXdc1HYCZMVUa/TYvzlN1CPlcHREF3MyoEdBQP9oASNdF/1da8b3nHQdfjMeN0b137x7Onz+PU6dOmb4rWp6n2Q7NBOkz1E5UkmHeiycV8TXNtlFH2U6I9glh4IX/871KdLb51TbCjuPgpXAYXy0dO7azs4Nr166ht7cXe/bsMd/X+ytI56Uyp7bJNuT6XN2nO3PkCP5X0SheLhTwOxJEJnD3PA+RUmk/eYc6Rhtr8dnqvClYsp0wgjKl0/eDQSRravBubW3ZePW3TUvO1Q7Qq/7TzykArLQ29mvU7zp/lX+9SBs+h9/RbCrpw8AZr0wmg3g8jra2tgeyUvw+aaZHzXGOui4ATLWOrRO5Nqr/AJT1eWKSh8BQx8LnZbNZpNNpVFVVob6+Hslk0uhwlUW1r8Cu3lV7urOzg97eXkSj0Qfskc3T9kW9zc/azyG41vFwPnSSPG+3qqpQKJikkAYpdP21coVVqbq/X/mB9NB5cT6BQADNzc0mqwc8fPuOTQeVL76mji6DaeocKA3ZSC4cDiOXyyESiRgeUMdJg7C242nLiuprfk7lYnt7G4ODg2V4kPJIDKW615ZdHT/HwARhdXW1KZm3g96cBx0o0lmrARj40mMybVuiMse11tdsB290dBRdXV0VbZPygvIM36/E60ob4mCgqDtUR5AvgaIjvri4iKGhIQAw2KOpqemBABR1lY0T7UCsJuPy+Txu375teIc86LouamtrTQKK2xPtvhJqK1VH8Tu+Xwx+5XI5JJNJ0yi8urranF6RzWaRyWSwvr5utgGy4nBkZOQBHiX9NRGq9kG3ApJXiEe1L51+17ZTthzY24Mq8VZdXZ0JvqretPEav8/eFgcOHCj7DrDbWJF8prabP290dGABxaalL7a0ACjaDW4dVp9F/RTqK66X+naV5Ia08P3d6gvaP9VvisP4nVAohNHRUaRSqbLtW//Y9ciBBo2oTI6M4Nft7Tg6NoZDd+5gZ2cHtbW1pu8BF4pMosbenrQ6HRRKgmqCJ+7vyWazODo7iw8lk5iamsINcZyj0SgGBwdx9+5dk1XnGDo7O+F5njlp4vbwMG4dOYJTU1MYvnIFY0eOoGV1FV/7yEdwYmICJ+/frwiY+ZoCNWDXIdTzmrUkaGh0FB9KJNDY2IiJUhMcjYw5TnmJOO+pgJr3ZYdSli2RyUg3BWI1NTVlJzfwxAGOV5WhMqQ6NOyHwTImG5ACu2VhDIaognIcB2cXF3Foagpra2sIRaMAYPZ+912/juHbt4s8UsqcK3OT70hvVs6Qx1Sh8Lt0VAdv3cJwqWFMuJSt4rEyCgb1Is/aCojKSUEC+26QXwuFAvbfvo0D4+NF50C6ydJwaLSaa6CZbY7BNn4AMDw2hv23/3+0/WlwZNeZJQie5wvgu2PfdyCAQACB2CMYZHAVJVKURJEilaKkzFJmZVZndXeZzVJtbTbWZm0902Y9YzNlZfVjzMpmqisrl0ptJMWkqNRKieIaZOwrAhEIBJbAvsPhcGzuz+eH+7k4fsOppGyUzwwGwJf37v3ut5xvud+9kzNkeeWtWwAoa8qTlDFbGfN1nR/BFgATOKRx0bW1HRnb4NpKX8ej87QNuM6Xz7VBio6zmFyqfrGbb9kBRM0U8V6s1uF3bJr9oS4abHVc2GSNwUKdq8qegngtLVenjYbNXl/buBIA8b7cK9zc3Axgz0CqYSYA+rT9gcWuYnqUQJPBLC3jtPmE91AjyR47pBGBHr9P3aeOge2Y8n+bfxmwZld63Vetxp5jKmbblPa2jtX3NWty99gxbHq9+GEmg8fze+sJ4BkYI9+ow+W6Lkb6+vAdrxcvp9P4smW7XNfFG5kMXkun8UcAXsjz05vZLF4F8O2SEnwjr8soS59fXcWbpaV4ORwuCKQrENHM16eBvWKvKQBUUEO7SLtmg0CCG7W55P9UKmVsL+/BfdMK4rVhNR2jf3Bd/DCZxHdCIXwrH2wir5HPKJu2nS62D516heOm7bDLg9WJJ+BkqbfH4ynoLwUUljNz/sQHBOrMqNLWK8DVcSvPczsJ/2fySE+5sZ0aXUddQ7UjvDQDB+ydPqE2inaRNjqTyZi1siv/MpncNj72n+Cc3nIcvF5aipe3t023e/685Th4NZ3GK66Lr+cDKgycU79pmT8v/q92gq/bQQet+vD59k5eU8dCeZ+fZWNABpU8Ho8JTvGHDRR9Ph9+lE7je5kMvuE4eNHrfWCsisF0vTWpRCyle8v5WTohvKd9n2KBBuV5NgzknO1AWjabq9arrKw0skFHh/NOJpPmZAR11lVW+Hwby+r+fq5TKBRCLBZ7wHlX2iku1ICD6j3lW16O45gtt+vr6wUJAuUJfo504DGGWoFkBxL0GfrD99WH8npzDdo7OjoKMATxEG2I0lOrx20bS9rSHtIhZaCBNpdbUGmnZmZmCoIMbBqpQUaupa5psflRJtX3Wl1dxdTUFOrq6gp4kFhVcUYxW2SvnY1PgFwV/1y+oaxd0Qfk9Ozk5KTZohOJRFBZWYmDBw8W3BdAAa5R/qFM+Hw+/Lnr4m+3tvDn+e3cALCyslKgF3hfXqzc4XopXYvN2eZ5VplxS2Ex3mblCpsNV1RU4MyZM6Z1wme5PnOggUcpOY6DD55+GiuhEC51daH9xg3TgIkDIsPY3SrtCCMXToGyzew0Ntwbrs4yP0cm5efVqeB9Nfp16+BBJKJRXOjowJe//30cHB3F6y++iEQkgsv5kwi4eDbjc7F0ITUIQ4DLo33I+AQ8mlFR481gigIxKgWv12sWld2YGY1cX183VSUEIWQKKm9gLxhAEEYDTgVk0x2A2ctFZ9gWFhtUEIzqpYCYJ4YQqKVSqYL9hjZo1yCKGmgFO+osnK+vx9mTJ9F16RLarl0r4DEFxOrEa0k1FdmnZT1ofDVqqA4MHRvNmGk2w3aqlf+1uoI/6iSpwWLZmmbSFIwpuOT/SkPKghpqBU2aieP9tYu2An1dN60E4ecoL8oLun4KDmzQpk4f78vvanWOzlnpqz8qu6SN3ou8QFqx78ynOc7//16OkyvNo3yqoaVDpSW3juMUBAlJX3VcVB7T6TTeyGTwk2AQX93ZwXPiMCi9dR8kAKNnuR3N4/EYJ4T/6/5QlV/Oo5jxsR0RAAVOIgBTvaIyZtsF20njujOzrM+ywYzSXsdYzGki+KcOpB6i7uUaOY5jTgXidxWo2Xqq2G/qB9d10XbuHFY//3l8NS9vwJ5NI92V5nTMPB4P7hw+jHXHwY+8XrwsAU7+fn13F1MAfpDN4kt5Z/aHHg+mHQffTafxddFDAHB6YQGlly7h+W9+s8DOamaX91YHzQbLqqNVZ5BGWrpK4ERAqjoBKDxyOpvda5jIRpYMBhA8A3s9LRikId1V1/9gbQ0Trou/TqXwzXwVJXmcwXbVX0oDO9jvOI4B58X0vvKerg/fVzry3gR5ak/5GdJPT1zRACRpqbyvNFAdonpWg5oej6fg5BWdh2ItXWvqVq6xPXfbjtrf0wArP6+Oh9KQr7+RzWLGcfAjx8HXZK4ejwevuS6m8/z/R2K7NQBiByYZzNFqF3U4bBu+vb1tqhEYHOAYtDKmmF0hfrD3n6td4Fy+T1l2XXxF5ETXS+dWjOfoYHDttRrUXg+lh/KfrpduQ2BwRf0BtU+um9seYm+VAvZO2qI/oRlb2gztXcH3qDs04KHBYFb4KA302TbPFsNGxWwF/6a+WF9fL8D4DB5yjKxKov1lFaXycrE18Hg8eNN18f1sFt/0ePA1scGanIhGo0bmyZfkvWAwaGSba7MjtgbY21rLZ/KHjqiegMRjPF3XNTrK5/Nhe3vb+Cy0XYlE4gG/zMZudoBSMZ5iT68315uG9lOT2dpnQCuJ1O4X8+uUD4BcoGF4eLhAxlVP8rkcZywWw+joaMF47bnYcqzPfLmkBF92XdTkT3AEcltZY7GY0Seqq5RflA+VP+056cWxUE9F88lYe/xLS0vY3NxET0+P8UXJx58VH/9eWyc4gEemp/FubS0ODw+bJiAMNCjg0lJ8dfJs46pCqdErLkg8Hkc4HMbGxgZKS0vNfi5eCmyKObvsAh2NRhGJRHBsZASX9u3D/uvXjfAcGR7GtZ4eHBkZKVhM2/mxBYN/24JAJaLAhFF6Xmrsddy2w8dopw10qRSV8agkbeNMAbRpxsixHQwqZlw0Q2yDSnWUbb7hmBng4DOYebEBLL9XrIRRL9shP9/WhvVwGHcOH0b79esFUW+NGHOeVBZcUxUu5Velib6nmWj+r+NVA6tgWsEen8H3VfkqD+n3FHwxy5FOpxHJZ+KUr3hP/tZx8/l2UIGBJYJUrq3u1dPKB3X4lF4a9OP91XDazopmALPZvcCNNr3kZ7XxquoNBdoMgHKOpC3nxuoFLZvWTBn3G/5zXIyA67x5Kb8pTba2tgzYooOgvXE02OPz+fCW349ZrxdvlpbiK6JblP/4Gv/m3rtiHZ/1GdQdxYycTTPytz6XpcJqvLUnhIJSnZ/KEzMsKh/UUSxDtmXPdlBUP+h7BCiqB3QcwB7vUy6KBbltnWIDRwAFtqzz5k38N488kpsL9px4gljdlmc7fQcHBzH+0EN4xXJ6+fcrXi++l07ja5kM3PzzX3ZdvO7x4BXPXm8fzYzzbwJ+rr3ykQ3ibH7m3ElL1Tn2+mjlAVBYCaXf4We0v4Pj7FUH8XJd15RvayCUFx2XPwkE8Hfb2/jT/LYU3dKjMqLjUt3Fsdi2bGtrC6lUqmD7o9pLpSd5SJ0S2hfytQY09GQW5T2OWWVOnXzSTivsVA51fMrDfI00twPGfCbHzMwn7609HxT8qxPHMdKusVqkWMCGW0JVv7zsOHgVyPG44LhsNotXHAffc128JJhN6aON83jpNp1iVQm6/qofuX6sQFAZUTys1QT2STsaEKfTRNq/4jj4biaDrwPI4sEjZ1XfqMOrVRmatdd10O/a2Fd1sAaKlGY8TYI2ulgjXo/HUxDA0zH6fLn+SZyrNhC1t2crf2pGXDEI76/fUb5hYISyZWevVT5s/cH1Io3oZNu0J//btGVAX3UDdR4/y3vs7Ozge14vpj0efN918fUiwcB0Ol1wSg7XnJU1xEm2/rF1o/KQ6hzHySUr6ZTqFqVUKmX65ITDYYO/stnctpK1tTWD+W17QByuGJPP4/qwcp7/a3DB1nm0Y+Q5xR7FsIryEn9isRiSyaTZCsP1dF3XJGjI29w2sb6+bviT4+ClCV17DFyTZDKJiooKM+ZYLGb0CHnQ9vk4V1bU2L6GLXvKtwzAJZNJxOPxB2ws1y8ejxudzwQ3T7j6LNfv3aPB4/Hg4cVFtOQbBjlyJqw6QTrQYgBMiayOO8sdFUxQWTGQwftolJwGgeBTx61OkeM4GBgfR//oaK4zaz7Dfmh8HCdmZnLCJgZKGYL/6/gvNTXhk9ZW9Fy9iv35LQD8nEYSuQVElRwFm4wEPLiHlfNVQ0fQQTDEMRKkkNlId2Zniq0pALM3WgVADTmrJ7heNijQuapi1R824+TzKIhcNwWnahQ04KRGjp9T5/bk2BjOtrSg49IlA8ppoFOplLkfM5SBQKAg6qq8lM3mzphVAXccxxyfR4d0a2sLt3t6cKu/Hwdu3kTfyEgB4LaDFKpkbWCi667v247J7Z6eXB+IGzfQceOGWfuysjKT2bUBOh1TVrvQWdXnaMR6aWnJOFGTR4/i5oED6BscRP+9ewXOpmYbbCCigETnrjzuEVnTtb7Z2YmhgQH0XL2K+vxRkyUlJaiurobH40FbW9sDThvvS2Pb09NTlGc4x3Q6d1wu9xcGAgFsbGwgkUggk8kUdBj/Q1+O45iqI+o4NWaUb91esbOzY+SQwIVrYMtjNpvFl3d38ZNgEF/Z3sZWHvyoE6drQ9ptbGyY9aURIc00i6vOB9/TZ/P6NPBLnaDVJLqfVPWu7SQRDCpwI18xC0Y7YOtxWzcpbzJaTz1GXaHBQNWrShc1/MX0lQIaHQuzoAR9emSW2gVepBnHr1nz3rt38X89fhwAsGUBrEwmg8+7Lh7J9yfK5On1TCaDL/l88GWz2M5mTbBSs51aAaU8prJrg5piPKDBb5tW5Hut4lEaZbNZo9eUpuqIM0imAI/2Sx035QfS+kslJXilvNzYTs1E67g/7fu2ntetLpRR2gweIaqyZwNE5TnaYgYWla8YsFtfXy8KrLm1zx4fP8fvl5WVGbvG19X5sx1zDVDwsp1QAnGtCLBtm9o/3s/r9Zr+LXyftFD6sNJVx/C04+Bp5OxhSnQYADwN4JF89VHK0pekD/WR6iB1zu2xKz/bc+G6UzfSgbaDReR5rTJV/iX9iWN8Ph8+D+CxfFXjLgqDShyz8gGfT1pls3uZTM3K6tryUtm2A1Jqe5gt14CoyoAt1+vr6wVypRiXgQY2eiQvclss6aIBAMV8XEs2H1RetGXADtjaOozvqd7i6yoHlPlUKmXkVgMdGvzgvBloSKVSJkOs608+5JxfcF284ffj6/lAja17HGfvmE1WTjBxAMDgHvVNNJH0uwKO5NvS0lIEg0GzVY3yQaxM2qZSKbNVJRgMYnl5uUBWi9lh1bX6vgbWARhspphEg9j294vJA2lmX5xvNF9ZoPyitpnrzXuHQiFsbW2Zk5qU3ykPtN2akNdx8nU+R7ev6XhVV+qYi9lfvYr5FxUVFUgmk8YHLEYXraIM5Y8sJd76LNdnDjRwEYuVmlMQeHHCdPKU4DoJW9CBvWNSlLH5bCotO4tFoK7MaANB193bE6xZdRvAOs5eBkmDJ7aS5DPOt7cjEQ5jaGAAx6enCwIDjOYrAKIS4KVZV5Y+EjCwMyzHns1mcbW11RzBd+Du3QJnQMemQRhmbBWw63rYtLjZ2Ykbvb04ODSE/XfumGoQr9drmmMpIKRCq6ysRFlZWYGQs9cDm1Xx2ZNHj+J6by/6Bwex79atB8AL56PK31b05+vrcba5GUfv3sWpuTkcnZpC67VruWMNxTjamSTSoqamBrW1tQ84AeSvxsbGB5wKBd4c02C+oeiNAwfQm288qY4taaulrwqUbV7mpXyuSnSwrw/JeByDfX1ovXq1IJKfzWbR0tJSoNBVltLpNJqamh5w6BTgkAd538H+fqxHo7ngxt27DxgFfkej4p8mNzbP8TWdazqdxrVjx7AdDOLGiRM4ODpaoAeo5FTeuW6u6xaUoGpwQ9eQ+ikej5tjjvg5doLWfiB/6IsBIV1zjye3r1r7K/A9dunmGrJUdX193Rh65QMAOLOxgUfy65xEoZFR3aqgl9uZFCgWK9lTGdX78irmaOqlDqBtZG3bYhtRoLCJHfXtwsKC0a8aNLQdCh033+Oz2fWc92fpPOejYJH7hxXcKD0+zdgrDWgf+D0F1er0MahMHUYgo44NnRW1L2pjHSd3ghEzF5Q1vXdlZWXBcwBgfX3dgHa7GZ3KvTqCNh3s9VOdwAygZly5tuQL1acKsJRHEomEWW911Dg2Nt3keqkD6ff7TUaI71EeFU/ovNnQzs6662eZ1dXxF9P3SiPiDtJmc3MT29vbCAQCpuEpM0vUWcvLywX3IybSyhzFLfzNeZaXlz+wXYV6iJ/j/Oj0chy23Os6axCONFKQa1dF0D4rPzNREgwGEQwGEQ6HTRJgbW2t4H4arNb1Je2ZyNrZ2SmQA21mTlkmBuQ6aJBHdein2TrdikfsrI4Rs/682LCcYyfNODY6b5Q32grqCNtp42+tuArne2Rls7kk1traWgHN+GzOx+v14nvJJP5LKoW/iMXwnfLyAplTXaV8pTZWk0Y6xmQyWRCo5loxIOO6rmnMzPkw+EAMqhVAasfpQ9TW1hr6amaY91N9rmus/Ky6TPGDyi7X0+v1mqaIpAvnzjVgZaJWa6RSqYKeKtQDpCH13hOOg8dcF+FwGNv5IIUGYLkOnAubySp9OSfSjb5bMR2hlQHKm6wS1+CI0oX4hGMKh8MYHx8vqPbQgJvyIHlAt9XYYyAOt+0NP6O6sFiQSNdX11TXlU2Xt7a2ChIOWuVEHQbAVHko1uSlCQpdT9KAr/OZ/AyrwlTH6Fx1zvq6rmexuSpt4vE45ufnC3YTKL/oGLPZXD/ElZWVAh/jn7p+r0CDZksJ6OzF1ECECm2xhbQJpMo3m83iRkcHLu/bh4O3bmH/nTtm8cLhMKqrqwuUMR3cpqamAvCljXQIym/t24fbAwPovX4dhyYmCghpR+W44GrEyCR+vx/HR0Zwvr0d/fkj4RzHwfX2dlzu6kL/4CC6bt0yY4zH4yjPHwWnAJ9lK+Xl5Q+UI29sbGBtbc08f7C/H8lYDDfzTi0ZlPSj0lDng44glTIZXw0KGdF1XVzbvx/r0Siu7d9vHGc6eFx/BRhUbAQ+aowBmKwDz2h3HAe/OHAgN4++PvTcvm2igwqybIXBcfP6uKUFq6EQLnV24sTMTAG44XeHe3txs68Px0ZGcGh8vIAmpIHyob02pIt9eTwe09SndmUFG5EIquTEDRoKVhEUkyUGowhobGWqZZycTzqdxv7r13Ht2DGkAwHMnjiBgfFx4wzxzHTbQCqQs+lKpc+/vd7cMVrZbBZD3d3Y8fsR2NxE382bBTQicON9bSOu9+fYOT+CVTUCtjPH8ZNvksmkKdVToKpGispcjwVTZaxGlO+Tb1mqGw6HDQCw1+4PdTH7w+NYOf7t7W3TGyIajT4AbtXp93q9xrmyAWI2u3ccKS87WGBn6vgcAnBtSqeOkwIKdSRUr+t6FnOq1DELBAKIx+Omf4s6H3alhMp4RUUFamtr4fV6zV5RZgg1UKIGU8fF3wTD2uPBdV00NDSgoqLCBIO1+kLnpnbONuz2s4oBnt3dXQSDQTQ0NKCrq8tkobWhGMeoPO84DhobG7Gzs4OmpiY0NjYa+eBFvudzf1Faijf8fryUyeB57GVC6XgQRKXTaTQ0NJgjyTyeXImllt/S3nNeOlcdA/DgaRxKPyAH0np6ejAwMGCcITugYb8Wj8fx2GOPoaenB36/HzU1NQXA1cYmdnWSLRtbW1tYXV2F67o4fPgwtre3zclWdtUKkCvvJ//aOpTPtXUtA3fU94oneC8GPB577DEMDAyYhr/Ko4FAAKdOnUJXVxd8Pp/pWq805piLlSNzzpSLVCqFxcVFlJaW4tSpU2a9eS+Px4OqqqoCDGgHuElT1Rfcz60YxMaBAEw1nc/nw9GjR/HQQw+ZY5WJtVw3tw/8ySefxKlTpxAMBk3iCdhzRuig6XGUGiQhr5eUlCCZTGJtbQ3RaBTPPPMMqqqqCvQFcaz20uH72iBb1xzYO7WE1XWqu7lOJSUliMfjyGZzyYHOzk5zihSTan6/H6FQCE8++SQ2NjZQXV1tZJo04xiUP237S+eQY02lUubZxewpaQYAf7WxgYlMBv97IoHvlJcX6E1WuyoOZeCNz2RDQFY/MpDA6kTaEd6X1YjZbG5rgXa353owAMi5kqbsMUQfhfdOp9OIx+MP9P6gs6/BpWL6SS9bx6scBYNB0zyWcq5BAAY0gFwAt6ysDNFoFBsbG+YUJeVV+xQmPo9rpDTQdV5ZWTFH1GuVODEaacD3aAe0p4rNQxq0LCsrM/1I7OoIBgtJTyZutre3Cw4L4G/F33xN8TBxozas5eeoH0gDvbf6bToPW/fYa837sHpZG+rb31M8zaBhMpks4Gn94fc1AaL01ipNfk638dlYRjEu1584pdi89SJ9IpFIQbU9n692U7Ej2xhwXJ/l+syBBh7H4vf7jVECcspypK8PFzs7MTA0hMMTE2ZiCkg0QmZHYiiM6lQBwOV9+5CIRnG1p8dkbnd3dxGNRhGNRg1T87ssfwb2FO3Ozo45cpH7e24PDCARi2Hw4EEcHBszhAX29suS6Dpu21EqKSnB0akpdN26lVvcvPNwpbsbiUgEN/v7cXRq6gEFQuFQJcv7apROn8MGQ4du38a1nh4cuHGjgF62c0lGVqYkKNTjNdnhmXP0+XyoXlzERiSChrU143TpudrFQLtd9sz3aVh4QoPH48HFxkbslJSgVJxXVa6MVKsS4Z5PzjWTyeDU+Dg+bmnBsXv3CkCPArsbBw5gPRrFle5unF5YMOXo4XwndWCvzFDHoM6dCqBtkEpLS7FYVQXX48FSdXUBcCR9lNd5P+UrBT9qVPQ76gjuv3MHtw4eRDIex40DBxAKh/FxczNOTUygOa/IFfTbvKyOKi917O7s34+rPT14aGIC11pbsRUIIJpIoHtoCF7J8tuKVo2gbQBth4v0pqFTRea6Lg5fuoRbAwN4aGICt/btw8ctLei7eRNT0SgutLfj1MQEjk9PF/A9AHMqy+7uLlZWVh4ABQx4BYNBDPf2YrC/H8dGRjAwNmayuywJVL3wh76+/e1vo66urqDEToM3AMwRvNlsLrPX2dmJpqYmdHZ2AsjxbSQSeQAQkHf4XcqL7WDw73v37iGRSKCjowOf//znkU6nUVFR8YAzYcs2naZijqV9qc5THi8rK0N/fz+eeeYZE7gj0LazHKxS2dnZwdraGjo7O9HS0mLopX0lqEPsoJXrugWBAnViE4mEOX74yJEj+PM///OCIzNtB4TPIYhm5kEvde5sXuTzHcdBQ0MD/uRP/gTNzc0PrBfXSXXB7u4uRkdH4ff70dPTYz63tLT0gDPJ53g8HrxeXo5ZJ7eP/fTCgvks9dXk5CS2trZQXl6OEydOFAAmOylAWgJ72+/UEbP5wn6N8/F6vSgvL8epU6dw9OjRgiwnwbCtN5LJpHGMm5qaAMCAWBvUaYCRF19TEJVIJJBMJlFVVYWWlhYzBgWyGpymI6PypCXPQGFvIdpl4hg+V98PhUImSNrf34+WlhZDX4L97e1tzM/PG13GIIk6gFwPbRCsiRJ1tBzHwcLCAnZ3d1FTU4OmpqaCLQfkN21ix6ys8qXaOn7PDhwqP+t3WMnq9eaqJuvr6wtsKHXg/Pw8HMcxx/pq6S4dWc5PnRqdO1+nc0eM1dLSYoC6HVBlE3LFFzYwV1lj1p0BQn6Ga0OaERPV1dVh3759BbjQ48kd8Tg/P28aaRPgK/0YWNWeBppYsvmW/UIOHDiAmpoaQxedFwOK6XQafxYO4683NvAX+f3iKpsqPzbOUAzAo5K5DbqkpARf/vKXTULDcRxD36WlJYTDYZw4caKA11SHkqZ0rPm/Vpqm02ncvXvX8HlLS4sJ4vFSPaHzYq8FXV/l72JOm9frRXV1NY4fP46Ojg5Dd9LAtjvE1q+88go8Ho+hi25hUmygeo1rzO+prFRUVBgHksEVO4HH73EN1SbraSxKV26rDIfDKC8vN1sj0ulc02TtLUAeovxXV1eb6mGt2lPbYzvF/C79w3A4bGhDfmYgU7/Pe5DfNMil97e/Y4+BOmlzc7OgITYxmv1dv99vgkZMmhDb856UW44f2NsO6bouotGokSdiY9Xdtj5jQJJ6z9Zb9ryUV/kaq8N06wR5VuWb/BwMBrG1tYW1tbU/fI8GAq5MJmPKaznJTw4cwGZpKS4ODODg6OgDROFvO/hgR4K5gHzt4K1buDQwgHRpKaaOHUP/vXsFoJO/+X0FxOq4fFBejt9UVeHk+DgOjY/j8PAwLu/bh/7BQeMo8R7Z7F7JICN29v2LAWsKaCaTQfXiItZDIQQzGfz9s8/i8PAwerN7x0+pU2GDN2VmAHi/rAy/OX4cj0xPw3Vd3GhqwoHr13FgZAQuUEAPjWrbjouWMfE9OrbapNJ1XeM4z1VUIBKJYHNz0wi5NnDjfZjpYkk7sBew8flyxygNdnXhak8PHp6cxOXGxpzzuraGA3fvIoO9PUwKVNSJKebIHLl/3/REyOYjgYygAjlBOTg4iMH+fpyemioog97Z2cHc3FzBnj2CgJqaGqTT6QJHlWPw+XyI5o+fKSkpQTAYxMmxMZxrbcX+mzcLnHnS33bqSR+CPM5bo7q6fvZeeQDou3EDQ4cO4eTEBM63tWEtFML5tjZ8YWgI4+PjBY5KNpvFYGcnru3fjycWFnDg7l3jFPGeVHLhcBiXu7uRCIdxrrUVp+/fx9mmJvQODhbMQ8uklQd1vXhfDQbYSl4/oyCx9+5dDIyPo7y8HP/p8cexGgrhem8vvF4vVgIBfNDQgI4bNwp4hoCVzWrGxsYMv9s85PV6MXjoEBLRKC7v24fDExMF77Ms+p/rOnnyZAFwUPoo2OT/S0tLSKVSDzQC0qAIdaJmPgCYjA0BF50dglGC90wmg/r6+oJ11bUrJp920zygcLuQvqdrzxJSv9+Pqqoq1NTUGABhZ5X4bHssLNskkFL50JJ1BXma3VFgTFBNp1fL0nWNNIBK2VUwaNu7YsEF/V8rWtRZ1QwT9Q7v7/Hkqk0aGxsfyFpwnXkffR8AvpNO4+/TaXwzEEBra6vJ1hDYADDdw3UeKkdKB9LUXmf+toMKxT6nVXgKzDh+Le3l90pKSgyYVz2mz1M6ay8ZDZxoRsh193oh0H7ZTVE5Lj7D3vdO+ug+aAa8VG5tPtIfLZ22s1Pkh/b29oI10vsqCNZAoW239X+uMddDZZifUZoUq5Cw9QWBMMdNuuvcVe9T7/FYQ302P0f9pL0OOFbaJtWrSl9dJ66p6hw68BqAoexpEEOfY9+Pz6SDoxVDSmfShnNOJBIP7HmnHmxqajK6THGjVkgob6ozw0sDsFx3zRgrL9rY/Y8CAXxTTnSx110xjwY07CNNFdvu7u6agA/HzHmFQiFUV1cXbH3k+mmVnfIGx6K6QteYp8BpY0mbZxWDqx7nZf+t+pxjDIfDaGhoQHl5ecE49L6qMxOJhNnKw4pr1S/Ugfaz1O6ofDqOg/3792Pfvn0FfKD8Xyx77jiO6WOm8qx8pT0uqqur0dXVhcbGRjMWtREqh7S5Z86cKajIVIfXnqPyNeVkc3MTk5OTRmZ7e3tNQJW8ZJ9E8kYmg/+aSOAvvV78Ub5aSPnb9uV0DD6fD62trWhubja9PhQz2H5uSUkJTpw4YXpY6GdsPKC2irJI+eZFH418q+vN8RG72LpV52TzrB0YJNYpZq/5mziPAR8mvov1/it2/V6BBhKC5VAsbUMRIdDGNySuAmg7Wq1Gn8LWNzKCmwcOYDUSwfuHD8Pn86FtZ8fs9VeChMNhbG1tmQgXCVZSUoL36+uxFgjg1wcOwOPxYODWLXTcuJETjuyek2mDeFXUOgdbKXGOBG3zlZVwPR7MR6NwPR5c6urCV4aHsbS0ZCLoHF8sFkNJSQmmp6cLFEI2m0VlZSXe278fa8EgPm5uRhbAWiiEwf5+DIgTZRt3HfOngf1ioJfXwaEh3DxwAA9PTxfQIpVKIZlMmtdIK2b15ubmCiKxnF95eTku79+PtVAIZ5uacOzuXZxvb0dPvpEh+UZpTEeIfKKGiK8reLWzJuSzAyMjODE7mwsOYM+waPaNdNJA106ez2ynjYEnKgcAODwxgd7hYayurppGogpIXNfF1ZYWXOjsxInRUdP3ghkxjfDy8zZw1nXyeDzYPzyMhxYWEAqF4PV48GFjI07ev28CXsqnjuPg0r59SIRCeK+2Fl2DgwVl2UpbVs3c6O3F6akpHJuexsHRUczMzMArhok8pzJtr6XKEg0en0O6UllyvLwXszSO4+DU+Dg+aWlB/9AQorEYzrW2on94uMBw8pka3VbQpuOibuq/dQs3DhzAsXwPCB2vvX/2D319N5nE321v4zuBAP5ITlvgOIFCfVlXV2ecHl50UhXoqWPBe6lDAeyV23k8Hry2s4O/2dzEN71eDOT3XSqtbNBAnlfZtIG0DcJ4qXzu7u6afgLcLvLq9jb+fncXf1xSgq/JNiJ1wsijbOJKJ4A8b5fGki66JY3rqs4NAPxwawt/5/Xij7NZfH1jo0AXK3/ZDowdSFT6KzCx5YUywfLW4eFheL1e/BjAqwC+ns3iywIMVN9x3cPhcEFmSteaY1OH/TnHwfP+vaPPtPLN6801MltbWys4wpTf5b2pm5UfiAeUNsrLNi+qk8Kg3vX8KVDaG0X1hu0IRSIRs81E9Q0rpXQt2Amf73NOCrg3Nzdx//591NfXF9g2flZ5n/P3enNbCZiZtWVD70MeUX3L/zk3ZlH9fj8++OADU3rP+9KR43YWfYYNgGmD7K0+ytPqzKRSKczPzxdsS+Xn9JjrcDj8wForgFW5IP/xf/Icacn3tHnr9evX0djYaErrOQ/NuCkQ5vxV7jkvO8NnO2xLS0umMozrqXOh7mDG1tZFmqjipTZI8Qq/R36lDvR4clVlZ8+eRW1tbUFDWDpRDQ0ND9hr5Sd1HPhsxcFcS9d1DU5T2tiJO1agcnuG0pxOpVa46hopDenEaD8J0vTq1asFsq36OhaLmUoF3k8DsLo+fL6Nm0i/jY0NuK5rHHnSR3Wk8o/X6zUNW3Ud+TdpqfqFf/v9fqyurpoqDJVZYC9wyXGxCoC4jffV6mdgL/hnB7mU51zXNf6EHVRUWVbb4/V6zXbbUChkZEwTf8T0GhhjQ3GOVYM1msywe/TZWET1oNKWOlcvVobv7OwgFosZPWVX1QJ7/Sf+bncX910X/2ltDX+U16N6qe+kcsL3Ghoa0NTUVNAnyHbIOb/19XV4PB6Dy5TWio1UX+s9GXCcnZ01611dXY3u7u4CG2ePmTyrtryY78fLxpqlpaXo7e1FW1tbwTrazyC2cl0XfX19D9Dyd12fOdCwkC+zLCkpwfr6uskcp9NpPDQ0hIudnTh29655jcbbdp4/DYgqiFGgcnx0FL/p64Pr8eBiRweevnHDdMTnFQgEEAqFsL6+bqJyvOLxOB6ZnsZbbW1wPR6ca21FV76SQRWJMisVmr3HiuP8tCgVr0NDQ7h+4AAa19cxFYng6MgIstncnmm9nzIZnVo1DADMUaIPz8wgnU7jbHMz9l+/bgRRFYEaeDUa+hkFAnzdVrp9IyN4aH4+t8cQhQ6PzchqJOxSIVXAh4eHcW3/fpyenETv/fvouXMHiUQCWV9hM0sbPDCqp8bdDl7ZmQz+zfnatPH5fKbbr46Tc+IJIRpN1jkzQJDN7vVRYPmTAi81fhc6OrAaDuNcWxv23bplwLA6wirk6ozbiorRZY776NQU2m/cyJXMSZdldW4ODQ3hZl8fHs9veVJeVv4pLS1F/507OL2wYPaL8n3NVpAu5B9+TgMmatj0Ur6wAYLKNcvkjk5O4vDEBJaWltDU1ISnUyncvXsXWewZYDsirOOwnR0C596bN3F0cjLXgMfZawgWCoUKMv7/HNd/Xl/HrNeL/7y+jjP5yhkdv+M8eIwR99QqT5CPbGBh6yXyqPKG4zj4L6WlmPZ48LdbW3hoft7cU6uJaFxIE/5wC5sN3nmpTlFHkM4Ht0CcO3cOfr8ffzMwgFmfD3+9sYFDi4sFfMLf6vionmD216ahBhr0SCwNTpAn/qq8HPN+P/5mawuPLC8bvfKPHg/eKCnBizs7eE6y05RDn89nmunZDrEtC6pH+Tob/n3wwQfIZrP4Xnc35nw+fD+Twan8tj/NaHL8AMy+V95fT6ngawSSZ6ur8W5tLR6fm8PphQVjA0mTs9XV+E1VFR6emcF38v2COB+153pxj67rugXNsIoBHZVz5cvNzU0sLy/j4sWLCAaD6O3tLXqiyFuOg3/w+/HCzg6+ks1ibW0N4+PjxuYrMH4nGsXbFRV4bHYWD+d5iev5fnk5fltTg8dmZvDo6mpB4CmbzZqSZgZSiEk4T+pB3s8uiVYco3LB7xC02fJCh4x0Gh4eRk1NDfr6+gwdvF4vtre3MTo6+kDXfmAvGPFuPI736urw1OIiHl9bM3ra7uGxsbFhymYpI7FYzFQw8spkMqZHjmbYVMaKBdtIK35GM8q2A0IndW1tDdeuXcOjjz5q7kPZ9vl8SKVSWF9ff8C+aKCApd7aUE6xgMqIHplHXaGVRBrAVQdMx865UEY1S689dFRHq71Lp9O4c+cOWlpaCpqHkl9mZ2cLtmN4PB5zMgfLrUlvjl1lSLFDKpVCc3NzAd9q4ExpUwz3KQ35HY6LiT6upQb31Il1XReXLl1Cc3OzmQ/pQodLm8SqPddAuTq5duk3kAvIrq6uoqamxvSS0HXSudE+AEBNTY05MUHpUYwGyvN+vx8XLlwwGEJ5TwPerpvbwlOeb65JB1X1u9pVtWuqV3ix2nghj9u8Xq8JFBfrO8c1ikajBYFqrqXar2x2LzivAZ7h4WHEYjHzfPIet1pz3AzwsFmm+ieUbZVjnZ/KEN9nE0oGc1zXLUgAarLkFY8H389m8Rf5cao+Uv1j2yql1cbGhukbxPHyb/5PR7+trc18n3pA56U8Q17U6im+z6OlOS8+z8YTiiuI0UgTmz/1PspXPp8PsViswK+x/Sj9Dnvuqe/wT12/VzNIKio2gKECGBgbw75bt0yzGR2YEs/+G9jLLhSLamUyGRyemIDf78fZpiacHB837ykhSVh2Z1YDEIvFcGJmBisrK7je24sTY2Pm8+qIA3sMrn0SVAHqmHnZwD6TyWD/8DBOLy7C7/fvNbCrry/oRaEGmv/r1gqCy5Ozs2i9ehWVlZXY3t5G582bWFpaQjq/d0c7xOtaaYmS7vva2dnB+fr6XAPF0VEcypeM69po+Z0619wHqkbSppHt4PD9vpERnMmD9w3LCefnqQToYLuuayL+dhRQacj/CS7YqVibEml0n3Oz+1hQ6AjYtSeHrZjUIdZIvCoxjimbzeLoyAgutLfj2Oio+R5LdPVeOkcaJs2IKG30swrsiwXDDoyM4HOpFKLRKGZct2C9eB8Fh/ocNQQ6N/1tK2obTNr8busIBTTFgIDtNPM+qrxtAKi6odj/5AEtjVUjb2eE/pDXoaEhZLu78WwqhYQAMFt+qKMymQwmJyextLSERCJhMqmkg8qs0onvMQhIHeTx5Bpmnqirwwf19XgmlYKb3cuCko//0evFP4bDeHFnB18UPlCnSccLFAJU0ln5g2tA+V5dXcX3UymkPB7Es1m8nM2iPN90jI64Zitp3JPJJJLJJNLpND6oqMA7VVV4JpHA48lkgWwDe6d2UJYymQx+FQzilxUV+FIyic+lUng2kcDPYzG8mMmYTJ7jOHjTcTDjOPiH0lJ8zd1r5Mrgxu7urgGMNu8pP9uAXUFGOp3G8vIyfurzYdvnQxmAA14v/m1NDV5Mp/Fl1y1w/Hh/AIYWzB4W4wHHcfDvg0Eseb34uLkZ/01d3QPA+d+XlGAlH9D/H6QsX6vLdI7JZNKcRqQnBah8KR9oQFNpwj3jqVQK78Xj+I/l5fimz4eX8oEbysBbGxuYdRz8JBjEv8iXcVdXVxcF0f9rIoHFbBaftLbi/5DfZsDn/r+2t7HkODjX3o7/wTpxhnP0eDxme5uW6ds8rZWb5G+uiepo3pv35Odsp0FtE+fObUUcw/b2Nurq6gxdbAckm83i/7m7iyXHwdmmJvzbffsKACnl0+PxmO2BtL27u7sIh8MF+97p0DEQoU0F7XmoM6CBN23iq4kDrgnXTfFMfX19QZAbyDk01dXVBTKgAUfeL51OY3Nzs6CSya7sUP7f2tpCaWmp2erJjCSdU+URtc2kN3Ex15dOo77GQAsDTToWr9eL9fV1U8Wh7ynPaWO6+fl506xV9Q4/Y29/4vh3dnZMrwK13QAKHMpiTeXsi+vC5ofEXjyGk3zFbXZ0ygBgeXkZpaWl5jQb1W82xnFd94EjA3VsXHN+nnPY3d1FRX4LsM5N19EO0tM2fppfUuziOoVCIXxYWYkfxON4xePBl/L+jeJCDXCvrq6abTOKN5Wvijl0uhXQdV3jdFdWVqKioqIgOcB1Ij6lrHDrIo9x1EovPt+uSie9gsEg3nrrLTiOY3iJfB+PxwuaaS8tLZmtSbat4GWPkzLBueoaMHDE+6guUV73er14yu/Hl0Mh1EhPC95Lx6DftRN+H3/8semLxc9qHwt+R2Vcg9d6f85V+UGTwHz21tYWEolEQeWarcM5dm4T9fl8qKqqKorF9W/9zb+3trawuLhYYFPUp+HnyLc8neqzXp850MBjf5qamrCZP4vYjjJSsZJB7Q6axRwPFSQqNzLaYFcXbvb14aGJCfzpr3+dy5iEwwXlakp43WvGxWPk/eDoKB5dWcH29jZS7t6+eQUX2WzWGAjeV8t3bOdOgTyBv+M4uN7WhhsHDuD0/fvYNzRkaGU7dsqgtuNM48vAiSobfbbSVJ04m7a6n+fyvn1YC4dxoaMDhyYmjCHQaKUyF5VGMYeTz9VSIRUYOzNK5aN0VSWh81M6sXTVLlW0v8fSKgIDNbjKq0ofrq0q+WLOqe1ka3aK/3OuSs9MJoMDd++i5coVRCIR7Mo62iBc11aVu96XMqLgV53yT9u7y9ftI/BsnuTn9X1V6Krw9HMqi7ZCs4GKzSs6DlXI/FsDG7bx53ft8ej9lJ8+7TVd+2J9Av6QV+OlS/hXdXU5QOPsNYHTtVCjSLlh8yUCcpUpyrpmE/ieOrTaNOyY34//3uOBE48XVHBQ1v7RcTDj8eDHgQC+JYE7gjGlz6fRyl5X6lnqB6/Xi6GDB7Hu8aDJcfDHeeBMXmNGhPLFebIRpuu6+H94vVhwHPy2uhr/bb53AZ+luoyfd10X/9PuLhYAvF1ZiT+vqcG/zGTwL9Jp+PNd5jmGb+7u4rvpNL7l9SIkvRuCwSB2d3dNl2mgsGJMZYYXwR/llU4B1/HjlhasAmgCcMdxMO04eMPvxwtWlYnKKbca8CQT28aSB17e3cUbAL5VWorySMSMh5/5djqN76XTeDG710yTup/00+Ae+VP1vc0H6qQU0wHAXhO/TCaDc+3tWAHwvUwGL4gucRwHXwfwGoBXJLBN/a7y7zgOvu3z4b/u7uLbfj9KJYDH+7zuOPh2SQlK8xlXtUHqiOilep/fUbBo6yjXdQv2oTtObh90eXn5A0fL2XqLn9ejlNUW6jj0PdL0ZdfFj3w+fFOCBTbdeWkmU3UO56QgWu2ULdeUSzoVoVAIGxsbKCsrQzKZLKCp7bzqOqtNte2JvSbKc16vt8CBV3tsB7mUJrb907FxLTSjqvyin+HcfT4ftra2sLm5ia2trQcy84rd+H3XzW1d0eBIsSC66+71BfF4PKYpptJFsRb/18C8HbRVWto6q5iTog6pBhp45CFxhjqstvNIHtjc3DRVcao3gb2gB+nP6gjlC71szMbXyBvqSGqVjWLnbHYve19Mrmz62LwTCoVw59AhJP1+/Chvy+y1Jq9Q7/EkOiYOVEbsdVW9p7h0eXkZkUjE0IgyzWeozeHY1/LN3pVfyCs2DyiPOE4uuHCzsxMf9fXhK1tbeDZvzym7aqtjsZjR0TxFhPRS51/na2MV8hyDy1qJwYu0Uz63gwu6bvZaqg/G34FAAH+7soIhvx9f293FF/JJDwZ3bN5LpVJYXV0tSHQoTxPz6HvqxzEQuL29XbCWOk67MohNRP1+vzl9SLG4yl8xjObxeLCxsYGhoSHE43FjexSXkCYcK8eg8vy7rt+rooGDIhDXLIed/VPnh9+zL1uQSy2Ad6O3F2vhMD7Jb3dgBliFAdhTSHwuCUBG1vPaaQx1odTB4LPtDLjt3ChzKuDyer24lh/3xy0t6Lh505Sg206tzWg27Si09tFK+rcqBTvAQufcVlgnRkfxSWsrjuaba6rjyN92FI2Cr6VAnLNtyIsZOGV83lefUQyo0pgpACgWgNC1UufFBhQ27+k68nm8tATRBu224tD1VMWugQg1LryvGjfSgNkuVRLFmqEp/fke56xBFr5/paUFf9/VhSfn59EyNVWgIFQ5c+42fXS+tpwrfTimTwOEuob6WZUFfY6Og7JrAwMF/Mobeh8bXKuDoE6CGnJtCveHvoqVAnKeSi9V8FxTO8NajJ62AVZgxXJKj8fzQN8HBcGu6+JF18WbpaV4xVNYnq1j5FUMnBT7DB0wrmU2m0Xf4CDunz6NbziFzpcGANVeaMlsJpPB17JZ/MjrxdfFCFIv2TTkuF7KZPCax4NveDwP8LQG4r6SzeJpOTOcdFIHjDRRR5+X8rDORXUq5/H43BzOtbbmnGkA33ddvCQ2yw6gkEZ6D9uuck2f3dnBS34/PHkwo1c2m8ULXi++4vViI1+5pnKlukgrxfQ93qdYsE//t3WI2sXTExO4un8/voFCPQQAX3ZdvGidomCDZz7/qx4PngXgy+41MeRznt3ZwSv50xpUn/D7to2zdZftoPI9Ww9xbQlKNXNFu6aOu/IGL/bv0PnZdl1/c2zP5atgstks1vP8TSdY70XesflWcR15jfNXetFG23ZRg/t0olnerbaN79vjKVbpR71sly7btsvGc3pvfs+muYJnXpyP7ZhzHIr7VLcCMH3MgsEgotGoKYW25YFjYjb40/BVMVvF0n7yGWnm8XgKEn0apLVlh/PUjDLXms9XWtg4zw6GADB8xqoAfl4dZD5Lm37az+Ya8bJxkGJ5fY1rpvRVnVNMN+tnbExnX6rDbB0QCoXQdfkyph55BK/kA9GcKz9Pu6s2PJVKFYxV11n1ih0g4m8e9ao6xJYrlVGv12uammug1rYd1DVqw7LZ3Pay6TNnsBUM4mc+H55ZWirgXx4NqTgqGo0iFosVNKfWCgeOVZ1uYO+UPH2P/pviT17qx6gM6Ps2nvi0KxqN4lIkgoTXix97vfhWvoKP32OAlnaEJxYpFlY+s/lJeYI8xGCDfl/1NtfExvuUB9UZxfCX8it/+3w+zM/Po76+/gE6qz2n/Kst+CzXZw40UInaTpYaOP4QjKgwKNPrpWBBSzQdx8GR4WFc7enBqYkJQ3gFjmSeS01N+Li5GfsdBz23bxcQhtFlVZS2ctAjoHhfjUJrSaoaVjXyqsSO5sf90MREQSaen9GSRAWrHA/f83q9BUf7qAJRBuVrv2vxFajaBpzfV2FURtUtHxyjLTDFgiech732qkR0fEoP0oyvKY3VabfvqdUS9rrq+JWPdL7Kf9p5np9hUywbIPI+JSUlBdUdqmAoP8UiuLqu+p4qDs1aF1PM/DxL4pTG59rasBYI4Lc1Nfhjt7Avg47R5g9bYdljLAa8lf/0/raR5Frb/FSMR5W3gEIZ0mcq+FG62IaZJ6SoseH600h+GtD4Q1yqS/hby3wVgCpPqqxr1kDLMYG9igTSRg2iron+r5UuvN8XdnbwjXw/E9UHdhdkW45swGJf3OfHcey/cwf/99OnAddFWsar9kF1ir22z+7s4JlsLgPhSpCRJbkaeARyPPZUMonn8sehZoCCgKDyE19TWpHudJwUNFImbVtjA0baSp3jmeVl/B87OoyN+WIm1yzTdfYCMDo2Pb5uc3OzYD1YXUja8Yx5OknKZxq0Ih00kKPgLp1O4yeOg+87uWqPb1j9FHTOGnS0gb7qLK7J8ZkZ/J/37cuBXMueZbO5bTTkPTvQwTl4vbmmlryvBo0cJ1cmSlpQjvi36tN0Om16Jni9XvxDJoPvZ7P4ViaDF717fRte29nB3y0u4i/icbwse9LVwSNY5jyY9VcdpEkD0pHPB/aqSskHdmUe15Pj5sVKS8cpPBZPg1Nqn9RhVZ1NR5zP4BjsYJuuNz+nwRLlFVt3UBbYe4Tvc056ZK8+w5ZTrh+Agsyn2l/HcfBmNosfAngJwBfzSQvSXG3Lazs7+LutLfxpOIxv5RtHkgftoK/Ni3q8Hr+jmUI+I5OXdQ3S6BYcBkNUF2k3eA1E8Dhyfk/3x3O7V7G15+dVR+h6qi3STCvntbu7m6vazOtd2gp1/LlmHo8H6+vrBbiM81Zn0NYBvBQn2NiV62evh+qgYr6IVjPYvGxjLn0e7xcMBtFx4wb+OBbDyZMnCwLJpDHlRR1D2nDFWSrbylv2XLlmdkW32njl0WL4SOmhrxerjgVygY3WX/0Kc088gS/kK3eU99Q2E5Pv7OwYf4Z01gp20lb9GP1f5YQ01/nr2BU/2f6D4tZifKW0jUaj6Dt7FuOnTuEbog90bHw21091ge0zcZ1Zdapj4hoBe4FaG6PadLKrNW1+tXUsL/VpgFxA5f79+wX34z11PtT3xSpKftf1mQMNLInSrLYqNwWCQKFTYQu17fQpsCERHcdB/+goTs7N5aItbuEedWAPQJxtbsZyMIgbvb0YkB4MvA+Vnq00lRH4OY7dcRxTykSiF2NedgUmbbxeLw6OjeHhpSU4joMlxylQ5ry/gk3dt6mCRQGl02AzG8tGbcdBjaAKFEHWhfZ2JKJRXOrsxMHRUQN4gL29X8rgCvrtqLLN4LbAEmSpk8QxanBC6WoLqK6Xfs+mF4GQvT+K47DBmI6X9NGSNAYyVGhV0SkdFAyM9PXhxoEDOHr3Lvrv3QMAAyz52UwmU3CsjDpCCvY4NgXANv2BvWZ4zFarkslmszh0+zZuHjiAx/NN4HjZDr32J+E4uTZaEkcQpHSzo6C206FzUIfMzmLo520FyZ4KNiCn7uD6aWZLn2cbbfu5m5ubZt+/XaX1h7yCweAD/G6fj0766ljpWOqcbP2hDfq4hgw8cV7qTKnuKBbQUN7m669ub+P7m5v401AI38h3/rfXrthrpCcb49J+aDMpynA2m8VbAH7g8+Fr6TSeSxeeha0O+pvZLN4KBvHi7i6+lN9uRtrYwJnyS3Ck8kKQpiD6xwBeCwTw0u4uviLndnPtbBDL8QEPlmsW+xzpPX7oEP633l58Y3MTXypiU1VuaUuUZtQBnK86LB6Px+ytp37Z2toqAEfUQ2+HQnjTdfHi1ha+AhjnnhmzbDaL73u9mHIcfM918cL2Nl7b2cH3Mhn8aSiEb+YbZ/2uywZNpJXt3PwoncabjoOXXBdfdh887s3j8eDHAN7w+/FHrovnsbc1jF39ySclJSX4qc+HH0Ui+Nr2Nr7surn/vV58HbmKCaXtj9JpvOb34xvZLL6aDzJMAfheOo3nZS5/vb2NCdfFf1pdxR/V1Zlx0clUPcTf2sDz08p7eQ8F7ORnrh8DSJRrZgs1Y6q8wl4lGhwgH7MfgY5Fx6zZTcqQAlEb9PNSWVLdojaPNGH1ZiqVMqXJfI320raDdHDpyPj9fkQiEfh8PqyurhboNL0cx8GrAKYB/MjrxYv5cbLaSsf3tzs7uO+6+OuNDXwjv0VJ52vrYt5fdRTljq9rTwLVtaQvv8f3+TdxhNfrNdummADR9aBuZcZYqy04nnS6sOGw4iraZeoXPd5SfzTYsrGxgZqaGvN5Yr8S2aKkz9nIn+6jmNi+v1b/KB7JZDJm3z/1nTqcb2RyFWsvuy5ewIPOFeWK9+BcKTOhUOgB+2+P3+YBJimSyaTBcBwz6aiBAm6do+7mvfmjySXeQ08LUd1t8x19DtWVXD/qSE1W2PqY81P7r4Gdjps38fmtLRw9ehTZ/OfJI6pzeP9kMmn0kk1//l8M73s8HhP8s5vQqi+kPqfOg/rBThzqeha7stlc4qLz5k38TwcPGlzF4J+ND9UX5v/qMzAYpMFDrrOtd3kvDRopT5AuHAfXUSvgbX1rz1XlPhaLFVTP2YkZGxMW06e/6/rMgYaSkhKUlpZiY2OjYG8KJ6cEJSEVwOnE9WJ0lQqFJUR39u/Hjd5enBwfx7GpKWSzub4PbAym93xkagofNjTg4PBwgaLRCC4XRcGYGitdTI5fgSTvq8KsCl0DG+xQShqQ0dUxphIAHjymjkpeAwW8X7HgjToKNuNznPyex+PBwNAQru7fjyN37xbcs1h0iu9pCaXSTsemClLvqYKtrytwVSHj+mqkU42M0sR2FgmoNbCkQkHAaQuR7XDqmGweVoCgPM2x3+zrw3o0iivd3ejLnziiAJHj4Bi0wZMGeGwQSrrYe+DI45yn7t3nPQ+OjuLZ/JGa9z+lNFLXkr9tYM+x6RjV2CpQUoVPGbKVPI16saCAjkXnosGzf+qyZYQXHX1933VzR2BN5beW/D6K9Pe92HiMhmJjY8NkHnmpcgcKg4jUZTTsSvttcbQpM3SqeRygrUsUNAIoKK1neSuwp1e/6/ViCsDfpFIm0GAHF9TQ6d80nsrrlEmVcwD4od+PGcfBGz4fvixyrwADAP4xEMCsx4N/cBw8l0qZLBrPJ2dzR5VBO1jM7vMqzwDwekkJZjwevA7gi/mMDEGtbov7tICr2hlbdjQQMnHqFFKBAN7IZvGVbOFeYq6DdnHn+Cj7gXx1hgJMXVM+P53ONcrb2Ngwx86p/L8Rj2PO48HrPh8ezzdsY3NAruM3XBc/zGbxSl5Hf3d3F/ezWfxNKoVv5veNKw+QDjYG0OAZkDvFQjN//0DaOw6+kt1rTqbOyRuZDKadnNP4guMYwMRTZBwnF7ja3NzEGwBmPB684fHgq+k0Xvd4MO04eA3A16Vyx3Ec/CibxTSAVx0Hf1Raim+l0/ju7i7+OBBAMM+vHo8H3/F68V+3t/EX8bj5vn2eO9eM68nx6PNUJ6u+428+zw7QMTumAcmtrS2T1VY5Zx8PoPDEEjptDOTa2Wq7Z4IG4/SzimGo84s5svpZdbKJzdibgo60Bpc1+GlXIVGGx8bGUFNTg+rqavh8PsPrNh76IwDfz2TwdccBRD/p+AHg234/vru7iz/N87Ztp5VGOidiyWK4QS/qcD2amraBvMNgkDZW5H5u4gIGmbRPBrBXWg/sVavZ+sr+Xx018lcxx0wxN20FeZMYVm2d6hriecUNlHGlMflBK4X8fj9+GQjktr85Dp6W02dc18VrPh+mHQevezx4ybvXh0SzxVw7rcyxA/nFHDQbG9CmsSmfZuoVw6lPRF9J7+X3+wtwCStRmCxVPqLM6jxU1qhb9TtcdwYdOW+uufKm/rZ9l9LSUtMfhLxHfsxms4hEIgUYVQPcdjBJ5UbHxTGxIojf13HSRipv2gFa0l3fVx1lr6XKAAMjqVTKbAVyHKfAFlMn6TwYNLJlmXNUvE7MrkFC9SdV9+vYOG/F1JpAtdfT9pN0rmwIykCF6gf1l+0qcj7vn7o+c6DB5/MZo2Xvv9OBkDh6jBGwl63WS5lQAwOO4+B6by8S0SgutLfj6OSkERYVLN7/9MICDo6OYnl5GWlZHAq57YCr06zAlwtlVz8o0GdgRPdIqkBRufLzBB08scFmcNKLBlXpx+idGnellSoIjkdBnY4JgKFd3717ODAyYqLgOm7OxwaHpJGWQNLw0RDYpbp2pkYFmmtSDHjS6fB6vQUNzlSZ2qBDacMfjRwrzzHrr3RSpcTf2nyIY7ez6fZ3stksDt66hev79+OwBHK0XJL3VkNEmmigTp+r4FMBu4Juu1eJHaDgZ8lLeuln9dJ1U+BpK3ulg4JmVZKqdNX42EEedSLtMdsgT79j85EqRB2Hfs4Gi8yKaZDxn+OivCuY4XjVKCpfch2pi3d2doxjSR3MwI06D5lMxjhfNAykpTo+CrC18kFpyjF8PZvF614vviNBBnsN9FLwZgMGYK9pr83vL7tuLislIIrj03u/mE7jDZ8PL7m5rMf09DRKS0vR0tKCra0tbGxsFJR7qwG3fyvYBICXs1m8ls3ixbwRpj7VShyb93TOyoe2ztCqoLZz57Dw1FN4WfjetmUKKv1+v9GPDNInk8kCW2vrCD7b5/OhpqYGXq/X0GdjYwOZTAbPJZP4aSSCL+XPn9c+JZzbVwA87zjw5+3Gn3g8+PvdXfxZvsmkPWdenwZeSS/VCwDw1e1t/DgQwMtCA3vtXtjdzQWitreRyFeKxWIxrKysYHh4GJFIBF1dXYjH4/gXOzv4u+1tvLC7i+2dHTzv9eKtQACveAv3sPt8PnzT58P3Mhl8Kw8CX/R68YWtLcQliOM4Dr4ZDuO/bWxEJpM7OlDnSPnUSgPlGxsT2GCSNoHrQLlloJRHxFJeY7EYgsEgPJ5co0A97YG6nbJXWlqKSCRi7s3O98Qe5JliuEV1sy3nNtYCUACsP82mKI+k02mjhxUTcfx0WNTx2dnZwfj4uDmNoaKiwmRCI5EIIpGImSMxy7PZLB5OJApOslBHhfz4UkkJvh2NFvQJAwp7QtCJZcBAj4Ej/mMG36YV56GnVdD+sBFiOp3GysoKEokE0uk0AoEA4vE4ysrKCmjp9XrN5ynf6+vrKCkpMcFIOgnEAXYwkmumJ8mp/Ns2n7zBPfi6FZBzsPWC1+s1R7SS15TfXDe3hUbtls/nM0GT2dlZfDcUwmIggFcdB39SX28qEnd3d/Gyx4NXnVzzV6WPHTDhs+jk2z0JdNyfdpEuTMjqEYuK1RR32kFt/s2gbiaTMcfPqo3n9tx4PI5wOGx0PRuPqs5V/KSOrM6LPpxu3VTaKB34Xb/fj3A4bPia89rZ2UEikUAoFILH4zF02NjYMIF5+gzAg8lR8nwoFEIgEDCVTQww2fQuZnd1zMT79pxtvWXrI/4EAgFzlKY+h42wtVpe8aLiJuV52m2uhwamtLKcz+F3GARWnwQo1K8MENi6VP8u9l42my2oclJZtXG+3odz+CzXZw400FB4PB6TJaCgkEl1IjoAZW47iq0OjzJz/+Agbvb14dT9+wXj4GIqUQhctWpAgYDu61THxh4Hja821wD2snv8nEZq+SzNKuqlVRValq4LSBpowENBJX/ohPLSsioFk1q1oaVm9rg0ims7kOqo6H4iVURqkG3a8rNazaERfj6Xn6fjxe/wc5q11RI5FSiNthEoKe8pvfiaHUXlnNWp1RInfk+zErzUMLqui57bt9E9NJQrtcKDRop8QBCoZazAXlSymENOEKLj57hVLkgPjo/ggvei0eFz1Aip7CiP2wbIduZ5X5uenKN+Vu9r6wANHHE8KrP2/TgHHa/SS+VNeYDP0/e9Xq/JGvHM+H+OS/m92Pg0oEjdxUw0Sz2ZPaBTEAgEjJNB48T58Ag0OkMbeSdSA0I2D5BGCkC4xi96PPiTfJdrXWcbwPE+tsEjeOC8VT/r85/PZvE8197jKdB5HHMmk8FXfD58ZXcXk5OT+GBsDJubmzhw4IABy7FYDJubm0gkEsaBUZCvYFaDqj6fD1/Jj2HXdeFIpkWBou0g6vwV2Kg+sXm/5epV/NvubrS0tMAjDrXqSQIndgufm5vDwsICXNc1zbYikYhpRAfs7aO35Y80DIVCKC8vx87ODpaXl/HM+rrJDrJ3RSqVMnaAfKn66uVQCP+ivPwBAFcM1Cu/ayBD9Qft33PpNL7hOMgKblAdsb29jUfX1/F4HsSGwmEkEgncuXMH6XQalZWVaGhoMM7Pi14vXo5GAeSO5/z80hJe8vtR4i08dcF1Xbzg8eCrsm66nradVBug+kf1iiY2VN507ipnlHFiG83wz8zMIJlMory8HJWVleaIuWQyidXVVfzU58P7ZWV4KZ3GoysrWFpaQmlpKWpraxGPx819eO9oNIqysjKjGxiYtJ0PGzvZGa1iQFZ1OOevNLLxB+9L/GFv71GMEYlEsLm5iTt37mB9fd3wciwWw9LSEpLJJLq6ukzPDjpm29vbJihHGmi1puotPlP3q+u62/aba0nakcftYCv5gjyfyWSQSCQQiURMoKW8vBwlJSWYnp7GysoKSkpKUFFRgfLycqTTaSQSCYyPj2Nubg7xeNxkku/fv49YLIaamhrU19cjHA4bh2ZxcRGRSMTYtrW1NQB4YOsM7UMxbGc7ZnQY9dhMG1/qGvP7GxsbBf/TcaNsq8POAMPi4iKmpqbg8XjwUmsrfgLglbxNoWOYSqXw5Y0NfG5zE6WlpdjNFp4Kpvp4d3fX2B/tVaHzVL1l87i+x/VkkEnf14AUUFgNzK1p29vbmJ2dNUcxM9kWiURMEIEB5p2dHUO/SCSCQCCAsrIybG9vY3193QTZiP9sZ5trreNUW2Wvl+1ghkIhJBIJMyf6hjyNSautstms2bbFygAGXfk8BsLm5+cxPT2NhoYG1NbWory8HIlEwmAW1S20Axr4VGffxlf2HGx+4FgV6zCgou/zvrZd0N4n9B9YEahYifqGn+Wxwepbbm5umsBosWAfn6mBaztoXwzP27LItdBKHMUIGjCknHC9i/lDxa7PHGhglJSlTjbj2UDfXnibgdXY2gRUAMx7K8jRyemZzIxI8tKmJFqOaZeCqFPOMauDq4tKRtJyQSoEj8eDmx0dGOzvx6nxcQyMjxcoC86d97ve3o7/3NSEU5EIDo2PFywwI2Z2gEBBDVCY/bQNoDpb/LwthPqaMqEGKDjXioqKBxTvpcZG/FVDAx66f9/0x1AAoWXxOn/eu1hErJiT6ziFkTbypJ1ZUQGhQtDIPeeokWAVxIuNjXivrg6nIxEcuX+/gLaO45h9WgoslH9tcGkHCTgfZqMJ9PkZbYKp82GgjwZYgR/py+dpdpqXGnu7rJdrpc4eFYyCJgZ5ivEXDbYqdF46DjUUXBebB7h+KqscN+cQz5cp6/iz2SwuNjbi4+ZmPBSPo9cCjvxsPB4vOv5sNtffoKqqCtF8d+F/rouOv51l55xDoZA5j3xubs5kpEKhEEKhEKL57FoqlTJyQPrz+9Q5wF7moLS0FKFQCPF4HBsbG8ZJtUv3qP8+LWhHvfdpARsbuNigTZvcseRUbYdtIFUn0TEg+Nze3sbw8DAWFhbgOA6amppQWVlpqlN4mpHP50NtbS12dnawsrJiStdVd1A29Zn2VjvVJT8G8LrXi3+VSpkmccWqgnh/+9L9qbw310qzpdRl8XgciUQCt2/fxtbWFkpKSlBbW4vKyko4joPZ2Vncu3cPS0tLCAaDqK+vRywWw/Xr17G4uIiSkhK0tbWhrKwMDQ0NptScTbrq6+tRUVGBhYUFrK6uYmdnB36/32RRGYinXdCmz7xsvVPsUv2uOsBuYkoakSeVTtSLzK6trq5idHQUANDY2Ijq6mpMT09jYWEBt2/fRigUwtGjR03VQSwWQzgcNlsIqEeYCVae9Xq9eNN18QOvF99IJvG8s7d9hr2ctPJD9bPyi4I/AjuVGbsCkwkKj8eDaDSKyclJzM7OoqKiAp2dnSgtLcXU1BQ+/PBDjI2NYX19HX6/H2f/u/8OCb8fr2YyyL73Hm7fvo1wOGwczFgshgMHDqC1tRXl+QBRJpMxmf/V1VUD7BWIa1UZx6s4ztb/ShPbwdYKSdoYPRLdvgdlwOfzoaysDJubmxgcHDQBlwMHDsDn82FpaQkVFRXw+Xy4dOkSEokEenp6THaejmV5ebnJqFO/EmMQ59Lm2QEO/Z/8a6856ceeP9zKQ71DmpHfABT03aCOvnXrFiKRCDo6OuA4Du7fv493330XMzMzaGxsxNzcnOlJsbGxAQD46KOP4Lou6urqEAqF4PV6sW/fPnR3d5sj8HgSSEVFBVZWVgpsCQCE80fJ2xhI19u2CZTLnZ0dRKNRgyv0pAHyPgCTsWdQnN8nfbnepaWlWFpawsLCAlKpFDo7OxEOh9G8uYmn84ETp6wMKysrJtjA41VXV1cLcITiHo6ZeoiybPsg/K04Wu0b9TbHa58WoxibuEcd2bW1Ndy/fx87+e2tlZWVplfI8vIylpaWkM1mUV5ejnv50+IikQgqKioMbuM2OI8nd2RmJpPB2tqa4TfdvkK985bj4PXVVXwnEMAr+S3fqs81yK3zcJxco8T5+Xmz7ZsOM6vsuG4qI1tbW1hdXUU8Hjd4g5+fmJgwx3RWVVUhk8mY6qRYLIZYLIb19XWsr6+beWqwRAN9qk+5dlxvGxurb2oHHugrrK+vGx2vOF2Tv6y+4es+nw/xeNwEDTY3N03QiFsu1b9jAp/3Y+Jgd3e3IHCkOI12mPNT/0Mvnb/Ny1zXQCBQ4Fto0IbywksDkJ/l+syBhsrKSpSWliKZTJoHaRZIhZFOWLFMlg5UFYoSand3N7fPPRLBudZW9N+7ZwxQWPbIMbjwcU0N3hsYwMmxMRybmjLPorOujKOE1feobBRkKgjg59XJ4ji8Xq8JStzs68NaJIJz7e04fP9+weIqQziOg48aG7EUCOBsUxMO5gGSKiYFJhr519JdWyiKKVEFTMqItgOm99Ln8ftUfHzfcRy8V1+P5UAAHzU2YmBsrOAZaogVfNvRNp2L8obyCserc+KcFbSqw0aDpvvTeI9UKoX19XXDr7x+W1OD5dJSfNDQgP137hga8XPalZjPJp200kfHpjzOselRQKr8lUdIKzsTpgEWm088Hg/KysrMc1XRkja6HUVlkyDbDvipAeX9dI3UqdQ1KxZAKMZ7NpC1HVN+XkEuI/n8Dtfgw4YGrAQC+LChAVUffVRgKHlfGkN7Hpyr7pP957ri8XjB3NWRjkajSKVSuHPnDlZWVhAOh9HU1GRK3ZeWlnD9+nXcv38fy8vLiMfjOHjwIBKJBK5du2b0ck1NDerq6tDa2oqmpiZUVFTAcfYakoXDYcRiMczMzGB9fb1AD7CnAbC3ncwOTKn+0fUvpvN1vbPZbEGzU8dxTDko18HmJf5QRphluHXrFubm5hAIBNDS0oL6+npsb2/j9u3b2N3dxfz8PFKpFJ544gkTYCO4Zikyyzp1v3o2m8U70Sh+Ggjgy5ubphGlnkLg9XrxaiiEGQB/lUzilfzxl6pTFdTY/KS6jHNjvwjdk5zNZlFZWQkA+OSTT7C0tIT29nZ0dXXB6/Vifn4e586dw40bNwAALS0tiMfjGBwchNfrxczMDIaGhkz5ZSwWw/nz5zEzM4Pu7m4cPnwYLS0tBUesNjc3o6KiAnfu3DGyomCdn6OO1Wywve76v/KKzlv1KwExAxsEeKof2X9gc3MTMzMzmJycRElJCerq6lBeXo7FxUXMzc2ZvcSzs7O4desWNjc3cfDgQXPkoOu6xjFh2TVQqFM45lc9HswAeDWbxXOyFYAgm/NVHcIf1UMEmKqDyU+23uPra2truHv3LkpKStDT0wOfz4erV6/i5s2bhodbWlrw0ksvobOzE9WJBN5YWcHDy8tYX19HVVUVDh8+DMfJBaMmJibg8XiMs9ra2orq6moTqCwrKzN2SoPqGvRWm6J4QeXWxkyfhglVDzJ4RceFY3IcB1VVVdje3saNGzewuLiI6upq9Pf3Y2lpCb/97W/R3NwMn8+HxsZGjIyMYHl5GclkElevXsXnPvc51NTUIBwOm20osVgM0WgUyWSyoF+JHUghz35a7yd1NMlXlAtuk2SVnNo6TRJwS6ff70d9fT1u3LiBVCplgiSXL1/GhQsXsLy8jK2tLTz55JNob2/H008/ja2tLTP/0dFRs1WouroaY2NjJlh96dIllJWVob29HY2NjWa+pCudcjr+DBYU0106D66zJkRKSkqMQ8h1BAororLZrAlo07bzHgz8zMzM4O7du4hEIujp6TH/d3R0mIqW4Xx/tlgshn379pn95j6fDw0NDQUBB2JC1TmKT22e5Ws21tbXOTeucSqVMu/bASjKVTQaheM4uH37tgkAA8ClS5dw9uxZTE5OYt++fThx4gSWl5eRSqWwu7uLt956C11dXaiqqsKNGzcwNzeHzs5O9Pb2miQJ51BbW4vl5WWsrKwY30a3Wf/A68V0JoO/2drCN/L633Ygi9lwIOeLbW5uGp5VrEp9zWQvMRcDYgw+hUIhrK6uYnl5GY7joLOzEwAwOjoKx8klDVzXxcrKCsrKykzAYW1tzehqWx+p3eUakx7EuMWCRzaWob6KRqNIJBIFeEWDDNQPDIhGo1FEIhEkEgksLi6isrISdXV18Hpzvbi4tYdbmg4cOIClpSWsrq6ivLz8gQSq3+9HLBYzcqnVMkDhaRescrX9Xb1sXM3/g8Eg1tfXDdZQfKf3tuXks1yfOdDAiC+ZihdBlzYxIdNx0T5NKG3nhJMqKSlB382bGBoYwOnJSTPpdDqN5eXlAiIHAgG8f/QoloNBfNTUhLZr18w94/F4QfUDx2o7cTYYLBZpVuWj5VW8L/8/PDyMa/kjOTlflrhptMvj8eCh+/fxSUsL+m/fNuVranCZPVIgw3HzeXQ2NYChQRQVIr2HOr8ACrrB0ijyf5ZwstRQx3k6P4fDd+9iZWWlYN09Hg8ikUjRjA7/5/35P8euyoP04vqp827zDuevr+mYmPkl0NAghs/nwyOTk/iouRlH7t412QF9hp3dVL5WfgBQAD7VaWfZlB2FtT/DsdvgXteVn6csbW1tYXl52TyX9HXdXOVENpt94H1+Rsui+Cx7ngqeFSjbzoPOX4MhOm573dQoKH+SL3SedKZV2QJ78nfo7l2sra0VVbjxeLzgKEelbTAYRFlZ2QM9A/7QFx0old1IJIKdnR3jPMfjcZOBWl5exkcffYTbt29jZmYGfr8f1dXVqK2txcLCAhYWFjAzM4OFhQVUVlZicnLS6J23334bVVVV6O7uxsDAAFpaWhCJRAzICgaDmJ2dxdWrVxEKhVBZWVmQWdbSe475H1wXry0t4c8ikYJTBn4XSFGgxrUj8CEAsfVcOp3G6uoqUqkUSkpKEI1Gsbu7i2vXrmF1dRV+vx9tbW1oamrC6OgoXnvtNVRVVWH//v0Acg0UJycn8dd//dc4fvw4jhw5gtLSUvP82tpaQ6f19XUj316vFz+NRDDn8+EtAM+srhqASt71+/14KZPBGx4P/jQUKtAlqud0Pion1Mk2SNWsLkHG5OQkPv74Y5SVleHUqVPw+Xy4fPkyrl27ZkDozs4OvvCFL+CRRx6B1+vFv/t3/w4jIyNoaWnB9vY2Ojs7zdYaACa75DgOVldXsbm5aRIKdMIGBgYwODiI3d1dwxdcM3UyqUt1jYsFnuyLcqygn9+l3iPA4nN9vtxpAnNzcyZL1NnZCZ/Ph8HBQbz++uvweDx44YUXUFpailQqheXlZXi9XrzxxhuYmZnBk08+CcfJVTUwwMSyX9o6BaoejwcvZzJ4zcn1J7HLftW2qaPK71MHsy8Ss8XqxPM+5I9AIIDe3l4sLy9jcHAQLS0taGlpwY0bN/DJJ59gfX29IPDyxS9+ES0tLZifn4f3F7/Aw9PTePbFFzHW2JgrH8+DfzodqVQKX/nKV+C6rsmYcRsGM3KxWAzz8/MFzrY63JpVtDGJ/q+VkdQpig9JM9X7W1tbpjKLgcUbN25gYWEB1dXVOHjwINbW1vD6669jbGzM2Peuri5EIhFMTEwgkUjA5/NhamoK8/Pz+P/MzOBaTw++6fXiW9GoKYlmpQezh6rPdH0Vi6lTzdL3jY0NEwhlMCwQCJh+GYp5FG85joOxgQF8ePAg/rikBLUff4ySkhIMDAxgamoK77zzDubm5gx+7OnpwUMPPYR0Oo2JiQksLCxgenoa3d3dKC0tRVdXF+bm5jA1NYX7+YTXkSNHDL+vra1haGjIVDft7u6aKrqlpaWC6gLbAVPepi3WQDD1FrcrMbiiwSiuu25b4brz84uLi7h//z5KSkrQ2NgIj8eD3/72tzh//jyeeOIJlJaWGl01NjaGmZkZ07D+UlMTfhqJ4KWtLXwtb1/oGCcSiYJnauUmUIhbivkvvOy/eY/S0lIkEgkTqNJkH1+rq6sza9bS0oLa2lpcvXoV7777LkZGRuA4Dp588kkcPXoUsVgMGxsbGB0dxebmJgKBADo6Oszfy8vLqK6uxpUrVxCPx9HW1oZ4PI6KigqkUimUl5cjEAhgbm7OzIvzeXF3F28Fg/iOVD+qnfpdVzQvP6yMYQ896hlizpKSEvPDCjmOe3Z2FrFYDI2NjVheXsbly5fNdrfd3V3cuXMHW1tbxnZRV3ObyMbGhrF9il05Hq24s7Hzp83PDiaVl5djbm7uAR9RA2i0lxUVFUgmk7h37x4CgQBaW1uRSCTw3nvvYWFhAa2trairqzPB73A4jHfeeQdDQ0Po6+vDzs4O7t+/j9bWVnR1daGtrc1sNeGWyWw2i6WlJWMTtVGoPQcbvxebJ/mBPWzUd+RneFKRftdOJP6u6zMHGqhItra2DEjl6/pQO1OtzqEqLQUoBHeaPe25fRuPra4iEAggkVfkzEJrRjubzeKR6Wn8tqYGfTdvGkbkM5nFsR0gjbbbGW+OR50Agl41mGQ43bs3MDaGU3NzKCkpwWYmU2CYuHD8/Km5OTy2uoqpqSlkrb0ubPb2aSCtmIG2lSX/L2YsVJh0jkBhdQGfRTBoVyacmJnBE4kEJicnsYMHy8zUQOs9bablpY1vaAiY2dLggx0oUcHQ4JKuIcelmXFVqNlsFifn5vDo6iqmp6eR9jxYDqoRUf2xs/X28zTQEQgECpxkggfdK8h5Ky05H47FNoDka7uLvPJCOp021QBcF4JdymIxRWx/XtdN11bl3gabdiBCs3z2pfOz10CDK/Z1aHwcX9jawvz8PCZEEdpRbgJMDV5wTMyqahbmD30x0EDQHgwGMTU1hcuXLyMQCGBgYAA1NTUYHx/HW2+9hdHRURPBDwQCePbZZ9HY2Ii7d+/iww8/RCwWQ1lZGbxeLxYWFvD4448jGAyaLEgkEjEBlKWlJQAwZyE7joPGxkaEQiH84he/wLVr19DZ2Ym2trYHKshIj1cBTGaz+Ov8KQP6nlal2M4E14I8yug9s36a6SVgDQQCJus4NDSEjY0NxGIxHDp0CLFYDLdv38Zf/dVfYXl5GdFoFIcOHcLY2BhaWlpM2ejOzg7eeustRCIRtLS0wHVzPQ3If9XV1aiurkYikTDB7K+kUniztBRPLS8jlQ9M2QDmuXQaL/n9qMk3xdSqD+DBQIv9OkEZZZNl/B7PXuOs8+fPY2hoCF1dXWhqasK1a9dw5coVrKysFFSLtba24tixY0ilUnjnnXdM8CocDpsgI7d+RSIRVFZW4v79+xgfH8dzzz1XYP80EHro0CH8/Oc/x4ULF3D48GE0NDSYjGU2mzV6S3Uz56xz50U60TbwNfKYVi+QV+iYTU1NYSl/dHR5eTna29uRSqVw4cIFXLhwAaurqygtLUV5eTlmZ2exb98+vPvuu1heXsbVq1eRTqdx7do1+P1+XLt2DV/84hfR1tZmtjJxC4mWHnMsn9/cxOeR20usWovrpTaVfMIfDd5za5Nmyfg+s1N+vx9PPvkkPB4PxsfHceLECezs7OC1117D2NhYgaPK4xzr6uqQzWaxurqKSCSCxx57DOl0GgsLC+jp6UFzczNmZmZw584dU95NB5nzz2azSCQSCAaDpuqnvLwc165dM+Dexh6KIRRbsRKF/On3+03zODsIr4Fj1RXcPnD9+nWsra2hqqoKR44cQTKZxE9/+lPcuXPHZI9ramrQ1tZmnLCNjQ2M5bdzhsNhXLx4EedffBHrgQB+sLuLY7dvo6amBpFIxCTS/H4/6urqkEgksLa2ViCfXGO1I+xrwNfV4QT29p0ziMhGf+QTTVrcO3YMyUAAf5tK4X/NZ+bfe+89nD9/viCJN3/qFK6fOQO/6+Kp7W2z/aC7uxtLS0vweDxobm5GKBTC5cuXjQOYTCZRX1+PSCRiMqTLy8vIZDIoLy83ei0ajWJubq7gVAGutZ7Yo4FRHuvIgDH50+fzma0b6uASr9AxJa0cx8H4+DgmJyfh9XrR3t4Ov9+P999/H5988gl2d3cRi8XQ3NyMpaUlxONxTE5OwufLNbd1XRdzc3P4fkMD1gH8wHXxXL4fELeuMGPLgBt1KHWYnnJgJ4OKXWrvWN2sVUu2rquvr8e9e/ewurqKgwcPYnJyEm+99RbGx8eNj3H48GGcOHECHo8Hb7/9Nqampgxte3t7UV1djbW1NVOx193dja6uLmxubmJxcdEk/crLy7GxsYFQKIT6+nrMzc2Z7Rm7u7v4/OYm/lVtrQkccs6fFjRW/R2NRgv8N9XTkUgE9fX1BdVJtD/sKxKPx9He3o7Z2Vn85Cc/wfDwMA4fPoympiZTiTc8PIyxsTFsbW1hcnIS7e3tiMfj2N7eNvxGbMD+VYr5OVYdu16KW5Q39fVoNGpOU+H6MIjI16LRKKqqqnD37l1jdzY3N/Gzn/0MV65cQSqVwvPPP4/29nbTx6S6uhp+vx+jo6M4ffo0SkpKkEwmjc25evUq7ty5g46ODrS2tprKQp/Ph/r6eqytrZlqTI5Xk6c6n2JBAXue7IWhbQY0QWj7y3bviN91feZAgzpUBIK6mGo47Ay1OuW2o8iFG+nrw51Dh7D/+nX03L6NuwcO4Mf5iob9yaTp0UAh5r1DoRBOLyygLV8ynJWOplploZlYdTZVIWi1gwIujvdqSwsudnXh8J07OJWPLqvz67ourrW14dr+/XhoYgK9w8NGAdsZWFvp2gygNNTP6f5RDQhQkVK5a9RNq1BUqOyoFV+z113LBXU86shy3dVw0hDpfQlGNPhkBy/sMtJrbW24sm8fTuS3xiivcY68L1BYiWJn3zkmVpjoeOnY87Iz9qS5zUtcWwbJNLDANVdHPhwOI5nnaY14c+6cF8enDWKy2SzGBgbwm+PHcWp8HMenp833OBd1PhT4kz5cE10XrWDRChTeQ+lrByR4HwXadqBAgZj9HF1r2zDwu5R5fpYn4Oj6Oc7e9hVde9tY0onjeDKZDK61tuJSVxcaP/oIjRcvGn745wo0cJ0pl2fPnjWR7P7+fqyuruLVV181WwB0njSu5DfHyR231NDQYMoxKyoqcPPmTVRWVmJ8fBzhcBgTExNIpVL4whe+UNAZm/xQXl6Or3/96/j5z3+OTz75BNevX0d3dzf27duHSCRSUGL6suPgRz4f/qUc5WvLsuoL1Xva4I18QnDHeRK8ZjIZTE9PY2JiAplMBjU1NThw4ACCwSCuXLmCCxcuYHFxscD4A7mtfrFYDJcuXTJBmJs3b+KXv/wlysrK8F5ZGWYffRTfCYXwkpRnsynexsYGvrSygtP5o5UzeRrTidTO2ZwX56w8rs6YBl35Od2mo4A+GAwiEAjgV7/6FdLpNB599FGk02n85Cc/wfT0tAGLrLAIBAK4f+QI/jIcxou7uwj5/Thw4AAaGxsxPT1tHKjKykrMzMxgbm4O29vbpkkcgILfrCDka1/+8pfx2muv4a233kJrayv6+vpQXl5u+stQh6nO0EuxgOoFBTFKI+oGBltWVlYwka8SrK6uRlNTE6anp/HWW29heHjY8DJt1cGDBxEMBrG6uopwOIzR0VGzDp2dnXj//fcxMDCA9vZ2bG1tYXp6GmVlZWbLED9bU1NjgrPcc0+500CC8rsCOC3BzmZz2w/ZkZ16m/Tg1hm/349HHnkEJSUlWFhYwJEjR7C6uorvf//7Zq8w9WQgEEBlZSUeffRRVFdXYziPOdra2hAIBFBbW4unn34aXq/XbDXa3NyEz+fD6dOnEc03xtRAOQAkEgnTiLC0tBSHDh3CD37wA8TjcTz++ONmm4XaJc6HNKEu4Gua4dYEFefDzFxTUxMeeeQRxGIxXL58GTs7O2hoaEBnZycWFxfx5ptvYnh42MgXt61++ctfRltbGzyeXDd3VoMNDQ2hv78fH3/8MV5Kp/FL18WL2dx2pMXFRYyPjxsHnE656gGW3FMeyB/aYJlOh2IG2mHOi/yvQTzKfyC/ResXJSV4fG4OPT09eOONN3Dr1i1DQ+q36TNnsBqJ4Keui6dl33dpaSni8TjS6VzDx2QyaZomHjhwAD09PaYnSSaTMZUi6+vrSCQSqKioMA7kysoKxsbGUFlZ+cBx4IoZ1W5rcEnlW6slmdFWmWDAZmpqCtPT0wgGg8ZpPnv2LK5fv45EImGy1g8//DA2NjZQUVGB+fl5DA8Pm1J61831xHj50CH8wnXxdCKBy8PDJkjW2tpqKnbq6uqQyWTMVhT2vFE8r/if87WdUxuvMANt2+1gMIiamhoMDQ1hcXERhw8fxqVLl/DWW2+ZBsV+vx/Jp57CG088geTKCj6fDwYODAyY4EBVVZWRucXFRQSDQUSjUQSDQZSWlhpsMD8/j83NTcTjcdOXJRgMmgoXxb1M5KkdKqar9TXVHeyZxX45ehodK3y4lSAajeLAgQNIpVL4yU9+ggsXLpjgbnl5OVpbW+G6rmkKWVlZibKyMszMzCCRSMDv92Pfvn0mcFFSUlJQ9aQndVAPcT0/zeH+tL+z2axpJM0+K7bfxe0qN2/exNraGg4ePIhbt27hN7/5jZGthx56CMePHzeBhQ8//BChUMhsUeT2nvX1ddTX1+Pxxx83uG5ubg5XrlxBXV0dGhsbTXC4uroaoVDI9O9QH8/e8kXZVD7Wv7merFzQ4JAGzHhxTSP5U6b+qeszBxqovJntUwdUna7Bzk4MHTqERyYncWx6umBCqqDsLPydw4exHovh9qFDOHL/Pgb7+5EIh/FxczO6h4ZyFQL5LpwKWu1ooQoCAw0EBVoWS2XHfWjMqHk8HlxubsYnra04ff8+jk9PG8V5ed8+JCIRXO3pwUPz8wacakPKqz09WAuH8UlLC/bdumX2vaoAc5zqVNrAjA4oL1UMnDPnEQgEzD43u3qEykPL/NTwk7FsASSzaZZdFbD9WY5LBZX0tY0U5+c4Dm739GBoYADHRkYwMDZW4NDwM9d6erASDOJiRwfO5CPw3PrA56px1yyRCprym+0M8PNKP9tRtcs9VTY4N/vZAEz5Jw12SUkJGhoazJi0Ay15kEEj/SFPjBw9ivX80a8Pzc8bhcC9gRo0sAN9mjHmpUEO/nA++qMyxkCI0lXX1+Z3AAWBNr2XbsWy72ErQ6WHygN5W+9hz5HPtOXf5/PhSnc3EpEIto8dQ+aNN0z/AgY4/9AXjeDm5iYuXbqE7e1tPPTQQ4jH4zh37pwpjS6W4afxXFxcxL1797B//35sbGzg3r17BlDU19dja2sLQ0NDqK+vN3sCR0ZGMDc3h/b2dgCFwRTqhC9+8Yvw+/344IMP8NFHH2F4eBj9/f3o6OhAKBSC3+/HlxwHf1lfX8Abrusam6BrpjzCwC4dQ2APwDBazhMVxsfHMTIyAo/Hg9raWrOn+OzZs7hy5YpxmLSMv62tDbW1tTh79qzp1D0+Po5sNnck4MzMDEZGRnDvf/wfseb14r/u7OCrzl4zOo43GAyan8nJSfw2GsUvysrwzNoavpDPkBE0azZI56MOOO9rA1c9kYc2gcHQt99+G7u7uzhz5gwWFhbwgx/8wHT5VieOVRo/PH4ccz4f3nQc/Jt8M8xUKoWOjg709fUhnU4jmUzi/Pnz2N7eRjweR2NjI5588kmT4dRKEwYxqJ9efPFFzM7OYnh4GDMzMzh+/Di6urpMxtbWi8UumzfID3yNzgiDOSsrK1hbWzOOo8/nw/Xr1/GP//iPmJycLAjEUm82NzfjqaeeMs0v19bWUFNTY3iru7sbg4ODOHXqFDKZjGkOm0wmMT8/j8rKSlRWVsJ1XYM3wuEw3sxm8bdbW/jC6iqeTqUQj8cLKtA0M895pdNps6dW+/KofrQDEs8//zzKysqwvr6O3t5e7Ozs4M033wSQayLLCiCvN9fg78knn0RNTY3hyYmJCdOnZ2FhwTh/N2/exL1793D8+HF4vV4cPnzY2HctlQdylQRspMou8h0dHbhz5w4+/PBDtLe3P1C5pLpNbRl5KBwOF8iGglfisJdffhlVVVVYW1vD6uoqWlpazGkYP//5z3Hjxg1ks1nTZK60tBQzx4/j3vHjOFBaioN5J8t1XdTW1mJqagpPP/00KioqMDc3hy/G4/j61laOv/M2OZVKmWZ8jY2NiMfjhidKSkqwsrKCd+Nx/GJtDX+WTuPb+UbB7AGmDirl3+v1FjQxVR2gmJVbml588UUsLi7i0Pg4jh49agLPkfzJPmzie+zYMewrK8P3Mxk8kw8krK6uoqqqyshKNpvbHnn9+nXU1taiq6sLn/vc5xDMV10Fg0H4/f6Coz93dnawuLiIsrIyg0/Onz+PcDiMo0ePFsisYicNdjPZxdeBQkxFfZdOp9HU1ITm5mY88sgjSKVSGB0dhc/nQ1dXFzweDy5cuIB3330Xa2trxl5HIhE0Njbi4YcfNk788vKysReHDh3C7Ows9u/fj/2xGP6FxwNUViIdj2N8fBzDw8Nm650eO1tdXW36+ej8OGa1abYus/UZ7TrtLYNqbMw7MjKCmZkZnDx5Eu+88w5+/etfmwAWg4aXnnwSCyUl+HVlJVrfeQeVlZUmYx+Px81pKgywnj59Gq2trUaGqRvC4TDm5+extbVl7G04HEZzczPGxsaM7DPBppUAyquKvXi5rmsy87RXnAe3h3k8uUaPyWTSbHfq6+vDwsIC3n77bVy+fNlsWWxtbcWjjz6KgYEBbG5umsa+ruuiq6sL2WwWn//855HNZk1wyXVdNDY2orm52eg/Nsze3t42WyoYYNjd3TV6w17D32W3eD9WrtEuMnhfWlqKW7duIZVKobm5GT//+c9x7do1Q4O6ujpz8g3139GjR03Qr6amxuDM4eFh06+hrq4OwWDQBJZY4VFfX4/6+nqjp4kFNeGqW3+LYW7F2HwtHA4jlUphc3OzoCLL/j6DWel0GqOjo0Y//K7r9zp1ggMiaFeAxEEM9vdjPRLBxy0tODY9/UAg4tOcsoGhIdzo7cXhfFOXw3fu4HpvLx7KZ7IYoVGnV7P3dqSRgRAqJAW5VAaMpFZVVRXsIzvf3o6VQACftLTg4cVFE7A4MTaGc62tOHbvnnHaNJDhui6O3LmDKz09ODE2ZjLrCjYLiO978IQB0tjn82F7e9vM03b6bvf0YOjgQZyamMCZ/P5TfldBMzO/6pyzhIxroL+VphoJ0/N51WiSZloNwO/o/iWlv9771sGDSMZiuLJvHx6anzcZMgUhj83O4r36ejw6O1sAKrWi5EpLCz45cwZdly+ja3DQjEsDSDp+PsOO7KlS1ff0IpC011QDMbwXy1JZPkhlqDSk8mPQwQ4wqLJwHAcHbtzA0MAATk5MACh+Sos6OZpxIQ8UC/5x3noPfkZ5xAbKHKfqBTt7SSdL78dLn8X72VsaNNBmj1kDa7ZSVOX7aUEvx3FwdGQEF9rbUfbee6ZjNJsV/XNd6+vruHLlCgKBAI4cOQKPx4NXX30VIyMjZlxaicNz0w8cOGAyrZ2dnWadWSLvOA42NjaQSCQwNTWF+vp6k63fv38/WlpazPYdDTSQfoFAAE8//bRpHjY7O4tEIoGFhQUcO3YM8Xi84AQQXe9iASY74KOOGF9jWTGQawTF4/h6enpQV1eHubk5/OQnP8G1a9dMhpCVGMwIxuNxPProo6itrTVbBfr7+7G4uAiv14va2lrEYjHcunULvY6DnzkOvoE9ftze3gYA813XdVFWVoZoNIr/y/o6Fvx+/CIWw+fz2zG04kj51/5t01dfUweTpaCRSAQffPCBCT5NT0/jzTffRDKZNCCSJckVFRU4efIk+vv7EfV68VfJJB5fWEB5eTmy2Sw2NjZMx+tUKoWbN29ic3MTXV1daGlpwYkTJxAOh022kLK3tbVlApbUS3yf+04vXbqEnZ0dHD161MhgsUAr8CAo52eZ0SX/lJSUmEZadCKam5tRXV2Nzc1NvP/++7h+/boJKjHAzsBsOBw2+3mrq6uxtbWFo0ePoqenB1euXDGZwWPHjqGmpsacpMUxe71esxe6vLzcANRMJoO/3drCQkkJfllWhhe9XiNr5EHVUbSJm5ubJlupQVzadtKFcphKpXD58mU888wz6OjoMPu9v/GNb5ggjFYOBAIBU25LHdjR0WHORafuc10XDz/8MM6cOWPWdSpfrVNMVxMjELBfv34dt2/fxvr6OhYWFjA/P4+KiooHbD/nTp4m37C5IOev1TLklbW1NVy5cgWPPvoo6uvrCzCZ4zh45JFH8PDDD5vtLUAOl/6P9fVY8/vxo0wGz62sGLlKpVKmAabrunjooYdMtlOzm36/3xwNGQwG4bou1tbWEAgEcOvWLXzwwQf48de/jmXXxX9JJvHN/IlVpBmda7VjKu8qWxwz9Qv3od+6dQsdHR1mzQ8fPoz+/v4CehK71S8t4XGfDxkAOy0t6OzsNBiVwayamhrs27fPnJ507949gz20Ioa2hfPhMbbvv/8+BgcHcffuXdTW1qKhoeEBG6z6QvFdKpUydlcrcDkH13Wxb98+dHV1YW1tDTs7OygrK0NdXR1SqRQ++OADLCwsoLu72zjDPp8PQ/v24Zf79qHO78eLgQDW1taMPayqqkJ9fT2Gh4fNiQUqdzU1NabxYCKRMBn4eDxuqjqGhoZw5MgRQye7MtTW57Yjx79Z4k58ypMD7t69i/n5eXR1dWFsbAyXLl0yAdDS0lLU19fjscceQ21pKf5uZwePLS6ivr4era2tuHTpkgmGrKysmCaC5eXlOHz4sOHBkpISE1jIZrMmSMd+ALRXW1tbpr+N+h/EXzb+sy/Okwkn8jVtMiviuAXn4MGDSKVS+M1vfoMLFy5gc3PT+F+hUAhf/epXzakxrF7zeDw4ePAg2traDN7htoHa2lqsrq7i1q1bmJ2dxZEjR0wyj8mQWCyGra0tYzft4CfnYc+LF+nB7Yarq6umnwkAEyCYmJjAysoKmpqa8Pbbb5uteezR8NRTT6GiogKJRAIrKyuYm5sza855jYyM4JNPPsHq6iqOHDliqjo0Od7a2oqNjQ3DA7FYzGAGx3FM7zXFVcqnyr+qj3iFQiFcaGjAX4VCeH5rC8/km+MqBq2srEQ6ncb9+/cB4J+nooF7SFXQ7OjXkbt3cePAATyxsGAGYS8qFdy5ujqcbWpC99WrOHbvHo5OTuaAl5PrdfDoygocx8Fqdi8TxAghnSe/31/Q2E+FgiCBhkb3g9HxcRzHGGm+dvTuXVxob8exsTGTIQaAEzMzGBgby1V3BIO4UF+PT1pbceDGDRzOH4XYe/cujs/M5KKteUCshlcBKJWz7TiRlrq/0d4vMzQwgEQ0inNtbegeGiqgL0EIsHf8J8Ewo8OlpaW42NiYO13h6lXsGxoqUBhaeqPZMw0qqZNJAKyf45pRQZDBqYC9Xi8O3rqFW/39ODoygu3tbWzls4UqBB1zc+jMBw8S3r3TFvicQCCAix0dSIRCuHP4MPbdumWyCur861YYAjBedskj6a2BNc7X7mOgYI40p4HPZrNGqXP9dJsDFT+NvcfjwZWWFnz8yCPouXoVbdevF/CH67rouHkTRyYnc1mXfCBJqy+4fYCBNhsI2rLIH86dcyOI0HurAXJdFxOHDuHeiRNILSzg9MJCwfdVDvW183V1eLeuDvuvXUPLlStmvfk8DayRprpeXFMNRn2aQ6eZYtLbdgoAYGBsDC1XruD69etY9e91SLYN0R/qWl1dxcTEBKqqqlBeXo7t7W388Ic/xHR+KwwBLGUrEAigrq4OJ06cQG9vr2lwtrq6ao49I312dnYwMzODW7dumU78Hk9u3+6jjz5qtk2Qtul0GqlUypTrE6TRmNHRv3v3LjKZDB555BFUVVUBKKxcsQMM+r7+z8w5sFcp57ou7t+/j1Q+U1xfX49oNIqKigpcunQJg4OD2NraQk9Pj9Ff7ErOSoxb+/bhX0ci+ObWFr5aXo5kMom6ujrU1taa0srV1VU0NDSgzevFC/leJfPptAlyuK6L1Xzjx0gkAtd1MT09jbaREWwfOoQTMzNY3d42R1QBe9VfnN9nucib6uTSVoyMjKC+vt440cFgEF//+teNbJBezNCzydYT6TROb2/DE4/DW1Fh1pYZdZ/Ph/7+fqMX6QjTGdStUQpEmPH8f09M4K3PfQ7H791D1+AglpeXcfv2bRPEUR2ojkwxmlD+VGb5bDYYKysrM8egra+vw3VdnDp1CqdOnTJ6lXaFdo332djYKNgCFgqFcPr0aeNgPvbYYwaQa/CmpKTEHOO8vb2NtxwHP3BdvJzNovPiRez096P71i18ODuLgYEBVFVVFegqBsI1kMb1olzb1YqqM9no9KmnnsLu7i4SiUSBTiRtqc9YcUGHVgNwrEDVLB731fKetA3agFTllbZufHwcqVQKMzMzpq+BriXxBfGhVscAe5iCAVz2auD73J5y/fp1PP/88ya7ptsMwvltWrFYzDzXdV28sLODH3s8+Gr+pAHek5U2nBdL2jkWDdhw7KlUyujd3d1d3Lt3D4uLi6h7/324jzyCP6muLsA9GgTgGvM3s8jUtVrFxXV0HAfr6+sYHBzEyZMnsbS0ZDAt6UbdTgzDoIJucaJTTN3O19PptOk/wftoskZ/M6C4tLSE27dvY35+3uh5XprhpqwDe1svSUtucSZvKXZKp9P4zW9+Y0rBy8rKzLg9Hg8effRRU+1HTLmzs4NXIxHMezz4bjqNZ2ULRltbWw4bJhJmSw9xD/mZ+p9j9/l85pjEH25t4b/s7OBgOo3KiQkTgOQ6FnPMFG/oa3zW7u4uNjc3UVFRgaWlJWxsbBRs3QiHw3jhhReMveW9XdfFk8kkngSQicfhRqPw+Xx4/PHHzZYXPpdb59ijhOvB4OnOzo6hqeu6pkHl2NgY3nnnHUQiERw6dAg1NTWIx+MPJHfIo/b8+BptD/u4UCYYXI3FYmhrazOVhXfu3IHf78eJEyeMDH5cU4MP6uvRls2ifmXFrGlvby86OzvNtgViWrsnQX9/P1ZWVnD37l00NjYiFosVfPbixYtoaGgwFQ92AsAODlImNYhEHbK1tYXt7W34/X5UVlYilUphYWEBy8vL6OjowP3797G9vY2enh54vV40NDTgxIkTKCsrK1g7BocTiYTpJXP//n00NDTg4YcfRl9fX4FfQPvMpKXX68Xq6iqy2azh6UwmtwWIRxXb81Bda/uhvAKBAM63t2PV58NPgkE8l9f9DOBkMrltrPxsPB43SbF/6vq9KhpYasUF0gyG4+Q6OD+5vo4nP/kE2WwWSYthbQJ80teHRDiM2wMDOD49XeAAaDk1v0PhUSahQmKGgOOhwtPAgjorrpsriwyHwwVOoeu6eGRpCYcnJnLGTzIvBCY0eBc6O7EeDuNGXx967tx5oGM4n83OugoUyLwsydGxU+loNAmAqUoIBoM4OjyMS11d6L15E6FQyAgXDZMawe3tbbPH7WpLC24cOIATY2O42NiItXAYgwcPoj1/NJqWISsT2kBJAYTH4ynYG8/xa9MQ/s05cI/xwNgYDk9MYHFxEYuBgAExfI7yAO9FJX65uRnX9u/HqfFxPHT/Pn7b1obdkhIM9/ai5/Ztsw68tDJGgR7HqwqMvKCOlPK+8jLHN9TdjcH+fhy/dw+HxseRTCaxtLRkjuWh46D7PAkSeLTW7u4uPjx2DOuRCG7l14XjVhClVzHFwjGzLFarS+zP2nNX594OMnDNeZ97J05gLRLBex6POd7UNk62M/FubS2Wg0EM9veja3DwAXqTJrpfUsdKIEH517Hq3NSZUcWq2Si+fq2tDefb21EVj6PsvffMft1iQZo/xBUMBtHe3m7mNDs7i2PHjuHUqVMmsECas0QxHo8jGAya0raGhgbjRO/u7hpgvr29jWg0it7eXiO3mUyu0/10Xs+S/2lIaEBDoRDS6TRu3ryJoaEhNDQ0mJNjFhcXMT09jY8//hjhcBgVeWeWtNbAns0DyhcEm/aaBQIBVFdXm2g/q7o6OzvNnkzlDXVqHcfB/x4OY9px8L10Gk/ls1l1dXUFPFxRUYHy8vICXRsIBAr2IbNJ3luOg+8BOJpKoeKDD/BKHkj8enXV7INvbW0tCLrwsoNe9uukGbeucYwej8ccY/ozvx9vlpTgpWAQz0gGnPqCTZnpxBCMUFcAML1IqIOp6/g+AxF0VFVH0Eayyegv4nGkwmFc6upC8Fe/MuBjfHzc9ELQS2XPDpbyf+pAAnluAywrK8PboRB+mE7j2UQCTyaTxpHW7Qf8Pu/Feytwtz97troa79bU4MnFRTySb55HfEGbTf74L0eOYDkYxN9ubuIL166hb2QEU1NTmIrFsLKygrq6Ojz77LMFJ7XofDUQD+ydjsMAh63faJ+++93vmnvQMdGtPdoomt8B9nCPZuvVvimW4GtM1DATS76l3clkMqbxGJBz9LWiiXPTgDHnwvmRxuqs0cHVK5lM4u/+7u9MVRllk5+7e+AAhgYG0HP1qrEdAPBUSQmSjoPXrCAAdR/HR7uhOIZ729XesUyamdfaTz7BtyIRvPDCCwU8zGdQxvhbA9vc4sWtWCsrK+bZXJ/R0VH8h//wH7C7u1twSgXnvbu7a3Ci0lHnybUnL+l66GeJK9mQmBf5gDTjaQ+NjY0FsqvrSv7QrKdibI5LbXw6ncba2hrm5ubw63AYZ6NRnLh5E0cnJwswJLc40Jk90tyMzaYmHJmcxMeyJYhywcCWyoWOm2PnuHlCyN+eOoXVcBiXu7vxq9deQ1NTE7q7u011ma1PftdFHUz601msqqoyjZinpqawvb2NhYUF04SVPMl+INpHi8EwJkqp02knKVu0jXbwFcjpGzq7V65cyeHtxUVsbW2hurraJCPsYBnnRDnT+Xu9XuM0s7l2NptFTU0NYrFYQXDM4/Ggo6PD2HDqr38fDmPe68Ubrovn8j2QGChjAJ12hk4678lj2puamkzlCEv6M5kM7t27h9HRUUSjUXNCBo9r1Xkozyh25rwZpNJKyvX1dWxtbSEYDKKyshLb29vmRCiuD5txk1ZM5DQ2Nhr+oCzu37/fYHX6mtpk2XVdo393dnaMXpqdncXu7i5+/vOfw+Px4OGHHza2XbG8jXd1rirXD09O4mJHB57PN11lEmpqagrRaNToptLSUqyvrxfYjN91feZAA5ADYHb5szpvc3Nz2NjYKOjmzUkosOBrnRcvYmhgAF2XL2Nzc7OgeaN2seZvOmK8VEmqoaAQflxTg3f6+lC7soLJmhq42SwOXbpkSusJ0uks6x4j7TifzWZNhm95eRmLi4tYX19H2yefYOjQITSfO2cWnvS51NSE9x5/HF6vF58bH0fb7GyB00VlQMBN5ufCf1RZiXd6e3FidBSn5uZwubkZF9rb0XnpEnpu30bd+fP40/HxHNjMN3LhfQjCOS8auHQ6jZt9fUhEo/ikpQUdH3+MnSNH0PjJJ+YoKV5c08GuLvyorw8nx8fRmz/2yTaw2pmZa8t7fFRZiZ/198NxHJwZHjbVF67rGke8vLwcdXV1phSIvGWDZxpHgqIbvb1IRCI429yMF19/HU5tLbbLynD70CF0DQ4WAJlsNtdU8mpPD55aXETHzEyBo0J+Uv7USxWRXZrEz9/s60MyFsO51lbUfPyxKTWqrq4uMBykE+fD+zI4dezuXXzS1obajz4yIMWWOzqLNPSqHClvAAqCcw0NDQUVDASTmqWwt7ZodpNjIBDJZDJo+fhjjJ08if7h4YJj0LhevIcC/t4bN3C9txedly+bsjYFxKr87N+cH19Th5nj1AAD76dKV0E9aXGpsxPrkQgyjz2GtmvXzD7Wf65Ag8fjMQFOHt/EwIbqERo3BqkIlJnhy2QyxlklrTl2RsOZteYPwSYNGUE9s0o7OzuYnp42ZexLS0uYn583AHd1dRX3799Hd3d3gSFTcMJL5cZ2LlXXx+Nxk91ZWlrC28Egfh6P43NLS3h0ZcWMWQNzNnjta2zEZns7+kZH8f7UlOF7dUCvtLTganc3jgwPo390FMBe1lZ53nEcvPr880jEYlitqsIz+WAL97xyO8LCwgLq6+tRU1Nj5lksGMj37P+3t7fNnIC9ZscTExP4r21tSJSW4j+vr2P8r/+64HvaO4eyViwox0ttsDofqsuow9UJY2B3d3cXbadOYfjoUbSeO2eq0vhd1Su67jZNFLRrwFCDXzs7O1hZWcHfOg5WIxG86vHA/fWvHwgq8mgzyhMAU4pLm6FzIU1+1d2NRCCAn4bD2HntNVNJ4vHkurQreO+9fh1Xu7vRdvUq1tbWCo6pTqfT+Li6Gn/nuvjv1tbwrUikQE97PHvHdXLMyvc6JnXOWaa7fOYMFp98Ei/u7uLLgkW2t7fxzjvvYHt72zQULhbc03VR55SyrpjD6/Wip6fHlMgDwMTEBEZGRkx3+2w2a46BVGzE+xQL9NDGqawqP+oPvzMyMoKVRx/F6he+gM+vrOBzUlX6q8ZGbASDGD5yBEfu3ze2sba2Fi0tLUbeNXvJwAG3fmiQxOPJVXq1trYaR31yctKUd6dSKWxvbxt9y/UhTlHbyfWjw81tkJTrbDZrMrKKL7LZXGXK7MmTWHzySXxhbQ1Pbm2ZIIvruvgojwW0yk7XgLZcZU8DrMpvPFGkq6vL0GFzcxMXLlwwe7R3dnbMUcea7LJ/E/8RPzuOY/wEfTY/q7jgZz/7GYb/5/8ZO3kM1379utnasbGxgel8jzcjx9eu4Wv5BngLEkxS7OK6LhYXF83JWvwMK9TowKbTaczOziKZTKI8kcDmmTPouHwZ6XTuJI7x8XF0dHQgGo0WBAKKJU5U19Fe0xl0nFxG/ObNm6YXk2JzBjg5Xt0OqDyqa6hBcj5fg0yabKSPwYpabodiMP1KczPeDATwb9bX8Y2806gyqfPVYAN5PBQKIZVKGdrH43HcuHEDY2NjWFxcNPRQO6xB0eaeHmwcPIjWwUG8MTJSUIFEvrq1bx+u7d+PgaEh9Oa31pO/aLszmYzR+66b20qZSqWwurqKd999Fy0tLejr60NNTc0DgW/bZtq2yuvNbZOjj/Tq9jZ+EYvh8fV1fG5tDYuLi1heXja8q74rv6t2gD4abQEDDGp7ia/JA5ooYBULx/lzvx8/P3UK+69dg+/cOcTjcVP1pXpV9Q0vxWxerxePra7iq6OjqK+vx+LiIsrLyxGPx1FaWmp6SpDPmez6LNfvvXVCGZrCQMI1NTWZTJStBHkRzLiui8dWVtD7+us54QsEzCRUeVyor8d7Z87A4/HgmNeLfbdumecr8FAQxfG+V1eHtWAQ66EQ3Pzrtw8dwqP5o1/USdaMO6NzWmLn8/lQUVFhSnludnZirL0dvRcuoHFwEN5IBHfyjQ2Pj47iYns7tvOC+0FjI+rOny8QVHaQtctuOa936+qwGgziXGsrGi5cwIWTJ5GIRjH00EMYPXECJ0ZH0TY1VdAUkULMI91IC92Dd3J8HOfb23FichKeujqM5/c00dkb6u7GjQMHTHPG6/v3Yy0QwEdNTegeGipYG8dxzNnzyg8KOH7R2IitvPI819aG7e1tXNm3D/uvX8ep/F4lboEBYKK2BGPpdBo32ttxqasLx+7dw4n8tpRgMIjTk5P4uLkZJyYn0dbWhocnJ3EWQNfly3AcB3d7e3O9DPKnM1zp7sZaOIx3PR50Onvl9LYStEv0VQY+rKzEe319GLh1Cw8vLZnGofuvXcv1Tjh4EEfHxtDe3l5gNHbyZZ28Ly91Ehi5fmpjA8fPnsX0zAwSmb39j5rRUuBWzAHnxUZSBMC6LURlh1lQG7Dqc9SRofE+PDGBL+7mzuBGXZ2hJZ9nO/3ZbBYNqRQ+d+ECZmZmsCQ6QR1sGzTxHnqEkj1Gfl6dXw28aHCC0X2+d/zePZxrb0frpUsG4NkK+g95EUwNDQ3h/v37pmM2x63z4Bw1c6nOhZZpk4/4HTvgy4t6hwEJdRBoAHn2NoMV7MhPQERZUcfRDnrp+PnDDt+UB2Z9tre3MTY2hhs3buD1r3wFqZIS/CQYhPPWW8ZmaHUWeYNbCBpnZ9F69WouGOLZ24ZDm+U4Di7v24f1/JazwK9+ZWxGJBJBQ0NDAT0ODg3h8r59aLtwAQsLC1hcXDTZSgZ/WlpaUF5eXiDbxdbOzpjwfeoFla/Z2Vn4/X48tbCA3zoOTkxO4ujRowWnFYyOjmJwcLAAxKnDrM/id7Rqj3qHF8EOq2T4/tjYGC5fvgy/34/yDz7AoV//GqWlpUjlndxIJIKenp6CKhHVb3y+LZN24FbpMTk5ifX1dXwpEMDPfT48vbGBz3/+84buruvi3r17+PDDD03DLNq4SCSC7u5usx+Zz9TnJ7a28A+ui77BQbPtiHTkfmDqXO9HH6Hu3LncuPOBeNrv0tJSDB48iHnHwX9aW8O3806J4zimIomyxopGYiO7r4dmwJmJnHv8cWyWleHX2Sz+lfQzmJiYwObmJk6cOIG2tjbj1Kou1/XX0nXqCj1BhjwdCoVM4MLn8+HOnTvIZDKorKxER0eH+VxLS8sD66uBzmKAnWNjcFyDUvw8t0IFAgEsPPEENqNRnI1E8G9Epx26eBGXu7rwEoDnnnvO3Id2TiuTiHkcJ5cpvn79Oh577DHU1NQY2jArq9nvK1euAMjt3+/u7jY6pq2tzcxBcbBWAPE3x6VbV2lzbLljcOzuE09gq6ICn5SV4f8kWynX19exs7ODJ5980hxhqskEzlXlSHWPyhjHwrWm3T179iwymdyxt2y+V1pais7Ozgdsq9oVdfZV97CygXOlg89ncm1q33sPiWefxRc3N/HYY48ZueFJEwyG8KqtrTXVQ/o6x7G4uIj3338fXV1dJnnhurmtU01NTaZSY3p6GlNTU3AcB+03bqDt+nXE43H48ideHD16FBUVFUXtGC87kML3eSwobdz29ja6u7tRU1NjjuYlTYBcAvfs2bMIhUKmFL7YGquM6fPIx/yJRqPo7OwEANMn4ezZs2YLHZ1yn8+Xq0gNhfA3qRT+KH9ShD7bllHyQTa7dzIUfYDNzU0kk0ncunXLnGjEJKcG2niSisfjQePFi2i9ehWdnZ2o7u0tkCni2R/19yMRDOLyvn2o+OADQzvHcUx/EvLf2NgYJicnTZU3qzSJYRTzqS/zaWvMiyfFua6Ln4bDmHUcvBUKYef11w0/M6DgOLleJ1xrlQ0NBOn62vKjNov4Rrd3rq+vY319HdlsFu//5V8iVVaGoYEBnHr7bYyNjaG5udnQWJ9RbJ6qM1j5wSSbx+PBzZs3MTExgbW1NVMQoL7TmTNnHqCXff1egQY6sK7r4mJjY87BunoVB0ZGUFpaWrBnVYmlhlSjrY7jmOPYdMK3u7sxfPQoTt+/j09aWrCZv++V7m5TLqcAhveiQc9kMtjY2EDP1au43tuL0MQElnt6kPb5UD43Z+ZBIGo7MVwA7hEjqFZA+ElrK9YjEdw9ehR9IyNwHAeDBw8aENt37RouHzoEOA72X7tW0CCHizw6OlpQoUFG2N7exv6rV3GttxfN587hypUraGtuxsixY9gtKcFqOIyPW1rQePGiCc54vV7TT4IKNpvNYmVlxezr8/v9iN65gyd+/Wt4PB788k/+BMlIBLcPHULTpUtwHAfX9u/HeiSC821taLx4Eb03buDmgQPouXED8/kTDihMdEx1fQkiKMiGF9xcBculw4eRjMUw2N+PtmvXsL6+XtDEjrzm8ewdg3fh6aeRjMVwvq0NLVeumIh5y/Q0Wq5cgdfrxbLjYN/SEtqvX889M7/1IBGN4lxrK7oGBzFw6xau7t+PA7dvI5FIGGWpgGRhYeEBBa/O3Ht1dViLRHBt/34c+uUv8V5nJ7YCAVw9cgR/+uMf48TMTK7kK69stBKAIFPvT4fpfF1drono6CgOT0yYPW6sRGFALJ1OY/TgQdw9cgTH852a3+/shJvNYmVqCr0LCw8EAge7unC1pwdPzM9jfz47TF5Ux/CD8nL89uhRpB96CGfykePzbW3Yf/062vMN2FTewuEw2traHtAT6gTz3gqqOfeGhgb4fD4sLCwU6IZMJoPh3l7cOXwYJ0ZH4fV68UFXFzKuiydHR3E6Hym3Ay7kGQVc9nMdx8HV1lZc3rcPkdparNXX49T4OMD1tgxtMaPzh7h2dnawtraG+vp6uG6uozAAY5zpPNy/f9+cBa9yrQ4k/9bX9X1emk0KhUKoqakxgIxdyK9du4aZmRmzLtSJpKnP50NdXZ1Zd31ujnwPZtLt18nL6hDs7u5icXERVVVVOHXqFBLr63gvFMJzrovnXnjB6MXLly/j4sWLqKioMIAzEAggGo0aB5O2gLoKyK3/1tYWLg0N4XJXF06Nj+PYsWPGGQiFQqirqzMB13Q6jfmf/QzRd95BNptFOt93haWc0WgU+/btM92jFUhoUM2mi66R9k9haWUwGERTUxMcx8G/8nrxF9ksPJ2dyLS1GQcwm83i2rVriEajeOKJJ0wFoWYm9Fm2Y0eaK/+wRFn7HHg8Hly9etXQo6mpCVVVVWa+QK6Mnn0fioEYO+jA3xpwUZ3Ac+6rq6vRW1qKFzc2EMg35OT8PR4PBgcHEQqF8Oyzz6K6urogi8f7amaJoNnv9+Pob38L5/JlPPnkk2g9fdrIBQNeHOfOzo45hrimpgaHDh0qSLiUlpYi6vPhDa8X/yp/SoFWLDDDy7mxMoT2WMfLz3AsgUAA7efPY+qRR/BnNTWozpciA8DY2Biqq6vxyCOPPHBMrwYaeH/KoYJ4Db6pA6rOME8mYU+WZDKJ7e1tVFVVFeh1O5jpuntNPosF7TWorzaF6+Pz+dB67hwWn3oK/7KiAjFZ28MTE/hv6uqwf/9+w2cagCLN1bFmNUJZWVkBr9JWBQIBA9o5l3A4jDNnzqClpcUE5jSgqI42eY+6kiX8nDtxo9pDlQ8T5Dp3DlMPP4xvl5ejpqbG9HRw3Vx3/YGBgQeqV3S9dTuM6h1bBohplUYlJSWoqanB8ePH0draau7PI4YBFMiG2khuaeJ4+UziFfISZVd7olTcuoXj4TCeeeYZE4jwer24ceMGHnnkERw9erTAxoTD4YKEiNp5j8eDZDKJw4cP4+jRo2Y7L5/J7/h8uf5GgUAAAwMD6Ovrg+u6JjFUVVVlHGNdR9Wfth+jdGbAjFs/6Dt1dnaisbGxoDGqz+fDyMgIGhsb8cUvfrFgC7JuL1S6ayWg8iKre7hOxEKTk5MIBAJoa2szFQg7OzsIBoN4Zm0N75aW4s/yzTHVJuv/5DN93+PJNd9lAJ468cSJE3CcXKUGtz/QOT137pzpv8A5+Xw+c9qJYkDO9WuZDF7d2MDJqSk88cQTRqYdx8kFh/J8tbi4iFu3bhnHnFUs0WgUp0+fLug3ovZa+csOqHCuZWVlpqrr6ZUV/LKsDI8nEjhx8qTpm5DNZk0y5ty5cwX9IriG6virHlT9oHrRcRxEIhE0Nzebo2uTySQ++ugj0+em6aOPMPXwwzg0PIwTJ06gvr6+wD9WG6yXYhGOKRKJYHJyEqlUCnNzc5iZmcG9e/fgOI45zYz8TT/7s1yfOdAwODiItbU1Uy5x7uRJrEUiuNnfjy6pMuDC2YZFFSSJSgdqaWmpYP/Z1W9+E7vhMN5pa8PBCxdw7dgxeBwHvdeuGaW2tbWFy83NuN3Xh32XLqFjZARjAwO4fegQDt66hd7hYfSNjKD+/Hncu3cPm//L/4L1eBwL+bOTA/kKCm7XoKCw+RQFlQ2CWNbMuXRduoThI0fQcfGiiWD1XLmCWwMD2H/jBlquX0fP7dtmMRdXVxGNRrGxsYFIJILl5WWz3YNKms7E1tYWmi5fRu25cygpKcEdjwfH5+Zw9M03cbOzE4P9/Tg4NIRkMolQKGS2GJSWlprST5ZGejweLC0tmX3c3I8dCASw7/JljBw7hraLF3NZqlQKB65fx42+Puy/cQMrKys4ND5umvwt5COSvMrKypBIJIyCYdMXAha/34/Dly/jZl8fui5dwsHxcYRDIVzbvx+HR0ZM5JeOD5vPJZNJcyxOKBTCwK1buNjVhf7hYdMheHl52QQpmGHi8TV89r4rV3Dn0CF05cte686fR/OVK3BdFxu+vUYrHC/XVh1xGhr+3n/tGm7m6bO4uLiniAHDm7xXMpkEsNe0h/dRQJJOpxEOh3HpzBlsxGI4396Opnw3d26rcN1cgzqWK10/fhy74TA+6ulBaTptAnEf1Nej9epVs+eaY7uSP5b1rVAIG8kkmvNbHAjMTFajuRmp/DPP9vSgNJPJ9fDo60PDhQumooJg+9MUlv7wsqsDFPToHk/27xgaGMBGLIaL+Ywj5/hJWxtOzc8XNMviWlG3sPyf+2yZ7QFyxuNiZyfWQiGsdnYi6/XifN5pTkQiGDl+HPUXLhQY2H+OiyCvsrIS9fX1BXNgSfzm5ibGxsbg8/lw4MABs8eRetJu4Mb5E3STPgQeChQJsDUbvLy8bPb8bW9vozwPeKlLSkpKEA6HsW/fPnP8G5/P69MCDnyN68P5ezwec4ReVVUVfD4fYrEY/ry0FP/a64WTL9tzXddsX4vH4zh9+jSOHDkCAMaY69Gfym/k9WQyidrXX8efjo/jueeeM+WFrHpQBy2ZTGJjYwPl5eUoKSnB0aNH0dTUVGDfSBPbibcdbqWT0op2R7Of9vgzmYzRrdQbPLbyzJkzePjhhx8oU9bnaBBVS/b1NcofwfW6bJHb2dlBPB5HeXm5OSKP393c3CzoD6TBRcUA9rz10uA9M/l0gvS+ZWVlBdu8vF4vOjo6CgJeGjixMy+UKwbiOzs7cezYMeN4aBBEG14zkHXmzBk0NDQU7C/2+Xz4GoC/rK42Dpnyh8ofn8sGhaqTlEcU9LZfv47n0ml87ZvfLNg25PF4UFFRYXjPlj8NNBXjPf2bMkm7xK08AIx9Zi8QrqvqeVbCKU8T2/F92hnSBkDByQ98XX/ar1/HX+a78Cu9KyoqDK15L95Dg5d8nWMCcttqbCfd4/GYORPcV1dXY3t7G42NjcahsY+Lo2xo0IL0If0ZoLMrTpXe6oA0X76Mk7OzeO5P/xTJZNLMMZPJnSLB40aJU/TSoKauvWZIORcGATY3N00gIBwOo66uzlQw6P1IF33NdXPVZbZD7vV6sbm5afaxc56qXzX7Pn38OP7TY4/BEwzieezZD27t4J560kFL9ak/dE0ikYg5gULlKxAIFGwhDOYrF06fPo3q6uoCHAQAb2QyeG11FX+RzeJFS55Uf9pyxv4KLG/nGjDQwfmQLxwn1zeIjWXteyvdbXur1Wy0H+Q/bisjPx87dgzHjh1DaWkplpaWzBYi7OzgSzMzOJUPqBWz6SordP55scEysZbj5JpyhsNhUxlH2QwGg2hpacGpU6dMM1nHcQw+toOEfP9fZzLoe/999Pb2mu0v5GXaD8p5OBzGwMAADh06VNDol8EAW1/ystdTZcp1c1tCJicnUVJSgq/t7OBrySRSjgM0NKCiogLxeBwbGxtmK/69e/fQ399v/uelMqi01K2IdlCHSXzS6ObNm/B6vXj00UdNDxHvyAgisRjO1dXhf/J48N+vr+OVfAWe2mF7bW3bHIvFsLGxYZo9BoNBg4F2dnZMMQF57erVqw/Qstj1mQMNo6Oj2NraMuW+ftdFyTPPIPab32Bzc9MMwAZcGmzgxDTKvrOzg/HxcaysrBRsX+Dv7VdfRdf3v4/GxkYkEgncyUcM0+k0br70Um5P/uHDiLzzDgb7+rBVVoahgwfxyNKSiaSGQiEcu3cPV7q70Xf7tlGuBDZ+vx/r6+uIx+OorKw0xI9Go1hYWDDvsVqipKQEA2NjeCJ/fFxFfT02NjZwcm4O/W++mStVr6pCbW0tHCe3vWBzcxN1dXXY2NgwgIdZGu6HW1tbQywWw+rqKioqKjCZP1mgurra7BU7cv8+2vPnI2/EYmZfM8fMPgd0QtjQo7y8HJubm4hGo2a9Ts7N4ekPPsD09DTC5eVYWlrC0akpHL5/H36/H6maGlRWVhoju7GxgebmZtPgMJPJoKGhASsrK+aoq5KSElMJkslk8HQqhcM/+1lOCZSV4eTcHE7Nz+eCRfl92el02syPCogBkZKSEpyYnUXknXfQ3d2NUCSC6vw+vVQqhfLyckOvtbU1VFZWYmxsDOXl5ei5fRsn80fzxfLnD7PJErOH5KW5uTlztBUDP3yPYMTv96N2YQEtf//3ubO+PR48dOsWLnZ24ujdu6iurjZAYHd3F5FIxFSURKNRrK6uory83JxXPTs7i1gshng8jv3Xr+NWfz9OTU6iqqqqoDJkZ2cH6+vrSCaTWFlZQdbdOy2g6Te/Qeqpp+Bms4i//TbeffddeDyegixnxdtvY/2rX4Xr9eKDhgY8/Oab5r66J676t7/Fyhe/CHg8SGcy6PjwQ2wfO4bqDz7A+Pg4ysvLC/apUsaLGV11ZFT+qSNU0TJokMnk+g3Mzs6i4u234T75JKo//BCOx4ONJ59ENptF1Tvv4Oz169je3sb9I0cw++ijaPzwQ9SeO2ccsZKSEgMoSMft7W0DUGt++1tsnz6N4Pg4tlpbUX32bO4zp06h5t13MTs7i7KyMpSVlX1W9fh7X47jGH7hmFnOyrVdX19HIpFAT08PXnzxRcTj8YIItVYZ2NsfbH2rmTe9WDXkOLnjkagvKyoqcPjwYTNGXtSXNqi1r08LOhF4auCHjpfOHciVUpeXl5u5svHQvn378MQTTxQ4JnTotFcNx6bl6h6PB/v37zd7C8mrBG0M5vl8PlO50NDQgPb2dnPMlTowbzkO/mF9HX/muvhG3gnXKg4FFTb4p56g/uY62vTj51ReotGoyQLZwFV/NMPP/1UW6YB4PB7j+KjTVVNTg7m5OdTW1hpHjQ4Fnch/cF28traG7wSD+KN/ojmUzSuaVVWgr456JpNBMpksWKfKykrDD5oR1/mqcw7sHSnp9/tN+bxmzpj4IE12dnbQ3d2N8fHxAt5SAEqesh0QpaEG/fg5VuXZQE8DQ2MDA/j40UexubiIL8iRjmzIdfHiRXR0dBSspfIe5YLjVNqTzyhvjpM7fUsDHi0tLbh+/bpxSsiL6tyxakGdfdVR9hpwnOxppQEd0pI/gUAAY2NjJkjkOLlS6b9ZXsbZcBiveL14UQD53Nyc4REG6hm8TqfTiMfjWFtbKwjEeL1eU83A9WhsbMTCwgKGhobQ1NSEaL7zPytB7ECROmjkOwasmFDge8zyc5x8jzrizv79+Jbr4sszM/iSbD2Ix+P4327cwMXOTrycyeArwi+Uf3WiuAYq+5yjx+MxHfS5juXl5VhYWMDt27fR1NRkdLHyEuVEdRP5pzTfJ4xBQuVxzteWGcdxMH7qFDbDYfzt5iaO508d8Hq9uNzUhP/N78c3VlbwspyYwd4HnK8GT6kDmGklvQEglUoZ/s5ms6bpMPWqBiQ9Hg9+kM1iKpvFX62v4+uSCbcdNtJVg6U8ypxjcBzHyA9PgVhbWzM6gc72aj4ZqbTiczQRwXHQj6F8qHyur6+bAFo0Gi3YksEkA9fJnpftw+l6azAVgEl0Mui2vr5utvgxoan2srq6umD7jPKs7SvyuV5v7njSyspKMy4Gb/jsbDaLWCyGjo4OHDx40FQfkZY/cRz8YGsL/zrfS4fPUZxQLMjCOVdUVJgt9dr8lkeoamW03+9HR0cHqqurH8BKGtTWii/7eeQlYC9R+b2NDfxtJoMvNjbizJkzqK2tNf4G+f7/5vdjxnHw/11dNfO056Q0VLozEKZHKPPZgUDABMrZN87j8aC+vv6B+xa7PnOgoa2tDaOjo3DdXKMN7/vvI/rOO2hqakJtXx+CwWABs5Lp7eirCubW1hYSiYQZ8Orqai5T8e67mDlzBm3nz5smW7W1tcYxB/JBiBs3cOfwYfTeuIG6ujr03riR61+QzwIy8r+wsID+6Wk8vLiIlZUVgK2swwABAABJREFULDh7Z90yo0alaDeloxNAQOe6rmmaqE0XXdc15cfMBKhxJzOSTlS4rJZgGYzruohGo6ZD6b3+fpz94hfhTE/j0Pi4GUdJSYmJPLF7LlCYweT/HD9LidfW1hAKhbC8vGwYp7y83ID5yclJ1NXVGaPKCDUDCWVlZZibm0NTU5NxVqenp1FdXY3R0VHU1NSYRiKzs7Oma72WeWqUl6CACpkVClyDm52d+PiJJ5AeH8epuTnjnDFSzfOv+T+zYty3WVpaagITPO6Ln2FAhsY6EokYHuCaUdkxy1ZVVWV4oHF2Fh03biAWiyGQB/7K86yI4dxVcH0+n1nngbExdN68ie7ubjjhcEG2emdnB1VVVZifn8fi4iJq/vEfsfrMM+i8cAENg4Pou3cPKysrSCaTWFxbQyQSQUtLi2m0Gb93D+Ff/xpjJ0+i8ZNPEI1GkUqlcmMOBDAzM4Py8nKEBgcRCARw9+hRNJ89i647d1D54YcIBAIYXlkxWZ65U6dw9+hRnBwdRfvGxgOGl/SkHlCDrJ89V1eH944cQf3775u99RsbG1hdXUXgl7/EmTt3AOT2jTVdumTuF6usxMrKCmYefRRblZWYPnMGR6emsLS0VBBZJvCJRqOYn59HPB9sOjY1hZb/+B/NUYpcl8aLF3NHuOUBiZ4x/Ye+1MGjTBD8E5ywxLq+vh4VFRUFDikNGmmr2Xg1mAqC1MDRmGvZHufMUj0aStJAS8tpJO3MGi8NOClgIj8TsGTzgWPlC3UWCZaoJxobGwvKvrUqgCBe+Yxj4b3q6upQVVX1QD+SjY0NAzo5xu7ubiwsLJgsvtKN933d68W06+KvUyl8Ix+o1GfbQTjbPtIpIR+oPeH4NjY2kEwmC8Bmd3e3OeOe1Wqcizqw+nytIiEgtXmKTcP47PtHjuDHpaV4eGoKNRMTqK2tNRiAwP8Hfj+mHAd/s7WFV/K2T505myfJd/qbfzOrzuf/MhDA634/vrq6imeluWckEsHg4CC8Xq/ZR08nl1seuOYMjtLJ3NzcRENDAxYXFwsCXnZ2nbRZW1vDe++9h3v9/fikpQUvpNN4Hg8eCam6gg6OfYqF0v3/R9ufBceVnWmC4Hevu2NxwDcsBAmQAAEuIECA+74EI0JShCJSGSEpFFpTUmZlZlU9tFlXd5vNPEzNw5i12fRYm/WMWXXX1GRnVVamlEotqS0UUkjKCMUiMoI7QYALFgIgQGzEvrrD4cudB/fv4PNDDyWrSrpmMAC+3HvOf/7l+5fzH+IDXTe9ho8fR7y8HD/OZvGqVJLE43HcvXsX1dXVqK+vL6hoUX1CHuM49DPKJ+QJrfBxnFxTxLm5Obzzzjtob28vaLbIz5Fm1AekJ59HWhBA246qOoM6fgBYWFhAU1OTsZkAMDk5ibfr6rDi9+MHAF7JbO7lXl1dxdTUlKHz4cOHsbi4iIcPH+LDmhpc3rcPr6fT+JLYfNd1EYlECvTp2toaJicnMTY2hrGxMTz33HMGj9gZSft/zjco2URWZJCfKBOBQKCgYW0mk8Gdtjas+nx4q7IS3/BvniBy9+5dXOzsxKLr4seui9elElCdd3VSSGttRM058Hhe8tzMzAxGRkbw6NEjzJw+jSuNjfhaIIAvSYCJa0Anl/NTe6W4U/uxEV/ba1/x6BGS0Sga8w0oGeR9KxzGlM+Hb8XjeDa/zZV8ygA8aVNTU2OOVOSWAA14cJ3ZQI9z6e3tRVdXF86fP296kHDMXy4txfcB/Hn+VAjVpep8qyzwYkWDVsrw86wyYFNQ8tzExASuXLmCCxcumHmSnwKBgLELxarSuIbqALMRZTqdO42ht7cXw8PD6OjoME2kuX66H1/tMC8NcNCGkPeJu5nU4Yk0AExTUQ2sBYNBXLt2DVu3bkU0Gi14Jhtico3oy5Df5ufnC/CB67pmC7Tr5rbNNjc35xKL4XABjviu52EcML10uI524Nv+n/wbiURMzw3KnB5AsLCwYBLxTMYwscmTMsh3tBsqBzoejln/X1tbw3/e2MA4gJ+VleETRfqHZLO5o35/WlqKv5BmkGp7bSxiz5dbk2xZZtCU+o3ro8cc/67rqQMNLGmmY+m6uQ7N27dvN5FBFTY7yq8TptGJx+OmezZLeDc2NlBy6RK2Xr2KdDqNufV1Ux5P4aAS23HrFuqvX4frukhks2js6sLz+S72bn6MvXv34vInPgGMjeHwo0emTIxgZXl5GbFYDMvLywXdprkwnpcr6ydjMfrNxipkeM36UVmSSVUZLy4umnPJebSlgl5GAmmAbuzahbVgENdKSnBkbAye5xlFoScwMPtvRwi1DFcNPsdNh5vbH/RYPQY/eNFoAjCO3OLiYkFvDtuB4feKASEqEg3s2ECosrISN3btwmowiFslJTg1PV0QAOD3GUEOh8PmmL/p6WlUVFRgaWnJ/A4Gg7hcW4u7HR14ZmoKZ2Zn4TiOWU82wAsGg5ienkZDQwMmJiawdetWPHjwAPv37zcl1TQWuu2H89A5UTkBMHyie3QDgQCGOjpy85yawompqYLvkxYmuvr++9g/NFRgtLXZTjgcNp38GfRqGxjAycePc2BiYQGVlZWmvDCdTmNhYQHV1dXY2d2Nvb29ubLIfBMfzq+hoQE7duzA5ePHsRYKoau1FZ+4dcvwhx1x1zXVi+O+1NCApfJyZM+exdm5OWQyuW7rU1NTpvKEwRg63azKefjwIWp+8xssfupT2NPdXdAMrbq6GisrK5iYmEBZWRm2bduGeDyOkZERQ0NG+G3+o4Kl3HycI/37utT55skOpOX6+joaGhrMUVhapquBRQUbSl8bCPE1Zm61kavneabqijqJlUL6fZXRYg6kbcA4Xp0vtxYRUDFbyD2twGZFBx1fOil+vx/37t1DOBxGU1NTQYn2xsYG5ubmjK4JBAIm2AkAl6qr8c4LL2Atk0FwYsIAc93bynElk0nMzs5iZGQE8/PzGB4exsL58/hNdTVe3djAp/JVW6/5/fhpSQn+NJ/F0WCCHWCx3+OPOiEbGxsmaK16k0FwXoODg/iothZ/HQjgy5kMPi30UZoUswfq2Gq5rc/nKzgxKpvN4herq1gOBvHbbBbef/gPCLz2Gq43N+PVZBKfzo//c+k03igtxZ/mnSt9pvIGLzvgor+JBWhvf+y6mHJd/DwYxJfzpx14noeRkRH8NhbD99ra8KfBIF51N6s6FhcXTSWZz5c7/iwajWJ0dBRra2u4tX07/rfaWvxZZSVel+2LxBXc/uc4Dubm5uB5Hqanp/H+li2Yd138pKQEX8Cmo6praOMc0pGvK2C3acT1YSZpb1cXRk+exJ/V1CCWB9LZbBZLS0u4u2sXxs+cQUkwiM/7n4RxdlBBn1OsPwQ/r8CdiZ9wOIytW7eaU294TzvTqXvK7VJgyrF9yoiND23sVV9fb/pGZDIZzMzM4LznoTsaxZ+UlCAiuKuystL0F3Dd3L7quro6bN26Ff/ecTDvuvg5gL+Qsm2OT200twhHo1FTbq6l/zYPq0xTZqljXdc1RxIyUcNAK7/Lufr9fuy+dQtT58/jq2VlyEr11Pz8PL6QyeBXjoOvBgIoy+MfDY6pg6LyrnxKnUvHm3Tn9oZYLIY3tm/HBIDvpNP4qm+zgaz+KB10SyjnS32slc4aUOe817ZvR9Z18aiiAtvyCRa/349X5+bwD5kMvuC6JshOGvEI3Gw2i/LycmzduhWLi4tYXl42uL62trbAWadt55iXl5cxOzsLAAY7U/+n02m8XlaGr/j9iFVWGhysslRMzqmn+cNMu+rDcDiMbDZrEnvEH5cvX8b+/ftNHwFbZ+sz1cZqooy63efLnWRFGUwmkxgcHMT8/Dyqq6vR2dlpMvHsI5DJZEzVrgYxgE1cz+o7tVu03a7rmj5rlCXXzTXnZfVSeXk53goE8IOKCnw2lcKfOJuVG4lEAmNjY8ZRZ9PTmZkZ4+QS1yqt7SOCp6ence/ePRw6dAidnZ1Gz7zu8+EHrot/kd8GpkF7+7Ltk+M4BZUb/H5JSQmSySSWlpZM0lR14wcffGD6cpCOpBnXSnv08bJ1Nrf3f8Zx8NOyMrySx08ffvgh2tvbC3pP/LHn4XUAjdbJQMXmWCzQorxAmea2+lQqheXlZayurhb0Enma66kDDY7jmLPlCQbZD4AD12wO/yeY48LyXmp0GOmjs8vyI96H0cl0Oo2RAwfQn8+CVl28WGBMp0+dwnsXLuD02BhO5I+TvLZzJ5aCQVxtasLhR49MYxKWxq+traGmpsbsf7MBIbOi8XgcoVDI9AXQ7Jc6QYFAAKurqwXKkQKqZYQcA49hqaioMOcrLy8vm71F58bHcWn7dpzLH8fICCGNL7dBrK2tPeE0kf7M3PI3lQx7CHCrA0u9EolELniT79KqQRSWpPv9ftMNmaXqdFAY+bPPZed6cnwcKxURS5PIF4lEArW1tYYGZ/POgSpBGjbyE1B40gIrB1i9UFJSgp72dqxUVuJSQwNOTE0ZHuQ8eHwX+Y6/CRY4J52f0p7z4w/LAakUadR0nN379mE1GMTlkhKcmJoq6qABmwGe8fFxk8kENvdMep6HxcVF9PX1IRQKGfqS9hqpJOChAeba0Vlj9cv6+jr8/lzDHp/PhwO9vehubcXxR48KnBLbeSimiEgvADj96BEu1ddjf29vAfigEu5pbsbjCxew48MPUTM4iFQqZbrbZ7NZ1H70EU4+foxsNot4PI54PF6gAGnwGdDjuLg/dW5uzvCL6+bKSZn1sEsLf9+XHR1WAKdVA7Ozs7hYVYW/8jx8KZPBi3kDQBpqdJ/0JR3Jq1qqys9Sx+q2ldXVVUxOTprA088cB48aG/FaJoOXpQyRUXmbNgqadTy8ODataCD9FcAAMCd/cPw0euPj4xjcvx/vd3biq34/Xs7zLjNd4+Pjpiw1EomgsbHRzOu9ffuwFAzirUwGf5HfO6lzIi08z8PS0hImJydRWVmJ6/X1GD97FggGseLz4aclJXg+nzF/OZ3Gn+ebEZLedqBF6aJbjug4Uv60KoUZA4JBVssxczM/P4+xV1/FfEkJfuI4+Lps81E+UmdX7+/mHXMNZNhl3plMBu1376K7tRX1H32Ejo4OfKelBVM+H95wXbySX8tXAPxl/vmUGXVG+EzlAw16qS63g7SvZbP4keviT0pL4eXBs8/nw+TkJIbPnMFqeTm+B+C1PE8SfFNPENT5fD7s2bMHnufh/5lOY9Lz8K31dXzWdQvGoTaWgatMJoOdO3fim8Egvg/gC54Hx5Inzku37igGUgdb9yIrPVR2HCfXo+Eb0SieqatDIpEwjtT8/Hyuh43fj28lk/h0Pviva6v8owEB2zGlY/L99XV8a30dX8hm8dm8k8R+TG1tbairqzPbV/RHE0AMcvF/xTzMbmoTX85deUW30rCSiA7xxsYGFhcX8clgEP+3QABeHiNoplzLsilfJSUl+Eo+q/mFbBbLy8uGVj9zHPwAwJccB5/Jr9/6+jpKSkpw8OBB1NTUPLE+HK+NeSn/iuuYvKJDxu/pehEfp9NpdA4P4/9+8CC257fQch4+nw8vpdP4ps8HL5NBXHpcUHZ0jBqk1PX3PA9vAPi+5+HLrotP5Z1oYr+Ojg58o7wc397YwNekgSLvSfmz9ZwGMOlo2vbBrrQFgB2XL2Py/Hm8XlmJZB4nu66LE1NTiPb348UXXyzon8CKBWasyWPBYLDAZyBmof0AYE4s8vl8WFhYQCqVwrFjx7B//35DJ5tWNt5UvifdlX+J7wKBANbW1gqCLRqUo07g2qbTaezYsaPAVqjetDPctrPPz3Id2I/CdXNZ/1QqhTNnzqCtrc1gUWBzrz3tMfE8n0Xd8LfxOP54fR1/JBUzLKXnemoilnOkQ87fb5SWYikQwNsA/jKwefJNRUWFOT7RcRyTIGNAhq9Rp5OfuK7E2ax0iUajht/S6TQ+43l4vaQEtdapHrpuNs01gMHAllbIMTkRDocRCoWMD8Tqubq6OrS2tpotl5rgVf+S62g/V+XG7/fjG5kMvgHAKyvD1b4+jIyMYNu2baivrzf31RM+eNmBk48LQJB3icfIl/Sb+Aw9AcOulvq466kDDYxIAZsghEyqQNY2IGRWBTwKNplFmZ2dNdkMzdxRUfCzD44eRTwSwcjJk1j9zncKlMHMv/k3SIdCuNLUZJy102Nj+Gj7dpx69MgoIAopy36KGUDNbDFrXVZWZhxtOi9ra2tYW1srqHAgSOE9uKeMZVPxeBxra2tIp3MNKNlQcWNjw9yHCujY5CRO5jOdi+IQE1DZex2pBHipY8y5AjD3VyBgAz/NlnK9/X5/wVYNACZbQTDB6JdmIjTIpAqTfEB6ra+vGwXMtT3x+DGO57OSG9jc583Ahq5FKpXC6uqqASU8ZmbLli1YWlpCVVVVrgnmkSM4nQ/epNNp08CG/2cyGbMuDPysrq6adeRn1FGw14HggXPTQAyAgoqRc+PjuNTQgNP5qhVbhvg/gyfc07+ysoKZmZknSsaXl5c3G8XkFQaNMgCMHTmCG888g5YbN1A+MoLJ48fRdeECdl65gq3XrpltJAxS0GhlMhnsuX8fzT09aGhoQDavcGwDbDsW+pufPTo+jj3372NhYQEbkpFkwPHxM88gWVOD8bNnUXv5cgHAU13DYBi3zbA6qba2FuNHj+LHJ06g/uJFhN59F49PnsTApz6FpitXEPjlLwuABI0VsFmuyLX9fV9aiUPDX11dbYJ42WzWlC4/PHECS2Vl+Jnj4M/yPWS43qSnrV8VgGr0ns/kM+i8ZDIZTExM5M4Vz/dFGDlxAnMlJfhpNos/zuthjlcDmfYaa6BBAx58nRUNHDeDbdrgk4CC+sPn8xleH3r1VSyVlOCHAD6f7xtAoMNtEZx7IBBANBrF3r17seh5+IHn4Yu+zaNXOWYCPz5venoaiUQCpaWleHT6NNbCYYSzWdRns3glH6y2q/ioZ21nzAbclGvtzE/gpjKs32M5IwNCy8vLeH5mBpcbG/FFxzFgS+VPsyWqc/m/OiP6Q5vg9/ux9epVNNy4gZqaGhw8eRIZ18X3PQ+fl+pCXWPym/KEDeI0sKDBJQWoBFevuy6+7POhIhjE2Py8yRQuLCzg2elpdEWj+HppKQJSZq6BcV1f2rI/Sybxt4kE/jQYRKUcWbm4uGiC78QK6XQaLS0tOHPmDDKJBD5bUYGs3Jc0ID2Vh9Tx8rzNhIPSTWVU14Z8dOTIEXOEnM/nM0733q4uzDz7LP5FOIwtVl8M0t+WU45RnRd+5jsrKxgD8H3HwSvZXGk3e6Q0NjZienq6YLspv0+aa8k+eY12j+upeoCZfdvpJp2qq6tx+vRp08OprKzMgHfHccxJIzaPqZ4hD6fTafxLx8FfCv+Tzj+ansZYJoPveR5eKy01FXOBQAANDQ0YHx9HTU2N2RKpTh2frQEy6hwGY7lNs9jnuUak5/r6Ourr69HS0mKcFeKOynxmva6urmBbij77u/E4/mZ1Fd8IBvHFfFJE14vP/Mf5eYxlMviHbBafyWcrKysrTUPI0Pw8Xs2f6KO04vzt4APvb1eoMFnBcWhFFnX01qtX8c18o1naIm49aWhoQFVV1RP+g9KPvGePU/0Rx8k196XDxHLvlpYWtLa2FgQDeV/qIdsBVFqq02ivLZthsrLlTdfFP7ouvux5eD1/nDxpMj09jb179xYEAFRG9fmaYOB8dVzE8MFg0FRq+Hw+dHR0YP/+/YaPFa8RxzLRRgzG9/8ukcAYgDfKyvAlZ7PaeS2/bVa3TDl5W8S+JqwgZ+DyE6ureLuqCp9zHCQsbEVfT/Uo5UlP2CB+pi3g3zyB7/Dhw+aIVjsQpBWcqhf5fP2tdGfCUrd+cWzpdNr4Hjwacn5+HpWVlca3sP0C2yZqUsjmJ/5NP9LzPMzPz+PUqVPYs2fPE8lg4kOVX85L76uf529ub0kkEqiqqjLYinRk4JTrzbX4566nDjQwKsa9P6rM1amyQRWwqYDUqNCp1jINLU9jVFQd5FQqhT03b+JeRwcqfvnLAgPvOA5qfvMbrL/yCs6Mj5sFOjo+jqPj4wCApOehqqrKVACwAWF5ebkxXGoMk8kkqqqqzBaLYDBo9udv27YNfn/ubGWfz2cyzcFgEOvr62Y7A5ArR9myZYtpQsXM7Pr6uukvUFVVhUAggIqKCsTjcdOwUJV3JpNBJN+FnfRhU0MaNaBwj7fP50Ntba1pbBfJH2MTDoexa9cuhEIh7N69G5WVlWY/e0tLi+k0TcOSyWSM8qioqDCnPESjUdNjgczJ+VHhEkyqk0LGZekbG9BVVlYim82aqhFtcqMGLZvNmsoD8kFlZSUcxzFj5/57NtgJ5hV828AAnslvYUnn6co+DhR2VrDwfvxNYWOgByjcq1ss2KDZDP4mCON1bHISR/J8yvnqPSkTwKYxs0t1CZCo2DT7wLIv8tLYmTPYiMXQf+gQWn70I4zn/x88ehTlb78Nz/MKKlr4XY6Pz1aDazsyXBte6oDajinvp4C1/uJFTOWDIcpHqkdY1cOtB2NjY4bvKysrMXD4MDaiUTw8cQJNP/0ppi9cQCoWw4OjR7HrjTeMnFD/sGxb+eoPcSlY0xJHluZlMhkM5qs4zo6P43ZrK14HTPBLHQg7yEMeAQoDQOpw8TMMsGazWUxMTJi13L17N2p8Pvwom8XnJGiqjqFeqvftIAQvvqa8z+COHYxg1Q/BDKsVVldXcW5yErcqK/G6OMqcjxpypYXP58Nr2SxeSaWQTqUQlz2pP3McfHd9Ha9lMvhqZSXW1tawtLQEx3HQ3NyMLwL4RSqVq+xgJRM2M3Q2TYsFChQQKu9rtk6zaax4YqaOje4W8r1SfD4f/nzLFvw/8j2MlKf0Oa7rPnEutoIv2jzXdfHo0SOsrq6ivr7eZPUzmQxisRjcz34W/2ZjA990HHwrm0XWcbCe3SzFppzr/O1MjeoLDRKqU069ls1m8TPHwY8A/GlZGb7gbJYKZzK5JlzPr67i3zU1mYA27690+GEqhf+0soJvBoP4bH7en/f78XreHhJgl5aWYmFhASsrK9i+fbvRnQxwvR0M4m9WV/HVVAqfyW6Wn2twhHhFM/laqaWBJdvxtudPnaB9jTQgU/vRR/jfnn0W2yMRPH78+AndS3vblKfPVL5yz9Y93Fryl5EI/t3jx/hSIAC/VN8tLCxgdnYWDx8+RGdnp5FdOzvHces4M5nc9ljiII5LEw+0j3RQeE+ecMP7kc/Ky8vR19dntjgSj3IteW3btg1lZWWYnp5GPB43z1LeqK2txb+qqsL/Z2ICX85XIFD/rqys4MGDB6bBtK6dBm5tp8UONhADabM4yp3qRfK28jiTE7TFs7OzKC0txfDwsJFlvf7PsjKMA/jbeByn8kfxqV4iXvxXsRj+/ewsXstkkPI2t43E43EMDQ1hY2MDYTlO1Q4gFbOLlDvF5JQtBij4nlYPBQIB0ytqYWGhoBfYwMAAzp8/D6DQoVH9WlNTY5JCalPIiz9MpfDtZBJfdBy8kL8Hk33ZbNYcMd/U1GS+Q/lUvapyo6/bQRfyKgNknO8/lpVhwnHwA8fB1/Pbk103VzW8urpqAkvt7e2mkkjXj3xEP0uD3PzMd+Nx/M3aGr5eUoLTCwtmyw8d5eXlZWxsbBh8rNVDGxsbBb0bdH5fKynBtzc28BWfD2V5W80AHiu+WUHBRNjWrVvhOI6pMCUfv5hMwl1cxBvbtmFLOIyvVFQU2EQ7CKL0V34ndunr6zNZ/HQ6jaNHj5pxKZ9yLuQ9tRX6DJufaUOpexKJBKqrq83Y6Ocw6MHAIi9WVhTjHb0U+6p++XEmg+9mMng9ncbLeVyQSCSwZ88exGKxgu2enJ8d+FEeKuafq91wXbegpQDvTT6iD6X9Tp7m+i/q0UBQqMZDy8ZVqenAdVHVCJHYZIhkMolwOGwWjlkeLeXYde8evDfewNzcHDbkHqmXXsLGSy/h7MOH6Bwbw0Z+sdTZYvm76+bKUVlydW3bNtw8dw4nR0ZweGzMEHdlZcUoDDqZbBxUXl5ulB8XgULFTDJpxqZydLKpxML5RjPRaNT85rN4sVlmV2Mjbhw8iOMPH+Lwo0eGhj6fzzTBCQQCZluE53m4UleH683NODY8jCNjYyYYQMeR2zwYNCgrKzNlgwQ5zOqosbnT0mLue+DhQ5OlJ4004qXN5FhFQUGkMmAZDteeR+exbJHOEOlGIxuLxeDz+Ywwx2IxuK5rGmQ1NzejrKwM27dvzzX7a2hAX2trruIlT0fyZW3+iDIe2cQ1I+9mMhkD+pnh4P62LVu2PBEpptGNRqPmc3V1dQgGg9iyZQvKyspMIzcbAFH4VTktLS0Z4dao9szp05h97jlUvfMOwteuGZDDEkMNnune+Np338Xs88+j7v334Xkeqt55Bwuf+hTqPvjAyK7Kqsq07RTp2PV7duRW39OLtLLB2rbr19F85w4qKiqwKhlJO+jE+/J0DmbLHMdB1dtvY+755xH59a/heR4iv/41Vj79adS+957hKSp36iJmU+1sye/z0rUtKyszwc/Hjx+bMv6+vj5s27YNf7J9O8IEevnxUZeoIVTAq3yoTgzn+Z/n5/F9L7ef76W8UzQ9PZ0DDZ/6FP66qQmfmJvD/48nDXib1VDqPGnwQvlXLxpOgmsGsHgvzZRSj/L0nLm5Ofj9uWa5bAb1lYoK/L+3b3+CnnawQ0uVbQeX4/b5fPjR5CQm02n8CMDn8kGrhYUFZLNZPH78GF8OBjHe348f7dsHD8AnspvbDXgvzQ7rmPRvyiHHkUwmC5r0AZvNA2n/tPFtNps1RyOz0q6iogLj4+MG1Kpc+nw+ExCfm5srOJpYs9Hc08yMViKRMFvxPC+37/Ujz8N8MIjveR5elEoJgg3SV+ms/Max2WumwVKuP/XU9yMRTHoe/jaRwGv5zK4Gq2jr6BzpxfX/66oqPPI8/MflZbTnKwN1SwGQszu7d+8uqDTj5fPlthS+kUxitqwMP/Q8vILNahnbyVCHivxA8MdAMemlusvgGNm+QztMnc3ydvIawfXi4uITupif2b59O7LZzWZlpLNmbGOxGL4WDqOpuzvX4yi/T5djnMhvWVTbbQeQiLU4XjvppPytjoTOndiI+o+2QhNSnrd5MhWbAiofUtfwaLZ4PG7oozLJZMY3tm5Fw40buebJi4sFAYSFhQV43maDaaAwg06Qr3PUkmrVP0pzzVraAWHOkXxHXqeDA8DoDdsB+2PPw1uVlfh6WRlSCwsFOEGDj19vasIzi4tYz/OOBj0WFhbMqQkMjtFO6jZp/qYMU1boAPJ/rru9tZRzZxNH/j8xMWH2vjN4sLi4iNl8Ly29MpmMaWo5Ojpq9AHv7zgO/mNVFSZdF//gOPhEJoOFhQVzAsXKygpGRkZQWVmJ7du3G0c5m82av8nbtv3QedgXHTLyRiAQyG0Bcxx8TbYcLi8vm6qH5eVlI3P9/f1GjjTAs3XrVjQ3N2NsbAyP8j4A185xHPx/q6ow5fPhb7NZHJKjPFOpFJaWljA/P4+mpiZTys8sPBOcxeyk4zj4YlkZPi8VmCr3rELX6iTSUPmBzcoDgQDeqqzEfCaDv1pcNJXnqkfLysqwe/durK2tYXR01ARE1N7yFCjaPU3S3L17F52dnaiqqiqwh9RPumbF7LUGPoDNyi+eHLd161YTzKDPAaAgKMhgeHNzM1zXNbpbcQqTwJlMxmyTUjznOA6+n0hgzPPwPc/DH4l/HQ6HMTk5aU5PsQPtmqDmPPX9jws6ALltLExoMVjORDSDsJoUf5rrqQMNLH1RZ0WPRtMFsgGovqdGGdjMSNJAsM/BysqKaZo4Pz+PcDhsABAVdvBLX8LM889jz82bGDhyBGuRCD4oK8NHO3bgQG8v2h88MEyvPQNoMAlWrp45g9XKSnzU2Iit166ZyBSBGZ3IB+3t6O3sxIG+Puy5f99sc2CDFy4os0daisSI4f09e9C1Zw867t3DvoEB49CnUimsra0hEomYaDLnCgDXm5uxHArhalMTdnZ3m+wJG6hxfjQOyWQS10+fxnIohGvNzWi5cweO45i9a2ROrexg8EEjWJwjj73r27sX19rakPX5cKWxEQ03bpgMenV1tVEubBqaTCaxmm/Qqac5kEfW19dRVlZmjsQhoGK54d1du3Bz1y4cGRzEwZERwyO6LozIum6u70QikcDS0hKi0aiJ/DILeKWpCUuVlbi6cyf29fcbAEBASPBAkE26skMz14tBEvJKKpXC9fp6XNu5EydHR3Ekn1lXwEgAxd4KrC6hUSH/2FljLVOisuLv2eefx0ZtLZZeeAEdQ0Pw+XxG0XFuVIoMCG1sbCDy/vuo+fBDAMBaOo3Qu++a/zecwnJbrgvvRcWvjgIBFD/HuQCF0WR1inlp4MAGMfpdZv2VPlwfWxfR+ITfew8V77yTA83ZLMLvvYctly/n+D21eZSdnZllRvkPtXVCswkMRM3OzppAJo3zxsZGrpw1f+xQMfpQnzErpY6r6lt+rqKiAv+4uIgpJ5cxPpvvVUF9925tLZZcF2+Wl+PzqcKu+Qq+VI5tp9q+1AAyA60ZC86N2TSuA/c5qx7ns3t7e83rCkSAXKahrq4OMzMzBpTZgQa/3499+/bhL8Nh/LvHj/Hi2hoWs1kTXCXIHRsbw0c7dmDF58OPAXxK5MKO6tsBFwUvmpml7lbwweo2Oly6xtSxLBF1HMcA8kePHhUEGvg8INcgj0cm03HS+dOO1NbWIh6Pm8A27Qr15+lHj/Cb6mp8Ts4FL1bBqDRWWbedUvtvftd1N7c+fCGbxY9dF9/IP3Nubq5g7tu2bXsCQGoAxfM8fNnnw/c9D5+TTJbqD9oq5emZmZkC57K8vBzPzc7in6JRfLWiAhV+f8HcuLYqnxpc06AodSQxAufNS8efyWTw+PFjRKNRIwPMrPEkGjpFSlN+n2OgbdO1t9eGuIW0ZzUBweSePXsK1tReazrYTFSwYR9teygUQmlpqdGpoVAIS0tLZtwqv7TjrCLlmAj0Ozo6CtZSHRt+XwMKdHzt19URZIk3E2kMdNbU1BSsj+otrc4kXdUmKkYgfqC9UQdAA18zMzMmSUBMFA6HEQwG0djYWGDPbR3zYjKJ/3H3biSTSQy7boFu4djJB7QFfA4bAXLLIXlCq2pUt2hSgAEp1b/8PnGB53kF60AaJBIJLOYDPMFgEMvLyyZDXV1djWg0amy+zXO8J6t6yQPqdH1uYwM/r6zEn5SUoCxPh3g8bsrD2XdK9Q/vrfdR2bL/13FxPem4E/99znXxOQAVefvJwAv/TiQSplcEK2YVj9D2kV+Jj7Ui7jPxOH4djeLPQyH485Vv1ElLS0vGttAZZg+JSCRi8KLqDNUTGvDgGjKgwirubDbXqJYVpnv37jX3nZubg+vmKpVOjIzgfkcHXkcO09rOPm2vHXDU99j0nH5TKpUyp8pNTEygoaHBJHD5Pd5PZbBY8EgxDf9m0pAVhZlMxthf8p7f7zc2dm1tDSsrK6aybWJi4gmc5DiOabY9MTFhAhWqp75YW4t/DATwajJpeogBuYDtvXv3cPDgQcRisQKcx4oGzk9/bPnUefL/6upqU92qiUrVI6z+0uTF77qeOtDAgfNvzSowKqfKhcyqpVTApoJfXV01e+vJmCwf4hEb7H1AA5bME5sKfPq557AaiWDk1Ck8Nz6OSyUlWPf5sBIO435HB57NR2XIqHR+eZHpTo2O4kYggBOjo6ivr0c6nTYgQ49qeevQIaxUVuJ+Rwc+kWfuRCKByspKc4QhANO4i6X4BP+BQADd+/ZhuaICvQcO4Hy+h0AoFMLq6qoBFKyMoEPrOA5OPHyI67t24Xj+iDGfz2d6GZDRqfxJnwtTU7jk9+P01BTq6+sLAh6qkOlAEyRotobMzwDETw8eRNbng5vN4tmZGezZs6cAxFBRMyKsTg4dRc0aa1NNjp1CHAwGcWv3bixWVKBrzx6czGd7S0tLMT09XRDwYqNGlj2zKoL8xVLQg319uL13Lw709pqSYtKFa0TDSMWlDjurbtLpzb4WXK/LjY1YrqjAh9u3Y2d3t+kbEYlEDD2YvWVPDttBYmZO9/AxOFRfX49EImEyGn6/H1veew9zzz+PpqtXC0ritPwWgHFCFdxrNgkodPJVfvk3n1msLMtef1t5qbHgZ20FaIMwAkDP80wwDoDpCq/BC66b7bzpxeeqLlOAoAE4GkV7n9vv6yKPU97o3C0tLRmgwcBfaWmpOfmA89a18/ly50zv2LED6+vr6O/vf6J7NOU8Foth9+7d+GwyiR+4Ll6WAA1p33n/PgYOHUJbfz/cXbueAFgEj7yKOZs2eND/CZRpFLnG6rDxOSsrKybQyXWura1FNps1TjEvdejK8pUYLDMlnbSSjiDgq6EQYhcv5mhbXW1sDp3/oaEhnE0m0R0K4QvYbB6qdLEDbDaAoa7SwASD0Kzai0QiBUd9ahAPgDlNp6KiAnV1deboZzp5pDVBsq6NBjPV2dLx0AYzaMdKsdraWoTv3sXJmhqcPHmy4B50+kl/Pl8dEKWROgx8zdYDvPdnslm85jiI5ulNp7OqqgqxWAxNTU0FdFWa8zmf9/vxL2tqMDo6inkro0q6aOaXjSS5t9l1XbS1tSHa24uvnzyJqPC9HSBVQK7BGK3aY1dvjlvpo2tNe9jb24vW1laUlpYafJBOp83aUP+pTucP99Pq2iuPFAsAacWmz+dDY2Mj4vE4tm/fbrKu5DnV07Q7DNDo/mz2K+DvbDZrnMOVlZWCyg7NpI+Pj5sqwEwmg2g0itnZWVTn+9RQDxVbd8o+Lzsows/yNZ56VlJSgomJCTQ2NprjcPVz/Ju8rhjK3k5CvU4nh7Rh5t2WUeq76elpNDY2msCHBmpII9tpUadYA8C2fNm2kY3s2P8CgKkk5XrRDmvQTPUu6cJ70kYBm0c9anBBeY+2ja9v3boVVVVVSCaT2LNnj9nWRHynW1fofNvz5fue5+HFjQ38m7q63ClC+V4UnE84HEZDQ4M5fUvpxTmpfNkBNpsGqsOIycmDnK/qmrKystzx45EIpqenzUkLtAuKqXS99XQ5rkk2m8WLyST++1gMwWAQ3dmsOUGOslxXV4dIJGKSMzoX5WnOWX03pYHqfj5/aWnJ+FqpVApzc3MmScBt4IFAAKFQCJ8eH8d/3LUrd5Rq/vvqHFOuFBfzudQftOkMntB34cly3MJAetr0t51uex2VNuSlqqoqPHz4sED36AlFPHWDAQQNaOn6KU+xktyu8qYOfHFjA//j/v0YHBxEIl+R4ziO8aO0pxflm/pe163Y/HQd9aqqqjJbVrkGDPCz2psBwqdNxD01ig5/7Wu43tJiSvdJXC3t46DV6CmQV+ezvr7edFllAz9G7UkEz/MK9peSae7v2YOetjY0rqzgcWkpTo+N4djkJI5PTuJybS0+2rEDJ0ZHn2gUQkVlO1gdw8M4PDaWG6u7ebYtAS7HfPzhQ1xrbsaJfDkPsAkctPsmKwsIDCnEmUwGx4eHzT3o3FB50mCp8HteLiJ6bHISJ7gXUwwNDZkCSgrq4bExdAwPw3EcbGQ3mxwy4EJHVBmVDo5mxGlEs9ksjg4O4ubu3Tjz6BEOTU4i5RZmotWJUMdOTwMguKZQqpG0FcHJ0VFcaWzE6fHxgpJEZkz4TM16UtC1dIsVFccnJ3FsYiJXUuYrLE+mQdOMI6O95HEaKM1OcfvDqdFRXGlqwumxMVRVVSGdTqO+vt44TwSvVKg0eJyv53lYWVkx/SKoKMrKyhCJRNDc3IyVlRX09PRgNM/f4ffeQ92VK4jFYgjEYqbbsevmyrXYhddxHBPdZqkbs5UMtjDARqXPdaUy49qSJ7ge5EU7w8PnKiilgeRFw6Z0oE4AUNA0lXNixoMRXgY9FQwR3HMtiwEwPp9/03DYVVp/iIsg1B777Oys6adSUlKC3bt3IxgMYmlpqSCTpYFdrpdmadWBUQPO64VkEq0jI7mtEnkQQsfkwtISzr7zDurq6grWl86DnbkqdtnP0yCWndli0ILGmxVZ3A9YWlqKiooKzM/PG2BInqDM22PhPamH1KnjZ9U4Ly0toampCWVlZaisrMTCwgL8fj8OHjyIGzdu4Gv79uG/z9uCDTxppO1gjB2EsD+vepvywS1tDKZqNVwoFDLOBunATJLKqgY9FDBTPhW8ajacPFVWVoaKigqEQiEEg0H4/X4cOnQI7733Ho4fP27urxlxBaOaGdOqHcUBtvwr36qTwM9Tv1dVVaGiosL81qOYtfGw8gTpoEFXHZM6XqSN2pZgMIjq6mrMzc2hpqamwHlQZ1Edad1SQZozQKD0V12kAWelE7u4q1NN8E47ryfq8FJnmv/bAJTroHyhWwQYGOecVMfwM8pzSle16eq82Sd2KZjVPkCu65rjB9WZIm0pOyr7Ns05BgX5GhAkre1AAW2H53kFDi7voVuF1d5Qjkkn1dHkD36X8s8xqCzNzMyY3l/kZQafuf4qb5w7MZW99soDOmfFMK6b20LFRBfnwOdrAFTprYEt2xYRXziOUxC0t51mNt4l3ek8flRbi//XyAi+6vfjWF6+dc3V+f247dukP/UIcaDjOKirqzO01cvmq/8SDEA6MKGkGXuOgbxE3FhWVmZO0dDvKqYmJlLnkXxDPtFAi+d5RkcGAgHs3LkTwGYFOfmBNtauqrXXWXWG0ph4mFXIPPnDcTYbcG5sbCAYDKKiogIATMCMFclqJzhvte8aGOM4bPnnFoJsNovW1laEw2HzedVv+prqgY+7VJ5isRh6e3sLbDbtMyuWGBwMhUKmoS4xpVY6cTxM/NLXtX1TNtpn4/+qqir4/X7MzMxg9+7dCIVCBbQiD9o6VvnJthW8KO/ckkIak5+4zYaYjMHup7meOtBwc88eLJaX40ZLC87m982qwigWhbKjgFQ8VAzaNKMi3xREASLL0unklJSUYH19HXfb27FUUYFUSQlK8qccMKK2d34eLXfv5gxwPkChRogOJMdsK3JVjgBMxYHnbTaWdBwH63nngJ+xSyIZRWSwgxm5zuFhdA4PAwASUhnCfch0nNQxZGRQwYg6G6Qj58J14CkHKlAaDc5mc9sQtAGeAgVG62jcfT4fzs3P4/RHH+Wek19bNVZ8BgCznUGBiB4bqPyjoI0OYiqVwsGREewfHMwBnbyAX9u2DVdOncKxoSEcGh0tyD5xP5GCmGAwWNCMSZ0mNYSqwDXqSAWhnd1tByabzWL/4CDaBgZyGZjsZsaK81MepACroBLYEEjyM/wu97Nv374d8XgcCwsLWFxcNFnw6upquK6LR4cP4+GJE6h77z3ELl4sAHxa0qcGhWumTgRpM3P6NBY++Umkrl1Dx9AQhjs78eDwYZwZH8ep6Wkz9mIGSveS6no7joMrW7bg0uHD2HXzJjqHh9G7dy/u7t+P0FtvYcvly2bedqk9ZYl/A5sGnQEaGmFdT8oI10AVMvmNAFFPxflDXOQ90ogliAwyBYNBxGIx7N+/3zjf/A4Ddbpe/J8OlWa91GCoI1pWVmaa0LJKqqqqCs3Nzbh58yZOnDhRMGY1Wuo82kac4yh2sQpLeZsOox0Act3cWfbsiD49PV3QS4XGW50n0kSrqSg7en8FqNR15eXlqKysLOjATpmkbNkyoyWwthOt+lD1HJ+vAVnqIIIG0pKVbtQHDLLREaKzoHLFe9A2qLNl22TyIpADNQxyUAfTNtTU1JhSVPKXBqcBFNhalVcFhPyfcyYvkA5akUTniYEW0pp6UB0X1Ve0VTo3DZ7qGmngTEFpNps1WeiZmRls3brVyBYDr/o9rq2t722gTrusvYvULtjZ6qzYEd5bHS4bePPZui667uRRHZ8GgjkG3m9lZQUNDQ0mK2c/n/9zbrquHD9pxPGThupg27KTyeROV+HYVXaVpspvvDR4pk6Yyp866p7nmTJgbbjHrY3qzDFY4DiO4UfSlp/RE7Q0MMXPUT/Z9on3ZeCI9KEca9CM39GMoi3f6iBzvWgvSSfqP9rCaDT6RMCwWGBZx6K8aCemlD+4XhrgdV3XlJkzgcP3fxkOYzqVwrczGRwRx1r5RPWePkcvO+DFhIr2v+G9dW76Y8uqrTv1ddd1Dc4idlG+5pYVTWzQx+Gl20wVK9CW2TLBMTBpobrM8zzT4I86k9t3bX9HMSnvp+tGOmkFQiQSweLiogkm0Wej/8AACRN+ioGV1pw39Q9pRJ9I5VZ7BlJXs7GoHtGpuk5xiupXO4ihskOeAmDurbTh9kL6r3TESUf6lhyn8hGDFK7rGh9GfVJ+RtecDepDoZDxp1R38P7q9ynG+F0X15i99nRbiNogVn6xKu1prqcONJwaHc1VCuT3yfPBFAheagx0Yhy4z7fZ0VhBn/4AmwupUfhUKnd8yIl8ZUHC58NiZSWu7dyJLZcvIxAImGwYM2JUKtxyYZdxMfDAjqscA5ldHT0yAI2HZjDonASDQcOMNA5kcjoLqnxIFwoWM7bRaPQJxlTnTY/Bcd3NI+Mo5Iygu26u6zgz2wRIVCYcExWOgg6CDTIem+xR8FWpcJ4KOqgAFCSxBN5xHBM5B1Bg7Cg0XH8KIsf10eHDWA2F8GFrK663tKDz/n0cGRszSg3YzOCwPJrrpg2NVAB1PUgXrrPP5zORRc6HDhOAgmCSRiSz2azZZ6mBCgAFjWxUBrjGupVDjW82m6ugyH7mM3jU1obyN99E5P33kU6nsbCwAJ/Ph6FXX8V6LIbFF1/EV0OhglJvlSvKKJVYMedgbW0Nf//pTyMVCmHmuefQGgjgvRMnsFpRgauBAA4/emTWs3vnTlxrbsaxoSE4joNrzc04nm9EqvzL9f7wyBEsVVZi8OhRPLeygv5DhxCvrIT7R3+EY3m6c9sIFTWVO3lFgRyNqzYc45Yg0lgdWgUgdGrIMz6fDw8PHED/oUNPqyL/iy4FyQSMfr8foVAIkUgEoVAI1dXVqK6uLjDMOgcF7lq2qE6L67oFckZwS/1YU1NjdLLP58OePXswMTGB7du3FwAcrWTRcVCWbCDP1+05k77q9KlOVv0KoAD0MGjI+Wv2lUaeekjLJu0San6fulCNOT/jebm9ig8fPsT27dsNTUkHG7wUi+wrgFG9wNfVGSM9NUuvwI7rzB8GntUW8776PQVM1IOavSVdGfBmEzPN1E9MTKClpcXoPepYnYvyF8dQbO58T/Uc70GQxs9qxZRiBW4p4hpwnppAUGdO10rBuuokDQaxcRfnsrCwgP3795txq/Oh2IDjy2QyRucyi8fPKr7RrD9pRbvKPgfcNpBMJs1WPs1WqaNhg3aVHdKNdKbtIbils7G8vGz0geflKuw4Bxu0U660Uo/yoUE0Pkuxjg36bflQ2WXQ18acGtwo5giSDuQLxaP2WqytrWF+ft4kiJg91CA/M+HKN+oQqLzR3tsBEeJY1R20W8Q97LHE3kN0TInR2OSSmIy0V1oQC6nsaFCN+IgVUdTNys905Ii91KYq7TTAQNpqMIRbReiIkmaUQdfN9eTgllTuc38hHsfF+np8PR8QsPUp15b3UJuh+IaYkp8jHdn3SLcv6nppYJ+XrrviGdv+0f5wDzvli8+gfiGPMzOtPKs6ww5uqF+kgVlic+IlbrUlz3BbQzKZNEktrbLROSmtGfDlvZUOwWAQExMTCIVCpgKd49Ukh+o26kg7EE4e5RZl8h/HopiDjjZPnmHGnfhNZV2DwhpMLXZf5WulA/sEsqcgfQOuHefHAAv1FXlX+Ytrplul7Werj0oZ5w91OOdh8y11rW5rtAMN+r+uAQNw1D3UD1w3DXyo/fld11MHGmZmZpDetg2Li4sYGRkpqDywL9u4ExCyuywb0HCySnBVyF07duB2aysO9PVh/+Ag7u3eja4TJ9B5/z5ef+MN3N21C9c7O5EqLcXYkSPozB/7oydFcPHW19dNo0mWrNMJZ0NCMgAZZnl5Gel02pTDLC8vY3p62gjJ9u3bDRNraRyVAE+msIFpKBR64tSOlZUVs5iBQACRSMQAYTpONGg0RNoEkmX+rBigQHOdNNug4JPGjUfUcC7cO7S2tma2gPB+XDs1+jwRAdgMsHA9CU45F23uo4aCARgt34/H4+YZLD+tfucdZC5cQKa8HMuhELr27jWBJhoGBcAEPzQqLD1cWVkxYwyFQoZ/qSDW1tZMgzYaK+VRgiEVcvIP+WtqaqrA0VdDr8EvjonGg8ocgDEgjuOgf98+9J45g6TPh2QwiOQLL6D60iWsrKyYTF/lL34B3x//MU4/flwQYeXYtVSd/KDGW4MBPp8Pz0xN4cNAAGenplBZWYlzk5P4sKEBpycmTOQ+m83iRksLFoNB3Ny1CwCwGAzixq5dOJ8HDgpsPc/L3Wf7dpyemEA4HMaZsTFcbmzEmcePsSvfG0CPQqQRUkeMf2vwjApYeYFGj89m0IgGQw1iNps7/uo/HzuG1TzI/n1fmv2lMVHnhmvFvd263UCzGVT6GjhUZ08z5AogCTRYNUY5CQaDGBgYwLlz5wxQJI1tZ9o2xAry7fd5sRkp5xoIBAqy83wmg3LUA3xN6WE713RIuI78LmmmToAafe0JQV4gOJ6bmzMnEujeWHVQtQyTNLIDO3bWiJ8hD9OBAAodbH6HupEZHi33VXpyfsDm3nnqP45J56s8qFlsgrhMJoOlpSW0tbWZoCn5jUEC8pQ9Pw1G2TyjoIp0ZJ8KjssOFnOeFRUVBkuQ5gy+kN6kKYEse/5ocIJjZdCGdor9o1zXNX1AeHSw3lttK/mNNFfdynlr1kmdA6WHZs7VuVpdXTWNwNjEjXa7oaHhCd3N9afsskcT76/8r9VQpCu34M3OzpqeJeqskXZq+yi/qptpH4lhSCNm+zUgpTJCR533/EEyiZ+5Lp51HLw4MwMg10uAGE95mTySTqdRVVVV4EiqsxYKhQpsXyKRwC/8fvw8EsEe18Urk5Nme68dpLDnbgdLOCd+Rkv79XvUH/oMrkEymcTS0hJ8Ph/Gx8cRDofR1NRktmPy0sAhMeHOnTsLAm8q78R41HWsBn78+DFSqRR27979hGOiziLtiDrtKusqH6rHitkIIGcLeTzi6uqqOfHm4NgY/ucTJ5BMJvEw3++MPKNz0QQbL61Y5f90vpjM40lGihfUr9GASTG7pnhN19V21lTP0fYyoMseJa7r4vHjx2hoaDABRv0OZZLjYqN81R8arOLaMCkZiURw//59AMDjx4+Nf8Q+PxpoVTtC3lQ/TmU0k8mYI+7ttSf+YBUe/SlWJNIPUz61AyvE63aghXq9rKysoKkse3DoWqpuoQ6y+dJ2uvU3700fUfE978EgB4OUy8vLqKysNPpOeYT8SBnnnBR3K17WxAJ1NGlTLOClfoZdKaOXnQzguvt8uS1zGmBLpVJGxoi3i23Z+7jrqQMNd/fvRzwcxu3WVpT+6lcF5YuMrlIw+R4XlY4iCc5jqpiRVKOsi3fzueewFg7j5q5dKPnlL3HjhRcQj0TQ1dqKqkuXEBwehtPSgkR1Nbr27MH2mzcNIOHi05B6noeBtjYMHDqE1u5u7OvvL4iuUii5J49jY8NLfo5H/6iTqOVTVH58jUzPclTd/01mW19fN85sKBRCJpPB7OxsQTTP5/PhQVsb7nV0YPfNm9jR1WUCAplM7ujFB21t6D14EHu7utA2MGDGZO87V2WkYIfrp9FH/pSVlRmDrdkFOjoaGVVmpLNEg8TPad8ECkZlZaVxJuhwsOswDePc3Bx2dnejbWAA40ePYujYMezt6jKN9Vjyy3lp5JdGBUDB6+l02vAwDRcj6ww2cO21ekMddjVKdtZdS6EoCxsbGxgYGMDy8jKi0Sh27tz5BAjVqDyPgep65RVshEJwl5fhm5pCxS9+gdXVVUP3YDCIUzMzaH733dy481FIKhW9v665KqsPq6vx7pYtaLh4ETt7enCypARn5+dzQB/AsYkJHJuYyBl9MbBnxsfxYUMDTo6OwnVdfLRjB86OjxsdYhvOYxMTODo+bhQctyaVlJQA+XFqlkV/FKjrOgKFDcHsAAffLwbMgUJDeTLfc+MPcakTwjlQB9GpZsZKM8w6J8qZVnwoaKJu5fx9Pp9xUBlc5bYvdqimrPGsaHUaud9T+63w0jVS0KWXjp3focwyos950fjR4ff5cqcO0JirrrWrkYBN3apAUOXQDjSFQiETzKuoqMDMzAzW1tbg8/lQU1OD8vJyk9nTShqVLY6hGHjh/8q3av+4NupgqqwuLy8b2o+PjxdkaVlGafMWA8MsA6ctUZlwXdccs/xeOIz3wmF8JpHAp5NJrKysYHV11QTLle4aVNBsvgZgOBY+U+nC/zXYwnXn+pCn6CAQqPLYMzrAzc3NTwAuPptHNLO3h70mHKtWanA/LKsIPC/XtZ3Ndm2ApjzF+/NeepKTvUY2z6pOsPUVT5hIp9OYnJwsyMjxPZt2AMz68UhI6gG1AUxUxONxNDU1YWlpCclkEouLi8hkMhgZGUFjY2MBX9pBFNKRe5WVz3VdlG+0zFtBrY3Jqqqq8ObCAqZcF78Mh/HZvDNKh86u5NA18Pv9psGlLZe0GSsrK9i7dy/S6TR+VVKCadfFxv79eObSpQIHJZvNFjgN6rjQ6VLdpgEEx3lymxBppmutzviWLVsMbk4kEohEIgUBNtXNvBiMo2OofMbPM7m2sbFh1nt2dhaxWMw4PhyDzklf14vPoS6ljGhSQJ0+2zHKZDKmkoZYcXl5GZOTkyahtnPnzqJODQOeVVVVBVtmlc+IMclv2WwWc3Nz+KnnofvUKSxmMviCZLw1gEC9pzJv6xm+rvacDddZCUs7RZ+ClXlci5GREcOD9fX12Lp1a4FcaHAaAFpbWwt0q9pOYuvl5WWkUin8MJXCTwIBNLkuPp0/TYD3Y7CCekPnqkFZXS++Tz4LhUImUKSnyyUSCczMzKC5uRnr6+umeeDjx4/R0tKCbdu2YVc+KWXTlNvkWdWpa6Kyy4AoMdMHkQh+tHUrXtvYwDfyVcN2gkRtkc7rd+ktBjWIlbiVT2WbgXmeNsEAWEtLCxobGwuSZqRfZWUlUqmU6SmhWJVjUN+ZPonf78fs7GyBjlHMpbKrOse2x2q39G9W+9gFBcprmrz4566nDjS09fTgfmcnmq9fzzWey4OekpISs+1AlTCVMrBZ0keBJ3C2y3wDgYAB2K7roq2nB30HD2JXVxdqamrQcf8+7nd2Yn9vrykpbr97F70HDmD3rVtYWFjA7OxsQRNIliCXlJSg/+BBrEWj6D1wAM09PSZYQPC4vr6OiYmJgjPEFQhpMGRjI3fspgJDnZsCMAoGsCnYCpjJXAyS0MgCQCQSwdatW1FeXo57HR1YiUQwcOQImrq7jfNOwe49cACrkQjutrcj8+MfG3BNp5pGU5W+ljGqoHEN6biwpJCfoTHJZDIFDZN4f95DS1wJ/Gg8eNrI2JEjuN/Rgdbubmy/eRPZbNY0LBzu7ET/oUNovHwZdVeuGNplMhk03b6Nptu3c85/XsFqMy7lQWaleHyeOl+cMxtfMVpHAeP51ZpFZPCF4IqZN4JiOoG20WKpIwDMzs5icXERwWDQdLemUdagxJ2WFtzcvRtb3nsP1e+8g+lnn0XZG2+g4p13co5Efp0YSX3mmWeM8dauz6pMOQfSRl+/1NCA1WAQfS++iHQmg9PuZqZXDZKCo3Q6jSNjYzj86JGZ7/HJyZxcYFNBaYBKjRjpxEvlzlaGtlHi+tnZZOBJYES9o46mPpffAYDDjx7h6Pg48PLLv0s1/ldddCqpU+jMeF6uemVlZQWLi4sF2wE4fuVZBhm0CsgGscAmaNCSe1YLlZeXY3p6Guvr65iZmTFOKfUYL+1zUsxQ28axGDDUkmrVp3pP/k9nZ2VlBeXl5RgbGzMdwisqKtDU1PTE+lLnseS8vr7eHCGlIEXln9uglpaWsLS0hHg8jsnJSePc0+Aqv6uzVYw/1WHgfNUhZzk89aUdZCdN+dlAIICFhQVks1kMDw+brGZFRQXa2toKaKy0JT80NTUVlOZyHNTRyWQSb1dVYS4QwM8dB88uL2NwcBB+f+7otbGxMezdu7dg7TQorVlNez2pL3SN+Ft5TAP2SjfyXl1dnelNQ/llGavqCQWkDBQUcxb4fK4FTxuora3F0tISZmZmUFdXZ5rrKm1JP9XxNm0YPCSOoOO7vr5esKWHPKu0Ih0Z6K+oqDBHRXMfPcehlT22PPp8PrN/WHnT5jGW6XOffDweR21tLaLRqDmJyw6wqCzZvKdJB3W6NQjKoL1+VwNaDGyWlJTgqysr+G4mg6NTU6jZubMgyGHbIt6La049q5dmxEmP2tpafHF5Gd9Np/Hcygr27NljHETVwSrbqpdJY03EAJs6T3sLqYyqIwBsnirATHQqlUJLS4upWLWfrfRTGqjupj3g84jZWKkai8WMM8858F4MsPJ1rhvHrbyo9GYgVtddeU7tBQPZdXV15lhTOuma8bb1l2ZriwW+yNue52F1dRWNjY3mvd6GBqyUleGNbBaflzXjfbhuWj6uY9Y523qfNOM2Vf2eJu/q6uqwsrKCeDyO+vp6o694X10/1W+kp9KUfoPneabnhed5+JXrYsF1kT1+HIGrVxGLxUySmNiBz9P76X2L8QV/c8sLt21wbLrdmcflMvDNJol8rq2/mNxVHGEHX+krMpi4srKCN8rLMR8I4KcbG/i692R/KuphJnD0GcV0qPITqyt40iDXgQFR4ibeb2RkBLW1tUgmk6ioqDCV4Paaslqyrq6uQK5tnUX8tbq6ircCAfx81y68tLqK1ySQwu8qriXfFJMhezzk4Wg0WrBFnrKwurqKyspK+P1+zM/PY3l5uei97OupAw1br15F8O234Xke5pzN8it2x7aNuBp/NQa2s0EwxK0HapyrLl3C2XzjwVW/H1suX0b99etIpVKYzD8zNjGB8x99BABI5bc5sCyJY0mn06iursbBvj50t7Wh7c6dgqaE5eXlxlHTI+WAHEPzzFneU51tNZQKZvjdYotqg/FiCoPvr62tmYaN+3p60NvZidbubhPU0ezfvu5u3O3oQNW77xqh0sg/702mUYBCp1b/ViFkuY4CKiqS+fn5JwymgiCdX1lZGUKhEGpqaozzenf/fqxFIug9cACNXV2mKZPP58ODw4cRj0YxfOwYfD//eQHN1JknvVmmRVryotLXOWlGhfQmH2YyGVMpQIDA9c5kMqY7M50VzpPf5/MVUHmeh5GDB3GvowPbL11CYGoKfr8fS0tLuH//vjkKjILNTNO1559HIhJB5tw57P9f/hcE3347d6xpNGrOOWeVELs4z8zMGDnSjuekGwGGRiU53i3r61h44QV4fj8enT6N9/79v0c4HC4wpKqUSU/NmCqwUKVHGrIczAb9nldYomeXkSmg42f4vpYKE+DymSp7pAsDReQddRoBoHfPHnTt3Yv/+eOU4n/DpePjuGZmZrC8vIyFhQWsrKxgeXnZ0HrHjh1PAEt17GgAd+7ciYaGhgK6KihiaS2dJ9fNdXcfGRnJBfaGh9Hc3FzglCrAsXWVDb74uv7mRRnk33RyFfTye3Q4qqqqMDMzg2Q+w753717zLDZrU2Cp9GQQhVsSbAAKbDZNKi8vx7Zt28wRysyk8txsG4SpLBUDwAq+bbug4FqdArvEkp8LBAImwMLqj+rqalOBouDYvi9/U3/b/ADAVLwdHRzEnbY2vOZ5uFhVhR/W1OBQVRW2XbuG3bt3G7nlnHSdVe+rbKodtt+3eUTBIx0P5TlmpsvKytDQ0GC6ttsZN32evR5cN7W/HGssFjPVmLweP34M13URi8UKtgbYusgGrWpj9X+WEBfjHY6PQWvHcQp6GtC2pdNpjI6OFjSU1EvBKqsv7aCmPg+AAet0bqPRKEZGRrCysmJ6FdCxo7OrTqUGDRRX8BmKD7k1Se02f5O2urUMAL4WCuGrnofbeQdGt2/aNokXHX6VRcUptN3V1dXG4frL8nL80cQEBpeW4CsvL+Bt/a1BBV62o0sHmckapcHHBVeBwp4SPE5OK1K0xw0vfldLpZUmyicMxLI8/61AAN8B8EIkggtLS4ZuzBjb+r0Yn5GedMz5GtdRk1tcG9KfuoRBMeLKuXzTeU0c6XM1iG7jBF0H0ri8vNw09t2yZQtemZjAz10Xn3c2gyie55kqCW7xoVOqNLXlztZptO/FGntyvn5/ricTtzywqk3xmM6XvhGdeNVpygfEA/SrXvc8fDedxun8aSasHiRf8u/19XXjO9kOuOoTpa/ruqZZ/uzsrNmyBAC/jcXwy+pqvJ5O45N5vV1SUoL6+vqC7esadFI7SbroGCi7fD75mBX0ryaT+Knn4XPpzV5QNq5LJpOm+kXti/Iz72/rDVZQsIqMYyKWDAaDZvufneRQWVEbzzUmLtVnEwNzTaiLf+P3Y9Hnw3vl5fjs3FxBbzr184pdNi6zec1xcqf2TUxMGJmm3uFJJuXl5abi6mmupw40LC4uFpwHSqBenlfGGumaPH4c42fOYMfly9hx61buQXmnkIQGCjvXMhtNQ6RgUPeD2EpfFyaTyWDy+HFMX7iAlhs30HDjhmGOdDqNPb292HXvXi5DJSctkOE9L1cGVFlZaYSRWTVmIAKBABafeQbTzz6Luvffx7Zr1woEU40E721HPDlWGwySNrNnzmDmuefQdOUKtnV1GWHqGBpCx9BQDvDlyzEZZctmszj06BHqb9zIRYTr6zHQ1obRkydRf+kSarq6jEJWJreNMZUZ10LL7yaPHcPYqVOoefdd1F25YhwH7o21BYeASbNZvNiDwnVdHOzrw522NnT29SEcDiObzZqjlg4NDODmrl3Y9tFHqKmpQSKRwOrqqikRIxCj8GqETTP2yncKDBUIK1DQHw2kKN2KAYZgMIimpiaj+Ph80v7e/v1Yi0YxePQoar79bThObivRzMyM2ZvHe5F+4V/+EplPfQpbL11CJBLB/LlzmDl7FvWXLiHW3W0qiwKBAGZnZ5FKpZBIJDAxMWEipep001DRgGrG5fHjx8gMDCA8OYnVl19G8K23MDw8jIqKCsRiMaw89xwGjxxBy40baO7pAQCj0Oi8qlG1wZNWALFREteAASI6NDTGHN9QRwceHDmCXTduoLGryxhS0om6Qi/KHTMxulY2OHCczT3FPp8PVz/1KcTzAO/3fWkZ+s8cBz9OJvHajh04ODICAJicnDQyYoMd6hNgU3esra09sU1H14I6jiWMS0tL6OzsNE726OgoKioqUFlZaYyKBm7I6+pIKf/bBhoo3OvJubLEUsEXMxwafPa8XOCotrYWjuNgaWkJW7ZswY4dO0yQmPfW52pAS6sQbANu64ZYLGYy2Ty6qb6+3lRzaSNXtRmkLS87O2KDc11/bXas4ycddD0ZXEin09i6dStqa2tNfxStSqNu0+eqDuaY7bkEAgGcnZvD/8XzUBkO4wuJBGYB3Nq9G809PdiyZcsTYyJYJCBU+ioN6JAWu7S8kxfnQBzAZwSDQQPowuGwcRw+7h6elwt+UjY0UaDzZ9ac+3upxxmI1pJn5SXlL9fNVbatrq4WZOX5XPIrv6PrwL/VedUye66h4zhGb6stYn8nnTvXgoCYR0Xa9PE8z1THMHDBvcia8WQGkvRTm6r2kPfUKjqlOekYCoUwOztr+E/1t+poXTOuFemxurpqjuqzAyihUAgVFRVYXFw0Z8Jz3rxvJBJBOBwuqCjivTQwozKuOEGdFMWj3Car2Imf0XHY8qh2n3NVB4/Jh+XlZZNAUl4CgC1bthgcpFtKVD5YDUx89w/xOB55Ht4MBnF2bu6JNVM+VMeD60bcyzF6nmewAOfMqgDVPby3BpaIA+zg0Pz8fMH3FVNvy/eO45rY/E2ZIbZide4fAziZD6zr9g/Fsoot1c4Vs3dKZz1unX9zfOorBQKbjT4TiYRpuM4G1rwH760nAmiPBl4+X+64SR456TgOvhAI4OzkJAYfP8ZCIFBwdLrytybIbAxnYw610wwIP378GPX19abK7Bfbt2Pa58OPALyWr8ahH7ixsYFoNGqC+kpbPpOVwmp71S/kkZmqs/+krAwXHj3KbXOTAAS3ULBPjy3LNhbU3zrfcDiM9fX1giNgueVefQA22uR7tp1WmtpYzrbPlBFurwiFQvi65+Fb6+t4Nb9lg/qCY9DKXZs37YBKsXlHo1EMDAwYflUeJ80YJHua66kDDeFwGPF43DC34zgFp0hw71hFRQUmz59HIhrF1PnzphEcGYQZVI1+s8kRiV9RUVFQpq7OkAq9XSrseR5uPfMM1quq8ODwYZT/0z8ZRchMOo861EZWiUQC6XTa7BPT8mI6tnRUSktLMfv889iorsbsc89hX39/gWPEZmW6wFQWHDsFmgabSpZz6f/EJ7BRXY3xM2fQcucO5ufnsbCwgEgkYpp00LlzXdesy8bGhokCLy8vY/TUKSRrajB1/jz29vYa0KSBFd5LneFUKoXJ48cxeuoUWq5fx87ubvj9flw7exaJaBQzzz2H/YODpgqE60XlSIbVAAxpwV4IS0tLBthsv3kT265dAwB07d+PgVdeQdudO9h97x62XbuGM7/+dQ7wRCLm+D9mZpeWlrC6ulogABrUUmCxcP485j/5SYR/+UtE3n+/wNngGmiE3RYsXVfSyufbbCpFQV9eXkYsFiuIDnNMHffu4cbu3Sh/802zn5V7zHw+nymLpROSTCZRcvkyai9fBgCsBIOYOHcO69XVmDx/Hs09PSYoRqd7dnYWkUjEvKbRY86JfMioO2k1Ozubqyj55S9R+0//BM/zsA6YDG/fmTNIlpej/+xZHHj4sEBhKV/xOfxbgbbf7y9wLAlcSCOb3gTsQ0ePYi0SwdDRo9hz/35BBoLPofxpkEMVK9dJDZY6zgREfr8fe2/fxsAf6NQJ7Znyw7IyTHgefujz4cWtWxGPx9HY2IipqSmzZ1VLd3V+akBIB+XfYhkIygP7IyQSCezYsQNTU1OIxWKmeoIBHDVABG22geb4igV6+NvzvAKAxM9T59jOYjabq1Cqrq5GJBLBRL5pqH7m4wxoNps1TWyVVnyPr3ueZ4ILLCf1PM8cNWpnCPW75Bd1iDgmnb+Oi/fQbBf1jsopaaOywIa1rLij/lbeVltLsKZOhw0Ys9nckVkrKyvwvM2z3b9RVob/uLqKM3kepO3TXhr8vN37weZL6jb7steOWIL2nryr298U9PFz09PTBcBX9Q7LURcWFozM2Y5BOBxGVVVVQYZPgwDq6KrsKX/reDSBov2KuF4aIFVeUd7hs3niA9eZssKgRyqVMk5dMT4kv01PTxctQff5fKitrX0CeCpfqp7mGutzNPljO2N2pUOxBIttL2ybq2vK7yjm0h97reiYK94hL5aWlpptJTbtNMBdzKlUHlHep0NDfVzMmVHcYesy1WM2yOf4WAmqzyXOYNM29mBQPUv6MOMK5HTJN8rL8XeJBD6xslLQE0nHrM+xkwjsFUYcpVViGmCyqz8oHwBMLx4NOFBH+nw+DA0NPVHlwkAYj57laRXKN0AuiExnWNeFWMReSzshpXMttvbKO6QxE4D0bdQe8xmkBxM/a2trxh9aWFgoqn+oiwcGBrCSXy/7c0eOHHkCD+kRiwy+lZaWIhQKIRqNGr1C/W/LpvKA2hfaAL/fj9XVVeO3+f1+vLi0hF/HYvhSHoNSj3M7EAAsLy+b7SK2bt6xY0cOt+e3yunaOE4uwMSgK4ODDOyQrrxYaUy+KzY38ratTzSQyEb+uiVc5YVrrH4x+Xtubu4Jfe+6LqqqquA4DmZmZp5oqgnkGlzqCTg+nw9fcF2cX1jI6Rs5ypM6iME/rgX9XVvPc+62TausrCzwyzWRQJn2+/2m0uqfu5460MDIMTMxnueZLt88ihHIMXRnvpfCkcHBgswuJ6GNl+ggU3GymkCZRUtHlBialSaBd924geHjx9F88yZisZj5/MjBg7h96hTaenrQMTxslCAd9YWFBQOa+B6fSwElwG64dAkTZ8+i8coVs6Bq5AAU7GcHUHAvlsAqWCUDpNNpbP/oIzw6fRrbLl0y+/3T+W0h7BLLrAd7GUxNTRWAS8dxUPf++5i+cAHbL182lQeaISDjanSYwjZ25gwS+cx75W9+A8/zUPVP/4T0c89h+4cfory8HKurq2Ys7HdBcKGGiQzK9UylUpiensa2bdvMkTHkrQeHD2MtGkXfgQNoun0b8/PzWFlZKSj34tGQfF5lZaVRqHyWXWKfSqUw+OKLSNfUYPWll7Dj1i2jePh5BgS0XFABss5p8vhxTD3zDGrffRc1XV0Fwui6LpaWlgzo0KzFgZERxL/3vRwgyJcC06DGYjFzVj3pxmZBDCatra1hy3vvYebZZ7Hr5k2z/YByxaoENkpLpXLHGTFbR/qwEoT7nAOBAIY6OjD5la+g7Gc/Q/WlSyZqy2yFAjjP88w557q+6nDaWRyuMWnC8dBYEpDzda73g7Y23O3oQOzxY2SzWTRdu4alpSWzbhq4HO7sxOjJk9h59SqaurvNc1VJ8zkcHx0EW4nu7e1Fy507wOc+V0wl/jddWiXwWiaDnwQC+GoggGg0aoJNy8vLBVUelE0tjyPtXTe3d08NL+etAT/P2+zAzzLVnzkO/vO2bejcswenZ2aMs6cywB919qi/tLqH66YGWAEb914CMHvPKdt2RoU/us2NgFF5z3ZUXNc10XYCWI5VnaLy8nKz9/3jDGZVVVVBAIL6WteQf9u6wnaOuAYaPON4qXc0Q1rMkVFHOJ1Om60dKmNcB/ZP4v5Zpa/yoeu6uHjxIjzPM2P4gt+PfQMDeDw3hzF3c7uVvSea8qJZOpvPi2VoyANaAagZGc206Trrd1UGNIiqfM+xaZKE3+W9NbFgA2yCV/vZOj/FIradp062HRl1bnXcyi8qQ8U+Rx7gM/m6/VkABcDY5k/Smd+xZVkDDfrdYp+jLrWrPejocI15JJ0CXFuGNBmk+pk6TOmnAWpiA96XPKtOowYFGAxRu2TrWMWanHcxJ1yrCMg/ur1Vt+bYjqwdyOF6cH0ymUxB9Sjft6tJNKii99TnkH6ZTAZfKCnBa4EA7j56hBUJXNq/da46RiZG2IBQ5wVsVqmok23bX+owW/42NjZMMEjlUIMaOkcdI5Czmwzo2qdvqaypTKlDpvJe7Dv29xVPswqIAUzem8ECriv5goFQ8qMmKIl1Wf2nWFPX19Y/fObbwSD+Yf9+nBwdxZl8o3nP85BIJMzxjDpO5T/bgVUacDxMqpWVlRmf5dmVFTy7soKdO3cikK8kyGazBSdmOE7haWCqFzl+6jjaTI6RDRh1vVTWbP2i60UeUt9NeZu0tb8XDocxMjJieFqr7ngfyiq33tAOLC8vm+eoHuF2f/p0autpF7keyke8PzGRrT+K2bRiNkzf49+sltTv8H+uix0o/V3XUwcaYrGYidDR+UmlUmafFzupRiIRNA0N4cjYWI5ZrEnaSpKE10y7TkoFV4miSkcZrbmnB809PTni5BcomUzi4YkTWI/F0HvgADqGhwFsZhXX19exvLxsAAGZL51OY7C9HaOnT6Ph4kXs6e2F4zgI3b6N5p6eHIP5/QXBFDIcz5MFNp0EKhSgsLGgMkZJSQl23b2Lxq6unLHMG+mSkhJT1cBsFhUR/15ZWYHP58PksWMYPnECDRcv4sJf/3XuWaFQwfFfFCIyomalHMdB+507uNfRgbo8APX5fKj+8EPUXb2K7du3I1ZVZYSJ97XnSgVJgaERZ68E7vkhzUtLS3F0aAhde/eis7/fHGPGxpGZTMbszeRxpVR0WpVhR6G5nts++AAT586h6p13jJAQSKpAUpDskmC+BwDTFy5go6YGs889h6qLF01UmtH9tbU1lJWVoba2tqDcLZPJYOvWrQX9ITQAMDY2ZsZO2rF3g73nijQl0CA9PM8zjjiQa4g2efw4lj/9aWz/8EPUX79ecBIJ13zw6FFsVFcDr7yCmr4+U3WwsbGBSCSCTCaDpt/8BmOnT6Px8mUTaKJyV4eTYEIDPjQkrKAgoNMMjWZrSe87+R4enufhhb/7u1xpoaVMSY+RkyeRqKrCwxMn0HDz5hNgTvtlKDBR8M9xjh0+jMFjx/5Z3fhfc/FZruviFQB/lm/cxLVg4zjOb3Fx0dBZ+ZHOYSwWM80MVQ5UtzCDwaNQyXd/v7GBKZ8PGy0tOJbfbqPBNoJlrhefq791TLoFwpZHAi9ePMXHpr/2X9F5+Hw+00dBQYWOgePY2Ngwc1VDys/o8cAM8PF+HDePNdTvcW7F+Mi2STZ9NOCj+9R1fGrv+JqCHt2+wOPa+D3ylOM4Zu8sv6M2VQOsQM6+s4u1VnsUA9L26wpobfuujqjOk89WoGeX2xNYcozKI9TPBKJKbxtc0tlgQNvmFXWMbdCua6Jrxf8Vo1A21N7peG3+URus9yFvaQDB1pPESzpOG0Da/M5n6lpolpng3n5fE0EatFR68H87KEmnh44X9btuj6Wd1fko2LefQ6ygNLXnq7/t+WgASoMsCvCJ/+xKINVlSk9mDTXgRt2iz7b5SsfEKhfiKh2bVsb4BXPauFiroNQuagAknU4XVERpkEWdTB0b564VXKQXq4H1pCLyEgPLtkOiepB6TPWq8g+3HmiQQIM6HBcdMl074kzaMJ0LP8cf5TVNmOjafZxe1nXg+8TkWqVh4yHSgQkfyoHKANeH+tBOhNp2K5PJIBQKYWFhwYznRz4fZv1+XGlsxKuyFq7rmu0EapdtPapYWeWCvSyIcZmc0oor0kLvw8CUBiRtOvKzul5KO23+zrloAEDtlCYSSSPlp2I6w9afjuOYyn71N8jPGngjT3IbSXl5udkWzs+r/eVzGDjjNn3XdQ1OoN7kZ9W/VNlRGqlu5vfsoHwxO8/kitKJl/p0T3s9daCBmWSWg7AEniWV1dXV8Pl8JnOgUTdbCFn6xwWh45nNZo3zsry8/IRxJ3HUCCmQVsNJoWPPhdbbtzFw+DD23L5tummq0uG+JRpD/j1+7hw2qqowfvYsai9fLhDmmdOnMX3hAho+/ND0anBdF1MnTmD8zBls/eAD1OQbVXJc8+fOYer8eWz94APELl7MLUIexE8eP46Jc+dQf/Eiqi5eNEqOjf6Wn30Wly5cwImHD9GZD5b4/X7caWnBtU9/GvW//S0iH3yAoWPHsF5VhdFTpxB+/33j4HN/qwoOGZvlS9MnT2L09Gm03r6Nc//hP2BhYaHAYdQMADMVD9rbMXD4MBovX0bDjRsFARAFP2rweVxjXV2dia77fD50Dg/j0Oho7jSKhQWz947ZDSo7lt/rkWNcM9spZ9Y/cu8eduSrD9x8+SCFURvSqQBRManTw6qTsdOnseWDDwyfl5WVmcoKGvOamhrTZEsrExYWFgqOGiTPcV8V6bOxsWG63FZXV6O0tBRDzz2HRFUVBo8exc7ubkM7VpWQ9nTE0uk0Fj/5SaRrajB94QLOzc8/sSau6yLZ14e77e3Y8uGHZu9qJpNBIpHAwvnzGDxyBG137uDID36QM56xmKGhBr+KOVlq/BXsqhxTD2jmw/M8tFy7hqFjx9By44ZR8MxC8t7UAY2XL2P05Ek0fPSR2c6lmRPbOOj4FEi6rosHR44gEYvhD3FxXPybMkZ6MFLNNdUMgII0AAbIp1Ip41jbBkxBK8tc+cyvlZTgP62u4mT+2EQtJ1UdrEZKaaaGWqtLijl9LA/l/bQzte3oqa7ifWl8tTu38htlmRkWHYsaVM2UM8CrQTsAJniuQKeY4bZfUz2nz+bneS89h1qBNfWnlmHbYECPBCUvfJyTz4w+aak87nkeGhoacOzYMfT19RUEfbT0kvQgTQnqmVXSYE4x8F3sbzoB/FvtkOovPl9BmoJKdaj0fXscumbqqKhcKV2os3T/tq3XFAjTzqp9tEGg0kl5Up/N+/CHtKDuozPC8SjPKk/yb7UrpAlppzTRcnwFmORF+/PK9/o8ZlP5XXXo2OxTm3/zOZw7bS77xaguYEmyymSx8ny1a0yi2LxC+tnVHNSlqmd13cl7yu/8mw4WaaanjpDu9hYe2uxi1Qocj73VUOepAS2VMd0SqTRU26P6nHqQz9Jtl7rVTx0qfRZpwp4ePl/h8Y6UZTpIGli0nTTOn7aKjrCtW8m3qq+0ylHlw3U3j1RVmmkVDfmeNNYAh+3H2PpeX2M1Gcv+bftg6x+OTWVKf6ss2gEhXRfelzJC3v+yz4dvJ5N4Jt+rhQk76hpuyVabYc9Xx8OLTdFZWU0epo6inbCrosibGmDkHFQn+ny+J3pcKBYnhtBMv9/vN7hJg4i8n81Luj42LXXN6E+yzxT1F30TBkDVfgGbWIPJPcUe/J7SivxDWpOvmRjh92iX1P7Y/KJBgWI4wp4fL8rp+vo6KioqzLgymUzBtkaO7Z+7njrQUFVVZZpbuK5r+hbwvFgbvNvRQQ6Oyox7D6mQdeJ0rsfGxorurbEjMVy0aL4LPwnsuq7JBncMDaHlzh2k02msZnJnnTL7TONfXl5uMmxk5N03bmDg8GFs+e1vAWyCesdxMP3ss1ivrsbUM8+gY2jICNTNs2eRqKrC4wsXsPv+/QK63L9wAev59xpv3zZGy+/349YzzyBZVYXJc+dQf/16gVMBAPdOn0YiFMLlxkZU5YMUPp8PV8+cwWo4jEenT6Px9m00Xb2Kh8ePo/7DDw1QpsDp2ahUwpo5HT5xAolIBH0HDmDr1asG7AUCAayurhpnkQo7EAjgwZEjJrCx7dq1AiGmAtXoK/mBTXAoTDTK3Itcnm/YSdqt5s/PDgQCpjogHA4bpcqLvEdFYQN98ocdZVXjw2AGjS0NbTabxfr6Oppu30bDjRu5cfv9mDp+HPeefRb1ly5h27VryGRyJ35s2bLFlErqdg9g8xgrdfpmTp3C6KlT2Prb3yL6wQdmfGpUKsfHkQiHUTo8jJmZGTw6fBizzz2H0jfeQLqzE6nPfQ7lb76J0KVLZp3LL17E0gsv4GB/vwnGKG1c18Wh0VG09vVhbGwMJfkqGjoDDw4fxmokgvudndjX328UDbMwVPZ29lj5l+tOWqu8KzCgkqWDfejRIxyfmsotbm2tkVnOTe+zY2YGp998M6d8d+409LarLcgnmhnWYMTDAweQDQZRamVBf1+XzZM0PuRZntXM5lAcn/I1LwUJaqj5HBoZBhs1Q5TNZvGFkhJs7+/H0tISpiVbrs/Qe33cfIoBUNKef7MyiXpHT/exQbACMuowHkGrAFF5zefzmd4nel8ABbJH/lFAxmez6iKZTBra2utjO1lKZ3XGlD42HTVbP9TRgW+6Lv44HsfLecfjV6Wl+FkggH+xsYHPyzYvBTGO4zxRik36URYpd6SHAlyVI61mIG8E8s3DOH4F8xrgVweg2PxJNwVGjrOZ0aEDyYAv14a/lbY2j/P+vIqtCe+vc+N3NKtml81TRxCHaHmzglidM8Gn67qmNJlzUf1oj1MdQupSVjPSTug4SUctedagAx060od0U360wbUt+7YTZZcm83XKlu2Y2/rCrtLQahgFybrOOg4Ftzp/vb+OS+enAQLVM1xHBeo2X+sYbTlTPmaggY660oeN4dTpUj1MObV5gmup24zU4VD7pltM1NbxeZ63WQJv63iOi/pPnSZb3/J1BuL4mXg8jmg0WlBSbuN2lX0NlpF/1WEkPqQjq+tpyzifo9sM6FQqv9kyRMfcDvLYa6+v27ylr6kDTH7XBIytNzSAaq+JrpvOsViVnfKGJmIcx8HrpaU4ODKCiYkJpEIhMyaVZ60AoX0mX6hzbFcNkH7c089Ao47JdV3Tr4B60dYnqsNZwaWBW5VZpYVuCVf55forHXW97GSDrQtt+mYymYITDe3TSLR6g/qBNNUAssq9PlvXQ3UW788+HrYtsOfGi3S1A1N6qX7X/7mtlVUjKs/kCQC//0CDRn8J5ghE7FJLO7qqERYFbPa9yXhksFgsZhq5aKmQbdSpjHSBNEvOMWlkjEofQMEeYAVbjuNgZ08PQu+9h5mZGcxlsyZb5vP5sPPqVYyfOYP99++bY0zS6TTa795F34ED2HfvHmKSEc1ms9h3+zYeHDmC5q4u1NTUFMxl140beHDkCJquXDFONkH5xsYGtl28iKlnnsG+O3cMvdLpNNru3MH9zk40Xb8On8+HrVevIvTuu7mjSPJ7bVKplHEwdb103ul0Gs1Xr2Lk5Em09vSgvLzc7AGm8tG1oLDszH+nJd8zgM/SKgjenwqD0c+5uTn4fJunilCYCSjs5mOsZEin06Zk2HE29zFphJGXzhUozKip88b3+F0GPLQML5FIFNCDP48vXMBGbS0mzp1Dbf5IVuVR/tBRotByLShHo6dPI5GvoIl+8IGhN9/3+/1YbWiAl/+dTqcxfeECUrW12Hjppdwc6uuR+fzn0fjoEWZnZxEOh7F9chLVb75paGIbaf2fYJhj8/l82Nfdjf5Dh9CRD5xRaRaTdQVpCqgURNtKkZUytnGyjT8/q7Ku61YMHKhTaGd1VOZZIuzz+fDgyBEky8sRfspzgv9LL9sQa4dgBj4Y3GWzU10vzUgREACbmXHeh/NXcE+9rcEdVpLxs3YfHcr8byor8UPXxdfLyvDlfBWYTXN1EHQMBH/q9FBfaAUGL117BUYasNRAhBpnjkMBucqkbhuiXtMAAACztYt6k8DBBo0KWHh/ztl2ergGpC/X4sGRI1jz+fCzsjL8cR6EvVFaiknHwd+tr+OzVgBc91jbgIMXbYjjOEanqiNDEELeV9DHALE62uQd0hhAgU3lOikg5Lj4ngIerh3Xi7126JSRVjYt7T34tAt6qZzo2tsBZtuBUN5T+6vypvJnf5/vk3fozPh8PlNhxTEXo5GNodQx1s9wrTSAwnEo0NXLrnKy9SHls5hepdzqOmsgQ3UZeZK8Rp6w7W0xMG8HKZQf+QwNGvMzfE2D2Lou/By/Z9sZ0kbXWm0KeUHnyr8V6KsOIRb5uO8pzqSc0TFWfuf3qMt0G4CCf+VH5V3b1n1cBQyfTbvLBILt1PCziqkor9x+rPT4OP2kc9MAFC/V9dRlpJEGUr6zuoq/WlzE666Lz0qyQysnSG+de7FgtM0nfF/pZK+l0l3nqBhe37NpyO/TXtlyb68T3wcKq7r5GW511fUivdbX1584rlYxNunFexeTP9JHx1hZWWm2/ZaWlj6RGNEq9rW1NXOCoW07KH/8PLd1KB0oNyrzDPDpems1mgaZ6H9ogF71rk13lW8G35LJpAlwkLZ6spHyg548wUoOfo/PrampKQgI2DaadkvHxblpYE3/Bza3TCrPFpujPU/PyyV8V1dXUVdXV0AnrqcdHPpd11MHGrT00u/34+6uXeg/eBDtd+9iX3+/WXiNSPNS42MbVBIdAPr37cP9jg7s6+nBtmvXTEmOghnbWAKbCli7s9PYspu253kY6ujA4NGj6Lh3D7vu3jUl9YFAAL179+Le/v3Yc+sWdt+/b5RoX2srRk6eROgXv0DwnXeQTqfN/uC6y5dxbGIiB/alZOjQ6KjpyJ/NMzONWduDB9if76DriXPseR4au7pQdfFijlmcza0NnHPtRx9h97172LJlC3zBoGGigyMjODQ6isePH2MDm0BMlRyNnu3ocR+R4+SyqMF33kH7b36DWCyGkpoaxGIxzOdL7fU404G2NvQfPIiKsTEsb9uGlqtX0djdjXUxTpw3BVGVNw2a53m5YxNXVgoighUVFaZbNMfPMZPP2BmezjiVN/cwks8UJCmoUH6kUaZgR6NRE7Hke/Z+YgVhDR9+iMlz51D73ntmDI7jYHFx0TzfdV3079uHu+fOoeHSJSM3VIjRaBSH+/vR09aGxlu3ngBXPKat4cMPMXbmDOouXcLi4iJq3n0Xc88/D/enP8199vOfR/OtW0YeKioqkMlkzDGdel8FmtlsFv2treh75RXsvHoV+/r7MdTRgYHDh1EzO4usl9tXvrq6WuC0aNDJdgzUaNOwEQzaIJBX7969uNPWhurZWcxWV6O1uxvtDx4UGFPNvKmxVb3Cz37c6xq1Z+ScdOm8fx9329vR2dsLvPKKrQ7/my8FNnq6AZ+fSCRMtl9Lbe0IOe/BdVD9WAxAASjomszv0nByDRUMkd6BQADfBzCezeLv1tfxpbyM2aBSDZkNJHV7jN+/eY44nSfyEbd58bOcA9ddq1qUj4DCzJVmCdQZpd6nfGuFDwPXQK75kwJSXTs7OGn/tp1X21HTcvT2O3cwdvo0XpWGha9ubOBnZWX4puzHpr3gmOLxeMF4NbMcCoWesCHKBzbgsMfOUlDN/JEHGWBRp852MPSyn8X/tXcEX+caKt6wkwzkJS0f1YuOhjpE5AfSx14Tfo/2RZ0u8h2/q6CvmPPBcerWCq4Vt5voemoAi/fis5TPeOl91enQeaj+1R4UiguUPrYcFZuH8g55gf8r5qC91P/1fnxNZVvnaAcnbB7THx2vBjl5P3WUdBysILDvpbq1GH9Qbm2ZopPHTLw6ObpemqRQp8ien83TmUzGNE3kd21HUwO4oVCowLFWJ5XypLKvNpzPUlugz6RjpViCa6lbVexAnc1Xem9+jjzNTKrn5QLhdg8Z6ra/ymYxksnguwBesYIBqo80oUW7S3xgr6XaENW9GoRSPWLPB4DJfuuWCAAFel/pr1hF8Y3aH45B+UZ1JrC5NVLxKp+VTudOimMgQudIGqgtVLmy5UCTlJWVlSbIRJ7guJLJZEGVKmmidsuWc9ofykQxHcvxAzANzYFNB1xlWteZusrGRrb9tuebzWYNLuHpWdq43LbNijE4LgYeVeYpi7RXvJ/ylgZ2yWP6P2lk8xHnaevEj7uUj3k8sF7FMPzTXE8daOhrbcWDw4eNI9538CDWolHc3b8fTfktAGQ822gUm5xGu/j+3fZ2rEUiuSaEV64UNB8jsXQh+JvKqXfPHvQdOoSdV6+isasLgUDAnEUPAMPHj2MtHMb9zk60P3hgjnIJBALoO3AAa+FwLhAxPGwE79Hp00hWVQEvvYT6GzcMk8yfO4eRT30KS93daO7pMQqAgseMFY0MGY5dSMk4CraSyWTBvl3OL5vNZbBmTp1Cz7lzOPzgAdofPDCKUru2p1IpTJ86hYcnTmDLe++h/vr1Aro7zuY5wgRww52dmDp/HvWXLqHp9m2k02nMz89jaWmpYCw+nw+Li4tYXFzE/S99CclYDPFwGJ7Ph+Hjx7GRTGLs7FnsunEDzT09uRMATp1C05UraLp921RmpFIp08xx4tgxvH/+PPbevo2d3d1GeDg3LflVB4sghQ6igi3Nvtn8wvfVMNt7ofn8YgJJ0KbKynEcbLl8GaW/+lXuOK18QMau7nAcB/c7OxGPRPDwxAmE3n0XPp8P8/PzpnFkx/Aw2h48wPz8PDa2bi3YY5VMJnPHl/7iF9j961/DcRzMJRJo7ulB8O23MT09DQBouHED21taMLa8jLKyMszPz2Nqagqe5xWc703FNH3qFKaffRb7797FYGcnEpEIBo8eRfSDD9D31a8iWVWFsUgEns+HW+fOwXVdtA0MGPoRWKgsFwsI6jqqQrSNd/e+fVgNh7FSUQHP58P9zk7s7O6G5+UCXA8OH0ZTXsb1GcWyAPpMdTgfHjiAwSNHsOfWLewbGHhi/2fDjRtouHHjifX/fV2671sbsQIwWyV0G5gG69TBVhCrYIif1R81/HagRp9HvcB76++vui6+l83i63JcGMeuz9D5cKzZbNbs2eXYuWaU2WJjp36nIdZqAN5b+Yn6V40t1x7YrLDjfTUbQvqrQ03a8kd1jQaxioFHmxeVXiojbQMD+LcHDuRey6/vy+k0Xvc8VFuZY9U7WqGmgX6W3RNcsS+RBoCVD3lPdXRYgqu05Wc4fhssFptnMQecrynQVWeMY+RzVIa5BuQFm7/1mepA6D5zfa6+x3vwfXuPsQJEO9CiwWpWZZA+BKkaIFM9qI6AypLtQOoPQXsxx0DHRoykAFzH6nleQc8s/R7lQx1he/uEAl07cK3OJteN86PDbScT1I6w3xcddDrz/Js0sPGgNmFUGVUnQnlRn6nz0vkVcxR126mOnfdSXWs7jspLGjAgBtQ5keb6ea2A0OCBPlsdHzvjqeMgf3BLqm5XUqdMnUviCfZ10Iv8RLyp+ptjVHnXyix+z3Vd03SedFH7wPH8SWkpvr2xgS/k8TXHp44lZZK6nXRx3Vy/H+I9zpnj0eCi/rarMrleyhOVlZVwHMdgfvKcBtFU/tVRVjqoPuR9WMmkTqfKNqtu7ax+LBZDdXW1eT5tYyaz2biTssb5ajCR8qCOtePkqtGYhFKeAmAauitfqA0nP9oVUuQ1bvmkjlBacy20Mo7rqrKuukwTSoqDdE3srbm2f7GysmKO6bTHYVeEJBIJIyfFKgzoT6ivq3LLcatPwnnR56MdUF7kOtq2Q/nW5mGlfXl5OYaGhgr0tPr4qjf/ueupAw1Dx45hNRTC4NGjOD41hUP5zGv7/fumDwIHrpMiwTVirpEqEtLzPNTOzyMeDqNqehqZTOYJh9A2GjbTDxw+jHg0itGTJ7G3t9c0C6SSPjwwgK69e3Gov98YTAKBg319uN3aikMPHphMUCaTwd6uLgwcOoSWri7s2rXLMOlbL72E9fzxj+0PHsDzPNxtacHA4cPYdvEi6q9fL3DA6FyrwQJyVQI1NTWmpMYGq/x8MBhEz4ULiEci6N63D20DAwVZlUAgYMp8J86dQyoaxfwnP2kaK5JOyWTSnHfLfX+Pn3nG9JrYfvOmiZonk0ksLi6a0wwUpIZ/9SssvfgiolNTWG1oQMuNGxg6fx7rsRiGjx9H0+3buRMAYjEMHzuG6kuXCpQL1/XRqVNIhMPoO3AAzT09OabMg2SugeM4GD10yBwnWX/9OqZOnMDMs8+i8coV7L53z4yL/KHRdhpqRhMJzhWgaJZKywFpeO0MEL9Hxc5MFRX8+vo6IpEIotGo6bSdyWTQce8e7rS3oynflNJxHLNFCCiMlGu52+TkJObm5lBRUYG9e/ciGAxibW0NY0eO4PpLL6HsZz+D95OfIPXSSxj5/Oex/sEHKB0cNJUSrutieHgYi4uLqK2tRW1trQEAPRcuIBGLoe/AARweGMCtPXtQffEikskktv32t5g4exY1c3OYam1F1ufDwKFDOD0z8wQY0qvY3i1Vagp29HUAONjXh559+1AzN4fZ6mpUz8/jV1//OvbfvYuh9nbEIxEMHTuG6AcfPOHIqFItFgnPZnPbV4aOHsVaXn47Hz40vKPA8g95qewWA8AEcep8aBBB56pASrMSDBDwOzRUBO+kC0EGt1Mo3XhRX74eyJ27bl9K52IATOWSINJ13YJta7Y+Vz1vBwtsx0qNHm2R0k0NvNKFWU11nKj/qEPU0bUzBzp3mx7253hptpfjozOljrw9N76uGVUGiWzwwCCOTRtdX6Uley7xNeUnOs2kI49tI18w86iBn2LZDhvo6BYJjnF9fd1siWP1XGVlZVFwy0A9x6H0pl7R17UrPdebfK2yYwM10k15RIN7Si9dKwa8PM8ztl97PKjOsdeaPGLbJH2fNNYz0rXnFIG7BtMUBOtvpSWfpbSm88RLKxJ1ewjpoA4Z70Xe4KUBMn5Gt8Xxs6QTx85qAc1uqy7V79Ih42sqcxwT9Z5+h7RlkkCrrVRuyGOkj9JcnQA6WOpA2XSwnVzeh89Ip9MmWGVjHPKGBhuL/ahTq2vOcXBbhm47KCa76oSqU8Tj6tnfRr9jV+yQr3T+uj5agcA1tPuOuK6L1wIB/MutWzExMYG4tZdcx2bzmtKNetUOHio/qnOrl/K24gfFsLq9SddVXyMPMYGm49OqP+o3+5kaJNBkmK7hxMQE1tfXUVlZabYEbmxsmGOcFQNpcFVpZgel0+m0OSKTTmk8Hje9fTSYoQE1yjExgK6pjY30mGqVoWIVslp9xSALANO0WHs4qQ625VAxpMp8aWkpVlZWjG/Az2iTWtV/DDTpWFSGOB/KggYr1FYqPVSeVL7tSmPVcxybbZf1OcqfFRUVpqeW3oc0/7h7FbueOtBwoLcX3a2tuVJi5DIw7Q8e5IyKKGBbmOzJkHFVaZMAc7W18Hw+LNTVFRBOF9mO8qkSa797N7f14s4dc4qERmA7hoawP+98JfMCy/u1Dw6iNd/kzhNm5lGTJSUlKJM9ye137qC3sxOd9++bDq5Dx49jPRrFzHPP4ezcnBFiKl5GZCngBAyxWAzZbBbz8/Nmvul02nw+k8k1r2y5fh2DR45g3927WF9fN9sDbFC168YNDBw6hN23bhnhUgdk/MgRTJw/j5br19ExNITk3bu439mJBgkGrKysmLPXOU5m6NPpNErfeguN77+PqqoqVFZW5ioLfD48OHoULTdvIpvNouHSJYyfOYPmGzfMee66jgCAri6MnjqFPT09BaWUFLb6+npks1ncev55pKqqsPCpT+Hwo0e4+/zzSFZX49GpU6jP96ZQfmETPfIKGxxRCWgmj5UWCny4/szAep6HiaNHMXziBLb99rfYevWq4evJY8cwce4cat59F5H33zfPmD51Cn2f/CSOP3yIjqEh+Hw+HBodNVsm+svKMDU1hd27d8PzPCwsLMB1XXPiRGlpKebn5zE+Po5sNouWlhYkEgn05I9WraqqwtLXv47Mli1Iv/wyKn76U2x89rPwtm7F5LlzaPmnf0I2m8XIyAj8fj+2bNmCbDaL8fFxrK2tob29HeFwGK1dXXhw5Ag6+/ux/+FD7Ovvx8bGBh4Eg0i/9x6OjI2hpqYGd1dW0LNvHw4/ePCEo2UDcZVZXmpANDOp7zuOgwMPH+LgyIih+z/80R9hJRTCvY4OtHd34257O/b09KChoaHACKhCLRYAIQ+4rou2O3fQd+AAOnp7CxoSqmF52mjtf+2lRldBG+lZjDa2LiTY5naIcDhcAIpUN7CLMI9e5aVgwM6Oq3HSYJydjSkWmFH+AGB0i2Z3CByZpWSQESgEcHS6qH90brpOBP3qSBVzeDkGbVCljhZPidHgCJ9JfWg35FJwwufb4NQGWhwf10eBsQJMvfgZ6kjSgHPSZp90BGOxmNFzHJ+CMgYO+NxiQVrSTYOIatOVN5XGNp/YzjnpymALdbNt5/VZGiAgb/E+Ns11HMVkCoDJtmpWmM6inT0ENm3uD5JJfGtmBv8qFsOX86XS/L5uHVCnSmnLzxYL7vE5Nk2pI3RrCL+rjhjnoLylc7eBajFHkDJqB+Z1LXTc1CXKRzbOo+7QbKDqbr6mcqCyxTk5zmaQRZ9HOvB1pRl5KpvdPDbR1mmcn1a9KA+SrurYUx/wCDrPy21fiMViGB8fL7CP/FuDRaofdIyqx/XEBGI6e9z8PHFmMb2ka27rcW67Vfop7/E7KieZTKbAnjCIoUEgnavqAb2Hro/fn+vFtbi4aComVJ+RzzXzbut50ovrFQwGsby8XMBfDKRyXsV0meogHaOuG9eOa5DNZg0+J47X9dG1Vb7ltir1XVSOKVvatFY/x2Amt0Hqe47jYGFhATMzMyaQwWMXy8vLUVZWZsaqfFXM/pAn+Tw2DmSQZG1tzXzX3iZBWqdSKXOyjOoNTfRR//DkA9XpnLt9FCNlWjGJ0od0UX5U3lFZ5Fh07cLhMNbW1p6oKCHNNHDABKOtZ0hHtQO61jZu1m18tt3ga3ZyVW2oPRc7mFPM1jL5aQfgKTtqS/+566kDDY1dXdh+8yYAYFUYhgRQBWI7GbbR1++owmvr6cH9jg7svnXLEMcWUPveKgR77t/Hnvv3c0ow/yyene33+80xjMeHh9E2MGDu6ff70d3UhFt79qC1uxt78j0a+lpb0f/SS2i+fh37+vsLGGBffz/aBgZy3fbzTL63qwsPDh9Gx/375pxjKm1mEwmmHSeXOdMols5FFSTLx4Jvv42zV6+iuroa6Tw41L3lzLzXX7+O6kuXcsLvFmZNHMfB1DPPIFldjYmzZ3Hy8WO03LmDhhs3MD8/j+m5OWQyuX4A6XQa0WgUfr8fS0tLJpruOLlSqZWVFaysrACvvILlF1/Enq4uvPitb6G8vBy9Z85goqMDLdeuYUdXF9LC5ByH4zho7unBC/mMfUaivgRHFJDm69cxdPQodt+8ifLy8lwFxbFj2HXrFiKRSAFI8vl8BQ0lu3fuRG9nJxo+/BBbr14tEEQVQPIaeZT3jEQiKC8vx8ipU0hEo5g8dw7NPT1GOCfz9Fx68UVcWFoy4/jJCy8gHo2iq6QE7Q8eFMybfSSCwSAqKirMcTzBYNAA+8nJSdO7oqamBhsbGxgfHzclbouLiwj85CfwXn0VZW+8kVPKP/4xUp/9LIK/+EUuqDQ+bsB8PB7H7t27UVdXh9HRUQwMDKClpQW77t3D7vv3UVpaimVsNvoqKyvD3NycAU90NlLpzfPBVWmqnKoDoY4qP6MAR/neBrCe5+Fgby+udXZiIw8uXvrOd3KGNJ8xUXCmClMvKkcas729vegcHi5Q1Dao/zjw//u4VL9pKR91BJ1JOsKq7+yxqcKnLlFgq/plaWkJa2trZpuEGn3gyQwOZYMyodkC20mwM+h24Ic8pAE/lT8a3rcCAby5sYFvxuP4Yh50qRHlnNTxUPujPGcHVDgu8gzpxPsAMJnD6elp04/HLk/XZ9n8q7yu4Ihj4DMIdphlIZjWoAFpzznxHmobdU+0glfNXmsG2OYj0kl7WPAzClQ1QGAHdm3ArHrcdvyLyWg2u9ngUjEE+Ya6RMehdLV5iHaXYyANtcxU+YcyYt/XDlzYDtrfra/jUTaL/3NxEa/LNhP+RCIROM7mGei053zexwUWSSd9TWmjuop6TYNUtr7VABHnYYNFdeYUNOtv6k/Sms9SvqeTq8EJlV/aB9u5toMSNn/ovTgH1Z1KJ+U9dXg5TuVb8gXw5Mk05FVb99nro84r14JrzupRBkBp77kW6tCpXNhype/pdyij6tCrk2U7LnyunRWnPtRgkOo31U0sA1cZAzZPAKBOYyYa2Ay8kb/02dzmofZHE2S288iAtVZFcfycsyatGAzheqoM2NumdHy2E6h6XddA76sOsOM4pvEhx0h+UgzK97RSzK600yQAv6fOOcdIbKanP5Ceruua5CfXKxKJIJ1OIx6Pm4Sibg9TOc9mswX9ZWhneMw8sSq3XVPPJZNJY+Nob7TfiM6pGF1Y5WbrfsqXHSRiEFr1ut7fxiC2zuAz9HV+PhwOm6Sw0ldlUW0ze+Cx8o/6S7dgpVIplJeXm2C78iDnyXkpP7Lxpr3WijtIB166nkpLW0cwsUyflTxPDKfz/Oeupw40UAEpI6hQ83/bMHLSGvm1B8kJ1F+/jm3XrmFo/3689xd/gb1dXTjw8KG5nw3euLh9ra24096OXTduoOn27QJDoWUyA4cOYSUcxs1du9D+4IFhcJ/Ph9v79mE1FEL/wYPY198Pz/PMVozBo0ex7do10wiE8wJyjMRmbm0DAzg0Opp7LX8sGgE9mZABBwZAXNfF8vKyqSJgEIbZJwXsruti/OhRfHTqFPblG+RR4G/t2IGr586h5do1rK6tYeLsWez46CM03LxpSuIcJ5cBaO3uxuDRo2i7exeel4u8T05O5pz9TAbRaNQ8nxmDqqoq+Hw+Mx8qfsdxMPupT2Gjuhp9Bw6g/vp1lJeXo//QIcRDITzMb6Pw+/1IJBKm+kOVu2YxuYfq3u7duLpzJ/Z2daG5pwfbb95E7UcfIRwOI+26aLhxAztu3UJJSUlBExWuTSAQMBHHB0eOYD0SwfSFCzj5+LE5tYK043cV/HueV1Dl4Pf70dbdjfsHDqApf/Qoo7fN165h9ORJdPb1meNe0+k0Ou7exd2ODhzNl+aTZ8mP8/PzhsbxeBwLCwuGtiwZ3L59O9bX19HQ0ICJiQmjWMh/Zb/+NcrzlQuZbBaBt96C/xe/gL+kBPNSjk2arK2tobW1Fa7rIhaL4dGjR6ipqUEgsHl8aXl5OSoqKrC6uopkMomFhQVkMhnc6+jAWiSC23v3mv4IGhDgpYDJdtbVyaAhshW6yns2m0XDzZu4tXcvViMRXDt1CqlUygQKbX2jPMC/+Xx2tO/duxdDR4+i7c4d7O3tLQBSf8jggl6Ua93Gw/FqoIE8qT0JaMz4WQWY/J4aG0bXmSViEyOum4I2BY5qtHjZwEvno4ZLwRxf43P4OjMVegpLIBDAz/x+jHse/m59HV/M94JIp9Mms0X+5/z5vza4JLjRclgdu+pv5RHNCNG5oO7Ue/OzpLntuOulwIU0VXAMbO7T1O8WKyPXoB35RgGvDWhJd61C0PXR4IE6ZrSNwCbYtOeoz9a58NJx25kWm5YKApVW/FGHR51Su9xZ6aA0pS63g5F2b5ZiwRiCUpU/Aq9vlJXh28kk/qyysiCIRjvOsmKdOx0K/V8Br9LH5h9bt9l6Ux11fV8dcg0c6JophtPX7YAgdQXvR16hrtEfvs85MVgdj8cLKlc0scL72s3RVB9Rf9gl6RyvfVqV0lb1D+fNdeNYOB9iMPIB6aaVBYpHgRxAp4PN/emKnZTu6gzzudziafefID01IOQ4ToFz8pNMBt8ZGsKflJTgk15hBQl/frC+jn8YHMSrrotviMyQ1hyzyhuDkCprujZ0QBwn15NgYWHBVCSwCo18qLaBto3bc1X2+D7pbK8f+VTvrets7/lXO8vvkyc5T5UzDRpQ/+haqHzZ3+Hzs9ksVldXC6ribGeZPF1eXm7sfTGdwGdR/9jyQn3D13gyoI6NycKKigpEo1FUVVWZI+LVLtm4rtjvbDZr+g+Qt1dWVlCXr0hXP5GN/TlGBhpYLQZs6mK1dcq7ivfsAK0drOQYNYFLGdbqAn2ejRntsXAckUgEY2NjRg/Y8q/0Jp5QPaM9+air4vG4qdrgdgVeKnN+v7+gCpNyQZyiVX46nmIYnZf9Ob5G/cux6fpokvtprqcONHCfJJ1qEt8GBnaEQwGmvmZ/VoHL0PHjSESjeHDkCA48fFhgfPk9Jcrd9nasRiK5bQX9/QXNRdj40PM8dPb24m57O44MDRmASOV0qL8fXXv24EB/PyorKwEAHXfv4k57O3beuGH22gOFCpaGyS5DVGCiEUxGiRTo8Pg6GiRlTjaUoeK/feIE1sJh9HZ24uDIiGGIW3v2YK2iAg9PnEA2m0UyFsP4uXNovH0bADDc2YlHp09jz61b2N/fj339/SgrK8NGKoXBwUGsrq6ivLwcS0tL8PlyXWTT6TSWlpbM+eaOk2vss7q6aoIGiUQCobfewtKLLyLy9tu429KChRdewNb5ecDzsLu7Gz6fzxwLyU7InHMwGDS8wb1Mfr8fN3btwmplJe53diL6wQd40N6Ox888g8YrV7D95k0TTVdQSR7SkrhUKoXW27fRf/AgOnt7zTYPCn08HjfKi4LFPYqpVApra2tmLXffv4/6Gzdyx/Pks1LBYBBNt2+jta8vd8SpOM7tg4PYNzCALVu2ICkgl85lNBpFMBg0EeDZ2VnU1dVhbW0NGxsb2Lt3L+LxuAGrC+fPY+rsWZT89KcIvPUWKioqEIlEMDExUaAglF8UyNGpCwaDppqiubkZ9/NHVoZCIUxOTqKmpgbV1dWmKqSyshLBYBAHentxp70dHb29BccBcg2y2SwetLfjbns7Ou7dw76BgQJFzfHxs5Qh1QGUCduhqZqexmq+8Wj/oUPYc/9+wX5Yfl+Bihoovd9gvj/D/Y4O7OzufiLCy3sWc6h/X5fqNAJnBeY0wOxUrOOxjS/ny9JFAMbgcN7Uc5WVlSYjxXXQrIiWzCoNybfFgr1KO+VDfo7/E3BSN+tWDQV+X3Qc/KPr4hv5agTXdY1hAwqbYzJIqaBMjbE9F36GGX7N0JIe3F/MwEcxZ1p53waC9jrrczQjp84a56YZRRtcc+6cIwP4Ngjk+vNeDGaqvdMgF3lK+w7YjqYdzNDgk9KFNLZf59/FwI7afqWBghgNAtOJ52VnpwnUFUxqZgvY5EuCM32N8qgOuX1cG+f5WdfFl2Mx+P1+EzzQ3hYMdq2trRnZLBO+Vt5XLMG10WAS6aWOcGlpKaqqqp4I7CoW8fv9aGhoKCgX1nXQbuiKbXQ97Qob7k/WTBfpTqdKX9f19TzPVOWpnrCfzTVg6bXqcCDXw6OmpqZgrPpd4pWtW7c+IZ/KHxpYUT1C3EpniTTQAIiOk7Rh5pKvU4fwtzYl1YAy/9deViqPpIHf7zenBui8HMfB309PYySTwbeyWXyltraAnrz+fn4eI5kMfuA4+KboxNLSUrPl2NbLdrUN+ZC8wYCB4zgGGwAoCApzjdXuEYfZl/aE8bzcUXt0YJWXiCFdN9fHTLdQcS01gUB5Vl6wG2RThsm71LnkY71sfah/854MNKjPYwctyIesaAiFQgUVGJxrNpvF3y0u4q8WF/Hl2lq84hQGaIHNqhJu/yPfvlNRgX986SWcm5zECTl5SquP7EChZslVvvhZrhXlYHl52fC6JpSDwSBCoZCpzqa+dJxcdbQG9Gy+9vl8Zkson80500YST6gO1XXmuuh2Ra6HPtfWo/ye4p5wOIx4PF4gnxwzn0V+oVPOdQmFQuYER/awcBwH319fxz88fIivl5Tgj/PNOjU4zHFSZ1H36pY45T2llW33eT+bj+2LiaB4PI7yfOKcc+J62ff5uOupAw29e/eiZ98+HBoYMEc3ckK6GKoY7f95KTCznaFMJoM9t26h7+BB7Ltz54nok20As9ks9t+7h7vt7dh3505BlEWViud52Nffj4MjI7nneIVlwa19fdh1925OmPNKdde9e6i9fDlXRi6ZNyWughhG9xSM8FKHAtgsyVRGj8Vi8Pl8pnMu/19cXDRM13r7Nh4cPoz2e/cKGPvIgwe4tnMn9nR1IZlMYujoUezp6spVAKTTGD9zButVVRg+fhytfX2mpGd4eBizs7Nw3VyGm8fcBYPBggz76uoqqqurc1slAOPURKNRVHz0EZp7euDz+XDzf/gfkIpEMAXgsz/4QQ5Q5yPwoVAIiUQCruuipqYG2WzW7O1S0OI4ucad15qbsef2bUSjUcw8+yyS1dUYO3MGjV1dSKVSiMfjqKysNGMh3bUiwHEc7Lp7F3t7exEKhQr2LQYCAYRCoQLQqRdBGsFiJpNrmsf/gRwAHjtyBCMnT2L3zZvYfe+eWePB9nYMHj2KM2NjaM03IOWaBwIBLF24gDvt7Vi7cwc7l5ZQXl5ecJTo+vq6CYQsLCxg8rXXsFFTA/dzn8PR/JaIxcVFOK++irUXX0Tgxz9GyS9/Cc/zTKna7t27UVJSgrEjR/DgmWfQcPEiGqemAADLy8umxwYzTI2NjYjFYgiHw0ilUojFYqZKY//gINofPMjNzypZ5brdbW/HcjiMO/v340A+EMZ1UeeMMqw6gfShUVZluVBXBzgOnExuixLBuH2GvO18qx6g/mi9fRv9hw6hM88T/IwadYKNYgr493Fp9RfnrcaeTm4xZ1Z1B/u98J4KhFQ3EzBks1mz1uq8MrOjTSJppJhBKwbWbafFHq+ugcqkZiIIZvm5z7ou/iRfiqlj1JJNNXpcKwWY1O326wxyaDbMdhpY2s65knbKI3YWXedezJlWenFc/CxpQXqrg6trS7qps8Ugi/ItARcBJ+mmoIXjt4N1xQAkHW3NJtlguljmzQY9yg8KZPV1OuesQCgvLzdgihUmwGaGvqSkxFQM6LO5Npzrjh07nlgXjptJFA1QqHPiOLnADulhOzGcj71+pKHSi3/b8qTjJ4Dk0c3FeoGoI6S9DuzsbCQSQTabLWjorDJM4MzvcM7U56wa0KAbdRXpW0z+td+IOqPkrbKyMtOA1K6g0LHod7jemhFcWFh4Qu5ot8PhMDY2NnI2sgityV/a74A/GszRudqOny1Tyt+8B3Wr6jn9W++lAR3iIY6Zf6fTaSwvLz9RTeG6Lr5WUoLvpNP4WiBgytd1vI7j4Ct+P77nungts3lkbLGxce7KM6q/dDz6GVZlaDBVecp2TpTn1BknD5GWmr3V+/zjxga+MzyMb5aX42WnsBpI7b5WNCgP2PZVZYtrqIkb1bf2+qnDyh4g2tS1mKPOuStvMxmnAWU+56/W1zGSyeDbAD4hvQ9snRMIBIzj7/P58LPycsy7Li5u24Zn+/rMMzXLT91kB2aVnkoD5RNWS1KeaYu4ZVL5mkk83pOOu45Bg1G6VVdlkvhPdS4rH5lssOW4GD5U/tS11OpFvq/95qib9V6qExXTua5rKiX5N6+/j8cxmsngb7NZvJKv/LBxMvmQfKv8qyfMeJ5XcG+VMf6tuNzmY/2O+mz8POfD5z/N9dSBhtt792I1HMbN3bux49atAkLaC20HEnSxuKAKljmJ+3v24N7+/dh59SpO/e//e84ZzZcTAYVbNJQgu+/dM3vm2cixf98+3O/sxO6bN00Agdl0jRYz8sYsMgV0oK0NvQcOoCl/qoGWT9kMSZChCo7Cq9FaLgo/z3ty60Q0GgWQY1aWM+nRUj6fD619fdg/OJh7LV/t4Pf7sa+/H1uvXkUikcDCwgLqrlzJnZ6Rp/nOa9cwmt9yQSEcHR0tiHhNT0/D5/NhdnYWDQ0NiEajGB0dNVUMu3btwtjYGGKxGGZnZ02EksGClZUVVL71FpY//Wn4x8fx49deQ+vt29g/OAggd9bt6uqqiXDSUdTSaSrzjqEhxH7729w8AwHsuHwZj06dwq6bN02ZFgCTeVSmJ4+QxvF4HKFQqMDAKagiuNXGNFwH8orjOCaLw6oez8ttgXl4/LgJ4hwcGTEZiQdHj2I1EsHVQAD7h4bMs6jUBw4fRjIUwr39+7H+/e+jrKwM4XAY1dXVmJubw8MDBzB57hy2vP8+IgMDJshU8ctfmqMsKysrsfr668gEg8h+9asIvv22eY7uu5s8fx6p2lpMP/sseq5cwfDrr6P+0iU4XV2orq7Gli1bsLS0ZOThSl0dHn7mM9jX0wNfvkxMaWeDdtK6enYWq5WVqJ2bK5BZyr9+h/dSuS7mvLmum2tGu28f9ty6hZa7d+HLB/RoQGwHzwaVfL7jONh975453tZzn9zPyou0+ENc6gBr1px8pk1cdY6Ud3UEOF8GwNTIU9eyAzaDerqvnyV8unWC9LLpqYEE/d/W/eQJdcRUl9lGnjbCBtbUTevr62bblWZ8lX9IT+oBAj0GMfm60sPOelCmmSVT4MPx2U6sBiFs2SBNVDdxvpRTOvx6DzpLurUmEAgU7GstKytDVVVVwfj1mQxWBoNBEwBXgEHHjetDcKJgmFtWuM9c15ljLBaE4qW8ovPne+rgaQWH2gVt/qaBACBXBbe0tFTAA8q/oVAIqVQKU1NTBVkdBYR+v78gk6u8p6CO89f58n721gTNHNnBCOo5xUXF5EnnyqoD29nd2NgwR8rxf95TQfj8/HxBhRPlhOOnnrB1tQZc7ICABlxs3rL5X8EydVixrJrej3Swx0vbRtuuOskeD3mYNFf8psEEO+ClwUwdjwaM7H41AMypLOow8vO8t1aoacCKz6G8895KA21qrWXuXI/XAgH86/p6LC4uYn5+viAjzc9+pqwM/93OnRjJN1xWO8pkisqABmB0rhw/nUt+h1vzWLpfVlZmtlUo/6gu0W11Nr6mnLNyUwPg2WwWf7NlCyZdF3+TyeBkvgJVeZT6i9V8tAfkF61WUlpQXsgD5B+7MsrWaWp/1Qco5sSq31BWVobl5WVzH61W03v8SWkp/n5jA68kk4b/KVuce1NTU0GgCwA+n07je9ksLszOPuEket7mtgrSWHWdrj/HzCAC16K8vLxAHjnuZDKJlZUVlJeXY21tzVQLr6ysGD5eWloqCMr5fD5zIh/lgXLMII7neZt9w1KbR3l+nB4mj6odoC21L+UHe43p7+iRkx+3pir79PVWVlaMDuSavl5aih8FAvhqIIDFxcWCwLXP58Mv/H68sbaGvwiH8YLIoQb/i83D1j027uZVTC49z0M0GsXjx4/N/3wOdbCNdT7ueupAw77ubvQePIh9t28XKFuNyukkdNI6GDK9RjrJwHfa27EWiaD3zBkEjh3D3lu3sPv+/YJojBKN93ZdF4Pt7eg/fBi7btzAzu5u3OvoQDwaxdDRo+gcHjZRs8ePHxuG9vl8JhvCExaYoek/eBDxaBTDx4+boxm1tJ6MQ8Dw8MAB9B04gAP5QIDSSIEGaUNGSiaTmDx2DI+ffRbpri7slCaD8XgciUQCIwcPYuLsWezNl+jz3ouLi4hEIpiZmUE8HsfS0hLi8ThmZ2dNBHbqxAk8PHECrV1d+Mx3v5srgfH7MTs7i4WFBRNs8TzP7P9PJBIYHR1Fa2srotEoSkpKEA6HUVFRYZzsQCCAuro6zM3NoaSkBMvLy0in04i8/z5C776Lqf/1f8VGNIqBQ4ewf3DQKIvl5WWzX0uBijYc0XOZmQXdcvky6q9fz+1jEjCqe6gVvFD5MLJORUXAznXg/XkPPpOO0dramtlqQMVN5UL+X+vpwYMjR3B4YMAYs42NDRzIb9U5OTZmxqTrz+06HUNDaDl61HSoXVlZQSaTwZ0LF7BRXY25T3wCJSMj2NndjabbtzE7O4uKhgazFrdFTtdfeAEbr76K8jffROSjj9DW1pY78eTmTTw6cwYdfX24c+YM1sNhTD3zDE4+fmwcwEAgdz7w7Owsho8fR7KmBgOHD2P3vXtGTlWx2Q76g/Z2PGxqgue6eByLYSnfGFMNrBoqGnpV4o6zWUqZyWQK1orvKyi3o/1q6FUGbYWogEZ/69yeNlr733LR2VQaMNBAkEInSLMJnKdG1HnEFGkBbDYC1E7WlDHSks5kKpUylSwaCAU2AwW2keYz+Ey1AzbNFUzScSB40P4KCrq5DgzQUe5jsViBIbcdTMdxTNdvGyip00i9o/NIJpPGOddyXHXOi+255ZyVl/U1/byWHjLYpM6vyo0GmTTLxmytPltlgJl6dm4v9plMJmOq3rSk3wZ16pTaTqi91vq30s3+LJ+v87X5154/bQWwuZ1CgxV25oVyTmdCn61BIt5LnTJ9jn7XzvQVC3Sqc07HTfUVnSRiAv1R3cOAmNJD52jzla6z8pICbZtXHccxFSuqF1V2dAx20Mxec6WjbTfUWVN+ULrrPPWHzj2dE9UJOkbNXANPBrtt/cU14Nj0ffIGMYLSk/PgmBTTahZb76U8qTQuJutKW+ITnSdlUT+nOsUOxPKy+d4OtirtbL7Wz6RSKRNMAGAcL91Ow7HoNlSbxlwj2iVNrFEXce34W9fztUwGb5aW4qt+PxKzs2ZODLal02mTEFPnSHWg8p2NWZS/bZ7RNeTrmuX3+XymmsjmMVvG6GTzWQzOaEAMAL5QXo6/rKvDgwcPsCLro0EhboWizXVdF68A2N3bm8PA+TJ++i/8LGWHY6Wjq7SxnXDen0euk/Ycj1YDr66uIhaLobq62lRb2DKiFenE2xsbGwWNRUkbbkvgOuox0byHBu40YaA6UtdQ11XXieOknWRQl9sxtFeKfl6rEnU7Fufg9/vxAoD/ae9ezM/PYyYeN+vIe/3QdTGRTuOvl5fxSam81i2Sajs1gKp2QjGtzl0vpUM0GsVgPrGt9CP+UF3xu66nDjTsHxpC++BgjvB5Qmu0VyejRFajD+CJ5n36nYN9fehpa8NGIIB4LIYHR4+iLd+t3444qmPg8/kwcORILjBw7Bj29vai9fZtDB45goMDAwaAz83NmQwvF1Gz1JFIxICqg/39uN3aip03byISiZhGkLayI6P0HTiAlUgEPfv2Yfe9e2ahbAeKTMYuocvLy5h65hmkamsxeOwYmrq74TiOKSUKBoN4fOEC1vNOu/vmm4ZuwGZnWb/fj5qaGkQiERP19jwPD48fR6KqCg+OHsXevj64rovFxUUsLy+bEmENnrCcnj0V6urqMD8/j2w2a5RIMplEc3Mz/H4/QqEQ1tbWsLCwYPbyr6ysoPzNN5F9+WU0371rjBIFgFUX0WgU5eXlWF5eNq97nmeenUgksLS0BADGGaLBJchQeqqhtcGL53mmg7yuDb9PHk2lUlheXjbv0QjRGWAZufI/TzAIhUJwhJ6dw8M48PAhqqurERc+4Np1DA1hb29vbkz5viAcs9/vx+6bNzF8/Dj2dnebPWo9O3di4PBhtPX0YG9fHxzHwYHr13GnvR1eby/if/7ngN+P1Gc/i1hvLyKRCG5t347hgwdxoKcHbUNDAIA77e3Y39tr+Md1c9k/7nktHxjA3ZIS7M8f32o7ULYMAsDQ0aPwfD7A81A9M2NoqCVratz1fmoA+ENlnM1m0dPWhrVwGDfPnoXneWh/8MAYQb2ngmuOzXE297eSR3iEqAIwlati2Yvf56WAV0vCHccxGXWORbd32OPUygQ9OUDHrtlNAhA9HovBDAbh7L2O1NkayCsGsmzjaAM1zeTZ4Fkdbi3jVLBPXuVJLLqe/A7vySwJy76BQgeIDU8JFnTcdAz1GGJgc2+8VkkUyyRoAM4G6vyfe9T5We0arZ+3g12cK9dybW3NgHFmU7UKgjqMmSbKmva0IWhQ3cbP6usqp3oP+2LwRrGB2m7+MDPC+WpfEep4tbPK9ww+JRIJc0KSAnjOm89IJpMFe+PVYSTtdI0IwLWpIO9XDIRrRlyBP+fxXjiM31RX4/zUFKoHBgp0k+pTpb/qRDoqOnaVPV7kS83AUq7JE7oe/L7tnNuOhNKd9+LnSBuVddU/pNNbgQD+4cwZHB0aQnN+i2AxcM/52cczUk74m/KnWJLAWp0Lz9vcLqU0tbGPHZwg36s86zj5OaAw6EZ9Sx6lnVGHUe+hegwozF7bTj/LzvUUD5230lTXTIO35A/dw0/9m81mjbOrepryoM+gTtZggVabKd9RPqi7bGdOAw+ct9oy2kUdD8d3fmEB/9e2NiwuLmIin+gp1quB8qFrR9rp1hYNgun66JppdY/aMNVlTJipbiGN7GalSlN+X7dqa6aefMmx6r014FFRUWH4LZ1O42eOg+/u24fnZmfx6bzd04AJtzhQD+t4VbaVN1V/8CQ66n/iGspsaWmpqUamU66Yw5Y/Bifo2JPnqbOZZKTzzwATeUedbE1G8ZkbGxsmGcH1U16x58j1JU8vLS0hHA5jdXW1IMDLcXI+fI3zpGypzdBAj/oxvO8XAfzY58NfhELw8oEIAPhhKoX/VFODF5eX8cl88FVlQ/W37RcVC/Tq+gJANBpFPB4vOEaYNKY/+zTXf9GpEzYIt0GEbRTUCeDfGiGyo7odQ0NoGxhAT3Mz7nd2Yv+9ewUCaYMVfs/zcvuuBw4dwr47d1BaWoqOoSEcm5jIMXkRgMVFINCkASWj7R8cRNPt20gkEvDlz5i194YpcDvY34+efftwoL+/oGEmFSMNF8E2Sz79fj8iv/41Vl9+GYcHB80xhhqY4bGfDZcvm2fSkJWWlpo9NNyvW1paaioQ9nR1YfjYMbTduWNKo2ZmZuDz+bB161bMzMzA7/dj7uxZrHz60wj+/OeI/+pXqKiowOjoKLZv3276BHDPPgV0cXERgUDAnMtbWlqKeDyOcDgM/8WL8Pv96H/5ZZSWlqKxq8sAeL/fb3orDA0NmdM2KIgqLJwvASONzfr6umlsqMaNa6kOgAJedXb4PAZm+ltb0dPWhp3XrmFnd7dREFS+VAyqhPkcBRx8ls/nM+XenJMCYPaZ0O7b2giquacHrX19Ob7Lz234xAmsRyIYOHIEbQ8ewHEc7O3rQ/Cdd3DpX/9rwO+Hk8mgdn4evf/236Ksvx8P9u1DIhzGvc5O7B8awr7+frT29eVkQOSITorneegcHkZHvmkq5IhAOloqi5z7wf5+fHj0KLKui4UtW4ySVIXG3yq/juMU7DW39Ugmk0Hn/fu4cuJErhnkwYNmOw7vwfsqkNbIuuu6JgvGtdH96Qr2NSqtgOP3eWnATPWi5+UCbRUVFQZc6R5jddRdN7e/PBKJmIxRuezv0+Av9WgikTBAh7RNJBJme4IaXjuYUCyAp+tk00oNreNsbj2yHQl+TtfOdtjYO4ZgSKP4BJWlpaVmnpQvdUR4X/Iv5w5sZq0powQxHL8NhDhfDQrwsx9nq5Ruun1P+dMGNTbtqbd47/Ly8gJdRppy/CpDOm4+kwCNgVxeCoDUqVY+tOfJtVOHjnzKv/W3BnLttaY8/LqsDG+urOBPy8vxmhx/Sfml3idNVH8oSKdc6DoBm6XV/LyuGXmBgF630dmJBt6LY2NvBYLFdyIRzJSU4P0tW/A50ZmkrzqClAOug/KJDRRddzMLaq+16l+tBip2D2ami+EbOiq8B+/Heyt/uq5rGoe5bq7vUzAYRDgcxo/9fsyXlOBGS0vBaT9KQ52nrhdp4vP5TGm+8praYI6LtFDa6PMUzNtOOh0kriH1hsqh4zhmHMR8sXxj0Lq6OtP8mp/5hd+PnwQCaE0k0HDzZkH2uliFhy2LKl8aJFYbrPyvvK4JE35Gg9ucM2VJeYGBaaWtfla3pZLHWlpajI1LJpOorq5GOBzG3NxcwfP4U1ZWZo6DVD7j83iygW17qPs4Jg2yKQ7j89ivhvTWJrzFbB5pTDrY79m2hXzI8ZSVlZlgsN3nQXU956FJB61IsZM15AG+puvN19hInvf5sd+P2UAAv45EsP/mTfS3tuKd6mp8JpHAp/OBFj2dR+VRg6aKySkDdPYfP35sTk2gziCv+HybJ69oM3H6MsV0H++hTeMV23EsZWVlmJ+fL8CwPM2EvYp4Pw3CKr1Uv+pzbHxJ3UDZC4VCBfTnXDWwF4/HUV1djfLyclOFQUedvoXKGfmO9/ujbBb/In9KyOjoqHnv79bXMenz4ZfhMF4Vu6f3sfW68qvN8za+Y7Uj19HeKvl7DzQsLi4WgDSNvPB1XbSPU5p02lR5Du7fj94DB7Dv9m0037mDHbdumb4KSSuipNEa/p1Op7Gzu9tsK8ig8Nx3PqusrAxbtmwpEFYABc2FGFXq3bMHd/bvR/O1a9hz/36BwtGF4hha+/rMCRmeKHAtYUN+XMvLy1hcXEQikUB1dTVC776LQ6OjaG5uRlqYhffY29uL7TdvIh6PI55XtlxsZsaojKhAaUj39vbi5OPHOUc+fzb84uKi2TPl8+X2Dc09/zyyNTXwvfIKqi5eNOXb7DhKOlNpkE6PHz9GbW0twuEwxsfHkclkjMCvvvwykjU1uNPejoYbNwDkGhBWV1fD7/ebigUqea2wADabunieZ0pINSDk9292nFcQRbrxYvkchYKZQAoKHZeu1lbEIxE8PH4cjV1dxnAsLy+bxjXkGVXEDw8cwOjJk2i9fRu787ziebmMKM/MdhzHRDX53J7mZnTv22eaSHqehwdtbRg4cgTbfvtbtOYrUFT423p60NvZidbbtwsyftlsFmU/+xmSr76KwwMD6DtwAGvhMHr27UPn/fu409aGjvzpEpwH+YaKqHfvXtxpb0fn/ftoz1cS8blKV47HVlL8Tve+fei4d68gEs7vqoFSAKd6RZ/DsXUMDcHn8+F2ayv23r5dAJ75eVWulBGVUS2X53YCpa9eamD+EJfqNBvQJxIJ05h1Y2PDlDWTdjovzZJ53uaRrHyGGtW1tTUsLy8/4VCtra2htLTUHElmGw/V8/p8tQfKH+RH/T6wuSWJn2cWv1g21A7+6Ckzek/byePc7M7tqlPVcdOTOviaGnjNPHC9bNp83N/Ke+pIO45jtivxdW5t0e/yfd1jzqAQZYsAwHYw+HkNoOg68d6a6VVHSudPvlLbokeT6bhtJ5wXx6A04drzfz1nnXz105ISTGSz+Nv1dXwhr+u57gS4PKec82XARQGTOkq2E6a6Q8EUASAbBiqf6Lo6Tq5CxvM292XX1tYW8Oari4t403XxyZUVA5pVTtVR0W1EdlZfHV0dP+/D/8kXytc272tmuBhf83layaTzpzzQoSL/BINBw7PEDq2trfjq2hr+PpXChaUl04CYDQt17PzhXKlPtCpS9YMt4/zR9VbgrvcnXeytvPyc6k9b95DGrILla6wson7bsmUL1tfX8YtsFtMAMp2daB8cNFWr5Ft+l9iV/Ej9SH2mFWeK/RSXq9zxhzLOABTvzYt2WPvoqH5UejJ4xyw114Ryx0RSZWWlcbR3795t+oDZ2MF2/rjmWkWUzWaN/8B7qJ7keHVOxHfEYKS1YgJ1MDXgw/EU67GgMqKXOsyO4xi7qjZAeVsbuSo/Kj3UnpLfNcBH/lA7RdprUOkrfj/+89oamm/fxujoKN46fhwLgQB+7rp41dmsuFFdrfyuNlRlzHFyR6Z+64UXUDE2hvdDIbwE4Jjnmf4x1I/kWQ0QUueS9ooPdcuJyr0m+3gPNuPXJIEG0JVmvLetD9RO6nxJA8oQt1RqFSA/xzUgHQOBgAk06HHaunakPW2XXc1Huqlv53kevl5air9ZW8Nn8riANogyQt7RudvratsQfY+9qshDpCs/q///ruupAw02g9mAgoSzAww6GSoKEpfEut/ZmevNcOAAdvb0GCLbIJogkPchAcl8k8ePY/jYMTReuYLmnh6T4SJA1fsAxcuG+P/d/fuxFo3iweHDqPnww4KFt8u/KDjMxOjCkXFoKJeWlrCysmJOkggEAlh59llc+qM/wtL9+9h19665TzabNccbMvpdVlZWwJCkCX9WVlYM8Hp44ABGTpxAZ28v9g8OYmNjAwsLC8Zg8dxez/NQ/ZvfYOGTn0TFO++grq4O09PTxgnZtm2bOYoxk8mY43rS6dyxi1VVVXj06BFmZmbM8ZGZTAbRX/0Kc5/4BGLvv29Oq9AyvNLSUqytrZmghgICKh1mWvT4G/IegSmNjjZmUYGxBdoWQCr75mvXMHLiBNrzJ0eoAtEjPlXxp9NpPDp1CvFYDINHj+LI+LhZ67W1NYyOjqK0tNRUbajAdn/iE4jHYhg+dgzHJyfhOA7ezlcsTJ47h4YbNwygp2Ledu0atl69ikwmgzXhwUwmg7Jf/xpbLl9G09Gj8LJZ9B48iNaeHrT09aHlzh04zmbgTh2SB+3tGDh0COmyMiTLy3G7tRVNt28/AWAVYNmOJWndcucOdt+7l8s0ozAzbjtbeg9bX6hTeHfXLnTnAyaf+e53c1UmApY0Yq3j4WvFItUKEGyQy+cXAxK/r0sNrAYI0+nccbcK1m2QoQaDxkWBkupP6iY18lo94rqu2VtLEEudooEc0lO3vdk2wF5Tm5Z6pB3HrsCBoM/OItDRpS7n9XHP4nukBXWcOtSRSMTQjs/lumhTTf0b2DxlQflNbYo9NgVLNohW557HHtvOPedDUJRMJguCvXYmznaA+UzqRo6NOk9L6jWTSFujY9axKd05f/IwP8tLZYnrwgoB3ofVaTxWmfP4kuvih66LP6uoMPe2qzM0S0t6c11JB9oddaB1XrqGfE+BO22arf8dxzE9jFS2KyoqzDGpiUQCf1FSgq+srWEOwMj27ZienkZlZSVm843ZVMZ52fqM76vcUY74eZsXeCmNgE07rOtDjGF/3wbOvMiHpaWlppGo4jXFeT6fD5/3+/HpjQ3M+/0Yyx9VuLy8jHg8/oRDz2cRWCvgVjqo02LzmMqDOrec00+zWfzj6Ci+GQziUCpVsKb83I8zGfwgm8XrnocvuJsVLho00wywrX8YKE4kEviC5+F72SyeX15GfX09kslkwakAvKfSTXmCY/r20hL+j1QKX3YcfE4cCq4z6WQH9mxb7ThOwckyxEnsZ0IbYOtPOk/ETrw35Zp4VXtkBQIBbNu2DYcOHUIikcDk5KR5LgBTmWYHuWkPGMxSvJNIJEx1C3WPnRnn3DlGzkcbkPOZ1InqUJEO1FG2jNr/q95zHAe7du3C/Px8QRUOeZT0tefKNVP559rwdV1r0lDvpVUYfP/10lI03LiBWwMDKA+FcOHxY1zesQMvra4iISdOaZNDxU6ku8omP/MPmQwWKiqwuHs3PJ8Pv6iowFHPw/z8vKm4bmxsLJgHaW/bepVDW+fSDpJe/A6D31wLrt/GxobpccWL8kWnn9hHdVuxdebl9/tzR9bnGzOrbGhAl/fjCX8ATH86xaa2TaVfWAzP8vnk1a9UVOC5lRUsJBJw8tXTOm5NpNryr1ex178bj+Nv1tawr60Np1Mp00NOMYnS9XddTx1o0Ei/bZhVeev7Cmz4eQVkHPDxoSHcbm3FiYkJtLS0GEZXRtZIvN5HI57XTp9GPBTCyIkT2Jk/XcHuCVFsvMWM/IHeXnS1tmL7lSvmPpohUPCiY9Loot6T489kMqZDayKRwKNHj7D45S8jXV2Nu/v3o/ajjwoUJIFgOBw2ws1smEYBw+GwKdGhkzt49CgSkQjutLWhc3gYiUTCnMBAunG7Q/YnP0Hju+/C8zws5EsfKyoqUFtbi1QqhdXVVczNzZl9/BsbGxgaGoLjOOjLZ963b99umkNu2bIFC7/5DSp+8xtUVlZi4JlnMPniizkn/Fe/QsWdOwgGg4hEIggGgwVZHACm8cvooUN4dOoU6j74AFuvXi0APzbAtxuW2J9Rw6sBIQZsdnZ3Y8/9+7nuv2LwKPi635v3TqVS2NfTg/5Dh3Cwv78AFMXze6loEAjM+PxdN29i6NgxdPb2GiXf2duL262taLx+HaFQyHxHS5aKlStp07CNjQ3s6OpC4+3buWCR8LyOnbw5cPgw1qJR+Dc24GQyiOUrYPhZ8rUaPQXsttK0ZUqdGMqAKmIFk7pWHHPXnj1YDYVwraMDXXv2oOXGDbQNDDyxF07XmJf+zXkwEGTPR6+PU8i/r4tGU7dvqEFn41XSVJ0yOuZKYwXepLkec8Wu2+R3DRYlEgmUlJQUnGig66bAnWBPZUwdTqWXnQ3gHLjelZWVJnBKPU3DT0CoPKH7gVl5YT+DwQufL7dnlO/pfRS02lVv+llmK7SfhTo3Sm/SQh1ulQ2gsKcR14/gl69xPpopILCg3qdTx/3fBOI6HrssWstOqYs4D64NeZFgkHzJe+v9FPDzO7bd5/woa6pz+VnVAXY5vOd5eAXAn8ViplO5rfMdZ7Ppp45Pdabn5Zo96xqp46pz5aV6SgGaPof8zD3Eam9sGeJVWlqKqqoq83o8HjcNPenMKH4oxr+8P+mm8qdro8FyW8epruY6aH8M0lA/S1tGedBAKSuIWBXKv+PxuEkmrK+vm/5LDKJx+2Y2mzXNpfWEDNKf/KqBVTsIYdsaziEUChmZUR78/9P2n0GSXVeaIPg9VxHh2sNDa61VRupMJAQJkCAIkCBIQlCCLFZ1V0/vWG9v7c6ObdvamM3a7OzO2vZsz3RXdzWLqooSLJAECJIAQRBMJFJGZmToyNBaa9ce7v72h7/v5vGHZBV+sJ5ZWES4eO+Kc4/4jvrx0RGWUyl8JxrF/9ekA/IerwBYBfATAC8YoIrkfZlMBuFwOEe2JJNJdVYjkYj6aT86wr89OkIsFkMsHoff71cRLO3t7VhbW8OG0XpagiZy3Ol0Gv/l4AAruo4f6jqeEQ6wB4EtdAhJns37W63WnPQNvk65wTlRXkjelMlkVG0tCVLweVyDUCiEfzg+xq/cbpzf30fP0RHKyspURK+kaVkoT86JXmnpMKSDwQweyXUgrUj9nLxUtvzls7kmfI1p1Tzf5nWSz+X/khY1TcMTTzwBv9+fI6dkeoI8l+RFEnQ3G4cSWOZn+BrPIufDfeL6Uo8tKChAUVEReo6P8Zm1tWyKgbFWhYWFKtJTzs1Mk+a5f0PT8M2jI1TFYpi12/HRvT1omqbqt/n9fiQSCbgMwFjOh/MwOxO5Vpwbz7sET8hLZGQK+YWm3XcUSN2Zz+U4SNuSx0te+qD/P/axj2FtbQ1ALiAt9Tjup91uV+mvTMeSgJKUl5Jny8hK6vWSVzMdUPJwfl7yQc6VNtaD9EYpuzmX7+zsYCmdxnFbGzxGRLfkSTwzH+b60ECDFH7cFIl6SgXBrBxz0lRGuGFOpxNOpxP1u7s4//772U0DchQbqXRIg0eGqvF5vVNTGGxuRtm1azm5u3yeWTmWmywJT9Oyee++y5ezB9QwMiWiKcP2iRpJRYj3IgIuiZmM3+PxIJFIwP/WW4g89RQa7txRoT+cK78nc+JYa4FKssWSW/GUeXPtIyOYOXECHWNjOcKfgjGRSCD8kY/g4IknUH/rFlKvvqoOu81mQ3FxMTY2NnIqvIbDYayvr6O1tRVOpxOHh4fw+/04PDxEOBxWBk04HIbb7UY8Hkcqlcq2VzQU/63HHkPLvXs5ubFcL1kl12azYeXCBcQLC7F+6RKKr11DMpnEYm8v1i9dQvXVq6gZGsLqyZNYOncOjbdvo1aE1Uvmz33l/pEuzUqhGeE3K85kINKgaxgdRcfMTBbxM84LmWE6nVbtbLj3vFfZzZtonpiA3++HboQctkxOouT6dUQiEdgMWiD9SJqXSr5UqilI5FxTqVRODq756hgdxWR3N5IOB1IOBw7LynJ6q8u58nlmoWs2TMzKsXyuuQAfhYoZUebrrcPDuNfTg6TDgUgggNHHHoPVakX9yAgymfs9mP8YOCCFAMdOw+1BQIMc04PW609x8bn5+fmq9oCkLRmOK/NO5RjNQksqOlKQUfAC9xF+OYbj42MVCigNE7PCZy4CJ8cilTbJa+VaSm+npmmqnSxwn6ZlwVWzIWsOcwVyc5J5RiTNybFIkI/PjcViObnBlC+pVEqtk1T25f9m2qHsk+suf6RHS4aJSuWZSgQvyljgfs49w5MlUCHna+Z5lCdyTOQZNMCkZ1CuuwRi5BnTNA2vAfiHZBLfyMvDF0SeqlkhNSumXKtgMIhAIIB4PI7106fxb4uL8ZKu4xncN4Sk/Od6cM14SRqlEi9BXbMBYVbc+bc0SPiejEaR+0tewpbU4XBYRc/wezS0mSKwv7+vijDTECssLITFYkE0GkV1dTUaGhrwq1/9SjkCuDc0qkgzctySn8qzJOclQTsJOkm+bvYucr6y+CAjGPgeozPZ/pTGTDqdbb/Kzi3JZFIZpuFwGOFwWNVbstuzbVu7u7vhdDrxd3/3d0p+cnzyTElASIYuSzqR9CYLzMr3v1pQgL+OxRDKZPBrmw3/WtAsn/OpRAK/cDjwnDGeNdb9AvDjWAyvWq34M68XXwsGPxCVKUEH6k47OztYWlpSaWz0wpaVlcHhcGB1dTWHTqWhyNf/hd+P/7izgxctFtiFASYNQvJLenr5OtflewcH+M97e/iEpuEvcR/8ZZSpBBCSyST29vYUHzg6OoLVakVRURHcbneOE05G4REIfWdnBzu6jus1NeiYmVFtN6VRTpnMSxqMlFmZzP00KQAfaPVJHkDeKveaf9tsNoTDYbWWEtCVRRb5+x23G68kEvjs0hKeEwV7zXJSylyO/xe6jp8A+HRFBU4sL6OoqEilK8uoLY6ZfJzGKQEfnmkJDsp1knYLZYWsPSd5gsPhQDAYRDAYVO9J26a8vBwHBwc54JSZZ5jlIAB8TNPwuM2GWF4eDg4OkMhkELbbUVZWhtLSUpSUlCi+SHomQCnTHaTskOCWWcbxdYJPZkeW5IH8nrRXf1tQgJ+Gw/hiMonPmJyH5rMiZdhPk0l8//gYX7Ba8awRlRSLxZTdIvUPftdut+fUp5POcSl3CMRQXnPdGf0qgTgZSSSdjFJGcA+9Xq+qKSGjJPkMCUZI3vOyy4VvRyJ42eNBXXGxkl+keyk//6nrQwMNhYWFOTmyQNbrzEuiMdJA5kLZ7XYVVs9DTMZP77VUTqQHz8wwzJPkwvUsLKB7fh6hUAhhr/cDSJDcWDMazsWVyr4MseEzzC0VOTaujQwnMR9ICuv8/HzFJAOBAAqvXEHX3Fw2fFbkvErDlv9LBZB/SyNPKh2NY2PoNXol64bA4Fq73W4kk0lsPPww0oEAZvr7cf7aNUQiEWxsbKg6CtFoFC6XSxU52tvbw9raGvb39xVjsFgsaGhoQGFhIY6OjrC1taXyl1jssOzyZaw/+ST0TAa1RpQI11OGacl1s9lsaLx9G3OnTqH82jV4vV44HA4MPfIIEoWFWLlwARW3b2Pp3DnECgsxe/IkKm/f/oDA5d+SIVERlAJd0zRsnDmTve+VK6i8fVsxtVgslk3NOX0aTXfuoGF0FAAw29mJ2f5+1N28iebJyRwwSQImpHmpOPDg7+/vq7Mw1daGmaefRuX776PJqKkg6cFsjMv78b3Zzk7c6+lBq1G3hLRqRqk51vbpabRPT+NeSwvGu7vRbcxDGmxy/FKpMa+zBNYk7f8xJVB+Ro5NIrLd8/PonJ3FRHMzBs6fh261Yqa/H6337qnvmD2A0igwG4Ycn1RypCED3Bfs5vX+U11ce5mzR4VJ1rGxWq0qB9bMV/h9WWSRc6CiybWQnmsqxOQn0pgkD5fPknxZ7pE8U3L/JNDH/ZCGg1QGpEIhDWRJe5w7z5E5ZYDjlDQqgQ8pm8yyJJlMwufzfcA4zmQyOSGOBCE4bob601CX35OXpHE5v4qKCqVorZw4gX9XV4dPJxL4xPH9Vpocr+yMRHnEsfIzZkNDRkBRSZGhulwXAoosXMfvc7wyOoPv8fv/YLdjRdfx7WgUXza8s2ZaMStu/D+dTsPj8eD555/HN7/5zSwPt9nwEwCfFPtljpLguZZODQKG5j2XXkvZYUbuheT95jk+aC7mZ9y9excNDQ2oqqqC1+tVZyqRSKjUiUAggOLiYqyvr2NlZUWNB8jWvmLfdKZMuVwu7O3tKT2BHlUZUSmNGhmJwLMlFU86Xcx8UZ717x8d4X+rrsZfxmJ4ySjYxnlLneZHkQheiUbxvKbhk4aDpbW1VdERnycNxYKCAmXMuFwuFBYWwuPxYH19HYuLi9g0Wiz/8pe/VIAndQ2ZbvgPx8f4biSCYE0N/sP8PL7u8aie8nKvOCeuk9ST5D4/n5+Pv08msXB8jNcLCvB/EAZFOp2teP9ELIYnjIJyjOCsqqqC1WrFTzUNawC+G4vhL0UnNjqeCDxJI8Tv98Nut8Pn88FisSASiSAajeKtt95SINlEczO+arHgG/E4PmfIh3Q6jfcCAfzPGxv4C78fPzYMQ+qUMgKRvJYAA3mSNOT+Zn8fS+k0XsvLw78UPJF8jrT1s3QaP0wm8aLFgq/4/dC0bMs7t9utomzlMxnVwHDtdDqNl6xW/CCVwnMWC/r7+1VL+a2trRwalIYP+dq7Xi/+73t7+FdWK05Yc7sREZSWRhf3T0acSVq2WCxK7yN/MctVnjO73Y4fpdNYs1jwisWCLxnAs+TxMsWC9MXz9dNEAqu6jh+kUth6802Ul5fj3LlzCAaDat4P0jG4lwxV5yXlLHk8n20Gral/yrplvIff70cwGMxpm83PzM3NIT8/X41R8gDJM3hG5TnTNA1jY2O4ceMGSktLUV5ergxht9udYy8AwDtuN/677W38q0wGj4i9NQMpUraaZTp1JpfLlVOXhzaaPPvSYfOjdBqrmobvRCJ4ynDqSLkr14Tft1qt+PtkEisAvp/JoGpwEOPj4+jo6EB5eblaI54FOR/SB4E72rdSt5GOcVljijJQ2qNy3yWIbgYmCRzu7u6qlHzpMOL3ZO04npeHdB2nEwng6AhXl5cRDodRX1+fs84SzPnHrg8NNIRCoRxPk8y1l3mrUuAR3eaCMBePBOdwOFRYnWx3Io0c/s/wKYlocePka5PNzRjp6EDb8DAax8ZyFFtJuPI1iZZlMtmOELOdnZj+7GdReeUKOmZn1TxZGIoHKxaLIRwO5yju3DwqduwDSwUVyDI1v9+v0F1696ikMByUzyH4ouu6UhoJerB7gVQopOHCvyORiMpNpoCqeO89bD7yCPxvv43Dw0NkMhm43W4UFRUhGAyiqqoKBQUFWFlZQSaTQUNDA9LpNPx+P3Z3dxUzPDw8xOLiovIus2exy+VCKBRC1a1buLCzg4mJCRQWFkI3lECZAiIVAR6IpvFx1I+MYHt7G8cGQl9z4wYWzpxB3a1bcLvdaLp9G7OnTqF+YCAntJNKCn9kCBW9wTTq6EG+e+kS4n4/Nh9+GJ3GvnNf5k+fVrUYWLxx7uRJRAMBLJ07hxMrK+oZNEAkfUkwhUKAdMfX5k6dQszvx/L58yi+fl15JaRxudrfr+Zfefs2rFar6gySyWQw3tWFqM+Hia4uVN25k8MMzOdFMvHqu3dRPzqaLV6j53qnpdCRRi/HpOs6JltacKe3FwBw4u5dVRxSGnhSmZBj4nu8n1wz8pu2qSlYrVaMdXSgY3Q0JzxczkXem88jw5YggxSS0gDi8/85gQYJ9PB/8p50Oq3aW5q9t5KGpLFFviEjeaRiwO/KwqSSl8iIKbmmkjfK3EGOQRqAXG+eNSno7XY7ioqKcjpNyDWWY5b7IY0IygEZqsy9ohebSiy9yfIzPEe/cTjws3Qaf55KISgMUK4L19DsTeEYaQxS4ZLGu3mP5PpLULOlpQVnzpzBlStXska23Y5fWiz4pJBJ0oAgX5EFZcnH5H25FgT0ST//GDhIhYYymPxOjlka39yzzyMbUv75TAYrKys5SjH3VJ5pSc9UaiKRCAKBACqvXMH2Rz6CT2QySBkec3n25BgksECgTAK7Ukfh3rLauTRUOE7pQZLgA+fDSEDJRzLGnKPRKEpKSpQHkjKYf3P/LBYLSkpK4HQ6VRrizs6O8mofHx9jYWEBs7OzOV1gRkdH0d/fD7/fr9adc5NOHAkiSHrmb3p+pRyh8q3rOv7r0RE2bTZ8OxzGC6JzjeSnuq7jVZsNG5qGn1ut+FdGZyHWF5FGNr34BMlogPJ9FubOz89HcXExdnZ2sL6+rqIiAeDOnTsIBAJ49NFHkclk8L1YDMuZDFYDAWRSKfzt0RGeFEVUJW1LsFGeIakP2Gw2/LnPh7/Z38fDOzvIFBcrviWjfXjuGhoalLNM0zT8RSyGb0ci+IbHo7zkpBVZN4u0yfEwtdTn86GrqwvhcBibm5tYWFhAIpHArb4+hK1WfCcaxQtGHS2LxYK3CwuxlUrhbw4O8Kg4G2ZwUxqFcv8kCPNnHg/+5vAQTxrF+kjXwP0Iukwmg1c0DVt2O35uteIvDWOOOh+BDO4vaUxGNWQyGTxrseBpiwVpqxWpQAD9/f1ob2/H0dERjo+P8UoigTcqK+GOxfDfCjBE0zT8xuPBpq7jv+zt4T/jg8ClxWLJieCU0T48x3ToUB+X0Q8yUuBBdPTZdBo/t1rxdY8HbqMTlAQf5WfNY/uzaBTfikTwmYIC9H3yk3jrrbfw+9//Ho8//jiKiooUv6K3mWNihyV5b/O+ci4SpOHcM5mM6vxAw5bpEAQtJa+grhyPx1FUVISampoPOJWlPCa9mPkMAJw6dQqVlZW4cuUKbty4gZ6eHmxubiJupAoB9yPzful0YiOTwX/e38dHhG3Je3I+fLY5FYS/uS7cf/l5roc895lMBp/TdfyDpuEbgQBKDZ1B7uEf2+c/j0bxrXAYX3e7VVT3iGGfnDlz5oF7Q54rIxWlPsy/ZXFYCQ5SX5X6gLR9KfPNQBc/t7u7C7fbjcrKypzi+XIc5j2W+qOu61hcXMT29jZisRgaGxtV2peMQvrHrg8NNCyfOIHRjg6cmJ5G39LSByqAm5UIuVlS4NLgpWddLhy/w0WTyLRU9qTw4OfIQEba2xHyejHV14czRr4bPycNcKksP8hbM3/6NGJeL5bOn4fFasXaQw+hc3QUbdPTihlIFJnCXP7Pe7LPNw18Xc/mQrndbmxtbSkPo9WaLSi5ubmJ0tLSnLAnIPegce34HIbgFRQUqPSHqdZWzJ0+jcbbt1E9OAibzYbCwsKc3NWKhQW0z8xk16WwMEepPzg4UIhxXV2dEiZsXVRQUKAMdoslm2/JsUlkTxbF0jRN1Yiw2+0IhUJqf6Xn36zwScWz5u5dBN9/P1vJ1m5HzdAQqu/ezd7DCOHUNE0xNCpyFsv9FkMcm/T8WSwWtI+OYrKnB61G8UQaVwDQMjiIaSMVhfnunePjGO/qQtfEhAq1Jr2RaUhPlFSO5cHmfrYODWGyuxsV164p8IO0wTVZOHtWgRvtRk92hrvZbDa0DA5ipr8fLcPDilFJQ868R1QQdF3HUl8fZvr7UXfjBmqHh9X4SIcPUmB4Dbe1IWEAZCNtbagYGFBrLAWUfD7XRfIMOU6zZ7x+ZAR1w8NZIyqTwUx7OyZ7e9E+MoLGsbEcw9VsqJjHIJ8rASD5zH+uSwoDqdDTeJR5i+ZcOAkUmQ1Madzxs9ILEY1G4Xa7FQ+VBoLkC/IcsvUYK0ebDRaum1Tow+Gw8vDyc21tbRgdHcXR0ZFSbCiUJUjNi/+Tz1Ehk2AIP8fxkkfa7fac5/Nzuq7jp5qG1XQa3wyF8G+FR1B6/BKJBHw+X87ZYAh7Op3Gd/b28FOLBZ85PsYn0+kcmpVn5EGCXNd11W3H4/Gg7L33sPPYY3goFAJEVAH5D8dGBbKoqEiN5/DwMCe8n7LN5XJ9wACRqWqcE2UOjRmpFJnnxNdJLx9NJvF0QQF8RtqE3GsAH6ARfp97lU6n8c4772BrawtlS0uoGxlBtKQEk2fOoLa2NuccyLQTyS/lOsnoIMlHrFarChk1e7U5NnN9An5X6jXyt91uR1NTk6oDsre3p86r1AVkDjzPEuV0eXk5urq6kEqlcOXKFcwaLXvLy8uxsrICTdNw584dVFRUoLS0NGfcV4uK8O+Xl/ElhwMXDEDNvNZsXWqeM5Abip9KpfDVggL8l/19fNXjyeFNEhi02+34mtuNv4vH8QW7XdHwysoKAoEAfD4fvh8K4acWC75kt+NZAfrLCu1UUPkcrtXKygrC4bDKZybf2dnZQSKRwHO6jlcsFtQlk1gsKMDnjHME3E+3pdHIiEVZD0LTNMXv6KS5ZLPhbDKJWH4+dnd3kU6n8SubDa+UluLZQAA1mfveYqa8cB3PRqM4b7EgHQphCvdBxd3dXUSjUdTU1OTIcJ5N6iVMg/X7/ZiYmEBrayuam5uBRAJvxuN40e1WdQxSqRQ+k0ziVw4HXrJaAT23dpmke0aCyNB7abBomoZPZjJ43OHAkRERR77v8/lUBJ3VasWLuo5XNA1fcjgQj8dVCLhZ35d8IpFIKGCMNCnHSSOS7/3fdnYQcTjwq3QaLxvFMRlN9jldxy80DV9zu2EzHJLUraSMJ4+UtM1n0ehjqo4s+m4GRLmmXK+vFxXh33i9OQYs91kac2Z9SNd1vOhy4UUDnDguLcVvCwrwejCIvEwGzwj7heMwe7x1/X60t1lfIA+Wjk1Nu1/YkOeK87VYLHglkcA3S0pw4fgYVcY+yZQXj8eD+vp6NTczuCBlmdl5BGR1teLiYpSWliIvLw93795FLBZT50pGTqbTaTybTOK1/Hx83e1G2kg/kLQlbSKLxaKc0nyfciQcDquoRKnX03EjaZQ60hc9Hvw3fr8CI8x7J+WM1BdfcrnwohEJojudqK2tRXV1Nb67v4//WlCAz+s6nk+lctLL+H3KPqa+81ySJ0n7kWf+QWeL86LeJ2uRmJ0kmUwGZWVl8Pv9Kn1aAi9yT82Am5QXjzzyCDRNU4DDr2w2/Mxmw5eSSZzGP319aKDhbnMzYl4vBhoaEHz//ZzcJmkUkuC4iAByBAuZfzgcRjKZVJ58Lg4nK5UkCRKYkSZJPLFYDB63G6knnkDT2BgODg6UgmJG6EnEcrxyPiempzHQ0IDya9ewcukSoj4fhtvakPfWW6qAmVpEgQBLj7pUVqgcz3R0YObECTTeuYO+pSX1HaKO8Xgc4XAYfr8f1dXV0LQHF7uSSK0sVhaNRlVNgBunTyPi92P25Em0TE5+gPhpfMg9JEhBA196JDbOnMHy+fNoGBhA4L33cjwi3D96+KgoxGIxlJSUoLy8HPv7+8pbSyXIZTBi5hnJvWdXDJvNBp/Pp16bbm/HTH8/am/cQNP4uNpfCoe8vDzVdzmTyRYtOjo6UuFV4XBYMSrZQzuTyaB2aEi1Uty13s/pzmQyaBgbUwUWE05nNrVjbAwNhnc9bQqD56GV9CsFg1R6qJzWDQ+j2EhhsRhFmKRXWdM0NA8OYuH0afTPzaGkpARWq1XlVTqdTjwSieBj165lz0d9vRqT9PbxdzqdrSHBsb539iyibjcWz51TAJRUlMzgiJxv69AQRk9n2U6LAfyYozkk05ZeD3kO+R4ALPX1Yf7UKTTeuYM6AziRPGK8uxtRnw9jnZ0ounpV3UcKDwluSIEilT9e8jyZvap/yotzNO8tlSEaUlSY+Rn+lkCApCUaxtJA575FIhHVOlPybAp4c/g99yAWi2HPKO4keaXcP64XP09Fl6lcv7RY8FOLBdVlZbDevo10Oo3h4WGUlpaqfFF6VswKjjwjUqmTIJo0OM0GjDQq6Yl/1WrFy0ZnDynL5JylYQ7cr4WSTqfxD1Yr1gC8lp+PPzMZ2hL0MSunlEPDw8OYnp7G9vY2Cjc2UDEwgGRREUZPn0ZLS4tqF0hwhTRBpZEtemloSbBG5slLg9IcmSF52/HxMV7XNPx8bw9dNhuGAJwuK0PU58P3gkE8D+DpTEaFwGcyGfh8PgU2mwEuKWu59vLMc00//elPI5FI4LaR8haNRjEyMoLi4mKVrwrkerCOj4/xO5cL//3CAl6y2fC4zfaB59Arze9I/iN1AeoHjECQxhNBgfcCAfx1KpVdA2MfXtc0fD8cxhcSCTxvyDkar7IAtazjRJnqMlpUS7o9ffo08vLyUFdXh0AggPHx8ZxC0ATnOM/LpaXYSKXw3VQKj4gIF2k4pVIpjI+PKxnLufNvjmlzcxOPpdPoj0TgsViwYdCUx+OBx+PB204nfma34/O6js/G4/i4pmF1cREzhgx9++23kZ+fj4ceegjf9Xhw4HLhe4kEzh0dKaAsEomo4mXpdFpF4MlojEAggMXFRYTDYTQ2NsJhGLczMzOIx+Poslpx0lT0eNYwQpxOp4o2JQ9kBK3U1Ug/PAvksyygbbPZ8BO/H+tWK35mt+MTwgCip5nKPtNCJb0dHx/jvffeQ01NjdJZMsa5AZBT4FcCxJlMBouLiygvL0fy9dfxZF4evK2tGKuuhtfrhaZp+ISm4WlDt9sVNG0GONlOURrhsn2u1WpVYA4A1fnkDYsFP3M48CyAJ429eyyTwUc0DfrREZb1+ylZnJfZUF9fX1fOKDku6v/S6cAik+eXl/FWRQWieXn4/83P4+lMBpWVlchkMnhofx+f9PlQHQxiMR5X3cskiKFpmoqESSaTirYkzUujk1GD1Bk9Ho86vxLAMNcNkHztQfxO7gEvabT9yuXCgc2GnyQS+EgkokBKOiLT6TTedjrxV+Ewvqbr+Fg8jkgkkhPBxvn8zcYGvheL4SOahoeMqBTgftF+Otd0XVdF3L8Tj2PDZsPl0lI8t7ur7pdMJvFWfj7edLvxL2MxfMHtVnxQnh+z/WR+X65DPB7H2NgYAKhi70xxoFx+OpPBJ0IhdNbUYNeI+Jb35fryvBUWFiqbQ/J7WUD36OgIPp8vB9ikc4LnLZPJ5KQ8yt9mA9wMnku7U4Kxb3q92LFY8FNdzwKByHVoMY08EAgo3ixtRr4mo+DJo+Wc6ewgDcu6OPF4XPFYXkzTks5OM30+SKc3z5XfqaysRCwWw6s2G9YtFvwglcL/C//09aGBhtqbN7F09izqBwaUgUvCkIM2GxLSsCLDobLECswejydHsZffkYvBhSfBm5+1tbWFvLt3UXz9Olw+H7YNZY33kOihvIcEK7jotslJdBpMzW63Y/HMGZRfuYLj4+Oc3sF8NoUbGZ0EGSRDuvfSS4gHApg7eRLVg4M4OjpCOBzG1tYWlpaWoOs6XC6X8o4TkQdyW4sC9xV9rh3nsdzXh5mTJ1G6u4sdqxXdU1Oq6JQkHhojUnmXTEPTNHXIAWDwoYcQ9/tVVw/meJHB81Dz/nwG8y6Zoxkzch41TUMgEFBjl0Wv5D4Buf1y506eRCwQwMLp06i5e1eFhRFsIlLLljKMoAGg0kNkeLGMjkmn08ojwNoULPhCJpBO3y/+yfmTtiWzlKH9nINsy8poEaKTkp7N9Mr7FBQU4OLuLp64fDlrgBjKrQTNZC6a2WiTY6Gxyn7LAHBucRE36urQOTUF4H5NEjPyKZUWjp21HpQwEuk/nJcZgJHINOcpz+HimTOI+HxYPndOeY6l0XK8toabDgdOr62hq6tLnQGeaz6TxhnPajqdxmRLC0ba29Fy9y6Kr11ThrTkXZK//akvuUccryzcRfTeXIlezkmCc6Q5qZRIY5O8hMg2kBvBIfM55f2t1mzldp538w+/I0EI7in3+o2dHWxnMgh3d6P+Jz+BzWbDysoK3njjDdTX1+PkyZMqL5SGGC8CDRTEsigvjXBJP+SXkgbkXD5ns+FfBINIJpN40xDWHDvXn4oKlWEaeSze+VVdx/eTSXzN5VJAsByz9J5LZZPr3dHRgaeeego//OEPVavjvLw8bGxsoL6+XvWvloqtHIcEH7jWpBHOh8A+lXzpMSKfIvgajUazOefpNNbSaaQAxMvLkc5kcGC14h90Hc+I7+Xl5amaDpIu5fXHlDd5BYNBNDQ0YHBwEFarFZcuXVIgA5UvScdcwzdcLmyk0/h+JoOHTTUauD6MPiHf5lmSF9fJarXmyI54PK7k8OWCAmwA2daERnjr95xObNps+FYohIu7uxgaGkJ5eTlqa2tzQBEJ7NvtdkQikRyvI9dpa2sLBQUFKCkpyUa2tbaq7x0cHODw8FDRaDqdxifcbvza48HHo1FsG2H7lGmUM+l0Gvv7+yg1WknK1qhcTxb2o5OAPEcq/K9arVjXNLyi6/i4kSbqcDhUIcCamhrs7e1hb28P/ZubGO/qwucBJesZCcVIquPjY5WzTUWZnurbt2/D5/MhnU7D6/Xi3r17OHHihJJDBQUFODo6QklJCWKxmFKuJZAmo7MCgYAyOP7u8BCvaBo+EQrh0aMjeDweFVUwPz+v9Igv5+fj7+JxfDaT9axvb2+jtrZW6RMEB4qLixVIRcOFjpXu7m74fD7Y7XZl/BwdHSmj8fDwEEVFRarOAEPp19bWsLa2Bk3TVL2rzs5OVFRUqMKa+/v78Hq9iMVias9ZeycUCsHr9WYdFZZsei7lQjQaRTweR15enqJxRowEAgH8PJHAuqbhFw4H+ldW0NXVhSNjnaijeDwehEIh5SCkjkMes7i4CK/Xi2g0iqKiIqyurqLKaOfq9XrxaiqFV61WfKWgAC8YhREfunULl0tLcWCx4L2yMvxfCwvVuRwcHMT29jaKi4sRjUaxvb2twrZZxyQ/Px9LS0vY29uD14g+YAt2dnzY2dnB6smTeK+8HN3hMC7+5jfweDy4UVqK35eXo2tiAr03b+JuTQ3GOjrwfDyOF4SDj2dC/v8gfma2hyTv65uexo3aWpTdvo3FoiIUFhZiYmICKysrCrT9eUcH9jQNf72zA+/oKObn51FRUaFkAYGW//rII9h3OvFaXh78o6NwuVzY3d1FeXk5HA4H9vf38a2dHbxfWYn+mRm0vfceTp48iWhZGdrGxvDe7GyOffX6Zz+LbS3bOcJco0XOSdoQEjyX/IyA0urqKuLxOC5duoT8/HyEw2GsrKxgb28PFosFN0pK8IfSUjw5MICH9vZUG0jKUwJm09PTsNlsSk7KKAzS8psOB94tLcX55WU8vrCg0s+SySTGjEhX6iKFhYX4+Mc//sB29dIBJw1s816abZTamzdx3N+Pj6dSiBjPWVhYwMbGhpJja2trqjYe9VEZnXl8fIz9/X2kUilVOJcRUewItrm5CbvdjqWlJcTjcdhsNvzG4cCvXC5cPDjA2cXFHLuUhTbLy8tz+DqfKV97EJhm/j+TyeDWrVv4yIkTeCcYxAsmefrHrg8NNHTOzqJ7fj6rrBm9rllpWBrrcgKyCI5ZIeZCeL1ehcLIIldy4tI4Nodr8b5DtbUYbG5G/a1b8Pz+96ioqMjpnyoVTv7P3xyXDOMNh8MKaQy+/z48v/99NhfM4VDFVB4EpEiEyoyKWSwWxCcmMNbZicbBQbW2DocDPp9Pefnz8/Oxt7eH/f19NUcSnDQeeUmjIRaLYeJTn0LC50Mmk8Hj3/wmLBYLtsVB4T3MqRh8neuk61n0d767G9MnTsC/sYF0Oo2GO3dyIgJIzByDBCxsNhtCoRCSySQ2NzcRCoXg8/mUt1AaxfLgUVmQwAVz0Btu38Z0Xx/K33tPARksOCVbbHFM9CYQbLBarUqRJ5NkjQtplBwfH2NnZ0fRnTRWEokEdnZ24HA4UFtbm7OWRFm5rlxz2dGDnQYsFgt8Pl9OH3IqfTs7O0pY5uXlqXlKxk76lzl+HK9Uvshk5dnhszhmTdNwcm0Np9bXkU6nsVtYqPLG+D0JfJgNWbMRI8+YHIMUVLxoNEqhZrPZcNJofXt+ZUUBHpKfnNvexrnt7ewXROqKFB4SRJM86NXOThy5XNkII6Mzi/z+gxSJP9VFjwnXgHQiW/tK8EiCNfw8cD+yKR6Pq/PBe5t5Hqu/yzxNnlN6GySP0XUd+fn5yijgGM1KFp/BfTGH/GUyGXy1oADfiUbRubmJVEUF9vb2YLVasdLfjytnzmA3HMYLmdxcSu5hMpnEZb8f/5+1NXwpLw+PmAw4jodGFJViGa0k+X4mk8EPIxF8OxxGXyCA/nRuuyybzYaBigp8t6gIn81kFMDFc+t2u/FiOo0vGwXdzOdDrrvZeyBBnbKyMpSXl+Pw8BA+nw/nzp1DSUmJ4nsEMqXi8K7Xi//Z5cI3Egk8BigAWSrCFotFGV8yQoTvSYCG8jkWi+HZZBK/dDrRZbfjbjyOc1tbODw8xHhnJz6L3IrjkkYe5PWR/ECeebkGXCMaKdFoFL/97W9RUVGBEydOoKSkBEC2PpS5c8AnQiH8LhjESwYvl06PdPp+mPvS0pIKvedZkqBdLBbD+Pg4kskkVldXMTIyoqJWWFfkmVgMvywowOe1bAXvVCqFT0WjeEXLps5t+/3Y2NhATU0NIpGIkkNOp1MpiJwnDTRza77x8XGUlpZibW0NTU1NmJmZwZ2qKvzK7cajW1v4it+vvK66ruPS9ja+EgggZrfDUVqqaISgPr129fX1yMvLU3m60mvLeXIsTMtZW1tThnEqlcJXjLP7Na8XRUbaozR+0uk0iouLsbm5iUuahr/c30dxcbFKfaEjSeqI3CeuDccSDAYxNDSEqqoqjDc14YcnTuDPfT58vqREgX8Mza+qqsLh4aG6ByP6tre3UVBQgLy8vJyIkp9FItgA8HZhIb4aCKC0tBThcBj5+fnY399HSUkJMpkMvuzz4VGjurpmyGWPx4Pi4mIFNjB6geloEuwvLi5GMBhUdUHsdrsaCy/qzYxccjgcCAQC2NjYUMCBz+fDI488okBeh8OBwsJCpbuUlJQgHA4rL7Gu69jc3ER5eTmKiooUkMD1yWQy6j5MsaUO4HQ68ZVQCN8/PsZnM9kULUYU5efnq2Kf3E/zmaaeVlRUhOHhYdTV1aGoqAjT09NobW3FvXv3cO7cObySTmPLasWPMhn8uZHa5vV68fDmJt632fDF/HwVCQIAfr8fPp8P8XgcjY2NWFlZUeBJIpFAIBDA9PQ0GhsbkU6n0djYiMnJSdTX1+dEy0SjUVyrrsauzYbhtjY8OTwMj8eDK+XlOMjLw3BbG6ru3MHgRz+KqNuNXxwf47MPSDuSf8vzawa0+Vm5Tl9wu2H9T/8pa/y2tCCdztaosVqzHTwymQyeCofxpsWCR/f2UFFRgdXVVQWudXZ2Ynd3FwDwTCyG39jt+JQhozs7O1UR96WlJXg8Hgw0NODAZsNIezueiMXwqMeD04ODWSM4k1GtJ202G84vL+NWfT2+bmoJzb/l3Mnzpdwx21VVVVVoaWnB5OQkgsEg1tfX8dvf/haxWAwWiwUVFRV4t7sb+04n3rbb0TA6irq6OqytraGtrQ3b29tIp7O16/b29lBTU4NwOIy6ujrs7u6iqKgo293C6NRwubQU+wUFuFpZiU+vrWF1dVUVj9/d3VURS/F4HGVlZTg6OlI0bZ7bg/6nzJO/eQUCAVzY2UHVN7+JopYWvGO3IxwOY29vDwCUbcIaFT6fD4lEAgcHB2hra8PMzAxKSkqwvLycjWgyUiLz8/MRjUbR2tqKpaUlFBYWYnNzU9WfWl1dRW1tLV5tbsah3Y7fBYMouX5dOUpjsRj8fj8CgQBKjHo6D5LJZllttrsl6JyXl5flQ1eu4P/S0ICamhp8mOtDAw1mRU325JRGmFkhlpOhAcb3KHAB4OjoKMc7I5U3fl8qdRRcQFaRHWlrQ9TrxcKZM7h4+7ZCys0L+qAF5G8ZapxKpeDz+aBpmgrJ43tk/FSCzUgn/5dgAxWhc1tbOGfUZUg0NmaV7ZUVBIPBnNoVmpat7muz2VTo0YMYm7zS6Wzhj5rr17H42GNI5+dj69w5tN67p/ZJGu5UtqQyytflmkz19iJq5Nyd+Q//AalUCoeCSebl5WGxpwdL58+j5vp1VAwMKIUyk8mgpKQEhYWF2N3dRSaTDWEbHx/PMUiJ7MuK+ACU8ZdMJpV3sXZoSKXvpAHMz8+r+g8yJ5K0JLuO0GBgeB0NK+aLEyDg3/RAWK33W/GwfSdDM10ulxL25jx3nheCWAxp5rMJuKgDaaCwu7u7ikF6PB6lqBIQkYo+AJWWsnziBP7zQw+hc2wMzRMT6pzyohdCGkU8C5wnLwJIuq6r1Azz98wgg9ngkufCDDRIWjOPz+FwwOv1onZ7G+cNICElvkNeYD7PHJP5zMtx8OfU/DwG6utxenERRUVF2YKjBr1xHnI9/pRXOp3G6wB+sb+PL+fn47PG+Q6HwxisrsY3Nzbw5bw8XBRGsPQcU7GlQSo911wD0pimafhxNIq/DYfRXlSEiwZNvxKP49tWK3rr6+Hf38+p0s/1I7gl+YWkGa61PMsSrOBnni8owAtOJ34TjeJuWRkODw9ht9sxde4cjlwuvFtQgOdEyK80mmOxGH5fXIyddBrfi8fxEWtuHiLHRFqUxfvMSiLv/x1dx7KuI1xejn8jFEIag1TSXk2n8UkBlsu5mkEX6cGWtGh+tozoIV/e3NzEG2+8gYqKCnR2dqKqqgoWy/06BFQSrpSXY9tiwXciETyG+3ne3B/y7GQyiYWFBWxvb6voLtm1glFZsVgMbzud+EF/P546OMD/02hj96n9fSwuLmJpaQnP5+dnPSKiC4ech1mOmuWr+ZI0RiOQY9/f38fh4SEODw9V0bzq6mrlrSS4lL+6in/b2opoNIpbdjt2dnbQ0tKC/f19AMDW1hba2tqwZvSJ393dRX19vQJ7o9GoqnuwtbWlDPRkMomVlRVVrK+8vBwvJ5P4usWiagYcHx/jS3t7yHvlFZSVlamaSsvLy0qpnJ+fx6OPPorBwUF4vV4UFRVhcHAQ58+fh8eogyB5YWlpKfb39zE3N4cLFy7gtddew2tNTdi2WPBrjwf/prAQ4XBYefuWl5eVEUaDl4Dg/v4+nE6nilSz27N93IPBYA7QACBH5lFpLysrU57ivb09vFBQgBPLy+g1aJI0xFB0Okmi0SgSiQT+8Ic/4MKFCwgGg7h27Rqef/55bG5uorCwED+ORvG9RALf8HjwGWGcS8CB/OMNpxNhiwWvAOiamIDX60VxcTGGhobQ1NSEP/zhDwgGg9C0bMrZ6dOnsbq6qlIDamtrc4DH5zUNP8pk8Bd+P0odDqyvr6sxptNp+Hw+DA4O4plnnsHw8DCampqUDkE5zagHqX/KTmqU/++88w4efvhhJBIJrK2t4eTJkxgcHER/fz9mZ2cViMX9LygogMfjQVVVFa5cuQJdz0Z4TkxM4Omnn8bY2JiK4piZmcHFixdx7do11NbWqhajDQ0NWFpaQktLi4oA4zi5voODg2hra0M8Hsfa2hoaGhowPDyM06dP48zGBp4uKYHV4cBSaanSyaiX8zyvra2hoqIih//T0UIDJx6Pw263Y39/HxaLRRVMf2J/H78NBPDnhYU5+/5ELIZPLi2hra0tZ8w8l6lUCnV1dbBYLCguLlbyjcUp6+vr4fF4lM6UyWQLlzPKorKyEl8tKMD34nF8WtfR3NyMvLw8fE7X8bN0GheM6Iiyy5ex8fDDeDQahbexEcD9FsjStpF8jvqIdNZJQ5WyqqmpSQEKFy9exNDQkNJbl5aWUFFRgedsNjy5swOnSDFYXFxEd3c3GhoaVF2Uz9rt+LrDgY29Pcx4vbDZbGhtbYXD4cDS0hJKSkrwlfx8fD+ZxKfSaZSVlcHn86GsrEzpr9vb29jZ2UFZWRlaJifx39bVAbu7ODLAC6tJzkq+LqN4HmT7+f1+dHR0KOArGo1id3dXReQkEgl87OAAl/Py8GQkgt7eXhQXFyMvLw+tra3w+/0oKChQoHFFRQUCgQA8Hg/cbjeam5uxs7OjwLqv6Dp+rOu4dHiIzs5O1VUjHA5nHQcDA7BYLIpHxmIxBXpQHzaDKKT3B0U3yPPv8XjQ1tamopSWl5dxZNQa4efa2tpQXV2tIszW1tbQ3NyMmpoaWK1WBINBFBUVoaGhAdvb28rBF4lE0NPTo2os7O7uoqSkBFtbW4jFYqirq8OT+/u47HTi1MaGqv9AULqoqAizs7M4c+ZMjuw2A4Vm+9qsU/O3zWbDww8/jNdeew3r6+uYmprCc8899wEZb74+NNAQDodzlHtzeLYMXQXuF5aQKJ+c5GxnJ6b7+tA/M4P2mRlkMtkwTxp2Ml+SlzRWeBEI6J6cxN3mZrQOD2Pt1ClcPXMGXePjaJ+eVp8zgx7yx2zA0KPO8D4Kk+n2drzd3Y3T8/OoHx3NyW+XyrEkWAohEjUVgLy8PBQXF2OiuRlvXbqEpsFBVA8O5qDPRJFkHqwZPeT/qVQKBQUF6JiZwfqlSwj7fBjv6kLn7Cx0XcdMRwdG29vRPjqKjpmZnNxkrpF5zzKZDHqnpjDa0YHuqSk0NzfneIf4+9qlS4h5vVi7eBEnjV7QTCVwOBwIhULq76KiohxDgM/mdzg3GlGHh4eIxWLIz8+H3W7H6smT2H70UVRcuYLygQEF9vD++5cuYf70adTfuoXqwcGcmhqadr8YExVyuX8M1QWgvMTpdFoJu0gkouidytxYYyPGOjrQNzWF/tXVHLRX5pZTEBNokEYhjWuLxaIQS65RKBTKicCQRh094FzH7UcfRdLrxUh7u2r1SbqTUQ8yN5lnlV4lng8W0Jzr7MT0xz6GjrExdMzMKHqTodw0GCTTogLCUF1JbxIt5u/R+nqMdnSgb3oaXXNzKufO/Pk/ZtxIAFIKBe6rVBB1XUf3/Dx6Fhay3zWqoJPWJED0z3EdHx/jp1Yr1jPZauqfNmgkmUzicm0ttlMpfCeTwXlhlJprDkhFhtEI0hjmujgcDnw7EsFyJoNQXZ0yOP8+mcS6xYJkXR0u7Ozk5DiShmWaAi+55maeKt83g6G6rivjhMZe4O23kXj4YfRtbGDLEMwsPEk+vLa2hpO6jtH2djwTi+HAyPcGoM4k9/f7oRBey8vDx8NhPGzkIadSKRXNQRnzRGkpXi8oQNvkJAYBBfQeHR1hb28P3ePjGOvqwsdjMSQsudEnEtQAclvLmmlTfl6uk6Zpajw8gxaLBZubm1hcXERzczOqq6txzWjrS57RYrEg092NvtVVDEcimJ2dRW9vLzY3N+H1ejE1NYX29nZsbW3B7/fj7t27aGtrU/9vbW2hubkZoVBIpaL85MIFHLrdeNNiQbvR0tdut2Nubg6zs7MIhUJ47LHH0NbWBuB+HjDn8yDa4N5wXeR6SbrJZDKoqqpSYAOvcDiMnZ0dAEBzczNsNhuKioowNzeHtrY2RCIR1NfXY2JiAm1tbZiYmEB1dTXS6TSKiorgdDqVV93hcODo6EjlRLvdbqytrSneVF1djYKCAvh8PlRXV6O0tBQej0fNk7yRn2caII1cFk0+OjpSivzU1BQee+wxLC4uori4GIWFhZiamsL58+cV2MwzzPVmBwoaxx/Z2cEfSkvRMDICx5NPYmZmBnV1ddB1HXt7e9B1He+88w56e3vhcrlUK0zZFUXS2uLiIgoLC3F4eIjZ2Vk8/vjj+PWvf43z589jd3cXy8vLqKiowM7ODqqrq5XXnvyEEZcAcmSQ3W5HdXU1AoEArl27ltNu8ODgALqu491338XFixfxLYsFG1Yrvh2J4DkjrF3SDz37TqcTL1gs+K+Hh3ixsBALCwvKYTE/Pw8AmJmZUZF+y8vLOHfunJLfpMuM4Z3Py8vDpzUNTyQSaPV4FJjPjhcFRhj/8vKyAi4o/9gZjHzYrDtSN6ScZbQiQ5uPjo6gaRrWjPQ+pqtUVlZmQeBYDN/s7sZLFgueN8ZBvjUxMYEnn3xSFQotLi7G4OAgLl26hIGBAXg8HuTl5WFychKNjY1KHyW/kJfFYsH09DR8Ph+Ki4sxPT2N+vp6TE1Noa+vDzs7Oyqne2FhASdOnMi5B3WChYUFFdEiL8p7RoY4HA4VFcEohE9rGj66soLH29qQTCbxg3AYf93QgM8cH+MvKyuVvOf9tre38fOf/xw+nw9zc3PY399HYWEh7HY7AoGAAlNmZ2eRTCaVAcZ0ZNIgANQkk/jXRpTT64bzyWq14mkDCIkDaBofR9/SEirr6vDG5CQGq6txp6kJfxkM4guG3i4BdF4SRJav8dIN450tawOBAE6ePInLly8rfWN8fBzT09OIRqOoqKhQQI3NZsPw8DA2NjYwMTGBWCyGyspKFBUVYdeotbC7u6vOzubmZjb1d3MT/9Jiwc2bN7FrRLbQeGVUsdOoUzQ5OZmNXmtqwvypU/i6x4MvGqlxZntLOqvk/MyfZWqSpmk4deoUlpeXlcNscnISFUdHeM7QoQ+bm3F8fIyDgwNsbGxgz0iloGNte3sbh4eH8Pv9ODLqh2xtbSEQCCCZTOKkxYITmQzGx8fx5r17KlqHQIPf70cqlcL+/j5isRiuXr2Kodpa3G1pwV8Gg3hJtBA3yzP527yn/Dx51tmzZ1FcXIzf/va3qpXvzMwMpqenMTs7i6KiIgQCAcUj9wxgPxqNqjaUBMvoZNjb20MsFlORThbL/ba4MzMz6A6FcG57G+vr69Dt99ve0vbY2trCm2++ib6+PjQ2Nio6kHbPg8AGCbZIXT0QCKCqqgrd3d0YFJH5/9j1oYGGzbNnca+3F82Dg2gYHQVwX9jwkpsgESI52Jn2dkz39yPlcCDhdGKwuRntMzMqfaCwsFAtAu85WFWFm3V1OLO4iL6lpQ94cyORCDpmZlBy/TpcLhdefe45RH0+VfleEgoXi69JZZEI1GxnJ6affhrV166hcWxMMdS5ri4Mnj2LjNWKgcZGNI6P5yifFGzSs2TeTIZxUThFIhGsfOITiPl8mOnvR41RRE8qs8yb5Dgl8cu1lR7NtqEhTJ04gdahIRwcHCCTyWQLevp8uNvcjNSrr+YYJnItJCCk6zq0iQl0/uIXgMWCaZFTRCFks9lQffUqFs+eRd3AAIbr6nDvwgVomoaLU1M4t7WljCGGQ5lTC6QxzvtzT/Ly8hTya7FYsPuRjyBZVIT1S5dQdO1ajsfP7XZj6OxZRI28/rObm2rduE4ynJKvSdTSbEjwM3t7e3C5XDg6OkJ+fj62z53DyIULSOXnI+V240ZdHfJ/+9scJFNGV0jDQ0Y9yL9nOzsxdeYMGm7dgmdpCUA2dHhtbU15n2QaDX+Ojo6ySPJvf4ujJ59E+dWrKu2DgoHjkgakzNGX+csyd2z6xAlEAgFMdHej9d69HGWLoA33kvckncqChny2pG9GTKRSKQy1tSHi9WKwuVmBJJIepDHCebFYnhTy/B6fJ88Ic79ZzZuoPP+22WxYPXkSo+3t6BRA5Z/6SiaT+EwyidcLCvCiEXXD1KeP7OzgamUlPi+AREYnELTR9WyUCQsCkp/QIJJrlUwm8WkAP9F19C0tYdcozPoxTcPP7HZcMpDwxcVFHB4eKiOFlxkseBDQwzMk/zeDSQQa2HoLyHaQyX/rLWSqqvAHnw9+vx+Tk5Oora3F9vY28vLyMDY2hqDdjq+tr8Pr9eLy6iq6u7tV6tL09DRaWlqwurqKnz/+OHYcDvza7UbJ9esoKirC5uYmTpw4gbW1Nfj9fszPz+Mhux1Fk5PY39/HlpHzu7W1hYaGBvz6179GLBZD329+g4pz5xDp6FChzxLwJQ80A+zSk/ggBYzrwTo8PMMEX/b29uDxeFBYWIijoyO0tbVhamoKJ0+exNZ77+GFggIcHBzAX1MDt9ut9qupqQmxWAw+nw/JZBInTpzA7u4uNjc3EYlE0NzcrNKwCgoK4PV64XK5MHNwgLd0HZ9KJNDe3o7d3V34/X5MTU3h+PgYe3t7mJubQ15eHqqrqxEMBlVI9oNkKXl3KnW/JbU8x1KZ0XVdtVKW0YFWq1XJiStXrijwqbCwUIWZ+v1+tLa2orKyErquo7KyEhZLNsKM9Z/IN2goMwKPldF5XgicLC4uIh6Pq9oD8uxx7BaLBb/QdfzXs2fxXCqFT2sahoaGlNJPUIo8l98hkCi9YfIckU/TWL0Qj+PzBwd4Y2oKePJJTE5Oqk4oS0tLsFqtGB0dRXt7O1KpFNbX13Hp0qUcepT7Mjk5iZ6eHkSjUWwYHblWDacAPXyZTDYnms4A6iqybooEimhEEMgLBoPIy8tToEBdXR1sNpsK+X8xkcAPj4/xNSPtiPTP3x6PB/39/Ugmk3jK54P7nXfw7LPPYrihQSnujY2NyMvLQ1NTk9pHggF0KriN9F46aiwWiyouTT2J6TilpaVwOp0IBoMoKytTIIDP54PP51NeaPJU6m3ks5KuSS+VlZUq+pGgVSAQgM1mU6HTfP53o1GsaRp+arHgRYtFATp2u111X+no6IDL5UJRUZHqDNDc3Ky8vDQ0q6qqAEDVCaN8pTzs6OhAfn4+PB6PinDh/aqqqlTtDUbs0pFAsIpgG9dPGt0WSzYFg0Am00p1XUdDQ4PiH5ubm2odvxUKYcNqxRt2O/614biQvNRut2Nvbw+Hh4dYW1tTqX92u121yOzv78f09DQaGhowOzuraoO4XC54PB4VsbS4uIjGxkbMzc2hqKgI+fn5ODg4UHzW6/WquUWjUUSjUVw5cwaHFgv+NhTCiyI1U54vfkfaOxw/6QKA0jWSySSuXr2Kixcvwmq1wuVyobW1Fevr68hkMhgyCo4zojSTyaZyNzc3w+Vyoa6uLtupp6wMOzs7qKmpUVEkpLXy8nJMGBGtdIrZ7dlOMYWFhUgkEurzkqZH29txaLHge7EYXjLkCt83y3SpG5C/0ybQdV1FQl69ehUvvPCCos3rJSVYPHsWnaOjeOToCKurq6ivr8fly5dRV1eHgYEBRCIRNDU1YWhoSDlnt7e3UVRUhFAohEAggJmZGXR1deHu3btoamrC1tYWHA4HPB4PFhYWsLKyopzEyWQSLpcL8XgcVqsVq6uruHrxIg60bF0K894+SOehs0fq8lwPj8eDg4MD3L59G6dOncIf/vAHtLS0KLoDgI2NjWyNubk5rK+vo76+Hnfu3EFtbS3W19dVDR/WaZH1Nujw3tzcVOl+BNrdbjfu3Lmj5sc5p1IpbGxsYL67G692dODpgwP8Dw8AUcxOEP5t1t24/0xH2draQnt7+wfOxIOuDw00TPX1IWIYw6337imDzWw8yc2RgyOzmj11ChGvF3mxGHzhMPpnZ6FpGqba2jDS3o5Ts7MoePttFBUVoaioCCMjI3j/7FmEXS68X1GB0hs3UFVVhc3NTdWDmQX16BVuvH0bc6dOoWNsTFVcftA4yUjNCz178iRifj+Wz51Dzd27ALLemcnubmSsVlgyGZwwDBC/34/y8nK1cdJINeevA1mkaWVlBZubm9B1HQcHB6i8ehWbjzyCop0dvPWVr6D+5k2UDwwoJFzTtJzcfAnwSOI4Pj5W+awNY2PoW17OFgxyuXB8fIyyy5ex9tBDCGxv496/+3doMkAj87glaMKDxCrnvGhA0uhx//736Hr3XQDA3b/6Kxwb+Ym3GxtVFwcAKh1FKhcylYCv8WBzjvQ4AEDNjRtYOnsW1TduqIrMVqsVi729mLxwASV7e7BYreidnlY0Kg3cByn9/DEfNLP3gq12rFYr7l68iHgwCHs4DPfhITonJlTrOfOP3CtzeBJpJRqNYrKnBzG/H5O9vWj62c9yUnb4eZvNptBiAizb29tZw91qhdViQYGh5PB70usqIzlI+zKShwomkPUwuVdWEPV6UbK7qwxamVIi95P3kAYFgBxFleG7ZNqMGOkYHcVEVxca79xRIYI0AJjTytcIjDAMnOvC+ZFeJAgh6ZvvSeWRnx1pb0fY6DJTfusW/jmuW7duodjvx78xCmf9NpVS6H3F2hr+1dISNjY2cK+uDgcHB3C5XNjY2EBTUxPW1tZU3rXH6AFNpcnlcsHn8yEUCqmImIqKCrg3NvAvDEE1F4tlDTWbDfXvvotIIIDBoiJluNNbxbNjBiDNxvODUH6zIJa8ixFRTBMCoNKwmA9M4c2cxYODA1RUVKiQyry8PBQVFaGsrAxra2uqvetzqRR+msngvBFZpGkaurq6kJ+fj9LSUtTV1cHv98Pv96O2thbt7e2KHgOBANxGSzmmFezs7KgIg6KiIqU8kdbMoAuAnNQRKQN58Tus98DWXKRreuoGBgayaRwDA6qWUTAYxPz8PE6dOgW73Y7y8nL4fD4VmqnrOmpra7GxsYHS0lI0NjZienoa6XRazZt7xuc/F4uhb2QE+fn5WAVU6glzTMvLy3HmzBlcv34dBwcHGKyuxo2aGnzN7c4pmGb2/kg5ZY7Ck9+R0TNcx1AohKamJlRVVeHOnTtIJpP4wx/+AJ/Pp8JgvV4vwuGw2v+trS1sbGxA13VV+E7uA/c4EomgqKgIh4eHKC4uVkXHFhYWlDGVSCTw0EMPobGx8QNgAwD8IJXCXkEBfgPgC4Yu1NTUhJKSEni9XpSXl8NqtaKiogKVlZVwOp1obGxUtG0+VzabDd3d3QpsaGpqQnl5OfLz81FTUwNN01BdXa2MXraYbmhoUPRCjxfDZiXN+Xw+BINBVQ+opKQEmqapCIxgMKjCoekxTiQSKtS4oqIiJ1JB1pjw+/0q0iMcDiMUCql0Duow4XAYqVQKH0skUD89jaeeeuqBNXEymYzaDwAqV3tnZ0cZoSsrK9B1XQFifr8f29vbSm9gZCP3njJSRqZS3qbTaawbAGZzczMWFxeRyWQ7cTBMm7WdJI87ODhAcXHxBxxNvDcNBZvNpmhye3tbOY1YyyOdTuNllwvfjkTwNZcLNsv91OFUKoXV1VWk02nllWdRuUwmg+3tbZWSQPnPYnJ0AEiwPp1Oq+J0hYWFWF1dxfHxMdbX11UbwoKCAjidThXxRDqwWCwKJGNYOPmIpDOr1YrDw0OEw2FYrVZsb2+rvaLux3SKdDqNr7lc+JYxd+pBUvciT4jH49A0TeXaZzIZbGxsIC8vD8vLyygqKsqJ/GIdNY/Ho+pesBsMi8T6/X6EQiG4jIK+ch6MVnri4ABXXS58w+Td/2MGmVm3NOvpdBSwI57Vmm3PWFRUBABobGzE+vq68pBbLNm0Xq/Xi76+PkSjUeUU4M/e3h6SySS8Xi92dnay9SiuXVMdgZLJJDweT05h43Q6jYKCAiWLLRYL2traoMVieMfpxJcNoAmAqncmdTyp55l1Ze4jdT5GNVHez509i7DbjeH2drS99hoODg4wMDCgaJN0GAqFsL29DavViosXL+Lg4AA+nw/RaFQV3b99+zZWVlZUuh0LzlKPZQc6Fgdlp8P6+np8Kh7Hmw4HvhEIqHmZ99f8N8+mGUziXm0ZafFOp1N1qOFaM7opGo2qiClGpm1vb6uUfIslW8SW9YBYxHl9fR3Hx8eYn59HKBTC3t4e4vG4ipTJy8vD0dGRAvc5xns9PThyufCmzYb/AR/skPIgm8d8yXWh7jM7O4snn3zyA5990PWhgYa2oSFM9vaibXj4gYiWJDI5EXnpuo7OsTGMd3aia2ICp9fXs0iprmO4tRWHLhdu1dejPxxW1f4PDw/RcPs2ls6eRdHly9jb21NM0ufzqVZt0ltaPzKC9unpLPIpBC7zfVikRxph/N9qtaJ9eBjjXV2oEwqexWJBx9gYJnt6cGJ6Gr0rK0gaykcikcDU1BRsNhuam5uV0ssiNqzcyzZVbrdb5ZKGQiHUDg3hkcND/ORTn0LM68XC2bOoMxQ/MltpiJvXluMnGMB58DUqzYF799AxO4vLf/EXiHi9WDh9GhcN45GflfeVnngyDKfTmRN5wLHIPc9MTmLwzBmkbTZ419YUkjfZ3IzJj34UnePjqDRCdCXtyD3g65lMRoWq8nPVg4MouX49W4fDfr9i/OqFCwgbSs9Lb7yB/f197Gu5BQxpqEpvtxmJ5rP5m0KKB5jtYtpHR7NRPnfu4NT6ejYv0un8AN3L/TKjwtK7H4lE1Dkrff/9nLamNProEfH7/TnK5LxRqHX38ceRCASweO4czm5t5Txf0rlUvrkGDNcijbJQ1lFFBXSrFRt+PzY3N3PWThPrK2kFwANBEhoUEmi419qK8a4uNA8O4tG//Vvs7Oxg2aBjKofm6AQ5J96fgAMjJeT4JFOVAlPej2OuunoVy+fPo3N8PKdg0J/yYrEiRiVQgJHvVRphpCx6VFZWplD55eVlVfG8s7MTs7OzKCsrUx5fFgGKRqOqWnttbW02B7WsTFUFX1lZUcXfaFSur6+rIkMEabnW0vMuwTKup1xvXtLjx9/0qFHpKS0tRUFBgfJKzM3Nqbl7vV5lOAeDQQD326KxBRo799jtdjyZTKJ9cREWiwUHfj9cLhe8Xq/y3CSTSTidTvj9fhwcHCASiWBrawsul0tVxaaSHgwG8fTTT+P999/H6OgoksmkCgHmmsh5yeg1MzjDSxokXEd6rXQ9G71WWlqKkpIS1W2ARsDGxgai0agK92Y3nNnZWdhsNuzu7mJxcRHT09OKV09PT6t6B7du3VKGFkMrdT1bF2R4eBh+vx+RSATBYBA7OzsqXY1ro+vZqudv+f04zGTw7UgkB2gwnyczACMNbPm6BDgZCUAQpLCwECdOnEAoFFJdOcLhMOLxOG7evIlUKoWqqiqsra1h15Bly8vLqKysxPr6Orq6upTRE4lElIepqqoKc3NzqKysRDweV0ZnWVkZYrGYKjAJ4APKmMViwctOJ74bjeJllwuZgwOlwPKzrCxOLy8NcuoeZkAOgEoT1DRNpVFYLBZsGzVqGBZOg1HXs1F2BIUJzpqLsYz+KAYAAQAASURBVNLQPzw8RCQSQSgUwvLyMgCofGLmbCeTSaXAplIpte/ktex0wMgBylLyZ7YvpwyR6Ycs0Ml7m0FIjpc1FVhIk8aQzKvmewTMmY7C8yRTO+Q6yxpMBJeDwaAKUy4qKkIqlVJt6EiPAFQKI0O5/X6/0j0lndjtdpSWlqqIOxZPZLtygkWc+0suF543om0Y1UMDIy8vD5FIRBnYnE8ikUBBQYGS0bKbG4tT0kEkQRxZh4cggsfjUREWdLy5XC4kk0lFA/QCt7e3o7CwUNWr4vnlZbfbUVBQoPaQ9+SzGMXFtNoXXS583tBzpR4pwUdpvJIGeYZ4HqxWqwJgDg8Plbe+oKAAbrdbFZZl6DrTOvLz8xEMBlVEKGnyzJkzODw8xOM1NfjXAKpEZy7zJZ01Zj4vf0gP1GnJ/1nfhIZmiSh8SvldWVmp5Njh4SEKCgrUPLk+NDLpGZepA9QNCBiwGCjbVufl5aG9vR1dViu+nEqhwrB5SN9mHd+sL8t948Wot0QigVAopMC11qEhrF64gOqREfXdlZUVBShubW2pwqs841NTUzg8PERdXR329/cRjUbhdDpx9+5dJRfIE1grgWfI7Xar86FpGoqLi3HmzBk8UlCA546O0G3UzJGy+UEyijJe8lXOl6AOu4LIccioKkaTOZ1OFX2zajhFgsEg8vPz4XA4FDAii86Oj4/D7/crIIYRywcHByrikrwmHo8rR8xTkQje83jwhBHlJOW02X6Xl9TruE+kB5/Ph83NzRwb5R+7PjTQUHTtGh69dSur2BmLzcWjcSgFMpmCOeywdmgIdcPDmO3sxHc++lGcnJtD67172ToA7e1on5hQebpEUDtmZvDwwQGGh4eRX1Ki2rQxnISCjARCBs0DTQACgGK0JB5pZHAuTRMTKL5+PcvojPBNq9WKztlZ9CwsZA+QCC1noZP8/Hwkk0nlTST6RoKw2+2YMXLcEx//OAbq6+F76y1Uz81B0zR0jI5itKMD7SMjymNNRkSlRY6XxgmFiM1mU+FfskCR1WpVaK/H48Gp2VkMtrTg9OKiKjTI8UnhLPeNAsht9NiVl6yloes6+ldWcK+3F0cOBw5KS3GnqgqrFy8ilZeHlNuN8a4ulN28+QGvn9wPHoaFnh4snD6NivffR51Rf2PxzBmUvfceqo0KujQqO8fGMN7djd7pabWXFHL0htB7/qAQMLOCTCYSDAaVguI0gIRkMom2qSk0jo1l1ysQ+EcBtgc9xxyGZbVa0TA2huLr17NjDATUuSLIoevZkGvZ6YNj1jQNle+/j72PfhQnZmaUImA2puX8JDPlHjOPUIVk37mD2VOn0HjnTg6NyB/e90FrKWlU0gzpZuTZZxHz+TDR1QXbr36lzr/VasXexYuYvnABLUNDaBKpSjyvbDNKmpHrLY08s3G40N2dndPAAIquXVNKcDKZRNHVq2iZnMwi/g9Ad/8UVyKRwMDAgCo419LSgkAggFgshsXFRZSWliKRSKiqvs3NzXA6narGAYVKbW0tgGwxua2tLdTV1eXkN3o8HrWv9IZYLBZUVlZibm4OtbW1WFtbU6HpPT09mJiYQCQSQWdnp8ojlvxH/lCxNgtgrrUEh2hsUHDyfPr9fpSVleGJJ57A7373O6yvr8Nms+H27dtYXFzE6uoqLBYLrl+/rjymHo8HRUVFODo6wtzcnEL0f//736tCf6FQCLFYDNPT0ygx5EY6nVYy4+joSHnzysvLsbKyAuB+kWKPx6OKCVJZ4vzMIIOkb86Z/0ejUWxtbSllvs6ok8F1kPfgmfX7/WhpacHVq1fR09ODnZ0d5UVlesX+/j4CgQDGxsbQ3NyMgYEBHBwcwOv1Ih6Po6WlBVNTU8qz4vP5MDExgebmZoyNjaGpqUm1n0ulUiguLlbpFLqe9UKyAvb8/LyKYuufnsZMfz9eFuGmDwJXzZekCxmZxzoAEizk36lUSgH0LS0tiEQi6O7uxrVr11RqRW9vLwoKClBaWopUKqUUdq/Xizt37iCTyWBrawvBYBA3b95EIBDA66+/Dq/Xi7ffflu1MCwpKVHhuaWlpQoUMI9d13W86HTiecNA2jIUyGg0qop2HRjgw97eHiorK1UertnbLJW7o6MjlVrEUGimnZEG2T+dRfcoiwAoT2Umk1EdB+Te0ItMo4fAJcfBNtVSuSYoxf3Y2NiAxWJRoIrZwGBBs5KSEgQCAXR1dcHhcKCjo0OlskiQ2ixD8vLycPbsWdWJ5eLFi/D5fOjp6YGmZbt+PPTQQ8jPz8fJkycVaElQhoY95b+ZrqijWa1WlTrQ1dWlDJdz585lnU3t7bBYsqkcTFehQk+PudkBwjV2uVxobGxUBl5TUxM0TUN7e7uS3azjwLUjvyCASh5+9uxZ5Ofno62tDZlMBj6fD/39/bBYLDh58qSqqdDR0QFN01BfX68KliaTyRxDIJ1Oo6enB+l0tttbV1cX8vLy0NfXB7fbrQACt9uNiooK5W2XhqrFku0YwLQRKdMtlmzaR0NDg/peX18fHA4HWlpaVJqA3BvJP6jLSVCBqV6kdeoN0kHj9Xrh9XpRWVmpCo7v7e1hdnYW+fn5iMViiEajODo6wtHRkaKtw8NDAFDniunbdBQyglemIdKLT3tC8ispB6WOzHnKYuekQZ/Ph52dHRwdHSnjkRfz8mOxGI6OjpQdIQtIUlfjenAe7EbCyOlQKKTsgVAolGOf7ezsoLS0VIE/pGmOnTqUlOV/zBiXn6F9yNbwBB47ZmbwF2VluDE/D5vNBpfLpaIxU6mUAjHJK1OpFHZ3dxUYarPZcpyedrtdpXjJbnPUe7a3t1W3H4fDge3tbbz33nvo7u5WKULSYSUvs65jNtD5GRYBjkaj6rPpdBqhUCjrrDM6+BCk5ftMCXG5XCr6hCBEOp1WaTGMVKfNS2cRQVUCGLT/GEERDodxfnsbXwsGsRkO59CjtD3kXCXAYLaRaWcXFxdjbW0tJ936H7s+NNCwe/EiJrq70XL3rgq3l0RlDieR4aN8j6jxXFcXhs+fh2614k5jI5onJtA5O4uPhMNYWlrCiiEwQqGQIlii2rL6K728JEpd11U3ABreZlROLqhUHnhZLBbMdnRgpr8ftTdvonN2VinF8jsU2BJV55y5GfRUMXwHuN+DebC5GWG3G8ePPYb4+DjS6TSaxsdRbfQTpxIu0wq4nua5SE8jCUTOkWPiGp5aX8fZra3s2hiHUqLH3EO+Lhml9C6bCZDPtVqtODU3h9uNjeidncXApUuIBwKwh8PI29lB+/i4EjhSOZJET0a9fP48EoEA1i9dQvPEBJbPnUO8sBDrDz2ECiOEdrazE0tnz6J3agovvP561huVlwe/3w+Px6PCy6UnVq6fXG+uJQAVCSIP5mRLCwabmlA/MICWyUlMt7dj2qiFwe4e5sNqNkrMB5l7JwvDcc/NkRfMK6ZXJxwOq9x+TdPgv3wZDxs1OQ6F8SM9PDKPTv7PZ0kwweFwoHdpCT2GlzhtKKYyjJiXjESRTIvzlEoJvZiZTAZtw8OY6utD88gIysrKsNDTg9mTJ9EwMIDVM2cQ8fkwe/IkTq+v5yippBN68My0I9dcnnNd1/Hu2bPZLjVnz6JpYkK1G2JayerqqlIq/zmu6upq3LlzB7quY2dnB3NzczldbmZmZlRtlY2NDdVTfWdnB/F4XOWZ/uEPf0AkEsH8/Dzi8Tg2NjaUBztsCBbyJxo+9Aqy6BDpsbCwUBlXh4eHOQqM5PVm3imFtAS1HgTc0XjifQgIx2IxBINBnDx5Us1xf38fLpdL5ZXOz8+r0Fd2KWIuMj0nNptNhR9GIhFVqdnn8ynl3WKx5PTzBgCv14tEIqFqBLE14d7eHnZ3d1FbW5sDZpvpStKfGYDY39/Hq6++is3NTZw/f17lLbMuh4wqAu73pAegAILBwUGVh350dKRkn9vtVp+hd8/j8WB1dVUVvuJ7MloAgPKeMF+7qalJtf6KxWL43e9+B03TEAwG0djYiPHxcTQ0NMC3u4v/xcjjlvxN7rvks5J+HnTRUKaCTmONRjVfs9vtCkBg3r/X60VTU5OqSn9wcIClpSUUFxcr+uczyCtoZDOMmaAPK7YDWU//M888kwNsSp2HNE8eTWCipKQEbrcbjUZHqerqarhcLlU4WCrCQG5XktbWVvV3Z2enKqrX19cHq9WKnp4eFBcXI5VKobe3V4Es9L7TEGR+OlNHLBYL/H6/avldUFCgCv2dOXNGFYYjuFtZWanOJaOIYrGYSrGQ4dfU61hbwul0qlz/dDqN+vp6xONxVFdXK7C8pqbmA3KYa0qgxmMUbOTeMnogkUigoaEhW0jWiGZNp7PtNqPRqKrDYAbPw+GwMnzIDwnI0RsZDocRDAaxsLCgohbD4TCcTicODg5USg3nSfrk2nAvy8vLlcGk6zrKy8tV9yieFTpsJDhOQ/vixYuYnp5GYWEhWltboWma4svRaBQ1NTWIx+PwG8U0NU1DaWkpkskkiouL1f1I59JIIt9IpVIoKSlRKQQyMiWVSqG6ujqnoBz5CoE/CXBxD+nQIh9NJpMq+oE8OZ1OK3qlDiBTLT0eDyKRiDLa6+rq8Nhjj+GXv/ylAnZkwU/q3+l0WkVoST2KKSc8C/F4XEU2ra2tAciGgNMbvra2ptZnb28PoVBI0Z5ZrnHOElSQ8tFsbxCI4Tow2pm2QWFhoTJaaXTLyCgWNSXgtLKykkOHQG6dLO4RdYxIJAKXy6Xmz8iwdDqt2tm6XC61jlIGkZYY6WU2wM3z1DRNtVKNRCIKzKFNx+LVvDdTq7h/7GpTUFCAiFH8OZVKYXFxEZqWTa2rrq5GJBKB2+1WQCH1HKZy0RaUde6i0Shu3LiBkZERfP7zn8+xC+T+msFzs1zjejNyhwAzASJGsns8HsRiMZU6wkgLOj44Xxa1djqdiMViqkMFo8SYikZnE8eTSCTUPRl9LXXAcqMmF3m5pBEz0CDnZLbvpT1ZXV2NiYmJD10s/UMDDfd6ehD2ejF94gQ6Z2dzjApeMiRDGhYSwbRYLJg7dQq6NVvrgDUa+Dki6kTyACiknYYWc1ZoyMuDzgJiDM39Y4aCNKglY9B1HXOnTiEWCGDh9GlU3bmjhMtsZyfmT53Cqbk5dC8sIJPJqAPBjaF3ncYgGSnzgPmdMwsLuFpVBeebbyp0mJEQZF4WiwWLPT1YOHsWzYODyntuVv75m2GJDHnmZ5kn6Ha7c4QK18Fs/HK/pMFMwIPdC6ThCtwPk+e+nN3cxGkjpyiZTGKkrQ2VV6+i/NYtlJWV5ShwfK4EK/ha/a1bmDt5EjXXrwMA6gcGMHfyJKpu3FCFb1bOn0csEMB4Zyd6FhbUvjP07PDwUM2/srIS+/v7CBvpOU6nUx1woooMReU60pOp6zqGWloQ8ngwc+IEqgcHMd3Xh2gggKm+PjSNj6txm8EMiQJLeuW6UljOdXVh8exZVBoRHFTiFnt7MXfqFLonJtB6716OIrW9va3OSjQaxZJRMJXPBu4DDWbGoes61k6dwtLZs+gaH0evUWSMXrcH5eGZvdu8rzzH8rPcW/lZ7lEmk0HHzAzajNQj3efD/OnTiPp8WDA6x4x3deHE9LQat0TYzaAX7y1BHDkmnvm+e/cw3NaGXqMKdzKZzAHaHsTf/pSX1+tFe3u7Km61vr6uOh7k5eWp3NyysjJsb2+joaEBq6ursNvt6OnpweHhIfLy8lTPeIbTLSwsqPGzEKDT6URra6uqwUBBT7pm2HEqlVJKF40OeR4lzZBHSdTbbGCbBTWjiSgLiM7L78siayUlJSgrK8O4EcnCKtlMK6HCDUB5N1paWvD2229jdXUV4+PjqK6uVkpWIpGA1+tFTU0Ndnd31Ro6HA7s7OzA7XajrKxMGRVVVVVobGzE0tISSktLUVFRoWjKLHw5b7PhzXNP/k4FkuC5BJMBKA8cW3uRxvPy8rBlgMMSpCgvL0c8HleF5kKhkKpMzuKEVHD9IpWEhdLM/9PjS4AcAGpqatDf34+trS20trZifHw8B4w0ezXJ4+TamEEa4L7ixnxVqezRU8soCnptaNxLz3UymVSV/Pf29lSaBHPYKS9p2DFKgAYLlTSOI51O4/DwUAECcsyMQuH9SMdlZWWoqqpSBSe7u7uRTqfR0NCgXmtubs5xisj1cbvd0DQNbW1tSKVSCqhIp9Po7OxEJpNBU1MTHA6HinRKJpNobm5WPLahoQHpdFqF68pIEYfDgcbGRkW/NODZwtHtdqsCitXV1chkMirKiZ5xXdfxS4sF341G8SWjCCbX1GKx4CexGH4UDOLFSATPCwOa8jQWi+GniQR+kErhBU3Di0ZhXvITKtOXL19GW1ubAv1oDElenEgkVJcMGmo/jkbxI13H8wA+JfgOU2N0XVfRS1arFT9Lp/FTiwUvaBqeMuhsf38fIyMjaGhoQF1dndLfmM/O87qwsIDCwkJV2FHytUwmg9nZWdXFhDTCOcgwbqfTiZ+l0/h+MomXbDZ81gDTHnnkEdhsNhweHuYYddQj/8v6On7hcOCLDgeeM3Svn8TjeAXA55NJPIP7Mkzqcjs7O6olKo0xXdcVTTJlqrKyUhlnPH+kPX6Xzg7yPoJ2e3t7qt0s66exNa2MAJH8w2Kx4OeZDP4ukcDTkQheNmokAMDZs2dx7949Rc+zHR1YOHMGBa+/DqcBhnq9XuTl5aGqqgpLS0vKWD8+PlapMWyFSfCEKXiMholEIqojwL1797C5uamK35qjMMhXqe9K3eaPAc/0ZpN32Gw2lbaxsbGRE31SUlKCVCqFUCiE3d1dVFVVqWjAaDQKn8+n2vPKumHcU8pLPo+2B1uyRyIR1bHMarWqdqdVVVUfAM7oEJRRJBIge5B+wPlS9oXDYeWFd7vd2NvbU3JQ0zQV5ZNOp+F0OnNqczEtIBwOKx5dUlKi0tUKCgpwdHSE4uLiHOCGPCsSicBisSidhg5Epow9SIeV9qHcb6nvyNcJPhJAAO7XQmNqjNfrVdELfJ3FVzlWRkyzBg5T8G02G3aMzmCkW0Y1E5xgVB3lIyOUdnZ2VHFes0PXbG+ZbRYzEMH9ZwoZU27/qevDp07s7iLkciFoFHfhgstNMRsa0pjkJk21teHYbkdeLIaz4+PoW1tDFPcrb5Ph2e12lX8kvXOyVSIJkwQhUTR6QEjscnxctAcpiOl0Gq3Dw5js6UHj4KAqngQA86dPI+zzYbClBV1GTjzzoTg2bgSZGcfB+RAB6l1cRPpnP8PS0hK0ujokEgns7OyoHDaOb+HsWUQDAcz296NlcjJn3fksgjDM2aXRTAVqsqUF05/5DE5MT8O1tZUNS66owLXqapxeWED/ygo0TcOdykrcqq/HQ+vrOGWgvXJ9pLEqX5dFnczIl8ViQdvUFIqvXcP29jaODVRZGrJcE/kMouXVg4MIvv9+Vmja7ai8fRuFV65kP2cwlPqBAcyfOoXu6Wm1j2S2mpbNlVpeXlY92ZeXl7G+vo7W1lbU19djdHQU021t2D1/HueWlhD/yU+wfvo0ts+cwYmZGQUgpNNpdE9OYqi1FU3Dw9lqwcPDmD5xAj337qmibuZzwdfMhhjfI90lEgmsXryIhN+PtYsXUTEwoJj7TH8/Yn4/xjo70TwxoRS8/Px8tLa2Ym9vTwnKtrY2dX8qEtIwkgr38fExrp87h4jfn007WVqCruuYaG7GjfZ26LqOzlu3VBSTGZjiHKQRIQ1UKXxoJEjFievBok9TbW04djiQF4uhb2oKbXNz6F9ZyXonjCJjcv34fPINM80SADPzgMo7d9BspEfEDKFMY0iCNGZA7E91UTDG43GcPXsWr7/+Onp6enD16lXFv/Ly8lSYanV1tVLSUqkUtra2sLe3p5SMaDSKsrIyFapNISBTYNLp+1XZ2Yea6L/T6URZWRkaGhqwvLyM5uZmFBcXf8BolvyWe02jUtK5GWjgd3muuQ8MvWNHB+Z12u121VGBHuGSkhLouq7SwNiXem1tTUVbFRcXq3BE5hFLJdjj8ah+3UyxY4spGuY0GljA7+7du6itrX0gUCjnR95MBT+VSmF4eBjXr1/Hzs4ONE3Dr371K2xtbeHZZ59FNBrNOZ8sZsz95/ipXFI5pWFGDyn3n8YM50aeQmOdIA4NHXpGWC9oc3MTeXl5KtyWZ0hGxNDQlrUezBEZEiyQ/5sVHAmWkk9wH0mv9JQfHh4qYJIt8GpqarC4uIhUKqWULIfDgY2NDVV7Qe4F18SsNEpllldFRYUylDh+rjMVbSrKr+k6vltYiC+lUnjWCOFl2iSVbBYvY04870nDVdd13L59G0VFRSoygbwLyLb7JCjDkFWG5dJxwBBxuRc8c3zm7OwsiouLVcRDMplUXnvJv7kXBGuY1/63LhfWNA3fi8fxMUB5glOpFH6cl4cNmw0/yWTwpGhLLMPMf2CxYN1qxSu6jk+LNsI2mw3b29uw2+0qlJ/GHemAXtHi4mIVxUBasdls+HEshlUAPwHwnOHoYccMzpeh+FarFT+NxbLdHjQNXzKU8GAwiIaGBhUJxLNJbyXpk8U0qV/KFrUWi0UViqRuI51D9GqyRsAPDw6wAuBHmQy+aoR419bWIhqN4rXXXkN9fT38fr8qzAkAvywowJbVih9nMvicsdc/1nWsWyz4CYBPCjonMKlp2UiK/Px8RCIR1RqUciWdzhbwKysrUzxE07LFG3Vdh9/vV5F/TLthdAI9trw/9Wav15sTSUY5zxB+eug1TcMPEgmsAnjd6cSfGU4tq9WK1tZW/NVf/ZUySJ8Nh5GyWhF95hl43n1XAQhsgRgIBJShzTMqQW4ahJR7jICR3yEvDYfD2N/fR1lZmaJn8i86OuVFmpAgs9T7ZN213d1dHB0dqRoc6+vr0HVdRfNR1m1tbSEej2N+fl6lje/v7yuQniClLK7M6CkZhUwezj3g2BkVF4/Hlc1ls9mUgU5HL4EM8k3OlWdbrg3tNgIJ+/v7ODw8VAWsZ2ZmcvQRufaMWqERTVuBKTScJwERggi6rqsCkJTtHKvD4VCFY7e2tlRaJSMpJK/lvkmdVv5vBpM4b7ZPZvoO66pwbQl8kV/SRqMDl3u7sLCg9o81NEirjLChjUMZS1pgShb1Bt5jfX0dTqcT9fX1ObRq1sPNtCxp2vze4eEhfve73+Gxxx7DP3V9+PaWgQB0qxVbhYVKMZPKgrykR1weNk3TMNrejnhBAdyHh+ienwcM5AaAyjvk5+mtcjgcypglIgUgp4iWDOGQhoxZ+ZVC+EFGhK7raBwbQ+Xt21kBJuojdI6NYaKrCydmZ3M2h8qImcHwkiFpEgChIkjlp6SkJCcXX9M0dIyM4F5fHzonJnIq40qPsjz0RLy43plMBtMnTiAaCGCssxOPHB7CYrHgek0N9p1O3G5oUKH2txoasFdQgPcrKnB6fT3nOdJ7LK+Bigq8X1mJ0/PzOLG8DF3XMVhdjYGGBpxbWkLv4qIyGnZ2drB84gQGH3sMdbduoXpwMEchkvtDT8LyiRNYPn9eefhlBAvXverOHdQbYfeaNbc1KO9LRJ7ABpVLMtXNRx5B1OPBzbo69ADZKAm3G3eamtA8MaHovHN2Fg2jo6rLSfPEBDpmZrKhjZbcriCSUckxmQ1f0m8mk0HL3bu419ODWiN/n7TD9IKO8fEcGnO5XEp5YmgxhT8/Q8YrPcnAfXAutr6O9y0WdI+PKwF7t6UFcQNhHzUieyRj4h49CKyTTJnPMIMr5tfIM8Y6O5EQ/CFt3GessRFDLS3onpxE19xczppJ4ITzNofDkWZ0XcdsZydm+vvRPTGh2uVyTWQo/h/jb3+Ki8ozDUtGHVy9elWF8jY3N6OiogIulwu9vb0oLi7G0tISvF6vqtJMRerw8BBerxcWi0V5ZViA6fDwEO+8844qQltSUoIbN24o70EsFoPH40F7ezvOnj2Lmzdvoru7Wylo5CPcI3k+ua5SHnB/KZwkryIQSjqhF4dCd319HdFoVBVDArIeydLSUjQ1NSnPFmsHbG9v56D4m5ubWF5eVgY5AFUgd3d3F263G6urq8rbACCnfgu9//TwsKgWz7XZK2/2dPG8fnNrC397dIT6+XmULiyolJVEIpFTuFBGLfCHBjLPEY0kenfpyWHBTPLBw8NDZVAw3FKeD8pOGt17e3sq75wK4LqRnnRwcKBk8MbGBubm5pRRzrFJg12uh+QDZoXM7A2Sn2UUIGUkz8XCwoIy2hjFUFJSkhNOToOanjEa1+xKxHPN1AtZhZ6h+g6HA6dPn0ZBQYHqSkH5zHBuKqVUvC0WC76Xl4cNqxXfjcXQsriIQCCQU5yQ6S5UquPxuGoHyR+bzaZqTMiieQQYZA459QSCF4FAQNGs2+3G4eGhmg97z7MWQ01NjSowp2kaAkbFdcoaGlfpdDqn+4TL5YLb7cZXIxH8fSKBr3s8KBVz0HUdX0ok8MN0Gl/Mz0ehx6OUfVmA8YuRCH6i6/hqQQE8gnYImhPwoeHL9+iNLSwszEmFlPL0axYLvhOL4Sv5+Sg0jC95Lt1ud0671ZcNwOTPvF54ja4CpEs6byQ/Y40JAMp453ngmaPRIHVePo9ngHTD8/6y04lvhcN42fC4kpexsOnU1BT6+vpw5swZNf4Xw2H8MJXC84YBkk6n8WIyiR+l03jRZkOBYdhLmU09iDUXCG5yf2gcMrKFRjkjO6lDydx48nhGWjjEcynbCKjItaAOxs+nUim87HLhW+EwXjTmxLWgEcr7fzEex/++vQ3nb36j5sJijtTx2HGC6QIWiwXz3d14q60NbW43uufnVVQY22UeHBwgFAopI5ueaJ5Z80VZqGkafhiJ4DvRKL5aUKBaJXJe3AMCObHHH8e11lY4jo5QaNCOy+VCaWkpvF4vQqEQwuGwWrv6+npUVFRgfX0dwWBQ8eRIJKJSUwj4BQIBBYjl5eWpqDCuJ8+k7IBgt9ux1NeHX3d0YC+dxues91PLeBbIX3VdxyuJBL4Xi+ErBQV4SXQLkYAz9dvNs2dx+3OfQ2UigbKbN1XaTWlpKYqLi7G6uqqAIRbhLSwsxPb2tlqXvLw8lUZB3ZZ8qrS0VBVIZiFE6iwEYILBIILBoHJOLPX14UpREXru3UOjERXJNEDuq1n34x5KWS/1YDrJrp45g+JkEsWGrrWzswOr1Yr5ri4MNjejaXAQXXNzar/sdjv8fr/iuYeHh8jPz0cmky3IGo1GFeDDSAjKC6aQAlC8k3vBMzHX1YX5U6dwcW0N/QYtkAc8yDEn+Z3Uz3n++Rp5KVMN/6nrQwMNhVtbiHo8CBrtO8yh2NKIMB9GuYFNd+5g4tw5JO12jNTXo9doJ0QGRGYI3A8rl9ENVJakAUHGNdvZifHOTtTduoWau3dzlDW5UHJxgVy0igKNoABRVQIQnbOz2VwggQjSey4L/FCQS6Va17OhMSRqCnyfzwcgtx0YfzdPTqLTKBYJk1eW6y/DmKXCT1TNu7aGuNeLCsMrBADnV1ZwvboaF43IBU3TcHFlBVerqnBpfV0hoPIg8b5ERAHgWlUVDpxO3G5sxENGq5Y7jY04cDpxo7YWJ5aXkZ+fryrFbj7yCJLBIJbOnUOHURjzQfRD5WLt4kUkCguxdvEiaoeGFHouvV9WqxWbZ87g9xcvom96WkVoSBokXcn5SGO/7tYtrD30EE4b4Fe98X/PvXs5tE5knLTC/aZAkc+SSpAMN5PGiRmIqBseRvG1a1maNLw1FosFzRMTaJ6YyHqehACm8KDXkQxrY2NDFYdpa2vD+vo61tfX4fP50NjYiLW1NdU7+mQ6fb8LiBGWdXJ2Fr/v7oZuuZ9vJ8crz5BEeaVXU6ZdyO/OdHTgXm8vmgyAiJ/XNA1Nd+5kW+gOD+fQ+a2uLsTz83HzxAmMd3bi1Pw8Oo20Kwl+SdDNDDhwD3935gwibjfGOjsVODbR3Iy7LS2ou3kTTePjDxQ2f8pL17P1ZFhUjN5UepcYxk5PNVNj6JnmWlJRY1Eo8knOm0KFwpR7srKyosIP6dXiOrGaNXOaSc88+zSOSc8PqvIuv8fzRoWI54fjJD8nUEYhSCOfgANrVhAgoCeIfI5FAAOBgOorT+OYYKfVmi0CNzQ0lHNvGQLMNlmck9VqVUKen5HjNysemqbhh+k09pxOxLu6EHz/faRSKeUxOnzkEXzn8cdxanYWvrk5dcZpDFmt96s6U0FsampSFfzZDQLIFo2MxWIoLS1FYWGhMlSBbOgoU+UINpJOaMzRcJFFgTVNU0YxlZ+ZmRlsb2/ntLaT+8yzR/4qZaoEE+X6mb8r+QXXnHQcCoUwNjYGn8+H/f19FUbs8XhUSzcgK4tpnIdCIRVmynQChrhSVpJ3st7Bo48+qs7Rr+12fC8ex1edTnzMCEPmmZO1Fr6eSODvEgm85HKho6ND5aNTh5Chw8w3JqDLMdMIoTHF6EgWbqMHmvtEw5ZdIAjU0cNnDj+WYdn0dBLsZDg5gUXyC3qoqbPYbDZ80WLB5w1POPcuFoshk8ngeZcLn4xE4BC6Fo1ufvYzViu+aKw/ZSZD9CUQTtkqjTQa/zJ1hefXZrPhGQBPi2J7VMi5ljLyUtM0PAPgY5oGVzqb1sRnEVAyyxGuiUx9ovyVegCNVxkFxHvKcTAl5HP5+fhkQQE0Y294Hshf2G6assFqteKpVArPGIY/1/azdjs+Fo/Da0SFSflFr7BcX545RsVxHgQopSyg8cI5SceerLtDmuP5pGOH0VPUK7muMsLohYICfAq5TgFZJ4L844teL1b++q+xvb2N4vPn8ed//ufqzEmghzKK8ufZUAhHuo71hx7C3zz9dI4c4hqSf3g8HmW4mds8SycH5/OdSARLmQy+G4vhBdGxgZ/n2l+4cAH/SySCzUwGvy8qwk9fekkBv2xTKGmEe+hwOFQK1YkTJ9TaMeKLNE9+R885cN+JR74tHaLxeBx2ux2fi8exr+v4jabhLw2QkudWRitYrVb8XSKBZV3H38Xj+LLPl7MOpH8gWxdnuLUVu7qO37lcePWzn1XP5XpShzcb8bT9SEc8e5RrMpWD+oe0tairp9NpTE5OqigDp9OJK2Vl2LfbMdzWhnYjUpF2EfUJKYsYbcA1ZhSVBM3S6TTeLSnBViaDN/Pz8eNnnslJ8X8+mcQhgI2HH8b/+pGPKPrhs7nnUi5KHUPqXXyd55RFInmmeVZSqRS+ZrPh0GLBUGsrygyQRz5b8kNzvQWp40la5DOfeuoppWv8U9eHBhoOSkuhW63YLytTxCwVDXmopHDgZ/j+iZUVzKVSOPJ6MdTSgo6ZGXXIeFExlN4eacyTQCWCk8lkcK+nBzG/H0tnz6LVMBB5CKWHW3pkSODSQydDiMjobDabyp8/NTuLjtlZNV4qFBTekkDkpjK8kyF5HMtSXx+mentRf+sWmo30COmtJaOTSKH5byrbElWmsnVQWoqM1Yo147mapuHU2hrObm5m98UgoNMbGzizuZm9ryAqyQTM63beACfOGxXbgSyIca2qCmcNtIvjD4fD8L/1FsJPPYW2kRHFYMzMXtJT4+3bmOnvR8XVq0oJYC94Waxv4MIFRP1+DLe14czGhtoXGlcME5SgDJnj8fExqu7cwSeNvRvMZFA3MoJPWyxZpNwYy2RLC0Y7OlB19SrqhodhsViyNHHmDOoHBtA0Pq7WTIZK8tCbIwrM0RzT7e3Z+hPXrqF+ZEStjVTsJ1taMNbRgd6pKbRPTyvBL4EHvsZQLF3XVZg2FTaJlHJsEkDqNooLjrS3o3NsTHk9pAFB+pVz4f7JfSTT5zmZPnECEa8Xs/39aJuaUnMDgPbpabRPT2cVKNEKTp01qxX7LheuVlXB8ZvfoKqqCh6PB7/3eHCvpwcn5+ZwdnNTtTsKBAKqe0Amkw2NrTg6QsjpRNHOjmLOQy0tCHm9mDt1Ci2Tkzlg5z/Htbe3h7q6OnVGAagwu4aGBqX8y1ZW6XS2/3Z5eTkikYjyspCeYrGY8upubGwoDzppgPyBhoEM+6VxY7PZVBVvedbN+yDpWMoCGeUC5OZtUkGl4kTvGXN42b2H7TmpWBcXF6OhoUF5r6xWqyqEBmTrXRDRZ42L/Px8lJSUKG+Bx+NBYWEhrFar8tYXFRXBZyhK0qiSwlTTsl59RozIuZG2eR6kl/8bHg/+1/V11A0NqTxtqzXbg37tqadwXFyMSZcLp999V32HYI80TlnMMhwOq7oNdrtdFVtjuO3Kygqs1my1aa/Xq5RO1l9gm1YW2wsGg8prQgOCiitpisoLO27QAynBAc6bPIrnxawsyff4/w/CYXwrFMI5Y83pwSRtvO10YrSxEb2pFE4sL6toBBqF4+Pj2N3dRXl5OcLhMMbHx9U5YU4rFXYCJwScaPhxbcabmvA/trZiJ5nEC0b65ff297GUTuN78TieE50MuE/c/xedTrxkFFkzf0aGtHINaLRSQeQ604gln6beQ6BAghdcY3nf/Px85SmmLsP9Iz1wn6RBQO+2LDJmsVgUAEAFm98haEUaYO96q9WqvJOkDc6JFw1ePoM0y0J31F04Ril36JnmsyjLZYoU14J6Hx1TXB/OVa4XeaBMPZJnkfyAa01vM9dXGrgAVD0I6iucD+/JdeA5Pzg4UDrAK4kEfphK4YvRKJ421ojrJ/fd3FFKrqXValVgIsE0WYdH8i+Ok+tIeUi63N/fV7QndQtJi0zNomyR70kHnNSNJZAJ3K99wvUhgMbrB+EwvhuL4UsOBz5jALKZTLagp9vtVp5yyjA5Rxrd37BY8K1wGN/weuEXaa5Sr81kMgpoZv01yeu5VpIHZDIZfM3txrfDYXzd4C3y81JmOBwOfMNqxbdCIXzd44HfALAlIE5ZLQFbmbLAPeO9ZfqhrA0nDU7OlTTJMcViMTgcDvx5NIq/DYXwFeOMSNnPMRG0+zOPB38bCuGrRo0BydcZOUa6e9nlwndjMbzsdCrHBc8/x27WM+TesS4I5Y6uZ+tL8YyT9sln5BkhL6itrVXFER0OB56ORvF6fj7Or68jv7U1x/4wy3f+Ji1y7eR5ALI60NfdbnwnEsFXnU4F9vH6RiyGb0ci+GpBAYLG+Lm3vB+jESWfNgMQEpjgmaKzlWsuAdK/CIXw7XAYX3O74RbAPddGrhXHy/clSMf3JWjc1NSUc0b/setDAw2BrS2E3W4ERP6m3FTpiZCD5aTlZ3uMQmwnjDaEjFogwyIazBBCTpbCg6ADPX18v3V4GBPd3WgeHFRImQQapDLFcUolmoeRh54MnfdYOH0aYa8Xg83NaBfeeApkEgsZkGSk/Jz0chBJnurrQ9jnyxrUt2+r9ZNGGr8jx8x5AVB5qolEAgcPP4zVCxfQMToKz9oaqq9fx9ajj+KUEXJuZlS85P/vB4MquuG0ocxyj2Ul3DMbGzixvJyTj3tqbQ0njd6wGeN+FID1IyOo3NvLMi/kErucH9eu8s4dFL7//geInOgvx9t05w7mTp5E78yMEgIUqlxLaUhLQU9BTKWEQprKDK/xzk6EvV4VjaFpGpbPn8+CW+fOodfozCD3iGOWQsl8cY8XTp9GLBDA8rlzKLxyRQF6/Fnq68O9M2egW60YbmtD29QUMpmMWls+Txp+cjxkRmYBRqEmBfRQTQ1GmprQPjKCZlF8Us5Nrq18TTIu0hmVaU3T0DU+jtH2djQPDeXwEXkfKkVU5E6PjGCorQ0VR0fY8PtRd+eOYsDHx8eY6O5G1O/HYHMzzm5uIh6PK+PM7XZja2tLeYXWfT5kLBbsFBWpvekaH8dIRwfax8ZUqKgZ9f1TXqwSXlVVpcCzRCKB/Px81NXVYXFxUYVtZjLZdl300FVVVSEvLw8Bo6Uq99Hr9SIWi6GoqAjhcBhHR0eKH9G7zXuQZph2YLPZ1HfW19dVH3KzwiQNyweBTPy8vCgfZI48n0kjSNM0zM7OYnV1FZWVldja2gIAFQZOuiX/1DQN29vbWa+WUXMhHo9jcnISR0dHCAaDKnSSIAS9AIeHhwgGg9B1XVWqzs/PV+Hq0sjY2tpS4erAfVBH8hT+D9yXI1/y+VA+MIDR/X2U1NWhuLgYoVAIq6urKJicxHIggPPr6zg25BhlFStW7+/vq/DdlZUVVQ2dbbSkdwMAfuNwYOYzn0Hz3buoHhxUxdC4FxxnJBLJkdEEHqmoM+KhqKgIewafZiXyYDCIV+JxvNHZicXVVTxj7C/5jQTxKZPJ08lPXz0+xo8yGbxoteKH6TRWAayUl+PFf/Ev8DHD+KY+8GuvF4dWK6Z6e/Hfd3YqRZF8gUarLBTNZ5JeWAuFsoK0IyNwdF3HDzY3sZnJ4NuRCF4wlMSXnc5sOLSomE+ZwpBsKVcksErPMAEF8hPuB3PTpZxn+1PqCQSFuMY0AAmOU8cBssatLJotdQR6e1nUms+SijLHQcODijvpknxE13XsGZGL0otIBw2VZLnGpAuCFYyuoP5jNkx5HhgWzNZ/nA91skwmo4q5sWilNGSZz51Op1XrUbvdriLILBaLKvop+VkgEMgBjaQs59rJ53ENqLcypF0aCZQzLE7KHGogG6nFvfheTQ027XZ8OxrF8ne+gx0DDF9cXMT09LTq+vFLTcMv8vLwqURC1WNgOpimaQpcJYBJTz15gZmuuB+7u7vwer2qAxIBIHqQpUMunb7f6YH3kIbj/v6+6hADQOlZNptNyTOeKZlrTjuARjMAfMvlwrrVir8NhZB88021PwcHB3jrrbfw5JNPKsBLnkMpl15yufBlY15S7zDLLhnFzLlI3fRB9s6LTic+awCCUo+WMpNjIzApX+M+SR1I8qcHzUnaLmZjXepUko6l/ik/+yWvFy8atSvk2PlDeajrOr7gduMlI4JKPsusC3KuXzA6rEibi+PlsyRYLecto7x5xszj57Pl8/kD3E8LIw9/KpXCydlZhEKhnGdyHIwKZhQF67GQPinb5DM0TcPnHA58wUg74uc575dcLrxoRIGRxuR3ASiAUNKBXGPzekl93UwP/D73So6Vl5QTD9pvs80l/5ey/cNcH769ZVERdKsVu0ZlT3kY5eDMyBsZOhWjmY4OjHR1QcuOXE2Ago0IjQQXqAxR2FDgSuLUdR0No6MovXEjK+yM++m6jqnWVkz29qJteBgtk5MfMPbMijQApezI4lQdY2OY7O5Gn1GjgYtNgUOmTSHL58hQQN6X3kebzYbO0VGMdXWheXgYHo8nx9somS3Haj6QVBi5jsPnziHq82G8qwuP3riBnoUFNFy+nJ2HyTiQIUKSAV+rrsZ+QQGuGPUapMEhi6fcKi/H1aoqXFhZwRmjx7aZMcqwZB5k7p00iCWDTqVSWOztxeKZM6i6dk1FCxD55p7zd83du2iZnERxcTEyomIuBbDMw5Z7zLGaUx/kWPg/C0HW376taKNjdBST3d3oMmpo8CIzlf/Lwy5BAHqx2kZGcK+nB9UDA8p7KhXipbNnoVut0NJpdE9MKNqSNTmY309aMCOwNpstJ+fSYrFgpL4eNx55BH3T0+hdXMTdmhpc6e2FbrVirKMDFQMDOd4drit/m4WFOV9fno3ptjZMtrejeXAQdSMjOH4AICL3jUZD2/Q02mdmVFTKzs4OjouKlDe2YWAAsydPotdIp+DcqRzRU2qxWHBqbg43a2vRMTamntU6NYW26elskSjb/SrS/1xAw/7+vhK8U1NTiEQiuHfvnmo/FwqF0NDQoPItFxYWUFdXB5fLhRljHUjXDKnm/2tra4rXsLiiLH5Fr73Mrw6FQrhZVoZ/n8ngIaPfu+RlZuVJ/uZZk/RhvnRdVyG2pA3SbUFBAa4UFuI/BYPoaGxEk2G8FRQUIBqNYq6rC292dODEzAz6jPbFFRUVqnihxZJttzQ/P694MjtK3GttxehnPoMGox0tC18xIo5Kt/Qal5aWqnVaXl5GdXU1No1Ir+PjY7xhsWSNZYsFzwmggWeeaS5Xi4rwi49/HB8/OMDJtTU4nU4cHR3hsVAIB/E4fllbi8dffhmX9vfVWEi7ss87ALS1tSkgRJ5l0tBXNA1hiwVbjz6Kf//YYzngozRGZaQOjUpZ4VoapZS/Eij8j6EQttJpvGqx4M9MOes0PCTvlbRhsVjwk+1trCBbsO+r+fn4f8fjSGka3isrw78rKVGgqq7r+FeRCL55dIQvOZ2oMQrhyTNJ43Rvby/HCxcKhVQuL+dnBhqkDEin03i5oADfjcfxZaPeicViwbM2Gz5tKGks6Ej5YbFYVAoFx0NvLg07gth5eXmIRqMKxGG6kgTZ5Jki2M0UTq4v15Ftjc0pIFLWyrGR98k8c46N9MDcdOpMfI0V79mdifRktVpV3QcJphM8kM/gGKVThFE6Pp9PyU3WTZJAFQ13rg3XPRqNKuCCZ440zXuw5bPdfr9gt0wP4fjMqQSZTEa1kOX9Kb84X5m3LsF+enTp+Ze0RgeW5JHU9Xjvjx0c4E2/H423b2NtbU2dm4ODA8waabt5eXl43efDhsWC1y0WPGsAU9xnnnWGeTP1mMVgI5GIaptHI5oRj+zOVVpamgNgk39zrpL367qugCsW6JO6Do0n6poyNSU/P1/pg7wP50za0TQNz8Tj+JnNhvbRUczPz6uzoGkaxsbGcHBwgN7eXnR0dMDr9ebwISmDzAaXlGu8zMaV+bMPMtrkueL3ScfSAJY0RL1E6pt/bIwSEOR7NPTIw0gDf2xePNtm8IK8mz/SqJU6NtcGMKLRjOiQL/wRgIf3MRut8jyQp8t1ksCItDkIsErQQ6YyyHuQj3IPSX+RSETxRmnDUc7puo7R0VFcv34d+/v7+MQnPoGioiLFw7juTN+WDjupJ8noCrneZl2Jr0neJm1ajp/3lfsvoykkPZhBF3l/Gd1iBhrMtGc+A/+YDfNPXR8aaGgdHsZUby9ahoeVgPtjh1AqGmQWXKCJ7m4kDMEy2NyM7vl5tUkUDETmqShQ2NKTw/A28+TvtbZiuq8P1devo+buXbXhw5/+NJI+H8Y7O1F87VqOcUtDjnOZ7ejAVF8f6gcGUD8yooSxrutoGh9H39JSllEamyc7X8iQQhnWx/9lPiHXxmazoX1mBjVG/QGIsZCQpedQErecfyqVUop7y9272bD0kRHk5+erkGHuiVTWJEOR+3dxZQXvV1Xh4srKBw6jDG+8VlWFfacT16qrcWZjI4fZcM9lniSfzUNrNlakcFg8exaxwkIsnT2L4mvXYLPZsNzXh7WHHkLp5cuoHhxUQMxKfz/WHnoI/TMzOL+9jcHqatxubMSl9XXkG2kIUkElI6BywX2RIWeSrjOZbBvGioGBbAVdI9e3cWwMzRMTWY+pyVh+kLefl3wGaaNlchINo6PZrioGwMW1tNls2Q4XfX1oHx1F28ICMmJOpAkaFcB974J8pmaiLwAYaGjAkdOJoZYW9C0tYbi1FbrVCug6gjs7yhAxA3u8p1TSzMCSGUGf6O5GxOfDvd5elN64kUOPvI+u61ju68PSuXPoHBtTrWTJJ3hfgj3Hx8eouXsX5bduobm5GZqhGEkvBOnParWia24O1YOD2WeJUF4aBzwD0gP5p75oTO7v7+Po6ChHGV5ZWUE6nQ2dl0UDacAuLy8rb73ValXeYvaKf8ftxvSlS2gbGkKdUQODACfXOxgMIi8vT7W5mm5rw1h5OTKahneKi/H19XWVMy73RxpEZlCRHjXyN6nsvJpK4UduN8585jN4bGNDGX88h/9TezsOHA7M9Pej0SjsZbVaUVxcjNfKy7Fjs2GsowP/59ZWAFl6r6iogKbdLxhVW1urAF1dz3o5v6TriAHYe/xx/KVRHZkCXZ4dros0HLlmpDkaYj85PFTG8p8ZxfQ4b3puLRYLfme14gDA7+12PGqkHrBA14+9XmwCuFJejv+uvT1nPJJfUIbQ68dzLvn38fEx/iwaxXfjcXzN5UKJIV/l/KikydBxGUkhXyMAKcFX0pry8gtjw6yEStCYSiQvfv9lpxOfsVqhA/j+8TG+YvJUZTIZfMnrxUsul6J7XhIM5iW95wQfNe2+R5b09CBF2mKx4Es+Hz6Xl4ff//73WDDSQ/kdGpbHx8c4PDyE3W6H1+tVXVsYBr6/v69qbJj5FY1OAg5sIyoNexpX/A57zmuapnreSxotLS3Nidw8Pj5Whi5wv6YJ21QSpGcEBo1zGvrxeFwBGdQXmAe8YugBBKmDwaAyrs1RFZw7a4qQdlnAkq3lvF4v6uvrlcxg6gRBIwIzgUBA7R2NBgKkZiWfqVNMiclkMqpttUy7ZCpReXm54vXkX4WFhaoAppS/VM6lgk7whjyYIBOjx/g/o6T+wYjo+XJeHp42nkvgyOPxoDaTwfmZGfzgjTeQNs5efn4+WlpaUF5erlrSfiUvD98/PsYX7HYUCZBZ8i7Z9pZt9azWbHqLpmkqPY6gzOrqKnRdR2FhIcrLy9W8Ce4fHR2pVnxSp9N1XbVNZEFS6v00fnlmWetDGuQEmwh8swYc7/GzVAq/SqXQffcujn/xC+xq9x0nlEOHh4eYnJxEIBBAXV0dqqurEQgE8I7bjV/k5eHT8TieNM6WTHnhb+m4lOHoLAZLXkAeYzampQNLGtC8v4wcZR0Gfp82jQRjyPNJ4w/S2+RYHwSOSN2TvJD6OGmE+yB1Vckb+VmCo5zTf3G7sW614q93dtCzsKDogHOk7JLRXDyjPDcEpFgnh8+Wxf+5FjKyV64511rq8FwbFlV+2+nE61VV6J+ZQe/sLILBIOYNu3NwcFCB9lxbGekwOzurnHOSBx0fHytgmWeBf5sNckkrlLFyjc3OTbl/jHw/Pj5WsoXzo0zmGSYN8nWzHcLvciycr3QQkY7k+tP+5t68BuAVTcPndR3PWixoNXSyf+z60EBD+/Q02qamsoMXeZoytIWXDIc3I2sdo6MYPnkSGoATU1M54dEUvAwfpLAh+kSmzb/lwbPZbNkWgIEAVi9cQMvkpGK+3jffRPipp1B365ZiTBIpkwQxZXRomO3vR+C991QvUho/i2fP4sTMjMrjloaq1WpVhdoYbshwRUmkBCSSyaQCTOTGSuKTjEQyBIlc8nN8r2F0FL2Li1mwxlCqAeB2ZSVu1tbi7NISTq6ufuCASaP/1Pq6qteQxgcRUu7ZxdVVvF9VhfNGUT05JvMB4zrx0JLIOX45ByCbDjHb34/KGzeU0N945BEkCgux9cgjaBwbU/S3YqQwDLW24tTaGm43NuLQ7cbVyko8Ig6gpFs+m0JGChF54HmRXih8lvr6sHjmDBoGBtAwNqaYzIPAG5maIA0XSUNUdMj8JVMi0NU8MZHdT0Oh40WFkDREepEClGtLo4M0d2ZhAdeqqtBrtAftnZrClb4+ZKxWHJSW5vRU5v0lrZAxmZUwuW5U5NpHRjBpAJayzZv8PdvZiYlHHoFutWK8uxs9i4s5SmYmk1EGhDRm5HPlHkrAheePfwO5tTdqh4ZyIqr+ua75ri6MdnTgiYMDdBgMnJXtWbguGAwCyKZZuN1uBAIBJBIJpeyRtwDICZv+H1taEHY4sHzhAv6PTU0AsqlGpCmuAZUPTdPwr410Ehuynmaf8ApSsTSDchK8JI0RIDYrZa+Gw9i0WHC3pQX/jzNnVA4wecBBKIRvRyL4SmEhumpqVPG7TCaDlyMRfD+ZxMteLyqNSItMJqPoUrahA6AUk7y8PHw9HMbfhkJ42e1GmWFsEZCTyi6/6zbCPEmzPH/Afc+BNJYlTwHuK5m6ruMr+fn4691dfNwI4SbQ4HK58JWCAvx9MomvGSG0ZoVJXlRypfeL/Jdr/JLbjS/5fIrOue7kc5Q3DO+W+8ZnyPMrQWF+Vtd1vGBUVScvlF6rB43b/BwZynl0dIQnk0l80agILg1zs8LF+xH8MIMy9M5KQIj34nmnrsH9IT+kkktlcWdnB0VFRWo8XGtGR9hsNuU1Jy2Rz0gvOAsmsiNEJBKBpt1Pf3EZIIquZ6t4J5NJRCIR7O/vo6qqKmc/QqEQ1tbWUFpaip2dHTQ1NaGiokKtP+cUCARUegKfW1lZCYslt0YUo8UymUyODiJTeBKJBKqrq2G1WlFYWIiysjLltachxPHxzDPakbyAIcg8byzkWVZWBqfTqdrLvWG14jcOB54Nh/Epw8MdDoexsbGhovQkjRAIk53JGCFC3iKN793dXVitVpSXl2d1Qq9XObdoUJI+gsGgSmeQgFwqlcLOzg4KCwvVM6inSj0KyKZ77O7uYrC6Gq/l5eFTR0d4IhbD33u92LTb8Z1IBO1GxAKQjR6ZnJxENBrF6OioWtP8/Hy0tbWhsbERLpcLGxsbcLlceMTvx+N2OxLhMLYET+A+sMsNo5Z2dnYUUES5z7nRQcUOE7FYTEVT2e3ZbgxMuygsLFR8lzou94K8i3VSyH8oN9hWU0bTUO+3Wq2qVhHD46PRKI6OjvDN5mbsFhRgt7oavYZzjjyHcohz2tnZwe7uLu7evYvq6mr89FOfwq7Vip87HHgsHFagluTZ1JGkLWPu2CMBd14yugnIjZCVeq0Eq8lvqA+Rdsmf6HyUTh0+X15cN0bVcJ0l7+UY5JmRF/eLkYbSTqFdRFCSYCfX5KPJJN7y+fD44SH2jFQDCcRJ3iqBcb7O8yRT3zlu8pajoyMV+R0IBFBaWqr0Te499R7WoOF9pJPpN14v9mw23KqvR8fsLDY2NpBMJlFXV6fGTYBkf38fy8vL6uzdu3cPOzs72HvoIbxXVoanIhE8zhT1gwMFzlZXV39A3nGfOW+OSzrJ5TkhnyFtSN2CUfBy/nxd6l7cR8okeTakPJEgmNwfaXvKdZSg3o/9fmzYbPhROo2zm5v4MNeHBhrkw81KgNkgk4qCREs0TUPb1BRa791TClfCWLA7VVW4fv48uurqsFRQgFt1dWgsLUXx5qbKk9k4cwa/bWtDnceDiNOJ3/X0oPfePdSPjkLT7lesbx4cVLlt+fn5KLt5E4Hpaezt7WHNbkdpaekHWkVyIVuHhnLaC8oQ1HmjRsPd5mY0jY8rxZ1ET2FPoqEQkgVTyER4uGw2GyZbWjDU0qIiMSjwOCZZi4DEZzZomYNntVpVFwteZDI36+qw73TiRk0NMpkMbtXV4cLqKiyahiuiReVgdTVu1dfjobU1nNva+gAyN1BRgauVlTizsIATc3M4TqVwrbZWES8LQZ5YXlbgRuvwMDLvvpuTw8frQQYiANQODaHy9u2sUDIYXu2NG1g8exa1t26pCtvc+/nTp9E7PY1UKoW+qSnc7OhA3GbD6smTqBgYUAecXj0ip9Lw4h5x7bmfmUwGky0tuNnZCV3X0Xj5MhaNmgqzJ0+ifnQ0x5Dn39IIlkoxACz29mK2vx8Nt2+j5u5d1Yqm/L33UDs8rAQU92+hpwdzp06hc2wMPQsLau0eBNKQEZkRVslA+Nn+lRVU3r6dRdptNnQYPY5H2trQPTmZA8yZjQAqGVRWJJ3Ii4pIy717aLl3LzsO2/2K5HLssydPqhSRzrGxBwJYBBvkOkt6ktER5jWQAlfTNIx1dCDs82FZdEKR9/nnuObPnMG+ruOqy4X/U3OzGsvx8bFKW6CCLgt/mdde0+53tqHhvJ1M4rvRKL7q8aC/qkpFf0hvP2maFcW/mk7j+8kkvup0qvZc5D0yF1AqV+YwTQosKj68h81mw1fSafxdPI6vGJ5w0jXv96LLhZdMIbf87uccDrxgGGecOwU2lQTznlL4ftZux8cdDjjE+xJs5OeB+ykGwH2FiPeRSiTzLSW9W61WrK2tYWtrS4EfX2hogPVXv0JtbS2g3Q9ddLvd+JjLhS8YhcNkKz3p/eM4JLhAY5vhy9wLKQvkOeV8ye8kAEza4dwkUEqeYlZgeT8acZIGJBAlP8vXzIYHFbH9/f0cpVjSQCaTyUn54Gvk27/Qdfzg+BjPpdN42lCcqFj92m7HTy0WfGZ+Hk+LPZSh8pqmqTPAArmBQEAVmKMcoLOBhrjf71fnlcocZRvnSHCcxhwNLY/Ho3gXe6+TxhghycgHCYYzdWp7exv7+/uqqCnBDkZdsM4VPXOrq6sqYkrXdQXKANlaE7JQn+SVLLIYCoWwsbGBlZUVpcscHByoZ9XV1al6EhwP9Z9kMont7W0A9w0q1lyhIbK/v4+fVVbi0GbDj+Jx+N9/X42N9Rkk8M415netVmtOy0vSVywWU4YbQVymmLFuC5Vt1rhiTRLOjWeAn2HdMOk80XVdtVOsqKiA2+1GOBzGysoKftHYiA2rFa/l5eETx8f4+NER3nA68YxwIDF67dq1a6o6PvnZ8fExrhYV4TtVVeg0HA0FBQXweDzKIUe6lACpTBWlw8tisaioEUbH8H+eM0Y9bRl6H3kBDW8JePI9i8WiwBCpN/G8cx1JH6wlxuKex8fHGB8fx5RRGJo0z0iP0vV1RE+dQs2NGzndJaQckTySRtr09DTK3nsPqfPn8ZwB4MsaENIbLHkTnZkShJT8TDpTSOtm8JZ7wdRGRruYeZsEg2XePuchIxsoJxhxJO0X6rZSpnF/+Gyz/kC+LOmckVF0QJiBZK7xs5kMPhuNAnY7dL//AzYLaVgC+tLY5jl2Op1Kv+G+EIgYGRnBvXv3cHBwgCeeeALBYFDxdplqyI5D9PizmD+Q1X8/r+v4h3Qan0Q2gml4eBj9/f3o6OhQ6xUOh7G4uIihoSHFmzOZbKHu7e1tDDz5JKJ2O970+fCSy4VwOIzbt29jc3MTBQUF6OnpUTRCRzJTjTgOrg9TILm33A/ZrlnKXK4dASHam6xZxO/LVGNz5IcEyQju0GaVnT9kRAhp3QwifT6TwU8zGXwmlVIRYv/U9aGBBqJZfKBU/s2KjlQ6JVPiz0xHB2ZOnED/7CxaJieh6zquV1dj3+XC3ZYWWG02HLhcmOzpwcOXL6vCR8OtrThwuTB3+nTWC+Z04k5zM2qGhrJG6NCQMtSXQ6GcUOijoyPFXLihDOeTaE3rvXuounMHuq6jwDgEFIpdExMY7+pCj2EoceO4WVJRI3EwZI8EQsWDzMzhcGC4sxMxrxerFy+ifXoauq7nCEFuvvQ2mZkf0U3pOZeXrus4t7SEGzU1OLe8jOv19dmUh6oqAMC+04mBhgY8tLeHgYYG7BcU4P3KSpUOAdwHOZgucbO+Hqc3NnCjthb7TieuVlaqe92oqUHX3Bxu1tXhwOXKpq1o96tAP8hYkszMzJB51dy9i9qhoexhst+v4lx99y5ap6ayXmCHA52zs7jb0oJDtztbIHRiQq1LVVUVxpua8J87OnB2aQnNR0dwOBwYqKjA+6dPo7uuDkc2G/5Dayt6JifRt7QEi8WCkbY2JA0msXLhAjqGhjDZ04O20VH4DG+iPNhcLx5UGUWh6zrmT51CxOfD/OnTaJuawvL584j7fFh/+GFU3rkDADlFkWb6+xHz+zHR1YXu+fmc9eI6kckzOkKi4dJ7IRkJ9wHIbRer6zpShtCRQID5O1TspDAzj+tBAAcvCWSk0+ks2Nfbi/aREbTNzkIzRSRIYIX3kyCCBF/4vlwP87noMGqktI2OKiHAcyzP2Z/yYoXiLxtFCDlmTdOUQs51kUa0XAeOU3oQNC1bBf9FMQ8qPBLQMe/B54xq3k5hwFIJ4iX5mnmPpUyQleV5vVBQgBcM0PZBNGIejxw3vRVArgCUYbBS0SMPlOg/PwfcL4AmwRm53ma6MoN35rPDZ9y7dw9vvfUWDg4O8OUvf1l1FWEv7VgshoGKCvysrg7fCIfxGcMYk55kaSxR6ZU1gGjsyFoscp4SmJBn7jVdx6s2G549PsZThkLC0FJp4HPe9I4wfJ3rIDsaUbbKqAmOg7KVeyjPkjyf/CzvLecuI04I2HM+pKG/LyrCps2GVzIZnFhdzQFIftrQgE2bDT8D8NjeXo6coZIl6cXtdmO5rw//m8OBjx0c4PzERI6SR32Cxig9R6xVcHBwAF3XVWj0/v6+Miap+EajUYTDYcRiMaRSqZwuGVK5nZmZUcCDGYja3t7G+vo6toxW45wLc5L5WxbNZvSABFq4lvTmy8gBGtYWSzayIBKJYM5oxUpgube3FxsbG5iZmUFHR8cHwGZZ44rvyRxkq9WK/f19vP322yisqED80iX4Ll/GFSMlkrRNJRhAjoHIfG1p3Emez7NKWrHb7dja2lI0QEVdyugH8S7pMaRRJHUzdgdqbGyEz+dTQHFnZyc+lUjglxYLPmvQ3ONHR6gdGsLJkyeRZ3TomJqawttvv51Tk0Wew9H2dsQNB5f1jTeUJ5dnT4KBUs9k5ICU2xyz1MelTJV8XvIPs/OMuiudIffu3UNVVRV8Pl8OqE0DmPtD44ppNPF4HFeuXMHCwoI64+wEFA6HcXh4CP/lyzh55Ur2/JpsDOpGUl7L/ytv30bj2BiaHnkEe0b3H6Z5SFqVPN1ms6l9ZgSG+TNmEFd6haVM4Jm12+0qBYVggVxv6eiSIIVs4UyDmo4q8iMpC6RM4toA9x2cUoZLJyn3iPo5ixJLfi3lnwQhzLKbc+IcZJ0W8kDSLvkCeQ2NZNo0jCLb2NhAKpVStpvValUpX+SPLFIrO3ZYrVb0ZTLoMu6/bcjj9fV1laLDc3bnzp2cQrcSpPO/9RYcn/wkHk8ksGFEhuUbXTcAYHx8XKWoMd2fMoP3I5/mM/kZ7g3BUsoKqZdwTuTVpCk6HMznnSCPtElltIrce9KP3Df+Tf1P2v4f0zR8xLhH8gG25oOuDw00mKt98kceQHm4ODip8PD3vZ4eRIzuDc0TE9A0DWcWFvB+ZSUqr11DeVkZbtbXo+iddxBPpbC9vY21tTV0jI5ipKMDFe+/j2AwiLHOTvQa6RfA/dCjRCKh8s/IeA8ODuDxeLB78SJuPf44qq5eRf3IiNocMszZzk5M9vSg/tYtZfRz8dumpnBieTlL3JqGyeZmDLW24mRFBQDgPzY24uTsLHoWFjBYXY2r58+j7949HNjt+IeGBnQY6M/fdHWhdWgIqXffhdVqRcfoKEbb29Fy967aWDNhSgZiBhn4eSKdMmqAPxaLBafW1lTrR82SLfh4YXUVAHC1shIXjBy988vLuFZVhQurq+r7UpE/v7KCa9XVOL+yApvNhgtGO8sLRqeJa9XVOL+8jPz8fJxfXsb16mrU372LQ+NebJUmLzJdaZSu9Pdj6dw5lLz7LqqNXCqJ0EpDdr67GysXLqBP7NGJqSncbW1F5fXrWF9fzxaKzGQwOTmJgY99DDGnE1erqhD90Y/g9Xpx7cknceB04lZDA2xWK8JOJ+42N6N/ZQWpVAq9U1O40dEBAGgbHkbD2BjqhoezyrehgMu14npJwQfcN8o6x8cx1tmJtpERWCyWrMHb2YnqmzcVOsv9t1qtaB0awvSJE2gbHVWCjYyVSK/cJ6nkyx/SEO/L7xHUGG9qws3+fuhWK0Y7O1Fx+/YHwgTl3Lh3FGBmYNEsqDlGzkEKaWVc2e0Y7u+H1WpFj6GESLo38xYp/M33lQYYLypnFosFbdPTaJqYyP4vQgX/OYGG5/Pz8XQmo4SdNJalsfX/p+0/YyNJ0zRB8DFzSXenO51aB2VQBxkMrSMjMyNlRKQWVTXdPbtz2NkbYHA/doHFYRe32Ls54HB7M8D0YHb29qZnanq6qzIrq1JnVerMiAzFiKAIBsmg1lo7tdPd7H64PR9ft2R15wDVBhAkXZh94v1e8bxKgobcT4k6y/20j1UqUlx/adjJ96gISLBKKhB2YIB/S6CDe0slhXtG/iy/z3PM96T8eHd7G3+9s4O3NjbwfHyvyJUEiyVACeyh+VSKmO7B8GHyd+ZN85k0xmjsUYbYARB53kg3mqbhD243PvH58GwkgpL5eZXe19PTo4zCe/fuob6+PuG5u3ABW243/mp9HTMbG/jM78eLm5t42vJ+yDxrKoMSMOQ4aMwy9J5KAfkFAXSux+8yMzHjdOJDTcPFSORH+22aplJqua4Oh0N5vBmlQkNb5jBTfkoPrzTuaFxKwISeSckXZeSX1C8kLVGB4vpc2drCp34/XrA6HlDRA4C3dR2/AfCW243CwkK1HlTcZVoa7//x7i6mTBNfZ2Tg1MIChoaGMDExoRQ8rrPb7UZZWRlKS0vV3GULTabBkB6k98gwDMzPz6OtrQ0zoq4RaZJzJmghnysBnoWFhaRCwPwu91Aa3VR+pcykscj78V6Sz9Pgo1Irw707Ozvx1FNPITU1Fbu7uyq1i+kjMqeb51GmYOm6jnv37mFychL509PIvns3sQYihUh60zgeWeuKZ5TnROoFBHAlnWuapgwAGp2kUbuMkbyVe8nXGXmi6zoOHz6MAwcOwOfzIScnB8CeZ/ltw8DbCQaCuGVMZGVlIdPqePShYeCvysqQXleHrDt3kvaHP9nff4/ZCxeQd/Om4nMyJJ00LYtjSrCaPFjyB/I8e1QX5yydGKQVh8OBAwcOoKqqCk6nU3WjcblcqoAk01JoMPGcu1wuVXMkHk8U6yVPGB0dhaZpOHDgAMrLy5GXlwe3241PP/0UU1NTSXJK7hHpWuoznJeUa9FoFN9//z1effVVdS6l/JfGN8ckaw0Yxl7BTgm885JrJYGI/YAD8gbJT2R9Hp4N6Rjg/Qjg8CwTnKChKMFDuZf8zXHwom1E/kT+yGdKgNjOEyT4IMP3uUb8PGW8BLT9VktP+/rdzMjA1+npeGJhASdmZzExMQEAKoVheHgY25cv41ZBAZ5aXsYlK8WKa724uIjV1VVsbW0hGAwiMzNTnReCO9vb2/B4PFheXkZxcbGSay4r0n1hYQEulwvhcBgulwuLi4tYW1tDUVsbnsvNRV5eHrYAzMzMqHo9BMu8Xi/WL13C3YICXF5ZwemNjaQ9IZ1JUJTrJp3RjJIjr5W8wM4fZFShXXeQwDj/ZySTlPtAcuqhPGP2z/G7dlr6+66fDDRIpNdusHBw9KrLQy4NGb5X3dmJ/sOHcXhgIDEIpxMn5uaQ+t132NjYwCFNQ+Dbb7G6ugrvkSPKA35kehpF7e2Yn59HTU0Nzi4tYW1tDavYq9YM7IXASsHJokKDTz+NaGYmJs+cQf79+4oIKHh6GhoS4fDNzci+c0dVJB85dAhDR47g8MAA6qyuE20HD2LF58NNK1dyPSUFd4qKkNvSgtuXLqkoAYfDgRWfDx1VVdA1DRGfD121tci0lNsDjx8j89athBJi7iH3kulLtBJIbiknDzhflwgkBbIkjiOTkzgqcgSPkqHriZaVJ2ZnE8/Hj3NtT87N4aQVImwCOD4zg2PT02o8bIcJC9xoGhtDT08PHlqKkwyxtINQ8kBNnjmDnXAY8xcvqpoYDIuyH9TREyewnZaGzpoaNI2NwTRNNI2P48zSEu63tmLb5uUsvXcP0+fO4ZAFVOm6jlMTE/g+Jwc5N28inJaGwaNHVd2CjgMH0H7wILLHx7GUna2eL/PtJGrIPbILamkQl3d1obSzE7FYDH01Neivr8fB1lZktrVBFwg1z1JlTw8OPn6coGtLgFOxlGi7VDCl104acxIBl4quYRiqGKQWj6PaSuGQ3lPS1VB9fQL46OhAeXd3Eo1JYMwupEmbkqYlQ+45eRK7lke+u64OjaOjSTSvaYkCgAxnlvQj5/jHeBUv7tdAbS0eVlejorUVlVY3j39ooCEej6vijXI8ct3k+Zfz4X5TIZCfobEsI1OAvYKjRNAZVSULd9LY5j7T+8SikLyfNAQlPdBAletPRV8aoDKvVs6Ta/0fsrMx63Tir6NRVFv1OeS8pBEtvTakfV3XVfszXhwn61rI9oIc69TUFIaHh+FyuRAKhZCXlwcgObSUNEx5+EkohFmnEx97vXiqp0e1j7x16xYWFhZQW1uLiGXYLy8v43B/P/pTU3ElGsUngQBmHA586vfjRdNU+eocN3NYuS5UKoLBoCrkeevWLczNzSEQCODSpUtK8WZoNdf1ddPEb00TbzocqnWdBC54pqS3T7bv8/l8SXyAvIzh6fSsSHCPgI9UuHlv0hmjJWgsSeBJhlyzeCK9YdzvF+JxXNvcRNwwsGtFzHCPXjRNPGvdf0GEQHMvpTebdHjV48H7TieuWgZTRkYG0tPTsbCwoCIUPB6PqtFAxdk0TdVdQuYOSzolEBGPxzE8PKy8uJLHk1eykCMVUGkwc6x8n3PgeKQORnqyOx7IR3gGpYHGcy55Po3Y5557DtnZ2SqdIRgMKqV8fn4euq6rSBmmvDAknzREWRmNRhGJRJJSn+yyZvrYMUydOYPS+/dR8vDhj+SpHaBhV4b5U6fQWlGByysreNoq2kYeNzAwgJaWFuTl5SnwMiUlBR6PRxUelWAWxyLbTZN22BaXoMajR4/g8XhUzQ177aJPHQ58X1GBq3NzOLeygn+fk4Nlnw8bp08j49YtNR9JN3n37iH//v2EweDYK/JGHs6Le+3aR3fYT3eU55WfI0+gDmMHtudOnsTnFRV4enkZz1j6Nml7bGxMteRlEUqGj5umqbqo0JO7tbWFzs5OlY6Sk5MDTdMwMzODmxkZ+PzaNfgcDqRdv65oVOpPBIrs4BjPkfzcxsYG7t27h7q6OnyXmopvsrLw9NISLliFmHkP6fWVZ4WGmNSzSYvSNpKGLY11KV+5nrRJaHtIoJ7vSZqVF9MGJADHcyxlrj0SjveSwEAsFsONcBjfZmbi2bU1PGUBb5S3pBfKWeoM0s6gPJTzp/7A8TD1xjQTgDbHTvDL4/Hg6/R0zHs8+DwUgv7JJ2r8TAuPRqN4EAxix+vFN5mZeMJKDaMTnCk4a2trOH/+/I9SE3imWX9mc3MTmZmZalyFVpopZVNaWprqOLW1tYWsrCxsbW1hdHQU09PTSjYz8iQajaI1HMaO241fZ2UBAM4tL6v5czwyYkPq3Uxp4B5RLkmeL/VlAie8H/ed51XKVPkedS9GXsluQvI5dtuCgNg3gQA+D4XwbCSCCyIK6++6/otrNNgNWjkoEqWdwdk/U9XbqwzZHZtCzXA2GV5DZdjn8ykwQxbU4IEgKMBQbxmmwtClwtu3MX7yJApv34bP50sKBdV1HRUPHmDgyBEcuHdP1WfQNA2Dzc3YSEvDw6oq1A8NQdM0HBsext0DB5B98yYCgQBGT5xAudU94tTEBK7n5KCkpQXh9HR0VFWhqKUFbrcbw0ePorS9HQsWQT0+eBCdNTU4cOcO8u7fV4o1iUOGJNoZKNeNBVFmjh/H2MmTqH30CKWdnUlEyH3k96SAl0KG/8t9lmttZ3x8Twp9eU8JMlFJI30AP267w++V37+PgSNHUNzSokIFyQRZlIsH9sCdO5g8exaHBwfVnH9E7BaSbJqJdpivOBPVmDutZ56YnYX/668xOTmJkpISPB+PY35+HnG3G+0HDyKSmopIIABoGnoPHULW7dsYOXQI4ydP4kBLCw50dCQZp/F4PBFpceoUiu/eRcnDh+qM2A3gx42N2AqF0H7+PMrX1lDS2ZkU7kamI/efaydDm+m15T5LRZQXvSE0ECnw+Fp9Tw86q6tR3dmJqv5+VfyVwpX36j98GOuhEHqbmlBvtZySey9BMvk391fSix1NBwDNMFBnARjyewCSWqtxb+X/9jWWkT48V0SQO6ursRYMorexUXWjkOH1/xDXvx0fx/suF16ZnMSLgrfKfvXSAJKePSoIcnycu0yNodBiKz3eg2GH0lNNoUJebc+Flog8gCSgD9jjK9Kw4R7YgSf5A+BH37mytYVPUlLw/OYmAoGAylmkEk1ln/Qtz5xMsSAdcE04BxrrEuyi4dPf34+ZmRm88MILKieUn2N6nPzO0ysr+DglBUUtLZibm8Pm5qZSoq9fv47FxUUcPXoUnZ2dMAwD/7fm5gT/83oRMAy8C+BtlwvZ2dlJXg3OgYXX+Dwa/SzcFgwGMTw8jLy8PHUPACrigWfsSjyOq5bRvmKFLhMAojImlTIZ1i9pQxrDwF6Ov4yik15SO1/nnvAcEiwkPfF7kufZax/Ie0mDmN8FoNIaZJQLxyZBEslvAOD81haeICDs9SI3NxcAUFFRoRRn5vfTKGZ6CemTz2Q9ANmZwOPxYGRkBFNTU4qGdV2H3+9HTU0NQqEQvv32W2xbERoStJaKoF2mU2Enb5N7JdeLz5TvS3kp5TJ5ABVXhyNRTJEdCYBEVfbZ2Vnl/fP7/Srvmgo2eS8NnkgkAsMw0NbWhtbWVmVsydQhznXy9Glspadj5vx5/EV6ujLeZFFXGm9+vx+ZVsvj/1NKCuYdDnwVDuNpK6ppcXERbrcbU1NTyM3NxZUrV/CHP/xBnR0CRVJ35Jpsbm6qtDoCDkBCdi4vL2NxcVF1hPD5fCgqKkIsFkt0kHLsdaO45XJh0enEF14vziwtoeDmTWwdPoy8GzeSnEPyrPC3NEwkjyMf5BmhTkvaIu2TD8v95TmxvyfBP9KJ0+nE9eJirFrG4NHHj5XOLaNiCLqlpaWpfHXm/JtmojOI3+/H3bt30dfXh3g8jkgkguvXryMYDGJ5eRkt//yfYyc9HfFnn0WOFelij/CV+iXnLfmApG9N03Do0CHU1NTg/xGLYR7A9zk5+MdW3QapW3D+8jcBesmDWEOFuqn84fhoYFPnlToQ10WClTQgCahLPsdLnlH7eGVEntw7+V2OhbT+vd+POeus/Hk4rNJadnd3k0BVyghGAfGy7wWja5iqJXUF1p0inyLdBAIBvByL4ROXC8fm5pTsYZ2c1dVV7O7uIv3rr2G8+irecjhQWlqq9p71Wmg8Ly8vIz8/H5mZmUk1Vb777jtsb29jYGAAgUAAp06dUrKI60TwPDMzE6urqwiHw/D7/WhqakIsFsPQ0BCWl5cVCC6N8sJbtzBy5Qrimobvs7Px3xYUqL2Qxr5hGApkJY2wdpxcG+lQkraq1JVJ3/weeSJ5hNQXeQ9GG/GzBN/JpyVN8T6c5/8Ui2EOwDeZmfhvLGfM33f9ZKAhEokkIeV2IpOKJLAXOmY3dHVdx1B9PQabm9HY14dKK8SUijHvQWMb2DOM5N/cCKlk2hVvCiwyJofDgZy7dxH87rtEaLpVjEaG4JV3d6O4owMA4LGYqK7rqOnsRG9TE5oGBhRDqhscRFFbG4aHh5GVlYXL29sYGxuD7nLh6NQUAt98g7W1NVRUVKC6rw8jIyMIhUK4vL2Nx48fY8Fan56GBmyHQhg/fRrl3d1J3l8ShlSGeEmjj0rb8NGj2E5Lw+OGBpRaIfmcm0Sq5cHiZyTBSqHDS/4vlUe5v9wH+Tc9/1QeqdBKZFcyKzKmwrY2ZN25k1Au9L0qrlJ48qeovR1HpqaQkZEBE8mFbiRKSYYn/7fPTSq1HE99dzfuHD0Kw/L0V3V0wO/3JxShcBjjp06hbnBQMS1Wy504fRo7mZmYOHVKdW2RCiOfUdHaikdPPAHT4cD46dNIv3lTGQPc/4nmZoyfOoX67m5VDNLO8CW4xvtzf6TSKgW0XbmpHRhALBZDd0MDACgvvxSipmmi+uFD9DY2oqG7+0fnXNIcvyuVJykAJS0ahoEjHR3orK1F9cOHqB4agmmh91LIu91upKWlqedmZ2cnCfSsrCzMHj+O96qqcGJ0FIXz8+iuqMBv6utxfGQEZcvLaowEVira2xU6Lsf7D3F94HZjzuXCB7EYTk9Pqz1jxA6VaJljx7NmB+mYL8615UXhwX2R0USkfbZ+o6AE9orHSqBL7inHwRA/0rPkv+S7cswEFKXSIsfMM/0Ltxt/puuIORzQQqGkytSkKa6NVPiBPcBJAjMEEUg3dqM5Hk8U35uenlaKIEPWZc4zFSjJM4r6+3GqrS1xDytiZHNzUxmxfX19aGpqwsbGBnw+H2ZmZuByubC+vo6ynR38d1YI7GBqKjY2NlSqh1wnu1ykt3R+fh6zs7Pwer0YGhrC7373O9TU1CAtLQ3BYDApZFcq6Ex1kEqhHRAi/+J6b1mdM2hEsB0d95gAj3yNtCTpUa45n/d9KIQv0tLw1NISzi0vK74l94nrQFkiW13LSAnSOyNa5LPkvcj/gYSMZXV+WZNJ13V87ffjs0AAl1dW0Dw5qUJPSSOk+2AwqAwDjiUYDCrPGZU9t9uNW7duYWlpKUlOZmVlIT8/H6Ojo0rX4r7Zc+rtYK7ko1Im85JgCnUiaTxK0FnqYjxjww0NGGhuRl1XF7q7u1UEi9vtRmpqKpaXlxEMBlFQUACfz4dwOJwkbzY3N+F2u+Hz+RCLxTA3N4fFxUXcs7qAEaix5xMDQGNfHwaPHMG1aBRFRUXQdV0VtSQf4D0KrBpRbrcbP1tdxa9iMVyyCo0uLy/jvuXE2dzcxC9+8QtkZmZieXkZZWVlyMzMhNPpVB057PVPCC7x/jKNIjs7G6ZpqkgjzsHj8SQVEfX7/XhtfR2/jcdxLRZLtCn+5hsc+uqrhLEkZI4E6MljJW1JOk5JSUFxcTF2dxNtNXm2SQMMoebnpYfb6/Wq1qfSGOZ3GPXCM++LRPCt14unLAMsYBW0ZfoJACwvLytZwz3nGWURyOXlZYyMjCTVKktPT8f4+Dg2NjaQ+c03mLt4ERnffJNkS9h1Ip4r0oI0vMiXMjMzVctLj8eDn2safhWL4U1dhwPJqZZSn5Uyje/TSOSZkhF1cs15L9opkt9IWSprxPG+jJ7YD6in7sW5S2CI35F6vrRv5NjIu7a3t/FSNIqPPB68HIvBaaVy0inHznjy3tLmk8/nmOjE4XuM5OLaEQwhiM9W1y/piUKDGxkZeCctDYaRKKocDAYVnwn09eH/srWVABSsPVpYWEBfXx+GhoaU0d7e3o7e3l4cPnwYJ06cQEpKCuLxuAIsotEoBgcHk+od6bqOrKwsTE9Pq2g8OmMAKFD56NGjGBkZUZE46enp2Nrawvr6Oqrn53FNT6Ts/czlgsMGYki5KAFyWXuBn5N7LOsT2XVTqTvz89K2o55FEILnns8hTyC/J43xGXa98xe7u/ibaBQ/d7vhdiWnwP+x67+4RoMdaOC1n7EvF0Yyvd7GRmykpqLj4EFUWgWXqDCwB3YsFktS+rlwMkTdHrZOAqYXOef6deTdu6fCiNbW1lQLHQpCKiLSIKLQlfMo7+5GzcAAUlNTsWMpn/F4XKGPEp2j4i3DpqSRTKICEpt/sL0dvYcO4aBl6MhDK71owB5ww7WWa2wYBsoePMDYiROofvQoab90XceDggLcKizE0cFBHLLy4iQxAUDHgQO4X1qK05OTKh3CbtzIsML9AAc7KEKmommJEDZGq9i9j9IgNk0T44cPY/zkSeTfvIlCy/NBRYnraVfuqJDax0SGLsOX+Fn5fRoWdgOruq8PME08rK5G9cOHqBochOH14mBHB/qamlBltUXkfOfm5hItdO7exfCxY8i6cQOrq6uIxWLIy8tTSDvpt6q3F263Gz0NDSi4c0fVGJFK+9jJk9hMS0N3XR3qh4bUvkmDjjQl916eSUbIyH1iZXFePZWVuGd1fuiuq0NuS4u6nwQa8u/fR1FbG9xuNza0PSDLDmDZ94J/272Jmqahp7ISXbW1qO3sRHlPD2CLfmktLMSD8nJUP3yIg48fKwR7amoK0WgUfr8fqampmJmZQculS9j0+3G3uBjOzz5D+/PPYysQQEtJCYrb29V9q/v6UNHdncjlFzS5H9j2p7qu7uzgY03DS7uJ/sgEFGXleIakyjFIJZeh5AwX5ppLA4Y0znaRUpm3e7rsbb8kXdmjLBhJQ6XEMPaqIkt+Cuzxbyo9Ozs7Se1YSRcEIlgJnZcM/5OAtB1YA/aARdIglW45Xhqimpbw6szPz2NiYkLlXRqGgQ8//BCVlZU4duyY4g92A8/lciE3N1flr6enp2NiYkKN0TRNFW4ZiUQUiOT3+zEyMoLHjx8jEong6NGjaGxsTDJSZOQY11fXdbTk5uJGbi4Otrcjb2gIS0tLyss0Pz+PjY0NbDz5JDpycnDBSnGThjHbW66tramWdzTaqVS6XC4sLS0pGuBF753P51ORDA6HIyl8U9d11W1BelAk/7Er4F+Gw5h3ufBNRgZeNPairEhvbNMIJCKZ2BHCzl9kJAj3jOO27x/pkbJkfX1dgTMs2uZwOPBFNIpZAL8PBFAwO6sAK+alcyxOp1PV1aHhxf3MyMhQ+zc6OoqRkZEkg1/TNCwsLKCzsxMjVk96eQZ1PVGdvaCgIEkRJZ/nHGXhP9I+14f34v6zawYNAzvfYNSoruu4UVuLda8XQ0eP4r+dnlYyJC0tDU6nE/n5+TAMQxmVy8vLCIVCiiY4B0ZdOhwOtLe3q9QxO1BEued0OvGS349aXYcrFIJp5f6zFgfBQIcjkQ7kdrsVb7limqizUu5YQDA1NVV16mChQXoSeTbIb8hTqFMSgJIOCHozdV3H/Pw85ufnkZmZmcjpLipSEbPS0fGiYeCZnR1sbGzg/2vpBKQN8jO7YShltjT4UlNT4fV6UVhYiJMnTybRP2lEfh6AokkCn7qeSH+RXQYYhUOez5ahfr8fh7a28PPtbfROTGDR0ud4XgzDwNd+P75MS8PPTBP/yNgLn5c1XJxOJ1paWjA7O6vAt8LCQoTDYQwPD8M0TaT/8AOC332XGLstQkcavNJ4ts/Z7/fjiSeeQGlpKdLT0xVtv+py4XUrtWlbpGrJ3/J51DUlsC2jT/gdCUBTBkmjz/55yeslqEAZKdM35Bz/2HilfmU3NvlZ0hoNfKfTidfjcbyhaXCIDlMS5CY/JT3Y/5aOAjuYIUP0eR/Wa2G9BK/Xi9TUVFV7IjU1FUePHlVdgAoKClT62srKiuIZlA8dHR14/PixcliyyOjKygoWFhZQVFSEHqsGl6x3tLy8jA8++ACFhYUoLS1FUVERQqEQZkXLRhnZSZutpKQEhw4dQn9/P1wuF4qLi+F2u7G4uIhYLIYruo43bHU3JPDKvZE1N+w6lrRLpb0lgS/uj0yL0XVdRZyQtiRt8JJ7KPm+/JykLXm96nLhVQG0/pTrJwMNUijKHz6MiqIkQIl2yd+1jx6hr6kJTVb+Ow8zFV9pUEpPgGmaSYUbpaFrGIlcE7fbjakzZ7CTkYHZ8+cRtEJl1tbWMDc3B4cj0QpJetilkUplme/xc6OHDmH4+HEcsTplAFB5o0Ra95s350eEmIefKJmu6yjt7ERha2tivqaZdLg5N2lES2ZKIuFhKLdaHzocDuwIQ840E509Vnw+tFZU4PTiYhJqxXW8Z3WjuFVYqApHynmZpon7eXn4oaAAR4eG0DQ2lqTk8fPtxcV4UF6OU+PjaBgeTqqAbTeEOUeJFJumiYlTp7CdkYHJ06eRdfu2AmyYB+t2u5XHYPb4cdy+cAHHRkZQNzio7k8a2Q9YkGAQxyS9ulwT/lQ+foy8+/cTir/1/dLOTtUFg6+trKyoHFvtgw9Q9P770DQNE4ahvJFkHhScpmmisqcH2XfuJFWhl4KVxSDrurvV3ORBt++BRJ/3W29e9jPdWVOjajRUPXyolBNeknnF43GlpOy33pIf0FjkM7nGUoh3VlcnCsU2NaGrrg71PT2qRoOmaWitqMCq34+u2lqUd3UleeDIfyiESlpaMHHqFJqHhxGNRlFw8yZmL1zAEUuZsYMwXMP9GPOf+nrF6cRLFg8wLWV0e3tbKeNUPtiijryDa8azsGWh+9KLAezVK+B8eD8q5+R7FGhyL7h/dv4gBZfkSQRDGXKoaZqiXyqaNFqAPYNHdgWiAUGexpBSjkOCrXLPeIZl6L306MmwT0Y96LqOlZUV/JCejq+KilAyPY3csTGsr69jfX1d7cPa2hpisRhqa2tVG8KtrS3VVpee1ezsbKSnp0PTrEia2Vk8KCjA3PnzqJychHN8HKaZ8HjSYM7JycGjR4+wsrKC7OxstY5UxOSacw1isRjuFBRg2e1GZ00NfF9/rfghUza6urow+1/9V1jzePBNZiaaxsaUEcV9IogjPVUejwepqakIBoPqs2xhKJVmrqs0pJeWltResQgXjaD9Qmy5j6TPnxsGfh2P4zUt4R0GEl7RlJQULCwsKLnBiv5Op/NHQIMEocjjZTSg3fsngUQa6aRZ+b1fmCb+ZncXVwEcOHBAgQvcEwIOmqZhcnIyqTgdQ5BTU1OxtraG9fV1tLa2YnV1NUkWeb1ebD39NN4pKkKu5RyRIAl540RzMzqrq3FlextPWnm1Up5KT7KUbeT1LpcLX3i9+NDtxhuW0WtXauUZ4/jf1DS8bxh40+1GeXk5PnU48Ffr63hxbQ3PW1E/k5OTCAaD2NjYQHFxMTIzM5WuIw10XdeRkpKC/v5+rK6uJnlB+Tka54zUCQaDSElJweduNz4LBPDUzg4uWwY6AQ4JbNCYWFlZQUZGBiKRCFZXVxGJRDA1NQWv14upqSllRMRiMXz22WdoampSPI5rQhCOhp/0MGpaIsopEolgeXlZ5Xgz5JtRIeRP5HcEe1mThTyBXlVJlwCSABpNS0RK5ObmoqKiQkUVzM/PKx2LejJ5Jiv3kzfz2QUFBep/8mkCsoyqAaDStajTsOK+fB9IyOcv0tIw53IlPLrxeJI85txYQI/8uaysDGfPnsX29jY6OzuTokD4PXrJSU/S6JbAFPmQ2+1GaWkpysrKoOu6aoMq9Wm5rvKSe/Cpw4H3nU68aBh4wTq3Uh7z2eRn9mguuTZ8PvfGrr8BwKe6jvddLrywtaVSKqXuJD3WUjfl86RuJwuGcm1II2zhynFzjFJXJH+m7sDXZeqtXf/keGg7GYahAH5GB/AcyHQQnjOevdTUVPUeAU3aeDy7pD1G1xmGoYqxjo6OqvSA+/fvo6ysDJ2dnUqfon40NDSEtLQ0PHz4UEUykea4n3dzcnA9NxcLKyt43tJ/CgsLsbCwgLW1NQVy/pCeju+ys7EaieAFQQu8nx0A3NnZUdGa5C8ymk4CkByP1MmkLOZakM9yvxkpJs8gZZRdn5R8TV4cD+ciz4m0I/6u67+4RoMURvJQ8cDxM/wtv8/JsEZDNBpF3JowKwHzMFBQkzkSFQP2FCQZ9qlpmqqCW/bgAUaOHUNZRwfyCwqwvr6uvOoMxeFnZVgiL6lccQ6DR49iMxRCe2Ulqvv6FCLFwyKrN8twTW4s58Yq81R6Rxoa0H/4MPJv3kTZo0dJShNRR2mMSiEkjUaO2W7MSeXu5NgY7hQX49TERBIBc38A4MzkJG4VFOCM1Y3CbrwCwM3CQiynpOBBWRlOzs2pZ8lxt5aXY8Xvx53iYjQMDysDhXUvJFPivTlPCvQDLS0YOX4chbdvIzU1VQlufk62xpk8exZboVDC223l/QF79R8kiCWFOOlLhlyTnuX4Hh88iEc1NSi+exfFVncQrjv3FgDGmpow9vrryLl+HVm3bwNAEtN1Op1YW1tTYboSlZfrZxciAFDR3Y2q3t6El1vkqtP7oGkahurr8e3x46hOTUV5VxcGamrw26oqlHs8qmYHlVtZpVsysIaeHjysqkJ1Zydqh4dhiBY6HA/HKc8258I1ttMNaUAi4LxIpwfb29F3+DB23W6sW9EbbC8qabjy0aOkUGz+lvtW3N6O0wsLyMjIQLuuo+DBA5xdWkJeXh4WrP3TNA29rJHS0oLSzs6ksUtD6U95sVUc19TOxLkm8qLgsRcBknPnPRn2L3k1FTQWPaOiF4vFlGEvjTcqGtfT0vBtVhYur6zgrOXptucw2/dW/pZIPccqcxUlbVGR+8rnwx+CQTy9vIzzloLL+3GcEtyiZ1ICTnwOQ5h3dnZUCPTKygq+y8rCakoK+pqaUNnTozwiXN9YLIbZ2Vk888wzSV4UyiLyDXo9pfE9c+4cNsNhtKWkIPa732F9fV3ljbJi/87ODlwuF27duoWLFy+isrLyRyG2UsHY3d3FWw4H/npjA/n376O4uBizlhzluvp8PlycncUtlwvHLYCOtEMlD0BSqLo00Bhpsra2pmSUvZbBysqKKvhGDxLXgaCQx+OB30pNJD1xPtxr0s+ru7t4yjJeeH6pzFOxJW0RROK+kxZkFI3dULMbBlKZ5njt4Dd/XnG5cE3XYbhciNfWqsgRGZGxvr6uoiZZhJMh7XJc0WgUbW1tiEQiSvn3er2orKzE78rLseHzYfzUKaR++63at93dXRX+/U1ZGZacicKjLMJFeid9OBwOlaphmnspfAyb/V1lJeadTvwqGkVpd7f6HvcrHo9jYWFB5VLruo6KQAD/oxVlNOBw4K8KCjDncuFjrxfHrRS+cDgMh8OhUrHW19eVh5JOI03TsLW1pbqbcC+lHHW5XCgsLFTpDOFwGPF4ohXrp+Ew5lwufBUO47KV5kTaXV1d/RGYyQ4YXq9XgaCsfeFwODA7O6vOa01NjYpo0PVEhEJBQUFSaPXW1pZKM1hdXUVGRoaSnx6PB+np6SoCbW1tDTMzMwpA1rQ9Jxa9jpwvACydPYvIM88g7csvkfrtt+pskrYpqysqKtDU1IScnJwkEJWgLgAF4nLfyYOlfh4Oh5GTk5O0/gQ92MZTGvn8m6DOxsaGAjeoRzudTry4uYnPg0Fc2dnBxMpKkp68vb2tctsjkQjS0tKQkpKCV155BXl5eTBNE7dv38bCwsKPIk2pK3COHBdlEWsJAAmZnZGRAQC4d+9e0rwJ1Aw3NOA9vx/XnE68EN9LTSPtkDd84HJhWtfxic+HV0SNDLu8lv9LXVjaSHyPayX1Pv79gduNaV3HfwgE4N7ZwQsWsHP37l0FzO1n9HFN+AzyURqPTU1NKCgoUC0yqT9LJ4OcG39vbW1he3sboVBIAeDSZrKD/vL7++n4wWBQ8S2Og+BVIBBQ4PKjR49QVFSk7LSUlBQ8KCjA14cOofvxY9QMDCRF/uTk5CAejyMnJwehUAiZmZmYmppCZmYmvF4vDhw4gBs3bihvPwE1Ri8UFxcDAILBIMbGxrC6uqpo5rtDh7Dq8+Ejw8BVa62zs7MRDAYVgJyamoofCgux4vXiE8PAW7Y1sAMNXG9d1zEwMIBOK81d1jqy2wISKNc0Db1VVeg4eBAvbGzgLYt32+UX68pIeUpZLZ/D+1Jeyedxv6Rtz+/ZQYk/dv1koIH9ZHlA7IqDJC7+bxfqNIQGDh/GkcFBVD5+rBQkMhB6qQGo3D0+l8xQKtM04IC9/NyK7m6VDx+3GH9GRgaWlpaQkpICv9+vwnfICCRzIHMOBALqnnVdXeg9dAiNVstLLjifz9Awzl9GZRB44PxIQIFAAANWkcmJU6dQYeW6M3dQ5plKAw/YM5CpKGxubmKyuRkzFy6g9tEjVPX2qnUnETVPTuKYFaUA237xIBybnlbdKOSe8kfTNJyZmMDNggIFWMj9JbGeGBtDy4EDOD46qtaJiqU0qOW6yzlqmoYDHR0qkkEXxc1oYHMPHA4HSltaMH76NJoGBtTn6EWgILUbO1RiScfSK0rAg9ejmhqshUIYPnZMGaP8kRE2k6dPYyc9HXMXL6Kyp0cd6s3NTXWIqfTw2VyDqaNHMXbiBHKvX0eRFdpPIepy7XU+qe/pQYNVfBFAUt54f1MT1n0+PKqtRXVfH9oqK7Hi8yHe1ITjMzPoq6rCvdJSNGVkwDQMfH3wII6NjKB4cVHtTe3AgCpsqbn2CihyXUgrknnZBT8vaRRwr8hMJUJLWqsfHkbd0BB6KivR29iIelHDBQAOj4+jbnAQi4uLiIt0A46NlwRq7DzILiA7a2uxFgxi6OhRlDx8mMQ8JQ38KS85BtK/5KXSGygv8gNN26upwLZhUlGiICXN5eTkIBAIJHXqcLvduJ6Whk9SUvBsJIJLlpdUgpC6ruP7cBjzTie+zczEW6LbiZ3P87zZ0+yk0iUNVpnvKkEL0zTxtWFgTtPwVXo6nhbFfakg2cPGpbIt059M01Sh7KRXfuf87Cy+y85GUVsbioqKFPgnDdvDhw+rGhg0DDg/GglZWVkYGxtThRu3t7dR0daGsZMncWJyEksWiL65uYmWlhYsLi5iyeqYFI/H0dPTg5mZGaT/xV/gfmkp3tZ1XBP8l+sCJCJhqsfGcGdyEvkFBQiFQsjOzsbY2BiAhNf94Pw8mq3WxLrwsNOzSp6iaRoCgYBKddjY2FDpHRL05XdJc3LPyV8Jiufk5CAYDKoom/28HlIhoiyVIIEEwCTfoWEloxZk9KFdfkj5InmNPHf0zsluFvbaIZKGCXZzLBwPAAQCAfWcaDSKYDCoDGiCUQSzdF1HZmYmjh8/jpqaGhi7u/jbhQUE/vAHrK6uqvUPh8OoqqpKKN3b2/jc7cbzomq7NMIYrSR5itRzPB4Pnltbw+fBIJ5dX1eRGaRldpKIx+OYmJiAx+NBQUGBcg5QEb62s4MPDANPWqCjbNGbkpKiQDWeJcpA6nPTVlcqvi4jSEKhEE6fPo2KigpFh+SHz62v41OfDy9ahrAEWRjRwzVxuVxIT0/HslX1PRKJYGxsTH0+FAphamoKgUAAXq8XoVBI8ZSFhQWlIxKsJG/g2d/Y2FAtKiORCNbX1xM1DCzPP5AAX6amplQq2OLiouqsMDY2pmjP4XBg9ZlnEMvJwerly8i+cyfJO89onkOHDuHQoUMoKSlRTiWpV/IsSrCFF8FC0n5eXp46m/SkMi1IeuZJ7xwnQduVlRUsLy8rr2xWVhb8fj8ub2/jZ4EATKcTpt+vAJvJyUkVCbG4uKhSWJjqSD2bhfd4SaPW4/EgHA5jaWkpiQ/FYjFkZ2ejzOrwZpefEmB0uVwIBoP4ly4XpgwDH3u9+DNLt5T6O/nt24aBdwwDP/N4kG5FtQE/TrEmQG1fd/IPaRNIQF/qIwDwc8PAv4rFENM0fOT14meWXTExMYH09HScPn06CQjhueP/5GESOPV6vcqIl+1y7byNc5Gv88zSbpKflfOSoId0JErbQep68ofgPXnq6uoqFiwHUWZmpuJPN9LTEfF60d/cjP+uqiqpTkpPTw+mpqaUPCRQlpubC11PRHK++eab+Ff/6l8p/cAwDBQWFuKJJ57AwMAAQqGQAgvn5uawtraGvLw8HGxvx+CRI3jb70eqFQ3o8/mQlpaGmZkZpX+dnpzE9dxcvOn3w2fxUtpnEqzj6xzHmBUVfurUqaSUUam/2vVZ0zTxYVERVl0ufJ+ain9mfY86oR3gMU0zqY6VdJBSr6IOJffWPg4ZTUT6+ynXTwYa2OJsv4mTYUmhJ40QKex7GxuxGQqh7eBBVPT0KGZBY0GGw7NFFJkM26LYW8IBe9VW9xubrusqbJXI/n7GB8cNJBstmqbh4OPHaBgeTqDF5l54kAy1J6OW68D5UQiQ4CKRCDY3N3GwowO9jY0ob29XHiAePAIVMpyHFw0IwzCU8jJ78SK209PR19iIqt7eJI/xfgYXX5f3lq/Lz0qQ4Oj0NJrGx5MMUPnbMAwcmZzE8ZmZRFiQuRcpsG71vqWiIefDNZVhnAwHsh8gPof7XdjWhpPz84k8Uev9juJitFVWotjytvybEydQ4/fDL0JcuXdkjvybXhQ+q/bRI3Q3NKC0tTXJ80bhQdopf/AAA83NKLx9WzF2HnCJCspiedznyTNnsJ2WhpkLF1DY1qbWnAyp//BhbKWloauuDg0i/F96r6sePsTQ0aMob2vD6uoqmvr70XHwIArv3MHIyAjuPPssVn0+3D1wIKGg+f1oKSlBUVubWtPuigp0HDyIgx0dqhCkXXmRtNVbVYW+pibUPXqEqr6+JEEkaVYi8RLgstOopiWinli3wrTOgmRw8v6SeXNN+ZpkhJIXydfqu7vxqLYWlQ8fqt7v9nPwp76onJMWJOgp50klmv2hpdeJIaJ245oh61QMtra2VG42Q2Lpef3Y68Ws04nPQyG8auU42435100THwB42+1GugAR7OvD8yDPlt3jwXMv+aQ9R1zTNLwVi+FvYzE8Izzr/K5doZb8iV57njPyDvJq5nDH43Gc3tlB88QExmZnUVBVhWAwiNXVVZXLXVBQgLq6uiS5x4t8QtM0hMNhDAwMqL3weDyo6O7GFWu+f9jZUbnYOzs76CMIbo1pe3sbGxsbaA2Hsanr+LVp4hr2IljIj0i7WVlZcDgcyMjIQDAYRCAQwNraGnJyclBYWKg8Zj6fTxkKrHpPoM/r9eL7UAjfZmXh1XgcL1j7QC+03+9XdSVIc9vb28oITk1Nxf28PHwZDqO+pweHx8cRCoUQjUaxurqKjY2NH0W9SN4t9YhPdB0fuFy4srGBZy0Zuri4qJwbrGtBmbm7u6v6nUtll2fHLpMk7dkVayARmiu9v9QTSFfSM+lyJfqt08AhQEPam52dVUpsJBJRaWVs5SjPfmVlJc6cOQPDMPCapmH0L/8So6Oj0JxObD/9NFaeeQbV8/M4Y8mYS2lp+Kc+H3Y9HsRDoSRFXhYskzJUnj/mY/+5w4ENlwumlaYiI1uYO83/Q9ZzJEB8JRbD0dFR7O7uYt0qfpqRkYGVlRXlpU5JSVFAWlpaGnp6etDY2IidnR3MWa2xCU5TxjscDhw5cgT5+flJ+gA9qRdWV3F5exuRSATu3FzlkFpdXcV70SjuuN34ucuFVwQAODo6ilgs0emCBm1GRgYCgQAWFhbgcDgwMTGh6kvQy1lXV6f0OeptEmBj+D/XS3ZWYPFLRk7k5eWpCI/8/HwsLCxgenoampZwgKWkpGDh88+x+swzyLTqEkgZlp+fj2vXrikwVPJW0qfkuwQw4vG4KuJKPkI5wc/IbkQ0Ppi6x+gMaUiyuOn6+jpWVlYUT1hdXUVmZiaKi4uVDNra2kJmZqYCDYuKirCzs4OxsTHs7u4iOzsbly5dQmpqquKtlIVy/qTR5uZmNDY24ttvv8Xw8LCqmq9pGhobG3H8+PGkcy+9r9KgAoB/ZBj4650dvGXT8+168FUAL4tCl9JOkLxGAn88TxJAl3yI8+PYJHD/mvW5X8Vi+Lk1Nwl0HbD0Nv5Io55j47pR3qVZhRXt89xP75cymLyK9gfHb4+O4RwJWkn9xR7dyvFRt+C6UVfhXsuo8EgkAp/Ph3PT0/guJwe/CAQUYMcfpjIwapFy2O12o6qqCt988w3Onz+P0tJSJatpV967dw9TU1OoqqqCYRioqalRqY07Ozv4s7w8NPh8cDmTa4VkZWVhYGBApeRX9/Uh7fp1PPeLX0C37GV7zSqp95L3+f1+eL1e1NTUKHvAvmZSfvG9vwDwt7EYfubcK+Ao7TSuJ3V1nn+Crfa9l4CDpBUJhNntZam//13XTwYaJCoogQW5EHKC0oiTBlllWxuGjh7F4YGBpMHKojFsi+JyuVSoDxUgXdeTQhI5aSpCZAjSGCUjjsfjqniafXwAEh7jo0eR/8MPKHn4UBmAmqZhqK4Og0eOoKm/HwcfP076HoUwBR/DKvk3gCRFiIfL5XKhuq8PRW1tiQ2zCEF6VzgXaSRJgiLB7u7uovTePYwcP44qq5Uan29HEO17RkKyK2l2pUwaaVII7KdAcs78Phms9CyxEJf0wvDz8XgcE83NmDxzBlnffouCBw8A7PV1tldInT52DLfPnMHhgQEcfPwYpmmivaoKq34/Ro4dg6briPh86KqrwxuDgyqEq6CgABkZGRiqr8eHFRWoT01F3DDwXl0dDg8MoMJqr1jV14fKx4+xvr4OzeX60dpQES3v7kZOS0uCVq1nuITAkF4Xu9F+sK0NvU1NKG5pUV5UaYBWtrVh6MgR1PX0JDERRtU4HA5UdHfjyY0NjI+Pw3S5cHh8HKfm5/Ho0SPoTieODA6iraICB9raEIvFMHbyJI6JwqC6rqOzuhrrwSAeHzqE/Pv3fwTEyXnruo7Hhw5hMxTCo9pa5D948COhJ79H2pVRCqRhvj9YV4fHDQ2ofvgQdUND6rt28M+e1iDp006LfKZcb/KGmv5+VPX2JiKIbN6Gf6iLFcqlwJcte3kxxJ0Kn/Qg00iVgBuNAa/Xq2h8d3dXdTRgASaexefW1/F5MIiXdvfaxfF9rtWLhoGfi5xcyS/keO3GuFxDO2AjFUsJPPGZVwBc2NxE1DCwrCeigBj2yArndkDI4/GoftW8H4EGKsjsuw5AtaOT3hqHw4G1tTX4fD5UVFQkedE/d7vxid+PV3Z38YL1DCA5R1nSt9PpxP3792GapipuuLCwgLy8PFRUVGDYql2jjKyhIQyEQrgSjWLZkodca+lxikQiypChvPN6varivMwBptyORCLY2dlRxUS3t7fxVWUlFpxO/A6Jft+GYSgwi/Qj6xBR9mpaIhz368xMLHk86KqrwzVNUx5yGr3Ajx0OdqARAD5MTcWsw4FPdR3PWgZQOBxW9Avs6R9Agl+Mj4+jpaVFgWh2euDeS74mx8A1T0tLw+XLl1XtB/Ka/QweTdOUMSZBU+kICAaDWFxcRCQSQX5+PmZnZ1UoL8NxqeCGw2EFDHDMW1tb0HUdMxcuYDcYxC2PB1cnJhS4LfUwnhVGBbCQGturSrCVQL3ks5LP8XNUjrk+6+vr6rxx7Mxtp0yPRCJJqUUMTdY0DaFQSPHqQCCAWCymalRQ9ra2tqp700igrkf+SMPY4/FgYWEBKSkp2N7exsOHDzE5OYnv/uv/GqsOB/7Tzg5OzM0hJSUFOzs7yMvLw8bGhqo8Pzs7i87SUvxf8/JQUFSEwDffID8/H01NTfjU4cBnubk4peuoFUbcFx4PvgiH8Wwkgmcs0DMQCGB9fR3xeFwVNVxaWlIpaQ0NDeoskJeSpoCEw0XTNJSVlWF6ehqh69eRcesWsrOzsSpkp8fjQWNjo6rjwrPIfafMoA5F+pCygmkXBA4oMygXqJMwNeR6Whp+63Dg+bU1PGlFlfD6VNfxUXo6jq6soHh8XOWYs3BfS24uWgD8IwDPWfoLO1ZkZmZiZWVF0XtNTQ2ysrKwbPGe3+7u4oNnnkGW243clpYkI+fo0aO4cOECTNNEeno6RkZGcOXKFZUiwZot0qCWdgh5KHnXK/E4XrdSCCUYZwfnZBq11PmlTSOfxdelfkY+wT2RQITUoznOqwBe1HU4re8xdYAAoJR50uHD8ZM/UD5IHUy2Ppe6q33NpLz+wDDwnqbh7e1tXNOT02OlXvehaeIdw8CbpomrgvdLGUCAVhZc5dj4+wuvFx++9hpOTUyguq8PmpaofZQ2MICf+/145Wc/S7JPYrEYsrKyEs7W2VmVfjE0NKS6+Ozu7uKvFhbwzZ/9GfyffYbMW7cQi8WwuLiIgYEBDAwMICUlRYEy6enp2NjYwPDwMOrq6mCae+1CySOvp6Xhty++iOxvv8Xhhw/RWVqKyatX4d7aUlEyUlcmr5Xgi2EYygkk66rws1LHkNEGpmniVZcLb4iaW9w/edkdPfwtdWq5lpJepXyw69/yOz/l+slAAwWHHXGRypBcIPtAKcQre3rQPDEBv9+PqLiHDCNjFAMLUtHg3xIhtEByMQp6iGmEcgH5w6JZqampSQiqHO/I8ePYDocxdfYsitvb1QYahoHepiZsBoNoP3gQpZ2dSe3POL/9cmBkeDDnJ/vC99fUoKu2FgdaWlDZ0wO/36/a/2xubiYhsXJeUsln6HxhaytKOzsRCASSGOd+136EtB/R2NdJgjbyu/K+dgbPnEy2O5JeaCoE9nEYhoGZ8+cRTU/H4qVLKO/q2tdTzrUdPXEC26EQ2q1OJqZp4tjwMO4WFyPzu++QkpKC6bNnceDePXR3d6OkpASlpaUYGhrC/Pw8el58EWs+H+6XlwOmia1AAG2VlSh79Cix/wcPoqu+HiUtLaoIYTwex2BdHYaOHkVFaysOdHQkGYHSAJQMg7QtjXFN01DW1YX8Bw8SDEfUYCDzYUqQz+eD5tqrREvFgYybhg8VD4k01/T34/D4uDJyziwuIiUlBasiN6u+pwcPq6tx0PLwS+Fr31td11HV3o7+w4dRyXQLca5l8UAp4CiMyT9k9Eh3fT0209Lw4MwZOF0uNFrpN3am+cfGI/mSnTb3o28p5P7YGflTX8z7BZBUxEuCuPyfYc12pULyH9IK+5enpKSoqIVoNIr09HTMz8+rnGUWN3w+FsOrlnJs6sntumSRRelhlIaKpG3uq9xzqXDZeQz3yC54yXMXFxcVPVMpJh1LpZ0XDUF5D+mlo6JFgyY9PV2dFYfDkRQqzdapzIvVNA0flZRgzuHAb00TF5aWktaJYK9pmgooJrBDTyeQUIgJdufl5an6GIWFhTjhcqHIel7ctk48w9PT0xgcHMTU1JSqxs19cLlcGBkZUR1k5JpKUI9zfbGwEH9wufCyZaCSLoG9tspcO0ZmUSalpKSoIoGvOBxoampKUh4lEG5Xtmgscf/fAPCeaeJV01SFFTlf0s4XXi8+cLnwqmHgud1dFZ5vmiYqKytx8eLFJAOcRjDHIj1oLCAmPWuM5JFRRlI+ylBySW8AlAEnwdPU1FT4fD7s7u4qI4iFLd1uN06dOoUTJ04k7Q1/7+7uwv/JJ9h44QVUjY5ifHMTWVlZir4pPyQgAkA5Orh3dMZQJ5JGRmpqqvJwyVpXVKjlGZKOAuo5LJpKYGtychK6ruNuTg5u5OXhLV3HOSNRqJEdWbhO5HslJSUKBCStbG5uYm5uDru7u2gtLERLURGeXVvDyaUleL1erK6u4l5eHv5foRAu7OygIRhEY2MjUtbX8YXTiSOjo2gbH0dfXx/mGeFoefH5/fuNjdhOScH6oUM48s03KC0tRSgUwjdeL5Z1HfdLS/Gz+XllvH8eCmHO6cQnKSmoHxpSkQLkW9RLuYcEQ6WnlzTIlqCalvDCFhUVqfO68eSTuH/pEkJffIHMW7cUeBoOh7GysoLU1FS1f+SL9iLqvMj7JD/m+svCgnT0SMPxPV1PzNfvx8n5eXWu4vE4PjxwAPNuN1pKSnBBROwwevO3eXmY1zT8ze4unhdAnExhiEajyMnJQW1tbVJnua8CAWy4XIidPYus27fVXHVdR2dnJ1JTU1FdXa1kZ35+PtLT05PkpV1flYYU/39nawt/E43ipd1dPL21lXT27Lowgf6UlJQkPYNjlp+11ymS9pCmafjM4cCHHg9eWl3FcyJCVwKh9jQE8oNQKITJycl9xyCBC0kfEiQFEuk+BPnkRV4h10uO4TepqZjWdfxtLIbzVgFk+Xyu2d8Gg5hxOvGrWAxnI5Gk/dtP7ssfuYcfp6Yi4nCgpaQEh63IaZfLBZ/Ph9OnTyfZCNRJCMZEIhF8+eWXiEQiyMjIwKFDh5BrRT996vdjJxDA5hNPKKAhLy9PpTf98MMPePLJJ+FyufD+++9jrLERU3/+5wgDeGVtLUmX1zQNX4bD2HQ4MH/pEho7OvBFYyO20tLwSTyOK1b7dMk7pd5k38N4PJ4kg6TBL/eWf1PWUEYT7JOpvlL/AqD0OLs+LvdDXhII42fI13hf+3f+2PWTgQbmoUjjiJccrET6JZFzckP19Rg+dgxHBgZU6gSZnSyEkZaWpoqVsGcz87qAvbx0IvVS2ZbFVvjjdDpVIS4q03Zjtaq9Hb2NjSi6e1flrnJRazo70X/4MA739yd5GHlvInXcHEZi2POQKaipMPc0NGAzFMLoiROo6O5Wlc9lXieJb8tiiryvJJ7t7W3MHD+OmfPnUdPZqQpLyv3gJcEhyQTsBLcfGCP3XjJn+yXvweJq9vtubW0l7TnXlPc/cPcuRo4dQ9GdO6qnLYUaa12QBg7cvYupM2dwyApLBoAjk5Mobm/HwMAA0tPT8arLhY6ODiyIfaGC1dTbi0d1dcj44QcYpomlJ5/EsZERNdbu+nqsh0IYbG5Gzt27ar8HmpuxHQ6j//BhZN2+nSgGefIkwl9+idyWFpUbTjoF9lrF8tmcx0BtLYaPHkXRnTsos9qTynMl90cedAlqyP20Mw8yNJniYwcmTNNETX8/Sh4+TNzD8tbxHNiFq8vlQt3QEGoHBxOMTYR27mdYcpwywkAy1Hg8juqHD9F29ixMhyPReWJoKEnA2pkuaVK+JgEEvi+FrxRSjw8eRFdtLYrv3kWpVZTHDjr8qS8ZjWEHYYC91Ciulax7QACHfEEaU7K1IL37up7oXsHCcIxu4P3Jk6TnjQLsu2AQfwgGcXVpCZetMHpe0mPMs0tll8qL/AwvyQfsSgcNHSqoExMTWFpawtTUVNI9aEBJQIPfA/Z6x3P+up4oZHbs2DGVl82QXhaFowyKx+MIh8MoKSlRqTSGYeBVw8CHhoGXLQOKPETTNFWwy+FwYGlpCYuLi3A4HLhy5Qr+8i//EqmpqYjFYjh58iQ8Hg9qamrw3Xff4cCBA1hcXMTBgwdRWVmZpKja12h1dRVTU1OqcBSLzfHMe71eNDY2JoW5y3BI+TrDuv9M1+GwCkbJEEt6SmmksoUdUzH8fj9e93jwOgBT0KF8ll3GcJ9kyoNhGHhJ03DVMGACMPbRIZxOJz5wuTCl6/idpuF5IWeBhFHPauMSEOX7Utbz7Mj+9RLklI4Muzz8zc4Ofrm9jefW1nBZRKZxfj+Ew/h1IIDLubl40spHp6HNcZimiZSUFJSVlakozFgs0bVhcXFR8R7f118jdP06co4cwVJBAe7l5eFfxGJ4eWsLT1uAAvlyPB5X0TzSUODrXGc+SxYClRXLuf+faBq+LynBuZkZHJueVjWCCI5/mZKC7xsbcW5mBk+srSE3N1fRx7/PyMC0puFd08QTwrgkMMJ878rKSjz11FNYXV3FDz/8gHA4jOPHj6OyslIVaPzfS0qw4Hbj90jIcgCYnZ3F14cPYyMtDfcDATy3u4v+/n5k7ezgfyorQzAYxCASBcPX1tZUrQkJ5qZ9+SWWnnwSBbdvo6amRqVRvJiejk/9frzpdqsOGjs7OzgxOopbhYW4ur2NvLy8pIKEDodDgWNDQ0NqXQGoaA4CwtQBaTidOnUKRUVF0DQNjx8/xuJzz2ErEEDsySeR/sMPPzqzBAv47LGxMQwMDKiIBF406innGVXhdDoVQEKZYppmoq7LwYMKgHhxcxOf+Hx4dm1NFeMjL7q6s4PPnE687nKhrq5OnQ8+8/XdXXxgmvgzrxeOWHKbTJ4npkHk5+cnybw3d3fxy7U1BL799kdRQ7FYDMPDw4q/Sf4obYw/pu9y7+PxOP4mGsW4aeK3DgeeETyCl9SleD4k/5B7zLFTH7U/X+rMH+s6ZjQNHzsceFkAsRybzJmnXcT9ZA0Nfobyn+ebfFvaZ/LZup6IcvwIwLsAXjMMXBNyWOo76+vrqoigpml43TQTQLAF1kk9SvLNNzQN75kmXrHANvILCcByTSR/1jQNn+g6fgPgNdPEq7EY/vPWFk5NTODixYvo6+tDT08PCgsLkZWVpVLVYJ1zAHhnawv/sb4emfPzCDx6hHA4jN3dXXz//feq6GnO6ipmL1xA+tdfq+dSlrL96Y0bN5CWlgZd1zF55gy2wmG8t7qKZ630MNM01Xl42zTxLoDndB3t7e2o0TRMnTuHN6x9kbaMXAcZ4ScdTGzdKQFz7p8E3uWZlmUEZH0HqVP/MeBLfpa0LG18/m+nadKTPDN/3/WTgQaZly4HKg+cHY3j4lCRjsfjGDh8GJvBINoOHkTl48forqhAd309ajIyUN7VpUI7/X4/OktL0fLEE6hsbYXf6nvOMCweNNM0MVxfj76mJhTfvasMNDILYM8oZweH/QxjXddR1tWFTAtJ1S2FnVdpZycOjYwkhBb2DHyGr0mvsR29kgocGQgPXvmDBxhsbkZlR4cKwSQhUljQCJBpEJJp817T585hOz0dvY2NqjOC3Riwgwq8JGFKlFIS292cHNwqLMSx4WE0jY2pPbUTnKQBYK9yNsM7NW2vWBOVLfld/uTfv4/0H35IzFswPklz/GzJw4c4YRkMu/t4iWQKjxROHE/j2BjOr67imzt3oOs6XrHWOmKtYc2jR+ipr0dJaytSU1PVvla0tmLo6FGU3r8Pr9eLyTNnsBMOY+XyZZQ8fIiNjQ3MzMzA5XIhFAqplAjuC5VFABg+ehRbGRkYPXECGTdvKlpyOByYOnoUoydOoKazE3WDg4o+ZPg96ZhzlYY3n8PQZzIvSa9c/56KCjysqUFNZyeqenuTwA557vYzxiWtSXTdDlJQgZKMkM+vHRiAw+HA44YG1FmV0Tk+e5iYZI6SViVaLOnETjeapqGrrg5rwSBGjh9HrZXSZVdW/tTXwsKCGiMBARq5UtBIo0F65yUQaRiG4kGs5ZCenq5y9KkwpaWl4cuUFHyTkYHn1tfxtAX0EayiEsvLMIxEuzKHA5/oOp7b3euuwnFJniSLMwF7yplM7ZFCTwq+37tceN/pxMuxGJ63FF6Px4OJiQlVhdrj8eDQoUMoKCiAw+FQnmJJQ/yb54DKGH98Pp/yoKalpakc5p2dnSSFzev1IisrS81jd3cX18yE111zOlX6FM8QO0AwEmJjYwM1NTUqjJzjyczMxMbGBiYmJpSnNxqNIhQKJSkZ+/Fgzo9g4crKirov6aeuri4pPU3SEf+nXJBhw3wO14xnlEYi+QSNTjtYwPHZZZJdYdpPkeGz7feR330lHsfvALwigEbuM8dipwNJC7xIp/Le9AyTtzB1QH4HAP4jgEkAn6Sk4EUhV7gH7wKY1DT8PhjEVes5Pp9P5Tl7vV6kpKTgySefRHZ2dhL46fF4EAqFVKoCsKevZGdn49clJZh3OPA+gCetiBl2wCA/XFtbU7VAJP+V0UjxeBxerxcZGRlqv0OhEGpra7G2toZoNIqbWVlYcrtxq6AALxp73VUYxXkjMxOLHg9uFRbi1dlZZGRkqJbTL+/u4kNdxysWuE4AglE/rI9QXFycBI4SlMnPz1e0cXV7Gx+ZJhr7+hCNRvH48WPMzc3hYmEh7vn9aHj8GD19fSgoKMDa2hq+//57jI+PIzU1FQsLCwgGgzAMA8ePH8ft27cRCoWwuLiIorY2hG/cQElJCcrOnkV2djZCoRCumCZesXK89VAIqampmJubQ+PAAF6z0skMc6+riq4nWtxS2ZdpSbKODAEdKS9TUlIUDfB8hb/8Es7nnoPvxg3lTDl9+jQKCwsVHaelpak93dzcxJAVYfH000//qI4AQUG7zkRgVBZeTE1NVZG2LxsGnpidTZxVS09hUdFX4nG8aRiJiGRLz5CRG6+53XhD15Ft1bmhLsIzQrnPNrYcr2mauKZp8P7wA260t0Oz6IZz1XVdFZykvij5teQx0gCWBh3f/0deL/7T1hbecux1geP99ktFkM+SRrnkzYyQknoY3+cYfh6P41exGN52uZBq2VNS15CdpChbuY+SJ/sEMCznxvHJ/ZYpxru7u3hP1zFlmvid04m3LZCCvIH3kbYGALxp/cSde21qKUf4nHg8jtdME68BiGsaDLHXUleUuqDcm/eiUUwC+K2u429dLqR++23CmK6sRHFxMW7evIm6ujrs7u6qyMy0tDRFd38TjSISDCJ65gzeWlnB2toa+vr6oOu64pHpHg98X30Fi7kqe4SRi4y0W1pagmmaONjRgYHmZmR+8w0+XVnB008/jYKCAnWWLywu4pLDgXBGBn5fV4cDS0t43TrLMRHhbqchuU+86NCQMlLqV5IuyMfZtpYRVhI8l7QqdXL5TClr7b/5fFk7Ue6fpN2fcv1koMFeKEJOSBoBfA/4cS50PB5HeWsrxk6cwBGLQXZWVyPi86Gnvh4XLCGZn5+Pzc1NfFVejvVAAMPHj8N98yaKiopQUFCgqqfTE9ff3IzNtDSMHDuG/Pv31QGlwiMZAIUmkKz4SI9y/s2bKO/qUp+VxhWFNr3UVDRkmgXvR8ZKVJeHnR4Ut9uN0s5OlRYQEwYy11XXdZX2wdBeaaRKQzptZgazaWnImJ9XAmY/j6L9+mPeW8lQAeB2URFWfD48KCvDqfn5JOHFz8l7cc8lk5HVglnYa2trK6ngG+dGw4pCnZ4BeX8a/BJ4oeHGz5BBS6WLxqhUfLnP0hDnmA4+fozyrq5EKzNhEJR3dSlaMZ1OVLS2YuDwYRTdvatC7rxeL5xOp6qsyxx4eqNIq2UPHmDoyBEU3rmjGA8ZzdjJk9gKh9HX2Kg8/Fxv9imWY5aef7uBRzrme/SYkMZbrdDS9sOHkdvSor5npyFN24uG2M+g4N5IZjtQW4veQ4dQLQATu3Gh67pq5elyuaA5kgseScbbWliIB+XlKA+HFbimaZoKTyXdEGDhPnNfTdPcKwbZ0fEjRPkfCmygANa0RCu0yclJDAwMIBKJKEWW4IEMd+Oa8xzxIujENnWZmZkK/Ze55t9mZmLO5cLnlmItq+fvVwzobU3DuwDecDgU6s+9kkXj7IJMrpucqxRmki9/oOuY0jS873Kp/E7SDxVmr9eL5uZmVFVVJdWRsINJdqCD6+NwOFSdBym/tre3MT8/D8MwsL6+jrW1NXU+ydeloc/xc2+2traQk5ODxsZGPH78GJFIBIWFhTh69Cj+7b/9t8r7QME9OzuLlZUVNDQ0YGZmRoGR+fn5CIVCSeeVzzRNUylFhpFoP9ne3o6CggLE43Gsra2hpKQkaf52hc6+/jKqR6aacF+i0Sg+NE186PHgla0tXLYMKH7eLldpTDrEeeU4pIIuazhomoY/uN34OCUFV3Z28JRVQ4P3ppf1NIBTFq2963Lhs7NnkbW7i7KurqTcValYyTWw06TkizTQmZ5kb/VKfeYlCwx7XdMQDAaVoUwa/3PTxH/c2MBzViqSnbcCUHRhd3gEg0FkZGRgbW0NDocDy+fOYfrJJ3EmHkeTruM108SHlteQ0TFdXV24efMmTNNEY2MjwuEwQqEQMjIyVFtI2d6S4zUMA+FwGNtWYcVgMIj09HSEw2Hs7Ozg6soKPtI0PG9FZTAKgjz3/OwsbuTl4enlZVVPa2trCx6PBxd9PjxvOUZWd/c6bvA8s4AfIxwoJzY3N9HT04MjR44oerm8vY0Lq6v4TwD+nzU1uFZejrcNA729vTj4+DEyMzOxHgziu+++w4xVeBoALl++jJmZGQwPD6uaNKzL1dDQgGAwiOHhYei6jqWlJRXtkZ+fD13Xsb6+rpwCJSUlCAQCah1JX6x3Qh7FPZbni9FANABId0yh4Gu8T/rNm/hFKIQv7t2DYdF/X18fjh07pnRdw/IWM0KU9NfQ0KAiQaTTj0CnLBZKOuf7kUgEXq9X7SXlYyQSUUY0n2sYiShQpt5JXZprQeOd45Q6lawBZNc7CZjLz/NvFvYjH+dZlilRdnCWZ556Ft97xenE85Z+HDWMH8kJyaukLiN5iZQxBJXk63xP0sMrmoaXbfq41EX3+y7HTaNYpkvagQzJZ+W9yOsA4OdOJ/4mlig0yXkbYg00TVP7zN9Sh+Ya2HmqXHup79nlpZwfL03T8LbTiV/F44nChkg0HyBQxeKjU1NTABKOy/HxcXg8HlRWVmJlZQU1sRj6GhvRPD+PQCCgOjnIQuwcj6xtxHPB880oH6/Xi4aREZy2ig43nD+PlpYWlJeXIxgMoq+vD5ubm8jNzUVlZSW2trbQ1dWF8+fPIy8vL2nu3A8pi6XdSTrf3NxU9GSPTthvX2U0q3QcSnqVOrV97+S+2fU+Sd/70bx0iPyU6ycDDYO1teg/fBgHOzpw8PHjP7qAciEBKAWRV9bsLM5//jl0XceWrqO2qws99fVoHBjA2tqaYjYulwtNfX1or6zEob4+lY/pcrlUSBuQUBJLWloweOQISu/fRzAYVELVTuRyo+TYuTGjx49jOz0dU2fOqAKN3PjZEycwcvw4Grq7UdHTg9TUVJimqdrz2FuuyfQI3p9epPX1dWX8yMrGdmLh2B0Oh6oOTC8H7yfXOpKXB9PhwFJ2dhLyKAnKTkj7/W9fN373xOgo7hYXI29tDf/6+HEcGx5G88TEvko4kFxdFoBqIyWZMo0pAEmVv7l+ZKoAMH3sGGbOn0fWt9+iqK1N3XuyuRkz58+jsa8PTRaD4UFgX3jpieW+SiYtgQeuLQWuVMglzUjQgntW3tWFora2hOfZosO0tDRVXZbKB5U+qaiUdnYi6/ZtOBwOJVQ5lqqODvQ1NaHaCvXimuzu7ioE1jT38sPJQCUgIXMxJQAkP2OnCWnM2QUtFRqukdx7eU+uv6ZpiVShtDR019ejuL09SaDLeQ3U1KC/uRkNPT2oHxpS+0Thx7PVWl6O1UAAfY2NOPj4sdqHYDCI4YYGfFtTgyNDQyhfX0dXeTlu1NbicH8/SpeX1dpW9/WhsqcHGxsbMEVoIPf2H+IKBoNKqVtbW8Py8jJGRkawsbGB1NRUPPfcc8jNzVV8hbQgf+Rr/NsenSIBlng8jmvRKD7SdVwTPeZ5vmXdEO7fS5qGazbjVa6PHUGX70ljc7/vyvP3upkIQzxoGPjHVmTDJeFdojJOg4FzlveRni25d3bwQyo9f3C78dcXLyL/5k08G41ibGwMj8rK8IfKSvwiHsczogUtgCQ+vbKygs7OTjy22jQXFxdjcXERo6OjSE1NxXvvvYfu7u4k/mCapvJCDg0NQdcTERednZ24f/8+SkpK0NjYqDy7VJIJJtBI4D5XVFQASPDO8vLyHwFQMpqOa8Z5bG5uYmVlJWmvpHGt6zrez8rCrK7jt7qO01bxO2CPt0slh+dS0oRUYnnJqAlN0/BJSgpmHA58nJKCZ631luCx1CV0XcfnoRDmHQ5sHD+OA1aKF/UBPtP+fHme5W+Hw6G8upTHpCUJ0GmahrcNA6+L6BQJIGiahjc8Hjwfi2HG0lW4TpQVQKL/uh2YkzKW0SJTZ85gy+/Hp/E43gLwmpaoSM/OJpL+PR4PnnzySeXtZrFGw0i0myRNUOdgYcbd3d2kgqg0np9YX8f51dXEXoq8frY1bNjYwPNWbZE1i4Y4h44DB3AjPR2vxuM4HYsleKq511qNsk0WaKWOxbB+6ckdHR3Frbo6bKam4ivTRP4XXyAcDqO6uhrffvsturq6EAgEcOrUKfRYrZDX19fhcCS6Paxa84jFYjh48KDSCWg8Z2ZmIhaLYWRkBIuLi8jKykJmZqZqixePx5GVlYXV1VVEIhHouq5SUGTaqqYluo+wjTXnQUOf8pa0t7u7i6GhIeTm5qouHVtbW1hYWEhKAVtaWlK1MPi93d1dFX1AGUxaIG2RPuilZbFbacBzPB6PR42b/IHgEj9PEINGWCQSwcrKShI47XQ68anDgY8NA/+N14sXbWMBoED04eFhpKenJ+kP6+vrGB0dTTrDHGd+fr6K1qIRZu8yx3FLnkHaNc3kbg+S10kZJQ1S+b/k39L7/5Fp4j2HA2/s7uIlwdMkX+BcJF+y80SeW85LGpqyBhBBO37HzuPkexLg5rijFm3tbG8jIsbD70rbgzxY8i+pCxP8khf36xNdx/tOJ16LRvGCzQlg58UOhwNRXYehaYju7GB9dxd+v18VVg0EAvB4PMjNzUVaWhq++OIL9Pf34+TJk/jyyy9hmib++7ffRmRuDtPr63CWleHevXvIy8tLFEW35BsjRhixKPeR8oy6cSgUwsrKCvx+P4LBICKRCE6ePImPP/4Ya2trqKiowO9//3vU1dUhLy8PxcXF6OzshGmaWF1dVev3x4x4rgd5B1O9DMP4UWQS6VHSqbRpJV1Lp7b8jNQb7U4Zu54kXyPfkroaf4Bkp8Hfdf1koOGxVeji8aFDKkd9v0FRSFNBYktKGVYrowTyl5aQf/8+AGDBUib5mdLOThzo6Eig30Lh4vO6KyrQ/uyzqGxrw8l/828SIYpWATQZDrUfuGAX8vF4HBVtbei3qv6zgjaF+uCRI9gMBvGwpgZFVqHIcDis+rzqeiJ89kFBAT4vKcGx4WHUDQ6iq7wc90pKUJWXh1B7O1oLC/Ho6aeh9/djorwcWlcXKoQyKpmfDOeUPc45DxmCv7u7C//EBLZCIYStFlscFy9JXHZwgJcdNZNX88QEGkdH8X+cP58U2SDvbTdYecD5nlQIiXCT2QBQUSLcZ1YUdzqdmLtwAdHMTCw/9RQahocVzU2fP4/tjAx01dWh0eqgQORdKnHxeFxVfyeiLqu+c9wU2hRQpmmir7oa3fX1KLp9GyUPH0LX9yIspKeIa8Hx29FuuxEm118yBWlAAUDZo0c4YHncNatAo6YlClExZ80wEi3VotGo8kBxPJKJyOfa980wDDS1t+NRbS2qOzqU54KflUxnP2NSrgH/lzRX0dqKweZmlD14kEQnkqEZhlV8NRRCV20tDo2MJAEzFFJOpxPHRkbQUlKCzOvXMTExgczMTKSkpGBmZgatly5hMxDAg7IyBL75Bm3PPovNQADtlZUotYRCPB5HT2UlumprUXr/PiotZXU/AfGnvLjmBBGp1LKoUXFxMfLy8tSc5fpKIS0vjtl+DqUn4kXDwHNrawle43TiY03Dew4HXo3F8IrNOyLvIV/nOKjo8UfWIeF3dV1PqkUiFUDJh8/H4zhnmvhnoRBmLMN2JRbDp1evovjuXThGRqDrepIn1H4/u3fHLkzfj8XwN5qGZ9bX8cTaGtxuN97LykIkGIR29ixSbt+G3+/H/MWLWPb58M7uLo5bRZ2kgmiaJh4/foy7d+9iZ2cHOTk5KCoqUnndkUgES0tLuHnzJra3t5Mi3b766itEIhE0NDQgKysL09PTygiKRCKYmZlBT08Pzpw5g+bmZqUYAFBAkK4n2oEdOHAA4+PjAIB/8k/+iQollbQiadlOd2yhzItKI8NtTdNMCoVnzjlplTxGFiSU8lYqvny2PE8c0+sAfmOBYIza4/jlbxpFrxkGfrmxgWJLb+B3JC3YnyHXUUYZ6LqujE7KI+lV5b0IPEtFn+srIzikl4nfk51g6Fig3OF9eJ7Ikwpu3sTkmTN4yjShW5X1OT/qT7y/9PLyvLHTANvi8Xk+n0+12pTAnJSZlJsy5Yf5/fzs2tqaam0oZc1X6elYdjjwgcOB50IhzM/PqxQf3pNgW0lJCfr7+7G1tZVolbm+jqGhIZUaNTo6irt37+J0MIjO1FQc6u9HbW0tNjc38c4776iOHn6/X6USHT16FDdu3EBNTQ2Wl5dRU1MDv9+PiooK1Vp7ZGQER48eRVtbG5aWllBQUICJiQkMDg7i8OHDME0ThYWFqqh2NBpV0Yh/vbqK36am4vzsLJ606paRLmV7SQIM3CfuLVNQuHeksXA4jPmTJ/HLc+eQZprIuHkTQKIT2j8LhXB5ZQVPWZ0ddF3Hl14vPq2oQMnysoqoJN+TdEX6lQA0dSEJ7kciEayvr6v2v9RrGD23urqq6IlRprJeBFP7PvT5MK1p+Pdra3gpFFIAMfm1TL+Sso0ARjAYVDoiDdl4PI7BwUHE43FcvnxZFcVkiooELaVuI/V+/jZNE7/d3cXfxGK4aqUP7heBxd88U38MOPhNKIQZhwPvxuN4wjIw5VnkWv8xg96u73MfedYIJAWDQQX+MB1J6ovSnuGZJZhMZ0I0GsU7hoEpXcc7hoGLFi3tZxMwitC+FnI+dt2P7xmGgd/5/ZhxOPAegEsW2CjXVvJlTdPwrs+HaV3HO6aJJ3Z2EAwGFR8KBAIoLy+HruvIzMxEU1MT0tPTlQ1UUFCQOCsjI1hdXUVJSYk6c+TvsuhsWloadnd3ldzgGS0oKMDKygpyc3MBJGpVlJWVYWlpCcPDwwiFQigvL8fY2BgikQguXryI0tJSBYqw5gj1Oakn23U20iTPmcfjUYAtacVO03bdOiqcILStCNby8zJiWvIcqR9RlwaQRKtMz5JOT6kX2+X533X9ZKCh4sGDRHX9trYktE0eaj5U5g4zp5jCNSUlRRUw2U/QcYIS2QaQtHH8Xtuzz2IjFELvoUM4+OGHCiG1hyXKBaZywGfyvrquo+ThQ4S+/z5RGTgrC4FAQG3UoceP0VVbi4MdHQCgmBwvHv47xcVY9flw98ABVPX24np5Oba9XrQ3NSH2u9/h0euvYzs9HThyBHA60V1Xh/z79xWiT8Tfnp9FQUZmzXUGoAzLtfx8mA4Hlq38N2kESKKViKJ9H+V+kBC5F1ynk2NjuF1cjJNWRVje0w5u7O4m+p2zt7hE/6VRtF/Va/vzHQ4H8m/exPTZsyi5f189wzAMFN66hckzZ1DX2/sjQ0saShT2khETQaSCxoJwVBK4Lo8bGrARCmHMKtrJeUoDWBrfFL5khrJKNw+tPQ1n5NAhTJw6hfwffkBBa6tiAPyOHCsv2VZMorJy/nYlmGeA45R0Zpomqnp7caCjIzEvZ3J7VLtA5o/dk8D9lsqMpmmo6e9HTX8/B59Ec3LMB9vbMdjcjHqrg4j93lQ0GkdHUTswgPb2dsCR3DKzoq0No8eP4/jYGEzTxIG7dzF5+jSOWf9z3qzRMHD4MAoePEgCaKSh9Ke8uD6apqkoFMnfmKNrV5wkX5MCyW7kkJ9IsJK8jHTtdrvxbnY2ZjQN72oanlheTjrHdoNe/nyZkoLPAgG8uLmJp63K45I2JCDC8yzzNSXd0Vv2hceDDQCp8TieW1vDp+EwVlwuRA8fRvVnnyUZtRIY5PPsKL58HQB+bZqYcTjweTCI5y0Bf21nBx9pGs6trqK9vR0HDx7EG5qGD+NxXLE8n5IvxWIxtLa24r7V9vXZZ59Fb28vbt++jZ2dHUSjUdX2joZVPB5PMj58Ph8mJycxMzOjOlNMTEzgxRdfRHNzM/7qr/4K169fRywWw9mzZ+H3+xGLxbC8vIypqSnVKzw3Nxc7OzvIzMxM6j5hB/75N/eU62RPEyLPY46uYRh4eXcXb+o6NI8HplWc2eVyKcBWVtiXgIbcF0lLdtrVNA1XolE8bbWmtueUk7fwt2EYCQ/ZF19gyIp0Yksy+/eAH3sOKU/+mIIk+QJ5t6Rt7gOwl05Kw+J6Whp+HY/jebcbL9k8QuT5IyMjGBsbUwUiuS/vRaP48s/+DNnff4/clhZk3bmDgtZWNF29ioioh8GoA03TVIg6c3VZGZ8dRLi+LH7J9dgQ9a7I6wiORSIRTE9Pq/VkBBnnzvO3ubmpCs/u7Oyo1qrPRSL4zuvFW45Em9iFhQVEo1GVlsDv5OfnIzMzEydOnMB3330Hp9OJAwcOIB6PIxKJYGJiAouLiygvL0eD34/mL7/E8ePHMT4+jv/8n/8zQqEQnnvuOfT29iIcDkPXdZw7d07pZG63G5OTk0hNTVXRCGtrayguLla0+MQTTyAtLQ3hcBjLy8uor6/H2toaHj9+jGAwiIqKiqSIBLfbjU99PizpOm4XFuL1+XnFk+hMY80J7i0NGYJYmpaIHPP5fOpzi4uLyMvLw80nnsBmairily4h89YtaJqG6bNnsZWSgu9cLrxspXkAwB+CQcw7ndhqaEBpZ6fS0aSOR31ra2tL7Y/kjTJygnQjgaucnJwkXY1zYJSy1OVY8PtnDgfe1TT8E+te1OtkHRxZr8puROXm5qrXZKH3aDSKpaUlVaAdQJJRJeldOjf203n/NhbDJICPvF5ctfEjyTfkfQnUyHsahoE3APzWNPGalmjvS75o/47USyWfZIQeL6kvEcxlXQICEBkZGUm8Xo6HlwQKSL+6ruNthwO/Mgy8oesqPZfPtes6HAOAH4EO/G3nu1z/100TvzUMvGbNUeqikqZoC75pmviNaeJ1TVMdNmhHut1uHDt2TMnYeDxRrHl+fh5PP/00dF1X68PoHAIRPG8cX1lZmUpPc7vdKCoqAgCMjY2paLPt7W1UVVWhsbFRpQ8tLi5idXUVxcXFaG5uRnd3N6ampjAzM4PFxUWsr6+jsbExyba1pxXT2S51FX7O5drrqshx8/uSFmk3SBohoELeY49qlJf9vMnPSVnGv1kPRNIEf8t9//uunww0lHd3o9rquKCLFlRyADLURioY29vbisk4nU6Ew+GkQygNLmkwSVRGKgBE+4ru3MHIsWPI/P57hdC73W6FKMkDLY0lSegc40BtLR43NMAzMoLN4mIU37mjPMjxeBzZi4vIvHUrEcKk6ypcnXPi/+UPHqCvsRHlHR1YWFjYO5RIVCFO/+orLFy6BOfgIHbLypD+3XeYmppSChCQ7AWhl0F6LbjBLDJFha/ozh1MnjmDg1Y4qWRCduKwM1b+vZ8RKdEsTdNwZGoKR6amEutnIzbSxfb2NmZnZ1VxKilo5RxkaoQ9DF9GIGiahpy7d5F//35iTwR95N+/j4ru7oSQskKiTNNUBgwVMXvFfGmE8LmcIw0zjrO6sxM99fWoEF5+0qbsIMB95NrJyv/hcPhHIBif4XK5MH32LHbS0zF97hxy791TaxmNRjHR3IyJU6dQ3dmp0kMcDgc6S0sx9i/+BTwffQTfV1/9yFDkXGSldXunFjuz6K2qwqOaGlQ9fIia/v59hbUEHvhjD4m2nz2uu7y4TnbjqLyrC4dGRhJru88YmbeXnZ2dtG9yXCUPH+Lc8jICgQB6dR159+7h9MICMjMzMSfGXvvoEbrr6lDa1pZUP0PSy5/6Wl1dVfnuVMjk2klDmutuP6t8z650aJqG38VieNfjwXPRKM5EIqrQJM8b1+jFzU184vfjRSHkJN/4g8uFD71eXN3exvPWd+PxOD4PBjHncOD3gQCuCF5BOrAj6fY95iWf9we/H2sOB3LjcVzTdeyurOAjjwf5Vr0TtnqTnSA4F/u5ksoe7/+2puFvYzG8YpoqzPx1TcObmoYFtxv/x+4umpubka3rODk+nth/qy2fw+FQUQqs85Cbm6v6x7NAGWlKgpRcG+aAsiXb+vq6AhI3NzexuroKp9OJ06dPq5z3r7/+GhcvXoSmaRgYGEBnZ6dS3tfW1rC+vq7Crqm4A8ldWKQiKtebn+NaUsmVnlaOX54t+ZpdgbZ/3g4I2ZVVuYdSb7B76iT9sPCVBIUpS6SnTI7F7n2T4Cu9+Lquq3txbvb6TNRj9lvXX8XjmDBNfOTx4DXrfYI3LBYXCASQmpqaFNJsmiZuZGRg2+nEzLlzCH3/PZxOJ2aOH8f/cvAgXo3H8Yww6sgzCgsLk9YLSG55SznEEGq/35+kS3HOEmTXdR15eXlKJrGbBdc6FAoB2Ovqsry8jI2NDaSlpWFtbQ2nFhbwps+XyGOORBCPx1Xuv6TNQCCg6scwfTI1NRV5eXnY3NzE6OgompqakJubiwcPHuDUqVMYHh7GL3/5SzgciUKwBFQyMjIQCoUwNzeH1dVVVW8gGo1iZmYGzc3NmJmZwezsrPJWLi8vo6SkBEVFRbh16xZWVlawvLysOn98++23ME0TWVlZ0PW9lK2fu934m2gUz1o8QNIKnQDU3ZiywELq3BPqI0yF2t7exuLiIkJffAHH888j7dYtZdAfGRrCUDiseBZ5+LWdHby3u4ui1lZFlzJlmAAXkFzIjZEJ/J/8Wnb14lxIs5yL/C0dKXyG3+/Hmw4Hfu50IiiMburIBDHdbjcikYgagzynXC+ed7m+Pp8PqampSh+Ox+NJqX+crz1CQRp1uq7jF9Ye/szlgt+RXNuK51kWQ+S4JV8BEnLsdQBv6sktAHkfyWclz5F8UxbzlkYcP8/9JJhLD7iuJ6fjSZ4q7yHB0FgshtecTlxjRw+RtszvyHtxjyWvJABtd6ZJHmQYiU5CLwHQHQ6YYn2l/iFp8zUAr4u1ImhKvhMOh3HmzBmMjY1hdnYWhYWFmDl+HP+Dx4PXTRMvWTxlZWUFhw4dwqNHj3DixAl8/fXXir83NTVhdHQUJ06cwPT0NKanp1FQUICamhr09PSgoaEB29vbGBgYQHNzM7xeL/Lz85Vcrq2tVXrUiRMnsLKygqGhIdzKzETLgQPwAGgQupWdP0ggkHvCNfB4PFheXk7SY+yyVdpjdlnO3/aaQHZ6k04jKXvtz5P0Je8l3/svuX4y0GBXQuTApNdUGhRS2eBiMj8Q2DMquIjSIyeFsFxoMlVd1+Hv70fmrVuIRqPYDgSQkZGhKgDLxbcbXnYAwzRN9B46hM1wGJvBIOB0Ysyq/M/UDyLxfr9/30KLuq5ja2sLBzo6UPboUcLYNk3U3LmD/sOHUXrvHmKxGPIfPED6zZuIRCJITU1NypMmwkwPN9cISBxeMm7D2KtjwQNvGAYKHjxA2aNHCVRrH0ZnN1YkY9lvfezfIwO2f1Yy15sZGbh58CBM00Tt3bsoefgQpmli6exZjD3/PCoePEBFT0/S2MmwOBbuMWtxsFikZJ4U6rquY+roUcyeP4/6nh7UDgwktQViBxOGSVGp4doEg0GMHz6M/+3cOZimCX9lJTaLiuCZnkap6GBS2dODAx0diflaCKkUXvaDbTeso1ZYsP2QEoTzer042N6O3qYmHLh3T9EZ7z9ltdrpb2rCIavtZndFBe42NcF0OGC8+CJ0Xcd3V69idWAAof7+JKNeMhXSksyxk8BAd10d1oNB9DQ0IF9Ej0hasL8moxfkGsi/Sad2ASk/F4/HMVhbi77Dh1HX1YXqvr4fGdrxeByLi4tJCLk0bvmapPNAIJAUNcNL13VU9faisqcnYUDaOtPI+/wpLyl43G43VldX1bgIisl89z/G2O3KFH9+63BgBgnP19NWuhU9blIRfgnAyzs70F0uOEQtHa7Tx04nZjQNn/p8eFModK8DeM9MVJmWwBsVCQni7cdn+Lp83ivxOH6nabhmRcCdXVqCdvMmpqamsGGtlz0EWN7PbtjLNQGAK6aJZ+JxQNOg6cme/I6ODmxtbWF8fBxerxfj4+OYnJyEpiU85vn5+Xjw4AFyc3Nx5MgRjI+PIycnBzdu3IDP50sqPsvic8si7YI8LhaLYXNzE2lpaRixzjE78AAJpeOFF17A73//e5w7dw7Xr1/HL3/5S+Tn52NrawtjY2NqD8PhMKanp39UOd3Ov+XzyYeAPWNRynEa2pKfsTMHz7mmafhY0/C+14uXolG8YPFuGZYpz788t6RVqTd85nTiQ48Hz6+v4xmLz8u9ldE4mpbw6jG0VjokJH3ZQWtJg6QLgjPr6+vY2NhQ7bRZwE7KT7k+BGxlDQeXy4U/93rx19vbeH5rC7qlh/AckHZzcnKSIjB4/zfjcfz/lpeR9cMPKmd/5Omnser34w+Ggbcsz5JU9Jg2w/nJYqHS+OJzaCTIiD1GRxCkoi7C52xvb2NjYwOBQAB5eXkIBAIqqmN1dRUbGxuqMxbPEtMDUlJS0N3djaysLGU02M88C0Lv7u5icHAQx48fV6CAaZpob29HXV0dVlZW8N577yEQCCAYDCIYDOLGjRvIzs5GV1cXXnnlFTgciQ4d9+7dg67rKCgowOjoKLKysjA3N4fU1FRsbGxgfn4ely9fRmZmJoaHh3Hnzh0cOnQILS0taG5uxvDwMCorK5U3k953j8eDV2IxvOJ0YmplBVFBYwS+ZAoRgKRUHMpz8ofd3V2srKwASESUZNy8iV8Eg7jx4AE2rb0OfPMN/mVxcQLg0fc6zDwTjaKktxf3u7qwubmpil5yn2lcMbefXk5GUMn0CgK31NNNc69lO2knFoslpUpI2pJ8nmeEZ1xG/VLnYHSDBPXs3m57VALBKclnNjY2kviCPP923ievV10u1V4yaux15pIgpeR/kn9IPsM5vh+L4demibd0HS/oyYUd5fc5L2ko2u0bqSfJ53A/eOYIfElARo7JDgQYYp4AkiLG7Tod78UxyrXhnlKHsPN47inpyw7+OhyORLqmruPVeByv24BmqVdT9pAnBQIB1NfXo7a2Frqu4//tcGBK0/AbAFficWRnZyM/Px8ulwsVFRXo6upCamoqGhoakJqaioMHD+L+/fuorq7Gd999h7m5OSwtLalUnJSUFKSmpmJqagorKyvIz89XdWzq6+t/tCahUAhNTU34S7c7UcfINHE1vldbyL7vkibla4aRSA9hnRS77illpQQWPjRN/CYaxc9NE1ewl4Yh6Z/3l/qQnSblc/gjo6TlWDRNw4emiXfjcbypaapF6t93/WSgIRKJKPRJhlfYJyE9WTSSeI01NmL42DEcevwYZV1dAPY8L1IhkH/zsMiFiMfj8Fl56tLglN5IO/PjmPYzCk3TRG1XFx7V1sLLiIa7d+HxeJTRSsLi3/w+xy9zN+V4C1tbkX//fgLYEMjy1tYWAoFAEgrNqAUZhs5QW1YOlqG4UukBgLmTJzFz7hxqu7pQ3N6exHRIKHZwR16SYcn3JeOR35FMnq+3VlRgx1Jkexsb4f/6a2xtbWHl6aexm56OgSNHkNPSokAVGQot98M0E2hvXl6eEo7yPYncTzz9NGKBAB42N6Ps0aMkBaqioiIJBa6pqVFIuGEYKCwsxA8nTmDTUgy36+thOhz4Mi0NZzY3UTMwgHg8jr7qavQ0NKD8wQMcfPwYhmFgoLYWg0eOoEoUSJXrRfpgfqc8CzSMJcJY3t2NrDt39mVUFW1t6D98GJXt7YoJdNXWwnQ4gFgMzt/9DmsvvwwzKwuP3G6c/OYbpQwzdYlCQhanIqjFMTscDmQuLmLN70eG1UbIDgTsBxJI5sTXJb3Zhb/0asvXSTebaWm4d/IkNAD1w8NJtEYl2e/3Q9f3Or5IumZIGRUdj8ejCsXKccv1t9O7PA9/6ouKfSwWw/T0NBYWFpQgzsrKUu0OOQ77OtnHbEe/39Z1/Coex8uWAAX2Kp6Tz0hBY1fIlAGkaXjHNPGGpsHp2KvefdU08VLiwUlgJxUY8iSJisf3EcKSXl6Ix/GcUIoJDDGyRFaLtu+LnUalwLYrs9LbwLHl5OQgEAigtLQUW1tbGBoaQn9/P6qqqrC8vIzbt2/j5MmTOHv2LH744QeEQiGUlZWhu7sbsVhMdQIgn5LeH9IoeRCBAiAhkwKBAKqrq7G8vIwzZ85gamoKCwsLGB0dRWVlJR4+fIjh4WEcOHAAqampyhBOS0tDamoqqqurYZqmep0X1+gjAO9pGl7XdbxggYumaWJ9fV3lrfOit5wG/OceDz72evGcAAEA4P30dMw4HPjA5cKTFhhP+pKem/3SGMlvSHOfpKRgzuHAH4JBXFldVZ+XHkpJKzLtjfxMdkWwGytSP5E0wZ7wBJ8JNFBHkEADfzPNSYJe/OyrLhdec7sxurwMWPyeslfyenkmOK8rpomR//gfE3zASp1s7O1FX1MT3nI4kGKlTcqwWXv9Hxq6UqnlGZTj4FwoJ6WRINfdMAxl1OTm5iqeZY/gk/yEYDpTt1ZXV1FUVKT0F4L+GxsbiSLbs7NJdQxWVlbQ1tam0iiKiooQDofx7/7dv8P8/DwuXryIyclJbG1tobS0FI8ePUJNTQ1mZmbQ1NSUqEBfU4PJyUnU19cjEonA5/OhsLAQ8XgcS0tLKC4uRlVVFTY3N/H++++juLgYHR0daGpqUuu4tbWF1tZW+Hw+XLp0SfEUypbc3FxVN2DHKqzLWhOmaao50zgkvW1ubqp87rm5OczOzmJmZkaBAnNzc2pfTdNUKR8BK4qY+0I6YKg4c6lJV6RlSW+xWEw5zmiokhdub2/jc48H7+k6XtE0vGC1UJZAN+/lcCSKmi8vLyfJKNIyW5Wapqny1xnVYRgGgsEgCgsLMTU1pfQiw0hEePX19e2rX7jdbtTX12N3d1c5mliv7P14HO8iIate0pML3tqdbXydTjtZh0vyQVlXiOH70saRhvzfulyY0hMpGZesMdkvyY/4nM+cTnyIRI0atpGVOgzvTxrzer2JFOm1NWWsv2YYqtUu9wHYS4WVgCj1wK2trX3XRa4P14S2jV0PlHXBJL8lzck0XQk8GIaBd4NBzGgafqlpeC8axcuxGF4QQGU8HsdXPh+++ou/gG9rC69aqV5SdwCAqy4XPnS7cW13F1tCvr4XjeLd06dxdGgIx6ankZWVhbS0NKysrGBmZkZFbJmmidnZWWxubqKurk4Vps3KysLy8jJSU1OTIoOkfiV/P+/14hOfD89vbmLFqgMhHW6U/x4BPkt5xj1OSUnBgtXlQspN8hwgudvjr30+TDsc+Hc7O/hPAK7FYnjBKn4v9R2Ow55yT7kldXQ7qC+jn6jX/DolBdO6jl/H43jSiuT8+66fDDTQayJRTGloSwOEQlDm2sfjcQwcOYKttDR0VFejsK0NaWlpWFpawryV6yYLxUhFgM8jUBCJRGAYhkLSKfz4OfsB4rjt0RjcDE3TUN3Xh7Tr11U4q9/vh8Py1HEsaWlpSXOUyr3dwJLGFz1ZCwsLiEQiiqC3t7exev48Zi9cQPHdu6qTgmRwLpcLcydPYujoURTcvIlcy0hnNwWGPxqGgYlTp7ATDuNxQwOK2tqSGIBkXlLJkWPmJQ0Z+b48cJIhkiEBwMmxMXxfWordWAyZ33yjFO+0L7/E6jPPIOv6daytrSmFll41uyH6xwwCKulE6qXBSsExUFuLoaNHcXJsDIfHx5MEAwsaUTHNysrC6YkJXC8rAzQNJYuLeJyTA0PX0VFdjYO9veiuqMCDEydgOhzob2pC3r17MAwD/U1NiQKpDQ3IuXs3aX3lmvIgS8UU2KvsbJqJYpMjx44h74cfkNvSouYtw19hmtCEV6Pm0SN01dUB774L55dfQtN1mK+9hobBQbUnkolIBdKu6Mq9XcjISNT6yMn5UScCuR88R3YjV17SiyUFsMxfk3s/WFeHmNcLGAZMhwPdDQ2otwp/cn9JzyzYKqtj855kilx/epLsRotpmuitqkJ3fT0O3L2Liu7uHwn8f4iLiqCsIM6xszuJ3cMgz539dSA5iuyaruO5WAzReBwxi6dI74Kcoz2dRV5XTBPPiLZPduAXgAI+5f6Q50p6kV4VSXdUinjmWQTqG78f37z2GjK++go5LS1J47QrolJJlIAozxCVEEbC2L9D+pmZmUEgEEBRURHS09Oxs7OD2dlZZGVlweVyobu7G3Nzc9je3sbw8LBK4amvr4fP54PX68X169eVMRGPx7GysqLWhe2Nh4eH4fP5cObMGRX9YBgGRkZGEIlEkJmZidHRUaSlpaGyshK7u7sqZ31wcBCGYaC7uxsOhwOVlZVJZ1vydl3X8RvDUJ6fl8V5ZpThfp5J3utjqyPE56EQ3rSUbwB4Q9MSES1aopAdFVjmUMt7SjlkGIaSCbxeM0381jTxqrlXX8BO+/yb8n9jY0PpDPSeS/4jQULJqzgOnhfT3Euxkx5/mS4ozwvBJPs9JZBAo5pyn2tJpX13d1dFNBJwlrKMNJ3y1Vf4/+TnqxamnAuNI9Z0kt5DniVZKAxIjpLieMgvOHa+Z49yS09PVx57znlrayupw8LMzAx2d3eVZ45jZNScx+PB9vY2JicnUVBQgMHBQTx48AArKysK7HK5XOjo6MD29jbKy8uxvr6OEydO4De/+Y3q0EBAwOPxYHp6GoFAAIODg8jOzkY0GkVaWhouX76Mf/2v/zUMw8Di4qKqFZGZmYmFhQVcu3YN6enpuHfvHrKysjA/P494PFH4zOPxoLy8HPfu3UNNTQ22t7fR29uL+vp6pQdGo9FEt5q8PJydmsLzloxOTU1V91hdXcX6+jomJiaUHud2u9Hf369qNa2srGB8fBzV1dXqXBNoIA+cOX4c/31eHl7a2cFTVrE4FrXz+Xyora1V/HW/1rysJRaPx/GFx4PPQiG8vLuLZ3Z2VBeN5eVlbG1t4Vfl5Zh3OPAbTcPZ1VVlqNqBN/IyrgXpRDr4xsfH1flaWlr6UacUAPjiiy9w8uRJHDhwQJ3d/Px83L9/X50JKcO/+eYb1Z7z2LFjyMjIwOLiIn6VmooZhwO/isVwfn09SQ7YeTzpnXqVPS3CbnzxTMtCjZInxONxXDUMfOh246pVK03yK36W51zqsu97vZjWNLxjGHjS6rDAi/Nm5EcgEMBTTz0Fn8+HnZ0dvGuaidpKpokTVn2C/WSiBBnt4Kg855yrdMSSL1Df5P5K+0fyC/JR7jV1C6kvulwuvBKL4X0AG5qGaV3H+wCeEdHa0WgUX2RkYNXpxBdeL56ZnU1ae87pCV3HE9Z6bQtd9t20NMw4nbhfVoZ/nJGRZKQ3NjYiLS1NFddksUypM2RmZiIjI0P9L2sF7qcTXgVw1dLhNMEnpY5DsMG+T9SdgsEgGhoaVDFWrt8fczIBwPOmiU99PmxqGqYt0P+iVULAbptKW4IygryZ8tBu0yu7w3aeXjRNfJySguc3NrAlIpb+rusnAw30JEmDngQnlRxJCHYDpvzBA4wcP476nh5VCXliYgK9vb0A9sJ5ZAgIfzudTlRUVKCqqkpV2meu1kRzMyZPn8aBlhbVEUBuFMcoc9DshPu4shKPrl1D7vXrONDRoV6XCjDD/IC9irhyE6Xibv8uC7qsrKwowROJRLB05gx2MzIwfvIkSjs71YZzHQzDwMixY9gKhzFy/Dg8n3+OYDCojCb5U3T7NqbPn0f1o0dJIbHSyJDGgVTK5HsSsdsPnbUfNKnQHR4fR2VPDzo6OrCxsYGF06cxfe4cQp9/jqb/9X9N1FcQY6ZSJHOaKQjmT57EwpNPIuPrr5Fz926SgeXxeJRgr7hxA4PnzwOahsG6Ogw3N2M9FEJLSQlKOzuVcmcP96KiWra8jAMdHdjd3YXX60VWeTnaKytR2d6Ozc1NdNXVwXQ4oMXjKHvwQJ2B8tZWDB09iurOThV+yLWWDEaeAa4v158CaPjoUWyGwxg/eRK+r7760TkbOnoUO+np6G9qwonZWWxvb6Ps0SMUtrbixo0biOk6Ur78EtXj42hoaMCIYy/3UNKmPfWFgmd7e1sp2g2PH+NefT2iLhd6KipwUBTZpLIgw6Dtwlwq4ZJJS6TcLrT4+uCRI4j6fHBvbsK9u4s6q/Am36cCRrCIVf2BPeFsF4QUlhw3mSfH3tPQgPVgEENHj6LQKsIpGfU/xCX3Fki0u2Qus72gp92okd+XtMUz/348jr+NxXAtGsVTltIPJIeuSiXoC68Xn6Sk4MWtLVy2GVGfezz4OCUFV9fW8LRVx0FeEmSW3h/uhV3RsAO9FHRyvnzGzYICRDwe7Jw9i6w7dxS4KHm3VB7l2krDlmsK7AE8vEjzOTk5qKurw4MHD1BQUIDt7W1FJ2fOnFGg+NbWFnoqK9Hb2IiLc3N4oqkJup7I6Q4Gg3C73SgvL0dRURHy8/PR0tKCra0tPLLS6egJYntTyoVXXnkFmZmZ2NzcxMLCAv5qfh4Tp0/j6s4OzuXlYWdnBwMDA3jw4EFS3ZcXXnhBGffSwJc08lY8jndME28JmQbsFTDm3kiFgzT1mmHgd3oirzlNdKl4W9PwppGcLglARc/IcyNpXUZ28L2XAFwzDJhI5PTaFV95SblkB8mkfLA7Puyf2e9edhqVird8TiQSSYpK4Ti4/pTd9ERzr8rLy2EYBtrb25GZmaloOR6P452tLXz9j/8xcq9fR25Li+K1CwsLypstx7q5uYm1tTWEQiFkZGSoMTOnWc5NRkBuicKt5DM7OztYXV3F0tKSes/hcOD7UAifZ2Xh2s4OnrJqMjHlcW1tLYl+opYHPBAIYHx8HEtLS0hJSUF1dTV6e3uxurqqWhdGo1EMDg5idnYWJ06cQDAYhGEYKCsrw+DgIC5evIjV1VXU1tZiZmYGd+/eRWZmJlJTU5GZmQmv16sAi5mZGdy7dw8lJSWYmZlRqSllZWVYX1/HgQMHEI1GkZubq9J2S0tLMT4+jrGxMeTk5ODRo0eora1Ffn4+BgcHEQwGceLECbVWfr8fy8vLqnp9KBTCr9bXMeN04kZ+Pl6cnFQ0HQwGEQqFMD4+jv7+frjdbqxbxm9eXh4GBgaQnZ2tQE3TNDE2NoaGhgYUFRUhOzsbn376qTJY5y5cwLbfj8+9Xly1aNTn8yEeT3QiyMvLU3KAxrAErdjNIxaL4dOyMsy7XPhI13FFyBnSwlNLS/g2MxMvx2IIBoPq7BG44GcZTby5uakiWOT76+vryhnIejazs7Nwu91IT09X9RlisRgKCgqUA297exuFhYUKJJF8RNMSUcChUAiBQADhcFjN9YWNDXxqeZSpH3BMEgiRcojygPKQaSF8Fj9LvVHyRV6ULS+ZJl60Ulcgon0kr5GAIu/9SjyO9zUNL1tghx3o5TO5P6FQSPG2qzs7+FjTcGVnR+m1+41f6mycK2WejFYhj5I6slx/XlJ/pDymncLoFNbQkel8lMWGYeCaaeIlw8DHmobfaom2vbKOSTQaxauGgfcNA9csWrTLNbkfkk+bpqm++7IVJWAYidQEXddVbab09HQF1hKklvqYXEv7+KUNybWQuotcIzstyD3md7hudnDNDhBJmtN1Ha/rOl6JRvGprifoKB5X85Pjkm09KaPsdEbbmPxcyl55P03T8Kqm4eVoFLCl2/5d108GGiT6JcNiuRHSYJAGPDfPMAyUd3WhdmAgsbAChechI7EzxMReOIT3YmXN9fV1GIaBydOnsZ2ZifFTp1DV2/sjJIibKA0duYDxeBxd9fXYTk/H1NmzyLbC18mIZcEiTdMUGsYqoXYlzz5/hijm5uZiY2ND9Yd1u93I++EHLDzxBEpaWpI2VIbelre24nFDA1wffpjkKbHvRUFrKw5PTMDlcmHdGj/X3q5g8bf8W9M0tBYWoqWkBKcnJnBsenrfgycVuP3u5XQ6kZqaCl3X0XXhAqIZGVi5fBn+oaGk8FRpSFGBkYrL4lNPIZqZiYUnnkDo+++T1nh9fV3lOmbfuYPREyewlZ6eKMTZ0oKhY8dQ2tqqlCdpiEllV4JDZDzlXV0o7+pKRBtoGqofPsTjhgaEZ2cxcvw4TMNAcUcHSh4+RFVvb4JRWdE33Dt7mLhkHlIRphCr6uhAb1MT8m/fVsq6pKX8Gzcwc/48ant7lZJLBVEyju3tbaSkpKCsrAyPysrwy5oaHO7vR4VALpmS0l9djX939GgCIGpvx8m5OUVXux4PTIcDvY2NyL13Lwk845jj8fiPWslKZk1mJQWeFIB2OjJNM1GnorERFa2tqBscTACcjr1ULVlEjYogz6idwXJMVIy5L/we73fo8WM8rKpCkZXbK/NT/6GAhqWlJTWWQCCgOhW4XC6lLNmrB0v+ZTeg5Fn6VTyOKU3D+04nzlkeEWk8ST5lmiY+SUnBjNOJT1JS8LRVaJf3+8Tnw6zDgY+9XjxphTDyXnIvgGQD9cuUFHzoduOl3V1cWl9XZ0wKaH5W7pO8z4XZWXyRlobQd98lgVN8Nr9vV0AkuCVBKNNMFFSTyqeUWRcvXlQGiMPhwKFDh5CXl6cUgcLCQuzs7OBfahqWfT7cLS7G/1kYcrquqxBzKq6/3tjAw+pqpHzyCbJu31ZFpCoqKuB2u9HU1IStrS2UlZUBSBQMDofDmKquxoLLhT94PLhi8YulpSWUl5djYWEBbrcbp0+fRnZ2dhKPl4oR9+YqgKti/SUt2ZUr6e03DANX4nG84XTCYxXBkmfaLvslj+NvrrMcj9x/+Xm7giMVyI8AvGsmaoKcsoU9S+XIfj87L5L0x78ZWcbnShBeyvL343G8A+DZlJSkcGVNS9RZYaQLjaSFhQXEYjHMzs4q43BlZQWtra04e/Ys6urqFO+6GQ5j2+PB1JkzCH73HRwOByabm/E/HjiA1wwDz9l4gaZpOHv2rOJVm5ubauycv0yzIKBhGIYyWKLRqGqrZpp7XQdomHwZCCTOvq7jZWuNGeEmW6kaRiJygOH7sVgMXV1daGxshM/nw8rKCvLy8jAzM4NQKKSiMenJZ8vNeDyR0siIh4KCArzzzjuIRCJ44403MDY2hv7+fpXGG41Gcf78eaSnp6O1tVWlZBQUFODo0aMqTHpsbAy6nijGfPjwYTgcDnR2diIQCOD+/fu4dOkSDh06hJ6eHrV//f39KC4uRm1tLcbGxjA5OanqqPj9fvyZ14u/3tnBi1YUiVyLjY2NhJNlYUHNzel0YnZ2VnVPYBQeAXOGTk9PT6vuKw6HA7VdXZg+exYvC+cbzxp/SyOIaQXcX5nmcnl1FV+mpeGZjQ2sixoOlHlPbW7i1dVV1aFCPoPnheOkQSjPkeQra2triEQiynCKWAWJCTKYpqlSneU5czgcSQUeeWap87Bbh5RjL5smXt7ZgaFpMK3i7bquqy5tUmaR50lZZZp70dMcC8+QrFkiIwY5V5mewjWzy0Oum90uec0ysk1beDo/R7uBz6Bz0+l04iqAazs7MAHERPq45P9ybNLQlLW5pMPLnkolnUt2m4n3l2AV78NuKvyuvL+810sAXgZgAIDQDZxOJ17WdbxkGNg1TRh6ciFFYK91pZwzx/YSgJcMA9B1wFp7uXfkdQBUutN+AIukEbvuup+847jIa+30Yqcd3ldGi9jnIvVUaTdIR/wL8TiuWGeZ68TvM9KUPIW0yDFIZ5WUvTL6n5fdDqe99lOunww0rK+vJy3GfsQnB2ZH2AzDwMihQxg+dgzVHR2oHRxUQn6/CckF4P88dGzbRg9d/s2bmDl3DuWtrUnojzxg+yk6EomqbGtDX2Mjcn/4AV6vVyFy0pCRYxkZGUFnZ2fSQbY/k3+73W5UVVUpNHZtbQ0AEA6HEbTSJYbOnEHUipaYPH0aFa2tyL13DwBQ3N6Ond/8JmFch8NJHkRJ/JKIuDdyDnZFRSqJ3M97JSVY9vlwu7AQR61uGPzhvR8UFKDlwAEcHx3F4fHxJCVPKr1ra2vI/u47TJ87h/SvvkoUWzt8GFNnzqCkpQXFHR0qFI7oM8O0NE1D0e3bGD1xAqHPP1eKJZU4IHGIiJjn3biB6bNnkX/rFsL37+O0ZRxv6Xs55HZvqGS2QLJhLBlCbksL8u/fx9d/8RfYSEvDQHMz0m7cQGpqalLdEruyLw+hvYidVGoNw0Dl48co7ujA6uoqoo7kFB+n04myri54U1LQdepUgp56e2GaZhLQAEABWR6PB501NVgNBNBaUYEjU1PKePV4PMjPz8cnTU3YskC9rro6XLD6QD+srlYRHIW3byelQXGfuNcyRFfSk3xNKrxUECQIybNOfrDrcqHn5El4PR4cGh1VHkIAeFRWhpvnziHvxg2UdnaqdZxobsboiRPYHh5G88RE0vlwOp2KZ2RmZqqCZnx2TX8/Sjs7E/nRjuRQ0X+oSxoE4XAYo6OjSd4TIDn/kest6VJefM8wDLyl6/i1YeBlM1Gc9X2nEy/t7qo8UF7kUa/s7uJ9XccrhqHqXnC/XjcM/FbX8SqQVOMiHo8npdUAyYrSx4aBaQAf6bpqJSkBKfuY7fzJNE2cmJuD/5tv0NnZCc3pTFJ+95u7BD3l61JxdDgcyuNrB8o9Hg9KSkpQXl6elM4BQAlpr9eLXyBRCPNVi463traUly8rK0spYYZh4KF1BreefBI1/f148skn4XA48NFHH+HixYsAgNnZWRQUFCAUCsEwEqH7byHRNu1VyxtjGIYKo6UsdLlcSXn6UrmRSoPcG64LeYEM5+R5MYzkrhnvRaP4bSyG10wTz1vP53e4xqwD8dr6ugollxe/84Fh4H2nE1e2tvCM6DcueaN9f3Vdx69TUzHtcOCdeBz1a2tITU1FeXm5amO2vLys7kHlWfJ7SXf87XA48JnTiY+9XlyNx/GiNWd6k+V3dV3HOx4PpnQdf0hNxVvxvVoOpO2xsTG0tbUhKytLtTNOS0vD8vKyAqmGh4eRnZ2NiooKBcYbhoE3DAP/YWkJmVZxUY/Hg+HLl7Hq9+P3hoFXRQ0KPpP8gvyR+xEMBpMiL6WsIx+UXjSeazkXTdPwi1gMv4rF1Nnn+/ye3Vjh+dzc3ITD4cDw8DBqa2vhdrsxOzubiFKcn8fa2pqK+KDBvbOzg7GxMaSnp2NkZASVlZXY3NzEgwcP4Pf7MTU1paIYdnZ20NvbiwcPHiAej+Ps2bOIRqN4+PAhioqKsL6+jnA4jAcPHqCmpkatMTtStLa2IhAIYGBgAFVVVbh06RK6u7vx+9//PiFry8pUtATTA9LS0rCwsIDs7Gysr6/j1UAArzidWDdNDAs5trm5iWAwiOzsbOTl5Sm+QuAxJycH6+vr2NnZUXJofHxcOQ6YEkQl3vfVV/i/FxYiMzMTsNGjTIuUOpzkq6Rzh8OBp9bW8JTl9d+0gEMagNQL5Pd4cSx8pnyeHXwk32TNG76Xn5+f5PBaX19XLU/p5adn/I+Bn+zWI41AeZ55MRp0P513P8eBLIor6dlu59gddpKfkJ9yb/b7jNRd+Tm7LOQakqf+xgJXn3XstVG36wFSj+LfMiKaNMDnyCKcvIdpmqr+hVxfOX7SC+8jgVxpH/B/aY9IeSE/xysu+CkdgZK25b6RX8TjcVWkX9pyco0oyzY3N9X6kZ6njhxB/+HDeC8axVMrK0l8UMojuVYSXJAyVq6DlJ329+U5sZ8bOX65l4axF63Ez8rofH6GeoeMIiUtSVBQ0ps865Iu7PtsP5P2ef19108GGqiAyoFxcvwb2At7lwvA3wOHD2MrFEJvYyMaRkbUZ7lAEmCQiiCfQ6CBC0LBln//Pg4+fpzwWFoHRXpged/9EBkuZEV3N4LffQcA8Pp8SUAD50emzDHIDZXrIItdcSNKS0uhaVpS3Qmmj4ydPIlYZiYmTp0CAGxnZGDwyBHk3b+vFB96XJh3x+rhDJXc3t5WwkweZskcgB+Hjsq91DQNJ8fGcLu4GCfHxn4EJHGuLRYY0XLgAJqsz0lllfuzvLwM88MPkfPBB9C0RD7W1IkT2E1Px8jx4zhgteGkYOCacl/y7t2D/sknCXqyVfrm3GgEp377beLzuo5YfK/lpGzlyNxgos9ScEha41x3d3cxfvgwxk6eRPHt2yi4dQujx48j+MUXqrYClXU76k8alGvHdZaCSDIongPu2czx45i7cAG1XV2o6e9Hy+nTWA8G0VFVhYrubiWkeW0//TSGXn4Z25EIlrOzkTk3h3g8jsqODszMzKgoEBo0Fa2t6Dx6FABw6PFjmKaJtqIiRF0uuDY2UH79Okq7ulQFdU3T1LnYT1DZhRLXk5+XOWISEJNnqL+pCVELEe+ur8fR6WmkpKSgq7wc7QcPIuZ2Jwp3nj2LM4uLyvCdOHUKm6EQ2p1ONI6OIhwOKyDSNM2kEE8ZNkl6k8ozPUP/kEBDKBRSudyjo6NKOXA4HMjMzFReB7vCIwWBXHN5Vl92OPCGxVve2t3FFIAPPR7Vhouf43dejEZxjQakLRTuxXgcV8lLbOCLpHnpYdJ1HW8B+LVh4FUjOUpBPlfSi2ma+FjT8DuHQxm05HOcpyymZPdaS8VbPkPKACpeduHIc6xpe5ExdtBQKgcvAHhyYyNh6Ot6YtzFxXjNMFBgFQNbXFxEPB7Hi4EAPtU0eK02W6yLcPz4cWRnZ0PTNLQXF+N/9/txbWMDl605H4vHccQa65R4vq7rKC4uhtvtVsXzpGdfKijSIJA/NLB/53DgOStdxq7Acl8Mw8C76emY0RK5xOet6BQa8uRxv7HypH8D4EIkoj5jj0p8Py0NM1aEzGVrrWShLO6HpG/TNHF1ZwcfeTx4cXsboVAIhYWFqK6uVnxediWS3jX+b99zvvexVdjqo5QUXBMAA2WEPH8/MxPRQm/oOry2cGryYeb7MnqKKQVzc3NJOktaWlqSE+SlWAzjv/wlFhcX4fR44Ha7cWZyEm2VlXjTArikt9Re1JXrput60lpyfTgev9+vaitQ+WSHI86H97timrhqgbSkG66rNCB4NgmElZSUIC8vD3Nzc9jY2MCBAwdQXl6u6HR1dVVF7rC2A/c6Go1iYGAAFy9exP3797G8vIzi4mLMz8+juroaHR0d0HUdtbW1WF9fR3d3NyYnJ/Hkk0/C6XTihx9+wLlz55Ceno6f/exn2LDOqWEYyMjIQDQaxdTUFAYGBpCfn4/y8nJ8+eWX6OzsRH19PdLS0jA9PY3h4WE0NjYqQCIlJQXz8/MIWG0bZSSArusKJCCYYZqmAhoIQhKkZxSDz+dTNRV8Pp+qJ0FaMs0EkL9iGUGSl33mdOJjnw9XtrfxrMijlilDbLm7tramZDJ1V4ILfJ1FMwlgyr0lOEuD/EPTxH/e2cHl1VW8aHMaUB/ivOV9yD8lcEFAjrrc366v4+Y//afI/v57ZN+5k+QkWj53Dv88HMaV7W28EE9OuaMM4nmUOold5kjDdHt7G1/5fPhDLKZSB3kfybfsHnH5nqZpqlWrXT7yPvF4oqAjQW45dnlJOfrrcBgzTid+HY/j8OxsUp0kGcnCaDrJs+X7PNd8jc+R/JZnkHyGn5d0GI1GEQqFfmQsy3WQ85f2ImWBPeLKXrdA8jhpx1Ank7qQrutJBZClHsloC1kvgRHimpZo8zp07Bi209Lw/s4OLuzuYm1tLene8p7SjpTrxrWSeyFtXj57PwOe/Nce7cLnsTsQHbAcO3k3ny11R7nPX3i9+H1qKp7a2cElq/YgeRbvI2Us7yP3AtjrHGJfAzsg+Xdd/0XtLfczLuQDufAU0BI1MU0T5a2tGD1+HHWWkcS8Qfsi25mB3CQadR6PJ1HjobkZYydOoODWLRQ8eJAUHsSUB5mTvB/gACSK0PW+/Tbyb95EeVdXkmInD6LbqgjNjaDCxfuwzyoPCIUcDTQaPD6r1/TOzg7Sv/oKkWeeQdn9+zABjB4/joq2NjV2udYshBUIBFQVdgIQXCPJBGVFbs79YUkJHpSV4biVM8nohOaJCTSNj6N5cvJHxCQZ4onRUdw9cAAnrBBjSQdyr7KzszE7O6sOcEpKCnKvX8fCpUsosSIO5CGmh1QCTjw0NL7l4WK0CJDIDaZAZ544xy1zDMmAmF8qaYLCTobSjp86ha1wGMPHj6Pif/6fceCjjxLj9HqTFElZKEvulx10sAMNnNvkkSMYOXYMmd9+i7Tr1+F0OjFx+jSiaWnorK5G+MYNFN2+jZFjx3DgwQMsLCzAMAwVMqvrOnauXUM8JwfjmZkwrbV98de/TnhDheCnAlI3OIhGy8jVdR0RlwttZ85g2+tFaiSCw+Pj2ElJUUr8fkaLnWHtt3dkulKB4m+pGBmGgbIHD9B35gw0TUOdVezO6XTiYVUVIoEAUra3kba+jmNjY8jIyFB0f2J0FPfLynCorw/xeBxZWVnQNE2F6bKgWltREd4rK0NlezsOW60Mu8rL0VpejuzvvkNOS0uSQWZXBP5UF5U5mSYBQNG03eCTl92zst/n+PebeiK64Q382Fu8n8Et91Ma3bq+F4pKBUFWopb3MAwDL8TjeNb63K4Al7i2diUlFovhvWAQM7qO38TjOLe2phQoronf70/qoAJA1bWQIa4SMKRhR0WPXQOkHOD6U5mxrwf5GWla0pVpmvgoNxeLTic+iMVwwSrMRbo9vbCA9B9+wLe3buHSyy+rNSwrK1P8rCU3F7NOJz4CcMnK57afJ9M0UVpaivz8fGiaphQsRsdJmpJKIufOfeV5+W0wiBmHA5/5/XjOCkOV85Py5NrODj7SNFy15IjcZz6Tn7lmGVK87GDdS9FoonCakEmGYaiaFXJvpAL7NoC3AcR9PhiWMc+IAEkPUt+Q51feC9jrQPW6aeI38Xiis4OVq0sdhuPj9Zam4UULaNlP6crJyVEplTQ0HQ4HiouL1ZgOHjyYBPxLmuX/XHvvJ5/gX/3iF8jKygKAJH2EsoeGIfUMKrucB5/lcrlUocLx8XFVSJLtn3mOeb7tynPUtq+skaXrOj4G8I7Hg0u6jiesaAXqJVNTUygqKlJ0QscIK7qPjo6qcHrqMaTXrq4uZGdnqyKOhmGgoqIC/f39uHv3Lurq6lBUVITFxUUMDAwoXuDz+eDz+TAwMIDFxUUlzyn7GRVBoGJmZgbV1dVwOBx49OgRlpeXcenSJczNzSEUCsHlcqG0tBQul0tFhPj9fvj9fnwE4Jf5+bi8vIyz29uqTos0tCXQHosluoiFQiE4nYmi47Ozs+jr68Pq6io2NzfV2sZiMUQuXsT/UlmJazs7eMJKRTBNEx+UlGDe6cSHbjeOWvUzeF75PLa9JK+KWw4YjoNpLuSVLFJK3kJdl0ASz89/CgQwpWn4fWoqrli1GKiXyxpAUu8mPfIe0tEjz/qDsjJse72YOXdOOf4oMx5kZ2PL5cJHponTk5PKBuA9JU/na/IMSyDA4XCotr2/t0DSj71eleYnIxzsMs6uX0ud1A4g2KOPabRJI03yEmlQXtnexqc+H96weIhd17bzHzswInkfeYr8nK7rWFpaQiQSUbJc3otAMu0t6szUByQYbDeE7bog95y6uFxLyl63241wOKzqNcj1lOskZaNdp+GYGIFvt4dWV1cxNzeHcDiM+q4udNbW4tzKCjypqUn2E+9l32/Jo7n/fI2yS/Jo6TACoFLwSReFhYUqotsux+S9pF7Ge/OetDXttu3/sLaGOdPENxkZeEnch+sq5awEkTkfrgHHK+la7sVPuX4y0CDDKqTxKQ+MJHx58LmIVb29OGz1fo5ZKLBkFPtdcmISoaKQnDh1CjsZGZg+exalnZ1JB5oKpJ0B2e9tmiZ6X3kF2xkZmDh9GrktLQoxmjp6FGMnTqC8tRUV3d0KKOCmSzQe2PNscB2kcs3xkACdzkQ/4eL2doSGh5GSkoJ4PI7Szs6EwmutIdNESMjhcBiZmZk/AngISvC5JB57oZn7paVY8fvRUlIC0zSx4vfjbnExqvv6ACTC01vLy3FyfByHRkaSAJ8HBQW4W1yMo4ODiGsa/u3p0zgxMgITQEtJCXKWlzEdCuHA3bvIu3cPi4uLihGnpKQgtaUFBQ8ewO12Y9PpxPSxYxg7eRKVbW0oamtLCikigUtaszMpqWhy3yVKaprJxVC4FtxfecB0XVdRIUQT67q60FVXh6KWFuVplozEY3mf9lOOpZFNBiuNctKDpmmJGhPhMOYuXEDgm29gGAbSv/oKi08+idDXX2NkZATemRk0WSDEugC5/v+0/WeQHVeWJgh+7v60jHihEAIIREDLgBYkABIEARKEBnUKZmZ1dVdPV03N1ljPTk9vr9ma7czY/tltW+ue3sqqrKpMpmIyKQBCEVQACELrAAgVQAAIBELrJ+JJ9/3x/LtxniOymj2W6WZhEfGE+xXnnvOd75x7LtenZ/9+ZHfvRtXgIOL19Zhx+bLK4qCjp2kaxuyIo7PNpmmi8fx53F+2DNMvXsTVadPwYMUKTDt3Ds03bpREyiQw5v9OAC7vLZUm51BGOB4sWoQHK1diztWr2P2LX8CyivvpLbuYz5K2NlyeORPL2tqw6OFDmKaJMRvU63qxPkmNXXiv13bAqCT9fr9yRC82N2M0FMKdxYuxurcXLpcLV2fPxlgwiPy6dZh+/fp/kwL9P3ux36zNQNBOEtI0i8WSPtA0vGaa2CGMkGyb1G0SHFG3bjVNvGQDwTEH00552GeaOBqJYEc6rao/O42ezFDJZrM46vXimMeD7XYKPAAc9XhwMBDA9vFxvGg79CTveC9ZW0GCPgDYqmk4HAphq739hw7KzJkz4Xa7EYlEVGSY9yAYoixJQE+w7LX37SYSCXg8HhW14TWZgaUMOKMMMhuG63hPLof9enHrSTQaVeNEAM6U1ObmZlX4TDqwLF6117LUCQ6yj5RjZm9ls1lVn4hz7/f7SwpwyX44Ix2WZWH7+DgO+v3Yk8+rPbXyftSDmqZht2Vhdy4H3eUCxPxJoncPgN25HEzLguE4qYb9LRQK2GFZ2GHvLTZFKitlmZeTDHICMDrXlB9JWko94wTjznWzvVDAhrExlJeXwxTzLoEcn8E55XzKtUE5nDp1aklWFOeDxz1SJ/KYQwZPPszl8Pk776Dm669Rf+kSLMtSRa9DodBTa9EwDCSTSUSjUaU7JAhl+2Vm5sjICACok1c4hsPDwwoQf+rx4IDPh+3j49iUTBZPKgiH8WoqhVfsyPghw8CHhoEd2SxesQnCPsPAsaoq7NImjiMkCc+5px4oLy9HMBhEOp1GVVVVSeCG45XJZPDo0SP4fD50d3dj9uzZqgbEzp07cePGDei6jitXrqC5uRnRaFQVkozFYvj4449LnDKePBGPx7F8+XIsXrwYhw4dQk1NDRYvXoxvvvkGgUAAGzduBFDcKjI+Po58Pq/qakQiETz//PMIBoOK4PnN+Dh6XS58HovhpWxWkSbMHiE5AUxEBun4UY94PB6MjY0hn8/jmWeeQWtrKy5fvgzDMND3/PPIRCL4LJfDFjv44XK5sDWRwFFdx7bxcaUzZHSXdoT3d7vd8Pl8CndKPCrXi8TPzH6U+751Xcf3TBPvWRZe13W1153YTGZXyS1qEp/JSvZsK/H69lQKv0smUX7sWAku8fl8WHznDh6vWYNX9YmsLrnvXGIO2Sep/7i+s9msyvT4oWXhvUIBr9uYT26TkzhO+iK8p3N7lgzCSFtL3SHvQ+da6hKpp141DPz3VVUlzq+0R3yuvJ+MSPM+TvsodYk8jUb6ak4/jtjZSbjL8ab+ZfCE78lgGwkm6ezyOX6/XxW+lfM3mf6WfZdjwe8xa1f6ZgDUWhkbG8Ocu3dRcfo0WlatgqulpeRzHAsnWSH1q5wL57zK78o54Xx4PB6Ew2GUl5erNvNyEim8JHaS/ZbPlNePTRO/GB/HmzahSf3Ltc85kIEl2VfKsRx755g7twP9oes7Ew2MSsvJdg6IZLV4OYEaOyfBmAShUhlTSIEJYoFFg9iOaWfPomPNGjTalZrlaQwSLMqBYnv42zRN1J86hcdr12LK11+XPP/RqlUYj8XQvmIFFra3q6JO8mhN3lcKtuwfGTqZrsZJ5ZFozAKRbZLkhRwnXdfVEZlUilLI5fYSjiuFRtOK0d/z06djdUcHTNPEhenTsdLeC+92u3F5xgwMB4M4O20aFj14UAIAz0+fjpFAABebmgBNK5IU06cDAEaCQYz5/TB1HfeXL0fdxYtKsXAO+/r64PP5UFdXBwB4uGoVxsvK0LZ0KaZdvQqXy6VS6R4vWYLHa9ci/OmniJ44AWDiZBKZIWBZlgLanO9wOKyAOfvOMSY54Jw3WXuAUYDGa9dQcepUkegRLD8VM8dMGnjOg4wm8ngrOY/8TC6Xw5xr13C3pQV1584hHA7DsiwEr17FtKtXiwbaPtVC1/USx4BRCcuyYBw6hPozZ7Bo0SKldExMbGciKSGNAJ0hKvWKU6cQOX4chUIBl//mb5CprMSj1atRc+6cYu87ly3Dw5UrMePyZXVSimRFpZJyOrYcX0kKPVy0CLc2bYJlGPh29Wq0LVuGuXahzXQ6jdHRUUzv60OTTQKktFJmVToz8vQPuZaYlbH49m1cmz0b87/9Vs3F0rY2XJoxA9OvXHkqRflPdfEceYJDTdMQCARQXl6uipq9HwigW9PwvmniORvATqZ/nYb2kB2d2RqPY7NNNDmdV0k+flpeXoyoe73YMDKi2vNVMIgDgQBeSSTwoq23dL2Ymv1VZSV6XS4cDASwzZavA4GAeo3kA4AS3cY5cxp1oJiqvcs+5z2lFyu4c3sYSQM6l5xX7jXn0Vr8kfqeZ64bRrFQlQQh0ug62+XU7YVCYdKtQ28B+J6uw3JNVIbm80dGRnCtsRG3XngBp/1+vGqPhQQFr+o6dhcK6sQFqR+4ViXAo74hiehyuZQNcRIKBAM8epJ67+XRUWxJp4vn3U9iu2T0XIIZTdNKHG1nUS4Jip2XExwecbuxz+3GLl3Hq0Incf5kUIHzIZ0VOceSZOJcyq14QOlRu3yftpx1GTh+tNVSZg8A+K1l4aVkEq8UJrYkyjljG/g8iXvksyVWMgwDnweDGLeLQca++QaGYWB43Tr8j1VVeK1QwBbbJsrMBW6ZlGQKABW9Z9RWrotCoYBYLKbml32nnfrE50OvXbfipUwGn0Yi6DUM7NN17LD3Ce9zu9Gr6zgcDOL1fB5vGQZ+Z1nYaZ/a5HK54Pf7Va0gSXjwdCNZ2C4UCikSpqysDNOnT0c6ncbY2BiqqqqUXrxx4wYaGhrQ1dWFFStWoLm5GQsWLMDg4CAuXryI8vJyTJ06Fd3d3bhz5w6mTp2Kbdu2qWM1u7q60NHRga6uLsyYMQMVFRVoa2vD7NmzsW3bNnVM7JUrV3Dnzh0YRvHo5KVLl6Kvr69I8CcS6tQIXdfxWqGA9wHsEPVGmEHBrbGS+KQzSNzg9XrVkZpnz57FtWvXSt5vvngRAxs34hV7jzkdl625HF6ys6cyYq1yvTCIRWJiMseRGbPyuFbKtK7rGB0dRSAQUCQFdc33NA3fQzG71tQnCvhRR/A+0vkEJnAIZZSnfEmybrNpoueXv8STJ0/UmjFNE9XV1dgSDmOljb8lyeiMWvPi2nM6S7R/zKL6nmXhbbuN8WnT0G1v1+R4yUweJy6Q48n/pb5i++Ua5fPl1hXiL6lPwuFwCQnhtGtyXfNZ3Msv2+vEnLJtfr8f06ZNQ19fnzpVj2Mm++0kQZyEhTxNI58vPUVDjgvtqMyukxlAcnuDs8+yL5PNN8fSOTYS62qahoqKCnWEOLcf8hkkBYhNJTEnsYKULep52W7pD8jx5zxrmqZwvDP4OJnNkGMux8BpG/l5TStuoX01EoFhGIgbBoaGhtRad+Iv/i8JFPmesz3OOh//tes7Ew1Lly4tAVA00JqmlaRmUYD4Pw3Z46VLi1Xzr19HY2urAmuywVRYHEg5iLquq7Q1yZzWXbqE6nPnFHsq0/6cwNEZIZF/T71yBZHjx4uK3z4uTNd1zLh8GQ9WrMDsa9fUwmO/JNCXykT+L/fOsE9lZWVKgUgD4GQcuRBYGZUKanx8XB0hw7SbwWefxcjmzZhpZwbIe7OP/Gnp6MCSx4/VOK3o6kIymcTg6Ciqq6uxyiYiVtl1NOQiWdHejgtNTVhtb7s4N306VrS3A5aFC83NmDIygu5oFFPtrRHhcBiDg4OKrexZtQrdW7fCffUqpre2YsalS8VItm1g5fO6169HvrISia1bUXXmzFORSPaPDoBlTUQ4AaC7u1tVnKbBpREmMOf2ExquaDRaLLwEqHvxezwminNFImT2tWuqZoKTaHM6vc5FyzmZ8e23mHb1KoaHh5F3TRTmojKhbPt8PkVEUFnx/dzLL6Nn717U3ruHhe3tTxkZaRj43EwmU1JoZmxsDN0rV6Jz7VoEHj+GpuuYevZsSZr8g5UrkSovx71lyzD1yhXVR0lu8ZKOsZNYJChrX7kSlmEA+TzMQgGJaBS3Fi3C1CtXSoA7x4L95f2cxtdp9AluNU1Dw+XLqLePKE3ZQLJpYADpdBrXV6yAx+3GjJs3FfD9UxEOXNOxWAyrV68uieqxEvzLuRwOh8N4KR5HMplUwJxzOVkEBwA+icXQ53bj02gUO+znUWb5Ny9d17E7m8Unuo5ddnSb+upQIIBevVj8bo9RWhX5NXPiCCka492ZDA7YTiOjsFLm2G6ngeR9OdemWcyaYMV2CV6ZeihlSDq48plS9ljTgG2h7pCRan7XCfjYZ0ZUeTnXsvy81E035s9HJhLBwUIBbzjWoPzNvsgxI1npnDcJ7p2knrM9LLgrQVE2m8VXwSAO+XzYlc1iS27iSDzeU/4tAQyBJbMrSKZPBnDpYH0RCOCAz4dtqRRetGsEfFRdjV7DwN/7fMjH43hZOPYy4nLU68XhUAhbRkawzj5FKJ1Oq6wOZpQ554Xjb1kWvgwGcTQSwYtDQ3jOPmtcyuLY2JgaH56tLqNSAPCbaBTdhoEj4TC2jY6WEDvykmMv9bgTpMr6FW9aFv5xaAixEyeUTbrz/PMYC4VwyDSxE0VwR+eD2EkSxVIf8pnMdqB9Y2HCeDxegglYDHB7KoVDgQDW9/Zi3LKwMxDAQb8fbwgS923Lwm/zeey11/7rXi9eGhtDIpUCjImtNwDUFk+uNTk+lCOJFwcHB9HU1ISRkREVdV68eDEu2dtir9o2ksUW58yZg0wmg/LyctTW1qpCiwAwY8YM9Pb24vr16xgdHcXUqVPR2NiIZDKpyIKKigr09PSguroax48fR2dnJx4/foxcLocpU6ao9k+bNg1fhULYN2MGdmWz2NrTg0gkghdyObxoY9PhRELh00KhoDJFnE4I1xTHIxwOIxgMKueGW0ABoOLUKex1ubBs2TLktNIUZxYNTafTSKfTKvslHA4rgol6gmMtHQU68GNjY+jv7y9xgigr3d3dmDJlSonOZp+oA6RNIu52Zi7w8zICynXH9U7/IRgMqpofJMZ27NiBJUuWqLVDOSKhJnWkM/BGOzAZqex0lkj0x22bK9eTU7fxh4Qd7ymfL+2aU0fI7YPSdpEQD4VCT+Fv531lv/gZucZ4TebA8rMejwf19fWIx+MYHh5WQdS0qFfBOWcf5ZYbaWcpZwCe8smAicKbsg+WVcxyYn8pn/KSNlI+S47LZDZV/kgs53K5VDH3ZDKp8AFll74I+ykDemwf+yWzsKU88n+ZHce2fBUK4f1CAT9MJvGGz1fiV0iCjvLENknZk5kqUvadf5umqQhDbh+lP0msJe0JA6n0faTvKrdv5ByY4Z+7vjPRQEGVgDgYDJYIPz8nHQ8WCLr8/PNIRaO4tXgxptqRawksJdsnG8//Gfnl8X1UZJZlKQJCtpHOoTTyUklLYoATKYWWkzXt6lXMvHmzmDZup5PKaItzsUsB52fJTvMcdqblkyyQ/XQyhplMRkX4neMjFdDQpk3IVlaifcUKNNpOu4z2Ohcfv8exv9HcjAtNTVjT0YHlnZ1Y1tlZVDZ2vQMawbRtwEzLwrLOTrTYWRGWZWH+/ftwuVwYHR0tMtJCkXDcE6+8oto5vbUV01tbMevWLQBAQTgQADDl5El0PvMMokePKieDBUE5BpKckECGZBCdaM6/U55Y5Ijy19DQgA0bNqhFRYXC55jmxPaXR6tXIx2L4c7ixag8fboE/EungW2TC1euFyoima0jjRbbImWZe0wZtbAsC+ldu2BWV+OGx4PmGzdKQA3n25l1RLDM9WhZFnrWr0emvBwulwsvv/tuUWnaa13Xdcy5ehV3ly7FLLuOiBPkSwMkx0A6D0DRCehZuRIFnw/uRAL1n32GgmmiZ8MGhO3tImS5ObbMWJJrQcqMfI3PZxuZRUD9xTYYhoFr27YhFY3i5sKFcB05omoC/KkuGlVd11W6oBOM7tJ17LDTOzV7TzNlW5IqJAbYn93ZLA4aBl41zZL91xwfCa50XcdeAHsLBWguFxAKqXl8A8D7hQJesyaO/+L3t5smXrGjqbDHcls+jz02OWeJSLITCEkd6wThlHkZYWB7mHIo9S8vjoszvVDelxfHj/eRxJUEifI+k0VVnI6j7BdJymQyiQXd3bgycya2ejzqFBwJQCUoZr9M08SXgQAOh0J4OZHAxnhc1VaRoECSCdJJkDJBMppjBBSjkftjMfQbBn7pcuGDQACbh4ex2T6yWQIor9f7VGFESc4w+i/3+cp50TQNB3y+4hGqgQBetLPxtqVS+EUkgoKm4UAggBcHB0vAEe9/JBRCn9uNY5WV+ElFhdLZlmUhFAqVFFiT8yQdkP+QyaDPsnBiyhT893YGngT6Xq8XwWAQyWQSFy5cUJF5RuINw8DrAN7PF4vGyfUjo6bss+w7LyepJQmi7YUC7v3936O3txeGXWPgud5eXAqFsMeWiWg0WrIOdV1X2Y4SAPJ9CcoJZAuFgjrZgOtJOgIvZ7PYPD6OZDaLzt7I2Ld5AAEAAElEQVRerDVN7Kyqgs/nQ94sZlHssCysGxsr2hx7zuPxuKoBwSyt8fFxRKNRJBIJFTWkHWZEWx4HSjz3mdeLdxsbEdm2DXP7+zE6OorKykpUVlaqgo6apqGurk5tcchkMurEl6qqKmzevBmzZ89GPB7H3LlzYVnF/dAjIyMqsNDQ0IB4PI6ysjJVo+Hbb7/F5s2bYZrFuiGxWEw5ke9XVWHY68VBw1Db7nw+nyKNuT4ZDWeUmEENyil1Gce8uroahlE8TpeZVyQAWKCRWwFlWn84HFZbZ2jbUqkUKioqlKMoCQLqPBIO+XweT548Kc6t2CoBQOF2r9eL0dFRjI+Pq21fMg2fZIMMyLCfUh9ZloXBwUFYlqWyOKR+kZ/ds2cPhoeHcfjwYXg8HixfvhzNzc1KJzkdTIn9uabk+pSYRBIMvAf1DfWax+NBRUUFAoGAwobpdFpt05SYjvZDrkmpC2hbaLckFqNOJb6R2XLEB8QrtG3U+7L9Us9NRjhIXW5ZlvLbKLfcEstTchjs5ZYfaUMlBpUY3Kn3JR5h2+W48Dtsey6Xw4Ad8OFa4Xjzon4gmcegCLc4EaPLunXMZHXieW7tMgyj5OjzQqGgxkCSJMTnvC8DHvI4ULl1XWJq2VfKz3uJBDotCz9PpVRdG85VLpdTxUVZlyqbzaosbY69JMEl9qAeJslPuWE2HL9HPVBWVlaCRygvktjg2mDmKP+WNuafu74z0cAzoQnknKmV0ohwsck0nJlXrqBt6VLMvnZNGXEqCN6T9+WAUHA4WGTme1atwv3ly9Fw+jQWtrejoqIC4+PjSpHxPtKo8zUpLMAEKHi4eDH6n38eVceOYdq1a0oB9KxahSfPPou5tkNMkCOPj5IAVf7mOcUq4myPRyAQwJPly/HgxRcx/cIFNF67pvrvBHJchPIZTpDj8XhQfeIEBjZuxAy7zsFkhMJkoJvvX54xA2PBIC5Mn44ldhaAbDNQBCvX7KJ8F5qaVCFBCcalApKkCJ3EaWfPovOZZzDj8mX1fDlH8rXK06eBTz4p3lvsGXaSOhxrgi1GBNhnEju8CMp5P7n9hKCahWvo1FsOZ0vXdTScOYMnzzyD5kuXVMSQ/ea95UKU6WUEfZIVTyQSikWn8acx6165Ek+eeQYzLl3C7Dt3kMlk0N7erj6vaRr8Bw4gvWMHwsePI5vNIiScRrkW+D+dBFlF2zTNkrUqyTles27fxqzbt4t9NSZSLSlbUladjD3liH8/WrMGuWAQgeFhzL5zB5qmYXpra1EJ+nzw+XzqmCxN09C1fDlut7RgzrVraLKPl6WBkI6fBN9U3JMZaSrVmhMn0L1+PapPnlTgV6Za/7EvKux8Pq/OJWeb5DjJbQdOXcM+S2MDAJvHx7GXhl0vze6S7Lecq8n6ud00sckumGXqeoleksBOkqrSMAOl22Wk/DmjOaZp4lOPBx+73dgxPo64puGYqKouiQGpZ+R6BCYiJvysNJwjIyNwuVwKTHOspf2RcgpMkCRf+P04HAphRzKJbaaJT91u7Pf51DGN7CttFXX2wMAAGm7cQPr997Hur/8aSbsOD+/t1NFSpzFt/fOyMrxlpwxzvJjdJCPz/B7/Z79YXJTrxLIsVFRU4I1CAR+bJpIuFwY0DZ+XlWGzXTeAAFY6/rw/2y5tnYx4kRyV657FIvfaDpxpmthlmvCk09hn1/qQ9yAGKBQKeB3Afl3HDzwexGw7QHJDZjgR0EkgRoD/dj6P9woFfN/e+iYJV45XNpuF2+1GTU0NHj16hClTpqhAgGmaeCWfx2obY2Tt78vz1+W9OAaUJY6LPGZMOh5cx1zj2WwWvsOH8f/auxfV1dXwijRqfp76Qcqy1B0AlB1hxXvK6ec+Hz4vL8fbhoFNgiBgdqrL5UJVVRWGhobU1k8596ZpIplMIpPJIB6PI5FIqPtLhwwoOjDDw8OYOnUqAoEAEokE+vv7USgUMGXKFDz//PMYHh5GZ2cnysvLcfTZZzHm90PbuhULL1+GruuqKCPXC20px3Pu3LnKORofH8eUKVNUYUMGekZGRpRu6OnpwcqVK9X89vT0wO/3o7GxURUZzOVyxeOmbbnYHgziEIDn+vqgaRoGBweVk5ZKpYo1qMJh+P1++P1+lZnGWg1sO2tXkPDIZrOYNm0aEokE7t69i9raWkyfPh0HDx7EqlWr0NDQAGCCPEylUqpwJNcp53psbAydnZ3QNE0VxCNuoXNPh21sbEylkA8PDyusxyxA4ld+dnR0FIlEAolEAvX19aipqVFZZ9Q3zmwwSXwRh8kgEL/DsaBTzi0kc+fOxapVq9T87bcsfGQY2J3LYYs9t9ImABPBJ64pScpJbMd1IjGqtGs8ESSTyeA38TguNDdjbz6P3baeocMpC8BTj1CuuDWFBAyxJ3WPDEBpmlZyXLnc9mSaxVpTmUxGBSh5TG0wGFTZHxK3c8wl6SgzUJgxxK1L3MpjWRaSySQ+/vhjBINBNDY2YurUqYqgoExIXUv94MR+El9w3jkuPKGH/qC0J16vF+Pj4/DZRddlhgGde9a04zN4FG0kElEkAkkty7KUjmLWUCwWU6fehMNhXL58GVeuXEFdXR0WLFiAmpoa5StKH9JJkEgfRNpAyp8kGWjbfmya+HkqhR94PDAymZKMCup2ykWhUFCZaLK//CzxBnVBKBRSY8j1yCwvzr3bLqr6m0IBa+7exQ8iEUyZMqVkvchgPu0o9ZC0c9/l+s5EQ11dXQlQYQdk2pvToSHDms/nUXfxIhbcv18UJtfE/k650Nl4TqzTIaDAti9fjmRZGbrXr8c2G1CwKqoEW5JpIqiXC1cysa0vvYRcWRkGN23CdLsYo2VZ6LRPHbjT0oI5tiMkmTHJ/EhGSJIOZI/8fj8eP36MeDyOsf/pf0K2shKPVq1SWx2cmRwEcNIJkAtbjl3FqVNovHYN5eXlyAoAxO9IACrJBhqBFQ8e4EJTE1Y8eKCUIPtJZWVZFpa0teHa7NlYev++Im0o/GyLBMRkalnbYLq9997j8YCJdZKppjBzsfEZg88+iwcvv4y6kydRdfasembfmjXo2bAB08+dwzRB2Mj+SkeE7wETp4bwNS5aLlye6EBQT6KNQHDqlSuY3toKn88Hj21wuCakPPOHCo/vcZ0QrHAB8zmSRe5cu7ZYK2TlSsxta4PPdsIlQMAnn8B/4ADyPh/O+/0lmT2SqON81NbWIhKJqPRFztW8tjYUCgXcXbkShUIBjdeulTiQEkA4yRVJLkhZlsy6nIPZV67g7tKlmHH5conM8qQU9pP3ubNkCRKRCNqWLEHLo0eTklSUH2cEQeqbu3Pn4uGqVWi6cAFlX3+NspMnET5+vAh67L2rkjH+U1yUYV7S6abMmqZZkspNQEcSRMoR12s2m8WXwSA+cbvxaqF4+gPnRM6N1FnSUaMMHnG5sN/vLzr7dnRDjq1sd6FQwGGXC58FAtiWSqljwr4IBHDI48HWeBwb4/FJiR4+8/d1dejTdXzkdsMqK8OAYeBjtxsrurqU/DjT1eU+fKcTzGfQWIZCIbhcLpyIRHAwGMSWkRFsSiYn1YvSmGtascJ6r8uFg5qGLcPD2BcKodcwcMDvxxZH5FA6jXy+rusqAqNpWkmdFemMcn5cLhd+4PXiPdPED/1+VNvRRAnw5LqQe0qdJDMwURCM4MPn8+FHhQJ+ouv4fTqNX+VyxXG2AYpsi3QinJkR1NN0YjgOEjgCwPZ8Hi/H48XPmRMZIS9lMtjM4xNF1I/P9/v9+FF5Of6Fq7TImbTvvJxOvSRuXvN48La79DQsebEfuq6jsbERQHHrHQBFBJL8IADmuEsbwjUodbeUR9lWOgp0gmbMmAGfz4fe3l40NTWhoaFBHSvurNgubUw6nVYOhJw3y5qoNZRMJjFlyhS13j+NRNCjafh1Lofn7DWUSqUUGPd4PIhEIio6nsvl0NvbCwCoqKhQ9kzTikUmJbFFh2TKlCmYNm0aLl68iMHBQbVdgfKn6zo+9/txpLkZdadOoezBA0SjUTzf14ezXi9eHB1FeXl5idwBEzVfJLhm1iUxHR2PgYEBAFCZsDzBI5/PY3BwEIFAQGX8hMNhRcgEAgEVDaXT+GIqhQ0jI8V+RyJqjikf4XBYRVq5naOsrEw5EXR0KfuBQEDJAT/rdrvxzTffoFAoIB6P4+zZs5gzZw58Pp/K6GQWA3ElI4t+vx/Nzc0YHBxUc8hTRUg2ulwuJJNJ3LlzB2fOnMHcuXOxZMkSlJeXKxmjE0dZYQFd0zRx6NAh9PT0IBqNYsWKFSpzgg43bQNlUfoMdAC5HmQmjdTtmUwG9+/fx8DAAG7fvg2v14sFCxagrKwMH3s8xeOaPR7s1ifqCHCLNnExnTKSAH6/H2NjY+qkGhmtpV/A7BT2n+vz8ePHODF9OpIeD97LZLCwvR1LliwpIXcpm1z7xIlynVCW6OxKgoFtcrlcGBkZQcLehpNKpdQ8c/skT8vg3PN0Eek/cGx5lKo8bYRbIvg3M63ZHqBInI2MjGBkZASpVArl5eVoamoqsasSe9Ah5lxKHEMSxLIsRbpRBwBAR0cHbt++rYhABiVJ0MjAiiQwmHEiHXyJ0dlOrjnODX3Wnp4epNNpxONxfPLJJxgZGVFYuK6uDk1NTSrjS7ZD9lf6w7wvP0ubIclwt9uNvr4+zGxrw98MDiKVSuFroa/lmGmapnREoVBAIBAoCZbJoANtr9vtVtsASeZIUoR98Xq9eG/pUowEg/imrg7rbtzA9OnTS07vkoQHs21kn/8Q9p7s+m86dUIKkWQu+Zt/k7nK5XIK3Enhk5Fa6ZhLsOgExfJ815mXL+P+8uVovnwZnZ2dqtiXjCjJqIJ0/GW1alk8cGlbGy7NnImaU6dKWMdp586hc+1azGltVQ4aJ0wqGAo1f1Nw+BwuchqiwMmTGNi4EdPOn580QkfDWuIobd2KzK5d8B84AP/nn6uxzOVyGF63Dnc2b8asK1cw5fx5AE8XbpFjK+cLAJbZ2yUAwBQgVQJv0zTRcPkyFra3l2S2SHJJvkaBZM0DGYGRSskZZZXRIcX0btmCXFUVutavR8Xp06p93evXI1tZicdr12LO3bsKgEhnSLKq/J994zzLNCO2i3LDVCwpq/IzUgFJ9lauEadDLskfSYrQOPA+ZCabL11C+/LlmNXaqhRxbutWJFetgnvfPhiffloid9yDy3bL9DpN05B9+WXg9dextK0NTdevlzgOhUIB7cuXI1VWhntLl6L2wgXVbjk3Uk7ZN+mcOV+Tssh7Tbt2DY2trSgUCnjY0lLc+nP+PGrsuitsN+ds1pUruNvSgpnXrqmq0c655fjT4Mu55lg/Wr0aqbIyPFq9GtNbW1W6q4yKSv32x74IJBj9k467nAsaGZnGKN+TTi37nUql8GF1Nfp1HT83TfzO5cLL8XiJU82+SgPMeeKa2FdVVSwS6fNhM7dMiXXudDo/i0bR63bjUCCgjgk7ZBeI/DQcxssixVXKga7rOBYKIeN2I2oWj+I03G58aFl41TDQ0tLylP6S6apO4oR9sayJbXVso9/vx3/IZtFjWfisrAxLHz9GTU2N0kuyP3JMt6dSOBgMYo8dDdmZTuOg34+9hQIikchTz5ftIlgneGefeVH3SHtRVlaGt71evCWMPi8+R24Lk0StBGa85LxJfZ3P57EDwG6fD52RCC5duoQZM2aoLTcSTDuzm3gfuW1CypEE39lsVmUKSVsvsQG/S3mMRqPKeZO2jOPH/jK70Ekg8H7O9SQjnRJjsI+FQgHTp09HVVUVxsbG4HK5EI/HUV5erlLJqftkUIV2ROoOKQ/SnvF92u9CoYD58+cjkUhgaGgIU6ZMQVNTE7LZLEZHR1XWmaZNbLEjpiC4JuDmvMiskmg0qggJANhm12LYNj6OtF2nByhu6QoGgzBNU0VNM5kMhoaG8PXXX8PtLh71uHbtWnX/srIymHZwgzo2lyueSx+JRFTUtL6+XjmznL8vCgX0axriLS1Y/tVXaG9vR7i/Hz+cORMvvPACfDYxQLzIeSLGlLaFeFJuBaipqcHY2Bi++uorZLNZ1NbWKqfb6/UiFAqprWvhcFjNH51mYjZmNXCd+e3TkEhKEFum02kV/eNvOS4kBehIULYePXqEwcFBjIyMlGQzjo+P4769JZVt1nW9BO/6/X7kcrmSEy+kI0JHh/0eHx9He3s74vE4Wltb0d/fr3QJHXGZMSLJmISt18vLy0tknRiDEV4ZOXcGrrh2+Ey57pnhoeu6qok1MDCA8+fPo7GxEYtaWjDe2IilT57gTHd3iT2h3MvnUTZIsDjxCvUJj3xffv8+pl27pvQKSf1Zw8NoW7oUKzs7EauoUMEh9kXqHKmf5FqnzpNOqfQjGFS4fv067t+/j+HhYSVfJI5os51ZjBxbzofMlJC6iGNxobYW39TWYl13NxY9eICHDx8ikUgoXS5lvaWlBQ0NDYqMkI4m16Rcn9IBJrahvpKnvXHrLwlOvl9dXV1Sr0FiPN6HpNRkdsGJ3XRdx1ehED4Nh7FldBS5XA6HQyFMPXMGDf39GB8fx/Xr1xUhUV1djcbGRvT395fID58vdT/1PuWc7WQWFde93KqRsI+wdruLRw7HYjHU1NSU1DCRz+Lcym3KfJ26kBkpQNFXvlRfj68qKrBldBTPDAzgyZMnuHnzptpFoOs6GsbHkV62DAtu3sTSpUuVHSIpQZtCgkyS3JQnWTD5n7u+M9Eg06okgyIHngrw5s2b6OrqQiaTQSQSQU1NDZKbNuH2okVY0taGGd9+i1wup9I5ZLRNbkmQDhxTqP1+P8rv3MGK7u7igrAzLWTqkFRqckE7MwZkRoD/3j3M/NWviqmAdjqZElpbWUt2iP2X7eezpdNJ4+KsJzH93DnMa2srLhChqDjO8j58VmbnTpi1tRjfvh0AMLJrF2pOnEDw1CkMbtqETEUF7i9fjppz5wAA386YgdY5c7D4zh0sfviwxJGQilAqRCdJwu9wQUlHzAnqAZQAyng8rljm3tWrcWvLFsy4eBFNN24o59GpkGSBEdmG8i++wOiWLaj95puSNtScOIG+55/H9IsXn3KCOW5yPHk/Kkr5GWCigI1lWcr5vNHcjDstLag+dgx1ly491TaysHILhuyXvD+fKcfamWnDi0Yxl8uh5tw5xL75ppjFYM/fk507UaiqgrVrF7xHj6rvABPF4qgs+B7fj+/aBTMWw/V58zDz5k1lkLhNZO7167jb0oJZ164pVlUqcOfeRKfClwBAzrV8T16FQgH3li3DeHk5HqxYgaozZ5SxcYIlaBos8Ty2Qc6rHFu2mc82TRPNFy6gfeVKzLpypaRtXPcSEP0pLoJ+WaSQxoQgRgJyYCKSx//ZNjLXHEfLsvBOPo/fWRYSLhf6UCQBdonvTeYMyXHI5/PYmU7jgN+P7Yw42+BQjifbZZomto+P45Cm4ZVUSq2jrYkEjkYi2GkDawlC+LfP58ORcBijmoZ6AG/YqZQsnEhwLtsoAZwEjHIMuM44NqZZ3Ff4fdPEb/J5vOFy4cmTJwCgIp3SCZU6fksmg5ftaGle0/BiJoNtNoFgCtlygk6pM/kZWbCWdojz6Pf7EYlEVMSE/ZL6gcQEX+clQYmcx8kcXjnXfL22tharVq1Sxx8SlMioDuebDugXfj8+q6rCS2Nj2GinGsttZ5Szo14vDldX45V4HBttZ4XZFYzUM4XW6ZBzLdLGy6ibrHEk7YkEYQRNBM8yoCEdE0n++P1+7DdN/D4YxKahIcROn8a8efPQ3NysCgTSJjoJJrZHyhHHTaamsp9sY39/Px4/fgzTNNHW1oaWlhZEo9ESpyYQCGB0dFT1TTovvA/1hJNcktmJrMUAAHlBWknycHR0FIVCsabD8PCwul8ymURZWVkJ0RK3s5Vk1DCdTuPBgwdIJpOquJ+UNwCYWSig1+VCuLNTYYDq6mq0tLSo7YhOPCSDARxP/i9JJTr0jx8/xgM7U/PmzZuYOXMmMpkMampqMDAwoORVZrw5sUgikVBkjbTfjKh+kM3iiMeD9aOj2DY0VOJMMzLO1HTDMBQuKhQK6Ovrw9SpU9UWYMoEx+PLL7+EpmmoqalBJBJBY2NjyalZrA1BcpByzKwRSTzpuo5YLIb6+nrouo5bt26hu7sbmqZh0aJF6tj0r6NRHA6H0XzxImbevFnM2rSJk/aFC/E/xGLYeveuKqwqdQkwQa5yLjg/JCIoY3yduGhkZASPHj1Cd3c3LKuY7s795qZpItDbi92xGJ555hl4mpqUE+TEfrI98kfiLxJ0Pp8Pvw0GMWwYuDxzJnyff16iUw3DQNONG2i6cQOLFy+Gy95axb7JgBOJQxlEojwBExjMuYWKZCz1f0tLi3JMKyoqFBaQ+pX9Hbfr6si1TqzkJH35zL/N5TBoFbdMPz82hrt2kI620uVyKWLjyZMn0LSJ6DzHkfNMmZMON387fSQZmOS2Jm7/aGpqwpQpU0r8LOdcUs9zHJz2nzqJz6HN+g+pFHotC8eqqmAWChjTdTxctQoNly/DsizU1NQgFoupDKDe3t4SfEu9wDFy6nrpB0tsw9c0TVMkIgPWTU1NWLVq1VPkjVxDXMtOP4LPpX8giT0A+P/mcugDcLyqCv+6rk7Ve6mtrVW+6NSBAaw+cgQulws3vV5EIhH1HGIMialJpkoM9kcnGm7cuKHqFSxta8PC9vYSBUOn/uLFi7h//75azCMjI+jp6UH8X/9rZMNhXJk1C803biiHlIaCAj48PFwyURzQJ0+eYGxsDMaePXi0ahWW3b+PlkePFIvDQSbrIx1X3oMLVQq8ZATdbjcSiYRKhXO5XLi+bh2SZWW4tWgRFty/D2Bi75SccLkgnP8zc4IASBpD6ahLJkxNkEucwvHRR7D27oXvwAGM79wJs6YGQy++iJZHj4DWVtxbtgzNtsPtcrmK9RTCYVybMweLHjxQzqEEsMDTx81db2rClVmzsPz+fcyxj3kCgFszZ+LbhQux4v59LHr48Ckl7gS0Xq8XAwMDGB8fx9CLLyJXWYkHq1ahsbW1JILojFhJxcHxKT95EvV25ekCJhR0xalTqL1woXgerb2PWaYqyX46lS2f57ykktI0DXdbWopbdTZswPTr15WilFFEypqTLOBznSSXVFSGYR8ZuWoVqo4dQ825c8poEYRz8ROgaJqGymPH0LN+Pdz795fImySRnFkYvPwHDsB6/XUssus9SOfEMAzMvn0bs2/fLt5DpETKOSaxQuPK9+gsOC+ZUUD553hbloXZ167h3rJlaLp8uST9jb89Hg9uLliARCSC1rlz0fXTn6p+SkPMH7l/mk4c2fJZt29j3r17Rbletgzty5dj+oULmHb1askalTLzx7xmzZpVMt6apimgMRkrL9NugaeLHRIE8vPv5PN4B8D76TT+fnQUz3Z3wwwE/mAmGsdIrt3dAHal04CuQ7N1onyu1FuWVSwQud1m62GDzE2JBLZyXVil2RS6XkxvjkajeBvAe6aJt1wu6NoEgU354iX7KGVsMlAjx0fK2m7DwC57/T9ctgynT59GPB5HdXU1gIn1QxZffp9647DLhc9CIezOZvGSnYoqx4T/y4KtMnrN/nEvN52Qg7qO3+ZyeDOXwx6RjcNxl4BH3keuX9oawzBUui1TdiUJw37SJhhGcR9xVVUVMpkMbt++jXw+jxkzZiAajarILW1tPp/Hl5qGXk3DF7EYfhiNljifkqw56vGgV9PwWVkZ3rSJSz5byh2Pbf7IJsresCy8YacxDw0NKQDt8XjUtkzeK5PJKIeWoJjkBNcOndNcrni0Hj8rATnb8oGu4wmAT3w+vNzTg6lTpyIajSo7Kgk6SWw4ZUGSpRIoyu94PB5VMNEwigXKjh49CpfLVbIllJFZEshyHjn3bBP7IR1wtkN+lu3kOLjdxcLVY2NjSq/LyH2hUMCTJ09K9nMPDg6WrA+uzcWLF6OzsxOGYaCnp6dkrrPZLL5tbkbB7cZYfX1JDaNEIoEnT54ogCtJNkaZneQjUAx08FhJt9utsgRisRh6e3tx+/ZtVVySpzWwVgC3fJBcY9YBn5/NZtVzKVdcB4eamjDg8eBEdTVeaG8vCXjRdsrUasoP95wPDQ1haGgIoVAI/f39ePLkiZIbtm3atGlYv369qqsgbYTEE5RP1miaDFNJJ5WOZDKZxHPPPQe3243/NZnECIC2JUuw/MkTVFZWqrWyr6UFQz4fjuo6tpkT23Rl9JxOMuVN6nLOmSSK6Hj6fD7cunULw8PD6nXTNNG3ejVaN2zAkrY2bK+qwqJFi0oykzkW1AdynTlthiSK2MZ3xsfx63we0bExfPMXf4E1jx+rArx0uKl3ZLtl3xi5lgEBmTUobYkkR3RdV9mNfr9fjSedbnkqgAw+UG9Ln0fqm8l+2O930mn8KpvFj4PB4ilmO3bg9JQp2JFK4WXbaZUkinTqiU8kzpsMc8v6bryXkxDh65qmKXwGoMRmyksSNtKxdvpf0uHXdR3v+P14N53GD+2tJr/JZrFhbAyj9v0WL16M+/Pn4z2PB3vzeaxwBNIkuU/9KWWXbZU6iRfHnlvO5NaRcDgMr9erTlCS4ydtv5Qn53hQNiWme8flwi8zGfzQ40HQ51Nb8cLhsPqMxOLSz6CvIYP9kwXeuM6/y/WdiYbBwUE8WLEC2WgUV2fPxoL790siAwDUOcV8uN/vR319PQzDwNjp0xjYuBFL2tpUGgnTgWU0gwV4JAtrGMVjcMbGxpBctAiFaBRXZ83C/Hv3SpQXB0FOMg3jHwLoljVR8JB70Vjp1+VyYeHNm7i5YAEW3LyplIyMZMjLSTTIiKOmTaT087nS+PN9uVjlItU0De4jR+D77DN139yePag8cQL9/f2IffMNVtvbPmALUcvt22idNw/L7t+f9Pgx+b9s+5WZMzESDOJSczPmtbWp924tWoREJIIrs2ZhgW1MJVtrWRa+nTEDlzdvRsOpU5hy4YJy+KqOHcPwiy9i6tmzauz4m8pMtsXZVkbYnMyejDryfyfrJ2VBzpscXwm6+CwCw1lXr+JOSwumnT+vSCgSa7KwEGXZmV7l7IuURTKRHWvWIF1ejt7nnisWwcREBCCXy6Fn1Sr0btiA+tOn0XzjBizLQvTECeQ/+qgY0XA4xFR20hBwTZmmicAXX+DZ8XFUV1djRDhGlC06Hvy8kzz7Q3JExTTZ2pAEoHQAOWZz7tzBrFu3ilEg2zGgEuW9F9y8icszZ0Lfv19FXNlup2GjPuFrLpcLq1evRjgcLoluti9fjmR5OdqXL0fFqVNKrnVdV1G4P/blBIcAFHCSY069QNA6mR4jUOF9ZabWHsPAC4aBQ9eu4WY0iubm5pJIhyQLpKGRERk5/1yD0tFyyrSMVEmmnq8ZhqGiGCRhtxcK2K5pMPN55IU8EYBKeXHKG6PrANTZ8HJspc6lM0PDOWXKFGzevBnt7e3ot8+lr6ysRCAQUBFBtlEa8s/9fvQYBj7x+bDLAQYksLcsSwF09lmmj5umiUQigVQqheHhYfwyFkOPy4Vf5fNY1duLVCqljrGjveT3JAFHICSzJUgccB7pKDmjwJLAYp9/n8lg3/LlaDx7FnMOH0YoFEJtbS2ampowdepUBW5ftyz8HsCbgpjhnEvn9W3Lwu8sC2+73QjZ8ufUw5ZVrKMRj8fxvmWhS9PwO9PES3a6ayKRKAFGLLpG+WSEjNF5YCJDC4BaI/Kzsio554/ZRgunTUNy2jTMsk/JSiQS6OnpKQHgJDHoSE4GEp3EM2WIsggUQfndu3fV55ky7/F4sHr1amSzWTQ2NiIQCKjv87eUPc67lH9JLmraRPo9/5dOQqFQrLXyXiqFmVeuoPHaNUQiEVWDg4USr169ioaGBhVttixLOVmcW46j2+3G6OgoZs2apXQcZfXleBwfpVKoOnFCgd0nT56gvr4ezc3NAJ7eZikdHKeeMm3HlyngU6dOBQDMmzcP+/btU9H/5cuXq6JpXN+UpVwup+osUH/QieC8sxAd188P0mm8b1nYBaC+vr7EsWSGgtSjkoioqKhAZWWlOhL49u3baLePp2ZfQ6EQ5s+fj+bmZoUjP8rn8ctMBt93u/GqLYPOtSyzb+Wz+ZlZs2ahtbUV4+Pjyqn2+Xz4YaGAvx0awpZEAq+++qo66Q0AcoaB9y0Lb/n9mGLXjeB7Eguyj5IwIFklZdNpM2pra0vkyDAMdG/YgHRFBe4EAlhin9Ih9ZyTWHCuDcoHP+uUqb1uN/a4XNhWVoYRnw+tc+fi306C8Z1jyHtLZ546UL7uJIUk8T0+Po5IJIKqqqqS73M9fJDN4t10Gj/wePCmcCjZNpmFK/tKGZQ6gTK42zAArxc/TybxQiyGr0Mh9LpcOBKJ4AcOv8BJUnCM6dzKMZK+jcTpzHLmtiC2m3PA7wLF4Mg/xOP4gceDrbmJI1klhnBiEDn3JH6JVSzLwouGgY2FAsI+H3IeD1Z0dUF3u/ErtxsDzzyD/33+fOS9XoxqGvbZ68mpXzifxBd8rrR3/I7cNiTxlSTW+V3TNPH7TAbvptN4w7KwU+h2aTsoD1IWqUOchZFfAbBV16Hb33e73apQLedF4klJKvDzzm1+ThlzBqz/ues7Ew0VFRWoO3UK3evWYaFNMrCzdEg6OzsVGAKgHIShoSGEPvsM2/v61J7LVCpVzFAQqUSM2FLBclD5HgCUf/YZsrt3Y6mdXWCKgQRKQZgEzc7J4cRTgOT+P7bJ4/Fg0YMHWHD/fvF7tiGSxU0mW4RyYciFICeGilQaAn6XilnTNHUMFNsFANmXX0Zm505UfP45ImfOIO/xqHRKRh5yuRzm3L2Llo6O4vcci9I5LvJa8eABLjY3Y9n9+0rY8/k8lt67VywEee+eclacoLp17lwkQyF0rFmDmvPnVepg1alTE9kRwoGVINPpoHJfmnQkZbRIETA2sKDDwYImUgbkvMjXpAEiiCGJQGMxvbUVNefOFd+3iyLJMWQfpOxIpeN0zmWEg5+ZcekS7i1bhuqTJ0tS1Hj/vueeQ6ayEl3r1qmaCozGTGYMZX8pe7Lv8j3JkrfNnYvbixdj2tmzmG6f6uBkZzuWLMGDlSsx89IlNLa2qj7JezvXhXQSnc6YE5xwDAlGZcravLY25D78EPfu3YPLPXFOM/shK0DzNWmQqJg1baLI18wrV3Bv6VJMv3Sp5Mhe5zj9MS+ZmSINqJM4k5fcgymNuRxTSeJRtnVdx5o1a3Dx4kW8OzKCq7NmYVsyiZdFtpeTKZcMvXRqqYskwVYoFErS/0lSfe73Y380il2ZDHZgYosF+73fsvD7bBZ7cjlsto+oktk72WxWpQMDEyQKC4VK55kRmPLycgBQ1edlmiPXtzx5SKaa3759G62NjXg4ZQrWPH6Ml/r6UF9fX1J/ACiCttcBfGCaeFU4ENLeSMBlmsVCbZRFOYcul0ulTKfTabxqmvjYNLEzl0M0GoWmFQtHsh+yYBqdJuofqbfkWuNxVwRfksji2uCJA9QdB5cuRTIUwoOVK1F/+bLKejIMAzNmzEB1dTUsy8JPAPxY2BWOs8xI0XUd3wPwfQeActo+ylE8HseKgQHEq6ow/dtvcbirq8RZ4ziw71zT0umXwFDOia7ransSQeP5mhocr6nBpoEBLO/qwi37uOXogwfYbJMlmq6jq6sLpmmqYyZpj3kvWZRMXk6nXzpQHIvOzk4cP34cmUxG1WMgaTY0NIQf/vCHat2l0+kSeZP2iA4K9eBklwTKcmzY/o8TCYyGw3iwciW25nKYOXMmfD6fqrUAQO3bp62i7LFvpmlidHQUPp8P06ZNQ7e91TUWi5WMyY8LBeR/9jN0dnbCbacTz5w5E7NmzVKgmIQMbYPUm1L/SxtD+eIaZ5u5B1zXdTWOziwhri/WGQMmtrpxKwt1azgcRjgcxo8DAbyVTmNwfBym3cbJgkYMLFDmfT6fIpCYidHX16fqZOh6Metr8eLFqK2tLcmE+FUmg07Lwq+zWezSSutNse+cX4mHpL4KBAIIhUJqrzjX0Rs+H1x2IUq3212iR9/SNPzY51M6g3Itdbszk1I6k2xLLpdT2TqUS24d4ZrifRrPncPjtWuxKZlEJhotSRWXpDnbL+WR+I7PdkbJiQU8Hg82dHfjzNSp2GtZMEQ9MWlLZT+kL8HX5Tp3Yh2ZycvPBexMQ+ojvs6+vZtO47Fp4hepFNb09SnySa5/BqQk0WmaZsmRw07c/G40ik4AR8JhvFooYJ9p4i0HaePUX9LRlP1w+jpOn4iYTmb4SNwpM7f+KZnEY9PE32Yy+MdsFjvSabxkF7Vl26nrpXxLOyhT+jm/kUhEZb3puo6Kigp4PB70PvccMj4fyiwL9ZaF11GKWdkP6laJreTY8JLBEInt5JjQT+Nrv7Tn+NeFAlb095cEzaXPKO89WRYd28c1VFFRoXBGLBbD2NjYU0XpZTvla8RVUgbk5yezdX/o+s5Ew/j4OKacPw/DMHBt/XokEgnMunVLpf3dX7AAN/7dv4P2+98j9s03MAxDASUWwuLE3WhuxqWNG+ELh+E+ckRFGnJbtyK5bRuMDz8E7H1pVPYEF/Pu3cOcAweKzqT+9D5LOYASXDgNklQ8TmMGTDCtVGCSgZQFUOQkOR0rfobCQOGXoHUyNkwqTQJu9t+yLMTtWg2jL72E2bdvl+z9kxVl5VhQKP85wWCblnR0YElHR3FcRaSn5dEjLOvsLAqZDfqlABYKBbTcuYNLM2Zgqi0rMl3QycjxmRwvqZA1TUMkElEFuBjRGxkZUYyh0yHjWFMRczHItGICEGBiuw0/owt5chbW4TxwUXcuW4bHa9eiSRxPKkkTyhcvJ6EinTfTNDHVLuI5MjICqi3KrmEYmHr2LB6vWYPab75R5FM+n0dmyxakd+6E95NP4D5ypOSZUvb5IwkMrkcZ+bzd0oJENIoHK1ei7tKlEoPC8XywYgXGYzHcX74cdZcuPRU1kTLuJI6cQFGuF/6+P38+7q9YgTnXrmF6a2vJPTgeEoTwR4I6JznC5/B/SVo1XL6M6a2txc/Z9QCkgfxTXNw2JudA6hwJhiSwkGBO6hOZQi51GfsSCoWwbt06/JPHg36vF/sLBazo7saUKVMU0JFRGukoOzMp5DxIYCn7k8/nccDrRY+u44Bh4A0BLnnf9wMBdGsaPjQMrLerwsvnEuDSMHJchoeHlQPOiu1yrdBAhkIhVbU+n89jfHwcmqaVOIYsEJZOpzEwMIC23buRjkZxqlCA/xe/QH19PTZv3qz2NLPvPwLwjvV0Wrr8TYNvWRbC4TBqampKMoVkNG9wcBC3b98Gurqw25bjg8bEOe3SfslxnAzscj3w2bo+EbWVuq91+nRcamrC6o4O1N66hb6+PqVXqkdHkV27FtPsE34sy0JjYyM2bdqkqrNLslQShiSX5SUBFzABBvl96Rz5/X7s1nVsaGuDKxBA2YoViEQiSk9xrp3OnIxYOsGwM7Ikgd9/SqcxYFk4PXUqttgkFu9VU1MDl6tYCT4UCqGmpgZ1dXVPEchy7qXOkLZerqGMfaQZnxWLxXDq1Cn09PSogtXBYBBlZWWoq6tDJBIpKZT3h0gN5zYQPs9JdDjxDwCVsfBDrxf/qbcX2zIZrFmzRpFktbW1JU4r7yErq0sA3tDQoMbD7Xajo6MDsVjsqXUqa0qMjo4qp3twcBAPHjzAo0eP1H1kcIhtljLI9UV9yOADq/bruo6HDx+it7f3KZmU64c4gfJFWW9paUF1dTXi8bhaV8yMkOSNlDvTNEtSz4lduLap195LpfBuJILlTU0InDunCi+Gw2HMnz9fkagMhuzOZvE+gJfTaXT295c48JZllRQPlPIodUahUEBjYyOSyST6+vrQ09ODhoYGpT+ampowODioTn9yBgUk7iY2okMp172Ufyfu5n045tL+WFaxLknlt9/ix7EY1q9fj3g8jjt37qCsrKxELqQukEEcKatyfLheSJpaloU1/f341/X1xfXlKi0MLfshda/skxxv59hIf0LOhyRkTNPEx4UC3jNN/MTtxg/8frzj8+Hd8XG8MDKCdD6vthdImWfQRJKLdGjZBs4pAESjUfwkm8XPx8fxUiqFnV4vduXzMLNZZBzO5WR4jW2VOJtz4ZRD4m/eg7LP1z/1ePCxaeLHbje+7/Phz0Ih/Gx0FAkAvW43jrjd+ElFRUkAj/hA2l/OM1BaYJkOellZGYaHh9XWWq75+lOnMLBxI77n9WK7LYdph2Mtt+RI/SltAO2ZlAv2V9qkTz0e7LMs/FjX8badxfSjQAA/T6Xwik34hcPhkufLNcv20EfhezLDRNOKBVsty1K1wCTxyrHhOpnMJ5a6yinrfO2PvnWCdQkerV6NfHk52pYuRfTECeW83HzjDeQrK6Ht2QPfxYuIxWIqlY7pfr29vdA0DZe2bEG6shLZl15CeP9+1fjMzp0wq6th7d2LrE1ASBAXiURQWVlZ4nzLjvNvtokTLZlFXlwIY2NjGBgYUN+hEDqdGeDpyqNOgM+oy2RGncJBYXMqPEksSGPJCBTblMvl4P74YxT27kXt6dMlQpHJZJBIJNC3Zg16n3sOS9va1BGAcpE6ldt/jZWSi0negxfbrus65t+7h+iJExgfH0fG7osERFJRUCE5FS1/01jSCPF4OKbMMnOB7DbJFn6GbXMqdif7zPZRacsx5/27V65E+/LlmHr2LGrOncPjNWuQrqhA+4oViJ44UbLwLMsqiYJKxeYEdpIAkEwsX2Pfq86cQdnXX0PTNKRNUxFw6Z07YdbVIbd7NzyffqoUjrNIi5Nsc84H2zT32rViRsOFCyVgXTrzzZcu4cGKFZhx+bIq1OccYyfAkEysBIbS+BBosChk29KlKgtGrmk5hvxbrh+p2Dl+nE+2zwlGZKRZkhZSzv+Y1/vj4zgUDGJbIoEX7WrOnH+v16sqiPN/WauFgIFAlenLBG48yYLGSteLkdAjbjfilgVPKoWaEyfw6c2biEajqKysRENDAxobG1WNCKmngInUS0nmyfHnezL687Zp4neWhbcMA167nZlMBiMjI0gmk3jJrv78/MAAevv74Xa7lQHlsV7ObWf5fB5J+/QMwzBUtWWp25kFI+WLr1EW6IAwI2J8fByFQgH1p0+j69lnMe38eeRyOTQ2Nhbrv0wSnZKyRdmVKZOFQvEEEBLlqVSqBGhRt5A0Yap4MplEKBRCtZ0iTGdMAlc6b+wTnQq2U4JuGQXlOnW73fidrmNY09A6Zw7KT55Ucuf1erHg/n3Mv3evKGOhkNrvfvbsWaWHJahm9pkkySS45JjItknCnqBpbGxMReLKy8vR0NCA2tpaJBKJkvor8v5yPqTDJy+Zui4dcU3T8GPDwH9OJpG0LHwVCqFhyhRUVFSguroalZWV0LTi/nUSA3QsgdLtd7yc9tRJNAClmVb8DAl1bhfzvv46Ts2YgQYHWfF+Oo1fjI/jTQC79NKz63lJ50baX+pQCSplBHjKlCl4w+dDz/792LlzZwlGcJIm0j7I/cm8P5/F43Y/q6vDD0wTe4UtknZf13X09fXh1KlTKC8vx9jYGI4dO6aKfjJgRVl3Yj7pBPC31y5y1tPTowhJ9od9drlcCIfDJbLFPnKeaF+y2SzKyspUhhDtBm2/zIKQssnsI+mwELPxOLpfZ7PodblwqqEB8+wIrsfjQfeKFfh39fX4MYDXBbDfkskgfOYM1q1bB08sVoIjZDBGzpWUN/Zr2rRpsCwLra2t6jhV+VMoFNQpVsQP76VS+G0+j52ZDLY6quXrul6SsSfHXDpgHD/DMJQuPqjrOLRkCcoHBlB99mzJWFHX89SMefPmqX5I3SCDWhL3Owl9EtmVlZXqBCsSsrlcDk+ePEFZWVlJoErKBts2mTPpxFXSTkjdQb0l5+TXgQC6dB0/T6Xwg0gEbwUC2GMYaG1vR9A+EYH3pV2UBOwvhofxs7ExvG0Y2GvLAMfD7XYjFAohm83iLbcb2y0Lj3M5pG2s3dPTg+rq6qcIJdlvuTZk4I59krjVif2kzqbMfOj3o1vT8G46jbcCAbwVCGC7WdxO8J+TSaTcbnwdjeId23GWz6E/lsvlMDQ0BADFbQj5PN7x+fCGz6eOmOXWDdriW7duFYN4V65g8cOH2PyjHyHr8PnYXrbZSbbIOZZzznl2EpCWZeFjtxvdmoZfZjJ4w96S9LrXi50A+pNJBAIBVFRUKP/QaUd1Xcfjx4+VvPNZFRUVCAQCalcBtwYyy5tESNw+ZlquCcqxHF/iJPZN6nu2x5kd9Ieu70w0pFIpAChmG+zdi7pz59QAFAoFRD/7DNmNG+E9cEA53MlkUi0Aj8ejyIrAoUPIbN5cdJgLE8cFlZ86ha5162AcOFAioOxgKBTCyMgIstmsqsxtGEZJ/QEOgFQu8jXp0Dx69AiXLl1CKpVCKBRCXV0dqqurnzIGUiHIBcL7WJal9p5JJo1toNPp9XpRX1+PdDpdUpl7ssgLCYf+NWsw+Dd/g+Dhw/AfP14EfocOoeLUKdRNnw5NsO98Xve6dUiXl6N1zhx1GgELjjHdkVkQTmfyelMTrs6aheXt7Vj88GEJoCYpI50OOT+apuHevHm4uXkzpp45g7KTJ0uUjTxRg+PnrCgvAdv4+DiSyaQaGxItHGOPx4O+NWvQvW4dFty8iXltbSXRJJmVwHvwN42mTCOVQJVFWnRdRyqVwsOVKzFeXo4nzzxTLJR47RraV6zAwrY2LFy4sITNlEwjI8IcK5nazL8ZcUmn02qNsE2jzz2HwU2bUH38uIruSyDj++QT5PbsQfDIERTMibom0qjx+c5UezkmnKNZt2+jsbW1GOkVFd3lGDVdv45pV6+WyPBk5IkzssbvO7M9ZEaFaZqYdfUq7i1bhnnffqtAJfvwz82lNIKTfcZpJAk47s2fj7alSzH9/HlMu3pVySbXxJ/i+sTvR59h4GAwiBeSSVXEDIACe9IRk860rhfTaXk2vNwnnUql1JzISH4mk8G+mhqkPR74h4ZQ9vXXGLOPBR4eHsaTJ0/Q09ODLVu2oKysTI2B1A+S7GE75Dw75+Ft+0c6eIx+Dg0NoezuXbwyMgKXy4UHodBTjLoz+4avy+JjsniWzAiRDo+uF7fgnKupwbGqKmweGkL02DHcunULpmmWbMWoOXcO9ZcuFQlOm8i4d+9eSeV/9o/rkLpNriVu5RoYGFBHFF65cqWEvDwRieBYVRVWtLfjFTsqFYvFMGfOHDQ2NiqgwfmXhIZ06umQyHmSkR7ZZl6apuFHuVwR7Og6vpo9Gx2rV2PBzZvY7fOpozhlNiGj7M6MMAAl61eCK2eUSTqFMhgg9ZKUMW61pK2SwPcPrXdpq6WtcZJEfH+XpuHnAB5bFj4vK8O/dLlQVVWFxsZGJa+RSASWZWFsbKyEwJHtZzskoSPbxDXi1L1yzUin7Hh1NZKGgQ81Df/Kmggo/DyVwhMAv8rnsby/v2Repe6VZBvXqMxykTrY5XKhpqamBIOUl5cjmUyqiJgcawZBdF3HQV3He6aJ7akUttvfJdZgFuLnZWUY8vvxfj6P9XYtlHw+jwOaho/37MEUu9izaZoYHBxUEXaeWOGzHYb169ejublZtZ1rV2IREnPU8V6vF1988QXmz5+vakwYhoFLly7h2rVrWLx4MVatWlVSoA+AqtMk5yyVSqGrqwvxeBwAEIlEVFHJkZERZGyCwPk9iS/kGiwUCujs7EQul8Oz+Tw+Ly9H86VLSCaTaozutLQgpev4ZTaLN+ysL03T1H736dOnq2wDiaGca0DqR6d9Zh2Ohw8fquNKSUCNj4+jv78fU6dOVTL4W48HXbqO/V4vtqTTCo9JB0sGdDg/MpDC9yzLUkGiY5EI4i4XMhs2FGtQ5fOIRCLI5XIYHh5WQaCGhgasX7++RK85s11kZotzzcl2jY6OKhzIWhSapqmtZFLfyPHjeMqMQydxINvndI45Vs5aBa8WCvjYMPAjO5Am55vP6+joQC6XQzgcRn19PUZHR1Wm3z/lcnhsmvindBqzOjqwcOFCdHR04KN8HseqqvCXloXX7Xsxi2l0dFSdvBAIBNT8c4ykbEldLbcTyP5yHTrHRZLEnK83NQ0fAPihWH+GYeA1jwf/mEigz7Lw0+FhbLfvwe1Lvb29qoC2pmm4evUqpk+fjnc1DY9NE++m0/hxLKbWCbPwIpFIsbD4zZsYHx9HKBTC+Pi40nHyRBcpK5P5Epw3Z0av/I6cQ5fLhTcB/B7A90W9DamfqUMe2AX8p06dCr/fj87OThQKBZypqsI/GAZeGBjAZjtAQp8lGAyivb1d6UyPx4P+/n5UVlZidHS05Bh3btFz4iZJoEk7If1nOcff5frORAMdwsAXX0D/6itUzZwJzTb+yWQS2oEDCCaTSO/di1xVFfD11+hZtQojW7ag+vhxNF2/rhZS+Ngx5D/6SE2CYRjF6IXtWNzfvRtaLgfj0CEFoLxeL8LhsCoKxerUHDQKNYtZSdBLsMDP3Z41C9fnz4fxwQfIJxLw+XyIxWIKAHNByDoEEuQR2MniWkwr7OzsVM+mgXe5XEilUmqfq8zSoIA5mU6+171hAwqVlUht24bykyfV53mvUChUIixerxcNZ86gc+1a1Jw+jSdPniiFScAoGX/n31efew7pUAgXm5ux0C5IRGZraGgIT548eQqwsa2WZeHO3r3IxGLoWLsW0a+/Vs/N5XIKFPC7zggzFyMXaygUKtk3yD1NMmJ5fcMGjJeX4/bixZhz546K6kmyyAn25DM5F5xHt9uNO3Pm4Ma8eVh0+zZaHj1CPp9HtKcHqUgE4a4uaJqmIn6GYaAgqhJLBSpBCy9nip+UFQDo6+vDkydPVGr9gxdfRLayEoMvvIAF9++rrTFKKR49ivrLl5FKpRAX4yedBBk5c0ad+HwZYTBNsyS1X4JVKhqCKQlmpUNDI8T/J1NmcjzkvWffvo3pra2IRCLQjNLKvtKQy3bJv9lP+Zp0WmXkStd13Fu6FMmyMrSvWIG6ixcnBQ1/7GtdVxe+njIF6/v78aizU53TLiPdjI4RMDkjxTxRAMBTWxy4BtLpNJI2kRFetgxjzzyD6pMn1bzzp6WlpXhkmK3X6DxTr8pn8znSIZTZAuwDP+fUeay9MG/ePIyPjyMcDqvIsTOjTDqlkuHn/AMoieYwGuF0uAqFAv4/moZ+TcPXU6Zg7fAwYrGYOrauvr4e5eXlKosuFoupthAMSvnixXax7VJecrkczp8/j0KhgPLyckybNk2NSS6Xwzfl5Rh0FU8H+u9sx8Lr9ap909zqwXHjemOb5PrleDij5JIwk3YMAF7zePBWIICRkRH8dMkSJPx+3Fq0CLPs+WQBL/aTxDAjSAR9HGfOOwGxnCuOB+VEFnsDUAJWOc5c8xLoDg4OqjXMS5IIThmVQFmm60pb+6nbjbjbjXJNw+aREViWhaamJvT396O+vl6NK/W0k8CZDHw7o/VOkpdOOj+337Jw9Ac/QM3XX6Ph8mUUCgU0XbiA/o0b8Y5NcnAM3gDwj4kEdmma2sbhdCjlGpFrwRlh4/opFAoIhUJqzz3rPO0zTXyk63gtHsc2oWu4dcDn8+G9aBTdhoEDPh9eFkRMT0+PKmq51OdD65w52C3IfV3X8XkwiJTHg65nn0W9vQ0vkUjghO1kMiASCASwbds2rFy5Uukky7LQ29uriug5AT0JH+LL6dOnQ9M0jI2NIRKJoKurC21tbao+QTgcVsQd9bHcFmFZljotjRlXdMYty1KZhDKzSGZuOvUW5zOVSuH+/fsIdnXhX1RUIBaL4XfC4Zp55Qr6nn8eP/T7S4hTearE0NAQ+uz9+01NTTBNEw8fPlRbI0KhEDo6OjA6OoqoXRS4q6sLXV1d0HUds2bNwoULFxAOh1UNMvY1HA6jvLwc4XBY9eH7ponfFgp40zAQs7Mp5JqXWM7pfEp8K21HOp3G7kwGH1sWAidPqoKbvAeDncyecrlc6O/vV85mTU0N0um0ygasqqpCPp9Hd3c3AKCurg4ejwft7e0wTRM1NTWoqKhQ+oQYMJPJIBqNwuVyqYKakwUEicF4OedXyiR1APUWA1My0KWIBpcLP7KzyHgvyyqeUETb3N7ejlQqhSlTpqChoQF9fX24Z+PRP2tpwc9GR7GssxODg4MwjOKJLwenTcOwZeGnIyN4raZG2QoGgeln+f3+Et+MY5/NZhXxy/5J8liOkcS0Ugc6yTwAeN3lwlu2fyaDEgDwSjyOz2Mx/MS2hyRGDMNAIpEowfknT57EpUuXsGzJEqTr67FhdBTZykrVVvlD4pp1QrhuSfrIOXUSKJPNr5wniemdZIWu69ij63jd5YLH1ruSJOUWcU0rkn+SsCHZ/246jR6XCx+73ej8L/8FXStW4Mkzz+Dtzk78P2trlbymUimEw2EkEgnU1taqIreUXUlO/qE+y7mW/ZRkw3e5/ptOneCJDMlkEvF4XO37oNJNbt8Os7oaXevWwf/55xjdsgV5+wjGmTdvKmUrQSkAmNu24eHu3SicOYPO1avVFgzj0CE1cWRnCEAk+GWalmUVj0WSAyAVH19v3bED4+Xl0F55Bf59+1BbW4vq6mrlvI+Njal0Ewo178+9ldOnT0c8Hkd7e7sS0GAwiOnTp6v0ZabqsJASz5omE8mz23nJ1HkCt5oTJ9D17LMIHT6sxoLAiaDa7Xbj8dKluLdsGaaePo2Gixfh+fTTpxwCmQbISzqa2WwWlV99hcFNm9Dy4IFSHmNjYxgcHFRbX/7Q2FqWhcpjxzD4wgtoOHu2pLgYjy5iWyQZ5GT5ndEn6XjI6FahUMDCmzfx7cKFmHf9unqPilE6HE5lKBWDVPJutxvfLliAeDiMS4sWoXXuXDRfvIjh6mpYhoGRmhoMDg4qdtBZHZltJLElgZ10cinH7PPNGTNwZfZsLL9/H7U2QWQYBqadPYvHa9ei/vRpZQCB4jGwbrcb2ZdewsM9e+A/eBD45JMSheB06mWKmxxrbkV5sHAhbi5ciFlXr6Ly9GmlxGV0Q6bLpe1IBkkHGTGTisoJwqk0ZfRBtonKlXJF8EfgKefNSYLIOZYGXt5bGgpd1zHvxg3cWrQITZcvl5xw8aciGQCg4fJl/MBuY689Thw/glxuO5OnFPAzstgbZYon91Dmx8fH8dlnn2FwcLAIEo4dwyK7fg6AEuKvu7sbZ8+eVZFsrkHOg8wa4Lw4545t0TQNx8JhHIlEsDWRwCb71AjLKh6FRwcuFAqhsrJSHZ1YKBRKCqFKnS31Ai8nsJWvSYPI97+n63ivUMAcAIe/9z08++QJVnZ2Yvny5copoV3ieBcKBUSjUaW3ZT+5zin/lCc+m6m+QDEyHwgEVAq1YRjYkU7joN+PPfa64lZDv+1QcH5/k0jgfRRPdtgm9CVtIOVdOjOUewl0SQ7w8+FwWO33ndvaisfPPINduRxGUymUlZU9VXBR6isS+tLxllEd2Q6ph/l93ksCUicwCwaDqhjoB9ksfpXJYHVfHzRNw6m6OuzO5bDdEb2VBL9sH3UKtz9IZ3d/oYAR08Q0TcOGkRE81nVEIhE8fPiwJEPxo3we73m92DI6iq22LEh7KsfJ6VxKEC51FO+9PxBA0u/Hk2eeQeXp0zBNE/WXLmGXpmHPnj3KdrpcLrw5Pg73kSOoq6vDk6oqlfZP4H/Y5cI+txu7slnsEOMudRo/85pl4ScVFaipqVGFUQFgzZo1uH37NvbNmoUBjwf7CgXsEhk2VVVVCo+9DeC9QgG77WAA18WH2Sze8/mwUNfxfa8X/7OuQ3e5YInjCN8A8O7YGKpOn4amTewznj59OkKhEL7++mtUVVVh6dKlmDdv3lPOVzAYVHJkWRZ+m0jg78fG8Nc1NXjTbsu7IyP4j9XV+Mt4HD+07ScdSFnLQdoSmYLPec3lcujo6ChxRhic4NhTb7N9zkw7aY+kvvf7/QgGg7h06RI2b95cQri+7vXiGU1DucgYoJzx58GDB2htbUUul8Of//mfI5PJ4MyZMyojIBwO48qVK+ju7kZlZSVmzJiB7u5unD9/HpFIBBs3blQ6rKenB319fRgZGUFHRwcsy0JNTU2JPnhL1/EWJk51IbnG8ZN2Qq4H5zonXuJYvJxM4tmhIfztpUuAyL7hliJiPOKWq1evYnBwEGVlZdi6dSu6urrQ3t4OTdOwceNGJBIJnLMLeW/atAmRSARXr17FqVgMF9xu/JXLhVdsPEJHmJks1PNsq+wT9YLEHpLo5uV0zGRGgzMgJcdEYneOnWEUT99jAIE/LHBI8uV7oRBe83hw9PJlZDCRwbn60SNcnzcPfx4Ol2BBl8ulHG1mEDvll/eQgTvpBMs+Tobp+BmJT+R9nPqJz38pm8XrqRRm1tVhdHRUyT3l5sNsFr9LJPCObTPT6TR2WBb+yjCgV1Y+lXHL9RIIBJRt4dqlvZZrSwZTnHPqvJzy7fxxEhRO8opjQFKH64lzw/+/Zxj4RS6H4DffIB6P4/GaNchUVOBgNov/TdfVNpF8Pq8wB/tEQo0kA8fReUnyWraf7ZTz+F2u70w0EARym8Lw8DD8fr9i5vL5PHyffIL0jh3wHjyIoaEh+A8ehLV9OxrOn1cTzs9LRzm1fTsKVVV4vHYtqo8fx5NnnoH+4YclwIF1HpwTKTsPPL13xrkwAKDmxAl0rFkD/aOPABRT5MbGxqBpmkotMc3iEVNkUeWgM2IjMwHIonKfJVkyGkIeo8U+EfRIgOmM2I6NjaH85Engk0+KVVMrK9Hb21tSCIztv9vSgnR5OR6sXIlsLofHa9cifPgwIidOlIyFjHixn1IxxL75BrUXLmDmokXI2ylHjx8/Vv2TC8aZLaBpGqrOnMG0q1fRt2YNbv37f4/AwYMIfPmlOjKUz5SpmLLP0hADkwM5vq5pGha0t2PevXuwP1BCRFF2ZNaFvL8cCxkpXHTrFlrnzkXW7UYiGkX7ihVYcO0abi1ejNnXr2POnDkloIiZLVRY7JeTEHOyh1LhXNy8GYlQCOcaG7H0gw8wODhYnPPjx7Hk1Cn0r12Lc3/1V8Xik62tCoikd+6EWVODwtatCO7frwywBLJyC4mMFEtlBgA3Fy1CPBLBvaVL8ezgYEnUX4IaggqZWULQ5vwc+ydJC9M01XqQTqSUB4/Ho44Y4xhSOUp5oNxRgUpnT87v+ObNSO/YgfvXr6PSLgTGfs/49ls037hR3Psv5sWpW/6Y1549ewBMHLkHTKSiy7GgzEuyU64JSebw85yfoaEh3GhqwuPvfx/1p05h9u3bmDFjBsrLy5VeY+VlkhehUKhkXkguUT/JOZJzRzlyu9046vXilx4PCpqGo5EIdgu9E41GVfYXSYXJjsmSxk/qOvkZZwYHx+4Pyd8WXcfr0Sj2JpNIlpXhoteLbfZn5BGW1AOWZaktE+ybM+ICoMTxkG0tFAoqCiPTMfn9lzMZvGyTSqbdB7/fr8geTSum7/46m0Wfx4Pf5HJY1ttbkrXBPnO8nPPEsZRyTODOYlHZbBbTr1/H/33JEvh8PjxJJFQRTSe5I4MEEtBIh17qXanzpO3nOpV7PAnqXS4XPvV4sN+y8GeGgbcNA7/O5dAJIFVZCdOyMGIYOOTx4C/taKrU71IW6Jyw3xyLX8fj+LuREfzrWAz/Kp/HT4eH8UN76xK3GX6Yy+Hs+Dj25HJ4OZvFb8Nh9Lhc+DQcxivDwyXkEteoXLOcb4JFSUJIB8OyLGwfH8f74+OotOv9aFoxulVjRx5dLpeKLpN0uHv3Ljo6OrBixQokk0lMmTIFhmFgv8eDbl3HAb8f/2N9PcbHx9HV1VWSwfSxz4duXcfHuo7/ZcoUtQefUctIJIIbN25gQziMk7W12GNn5VDWKOe6ruNtXceORAKmoxDlPgBDuo5LixfjvtuNt3RdHQXLcXhV02Du24fW1lZoduZWQ0MDKioq1FzcmjULR+bNw7Bp4odCXjRNU1F2jvc/dHfjUT6P/6O/H2/ae9n/dmgIPS4XfjY2hnfKypTu4TwvXboUFRUVynZSViaLvlJew+FwyZrmWpB4keMjMQH1nXN7bm1tLR48eIBkMqlsI9f51atXsXLlyqcII+mIMODHbZiWNXG8KGVPvp7P5/FFIIDfrl+P5/r6sNFuH2vK1NfX4/z58xgeHkZ/fz9u3bqFefPmYc6cOYqkkfaVa0ziDic+lySJ0/GjTFBHyAw2jhvtBm0967RJW89xCwQCsKyJYogcc2aCnKqvR7+m4afDw9hsFzulvmAWIedW/payx+dJfc95lt+TDqjTKSfulDiV+IiBXPlM1vmR2Q3OjIx8Pq9qmsTjcWXDl9y/j7+ZMQPNgYCSMc6dZVmqzoHcOkF5o37m9gM5v5RDWRuE8s15PWQUj4J+NZXCVpGZyDXg8XhwyDDwga7jX1oW9ooMdTrKvK8MHL1nWcVtZNksthkTNbxI1kv5cc4jt/pEo1Ekk0l0d3ereZFZSlLOpS2UdlYSMnzdEO3hZyQxedjOSvizUAg/sIsdMyuKn3GuKcuysAPAJtPE/37lCkYBVB47hrGXXsKbdrDG4/EgkUiUZKRaloVP3W4czGSwJZPBa5OsPalbZOY11xT75AwcfJfrv4loIHPm9/vRu3o1WnfsQOzzzxE+frw4cZ9+itg330DXdWR0Hd6jRxH66itUTpsGy95HA6DEqdY0DdHPPkPylVdQ/sUXGPvd7+D+2c+KnRDsSTAYLGHPJLhwOr4SyPCSDnHwq6/g/dnPiqBJLxYgisfjCAQCqKurK5lgp7KUi8Pr9SIajao9uCMbNmDspZdQfeIEas6dA1CMag0MDGB0dLTEERsbG0NPT48qcCSdOvZjcHBQFU7jYuHnZDrp6Ogo9A8/hL5jB/T9+/Hoe9+DGY1ieM8e5GwyhcpdRnJocPk3hcg0TQwPD6s94ySZ5FGKky08Ggq/348nzz6LbEUFtB070NjaqggYtoVzSUG3LAtPli9H+/LlqPn6a1SfPfsU2yvJEjm3cixoqAiIpRxI58hpAKTBmtfWhrl37+LWzJm4Pn8+Ft66hVn372PGzZtFh8NOaZKMNBWDvJfTIZELVio8TdOw8sEDXJ41C4vb2tAwdSo8Hg8etbTgyTPPFI+VffZZjFdU4NHq1Zhx86aSA+8nnyCzcyc8+/eX9EsqCL4mGWO5luhILrx5E1eWLEHW48GN5ma0PHpU8h1JrDkJJ2efpEzIMZZp1zISKsnHW7Nm4e6SJZhz7RqmXrlSEpFk5IEED8eeil6meksZHd+xA2ZtLe7qOkL/8T8iEAggHA6XVC6WKZJynf8prrKyMtU+GR2ZbF3J9eqUHycrTuc4ny8eA/lw9WqkIhF0r1+P/231ajVWXCe5XE45LpLEkEQDv+PUGWyDrk8cQ+rxeLDfMFAAYFgWXrMm9rDSUec88vskd6nzWMOG/ZEyxDnifPGSc8j7yhoKxyMRfBGL4Ue5HF6zLPz9yAg2xuNYunQpjh8/jsWLFyugxvGXMiWNq9PJ5yXtBIEb20xQw3GUdVycTrKUY5fLhfU9PfiyogJv+v1obGwskUkJFJwEg2yj1P/RaBQej6coHw8f4u7duwCKxZrLysqKTp8DKEvddSwcxhfl5diaSOBlG+zKfshMNn5fkhb8rDMCJtfDR34/ugH8PJXC90Ih/NDrxbvj41j8+DGy2Szali7Fn4VC6p4HdR1/NziI10wTe10uHNJ1/Cafx1uGgV12u2prazE2NoZEIoH/XzqNx1Zx7+8306djt92Gk6JNZ6ZOxYDHg4MuF3brOl63LPw+n8f2dFptWaQdkPqGWELankQiUVLQk7LF+d6eSmH4l7/E6Ogo3PZ555s3b8aSJUtU5gB/gAkSCwB+9atfqer5g4ODmLthA1IzZ+L5ZBKhOXOQSCTw61//GjU1NerkgrUNDfi6pga7xVqU9pV69fl4HHvFFgK53mSGj5RBzvHubBbvaxrGNQ1PAPzONLHTQcpImaBc9Pb24urVq+pEmNa5cxHXNPxsdBQ/jEafsqnS5v5FLIb/o78ff2lvpwCAf1NZif/348f4iZ3N4MQUzB66e/euiuh2dHSgoqICLS0t6sQTypBpmqp2BLNUPy4U8CvTxHaXC1tt+8a1J7EIdS91azabxRd+P76IxTBr5kzg+vUSvUXCWB5lS13B9ieTyae2q1G2ZAaIPE4PAPa53RjyeHByyhT8W3vuEokEAoGAqtNz69YtlXHR1taG05WVOBqN4i1dx9ZcTh2rTmLAKUOyz6zR5SRDOT75fB6HDAMHqqoQW7YM1faJN9xCyLoEuq6rmkbMKmUfZTYJHWy2LZPJIBaLIZvN4tmuLpxvbMS/iESg5SdORQgEAujp6cHt27cV6cV+OfGNZU0URHTiDScOk5hQ0zS1dgEoXC/14aceDw74fPgJgNftuiJ8PrMXgIk6IrKGDe0Ys3vz+bza8sBxk3bVMIrbRc6fPw+fz6fkne2XDjvXj5NIksSXk3ApFAo4EAyixzDwgWVh7chICZ4DioHY34ZC6HW78Q/xOF6rqFB+BzPmpa1joO010yySE9Eohu2aMDLTUzrIzrXPbQrl5eXo7+9XhAbxKPWs7Iu07/K1yXxTOVacVykXH3m96NY0/GJ8HD+wfQr6SCRCeS+Xy6Xqy3Atc86rzpzBv1+4EPPnz0ehUNxaMjw8XBJ4M00Tn/h86NY0HAwGsT2ReArHyPmU61JiIkmYy2Ddf+36zkSD3A7gcrmQ2rYN+epq9L/wgnJmLWuiui4VuKZpqsqlpmnoXbUKPX/913B//DF8R48i+/LLiG/dioZvvkH9zZu4OQnzFAqFnlKSk/1N5epc2PIzNBJSKNLpdEnNBQncnUICTJxnyhRnXn0vvoh8VRX6nn8eNefOKaOQTqfVkU1cKF6vV0WUuSfH5XIp0iGbzaK3txdjY2OqGFP/2rXoXb8e0c8+Q/TECRQKxb2SfX190G7fRuTTT6Hv3o20vXfP0PWSAk8SoFOIyFDKy7IstXdPKlCpCOUYGYaB5KZNGHv5ZVQdO4apDx5g6pkzeLR6NSq+/FIpPjmW0pGi8eCxid3r16PWPvWAz+XfBHWTzTfvJfsho8PSMXIqWoJGGg8AWNDejrltbQCArD12BGXO6LOUEafDKPsqFzKf7Xa7sbSzE4sfPQIApO0CU93r1yNdXo6eDRsw58oV3Fu2DLOuXUMmk0FPTw8ymQzCx44h+OWXxfvakWmy2pJkkG2U8yid2Pn37uFySwsyfj9OzZ6NqjNnSsgE57rgWEmwzT7Jz8m1Ku9D/SAZWwA4tGIFkqEQ7i5Zgtm3b6uK1M41T+MjnyHHWka0Pfv2IbtrF/SPP8bt27cRDAYRi8UwdepU9K5ejTstLaj75hvUnDs3aaTmT3E5x1bKu1T8fI3rVsqWM4LL/z0eD1KpFJpu30bvc8/hxXgcyXxe1UKQMsF5kOQf5ZtrTDrHk0WrpAF/A8D7AF63LLxiWciL9S4dGu4TpL7TtGLa7s2bN7HEjq5zfHjJzAmZyiu37DC1VsrCVx4PejQNv8nl8HeZDIb27cMLL7wA3Saae3t71WclyUKdzP5TbxLoSDJKOlokTThOLKgmwZIEokz7lMVxuXXvR+XlWHnjBnK5HC5HImp7HseHOoZA0u12q6PfpK7SNA37LQu/HBnBm5qG74fD+HU8jsMzZ2JBNotTp06htrYW386YgfcaGrAjnVZEgtSVX0aj6LUj+9tGRhRRxH7xuRKkOPUiX5PgS66D1ywL+zQNP7ELo77m8WC7aeKznh6kUin8x40b8fjxY1zs6QEA/HTGDHSYJv5pfBx1t27hHxYtQq/bjZ8lEqi+dQu6rmP79u14/Pgxuru7sSkaxVdVVfgzUWXf2aat8Ti+8nrxllE8aeJV08RGu4aTbp+8IXEOZcaZPi3v6dRVBLaydhJl6ezZs5g2bZo6C30ypyWbzaptrKzrsvzJE+wEVAQYKJ5kUlFRodb/dtPEjp4eNDU1ldxXzh3bDkzYVT5XEn2fAPiNZWF3Po/tIu183fAwZnR346tQCFdmzsQe00RSONEAcADAr196CZVuN6ZeuaLA7OPHj2EYxf3/qx8+xN0lS/Bj+zhbaf8B4LeJBH4Wj+Mvysvx41gMW3M5TK2sVM7cj2MxNN+4gQX2cZtOp6FQKGBgYACdnZ0wDAMPHjxANptFIpGAYRhYs2aNkhGPx4OKigp0dHTgvWQSvwHwdiaD35kmnqDovK+3i/Jx7U2WGSRt5ldVVRj0eJBbuBALDhzAxYsXFSa0rOJ23Xv37sHn86k5pQ7StGK0PhwOq74w4s1+koRgcWvK4NuGgX9MJLCqowOuZcsQCoWQtKveG0axyPrY2BhGRkbU1rYPdB1jAH6Ty2GTPReUd/ZV6gE+i9FaZ+BBOkK6ruOLaBSjhoH0unUIffWV2ibgcrkwOjqqsCJ1Ecc2m80qZ0yuK6n7WNPM6/Vi3dAQfhyLYdW8eRgZGVHj6ff71Qk3DDrSd3HiZIknnTjEqfOkM24YBoLBYIn9dzp5B3w+dBsGfpnJ4G1bR8lgH3EogzbcWkKbSPtBfcT9/nSmScxzbvL54rGZU6dOLSFAZUBIyi0vZyBmMmfVMAzsyeXwkWXhJXvrB8eG7Xa5XHjbMPB7AD+2C+tLmybHW5Lw200T200TMyMR/L04WYHyIbN5qTM415FIBKFQSOlPua2RYyd1Ldea1N9O+882Ey9IeaC88v23dR3vA3hHBHT5m+0m9uWWaZJ20WgU0WgUg4ODikzi2vf5fApP5fN5VXNjdy6HjwHsNZ8+gc0pu+yPDCxShp1j8l2u70w0SKGxLAsVX32F4RdfRPTLLxEIh1XhGBZqYUMYNeNkdz77LHIVFSjs3Anv0aNI79iBQnU1utevR/3ly5gyZYpiLnlFo9GSTrI9spNOZoavOZVD57JlePwXfwHt97+HdvAggFLni86TdBoncx7JkEqgHzpyBKnt21F36lSJQJJlkqCcUUIKF9PEyDjyKDjTNNU+5rsbNiBdUQHzxRdRceoUCoUCRkdHMTY2hnA4jFAohMevvAIYBpDPo+7oUUTtCIBTWLgoJHvH8ZNj6SQoZORZRmVG9uyBFY1iaOdOVP/2t9Bv3MD4734HABixLIRCITQ1NcHlciGRSOD+/fsYHx+Hz+dDfX09gsEgZl65gralS1F/5gwGnnkG1555BnOvX8esW7dKFLVM1eV8sC+cTyeZIQ0gF6Q0APz/5s2bCIVCKoIjSRmn8XQCZ87tZH87lRLfd8q1VNTNFy+ifcUKzLh4Ec23b2NuWxsKhQIejYygp6fnqWiQXKdS9qRTLiPiHCcCCwm+DMNAQ0NDyRriGEglyPZyncuI82SGWK5RCTJIuABArK8PY34/KgcGSvbMSTKI61Qq93w+rxw19kEd/3TkCFyHD0PXdXh9PjQ0NKj6KW1LlyIZiaBn/Xqstc8jp/Mrjewf86LTyb7JVEMnmcAf5zzwkoDF7XajvLwcbrcbmUwG01tb8b8sWIDq6mp8+umneOGFF0oMi0yFkzIodYIEWTK6IdcO69tYloWXdB0vwc6Cs0GglDuObSgUgt/vVwVQC4XiSUBDQ0O4e/culixZovafS0MoZYfjISNkMuMmEonA5/PhzwYH8YvxcbxlA+K33npLbSHp6OhQx3s6++l8nlN2ncBK6gQCO9M0VQqmEwhLR5VgPJlMwufzYXh4GBUVFQgGg/B4PHj48CHi8Tjq6+tx8uRJzJ07F/X19aodLGo5MDCAR48eqTRqjotpmvjH6dPRpet4t1DA614vvqqoQNzrxc2FC9F47RoePHiAy889h0GXC4cCAexwzLOu69iRTuOAz4e9NnBn2ym3HB/iBakrJXibrPAWSd83APzI70fAJjGcn+P+ZILQH/p8eDedRktnJ0zTxCuJBI5VV2NlZ2fJOiFQWzc8jH87Z85TWzKljn5+bAx/UVtboqNZz0auR5n+TJmT6wNAyXqS65mfo/3nc5iGz2P3nOBWyiUwsVfesoonY0ybNq2kTfws54nvybmRukfa/S8CAXzi9eLV/n7ssCZqLgHFoMtvgkF0GwY+MgxstE9jYMZDPB7Hs5kM/swmONIoJec/jkSQCAaRW78eDZcvqz5SlxmGge+Fw3h51ix0dnaWZD9xHP6huxsPczn8dHgYP7GL+0n7QwdB6jgJmFkAMxQKoba2Fm1tbcoxv3PnDubOnYuqqiols6ZpIhwO47fxOLo0Db8zTbxlGPhNLodtdsq7057KraLS7vv9fuxKp/FBLof5336LliVL0NDQgI8++qgkA4CnvcjsCNbuoYNN/UT7RSJUbs8jfrIsC2/4/fB9/nkRSyxZArfbjUQigYGBAQwPD0PXdVRWVqrxLCsrw5qODlybPRs/8PtREQ6rLTeci8n0JnWBbJ8kt9mvRCKBPbkc9gGInDuniFTKC7MiGPWlvZdZksQ5Uo4k4SLtnfRL+Bqzh5qamlBdXV2C0aWddZLfzmCaxD4cH3mimMSDUk55valp+D1KnVBd15Ws0oZybqTzz/8No1gwMZvNKoyXTqdLovxSL3DrpNPBlm2VeoL2Ss6xU09R9l4cG8M6eyuP86Q8rpe9bjde1TSU231mP2Xwmv2jDHGMgWKUP5FIPIXXpSxSnizLUqd7xWIxRWjJbE25hUaS/9K3cM4n2yznTOp1/m0YBl73eOCxLLxrE2l7XRP1ueR3KacyuOnM8u+3jwfXNE0F5V2u4iEEPp8P4+Pj2G6aeG5goJitNEkwximDklzhe1J3O0mmf+76zkSDdN4ymQwiX30F/+efKwGUafE0mtJYcCIqvvwSPevXw3fgQPH9ffuA3btRcfIkstmsOmXg4cOHCqBNdrSY83IadrbF+fknzz6LQmUl8OqrcB88qMATwTIjRZKNkoaBrDYXsHRuw8eOofbCBXXuOtslAZUErPyejPyQna2urkYymVRGLRqNItrTg3RZGYK2waVyLhSKFaPLy8uRO3kSPRs2oObECVSfPw/DUTyJSkVGBZyOqvws2yvJByoa/gQCAYwaBvIALBTTREdHR5UysCwLw+vX4/DWrWg8dw6J3/xGRfs0rRj5mzNnDpY+foyF7e0YGRnB8T//c6TKy3GnpQWN166pcZfnBgOlR6YxFZEOptynxXkyTbPEwEjQyJRBKipe0rl2jhMwobBZmIdEkZQNOf9SAXIM5fqiIpl58yZm3bpVnAtjYj9wwk57kkbUqVilImYf2Ba+x7oZEhCsvHEDrXPmYNn9+6q90iDJtSDl1hlVl2PEcZNbJqSh4f+5XA63Z8/Gw8ZGWIaB/ooKGHY0ccqUKSV7YGnY5JxSHuT7TtAj0zaDdlRywbff4uaCBWi5exch+5hFaVT/FNft27fVPmSp3OWcOiN38ghDyYzLvvG0Al6VlZUKPCQSCXXWNJ/rjFgApY4Ts6YkyHIWyAKAT91uHA6FVBQcmNgSIvcj83WuYU3T0NXVhcOHD5cAgL6+Ppw6dQrr169X2XQ8xo068+NCoRjxMQz8wK6FQwNrGAZ+m0zi54ODeEPT8INIBDsBtLe343Z/P+rq6uB2u3HhwgUV/e/s7EQoFEJ1dXUJMJF2hP3gupG6UaYaalqxjhHf41ojaSLXOm2Ez+dTjgx1GJ+ZTqdVQeGf/vSnyGQyqmAsAdKaNWvw5ptvYnR0FAcOHICmaaiurlZHQbtcLrxcUYEvYjFsicehlZXhhYEBHAqFsNCO+g8NDWHF/fu4Pn8+9oi1xD4BwCv5PLbaWYrSvkj9IGVD6gFeTvDlBLEE0PKS41VVVYXu7m41B28Hg3jT78cHnZ2wQiFsTCTw/1i+HB+cO4ecvf1H1olie1lLiW2Qjn42m1WFpjVNw75CAe9VVGDzyAhetms0SMdOOlJsr2VZ+CoUwgG/HzvGx7F5EmcDAH4Tj+Pz738ftSdPot52uNPpNG7duoWlS5c+NbZSz3PsYrGYIvyImTjusm/S4ebrXJPj4+MqbZ7Y7VAwiAGXCwdME3vtz5GgMQwDb2gafm+a2GGn2NO+FAoFjI2Nqe22UuZ5vVoo4Ffj46g8ebKkbSSBpcMs7Y3s/7+pqsJ/6e/Hvyovh6Zp6jhEKXu8N49J5KkyXLfMXiorK8P06dNx7969Yo0sO62aRVMZHXS5XNiZTmO/x4M3XS684fNhj2Ggb2wMLtFXqRsk4c4xyGazeH58HKGLF3Hr1i1cHRtDZWUlwuGwOglg6dKlqK2tVbpT0zRFWOq6riL+1Nt0+KSjQhmlw8vP5PN5jI6OoqOjQ5Gbuq5jtb3N7vbt2/B6vZgyZQoWLlyIpsFBfK+rC8uWLVP34iXnhL/lnEpMKbMf2VZGqLel0/gvN29i1MZmdGqpGzmexOq0KzIziDja7/fD5/OVBAX5PtPAJU6VJJW0tZxLJyam/WT7pf2WWEniVCkbTmxFzLJT07Bb01AmUumlTyJJGLaXQUoSVKynR4e5UCioug7skxx7tk2+58SUcq5lf6VOkXqKsk6SSGIb6puAncHglAcS83Lc2Fc5drwikQj6+/tLtu5JMleOscvlUoW2h4eHlS/F/tDGywxKp8/G8XH2id+XxAIv2W7TNPGrXA6dloV/SiaxNxpVMpzJZEpID65f6VMERTYei9/TZ2YgJ5FIqFpnXq9XHZYg14zEN3Ku5etSrp32/rtc/81EA5UyiwMSRHJSKSBkZajAgaJStfbvR9n776v7eo8ehf/zz1HwepGsqFDFWpjKwv1IUmk7OzuZ8+d0jHjVnzqFR6tWwfrggxJgI89jZX/lb7mQqLTo1JBwcLvdGH/xRTx45RXUnz6N2gsX1LjIi8Io+yBBGUFCKpVCoVBAd3c38vk8RqZMgWUYGLejFWNjY2qRxONxxGIxVJ05o54L4RDIcZlMmTpByGRjKsdD7ikLBoOYefIkOlavRtPFi8UoYUsLev/6rxE6cgTBL7/E4KZNKMRiaFu6FBU//7lyQDRNQyKRQFtbm0r9MgwDDadPo2PNGkw5dQpDQ0Mqq4XzJRlk/qYy5qJauXKlirIXCgW0L1yIe8uWQfv971V0m8qhpqZGna+bTCZVRdpCoVibpLOzE319fTC3b8fAxo1Y3t6OlkeP1BhwX188HkcoFEJLS4s69skpk07iRhIRkuEmyJLGhbLKEx+A0mwN3kMqOwl+OW9UpBKwAEDLo0dY/PBh8XMOpSOdWieA+kPOBtsnQSI/51wDmqbh4NKlsAwDumnimc5O1NfXq8JIEsjL/tE4y36wbdRPmlYs9BqLxWCaxVTDZDKJx0uX4tH8+Zh99Spqv/0WI7bxlEXk/hRXV1cXZs6c+VT6vXSwnOMtgYt01AAgFoshEomgUCgoUL9ixQqUlZUpZvvRo0eor69X59DTeMl6CSwkJaMzMvIuLwn6P41E0GsYOKjr2GavN5fLpdhwgh8ZvSdI/Nznw8E338Tsa9fQfOMGgCJZqWlFEvLmzZt48OABGhsb0dTUpObnl3Pm4Imm4X3LwrbhYQVmmF3z07Iy9BgGfgPgJ3baaUVFhTop6ObNmzh9+rRyPoAimRP94Q9xJBzGnlzxVAMe/0VZc54CQrAnCVyOG+eSTpNcH1JWZYSK64V2RtO0kowRnhZCsJ3P59HR0oLjCxYgNzqKLtPER3v2YPGdO0jW1eHrmhpsjcfx3NgYXnW78X+trsbFjg5o9fV4dmgI1iefIBAIqArlSx8/xv9lxoyijbMmJ6Gl0y/JRY6Rk6SxrNLIqhOATQZ2vvD78WE6jT+PRLBd2CU57tJBkDWQpGxyrKW+lACObZb65fLly+q4OBI170Wj6HG58FlZGbbZx2zKtcM1K59rWRYOBQLoNQyVISL7S2fvXCyGtMeDrvXrUXvxoqqNQkJL6j2pX6UDXlVVpTLdCEoJbClTTrAsx4XrUs6druvYnkrhgN+Pd0IhxGxiiUSMx+PBD9xuvJ7JYDSdhk+k7I+Pj2NwcBDl5eVKJ0kbbpom9pgm9M8+w4ULFwCR7UMMKYlhiVGkLP44FsM7ZWVqffyhI+qINR49eqQyPbm2Ojo6MDIyojKgYrEYqqqqMGPGDMTtLA05Z6Zp4oVEAtsDAVU4VRY85jhK54rOr5wbiSsTiYQ6ipht03UdDx8+RCqVUsEl5zrg1lrqJH6Gcs15dpJhzIQIhUIYGRlRATSPx4Ouri6V4UE9lEgkYFlWSRYQ58Bp851ZeXJLspwXXpRLZ40lOT5VVVUKG3O8KR/E41J+JQEgi2JyTRF/Sp3tdruVTuXzKa9y/iVep96RmFjiPqkb2S7KA+eG71HGJ3Pu6UeRSJRZqvJUPo49A17SpsvoOGWAay2fz2PErp/AMWab5FjwNVm0kH1xkprM6ONJChwHfoZZscTbzAZz6nmSJfyfa4zYs1Aobg+R8idxqRM/ud1utZ3ENE3cmTMHH86fj+FsFi/b2w6cZAF9AdoaKf9ODCcJGCdZI/HlHrcb+zwe/FkwqLbG6Lqu9ASzOehHyzUjj2ZNpVJqHGQtIPoyfX19+DIYxK9iMbySSOAVh9/u1KmcJ8MwFOEpZcBpO/5r13cmGnjCBPcIUVHTkSO4IwijoGraxB6n0dFRJJNJ9T4nhQI3MDCgCixy6wBZG35WLnb5fQnQJ2NlKGRTr1yBuW8fOkVKJR2P6dOnq0F3smXSIUwmk6qiL9vOxZ146SXkKyvx5NlnUXfx4lOOvtOxcraRE3rz5k3FzCUSiaLAvf8+/G++iSnffKNqOHDPFRVEbW2tSqOjUDsdUDmWcmzluDoNh3PBcEzY//KTJxE5fhzZbBZ9loXBf/kvUaiqQmr7dkRPnEDw0CEkt21DwN6uEra321DReDwePH78GLquo6KiAg2XL6P2wgVkMhmkrYm911LI6ShL4Mh2MQVXnnXcuXYt8mVl0PbsgX7wIKwdOzC6dy8qjx2D+5tvVA0NyjMJpK6uLty5cwfpdBrjq1bBjERweeZMLOnogGkWz39ua2tDb28vQqGQOvpLphPLMeN1vakJl2fORMPp05jX1qYAmTOlmspc14vRC86rnN/JHG3pDMixkUqc8yyZZKdilMZP3kvKgAT8TidC9l9+Xyo2PmNpWxsuz5qFhTdvovbbb3ErkUAmk4Hf70d9ff1TfZMgVBoyzr8Epj6fT6Whc1wfrFiBZDSKuy0tmH37trov599JEv6xrkQigUuXLiGbzaKurk4ZSY6jYRj4MhjEAZ8Pb2oasrkcPna5sGN8XBUbI4h0uVyoqKhQhGRjYyOePHmiwNLFixdRKBRQXV2NhoYGABPHM8p55VjyfHbKhySTKTdShwPAa5aFDy0Lr5oTFbENw1ARQFkhXO7LLBQKOBqNIunxoG3pUsy+fbsElB85ckTtT04mkzh+/HhRJ77wAkZnzkTM40FdPI5tmQymnj2LRQ8eoLq6GtebmjCycCGiuo7doigjwRmPHHa5XIjFYmhoaMDIyAg6OztxKBhEr67jY7cb22ygRBmXABhACaiXDgXbTx0+GRknSQMZsdM0TdlbporLAnKUUeoCALi7dCmSPh9+OjKCjGEgHo2idc4c3PF6MeB243A4jOfjceWESRshnXJuJenp6SmpnSEDBzKqyvkFSnWb1MnsE8EfANV3qbedY/Oe14se08Q/xuPYGQ4r3czPSVJG3seyrJL94Py8/L7USU6CwjRNPH78GMuWLVM4R9d1vGZZ+KBQwA6xRdRJLkmAyue/oWn4wLLwujZxSoIcr0KhgN3ZLH6VTKLu9Gll11544QUsW7asxFY7nRYJvtvb21FRUYHq6uqS9H3DMNDc3KycaGAiO4rfl2m5Tmf+xVQKzw4OYtGiRaoddBxl22Q0WdeLW/JS9lGpEqg6ASrHkPItdUttbS1mzZr1lKMjx9s5n05CRtqcUCgEn8+n6jFYVjEqf/v2bfh8PsydOxeVlZXw+/2oq6tT6eEA8O7ICP52aAh/UVaG70ciJU7PZAWOJbEjdQDtCtfSsVAIn2zahCpNQ9jGULynZVkYHBxEf38/amtrSwIFUgfJ9GauMzrlnFMnCSoL2LH+Wi6XQ1lZmZoTwyjWyejt7cWdO3cwY8YMVFVVqayIsbGxpzKQpP3XNE31x3lcrsSYHItPPR4cDgZRPns2Kk6dAjBR8LCnpwdjY2PKwWJVfWavUF+yv/J3Pp8vKapO/SwdY9nmrq6ukmCDzK6SOF3q5FQqVbIuKYd0SuXfTvvp9BOIiaPRaEnWKMeTW0Qpc1L/8Rks+MuMF4lHpR0vFIpbu1OpFIaHh9VJeXym02GWJJasnTUZ9stmsyV1JSR+5dhz3Fi3jkEJOX7EOdLxl4RiPp9XZIXMoJRZIOw/cQADHYFAADcXLkQiHMbhQgE77AxOiY0llub8Sf9Jku5sIy/nFjb5+5V8Hj8qL0fAJmJ1XVc1FrgFgnNsGIbKdDBNU61TBv65DgyjmK3MtcEjLn9nWeh1uXA4GMQOO7OB959MZ3JOpaw7bZBTbv/Q9Z2Jhmg0qthypmuyY7quq6IVzmPrdL1YkJDstqyE6ewUhTKTyahUkIit0IGnU7Lka07GkZM/mSFyAhBehUJBCT2Vkbz42UAgoCaWoIbPL//8c8S3bsXUM2eeao8UsMmez8nu7+9XW0fY3kwmA23fPky7fBk1NTUYtsdb9mlsbAxutxt1dXXqKEDnM2SbZLu4MCXR4ATGzsgCX5OAlQpoir2Fo+Krr2BZFkJffQXfZ58VlSOgivJEIhG1ZYXMeSaTwYIFC+DxeFTFZTKiUqnxqB+g9Ag3ma1BJe12u1H7zTdoX74c2kcfFRXYnj0o1NQgvXMnltkR33w+r1LtSOKwgquu6yj//HNkdu7EsgcP1DOGh4cxMDCAYDCIiooKpeykky8NH8f52pw5SNhFDwf+8R9RVVWFKVOmwO12o7+/H0Pr1qF/40YsunUL8+0jPAlgpMKVMsB5kHJHIOJU4HKOpVGWCpNz7pRTuSYl+Jdtm6yNk13y84sfPcKC9nbkcjn0JxLo7OxU+uDevXtobGx8Cmymt2zB+I4d8H3yCdxHjqhn0pClt2xBZtcueL/6ClO+/VYZm3w+j8bz5/F4zRosvHVLbaUgWfpd2v5/9kqlUujs7EQ6nYZhGHjy5InKqiII/P2qVRjSdXxkZ5d0WRZ+nc1i/OOPlZG9O3curs+dix8ND2OTXaWdWT35fB4dHR04deqUWlss7GUYxYJUgUAADxYtwoXp07Erm8UWoKQmgpQPp2PBudM0DbssC7tME5amoYAJR5LGUjovUnfk83ls7O/HwUAAc2/cKHlGNptV5EkgEEAgEFDkX+fatci4XGjUNNx3uZAKBtGxZg1eSCSwZMkS/La+HmmfDxVW8agsGdHlnA4ODpYQCLqu41FLC5IAIoUCto+PI20/j3ZBAg35W2bOEPj19/eXpKSPjIyo9Faux1QqpYgXj8eDDzIZ7Pd68Wf5PDZrmnIsJts3yzTZTCaDGZcuoWvdOvyrqip0dHTg3XQas1pb0djYiFN1ddhk61Fn1EVuP7IsS9UE4B5QbgGjTDhJJ46rM4o8mQ6xrIn0bamvJeiUOvt1q1gQ8kcitVaSmk4SVwJiabvkEWXSqeUl8Qq/X1NTg+bmZvWZQqGAN0wTLw4NFfWfTZSR0OTz2R/p2O3VNLw6CQ7h/5qm4ZV8Ht3/9E8YHR2FYRPLra2tmD179qQEFe+vaZoae8MwEI1Glf2orKwsmae+vj7lGHq9Xvh8PnwVCuHIrVv4cTCois5JTOVc/86xpl1kEEpmY9FeZ7NZFXGjPNCOHQDw202bUKFpqL90qQT0Mp2Z2UROHTIZngKAn3Z34910WhWHlI4hC6WOj48rcoBA3u/3o6amBl6vF21tbbh37x5qa2sxY8YMaJqGnw4N4WEuh78bGcE79ukdcu38amwMH0ejeDkexxY7EikdwckIMU0rZoONer3IPPss5h0/jhs3bmBwcLBkK8Tdu3fhcrkwbdo0NS+y4Bsj+9QXlA+Xy6XmyFnUlo5XIpHAvXv3lGPK68mTJ0in0xgYGEA4HFYFlBOJhDpFheQEiWP2Wa4pngoh17CUZ/ZT0zR8XlaGAcNA8plnELKxIwOYxIExe07lNlXKK5/BQAF1CokbAKo9nH/poMvPOuXfuQakbptsbVDf8CIZ5ySd+R5140FNw4cuF/bm8/ixw15qmqYyvZmBzXZxyxP1gs/nQ3l5ucparKqqKtnaRBlh3QtN05T+kFhSrjsS3k7yw0l+8X2/3494PK6yn0gkOPU9a0s55YP3p7w68SrHjw415UDOK+eRn5dkTyAQQG9vL1pu38alGTOwyw5cyrVNmXHOuXO+pVzIdkm7Itelk4yXNioej6O6ulqVEvggm8UvMxm8Golgj92+mNBtyWSyhESZMWOGWt8MWvzQ68U/JhLYY2fESLwvfSZpr+XfknBxfva/dv03HW8pBY6LRhaBo0GXWQ2crK6urpJKus5LCpUStF27cPe11zDt7FlMvXKlZAKlcnBecqE7HU6CcPlcEift7e2IRCIqTX+yi0JP4EflxPtHjh/HnDt3iv0U/Z/MYZtsLEzTxNDQUAmJIBX08PAwysrKSsCUBAYDAwPKiXeSBlJwZDskY+UkGJztk8SCU1nK+Wm8dg2N165hZGQEg+bEHlBGCHIvv4z+jRtRdewYvIcPq3oU0kAxbVUWcpKMmlQ6EmTyPZmemM1m4Tp8GMG//3sAgBEMoq61FT3RKJ558gSNjY141NKCq83NSFy+jGlXrwKAAvJerxdlZWWovHMH1b/4BWpra5GzjfjAwADS6bRyIKQRm8xRY1+W3L2Lc9OmQd+/HwAwOjqqCI5sNouhNWtglpfj2wULMPv27RKG1mnwnPNF0CnXiRwnsr+SzHKCOadjJg0J2yG/z7mZbO1IeXEaZSfwIjihDBtGsU7D4OCgcpJ5H03T1NGV+T17MO3qVVVokW3J7NwJq64OQ5s2of7SJdUPn8+H5hs3sOjBgyIgE8e8Odv5x74YzU2lUrh48SJGRkZUHYqxsTHU1tZiWXk5rs2di38Zi2F4ZAR/NzKCmpMnEQqFEI1G4fP5sH/hQoz4fPgAwNpUChUVFUrHHXG78Zv581HV24uYnQXl9/tVtWLTNDEwMICvnn8eCcPAPo8HLwndCDxNMsq5pD5xElwE1ySOmeJPICjlyOVyYXVfHwqnTxcBoz6xXYMgmeM1MDCg1nTN118js2sXfhQOo7WtDUciEUw7dw6maSIej2PVgwe42NyMN/x+uG0nmXNKx1jTipGrXC6HqVOnQtd1tC9fjoTLhZpcDs+NjiJpTqShM2pIgy4db7lGJLDg84LBoDpRiODN7/crsEiAsM/jQZem4RepFDZZE9lVsgCu1LtMCZ565Qr+1ZQp2Dl/Pi5lMrjzn/4TqqursToSwcv2HFgCWEqCgb+5zhYvXoza2tpJwZ0E4MwmoyMgZUbKCseFZA3nQjpFJCEkgNlrWXgnGETQrqPgvJ/Ur9KRI3Gj9L5roho706KlUyTbSnvu1GF0jmnznbpStm0ye+8kP/ia1K/yHppWPK2LdvEPOdi6rpektt68eRN1dXVobGxEb2+v6tO9e/cQi8Uwf/58ZDIZVFZWwrIs7PN48CSbxT+YJt6wa1Oxv7zn534/9peV4fvDw9hjGNhvWfidaWJbKoVXbCc3m81ifHy8pOYP8R5rrMi5pgx9VFaGsWAQmXXrUH/pkporzs/Y2Bja29sxa9YsRCKREvJejqG8fp5MosM08XfDw/hxLKb6Y1nF7NA7d+6o4JCuF7MEGQnnqQvV1dWora1VGXAA8N9VVOA/9/fjr6qroes6mpub0dfXh0QiUdTFHg96DQOfRiLY7nBK5Vw7senWRAKf5HKoPH0ac+bMwaJFi3DkyBGMjIwoArKurg4V9pF/AJR+5fxK3MTx8Hq9JcX/eBwk58HlcqGqqgouV/G0hXQ6ja6uLsTjcZw/f14dyc5sgGnTpqksNW4FY6YT1zPbRhmmjEq5ku9xPdEhe8008aGmof7mTRg2gcBtISRc6ZRzmzX7z0AKdax0EqXulJkcTtxDAqOurg7BYBAHNA0fGQb2FgrYVpgolMrvMzPM2SfnPaVTRwdQ+jtss2VZ+DifR5e9Nv+FI+DJrR0VFRWYOXOm8sMAoK6uTm2x0bRi/Zn6+npFbtBpJQkj61lRVigLHEuJxYlrpVxLR9Q5nhIPcn6DwaCaR84ta0uRAJLZtZwvRvKl/uTFtSAzAJyn5FAOJbYLBALw+/3o7+/HgvZ2eI4exZZ/82/gtTM6ubYMwyg5DYptkFjRaQecfhTbRTtLW8fAEDPNOBfxeBzj4+MYtmsBvet247Fl4X3LwtZ0GuPj46iqqlJ6OpVKqeA/iQfOcSQSQT6fx2seD+Z1d6OsrAxem5SR2EVeTpwmx32y1/5r13cmGkZHR9WN86+8gq49e1B29CjKTp5Ue3ukE8LFY1kWhoaGFOPCTnCQeUnlF41GEQqF8OTVV5GtrMTjtWtRd/GiYk3p2FNJcQHQgElh58RKh4sF5eS+Jgr4yIYN6Nq2DQ1nzqD+0qUSMME2E9wZhqEYOgoOlYlzATqdQSdhwiubzWJsbOwpZ599SCaTai54ScEuFAoYGRlBLBYrUYBOxScv56JxtlO2RUa+JxMy6Ujn88VKrmTQuS/K7Xaj/YUXkK6owPCWLXjZNtYDAwNoX7gQ3Vu3Inz1Khbcv6/64EwPk6BFGm+54Pk9GplUKqWOggqHw3gxk0Hs8uViO/N5tM6Zg1QohHvLlqHs669RKBTQ39+vClf6/X70rFqFK+vXY25rK2bevInbs2fjzl/+Jbz79yNz4gQSiQSG16/HhU2bsPDWLcyxQY3cv0fQOufOHQz/4hfo6OiAqetIbtqEvldeQeDQIbiPHIHvwAEU9u7F7Js3kc/n0TZvHm4tWoSaadMw7euvlcKmY0fZB4pEw9y5c1UavCSj7s6di3tLlyJ69y4WPXig5FU6SfwOf6RjQiMgn8f5lnLglBcpF/JZk5FcZOhdLpeKngSDQaRSqRIS07IsBA4eRGbnTjScPYuqqiq43W4kk0kVQdY//BCFV1+F9cEHePDgAcLhsAKPXq930misJFb/FJc0+NSNsVgM/f39Kvq9orsbf1ZVhWfmzMG9/n48+tWvMDQ0BM/ixYqNf76vD8erq/F2IADLslTEwjRN7HO7MezxIL1mDSLHj8M0TSX/Y2NjyuiX9/VhPBpF4/i42lOp6zo+dbux3+fDtvFxbLFfB0ori7MvlCE6p0e9XhyJxbA1kcAmO2WVoIGZQqZpor6+XmWxMZVRMv28L4u39ff3Q9M0TDl/Hv/zxo2YU1sLd1cXhn7xiyL4nTULuq5j2ZMn2JhIYN26dUoGuVZY94d6vLm5WRHkTRcvomf9euyyozy0Y1I/kiRyRiOcoMOyLHV6TdDeg0ki0tk/vveWYeB3llWM4tuF1iTRICM8t2fPxoMVK4on83R1qQi2x+PBokWLMH36dMyYMUPpUMMw8JnPh18PDWGLz4dm2+nm/Qh2uSZkH2W0kH3gOpTFFfkctlsSPPLnnyMd5XuUBz6DYI1zSTssX5MEGG0zbZB0ErgGZXvoHEsHlv03zWJmSjKZVGm+kxH3/J92m32TfZT6sFAo4LfJJD7/0Y9Qe/IkGi5fVs9va2vDkiVLSsZI3tc5hhwvv9+PO3fulIw/X6ctKxQKeEvX8bHLhXdsveE8Ss6yLBwOBjHocuH9QgHbslm873Kh2ygWiXxNOHccd+oHSaw5K6nTHu7MZPD7TAZlJ05A04pbS5xyQozHI96cOMX59zt+P36dy+EvystLnAzTNNXpVyMjI+jv74dhGOjt7UUkEsGUKVNgWRbu37+Pvr4+zJs3ryRb5Z3ycuzUNFTZDn9NTQ1GR0fR2dmJqqoqvG4YeL9QwG47uurEZZRBqTsty8KWdBq1Fy7g/JUr6MhkMGvWLESjUZXt1tzcjLlz55bYKUmKsfDbunXrkM/nVWR48eLFSCQSqKysVLp22bJlcLlc+DCbxc8GB7G3pQUvplJqqx1JmEAggNHRUXW6Szqdxv3791EoFPDcc88pkpG6nLpJyrokPZwYUzp8kvx8pVDADgAfPH6M61opwc25oL5MJpMle/qZxceL40MHtlAoIJlMltQyME1T6XPn2nW73fgYQJem4WNNw15bZtkn6kzn3nnnJW2EM5jm1I8A8LZl4T3TxPeM0joztN+ZTAbxeBxXr16FrhczVYLBoKoRVlZWhtWrV0PTNLS1tWHq1KloampCR0cHMpkMFi5cqNaDdOKZlUQdMZnvIu3cP4f/ZVCBhBR1JwkBXS9uf2bm3GR2kZ9Lp9Nqeyn1OvUL8S/xgzO7ivPMvlJGGcwCJk6FSaVS6qhYSbBIv8m5fp3jIMdA9oN2SOokiaE5Xswo8Xg8CiO+4XbjA5vwIlFFHEuSl1vs2b4x+yhRXgyW8NlOAob/0zZJAsK5dqU9+y7XdyYaaMQ1TUNm1y6YNTUYevFFWJ98UpKyQYAOoKSGARvITshUDTmBTANLp9Oo/+Yb9GzYgKlnz6pJ4cCxUBAZIJlGJNk1RjAIRAYHB0tAC9vONrHGQuczz6D2woWn2J58vli9ncwRU0tp/PmbC5nXZI4wFYtUxjxFgt9nG6ViSqfTSmDk/SkA3G8nC+dMtlCcQiYdwz9kzKUAygXoZLkIWmT9CmYFhEIhuK5dw71ly9Bs71MPhUIIBoO4uXMncuXleLhqFWbfvo2xsTF13BsXhyQznG2T7ZBkiFRO0WgUU6dOVcwtx3d1RwfOTpuGmdevq0hCTU0Nampq0NXVhb6+Pgw98wwysRhuL1qE6IkTaHvjDWQrK2Ht2IHykyeRSqXQs2EDcuXlaJ07F5Hjx1UFbc65y+VC27x5uLlgAXz5PLTHj4sk0rZtyNfUILltGyKHDiH01VdYbGeoZHQdtxYtQrKsDL3PPYdlnZ1/UMYkyCXQpqL1er24v3w5EpEIrs+di7l375aMkZxvqXwo+5QnGZ2R8yCVlSQPqPSd8jrZxTm+M2cOWl96CeFPP0Xk+HHE43GkNm1CYts2uD/6CP4vvoCmaQh88QVqzp1DeXk5TK8XXq9XkXWFQgGuw4dhHDoEy7KQsKNW7ldfRdfrr8N16xZchw8jkUigvLwcDQ0NJac7SADyx7xywpGlPorH4yplFigCK6ZDci3JfaM+nw/rhoexYXQUK1asQJdNxtCYvW5Z+MdEAmXHj2NsbAwejwejo6MoFArquLaGhgYMVVWhoGl45PNBSybVPO8PhdBjFIvYvWhHo/kjyQbKA42ky+XCp6GQiu7txNMyRaPq9XpVtIPj7YxSejwelJWVIR6PK6Dj8Xjg9/sVeI9EImhoaFBFXV0uF8LhsNqfSEArdWEul0MwGERZWZkCHtOuXsX/bdGi4nq15YBAUgJTVvSejJijzEgdzBMlOAb8oe7h/vbduo4dhQJqw2HExXjI9E8WrG1fvhzpigrcW7YM64aGlLOXy+UwMjKC9vZ2DA0NKSfPsiz8YuNGdGkaDgUC+AtbR+u6rmSLF7NQCMAYlZRr2zRNfALgA58PrxYK2CHsgrTz/C7nlf87dQbtJufosMuF/QD+XNexVxD5uq6jr69PRZtIIjHYIEEcx5kRUNpQfiaRSKioqMyouHv3rjoymnN2UNPwcSyGrW43ttiRYmn32Taph51RIaA0A49j8E1dXbEY5LPPovrsWeWYSBJHjpPEOJPdkwCVl5Q1SQjtdbnw7+fOxcDAgGo/x4vP2JpI4IDPh+/5/fB7PPieZeG3ZvFIxzJ7u0UqlSrZgkSdxQxGjqOc70KhgD3ZLCJffolT58/DtNeAJPZISrAP8rfz4udf83jwPzQ1lYyd3OJTV1eH8vLy4raxBw8QDAbh8/nw6NEj9PX1YWRkBIVCsTbT/PnzMWfOnJLx4EVZjUQiMAwDuzQNG+3tq5peeqwd28H1LMkPOd7JZBL9/f0lx+D29vZiaGgIZWVlJZmS1LXpdBqjo6NobW1VBW+9Xi8eP36Mvr4+xONxNDQ0IJFI4MaNG7AsC38fCKDDNPHLTAaR8+fhdrtRXV0NwzAwffp0LFiwAK2trbh69WrJGk7a2/PkHMg+yrXvXN/8OagXtwO+aprYbuNzrl9Z+Jnf5W9mZFBfcjstSWP6B5Lo5DhJ/4HYmdsJeXqF1BVcV69bFt5HsQaR6cDt9B2kTXauR3lJHOT0AbgeNU3DTgCv2kQm/SHKL8kdnoJiWZYqGEjMKqPlPMGAtpaR7lgsprKlTNNU4zosTtPhvDO4yz5LXffPBZT4+7DLhQ8jEWzK57Eqk1H+iWEY6oQ/y7LwqceDj10u/EjT8P1wWOkSkiDy6HK53Yb/s54RbRfbI9ebc06kfdN1HV1dXSorS/o6nBvps8o+yjHhJWtzSP3kvEc0GlW1e9gO+niU7R0A/k19Pbq6uhShFggE4PV6MTo6CtM0SzLgSCRz3dL+cTs6dbPTh5S+OdvMSxJjUn99l+s7Ew1ywtwff4z8nj2IHD2qjhKShkYanJH16xF/+WV49u2D9+jRkv05EjzRwBQKBfT09EDXdTSdO4eF9l54TTCX0pnh/j8p4HKyqWQoKCQ9OAEEQNi5E9Zrr6G8txdJjwdNFy6os4vlAupYsgSPVq2Cd/9++D7/XCl7GQElwJTRDOmcyyiZLAAGQBWskYBALhBN0zCyfj1SO3YA778PfPJJyeTLZ8nFIL/PS7KofIYUPgmGZf/kQpFpctKwFAoF5ewZhoH0li3o3rULwStXUH3jBsKtrZh29SpyuRzSpqmOlplqnzZRd+YMOjo6MDQ0pJi7PwSqeEnQL8fMsiw8Wb4cvX/1V5jy9dcIXb9eUiipUCggnU5j/r17WNLRgXg8jkd+P8rKypQzE4vFcOnSJUSOHkV861Y0nDkDAIh98QV6n3sOoaNHoWnF/XPhI0eQ2LoVDefPw7KKxI88lhEAru/di/GyMoxv3ozIxx9D0zSUHT2KxCuvwGM7xRznB4sW4d6yZSjv6QEAzL5y5SlSxenIybEg2cajbuZcvYrbLS2Yc/26KkbnVMBOMCdBvlREElg4Dcz/n7b/jJLjPNME0Sciva/KLF8FV3CEJbz3AA28JSlKLUpqSd3q6ZntWTd7du70np25M2d2790zMzs7vSO1Wq1Wy1IECBIAARIgvPcEQIDwvrzNyqz0GXF/ZDxfvRkodlN7pTinTlWlifjMa5/XfBRGciz/ED3an3l57FgkgkGkV6xAweqr0b9tG8xIBMWvfx0NVk1vsVjEwIoVePrqq4gdPqzKBKR8kYrGNE2kN24EYjHceOklBP/P/1PJr5aWFixcuFB1Ev8yo/b/34vylPd3uVwYMWIEnjx5omQh0X46DVQUlJc0HFgGAww1gHK5XNjicKB3507cunULactYqa2thc/nQ2dnJ2pra/Hw4UPMqK3Fg1mzsMkqEeJeb0inS53yhfEnHQnuPx1w2Vl+h2HgfU3DNguZ53ekM8n9oLEo6YTvjxs3Th3fyK7wHo8HL7/8sorUNTU1weFwoL6+Xjn04XAYhwMB/C+JBL7ucuHPbGM2DKOsZ0F/f7+KBtCwkhl4QHlkShodsvmUBGJkiQUdCQky2HUC70vakEAz9VYul0NfXx80TUPD6dMlx/TMGdy7dw/Tpk2DaZro6+vD559/jhEjRiAajarITzabxVuahp26jvWWsSczFWi0Pn36FLW1tWoe0pE+4HJhfyCAtckkXs/l8F4shnZdx07DwBKrfwH1gn3dpDMvdY3Uc9IZ21lRgXbDwE8SCWwMBhWQoOt6mWHGMfKSkXMpa+RxYfw7KO7LCCiz78gLdD72ulzo1HV8EolgrZVJIU8gkbKQ5SR0EuwyUPJ/sVhqBvnLwUHUnz2rjl2bO3cu5s+fX8Zv9sCM/Jv35vyk3QKgLEWdn+V6St6gfOLfa1IpTHnwAAsXLizxtq5jq8igkzKS+yBp3L4f8vmMdvL9dDqt9pZ6+1+NHo3n3d34ptWYU8rln3Z3428GBvC9SAR/FA7jFwMD+FE8jn/m9eKbkUhZtFA6DaZplsk0lkFQDmhaqazKNE00NDQgGAziFwMD+Ou+PvxTTcM7FgBM8IKgEOlcRkLlntGxlQ7T0VAIexYtQk0uB88nn5T1pABKkcn29nbVZFf2SZHP7evrUxmvDQ0N6phx2ZC3q6sLAPC2w4HfaBrmdHSoMfFYX2a+VFVVlU6jsRzyL8aPx96ZMxGPx/F2sYi9AH5VKGB9KoVXxJhJF9IJ5bpkMhn8euRI9Lhc+C2AhT09ivd4PHgymURLS0tZs0ddL5UIdXZ2YuTIkQq0lScNsE6fwT8CTQRtCChIUKFYLKrsEdL/pYYGnG5sxFarO/8Say79RnmmNLOpSEeS32QQUfI+XyP9S16T8onZ0rw3wWJGsFmyQZCYTru0MwmAEGjw+/3o7e1V6yrtYzrc8XhcBTekbh8uAs49tfsp9jnv8njQ7nDg08pKvGKVUNGGJDBSKBTwvt+PNl3Hz7NZfM3qy0NalNljlGWcI/lBHg8tS9qHkzsAVJkIdRGbMJJeaD+yvEPKd7uskw669Gnls+3Pp/6y26pSHxMA4nek7PN4PGUnwKRSKZXNwRPquP/Uc7JJsMzCl4ABx86/pa0jaeZ3CcL9zkCDpmnwHTqEkIW6ZyyDjU0gubm8Bl5/HWZDA3KbN8P98cdlaBkvu0AiQ7G5ISfMjSDhy02XiyDvLZEvn8+HmpoaFdUjEeTzeeS2b4dRX49Btxvz/vN/BgBktPJu/KZp4vmiRchFoyhu2AD9o4/UZkpB4rGiqpLZuDGSce0OfCqVUuecSiPXjjol16+HWVcHbN8Ox549Ze9JIh6OuPm3nUgkSGFnzC9jFHvGgP2SCPTNLVuQq67G47lzMebGDbX+hUIBnZ2d6O7uLhmit26h8le/wiCAnKXA7WftyrnYDS/+lsAYALQtXYpiVRW6V61C/cWLihFp3CSTybKMnHA4rIQQ07JGjBiB/LVr8N+7V1JShgHvoUOIvv8+dF1H3qIVx0cfofH4cVTX18MUx31xXMViESPOnsXT+fPh3LsXul5Kf8voQ3XGAJBctQontm1D0edDPhCAaZpY/bd/C03TkDPLyx2AFzNX7Htrmib6+/sROXECS86ehcPhQB9QhsjTKZJ0L5WmdDTtfCsNV649QTW7QWoHNiQf857Tb9/GpeZmOPfseeHzTqcTS5cuVUrlww0bkK+oQM+qVXDu368yaezRFiVH3nsPeOMNRI4fR876XLFYOkqW5RVS4P6+L87RnpUFQIEIbIDISA+jEpy/1+tFf3+/avzK+9HYoBPM+3s8HtVASNM01fR0blsb/lWhgCIAh5V5UywWsS6Xw7ZcDgXDAIQRRLrgHOyOoqZp2GSa2GTJMMPpLOMteVQVjzGW9CcdTo/Hg6dPn6qmkOwUnk6nFcDb3d2NBw8e4P79+/D7/fB6vXA4HHhv9Wr0GAZ+kcvhB4IvZP0+AFW2QMebtENakXxLwEeCbfaoCWktkUiozyWTSfRYRjV1Whl4bzm2bEjKteBeSN3HcTVevoza8+dLn7eOhAag9DHlCkshWKf5DY8HrZYOlP0K2JSXx9gxkiRB6APBIDqcTnwcCmFDPI6NmQz2+nzYbBlonLvd2OJ8+b49W4K/ZTT7LU3D+7qOd7xe1beGRiWbRBNkkCWQvJdd9xL8l+WXTqdT3dfhcCh+GTlyJBoaGsqyAN7O5fDLfB4bLNpjlp2MmEpZK8tPJN/bDXNN07AulULrT35SAtwtfnn+/DlmzJgxbISYaysdO5mGawdZZd36l9kGUv/TNpHZoHY9Kw1UgtlyruRzKb9klI/PYKNIORbO78q4cRjQNPwkkcA7FRXK4Oee/qi/H88MA38zMIB3KirwN/E4nhkG/mtPD/7ICk5RpspnsE9FZWUl+vv70djYCKfTidraWnR2dqrxScfsb+JxPCkW8X93d+Pb1jHJdOB8Ph9+0tmJD0IhvNrfj9dFLbvU/bKEhzbegfp6VeI24eBBPH78GNlsVskhXS9l2ESjUdV3iZFt7mkoFFL7IU9/k3KXstc0TezQdfxpQwP2XLmCuAVCspTOsAI/V69eRSqVUqW6X0ydisFgEB8Wi9g0OIhf+3xocziw1+fDK1afEAm6ydRxZlPG43FMvHYNt6dNw3qLPvhDGciyQLkGBEp52gUBCOkUSnBPymjpc2jaEMgIDJ1WQPp0OBw409iIHq8X+1wuOHM5fOjxYEsuh7UCpJP3pi6RGVGShiXPUI7abSrp+O3VNOx2OrFF0/COxUvM8qZjLZ9DPUZwgjzO55A3mRFgz3SirDJNE7FYrAw8tdt2dj4ts6lsdiff35rP4wNNw9ZiEbFYrMxmkHJsh2FgJ4C3bD002D9CjpnrT11O2cNAC9eB70kfhuN1u92qWabP51PlHSx7kvYTaZpZPbIJKdeFwRKZ6UL5KWU9fyTty7XkD4MLpG3SiiyHoL1gGAZ6rCOX5VhoX4VCIZWJydIo2uXSN5WXtGUkCCbp5qteXxlo4GD4m+ec8ygmYMhh4cRN00R+3z6k1q+HY9eusoiGdLilAuQGSSNruElJg3e4SxI9hSvRfJ/Pp1LAFIq0Zw+KO3Zg0s2bqKqqQjqdVkiTVN71J0+idckSRI4exaBleAJDNeUyvW04cGQ4xcdN7u/vV2OVRqz9f+f776OwdSv0XbvU3sj5Zl59FZ+99RaaL1/GiKtX1TM4B1nLKg0PiZZJIuP47fQg94EKTRqlfr9fGYDNly7h0Zw5aDh7tqwutq+vD52dnSUl8soryG/ditjhw6g6c0ah3NwHmdprH4f8WwpEXo2nT6Nl0SJUHDpURsuskWfDRyo32WCIDF5VVQWn06kcCI6L9EtHj+lOVAx2g1vXdTRcuoTQ0aN4+vSp6mDe/8oryNfUwFy3DpH9+zGwcSOMqiq4Bgfh6+3FiHPnytLdmAbHunsKXalYKUxk2rBdONuzfGSpE41UXlRCfE0qTTpQ8khRuS92R8BOX9KpA4CJd+4g+957ePr0KYoW7fvffReZTZsw+vLlMgN/ys2buDp+PFwffohUKlVWryl5SMmNffvg/fRTVI8Zg3bHUKmTrutIJBLKiBtOef4+LtJDMBiE0+nE583N+I+zZ2OcpmH14CCi0aiK0BeLpXq8hQsXKiOMyH0wGERlZaVyaGiQMRWedEy5TAUmDQei4lLpSWPYLl+ocKQylmAV8KIiojEowRJZX0+Zz/3UtFI/mnv37sEwSlkLra2tiuafPXumxvfw4UPcunULVVVVaGhoUEcSruruxrmRI1WtK2sZOWbWjN66dQvhcBifjRiBW6tWodjSgrcDgRcAN64LnW8Z5ZEGjJT9NITJM5I/7ce9kVaZgSCVu3TSua7cG7l3fD5pWRr/EvTj5yVQrmmlBmKTJk1CZWWlyvjjsb+GYWBLPo8PAGyysmY2mia2WPQE0RjNrrO4jrIEk839pPEinfNvOJ34jhXxSqfTir4oU6WelSAM10oGLrgfKoMR5Q6g3DMCEPJZxWIRW51OzOvoeAF8lOnQfN5+pxN7XC5sKxaxUUSgJe/zonwmz1Fe9fb2IpFIlBn8cs9JOxUVFSoKP3PmTIwePRqhUAgLFiwAUJLHs2bNUrzOfXA6nfjQNPHOzZt4x+fDWxYozvtybKZpIh6Pl/rDiLTzXC4Hl8uFAy4XdrtcWJfJYINo+M1TEXjiGPdZ0ukHhoE9K1eiWtNUw2/JVy9dv457M2bge01NL+h/0zTx3VAIf5dK4U+tKOz3Kyrw43gcf2I1zLYHJzRNU5HLYDCoatJNsxQ9rKioKGuMN3XqVBUB/9NoFD/q7cWfRaNqfDJbZY/Xiw6HAwcrKrDOMvplJpPcb44fADak0/igUEDV2bMYN24c5syZg7Nnz+LRo0eKdwKBAAKWTOI8KFMoR2m/kKdlOQI/TzqXxw3b7atCoYCLFy+ivb297Pjxidev4+GcOdisaaisrMQ7AP4+k8Hr1pH1/GH0nc6hYRiqNOf69euY7nRi9eAgOjs7cbepCVOnTlX87HCUmlg3Njbi/v37ZQ4tQSsJUFNm2G0cgjrAUB8mSfeUCfwM18fhcGBldzeO19TgTYcDu7xetGka9ng8eEP0EZBygzxiz8ojjUj9wDna+21w3Lqu44NCAa0A9ng8+I5j6EhKZjlynlxnvi9LpTknv9+vbEaZPSABAhnwICAkdZ6UjZJ++b4Es+R8+NlN2Sw2sjeCrpfpG35f13Vs1jRsMgz4HEOlmLwvGwlHIpFS9kOhgL+/dw9/HAxie22t2mcCDfbxcN84T/7PHkoEuVhiagelpE6Vtop9zhwz+YvfleAI185uX8o9oc4jT0kfkicy1tbWoqKiQtEWe5mRFjlW9sXo7OxEVACksqRS2sYAlAzhc7ludrDmq9rHXxlo4A3JjKwfAVCW9iSFq2macO7fD9euXaXvW5vAz8gmgcFgUCGpFBJdCxbg0bp1aDh9Gg2XLpUJBYmw2yfLBZBIkQQ1IpGIivrx+95Dh1D92WcYPWUKXFaqDAmGhrtpmmi6cgUNly6hvb0dSZtAodPHNbEb29LhkY47nf++vr4yIWVPzVGvf/QRtL17S/MSc+Bn8lu2wKyqwsPZs1F99qwStNLB5DNZq2N3FOS8TLNUMvJ43jw0nTmDxsuXy8Yj5yK/09XVhYqKCoRCIYy+fh3hY8cwODiIfG2tEpiMGJimidzmzTDq6tCzenVpjtpQehsFA9dLGiTyufJ1acTWX7yIyPHjJYVXVVVWM8x9I2r89OnTMqFOeg2Hw2WosoyYS5qUwox7I40e0yylhD6dPx/hTz5Bw5UryGQyiBw8iP5XX0XwwAEAgG/vXhjbt2OCdYKHYRjQLT4hHzE90OfzlfXt4Pzp1LDzt2maL6TzSoOJ45UCVEahWDNIpSrRcfldjksKZXvWxP1Jk3Dn5ZdR1dOD7lgML924gRFXryIej8M0TUSjUQUYqFS7jz+G9+BBuOrqkJ41S0V2QseOIfQf/2PJuBLRPUmbku84d6ZbMqJJxfPo0SNUV1cjFovhD3FROVdUVCAej+Ph7NlIh0K4PW0aRv/yl+poxMmTJ8PlcuHZs2e4ePEiNK3UMK2iogKxWAydnZ3qFAoqJNJ2MplUUWoqdzrKuVwOyWQSj6dPx/lFi9BrmnhN1OnvLhaxPxLB5mwWa6zu5nLswJAjx9IGmRbP35QzVMB2kKqyslIpNblHdr5nxIH0RH6lw8N7RCIRmGbpVJVXMxn8ZWWlSkXOZDKqRItphjKScfvll5GKRHAtEMAfW2UAPDmDjeho0APlesZuVEqQAhjiTwBlSlvKLBp55BfpcNGZpYyRck5+1zAMpauG4z2ZiizTi5luyegOx07QhvJro2niFSvT0GnJkeEi9lL+SP1rzyaxAzl2Q0a+JudLOuF9SS9SHnPd5P3lPe38yHtKnSZ/7y4W8YtoFK/F41hlNd6S8+aVyWTwXl0deh0OvGcYWGg1MLXbJRIYefLkCRKJRJmsM00TT58+xZw5c8rWQM6d+82I6s2bN9Hf34/e3l7E43GsWrUKuVwON2/eLPW9amxEsVhEZWUlwuEwfhmNolXX8dNiEW9b0TG5Drx6enrQ2tqKuro6ZZ+Rzvd4POjQdRwIhbDeqheWUWpGJblOpCcAOBYMIuV0on3pUjRduVIW3XU6nZh07x42mia+NX162X5y/b5bXY3vWmM0TRPftEooJE9KfQZA9YUxzVKpEEE1l8uFu3fvKtqkY04++k4shj8WR4ZKegSA7UbpxITNhYLSI9xv0qbkZdoeqwcH0fzoEU5cv46OVEqViDHNORqNYubMmWUykr+5vpLPqa99Pp8K9vCz5D/aD5RLBGA51ps3byq5zZT0KQ8eYE5rK5YuWwZvfT2+7nTilXQaccOAy3Js+MP50VYhaMLmlAMDA+jr60M6nUZTUxMCgQBaW1tLJ3tVVSlZJG05/k/6OBwI4N90dWFFczOW9vXhaDCI/+7xY2ytqsIKtxtHgkH8D0+e4K2KCswePVrJ3VAohEmTJuFjtxub4nH8wOks6wGzPB7HykRCHRH4WwBvaho0275LnpRZI/bgp7TJuMZ2cFnKua/pOt41Sj1Q5D2Y6c2sZ5ktRKCB6y0dZa4f+yGw4bPkCek4251nO89KWU29K9dEzk3eW9oB/I7dd5B0Aww5vNTxzDj823S6lMkUj2O1cyhjnWCBvUExeU/6PPyO0+lUZcW9vb1qDnY6lgAF524HG6QOkkFqe3CGtueX2eHMZPX7/Ugmk2W+Mm0ETdNU039d19Hb26vmxwxwTSsBvVVVVUilUjgdjeKX1dXYbhjYYa27tIvt9jFpxz5Xu47+x67fqXSCwpsCjwsgN0UaR/YFlkaANHzpCFEh0XhNrVkDs6oKLYsWofb8+TKhyjHZnVE6ViRSu+HLTSTBS2JOpVJob29X6VtEfSiwGREbHBxUTRvlgjM6bo/ocnzyIpOR8NjIQ66dXRgAQ0Y+8KIgoJJx7NoF7Wtfw+jLlwGg7NgT+/7YGYJjsxt+T+bNQzoaxXPrBBCJykti4x7QkdF1HeFwWNU+ce0qrY7QMtXLt28f0hs2wG01GKVDYgcTpECU6283LOT3eBGhpHAmiESh3dfXhzbrCBjSBR1zGX0tFksdjBk1oHNHx0oaF1xz8ophGHi+cCGysRiS69ZhQjKJB1Om4Nm0aZh69Soaurpw6+230b9oEcZduIDme/dgCAUgHRmOSUY0aQQyEsO1pOPJ0xx4LrXL5VLpYhRU8jQG7o9sSsSjtaQTI50BCb5RSMnLNE3cfOMNpCMRJINBmA4Hrr/0EtLvvquil6ZpqkwNuzAcHBxUJycUi0VVemOPLkoetPMTo9qhUAjFYlH1GBgYGEAymUQikXjhRIrf16Xrpdq8eDyOtrY2TLl5E3dnzkT0+HEFHlRUVMBrpY13dXWp+slQKISRI0ciGo1iYGBApSNKoAGA4i3SeqFQwOPHj9HU1IR8Po/u7m48WbUK6UAAOzMZzLeOU3M4HPioqgodzlIzvlWDg2XjtstyecQX115GbWXaNveCOkAqcc1G44zySsCYdE4jnPTK94LBoKLpUCikAHDyJqNg5IlQKIRp06ahpaUFcx48wIUxY7DdMliAklPCqD4zffh8jknOW46RsoXAiIycSHrmuhLEsOtIYKgWXDrX8vl0CKhTmdopjzmW583zGZQddudSPkemag6nJ6Q+pvyVazKc8SmPxuR+S5CG4+OcpIPOew2nyxhRpfzl56lLCBxJOuP70oF/8uSJahjI+//K7y+VjYTDWGWVl/Cio8pryq1buDJ2LF7J58v2cjg9JQ1PrjVpjaCr5BvSIPmKa0gbxul0orGxEXfu3FH7kk6n4XK5UF9fD13XUV1dDYfDgTcB7HG78S0r00Gm03IdgVIt/MiRI184pcvlcuGbhoFf5nLYVCyWlbSw8R4ABdRJ+jAMAztME78cHETFqVNl+ySvLwOH7AACPzscDfJ58nsycGGapVKn58+fq4hxPp/HF198gebmZgSDwbJ7S4eBTt52lwtrrTIQhwAa+Xzp4MhAhxzXwMAAHjx4oDLZgFKT4GfPnmHSpEkv6FLeS/Kh7KzPwIjUDwCUPch7SF1uB/Ps9Hvv3j2MGjVKRVPt9j75S5ZyAcDz589hGKXsBsrgz0aOxL6aGrzS34/k4cOl8WzejDNLliCcTKLuwgU1V5fLpZ7p8XhwyOtFV6GAw9EovhEK4V+73XiWz2OnrmNrZSV2Fotoyefxa4cDy6zSU+6Vx+PBh2432opF/KivD9ut/hT0QRjg2OR2YxNpSOwl9YkEmuxy2U7r1JvUa3bQkLTxumFgvcOBj0wTWwcH8R3TxNes8RMUAoZ8AfYWYvmfHAsz6mTzP3kig92hjMfjZUctSr2g2ebPeUqQgeOSjj3nKp113oe2Mu3yTzwe7HE48M1sFt+wggnskwZA9Tt7y+HATpcL3/b7kbFOPKTeo/9IPUdQUfKMaZaA+EAgAADq6PTnz5+jo6PjhWAA14d8xLHLNZG+qORryU9cH46XMpzAogzkFwoFBYQQIAxZTTIJ9sZiMXXfRCKBZDJZVmpumqY6djiXy+FdAO1OJ97L5bCss/MF/ue47GO161xev3eggc4Vo2GaVkp95Ln2TFfmw8k4qTVrkN20CY5du+Dcv1+9J53IQqGARCIBp9OJ5uZmaJqGtrY25HfvRn7rVjj37UNfX59SjnJT7YqGY5ULIY0dEhgNEan4Zdo4o2ASYfd4PKXuxNOno/Of/TPoO3dC03UUt22Dc/dumAcPljn1wJBhLgWQ3ETOhRG24eYjQZEvU7plxuv+/Wi6fh31I0cCNmPMLmCGe56dSUzTRNPZs3i2YIFqgihTAu0OPYlb27wZD1euROHcOYy6dk2lfZMR6JRzLYJHjsB14EBpf0TUPWjVH0tnxf48++t2+qCQY9SfzyUN3Bo3DjcnT0b08GFon32m6rCcTifiy5eje9UqjDh7FuNu3VKpVfKMc6kouMbsHMs+FXLMjWfO4PmiRWg8cwbt7e34/I03kKmowO3p01Fz7hw6V6xAIRrF0/nzMfHOnbJ9sgt/eW9GDSig5MUUW0mPsVhMpcC6XC4MDAyojrVsqpRIJFQ9ZDgcRrFYVAKNHbuBUjOaYrGo6sa4FuQ5jpPKpenMGTxfuBCh1lbE6+pQ+emnCrVml/JoNIqGhgblIGSzWZU+397erp7DOlK7Iyxp2K4UTNNEz+LF6Ny2De4PP4Tr1Cl1hCaNvra2thf44/dxuVyuspTVGa2tePnpU3W8WCQSUZHlfD6v5sdmVnQ6yVMyFVYCS3ZHfvTo0SpVOBAIYNT582hftgzrrWwGOsObslns03VstEA4ri0vGcUZ7jQF0oRd9jATQaakkx5oHNllj9Q7lZWVSCQS6j6kCdIBjRYASmYDQ44650fQuFgsqgZiM58/R+TECaz//vdhiDRjKcM5Rqk/pOMgQRGOmU6bjDRIhT2cY8Tvci4yMk9ADCg5cNOnT0csFkNNTQ1M08TIkSOxfv16xGIxZZgbhoHro0fjPyaTeMM08ZrQC9JwarWa723OZvGK5Zwy1Za0dNDrxUGvF+tTKay1nFx+XxqnMnLFPZBOEYEwyjK5h/zNyLB0QqXRKo1U7oE8rk4a8vzfDnpybHaQi3MASvppaz6PXYaBjZlMWVRJOmlffPEFuru78WZzM8ZZ/Xc6pkxBNBpFwHIW7GAKAIwbNw6VlZXq6EyHw4EZM2ZgyZIlar4yWibphj+kSQlI2fUgnQ+CYRtME/966lS0t7eXlVTa19vhcJRFmbmPmqbhLZ8P210u9Fu9DOxROgkSycs0TWw0DFScO4fDFy/C0Evd1mXdvdPpxJgxY8oyWLlH8hoOcJDPlGtAwJE8xaDRtWvX0GE1R+T4U6kUrl27hkgkokrMZPYnARXafBKs49oBKNXdh0LYWijgdUvuk68OBwLYO2sW6gYHoe3dq8BQZhnlcjl0d3erdZW2G0vl5NzsqfT2VHkJHjocDqVP6RCapompU6fi/v37GBgYKANECoUCurq6SiWfApSXwZ+BgQG157SVxo8fr47f7e/vV/c7NG8eBvx+7EmnMdXqjXG7uhpJnw+DS5ag2rI3aZs/ePAAM2fOhNPpxIquLlwcPRrznz7F+YcPsWzCBJxubMTGVAr3nj3D6kgEx2tr8YZh4JZ1RHhjYyMCgQBu3LiB+bEYLjY34wdW3yLSzaloFB8FAljZ1YWvWSCJ5CPKQq6Xruuqj4ekOZm5JL8j95E0IgNStLV+7fejVdfxs3Qa3wiHy3gcgLKlub/MXOH+SoeVYLkEgki70glm1qsEG0hP3DMZwB3O/pXAv11GynlSF8v1+NDtRpum4Vf5PL4pnFsCFpQ/G00TfzFqFLLZLB52dysakWWZ7ANEm1fqW9oiDMZ1dnaivr5ejY/jkbrCriPoA0mZI30M8uNwcorrShqRPW5o1yWTSdUniOvu8XjU8ZsOh0Nl3Mp152/J/7QjvuF04mdWuZNssC/tHNnDRMpCCWba5/OPXb9T6QQjQ9xsACptj4YRB67e37gRaGxEcft2aHv3qg20N9TSNA2BQEClenm9XnRfvYqeTz8tRYSamsqIlt+Ri2D/f7iMgGKxiM4FC9DzF38BzwcfQNN1pDduhPnhh3BbZ83bDUEKbxofT+bPh1FbC3P79tJz6uuR37oVmqbh2Y4dwNmzqL94sczYAVB2P0nIrMfhZ6RzbG7ciMLWrXC8/z6c+/eXKXB5T0Y1+H2JtvI1O6Io35dGgdxz/t94+fJQyYQNXJD35bPD4TC+WLMGhaoqtC1ZgrE3bwKAarxD5URBxftJQCaxciVS69ej6exZeK9fL2sEJZ0SCrmW2bPxbMECNJw+jfqLF8vmQcdIHpFDgVMoFHBz8mQkIhHkly3DjIsX0d/fr3pm9C1dCqOqCs8XLsREK0rEHgzcT8mInEsmk4HX6y3rDDvcepoAGk6dwvOFC1F/5gxSqRQaTp/G80WLMOrixRf2hr9pCEqQ5eHUqbg3cyYmfPYZxn/xhRoLP9O9aBHali5F9ZEjqDx1SpWHyP1IrlyJpwsWoPb4cWi6jvZlyzDy7FnoR48qwel0OlFTU1M6KtI1dEY6jVyi0HTmCNIRuCgWiwgeOYKpJ04oYyebzcLUS2ntbNDJo38IiND5Z/YPBTDpfzhjT669XEtN05Bavx5mVRVya9ei6swZBZwyGs00xd/3RX6kYg8EAuiyUqzpIMssFY5DOl00cB0OhzpBgXNn9hAjNE6nE8FgENXV1ejr61Nytu7CBfyTpiY0NTXBtBxuXdexTdOwvVCAoeswra7l0tgin0tZJi+7UpbyQRoWNJLt0T+JpLtcLnR2diplzP4VNJQAoKGhAePGjVMnUTAjhDwi0y5dLlfpqNRUCl6vVxklrN/l56XjbDeaJAghZRH3VTaB4lrReJAGjJRlBGBonFM+Ub/Kcj4aNMlkEvfv30dnZyfmzZuHyspK3L9/Hx999BGqq6sxY8YMdXzjB/Pno88w8BvTxFrhvHI82WwWzxYuRNbrxUdOJzYIAEA6bocqKtDhdGJ/MIh1/f1qj2UGBvfQbmjKYz75PZmpKOnIbtzIsgJgyJiU4BRLOinvSVdSF/N/Sbd8jT/jx49HQ0NDWd33lnweK/r7S6CRritDjXvV19eH1tZWaFqpTCcQCKCvrw/37t1DTU0Npk+frtbi+vXrSCaTaGxsVOAfHRXdurdpmqofC40/0idfk9mIkmYGrSwkqdv5Pj9LQ9kwDNUsjM/TNA319fXKiZGGvqR7KWtpKHM9aQ/KCK59PHSC6Bhx32SZJ/tKDGf/2fUi/+c+yqwWPpeOHOUrsz14ehlpkd99/Pgxxo4di8mTJytbg/OUWVmDg4PKiZbGuqaVjkVtdzqxyzQxS0QS8/k8PorF0OPxID13Lsbs2YO2tja1V7Q5nz17hq6uLuUAktYZHJBrwLLg4crZOGfuq8PhUKU2n3/+uaLd8ePHo7m5GR9++CHS6bQCd7mXz549Q2VlpcrQol/A0ivSC/kvm82qBtuDg4Nob29HIpHAvMePcbKuDtGjRxVgHDtyBLmlS1H56adKP5Jm2TjU4XBgQWcn/r9Ll2L/jRsYcLmwpLcX/37hQty9exfPdB1rUin8ZV0duru7ccKiQ+pLAJjb3o4/HzkSk8LhsiDpJ+Ewup1OHKuuxjcs0Ec6Xtx3Bl9lHT0/Qx0g5RzXX/5PXiOYJ+nuTU3DTgDfFD1yHA5H2QlUfJ3OJG3kTCaDYDCoGmPKPkpp6+QHzlcCHgTUOBYZUOAe0JG1yxfJD3ZZS3ktQS/pR1LvvQ3gXdPENy1QRIL37FnA+/JevAcDVJQbvOw2IWWPpmnKPu/r61O9uYChjDup76Ucse+r/F/amLInkVxvjpkyjesmbXvDMMoaILNfHX+4X/xsKpVSvcWYrZlIJBTgAACbNA0LEgkUikXo4tQdaWtKXSHlPcf//+T6ykDDqFGj8OzZMwyuXo3cli1wvv8+Bg8eVB1386+/jty6daXMBavG3DRN6Lt2wdixA9pvf1vm8MrIupwEicrn86GxsVGlwsjFkoJTOqh2R0xGl6TR0rpkCYqxGDKbNkHTdRh1dchs2oTg0aNqwYES4bbPnYvnixej6cwZ1SfCs2cPCuvWwbd3LyqjUXQuXw7Nyr4wamrQtnQpJty+rYx2GY14NG0aHs+di0ar1wFQigRTgEqFaBgG8lu2AA0NMLduheOjj9T8pPNsmqYy2DVNUxH04dB1u5KWTpdcX7luQDmyRaOhbe5cPF2wACPPnVNzkeBJ7fHjaFu6FKMuX1Zryl4HsrEU722vP06uXYtCTQ1alyxB4+XLZREdue8Uis8WLkQ6GkXLokWoOXeubG4UPqxTk+lN2WwW465cwd0ZM1B7+jQKhQIqKytV/XzhwAGkN25E85UrKsJHYcsfGcmRNB2Px1XDP7me9gyV2vPn4T14EBUVFdCdTgVE+P1+6FaERwIKdF6kMW8YBh7Ono1MZSUezJ6tgAb5vY5ly5CvrkbvmjWoOX9evU7jPJvN4vnixcjFYuhYsQIAkKuuxvPFizH24EG0z5uHntWrUXPsGJo//1yBFEzz6l+2DI/nzUPjqVOInTmjyo7Yf4TGM5v2BINBlQVEh5llHf39/YjH4wgEAqqcIxQKob+/H8lkUn1HZlNJGSAVnlx7Sc/6rl0lEHTXrrImQplM5gUh/fu+GFljlFH2uKHiJZpPVN7hcJRlOjDDgSmP5Hme2sP5876cD41Gpg/K9QJezJyScsnunEllL79rlx3SEAOg9plpnVx7wzBwb9IkfD55MiZeu4bxX3yB3t5eNY7m5mbMnj0bvx4cxA+9XizasgWvDgyojuwc40cOB3Z1d+NbPh++bjUtpoFGBZrJZPD8+XOkUilcbWrCrdWrkW9vxxbhSMjIlZzzcAYHHSYZRaERwXpaGXmSxgUdLumIy8gnf+RnmA3I8hg6PASTGhsbVQbL8s5OXB47FtstuSGbKdIwG3H2LPpfew1bCuVH6Ml5bs7lsEfTsMnSOXTMSFvS8ZcGOuWkPShgd/j5PTuwQ5nN9eRRp16vF79KJvFfNQ0rp0zBmlQKB71e/CqRwIpJk/BaNovztbX4N0+fYktNDVYHAjjk8+H/3daGH8Ri2GHth9SBlKuyGZjd2ZA6+JDPh13BIF4aMQLNn3+umt8ZhoGzVVV4NGcO1iYSmHz/Pqqrq9HZ2Ylbt27h4cOHqKmpwelYDEe/+100nj6NUdeuAQAePXqEzs5ONDQ0lDX5I/3JyKKkRZkpaOdJ/i15HRhKv2avh2KxiOfPn6OiogJ+vx+XGhrwS9PE9sFBbLBomTys6zo+MAzsdrmw0cpy0fVSdsL10aNxfeJEOAYHsc6WRZjP57FX07B33jxUDQ6i4dKlsrmQLi9duoSXX375BZtP7pedD+02owTlb9++je7ubkSs4y+ZZTRlyhQ8efIEHR0dZXpc0qL9krYrAQu7baBpGtYNDuKjQADrrExJjkXXdbwaj+NAKIS6CxdQX1+PqVOn4tGjR7h//z4cDgd6Fi/Gw1dfRXWhgGbRwI36jwFApplLm4T7RBnGS56yEIvFyprotba2IpVKYdmyZapk5vnz5wBKgO6ECRMwduzYsp4wXHs6K3b+djgcSCaTCIfDqKqqQjAYRHd3N1Z3diL5H/5DKb3d0nsjP/sMDZculRxLy8lm+jszjaXtK4FKAo8s2+E6cHx00lUg1Fo7OuLZbBabslm8Xyhgq8is5CUDY4ZhKLBHOrvSz7Hb0na7W2Y52UHZt3UdXzNNBMSJa5RF8ohKmV0h7QMACtil7cvsPQKoUu7Ksj05J+lwc235Wwb/7FlLw/lj7A8hsz94X03TsM3hwDYAAWssBMUImMhgHucsy71YBss1kdmWlAOkJYfDoXif8pC2fTQaVfOgvLGX8NrlgaR5KW8lACs/R7CFY5e0ouu6Cj7HYjGYpqkyjSORSNncaDuSjjVNw8/6+vAjTcM3YjFs1ob6swwHEsmm01K+2umSY5d+5XAycbjrKwMNLpcL0WgUyTfegFldjcK2bXBYNVWJRALJdetgWpkLro8/VoNwHTiAgtW4EGIT5MKTIZkVQYMZgDoBQkZLpJEmN47XcIwuDaL6EyfwZMECeKxj84zNm+Hbtw+VlZXqiDneo2XxYmRiMbQtWQK/34+7L78M49134fne9zC6uRljxozB03/37/Do0aPSJu3YgfqzZzE4OKgQOLmpj+fORToWQ8uiRWi8fBmtc+bgyfz5MN99VzV4lBus79wJY8cOOMQJE6Zpwvvmm0iuWwft3XcBK1OETtykSZOUAJAEZHe8pPCwO2H2z9mFoGEYeDp/PrJVVXgyfz6y2Sw6V6xA05kzqL94EblcDuFjx+A7dAhjmpuhCWINBoNIJpPqGDUqDZkaZZomggcOYHD9ejScPfuC0yLXggZXw6lTaLFAId6DdNM6Z06pVOHUKUROnFBOHI3t0TduYLR17KZz5Eh0dHQgm82WUPtr1xC4fx+1tbVKwMqyCQp7/pap5kQf7U2Y7HQrDXM5R15SOMn0SCnUTNPEuKtXcX/WLIy/enVYhV974gQ6li1TJTDSmaDzMfLcOTyZNw9N1rq3LFqEpnPn4Ha7SydjVFWhe+VKjL5+XaXU0gB4tmABcrEYWpcsQeDIkVKp0csv49nChfA9eYL0qFGoP3kSbpcLTxYsQPDZMwyOHInY4cPQdR2dy5cj8skn8Hz8sYqeDAwMKIecz5JRPwDKSOAaSx62O4RSTuj79im+6962DZ3btyOwfz+Ku3cjn8+rc8d/3xfpkqmPTEE1TVMZBKwfJMoeCoUQCARUAy/22yBvsl+FaZrqKDFJl1wjli5RrkoFSkdZ8hbXSipErqs8JYfRFqZsykirjLqQF05UVOB9pxNjmpsx7fFj9XnDMPD5pEkYCIVw46WXUHj/fXi9XgwMDEDXdXz++edoamrC348YgRYA+4NBdPz4xwiFQupI3bq6Ovy6pgbtVqrgm1a5Act9CFQxGyYYDOLOjBlIhcM46/fjLesoSmk4cx5yvnLMdjnKdGSXy6UMPspSqc9osBJcYgNPmUop6ZYyUkYiCPTRWSwUCiqySQNjWX8//rVVPuV2u9HW1qYMDRpyYz7/HN94+WVUVVXBtPjLngq/0TSxkdlcllNKOpKgJ9eIa8ExAy+eOsPX7M4J6U5GpXiP0aNHq2f/83gcz00TR2IxfDsaxV8WCnhqGDgcjeKbxSLedzrx3Krf3hwMYheAlkIBP+ztxZsiPdVuU9DZ5dhl6aic6x6vF71OJ+5Mn471VuSVvHFw7lwMBIP4IJvF4/37VUmaaZoYGBjA1atX8ZuVK5GqrMSzBQtQawHApmmitbVVnXxAuSGDF9wbrg2bm/IoW36HZTU8NpfHnMkmjXY5SccTAK6OG4cBhwO7AKwS/anI0x/6fOhwOLDP58NqK8vN5XLh80mTkAyHsa9YxPpEoswW0TQNRyMRDLpcKCxdioZLl8rAes41n89jYGBAZQYOZ+tJPpLlPtJW4HcDgQAeP36M27dvq5T8TivLYMmSJbhx40bppCNLVwcCAeVU2/WytGNJl5w7wR7TNLG+WMS6eLwU4bRFL1clk3j5yRMcvXYN8VRKpfg/ffoULpcLXStXIhMO4+NCAW9aDiZpi6nh3CuZWcx9lnYbQWw6mtQ1BOsdDgcqKiqU7He5XGhubsaoUaOgaRomT56sUvDlZ6RMImDKSwLNBEMIklN+SfuPQKzMPjBNE89nzcLFJUsQzWbxiiWPBwcHFS9y7pKXmSLPDEBmYEs+kuCDaZrYYBho/vxzjBs3Dm7rZBzymtSDnD9pVmZVShkvbUKuB1+36xEpCyUvcv9IV4ZhqN4hXPtQKISpU6cqsAEo+U8TJkyAy+VCIBBQ+yfBCX6f/R+kbcmxcMzSsee6cy523pTzkHYmP8c9krpT6hnpa/B/eSKEBEeYaUHghvQkwQ37/Q3DUHKQc2GfPSkv5D7b5yhlr7y/tAsk8Cttf+77Rw4HdmWz+JbDgXcsYI3f9fl8SCaTSFlyIRKJIB6Pq9OIqqqq1PhpR5qmiR/39+NpsYifFQp4BaVsEJ/Pp3icgAR1GnnNvl8SQOPrcu/tgPWXXV8ZaOjp6UGhUED4wAH0vvIKHO+/r1J3i8UinLt3o/BHfwQzEEBh3To4PvqoDA22pxFJJ5gTyGazqheDNNqYei0XQBK5XUFKp9r+vsvlQuWpU4j/4hcKGQx9/DFC3/gGWv63/w3+q1fRdPWqEt4Np0+jZfFiNJw9i7sLFyITi0HbsgW+/fsRiUTgcDiUU69pGqBpqi6fDCvn3nT2LJ4tXIims2dLjtnChcjFYtC3bYP3wAHFdJyDc/9+aAcOlK2TpmlIrluHQm0tsH07nHv3qrnzRAC7MADKz+2W9xou8isBGvnD7xUKBUQPH0bPqlWIHj6MtlWrkI/F8HzRIjRcujREFwJNlmBPOBxWfTl4BI8U2gAQOHwY1WfPlprdCQaV+wkMGfkNly6h4dIlWJMt2/vWxYuRq6pC69KlqDh5UtEkgQauD8dcVVVVOg1g6lR0LFuGMZcuoebOHdyfPBn3ZsyA64MP4Pnkk7IomzSQSLep1avx+aZNGHX+POovXVLjb5s7t0QHp0+j3mquaTfoOTc7mMCxylQ7Pnv09esKABjO0a45dw41587B6/ViUBhosvlN/cWLCBw+rBzXypMn4ff7kTBNxQ+1J04oOpXGQeOZM6Usl/Pn4ff7YRgGWhcvRr6qCoVYDKbDgY7ly0uKORZDv/Va7+rVgKYhX1WF+GuvoWLfvrL0cyK2NCxYGmCPfEpDQF5Sbgx3aZqG3ObNMGtrEX/tNbh+8xuYpll2Os3v+/J6vZg6dSru3r2Le/fuwe/3Y9WqVeq8dNbf8Xg61m8zauZyuXC5sRH/JZnERocD37XqMWlEyihlsVhEPB7H3bt34fV60draikKhgEfTpuG/iUbxejyOJX19qnMzUB6d4RpJkEFepEMqTzpj/C7pkfzidDqx2+lEm65jcMoUzHj2rMyonPrFF/hswgRUHjmCYrGI6urqUnaRFXHQNA3fCQbxX3t6EDl9Wp0MUVtbW4qKjRyJt3Qdu3Udf2wdY8exBQIBdHZ2QtM0VFdXo6amBvfu3cPkzz/HralTsU1kk0hnh9E7n8+nssYkzXHslI80MpjVQ1lIw04anCxvsdesMgJBB0EafHJPaPBLfZNOp1Xki5lCBIB8Pp86wjkYDKqUZmkk240r7jHnKXWLXafYdTsvCSTYLynzdF3HHtPEb+NxfCcQUBkmBEb6+vrw4MED5bB8r7kZ/3dPD2bcu4fzPT3YvnAh3tM0zLh3D1f7+7Fm3DgcqarCmt5ePOjuxiuVlThSXY1vi7I2KWN7enrQ09NTBrp9pOvYXVWFV/r78YqVfsx5vD4wgH1+PzbncpgxYwa6u7vR39+Pvr4+zLp3D+dGjkTFoUPKaJSG7alTpxDKZpFYvBjRo0cV+JjL5XD//n2sWrWqjJf4961x4/D5pEkYGY2i3nLSp0yZgirrVKVp06Yp2T5y5Eh16srAwICK5t0cOxbf/uILfNvvx9eDwbISTGkLzLx/H3emT8dbeuk4TcMolX1RHm3L57ELwLZCAaFQSO3ztJs3cW3iRLxhZb5K2yafz2NLLod3czlETp4EAPUZadDSNqDzZAca5GuM7sn1ovyhYzXKqu3WNA23b99GOp3G3bt3kclkUFtbi9WrV+M/PXyIXxcK2JTJYHNjI+rr69Wxl8Ndkm+GC9SQb2X5Cmld8ksqlcL9+/cxfvx4AKVoaN2JE+h/9VWsLxZR0MoDB5qm4WJ9Pf6uuxvLXnoJ89rbUVNTg3w+j0AggJqamrKym3HjxkHTNJytrsa/evgQW2tqMNcaU79VBsUUdMpSh8OBkSNHqmi4lNMS7OQ6UwYxM4bzoAwjqE4AVIKUkUgEDQ0Nah24jgxspcNh/GRgAGtEfbxHZHzSNiANETgHoDLKCEazPNKehcKSJOkg2p1H7qfsLySdTOmw8TVZxrJX07DL5cJb2Sy2irlIW+YDw8CvCgX8scOBb1h1+TLLoKenB/fv34dpmqirq4Omabh79y4AYMKECaipqUEul8Pt27eVXr969Sp8Ph/q6+vh8/lUqj0Bw3g8jq6uLnXakJw79YHMpKKOkbaB/J7kZXs5jZQxDMZR3xIM4ZrRIWZpBPdBOseFQqEsO5P2jywvk/qce017J51Oo2vhQvyLhga8kU7jdYsmJBgi/U7KF77HcfDzfF82PJbf5ed/G4mgDaWjYr85jF+cSqVUqefIkSPVmDl+ZgcZhqFKt74XieDH/f1Y1d2NuDbU74hBpq6uLrjdbmWjy73iHPh86btRhtrl6z92fWWggcrRc/Agwrt3lwx/bSj9I3j8OPrfeAOF2loUtm6Fvm/fsAaFHLgkNG4EETXpYJER7OkdFHAcAwlSCnQuDgUe00rlZ3w+H3pWrVLN+IJHjypiqT57FtVnz0LXdWTSabQsXgx91y5lsPX19Q0dEbZ5M4zqajyZPx9jbtxQET5uEp049g8omiYaLcet/vx5JN9+G90rV6p+DHZilkZf4KOPkFi7Fti58wXjX6JMnKfdILQTvR2AkPewI/mGUaoH8h48iNFHj5Zq77xetC1bhhFnz6pmRjRo7SiZaQ6lzxMNp3C3C2W7gJDGj93hsc9B/m48c6ZUUnH8OEyz1GH6wYMHSCaTChUls+m6rhofdq9ahawVaZp8/z4ezJqFdGUl8hs2ICR6Fsh1l8ottXEjclVVeLpggQIaNE3D80WLkIlG8XzRIlSfO4e2uXPRvnQpGk+fRu2FC2Xzs6Ox9n0cDgj6Mt6zr4v9Hvb37Gtaf/EiYqdPl/4X68ar7sIFBA4fRigUQtpSKo1nzuDZwoUIt7Yi0diI+hMn4HA68XzhQoTb2jDQ0ID6kydhAqX+EcePI4chQ8aOfNOgo+FJmcE9sI/ZPj85b3k53n8fxe3boe/cqdZK7u/v+/J6veqIr4aGBvT09ODcuXMIh8NobGxU54kPDAzgxIkTiMfjCIVCGDNmjDracn9zM3pNEx+43fgzq8s3jTZGqWRGDUuHWEvbsmgRBoNBHPN4sEmsE9eazjGAFxQM+ZrGIlB+TKD8DHmCDrXD4cDbDgd+kcth3BdflK2LpmmYfP8+wseOobW1VYGCMuIcCASwPRzG+C++wI8uXkQWQwYpz9Pe6nDgO5EIgsGgAmnq6upQX1+P27dvAxg6Y93pdGLKgweY9ugRNm/bhpzltPPi+jGqJCNx8nMymkZnkam98phRrpc0WiVoQMCT36ORJuUB94g0ynvLrvlMkTZNU5XLtLS0qDITRoJkPw9+j3KY95cGG8fKcclLGkpSJjLVk+so6cy+/7qu41eVlWjTdfxdKoUt1rG+/G4+n1d9XQzDwBteL2ZlMrjS1QWXdeb9m5qGT1pbYfp8eDWTwb+sq8O5x48xmM9jSV8f/l/Tp6usKLuT8Pz5c4wYMQJhqwmbYRjY6/OhU9fxaTSKdZ2dZU7Ba9ks5ra1lY5rDYXg9/tRUVGBSCSCKRcvAnv3oqenB5qul4G6vMeIq1dRc+5caQ+s7Be/34+mpqayz3P9AOCGlfVzf9YshI4dg9frxYULF9DY2IhRo0ahs7MTW7duRSaTwaVLl5DL5dRxubFYDMFgEO9pGtqyWfykUMBbVm291BNc85efPMG/eOkl1VyUThiduk35PNYkEqVot+XYFotFTLp3DyM/+wwbv/Md6MKhoq22oVBA9fnzOHj+vOrPIx0yZtLKshy7/SgN+uFsGalHgJK+eP78Oaqrq+F2u5FKpTBt2jRcu3YNvb29GD16NPb5fOgpFHA0FMK/GzGizBG006p8nuQRaXtJPqVskIa7lDHMTqAMqD57Fot7erBmwwZkHEP9Ffis86NGod80caymBlssIEjTSs3aa2tr1Xq53W5UVVUhkUhgt8uF1kIBvzVNTMtkEIlEMGbMGNy4cUP1UOKxxRwLZYUMzrxfKOC3bje25HJYZ/Vk4okNEvDfXSziaHU1NvX1YYl1pPl+pxMnpk9HrKsL1WfPqvUIBAK4NnIkbmzdiprjx1FtZVbSVh5x7RoOWEEq6jpmcwAoK02RTRApHyVgRd3I051o+wHAkydPygJ2X2YjsY+E3RaTdrSUeQCwMxJBu67j3WIRq60oNOmU9PJLtxutmoafpVL4pqjFl4FbNuCWGRc8KYcADmnFfpIY+5JI3mEmHEtG7DYoI+Gck3yGlPnyb66VBKT4fD7DaQNbKAPoG/D0J9oOlD9Sl+bzeYRCISXTqW/tNgvXms+hj2IYBjqWLUPG78feYhHri+Wnith9MSkL7BkoMqtHjoG6hGuqaaUSxD0eD96x9k2WlZCmSWPSLpCZjBwbgzFvBwJ4OxDApbY26FYWcDgcRk9Pj1ojNveVtMt9tetvCbjb/bCvcv1Ox1tS4dlT00KhUKkpxZEj6FqxAs4PPihDR6RykQOTCo1MyPRnaZTwOZKAyYzcGLnR0jGhsSfTd8mcnFNVVRU8x4+jY/ly1J44UXbkIsddLBZLqeB/93cwDAMh6wQM3kfTNHj37EFx+3bUnT6NfuvIFenQc5Mkil1/8SLqLlyAaZq48M/+Gcy6OhS2bVPp3Hw+MFRn53A4kNu5E47f/AbF9euR/+lPoe/cqZpF2o0YKSzsSlruj/wtBal9DyUTBQIBOJ1O1FgRe6fTiZw+1D1X1hvz+3JdpDKQ684x2JlL3kfSjXRC5FrzXvUXL6L2/HlkMhkUjNLJDHV1dei0DEZ+tiiEi8vlwrgrV/Bg9mxM+OwzOJ1OTLh2DbenTUPViRMIWsd2EhXn9wrr1iG9cSNihw+j8fRptC1diobTp1V6nqZZZR6LFqHh9Gnouo6OZcuQq65G27JlaLxypYxf7HtnN4pJ88MBOsMZZPYMAH7fvu7DGXSSfyXCK1+nwcA5MEMiHA6XKfPgkSOqCRnXPnL8OHRdR6sQevJ+FLBMwSTYJ2vPJN/+Q6CJFJpAKYPGOHSo1CzJNF/4/u/zonHQ2dkJt9uNcePGobe3F+l0GrFYDIFAABUVFaqhJsEUt9utokvxeByv9PbizIgRWG9FF+kwsrxHnihSXV2NWCyGx48fY8SIEWhpacH4zz5D25IlWG8dCUkaYoRcprpLeSJpgzKJyohryj2jMcdx8J7b3W6MvXULp65dQzEUUoYBHX/KV2ZoyT1jLSZph6fS0OiUNOtwOJQBTDqJx+NKrsgGU7JO1e5ESNri3GTHd2n8yyM1GTGTgAkddBrtlJXMlmD5CdM47eC7vLfD4UAgEFAyyO/3lx1FyBR5GlQyJZl7xeh6IpHAjRs3MG/ePGWIyznbeUwae3Z9w/sDUAATX5fZbqQte+RoR7GID51OfNtqBsj5OxwOVFVVqb0lbVH+kxYkAMIoGfud8LtMPZfyRdd1TJgwAXV1dWVR6rcNA782DKy3ooAyZZWGPMdIYz8ajZY1ipMRYJluLulWGpr3799Xhr9dD0y9dQs3p0xBjZUdyXXkfofD4TJ6AaAAEJYZbSsUcCAUwrcsY1U6ApLm+SNTzu06SOoKyjfKdQmmye9KR8RutNLWHDVqlALG7JeU9Xa9JnWWHHOxWERFRYXaV+l0TJkyBU6nE39SWYm/7u/Hn1rHcNt1h7Q5yMOUucwi4mclTXOP8/k8jgQCOFpdjZVdXZjS1qZ4cWBgAHfu3Cnr2dLd3Y3Ozk5EIhFVOkg+nvvoEe7OmIE5Dx/i7GefoaamBuPHj0dvby8uWcEN9lS4evUq8vk8VrjdODtiBF6Nx/Hw4UOEw2HMnz8fLS0tyOfzmDx5sqLR/v5+pNNpjBkzpsxpMk0T7/l8aNd1fOB2qxNoJCjLNThZU4M+txs7s1l0vv8+amtrcWjdOmRDIWQXL1Z6nw7kF9OnI1tRgfZlyxA6ehT5fB61Fy6g4fJlRCIRPLYCDQcPHlSygDxMcJS/ZYapPKJY0qbdbmamlzyqXtKVpAnuNZ8pZZkMmEl63JrP4wPrt6Rb/i4UCtiqafjA5VJHz3JsLB+gTUzaczgcqhyFc6JcZLkcmx+TJqX9x3FEIhElrzlv6i3KUs5J2gt2GSDfk7aiXY9wnPagsdQrzMqz+1OUSXK9+b8s6+Q85HdJK8FgEMFgEA6HA6MvXsSzBQuwSYAyHCNtT3lPaVNL2cBSHemXcJ05Pq7JukIBG00TTVYmGnWCy+VS5bHyFCQ2PDZNUwVP7lgn09HvNIyhMjLep66uDqlUqmxNSd92oETus92eknbfV72+MtBAZqUxyroXYOg8eu/Bgwi/915JyegvHsk4nNFud25Tq1fj8zfewNgrVzDmxo0yo4xIKQk/l8up+0ii/oeeQ0OTi0mm8pw5g9rz50sLawx1D+e9TbOUwsLn0HiUhoN+4ACab94sHQFovJiOYjempOGYyWTg/uADFDduhLZzZ+m9jRth7NihQASpnFX97ptvAvX1pYabBw6UpT3z4lylQJWAh3297ApdEhWNaKZoywimVLCysaNMl5LPJC1RoNjrGnkxciIvu4Ey3Bzsxi6NjHw+j/b2dnR0dCikkClETD8lCt78+edotroxF63/Y6dPl4xTSwD09fWpNDxN01Bctw5mXR3ir72G8f/pP6kMFhNDBlzdhQuK3gCg+tgxdK1cqUpq6Ixzn6SAlMYcBZ+MNMr1k5+lcLEbuVJ4SzCBdGbnIXlfKYTt0W+7QrYjw/Lie1SYHBvpjN+TDpKmlaK2RHGHu4ajDfs8gKEjywh0DjfG3+dF/jBNsyziommacjRZy816b5lWSCW6tlDA/xgIoNOK2LMWj06iTFl1Op0qDZmRqcn37+N/mTGjNGfni82OJIAm11QaG3KtpLyjopVAgZ0vi8Vi2ZnP9iiBjMhIeuNJCgQQWIvMdZElFqZpqi78HR0daGpqUmBzZ2cnzp49i3g8juezZqFl8WL0xeNYY8l66QxLp4EZbXyWPQIxMDCgHAXDMFRjJ8pljlc21pKNIu2ZUlxvGpME/UeNGoXRo0erbuGpVAojRozArFmzYFiAand3t2o2zLlI+S+vQCCAsWPHlvVJAvCCbOA+ea2aUjtvcz2Yai7TZbl+sqElv0c+dDgc2OZy4QeWE8F95felQcpyQen4yTFLvUcH2D5mSdOappUdfcZxbzcMrInHMZjNQrMMOMo98mUulytzBDo7O1U6rx1kcLlcat9uT5iAm5Mno/7kSTRdvar2nWChXFeOddK9e5j57Bk+//xzFLWhxmmSt+zzlOvncDiwNp/Hv500CZ2dnUrecx3tAAMBD03TVK8Xfu4Dw8Aevx+bslm8bulU9hqhjCbNcW65XA4fu93YN3cuKuNx1F24oOiG+5dOpxXwxZ4TUrdJmpS6Zjja5ffoVKdSKZy19G1HR4c6NSIUCuGdigq8Y2UGkF+4tvLezKzh3ywHkHwi5ySBt2M1Neh0OnGkqgpz2trg9/tRU1OD/qVLcW76dIQOHEDs9GkFTMhTBTgOTdMw/fFj/Ne1a/HhhQvosOxS7pm09eSY53d24v9YvhyXLl3CVeHk+Xw+VFRUYNq0aUrOsOeNPBmFuujtfB6/KhSwWZweJMEoyq7txSJ25/OoungRPp+vdJzgyZNoW7YM9adOKXCZUfc5Dx/i3MiRqDl+vEzeAaW+A7quI5lMoq2tDU1NTWXBRfIjgQbpVEn5bXd6pSwyDAPV1dVlkX3pY9gDkVLuSNtHOpqyh80OpxM7AMAak+Q1PmObruNNhwPVlvzg85mZRvkj9QjXXR6rzP475AN5+oPMMmD2CjM85HztfGa35WXE207rUodKOcS/+Z60H6WuJV8xq14CO6Q5OvbSz5DBJ+pTrpG0d9n7KpPJoPnzzzHi6lXMefNNeJqby8AFuQ6cgxw/Pyvnag+6yXnJAMdwa8JSLYIglEGcK+dVWVmpvkNbmGPlOFjyKeUvfS67PTycrSfX2r6/X+X6ykADH8pzWSORiFKCUuGQUaWg+TKhL41SLlRm0yYYsRjuz5qF8LFjZemiXCC5uXbni6/LxZHPpIGdefVVFLZuRfHkSTitEw3siorPkWlucm58ncYOj8Ozd9/mZ+QmSZQPKKXG6B99BNfu3UNG5htvAA0NMHbsgPnRR+rZXONYLAbX2bNoXbIExq5dan+4XpLApSDomD8fzxYswJhLlzD6+vUX9onjlSgs946ADyOl+Xwe6XQaqVRKETyPMeT+yOOtJHHSkCOK7ff7VdqedHal4zMcsib3dzjjmRcNb0aw6HAVCgXVDIrpqjyGjs4+L+lgSQdIMqu2cyf0N99Eg3XyxXCOraRfTdNQefIkmq5cKa0dhtB3CQ7I6+nLL+Ph3LkYee4cmq5cecHAkvTP9ZWpd1w/KnimG9LxZWScr0lQTBp69kgr+Zo/0oiX3+N+Op3OMgWt67oywHnEpXRE5Pf4tz0qIdHzLwNH7POgQyt5wP793+fF+xMskhFVNnSjE8coGVF/Ccix4RcjdOwPQ56T+836Wq4x91/KBq4hHSHJh/JvrhuVmgQ/NU1TIDD3R3Z65vWJx4Ofjh+PKbkcpj9+rBw0KkHKLTZsooEYiURUtFbXdVRXV6OhoQENDQ2qFvWzkSPxv3s8+GYqhe9bNeMOh0MBNky75319Ph+eLViAVCSCg16v6pzP+cqSB74uoxuclzTuKV/C4bAyhrhupHWmhFJuc/2kXJGAYi6XUzrG4/Hg8ePHuHbtGkaMGIH169fDMAxcunQJR63yvxEjRuDtt99GMpksa57K8XE8fDZ7g4RCoTIHSwJjUnfZ5TCAsnmSVuzyy87Dclx2p5/PlMazBGLks6WcGA54kAC4/L7UxZIP7HMjEMn7yxReAOju7lYOTSaTUYCWfK6kmXA4jHA4jHszZiATDqN16VI0XrlSJrvlmsl5yuiyTFXn/319fWXOkJRlkofluklniPcHSjbQrVu31D4yS5Tf3RsKocPpxF5NU/0rgsEgwuGwAvO5/oyiud1uHI5GkXC7kVu2bAiQN8vLgthQ2b4OdgOd733Z3sn3mX1CQLe2tlZFNqVekDTM78vABeWxYRjqnsxC4PjtASveZ2s+j71OJ2Y/f670s9frxb2ZMzEYCqGwZg3qLlyA0+nEggULVLYV5STXn8/gupLeHQ6HyqSS0X5+n5d0PAkwcu9pH/B0ORl5drlceDufx+rOzhLdiOgoyz+YffFqJoOFiQR+cvo0AqNGIRqNIvfFFxh1/Xppna3nplIpPH/+HE3d3ZhpBS0h5Kzb7cbEiRMBAF988cUL5U10NqVuknYnSwRkQMwuS3hR7g4HSNoDO3y2tLWl3c+/7XKN/w8ngyS4LfWspmkKXOd8GeSSpR7kMfom3FfKRLttRvqyywbJC5L+pWyx+wjyPvb58DtSj0j/iv9Lu5sAnhwLZZ7MGpERfAbDpS0s58x9lydxkQZJG6QDXhwnAUR5L64Pn8U1ph6y25LcuwMuF3Y7nXgnncY71pHctA0GBwdVc1Y+WzbALBQKKqPHMEo9GtjTgbYhgxjSzjsaCuFAIIAdhoF1QndIXhnOPrDvs12vf9n1lYGGUChU1jmaqXe9vb3IZDIqciE3Um6m/TJNE+Fw+IWj6Vy7d6O4YweqTpxQzam4wXy2JFapcHhJ5SOJHRgSyrktW2DW16N71So0XblSZhzZmZwXa7uYLh+Px8vqoYwNG/Bs40YMHj6sOihzI4YbjxSKdKj4vsfjQfj4cfSsWgXz/ffLBBqf5/V6UXvpEowPPkBbWxsglIidyaXyfDJ/PjLRKB7Ono3Gy5eV8rFHE+TzZBd0lkvIkhWZgsznF4ulOj/Z2VoSctvcuWj9i79AxcGDcB4+jIEVKxB/9VX49+2D+8gR9dnh0FK7kJYC0D5vfm9wcLCsEz9Q3lTRNEtRmEtWc0an06nKgphu+nzWLDyYPRt1x48jduaMEkpl9Ld3LwJHjiA6fjwMUQJkN4AkImyapupuLddfKjopzB7Pm4dMNIpnCxag6cqVstpaaXwCwJOXX8bD2bNRc/w4AocPq2wcnjrAaKs06Dl/qQwkDVGwybWnUcIfGd3gWvO3HRTgGsp5U3HY91L29OD+8ZIKSSpMqVDtc5Fra//5Q10SqJR18JqmqfRHKr6k1eVdnv1OJcOjLfv7+1Wfh46ODui6jkQioQyUnsWLcfuVVzD+6lXUPH5caqY3ZQqOL1yIQiaDtVZqHud9LBTCxx4PNqRSWGPRh90IkuPgxfcymYwCr6Tsks7Lr4tFdHs8uDZhAmY8fVr2WafTiZkzZ6r1icfjCIfDCAQCmD17Nurq6mCaJqqqqvDyyy8r3uH993i96DRN/H06jT/Vh449I81Qlo0YMQLTpk3D3bt38dKNG3g4eza2WPKVMo/GC2le0jTHLI070hj31X4EmoxmyCgSeYhRHN5L0gafIYELGvPkmYGBAaVTk8kkQlZZSoWI0Eqa4/1I7zKdkp+VgK9dDtgBOc6T+k0a5ZL3ZCCAlwRE7UEK0pkEBe1GoFwfGqNSvkvnnHOQ4yNPplIpJJPJMrB8j2lip9+PV/r6sMJqqCj3v1AooKWlBY8fP0ahUMCTJ08UIA8MOYB8XrFYRF9fX6mz/6VLuDV1KmJHjqg+BezTwHHaARXShgTmpa6Wl7THJEhuTw+2g+oS+Kmrq0M4HAYAla1A22GHaeK9fB4brLp+XS/1CmDjPTqJ5CUC6esGB7GnUEDA6p0kS5ekQ2/X/cP9/WUX18q+JsViEV6vF+dqavCuw4E/GTUK0wKBsqxKqdflOsl9kHpSZqzILB5eErRcmUhgE4AbPT3IW/vS2dmJqqNHoa9Zg+CxY+o7vb29GDNmTFkvKzvoI2Ur5SbBRY6PF2mR2WZcDzbTlPPjetjXgrpY8pS0q+T7plkCCBsbG7F06VI8evQIFRUVcLlc6OnpQXt7uzqOOR6Po7e3tyzbgHNgby/KhlgsVpYdRkB+OGCBAAjnzXvKfeY45Rzt4IOUx/I3PyfphzJJAh7kP2n/2QNkvNcBlwsf9PTgO4EAtlvjIoAUDofLyrc4LzaTlc4w7UOfz6caBkr7zTAM7Hc68fNx4/Bqfz++nkyWvS9ltYzwy+CrnAvlIWmAQQIpq+w8JQEXZnVK0DMej6vPk58pVyQ44fP5VPNECcbZaZd6lyUyDIbk83k8evQIlZWVZbTM+9uBR7lf0h+RcnY4X5W/d0WjaNd1/CKXw7e0ocw7l8uFRCIBh8OhMq65/7RHisVSGRjtiYdTp+K7Hg++nkrhn2uaKrGlruSaHggG0e5wYCeAlRYwLOdq1zeSr+Xcv6qN/JWBhurqaqRSKZV6SyLKr12L9Lp1cL7/Ptwff1w2YBrNUoFzoZ1Op0JqKNhM04Tr44/RcPlyKU3OlootDWE7iCEZQi6S3fnks3179iC7eTOqT50qQ+CGizoBJcFdUVGBioqKMmXNxY5Go+jdsgXFmhrEX30VjZcvq++RoCTB8f6sGR0YGFBr53SWTj2o++wz1Jw7h4cPHyKH8sswDBU9oTCVTpcdfJFXw6lTaF28GPWnT5c10hvOGJPKkqh5LBZThgZfYwM2adxpmqbOeGcKpYy8dH/3uyjW1SH+6qvwHTqEgddeQ7GuDpnNm9F45YrqJM8sGrvhIRWK3fiw7zsNcIlsAkM1xjTuJWrMJmkdHR0KXOv79rdRiEbRtnQpfJ9+qo4k5JrzXrlcDqlUStWWSid3OEOX9Z3slEtHUxqacj/GXb2KezNnYtSFC2VNfvhdOi0ASsBSRQU6li9H8+HDyhjK5/MKJKRyYPRZHoNDh1LOExgCQfjb6XSqTsZ+v1+VvLTPm4d7M2di3JUrqDhxQu0HeYKXfAaj+FxPyW9M4ZQ8LelC0sFwjpCdTuxRzz8kwMCLEU/yD9eZKdvBYBA7czn8ZGAAc6qrEYlEEIlEUF1drRzqh1On4jehEN7M5fCKRVP5fB5utxt9fX1le9y5fDnylZW4PX06XAcOYHBwEI+3b0emogIfZzJ4zTrGk/R1IBxGh9OJfYEAXhPZCVS2dqdZ9lAoFIa6P/Mzw9H/N1wu/E08jlnW0Zby/UKhgPPnz6OtrU2t0cDAAAYGBnDx4kWsXbsWPp8P9+7dw+HDh+F0OlFTU6N0yrJIBOdGjsTbIjOC0Usqf0aUGcmcdO8e1heLmD9/PnKW3uKa8Hvcp+EAQPsa8H86V3ItaCzINef70imSTqOUaXw+9bEEGmRn/GQyiXg8rpw4aaBJfUzd+WjaNPx5JIIdhoHNApyhbKTul8ALf8sxaoIeOQ+Om3KGgIbdIOO9D3m9ONDbi2/7/dhkzYc8K3mbOs8OIHMvZHTMToN2w5jzuHnzJioqKpRjbZomdodC6HQ48Gk0ii3CsJbPmT17tlrj+/fvK15+8uSJ0ldSXvr9/lKqeFsbXB9/DNM04bZSxdmBXxq60p7gqRNNFRWov3gRDocDY8aMwejRoxGNRlWGodvtxty5c1EoFFBdXa2e6XK5cCQYxH9z6xa+HQjgTSuqbDc0uVbRaBRBqykn6ZgOwdZsFku6ukrZR1YWYCKRQFdXF54/f46BgQHU19eX0bDb7cbrmQxGXL2KA+fPw7R0LumUe+n3+8vAZLtDNpydY39dPlfyp8/nw8XRo9FvGPibeBzfsc6sl8+Rv6WTIwHz4a7hbFW5l5/6/fjY58MrEybg5SdPVIlA7PRpvOXz4eNz51C0Pnvv3j3Mnj1b3YcyiXQPDB2rKZ18zoWfcbvdCpiQNhT50p5dd6WpCSfr67G9WMRbwhEjmPyhaeKXTie25vNYa0XNOW/VJN3KSCGodvr0adTU1KC2thbRaBTTp09HX18fnjx5onrStLe3K7kFQMmdRCJRJktkQzvKJ5kSzs/KMi0J7ssjiLk/5M2enp6yhuX2SK+kM8o6OTY77dnBGEnjko6kvbLT40GbYeDvUilsj0SUrGeQiN9lZi5pTdKbzLAmiCuPfOT43tM0dHu9OFhRge1WLT/1Ap8jZYK0A3hJv2E4AMoue+28xb9pO/J77NFAcIABFNK5XGfaU5/6/fh3fX34I48HbznL+4ZwH10uF8LhMFwul9KbHo8H9+7dw+jRo1W2AMdA/9EOMEh9I8cig77ke7tvtSWfx4eahretUtnfZjL4m8FBvKVpmDM4WFaCmc1mEQwG1dxZXcBTVJ7On4+0ruM3hoF/DqhkAI7FZzX83ZjJ4EOPBxtENYLUo3LP7Lb2cEDLP3Z9ZaCBDb3YwTuVSsEwDAyuXw+zvh6FrVvhOnCgDAV3uVwq8vTs2TOk02m1yD6fD6FQqCxix42ShpdEy6ho7YarnLB9YyUYIBkwcOQIQseOIbV2La7/i3+B2mPHUH3u3AtgwHDKRkZpgBJR+f1+1fiPNWd0YiRxyTlxXXt7e8tKTgKBACqtJkQUDrLemPMvFArotTr4yrHxPa6VZApd11F7/jzqrJMNDLwISkjBIo1SadTR+OQ9ScB2YZfJZFRpBd8jiu7ftw+DGzYgcOAACoUCYocPo3fNGtSdPKmUbiAQKBPaEoGWjMFLChO7sUnhKuvFuVb2PebFtaGixG9/C+cbb6DuzBn09vaqlDXuMZ9ZLBaRTCYRjUbLDHkKRiov0kcqlVJHojIizwY+kt44vxFXr6L67NnS/EW6nBRmHP/4K1dwZ8YMVFtRI2kgUZiwHkw2TuWe2evCCGzRgOAxeXwvnU6jv79fpU8+mDUL2VgMD+fMwfQjR8r2zR61lCg519We9hYOh5WzzX2UDg/BI7sykEaBlA98rj3a+Ye8dF3H+PHjUVFRgcuNjdg/fz4WNjdjycAAwuEwotEo/o9kEk8NA5mGBnx/2jQVKXv+/DmKxSJOjB6NHgC/NU2sE4YVoxrcZ4/Ho45Ie+n2bTSNGIFIJILOs2fRs2oVtpmm4jeuwbZCAR/qOrYUCqouWyoZaVjJRn8E2eQ8pbyT8miH2w3XmTPIZDJICPkGDJ0ORIeUx83y+exfkUgklHPidrtRWVmJTCaDxYUC/udQSBmcksdZ4iZ5m7RAJ5eyTp6zLY2D4dZDynrqR+pBAhTysjsBHzkceD+bxTu6jnUYampGfuNz5MXaehrCjOrx3vl8Hk+fPkV1dXWZjuR7fD5BqdvTpyPtdGJXoYCVVgSJPEi5Zpqm0keUDTLyJC+7ziINyB/pkMnX9vh8aCsW8XeDg9gaDqvvS0BHyg+OU/7PMcsInsw0tMsDoOQkjB07VnXsp2H5lmHgN4UCtlg8RrqQ45BjqKioULzIo4Klga7rpQhrwjqtgaVSpLmqqipEo9EXsjh53XjpJQyEQijMnYtqq9cAjwjn0ZNci/b2dgwODqpjXb1eL6qrq/HzCRPQ7nDgJ8Ui3hRnuNvloHRapTMm917SNe2FQCCAYDCIQCBQtv+85F7Zs1+AkqE8ceJEVFRUlNGT5Acp3/maPUIswWrSOmlj5v37uDdzJr5vNd+V9+Hf9jWRsoDO+Ue6jg89HqxOpbAqHlfjsN+POm5fLIZ2APsDAYxNJtVncrkcvvjiCxWAczgcJbDYKu/kekmwg+ste5lIUE+CeLIkjqVjBDQZXCAtn6yvR7fHg52ZDDYMDr6QofsrrxdtDgfeN02sTCbLnFNps3CMHR0daG5uRm1tLbLZrDr2l42KObf79++jtbUVcWsdZaluf38/AoFACZSz7CO5nzID1g7SEjCVQKncc5mRyWCN9GkoHyTP817Ug3Y5aNcT8nUJHPE1yj9d10t62ONRx/ByTCxf5p7JoIUECekP0Ennc1KplMpcpS31LZcLf93Xh1ficUAAL7JPk7Qz+bq0qyRIYg8eSP0ogRu77cxLyhvKH/qQfF/6PlxPt9uNbDaLo9XV6DFN/DCbxc+zWfxTtxs/aGhQfCF9mu7ubnR0dCj9OTAwAK/XW3Z0KsdhlwXSxxzONpB0KG1d8spWy84KW3v8t8kknpkmfgPgZcvGknYHwSNd17Ezl8NPTRNjly/HxNu3gcePcbeiAt+wAiXsy+D3+xW/5awTYhb39JTuI7LZJe1K2rQDtVIGf5Xrd+rRwMWmkVcoFBDcvx/Jdevg2L27bCFpFNvrQvmbkepwOIzOzk41KU5ANlqSxCmJVToa/L5EJrkgRAGdTidisZjq5m4YBu6tXYt8dTU6V6xAlXWMjvy+vKSikA4tx1N99ixqLLCiOIzDa/+7WCyiv78fA9bxNoZhQNu8GYk33kDviROqWaDXOp5ERo04FqbukzhYj8P3iT5LZSeVxXCOpwR75H7yb4mmEWGUhCgVF9FFMgiZ1+FwQD96FI79+0sZET4fIhcuIHr6dKkTuNW8xD52O+gk98buzNiBJios6XRI45BXmZO6YQMKO3ZAe+89mHv3wrFvH4LHjsFfV4duK5vBNE1omzfDfPNNaO+9B+zZ8wKwIY1F6Qzw+cwikAY391euLef5fNYsPJ43DyPOnsWoa9fKwCEpEHVdR+OVKwgePVrqqeJwKCdL9mbgZysqKhAMBtHb21tWF6/ruoo2kAeoqB5Nm4ae1atReehQSWivWoXqo0cROnq0FKV/9AjpSAT+p08xODhYBrKQhhihYd08nSwZIdE0TZ0NLrMmgsEgkpahZjfs7KCL3XC0G4GSXv6QV7FYxJMnT9DV1YVHmzcjFYngVLEI14EDmDFjBlKpFL43ZQp+1NeH8Z9/jtOnTyMQCCAUCmHWrFkYHBzEy3fv4s706dhaKMAwhxqd+v1+9Pf3K+PS4/Egeu0aNpil7LGMBfKOvn4d36+txUsvvQRYjjrpYqthYJthqPpY0odUouR7mfUkjQ9pQEhDQgLJUlbJfZLOLUE+KjxmUBEMy+fzCtHXNE2dbCINlHw+rxr1kf+AkuGZTCaRzWZxe8IE7J81CzsKBay2Mr2Ypiqz3qRRKudK+jMMQ0XjCNQlEomy9bNHezRNw2+rqtAO4OfZLNYJg91eRsFnDgwMqNI0ylTylJQ3z549Q11d3QvHsJG/CBrkcjk0X7qEzhUrsFGcIQ4MGdcyUgUMlTpIQ4W/pU7n3snnSx6TIDY/+xaA9x0OfMvSA/aoMNeWayKNQd6Lxr9XZKhIx4F7mrScJI4vFouplH/Kqm3FIpZ1d5dkqzbUhEzqRdJMoVBQp1ZMnjxZnV/f29tbVtJEsIxlonIu/f39qKmpKQNppK6advs2Pp80CTVWZqKu6+jo6IBpmqivr0d7e7va19bWVqTTaUyePBlVVVWorKxERUUFNmcyOBSNqlMnuF9yrbhGUneyITT54APDwG+iUaxLJvFqPK7WnHZiT09Pmc3Ge6TTabS2tpaBBdKAzefzuHv3LubPn4/q6upy3TyMTTXcb/lDmiWthEIhvHz7Nv778eMxdfRodX+ut3QcpKNp17e6ruNDrxftDgcOVVbiVUv329PwNW2oj9ZrAwM4EArhFctxJi9xnlJ3EdBl2QDpmz0VyNsSXOPaEOgkLbOPDe/D90h7zFTVNA2ru7vxaTSKjZkMPMGgAqO5Pt8wTfwyl8OWQkHZa1wfWUJAGzwUCmHEiBEYLdba7vBHo1FMnjwZN27cUHwho6rPnj1DbW1tWWYO78OyAe6L9Av4PJ5eIPUMgTlpz/r9ftWoWEaxpQMpfQs739htfdKO3FspqyXd8f+t+Ty+4XYj7PXi3VQKP02n8XowiNesjFM6krw/5ZG0a3iEa8Hao0sNDfgrtxt/PjiINyyHVdM07HC70fjwYQlcsRpuSrks+UHOj3OhTLfb6FLfkA+5nnY+lYCt1AfUb/yfc5S2PPejoqICfr8fX9N17NF1DJomnpsmftjbix9YGWKSvwOBgLIpZO+qcDis5IRdZ0h5wPvY/VIJbtq/J+ctedY0TXwvHMaP43F8w+rTxbIJ2ly0iQDgp+k0nhkGijNm4K9efx2maZaVX8vGxIODg/D5fMjlcggEAiob275/0haWY5R7/7uCDb8T0CCNDQIB4WPH4PnkE+WEAOVH7em6roicl8fjQSQSUX8Hg0FVOwWUN0qkcOS9pKMsF0Y6xkztlAvEjZdoJwA0MQvh9GkVUZDor3RAJSFJZpD3szMWiYhEwo0jUVHoU/hlt26FUVODjmXLUHny5LDONBUHiV8KPhq0NKw4VrsRz/3h+DlnfsYeSeN3ZEow52RHNisrK1FZWakMbhnJobLkfjocjjImB4Yi+RT8klE5ZikoWmbPxvOFCzHm4kWM+OwztRZc+2w2i56enrLIvJyvZCg7CGPs2AGzoQHYsUMdOZpYuRL927cDv/0tsGdPaQ/efBNGfT20N94A9uwBAAyuXo2r27dj9MWLGHPjRpnwIbAglbqdsaVBzbly/57Mm4dMLIZnCxei/uLFF5xkCbZIx1DTtDLnjA76wMAANE1TGUsEwdxuN0KhkHK4SPMs6Uin02hdswaFmhr0v/JKSXnW1KB71Sp4Dx4slZ+MGAE4nRior4ff6j5Ofu1ZtAhdq1ah8tAhRE+dUuOUSknuEZHZlEjtC4fDSCaTSCQSL/CLpBt5P7lO8nX5ua8qRP+fXMViUdXST79zBzenTEHdmTMwDAOhUAihUAjrKiqwIpHAjz/7DI+tdamsrERtbS2ePn2K5fE4/o3bjZ5EAk4rpZlz6uvrK5OhXHPDMFR3fABlDaDkOtmBRilnJeApnUfOi84WDT1p0JG2P3a78f7gIF4aORLzOzrUGPkMOnkcSygUUnvu8/nUsYSMcEinQ0ZgJA9QrrCON51Oo729HWPGjIHH48H9WbMwEAjgw1wOS/r6VLRHOtzAkCyUr9kVNDPNCLrKHjaS1qSC35hOY38ggHe8XhjieEwpqxhJZ6R87NixqKysxKRJk/DzeBx/lc1i4rZteO3KFQUK1NTUoKGhQTUMpeyUJ6zQIB334AH+11mz4PP5VIO2dDpdpk8kD8noIIAyI0tmTkhDk+sg+UvyHGl1s67jT6ury2SlfAYvOgZcJ9oPdI4lKF0oFBTgxL3kc+3ZexLgtYM4pC2uA2mPc5SvP3jwAKFQCJMmTYKmlQIHbW1taG1tLQNgurq61DOAoZRzro+kFwCYdO8eZjx9iuvXr6MgxiONdcmbkrfoML6WyeB/nzxZRcvl3OS6nq+txd9GInizvx8bTVPpEMqP30Yi6HS5cCAYxLL+ftV0m+tOgCUajSIUCpXJEzaAloa5dNAGBwcxMDCg0pjtstkuo+Ta85L2gmmaav7MtMhkMmX2mVzrn8fj+GFvL/4sFsN3YjHlSHOczNzbmMthn8+HdVZau8ycoePNY2az2SxWJZNYbunDhNOpdHJTUxOcTidaWlrU3GbPno2IlTpPOqVs5BrRrrH36OA+8POUKz/r7cUPs1lMr6vDwq4uFAoFVV7K+a9IJDDt8WPMmDEDwWBQgXiUsV83DKzt64NmyQfyPoNMXE+CmQsXLsTIkSPVqQZcY2ZCcqxer1f1ArPzYSAQUM4XT/2grV4sFlXZJ4MhtDulXctMhTOxGP5TTw++6fXia0JvSF9Aynq7HTocffF/qT+lzOL3pB6l7CJNSb+D+/2zTAbPTRP7/H6sSiYRDodRXV2t6vgpW2QPi3fTafzVxIlY3NqKV6z3TzU0oNvhwN/E4/jmyJEoFosqo5H7m7GO8LWXL3B8wwW/5Hzl63Z9J+UTf2RWySceD/bmcvh+Po/vhMNldoP09chf0gf5VSKBH9XXY+aECfgPo0bhfw4G8fN4HH/d14cfWBkznB8v+mB+vx/5fB7PZ83C84UL8X6hgM3Fod4mnIsEHWTQmfsnS/DkfkoAyB5A+9Tnw958Ht/LZvEndXX4ulUecburq0xHS+Asn8/jj9xu/KpQwJZcruykKI7D7XajoaEBplnqAQcAra2tqK2tRTweVyd5kJfsc5H2SzabVWst5c9Xub4y0CCVGCdSLJY3mOHr8nOMOknhzSMleQUCgVJaOspPGZDKXhohkgnt6AsBAt7L7qTyM/y/8coV1F28WHqWs7wp23CLzrFQYNuVoxyHXBsZkZBOsNPpRDQaVUBN5pNPkFy7FuGDB5UhSAeZc/P7/Uo4yFMmDMNAfPlytL/+OupOnEC1deoBUG4A2tdNrq19nSQIwc9S8HE8Mq0yGAyiqqpKNR9jUx9gKC0NGCpxYdYLgQYJMvFzduOc607Ge7ZgATKxGB7NnYt6qwknUGLGJ9On4/6sWdB37YLnk0/KaFVG1aVCkM8Mffwxsps2wfHRR8hYa5LbuhWorwe2b4dj7154PB5UnDyJ3tWrET15Ej2WEEhv3AizqgqP585FxYkTav35DO4Z10ei3HJ8FKqSdutPnULrkiVoOnNG0SS/J6MZ0mjsnD8fzxYtQuzTTxE+frwszU8ec8N9SSQSAFDmzA0MDCgDgII+sH8/zLVrUX3sGDRNQ+fy5Qh9/LFC+quOHEHPqlWo/PRT9Sxme3SuWIFCTQ16V69G8MgRFaWUpUKSHlOpFDo6OtT6MCLIecgjjSQND2eA2u8vX+ea/qEuGame+ewZxty4gZaWFrgiEeUsA1CReO67PB1EIvCBQEA5YzTe7SANn0lZ8HzWLPzbiROxtVDAq6ILNS/prNgdbSlLpALlZ+1Ar+RZp9OJ37rdaDNNJMaMwfyOjjJAAig/kYVgV1tbm4qUulwuJJNJ5cyT1ihfpKEvjUUCDDRQmpubFTAy5uJFPF2wABstQxQopwU+h0arNLok7+RyOfT09KgsNDYVlDxmN0wBYINh4C3DQKXTiYT1Xe4/n0V5yUZXdXV1qKysRDgcxn/o68Nz08Tg2LGYdO8eWlpaFMja3d2NZcuWARjqtM55S53ucrlUBhPX0Z6ObAeceEn6kTJLNmSTBrsd5JP60k5zUlcNFwEkfUtjkEagBFfsOpqfZa8BfpZlZJLmPzRNvBuJ4NV4HK9bPXikAyEN5nw+j1Qqhba2NqxevVrNj6d5jB49GiNGjFDj/uyzz1SQQALira2tyiiWneL5Y88UkYAex8R9lEY+Py/3U54kIemtWCzixqRJiAcC2GsY2G5FyPr7+9WabUilsNfrxVarZpg9iqjngsGgOqqysbERDQ0Nav+am5tx9+5dZU/IbB+5//wt5bZdttv3lvLHHsXkxca77e3tyGazZRkI/OwPe3vxOJ/HD3t78R3LWeFFOeJwOLA5n8eOYhGJfB45vTx7i/tSKBRUrzNZJ88TprxeLxoaGvDgwQMEg0EFnrOBnzxyVe4jS1R8Pp/qK0J9Qbrk+nK//2ZgAM8BpEeOxKLubhiGgaqqKrVnpEOOnfLbDuAyK+LL0spJ+16vF+PGjVNBSruMoG1EQIYZMZwnM6BXrlyJ2tpaXLhwAYVCQWU2MCBCgIP9z7jPlNtOpxOBQABerxfHa2vRZRj4ZS6Hb4RCKjvsUkMDDlVUYHMuh/XiWEU5//1OJ3bqOrYVi9giyjXtwIHkO2m3SjBIgkd2HqV9/N1QCD9JJLDNHMpUrqmpQSAQUKVF4XAYtbW1Cmz422QSXR4Pzo4YgVwuh1gshrfjcXygafiuJYuePn2KaDQKj8eDczU12OfzYUVXF5b19JQBQrJMijQ/nA1l33/pH1G+8TdPw5ByendDAzo0DT8dHMSfWMeqknYzmQxiFthHetF1HbW1tQCAv2trQ5uuI9XUpI7UfdPrxdtNTQCgovnkI+4JM3xM00TL4sXIxmLYl0phVVeXKl2XPVDsjr9dL9nBIjvAAAwFZZxOJ3ZHIujUNPwsncafACoTwe/3I5PJlPW8KhaLiEajKBaL+BNNw585HLh586aSIZLWnE6nyh7SdR3d3d1obW1F2CpFjMfjKpuK35GXLE2ibS4zlaQs/Yeu3wlo4KbKXgvDZRdwENnXXsPNDRug79wJ88MPFYGGQqGygaZfeQXZxYvh2LULzgMHyhZUMqMd2SJTS2PYbtAAQ/V5HKddAMjIHDAUIZHKjELGzjQyjU4aYvJ/PpdXx/z5aFu6FLXHjyN2+rTK6jBNE7h2DYZ1jrbpcqF70SL0r1yJ4P798H36KQZXr0bH+vWoOnoUobNnXwBlWtatU6Ug1efOqedybezEzz2UwmI4sET+LdPwuO4EPtj8kGvFdGZGwekQc9wSIZOZJPKkCsnEdiTQMAw0nD5d1tySe2cYBh7OmYNcVRW0zZvhO3So7PsS1ALKUWfub/3Fi6h+/BhtbW14atG6vnMnDKucgsJ+1BdfwPf4MfL5PK4GAujv74e+cye0t97CaOvYStnVls40BRIdFF3X0T5vHjpXrEDNsWOIWv0+7IqKZTqmaSILlM2HAJVMNzMMA88XLUK2qgrdq1ej0ToSU66tpOu+pUvRtXQpYocPo/LiRWiahv5ly9C5bBlqT5xARBxFFjp6FCOuXlVGWsWJE+jt7YUrElHNrQKHD5fq1q1sHPKy/6OPMLhuHQIHDqjxMi3Xzqtut1t1CM5kMmWlHMPJIvkjX5c0LmneLmT/kJfkGzY5ZHQYGDrhhtkbnB+dQMMwFI/Q+WQ0iI4SIzpspNTa2qqc1IqKCjxbsACDXi8+yOWwYmCgrMzKbujLfZCyUQIXUpZKMIKygnteLBaxOZvFXq8X4+/cKaM7mYpKBej3+xU4EAqF0NjYiPeyWfx1Lodpc+diaqGAhoYGBAIBVFdXo7a2FtXV1crg5HMLhdIpHSw383q9KrrqdDrRcOkSvlVZiREjRqDocLwg/2TqpjS2Je2Ypon+/n50d3erI2L7+vrQ19eH+vr6svWT9M370oHn/kl5WSwW0dPToxo88v9oNIqKigr86erV+L86OhA7dw6PHj1Cd3c3dL1UJ5/P59Hb2/uC/APKjzPmXO2p11JOkS5ogHFs0qHjmux3OrHb48HGTAavWVmL0vmTBrVdDx0OBLCvqwvf8nqxSeh7++ckrUpniPsh+2NIG4HvS2eI/Hfz5k1UVlbC7/erse30eNDpdOJQRQU2WGvJe0gaMQxD8aqmlZoosqGhHDuf5XQ6MX78eBw/flyNw+l0IhKJYNy4cUoWSFtIzlvyFenp2qhRuLp6NSb39eE1QEU+qXM+8XhwwOHAOqcTa6z7ybWlo0njdda9e7g5ZQre9nrht45klYDHRsPAZuuzGQw54JLGWSZ77949OBwO1TeEcp97Yg/GkI85Z152GrDLfPns4YIoAFQPKJbsyQgdP/ODaBQ/7O3FP6mqeuE9OlyMokveluspM35lhg55ivRAUKaxsVE5IX19fXj48CGWLFmi1gdAGahOkNXlcqGqqkrZwLK8jUCXYRiIRqP4fkUFftjbi3ldXeqIyMrKSgVIy3IsWTZTLBZV0+rdxSJ+WSxia6GADZYsoBMp7XUCCJJWgSEgM5lMKqCL2Yk9PT1lZWu8bzqdRiQSQWVlJQqFUuPhWCymwAnaoczEKhQKCIVCqKqqKuuDUldXh22FAg74/fjjUAjxeBw+nw9erxefRqPodDrxfrGIOa2tKqBG2eF0OvFuZSXadR07NQ2vDA4qmrT7G3Y6JS8zgMBgoXRQmZVNPe90OvGtykp8MxJBIpHAxYcPYZomxo4dW6Y7Q6GQaohcLBbxRy4XflYs4m3LZqqtrcWfBYP4gWX3USeyXPVAKIROXcex6mpsMIZS9SWQL3WAlOH8337xNeoG/vBEGnk/0zRLtoGm4XtWn7rfpFL46/5+NFRX49nAAP7M7cY71glKpCnaQt+PRPBXXV0Ye/s2CtGo4nvuRzqdLjveNZPJ4DeDg3h3wwY0nD6N+osX0XTmDJ7On48ZbW0INDYq3pT8TFlvlxfSn6BTbvennE5nWaBd0zRszmaxR9PwHQsk5OXxeFSggj/MoOLz6DPJwCHn19LSopqwOp2l07vi8bjiFZ7WJbP3CSDJ/aMsofyR8varXF8ZaKDA4Y906KXjLweRXLsWRm0tsGULHLt3w9y4EcU330Ti9GmEL19WzNG1YgXMmhoUt2+H+5NPVMo8L26sfI0bRFSIm2BPLZMLJZFGOVap2MgU9swGexYF14T/y+iMdHSlM87Pty9bhmwshs4VK9B4+XKZUpTRbQDoW7MGhepqpDduRPPNm7i5aRMK1dXoW7MGTVeulI1f13XUnzyJjmXLUHvypJr7l6GO8reMBkkj0G4AkrntgA7rTBOJBKqqqlQEQ96PJR/cU75GFJnMzBRnuRbSQZRgDgBUnz2Laqu/RgHlKW2NZ87g2cKFcO3bp/bBbuxznUjj0hAkLYbDf4IwQwABAABJREFUYdUrw7l/P4x9+0r0sm0b2r72NQSuXMGIq1fROns2Br7/feA3v4G2dy98n34K37hxKAaDZQK3c8ECtC5eDM/jx0iPGoXIJ58gd/QoTNNEx/LlKNTUoH3pUhTef7+s5lh2QLZHGTk/mbLK7xUKBcSOHEHXihWIHj5cxgf8nKSJzuXLka+qQt8rr6Dx8mU4HA70rFqFQlWVOhIWALoWLkT7kiXAyZOou3ChRAsrV6J7yRI4T5xA45UrMIyhkxVoxHCdPZ98As8nn5QMAssIoJMl914KUEYa2b+CBqKkCTutD2dkSjq0A1j2z/2+L+lUB4NBVerAqAsNQqaTytRI0if/5vrmcjn1Oa6hBO9isRjy+byKctWfOoW+NWuwwUqTBIayGKSDKQEErhnljjSoZJqrdND5Wd7D5XJhE4CN6TQOPXsGh2j0x+vh1Km4uno1qo8eReTKFaTTaYRCIdTW1mLcuHH4/yQSeG6ayIwZgz9+9AiFQkEZ8/l8HsFgEM3NzdA0TWUFpFIp1UhLOqNsrEWnhnsjDVwJAsgIuZS/vAi2hkIhlRVUUVFR5uzYaVLXdXzsdmM3gD/KZjEnkXjh2C4+X2Zy5PN5FQn/VmUl5rW349+cOoX7bW2qXwcDA21tbWW8xd+MMBWLRVwbNQoH3W5symbxqhUNlA6DLB0AoNIqh5Ojuq5jdzSKdqcTe7xerBIN76RO4nclr2uaht1OJ9qKRfwsncZmq/kbaZnOz3AGuqRTCTxImWinZcpYgsHjx49XUR9+dluhgJ2ahnVWPwfZKE7qJ85jxIgRiEajqKurK+MNO/jJjBECCrpeyrBYsGABZs+erfhcpjHzPtwfglHMCLg2YQISoRD+ur8fawMBNDY2oq+vT4Ga+0aPRoeu47fBII7cvIlv+/34mgWqSBCqWCx1Kp/54AG+Hgph4sSJan8ol+gQE+SmUypBKPZNqampQTKZxIMHD5RjSxofLk3d7XZjypQpqK+vL5MPdhtA2hl2e4d0INeffCwjlHb+5PWdWAzfjERUhF3em/v6kcOB3wDYnMthschUYLNkAqW0n6gDpb3M5xMIJk1UVFTg8+Zm/OXo0Xg9kcD3LPn1fqGA3R4PmseNQzabxb179/Do0SN1uofb7caMGTPgcrlQXV2NYrGIMWPGqKOH5weD2OF2o9sCqsPhsAIQOE+nVdLxxRdflPU5IUDyi1AIbQ4Hduk6llvNY+28xXXgxbnLvguBQEDZh263G11dXejr61NgDMEBoCSvPR4PAoGAoqslS5bAFM7zuHHjyurZq6qqUFNTo/be5/Nh5syZmKVp+J9QsiXSVrac0+nEpkwGH7hcWNnbi6Blu3EulBVbcjl86PFgqyVzJN9Qn0q/QNIN91b6TtLOkbpSPps2AoFjykKuiyxpHRgYQMPNm/hLC4AaiMUUv3GPM5kMqqqq8It4HD9JJDDRNGEWCthoHZ8p5QznIOWnzGbmfO06ToLWAMr6gtntiHw+j3WFAl4fGMBLDQ0oFAr4cTyOJ4UCnvt8KAL4UW8v1gvdxTKuVCqFUfE4/qeeHrT39iKTyaC/v181que6x+Nx5HI5BINBDAwM4FfFIgbCYeTnz0ft+fNovHwZmUwGF159Fc2ahlctAFVmcJAGpKyS8oY8IgPUEpzlWCgzlyWTyPv9+InbDcM08Y5lL7hcLvT19amMHJmNyPkTtGW5mvRzU6kUHj58iIGBAdU3aKRVLsMTVdhAHBjKZpb0SrqT85PA0Fe5fqeMBkalOBC/34/u7m6F2kgDVNO0UuR3+3boO3fC5XIhZ9Wxd61YgVHXrinhHj18GB3LlkHftUsZ05K5pCEnJyjBAH5Wpt1xTFx0KjIuqFwwCj97yu+XOSVyLPKe0gCVDWikEGo6cwbPFy5E45kz6vMct0zd0zQNTWfPomXRIjScOQOfz4fGM2dK0ftTp14gdtM0UX/xIhovXy4ZHnp5iptdico1tAMt3FOuHecpwQXpnPCkjFQqpc5DljXzrIWUIBX3lExIBUDlwrFKh5pMZL/4vlwL0zQRO30aoaNH0dHRgbR4XTIR52LfcyLJNEoCgQDS6XTZ/hS2bUMxFsP9WbNQefIkHs2di3wsBt3q6ZDJZNDd3V2WXaBpGtqWLkU2FkMuGoXpcCC5di2aP/+8FEU6ehRdK1ciaJUf0FGiEJAOCNdErpl8DRDlJx98gJq9e+H1epERnfTJa1LB1Z04gbZly1Bz7JgyvutPnkTb0qVoOH1a0UnHsmXIVVWhc/lyNFy6BAAKpOhcuRJNV6+qedfW1mJgYECdssL5MBpBQ0M6U5qmIffaa8hs3gznoUNwWWAGaUkap3yOXfhJHpROx3Cggt1IlcbQ7/OSwpu1c8FgEBUVFaVSnIoKZcgznbWqqgp1dXXKcKyvry8DoAjsUeHQUHc6neoYx2fPnqGpqQn9/f0Yff06/uXUqaVTJUQ0Tho0NPLs60M+kyCuNNjlZ2lcy7Roh8OBVCqlZD2fyc+fqK3FQCiEzIIFiBw/ro7szGQyGDVqFL67aBF+1NeH2vPncfLkSdTW1sLhcJQpUdIX5XoikVBz53g5RymfZOqwNK6o/6SjTlqRjvfg4KCaowKM+/pUphH5UYJjuq5jZ1MT2gH8qlDAfLEfTCXm3toj24mVK/GTlSsR6+lBu2ni7J//OSoOHkT42DGVHcTxm+bQyRqcazgcVtHDh3PmIOXx4COnE+sFb0mdKg1DOsF2/uL+b87lsEfTsCmXK/V9EE4MHT3qZhn9cTgc2FYsYq/LhW/aDFMZ4JBZApR3/ByfI0ESrufRUAj/6+PH+F4kgjctGiwWi6pXVFVV1QvpqlsKBSzp7i6N3XYaCS/Z3G3q1KlKrklH0m6kUVbLdQOAq1evYuTIkSpCbQcoSB8TJkxAIpGA0+nEkiVLMHr0aDT4/dhlmvjTykr4nU4sXrxYyRO3241ksYifJpNImCYeZrP4SaGAty0e+MXAAP5zVRVmNjZipVX/ncvl1BHc0uCW0W7W8VL+0CgGUHbKS11dHTo6OnDnzh10LVyIE7NnI9TZiTqRJccrn8/j9u3bmDFjhqrL5/qyjlyWJNl/y8vu7AFQMskOIHCN/yFDWmY4vQegzeHAbqcTs6ysSh5PHYlEyoBKCZDpeilCGY/HcefOHbhcLowZM0Zl/Q4ODuL+/fs4NGcO+v1+HHQ48C2rtGu304k2hwPJl15CMplEV1cXdF1Xx9k5nU5MmDBBjTGdTqOhoaFsbgR/OE9GS9lsGSg54TU1NYpWmZ3qcDjwdQC/LhSw2co8I1+TH6mHWJok7S/5QxntdDpVn4rJkyfjwYMHZcE/ZjQAUM1V2TBS0zQFlEg+Id26XC5lRzGSWyyW+luQ12kXbAIw9+nTkrywwAdpK7ndbmxzOvGmpkHzeKBZmRMyOCf53e5M2+dOncMf8hOdcsomzoEgAEErAu0EWmgLjBw5UoEadj5xOBwYtDIxfhyP46lhoOBw4KeZDPKmCYdVKkD+Z2CHtGwH56RNwzWQfEKZPJz8owx0u90IBAIwjKFGz99wufD3xSImGgZuA3jbyqSjLuEYCT6FQiHEYjF1miGDltK/Y6mEz+fD11Ip/DKTQfTMGbUuXStXIhuJYG8+j9WpVFkGnJR/0oaWNGcYhirxlHtqGKX+VHu9XmzO5fB6LofKykrouo6DDgdaDQN/MzCA5fG4yogoFotoaWl5IRgj9TLXUPrKtFXGjRsHANin6/hrlwvbDQMbnzxBLpdDhZUZQjnE+cksCpmNJANRco//sesrW9EypZU1M8lkUjWhIRPJBcDevfAeOIBgMIjYqFHInjuH9qVL0Wg1PeOCBI8cQd/PflZCiiyBJBWZHSHj3xRi8vMSTbVH6UgAUnDJiIQEEXhJ9E+CF5JJeH9+TgIPcsy8d9WZM6g9f75k3AgjTSof/h5x9SoaL18uDcbhwIirV1U02bDQdBpecu4UjNIotc9PGsz2iLbMYOF3i8Uiuru7kbY6sldXV6u0QabhBAKB0ukGuZyqv7Ib3VxruQ+ytppHt9jX2e6MSAOP13B7MBwNMV2Nn/V4POpIOmlEG8ZQhgCjzfI+oYMHMbh+PWpPnEAul0PdiRNoXboUocOHkbAiZP39/aqLOedQf+IE2pYuRbClBcnGRtSfPKlSH8fcuKGAuKJVx0shLeciUVI2DuO4pPCT9VdUEhLI4zrIdQsdO4bQsWOl+VvPCRw5gnHW8ZRpawxVR4+ia8UKVB87pqIW1UePomPFCsQOH8bg4KBKufN4PKitrVVrQmMsGAyqPQeg0pXjVoQks2kTjPp6DLz+OkZdv142bu4LDTjSgDQS5d/8znDv2WnO4XCoprW/76uurg7Tpk3DpUuX0NbWhqamJpUmHYlEEA6HVVrj5MmTlSHc3d2tjqYKW42SNE0rO56U5RJSvlGRc+5siEUDUM5f0phMB7avq67ryqlgtOvLkG8Z9eZ3afBJhcWsjEmff44rY8cievgwKisrEQqFVE24z+fD10MhLOrpwV9du4YOSwb6/X4Eg0EAUEfFErzK5/PIZDKqtwOPZH348CGampqQSCRQU1ODS5cuYc6cOcrIJo0AQ+mTdoPCbmgxWssmZdlsFs+fP8fUqVPL1og8yL3dksthj9eLd/x+hEIhxcd8n0Yyv8vvx9euRcHvx4/6+pADkK2qwuC6dRh36xY6Ozuh6zr6ly3Df1mwACaAmUI2MopPXpx04waeL1qEzVYZFwEKqSftgADvZd9/TdOwyTCw0cqM0C3wR4Lr8m/SLL+7WdPwZ7FYmQwmHcoGm9RB1B32sgg+h6VpTqcTH4fD6LCiZdusqC3XAnjxmF3OUaaUk76lTpU9iGQmmjTAJZ/xR4KDNE7T6TR6e3uRTqeVMc1L6rVgMKiO/J0/fz5GjRqF+ZqGf2nNP5PJqCwbjuGfaBr+CYC/7+vDX3V34zsWyOBwOPDjnh50OJ04P3IkVlq1v4lEAt3d3eju7kaxWMQnHg/21dZiUzqNldaaSD3Ospq7L72EzzZtgru3F1sFyDh69Gj09fVhd0MD+n0+5Neswchr18oce+53Op1Gd3c3RowYoebd09ODTz/9FJFIBMuXL1dOLucnZY3dXpDPkFkGdvkmvz/cJXn/bacTP89k8OrAgCrvq6ysVLTHSH1HR4c65aaysrLMtuBn+/v71XHXAwMDOHfuHKoGBpBZtAhTnzxBbsIEaJqGjVZEvemzz5CbNw+1tbWqf1FbW5vaT46RJ+xQFtN5lTqUgFBPT08ZH/A4Se4to6FvOxx4va8PmssFhwU6ykittGXp4NGWtAcAuQeUA7S/KJvq6+tRW1urbDU+i6Vw0o5hZpNMCSeAYpomQqGQshc4tkwmg135PH6Zz+PNcBivW0EvriP3ZjiZL+mJ95Q0KG1gqUP4W4LaHC+dQKB00gZtvQ8MAzvr6rCjuxsbbd9va2sr0+HUs8yqGRgYUOtJR7anpwcbXC584HZjkwUaUsZms1m0tLQgHo+rfkD2udHHkP6FnKOdX2SgloAK1zGZTKqTlAjYLzZNLBU2DFIpdAswh0ACZbeu68qu4Ng4J8oU0kcmk8HrmoYxd+7gg0uXULTGVnv8ONqXLcOEx4/R4XYjFoupqD/1hLRb7DampEeOg8/+MBRCh8OBD1wurLLmGwgEsN3rxS5dx/ZiEQVrPTg3u53OeQJDjSEl71HekYcMw8AHLhdadR07TRMrrSAwdQvl9p07d/DZZ5+pk2EiVs8wrp29P4Md/Piy63cCGji5YrGolKA0OiSREcWsq6tTaWCOq1dRb9V7SyePCKVESaTDxB+7EczFkUaQjHbwM9JJk84uDShp3NidkOE2lpckLhlll07QcGiXNFAoeCTzDXfZ18Fu0Mt1kWsikS47oCGJxz5O3teeIZJMJssaPFZXV5cJagpzpk5y7PI+RBmBoTomEjuVhB395twk4CP3TK4LvwtAGaFUqHQenU6nQmiJcnLcpBuZmk70k6mEnEv01CmMvn4dAJAHED19GrEzZ1AoFHDfEgDZbBbd3d2qbrxYLKLm/HlUnztXtubGMPOSeyhRda4d/7fXgtmVIS87Cso1ksayFMjyt6RtvhY5fhzhY8dKzcus9yMnTiB8/HjJMMeQga7rpeiJ0+lEj3WG7+DgoELfSSNEg+k4e/fuRWbTJgQPHlRGEYAysIh0JXlM0o2dfySgINdoOAPhD3H19/fj1q1byGQyiEajuHv3Lm7fvo1Ro0Zh5MiRmDhxIhwOB1paWnDixAnVs2DGjBkIhUJ4/PhxWXdp0jkNc8o17jMzpcgH7CBM/pegn3TmIPZP1ujx9z5dx8GaGqxNJLC8v79szQxj6KQT0pHMmuGpEdIJIg+Pvn4dyV/9qiR/a2vLAEs7AAgM1Shzf5nJQSdO8hDXCYA6HcflcqGtrQ1z585FTU2NkiFcD9KFzHCRtMUrmUzi/PnzaG9vL5Mn3d3dZU3aJBDKz2zWNGzJ5VBTUYFesR7cn3uTJuHR1q3w7NkD36FD0LQSYBo9fBjFbdvw3cZGtLS04O/TaYy/dk1FhxwOBx6uWoWMz4d/PziI73m9+GPLMHM6h5r66bqOaY8f43+cOLE0d6skSYKSEoCSzhtliNRN9nlKY/vLQHjSIDDUp4QXn0/jmNkQEnxubGwsAzNqa2tVLw7yiaZp2JhK4WBlJb5tRWZJG6T9TCajGslxnHsA7KytxWsDA3h1YKDsWD3KH2lIkw4lWCflD2mrWCzi7/v7cekv/gK1J06g8fJlGEYpynnv3j3Mnj1bOdI0trmWhULpyDp5/LU0dKWBLefCn3cqK/GdqqoyOvx+Lof/0tGB5ZbTTL3MDIJAIIBPPB50ahoOuFz4eiiEgYEBVZpEHQ4An0+ejFQohDM+H7Z3diqwTNd1tLW1oaanB+l581Bv6Uzyrd0pkNkRmqbh6dOnePr0KRwOB5qamjBt2rR/UK7LS9oPEgiQtt9w35H3k3tumiZ2uN2Y39FRGqs1Px6ZyBp42kQ8lpagqtR9pmmip6cH4XAYiUQCd+7cQW9vL8LHjmHh1auYM2cOHlv9eHD7NlYnk+jo6MBxy5H0eDxoaGjAgLV3cs0AqNM1ZIDi+vXrSCQSGDt2LILBIFavXq16NgQCAcyZM0c1WjRNU/X6kOVg/G23w+xNdYEXAW0JyEkaZ2mB0+lEXV0d1qxZg7Fjx8Lr9aKjowMulwuRSESVHUp6kbYT7yv5o6urq0zWcA4/8/nQquv4lWFguVVyViwWkUqlcObMGfT29mLMmDEYN24cKioqFJ/a9a2UQXyOXa+Slmgj0tdgdmo2m0VDQ4Pic85nF4BWTcMu08R2h6NM75FXJIgpx8cxhkIh5RMVi0Ws03Wss+wwe/T6yZMnuH79OjweD+LLl+PWlCnYmMlgtQWkS/9C7i+Asow1aatJfpI2JoNltFEYLKC/wIaMXEspV3kfmYXJ1+QYZC+Dzs5O5PN5FdSiTRE+dgzegwdRrKrC0epqNDU1Yfr06WX2C2l8uL3me5KmOcfX4nF8HA7jVUsOGEap2evYTAZ/lkiUwEWrsSu/E4/H1XHFkt+kTpT0Tb6SfDlhwgRkXnoJc1pa8Hl7OwzDQGdnJ54/f67mXSwW0dvbi66uLrS0tGDmzJllPV/Yd4rr93sHGriYTDXq6elRhCCFMzcgFAqhpqam7BgaSRyS0PgeAOReew3XduzAiDNnVH23XDwpOORGyvFJYqZSBobSLuWzuVkyMiOJl06v3fmQ0RV+nt+RRof8jPyuZAB5D47BnmLE79tBC0YvhwMb7JFKu9Cl4SXLNeSYGFmRe8Za8EKhgN7eXgQCAWUQMt2deyqddp/Pp1BoRrcl0EHFQiUoacruOMuxm6aJjvnz0bl8OWqOH0eVOIWB9GDves5oLteO0RipKOV6AEPCg/PnesjGLBwr36OzrOs6BizDVCpnpmxKI9C+R/yM3WCQtCqBDznmf8jgKhaL6Jg/H+3LlmHMxYtounLlhegDvyeFt90AHI7/pZLrWriw1DPkxAkErX4koVBINWnyer3K8OLzSf90KH2HDsF78CB0XUfCWj87ICD5yT5fTdPKunEzo2I4JF7KAxpVf4grnU6rVMiGhgY8efIEAMoygwqFAuLxuIqi+nw+VFdXq3WjocMMEAIMbCxJg0XXS0cMp1IpBe4w8+HWrVtoaGhQoAXXjuCEXRZKYwoADtXWotOKEL9iOTs0ihw2I0jKUZ/Ph9bW1rKxyFR0yhHyPTMQOhcswH+aPRuFgQF0Ajj0rW9h7OXLCLa3K7lzpakJP/H58KfpNL4RCqmaXo4padXY+3w+jBgxAr29vXC73bg/eTLOzZuHrYUCXhdHuTJSK4EG6ahIXgmHw5g5cyY6OjpUVNflcmHhwoUvpNNKuuW6yvIK7iHX4u6MGchHItA2b8bYmzcRCoXUsXdLnE6cN03s8/mwPZnEmrFj4Z40Scn3j91u/ARAAcCHHg/esWQsjVjKEk0rnQolI7zyPXnZ5SX/toOWB1wuvO9ylTq4F4tlp2iQrgCUycKDHg8+BvDHg4OqtIGfI+339vYqmcwIKwC11x6PB9FotCxrqrKyEgDwtUAAfzlqlCpLk6e8GIaBG9ZxxDLle2dNDTpdLnwSieA1q0SA2Z3S6LPrKa6P3U4g/WiahgujRiHr9aJj2TLUXbiggFnKAYKqg4ODaGlpwZlYDCenTsWUW7cw7dEjmKapStDYg0QCDaQhCTLQwctkMujp6VHHbU7s78e3L19GMpnEXYtnBgcHMXXqVAUSvxYMlrrT9/ejzwLt+/r6SmUZVpTO5XJhak8PPhs/HnM6OtBuOag8NevOnTvwd3Rg7P79ME0TWStrQ9pW5AtZFgOUMsI4V+pGaSNKMFTOWd6btgjBEfveSDrn7+EMa2kjMjs0l8up05B8Pp/iH9oBtNnYu4F0BEA1tU1aIALHlUwmyxqGyuvMmTNKRu3btw+jRo1CfX29clTtgB/1Ah26Gzdu4NmzZ5g7dy5CoZAC/xkEYkZYoVDAsWPH4PV6MXr0aIwaNUqBENK21XVdNXIcDmykDJAOGfUVgS1mXzBQc/nyZTRZpwfY0/Ltzh73X5bGSMeb36XtxH1bn0phj9eLtakUunt7lW2STqfx4MED9Pf3o7W1FXfu3EHnggX4bMIEbMlmsVY4etLhlj6A3e621+xzDbjmHo8Hz549Q39/Pzo6OlQDy+WxGA5VVuKVwUF02uxC2rf27Ce7/5LJZMpsdJbbeDwePHjwAPfv3y/L8KU/cP2ll9Dv9eKA04l1hcKwOo28yDWwgzn8Mc2hLD3a4ewp1NPTg0AggGQyqRqccn9JO/LZxeLQUaZ8XQY3qAdlz49EIoFwOIxYLIYvvvhCfUbTNFWK1tTUhBUrVsDv95dlbdozB+x2pwQWeJG21xeLWG+dsqhZui1iNU6X5cPUPW63GwcOHMDo0aMRjUZfCMLafWqOh6AT+XiKy4V/MjiIQGMjinV1iMfjuH79OvL5vCrfJXA9adIkNDQ0lIGSQKm8V4IMdr/iy66vDDTQ8OQGMX1eCnBN01T9MBFRu3FKopMKWA42s3kzjKoqPFu0CJWnTilBxclJpNLurJOBpWDj96UCGc444jOGQ8uAoaMG+Z7dibcvOIWbZD75WXkfMrNUWHbnR85XOgTS8eOch0NSJdAh5yaFv1wrKTwkE9HA6+/vRyaTQVtbGyZMmFDWyyCfz6sICw0aOji8GNkfbr4UCPI1zo3jkWvZuXw5stXV6Fi+HLHTp8sEOo1FeXwNGZmd+mV6vxT+FF58neOWa8i6OEYOOU7DKEXeeTQRa+S4jpJnJH3aFYKkCUmX/K7cQ7nH9nvKdeRr7BPxaM4cRI4fLxNWkoYkeCINZMmbkp8kH3UuX46s1cNh7M2bZZk/XHO/31/m6HKfUqlUGRhQKBTQb0XNGfniZQc97MYinQ5+liVfko/kxbWT4/19XqQzKkGpzPl6NpvFwMCAWk970z1N09SJFKzLdDpLXcrZhIzdzZmKS8CvsrIS3d3d8Pv98Pl8ylGTNCMBF7sc4xpvETX4wWCwDJiUn+Oaki69Xi8GBwfLojtSnsuSL+lwPF+0CFmvF3+bTCLndGKwogJP5s3D8kOH0NvbW+rIPXo0enUdP0kk8I1QqKzZUTKZRLd1lBsA5ch7vV48mj0bKZcLHzocWF8s700jwS07r8poFvthhMNhGIahOp/LxlqU0XbD1zRNxONx1NbWqs8RhDVNE1Nu3sTNKVMw6e5dTJk5E6NHj8a3v/1ttUfff/wYzwGcbGjAvx89uowv5wIYMzCA/9LRgS2WXCMQRKeAjhBBEqAc9KYckhF1yvAvo5FisYiddXXocJTq15dZqccS0CcvSIP4o1gMnQ4Hfp7N4m0rbZtzcblcCIfDZaUohUIBU6dOVXuRSqVQW1urGuNRNs+YMUONTzbkk85OPp9HLBZDMBgsMz635nLYbZrYkE7D7/e/EDHimkhatmfASDnO14rFInYYBv6+txd1Z84ogG/mzJkYP3684ueuri5cunQJfX19OLJpEwaDQVybOBETbt9GRUUF5syZA13XlZOYz+fRYx1RJwExrjGbyhmGgd7eXkUT7AZfFHQya9Ys1NfXI5vNIhgMYuazZ1hsye1+q4S2u7tbyRACGBMfP0bFiRMYO3YsBq0TDVgK0WY1LOX+ywwrGfzoW7oU/111Nb7T2Yk3LYD60aNHAEqGb319fVnqubQJ5W+7wwUMnfQinWKpC+z2j7SNKK/dbjc+NAz8NBLBqkIBa6xUfimzU6mUymrg2heLpUabDMaRXgqFguqt4vf7lR2RTCbLbCRpw9Fe0rQSWNnW1oaKigosWLAAzc3NZSec2Om1sbER9+7dQ19fnwoeMaNH10uZZwSPmE3kdDrR29uLnVZfl83pNNYJoFCCXPYMU+o2jof7TznP7JlHjx6pOSWTSTx8+BC//OUvMXLkSIwaNQqGYSASiaj+OgTO5H5Jx1DakxwfP09AY1pLC5ZafZJOXr6Mp0+fqma6XHePx4PJkyfj47Fj0eV04kNNw+tWQ17pRFN+2iPA9vWXvoSUt16vF9lsFjU1NRgzZgzcbjd8Ph8mOxz4gcMBt5XWLp0+e1andDql/QxAZRzn83mEw2E0NDQgl8vhxo0baG5uVgEI3tftdqNJ07DHMLDNKD9qVfKK3T+jTJBZQ9Lu5zrRtuYa1dbWIhqNKp6TAWl5kS/twV1JT6ZpqgwfZqoZhoH6+noEAgE8f/4cwWBQ2QZ0/mfMmKGOczcMQ4EBpGWpO+TYpI3OtaG+sIOmpCk2omYmD3lE10tHAI8cOVL17CCdSD0q/QTpV8rgj5RdTqcTEydOxPjx4+Hz+ZS9KYNElOPU8/yx0/k/dn1loIGbxqO6JEHL0gJZJ0umlkan3YGxR2sD+/Yhu2ULaqzUaylMpZEmFYo01O3Ea3ey7E60XRnJqIr8HscnwRL5HLvDIAmQ35W/OQ8i20S7pLMnGQZ4MT1VbrQEIL7MOZLAhH1NODd7Boh0VBUTb9yI7IoVcL7/PswTJ9R8SMwsVZCGuBwf5+h2u5VzZE+plcwsIzp2Aw0onSzRsngxGk6dUmn1RHWl02IHaojiESGl48v3mV4u6cPv96tIhVRe9gyUYrGoOikzaiKBBj6DSthOk/I1uRd2epD7JwEGSWsSjJD0WH/yJNqXLkW9dUKJ/JzkJ67dcDQo6U/em/eyN5CUtERakw4qn/V81iw8mT8f7g8+gPvjj9W8afSwVpNOBvnITtN8TTpTpAf7evC3BNv+UBdpV5YWOJ1O3J04Ee9OmIBsOo10JoO/nj0b4x0OjG9tVXNlCjPHSGONWSAEvzRt6Oi4mpoaZSQ2Njbi/v37qKqqwoQJE5SjLUvRuIYcF/+3v7/BMLA5lwM0DQ7LOeG6S/lodwC4l9L4oEy8OXYsrr7+OmKHDyN09Ch0XVc1z9G7d/EsGsW3AgF0JhL4VbGIhS0tiEajCAQCqKmpwaZsFp8GAvim1VOFvK3runL47aBooVBA86VL6FqxAptFdEpmZUkj1U47kieYjsn/+5ctwz+NRLAxk8EaK0ok6YBy84DLhb319fijbBbrRA0t13zms2eY+vBhSR5a0R9pOPxxKIS/TSTwPau/h/36VmUl3g4EUCwW8cUXX6C/vx9OZ+m4aWYo3p4wAcfq6rApk8GrVj0zZS8v2UySek8anBwv+XxjOo29WqkxpL0cgnvBtSAdvD4wgIORCL5tnfVOZ+3WuHH4Wj6Pb3V3421LftNZpk60R0slIMIsEZlVJ8EcGviMMHFOTqcTG0wTC9vaEAqF4LH4xZ4+KveDjqu0faSMk/bLpkwGj3/8YyQSCZimiZqaGkyaNAlXmprwgcuFNzUN62tqsGTJEsTjcWSzWXyay+H1QgHLli2D2+2G1+tFe3t7mU7KZrPKsCY9S3kjjU9G00yz1MiStG6apd4wl6xmv2PHjkV1dTVMc+gIZdmjgXtIvc33o9Go0vEEcwqFAlpmzcKzhQtRf/IkQlevlsmCYrGITysrMQDg/+roQOroUeTzebVODocD77//PpYvX45Ro0aVyROu9d/19uK/9vRgh2Hgv0W53Jf0YXcApB6V8ouvSd3481wObQ4HDlZUqBKKa6NG4WAkgjcArBKZk4blpJE/DgcC+MDtxqTJk9FsZSpVVFSoDE8GZQjMct9o65APCT6wN0Jvby8OHTqEmpoaTJ48GSNGjFARSQmK+f1+LFu2DD9qa8O/feklrOzqwrL+foTDYbhcLpw9exYd1pwIsDHS+mD2bHS73dir61jV1aXW027LyvV0OBxlpUC05aQzHIvFsGDBAty+fVvZTel0Gl1dXaiqqsK8efOUXpC8L6O9AJTzZnf8ydvy9cHBQQSDQVRVVanjNidNmoRx48bB7/eXOWuRSARvahp2mya2FApl/bskT8m5SztNvmYH5MmDvKdhlI489fv9ZdmrUncMd1+55jKLQ2Z9S51Ou6G5uRmxWEydlsO+FrquY7zDge3FIqBpKA6zx3So5Vjk8ykXJJ3I7Dm5frlcDslkUh1BnUqlXshYB6D4lnYRgBf6GWmapprU88rn8wqQu337NhJW41vagP39/di/fz/mzp2LmTNnvjDX4fxLCbAPt+92oImf4d8MxmpaKauCesswDFy6dAkvvfRSWZNurqf0FaXOkTY9gy28L/nnwYMHmDp1atkJVXJ81HHy+F/p236V63dqqd7Z2Yne3t4yp1wSqqZpqluunXmA8hQeaZxJA7Ti5ElU375d+rxplhm50rmhErIzMx0YPoPGuSnuZW+aN5zhyO9KUMOOCEoH3Q6KyDGRuYGhTZMEJ0EJ3oOKwG5E0fCTF+dnB3HsaYi8P8cnnT7e357KL3/zPr1r1sCsqUFh61aYx4+ruj/JdHJviBYPZ6gztc2+z5JWJINLo41KpfHyZTRculRaR8solgKDqZFyv+U4Ze8FGmg0PGWUF4A66ojKyul0YmDFCjxcuRLVR4+i9vz5MpqmE21nXnlJI1463FJYyMwUOQ8aHHLP7GUW8vlybDXnzqmmpKagB4lGy/HJ/ZVKSo5VjkvTNNReuID6S5fgdDoRtM5FJo3xOEL7/hiGgWcLFiAbi6G4YQM8n3xStgepVEopINkIksdhcRxyrbPZrIoqMVokFYCkOztI8Ye4aPBQNpBmL48bhz6vFz9NJlE0DPQFArjz8stYk0ohkUigq6sL52pqcGbCBLzjdmOdAPeovPv6+sp4j/1G7IAY0XEaBzLyxP2kYUteku8BQyVFEqDhPTlPqRTl3E9WVuLE+vWYcvMmxlupiw6HA9cnTkQyFIK+Zg0WdnUpoG7SpEn4o7VrUVFRgWQyCYwYgW+nUgg2N6t+Kk6nE9t0Hf9a09Bu1SHyYlmKy+VSKH13d7fK/tACAWSzWfQnEugUYI2u6zgcCODjcBivDwxgpVVOQ5qRYGQmk0Fra6uaJwD0vfIKCm439gJYaBnjdkPR4XBgj9eLDqcTv9U0vFNVhYGBAXR1deHR1Km4NnEiJt+8iYl37ihgzePxKMMgk8ng9VwOO2IxJW+5DxLwSafTePz4MZ48eaJSglnyVl9fjzMLFyLu8WC/w4G1ogeGpBMpg3K53AunRJFGuD4bCwW82tdXAkYs50jqf3tNtaZp2GwYeLtQQMwodR/fmcvhJ2PHIqVpGATw95kMtjmHjq/L5XKIxWJKnshTHEhvfBZ/UqmU0pG7i0W8axh4ff16TLIcO6fTiY6ODrS2tmLkyJEIBoPIZrNlx17ajTqpazVNKzOq5fVltgJ/8vk8nj59il1jxqBd0/DrQgErrSxBn8+HrQ4H3kyn4fB44LAAJzr55HlmIxBUkPqMNEcelrKIkXc6NaZp4vr167h16xZ8Ph9aWlpQX1+PxYsXQ9M0VQZHx4jrwcyEJy+/jNMLF+LVeBxrrVKVrq4uVeL1bNEiZKuq0LZsGZquXlVrQbkx9soV3JsxA2OvX0dLS0uZzeJ0OtHW1oY9e/agrq4Ozc3NuDFmDH5VKODPDQNv+Xz4YU8PHufz+A2A/xYv9lkgLUp7Vsp+u30o95jr+w2XC3+XSuH1ZBJVVVUwTRMHIxG0ORx4X9OwySrdcbvdqqEvULJ/dmoauh0OZMaPR9XZs0gkEkgkEqiurkYkEkFHR4eaK0urmKVl1+l2Gyufz6O1tRXJZBKRSATjx49XDVPJz3TiblZWIhkK4aTDgT+tr0csFlPZy5MmTcLLL7+senYApayMKq8XvyoWsSGTUanevK+UgVxfYKjZM2mOdid5pVgsqjInCciQnplpJH0B8rg9q1GWxFDHcv1Io1I+JBIJxSOjR49GOBxGTU0NIpFImUzVNA1bNQ1bikUUAejCubbbHnb7WQaI7BkI9ovzpizhGDh/yaPSnuEzZc8GTRvKNJXjwv+Pt/8Okuu600TB7970mZWZVVnew4MESRgBJEESICGKFCVSFK0otdQS+3VPz+vpnZ2eebETEy9m34s3a/55GxsbsaanZ/p1T6ulltQtiaRE70QHQzgCBEkYEkAB5b3LSlPp7v6R+Z368iAhUbHsvREVVZV577nn/M7Pu4OqTsUS59XVVRw5cgQ7duwwJxZw3nS4qx2h+p5NO6pvqzOeY+g8WPpQLpfxVjyOXwcCeNrnwx/6/VV5jzVb4Neeh3/0PDxeLuNhZ81xRpqgE07nVS6X8atKBb/0+fBEpYIHak3sC4UCxsfHcfHGGzH95S+j//BhhF57Dfl8HleuXEFLSwu2bdtW50BRPdt2KNMZopl+3B86xlQ/4s8b0SieDYfxdCCAP6w5urUnQiqVQktLS93JiMxY5VjkE3w3+TgApFIp8z9hnc1mkclksLS0hGAwWNeXiDJCHcZa1k6Yf57rczsa7GPpbC+O41Rr0ciIOAkKPiV4ZS7qRVavLHCt4OYmKlHquHxGN5EIQCRXT5MKexU4RCI7mt3obyUWjT4p41cjUZGMn9nGs86RG24bqIQvEZpztSPrug/8W+GnHk4qInZjJJuRNb/2GmbvvRf+Z581NU+aRsR0G51/oyN9VCDZ+9rIOFZjlDBRJ40qr0r0KlBVuVIjAag/E9jGK52TGmie5+Hyl7+MQns7Zr/8ZXQfP36NYkLYXE/gKL7zfo7PNTV6XhmcTWe2oLMdWLZBoLjfSBDpXqii18jZB8AYUPyMddRMwaRRxmi6TRud776LiX37EH35ZXieh9z992P1m99E4LnnEHj5ZdMAqr+/v6rwtLVhamrKZNLoOjzPMwaZ3++vK/sizamAVsH5z+Vs0COZisUiUqkUEokEUpOTONLfjz9ubkY6ncbfrqxg95UrCIfDRtAc6+/HXCCAX7guvlLrP0DDKhAIYGZmxvRX4R4zY4ERHvZE0f1rpChRYAONy930NwUYL2ZZaL8FwjaZTOJQby/SsRg+2bYNGz7+2Oz/rVeu4Pz27djw2Wcm8hUKhZBMJk09sOtW+wClUqk6OtE5AVU67u3thed52LNnDzKZjDlOzu+vNkJMpVJYWlrC5T17kGlqwluhEL6JeofKa8kkpv1+vJZMmk7fenEOPP1DeXrilVfgfvvb+GaxiKampmsUX8L6oUwGryWT+EE4jB/Oz+NvVlawYXYW52+5BdlEAh9v24aB06dxcds2DN16K5K1FGcqFOTlyjMXFxdNx3ga157nob+/Hxs3bsTFixdx9uxZUwMbmp7GoUAAX681yiU8CXM6ZJnlwM8UVrZyRRpTOlP+Qz7C1GB1As/NzaFSqeDvw2HMBINIVCrorhk248vLdTyaHfM1Y8mWnaoLqHz6aSiEcdfFi01NeLim1M7NzeG1msI5NDSEnp4etLa2mn4i1C9UkW1kkNpyne8kXy+VSpiZmTFRVKalnz17Fndv3Yq329txx/g4Fty1co+WlhYTcFCjgk5X13VNozEAdVmDKj/4fKVSwcTEBLLZLGKxGNLpNC5dumT6cdEwZhYSa5tjsZjBB5XlPHIwGo3i4pe+hGw0ijf9fjw4NYXm5mbj9CsWi+g9dAijd9yBzvfeq+MnnOO6M2ew4eOPq2MLnmnWzsrKCoaHhzE5OYmfJJNYiMXwl7Oz+M7AAP5VWxv+cnYWT4psVPwjXAgvW6/zPA9/v7iI/7KwgD9va8Mf12hF6fhb4TDunJ3FUjaLQM3p+c3VVTwXCOC70ShccfaEQiHjKF9eXsamoSF4u3bhls8+g8/nw8jICIaGhrBz505DZ9w/4NrMTDqK7AADaYtOp9dffx2e52Hr1q3XNDJdWlrCDefO4aNt27B7ZARL0gBu8+bN5iSN1topMEBVT3kiEMBXFheRLxbhl+71XGsjGcrPlPdRJ6Gxo9/zu0AgYEplaBgTNqoPcf+09wb1Wl5qlKlRqCelrays4NNPP0WlUsHNN99cFxxQ/UdL85T2r/e/4naj+7iX/F0qldDT04NwOIx0Om2ydWmjlEolk/JPp0SjzGzCnTyEPQh4ehXhyH4E0WgUi4uLZkw6twk7m5+pDql7S9mjNmOlUjE8lN+rvfRsUxMmHQc/LhTwrdXVunn4fD78YziMUQA/zOVQefVVk33M3jBA1XHBMqDPbrwRp7dsQSEQQD4QwDOui+9Eo6avzPbt2/H8jTei0NaGyXvuwdZ33zVH9W7cuNHATm0jNcQJT+4XaUBxXnmKygne90vXxQSAHxUK+MMa/bJPRnNzM65cuWJKPHhppojKNOUVxWIRzc3N5v7V1VVz9GdXVxeGhoaMXlQul+uyfejE1z5afBczrT7P9Xs5GkgYtnLqOA7KDz2EpSefRPzwYbQfOVJnlCiSKTLZhiQA4/VVgUMk1EWS4NVwVe+KGvF2I0p+rx5VXrZhqynKuqHKaKg02Cksdpq5GnX6v3rGVEniHHmv7VGi90mVJyKtrkONZ2DN8LWj4+qo0CimMpRCoYDgK68g8tOfVuHoX+vU7ThVD/rCwoLZM10n4a5EZxumXL9G+rSWnutVhVrTx7gfjS57n3XdipvcO408qtBUR5vruug5dAgT+/ah+9AhY+DZY+q7bFxUYlUFRp9Rw5efqTFJmKkTwDa4VKmyjWqladsBp/hF/FEY8u9GF9/BlE0elUZcUseXjtF17BgSb7+Nubk55LF2xKXz+OMIvfoqSqWSaWDU399fFyUoPfggvCefhPvLXwK//rUZX4/BMg6+b34Tpcceg//ZZ+G+8IJZYzgcRlNT0zXpdl/U9dRTT+Hw4cOmU/u/+3f/DolEwvTyCAQCyPt8eLqlBb516+ocIv96ZQX/WzqN/y4WQ2562qQgM4LIDCNbwVAlZHjnThy/+27MZrP4cjpd5xhqxJdVMSJ+/KapCS/H43g4m8X9NSWdP3bEQ/lgIBDAz7JZ5P1+hHI53Pjxx0bRCQQCuGtuDv+XcBh/eeYMVstrTbzYW4NCj84j5RWqfC0sLOBCLQOgWCzi9XAYb7S2YsuNNyJ85QoCgQB6e3sxPj6OWCyGwaNHMXfvvfhG7cgn5VOPFot4znHwSK2Bp+2wJf5yXkovTb/5Df5syxZs2bIFFaFLprsDVfr8ls+H/6G9HeVyGQ9lMliMxXB5zx7cNTSEk+Ew7p6bw5133om3brgBC6EQflYu42u14+yUl6usYmmIoY0ajjC68+zWrXhk/Xo8WOMdo6OjiPzwh8h5Hj7YuBG9vb1obm42DirXdY1SrSU7imeqdBJv+KP4pZku7I3A9GWV28FgEI+Xy/glgEdWV/G1mjOr5NU76qk3KN7RgaH4qzzN5/Mhl8vhmwB+FQziwVqvgWKxiLGxMczOzsLvrzZAXFpawubNm5FIJOoUaYU936s4obCx5Qifm5ycRLpW583vJiYmsPn11/Fvt26twqa93cCGJQvq5OGcSH88otV2aJPfqwwrFAqYn5/Hli1bEAqF8Pzzz5u1b9myxQSQ+M5169YZxbpcLpsz49PpNFZWVnDhwgV0dnaiUChg/fHjGL/rLmz66CN8ND+PvXv3muyHQqGAwQ8/RNNvflMtZbHKC1V+2jC09ScqyjefPYszN96IvUtLWEml8P1UCn8Qi5mGu4ojdI6yx43qk6oP/dX8PK4Ui/jL2Vn8Sa1shLiu+q3K7wdWV3HvygrWJZOo1IJoyuf486XRUdwyNIS5uTlMzs/j0qVL6OvrQ3NzM2ZnZ+v0P71UnjNIpxdhRl0mk8ngnXfewfT0NHbu3In29nYDx3A4jD0TE+g+cQLr1q1DNhpFe3s7gGomMw3ujo6OOrhTP1O+y/eqrkoe/pLfjxccB48Wi3hIsuF4D407AJiZmblGBpVKJYyNjeHWW2+tw+doNIq5uTnMzc3V6TKUGwCME09PPikUCjjS3o6DPT34fiiEO2vHXpZKJXR0dGB8fBzRaNQ0ZbZh/3IggF+Hw/hmjS+pg4S4a+OzXrauyEudHrlczjSEZuNWOvZ8vmpz47/6q79CPB43fZc8zzMlptPT0yaYs7CwgI6ODszOziKZTOJf/It/YfZ0aWkJlUoFGzduxNtvv41169aZI1oVt9XRYjujuVc2veoxjQoXOjdUv3UcB4+Xy3jWdfEDqxkuMxaeLJfx40IB8ddfx7lz5+rWyswaHiE7Pz+Pkw8+iFw8jmAmg2Q6jW8nEggGg+bEq8HBQfQePIiZe+/FrUNDKNVKZQh3NhEmLalupTTfKBijPNm2y7Rc9fFyGc+5Lr5fK40IBAImA7NYLOLjjz/Gxx9/jAMHDhh9T+GnPIjvZPaD9slgb41Sqdr48ejRo0gkEujs7DTz0X4YDP6TXji3RuWZ17t+r+MtdfDi17+OwqOPwv/LX8L36qsoPvYYvM5OTO7fj86jRwHU9ziwjW/dACIXCY2KYyPngAp3NYSoWCuy8jt9p85H79dNUieIpjXzOV5quKoibXsSSYAamVavoG0YqsLAd9tOAZ0T50JEonDR9ykh2PvSCL6qENHpwH1hVIzv0VpWz/PqIpiclyKupmARTnoMjqYXqRGuOKIOGHqVNQ39epF2dUgQ7mpkqRPGZg7KaHXPe06cQG/tRAW49SdK6P6rUd8Ih3WNtlPCVrgUv3S9ej/HUxioY4wwUXxX48mei20o2HNUx4nOMxgMmk7jwFqkm4pWIyWc45DfRJ5/HqXHHsO6kyexWhMe5XK1Z4zf70dLS4upTfWefBJeby+8b30LvprzQOfryh7lHn0Ula4uVB59FOEXXjD7EovF0N/fj9nZWfxzXGwwFIlE0Nraiq1btxrYlstl0wlfs2cIzx+0tODpVAr5fB4XaydUFItF01STsKMh7nkehoeH0dbWZhrEjT/1FDKJBF4PBvGAZIHYOKb0YnvoX47HMeX344VoFPfV3qW8SvkTlVLHqTqMX4hEkPf50L66iqfCYZyv1WFu3boVvb29+OCDD0y2VCwWw+7du9Hf31+Hw8SPmZkZVCrV0zdmZ2eRSqVMkzZmcpTLZbwTj2MxFMKnO3bgm59+inw+jxMnTpjo2MZkEv/xlluqHeClft3zqr0oHqgZgr4GabLErWw2axRE26CxndxsAserUCjgypUrVUeD349fhUJ4BMD93d0or6zATSTgJJN4tFjE8z4fHsxmsSw127pfVPoJJ+4F0+odx8Fzfj8mHAe/DoXwaI0PHjx4EGfPnkUwGMTVq1cRj8fR29uLdevW4dMbbsAbsRgeymRwX60JKU9OURmmssxxHLzs9+OlZBIPrqzg3trZ6DYsKFPoYGBzQRoRW3w+/J8iEcRiMWRRL2/5PjUUVU7n8/k6un8tHMavQyE8Vixi3/w8JiYm8JWBAdxXO8Lu7ZkZLC4umqwfltYxmkrjSteszgtVpFVJ43xVcee9N9xwA5qbm82JSK7r4uabb8Y999xjTlgg7ZAWtQEgUK9v2YEX3qOOe76bMjMWiyEcDpv/E4kEBgYGsHPnzrr+Qq671rCMJRN0fjCrJJPJYGRkBAsLC0hevoz2I0eqY2/bZuBCpV35iirrumc2X9L77Wc3nz+PbZcuIRAI4MXxcTz44IOmObne57rVkrPFxUXD4zT7U3nYn7e24j/Pz+PPa2URvDiGz+czjS2ph70eDuOFRAI/KBbxaG3/n6tU8A/hMO6PRvFQzfC95ZZbcPXqVbiui5mZGbS2tuK2227D4OAg5ubmzHv4Xq1r16g4dQ6WVxB+muHCY+kvXLiAm2++GT09PfA8z2Qsc71Mofe8ajPt5eVlJBIJcw9x+kWfD7+KRHD/6qppiMj91YAi5/rrzk5MuS5+6fPhQK3xnO4xZVahUMDly5eNU4D7w4zFTCaDUCiEQCCA2dlZrF+/Hs888wwuXLiAeDyO8fFx9PT0YG5uDq7roq2tDcPDw+jv78fS0hJKpRK6urowPDyM8//xPyID4B+KRXy9ViLh9/uxceNGvPbaa7h48aIxwlSHdl0Xz4fDmPT58Lzr4mGL7yht2nirmbT6PddJm4b3EqeCwaDJCAJgjulcrDXZJd6qDk4dvVQqIRaLmT56DP7wHTz5hCXBw8PDuOGGG4yuTnpVfFS9z9ZhbbqkPLftHR2PuPL1YhHfqFTQ195eV77JHj8PLS3hhqtX8deHDiHS3Y1MJmP4fLlcRlNTE9LpNLLZLIrFIvqPHMHYXXdh3bFjuH1qCg9///t1emc4HEb/6dPYMTyMHTt24D0J4vEECrVb7azgRrac6uWkGeVvasf5/X485vPhO4EAYuIYZwaZ51UdT+fPn8eBAwfqMtTs3jKEub6f8lVtMT6fTqexuLiIzs7OOrtFbS8NGnjeWm8eG6+vd31uRwOPWWLkIffYY0BPD0pPPIHgq68i+NxzqDzxBLqPHKkz4m0A6+Zqsyu/3w9885u49Oij6Dl4EJ3HjplNVeODi7WdCrY3RwGuTgd7M9To0nepktZoXHscXnwHCdP2YqrwUkPbdr7YTIPfaXq/vtuGkx1xUxjZhqjuja7DjmhTaDEyyn3T/gxsrGavl0KLCM616L5o6qUKfIWlKpF2ZN1WctU5pMqYGgC2UqFChAyf67e9z/Zc9H/tAaBGtDouyARUidP90n22DT9VaAnT69GZ0o7+KMwU/+wMC3sOOi/ClJ+rAcEfNpDS2nLCWMs9dCy+i9GiyOuvY8OlS8h/9au48r/+r3B+8QuEXn0V0WgUbW1tmJubM3Tif/ZZeE8+icRrr2HFudb5ogZK7IUXUHj0UQRffBFlwQdmVjGq80VfHR0d5l3sL6B7xahOI+FN2gmHwxgcHDQlELFYDFNTU6hUKiZbhAZAc3OzMagDgQC63n0Xc1/5Cr5Rc2go/pIWVKG099x1XTxWi/I/ah2PRFpRR5uWobmui6ejUfxDoYCnIhHccMMNSKfTyOfzWL9+fbXpYyplIslNTU3YtGkTeCwp8ZX8wj6qiw3wPM8zpTKVSgXfamrCs+Uyvuk4+Oojj+DKlSs4dOiQOdmjt7fX9PEgTapDk7AnDtnO3Jf8fvxjby+it96KxNtvG0OM0QQqycT3+fl5RGrGs9Kt4zh41HXxpOehEgggKKUPzHx4vFRC2edDRfiaHaWmIar0/0Ykgl+FQrhrbAx3RyJ4o6UFW86exT998gkWFhYwNDRUl1VWLpeNkfvSnj1Y8PvxYjSK+2qKi61sqNwhLr2SSFSPQE0m8TBQV1/OZ6lAMkLe0tKCzs5OuK6L5uZmg6N0DHMvFD85nspYm/85joOX02lMVCp4IRLBA7EY9u7di2QyiUwmg+XlZbS0tCCZTKK/vx9dXV3mzHm/349M7ex44rRdeqdBEp/PZ2jbNjpsnkxjUB3ePp8P8Xi8zhlAuaENvVQnIi7YtbSkFZWZlB28d3JyEj6fzxgiU1NTGB0dRXNzM7Zu3Wrgp2n8Pp+vLm28Uqk2rtuwYQNGRkZMRo3f7zenaNCYtOW5pk/b8FEeqEEXlaWqb5H3jIyM4Nlnn8Xi3XfjZ5UK/vXiIp6QzNjrZQHYn/1Rayv+qLW1bg8Jb57EUSwWsbKyAtd1sbKygl93d2Pa58NPikV8o2bs/axcxoTPhxdjMeytnarw6aefYmpqymQe5fN5czQ2YU5nHo1N0gr5D/l3JpPB/Px8Qz6lez80NIQTJ04YJ9rq6qpxdnH/I5EIPK/amPTs2bMYGxtDX19fHW0/Hw5j0nXxWjKJB2tGvTqPyD/592PFIn7luvhmzaFm65x6XPodd9yBkydPGsOfDngaxdFoFNFoFMvLy6hUqqUvmUzGnJpC/AyFQiZDEVg7sYpG+8YPPsDMgQP4bjCIoOg71E9GR0eNYaUGPAA8VirhWQBPVirGScc+ChxD8Zf/a+mK4hNxV49AZdNaOpTJI+nw4x43NTUhm80a2c9Sk3w+j0KhYEosWcaqeh8z8RhIJF7TYUTdjTBRY9q2O2y65eekOXXYaAayrTtT/rCPAh34fr8fsVjMNOwlnLl2ZmFQFvl8Pmw+dw43fvZZ9R1tbYZu2JuAeMITODRwvby8bBy7yvtsfmEH8GydimtWh7DaJYQzxwyHw+Z4z0AggPHx8brTq/Ri0KWRjk+54rquKV+mA4+N+/V4Xc3iV9nDv7l+/ft3Xb/X8ZaceKlUqtbnP/oo/M8+Wx3opZfQfPgwmjs6UMQag1NPb6PL5/OZI0wmHnsMhfZ2jO/bh1StSz3fbW+CepR4DxmvGke2kamfqbGnUTiNvilzUMeJChqNEquSRaJQZUCfVcOQa6CCqIY2iYmEp4it6UgA6tbF/xUWZuNrhKPwU4ZhG5+8eMSSwpTrYsM9NebJqGyDXoUemb7CW+erqVqqcFF4Kp4o41LFnYTD59STbis9NixZ/2yXACmM9DjORvvDd6sypXNTI72Rt1j3SXFcPcyktUbOLl62Q073RUtQ+H5V6m0Y61oVvxXONIiZdQKgrjcADTBbYPHdgUCgTnB/tnMnVltb4T76KNoOH0Zvby/8fr+JpPl8PuCll5B6//3qCQxOfcTZZvyR11/H1k8/xejoKJZEISAv0R4FX+TFo1V5mgLhQYVpaWmprhZPDQemf66urmJ4eNjQ2dLSEk6fPo1cLlfX1MpxHORyOeRyOTQ1NWFpaQkdn32GP+3srKZAW6cz2EeHqgJPOnFdF9+oVPD1WtNEn6TwsWkV8ZRdoxXX7l1Zwf6aYp51HKxfv944BWiQt7S0oLm5GT6fz5SJMRqle0h8p2K+LJEyAKZ52COui0crFXiBAIoAjh49isuXL5sjKD/55BNMTk5i3bp16O/vR1tbm1Hqyd9UFvAiL/h5WxtmQiEE9+9H4OWXDb9m+iNxVA1PHimVTqcxNjaGJmkax8ZPzHzQaILyB+K93eiLKaQqs16sVDDjunh/YAA/8flww7vv4uDBgzi0axeuPvggbvjwQ2z/7DMTVY9EItixYweSySRQLOKlUgkP1sp9aGSrrCRsuPc+X7Xx1jOVCh6vKY10fukzWi5JpSuZTJoGj4SxNgxTWacyQ+lXZQ/H/gOfD/9QqeDJGo9ZWVlBKBTC6uoqotEoOjs7kclk0N7ejv7+fqN0MkvIdV2zfjsAwc+0X5PySFse63e2s3+olk7f19dX19SLTcL0SFrbqaCKsuIAnSTqmKCuMT09bU6T4PGGfn/1+GE9mpqBg1gsZva6UCgYp0IgEEBX7Zx2lrcFAgGTdZZOp/FmLIbDf/7n6Hj7bUQ+/vgaWKhupuvzPA+XbroJF3ftwpYPP8Tmc+cMHVDnUdnhOA7Gx8fxs3QaC7EY/vP8PJ7o6jK0RD5HOaTy8e/m5/FX8/P471ta8HRLi8Fn1f0AGOOcRhhx+esrK3gtmcT3wmH4a3jwB34/fpTP495ab5hAIIB33nkHw8PDSCQSKBaLaG9vR3d3tzkWm3tHJ7E6SVTXs52Neild6vy1Mfrk5KQxZOhsIG3S4N6/f7+RB8FgEN8NBPCTYhH318ohQ6GQaTZpO7wqlQoeKpfxaLGIiuOgUjN2dc+Io+Vy2WQT2Vm6s7Oz5nhqzl2zfJqamjAzM2Pwk0YkS+84JvWlwQ8/xP/ypS+hL5EwpXnlctnossQH/qgO/o1KBV9dWanLDmhkN/BzNQK5rxrYURuBF/V/x3FM1h6PTU8kEkZ+MbuzXC4jXjutJ5PJGOdHc3MzPM8zfR7UObe0tGTeTfm5YcOGOqOTl21jKb5xT5SOde0ayFP9Wnl8uVzGK8Egfh0O449yOfxRzQgmXVGfZPCbfQuY5eHz+UyGh8/nMxmfMzMz6OjoqNPd6STkKS+zs7O4dOkSIpEIQqEQZu64A888+CDCpRK+WsMLe32q39vNyNVeUHpQ/CBts7zz10tLeDoSwVM15xhx2O/3Y8eOHQbmDNRrFrf2kKD+bWePu2719K1CoYBXQyEc/4u/QGh8HK21JuKqa9j8lxnnv+/1uR0NwWDQRJ0AIPjKK/C/9FJ1AmL405umwFAFhMoaJ00vSzweh/fee5jYtw89hw7BcRxM3XYbpg8cQO/hw+g6dqxOkFBYUqhzsxQJFMF5EdC211w/U4VYLxV8dmq8Kra2Eal/244CPVWCvxkFsb3BjYx2fkZD2HYYqGHOZ5TQdE28XxUeGqBEVDJfljewsZrPVz2Lmg2VbKcBLyUsjRLRU8+jW/RZez6Eub2/ysBsPLAVc/7Nd9hCwDaiFe72PY0cAjpv3Us11Pg5ccEeX3Hid+E1x1LaU2ecMjcbr/V+Gz9sYaE/6qVWGtH9owGkJz1wvTQcFCaVSsU4Jikkdazew4dx5dZb0fz669i4cSN8vmqNIrvzqgNJ593IcUahZws+ACYzRw3uL/J64YUX0NTUhDvuuAOJRALnzp0zhvbCwgJe8vtxqKcH3wsGcU/N604DXmvYmd6dTCYRCoVwZft2vHXvvdg/Po7HrlzBRx99hPn5eUxPT6NQKGBxcdFEWpXWgGv5mOKK8ixbaAFr9KVZavxubm4O4XDYCEv9nvBlsyLCXxUc5bfkuTS8FDdUmSavnZycNMZSPp/HxYsXkcvlkMlkcOnSJSPPSqWSOTIvHo+bsVOpFEKhUF2GCHHf5rffyGbx63AY8cOHTT0tAFP/yPPedY6kj7a2NvT19aG1tdXIAMo28hFbcSXMNctNoyhKa0CVNr+9uIifFov4k2QSqVpmUSgUwuT+/cjH45i85x78H2vKjO6xz+fDw56Hh2rRQ0ZCFSYq39TB+lCpZOqxvdqeU1arbOR8S6WSibAx+h0MBrG4uFjndFRHArB27KbOQ3kecedrfj++WlvXWC6HfD6PZDJp5NnVq1eRyWRM7xeOwf1WulD+pM7C5wE86/fjO6XSNc1Dbd2jVCphamrK4Ddl4+rqKiYmJtDW1oauri6T3s53xeNxxGIxfFLLRmGfAa6TDtxSqXokbjAYNPw2HA5jeXkZra2tePzxx+F5nsFZz/OM83NiYsIosBq9ymazpnyCOoLeR+dvOp02zlPCzufz4ejAAPLRKKbuuQebzp6tkyGEq8pNlemf7tyJbHMzPt2xAxs/+aTO0aS4p0cB3nzuHD664QbsnJrCB+PjpgdCpVLNwFhcXMT4+DhuueUWsy/sy/BfFxdNA0h9D2mLeBeJROr25yuZDB72PHR3dxv8fsznw935POZWVxFMJMy6NYuqv78fyWTSlNHou2zc0+wB1W+Ul6p8VeOH/If3sXcSdTqulfqd6m7kNU8EAvhqPo/pXA5+OX2Gc/1twSx+Zhs1KsdpJHEs7UtAp2M8Hjc82efzYWJiwkTBKSvn5+dND5H29nYsLS1hbm7OjMU5j46OmlNDFhYW4HkeBgYG6hwnali/5PPhmaYmfKtSwddqxjvXQ33HDooS9iFx8KuhyD2jTv284+B518WfBAJ4ola3z2aq7PEUCAQwPDyMwcFBk0FEXTwajZo1khaZVUr7A6g6ndra2jA9PY0tW7aYY485H9uRovaN6m/Kc/V7OrVsHdTOACmVSvhVczOmfD78eHUVT9fGjkajppx0eXnZOHspmzkH9l3RZqBqh1JuEB6UZ4FAALlcDiMjI8aYXrj/fhRbWvB8sYh7a85W245T2cLv9Ye6i8pupRHVT58NBDDhefj7fB5PBoMm2OTz+XDzzTeju7vbvIP7R5vN/q12BO8jr6He8abfj3QggGOBAL5eu4cOZs6N8pj2JgMfGtT8Xdfn1qLpTeXgXAw/Z6pSNps1NXsKQFXQ1LjN5/NYXl6uCsVf/Qodzz6LoudhrFzGzL59KLe3Y+yuu9Dy3nsGwMosVXhxE22vmj7H+9U41+9tpwMv27tuRwPUi9ToUsapBhBhqkaeMibCTh0BqqhybNsTaq/Rdpxo2qUNJ/W08TMlzng8bhrlMWWatVB2JgXT3XTNmUzGpL8xbZhnRGs9FpFdBaMtLAlXJWI1mskUVGDbDhxehJHtQLCdALzXdgxwPhzXdmzYuKDMRxkV36/NlWwmbjs6bKeC7oG+V+ek71f6VEeR/s0xqWjwe1XqdZ2O49Sl1GvkBQBGv/QljN5xB3oPH8bA6dMmwsA0UnWW5O6/HycfeQSd77yDTf/pPyGRSMCrHaumAprrcZz6kiPdX/2Mc2FqJL8rl8smbe2f44rH41i/fr1JbdQsppaWFhxOJDDp8+EnpRLuAUyUxXXXUhtpjDADwvM8vNvZiblAAIf7+/GlsTETmWGNXiAQQF9fH7q7u43xTXxk9obyGDUudP+U5wFrfFCFnV40YuZrNfE+nw+RSASFQsGcVqROU15UpNVYJr7SeaF8Xb33c3NzKBQKWLduHZLJJN5//3188sknCAQCKBQKJi2ehmwwGERPTw9uvPFGMw4b9DFKp04Y5SmO4+DhSgX7xsfxozNnMF7DScdx0NHRYUqGlE75rEb5U6mUccaT52qUQvFWeZfNx1R+6Du/ks2i78IF3HLLLZjz+9Hc3IympibcOjSEY+vX48FKfTdwwlXlOBV6lbm2A0QNUD5XLpfxguvi2UgE3/KqfS84X5tvUhFn+igdKsQHygrihK3IEQeJU6qQAUB7ezsSiQSWlpbM8WbhcBjZbBbRaBSJRALhcNiU6niehxdcF8+lUnhgcRFfq53Zrkawrv+Z5mZMui5+VqnggBzNpvujxvQnn3xSd4Y6o3ejo6MYHBxEZ2en6RbuutVMk4mJCWzatAk/+9nPMDIygu7ubszPzxucv3TpElpbWxEMBjExMYF169aZ7vS9vb2YnJxEW1sbnnjiCZNZymwzZiek02nMz88jFotVea63Vu7FSDdxIhQKmcgi8Zk64i233IL+/n7Dl788O4uXm5rQefDgNSdS8R2ErzoRXdfFxhMncPnWW7Hlww+N7CN+Ec40WEiDW86fxy1DQwgEAnjfcZBIJLBx48Zqpg7WAiqUQT6fD3+WSuGvan0ZlJaUzsgnyXvstHDqNZoVo3jyaiiEl7/7Xdw9NYXwa69dox+rnqBZKvoejqfZnYpnShONZCDfw75B+XweV69eRSwWM6nWoVAIN9100zVw1rXyt+p7dkakwpB4pkdNKi/7VaWCw3/+52h+7TV0Hj1qDPeRkRHTl4G6IstNstkslpeXsWHDBqNDkD+wHKVcLhtDtampqU5/mZ+fRyKRQEtLC0qlEnp7e9HR0WGcDqRb4uwvOjow7fPh5wC+XEtDVx1Y90N5NLOCbB2N+EonUrlcxq+amjAB4O+yWTyRTJoMOR5vzKxRz1vLPqHBqEYl0+RZGuC6rpmD3+9HOp1Gf38/mpqacOONNxqDU9dCetYgjeqgqu/azhW7RJj0YvNRAHi8VMJzjoNvi6OJWXbkDez9RdrjiRPUKXw+H7LZrGlU29zcbNbMjCzP80zwpr+/H5cvXzb9rgCg/8gRjN55J74ZiZhgiJZwqH2g9p/qxqRBGuzqGOX9zDRj/6U/lFOdCKtUKmXkGPGQP3QYqV2kv+lgVl5ZLpfxUKGAF6JR3DExYeZGx4yWNZN+yHeoe37hjgZ2wKRnSlOySRirq6smNSkcDhuBoz+20QQAbcLIybAqlQq8t97Cwv33o/3tt+s2TI1x25OkBra9mSrA1GDTDVVjTQ1S2/Dib80AUCHEi8/xPs5ba56YAtfIcNT1KoLyXaqQ8n+ujxEYCk9eanCqwmM7bOgJ4zxCoRC6urrMd6yTYjpptNapVRUpwl8JEqiWYGRrR6gpA6fhqIqtwlH3RveCii9ho8zONirVKcB79Vg+zlVxxoaX4pE6PtSppkozfzhP9Yzb49pCWWHJ7xRO/FyNEc5ZHWKq5CjtqkDQRjH2XBTv+G7b8aPGus/nM95P/eF6RvbuRb61FRP79uGWoSEz32g0aphesVhEPp/HmUceQaG9HdMHDuCWo0eNEqfOGL638LWvYezxxxF87jngV78yvMqmcV2zbZyVy9U6aKaSftFXX18fAJhjOm3l4qFsFi9Eo/hGLeIK1Ee0uM8UtMSHBzMZvBSL4d65OXz22WeYmprC4uKiiZTSY//ZDTfgHzo7sX9yEo/VeCWbmml9qO3oUlxQpZMwYyTANoyp9EWjUfT29pqjKTVlnvunuKdOMMV1vl8VfYUjaXrPnj3o6OgAAONU2bx5Mzo7O030knAnfvD95XLZpFDyUh6vZXFANdX98OHDWF5eNtG2SqWCoaEh/PznP8fXv/51rFu3rk65VJlULpcxNTVlDB3uicoGpS8qiCrz1BhRmqDCyEAA7x0dHa02XXzhBTx9ww246667sGplRdin22jTLdvpREOSuMT3U2/4ZU9PVTEvlXDXwoLZR4VFpVJNxSduf/zxxyYbZHV1FTt27MDrr7+O999/H7FYDOVyGfPz82htbUWuRi+pVApTU1PmWFT2GmCfj3/5L/8lbr31VjQ3N+PSpUvwPA8bN25EoVAwR/Du3bvXwNDv9+P5YBDTPh9ea27G12dnrzHw9P/HSyU86zh4vEG5p62HlEol3HzzzTha42ukwZtuugl79+5Fc3Oz2UNGM/1+Py5fvmz4IPUvOgAKhYIpOwqHw2ivNVVjhJcd19mBnDyZ59UvLi6atWQyGYMH5DWkccUzzpulPXTE0aDT3gIHlpex8pOfoFgsIhyPm473ineKO/r3wIcfYvP589X/11iqcdiyQ7w9Rxqqrlvtvn/q1Km6Y1rtcpenW1rwvXjc1MErn1LHBw0InSdxmMq66lV0Hnqeh+fDYSz6fHg/GMQDtVIJ6mo0Zvg+24lAvFS9Wdeta7HlfSNdlWnahUIBMzMzCNYiqqFQCBs3boTf78fMzIyZE43Y5x0Hv+rqwgNLS/harQ9QttYsVmHKOajOTtmrfWrI855vakI+FsPcvfci9uabBqdmZmZM1J2ZfmwOWalUjK1Cxx35aSQSwcLCgsnaicfjxjDl/jlO9VhLNmDdsWMHXNc1Mph7Tvn4jWwWL8ZieLjmeCS9Ke9VnYx7aeMRL0bsNZPyqUoFP/c8/KDmWLd1M8KPTjPq/eocJOzYIJn6AmUlS/sAGKc6DXSdqx0EtO0vvdQuUT2a+E9+mM/nzZGaHOMbpRIezOXM0ZuqB6vsYxkM6ZxNx4nH3HfiAWmFmV+UbX6/H62trQY2fHb7lSuIvP467vuf/2f4ahlQjewJXZvqLtwfXjbMqD9QX3hgdRXfjkSQkGwXPs8MDfIOZm6o/FUepLoTLwZOyJ8O5PPYv7CAsYkJuDVdXemWYxHnqKeQpq7XEsG+PrejgV5ZeorsLtraHZapMJFI5BpDUo1ACjxVIvWM18SJE+g5caIKNCt6QgDYm6x1a7aXicyVRqVtDCpCk5GowaqKgip02jjMnhcRWZmsKn6qeFBBUERRRU4/VwFCYaZCmWu1PW+6bs7fNl4IZ40e8n/CoVyuNgnLZrOmHlybxSgx2oTFy/a+ER52FFUJxU7XUeeSMjpbwOpa1COrY2s0395/dXropYzFvkdxX8dQQUXBQRhRIOhe6N7b61McIgzUa8kxFIfUGcRxFQ/VWOP4uk59n+2l594xnc/uwK/GSfd772Hi7rvRf+wYVlZW6k4mUeU1FAqh9+BBTOzfj+6DB+toihER5UXFxx6D192N1W9+E6Ha8Za6FyoAXNc1GVUqIJhu/HkZ6e97sWyBxjxLAfh7y+oq/nXN2XJaoiS8t1KpGONLnSjLn36KHek0xldXMT8/bxqMsXbYdV309PTgyIEDSIfDOB6J4LHhYYOX3EOFp3rVbeef4jv3SnGR95M3h0IhxGIxpFKpOseqOs50v4gzys/UWaUGLj/j5zyyj3OKRCLo7OxEZ2cn5ufnkclkTJSA79M12Q5ypQPFUaDKl958803TPV6PzZufn8f58+exd+9erFu3ro5XKR29HAjgF46Dx3w+PFp7ZywWM9382b+CslYj+Yx0UBHR+s1Dra04OjiIP25qwtficQSDQczMzCAQCGB6etrI72AwaKIUVCboCF9eXobP5zOGKlPiHccxpSWet9ZrgY4PKnyUhd/IZvFSLIaHaoaIbagS7tzzSqViauBjsRgOHz6MHTt2YGhoCJ988gl6e3vheZ4xgFZXV+tShwuFgqnd1RRdwrCpqck4KBh91p4x0WjUzPHJchk/r/WoYMd7ygryaeo8T3kevuV5cEIheDUdR/UH5eksedDoHstp4vF4nWOashYAenp6zP4zKre0tIRMJoNEIgHXdU2m0PLyMlKplOEFzNZgU0nP8zA7O2uOsaQhEAwGTe8A7ikjqVyTzVt5X3t7uzG66Oh0XdecDmLLTu697WywaU2fVT5FPqL8kHTLSDPruzknn8+HlpYWY+CqwW6viZ9xnvxNmiA+EO/4mZ4korzNdV08WiziWQBfz2bh1viz1nFrFhX5P/m0Bm9ovNmGhdKhws3WKfgu4ujy8jIymYxpykr+yblosO6FSATTrotXk0l8eWzM2AqK5yo7NOOZPJuXZm88ViziZ/PzSLzxhtGRuCfLy8tGRq+urpp+R67rorW11WQu0KnK01qoZ0ejUXOyBrNxuS41Qvkc16Awq1QqeLBUwtcWFqpOl1qJXiOc4T5w3fze1s2YWaf652PFIh6pVNBWc0Dkcrk6nZE8OJfLGX7nOA6SyaSRA4Q5MzUIRzqNg8Gg6U/humvHGKqtRrlFPLczW1RXIJ2oXsvfmqHHNVOeqX1DfCfs7EAr152vOXnYs4JHdxLGPPqUTX3pSKD9SacYgwqUZbFYDF1dXTh79qxxNLFUw7Yr1RhXm8Pm9bzfdvSR/1Qq1ROYyLdVXgSDQeMkU/luO/tVL6ds4rr5/mw2i3K5XOcMtXue5HI5Q1NaBmTbop/n+tyOBnpa/UJMRCplzkQKZVpcHHBtqrsSGxerhioXxO9pwFDw8T4Sgl1LaBvRGoHifPm9rWDrTyNhw791HkqE+rytuKthSsRRZ4Beqqzbc7GVXmUAakwrAXNuuh+qIHCeShCNhD8NIwogRTwVlKpg8Z0UJrrPymhsxUKfV5hq1IxrtpmAjk0cUXiQiRHOJGB7Pfb6dU/VcUTnjr7XHkMVVM6fcFKcBdbOh1cBofO3HTb6Gf/XzBSdl42b+l6FqzpnOKbuEZ0nZNbEiUwmg+npadPNn1HKQqGAloMH0XbkSDViLF1v1fnHNfacPIme2hGik7ffjrG77kL85ZcRef1142wwc33mGZQeewy+Z55pqLhy3EAggGQyifb2duRyOcN8qdg7jlNnnHyR149+9KM6XqiKG+FJmDLCQSWEjbq6u7vR3d1t0u4uXbqEd955B/F4HH6/H9u2bUNPT4+hQRpHqVQK7fk8XvL78WCti77dBdtWilUANRIwpB111ikucw4UnrOzs9dEAZW3Ka+wlQ97bH5m7202m61T7kZHR7G4uIgPPvjANKhUpYHz1jRERsV0TrZTk9fmzZvx0UcfGeOQ8woEAujp6UFHR4c5jkz3mPv6j8kkxhwHPy2VsHN83OzJtm3b8Morr+Ctt94yjS3n5ubQ2dmJYrGI2dlZkwpfLBbR19eHkZER07vjnT/9UxT9fvxDoYBHaw2mGPXLZrMmIjg2Nobdu3cbeClO0khVXu55nlFgWSLFZ6kjqEEYCoXwqOPg0VwOHgCv9qzt8Fa+zKwollHovpFPBwIBxONxkyGnDekKhQKWl5fheR6amppQKlWbP2rJFTMxNVWUNEeF3/M8POx5uHNuroqjvvoTDuwsHJu3Kh3ZDnzbMc+/T548iY0bN6K/v9/oRXSC+Hw+dHV1oVQqmYyB2dlZ+Hw+o7AyE2dhYeEaA3pqagoATJZBqVTC0NAQvvSlLxk+wUxTGm7pdNoYMc8DeN3z8N1iEd/2r50oo+/o6ekx9EeaoQzTXi5aJ27Tla2T2HSuco57poYcfxjA0XK6rq4ubNy4EUtLSxirGciaUQugTgFXXqX6G50INGoJd+paunbSFfWDB0slfLVmLJ9qa6uTO5pqTrx0HMcc1Un46XfKR+09sfUZfua6rjHCSFv5fN4ceUdHCjPQSJvk6U8Wi/inchkP1ZoSEqeVV6jOSXnAv4l/mtEKAA+USkj/4hc4c+ZMnSxyXdc4FOkwjNWOeWaT5GAwaHQQ7a9A3hcOh82+JBIJQzNs0mzLJmZvEDdsB7Stm6rhx4u2ifIKRqnVwchLeYmWXNsnUtHBGQqFzD42Nzfj8uXLdVkNyWTSNBxmDwvyRupq5C9s7qp0SH5hB3psh5dNO7rPxAWlT8KQTm7+UA9jH6rFxUVzogjX3tLSYnTLthr9xGIxcwoMT+2xyyzprAmHwxgfH0c6nTY9M3R+XNfExIRpjspTTjgWHUOO45jsGLUzVJehQ8HOPuR1MJXCm4EA/qhQwHdqe57L5erWTNlHWuE4ts3KfWPQgFka6qTis8wEpGxRnFXezOw5vq+Rvdro+r2Ot6QHRI1o2yiikUUkUgLkAihs1IAmclFp5jNqcPAzG3lVmOu7+L82BeL36ulVA49GozJnetL0tzoUdA1q0CmjV0Wclzo9VMFSocTLNi4Jaz5LAualQlgFI6NetpGvF9dkK4G2Qm7DTZUlrrfR/3xOx9HP+YymS9lpp9xj9dAr3ujz+oxtTHNuilOet9Y9tlEqMt+hwoYGqcKa46vhzz2gcsLPlBkp7gNrtYu2QNO95rM6JoV5pVKpE2iq8PJ5NeIoAJUeiS9Uxvi8wpHfc28YHeB4wWCwLkWShj3no5E0Xa8alJVKBeP79mG1tRWVBx5A5PXXkbv/fmS+9jX4nnkG/pdeQuDllxF4+WUDH1VGde/i8bjx+tNYsWmpkUH5RVz33nuvKTFj/T5hTcPf7/eb39wbwo6OB8KvXC4bw3rdunUoFovYu3cvuru7jWAk/3EcBw+sruKhGj+v+Nea8ymfsZUoNT51f4BrG5UpvStecnzWgpOGiR9KPxoVU0VUDQc7dbNcLqOjo8Mcf1YqlTA5OWlSbglPOu+Wl5cB1B+/RYWBRpU2ONb1K9/xPA+dnZ0Ih8NIJBImSkKYtbS0mDIchRXHcBwH33Fd/COAr9ZSRuPxOM6fPw+fr9rgbGJiAt3d3UYBI58Oh8PI5XKIx+NYXl5GPp9HPB43+zB49CgW7rsP35dGk4yUsmEYHazM/gBgoj5qvBGHqBhSAVc+rs493UeVOYo7dr05FXt+x4hVNpvF5s2bjRLseZ5RfNn0kxF54huj+6lUqk5eEY8mJiZM4zt1DqVSKeNc4Xy1npwOM+IEDTBbRqgirUq4Gl/lcrnuxAPqWY5TPVqN2Q6O45j67Pb29rpMxmKxiLm5ObS2thqDkP0YGCFjOvny8jIikQiCwSBaa0c2+v1+zM/PI5vNmpTyPXv24PDhwyiXy3Vp1Y7j4IWBAcw6Dn5aLuM7Ik+VlwcCASQSCSSTSaN32DqM6k9KW8rvOC7x2ZaD+gz3xe7TZethlUoFW7ZswS233IIjR46gUqmYDDbeozKykcOD43PelLHk4/y+XC6byDsdW/yMODA6OmqMn+Pd3Xg2FMKT5TK2ir5J+LExKPeEc9Ksgd/Wn0jXYMsTOjAoT1R3Jp+2dbSHPQ+31ujP1rEJP+4/9WeNjKrhAqAuiKi6ON+ZSCQwODiITCYDx3HMcYacf2trq7nP5/NhcnIS7e3tRu6EQiHMzMyY0330ncFg0DRWJV9Kp9N19gTXofopdR4tz1F7Ry/CjUYnnYI08HXfNKuM/+dyubrMbwCm19PMzAxc1zXyb2VlxfQkIH9fWVmBz+czZWSkR8pZ7t3ExITBSe6BLee1tJLzVl2X3xHXiFt0iKozjjBzXRdvxeN4JR7HI6ur+KOac5iwWVpaMniYSCRw/vx5JJNJLC4umlIYBm55HDFQLddmqQ1772hJlwbTm5qaMD8/j6tXr6JSqWBmZgYbNmxAuVw2pVRK/5STdF4ovMgLFcbqTFF+8n/2+zHpuviHQgHfru0he/PQQaCZV+pkIA/Spsj6XSaTMeNpE3bPqzqF6bAmj1edXt/XSDf8XdfndjSQWDRFvtGL1PDU72xmoYq8Iq4KT26UboSeN63EyI2kUuj3+40ywg1uZJjr+Jxzozp1RSzbKON6GAFhpF/rrFSY6sVnNTWsETzVu6YpRI2+4zNAvWKsBiCVTHUiULBqFEeju2pcq6BXoUziUQNAjXVbwVT46p6SSOz7bOFvO5m4Xt0/Vfh4L8ch/G1HhO0Est9HpqnvVqNL52krHbZSbRv9tmPG3j8+T4VZlTd12qly4XlrZ4bzOzvqSmOXtEQ80fHUyUjlQVP/Pc8zjDaTyRhHQywWg8/nMwo06/94vrPijO0YsBXUvsOHMXrnneg/dgwdAwM48fDDqLS3w3vsMfhfeqluv3RfbIeRGpzcJ97/2xxxX8R11113mXcpPLkfykOpjNj0QwWCuEhBms/nsbKygrfeegv33XefERy8lM5UEVB8JQ7ajmPinGYBqbKh/FDfx9+hUMhkkFy8eBHhcNg0XhocHMQnn3yCgwcPolyupr3y2K1gMGgMwkgkYlL5A4GAMSaJi3/yJ3+CjRs3IhKJIJ1OY3Z21kQ+mG7LrDimxgNVfGCJQqVSMfXuAOoMCNvpoA4HAMYg5Wc+X7XkQJvpqTwhbT4ZCuFJAJ8tLwO1shimdVN54rw8zzMniHhetd54ZmbGRPqYaRYOh9F2+DC+n0zim9/8JjKZjDkFxK7D1FIcrcdXucF7qZSrUa/GIvGUOKGy2DYUiUsqA18OBPC85+FPs1ncXHM8MGOKdO3z+YxRoLXHruuaE0SoONFg4mkJ3PfR0VGk02kMDAwYx9LygQM4ctNN8JdKuH9mxsz3lWAQz3d04MD0NA7MzRkeSUWS4ysd2HqMXvy/VCrhv83O4vhf/AXafvMbdLz/vokafvTRR0ilUqZRn89XbdrIEgDdDxpmTINWfYeN8Ag/u9zTdV0T1fL5qkfC9ff3m4776pAIBAJ4ZHUVrwSD+LY4bfQ3UFXs+/r6UCgUTFYb6+jVaag6i8pr1RFtWdZIt1QDjpfKFPIrx3Hq+kio006f/dHSEv56aQl/trqKH9Tqs22ZofSjuhF1Uu4vDUQ6zshPmHUzNzeHtra2Kl/ctQuZcBi/KpfxP0kDWcJmeXm5Tg430lNsvqS6jeqH/IzHltP4oPwmv9S6fqU/xWutlac80RMBuGbOUWWeRmjZxPBIezte+M53EIpEkHznHaMHUHb09vYaI5wOVM/zTMYSjz90XRdLS0vI5XJoa2szEfCpqSljXNExrKXc1OWvXLmC/v5+k2Zu65dAVXbQoa+wsfVv4riWRqjuzHfTwfCy34/nQyE86Xn4fk0GcK9isRjaaqeh0Mnc1NRkZAYAzM/Po7u723zm9/sxMjKCzs5O4xDnu9nHJR6P4wXXxfNdXfhuIIAnrWN0VdYrzaoOqvSgOKW6GPkOcUCN7/9pbg6T5TJeDATwRDqNZDKJQqFgdEpmtiUSCdNLhHsNwBz5qboJyzbZQJt8kWUWRzs7ceIv/gJ9R45gXS1zbWxszPCLG2+8sU6P4rqVn9v8x6ZNXXsjnvYn+Tz+LpfD92s8hk4PZiRofyS1tzSDSHGKcpUZOrSvSYe8N5VKGdyh01z5heKvrv/zXr+Xo4FA5MKoiGhNmTI/W4kgkNVRoYtt5PG2FRWmatrCSeelhn8jBZ3zUkRQpwPfS0ZoZyHwfUokZEDqCSW8OBY9mJyXIqXOTYUEEUkVfz5rz4WE3CiypPfyPfSS8391JJDpk/FqloMqjOo5Vw+dwkqNbEXg663bNnyUMIE1BZ+wsB0IyvSIu7xs5sDP7MwF7r3tEODF6LsyTxXeim/KAHU9ilO6L7oOzk+VHJ230qKeTa/wUQXBzkYirMrlslE0dV5kYOo0UyVBDUqfz2eiyKxzpPC2BQxh16h2VR0qupf8u/fkSfScOAEAyOTziL30Eopf/Sr8zz1XN4bilv7YdK54qHTZyDj4oi6eL89L8Z6wtJVBjRw/Uyrhx56Hp4JBPOx5RlldWVnB6OgoPM/DXXfdZVJBFfakU2Ctg7I6FG1j0KYv8jjb0UccsaNQWpbhuq5xhtCwDYVCmJiYwIYNG3Du3Dm8++67iMViaG1tRSaTgc/nQ1tbGxYXF9HU1IRoNIrBwUGcP3/enFhRLleb4rHBHfkbu+4DqGvoVSwW0dzcbJxdnDcFsutWox3M4OM6VNHU/aEw14gHYc41qkFOXkr5ok5CRiq5TqYIqyLHNEeeaEHDhtFrKpYaLfI8D2NjY5idncXAwIBxnDAr4aabbrome8rmJbouGw4aIVEjAqhvuEpZyL1Qxznn+4uBAcwA+Jt0Gn9VO3pMHc40VJeWluA4DgYHB80pKjw5AoBJ++beVCoVU1oEVBXS2dlZbNu2zaQeH9y+HQuhEH5VLOLu2nnsjuPgpZYWzAYCeLezE/fX9oTrZHOsXC5n8N3OzFLjROVMsVjERzfeiEIkgrl770X38ePw+Xxobm7Gjh07EIlEsLKygmQyafixGuTc397eXgQCAVNby2wPZg8xc4sOqEwmY6LC3CPuE497I41ynY5TdUo/VC7jqWIRfm+tsTVrn+kkOXv2LBzHQTqdxtTUFI4ePYp9+/bVwUZln+IPcUH1CeVdtq5Jnqm6WiPDh2sNh8Noa2szjko+r/f9b8vLuFoq4b8sLOAHtY71qrfwb+4l5ZziOHW+5eVlTE9Pw+/3o6WlpY5GPM/D5s2b8corr2B8fBzdTU1If/3reESCakrDk5OTJhVcswFsXVJ5N2nYnjNpNBqNmkZ6pJV8Pm/ww+fz4eLFi+ju7r7G0CTPYkTZ89aOVC0UCibIQNyhA5f3cW6a9ZLL5fBcTw9WYjGsPvAAEm+/bWSMOn1d1zWZVcS7mZkZk/lFHJqbmzNRW8Iom81icHAQPp/PZHaxf9DzjoN/am/H/QsL+MP77kNLS8s1OqDiJOGv+KnOZ33W/tvO6NC9+zdzcxgvl/Gcz4enauU0zC7PZrOm+T7LKQcGBlCpVHvP5HI5xGIxdHZ2miMtyRva2trqemJ5nof+/n5ju7wQjWIcwD96Hr4vvfYU93XOigtKI7yHe6/8SvUaHdfzPPxRNIq/y2TwaC1oSweoyoimpibT4JcZQ5FIBPl8Hh0dHeY45NbWViwsLBieR37NvSN+Ph+JIN/UhJG9ezH7wx+iUlk7/nN0dNQExRQH1CayywwUXrq+RvYofx7z+/ForSmrllpRb0qn06aUQvkhdXS107TBPXUeDTZqkGJ5eRnLy8umebv2JrF5Hsf/fa7f65B4VS60fofKBhV0IhgXrl5Qfq6ExoXrgtRIoxedwNQ5kKgJFDVqCBQ7VUuNbhVSSui6yeql5Hccl8yEirreq4iq79C1NSI8NZZ1nWpYq4GoDJtCSb3NdnSRTJ3rUwVIFUq+Rx04fF6dOVxTo8iAGur6bt0HWzjbBrbCRI0dwtSGl67L3l91yCjOqWPCcRxM3X47JvbvR9e776Lz6NE65tJIsVDCVUHMfdGSIOKNKlac+/iePZjYtw89hw6h69gx804aAxxX/9aUe8KM8FWa0KNxiJs2Hdu4rs4tXb/ik178jg4EKvTKVJVWbSeC7gnvt//ne9nsz3n+ecR++ctrmLwqVopPulblW/qsKqz/HBeVTcU/WzAB9Z2bec/q6ip+XCphFMCPCwVsvHgRoVAIly9fNvPOZDJYWFioO0LLVnQcZ63bN9/FjDGNxNgKlkafiYu8RxUrCiTiKvtN0CBkEygqT5VKNc1b+RMA9Pb2YmZmxhjF2rytUqmYkwVojHNcOsE1CkD80jIert1x1uqfiR8ajVO+yLnp3uhxT+qEUIet0pHuLcdm1D6ZTBqDkPsCVI/sdBzHlP0oP11dXUUymTRrpNHHM9eZMjo0NIRt27aZNPwdO3bg4sWLKJfLdVE55Z/8n3uqvEYdBZo6rDhHWPJ//Vt5POHwteVlvJlK4Q9rqc7kfxqdAdaaztLYoUJGpyydnsQ94oU6Nhgh9vv96Kp1z3+tuRnfFKMZAB7MZPBCNIovz86a6DydvMrfuTY7aqkyQemkUCjg/sVFPJfJoOPdd80aduzYYbrfM+Wb56orLnHMdDoNx3HM/Z7nmSig67omwkdjg0q47q/SC/eWtK7lfur0pn6mOoDjVI91HR4eNjjb09NTh08+n68uNb+RIWfLaf1cZfv1ZLnKC8U51uurbqnP+v1+/Fkqhf8yP48/a201+/zbFOyfr67ib/r68JX5edy8uGgMQPY/GR8fh89XTRmnYU/84tG24XAYTefO4U/37UOlUsG8vE/1RkbXVU4rXMkvVA+ydU3lffl8HgsLC3UBFDbKZanV+vXrkUqlDC/ld47jGJ5MQ29kZAShUAidnZ14JRjEL10XTzkOvglcQy8amONVqVSwUCjg71dXcePMDEI33YSh2slUpVIJW7duRTKZNPpLuVw2x3H29fUhHo+bfZyZmUFPT4+RMU1NTZiYmEBrayui0agxSHO5nEmZ/yfPw5jj4PWWFvxFc7Ppu6F6xfUcOrYBrXunuGvLEN1D3v90OIwf5vP4w2AQkRqukI+xXNfzPJPhlMlksLKygmg0iqWlJQDVk4ZmZ2cRCASwtLRkaH9ubs7AkI5FZqB8LxzGT4pFfLfmhFHeZmfTKK+3A1K6XoWf8i61U2gf3pvPI/fii/jwww8xnUrh5ptvxo033mgcJJQT8Xjc9DMhHrtu9QS82drJQMyE5JwCgQDm5ubqjPILFy7gxqUlLPX3o/PgQVOeQr554cIF/M3f/A12796NrVu3mjWRL2i5D/kucZX3UH7p/tM+VholPlcqFXR2dtbZSOFw2PBMvo96DjMZlfdxbhyP8yK8+c5MJoOpqSksLCygubnZ6BaKj3yfZjEqXv+263M7Gig8lNGSmDTSYEezbANeDQo1zJVA+R0Rg4tUDwsNLAKcTEX/B9YcFQSseoh1ThSk9nxso5cOBm4SYUP46IbYhiQAsza9zzYEOAcKEq0b1DRmdSDYGQcqVHStOm/byFdHg+0oINJq9M/eT5tY9D22EmAzaV07BZHneZi87TaM33UXeg8dQtfx42Zf7SyTRo4D3V8bvtzzydtuw9idd6L38GH0nDhhGOnk3XdjtbUVU/fcg4HTp+vwnvPTGnpdg32vKii6N1R6iT+lUgmT+/ebIx/7T52qy/Kx6YHr0pMFbAGoiq5maOhlG7gcQ8syqBzbgkWfnZubQyaTMQLcjjCpo4zOD1WYNf3LNvjVOcL6Mnph9QhAmwEqLtr8a2bvXkw8/jj6jhxB3wcf1K2pEZy+yCudTl8TUeA71Zmn/JN76PP58L1kEv9tZQXfrCkBFJ6e55nUf89bywBTulR8VScBx1aDwjasCVPOSfsq6NxVqef7iQ+EMRUARmKYVUOZwgZQi4uLWFlZged5pnaeSufKyorpip5IJEz5T7FYrDuJgEct0/i84YYbjKGjTlauWXm6pvWqkaj7VqlU8F5LCy79p/+E3MGD8GpHDwJA+sAB/PShh1BaXcW9tWwFpWG/v9qj4yW/H8+USvh6ezu+U+uKTlhRqWT0LpVKmZpSGr2u66K5ubnO8UBnBU85yeVymJubM0ZCKBTCzp07MTQ0hGg0avaTdN5IKVZHA3FI12I78xSOyjOosNBYYQ1xOBzG1woF/GGphJTPh9mawU85ztJE4o7P5zOnMTACz4gsjTjOX3GM61tZWTH7HQwGce/KCvYvLBhnIGXrV/N57Bkfr56aIDJA+Zoav1RUbVlky2DHcbBvfh6XfvjDqt5Ty1A5duwY1q1bZ6LIGuCwr3w+j/b2dqPkxuNxjI2Nmejl1NQUOjs7kc1mjZFKh58e+5bP501qP+enfJq6y2vhMF4F8C3Pwx9g7RQPrf1tb2/HqVOnUCwW0d3djb6+PrN3fB8NC8JGdQjVXWydweb3/JvwVJzlZesply9fNjjvumsNBhkt/H4yiadrR2VyrppyrLoiAPxodRVTgQDe7ujAozVjZ3x8HMViEdPT06Z+nkZrNps1jk3qeXQMaaYT1+v3+41zVEuaFCb8bcs6e/0Kb+o8IyMjKBaLaG9vh+u6uHTpEmKxGLZu3YqrV6+aZouKiz6fz5QmkWf4fD4899xzyGaz+MpXvoJn7rkHY56HXzgOvluLHivsNLtFjddvhcP4g6Ym4MABzG/fjv/wH/4DcrkckskkWltbEYlETC8d0r7P5zN9atj4MZvNorW11TQ+VJpmxoaWfcZiMXw3EMBPy2V8PxQyPU2IZ7a+xN9q5BEX9X4NRBGnmI2m2b6kDc/z8JVgEPe7LpZnZzFSKiEajdbRC+VBS0sLAoGAieZTtqZSKXieZ/p6eJ5neGRnZ6cZi05pBhoecRzsS6erhnylYmhEac/mD0pvtAWVTpSWVbfkd0pbhUIB7e3tWF5extLSEoaGhrC6uopHHnnkmqPouf/ssUK+xkywUqlk5D+zVzhGpVLt5XH16lUU33wTN9X0qWh7OwCYMRcWFvDGG2/A7/fjjjvuqLMzbLuNe0/HD0/tUb1OYeZ51SaLsVjMPGc7rohDDNSQ16oul8vlTL8SfRdxSeWG8uBAIIDm5mYMDAwAgHFqcw62/aQ6Junid12f29FAhFSgKlCUgGzmwfRd9aDYRgSRjwRHQ1M9abzopdFok3p27SgtmTQv9V4rYyNA1XDl/7pG1vXZDFsZiP0uCmnN5qCCy3uU4QAwTF3nyLnwbzI3nYsiKsdUxUqNFx1bBZGmaNqMQp8hHFWJV5yYvO02jO/bh56DB9F78mSdYsF3kVCIFzru+F13Idfaion9+7H96lXzHC9VdBs5beyLCiUVng/27UM+lcL4XXeh/9Qpsy/9R45g9I470HfkSF0nYM6XhKopzXzW7mGixquO04jw++S9Nt7ZCr8yADINVWwbKVvKtPis4r0d7dCxKBwU5/jD2m/WwfO0A45BXsH32M4v5Qm2AOffxWLRHEtI7zyNCa5VFWR1NiqeGcb88MPwOjrM3tvrtp02X+TFhnaNjHQVgjTaFQ6O4+A70Si2X7mCeDyOaSkXU15IoctUWDttTh2h3Hs6A+z3EibKK2xatZ0XNk8F1hydNJJZvsBjpXjOs8/nw/DwcF3ExefzYWBgANu2bcPJkydNCi6j3fF4vO70gAsXLqCrq8t0Tg+FQti8eTOGh4fR3NxsHDPq+FN+q8oBs3+06R/XSBo5vn49ivE4Ju++G93PP28+X37wQZTjcbxQKplUfDWQuKe/cByMeR6ej0TwXXGI88hFGtaM2PMz7a5OAzcQCJiTH3QdPI6YpU0+nw+JRAIDAwPo7OysUwBVnlGhUHqw5QEdMcoXlKYoI2y4EdcUJ1VecW+03pvylE2y4rVjOwGYDuIAjLOGjbC0BLRUKjXsf0JjnjBSJwoNK8KSXfbJFzk28Vz5CHUaPUqXdPebpiac+ff/Hm1vvYX2I0cMT11cXEQymTRwY88bxUPCl3Bi/4l4PG6Od+WcyXt4rGlra6vhN0wNVqfUyMgIksmkgR3vfb6rCzMAfg7gWzWZT7xTZ2JnZydmZmaMnCyXy6Yzu8Jca8Ab6RuqU6gepEZjI96tJUu2UTAwMIB8Po/m5maMjIzgUCqFv56exv+uvR1/lEpd85wth21d47+LxfB3mQyejkRwc1cXyuUypqamzLzsrCjVWehEo1zQbDE+S+cQ6YzZPMRl29Ah7FTP0vny4ntVfrKWv1Kp4IYbbjAn3Njz9vl85rQCoKr/XL58GVNTUyiVSnj55Zfx4O7deDEaxZNAXS8I1QEaOZuU/kdHRw1fdxwHQ0NDSCaT5nvKDPYiYUNIZjoAMFlduVzOZAex1EhxLxqN4hHHwRM1B+jU0lJdRovyf8UH8hKerGUHWckbFHeVv9l6PJ9RhwHphw5UzosnLTCqPTU1hba2NrM/kUgE09PT6O7uNnKWcpcZC9FoFC0tLWaNAwMD5m+/v/4EHpueCAO16fTS+9Q+sXVUwiIWi+H+++/H+Pi4cby1tbUZec9ANA3rcrmM3t5epNNp43ifnp42PJnOMPbiIAw5X/Y3AmBON+LJHCrbzp49i7m5OeP8Jb3ZcNH95Bqnpqbw5ptvwnVd7N271zT0ZRYRszz1srO/Xdc1TaX5HeHJvWfPCc5FdXd18qkdRZrOZrNmTrZtQN5VqVQwPj6OmZkZ3Hzzzfg81+d2NCiBcWG2s0GVZVsAKiLaTJsAUcLVyIlG4jRCop8RmJyrCnpeKpjs71TxUwah0RtlDMosVLninHQDbeXdXiMVKxXSahxxHirguAaOrc+pocv7+a6p22/H2J13YtOpU9h09qxhZBrZVAZv7yNwbT8HXlRqtXvzqf37kU+lTIRejXY+w8uOfgNA//vvY2TvXsTGx/Hy976HDSdOYPO5c3WZBCqwdJ+Uqel9hNvwjh0oh8MIZDIYPHasTgnpOXECvSdPXgNHvWxj2XEc4+SyjXn9zb9tr3ClUkHvyZNr77X2/XqXrUTY+2Yb7Jw7916dX7YRxEtxgt95noex3bsxescdSL72GnwvvggAhmbV4afrp4HDPaJ3lPXDtjONY01OTmJ2dhbNzc3o7e1FNputKw1QJwMFZzgcxuzs7DXwBwD/s8+i8sQT6H7//WsMA1vwf9EXa2wVxpyjOmjUAAbWosCMgM3NzWFpacmcMkC69PmqfQ0UN+y9b/R+fq8CknMhPSkO/LYsMT7zaiiE58NhfKN2KsaLnoc/8PlwQzSK5uZm06CODdNct9qolg0fd+7cibGxMaTTaczMzBjDmY3V5ubmkE6nkUgkTNM6oGpwRqNRbN261TSCuvPOO3Hp0iXDowgrKlGcOyMf5XK5rvEelV2bNvx+P7afP49jg4MYOHYMvevW4cyZMwCAxMsvo/Ktb+GBmiKqDj3NinrKqTobHi4UUBaFg44GYC0qxnpoGh5qOJP+iNOkw1KpZBq8saM8lTDFD9s5QIe23gOgDg7EDTt6orJe5aLKFx2Lz9Pw5PjlctnMk2upVComi6VQKKClpQWZTMZEM7m+WCxmshaCwSByuRwymQzGx8cxPz9vDCg92m12dtZ0ai+VSsbJ5PdXG4Yqn1XDTmvHydO4BgYoGIlSx96bAwNYjccxfeAAEm+/bbqfX7lyBbFYDMlk0iiBjlNt3sf0cOLI1atX0d3djdbWVtNZnCfRdHd3m7kXi0Wk02l0dXXVwTkajaK1tdXA+ty5c+jo6EAikUBbW5vBtUAggD90HPwcwJOedw0foBHs9/sxOjpq5qo83dZtQqGQUYaVrlQv1J9GBp9+xz1QfcDG7ZGREYyNjZn3HVu3DguVCv5ydhY/aG6u00f5o4aUOhCCwSB+0NyMJ2r7TUOG9M0oMjO1tLGs46w1p6TDhpFlpRfiJmmRmUDKo6+nB9hOEo7Jd0QiESNLiZulUgnxeBy5XM4YbSzJIb0uLS0ZuITDYbwSDOLHGzYgdvvtSL7zDpaWljBw+jT+9tZbq/suQT6u394vAMbYAapR5b6+Pjz99NP467/+a2QyGRw/fhylUgkbN240xw4yM6xSqZiGyEw99/v9pkSCDgYA5rQa0gkA0/eGsOnv7zcOK9Xj1XZRnsbSPdUfdC9sndrWTe0xbR6peE8Y0bk4Pz9velEwys9SKdIUYdrU1GRK5RgMVtkaCKydBKX4ovYcx2xkG9l6h+KxzQNsfdlxHHzyyScAgE2bNsF1XWzZsgXAGo8lz2lra0Nvb6/Bqbm5OdOPibYgnXf8bHV1ta5ngdIVZb6evsCsLxrYvb29dTxF11upVIyzm3LW7/fj1VdfxdGjR6sBzg8+wBNPPIGBgQEkk0lMTk6ak4E++eQTU3p0yy23mDJK8tVKpWJKZFR+l0olI7cYmCGseVIF7UPym2dLJfyj55mmn5SB2iRb91htlnQ6bfrb/K7r9+7RoMhCpm7S2w8fRu/Jk9cgoE1kwJrCTMSkUKYCzYuAooLHFCc+N75nD8bvugs9hw6h74MPjAeLKUkECgWqZgzoHNVJQkLRlMKZO+7A1dtvx/rjx03kW41mKj58jxrs6nwh4tuMhPfSeLKNblVYCD9lziQINVJsZgAAo3fcgVwqhXN79+Lil76ETR98YAx32wnAd5Ox6Dq4XkVGrouNeS7ddFOdIa8GreITL2XAXFP/qVPoP3UKh//8z5FLpXB5zx501ZwCttC3mVsjI1HHv7BzJwrRKCJzc+g8ehQQA1fnos/Z1/WYqW3UNnpeDTqOxX2ynQSNmLxtCNrvtpUk4ogao7Y3Vt+lmSK8NOroOA7G7rwT+dZWePffj5433jB14DQC6AjUtTnOmnOSaaX05iaTSSwfOICpe+5B/5EjJguGBggjEgBMtJv/217fjo4OeJ6H+fl5I2QUdoGXX0bi0CG0DwwA0ttFYfXPdSkPUrg3+m07b9WRMjU1BcdxjGBh7XRbWxs2bNiAfD6P18JhvJJK4YGlJRxYXobjOHWCmPtPvmsb06ro6PupcJGXq7GhOPh8MolJvx/P16LLk56Hn5bL+H8kEqZrtB75Rvzw+XzGKOPxVkz1VlngeV5dV34+RyFPnpxKpRCPx7Fp0yZs2LDBGJhq/FBGaCTE5/OZaBVlj+4NcXDg9Gms/OQn6OjogFtL1SyXy4i//Ta+1d6O7du3w7Eczeosfdjz8O1IBBMLCyi5a0fU0fDgGfGO45jMBjb0a25uNnvhOE5dmjCV9mKxaKJDbJ6lc1F4cm9JpyqziCfMXgPqe7kQl+wIGPdBSwrUkaA9Y8LhMFpbW+uaLNrGXrlcNg4XnipCBYidyePxOJaWllAsVo9/jEaj6OzsNM8z+uk4jqnL9fv9WFhYMOfNE9cTiQQCgYBRsGggMxBBOLMfBABjQNMYJU3QKcOsnB8Ui/hJLoeWo0cNH9y5cyd2795tUoE9zzNRTMpq0qTruujr6zOZQuzHkEwm0d7ebprBMRNicHDQKMSM2vX19Zl7+vv7MT09jc7OTpPSS9pIpVL4LoCH02lUhB5c1607EpVZg46zdloY8SoSiZisHOIZYa9y0dbVbHmnlzobSqUSNm/ejJGRkTp5pjLx/PnzdcdS7xkawqlNm3DnwgImm5rqopaN3lmpVPC3s7P4f8/O4tuOg6dqDknyMN5DxZ5rodwlj/c8z2TmkfcQ3+33EjaEGT8jXmg5nu1kUHqzDVuWMtLACIVCuO2227Bp0yaUSiV0d3fX9dThKS806IvFIpqamvCrfB5LwSDy996L+FtvoVwu49KlS3jggQeMDst9UJ6ihrk6IUhD8XjcNDwk7zp+/DguXLiAW2+9FbfeeqvpTTIyMmL6X4RCIVOPXygUTKkV+Smz6gqFgpGhnJ9eNkwb6a7qmNLndQ/4v65dx7WdS7pvfC8zjjSDleWOdIaSn7OJrGZNp9Np01CSMpVlYrY+8Gy5jJ8Ui/jjpib8Qc2ho7iomShAfQReP7eDX7ZTUeUqg6xnz57FxYsXMTo6img0ivb2dvT29ho6qVTWmncyY4GlX7naEdH5fB7z8/MmYMz1sdEp6TKZTF5TMkq9gLyWJRd0rlFHsfUkv9+P5pqjsrm5GeFwGEtLS0in02hubkY8HjenWxG3Ozs70draikuXLuGZZ54xAZXFxUU8+uijdVkGKpcp8yuVijnmuq2tzewtdWzqIrzf56seF/1Tz8O4Wz1O88Fa4CqbzaK/v9/wa9p6LEHhXmUyGVy6dAmf5/rcjgZlUjQmqaRNMGq9fz82nztXZ6yrQsaF2oSs95KArmfwERnpMZ7Yt8/UtK87c6aOMO16U2CtM7Ft0KoBosyXBHL1ttuQbWnB1dtvx87h4WuUU31Ho7/Vc6nvsxkWO6if37wZn+7cid7Dh9F/6tQ1hon9LOejl204eZ6Hgfffx/DevSiFw8i2tOCznTuROniwigz+a4/0sw1XhVGj92lk87OaIR+dn6+mplsGi+KCPVedf6VSqc779tsx8P77dUxJcbLRPO1L96v/yBGM3HEH+t9/3+CEjsOxbIZvC237O12H/b++n/BSYXu9eauwud76GsHRVjaIJ/Ze284GVUQaGdw2PvUeOYLWjg6srKxUmftdd+HK176GnoMH0XnsWN0c1FBTpbJQKGBhYQFT+/ah1NaG0TvuQOfRo+a55uZmpNNpozRT+bl8+XIdzTY3N6OtrQ2RSMSkGttr5WeNsmJ+1158ERedI1rXacNGhbbuH/Gmra3NRAtZs8r+GCsrK6Ye++W2NswEAnglkcCds7NmfDubiQ4DfQ+NR36mcNLIrSoOvJfjfiObxQvRKL6Ry8HnunixqQnfFeVbeSmVdB4Hybpp8vOmpiYTVeM6ksmkSf+kUsWofaVSMQY1hSSNQ8ob/c291/2376EyZMsNGk2apq64pn2H+B3hVS6XkUgkjILIhnHEEb6PDdoqlQqam5uxuLiI5eVlY+xSGVhYWDBKqfYyosOkUqmY5omMWP58dRWHe3uxf3ISd87Oms7TfJaREc/zjGGiDjzlZ+qkpBzWCCawFplSOL8RjeK1ZBL3Ly6ir2Z8sYShUCjUnSLC7wCYRnDZbBaeV82IYLkMFdNYLGacBdRH2tra0NnZacoTUqkUcrkctm/fbmCXSCSQTCZNqnY2m8X8/LyJ4mpKN2Ggze4YLdN6WjUSXdfFD2Ix/EGxiE+3b8d/OXIEjuPUlT0Fg0F0dHQY5Vr7dwBrgQdmdPT29hoHQqVSwdzcnIE5jQs68Uj3LI/g0YI/isfxUDaLB9PpuqwZDW6ow6hSWTsNRXHHcRxTcqHZISpbaGATTqov2XpgI2NOac3mnzbv5LhahuB5HnZcvYrbp6bg9/sx09qKnp4ew3/5m/MlLv/V3BxGKhX8revi5/k8no5G8USthppKPp2/3H8NmpHfOc5a6rPnVTOWent76zIkg8Eg4vF4XSCORib/5v7Q4WDLsEbGMHkO6YiOrI0bN5rjEzl/O5hEvhSq9TK48cIFzPX0oPfIETQ1NSGTyeDEiRM4e/Ysdu7cWfdO+7J1FZ0n50A9jXKSZRv8XJ3tjlNNBSePoDNmeXnZlAnR8aAnV6hOxnc1kgU6R9WF1fmnOGsHlhrZHLxXv1da06a4qkeSD3V1dZmMrba2NgAwDQTJGx2nWlZVLpeNA5/9LchLuJ6fzM1huFLBf8tkjKNBg6CNHCP2Gq+3PnvvuV+rq6sYGxszPUNmZmZQKpVw880315WwlMtlc0wnnUZs7Ev949zmzRi+/XY8VirhG5UKhoeHcaTGX9nfg3KVPIp9Wxh8JY8Aqrx8cnLSrEkNcfJ8z/OME/WXxSL+bmkJX56ZgbOyYk4BaWtrw9atW02ZDZ3a5XLZBEgSiQQmJycxMTGBnTt3GpuX2auEBXUfyj89upfHfLJfRbFYRCwWQzQaRSKRwB8tL+NvV1bwx8kkOmunXYyMjJhTVjyvGqhjJiADXI7joLu7+5rT0653fW5HA72RynSJWJtPncLlPXtw4yef1HUxJqIpYvJZIpsSrCJdI0OJ95fLZXOG/Lrjx3HlttswWOvQ38iJoGUBygAa/dgZAlzvwNGjVUP3+HF8tH59db0ffWQcK0pYNkNRAaXGokaAbGH62a5dyLa0YOzOO7H+o4/q5qwMTOGp76Myo4qxz+fD4JkzWPfRRxjZtQtXbr0VA8eO1Qkq21jV33bJiSrOaiwZI/ToUVy9/XYMHD1qBB8VTcUB+zl7jT6fD+vOnMG6M2eqyoM4VZQp6/+Erb0Oju3z+bDh44+x/qOPqvc69Y4KFXBqiCqsHeda54bivf6tSlUj5YnrVBjYa7L3RmnQnk+jdQCoMyb1HsWdRoqJ/q+GWM/Jk+g6fry6rlpTIs/zMPrggyi0tWHi7rvRWctAaYRXZIjA2vGBeOcdzH75y+g6eLBOaHN+5XIZMzMzSCaTAIDi17+O4qOPIvrii+g5fdp03tf91rXp+vQ0D9tx10iZ/aIudagqXXFujLZrloBmk1C5oNChorFz505cvXrVOB58Ph8ezGTwiuvi67U6dZsHcV+VTwJrpRE0lLT/CmFERddx1gx5O2vs/nweD9S86+FwGH9ccxTM1IQ9DcdcLmeUbTbpovE9MDCA1tZWtLe3451kEif+7b/F5g8+wK2Tk9i8ebM56jL95S/j8UwGj9TKCDxvrXRAu2qrgq+RFGCtZpnNlQCYbALFFa6V+0kji/WwhDEjaDyKUWudK5WKKRfiOeZar82UVu4BGzmuX78e2WwWwFpj0Y6ODjQ1NRmFYmlpyRjXfC+jFVQ8fD4fFhYWsGHDBvzNwADmgkG839+Pp1ta6vr0qKKtTm1b4ea6NBCh92jUSHGEhu9bwSCmHQe/aW3FozVnCS/bKU3FOhgMYnl5GYlEAisrKwiHw+jr60M2mzWN3HjCQjgcNhlOpVIJAwMDpsFWU1MTtm/fjtnZWaPMs/SCdbE0svg/P1OZS8ON9GI7bZXmV6zmoOxxk8/ncfToUczOzmLdunXo6Oio63XFkzTohKMBxbkyk4fGZ2vt5AQ2jWT/FsLV7/ejvdYArVgs4meui6lAAC9EIri31lSVe8fmqnQIcE2ULcyK4JqojNOIVqeMLVuJc+N79mB83z70HzmCgdOnr5G/ypdVn1B+f+XKlToc0zEAYNu2bRgdHcUnn3xi1hAIVI/EzefzJnWbvJo0H41GcfPNNyMQCOC/T6Xwl7OzWPE8XC2X8XeZDJ6qlZ/QmRcMBo1iD8Dw4Gw2a+QMjQIaLcvLy+jq6jLRU40e29FihYctv/m9fb8tb3hPMpk0WStdXV1GzlCHV8ew7hkAvPHGG1h57z3srOHUas3hls1m8eMf/xjhcBg33HBD3T7Ze6a2gp35qz0pfD6f0RdmZmZw9uxZ4xxMpVIm248ONNetNg2cmZkBAMNzY7EYpqenzd5oAIZzUvrVHw1OKawpN215au+XynL7Hl62zkSeRBlB+orWmgeXy2XMzc2ZjCHW/5MPkp+5bvWUHmbB0DmjMHAcB38Sj+Nv02n8Ua3PRSM918YjhZvaQ2qb2bqZPhuJRPDZZ58hk8nA86oONjaD1nt9Ph9+vLyMvzlwADd+/DE2nzuHUqmEtrY2LCwsIJ/P48ojjyCfSuGVfB6P1rI7KFsPHTpknMxtbW2GVzKLkbJccTMQCGBhYaFOnycMVM65roulpSX87coKRj0PryYS+B937cLKygrejMXw8Y03ItrUhG/XAh7cT/J5AGhvb0cqlcLs7Gydk89xqkE3AKYsJpfL4QXXxd+Ew/iTbBbfr5VbrKys1K2hpaXFlL+Vy2V8P5nEbZOTuKGvzzhc8vm8kR2hUAjDw8PYtGkTZmdn0dLSYhqydnV1YWJiAp/n+tyOBjWSVAGuVCpoP3IEAHB2926Uy2VsOnvWIIUaVTbR2Yp9o3faTLVSqeDitm34bNcubDx5EgPHj6Pvgw+qyG552hoZjkqwNpNVxNdnK5UK1n/0EdZ/9BEA4J0//VNkk0l8ctNN6Hj//YbrsQVjI2al39nG4uDRo7hy221YV3Og2GMrg2oUWbcZopaqeJ6Hvg8+WBPgTr2DQcfS/xsxQTVUbeO+69gxtB46VFUyat4+xSMVKo2YdSMj2vag8v5GnlU+q4xODUg7Pdw21hsZ+43+bmSk67v1GdvB0mgc4oO9zutdulZ1QNiZI6og6LiNcMtepwpDe/123WKlUkHf4cMYu/NO9Bw6VJdCzflpZIjMj+PGjh5F17Fj1ftkL7jPnueZNOkrV66g+B/+A7zeXhQffRSp4eG6ens7a6YRXisMFY6NcP6Lumw4k9fR4GVKL3GBXmoApnaQAoqGjeM46O/vx9DQEDZt2mSe+Uomg/tq0QyuRp0umoquDg0aYzR02ddAS5bo4aYhosqFOlA4X+4F1+g4a5FbZsM4jmMaWQUCAQwODposlUuXLuG5YBBLkQgufulL+BdjY8hms8aA/kUigTHPwy9dF9uyWRO1p5Gfy+WwuLiI0dFR4xgB1krfuHZ67ymQmWWgKb0q22hMRSIRpNPp6rGrwrOam5tNoy1+xnH0PmbsBAIBc0wZnQyMGJOfAzBpkUxl1p4P8XjcRChZfhIIBLBt2zYTtVm3bh0qlQr27duH1WAQ/wTgO7XSG5sXkl+q0aX30KGispURVu2vRINT5QEjUY/7fPiF4+D+xUXkcjmT/kycVVwF1pxToVAIi4uL8Ly1tFgq0MwIyGQy1xyp67qucSJFo1Gsrq5ifn7eGIMsN2GkiDhEJ5aO08gYVvnOTBtmStI5RTzjfDds2IBPP/0Uy8vLOHfuHOLxONavX2/GJ36ooU86ZBkSM2vi8XhdSjGdC9Fo1GT8kKds2LDBRDa/Xy7jx4UCftDcjB09PQbn6DAAYLKmABj8Y18cOvU4PucArPXVIt5wL8kbisUixu+6C/nW1mpj5A8+qDNQVIZzLxXmjLKzNEX3R3nuxYsXMTQ0hIsXLxo6Ym+Pt9566xpDkXicSqUwMDCA5uZmPN3SgofKZfyyWMTfrqzgB9I3oVKpmD4x8Xgc3d3d8DzP9AwhbjG9XUtN8vk80uk0pqamTEmRnZZOeOi6eI/N45XPNNLzSLPk4dxPdRDZuo7S0YkTJ/DBBx/URX8ZnS2VShgeHsZ//a//FX/2Z3+GLVu2XNfRoXxDaYi0T6OJe+G6LiYnJ7G0tIQ9e/aYevdMJoNKZa2kROVYKpUy66Sc5Xxtw191I1sXaKSjqd6nup6tz9p7dz29hPfZe6h6C2HBozqZxUUDlMEUlvCwCXIoFMLq6qopA7ODhQDw3aYmfEeCEzon0pj+bxvgulbFTTtTjhczoWZnZw3eUZ9744038JWvfAVNTU2Ym5vD8ePH8Z83b8ZCLIYzN9yALbVgA51MgUAA3e+9h6kDB7Dp7FmczuVw8eJFIx9Onz6NPXv2GHzo7e1FqVTC9N69+Mnu3bjtyhUM1BouU1cZ3rkTf71vH8rj43i81m9FM+fJix2nGkR5vFTCL30+fDccxu23345CoYD/5+oqpv1+/F02i+/VggDBYBA/XVnBXxUKaNu3D91HjiAWi2F+ft7AXssWlAfG43GEQiH8cG4OYwB+mMvhj1Ip+P1+o0cBa5kaKntJU6pbNknmis+31miZJYH5fB6xWMw0Dv481+d2NGi5g21AeZ6Hz3btQq65GedvuQWthw5dg7Cq6CsSaoqlfdnP8LlPd+xAtrkZF3ftQu/Jkw2J0PZ8q2Gj/xPZG/1tKxJc9zpxApA52d5yOxXfZiI2UVMQ8v/BM2cweOZMFU7W8zp/JX5V7HX9VEZsONOwuJ4xrUaXvsdmDpy7He2j4GKapx3ZaWTU6pxtZcJ2TNn4xbF1vjbDVzzS/dJnbYGsArXRu/R/G1fL5fI1WQT2HPR9Nmx1nvYe6KV7q5FZRojsOdnzbCQ0Fe/1c85H52DDpOvYMXTVnGT6jP6t42q6ngokG2bsrMwaM9d1q00dn3wSve+/f42HnDjZCPZqNOlFurDh9EVefL/yJzUaqBwRHxhVUxqj8NP65qamproznNW4t99bLpfroo+qDBDnKWA0Pd92ZLJHAVOR+bz+VuMUgHFc8Igyn2+tTpt1pJ5X7Yo/NjaGy5cvm8jMl7q7cWzdOqw7eRJvf/op/H4/Pv74YwwPD+POdetwpL8f9y0t4UI2i56eHlQqlTqjh5Gxpqamuqg90w8dx6mjG5VRVHgdZy0677ouflks4tDGjeh67z0UDx408weqytOmTZuwdevWa5zKXHMul8PCwoLBD8KNfQO4952dnVhdXcXS0hI6OztNI9D29nasrKyYJleZTAbJZNI0QeOa+/r6MDAwYHBs/fr1aGpqAgB8NZ/HN4JBuACKonQT9zhXjUxrX49KpRqJpXJPfCZ+LS8v1+EasMZHqOxu9vnw72qK4FRTE5qbm+tkI5tdqsHO1FBmrnR3dwOoRmdzuRyWlpaQSqXM/hFH/TVF0XGqHcEZAe3r6zMODsdxTASQadYKD+KBynvCiQYAcZ9rJx267toxb1zfpk2bMD09bebS0dGB7du3o6+vz4zVVEtxpbOJ/GNmZgbRaNSUD/F79j9hqRKNLpaT8N2alvwtvx9P1RxXasxrZJs8SMvP6HBTnUhLBTgW36+GCpudeZ6H7vfew/j+/eh4911zEo3ysuvpDZzb+J49GLvzTmz58EOs/+ijOiPA86qOah7HzMaZzDzQXgfKO3lls1kcPHgQ+/fvN8r/H8Ri+Fatg7/Kk+XlZbS2thpHKWH+VjyOdzo7cd/8PO6cnTV7w+Nd8/k8pqenMTw8bHgAeb9mhOj+q059vUt1OsLdcaop65rtphF57ftg60Su62JxcREHDx40cNKeO0xNB6qd/f+vH32E2fZ2PB2J4Kka7GxdE1g7ZYN0onog8Y0lZKQ5NsEjnlH3nJ+fR6FQMJkNPGkhk8mgu7sb5XLZHJNNR4atE9h6pep+jb5TfU51X8Luesa4rXsqzPVv0pjiPpu7uq5r5HEqlTJR/Ewmg3g8jo6ODmOQ8xhylW86f3uO9jzs+SuONQo08Hs7u4H3cC7UfYgLnudhZGQEJ06cwN13343f/OY3OHLkCDam08jfdBO2f/qp6dHhui66aqe+rBw/joEPP0RrayuGa8ezUm4VCgU899xzuO2229DR0YEnn3wSruvi33d1YSkQwIdbt+KpWq8doMpX/se+PiwEg/iHQgF7xseNPLh69So6Oztx1113GZgEg0E8VS7jsWIRgZrjoVgs4lueh1+Wy3hKsvt8Ph/+y+IiRj0Pi7t2oefECbS2tiIQCGB5ebmuYTp5CPm151WdEP+yuRl/vbiIP631h2BgSPdGdVuuKWzRoZYXlkoldHZ2wnVd45Bh/wk9avV3Xb+Xo0GNcI0OA8BgLU2+//3364za6xkXipCqBOulwoFjuq6LwWPHcPW227Du+PE6Zd026HUc24hWYrJ/61y4Zq2L6z99Gv21bICKc62h2+j5RnOz10tY2WMo3JQR8CIMtC5U30FmbcPKnocK6kYM1f7eNpKoLKhSonBR50oj49VmOKqs8DkSil2nyHnYY+il+9yImatw4P2NogIK80Y4y6iv4zh1UVN9/nr4ZzNkhbvC3HYe2MJamZBt/BGGigs6hq5N983GhUbw0mg3f6vzxIaXwoXz0YZgiqdU9nk2Mo3CwMsvo+PkSbR3dKBo8RutpW/0znQ6jVQqZc4nJ2y57uvN9//Xq5GjTWFN/LHTk5kSrcYzU6ldt5qqyogm98MuudBoKu/XVG91vvC9uVzORNbVQVmpVPBWPI4Ptm3DvokJ7K4JX0bRgSrvPNLejsMDA7hvYQF/VjsKlan8Wv/LCNr09DQcxzElIFNTU1hYWEA0GsXG06cReeON6rwcB4uLi6Ym8u7FRfy7jRvx2coK5tvbsXnzZrhutTmo61bTMh9//HHT+V2NJ8Jf6YU/hInSnyqMP8lmsdLcjNE770Tzu+8ao5f7yVMPOBZhR+WDdcTBYNAY6nQY6VGoGqlfXl5GOBzG8vJyneGtEXJ2+3ccx0QpGYWggn3hwgWEw2GkUqlrIv6Eiyosyo94DyNRLIMiXBUX6OzQaCjhwTTeXC6H9vZ2OE41UmOXNZGf8WhPHneWyWQQDoeN84iKNZvMslkY1xIOh9HS0lJXY879iUQiJs2YWSo0RDlHwoFr0CCBOglZ4mBHx/ks30/DwPM8Q+OuW01vfvvtt5FOp3HHHXcYA06bTnM84hxLymigFotFE30iPlFGc5/pfNA0W/2tMkKNfQ0s2M0f6RTStF/+rcfGNZJ9qUOHEH799arzyqsP+Nj6gy0nHcfByN69KLS14bNduzD44YfmGW0mrA4MGqCdnZ0GF1Rf4bvoQLpy5QqKxSK6u7vR399fLRWo8VHluWxM2tXVVSffDnZ2Yi4YxFvt7fhazdHa39+P5uZmXL58Gfl8HqOjo5iYmKiLujeSGZwb98qGka0b6B7y8vv9xulE+cC10phSeOtYFy9exPLych2d2jRL59LFm25C0fPw9/k8vl07MlfljO6x7innpSfAke7Y9I48wHEc43AnL2TDWMdxDN23trYikUggFovVndqkBq4NRzXYdK5quF0vAGPzT4WjrQfqZevadLDYe/ud73zH0B/31w7a6Dson+kYsssxrqenq+6tc+Rv/ft6uibxuBE9u65rjiTVYJDrunj//fexe/dunD59GgsLC7jh00+x9cKFquOylsHHzBX29onFYojFYvjyl7+M3/zmN1haWgJQDXZMTExg06ZNBicCgQB+UCrhp+UynvT5TPkQYfdkpYLnKhU87nmmRLdYLOLs2bO4cOECbrvtNjiOY3gB9ZLFxUXjyPtWKIQHlpfhq/WE4Ph3jozgxaYmbDh5EsFgEHNzc9i6dSuSySRmZ2fR1dVVx5NtHfUPEwn8oOaYpw5Iuar2cz6fRzKZNHsQk7IYoFqGq7ZDuVxGJpMxdKa4YuPq9a7fu3TiesjXc+KEKWGAW3+EkU1kdmQaaHzWvb5Pxxg4fdrU1nMGNtLazykgG313ve8bMWwqCQobfS+ZkA0voD5t0I7w2/PgdzqWRgYpVDQa3Gj+Onf7b/se/tZ32DDk/2QCmqKvnV31Ug+m7pdtzPI7votrVPjpXG3niUaRtLzHXqcN7+utsRGuN2KQ9MISBjau2Ou0x1X6UkPHvq8Rw9b/Hcep2wN1MHEMKqkc16YvpVMb//hOxSE+x/9VsNDw4T0Kd4XJ7xLEujeJRKKuY+/S0pKhC9vJSYXfpgfChV2J1TC2nXL/HBfnZP/W1HCFG9dTqVSMIbO6uopsNotIJGIyDoCqUTsyMmLOU6YBT0Hj8/lwpL0dh7Zswb1zc7hrbq7uODUqKkyrc5w1o08NRuLAD+NxLPp8+GDjRvzF+vUAcM150P+fchlzjoP3QiF8vxZF1LlRKNJR2dbWZhwbitfNzc2mgzObYbKzM50WVCZvvPFG9PT0mA7ju3fvNl2fqYg1coQrvdLA1xMWFC8qlWqjve0zM5hJpdD93nvmZAfSQTabxYsvvogrV67glltuMVEJbXTJ0w8Yeed7uf+kI0a0iQtM+aRhw0hdS0tLXTMr8lEeFUZ8i8fjCAQC5pQHrs9WfBspoMr71WimI4P0xO+5Nyr3FI5MuadxqtESzpnvp5NtZmbGHGvKtfI+n89nTkzQHhuMLjPzgXvA6B5T/Dk/23gjndpynpfjOHVnsHN9VGQVxlTY6GSgc0L51OrqKqanp40jiT88ftXz1jIH6RRRI4mG89LSEiKRiIn8MauIeM0ospaeqVGi2Rl8Rsul1DgnDbHJGmus1emgOowGJzzPM/drYKERP76ejpJ6803Mf+UrSL39tmmIRofs+Pg44vE4Wlpa6k4mU3q35bXOl/MfHh5GPp/H4ODgNQ5L8m+OWalUTHPeZDKJB5aX8Voyia+n04Z22VeFvV48zzPOIvJa28lL3CJvt/U6xU/ShOI1cZk4y/KqbK3sTHuTKE6oQ4E9YohP3GfVuwj7yPPPI/7d7+Lp2tHLHE91f6UnO+hjN9pV/pBOp7Fp0yZEIhF0dHTU9XMgz9TGtHRKcL48QpDj2XqIwpYwUIcyZbPOTwO0jdZq8w/FO127/azq50DVfmpvb68rSVC+zdIw/Y7jkl/Y9pOtW+q8bJ1N4aNOKVtHtXXIRmsEqr0E+B2DD6FQCOfPn8fo6KhxvrHpJYMNMzMzJmjEQAblNxs8qqOUUX/yONd18a1QCN+uGeQrIv99Ph8e9jw8XNOVHNEXmWGbz+dNLxzClCdreZ5nMv6o79CZt7q6io6jR/HA+Hi1KWVN11pcXEQwGMTFixeRTCYRj8cN/BrZWbb9wD3XrOpwOFynE9vjEI/Jw/g3db9sNmuOiKWD7nddv5ejQZHBdiTYBoEu3DZU+DfH4vjAWvdk+xm+i9/ZHlYbgRXxOY5tUNoEoversWEzQF2/zkM9sbaBZr+TiKLfNzKs7DkC9WUsnCthqHuiCrMq1ZoSzs9tOOrc7HHVS0pEtBljI2Zow89+v/6ve2uv31Yq+KNMXveMCqvN1Ox56v/237bg5h4q7vPoMBt39b2N5qC4o9/ZQkmVet17VcAbCWtVWq+HkzqmXTtu0yBxiJft0NGr0fOqRNh0qfPT76gAOI5jotGeVzWUlu65B+MPPQTvvfdMzxTdGxrNHJs8hsbj/Pw8isUiotGoqT37/4ejgdEYxSXiMAUbDRrXdY1xD1TTobVDu+dVjY1feR6O/PmfI5lO41u1yDgFBEsDAOD/tbqKOcfB4b4+/NsNG+pw8Ho0y+gy94mf/2B1FT8pFvG9SAQtUnrANZbLZfxBLoeflMt4rGZs8ZQApimzhpvKIBWFpaUlTE9Pm/r7bDZrGvbNzMzA5/Ohr68PJ06cMCeNVCoVvBIM4md3342K5+Gh2pwXFhbMOepscEYDR/eA2SE2LiouqeH161//GmOnT2NPUxMqlQoma/X7XH86ncaZM2eQz+exf/9+091fyxBYS0sjj2NzHBqphC0VGpZHUKliBJ9lLHyeyo3W5nOfqTTYCozyHY6lxqTSiBo6Nl9dXV01ZQg6Fp/h/tPJpP0oiLdcHzPEtK5YaYbd44mj+n+5XDY8mjzAdV1ks9k6xwYjT6Qv0hsAMxdmnSjdkCeqbG+UycDfajTqfTRm9QhLLenhb8LQ7/cjm81icHAQmUwGw8PDpsdGpVI9lnO41ruGaeN0NGjUt1G0TPmVymrCXuuGKQN5tGAkEkFPTw/y+TyWl5dNbS/f5/P5TKaJ7glQ32ha+bhGjnnZshkAUgcPouW996pjdXYa/sIm4uTxhCfHIZ/l3qksVF7A+WsDR3U20IEaj8eRSqVQLpeRTqeNUfNQuYwncznA74e/tRWffPIJcrmcMZKmpqbQ1taGaDRqMnEoB9kdn3hCPsN7yF913rxf8ZJ7zpNXyuWyafiWz+dx5swZ7K71XaMjjJdmotA5mMvlsLKyYvgrAGOIM8Ibfu01PBGP46l/9a8a6nyKXxpUI1zZENXWC6l/7NmzB3fccUddJpSeMKTyjTjNz6kTEO/s9H41sm2Hj/I8dTSoQ0a/s/fFHkdho/q6fsb5abNm8ix10GvQjZkqdD4Gg0Fz+gIj1sQP4hOf45rs+eu9eo/Sp8oMwkBhaOuHbMKs35VKJSwtLeHTTz81GWDEL86J/L1YLGK8ZrSHw2EkEgmMjo7WZfrSAXPx4kVDN3SSO45jMhGKxSLeamrCC9Eo7pmexr210yMojxcXFzE2NoaNGzca/GRfHwDG6c/Tdpj1RP5D3GQ52ZXt23Hlttuwf2ICX08m8cknn8Dv9+PEiRO4/fbbTfYg4cJnKcPYgJbf8T2Kv+QhxA/OiTDXXlwqGz3PMz0c6MT4PNfndjQ0QhrbgLGNEdvQVYGkCE0EsYlJjVeNbqhgUCFOBLeVJX6nhGczKmVEJBw1XHX9vFffyfeqMalzVLjZSrzCSYlW/7+eEQpcexqEGp4KQ30GWDsHnfMZ270bw7ffjvUnTmDdmTN167MNcipXjRRy3YPr7VUj412NGGVydhmFjqXP8LeNY/xcDSmgvo+G4pemmivMFYdtrzXHtj2Fihc2Pej9Opa9VzaMFHaaTaL32Y5BxTf7UuFm46K9fl7qWKBiZsNIx7NpQtfYiFb1snkO10aDL//wwyh3dGD6wAG0HzlSB4NKpVobTSPWcaoR15aWFkxNTRlDOJPJGOZq1z7/c1zZbLahkshIP5m9wlbXwnVQ4Q0EAmhra8OPIxHM+/14LxbD/yGRqKMlYK2c5LulEn7mefiuJURs3k6Fi882UgKfCATwhNVci4YaFaG7cjncXlOSC4mEmbPSD41Hn8+HdDptUt7ZyLGlpcWkKDOCkEwmMTMzg6amJnPsHwA8FwhgPhTCT8tlPFwbk8oBj3ditoaWAXJtXDN/q8NRBe/S0pI57kob+qlyS/xPp9PIZDJobW01gp5w1yZnhD/3jBHR1dVVc7QrhTyjiFRi4vF4nUEVi8VMOYHyRUb/VWllZEtLh0hvGvXm3Pi3wo77rYYwDTGVjXSUaXYHO6MTX4l7ajBw31lX39bWhkAgYKL1hUIBgUD1SMqlpSUsLS2ZYy5ZUrCyslLnDPD7/cYwosFLOqEs0AwYW15wv9QIJoyV9jS7SmUPFUGOQ6eMym/OSedBhZowpbOKqeCM1hUKBTQ1NSEYDNZlMvj9fgMbe2yVzZyLOpsUL1WhJT6XSiXMz8/XRQxHR0fRVotkv9vcjGP/5t+g7/BhdNZOpWJmCWGiddqEjf1j6xlqrHEuCwsL8Pv9RkGn04H0yIh8Pp/HlStXjKHG52mo2Y5+13XR09ODu+++2zTZpBOnXC5jdHQUmzZtwpYtW3DhwgUsLS2ZdP1UKmV6fvh8Ppw9exbpdNrUZC8vLyOVSl0jg5k54HlrZRzcMzo3mJmjz9n6Ep2aPp8Pe/bswdatW3H48GEsLy8b2J86dQqO4xgDSvGc+x4KhcwxiixjIu7rkaaKO/Pz8wZ3CU/KQcW9RsEM0qWtM7Jvi/JrfqfHy9r2CJ0LnucZmqCcJQ+y9WiuheuiA0z5Gx2Sak8oHdlODOWrOn/+VoeY8hF9H41j4gX5lvZQsfU7x3EMz1WjnXipMpxzteeu5XDEEe6vZiKp45nyTssLnncc/LRcxg0APl63Dl27d6PtyBGzjzymc2lpyfA0NullU91KpYKFhQXkcjmjJ/X09CAWiyGdTqO7uxuxWAzHjh0za6Msol7ANdB5FgqF8EoyiVmfDwe7u/H4zIzpbUUc9Pv9mJ2dxa9+9SsMDg6aE3yWl5fR3NxsYEmHneNUS30uXLhg8DaZTGJiYgKXd+9GJpHAyWgUD164gN27d+PUqVM4d+4cRkdHsX37dpPZsLCwgHQ6jYGBAaMTfPbZZyZDcmJiAuvXr8fk5CQqlYo5ZalYLOLy5ctGb6TTZnx8HMlkEqFQyDStnZycRE9Pj9mz2dlZ4+gZHR3F57k+t6PB9iDztzJ1va+RsaAGHj9TZcI2dkh8tlOAl81EgXqjTv/n37ZBeL1IjBKkrkfHvR5MfhtcdD66NjU2CCN7HjZMeU8j41HfZ198L1MxOdbw3r3ItrRg6NZbMXD6dN0ekGGpQND12PDTOdkKC+Fk73cj+Ct8OR+9X+/TtarhbcNZ58S/7cvGt0Z7qWvWOfI727hWHGnkhNP7OKbSk/0uFWQKu+utQ+Fgv4fXb3MI2AKD79Y1KfwVDnpfo3fZMNB79HPinyr6ne++i+l77kHbW281jBjoUU+svVZvPumfmRI23/nnuAg7jSTqPvKHAl+jgBT0jMrxswMHDsALBvGPlQq+WzNy6CXXLt6VSgUPFAr4uuuiUixivhZpY0RZjYZKpZod8FJTEx4tFPCQGBmKu4oT6hzWqKCm1rOGkgYADd1gMIjm5maUy2VEIhGTCslj+3p7e9Hc3GwUA9d1TY2llnd81+/HzzwP3/P7Eaql4m/dutWkUGpkA1hLf9ToDffJxn3yIJ/Ph6mpKYyMjBiFm+dKM0pPuBQKBXOmu2YzqAODxg3hRYWRBhgvKi2K4zQOmKLM9EgakWposJs8HQtAVSGiE4W12kC98574pHyUMLIdJK7rGtymIqkKMFNLV1dX64xVZhdwTM5b05y5R6wVZZo3nXJsbLe8vGzG4Vqp2Op+cv+Vf3He3G81/hUmyqcVVzQK6bquUXpt3qZwpDFLWtFsJsU5pTPVG5itwsZ309PTcF3XnMfOIy5XVlbQ1tYGv796wgXXRVxpJLfUuOS62GSO97P8hkbWoUOHkMlkTKnTmTNn8NhjjwEA/pdCAVnHweT+/dgzPo5sNouhoSFjxLG/hu6HrVfYeoN9cT/Zk0GDOOl0GrOzs3VRzJWVFVy4cAGxWMxkR/l8PiM/yB8u33wzzm/fjl2ffYYNCwuGdwLAxx9/bIyVyclJhEIhc7IFnacLCwvX0Mvs7CwikYhxhjmOg+npaVMiRHoCqp3hmb2jRncjWWXro7yH41IGbtmyBY7j4Be/+IVxUm7ZssU4TVZXV7GwsGDKm9hzhnDiPYyWa7NPOka4R/Pz81heXq7L/lCaUN1OM7tIDyzPULkYiURw7tw5PFcu42BPD+5bWDAnLa2srBia4JyY4UUc4v4x4ky5RJnId5EfOI5jeLg6zFUPojxj9hPXZ2dEUT4ofwXWyqyJ+3Tk6H7raSQADC/kfjPqTt6rOoTSD3lJoz3h+9WRWqmsHcvMsfic8kDKwEqlYpzAxG/iI/nx3/f3YyoQwITnoez3I7N7NzqOHjXOjFgshmw2a7IDXbd6mpQe8UgHA3km6YmOaGYh0fHgui7Gx8dx+vRptLW14aGHHjK9PFS3frRQwDO+6lHheiQ6+Sb7XExPT2N0dBR79uzB4OAg0ul0XVYdG5mTv/f19WF0dBQrKytobW2F67rYcOIEJvbvx0M1Z/r09DR27NiBhYUFnDlzBgsLC3jsscfgOA4mJyeRSCQwPj6OXbt2YWFhAYuLi0gkEjh//jyGhoaQSqVMucPq6ioGBwcxNjZm5hSPx9HX14cLFy4gGo1iZmbGlJ2eOHECyWQS8/Pz2L59OwKBAD777DPEYrHPfbQl8Hs4GmyjTJVWVe5tIWUbI7Yg1r+JrHYDN1W8GzFNvdQQ0/+BekVePXCNHAaN1q9KtR1htd/Pd9jC2TaeCT9NH+Sl81LFSJWaRvtCZmfD0J6H3gcA648fx9CePeg/csTUibruWl0b52uPpeuyHQC2oLPXpvMhLDknVW74o82A+Dyfs5VAnYvt4OE9uhbbgWEbvxyTzNU2ruw12c4Svcc24m0HgW1g6722cmvDsJFTQmHf6G/FJfse29BS4cu1Uxm197kRfuo7FKY2vFXpVXygocjvek6cQHetMWzFUsRd1zV1yerd9/v9aG5uNkoC8dxOA2ykwH4RV1tbW50xp3zE5htqYFAp0saNjlP1js/OzmJ7UxO2o1q7eKSmHNCYo6HL1HkKfzX2fL7qcaNbtmxBX18fUqkUXvP5MAngxWgU/7JWs6sORlUWlKeTRng/+Rx/qzBnajzxaHJy0hwHR/qIRCJYv369URCYFul5HsbGxtDZ2Wn28KlIBN9uwBM1O0ppRfFQo/+e5+Hn+Tx+tLqK7wWDeNzvr3MQpNNpg2dU0Hn0IZUzjkPFlIqvyk42+dPoFRVFKo6RSMRE8BmlZVqo53kmUsiU+FQqZQzweDwOv99vmrZxbEZ5aYizbEDnrnSg+6EOYcKQiiT/Vl7EMahUE5epCB9MpfBmKoWvzM/jy7U9VqcAa66Xl5dNw0Rm0ESjUbS2tmJhYQE+nw+ZTMbQfWtrKyYnJ42iy7nPzc0ZmPMkB52nwkF5j82HG8lc/pBH2rXlSt/aU4hwZNkIHaJcp+0Ic13X8DZmuvT09MDv92P79u3mzPPW1lYzd7+/evQp99pOndX9Jq+hUct3AFUHblOtXIj7YuMG56elEj6fD98D8F8XF7Hu5Elks1nDDwgXzRqyZbitg+o9tmxUHKKzQJ3NnBcdkKlUCp2dnab8ww6ABQIBvL57N9KRCD656SY8fuECxsfHDd/68MMPccstt5isgrGxMVy9ehWO46C3t9f0nlFju1KpYGpqqi7Cajsi1dGsdEc+ooY45TMv5X12GR2P4z19+jTm5uaQSCTqort0AJFHEX6ETTqdxqVLl7C4uGhOvWlpaTFOfe6BZgB3d3fjo48+MhFe0qTqfdxDOlrZ8DaZTGLfvn11TnoazpFIBO91d2MuHMbrLS340uhonU7C0hUapsC1TbUJF/JHOqFs3Y16EMsN1K5RPVD3TeUle5Dwf81wA+rLChhYYVkAL2a3aKkY+Th5DZ0yOkcNXhAG5FekFdK98jHCgc8wA8Q+SYhyg4FM4rHqtBxLafTeuTm8kUphXS6Hq5EI1n/ySR2v5Jr5rsnJScMXN23ahEqlgng8jrGxMeNY2L17N/bu3WuyAalnNDc34/DhwygUCjh//jxKpRJmZmbQ1taGb3zjG8bWIL19vVjErRMTVbglEnW8nMEqz/MwODiIq1ev4uzZs2hvb0cqlTI9mwgr7pvjVLMPrl69avClXC5j8MwZ/EFTEzZs2ID5cNjoGKlUCqOjozh37hxuuOEG9Pf3w/M87Nq1C4cOHcLU1JQ5wnjXrl04ffo01q9fD8/z0NLSghtuuAHvvvsu/H4/hoaGMDg4iFgshosXL8J1q8fEbtq0yZRp3HzzzVheXsbevXtx/vx5jI+Pm9Mutm/fjnffffcavL3e9bkdDWoE8H9V1sjMGhlqtmEIXHvUnTIjFfiKwPp+24DmOPb9KiT4Gd+nCoM9hr4fqD91g0yD49nMXxmEztc2GBUe6mH8bWu1jXz7M75XhW6j99mRi0qlgu7jx9F17Fh1LtJ5lN9zX+058X9lxnb0wVbAGhn2ZCjqPVVlTMexDVeuV9PAG92n3+keqaFnP2cLPl62I0j3jDDQy54LcUX3S+Fr4wyNS23sYq9B565zsD9XmNtrsHFfFQA1hvRqhLs6P5vubfpQGKgCqThC4WU3HFWlXTsiKx7Z8HAcx0TKdd90P+z9+yIvdvHlvnJv1cDj/0w/Vh5BQUXFnUcj0kjWngeVSjWdcGhoCAsLCya1nBEMlo4QjvRad3d3Y926dbh/5068mkziu4G14xyVVxNu6mRQ+cD7uH+uu3ZqjBomXIPnecYZoorb7OwsTp06ZQwkv796djgbptEQ5HwYEWXkiDBTpy4j1XbGB7B2JOcPczmMOQ5+vLqKhz3PGGWO42BlZcU4qUqlklEKlHaVbxA+mjasRqoaWjQMqCwC1Wjm9PS0gSWzIwg313URj8cNDtB5QJqhEq20zjnYkTr9jBffRdiQ59LRUqlUTBmCLc8JNx2bsC4UCnijpQWzoRDeTKVwz9JSnVxVByfLRIBqNIanQPBoQNd10dnZifn5edNAy+fzIZVKAYCBBzM7VHapXGAquy0jmF1h6yfAtU2zidvqROR9tjOGCn82mzUp7Jw74UWYMiOoUCiYSNiBAwfM3k9MTKCjowPhcNhE9VZXVzE5OYlisWiOf5ubmzO1zHyWe6fOEUbiaJwWi0VMT09j48aNSCQSWFlZMTXJjuPg1wB+dM892HXxIjref98o8idPnsSRI0cwOTmJrbX651G3vkSW61ZctfUObRjKfaMTRPGRRoDqD3SwMT2YR6g6jlMnX4h/2oTT5/Ph7okJvNfTg7snJ025xdWrVw1dl8tlTE9PI5fL1fXS8fl8mJ6exqVLl9DS0oK+vj6TebRhwwbjBL7pppuMccbO+aQ98mk6M9URR35BRzzXqT88CpYwoSNobm4OlUoFmzdvxqn+fry7bh3mR0dxay1qSfho7yDCk3Oh48ZOpdfs0vb2dmzatAmLi4vGicCL95A/27pQuVw2p0Q00ls8z8Mdo6M43NuLvaOjBna2/aFlIKrvUL5qOj9Pt9F7VafSy9a7uWd2TwmVjbpGfY40qHioGT76bttpzf2y52fLGPJydYKoc0F5WKO1U3dRJyi/o4xXvqdzVP5ZqVQDHrdNTuLWiQkzz4+vXMGl8loj64WFBXMs7fbt2/Hxxx9jZmbG7Ofly5exa9cuoxsEAgEkk0lMT0/D71870ShRK93kejh+uVw2UXrVQ/nDTB3NXHEcx5QKMpsiHo9jbm7ONKqmM5+8h7I/GAzi0qVLGBoawqZNm0zjSjqZOf7q6iqmpqYMjjmOg2effRYdHR3o7+83mVDZbNY4330+X93pTUtLS3XNwQnzjRs34uLFi8YZzFLLqakp08eD2UtjY2Po6uqqlndcvmyy1T7P9Xv1aFADiZujSMoNUERUJqCEZCN9I6PQNox4Xc9o1uiRIoh6LXVu+k5FHFU2+Lcq0mpgcmz9TD3vtsNA52av5XqGqcLR/s5monbURN/RyHhST6M2Y+J49jx1HfZcbUZJuCmzt2GvjIfzUZirU8dm0PYc7Dna87OdSwo/26jnPZoKZuMw56iMWPfrevvUyOi332u/RyNqiov2Wq+nANvzs6NDeo+uVWnBTu3mpbiv69Hvde28X5Vufm875+z5KI4qjdsOCb6H77AzFWz+pHDkfOx1fZHXf5ubw/v9/bh7agr7apFoRoOpnABVJYLRLvYY4HpYl8qUcwo17m02m0U6nUY0GjXNiWiUU5BRaWVEnWl+nueZxnLREyewLxZDrqsLb2/YgN7eXnR1dRmlXg017guNehu32XzOrn9Pp9MYGRkxzoOWlhZEIhEsLi4CgPGks4yAChHXm0wmjRKtioA6W4C1lHj9nPKM32kEplKp4Fueh1+4Lr4j8orrpHEFwCjc3BPipSrcNCz4Dq0dbiR7XNc10S/Pq0bCwuGwiTguLS0Zpc7v96O7u9s0ziQu85QBKtAayWIElPeS19BxYzuUyIeUH1CpYxaLpvsrbhA25KuM7PA9t125gsN9fdg7OWnwg++l44w0cf/99yMYDKK3t9fcwwgkFc9KpYJMJoNT/f04Wuv0f38uZxxpS0tLBvZLS0sG77PZrKGXzs5OUw+7urqKXxaLeD4SwR8Gg3jEWTsdhPvKH5bKsCcCDRjSBOmNzo9nSiU8Gwjg8VIJzeEwNm3aZI7rZMbJoUOHDIw5RrFYxMaNGw1Ms9ksXNdFMplEoVAwzVQpN7hvY2NjZi8+++wzY4Bxj6gwM4Mwl8shEolg06ZNSCaTWFlZweXLlzE7O4vdu3cjnU7Xlaf8U2cnluJxnNq8GQ+dOIFIJIJgMIjZ2VmT+cCSBPIdxX1GqMnDye/oIKJirKVndLqqQ5a4yB86dhitJE9QeU+dUemBPKxYLGLz4iI2nTuHSqWCSZGjzJg5d+6c6QPCBok+n8+cdrG4uIh4PG6cKew1ksvlMDMzY2qo2Z+GzouFhQXTbwCoOsyamprQ1tZm+J9GvjUgYeuJ/K2fkRaOr1uHhWgUR/r6sGd83Kxd9UmbxvV4WO29QtrmfcxSU1qw9RV9l62fqYPTXofjONg9NoYv1erGK5aurnup79VASqPSAf27kb5AXtOox5M27lS5pDqKXpyX8n8bTqRjzpnPKZ8l/OyMDXX+N4IHP7dxg/fxHfxfx7cz4BrZK+q4VluK71DHbldXFy5fvmxwKpvNGl1h3bp16OjowOuvv25KxNrb241cj0ajhh+xufPIyAjK5WrpzaFDh7CysoJQKGR6qNCBTUcFbSItM6IRT9g5TtWQb21tNSfGkPbmaqd5dXZ2mnGoF1HfYP8kACZDw3XXjvdk75rFxUWkUins2bMHly5dwujoKKanp5HP5zE7O4tEImFOW5mdnYXf70dvby/S6TT6+/uNg6StrQ29vb2IRqOm31M8HkdHR4cJFLS2tqK1tRWDg4MGtuzzsHXrVgwNDWF5edlkCn6e6/c+dUKNCPVUKnGoA8C+GhngSjj2+3gpgfJ/22jU8ew56PPqULAvm4nYxKDr0Lnq2uxx+F51ylzv/Y08fjqeMpPrzd2Gm63EahNICpjrOQpspqNzU8Fjr11TrXTduk7dO46lnzcSOEC90aj7fL318zMdW//XZ3Q+nLvd1Mues42XjQRgI7ja+2h7t7l+GiXq5WwkeHQM3Z9GtGBHKnUe/FsFLBkjDV2bDvkeG//1cxufbQWDl50hpOPZxg8/v57jqRH92/tkj6HOLtsx90Vd7w8MYC4cxsHubnxVIu9cvzaBzOfzRtBx3hRahBOP6SSOUADNzs7CcRzMzc1haWnJwC+TydThDiMmfBff4fP5sLy8jHA4jJmZGXz22WemmebAwAA2btyI3t5eY5SoQsiLRgwN4nQ6bQwNGgV0dnAuGzZswPj4OFy3WgpAwdbV1YXW1lZcuHAB+XweIyMj2LhxozHm6Hzhvvr9fuO0URpRZwhpTNPqFacfBvBEDSc5BpVKZl7Q4UCY2k5NljnQANXeIFqXq3RP2LCXBY1opsOTL8Tj8TqDt7Oz0zhfstksNm3aZOZG3qlBgWKxiFdDITyfTOLBTAb3iYOAyiNhah/9CKyVQtCBxMgMYc8obCaTMeulw4uZPeVyGREA+wsFRCIRfNbVZQwuwkJPp+CzWnJAI1OVRJ/Phxe2bsW834/nw2FsPnfO4OLk5KRxADCyqv0hAJgyDcL3mV27MOu6+LtMBoO1buWkWeJYPp83tKk0ocYDnT7M/vjHG2/EnOviZ+Uy/veBAHbs2GHgXCqVEA6HMT09bfCX33F/uAeMLmomEHFIHXykSZUh3ONCoWCarvF0Fj47MTGBxcVFc5LE8a4u/N/LZdy3uop7ak1bV1ZWcGexiPe6u7G/lvXQ398Px3GwsLCArq4u0/Dw4w0bMH7XXeh4+22kDh0yUTU2dtVovDoRVHbZ0W9b1tBwUAfE9XQEwlu7sv82vUgdGryPvIQ0kJDmt8FgEB0dHcbBpHK9XC6jq6vLNHMFYIyb1dVVhMNhc+wfcUcdCzpX1b0b6RWNnPy899YrV3Bs3TrsHR42/FGDRaqrcY79/f2Ynp42+6Z6H8clXNVQLRQKpmeP3q+w1v209UN77nbkXPHdxhv7b1tv0+9se+N6+GPPi3yWJ51oZrTey3XZjgGdJ9fVyB6x5S5leCNc1bEII+V5jdbSyGay/1fY6Pt0z+yGnLpOzoV7GY/HkUwmEQwGMTc3h1KphFQqZforLCwsIBKJmH4u2sT3g95eXHjwQdwzPY17anqP9jTr7u7Gjh070N7ejsXFRTiOg5m9e/F3Bw5g6sIF/A+bN5vMINIi5Znqpp7nGX0IqPZ5aWlpMc3Ff5bJ4APXxR3Dw9hfmy+d8cwaoyNkZWXF6IAaUKLul8lkEI1GsWXLFoPXk5OTeOmll9Db22vmNTs7a0orFxYWkM/nzYlboVDInLzFHkaUrZFIxBwRTrzlseHt7e115WXxeNw00f081+9VOsGrkVHXyNi372n0/+cxyvR7mzD5Lk2Zb2RIAmuGkxKU7flrJKgaEZRtAKlhZ8NN1/jbjNxG9zear60o6vsVjjofIjZhoIJXGZk91+vtgS3UGr3b3meNIOrzvFeVRFuo2XDTZxvtkY1PjYxh+znbcaIKjL1P9r7Y71SjRoW64pa9BluAUoFUpbCRgFMF1p6HDWtbMeb/dimEPkempiULjQSvLfh/Fz7wf1v42jhi0x4vFZaNhLc9lv0O/q37YM/f3vMv6to7PIzDfX3YffUqRsfGzBw1g4YKPyNgjOpqajujeqoMuq6LoaEhU6fPLsGM2FAgUzAprhOHOL5GeJgm6fP5MD8/j6tXr+L48eMmDXjdunXo6elBS0uLSS8H6k+rYKSLhjMv/l0oFLCysgIA5ni8TCZjshWoWLS1tSGTyaC5uRmOUz0vmor35OSkiZZ6nmcMXMdx6px1lUrFNGjjnGj002GhChwN3Hw+b9IGh4eHkbv/flzevx99hw+j/cgRk75Pw2h8zx6M3XUXWk6exDvvvGPGZBr6xW3bcGLDBtw7O4v7ao4Lws3v96OlpQUPPfQQ2tra0NzcbKIQTB9nqrFmiDBSzOyNU6dOGYcF8YRR5Fwuh2d27MB8IIDnAgH0nD1rjH9Ni6aDmnwpHA4jGo1eY+QQb7Qsh/03FL/4DLMBfL7q8ZtUdNThxWagxBFtRqplJsRV/u/z+bD36lUcGRjAbVevGieAGiWNdATCn807eYLHnWNjONTTgzvGx01knutSJyh5kzqU+B7Oj04JACbl+9arV83JMxxPacnmicQRzYxR3mkbJdqL4XrGg55so0YqyyX4TEtLC05s2IC5QABvtLTggdq8AVSbzY6MVLMq5Mg4W05M3XMPVltaMHfffdgzMWGcafzNZ7T0QXt4kYepPqR6Fu9RXch2xuuc+J2dDaBywNYV1TnJOdh9xtRYo0J/+fLla/DOvp9jks/z3HqVWZrJSzixCz+vRnjRCE6e52Hn8DB2jYxUaU9kg87N1i8HBwcxMTFh8JCZB8o/6ZAlX+U8tNxR9Qq9bP1FHTu2vmg/a+u1OqbqcHy+0Z7rdT19WHmWwsrzvLpeD410s0a6TqN9s2Fgz+O37ZHCgvNtpIcC15Z4qYPD1sHsudnOPP3+enzMvsfzqg6/jo4OLCwsGLqpVCrYvn07IpEITp06ZTLGJiYmDE2trq7i7L/+11hNpXAsEsGBjz6qo/lQKIT9+/cjkUggHo/j5z//OSqVCib270e+pQUveh4euXQJ27ZtMzaS4gf5jdI4MxBYnkEj/MjGjVgOhfB6SwsW//7vjS7teVUjvqenB6VSCYuLi+jq6sLKygou3XQT/uPAAB7O57GzstYIljpcKpVCIpHAli1bcP78eZTLZZO5xv4LoVAIKysrGB0dxdDQEC5fvoze3l7kcjlzdG4mkzEOb5ZdMCPO5/PVldMxY6lcLqOlpQVNTU1mPz7P9XuXTthERqS0DSBFeKD+yECbGGzDyyYcmzkoo1QltpHxowRDxQeo9/jze3ud9rt1ffbndgqSLcSVEdrClt/bf3OONiwbeeWVceh3RA4ij9b02XPQ+TViFva+EW78Ttdsj6UC2TaM1ci136l4cz3lyYav/UwjvND583MKbG042QjfG+GDzsOeI38aCTz9Xr3xVOxd163rNKwGMi/7M9vpwzk2cvQoE220Ds7HdkwojigcFOb2+nSO9vvssdT5QDjoPYyK2PTfaHxVIm1asmFp783nZaS/7zX44YcYOH0afr8fGakXpPEIVCNFXV1d2LRpkzmKzfM8k3LOOdrd/bn/ra2txui9Xi26Go9U3tRpwb2nUNWoEz3hmUwGExMT+Oijj5BIJNDZ2YmNGzeiu7sbzc3Npgaae6CRfD0GkrX2jKKurq6aEwRmZmbQ39+PiYkJU8NIg6xUKmF5eRldXV34+eoqfhyL4eFcDg9LraM6pEjr7I3BddGBoxEHRjOKxSImJycxNTWFpaUlLC4umhrwmaefxmpzMybvvhtfGh01+M4a8pM1Bebirl0Iv/YaPM+rSwU/fu+9yIZCeLmpCfHaySkcg832VLDn83nTrLdYLJomZ2qsqENaHVCs11d4eJ6HnZ9+ipMbN2LnxYuYmZmpKwnhmNx3Oh/Y/Z54wvdqNg5xTDMF7AijKuqfbNyIDzZuxO7Ll7F7bAwA0NTUVLd3anTyt/6t4xaLRWy/ehXbaw35IHtL3kfj23Ycq9OPxvyukRFjhBVl7noGPdet8ph0R5wjP2VWyvYrV7CjVuePGjyZMq/lUCrX9RQPhSMdI4ygKx+ns0ydUiqXdf62bNDnaYjfMTqKQ729uHVsDEO1+mZ1Zqhz0o5WxmIx7B0exslAAF+6ehV9fX2Gr9iOYNUTtS8DeYfqoLZupPigPQYa4aPKEeLk9ehAZRz5GPfGlh227FdjW+lVZWejlP1G+gjlo63rEN4KO9tYUjmu+tpvMy5tePAddGpyvWw0rOVuruvW4a3KI8JNcdlecyM924abrXs0gpnCQj9TB4zqqbauqTqvTX/qbCS+Ku0q7Gz9SdejTqRGwVSlgUZ7YuMqnT26Hhum+m7ioOqJtp1kr4sw1e/1akSX+j59P/GTZQPMrmxubobf78eOHTvQ2tpqytAymYzpy+OMjOBUOIx9tX5GvLhXPGEnk8kYvarrvfcwuX8/ut57D8Odndi2bZtx3gL1mbZKz0C1V5DjOKZ0gllwN509i7M334yOw4dNFh9hySan3d3dGB4exvr16+H3+3F5zx5kgkE8Wyohcfky5ufnzXGSCwsLSCQSpmyO/L1UKiGTyRhaY/ZqoVDA4uIixsfHceHCBaysrODTTz/Fhg0bcOXKFTQ1NZnMvtbWVszOzpqeUPl8HsFg0PRToTzl6S12r5Tfdv1epROKKKq8N/q/0fONlHw1DlVwXE9xaESsNkPRz1WJaWRgqRFsj6VMUMe0vZZcnyornGcjRt2I8fHzRhFjhS/fr3C252grDmR0qqzYzEovJX7dd2WsKkx5vyq6NhOyU3A1emrjiO6JzkfXanuieR8VA1tJ0DFsAa91w3QyNNofW/g0GrfRXinONfIa21elUjG1WhrN0nfo/Brhkb1vNo7bMGi0DhpjeiSRjbuNYGQLQNtJYM/N5iO2wqS02EgRUZg34hEKV3uOjeDKOTcS4l/UlUwm65R8dQYCMEYCaYafU8AQDswMoENKSwX8/upRdozKcp12tImfaY0lhZQqSXyO42h03HGqxlYmkzElFhN79uDSnj24d2QET7e0oKenxzg7Wc9MQ6tYLKK1tRXJZNJE7guFgqk99jzPCPOmpqa6UxKY2tfd3Y3/WyaD6UAAv3RdbPr4Y5MmyNRc7q3jOKaEgZF0NnPiEW7lctk0d+TRUDyuitEF13Wx5dQpfLZrF7Z8+GHD8rj+I0cwtGcPWt5806RJqqN83dGjGL3zTtx8/nxdf4FYLIYzg4M4sWEDdnz6KXbUjGXl76VSyeyv7g1/E0eoXM3PzzfkU70nT6Lt8GG4rotcjQfaznLSqfJVPV+dONeIH2uGge20VEfmB5s2YTEWqzobxsbqDCKOqUqv9ghR5ZTzttOxqSwp/9asHeUNHJ/0yPt1LM5DHS40xDXDgveSn6pjiPDhfDlHYE2BbRQU0RNDSCPcA9t45BzJS9TBpuvg+kj/KvPZqJH8AgB2XL2KrRcuVFP8RW9gijEv0gp5Cmlg18gIdtZS9J1a80nyFt0nWxdjRgv3js4N23hWmaG0o/pHIye2zlv3XeGjMlV1pUZ6pO4b4a66nG2AcRxb19T71CDj/5opZOONvlsvrbdXPc6Gl63P6Fpdt9pUcrTWH4Hj0tGk86YTTPdEeZHKcNuQUR2Z/3Outly3dRHVf3Q8/d/eQ66jEdztwI3uj627KD7Y+2HDgPOw9SLVAxplwNiXbT8o3jXS5a4HE+VdNt8g7ZC/AKjj+bpnjfRvhafiGz/v7e295sjOXC6HlpYWdHR0mHFc18XY2Jg5mWnf/DxuP3gQyWQSrt9/DQ0Svp988gkWFhZQqVTQdewYuo4dg+M4mL7tNsO3bRuHGWm6jkQiUZWdtQAAP7/p0iXcNTeHD8+cwawEe3RfaLCThvqPHMHw3r3oOnYMp06dwtzcnClPZQBAm18z4494yia+lEGxWMxkqXmeh4WFBWQyGUxPT+Pq1atYWFjA5OQk+vr6cPHiRQQCAXNKU1NTE8bGxtDd3W2aFPv9fszOzqKjo+MaXnK96/fKaLAVDyU4W1jZyEXg2hPT+21mYxvtjYidm6WXMkLb6NDPfxuRNmLIjYiRa1cFqxHTtImazxFhVWAR1jb87DXZsLURWCMD9v2/a1xlJrYBp4RkG/y2Q8o2aDzPq0vBt5mc4pKtBNl70UgJbsRcG82bc2WtezQaRSQSqYOVwkZhqUoMx/pdhrIqA4oHNqwYBWD360ZCwRbGOj/FSX2O87NhaisiOmcendRofY0uVax+F53xe9sRZz/HOdq4bNOTrRw1mpuuj+v/bXuh/3/RF1NbFReBei8/hQONDjVOHGctZZz30hheXl42UUQ9Z9vm0fybhgHrylVZ1yaJOl9+pvuoyp7jODh3yy3Ix+N4tVhE6Gc/w5e//OVrhBNLEebm5nDrrbeiq6sLzc3NdYY0/x4ZGUFTU1NdxJRe/HK5eiTmTT4fVjdvxg3nz+PipUvXGLbk1Wwmp/X+XIseO0fFgXXErOfnSSCe56H9/ffR/v77AIBFdy2N2+fz4eqOHbiyYweaX30ViXffBZOEidPlchk3XryIAzytAmt8NJ1O4+i6dUg3NeHkxo1oP3Kkrua4ES9Qmai0pQ5i4o/yC8dxTIM8OwJn061Nu/xcDTblQ9wDOuJ1zpQzlJ+3XbmC4+vX4/bh4YaRwEbr5jrUoc5LDQL+KM9Q5y/Xo85wdSI00ilsWgLqDX3SCd9HXFTea8sLVRh1r/TiM3rcm63HKF8hjSsd2PNXPOGlae2KW8Ql7jOd9LacUj4OwKTkUtaqsaYZJJyLXRLCsdVZwbkR12yjWOGleg3fb/N41V1tHNN38V5+ZssnXbetlzQKSNm6ks7PlnHX041tw9q+R9dqr6GRQc3PG73PXk+lUi2N4P10hFGmuG61Lwqdbbaewr8VD1SuKJ3apRMKY8Udna/thFDdzOYBjWBp68z6XSNatvfN5pe2bmbjotKQvrMR/unfih+N8ME2+hvpZfzNskzCVB3hOgbpihe/516qY0Lhq5euXZ/r6+vDyMiIyRZYXl5Gb29vXQnCysoKSqUStmzZYgz2ubm5Or6ie7G6uopIJILR0VFT1qfvX1xcvCZTCFhz7tr7zYAgs/2oN8disTrnjDbJprxKp9NoaWmB41SDKM3vvovWQ4cAAAXPw/LyMu6++240NzdjaGioTj9nZid5JB25PG52YWEB6XTalFKEw2FTMuHzrR1ROjs7i87OTiwvL2NychLz8/NYWFhAa2srLl26BNd1MTw8jJWVFSQSCYyOjmLdunUm4/N3XZ/b0WDXpRFpuAGNjFIlBhX4NlNsJCT4HH/ru7jBtiHOq5Hw0PnoZ7omftZIOOgzdgrT9QjVXn8jRYVIq0oa77ej8zaT5NiqDDRiVvYztqKmY/F/EpmuTQVDIyWU+8Q5aF0vFVmumYaQKs22t1zH1+8a7WUjZcteu+4zI8Ou6xpvpI0z6jyy96GRga6X4hH/tpmZLbyYbkUjyFZACVuNhKlSZO+77XhRutX/Gyla9IjaDpJGyoWOoziil52epwqtrQjZ9KdZJmq8EG62MOCzjXiALfz1O32nvuuf41K61a7/bIbGowv5w3WqMaX4wDEikQgWFhbMdz6fz0TodV2q1BIWdrdyKmtqoBI39IgwPWqO/4dCIWz84ANcue023HTuHNra2jAzM4OZmRmzVjpR6OQoFouYnZ29Bk7KJ9LpdJ1xoUqX3+/HzUNDuPHixary2IC3cM5suKZC21aCuBbP80wWQzqdxtLSkjnXnfBIp9OGPzQ1NRnYf7ZzJ1ZbW7Fw331IvvOOmY/rukZBAGAaZCr+VSoVbD51Chd27MDGDz64pu6b8OG93Ev7eDnKLOUdtmPIdd26MhP+qPKovJ3P8R4tZ1Cnpj6jskPv43OVSgW3T03hjpmZKn4L3dt8WBXnRvIRQPW0icFB3DY0hB3Dw3UwU56sY6syrt/rPJTnAaiTa3YWHe9RGlY42CWc5At2VoKOyft4rzpQ9F6bZjVzT2nIxil1PqqeZRtU6kzRbCelSc5Px/9o3Tqc2rIFX7p4EbtGRur2wHaiNdLReNk43kgXU2eb7qvKxkYyUZ/RddlZL/a8bB1LP9d9bySrVN400idsfcbWgW29RHFC9Q8bjoSLygLN/rLp19YJefGkDcqQlXvvxcJ99yH0618j/tZb8LxqNHVubg59fX3XOGa4N+pYsnVTG8YKD9URbZxTGWbvs/3+Rt/bV6P9tOeqMgeod+TYupnSur2fSt+UK1yT7TzmGDaO25cNu0aZO+SrNg02ok0dS5+zI/+N9kXfpxdx76P163F09250vPMOYm++aQIilM+FQgHz8/MYHBxEOBw2+pA28bXpm/o/eykwg5EwZ4BG95Z6j2ZHcv7hcBjBYNDoMjTiARjnguu6mL3zTizcfz/a3nwTrYcPw+fzmSaPLEtgmR75Kss29WQIlsn6fL46RwN7LQwPDxtnLo/gnpubw+DgoJEvbETJ/gvsjQPAlKvGYjHkcjmMjo4avae5udn0Y7HtoOtdv1czyOsxQf3eZoi8XzdG79NI7PU80PodP7e9TbZ3lGPwffbz1zNKGhk/nrdWZ6XEQkLgczpPRdpGQtJmlgpH+/NGgk8JQZmPwt9WIuwxlbECjU/osOegjAO49kgb3ksDyfPWjrFSWNsMVnGDc1LlX+eraebKUDWNm+PZgpeppuzkfb2omT1X/tiRdVuR0edsD7petlJGhUw7viqcbfy0x9J52gpII4GsCnGj/aPR28jBp+PZnnOlMdKBCqZGysr11mLPyXZU2vulc7LLeOzrevhtC0XbIfFFXaRfxWHCiI44x3HMEW82P1HHLVClFRrh/G5ubg6ZTAYATCM9dVbQkcTSC653dXUVKysrpmeB3+83GT+x/297bxJc15FlCZ73ARDD/5hIcKaIkSQ4iTOpUGqISClCFZGDVYaVlVkvq1e9710tc1G7WldarnpdZllt2VmKzNAsRUiUOM8TiIkzKGKegT/UAjiO8w78U1I1o9O67LsZDMD/77lfv3793nOvX3fPZpHJrGzp0Sv9mFFRKpVCJkbX2Bje/fjjFaeppgZjY2MhrVz1KPWZ3qKhel+dJJU9TdHUulS/x+ySb82LgSKV7UKhgEu7duFcRwcO3ryJTV9/nZpD3L7Aq6qomzKZDHZ/+y0GT5/Gxs8+SwUAmG3CrAbdgqCy2HXjBrpu3Fj5TJxpfq/zgW3yO/aP9cecELVjBFg8gZoZDspn1Rt0sPmuyqfSQRlV0OrBQV35VBlgHUmSpALqfEdXq3zMv929GxMNDfiuvR2HBgfDe4oL1Ilm/XpmEOVA+0OeMjijttF1F59l3xyg89R9ndfsv/bN7RqfZYYLt9qVs8fklY69O5F8Th3q2tra1Bxyu1UsFnFx505c7O7Gif5+HJWAjvJAbdKVffswmc3i8p49OPX06Tq7EbMjjjvcxirfFcPpljGd0/6syo3SoLqWcuv40fuq9Zf7/mX2x22/0hbLaPK6WXRsqWdizzld5EmMPuWDyhrtD+1KkiSY/OAD5DdvRumv/zqcPVMoFPBNWxvG3noLZ4aGcGz1TBuOL22ij7MHuzl2Kiuqz3wuAulFPT5/cedOfLNrF04ODOD4o0frcKkGHCk/V9vb8e3u3fizx4/xhtwEo/yoqqrCt1u24I/bt+PU0FAIqJHfMfqvtrfjXEcH3n32DG+Nj6/L2nDcWSwWwy1ROk7OLz7PH+W14hzqBdoR6ifHYFxYiMmfzhXqp5jTHpu3rFvHDwD+uH07Zhsa8PStt7D/q6/Q2tqasjljY2OYm5sLWQGZTCYsXHBcdGxo577//nu8ePECi4uL686wampqClvHGLj4fW0t/mnXLvybyUn8amICVVVVqUWwjRs34unTpyEjktkFIyMjWF5exuLiIsbeew/Lmzdj9L33sOW771AsrixScKuoBhp440w+n8f9+/dx+PDhME51dXWYn59HY2NjwE319fUhYPD999/jzJkz4cpMvQqbt9gQp9XW1mJ6ejo8Ozw8jOrqajx//jzwjgdJ5/N5NDc3Y3R0FLlcLtzW9EPlRwcaXPG54LuDrg6gFgUQWq8qm5iDFTMkVLzlDFJM2bvAxwxDOeBAOtRgaQBD6XWa9TtvS+vzEjN6nKgEtfqcjosqytgYkF/8npNX/1fgzknnykX5XyqVUvuHqLRcGSp/3NgrsFUgqPRr+ywKnhVMslBhlEql4MC5vLrceh0OovU7DyzoswruHOCyTV0BVH74+Cq9ypsYWFNw4SDODZ22o7IfA7veTwVK/O1y7QbLAU1sDur2EdYRA0Jaf6zE+uLGTul6GaB8FYV3q5M2/vAwn9s9Pbh75AjOrB5kR6Otc5BReI4V9+Dl8/lwDRpP8QeAO3v34ubBgzh48yY6rl3DwsICZmZmwrkDNKy8DWHTpk3hNgdm2VDv8IA6AgkNdGg6Nw2jOm/MJFDdwvnB52IBNX7O/pIPvlLFeeHOLMfVwSZp0Ns81MkrFov47u23MZnN4lpvL97+8suUruGqCXnPlYZMJoONX3+N2o8+SqXxkk4AKdDnelXp1p8YQOO88DnGsdLtC6TNb/jRbBmVTbdNqh+8qN5x2XZdrDpI/3d7wHo1yOz63W1EkiQ4eu8eruzZg5MDAyldrDSxLqaQKoBWml3vA+sz3vTvYnHtOk/OG/2OY8XUZNpMnuHBuajtsg3+UNZ0iwV5qbLlclpOj2v95CUDGI6JVNYu9fRgMpvFpZ6ecK6G61W1h6cGB3G+owOnhoZSPHGb6fzyrNpyQXCVJ3VIfsh+8RnFVXqAW3V1deqGH44/dYbLiY6zBtvUxmgwU+cT66P+ZJuqRxyHJEmSuq1D21RsrfzW7TSslzpD50sM5+hV18+fP0/dwpIkCXouXsTjt97CxrNnsSRB0gdvvIF8NovzXV34s7GxsjKpv90Z1GeJGcl7pUHlVYNklI1vdu3CeEMDznd2hkNetfj8KBQKOLtrF8br6/HHHTtwZHh4nRyRR3/Yvh3jq4FOPqcyrX0uFosrgdH6enyxdSsOyo0kKlc+r1hcJrQ4Pqf91cJgp54tw2fV4abNJy/dXvsNOQweeGac+jFaiIOV9hMDA/h6505s/+Yb1NXVoa2tLSySFItr1zur3SfNSiuxUnV1NTZu3Ij5+XnMzMyk5gvnflNTE+rr61Eqrd1a9c+5HL6vqsLvW1rw29nZIHOcy3v27MGTJ0/w9OnTgAFyuRwuXLiA6elpLC0tYcuXX2L617/G5rNnw7xkMPTRo0fhumOOD29pefHiBcbGxrC4uIhHjx5hfn4+YOP5+XmMjo4GDNbW1oaFhQXcvHkTDx8+xL59+8L15MPDw5iamsLIyEi4NpyZCk+ePMHc3BwmJyfR2NiIUqmEbdu2hbFiNgfxJOf7jyk/OdCgAu7gXycNf7vDUK5O/k/h07pccGPtqgLVgVIj4qsk7lRqtC+m9FliCtEVpdL2Y+rSvjqvnd/aDzU2nhYbAwdKh6dcafqjg2KvD0AKjCuPaaQURDr9ymMHOS4nLwN8rpjUECloopHSK/XcmdHTkLVoWi7bUKDDtlT2XLbV0KvDQB5yZSwWaHDDSyCvshWbWz4ntS69dk3ngUf9HZB5W85vjm85XcG/CQJ8rLSv6qC67MXmkeoAHQMHSk4Lf8eCds7XV1Uoj5wvft7BnSNHMJnL4dv2duy/fz+8RyCgqzscS64+EASTDwQL1/btw1xTE67s2YP8f/tvAbxv2LAB2WwWuVwOdXV1yGazqYMeGdHmAYy8Zo1zhQ5TTP+zP7HDKHXeK4jUwJvqaXegHHy5fmH/fa75PCQ9+pnXs//6ddw+dAjdly+H8xnYRwZWyq0KlQsExoKo5Ie+p2dzKBjUTAyOgToBBFWUa3UcvJ+aKu3yr895gIafu55RnUIeq9zreMQyMUin2medj+pEqQyyznw+j8ODg2GFPS+22PkBIOVYafuuH1h37CpJ9ilJ1lbS/bwH13WcXwBSK38KkGPOQ7G4dnWmYyTfdpckSai7trZ2nfzr+9p3lSPPxtCxOj00hHMdHTh27946O6l/8+fogwc42N+/wi/Reyo7KkPAWvYK6dFxJL2UB/KUdl8P0YvZBsqnzyHOOzotHqjj5zr/PaWadKoupZyqrXesqPiBdVRVVaG+vj6KM5kdFbO7jnFZVB8oLtJ5xpVY0sYfXbDL5/N49OhRKpU6k8ngzdFRtH71Fb6fmsI32WxY6a3/p38C/v2/x6nV/fHepv+tY6w8U/6oHlB5UdlgXZodeHJgABc6O3FyNeMphkv17yRJcGpV3k8NDaVuD/G2Tg8N4Vx7O04ODq6rS+WE350eGsL5jg6cHh5eh/lIt9Plz8Vwi8tEDAuRX5opzsL/iQl8fPjDYCnHgoEoPaBabyCK6WPH0gDQfvcuqj78ELOzsyhmMqivrw+H3zLbcmZmBg0NDaiqqsIfWlvx0Z49OHL3Lv5iVed90dSET06fxq+np/HzVTnkIgyDqRyX6upq3L9/H4VCIZXZ99tCAf93JoNfTU+jprY2xY9MJhMyKhYWFtDW1pZahKEdeHdyEu1nz6Lv4UM8XLUhmUwG2WwWdXV1IbAArDjyzEjNZDIYHx9HTU0Nmpubkc1msbCwgKamJtTU1IQMDL4/NzeHJ0+e4MWLF7hy5QpaWlrw7NkzTE1NYX5+HoODg+EAycbGRiwsLGDnzp2YmJjA3NwcduzYgaGhoZC5WiqtLM5OTEyErMepqakQGPmh8pMOg4wZTWC9cx8TUlecKqzu1LDO2PPalgqpgwL+1pQZFgV6utLGd8o5GDFlWE5BkjYHv97PWL9iADXGdz7j32tf9H91UByY0wD7SpWmJatRBpAC1jSEWpfyQg2pj3PsWdLFZ5Rm/1x5prxXY6OnhmvfXZ74t/92Z8aVvTvbKhuulPUdAhcHoQ6ilPe6QqFGS9t1OVCDos+5c6UGwmVc++6yrMbexzSmF5wujreCX5cpdeJiMsR39LcH17zvsb74M3+K0tbWlqKRK2UEx0fu3sXVvXvx+t27ITUOWLtSUvnhTjJT4xYXF8NZBMvLy9j02Wco/Pzn2P3dd2hrawtnN9C4cjWM6XvqoC8sLIQ9h0mShL2CCtwpn+qQ6m8dBwe+HFPdXuA6kLpJgxZ0BpQO1u2f+XzWul0nK6AtlUrouXUL++7exejoKCZWAy7A2jWD7HtMllxnKx1JsnKQJw0221V55o86WDGesi/kpzsVsfkYyxrTYIPqy1jAFVgLhKjzwdVz5QFlSXUc29WgktPk817lPeZEKB/5rPKMvKQT6ltAWHQcVdYymbWsHaXP5UwP/vLgm+vjYrEY0lhjgSfnPe2bbm1QfaCAn23Mzs6G1TvaDK5OaZ9ZZmdn12Ezty2ZTAZHHzzA4cHBFcDe0JDincqSjsG19nZ819GBE/39OPbwYXhH+0C+aPt+XS3fc3usbep8V14qzzjudKwpqxowpAxokNhlnM8yEMj/uWIMIBVU1i25LtMaAHFMwn4zcOEBee+/OkQMknFhhfKsAU0GMzOZTJAx0uKHiiZJgpmZmRQNbW1taGpqQpIk2LJlC/bt24crV64AAKo++wyvv3iBo0ePoii6SIPnji18bBULsn/+rut39RUoA0cfPAjyp8WxpPL9+KNHIfvBsbLSfeLxY5x88gSrDF2HP4D02RhHHzzA8UePVuiM2AsPsFDO/Dktmu3nc0YxGudGrP8qN5r5wDY1iKwyRJugWyJ5FWWxWAzZD8wUUj4SE/H8BbaTyWQwMjISzh3YtGlToOvp06fYuXMnPt20CeO1tbjY3Y3TX3+NDRs24LOuLkxls/ioqgr77t7Fxo0bUVtbi8bGRkxNTYVAG/s6NzcXbqBiP36Zz+ODxUVMzM6iKEFt9keD1Tt27EBfX19q6wgzRKenp9Hb24sXL15gcnISSbJyhefp06cxLJkvPHuhuroa9fX16Ovrw+bNm8M22P7+fjx69AgNDQ1IkgTPnz/HgwcPcPPmTUxMTCCXy6G2thYPHjzA1NQU9u7di87OToyNjWF4eBiLi4vYvXt3WDRilsOOHTvQ2tqK2dlZPHv2DNPT0+Gz8fFxjIyMAFi5OU0P0H9Z+UkZDSzljIDvHXPhdyWpf8cmIf93QKFgy+vl/6pw+DcFFVh/XZUaJ76n6Vj6jLdTTjH6aowaSDWm7rw60NDvPQJeLuWWbSvtykdfodC9sTQ8MRqAtatVFKgqPwiCdLzYX46bKjp+piBReawypX1Vmt2oUDmoU659UV7oeCltmjXgqyzKd5ULN2rKe/2bADcmYy4Xamh8XF3uXOZULjjOPl/YL135Ub5T3rRPWtQxUoOlfHU5jfHMi6bcqcPl81B554Bf6dEAiBpgLbE6/hSlUCiE6xUVMJCGzuvX0Xn9OgBgUs4n8AAK5VSDCrOzs4HfPDuhvr4eXc+fo/Yf/3FFBlavhtJAHOf9xMRE4IE68jF9FQvUqaOo+sHnm44L21C94QfJ8bcGFpRnGsz0MVT6XHZUX7pdYv/ogDBoo9cZup7wdnXuaF+on2prazFw8CCuHziAQ7duYX9fX7A/dESch+SP6lA6Bj4OHkBQJ951qo4n32GfdMuLjgGBlDr38/PzIVNHdQqzbWKpzj7GdNQ1gy6GKxgU473invbteIEyVyqVUo6rzyttU+2Jy5DqMQ/66fYmlSv/m+PNzDaVWZUpnSf8XBeAYgE+0q806P5lrgqqAwasHXDmn+sVbjrXAKS2S3KFlP3UoMW5zk5MNDTgYnd36lyHcuOlARENRKu9dfut9sP1BT/X8dSAF/laXV2N2dnZ1FYW1q2reQzGkmbyjY4U63PHnwEr3RIRw4rsA9+PZZUqLap7vE7OYx0jn4/kYSzww7p1qxX3xFN+u7u7U3agq6sL/f39mF1NOR8ZGVmnb3X89W+lR5+LBRq9Li2xZxw7xHC88kHpcpoUP/N/raOc/dP55XzXhSHqIg9GKa7hs9qfGCYsF/jUZ1SuOMasV88U8v7oljzVG64nNSChiwe6nWxqaipsCaXMMbDGlfZMJhNud6ipqcGfv3iBT1pb0Xv7NlpaWlBfX48/f/ECnxaL+OXUFHK5HMbGxvDgwYNwRoPiweXlZWzduhVLS0sYHx9PHRz9aTaL323dit/MzOCXqwc4awCuubkZc3NzuHDhAiYmJrCwsBAyz/bv349SqYTBwUEcOHAA2dUsn8XFRQwcOoSvjx/H9rk5NN64EW6U2LdvH+7cuYMkSTA2NoZNmzbh2bNngW/Xrl0LN0OMjo5iZGQEExMTqK6uDrdxUc/s2rUrZBUlSYInT57gtddeC9shnj9/HrIxRkdHUVtbi+Hh4RBw4W9mOjDL9ceUHx1o0Mi9CqYKkwuSKw39rX/zXVdyPnn0c53MrMPbUKWoK0KxUk6xaz90wvIzzxDw72Pgzf8maADSEW5fXfE2gbTjqCs+qnxijns5Z1YBLIvST/CfJOv3nup4Kz/VQMd4xWfKObMxY6KKWI2myo0aQuenA0YCdX1WaVYD73x1Qx8DzgougLVAl0eZ2ZbzVHkYc7bVIOgc0jHxvmjRoIzOLR0zbSPmnOjz6sz4nNa+uhOmcqDOt/KEssX6fV4qXW7Alf/qFCh/XWf8Kcr09DQABKeV/dfDl/z2APZDT1RmpgEVfl1dHZqbm0NWgqYpZjIr+yUJ2kulUjAWc3NzL9XZNTU1qYOalMdav75HmVcHjkX1lAYzCDoUcLGoc6Ht8DPdM68OMt+N6UKXWZdbDW7xWks9g0L1qPJO54i2p7pa+3L9wAFMNzXh5oEDODI8vC6gqE6Vz2PtF8dH+a46g7S5jqIjrYET9k+DP+6M0/5znnHsamtrU4EOOjMe+HVHBlhbGeOzLO5Mqg7PZDJoaGhY53yyjUu7doU05sOrV4RlMukr8HRbhwcMPFOH7TOV3e2L22Tap4WFBczPz6fsrF4ry3nId+rq6tDU1JQKOml7HiApFouYmppax9NisRjGRHmXJEkIDqgcM9CjWUQ6V7h/me8UCgXc7O7Gt7t3h0P+PM2a9FZVVeFEfz8udHXhZH9/St5i2yI0sKLOOreI+GFzfEcD4IrFdJFFZYz2P5PJhHNq2Nd8Po/p6ekA3i9s346L3d04OTiYujmDtHK7AR0izXBgW6VSKdWmFtUd6mA6RtZgo+pbxxA6Z7lAw7p1LnOc/PBYHz++wzbpiC0tLWH79u3Ytm1bah7U19dj586duHv3LkqlEkZHRzE/Px/OytL5E9Objmd+CIN5/7Uu/dxtTAy/sB3KfyzYqGOlsqi2Ue2KYgy3u1pi+E1lIbbg5XKv/oLrBbcN3q5jddXXMZ+HGIN2iP1TmpgFqLjHAw90/kdGRlJXKGYymbCQwgytHTt2BDw9MzODPysWsef2bQwNDWFpzx7U19fj7fFxbD9/HqdPn0aybRuGh4fx8OFDFAqFcMOX2pG/+qu/St1awQyK323diuc1NfhdLod3p6ZCv4kJzpw5g5mZGdy5cwdTU1MhANfW1oZNmzahqmrlNor6+nps3LgRz58/R3V1NfpOncJcQwPmT5zAkc8/x8LCAnK5HJqbm7Fp0ya8ePEi6BMG02tra/HixQvU19ejubkZU1NT4XwGnqfAQDIPrB4fHw9ZKQyk8KrPqqqqcG5DbW0trnV04PzJk+i5fBmNDx8GjEl96OdOvaz8pEADBcmFkkJCBa4OlSoC/h+b+DoxFQCpU+UANmZ0FYxpdDG2cqJAxOniMz7RtQ19Xg29G3JXoBoBo0HSSelOmAIfb5t/xyKb+nwscqlGw/untHJ8S6W1+2HdIVFFxLocWHv7secUzOjz2k8H3FoHx1XbcTpdFrWvKmsKTlQWvT6l0VccdOwoB541oEVpdEWvfClnGBTw8HPN5OFnajC4R00DcQ6ctf6YIxibX+WUUMxAqwxTl3iwTPnigSyt2/mn88DHw40/+eLBtlddLr/2GvqPH0f3pUvounEj9FdXA7m3j0aZ/3PbQiazsl+RBzdyFY18UP0CrAXTNHuBuqiqqiq1Ak0ek0fK/3JBAwWtQPpGGjoJ6hipzvRxBNYHpekUxORKx9V1kOpUlVHtowN+1dlJkoSMEQZFvD2XfaUrpov4fFVVFRobG3FyYGDlML3BweAQan0xMKoyzXYIAGLXKWqavjpuBEmqD3WeKd06d7hiHAvO8LfSoed4qKxq8Iq3knB1n20zqKZzWQ81S5IkrLhxHulNK+fa2zGezeJCZyfeeP58nT1khpGCXx8nnxe1tbVhn6vOAQXTxeLaQY86B9QBARCuhFXe3+rpwbV9+3Ds/n28PjSUOgtgcXExpB6Tz8xo4UFeKhd60Kvyne/ryhTf49YJD3bS2Se9rPN8Zycm6upwrqMjdZ2oYjXWcWR4GMcePlyRmVV95w6SYwHHOqyb8sS/NZii85u08n9+p2Olc0Jxz7lt2/Dd/v04NTiI14eGcKGrCxPZLM51dKD33r3AT8dqDC7p7SSOaWI2WjMWgLXD+vRZ6izFwHRGVI6oF1R22b46gqpb1LFWu+8yQseHtx9UV1fj1KlTaGxsDHOB7XZ2duL+/fsrK9Xvvov/6/338bOHD3H80aNU5okWx1s6N/3vcr4F33Usoe85plWMyP99a0SSJLi0axe+270bp4aGcPLJk3VBBrc3/rfqS+2v6tAYXnSspv0kH9X2qr5hvTGfyflVjtex79mmYgofD32HckpnWOvltgk924FBBtZFR5r6PpfLYWpqCg0NDSEQwTMHampqwk1aPNz60KFD+Oqrr6J9b2lpCbzSq4Z/OT6Oj1ta8N7YWNCPejA2n3/zzTexY8cOfPLJJyEwQozW1tYGYCUocvfuXSRJgtaPP0b+F79A2xdfhNsnZmZm8O2336YyvWtqatDa2hoClc3Nzbhz5w6KxSIOHz6Mzs5OfPbZZ5ibmwuBao7z48eP8fTpU/T09AS8NzAwgMbGRhw/fhxbtmwJt10AwJW9ezFVV4ehU6fw21XcqNkfnBM/pvzoQAMFk0LKzzhAvkKjk4pEAen0R1Vm2gb/5nsOlDzS6o6c0uDATttSpU0Dpn3Sv0lzbIKrklIFQ9oUUCggT5IkRJK0nzHArRNb21GwrooopmT4fbm01RgY52+CRB8z7T+NrNflMhFTkMpvbUfHsBxgd4PmcsMfj+TGgkYxJ8YNEWlzo0g5oowon5Q2f155oXQ62FUe+mdaj46Bjz2QDqhwBV1vdygnbywEOMqvmIF3/pTjr9KsdGtAI/auynBM77isvYxWrzc2D19l6T9+HLOtrbh//Di2nTuH5eXlcMI5jZZGjQks6+vrw6GNdMbUYSD4Zz/V6dFgkmY78FnlCwMfuhriMupZCDruLjsaGGJRXaS81gCFb/XxoJA+V05WdPsN7ZQ6I/zMddq9fftw58gR9F69im3nzwdg47rDQSX7qzbL5ZN10Fk/NDCAQ6snjefF4eH4Kx+176or1DFS54FAjP2719uL24cOYe/Vq+i+eTPIjv54EDpJVpw0yht1GR10HVuCQupIDTBkMhnc3rMHl3t68Objxzi9mgYaC1iyHwyG6na+JElSQXodb9I2Ozsb7MP+Gzdw48ABHL53L6z4a12kU1P0S6X0dYIqnwz8jY6OruM7x0vnFzOC/NBJBg+IA0hPkiS41tuLyVwOV/bswbGHD1PBGM51thc7bFuLHvKm4+dzRoNNfMcdOPKLmRjk8/H791dO779/P5VmTVkkrZ594CtjHDN1ptW+81mCZf3tz/A354veIqHBNnWQKEOcA8ViMVyVeq6jA4cHB3Fq9eC+4/39oY+UA22TRQ9fZtuKEXRrSyaTCdswlNf8W8dFx1D1l+55V/vvtwSobWEfrra3hwMSjz96FMbEgwHED48ePQpO17Fjx8LKrWKJ6upqtLe3Y8uWLXjw4AFm//IvUWxqwoXOTpxZ3fOtGLKc3VadqsX1B/9mHYpbY9hVf1wO9R09g6BYLOLsrl2YzGbx3e7d2Hf3bhgXZiDFfKLYfIphLu93DCM5z8rZJv3Mi9os5VcMR/F/FvcB9Z0YDvD39Tpoyib5w4MHp6enA67hOXM8OwFA6jYdtke9zfHivB8dHcWmTZsArFxH6dkVDLryVrBSqRTaXFxcxMm5OZx6+nQlM2E100Jv5Zifnw/68MyZMxgfH8e//Mu/4Pnz51haWkI2mw02K5fLBT7kPvsM2U8/RX19PYpJgt7eXjx9+hTDw8OpOfTw4UO89tpr2Lp1K6qqVq41HhsbQ319PbZu3YpcLodMJpPa3k5+0162tLSEjKMnT55gfHwcly5dwsGDB9HR0RHOVvlgYgKfbNyI96emsHHjxhAkz+fzaGhoSOmYHyo/OtCgBsIdVH7vIAuI7+1W0KkDyefZlrahdSigVaXtdMUcGH1Gla3So/WrwtP2ve8K6Ji6qgpOI/ZKixefjKoAvB9KswaCfGVJ3/MgQQyce5Tcv1Pgqu97BNyVd8yZiIEjdR5d2blR1b47T2LPxhSm0hlr24Mr5WgqN56aqhjjsToFMdmLjaMboxhtsT6q7BNoaTQ5xjv/X3moINLpjo27fq598XGIbXnQul2J8pnYnkCtO0aPjpuOpc+VV1naPv8cy2+/jZZPP8Xjx4+DISM9jJQzY6G+vj4c1qirXUzNJVhUcKOr73r3tc5hBQPKYwXiCoZjAU2dO4y667yLzQ+d/3xWAxcvm28cZ9btQQOlTWVWV8lJSyzIwO/uHT2KmaYm3Dt6FBu//jp1ejSLzkV915/Ros8XiyuHAN7eswc3DxzAgZs30XXjRjDo6uzrHFP+cTWcgI1tajBJP7975AimczncPXIEe1f3fyqP3VbpanmxuLZCT5nTgyw5tsD6A3hZLnZ1YaKhAX/cvh3dN28GufeMHB079o30KW/4eTlgm8/nsef2bRweHFxZfZJxVn3LtGe+o/ORdev+YwXIGqiN9dt/q0NP0OZz6vTQEM53duLU0FAI8KjOLuewxIoG1Bx7KG3KRw2m63eUWT1/I0kSHBkexutDQ6lVxqWlpXC/O9vxbFTNPND5pDemKLaivdKrJvUGCMV8msoOrDg23BKies3njgY3isXiyu0EXV04fv8+8vk8Xh8awpFVJwCrc09vqXDb4TaOgS0NUGlQ2XWsjgUL52WhkL7JwlfAVWdrVhnlianVDLica2/HZDaL8x0dqfFkXRyj6upqfP/997h27Rry+Tyam5uxc+fOcDBkqbQSQOOtA6VSCblcDqVSCQ3//b+j9O/+HU4+eRLlFX/c4dVgh+M79q8cVqIsqA4h3922uf7hnKV9YcDwaF8fru7di5OrmWgexNYMKddt2l/vi9MfC6To5+SJ42I9T8jf5TuqE1iHZ2Tr3HzZWCnN/lxsnPlZPp9PbUskbTt27AhzinXfv38fGzduDLjo+++/x6NHjzA9PY2Ojg60tbVhbm4uyIPenPPw4UOMjIxgcnIy6CUG8fT3hQsXUFdXh5aWFmzYsAGjo6Mhg47B6Lm5uTD3yDtuCWptbcXevXvR0dGBqqqqcN4EsHb22NOnT0P7DQ0Nqa1q7C9lk4GWubk5fPnllzhw4AB2796NhoYG1NbWYsuWLWHbA+vkTTQs5MH09DS2b9+OAwcOoLW1FcPDwxgdHcU/lkqY2rsXfz46irfGxnBsehrHV68rnlpdZOFiy+zsbCqQ/kPlf/owSDdw/JylXPaBOxYKFrQubVedR2AtAq5CTmH1tvieOi/ejipRVS5qgNWZ8rr5v/OBho+GjgbSV/G0Pa/PJ6wHUzSyXQ7YxxwyNUL8rQc38bc7Ft73WPTU++HjqT/OO1WI/KEy9LH2bR9Ou7epIJHPqLFh206/B6QI9Fx5K7/cUXDwoKf26tkQOnblAnQ6ttquGmEFV84jrpAAa+De+xEzeCr/Lk8xGfP23dB6UIAAQA27yrYDCBbVRwQMvoLh81PnhoJK7Z8D+VdZqj78ELs+/HAFDKy2w313tbW1qKurQ0NDQ0jzozNCnuhpxgS2xWIxOEexVGRNB9Ztbvxe578HWck/BSU+b1WPxEAb6dLv1UlkmwpudCxiq0QakFLHRPWjyxM/izmE2q8TAwO43NODY/39Yb+mgiWVLxanm3UpYFReMfhx88ABTDU14caBA9h27lyqDg0w6LxnW7q64HKs2yJI78EbN3D9wAEcvHUrlammZwDwJG91oMlvrvgoqKZcuSzEQOnrqzeqHLpzJzglpF9tu8/pUqmUumqPvKBzoUEmylOxuHIwJVeq/FA/dbocVDc2Nq7L4IiBddIIrF2T6QFXHX8H9HrWkf24biYAADd3SURBVNZ9ZHg4HJRYJSvRlF2VVZdDHweOr/bBg+oq1xp40WAcfxcKBbS1ta2TD24VGB0dDf3TO+41yFlOl5RKJVx57TWc6+jAGw8f4vDgYNShYt3eb/bdbT11oOs6pY38IR0E60cfPMDJJ09Wsmok6OY2VeVPdav3j7pXwbqOvdonrS9J0lcjahaHyr/2x4OvxB58x/l/YmAAl7q7cXI1w8rHnzxqamrChQsXMD09jerq6nDwnso/5x4L8UbDJ5/gL+vq0NPTg2R1ZZc8ZDaHYr7Lq/JwavXKWrU1HqBmQNgzq5yv1HPMNGJmjJ8NpfxTPyBJErw1NoY/O3t2pX2ZawzIum3hHFHb4CvD3lYM2ype4t+Oeai39BYx1ZMexHaeU686xnfMpzLK/rFv6gf6gpv7FqXSyoIIg7x07Ddv3ozp6ekwnrwGEgC2bduGyclJzMzMYMuWLejp6QGwcmBtU1NT2ELALd+PHz/G5OQkZmdn0d7eHmTmtddew8DAAEqllS1jQ0NDAFbOvKL8TE1Nobq6Gp2dnWhoaEA+n8f4+Di2bNmC2dlZPHnyBHNzc+FKy/7+ftTU1KC3txfLy8sYGRnB2NhYwG7T09Po7OxEd3c3Dh06hL//+7/H7OwsFhYW8OTJE1RVVWH37t0hoDI1NYWxsTFMTk7i0qVLGBoawvbt23Hw4EFs2LAB33//Pa62t+PWf/yP2PLFF9j57bepLL0NGzbg6alT+Jc33sDo6llFra2t4VDK/+dXv8JsSwu+rK5Gz61b6+yL6m/VYz+m/KTrLXXSsDj4V6LcKHMiuFOj9agx03pZygUkdCK40eBE0ZRcNTD+mTsnnGwKjGN8UKOjkVUqaueZt+WOXjlesC86wVUJsW4fM6dbwVaMVuWXKjVmbGgfXOk4XS6c+uP7E52fWpcaDHe2lSfeT21bnUjW46sA+o4Dy1gQjeCFdPBqGpVXdRo88ODjrjT4dwoS3BEhfTF5chDOveBafLz0M5fFWN0xvnugyINGyleeHK9jqm3zWT34T9vV/X5Kg9LqoLfc/FPn6VWXh//pP2HTZ59h89mzIbCgGQw0ENlsNqRdkz901nkIpDpVOn6xPnAsdJ7r2HE+lJMd12M6r70dfv7g6FEMnjyJzgsX0H71aiog4HZCU24V5LlDojqNKfWasaHPuxNXV1cX9jmWy6wolUo48fgxjj96hPn5edwQZ9fnhr6rQErHQuVXnbjFxUWMjIyg88IF3F89s4POgDoXCki5Kks5JyjzIAT5QGeW8+rIgwc4unqlWymbjTp6mg1RsL5T5vS8AR8/lR//2d/Xh32re1MTGbPYqqLKk6b/c9z5rNNIoExHecOGDbi/fz9uHT6M1+/cweHBwXWHpWr/VS9qmyrnfEdXnWJBJtdzSrNeq+b4SrEG5Sqfz687y8H1PWnj58oHlW/vt84ZnXd8R20W9+vq1h7WU1W1ckgm5yX3RfN7vW1JbZguFH3X3o6JbBZnd+3Cvrt3UyvRpFOvUCXN/FwxDHUn545iUe23jq+Ov2a5OM6LzX86QxxXyrbbdrVlqit0zvt81nmmAQAdM9ZDHmg/+VtpVd2eJEm4vjGTyaAUmcvLy8vIZrMAgJ6eHkxOTmJubg7btm1bt3Co/cxkMmhtbUVdXR3y+TxmZmYwMTGRCnxnMpkQJOBe+ws7duDL3l4Uq6rwza5dyH76aVj91qBVTU0NmpubQ3p6bW1tOLQ0JuOFQiE4oCrXflWov6eyAyAcvEd5pwyq/JDneo218qa2tjbIJTGMYqUkSQKeVCy8sLAQcAHtJecqZUk/1/lI/ac6l+/5Qhq/X7KVbfaX7Wi7Ojcon+S3L7aSx8ViMdyURd2lgQfPTkqSlQWa3t7eFR1//z6GhoYwNzcXMkCTJAnbGVpaWpDL5YId6+npQT6fR2NjY8i2YfZoJpMJ2Kyuri4ExTXTrbm5OWDpnTt3BrnYsGEDnj59ig0bNqCjoyM1B6nzN23ahFwuhw0bNuDhw4fYvn07BgcHUSisbEfs7u5GNpsNAcxMJhMO/6Z8TExMIJvNorq6Go2NjRg8eRKLuRymf/1r/G+rn9NOFwoFnH/zTUzncrjU3Y3Xh4aCrqqrq8Pp4WFcqqnBif7+IP+Kw1TfxHywl5WflNGgIN2dJFXequxIKBmjgqKCpp/RMHLClUv543surMocYP0+X3U4HDz4yozWSfq0Hn6mYMdBsr5TDsQrmNA2y9GggEYVtY6Hj4PXTXp5LVEsIq7txpxjBVGqYBzs6zs+Dt5Hlwn2gUZF+xczBup0eR18V3n9ssnifabCdLDhPCCo18CIAk19XsGDzjHtt8qsjp8D0ZjMKP10VJnSqPzTMfEgnI+HO/6qHxxQ+Ti4/GqARN/T8fLiY6I0qvzFZKncnKTB1LFROXmVZXnrVkz86lc4cP8+Ghoawkoo53OhUMCNri4MnDiB7kuX0HHtWgo862qD9tX1r65eqA51XVoO1PoYq270iDfbGDh0CHePHEHPpUvovH4dQ6dPY66lBUOnT2N/X1+gR9OGk2TlvBpeswesgekYIOG7XHFkX0mHOwAqh0tLS3j27FlKxtSRJYDt278ffceOofP8edSvnjZPPrAUi0WMv/02pj74AM0ffYTmL78MfJl8912Mv/8+Wj7+GC2rh06ps8LMgMbGRrx2+TLar14NfaMTR33HNGmCQKVX913z2j2mjBJQltPZWpjqSzp5OwJ1hgfMCS65qq089qAk6yHo8ZVcHVNPb3Y96TKrwYKamppwEwRBHb//cHXLyM0DB3BmZGSdflRbqKeQx4KNCqg5DuSJzhXlh/aXDrAHor2Pase4z1+zSHQsY/MVQAhCqWOgOk95oHNFt3O4zuQz5DX7r2fMMBUYQCr1mc6k2gt1ikulEo719eHSnj042te3Dkfq2Md0fCylV53LGJ8Y5FDnzHWit6v2U7disE7lIwE/dbtiBeIFBfSKL2L0u86n7KkzoPIYs3l+MKljTg38aFuZTCYcSLxv3z50dHTgzp07qK2tDZlOfF7P36iursaWLVvQ29uLFy9e4NmzZ2hubkapVArB9Gw2i5aWljAXgZVD6YpVVcgUizhw40Y4K4YZVwDCZ+o8c8w0s4G81mAky7X2dlzeswfH+vpw7NGjFJZ0u6JYX7PsqNfZLuVWZUrnIR1e9pW4kbqSGWSUfQbH2Tblh7pGx5tzjzZSsxh1q4ziHbWlej6NZuBqQFd9Psek9N/Y//n5+UCbzhUNxJOHV9vbcbGrCydXD1/lHPPD6JMkCTaOgYHl5eWgl3iulepgtk0+sI8dHR0hgOKLH6VSCdlsNhw6q0E6PsvAGAtlQQPQGtRZWlrC5ORk2Gq6ZcsWAAiHTs/Pz6NYLCKXy6GqqgoNDQ2or6/H9u3bASC1lZa0nRgYWLkNp78/JYscl5MDA7jY1RXOliFN1dXVOPbwIY4+eLDCm1IpJWuUFc0CVR/7h8pPOqOBRR2AGKBQg1pVVYVNmzaFyM/i4iLm5+dDCgkJVkDPwdI7md14UmA0Ih4DDqo4VZCdXgUrzjyNIKojrcCRoIET0h0dIJ3Gq4PstPnfDlJYVywQoAZBnU93QDwi64ZbxzYG2lUu2GffJkB6tT7laSySyjq9Dhps5YEDjFgf9Fl1bv1ZHSv/XovyQ/unY+nAWwGhrzLo+w5oVQ4VzGnftS6nOUYbI8a+bcEDBy6f2g8NoCkAc+DoshIDRyqrdJRiQQx3fvVcF58vuoKgvHA9ExtHlXXt/6suDePj2HftGnbs2JHKwKDxW1xcRP+JE5hbPTBy58WLKTod1LI/dCJ0hUh1BkEJgabzj88pjwhwWCflh58pAAOAj44fx2xjI+4fP449t29j7+XL6Dt2DHtXHWkNqPG3r66obtD+qTxpxlWxWEzdG03Z4P+kld/xcw+M6YrRvaNHMdfSgv4TJ9D1j/+4rm7SOfnBByhs24aJX/0KDZ98Evg39v77yG/divH330fDJ5+keE19xjEhMNN5x7lWKpWCw8xVXF8J177Q4aBzo/vWNZDO//sPHsTtQ4fC4W/kaV1dXWq/uQJstSeUI69X5dNX3Vgvx0LBn451qVRKORGUNQ2Mq+wlydr1r7W1tcGZAhC2bBzp60sdVkgafU+r6xC3FfzcD3iMyarKqeoUDxQ6z3S+E7Qq7tG63WlXOmNnOvAZpUWDh77IozKUJEnYc0ydo8GMXC4XAlRu9/UzlSPSkyQJDg8N4fXhYX64boxVn6tt4nMqTyq3jguUNvZTt5goTY7N1IZ5loiOMW9SudXTg0s9PTjW14eDq9d6UlYZGNStKDr27sB4EID0qm7y4JI+y3rZzo2uLlzduxdH+/pwaGAgPK+YUvm2sLCAxcXFcMVga2srJicngy6nPKiTxe1Yp0+fDgfd6VkfdPL0OkMAK2eVrG6jObawgHxnZ+ARD4llG0tLS5ifnw9jThlRPU/nUvVNVVUVLvX0YDKXw6WeHvT29aXmLPmpC10qywxO6jOa1eyBPc57lz9u0ePfrldYty7Eqq5hpluhUAhp/wxi+Io0+6byrNd+c2sSf7MOzWYg3fos+UOdyvpqa2vDPPR5xXlKXHK+owOT2SzOtbej59atFB9p11i0Tt8OGbN/5KvWQ/o1iKP6k4EXyqYHYSi32hcGEPwgWJYYNt+4cWOon5/p+Ui5XC7cKkS+M2i0tLSEPbdvY39fHwBgWgJsLPvu3kXvvXsrwQPZSk576NtpiCUUpyvNrzzQoFF7N4hKmP7Pz5jCVCwWw/3uL168CO/rPlKmeQLxbRUxwx8rPpH4mace8Rl3ABXAqIJQ46YGlv9rve6scbIq2FY+qTF1HlJBePQQWH+tj/JGjZVGdhlcUIDKtnQlRvmpjosCQgdgTof3FViLvpejWSOuNFYqX0qLByy8lBtLpVkBmgL+2Dgqrc4fb0sVudbpPFH+l3PSY+DIZU3f0faoQDW9PBbMcdCr9fpYK29j8420+pzSog4Mlb8qa9btTvEPjVFsHvF9D8hpcZn6UwUa/uYf/mGlr6vt0SDS2BUKBXSeP4/BU6fQdeFCSt9oH9hXdQicH9ybTlCmc97BtK5oZDKZcEaEGywaNgcqpVIJ3Rcvou/oUey5cgWlUgldN2+i+9YtAEDN6oqE6i6OOUEnZVOv3FMHl/3iCqGCdbcVOtbaTw+mEDAoaH/t7Fk8OHMG2/7wh9S8Yp2U2ebf/x5TH3yAlo8+CgEAAGj9+GOM//KXaPnoo9R8Z5/ZT7Wr/B1z7rgvVeVf9TCdcXfyY3OYer5UKuH6/v2YbWzEd7t3Y9PXXwfe6dkByn/Kh4+HBmLU5vFvd3b4t9pfZiToSqWuYqs90z4SQKpToFstkiRZCXrdubMC4k0/5PP51M0FGgxTm6/06zxR2VB75JhIMzoo367rtagOZLDJM33cTutcVWfFsRD5xkCO2zDqJa//8muv4WJXF16/excnHj8Oq4wsnBfOC513Pj/VFrmsxHCSypvqE9Vt+p4vgpTDBF5i9lWfowzoePmcow6+1N2NyVwOl3t6cHB1RVEdmLm5uVQAV+n2MWNfVP96+x4k0D7ruADA1b17MdnYiCt79+L1oaFof0mvvk+9nSQJmpub1wWe1cYAKwfoUbfs3bv3pUEd/s3zMaqrq1G16kv4eHErIU/95zkNdCYV++uVz2yzuroah27fxvX9+3Ho9u1wtgQdfm4V4tio3i0Wi2G1vra2FouLi2Fcqac1u0Btm28jpBPPwIUvGPhBo+oMEjfw88nJySAvevAogwnaf76v52Mw80MXJ5IkSen0mpqaENSlPaJjzUAX2/ODWmmzYuVoXx+u7NmDI/fuBXpiCxF6/gDlhQEYlg0bNqQCJSqjyn/1DR3PMpjEz8lznU9sm3JCvagLjxrcYB16kCvxGQPKtI2UJ+obtsksD9ddpFfnueoB5QH7TXvJcY5l8Kjedqz/spKUylk4K2+++WYQjhhwixkTloaGhlTH5ufnUwJNpitD1Ji64wKUB1ChY2ZQSa+mmbI4IFWwqczkZFFgz7YUGLqi1wHSNhyAeSTN++Lv+KR1YdLJwvRJXc12I+J8iRkppV0Nl6Yb+XsxEKE8cfo1yqwTS4Gl80M/c+c2Jg9KS6yPWmKG3B0ev8KtHD/1s5gMsDgf9XPnq/7EgJyn+fF7HRflj/czNq+cFv/O5ZT1OggkmKUxdyDobcXk22XDlZ+OF5/3/lKpOxBbXl7GpUuX8KrLb3/729C2H2JLg+qFRovzmGCEtHtabpKkb56gXmUbqnt1K4M7uWybdOkhVwROrItG0W0C5zWj/wrUSAP/V6Omhe8x+k66XA5Vl5cLqHhwWOtjH4AVuZidnQ0nTLtzzfEj2HKboHt1lc76+nosLS2hsbERs++9h4c/+xl2nz2LXZcvp+Sbxl/7SfDKMdTMP/ZfadC5yOdJa5IkuNXTg5urh0N2XLsWQDbbKhaLwV7rWJAHLif6DIGSrzSRFtp5dzj5TsxZ0vYot3Q0mFLN79iGyqWv0ivwIl2xoF2sX27zyROVJ5UXBhnKbQFwXcsx0H7oGNO2c/4r4NXAkOp6HddCoYCmpiaUSqWwXccPc/UsqE//w3/AXGsrGsbH8b9/9hna2tpSWE7t0+joaNBvbmt1XqrsaDBBP1f++iLLy2w2MQTbc3zgmMbbdJwSo9ntk/8UCgXc3bcPNw4cwKFbt8IWMn6vY6VzWx1x1ZVuI/V70qTBTdV1ykv25Vp7Oy6sHgB5ZDWTJCabjt10jNT5ir2vPFPatd5yc8776dhAV9K5FUEDoqyD+90VY3owgEFH1seMFJ3vS0tLQT/RCSRdDGx4Np0eQKryps+4PGgAlnZXv+f/dILp2Csup9xz3BjM0qwI9lttH/vCectgCx1j0qdySvncsGFDyCzh8yoH2le+T6yieIJjznMleNAi+6P+jAaAgPRByI7rPICn7WlwQbGRBlcoVxwTPVNjYWEhBBC0Tceg8/PzYauoBk5pI3T8SKfaCMop31c7T54ww0X1vwZd2B9+rjaSZcOGDcHGkHYNRvzt3/4tfqj86IwGnbDuOLJQmNzJm56eDszWLQoKiD0V0AdGJ4vW/TJgrDRqe+7U8Xn9W42ArxqpQWVxI6WKN+b8aPqgKhvSR35pSpGu4qqhVYOm9BCUJ0mSWq1x8OmGX+vSZ2NjzqKGQvur/GNfy9XHiVEqlcJBhWp4YuNLpREzvNqvmMPvnyt41DZ87JweB64qx7F3vR591g1QTDn78z72rJf9ozLUlSd+5yBOAcXL6Od3GmX2upV2BYksBK4EBDrePiakJ+bUUG5VKWvbsbnh/FXZ8TH8U5RsNouZmZnUfcdsWw2T6gHyKQa21AHROeDP6rhrgIOHSvE913vqvFCv8Yf1ci6q00OApXdV+5xUpyYmSz43XK+rE+agNvY87Y3qOjXyunpAUOlnYiiN6nSzDfIglmXBZwiEHp0+jcWNGzF05gwav/giNX9UT7MOAjOu9itfdOVJdSO/b2hoQKm0lkUIAK9dvowt336LQqGACVu5IPgD0kF65bPPTwWQeoAli+puDYSpbClY5o8CUJfJJEnCuTP6PrC2lU3lRB1O8pN9ot108E8+MEOIBxzq3NLx1/nJPvp4UD54d3kM37CfuhJGu768vIzx8fEU/73uGJbQcRwbG0vRT6CrgTLq5uEjR5CvrUXN7Cz2XbkSaNF5rW3yfa1b+6dj73OE3/vqocqe2zDXFzoXaa8cj6idK4chtU/l7IQ7Uzr+pVIpZNSUSiXkgXU2z22cyj2/JwbVtjWIpkV1v+tF7UehUMDRhw9x7NGjlXYjeEr/VxnWOcO6tH3H28obdWjdzupzjsc8IMFx1eslPROANmhycjLY3Nra2oA3ufdenVbOJT3YE1hx8FpaWoIOoS6tr68PWRTUf5yzDFawXcX4Ok7qQzF7gfpLV9TVyeRZKOqXkX6VEfZT0/LVhpE+BhQ0C6Ouri440PPz84G/nPs+lsSdxWI6g0QXG5JkLZChfiCLOs+FwspWENUHmUwm0Ek+si7S5UFjBoSYUeZ4X28/qqqqwsLCQpBfjqtmu+h4xQJb1NGuC7gNUu286m7aFseAXo9myZMv7LdmzHkghTQoPap72DfaRrXp1AU6Vj9UftIZDe5g62T3gVbFrExXwValpR1RhrDTMQfEFZB/rrQrGFNjov0gA9VYafveX28nRl9MMQLxU3lZNOVTDYgqGlUomjaozgAFSEGUj4c7/e7AODBQQXPQp3zUenXstR0FPPoOJ7tGER1AxBxO/1sBnypcHwuVnXJGXvmutHggyRWBAx7loY9lDLTqc5wzqrRVsfn8YZ+YXhVL+fNAkmamuFz6OPp4Kt0xXeFOCMfG5ZxKju/F+OMy4HSyHnfUlKc6P7XonC3Xxv/bMjIysi6ooKBFx1+NpRpVDTzpygL5x7EsFtNX1+p7mj3gskzgQR7o9pXl5eXUVZpAOlCngIB/ayBD61U5+CHdos6mZmooD90W6Pzjj76r/7OQjmKxmLrf2/VloVDA9C9+gdnf/AaN//zPyH3++ToZV3kjTQsLC6Gt1k8+weh776H100/D/d8x3UHHXVPolQ/eZ/5PPhaLKym+TPMlIFNHToNMviKk5x2QRl0FdLCl8uRBpnLgR+VDbWShUAjbLlUuWHRfuAYOXHbYfsxm8jul1eWVWzsaGhoC/cvLy5idnU3NBZ1X7AuDeToH6UxMTk6umxOq71mfBhu0LwSuOtZuFxRIKz/UUeW5FqXS2p7qfD4fTu9/8MYbWGpoQOPUFI49eoTSqoNGXijQ5bjMzMyE/vj8iWEF1UHUG7pirHNU7Uw5O+9t6aKA81L1Pnnr+NL5qjZJgwLkuWM5lUPVczFspX3x/mvRdrWOGBZ2Pcf5evm113C+sxMnBwZw9MGDlGPjWMCxi+OucnT5Mz8UDFH+xYLmfIdZ0gwyuuPLn2w2i+Xl5SCTHGsGHHSLOPU+da3eNsDfdPKrqqpCAJIZrmrzKMMqExrop26h86u0cN5Tj+hqOp1JDUgoXtArV0ulUnCci8Vi2MbCbAK2z0MI2SfyQuWK9XA8uOWThYEbxW7kDfWKznW3W+SPOsDc+qlzVc+F0ECJ2hx+rrc38G/ym22676ryyOCz6mANDiZJ+mYQPWxVA0GakaTBBtVPyl+lR8fS8Y7KpuNXn3uqd0gr7YyOC22U162Y8ceUn3zrhBKrSkaBjRpyBQD6rCsuIB2FYycUMLsRIh36d+wZtqvAx0GNKzjWp3R7EMMBkxtMd24cSCsA9frcECnPnS4FvZycTI/SNtRY6tjp+KhAqwFVZR0bf6UlFqWP9UEBkb6ndWvbbmCUPi2xSeYG0x1sd9y8bXXKVXlov53umOF0QBOj31PotGhb/NFggz7HucNUYgVNaiTc6VcexQBGDJDF+urKzHmjq94q7/pb6yNNCtbdQVFZ9D6xn+Vk0oNMf6ogAwBMTEwAWNGPGkBUkMTvOZc5T3WVWftKMM/PYhkeHBNG7D0wVSqVUhF4vSkASAfJFEQpcHL5jo2Pjp+OP7/j8w6wda5ouiULDaYGDzQw4TKvRl/lhfV6YMKzPJIkwdxf/AUK27Zh5je/Qfazz9bNXdUjyh/S0PLVV2j9wx9WZNn6rOCSZzBwjBQUqG5XwMOrq1zHE2y7/dK0X+W3y5O+o7zRAANtkdoqDQYwc4SBAZVxrtbp6qAflEcZiNkLdWzdfqn95XdVVSunegNrwXuuxJHf/E2nxIE56eV7Oj4q+zrvyLeFhQUUi2s3iricsX6Oj75PHlF3UK40qMBxUSdEAzqcgxqo5BhxW9DS0hIO3bqFmwcP4vDqmSvF4to1ySoLetYMzxbh824zYmPkukIPDncM5nNMsajyjnLP91RvOn6N2QOfl5RTP9tF+et6jOOvRWkmz1kc07rzEdOLrgtehqfUDp7v7MRENovznZ04NDAQ6qB88X11xrxNl1l1+JUXzlfnk8qJ0up4RHU/sLaNLcY73j6g5w6xzuXlZUxPT2Nubi7ctqOBPeoq1VGqD3kOAMeITjzb1oN86VzyCkcG7T37jf3hGHBrGJ31YrEY2tUDA2mLqYc9uFkoFEKwVBck1Tbq3761UwPcmtXoQUz2g3o7FmDQM2SIc3jFOYMDegg8n+H/ehCvB+30ylCOH9spFAohO12DOKyXdOkNJRpU5//kP+WFWResV/U1dbsHUNQeKUbX7CvKG4PKik85Bhog5/Nqi1kPv+P/Pr98XlKvq3+mdf2Y8qMDDWS2EhZz5FTRstOuILQz/F+FW79XZ5nPsl62r3uGSasewKHt+m+NsscOynPF5oOh9ZEuj3KpU6uK1PuhxkInJAVcFa+OAYFPdXV1AJUKatwAK63qQJRzamO812i3GzxfNY8Vfq7GMGY0lVbSEHvWx9nHRN9V/rqM6GfuxLLoiefaD50PCpj0M+2b8kaj2EqH88b75ulUSguj1LGxLecEKUhSgKfPKr3Oc+exfq881oCk7mXT9C9VgA6WYiBQ5dT760E/LT5OrNffe5Wlra0tGGHVVTRwPj8ITtTgEDwoQKHz7U6681z3YnOcWI/zgHygM+E8dzBZbs7zHdUPHmRQB1YDf3xP56UaZsqjgimVh1ifYqBb9bXyNVYPeZr73e8w85vfIPe736X0E593/V5O1znAS5K1Ax5jQaFy+ppjxFW1mZmZ0I5evcY6lEbqDgUV5YLiSjOwtqjgcqeAxL/jFhCCusHDh3Hn9ddx+PZtHOzvTzlxihM0cy+fz4dMDdWjDBYxiMG+6soh+6mOLJ1rFrd1KicKwnWe6jkrpJfZAh6AVdkrFosYPnIEQ6dPY/e332L3lStBBpjCTF57dgf7yrEh4NWD4LQt6h5gbd+uBgpU9jkXum7cQNeNG6iqqsLN3l7cPHgQr9+5g8ODgyksoVkOpVIpdZOA61+VXX4XCxKro8BnyulnrU/tMvvCE+J1vqjdYSGI1/mg81MDZDG5Jz9Udjlm+tvnl/ZL9UUMc+v7jhFVN8bwrMrf0bt3cXnvXhy9dy+MN2VaaXAeqU+g46NBEdeL2jeVSw3AlsMXzhNtO4YX+N7CwkIIDGlAv1RacVg5txYXFzE5OYmZmZnUgY4qD5QXtTWc39yiqvqZVxHyM81G1St4VUbYD9rLXC4XdKlmIWhAlbqHbTFAqJkei4uLqbMNqEfV+aX86SGxGpRgH9TmAkh9x3MlNIjhWY1850ZXFy51d+PEwABeHxoKesuDGrr9hBkXzJ7gOBUKhdRn7I8vyNFOqI3SBXL2iedN6PeuD5ixpjZHx1GzH1yeOY4aQFH9p/WR3+pjqqxQBjQbkf1XHcI6Y8FA0kWbw6AVv7va3o5LPT04NTiIow8e4MeUHx1oICE6UVSR6URXsOiGQ+tyRRIDsf68CjYHxtOkNLqnA6Crfbp/EUjvIXXHzEGq083v9Hs1uuQRBbWcwlYh5G91EFShFgqFAGrcWVFe/xDtbhyVVrbjho7PKL9UFnwlwd/nZNTzLnSc9W9VcOWejRlfrYfflaM59myMb6qAPbjiPHTeel1uiPx7rcdBh9Kp80TfobLVLB7KnwbAYvXxMw0y8HunV4GMG3sPpKjs0whwvmo/FOTHxsY/i4EK/k95VEDlvFKZ/f+qKBjRFVCmUtPQcmVQHSIHZuwPi6ZlKxByp1KLBsqANUOrhl7ln8bVZVzHgXP96alTGHnnHWz58ku0ffNN1BbwN42ktqW6nDKk46rjx3b9O9bjcytmk/QwPO2PO2v8yX72GXKff76ORuWDt0m5W1xcDGBJD4AjGNbxicmu855ykyRrK2W6ist3XacS7DiQ1vf0f9LJwvmtbSgIUx6Xk9WGhgbcOXwY001NuNbbi23nzqXkiHxUHaZBMj1Mlo4pZYoyQGdAFxjIV11p4o8HJBgQoY3U/dEEk5Q7/s39wDU1NeEQV25Z0cP+yJ8Hb7yB+ZYWPHrzTRzs7w/jomm+Pk+drwCie/kpm7yFRgG6BkkIVJWvKs+lUgnX/u2/xXxzM6739uKtsbGUXdH5try8jGfPnqUCzy/DUm57+w8cwJ0jR9B77Rr23b2b6qPObcedHHfXeZqppCuk/FvHmmPvWZr8XmmlHLi9j+m6WL/5ty9yOW5QXKj90v7q867/VF5UJvbfv4+DAwMrz0TwQaxP5fAN//cFCS8xfezPa336vQdQXJ/H6uEWAA+C8F3O0U2bNqGlpSXFK+cvAMzOzgY9QOef9TG7iY4xbRuzEjg/iYV1pVyxrQbRVf/pajWfp67QjDd+p+fOKM5gnzTwoCvx/I5BUuo8blsgT7VexVUM6pGWm93duL5/P47cvYueW7dQVVWFi11dmMzlcL6jAx3XroX6WGehUAjnFiwuLoYbqqhXqXs88Ms5yW1reqgmgzT6POWCzrUfgqj6hPwgj1g/7QDnFjMoHCdzDIvFYthKyc+ULg0GAAjnUmjAwOVd8Q8zIxTv6lY0DxxR53GrC/EJx/NiVxemcjmc6+hAz2pm2w+Vn7R1gr9JTGyVgs+4gVPwoQoy5jz6wPJvZaQ7NQo+9T1Vlupo8X+P5gDp08RjSt/rdBqcRgeX7oBrQESBcczxIW852fVAKq1DeaA0uOKPgRTvq4+xj4lOIFVcOulJN1egNM1TZct55mPvchAzJr4y4Q5DuTGK1QesTUCNUvrYxXjndPN/P3hFn9HP+HdMHlSOfA4prTFDrO84X3wsnO/ajxivYoEYp9PrcifWAVJszPWzWAaE64GYnOi803ecL3+KcmfvXjw4cwa7zp7FLrvVgkZLlb7TTWOofAKQ4qePgfJc5y6wfjU9pu99HHylS8eNf+fzeTx75x0sbd6MkXffDVsE1A5om1qHrzKQL0A6MKxFnWrtG38rH3z8NWNDV8zZDwVQqsvJd6VRZUr7pbQRKKkt0eCNgkwNXvi85Gqc1gOsBaMVQOpvdah0q4XKAR1PXaEmnSp3etK722H2XfW9p9CWSiVMTExg8xdfYPmtt7D97Fm8ePEiOPbkG2lmEEZXttQpVN5pFgF5Vl9fnwrgETjr/t5MJhPOYiCgY90EfwS5CrBLpVI4cV11JQN/OrbU0wrsuy9eRP+JE+i+eDEEHwGEVF2O8/z8fFiV0rmoDoam8SZJEpyNUmntjntND9ZMF46jZgdlMpmQdbXtyy/x5O23sfvSpXV73jlPySv2PWardO57wCCTyeDOkSOYaW7GncOH0X71auCF2yDX2TpH+FtlBFjbusZ2+b3qQ32P9aotVjnW9pQGt22uM7UuDaTFMIEHQRUnuJ7Rova3HK5Vx8mxrvZV9bbaANWHsXacLzHa9H11mNwG8fsY/32MYrxWG8tnGYTk3KauYeCNAWHOFc4jzmFdHKDNYMYV9d6V3btxvbcXB2/dwoH79wNdfI+6SVeydesF+aXb3HTe6NzQeahOsGIDyvXc3FxqvOhsM0tDA8nqvGp2pdp01XW6pfvavn2YXr1KtfP6dRSLRfReu4Zbhw9j//XrYUy4VYT0zM3NBb3PYEM+nw9b3jSbgDRRP6ouY2YYz0NyXaJzlnxW2SaPON94naXqWf5W+dLAhOpJtaPst+IefZ51qk2gjfdgC+VBs9NUf+j3fI/BEvKB/NF+Hbx1CzcPHMCBmzfD2UQ/VH709ZZnzpxJCXnMWVHFo5/FFKsaFh08vuNKgoxWB8oVukeCFNQ4jRo1dMDDElPcOtixYIY7VNoXCpk7Vd5PN54UDv5ouh7pVrCsxkmNthp1fkbA5KBQFZePpdaj/XbjqMpMU9Bi8uPA2nmh8qNtetTWZS32rvZf39X+af2efeGGVcGDgwafD/pcOYOpshsDUg7S1YHy9hVwAmtGKEary6N/70EV0hkLmGgdbtxID+VV+ae8cdliUTpc5nxu6riW01kO2twIXLlyBa+6NPzDP2B+0ybUj43hz/7Lf4nqNM4Z5YdmgrCfqlO07wr2tG/ab/2OfNE57nW5E6EAlfzyLQcT77yDsffew8ZPP8XGP/4xJbOup3z8XZb42+mO6U3d36+gl85VoVDA9z/7Gb7/+c+x9auvsPW771L2YmZmBpOTk6lgg/JC5VXrV52kfVRe0YDzqkmmu1Kva4YaaaZT5LqWQJHAi4BH21YbGDP5pdLaNh630RroitkKXWlT26rPVFdXhwMFlWamjHJlylOINThBB9HHmWPjKzfsO4PyLG4D+B55qllEdBhIj9bPujwI5XZJ5U5TiR3LuA5zB0i3f/hYevvlMI3vD+Z+aOoVzkWlg+OnMqc6uaamBs3NzanT9PU58n52drZsBobPGZ3bA4cOof/4cXRduIDdV6+mgo2O40i/BnHUgSbfXb6U1/63fqY0k/9qO912kVbX7z7HdEw920b1rdbP/1Wfum5i2yoTGmhRvBGTHT6jeNDxlpdyutvtm9fn80rfc7ut7bpd8+fUPjpNrku1Dscfjs80SKWZSer4Ly0thXMf5ubmMDc3h3/4m7/BdFMTcpOT+Ov/+l9Tekb5ws+pn6iTOHcVVxHHkx9A+rwnOteaBs9nkiRJXZWtusHnST6fR9/+/bh18CAO3rqF7ps3A606hooJXI/f6OrCrYMHcfjOHRzs7w/0Eu/wnAPlt+p99S2cNtqf652duNbbG7bg3dm7F1f37cORu3fRe+9e0APEArqNVOtVmaSuJP80OJzJrJwJwS1itAsarGAwQPU0+cyANTNd1YYwSFQqlYKdpM1j31VGSK+e8ZAkSein6w+VV5XBJElwq7sbl/fswet37uDQ4GCQQcpoPp/H3/3d3+GHyk/eOuFKwQG7R2diCiemzFSoVFh1UHSSaSYChS6WDqr7+tTwsw7tg9LIutkPLRodU4XNQVM6yjmzpEnr9+cI2imwKuTlDJ72Jxa9UiDs/PMxduWq7ZYzBGoMlQ4AKfCsfC6nTNyAsjhwjwV89Bml0+t2XipI5fN+CJnLufaDcqj8UPmJgUQfw3LGUuXWx4NKXR0DV2oxHpSjRet0WfNglcuIgwoHMOqw8bcqVu9/uc/0O5UFbVsd41hd5RxWb+dVl93ffosHP/sZ2ledW52rmpGgxoZF57DSqgBFecXnlS8v3nxzZTvDF1+g7ezZlEHiO14vjWKMR4x6+1zIZDJo+eorNH3xBQCgEOGn2g6XGcq9t6X6TOeXOqV8vpxtKRaLePGLX2CxrQ0j77yD1j/8IRhbrtDrYYbudDg4jzmz+plm7vB9pmuPv/02xt5/H5s//xytf/hDarWKhSn3CjT5W4EH21YZodOl9HBM1a4oH9UOeCBIHTTlC38IcnU7COurq6tLyU8+n0c2mwWQDoT6gZfcZuJyqaeJExwr4OK80lRj2m5NO9aT05+eOoWRd9/F1i+/xOazZ9dloRH8qX0n3bq6xL/1mk++FwvcPj5xAo/ffBM7v/4a286fT8k6ZUgDOVylZKaDBkwU2FL2de+xAm2+p9stCJZ1vmj/lbZcLhfOnSk31+bn51MH9Xm9rldYR/vVq2i/enVljFflin2mo0dZY7+TJEmdwk/ZYtv87dhO5Sr2DkuMVn1GsznVtsQCRfoZeeG6lGNPmlSHqP5RDKS2UXUgdaR/H+NH7HO1I1pIv9tU1d0xvO12W/G1BvxiODqGmbQ97av3yXmkfdN31MZwDDlGnAecR0o3n6+trUVNTQ1yuRwWFxdxanAQFzo7sf/mzbDdggFP19f5fD4cFKuZWdRd1dXVyGazKBZXDiL0hUb+r7aMmQKcI+StrmSXSqWQ/aSYNpvN4vbhw5jK5XDn8GGcePw45UuRZvadvKmtrQ3Y4cTjxzgzMoLl5WXUNzYGvcvtJR4QYj0a+FA8RBp1a9n1/fsx3diImwcO4NDAAK7t24epxkZc7+3F4cHBlJ3kGLGf1JFue1W/M3uFhfXx+mi+ywwV9Yl0ntBWqYyrz0N9DCAEYCgvDQ0Noc+6CKF6Qs+g0EUK0sG6AITDM5khkSQJru7bh8lsFtf378fJp09T/NJAxg+VnxRo0EnsKwuxwk6pUXHHnMUDFO4wKA18nuBBgaqm8yjzXSkpaCCt/N6dVFdCSqd+risuDswU/AHpqHVsTzoHvFRauZKHgq6Onyr/cjT6d268+L0CSgX9biTVidUMEv7W+mks9U5zfsd2+LcaQvZNx6xccEGLyoZ/rvWoPDqoUd7rgVtej/PXadcSkyetyw2xFzWYXr86ZwRb/vnLnOmXgaUYKPLvY5+zHjUanB+eyqfFVxadTucV+6ztx+hwx1t54ABXQYcC61ddNn/7LdrOngUATK+mMDvdMXDE/qqSV32jIMeDRDoWI++8g8XNmzHy859j49dfp3Sn6l8Hah70I0/pvGUyGUy88w7G338frZ98grZvvglt++qjB1hVXlVv+rxRfcb+Uh/5nCXNGsThu8ViEVu+/DIEXCizGrBj2r4GHJQWlZFyetQdBQUWDN68+MUvkF/dXlLzz/+cGjP2QduI8U5TwPWgQNWxBDasWx1Gf075rCCW7+stGK5LSfPS0hIeHj2KgZMn0XH+PF67fDnIC1fRSLPeia5AXmVCZTBJkrCHlgCX/VWApcEKBcVKr2ZyhPmxGoDafPZsyuGuqqoKwBRAcDBIU11dXerueDr0sQOqGdjiu0/eegvzra148vbb2HfvXljhoqxwVUoDe8Q5fsUc641lYag+cZ6QdrW7ejq8yiLbGxsbS60s6jyi00I8o3qAdQ0fOYL+48fRfekSem7dSvFaV4sVQ3KuE3d5wDFmEzxA6VhQ9Y3qC8UOXg/5emnXLpzv7MSJ/n4c7O9fNx8U88Vsq34ew0Tkgx8CGtPH3m8WtR9eRwwPqH3UOsmbGIbXZ5SWmOxzLH0Fm++ovophXP+sHK1uQ15W3PaU46lnILgseZ21tbWora3Fu5OTeJdZkt3dqVXmhYWFcBsQV7rpAFZXV4cDK9WWM1ih5yrpNdm0US7jPm84BppVxwABdXl1dTVO9PfjUnc3Tg4NBRvJNkmb6lNuK+NnSicdVmbjMSuK/HW5ZD/47tzcXGqLe5KsOPtH7tzBtf378frqmS7HV2k+1t8f6GNxvUfaaJOog5l5pzxMkgTz8/NB/zKoxKBKoVBIfa96Vq/m1HnPrRicY3rrD/GCBmc5boqdSStxBt9hv9RPZp/1oGgGn0/09+N8VxeO9fUBWMsiI80xHRMrP+nWCR0YBSux1Q41QlrUgeQ7+rkqBI3OufOhtBBYkNGckDFwon1xIFvOGLujpe9pBFf7w+fZN//O/9a6qSQ0AqUpOaRTlUSMb0qD0x9TjhxX5ZuPl6fgap0eKfMAQ4wer0uLgx0fH29f+eBGT8ffgb/Wx/559oi2o3KqbTnd3p+YIXeeAGtngyi9nvLGz5X3KgM00kD6QNIf49A6nfp/zGDHAB2wBrDV6eJzviqtxk+dQW1XQZr2zw18ucCKfudyEnOoqVP+FGVhYWGdzMVSehWcJkkSVtsVhCXJ2jVR3k8gDeIIjlo+/hhj772Hlk8+CScqq2yQxwQjGoHX72i8dOyHP/gAy21tmPzgA+y5fXvd/FHnm/1S2lWuWD9XyFXfuu7W/nLs8vl8OBSRhpqlVCqh6sMPsePDDwEA4yJfygM1yGqH+IzqSM4zdRTYdzqHHsjJZDJo/ugjTH7wAVp+//sQ3OA4Kj84p5QOBRvkDYGOrpT4qjzr11UyTQUmfa6zWXjVI3WTglsdv75jx7DQ2or+48fR+PnnqXHSW3EULDOTT+VKVzV9bqu9og1T3rMtzivNyNiwYUO4pqyurm5lhfDGDfQdPYrua9fQ2dmZSq8lsCdNXLEimOPKHPtEW87nddVM9d7S0hLav/sOw6dPo/3cuSCzeraDYi/yKUnWViOpI9wGcWVM5Y9zi0EB6iHViRrI89PdtSwuLmLz5s0pHlOmdMwmJydT9o1jMHjyJGabmzF46hROPnkS3qMs+tzzoIPyMobR9HN10vicyrn3T+cx57yfP1UqlXCuvR2Tq9dDdt+8mRp/rct1ltLtbfl3Xg9lR4Or7I/3T7+L2V72HVjLBla8F8PuSi/lxlfV+azTR14qbnTcFMPeqmdZl9oF7ZPjFg3wKj7Son3UBVXlkdZP+VKcqs495+zc3FyYGzx0sqGhAblcLrSZzWbR1taWCvDNzc2FgGoms5KRwMMDb/X04MaBA9h35Qrar10Lz1EHagZVXV0dmpqaUniKOKSurg6zs7MpR3V5eTmcF0C7s7S0hD23b6Pn1q0VfQCkDjKkbuMhmfX19akDOKlnyffZ2dng4C4tLaWu8CyV0tvha2pqQlsNDQ2Ynp4O2GXDhg0hUF0oFLDnxQscHBhYeTdJ0H3zJvbdvYtMJoOl1SwR6v2pqalUsJ1jRdman58P+lkD+ewT+bS4uJgKYLAftBuqf1QWiefYHg9f1EAL+am2nlld5J3jYT1TCACmp6ejPoDqA8o3r2zunJhA+7VrAIDJUnobXSazsl3kx5QffUZDpVRKpVRKpVTKD5W/e/wY//nRI/yfu3bh/9i581+bnEr5Vy4VeaiUHyr/K8jI/wp9qJT/f5U9332H+/Pz6KmvR9+ZM//a5FRKpURLJdBQKZVSKZVSKZVSKZVSKZVSKZVSKZVSKa+srD8Jp1IqpVIqpVIqpVIqpVIqpVIqpVIqpVIq5X+yVAINlVIplVIplVIplVIplVIplVIplVIplfLKSiXQUCmVUimVUimVUimVUimVUimVUimVUimvrFQCDZVSKZVSKZVSKZVSKZVSKZVSKZVSKZXyykol0FAplVIplVIplVIplVIplVIplVIplVIpr6xUAg2VUimVUimVUimVUimVUimVUimVUimV8spKJdBQKZVSKZVSKZVSKZVSKZVSKZVSKZVSKa+sVAINlVIplVIplVIplVIplVIplVIplVIplfLKyv8AlHXM0R3S/v8AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_images([img0, img1], ['Image 1 - detected points', 'Image 2 - detected points'], pad=0.5)\n", + "plot_keypoints([kp0, kp1], colors='c')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CtkevloydpC-" + }, + "source": [ + "## Matched Lines\n", + "(Each match has a different color) " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "oTmOvqOldpC-", + "outputId": "7d091385-94df-498e-fea4-0b5032729cea" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_images([img0, img1], ['Image 1 - line matches', 'Image 2 - line matches'], pad=0.5)\n", + "plot_color_line_matches([matched_lines0, matched_lines1], lw=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kfXg1clhdpC_" + }, + "source": [ + "## Matched Points" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "6Rfv5FvOdpC_", + "outputId": "1af0439b-77db-4f55-f7c8-c0736cf7c7aa" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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ooICzD9+JvsM73/lO/MEf/AHe8pa34Od//ueHpqXwHdYGhe9QQAEF/FuAIqPhWwCvfOUr8fa3vx1vetOb8PznPx8PPPAAbr31Vlx44YU95f7Lf/kv+F//63/h9a9/PX7+538eW7duxa233ppt5OOiyL7v473vfS9e8YpXYNeuXXjTm96Ebdu24fDhw7jtttswMTGBj3/847k4ffzjH8++xd1ut/GNb3wDv/M7vwMAeNWrXpW9LThbsG7dOlx33XV405vehOPHj+Pd7343du7cmW1WVSqV8I53vANvetObcP311+P1r3999omq888/H29+85vPCh7XXHMNAODXf/3Xccstt6BUKuE//sf/mBnrsw2vfOUr8dd//deYnJzEFVdcgbvuuguf//zne9bXAqkhDoIA73jHOzA/P49KpYIXv/jF2LRpE971rnfhJ3/yJ/Gc5zwHb3jDGzA9PY3du3djZWUF/+///b814fOjP/qj+NCHPoSf/umfxm233YYXvOAFiKIIjzzyCD70oQ/hM5/5DL7ne74Hb3/723H77bfj5ptvxnnnnYcTJ07gz/7sz7B9+3Zcd911Z5NFBRRQQAEFKPCd5jv8wz/8A375l38ZF198MS6//HL8zd/8Tc/9l770pdi8efNZ5UHhOxS+QwEFFPBdDN/aj1x854P1iarR0dG+stdff32ya9euvuvnnXdecvPNN2fnjUYj+cVf/MVk69atSa1WS17wghckd911V3L99dcn119/fc+ze/fuTW6++eakVqslGzduTH7xF38x+ehHP5oA6PkcUpIkyX333Ze8+tWvTtavX59UKpXkvPPOS173utclX/jCFwbS6T67pf3JTyw9VXCfqPrbv/3b5Nd+7deSTZs2JbVaLbn55puT/fv395X/u7/7u+RZz3pWUqlUknXr1iU//MM/nBw6dKinjPWJqv/6X/9rX33nnXde36c7f/u3fzvZtm1b4vv+wM9VDdvHFh6nT59O3vSmNyUbNmxIxsbGkpe97GXJI488ouL1l3/5l8mFF16YBEHQ97mqf/qnf0qe//znJ7VaLZmYmEiuvfba5G//9m8H4vnGN74xOe+883qutVqt5B3veEeya9eupFKpJNPT08k111yTvO1tb0vm5+eTJEmSL3zhC8n3f//3J+ecc05SLpeTc845J3n961+fPPbYYyavCiiggAL+vcK/B9/B2V7rjz+x+FSg8B0K36GAAgr47gcvSZ7ibjcFPO3w7ne/G29+85tx6NAhbNu27duNzprgX/7lX3DjjTfiwx/+MF7zmtd8u9EpoIACCiiggH8XUPgOBRRQQAEFfDuh2KPhOwzq9XrPeaPRwF/8xV/g4osv/q5zFAoooIACCiiggKcfCt+hgAIKKKCA7zQo9mj4DoNXv/rVOPfcc3H11Vdjfn4ef/M3f4NHHnkEt95667cbtQIKKKCAAgoo4DsQCt+hgAIKKKCA7zQoAg3fYfCyl70M733ve3HrrbciiiJcccUV+OAHP4gf+qEf+najVkABBRRQQAEFfAdC4TsUUEABBRTwnQbFHg0FFFBAAQUUUEABBRRQQAEFFFDAWYNij4YCCiiggAIKKKCAAgoooIACCijgrEERaCiggAIKKKCAAgoooIACCiiggALOGgy9R8P9998PAPD9NDbheV7PfXk9SRJ4npeV4d9hj7X6uG4uwzDomjuWK0iGrcc9J3EbBIxrkiTZn+/7WR2yvTiOc9vPA9eWLKe14a5JeuI47sPdXdP4pvWBbItxleWZRqss42fxnfvTwsPCWXuGy1q8lXhafLfwyyvHcqOVXUu7DtbCe5YTWUbKhoaLVi8fy/Hg6mI5tNrSnpfXHK3D3JfwAz/wA308e6pwww039OHhxr+mA33fz2h2ukLrT8kPp5PddQbX75b+4ue5T2UbPBb5OuPIx1yvHOfcNuvQOI57cJXyInWFxI3linmg6TfXBzzGNVqYTt/3EUURACAIAsRx3MN/1i/Sxrl7QRCossttWfgyaHrK0jmWTrN4x7Rp7VnyY9k6TfdZuDH/NL5Y9zV7oPWNRZe042wv8+hgkLzR2pX1uTpcfztZkTS4OnksufGj6WpXJoqiHvol/zS+RFHUI69MvyVz1hjPkzke/7IPuIyjxZXnscy0WXzjvnR9JPteo4evuWelruNnNTlzz2n9LOlgPJlHbEedreE65ZjRaGFcZX8wSF7JOtwx84z71sLL1anpVfmsVp/GMzeO3JiS+GrPsB5n+vL6g+vm8eVkWNbr+t7ZFR6PclxYukbTMW58yD6RsqLVyTLj8JM0y+eYT06/sPzLY02+NLoG2SqJgyzv6GD97eqSY03qF8kX9lVlu9x/st+sMSdBs23Oj2D9reloiS/rPB4nnuchDMMeP4X57I4/+MEPqjRLGDrQoBkKed26xs9bzoRmXJgZWjtax1t48zN59Gnl2fhoRsE6H9SWxjNuhwXCwlNrX6NDltMEj5+zFMogOiXOfF8zbsPwTasvDyeLFxo92jX3XJ7ycngNww+NFpb9PJry7lvt8HVLvphGHpfsXFg45Z1r7Vi8s+Q1r4wF7BxoTo3mnJxtsJS8AzZK0hA6OhhfHm+Sx7I9V05zBmUdWlCR+4uNM7cnDWCeDmCniOnWdD47dO4a0yKPWYcz3q4My6bks8PPMr4aLrINzanSdLzWjnQW2Mnm8Ssnh4PGljWmmQ5uh4H5z064vCefz7PfeTbRskdsv7SxxnJn0cAOJNtg2V8O5PiVfZZnQ7SxlUeThotsU/JTexGk8ZjxsvwNyXftfhAEfbyX9UkatECBbEMLYnLbjIN0/mUfsizk2Uy2FZYsMu2abdVkyl23JgmaPmI/SQZNrKCB1BEc1GD5lXJq8UqTPaZdtiPpdLJh0ef6zE0+2Q4xn9xkTfJN9k+e7tLGNcuWNqYlsM3TdI3WdzwGJO1uPPA45cBFp9PpoUniZOHIMsx+hmzH1a3VqfUJjw2mjWWC8WWdJWmTk2m+B/QHNXhcM05Mp6RF8szVzbQ426qNcx7f3J7EWdKh8TDP9lnyzbZxGPuo6RYOMjBvgd6XOnmw5kADKygmTiOYkdOEgK8xs/Lq0p7N6yCrM7XrluBruDD9WhsWcL3yWl5HA3qEdlAbFm1aHRrNmnMhjZSrxxosGi3D8llTmIPolZCnlIfheZ5cSFliYyLL5l3X2s2rS9KhyXIeDJItiz55zSqfd9/hbNXLTkNev3AbPCnUjId0zjTeDatAzxQsB5npk46ANAraBMmVB3qNmFavu6a9cZX3Lb0u71kZV3njiOXZHfu+3/fm3noTorXNziOPR3nMOsXSaZpd0aL88j7XlyeLg8aQlFF2VOUEcpC9lbSy0+rGjDZ2tIm4pMnx3cKDx6gm85Z9cbyW8s/yIJ9hf4Gf1Zx3y1eQDvIwdpj5455lPkh50bLJhuEht8Py4WiTfaVNorSxxRMNV4fm7DMfZPsyoMZjzsqIYFqsSZemP9mp5gm41Q+ybc7ckP2R5xdZdl+TVXboHZ3cHuPmMhiYFu1c0shjy/UlByasvrXGrtZvrCtZ3mQd2jFPjphGHj8OZ01PMg6aHmQ7xHVr9WgBA4mDpkM1HS/Lu7pk9oRlVyXdw+gmywZqZR19kjbuC20sWjpWw022m9cH0v5ocuj+rOxWxk0D1rd5v2wLmZeDbDrzUupVWQ/QP6fT2tZ0hTZ2tHs8ztj+a0FZB3mZORasKdCQx3QWXE2JW0bUaofLWAbAAk3BMo4OtDfqGv5ssFmwNGVmGSqNB5oADirL96SByzOQeQpQo0XSqeHATgr3EeNq8WPQcxod8pwVlawzD4c8uhksxZeHs4WPZUxkPdqb10E48fVBRoZxstrR8LTwHgYvxsGB9Vbb3dMMvyvPTqb2VidPdjSjdTZBG0fuVzqUDldOs2NaNN2sBWqYj5oxcufyzZMmp5q8a8ZQ6gfZpqxDi77n6WxN73KmgIabxoc8OXRGV8oE85r5wGPV3bOcx7yxztelHEjHmoFlOG9sy3Pp9GhvZTTaJa+0fpGymoejvMZ9zY5OnmxofWv1oSxv2Xamm5+T7TC9Ug9pk05Zl9WOZScsXlm4833LwdXkTgYZ8mSeeaK9NdYgb0xob8fz6NN0N/cJy5ajQQY5LT8kj8eaDZFlBukTBpZpaxxrz1q8tfDTyrK+Yvmx7Inmu0re8GRc1u3snyWr3B+DdI2VHZjXf/K6xFmj2Z1z8Cwv20pm2mhtyzJMn2ZTuX72h6RPJK9bkCdncmmFpvNle9w2420FEpn3zDv3LJAf+OG+lvQkSdITuJNtMC+07AeNZxqeFn+tvmM8+dy1bflMLEOWjmAZdrpJu5dH9zAwdKBBTlzdLwuAvK45AnxNPsv1ATaj8hSzhqsmxLJ9rYxW3ro/qH7Gn4WIy2r0aYZdPqM9Zw1eq04LWJHyc3n0yzq4/UHCnGfotUGtyYllSLU+0XC1BrGGP9M2yIhr5Sxe5uHBNHM9Vj9r9cjr2q+8rxlAxplpcWU0Z08D17fsrGoyIuWUnQNXhhUpjx/WHWyIzhY4h5Zxd207XNkR0XToIFmQ5dn48QSBx7XEB1g17nzfMpzyN288SDnL6zM2pPJZ7iuHU54eYbysMSppsRwjVxfLqqY3Jd5aoEzyW9KsOS+DdCrLFePBwQXJN21yzP1o4ZSnX+Qfj8m8N9ASFy0QkscP7u9BtktOeKTcMw3sVEoH2MoY0mRRm1A70HQejzltvHJ5Lsf4WG1YY1yjTdLO+luORSlrss9lv8rghkW7phessWnZWzlOmYeD7Jwsk4eP9jzTInkl31pzWxJXLYCp2Wt5zPdcX8ilGFwX456nBxkk/e5cC0bIgJzWzy7YKmkfNAZYT2j1Wllw2riXPNHa0/BhXjha3a+jTeMH37NslByr2gsIxwspL5bd5jbYD2T+Otlxz7tr7mWF1PVsD12fa4EZqRus8SfpGOYFq9Zenq2SfeFoYr6wXLjy8h5nCXD9VrvynIMqkt+8jEjDQXuWcbFkSQLzexCc8dIJvmaVkYhbzGPB1a5pz3LnynY0J8uBZmjz8Mx7fhhFO4wB0PCxMicsY6nVqQ0eqz1NUcrr3B9Mu3ac1/4gGNQPEqxUd63f5HVWnoPay1NIWt8MY3x4YOe1n+dU5zlEWl8Ncjzz5AoYnGXBEVfLIcijdZAzY411Szew85bH76ciu4NAa5fTvJk2+ac5IMxfzRBYsqbRypNcSx9L/lr1OGAnx7Ut63bOrrwvHQges7JNbUmJfAOijXPL4bB0mHT0ZT9p/LYcYXku62NcrXGr2QON95oOGGSvZFmLZ0wz42TVx33GgSvZjpYNIK8zjayrLJ5oekKjSdat6Q55LY9mPpbX5HUrK0Des9qQ/WTJRp7dsWyjxrs8f8bVKYMMWhmt7rwJkOXUanpIPsP0aONHq0drQ9MRefhI/lh4Mo9Yl2nPAuiTu7xxLe2KnOzwr0Z3np8gcRkUiLNw4La0NHitT7UsGStAodHDukKCNv5kHazLuB+sdiWusm6WR67fokkGpLgdq27GQZN/DUepm7QNSPPssbRvXKfEUZMz7a291h73hTU+eX8QSaems9ge5eknvq7pYnmdaeYxrY2RQWNN0uKuaTx01yy7oo1Ny54NA0MHGjSCJOSlWWrIWoo3T3laBkArYz2nMS2vTqvTmVb5fJ4yse7L+jSlpJXX2pBgvfEZRkjyFCPzU1NmEgdtwGhptJYjpOGUJ/CW0udnNCOm0ZbnjOTJQB6f8vCwwDK6WjlL2bFy50mfRZuFn8UTa6wyLXm0M35c1hk8VrysWK03xlq7eQ7D2QRNpjg1Wd7X0gOZH9rbZ7kMQ5bjOmTf8TWNV1p5SRvLkNb/0rBKZ2QY2ZH38uROe5ulgeQ5p8g6OcyTI89bXd4y6E2JNjY12+B4JN98Mb6WHLvn2XGS417L/OMJKweJXJ2DdBbLgyZTsqwm81KOmRcSPz7Om/xLnNyvxUNrfEgaZBucEi/1rXxW1qFNxDWnXDuW9VuBOM2mcp8wfswjjWeuTQsnzW4yrxzIjdyYz3ljX9LLNPAY02RBG8tMn5R3jUYGHjO8kZyTT25b/vHyDaaVr2k0u7Hs3txq/HLHWj8yT1jeGG/tOSnjckxo/JY6h/kpv9Lj6tP8R2sMS9B4x/Rp2Q2D6tDGQd5YY9qZ17Id2Z9anZrsSp7wc3l4O5D2wZJzd11mMsiy8i27ZvOsujX+yHHF8stBJ013WHqDdZomhzy++IWQBDm2B2XbOJl2dbF9k+1qOkjySGYzuDKajrJkUPKM9ZH2skoLXuTBmjIatPQdRpyFgOvQCOVO57q1AWMNSgt3LqO9CcxTFlxXHlhvcjVFrdXNg2qQQuE2uC5NQIcRQL6fhy+X0wRU4prX35YxsPDTlEIe/jygGG8Nt2EG1CBjzbhoEwBJRx49Fh+GLS/LyonVoPoZLD4xLtyGZVy5Xo4u87GGn9bP3Cb3twRt7d/TBa5tfoNtybSMums8s3gkx6Q0Ilo/MG+tMcD1D6M/ZX2Sbu1cG6cM/LwmTw4PF3BhepkXViYH84QdMXfORt/CR3MGLEfJnUvbIPtTPs9tav1rvT3h8Sp1OE+kh3mbz/3BaeHaGxr5vKTPOWZBEPS8XXKgjQnLrvFzzHuWQyutl2m1xrAmbxouectGNPvgjh1+3EdcF/NG86msOpjn2mTEylZiHmh8dMdslyRI/SUDsxIfy/7JtqR/otli6xkpH8xLx393LmVB8oE/R8hvH/k5ro/x0wJwzOc8O63xiOVdk0XuY0kj05t3X7smcXHjXvaZxiPLTki8ZR1aSji/1bZkw8JZyq7ks6WrtYCvfI7r5f5gnFhepO7Sxn+eTOXRKH9lWS3TxBr3eXVbes+VscYht6PJsKZXHB1s/5mfTj4sf0S2q+lyppv5nDemtOuWvWfaedxaY0LWw/ZBCy7k6VoJZ7RHAxvNQYbEMnIWstqAlvVoxDFO2jUNz7wOs5hoXdeUgfy1BEmjPa8NrjOPl3zN4nXegLeezeNz3kC0+sN6Rutbi0dcF79dtOq1aOVrg/jNg1t73h1rfZg3iK26NNwsmdZgEH8YL4a8tnm8snHUaJBtu3otgwrYazAB+4sIspw2BmXbTwfItzSWDLlJVZIkPRswWX3sQHtTbdHljIx0ePJw4nPZN3k6w/1qS1YsvKSh0+pmgylpZ3wGOaDMU5Y7pkN7a8s0am0xjuyMDMpakOeak8LPuDq1e5YT52iwbAPLlcVnC3d5X/5Z/JX4DnrLzHKt8UfS5cpoEzWJD/NI8pfLsGPPgUtZXmbKyH6V8s94cV/LPmA8ZPsSJM+477SsN8ZR8kbKjBZ8svqc29XkQ7NB2jXLjmh0u1+rHp64M1i0sZ3SsgU1/DiTTVs2xWOI7ZYmhzKbQpN7V5/Gqzz5knLvggha4EXy09UnZVLDxQG/hdfGvRzfrDcsfnN5+YyjI+/TrLK+vDfG1lhhnFhPa+NQltfo0PpQ09mDfCH3K/fAsMqxbObxK0kS9ROReXU7sDIEJN+0+1q9sm7N/g3Sb1obGp+0sZinB+QYYB+M+5B9Bm7PGtuyjNT5WkagpEvLYNLoGwRntEeDNNCagOUNLlmHVtYy3kD/Gqy8NmRdmkOl0cN1a3UCgwcKH2u48kDJG4QWPzVlwm3JMpoxtpSRhj9DnsHKG1ha3XmDlw23VkYDWVZ7SyN5r93T6NX6jPG06JDXLX7lyfOw7WjKkx0HWY5p0ngky+cZu2HGJusMCydrkpnncDJIJWrxUKN/UL1PFbQ+5IkIf6KS39hZekWOS1dOc8qsftMykbTx7K7Le5YDIvub+1VLVZU0yjp5HGs6ntvj/tT0noanuycDQlpQS9JgjXnXB5w5pI0l7m+uRzuWdfBEUU6ctDdtsk3NMdHalG+35YZfzBMpW5be0Pgt7zMfZL9KedKyYfJolG1Y/gg7grI+DbiftWAHO8+uvOSX+9P6y+GXFyCUuoL7g/WKxRMH2tsu7iuJk1YHwyDbaNkRS24tncZ6Q5bV7DePIQkyiKLpnmHkKs/XcOWH0WsWLy2eWfo9T5Y1HJguniC5OjlTyck8fz6U65PXtb7V6OaxzWNskD8i23fHPAHT+m1Q/exDsR7V+K49o8mrlcmnZYjltctZSsPKF5e1rmsZKFxeq5vHa15b2vjLA4vHlmxLPvI1a1Kv0ajJkCwvwdIRzBPmD8u9NjZYbqS8y+c0faEtwxkG1rRHg2QAK6E8JcDXNcWlKQ1HjNUR8lczPHmGiunR6s7jgaxLUzDawGXB4Gt8fS2DxzIEg3BnGvhNEtOnOSAWrprgar8S77x6hqGfQcoFD7q11HOmOKyFFgs3q0+tsSXr0N6iMe9lea2c9gyPGz62ZNwaM3nlWF6scSHLaG/xLJ5x++x0PJ0gvz7h8NF0qjzmSYSFoyYDbBD5LUGec+NAe0be03CQ565/GH9+28X4cf3MK+aZ9YZDw8vxVFvKAUDdAMsB38vTwZaj4crLN+xaPZZMyHocfto4kjhI+vi6lANtnbnsH65fw0+TaU5Tds9YGUrcxyyv1ptopks+zwEkDVjnsWPGv+yMSfyiKDLHmzx3fNF4rvHGwtka67Kco4fbk/2k6SU5Th3k+VwaToyH1pea/EtZkPelXtH6XOOnHGMsZ9YyKGtsyV8tVd6a5Fv2k/Gx7Brjx3xkncL8sHCRNFmZcpI22WeyzkFLHyTNFh4cLGV+yYBwHk3cX5pfw2NR0sn0Wz4NnzvgbDgeV2wPmP8A+vY+4Iwjra95Usw0y/Gc92LO/Wqp9NyX/Jzki9b/Fj+1jCqmjXWbxmf5KzNXZHuyfhcIdpCXkSl5q92T/o3ml7PNdmXdNfnJc9lv2nh09zXaNH0sA955dch68vwrhjP+6gQbQ1mGj/m5vGcsxeB+LcK0OmV9QL+iHwR57TGOso1hFI6mWGVbLIh5Thzjqg1k7ZlhUo1kPdz33LZl2Lleqz8tY6DVY9FkPT+sEbBoZ2XK5bVyQP66JsvADqJbU+IW5CmfvOet8evqlMZK6+883strltzkjX9NziWOXE+e8RtE97CKdK2g0c145vFGkzftWYteKyDA+lI+y3iy4eZjazwA9hcguB12sgbJlaUD82RQq0OzITwhdnRozv8gudHwkjRbY0GC44l8S8hjkftOCyRr41jKkpXGyXRqGRvyniYnTKOcTFiyqGV6ME8k7ZbekGXkubwm8ZZBCc4eyOsryVe50Z8sb9kCWQePBe3tkoYHB+5knVqghfGQf0xP3j3XtgNOUWd9ZWVHMj2se9yzEqdOp6MGTrRxJceF5KW7d6Z9zbhaAUkNL0sfW7zme8P0mZzEWJkzEgftLTqPL4k3+7DcvyzzHOziALmsg2livcGTsbwXD9yPebZi2H5hPavhynVoz1myxv2gZX/mvRDgseeWZspAEMsjy7bDN4qiHtnmOjRdx/rV0n3yWcsOMo9YX/M9rkeb9MssM0seuB81HPPGu+XLyPqsFxwym5Cf12RU4uzGiPMbOFNLA6tfhp1Pn1GggRsYZjDIa1o5Fsph6s1TCBYNLHDaIOA2WFloxmGY9hms5/OcVA1/rXwejnnr6ax25P288vKY+WvRNWy/a22yItAMfd7z1j15njeI2SAwPXlKVsPTcjJluTyFa/FyLTJr4TzsONfGioWPbNPqRynnWtTVCphpzpUG/FZX1rWWwORaQOtvra0z0TcsV8xzmQ0ho+sO2BHIkw9Lp8pxIdsaZKCk8QRs51bikccLaUSt8SbpkmPI/WrBQuatNVZk3exkWGMhz96x88oyzn1r1cfBgzw7pOkVps26zzx3jukgJ5h5JvtEezMn9YNld7Q+cfpEC8C4MkD/TvfaGzZ5T/umueSVrNvh4YCDEVyPlTYt6bXsnDV2hx0PVnl+g6rhL9vm/QM00MbssD4Ey4HGG/m1Cx7vsh6pAzS9ZdlnbfxwHcxPOUa4DY23w4xLbl8GlzS7zrqB8ZZ4WPrDwofliG2N5AvbvUG8l3hYmRODbKjVdxIPxtEK3nIbHKDUcGGdzbaObYE2UdfotgL7Wv9ZY4Hva3aV67bGn/biwMIjzw5bupXb1vSa1P8sb5qe1PpXPmfx2KJ9EHAbGl55gRDmD+Nr2TmWHa4vr88tWFOggZmoEWMJsjZwtXL8zKD2tDby6tbq0hw2q6ykQVMCeQNnWDw5kjuIX1qqqQOtLe4DHkCaQclr35W1nLRhhV0bzINkgI1oHo7MDy7PEy5L2btj+WYpr8082i3ZOZN6LLni5870eh5vuJw2ec0zhHkyYhl4q7xWp+UkabiuJSXsTEFT5g4/PmZHSdITx3HmMPPkzRkijT436ec2tHHFb5qYN/LZvH7k65JG65yNvGyLJ35W6h/z0/N63+zn0a7RkWcr3H3tbZzEVdap2SANf0mbtoZVts90ucmLfHNljSlZr0y75jcf8pjlUuIhecbOHPMxT7YsPDW9ZNkMa6MzrY8sfudNFqx+ZH5I3Nj2auUsn0DT/fKa9VZf9hc72hIX5qGknfXIMP3BOiJPb8uvxHC9zEOJj/WmkeWG194z7XLZlJMd1oWyPoserW+4/3hPAFle46HGC63/ePmJhnueTDKPNTodWNkemmywXtfqteyjfNadW5kA8hntzbWlhzTQxk9eOY3GvLE+CIc8G67pSe05C2QmiPzV9JnmZ7BNkMDBvLyAkebruOsarzhzhY8t3ln2RtatyYl7nssNs6RJ4mbZCFmO5dvRyzxkP8LCjXmsySTz0C31Y5wH6WSGM/68JRPD1zSCtXLy3A0+FlYW8jwcLYYMok2jKe9Zjfn8nIVLnoBpSt96ZhgFaQkRl9EGpkZfnrEfxPM8Y5WHm2Y8JI55/MzDIc8h0MpI5SZxt9Iu89rj+8PIhkWPpuAH4TTIUA7jHLEzKpWtpfhknVqbVruWPHJ77lg6WYPGNytjqUeeLrCcDwnyvjWJ5jduQK8hZKcA0NflSx5o/TZIf/A59xk7VnLiKuuX55ZzzOWlnGt4azyXjovEi986cRt5+Lpjh+8gh0wLFuTJheb0yefcW1DJV20sy/5m+h0wHyQP8/gi22Ecgf4NLpl/PGFg3LT1qVr/M2h2xF1j+ZKTPheg0fSOfCMu6cjD0fFdBgflhJZtkqRrkKNn6SzNl5LjEEBPloll7+X1vL0QLJmRZQbZWm1suF9um3WKpE/i6/paG7OsT1h3WX0p+0Hiq2W/SBg0MbFsHPeRhrd8nnnNQTLubw5OcFnpn3MQKy9LjXF150yfxWNNL2qyxu3KLDpLN2j9KWVQ9idPMjX953n9aenyGo8BfjaPdxrOlq3iICD/WvaI+aqNZ/k8L1vR7I1FqyULkibNjmp8lLixvOTpSeYrP8+0s12Udpf1hWZfZJsWrzR/w7Wh4cnPaTKm2Q6rb+R91gGSprzxJ+GMPm8pkdUGhkacvK8dyzbcsdXZ1rVBwizxzqPFUkjDgiX82mC3BphWz6B2NB7kCZoEzUBxvZZxs3g/zODWhD+v75l+61wbsHkKUMPTwsFK+9JwkOWG6fdBspcnE5aCzRtDw8pXngF051p7PEG2xgHX60DjnTOK7rr1FltLacybtGrtPFVdYAF/3lIbn3m6VjvmOrR+15x0ud4vSZKeNZeD5J/l2XJkLeOp6RWtXe26Jg8MmvxobUsaOHim1T2MHdRATuAtB8Qar4PGs3Vdm5wwrZpD4a6zw6EFWuVz1rjRcMyTMcumSHliXaP1K5fnSaclE5JWd9+1w3yT9/JSaNnOyToYT0um2EZZKfBybPBYz7N/zAvtWUmLZfMYHz622nXnVho5r1uWIHFk26S1wfWwXXDlrEyTPND6VV63XgZYbXAmlFY+b3xJmdP6F9CXwEjeaXqUs514HHJblgxo/NOyU1z9WiaO1FHMtzxeMB8lrZaOt8owD7htWYbbZd47sJ7Ps6lsGzX5yRu7lp5lnrFt0Oq16GXarRcjMoNDBgul7tPGvqw/L3Au6+E+dbjweNZsjDznvuNrcomcJkdyXOX50Nx/eXZD+px5/Sr5pb3EsmyUBWveo4ER1zqWFQorLCl0kjitHgsP7f4wRLOx0Z6zBt+g+vLwta5bioXv87ED5qMlQBbklctzXgYpIK2uYfHJc34G9Qe3N0xfsnxqPBzU/xqesh5+22/RqoE2Wddw0BwHq780A8W80NqS7Wjnmjyw8bAMq8SD6dN4auHCfMkbQ3kGYS3j6EzAKXFu2+qLPD7K53liLOVHc27dNX4LI+tl+ZFG3+HDxt6VY1sg286TG4ebA2vtuuaESR67Z9zz7Jy4Y3YOtTd0PN41Pag5KGzcLb4wWA6dhZPGg0FLuyx+sj3WeO/qdLKTp781mq1lCJqcc5aSpjOkI8pv9AYFeaSs81tojR9MF8u0a1/uOWLZF1eewbKzGp9YPuSzLHtuOYDsA56QMB7Md4tHGmj9KeuRPGWaNZqYJ9q45GuaHWa6tMlrXruSD5psSNDw1fSeJmvuVwsaWrg4eiy7IceB5IHEyxrzXIcsz5vTMp0SP8sWa7RJ/FmnOd5YwSnNzvOx5Lu0GYyTpeP42TzfQcqoNm54HGi61R1rNlfLauSJo2ZTmD5LzzJvNR0v28zTDVyfO9aWKMixoAU1uD45BrTMImA1E0OzLzyuGU/JTw2PvPFqvSDjc9lP0qZJnrAMc1vyXGuHn7Fsjmazh4E1BRo0wbJ++ZqlWFkALWZr+AwayIBuqHnQagN4EF6aomEBtBTlINCUpKUwrbo1fPMcFlnfICXJz+fhwfXyYGde5wmuRh/zxXIStHo0Izeo3yzDaOHPA5PbsORmmHFgGU+LDou2vDa0+gfJ+qCxpRkzq/28dEzLWZHXNVzcr5x4MC7SSD0doAUDNP0qz+WkeJgxw06PpJnHpMOBn7OcTG5f1s91S/nn9GamLwiCLI2bjapcs63JujVmuJ85LVt7RuLFtiTPsLMxZvnUlowwb5nPmu7Py7LgybRVF/OAcZF94CamclIq6eO3ZZbek7gHQaC+bZTPaEFWS69JGlnOBtkFjR/WuNOeH+RnSB5bb8f52UFLV+QvB1c02eOykjd8zs/IsaDZXjnmnbxInvBY5PLumCd21tgaFAS0eJv3BtT1jSUrFl8kD7ivmQe+72cTcTmpcfRoAVxZPweMGU9Jk6ZnpHwwfyy/h/Um08d6wPJ32KYwT7gPLfzYBkrauC+5Tm3caP2U16a8x/2g6Q7XjgNNV8h7chwzuPYkn3nJFesced3xSeKq2RdXTmZ9DeojSSePA8kLWVaTJXfs/ACJf15fMg9lOUejHN9yiRAvk9GyXSwdJumR7XAZ1sWavmCQfe2e07J88vw0iY8bb+wHubKczenusQ3Nk2EN1rSlukSEO50Jt4y2vKY9B+g7nXP5PIWkDTDNUFp4WkZsEFOttplODTerzDD1AIMnwNweGwxZVuOPRa/2vKa4Op0Ovv71r2NpaanneeY1p7MOy3NrsGpKaC20DNNn2v1h2+NnrOeH5Qe3yUpI6/9h6uV+YnwHyYkc08OOEcu504xP3vjW5EmTM367MwxfngqwnmOnhw2vw1HTj563+rUGWUb2Px+7sgyunOw3bZyx0WHHl8tIw8U0yAksvwVw7VqBFqtvXZuSjyxP2qTLmmzwNe3YctoG2R7poEgaNBzlfYk3jyWXJslOtzZJ4bf/Em/+1f4YX+0Z5rvGRwdaH3D9moMv+cdyrzmGcmIr92Jwsqo5Y+5cTiLy7IHEwbrO/Jdj3dJLcnzHcdzzpQJJu9Yfsoymb6zychxruoDrlsDPMo08Diy+a7/WuGT+yee4/+XzFv7DltV44cajvMbynDeOtDpZP/D4Z73kyrAu52fddTl2ZDvam2umh+WD9YTkRZ59YXosuXN/LoCp0Qj06wYr8Kvpa8ZLZgFqfeTwYV0pg6w8AWea3DHrZ00XME2aPme+DNLpkm+aLrfq0uhgnZwXUNFsBPNBWyKjyaSFg8xmkG1pek57nut1Y5wzJfmlidVvPAaZRqkfNb3G1zSbYwUbJX7MV60tJ9fDwNAZDa5yTaHxuXY8CKE8xWe1ZRnGvLq1e9rz/OZdey7vHisEzdlnA2q1oQkF4zgIH01RMf/43KLHwpMVjIN2u41jx47hjjvuwBNPPIEbb7wRGzdu7Hve4aDVK+9pbeThqb1B5Xq057gct6sNVO2c6eN+z8PL6iurnIU/t6PVL/HSZMOi0+KH1q/aBEAryzpEk0mrfJ5i13SGPJfGf5AOeCpgjRlrAmbpV3fOb/slfWxYeDzI4zweSaPHjpWUI+1NOtNq8Z/x0q5Zu9E7Q285fdyfkiZrXMmghzZmmBfyPjuQnEnB4022o/HN8zz1bSjzUDo48hqPE3a4+K2Olmor69f60vFDpk8z3Zad0AJtbK8YrHGhpcAyzxkHXuJg0au1p7Wl4afJOuNj6fxB9oHr03gm+0zKicZTKWcW/vyMpbu5zkH1yHJSLrTxo/HT4omkndvTQOtztpWD6tAmbED/fgiuDom/y/KyQAsU5/lAPMa08pL3ki5rrPFYkb958q5lKln9Isez5D+/ddcmZYyfxIezNPLkmNvX+Mw8z5vUSX3JvJG4sg2S+Fs+Sp7N4uuWrtDaHOQ7MLDc5+HKusH9Mn9YbjU/xZJty+bK/tX2p+JxynW7X5ldoulyjV9crzavk+OA5cM9o/He8VQbz1L/yyC1yyTR9CXXOyyclc9bchm+ptXlYBiFb13La18TaKvOPOHRDLWmWC3DyZA3KDUc84R7kBNm1cnHWr9qdGvnmhLlgfDQQw/hk5/8JGZnZzE1NYWlpaW+QIPlOOW1rzkAGl81p8A9w+lgebRrjo2Fk/VGzTLazqlw55ZSYjo1fg3iJyuovFSsQXKaJ5/ymkYzl9H6STMiWvta33B7Gp8tWjTFfbZh0Bo9h5M8Zv3DsslGFOjf3FDy2pKTQTKvvUnIM5iSBi7HDqdGu9bHmjw5p1xz8NhIa5Mo+SzjobXPRtgdSwfYklF5TfYRv2Fjx1KCphcdDzT5dw4C46HpF3nNcnAkSD5pwV15TUujlfI6SAfl4cZ2zbUnJ2yDdDG3YzmuGj7WPVmPWyLEtkGTaS1gZGVDDaKBZdH1izX5d21x/1g2UMuWkXhI2WCeaHZEkyWtLeYH2wsOVrAdtfQl42/xkmlkPcz0aHpQ2mJtAi1/pbxJH0bio8mUpJPpYb5oEx7tWXk9zzdinkvgdmSgX+OZNt40W8nXmUcWHlaWg0aTK2PJgVW/JkO8tM71uaxTy/ZivDS8pR5kvAdlmMmyzH+WubzxrY0Jq12WqWF1LJe1/GrGW2uHX1xoS5c0Gpwd1vjr6rHosTIpZJ0OHB48Xtw9a5zI7A+tvTx+JkmiZvLkwZozGrTfvGN5TRt4eWXz2teUB2ArV9m+5fANasPVZZUbZOw1Woeh2+JBHn15z8rnZLuWYs47H4RrqVTCVVddBd/38dBDD+GVr3wlxsbG1Mi61idSMVl9InnA+Ofhahk9Sxlxn1sKJk+22Alwxxp9Fu7seGqOGJfJw1uTizxnII9Xg8auxINptqKyWt1Mi4Wvho9lJJjOQeP1qYLkg+xDd669TQf638JIvN11zYiygyDxyCsv8XT35bNa4Ibb0+Sdn5HlHA80XBhHiZMro6XHcl3OQMtzjS/MA+a9O5aTNe5Pix4Z1OC+yVv2wW8c3DX3nIQ8vcX8ZD5qoOk92XeSNut5Hruyba0uiaN8zrJpHHDiLBiHn5MNLaVdo3ctNiZPJ0m6rTLDTNq1Z6WDaWXraHxl4LXKlj3ktfEONHnU+ivPdshxKq/ljT9ux9IVsn6r3/PGwCAbYvWzlDtLJzr973lez9tGSas8t3CVcm7pXNaF3JbmR3B9mi1j+t0kd5ilN5qt4ja14KfV/9a5RrdGO+OUJEnf5ypdGe0648h0yrGl6WH2C7SlXZZ+l/VZ+xwxXwH9JZQsp+kNS864rPW89HkkPlInct0MebLk7nM7mn2V+GgyPUif5I2LYe2MxgPGWdNFLFcaX9w1roeDyhzocsdryfg9o89bDqO0NKZZCoPLWcd5DNOe42s8+dI6SFOqeXVr9PCxNvD52GpDE2aLPkuwLFwl8GSVB6KDPHyZpwAy43LZZZdhfHwc4+PjPfUMy19+RnN6rIGYB5qylfesNgfhOkx7gP4JmUE81/DRcHbP5hldrY08B3cQXYPakoZMo5OVvSVvzCse15wKPsz4dL8WfmcTNF0o70lZHsZgOJDfdJZjgccl153nqOW9AWecLTr5mlZ+UJ/m6TnGV9Yt5WCQjcnjj7vPX7+wZIsnaoPSkbXx7K5ryz7k5FjLIGCea21oehTotfdu7wLNfnIbmo7MG+9Wm5pj6n5Zx8sApSa3mpwwfq5NmUZqOYaWfpVOGdOnjTWJr7b+3KrHlXdjXFuiwzxzn6x15V1d2ts+CdYYseSLeZQ3RhzwhFuTR8lDd5/718p8kHwf1A+sZxwM669Y+pR5kafDJS9YJiWdFh6Wznd1ch9Ie+nqZ32l1cGyx/2j0TUoi0+Te9ke60H2m5h3ms1wtMlgvmX7rD50bWu4st7gzDY5BrkOrW9ZXjTZ0oBfVOTZP40+lhUNV20SquHJY1rSrtkJSZ8WJOYy3K7FGw1PjX7mHfeBtuxH8kXTrZZdl7zmY+aJ9CmkHMmxO4g38tzih6tT9jMH7QfJn4Qz/rylNigdsEBaytRSAhajtMGyFhjGWAwyLrKcNrAsvmjRwUF1WvcsxZeHqytrDbI8p07DI2+wyutOYA8dOoTTp09jy5YtGBkZUem1jKDEj/mrKTCr34bhH9+36tMMsNVOXntWyhorHU02LMWh0eLqYeWuKT/pMGjrSC26LRoGGUtLPi06Zd1chmnUDILVv/IaP3u2QeOJ9gv0T4S0Sae854wCl9VkWeJg6Vd2bPKch7yJlhX4cb/aso5Buo7xsu5ZtkSzVZpcavzQnuFJr9W/WiaFdHI0W8EyY41pbcKlTSgH2T75vJZtxLqCdbmrU8qg9vaQ5YlpZPw0+yMdIsaJ9Y18TqOZ8eVNr2R77lw6YtLRzJsUWuPRGmfyWAaZZL+7c4kL06S1ZelhLsN9zLKrybMmcxqd8r5lI1yfMC+5bXlNy1jRMv60drXr8hrLs6VDZTvWPe4Decwp06xTNdw0uRnUvuMNB2tlvUD/JxQtOdDoG6QnnHxrsinrsrI2GVceH+5Y47PFN41nlm2yxrWkZZCfk6ebpX7j5zjTTWtHs8OuHl7yxzhrfcfn3G/D8kYG8FmeNF3CdWpjh9scpi+1dplWtgH8nJXFxTaG8dbolWW0ZS95eFhjRwL7k278aS8W5FjS+GLBmpZO5KWtWNe0QSzvs9ANkxrDz8pzFt6nCpZQW4OHn7HwyRN4C3fmpVYHCxin3OQpQIZBjoQso9Htys3NzeGb3/wmPM/D6OgoLrvsMhMXdi40I6bRa50z7/Igrwz3u+YcaeUtPq8FTzYoGj80/PLqteRV3h9kFPPqs9q3+tNSmBIX2Y4ml/wMoGfmWIo+L43+bIOWyeJAjl35ts/hbjlB7ERpNMtz+as5LayzNQdGy3bIcwocfloQRzOInre6uzHLN/cjy6AVkMkbg8M4Txqwc8b0W7pOPs98z1sCwfVL4EmolBWL/jybIuuVdlqTAW3sS3p44s0yC6Dns2YanRoPeVxoPJDtWLyQ40TKEJfVUnw1/cX8lPjJftH0ANMk6dEcvTw7NCg7TGtL8tEFXGSdEpc8/0DrxzzdzrSwXpDX8vwQDi5o7WrPWPIG9Dvlg/SCxFt7E8lj1N3XsnssvaWNY8kTy4bL+iVo+1bl1a3RyvVLGuUzUsdxoM5q28I7SZKeTWg1u6X1ucYbKe95QVbt+TyeaP1t8VEbLw43qw05ZplWuRzAgZb1oNEocWLfgevSsuuSpHf5GvsI7EfI9rguWYfsW27PCvRqssv0W8tpBukPS0a5LrYdefTKPpL9y+0yPzQ7qmVYDtLVFn2WTtFgzUsnNNA6RHt+ULlBytoi0lJK2i/XNyyzBim6Qfc0wcwbyJZAA/bkyQmHZnQ1PAeBpcwtpSR/ZVsrKytYXl4GACwtLfU5ZLJeBs0BBOw3ghbftH7OUwLMB/lrKQlZfpCSthQKt2+9idLO89rOozuPLsljdz5o7fWw403SK3f6lfKbh5+UeaZPGgxtssB4MT5M99MB8m0p6wWJlysrHRSm3ZJJ7o9B44af18a4xCNv3LFzxfXIX40e7deBtrsz88Xdt7I6JD5auiunszPwZIFTjeWbbWtsyYkc4zNoMqnx1f1Za6Hlc5ZjIbNieMf7PMfEAQeSmP+aU6npeYmPo8vKEJCOpTU2LD3PY4Hr0iaXWn9zP2hjk9vXxpq7Lt/wyTZleYmfhSOPIeaFRYOzSdbba+4HrT/5ujUJyuOF1Q7rxyRJUCqVhlr6xrIlgYNo7po29thOSlzy6LF47p7X7I+15EXqHy1Qz7pQ4s888bzePQa4Ho32vLe3zC8+5oku4yLx13wLS1/nvTRg28XXue+ZR5ZPYOlZ5qFli5lX2rjV5EVrX9LI+iEPf8k7fs7qW+atNYFlvcj0ctadJZ8WvRY9Wp9ZupNtjCzDbcuyGo80GjUdYuGTpzMsXsiXU/zyQpMLWadmexm04M0wcEabQWrnljKy7lt1yuuaArLa1pw969eqxxJGTei1ZzXjwXRoPOH2eXBoQqwpSete3vVBNAzDf2sQAsDMzAzuueceNJtN+L6PkydPYnFxEdPT06pCtvgyzCCz+M/XBtEwjDGR5QYpmUE4cn2aTMprluIbBEyjxF973pIVzTlj2iz+ac6l9nZQq9NSwhx9HnRs9Zc1Hp8OcHS7Y63vOdWTjUVefzINzhHT1jvKY8dL3oeA8ZLH7JjKttnh4NRONnaD5Fjq0jx9LcvnObCA/ilEnvhruoJp4X5x14fRpVIXum+sc/qy5JfmzOU5pHk2iPuW02elc+Ke0SY92kZjLAMary1niPtU3reCOu6exIFlWT7jZNmV4f0o5B8Hjlw9LBc8prgftP7nIInVpxzEYZnTdBwHergvtGA21yPbZb0tQZucyTepWqDE8gOYPgviOEaz2ezTJ7INzdZZOlPKtpa5o/WpDHxaY0WjW/arPJd1S7vLPJVtsW7Q6OegEfezq8eykZrOtbKEZBnreR5HWllLzzNImnmpiQRHn7Nbjmesv/JskTbO5ViRNGgv1Sw+aMFly6668STl1PIPBuEu9bjUR5Kfkl8SDx5TFq2W3dF4xPXz81bwWQbJpZ6zsony5C7PDrlyVrBZ0imfZ3vqeZ6Zwccyxc9LnSF1OdPHdMg2NLuj8VziYQUH8+CM92iwrjGzrDqsNiyhtQaMhLyBZQmBpsS0+iyBl+fW4NboGSSUFo3Ws3zfMhAafRZdWl2sWDXFI+taWlrC7OxsZozvmbkHd919F3acswOhH6LklVAOyumxX0LohygHZZT8Uv9fUELopfdDLy0feAEqQQWhHyL0Q5V/rBC1QTJIbizZZYWgPTuoPs3wWDyX51w2r2+tcnm0Md6WI2HRpsmONKTDBCzycLQMmOU0WXWxo2vhdbaB+0MaC3fOelTrY22Jhbyf97z2BszdY/5KOWActTdImm7QMjNc3ZZcxXGMMAxVoyj55Y65/yVNFg/5GW0Sxkbcwlm2MQx9mhOg6Vnp2Mi+cOXkRMMCOSFnvcN8kPc1p44dHFnO3dPkiK/l2SGJp6ZrXaBL0xVcL/cl0L/e32WCcDnWW7yGVdbBOkTySdKmTWitsSrr5kwHbYmApqslL7SxLYHHN+OhTZ5kXezoS3lweGhvAzW9666x7pATimaziXa7jWq1mvFHTsK0+lkOWRZ5fFhZTpqOYd7y20XZJgPjogV5JP6WDnI8ZpnTJkeazeVsFq1+9+wgv5dtHOs6rkN7Rj7Hxxpe7pj1Bgc7Ha3cd9xnlt9g2VB5j8cey5mVPZDnA/Bzmm6UuGt8cc9KmeAgt2ZTNJ0lJ8w8hvLGtiZ7LLscSNbkh22z5DXra6lHrD62xgi3a+k9SQs/L9saRn+w/XU0WXiz/tBsrmub7Tq3yV9wkm0MC09boIGvsZBpAuGus6Ng1a/Vo7WVpyQsfPPOLWWv1WmBVpfEK0+Jyra0dvP6Yq24DsMD7dy1sWHDBmzZsgX1eh2+7+NE6wRuP3474uMxWlELnbiDTtIZCpeBuMLLAhIyQBH6Icp+OQtUlPzV4EbZXw1y9AU1cu6XgzICL+i5n10Lyih5pdXgSTc4ItsPvTDDk3kcRRH27t2LyclJbNy40TRCef1oyYBVXpMLrQ5rEq7JoyWbec6R9rylIyxcmF/uXDMCrj4tjXEtivSpgGUQHA6etxr1thS9pvNkHe6X62fjwUaPHT7tTbt0RNiI8T0JTLPmPMg+0Rw/6Ty6CYYMCLC+Yp5xPVqWieSVrMuBlC92mrVJgpx4yYmsdMy0N1vcv9wH1rgB+jfast4yW89J3si+lHxiXLXyzB/+lTRLR4xx5GNNRiVftTGu2VLZJuPm+lduyDXI77F0ted5WVBDtst+hZRvSTPLMtM06C0z06vpHB7vlo7SQAsoy7HLExvGQesTOb7dPRdgWFlZQRRFqNfrqNVqfRt4avXyuXWs8V0bY9o4kPpI82eljOUFFGQb8r4WrGG7KPWv5J3Ez2qHxygHezSeMl2yTsafQbM5Uh75mjaGGW9uT5bPswuyjGWftDq0saHpRqmTNJ5pfJY2ReJo6SNZpybfVlnWQ1rfumOHj/yqjaMxSXozRxgfWZcLGkv+aLzT9CvrBQuYRkvOmd8aHyRNDmcZqLbGusZbLfjHujNPfoeRZ342LwArwbKba4GhAw2sZB0MMrR8zXo2zxBaA8MaXIPAUk5cZhjQDI28zop22HqtctIo5XW4pgi1gZKHK9dj1WvhfvLkSezevRuNRgNxHGNlZQX/ceN/xJt3vhmncRr7WvswOTYJDx58z4eHLo7o9jkSIAESr6uU0I1OJqkii+IICRJ04g5acQudpIN21EY7bqMVtdCO2+k9dyzuuzKduIN23MZKZyUry+V6/qK0nmbUHKIXh4PAC3qCIqEXIm7H8BMfk2OTKAWlNFDSvV8KSj2BjJ6gighgZOW91UwRF+iQARhuP6tH1JllkvirmSSD5J1lSQNLL2j1WsbPUpR5hkoqWx5P1ng5m2DhbOlZfmsP2F8SyHM+NJ3HbTFIflu81yakGi813spzfkPu7uel+DHvrHRGdhpcGy6I4+qTbVkBKKt+zaGyHBXN6ef6fd9Hp9Pp6zN3T7YvnRzNqbGcHEvHazKhBe2st56a48S0M92yfb7vjvlZ5psMCDDf8vpLOpb8nKSd5Y5pzrPxmg2Wcuv7fo8sSmBZZJ5zIFKTfSdLjLPDjWWe5VkbD5bTq9XvnmVcLZw03snnxsbG0Gw2s/NKpZLxzwqKMP7WpMYK8OeND9k3Uk403rAcyDeyefaIZZbxto6HDaZr/evKyYAl75OjjW8nT8xndy7vyY0c5ViwxhP7BBpfz8SGc3knS9I+ac9YPNXssWabOfDIulnymoOvQH/GjWaruX3NLkl6NF3H40AbI2wTLJ3HoAXOpF6X1yycrBdNTJ9sR5YbpL9lHZJe5oNmcwfVy89oL0y09t11t8RF2oc8O+V+tQCi5JNsb9hgs4Q1ZTRoiEhkJJM0o2CVt9pzZfh5qx6trCVg7lgbaNyZeXVoOLFx0QarVhcbFKZfU1iD8NFoGoYGiY/1nMbL5eVlHDp0CCdOnMCRI0fQaDQQhiGazSZWVlawtLSEffv24ZGRR/D3nb8fiPcw4Ht+zyS9Z8LcXVLRd82VC0JUggpGS6M9E3dXJgy6v1R34AUI/AA+xA7J0AM3SZIGTxJv1ejGSFMY4yT9S5AgSiIsrSzh0T2PopN0EFZDbN64GQjQF+hoRS3UO3UsJotpUCRpZ8ERFzyRf+7a2coeAdAXnCj5aQBEDVhQcERe56AG19nXr16pL5vEBUOyQAiCLLDiymrBEc0pWKtjcibAb901p8DhyW//NPyHcWDlNW6P9YNmMPMcbi4vjZeGGxtTV9+wk3KJo/uNoih7q6K1K53ePCdM4mfJg3aPJ36WHWD+SwPOPGTa2bGR5Zkv7h73A9vxQc6k/NVSn7VnNPllh5EdwLxxmefcWJkWsl7HX/fGLA937b48lw6+lgGk9bt8njfq1AKGjIsVZJNl2bm0dJvlN0j5tPoqT8/INuVEwwXyrCwLljHL/7DGYbVaRRimrqzbDNLypeQ4kzxnHNj55jZ5WQZPMiyeMv2ybc7Ysnw/bUw70Hg7SKdpbbFeY31kpVH3+DykZyw7IGVX6zOJB/OXaWR+acFB1qdsI7VJPJ8znRK0c9b5Gq7asxJ/jcfDTCa1sWDJRx69HCiyQNanZZFq+lMb+1KHWHbC8pu4jMu4cM9rY4D5po0R2ZdSTizdJOmUdiEvSKyNA6aFZcHpoiTpDbBLuuSyKbbRFt4a3/Nk34I1f3Uib1Bpx1Z57T4rVGsQa4bJcroGMWLQfUtpD9tReQObn7V4otFqtS2f04xZ3jNa21Z5FkbP83D06FHccccdOHXqFBqNBprNJpaWljA+Po6RkRGMjo4iSdJgxDOTZ+KasWuw4/wd2PWMXYi9eHWCHImJcTfLIEqi3gkz/cpJuLzWiTu99Yr77t5KtNJXtqdM0n/N4eOyK84mBEgDGMFKgNrBWraMQ2Yn8HnVryIsh9nEPQuUiLKhF6Z1e372l2WTeB6QAPAAL/HQjZn0KhqIzBIkWZBE/kVJ1PPbjttotBtYWF6AV/YQeREa7UZuIISvteN2mt1yNnjbzd6QQQm5nIUDFyW/hA1PbMj2DSkH5f7joGRek9fltdmxWXixBx8+/MTvPY48BF6wet33kcT6mmbLKA7j/HGarqY7h3GMnOFkZ0Fzbiz9zkZZOrJaWqak3z3Lyze0X0cv84Tr5TKyPWewnSOk6XZ3zg6ERj/3CRt3zYZYb1tdO5pjrdkb6fy4Z60345oDIvlv4cuZFlpZWSdnncg3nQzWBJhpZfp4osRlLTnXfBIeb64dQP8CgbwmHURt0iqfYWAnU2tLypTMltCWokjHnseO7AutfS43SG44cCIn7pYekeNN9kWlUsH27dvR6XRw5MgRtNttlT521N25TO3OG2uyDk1G3DUt40bWy3VoPqXmJ7KsaxM2zbeU/WaNI02mZZs86dPKsz7T+C/b42CbHAdSX0h+ykmW1keWfy551el0+jbcZZuY10da9pbktxWgkjhLebf0iXZN3tOy/iTu7lizsbLOvKw9TY/yPe2a5jtYOs7ik0UHgxagt/SxlGfZFtPAbVnjUsq70yWuTy35kzpI1iPvS7B0v9S1cRyrdlLDW2uXx5i7J4PiUg9qAaQ8OOM9GixDIonTnnMEMuJWOwx5wsn3uW4Lf0148sDCOY8W7Rob8zzgNvKU6qC2LJrYwctzFBiiKMKDDz6I2dlZ1Ot1NBoNHDhwAMePH8fo6CguvvhijIyMII7Tjd3mT88jXAwRRiF2XbAL52w5py/6LNtjBc24M54arppsaM4CP5OnENxkmoMUbgmGFRjJAhjR6pIO99wTTz6BQ0cOIUaMKy+5El7g9Szp6AuWJL3tLsfLGT7ynjvPcE3E9bO4V4aEwAsQIICXpJPn0AtRDsuolqo9wZCRcKTnPAsIuACJlwZffN/PskgCrxu57QZLkChjrhs8QZJmkbjfJFkNliRJghirwZE4SYNe7hoSYLm1nAWrWlErW47TilrqNbcsx4RnrI2PXuzBT0RQIvHhJV7PeXYt7vKIymnPyiBHdo/b6gZA/MQHIvSVL/kleLEHxIAfd697vZvJuc0cWQ85Y8aOmjTi7FBpzh+g76/AY50nS7LOQZMk15Z0iPOMN+sK3rXbXR/WBmhvT9gB1t50arqOHT3LLkhaLEeVnVX57KB0V14qw06Vkw9Jh3yWnUwuJ+t2NHPfany3nL+8zAo5EZYOnNYmTwA0h5Cft950sVPINpInvpJf2njSeOLuaV/w0J7T/BW5SSPLq2bfNRrZOfZ9H7t27cIrXvEKnDp1Cn//93+PmZkZtX9kexJ3zU9ieWR6ta/yaDpJ841lO8w3qV+sMhy8lPVpWXJaXZw9wTi6Z1x9eW+DJf3W2JC0yzLMa8/z+rKOuM/ZP9Wy4KReYl2t8Vz2u8SPceP+k2OHn7HakPjJMppfavHa9bXmp2t2TMNT0pwXjNb0pGxbG6cSWG9xP/E40spxnzPf3Ll8Ri6/yfMpuF0LZJBCs3NJkiAMQ1U3amNX09Nan0tZduU0ujV6XOBDfqZa4su0sz6U9pj9NMeLYeApf94yzzliwdAcJK3eteCQd112hHZs4TJokPM5Dw5uexh6LANlGXxWcu7esO1x3ZqxtejV8Dp69CgeeeQRLC0tod1uAwCmpqawvLyMiYkJlEolRFG0GmiYn8fBgwcxMTEBz/Pwmte8BrVarYcWHoiWUl9aWsLc3Bw2btyIcrms8nAYJ0re48Fn8TXw0yUUeXzne5oiXV5exuHDh1EZqeB7Lvge3L90PxrtBv7TJf8Jk+OTqnOnKd0zaV8qvk7SzdboLseQgQi3PIODJFbwZHF5EXOLczh4+CDqrTrgA+2ojQABpiamMD49ngZqlIBHO24jSqJs74woiczgSF9ApXvtbGVB4HiX5/BW97+g31qphonKxOoeGmLZTuCnARbf97MlN1+986vpUpsE2ZKb1Q5abQ8eyeTqzbSo13UkkKR7mXR/Yy9GEq6ey7/YS5fqxH6cnctjRudMwUu8LPCRBUVcIISCHdY9L/HgxR6CJEgDSnFvORfw8OK0rSAJerJEAgRAhKyuAEFvQEUETNI4VAIk/W+ueKxpekHTya7fNB2S50zI+3mOiwTGWXNE2N5o7eWljGu6RDqzjIOWhuzq5U9JOgdQtuGCM9oklWnReMB8GMau5NHNwSLuN65Xs6MyGJSXxcAg+SzrlHjzZFpel5+h1WwATypk/VoWiIaPhpM75wCfJevcFvNJTjbDMMQVV1wB3/fRbDbR6XT6+lmjWfJUG3uyn7R+BXT/1pok5fm4eT4Zj1PNL5H0yToGyROX4f5i3DR88wJvXJZ5n+fLr5V/Vt86HOXz/FUSV1abbFu6xpofyGe0CSn3HX9iU+Kr6Smmy5Jli29ybLDu4fblPQs4uM11uus8SZb8SJKkZyIsaeTxK/tAGxtW4EXDO09/aXIk6XPlpO1im+BwljKk+QiWXne0sU7lNpx9lLxwwF8ocmXYTmqfg2Wbxvpay+yxYM0ZDRqxVrlBZfiYywyjKIfFfZDy0hS2fI5pYoNrOSnyOU0ZWmW180HtWG1ZeLtyGp/kNTlgtLriOMaePXvw6KOPIo5jrFu3DtVqFePj49i1axeq1SoAIAxDeJ6XpTZWq1WsX78ei4uLOHbsGC644II+3mv0cD/u2bMH9957Ly6//HI897nPNaOVzCOLB9pzlsK22sgDbn9xcRF33303Tp48Cc9L3/7evXg3/sH/B/zW3/8WSn4J1aCKalhNf7vHtaCGSlhBLaj13u/+yvuVoIJamJarhTVU/MpquTC9X/EraZq/V0YtrGV9q9GugbzebDbxxS9+ETgJ1Jo1xHGMcrmcRnzrCaZXpnHjc2/sCS5ZsjkMb9mweZ6XBiai3iUzVoCEs0PkUp0d5+3IshnW9EvLgNpRG82oiXarjVaphcTvTvq9/t/s2F8NEJxtcJNzP/F7ggFIVifmSJBOxrtBAyRYzR7pBkpksCQLmrh4iOepAR93LfESwOtmm3hAJ+xkARN4WA2QoJc3fTzzzx5/+jJEiD9ahoiVYZIFOJIgC5pkGSLdOhGtLpfiZ/3ERxIlvQEUF1DpBke82EPgr67L5MwR6chq9kQ6TVpGhGZreNlIHw+NMSt1HzttEge+zsEPOWnW6tdsPbfDdcvnebIrn5NvOTWnX7Of8pydeNmm6yd+lvGX+HJ/y8mE5SQyf2VbPOGSE4VBNpFBs7Ua7nl0ac6yKzMyMgLf99FoNNBoNDA5OYl2u42lpaVcvNx1dvwt+WHaJU/Z38iTPUeX5lPKvrdSzPN8Z+Yt4y7/NP+V62D8mU+SfxpO1jjgcchyx7SyHmB6tKwYi0fuvvYZ27x+1niiTR5lHawTtMmrLCPT1JnvFn6WzDIPZD2S1yz3XL/FE6cTWIdb/Z9XN49vyVMtMCH5psk8gybDkqeWveOxq81/ZL9yO9w/Gh3WfY338jkOrjB+2jkAdQlVnh6X8uFsrhaQGgRrymiwEGNlZZWz7uV1slXGeka7z89bNDAt1sCWAyBP0DXDaLWfZwwZN41GTRlZdFrGQetDNnSMV5Kk0cjnPve5mJ+fx6c+9Sl0Op0ssyAMQ5TL5WxnaPfcjh07sGXLlkzZHzlyBOedd57qfFpGytXXaDTQbrexuLio8lDDXaOX29AcXllO1m1Fn+XzWntxHOPJJ5/E4cOH0W630el0EEURtpW34bXBa9FKWthx3g5MrJtAI26g3q6jHtXR7DRRj+podBqod+qYb82j0WmgEaXnzWj1fitumXxhyIIaSmCj4leyoIQLVmTnQe/1uBnj66e+jla9hbgVA22gc6qDMspYN7EOQSnA4uIiarWa2kcM2pjXeCzrCLwAQRigGqaBLo72aoafz5MkwTXPvGZo/g0LL3vZy9RJFY8rB3ESAz7S7AN0J9Z+OhHvJN3JeTcoEfvppD32YkSIsnsxYqQv+KN0Ep8T6IiR/kZeBM/3+gMh8lk/RuRFq8EAP9GDJk9DUCCDbsCjLxsi8dKv2LhACbplXGCkGzwBRODEHVvgpYGSyIuAENmeJVlgxP3z6JrCk7OePWIEP3jZTBb4cMe0RKZviQ381WUxtMzGLY2SwRh3L/TCnroRpbiGSIPOHrw+fcwTD6dfpYPPXwpx5ZwzxGmunOYr22Dd4Z6T19yxrJPbk5Bn+7UJm7zn8GLnW/M9JE/cudMb1ttmy8ZrS0zyfDrL73N1yMwD7b7EkScYXK/UlTIl+lnPehbCMESr1UIYhti8eTPGx8fxzW9+08ykYfw038JKY5bAgSFuR+NPnn+Wl84sP/vn6tFSqbl/8lLiNRnl8eHq195u5k1qNPucl62ljUGuV+szK6g6DO/5WMM7r/+dHMrggHwzzHVZ44D7TZuwavhptHEWj4W3KyP7RJMVV4YnlRwQccdOLzPOcmxrL68s+obJFJOyItuTeGpyJW2KLKPxRrYt+cW8suRaluH6mD9cnxUklrpSq0sbU7zngqyLx5HlH1svJPLgjPZokEzgc4vAvLq064M6SKtDG4TsvAwDg+oe9p4mVFb92jPMyzxlbglwHr5W3ZZyzpvYjYyM4Prrr8fmzZtx5513ol6vo9VqYX5+PkuR9TwP09PTmJqaQqlUygR2cXER9957Ly688EJs3bpVHUyWsfG89BNXy8vLaLVaff1tKWtXV16faIZB45Erq/FH8lB769VqtbBnzx7s2bMHJ0+exNTUFHbs2IFNlU3YEG/A8vIyJg9OYnuyHVdeeSXWrVvX11+DFGKcxD0BCHcsAxONTiMNYERN9Vw+N9+aRyNqZM/L456gRgnAVB9qGfzPL/9PPVARVFYDHEF14LkLgrh7ri53v+SlSxys8cCyYvXj2QRtzLFc9hjO9LU/AqTLMBD3OylsQHgdpZYSLycCmsM8LC8s/SffejiIk3h1At4NmLigiAuiuKCIGgTpBlt6Mhy6gQ/46Lmn/WplzKCIpwdPeup7OkQkRjahB7CaaYLVbBIXREk7oHvNBUu89NwtBYnTCtPgiOw3FxCRx97qsbb05mxm15hBEZFJYi2zccESM/OkG+Bw2SDa/iSunFtW03OeBL3BmW7Widt/BLBthHNknYOeZ7fZvslylt+j+QK8n4ALyHA7bFNlO4yb5dvIOlwZftHh9I2V2mzVJwMJrqy2v0kYhjj33HOzTMm5uTk8/vjjmJ+f7+Op6w+3CSDjr/Vf3jXmjeVDaQ67vGfhIq9JHe3okG1YdEhcNT7z5FxOAuU1TW7kxNPaIJTb1N5O8/IFCXn+mcPPTRbZL8wDra+sCbfmY3MfOT+WZdvKrMibX/C4tgJF8prsK/csB0BkOZYbOeHW9Is1t5A4W8vtmEaLrjz9qPkRLDNctyU3sq/4PuuMvHscfJE4c1+xXHLQm/tO1qHpeUsnsx+nPce84/7XeKplMOThocGaMxo046Tdz7tmDVzruWGJ0erSOjNvEjpM3YOUxjC4sbLPKyd/8+q0zq1JgHZuTco1nBgmJyexZcsWbN26Fffccw8ee+wxzM7OZgY+DEOUSiVs3boVu3btwvT0NBYWFnDgwAFs3boVQP66Rwnue7GNRgOLi4tIkjSaurKygsnJyR48LcdhEE+GcTbWIj+agZ+ZmcHXvvY1HDhwAJ1OB+effz7GxsZQqVQwPT2NiYkJ7NmzB/v27UOSJLjuuut633QbmRSyPwM/wIg3gtHS6EB8LadIM4KaMoziCI2ogeXWMp7Y/wSeOPAElppLaMZNxH6MZtJEvVXHxIYJbN62OQ1ixKvBDHfe6DTQiBtqUMMFPdaSqRF64eqSEResCKuo+r3Bi5773d+7OnehVqphpDSCWtj9Fed8z6WxD+Kz3GCL9almGOSxNPKyvGbM+VlNRrSJA9C/+Zv2rHxrrDkJjEPgdd/6JOlEM4l6g2ZaSqrkm+Od5jSzU8s0s0Mqj6Xsu4mOc6Klg80OQ4I00BElafaIF3pZMCTLKKEASRZEQZoN4jJM+srJoIgS7HCZJzK7JGtPBlPEHhxyGQpff7qyTWR2CQdMeJ+SGHGaRdPN4umDLLayGiBxwRE1SCICKGeLnr69RURQJNs3hIIhbn8QLdAhs0n4XuCt7jMis0f6giehn2aKYPU+kG7Q6vtpGW2MA1DHDl93TqnmsHJ5oH+tN0+CePy651kvueucIRHHMY4fP45nP/vZqFQqOP/88zE3N4evfvWrPfhpuGqOtQO5e3sevyS+lt1kPBi0/TQ0v4P7QWvTAsuHtPgs2+Q6WF/KPnW/HBSy3qAybzRbIWXFsn2WnLm2pT2x5MDiuzX5dHLI/OIMC+afJnM8pjQ/1JIp7Z7WnsNNeymlre+3MsAsm+5o1pZ/SX6xr8Oyp8mF1W8OZ5YrbSmZ+9P6R8t0dXMWBilz2nWmJUlWszw0P0PyhpcFabyQPNfONV5qfNT8RR5vXLflPw4DZ7RHQ959TeitsoPAUp4sRMOC1WGDBH2YNq2BzvetgbQWnPOM5KBnh8Ejz6gyDvy7Y8cOnDhxAh/96EcxPz+ParWKTZs2YWJiAp1OBzMzMzh48CDq9TquvPJKRFGETZs2AQAOHjyIjRs3olQq5dIRxzEOHDiAb3zjGzh48CAOHz6MarWKubk5fOELX8Dzn/98bN68uQevYWTSkgM28BoPNL5x3dr9vXv34sSJE5iZmcFznvMcbN++PQvIXHjhhdixYwdGR0dx9913Z29jGCcNDwmaMR0kP5Yi056R9wI/wKg/Cr/jw1/0sbx/GaWwhInaBJIkwYkTJxCtRNhQ34Brt1+LCy+90HzLJfHVHMQoibIARDNqYqW9kgYpuue8hCTLvhDBiiyDoxvUyK7Hq0GP5qNNtKK1LT/hYASfP3DxAwiSAEEcwI/St65BFKRvXyMfQZweex0PYRKm590/P/YRJumnTV1mg+bsW4bUcu6sN06S/+wkuOcsh0yW155zzo9M92Zgh9JtKms5Pdo5O3YOZ5mCz3Tn0cFOTfb22/eBtv52xHIKZbsa5DmY/CyPGevNjXbsed7qp2u7QY4IUd9v3/KPoBsMEctt3NKdniU9lA3Ss6xHuaYGU+SSHVCQhPbyeNqyTRKsttMNbnjw4Pkke0lO4y4w4qpUNnV1WT9nC7S9Q9TghpZZwkEO7Xll6Q1ng6CD1Xq6dfZkn3TPs2cVR9j3/cwWPvroo3jmM5+JTqeDdevWYd26ddi5cyf279+f6Rb3p/k0WuBEju+8yYh8htf8y3ty3Gu2jnWZG7ecomzZXcZVG9+sK62AgebjyLbkdU2vS3vh6HA78Wt6UPOnLP9Tnru2OLgt7zN9Gv2yfiuTQQNJn+xXTXZ42Y+UBW0vBY3PLBvcb9pyBImLfE7jq2av5J/lB3C/an2g+QB5S40lWC8eJe+lnWNw7fMkntvlfnOf02ZeubpcOZkxlienjhbmtTvP86m18pY/r9Up+aRlvbJvZMmB1fYwcNYCDZZAWQ6wphi16/yMLD8soRpTWIjynDZrEA1qcxh8NAGx2snjGz9v0Wc5uhoOefS7X6bl5MmTWFpawsaNGxFFEc4//3yMj49nu0FPT09j7969WFxcxJEjR7Bu3Tps3LgRhw8fxiOPPIKdO3diw4YNKj9cG6dOncLHP/5xHD16FEA6ONatW4d2u40jR47gm9/8JtatW5ctz7AMhyWjWtssk8wHvi4VmyYrruzp06fh+z7Wr1+Pc889F77vZ995XlhYwBe/+EXceeedaLfbGB0dRafTQaVSUeti4HYtmdNotMbcIOPgaH/ggQfw8Y9/HHEcY9OmTWg2myiXy5iensbIyAgePP4g7vvaffje+HsxOTKJkWAEI2H6VwtqGC2NZptc+p6+jjrwAoyWRjFaGs2lmftAc3i4vyTdz3rWsxDFEeqdOurtOlbaK6h3ur/tes8x3+s5F9cblUa6t4EfZb/uby1vlf04fZPqAhAyICGv+bGPMA6zIIUfieei7jVxHsRB3zUPvUaelwKxXpB9JYMJzGMtE4L7WnNcNFkcpN/Y0dHWnmopuJpdkG1Zn660jtlRYkdI4iGdA8ZJOj4W75h+TZdl97ufig3S6AHCJAR6V1v09JuWgSLvaW062rUJFjvELFfs6Dj+WI4mPKSf7XX7m3gxvMDLMlDUoIWxXMYdR0nUE/DoW37jr2aTuHp5P5SezBJfb1/+nY2MjMRPAzmR7NCueMhlODJA4hmNZkESETDhzJKzAsnqnip+kn61p+yXs8BFJajgH277B1TLVdRKNSwvLOOlF70UN154I4B0o+UTJ07g6NGj2R5OWtDAGhNS1nhiKMvLcZY3oRsErI94UiknVZZ9t9q1Jgnst2vjVuMPL0XhdvPolrx0QRpuT5uASRx4Msj3NV2h1c08zbM1Tt842yHr0NrT7AEHtjV9p/ksDthXkXaMA+QaaHZV3nP1WPMMtoWa3deygphPTIPWHvMzSXr3ruI38LIvOfjDfjq3K5/R8NBw5ywrje9OTi1ZkLzXfA5NJnhcSL2k0SXtKtel2WDZhsweZT6bdpfgKe3RwAJoPccDX1PmrAQ0wRimDSBfAVt0MD5roX8toNUjcRzUtibwLPwOhqE7D7e88lp9nU4HDz30EB588EGce+652LRpE/bs2YP9+/dnbyFdYCGKItRqNUxNTWUT0Fqthmq1ik6ng06nk32tgnE4ePAg9uzZg1OnTiFJEpx//vkIggATExNoNBo4efIkZmdnsWXLFvX5PAUqgd/UarzQ5CfP+eC2fN/HyMgItm7ditHRUTQajWx/i29+85uYmZlBuVxGuVzuCTBYStzqV2u8WYpV41neGJXPx3GMkydPZvtxuOthGKJWq6FWq6E0WcKnkk/hU/d9CoOgFtYwEo5gNBzNghH8J4MTPfeCkey6+62FtXQihd6+Z8Mu+Rb4AcbKYxgrjw3Edxh4+V+/vE92Mn4iQeRH6HgdxH6c/gbxamAiiBEH6XUOVPQcB+lxK2wh9uO+gMZTCWpkgYzERxBRkCMJ+gIeXuRlwQsOYMjghyuTTXrICObpOQ4qDuPsW86sXCcunTfnaFrrM620TaZFw122qzkJvH6aAwwaT3gsW/ohT5/J61oKqGZvNUdV2i3LfjHO8tyy8bwESR57nocSSuhZppEMDrxzv0mHijfCknyVWTeuj4D+YJrFDylv7ITGyWpgo4POavYDb+rqJ9kymCzgIQIqfN6TPWIFT1yQhZcCic1pZX0y86WnPZF9MlQwwksDRECaTdNGGw00Vu9HAJa7f0j7+LEnH0OIEOWgjEpYQTAWID4/xitmXoGVIysZP9mGaRMg19+WL8ATgUH7Jcn+dHW7Mpp8MC6D/DytHUtvcjaHnMBqQUBrXGq0av675kcwzzRdIp/PmwhqtDuQgWDZX3lBEk3XMG2Sj6wHXDvDvE3X6Gd54D5kHcF2SpNVrS/Y13FtaFkfebzQbA37h3n9pPUnjw9tOYNlO6QO5yAh63emxfJvB9kmTYcMo2NcHRxYtIJZEtj/sTKhkmR1yalVh6QlL/hm8cOCM96jgY+HfTZP0GTHWMIvgQnN60TrWp4yH8RIa/BZbWnPcnlLuXBZC69Bg8HCIUmS7BNRExMTfRE6jddcz9zcHE6dOgXf99Fut/HYY49l52EYIo5jzM3NodVqYfPmzdmSCgAYHx9Hu91GvV7HoUOHsGXLFlSr1T5Z6HQ6OHjwIJ588sks0DA9PY3169ejWq3iwgsvxNLSEh5//HGsW7cOlUpF7VuLt3lOdp5h1cpbbchrYRjinHPOwcaNGwEA7XYbURSh1WqhXq9jZWUFGzZsQLVaxbOe9aws2MAKTMqEVArSEGnGgMebNh4Yb8vhd+D7Pnbt2oXZ2Vl0Op0sS6NarWJ0dBRLS0s4d+5c/NP3/RO2nbsNzbiJ5fYyltvLWIlWsNJewXJnGSud9Hil0/u33E7vLbWXcKJ+Is0UEPfrUb0PJ4ZqUM0CFFmwIqylgYmwG7AopYGKnfWdWZBB+xuvjGO0NIpSUBrYruSR5GOPUYeXLqMQwRDnsHBfa30KoO/73JoujeMYfuCjjXYWlIi81UBGG+0sOJEECTr+amDDBUEiP0IUdJ/zY7TDthn8iP1+J9zkjwxqdJeV9AU55H2XgRH1ZneESdgT5JBBEFeWMzUkcKaC5dixU+L6RjoB8ro8lrpWkwfnHGhjWXPstbRoy2nS0r01PaCtbbWcS37W0qdMs3QutfW8GrCTqN1zkLehmHTO+J5Fq1YPpwSzvGhvqLluuaSohz/dDUB9z4cfiXXJHfuNINOQF1hmueGAFz9jHWt2RauDMzpkUIOX0CAAtmzbghde/0IcOHQAJ2dPojpSxYbNG1CulXFi5gSOzxzHnr17ML1hGovLiwhKAS7YeQFGxkdw4NAB7KztxIPHHuwZl1ofcv9aGTuaHbSWfmlyLuvhstokSEuZ56Vflp/GkxUOlEo6uV1rHGoyxzhKebAmOfJcy95iHLQ+YBlnvS35JydQ2sZ5mmxofhOfW5lYEjS/OW+eYAW4ZH3W2NP8Otn3msxq/c+yb9kNCW4PNUvuJW0avySemt5kYHkdBJqd5XFnjeVB7WjLWCTest+167Idea7pIc3+yXKsg3kMSVpcOR6nMvOIrw0LZ5zRIK9ZypHLWcbZnecJLpexOs5qx6JFM7p5A1sDS6EMK/Rcj4bfIH5puAxDg6unXq/jC1/4ApaWlnDddddh+/btZr2SP/Pz8wCAiYkJeF4aCAjDEPV6HUtLS6jVatnb+HK5jHa7jZWVlew7161WC41GA7VaDR9sfBDv//T7ETUjbFy3EVNjUygFJVSCCipBBeWgDD/x8fDRhzH/jHnU5+po1Vs4uOkg6mEd49E45hpzmJicwNee/BqW1y9j66atKHkllINy+uenv5WggpJfQskvmTzLk8dBfas5efI5d390dBTr169HqZTiUSqVssyO48ePo9lsZssN3F4WmtHTnB4t+psHljxxGzzWuW3f97Ft2zY873nPw/HjxzEzMwPf91EulzM6n/3sZ2efMy0HZYyXxnvw4Da1cWCNsSiJ0mULLjDRDVost5dRj+p9gQv+m1uZy56pd+po7G1gqbWEQVAJKrkBCfm3b8u+dMIbBQjjEGEcIuh03/jHYRpoiIKedcqcHsn8kIaRJ6psGLI35/BQSkpAp98hlnzW5MHSCdw/Dp8ojnqyLWQAouN3EHm9gYmO30ESJFkAxGV2uDpaYWs1c8OLegIbZxzUiFf3yZCBi2zvjKR/aYpbkiKXm2R7a4ggiAtqaOPX9Svz01o2IZ+Xz8hftpdWn+XZO+mgy93lLVulOeXWseZM5Y1vTefwWlMO2mg+gTb5csCbgA1yvNkH4udYb/LERvYrv0Fi3jDtGl6yvDZutf7Oc3wdT2W6uBYE0XgzyAfysLq3SZ7v5nkeyuUyXnTei3DN+mswenwUO9ftxMTEBJaXlnHptkvx9Se/jj237YF/0kd7pI3JyiQuv/xyXNy8GC/4nhfgwfaDWFxcxNTUFE6fPt33PXmWXc32M3199Cj+sDXemFaWXVdGTlo0597il0aDxJsnK5YPy8eafGh4aOOW69NkXMtO4jKWHpFyxy/KOFir4Sj7jOXYwkHDUaOd78u6NLpYJzhwek5O+rgN2b61zGZYf1bTIZYtks8wbjJQIMuZuiFHr0jea5lzGn2W76otceb6tP7nY00fW7RIWdOWXEi7xjTxnh6SP1EU9dguzT4NY0e0+4D+SVPpE+TBmjMaJDJ8bjHXMmxaXVZ7bLi0QWnhNQzuXI92rJ1r9VtR0kHPamXzaLB4pw0Yiwb3u7i4iNnZ2WzTRotWzfn74he/iAsvvBCTk5NotVpYWVlBEATYuHEjkiTB2NgYJicnUS6X0el0sk9ejoyMoNlsZpHPcruMZtxEOSwjQoTZxixaUQvNON2MrxW1UG/VMRfNId4ao7W5hdiLcdw/niLXArBPIPvlgawGgCyIUfJL2XHZXw1GuCBFJaig7JdRCkp9AQt3Lq/J512ZSljJypa8EuaCOcx78wiaASbHJuFXfHTaHdTr9SwIMTIygomJiSw7Q/aNJld5Slaes7xwn+eNZ1lWczxKpRIuuugirFu3Drt370az2cwyWy644AJceOGFPU6Fhre8lucQMi8Cb3WZg2b0rLHornNq9FVXXYU4iVFv17HUWlL/FluL5r2l1hIOLRzqOT++7Tg6QQeD0oe9uPs2PuoNQGTBiag76e0E2XkYp8sTgnZ3ght1n09ChFG65t5NeNnh1HS1li5v8Q/odyqyertBjVJUSnfRV9IC3fOanGl9acmJC2q4wETPfhgBBS66WRwyS0MGNeIgRttv95Q/K0GNhDIzjD+3tEQeZ0tORJBDBjuCOIDv6W8XHcg3/Dym5IRSrnllyHPGeDzz8o+85zUdxnVzHXk6kTczY7rlmNcm0cNOGCynkuvT6GAnVPoSckxYY1Q6oW5scZaG5nBKHjBerg5JI8uN5JfmvPIE2VqXzDTJ+mq1GpaXl/HZz34WR44cwVVXXYUwTN3X+fl5jI6OIooibNmyBaOjo5ifn8eePXtw9OhRLC8v49ixY9nG0wsLCz10MW/ycNdsneZTaj5anu7k+9YE0V3T+k/qaMtHlNc1X0LLsNDa0UDTzZrsOZCBc0kXjxumYxDvNTuh8cnSMZafoMkI21BJh4Yf88cBTxDdrxaY1NpkvjA/Jb5A/icQrX0erDHueV7P15lc/ZZc8piwxh6DpaukHGkyzfKkjR/GJ6/9PN5p+LMe1eRX4qftA8OZFxqO7pqbT2mZKrIt9r/4RRbTp/Fh2JeZa8posBSMVtZBXsflPWsRqikx7RrXMQzueQJmKR++7z5XeOmll+K8884zB1EePpYgac9ryj9PCDUaPC99Y+DSnarVqpkayH3SbDZx9OhRnDx5EmEYYmVlBVEUYWpqCqOjo1heXu7JaACAWq2GdevWIQgCnDx5EpVKBbOzs/jeke/F5s2bsX79erzwhS/E9PR03wTkG9/4Bj75yU9icnISd997NyYmJrBt+zYsLi9iZGIE7biNyItQqpbw7Oc8Gzsu2IFmtBqocMftuJ0dy/utqIVWbF+rd+qYa86hFffe43LtqJ1uQjYseMjWmWaXEg/BZIBgNkA4F6L2kVoWBCkFJVSDKspBGbWghkpQQS1MN090wYwsSOKXewIhfffEr8vykAEUmf0R+IGpKF0fSaUZhiGazSYajQbiOEa73caWLVv60rsY8gx1D49oXOUZsWH1CdcBAL7nY7Q8itHyKDZjcy5Ow8DNN9+cbpoWYHUSW0onu22vjU7QSSfE7q+7RKHtd+/5EZqlJjqVtFzHX/0duO9CAoRR2B/AEOcyaBFEAfyOn12Xf6W4lL31d/vDOX7L3cYtvc0OAIOmN6URtRxxD146Ce9+AhDIfyPImQMOH2sZgnw+TnqDEzJgEfu9+2n0LCvxVoMh8r5bfpL9eWce1NCWmPRlaHS/dpIFMKL0M4m8KWgWwOg+J8/ZsdOcOnZwLAdelpXHls7RdAX3IT8jAwuyz7U3iIMcYW1SxBkA2hs9C2cOGDCww5/nxGo4azqRN7izfJwkSbINADl9WksZ1vjF9TGO0tF1b25dht/DDz+MDRs2II7jbH+nIAgwOTmJH/iBH0CpVEKj0cCJEydw6NAhnDx5Ep/97Gdx6tQpeJ6HDRs2ZMs1h7Ut8tjxmnUQywhPRiw9pbXD+7QMsmM8wZK81ZYyMD7W2JPZQlrf8TUZgGJZ0HS81f8Ob1kPT4SYh9akin135rusm/miBUaZXs5+Yj9I8jPPp7dsoaZHefxrdGrZjcxv5rm8Zuleq/8464nxdXiw3uJ+yxs/mg9hLbHTArSSN8wLTY64jIYrZ4tqY8OSc9Ypjj88Fl3bvBRF2guer2nyyrpa0sTl5XWNx46Pmn3SYE0ZDZqArrW85jRYCndQnTwwWWj4+jD45ikWVhzuWF5/4IEH8MgjjyBJEuzYscOM+Gh0aMpfM1DyeU1xaA6Cpujd+enTp7Fnzx5EUYT5+XncdddduPHGGzE9Pa3iG8UR4ihGGIaYmprC+Pg4Tp8+naUmVqtVJEmCkZEReF66nKJer6NSqSCOY4yOjiKO4yybYXJyElEUZRkPs7Oz+PKXv4xnPOMZPcGa/7P7/+D9978f9bE6wjhE/bI6xmvjuM+7DygB1bCKWrkGdIC4FWP59DK2BFt6Jthu0lwNqpgoT/RkGJSDMip+935Y7ZmMuy8f5DkMfB7FEdpJezWw0Wn2BTH2PLkH9953Lw4dO4T1W9fj853Po4MOgkqAdtJG7KVr5ZtxE8uN5b62NfAh1lrCW90VPEnXwj4VCBD0LUORQYqe46CM5koTS3NLQAQESYBKq4Kpx6dwXv08VMNqFjQpeaWeTJDQD3sDJCI44pbChF6Y3Zdv9Cxjqp3zvbyJwNkEz/PSvokSlKMyqn41lVsyWnn7K0j8JM2dpJMGHErpvgodv5MFLuKwG8zw21nwwgUuOn4H7bCNelDPghYugDFwgpsgnZRSgKIncOGWikQUyIgDlOJSzzW/k05skfQ7FUyv5fi6cu5XW4YwKA20r88UR8D30mBGkKSbXsq+0d4QSkeH18Frb0vks/DQE9Rw+2VwdkYWoBD7Z3T8Tt9v228jqkR9gY0zDmok4usmItuiZ6NQEdzI9tHoeL3LTJL+r5+4wEYS2y8WuJ80h1eWcc9ZEwHLOcwD14fS8daydNgh1yZCjIsm19I5t2w8y7u2Xl6jkf0Ga0LmymoBj7z0banjJPBkYMOGDXj2s58N3/dx6tQpTE5OotPpYP369Zifn0ej0cCWLVuwsLCARqOBkZERbNy4EXNzc9neU7VarY/mvH05mOfunIMAnG0iQZvc5fFYm9DxM9I3zOufYWVXk3+un2nVymt6mnG2AlkWHwbJvANeOsV0s7xq+x1ZPOJsIY3vebpDPsO4s13Snmd+aDqNx7ccP1Z2AQdSWH7z9IDWtqTBjWuZtSoDURYNFh5sQ/meNs4kzrKcFby19LADzRfTJv+SFtaJPFZdOcknTaakfDM/3LPaXk4a/yQfZZBCtsn6g+lzdQ67V8NT2gySr2uDYVD5YZw8FhYNl0HKIm/QW0ogrw7GzUEQBCiVSmi320PRJOu1DJ1Fs2ZMuJ48I9fpdLBv3z7867/+KxYWFlAqlRCGIQ4cOIA777wTN954I0ZHR/v66H/c9T/w7q+9GwEChH4IL+6u3Rrv7kDv++kEKikDo0DSSVAKSggQoBSWEHipQ76yvIKxsTGM+CPwJ7vf0m748Os+qo0q1tfX46KTF6ESVhB6IR4//TgmahNYWVhBo9NAUk2wHC4jTuJsF250urtUV4E9h/egc7CDdtxGK26hE68hw4DATXyzpRVBf5aAXF7BwQ1rWUWn1EG73EZlpIJ1Y+tw3YHrMHtyFldediUQAROVCYxVx+DDx4UXXYjzLzwfMWJ0og4aUQP1KP28Yr1TR6PTSI/FtXo7PW90Vsu6DRazMt37zajZ8213DSJEqEd1tOIWQj9E2AkR+AECL03V9r30e+oAsk32Wn4ry+5IkgTBSoA7Hr8DnaSDKInQiTtry/5QwAU5OIvDLXPJgiAyYEHX+HrJTwMgu5PdqISr+4S440pYyfpUuxb6oalP+C0DO4qaY8Pp7NLIuXbiOE4//Ratvsm39Is10XH3epwVb3XfhLbXXg1aiEBGllXhdRCF0Wr5oI1GqZEFNdymklEweH2fH3UzKaLVYIXMxujb56J77AIXLhNDBj+kw2vxQgI7YlZZdqYlTy09zhMtlhfW4S5To+SVss8QSqfZCpRba2QlfZIGz0szNWQQQ27wKb944gIbHS/dU6PjpfttuICG6+tWqdUT9MgyPLwzC2r0bOwpAhp9GRhasCMKECLs+xpKiFRmsgwPr98x5n5k+WD+j4yM4KKLLsKxY8dw8uTJnn6XwI6dJl9av1kOtraeF+gNgMhzWdYaB3mTUpYtqZMYB8krzUeR7XhemtHQarVw+eWXIwzDLNvR0VkulzE+Po56vY6xsTEEQYCDBw+i0WhgfHw8q79Wq/UFAjV/1eK/5FuSJKhUKgiCIFsaqNHLfGY+Mi+YV7J9d0/z6Sx5sOhx1/IyBST+Ei/mA587cLYq7606l5c4afjzRErzozO7ZUykNB4yLlafsLxLXLVMDB7Plt+fp7/dc9aEkduxntf4btU5iC95cy454dVsjhWc46wIWbfjDwfvrfEA9AcCpG7UbB6DdZ2DT9pyBfe8CwBI+jV8ZPabxI0DFbyUzR1rL00smuQx+ygOX+aRZt802dHgjJZODHKGrDLcsVY7/EweMXJgaU6dxWjL6WPQymmdWa/XMT8/D9/3USqV4Ps+PvPoZ3CkcSQ99/zsG+W+l34P2vPSt5ouHd2Hn705d2+ls8lbdwLneV7Ps+5ez7F7Dn7P8+4Z9+/JfU/i9i/djuXlZWxcvxFttFH2y0ANOHb6GJ449AQuOP8ChEH6ZtHVc/POmzHeGccDDz2Qvj1FBxEitDotNNoNxIgxvzSPkfERJF6CFlrp8yUfsRejE3fSHaYrCeqlOlaSlfQNf6cFL/DghV761moO+NKDX0InToMF7jf2Y0COZw+ATBxJun8NDAUePIR+iMALsklzz8RZ8NjxEAmypRIpeV1Fgq6BQ2ow4yTO/qIkSp33OJ1gR3G0mmGwDkAdwMb071/xrymN7e4fANzf/euCtjeEGvAQ18ZL4yiP6oGQ0EuDBo6HMWIgSTdXdDR1ok4WIJB90opaaHQaaMSN1aBHVMf8yjzanTYSpJ9ka6ONqDPcBjLuO+na0o6SX8r6KfDSgJccI7JvkyRJdzhHnC1/idANcHSDHI6GVpz+ufNO0gHuG06ONLmyAhQnrjqBuBPD66Tfh/djP91RvruG30/SPy/2svMgCbLr7u25D1E+TsuHCLPnAgTZdXcsjZ719j1JaPO/JJ3gBkmAml/r0/XSQcqLcvfoZt9DC60sYNH22ojDdD+EOIizgEaWXeEyL7q/K+FKb4Cj+zsI/NhXgxO8nMTv+CglpZ59LrL7Sdi77MQLsjRvzRZK51luRMeTGwmaUdcmcLI8vzGU/en+tOUBbplLj1Pm9tRISkiiXtsqcZDOlBbwYIecHZY4jhHFEVBCllHhvoTSRjv76kmWgYNO/xKUYHWJScdPA11yvw2ZrbHWT7pqy074mhbUKKGEECEuu+gy+Of5WOgsYLY+27dUxWVvuOwdbYLCExV2niXkTaakHPJkyd3PS5XnSQGAHrmXxxLXXB5Tho98K+qeX15exmOPPYZNmzahXC5jYmICcRzj4MGDePTRR3Hq1CnMz8/D8zxUKhW0Wi3MzMxkm1PXajWsrKyYEyQ5SZB6j1OSmY+XXnoptm/fjq985StYXl7OnuExavUZ+6ky04Qn/toEXOsna6+NvAmspmc4Ndrz0uVwkj/aBEp7g5s3cdXkV06+tPtcH+sfBw5/K3Mtz6fX6tGe1WgCegNO2iSN2+f+snikTbS5jJv48gSUr1k08BICh5fU7RoNcikB81azj3wu+aAFp7S5W95kXOObe95lXGjzRneuLbPQxhNvYpo3zphv7h4vRZM4yPLuVwu4yWCFprMlj1jOpM5iv0G2yTplGFjz0gmt4mEa0wbDoOcsg6AFESQzNUFcK86Ws8h1JEmCpaUl3H777Thy5AjCMEQYhjh+/Dj+9sm/xVcbX81t5zsCRpBOciU0AXw6/zE34fY8b3USXvHSN23TXR4ngBd4iKM426DMBUXicpxOmhDApQTHnRi1oAbfSyeZ4yPjqcETQZM4inHq1CmsrKygWq1iYWEBcRxjpDaCcrWcvVHfsGFDtmEUIIyieGvvAgPuX/pfvGn20mCBO3cBBHccJ3E2Ic/O6c8FGOR5lERPeRlDO26jHbex3FldUpGFkbzeXwsk/XESD8xoyAMPXjbpL/mldPIf+0j8BDXUEHohQi/EWG0MY7WxNLjDgR2sBsQcPpJvLkjjAhzNqJntkeGOm1FzqE9cOij7ZVSDavp5y3AE68P1qAXd/S6CCjZOb+wJGJT8Urrco0tj4AdZ8NDJt2O511260ok6KW6ddNnMQ52HcGL2BBZXFtPlMUl3aU3QQuyL78/7ce9v9zjxnkI/JV5PMMMde7HXG9CgoIcsK49deS/ysvEs63Q7y7trWSCkW0c1qSJpJkji/nS9PCcA6HWK4zgGPGQT1Lbf7tnnwmVfxGGMFlo9191vq9zKghnurfuwm3bKvSu0TTtdpoW755aMlJISvI6X3ouDdHIfl7JNO5lOpp/5w5OUQQ4zO65a8EmbOEp85Bs8DiJIB0Zb4uR5abA96SQI/bCvPm2iIZ9nuZDtspwASD+f6AIVYlPQjp8GzOUykuxTrmLvjZ5MDa+bqeGWs1DWR+InuKd+D/AIgBKAC20Z6gtqaAGNWGRrcEaHW46SBFlQ0P1mX0/p1ueyYTQe8TXpeDqZ0N4USyfU8rtcWZYRDRd3LY5jnDx5EgsLC7jwwgtx3XXX4eTJk/jyl7+Mr3/965iZmUG9Xke73Ybv+1nQrFKpYOPGjSiXy1heXs42uc6btGhZB04egVVH2/3u2LEDO3bswLnnnovHHnuspw2mlSdN8r4DvicnUjwRkGnyEkfZH9w/8r6bjPIEhwNRbgK5bt06XHLJJXjggQewuLjYN/548sp9mQfahFfWLemQfNAmg1p7Wj3unHWc7DNeZjEIb/es5AW/7c6bS3BqvCYPWh15cx7Jp0HPWfIqx7ask5/RloFo45r7io9ZR/B1KfdaP7JN42fksRaI4Ym2POaxxPgzz9x1twceZ3rIOpnnrm62mSyP7lknq6xTmb/y1x1LWZe8Z/2iycMgOOM9Gtb6zDDKRoKmsCw88ozaoDJ5Rofb0gbhwsIC7rjjDjzxxBNZtNfzPDQaDbym8hr83CU/h5tuugnVWrV3shlHPZO7KI5WJ1bx6jU3AXb35eQrScQbc5rkunvas+12G7sf2I29+/bixMkTqFQqmJyeTNOx4witdgvtThvr1q/Drit3YXxifPWtdrfuVruFO+66A4tLi/B8L3sWHtBsNeEHfprN0Gqh2WoiCAPESYxqtQrP99CJO2i2mmh32vA8D5VqBXPzc6g36ti8ZTMqtQo2TG9Iv1zR5Ymkc2lkCXv37kXcidFYbiAshVhfWZ/2i5f20frq+qxd5pPkD993PHfHAHrO3TE/h0S8ORT/XGBFgu/5PX3+VCb4PfIsAibdC98ySJCkGS7dZR1KgRSWun/fQnATNhcEkOdxEqfLSqL6alAmAeB1leoJkeYnMlbiZG2Bomx5RlhOPyfpJ/BGPJTDMuJ2jE6jk36NgSbpPFEP4iANyCTpZB1xV085dPw06OW+SQ8vXU6UoPc8Rrx6rxvYcGVcUCMKo55v3Gffvfd6r2XBj7WbiNU+ckEJCoQEcZDR6sd+ViZIgv5gCB27DA8XCCnHZQStAKPRaMpbCN52M0C8qJv9IXib+AmaaCIO04mkzLZwAY04TLMw5N4WHb+DZqmJdkXsidHNzhgYLEqwmmkhghbaZp0czHBLToIoQDkpo4RSuiRAbNpp2XPpcPAk0/1am3WxY8NOHKdjMh7sZGnr99mB1NJtVfkSjlGQBEDU26bVjuYDML80x9j3fUxMTeAVr3oFkiDBZ2/7LPYd2pcFI9peO82sEvtrZPtndLMweDmKC2rITUJlUGNYcOOoL0sjFoEK/pIJfarVLVFx2RsuS6PslTFaGUXcjBE1op5gGfOV/S9tQhkEabbQddddh/HxcSwtLeGOO+7A3XffnX1BYt26dahUKlhcXMTi4iLq9TpOnz6NJEkwPT2NUqmE9evXIwxDtT+lcy771U0MtM8j1mo1bNiwAbOzs3jiiScQRZE5IdXkyIEVXJC80pYv8Jp3rW6ePHB5nvxqk3U3trZv344dO3Zgz549WF5eVlOrNRzk+TB+OvPF8t15AqvxXuOPBK3fGQfrnkYTy7DkEU8kmUZZnxXUkLyQk0ttQu7qcpkErLNlGV4GYy3n0yaw7lybnPMyAEtHs57neZbGB60ObbwwDRovrbJsAzQdxrzVggySRkumtDGpZYpyf8hzmdmj6VJJlxbgcOU52Oiym1gunpZAw1pgkLJdi8JZS3s8SLWAhaYQ85wey9H4/a/+Pv75gX/G8tJyOpmMfJTCEpI4nWAH7QDBEwH+z+z/waYNm1aXL3ieuqxBXvM8r2c5hPwDoF7n8lkdSIXE6y7TmJudw72P34uV5RUcO3UM52w9ByP+SOoQ+Omb8oWFBdTjOsZnx7GtvA2BH6yuby+VgQbQmmmhmlQxNjKG+kL6RqFaqaLdamOkNoJatYYojBCUA8RRmnHgcGo1WpiZn0k/P7W0jIXGAkp+CZO1SVw5diWmwim88IIX4vLLL0+XliReRoPv+Wg1W/jAkQ/g2NFj2HtgL8qlMq6cuBIb129Ep9PByMgIvv+l34+RkZE+pS77d1jIK6sZZ0t+tHqWl5fxT//0T1hYXEC708bi0iImJiewUl9JN9ucGMczr34mNm/Z3Bd4yia+3WBHFIuAkCubpB51T8ApWb2XBV4oCNP3hxhJvBpwyQJiSYRO1F0OggitqIXF5UWcnD2JU7OnsNJcQTtuIyyHSJCg3qqjNlrD5LpJlCqldElG3Ek/SSiyFtxftsQh7qCdpMs1siUoiLLntOwRGUiysjd6AjM9Hdj9HW6lx0BwGSgrnZXeGxHSZTI148GnMHn/boLESydUHrzVpVBrDZRlcaKk79qZI4bV7C14q2+DEy87l79Iuve6537ir+6tEJfSPW26etmhKWXRg5cGIRJkwSIXAGoFLTTDZn/Gy7CZLglWg1XuN++rFG5iGa1OMIMofaYUl7JriJHZmT5+dZcv+ehuROgF3W7xkERd3Rkn8Pw0WOb7PqIknaD6Yfftude774t0jjm92nKg3D1ZxoHmNEnnzkGeU5/Zdt/H+Ph49uWlbRu2Yd++ffDmPayP1yPwAnSanWxiKu3HoAwSbVKePe8lqwELt1yEPsWqLSXRghYdv4Nm2Oy5ni1NeYpBjUyOEv08C3YkIUpIN26u7azh2OQxHFg+gM7pDg52DmKxsojJcydxxSVX4LrnXoeklWDf3n148MEHsW/fPhw+fBitVgvHjx/Hli1bkCRp9uno6Ki6Rpz9RC11X/ZBp9PBnj17cOTIEZw4cQLr1q3rkRNNPrQ2rQCEK6ulSLuy0vnXJntyHFh4ybatAEGtVsuyQ8455xzMzMz04MJg0afxPa+8u8cT2mHGL+PHEyf5a00YGUdrDwV+3qXly3JyOYOFuzaZtO5zkMTikRYI4Uks8zpvqYiUY1kHZ9txJoi1XMOiVcqEO46iqIev2vxOPjtMGxYPNNDGohVo4vZZv2uywPjn6QZ3LW8pjesjlnN5n+2KvKcdS/zylsn24JkMOdtaWVnpa5SJZ8S0yZf2vEaQiiwRyXVonSY7153nKTIGpkO29b++9r/wuUc/h9nTs2lqXtRBpVqB53totpqAl0aYYsTYuHEjglKQvQV16/PlpE5O9vgaT/7k5JKv81t6rd5/D8CBm559L7r33P4YWlDHQ39QSCvbU7fcZ0MsBeBAELAaLKov13Fq5lSWZh930uyIdquNyYlJjI+Np8tAgjBbniDx6MNxAN4Sj56y3UCOLOfB61m6kuEt9gtJkqSHH0uLS3j0kUdXeeDKIzVuzUYTtWoNG9ZvwPZt21Gr1lCpVHpwkJtKOlzcEoUk7ma4RDGiToR2q41mo4lWs4Vmo4lGvYH6Sh2NeiP9azTQaXXQaXcQdSIkcTp+wyBd9hCGq3tTuKwc93nSleYK3vHOd6RBgqid/bqNNlfaK9lfvU0bcLbT/SqanWa22Warky7tOHriKGYXZrGwvAAEQFAKUG/Ws4wBeL1ZCW4C6Sad6TzuLE6m1wI5FoMn4dnb82R1wu3DRykooVbuLpEKK4jbMaqlKpJOguZKE616K8vQ6Jncp4wBPPSkf2ftdNt0waPsbaqXFUg3ixX8dZkZckLvsj0k/9297Nf1iXLu+ke7tsqsp6cP/s0HpjjgQ0EN2e+yjFbelZHPymt5z/Kx+1cKS9i4cSNqlVqmX6JOhIX5BcRRGjQJFgKcv+/8zJdw/oWWOq2yIMdn4eel/8O+kPXGV/O1pIMao/vZVnRWl4wEEWrjNTz/+ufDr/i4++t3Y/+R/WmAoput0RO4oEBH3333WVdveH/FZTmVkWaNtVfa8CMftbCWLuNrJxirjGUZGtkSk2T1SynZZ15jHyWvlJUJ4nQD0YpfweTIJCYnJnHllVei0WjgnnvuQbvdzr6kJR13ecx85n7kyYB1X06Y8/pL81+19rVgi7vnlk288pWvhOd5uP3223Ho0CE0Go0+ObUmLPKaFgCU+Di6JK78JlXyZVDb7lxucsd7CWjzBK0tDRgXLTDiQJvAa3MTbQIsgWmTz2lLWFxgQWZ/aYEGue+KNSHWgheMG+PpaOclQBI/WReXkRlunCVh9b9sn+VCy5RjfPPGDz+btx+HrMPt5cDLnzSeSrnUxrcc//Ke7CvZ35JGd11bYijr0MaUbDtJErz3ve/t4yPDmjeDtAIIecyy6homyKAFMuR1bbBpbWn1Wdd4cFl4/dxzfg7/7Xv+G2ZnZ/GhD30I9Xodo6OjAIATSydQq9WwML+A48eP49VXvhoveclLBqZ35oHFM00INfqdoLfbbXz0ox/FE3ufQLlSRlSLsLe5F0GQrq+vVqqor9RRm6zhnEvOQalcypYuuE0Ml1aWcOddd+LkzEm0o3b6RtZPFVRtvIZKrYLE7zrgfpJtvhchyt5gu0mb2xwSfrrxoB+mm0Z6gYdSuZRuPhhH2a97Qz8IsvT2vIlRd+Lu+766AaSP1Y071eCCmOz3TNCdQuoeZ06rPIaHTruD5ZVldNBdB54ASZgaw6gcoRE1sJKs4PT86bQPu2/l3WSqJ6gkAlLZW/uk/35fIIqyIrSAVZI7szkDWO7+HTi71apQ7v7lgTY56f7+4//+RzUQ1RNQysks0srONefQ6KTLS6JmhKSVIIzDPnnykt5JdM8kSU54IMZ/V51lfesl2eapbnkEPGSTbTcB7plwGxNwQA92yHvZb86Et446FqKF9MR9cMQNa9dfTuSejolzgtWsA5l54AIibg8JsVGn3IwzW8qBtKy7n9UXd4N33TLumQABqqUqNm3YhM2bNmOkNoI4itFsNbF3314szi9mGTtuDLqAh+uzrH+6/+RSFvdZyshP9azb/DDyonRvAvfpSs6GEOdPOQAixlIfOBqG0ScJevuEfnmpTM/eImKZTdYvsQfP9/plFun4SBIh096qfpVlY3TTX7lMl66wFGJ6XZqq347TDS3rrTriJEYrbKE0UkrtVxuZLc7ITfo/RSedc+l/aQEC7W2ZTHl19WuT2Z4uyvHvsmN4CDopv8soZ/hsntiMZ008Cxs3bsT83fMoL5azCY7mv7Dv5XkeRkdHsWXLFhw5cgSjo6O45Q23IAkTLKwsoDZRw+6HdmP9lvV44OEHMLc8h4cffxjHTh0DSkDH66DerqMyVgFKwHJrGX7ZB0pAG234VR/1Uv2sBDVKXgnhvjQwUdlRgR/5SFpJz6da87I2ZHAjSNLlJ25z0Gw5SjfjSI4nzYfN86MH+dXWNTfBKZfLmJ6eRq1Ww2OPPYbDhw9nciUDA1owgCfuAPq+lmRN+oDer1Ywbjy/YJ+dAwyynHzLq7VvjQ9tTpO3zIsDB9pchXmWF/DgJQ5Ms7ymten2CdD0jPzVJtbWvMW6N8yEX7bF/HLnTn8EQdAXINF0iHteo4XnTFpfWTJl0cbPsG629Bwfy6VQXJfVthx7Fl5583Lmt5Xp5XleT0bOWmHoQIM1mbeiMHnCpwlTnqJ011qtFg4ePIht27ahVuvd+fxMJ+8aaBFBxleez8zMYH5+HrVaLbs+OTkJIP2EZBzH2LNnD2688cY+pcc0WoEEDR+rjrxzN0gvvfRSzM7Owvd93N+8Hx+IPrDq6Lvl9TPdPwtqAHbot/xkdaLux90JO8Tacs9PnTMf6QaCcZg5+FWvmpZre6gFNYzVxlCr1LB+ej1KQQmhl66zPLj/IE7NnsLC/AKmJqZQqVQQxzFq1Rq2bd+GifGJVf4JxzZBki0BAJAtAeDJN6fkuyALb0oo/9wXC3regIu/3E9sSrHoBm4AAAt68cALVr/CEKQbE5a8UsajcljONmbkv9APs092ys0buUzZL6dv5bww+9KD+0JHyS9lmyCGfoj9+/Zj93270wlCNwMi9EOUghIqpUr2NhtJN7qdxKg36vik/0mcHO1+9i3xsjTtbFf2rlPmdTxU/AqqYRXVoJqu3Y89xI0YUStCEAcoB2UEcYC4HcNlY8DrOk2BBz9InRY/7L4J8tOU42xS56eOUKvVSicEHnDLy2/R9/Pg7CK+rpR3ZY+XjuPkyZM40TiBlfZKavy7z/iBn+5xIiYxPRMeD5lDLCc5PRMjCgzIslpwIBsfXu+z6aX0X5Kk171EpPZDXJP1n43gwNMRYBB199B9JnAmj0qaZuneZPfvDPEY9m0+Z524N7Vepxsk8VaDWxzMku24wGh2vfuck0MPqzLheekkv1KpoFqtotlpYqWxki6XidIAtJZVknhJtmnjWQGJr1zykng9+57IvUHkXgRuA88gDlCKSumXR5BOIitBBc++8tnYsXUHZo7NYKQygisvuxKnT57GhukNOHLwCMZr4xgfGccXP/dFtKfaOD17Wk1/dnbb2tTTAnY4NSdTOq9aqrbVnjb5cMduH4Pp6WmsX78ec3NzmJuby97gMVg+nud5aLfbePazn41qtYpDhw6hvlzH+eefj3JSxvqJ9WhtaGG0MoqvP/F1zB2ew/mV8zFaH0XQCrC8vIylpSUkSboX1Xg8jsnJyey8Vqth/fr12ecywzDM3m57XpqpYS0piYMYW7ZvwTnnn4OFlQXsPbgXS80lTG2cwvj0OI7OHMWp+qn0q0bdr580/WbvUhaXvXGGy09kdkXfOe+l0ZVL/qQrl8u+kNK95uxmEATYsmULdu3ahfHx8eyz7bOzs5iamsomOJwibx1rssMTa03u5FvsvD1e5ORamyBq5zx5z/htvPFnHB3tbhIs69bKMw+08kyHVQfzz+I5847HpLsny8p6NF2SFzBh/LhvLX5qcxfHW1mPxWeeI1lBDFcH39fwsGSM68s7l7LIQTam06pPmxM6kNkRTAPLNn+6Uluq5+qXsqAFEdcCa8pokERbRi9PYXCZYRFOkvRTaw888ADuuecebNu2DTt27MAFF1yA6enpNdXJSmoQDdqzDnzfR6vVwp133omVlRVUKhXMz88jjmMsLi4iDENUq1Vs2rQJ559/fs+ni6z6LYGyolJWuTynxPd9nHfeebj33nsxNzeHi4KLcMuBW3DxpRcj8RIEpQCbtm7C5bsuR7laRpREaLabiBGj0Wpkm8PdcdcdePyJx9FqtxBWQszOzyJOYkyum0QQBghKARAgm5Rnb+KSOF3Ln7RQ79TTzeu8dDKf+AmSToJGq4GwHKLZStPNS+1SuiO8mNyfap7Csr+M1ngL85X5NO09ABABDxx+oOcThu24bfLjbIHv+dknIkM/zI5HS6M9n2AMEKC+Us9S+REjdei97ttsF4jpXiuFJWzdujVdm8azr0RMHLE6Yc5+u8cuCBLH3b0cXAAljnr2Q4jjOMscacftns9YDpVJMuREKXs7WUtluBJXVicwSCeBba+dvWnseYsuJiHwAIzabQRJ6lxJB6uMMsIkRNyMMRaOoeJVgAbQXm5jx8YdKIdlHDl0BOtG12H7+u14wzPegPHyOMphukfJMA7/ILjzzjtxetNp7Nu3Dw888AD279+f7YxeqVQwPj6e0qAYYEDXUXnRbM0Zk06KZsQtXaIZOm4zPemWF4GLsBRi+47tuOHGG1Aql/DYY4/hnq/dg1a7hec973nY/Y3dqU5ptVCpVlAbqSEIgyyAkb2R9rrt+sgCifJfhKjnCwCRl04U4iD9fGb2GUTxaUS3vl172+8ys3iTzaFBTtwp8HnGICfNYuxktynAlN33sRoo6N7PggJ9TRh0PpUh4ALZzvvQ56JPDwi8n3KgSYE7Z+/sDSAdMQpOI5WFTb3ZGTJrJkxC9esuahaH++St+IytOw+9MPerL/LcTTSB3nEuU82B3nEeBAGq1Sq2b9+Oa665BkC6pv/CCy/EgQMHMDc3h1arZTr2/OuCUVdffTXm5+cxMzOTZkjMz2NlZQWbN2/GwYMH0Wg0cMUVV2Dz5s1YWFjAqVOncOTIETSbTezfvz9bz+3oWL9+PUZGRrIJc6fTQbmcprpl+310eYAE2Sdd3b1yuYyXX/pybNu2Dbfddht21HZg3bZ12Lx5M5aXl3H/zP3Yt28fyuVytumkTFGWdCZJurdREiZZ4MF90pUDHFmggvbR6DkPuntqyPJnmKnhvmpS9sqYGpvCyKkR+Cd8hHtCVIIKZi+ZxfTYNDZEG7Bl75YeudAmuQxsl7RJIcuHdk/LDpb18oZ28lju7yLx56wgxldOyOQYsGyvZTu1CatsR6Ndy1gaxDMNb8knxk3et+YU7tfKfpBtSf5qtPEGmdachttmelju3Jhz/Swn+PI5DTTeMl+0gJcWKGH+WsfSH+OAjhbA4Pp5CZUVuOGvV1j0yro03mrHebDmzSCtSawmRPK65bSyAMljR/zc3BzuvPNOHDx4EEtLS3jooYfw+OOPY9OmTXj5y1+OTZs29eGQh5fWAcMGICSeS60l3Pr1W/HV+a8i3BDiOI6jXC2j2Wii43Xg++n6+8poBbPTs/jSwS9lb4A9pGvOAz99yx8EQd8GkT78nk/nubRqd03+ubR/+Yk9t/8A05UkCcIwxOzsLPbv34+tW7di+eQyxi4dQ7lUxsrKCs4fPx9Xbb0KpVIJnU4HX/rSl1AqlXD00FGUSiVs27YN1226DutPrcepU6cwNTqFFW8FKysraM+1MTExkU3ogHRSUCqVMsXTaDQQxzGW68sIggBjY2OIoghRFOHUqVPYu3cvNmzYgJ07d6JarWJqZArf/7LvzzZyarVa+Ju/+RvsOZRuxnTttddi79692LFjB67adRVe8IIX9HzaEkid5na0ulyjk3SyTQhbUW8Qw309IUqi7Fj+uqUcnNHAn1+USz7c862ohUNHDuHEzInVCZHImJB1uK8DLDYWEZSCvjYcrn3tiHJPOySrKc5I0LuHBFb3epBLRpCkz0VRumcCIJw9sWmqe1vfM2kVwRS3MWWWSp6kk89swulFq8tS2OeRyykqACaBh/Fwen756q33/9H7+0iWYy/L8Oh+8rIclNMsjqCCSlhBNaiiWup+PrM0gmpYxf69+9FqtJC0E3TWdbAULKG+XIcXe6iEFZSC0uqnX6NV/rqvIWTp+clqWj9i9Kf2i/sueOUi53KXdCv9UzN4lv6XxszttdHT3wDCJMTCzALqc3VMnzONa6+6FmPhGL7+9a/j/jvux+nTp7FybAXlchkVVFD1q6hUKn3OlmuTHa+8YK0WYJF0spF3YKX2Jkh6Pn/Ys/bcBTLEJnxxEGPdlnU498JzcfTkUdSjOvyyj5VoBacWT2F2aTYLfCRBkj1nTuq9XlwA9CwF4SVAWV+48t5qEEgG8Z7yp1NJBkt+CeOj41mw1Us8tFtttOotjI+NY252LpUZN0BlMCahlxwOz0TgT5k0vLdJRqP4mkqmM57qUhGLD6sKKz0XwaWe7CEvDYpFvONsIuqRx1S3yyzSlnGcMSToDXbQJ25dcMN9tWW0Mor1U+txcvokdh/fjeB4gNHqKBojDZxefxrxeIyl+SXE7bjnKzEBgqwOP0mD9CWvhKSUYP/p/Thv+3m44llXpGMhScfg7t278cIXvhAbNmzAddddl37FyvNw4YUXYmZmBps2bcIDDzyASy65BEePpr7KwsICqtUqrrjiCpRKJRw8eBDA6oZ9eU69nChPTU1hx44dOHnyJL70pS+h2WwijmNs2bIFF1xwAaamprK9zJzvISecrG8CLw0Elb1VQ5R09LfuDngipk0CeKIMIFtG5YKqWaAiiNBO2n2fdE3CBDsu3IFN2zZhbmkO8yvzOL10GqVKCeu3rkcHHTSWG5nOdG3JT2auBayJEZeRv9ZEW5uHyOvuOM+euTK8JIQDJ1q/8j3N/mh2iiezefgzT6w2rXJawEELCsmyFm1yWZY238qbmPJkmXnjymgBIK0N7itrAq99VUPSxOM2rx8t+dGW9/DSBE0OtcCQxjMNf7kPh6xHtstLkdgHlG1bwR/m/yBYc6BhUMVWRIWFlK/l1bV3717ce++92TeKK5UK2u02jh07hieeeAKbNm0yAx0SNBysNq1BLduZWZnBm29/c2+hZvfX9WP3bevf3fF3ubQ+XdATnBCBCyRI3xifn06a4qtifKn+JQSN1PCW7ytj5JERlMI0Xb7VamFpcSlLfY/2RqiUKukAnkz3OYirMeJSuidCKSilgQ73z/eync7jKF1X7Pke4tE0xb0aVNOJdLuD05XTWNq2hPGxcRwoHUjfRM+FePCuBzExPgEPHo4fO46j9aN4cuOTWJxaxEPxQ0i2Jyj7ZXzmyc/gA40PoByWUfbLqAQVlPwSKmEF5SC9Vg7S69WgimpYRTkoI/TCbJIrAzcevHTiiAr80iovrWCPfN6V8zwv43/ohzh29Bje8573oFlvIgxCVMoVlEvpWlfnPLVaLRw9ehSVSgVXT16NG55/A8455xzVcXHr7qIoyjalqtfrWFlZwcLSQs/f0soSlupLWFhaSDcnbDfRjtpotBpodVoISgEqIxW02unXI2pjtTRw0mmjMlJBgjRDot6sp5kpnVYWwAlKAabXTyMsh1mGih/46CSddEwESJ1iP0Gz3USz00ScxGhHbYSVEF7gZedBmG6k2mw302UPYbobfaPVQKlSArwu3YhTWRL/nta0eyBbCuHBS/cogZhADAOuqIc0yLGx++euSTiLtMh9Ccz17/HqvgJyktGzbwGVdcdy7wI3mXCfiQy9EKPVUXjTHp6In8BSYyndEHKbj4vKF2Hvnr04sHAAS94S/Ha6EVs1qGZLnNyGnUD/pEALJrPjpjkqsi7LsZVlepwceNlkLPsMpjEBcAb+nPAc3HDlDThRPYHR0VGsX78et99+O06ePIkDBw5gYWEB4+PjGB0dTScr3Ylzx++gg9WAhntjyecu6OE+kygDHn1fF3CBkG4Gx7DgR6tL4rLPkXY3+uRAh+/5qJQrmCpNZV+PaLab6aafrSaCVoCVaAXwAT/0e4IBLqsk8nKCLcPgS588LSUl/XOo2niAWO5Fvy4IIoOopbCEzVs2Y3F+EWPjY5g9PYvJiUmMjY+hUq3gxIkTmJieSDMAl2bx2N7HEFbCbOLnlon08EH5/GySItKbcXK29IS3+vnbTtIBgtVgVc8SmmSV5kPNQ0iOpMsNs/RcpE5vHMRI1iW9uA6ADz7xQeAJceHhVH/4iY/qZ6qoBJUsMOFsNzpA3IpR316HH/uoXVjD1PgUjh85juWFZexZ2oNyUMY5F5+DybFJtBfamDg4gXa73bfpHNA7/n3fzzIgHn74Yezfvx/1erqk45xzzsHc3ByOHj2KKIrQbDazpb2827t06qWOkHsR8CSdJwnWyz5XxuHeM4nsLl314n79KPcrcBOykZER/MizfgSTk5N44okn4E16mJmZQblcxtLSEnzfx+zSLPYF+9DpdNTJat5knt8Ic8aH1LUcCOLJqbwn+ek+TZo3QcoLBLn71gRc47t7xgp2a+W5v4exQ76ffnrVgQyYWTzPCyi4OrUXDdZE2JqgWksbeO8J5hPLg5wQaxkE7jlZJy/9cDziQIVcTsK8cte1a9aeCVbQQQuA5AVeJC80uXBLmrgPrEAV6xvmneVDuWO5eaW8N2yAwcGaAg3c0RpT85SLA2sSz1EjBwsLCzh69CiWlpYwNjaGSqWSrQdcWlpShSUPD02hWG3nKaDzJs/D7Jtncd/u+3DXV+9CvVFHWA5RqVTgBR5a7RaWlpew8+KdeOGLXtiz2Z5MXZcb9XXiVGm7t9uubJIkPZsxxuJThlES9TzvJkGuXJykBt+9NU+SBEsrS7j3a/cCQDbBHKmOICyF2dczNm/ZjLHxMSRIsLC0gEcfezR1FgJg+/nbAR94Yu8TWWpzHMRoJ220O21E7dSZTZAuxfB8Dx10Mlwjr5um73XSMnG6xq0ZN7FUXoK3yUM0EmHBX0jXrscJHtv/WDppjdJJbpIkqAd1REGEY/Gx1ZTgxe7fdzqc239J7oDuwQPWAUiAjyx+BP4/+0Cw+hWGnjeXSe8kMnP0E+OrFIGHZCzJ3pjFnVRGsnrgIY5idCqdNKAkNjkMg7DnSxJhEsJre2gvt9ONPMsxRqdGV5eB+KsOusxwaCWtdPIUxTh+4jgmxiawccNGrKys4PjR4zhnyzkohSXU6/XVLCAvwJbNW1Knrh1hx44dmBidwH1fvw9TU1O47vnX4fChw6iv1DE+MY7zzz8f/3rPv6JUKuHqZ12NKI6w+xu7sePcHTjv3PNw5NgR7N+/H7uu3IWwHOL+3fdjZGQEl1x6CU7PncbuB3bjNa99zeqY7W6Kau3R0YnTjJVGp4Hl1vLqVyi6v42ogWaniSeefAKRF6WBlq5Tn3gJkmCNb5ZlkSF0v6y37y3qMLDWl90GTh/e92FgH12sAHhO9w/APi4gZFOd3IpJoofVjI9snxiZ6eEmmPDhRV72dpU//ShTzt0bSLcBpHPcezaGjFavZ859N7sMSG3ZwsIC1q1bh0svvRS1Wg1jY2O46667cOzYsSylO7NZXZrLSRmlpAQv6rfB7GxpjmFPl3irbx+zOnyv7xOGLpAhN87rCVp0z11WB2d0REGElt9C3a9jdmkWzbjZK9OjwFwyB0woYhOneqUUlVCNq9keCX68GrySfdrTB12dxhkAnre6xAZANoF3X0VwwY2Ol2aSZUEYEfiQ5Sx4sPkgUAXQBjCGdGncfPcPgDfroeyXgQiIJ+J0iR0tmQiioCcYki2H6P7ycgsnjy6jK/bibN8LGbzIlhF1AzjWFyA4ANWzDIcgSRIEUYAkSlAK04l/FozxgWajmWWnyKwOuaQq8RJMh9P4uUt+DqdnT2N8YhwjEyPwSz727t+LyfWTKFVLeOLJJzA+NY7Ii7DUWEKE9PPZCysLODV3CsvtZQSVAM2oiaktU5hdmMXy6DKSiQQL0QKaURPHcAx+08fE6AR2lHf0vIXnN6JyAlGtVlGv1/Hkk0+iVCqh0WjgmmuuwWte8xocOnQI3/zmN7GysoLFxcWeiRs7+LLuQb40P9M3ToZ48WdNat09ubGjw3vLli0YGxvDZz/7WTz44IM4deoUarUaJiYmEAQBgiDA6173OoRhiMcfT5e6uYl93qaQ7hrjnTeZ1+Yd/MaX+ciZb/zr7gdBgEqlkgVQnO7libA2B7CCUho4HjBeWmBc1i2vy3Y5e0DjDU82Nb7yPWsOpQUG5O+gNnlizDjIOhyNsi6NbxrNGi5581FNDrU+kQEVDlpIGdfmsVIXcNuyvJYFJPFhkO3L5/mLIVyWxyLXmcfXte4Z5GDNX53QwIrY5XWwNrHXBNDz0o0Ln/GMZ6DRaGBkZARBEGB2dhaHDx/G6dOnzTaGwTUPb4kb4+XKVUtVTI5Mwu/4OLLvCC666CKMVEdQKVUQVAM87/ueh3PPPRflctlMJ2MBtQa1xf88Wq3rKysr+OiTH8XS0hL2H9uP0dFRVFtVVP0qJiYmsHnzZtx0000IwxC+72P37t34/GOfz9ZivuxlL8MnPvEJ/PND/4yLL744jXB3N5dstVrZRksbN27EaDV9OxdFUbZRU6vVygbgysoKqtUqpqen8dX7v4pjx47hBS94ATZXN2eR2+XlZYRRWl+73caJ2RNZP7Tb7YzWcrmMRqOBF13/InzvC74X7aSNldYKGlEDy+1lNKL0k4P1zurnBxudRnq9OxF0nyJ0k8Jm1P3rHrei9BOFrbiFViT+uudu48e1gnsDGHT/efDSTSvbcZox4sdouAXOmrh7ynWanMlNz9wyB9/34VdWnXSXwuw+Ixn5q46Y762+dYmiCGEpTDd19Orwqz6CUoBZfxYH44Oryxri1S9hyH9xECMJuscXdR0yLwHGAYwDD+GhlIYRoqkFYH33uA1gDsCF6enHH/n4ark5APcDKHXP7xN17O/+Ofi6OK4DuLN7HAD/8Pf/oDCbsoV4CdOga9UAUTPKNulCkvIbCbJgjuszmWoN9H4pQjrqbsM8+bUI+TZ0YEq1CFZZ2Q48yUe8+oa3J/gFr+/tqx/4GB0fRb1ZR1BKP/vXjtuoN+podVrpRqndIAu8rq71xcSE0uCzN91nMePjaQPHH8/DBw99MJWDPWJiHAPxtTHarfbql26SXl73TarFchr+Ska23r67xCZE95N97roLrkQifT1aneSWktLqev2k940XYPsDbLd838fExAQuvvhiPPb4Y/iBH/wBhLUQh08cxkOPP4SvPfA1HDl5BEE1wPj0OPyK35eB0fHFJxRFsKNT6qyW9XqXqgzbJz1fBBAb6pXiEqqdav8me45f3Q32vDgNfrq/C86/AKMjozh44CCu3HUlzj///DQLq93AYn0Ry41lHDx2EBu3bMSdd9+Jg0cPYnLdJLzQS+lD1BvQ8NKvVciAh/ySCF87U3AZSS7gUYkqfZkeWVAbq7I8MpruebCwuIB6o47t27fDCz1EcYSV5go6SQdJkKDjpQHYDtJ+1MbsyeQkfuPR3wAA+Cd9jJfG0/2N4gC1xRomqhOo+BVgBlg3tg61oIZ4JcY5E+dgZHkE506dC7/tY/bYLMbKY3j5M16OD9/6YZSSEqp+FYiA0ZFRXLzjYlx22WU4ffo07pq5C9VqFXNzc30TZZbvbdu24dFHH0W73cbUVJqh84IXvAATExOYnp7ONk0MwxD1eh379u1Ds9lU67J8XverTQq0CacE64UbX5NtaAEAz/OwZcsW3HHHHfjwhz+MkZER3HDDDbj00ktxzz334NixY1hcXMTx48cxOTmJZrOZLRXRPlloAU/K3C9/EUHzibXnJb0WyElYGIaYmprCc57zHFQqFdxxxx1YXl5Gu93OfMq8iZn1lp3LDZrgcn9qNFrzJZ7AMj9lUEVL23ft8uRSw4E3HdQCJvJ5bR7DGSaWDDCO1sSeJ//MU5ZDec5f4HF15m2Qy7ho/eeuWUElLZAhn+cxyzrJ3XfZBpJHWgDTHcv+0/pZ8kjjoQz6rHVOekYZDcOUySu7lsn+/Pw87rjjDkRRhFKphLm5dD3nyMgItm/fjm3btpmbwqyVBtlhw0S/XFmXenf48GFs3749++JEEASYmppCGIa5a9YG8czCZxiwHMJKpYJ169ZhZWUFQRBgfHwczWYTBw4cwCWXXILjx49jbm4OGzem+dz1ej1b09hoNHDo0CHcdtttWFpayoIItVotS90fHx/HyMhIhr+M2gVBOsloNpuZ4j9+/Dh838e5556LCy+8EJs3b0aSJGg2m2i1WlhcXMzWlIdhiLGxMQBAu91GuVzO3gQuLy+jVCrhgW88gOd8z3OwYd0GAL3r0eWgzeM9GzipbJ0xarVaaDQaaDQaWFpawsrKCur1Oubm5zC/NI+FlQXML89jqbGEhZUFnF48jfnl+TS7w4tw8eUXo1QrodFJgx5+Oc3q8MI0A2R2ZRZLjSW0kzaaURPra+sR+zHaaPdtGMUb2KVEYPhNz/jteN4mbQGAErLAhV9N12J7Sbr5WBlllFBKU1tRzn7LKKPiVTDijSCMQlSSCqpeFZdfcDm2bdiG3ffsxjN3PRNXXHwFHtj9AE7OnMRLv++l6dcpPvVJXHLpJbj4kovx+J7H8eSBJ/HCF70QURzhK3d+Bdu3b8emLZtwx513oN6oY2JqAs+8+pl4fM/jiOIIl19xOVqdFu67/z5s37Ed0+umcXruNPbu24tLL78UAPDwow8jCANcdPFFmD09i0cefQTf/5++vzdLSGQk9WQlaZlKRtkHHnoAp06dwqn5U6g369kmnZ7vAR6yLCCg9wsT2WSb1qVn69GTpKfPZTn5WcSeNew0gU+QZBsfuv0tVPk5A5V0snMylZ0YacAISK3QmhfxnQEkWJ3wdyfQgHKeeBnNcn8DVzatKum5pr7plW/vve6+HsGqY+j2f+n5lGV5tXzumP1WBlfUrveye1nGAH3Zwv3zPR/VThWlnSV87qHPpfuOxABCoH1FGytLK0AHOOWfSoMm4lOiLlDidtivRtXsPNsvIO5mmMSr6/09eGn2HpJ04puIt/viTb+bpGcbh3aPXdCiFbZ6sznEsdYHX25/Oc1cmAQ+cegTwCGg5KdLgCpeJV3Gl5SAfcBKeQWN9Q2ElTDd9T8O068IdNJAj/vqjgyEuGN3L/skIrqbNQZpplIH6ZIH97lTR2cHnX7aBQ9cACPyutmQzCP02pmgGqCZNNGpdNBMmjhSP4LISzcQbkWt3mVsa9j0M0aMpc4SVqKVNBjnefCaYiK6lI65KImQzCa9exF1x9B7v/heYGuvzJa8Esqnyyh9tfvlkKkSKn4FjZFG+lWGOA0yZX9RumSyklSwrbIND+57EPFkjLgW49nPfTZ27dqFmZkZnDp1KrU/mzbhkUcewcGDB3veNGr+rhZ8GMZx5+UCXK8F2os82ba77gIln/3sZ9FoNPC6170ON998M+r1OgDgtttuQxiG+NKXvoQrr0yDaS5TC0BPRpacyMlzKwDCOLFPbtFrBSv4vgsmlUolXHDBBbj22msxPT2N5eVlXH311YiiCDMzM3j44YezJSESX80X12jjcjK7xaKDJ6+yjPwyinvGyZYmD64fR0dHUSqV0Gw2US6Xs5d2bhm6y+Jhenii6n7l5FbL6OAlHZIeuUTH3ZPXJE3a5Fkuo3BtWUEOV48EDloNI5dWYEme8/yTJ/EcJGEeWfMQLUCijVvHG23/kGHGDMskz40kv50ccv8PAi8ZMiTRaDQGFzrL8OUDX8bPfuJnMTMzk+6473urb3m7b1cnJycxMZHmXmaMgtdzPPCeKMPltfvy+QQJ5k7PYWFhActLyxgbG0NY6n5dIAiwaeOmFHelbrPdpL8MkDqqCZJ0jwUBg+jNytG1E8dPYHZ2Fu1WO9twbWVlBeVSGUEQYOPGjdiyeQv8wMepmVM4dPBQ+jUJP0AURzh+7DhGaiMYHR3F0aNHMTU1hXKpjE4n3QjT4YskzTTwvXSARZ0IftBVjlG6V0Oj3kC5XE7XNnbpaTQa6cD0fMzPz6f8FgMgDNJsC7fDsxvwSwtLWJxaxFx1DuWgnL0BHKmOYHxkvOdzjqGXbuQXeuHq9+7jbj93vHRJgftrx/Ci9FrUiZBE6eTAOdNuMgGkgzMM0tmT2+Sy2Whifn4e5XIZSZyg1W5hx7YdWSqiU+RJnGTBmLm5OZyaOZXVPzoyimq1uhpVTJDxutVqodlspkt3PA/zi/MYGRtBUA4wMzuD6kgV1dEqFpYWUG/WsW7TOsSIMXN6BpVqBbEXY6W5glK5hMhPnUW/7GOlvYJ2nO6hkAQJ6q06vNBDWA6zfRrc/gkuc+FsToTcsg8kyD7j6SYho5VRhH6ITqOD0eooRsujWDy9CD/xMV4Zx9b1WzF/ah5llHHR9otQRhlH9h3B1g1bccVFV2DPo3uweHoRF19wMcZGxvDVr34VtVoN3/u934snnngC+/buw6/92q/l6ohh77lzz/Nwzz33YO8Te3H/7vtx+NDhLFgZBunSq2wj0652TpKk581ykqzqgSRJZTBb+kLGBUizJfpwIV0DAHEU9+i5noi752d4wAM66KATdBCF6a7n7aCNlt9Kr3V3Qu8E6VvnTtBBXIpRHi+jNFpCI25gpbOClc4Kmkkzf1KdoO9NchCt7qzvJpvuvhvDLs3ci73VpQ6OLg7aYDWTgu/J68BqRolWNk1O6AYyxSaL8IBqrYqwFKJSq6BULqHdaWN2bhZRFKXjKE6XxSVeGkz1Q7+njZQVSnBICyjJbJJU8L61wYnvZjBEkYNTfYEqDxgtjab7DQVBlv3lysRxjHYnDYg3GulyRc/vvpVUNqiMEQ+fpZCgbzz0ZGp0AzXuc4juOIzDnk92Bkn308KuXPfY1ec+f+r00E033YRvfOMbePTRR7GysoLf+I3fwNatWxGGIZaXl3F6/jS+8KUv4Mt3fhlXX3M1RiZG8PDjD6ebL3vpJphJkAaDwmqIiy65CNvP3456q45W1MLC8gK8kofIi3B85jiCSoCl+hLq7Tom103i6ImjGJseQ71Zx8zcDJbby/DLPrySh3bSxvzSPGIvhhem2VFne3NkD166Ya1XwVh5DFW/iqXZJfgdH7WgBrSBoLPK71KcBpDCKOV7GeU0wBF173cDTJlsKRNx7c23K6tNeNyxPLeWbvi+j/HxcWzevBmf+tSnsGXLFrztbW/DxMQEZmdnsXfvXnz+859HGIbYuXMnPM/Dhg0bMD8/j8ceewyzs7PqixyebMnJC2cwSHy0CaD1KUZZnnnnnnMT8Oc+97m45JJLMD4+jiRJMt+p0WggCAJ89KMfRavVUrMFmM+Mr3Zd8iDTCTn1WDxh/vDbd7kkZHp6Gi95yUswNTWFkydPYnJyErOzs7j77rsxMzOTBRvc0iHtjbyWoaJlcjjcXPuaDGj9Kq/z5Jb7kQMqpVIpC1RMTk6i0/n/2fvTYEmS9DwPfWLP9exr1Tm1V3d1V+8907M1ZunhQLMAAgiIEEUKNInGX6LMZDKTyah/oumHzESTXSOJK1AgRUkkdQVegKDIiwFn5cx0A71NV6+173Xq7HvuGavfH57uGRkn8tSpmSGle01+7FhmRkZGeLh7ePj3fu/3ftGAQzKvffPOexgTSNVFOVMPAxaGXWMeOJX3XZadokCddN+oa8sDs9LtlgeYHGbqZ9s6ry/y+hzg937v94YeVx//qECD7/u5iFveTZBX4bzXdMlrtMtbl/mdt36Ha9evSSoTfQMsEQlhGLJv7jM+Oz54k/SMv7zX1E6D58/zSA1bcajNhqx3rVbTMWqWZWFavdh1x6Zaqer9cq8777w5+w6ry2H75HWtQBBFkfbIx0kvTrfXPgrhVDoYURxRKBQQiaDRbPQnP/pgTyISWq2WTuskRH+Bm56A9MMNcaDeYSD713FS6QON/uScRskNs2cgmZmVs4H2DsbEA1799D7asOiBIOnvjlyOuP47cvm/jYD/u/z/axlw7PcfWGmgJL3ozZYDc3fqXnlspkWmLgOGIxzwyqf3S++T3T9b37xtpmlSrUhqtYGh2V9RFNHtdOW82APREQzMhWkGwaPOZww20NDvsm2X992B53r6eWqIgWfhgfMZcrG0UFngqe5TnDl3Btdz6XQ7fPTJR3Q6HTa3NumG0olRKpfwip4OuRpgaDEojKg98EY/lakyzhOzvz8GgyKKKRAGDrKFgAMAzcDzQnfDL4bhM7T8rM+lf9NF9F8NpPaIiCTzxzItnp1+lhOjJyg5Jc26uHnlJpsrmxSsAs8//TxLd5YwIkMDGK4h9UfOnDjDL3/ll5kYmUAIwZtvvsmxY8eYmZkhCAJu3brFzZs3eeKJJygUCpw5c4aPPvqIV155hfX1dX784x9TrVYZGRlhcXGR8+fP87u/+7s8fPiQv/bX/hqlUomxsTHWt9bZ3N3k/sP7JEaC6ZqsbKywV98b1K9AapVEtgRKz06d5dzkORI7Ya+9x5kLZ2iFLR6sPaAZNLGKFpv7m6xsrdAKW8R2X+MkNEIJuPaYMo8qaSBI//dCehzh9MGhOAVWJH2wwkmcPogk5G8UMyTreYe+IWmaJpOTk8RxzNWrV/nWt77Fr/3arxFFEbVajevXr/Pxxx/z8ssvc/bsWd577z3W19e5du2adCYNATDSxl8abMgad1kveR6AkPXA5j038gwmdfxKpcK//+//+7iui+tKx1i73ebBgweEYUij0eCnP/0pjuMMHF/VOQ0UHBZKlg1VOMwAVUzfPGAm68HOcySkrxlgZGSEb37zm5w5cwbbttne3tYOjCtXrvCTn/yEdrs9NKPSsLoO0xnIAgt59mG6XQ4LTxj223S7z8/Pc+7cOba2thgbG+Opp55ib2+PH/zgB7RarQOsiCxokwbc8kCNdPuns9Okf5c+Tro9hoFHhwFmWWaKZVky85YnBfdVyPjKyooWfs8DERVTwrIsfc9pcd6csZvHgoD8TFvp79IhK3/v7/29A32YLUcmrOZ1Wvrk2coM+5zengdMpDv62Zln+R9+5X/gR+UfcenSJZIk0TFw+/v7dMMuP5n7CTcbNwfriDjy54FFxCN+M+y7RPQFkwxhIKLedUXIeO//XysmMvZdyQyo1+xi0ki9ViDkEboEhy2ePPni67Qdqf0MBlMRHlYed1H2sy7i/q+0+Pu/SkkbcWnU+DBDiL5HMP2AMg1Tg2CWKTUM4kQiyrZlS1ojQrNGwkgu5A4rWntCSIPBxNTeYPWdynDQv6QckPLfVMk7hTJwxWC7HdiWNoTT2/LU4rOGc/r1MS8zbXgeaX9DGiTlcplyuSzTsYbSw5teIKgxkAYd1TXJl/xzGhhDNSwGvPzq+2H7qLCHI4AWeekk1ef0ayISOkEHN3EpForYyGwVkR9JDZZYgry2YffHZHquHYp7HwJeH1Z/dcx02+btP+QY2vBO/y4H1G3FLeaemSO0QymQmnQoTBfo7nUxOgZu5OIHPp24w0Rngunu9MDiVVFND1uUDls4pffLLjjTv8mjwabXJ4ctLNNjdGR0hJc+/RIvf/plsODe0j0M22BsYoxu2OX2vdv89P2fcuvOLbCgPFLWzAUFjmczTijwJKvJoEMZjH7IgzpWFpjJy1yRBWCOVFL9LBAyRKO3ioyIeG/3Pd7bfW8QtCoIOCt/83HnYzg2/PD//Rv/PSYmnukR+zEjjREq9yt4pker1iK0Qgr3C9jYTK1NMTM2w4d3PqS936Z8sczDrYfMjcyxUluh2Wgy8eIEm/Ym28Y2ZtOkRUuKHU7OMVoY5fLly8yMzNBNuhjBQcpz2kEyYUzw1JmnaLfbvLTwEi+ef5FarcayWJaCxZZFw2xwv36fzdomnU5Hj9sBw4U++KDCcCJLflbbQjPsgxOW1ChRQEXX7A4AF+r/UcVKLA1YaHaFYlEogEI4LMwsUHWrmGWTvYU9vvfge1ixhd/wccoOp54/BUVodVqMjY3xwQcfUK/XdZhwnhbAsBj69Ods7HjaEM8zANPHyYIUWWM1Oy+oUILt7W0Mw2B3d5dbt24xMjLC3bt3abfbjI2N6d+mx0VeCIB6HeYJfpStk/5Ntl3S15BmDKTbK30sy7I4duwYCwsLmKapNTS63S7FYpEoig6wGLLHSPdZ3nyYLdl5NR3mkG37PCAh62hInz/d3orB/IUvfIFjx47RaDQoFApsbGwwMzPDwsICy8vLWgsu3fdZz3z6mg4Dq9LnzgIQ6XPkOUuOCkRk+8+2bV599VUWFhZoNBrMz8+zt7fHj370IzY2Ng6AhXngWx6gk34dZpun98u+T1/DYb/PlscSg3ycfR51c2X3Gdb5lmXxxS9+kXa7zdramvSuC0GlUuGVV17hv/rCfzWQZ/2wklefo+w7rBiGQbvd5vd///ep1WqS6o/B2NgYQgheeeUVXnzxRW3YDAxEZQtkwIrs+wEwJAt0pMAPlWc6C4yoz+1Om3/xL/4Fy8u98IceyhVFEUEYyNcgYGJiAoHgiSee4M7dO+zv7TMzO8P8sXmuXb1GIhKdfhHAMA1Wlle4eu0qY2NjvPjSizIOOZYx6QqltSwL13OJo5golucSQhDFMszi448/ZnxinFOnTkn2gmkQhiHb29v6oRDGIY16g2efexbHdvRvbccmiaVxEUURW1tbrK6uUqlWKJVKGKZ8kLTbbcrlsjRWHVtP3JZl4bgOpmUOoMoY0nCJE4kgYkgvbL1Zx3M9CsUCYRjSbrcZGR0hDEKWHi5x+vRpypUy9+/dx3EdZmdn2d3dZWNzg5MnTyKEwHakMeEVPQIR0Ol2MFyDSER0w64WB8NC6jNEUrchNmKqY1V29ndIjISR8RE6QYe9xh7lkTLCFOzV9zAsA8M2aAcy7EGYgjAODxjSh45vDBzT0eKURa+IhUXoh1RLVTzbI+gEmJiMVcfY39kniaRwpWd5WnU/HZaihO6Ucr9tyhRlM1MzdDtdLTB1+vRprly5gmVbXLhwgVarxa2bt5idm2VxcZGlpSWazSbnnzyPSATXb1/HHXNxig73V+4TJAGma+KVPXYbu4RJiFWw6IQdWkFLL9b9RAJbeRkYDKTy/UhxBM/28GyPgi1TqzmWI9vGtPqpTHuZOdS9HsWR7rsgkmKhKna53qzrDBXKg4uBpumnKnEADO1PDod23uOXHvgwYDBnjOX0PsM8/drLn9lX/ZmGiRu6TBQmMBKD0AxpBk2MWN7zGPSNvhwjOg/4Ue2X+13aiMoY09orn2kvPY/maVmkAIs8L7nWwTAHt2NAW7QHQVyQT2EVv67ajIPtLQ/Rb2v9WR++3+7DAIOB55refchAMoZ/d2D7Ifu24ha/e+N3df+oLEiJlyAWB8e2tW0x3Z0+QFk+ULXMYicvlV3aK5O3yIT+IjS9qB24zhzjKLso10aHgG67y/K9ZT73qc/hmi6j5iie4zHlTSFcwW6wi71hs8gi1VKVsBMOGKPZRXfWiMm75jwqb7bu2fWVOl6aZYjBAQYJppwbY2INYEh8TD6bTpw+wez8LNNz01ofot6sc+P2DY4tHkOYgqs3rvJw5SF2wcYyLc7vnCc2YgIREBohwhYIW7B4ZpHyaJnSaIkHaw9459Y7FCoFxmfHqbVq4Mt+aos2whY0ug02OhsE7UALPPuJj9joXfdG74KPwR9f/eMDY8jCwrAMvD0Pe9TGrJgDxriZmPq9JSwKZoHt+jbba9sYdw2+5H2JsBOytrRGp96hYBWYGpni2PljnH/uPD9986fsbe9Jkd9UH5qGiZtIvaL0Yyft5czzAKv+T6cy1AaG2cviZEaEZkhsSTZGYAQauFCvkSH3SYMUvuPr96vxKn7bp+N1+N6V7x1ot3SxsTFnTOzpFGChwkOSPgMjzbZQYrNqu/psJ1LbSQnk5gEKWWMqe2+o9kobidl7fmpqCsuyuH//PsvLy5w7d452u02lUuGTTz5ha2tLpyfNZg3JAzPyjMe8PsyW9P2XvobsPJa+xvRn9YzMaj+4rsvLL7+M53l0u13CMKTVavHJJ59w8uRJNjdlyHS5XNae7+x1pMdi3jVl65OeR9W1Zcdonm5Gul2z15Hua9WPpmkyNjbGxMQEcRxTq9Vot9sUi0VM0+TcuXNEUcSDBw8IgmDgPNljZuuR14dZoCRv7KW/GwZ2Z+fo7PMo3Z+2bXPmzBmeffZZLMtiYmKCIAgolUpUKhW2t7eHhnsc9iwYFhqTbY884CLvmv+NAA3qwEcxwNOVOsoxH1Vc15WUt/V1aSTaNi+++CKf+cxn+rHMR6jvURsmfePndYIqSZLwzuo73BP36FgdrIpFFEQ04yZ+x6dzrYM1Y1EuyThy25TaDbYpH7gmvfeGJb/vKc2n65o32R61pAfGRHGC5849x5WfXtGDTN28hUKBolXEMzzufHSHiYkJ3rj9BuPj44x4I7TWWlx/eB3PldQrL/YGBuLiyCIrYoUJc4Jxxgk6gRYN8SxPG6yu4YIDraAl1ahFL51nLHj55MvEccyUmCISkTRoCWnWm9y9epeTJ08yPTHNTGWGeWMe13LpBB0qxYoMWXF6wImIqBarBGGAXbepiiphGGJv2Dw5/6TW81AhHu12G9d26Zgd3kveo+JWqHgVuo0ufstncW6RsB2yvLTMc089R9Eu4u/6lI0yi5OL7G3t0Wl3mPQmqdfrzE3NUU7KtNfbPFN6hgsXLvDWW2/htlz+wy/9h+zv70uQpbf4GHPGiOOY/XAfR6dHgKJT1ItXy7II4gALi4JX4EuvfInLly/jeR5f/vKXuXv3LpcuXeJXf+VXKRaLfO973+PEiRM89dRT3Lp1i5WVFb785S/TarX4/g+/zytfeIVrt67x8bWPsTyLZ158hnq7zs17N3n6uadph20+vPwh4zPjuCWXq7eu0gk7zM3OERFxZ+kORbuI5Vks7y0TEtJwGjRp4hu+9L4kEerv0KIWWuvyxS7YeJFH+UEZCmALm+X1ZRzDoVVq0RVdGvsNWkmL2IrZ6eyQBAmGMCgFJc7MnaGwKRXGJ8uTLMwt8LDzkKJd5PTCadr1Nrubu1QLVc6dOse92/foxB1iV6bi26xv0o7b+IZPO27TER0+8/nPUPNr7Hf32e/uD7zf7+4TxEHupRkYjBZGGSuMMeqNMleak+8Lo6zcWSFshOyt7xE2Q9zEpWJXcGIHK+oL2glTKrarhWNsDaakS9NxEysZ8Hwp2m42JeHQuO8MC0BplSjmhzJ+0xTyASAk7SV9RGz5JpvcXb87ePpKSvn+MV+NuCcYiK3fq+9VFgZbyO9s7IHv1L6WYQ08wLN00GGLjOy8nLdQVEacUTAojBb41m98C7tss7a7xtL6Eh9e+5C13TXZb04idS9MqXuhDQKrZyBY0eEeaIFc7Me2jv1Oi9xZkYwJd4Urv1Px4bGDGZm4uNhxb38cCXbkLITT155d4EA/xrRYLHLq1Cm++MUvcvbsWdrtNqurq7z99ttcunSJdrtNHMdUq1VpRDH4zB62cMpbLA37Pq8MAPM5z/zs4iz9u2GLfCEExWKRdruNaZpcv36dK1eu8Fu/9VuMj49LnYYwpFwuMzExwfr6er/bchZ32XOk20K1b7pe6WvNtkX2mgcWyb0/C0s/mwCI+9ebhL1j+2jnwUn/JK8uvMq5c+eI45ilpSU+evAR0U7Ev/Opf4fR0VFeX32dt/bewvO8AQp9MSnKcwfyWOZ1E1EQfP03v87ljctsPNhgbm6O+r0644wjhKDT6eA4DiMjIzzxxBP89jd+G0CnlBRC6IxRCoxYWltidXuVWqvGrfu32G3sUqgW2K5tExohpdESTsmRAsupDCa+7dM22loENDIjrm1fI3RCYifmex+mjHD16G72/gHGwBg1Doh4prOYDGhmZEQ/1f+AEGhs4eAM6G44hrxHlSFfSkoQ5RtD2bGRHdO2bfPn//yfZ3Z2VmpteSaxFdOO2ixvLlMcLdIKW9xZvsPb77/N5PwkN+/dRDhCsjAINYjRsTs0jIb+rFgYj2LPGMLQbIsB9kXmfXqbHfcZGQMgRmLhClc+I3rX2O12uXfvHtevX+frX/86Z86cIY5j2u028/PzfPTRR3S7XR0unlvHzPMh3YbAwfmfg6r/ar/0a/Z91hjNsiSyRqFhGFSrVcbGxlhbW2N/fx/XdVlaWmJ5eVmL1gdBoLW+ssfIGqTpumSv6zCbRO2XFxaSPcZhbZh+ntq2zXPPPYdhGNr49n2fQqFAt9tlaWkJ13WpVqvs7u4OgEXpY6XPnQU28uqSV890yR4nD/hKX3deWxiGged5PPfcc7z00ktagyIMQ32cUqnE8ePHdQhFtk6qrbP1TV9/eg7OC504zOY9yva88gvR+k434qMG3mGVzusEgHa7zdWrV2k0GgRBoMXzlLE47HePa5hn65NX//TN88knn/Af/eA/YjPaHNxRPWhWgP/X459bgQ+WKb3Blmlpz6kCJfRrL2We2uZYjk6pp7YhIOgGREHEzokd7akkgTiKZYo6w6TgFIieiPik9QlGQS7WJ4uT0qsfxpLKm0CRIpZp4djSqxubMe5nXJJiwnX3OsKSBnIURNiRjYgFJUoyDzwGUSxBhtAP6Xa6NOtNRiojdNodRCSIo1gi36ZFy2jhjDrUghpmx2RsZAzTkboQYSR1JtS4Ujd0qVTi/PnzWtjHcRwcx6FSqdBsNrl37x6f+9zndNzn888/j1kxuZfcAxuJ+I+EJKMJP+WnMhf6M/ABH0hv4Qk1SIAp+W8KE3vMpuJVMBOTpJQwUZ3gHfMd9p/cp+JV+OPCH7Nn7mG5cpHg4FAMJcBjjfQXEwW7IDM2mA4iEBQokEQJFbsiPeqex/T0NNVqVV/X2NiYRuFV7KEQQqfXAmS4UbsLPlSMCiPJCEWKLNqLfPLgE8Z2xzgVnKLVarG1vcWcPcezx5/Frsn77Bunv0GxWOSfXftnPHH8CZ599ln+2T/7ZxQKBb722a/x+uuvs9/ax/M8nYEEA4QlCBLpdYpNqXweIrNoJGaCsAVTc1NERsTS2hLl0TLlsTK37t3CT3wm3Anq7TpBIsXBAiNgp7lDQMDd5bsyJWncJapFhJ+EfUNlt/evyrXU+xD4BJ01w/Klkerg6KwZriUzZ9yv3adoFxkvjHOseoyiXaToFPWrbdo6q0SURISxTG/qR75Oo9oKWzTDJk2/yZ3dOzyMHlI36zTnm4fSXq3Ywoml+rkd27ixKxdYka23l8KS/l5tc2IHK+yl3zP7okJCSCBBAROhEWrwIjRCDVbEViz/U2kD06kF1W/Si/DYio9MwzYwcE1XesR67JY4iqVhgxRvVfeYQNZXCcelAZE0DTwx+ir5R05xmK6T+NmBjjRgkQYz0t/ZvT8jNsCH+Yl5xqvjzHXmsAyLpcaSjr/MAzb0K/J6FfCQ2AmhGRIYwYAApwIlNP3aiujaXaJCah8rfCRooRf3PeBCGwFxf3s6Ljy7bzfq4t/zOXXqFE8++aSOPa1WqzpG2rIsPX9nF2pZr0/WaM6Kq6nv1eJWCXkN89BlPXNZ4CD7Ob0GSB9DvVYqFe1tq9fr3Lx5E9/3qdfrrK6u6mOlU+ilrzPvWvKMxbwFex7gkz7+YY6LvHVTdt9s+6yvr9PpdGg2m1y/fp07d+4wPj4uxbFbLeI41kLiQvRp0Gnqt6qXYlWGYUipVGJ6epqJiQk6nQ5f+MIXmJ+f50c/+pGON1fPeEAr7O/v72MJCzd2sbCoelWmjk2xYC/QarU41j3Gu0vv8tzic1xbu0ar1WLemsfu2noBruqVBW2U6Nyf//N/nrfeeYvnP/U83/7et/nh6z/k2Zee5dd/69d5uPYQp+Tw1ntvsb6zLoFfI+qDGGYPMO69qlCIyEjNo6k596glDTwMvOaAFVrsM/XZQRrtruHy8e7HTMVTPHH6CQhhzBvDTVzGpscYGRmh0+kw356nnbQ5Jo7h1b2BOPDs/TIwppEhQgp0iK1Yh4ykGRbq80AIiRnhWz5Np6nbToEYWo9rWBH0GRc4FJeKTMxM8M7td/DueJSdskylikXnVIfOfgd/22ehsZAL0ql7IfdUvX2ycfd592b2/s/G6w8Te8zOTenSarVYX1/n6tWrPPnkkzz55JPMz8/z4osvsra2xpUrVxgfHx8AadP9NUxQMA94zZuX8toiXYaFa6S/y7aBel+tVjl+/DhbW1tsbm5iGAZLS0tcvHiRpaUl3nrrLZli1xgEgtU5ssZ3Wichr+7Zfst7DmTbIw/sTTNj0r9NP7tM0+QrX/kKTz/9NIAGv3zfp1gs0u12EUKwuLjI7u4uzWZT/zbdfnlswDRj7zCgapjQZ7auj2tj/1xAw6MM++yNeRT0K/07tU0ZVs1mkziOaTab1Ov1oZ2dd+xHIVGHPdDT++7u7vLmm2+yubnJysoKvx7+OpGQHlxhCK0U3g26zB2b4yuvfYVCqUCURNoQiYUMH0iQ6e70tiSSyuOiv019n5AQxZFOiaYUytV+2WOrYwVxQKvdYnVllSAMEIbAsiVlEFemWDNMgyAKqJt1IicicAOCMAALNu1N6aV0JPsgSiIZqtCjewshjQBGe42kno0GWndBo/3KnrLQ6RGpANOZhk7rMTzV+0+XoL+fEfbSK9LPSW9iYlQM6SFVOemrMt95MpEgjgtuWDcQtmDkGyOssUar0WLWm2V8ZJztzW1KxRKzM7Osr6+TxAlT01PU63XiJMaxHSanJtnY3KBYLjIyMsLO7g5REjE2PkatUaMe1SkWiviJ9IoEBOw2d2nQkEYevfjaXvrDWFpY8j/9zFROKw8JbDThb/zLv4GNjWu6lH9aRkQCMzL5W3/4t7CFTbvWprJUYf7GPFurW7iGy3ej77K/tc/+9j4fv/MxZa/MNtsUggLGrsHd1l26UZfVcBXXc/Ftn8JogenZaebnZY6whYUFdnZ2mJiY4MKFC4yPj3PixAls26Zarer7R6HNSsBTTXB2LA0tV7h6gvM8j7Jd5nTxNI1Gg0V7ETd0mWaaxWQRwzCYiCfoii4dq0PgBxiBob0NKqOHEjiNk5ggCtip7LBb3KUlpChXYAf4+PiGT9D70zGxvb9ADaz0FCDg3rV7Wi/AIDVJI/T9+jilYBcwIgMzNinEBYgkUGVhDYQiKAp82phuu+1+nHaKpTAsVMJIDA08uInbByh6qdvUZzdxNZBRDIrS2x3ZGhzMzrV5SL9pSgBQGKLPsrATvahO7ITEksDSwpkFZo7PsF3bZre5y/rOOvVOnYAA4YiBFIOK9ntYWsEDRfRjkq2kZ+yrjBTC6KdBVKCr6Ht1D4QepIGNHkgTWJKSrPohHUOvPh9W/ujdPxrcUALjqeFAx9BtwtRGRpqh4SQOhagwsM3Gltcd97YhQRHDkOkfNQtmCFih2BVqW9fu9kGMI4AW//TBP6X8v5YpWSWKZhEjNPBP+RhB34PpxCmwogdmuMI9AHCY8SC9Or1wyvPIpRdSaQN36PBJeXxUmjvF0lPfp7elF2GFQoHx8XEajQalUompqSlGR0fxPI9ms4lhGERRpNMFpuuZ/nzYIj27b7ZkPWZZjYm8BfGj2iRvPdftdul0OrRaLdbW1rSA3urqKp7nsby8jGmaWtg0G2s9NjZGq9UCpPhpFEW6jVRavkqlwsjICOPj4/zSL/0S7777LouLi7z44ot4nkeSJHQ6HYIgYH9/f8DAsG2b3d1dbNsmDEOeeOIJLl68qD2DIFNk3759W5833W7Zdm61WtRqNZ69+Cz4sH1/m5Jf4kzlDOcK5zi5cBLLsri2eg2v4x1ISwgHFfXz5lJtlPdSjYZGL521JYH6bKrVNHhxAMwweveunb9P1kj/zr3vyDcfDva/ickFLvBHf/GP8H2flZWVAUdPun/zitpuG3JOJoEk6N+Pw/bPttMB8EJIJmtiJ32AIgVOKDBdbfOqHrPHZ9lt7rK3tUdkSeZGO27T6DZohk1CQqrFKguNhYHzZF+HhWSlr2HY98Oep+m5JHvN2bkmC+gIIbQDbWRkhPPnz+P7vs6cMD09zbe+9S2Wlpa4cePGABMofZ7DwM+8bdnrzV57ts3SXv6842eBFPVvWRZLS0vcvHmTIAgYGRlhbm6OmZkZHjx4gGmabG1t6cxr6WvIggzqeOlzZ4GfPGM8D3DKm0vVedJhfen/LPPRtm1GR0cJgoBarUatVmNvb48bN24wOjrKrVu32Nra4uTJk4RheCjbLd2P2XCWR2k3ZMGJ7PGOCi6ky2NpNOQ9DA/bP+/zUX6T3afT6bC1taUfzJVKhaeeemrgwT/spj1s21EaLHvdxWKR/f19TUOasCZIEmksdjodjcrPj83jbDiYyyavvvaqRnzTg/BRQI0aGAppVyIu6hztdptWq0Wj0ZCvrQbNZpNms0mr1eJq5yp/Yv8JJNLgEAgcQ+aNtgxLo9i2sPHwZFyiYdLutvE7PhYWk2OTOJZDfa/O1voWIhKcPytTAZrCxLM9Wo0WG2sbdFodjs0dY+H4AgYGYRBKVepYIBL50LctW8bxWxZRGLG+tk69XqdWq1EsFFlYXCCIAqanp7Eci47fYXd/F2EI2p02tmNz+uxpLMciiAKpn0CCYUkthTAOJTDTE78S9FJamYAhBSun5qcI4gA/8Dm2cIxWu8Vyssz88XkSEoJWwNjEGI7ngA2WZxFaIY24QSISbGGTdBPq1OlEHerNOu2kTZzE1Bt1/NDHN31a7RZxEuPjI0IhwSAeTR08SomQoE+729bbNms9Zo0BhPDx6se9gQRv33tbvi/A6zuvpw8En/Teu/A/X/qf5XsLuN/7Rxph//k/+c91vL31Q8m2SeJEind92yMOYoQrBujqlugpPChWDj3PrmHrBYfZMrE+sXAMR6ckFGuSFWNiYrfsforLBEiQbBrLwXItzeAxMTVTZzKZZMaYIQxCzNDESZw+CCUMoiDCNEwcyyFOYsmKSLqEZkgn6bAltrhZuUlkRXglj07YoRt1h8ezH1Ic08G1JEPCsz08yyPshIRdycgRsRT+S2dRSee6zxqzhwEL2SJMQWAEhFZIh85AloQBbYFhxxPoWGUdv6y82ZEEKNzYxY3kvxd5uLGLl3gSqPAdCfSl5t9CocCZ1hm+cvwrcBy2tra40r7Cx7c+1ulZ0wuRAS+ZKcNJAnqxx4SD4STWoFcwsRO92B5gYZgxvukf+E1sHQ00UsCFpufGbt9rqMCNuKdJIiwJbvTCMyrlCs8/9zwillTwvd09VldXJQPLlqEDuq+NgyBGbMownwPb0/v+DIwOBAMpQvPShqptbuxSDIsDQIdKI2xIMYYBPQvTMjn35DkK5QKNToO232a3s8teskfX7ErQwkp5NM3w8PCbHtNCh3ikWRaiB6IJW4ciOSLFsui99/Bk2tTE0qCFKtkFcl5sdtpoVvsWCgVGR0epVCoAXLx4kc3NTWZmZnTqNUADGHkLvHQdsudM1y1PAT67kM4eJ0uVHuZdzC6W1bGztO8kSajX6/i+z/Xr1/m1X/s17t+/D0jl+3a7zbVr1zRVO32M6elpfumXfombN28yPz/P2toaq6uruK7LuXPnePjwIVEUMTExoTMDnDlzhpMnT2pGTLvdxjAMHY/uui5JktDtdvW6qdvtsrW1xezsLJ1OhxMnJCUxiiLOnTvH0tKSBjZU3dRa68Cw64ENn/nMZ7h27RqWZWkGpep/JVDX6XSGtmXaiMkeP73Npp8G0zAMjPho7OG88ZPeNtDfPfA0NmMJAp9e4FOf+xSJnfDdH36XkakRTp49yQ9e/wGFoKAZxYrdkKVrp8dZepvarsZu3vjMAoPZtsnrD5DPWBELPMPTzq6sgavO/8ziM4xZY0ydneL48eOUy2Xq9TrNZpO3336b1Z1Vbty4ge3YMHfQAE6XYSDJYfduFhAdNg7y2id9jPR36fq5rsvc3BwLCwt88skn7O3tUSwWOX36NM1mk+XlZV5//XXCMNShxOp42ewK6rssk2JY/6X7PcsEeFSaxGFsrPT1qvr/9Kc/5a/8lb/Cpz71KW0jvfrqqywuLlKv17l//z5ra2sHzpFt42HfpcGc9Gf1uzzDOwvCpNtB7ZtlWaTbMI5jfvzjH3P8+HGCIGBtbY2FhQW+/vWvA+j0q1EUDQDgeW2fnd/zWBZ5gHt2XsoDsh5la+eVx8o6cZTOOmy/YZXKQ7bSxXVdKVLYG/CK0fDzlmENdljDFotFnn32WT788EOCIMC2bU3NL5fL+ibc2Nhgenqa733vezx8+BDLsuh0OkxPT/O1r31NU0Z93ycIAjqdDu12m06no9+3Wi1arZYGDrICLioVpRowarJXMT3lpMwz0TN0gy6tbgsscDxHhkgk0jMYI5kRiZHgCx8s8Iu+XPgJGWcXi5h2qU1yVhr0n7if6LRiMTFiVGhGwwMepDqu95odZWq7BZwe/OoKVzCEpB6bholVtDAKBiIWEEs0fClcknmokYtb25DCTUbS81T2RAgdw9FMB2XYOpbD4uQijf0Glmfx9OzTdNtdRmujvHz6ZRzD4aPaRzz/5PNMTUzxYfwhE6MTzE7P8uYbb2IkBsenj/NLn/8l3n7zbV56/iUW5he49NNLVIoVXnr+JV7/8eusr6/zla98hZ2dHd566y3GxsY4c+YMb7/9NmEUYlqm7gMZkdJL6YYESAzT0IwWy7Ek88QyKBQLXHz2Ih9+/CHTs9M88eQTXLl2hc3tTV7+1MsEUcClDy5x7PgxpmamuH7zOoZlcPrMae4v3Wd1bZUTp06wW9tlc3eT2IwZnx5nbXMNP/apjldp+22anSZYYDomzW6TmBjTNvEjXwoaJoE2JmIRS4FFIcXDdJx+6m9oyWcf/uzFyBzTGbJf8QiH6oEqIhLYps1YYUwbzSo1Y3piVuwUBTwqplEsYilCGbYGT2AC5SNck0AbcaYwsUNbvsaDr0psUxt8PU+9bheDfFV7IxkAMQZCEHrvAytfh+JIRbEFevWyhEXRKfIP3vkH2ImNEcr47+RUghVZeMLTbAs3cnUsriWk8a4W31YsPfd22Ne2gUHDKP0gT4PSwxY7iZDAhA4hUUBEjk6G3k9RflP7+5ZP7A0CG5EZaQDgnTvvDLZRhtWlQIs0cJF+dSNXp7lTxrKO844tzVzQ4FrSF6LDkACUCkfR/a6yGCiwwkqBHZkxotTxB1gcGcAjW65sX4HtzMb0+E8BHV7sYUY9wLI3ptUYOjC8eukuIztC2PK9Dg8yZD8eOs/02C+a5ZPSs9D6Fcngdg1sRDLMSoneOa7D1NSUTvU3NjbG6Ogopmlqb7vjOLiuqx0nWWNoGACgq5vxvKVLHhiRt7bKLjCzccRZQCL9W+WhU/tVq1IH6bOf/Sxzc3Osr6/z9NNPMzc3h2VZ1Gq1gbAEdaxut0uSJExOTjI7O8vt27c5e/aspgSfPHmSRqPBzMwM8/Pz2rGkQm7a7fYBR4z6rEIHwzDEcRxtGBeLRQoFaSyfPHmSsbExbt26xcWLF3X6P8uyuHHjBtvb27qPVFumqdYK2JidneWzn/0s7Xabe/fucfXqVfb39/upvlP9OwzMOkzELd1PWQMvz1g6zMjIA8mSJMFJHDzkPSc2BCesEywcX+Clf+8lfN+nVCqx9+M9nn7uaRzHYWJiAs/zBpjF2WtNj600YJxeW+eN87zwgEcZONnxnAdKqDG7srLCCy+8wOjoqGba2LZNq9WiVCpJp1exqMHC7Pmyz5a8kgckqDqm22dYJoTs8ylb8tpG/Z89exYhhPaEP/vssxQKBfb29lhfX+fu3bvU63Xa7TbVanVoCEjeMzTbFnnXnVfvPLAle66sMZwdx+p+rtVq/OZv/iYXLlzQOhSTk5PYtk2lUuH+/fvU6/UDTKJh64I4jgf0bg4bd+l90nMsDM6h2RCF7Pu89kyShEajwe7uLp1Oh69+9assLCxIjbYg4Fd+5VdoNBrs7+9z69Ytbt++PRBqmQUI0v2aHk957ZIHksRxrHUihpVfONBwGEjwqN9lJ9DDPqeLAhW2trYYGRmh1WphWZbOkXyU8z1uyd70ahv0b55nnnmG1157jTfeeGOgg4MgoFwuaxR+Y2MD13V5//339Q198+ZN3nrrLUZGRjRqrgao7/uYpjmA/KuB0Wq1uHz5MufPn6dSqehYRnWMOI4PqKy6rssT5hM6B7Jt25w+fZpiWNR1VYNOARaO4SBcQSfucOvWLaamplhdXdVKvBMTEzz//PNYlsXy8jJ37tzBdmzcgkuxUqRYLlIdq8pQDyGZBV7Jk0BG6GM5llTh76ntB1HAw9WHmI7J5PSkzrrgFl3JUhAxnaBDrVnTx5v0JrUBJwxJm4tFTJiEOiwhMZJ+yImQYSsqXOGjex9JNoSIiTd6vxMx/+hH/6jf6W+kBsByZpCsAn+Yep8u78oXU5g4f+xor7+75+J97BFakvLkmI7U0FB/Rs9jmHklRlOkncShGBXZXNlkV+yy392nvlrn/vZ9Ij/izuYd6nt1OkFHxq8lBv6eT8EtYO6bWDsW82Ke0f1RZt1ZjjnH+Dj6mKSRMJlM4gqX4n5xgGJvxRYFqyBZAL2F3JK5xB+U/2DQiO29OsLBE56kO4sU3RlbMmnUX88DKmKBa7t0zS41o6avNW0oK69ohMzi0I26WmNAi3cZkTYqHocxog34nudZ0+mT3nbD5MyxM1orJa2fol4Nw9CZJ1RoRfpVtU2SJARJIENUNlfpBl2afpNu2NVjO52SLqtDoIy+3JKl+//bLgI9HtQ1G0o4Uu8itKEaxAG1pKa349IPtfoZz68M0TTQMjCekj7QYYpB1o16r8MMRD/kIg1yqHtDHZMECT4hmTSI3ueUYaw/C8nIWDi1wGd/6bOEhKxur3Lj7g2WN5ZpBA2EJbQhrwEKIxoAK0IrJHIO6mZERvRIEU7VVgPAhbAPfo77r65wB9X4I1MK0vUADtdwIQSXHquDnlfMlMwMYQqEJTh59iSvfOEVWn6LzZ1Nrt66yv3l+zJbQW9cpFkc6n5OzGSA4XEA3EgBZIcBHUOLgQaPfNvvZ1TRzSX066Hsn17b/k9v/U8U3ivgmR6e4RGFEX/43T8kakVQBvuUZAN1a10ZGhK5eLHX11gRjhbqPHD4IZ6m7Heq5Hm50s97tX96HQAH0+ylz5H1hI2MjHDs2DGmp6clQ2dvT2em2tvb02no1LnUekk5Vp599lk2NjZIkoQbN24A0rk0PT3NxYsXqVQqhGGIZVk0m01c19XGoWHIML0kSYiivtZNFEVav6Fer7Ozs8OxY8c06NDpdKhWq7RaMkVjoVBgYWGBsbExtre3sSyLd999l06no9vJNE0d8tFsNnW43vj4ONVqlXv37hHHMa1WS9ctS5vOAxyyfZP+Pk8vQpWs8TssY0p6LZnu/7Rhr/ZTceHr6+scP36carVKqVQiDEPGxsZYXFzEsizGxsY0mJM+ThrcyoJXeQBv9noOAxbS432YMZ63PXuudrvN/fv3SZKET3/605RKJRzH0V5kkJ59zxt8IKUN3zTb5SiGd55dke7jvH0Puwez+6d/t7+/z+zsLM8++yyGYejwIiEEjuNooG1sbOwAfV+NiywbIe9c2TocZnflzU2qZMdI1iGQHq/lcpnPf/7zxHHM97//fVzX5cyZM/i+T61W4/333+fatWvcv3+fU6dOaedv+ljZcai0fPLmyfSYzHufV//DQKj0fmkgIH0/Tk9Pc/LkSSqVigaFJicnGR0dpd1u8/DhQ65cuaL7M9uWeefKbsv7TXaeSrPw846dfn1U+bk1GvKQqbz9HnefRqPBw4cPuXTpEnt7ewDaS7CxscEHH3zACy+8oGNxHjUBDTtfdh91Q6oHVxiGmrLS6XRoNBrU63Xe33ufNybeQCRCUrZsr7/IdA1COyTyIhq1BpZh4YYupjCJo5hKuUJZlPvCaTGIWMgUjQFYQX9h6lhSFLFYLJLMJ2zamwSeTN2X9twbwqDb7iJigSEM2q22XmytbaxhGiYnZk7oWNEkSbQnQD184zjWsT+u69JqtbSok4qRPH/+PKVSSe/fbDZ55plnsG2b1dVVTs6fpFQo6faNoggr6aVjMiQVMDEl6JAkCb7vE+/HzM3NMe/NE4YhzU4TL5CCgpZtUWvVuHT5EvV6nVOnTvHZE5/F931p2PYQtySRIIJ6+GUnLjWZGIbB1772Nd555x3K5TK//Mu/zB//8R+zvLLMn/vlP8eVa1e4cv0KFy5e4PiJ4/zJd/6EQrnA8y89z4cff0gkIiamJihWilz68BKnzpziwtMX+NO3/5RipchTF5/i2o1rbO5s8vSzT+OHPp9c+YT5hXm6UZe79+9KgERI0cuYmDZt7lp3D6QBBAbfCwO6IB72HsZtgViTjIKEBHE1NZb3e/8ghQ+vpAZ51qvYSN8AvVebwdlBgFHo183G1t5FU0gvqTK0YhHToR9/jIXOoACSRaMYMY8VRtLb1fT63ntlBJbiUp8yndhSWT+SRpEdSwNKBALhy3FRGalgl2yZZi0JCAm1RzokJBTys7AEo94onahDO2zTDtsylVrY0e+PWgwMLSJpJRau3cs00XKIOhFEg55sRc/XMfgqLl0ZPGY/HZ0ytnzTl3OPFRHaoY6nV8bao9r20BCKnnGd521XBmbaYFcpTRXzyLItvIJHsVzEK3hMTE2ACa12i/WtdXZruwRChgQoA3PA8OwZn0oMERgQn0y/H0hXOmyI/Z8IzPydS39HpkTFlAKXs6BChTQoIYwBb74CT9Q2J3FwYzff298DngzD0Clt0+yibFrOxEjwLR9h99kuqp3T7JbH0cfQ2Tx64MWoPcrrH7+Oa7jE3Zhm0CSshDJsL+6DHYWkIEFOxVpJKcg7QrLU8ha46bR/iRgUB1X3cmzIGPc8Fs8BsEPtk2J+qM9p8CcdsqJSmjaCBg01sRqwsbPRbx8TCaxltYmGtGWaDaTvwR5YpkT/FCMjm2HEjV2tyZLuEwWgGUk/W4tIDoZ25jmFsgvYer2umQC7u7ssLS1pR9DVq1cZGRmhXC7rWHGQ6y7P8zh5UmoaKM0dte3jjz/GdV0mJycJgoClJZk2enR0FNu2qdfrTExMaMAkiiK5RkqxDtrtNpZl6TAKz/OYmZlhbGyMjY0NSqUSV65c4Q/+4A8ol8v8xb/4F6lWq1QqFbrdrgz17AmJqutXivZqHdrtdvnqV78KwPLystaESMdBZ42TYevhrAifKuoa08dIb1PHyBqcWYM923dZY0ntF8cxDx480Or+yhh76qmnqFQqmrqt1pCKFTCMGq+H/RCDOls3PfRThnh2HA5rv+z3WdsA5Jr0gw8+YGZmRjNeQAJmMzMzWJbF+Pj4wO/T9VPpILNZibJtm+c5ztYr+7t0W+SVw/pN/XZra4snn3ySDz74gLNnz7K4uAhAqVQC5DiN45ixsbGB32WPlXfsYUZ0GlxKX182lCKvbbKaDdmxnGZlTUxM8ODBA06ePMnnPvc5KpUKlmWxt7dHEASMjY3heR6e51EoFAauI49hkh0j2WtOlzTQlL630/VMgwfZMLNh4zt93osXLzI3N8fGxgYbGxuMj49z8uRJhBDs7Oxw79497ty5w8OHDzl27NhQjQZ1zPS5VX8MC1E57HP2Wh+3PDbQMOzhc5TfwXAkM135JEm4dOkSd+/eJUkSraavJtggCHj//fdpt9t86UtfwnXdgWOnX9VvFHCgwIMgCPB9X6PqKkYrrXGgvvd9X9dL/S+Vl9gsbUqj0expKQgZO52QEDm9FHOFRC/qetmpB4TFHqtceMT3pSHbT/faWUjKtxmmxBNto2/ECAPT6tFtDZPwxZAoiKSegy098Je8S5gd6emN5iLElKBeqGMIg1apxR3njqRyG/14+ciX6SqLhSJW1NNsEJLGm9gJzWNN1p11bndvYxkWrbiFG0gjzMUltEP25/apnKlgzpjcSm6BKTNliFBonQDbsDF8A8d29ILcseRDUGkFOKZDK2ixtrnGxNgEpmkyMTHB6uoqk2OTnDt5juV7yzx98mnOnTvHnQ/ucOrUKRZmFtiytkiShJPVk1y4cIHiRpEvvPQFmee2HDA/N88z88/gXnVpWA1em3uN27dvM26OMxPNUCgUeL/9vh5jnudJwSvHZ7wwTjfukjhyUawFC6WrUKpT9/4UaCMH+fDhYApTK907hqPTYnmmh2M4MjylZ7CLpD8RJsjJKybW4zUSkfxsSIaI+otFb6FN0vde8givX7re2fof9rvedzrHO/QFHB+n5LSZNtR6YQppz/jDukyPWXbKTJWmqLpVRrwRRrwRSk4Jz/JwLCd1+N6cg8xEkRZ4Vf8bWxsEcUCYhKx0V2iYDTp06JoyLVs61l7rDxiHpKbMa66eEWElEuhUBoa6N9LCk8LoPYxM0T8vmVAKU15TZOVkyXgUUIHMW1+wClScCmWrzOb+JlW7StkoM1OdYcKYoLZRAx884UltDRVC0ROn1KBbzmIyNmJipwe2GCGBFRDZMpd8ZEsmQGDKzAyBJXUrQjOUr1Z4qD5DWlQxzZYABjzgAwyOtDFv9o12ZdhrEVjZOI8uvbGZ7rssyDAASh56qMF66m1G33OfkAx8PjIwoxgCDLbnfrTPg8aD/oYSw59Zh1c+dSqj3xaZ9hhguNDvM7XNEjLMToNjwhz43uiFnWjhzAzgo75X84Zt2lQrVT73mc9RcAuSAWYIVlZW+NWnf5VrN65x4uwJrt28hlf2eO/D99ht7RKYgUwL3GOrhGZPvM4MBzIR6HvRSKRGh5FaSxxlzj1Cu6rrO6DRkUrBOJDRwLC5e/suz1jP4BgOu1u7rHqreNMef3jrD/n2nW8zXhmn5bQQhhgAN0qVEpEXUevWMB2TYrHIe++9x9raGru7u3z605+mWCxqev6lS5d4/vnnabfbA4a2EEJ7+0qlknYKqTSjQgjm5uY040Glxbt+/Tqvv/46q6urOI7Dj3/8Y5599lndHCp2P20kHDt2jLm5OYQQ7O3tUalUeOGFF2i329y4cYPnn39+YK06zODKGipqe9YYydKi04ZsVmBOd2OOUZ4+d3p73vpdCMHy8jL1ep2ZmRmZArznwFHCmsqYyzt/npGdrVf681GN8/Txs8ZVtj2yYEq6HZR46NbWFltbWywuLmqmSrVaZWZmhv39fR2CnO6/PGM475rTbZLdL12yooh5xidwoF2yAoRpkCeKIt59912OHTtGpVLBcRyEkNlw5ubmcBxnwOGY7YthfZbVb8jrt/S1ZMfaMAAlG+KTB26o/VZXV/nyl788wEaybZtiscj29jabm5s0m029Pe846ZIdQ3n1zRtH6X2Gjb3099nfZL8TQqZFDsOQ9fV1nnrqKcrlsmZG7e/v67S+cRxTKpX0d+n5IU9LKO9602M5bzyn+zdvLGdfH1UeG2jImyyyjToM9RoGSKibf39/n3q9rlNC1Wo1oijSIEEQBMRxzC3jFjeLNym+V2R2aRbHdhCJNDiTOCGJkgPv098bGCRxIqnPVl9MLr1oQSB/IxICIRX04zAmiRPiKGbUGuXFwosyNaRp4pouQRhQLBQxhLzxukGXTlsKsBU8ufDotrtUyhWajSblcpliocjKygqTk5NUqhXu3L3D/Pw8I6Mj3Lhxg/HxcSYmJ1hZXeHY8WMUi0Vu3JRCNSru6tSZU+zt7+EHPi++9CLvvPsOtmPzymde4cOPP6RQLOAVPLpBl4QE27GJRYwf+pi2iTAEYRyiNRt6HvK2aBPbMX7gIxKBV/RwcbUAo0o3FyANppbRoiskq0J5v4QpSNxEe/GVKr1wJDATWzEUGABimMoMkCKDIEt32ODsvabtoDzb4V/1XnfhP/t//mdaSPC//cP/VtL5TcHv/NnvYPyZTOvpXHHwbnoE3QADA++eh3nXJIkT/t6P/h6O5dBtd7F2Lcp3y3TaHUxMfv8Hvy8FEhOB+bDnjS7J9ggrIbZp6/AJkQgJAoSe9Cz16PtJnFBwC5QKJbpWl2V7WXp8k0iL3GX/lAczMRINWOhiog2WgTYzOLoxIXqponqUaydwtOfRMz0tMKo82gYGtmVj2fJeE0ZfzyBMQsI4RNgyNCKgZxSm1KND4xEp+Hr9bMQyk4ORDHp3FQsAg36stkXfM24Mvh+I5zbg9u7tIzTKz1kKvf+coij/dmLra9OhJdAX4BOqykM60ehpNJgpgzIFeg54sdV+j1EMFT6QrUNvbMXEtOIWrbiV/1tATB8+Bs3E1AKAbuziRI4WnnSF/Kwo6HZsUwyKjMQjMhSo59VVGUSyz6so6auzR1akAQgFUMR2jG/4A9v1fw+wGFZ3S1gUzSKj3ihjhTEqtgRbHBwZFx0IOo0O9Z06SZjo+1+DX4bR97abKU0FFTZkJoPfG4P7qvePyyBKp8pLZ7dIZ704EH4iBtkstmlTckuMVkd56omnGK2O0mq0eHD/Aeur60RhpFMtp1kGWgcjfW0p9kCadaAYCQMaJPSBnccC9lNrgDSz7PCf9O+pn3zykwO6NH/B+wu8+tyrjFfHOVc5J0MBli1u7NzQBk2ecabKYcaXGr86O4EtxUIDIyCwAwIrkOBbL2uIb/kD2VyygEZiJBLoIBx6vRpcE3AnvMO719+VYF8SIzzBv778r+WOk70fzOQf5+9/++/3j4mB9YqFCAUswJsP32SqMUXsx3QaHUQomPrpFNPj05L9JSwmRiZwTZdqoYqRGHimh4gEzb0mnu1hxiY7tR0cw6Ex3qC2VWMynCRKIt6+9jYf3/6Y2IpxLZdiqahFHLMLb6Xx8Nxzz+lQjkajwZNPPsnc3Bz3799na2tL0/CzBnjaEEgbaNmFfJ5HNzsGhBADtOas4ZcVmxvmKc4zGNXnbrdLq9XSDFfD6IeobG1tcfz4cYrFomayZOuavpbDjC9VFHCUBVuUEZfVmcg7B+QDKemS3q5CQcMw1OCUEIKxsTFqtdoAODRMP0B9l+5b9V3aMM/Gy2ftpmxfpM+Z7bNHGX1KDHV1dZWVlRVmZ2d1HSzLYnJyMle7ZVj/DTvvsN9lrzN7zaouaaZQ9ljZc6n2832fu3fvcvnyZRYXFzlz5gyu6+pxuLy8jGVZVCqVXGM6fV1pcCB9zux9mq5HHoin2lwdM92n6XNkmQTZbZ1Oh3v37vHNb35TJxaYnZ3Ftm1GRkZ0aM/W1pbW6ktfT16/DQO58uaHdD/lzSnZ4x2m3ZAtjwU0DGvsvIE/7AZXRV1MkiSEYUin0+Hdd9/l0qVLAzdYmgqnfucnPjW/RiNqsN3ZlkrkvUWEZgwgtGdWCeyp94BMsyYDwKWIXc7CW1gCbBCF/u8OLFbU/CGQwnNpwzbPYzPRex1PbXsi9T5tUD+Zer/Ye/WBk4PHe5u39fF+vPJjkFmbeP3e61Dt7Rf1+gUDwp4HyOgvZpV3Rl5Sj/Lj9fbzZCy9ZUp6pvL4xHGMbUkV7yRJcDxHH8NIDGlgGyaO7ciwjHaI67ja8CTpLaDDWGanEDJe3w98gm5Aq9WiWCgyMTlBFEYUigUKTkH2kZoU1IJOMBAOotBM1W8GBoYpJ/+x8TF2dnakQvjYKBsbGwghGBkZYXdvF4GQVFIhjV8RSzHAgACBwIhlhgs/8bnZvqm9aSYm1Om3QZiaqNTC1TQQjhxXau2qx11BLlbVeFX1z10k/zzeKyPVbsOOcxjTwEACAITSE+vm7D/kvIeW3jHSKv12IseXirFHINNihT3KWI+lozM2GL25JWVcCFOQWD2DpKfr8CijwxTSa2wKk4Inx1wikoE0sv/GS9pTq8ZRKrxGGXNpD6sySvu3Rf8+GfCwpwEGDgpEPvb4UmMqNaYHv+7PL4Zh6Lk3XT91nMNKYkpvbkBAi5Zup7z65JYeyKYp/YqS3xMCdGJHghexRyEuUIgKVDoVGUcvPC1Cq68jveAUMr1a7MR91oQZUpoo8fxnnqedtGlFLRphg3pYpxW12Ag2aIQypVrdqBNN5rBFevVWFHg3dgfq68YubtjbFvX26WlJKIDFTVxs09YiiSo0SIWpKG2IiOgAYKH2yQIXCgDwTX9QUNIYBDfS1/CdG98ZvK7JwY/pLBcDr5ltOhSgt81GprxMh2oMHEulNlVrmN56QF9LT/wzK+w5TCcjL71gNk1gunzze9/U7wum1G+wEgvjtIHwxUGdDMUe6DEI1PdWnBqzPYZaWlujIAoDzx384V5h3S2Z9ZsQvbnATmTGGjPU/zq9aS+tqXAF3ojHzPEZ6n6derdOJ+nQSTo0wyatsEUohgMWAK7hysw8pgsx+IEvM3JYcp3R7rQRlpyrOmaH5cayTCNuwoO9BxIgTELCJMSPfYIkIEiC/pyi1mj30VmUALkW+3f7H69xjX/4h/8Q15TsLyxIZmWWKQuL8eo4l9Yv4W17eKZHc6LJd48DdwABAABJREFUf/nif0mSJFQqFV5++WUmJiY4deoUm5ub7O3t6TCDrGGaXjenjdk8AEC9VyGgak2c3jd9nLw+TqvdZzMl5I0Pz/NwXZdutzugJ7a2tsanPvUpAI4dO8bu7q7WCzvMGM0CAnntkTWos/bGMAdm3rb0vlntCHUuy7KYmprSrObd3V3u3r2rM8qlwYthYE7aoMvS2NMG2mFGet615vVfntGobKjsvkIIfN8nDEMdoqy0Q06fPk2tVtNMbfWbdLtlwZF0H+QJJ+b1xzBAJH2erEGcBQWy7B/DMLh37x4AJ06c0Ma9CnMaHR1ldHQUOCiCmgWh8gzlPHBpmFhmNqQpDyTKM9CHhRAJIXVr3nzzTSYnJ3n++eexbRvXdWWWvijS15kOC0mP07QmTF6IRPp8eX2TBZayYFMaUBl2/LzyM4lBZlEP1Wgq9aKK9e92u3qAdzodms0m7XZbsxZarRaGYejtjUaDRqOBbdt6gCl6OaARsIm9CV6svUi1WtVhFembzbIsjd6pfzWxqEZTcX0qpZFpmlpsUiklT01N4TgOt27d0ump7t+/z1NPP0V1pEqhWKDrdzVF2rItmU0gltkETNscWIB3uh0MU2YOeLD0gDAKGRsf4+KzFzl1+hQ/+OEPeOGlF5idneWNP32D5198npHREV7/09c5c/YM165fo92R7ffcC8/xYOkBn/v857iyc4U/+OgPqI5WSYyEWqOG4zkyG0CnheVYdIOuZCoor2UPkJEdmu3s3mv6/rIy3xvo7BGGMDCsvkGtDaGeOB4gv/cY8LoahkEURsR2rPvGNE0J2JQgGU9oiAbb1jaYkmFiBNKI6ppDUg2a8ti5N0AvNSK9LJC0ev+q7KbeZxn56c9q7ZwxqmKOnnbwyEVkXkl511KGKDBAF9a/SRurPYBITx4purW+n0V/AlSaKFljUIMfPcAnFrE2Xg3TGDBkoQ/Oqe29kx4svW2xEYOQKTwfWX7R7d0rythOSGiGzcwp+20OB43qw4r23B/xN2kw4NE7cyQwR2VmsUUvsKYH5jiJFPLUWR56KRrTXm0jMfr6ET0DMElS4WFGgu3azMzPUCgXtKArdo9hEwVawFWxolQIQSfq0PbbBElAo9Og1qohLNEPN0ilc0zrCAyk6XxUWxhoYdHIivDxc3Y6QjtnD6vYJRlNBUtYuJbL3Yd38SyPglmg6BTxLI9xb5y50hye5elwpk8+/IR6o06afRKJHhCQyggRWzFdu0vTafaNXSsaNOwzRaeCVIBKGrTIxPMX4yJ20MuqkLi4iTS+8haR6cV0+hlbKpX4y//hX8YreyRmQjfu0vKl4dmNu7TDNn7s0wk7/Mn3/4R22D4AUmiAI7Xdt/0D235W1kYadHoUwOHFkjmjtVNEz9gXFq7pMjc1x+lTp+U8qAyI3nwYJqFmAvrCl20Rtrj94DadqKPBChXuo0EOow9sHOW60gCtDnHIZi1JgRdZIEMz1XpCn46Q46IUlrTBrcaAbds8//zzTE9PMzc3N5CJIu3djUREK2rxg9d/wL3Ve4zPjnPqyVOs760zMT9BV3RpBA2aQZP9zj5b9hbLW8uMjoziRz4d0SEgIPESdoKdQ6+/aBUpmSVGjVGKZlFfy/jIOJ7pMVoZpdvoMloZpd1sc/3qdWZnZjl7+iwj1RFEIkW9m60mD1cf0o7aTM1N0Q4k2BH5EVEc0TAbjM1KccRWq4XjOHzpS1/CcRxee+01bt26xY9//GMdz5+3aFf3j7qf8hx16e3KwMsLsVBlmOc0fa70tjxQwzRNjh8/TqlU0mlD4zimWq3i+75ecz/11FPUajWdhjTPYE4brYcZ3nnXMjCuM+2mSpZFkNeu6WOobcpeuX//Pk8//TSlUknrfFQqFWq12oE2zPaJslGGGW2qful2yB4rWy+9BobcsZM1erO/TbeFZVnMzs4ShiG7u7vUajUajQb37t2j2+3q42XBkfQ1p9s+2w95AMNh43JY2+TtkwVq0kWlBd7c3NTpbEHORwsLC8zMzEgx9BwQYxigoeozbN9sHfLAp2y7DLu309eaFWVNkoTV1VV83+fEiRNMT0/r/U3TpFwua7ZGGmBInyf7n27L9Lnz2jw7jrLnyLsHj1KODDRsbW3h+75Ov9hoNA6kZEz/B0GAYRgaUUtfkErnKITMjKA+7+/vs7+/zxNPPMH4+LheuCjgQE1mKiXk2NgY5XIZz/OI41hPglEU0el08DxPCwKVy2U2NjYYGRnRwoWLi4uEYcj29jZnzpxhZWUFkEjtxsYGcRxTLBbZ3d3F8zx2d3c5duwY5VJZqo1jUbD7yBJIQyImljRA0VelBygaRUzDJG7FHPeO863f+BY3b97kV7/0q9i2zf61fb7x4jekuvJUky+d+RIArfEWL5x4ge6VLpEZ0bSb/Mr5X+EWt/jGs9+g1Wrxwt4L/MZv/AadTodvf/vb/Oqv/iobGxt897vf5etf/zp/8id/QrvdBtApUVRay0hEhLFMWegWXAxbXkM37EqRsMjXqQ5rzRq37tzC8iym56aJkgi35FJv1YmFTJUYRAF+5GO6pjYmVGYHbSio7ab0jA/ELhuxNl5j0V9sHtWofBzD71FFgyYilSquF/OrxDhV6IVOr5mO9U3kv5mYJFFCx+qwW9zV8c/6uoZdm9qeAg2y16o9yRmv8qGldzwS+kwB0X9vGfKeSwNH6TqYop8RRaXOg959YKS81gYcAB2MQdaGZmykrkGDG2px/W8IUPhZys86vgyM4b/NY5D8DKyCo+yj0tKGRs/T+ItOMQoYoYG536Mf9kBFE1MLIKpwNRP5ahmWTEFrSpC5HteJiPBMD9d2ZZYPGe+i77X0fZi+P3VYSTIIfGqAIfUXmqF8Z0ZSENSMdIpLlcHmwD2abWfRH6cDYGPvtU2b/cb+0RquxGDq1V/guFcGa9fu5o63o7CcsmKEacNcGa2eKUPsyk4ZZ9OhbJWpulVKdgnbsPFsT/a1Y1P0iown4xz3jtPoNCRTLogHhC/VnCpi0WfbqWrnLHwSBsNINEtDZagh0gyndNaabGiKAjMiMxpgbQywO7IZLpYe3Q+O4eAaLjY2kRthO7YWZ7SFTSEuYIUHgQ4VkgJ9YEvQS1Eo+sBoWghTp/7sARm+7dM22wdAjMiIjjTWDGFoYMIRDj9o/YCRZAR306VoSaFbz5SAWsku4SDD6QhhU2zil3wmZicwQ5PdW7u8svgKp4+flmyGAEI/ZG1tjeREwuLiIm+88QZ73T22t7f5rd/6Lbb3thGOoDBaIDAC9jv7tKM2+519QiNko7aBL3zJHPIbNIIGsRtTC2qERshd/y67rV3C/R4j7xjc5S5vPXzrYD+ZDo7nsB1sUzSL0IXJ6iQVo0LZLlMwCvydD/8OJatE2S5TtIpU7Aolp0RohRx76hhWZLH2YI1Wo3XA6M16DaHvJVUlu8DPMyiPAlYcZjjlGZJnzpzBMAydhUJlUhsfHycIAoIgQAjB6dOn6Xa7rK2t6axnh4EdWW9unhGWrfOw+z3PWM8DHdLny7bJ9va21mFrNpuaBaDqmmeQDjt+ug7Kuzzs2rJ1Sb+qdIt5fZkFLNLfZ/dJkoS9vT1NszdNU19nWjg33R7Zc2TbXRmsWZBgWHaUvOs/DIhQ7/MEQNPHtyyLalXStVVIT6vVot1u6/7LtlEWHMmrx7D6p+vwqOvI+406dzaUIl031a5JkrC7uyuF8nu2mrrnZmZmqFar1Ot13R55gNCwvsyCEXkAXRbUyrsH8q71sHJkoOF3fud3dEWyF6UmJMVmUKI7SnxRoYeAznGcJAmFQkGzF2zb1hSfcrnM1NTUwOBSN54CGdRgun//PufPn8eyLB4+fMjFixd1Osfz589Tq9W0SFCj0dAquUrRuNFo8ODBAx3b8/zzz3PlyhWeeOIJXn75Zd566y3+6l/9q0xNTfGTn/yE3/7t3+batWu89dZbAw2dzjmq6pvuEDWw0hkfFhcXaTabjI6O0u12mZ6eplqtsra2xs7ODr/3e7+H67ocP35cH0MhYLdv32Z9fZ1Go8H777/P0tIS6+vrXL9+nbt373Lr1i1u3brF/fv3uXLlCmEY6rqqNlef1aSjbtKiLVe6ISGmYbLXlJOVYzi88dM3WHAWOD93nvGSfOiMlEYIbCmwJETPA+kIDfgowEapTqsJWMVWGYZBo95gYmJC10WxYpQux/7+PmEc4niO9JD4LWJiLLdHcSSh2W0SxiFLE0vUKjUpDtkz9FX2Dsu0+l53dZP1GB5p0b5Y9LQqRN97miA9jMr4SHv1FUVfl1+UgZBigKhjDnjUFQOh58E16KvMH1qHlOEkTKFjwdUxEyOh2xPDSIuNHTVuX4c94OhUl/ozLmZsYkZm34uW2LiGK1NphgZmZEIIiZ/098OhYElgTwmtmZaJV/QkfVaEUlSNSL5aUuMhtiSVPbF7oRNWPGBYamp06j8kPJQKrfrBsRyd+lLF/qdZC0L0Qi4SCejFSUwkcpgaeX2VSKPaMq2+eKvRN5wxZF8pYGYgbEzkMJf+LRaB9MDnMXKOVDx+vnSX/zbL/xkg2GHt+QjQcgCMTDPM1MaszkaGHaLTrKaPN+T87197/5CKpkqVfpjfYUWBHZmwoew29XlA+DG1b5pxosFjYUqdmdjDiFJikWLwODplaQ9ANg2ZknpmdgY/8pmdm5V3odnXolHMnUhImn87bHPz3k3aQRvTNSUTxQHDMQjMgG7SPai1kXr9eVgbmr2QOH2GhuinHNbstwxzCwP9rDNMyWIcrY4S+tJRUBM1dpGZYwJk+IIvfHzhE4hAAprj8N2d78IOMAl/8NEfwEf9UziGBCaKdhFnySEOYuySjXnc5MbNG5SdMmW3zGgwiiUsCmaBildBGIKCVeD85HnmJ+fZWtmCCPyGz7HpY0xUJpgZn8F1XP7m3/ybfPnLX+ZXfu1XqHVq7LX3qPt1uqLLvZV7NIIGWzXJqgjNkOPHj+MbPqs7qzRbTVpei067Qztu6/8gOUSUeBrsSfn8cxN3gEGW/beTntaMcPS+diy3qZSztjEoAgcHjdc8gyP9qkrWmFavIyMj7O/vD6xjfd/X51WhqSdPntRGdZIkmq2cB6qokmcYDYzXQ4yc7HWlf5PNfJA+xzDvvGJTLy4uUiqVWF1d1SlNs5T9wwzp7OdhIouGYQwY+VlAJW10Zo+fd/50/fIApatXrzI9Pa117pTds7q6OvDbbMkLm1DXle3PYeBP2u7JGtPZc6e/zwvNSIcgqHZQYq3qN41Ggzt37tBut3N1PfJCaNLnyAIAqh2GaZ6k2ybLDEnvm9dP6TGeV5StqJz2KiJgb29PZ+jJ3gPZOmXHQ17IRrb+2ffp8Zy1/VUY11HKYzEaVIOryqfZBipFojISC4WCZhqozlM3sGVZehLzfZ9Go8H4+LhG2qampiiVStpoV4ap6pzR0VHGx8exLIu1tTU++OADFhcXmZ6eZnx8nG63y2c/+1n29/eZmJjg85//PJ988glf/epXOX/+PK+//jqnTp3S+ZKnp6d54okn+NSnPsXFixf5p//0n/Laa69h2zabm5t84QtfYGtri3PnzjE/P8/Nmzf7rIA41pOuAj+U8azaBtDtkO7MBw8esLm5SafT4fLlyywtLbG/v89HH33Ezs4Oe3t7Os3k0tKSbs9Op8PNmzfZ3d1lZ2dH55ANw5CpqSkKhQJzc3MyO8bSEgsLC9y9exfP8wjjkJ14B0c4iEhITwo2cRjTaXc0Va5QKAxMDKZpsr6+ThRFLCwsMD4+TrlcplAoaGqdEu9UasRBEHDz5k3CMOTixYuaWZJ+WBmGZL20Wi0ajQYjIyN6nCmQqtvtEoahjFUS0vOp9rFDGZsbhiFGW94IyxPLJJE0lAU9kTuFFotYAgo9ZsVASQkFDhSRStcWW7iJKxeYcf9fxcsqg9gxHE1RV/1t2j2GQE9TRCnRY8pzJkYi40tF0Bd5NCTFXKVk0xkejESnhxxgBBy1qDkqBTbIzb8Yo1RlLFDU9IEFq0CGx6Sr8xjMBWUUaDV09dfzstnIV9dwcXEpGAXKooxneBSMAiWrRMGQKfRcw9X/Hh6e5ekJNIxDOkmHv/6f/3WaQZOGL+m9iuab+znM394IGvjBo2n6RiyNFy0AadqUCiXNLtGAI31WkDJiQsIjhViYyPFqIxetapxqFknv+JhoIykR/fP9POCFDvNJsTseeaweM8ExHd0O6neqXrngTc51u6aLa7r6/nQtF9uwcUxHsyoUw0IxLkzD7IeBCfp1EOjsIgqUVEKnUSJZYp1uRzN6IiRbQjG2tKAuqf7shbep46YaLtuQv5iSBxYMAxD+r1AM+sLBIhlgrKSZZ7qvMnSd7LjT2kvqL61hop4dR2WJJci5fPPw3RRoKMaFZpSZQmZNKnklmZUpkuCvI2Q4kxmnMl/05j91Pfq6hNDMHQVAaqaKYpHRD8VSz43ACgY0WrQgZOY1twwRZlasDddwKVklHMNBhILGfgMTk4JbkCCCJZ/llmmxcHwBz/OkwyqJ6fpdtrvbdMIOwhDsR/vsJXuE3ZBgT4IYQRzQiToSyBhWaoP1Sl5KeL3xOr/7//ldKgWZ2apgFiTIHRlU3AqF0QIjxghxN2bSnmRuco759jwFu8/UKFpFzeKwTZtEJPi9v27SpRW1eLD+gKX1Jbqiq4FvpXsRmREtu6V1L0Ij1AKdhxWVvlSDEr33TuL0AQrRAzV6IIXaV4EdjpD7KrYZDBrxW1tbCCGV8JWx5fu+Ft1TMeMA3W6XY8eOydTkzebAsYYZoaqkjeysIZr9PXDA4ExvO0z5P68IIVhfX9epT03T5NSpU6yurrK1tUWj0cgFG7IGY/q9qk+6/unf57VDnpc8a6DmHSPP2M+2jcq8UqlUmJycxPd91tbWDoBLefV5FHiVbu88lsMwECnbRmnDNg3CZI18dfwkSbBtm+vXr1OtVnnqqacQQqbGnZ6eplarsb29PTQLSPa6s+Mt3e7KBs3TyMiCNHnjLg0qZAGOvDZW21SdlH6cAsUePnw4VA8lOx4f1Q95/Z4F1YToa3Jkx/7jlCMDDSpnsfKMq46N45hWq0WhUNAUFiEEDx8+5Ny5cwghU+WcP3+e3d1d4jhmfn6e/f19SiW5gFbCfK1Wi/Pnz2uQIY3sqYZV8TmKATE1NUWlUuHMmTP85m/+Jtvb29y+fZtvfOMbfO9732NqaornnnuOWq3Gq6++CsjQiOPHj7O1taWvbXx8nJmZGVzXZWJigsnJSTqdDrOzs4yNjbG7u8vCwoIO4RgbG8M0Ta3Km554s6ii6rT0YLVtm6tXr3Lv3j3OnTvH9evXWVpa4vLly9y6dUtfx/T0NKOjo+zs7HDmzBmNGgdBwMWLFzl58iRbW1ucP3+e2dlZlpaWmJqSaRtu376NbdtsbW1x//59fN9n39znny/+837HqnvMk6ERNtLwsGP5QLGxsUalcng4EWJ/wWazsMmH5odYgVyoO0VH/06lVEwCGZO7O7KLYzgEfkClWMHzPYzYIAkTGeNqOBBDYAQkccL28jaOIR+AcRSzu7tLqVSiWq1iGMYAHU099NJgVBAE7Ll77JX2Hj2oRU98LO7RUuN+qjPblHnFdXhEhsomrB5Q4PQMMjOW19ADABQd9yie8XR9tOhXSqxO55Y3HO19coUrvW6JRcEpcM25hhEZMrVozwjXxrbp6vhaFc6jxmxiJP34YAJd58iUQnFBEkhWAH3KsKLlHlrSc5voGy1pZsZhntBh7QNoOnA2fd5jl7yUgpH8V4aKIQx+93/4XWzTxrEcKVpmubiWK2PrbY+CXdD/JbvEdGmaU6OnKLtlyk6Ziluh4lYouSXJOBEJN27fkOKBSUg37PL+h++zvL5MSCjj8o0I27MZnRql4BSIrZhm0CQ0QwICGduedB7ZBiZmfwwYEpBRBjTIlHxqXGvjN+kDF0LIjDSHKdCni4PT9xAb5sBDXBk6ChxQrItHGrSmHCuBCPKzWjxGEYmQjCViDGHgG9JLl2aFxMQ6heuj6mZj4xlSY8EzPDxk2ICLS4ECE94EI96INEYSua8yCEp2SRqShkfJLmFFFt16l5vXb3L16lWiKKJUKjE+Ps7Xv/F1jp0+Ritu0U7adESHVtyiGTWph3WaYZNm3JTCklFTf9cIG7TiFp2kM/QaLPoAC/SZOKodDmsDz/AkeGNI8EYZ0UkoF2iu7cqFliFT6CqgANDjTP1FIqIbdImSCMOWizPDNDQIk2bsDDX+H3NYpMfRzwOsnHXP8p+c+k9Y21ij5bd45rlnaHVbYMJubRev6NEJO/iBj1f2uH7zOldvX6XV7TPzDMvAKUhtpVK1RHW0KsWoexlbFCNCGL2QQqIBoDH3NfX9L5p58zcW/wZBLZCMx9EyXsUjIqITdfBjn0anIdkNIgAbVhortBtSh0NUpHZDJ+ogLIFdsLnfvU/QCehGXfzEl/2REtPeaw1/pltGP22za/cdEiIWFNyCFKW2HBrNBtutbRxH3n8IaEUt9qN9Oe4sA7/hy2dfJOfZ6GFE/PDRzxoDQwLWvbnAjE2CVoBbdCk5JVxcqW/jm5TjssyQkxH2VK/pMCYNQvbWGDobk0rN23vfdtr97T3g4kigRY9doUALB4dPVj+hZJWouBUcIcOfSlaJkl2S6YmdMkVDpnwulApMV6bZWNug1WodUPPPM6yz29LCe3kic+k1dNabmzUA8wymrLFs2zYzMzM899xzzM7O4vtSgDQMQ1555RWazSZXrlxha2uLVqulnV7DNCFUyW7PepSz3vEsaDHM6M+WtGGvfpfV7hBCptY9efIkZ86c0aEtyvaJ4/iAAzR9HcPqkBevn73G7PfZ1/Sx08dQ9lW67bLtkh4fyhnsOA6dToft7W22trYGgKe8kmfsp4+dbs+0Nkq6//LsvGHnyH7OghHZzC1hGPLBBx/wxBNP6KwhnucxMTHB/v7+gNN/2LHTJQ+UUNeUx2xIj8d0exwFxMgrjyUGGQQBe3t7LC4usry8zMzMDIVCgdu3b+M4Dt1uV8eQ7O7ucvnyZSqVCnEcU6/XAXjqqafY2dnh5Zdf5ty5c1y+fJlvfvObbG1tceHCBZ0fdRh6lp6kDMNgcXGR1157jeXlZaamptjf32d6ehrXdalWqxw7dgzbthkfH2dycpL9/X3t7Yd+yIOiqmxubrK/v4/v+7zzzjvcv3+fW7du8dZbb2GaJtVqlQ8++GDgZkiHdpimqRkBaQqQqq8CSzzP48KFC5ru0+1K18A/+Af/gMXFRSzLIgxDDX4oOpAQglKpxNjYGNvb2/zLf/kv2djYoNPp8Pu///s6zuyP//iPtY7Gn/3ZnxEEAZZlUQgKvPbwNcojZYQlCEWoF3ktv0VsxLglF8Mx5KIPaXDeL9ynVeyrvBtJrw+inEEtXUmIEQGjRx1hBw4ij5+humq6ai8+2IgNiJAenx6joFAvcLJxsu/l6i1YVXYCw5CLV0X/FKakgkaGpNfHRowwhTS4kca1b/jSCFfZFo5wf2kxuNjtp4hDUm6VJ1nFIac9pXoiUx42U0jGg5lQN+vUrFo+dfpnWUim2zlFPVZAhRZB6wEediKVzR3hUECCH65wMWKpT2GZFpZtYTs2YRweiHlWoQl+Ihdzqo0TK9HpLQMCQkKpjN9bTD8WYyPbNkcBMNL/enOfBRNFEd2oq40w5fUXoh+m8HOVCnDu4OZ11lF6hYqy7VgOtmlTFlIrplKqYBu2XDQ3WlQrVQ3E1vZrFEtFHNchEQmdboeQENu1iRIp0qY0WEIRHokZ4BgSWPQsubBXIpGe48nsMbGg05Z57B3X0QwepQwfJHIB78fS83eUounqPQ9cmn2R/RtWEpJB8UejDyily6P60sLSSvmqLUzkAjkSEqirJ3UJXEQJYSDHcShCAhE88vjmaROOI0OGelkGLi1dYnJvEhdXM3M8Qxo0BatAwSgwLsaZN+dlfLxVwnVdHMehYMoQxVbUokMHH59aUJMie0ZA1+hKMEJ0aCUSlOgI+d8WbTp0hgJNvpBxpAGBnF972gFRHMlngJD9flhblqwSJbOE4zvE+zFe6OHEDiPuCKfmT7EwtUDFqVA0pNCfjfT8KxZKEAcybCr25bxBSC2s0YyatEWbLtK73I7btCNpjPmJrzMUhCLUGkIKBHvccie4w39x87/oMxM+lPUs2AVcXKpeVQqBmkVGkhG22abrdPHbvnyehRLkNjBwYskOLY2WePLck1w4c4FKoSINWNMj7saISFCtVgcW9mqRrrxw169fJ4oinn76aa3jEBsxYRziR75m0MTILCSxkN8FcSDBzkSCGkESEMYhzU4Tx3MoVookJHx6+tPsiT0e7D3gmHuM49PHtQaWCnssFAo6VeAffPgHvPfee4RhyPnz5wfixf/cn/tzvPqpV5mZmWFvb4+1tTWWVpa49/Ae7330HvMn5vn1f+/X6YRyLFoFS2cL6UZd9hp7+IkU2fRjn3bYZr+5T9NvUigWCJHPosAM5POpZNNMmvihTyfq0I27R5r7VDGQ85FNnw2VZkAhoBt16dKlLdo0kobU4rB684QK0TNk9pejCn1mBTzT78txWWulaFBDhRf1AH69tlCsIDPBtE28skdoSAZfQMBevMdGssFebU+m9VXhL4cUO7Fx5lJhIEKmGXbpC846ohdKGfdDQlSoiB3LervC7a9LjEGKeXrtnzaEhjEY0h7+9P/MzAxf//rXmZubG9BlGBsbI0kSJiYmGB0dpdFo8N5777G2tjbUkZiu10B/ZQCVYfXKMhWGARXZ8+UZfGk7ybZtPM/TKVdBOlVfe+01NjY2uHfvHisrK1oLIO3YzSt5bArl6Dtqe6RBiuxvsgZ+9rzZY0dRxO3bt/nyl7/M2NgYvu+ztbXF0tKSDhVRDsg0zT/LThh2rVlQK4+pki7Z0ITD9k3vNyykQ63hVLYQ27a1qKfneXq/NMsnCz5kAbesuOawumXHXLYP0iyio5QjAw0K8RNCUtkdx6FSqdBqtXjllVeYmJjg+vXrlMtlAEZHR/F9n2effZbf+q3f4urVqziOw6c//Wn+6I/+iNdee41ms0mr1eLVV1/ljTfeYH5+npWVlYHGsm37ABqWHgTFYlF7fUzTZG9vT2se7O3tcfr0ae7evcvm5ia1Wo133nmHra0tRkZGAIkchWHInTt3mJycpNVqcePGDZ5++mkd1uH7vhbE2dnZwXVdrcifHsBCyJy8KiREUcxUnVV6kiRJ+Judv0n4ViiNzSVJC7aKFlyAoluUAnufFzz0HsoFiGdQCCQyb4wacnHrm5i3TEkHDBOiuxEltwQxOG0HIrAN6eGOipFM2WgJqcgcFbEiq2/8xgbNWhMRC+1JA9jc3CQMQ6rTVURFaJXw8kgZwzVk6rOe9y8xE/05NqTSvDIkQ0Lt6VcCWoqWmStGp7zePW/OY5dhz+2f1avTo+FawsIT3oGY37TQnfLg6Z+KXlyrZWjwICbWbIfESLRBrV4fWQw0jVb9y82pCSKlxq/EJ4dRsLXXLB1XP+S8j1MMUsKZPS+T+lMK545wJHgR97IexA6GbyBCQdSOZKqxCPyuTxzFRGGEH8g4NaXkrlJYqlR9wu6ltLQSEkdqhghHMlDUd/rV7Gc2EGbvP0Wvboftx7voVLEMqd2gdRxMOU6SKJX2SKDDjxRIZ1qmzElvioF+jJCq5+n+qHckiIsAwzSodWpYphROjE3JhHCEg0iETn/kJA4C6VUMkmBgvD6qhEIac50o4yVPR4ZYQNj7f0RRi3Z1/0RBJDPMqHGdQMErSBovJu12G8/rhbiIhK7flfOsBUEc0I27sj+PAP4IxGOzI2JiaYjH+SwBDRY+4rCO4eBZnmacOKaDERtsrW8hYiE/GwamJTPxdJ0ubaMtAZseaOHHPt1gSAaezLkKZoGiVaRgFjRgUbJKUqnfKzFu9IGKolWU+yDZFp4ttYX80JfecRO6dAeYFPWgTj2o04gaNJOm1kZpRs2hQEVCgp/4/ZC2kVg1IjWzxqpYJdo6fE5U4VEFo0CRonxPAU/0QqXMEmNijFkxS8ksUbErVJwKBVsa/gWjoO8NkIuoiIhW0iK0Q3a6OzREgw4dumZXgy9dunRFl07coSu6BCLQei97Qc/7rmyz1BSi2ERiUcBCKmws5xZ8gzfg7sHtBoYcPylGiWu62qsfd2N2N3exsXlh7wUKlkypqTKcqN+oDCiu4VKyS3p7ySlRckryO9PFMuSa5q233sKqWzIb18YtWq2WXn+pcEvl1DAMQ4fUdjodnX3MdV3d1rZtE8cxb7zxBl/4whe0QZQkCQWnwER5gpeffJlvfOMbTI5PIoQUEFeLaHXOer3OyMiIFih3XZfl5WUKhQLValXrgbVaLW7dukWlUuHzn/88QkhtqjiOqTVq3Lp7i+t3rrN4ZpHx6XFM12S/tU8gAv7V9/8ViZVw4ZkLjE2NEYiAdtAmMiJagXTStIO2BBo7dW5u3ySJeqKcsWSGjbfG+Q3jNzh27BiVSkWzhIUp6MQd/MTXYHuQ9F57oZS+8PX36jUk7GtiiAAfX79XTpGjOkYsYenMOCr8YtKbpOJVKFjSuWBialBbOUbiJGZ7Z5tGs4HlWDq0U7El20ab0OkzLwIjeCTTwkqsPsNC9ENCNCDRAycUgOHRZ4qlNS08vAGWhLIdnn32WcbHx/VntYZXIRRxHDM6Okq5XNbgl9KcU0yAwwy2w4CHLDCS97thTIfD2CHZcISFhQW++MUv6hSeIB2r5XKZkydPMjc3x/7+PleuXOHu3bv6nkwbpnkO3qxhmwd6qN9nAZXsNWSN2CyLYZj3XIGq3W5Xi116nseTTz5JpVJhe3ub9fV1dnZ2CMNQM+GzbafAlTzQ5zBgIduP6XqmrzP7Xfqas+CQeq++29zcZHx8nAsXLmjGxsLCAo1GQ2vXqTCSLFiT137q+Ok6Zn+TBSJUKMswZshRy5GBhr/8l/8y7777Lt/4xjf44IMPmJ6e5uTJk3zve9/j13/917lz5w6FQoFms0mj0aBUKrGwsMDi4iJzc3Pcu3ePqakpbNtmbGyMsbExoijixIkTeJ6H53nMzMxw584dfaGWZQ08/LPxMlEUUa/X+fGPf4zneZw9e5a3336bubk5AG7cuEGxWCQIAu7fv8/a2hrb29ssLS0xMzOj1Twty6JQKLCwsEAYhjx48IDTp09r4ckzZ85w8+ZNyuUyy8vLdLtdkiThg+QDfmj/8IAQlWVaOLaDEcgFtPqzDRsv9DAwmBSTfcTMlBT9xJCGEUmv8y1BPaz3fXVBKue9oh8roUK399/bdqAcRWhrFvkASQlqGYspBkGPzk8sKcOO4WhDgFCyHFQohBVL8MKObUQkpGGeSPqiERsQg2d5lL0y5WJZTwSOLQ0g13M1Am85FqZrSq+V8KVHSviElkyT5gv5cFXq8YnRz/uutA10/GnPO57OegA8+kGsKIzZts25/zTym9UlGPh4BMr4YUX0GQ/quOnzaU9tb5OKwT+KAZT+3iSTeSJVdxWTfng1+wBGSDjYXlngQy20HaQQ4GMwGFScs0q9qNO7xRYFUeir4gtbKrr7fVE0IzEQvoBQLnBsw4YYbXz/7b/9twEIYzneOmFHpmLr7lPza9S6Nep+nUbQoOE3aIUtWkGLdtiW3rKoSzfqSs9rIr2JYRwOek8tDoZy5IV2ZK+7F6JgmT3AM0GzHuIolgY7vawnIpH7mzIbTJRE0jiyjhCGIpDhFxI9xEgMRkojmMIk6ATEfsz05DSu7dKqt4jDmFMnTuG5nvQU97LcRCLqC64ijUvlUQ6SgE7QoSEa8l6zEn2vtswWzaAp7xkbmWJWVdmU9dPZUB8zi4aQN5PubxO58LQt+XhMRKJV/Y8KXhylhCIkjHIM8An50hU9toe6D4ZIfFjComAVcC25uDYTk9HyqPR+BzGEMDYyJo3FwNf3rR/6bLY2paCuA524Q8NvEJpyfn1UcQ0XB0eCFXYRRziEnVBSrYMyE84ET596mrJTxjVk5hCQKuGGJUOIgkRmKKp36rzxzhsyjWNBIDyBVbYkE0oMBz8VwyUxEnxDPhsaNPRzVXvpk0DeazES/OoMHqNklWT2AKss/+0yZVNmyyhWixREgWpSJWknzIzOUHWqjHqjkjkSS0bXpUuXmJiY4Bvf+AZbO1vUgzr73X06ooNdsdlt7VIP6iRuwvtX3+eTu59Ikdres1sUBJEVETmSWTds/rOwdCiUCldRzJE4idVOhKMhrajFB/sfEMaSrRSJSAOFj8PcsOin8iz0/hzDwSk6mJHJ5MYkxZ2iniMqbgVLWFQLVYzIwIgNbng36JySoRJb5S1EIGh2mzTqDeyaza2Ht5g7Pjew6C4UCoyMjDA/P8+bb77JU089xerqqvZEVyoV7ZE2DEPrgKl06QsLC3odt7GxwdbWFs888wyrq6sDhlUcx9imTdyN2bi3wYNrD/hLf+kvMVocZXF8kTiOeeA8YGZmhicnn+TCkxe0XpZt29qJpBxLW1tb/Df/7/+G/f19vYgvFKSQ8fvu+6ysrPBLv/RLep2pAAfFbFWC3cqZpdaK6dBlRblO0+jThoTy6lqWJRkrlkwhrACKbixBslbQojhSpBN3pGCymVD369xbvkd1sqqZh62oRSfu0AklA6SbdOX6K/HlM3s8f+z8deuvM2/M9+83w5D3JBIY6SQSqPOFT2DIbV268jsk68o3fflv+DRoEBiB/o+Mw5koypnhJn3NpvcevkdxpUjZ7oeElK0yBUuCjyWzhGd4FM0iwhOcf/Y8Li6hH3L16lW63a72vqv2Tnt4h1Hpsx7nrGGn77feOMoarVm2Q/p8abZAtVrlS1/6EvPz8wN2ThqkK5fLTExMUKvVWF5ePjS0Q5W8sInsuEvbb3lOYnXd6ndZpkGeQzndhoYhheQvXLjASy+9hG3bA1kMFxYWmJub4/z58+zt7bGzs8OHH36oDfPs8bLC/emSrV/e9aRf09edZoikryU9DrLXmz6OYtsrnRTDMHj66aeZmJigXq+zs7PD+vq6To6QBjCG9U/2+vPGVxZ4GdZPh4WmZMuRgYZiscjc3BwzMzNYljUQkqCyDczMzMjsAD2v/uTkJFNTUzprwIkTJ1hfX9caA5cvXyYMQ1ZWVvjwww+Zn5+n3W4PIE2qQ9L/qsGURoPSUxgfH2diYoLFxUWmpqaYnp7m4sWL7O7u0mw2OXPmDEtLSxw/fpydnR12d3exbZtqtUq5XCYMQ65fv47ruly7do2rV69Sq9X4J//kn7C/v8/29rZuC9M0GRfjXGxf1BkJEiMhiAOiJMIreRrdVQseFXuoBKaCKOhnMej9KWM4bRj/3LHo2dIDE5SX3hCGXOCZ0ghV13PY77PFEMbQhdHQeNrHKGlKv06tplJLptOtIZFwK+mDO4q1YSGpjq7VF4NT39mGjW3ZMv2okJ5kTKQhRqzToemFmgj7XgThy0VeL5VaRF/AMRJRX0BPpLIA/JztkdVAkC+pCSTvHD/DOQ8dBzl1OPQ82e2ZUIUD+2bAmcOKsMTh94qiYubFZD/iHK/8g1cAaYgqT1/JkcZV+rP6nyxNDv2u5JQoOkXu3byHa7qYkUnYDfnhD3/I2toaYRLiVT3OXDzDg40HhFbI4vlFHqw/YLuxTWzHjEyO4OOz29iVYRAFG8MxaMUtGf5jJpLSbvZT72mj1UICg4/BDjcSQ3vpdTo/A/bCPbmINUOMokG7K1MJB2YABVjdWZX06yMY3q7paq+ZEztSbySxKbtlwlaIa7icOnaKvc09tte3GauO8fSFp9nY2GB5RS6STp45SavTYnVjlbGpMSZnJ2l2m2zVtggJqYxX8IVP028ibAnkKgq9Zv2oYkogQLcBhp5vRCwQiZAhd4apvVy2Y8sQrESKPtqWLe/9pK8v8FjliGM/JqaVtGglLb1ts5VRJNwf8mMTrUuidTuEzYg9gm3Y+C2fsltmpDSChcX+9j6TY5NMVCaIooilB0sy5XOlTLvT5l7jHjPHZhC2YFfs8q/X/jWdqEM7kur8h5YTvRC42MKMTEYYYb40T9kps7OxQ8EtcHzuOAYGqyurVKoVSuUSiUh4sPyA0YlRLM+iE3XY2t/C8Ay6dAmT4Ua1eiaIWLKbWnGLui9Th1m2RdySaZ7VnC8Q0Mw/TmGsQMWs8L//2f9OyZCCs0WjSNkqM9IcoWRKY2bSnOREcoKN1Q0M38AKpec1CiJMU2bg6vpdnIqDXbYRnsAb9Th25hijM6MEZsBWY4vYiQmtkGbUZK+zp420VtKiFbWI3RhcaAWtgbqamBTNoo67V+lIFePBoa9Fo+aKOOqlDy/IsImxSenR70QdgkR69reDbbpRVxuxoQi1IRokAZxH/gP3uHegDf/6w78OD3tsDXpsN8MhtmL+0Z/8I8J2yHRnmpJTorpdpVqoUlgtYMYyNWhpuUTYCRkrj1EpVIiLMffv3mdmbIYkSHjrjbdo7DZYa6+x9nCN+ZPzTI8N5qkXQtBsNvE8T4ehnj17FsOQKR9nZmY0a1etT7vdLtVqVTN+1YJcGXaK3aGM0ziO2djY4OHDhzpuXoXvNhoNXNfF8zzNwnAcRwtnq+Mp1f00szZtNKSF2hXLRI0tPQ56xkOtVuP69euEYUipVNIh0l898VVmZma0NhhI5p0Sb1f7b25t8t/9P/47tmvb8pljJ+DA8dPHeebFZ3i6/DSucHVdFKtXGX552gLqOnZ2digWi3q9r0XDqyMUi0UMw6DZbsrnnR1T79blfWGEGsToxB0iq8cIMXpZUKKAhttgw9+gE3d0WNWjQvkcHNxxCVZMmVP8x+5/PCBanzXi0pT4vFe1X3ZbNmNC9rjDjDz13fHjx4miiHv37g0YwwqcUmPEMAzK5TJPPPGEZmq0Wi1WV1cHjGRVsmBKOgwhj7GQB6akjdr07/OM9mxbqnp/5jOf4fnnn6dSqQwY2ereUeChsg+vXbums6ZkQYD08dP1yYoh5o3RvP5O998wvZHs5/RYUak7n3zySZ544okB3RN1DRMTE8zPz3P+/Hm63S6XLl2i0Wgc0MgY1obZuud9P6zf1XXl9dOwcmSgodVqMTs7S7vdpl6vUywWuXfvHmEYUq/XuXz5skZ4VQf5vs/t27cZHx/nk08+0YPg1q1b3Lt3jzt37mCaJk899RTNZpONjQ263a7OWgAMgA2qMdS/ykRQKBSYnZ1ldXWVVqtFHMe89957tFotPv74Y1ZWVmg0Gvzzf/7P2dvbo9PpUKlU9KC1bZtms8l3vvMdrbHwwx/+ENM0qdVqrK+v64lEUfXiOGYqmeLZ/WcHJu92u00QBNi2TblcHkglaRgG09PTev/0NaWRaoVs+74vhWhiSZdOjAQsMCwD0zG12nq9WZc9aaK9N8KQtGiVijGMQi0spY4jDIHpmJKebUtNAsMx5H6W9BxqRoA5qHI/oJhuJNrrpzzsjxTtesySDjGQjXZwH6UoPuDZz95sWWP8sJL+Ou1BVcdW+hHCkMZY1HuNDc3aMBIZc2vE8ntiNENEpxBTEy9GP3WYbQxko1D95UcyfaOwBLZnY3v2gAClZrkMY7Yc5Vp/lv4a9huRed8L+cjd9TFStj32ufWmo4ELeSURCc2gSTNoSpaAYQ7+Yx6YyFUGgkhI43NoOdb779X7p/FPscYlEPaw8VAydLwYBwcEjNljxH6MndhMe9Ocnj3NrSu3iP2Ykl3ilRdf4cblGzT3mriWy8svvEzX7/Jg6QEXn7vI+Nw4zaDJ3dW7XLp6CbtsM3lskrvLd2knbRIroTxepuk3aUdtYjPGtCWrSMX2hkYoDZH0U0TQZxQ8xvALEsn2MDDARab6w6BpNBGO7LPtYJuoEhGdidgwN7hRu4EoCDgrj3FP3IMCcBJWWNEGoVGS6f1aYYuSU8ISFqPOKNMj0xSsAlEn4uG9h8xOzzI9Mc3t27cRsWB0dJSF4wvcuHmDMA4plUtMTk/yYOUBQRzglT0q4xXWd9bpii5JLO9TBfJEyXBPm2VIFkLRKupXx5TK/BtrG4hYSGCrKBf+btElMRKanSaGbVCoFKSuTrdFYiQYjtSdieJevLfZZ2rIRnh0H6jxGYiAIOpx/l1o0WKz3QMuSrARbMBu70fjsNpZ7TMEJmC7u33g2IoRZWFpBo5K3arA4najLVk6hoHlWHTjLpvtTWIR0xZt7MRmZXtFagwYPrQhbvcm5QIyPEFhGRaa4WJj4yUelWJFgnuYtOttxkfHsUyLeq1OHMeMlceIRUytUyO2JNisDJVh7WdjY8QGlYIMxbBMi2bYpB7XdThIREQQS7q7Bq8M4IuDxzIjEzuSsetJJ8GObKldETt0a12W7i5x1j/LEyefYPvyNp954TN85uJncGOX/+V//F949ZVX+fKXvoxlWfyPv/c/8o1f/QaVyQr7/j7/2x/+bzz3ynOUJkpsN7d5/Z3XOfP0GWInZru5zfX71xmbHcNPfB0Gk+sp7lW/3ClTdaqMuCNU3Solu8SsMysz/FhlihSpulXGCmOUzBJmZPK9b3+P29dvM1YdIxEJm3ubREaE4RiYnsnk3CRf+dpX8BOfW/dvURopsbyxzNr2GpXJCmNTY5iWieVaNIIG9aRON+rSDtsSMFS6G5uBZMFky6T8/27juzAGf/87fx/ohzAVrAKWsOg+1cVKLD5e+xgbm9HtURzDIYoiJhoTTDCBV/ckqxMHMzYZK49JgK40gmu4BJ2AdqVNJ+xgxiYilGJunu1hWzLeemdnhyeeeIKRkRH9zFDCb1EUaaMhvS5Ur2EY0mg09PpS7ae812o/lRUtvZ5WgIhah6qMYfPz8zpLQbVaJQxDxsfHEULQaDT0erlQKOjjhGFIwSsgfIHZSYWO9oySJ158Quow9MKN06Laqo7pTBZp0MGyLEqlEpVKRe/r+z7dblf2xcQEzWaTer3ORHWCSqXC5uYmoyOjmk6v1tDFYpFut6tTqNu2rRnPlmfpNo6SSIdC+fgynKWXRaQZNrm7cpeaX6M4WmR+UraX0qFrNpsazDFNU6dvT7ez6h/TlJn6soZqGqxIgwLqO9V2ikWTdcaq9cfo6CgrKys6PETVQY0rFR4ihByXp0+fJggCgiDg3r17jI2NacaP2jetWZAX75/epgz9NNCijgPoEClV0mMiPVZhkCGgQkIuXLhAGIasr69Lhkzv3lDXpooS6T9z5oxmoodheEBzI90Hqr2HsSlUm2RBlOy+Wf2CrOGe951pmoyNjfH5z3+e48eP64woKqVluVymXC4PsD+SJOHq1au02+1cMGRY3fKyYKRLVqMiPc/ksV0OK4Y4Ivfhb/2tv8X4+DgnTpzgJz/5CV/96le5du0ae3t7/PIv/zL/4l/8C5rNJlNTU4RhyPb2NuVymYsXL/Laa6/xf/wf/wdf/OIXSZKEjz76iG9961v85Cc/YW5ujoWFBb797W9z8eJF3nnnnYHJRg1WNUDTE1Oj0dAaDQqgiKII3/dxXVdPyI7j6JtSHTtNo1Gf1cBIx2Kpm900TQ0GKEQ2SRIajQae5+nO7HQ6eiJ3HIc4jvVk0Qpa/ODCD3S8uvLAm8IcSGknIqG97aYwtdGahIn20CvD10gM7aU3RP+9DtXoKeOnPfcWMtuD53gyVKE3BNRgVsVxHP3QUjeVagPVfoVCYeCGUxNtHMd6UjNMAz/wqbfqlColnIJDN+jiFl2wwXAMhC1oBA0d45rYiU7xqNgEaYHA9J9W5CYaAEHSr0rlPqtc/osCQh6rPOqO+3nr06PUq5CC7KvWc+jtI0/ZBz1AGv0GEuxIjB5tXGkYGP20b4oFc9R6PbL8Aq5dHqZ3vf24koNsBtEHNwZAp3/b40FWQL8qAVMS+VBwLDmPqCwojiPBhjCUsbe2bUsQKvYlo+OI9bcMSYcmlIvtsfIYfsOXi2LDY356nrgbU9+p4+Bw8thJXMNl+f4ytrA5Nn2M+dl5Ln8iQeax8TEuXLjA9ZvXGRkb4ckLT0qhtvYe9aCuvcXNoKkXcEES0E26Mh45CQji4GgMLnGQvvm4/abAIYAk7tGPjX7KyiO3Yy+kzDVdqsUqcUcCQsdnjlOwCjLvPYbWcVHzp5p7VPaRKImotWusbKxooTvTM+mKrjR8H3EDuYaLERq4wmW0NErBKNDabeHiMjkySbFQZHNzk+MLxymWi0RJxMrGCvuNfUYnRmn6TRqdBomV4BQcKZwopI6MMI8YdvWLKmmAJHVPK6E93e9myvv3mCEzP2/9VF0AbMvug8W9EDUFvh/KCkultzSTFPjcYxApzRZM9JgYCsgKKNtl3MQFHxanF5koTVC2yizdWmJubI5jE8eobdbYeLDBZ1/4LAvTCzy48YBbn9zisy98lqfOP8VHH33EnTt3+At/8S+QOAmvv/s6a7trfOs3v0UradEM+2Fjzag58FllPWmEDVpRa+iYNWJDsznM0MSJHcYKY5w9fpayVWbl7grjxXF2V3dpbjd55twzfOur38JNXObG5hC+YGFhASEEOzs7lEqlfmpMpbdw/xZzi3M4JYdLH11ic2+T9Z11bty5wdPPP011osrcwhwbOxuMz4wTErLf2uf2/dts7m0yNT+F4Rh0IilA2Qk72EWbUEh9GpVdw08eHWY0MG562a2UeHXRLjJSGqHsliGCglVgrDyGa7j4LR/P8hgrj+Hg4Ld8vjr/VaqmZOE+fPiQhYUF7XVVQuPKyFasgyz1WbEW1Br1xo0bVKtVPvvZz+o19NjYGJOTkzQaDZ3qUa2F1VqwXC4jhOC//q//a834VetsFTd/7NgxXnjhBa2jlp63VchsnhkSRRGNRoNKpaKNYEAb6EkiU26urq5qPYU4jjXLQ61L1RpcXbtanx8/fpzZ2Vkdop1dx6prUfP122+/zY9+9CMsy2J+fp7Pf/7zjI2NDWQ+U/Xb2dlhdHSUkZERSqWSvsZOp6NZGpOTk7qfXNeVwyNlLKe942lPvLJX0sCDuj7VxwrMWF5exrZtDXoow1sBOeq3hUJBM8/feecdZmZmWFhY0OxtVbf02j5dJ+g/j9PhFapO6f5W36UZA8rWUteV/p36rbLRLl68yPT0NLdu3WJkZEQDEWlwLg1OqN+pPlpdXWV1dfVAmsgscJJlKGTZCnlMk+xYzgIxh7E21Lh76aWXuHjxomSH9sZ8GIYHNCPS/XL9+nWazaa2S/f396nVagfGT/a8eXXK9me6n/OO83f/7t89cC3ZcmRGg2malMtlFhcXmZyc1LFlvu+zuLjImTNnKBQKmg5WKpWYmJhgdnZW07J2dna4fv069Xqd73//+ywvL3Pr1i2KxSKNRoPvfOc7+iIV+qkuXF1cq9XC931GR0cBdCxeFkVrNptykd4zltWNm2YOKLS3UCgMTExq/yAIdChHEAT6JklT5FzX1ZOuQo7VzZJGjy3LIkxCFrYWdCaDMAkxbVNSkS25kAhFKNkEFnJx16OEa50BJaLY+/uZvcCqX1N6DGZ5EABRoIaJTFmpsj6oVJaKdprWoLCxtQClzk+v6KmRjLt1bAe6ki7t2R4FR8YXB+2gf05hyjRVPfRZTZAKRVdxl+q12WwOfI6iSKdbVYCT6os0iAVg2RaWZ0mWSA/UMF1Tsgoc2Q+GZ/QFBp0EbCm0l1hSzCwk1CKEWvCyJ3YZGz1dCDPFNjD7Oc0fK6NCumS73ui/agPa+vnGxy+sDNiDKaCjvxFDDOa4/3mMf2XEHXp/pIyYvHr+LOft/6xvdAzU6bC69F6VroXSsNCe8V4ooV7Y9mZvX/iPFWai6yMEAQFYyPSmfiA1YkxpRO139uVcVpHj9177nvTSTsZ942kDmOkf80/u/4lkJLSB9+U2E1OKzPXCI1zTlQKEVolJa1JmSegJz9V36izdXSIOY0xMRqpyIVEsFjl9+jSXr1xmfWMdy7F44eUXWFld4f7KfWzX5uxTZ9nv7LNZ2wQH3KqrY5BVJpl0GTACTRkmZAr58LaEBIdtw5YsAXoP2V5qwXRbx0ZMbMnMC81uU3+3srWS6t7hzKoDJaWno7LBePRE/vwYU5gyVbDt0aw3MYXJWFVqMOy39jEwqJpVGZdvy0wuoRESBzHNYpO1xhp+LeVdL8OqvyrfF1XjyDFgC8kGGC+OE7ZCCMG1XOan59nd3iXwA2zHZnp2ms3tTYIkYHJmksRMtDaJn/gEcY9m/wjl+lxmldFvs/R9ocujAIYUeKcyAyRRIoUO1XNH2IyWR7ETm069g2VYLB5bJAxCmS7NNJicnWSntkPLb4ENpZEStVaNUIRyoa7SoiZRvkc9r5jy+fJzhUim2qwTy0whhmPQrDcxGrKh4kLMle4VxFpvXl2At7bfAkU+uQj/KvxXmFeluLR31uOND99gtDhKq91ifGKcn6z9hLHCGCPeCKPuKAvlBUa9UUYLo7iJixVZVOwKtmnTbrcJIyn+5+PTjJr84z/8x1y+dZl23JZMJA8SL8EsmpQmSowfG2fL3uJ29zbbo9sEZoA4J+Ac3OUu//Kjf6mv08Cg+n6VqlulbJUZ9UYZcUdwEkdnJxlxR1hvrjMajUIF5t15RFvQttr8uxf+XZ48/yRbW1v867f/Nf/Bp/8DvQ7dn9nnzTff5MnjTzI9Pc3e3h4PHjyg3q7z27/52zoVu1qkd32ZnavpNwkSqRlQa9X4h//4H7Jd25ZrhR5DJjRCaq0a5bEyVsGiG3XpRB3MGRNnwqHltwicgLAQ0o26bDW2MFypIdSN5b6fnvs0L5x9gTiO+f73v8/JkyeZnp6mVqvxgx/8gG9961t4nsft27e5evUqv/3bv43ruly+fJnNzU2+8IUvEIYhb731FhMTE5w/f57FxUVOnDjBzEx/MnddV69dVcr5tJGZNlSLxaLWqVDAhhJEr9VqXLt2jXPnznH8+HHNUFBx9dPT0zSbTarVKq7rEkURrVaLcrnMysoKx48fBySgrkTwHEcK5apMd+VymZGRkYF1vVrrqXrGsUyXvrGxwcWLFzEMqZuhMk6kj5lmKSdJwubmpo7zV6Es6rdBEOA4zoCOnOd5tFotLcKYdmIGQUCn06Fer2s9EWUYKzsmy2ZR7axslHQYCgwah4odsr6+zqlTp3QoQdoGUnVS+3uep5nc6+vrrK2tUalUOHnypAaX1PHTrIt0PfPqqNbcqh3TtlPawFXb8kCLLGOj3W7zwQcf6FD9brer20+JzyqHZ/pcilFx//59relXLBYHxkcaAMmCO3nhLArgSLMd1O/V8fJYGll2iPp+ZGSEU6dOybDEpSWEkAkP1DnTAFOaWTAxMaEzpiiHsQKP0o7iNAiSZTVkGSl5IEy6//+NaDQ4jsPU1BSffPKJRlBu3LiB67p897vf1dkilIdbCT++/fbbehL6sz/7Mz0BXb9+XVdSxaAomk8WUUxPcCoDhApLUKEMyhhNAwbKuOx0OvpfdYQyWtVNphoe0DFhqqEVCqm+U9tVysn0BOM4jp50DKMfM6Qm3+PJcc2yKJfLB24MJezh2hLhNDBoNBoa0VIxe2rC6gZd7XFSYk+WK8V/DNvQ24JYPgBN25QMgt7ixrANCWaIWMZRWtI41er76ri9EAq1IAoI5AKbfs559V7tp8Mc9CBC0qkD+ovDXnxwbgnQ6SwN0QtJSFLhCSpkwTQwKobe1zEdzeDQoEkKEHFMpy8cKCSI4tly0ZkEUjDMs71+xg4/0in8jMjA7EjARSR9pFU93NJ0ODUu1aSrFI3T3ydJIlNe2aZOtZaYiQ5fiZGpIZvdJtv72zL+PpY6H4YrQ2hMz9TpL2M7JjKlcRFbUoBRpZdU2RUSI+mzEpQh/osAOlCHMg4yCjLfq3Nqg0v03/9cIpk/j8c1a8D8AoplWrlhFkmcyHYwpDddo+s90Mm0TG14PI53/XGKNrINdBhW2mjz4x6g0QM40poF8mcZit6QOiYkUjgs6VKP6rn7pIEZMSeyXwLwp8t/KtPlSoyZ5d1lSZnvhU5sNbekWGUReY9jSi9/ItPCTY1PYWFR360zNTnFaGUUC4tWo8XGxgaLxxdxbIeHKw+J45jJ6UncosvK+grdqEtxpIjlWuw0drRw2lFF9TToqtLgpdgUitWQiIQgDAjCoA9A9jzZMbHU2ZCPBZpJU86jhd61R1vyTQkQsOavyc+977UNazEQAmYi5zLXlvH4IhKYwqRcKhP6knqtBGFDpKBc1+gSd2OaZpOoEBGbMSv1FXxbjpeV/T7Aki1Fq0jRluEiBbOAbcp2icOYjfUN6fE1TVzPJYxDKmMVGp0GnbBDYsoQkW7SPRxAFGjGi2EYWvg3FnEfAOjp7xCh23Qv2pNvKvJlqbY08Plu4668PwpyvNpdW4JyiYkjHEpuibJd1hkwKmaF6dI0VbPKpDspP1enwYA333yTSx9fQrhSADJ2ZIaO2I4JzRBcMIumzOziSUM1NmPJcImkYZs3d6u5Qon1Hil0JtVejiUzWSAgMALqok7Ta1I36qw/XJepI6N2f24Y0sclq0TVlkBA1ZFgwFJliXAuJKknMoOA7yBqghFnhOdHnuc//dp/Sskssbu+y3e+8x1u3rrJX/orfwm36vLWh29BAU4+cZK9zh4btQ2EJ4idmJpfoxW1qPk11tvr7LZ3acUtmbo1G3NfAF6E7935HqX7MgOJMWbwwz/7IeOlcR3yIY4J3rv/HmeDs9ixzQc3PuDk7EnuN+8zKSaxIrl28DzJCi27ZSm+2vOod+wO8/E8VnvQgKxUKrSDNrvXd5mcnGR0dJSdnR0qjQpnOcv29rakhp+6wN27d7m/fJ9SqcTLL7/M9evX2dnZ4enPPq0zd5TLZTY3N7Ftmw8++ICNjQ1WVyVoeP36dRzHod1us7Ozw+3bt7X3XWVRW1hY4Omnn+b8+fOajq886srTrNanKjRa0f4BzSBR62IVpmAYBvv7+4A0PldXV7ly5QqvvvoqL7zwAru7u0RRxBe+8AUA3nnnHV566SVmZ2fZ3t7m1q1bfO1rX9NhxM1mk5MnT9JoNHj33Xf55je/qbMLrK2t8alPfYpOp6PZDefOnUMIwZtvvslzzz1HsVhkd3eXK1eu8NJLLzE3N4dt2zrzR9rYCoJAgyzKeFTtFQQBc3NzfO5zn2NmZoZut6vX9kEQ0O12GR0dxbYl2KZAB2AAIEkb4L7vaztAz1k9r7zaN+2lV30EfcNf2ToqhEPpLqg+TYuGKltDHVs5Xy9dusTdu3e1LVOr1bh8+fJAOEL2NZ09RvV12uGrgAxlM6WPpX6jrj1tzKrjpIGUdH+4rsvHH3+smSFpm0zVS9mC6hoBPv74Y+7du8fIyIhm6Ku6KAaBai91DWmDXLV5dsyoNska3mmWjLqONKs+zQABae9ubW3peztJEjqdzkD2wjQwohyx6WNsbm5y7949zp49qxk7lmURhuHAfZ1m26cBOVXUPmmWiaqzCglKgy+HlSMDDfV6nR/96EcUCgXa7TZ/+qd/qg1eRftJkkTTN9SrYgWoC1QDSbEDFBVHUfRVZ6QHTfqGazQaOI7DteQaN+IbWJ1eekakoUnSUw0PE5Io6acgxMRy5L7CETJWXkh6sohlvnMzkmELoiGIQ6lC7DmePG4BmTHBkqkoi16RykRFLmJNm2qlSqfT0eBCmubleZ5uKzURKNBEgR9Zuo8aOJ7naZBFdarneQMTYfqGjONYU7VU2yVJorUrJkYnKBaLeqKBPrKlBp46vzKM1U2a3kfdaGEY0mq3tDaFip9LkoQwCun4Hdp+m04gRaNiJJNDefsV4KHpuaaQo1Kp8NtgWIZ87YEimEjQwpTGeGL3FpI9FoFhG8SmXLCp8yitCv3ZOISCesRiCEMzPrTgZBrgMGSIimKMuKar3yvgw0TSKIklnVR975gyZWkSJHww8gFiVGBM9oCOSAqm2YndTwEVyfzUbtulbJWxIxsnkZlPbGHLxa9VwKAPfql7zzT7eh8qRWkoQoQt6IgOraTF/5e9Pwuy5Mqyw9B1jk/3+h3ixpQZmcgBiSEBFICqQnUVurqqW91Na5PxQ6KJLaMoso0yk0wmmvTLDxq/+KVPmT3Th75olExtxkcj3xMfH0lRr9nq6n5d1dVdEwpzJRLIBJBzRGRE3NHnc97H9r39+I3IiMhEoiQ+8WSG3Yg7+HU/fvz42WuvvVaucxR+QYthj/o2qRKxqip1I5LJLA5h4rh6EY9a8D4p0FGXibScUmxjqSllJMvfZUEAlVKyKBcWRV3fzmU2vUGPPOjZPcFW9H5rReSTdqfNXrA43pe61ZZm4lOVQCwHERx3KDp+BsI0anuvulzE93z5TFmVqEwFpRvKd6uc6IQMc+v5L9Ba4FJ9zhQUORVUQBiEUFDkpFED2sbSHFNWJbSnGyFGbZCqFEleCwfU5aC78zp9GwG3Zrfaon594Ob4Jv1eB+efLD4hZkZQ/7Bdp6I5ia9vFszrRB259ygoDHoDKpWwdfaBx2E9/iysjKXK0rzIAK71TmY5KSiiX1sPvahHDiApscKG/SHKssRsTsy+Xr9HWgfJAp24Ixo+SZ6I1k6BQqj5i2JB82wdhI+zcWuMjrNx2xXliGEu5SJaix6DhkZpSrGIZLCqqioknaQpx9JA5Vek96Ca/Vhm7viqdo6piH0y7A2RzBOYikD5uBtjOptC+xpRN6JSnYIywyyUemSzaNgPvJD0dIsFU9pSrpUsz7CX7z16e8vNB/BG+/sUaIywhk+gAiitENkI3YKcL776zFexd3sP1bhCV3XxW9/5Lbz3s/cwG88QhAG++1vfxV+89RfYm+3ha29+Dda3WJgFfvrhTzGv5hicGeDW9i2kNoUNLHSoMctmUmKYmQyZl9G5tQBSNOf9CC1PDS3gGf/4muwPkypBmqTYTXdRGWLT5JdzusfUzCkAuId7uIZr+Cf/r38i2wyHIbzXPXxw8wOs99YxzaboZB1cfXAVsRdDVQqb3ibOjc6RBkSuEdoQSIFyVmIxWRDLNu7A63l47+P3sDAL/Is/+Bf4+re/js2Lm3g4f4hxNsbD+UPYyGJezvEgfYAbsxtUClKM8b99/r/ROaZ4BP/zv/yf5dhDHWIQEJDClqnDYIheQBoVH6x/gNRPSXej9BHZCJVXYbQyQoYMfuBLacH58+exsrKCyWSCg4MDDIdDWVtnWSYlyf1+vxUc9/t93LlzB8PhEHfu3EEURXjnnXfEbYM10fb29iRZ9uMf/xgPHjxAkiQSzHQ6nZbLG4DW77z2S5JE6vc5k8wJlvX1dWE0d7tdPPPMM7hzh0DHM2fOoNvt4q233sJsNiPL9MEA6+vruH37Nm7duoVz584hSRJcu3ZN7qkXL17E3bt3xZL0xo0buHPnDu7fv4/RaIS33noL4/EYnudhsVjgww8/xCuvvII8z/HZZ5/h7bffxrPPPoter4fPPvsM6+vrePPNN2W/OfvL17gb6LtZ+yiK0O120ev1EEURXnvtNQDAv/yX/xK//du/jdFohOvXr+Pdd9/FX/2rfxWe5+GHP/whLl68iNdeew2TyQR/+Id/iN/5nd9Bt9vFtWvX4HkeXnyR1FHfeecdXLp0Cevr60jTFD/72c/w5ptvQimFmzdvIssyXLlyBVVV4cc//jG+9rWvodfrYW9vD59//jmef/55eJ6Hn/3sZ0iSBK+99hqKosCVK1daZRl8Xt1gno/9X/2rfwXP87C6uoqrV6/itddek1iC4xeXReHGcKxtt8wkWC5hcYNzN4vusg7cEm23VJttaPM8x3w+RxiGYkrgxk4uA4RZFZPJRARYedwXRSE6gfzd7jZcbb1lkUX3uHh8uOwEd5tu8tH9LL+fAQAG8/g99+7dw/7+Pp599tkWW8ItP+FHfv7+/fv43ve+J9fpyy+/LAACnxfuT1cuwI373BidYzyOF/M8h1JKwDV3jjiunRpoGI/HRLWtbVK4gxgFcmk4Lqjg1ku5P5y5/8x+homa0OItp8UC+8yneYpu1JUMtPY1xv4YVV5hu9jGrk8LR87+WG0bC8PIsYI8KoP7tJuzUJAFQ+3ooGwtghVQdl316DnJrNcBqq8p+FSGMqBu6QEDKAx+BJpsJCkuarQelFXodXvwVG3bV1lUOWVLyqxE7ueIbARdaFJPryzyNIcpDPIkh62sLJxtaZGlGYq8kPPN580FP3iALiNrLjMl9EL0gl5rYAPNJOXa7nDZTJIkUg7BdWQ8tgCIYKZ74TJKzSieC265k5zv+7RwrMeL8hUJqrHbh6Lsl9EG1rMEcmjbaEKgcZhIixRZmUk5jFFG3Cb4fbnJkeq0caHgcVqDK5VXibUUl1kcCjRCfLEmcWETiMu/ukZYGCCmLn8xvgRYOtNiGakLDZUrxFUMZRVCTZljUxixOVWWHkOPSmSUUXKtGs+g9ErK6GnK5OU6p5/azqrQRL0tVEG2pZ5B5VXirFCpSkCBR1GP+fpjhgUfv7AnagbBcayBPH003Tv0QnT8Djp+hwTFnN9DL5QfX9O8xoHVzsMdyk7YErNkhp29HcoSagA+4IXkWMOir3mZN/vIGfBH7LNQzOtLsZVxV0BhliwVXer58vZ+iXoVAjZoB6gBlZXJPtf7mhZOppIDXj7M5X0+ApDxlCeBrzU0D3bDLinsFzSO+nFfGHCwQC/uoTIVkiyBsQZhEBILoXamYcvS0lK2ebqYtsGb07ZH3JVdlpDWWsAKow0SUwPWnoFWGmmRUvleQPPJLJ9RFty3GOfj9oZ1u+80NCIdEZOsvievDlbFxnR1sIput4vxZIwkJbenIAwwno6R5ilGayMYRboTeZVLOUFpS7J3rTIBWFpjs60PdnyrAYfK1qCxR7T0PK/LfxQFCLN8JvoWNrWNIOsx5RYMFsdhDBigyGnsdYIOsoKsaZn1V5ji2GskQIBQ12WgQt4ixykuyWkJKNesM4AYBQiAmYOI3Xhwg/ppg/7+w2t/CPRAPxb4Z+/9MxJSHSn8ycd/QmCYDmAUuaCsZquw2qKbd7EaruKFMy/gzid3gBTY7G/i33vz38Nf/OlfoJgXeOHKC7j60lX84Z/+IfRA44WvvoCD/ACTYoK9dA+zgvQZkorsftMyJc2CMhPx29a493H02K7fwkAFA3KFLrCdbuOgPEBapVBa4ebdmygNjaPjynB85SM+IMbCIBwgshEiRLg/uI9wFuLq4ipiHWMr2sLl8DLWe+vYGGyg7/cx6oyQTTL88R/9MfYP9mEDizOXzuDS1UvojDqYV3PsTHdIe6aYkO5MOcO8muMgO8Dns88xTsfYH+0jW80OlWwBAJ6le1NoQoQ2xHpvHZGNkK/niL0YO7s72Iv2sOgtsDnYxJ8f/Dm2e9sITYjdaheL6QLDaCis3q2tLbFyf+mll7CxsYFPPvkEAHD16lU8ePAA9+/fRxiG+NVf/VVcv34d77zzDtbW1g7VwgNNEMPrOnbQ4Ez9cq2/7/tYWVlBFEV4+PAh1tfXxY0OAAaDAUajEXq9Hp5//nl8/PHHUqIwm81EyDGOY0wmE5w7dw7z+Ryj0QhxHGNvbw9pmuLBgwfo9Xr45JNPcPnyZdy5cwdlWeLBgwd4//33kSQJPvvsM2xtbeHDDz9EWZZ47733MJlM8Pbbb+Nb3/rWoYDUzUi7zX2exSxZrPPTTz/Fp59+ips3b+L8+fMCeiwWC3z66afY29sTIfzt7W384he/wPXr1/GVr3wFw+EQ165dw9e//nWMRiNZ0y4WC4RhiJs3b+Ljjz/Gm2++iSRJ8P777yOOY7zwwgvY29vD9evX8d3vfhfdbhcffPABHjx4gG9/+9tYLBb45JNP8Df/5t/EhQsXBADiBCgfMwe+bnbb932cOXMGgwElTL/1rW/h0qVL+PnPf46trS1sbm4KqPG1r30N1lp89NFH6Ha7OH/+PPI8x/vvv4833ngDvV4P0+kUn3zyCV599VXEcYwbN25gfX0dnU4Hk8kEe3t7AhI8fPhQ1u2s77exsYEgCDCbzSRo5jHCLPb9/X3EcYznnnuOphFn/Lol8nme4/Lly7h16xaMMTh//jxeffVVXL16tRW3LrMPOMHqskpcDToeI6yL4iaV3Mw/v4/3x03cugwVjov5ulhZWcGlS5dk/5YZFi5LgRlEP/7xj7GzsyNWuh999BGCIDiUmGbgiK9hBi9chgP3J5dE8XG7Mf5TZzTwBcHIFLMV5vM55vO5BKAuisWZUxcZcWk8SZLgB5d/gHuDe0d/6eCUzz2q2eaHxfDY1lFZUrY2yrTo3e5g5ZukBCYu1Ztfd54TuutTalLLDnUoe8TZyhZVh6nfy6xGD1R72z3ieQ8nB7AGTZmC47SgbROkCiWYg1YngGXWCGfw3c/CoJWJZkBFWw10gTIvic4LyvJHQUTaDbVSeZFRmQkHc1EQIeySx7inSDci9OlvGNoe6z/4ni+TrAtGuOi953kweVP3xPQ51okoyxJ+4UMvmhIad4JxqXLMInBRUraxYjSVL34ooDIVJosJHuw+QFqkGK2P0FvpCTgRxRHiQSzlMZnJhG2QgWycMmTIbS7BOpdiVLqtIVGqsmGVKNKakDH1ZTTb1gcRFohpGCFsWxqZCFEZwa9Iv4Pp3QzICS2tduwAAGjKOhtrxG6UwQlezDMVvERJGgU2P/F4WVzVBRMCHZCSuPaFFq+UQmXJt3xezIUaX5pSRP8KQwyQwhRI45SAk+XGT7n75QTOGprADB1CVQqhChF3yFmB61njOCaAoQYKoziicVBRSY4FAWmVqYEyawR8+UJlLEe0Q4KcX7QdxbhYynofBZ5UIEcAaR6QV3XgUt8VZ/lMXgPQBOg8F3PJF8+7y0DHEYfnikJKtt9hPVRlhbzIpYTA0x6MpZK8ytT7rCiQYlchY424gVhVgxp8aE8gkGhAwAUAuTccpAd0XB3gfnGfwEcLIFpynfCBe5Oj7+c8Hwc6QKxjhJqunVCHsKXF/s4+YCCMwSiKsDZaw97+HpIkQSfuoN/v42B8QEmIXhdBJ8DedA95lcPvkANPWqWiX1SZqhnHpxxuzMZiViKf30Vep/Qfw1WlAIl8erYpoZJtepBr7ZHzjiVnC11pRH6EXtQjkb+8QNSJ4IUekixBXuZQnoLyFIqqaASS6/sCf8dkMZH1wF3cxft33icGT83i+dc/+9cNcDEH6az06Fj12w1Lz9e+AKs9v4cznTOIdIRYx+iHfcrye330PXLjgAHe+8V7uH7zOibZBKVPzLhMZ7CRRTgI0V/vkw1qTiKkpSaAvlVqWSx30NGttCVmFQX/u/mugHTVWoUHiwf4/rXvE+h/jJZGEATwVj2sRCvY1JsY3RphdXcVw2iIDshdY62zhq1oCwOfHDY6qoNhOIRJDP7VP/tXlOE3OSq/IrvRCIjXYkzyCQqvgI41Cq/AxvoGEpPgtrqNXeziR3s/wl61h8VggVKV+Oc3/rnMS//jH/+Pso8RInSSDjZ+uIE0SuGv+fjJ9k+wlW/hfuc+IkRQnyvYxOJT9Sl6podPp59iL9+D9azUqLtrElcbDYAkEz3Pw8rKipRHeJ4njF3O6I5GI0ynU0kMraysIM9zdDodBEGA3/iN30BRFLhx4wYGgwGef/553L59G7PZDOfOncOlS5fwwQcf4OWXX8ZoNJLA+MyZM3jxxRdx69YtFEWB8+fP4/Lly/jggw9QliVeeOEFyXafPXsWL774Ij766COcPXsWzz33HJ599ll89NFHeO6552SN58YlHMwtZ/mBpuSBg+EoirC6uopPP/0Um5ubSJIEzzzzjDCLWXdhOBzi7t27WF9fx/3798UJYjKZYDweY3NzUwK8MAxx7do1vPTSS/jwww+R5zk++ugjjMdjPHjwAEopXLx4Ee+99x7yPMfPf/5z+L6Pd955B2VZih7Hw4cPJUh3E7yucCTHaMvigqPRCFtbW4jjGIPBAB9//DF+/OMf4y/9pb8kbJHt7W2xznz77bfx9a9/HUEQ4ObNm7hx4wa++c1vQimF999/H5ubm7h06RKCIMCNGzcQBAHiOMZHH32EnZ0dvP766yiKAj/4wQ/w2muvYWNjA3fv3sW9e/fw/PPPIwxDfPDBBwjDEM888wyyLMNPf/pT/JW/8lcwHBLItr6+3oo73fILPndxHMP3fayursqY/cY3voF+v49f/OIXuHz5MobDIabTKT799FO88sor8DwPn332GVZXVzEajZAkCW7evIkrV66g2+1iZ2cHQRBQKVQN8Fy4cAFxHOPOnTsoikJYJjs7O8KGMcbg9u3b2NraQqfTwcHBgQivzmYz3L17F57n4aWXXkIcxy3tERcoYMdFF3xgJ5rV1VW8/PLLePnll6WkibUEGcxioIHFYnmMMKDI40QpJVIFXN7L0gCum81J7dRAAyvKpmkqtItlqohba8MnPU1T2Wm3JIDf/50738FgNIAXeKhsBS/wiLqZLlBUBYIwoGyyssiLHJP5BBZk7dey8qp90CtLVo6lLvHTcz89RPN1F7enFl9yas3dR6lFd97X/lgTrB1Fqz75aw/v86F22kDwqAX5aRtnGGthwQpVa3vL2eLW84d292SleAZr3Bryp9LqbLF8tSV9Bw5aW3aVVkFF9d9akY5CTRN13+MyRyj5SuwTZqdoS1lsUxgCSOA1gIoD0LBnOb8+VVNkNsNCL0irYVHhYfkQ+TiHpyjYzv0cmZ+hKiqY0pDbiKWF4NBr1Hjd8hoGAN3aN7cOy52kjSXgYVbMMC/mKHQBP/bhxz5KvyRAQ2W0aPQrVH6FFCm5hej6R5VUblEDHG5dMZdVtE8+ji5/WL7WnOuagyueAzjg+kJDpQbClFXox31hJLishMpUZAXJ6vIOkFCYgjKfJ1znvvIR6QhddFEmJQIEWO2vYqW7gm5A9eyhovPqw0c/7ov+QJqmiLsxfM+XUjMWMC1MgWkyxTybQ4dkS5lVGabJFDrQZANnc/iej1JR2UtmMgFPH6cpkECs1uTaYE095qBaJSlcKnBie5y5ynmds6EKVGvOFG5TGbpuPaLt5yUtmq1q7EfdufZpgytuE/YN98NR3bF8V1aOAGgNcuSmDoI5AFtmbZx+h+hjdQmRMCaUPrS4admzqhPuS0c01kh4pEr/chLBAtivf+/Wf08hOgmoQMGwApXWlfV+g4Bp31IwbAuLQAcYxkP42ocpDGbTGYZ9WsDNp3MoRZZwXM5otcXK6gqSLME8mcNogyAKiNFgCjoPGi1g7tFdTMwlOSanDx8FILrvKVECPpAjxzSb0vsC0ulQaX0/5XFwFDAJms9CRWKspjDQlcawR/0xm89QVAU6vQ6MMhTo10LVpaW5gcvEjCWAFhVOHfS32rOHOkfue0mSiDV0p6JSv621Lax0VwiQqt202PrVwKDSFbYPtjFejJHalOY6FJiXc2QmEwaiNIf95N5TjhrLBQoUfoG0SvHg4AH1o6OtcuJ89gzgnfPglR6VOlY+giogXYhEoa/7GIUjDM0Q39n6DmIvxvvb7yMsQ/z17/x1bH+2jR/98Ee49Owl/Mp3fwU//8XP8e7H7+Jbv/4tcfx4/+P3MS2m2NzcxIezDzEuxxjbMcbJGNvdbaRI8cNPfkjHN6Td+kff/0f0SwT89//v/x6DkGxKV6IVxF4swpqDYIDV7ip6Xg+60Oh7fcQe2a2eCc5gEA6QpZm4GgBAHMcSzAEQwUR3LcJODSzWzWzWyWQiQc9gMBDWhKu+D9Caha09OVhinQiXYc1xCNCIy29sbGA6nYr4ZL9PAixMCXcdJFwtCs7iRlGEM2fOYGNjQ1gOW1tbWFlZkeD+7Nmz4ugxHA7x4osv4sMPP8RgMMArr7wizgdnz56V5FS/3xfw5L33yMXp/PnzUvIRxzG+9rWv4datW0jTVBxBWFPh6tWruH37Nr75zW+i1+u12ONuSQD3H9PeXc2AwWAArbW4+N27dw++7+P69euw1uKTTz6RMoQbN25gPB7j4cOHGI/HeP/996GUwng8xs7ODn7yk5/gb/2tv0WXmjHo9Xq4efMm4jjGz372Mymz+eijj/D2229jbW0NURThe9/7HrIsw7179zCZTPBnf/ZnOH/+PKIowo9//GMcHBzg3Llz6Pf7ksTLskxAE7c8ZDk73+/3cenSJVy4cEFAoI8//hgvvfQSfN/HJ598grt37+Lll1/GdDrFW2+9hd/5nd9Br9fDvXv3cPfuXXzta18DANy6dQuvv/46tra2oJTC3bt3Eccx4jjGeDzGbDbDr/zKr6AoCmxvb+MrX/kKNjc3cf/+fTx48AAvvvgiwjDEz3/+c6ysrODKlSuYTqe4fv06fu/3fg9nzpwRkVUWs3d1FHiN7pZDMDCxv7+PN998ExsbG7hx4waeeeYZnD9/HlmW4U//9E/x7W9/G51OB9euXUOaprh8+bIAR1euXBEw8datW3jxxRexsrKCnZ0dzOdzcfp59913Bcg4TTs10MAencvIGCNyrliEaBGYGZIowcpgBX7gw1ZWMjrKUB09bE1RsXVwZuhG0sk6iGwEv/QlCMpmGaI8EhoHTzLuhSSUfmvwWzd+C37kk9BelcMLPRJvCjRujm7i5trNJnjkGtI6MNRKiwI5i2HJhcvBhj3MeuD9kODDNOJMfGOSGm8OjNgqkO0DH8cy8DTNCdQ4QBbwxDav0/+6xMC5AfNr/HezWXtoUeS+5ygWyJFB4C+Rnu2uJ6TPnf1ZBo6UUu3FIT/P7zsqe6nQyqoe6g9bP54mGN46xTEd1WrWDtAE7gptLYNlwUwGOVqsFWgpv/GUh8CjBZ+IYRqiB0dlBF1qrNpVWQzytgIdwJYWvvbRiTpimecFHtHMtZUsYqUq5DZvtCJUIYKWGcjdo9QlclWzNI7yel/qh8AG8C05ovDvXK4UqEDAI5ep4/bfb3/7t+WGBVDmO69yCtarHFmZPfI5l07M73FLF0pboqyIwYIukCLFtJq29QMes3kgVoXUTNdZZM4gR2WEntfDqr+KqBuReFsQo6sJ2Ah0gEhHiDxSqy6LEoFP9eLGErtH+/ViW1lkRUbsEaIjYZEvKKOYzZAWKTFqaqpzbqiP2BaOXyssZX4fx55zucmiXwFFVbSD+JrVpkCgtTLN/PY4ATO76njKI1aU9mFKEt0LwkC0aYw1UB6xWipTz/loW+w+svH84f79JTYGhdy/3e9Vqn1vkLf+Muft45ozz/L4MSAmEypIfx7MD5rPBMBu1mh2AMCD5EHztyVxUQDNKokv2xNKdRRUW7dAezJm3PtqURRYJIvmHqkO35NajkRH9PtpgUGjDFKkSKtU7DIP8oPmeDTawIFbTuN8J88lvvJFyJPnUF5H8TqI2VqZycS+VgAXd7seJS8SJLJvCIAppnQOTjsXquYYWN+lg07jgqV9lFmJTtCBpzzE3RjaaqQZUZWTLEFmMuhQo9IVkiIhRx+3H51yHw8egcDKa4HznPCqbM0eDNtgxB72iCUCyDrhj679Ef1dr9v/4b/5h3QcawrBLED3f+9SaaPW+PMP/hz9oI+VcAWJTXBm9Qye6T2DaBjh4PYB/vKbfxnn18/j53/+c+hc46/9tb+G29u38Qd/+gfw+z5e/caruPPwDn763k9x/vnzqPwKs2qGRbXAfrKP/XQft6a3MMknmBZTTPLJ4fNWN0956Pk9eCMP6hUqBSmiAje8GxgGQxjfIBgGMOsGKlP4xHyCLW8LdsWiTEsoT+HMmTO4c+cOwjDEYDDAcDhEmqaYz+etsg3P87C5uYnt7W1YaxHHsehasCbEO++8gywj8GM0GmF/fx9JkqDf70sGuNvtIo5jKbFVSgnV3KXHL9flM32da9YZhBiPxy3hxcViISzV2WwmgSD/7YrAc9wURZFoWzAtnsuCOb5xNQEY5FBKCaMkDEO88MIL8n6Xju8ydRn04UfeB9aWW11dxTPPPIO9vT30+32cOXMGL7/8Mm7evIkkSfDcc88hyzIR+9zc3BQL0k6ng16vhwsXLkhQWpYl+v0+sizDuXPnMBwOURQFLl++jLt37+LixYsoyxJra2uI4xhVVWEwGCDLMmxtbSGKIly6dAnvvvsuvvKVr4jhANCAJtwv3E/c3NiQnVOSJMF0OsW7776LxWKBxWKBvb09fPDBB6iqCjs7O3j//fdx584d3LpFwtDf//73hZ1w7949fP/738c3v/lN+d7hcIhPPvkE/X4f165dk7KWO3fu4IMPPkC328X+/j6+//3vYzKZYGNjA4vFAj/4wQ/w7W9/G59++il+9KMfSQkLM4BcswE+XrfxeVVKodvt4uLFi8KU+cUvfiGsnt3dXfz85z/H9vY29vf3kaYpvv/97+OVV17Bzs4Obt26hXfffRdnz55FEAT4sz/7MwFkjDEYjUbCkL127Ro+//xzvPjiiy0A8Lh2aqCBGQlZleGn5qdI5ynKoqRsamUk8LClpYyWVUjOJZh+ZXrar2i3eqHvBkit3+HUmdd/C2CgnICp1jXgWlwuoUiRYpgPob1GgRVAW9ytBgxKlC3xN8maotGBcH+ehK566NhrYUC2enQzrPI7mqBIAqM6QwA0QIIAJ7rxXeXGQIjWlLlXul5UsjhcnfVzWSOsd8EgCdfHu30j4n9Pq3EGwhkTrue4LJZd9slSrZG0ZdBkGRDgDLkDECgoWWBYbQ999rHa8me+jKDCATJOZO4cG/Ms7ZADoLhaB/TS8UyVp9IsWmM/tGEDBDr/mtPTtszMVY5MZc3YhmkxIY7SPXjvR++1/mZdhq5fB+h+F3EYIw5ijKIR+mEfvaCHQVSrrkcDDKMh2cJFKxhGQ3zvD79Hop3wUZUV3n3/XXxw7QNYbdFf6eP1N17HK6+9Io4xvGhPygTzao6kSjDNp1gUC3J0qMhGkB9zUwf3zmNSJUIXPlQf/xQbz7kclDDIEXohARhBhH7UlyCFs5W+8hGoAJ/e/BS727uI4xhbZ7ewurIK7dfAbx3w8vwEELOksHSMWUkgBgc2Fei1EsQyYZ0A7ofSlm16/QmtBJUpwQLzfN5+MVlivplTstKWGs/jXIrjaU/AbQalLKzoQZyWmcfXQiuL64Dg9ZsOXcctBtuXcF3zvRyo2TBQssB2y9mUqjVebKO18Eig6AgA/DF36okbMxgkOFs6Pe58ZXzTBq7R3OdcYOHUdpnHNP5OBgKepI8EGP0irV6v8O9yvFxGqetysDq4rAxpbLBgKl/LJzVjjZQQAiDWoQ9M7IS+37183VKjEw5vWfyS11eyqRpc5HnnUY3dcDx46Abk1FBkxJYJwxB5SXO3tWRtyfpPDycP2xtKAdSYGIbAP3/7n7de/vv/978va+JgL0D/+30ENkCVVzhz9ww2BhsYhkOsd9dxbvUc1jprWIvWsNndxPn+eSQPExQlsRenxZT0KMo55tUc02KKeTXH/mIf25NtfHTrI6QqxdgfY9KZIIkSVEGFd/Q7QAT8rx//r7RTtcDu//Bv/gd0dRf+0MeoGOH3/+T3Ufol/uCjP8DZlbPCstgb7uF729/DPJpjr7uHF7ZewH6+j/Wz6+j1elhZWWkFXMYYPPvss7h9+zbOnDmDIAhw8eJFya4PBoNWUOqyt5br4QGIcxxbfYZhiMuXL2N7exu9Xg/9fh+bm5sCmJw5cwZRFEkJyerqqmhbnDlzRkAJDv7DMESn08FgMMCFCxfw0UcfoSgKjEYjobl7nofRaITJZIIkSbCysiKABAfzHPi5+mmsccaJYddxwtXicAUfeZ8YHGH2AADZHjttuMyJJEkwHA6xubkpLBcAAoqw6CiX6MRx3Cq/cfshyzJ0u11sbW3hwoULuHLlCp599tnWmt6NaVyRRNZmcHU3eH82Nzdx7tw5jEYjKUHo9XoYjUZQSuHChQt48OCBsGYuXrwo5RPD4RAHBwd46aWXDo25OI6xvr4u27l8+TLG47HojGxublIZK4BnnnkG9+7dw/nz5zGbzbC2tobxeIw33nhDzo8rEMlgkJvw8jxPBFm5P3u9HolAz2a4d+8eZrMZtre3sbm5ic8//xyLxQL7+/t4+PAhjDHY29vDiy++iHv37qEsS+zt7aEoCrz33nv4xje+Id/F9qdZluGjjz5CWZZ4+PDh0y+d4JKJuZ3jF1d/8cWD6ZOaagI+oAmWXAV5N6hYDogOKc0Dh/bZwrb91TmIOk3G5svM5ig01ob1AkMWsEvHdVR5gtskM7KUVf9Cu+cCPka1QB3PeIhsJLoO2mgJDjl7zplwrlV2M7DWWiojsLToNKVpZdSkf5gVUD8qTUBAZSrJ0IjrgXLAIO2wSFQjHirAkWrAk6d6jus+kEWmA5K4C093AWYNTfjGNn3QnAQ0AIlzjg9lxOq/n3QstxbxDshwKCA54nibzR/+AjfoORKseNQ+qaak5khNgydpRzBWWl/JixG0GQ2T7GirxidqIwC/2vz5J/M/gfqRamr5VXOteLpRd/dBmcVQhxLMD7wBtKeljMezJAinLAn7edaDNhpxJ0bkR6hMhahL7AW2xM1LAibSnEphcpOTIn2ZITWpBPJuUJ9XuVxLucmRIz8UZC1nezVqdWZncs69HOUmBdSfpZ/Br3xxZhCl/y/QGNzwlY+u1xUgxANlnn34YPq9hRUwFopAjdLSIk7A1rr8wqCeS6yRubtlqXjKxtdrAWJ6HMIRnvDwl0GPFiiCx2N3uPOW+5y7HQFmT9imMBEAukfb+n7v0NsZSNa2Fu9idoCiedI9Nu7/L9Ja4DXac5jce+qA/XHHYwvg8x79vqUdOrEJO1OR9hEzbjzlocorxFGM0CdgNksyjPojsjW1wN7eHoYrQ9IqKXMcTA/Q6XUEvEuKBMpTIup50piOVCTXwKH+cddYaI877psc+fHW16dofA2zZo6yqnW+eK46rvG9mkEvAHK95zhdNq9133Sf1uT0UqBAalIaY/V4UEXDEKMvlR2SxiV1HjwMugPkSU4llEGIfq+PyXSCylTo9rowMJgmU1S2wqycUXkfCtyf3IednP6aZ4acr6nkL9QhWdZ6XUQ6gh/4CGch+nkf62Yd8+05wjLEhY0L6PV7VOPe7+Lt995G0Atw7tlz+PT+p7gzu4NerwfP83CnvIMHyQNsYxuzcibAxj/+yT+mHekAv//R7wMf0Z8+fPy3/+y/RQcdmMBgiCH+5M//BMlBgjzKcX//Pr7/1vexd7CHW5/cwmvqNQyCAdZ6a+j7/ZbdIAdtDG662XIW/Dtz5oyUonIgyiDCysoKVldXJTh/9tln4fs+tra2sL29LS4QXPPOAWUQBNja2kKe5yKaePbsWXQ6Hayvr9Nx+j6ef/553Lt3D5ubm+j1ejh//jwePHiAIAjkNX4vMxmUUkjTVHQmlgFcdrZjtgbT88+dO4fr168Lg4N1BLTWOHPmDNm4JgmCIMDq6ioODg4wn89x8eJFAMBsNsPq6qrU9XOgHkURZrMZFosFNjc34fs+Dg4O0O12RSxyPp+j36dzMx6PWyCFK1wINFl9GaeqDVTze3q9ngTGDHywCOfa2lqrbJhBTmttS4Rea43BYICvfvWrLVHIOI5RlqWM4W63K3oe3W5XtNh838dgMMDq6ir29vbQ6XQE+Hj11Vfx7W9/+9AxucYLfN6YqeKeSz7uS5cu4Rvf+IY4bly4cAGj0QgbGxvIsgxf+cpXcOPGDdy7dw/r6+u4ePEirl+/jiRJcPbsWQyHQ3zzm9/ExYsXRfuB9UnOnj2LtbU17O/vY2tr68txnWBBvL/y87+CIAwQhAH8gLyklVYSnGlfww99qYll2qgFqS1HcUSLC1O1XjfWUVfXaG5Suqnv5G0aS2rkSqvGb7zO1LGmgGQoNShAUUYADGMNiqrAQlNGMC9yeR0evZ8/x/vQqi93SgAeGbw/jUD1iGz7ie2kDPUJgVX7pcOBnywklfnyAafHbUcmtw4zIJZZMT78RmDQYcm4ziHKqka4rX4NNYMk8AOy4quzNcKgqTNTHCgCkLp1pqILCFLX2jJgUlQFsiIjoMGnRQi/t7JVM5YdcMQtxXnqrJLjmBhOX5/UltkiR277izQnSyYsIIf9xP8k0OVSIi6Tsc42lMKLL7zYCjjcRbFbJsVBKf/N8xHrNrg/82TeotJzFowX4XKNcV2829dPCV/BE5LNnqS5/cfssKOyfQKU+Vb6QkQaH9H4XDI93aWqy3PwJAjjhTIDnL72WxocvA+lLZFmKZX4MXBpiL1ndB1s1HRwZpw8imL8WM0ZfywWCaDFqDtNc5kCvI3lUjDr/OPnTxM4t8CBL9gEZEWdda73110o0u5ZCe4fpx8e9Z0yE+hmPuAxo6xqxgiPr5oZKWCZR6/xOE2LlFw26n8yJmrNFhalfVotUIHMEbLesUbOY1a2af/76X7r79vj2+0Njpe+YNb0lYKCqpS4WESIRCsGJQS401ZDVxrnB+fR9/vo6R56Xg+xjtHRHSAH3nnrHdy+dxtKkyB3J+5g92AXft9HZ7WD7qiLrWe3MKtmmBQTzPIZFuWCsvp1iRUDjo8aqy6YUNnqyHvVaZow35x+fZKmqmb9wfonSit4vofSlMJaWU4MHbmt+lpxXTdmSX2yfAAWeDB70KxlU/kg/dRTlFZaSuRCTc5QgQ6aca+1gHiVcWx463GdmxzjYozdfFdYYegD6APb2MYN3ABW6bt+jp/TL9v1TwxaN30awIcPdIBwEWLNW0OpS8QqxoXBBcQ6xkqwgp3PdvD8lecxnU5x+/ZtdLtdvHD1BVz7+BomswnOrp1FqUt8vvgceUH7tWN2cOAf4IN7H5CWlC2Bm6Afp3nKa+xJa22Kvt8XxxL++6P4I5iFQbqbwnxusL+9j2kxxUfXP8IbX38D4/EYVVXh008/Fa2Ig4MDPHz4ENvb2wCA27dvY2NjAw8fPsT9+/dx4cIFKZW4f/8+zp07h52dHfi+j/l8jtlsJkKZOzs72NvbE9HKnZ0dZFmG1dVVTKdTZFmG+byh6QRB0HJfsNYKy4JfZ0YFl22whlev1xN2wblz56CUwvnz5zGfz7G2tobV1VWsra1J8P/ss8/i3r174lzBZRMM2jCLJI5jXL16Fe+//z46nQ6eeeYZjEYjKRW4cuUKDg4OBJw5e/Ys0jSFUgrPPfeciLBzEM2lNXxMco044u7MTun1ei1bx/X1dTx8+BCz2QyXL19Gp9PBbDaDMQZra2sIwxCz2QxhGGJjYwP37t0TsOTu3bt0ndZMijiO8eDBAzz77LPodDqkl6WUaElMJhNxZZlMJkjTtAWkRFGEIAhI9NkJ3rXWmM/nAnIw+MCi8SwdYIzBeDwWnZNutyug0nw+F1FOdvLj0h0GjhjEYPCHASCXPeEK2mtda4TVDI2T2qmBhitXrhwSSeEdcRGWLMtgSgNttKBqjEAZY6BLDX/WUHc6uiM77tJg+CLgi5BRt8AjcUgXmeN6qWVvUP5eHnguUpkXOWazGfI8x311H3uTPexu78IGFg++9gC+9SnorCj4DGwg2S4fPrRxLCVrOz9+PVBBs0CxVNfO4mRakaWiUorowFpDBaT4nlUZCkv16BUqqqess/OlqgXmTCH17EwDZgsxyai5WXonk+9mu8VSS7WffyoB3+PekJ9mkHlU5oAXJKcIlJ9k+7/0ZtuB23LJgGc9BAjapSWmAUdcVoUAKfXimdXvYZvAhkE3KTHgOnNFwYYwQWowUZ7j8feo/j/iuA61J6Q9ywLxuIX9oUTb0V+2c2unfvuTZTAf1VgXw1MeVKWAnBaknvXgez7Whmukq1Bn4HkRyEEXPwpIW+TS/wJgqHo+qNkATLXnx8IWpwoYeV9dBpKrXQPTdkkQQUjVLl0B0AqMXJE5GT/1opYZLwL2PqKJSJ2t6dRPC4h5jOaykzi4X87GS19ZSCmEPIeGrVaWJS2mQLW4fDN36e68AHD7Tsr7HPCK+1eYIE9n6MrxyfHqpiSAdhBtcNo2Y2C57MFlPzBwzfe4L7q/Loghj6p9nTMDIrfk9nHaMprjvnMZuOiqLiIvgg/KBPsgZtGD2w9gMgPPkNirKskqOESI0XCEb7/5bYQBUX61JhCstCUW2QL/wTP/AdbCtdZiOi9y0bopUZIAbDbFQXaA1KZIbYpZOcM0J8r7vJxjbuaYZlSGVeoSmc0aAM0pM2LglB+TKmn6aema+3zy+aM76EL9w82CLJMtgYD9bh8HkwMSyfW72Iq3EPsx/Xgxen4PkY2wFq9hFI7Qj/rwjY9r167hj773R/jOd78Dz/PwYPIAF16+gJ18B/s5WVEy1T8pEyRl0gIKpYTqKczx7j3ZWrruhI1aJyIin4I+HutZlR07Fyuo5n7gkS6GNQ6j00nU8VzfEnBdagYGqUkbcdbsdCAjt1iTrs+Gv4GBP0BP9TDZnWCyN4GGRrfTRVZkiLoRLj1/CfuTfYS9EIUqME2nmGQTGJ9cYlKkmNgJxrMx7xyu3bvW/sIb9WNEr+MX9d8dAHv0q/Y1AgSIpzEiReNnM9jEudE5VEmFV0ev4j96/T/CQXqARbXArJphe7yNg/QA02KKhVlgXs4xzsfYSXboWqnLRdIope8uAMZNMAQwBno/6InY9+qNVWwMNjCNp/APfLz3+XuYqRkm3gS7n+/iW6Nv4WHnIW6Pb+PSpUsAKIPOmghRFCHLMty+fRuvvPIKfN/H3bt38cILL4g7wCeffIJvfetbKMsS8/lcLCK3t7eRpqlkzxlcACC6DByYukK/7OxRVRUWiwU+//xzocuXZYnd3V1sb29Da427d++KKORwOMSDBw/EGQMgdtTu7i62trawvr4uMRy7HuR5jpWVFZRliSRJUJYlzp8/j/39fRhjEMcxVldXkaYput0u1tbWxPVkOp0KWMK2qlzusazV4Gb5GUCJ4xjDIQmlv/DCCwL8dDodXLlyRYQ3L1y4gDt37gh75erVqwIIrK+vtxgHrJnBzIXz58/j7t278H0fzz33HD799FMMBgN0Oh1cvXoVb7/9NrTWuHjxoljQGmPwwgsvSDKfRSBZxJLPAwBx2WCmCgMqYRhiZWVFYumLFy/i9m0Ck9ke9a233pL+ZlZNWZY4e/Ys7t69Kzob/X5fNEEAtOLszc1NccvY2jqdiNypgQZGQlx/UP5xETPuBBayYHsNPjFlWYqvJw96AIcGDlNbGLUKw1B+dzUU+LN8UlgMktEe9iVlwCPPc3kuSRKkaYoPvvMBFhcXwKsndMIRdHDbfvLJW32zkKwrZ9StJjVtU1v/1bRnz1Jw4sOHb3yEVdgELlZ+a4KUGvyo8gq2sEAJAlIcwMSznmT3+By5fuhGGehAw3pWaseVrwj8QEWuFJoWiRzIsLilUXU9sWrKGSQTX1stGjS/82LJBU9cXYgv3JysNwcz/PxyUNwKlB5Bgzxq+82pfbyBceTNfqls4hAj4Kj3ue1JmCe/bCDmKbMa2ps+TIEGaPH3KJbQMh37aTfWNoFFy2O+RIkMGebV/JFB81GU9eV9flqNt++WBSxv3sLCVhamOl2QJnOTChCpqCn9UCHm+3NM96bQpUaECGdXz+LKuSsYdUfoqi46qtbH8Gihy4JspSkxW8xQmYrEKmtmGotNVpaCSRY+y2wmwVdmqJY7rUi8sgTZnSZ5QsKkjhZNZRtNGre/l+nfTuc0YYcTbBzVBPSN6A2ZyjDLZk/1fD6txqBWhQraaAmSlhkSj9WeMpB7ZNb7S+5KF+xj8MvCHl0GcPb4bf30xk8PPceg8O9/9vsCQPI1FHkRQoSIPWIQqEKh7/ex3lvHqDPCeryOZ/vPIkSIXthD1++iF9Bj7MfoBl1ZtLPYnZvw2d3dFR94rtG+f/8+aQqYHAeLA6RIkQQJDsoDYiWUE7KaNHPMyhk+v/c5ZvkMxjMwniFHJ0UJF2hgVs0wXowbAO1xWCBfB342/5n86V3zWoCPaMV4EaIqgpd4GHQGWI1XsdpbRT/siwNDaKgf796/i9HmCCpSuLd/D7d2biFVKTKbIbG1Zk6RiHCxADNOssddafN1kpv8VBapbmNmTGpSKTvjxvepI8tVnKahhcUQ6hCBR8wUTzdipZUltnFSJA1Lx7YZOQuzwMIs8LB82FiqdwCcc76s3r3rO9fpl/p9WmlEKkJsY4xCqmGPVYyVzgp6QQ8qUxj1R7RvSmP/YB+DlQGyMsMkm2CSTZDqFNN8inE6RmKSRqMIGbLcYfNkwPUH9P0/mf0E//rhv0Y/IAZDT/fQUWRV2vN7OOOdwaA7QKwI0BoExGgYRkNMH06xM97Bolrg8kuXMc7GuLV9CwuzgIoV9uZ7uLV7C37PR2IT2KHFdrKNf/Pg32CSTzDvzoEF8D/9+f8EKOA/Kf4TfAffkUB1c3MTAHD16lXkeY5nnnkGg8EAV69exd27d0WTYXd3FysrK+Jo8fHHH6Pf75Om0dZWK3EbRZEACW5gnGVZS5+BtSS63a4wIc6fP49Op4M7d+7gG9/4Bnq9Hu7evYvJZIILFy4giiIRBex0OrDW4vr16/jud7+LLMtw//59XLlyRYL9g4MD0hupY687d+7g7NmzwgK5ePGixJm/+MUvsLW1hf39fRwcHODy5ctIkgSffvopXnrpJRrvS04aHAxz3OgCDpyxZ0bB7du3cXBwIKD+7u4uDg4O0O/3MZlMBNDY3NzE7u4u9vb2hKWwt7eHmzdvot/vYzAYiNUjax6UZYnNzU0peYiiSJxI2HUlCALM53NorfHMM8+IgOiDBw9w/vx5WEsuYt1uVxgnXOrBczMn1flcep6H5557DnEcw1qLtbU1nD17FhcvXkSapqIfsbpKNKNz585JPH/u3Dl8/PHH2NjYAABsbGwIuMOt1+shTVM8++yzuHv3LobD4dN3nWCVU6Z18MEy5QWAoFYAWpY3y44U7ONZVZXcxPhGxu9lZCfLMgE4GEDIskx+GGBgCxC2uuEOdJEtvoG6FiFaa1x9+yop2NeBcIUK3WEXG2c3oDwF+IAXehL8ckaQ32+1leyg/LO0vVKVFCBr0wq6jar/XhKSlODjcdtxi6cvsHhrZcIdcUqu/ea/cy/HIlgQeOG4GDDLg9/nWQ++ob+DihwAAksLJR8+Oqp2GzFkBRWBvLk7focyuqCJRHmqWfjX5yMriRHiheRmwOBGVmYUKKiqydDU5xIeoPxmW1x/b3STEXb/8XZkHKCU51yRTDd4fepBglQbNHXSrXIPq8U1xS0TYQYDgOa5mgXhZlYZbHG1HqQcg7PQrHGBJhPNJR3yN2/DXex/0UDitON86XseG5hxP3tKwZtfZnOPo3VMdulxqbWAFgdcOwpYa5LTT48mz40D9tSmh1+M65+67WAH7y3eAxan27YLKgk9XjX0eB++lEsxEy1UlEWOVIS+6guDrUKFQTigwKSuSWbRyk7Qga99Kd1bJAtMkymiOEKSJ4APGE3gaWHJdSM1KZIqIf0Lm5KQJz/Wv+OIGnb32CIdIUIkIEukIwSKdCZ82zgBKCgBnlkHQ2rWUWd60Mx9TImuLN3T8rLR3ChtKToUrhZFaUoUVYEXhy/CD6i/iqqQ4EQEONmBoMxojlBtPQs3U14f6KFzetI8qkDAu9x36rKZ5RIJ3gyzZipbz+ectVdO6ZMzvz1ue+zPLL/9EXMTsxpKWz7aKnS5HZx+N1xGhusuEaoQHU22k7FXMwx0jKAMsOKvYOgP0dVdeMZDH32cj84jUhE846Hrd9HRHWir8U9+8E9w/fp1ydJxkuk73/kOvv71ryNNU0wmE1y8eBErKysASBuFr51JOUHmZTjIDpAgwSSfwIaWstHFHIlJsCgXSKpE1mLseJOYBNNiKkAAAuBedY/KyI4rJds9vr88eOJII+AniFmYHCRIJomUZYaasp///tf/ffTjPnZSYlyMizHGxRizfCZzBIv5HioVOWGeP65ppVu2voUpJLMt5RGmmQuOO+4INC8GOqB5xijMprNmvzQwHJH2B5e9FLYQ8d4kS/Awe3j0F2w/+u9IR1LesBaR1gKXPXS8Ds2FXOrEbGZlUVakTZFUCcbpGHvJHnaqnUbgsqTx86gWqhA9v4eV6QqBFX4fsY4xMiMMggGe3XgWG/0NDCPal/QgxXPnn8NKtAKbW3jagw0txukY+aQJHgHg7NmzKMtSnBY4QH711Vexvr6ObreLbreL73znO1Kf/5WvfEVq/rXW+OY3vynrFQYY9vb2kCSJZPw5hmKBR9eB4rvf/S663a4IF77xxht48OAB4jjGlStXAABbW1vodrv46le/ijt37mBlZQWdTgfz+Vwy91/96lcl2cv7wpalV65cQZZloj/x3HPPYXt7G3Eci8BmURQ4c+YMLl++jJs3b4oY58HBQYuR4Vqyc+zoxqFc2sBOIiwUyloLWmvcuHEDV65cged52N7exs7ODjY2NjCbzXDt2jXRTTDG4MMPP8Q3vvENfPLJJ7hy5Yq4MjAAxFaRe3t7uH//vpQnvP/++3j99dexu7uL2WyGTz75BBcvXsR8Psdnn32GM2fOYG9vDx9//DHOnz+PJElk/zkZLxox9fexbgXH2VEUYWdnBw8fPsRgMEAURdjb28Mnn3yC559/Xsp3Hj58iDiOsb+/jziOsbOzg/F4LG4cbH155swZvPjii/LdRVFgd3cXu7s0GbLQ5Msvv/zI64XbqYEGrtNZLp/gWg8+6Rzo8wBmoCDLMlGtZFbBYrEQxVS2xGR6iPu4zKJgtM5lQbjKn0CD0jFthvd9WeyFwQ7+nod4iA+/8SGMb/BB9EET1NmG3tqq2a/LI2QxY7QE2oEJ0LEdCo5zJYtA35CNp640AhPAM55QpkMdyr4aY6juH4Dne/B8j1whfGINFBWpEHM9KIMYlaokg1eihPUsjOcEyE6gzL8zU4B/F0V+vURtPaktZ84fdSP8IgGnE2jzo4aGiuicSNlKffPXfv27JYAjtCECSwyPECEiExHQYX3oSqNICrpx1llXZaj0ACWaG5jRwggJvVAW8DwWRdXfg4BRDH5YjwJwFagWIGI1naekSlB5FQpVIEeO1KaYLCYoVCEMEAax2BrSLZfh97lB/qlKF57gPLSCuTp440dZfNl2yZGGFrFQ1kewxrYXTzXYoUA3EGscVoFuaMSVobmIQTuh96IGkTwlNdMM7gmIVP+zygpQ9MtuDPaYqqkBdlWchW1jD4Ml7sLzEJBzTJByxE78/1VbLivh/qtffLymgdNqvkk7IfbjecsFCTlL3Vd9lEWJIi+kjGnYH8p9AGh0FiwsUptiUS4AW5cbmIZ1weP+pBYqAlg6qoMI9MgCb33dFwDGfZ7fG9gAxbzAC+dewOZwE12/izIvkaYpjDGSVGBRqf39fQBE5eT6Xr7fFUWBf/AP/gFu3bqFTreDb/3qt/Ar3/oVWGUFPE3KBImhIGxeUGCQ2QwHyQFllHnORCoiexkyynhaslRlFotb495qzv2lq0nkrqvpmD3rQRUKkRch9mMBNFRFzKiqrEQ3iu+faZFChQqZyTAv5xQ8gmwfkyJpWHq1ttRp2GdHsZhO245j2PGcwmMot4+fdT/x+7+qoF5TQEWlYroiXYeH4UP82ezP0PW6KFSB0e4Im9kmhsGQQL4a6NCVxsAb4HyHgAwdaaz11zDoDKCMkrXcZDKRjC5nBTlrzOvJrMxwsDjAnZ07CHoBbEAOD0mV4M7eHRR+Aa/nYVbOkNgEqUkxy2ek62VJ8JZdfkpbUmnKsh9nrVuw1NH4hzf/IelYBD1ik3hkLzzqjNALelRWbHxEitYn7JhWlqW4g7FWWYUK8IHMZhiXYyzsApNiQpn0Yk5jrXYdEpAKWYsxp+BYt+NkgN3CIgUJA2ujRaC16NYsnjpRsSgXZAH8iOYrX46fwVxP0VqYv0crjVeGr4gwL1sGl7ZEUiVY5AvsJDu4Mb2BeTUX4OBR10bX6zZshXCIS91LGAZDYjn4PQKKdL0PitYnDFR8eP1DmMKgE3cwK2fYz/dxe3abSpHqciQp4wOaEo+69Tw63z3dQ/9aA1ac7ZzFf9P/bxAEgSRIFwtC13u9nggvMts7z3MYYwSMAyDOERyMJ0mC7e1tzOdzETbkensOuBno8DwPV69elQw9gx6c6B2NRnjllVfke5577jnRIAiCAG+88YboJ1y6dAmTyURcHay1YpF47tw5ASSKosDrr7+O8XgMay02Njbw67/+63IMX/3qV3Hp0iUMh0MopfD6669LHMfjsygKpGnacpjg9ZOraaC1xmuvvYbFYoHhcEhJ5tqW1PM8vPLKK7DWYnV1FaPRSHQN2F5zMplgbW0NKysr+LVf+zVhM/D39ft99Pt9PPfcc8jzHFEUYTQa4ZlnnhGGwZUrVzCfzxHHMdbW1oS1EccxfN8X0U6XueCKWy4Ll3KS332dtUJGoxH6/T7u37+Pr33ta1hZWUFVVdje3sYbb7yBXq+HnZ0dvPDCC9jY2EAURbh9+zauXr3aYkq5gpNRFGEwGAgL5bd+67ceeV27TdlTpuv+7t/9uzLouQQBIOZBVVUiRMJlCVz7wegSswq4toS9cX3fb3UU0194MLOyp7tocT/Dv7sIFwMWrm3lMrDg2oTwjcjzPMwwwydnPkHqp1j4CxhtoEPdZiU4ugatgM5xyfjSFu/OIkiy1zicyWY7UJSkyht6oWS3OMj2Kg+eoWBaV1Si4RvK7vnGhy41snkGrTT6PfLBhaX+mi+I9mNgoH2iPYZxiLRIadHkg9w8PLSy/kVVAAE9V4AWhpWqUOoSRlOQXKGSYNAVNpSM+pcRNH/RVp8XyZ6y6KCqWRx14B3YQH4PEQrIpA0ttgIVkHBUzQyxhRW6J9ewBiqAthpVVlHdr/JxI7oBv/LRrf/1vT66tks+4hV9n9IKpSpR6rrvdYFc5yi9ErnOkaF2FFA5Sl0iV7SQKkAZoQIFfX7pHzM9TttPy0yKQ295HF2HExrbwUp5EJqMNpcNSQa0FsFShr6UtQi+8fVvUGa0zogqpVriegyISN28NUhLEobLqowea9vFrKRF6ed3PpfSIskQc/b0KR7/Uf3R0gpAI0zGzAZrbRMonSKIEVFFNFlk+b3uW3kPB2amtiOuXWh4HJjS4P7OfSRZQgGmr7G6sYq4FwuQxBogroiw+5gXOWbzGZWjebWdn3YAmVqbBkAbXDoBpPm/TOMuecJ+EPCRXVJqIUUBHuGj43Xkp+tRtjtSET58+0Mkewk6toOvXPkKvv7y19HRHQQ2kCCz65NTiKmMWLTNZjPcvn1bFt+9Xk8W0kBbl4ETCwYGKiSLydIrUXolBusD7Ix3cGfnDryuh9IraZ60ND8WqkBmMxSaHnPkzTypihP7zLc+Qkv3V7/yBcQJEULlCoEJiD3j07zOAZrv+0gzYrsEUYCiKqBDLcBJ4RVIbYrEJCi8QkCXAifbQDIwz2wPER9Fk2TgOepxx4GbDJBrvNYSccW1n1ZTUC0trVCHCNCwMQbhAF1NZVc9j0pGqqSicpMgRi+kINCrPMwP5uhHfVw4c4E+73XgKU8s4Fw6Ogvn8fpzntUWxNkUP377x3j/+vsodUm2jiGACNCRxmB9gM1nNlHoAsYzyJEjKYntxCBGZpufk5oHj0BBr0PMivq4uNQs1CElvmpQwdNkH5oXObSvhdVT2lIEOBfFAgkSLMxCAJbj+h+Vk0RQQN/28fLwZaz2V9EJiJnqadKX0ErLvJ4VdH9MSvqupKLHRVX/mMWxDItQE9ug5/cEMBB2g/LF2cz3yVUoL3OMZ2Oyj66Bv7RKiRlTHc9siBBh1BkJWNH3+wRa1GBFR3cQeRGtCyorrmGVJS0OZk8cpAeYFTPMqzlWohX8vSt/DysrK60MvWvdyEwHoCkZ5zmQA2YXQOAxmaZpK0nLwStv32WVK6Xwve99D88//zy63S6SJEEYhqKNwFn26XSKIAjQ7/clGQxAYjYO7oGmXIO3w+/h/eRjYICFg/EwDFvBfBiGwoZgMGR/fx/T6bSVVHYdGpb7MQgCnD17VoCIyWQibAu2tGSwZzabodfryXEkSSIijq4GoO/7+Pzzz3Hu3DkYY5CmaYu5z+KPnGRn7QUGSeI4lpIPBm54u/y9HOMua1G44MNsNsPu7i4uXLiA4XCIhw8fYj6f4/z580jTVMpuRqMRdnd3sbOzg3PnziEMQ9y7dw/WWly+fBmTyQS+7yOOYzn2/f19ua9+9tlnsNbitddeE/DpuHZqoOGrX/1qC1lxBxyzCtxHd2AwE4EvCvajZREPF6FyxRw5+OdByDScZY2H5Voj/ixbt7h1JnxBuMwGt9Znmk6xi12yCioMqqyCyQ0+ePMDTDsNt47LADzT6CKwc4FbUmAr2wjw1ZOvpz3JUHEGw1ortXVW1/uka2Ev7ahK18JunAFpuQ04gfkvLSh3afy2nlSwpPRfZ7EZAOFSCu4jH8TwCFVIuhPGR8fvIAQtEiJEsijl/mbqf1mW0EqLDSQUxKLP6FoXQleSfeAFYalKVH6FAgW9T5VNyQsoGJeyDJCoElNq3b59Eir+afrzqW7T2a4rUiXnpGZ/aEPnyrek++EbH15FzJzQhghN2Brj8micBaqCKGnzWPZDX7Q+SlvC+lYWVQUoA1npSgCQUhHIwb+fZgEPoBlLDqjAjgMyHuvFBgDJ1Lm2RnLt2KYcZGNzg2jfNWjAwAEDCV+0uaVGfI0IAKL91r4DEGcL7dfzpGky2VLCwromzrywXNJzVBOBUDR6AgxMCHvC2kNsmac6zyyDqU7Q6jKV3LKsAAE6PgWrNrdIJgn8yscgGkj9uqoU4iiGMgqhF0IZhSItMIyHWEwXWB+tI1/k6Md9lFmJ1dEqQj8kVk1ZoRN3yGmizBGEFOxZZaE9jazMsDfbgw0tEAGZoaA0tSkW1ULsPpMqoYCV6dz1j5QX1CUGBqdz9FlmRQB19iYnthyzYhgMg6q9xxVQVmWj98LgS122EXgB1oI1KZtwa85ZaNJ95O2IgCy+hJKxk5oFVKZkXrKlJQZCqWSesrkl+9LSCgiGsrFf/pWv/QpggAd3H+DKxSuI/AidoIM3+2+i43ek7tkYg/l8LuWavEgsVQkbWmSWaskZoFhUCyRlImBFYhLkyInhYHMUuiC6f/0864ecxCT04QvbpKPpnskBpjwfdGgtU1bQ0Aj8gOYPT1OQWeYiKJ3ZDJVHczEHlpnNkIKYIcde4xYNcxDNvOv+AyBii0orWcswYF2q8vTziANUy7bRBhRPs416A6dqrfkHPoFgCAQoC00IXWoSrFTEwlBjhY37G5JF5qyzMQaj0QhXrlyRccXBk7t+5YRZEAaoVEWgA2o9GUX3zqSicTNJJ6h0RWOuXGCaTVF5FbEu6nHFegZJRY/HAQfcWEunozt4pf8Kfm/193Bv7x5MaJBYYhmlNsXudBc/ee8nyFQGG1qYwMBGFqqr0FnpIFN0LRxZMuf0cUd1RI8nQoRO/S9UoZS7edoTm3SlFeJ+TIzOWpOHrzv+mVdzug5Ncux1FagAHdVB3++j65OIK+sAecpDWZA44rnz52CVRezFyE2ORUUsErbkTKqjgQoFhdiLic3i9dD36HEQDNDzexhFIwEx+LGjOliP17HR30CESJK4PF5EUNgJspm2v7u728r2uwlYTr5yrMXx2z/9p/8Uv/3bv40rV64IeOsG7wzWcgLZZRgAEOa6W+bAyek4jhFFkZRW8PdygpoDdb4mOKh3E8tsGBCGIUajUQvo4H5wS+b5M1VVwfd93L59G8aYluihy4LnbTAAwzoabuzIfcgAjgtsL/cZ6x1w/7pMD7dxvMvbBIDpdIrJZCJMEz4uAK14mfvJWovhcCilIYvFQphd7t/8ftc6czweo99v6Fc8B7HLBFcRsNZGt9vF66+//shrSc7HaYGG3/3d322VHvDg4gHHdB4+oS4K5zIIuDaIRSIZreIDYFCAQQCmjvBABCDaEHzBuMgY7xejSPyai66x0ikzM3hwZ1mGh95D/PRXDwswtTutbY+ojvgnJ2qp5tStZ3ezBU+ySHcztBy4uzd6VSkK3FVTi8z2jDCgm2z9aIxpVJdBlHTUVovQIGYCqxjXVm9MQ2dXi0MWi84xfplZ2uV2KJiGA3SgHcgp09QxM7Wfqf7MNhCGR6VFU8KrmnIXbbSwDmABP/KhOxq5ySmzrQnc4MxXqUsJoAtFLiN5VQMgKJFq+kxr4a6+QKZ/qQb/C7UvmO3kbeilf94R/9ysOC9aBTioGRFsR8iZeLd23MAIM6a0DgNDEcDErJrTlEwEOkAv7KEX9FqP/aCPOCANkY7fQeTXdmE+jQfXLcLXtDjSSuOdn79DN6YanLHaUjBagxju9VTYAkVFzJ+szBqdkbr8qagKWVyxMCu/h4/7abQnoWsLiOWcZxd0BUAMEgUJhD3tybxRmcbBhEFWZji5c+mX3ixa45TnWJcto61uuQ9xGRfXLrMDEWfYBLQDBcW+ovkHFVBmde164MEP/aZMzim/Mp6RMZ2Uicwt82KOQhNjqdSlAKyVOj4jrS0JtHHdtW98KaXgOa+jOwQ61v8iREAOnFk5g0hFGMUjZJMMW2tbCFUoi6JFsgA8ysZbbQmwqbO3B+kBxuUYs3KGT+5+QplLr0J3tYuwH2JRLgh8tHkDBLNTgHX0c+wpgXVmVbHDTt14/DHQKCAMgP9q87/CWrCGju5grbcGr/JgUoNhZ0haHnVgyExNXvy7C3pepAZBIOJeHDCsrKwgTVOxMuv1elgsFnT/qP+pjhIgYFbMkNoU03SKWU408dKjPk1NKhoFi6p5XJSLNqX7iOYpT/QXurpmmugIXUUlM8Ku4+u5TpiUVUmAFmena72k3OSUla5BlMRQ+UFikhOtYDuqI0FeqELRaBL2RQWxiuRmrGnR6itUdF0wg7IWazwtAy9GDN/6LQbfqT7L91xLYNCG3YCuNAH3lhgWzNIJQb/7pma4eFQqUS5KAo68GjiqrSh56mWHgE6ngyzLMBgMRNvMZQczMMZJQg56OOvc7XWJpVMzTI1PSYB5MYcNLKxv6bxVNBbX/DV82/+2rOU9z0OWZeh2u8jzHB9++CEWi4UEsbwfr732GkajEQAgjELkioC2ST7BvJxjYRYodCHAxcIsqOypLlnhf4lJ6DmbPvI+xGVPsVc7legeuroLv/LR86g0Io5iKh0xtQuQroO3WghTh5oANwekmJdzzIoZxukYhVfAwuLvX/z7+Er/K3Ta65hFaxLHTW2KWzu3MCtnKIOyKa+ot7ewNQhi5khsIgKbC7N4JACkoUW8ki1kYy9G3+vjK6Ov4K8//9elzxeLBWazmcRVrjD/cgk77zsAEfv7jd/4jdZ5ZAcLAK2EMAMVXD7PNpTLehHumOT5kYNvfj8H2wBkv935kxt/X57nmE6nsj3+4YC62+1KbMjgwTvvvIN+v49nn322FauyqKNrC5plWcuEwBVlBCCgAcesLmthGYDgEn/3OT4v7vFyPwZBgF6v1zpmFzhxwRSXEVKWJba3tzEajYQ9wp9fZvkxqMN9w5qHDIoy2OmeB/fzv/Zrv3bkOHXbqYGGv/E3/oYcnCsi4lJ8+GS4aAwH8NzpPMh48uMBzDdgvmHzSQMgg59LNFgHgr1qWVTIpQIti0OyOCXvr4tg8XForVGpCgfBAQXWipwVKlWhN+xhtDESBfLc5E3G/AjNA0bosyqjGnrdOCnwa+5nnqhZJ6Nla9qQdYAOlxWs2jZz7j9hQTzJgt2icbmwTVa80lUTKNqmVp9vsp71mjKAmsLnKY+251EgyRna0paStS1Ms+B2a5Fd4UZeDHCgJqKFqg3y/FKAD5fx4WQflwNsycYrDxkyTNVU6OAn7mN9DiSTxEwR1WgicJBOb2/OO4NAslx3xi/34eMeq7RfBgX9KX8nA1FuvWqv26NymPpHqcPZOWONABylaWxnT+NWoaGF3hsgaLKRtfBahKY+2bc+ul4XPkh0rB/2EYBEVVl4rR8SdTMOYgLKwhA60Jinc+RVLuK182wOqyzCbkhZdVPK9aN9jRIlFukC1rPiMMPzYWlKEVfLqkwotZnJxKa3MIVsV8RzHQ0NN/v9tJqAGqq5xmRedEpegMPWkG6G/iQgxb2OWXDQfQ6AlJm52Vbetoggwsj89UWaQiOE6ALQUrZiagFfRWw67guXyVNVNbhsHdBZ29Yc2yqZOum+ZdGMZ0vlAQxUBDaALjQ6ugPkkDICr/JEC6If9hGpCMW8kPd62pP7NmfIjDHodDqU8Ql85FUudfTzco7eag+zYgbjG9jAkqBgNkGGDH7skyaOX0m5mIhyBkBikhNLBphV4DILfOPL713dlddDG6LrdxFacjTwSg+9sIfN4SZsZlHMCoReiOFwKAxQdxHsskiZ1jyZTKC1FlV6KeGq1zjcN8bQmEsr6hfjGxJHzKZYlAQGlF7ZBJrVnGjzVSIaF6mtGQ7mBJ0LZ1y2+kZR5p+1PjToPoUK4nqgdEN/5nu/0bTmKmwhGh0LQ9T+xCQn7keoQtEW6eouen6P7o+VRi/qESisAqCC2JAHfoDLvct4Rj2DYThE7MfQBZWRJmUifVMqKq9JygSTdEJjRlM/Z5Zo8lmVicBjUiWNMCy7JdRisCc17s+O7gibgu8PsR8jsAE9x31cj7UAgbAUkYOSJobuJb2gB1UqWSdzcOT+cLLODbrcgGexWKDX6wnzmBOILELIcQGv9znYcpnM7A63ubkpgWHrOquZ0bwNY2j8zooZti5viZZGapryh1kxozIMk2BWzsitwgErFtXi2LlXgAq/R9oKXoyu6uLzjz5HiBCvXn0Vo5BYB7GOMQjpkUs3uqoLUxo5flfwnoEfDvq5T/k9RhkkSGTfx/kYpU9gBdu0sh7FrKTyixe6L+DvfOXvCJucs9QuG4H7uyxJS2exWAjDhvuVhQl/8zd/U1wM+By4ATtA88z+/r4EuJzNZ0YDfzeXGvG17fs+Op0Oer2exIMuKMD76mrquU6HnGXf399HURQtzR/eL2MMBoNBiznP43J/fx+vvPLKoWS1qzPI58KdQ6X0rs72u0ALAEwmE6ysrEhw7ibgAQibw2VSuMG/y5ooyxKz2Qx37tw5xKpwgWy+dnkbaZri5s2bePPNN0Us0v2cOwZdFyFXYoC3yaAQn1+3lEUphW9961snzlunFoNkP1H+ArdMYZnKwZMHT0x8slz0qigKzGYzEY3i7fBn+OB5cPA2Pc9r0UBcXQYehJ1OR2peXDYDdwzTergOB4AsWjx4OGPOYBbOqIZbe7g1uIXba7ex7+8jAOkb+J4vgWFoqdbeV7XwnfIRgOroTW5gC4tu0BU1cG201A96ljKcrNTN1pAFCslywicGQW7yRr9AGxEBZIeL3OZNFrRG7kuUjQCe45hQoGhKA2qwpLTl49sgKohit1v3VaF6POoiN33Ec/z8MfumrGoBHr6lc9BFtxEjrMUgGQzhYJzFHn3PlyDFzZh7ntdkzZ0AXQAjZncwBZTBJNUIsnFQ4T7yeTrE+Fjq3xMbn4PT1NCexG5wgRFFDA6ptWZQxM3s1hkmrsFn5gFs409fomZw1NnIFsjm0LGfFCTgYE7+uXRdnrPq87l8rK4WAQeCJdr03TQ5eQH4RZqBIUsutwaX2UZO0m+ZHnza5uok+Ev/AgToeB1ZoHY00VNZ/M4mlrKbqotRd4QYtPDisiaZ90194/I0ClMgCANZkLq2WmyDxQsrF2xOkZJtnKYsFguaJRUtCnOVC0W3UIVYUXL5TWGajGUrc3uEEKRbkiG6GzVowGPXHRfL1xY/x4yTR7bHOFVSN83XkmPL52tffpfn6t9hSFBN6+az2ta1uB4tKipD4BDqOmGrGiu8CpVkgV0GDd9XpJTjcZuCZJEXaiHHx+wVq+s5NThBDyRsftXQYovK99lABTKOQxUiMAFiPyaBX+PD8z10TRejaIRhNESsYqhUYeAP0I/6QAV0u10Zy2wXdunSJXz44YcIOgHRz5Gh1CVUR+Hh9CH25nvQHS2CnEJJr10PcpXjwBzgnrnXchY5NF7mAPabPz146M66AlQwy4RBi9iP0Q/76GU9RDqC7mnEfoxJOEGkKaDv+SQm2PW6iMO4laVaC9boe+o1FbM6ebHNfvCuz/2yttZisaCFbyfELJvR8dalILN8Rs4JJsG8nFPfMd2/do5ITYpxNRaGAwMXmc1OnN8EuNAdDNUQZ4IzVC5S6xgx8MeAAUDXc1mVAqYaa5DmKWbpDDv5DjkRmEYb4bjmwRPQhFkfsRcLiBIiFAZIP+yjW3bFiaIbUeA/CIkmH+pQ1scWFklBgE6hCtiASnAWxUKAM+nLWrsht7mMu0IVmFX0vqzKkObUr5nJHn39lvSjQG42HUMlC12fhBm5n7s+HU+kSAQ1BGlW8Osb/oa8R5fE3IhUhK7fpftsHTAycMYBGzOVkyTBH//xH+POnTuSSXWz03yP0FrLWp/X7hcvXsR3XvvOkZliDuY4UNre3hal/jAMMZ/PMc/nNPZU1gAQNbvC1T6ZlWSLPCknGPfGyHWO++P7SOzx7BxmBHVA13PP6yHWNWMIzdjpoL6+a8CCwYuVcAWb4Sa8bqO54OoycDIXgFDn3XhnuXxgWY8hiiIp5+GyHaUUPvroI7z00kviyLAcMHO5AmfweZ9cZjrHe9Za0ZfguQYg5wIWqtRaC8jkJqrdAHexWGA+nwudn0Uieb/cY+f+4G0saz1sb2/DGIOtrS0ZJxxLcvwJNPoSQRAIKOP2FYNI7hzKY4z1grgcgdlqbqkHb8e15mRWTJqm2NnZkdfd8+Am0q21IifA46PT6cj+ugl117aY+8U9fgb8uIKALSz5va7gqMswOa6dGmhwL3IXuWJFUqaYMArvnmS++F3mA6NkLsgQRRHCMES/328hpi5Fxx2AzIDgRxcA4YvLpbN4nofd3i4W3QUJ8FmiN11fuU60NtOI9n289vHRHeEEYodfOiLzHB5625HtkGCdk53mH6mdrxdY7mva1A4WLk23tqQKdSj2Z54iLQQBRWr6bqhDlAXV1euQVPzf9d7FD/CDlpihBw+7apdEptxjVThyQb68uD/0t/tW1Zxz3uZpm1VWhAof9f3L33noHHKd8WMGcu72XIcF3zTU6MhGjSBkDVYx4BSooFUCw4HCT/RPMFOz5tw7n3eF91pBPmdQVRMsCSjiAB8u0GTcf8pIlr6ypwSLDnfE0Z87YVtHlbsAEGHTI+tw0ZQnuewcCRKtcz6Xv/+UFOsoIPuuwAvEgz30wla5RMejx0AHbfZD/SUSzBkC9B5sPxCKpmv/J+UO9fPM2hHrvScI+Hgbj2zuJXPU2/j1Y+LpZT0FN9PPrKZABTInsSVjx3YaBkfN3uiHfVns9rweUAGRF8l82A27UlvPIrc+fJjKyGKmshXRcesAkDPZmcokKOKAkIXHXLE/oevWwc9xTUER8FIHvDy/ssUdi2PyewGQUnxViZWkBZUTcFkAP7IFKAdH/O/L0j9QaFx7fEWlE33dhwcPoUfHxUw6mXu0hinJIcnXtFiyprblsk2WRqs6g8fsn4pqUblMoTRlC8Rl3QCjTHM91GBIbsmNR9x9gPY4PipWnJ987HzNesrDG9M3EBcxRt4II5+cAHp+D6u9VZzrn8P6fB1ba1syXju6A5RoZancbK5Q16sce7M9ynYXRJk2nsE0n2KcjCnjrYi6PitmmBc0jjNkmFUzbFfbWMwXwkI4yeKSVf1jL0bHIxAi9mN57Gi67ji7PayGGJohXY+DSNxF+DOBotJXXsfxwtxda3EAwZZ8TLnmxSwnhTir2lpoqxLTfCpB9SyfCW19UdYWlo5FrGsVy/X/SUUBe1KdwEjxgMAExDrxIgzVELFHbiI855jCkI2ttZRgqoM7Zq9xcmdiJ1T6ZjPRt0jNo+n9AF1HDOoyS4Gta3t+T8ALv6L1ROzF6KOPrWCL5segh8AE8CsfK90VAXA5mJIMbpHIOFoUC0yyCTF2kcMGVspzckvipoUtpI8ndoKH6uEhYcrMHgNgOE1cbeoynI7qCIjxl9f+Mq6GV7G9vQ2tya2AS0E4aOV4g5OKAFpZ7Zdeeklqxjn4doNsHndAW0Gft+sZD0NvCAAoTF2v7lEA3ul05PsACOjxj37wj7C/v4/f+73fw+rqqrD7FmYhwIQIWZoFHs4f4kdv/wgLtcAiWqD0a40wXaD0SYjWeI/uy0v2Ev671/47CUr5euP+cMUUXRF+3l+XhbIceHPGG6DgdT6fS+b84cOHoiHizmc8xzFYxNaTbgDtvs/VD+DXOJjn8+UmkJn9wOffTVq4JfTcB3zMcl3V+8l9wv3kxok7OzvY3t7GuXPnWvvmjjsXeGB2DvcZrzVcZgrQALgMjCRJIiBvVVXCgnBBIN5XZoLw/NjtdqUUz2W0uQCaWxbBIACfy7feegtra2stodDl/uFjXCwWAl7wnMzXDyflGWh2WRe/8zu/c+IccGqg4b333hPHB67L4ovaZQ0wUsidx+iJCzqwQCPTNviEugKP/H6uyfW01xJ25M/yAHEpNSxwwd/v7tOHow/xefz5aQ/7cJOqhFMs9E6zFmTtBvUYWd3HBTtO2VSoGrDDUqasUlW7HAMQaj5bLR4KzpcAiEft26E+fNK1s3UWiUvZbDfY4+90s9eAo5vxBUAG3nZL6VvxQ12D7tCoFRTZL6LtDy97YZtSFgNzKvXwpZ1p/c6ZfmEj1OyPCFFjQVmzQJjmHFqqjY0QoRtQnShnJLlfK0tCU5xVZhX21KZiMceij24QxayOFiig2udFGgMXT1Nr4pR9Z2GRlSSG9mU2XxHDKfIo4ObyBxaj6npdAjZ0RwK+yI8kiwcLGfvWWHSCjmxbKQXtEXU+yRNhTiVFIoyAtEqRFpT9KiwJX2a2EbyU7LYtmuy3beriW9fUEQwCaU9YJXba5oIdLKYpDABWoVek7B+oRjujE1J2NA5jEY8MbCCARhAETVmBJV2MtExb5SZ5RQv0SleSbWSrvNSmQvUuUR4L2rhlM5zt6gU9KSmQwC+I4cETkT+lCMi12sr+5DbHtJjSNVmlSGwiwnC5zUkXxBGktLAC1godnc+jgQAnwgSp+5tfb5WJOMK5zG46FPQ9rnUoICBIiLBxs3C1XFTjaAJA+oXnEZlzbKPpwkK/LqB3Y3qDQI+01oeodVBO2jcO6rteVx77YV8C9V7QQ4gQ/aBP7/FjDDtDrEar2OoQcNEPyFbUN03mllXYeWHLi8DxbCzndFEtiC5eCwG6VqCzYoZZMRObzXkxx4PsQQvMmJfzE4ELV8ehH/YbTYf6eENFwsGe8TBSI6z76xjoAbEwOs44jiJ0bAdraq0VuPDaktlPrhi4S52Xcwu0FulAk61TSiGrCKyYZBMCImqtgb35HpUrRMCiXGCSTkQsUTQlqgQzO2u0LrLT61wMggHO6DOircAaEywIrpVusu0MNNaA2sIuMK7GMoewoOFxgb2Conmj7l+2UeQ5g4UVIy9C0A3ofKlmTPb9voBNsR8DBrKWdkuZuf+trecZm2OcjOF1PSpXymlsCYOnXgOw1kpq6T6TmQyz+Qy3p7fxwx/+ENaSBeDGxoYEPxzMMZM5SRL5uygKYd5cu3ZNzjezk3gMuU5z8/lcYhJmPXOikin/HOgyo8cNApkRHYYhBoOB6B94nkf3btWhuChqyseVUsjiDNn9TCwqXWp+p9NBt9vFp7c+hQ0tgn6AwitESLMKKrz+4usSmLul4245AAfcHOy75StuQAs0IIArSugG+fzDZVnuNcbfxc1ai263i48++ghvvfVW69pz6f68DVfnjwGx1dVVfP3rX2+dA3f7bnkD0A6UOa7kILnT6bQYDRzku0CEtWT1yQAXv5+3zZ/lY2BwgAN9t2zEZewzIMDPucwcjnFdwIYf3dIOjrFZX4IBAO5Dl5nhlv/zdt058q233sIbb7yBs2fPtubNZSaCWw7C7Ag+d3yuXUYb/33admqgYWNjQw7KRbd4J10UiDs2UQnG3rgR3KuzuLwY9TzyqX3oPcQfPPMHrQCIg92xPyZbIA6WXGo0q7HXNdW+amzrWLBRMvF15j+0IV5KXqL31pnSMi8R+qHUssMCZVEjSIYWI8pTUIFqRMkUKSTzItPNFosoIusE2KpVDy8ZqScNmh4H7KA3nm57yh6ijZ+0zVbgzPv0pIKBX6A/hE1xirKAJ/lOV2PBFbdzM7n0FUv6Fw5TwF14n/i9HFg/4f4uf4Yz+65uxTLIcapmH7FfS+85BGrUrIzIRujZHrE5alaNNhrKKFqIeZSZLqtS6KRcMw4PRAXXFblUGHIUKVGKa0Wl6DWjT8i01FlZF4ThgIQXfvTfIupEkoWtTNXOoj6lVtoSZUXCU19mU6j7uQY2Qq8RGWP2U8frYL2zjn7UF2pwL+hhGA0lo9nxOg29VkfEstE+opCs+dKM6LtMvV/kCxGI8yIPSZVgnI1pUVpSLfiipMU008/TMiWrt5KC88LUYpe10BsHhm5QKyUNlsqSvmxg4zRNGEeaFqM93ZOyCM6eu4CojEVFbJSJmWBcjEUDhJkvLLZ37HcrD13dlZr4rtfFSriCfkDBbOzHEvD6xkc36NJ9vQJMVVMq6/nKaoukpJp+zh5zcMqBBdPjuXb+pOskUIGMoUgTwCMsPE01892gK+wYbbWUuHmKStoE9DQUnAigaUsUlSMeaQopEWTgQ8q5bCVAAtOgp2ba2tdfzX4VbyZvYpbNKNNezFDqEl7sCc2dXQCYgl95FebeHAf6QByPmDWTq9oS85imoKQO39WA4HPJehcd3ZRLcFY8UhHVlwc9bHgbQvEPogAmMFCxOiS2plQt6OhVIio5ySaiJzDNpi07T3bQSMoEs4zYFqJFUJdFnMQI0tACqnU9yuR3vS4G0aB5LuhJ2QgzSwbhQCwre2EPtYwpOT3UAYjv+zTWO31sVpsAIE5os9lMFvNKKezt7QGA1GMzPXs2m8n6lpNfqUkJtKnP47yYS3kDAz2LikoeWJiTn58VM6RlKtdOWh0PYjN4tRauoet3iWniRXKN8NrD9/ym7MvSWrO0JRKTYFyNkZb1dVlSSctJgAmPsVgTkCQsGAaWVDOndAYdDKMhjUPTk/HHoGjoEY08DMOWJePu7i5+/PGPcXBwAADiPPDMM89gOBxKwMvZYGbMKKWENl8UBabTaatG3s2Sj8fjVp06B+ydTqdVzgFAEqZaa7E15GQmZ7R938fW1haqqsKNGzda5QbMynZLu5lpcfnyZTqf9TaYDc6f6Xa7iONYssdVVSFJEqyurmKlWsGNGzeEBeQG7672XJYRmMEAC7POOfh2gQIW+RuPx/Ia72+SJBgOh7CW9Dc48OTPs+PA8tyRJIkEytwHSimsrKzg7t27rTJ7Bnw6nQ7OnTsnx82lIK7DhCv0z0wGPl983Bwos4sCv86MABf8AKhcnt0cXCCGX3dLOLjM5s///M8xnTb3hWW2Gn+O95FfB4Bnn30WZ8+ebTEZ3P1xWRfu9l3NFP6sW1LCfe2yFXi8u5oZLnjD1xJvj9kT9+7dw97enowpV4+RiQYu0P3USydY6IXRQdcSg9GQJElaQMSd1Tv4/ub3T9y2ZOLqReJSxTUCGzQdD0fQsBaxo3jBoVNzf542KItP2wtHNAcAWRYE498jFR0S/vPgkasBqMyBnSB4McXBGgwE/Gg6jLLclaJjr1SdWfRq6nid0RKBRHVYH0AEEuvMzpOCAk8UqC5t44nbcV/9BNT947+qKQs5VsX/OHDgJBDEtt0YJHsILXXYHPy2QIz6PMs5PkrA8SgmiW3E89zrDbV2ASxkWy3mQYMyPfpwHIHJQ+87YcgsM1BO+q5HbsexWOXrzgUq+dpFXWZijZXadQGHYLAoFieO89iPEYcxebEHPfEu7/rERAi8QET4FBT++I//mOatWtuDdVeYJcQgpdFGqPVWNSyXRzKITmgWVqwWF9Xi2Mz6F23uvOjqDbAThwcCmt1AM/YoS7oRbpAwVOXBr3ys9lbltZ7uYRiSX/kgGDTK92VJtpO1J3pmiJrOglnjdEy0VksZ3sQkFAgURHdNq1TELNMqRW5qNocpTpXNXj5uoEH9K1sJcGarL6fs4ahW2QqzaoZZNXvkex4HcGSWCJ9DBqpCFaIX9XAmPIM4qDP6moIiZuB4INeNwKPyoiRLSC+oFg1lkUIOzJIqwcRMsJPvYF4SXf40QVnX77ay7F2vi4E3aAWyvYB+OIjthwSqBTagQDfoUj29F+L23dv4p//Pf4rABniQP8Bnn32GJCHrOl7UuTRmZlSEVdhifQLtGmnf92le9Q1KTSKMxjcovRI2sKj8SqwmK6+CCQxMYDDzZ5j4E5QePV96pbyn9E5OFLAQIF9bXuVRSWZNz/dKD17ltZ5XhYJf1WLONdMiAjHfYhujp3qyUHczp0oRAGR8g8qnx0IVZH1Yi07awBKFvD6eyqtwoA7w0H9IlseKHFQYmDkJnIGFOKJwWZYEvqrRuvAqD8NoiEhFJIiYK9IfsPSZftDHIBrA5hamMrK458CQ3dNWeitYwYqMB0QAouba5wCbGRfLgSg0CJyqwTvXJYTBHgYtmCXA70tMgklOOjYMXCzKk+9XXa+LvkesuY5PpQyhF8LTNCcLO4qp9rXV+rSaYq/cQ7pIielXpZhX8xNtniMdCVDBzJd+0Ec2yXA3ugv7ghXnjXu4B7Wr8NKVl/Diiy9iZWVFXAA4q839x9cSX1tuxp6d6abTaavsmwMkN2jjIM4Vu+OyIK7P56CN+4Tr2TkeYnCq0+kgCAIBVGazmWwHgDC3WcSega+NjQ0J3mezmXwvg2BukA5AnPs4JuNjH4/H8l2cHObvccvYlwN2Ho+LxQJBEODq1auIogjXr19vaWLwdb1c2jKbzfDqq69iMBggiiJhIHGwvb6+LqAea/cBkD7b3t6WoNwV/mQmADd2SQAIVOIA2GVD8VhhFovbRzwO+HfehjtGloN73o+bN2/i4OCgxfCRe3w9NrlvXVb/2toaXn311VbAz2PYZXK5QBCff5433PKJZV1DZskwwOaWYOzv72Nzc7NVmsJ95B4HAHz22Wd4//33BdByrzUW7HTnNd/38Xf+zt859toHHgNo4BaGIZIkEdoRT5pMUWHVUADo2z4u7F8gJkDtFFDYAvDqQKQWhMtNTplK1QgTctAk9mweZBu5yWnhxtmJmmHgWsJxVuuxlfOPanZp8bh8J69pmUx3l+AdeHRg8GVTwF0GiLPgZ9ppC/hYCm5bj4oWlqhtMJVpgx7GmAbcUXUJiDEtZocAHLV6OWfWhbLq1B0zff5U7Un78GkxBR61raPecxoGQw0gHZkFZODwi+z7o0AxNwB3QD+mFltL54uzXTgdiCmWf8oqGjeogxqe2BwmDTN8RBTTee644zmuD1yw45CmyFKfHPodzTWrlW7cPx7RuF76qHYkcLL5yE0RuGgUMT1KetRV88NBgFd5UIWCLtp2kUKl1BWsZymICQwFIj7VglZ+BeMZ+WFhvke2Gvhkpseh4HS5XMrROzGgeVve/rRZBkunxZ2nFRwrYtvMf64wpggKWsquDzBoaMa2ziJ7lEVmtgvraxQlueAoTwngZ33bsosrVdko9ddK9AUKqXPm0oXTsGR4/1mjhbVAmB3hHj91TduqkbOdMv8yU8dWj557nMZaIyfR679I4/PGTA9P032KAQMWYmbdFNaqcUvTGDyvDKn/z8pZAxgZYoJkhgKlk+43oQ6hn9Wi2m+fsbCZbQXqfC1ycM62n4ENCECuBfJUoRCYAFVZtbJFsIDNaZsctCyzRN1yVV5Yu5k4Zq2VKOWadwGIAhSw898c/HNwn3kZFtECVZde59esPr5/lFEtcEJ+SvrRpZbj16WGV3gIUnLK4c9FNkK3JFYNSlA5kLMg5x+gKVG0AYEVlVfBRhYIIEBN6ZETRKno98RLMPWm0h8C4mT090lzn3tcLigTmEDcUrzSExAmMOT8wMfnVz50SWMo9mMEftBaxLsBhxvEufXYXd3FwB+09Al4Hc5ZX4Due8YzIl5a+cSwkTJGXTSARblAZSvkirQlUqQYY9zMV/XPaZxXYp80QNi+17VUd91+rLUoKooDdtNdjOMxqkGFKqjk3rT2wzUcbB/g+vXrOHPmDM6cOYPhcCjjwc1Kc5DGWX4Ownq9nlg8slifW2btBu5utt6ltmdZ1rrO3KCW4x8GFPi1KIrQ7/cli86xEQeODFIVRSGlPv1+H91uVzQhmJXBjITZbCZBOMdZ/B28DQGFauo72+gyOMJ94AajbrkF960rFglA2AVu/3Ag6pYrDYfDFlsoTdNWiUav1xO2kFvuYYyREg0O9Lk0xLXJ5O/lc8PaCRzwMvsFaHRyyrIU5gaXhfCx5nmOs2fPSmC9rKPg9gc/vvbaa5hMJqKdwGVFAJWoZFkm7j9s2ZnnOTY2NrC7uysBuwuccF8ul+hoTSKUaZpiNpsJsLnMJlgsFojjWPrLLSXp9XrCcnEtl3kbLiDC/bO6ukpaQjVzyBiDzc1NXL58GZ999plsw2WOnNRODTT0+3256blCEDxoXXsP7rxu0UUXXXimTc8LVKNWzoOVAQsemC69ZLnuZj6fi2sEvzfPc8Rx3LogBBHUSqiTSZ4g7IakPeApzLM5sSh81dS1Vjnu9O7gg+4HdDDOovlQq6nWEqy5mdL6daChYwvzwrZr079oOwSEHMJCmqCrxf6o98XZ0KHja91jlp0fjhpnpxh7y8Gu64/tWU8C3+UggQMeT9GFrGwt9mIqVGWDWIdRKMfIIIcIH7KVHdpMD84YHwp0j1uEfBHAaPm0n2ZbR52f0wAZ9WdbtfSn/d7Tbp/fC6eURjV9yFkSPu/MJADQgBoMWp3ie1yQZLk+nMEslwXwqKag4JmGms0lVAyqXbp4CQpKxk1ZEdW6rGo6f1U02e+qODRPPHaJkwKsR/RlBEvv+aKAmFsqYugxKAKqvzckJAvjCHDWrCppzMJgpkVNsza6AS2O20cXLFFVA6Kw8wvPn9IPsPR9MFBaESiirIDKvA/yyMCuc/1W6pSipu577NLvtTr7I/vW7aOsed5lz2irpc8ZhNNGIzQhojKigK2iyVNEYbkkyywx9uoxInOWZ4UJU+lKSogqXQeLp7jHKEtBsGeIbedZ59Fx9FGWyseYbadso49jrYXSSvqf96XUJYymbLaw7JjJ47CHXPcdYZBU+GLz7BdsucnJGcorqO/79OMbH/28j9zLUepSfip9fFCmrBLQIjCN7WBgg5bwn1gXgsojOJj1rS/sC7YuZJDRpcNykMNBlUvxzvMc+SKXzC+AVvDhCuolOQmlZiYTwLLQFLQa3wAhSHlfle1AP6BAP/VS6ZdCF8eK3nH/uAG9Z2pApz52BncCEwh4EVjqK9/6CHLqU1Uo+YxCc2y8xuR+qUxdButVND79GmjRBMwyWFH5lRwDgxWFXxCA0aX3M4Bx0nhtARdVwy7RlaZ9rnz4JR2rLrSAWAxg+JUvtrAMZrmNgzY3cHStKzn49byaoaQC9NBriffxdoyt59nAAiEaJo0uG+C6Bq/kUVdI/ETYNsxMEfaNqvBf3vsvsX5vHdvb2xLEa62l76qwwsOHDzEej7G3t4fnnnsOZ8+eldjDzRwzXZ0DUAYBXD0EHvfutcCBMR8vB478XnZX4ZINVy9gNpthsVi0xPi01hiNRhLccV9nWdYqPed95vE4Ho/R6/Vk+wyg8PU7m81kvzgmyvNcAlm3sZ4Al6EsfxePBZcF4QbrfCz9fh9KKcxmM8RxLOOFhRC5D93+ZZYFx2gcE6ZpKqKRQGN76vadC3a4zC8udWGglTP1QFNm4JYt8JjnQJ/nRVeAlktE+Hc3dnUBB/d64hIQBj74XDGbQ2uN1dVVAZCZEbC+vg5jDD7//HMBPVz2DX9nr9eTyoHlcof5fN6ypux2uzL++VhdTQl+79oa6d/cvXu31c8cN/N2OA7vdDp49dVXZfxwCQ5fW+fOnYPv+xJbP3WgwRXr4Q526SZ8c1r+8uUdcRF4lwq1TMlg4OKoA3HRSbfuhC9MoKGUaF2XIhhQjXhFNeJczxabWChFSlFWSgcaV7OreGP6BkpVYqezgwRJaxJm4KCVia9ZGqWiRZXrN26UEUG8CpU4JJQg5d8nqfl2mQcSdNULW620BKGtDJ9qQAhZ0tkmsHaVzXnxdyrbxJPa0n643+8e+yFq/mnKDrid0uHjuH2Ufa2DAQkW6vIVyfrbJngAIIwPd1uuyIz2a1BO1xnR2g5QrOaY1aGdc+EsvA9ljI/6/Ysc83HbOw0Qwe9bGluHtrG8rcfdf5dNU49RAULcr3PKjRRUC/BY7j8Dc3jx62zr/Z33T9wtT3no+B2sRCtEHQ9i9MKGoj0IB4jDWITefvD//QGVU9R2ijD1nMU1+KiFF2vGV25ymUOMNq35g4M5sbzFEQCL0/9ybk7JTDmqT55o3NmaZVJTqN05wb3mT8NmUYbAIa+q3VuKpYCkXqQHBSmyB2UAv6SsojKk54MKpOURAIVPIo6FV0gWkAMmXigbzzSAinZKW5Q9/IM2QwfAF2fWPWljIMSdv2pwTkC/+jV6e6NzIeeBwQ3nGOXYT8h48z5wYOWycoKKMtu6pGCK3Z+8ykNgA6iSNApURfvNfa48Bfhkn2k9S9l7vwlomLHDAEelq9Z5OxHM5OFY90mQBdAV3Q88eAiDEINggFf6r6CruwIOsLhjFFLWyvd8VCAHlEIVmKQTEuZkq8La1pa1DFjzYGIm8lxqT8G6cJT9WaOhozuNhkPt6BIiFHHAftWXDHvskfuEV3pATroYLL49m80OieLxwtTNuvPrnBRyxbo5OCpLKum0vkWucqhIkWZF7fiS2pRsbFWCzMtQhdRHBQosqgVKj9ZLMztrxIeRnzgfhfU/BnbYhjK0ofRRqEL4lY/YxtClJqZC6bVBIOOjmBUoZoUEPKzlwIGJhcU0IR0Tl0HC41J3NaxvZW5hlkEZEgsjUxlmaiavF/oRbDynMTDDgAWDVwxOCYBYH4/NLFSuBOzyDM2ZXuUBVZNpBuo1fAWgAHRKrIxAUUa+ozqyfncV/QG0+6QGADhrn1c5bqvbWCwWLQaH+/nlunzu7wsXLkjs4a75uYSJ/3YtAnn/3GwxN3c/Oehi5gG747kJUA4UuURhWdiRs/YcBLJmgiuix6UYbqmHm+VmxgPHOczOcFkwbgzmOh1orVuuFByEApDgnUX+mK3gggZuGQmzOvI8x3xOtj29Xk+24/Yra1owQOP2bVmW6PV6ODg4kLmBA3yO4xiA4bnEZS64x+ieN+5jPrbl0gBmABhjMBgMWpocvL8uQ8AF5pY1D/i8c6afGQSLxQJ7e3tSMsJji0GStbU1TCYTnD9/XnRFeKyyjSSLnfK54lIh7qM8z+W88j65TAoeQwwcu+wP3l/uOwaM3L5kIIb7h5kwDEwkSSL75zqa8Lg6qT1W6YSLHrkooGysRsFduhAACba4Ps0FGVz1VBfJclFHHvB8IaY2xcybiSCODx+fR59D+UqsH3340B1NdaQ6hDaUKY99si7ShgSvBoOBDAqXrvO+eh9/sfYX8KyH1E9RqtN1qBv8L3vW+/ARqxi+rRWzQRTLoP7najIw3cwYsg2DbbyglVJQvmoAAk0AR25zEcIrQDXFLHYl/+oAhZ8vbHG6haLbLFoimyysKXZnzj8J+OpAiqnzpmosdoxptCIqQ9mAldUVQAFZkaEoKZPEZTTWs6IzwSDO0w64j82Ef9Fg+UnaMgiyJETplsZwtt/9nGgvYInNwo+qod4KGwCUKQXQDjrcdhLj43GPj7f5qHZKIKTF0jnNdr/Ie0H9My9IFO9UbbkURjfPuyr6oapdEmohuBWsUGCgYvR0D7GKESPGKBxh6A0x1EOshqtQlmoTjSLtlhIlkjxpwAhlyFmi1iBgRkZik2axX/vKs5NKYYnuX1iqqWfQ1C1VO7b0qQ5U6bAfg+VxBNPAKkuBpX+6efm47bfKy5hpAE3U7lrENKgCySozgBEYcmiJLN2HPMPFaJ6UvKCEOFcoqwRULFXZWJx5FIywgKBQvusscKlo3hZ2QA1ki46HOkFk+FEAoMXJArpPqylQttevDgOCRzSZe0+zbxYthog2WgCowAQSQAnQUVCwJHpIjP9w+R//8+oSQM8IAMUlB6Uu8VA/xB/jjxvdCE37gqL+qacCFuRku8Ku7qIX9RpRQ93oRwyjIbyS3IAiHYkTQBwRRR6a3EAm6UTGS1KRuF9SEvNgmk1F0yIpE0yqCR6YB1Lzn6RJW2/kiCoYnWt0UxKCjVSEKIwa4b/aclGAjdrlgN+rOopEA1XcEqw0lRGwgTOkvODmoInXA7yg50CegQoAEihwIFCUBYI4EAFL1jBgBw0WpXTdJNj6MqkSjM1YypjYzvbYxM8mrX9Yy0FAHRVSmZXqCKMiQoQYsWhG9MIe1gfr6KquMFYCE0BbLWtert93A+K0JJHJ1KawgW2JUdrQogxKslOs/1VhXQ7BIA0WonGRq/xEhpNnPAEhAgTCLglMIEwSBmqYZVIlFY3toCcikMzWKPMSk8kEWZYJS4C1Ntw4go85CAIMBgOsrKzg7Nmz+PVf/3UMh8NWYOgyd1yWNWfuWQ+AM9duyQmDZC7rwwUp3KDPDcKZeWEtOWRwsMXbd1nVnBnnEgKOfZihzVl1Bi84vuHA3U2auoANgNaxurX0btKXA1QXFHTjK94/oHEy4PPB4A4zERh44Yw8Ayy8nxzUc9DLgTO/F4DoTxwVnIplrFPGweOBwQ5mMTBTwQVe+L0MYDDYw2OEj59f5zHBnwPQEh5d1kDg42HgiANzZrbkeY6iKFqlNW4cO5vN5HVXcDTLMnme50IG1Rhs4fjY3S/uB+4T3lcXeOJzySwVV49hWZvB1VZ0WSpuiRLH/a5OhHuujmunBhpcNJu/xFVq5U7g57ij+GD4YmTEh08u16u4NVKuoiofKIMAQRDgWnUN/8L+i9Pu+rFNGAA1G0BDQ0c1IlVnBjuqA4tGpd8N0lwWgFA+63rw7Kg7+HHzu8LhLONy5uURZ0ybRuFfl1qormxZ6FkPXdMV8bXQhpShyYEiK8jmzfMRRzENwpqq69KtirIg3+ga8MgrCjZyk9PiVxm68WkL+BDQw+haqNJ5NJGRIPaoNsaY+spvH7M4GMBvHau2tKgv0qKxkKoF6GCAMi9RlZWcY2usMDy0diww6weua9Za0025poy7P5zRY8cRppPnYX7Y9eBpLebrjHSljrnAXYbB02qP2pYDgCyLSrr/jv6obTQ+OBByAA/+3hbD5THKRP5tbRy0A8DCHqH78BgCjhJA1zR81lwJVNAo/KuQhPMCskQb+kNsBBsY+APEmgS8hgGJLw79IWIdk62ms0BcXvx7nkeWmZYW/dNiink1x7ScYlJSkDQtpqTEXvuNzwvK+uY2FzFHtl9sjYFHHKcAbc5z7rx8iGVTb899/tjr6ik3Vw8nUIE8cn1zqEJ0dIcW+rX9bAg6Xz58hF7j0MC/e2gykmVVwlQGfuA3QbRtQIm0TLHIF8iRNy4IKCQ4yZELWJ2D7C8LFI3uzuMwNBz2CgC5l7rn86Rz/MimHHvox2XpnKa55UbOOAtViJE/gg8fK8EKLnUv0RyonDlPQa5lLt8sLJWPzMs5dvNdEftjEUB+/6OaWEsG/UbY0icB2n7Qx9pgDc/oZyQAjn2yWgxsgI7qYBSP0A268D0fJUqMkzGJo9Y2rPNijsxmmOZTLMoFxsmYAvVafHBRLfCweiiinaxPcxJ4GOlIBEK7+wS8sJtBx+tgpbPSsvwMNDExVuNVYWr0/B4G0QBdrysluJyh47UpB4yu+B+AVn03J694DmMxc2ZjlCiRIcMsm0k/TLMp9uZ7EuAnFQE7SZWg1KWcw7EdU18p6s/UpA2wUwLYP9w3LXcY00Un6Eg/xF4DVHT9rjgB9W1fAvuO7kAXJDwe2hC9sMnWMs2eA9CqImvjeTkXZxQ+9+wcw2PRBWDYljI1KWaW+iAD2aoex3r9297fRme3g4cPH2I6nUIpJeCRG+xwQKeUQhzHOHfunAgIvvvuu6L/xsfFNH2XZc3PFUWB1dVVARM4uOcxwPEE08PdTLpbRsB/8/65wZwb9HHgyTGSmx12X/c8Tyw2OSBlxwk3o+4CG0ATDAONvajrxsEBNb/XBVRcO0we7242mo/D/Q4+fhZe5Ne63S6MMSJwydcSgJa7g8v+Zto/X6MMPLjAjwt8uMwE3n8XXOL+5OaWoszncxlf7jGxNkKapnIdMDjE44HBJfec8/czcMXsCt4fZh6UZYnBYCD7xcADAy1pmiKOYxmrzKLn7+eSFJcxxu/j0ht3LDNTg4+H96vX68lY5bIhly3ishncmJ5BBABS8sP75mqJcGzO+3xaoEHZI1N/h9vf/tt/+1DdHyNxfEJZbdW94PigeJC5dBgXpV5mN/DgdQNdRs5meob9YJ8WO5poeBXIu7zUpWSAClVIRkjsKOtsnmThnL+dooFfXqCylFFb1lrgBZkszGybjuuWb9Qfeuzvl/YEn23VdBuivmpb113XWT3OLokolKH6bPmBksA/CmmC8DwPFy9ehPZooC+SBaCA+WIOL/SQFAngA37XFy9n61EAnlVZQyWv65b53Avg8QQsDq5ZZnYM/2hDx+TDhy0sUpuS+jcA7WnSCKk9sq22UL4icdOaysvsjOOaMkoAlRZjhJkKqq7N1XQSS0PWbgC9xllP/lvO9dMc51+UWv+022lP8f8Z9vXf4uYCGVrVYoU1mMGss9Aj20y20oz9WHzHORgKqxDDzhCDqLar8yIJupWq7QtBLgVpmWKezpvMZb0YTqqEXCbymSi383v495MsIcUJgy3kFJW4eLrRhAEgei+VrcRyk394XyvT1oX5P1PjoJndQFhUMVDkJsGgBwNSrNfANP0AgQBXURSB6+Alm4OaoaYbhlhR1WC1MgRiVI1YZlZlKFGKaGZuc2HSnJYBo6DETlTmSDfwr5u1bUvUVhnko0Cpo77LeDAw1C9VeGqdBm6+rWnuti79qR+1aYv0auhW7S2zg4xu9IfY6pdFSHPkJzIxffgSxEag65HLDNipITCBZO9DGwpLIfZiyeh3dU1R9o3YOJrASGkDX585crpOy0TKCTJLwWoGEkZlxsFJLi8cnLPdZz/oi+NI1+tK4M7uIv2gj37UFxvQXkAMk8AGGEQExHQ73dbadjmz7Hrac5DIAZExRrK2HBAI+9fXKHWJWTGjEppsIowKnpum+RSLguxExVWiIoccZmIwqJNUyYljk0GcZQeWrq7PX9C8xm4bvaCHle4KQksCl4NoIKxbjQak4Uw6B3LTZCqgU67oHM9zcvZ5Xj+PH/3Jj3Dt2rVWSbRb4+6WT8gYR5O1dcsp3MCXAybuZ/dvzhy7gZQbfLmBEoMA0n9OgOoGZbwPru6F65TAJRIizOkEdxwcZlkmVHwGVTibztlnN7ZyyyVccJ/7xdVTcZ/nz/J45H11yz9cRgjQlJ+4wID7Pj6GxWLRAmDc7TPbgANlV4TTZbO75QpAo+HnAob8Xj4WPp9u2b07L3I86ZZjMIOKwQUGAXif+Rg6nU7LTZEZG67mB//tliFMJhOUZSkiirxtF8iZz+cCRLgsBU7OMPOAj4EZBDxelqULmK3D72VXEXaWcHUVuLkAkAvm8PHwteXqQ3ACia9Rd/zwmPjd3/3dY+ci4JSMhqzMkJxLKKCqFyK2sJIt9kA3xrzIkUwTyRS7CqJ8wHzS+MCttYI+uRMGTxI8IbnUktjG2NJbrXocqeVBo3BrjaUgWDfKvTyhSP2LbsRZeD9v4zZ+qH9IGTVL9kt8AxeP7po2/NhCjk5mZ3nxwwvRY0GDpx0Unbb+/lEva6r1fiwdh9PoAgC4hmv0SwigVz+5jqZ8oA74RaHfUD2v59F4DMuaspkamNxQdlAFKHWJeWcudGddEk1a25ouXVOfYeux4ylRaq9UU+vLomaVqqACRQJINiE2RO2SUmhSpTdBU9v92Oewrq+2sI2egzIyfmRZ7Gg9wMOjs3s1aOJbKiHyTV36YjzRpgg8uul5Pi2kszyT72W3GC7DYRDvuOOSoMax0wRwyOqvVdMOhxL+JIKpy/vzqE38HwGQPM7hnJbFgcd431NsFlaCHdiayVW13vCl7RcHlm6wHOigKTnxInS8DkbhCGHUAAieai4O7dH874630pDgZ2ELKjGp2RWpScX+clFSxu+4FuoQfa8vgU5Hk4NF5EcCYDBTh0GLEvTdeZXLd3OZS1Ilsi/HNXZscO+H3Cwa1pCxRkphWu1LtD09al/5J9A1oyMISPBQBQ3woam/Ai9oShM9X45VKQKsGTQoq1LmQ3EJqV0nWiVDNXsmM9mJbAJuCkpKMrXR6Hh0b+HA3bdUKukpYph42kMUEhhjbb120jUt2lbtsWdLAV0YRM8tBezMNDkN8OLDRxddYTRyuaPoOTEAo5oMYW5yuofBCFOvsAQGiXMP42VLGIayqqWDEC4IsHAdGnSpEdpQ9AH6po81rEnwy0KHqlBIs7pkNQJUpFD5VB7KZSxsdZkjFwvMA32AHb3TssXk9xX65EHtW7/RbGANh/ofazUEhsqoAhuIHkaIUIANtmnsel1EfgTf8yUL7tKRwzCEhsYAA4y8kewDZyMBIOpE8HqeZLABCnStIqFVBm2m+RQ2sEhMIswwF3xNTIJFQUDrbr6LaT4VlgqXm5wEhDKgw0wLAXOYpaLJzrkf9BF7MVZ7q/id0e/g4ODgkOAjB7K89nYDeg5wXNCSAQc3687v5wCKBQZ5G7ye5+DWjSdcdrZLGXcDM5eqzoEZgFbw677mHh8Hc27ptwsUuLR97gsOejko59ddIIABjOWgjz/H++uK83O8w991VKmKjH/HncHV2+O/+XVmOrgWmQwsAMR84FISfp23weeI9315n5b7ygWWWroh9f5zgO2OGw6cXdFI3o4LeLi6IC4Ywn3hluhwKc0y66LX6wlbn0ss3P60lsQxuZxjeay74MMy44q/gxlJfMwcDzMIwLIEfM5cQMc9z+73Lo9lN05298MFZdxzuXwdHNdOxWi4Nb6FS/+3S6faIADJuopSNbzDvytPlN6FBm+1eKsrq4ROylZkYkmmG3ur0AvFTkcb3Sw2FdW9+YoophyIHoWg8e8udYcv2nvmHnKdI9IR0lmKfJ5j0B1gJV5BsSA0KS1osk/LFMP1IdHuKkLkkyoRn3AuMeAb+CGmBdeEOgru8lNn4Y13CiGr5ebQ2ltPu2jXcgbny8h4H7N/R37XSWUmT/IdT2MbNdDBPzwmPeOJoJlvfBGfQwaEFdGdUQJxJ8awP0Q/7qPbaVPmirzAfDEnJeyqRF7kMp50oAl08SCCgMzWKGwB+ID1yPorN3UuqcqF3fHY4+Zx+sUS4Bjo4LBFKjwBGpbBkaIshFlUqUrsAI9rLMzFdaI+aAHI13i/00c36CKZJTSP1AGKRk13NEAYhChtCe1rFKbAPJsT46Rmxcg1ihIXr1xEVmbiSMPuEnmVk3BjzVZhB41/a9ujrrenNQd8GSU9T9Ke5ryCRjOF71/ilOOOd84KLYFoXIIgorvHgXWWgNTAUt00C9rxvc/VbtFevfhDreGDJmjkLHNq06PL+5zGpRqsI+SD7q2szRPogCwoaxaBtY3Ojlho1kGr6AKZBrBv2R+7ApS/5KagZI4Q4EMvaQ7VNqJaaXT8DiIvwng8RlEUiL0Yw2ooivrCqqyBAVf4mctPTtu430OETd87ayaZY1VdAlqz2rQiBf/KVAK+uLbfbPvLaxEGM07T/8trMt4H1pNyWZiudapoRdW25Ccdt9jMsiZErYHA4pXsvBEhEjAj9mMMo6GUFYQ2JOakVsL2KHSBeT7HQXIgTKcChega5DaXzDyXE2WmKRXIQa+fdB611eIaIo81eNFRHVofFBCbzI7utFwo+Br3Kk/ueXyduYElBwDsbrCseeYGtstlbsyQUYEih5qQbEMX1QLGN7CBFVHLQhdiEWp8YqyUuhR7TAZ2cuQ4W57Ffzz+j7FYLPDuu++2lOrdoNfN1vPa3M2CDwYDDIdDYYu4WWgOhjjjytRuzkZzgMYBJgMKbnAeBAE6nY7oDrilN/wZDrwAtMAHLgVYdi9w9Q84WOOgms+Fe54YTFkugQCa5CwzyV3tAT7H/D18TLxNd2y4oMtRgM3yc8uNt8mZdPecuawRl/nhMipc8MDNrLtjwGWC8GvuMbjaAQx08DlfBokYaHDHGWf8GdxxGTQu8MA/zE7h7TBwoDUJRLKzh1KqVdrD54lLsfi6ZJDADfRdkMEVaWR9Gh7HfI65X/r9fksnQ2stQpDsqMLHxN/F48g9by6gseyIwZUGvD1u3G9lWeJrX/vaobFyaOycBmgoTYn/+u/+11RbaOuAoK41ZCo4L5hclwV+TsT7dCPeZ7Vt6hWdMgb+5wqN8YKEn/sirQV4LIEYvFBUFYlKetbDfX0fc+8Ygbc6iNfQUJWCLa1YtXGJAFPr2dpNGw1daFEK1qWW4NSWFqpUUCVR5VVFv6tKyeetsS0AgrUPgjjAcG2ItbNrCLoBZcBqAGSSTCgItQWsb6Ej3dTc1loKaZk2QmP1tlm1+7GDVNMIfTH9k3/n7DuAw+JxvwwWx3J7nHXtF9mXJw20XLEzNKwL31KA7VkSOYtDokAGNoDJDSIvwvbdbZjc0BgyCqhoAnvhuRfQ7XbxwfsfyKSitYaFhed7OLN1BkEngBd5WBQLHCQHtMio6a682OIxxCKdzG7hRe1pKMjH9hWaEiJPNbanSqlDdd+cpbW6BjHs8WrdvAiUrFvlk7CfqUWuDIkA/md//T/DMCJK/zAaHvrph31opVEZmhfzKhdgIqscgMIBK/7x/+MfAx4o+GLr3SxpZVrhkc5JbnMBYFKbCogkrhNcAub8O5L+/X90gP/vWrs9beDllEwxfi/ftyRgdaxdXSFfrQi84FIDKDT3bF1JucBxjUXkQhuS00QtLhcauvY4WAxtKJaGzFBTRkkQm+ZU754bAlCttlAB2Z7CI6A1Nc19rNQ0PxlFFnxcOleiFFHOZUFN9/dH9eFKtYIIEYEmqsRL0Uv4L9b/C/FXZz2p5cwpL+rygoSbCzSaJAUKEnSsSBsgM8ScKVE22iVVJiUlomNSJzEy29Y14ddO23zVgBmBChrQ2GoC0msAxgXPeJzxnGOsadZrjiYFszJOUz4koMUSyMPf6Za6uGK0xzUFhQ46AlREKmqBFMxK4FIRr/SktCK0oTAXxI2iZsIWpra6rIUoRdegdtEoFAXmqa3LgRjIqAEn1j5oOWic0Bh0YeAlUo32BYMyHUXilGx/6lfEsBiEA1pbFh5sZgmgt1rGrMscmE6nLbs9DsS5ntylvy8WC7E05Azow/2HuH/3PpIkkcB7mdbPQdeyvT3QBGEXL17Er/7qryIIAvT7fQkssyyTIH/ZhY7HJrMc+Fp0A0q+Lhl0mM/nLfcJPtY8z8XOkanxbmN3CA4iGfwAGu2PPM+lL7kvXItRV7vO933px+Vtcb9xP3EQyX3B++AyQdxyBO7XZabEMvPcBaokCcZ2qI415vI2uF/lWnXCSxdAcT/Lz7vnzmWQ8Hl151E3U7+crXfL7F3GhpvNdwN69283mw80ZTIu2OSCI3x+lvvLBahc4MHd1+U+cr/HZdMsM/v5dRc85Pcw64Tf67L33W0vs6tcwU+35MgVIXUZIi4o+Pf+3t87YpZqt1OVTvjax7nuOalR4S9yT4bb8e7AWkaiXLSGhSyEtmRs6/2tAIgHUU1hZx91zjxCQ4AKzuDwIt5oI7WzhSmkPrS0pXxGHBosofvwSOm3l/UQ6YgW/ez9rYwALpyZKlEeEi780lqdQXYt/KCazJqn6nICT8PTHipU8AcUlPq2RsszH17hIcgC+CndhPzKR6hC9DwSl0IO7N3ZQwiy+TGeAXwg6kewnsUiX8B6lsSAav0DthwTL/elUgPJxNdgxiScINVpC3x44oV33S/cWormy9s8SoTsUe0pCjme2I6i8btiZ8CpFiNi9Xnx6O//BX5BFNhXHr0J9jPXSsPrOBksFhg1HiITEQhWavSjPkbhqFmoKhpvoQ6F1cA3rdJSdm+ymGCRL5rMWm3nxYF1rnJRfpeMmGpnQh/Jvjmhv40ySOt/rWt3aTi89f9568QxEugAHb+Djt+hOuEgJjvLoI9+2McgGmCls4KVaAWrnVXs2T2s6BUM1AA91UNf9RHHpFtQVRXyPEccx7KoctXZeSHH86Z743AzRazw7iLxmc0wq2aYllMRK5tXcyzMghwn6sVyalIRB+PAhQMadpwobXmqOvZlQVBXX8Z9/L9c+2WXwS29V7LMp3RUerxdWXLG0RpGG2Qqa5WbuEGjsDqOaS6TI0QowWOsY8Q6xsAfoIu6bl+TQ0JQBeKW0PN6iFQErZr6U7ec0l3cc1DL6wTRACozjIIRBsFAaKvD4RBlWeLMmTMicO1mJAGIzRgHEq74l3v9WmsxnU6RJIlktVyrSN/3pUbXXVfx4tRVLLfWSulNbnMCMkxto1kmAgIsioUkIfhad0tKuFyIn+cSInaiyUx26utYQVFAX5fJsDZIi/mmlNwveJwYaxrtk7rEqDC0ZoM9vnzTwiIBzXfaaikrWmbYGVsD5L5tryd40/U1FtqQHGd0ROslr0fzvh+LE0doQww9YlawEGcv6CHWMbHxamYDM21934f2NGmWIBMxXVekc16Q7gGzKxbVQn6fVTPsmt3GPcPQ40nXlAcPYR62mCMRIqhSoW/78pw2JIDa0z2EJZWKRJqAmU63g17Uw0q1gqqq8ODBA5Q5BdnMDuCxz8EKB14cIPFzy/eyxWKBF154ASsrK/Icj3EOgDk44u/h+2KSJK0ssxuEs5YAU9r5vUDDSLDWSvbezSa71xy7p3C8w9c0B4Nc876ystK6LheLhcw1vV6vRUd3a+P5/u+6R3CfRlEk9oNudpqBkk6nI8Eyz21AE/Tz+4uiEKDCpcfz864dJX8P9zHQpt272gRcBu8G/vw53gbQaHG4pSZ8rpbBEJetwcfiskxcQUr+HDd3zC0DHUfFti7AwfvuCpIugz98LMtAxKPYJsvMJHd/j2Ku8Dh2dVLc/nf70tVRcEFB9ztd8Mc9Bv7+5ffy2HC3c5p26rB4NBq1BrCrisq0Dnfn3IHnAgx8glwxGb55uh3FB7+MOvJ3AA0YUVUVZdBVowvhUpK4g/jid/eNJzjev7zIce/ePfH77ff7mE6nyLIMOzs7Im4yHA7h+z4mkwmMIQubIAwwXUyhQw0d0E9apJgsJkgLysToQFPG1bPyHgQ1iOFT1qVShJBXuhK2AQMixjPieMCgSu6Ry4HUptbAiyw8m5Khx29X68el8cQihABazh0iYMXCkEYLq0NXJAaJkny/1/pr2Eq3UCUVqqRCpCPy8S7IPUJ5CsY3iPoRvMhDOAiBAJKlymyGaToVS7hKUYbI1U5wM1THtifNLtbzg1ZaKKOwzkQunYT23+3OPPr3J21Pso0jdksANIWT1dzdBdnjZFaD+sd9u1WNg4qljGtoQ3RVl4CLmspdZRUxOlDTdQ1NfMPhEEEYYG9/D3mVizOI6w6iQiVjhQXnGMBY3t/lc+bWv8t5tZZU/AtaPJxK+O8YPS8NDT3TLf2BQDeZRt/UDgS6qQ3uKqqXjXWMruqi41PduFd68DKPFoUqIMZW4eNMeAZxENPCpajpnYEPHWmhegKNFzbPkSyoFUUEOE7zKebFHKVXikL7vJzDBhaVV5HlXj7FNJ8KG8ZVOM9sJsDGSS1A0Ij9qXb2SgJW22Q6H9WWA2FuLgvktKKA/64dbhb26P7/gl3J1+qhbfLldsoEvrA5uByzLrfkMqwARGXvel0RSeyZHrqKnFmmdoq+6ZN2RC9AXuYY9UfwgrZ4HdDUYS9n7njtwc+51OVOp4PJZIJer4dut4tut4uyLCU5wxRZNzPIi1q3vlsphZ7tyRrIFRlzM7OcQR0MBiLqvbxdDjQ4UEjTFLPZDLPZDKPRCFEcoUCBWTYTYcrMZiQKWCTC2BA2RpUJSJGZDEmRYJbNkFZpY8utqdzHBTkedV3/52v/OV7zXkOOHH63nsdqq9hFQUyDpCTLT1ebQICTmgGyrF2QmYzADKflINB1iil2q93WuHKDuNPMH4EKWnoHLHDJ5SE8twc2QDfqYsVbEdcRl3ERIqTSTSeDby2xDGfFjGxRswmBx8hbrhIswOn+PS/mWNgFHpqH1Be67o/i6HKr/3TzP8V/OPoPsb29jR/96Ee4e/duC+hmQIHHshuYL1PW+X7DGdo7d+5gOp1Knb8bQLGlIrMCXCo6B6/u81EUIc9zYWYwSMD76Nbiu3aEAFq0c37M87ylPcDXIH92sVhICYgrtOcKUXLMwkkGDmKZobAsYMlOBJz8ZZDEdbNwr3W+9pc1CDiWclkZbgDNYzlJEoRhKIKJHEu5DhjcZ1VVIcuyVgbeDVLdoNotkXHBnOVxwPvC2+f+4LYMsi6DBq5eB/cBnwuXscFAAI9Hnvf4egaaINsFkngbPK6YTeACJbzv7vG4YBL3Ne8fjw+XCeTGtDwHL4Mfy1om7nzE23P3YVmbxH2fy2ABmph7eZsntVMDDfv7+626JndScAeTS9dxD4Q71R30bp2O+7d7oIwm8gngbXK9iIsQuheQ26EuDcSl1fAktVgscO/evRYY0u/3YQyphc5mMxwcHGA8HssA29/fR6/Xw3Q6xdraGuI4Jqscv4MsyWjC9zRUouDPfWx2N0UsRClyV1CKSi181Bd/PX+7tCAXGXXVR8OQWAbFeoH/5cz/0jpXyipxd+Ca2o7XIeGsOmtQFiWJLVUllFZNxrwOKpm1YbWVjDq7FyjllD+oJhA7Fjm3OBTA3cVd+qV/2lHobItbBKHQt6iWdYmBMqpxanCAEG01Aj9oABNLIFOWZ82x1roYfseH1RZZSQJdrsUll5Qs02wFcOB/S37xUqv9OBZxS8evrDo9gHFSO40o6HHveRpgSf0dDHAcmWl1t93B0YGLBV1L8WN8d80IEheRWuBWQ2PrzBYCj7JvWrcDXBHwY6G5qhYPLDOkJWX9jv9aJaJxAl44+2RB81puc6RlKoH00w6ANbSAGMxGiTTZ43X8jrgvBAgQ2Qg+fMRljFgR6yKIaoeCXoiBGmDFrGDQGSDySHyRtxkHMbkY6IainWUZ4jiG0opEFqtGYX2aT7E33UOGDKVXtlTa5yVZ8o3TsSyOXRu+yhyf4TQwdE5daraT1TxOc6OrumQ/CRJ3jFWj6h6ogLRbastgowzyKpfStNSkSCztI2eaS1uKe4Vb+vLv2pfTXM2ADNkhNpw0dwo6SUtwF3j14avY6NTWsHUwGHsx+j49xl6MfthHV3UR5zGG4RAdr9NaAPMC3Q2oOBAaDAaYz+ettZK7+OQkjVtL7D66i1J3nRZFERaLRYs5BaC1jnPXd7zGGo/HACAMrMiPMAgGrQwcZ4/5bwAtVpZbl723tydCc71eTxhdbEN47949jKdjhL0Q1rNQoUKpSngdD6t6FWFR77+lUglf+1hZWZHghPfL7Tc3uHEzgfy3taQzwWyQ1KQEpNZlOrnNSaTVBU2qRARjV9QKfu+53yNdiHKOWTETh4ZxMibWgknkeX6cl3Ps5DvCMmMXiuNAbA0t4ETsxQQ8+7E4bYQ2bKxRO8TE2PA20PN7UrrU0R0at35MwqvO+llrKq/kfcmRY5JOkJgEW50trIQr+MUvfoG7d+8KY4DX3Tx2OJPPgAgH9OzYsLymr6oKf/RHfyTjgbO5y0KLbuP1PY8lt5TJfQ+vo3kMM0DAr7sCiMvlCAxUcHNp6izMB0DsHIfDYavEg4+fH7MsE3CAx+ZwOGwJLLrgH3+fa5HIDhbMmOL3aq1blo9uKYB7TXBzk78clHNfuIwpN9vvZuldxxUOut3gfXl+4c+4QTU/555j3vdlEULef46RlpPWPI7c1905io+FGwMny8CAO18BwGw2EyYI7wPbqHL/uQAM9xV/33Q6bcXFrn4JXzusj8DHwHO/KwS5rEPCP7zfDIC5cyB/j3uc7vlxwTc+H245Bb/3NO3UQEMcx4eQH5da4d7IeCeXBwLfbFw0cnmw8gEADRo1n88xnU7R7XYxnU5loFdV4yl69uxZQZDciQ1o1EN5knCpWQABEmfPnkW/3xek/8GDB2SruFiIn+zKygp6vZ70QZIksNbi5s2bGI1GyLIM6+vrYnPCF75SCvP5HEVRSB3MmTNn4HkeptOpXBDuoHf7waVP8QRjrcVgMIDWGr/5yW9ils2gO5rUhk1Gwj0ooDoKfsdHd9QVsT0WMsqDHEVYkAhlXc5wUmPxw8AGjWMDSFuCBcnY8jLu0KR3sHdA56wGJ6y26PQ66MQdVJqoqIUqGktKp0TluDpZ2iFIQLacdThVJt5trrsFf3759cdtS1oD/BwLdXnwJPNxKneFGrxw66mXLdBY2VypmgpcW8txltGoJxSGfNo15cvthJryI19/ivsizIdaJI3Hzsf7Hz+9L1n+TlgqG7FloxHj3AgAiKAeB6DHNQ0SwGUmBNfXuxRhVuE31jTCbDBiMXjUNo/aFu+nsAjqIPlxGqv380+kIxH4ZQaGZ8npqBf1Wur+6+E6BfWRonpkHSLyI7Lf86hcp6oqJFkCP/ChArKZzctc1Pw5w+eWizBQMctnmFdzqa92gdTEJkhsgjHGkk3nvjkJJPCVj0jTfsZBjE1vE7GOsdZZw0q4grVwjaj5/oACVBUj2U9wZuMMipKClYPiANNyit1kF5mXYWEWmJZTTPIJZsUMc0vWn5xJZgHGfxvAi0OixU+wz8tj/v9H3Z/F2pad52HoN8aY/Wp2f9pqTlWxilUskUXJJCVSguzLCwhCJFm2HEsQENtAgMQG8hIEwn3IUx5vkKcEuBd5MOxc4MYxEiA2HFkKaUuyLMUkLTaSyLJYrPZUnVN1ut2sdvZj3Id//mP9c57dnSLp5I6Njb332mvNZswxxxz/93//9/HP4bafdPtM+WeBWaU2zD4Hh+P6GCf1Cd1XtvGuFue5hCgojAIqV8xM5n+PLAWFk4jGwSgYeer9uBz7YHAUdEyLcNRb1wx/ygQRrzGGSuYSUJABBAMUvO7jtZVSypeNyISQpEfzmkXWpsvMJ4MbHNxwTTyvoVarlX+WMc2dKeOBIzBvmkwRx7EHPmTyS/rAy4CRwRIORvm4+X283rLWAg6IEWMUjfx5stAbByLMApF16gB8cLOX7vXezxlJeT34dQ4weD8yuDhaHBFDAWQFyuywZbNE3uRYNsvePFa0JFZ+2Bx6FgPPeRdZiMY67tmF8u+jkECKLMi86Oaj9SOkZYr3w/cx35kjtCHapoWqFWJH900cxX4NzOv3OI7x7LPPYmtrywNsEgziPt3e3vZ0fu6boih6zGQ5Lquq8ut3Y4wPAHk8WGu9YwEzD7gxo4/vh6qqfEDHMQ7vRzKwOQiWMQhvl2MPBh54fA+z5jIY5fcx+MhxjwQXOIiU7+NtyaBYgpBDBpU8D/4cxyLcB2zJKcsdJIjAxyH3EUUR0jT19zPf/xJM4POQ7Pdh4poDXj5WmYSVwb9kwnAbzmN8HHyOEjySgIFkcfHn+Tj4Pcx2Z2ZJHMceuOL7lcfC8Lhlgp3fb631bDX5ee5vCfCwreWQHcTvlddTxsXyesvxxnF1URS+ZI/nSH7v8FyGeiVntUsDDUVR+IOWg1N2EB8II2AMCvC31hovvPCC70i5oOYBKNGjoih6+1ssFkiSBHt7e34SkMgnB/VyIpelErx9+cAD6AFhtyzezd/F/OEcqIFiVWB2OEPgAoyTMfa393FwcIDt7W1oTfYuH330UQ8NH41GWK1WPfoiI4ts88IoKHuqWmtx+/ZtD94M66jkw5pfC4LAUyqNMTjID7DT7CBC5AVmlCJ/3mk0RYYMu3rXDz5jDB4uH+Lw8BBN02A6ndKgtY0HKLxXcwjYwFIwpIjKGI0jX+O5rJZwgcPx6hiVqmCDTkgyslgmRKGsrlSPgRgLLB4bhcop7yset+TdHNgAutX0kArijbq7Mj7Itu0mu2MdARNOOy/O14DOq9GdEriqe77jreoCiMtk9dEvG+Fr0gsuhGbAY2KXZzQvctgBBrQ7RxatgjXiAQN9gQAWH0N49lvOOr/HtvN/hdjktGvzwwAf8rPu8eCmt2utPBj042oMArFFZO8Yn6BZ2I0A3I/gcGWdNP/d+79S/j68zLYi3dF8TeQZFAyaxWG8KWdQtM3GEgOgaRscNoewsKhbos974bvu+0lBDqOMByjkdwhyM9qNdnHD3EBsYq8z4qxDoANiX+QFjQ1RssFflavoGFH6TKgX77MVVu0Kq3YgMjy/4IBv0w+m+ceaaqfTIMUknGBkRhiHY9wc3cROvIPtYBsmN7g2voa9cA8jM0ISJ6hQYdWsqJylXuDh/CGQAIXbsElW7YpqvlePUCmiWa/alaedX6i237FEGHgBNlTyi67Tj+I+Owvs4dcuM15Pa7WjtUPt6h4DicUyF27hLSovOo9ABRtmjw4BBxQtsaAOy0PSI+hsY72o4gWABbMmJHCR6YwCQwYkAhonHDjK903tFGOMe9lJ4HShMl6MywWpPzeh8C/LbPl3phhL6vRqtfKL6zRNvVI7r9fCMPTrJnYgkJlNDmD5WIHNGoozsrx/FrYDNuW8vD0O5IaAicwUcj/ILKsEh+W6lUtskyR5/Jp161JZWiMz+RwMy8TcKBqReLhYA/NnpYghb5+PZb1e+3Uq2w9WbYVFtUDekkXmoqTfCxQekGAGWe5yz7Q4ro7xof3Qs8qYceHbz58yPi1pPpnGeDFY3WiENsTdyV3sjnfxYvoivjj+omcrcD26pIRLwIsDyiFrgseRDLTjOPYJO+4bdiHgIJH7inVWJDt6KPrH11Z+TgZvHAfxelxeX8mw4HPiMSXBFRlYcmAnyxoYmGM2hoyhJIgoATDen4zZJLucs+Qc4LNGg8z6ywCYWSnc53yOMkPP98fw+GTgK+Mcef4S5JBsKr62DCpKkFDqO0gGh/xdWq1KFgvPBRI0kWCiZIbwOfF8JFn47BghxRUlQMR9lqapn3MYtGD2CM99fN6ShSCZB9xnAHr6QNyPkv0zZLTx+TKQxtpAEkTibQ3Z9kNmyVnt0kDDw4cPe8gKdyr/lCiU1tor0HJ9IZ/4ycmJRwRHo1FvcMpJM45jf6HyPMfxyTHctoNONAWWjYZqFOIwxtWrV1GWJY6Ojnq1OHxTcYdJmodEJMfjMb4RfQP/Ov7XwPY5neUCTx8OwgAucUADqEYhMQlUq4AGXixPW422aBGpCFmY+UxcrGNUSQXjDIlMHTgkSKjUwZFVGQsWMd3q+PjYI7k8gfL3kILFiN5iscBiscD169exv7/fo0ItFgu89dZbSJLEI8mBo1qcFCkSl6B1hEa7qt+fB9EBlFLI8xyz2Qzj8RhvvfWW3y/fDC+99BKSJMH777+PD+5+AJ1ocrEwFjtXd7B9sI1lucTx6pjqufWGcdFq0qmoXIWlW2Kt6UEn7Qfr7quNLh7syikvhslq0wkSb4sYmxh1XsM2FtqR3oJ2NCFtT7ehtMLR0REAQBvty0iccgjSgAAVS2JbrdnYNOY6v1R5hC9FAQWLGrqn9SDLMADKcnsQ40nXzN12NTRcuwEwZFDA4Akf2/DzPzZWA2//tMagx1nij6dt56z3DEo9/DmeplXhzg8YTt98lzkUlnhGdRO8MqirevM+qE2ZREfdP/XnJRgNP+rm4Hyw88Pu2sH5YJsp6TLgU3lfmO28ZhRpTsQ6xna4jUSRCGeiyHYwNZ1QGULoViPWMQIdIIlIHIsFCvMihwrUBrBwFYmTdiJ5DRrMmhm5hnQgAYMcLIZ3UVbwtBYg8MAG2ygGKoDRxruqcD+0rkXVVhvtia7EYt2uKbCtLVx++YvD7gFcuhKpyGfGR8EI02iKrXALz8TP4IXwBdyY3sBBeoDdcJfU9xU9l9fNGm3QYlEvKODo6N+LeoFZMUNjGqzbNZbNEqtm1fte1sQWOQ90MDAb9wPhdsCMLWazeXaOa7xY4I8LzGBA6UdxGzauQdM+Tj2V4MVlgBn+TKACODivZzCrZz1WR+v6ThDnbSs1qS/34Gz2KBhhHIy9c0NqUuhKY1yPMU2m2HJbGAdjTFpy54kQ9VimHHgptdH64uCpbVssFgtsbW1ha2urR/UGNovc8Xjco2HzmoZZEQw0DOdsDuiBTdAIwAewkrXBx8WMCwA9f3pgI27Oa1ZeG3HAI0EHYCN+LksDeP8SGJF14jL7KTPMkgkhAwoZaPFnGMyRNHbejzEGcUAMsrZtsR/sA9nmeScBjKZpen3A+5b6B03bYFEu8O3vfRv/6z//X2kdF7RogxY2tNCJhotIt8eGFjYkV5gmalCMC9zWt7FltrCzs+PLCLIseyyrzXHHarXya32OFdq29SwGZpywDkOWZf56MmjA25QBsKSyD8sluB+kvoKksUvAQSnl2SwcKEvgjv9mgIABA6lXwUAF70P2BR8jj1vJ0pGBMf8tS7FkgpbX9kMwgI+Dj52Ddx4bEnyTY5H3J4+R+0yWLkhggV8bxpRyH5yklZl0qdXA7h4S4JTZf3lf8hwwLI/gz/DYlu+X13uoEyF1Fngekq/7uVVvnEXkvMif4f/zecqYW4KcvC95P8q+lgAWz4mS+SBjx2EiXm5fAnySpSXLhD7/+c/jonZpoIFBAXkCPLjlAQDwKqzj8RhlWWK5XPobiQNbPgEpeCFZDUwL4TqrdC/F/3Lrf3nsuNhb3MQG5qCzk3SkT+DrrdkmUhMQoKzylpGxjvFU/BRixPjcyeeoUww9sJumwWpNWac4jTGajghg6OjOa7v29kRad8JDUYNKd9ZV3SLUmk7MUZ8SECcAtk7pcAdS+dURdKthr1igBlRLtplJkODN0ZuIVIT8Rk5WfCqEakiEMTYxzJ5BndfANmC19RZOIUKYA4O9W3vYHm1DR+TlrKB6A19OQMAGxWIkmW8YXhTIyYaVsXkwJ1GCOKSFg3MO19V17GMfhSuwtEvo+nHLF6018jzHn6d/jj+9/qePdZGGRgyyqQpsAFc56Fb77CNbbEZB5L2nffCpujo+tFirNe7r+2jTjR0jO5r0Shn2Tr83lFNkkagDmMCQS4fLENgAqLuHiunq/EDK2dDwzilsC8vsCrYsZOHCM9sw8/0EwT/rWDg4YlE43bMgNcb0gI/Wtt7yzTNAflztRwliyPXmZbb7I9o3aytwecKPo7GgoVYb0Uhv2asCmgOU8YwBo40/rta1UEZ5fYkWLVpLgQg7+3A5hFNuk613P1qwQ27rSbbLgfa6XV/85gsal4b0BDcVaU6khmzjRmaE/Wjf1z2nisDYSTghEU6VIgoINGgtCYomSQLrLAng2XrDCkODoinIxrRT+C/qgjQ4jEVtO/G7DtSobIUcOf2NztrRXc7W77RmXZeNRoM1uv6rnzz45rHHYECsY3JeUQnGwRhb0Ra2w208kz6DvWQPEzPxGfREJxgFIwQ6QN3WmBdzuNAR06MDIxbVAg8XD1Epoogv6gXVr4vMKlPAzzt2rllPTIJEJ0ALZBHVnwc6IBFZOAL/NKCMooC87axnXY28zsnlwFEZyscpE7ps+zjbZRZN1XaMhx9iirawfvydqJOeOwMU/PzhAcgzmob2QEVmMgInVEo6ASZD0AYYB+RqkJkM2mls5VuYRBPoSmMv2kPiNjTiIcUYgF+cywytZATIUtlhIMDrG7ldGZgP67MB9EpIZGJMZjf5NRm8ySBNrqtkUCkDNbn45wBSZqYB+HWYfE0yRxg08UCACJI5YJVaZ7IfZCJuCIzI4FQGinBAqlMcJAeYlJPHlPazLMNkMkEURdjd3cXBzgGt7dMUP/fZn8N4PPb7KorCM1pkf/sx2p27XIdyNnlnZ8frofFnuRSiLEv/P7nO5N85WJVC9ev12rtCLBYLjMcbUTE+Jt738fEx4jj2jJH1eu1Lc5hJI8X6uF+DIOhpKvD5LJfLHusgSRJfNsQs6TiOMZ/PEUURRqORj7W4/2QfyXEpgTkGYLif+fwZBJFMAgYF+P0ysy4BA773+Pz4fh2CCxLYYvaHTGzyuJb3GvePjCPTNMVoNOrdBzKOkdvgY+efElg5TYuQrzX3DY9tnneYecBsBC4N4r6RbAbeFgOskqUi2SFybpJzDfed1Fvgc+ZrLwEv3o8EXiTQI8si5LWQbC4JuEnNBuccfv3Xfx0XtUsDDev1+jHUSSI73BlRFOF7o++hmlRIggTL5RIW1lNBTGugTyj4T4PUZ/E5k28sZf8Y7cvzHKvVCsk4wX9w/z8g26HOio5r+eNRjNKSA8FHjz4ikb6QgmurrbdadJGDS93GxaGzXrytb1Nwt33KQ3Nn8LdczyQdHayrITauOxeQMn5sY3JYaCMYtwkCNDSSOAEc0DYtmprsmXhSsZYW82EUYnd/F3mVY12ssS7XUEbBaYfQhKh1jbVbo0gKf46N6uz/dNvPpA8ZlxrAc/3zCkDXwTiithlHYE0A8jU3tquVDkeky5A56JgC++X1JXmft8ozP7aCLQQuwCyeoRpVUFr57WmzURuWysAA8G+Sf4O2bhG5CCYziBHjy+svI45iaEPaBpzdrVF7EbilW6IyFTEKdO1tnhpDJR/n6h846hOZ1b9IM4Br6r2gXJeJalRnh+ZIj4J/cpDOziIXMh26DD6zLoZjqFeH7ByUUWTD2nmYt6rLhCrbBywUNkGK6c6F9R64HEUcmwV9/lIaEn4XpOURINjU+EP3jts6i7qpAddl3FTb97LvAJ/e9XhSIOCHAQ663WotFtpdu0zAraA2ddx+k92k/SMIUny5hes0JYaH8yPCg5RTHsyIdLQJyDt7uqGAJJcacFmEUcYzMzh45qxq7UhAkxkFrWq9FbEEPlq3AQF/1KwOBlBqR3PJj6N5lwNmEhiyaYxN7NXlI0TYjrexHWzTgqUT8Y0NWcs1RYOdyY6fhyNN4GaZl5hOp1QPrRRMYLz98snyZGMnrGyPlbGu1lgWVPrWqtYDGXnTKc63a6ybtbc19deluxbcb41rNv12SdeHM/tI6cfHlIoJnDAkvLkb7CI1KVSpcHXnKsZmjCRIoJ2mshansFgvSJNDNVSO4EpiVrQrHC4PYQNLgEZJoAZrdJx3bKNghGk43ZQl6AyxjjGOx4hNDGUVmrJBHMTETFEbEWCnHI2vrvRk1a6odr4TDeTrUtrS6zt8HBDpR9V4XvlhbjULi2W7xLJd+jlfPrOAjWaMv6dFRSUe0g8FRSCFTjE+HCO0oS/74O9JNCERykqR9SRipDrFPvaxF+wRqOSCXsAqyz9kplBqkPHrwIYVMRQHBPolJvInsKFg8+ty0T4MmmSQxUEAJ9wYVODtsOaZ3LYMSrF7YC4AAQAASURBVOU6XQooytICGdTye2UQwecmmRv89xCc4GNg5sNQ/O6zn/0sXnvtNYzHY0ynU+/oxvRxGUhJFwNeJ/I++BpJCrjM1HI8wowYPhdmN8s6ewkSAfAUdg5SlSINEr7+XPYt2S38P4DAZRZklAE7Xy9JgZf9yMc2BMxWq5UXA2SQggEM1mjjUiQOuhmkkSDVkIHD112CG7Kv8jz3QTsH1rI8Qo57vhZyzPE9xUzruq7Jma8bwxyQy2vO25bXQ4IFsuxAiskOATvua1myz8clE+Tyvub/8f6G9zW/Jp1AJLtBOnZwOQ2zU/g+kcc41NmQ58z7G7Is+H28Lwk48jfPGXKMMvtDshf4GnF/DO9VOb9JAEn2kwQgL2qXBhoksiRVQ5lxIC1Pbr9wGwuzIEXnrdIHXpdtxhkqT3BUn8/Ba6xiX7rA/490hAkmtAh2Ea4n1xEiRLEsKKPcAKpWmGZTPPfUc7j91m0Ui4LKAVqH7e1tvPbaa3Tc799Gq1rokDy/i7bA8eIYi3yB/av7uPLUFahAoUGDZbHEnXt3sCyWSCcpkklCdZkBBZUNutribpFXqcoLzTWgoLfRDdkyxt1r08ef6u/hPVLPP0NB3zhDmfQOFIhshMSSYJp2Gq5xSKMu29bRTxUUnHWwbSc61HT0nKALijTQtJTRcRSFUtbdNCh0gbmeU+mCrr07QJM1jwXO/wf+Dwr0bnbfcjx1opJBEMAkG4AmcAEemUc+qL1MaUCgaWFqDLEJYsRU3+0aRE2EUTMiGyhEBF6A9hdqolU76zDOxnj46KEHpipDlplN0ECliuqXm7UHcaDgxSprliNXIABnUPN/mXPQ0D4w5y9jDchtUcG1Dkor7zThg3DVZZc6YKlFiwrV5lqcI4apoDZlOq4PXBgYv/izynqhzYsWv70zEACMzIpxVt1TgztWBwNllwUz+Li1016IFG4zjpu2oWyldn4ssbiod0w5T0Oju2bnuQ+c1xyc93inzfW1Di4TMIcIvShioAMv9OjPVRsf7MlvBpK8L/wlKdhnncdj5RM/phiIxyDbV2p0+iyIoZzyIAbXtkc68vdKGm4sPflZYa1F1VQwgfHiabmlYC9vci+IVqNGYzfCmEzH/1GBG97lwJHl38fGme492dsD0HhhTQwW2Yx1jEhR303iic/274a7lI0DPVOyIEMSJh70iFQE7TTqdY2d7R1/nbh/8jLHo/Uj1GGNeT3HvJlj1awwa2ZYNMRIWDZLUuvvSlVqW3tAicfvY0H/WeYts8v1A48pBjwjQ4BZpCJMwgn2k30qgQw7/ZAORNNKo6nJmQmamDR8vPN6jspVcLnzteyrZnVuWUKkI8/qGAdjZFGGK8GVnq7COBx7vY1Qh0ALAl6C0IPZbPlYtCQEyIJ/y3pJNfedSCBrLRUtMWbYwpHH+r+vUqwf5h5ycJ7FctRS+aIsozl3Hw82vyooEjI0neimGZGVpEo842IcjhG5CDvYIU2Lzm5yGk2RKQI1ZHZRsickjXo2m2E0Gj0WkHLAN8wq8ueHmUUOSvinDOasJaFB1giQQaQEBGS2VAYnklYuAwnfX4JiLrUSZEZ4+M3nIUtYjDEoyxKvvfYaXnzxRQAb6nWSJFiv12iapufgwElLCQwNA2IJrHAWmoPqoih65RacYWYQQgaQ3GQALYOyIW182FcSLBieg9SQ4OBUZshlk7oaHMBK9rh0MuBzZX0BFrRkYIHBI9bWk8c5Ho89mDIEXHicZVnWYylLUEkev8yUy3tCCl5ysMvCnzKwltdRsojk9iTwNQyMOejmUgP+XiwWmM1mvnyfxzEDMMaQuCj3H2+fwSAGPZgdwOfKAAkzYyTLiPUyGHhhcI/HX5JQ2eawjIPPW54vj1E51/BPZqpEUXRq0C/HLIOJfIwAfJ/xnCCvBe9HCvZKMIeBWXmdLtOUk7PgOe0v/+W/7C8475RPnDuSB9BoNMJzz1G6/P79+3RzaIWyLT167wKHrYMt6Eh7FN8aiwqiBtaWmK1nWNfrTVbGUF0X1+i3mpwK+HM11QBc7uRtB2DomNDu2vVADGM7i7tGIw3JtoxLD2Idw1YWqIBxPPbgSKxjz2io1zX53QfRpq60u7hsn7lYLGCMwXK5xPHsGFEW+eyeiQ2euvUU7h3ew6PjR1gUC5jIwBmHdJpitDUi7+l8CWccGtVgqZdYBVTuwcwIHWgfBLegTFfr2g1IgCcDgjaDR/lFvrcE7AI/A4MsyeBah6qo4Kzr1Z7GcYwwCEmwsa59ttrT9DUF1lxS4LTzgBWXFjzRwuWy5QVikxz0cMCMGhvWSsf4SDTVhscqhmoUXO0ouwXl3QtKV8JGFqUqSWQJJWq9oVKfex4D0IJeuhi4CB1lA3WjPWPIOLo+gQowGU9gG4vVkhS9mRLLAI+JjGdCVK5C6UpSxFD1pS052Ur1tAzWRUEv9zP/5ODIwMDVDm3VwjYWRhuEQYim7cSJNKADDWUUsTl056hirD/+JwEzlFVIIrJJU6qvkcGZdhZt+7iAxI+7sXUli8+xIKO0nvTAkKMSC6U71Ns1fi7mwIb97JmZwEHiv2/9iB9l6+VbleoxELisItCBz7KzrksAUr3n91nbzV+GMthlS770nsHRidM27QYM+v/nfjutcUmPUabXZ95VpPvJc2eiSSsn0QnGyRhGGXpWWWJkrYoVlKH1Q4PGa0HMqzkKFB44Km15Zl/yMfnMkdqIU/6w/f/Z9LO4Ed5AoilgNc54FpDSXZCi4S1wG002rWxxWrhi427S/X5ROUisY2RaCD+ajWNFalJkilgyrLPA2gpZQL+nmqxYG9sgb3I0uvE2jUWzYVrwnJk3uWe6MFi3btc0JzgCOSpUZNfKrCVXf+y+DRH2dGvktZqaKQId+PIVnnv552W1bKSbDrABwC8qi9HQSIPUgxXjcOz7ma+FdBBhtgVbm06jKTJDbBhvnylKiKUgOFPjh4GurDHnLPuQ5iyDNP4c0A+MGYjg9bxkCgAb7QcZxMnGZSb8/qqq8PDhQ/zbf/tvvStbURR4++238bf/9t/Gq6++6s9LBnNcAsP7lNlc+TcHUKz1IR0U+D3cL8YYjEYjH3RzgMQABoMTHETWde2tCmX2WQbZ3I9DpovsK+ecDyplkMwBomSHyGPn45QgFAvic3zF2XgWYR3uW2a2GYhi5gsHyVyiIt8nnV+4yeBWAlZDsEkCIvy6TDzLQJa1HngbHPzL7Q1BCwngycAYgLdQlcfPoAYH8JJVwOec5zmyLOsJJ57GNuJzkawLpZQv+RnqFZ6cnHhAiYUVWSOEj0NqtfA5r1YrPxfwNwMzzPbh19lpZTwe9+5l3pYcsxJgkPcbg458DfjeSJLEl1cxC4XBGB6fi8WiB0D82q/9Gi5ql2Y0fPrTn8bdu3d7dKAkSfDiiy/i9u3bXiySEbUhbYi1FAIdIEOGJEpwNbnqD55vTolStm2Le0f3UJalRy15QPONGsexF4958OABoIDa1rh3eA+V22glHNw4wNb+Fu4d3sPh/BCreoUgDhCNI2RbGdb1GotiQTRSTZnVWtNDdhksUURFTzTOwpK+wnmNrRK7sgTjDAWAIkuJCVlmIQL9rmMKCDtwo1EN8jBHndWITITUpDDOYEftYBe7aJoGRVEgC2mB86fJn+LfxP/mEhc+QAQSRAxaEq+MFS2ctdVoy5YYEibyD2Sm1UdBBGfpRgijEEVekJaF6XQHtIWONMbZGHnb1bZ2AR4DQ0uzJHaHbtDEj7MhLtMMjO9XA0NlLN2CX0MDFsQEUMovJoBN2QAzALiEgBkcnk3RZbsr5gNf5OBgAMSD1wbrHZ+x7fo0ReqzjhzwGWz0RVDTwqZpKasmM//soFErAVp0ATS/1rvDJWhRd6h+hkuzLUIXIrMZIhdROUfHJEBD7IJxNkaapL1zdnCedcHZdbZgzR15ifPYkMfPpU2ntq50SnTqmU25TblOYANkbQbTEEA0jsYYhSMYa9DmLVSjCCxTGm3TKZNrg7/+a38dVVttKOX1Gut63ft9Xa+xLJe91y67yH6M/fIjbt660pVY2dXFH7hEk7oGmc4IwGAHh86eMtQkNMgPOi6HqF3tgy4OwBvX+Cwr0/P5++P0iQS42P2AX+9+8SU7APrBjHMbAOD/RAyASwmkgwMHy37B7wj0ik1Mmd82/1gOHD/KJkt6vGbAj7lxqVZsYs/wCzUxAGTWhQFCp+gY2YKSS0PO2rYsFeJrwOPn7eJtvF2+7fUcLtJkCVRAmXSd+Ix5alLsGhLbDC2JFceGWDxaa4RBZ21nN5oqFlTuwzoV83qOe/behrXTgRjn9RmXJHBm3/9U9No4HJOuAiKM1Ag7dgfTaIrpaIrYxUhUgsDRZCzV2DlYjpII63q9cbLqAIvSljRWO42SGrVnWxSuIDasK/tuLag8yFk19Hs1qNX5lZ1fwd/c+ZsAgKqtUNjOPQEb61pv7ahIZ8sznDq3hcIW3mWBrSM9axE0nzJ75WFX3+HvTcHauwjwUFBIdOLHwch0LBedebBiEhFAEdrQMzDG0RjTeIqRGSFylEGX5Q8yyzkMkAH0MsMcWHEgBPRLojmpyNtlYIO3IwEOzt4//fTTuHnzpp+j3nvvPdy+fRtFUfTqxGWmdcga4IBJHvMwayvfM8zKyjIEDv7l+4ANvV8Gi0PNjNOAGxm4ymCYfzJD5KzyChlM8utDEEj+T5alnJUFH15nyfTg/8dx7AP10/RH+Dw5gJR6EnI/soRCgimSrSODZQkG8bb4nNhZQfb3ENCSxymv8TD7z/vn90qwYPiTS3OkbsJp+5PXR85vrKXAP/las4kBf4bHlrRflW4mPI5Y0046zKxWK4zHY6Rp2hsTDDzKbcg+keN2eA8z+0WORQZF2rb1LCx5nVlzQo5DngeGbkNntUsDDePxGIeHhz2aCaMqRVEgz/MejYQfNvJiyZtc0k4ksCBvWO4EWavFbUjx4AubpinadYuRHiFzGZwjYcln9DPYxS6mmOLO/A7W6zWyLMNBeIBr+hrWdo0HJw963sjWkuPFveAePrr6kQcgOMDzD8nudx8cDQMfBc8eKAccUBV0S2KtfJCqoHyQ6xoHN3Ib0OKxC9N9d43LJ0KEFDRZymAzDdQ/DLv5jxdcUECpSuQqJ7ZIQplrySJ4LAvMQEsMYLtjOFjKnseIUbrS13aHbccVscQK2Uq3MIpGULVCm7eIdezZEHAg+mpAyJpTpAWhArXRogAFJH6R0pYomsK7UlhtaZFhS1+f7D/Hwot68zd13kV3gWgOGwaH2vRpL3CRtGuRnfE06uGCdLgm0XgcuDjneOgj3TW2VGbhmr7Yo3aaNCzi2i+44XBusG4Vlf5UqLDC6tRj9Qus5nLlABF/uQjjdkwAnN2UQ3HfKqcAC6p7NDSpr9YrLFdL6FATWyGwyB25fVSaNDpaQwEt35OFKjazXSQOhrGMc0DDb/+rb1OwYcj2MDYkepeFGbKQslRPT5/GNJ5iO9nGdrKN3XQX2/E2JvEE42iMcTT2weC6XuP3//j3UbabxXbe5J4GzTXca7vxPPeU5y6Y+bhCgD+qJnUNAPzYSim4GRjEKvalFBx8c5NAAS/wpeUkZyl9xvNyRD7fhgAhA1JGE6hpYDwTgoNQvqe8pkEHtHkmzPCYBjeVdzigEzy3reyqV3YSIOi7nYjsrXwODAELz3TrgB9mYFwG7LlsOZA8TgBelFa2YRabt33W9h1Ir6du68fGogRqJCsJuByrwcF5MOKcN53ZIhWR1oaKPVjB40eBwONFu8BJc+KtLFmvoXDFhaBRrGMPVqQmxW5EGhbsDMFlIgobzQilicXmLaBtF9i3JXKb4357n0DTco1Vs9pY5p7SjDI9dkVqUh8sT4oJYsSbspB0jBgxttwWrulrHsgYBSPEwYaqzQtjFuZjCrMMevnatKr182OkImSg8obUppjYiac18z3PQTmv8ySbgLfLWVb+rhsCdBpDazguS2FB2lVNwAOXZ7Hmx6pZYWVpLi8sze/clw7OA0LHOO71KbM/+X0XARaRishxR8denHZsxhhpAifi7ivTGSWYVIJpRHojoQ3p+kSjHmNgKMDHc4VkWPC14YBNzqucOf7BD34AYwxOTk56QeVpAAEHf8P5WYImHEANgQgOjBj0kPuQYIGMM4ANo0GOhdMEAU8DboZxDR8b/85jVQbfQF/TQh6X1LuT75fnJ//P5yKTurI/JTtAlnXwNiXI0wNkxb0wPL/heJCsE/6MH5uqX9/P5yiz8LxfGfsNz0OyL2Sf8t/cb/Ka8Wf4Nf7Jx8mAkNRkOO1aS6bDUDvBOefH7PC8h+CG7FveBgMUw9IP/j/H23wth+c0ZNXIayTHjQRdZFnO0GVDWu3ymBmCOAyaXLZsAnhCjQYWB2G02nvxdoIb/L4kSTzFg18bggRSDER2xrD2TXY674MfHEPqkTEGaZp6H1DuPGAzOWpNYh1ZliFNU1/bxFQb+UBjhsa15ho+ufykR50YlWK1UKa7AUCcxFi5Ff4k+BN813zXu02cmrEXwadf+7g+jdegm6TEw4bry09rHFh5pF+ht6iUmT6/xDLdAp2PkbUGzhsPnClWAdUbt/0FHTRQohO2ChvYUIj7KbcJ/qO2H/id0gIX+MA0dt0jU5GDRgBiYwQIyLLSUXYgVjFsY9GsGxITC0dka4kIqqaAO4mJQZMXOQ6uHuDd998lQcLAeYXxOItx9amreDR7hLv37gIBfABb2QphGiLKIjjjkNcdfdc4X47CQSFC+AU8XyMJejxxO2Pd4csS+PqdB54osZ0LwIaLD+ficg5ubEu6wopYIoNylXO3qdED15TrxEctOY9ENkLSJEhtSsJgPG4QQ9UKTdWgWBcYZ2Ps7ezR/eyIDlbbGpWpUKDAyhJFuFQlXvr0S1hVK6IN10QtXpQLHOaHlF20T5Z154CVhe9kSUOsaWynJiXhOZHl2gq3MA2mmJopdtNdjMOx12poXevF5SpXYW3XWLZLLJoFZtWM/u5sBlk8lbPfsk7eu078n5gRP621aLF25JCgWuX1ZqS4qILqjWkZxH+ce8yDdgwcAFTe5TpHGuu8Y8HHZaLwPmIVkwgi2yfrwOvIBCroM7WE5oeF9eKvrM3hM+sMSjmyCfbAxiWp5U9yDvInH9sQHOD9esD1CUqNTjteZjH4siAV9SykuVzDM/LQF9yy4kuC1pXtSsWEaOlpoN5OsIP/8Mp/iNl6hrItMTmYYN2s8ez4Wbyy9QoWzQLzeo5FtcDarTGv5piVM7L/rLqf9Yw0K9rTmQdGGUyDKc0BAZVDxDom0FOT7pLRXebUwYNqlauwalZ4VD3yop7886KgNTUpsoDcVa6EV5Bm9DePxUhHG8DGiTKy7qtqqYTisDnE3eouBdudm8hZrBEAG5tVUXqQaTrnBAm5VQQZxmZMOhadhsIkmmAcjDHW1EdegFMs1vlvXljzek0GhhyUSVpwjyLfKBi9qfvnzwyziUqpnggdBwRhGJIDW9tgXa+hEoXWtFjWSwIr2EWlIZaFFCnNXY5lu5m/c5t7poeD84y106btyzLmNLR/BjFwNTIj/wyamAmm0RTjZoxEJ5hEE8RhDB1oYlh01yPsqJ9VVeFP/uRPcHh4iFu3bmFrawt5nvcy/cPyAskW4P7l37lPJXX/LCV9CQgMxQv9+YrYYRiYDhkJMrCXscmw9IV/z/O85wjCWWCZheft8k85FmU7LSiUGfth8CkTtfL4OYg8DSzh3+Xnue/kuB4G27IcQbILJBgk+4/7gV+TJSR8TYbgCu+HG78uQRp5rvw6634MAQvZhoKqvH3uK3lt5Gt8nYfjZAhgyXOXn+H+kmNTbof/PwQf5P+HoAC/Ls+T+3fIfpHHy59lkU3WV+ExJm2B+X3yGl6mXRpocM55T1v+mxEyKYTDIAIH/fKk+L1cN8U1XHwSfBEY9aqqCkVReHSVgQmJmknRjdzkuBvexTpZA4YeXMYZqEZhnZDo17JZIoyJPsLbGg4ISTmStSwStWW2Be+fB0YURnhfvY8/Cf5k0IHwwbAB6TkYZ+BahyRMUBc1YDswxdH744SoTnmVo6xKYjl0logqVBsLTdRnuxiox+2y1Blf2uleRvC8wNEp5/UdoLEBMToPeAND2gQgXQ2/vFQbQb5LtW7xVIMWe6UrfQlHbwGrHWxs/cIbAJB236c0LuUJXQiTGUR1BLtjoRsN0xqoussSFAm27m6hWTfIlzkBHCqCzS1SpNgd7+Lp8GlkLkO1rlCXdQ9A4zHN/t+MfkplYgcHGFA2qc4J+DEOi2KBZb5EgwararXRqcAGpOBSlMY1QAi4wKEEObCsKqr7tcZ68MRp2jbFK+7C63xm+yFAifO29aQBkFOOykS0EGDjTZzGBhkeN793Qj98eVMbwrQkMLsVb+Gp6VNUaxuRR/wkpt/5ZxZmlN1Wmuqe6xzzco6j/AiH+SGO82OcFCc4KU+wKBf46OgjTyHmuua8yXtlA0/avKtBFxCwrkViEp/tzMIMB+aAzqMLYFKd+hp5/kxqaMHYNA1y5Jg1M5zUJ5jVMyzaBZYNqckv6yXRj9vN4tczvTrw4kcZ1AI0Rs4MWJ5gVwzkMkOBRWU9TR6BF+BUbuMgopTq1YNzyQc7B7AO0WXADRno9o79h2SIDB0cYhVvrq1KffaZa8XZLYTHL5eP1a0obwGdY97kxLTpLBClk4gsfWFhzR9X8ywGd4rjyhM2vv7cF+Owq7nX5DQxCSYbrQOTQFmFLMhwa3ILIzNCu27x6ideRRZlvUBCBlDDBSOvcYIgQNmUmJUzD0DMqzlW7YoENau5d8yYV3P/2qLeABln9XNqSMhwK97CM9EzXh8gDVMqdTLkXKKh4VrnXZ2atkHZlMSsqteeZbWqOpeOhgLjvD3fpcUoEhQdBSPsJXvIAhpvQwCNm0UXQCi6r3Kb47g+Rt7mVJJmCSw5b1ylJt1oU4RjTKIJaVgEmddTSFSyATMiOr7tZBuTjObzWMWPZTo50OVrKOnQHFTxtee/5cKf1ejrusaBOfD2jLKFYdgDQTgBxoEiU9F5Xdo0Dcq2xKyYbcplbIFlvfSlIqtm5RkXy3qJk/IEa9f1Zaez4XV4OtbF7LIqq2dc81SnCNsQzbRBup3iYHqAd8w7mH5E4PkkmhCQ0fX7qBnhhdEL2El2egENsKktT5LE61HwukoGwMPS7WFQxq8xWCEDMN6PDBJl8Ca/JWggy1QYhOBATdoM8hwwBDfkGJNAg4yZuIae38+xB48NGUNxmbk8dqb3c+At2RTDJDBrGkjBRtbtYEHNs4J7uQ0pTDnMxHM/SU2KIQDC2+T+lteFz0XGolwGI9fcfM8M9y8Da16Py77lYxyyAqTThRxvkrECwPeX/F06xsixJAGdIQDEP3m883HJ/h8yUc4aV7wvydSRfcHxrbSk5fhYVg3IZ9hpoNhZ7dJikL/1W7+FO3fuwDkSNbHWYvL0BNHTEd747hvQLVlT2sAiezrD3mgPqlBo1y2MNYh1J5ZXOloMB2EPmeMbUN70VVXh0aNHcM5hNBp5YIMnZ2ZZRFGEqqrw54s/xz/b+WeXPHGi+UeKaou11dCtpsCzE3Y01sC0VDufhSKboKj2mIUjdathC4tQhZimUzjl8LB86EUabUAClrWqUaGiAN1QBmVdr6FTjWW5RIXOmpFF6y6yZAR6AIa0poxMB7JYonwbZYgt4boyDUHzr23fXYLpsjVqf9w1utrTywptdgtzuP6k2WNOAE8EODCA4TPCsibSdRkyBV9fzSUhl25WZOJcR9dld4JLBuTaae+gETrKsocu9CyLqCXaYhZmPnOdGvofj8egDZCaFK50qJYVtNMoi7L3UAM2kwNAE9poNPL1XG+++Sbu37+P1WpF9+pkgtlsBqUU0jTFyy+/jOVyiXfeeQdOETU4SAJk0wy1q7F/bR+7V3dhE4t5PceD+QPUpkalKxK1VAVKRXWztSagy1vOYuN08MNke89sl9ncjwoIGTA9JBvooqYVCQZyeQV/T+MpUALTaIpJ2GWJQsrSZQFRkFNNgYBWGi1arGoKMk6qEwr4K8qEzsqZrxde15QJK9sNeMFB8A/TFDY16rEhEb8syLwg2jgYe7GzNEgxjsd+XCc6QWCobIvryxtL4GTZlFg3a8zKGZbtErOKAi12JuBMX9mW5yr5y+ty2Wvz42gKamNfqUNvx5iYxGdqM0PlNjwn+3m5KyVwIJYEg03ret3LdnqngU6U0wf3DA78mM+dj1OeZ6SpNCAJEspAqwTTkILbBORUkYTJRiw5oPHATL3WEfV92Sx7tpp5S4AGA3JcTiBBjdrWF4r3/ftsfK94wcsO5OPSgUk4wSSYYGIm2I62MdEEXLI2QqISjMIRiU6bsJcIkZRyuYhvbYtlRffM2hFzaVEvcFKe+N9X7QqLZoF1u/YsikW9wKJenMummAQTjMNxj101DsbYirbovo8m9LxTtOZwzvlx3KqWhDurpQcn8jbHolr0XDrylkoNmGF1XgtVSHOPSTe6MF1pSKADbyvqHOlZ8HqAWRaVrTxocZ6VrYbGKNwIPXrgKRxhK9ry2X4pBMmgxiSakOZCMKLneXfdwjDEer1GVVVedI0X8BzkMPNWKeUF9fja89+cyJOZ/KIoegEU0F8nc8AolfBldtMr7bsGi2qBeTHHsl5ugItm6eciZuYsmyUBz9XCX1+lFP6jF/4jrJs1vvadr+Hu4V00ugFiwGQGSEDW4135iZzX/8un/0v8RPoTPnAbAgYcKEnqtyzTZiBIlk1ITQXJhJYsaO4bKTTIn+fjkIAQ9/vw2g2DThnUyWy/TFYmSfKYtsEQiCiKwpd0S2FOuV8+Jg6GTwM3ud+kdgWDLtKdQLIceK7h7XAWfegIkud5D0Th4JT7lPv5NGFRBpGGjAMJvJwF4knQaBh413WNxWKB3d3dHmtJggl8L0r3DcmkkACS/PzR0RGyLMPe3l4P+OKY9eDgAFEUYb1eUwK6A2nkPSr3x4l03ie7drCFqQSzZB9y33CTsZYEZU4DumRFAI+Bqqqws7PTG498LDyvtG2L9Xrt56Jf+IVfOH0SFe3SjAYASNO0h6w93H6If1H+C+ClU97sQBnF07KKYmHF9a38xdRHVvZ2Iwdda0QqohpHG5I4kSMa/Z7Zw8iOAAds2S38F/gv8P7t97FarxAmIdXoG4sXXn4B82KO48UxHhw/gIoUVKSQTBKq8W5JmI7r9mtVY23WaALKGrem9U4XQwEiBOjpJACgmu9kkyH1FP+uDICtHJVTmGCCqCaBvUhHZOlZK1zfv47QhDg5OcFivkAQdnWWRiFOY1qsVxTsIcBGLAklqe6jQqUrFKrwqtL85bRYkA7p9R1AFjrK+EcuwsiNNseuAjRtg/vmPgIVeB0A4wwF6x24EYcxmqpBU3U2g47YDMWogIkMBaWuH5SeGSB2wf55WcJTaTxPWhLAtGSrvP6C1mSB2bQboMWXr2h4+0Sgr2cA4PG+NfCMFf992inxa2N6jxkbL3rJLhJ8PSpUaNFSEIMMqUpR79ewicXUTDHRE6QuRZzHMC3VugcRPTyff/55L+rCojDz+RxTM8U+9jEOxiiaAg+rh71JiZV9eeGilMLNmzexvb3dmxj9ROpaVKryC4wSlIV57/57KEyBwhTITe7BjFrX3j5Vlh8xmPSx2zAOu2hbg/8/SSBnnfXiZ4f54aU/d1ZjxoIUXOTAPzUp9uK9ntL5VryFSUCBTBZmiFREAGun2M6LxpPqBMfFMRbNwlsQcvaLbSDLtvRZzLPaaWVZlzmn2BDglgSJP/5r2TUfoKdhSiK5XemA1pR15WCitZQ9L+qNiNy6WnuwwoMWF9DGfZkELle7P2zMtGgcZWLnzfzSnz2rceDK2fbEJD4LOI46CjkHRGZM2cFwRGMkoLkarrNBtRZFQwEeZ805uOdvBne8FWJLbAXprMJaDhWqH6k2BwM1/M1gTWQIyNiKtzYAV0BBHwfw/LovPwpIVNkoA6edt3/Mmxx5TZlfBjWWNQVRhaX3sGhh0RYEctkNsHFRk3oOSyx/qP7Q0J5lw2AOgxaTcIJpOMV2tI3teBtb4RZ2oh3sxrvYyXbwVPAUYkfMjCRM/GI/DMMerV9rjaIucFKe4Gh9RAK9do1ZNSP2RDXH2nbBZTXDvJ7jo+IjP08s6rPZFJnJCFgJqbSBwYq9aA9b8RbpB6jUA5XTcIpROPLP7FqRLsKiWmCWz5C3+SZTbzdgAYNwbFfLwMW6XV/IDGP3k1jH3taUWU5a0/PfOot1tcbczgkoBbE9mNVzHggaqKBnZ5rqtDdHJ0i84GNmMuykOxhH9L7YxYgd6VtkSeYDHmZMMoAhqc1aa8/WlZllae84LHUeBuUZMgQ6wF6y55N7MuiXzAEOXGVwG0URVqsV1v9ijeSNxLOceRvXrl3DT/zET+Dg4ABhEsJFDvNyjrEakxi5WGfI4IzBBD4Pmf2vKlpvyVIMzmZz4+OTtHJex/DfQ0YCQGXXSZJ4McsgCJAkif+czFTztRnS0yVbmxX8uW/ZTUFaV0rqOge1fG5DIIbPWwKRQ60JCUzx+Q0ZFBJ04fdLhwjJ5GZB0CEQI8fVsOxCZvLle4elBLIkgMvzWdtDXrthgM3HLv/m8cnAiAQi+HrxvDjczlC/QLJU5vM58jzHZDLxIpEAsFqt8ODBA6Rp6p0FJbjD/S/7gffn2ZJCr5DPR4IyfA0ki0r2rexveS3kPS77XV4bvseGxyjHM58TH8tl2qUZDX/v7/09b2vBnf7Sp15CEzT409f/FCqiejMXOUyuT9CYBrOCsm0lyn5mXDcwifE17NL3ndWUfyTZGdY7cLRYU64TQ2qIBRAgoMWJiqAaBd1QrSyr6gct1XxPwgm2oi0vdKRasjwESEl9Xa0xW82gIoUwC8mpoqAFHAMXNiBhxdrVcIHz9OKiLWASQ7RpFprsHAQuaznJNpNsuxi4Tn3bEbjBD1K2oowUlW+4luo6m5pu7MAERJetO1Etgw2FtqOoNrpBa1qs3ApH7shnsD1w8aRBIINO2Nhiaqe9+wIzFwJDIAvXozpsgAtWOGf9A/9aN5b8OPoYx0Yf69d8+hpQJbb7BMwMFgLzQMZArVrWMLNtoHfC+LilDmccS694xnZWnnYD7GWaFK91qZGpDLEljYyojRA1EYIqgK40gjbAczefw+50t4ca9/ptgMKu12vcvn2793CUGQn53r29PWQZibu2aMmvviuZ4IUnZ0pWWGGNNQpVeOXwEqW36ORa7AutBS8JUskAdfNR58fp/5WbL5tSGxeJQAsbTJN4BkOmMyRBgkhHXluCg3+llNcEqF2Nsim9uvu6WRMFu6sTz5v83Ay0gvLitcNaf29jd4kMtoYmAMNsbBTZzlMGtLxPp1yv3ry2tbdk5kAmb/NzA04pUinnqMtcB/7Mj7tx//rrbCJfOhHr2AfuDGCMzIjo91EHXHUBLtPLq5Y0QRbNAkf5EY6LY68RsqyXWFSLHkOFA/jKVp6R8HHdRZ70vHslJWZTJsOgXWziDcDVjZ1xtMlaZ0Hmx38URNQ38RQucLj74K5/rq/rtX++z6u5D4g5g88Aniw/+XHqo/jz1hv2AZdIMYtqEk6wE+9gL9nDQXKAvXAP+9E+9kf7mMZTmLYTsFTKAxZN2xBg49aeIbFouuvdfTOo5ctCagK45tUcpS1PPV6jjAcfJhExQBhMY1bINCbWTKYybMXEsNiOtzEOx1QG6hwBRZocL9iac92sfakBA5A8N3GJQW47O88uU88g60X6FomhOcbPkTrwAqxKKWIrYQOO8n1QNMW54r69cp6A7stJOOnZaGY6oz5JtrxjSAICJqfRFGmQwjZ97QJJlefvsix9FlOK1LHtHQcYURT5YBLYUOKVUpjP5/j7f//v4/Dw0DMvOchhMOTKlSt49dVX8fzzz/cCZmtJhN2PhYFgnsw2A7Q+KMvyMc03CTRwMMnlBhzYc2DLwRtvW2bAOZDk4FP2haSn8z6YjS1ZAgzuSKaCzBpLAKGqKn/+fPxhGKIoCh+Esw7eer322+M+5nJzBh/4WGWgzQAEAzdKkeW8jO8k45w/x/0kyx6GVo3ydz5HBg44sJfsBL6G/F4+HgY0JOOCj0cyAnhfQ+FEOUZk9l6uTbkvJEOC+8WzfERALVkvwz4alhScxjqQLAwO7GU/8XvlecixIu9XeR58LKeViPAxSZcVaVUqrxVrqPC9JgEIvibOOT/GAOBXfuVXcFG7NKOBNyoH6zgZY71eY9pOEdQBdEuB+i19C1ppouqt173PMXL27LPP9miBQ5SrbVvMF3PMljPM87m3nWSqdqUr1EENkxmqSa8XyNscLnRYlAvK8nZBvjMOJjDeh7s1lF21ypLarwKJ0l1kXwhsMjjieaNCBbW9WcQYGOhIQ4easirO+FKLwAZIuq+0SbFVbmEv2MP6aE3e1y6BaQ2qtsK/vvmvYSxpTKDBphRChxglI1ocC/tGT/OBQ6hDNJZq8gtVYO3WHsCo0NXUagJ+XDB4eJ7hdMA2gczMSOsU2mrY0vqyE9c4KKuQxAnSJEXbtJiv5pT1F/oSo+kIVltf3+utJjuRxFrXpwNOZ63BZLAvwAtmARgYKoXpgAxJWy6LEq1t6doZ43UkHEi00pmOyty5WbSq/fgBv9rYa/Ln+Vj5957+hBJCoE/IzPCuDdx9XR/JTK0HLwDyeicUDmusN9vSOFPr4rGWb87J18AzWwlCqK0rTWr3WoQ2RIyYgD0XEfuiy+YkNkHiEkwwoXtDG4zSEaaY9urQ5APutCbr6njizHPKbha2wNHqCMtm6cGLEiX9HtT48i99GYf5IWksFCdYVAssygWWFWVC8yZH2ZQ/skCpl1mH8zolFzUOVocifOc1GcA3rnPF+TE4SPggT2tvfWn0Rk8i0IHXRGB3BD9GO3p9YzcLc9YKOC/oZwZUZSvYxqLQBXTTF3f0Npqd7WZlq0vpY2hon22PdOQDOOluIMFCDqplgD08VtlYvBDApYJPCVYCm/Fy2hhwcDT/tzXV2J9flXLpNmQjyBICZt5cTa9SZteknnY+jaY+kAxViDggByKniInAASAL4y1rAi3m9dwHt8tmSYG73bAQhufO5XSta70t5Gl98yQtQIAJJl57YBJPMApHVB4DKpnLZzmyIMPTu09jmkw9eMFCtfWqxje/9k18+pOfxqsvvorQhB7UX5XkZMBMnVW1opKIdum1GlbNygf33Ees/SIFQa2zBJyhOJ1F9wRNgTSOxmaMWMde/4AZJtNgikxlGOsxXtl6BXs7e5homsNDGyKLycP+zkd3cPvBbeze2CUR3naFk2KjIbBsl77c40HxAO+272JlV16z4qzrlerUl3lMQxIqHJmudMUQg2AaTrFjdvBM/AxGGQXqk3BCDLE09dlFrbUPvJflEg/nDxGOQg9aeCCj00IoXLFxpeiEHdfN2ltp8ng+bfzJxmwvBi0MaF3Y2AazeraZty1d46qtzgRufL90+hW+5COg0rdxNPZlfIkme9NJNMGO2/Eg88RMME2mmOopAPjSj2HAA2zW++wuxwEK/1RKYbFY4L333sMXvvAFZFnmA0wuERlmx3n7MpMr2QsS8JClDUA/ow9shD850JMMAFmaIINi+bsMeLkNgz9J65flEfw5qTvHgb8MABkQ4eCfPxdFkQd5xuOxP25ZRiLBiyiK/Pjl+IvBA0mL56Ba6lbIvpTHxK4NVVV5QX0A3slBgpF83XiNxvuSbAoZzPL1YB0/zsZz38k+5b7ifQz7jwUOh6UXzBSQVq0STOD+k0CBTIhJtoM8HykgKcuThmCGLP+QgBeXWcixxf3J22dQRW5vWO7C5yMZNnzsvB85HvmeTNPUjye+h/lYJFDDoNSpLPJT2qWBBqm4yRc4SRKcnJx4JE8il9zkzSkRsKG3KneO7Cg4QLUKYRP6xZykjLnGYS/cI1CinJPQZDDCw9lDr57ZNA1GoxGev/I87ty5g8VigcViQYhmHOCp60+hdS0OTw6xrJbEzFAtalVjZVcoVEHqwGGL2tQ+M1qh8tlRFkBzhhYyVtmNUB/OCRQDbAK4K2d0vOm+B84MflFpxOJouP1THAc44AsRYoIJBXyN8VoVLFDJ9oiMxAOAs3RTKK1QNRVOFide06FVLUpdwqYk/pjHOQ7NITE0pjWs6S+UD9FRyU9xtwhs4I/RO06oDa1RZv+8VVwXKDhFZQytorrMxjXe2rJWdS9wZ12KM61DZRPZf2ZfaEugEmd2uc8MjA/wrevq+IzeaBd0rhucVeefjzXV3/9ja6rz7nFFIIUEXSR7AYAPhpgx8UQsosHbPDjCpSViu/UwkpFDIb3gPGSrab+q2AiOami/COOSqxCkocLjJgb5vSdIELYhbG7h1o6CzoqyUKolO7SxG0M3GrAgFo1S+G9+4b+5uDucIzeKaoF5Ocei7H4O/j7Kj3CUH+H773/fBwa+Hr0T46pt/USK/P4YRGAu25PoFwytAPnczrJgPK2pMy4oZ/JqnB7k/Tha05J2gdZdIN65UvgZwLlLMSSYWs1MDy8Yyfe+om1bu2Fc1JaC+cpW3sr0MvT7YQnBEEBg0EWyAE47ft4OgEsxK3quQcClrrm3+2ShzG4eYIvGhVtsWFnu47EUPYCvjRe39OUEAbFVroRXSB+kKyUZBSOkAQmaJkHiP+NAApccnHFJ0LJeesV/LrdZVAsfLJ6lY9CgwTGOcdKc0HFWmz7kRWDdUumJm50DFu8BeETfGppKQxWxEtlhKULkGYrM0El0gpEeYVftkuBnGPt+0K2mea9jJSVB4hfgAI2ZUpVYtAscFUc4Lo9RmQozO8OsmZGWQ7spVWBNEO9gYi0WdoGZmsHVDjY/Y3x9cM51BVkxJ/PEM2rY9YBFA8dmjFvJLXIKSvaoPCTcwciNoLTyAMWyWSJ3uWdMsEXwsqXr+WHxIZbLDdPiXDZFMPFlH5NggkxnMLWBXVsgBw4mB9jNdrGb7uJgcoCbo5vIdAZVKtz78B6UUrh165anVtd1jaIoUNc1jo6O8LWvfQ0/+3M/i+nBlMpDygVmxYx0C1SNVU3lTewmsapXmJdzGouuIO2STuiRhRwv0rcwigKKvMlRtRWO1fEGlO7mB74vzpunFJRnwGSGtGdSnWIcEfspRgxVKVy5eQXjeIzpdIrVaoU333wTTdMgjmN84hOfwMsvv4z9/X2vS8H3C2fIh3EEN6kxcRr9fph0GAZJMoPPugYSgBhmhTl7z0GsBCPk54ZZfRlUysz6UK9gKHrJgbAMUCX7hPUM1uu1z7azsCizKWT2e1iKwnMAbxPoZ+rlcfH/ZdmGjAN5W2maemHbuq6RpmkPCOLjybKs59rC/cf6ANx/DJAwGCT7WZa28HFJAEsGv9Za737I58jBthxXDGbIEhS+TtJhgf+W+hPcF5LRIxkI/H/eJve7BBAk44T7XwJqklUhmQl8jEVR9AAeyW4YMjAk+4aPjUUxh+NYsnAYnGJRUW5f+tKXzpgpNu3SQAN3rERTeFAxDckYgyqrcN/ch240KlVBB0TD1o4eKrydYe0UQFYjDEJIURAAPXSRO07SVRi1Wq/XWK/X3nazaRpvecn/axqiKP4Pz/4PUCALPDM1vtTACyta0lcYhSP/sJ+qqf9frGKELoRqFZq8wTSZYhSNENgA5aKEqxwp0fON7ai+L8xC1KixKBZ4//77WNs1SlVCpcqDGY1pEI5CVCCrrcpVxOpQLZxx0KHelAd0bAA/uZ4TuPFnCiUWTnIUODwe/J+2PYMeOKKcgra0aDCO+iZuY4zaEUxD/RipCK50CFWIve09GG1QFTQpGi1sY9DSZAJLbAezsahrsLEh468KFSpVeSChd4xnNOXougc2ID0G29VjthaBDjzt3Tnn9Rg8aKSImWFhiZWhqg374SzQ55zGmX/WK+EFmHbagxeudR744Yy3g/O6Ba1uAQPvBOKDe7XJ5A2bBB94nxIgODcoGIIgoOzeMLPaKwXBKZnZJ2WFqM2D3CoLBdXzeD+P1eDbWdoxsjFA4zT+yf/znyAOuqxsQEKeWZhhFBGFdRpP/fdWsoVxNEYWdrXkYYaro6u4tX3Lfy4LM/z+V34fkY6AGnjw4AGiKMIzzzyzoaYGjjJjtvBq4hwMrdoVLT47j/aT8oR0FezKuz9IJfEnCe7YEpL74OM2r6cgx2t3T3kWwQXUCQ6UeRseSHGCcXRO8Ooz2GjPZkJdsnFf/rBNgggMUnAJA5ej8OuRiR7vwy6I51IOzvgzW0827uvTjuE0LY3etR+8X25TNv4M16n7OUXRvNVzATqjhQgJxOnAZK035WSSccMgS+ta5E2OpVuiLTbaER+XUcRgSaD7ZUORjnAQHyDNRNmNIVZioAOkYYrjw2NEcYS8yHF4dAgTGaTjFDrS3sb4w0cfYl7O4UIHlSgUtujNWcNmYVGCAkdmpQ2vGSrBduuA64uatwLutJfYdSl0Yc9CmpMPxhpst9sIbFdyyVbBKsaXPv8lqFphO9vG22+8jUcPHmFvfw/Xbl5DkAVwocO9xT38zr/+HeQqx9OvPA0zMr6cLXc0n83WM8zKGaqmQq5zb3/dK0s8r4nyRl9+2bEYI0fnwvbGkYuw5/ZwEzcxVmOkSEkLzGmEOoRVlkrtQKyYoipQoMCJO8GH9kNigqTAOqR1pFufPt5CR6WG4w/HuLp1FdvJNjEFHOmM6ErjTfsm7nz7DtlIgpxBItsle7Tx9sbAJjvKQRcHyeko7VPwtUOjSWi8dCUWFbF8G934BBmXhZSu9M8XBi/4WbNu17Dt2SwqB4dVS4wMXq8429k9mgBb0RZWzQr/6G/8I1oPBwH+8A//EO+88w6qqsJLL72En/u5n8N0OoUxBuv1uqedIIMkzqTLQHMoZjnUQ5CgAAdUnIXnYIsDRg76hoxqYAMK8Pa5Pp/jHRnE+XtMgB/AhpLOvwN9oIQb/81xFAekMlbiPpDClBwMMnOEs83cd3zeDApI4EBm9iWgweyQJEl6AS5vk0sn2BSAAQHudxmgDlkLQ8FNfg8zKyR7goNZZmjwcTKbQsajzJ6QfcrsDhZ7lOfPQTW/JjUaZLA9ZC4MwQQ5Dvj3YTkC/09+Vp4PsNFeGDIk+DyG10ECLKy74ly/vEiCULJf+R6QwqoyvpdMDr5u8loNx/tl2qWBhtFo5Dta0inKskRVVTTxOofvb38fbzRvdB/afJ7tBDlATxZJP+uoOjeHnF4zztDCIqJFxCgc+fcFNoBuNNET2xBogZPZCX7wxg+wtbX12ISV5zmqqsLx8TGKokBRFNBG40sPvoS963vImxzzYk6TtNpoSVSqwtqssVQdpdptXm+MWNQZPB64bHcd3D3YAwQIbeiVt0MXAjGwmq4QIYKrHBKdIHQhpm4KUxvsx/to8xbVukIxKzzanwUZPvn8J+Ea5ydpHtzL1RJxFiOZJJSFCYE2aKEihZUlP+va1PSgcIVfqDaaGBg1alL2BgXtjWp8zfJ5NH6nHFrTBbvoKNjnNa5/zy4YeKxpAHI1YbFQXvBMMEEMEgk1jdko3bsA+SJHWVDtHgskAZSp06FGoxo6/4YW6tZQwF63nUaGbv2YaFTj+6NGfekAmRd3vpa9s/+k5GqXOewe6Bw8NWg2IAKDFwyaXGa/XAkh9B96AEYHaHimQxcU0Ecd6qb2pSKtoj44Vy+Eg/9uccj79OUgIshksIEDwCdhUPD2TmN28Pbla5fd7uYj6rHXLSyW1RKL8mzRsx+28eJY3VWkFWM1zRMyk+lCP1duZVvYmewgMQl29S4O7AGiIKIsp0m8oCzPOwqKsre27OlV5Db34pzrtqP1ujVyl2NWzLC2a79I9a4zFzTuQ58R+yGrSXx2CK6Xbe92BraGvOjYNDRpM2jzuCaK21DqL2PHyYFpjyU0oC1aZTeACIOW3RcLRl44R16yyTIlrgf3LAAGNHQHbmDzO7+PWWEsdCfFD9na8rL3qb9HnuC61yDmxwqr3vkMgc+zmob29O5xNPZaSqEOfbkOHZLz7Dc+t8ZSaU3VdoB+S7oKi3rhnTwubKyPmnQ/19335gBhso6NgRAjM8Ku3vUMmdnRDOWqs21uN+NJG00OUQo9a+tWt14gl0WqL2o8x7CFNYP2ucoJ0NGbUjqrLGy4KWU8bS79n9/5n3t/B/vkmDQ5miCbk6NSrGKUL5QYhSOswzVMabA72sXT8dPYHe9ifjTHm+++icXhAuWyBCrAtAbTdIosyGCsAQyQuxxN0qCKKtRRjSokkes6rMm9y1Sb5zSvY1SNUpVPVnrYrUkYYGZB6MB1ukWIcDW4ijiIMYkm+Oj2RwgQ4PqV69jb2gMskKUZ7t27h7tHd3Ht1jViU6w2lpLLY9KmqA5OAZoMoKxC1EQIy5CABxsRo3fwe+KSjVikGSGyJI6uoXuZSlliMA7GmKhJT2BO0tmlAGFrW6zLNV757CuIpzHicYyPjj7CveN7qFAhyAJfEnL30V18dPQRWtPihVsv4L/6yf8KSZJgMpn4/TtH6vVKKXzmM5/BtWvXAPSDZhmASrFHWbogz4s/w4EoZ3clSMEZag4Ch/R2zg5zwHdarbtkSHCgxoGcBDtkSYIs3VCKNCSGbAHWi+Bzk9l1/pbCntw3HExKoIDBDz4H7qchZV8KAspMugxkmVnAjbPsHIBK7Qm+DnyN+XxkEC/badaj/LpkDjDQwPuTyWV+rxyvkn0hz1ceJ5+rZGLIcS8ZAHze8tz5WCU4w0H8cPzKccL/4588Zll0k8cSg02S5SL7X/6fz0MyQrh/+BiHJRISgGDmgxzTcqzL/fNYkn0w7POL2qXFIH/zN3/To2yMar366qv4xje+4S0olVK4/uJ1XHvpGvI6x/HqGEVb+AdiBSozCNIAOtY9F4Ta1d41wf/sMtcXWjyiy6h3FptsS8k/WQzRlhaudFSO4UJsj7bx1JWn4CqHalUR2NHR9U1roFuNNEixN9lDHBCSwBe6aSmzzpmlRbGASQ0QUfZrtiaxIastGtN4J4tGNUAIT4dbFAtiKpgWLugQaX05G0l+CHJmIrABUcA7a7nA0cOfRZ84g58YAjR0q6FbjTZvSfAtyhC6EOWiJMFM0y9vsdYiSRKiI5UF7j26hyANYBKDZbWESQ0qXWFezlGqErnKYQOiZ0rbToSka1Gh82DvQA3//cMIOF62DUshsHE/8cCQI+2AGDFSpMhUhhSkH8BZIM6eNG2DqqxQNiUQYMO2cBUQAkEW0ILWkXYIf/P7GrVhavDPywR2fC7abYIev2DiSUl80dv7fz9JnwFdvzm1AU6634Mg8MEOnCjN4Ex293Wpc3PEkODFsf/mINEJ1dxuMV7b2otmei0N8wTaFqd1x49w/EmhQJlpdu5yxyg1GAA88TXk0pJYU91vrGIPTMQqpoyyCoEaOHlwArTAdraNLMponlGbwC0IA4wnY2ztbHkLzcpWyKucSiNcQQBNuyA2RkdlZmHFi2i+4FKhYb98jOvR085RfRYP7WqTMb8MaMEOO7yNKIh6Vma+RKSzYbzo2Li+n7PotrZYr9ZoG9qO1hraaB9ssoYMi/A6vaE+SzbSx83wn9kGoJwHODrwhllpQ9BFlqkwA0Ie78fZf3cQ5zbJArhM2Qg/NzOVATmQIvU2s6EKvY0iOhvkvZ09nCxPYCKDDz76AKOdkS8zUInCPJ/7ssrSljQnMcPmsuf6BHMXswq5rI/nTOWUBxh6H+lABR5XXoNIX2AL7chmMlThxglJk4PWKBvBtXQ/Ka0oaOnK+FrXPfNsfaH4ITdtdW8tF9jA/83gKzswmcb4xE6ECLrpdFS6dU+gAjhDuh9VWKE0JUpTogoqlLoku+bOMYoFzPk5IpkWlwIt+PgFW4b7rFpVcJUDGvjyS55b+dkKTfcRrxMaTYBKZUij7Kz9h3YDREQuQorU/52AwImwDXugRexitKsW9br2OlXWWiyXS7z88suYz+f4a3/tr+H3fu/38ODBAxwcHOALX/gC9vf3UZYlTk5O8MYbb2AymeDg4ABVVWF3dxdXrlzBwcEB3n33XXz1q1/Fv/t3/w5KKfzKr/wKXnrpJR+QcfDJQAFnrKUAne9PvbGrlDX4EgwA0HPHY6CBm9yPZD7wekkGnRyQyuCTj4ODwyHzYjj/8X5kUM7/k1nvYZlB27aeLQBsStY588xAA7MYJLt7GDAyECKPQwbCst/KsvQlGrxfmdWWjBDeNzPIpXsCH6fMhLO4I++L+4FLWBgQCIKgx8zg7cgsPr/OZRpDtsYwyJbXBEBvbMlAnK+RZMvIayf7TFrUyusoWSh8XXmMyTIJOdbkGOR9Df/PjcfHaS4nsr8l6MF9zsfE25faD/x+eS6SxXAauPTpT38aF7VLMxoePnwIgMob2Jrlvffew8OHD1GWpR80mc1wAzdQuQrTeupPCs1GhOMgOcA4GPuLPaRncEeenJzgZHaCsiFwwoXOo9U1aqhIIZ7EWNdr3H14F0fLI6hQoXQlXOCg4o5WHQNlSA+VIiHgwxqL94L38K32WxSMTM8/f+20L6tgemGg6OFhAgOTUjY9RoxABURlbEGuFW5ClDhL+ghbegtt0WJ5tMTiaIGrO1cxzsbIsozYF3WFKI0wW8+gYgVrKJO+qokybRKDZJpgWS0xL+ZQkfKB6rpZQxuNBg3War1xsbAbe0u+VgAoUz605tztBgfTLDsRS86yBi5AEAawoSWPdJ0gzEKoRhFLJY0Q1iH2zf4mK+soGxu0AdlumbG/xsObyFqL7e1tmnDRIsoiYmbollgIrsDKrgjMcPnGoqxjZrCY37JeonIVrLZ+keeBDDWg88sH98cNLlOcuhj3JRGGskqh2mgIJCrBxJH9ZIoUYz3GCCOM9AhhE6JaVqhzqu+EBo3r7nrLb17MMiOnsgRoqEgBIQEfRVsQmNWNiScB8uQ5AR2DBf0FaYmOps+bu+xmB/RXTxV2m5pvC7spi+GFK7uKSKrtWcfdMUvYnYVFWiWA4QNP67y2hnUWz730HNmZNSRAWDQFiqZA1VIm9KJgkttZ1HTfB2cFUkL3Qv489TwlvXrQGnTWi23uBR8V+uU4XFvuJt183Dkx9IICC6Dsvh+dfgyJTrzuCwMZO2oHSbx53aBb8OnO8cY5fPThR1itV1gXa7ovDZCMEqSTFGESEnDZ2cpxmchFoAWzClq0vo+fNAj3gGRnd+tZDR1w0bYtFKhUAJ0GxGWS4Q4OVUvK36UqaVtNJzwbdBvQAAy8o84pG/Et0hHGZuOQkOoNy4VtMj2LwbbEXutEMPmbbS2X1XKjOeSa3j0mgQwPmrXnj7+P2zQ0Up16JhivF/h+8uDFAFx5ErDFwmLVEuOv2+mm8b057PqH3c8cQAaokhIYutZI1ym2o22M3AhRG8HUBiiBZ648g6cOnkJTNGjKBmVe4utf+zrCIERe5EAAtKaFTjQQg5IVoUUbtvRtaFy40MGaTqjYtB6AavU5QEYHRvReYpvmC9rYjvHLO79MzyA4JKMELVp8dPgRFuUCta5xtDpCYAIUlsQQK1Uhb3NKLpzf+T7pYZwhVkcHmsh+V0r5ZESlK6z12oPK1tDrrenA5QuacupxAKMx/ji29BY+EX0CPxP+jBdn29nZwfb2NsqyxLvvv4s2bFElFU7qE+w9vYf3Hr2HQhV4sHjgx1IwCryeV9HScyPXOVz2BOUh3E4Bkxj05ESDpguEVrXIFZWBWGVRG9LRqnV9ZpmNdtqDEJGNENsYH259iHQ3xYc/+BCzcIa9T+6hbEs8lT6Fu4/uInEJrmxdQZzGuH79ug9+tre3EUURqqrCH/3RH+HP//zP0bYtJpMJoijCcrn0NHimvjP1eyiyKF0NZGDvh8+gFp1fk8Ei1/nLIImDqOPjY/83fw+DR38JBDAwLOvgfcvz4QBfBpe+v7vPSuCDt8NrdM56y4Bd2lPKYJWPUzJBJEgC4LGAeFgewrGd1htmzGksDykmGUURwjBEkiSescHAEYMm/Dk+dmCj+8d/D4N9tmQdMgMkC0CCMRKQkJl/+VNeQ3nNhufIATm/R44HGcRLFobsY6mrIAUcGQgaAgN8DFI7QTJcGBDhY+BSCQmUSIBAsh3ktiWjYwiq8f74/pOx+bDfZP9epl2a0fDLv/zLKMsSq9UKxhhkWYabN2/iO9/5Ts/79Gd+5mfw7LPPoixLLJdLf1CShnLlyhVkGXHmh/QWbnVd4+TkBPP5HEVRIMuyXn2Pcw6j0QhbW1vI8xzf+9738OjRI6Rp6lEevknH4zHG4zFWq5W/ERaLBZ555hlcv36d6gRXMyCCz7qzDaeONFzoNiUVqvZsCy8E2TE1GARhRgZnqi+VrXTK10tqq31dJAf5xtLDULca29k2JskErnKoVzXSIPXWltWqwsH2AUbhCIlJME3I1igKIj/IWksLllVFKsmz9YzKHrqA9GR9gsIWBHBwOYlgI3jniq6fuE9YL+Ey58vMAc5MMJOEmQJpkPrXsyDzglYe7OiYGbGOoRqFZt1symlUCGcdHj16hPV6jSRJ/Hh7x7yD78TfQdiGCG3onQ5G4QijeER0W7cpcfBZeFdT/aYikGPt1hvgpiuz4FIHDoofYw78MNnxU+5SCWTwgsPA+CBaNcovohKXwK5IyCq1KdI2hVs5TIIJdsY7G3GZyMAkpsew4NIJBjL8eBBfrWkRjSJazHTlN8xWYpruD3Ou3Qn/yPqMF9yeii+y561qLwQutNKk1B1NekrzcUAsgTjo6rnZBtc2XhywbEu8+8G7FPTXOVYlCXq1oEWyz1LLY3hCzQ+ZsWcWxaZbNiyTi2jpH7f5oFzQ+CWYMTwWrr2v7OXmD4DOk90MjNtkDbmsgYEA3g//5FIBLgtgscYzRVlP2a9sTxpUD/tGljEoKNRVvVHrVhbQXT3+BewcBQJ62bmDhSQZrOPSgcY1Hni+6DyzgKzzIhf50r1IRzS2hdOGTxC41mfKq7ba2IOye0S7uhAYYq0fyZyKVIQ4jJHEXUas055p2sYzargc4uMIjQ7H5I+lncbQGTYLqLYLJmtNWfqmy9ZXAYEWlUHcxGQ1bGMEDZWScjmcNRa1qtEGrX+Gt8EmM851/AxYNEFXbmBq/75WXy5gZ7ZA6ELYwkI3Gld3riILiB2pakV6ErXFC8+9AFggX+dQWuE73/kOoIB1vgYMoENNpZeajs2GFjawdEwBASw2sI8JSw+bsgJEsIb60m4CcwawAZDFN5emwPmxq4zCi/GL+PWdX8c4G0Mphd3dXUynU7z77rvI8xwfffQRAODtt9/GZz7zGYRhiOVyCWMMiqLAnTt38Bu/8RtQSuH4+BhBEOD27dv4gz/4A6xWKxRFgSiO6JkaNGjCBnVSo01a1FENpIBL6Po0ht5jA+uZFvztgfYneT524FmPkagD3NA36H5WVK5pEoN1RU4m82qOSp1970YuQqYzbMfbZDuqM+wkO7j9g9tYPFgABRC7GFvRFvZGe9iKtzAyI2wn2/jspz6LLMsey97LYF6WDMjgWrosyMDp0aNHPiCTNf8MBOzs7CCOY6xWK69lIJkSQyFCqdEg9RJ4XZ3nOcqyxNbWVi8zz/vlDDozOPj8+HeZ0ZYlEUPGAx8nW1PKbbITBL/GJSNlWaIoCh9n8Tak0wOzBWRWn4+RX+d5ngVO1+s1oijyrAIZoEsRTd4H94XMnvM+ZLDM7GlZ0jEETbgUZzwe90o3JBAgbThZk4D7Vva5zOrLa8Kx5pAhIvuM/5bHxs0n2kV/8na5r3jfLLI43Id0HOEmGR28H96HrDw4DVyRpUVSQ0OOL1kGxFqHvD1+D4/NL3zhC4+d97BdmtGwWCyglMJkMvEHxf6yTJOJogjjMU3KCyywUisfPMP163Gk+IakxfD2pBYE8HgNCgDfoUzf2d3dxdWrVz2yxqKQDDgcHBwAoAmDQYogoAxn1Eb0EDdmc6wWSMMUpt3440pgxFpS6W2aBkVRIEkS73RxeHiIsiwRJ7EX6SldCRUpmNRgns89tRIhYAOLwhLbwgVE72sNLRQqU6EKCcGvUeOevkeiiGGFZmcQwG0PLlxN3wYk5hQquh6emYCAmCEq9oG8DawXh0pV6gEBYw3SNkUWZVSO0n0m1rFXN2/b1mtZ8KKGwYi8yXG0PNroQHSgjNWUiahc5cGcGWakhaG7spOW7Jt6gYA89YE9aahDBFcImOEMauhCNKrBQi0ooOsWFa3bgARnNQaCYkflNaEl9e8tbCF2MaKGWBuhDRG2BGCElrJbW/EW9sZ7ULWCDjRMYjwI1eiGlLHVErmmGvmVXWFhF2SbiNIH/KyZIMtLvBCocpvMEc93Q7vWrTNPb9POoEdzxoSzJyyIypTVqI2w63YxMROMVGclFkww0iMkKiEARG3Q60Y1OFmd4KNHH3mmCoMbRUtiadbQotKPpQ7sa02LxmxKkRp0GhqXYKY8luX8mOCPddZbXN5f3d8Ejt03Z7V97bs9B2iJqd91Q8FF0BI9OLQ0zsbRGNeuXMNkNEE2yuDgUNYlPvjoA8RpTBmrToOhdh0Q2pXpsIL4pejal2wSOOBz9Vl8bPqYLTOfiMZ/AWVbQYgpmsArqVcNieU2aHqUeXk8LEDpldWfAPxioblQhz3QRB530zabfZyzbc9scWcEuAqbev/LHJfqgEapP8HPMNW/HjweK1wMMsSaar6rtsIiXyAKI7reanMerWs9U+YiR41ABUh1iq1wi5huXamIUcaziqqqwuGjQ+QlBaNKK2LdBUDd1GhaKj1k8brzWqITYnR0z6hId6w6IbrJOhYMLPMCKggDjPUYu2/s4vqz12FDyqKzGOuqIbB5jTXevPsm1lijCRssa9JzsobYOD0Qbzi25S3B/9OgMga0aOP2cceeyzSHjQOSoyy9LznogAtdaYR1iKiIkCFD6lKM3AhTNYXONZqiyzZ2DKJsO8Mv/tVfJD0jEHj0g9s/wPsfvY82aJG3OR6cPECtaqT7KZWO1HOs3AprtUYRFvjzh3/eB7dePP3wdaP9NwPl8Tr2ujO60T1XLI0O6HIEGPTKjLqSEGaCNKZBbUjbodX0HGF9imH7Fr6Fb62+hTiPiSG0IO2JalkhRgyEpCmRH+S4397HC9deQLBDoE8xJ/bgn9z9E9zYv4HFagFVK3zt61/rZSOdddBOI25jJFUCtd5kbI0xnhI/pN2flnF32sFFjmzLTU3jJ6pRBiXqsEapS9KzMI1/hrKmhdXEmr2r7pLweLf+7Q2/boxyIuwT6Sfwc+rncFKdYFbOcJwfo1Qlbjx/A7Nyhtl6hqPyCB+YD9A81QElZ4BEwXsBRmZEOhKdy8fIELNzHIz9eiIzGUaarEhHeoQECUbBCMqpXuB2dHSE733ve9je3sbOzo4PQLluncGLp556CuPxGPP53IsGSpCDg2POnPN14wCSnSuCIMByucRv//ZvYzQaIUkSH+fw9Tw4OMDTTz/dCwQBindYi4Fjn9OcBTgAZ1Y5AKzXa7z33nu9sZCmqQ9YsyzDM888g/l83ivvk+UqMvBmSr5kR8jfnXM4Pj72JRBt22I0Gj0mqMi/y6w5C/3L/fB7eDwzcMLb4ThQZuBZOJIz/XVd+2vCdpjcuK+ZecGxoWRRcOzJoBGLcA7LSiRoIDP7fH34PXyfyrEk41s+l2GJCwM/wzIfWY4iQSoZOzNLQWpO8N/cpOAkH7ssXZLbZKCNj1keE79Xfu6idmmgYTqdIkkSL2qSZRmKosDTTz/tWQQ8AK21+Of6n+Pt7bc3HeY2meukTZAsk40IJCsDdzT7WNFDpQrJ/1xPSCshNZssd4SIkPfupuQbjCeZLMuglMLR0RGstR4IkRSmK1eueMSNX5OD/LSLMqzF4oEg6UH8HmOMD9JMa0j52EYYYYRr0TUgOpvKJMEXSevh+qk4jjGfz3EyOyGFa916n+3xzhgqVnCBo4erogm+dKVXT29UQ37brsQaa8zVHMc4JhAgKjdBnLq4lh7thqHAJQEhOrZAS5mwwAVA1U1ctaKsmAvpoRFkJObYEm0yQoRJPIGxBuN4jFE0QhSQd3DVUEkAUzIb1WBdky913uQk6Ggo+360PMK6XkPFXflA9/5ABV4nQDJYrLNnBjpOOV+/qbSCCjdBOAcIvub4rHsv6u4DS/0U2xiJpdrJTGcY2REyleFAH+Bp/TS002jXrV8cJopqLAN0CxRBjZPoJIMmLIg1q2cogxJFUKDUpJ1Rqk1NKgfyVne2rB1F2+sdwG4ExwZ0ftlu43Yf/LnEGlnt9jOXPdvQTvSTa24zZIhdjEzTz9SRXkaCxANinAnlLFWrWs848YBNp41ROaL28mK41J1YotqIHzZoYEISx5J2gjT0O8DxY1hRPt4R8Bk7GbBpaBzrY9wp70CVCu5ooycAgGjbp23OKWIL2RBjN/aMIe5bZsEA8BoAVlmESYhkmhDI1ZZY5AvMqznpyHRgxWnOFF7nRIgkSkbPZVwmfBPZNg7GJR2Tg+eqrXwAbZ3Fk5Bmen0lGCD8FZoQoQl9fXW+zmGUwTgd90omNofsUFZlT1RRMt/OPlU3fIGaHFIKZ84p8lpctoTnvCbPP1CdVbUjYM0Ext9bXHrRoLnQ2USBhKADBIADie2iwRprypB015DFGZtJg3brHJ2AdsO2CCzpEMlSRg++qMFcYFss7ZKA8A6YK21JbgJntQRIH6UYBeQwMwpGve+ojvDc5DmMwzEOpgd48MEDHN87xigY4e/8xt/BOB2jKivM5hR4LZsl5uUch8tDfPN738TR6oiCwrBFE1FW20bWB4E2sOczC04DKxQISGe2UqcJ9dj7zmsWiNvY1/xPgymO3juCrjQF3SaDgcHV6VU8d+053H//Pt45egemMvjP/9p/jheffRFVWWG5XGI+n6MsS+zu7uLh4UN89Q++ij/7iz9DYxqoWKHRDVzo4AKH4/XxhrnQMS6icYRkmnhXhNZ0AsWm9cmYixoDtzwnxg1ZHmcug3EG1bzCU3eeAqJOPDtW+NJf+RKO18dYN2vUmp6RhSvw1uFbKFyBIAvwYPUA1XaFE3OC7x59F+tmvZmbMwBH3Te3L4AcPTrQBzXIErUDmXWj8fTsaVwrrvWyuQB8hnqohC+TX9pppG1KyQAEUNWmzpoDWmstVqvVY8Hk3/pbfwvT6RRKU2lmqchu+c/e+DP86V/8KSZ7ExytjvBw/hCNafCX/9Jfxi9e+0WsViscHR355Nrnbn4ODx8+RB7keP311/Fnf/ZnXvjQxAYqVXjx0y/ik699EtlehlKVOC6O8WBOJSeLeoFFs8CsmeHD9kMs2yU5LLWrM+eZzGQYm7EHK0xlsPfaHvbH+5hHc0yjKSbhBFvRFibhBNNoiq1oC82ywbs/eBc7Ozs+gJWMCY4LOE7gWIN/cobeGIM33ngDy+USu7u7eOWVV/D0009jPB73YomhXgBfO0lD55IAGVjKQJwD4jzP8cYbb8Bai6tXr3qhRN4Wb2e9XuPq1auYzWa9oF0CIbI8hd8jdSJ4bdk0Df74j/8YeZ73Sj4YvLh27RquX7/eC3CZnSedHni9yn3Ztq0H1STQBhCY8vDhQ5/Qruval2twXPf888+jKArft5xcZkYKO1LIMhtZfsLXiAGG+/fvw1rrASCZ7Wd2jDyX4fWVwb/8nIwph7Gm/F0eG48TBi7m8/nmfhKgFV/XYUKctS6kCKwcw2ma+mPnY2AgSV4fOUbl9i7TLg00vPDCC4iiCOv1GlVV+YP45Cc/iaqqUJYlsizz9Kafb34eny4/DaspwK1VjdJRgBuOQp/hr1yFNdY4dsc9ccjKVaiS6sLMjoJCGIUwLxDyzeJAHPSGV8Jetp5Fz3Sj8aZ5E3ETo3AFVKAwCkYIWxJJRE0ZGGZocOfKn6ddXBnw8U0sUWimMcn6NEmZYjRuiFgrpVAURa8cBegEG52mDDsSxG2MPbeHoCGbrtSk/mbhwcmDhm1ljhZHCIIAWZbBWusnJKVokdaAFgI1ag9WcICuEw0VKeRNjlW9AkLSESht6W28iqjwivdt2qIdt5uM/kVABg/UJiDxok55X7IzQkdq8sZ0rA3baUq0IcbVGJnLMHZjPza4DCVwAYlkdlm11rZ4dPII63pNoEDHPGgNBauVqmgBF7oNU0MAFRUIBGFAgoN4DnY58G3R0uJWLvhOWysp9JxbfHOdijhMr9wksAGJPLU0FiIbIWqI2jq1UxwUB14MyjQGtrEe3WUUnhtPVDzeJWXRgSz2Kl2hCuj+LVSBeDv2rIw11mQT5so+K6PL9nprVgYzlNABOMeW9Ima0H7gYFXanwUg8UpmqKQqReYyJIrEUsOWxED/9q/9bewkO9hJdrCVbCELMy98VzQFDteHeLB6gIerh3iUP8Lh6hAP1g9wmB/ipDjBvJxjWS2xqlde26Fua59x7l1v/tnRrD+Oij9AgVVjiHKbu1PQiGGAwvtu0F8YA0CwqZOP9UZAMtIbmr6GRtN2gG0nqtjatseuWLfrc8EG3Wqqz7Yk7CsFR402mE6nSNPUB1AWxChbV2vkNWk1XMbmj89fUqjpJeeV9hvbILe5Px9e4B6uDy+3/a4xgMFClNLakm0sjeoEZcsG+TpHW7Vom3bDTgBgAoPR1gjpOKXyJNcv+yiagrQSfpjyLBCAYJUl5oBsIVDWT+6SwUwmL8TZPVNYC4T7ls+lUc3j9sqiKRBIHZsYoQ5pjNUVVtWK+qrTEGA6Obs09GyPT2nakavANJ0iNjFZizqNk+MTHGwd0HMepNkyr+c4Lo9RWyqDWrdr1E2N9WpNK6qnaJu/+4e/29tHpKINw8LFKLdL1EENlBQIx/MYYztGaEO0eQtVUgmArjcMmun2lLRKIgudaZzkJ6ThELRowgaloW3WQe3BilrVlyoH63cISCCxc0Y5whHeO37vsVIcAMA9ABGA1+jPP/rmHwHfBAlrmsxnp9PbKdzaoZk2mN2aweYWYUOsv8Qm0EuN7DiDrrVfhxltsL29jWvXrkFrjTRNffkur590oIEIxHAJ4S3BC1fABpaey4bKL0pL4Ho0iTDeHZN4Z5tjqZb44viLGI/Hnh37zI1n8P3vf58SUDpAEhFjNfxs6F3W/vzP/xyr1Qp/5a/8FXzuc59DXdc4Xh7j3tE9zIoZXOTw5u038f799/Gd179DZSCmhYscbGi9/aQNLZqMwBPtNF6+9TKapsGDBw/8+coMusyoc2DCz2kOvqQg4v7+Pl555RVkWYbVaoXf+Z3f6WUsd3d3MR6Pcfv2bZ9VTZIESincCm4hOUjwqU99Ct/4xjfwg3s/wPb2Nv7jn/qPEUURiqLAeDzGZDLBdDr1r33lK1/BfD73a2IfNNUas7dniK5H+MTuJ3Dl4Arqusbd5q4PeDlAlMFUa1usW1pXrN16IzCMAotmgUW9wLJdUrIgLvHAPsBbh2/haHWEHPmZLLNABRjPOpDCjD2rYhyMMQnp5zgYY5JMiGXRfW9FW9hKtmAUHd8PfvADlGWJ9957D48ePcKNGzfwUz/1U7hx4wb29/cRRREODw8xm8161HuZeeafnGmWjAbfD11S0zmHz3/+856pzYwMjid4O2VZ4t1338V6vcZoNPJrewYllFLI89zbJDKrYAiyWGsxn89x9+5d3Lp1Cy+++CIODg6QJIlfKzJIINkAbIMJwANlQ90JBkkA+PIPPvY333wTALC/v98DPPi4AODk5AQHBwf+HPh1Pg/ZLyw0yduRATS/55133sFbb73Vuy4c3z3//PPY29sD8LhWwbCkQcaHEujg8+W++vDDD/HgwQOvdTFkcUynU3zyk5/E0RHFamma+m3wOcjty3hvvV732DN8fR89eoSHDx969g2zYJjFk2VZj4HyGKg5YEyc1y4NNDx48MAjqovFwnfE4eFh7wABCkyu2CvYs2T5I5G6IAhwNb7aC775d4nsrNdrPDp8hMP5IYI08Bl6BixqVSPMiObPyHZPM6ELbhjIKF3pgY0KFdpILHjPoJQrpzZOBB0dXnpJRy5C0iYITIAgDjBSI6LLK90TSuSsbGhoAdHMGsABaZpie3vbDyw5uPgG5AmjLEvMZjMvosPooAQyeiKP6NcEybqlIU1GDhiJuPHnYxXTQpHr59DZ4liHZka0pLqu8ejRI8znc5okXYDtcNsDNUEQYGdnx090QMcSMdozLEpL7hQqVggSsk9qVetFQBu9EbSsXe2vaY0aC0eCVF43Qteotinwv0zzQoGTzsWDnTykIKajUoxYketE6lJMMSURzA7EiBB5gSnVKtSrGtN0it2dXRR14c/HBhaVI5uoRjUbMK7LslWosGyXWNQLrNu1V8Gude2z7a1uic6L4skWkdxcPxMdiC/2II9BmR+uB05Vioy/bIZtu429dg+ucriiriAMNvS54Vjzu+3+V9c18jz3YyEMQ9S2xromUU8uHWnCBmtHC4wmapAjx9qu/UK4cAVR4VkcE0Rhl4wML1ynBnTkc4IaBjx+75/+3oVdyar7RhmEJqRAPIiQBAnSIMXB6ADPBs8iizKMwhEe3n2I22/eRj7PfVDJ7gXTyRTJOIFJDIIRia4umyUtqNoF1s2asmzuDETZDX4Cjweg5zBTTmsWFrkl8cWPyxzo714h1CGVOKkAbdmiqRq41m0sYDWgDAlSNkGDI3uEclmerinRnYdpDEzdB52No7Ed6hDbk23sbu9ivVzj+Ph488xR8LR1hMDW7hYaRWOwaAscLY5oDkJzdr+fco6S+cClBlxm4txAwwUgYPE0cBHAMY77tonYABlc2oUGQEuLZ352GUdAUKQj7G7twiiDxWKBsip9+ZV3INAOJjb43FOfw0iPUDQFHi0f4a07b2G8vwnM2D3kopIYtg0FcDFYdt69iE3ZjIbesGS6+9wDTBobgOGi4NphIw6rDOKAAAaozsHGWhQocFgebkpOLJVPXMRiijTZzcY63jgKdFR/OMq4LdoFbGrhRs6LGrK443l9oS2VVwVtADOmZ01mMsSIkTUZgipAmIdIVILd0S4CG6Cck5OUVtR3QRggSAMgAka7Izz38nOYFTMcF8f4aP4R3rr7FiYHEyoRadf+mp9r/zrQoMjbHHmb4xADcC4G8Iy/qGfPQZ1IJJ9r0AQEnMeBFyyMHIHpUU2/q1IRw02nGDUjApW18UEbL6p/8ed+EU8//bRfpP/jf/yP8cWf/SK2t7f9Yr+qKp8JZhHDOI4RxzFOTk7w8OFDXLt2DY8ePcIrr7ziA4rEJNiNdxGUlNRwocPtN25j98Ndv4bioFFmYY0hu/it3S28/tHr/liZMSup2Py8ZG00ZvYOBeTCMESWZfjVX/1VbG9v+0Ds9ddfx5e+9CW/Ha65d875kmIO+rMsw/PPP4/Dw0NsbW3hU5/6FJ555hns7u76rOZkMoG1FtPpFOv1GovFAnEco6oqT5uXQfV6vcbXv/51fPvb38b169exu7uLNE19JprXt7w+5Ux3FEWYhlNsqS0440hTalBbf+vWLUynUy8+/7WvfQ2f+9znUNkKs2qGeTXHol5gXs9R6QpH6yO8/+B9zMoZ8jpHXuW46+4idzmJjXeWz2c1Zpq2dQv1GtmTRjbCtek13Jndwc3gJq6V17CdbGN1uIKpDCYhlYcEbqOvwOdaFEXPmlKCDzwWgiDAaDTC9vY2jDH4xje+8Vg/8zgBgBs3buDmzZtYLBa9/0lQgo9BAiASwFJK4f3338dsNsP3vvc9vPPOOzg4OMDP/uzP4sqVK9jd3fXx4WKx8P3DY0xeV8kaAeDLKpjZwNdyNpvhi1/8Ivb29nrgBN8vzCgpyxLvvPMOrLW4du2aBzd4X1x+wEwJZkYAxNZfrVb+/imKAt///vdx5coVvPDCC7h69aq/F5gVNAzeZX8NXVBk2YS0g+TymEePHuHu3bt49tlnkWVZr5yFQbrd3V20bYvr16/jwYMHXspAlqvI8gZuEnCQycKmaXB8fIw33njDjwM+Tuccrl27hueee84DNXJc8HFJgOWidmmg4cUXX/QHLidLWcvBNwGjkcPAVyqEynoVeVF40FtroaCoPq+JEZuYBO7URu11YidARZ9hNoVkAsgbhDvZOUcik6s5irbAu8m7uKvuUgCna0+brlXt/aobdYrnuexf033zgzbA2UyMKXyA58tJ2i4r3xpyH1DkXqFq5amhrqRs22R/QtaVXUbetMZbW7Z1S+BIN3nIvh2yMYYTiqRzcb/JQcY3lUSmeT/s2Toej3HlypXeteWyFQmIyH21TUtlFDZE2IRoyxYZSIAM6CYgHXifYFmi0rYtdEgCNGVT9h5SLMZUViWyrQw60Z5CL/Uj+DUuPcmb3NctsuhloQos1ZI0AsLa/48zRZdpgQsQRIEv+2FWRlzHnvrry4i6/+2pPezYHWirMY7HCFwAVzoSHVQU9IegzI9zxDgpmsIzghpDQfnKrlCYgsQsUSBH7p05vPZBlyEtVOFLJ3pBkMbji18JZmYgIbNyk8H1bAG1KXdip41EJQQWxSVGGGGsxhipEbm2IMDYjaFaRQwDm8BYg7IoPY1PTubydx4jzBoKw5Amd022s6UroWK1sVlE5QXHClVQnwQNiqjAYXyIVrcI09DTus8KMCw6detOXfxS7Wr3fV5bDbvceKtGrsFu6xbKKmLFOMBZt6n/7xbyQRRgsj3x9fa1qs8MZjQ0IkMuBbaxaOqGshZcKtKNjda1Xu3/NNX/85qD8y4H3U7JtYX+uWmq96HzNkgZZ23hIofGEc25p7KvgTv2DtqjrgTmDLZQaEMcFAck9BlOcC25hvQwxdWtq3hq/ykq9eqs6iSopKAQmABRGqFoC5Rt6QO0vKGga12vCUCzVOe+btbkYtIWOFwekg2iObuE66y+9PokATHKPJg22M6H+Yf0i8Gmv4etBf757X/ee0mlCvNyjlDT+e5EOyQOaWLvaMFjJjYxAhMACn6MVC0lAcq2pDr/tvTlOWVbXlh+wefptSDkbajxuB7NKed+6qlaOj7tKINYtIUHgFrbUl17ReP0ouOLEJEQoaOSvziIvV6GtcTysdaiqAoCLZLqcqKLTrgydPc9s12soYx9qUsCVcJN6Rw7MQA4dawrpxDaEPvYx7ff+TZGZuTH9tRO8entT2McUlZ3FIyQGmJjjNIRlO7WGVrh3sN7uHd8D/dn95HtZIi2IjxcPcRRfoTj4hjzao51s0ZuybGnVmfbM/ou7oA/qy2qsOprirjB+y7QdWGGIwMTt67cwgcPP8B0PkWqUkzCCX6Q/gA4Bm7qm5hEE2xVW9iKt3DtKaKCcxaUKdvXr19HlmUYj8eeDcDJIV6f8Lpnf3/fZ1B5fVoUxWOMWGCzNnLO9TKzSimfsZZZ2U9/+tNwzuGtt97qCR8y++PGjRv4zGc+g93dXb9/BlyuXbvmy4UZrOB1c5Zlnj6ttfZMiKeffhrOOfzkT/6kB0v4eJhhEYYhDg8PcXh42Ksvl+vyqqpw7949H5hyXx0dHfm1nsy+8mvSCYHr6mXAtbu7i9dee80HTqytFgRUNpgECQ6Sg9569IMPPsB8e94TXuRrwH1vHQHtK0ulHWu7xtqtsagXuHdyD//unX+HUtPawcUO7ajFg8kD3C5vY/n+8kwBXOOML51NVYpMZwSUIfVsipEe0T1oOk0KM8Indz6JG9dueABqPB7j2Wef9YE3r9O5b5Ikwd27d/Hw4UM/RrlfZNAvk5PcBzxGZ7MZvvOd73jtu7Zt8cwzz6Asy414cceIXi6XfpuSsi+TyrLUgK930zSezZAkCUajkR+73/zmN/014u3J7P3e3h5u3LiBuq6RZZkvXeA4hj8jxQ65DALYBP55nuMrX/kKPvjgA3z/+9/H888/j8997nPY39/H1taWfw/LCMjSCO57PldmeMjz531LNvEv//IvI8uyx+wn5ZgsyxJvvPEG6rrGzs5Oz7ZV3vdcNsOxGYMVfN3rmlzs3nnnHYxGIzz77LN47rnneveYvKc5ruJtyGs4jPHPapcGGnZ3d3uIl0SjJErDYIMU8eAByJOcDDSHTe5DdiCfoERSZrOZHxzWWmxtbT1W38Mdy3VNjLbOTmYAgOeK53DL3fJ1KjIwj+MYo9HIT2K1I3o8Z85zR4vIRUVZZ5UoVKgwr+dY1kuaeAxR7r2TRZd1ZVG/XnZVBm4OQIyNBkCCx4UeT+s/p3xGPrKE9KdN2rNT5KAvBDks2NBS4IoUBgaVqaBqhbTZaGLEOvZUewk+MMLHtU38QJUPXB4rPB6GY0hSjWRdohQ14WszrHED+kqr/NDkmz9LM0ziCaJwo/zrP9dReZ11sI3141eO0+EYbxoKurieDHpjHelrslF5Yc8aNRDBC3xyYC8dPUqUxMjg8dX9rFChjQdAxml3bADPpgjDcHPNWPCz+1+kyE97iqkvH0kVLR5Vq+BKR6U2HT2+zVsECDBKRlRXXB1i5Qi0KE3pyyNyRcyDIAtQqcqzTBrQua3cqk+ZP21dzYtHKYQXD94zgdc8YS2HwAVeNDG2MQlztpFnH7FTi6sckrAribAUJMYuhnIKcRsjbmMvoCcXRdZa/MN/+A97h1E0BY7zYxwXxzhcH+KjxUe4v7qP+6v7foF9lB/hpDzBrJhhWS2xrJZY1+tzs4E9sO+M93GGuBdIRoM3nTK9liixyjvU4gK2m4Xd2HY6qveWokY+sD2jGZDVbxqQtWIapBTAaOHC4Rq/j9rVeHT8CHnVMZg0Zaq8UOslXTFYef3SwpOnNQXUpqaAPN+U3bjY4c3yTeDuRlzxvPPPgswDFdNoimk4pQAm3sLN0U3/v3G4qSv+3X/yu/iLP/sLyoZdu4a//jf/OlzofKla0XaaOvWaAuuIwLOj+RG+/873qeyroexzq0hU2Nexd/XuYRoSY+oC54d+lxCrhMvhflyNy2aMM8jijHR5dASjyRIYQuyvRYu6rbEsliibEo2lciyr7cXB5+bEeloGs3YGtOgJfVplCcS7RKtRQys6trzNvTaHc85rvNS2Jq0X7c5mLbDuUeeYwMKHym2ehWEQEqPD0jmzZTe71px72l1pknakZWGdxaJaYIEFgTlNg1W0wp0P7xAA3d2jZ3cjsWkiG2G32cV+sY9RQKDFrfEtn0wpZgU+vPMh1sdrVHkXRHfsHussgWShozKQoEEdkaChShWasPEuGrXqLBrPY8Dws6S7n2tTI+8EbR7OHwJzeKaRo8wPfvv2bwO3H99UoMjhahSMMIkm2I63MdIjnNw7QbNscGV6Be/+xbvYTrYJ/LchiW3aEMt7S7z7/Xfx8OFDvzYdslVPa7ym4QCRFfZldvvZZ5/Fr/7qr+LDDz/E7/zO72A2mz3GePilX/olvPbaa74cVj7fZAaTt7m/v98T95N2h1evXoW1Fu+//34vsOGAh0smlsslbty4gd3dXZ95BdBbvzGIwGs1ZmTwum1vb6+XLJxOp/jpn/5pzGYz/OAHP+hltZlKzqCKDCwlI0TW4kvW7pUrV/x7t7a2/DHINoxleH374MED/I+/+z/i6fnTvSD2s5/9LH7pZ35p03+xxrJe4o3338D//q/+d3zw8APYmARm2XGkMQ1mwQyHwSFqU6MylR+7w/ZfT/5rvJC84ANKzoQPk3wAAV6z2Qyz2Qy7u7t+LEnND742MkMt/14sFvja176Gw8NDKKW8LsLP//zP48qVK71xV9c17t27h9ls5kt7OKBntgLHbbI8hIEGXsdfuXIFN2/e9NoZu7u7uHHjht8Pay+wZl2SJLh37x4ePXrUi0k5WGZQTY49GfRXVYW6rvH222/j3r17fnxdv37dA1Y8jmezmQfR+H38f2YQ8L5lclaCPMxAuXr1KsbjMQ4PD/H66697B4+hQwkzDdI07ZW3S7aCJAEMQSO+ntZajMdj5HmOu3fv4vbt23j99dfx6quv4uWXX8Z0OoVz5NoiGcdSDxHYADOXaZcGGrjJQJwPuqoqnJyceMVPRojkzckHxyiJnGT5fbKuZ4iW8L54YpR0GKUUlsulv3Cr1QppmvrM9mKxwPb2tqeLVFXlbwCeHCQCyBeEa1T4WNm5gOuzHajmPrc5mrZB5jJopbEoFp4Sx+gQn58XCAFlnRrTYFUTPbFwhRcc8uUgqvIK+6Ui4SpmX7BDQ6Man7lwyvkgdcXpUFGPrMXT2WcoE5FBC/B4gMf/6oLW0PbLCgJH1EbdaqKKWrLo9JkEFXn2RapST2eOHJWguGaDCvINIlVpedKXD1r50JTgB48VAN5mSFLr5MOGH/yMmsuHkQSw5OTLaLxH/wJynxjZTcpI1mEBHfPCatRNvam1DOMelUzSvPhcy7LEcr2k8p9mTSUmHTiBEMTCsF2NsCMtCXb0KB1lRitXYa3Wm7KC7vON6oTHOONlQKwE2UQWLEgCoinavi6EsZ2SuQtwgAOyUmXmgkn8e1nBnoX7nHJYrBe4/+g+qqBCERYoTIGVWtEDthvflaq8dzrXXrPOBQweZxptBvemXSZOEHoOrIDPNqHf+H99A+NojGk8xTSeYjvZxm66i71sD/vpPibxBLe2b+HVK6+STWo08j+zMEMWZt4CcFWvcJQf4b////z3+MNv/CHuHt9FEzSIt2I896nnEE5DLJoFls0Si3bzM7enUDc7LQdv3SZYDXRKzpcD6EBDBQq1rS8ViDuKAMDipszkuAxroUWLZbvEsl1eouO7xhl2ceyB6oT+dMfyMSQsmwUZ0rADMcIUs0cz1HmNR/ceUTkRKq9PwcEJf+uQRC8bew4FfNAP3AeXBTBatFQz3CwufrNsOwD+it8xfvsvfptEY7tzHpkRJoYAiv1kH7vpLq6Or+JmfBN5m2O6nKJe1ihOCnK4cZtnbxiG2N3dxW/91m8hDEPcuXMH/+3/+7/Ful4To8tQwKoihZ/5+Z/BSz/xEubrORblAj949wd4+/238fKnX0aNmijxNvfU+LzNSVi4AzDKln76kolLNmccnOmCfjc7U0z2MaeO09hWZzQD0qbxNoeKgHylFKI4ohIGa3tsnSdh6bSKdDKWzdKXzXhnFnH8zrmz56WOmdLaDqzvnt/8WaUVWVJ2VqUXzW+RiuBq520zPSOiCzbGEWXj2R2ntZQE4TK+885fQSEyEYwzcNoREL0kMMq5znWn7UBFV6O91gLXzj5WtvTUjYapqSwkbmJkVYbdEbka3b99Hy53UE23yIaCCUjL4Zd+5ZegA42v/quv4va92yhMgTqsKZALGrjIIdvOSMOhLS4loNq4BstmiWWzxP3ifv86TYDX3ev4gz/7g7M3MAXUFwjcYWFnVSmEdUhlXnWweb1UaMMWCRJsp9sIdQh36GBLArxk0LFerz2LgNfhMsEXBAHW6/VjQnQc8FVV5Wu9fZlsTOsSXuPI9fEHH3yAP/mTP/G2gzL7z6DD22+/jW9/+9ter8CPAxEEycyvBB6Y3fDw4UPcuXOnB/j/xE/8BH7+538eH3zwAb761a/i4cOHvXVZmqb4O3/n7+DZZ5/FbDZ7TDSzKGhMyoBWHs/e3h729vb8+msYowCnJ0HX67XP8HPwPR6P8eqrr/bWkIlKsBvt4pn0Gdy0N7E+WvtSg8AFaAsCnj7/+c8jiiK8/vrrvv+UUWiDFi52mF6Z4rlPPYfP3/y8Py5eN0uKvLwuzjlMp1NiUxUFtre3fYDP62eZ9JNsdO7DP/zDP8SjR4961+T69eu+RIUbj7u/+Iu/wO3btzeAbcdslutsHkMyk84Jw62tLbzyyiu+PIjHLSd9+dhGo5FnrhwdHWG5XGJra8trWXCwLnXxeM3P58FAhHMOX//61/Hhhx/CWov9/X381E/9FD7zmc88xi4AgHfeeQdlWXrQQo5j6VLIY5zfw1pok8kEzz//vB9LURThlVdewfb2tu9LZoRwPzFT+969ez0gSZYZ8fb4damxoZTCYrHAW2+9hQ8/JHbjdDrFl770JVy5csWXQGmte3odPD5krPRjcZ1g+0a+SYuiQBzHKMsS8/nc6zbwe4e6DRKxlEgQN/k3gwhS1ZK3LbPdfOGcI4HFP7V/irfjt+EKBzdzCC35OMeIUTc13rvyHsyVjfhb6EKq8WsjxN1X5CJfjrAdbmOCCRKXeF2G0IVILTlgeETcbTxJ+aLyhZATlkQNnXMIDWVhx8EYKtzckHme0wOm3gTH3EeSytRjAxhgls9Q6xpbB1uodU0Cjl3AxrXsrWlpUQgKRnNHi0am0jNIcdrihQPUQhW+tn8oqAbVnSdbMF5CO4DtEjl49ZoWYUjU+ZKsNNOAQIrIRR68iJuYrBct9U1SJciCDE45xElMNHPBTpAo+fB1AH7CkQAD9zMj4/wai6BKUIy3LxVnp9Npj2Ejswh8LPIB0dt3Y3Hy6GSDACsCaRjImKiJpzYNEVuJdEpUmV8vS6LbusD1bEjZoWFu5qhDytJZS2AUv49Bi1rVKE2JQlNJRtM0vnSjruvznQYMqHTACRBLAFhpm+IoPEKt68HH6B6Wgo56EGmwuKQUnWzQ9PQaeq0bwhwstK71dqF/8egvLhzDF7XIUM02AxDr2Rr5tRz1pIaxhoK9oMVUT7EdbyPNUsQmpnFv6Pcsy5CNM9RtjffuvIc3330Tq2qFe8f3qOxDl5QhCRu/sGb/+RbtY4Kjyioq22I1dhXiys4VbE+3oaCwnC3RNi2SOIEJDVgssbHkWLNu11QW0AWalwksh44ADGhwUMVBfQvK1pYoN8d9XkygAVw/5XUZ4HVsr1jHGJkR2rpFkzeeHcPU9FEywq1nb9GYcsCjw0ewsIjSiCjvncAlB2JeyNA1l2ZfXNgUqLSk02o5rC8QoQyxqXuX596VlCinECDAv/wX/9K7/Bx/8himMlTT34YI6xAZMqhAYT/ex63kFpp1g6ujq/jJaz+JLz7/RT9PygWXFDbmb6WIbszjY1WtvIZS0Ra+hCRvcqybNe4+uIvf++PfQ6Ur6FTjqRee8swwBk65/KIBBcQfx2WjRUuZczkwul/btiVQABswwz+7n4Alk+oUO9EOioYYKLWt+2PjsmUxqmNUuO6Z5agUStZ0G2s2z9kzwJbKVUBAAYsHWMT5VavKgwz+GDVOFSiOFAlxxiZGHMReZwUOsC2tydiRpLLEWLLOPjY/n9W4bKgNWjQxMR5ylUNrjePwGK1tUT9Xe/bSsH39W18HQMC4uqEQNIF3eUiqBHEd46df/Glc276GUUilesvFEu+8/Q6ef5aCRNb1KZtOK8kt8XD5EPN6jkWz8LoVq2pFekK4GLRk7Y02ah8vA5HttLHxfPevVhEA04ES77n38Pd+/+8hUxnee/E9lFdKKl2sNZXeQuGD6gPcWd5BW7ZkA6mUL8mQayCZrQc2CUVO+Bhj8FM/9VN+HS+F+Zxzfg5I0xTPPvss3nrrLS+YKdetAHprMP45mUxw/fp1PHr0qLfe5fuPbSGZrSAp5hyAylIU+c1xiwws+bx4O1yWMjx/f1lEQM5/M4s6jmMcHBzgi1/8Im7cuPGY0J/MPLPuxv379/HgwQOvM8AlAl/60pcQBAG++c1v+v9LocTPf+Lz+LUv/Bo++uijngVjEATI87zXZ/w6v2dnZ8c78w1tPgE8dn24b1g7BACyLMOtW7dw7do1vPTSSwA2eiC8xuXrwSUdfOzMTHn11Vdx9+5d3L9/37+PPxdFEV577TW89NJLvbEpE3JyzczH7Zzz+2qaxjOrebwwq52vo3Rt4LX8H//xH+POnTv+WOq6xpUrV3qJbWYbaK29EKwsdWBAgDVeZOKQ7TVZPPPLX/6y1+djbYrVauXX80PAqKoqHB8fo65rL3zJ9/AQGGNghONR7rPDw0O8+eabuHPnDpxzuHnzJr7whS/g5s2bvbIOHqtf+9rX8OjRo14sPyzr+Bt/42+cMmn126WBhrfeessHN0VRIE1THG8d41HxCLomoalxPKZJ0G78qZ3d3KzcGbJmhycgmXUeZp/55OU2ZBabB53Wmur6pha1rrFSK58VZXcDi65cYUgfdugFzLzg7dUDcmuxsXREiCAJEEYkvhQjRpAF0KH2CtOerq0JufM2nh2wEbkIqlaeui2ZHLJsgPtsmOHn/tqOCamcqAm0omsyDKiNIUoy35xlWaKoCt/HZVlitV55txAXOeSWFoMsssk+zMy48NnnzmmBy0sqRe+/SMeABclqVdP1cbWnkvMi2TkHW1kfMLboWw0iQH80d78bazasCxX4gDZoycWCSwiM7dS9u1rY0Iab97IHeUuZFtUoJGGCOIp7NzhPhsDjYinygSMnySELY4iYK0W0OB4L/Do/LORneSLjyZP3JRkcsv6RhZp4IpeTujEG/6z9Z/i2+XbvHolchNjFvkwhtCFG9QhxE+PG+AZ5Wyuym8xURmNfEShgHdUTs0PH8fIY9w7vkaVYl1WVGho1aqhKoTENEMF/lm1JS1xc133KYHvMilFD0z3d3eu8TV7AjyYjsuRrGx80PGmr2gpVW2FWzjYvTgCM6VflFA7Xh1B5Xzjw3POL6FtlZGWpW7LR1a1G0AQIChrXIUJc2buCl19+Gev1miyxXN+6lGvFS5S4u7qLRUW2li3aU8UfM5NhHIyxHW3jmfAZsguLt5AFmc/Cx2Hsqe9GkzUiB5+rekUBaL3Cqlrh3Q/fxYOTB6TIHjqk05Syjra4lPgiW2Ge1diFoHIVqkYs9E/RKTjCET6498Hj/ziHoMAACotOBjogEToVeGcOFgvVSvsstxc1tC1mixnW5Zrm50BDhYqywJdlBsi6df6pNqyMChUelY/gSUDT0zfz3YffxX/3L/+7x//xx5tSEq20d86IDYFh43Dsx8F2tE0lI9G0X0ITZkhMgkk0wUF6QGKpJsV93McHDz7AarbCdDLFf/p//0/JYaTL9Mja7N3dXV8K+S9//1/inQ/ewXsfvodlufS6F1aTPaKKFeJpjC/+376IZbvEnYd38L03v0fMLtP0Sg7icYwWFCCzHsvHKcPJbY68yL14pVEGiUr8PONqAg20014AVV4rX66jHR2bdpuSCEPAgdeKuYToN+tEyHtEMhwTkxCTxJKORNVUZwbPfP8smgVMRefGx8uNQcILQbem056wesO26J73cJu1TxAFGGUjNK5BXuVwDQlntuZsl5VG0TOjCRsPrvCa4iv3vgLcI6ZCj+F1StlEqEIChw2VTYzDMZ4eP03Wpp3mQ2pShIru+bYhUduiLPDw8CFO5ico6gIrt8K8nqMOOivTgJwm2J7zMqUgHqyICaxYY42v3vkq/f9G9w30+uR1vI5/9P/9R/7vWJMDm9Ya/9O3/ydyTojofp2EE0yiCXbSHUQuwk66Q98ZuS3FLsb1G9fhrMPh4SGstT6jv1gssFqtcHx8jAcPHmA0GuHoiOyLeA0yBALkOubZZ5/FlStX8K1vfau3ZuW11Xg89kG51GMLwxBpmuLKlSsejJAB83K59C4Pw2w/vwd43GpwyMSWbGu5hmPK/tWrV/Hqq6/2AmEpQMrrKp7LGOhhyv7W1pYXw2QHj9ls5j/PwvHPPPOMj6F4uwC8uOEwHuLYirPsMmbidaMs6RkG83z+o9EIN27cwJe//GVcv37dlw5L1gAfC7PJHzx4gPv37/tt7e/v4yd/8ifxq7/6q/jd3/1d/NEf/RFWq5Xv0zAMcXBwgN/8zd/EeDzGyclJr/85yc3vl4xnPs/pdIrJZNKj+fPYk/oVMoDn7XK/TSYTPPfcc3jqqadw/fr1XtKOtxXHsdcC4/HKmhK/9Eu/hCtXruArX/mKB844MA+CAC+//DJ+5md+xgtMyvW7PD+ZlObfd3d3/XiQIA33MTcGWCRLW2uNb3/72/joo498fy2XS4xGI98vvG++15bLJYqi6DEzAHiAbTYT69lz2qWBhvfee88rgwLAcrnE17Ov48PdD8/+kIPPTkeOfqYmRdImmyC7YwqkbeoD8MAGKFEiD3JSHjbaB+bK9TP5MpB6rngOLxUv9W4oXwPmWszWM6hEUYDMzhRsRYgKbdBu7DV1hWAUkIWfqsgyB6W3NJTZUij0H/gxKAA4Dbg4pxnX0dIh2BY28gGdaUwPoGDmRaITWtzaANN4ihIlUrVZQfNEM3SeGA5O/jtNUj+Ah3QjOdjkZ6uy8sjZULPDKQcX0kJ31awoy6Nr74ddKioJYKACEXy5SGMaH5hy/5+bJUdnqWYN1fvCEOMBClZRoFsFlb8mPthCe2krNIAWajID7+DomoGuF1+70IbIAhJvNKFBohMkSOgecDHCJsQIIwRtgCzIiA6qdA+w4ElOlo2wyCE/AMfjsReq4YmX0WzJrmHRHuecBzAkciwfVL9Q/wJ+2v40jtZHOFofoQ6ofIfdHkpVolAFViGVO7zfvk/14WdcHwMSPkoVCZ4GJkA7apEiRdcrSNsUkY2QICGLs1JjHIzxiac+AQOD1Wrl/bqdc6jayutj2MAS48Z2GhJqTXZvIdU7NkHjx50LnS8/adB51jPbYXCflvnFtn4ymJQ13gCVHXAw2bp+5l4yKWrU5wbLw8ZaFWiJnaAsjZEgDGhhbWkOstriKD7Ctx9+G6tyhcVkQfPdKQDgg/zBY69FqrOyxCZoZlbCulljWS/x/vJ9L9THdpanNQWFTBNIMQ2mmAQTjPQIV5IrsIVFNaswDaf4hRd+AdvJNnaSHWQmQ6ACKE3OQ4UtEKSkBzLP5/j6t7+OEiXe++g9r4UTT2IggmfpNJrA0SANfDBa2svZNT42j5/SLKwP/uq2Rl+S5JIXVeqT0EZPPRZ2APLXwSnYtrNV5oS0IpbY0FGCy2eetKxBngvX1DPg5ktkzhZmv1z7QvfTAn/w3T/olYxkYeYdHLYyArRiHeP+8j7qqMZiuoAtiaL84nwjXB2ZCLvxLv6TV/8TAJQw+aff+ae97EwQkLXzr//yr2N/f5/A96LA7du3kec59q7ukbguux65Cqt6RUKHzRwPFg8wL+fIXY5wHGLVrDyAltfE9smrnJ5jiuYhBjFdfPG6QLZABWTD7UKoRqHOa7RV60VinaLro7RCOkox3hqjbEocz8mOU1p+8nuL8nK6GwwweUvWDqhVjjQjjDbe1cJaS+Bsp8VSulPutQCwgd0AEnZQIqKUtys9qU74IB7XpAGAFghBYyMJExTLAsW68HMgOs0GrTX2tvagjSZWUlN6odbTAM3a1TipTnCCE6hcIVBBj/XiVHcvnFaKZUDlUN25MbNCV8RKiMsYpiV2QmhJV2mSTnDl4Aqef+55VHWFO+/fwdvvvU3gj2lgI0slj5GFix12ru1gVa9wsj6h5MsFwqLMwgKA4+Xxue89qyWGWKPjYIxROMJYj5Ehgy41qiXdH/opjTnmsLn1rJKgJeZUpKJecPrss8/iZ3/2Z/Huu++eWlotBQu57IPXRxxAf/7zn8d4PPZsBd7OycmJ15cDNqAH/y414+R6eMjckp/jxuukKIp8wCnfJ9nc8/kc29vbiKLosXIVAHj55Zdx8+ZNnwy6du0a8jzHdDrFX/pLfwk7OzvY3t7G9vZ2L+HE67vDw0NMp1NPlx8CLvw9THbJNZ/sF9kfWmtfonDr1i1Yaz3DQTINqqryfydJ4oW7ORZ77bXX8MUvftG7KMg+ZsDm1q1b2NraQlmWvaCb9yWTdKwjIrPw3LfyWnMcKPub+47fy+yLV155BV/4whewv7/fu57M2pCggFIKH374oY97WBR2f38fTz31FKqqwnvvvdcDY+I4xiuvvIJnnnkG7777rgdlTmMg8+8ynmrbFtvb5ObH4q9yLPJn5HkB6CUUR6MRDg4OvE0nu8fIMn+AAKMkSbBYLLBcLpHnOYIgwPb2Nv7u3/27mEwm+Af/4B+cMkM83i4NNDz//POeisEX/q+WfxXto9ZTHCtFtMdWt5vMtqpRuML/rgON2tVYYun9jj0VFcK67AybL+OMr/0PXejZApylTnRClpIdsMHlDokmu7gsyLy6vWkNIh0R7a8L2KRd0MRNSEm7C+p4wAIUPBS2QG5zzIs5Vs0KLnQoXIFFuaAMrNr0C5cuMHAhmRa1qr0dV6vaM+vOmToJdJPYRcBFR4eOisizLVK1CepiRXXzOtCUhTYp0ADFuiDrrNYg1eRwYUtSn2fKFU/8PKhlOQffNHK88IQetQSQAOjR3uRExwEzTxxhEPrJyDmHsqFAd92Sbem6XWNZLZG3OWxIbJZltcS8mCM3OW6Pbj+WdeHSDq/WflZzZIEWutDT9Jmqz1lMaOAjfPT4dkTdPwC4YHDN+LnFMVk3nxgQoBS5CMEogEmNF3gMLblMMFPGOAMVKUziCbbNNiJE0JWmrHIde5bGKBwhUAHqqvZlIFLzZDipMao+0ROMmzGm62mPsitBDq7nfP755wnMsKSCzpoi65YUmkvVleogR+EKzDHHiTnByqxQm5qo/6o6vdymooVkEiRkR1sCQU2WZ2FLjiWmoXrXsCHxsbiJMWknNJZr4xeI1lpMJhN8+tOfRhZnqOsaDx89pIkbjsaUXWNplzipTvCf/T/+My/0eJgf4mhNauon5QkW5QKLakFBRaeif1rjBXqgAhIQbepLgY/nNdaukJmwMzUrLID55k/OqBpLoFzsYsQ6xtMHT2OSTBDrGIujBWxlcWXnCtGjVedR3y1WS0tzGrsqVCDQp0VL4qKnBNgODitLQdj96v4me+sccA1w16lPXv/o9cc+q6AQa8qes9jiOBhjhRUSl6A2NeI6RrJOsFPsYKIIxBjpEa5fuY5rB9fw5S9/GcaQveM3v/VNfPf730UbtNi/vg8VKzSqQbqVYv/mPk5WJ5jlM7z53puwgUU4CslatF1j1a6walc9fYLSdjT5j+v/yaUOnQ1iGIQetOrNX2LMWNgNNb+zRwToOamdJtck3WyO6RL4QqhD/MYLv4Gns6fx4OQB7j68i8IWMJnBsl0Sw62lfsht7t1DLmv7eWHTIOvitsS8nT/+/6PB3wYAWclDOYXbV24jdjESm2BsxjhIDvC/vf+/YSvcQlEUmDw9gcoVJskE+3v7fkHFmj6SMq61RqjIDWmcjhFGxKzQmQa26Dn84YcfYvfZXUynU6Rp6pMgh4eHaJoGk8kE3/3ud5FlGT744AN861vf8vXzs9kMSivSZAhaRFsRTuYnsAWJdyICbr10C5/67KcQZAFMYrAoF7h97zbuHd7Dvfwe1u0a/z/a/izIsuw8D0O/tfa895lzrqqsqu6uHtBoNAaSAAkKJAgQoiiBGhymPIQd14pQyEPYD46rByvCDuvhhm/4xfaNsB/4cq9CihDjUqZJyhxMEgIIECQFoNXdQAM9Vtecc+aZz573XvfhP/8665zMrKomdFdGRp7MPMPea6299vq///u/z4kcAtbY3lkUmMop+pM+CX+yM9YlLbAp0cNOM5awyKqYSyHnDCtL0Tqa1Yt9W6UqVNlTiraajAoWuJyDFXVJ7h9CCVjSQrPRpMDJklSulafasUWLfhpzoECxxNgR4fJ1w4mF0ezxWTgbNnzbJzcZSXbVmqUkqBNbVgu767uIy5jW/2JGgprFwn54qT8kULnzxFSw6APNmBXzwGgO/uDe4rzw3HL/WTXtgV24aDgNXAuv4XhyjGJakA0obLiWC1Ur9Lo9/PTnfhpJRjpi03yKs8kZTienEKEg7YliqrVWWF/lceBoWtGa389WL0Q6T20Xv3HJG9SkK+QJD5Ed4SA8wPfvfR/VrMLpC6fIRhkl1eaizk7l4HZyG+/334fKFIRLOiUStMfsdDp4+eWXlwJ83nf6vo/19XXq7xUGtamLxUHtReXO5mv5b/yYmQbmfoifb8YMjQZRF9lulINL3iMHQYCrV6/qwPnTn/60Fm986aWXlhgSDEbwuRRFseTIcRELGsBSYG0COfw77+NNQXb+exiGGI1GSywILm9n1zkW03QcZ6lkgsei0+lgY2NDs3RNtoXv+9jY2MBXv/pVzWQz2dp5nqPf76PZbOrj4tiBn8v709WxMs/VjFNMdodlWWg2mwjDEFtbW0vHzXNCSoksyxCGIYIg0MAWAy18DGdnZ7h165buC2bdBEGAj3/843j55Zf1ca7qZPDnmSUxZj8yA4bHwGRimACTCeKYycp2u40vfvGLuHXrli6j5ucx42K1kmA8HqOqKq3BmKYpyrJEFEUacHpSe2qgwaR+m/QiVn4PQYJnvvJ1vbwpQMHP325vL10QfJI8KSpRYZSOcDo+xR/IP8B9Z5nPVmOOxkuqF17dWLOCtJmlflzjul0O7Dgr7SqXSh8qH77yF/oMIJ0AX/gkdKhcVHWFJpqIEEEKibfEW+g4HQrSq4V4YmAFtPkrFxagXPsDSfXvSZWgsiqtoWACNayhUMhCu1nkFmVaJt6EKOZGY9G8FCnGaswdtNyUMQs4o3aRNaeCFsfTIA+r/c+p9GyzqYGg2qHzh0PQhqQ+k7mEZ3l4LXqNbjZWpBkAdmWjsisCRISPSEZLCKxSCp7tIZABGmWD+q8uMC2myPMcPohOFie0iSudEl2rS4Hs/Ku0S61bkSChEpHLtCTEYl5CEMDEm3a+GVeigqMc8hg338fIVpv9uMQiueCGXqFCggSJSAA5XxwNSzOz1ahRh/OAk9/K3FA6WFi/KcByKLNgCcquWDWNqSykLgtxagc70Q66NVEoU0XOEiFCuHChcqW1NOzaBqoF9VAIQfW78BZrhVxkD3XXCIFROsKjo0dagMq2bdSKSnZSECgX1zEqp0K4FiITGcb5GMfZMXKZo4hIFDWVKUZyRH+TlwQ787IPt3Ypq1K5eNd6F1ERQebkvBK4wdL/vcLDZrGJv/Oxv4PIic7dnM99hCKxR3akuPRnOsCf/Zs/wyAdkJ2oQ7W7l1FnWRTRs+elCIKsPmtVIy9yYkpgnqGUSmexTcbEUhPQG9pazi05QVnN4+F5RgPOVl8udOkJK7IHVoCe30PbaaPjdbARbGAj2EDP66HjdeAKKqGAoI1qUlDZRFzG6E/6+PZ3vo3TCYk5wgXsyIbTdCADiVzNAVtFNd+jYoRBMVjOoEs8VmQOCpB9Cfe3SVzSgYMyLSHCuYXyqQe3dhEgwO76Ln6i8xNoyiauBFegfIVO0MHVzavk1GKTowa7AJgbAc/z0O11kdUZZsWMykRKAqHighwhRsUI/bSPQTaguu98gmE2xP39+5iVM03l53tdLeon0/h5rOdPqVEjQQJZz7OnyoNv+7i6eRWucJFOUpwdnlGZDWwdQLquixdeeAGf3/48boQ3MPNmeDh9iMAOsHttV29AzA2dqcfkhA4B7nWqwd9pPiVNjzKm+vaS9BlmxYyy/+UMdw/v4tHZI8r22+RAUMv6iey11T5QQuHIPVr+ew384Xf+cHmucBJjMP8GYN8lxo4t5wLGoPt867AFDx66fhctr4XIitB222jZLfT8HqIgQqPRQOaSnogrKHlh1tDy/inPc7iuq/V9dNloreCUDuRAwhnNPecrcgVqn7bxC9u/gE6ngyiKkOc53ivew3un7+FH+z9aUoLn+bixsYEvfOEL+PKXv4z+qI//8//6P/H27bcxTsdo9BpwIgeFKGCHNl75iVcwSScYJSNM0gn2TvdQyQoykDRvK5q3cRnjlzZ+CX/v+b+n2a282efsWpzGGBQDDKsh3rr3FhKZYH+0jwcnD3CWnGGUjpCCRK8ru9I2nJWoKFsvSz2WcRWfB8ZM9qg6vxYFbgDXcpGnOfI0J9B+bj0rBD03akaU0JlbrMZFvOSmUoLEH7k9HzyPjXADaZ1qcPFLV76E//oz/7V+DtOoeb/73nvvoUSJ2w9v49vf/TZdC3VCon6OQmmVyFSGyiZtHrjA7nO7kIFEJjICLfIJxhnZsNfWQt+Dy1ZTpPjR4Ef4uZ2fQ2EXmEQT2hvKUpezAsA3X/vmhZdLmJGNeMNpYNPfRORQ2eMX3C/g1fVXFwkyUEJNhhKTfIJBNkA/72OYDzHOxziLzzAtpnRtz7VXKvGYsr+5bWmCBIlKcDo7Xdg4b2ABUKyUgPyzf/HP6JddQFwTuiw2tEL88Ic/RNtr48rDK7AKC92AmHAyl0gGCXbFLpW/OE26hu0INuylgJRp/CZzwSwfMJkBJkPgi1/8Ivb29nRtvflaBi1NpgHbpLJjAoMFLLIppUS329WUdjM7zeuIuZeyLAtRFOnr32TDmvPTpPBfOCxGGYYJpvB6wtaYvP7zdc9Aw9WrV3WwH0WRTtjyOSZJot8ziiL9XAZQvvKVr2BjY4P2+J53TrCS9S1MS1Uz+88gCR+X6UBnZur5XE22A0AaFg8fPjzHlOdm/s7gkVmm7Hketra2tABrr9dDu91GGIZ46aWX8Pzzz+OFF17Q/c8lNDw3RqMRJpPJOYdHPl4+B3MszbnB52fen1clBvi9TetapZTWrFBK6XuU4zj6fsNlHgCx1EejEW7evKkdW57UnhpoYLsZM+ADLqYYceMLUWdQ7RqWvbAyWaUiSUlZ4ggRirrAX1N/DeNyrN0VTGvJyiLF/VxQoG0KGfLXEkPikqYEeXMrKBSi0D8FiD0gQBRkpoyamhO6rQbm5nW8mklQVApiuWRdxRoBHJjrUomShCL9mij2zaqJNbVGWW5lo6orLYro2A7Vloocv331t1GKkrLwcBbK15C6L1jUjcGYShGTQtd9XhKcMHCRWxfQop9G1Ogv83wFOMXcrrOgMhtf+JpuH4kIgQggPdpssNdwbRHF0Bc+Ppd8jvRC5os+I41MkeVzYzAiUZRxz0Smb7RcZsOuH7mk8prKJgCiQPFY4Usui7EFZRwYNGBBPNac4HKccv5F028ukHXRpvtJrGxFtHrOFHH2SAekij4T3kIrQwmFM+eMhLdQoG7UWkvgcefnFR7cwtVWqq6gMh8G8jzh0XydW2+WKDGNpggtokG7goAmqSRc5SKsQjTzJpJBgmvuNXS7XczqGY5mR7qGbPXGX6lKj19u5cgECSRqdoWKEdfErqhQ4QQnmMkZkkaCwiouLCdo/j+bsKWNjt/R312/e/HjYPF4u7GNl9ZfQsfvwLOXrVz+q2/8V/jhD3+IwWBAVMmdbfyVX/grCHshaq+mzWY5wbSaYlJMMKtnUK7CtJpimA3xMHuIWMWoRIXcySnDd0ETFQk+dsMu1lvrqJIKo7ORVjAXcs6SkgAsoNVtIVMUJE8zcjxRQmm9CP7idSNXBAahAPbSvSdMRuO45ht/SxBDqO7WUJEiKnFtY7O5ifVoHevBOtpuG027iabdRNftYj1Yx053B67noixK/PHX/xhJmeD2g9uI65icSxwCYQuHQNnSoZrtdqu9ECMUCSp3QSHnNeid5B380et/tHzAAwArVYISUgelnkW1z5EToeW10LAbaDpNrUkQOiFZflqkV3C9eR3Pt57Xf6/TGv/76/87Hnz4AJ70cHXrKn7qJ38K165dQ6/X00A96yxkdobYinESn+DdB+9if7iPo/GRBuhy5Mu6J7JALGKcnRmoURcXtrcP38ZvH/72ub9bx5bW3/AtH6EVIpDk9x7ZVN7QCTtouA3SY7BCRG6kGSg9v0eMQidCw21ogV/HcvBHf/RH+PXf+3VdZ/sTP/ETeO6Z58hD3QaxBysChb2mR5bA6QTf+8H30J/0cTo5Jd0PQYyp0i5ROFQPrxwF27e1nWqtjGy4cd9hkclzWwa+3X1EExFmvTmSXJg8SeLEeBYQpUCd1pCFhFM5sEsbkYzQ7DeB6ULM6xOf+ARefvllDWYByzZlvDE0a2ht28bBwQGOj4/pGoOFk4cnKPsl/NpHMSExXyklmmtN/Oqv/iqqqkKapsiyDO+88w6azSZu3LhxLtDiDS1v0HnTzEGFa7vYdrZx07+J5riJRqOBftjHO8N3sHe8h/39fQyHw6VMI1OxeePc7XaxvrWOlz79Eno3ekisBAfjA9w5voN7x/ewd7ZHa59DQTrrHFRWhUQmmBQTKhNxLmGMXTCOAkLrqdiCdFZcy4UjHAReAM/xsOVtoeN10PW62G5u41sH30LX7aLpNbWYuG/5sC0CYDzhoSmbCLMQiAE3XwByrFbPAniNRgM//+LP42d+8me0yPpwOMRrr72G7772XZz1z4hxJUsojwSclavwq//Rr+KrL34V/+v/63/FO++QaHGv18Mv//Iv4/f+4Pfw/Mefx6/+R7+K49ExJsUEg9kAB/0DHA2PEHZDYmbNwT5mZ/Q2enhu9zkNXnFGlwNp3kfxHGA3gjRN8eDBA7z55pukan+4j0IWkIFE7daonRq1W0MEAp2rHey+uIsYxHYcF2OcTk5xNj3TZZC1PM84bbttJHmycBZCiQQJNrNNTOoJbg9uoz/r030znyCp5mj04fkxd4RDQIvVQGiF+Ifb/xDb7W19LZmifGb22IyD6rrGyckJ3n33Xbz88st6T2I21sIy6/vDMMSVK1d0XLW1taWtOPkzt7e3IYTQZRkcXCZJci5pY4pP5nm+lLXn//HzJpPJkm7b6jmZP4UgPYiXXnoJ3W5XC6KbWfSyLDEajbC5uYkwDKGU0lph/NnMBuDPZfbAbDaD4zi4cuUKXnnlFd2nDCgwU4CvEdZaWHWaW9Vd4PEzy2zMPuPfzXWo2+3i4OBA94nWtFvRJgAIJOh2uzqOFULgE5/4BL70pS9pwOrq1atot9t47rnn8Mu//MuaIcCv4fFmgIidocy42AREeKx5Hpm6GOZxcDPZGvyz1Wrh+Ph4CUxh8wLuW/P4TG0Uz/OQJAmKosBgMICUUjtkPKk9NdBg1suYA8cHZKImPAHNCWvbNn5N/BoOZ4dkfSd8ovMLT2e7PdBjq7IwdsfIRQ5PeRRU1j6COkCjahB7II8oCzunlrLCJ3/rC1vVOiPGVmdJneiaXRbkYd2GmZzh1DuljJK1rFz/1O2C4G+pzlcolKJc+F3/GPRp87OkkhocKVGiEpVWe5aQGmFmgIED2cs+Xyqp6fkWLNiWvQy8CKWz/UsskjnN8rHn9bRAxDyIL1AgVvHlgfWqPaOLJbEz1lSwlKUDXtsh8SY7n9PtS6Lbe6UHu7AhM8oGuqWLoAwgarGkShyGIV599VVEUYQ0TXF4ekgaIA6BPolKKAjzoMtmKps2/alKwV8ZMs08eRztmtkkNkikkjeyqlZUH2tLckuY18bWgm7WWuzvKRxALhsLdgZhzQtW6LcUAWa2sNEMm0t1uxyQztQMAwxInZ/LpUBBIJ4AiApFmQtPePAyD47lQGwsWEhaz2QObHA5lVVZmnURqhCtqkXsiwIQuQAK4NqVa/B9H8N4iH6/T3RARSASj92snuG//L//lximQyqXSIf68SAd4O7wLgbJ4u+X1b4HdrAERjyIHqD4WIE6ruFUDspWifvN+2iqJlqqhUhGuO5dR8Nu0DrpeNjZ2dE37D/8wz+EsATe+MEbyHJyPohVTAwJmxgxucyhPAXZkHjh1Rfgd3zcy+5hYA2QO/PyFrki1mow1R0QW6nrdykTZFMAHdohlaJhftOc132XoqTNajGhbFdBJWVplS7mpAF28vpcoNBirsXcWmKKKe4kd56u7l+BFPVvzqnYtVzM0Tlrx65sRGWEz2x8Bk27CR8+3vizN5BPcy2kKSDgBR5e/NiLeOGTL+BseoaT8QnuHt1FKlLkdr5UMpHUCdIqxbQ2rDxTLAUyplgeCz9emum7Of+ez/vfTX8X3h0P/n26P/qWr0tHml4T7aANT3oo6gJN0SSGRikgCkFuGqUkseKaroPnbz6P/+w//c8wnAzx3Te+iz/4xh/oOVDYBQFWQY2d53ZQyAKjfIRBMsA0n2onGXYpWl2muH6fy5N4bJ+GTu9Kum7LL5SwaguhFeJR5xH8oQ9vNLcztYhF4ksfG9WGBioiEZF7SuIgm2VABpRxSY5ENjGltra28I/+0T9Cnud47bXX8Dv/8ndovspSB2xO6OAX//ovonIrHE2OcDg5xMNTAvNKu8SsnGFaEkMjrVLtNmLaYK42vieWNXXWrJ4tNAY8XFgail0AnwTRyyHxHfs78I99BGcBia/6HYQyRB3XKKwCo80RUjfViQpmGgZWgMPkEMNsiLxa6CeZVF+TlmtSv839G4ClDfdq3fdq9pefz/tBz/N0aSSLsZmvE0JgbW0Ne3t7ejP86U9/Gj/90z+tX9toNBA3Y9wr7uGdw3fw1qO3MB6P9f6SN8Js39put/Hrv/7r+MaffIOCVqfWFsp1WOPVn34V4XqIs/gM/bSPYTLEIB4sHLkqckaZlBOUdYmH8QXCsI9pDJ46ksSmy5dLWKVFbMGSWJ7dURf+HoESnW4Hf/tv/W1sbW0t9S+PgeM48FxykfCVD5lLiELALmz87NrPku3vPAPMfb6+vk7uQZnAhruBtbU1lGWJJEowdIfYq/bw3I3nNFBkBiS2betsuzlW5lwxyxOAZRFF7fKg6Hyt2IJTLDL6n7j2CVzpXcFP3vjJJZr9O++8g6997Wt47733FkGjJSF9iY2rG/h7//nfw3O95/DP/+k/x5//xZ+jkhXclougE+Df+fK/A1/6+MVf/EU8evRI23cORgO8c+cdBJ2A7o8qxaSYYFpMMc7GiOsYiUowLaZ4dvdZ9Fo9Hdesilcya4f7iq+Vr3/969jb28P3vvc9PPfcc5p6bzILzKCXGZxnZ2dajG9zc1PT0vn6HI/HkFJiPB7D931kWaZdCzzPW3LlAyhG42NktoCZ6a6qCnfv3oVSSmsucEANLMBCHkcTgJhOp3j99dfx6U9/WpeHcXY9jmPMZjPN6GEHBc/zcOXKFTSbVAJ19epVxHFM89nz8Oqrr2IymcD3fTz33HN6fnEG3SzVNfUH+OdqkM0gSJIkGAwG+u+mBtlFiXL++/b2traK5f+Zax6z0Pk4W62Wvu749xs3buBHP6Kyz1u3buH1119HGIZLpS4M1FmWpcebx4yBPP42j8VkU6w6fVxULnPROK6vr2tbXG5cMsGfyWUtAPS1yZobPL/Ozs4gpaREwFO0pwYaTBpLWZbal5TrsxlZNyeviTqVZYnPb3weds+mWl6V6u9MUa3vVE2Jpl0lmMgJBffcHxfVFxo18Jyt5cCHmQJLFP/KI6vKkiwta0nq3mtqDVEZaas3e2yj0+xo9IoHg1tVV5iqKcb1GFM1RT/rI5YxcplT/XE1W6awsYCkqLTF3qU2e0/ROIhbbWaZyJOcHs61lbdjYIQBCxYhAxYbCYiFjeCT3KtkNR8TkEq5FBJ1VRNDZE555T5hJJvf+6lqP58AXDBTpRBzmrgAzf7mk9/aeJPFHFMSdm3ju9Z34VUeiUS1JEIrRIgQgQoQ1AE85WFNrdHcq3y0nBakWiwsTKvT1DlVUslAFRMtcXqGYTqk+TQXM8xEplkVXA6S1USlzwQFDZeOw5wtwGUPUhF1WihyBKjVXKHWlRCWILHFMiXVc0F9yHTX1T4/UAfL88gEwdjpYe7U0hANyEqS1oKyFzoYc6cPZoCgBlABjVYDlm+RJko11gJ/iSBrVtY64frkpxH0tJRF5ToN0sFghxEX7sLFobTwg6MfIHIpC7sRbujHnJmN3IiUyZ2ISgOKFKNsdB6cYEAiG+Jh/RCZlyHzM1RuhRPvBG/uvXnpsYZWiLbbRsNpoGE1MCtnCGWI0daIHCbmehUyl5CFhB/7aBQNUg+fdvFf/J3/Ah/72Mfw9a9/Hd+7/T2NoluWBWUplHYJGUl85mc/Q+Up42Pc3ruNvf4eWo2WBl0O0gOq663IV/4yQUVHOmg55DpwzbmmHQjabptU2qWjQanBYIDv/ZvvYTAbUNDXdrB2dY2AinqmdQAutZETC+ATIGrx0vybz9MznOHBnQeL15k0XaN9c/pN4M8Wv0sQkOZJ0rFwpYum3cSWtUVZfSsgsUoI2I4N27I1qFCLmsTwKrpGkzLBrJzRZjefPlY0k06BGHdJlZDI2nyhFZkApnQvijMStszXiMVwWfs6vo5f++e/BoDq0MUzQjvuMJOEs+7b0TZuNG8gn+ZIJykaXgOdsAPP9bRbRqlKAk7mZYxsjctg06Qg55JhPtS16xe1vM5p7beVfr+9ZE+XgnA5ZKUqcgp4WCxbW3oArl7Qh7XQ1/O/+u1/hcAKiNGzO9LgE4ORkRvhzvQOOlGHXDNkB+vxOtp+G1c2rpAVopy7RVneUn0xK46XioRoT6enOCvO8LD/ECfxCXI7xwcPP0BmZTibnmGQDAikqqmEQFl0z4OFBatQElAR1zHi3Oi31Wz85vz7gvvfN9U38T/9s/+JflkHrDUChq16Ljo9ByTe/sbbJNBqNRBZEbJBhk7Wwa7cJVaKEyG0Q4RWiJbfQstvwbXcC8WheXPLNeMcvPCGl4MR3hDXdY2NjQ3s7+9rzaYoihBF0VIwc1HW1Qwg+L3M59iWDVlL1EWNoAzQSlpoizb+3Sv/Lj7xiU8gyzLNHHjw4AGuXr2KIAj0e2ggxgIG8YC0WcoZRsUIo3KE0/QUZ+mZnuPDdKiFQCfZZGHL6ubnRD+Pto6AFxa//8H9P0Bjr4HIJneLyCLBz1k1w/TaFGW3hF1SqS6LR3rKw53RHbg9F05IWhYM9DHVeTgcLq6Heb8w8GM2EygwNQBWmctmoG1S6HkPw2Jz/H+zdFoIgSAI8LnPfU5n64EFsMVjz/Z/eiyVRFiFeGntJf1ZdVWjzml+eNJDNsnQXKfNnFm+4NouIhlhw9vQQRw31qPioLjRaOg+MOcSB2HsCmAmV7mvqqrCw4cP0Wq1sLGxsRQErzoDcCZ7NBrBcRwNNnz605/WbASlFOI4RpZlmEwmOqbiLL9t2+fq403gkIN07l8z0GT6PMdnaZpqqrx5rZZlqf/+7rvv4r333sNsNsPzzz+P3d3dczFemqb6emdq/v7+Pra2tnD16lVdYiHEgrrf6/XQbDbhui4mk4keD86U8/ubDg9mQM59wedaVRU+/PBDAAunEtOS3gzizUCc+/bNN9/Exz/+cURRpOeBWVpg9nWv18PW1hbW19d1KQWXTSil4DgOrl27Btu2tQ0msxC4fIbP2WRRsE0pz2UTcOBzjOMYR0dHWlOQNTNWdSpW10eAmBtZlmnAi+cpfz4fZ13XCMMQjUYDvV5PA7rMflNKaf2TJ7WnBhref/99fWAXaSvwDYEXstULTUqJW7iFDev8BW+yIRjJGwwGiNMYb/TewMAe6FIGDjprUS+ytvNsWoV5CYBVI7Op/v4yJfknNhbm4hrA3NJBkiNokXdBNb2OcNCoG2irNjaLTTipQzRItwlf+hCgi7rRaEAIopgxOqagqJ63mmEmZ5iB6J98/IUkkbxULeoaKzn3qEahz//HOs8L/7xMJ2ZmAYAnz5pV0EJR1rOSlbbTEyD6Pl8QWg1bPIEJASwy2XPbRKnk8uZF1Npei9+TP/exWX3zX5eUj/B7QgGZzDDDbKFz8TRXkzJo4xUBL1wa4mcGwwckaCos6p+oitCtuojqCH7lw8bCjonR0atXabd90j/BKB0RW8euSLtgXhbCYqS5yHVpSCYW37mgkhBdK3uBiJhUkjQ55hocLDraCBs6c6uDA6MMpFKVdg1h2n/lGxneJ83dmj5b+EKzbBic8GoPjbpBtehqLgZbEbPBKi2oSpGoqKQbaCUqhO0QylGYFkSzrC2yWC1FSRleO0fhFfiNt38D03yKWU6byCc1AbEAHwxQwnwcVAHqcY0wIwvQm1du4tWPv0oCaZJKQOq6XnjRWzXcyMUkmxBgUQ9xKk4xbA1RWAUKq1jU8a4ejxL40zf+FJ13OpCZRL1O6vw+qAQpEAGadhMb4QbiMiZrwrCFtt/G9eo6fu6Zn1tC3YHF5rASFSbFBJVTIRUpaQ7MA8xRMcIkn2CUjzAuxjiMD/H+8H2MizHG+XhBawUW1mzz+TXLZohkhJ7TI3VzK0LTbiK0Qvi2j/XeOlCT2807H7yDwWyA0/EpCa65lS6bYbZaLQ3dio/YahArLq9yTKqL+fO6FIqzuk+ZzQfmGhzSRZmW5CACgcALEDisGgdUqtJik0VdELC4+v7zfRAr2etrsyIh226rixefexGqUjg7OcOjB4/0tadA+h7Sk4jrGJMpCdyNkzFmxQxFWiAbZk/lVmEJSyvTB3aAjtfBlegKfGvujiRsXSYJ0LkdHBzgYHiAWtYIGgHaXpsyy/OSibiKLxWbFIoABbuyl6wSGdj0XR/b4TZZiMYjKrWUGVG5Za1LTH7wzg+QVhc4MNw5f35cBhPaIX07i4Ccz52dITb8Dey4O+hFPYzyEQ72D1BnNQZHA9RpTUwV4eHzn/087t65i3v370G6Ev/e/+3fQ9ALMCioJn5cjZHKFGfxGR6dPsLp7JQ0D+p0UT4w1686N9eN0sfV9Xzv0QUlT2cAHpPI55IQ13K1dSn3ScNpoOk2ITKBtcM1YAakZYrMyXDaPiUgPIUGu+M01gHkZz7zGXz605/WgZxJATczgrwxNjfKvAcFsERhN+u0V7N+JnuDN9nma5RScISj9Tj4NQzUmsfI2dqyLHH//n1IKfHgwQN87Wtfw3Q2pXuHC9gNG2E3xL2Dewh7IV765Etob7a1yGyqUoyzMUbZCEc4wqQxQdYgbanVNf7br30beA2AB4ifFrArG57y8N23v4v4SoyG3cDxnx5TKZfbhKc82JWNeBRj/2gfnaBDAJLbQsNpoB204WLBPDHBHA5qiqLQAR8HgpyIMxX2OaA2A122xTOzxWad+er4mAEQQAEeOwuYtPmyLHVi0ARR+PGq1SEH2sAiAOPzNbVVOI7hz+Lgkx8DgO/7cBwHv/iLv4hut6sDcD5eU9/O/OYMfFEUODg4QL/f18fbaDTQ6XRwcHCAyWSinXHYKcwMIhnA4Xr/iyj0fLxsDZqmqf6bFrg3yjtM1roZYI7HY4xGI1y5ckV/FgfPLBLIAExZljg8PMTh4SHeeecdbQHKQMfx8THeeusttNttPPvss7hy5QpmsxnCMNRAlKkrYZbK8nmZZVz8/K2tLQRBcI6ZZYqdm3OBz3UymeC9997D0dERXn75ZbzwwgtLc5LXFwbqfN/HdDrF3t6eBlr+xt/4G/A8T8/nfr8P3/e1loXneWi1Wktr1CpQywwyEwwy2Qx1XWM4HGoBY34uX2smY4PH1byueCzW1ta0vhK3siwXjCRBAp7r6+totVq6n3gNqOv6375GQ6PRWBJxNBcPcyKbdBUTATIpJ2bHrXbwEl1kbnVUylIHR+zYUIrHK3u7cBEipMo5QXaDEnM7OAWyShMKs3RG9ayoFjTz+WMOKtl6LkeOBMn54NxdebxKieQ4SlGAJBwBYQkS4qrJ3cFSFtH3c4esPjMPcibhZR7aVRsiF/BtHy9/7GWEYYjpdIrZbKYnRVVVKMoC7fU2lKsgfEEiPlaCaT1FIhNMyymp/891BzSjpM60zR+XifylFfEviOM5uNcZR7YE/YjvzxRkZjnkKtd/W/0cbVP5mM9g1X0bVJdpKcOasIa2rlqyv5yDWfocnrYfDMDGDMQLFDSn+DWrYEcArVJ90fszm0eDYHBgtSzIUJKzCDwqP1JUfuTmLjbEBhpoLMRPlUt18vP6vrIssb65DidycBaf4XBwSNedReKLSZ3oWvDSKhclIXNRQS4LudQBAbRRdeHCL31N+WWGBa8NSiitD+D4DimLiwJZmWlgMREJba6NjPZTtTk7xYYN6UhYcj4P5uKtQR0gUhGc3MF/8pP/CdaCNbT8FiIngmuRIKOUUgMraZlqIMIEJc49LmboJ32cOCdIeylKi+jbj+xH+Ob9i0W7zOZIctBRUpFzTk20VAatAjeALW3KBCuyuXM9F1c2rgA2cNo/xbgeI1MZJmqibXsTlUCNFf7ff7ZiV+QA//Nr/zMadmP522mgaTfRclpouk2sRWvoBT003Sa2wi00XdJUaDgNOh6z6+fjW9QFhtkQr/3wNfwfv/9/4GR6gtqrsXl9E8+8/AzG+VgzxM6KMzxIH1CGr5oi3TMCQgHSEGlQFtupCOhtlARQBCBR35bbwpX1K2hEDaAiy+Y4jzErZhC+IDDOyqB8hdqtMS2miPMYWZVdzqaYNx30MzgtiPkkhCBXI1wOPJSqRFmVVP4xz+DlIifQwdgwmi45UshF9nulMbiir7/5MZ2IE7x/9/3F61aTEYpAj8lkgt3GLta8NWyKTYhMYKu5hbVoDZEdwbM8rcjPAaflWPq+nNbpQkSwSLSQIAvvTcoJCYJWiRaPm9ZTFBsEJIwwwuH0gqLqeXOlq+vn64I28XVVa7Fb5dLayuzBsRxj/2B/MX7h8vvZIDHrjXADba9NdqrKRh7naLgNNMMm3R8k2ddCzfcs8zWcM9dpnWJaTHE4O9TnPM2nyPaIyaL688/fmn/w9eXj+Ib6BuznbOA6gUUPDx6icdZYAi94DbriXkFr0kJ/0Ec+zbHeWkfkRpir48CTHl5++WV84ee+gLPhGf74W3+Mg8EBrd9IEIt5mZVXob3dxiAZaPeEWU4W1I8r46tRa7eRSfGU4hUBgBfP//nP8GfADbof/4X9F/i17/3akg5I5ESIrAjIgEQlGG2OIJoCHacDDx7toaQFFSkM0yFadQutVkvbtvHmHIBW/Dfp8Ry0muUlZlsNOs39Lwelq8kyrsXngCRJEmLQFDb8xEfX72LUH+GF9RfwH3/qP17KyrJdYpIk+Nf/+l/jg4cfYDQa0f5ZQicHhC/wla9+BetX1/HHf/rHePfuu6SJEErc2L2B9wfvoxIV9qZ7BKYXJDA5Laa0jlyg+wtAW1cyYNRwGnRd2CH9lMRu6fgdcr+qHKyX68jTHFNMIV0Jz/eWgmrO/H/xi19Eq9XS/WZmak0mBYAlmz4uPwjDcEnIjsdhbW0NrVbrXOxhWZam+jPYkKbpUjL0Iho9f+YqK8B0ZODXd7skdmOyCbiZmWr+DGb68Gezk8BqdpjnxPb2Nra2tuC67rmMs9k4aOYyd/48jqukJH0H3/eXztvUXVk9Vs5es5DjT/zET+D69eu6X1h/wnVdzGYzxHGs6/l7vR4ePnyo+z3Pc2xsbOj+6XQ6KIoCSZJgPB5jMpkgjuMlkMAcD9Ne0jw3Hgu2j2Q3iFX3DLOPVoEYFtSUUmqHCH5fEyTi9+TrmsvBqqrCYDBAVVUIgkCzC1zXxeHhIc7OztDr9WDbNjkNzYN4M15mwIiZGzwmJqjETLFOp6NBJ17D+HjN15hzkN9jOBzirbfewrPPPotPfvKTS7awpttSXddatPPk5ARnZ2e4d+/eUlkHH8OT2lMDDS+++OJSrYp5oZj+ppPJRKsgr6KT5gVodp6JTJliI0II/PTkp5dUNPM8J7/SVgODeID+tK/p46latpJkZ4FMkBVTZhHlvLIrKJds7FI3fTzNWkGDFb7wtaidrWgzXytawJQkrYJCFbQxFeRAwJaV7JvN6vCwPkrvL47lX+FfUXDpC4i1RbkI1++HVogAAZolWRO20UZDNXDDvoHQDRHVEanxgibSdDrFdDrVlMHxeEwLlYC2Kc2QIS5j+B0fmczQT/sYl2MgAG3OORPOlqYolhkmT7KQfOrTV5dv9ldLNy76PLX6qyKWCGjchCAQQ4BAKP7J349r7N6gFdznOgUAYEtbM06YZlyipD5ST3ZGufCUxeIcuOxkiXFiLT/viY0DJCUXVPHKg+3YEB0BrybAwq1d+ntqo1E1dFlSy2vhVvuWdmyxhEX6KMg1+JAJui5ZrLGf9HE2OUNhzQX77JLmkj0XfZ1/nzuH+XUjFDlkBFWwYFdUVDrFzjeAYQNpAcqaa4rIGnChRVRzK6fnrAoyR8A//ON/+MTusyVZogV2gMglNlPLa6Hjd9Dze+iFPey2dtH0mmi6TfyTP/8nODs4AzLAlz5+5jM/g1dffpVKlCxLW0UmZYIMGezAhttwMc3InuwH7/0ApShxNDlC7RCjq7AKcq1R1ULVfa7ofpFd5GpjCrkjHQJ8SoWqqNCIGvqmNStnVLuclCjqQgcbpmr7aovsudq32yJgYv646TTRdJoYjoaYeBOoWMFLPezWu/jy+pfh1eQuwxsOgO4jYRhibWsNg3iAOwd38Kff+1M8PH1IQIVDIrCFRaBX7MQY2SMSfZQK3+t/D/HJnIpuYQnIE4rsdztlB9vRNnaCHdilDSRAL+xhrbEGR5DNnRACeU0lTpN8gmk5xbgYI0GCWT3TDhNsF3cZKGA2dg1hYLuua8r6z+d/rYjFV6rHgOwK2rLUqmkd8m0ftrQReAHarTbqqsZoMsJ4OkYtaghbLFhfc5D5+fbzGOUjHM2OMCgGeHvwNqan02UWijl3pIuWQ6UxLbeFjtfRv/e8Hm42bqLjddB0mmi7bSpP8Dpo2A040sG/+Bf/Ar/7e78Ly7fw8U99HF/6a1+iua8ybafHYpDSk1RDn01w+8FtnI5OMc2mxJayK1pXJTH/YNEaKYW8lI1RosQYY2zKTdxo3dBlTqfZKdIsRTwiK8+LmoBAZEf6vFtuC2v+GgVh8GGXNq6tXcP+nX10G12cHZ/h7u27ULXCdDxFXuVk1+cKbF3fwnA2xPHoGJWo8NLuS1C20roQw2KIe/E9xGWMcTpGbMXIr+SXr/EHAP6/wNe++jU86zyLoCKdIZM+v9Zaw9/6yt/CdDpFHMfI8xzvv/8+Wq0Wtna2kFQJxhmxlIbpUIPKk2KiRQTZOWWUjzDMhlRaVRLAellpEE3oeYnQ/P6mhCLmUJFf7rIiscR+Wm3/9A/+KQBy/rBv2tri3MOcgSoD7D3awz9+9h+jLdt6w2xaHAKLIGw1u86bfWB5gw8sap05KxgEgf6b+fpVUMPMspuBIu+ZOYC6efOmLong4O4nN34St67fQvRshB8lP4JSCs1mE5969lN4J3sHQRDgy1/+MgAgTVNMJhNkWYYPH36I9mab9IjqRFvWzioqVRtnY0pMVTHpGeRj7E33tL7BktDiBU08L2DdJDYhs6uc2kGRFeg+6KLhNBDKEN2wi41yA4EMyCnMHyENUoQihC98WHJRnsGaBKxRwLX+QRBoa1rOWJtlIMy44Pp7LlXwfX/p+SzOucqeMcffBK2ABUugqipMp9Nz5Rccy/B4K6UQBIEWTTTH3MxkR1GEbrerS4rMecZzkOdHlmU60GdKu0mXZ6CBSxrMtsq24Nea8RiLArKAOgfrHPSaJU7cX6ZlvRm7sSaBlBLNZlP/P0kSHB4ealcGIYQeL+43LtU3gUDzsQlQMZhoMp25v/inGeDzNeb7Pj75yU9iZ2cHSiktEbD6mSbQwOc3mUwwHo/1OIRhiPX1dezt7cG2baRpCiHI+cEsX+J5ZZ6P+T8TyAQWgByvNWYzn3cRs4X3T8888wyef/55Pd/NEhXuv7om21VzjrErSr/fR5qm2hnlSe2pQ11GWZRSS6qmjH40m0194ZqIx6r9yGpH8IXOHcwZVbOTTGSGO1qC6NvFaYGTo5Ol95NKIkR4DujgY9ja2sKLLxK0/uDBA8AC2Q3NnQVKq4TTcFBapc7SKltRFhcLyx/tUGDNQQ0xp7Ne4lctlCAV/tqBLKUWsLOVrWvrtBPEXM/B/MlZ9VpQvSoDF4VYBGMjjBYfWGOhoH3JPUFYAqItNM1UdinL74Ay3X7t64zgprOJoA7QrbroxT3siB2EdqgpwyMxQpzHFIzCg6hoE/GB9wFmcgZRC7i2izAICSWNEwhboJY1citHKlJS/ncoG8cOI/zFjIvHNoXLN15i+XmaNjwv2/jIzagBZ0ApR65ZBvpj51lNBgRWKa2rTTMTlEWbsZp5G0Z2c860MQGdpfrspwBaltqcacHzTDMtJC62O72orewprfkXO6C4cImuLwJth2vVFsIypPpw4UHkQtvGMqBX5AW2d7fhtlxkyHAyOcG0mKKwiuUyEJmdc5rg/z8OKPLgIUJEQMqc5cGlGRISn3r1U5S5LDPSrGDXgoIysnFBWfGkICu/k/jkki5e1N3jKpbqyt/CW8CHi99Z3T+QREfueB2sRWsIZABRECBm5Ra8iYem21zYk+YCsiRtiTqtEVohdrZ28Mu/8stQtsLX/vRrOJucUbbXrslK0AWUrdDeaGP3uV1M0gmOh8foT/voZ300nIbO2OrAr6bg7zI6u9mSKiFbynykXXAUiC1UqQplXaJ+bnH9vY238Rtv/YbuM+6HyKLMZi/oYfPhJlp2CyITOMQhxvkYVmGhXbdhz2wgBapZRWvZnHZ548YN/MIv/ALG0zFef/t1vHv/XSSKnCdKu9RlF1efvYpGp4FhOsRxfIxBPsDbxduYDWaXBpwSEg27QToUXhtbwZYGVvgnlw5w6cOsmNdzFxPsD/fxwcMPkIoUylGwAotAMEX2pU/U81F0f+E5xqwvAUFsiVpgls0wGo2QlRmSKqG68Qtalmf4vQe/R7oEimyLd7wdNJwGAiuAZ3mwpa2FLmvUuoyGbQPjMsb99D4mJQWgo3y0rKtgtNAO4VQOip8t4JQOTqIT7N/fp/50qO8iGRFIZTdxY+sGOl4HspD49uG38d2739WZNN4cc9bw5s2b2NnZwRe/+EWcDk/x/t338cYP38Dp+BRu5JLNIwpIT+KXXv4l/NxzP6fr9t977z1sbGwQa9ASpJdQxbo8iNk1cR0TqFWTrs4wG+LD8YcYpkOMshGmj6Z07nxrvrE4d1ELqrmvHBxbx5A+BR2yIEbaZrhJQJ3dRMfvYL2xDh8+9u/s496793Dn9h1kZaav4coiq8SoG+HKzSu4+cJNiBnte2azGabTKbrdrt5gz2Y0J8zsLm+kLWFpMLCqKmRepoMkfg4HDXVdw3EcJEmiM5rvvfceNrc38dpbr+FweIij0RHefOdNYrogIVDQLjXQXDs11q6uAT4BmuN8THpXj1ljZEWC1exa1Gl2ELohkjjBcDCk9cP1AAFdKhiPY9wb3MOzrWehykX5JtPYee5cxLzlnxxcmcEM7095T2xqGPBj8/UcgJqsCjNbzpR1DnQePqRaFlaJd10XH//4x/Hss89iMBjggw8+gFJKZ5Nv376N3d1dsv2eB5gMMvnSR8fq6AQT172z7gg3MzADoAP+uqZ97mA2wOHwEMpReHD8AHunezgcHuJHt3+Ek/EJAVNORfcat8ZJfoKD6gCzilwuZtVsGQD0APz04lcWjg9kgNe/8Tq+euur+Nntn8VnPvMZdDodPddY/JEz7Hp+XFDqEEXREqPBbGbMsDrm5mMzxmGmdxzHSyCFmTDVSY/5WAdBgEajgY2NDf27SZcHiH3DWiUmK8GsyzfLCnh8zPnE75XnOQaDATY2SJTITBSzFSSPcVEUS4ADQOyDIAh0eQSzFGazmT5HLovh68iM/XjOM1DhOA7a7TaklFhbW9Pny1R8vm7SNNWggGlbaYJx3K/cz1mWLWX7ue94HptrnZnEZoCHS4P4/yaAkaapBpOYsZQkCeq6xmQywdHRkWbNtFotzTgvigK7u7uarWGWhJhAED+Xr8dV9sU51v8KEGGCRatzmM85iiK0Wi3d16s6KubzWQiS10U+njiOEccxGo0n2NHx+zzVswC88cYb51BenkiMAnEdD090cxHmTjUXXLMjzOeYr1vtuFUEy/d9fbGaqKP5XHOhrOtaD/5kMtGojKMc+IoWY1ELdNHVqupCCPiOr9/LBFmSJMF0OkWz2aSanEkfJyPKrNVOTaJ9oMyXHdmo7ArTfIppMYVyFWkwqIzYFqJYYgY8LjiSkEuUc6emWvXADWgDOC8NMWvk9Zcql8okllwoLqjJX2oWyBaty4Ow8n8T4JpnyS+swY+Wn2spi+w94SIUITzloSM6uvylYTfgg6iU7HIgLRKULIsScR4jrUhxP0OGs+QMiUoouzkXTmRhzlKUOkB/6vKHSzQbLvr9Qhr/R2B0MLAiICjQF1gCGbg9qTQECtqlggUshRJQtVrSUmCghfUsuM73sU1dcEwCSwERz698FYHgp7ggIbPHgUP81moeQCsLtkc3RVeRFaxf+/BKD0EdoFW3tLCdXdn6+rDE3INaEbMoB22AorUIylUarNCMC8zFaZHia3e/hlE20urxF7WG28BmtEke3U6EwAng2/OadMvWmWmlFMqqxHff+C7iIqbAwAHs0Ka1YC6uuKQJUAD30/sQI7EI7tyaKOLP1U8UYkUN/PZ7v43QClGEBYQ91wepqI/c3EVURwQi+l1s+VvYLDZJvDBP8MnrnyQLUkEWjhKLDQjrbkTdCLVdE+hSzpCURJmfFYvHcUlZ/rRM9eO4jLF/uo+z8RlySbZmcKB92BUUgRp1ikE5oPOJQTXk3GwsBW+r5y5rqe0Ff+eD3yF3o56Pk9kJqaFnkuZPHqDn9vCz0c/iF1/9Rfjwcf/ufezv76PRaKDZbCIv5yyGckLshWqGRBGLIRUpKqfCrJ7pgPPB9AFpVOTjSynmUkiEMkTlVpC5hJd4uN66jq3WFhpWQwd7AkJrNchAkoByNsV7e+9hlFI2ubbJTaEUBCSdczbiS5F/N8r6hBJURuCH8GwSuK0KUuKvigrDckisCpSaxXIZ8ALQPSpyIjScBm42biK0Q112wUAFA74PHj7ASXoCZSlMxRTvjN7RwUhcxxeWnUhI+MKHfE7CKizSjSkdWDnpUriVi9tnt9GNu6iuVUgGCY7uHeH04SmVfUwlKpAlY6vVwvPh80uZwKUstmWj63TREz19/zdtw4Ig0CJ2rA4/Go1wdnaG3d1d/OHX/xBhN8T33/8+3rn7DhJFNoypSMn6z63hdTzMSrJmrYIKfz7+cyQjEg+9sOymCVivWtoekx0NeE08PjlG3shxO7+NR/ceIUszuMJF/7QP/4xcv9qtNk5OTrC+vr5UDgssi+rxfsfc4HK7jHpuWRY8x0PDamDT2YRQAp1hB626tUQP5gDEdV387Rf+Nj75yU/qPrZtG17oYRAPcPfgLr713W/h9qPbVKo3B5orlyxMK6dCM2iiEiSQmkhy3ilEsbgOFIAZ8Hf/6O/q449sAtSaR01dKqAZWG4L3bALH1R61faJudJ0m+iFPbS8FkIrXOoLzggC0FR1Dtg58OHsJgdeHFxwv5n73SzLtKsa74tZsf7OnTvY2NjA6empFqLjfTnvTR88eIC1tTVEUbQUmHGwtprJVEot0fS5ce05XxuORYK/yp0HL0kLm8kmpmoKkQjcfXRXjzMHxv/hJ/5DPPvss0uBrhu5iOsYe6d7+JN//Sd4/Uevo3IqCE/Abbr6Xnd1/So6fgf5MMft27fRarW0TSIzFNbX15eCJp6bZmBqBnsmMMDnulqSwMdpBqT8N6WUDrZYnJH1Ojj458bjy6KXDJA4jqMp86brgeM4iKJIZ8i5HCeOY7RaLR2wc1DObVWXoCxLTKdTlGW5JGrIoAaDDGapAoMZ5th7nofpdEqMy7nmAM9VE5SQksQO2+02Op2O1hEIw3ChTacU1tbWsLW1hZ/6qZ/Chx9+qMeO5+Lq+sN9zudsziE+XrM8n8/RjCFXA3Oz3B8Ams0mkiTRJTwmSMXXr8mwYHCVY9d2u40wDPW8Z1HLIAg0+8Fk8fP5muCWabyw2q+ryXNz3pr3K7OZ8xUgi86TkxM9Fua6ZBo7cGu1WhBCaD0HFqMcDofY2dnB07SnBhrMGp5VwIDFUPhmxIuKiSKZViVmpwELlMa8iHlh4M9llFgptYSk2La9JIixigybNz1+H14wWZHXRLhWKVMX1QqZ77sqoGLVFpqyibqoyWrMOJ+txhZELTCYDDAYDHRtV5qmGvzQ4iNQVIYxLwPJQHWgXos2I/1ZHzKQi4yuyogNMLdJZDuyDGQleGlGTFCQ74GyuVZJCuQOSEBMZ8hEBcuztIJ6oYoFDd0UXTQpwiuB5+NaJaiuOEWKsemxx61c+bnabMCyLZ01tzyqXQ9ViEbVgF/58JQHq7SI1u62IHKB/nEfjj23d5EUgObI9SZmCfwxsuU5ci3OVIjisfWsAB5b/nDpS8QiSPhLOZQI0jcxj81kRqyCDY9rElIzLWxha8cAPiwedxO0YsDkSY3ZPGZbLZNh0IxZI+fAsMs0MS5pHFQ5cIiBY4hwerWHJpoa+PrVX/hVstOzvSVWQlWTKGte5kQdTkcYZfPv+eP+rL/0e17NF/AL3E48eOjaXQQW2fi5kurQHcuB53hwHRe1qjGdTdEf9pHkCZIiAWzQhtsiX/lzIJEka71ZPYO0yU1E05a5rwTwbvoufv97v38+qPnhynHOGQbsuBDZEVo+bb5ZEC+yI0Q2PW65LezYO1RrPVdVDyx6vStc/O7v/i6+8fo3MBqN4HkevvKVr+Bzn/0cJulCuT1TmX6sbAU7shEXMR4dP8L7997H0fAIs3qmyyYKQWU3tUWCf8pSSKwEcR4v5tUlOkbfOvwW/rvf+u+M7pOwTkjw1BEOXOnqGnLNtLAjLXr4bPgsel4PXa9LgsCWT0wA20NVV6RcPwcfRgXNjdt7t/FnD/8McR1D+QqJSvBh/KF295hVswvXAAFBwrFzvQQ/I7p+OZmr1FcuZCHhCAfr2+t45oVnUFkV3r3zLvb7+yjdEpVToXQWVo95nWOWzVDWhi7FJXIrAgR+2cIm/RDLh2uRhoIrXThyLtorFrXW04pKMJKSgu1pOSWBwPl4jDFeYuC5wqVrwvLhCheRH8GVLpUfTKak7wDS14itGJVP95JK0vUAAF9/++vzyQvgFUomeLVHTlOVgxAh3n3zXdzYv4FIRrALG/E0xrXgGjaxSSUfDpV8uJa7tAlkOrW5WQSWN3i+5WMj2MC6WsdavIa6JtFrrhe3LAtXrlxBHMc4PDyE4zj4B//gH2BzcxMKVD6RqAS1W2OYDvGjOz/Ch3sf4t7RPeSSXLyYmTOzZxi7Yxw5R3jz4E0qa+qAvs3jUwKucvGb3/tNdPyOFlut4xrtURtX0iuakRPJCEiBbtbFNesaOn4HnvTOOT+Y58wBgblZNssNTMou77lms9kSHdhxSHCy43Zwxb+C7Xob4+lYK7kzhZvBnr/7hb+LNE3x1ltv4c6dO5SdBQHpmcjQ3elic3cT11+4DvjkJjHOx3hw/IDK96wSs2qG0+wU92b3qHSgpMTQZaU3UkitWdP0mpr233AaSEYJ8maOeDNG7ufa2cwqLFRhhbEao0CB0AqXAhneT7uuiziOcXZ2pl0KmAJvWRYePXqEq1evYn9/H2dnhL7y/2zbxtHREe7du6cz/2b2kpkRvDc2AxpT0+Cy/THv1/lYmTnB9fyrSUKlFCaTif5sgPbvoRMiEhG8lof7zn3c7d9deg0HdL/yzK/gVnULHx5/iH6/j36/r8+LA7Y0TdFut7G7u6uz/xw48vmYjBLub1Mjg5t5jqt0dDPWYFYIB96rfbrKjHFdF41GA3Vdo9/vw/M8HZzWNbkOAMBoNMKjR4/QbDbR7Xb1MTJrgK+loig0m4uvMT5m1kCwLEtrWPD58v9N5oMZs5nXMoMj0+lUs0ZMDQtTJJqPjYPrRqOB9fV1NBoNpGmK6XQK13Vx5coVPP/889jc3NTAVr/fRxiGS6x5Zsqb+h08P1ZdGcxyGX49/311LFZBZSklWq0W+v2+7jPznMy5LiUJwrbbbdy6dQtra2taC4THRCmFTqeD3d1dfY9gRgYfByfn+VhWAR/zuLmv+Xez/83Y2QQf+H/8UymFdruNvb29pc8wgTgTTLEsSwPoruui2Wxqd5HxeLwEBj2uPTXQcO3atQW6v4LOMpAwnU5xcnKi6zZWUUDzoMwBNy8QXuDMGxA/x7yZmZ3Bf7uI/sfoGA/KO9Y7SO0UzaqJmT+Dbdvoo68zAbKgm4apO8Hvad4gzUXXfA43ppoAWEIMzUnOzzGpiJqSJ0i40oOHqIp0bUxLtRBnMU6Hp+igoydUlmWwLAvdbpcsaeTCt1hIQRlSSVmoXOS6Jm+Sk+1YZVfkMZxPKIi2FgKcSyUMVvFYxoNVW2TBpCjjzNoFoqKMmeM6EK5AnJKKeG3Vi9IQsVCH583BUzdBGfRk/gUXC5HOy4LQAMuZUEV0f1vZmuES1AFCFcJXPtpow1MevJpAmUhQFjgQAfJJDlvYqAWxWFg3JEcO5Sr8ifsniCVl/6TxxcEeazhoIc6P2owyjgvPVT9tHsBf9vxLWo1an9OCoCKWamz5eU9qNmzdz6wJYcFaYr8weMWibmwPe2kTy9RxpZRmSpzrT0HABQMhMVayshL02vk8/8bvf+OJ5+RIB4ETaMvLpkdg1rXWNXwi+ATVrHst+I4PS1j4J/+ff0LUXkkZlk9+5pNwfZeyxYquz1k10xvd0/IU8ZSu16zOzukL6FOrBOzMhiwlkEMLRHZbXQgI9Ed9qHqhO6Is+oYNSJeAy8tsCAESC7RgaaZBoQpMqylOi1M9f8u6RFmX2tLxcU2CqM/4LMgbvrZxbB3jD9/7Q3jC07Z6rGgfWRG6dhc9p4dNbxONYQNu5cK+S6U3UkldL2vWx7qui4997GP48pe/jKgZ4Ufv/wi/+bu/CdgkroYGULolvK6HZz/xLJymg0E6wMHgAJN8gsqiMoaszogiX47J9WjVpnj/iVMFlrBIsV+SVaJv+ZClxLg5BjLAUx62nW1st7fhSeqDwAog5AIYcgOXQOUyxQ/f+SEG6QDH42PABzIrQ9JOUDmVBhEggA/wAf7i9C/oPeaMMquwtD2ql3poqzY+/cyn0Qt7iGSE0dEIRVagt95DriigZcrzuBhjUk70PE2rFLNqhmExXAYpLmkMUriS7hNVSqUuzaCJ9db6kuikLWwtgGn7NtKKlPmTOsHMIhZAKctL1zMuwZG1hMqV1tBRUMgklV39cPxD/DD+IdKStFFKVS4zZ+bNt3xdxtF226RF4bWxHq2Ter/VQC/owYePclpCzRRiFRPIJ4iey7XKZmMaPYClfQUUEEhyhmk0GqijGv6Rj6AMUD1asAx4L8Z7tBdeeAGf/exn4Yc+fusPfguns1OUVqmZN4VVQPkKO5s7sCILk2KCUTbCSX6C2/lt/Nnkz0iP5RJNENPCtulQaUdkRWh7bfSCHlSisD3bxtn4DG7lkqZEM4dd2KhUpUVizaDFzKRxM5XipZT6Obx5Nzf3Z2dnmhb83nvv6f6rixqe9NCre3g+fB437Zt49vqzSJIERVHgdnkbURTprCr3Jdf25zklHbh8huc8a1WM8hGGyRBxFaMf9zFMhzhKj3BSnWDqTpFsJCi2zusNvYE38Pt/8fuwhLVw13Ga2uIym2U43DxEGZQopgVkTuVxPihxMpADpDatw8yS4Cx2nucYDocYDoe6z0wniFXQx9xP8z7UDGB4DLjfeTyYscFBCVv/PXr0SM9HDnLieHFvYbE8/gy2xszzXNPROTvOxz0cDnHv3j2d4edrZjKZYHd3V1PbzTFst9ua7s/nxpl43lcD0P1yWbLSzITzNcoBGM8/7hsz8GYmAcc03FcAlTSwzaqUElevXkWz2UQQBDg8PMR4PMYbb7yhnSeklNoFoCxLvPTSS/pziqLQmXQ+V9MFgp1SeNwYsGKgxjzf1djLsiw0m01tqcj3VPM1RVHoLDkzNYqiwP7+vj5XtgDlePH+/fu4fv06ZrMZhsMhGo0G9vf30el04HneOZaEqXfC64I5N/mnOcarQM/qWJpBfKfTwQcffLCU/L5sDnCf9Pt9zGYUS7KdJ5eUcPnHeDzGlStXluabmaTm8+L5Y/40P9cEyniMVpP45pissnOYWcLrgXkPYoKACTIKIdBqtbC9vY2NjQ2EYaivYV1C9RTtqYEG03HCpKeYQIIQC1oS/3+VqWCiiXwzNQ92laJnUsVMRgU/L45jXVfDKK6J9LBtEr/20DvEbfs2MpWh6l4euNiYUxDLueVgSrXlbAvnKtIhkELC8iw0rSYCGVAoVmWarmtJa2nS8+DwOZrIqnm+JmOCf/IiaCod82vZ2obHCFhQ8KQkPYtQhkvODEVdYFbOiOVhe8iLHIPhYGmC82fZto3t7W0kWYLDM6q3VI7SFoq1XS85g1R2hUJS7WWOhWXizKaay8zLnso5hEWcPHiUgZ7bo7ErRKnKJQHOUi5KQ1ilfSk7/rjAeg5WVILKL6aYLv73pDKK3uJ5rE3Ajg6BIl2LLbUF13IhxXzxmzsqcElLIaiMJp1/XXqYSsAWVBIh1QKsYAChxsLu9dLA/8cpGdFPe4w452Man28mMnp/4y2WzuUpjlPWBMiJeu7oMqeAm6AF273Vol6IxD3hfTVoIUCK5kppXYGLzrmoCxRZgXF2ARvnorY7/563D0cfwpRX4cy5L3340kfLb2E73EbDaSCf5JgNZzg7PkNd1ks1jM12E0oqgtskiRF6ngdlK1L7b5Io54XX3nw5tIVNaxwoyIvciLLW0l4CllbLrrI6Q1qmC1XzCxpbA/qWD8/y4EgH0xG5O6ha6WM/yA5Q1nOh1rqgILdKL2bI+AA+Sw9lLbUoKJeHWJUFWUhMehP0J33IvsQsniG7mQEZtICpXdlYL9fx19f/Oj798qfhSx//5rV/gziOtRUUUzRNYLhChaROKCsfAYNigLPsDP20j2FBdfrjfIxxQVaRcRkjLsnNYqmvmtBMl8P4EIhXSs8e10IAAQG9dmUDBTkXuJmrr5Fuq4vdq7vIsxz7+/uYJTMtUixsshueWTO8efYmstNMK9UrKJhLof5IK6Qsrt3Etr+NjtdBx++g7RG9PLIjOMLRgEhSUhnAtJhimA8xykc6UDsrzigjLykQPpldrHOyOpekonFwSgd+7cMqLciS3JzsmkrGmm4Tr3zsFRRlgUcHj9Cf9FFKWm9LixgwlSQgKc1TZNXl4BiXLiVlgrzKMcipnIdBtryikqel+f/W/OcxSHfmGlkaW1csiJSYFW7l4sQ7gUwlYhXDVz7uZ/eRzTK0nTYabmOJOckb+9VsFK8FHJh1Oh0MBgOECNGpOkA1D7RymsNe4eGXdn4J165dA0D3+/fffx9RFGFzc5OCf5VjWk5xPD5GCir1SEAuVmxlOy5IT6Gf9nF/eh+TYoJBOsCsXGHifNzoSxYKLG0NeJ2VZ/j+h9/XQXfHJ22apt1EndeYyAmVparFRtukJAPQNcW8tzT7hZmj0+n00j0Xvxf3N/dn4ARoi7beU5nJKK5L9zwP/X5fB5avv/46Dg8P8eGHH+L07JRKm+xSCyKH3RA//9d+HrnIMS2mVIY1L8caZ2McV8fot/tIWgnts5zldfUdvINf/+DXgW1A/HVB/VjZ+B9O/weUWyVUW+FufBffePsbuDW+BV/4CGWIOInRFV1c864RiOU00fbasIV9LoHG52gGMwC0JoKZheVAhoMWYBH4SCl1tt78u1mmZNLmGVTiz+33+3juuedw5coVHdhz2UlZllrFnwURAWIPMMuCj2GV6s/HsuogsnqM5vmYfcEBcBzH+vo0gYbVoJbBGQCalcOlQ71eT4sKsuBfEATY3t7G5uYmoihCv9/H0dERrl69qo/Bsiwd3PN1zOfEjCkGGoBlZ4xV1obZzKC92Wzi/v37+prgoJnHbTabIc9zhGGoE6hCUHnQvXv39HOfeeYZHBwc4OjoCJPJBG+//TaSJEGe55hOp9jf39fXVhRFuHnzpu63oih0Rn2VicFxUFmWek6agf3qmJpzm8e42+0uzR9+Pn+bbBHHcdBoNLTtZ1VVuHfvHm7evImjoyNsbm7i6OgIjx49wvr6OprNJgaDAaIoQhAEurSJ1zBzrvA+w7w2VuPo1eO7aO6ugilKUZlTlmVL48fvufp8IYS+h8xmMzx8+BCDAd3zXnjhBXz+858/N2cuah/J94AX3yzLdKfzJDQHwQx09QcZLAizk0zKofl3fr1ZosGTzQyCkyTRaqH8PJO+ZN5s6rrGl2Zfwt9q/i0opXDnwR0IX5AQ5Lw+O0UKp+HAiiz6W53qzHQGsoTjcoYUKTIrW87wt+ffAKBA4nK1A095iCSJ0KANVHYFX/habMspicIdWiG8ihT+2Q9d1EL7BDNKyDc2RsHMPlqdbGa/Mk3noknLFw9T9rifeYyrqkLgBbi2cQ3rzXWNJnMfLy3A5QJNZbSWP7/dbpPirCRKKOtUsN1mWlPGRblKl4zwl2kzyq/JkFGtO7dz0gEkwsmghQ0bsprX+wtbZ3hZWJPLDVZ1LHT28nGaAsLQJhA5ZphhgMHyc9TKz9W/A9qKVSqpBa9442zBog2vErThkotjr2SF0iq1cOiFjbUbmFEw125YYgMICtALUSzKYi7ThPgI+hMXNuP1Hwm4ECBq/HxTvxSE/iVKKSxQ4MI/Oeu5uba5AHDqksqHCrKsS6vLASFgPo5ifr3NhfPqmjbKjzumQhUoqgKzagYA2MtWvO4lgO3HnN+Ke4aTE63dcz20rTYJ+9mkIyErCVRAFEZwbAe1qnHWP0OapVBSwbZsDeYlVaJVyh/nNGHB0mUUnuVpUIEDNdarqOoKU0ypDMuuUNgFjuojlOnlICRb3gVWAJUrVFmF2WhGgaegcdvY2ECtamJzleQoMrbGeG3wGlkOqgzJ9eRCQOS33v4t4G16zE5D0VmkS1q4JCSQAf1vXibS9Jq4Yl1Bw2vgBf8FRGvEbgmtkJ5vB+c27HVdY5pN8a3vfgu/9X/9FkbZCDKU+OzPfRZWZGnBwWk5xaScYFxSQJcixaRYUX4XC2tLuFgI/M6D8UxkOBme0Ga+Of++gEF1UZD/3976b7HlbWFaURnHrJ4hrue08mqq/346OcX4jALPcT6+lG4e2qF2argSXYF9ZiPux5CpxK3dW3j++vNwbAdSSKQlCY/yWl+4BYbpEMN0iLPZGWLEyO2c2HAkHnOuvXPwDj2wAazxQyqd8i0fXuVh3V/HVmsLkYzg1i7SUYqtzhYiP9LXLgQBpWmdateFWTlbqPXnY20heNFaliFD5mQQtoDwBDFLWNtn7jrCAOT373xfv05CIrAC7eRhZRYyL0N6LUUgAkQywk53B65y0XbbiCTN10pUyLIMV69excbGBlqtFuI4hhACm5ubODw8XHJAMDfd3Bw4aIkWHNeB7/vodDpLFoO8f2Dle96PHB8fY219Dd/+3rcxykY4GBzgtR+9poPo0i211kImMtRejT76eGP0hp7v5xhRIYCfAu2tahe+oj1UKEMSyTzbxw3nBvplH+XzpRamDhGSZk/HQVIk5/adZsCyuh81s8D8HG6r4AR/s3AdZ96lpDJDq6QyVSd34GUeWqqFL7S/oIMyDpDCMERZlnjttdfw9Te+jpOTE1iWhbWNNYyzMWqXEjsb1zbwiZ/6BF5/+3W8fedtEkwWGW5cvYGD7ABjMcapPMVRcoTX77xOuiDmPeuD5e71pKc1YdjekrUqOn5HMy0iO0I36EKlCtudbURWhEk+gciF3mOaugHcT7PZTAM3DNCYSTLub9Y44ACZKfR1XVPCa8544GxwXde4cuWKDmCvX7+umQ38f3M/u5qJXQ3OVv++mvE3n+u6rrYkNVnZ3Bjk4jnCDgdCLJwVGGDpdrvIsgydTgfr6+u4f/8+ms2mFmOcTCbwPA9pmmoGy2rpC8c9zG7g0gPLsnTpjQkirQaaZsbcZBBwQpcZGpz15ut9PB4vlY+wJgjHKxzPhWGo3e7SNNXWrbPZDEmS4Etf+hLqusZgMKDSMbUs5GgCDKtjx+e4Goua42W+n/k/zt4zKMVMdJPhYo4jB+G+72M8pgTT8fExxuMxTk9P8f777yNJElRVhdu3b2vLzLIskWUZTk9PcXJygpdfflkfg3m8PEf5+uB5xECKGdtdtG6tgqf8zS4pF5VN8efyuAKkW+E4DqbTqWZ5SSlx79493L1796l0Gp4aaPA8bwk1MhfqVdRO0/9XShlM4GGV9cAnaGb6TQsQ/p/ZKdzpLMJiDg4vQFwrw39XSmkf3yqrEFoh/MpHpCL9GRvBBpqqSZ9jQS9UPJFd19XiPFmeYZyOIQKBpE7Qn/XRj/tIVUoBn0MBp3IVbMdGIhLEToxpY4ozeYbSLkkE7TFlApayloTv3C0XzroDX/gEYsCD2yZAI5ABWqKFUISIFNWadqwOBddGv5kopjkJufSCf+ebQVVVmoZnIqPmTcO8AJl5AkBfYLyA8e8WSMDLVz6iOlqaB7agDIIQAtK6uGYJIOAiyzIMp0PaYHukct+fke1pUie6/KO0iPHAgIbwBQFIctme83FjIZSgoL+ijJmsJGzY2u7UZEWUotQ14hysc0nIk0oMuHQAAoDEYxkOFzbOyiuhQQtb2doFQltwMrggFQErko47E9ljSxUcRUKkVk12ko5w4Dv+kgaEwsLPngGip3YP+XHbRwE/1Jwdg4up1+P+U7IU5s2WNtWnWw7VqFsOLGnpQBgA9g/3USqyxYUNKmF4AsgiYayzzCZ5yvMsVIGinAMxJS6uub/oNGvgIhKBK1w0rIam/vuOD096cCxHU93p5eQswXoWLAaZVHPHjiomoPYClyQBgUAG8KRH7y3JWtK1XViShASn+ZSsPb2CLEpljdqqcZxfYBJfLo49tEI0ZVMDAZay0ApbxOAIIg28HR8foyjJv70SlWZQDcoB9qo9Oo9qLnJZp0vOIavNEhaBEnOtCv3TjjAdTfFo4xHqtEYgA5ROiYbVwJq7RveoObMltEJ0wg6evfosXNtFnMT4nT/4HTw8fYj7R/dRORVaWy30dnpobbYwSAYYF2PtiDApJ1QiNy95eJzAaWAFem377aPfRs/roek0tQtC02liM9zUdpYdv4ON5gZaLjlsCCEwKww9ipWfDEaMizEOcIAkSlC0C7zuvI6/OPyLx4IUTYcCno7oIEoj2KWNAAE2W5u4cfUGaUQ4LsazMbI6Q23XOItJIHiYD7WtX1zGSFSCHDlOp6d4d/ru8ofNLu4bW9jwLI+0RubHsxFu4IXuC+jYHWyGmwgQIJkk2FjfwPvvv4+1tTXcv38f+yf7SOoEo3REQo8uub+IQGg2G4vhseNCjZoAnmqGg/SADsIDxJXFWvumeHP5ICvgf/zz/xEWLK2d0cpbaIdteJWHK7iC3o0eDpNDXDm6grbXRkM2cFweo1f2sIlNAqqM7D6wSAABi829ualdoisrKgW6El6BNbFwXB1jNpuh3+/rPSHv4bi86Vd/4Vf1vkJJhdIqMcyG2Dvbw1+88Rf4cO9DiECgudlE72oPsYq1zeZRdoR7h/cwraeY+bPz95kKwAhwxg7aD9tUjuI0IDOJ9riN3mlvyYq37bWxFq3pYJtLRcx9jploAaA38HVdo9FoaJG/1Ywxn/dqfbQZEEspdTnBtWvX8DM/8zNQSmFnZwcffvihZlo9+/yzmO5M9d52a2tLByiPHj2C53n4zGc+Q5nndIa3P3wbiUrgNByMshFdC3VMpVF1rOfatJhS38/2CPzJicGS15fblgYygB3YxF5JQayyOXvlTJ5heH+IhtOgb6uBT1/9NK551zT72HVdrK+v49atW0s6HBwwp2mKGzcWNa+NRgO9Xg/vvfeeLgfgQJRjBjMpthpTmEE3xxfmXOd5zs0E4xgsMK0XzQQsv9Z0YOBz5OuGj7euSaNge3sbURQhjmMEQYA8zzGZTHTm3HVdFEWBw8NDvPTSS/oaNGn2wIK1YF67XFLD52EGpeZrV/+uFLkscDxlsjO4n9bX13UC1LZtXRIOQMcUzFwASGSw3W4jTVN0u12cnJzgU5/6FPr9PjqdDk5PT7G9vb20rqxqAqxeUyZQsnp+q9fqRa/lsoKLGAwXMcqjKNLvWZYl7ty5g8PDQ5ycnODmzZs4Pj7GxsYGTk5OYNs2dnZ2sLW1haqq8J3vfEcDCNx4HHkNWbVf5b5cPafVMhJzPpvNTCIzk2I1Ga11Ao35ynGv6Sx0//59fPDBB0/FanhqoIGRWQ46GQVWSmnkkCcZU8jMxXWVzXBRyQCAc+i6+Xru+NWFIU0pCDMFTHhSrE4282JstVq60/m92DaIn28G4mZWXg/unLXgVR4iFaHn9eA0nKVj4L7pn/UxGo2WUE99U5LkUsFUTg6OtVvC/NuObNRujczKMFZjbbPJApBLgQfvIZN5Vj+jrL4vfM2msFxC2EMrhOVYlB2oQsoWzJ/jKnosXbIGNS1iLkKKTeDJBI9Y/XY8HuvnMX2HL35WbeX5sMp+WUVf9bgq8mz2bR9BEaAclkvItankynOGfZl5XtQ1Ce05vgN40K4hGTKia9o1WXHJUutWVDYJjmk9C1Fom9RCFE90DmGWBds/OsKBBUvb/mVVhlKVC+Bi7g7BYMVScGqO/VybQFtqAufdHx7XFM5pMUjIhe3jnAmhafSChDzZZYJBhcuaDRtuTXR1Fh51lKM/i0EK1u5gwU1mbjDl+6lKLJ7UzNc+ZdnI4xprFDxWH9TH45kx88bsFcdaiHAWOTGZ8ozGU2dC7QWb5knNdAx5GhaJOS7AwhVjWs059ZfZ5z4N9V+BLFzn5S8NvwHXWjgTmDaKtaqJBVUU2mY0ExllOi9pvvQ1k8m1XALbpA0IoKgKcjsQCgfFAQEyqkBcxsu16calIyER2ZGmd6+764gsclfoNrrabcSRjrbz1Ovh3NKTHTk4I35cHKPf6qPsklvEveN7l9bGc2P9BpSA3bQBlwDAptdEO25je7yNQATYDrYhS6Ihd9tdyEJivbWOXtSDKhVOTk7QbDfhdTy6/9gFBskAo2yEDx59gGkxhRM6mBQTnM5OdbZ5Ujy5fr/pNpfq+M0gbifcwYudF9FyW7C+Z+HR7UewCxt/86/+TXzupz6HHDnpBnCJRT2jsh+HAs/j8THuxfdwLI4x9acYWAM8kA/w7f1vX3oNeNJDKEK0nBY6ooNtexsbrQ1EMsJWdwuhE0Iosu86Oj6C1/BIbBkZptUUo2KEaUEMk7ikGv2z7Izm+PCSgWLwiYltbUoecEmPXZFoZ1iE8FIPjVEDgQrw1S9/Fd1Gl4BhacGSFhrNBib5BB/c/wDv338fZ7MzVDa558AHcivHTJF+wKyYIVVUvz+rZsSuywYLkNGUYrl3wXF/MC+jYjFT4aFpk9vCmr+GtWANPa+HnWgHG+EG1sI1eLWHjtfRTgyc2TaFIU0HMP7JewWu/9WbeyXRtJoIvRDNZhOH9SHquEYkInws+hhudG4giiJtA7i/vw/f93VwX4gCiSIhzXfvvYutG1sa4HIaDobZEJNigr18D2fZGfaLfWLpzL8va5EdaSvblkt6HQ27gV7Yg13aaDkt2KWNQTwABHBqnaIIChLbVtbSObPy/6qwJu93oihCkiRaAJ3F5V588UUMh0P9nO3tbZyenmqrxCzL0Ov18OKLL+Ls7Ezv1x3poON20EEHvWYPaC6CFg4mOJnEezmm5vO+N69zDJIBDvoHqJ0ap7NTHPQPkKoUg2SAd+6+g4fThxilI9ROjdzJkYQJJt4E9w7vIa5jDST+N85/g3+/+e8DoKTU+vo6XNfFZz/7WbRaLayvr2M0GkEIgX6/D9u28eUvf1kzHdjxbWdnB9///vc1O2SVkcL9zXEEgw8XCUGuNjOoXg3Gub9ms9lSObQZqJrfnNUPwxAbGxv41Kc+hTiO0el0NPjATiONRgOO4+D09BQ7OztwHEfb0aZpuhSPMWhn1tnz8+q6XtI44GYG3Wa8ZSb9AKrHD4IAZVnq+cU6M5ZlaV0+Pm52zOBAmsvXkyTB8fExbty4oVkP0+kU7XYbR0dH2NrawunpKfI8R7/fx507d3D9+vVzLJSLWEWr52MmU822Gj+ajWMPjgNNZoSZTOf3iaJIx5me5yFJEqyvr5P2X7+P3d1dHB8fIwgCXL16FVJKrWW4ubmJN954A1evXsX6+voScHBR//P6wEwSHguOmS9i3ZiP+dhZS4XdkczGz2UNDu4Hx3E02MXxWpqmS6VQj2tPDTQwCsWTmjthlV7D/+OD4+dxvdVq8M+NO1RKUv7ki26VnsfoZL/fR13X2NnZOYfAcMfzBcuPGfWJ4xjb29u4ceOGrvthio85YKsDdBF1zpyIvIjxea+eI1ukmTcUXhzM37mtfk5ZlhSkzxZ+zSZA4wc+lEOK7Jmg4DipSRQsqciyj0s+2MZvhhkGYkD1qRb9r5aXWObZixpop3KWfvJjWUhSO5/XPDvV3H5zru7dcAnJ5tiDWScmiMTffDMwaU4858x5sdpPDHQxOmj2Kz+f6/nMsZ3NZuh0Oho4M9+TP/8cc6ememDzb1qEriKAoHYpOIILFFaB2ql1OciSa4hcuIzkyJGKFKlMNWjxuKDUUmTp6CqXHiuqpxdKAJICUM7alyDAgpkXF2otrJQzaHbCE3Q1HtdMwEJC6vIOtvIUcg72gURBH3fOUkktysnlSVZNbA2d8Z+Xw9Si1uUwhSw0YMN9sgRW/LhlIB+lPcVnVahQqQp5eQFI5H20j2PQgvvfklR+AwUIScyhWtWkiVDlFEQyrfsvqcdhvsaCpctItOjnPPBmUcoaNQb1AE+hKfrYZgtbl8E4oJINpRSyKoMlLbIWnfdtgQJFXUBVl5+fJ+blH3PtDFuSZWmhCgyKAU7zU9RJjWpCdfpJlTy21p+z4A2ngZbbItHfWQSZS4RWiL/yE38F7aAN1AsBTpPRFbUiTIspxtkY73z4DmZiRgKVVgnlK4z8ER72HxKjCyQCXKNesFZOl49HnAp4wtMOIlzuUeQFPEluKJvepnYZCe0QoQzhWq4eS2EJOJ5DJTZlogO6SUGgRD/t4/6E6vc5YNcuRVvzbwDfzb8L/y98NOzGkkBew26g7bWx2dykbHOjjY7bwbgew699dGQHr1x/Bd2wi72jPViRBbtpY1bP8Oj0EWIVAz5wOj0lFpuVY1bNcG90DwkSxH0qBVnK1hqOpJ70NEiyHWzrMoaG3UBgBWiEDTgWlTZCAFmV4WhwBCuw8MHeByidEqfTU8zqGYkrWwWUs5hzffTpwTYABfwve//LhXOH3U/syIblWQhEANu2sRVswckcNGQDG+0Nsh5MFVSssN5bx3QyhWVbUB7Z+R6NjpAhgx3ZQAiMihH6WR9HoyPEdYxCFpqp08/7C4bJ5fH3UmPWh60IrLArG8VOASu3oDoKTuHAKz24uYt6WKNRNTDLFxSS1aw0sLD840yiZVm4d++ezqg2m03s7e2hKArs7Oxg1B8hiiJ0og52nttBv9/HNfsaur0url27ptmqP/rRjwAAV69eXfjFWwJxFaOwCi2UOaspyz+rZ5iWU4yyEablFINkgAfTB3h79DaG6fB8icK1+TfmGkulDZlLOJWD//7D/x7rR+t6brGwZihC9Is+jpwjpEGKwqMyxvfeew9RFOk99unpqa73FkLojGySJLrEYHNzc0ngjfcx5t55dZ/E/W/u7/l/nuVhK9pCU5Bw4Xg8xtSfwvM83LlzBzf9m3jt6DU8fPhwifXa7Xbx9//+36dMvcoR1zHWW+tLAXmj0dAMAd4D9no9XSrN7gVFUaDb7cKyLJydnaHT6eDll1/WyTx9vczH09Qw48/ivRonPc1s/WqwZgZ/5v84MxzHsWY1cAKR12ym83M/sJPE5uYm2u02Pve5z2kxR3aKSNMUzz77LCaTic60v/nmm3jllVeQZRna7fZS0jLPc12SwftQ7kPLshDHsdYVAC4us14FGrgfGEzg3zudjtbn4HJujqN839faGI1GA5ubm7h27ZoGFrIsw/HxMZRSeOmllyCEQLvdRrfb1Uz2wWCAmzdv6rjK/GmCIjwOJthiAjzm+Jk/zXMzx5TjBtaaMBv3NR8jMyC4pMQEJjg2raoKYRhiOBxqlxSllJ6jVVXh6OhIx6KrrHP+3WR083VpWqGawMpqXGOOJ//ftOA0n2f2Dfc5kwYYWOHxrapK23c+qT010MB0d5MGxHUicRzr2r1ut3su8OMJYtp6mCe++jdeRM1aEf5MHeDNF0Wm5Jj0PbOTzQnJHWeq8vKCZp4XX1Bmtt4cEHNRMhdIpRSGw+ESXYsXG34e29pw3wA4V4oCYMktg4NntpUJguAcLYppep7jIXIiopDZJIrk2z5t4lcuviRJkOSJDsbjOMZ4PIaSJPKYCQqEU0UCcppZMWdc5DJH7uTIXWJdJCLRr8lF/thAinUrTA0LV5H4XQDKnvi1j0hG8KUPkQgEVgCnctANumg4Dbhw9eJmzjUeL/4bj60JTpg3TmChasuv5cXbpDGtXszmZ5g1eXpuQ8AV5MARKBIStQQFwxBzAE5IqkU2bvJKke3QbDbD2dkZkiSB7dhQjtLlH4WkMSitUoMU/De25NTMClVoW8hczNXZH9OEEhSkKcq+SDXXGaiFZkqwZgWDFpxVf1wwagIW1Om4GNC6+MXLvyraLJeiRIIEwrqgZENcrkKvmUi1B7d2SbizdrUuA+sIVKjw/Meex6yYYZqTaGFcxpRJLzMUdXEpxfvfVjsnkPmXbBxYG39Yfrx6Gpf0nYAgEVxQllWDFqtslLrSzBxmWlSqulg/QxmPL2kCQn+mJeYb5Fpp4EIpQ1zVyLQn9QrqXi/e72n7tFQlVEWaJWlNtbECC5ZCqUrtQnFZc4ULz6YyENdyYQkLZV2in/YpeGnNUIsafauP3zj6DWR1dunx2ZKyppEdwXd99Do97Lq7mjnQcltETbYbWpiR2RxSSEBAizPOShKonBXzIKqcYZJNEFcxDmYH6Jd99Ed9xBXNfV0m8pjmSGfJ5jRy6Puad03/HsgAjuVAQuKb3/wm+md9CEvghY+9gI2tDZRqLjBak9PFUXqEO/EdJGcJRvlo4Y5iWsU+oHFtOA20shaaU3KGaPrUJ223jRu9G7rvmB7fdJoIEKDtt2FbNu7s38Fbt99Cc6OJ2q2JwTFncUzKiRZCvD+5r38fF+NLwSUbNppVk37WTTiVg3pWo45r2Mom4EpIFDnVMwtb4GOf/Ji2WxyXY8zqmS73SKuUSuscYIAB9st9vDV4a/GBq2DA/HcJqUs+IitC02qimTTRUz1sRpu4Gl7Fc/VzaLkt7G7souW0EEoClVSmoGwFK7Lofl8TmNTP+hiWQ5xlZziLzzDIBtruN1MZpukUw3KIoi5Q+zU55bRxYXsNr+E3//Q3ISFhC1s7s7A+ihM56Hyqg5bdQt/r4356H+21NtbddRR+Ad/ydYDCAYyZqGq323ofxwkOgIJpk10JAI5FrBwz4OD3Muu3mZmZpinCMNSsgkeHj/DD2z/EIB7gtR+9hqPxEQpZoHIqpCJFUpNdabAVYFbOcJQdabvXaWnYaXbp+/fwe8BtWkciKyIWld1AIAKEIkQbbSpDtSM08gbWojWEKkRkRZgNZ5ChRNNr6rXTFMzkcwOWBcDNfY651zT33MDCZY0TigA0i8UsJUiSRJcEuMKF79Dx8v7ctm20Wi3UdY1er4eyLHH79m0C0ra2kOc53n//fTSbVNrM1pUA8O6778L3fWxtbS3t0fgcOTgyx5EDNzOIvWh/aDbzOQB0hpjdPZhqvpotNzPhHLwx0HXv3j1sbW3pzHGaptqZodls6hjl6OgIu7u76PV6ukyGG8dMpiaFZVloNBpLOm68X2XgyQQVzGy6WQLFIAGfFzMyeOy5pIVZ2nxNtVotbG1t4caNG9jd3dX9PBqNYFkWnnnmGUQRxStXr16FEAJXr17Vpdpm2TofIwf1PF/NGIDjLjN7z8ezCjaY8RDHUwzezWYzzdLg1/BzWGBTKdLnaLVauHXrFnZ3d3V5SRAEGAwGqKoK6+vrcBxHW5lGUYROp4P9/X00Go1z64vpDLN6zZnHzXNiNRlvHvMqcMTrFWv1mGALP99MYDP40mg0tKMPx7Hcz0/TnhpoWA1SgYXIyWr9GU8MXszKstRBnBn8mxOAFwMOqHnC8GtMK42qquB5ngY/+G8csJtCFwwo8MAxwhcEwdIg8DHzRDAXolW0h9tqf/DFxovEKjiRZRkF8gaSbPYF04x4sjGKxJRCLvNYtcQsigKTyUQHq9wHWZadKwUxJ5+JjvHxhGGowZtVhLOqKqAGXJtQubpYsAv4ebzQCCmodtoqljP3gnQTUqTatzrF3HJTJJhYk8VzYajjc/bWBgUK2VzjYf7lhHOHBxHAtmwIS5AVJUhYk/UsAhFQuULt0rdyoarFglTXtZ4vpuIxeyXzfDBvJLzRMOc/94UJXpgMCxOkMul7JijF88C2iTorlUSgAh0UCiGg8sUxrIov8TXDx/N+433M7BlRZwvKONli/t7zTPNt9zaO7CMaO0GlO7qZ5XFPERgCc/vCeWkEu3Fw4FzWJQWjc6FJ1qU4lz2/gGmgLVD/Mqnv+VvzPOOgkf8nIBYlJxK4f/f+hW8T2iG2oi0SyfKaCJ1Q0+Y58C5ViawgBf9pTt+zfIbj4TEFCtaT+/DHBRiABZsEgNaJ0NlkfDTGggIF9BWqBTjxY2Atoha6VIeBN3PjoEDBPANbj9MVeJpmCwrsHDHXzpjXoWuQRC1AkrRMHw+SfISWqxx5kWOKKYlWclnFfJ7U3rwUSuJSRocFssb0bR8SEmVdIpYxkjjBw9lDYmjUBfI6R1YREHZR42C84TTQclpouA1d4hDZEdb8Ndywb2An3QES4Plrz2vbvabThC991KKmoFelKGWpy/3ikmrmuTRkmk8XYEY5w3FyvPT/WTFDuVECG3Rsj8pHwIr2qSc9zbLoeB1cCa4Qnd9egDaWZUFV83KxOfjD7JxhPsRBfKDLHUb56NK+sYWNyCJByE7S0Yr8DZtqyrt2F9eD6xToWRTQbXe2sd3Zhmd56E/6GBekRfHh3ocI10J85/vfQdAN8MGjDzCoBkTn9xKkXorSKVHapOdjtkejRwCIwcDsjp1gRx9HwyWNFBtz2j1IHLhChaRIdMA6zIc6gE2qRH9rBsXKvNDrwN65f8MRDgWyLgFYXbeLXkAgxZq/huvRddLPkBGcysHV3lXcfecu1hvr2Lu7hzfefANJkeBweIjCKVD6JVI3xRRTlF4J0RDYvrmt3RfSKkVWZ+jnfQ3q1umT13wJSde4JLFP35oH4PP53nE76LgdrAfrWHPXMItniKwIXuEhsiIEKtD361VG42qSwdwT8J5KStJu6rk9BFWAzXwTarSwssuyTOtd/Qef/A/w6quvAoAWegvDEHEZ42x2poVWh8mQQK1qqh1h2O1jWk5xGB/qMWcR4aU2x6EadkOLZ/b2emjYDdJcmZc5RVaEzdYmOcjMBVu7QRfrch2hHS5lWoEFQMGBPO8JOYsPLPbYRVFgOp2i1+tBCLHkEmDbNq5fv45XX31VZ43NvZVSCtevX9f7a97bcMDHpbBVVS0l9Pi1q6AJvwf/vnQdGM9b/d3co/H5M+OA996rZQi8f+a9daPRwK1bt+D7Pl566SUMh0OcnZ3BcRx0u13s7+8jyzIEQYDRaKSP9/Of/7yOMXi/x3EOn7sZl0RRtASimcAPJ1pNjQA+V7M/+DUs7shxxWp8xaXzfL5BEGhwIooiPHz4EDs7O3ouTKdTuK6LJEm028NwOMTx8TF2d3e1LgePGyeWuVTeZJ6YJT5sm8rnvZr1X31s/s6fyUKbZj9wbGOyHXzfx9raGtbW1nDlyhXNxnEcB0dHR3oN4dIItn+1bRsvvfQSPvzwQ2xubup+FkJowc5V20oea57zfG6rc9Ucs4vGta5rtFotzSrhebOa6Ob+dBwHvV4PaZri4x//uNbNYNvOp2lPDTTwAsoHy4FWVVU4OzvTtWJ8oZnPsSxL2yfxifJ7mmgV132cnJzoQV9F4vhijeMYe3t7S4E8ozwctJuDwoPPiNXqhOOFwaSSmQvEKurDj80JDlCNj6nMadrcmGjq6iTgoHQV2OCF1gxO+X0ZUFkNcs3g32RLmIukiZRxP5nfF6G5fFwmEmou9ubvEhKu5WqhR7PvVlVo+bUjMYLt2nTjc5sUADiS3BvmolmpSlHZFRKVIENGWbY6xrSaatbFWI4Ru/FC4+IJWgksqOkpj5gUWLiBcBmIL3zYJekIhCIkNXpmY8BDrWptW8nzyvzJ52/OI64jXVW/5b42gQcWOVplVJhjZo67OTeYLndoH+KuuIu8mV9aAsElCaEKSXh0fo7aSnNOtecAsFDkGAIPUC5ZE5oOITVq/fvS57CwoTXvp3mpw6VNUZBlK1vbWLLGAYMCrNtgPl50Pi5+DCyXT3yEFpcx4mmM/en+uSAeCuc1NMzmAKgAq7BI4C/owJMkrMhBKNR8zVMVhCswyykwS2tyZsnV4zU3zKDBBBJMgOGjNhYQ1V+rGS/Ui59PC1zI+fMUfqyAXpfmzEEznmP6cwzQIq/yjy6wajTWeWCwzhak/+Da7mJuG6BFXucaAMirOcvIZJc8ZdlOBRLWTPPlY18VYX1c30sh4QqX9C7mNo39rA9VL+xb2VJUlxG8e/59XOnqIKXhUFmDrlmfgxJbwRZutW6Rgj2r2c9ZFqFNm7WqqvCP/x//GHf37qKyKvzVX/mr2Lq+Rc4gNQXFcRlrHaJCFhjGQ8yKGfp5nxgWKtW6BHEZP/H8QztEz+1pYFBWErZF5TCWsEhQuQohQ4lEJZiUE+wn+1qf4nEWroEV6PP3lY+NyQYmxQRXiitwlIONegNIgHycIxkkcCpi9Fm1hSzPyMbQq/GVv/kVWJGFaTVFXMcYZkMkKkE/7mMv3UM8IdG+STm51P3GFram41+LrulAMrRDONJBlVco8gK2M8/WqQqThJy1cplTiUs1B4qqGIUqMCyGGBbDJaFMU4fksr4XELBv2nBqByKl8gGv9OCUDlSi4OYuolGEX+j+AjaaG1Qq47bR9ttQsSKh2XnSRAYSp/kpjtNjnKQnOM1OcZaeLdmmmsBKP+vjKDl6ou4JDEyZrxPP8hDYASKHGARNp4le0CNdCn8N6+E6QitEIAJsNDcwG81w1bmKcUKbcD5mc8/Ee0YhhFamN/cKRVFAlAKb/iY21AbtBzrL5Q7AYi9mBv9CkJ6NBiPysdaiYBDiYHCAUT4CPGBSTPAgfoDpeKFNcc7tw5xPc2HMttdGZEXohT24tUu/ywjjwRjSkjhuHiPuxbAKC1ZOgG5d10saXav7nZ2dnaWkC+9puHHAy/tW/r+5Rzf3otw4QDT3qAyEMHOVn3cZG+EyloMQQjtiPC45ae67r169ihdffFHHOWZiVQiBW7duLenR8evN4+TPMW1uTdFJk5HA58B7TRNE489cPdfVOcv7zziOl9wn+DHvO3ksXdfFK6+8AsdxsLW1hdFohDRN4Xme1mIQgvRFJpMJfN/X4MVgMNAAFF8rPGaz2XkQzUxKmwASn4/5eHVPbj5HKYVms6nLkMyYywTGuG854a0Ulen3+31dXpBlGSaTCdrtNvI8RxAE2mmDNTkYHONx5s/ixv1pMq8YsFtlwa82831WY1ru81WCgDlvzJi43W5jOp0iDEOcnZ1pvZCLxuKi9tRAwwcffLCE3pp0Ddu2l5CcIAh0J3BW2ETdVssozE7l3y3Lwhtbb+Ch85AEDOGTVZGix/zlwkWAgGiYlYNG3oAPn4KRObV11aLEpNObWWUIa+iDAAEAAElEQVRzkeGFy6RTma83AzlTV8F8bz4nbqzGCiwDLgCWkDdmNZhWlubk5vddXRy4mYir2cc8DuaE47+bx27evPh5jFTq+kWjz0wqDz+PAR2zb/hCZVRfKaVvskIIfHPzm3gUPNLPd5QDX/lkYSU9hHVIYpUlofGhDBEiRFu1oUoFT3loOA2oWGF6OkXo0UbWdmzUVq2FM1lwU7lK6yIkimjEzL4YyqEuDymteW2/f/kmRSqp9QI0Fb+al4KIgI4NJPIV1GSLZ7kWZRcQoqma8Gt/YYVoLHJaMHQFBDLnJs8BvtkwlbHT6UBKieFwiJ8pfwY/g5+h95YKTstBYRWYVrQhSQWVyCRIyA3FIgZKLGNkkgRHM5ldClJYsBAggA8fXXSXhC41m0EsgqE4iyljbJHAYyrILpYBo6UmFkKHlrCWQQ/OYmJRwvHYkgwFXRLCVHKlFllzJZbfj4PGxzX+/0cqo7DIjnCCCSb55PHPvURzx5c+fJscCTzbI7cLQ4hQQaGsS+30kJTzoO0x1PdV4Ufz3M8FV3MmwkdpbPkphQTq+bpfKxoXy6IyoXnAUqmFteyTmi7NUfWPxTzgJla+gOWNSVmXGkwDcKE7x+OahNSuJFVWocxKyJqo7WudNSqzErYudWC2QqEKFCiQFgQ25Yr0NJ4W2KkVAbY/rg5GrWoCvqoUZ9kZZCz1tVRhIWZ72XFZwiIAwm5icnOCerOGXdr4VvEtbJ9ta2p4ZEdYc9bQ9tro+B1irbVtWl9sF1EUodfr6YwyBJBWKcbpGElN63pSJZo9MS2mSz+Ph8c4m50hLVIUosBMzfCp3qfwRfuLuHHjhr7nmXsTpRSmOSnyVw5ZNOZWjlE6wvHkWAd1B4MD1LLGoBygP+mjL/pIggR5mJPN5jMrnaKwUOg/PkMv6Gkdj1CEaNdt0sqYl3nsdHfQ9bto+k0Cr8tMMzbYGnVcjLWewLgY4zQ7xWQ60e4fqbp4LbCEhYZFgfXV4CpCGRJIFLTgOz5ssQgCpCVR1AWSnCx/h/kQ/aSPSTnBMBkiqRMCsCwqvdT2u2rlpwB+dP9HFx6PAyrHadgNdL0uOm5nCdjaCXbwQusFrZvRclv6Z9Nt6j3o2x++jcPRIZSvMK7HOMvPcJqdYlSOULolaYsUY10qlNYpztIzHCaHT31tcBMQcIQDcV1AXpGwCot0qwobiAG3cvHH6R/j0f4jAoAsOr+rzlW4lav3ZFEU6SSWCSqYJZ38O0BrS8tpoWE10K7b8PoebgY3Iew5QOFRMHfr1i0IsWxJads2bN8mAdZqpoVY+fEwHRJ4kQ4xSAcYZmQzO6tmGGUE8tSoiaG0sdwfspJ4c/Imem/1NDOHx48ZQk23ifVoHZvNTa3L0vbaVGKBBb2b96acdOOYJI5jXX/OgTiwCLw4ODaTlOZenPdeFyUXzeeY9wIut+Y9sknZB85bagZBcC6OWgU4Vvd2fA6rx8v/8zwPg8FAB8nMyuUMOp8H7yc5c25+lhlwm2CQlFKzeofDITY3N5f285y8Nd+j0+ng53/+55dKgnkfy2UTF/UtjyVn9c1zNFm75vmbx8r7X7P0/DKQyOxjHtdGo6Ez9SbQZR4rJ4sdx8HnPvc5XSaSpqmek1tbWxgMBlqXYTabIQgCPPPMM7rf1tbWNIBj9tHqHOS/m4CHmXDkY1pNcK6em3mOZunEKkhnxrBCkCZHkiS4du0aptMpBoMBhBDo9Xrn+vSi9tRAwzPPPLMUaJoHzhcto2TAedDAPKHVhYIHkxdV7ujtck7RQIpc5piJGfqyj0xQMJIhW85a2tAUe6nkou5//uUpT//NUx4t+KVNtPrKga1stKwWBSGV1LVsptaCWQtTFAXSNNWLjBl0mwglAM20WK2v4dexUCb3K/elCQIwLcgEcBjdMsERbma/6k2Y8T9zUvKxsZKseYGvsjbM0pggCLSHcKPR0LoCDECtInTmomAuAFJKfG7wOXy2+CzRSN0acR0jt4jeHtcxKlFhoiY4rU+RgOqE9RwwdVtcAB3Aqhe2oF7twakdXcPow4dd2BCJgA8fO/YOIiuCVVjkuAEfnuXp80iSBHEWI1MZKqeCFVrIRY5pQUyKWbUQ+MrlovRjIid6zuaS/qYXVhu0wSqhHQo8LFgVjnQg2gIymLtTzM/FVe6iZKRy6FwqG14xF0eUrq5FHY/HGnFnkSHP8+C6LiZnE8oitHbwbPgs8jzXgkY87nrjIhe2TXk1L4ERCVGlVQy7acNre0hACt/8NVMzGiukSJBou7b5yS5fr3O9hG7dhVM7xBaxSYAP1aK8Jy9zjCYjTJMpChSQoYQVWihsAiuYJXGuKUCUgmqi5wJ7co5IMBOhVFTOUctaO308LoATEPBtX4sDcjaprmsqDVEVARjq6TP8H6WldXouu/1RmoCg68SizB07WzCtXwpJYpTzLHxap8RguaRxYGyWIbBbBDf9O3eHodVRi/pSSvtqY1FLKaTufw3UYe6KBEUio3PQoqzLpw7KL2QG/BhDqAELsdDdKBVlSCpRkRMJgEyQZfLTvqctbAKaJAXenuUR+GT5UAWpzLejNlBT8KhqhVkywySZwG24mGQTYgIUMXKVk8XtkzK/fOzm8z5i+UylKkzyCQlzehWt2wr4fv59/OD0B1oo9HEuKoEMdCDOQWXLI1E9q7QQyhDXN69DFhIbjQ1ERQR7aOOF1gvY3dlFNs1wqA7x7sG7eP7557G+vg7P886xMlcDDyFIM8j1XC1OyHTtyWSy5DV+8+ZN/Mt/+S/xyiuv4Dvf+Q76gz7iNMZ+fx8n4xPAB2qv1qBvIQsoT+Fm9yYsx0Jcx3gwe7BgU5iClQ+W+4PBgdAK0bSb6AZdAn79LoI6gJgKvLLxCtpuGyITGB4N0fE7ePa5Z6GgkNfkJKP1KIoJJhUxOAbpAP28j/18X2tSzMqLM1qWsHTJybazjbbXRjkrkc5SqEJhNplRqZQkMDQrMyojc4C1a2tIZUrOHtUCFC1QYFSOMCpH2Ev3aO0Si005rzUXNQFBIqZyztSrXVzpXdFASjNqYtfb1Q4SoRVq/Y5e2EMzbCLJyMp2Wk3p3laTdeswG2J/so/T+BSjioCmuI7Rj4ltk9UZWW9bOC/gq4C9eg9f+/BrS3/+XPtzeD58nhIRIkQ376I5JSbFerSObtiFYzna2t1xHJ2Mmk6nyLIMrVYLk8kEJycnWiyx0Wjo/dz29rbOhK8mnzzLQ+AES3s/rovnfSbvF8MwxMnJCVqtFh4+fIj9/X2MszG+9d1v4e07b5N9u1cDHlA5Fbae2cJ2Z1vP5YP4AO8X7xNzp4rPa+oYY6hLveYMKR4jHrem28TsdKZBsYZNiUekOLcPXg3KgEXZ60UZ/tWg3wwGOQbI83yptp+ThhwETqdTrT3Ar2MRxNVAffU4uc810DkfZzPBx+wC3qsVRQHf9xcAkuEYt6rRsBpsmusdn0sQBJhMJvr1DFgIsRBSZL0SZovwMfOayPs4/jYZ0atsEDNpyoEvswrM9+F5yrGMmYS7aNzMZjIDGDB4//33l8ALTvgJsSgdUYpKIT72sY+dC/r5c65evarnFP9kfQeAwBiTlczjbn4298vqufHzVp1EVsGT1casIL7ueXxWE8umZs0zzzyDj3/84/B9X/9/FTx7XHtqoIEH2ax3AbAUHPPf+du8aM2LaBU5u+hxXdd4Nn4Wt+QtPfnu2nexL/cRCspsu7WrmQtSSJQVeZxzrSgDEqVd6sdjMabHMkPuXk4hh4MlkMKv6WdQU3bahUsifr5Ey24hQoQCJLrnWwRqyHoxSRlh5cCVBVBMqhGjjwA0vQbAuYDPREH5AjQvSB6vi5gcF42hOcZZli0hq4xAmoseL15hGGIymWB/fx91TWKUURThxo0bSJIEWZadQ15ZuMVcJLi1yza6oou6qOFb5C3sWR4EKLgM3ZDoVJa9sEG1JGblDP1Zn2pdrVJnbpiRUNgU/NdOTXRQNSIyv58hD/JlsGoh+wFRCViFBbuyEbgBwmCutF65sEpS+256TSAF3JmLsCT6ZCCIXYMS6LQ7GuFut9skiFQmiFWMuIpRWWQLWbs1clAAzzTgSTXBuB4jsRLEkkCXQpDo4+MEHZld4cGD67tADtiljTAI4QUe6qSGlVrwHR/dsAtXuZBDiYbTQKfVQaQixMMYWZLpOWA6qfgOXQuNqoEiI9R83V/HlrWlx/myxb1UJG6WIMHB8ADDlDKCmSQgJhEJATVWjsIrMBVTct9w0sW16qyMUy3gVi5kJhHkgfbtFhWJWgZugDyj+njLsQA5L5dwFKzQIrtYZJrBcRGFXQqJpttEw20gciMENtWHO9LRQm4AZXnLmmrD0zJFWqSY5uQ//iTWAosdmoG6UkYpyP8fgAoFRY4cqlyI633EZpYRWNLSQT+LPFaqQlEVyOpsGWjiJgDUNI6WsOA6ri6L4UDzoiBCMx0UkFePKSN5wr1QYC5+ajAI2J2DgQHAYK3MyzuqmgLy8rE+poumgYvLhvEpSicuahzwZ3UGlJdoeowuefGAfkgldblbaIWaicTilVznzq4bLOLI46xZYdVCeyGpEqRlSloRF407DNDJYCBlyJ6abZHUCZIswVl2pgEm/tIg193L38wCgdFNt4n2YRvhMQlYcoa16TS1aGRkkwBfx++g5bYQSFprWPMJoHWON968qTc3cXzvtoSFQAUIs5BcmzK6708mE31//ZVXfgWvvvCqfr1ZelmixDgfI67nFpvxGVHeRYaz2Rn2zvagbGIM7iV7uIM7yCQB4sn9ZPlaygC8Tg896WkmSdOmc29Y5Pyx6++STkR3B+uNdW1f6tgOPN9DWZc4m51ph4aT6QkSleCDvQ9QORUOcICRNUJiJUgDKoG8qPWzvv78dW8dTbtJji8gtw0BAd/zab9XUVlkWqaYpBP0Z33UXo1RMSKxRTHf60BRWcBcFdOChQf9B8u6LKq6dI01HUc6XkeXDnSCDjpeB2v+GnajXWw2N1HNKlxbu4aHHzyEW7tABvzJn/4JjifHGOUj1B7tQ4bZEMpT6F3t4fqL1zEqad8yLId4mD7Ee/F7mJSTS1lyzAgIRIC224aVW9hsbsIqLMhM4vrGdSAF0kGK7fY2tre3EbkkzmoGm9zMgE2DN8bez8yccv0764ZVVYXxeIxHjx7h9PSUbMqLEI1ZQyfFONj91Pqn8CvP/MrS/tM8jiRLtM1uLnOUdom4jnE8OcY4HyNRiQa5htkQh/1DxHWMcU5MlMtAUvuRrV1sQhlqkIkByq7fxWZrE92gi8iKFiUibhtNt0kOO0YQbsY37ADA7Akz4chBKgt7c5k0l4FPp9MlgffVbLZZFsHrQJIkuHfvnhafZLY0fz4fw6oVvRncm+xrBpLMMgE+Fj5Py7IQBIFORrLDhvl+WZZhNBrh9u3baDabqKpKC0JykvRxQfDq/1hgkxO3JuvbBEDM8zH7bDXWXC1bN4NrAPp4OaluloWYjBhmTbAAppRSAy1m/y/NvwtKpOu61uKpPFeUUlqHhLUmzETf6jEzo2cVoFoFH8zrme9XXJ5iCjsykGEy8JlNxZ/D8cBqDHlZe2qggU/cnPR8MvxtWZZeVLgTzcyo2UHmxcQdY7IczAw6T5hD+xBve28/lr4tlKBMaG0IASofXu2hiaZmNTBQ4SiHalMr2rA6kQN4IA0AIwvNtPuRGukMbWqnyGwjMGliSQHbUtaS6CBT6K3Sgl2QorLe2NVEeQ4QUIlI6KEX9qBqQi65z3lx4gvNBCvMmwRf1Cb6tToxeOKZFyOPg7k4KaW0foTjOGg2m0iSBEdHR0uWpcyKGAwG2oUkyzJMp1P9/jx5V3UeVgEqs56NX8cZeS7NsSwKypzKQaNqaDrnMB1qkAYgSlmr1YLKlAZz6rrGyckJHu09wmA2gN/xUTkVxtkYhVVAhAK5yGE3bV1qMapHmAQTyEhCBQq5JAtK5akLVbRFLajsR1FgHtUR/HouSgkSpQwQwFc+WqqFpmiiYTVobgoXWZrh+OwYSZIsNqjzGwIsoBCkYF3Z9J0ixTgbazeKWTUjMal8ijqsUUYlKqtC4iTkXGEVl5cYBFS6wswCtyatBrM8xFUuZCEhc4m+3ce4GsMXPnyxYA85WCxISinKLCFCQzSgCoVgGugaVn6e6xIjQ5QL5tCjR49weHKIXOZQPlFwuZxD+QrKV0gFATaZzFD6JSqnQumU6Nt9yihdtF7UAk5JTJdO2dFMkSqtUJc1pJL45b/2ywCgAYSszJAUCcbZGIN0QN/JAMN0eOFGVUCg7bfxTOcZEpXzGnjzu29CVAKudLF7dRfdVncBvM0D7Bo18pqsEuOK6rHjkjI+j2MVUEcaGXQFDaYp+se/1ca6ARmyv5woJEs0CEW2o/WcmVWDbDeVfCKrxJNz0EfYdL61Ql7mKOsStaQSmEpcfHAKSouRlmpxw/2owI7piMHHYkt74ZJhlKQotcywmKZTzaT5KOPzbwN8Mm1HC1UgLwh8VUpBFU+vX8IZZlsu3AJ6Xg+BHVANux0gtOknMy486QEl8K1vfIt86O0ar3zmFVRuhUk5WZQ+1ETJT+uURAFXJhoDTgAJFi4Fjo/pzwoVEpGgrEuM0/ESuMTv8Tg3EQGB0Ap1WUfTbepgpuN3UM0q7Ma7uFvexcnJCQ7qA+RWjlrUyG36ad6HgWXBbFNom0F6KSUc5aDn9tBDD27LRdWptMXhweAAD4cPsb6+DgAYp2PkeY7d3V0IIag211WI1iI4DQeDdADlzQPxudDgtFqICp4VZ7gX39MU+njvckCS2SVtt41QhugGXQDApreJyI4wy2aophXSYYo6qTULLE5iSFcCAfCZz38GTsvRpR/MnpgUk6Xjumw8/JwYC52ig5bXgic8oKC1PvRDdLodCo4UicsWikDQuIqpbKIcn9P5yOqMtCDSUzgzR7O+ACxdx5c1Z9eBUzqwckszaatZBbu0URc1rofXtf5Dw2ogAF0zbbcN6UjtenI2I2ePYUqlKXcO75B2RqPAUXKEh9OHxOxDivRBujiHU2hLW0tQyWbLnrOA5hoebNfadJrYaGyg41FpStcnwc8tewue5Z0rneX9Ols2MtPVBC7M/ed0OtW6AmbjBFczalIwV87r0xWJCZ5OT3E8OsbW1hZkRHbkdtPGYDDA2toa0jSlbLsNdK90Mcknet4kKtFaXuN8rEuLRgWxY+KarG1n5ezSa50tiRmAYKCt7bYxTaa4t30PXzv7Gm46N5dAuqbdRGiHqEt6Xw7alCLHPhZ2bDabaDQamE4JDOP9Nvfx8fExmk0KMI6OjvDw4UNsb29r4Xil1BKV3dRNMBnG/M0sGDPoBc6XhfCxSEm2nJPJZAmkMIN+tlQFoB346rrWIphhGJ5LOpuN40chhNbhS9N0CeAwgRCTtWDGELymMuhrxkCrn2keR13XukyA5+3q5ymlcHJygjzPNcjgOA7W19eX5rqZ/OX+NwFjPqbRaKTH3IzZOMFnxskm2GA+NoGV1TFcBYuEEJqdFMcxms3m0v/N/pvNZjr2Z6FMNiIw71lPak8NNPR6PWRZhrIsdQ0UdwYDEHwCHJTyJFu1ezEnmdkhZumEqQXAHfhT6U/hc/nn6L1RIa6odtxu2JhVMwzSAURAAeKkpMWlsGjRncgJBScio4BEXLyYWMoiBoOg4JDZE6EI0REdorRz4FU5qOMazYisdiaTCcYpeZlnkmr9ladQORXimsSsUpkid3NkXqbdFmp5ScZFzS341ufsjXl9HzMtOOCTlkQgAkzsCTrooFGT9aNd2QishS0psFzjxSihqdZrAkKMVJdliSAI0Gq1UJalVscNggBJkmhV1ZOTEwyHQ4RhCKWItm/bNtrtNmazmablm+gbf74JjvBcWUUoAZxDRc33YqpWnudI0xSWZaHVamkKoeu6cF0XR0dHODg40AF8J+igE3S0hUu/30ezbCJNU2wq8p9mERdTnGZ7extXrl5BWqaY1TPkMsfp5JQAKosAKqfpoHIrsrWyK6QqxVAOaS6oFClSCoAYiDYSs8IWcHdoHLmsgJ00fOUjtMi6SuYS9aiGDx+b3ibyUY7jB8coD0qsWWu44l2B53no9Xrodru6vmo8GWMwJSuysBuiud6E0yDdhsqudGlDYVF5RKISEua0Zgu2iMmuuGAaS0gdvOsSJg7mwwpKkrYGgxihDLHeXEchC/jCRzWrsH93H2mcohE0NNgSBKQK3u/3MTmdLNXzmeJKWkTIdxCtRahcEhKd1cSqYHZT5VTwOh4Kq8DMniELSasikxn+t+/9bxdenq7lout30Q26eGn9JXT8DhpuA4EdwLXmWV/ejNbVAqQoEwp8gwqFX+B99T5tvC/oQAFBVmZ2hIZsYCPc0BtSXxI13rd9eI4HSyyj6aUqkdapzi5PchJ5mxUzxDWJqKZ1+lQZeS4xeZLQ4F+qGUKILB6qP+IpMtsKSgtkLmkqSBKafJy+g4DQgm+OcDSTgY+lVgT25Cp/rN0kn4eC0uUQpmbEE/uM99xzzQtbUmkPl7Kwgj7/zkASxLz0DXN2RT1n9dUk5Pg02hasfbJ0iB9hiE0RToDmelxTfXsffdq4PGneXF987uHkULNLHEkMCl/6WPfW4QvSvGnYDQQyQGAH5D4hqeSszmt89spn8UzrmQXTT5I7BlswniQnOEvPMJvNcLN3UwcYLITHAsNxFWNaTBePy/MCkAqKWBzxDCfpyVLZETB31tmbl6Lcmb+oMf/eAvAS1a3blQ2ndCByEkl0axezdIY377xJDAu7Cad2tKCmBw9tt00UekX7q06ng06ng52dHbz6KjEhRqPREtvCDCZ4o2hauZn11AA0K7IoCgwGA0gpsb65jtqpdcCfgIK4UbpgMQzTIU6mJ0irFIf5Ie4N72GQDxBHMcpGCWxfMAdqYt4dj4/RK3q05s2z9hvBBqIGZaIjGaHltcidZt7PSZZgmk+RyWwp4z2rZ7oMZFpN8ah8hNnZ7FJAOLIjtOwWdtwdsmd1IngOlSJJQTa+XA5XKHI04bV1lI2ISVGep/4XFt0nhU3AKQCoNl0TJ9YJfnDnB5deH65wdblAy2nR/LcaCLwAz1x/RrtCOJWDjtdBw24AKdD22wjCAJVdad0JdqkY5STyyWUy/aKvwaRxMUZSXVy+4Fs+gWpeB02bwImO34FXe9hqbyEdpOj6XfiZj2FjiKpTQY0VZDXX5ZESWZYtWV8y9d+MEzgwzbIMg8FA79n29vYwmUz0nmxzcxNKKXz44Yfa2rLb7dK+zuqg9haC56zXYIog8rXA+9lmqwnlKAzSATEk5t/cL/x4lJG+yf5sH+8O3iXwZ3eId2fvAh+e7zcGJJkdpJ1j5qCkDx8Nu4HN1iZu1bewf2df684cHR1ha2sLdV1jY2MDw+EQBwcH6PV68H2qt+Nr2dz7MMMiSRL9d9Oikff6Jiv9osCR994MNJhi/ebr+VgYvGAWdxzHGnRoNBpot9uwbRv9fl8/5hKg+/fvY319HXVd4/79+xiPx9jc3Pz/MfdnwZJk53kg+J3ju3vsd8ube1VlFVAFgFhIgJC4iOS0QFImqkVKJrE1D021db/IaHrtR82jHmRt/SCZUdYjU7c06s3aRqIoUuwhQRIk0QBBgAAKqEKhlqys3O5+Y/V9OfPw+3/iRNyIrASNGhtPu3Zv3iXC/fhx9/N//7do406z6QksQQKTpc0AiMngNgt+EwwAsFKbApTUkee5rkvMeyXPITay5NcPw1BLKhzHWZGRcE0DQMuw2Qg+z3Ocn58jTVN0u92VGtmM9jRTRbi25lqHzVE31dQm8GICMlLKFcn7umSoqipt3mkmnOzt7UEIoZ8X62Dhtu25gYbFYqFjdzzP0/ogs+vMA2EWsiZ9xywK19kNPCimgYoZyWJOLB7snWAHUkrEsxhe4eHAOQDmdPC+7wMCiOexprpo7ZQAKlCsIjMX5uUcR8ERHM+BsARphVGS4RViXKpLpKplMqDVgVsgXSlvA8BubM1iCCWBFHZlI0CAXeyimlZILhOk4xTduAu7stELenA9lxAyu0KmMtgdG739Hry+R51zlaGwC8SIMQfp/tlY6YpxXnuMcKgr7SkPQUEmfcyq8EB6bASgWCflofEa6ggIX3sahH6InWhH3xiUogzdy8tLPHz4EFVVYW9vD91uF7Zt4/T0FJPJBOlrKZphg1CEsCrK8o36EepFDZUp+IpSHESzemGYpqG+72tEkY1G+TNffAxc8A2PKT91XWuEuCgKeJ6HLMvw/vvvI47jFbdYfr/ZbAbLsjSoxtnIDHpx5i0zd/gGcffuXYxsimvqOT1cXl7quX7Nuga3IgbKMBjqRRsjm5ZloUIFEQjypJAFUkXRZOeLc5zNzxDXMSqbGAilXSJzMpxb58SyETlqt6aFK299ALegjcXYfIoBMiu0YJUWgr0A5aKEKChebxJPIMfEELlzcAfXhtdQzAu4rqsdgfM8R5ZlGs0VQiDNU4wORxheG1K3vZV/FIKOJVNLUCUH/WymZpg7c2Q2xZwWsticOtEH8CnAqi0NRjBYIZTA5NYEKACRC+oWlQ6cwoGd2XBzF07qoOt2sTPYwU5nB7ZFtzx+AMdxrB8Q9hMb+/v7CMMQZVlq/eP/9P/6n7Tx1ThdMhgu08vV72VjHC+OV76XlJu7f2KXZDlu7eLQOaQOoB1qWjp3mZmNUDalTgJImxTnybleQMf19i4M561Hkq7xvtvHnr+HUISaPhpaITzbowVwA5I/ODYqWSEuY7pP1lRMsIs7R64lNcUbpg3R5J9H2/+fcvtBgRAFhVzlpBH/ATc2enMEAQC+7VPqRGt0qaB04kRWZ5Tw0GyReChi4wlFBbstbCgo3W1VdQta/IAOjpyiYKZjcLSn6cchITUgUNYllEV6/bzJkTc5iqbYSuFeMeH8824twAK0HkWC7uV5nZO5ICa0nniO4/+1k1+7cuyOdJYSEEkgrdM4OB2fomf3qLj0D9G3+xjYAwydIXpeD3VWIwoiTXVOSmoYMGi3qAiIEIFAZVWYF3OM0zFmORlRns3PkIscx+NjlJI8GK5IgKyGvIjcAvDbeQCBcT3G649fJ5nOM2j9EpSiEVkk8eBuateh9cXAp+KTgelQhtrDoeN0aJyM4stcnJrMVQbvVa0w7A6xI3f0gpg7ryz7KIoCFxcX6HQ6eP3113F4eIivf/3rePToEU4uTnCxIKlH7daobJKBKF8BHnDz5ZvoRl0CfsoFTrITzKu5jnHcNgdc4WpdvlnMXfeuoxuR70IoiH3CTKO6aaO4W+mY6U/BiR6TZKJNNeflfOs87zpd7Pg7CEWIrt9FEReIvAhlXuLs9Exry5UkBlXe5BRp6gFO16Hx2EAJK1SBWTXT6Rkm44YjXLfddwWEHhOWCXRtkgX1vT5uBDeIhWMRiBFKYtde27kG13EJROGxb8dmnI7JVDQ+x6yc4cHsAcbZGIsT8vHQ+zIE8CPLfWEmb4AA3/3Odwk8sTvoWl0EMsDAHSx9VuwlyyLqRNj1d3Hz5k38yI/8yEphyMBFVVV6fW8WlWY3n4ELs2PMRZdmEwmJ0KVUscPgcLnvhl/apk78u+++i3/6T/8p/tKP/SW89pnXMMkmBDy1zAmWODE4F9cxEpXgMr9EXMXar6J6UuEfjf4RAoukFEmSrKQaPHz4EK7r4mMf+5g+XjNNI4oiTfc3oxSB1WQ7/npbccqfzZ8DwHA4JOZZy1IAoMef34e/z4aXfD4WiwUuLy8RhiGyLMMbb7yB/f19+L6vPT7YJ+zs7EybDzLIYDIBeO1sGj4Cy7qSfSIYZDXZ3puO1WTf9Ho9NE2DNE014LXOEDCZ4lyPjMdjPd5hGKLf7+Py8hKXl5e4desWLi4uMBqN8P7776MsS51usVgssLOzo2sfBuHWzSxNvwizvuZG8SZwgefuOmtOKYofTZJER2+aSgKuo4BVn5HFYqGBEmY3PM/23EADF1gMLuzs7ACARj14IrDG3zQTXGczmMDEOoBgUrHMQQWWKBybm7A+xvM8zOdzTKdTeJ6HnZ0dTd9h04vZbKbpMDzYtrLRUR100EGv6uF39353Y7HjKQ+BoEJ9IAaaEi5ribqo4buEdidJgrJq6eg2GZvlIkdmZThtTsnNf1ih2dvOYHAaotiJXEDkAkEcaE1oWIcYlSO4tYtiXsBrqEts1RZ810dn0IHX83QcZNzEOnKwlCUVBEiRIMGFukAqU3LAFkThNHXvvAklNHPC2yXQpFoQBd9/gSjyl9YlnjRPSLcVFhCWwBPrCaaYAi5Qhka85Np7WA0Vwhzx1XN6cJWLSEYaqAkRwoOHnujRYkJ1tFki07/WWRKe56Hb7WrkcDwe4+nTp7AsC8PhcEX/xLE0fPPsdKhqT9NUzzGmtjHKzNE1aZri/v37uHPnDsIwhO/7CIJAI7jr89w0x9EgXC3RER1EIoJtEYpZWzXGzRjvn76vb3i+72N/fx8WLGRzcrd1HAeTxQT3n97HZXJJLAQfaNxGf7Yii8wbVZsm4RMT4dK+RCYy1PLq4ubP8Gd6PrqNq2VIjuXA9YjN44HmA3Ig9+i+4AsffdHHgTiAL3xKhsBmw6XHR481uFNWJebpHIUosHdrD/CASTrB+fwc8EEGoU6Dxm7I0wE5FtYCiZWgdmuoznNIAtousIQkQ0j+qG0NCL7XvIdIRFALhaZo4Dc+vnf+PfS9PvbCPbw0fAmOddX5eNuWV/kVkOIyvcR/+//4bwEf8AYe9nb2UKgCF0Wbmd6Qxn2bPMIWNhUJVgcH9gH6YR++5cMVLhWQcnlbr5saRVUgr3PMszkSmeCsOdMu4kmTbKeKynDp/N+CFV27i5E9wm3/NrpOFwNvgBujG9gJKRXAl+SgnDc53YOqpWZ/UVC6SVJRp3hezPHWo7dwPD5GLnI0TgMrtFCLGoUq/pN4UvxFbgpKpz4AwLSeAj8AXuEIRyeEZEkGVSlISLqXex16/qkGZV3qGNlnpYVYwtKsDHZnZ8ZDAypsUqRoqudL8bjy+oYXh7nvnHTi2i6BVMLW/hxFU2iwIKsz7dtwpTBaY7WUTamPg00+GzQQSjzTHJJeSiwlEGip7TUVkux1oufW4sOPW0tCRCt3bBkWgSQ2RdftYr+3j/3OPnaCHRwGhxjYA9zo30B8GeOF6y/gD3/nD/HpT3wa3/zmN/H09Ckuk0scj49xOj1F7dRo3Aa1XVMh69J1MDgYYLA70IwLnYRQJysspAaNZmOc5CfLVBfIFRDoWefclz4CK9CFKBejHaftvDodZNMMXbuLF70XcdgcUhqA38dAUjSvSU82QQteywG0aO0GXaTzFKIQsGpaXMux1IzDn7zzk/ixuz+28szgRbdSisDOtkE0KyhR4zK9JEmrylYkF6flqTYZTJpkq+QCgAZi9Bi4Xey4O7jr3MUoHGEUEsvCs8h01XVdzVaapcuu98nsBLnI8Sh5hEk1wWV+iWkw3Q6mAwiaAEOHvAECmxg6DAZqozuQ3Ip9T7KGrqc55qjq7UBDpSqdDnPe6if4tcqGDH4/1JvCbb0pPJJX9L0+rkfX8TH/Y4isCG7j4sbODRw/OMbh7iEePXmE17/zOt59/C4uU/LPqp0atVtDBALdwy6SKsFJdqJTr7YB5hJS+6WwFEEzAlqPikhE2Ovu6cQRfj5azZKhs240aFLEuUOcZZle/22by6bxILD0aJBSosgKdK0uQj/EnrWHWTWDH/oaiDN9DVzX1exffq+0SskEvF03sX7fbLjyupHfmwtdbo4wxd3spnMzRUqJMAxXOuBmUarnzVohzmvWfr+vdf5sMM5JC1lGz6ZNRvLMOPU8D3EcYzKZoN/vazPEwWCAXq+n/Sf6/T5u3rypvS/MYzYLbj5v6z4GJpi0fr6uXCNrQEIYhnAcB0mS6DqBmbQMYpjeFyaIURQF0jTF2dkZmqbB97//fVxeXuLmzZs4Pj7GcDjULAQpJaIo0hI3loebrBOzyc6A0jrrjMEBroFM4IzPnQke8c9YAs+qAWavmX/Dr2nW5UVR6FjL4XCIe/fubRxXc3tuoOF3fud34Hke9vb2cPv2bX0yB4MBqqrShT6jhuZJNXUfm04sDwBPHBOBZFYDTwAuDJmikqYp3nnnHYxGI+zu7uLi4gJvvvkmOp0Obty4obX6w+EQZVlqLcwmitDfffh30T/oa+09G9EwxToTGVHIkWKuKA4w9ehntVjrKrebVBRpZDc2GUkWHpk/NTakkiizEpakrlJZUZ614znUBVMZGrvBGUjb2XgNcr99rw2pIlJJ+Mon/b9FhpVdQb4UESLsiB0EItCFoyoUylmJnt+DrCUuZhcoZAG/7yNDps2laqfGolzgMqHCVLkKzaDBzJ6RDt6unmlOKCqh39NWNhxQMWQJC1VZoanbRZAEja2Y41ScohSljpuEgE5m4GeRgCAPjha5ZbZGs9eQWZcIEHoh6kWNOIkR7UQIEEA4Aj2/h8AN9HytqgpHR0eYzWbodpdRWHwhMi2VtYfMrmD60eXlpWbtZFlGnf401VpEzyO7aZZ3CCGu+FWsI5JVVel9A6ABlSiK4DiUKT6bzXBydIKhO8SwP9T0Qx0vKi3ShrYsDL5uTMOXtEg1Y6K2a52cwRKQtCGDxlwS82eGmfY/YEoogI3xfhw/a8qRAtB5yqMcjWjgKhdWYaEoC4QyRFVU2PP20G262Gl24FYuUAFNssqcYvMiHf0qifKY2AmKoEBqpchdkkbkVk6yJY4slcSiSGVKXaX120EXQNvQ+PQ///TKjyxhwbM9hHaI0A1pIeaTmdROuIO9cA8H0QGGwVAvxnseUUxv92+j7/fxzRe/idFwhCiK8NJLL60wwfjBlhZkGsodpUQlGiBYVLQ4G2djpCrFtJ7q35tX8+056JmtF2g3nBuIZET0YMvTVH1eVABkNMg0/LRJ8Sh/pBfx82q+tXjRnSl3qfvtu330HPre3eAusSkehHh69hSv3n0VA3+A1158DYcHh7RwqcgUdZZRMWGFli60kiqhTlA5x6wmzS1TWtO6TaRpcuR1/txmjf+/3kpVoqxL8rWw2w8BZMgwSScf+vc2NsgqpKULTX7e1qomoKImHfq2osIS1tLrQtornhI6AaL9SJoEdfXsRIhtGzNBXIsAChs24llMvg2Oj17U02aTFiwtSSkboqoXqliCFk12hSWioJb7pZbMBktSxC57ZDxP+ogpCalUhaom75srcpCL5zjwP273JbQgAwnZl0C5CrYjA3p2D6/dfQ3X+9dxd4+uk0BSJHJoheh6XVRNRYlHIJPrRUna80W5lH8syoXWp8cVxXnO6zniiphIpkEnS4/G5Vgftwas2n+a0XJ69dAc2UZPOh1Nqw9FiIE/QD7LcVAe4DQ/RePT/XrRpcQgTxGz0vVcRGGEz33uc3jxxRdXnNkB6K6mZVmIVLRSnHGxUZalNic0/QOAZUe6rEttGM1d5gwZsRiK5f2TQdgnxRMs5gvE5zR+2yQFUsgVb4pROEJgBbjTvYOkTjCdTFHHNfI4hyVo/7Isg+d7uPPiHVy7eQ2JSjRDhj0Z+D6rIyM3bIEMsOvsEjNNUsSxDWIu2ZatQYqqqUgCVuc67jVV6XbJRuuz0qDR8rvj5FgzKfIq12ytle377ecO4LzsQBbtGriyYZc2AdhNay4atUwbGcFpDGBFCmRNplkkhVUgbpYA0mV+iQ/KD7T8Y1tEqwnKa/Csff7x1+xNMQyG2Cv3ILsSbuPCVvbKmswsVs25yQ0fptXzmpLZEyYFn4tILojruobnefrZHzrhynrP9FAzG64MGjC9nlm8vG41m28sN2AggtlZ/FrbzAOBZQIC/59ZB9xUZgDQlGow0GF6zfDPGDA4ODhAWZa6qN/d3V3xmjMlA6a0g4/ZLO7XTRd5M2tJM5GPx9P8Px+zUkrvPwNIZmoi33fWY2X5Z9wID4IARVHg1Vdf1Y33w8PDlRCA9dfm42CwiF/X/DCZNXye19kW63PTrLvN7w0GAxwfH+t5sy4hYXkF7xszHLgGiuMYJycnG8d+fXtuoOGll17CZDLBo0ePcHFxgRdeeAE7OzuI41ibwADQ5oDmJNh0YtcRFp6wfHGYRpI8uVjLJYTQDrfz+Ry+72M6nWogIk1TTCYTTCYTDAYDXLt2DWma6sEtigKLxWJF71KWJTzXw0iM6CErCQmz5Wp0CBeIVU1FYJqmGA6HmKdznM5OEe1GRIGyKzw8e0hdZh/UlXUqKuTcEjPMSOM+bKUPW7qxbMbnVA78xMeetYfQCmEpC3Veo8gLqFqh0+nAcR1YLi2kSpQ6WvASl4R+t9IP/cBq90tPhoGtTTLdxoWCQiISqFQBJUUehQgRFiGiLEIPPew4O3BzFx23Q9rvOoHTdXARE2gRVzEykVEKCHKIkOIaK1ERUGOnKFzqsG9E+wWxKjyQQaKL9iGKpbN9XZMGtgClFqRuiqfyKZSjUMoSzU5DmeVrm6UsMg2NPLh9F95d6tqPqzGZdrZRlwECzIs50ipFuKDYJF8QuDEajfQcZcBtPB5jMpmgLEvcuXMHwCo6bl7w5k11HXFVSmmJQpqmmM1muHbtGnq9ntZ3eZ6H4lWiONulDduxoXJFpqjSgWiWhop802bkVMtVYEGWEk6xfFgCS61dHMd0jQQlvviJL8KpHLi1i7AM4WYu0XOjASIv0nRs1ShdIHByAJuqTjBBihSLcIE8ylFZWwrBDiB3JKyKpB5WaUHkgo6zsWGl9H9eyPCHXdn0u4VYGrLCRV/2V+5LLJ8BgFrUiJuYXK1FCoSANbCADvBzf+vncJFc4CIjE65ZPsOiWCApElxml38+yniXMtT9hQ//u76WOPTsHgbuQHfXei653QcygC997Hl75APR6tQ9Z0mFZJ2jZVnIymzJWlBkcsYL60TRZ17IzuoZZgnpdxf1YitI4QhH029vRbc09TryIjiWoxMbdCHXVHohGpcxTtKTpe61nFHhIgG8DPwB/oAK7neA8H6ou1Zdp0uabKeHzmJpYOYrH3fDuxgMBwRYCDLqEkrohQ8vSMqyRGM1mGbkRF9bNXWAi4Uuvi6zS0yrqS40OGbNlDxUqvpzFdb/qbYKbdpE/ec04cTSCJJlFdy5ZwkHU7SVous4rdOtEgoBoc0edRqL4e3BYEXZlNpYlcdW+cS8gwKOFkfPvf8atJCuBi+YYeEIkkvoBJG24FJCaZYIm63yx0bQwjheBi2ENJItVPNc84KL9hoUmwsHUA7rRZa/d4pTvJu+C6QAjje/loTUkhCONvUFyUI6NhX7PbuHG/4NDN0hunYXvvAR2kQLj+wIlrKQ5RlmxUyndC3KhQYy0yZFjlwzAeKKCj3z/3lN94qyKTEtiCr+JH6ij5flX5gQ4KXXHLtrB6QIcPm6/XV0T7vojrvoyq6WeIUWJTpxZ7vnkUQkkHQfjOxIF+9SSr0OZed43Rywlo2OftPHdff6le4k69mVUhr0ZRO7oiY5oPIUAbz5VJs0MtB5vjhHJjI8nj3Gu4t3cZ6fYx7NkXWyjWucN/AGnCcOHYeMtCfPvr2PF/wX6NzBJwBB0rXJ4E9e5FC2opQqlny09/24aD838VZpRWAFGFgDmhetOatjORqkAAyJhipJQlcmSEuSd2569gkIeIKkeEopuse7QOmWNO8j4E+zP0We5MiqbOu140pXP2PYsLJjd3AtuIZ71j10HDLN7Dr0O4EXaLla1VSaYcJmorOSAOnz8hwP8taPopptlbMFNsk5dIRmC5IP3IEGlfizyAT8Qx/9gz4c14FXeSiKAr7vr5i3m951Qghd0K5c2wbLG1gWm+sFNrBk+pjyYtM0lv+evfK4a52mKTqdzkqMLzMuuNheZ3QA0EbsTKPnmm06naKua/T7fe3TZxbmvA+mlKJpGp1MxzUgH4dpfs9gBW+mbIJ/z0xp4NdmwIbHy2Rn8H6ZAA6PuWVZ2h5gNBqtgDImS5nPKXf8+bXNtQf7LvB5B3AFDGUAld97k6mjWUPzxyYDSPM41pv55tY0DbrdLt57770V1YE5Nvz3poSDGTbczPwLl06UZYlerwcppXZ3raoKh4eHSJJE58GyRKEoihUTlk1IIA8OnyhGaPj3eMKakU8XFxd4+PAhkiSB4zj6o2kabdgXRRHCMMRkMtFF382bN9Hv93WHeTQaYTweI0kSKKX0hDcRIpMqtYkuxSegLEtUWYVdfxdO7qAv+nj47kNE5xGu96/DKR14hYcoIq0nSqLlP3nyBEIIhFGIQhSaApg0CYaHQ/gDX8f+sXlbgQJTl5IvUpmi6Gw3k5SWRNAsvRlGckSpFsLVso+6qmFbNsVpZgkcz4Ht2ahEhbE1JqO8To2m23pViMvtk0S1C1clYQ9snVrAppqjZoSgCdBVXfSqHsImxAff+wBN2mB2McPBjQPcevkWaoe66pz+kYIWPNxlZxNN7Zkh6UMnkWyY1VJJWI0FS9GHbCR9TxFaykVwIxvUbk1Z8m1Kw7ZjZQMvu6LPspGQnoRz00EzauA1Hj7ofYCO3UFf9DFshtRxgLuC9q7T1HjjGxvLN6qqwvHxMR48eAAppdbAfeOvfAOFv/lhKSsJp2z30SGvBrtuJQOFDZELVPMKVmHpWCynou6UYzsrAErSJBilI3R2O7A6lo7pSpwEiUUdslSlKJnasLamcuAs2Q3Kh1u4QELSJKuxUBc1iqSghx0UHNuB4zmAA1SSImpLq0TTbcjboY0zrexqK1DHRmt2ZVPXsHLo/yWBEaEdwmsIVKpnNfzER68mraozdSDHEv/D3/gfNr94e87SKt3o2XCRXOB4cYzT+BTn6bl2C5/mU1zMLlBK6tDO6tmV19URfR9CcXcEnavIjpYxfC1AEVrL70dWhH7Ux6F1SIt0l0zlui45YrPvRtM0aGSDyq605GNWzLBoSOoQN7HuMs3LOS3a0gcaQNjW8XOkoztHd7p3dFb9o/cfYT6Z49rBNbxw5wV6ILcLRfYIWJQLPMofYbZY0pO3FbuhFWrDvK7d1fnqfa+vgZydaAddv4tbHQJLBv4AkYzIjK6NCDNNjvn/juOgKAk4YSBiUVDhNc7HuCwucZldYpyPMS3ILIylI0lF/hZ5nVNqwrrm/i84CeR5N47q3BY/+bybgCA2jGqQ1RmqpoIllx1xNkMtm3K7TwWIws9gBQMGDCizlKRGrV9Hf65LYmtUf365DaeXeBZ1hT1JJoCe7WnJBHdcAVoDDL0hsQeKluXTxtgmVXKl08vnWxebrS+HgoJoxPL7hozkWRszLJ7FUnmeY3akA8+iGFNOCAktYif03T52vV18JvwM/vaLf1sXK/qYhELapFqacDY7Q6YynMxOsCgItPjg5AMoV+H+k/t0H2mNugtRoLZqzSirrRozNcMsn61IkMzz/6zjdISDwAr0fY/vNwxOdl26Hwz8ARWmbqC9CkbhCK5wddcQWMaK87ovjmP4Dq2lGOw36cRcTMRxjH6/jz/90z/FCy+8gD/5kz/Bu+++i7PzMzw9fYrKqdC4DRlBOzU+91c+h3AUkoa/bL0gWtDgKDvSXhWLarH1+AMr0B37yI6wY+/gtn2b5G5OF72gp8EDS1qoajKOrVWNvMkxr+Z0nzcYA2x+WDab7w2+9NHze1pOEjgByrRE4JH2e7FYIK1S5GVO6yqL1lWLerE1jpjPIV9vbHw7y2eY5lMNVOYq1/KRbfOafUhCK9RgQd/u48A+0POg7/VpPSwkXIekMK7vIi7pOTfNp5jkEwKRiinenb6rv3/lOXQA/K/j/xXii2LlGdR1uuhIAs2H/lAzKXbCHYzkiOagS2lUgQz0erDT6WgGNxfgZlFpNq1M40Geu8Cy4DRlFOsSg3VjTLPm4dfiAjQIAnzqU5/Sho38t8xQYCNELvjNZhpvvKY0gQHuqJvFMYMI6wbwZn3GjA5+Xpv3JpMhb77uOgNiE7shDEO9HuLfWWfcm7Wg6aNg+kmY+8FreAaXTKYIm09yk88MQ+Dx4SKfx9DzPH38m+Qh2+Qw/JlN8rlBxePJzXQTIDKZ2maNbo73szahtglX1raf//mf1xTlwYAc+tM0RdM02N/f1xrysixxeXmJ2WyGuq4RhiFeffXVFcoQ7yQXWkoprWuZTCbodDpau8MDc3R0hAcPHiBJEoRhiCiKNDIUBIH2bOAJ4HkeZrOZBgS4m3vnzh04jqO7tOPxGEVRYD6fY39/H7du3VrRyHCUh3mBMuV8sVigKAoEQYDT01Ocnp7ihRdegG3b+vWFWEaJ8GvyQ4m/Z14gPB4mFYcnMF9MQghMJhN88MEHGI/HyOscn/jsJ2B1LMhQUmeipVZbkUUFuVgaX6Yq1SkCGbKtixq7scl8Ty3NIe3GhmqWxmQ1atSi/ZA1ShCVvpIVRcYJipZ7Lv28ErBBHQcHjpZCBAgQqhCRijC0h4gQoWt14daUyGAVFrJFBtuzsSgXeP3t1/HOnXeQhilqpyaph9VAyS1T3fz2tv1sANlIWHULUjRE69UgQUtN5GNu7AbKVlvf04ZNMpf2GLlrwcacnvJQzkqcPz5HHddwGxf5JEc5L1EuSgR+gJ2dHQwGA9i2jazKcJFc0CLGa1DaFP+ofAUZSlRORYZj/CHpc2ltZtOIRmg6r1UQAGGXlH4RIEAoQh3FahUW7hzcwWH/kHLDW/p3JjIkoHnGgFGqUv29y5QMVku71Pu1TcPqNsSccOvlfHQbl+JCawJ5RCN05xKAjjSs7ZqOt03JYBlIjlzLRLa9r2gE9rp7GPiD1Q+PPvf9/tWfGR+BvVw4mNvf//t/H7/4i7+IxmkQ18SkiJsY85p8KsbZWGtXx8UYs2qmncI54nIrBd6IK1SqvU63FOYALeQDixbdvEjnmL5ABug4tEgPZIC+118W8y1YsdfbQ8fpwLVcVKrSnU1ejE2LKS0Uyym5s7dF+CSf4IPTD0ib6zYoxeYFLdNfuasVilAXRa4gXwAoaJBCexrU+Ypmm7tbW7t7krpj2lSvNSPjCLiBP9DMib5LsW8du4O97h4821txxgZWaYsrc4q7US1D4PHpY/zT/+c/ReEU+OSPfRKIQOe7nBJduNXoM2MsrVOdMNHgz+e38P/vmy1sXPOu4UZ4A2lFNG82UXtWwWULm7rfclmsOILkGfp6EI0GPbjQKlShJULPK7VxhINXuq+g63Yxikboe310XTJidJWLUTTCxekFDg8O8a1vfQsv3n0R3/v+95DnOc4mZ4jLGPN8TolEVo3aqVGgQG3XcLqOlgrdDm7jb+/9bT2Xc5XrjjUnx3BqRtIk2oA3r/Ml6PuMjb0sPgyssGDBszxKg3CXhdNusIuuR9eE0zjY6ezAKi0q9t0eLp5e4ObeTbz1rbeQzTOcHJ9okzTbJtPTeUHeFNduX8Nnf+KzaNyGZFOtHCRp2gSQOsG8mWvWUdIkVHRuYWH9IMcoIalgtgzmhNvDwBlgJ9whmr3bQ8/rYa+7h1E4Ip8afwC3cdH3+4Ai4/R+v49vfetbuHPnDr70pS/h8ePHuLi4wOPHjzWAyeyLL3zhC/jUpz61lDq160Zz4d80Daqm0v4UulNfzPQ1wZILvl/Mq7lmnyzqxVWJg3HcHaej73c9j/wYuFiOHGKsuZa77AKrBlmZobEaTOKJNj08mZ4gRUqx2vVia6SwK1x6pliB9hdyHZekUoD2FCmbVvLVArXzcr4dZLbpuvcsAgtt2TLsGmIxNaoh6VVTIKlIfreNTeFIR0v+zBhLnhP8fdei509VVvjKV76C1z7xGrzQw6JaYJJNcJFcIG4oEU8zCNtzt2mzhKWNMFmCFIgAPYeSRXpOTz+TvMbDTrijn1V1vuzsR1GEoiAjb1OCwfVRnue4du3aCjDBxbTZqV9n3V5eXuLP/uzP8JnPfGZFlsB1Ctc2LCdhdoXZdObnH9c0POdNCQEXusxkN9kA/LcAFeiu614BWJjB5HmeHot16YDJClkvwr/61a/ixRdf1PKGdWNGfe20x2qyJ4ClgaL5N9zIZlmYCV6Yr7GtgDelM5yS4rouut0uXNddASLWwaN1SQQzUn73d38X9+7dg+d5ur4399c8X1zv83nkj1/4hV/YOJfN7bkZDRcXF1pbwxEXrutq53bXdTVDwDSV4IlhUrXXF2I8yIy6sS6+2+1qU46yLHF4eKgNg0wtOr8GZ8wyssamkUwLMTV77HQ6mUxwIai7GMqQCkQoqGapAVy/QNaRtbIsURQFZrMZjo6OVpAgE43i92ZaH38wesXUFFPzZI4XsKRK8eslSQLf99EVXUSgCDwAqAQ9lIbWcAWl5Ncqy5IMC5sajd3gncfv4I92/wiLneUNsJIVKlRIkFCmfeuKzpR4SGwt0KAoqsoryWTPrpeUxZUOlyrpYWQD0pfUvRYVcuRkJrktPYWfDxaAABC+II25soFPA7KQcEsXTubAr330LTIJYrooFCAsASWJysrFbm7lyG3S9RcWaflriyjejSQAwTxGTQ1W6tmAiqKFqa1sLf0QirpYKUhr3KiGxkWVKFGiDEvgle1j69QOQhmSMWZrIOoril/1ag9hGcIqLYwwQg89OIVDaR9i6X8ym8+gPIXKpkjWFARCNW4DEQlkyDAVUyRugjIosbAXGNst02XdRLL9r6OIubD+j/0Z9tQevMZDPI/RJA1R3xHCVjaSIkFhFajdGpnIIAIBhFh6hlhtdKiTYyqmOJEnpFOWmxeaUkny8YCvQZIByNDVKix4ORm9ykaiTMuVHOdSlJiXc/zyr/wyJtlEsxEu00vcH9/X35tkE1TN5uLElvZGAOLL3pfx3T/5Lm7t3ULP7cGDh0hSzFXP6eFAUNfFdVyISKxoLoUQkJYkrXWToLTLJU20XiBRic4F5+i5RbPQlNpUrbIOGjS6iHOwzIhnnT8DFZWqnlnUetIj8zino13vufDqOOR8vh/s48Xui4gcMhD7vbd/D0/vP0XP6+GTH/8kbt6+icquMK/mVFi1utwMmU66WJQLnNanOhedi42N4w9bd5D2rD3cde4u0z2EAzQgmQ/IOFMJhQoV8pKM5o6bYxq3duy2LUx9SbFvmlLbLgh13rpDrIq+19egSd/ro+/2MbJHcCck70m/luInfuIn0Ov1rpgqm2k15uKoLEl2FlcxLooLjKsxptUUk2qCRUPzIROZni+povtN2dB9pla1Nkn8i9h0vGi7/aBJIAB16x9nj/E4e7zyupawCIZujfI8SZ14X/or8Z9SLJkxeZPrY06b9JnnUULqJBaOzfRtX/th8HUh0JpSCgKMjuNjfDD/AEmZaGaDLuzebV/8jfazBYgRAblWZZEbf8u6skoLfukjyALcPriN2we30bE7WBQLeMrDoX0IT1CUp2u7cC13ZTHLaxb2r5G2pJSYkorPHLmWRGRNpr0c0obAiaQhD6q4ivX3uJDnSPGkTnCWnwEA9v193Ore0sfMwMCV7Z32HFoCzjUHzl4b5VnT2kAWxDy7FJeI78d48fqLJBerfPQaYmm5jothfwhXubAtW6+FXJcivgsUmOZTbTTLoHLaEM0/B82Deb2k1CdNsjxGlWsJzSUuSbqyNq8/bB57Fs3FjtuBXdvYebyDMi4hQ5ozCRIdU92k5E/0x9/7Y4iOwG53F45w9PnktZoZWchUcqd2MFADdOuudqlXUMRudYHGXlK4ARD7QipkyDT4kAt63mfI9P2fQappOsVRckRxjgWN1zZmAxfl/By7Hl7HXrMHlSok4wTTyyk1alrwu9Pt4KV7L8HyLGR1RnOzibFIW2+R1ndhG8vKBoGJLBlyLbqXC7Q6d0H38kYRWJXVmWbdbNosYRHIbockD3ZCDVJA0f1rnI9xmp5qKR17BK1sLvAb3/8NAORR9Bt/9TfglOSlxZ1q/rqsSzRuoxM8OH71Irkgdkk509Kci/wCD9IHmF8SSL8NMPKkp587A59ACmZQ8DNo6BEw+KPXfhSe52nTfLMWM+Xu5v95/8/OztDpdHTsfZIkurbg2oxj7xmI4P+zjMRMs3EcR7M2AOhEQ05/MIEBrve0JNKg+pusDa671uUDJvi/zmQwGeye52EymWi/Nl57cV2ZZZlmwXODna83s0jnZ7XJBON9431nqUZZlissqU3SDtMYk/d5sVhgMBjon69LRczj4+Nm6fBrr72Gvb09/X3eV5NJAUCzOs39eZY0Y317bkbDP/yH/1DTWEztBn8dBAEWiwUuLi40m6HT6WA4HOLOnTtXKOLrg7FYLHB0dIQkSTAOxpgOptgJdzQtPRABddUrh0wB2702tTF8Us2BMPU/DGYwRYVzXr/18W9htr+kL1vKInp3Wxhx95ZTELhoQQqimcsQs5MZjt4/QhEXWmayv7+vX5M1TCZSZZ4onlibkEQeS6YZLRYLGqcW2Dk4OMCnPvUpRFGkNeeMCkZRtIIgmhcunyfbtvHw4UM8fvwYWZlBeQrKV4h2I9hdG7nMMStnZBhoU8RiIQsUdoHSWXaINxbZioo9juzSD0RQ538rm0JRDJIHSviwlQ1LWbTAAC2MKywz40uQ/q8SlV4AbtqXrRT7RsIGMThc5VKih/IRNAECFZAnhDI0by2FV3q0wJkV9GDIZKaL4a3eGywxMZzRlVgyRLZtspErsg8Jqc2euBhsRINaELNkGwhkKYtYAcqDW7u0oINPXga1C6eiBJCu04VTO2jiBipVsEoLHacDx6bFUNGQ3CduYgwOB7C7NhJQ/BsbqDJ7IUO2/JlKN+6bUK1paEP75dSOvv585cMq6LqMZKS/59b0+1AgnXELlrB5ayYyLT/iz5lc+pXk1gbAhMepIb+HezfuYRSMMAyG9Nlf/czMBTZ1rVWNRbFYASJMoGKSTfDlb3wZyiO2SYbtsZASckWfHFl07Fqb3H7Ph4+u09V53BxT5igHvuevdNvrptasJm2Exp0ww9eB9b2LinwMWLe9bT99e6kjtoWt53aDRgOLbNC4bcHOlFf+0CyL9rMvCEiL7IjMXlsGhitcfWxFXaAUpS4k2FCT5WfaULOlCT8z3cNajYQLLGJRcCfLQquttC1YrqWZFGzOx5TbZy3UPUnPErsk8PDW3i1cH17Xmm3OW+foQo4t7FgdrYM2dZXAkjnHz0emWJoMO5NRyO7h0pJIkOCyvCSgov03r+eaYTOv57q7zGaMZVN+KHPmubb2Hm0CFaYh4V/EJkH3etPHwRGOTm3p2B280HkBaUMsLAa3NLOkjrdKhABoYK3v9SEriV7QQzJNMOqPML4YQzUKWZKhKAuilSti2zWyjdS2G0rYcUjC9KwxdYW7cn3wNRJKui74+vAF3RcY8AusALKQ2OnsIHIiXQTyfOEiwVysl4rYiqUs6X5eJRj6Q3x8/+O6gzeZTBCEAU4mJxgnY0yzKe4/uQ8ZSrz+9uuY5TM8vXxKxWw+RW0vGWe1TayOxmlQWc8GNQNJ67HQDnU6jr4n2KFmB7IBMftSdOwO+n5fmw6zHpzd/63AomNrARiWVBayIOlYm/4xKSeaQj8vSOaQVpSo8kyG0TPWIJvOJ3vVhFaok7mYORNKOvZhMNTsM1dRY48bYAB1faMo0kWDfq+2aWeuSVnnz7+/0sF1oCMbZ8UM54tzlFaJk+kJFtUC43SM0/kpClngycUTGpvWjPRZ8kZPeDrViJ9pzEpy4NDcbD1jhKDo+RrUkEmrdMl0YiC6XugkoPXNgkXrHUHPKtdy9bNKSvIXshyL/GPqFGlFIN1WHwcrQMel85NMEzjSwaA7gAULP9T9IXREZyUFI5RUSziNAYwAK+t+lhRwUZemqW6aVhV527DnUuVUmOQTki5WM/1cX1QL3XRg4Cauyatjx97BP//oP8fdu3dXWNLrW1EUGuTiZ0tZlvgX/+Jf4K//9b+ui1VOGuRinGXrTdOg1+uhqipMp1N0u130+/0rnf5Op6MLdSmlNplk1rwpS+p0OvrZVRSFjq43ayU+pjAMtYzDbPSam/k9E6z93ve+B9u2NevDbDqbzVoppWZMmIaX3W535Rrk+yiPke/7KymNjrNMMtNgopGoZ8pUTIY9v7bneciybAXA4eMza+11cOXb3/42Xn75Zf2+Jshhghl8fkyDUZaA/PAP//DGa8PcnpvR8Pbbb+suO1M8NhlaMJ2DC/t1VsD6hcWDwZOmLEucD87xnf53turjRS208ZtTObpAsktbFyhWaaGaVxCZ0D8PJeUbC0GusEmSoCgKXPv6Ndzo38CNezcQ7US6A8SIb2mVmGCCo+ZIF1GlKAHTB6MD4CXaN6d24FYuPfQkPSBUrCgiUxHVmwu6AIEuqBxJk43HiCNxWPbBHhmnp6c6vtG8sNZ1RCaQsU7jNS90RrGiKMLIG+mLaSfYwag3QtM0ZLbptgfcAKpWqLNas0ga1aAA0dPn9RyTfILGbQgxb4s9HstU0MMhkxlKu9xY6FWiwkJRJ1NLFFomhZZsMKBgrf2xIgDIUfRhKYsKoDY6slIVdS4FMTYqEEDB+39l42F7BlvB8i0dBRmoAP20D7+hmDpLkTM83yAZpMirHMpRyBSNA8sKEmyOHGxkQ+aOkt6TadMK1IVlqcCz9pMBmwYNdXFkjoUgKnJjkY6y8qpV3w8jTcWMO3XqpT/FyB+hL/rwBYFzPfT0Q5WlIKEIYQubFtgqx4Njyt9u/AalVRKbpAUI4jomgMubkVeIlaMIiq3pJlbTGnu2kgq3agGj2oNXEWjUb/ok96hdqEShSRrsdnYhLEF0z+QC02IK5SmIUKByKqQqxc/8+M/oWMrvn39ffz1Ox1uLx57X02CECVC8MHgBQ3+It/7fb6Hn9PCTn/tJfPT2R+FKF0IJVA252s/Lue4+z4oZJtmEgJu2WD6uj5GUiS58ti2suHDnODC9gG31o+zfELohDjxiUXCc78AfoOt2NUDIgFZcx/o+mMlMU3d5ATotp9qzgYtsdi3fuNWt34lwMOgMqIhvtbnchT4tTrUhGbumP8tXwBHOyoKdJSB9t4+bzk2SKAk6TlcspW1KEVOrVjXyOsdFQqa2DLxMqskKBXYbZduVrmYzsBdEx+mQ3l+4y46ZACaTCb7xrW8Q8NomTzxOH68Ydm47Vjb/YwCCY/kYIOHz3reXsW+WtBC5pKs2N36G8yalxGw2Q1mW6HQ6K0WnqR9lnyTfJ2fhWtWYFwRGXOQXOEvPcJlf4iK/oGsnu8SkINBtUS0wzaaYpTM6foklY67d/jwAg1yjwplARYP2WcXFw4ZlxjcW34CEJCDC8uBbPkIrxLXgmpbOsAld5EboB334rk9d1CzBZXKJUpZ4fP6YOpcY4yg7wllzRrKyIFtdP5j73vrKeJWHe6N7ml7uSUpokFgC9+znwNr1oimQVilOmhPESawTJrZ1cwG6R2gvl9Z4NlIR/rsf/e9WqMpZltEcbhfvAK4UKZx01HW6CKIAB/4BvKmH4XAIx3WQVAnem72HLMtwdnam55LZAb1+/Tp+5Vd+RUuFMtUW+uUcs3ymmTlxFeukCE5nOG1OEadLudGz7hG+9FeO221c/OqLv4p7w3t6vcpdWN/39fqJKdl8DTiOg8ViAcdxMJvNkJQJKrvC699/Hd29Lr78jS/jMr7E04unOJmcEFPSqZAIYqMpTyHqRvivX/uvNSODi+d5Sdf/cX5Mx1THiCfbzR0lpL6nR3ak2TmdaUff7zkRKMoi3Ipu4WO7H9PHwmsUM0JPs2Ski4PwAIedQwghdBTkeDzWYzCfzxFFEf7sz/4M0+kU3/ve9/Duu+8SmOYo7U9x46Ub+MinPoKkSUhiV8y0/1bSJLgoLxAXsY5t3ebFwEA8x5IO3SHJ62SogUQpyNS9UQ2GzhCFKvS9VY9z64mxqBbI4u0JFpFNzAff9pfpPELQs0pkiPMYyqPY4/vp/a3+KRzZacZ0MqDcsZbJLb4ioGyAAaKaZLWogb1gT8+9Olga6DOIzEWhCRY1TUOGtyC5N1+7cRyvsBH4vDNDgGs1LoCn0ym+9rWv4ebNm+RbZBhIWpaF09NT3dS9uLiAlBJxHCNJElxeXup9Zb+C0Wikfx4EgZ5zk8lES9B5n0z2t8kI4O8DS6PCIAjQ7/ext7e3Unfya/H/TdkFv4bnefjGN76Bl19+WY8pN9QZQGCgAFhl7APEmNfJXW395vu+lnqwEWae5+h2u9qPoyxLDSyxTwQ3DNaNP/k69TwPOzs7V5gb5mYCDHzcek6k6YoEJAiCFVKBeR8wwQ6TIf9h23MDDawnY1qJlFJPzLqutV/BbDaD4zjodDo6pmNdL7JpAEzQ4cWLF3H4+BCT+QSVXSHaieANPBRWQZm8doXabWP4rIL0vXaJhbXQWuxtBYlQAk7jQFYSdtGyJVQAp3QwjaZQjUKAAH30EShiUQwsolp7lqcL87zOcZnQvlROhePpMc5jQnhrh6j4MiJtfGqnSNxEx2Zu063ZinT73G1murnbUMEUyhBu7aIclJom71c+RLacDAw08IVh3myAVZ0PAI1E8oXAUhP+Pz9wOU6RzxXfzExkzWkckm/UHQwa0i3q81wRmMSd1YuLC001UlIhUQnsrg34IJNBq9DghNb7q2T5vfb/24rqRpDmu1Y1BAQlCSgCMCrZxpptYzcouQJSWIpMzaCgqdUVKu1NUckKtSSPigztQ8oDyTNMbej6+1nLOclMmS662Mc+USuLhh56DcgBWhgxb00BZSsITyC3ckzcyeaD0SegNR5TJBWpm1ozKvhB2YgGlai2movyvjZoUIjW36F1lB5bY+0qX6Ed3w0v48ChAg8BxIgSJHws2RVO7aBf9tFLenCVi70O+SN4jYcqITBFeYqYI7KNrhQtQCGXDIbUSxGLGBfiQoOGGxkeiswovcbTHWUGLHzlw8s9fPrap1cYDQwa+LaPuIyvGEHy1wxGXGb0+cHkgf7e+IfGgAC+u/gu8KYx9yBXFh99j7wA+v0+rnnXKMO8/dBGh24ftrJ1BCVrd+flXH/mnHI2Gzsrz3A/u69/9qwObWiFBFK0i1StW3W6GAQDLRl4sfsiem5P/w4XYo5FmsSiLrRvw6yY4eHZQ/zm7/0mjqfHUJ6CP/Bx68YtvcBmCumi3t5R8qS3XPxZPnyLFoCWtAiswDI5IK5jjIsx8nQZ77Yot+v9BQhUC2SAQTDQBcmOu6PlIb70UZc1fNdHGIW66KtqouznJenpp8UUp9npCviyQoG9sfzyIr9AT/XQsTrYd8h5vuf1yKyuFhj0BlT0t53+SlVae7woFzgujvFO+Y4GfraNnW/5K14UPXfprj7wyVn97IMznHxwgr/6E39Vp390nS4Ch5hz60C2EILkQt4AQ3+Im52bV+ip5u9bloVvfvOb+Gf/7J/p6N5f/dVfheM51FWuFkhVqhkFtV2jcRuSMOWXmOQTTArqLi+KhQa08jrfWow9z8ZjW6gCZVViXs31XHrev3ctSkiK3AiogZEYYQc78IUPkQpUcUX3tFzRmkRJVA0Z3MpAQoYSL7zwgi6kL8tLfb0+S6tuCUsnMYy8EW7bt9FxqOBk7botKP4QAIRsu1atZ0VSJHj08JGWwfLC2jQqA7CysDe9pHh9YOqlec1oAlkmldikaPOCOrRD7PX3VuL/uIAy35+7nax95u8DQNG0RWXLRmAvC90FbyUl82KOB0cPcPL0BPeG91Zi/8y5vW6QZprRMdjG3mEv9l7E7YPbSHspJs0ED5uHePf0Xd3FnM1mUEohiiJ8/vOfx88d/hyaplkpmngczW6j4zjIm1zLGvh4zHs9Xy/8/9PyFPfTq/f7n+z+JP7JT/yTlUKE32+dXcs/M2nmPBbc6GJpgNl1VUoRs7VNtrJzG8PpED918FOUkJRl8DwP/X5fHx/PC96XoqZnm2aRtM+2SU4pUCYoMytnOKqOsMgW+lnD94J//kP/HC8PXtbnVYjVyEnHcdAf9bUxOyckrQDnxj3cfJ6N5RiFX2BaTK9ck6YP0q++8qtY1AvtZaTNlas5nqZPNcNwq7GycDS7zWRLMNgciQg/v//zCKxAX598vG7mriT8AVTbcTSsuebnhiwDDtzFrqoKDx48wM2bN/X5YgkBfw1Ap/kBuJK+wYV5p9PR9wZunPJcN40O1z9MUNycn3w++cO836zXnuvNWJ7fSlGK39OnT/Hxj38cvu9faeTytc/jwUAM3zf4fmnKRBaLhf4+fzBQkySJPhdZlq2ADHEcr0hB+JyabJR1OYb5rN0mb7AsC4vFAu+//75O1eFxNe8FpoTGTEzhY/4LZTTcvXt35cbLNwGmgMzn85WkCTbx4IlnyhjWTza/Hv8d37A5itLzPFwfXgcAnGan8B1fO1rXRU2Fix0ANRW0tm3jP6r/iDOc6WKkFCW5HIsahVVQkdc2dMYYAwr4AB+0O7VhAARR113lwgPpQl3fRUd0ECECHKB2aoQqREd14OYudu1ddOsuRfApqbNlS5RL6rbMl1Rz2dK9Der5JS6RWikym4qlRjQbOyGOcjT66bsEVgQIENYhQkU080iRSVWIEC5clHWJntVb6UyZdJkkSTQymWWZfqDw5GM5CDMgqqpCWZf49Y/+OlzP1UCNZpvY7XsrF43XQNUKTuFg6A3h1A6G9RAddGjRBYmmbFYWMHme63xaz/NQNzXiMsakoG5v4zWY5BOczE7I+M8ukYmMaHshUFolFliQ2dcz6IuNaKh4Rb4CFpigwTO9KVQbvwgqdpj2p0QbbdaCFBUqbZaZtf8mmABoO3IuKAKN32/LPlvK0qaIrFVkiqaWU6AmjwlJkpNGNsuUjmdtbITedtAsLIEXCal/zvn0AJ5pQMZGayVKwAaUpZZyGjbSFJuBDtEnkJAZEgwQcByrV3voNl3sql3NGvLRMogaF8IWxBxpTSmnxRSN1wA+kMlMexoUboG5mJPcopfjv/r3/9XG/fFtX8snNkkrbvZu4hP7n7gCUvS9Pn7kR34E/f0+fvYXfxb+wMe8nmud5qIml/FYxShkgYviAg/iB9pYcdsCxJXuauHIVPv243ZwG71eb8VHgAELy7a0vvrB8QP8L//2f8HNezdx+OIhZvlsRbcd1zGeZk+xWCwQn7WmY9X2jmloG0kQxofMJabOFI1o4BUeDooDfOHgCzo1IxABAhHAljZ5xTQJarfWcZ2XySUtBlnjypnz1RyX+bIo29bVDKwAu+4u0Y7tQHeOfdfXUZ1vf/9tkgBGQ9SKFryTYoKn2VPdLV6U26m6AGmYu05X+1bc6d6hYt0K4Fou5pM53nzjTe0LdO+Vewg7IZIiQV7nKFDgPD8ns9BqjsV0u7GbK10tszj0DvFK5xXtNeBardmaWhbMVdPKz+oCSZngQfaAipY2Di6vc6AD/Ptv/vsr78MLW55PA49MUhmwMM3UTE8K3/b1M6aqKu1ZxAujpmmoWG4Xz2bnpN/vo9vt6s7a2dmZLjCBpVxSSokaNdImxSSbIKkTXYykDQEX82pODJX22kuaRNPms4q02H9e7woFpc9dklJX83R2yj8Egvbj6h/qz1JJnJydwLd8PXduh7cx8AaIEEElCqFFz/LQCmFbNvI6X/qbtMV0VmWYlTMc18cki2o1+s+as67l4u/96d/TMZIc6dxze9iNdjEIBtp3ZLe7i4FPwJLTONh1d1e0ypweBkDrf7lYWC+ohRCaOWtKfLhwPTs7w8XFhW6ImA2VdSCA3yvPc6hGUXSwHKCqKip6PBvD4VAXOr//+7+PplxdRK/Lkfhveb26zhQFllpnHgPeNy5YONnMNMtbLBZ6MW+CcKzB5qaOOS6RIvbJgXdA7xss9dPm+Ju6bcdx0Ov1kJUZzmfn+NrXvrZy3fFxmmkb5s+5kFvvqjI4wvHZnufB8zxtps7gA792lmW6WOLfNQEWHiO+9i1Y2A13sYtdXeyY+2bWGTxOPD/yPEclKnz7+9+GkzkrxZQJpPC5toWNHX8HQ3e4cl/ZVLCZ3fBf//Vfx+/83u/gb/ydv0HP9DZqlFlpi3oB4Qj8nY//Hf03PK5mTcWvm1e5lqpMcpJesqkxgx0MVJwX53hv8R6ZcpYxvjD8AlStVpqDPBf4/dI01eAC0+C5nuNCn0EB7noza306nerXZQ+6+XyuQZs7d+6g0+noeHYhlv4KfN83543ZvORrwkxaMK9DHiMG7NYBB1PytQ5mbjp/m1jf7F/BSYF8r2L2PY8F7ycDDizHN00ueR+bpsFgMNDf49dgEIaPia9vBmk8z1thFALLlBEAmklojtG2Y1zf+Po4PDxcARgBaNYGny++3k2wxyQRPGt7bqBhd3d3ZXFgdrbLskS329UIDF+UPAk3XdDrN2gTbOBB54nISFhVVfA8Tw8s/76pb1FKoaxKfDb/LBUiLj3gGF0TtkBcxXhy+YS6z20HPVUpvIGH2q4RqxipTJFZVPzl7b8SpQYCAADLtwW67cem8xm2HgDK1rRzr/HgNz6CIkCIUHdR+6qPvWaP8qNFCFlKFCkBNkIKFE2B4+kxSruEFZGesLAKeAMPmch0BGSKFOfynPZfkQnSCh1fAhjQl65y4fgU/ec1no4B7Ht9dKwOrMpCXMQY2AMM5ZCAlNolyvmCblaDAT3Ax/MxPnb2MViRhdqtkVs5MivDzCVqHAMr2Lk6TKZxn698BCrQcpNAkAZ+iCEcy0EXXdpvOOg0HQzEAG7tYifbQXfSXXkwSElJKexfkSQJvNBDZVfLZASRYi7miEWMWMaIEVMnHNQtL0VJAME2/wfexJI1wSCFaY7G0o+toAEsMh6DBdGIlQu5kcuUDzPNgpkVmcy0d4NQxvs9o3gHlukiUkmUstTZ2I1oNMihhNKv9TxO5rxJSO1HYa/9q6oKTd2CkKJBLWskdqLHcX1TQpH3hSKwgE3ZTEp0jXprYWCD2BMa7EoaBAjQsTrw4WNH7ZAXTOGhmleUZlFI/Mtf+5eUppCREeQVxkJ6qVkLprTiMr3cahJp/bwFt3HxweUHGMQDTZuMZIRQhjh0DzHwBri5cxMHvQN0LHKZfv0bryPshiitEnETa/8B1s/z9+Iyxixvi4vWUDFutpvgRXakwQendnA2OqMo0bRBKEPsOXt4MXhxJXFh4A8oFs6lewFrlWfFbFn0F8sO7Kycae+Ck/QEF/EFTnonyPs5aqfG23gbX/rOl67smyc9Gp9W984FbDEvUM5L9L0+fPi4Lq8vM9Xd1lMHHhV9MqexaU0yY7X0okiaBHEREwDlpoizWHey6qim4u9sdZ9YPz1yRrjp3kTX7WLYGVJRb3uwRAsytvOzUY1mIiVVgnkxx1FO0XUX8QWm+1OtY34SPwHiK8Og9fY3whuIHJI+pLMUqlbaqBCidWwvG9RljbOE4gYLVVA6gYq3piqwP8EoGOFm5yZ6Tg+zsxnm4zlee+U1yPaffg/VaNlZUid4HD/Gm5M39djl9WZZiWd5K0aZdVzj5KMnkIVEIAL85vlvUuSoFemIUl+RB8lADABAF2BvvPGGTpcy475MFoVJmw2aQMuouLHBC76PvPoRvPTSSwCoUPt3/+7fIc5ifOyHP6bp7LnINWiRIaOFfkXxr7oAyKaIqxhZnSGrs2cDFeuafbH83KgG03KKaXm1Q/rM1wM9R9gc2Zc+XJABYVRHOLAO0JVd3XDo2B18+hOfBgDkTY5xOsaXv/5l3Lh2A7VT6475SXaC+/F9pJepjlzctvmWr9lNnvIwfDBEHMcIZYhpbwq3cZEIMkZ0Gge1XcOu6P4sU6JaSylXdOLMhPzmN7+JLMu0TJfXmAw+AFgpVlg3zUUNAwgvv/wyDg8PVyjYvHjnzVxoc5EBLDXXmxpoXCCZABkXUeb/uRBSSum4eGBZ8PPvvP/++7i4uNCSFW7G8Xtu8iQz19hmUXTnzh0ai9bA0Ve+Hg8T7OPXM7/m8TS7x8Cy2OZixDT+4808JoAc7PkcmUCObds4OzvD8fGxLpJN00/WrZsgldkc4+Pm39/d3aWaQfrYcXcwjsdX2Bp8DOb31gvTTd83m6QAFXyiEQhViEP/EPCXrAw+l2bnmBt45nuYny3LQoQIkRthX+6j8RrdDDTnkSmdBoC33npLyxKUWpoI876aIA+DtOsgAEsY+LrodrtomgadTken7GVZtmKEaN5rO50OOp3OyriZ88AccwYHTFbTOijA84SL9Lqu8f3vfx87OztXGE7rLBz+mdnwNsfPHHOeV1zss1nm+nk0pRkmUMSvxewGvqbXWWB8XzEb81ynmPNsMpkgz/MVgNasqfk9zDltHs/6WJvXtFIUoMD3FhOkVYqSG+u61tehCVg+L8DA23MDDWVZXkHF+M34QE0NiZQSJ/4JbMdGIxsyb4Ovd9S8qHnATJSYJwYjX+fn56iqCnEcrzx80jTVtBONAGOB3/nk72hatN+Q/MD32yKjdpGXOWQm4VUe+uijX/dxp3MHXdElyn8DRFFEg9QuXpqm0eZAi2qBy+SSaNwiw0VMMTaVWxFIITPUbo1CFARSiNZAET+4BwBT+V20XhQjG05JMgWvIO171EQYNkNEkrSGviLtblPRzaGsSsDBisnbrJyhtEokTYJZReYyHH2YBRkunUsCB9wcKtqwWOpTp1sXkcqGoxzdafaVD7/00a/78EsfURVhZI/gVA6mkynlSLskMymtElbHokLSLqn4R4qpmOJYHBNA4WfUhef7h91++G3SgQrgOi5kKMkksJWfuLVLY6Qi8vSQLkZiBL/20W266KruClLIcxrYQDsSwBe9L+J173V97uyGjCI5mon1wFyg16J1dd/GgjA29p4QEBDSMAtlQzTxbDaFFHQuuAgHsOJpsanQrCQZbzHAwb8iQV3BWtXb0z/azpuOU1z7Z8bv5VgrPLwNr9e+5hXZCW9iKV9Z2YdWBiIhtbHmleNERdZ2agFLWBT9yRntUMSy4bHtLf9u75/sQQpJxaTBWuAu3sAf4NXdV1f+PwyG6Ht9eDYxb5IyWQEp/tE//kfwhz7u7N+heV5O8Th9rEEBbbp4f/UYpJLwz31KdGlTNPh6MyUfAzHANXVNG+h6yiPzKUmypBQpCquACAQO7h6gu9fFOB9jXs7x6PwRnsqnOMEJHl080kZS27SmPXdZNDJDwvzc9/q41b2FgTugvHCbvBIev/8Y//pf/2s8efIEtmPj7kfu4qd+7qd0ZBtrZ9OGkjNykaOyK8RVjLP8DEfxESbFBFmePdNszGoszXrhD6dpQVXVSmWUh2uda/jpz/809np7GnD5tV/7NXzktY/g1iu3yGuilUGw6RhHyqVNigfzJetkXs41YGduAoKi0hwanx1/B/2yj6ePnsIqLbhw8fnPfh79Tp9YRELoOGFYlL6SixxxSbTvaTPFZXyJuIpRO2Sst83clOeP13j6Xm0rSlOwQAVEN+gidENUTYXT9BQX9QXSIMX52TmSOtnaBWdPir7bJ5CCi0yOmmuTbgSWxrVFXSCvczyMHyL1U1SdCpfuJf710b/+UO+LvtuHpzzkixzJmArW/d4+ujY9v0NB4D2bOnesDjn618uCxSyegiDQxsm8zgGATtDBYe8QwGozxFzgSym1SRl30GezGbrdLt588028+MqL+O3f/23c+cgdfPHLX0TjNDidneJsdoZxPkaqUtRejcJppZVsimg1kL4kmdzzghXtZ77XFyB/Eb3Zxt/wVgH/5pv/ZjlHICE6At65R5GBVkCyIRlh193FQfcAB50D7Pq7dL+LBnBsh0xwE2KFnc3OiDGSTnA6O0WGDCfqBBfNBSadCcled7bHCv/u67+7lGC5S+ZMHdc4qU+QzlPsy32MrBHc0iXPldzTRs52Y6OpG01dNgsP27a1HMMsNLi7zoWMuVbl8226wq83M8yOotlZZ4o1/x0XZbwJIXRBw3MKWDIIqqrCN77xDaRpisFgoD1R1hnGvJ9mN58705ZlodPp4Id+6IdWihCTXWEWfetrdJOhsd4k5MKEo/t4vE2Wgnm8TdPopqRZxPBrZlmGL3/5y1pSwce5rXvN1yAfB9cMN2/exN27d3VnWCkyQB+NRisx8ybVnoEoMzJRXxdG4boOUDDQaer2+Vh5M/edmQLf/va3NfOb34N/ziAAzwEAGljj8yslmREeHBysAG5Zlq0AZGahb3b5TfNAfm9moTBDncfa7Kqz/x7XhbxPJnjB75skiWZE83uZXXKuGU2JNx+HWcTzdcv7EMcxhsPhyrhtm5smCGAWzesFOP+MAZ0kSTTIsr4vpncDn08eR7PRbkrL1oFJ/h2+Rpn5xeNwdHR0BYAxQQ9T1mLOTRMU2ASc8Xv0ej188MEHK8CHeW9jSZN5bsz928QS2bT9QECDeaIAQq34hHMkCZ8kAPjynS8jt/MVsyUfvk5x4H+e8iBCgbqpYZc2/VxQgSiUQCekBUtRFHiv8x5RB2VEmm7fIbddy9VmiYtigS+MvwAVKG0+mMqU3MZljHPrHMluG6O3aVGmaD8DRfsXVrS/vlruuwsXEEBP9TBqRhilI8hCIvRJ12DSY8bVGGf+GezG1oaPAHWoS1lqr4lEJHpfM0nd9EIQOME6dEgQkyJY3d9ngRSucOHa7pIpIALKG5YeQhUirEJEWQSVKt058oUPx6KLbZpOce6ewx7ZSKwEC7FAjLYDKNIVxkcuKHZQqe1UfwDALnRhKBsJq7HgCx8ePM1iGKgBoiZCp+lgaA0R1RH6Xh9FRWAIx9vNyhnFLbo15iXRYQurQGInOLPPtI5/0/5IJVcSRgIEZJokI4hM6ILOq1tDQxnix8Ifw8uLl3E+Pyc2TBsJCR9EMW/IyJDTObaxCaSSFCeoLAIV2rhLDQwoA6B41ljyJmiB2ahGA0ArPxYk4dhoNIkGpSqXQEZb7D/LywJogab2OK4kDRiL3a1b6x2h/2ssPJ8HmOHjbtAsZR5oxxJKj+l617BGrU01paIUD0tYevwbNCuskUaRLnyaT/Fg8mCZrMDdGFWjrMutBYFneeh5PQ1ULHYWkK6Ea7kYyiEin5gMHYsW176k629nbweWa2GWk6fBH339j3CZkjZdeQpiJJB7OcYYa8nV1ghG0LXPbCnuGLvSRahC3AhvoGt38UL6AtQDhb9+76/jxcMXIUoynUtqAv/Ya8Y0dzW1q+fZOe7P7+v/b5NVOMKB9VEL4gUBp3LwKHiEo+Mj2jd2IJcRbgY30bW72O/t43BwSCwKy8Xv/d7v4dvvfRuvv/46bMfGX/7pv4xrd66hkIXuwqYqXXaim0SDFsxuGDdj/X8Fhd/4s99Y3clXAKuy0H27q03HIrk01OzYHYycEXajXdzcXRbYkR3BtmyUVYmyIY0/j5HufLf63of1Q8y6BPpWToV3T94FTq6OF2vvGbzpu3100AEywL+gYvqv/OhfQc8nPwcpKJVGKdLfp02KUrZpHG1XPqmTFUnMtJni6ezpKlBiA+skCE96xN6wPG2Kxpr/WTHDOB+jaioUTaEj4bYZp9qwIQMJu7IRFRHu7d8jPyLpwrMJpAAIRJWWhOVYqFHjfHqOR3iExEsQuzEWPTrn24zjmLGh40fdpcyo7/XxcPoQvbSHgTeAr3y8M30H10fXkVYpfMtfeS1e3JkLUF50mawKIQR820fP6uFWeAu3rdtwbZd8aGY9TKdTpGmqC7LLy0u6Vlua/T/4B/8AvV6P5Jbt3M2Q4ejyCH/0tT/Cg5MH6B/08dInXsK0nJKvSTXTfgRpnSKrs63AzaatAZlyJk2CpNhw7V48++8lJBzpwLOpkxyIAD2vBwcO9pw9dM+6CFWI+CSmqEphUyGiKI7TiRx87ic/R5HLIicfgnqBRbHAeX6O0+AU2Y0Mj93HW9l1Kya4RtoGgxd9r49ZPcN33vsOek4PgQzw1uQt9Nwebue39fwzF+gs2VwvGBmYAJaGqlxAMFuAG2MmE4FBAQBapmoWYdyVN403f+mXfmnlNdY7mOsACH/P1HRzcWgyLPj3zMKJ99H8+Xrn3yzUeM1rxvCud+BNSjlfI2ZhygXsxQVNso9//ONXxpv/nveJWdQmyGJZFg4ODvTx8nuvd8zXG6cmyGgem1mgmuNhblyIM7hj7ieDNOZ4WpaFN998E2+99RZGo5FmqqwXoub+8rlksNS2bXzyk5/ECy+8oM+r53kYj8e6+OW5xKaCYRiuMGP457yZwA7PXQYFLMvCrVu38Oabb+pOOwAtfwGW0br8movFAl/96lc1WMXHyL4Q5jww5x6DTDyvXn31VQRBoAEt83yahpHr54YZTQweNk2jJQqmFNz84GtlOp1ib29vxSPF9H0w3+vdd9+9wg4x98sECFakOgbI5Xkeer2lnJ3BBH5/fi0ThOJjM68jntvr94n1aziKIqRpqveTr791bwsTTJnNZivKhOfZnhto6HTIet5EPsyd4EnGE6aua/z8w5/H3u09FDYZ+2UqQyqoKEsVFakxYpzjHLETIx3SwnXTZinqSqXWmj65B2C/LaiVq6n3Yfuvgw4CFaBTdLCb7pIJXUYLW6dy8Kj7CA0acl6OIkCSOVKmMjRuow0I52KOE5zoeL7SLkkqwVtb+DObwFc+FaeNh6RJ8DR6uvG4dIRm69AfipAc+1uwJRABkAHlrIRsJBbzBV3UgYdxMkaKFMpXaIIGiUiQyYw+IyMQAyUKUGEww2y1M21jqRftre2YgqZe2n2i1g9cMuVjJsAhDilZwerg2uAaVKyQT3MdsVQKiht8vfM6voavrb6+WJq01bLWkVnmz69sPFut9oYrWrTQseAqF77wqVhsFKIygl8RXW4gB3AyB7KUqFSFyq5wcPcAJ7MTnM5OKdFAFigsij7MgxxP7aeYY47c2RB/WNOY2Z4Nr156BHREB27pIigCPQ+6NhWNnksdZWlJ5A0BSHETo3RIjlMKYnHkIic6NxKUYktEZesDYTWWLqgBmrewKIO+AZlhPhdA0b7myulhV3MFbapX42q3VMmlOaamVmPJ6tgW9WXBgqylNtu0YNDMW3ClFjUZej6jQ7uprjfBkWeCFYLkIWbneYVFsQXAExDkRG/7y4QE0cq2akpFyKpshTqe1znOkjOcJS0H/wCYqzmOJ8dkxGkwP1a2d9pFs02xdXXYmpQJwFc+7g3u4c7+nRVfA1fQIoTp7WyiNS+pCDH9DE7rU9wf38fslL6vt08BXzn/CnBO5yqyoqXhVKvNH/pD7EV72Il2cC24alTZc3twpEOyk9bAa1JMqODOZ3j93dfxjTe+QQk1XgMRChxlR5o5MK/nWw39QpuYaeKaQNWp4NUerL6Fm/ZN9OweumEXQ3uIQxyi71BhPvAGCCRJsEy6ND8wS1lCBlKnfPzh1/4Q3/7+t/HSx1+C03GWsouWbXFanuqv4+MY9btbTH6FvezKOj39ddfp4jA8JA+KI5LqRFaEv/nzfxPdThd1RUBj0RQ6pnNRU2xqaRN4cdwc48K/QHKY4Ng9xlvHb23fB8PEs+t00bN72Al3cNe+i4FPbJO9zp7+vScPn+A3fv038OonXsXLH3tZm8slVSsdUJlOQUnrNmauJB8EZlw8y2RUCgnf8iFqgQIFKovYgPeT+yTLUBUZXTbFdpd9KWEHBAYfeAckubBJVuIKl+65ghhXCgp5mQMWSQTSOsVxeoy3Z29vNkW1AcwA/AGBFDrVox0/Bnx6Tg8HvQP9dd/ro1pUuO3dXmGAmEWQuWhd7x6bi0cuBl1J/hohQgyHQxzWhzitT1GdVLjXvYe/99LfQ9M0Kx15BjzSNIVt2ySdaVNqFtUCSUVzKqmJlXMen2NaT/Huk3cxK2Zwey6t2eqUnllbTEXXtwYNGaEWOa05AICHNQMwar/eIJ9sXwBvPn1Tp31EdkRzNdjBbe82wjhEcVngJ374J/DRlz6qZUONohjdcTzWcZR8vc4rMoM8yo7wzuKdpSlktaZRygH8MX3pSx8dp6OvWb6ndV26PoYhsdYG/gAdu4OdaAeiENgJdyBLuXIezALG9Jbg887nms8ZJwHw+eTF/2g0WulmbwMa1lkXXDiYBSUXRswcNv+Ory2TjbDOnDCLHy72uDDiGoDd9M1uLoMRaZqi3+8DIKClLEvs7u4CoMK80+ng85//vB67dRCEv59l2YpDvgmq8Jg3TYPJZIKyLFc6/yZT2yzQ+Ph5M6/N9THnv2d2Rp7nV8bH/FvT+4ON83/pl35JgxN8Xk1zQQZmzHPMr7G/v6/3k4tyLqTNc2NGRbKEg4GYdW8QHiNmQJj+AIeHh/o9eP/4vXjemjIgKSXeeOMN+L6vi+h1EIfP7zr7hQHYwWCggRzzXJlzgZksfH81QT+elzxupszKBK3MfeN4TbPmNaVX63GQT548wRtvvKFZHeZ50re29hyac5BBI9/38ZnPfAbXrl3DYrHQr5Nl2QoYpJTSniemX4I5T835uWnjfQvDkDxsjPnN+2mC6SbQ8P3vfx9xHOv70Oc+97mt78PbcwMNpsbFPABG1Tjyhw06lFLo1l0cykMd7acRFmPxzojk5fgSFxcXVBj4AHwQ48CrdbE/ySZQoQI6wKKhjNgUqe78s3/CFBs0jTY2x0ltKyaUgA0briDzxwABeqqHQxyiozrwSg8qVwh8igKZzqZUILmKJBMciSZipFaKTtOhLvcaAs8mgLNmRtGFxj41IPM+009BP6QV4HSWhndMFQ2bEIN6AFEIDL0h7NpGz+mRP4TVZs2rWnewKqfCXM0xLsdIRYrCpgVfYRGTohIVcitHLGKMmzG9t4WrkZIs1+xBd4ktRZGPkYpw2BxCZWo1ctJxkaUZGtFAWAJwicZfWlR8p4IMM6+cnxakMD0TUpGSkaJHc2fr1lLyrcKCdCXkSMJpHPIpqF34DWn1h2KIYlbATm2ETQi7sVEqShTZubmDcTqmlBGb2Cjs4zBxJig8AtYKWTyTReE1npYUddDBntgjtoyi6NNqXmE2nsG2bFi2BWlJMrSsFxABJWnwWBVWQUW5qCk5YguLwoIFu7EhmlXfCO3D0Bb4ynpOFkU7pmx8yZ4M7EvBwEGNmjLi22K6Ro3aqpfXgyGVALYDBMzQEZWA1RBIIVUrExEgoNAVupNayWqFmbC+33rTl57a/POVP1OISyqq6E/bBR2W7IZNm4BA5EToel1cnFzAsz30/B6NU3tdVqpCoQoUiiJAGbCJK/INgQv6aEHO4/QY+ADPzG63hU15322aRc/poe/0cSu4ReZunV30vT4iO8KTh0/w+7//+7h37x5eevklZFVGJlTNQuvSYxXjND/F++n7WJwTo2hbERLa4QoAwf4KfaePRbaAndnoTrrwlIdPdj6Jn77z03AbF5EdoWka6oaj7eSKjBJ9nBJxHeNPv/uneJI+wUV+gdqrcdQc4YPzD7T51qYx4fg3Nrhjo0JOWtiL9nQ82pP3n+Cjo4/ip2/9NHpOD54kMH19UV9VFUajEXq7PcyL+Urih+lLwV+zC/5RcoR5Ocd5eo74pVjP0z9990+fvc9eTyd93LBuoJt2MTudYSfawc/9zM/Bc4jezdc1O+8zKMJg00l2gvcW72njx40Gk68Av1v8Lrrf7a4Yi5pMgJveTWLqhCMyg2xNSAcepWMsisUySSRvE1Bap/9FtcB33/kuXn/4Oiq7gh3Z2N3d1WBGUicbJSi8NYISp2rUeJA+gMxagzVOwGmqrQwfRzi6kLzmX8Orw1fRcTrwLR/j8zEuzy9x74V7kFYb/dUCH3mdI65iHCfHeGf2jgbttjJ3vkPx1YOvDqBKhaiOgBBQewqqo9DEjU7dyZHDKi2UdQlPeVgsFrrJwwtDnoPmojDPc6RpCs/zMBgMsFgsEIbhSgEWWAE84WHH3VlZBPNiniWp//7hv0d/1MdnPvOZlc5kmqaorRpuz0XjNpRsUC5Q2ZWWkp7MTzCtpzhbnGFeznVcI7NatvmD0EFB38NLlChretadF+f08xazwJA+3pq8BXzj6ssIkKGfJ1ugwiIjzaE9xAvhC9jxd7Dj79B9KOhDQiJexPjDP/xD/KW//JcgbLECKqZNy4atYpxn53h//r72oZkVs63zk8E9jgsuOyVwC1C7iuS5GSBySpzy4OGbF9/EXm9PMy4c5ejCjxf8XGDweed5sc4y4Dmyzkjg71uWhSRJ8PLLL+ui1uygctFrFvjr1Pf1TjsXTk1DCQRpmiLLshUPNd7vsix1AQVgpUDk7i4XxiYYx/tkHg9/bRrZAbhS4L311lu4devWFUDFbJwCy8KTN3NM179v0uTZOy7LMg0MmMUpQIX4yckJXnnlFQBAGIZQSmFnZ+cKAMVAg1msrheuDECZjJC3335bA5QMtkgp0ev1tMycTRdZ0mP+Po+LCWbw3zD4VRSFvjdxkczpFXmeI8syjMdjnSrhOA4+97nP4WMf+9iVOcrjz3POnGPMqOCCmucKF9s8V9fngnkM63OBAZZ1YGF9bvX7faRpque++fsm2MF/z94Wr7766srfAEtjRZ5nDFKY0orhcKhlGgyCeJ6H+Xyuj9+UgTHowiwEEwA0Qcb1YzMlV8y+ML0Y1hkeZgKP4zh46623EMfxinzrw7bnBhpMRGV3dxez2QxxHOuYjl6vh6IotD6Jd5jRMPOiNA+aT5p5Ubq1C5lKRDJCJCIkSYKmaTCfz3H9+nWMwhHu37+v90kphTAKcffeXbzz+B2cxUSXL60SpV0iGAVIVILL4hKFUyBWMfkCyBI58o1FmRIKpSJGQCxa1JvHVGC54AdIysDFbdttdpQDp3EoHq0IMLAG2LV3kZ6nsCvS0KuGFoIH1w6QNAmOJkfIrXyZlGGXaLyG9lesMT0EpSjUskauciyw0MW/chXqoN5uXKgAD9SJD0Ddd6um5IJ+TsZqfbcPt3ZhlRaxRBwfN2/cxHsP3kNcxgREiAKxjFH5FayehfPknNysrUIbIqZ2ikQktG/Rhn1Zd95mJgWIRRGBJDKuIOPHwAq00RUUuaZnVYbGblDJSrtqZ1aGytqg22YmRWuqCAvIrFUmxQdUuekFjblvAHkyWK4FaUvyzTAMPneKHYRNSN4UkooBFy4sx1qJ50xVStRzu0Ru5ZhiiiN1pKM7S6ckUGmElY0BEdbiR1ZExmalhU7TwWF0iPnpHHVaQzVKyx+UrRCMArg9F08un2BezmmetWyOXOYEVuAZLIqGTBxXjCbZld2CBhOe26WdZQuQWr7AXgtQxNDgxIxKUMHQiIbAp/U7l+lzwgtW43dEIzQLhDtlVVrBEhbRy9s5kTc5ClWgsRodW/phgIvC5oXIpt/jxTlCoECBRbFY+m9sGTcByrn34KHJGjQ5Gf3Z0sbB/oFmL5SqZVM0ZEKX1ilKlKhUpdMY1l9343u/CnwL3wIe0O+4woUvfZJ22B10rS4O3AMMvSH2u/vYiXbgW/4V80M2PmTfApYOHCVHmOQTnCfnSO+meny/i+/i37xBWnGdM27R+zEoMApJpjD0huhXfZTTEs5TB127i1/57K/gxesv6gSUWTYjY1+V6phM9k9YVAvMa/o8LsZ4lDzC4pJAE93lpKAj/M/f+58BUOGwnnnetQmU2F/s43BO7Amm5d/q3NJ+AtqjQFydTP/7//6/43f/4HdRixoykPjP/+5/Tl4Ubec5bog9kKoUmcq0IeHZ7AzH5THmnTnybo73rPfwte9/7crr8/xhQ02mkF9zryEMSa4zCkfouB1EQUT3DQG8/fbbeOvdt/CpH/kUeVG0qShxHeNp+hRvL97WLIdt1Hzf8q+wXAbuYPm1P8B16zoenz2GVVq4MbqB//Kn/kuKdbZcveDKmxyLaoHKquD2XCzKBd57/B6+9b1v4d3H78LpOrj32j2S9ahMx/txwkRSJ1ckFaUqMS7GGBdjCAhcL67jIDzAvJzjLDnDwl3gW0+/tfG4TIbDQXCAj3U+RmCU5cERDqqiQjfq4uz8DP1BH0+OnqDT7+BR/gg5ckzEBLEfIwsyFLsFanszGPInD/8EnacdDTINPIqTdWsXR/4RLq5dIHVTXL+4jkAE6Nd97IpdMlXMqNNoLnB547UWL5yDIFj5PzePuLDlRXHgBuh7lPwB0KKVU0C4E9/pdDS9drFY6NSEN998E57v4Utf+RIap8H7R+9TFLlFc72yKzReg9qrceOVG6jcakXiw+aa24Aj3hTatRsDFThfMiqetY2A33rrt4ixJr2VtI+BN8BOuINr4TXsBDs62nfokzm1amjRPp1OYXs2TiYnWFQLTNIJnlw8wayY4dHsEWIrRhqluplT2Usg/Fvvrc41Bhh94aO6U8EqLTz+zmMaf6eLG94N/OLNX9zoHbBeJJo6eC60mIVsasv5d7kI4ddcf51NQINJuWbDQJ436wkeSpHR3c7Oji4kudBhU9fFYqGTEcxifZ0ZBGClsDcBD7NbHoYher3eleLdHDuT6WYCDiaDxARdeBOCGNGWZWE+n68YMJqUff49HgeOMTQZSOtAgvke68Uj7xMfu2VRZOGLL764UnCaHf/18TTlGua4mGPL6UBckDLQwOBKmqYrkoajo6MVkIFNI03KP59L85xuOj7TKJLvM5PJBE+ePMG1a9dW5iADECaDZttzd31cTZYFQCz+k5MTPRfWAac8z/H48WMt0el0OtjZ2cEnPvGJldcFlvWzuV98fHw9mvdq/vl4PEaWZRps5tflzfR0WR/XTce8PufZ86MoCgRBsHFuM3OF5Xyj0Qg//uM/rq0Tnmd7bqDh0aNHelC63a42YeSHFKPqZjTOulvlOsJoolnr1Cj+XTZtYadU3/e1FwT/XtM0sC1iHwRlgJ1yZ2Wg7wzuIM9znJ+fw/d9jbhWVYXZbAbHc0jvHArdHS6sQneLE5EglSkWaqHp7amiRfymQrYS1PlIZapBiSMcUfG6VjjSYLRa94EkH4eGQAq/8bEv92FlFoI6gA8fTd7AkhbSPIV0JeIihggFKpf0jJnMdBGrIxrXNwGtxy9RUo62vzTw4xSDK8c2o/3XcYJttGAkI+yKXeyWuxglI/19Lobv3LiDLM6QFimkJ7Xh3aSeYIEFcjtHbpPzfCGpM1+KEolIMMd8+yyV7YdhcCUt0trbjY2gCmA3NkI7hCfJ+E02EqIRiMIISZ4gK6nrze+byxyFU2wdN6il0RYkMVK40AawnU2hlp4ULElxlANPeDp6dAc76KCDLrpwCkcbnUECjdto9kQmqdhQniIKtTVHapG5X57nUH0F9Nd2XQlKWMg7EBaxAXzlo1f0dATpKBzhxugGTp6eLOlmTYVCEDsnQ4bKpQWSjmZtk04KFFsXgBbI5I5BBDaEqxqSeDSCvEo+zA9ifTxlI5dxm61XhC1b3WFTIS9znWbB8pzSIonOVE2XINczPE7QAF2vi8iJELohQjtE5EYIbPKFEUKgrEskZYK0TJGUlPiwKBbIqmw73dhgUHwYMKOgyOWdwUSDmTXNplDZdoAilOQr0LE7uqNpCUvHN0op4XoUdZvWKU7GJ7hYXMDpOJRUoCi1Jq9zTOsp1iXR4kg8c/9tYSOwyUyOu8d7/h5e7r2My/QST959gizO4NgOXn31Vdy8cZPmBirkVa7TMhb1AuflOR5NHmF+TgkWmcyAO6APAH9y/0+A+0R51t1/u7dCee9Y9L3rwXVKNGjZDMNgiFE0QhRGKOoCJ9MT/Pe/9t/j3ifu4cZLNzAtprrInhQTHVd2Vp1hVsyQzBPMPtjO7AjsgCQca2aZA3eA9+v38eTwCazCQs/uQTkKQ3eIm+ImfOFDNfSM5UVbt9vVRcJv/dZv4dvf/zaePHmCm3du4m/93/8WUqQECrRJCYtqoT+4w7yoF3iUP9LHEdfxZkbDLvDVB18lbwpmVTgdDNwBbjm3NKU8ciN4dmvc1k6HWtUa/FqUC0xyks3cn91fyaAvmxL4DP3Nd/Ad/Paf/TaNmQz0uWNQZ+ANsDvdRd/tkwyiBPyZjxv2DfzSrV/CTrSDvteHLW29YOJYZGlLYlGUy0512lCCQlqneKH/Aj6//3lIKfHHf/zHePfdd/Gf/dx/hsvkErOCIl6ZvbBolmMa19QIeJo8xSSjY5xkkyX4wn4bbUfeBnXb7cam1Kk8hFM7sGGjTEuoWmlz3VdeeQW9QQ9K0rM6rVOcZWcYp2OceWdIXkzwvv0+vvL2V66cOktYmoGijRVZ+mGwUtg3JhABek4POfKVjhk/C9jQzaTTAstFrVl8mh1WLraahtYurnJhNzZ6WU8XBf6COqvMkP0vPvtf4MUXX9T7wTFz9+/fx2/8h9/A2w/exs/8tZ/BrZdvEZDURnbOihluh7dxJ7yDTGWYllOc5We4LC4xLsfIqgwf7X0UGTKcpWdYYIFJMcFFeoFHZ49gd2ykdUreFmWGSTl5PpBibfPk0kiTvb3QAL2ihzBuZV+5gMqUNmT9whe+QNIBScbJ82KODBmeXDzBGxdvkOFrU+Np+hSL+QJP3af4G4d/Y6X4N88XcFX+ACwLFS4weM3NWnEAK+fQLLA2+RjweTZlCCydWI/KFGJpkhnHsQYZzI2bk1xPmB1bpvmb3hX8M9OclQEPs9PLDB9zDDbJI9YN9ng8ze+ZgAl/zXN3Pp+vRHCavhWWZWlWEmvyi6LQTIP1Qt/8WAcguAjn4p6BhW63qwt6lmAxqBAEgQZp+NywnII3/n1+T/7gQpRZJsyS4PfhQtS2beR5rgtRBpFms9nKvcJkyvD+rfsf8Biaf8OgxP7+vmYSrEskeA6wv8G67GYd0DA3PsedTgfvvPPOSp1qAgX8vryfvV4P77///hUQzjwW8/64Pve4hjbn12QyQa/XuxIvyZvJQNh0ja8fk7mZoHKapuh2u1fu4wBwcXGBNE3xkY98BJZlaQkLs3GeZ/uBpBO8A47jaGMcptLwIDFNig/ANOwwJ405GOZFaaJXfEL6/T6iKEIcx/A8T+u5eDNRQL7ZmZvv+yiKAt1uV+tuANKF8cXjWR46DuW+WsIiuUdjmPhIZ8XAZjabkXllYOMyvcTZ/IwMAQMglzlkJFE5FWLEmJQTNAF1Yhf1glz+5SpqxJT1WlAkJBciR83RkjkBbGQA2LCXqRSKvAF6TQ8De0Bmj23MG/06dRtn+QyJSlDZFWbljEwd2652LnICGjbNA0Va10rQMaQyxVzOcVKcoPAKVH61GaSI6MNtSIri1R5kITVldFgM0UOPimG7Qw/mjDwsLFhwXIeKUU8hVjFmaoY55pg3c8zruTZ8TBsCgEqbjCmVUBhjfPVAFEhm4S3/z3IPHz66oosma2DVlnZnl42ELWwM+0MkGWWtK0uRbAcZxWFa+WZvhFbDz1r8UpCJHp+TK8WuiyWjQS3HXoLAKKux9Dj6tY9e3cOOu4NrnWuYnk7RpA2shrwDcklu/cFOADjA08VTikW05iicQieN1LJe1dC2m9M4K8CRr3wEdYB+0Ydd2nAqBzdGN9BxOzSOUi477G2caYI2o14l2qNlrubLubZNvqRsDSQA7cJAkg9FLQjwKeXaePNYuldfE6o1KxMOUC3PORtZ1k2Nqq4046WxKJbwJD6Bip+TqQHSnwd2gL7fR+RGiBwqtEM3hGd7+MqXv4KdnR1EvUhHD+YNsRGKhqQTG2nGa+P0rCJfQWkN/Vm5zGfcmOZhbgH5P/TsHq5b13WR6UoXrnThWA4c6UBaEo5LbIqspi7ytCBDukVJ+uesyrT+feOC/YXlMd3HfeDJ6o+ZTcHO9wNvgBd6L2DoDvHo7UeYnk8xG88QhRF+9C/9KN0nmhJpSZK6RU2F4Vl+tiwSnyGr4PSMyIpwee0SJ9UJbl/eRiTJA2Pf38fd4C5FW4YjdGQHbuPicO8QURShaArN3mD2xCSfaGPMWTmj/5cznE5PMS2mOBWnSD+SasbcN95d8sEFxDLm0VmyAAbeAF27i+PyGGfdMyR7CcIoxKSaoO/1seftwZf+SicOoKLb7GDyQth1XZJOeUqzF7745S/ivSfv4ROf/YSmiecgD5lFtcDTjAqeD5MOABSfanpT3O7cRndIXx8/PMbbb70NoQQGgwE+/elPg/178jpHVmdIavKnuL+4j+/MvqOBiqbTAJ8mNsz/+c3/c/l+LV2+Y3UQypDSEbyBLrh7Npk+9twebrg3sNfdwygYEQ2+gY4xdISDkTvCyB2tFAFmB9dxHNy8eXNZTFsWnj59it6ohz/82h9i5+YOvv7dr2N0Y4Svv/F15CLHyfQEk3xC1ycSZD7JgrIeJagom87XUXEEnBrXg3RJ2iNCRHUEZ+qg63bx6kuvUrqHXCYO1aqmhJ5W7pHUCS7yCzyIHyzPWb3hnNmAvJTo/X5PAzwsAejaXexEO9jr7Gl/goPeAYb+kJg7FXXKq6rSnU+zy7xOx12n6vKakOMPzUhKXeRBQJYSURXhhfCFjcUfU7Fvh7dXzleaprrorOsa/X4fQghMJhP8q3/1r/DLP/3L+hwXTYFUpbRmc6iRM81bw81ihkk5wWXeRhtnYy0VYU+LvMhX1x4haP020Bf3yvbt428Dx9DzLrRCdNwOfOUjczKoUmHX2cUwHKLrdOHUDr548kVtSsuActeh6O918GGTP8im1AH+/3phbbIkuFAyu/W8cZoE68+Znm2eH1OqY9YGDBa4rquLRDMRY12ebRZFZkfcbFTy65t/Y85DBkbW6ekm0GDWMOubKSnhIpv3yax5uDYyx5Yj1xlA4ULafE1+Db6WAGj2xDoYyLGWvN/8O5xswfWMSd03QUXe1u9zZv3nuq4uSs0I0iRJ4Pu+Bga5Aa0UyUqm06n2rzALdi7aefzWgSk+P8yc5/+b4ILJvuD9WZeBmK/Jm3mOTUaDEAK9Xg+LxUJLYUzmCBtV8txm2cR8Ptfzk/eDNz7GdYDDnIuLxQKj0Ujvc6/X00AVz0EGUnjjY+U5sc1zwjxenrd8L1wsFuj3+ysgF89FrsEZmPJ9H/P5HL1eb0Xu8qztB/ZoMOUQvu/rAzGNNsyJz2gVf72O9JioG58MBjB4QvLNioEMfh2z8GfaEdN7zP1m1Mk06DBpVlVV6QfPOtK5jgyZ+y8gqMBPXQzyAXasHTg53Uy6dhd2TdFKs9kML7zwArIsw4MHD+i90VASQgDc+cgdPLp4hKPxkZZM5FaOwi1g9SxM8gkymaEQxVUWhYDOMk9Vy6D4sK6wAOBRp9kBOYY7NRWT3bKLoAnQFbSYcZQDVSp4gYcgCvDk/Imm9xdWodMeCtECFFuM+6RqC2Rl6WNvHDKw5K52LevVIn1V2kdaxybQBpqeIubEsB4iQgQfPqp5hWySIUCAyIoIfPFcNFajnfIzN4Oz4+A8OcesnmmpSiFaVgNyxCpG4zabx7LBqnSmLV6tpmVSNAFsRV0rT3qQFQEUsiEXeABallNbtWYGJEg2JzS0TAollPY2gAXS7Bu/8w7eoY6zKflgkAK0D07uQHoSdk0Gav26j6AMEKoQo2CEnc4Ojj84RpEUZMjmVni8+1jfrJj1UVt0rgpRLAt9vqyb5XsGTaB9KCJBNNB9sU965EUOpyR5Efs6AMQYSeoElUNzOm5IjlO7NWQkUcsaMWIkSLZ6E9iwKdHDeF1tMgky2atk9VyJHllFnV5LWOSkbvnwbR+BE+jFvRDU2a+bGlmV6Q+Otbzi23AA8pJZW+eb+uKuICZFz+8htEIsZgtkSYbFfIGyKuEGLnYOdlCoQlPD8zpHqcpnaqE/VNoigExlyMoMp+Xp2o+ezWAI7ZAkDt4I93r3tEGeb/k6ocAStKh5/fXX8f4H7yOrM4iOwO6tXShfaap02tCCfVbPMKtnQAEaL167uwAOoSUObx+/vbIvElJfg4FF94PdYBddq4vIjhDIAL7tE9NGCFi2Bdu3kVc5xvEYk2aChVjgO7Pv6MJsW6KBJz1duDJzgZMh2DjwXv+e7sr3XCp0Qxnif/yX/yO++idfhXIU+tf6+Gt/669Rmk4LTizqBQpJ5rHzao5xPsaj+BFm5QwX1QXywxw4BN7AG/iPf/of9T7ZwtasDvbnCERA+9R2uiMZoecRs2LgDXB7/zYO/APc7d3Fe817QA781Z2/qrs3YRjqRR4vQBmogIT2XWDzT/aq0J8LYqOwR8Xb5ds4LU8xuzVD7dR4hEf4zsPvbBxfLqJG/gh3uneAFDh9eoqL0wsMugO8+pFXadEJhapZGkmmVYpZPsNxeqwlRItqsXEeSyHRsTtkFu05+OK3v6glQ+vsmJ7Tw9AfYifawbAaIrSWHR7HcRC6IfbDfbzQfwHn1jleHb6KpEwQRRGOyiNcjC8wn88xmUx0oXNxcUFAkCMgQ4mf+vmfws6NHZLbGYkvx5NjvHn5Js6aM8RWjDdmb+gUpm0RpJy60bW72HF3cCe4g45NnhTdoKsTsd579z24nou9gz3kdY5KkQfDJJvgcfYYyTzZbKTYbpawtH9HJClNp+f0UM5LdOwOznpn8JWPeCeGyAS8hrq6spCom1rTv7nTygWFuYDmtZ4Zm8c/A5ameOsLYU74MH/XLEzZM0BKYkb2rT5G7gjdblfHnZvdZL4O8jxHGIY4PT3FYDDAw6cPUVol3n70Ni7TS5zOTvH6O6/TNRtfoHZq8p6RCclF3BoylOSLpSqUqsS0mmJaTWnHAwA+cHJ2QsevtpgH8zmARew7i4w0e25Ppx71HPp6Op/CqRy8NH4JHatD9yuvj57XQ0euUrXNwtks5s34RO6Wmy76JlV/vXifz+d6PM3Ou1JKAw1s9Gg2Ivk915uY/L683q+qSpsPclFoNiL5/K+zdNY7uvyzTVILE8zh+2KSJLo2MYEOE/zg42agIUkS3SE25zrXQHzM5uf1ucv7zTGbzJxgMBQgEIgBHH4PHvP1tJx10IXnved5CIIASZJowEoppYth3q8kSbRUJQgCXF5eXrlW14GhdUnBer3G54RNUrmgNwEak8XDHzwnNhX56xsfb7fbRVEUK/PFZM/w+ebXDsMQWZYhTVPtfcF/y9eDGSdrnjP+MGNO+f+mL8v6nOT3NsGzTWwKc8zN95RSYjQaYbFY6AbEpnExk27CMESapisMjA/bnhto4JNoxoHwSeULgTc+YKaumANuHsT6hQ4sY1LMiW3qj1jHsn6CzEgVE+TgSdE0jTafMalCJrDAx2LGvqwj7ubxmR0hz/Pg+74GQljywRQxviD4JgC0iRNOgFvyFqzSQjfv0o26ocIllCHu9u7inXfe0RM+SRPAAVH9bdIgOn0HlV1RsapI5hEj1rncGQikWKG2i1UJACQ+HKAoAfSWAIUDBx48RDLCUAxxW92GXxII4MOHIxw4tgPpSDw6o4VxYRWI7RgX0QWUpZZxgmJzUS+U0GwNW9iwQNFmc8wxVVNUskLlkB9EgWKjrwGw7Mq7NWWsD+shwiDEbXFbp3tw6oen2s49fDR1QxrSJkaiEszEDHM1x9H8CLN6tpTZSEr3qGSF3M5Xj2fdONPcTE8KuOigQwVybUGUAqoiA0000Br4CmQYWNkk+SjslpFgbQB5jDHldAhYoLHfVDBmAA6W+/as1zOPwVZL41QP5PhuG7eXBuQ1MlVTHdea9JOtppV2bevz4NQkIwrrEP2qjx2XPAG8xoOtbC3HqBpaoMV1rNk5PP8TJFpSlIpW9rTBx0Y0AqImaYkQ5BWxM9ohI7imQlmXiMuYkhKeYVK3OmQCrnQROAECO0DgBDh+dIx+t0+aeEbjVa3d3QtVIG1SzPM5HmePly/mYGV+6xQLtNeldBDKkJJO4METVNybiSANmiWtHTkqq0JSkgZaU/+33As+DKRIqgRJlZBJ5doYXPlbG8BLIKCulkibFKN6hKEzxO3g9rIYFhF8m8CdbqeLwKOIq69942u4/+Q+zuIzWH0L+3f3KV62jbXMFY3lrJlhUk2eud+bNtmT8GoPYRkikAGG/hCRFZEEpdVwB04AoQS8wIOSCkVNhe28nOO92XsrsZ+bTEIFBNyuC/HjFKUbyQjJUYKORdGekYzQER2M/BGGPhUJ1wbXcDg4hCc9/NZv/Ra+8c1v4MnFE1x/6Tr+8v/tL5NRXb2UROjiul7gKD0iE8+amAjbitLAonhpa8fCl77zJUQi0kyKgT/AwB2saNeHwRBDf6hBlRvhjSsdzG1rgH/7b/8t/sP/5z9ASIE7r9zBz/3Nn9OsClP6wf47lUWeI6fVKc6dc8z355h4E7x7/u7W+ekISvSIrAiHziFCn84px9Q6Frm/SyFRqxoPnzxEXMWQSmJSTvAkfaLHLK63F9gMMIUyxG60C6TA9cV1jOdjvP7wdTxqHmFYDpHZGTI3Q+mSzxKztriIcIQDJ3cwKAe4I+9ASgnf9zEcDtHr9XB6eorf/N5v4mvf/BoODg7wsz/7s3otVKpSJ2M1XoNZOdPmhot6KTOIqxhHxREWzQLZlJhHWZ3RfbEEYNx2XOnqyNk9bw8f6X9EM7Q6Xge2RfGmRV7Asi2kRYpa1Zhnc8zyGS6LS5yX50iKBLP+jMDpLTJSu7bxnfo7GL090sDIKBxhJ9yByAUeDB8gvh3j7fJtyBOpQbTIjlYW3Gb3HoDunDJdnTt4vKDnbjmvBXkNqJRCnue6i2ku0jexMni8dru7SP0U153ruCgv0NQNFskCj588hm3bKIqCPB3a9eYv/MIv4FOf+hQB1IqSQCq7wtPxU/zBV/8Ax7NjfOSTHyFD6Kr1k1GUzrOoSf7Da7watb7uj/NjyJiAEylJ2sTSRQW1Ma6UjYv7Tl+DFP/4L/9jvDR8aYXVwOttbiaaDcJ1FsV6EbtYLHQhbp4rZjQ0TYMkSVYkEgw+cBe3rmtdIHFcIa+zlVI4ODjQx2R2hvn1zOLfPMeb7lNmzbGJocF6dzZFNJkDXL/UdY0sy3SjlAu4JEm0JJz/Zh1QWWdVMEjBjHJ+TQa+PM/Tninm+PIx8biZoMV6bWMyA0zWCbPETXDEHBeW0vA+RVGEDz74YIXtYTIlTAY7zwFTVrO+D0IIzWpfZy2Y84yv+3WQyDy/5jk1z2sYhrpu5PloAjI81lzsM8vDPFe8mQki5vnkMeDv83vy7zArzLzHmMdqHrP5ffN8bjpWc2z6/T5OT09X1ATmfDH3USmKxByPxxvZPdu2Hwho4IKaqSMmA8BkO/AOmRftphO5PkB8sTLFDoB2pzVPXhRF2NvbW9FhDQYDSClx8+bNFeoNT9g0TTGfz/XF7nnelQeGlPIKKscn3GRq8L4wqMDHzReDUhS5YyKPUkr0+30Mh8OVSaaU0rSV4XC4Mn5CCMRxjOl0ugRc6gaqUvBsD52mg27VxU69Q3GB7Q2GAZ4wDPVDNYoiZEVGelSRYpJOUNgFaqfGJJ9gUS+QyQyZneGh/3CVmWDS+g2AIkOGOeY4V+f0O5vSKABatPTbrr+yYDUWdZJbuQJ7F1S4at6ohEKBAg2aK/GJbBC4rWDkhAsbBFBISIo2EzlOm1PqeoEAihz51kWqV1PqCIMRgQigpMJAUdxngoRYC5mNqI4QliE6VQejYAQrtGgBZ2VIXepgLLBAIhLEKl6alirypZhjTh132Tw7PaM9LzymjnIQ1VQEcZHvwCFwRlnLiMqGDBtzQaaUnNyyMY+8ZSkICFoEiw2mhy2DhkGMhFv0z6kysJo2QrWxCaxoXFp0N3TtSVuisRqkdoppM8Wxc4yi3nK+ZJvoIUkTy8BRIAJcE9dI8tFGyfrKx5NHTyAFeRRAAmmTYpyOsWgWqN2aPDFkgRfvvYjLlOixcRmjqLcnLHDkJcfpKaVQNuUKSFHFFdAFUqQfqv+VkFo6oEoF2UgUcQFVK7iOi36/T9dC+69Q1BGbVlMdc/ph4IAEeVu4lgunJpBr1B2RjlzYqyaPaDQVO0Om9cxFU3yojGPrJoDGbjBVU0yzKbCZNLBpx2HdsGBXNgIVIK1TdO0urvvXtYkkd+0DKyC5jKBEgkIVVMTWC0ybKebNnLxiRE7AQDbFOB5DQWFaTXGhNqzGn7VrQsKVLoERdoAXui8gsAOaI5YP32rNDpWFN994E/N4Djdy4Xd8zOoZnhZPycCyNTTctHmS3Ozd1yilZubPMLuYUee97brvhrtkGNuyGJzawcAf0J1USqQlUcNn5QwZMrg9F+OcaOBvvv8mLpILuIGLRb3A0/Ip3s7e1h4Z24CTrtNd8aEwzSD7bn+Z9NH+f6ImCPoBXnv5NXzyhz6Jl3ZeojE0ulzms57ljufn5zg+PsbJyQkcx8Gdu3fIGLk1qNwEUnCBvaipI89+FXEVY3G+JqmxAEzoS0eS+eOuv4u79l0EdkBQu3TgOq4GDOuG2FKLZIGqqXCRX+Do/Ajn6Tm+9OBL5NtUgGj0hteKaChy2yotYlPUJEf7yvAreDt/mzrO6GM4HWK33IWsJV754Vdw+6XbCJ1Qu9bzxgWB2QxZL5DMIrnT6VCH0hE4nZ0iaRI0fkNMlHYeTnLyJkkaMng9So8wn1ExO82nmz0+QEV3x+7Ag4eBHCDMQ7iNizIpgYqOva5a4zopsHewh8ODQyiL0nZm5Qwn8xMsLkmaFUcx8ArwTXwTeGRcc615YsciGQGnn3TsFrSzyVw8UhHJi/I9TE+nQArsD/bxsz/7s9jd3V1ZYPM6lotds9Dh9a3ZKOO1Y5Zl6Ha7msrOxZNZmLhue/9WCrdv38ZLL71EzarawcAa6LXwPJrjI5//COI4xvXr1zeum3ltnDe5BhhNkDFuYi3v5Pjey+yS5DutFMq8nk3j4pP0BLa08d/83n+D3WBXsyIG/kBHHjMIdBM3cV6dIyopKt73fe0tw5RvBhL29vZWOsQ8hr7v4yMf+QiUImmB6W7P54Mj+Xge85iylxvXKPzaVVWh3+9rZpbJojBlE6a8h8d3fVsv3szrKAgC5HmuZSGmCSAXm9wFns/nGAwG6Ha7iOMYeZ5rtgEX+QxYmKAF77u5L+Z17vs+xuMxPM/TzVUGXri+4THgn3EtZ3qq8HHx+/C+CUESN2bxrLMj2DCSx5MjPPM8v+JBsF4o8/fMNAlu+JqGtfx7XI/xGJivbdZt5nGY90BzDM37IkBADsd/mowI8+9MVj2zERaLxcqcXmcbAEvgxHwdHi/zfRiQXN933kc+jyawZ0aLrh+3ufH4dDodXa+aY8E/N9mLALSNAe/X82zPDTRwHIvjOEiSBOfnFDnkuq6mUpj0Ci7y15Gr9egNRplNXZL5melxPKhlWaLb7aLb7epJzX+bZZnWJ/HgF0WB8/NzKKW0vqeqKj15zRs2AI2SbUL2zL/hi8nUSPE+MoLHr7F+A+GLw7zJ8uuaKJ35PmEY6v1mypI5TutolxnfwhPEtV300INf+PAqj6j8DZnXMPvCtm3MrTnQAWqHokUXzQKIgMzOtCFmitYBHflmuv/6JrBi/KfBiw9jUbSFrl4AKlrEszeFqUVd+TOpUKgCtappUS2WAAWbzW3eTQEfPnzh60JdQpJHgKCkgFSlyBxKhigFFe0Q2JyqwWAAG0CCpAI+fLiVi1E5wkAN0FVd7Lq7GGCAQAWo4grj2RgX6QVyO0dsx8i9XINBmczQeA2BFKJErnLNZFkpANTaZ+P7zKTwhKeZFJ7wUGXVssiEBTRAnpHfhbKUTqnIBBlCrvuNmJsJDJmf+fyzz0JurVGweH+3zA8LBK44YsmsceHCEY42gssrSmOZYKKlPbnIUTft+FxffU2ncWBHNmQh4VQO+U/kDn7s1o9hFIwwCkYY+AOETghb2trvJCszjLOxBiMu00v9Yf5/m4bdggVf+sQ8EMsFVSOIHls3NTnuo3Uob6MtY8QYVxv8R9rNFjZCERLTRLYsE0GdR1NOUqoSWZ2hkQ0yl1gNj/PHdF19CEjBfjZdp6vlEfzZla6+r9aqRlmTMWBap4hLMm5rsEWe9JxbLWvUbk1O/tUEz0rPMzdXuBT5aXUwskfo2310VRfDaoi7uIvXXn0NPaeH/c4++SE4XUhIZA2BtfOCiqtxOcZlcYmz5Azn6bn2ZZiXczIIrVNcZpc4bo63j2UX+pwCAGZ07lyLWDBDa4jADjRA4Vl0bRZpgaZu9PjmKsf7s/d1Qb2oF1sNWrVJqKSijNkTHJO819nDz7z2M+i7fYyCEXpOTxuK8nNrUSywaKj4W1QLXKaX2gyRvSjm5RyTYoKHi4ea2TEv51d36HPA7+P30T/toz9ZghLsRWECFh2rA6dxkF1m6Lk9HFw/gCMdlEUJCYmu6qIjOzoy2WQ58mZ253h9UlUVzufn8Ps+evs9DVZM8+kyzjCfUfHW6vEvi0vM0zkVwTmBe5s2CYmu0yVTZ+mjzmrIRqIpG5RFCY7IVUJBWALKVYiDGG+kbyCeEyCS1psBJ0c4iC5IohDJSHt6RJKK7ciO0LW6+uehCGlfEBDQLwTOzs5QliX29/fxsTsfuxI1Zy62pZSa0u77/tIkrilwPDlGLnM8vniMDBk+OP1AmzR+cPoB4ibGE/UEsR0j7sTadLuxls+Qh3ioDTRd6RJg0II816xrBBw6BPRYotXlYyknKBuSJaZNSjKj5JFmoyTN5nswn5+e29OsnL7bR9elr0fhCJGItBxqGAyxX+1jFIzQcTor6yxei7J8wyxSWFrARQAXT9euXcPLL7+8si6UkjwlTk9PNWOWF/hmccFNLl5jBg7d12pBTQXpyhVTxkLSmL/22dc0xbuuiU3HoFGsCKgYp2PtScFjOCtneCd+R5uebpz336ZPHjy4Qxfi88TY0j5P8HH3tbv4v8r/Cz300Lf68KWPdJFCpQp+6OOzn/2snmtm1xlYmgZyYW3KJfhar6oK7777rl5H3759W+vYeVtvfPL9gL0WnsVoMDcu3vf29vAjP/Ij2sjUlEBwPcS/H0URHMfBL//yL+smK7My+O/MesEsNE35Gnfb+fdHo5EuIBlc4drFrD0YDDALYgY3GJgwxzWOY0gpEUURhsOhlkZwU9P0FlBKaXCJx+Xg4GCFEcDHsl6D8b7w33J9GEWRHhvTyHIbCMA+MSbIZb7++t+s7wOz69M01dcpnzseT3Njo+Y4jnXYgOktY4JSvP8AdExn0zQ6wccEgdZlI+vsEj6P3PxeB5TXj5PnKn+v2+3q9Ejz99ZBBp7PQRAgyzLNxnqe7bmBBkZl2fSDJ3rplfijzh9B2hIhqFPoCQ9WbSFCpCnh5k3RROXMC49PoHkRB0GATqezcuGZxbnp+rmJoQDQRbJYLBBFkaZj8YOTgQp+DUb9GHlknZP5+usnEYC+QPkiYGdwU9tqOvry8a4j0+ZkBqAv+uFwiLquMR6Pr5jArDMv1t9jncbEP2P0lveb37cv++ioDpn7gXSN+9G+vvnyWPDrBEGAyqpwmV4iVSkSkAEd09RjGeN4cUxpDm1iR458e5QisAJEsDP/FU3iswqU9mW5kBHGP2Y3cPLG6p8p0sKqQpsv8vc1QCFwlbmh2kJV2ZCqdXwWkopzKB3TWIoSC9F2zjxs3gSADoDIMCs0Ov5u7cKvffSrPkYYoS/7aKYN9qN93BjdwOnDU1Q1ySoSmWAu54itGOgCuZ1jUlAXg1NJOMK1FjUa1UA5a74FFlb9MlrwRPtuVBb5FQhP+yKYLBLuhpcgqn4KmiNpkz4TpOCYS/Pc8PiwWWO2qf3NL7khAlNLcaQLUQq4cGm/FZlD5lmOsirRgIxO4yDG//Hm/4FxNsYkm2wsFm1pY+iTczs7uI+CEV7be01/PfSH6Pt90kEL4OEHD1HVlS5aJsXSMJDd+KdlS7uvthcwLij6lRfcTIvV8oiGUjZM35oP26Qgb4OeTd4QgR1oE0gLbWcUFCGXVinyigz78jrXKQKbut3rm4CApSy4wkXkR5QMIx0CuVowhPX2eU1Gr0mZIK7iZ+qTn2crVIGiKjCtpniSP7n6C9/c/HeOdNBxOug7fewEO9gP9rET7KDn9nDoHy4LEY+6ftzJd6SDrMp0t31ezDHOxng8e4zf/NJvIpUpbrx8A4VV0KK+XCCpWqCiukSZPt+YAq3Ph7Q1a8KVLdDUfu0IKtAkyLRVCYW4iXFWnS27/fMYONp8/D2H/CV0MeZ0tayj7/ax7+/j5f7Luus5cAeIrAiORTeRqqkwL8gkc17N8dtf+m28/fBtfPTTH0Vnr7Oc/8UUT+OnmOZTTMsp5sV8a7HtW75mSawwJpy+NoVk7wwGjrp2FwN/AMdy9OLtVnFLd5WAZfTbpmc+A/NCCIxGI5RliWlCQMqD4weQocR33/0uot0I337r2+jsdvC997+H2q5xqk4pucfOKYrZrlDapY46BICLdMmisQQlkoQOyaIYcLJky9Rrn5O1qlHUBY6aI2RFhiRNsCgXW9NQ9Li1xXwv76F33Ftln7TFN//eKBwhktGVtZBneTiIDuB5HvbFPjzPw5l7BiEEsizD+/X7JHl652uQUuLs7AxZllExWWVAADRegxdffRGv/fBr2hdlVsyWBoz5BNOSrllmW2yLVmWQoudSygyDZb5Fsk5LLuUqQlLDwjTOvMwu8WD+QAOH83K+1dej69BcimSEnXAHVmnh4OQA1bzCTriDYlbgQl5AOQoTbwKncuApj6LOpcRsNruieef12c2bN7XpnLluNBkSGtA1OtHmPX+d9m02o7hoitwIHa+Da+E1rUHnxhezmdmHgY06uQkYlzEeHD9A7dZ464O3IAKB77z3HVzGlziaHOE0PqUoU6fCxJ6gtEr8bw//NyzuL7be19jQleceM1O6Tnd5Xdsd9K0+epLmaOCTmTg3G7h49jxP+8uYoJDZTDTX4Cb4wNv612adwrVBFEW4fv06hsOh/r5Jkee/467zbDbTrGOTcc3nzDTKN9+L95n3m9fxQgh89KMfxcsvv7wyD8yCflP3XAihUwTNYtucV6bHxd7eHu7du4cbN26sNGPX/56P1fM8/PiP//hKcoJZ8K4fozmvubZJ0xSPHz/WNdarr76qGQfMlFhPImFZEo/ZiqnshlrO3AfbtnHnzh3cunVLe32YDej14t91XXz2s5/V14n5O+bfmmPOX5uyBd64RuN5u4kVwQCWaYy5PlfX56zZ+FdKrXgtmmwQPi/csOfvR1GkG9/ms/JZ2w8ENPBAMB2q0+ngHOd4ZD9C0kmou7muuVYgXXwdIkSISEQIEdL/Wz1845AZjgULlm2REU29PHlRFGkTE0aYzJPBF3iWZRrh4gFjRItPjDnQ60yETW6e+jAMQGT9psQnzUT9AVy5gC4uLnRkD+9fr9eD67p4+vTpyg1BKYWdnR3NmjApQDwxTSqViZqZ+2xe9Px5E41m002TUXIeiyRJsFgs9O/xWNk2deqL0wKogRAhAhXo4xsOh/hg9gEGgwEsy0IcxxoRk65EihRWZEEFFONXOmQ4GCPGAgukMtWRjrkgBsW2VIx1poQ2UER9tUu+xW+AF20N2nPd/uO0AjYTXNkEUFpUDEhFDAh9k2679tvezwMV6MzQAEjiUKkKjWpQi1qbgG01L2S9awxgB8u4xzZG01UuOqqDnurBr30M6gE6TQcd1UEIorLudfYw9Id4+MFDeH0PhV9gqqYYV2McLY4IKLJznfCRgxI2KqvCDDPU+PAO+DrDw64IQNESCpDUg2UyjuNASNIdJ3VCzuzIEKt4O0gF6AUknxv99m1MXInyitkoHFxNdQFwf3IftrRpkW/7CJ0QkRPRwsztIHRCLZeomur/S9ufxcqWnGdi6Bcr1rxWzjv3cPaZpxoOh2JZFMXSTLUkUNbQbqDRgOG24bd+69f74msYuPCrbfTLbRho9IMNCAbasloSREIcRLLFUpHFqiJrOKfq1JmHPee85iHuw58RGZk7d9VhAzcONvLs3DmsFRErVvzf//3fh7RMcW9wD5NsohgNUlBytfmmv6CWzzfyfa+Pa61rKqALeQhec9jWXIcGNfIqV58tqe9S5G5SkvjeOB+fWeZhMQsOd+BwRzl6gFFmXAIU04KEBz93TPXPNSz4pg+f+8rezeb2UkBU1RWSIsHe8R4Beo6BpEwwrsYoxJoSnjWNgcEyrMUPo0e91EOeR1IlSOv0zCBVfh7w+RoURV1gmA0xzIZ4OHv4wv1iGiZ8vhDK7Ht9bLgbcIULkQsERYCXipfw6u6rKjBumA1iVMmyvrkFaVRFGMQDjMoRpmyKUTnCqBhhlI8UY2BWzEggtCKLvxcFfwC6Pm1mUznNHJgwman612QEpsZpjDiL8ah+hFKUlLmWdpVrmrQ3lToGcn4/L59jjDEOnh9g09/ErreLhtdQThuWsNR9rhA0L6fllJgsBpW6qGB0DlCM8zHuJffUa88q9QCgdABCHsKqLHT9rqLdt+wWMT7m2ezQDEmnwmmTxoNJLgsym+eZHhpuA5gCGxsb8E98XLl4Bf29Pl65/gq+9/h7aDaauP30NvI8x2QywWAwUHuWOI9RmAWYy7B7bRdXXrmCghdKd0QKkmZGRqyVYmGzOSvPBhQA0DXJqXTHNmh8FZAuBCbpBEeTI9S8Rl7nyOoMcR2fHQiaVJLTdtvkQDFnn3S8DlzhKjp9wGnPN67GFFibBmyTAgKlEp9V4CWHGZkYfzBG63wL1/vXYbgGartWwe26jXCJkjQKRELaHvPxnhZTpdkyLaeI0gjH5bHSL/ksjRKLWarsom22ccm/hJ7fI2aRRewzi88dMWpiawlDYDgbouJkQXk8OMbx7BjRCZXwCE/Q/UXXphCkSWGVFv7iW3+xBIKFZkh6V8LF+Y3zKthu2S0l4iotivXAAVhk51eDqLqucXBwsKQPIDOjeuJOMlUkW0XvcxlUyuDHMAx43MNuYxeWZSHoBWi1Wugd9ZBYCT4++BgfffqRAi1kcPSHf/iHuHXrFqKSyprk2hXVkRKClmCTBOAfpY8wmS7EZc8CnCU40bAaSltEB89kH+vPtd02mk4Ttkkxhx60rcu6y9/1gNGyLIxGI8XCkMCBLkgvmctRFKl1Qy+TkL/r4ykz3rr4n95kMC7jidVkoHyPPF49qy7BPt/31TUmg1gdsNKTma7rqvHU9Tnk62Xwu6rRpx+X/H1d38oEqd4kMzzPczSbTVU+r5cvyM/Vz0NnFa02PXaSTf5uGAbOnTuH8+fPL5Xzrwbk8vym0ykMg9xUdNtYHWRYBblW2Q6TyQT7+/tqvPv9Pm7evLkEcKweswRldBb7uthPNr2fGKMSsFdeeQWXL19eGsfV75BJ87qucevWrbX9eVZ7YaDh6OgIQpAa7HQ6VbU3/bKP/2by35C1ieciR45ZTToAbtclgUJG2e1ILAT19sQeWd2xmCyqwuXvs2qL/Ifhk4WYRfWuvbSHaloRBat24QkPbbuNi95FTKdThcrJJq0x9Q6WC6W+kOiTVSJmqzVWwOLCWIdSyaYvPvJiFkIgSZKlz9MnWZIkSwDEus/TkTSdvrNqZ6KXZOjnB5xWc5XfqV8E+sUgz3n1ffL1eh3UKlVoFRXWP1/+zgWHX/vwCg+BHaAoiyVbUYmaSYS9rkkgpagKReeTjgSxIIX/aTlFbMZI+dxucl5rnyNfr0UAnAIhFKWbrf/7We8XggAKxpjK5nPBlcjYKSCOkWd2IQqVZYQAaqNWIMNZAIUtKBvPwSGq+WbBYMgKAvwqRt8ndSEGbABUoID6LCAyA9gWU0wkGzYF0a4Bt3YRliH6dR9e5YHnHNWMrsWXL7+Mlt0CEwy5yDHBBBODLEhn83/SKSIBCfUlIkHJSgIoTU1L4Kw+Nih7JMsmpIuJDlDYzIbv+ChyupFDABWvUPKSBDs5iah+JpNGHQZl1JlBQpNlXSIqoqVM4+c1zjgsbqHltKhG3/IR2AGySUYAxXzDDwBVXuEoPcLD8qFyGIiqaG3wyxlXYolyU9o0mzjvnEfohypQ9QyP9DkEbVYqVEhFiqimzPW0mGJvtIfCLJAgUfW8eZWfSbt3mAPXcGFyE9yYM8rm4mJlTUr/cRmjSj8nsNXsR1nJYDMbTd4kpwhGPzaz1bUkWRS5ILHMTGQUDBUxsZN+CUCEg6ugWYETgqEsSliOpcQy0yo9s8xKXt/AAqA4a+Nb1iUm9QSTYoKn8VMsOe5uA2DAu+N3FeVY/w6b2QRS8CY6Zgcds4PQCBEaIZzagVM72GE7uGHcUGK3pm2qNVpuJAGQ7R4yzMQMw2pIVyabYSZmiOoI42KMzMgAC6p/kzpBCQI9X6SPOeOqREcHgzgjrZisynAUH+FAHKAUJYb1EHE/xof8Q/zNg79Z+3kN3lAlHjJQCHkIszDVOYdmiA1jg0rf4MKEiZrVYDaVUFS8UvXo02pKzLq541AsYkQF7U/G2Rh7yZ4KSOMqXp/JntPt23YbvYBYLU2TlP3NwsT28Tbi4xhXnat4nj2HF3uIjAgBC5YE2GRjjKFMS4hawEotTG9P0eg2cOvWraUsZpZlmM1mpNpvgIJxyco0atI1yCdIRILKqpCxTAEVuUGlf0mVIC5jVU6QiAQpUro/nlF+JK97zyK3FtOg66YWNU6SExwlR8jKjBgBRYy4itcL5m6TzbXZMMELDruywTIGnnMS/hUuBh8P8BpeUyCQHPuG3UCapJhOp0uBEwBYwkKXd9ERHUX11gXlDMsArOU+z+scPOCoHXKlimuyX57kE9L1ELECMZ7HpJsyyScY5SNk1XomhcMdKsnyumibbVzzr6GKKiTTBHVeYzqegtV0DGVVksYUByUCGk2yI06OlsRHxeHp+cfA4DJXlcsERqB0pGT5jG/4ZPHKqFTKhQuWMty4eAOe6S0l2iTgIJvcb67u+/R5K9+zSmWXSTeZYdZFGyWr4p133sGFCxfAOSUXAyNAbdUqqMlyEg7kNqf9gbc4VlW+VcwUADfOx4hqKvmQj5NiglE8wsAa4Nns2ZJ+xbq1WtoJS+tWCYyqcpo5QCqZatI9qGWTW4dlWXj77bfh+746TmDZqUTul7MsQ6fTUX+T5cty37uaNJTXuL43l02yjY+OjlQpibSzXKc7p5eh6yUUciz12Eaya/TzMAwDd+/eRbPZVN8vy8sty1qy2JRMDT1pqgNhuu3j6vnJvtI1DaQIpQRz6rpWzHM9+y7vfVVVqePR47FVYE4er5oL82OMomgphtTXbvm7DPQvX76s3i8ZKfp56WuPzgTRrzGAQBXTNNV5ye9bBTv0uG2VIbIultTPVX6OaZpoNptK02QVAFl9j66Z+KJM2V9KDFIGpkVRqMmsT2AGBqu20KpbaKGFPvpET2MLPQadPgQAcRLjaHKEg8kBUp6Chxy5mVNAwhLUbo2c5ZiyKY6sI3xafYrIj04HbDFguRZcy1UghFM56BpdNMsmCrdADz04cACTBAFDFiprM2AxwXWdBDkZ9IFbZTroqJZOXZO/S69ZXYtiFTiQC7D8TAnq6BeEfK+0O5HUGv1mKsdKpyjpdV95nqvyBwBqYq6CI/o5SNRLCoas0mr0PloFXvS/66/RKYA6iisXOvkaWY94CgWsgYAFMCui6NsV2SEdHB8opWLZN81mU1GfprMp7NAGDzkeHT5CLGJiSxhE6U+MBKVXYpRTfXFukKNDwV6gzANQpRIA1rMo1r0Xc9rrnBEgxRcNGGA1I9BhDUCRs5wAChDAIEGK0ijPBigAJZJpCUtlswUEmEk1wllFQEXKUkQsoo2Qv/6zZPs2yMNesj4sWMqG1IOHECHaaOM8O6+cJKZHU5iFqUTqTGZCMEGBD58hcRLUfq20QSbFBPDovCMRoWQlcpYrLQN1vjVoZWud7mdDGEqLQhQCFrOo9GAuQFmkBcq8JC96g6EyKlx/+TpG2Qgn8QmGyfDMYFKOg6wXVhoOc6vLcTZefsNniB4yMLLTnLtG2GyeWa5N8IpT7auwYRQGRCEwrac4whHNYUGg7lkBsi3sJaFMDx427A1cci4pamrTasJm9qLECoIABBGrDbh0uZEZZPlz1gbcMix4hkdUdUFCt3KjzTgDsxg5bVTrKcpnNQ5OmifMJm0IUKmAxSyl51IzytLmoEyt/InFShApFfc/o0kLUglQcMYJcJmzNfI6P5M9IcsaJHAiBJWGCKExlbTrtkZNVqNlikE5wMPs4Qv1iXQ6cQ2Xgh7eRc/soe/2KYtntnCRX6Qs6RyUCowAhqCa6H6/T5+jbUwlm2aaT3GcHePx6DFiHmNQDDAux5hW83KIucVlUlJAG5URiqpAKcr14ypdj+Z/atkttO220omR9rFVRaVdUzHFSXpCIEhJWc+zmDAe95QtZdMgxpC0W2xaTew4O8puVLIa2k4boRUq4JxbHHFF835SLrMnpBZGihQnyQn2433cndzFSXyC2UMqf4EcsrnuADKA73A4tQOrb4GdY2QvXdkQsYBZmFSCKhzcY/ew6WwiMAIV3FRFhe3t7aWyUQBL98g4jhXNfVVBXM+MDYdDtU/hnCPJEghHoODFkvPHOBvjaHqEuI7V36Ruxawk0cBxPkZUrHfkAED6RIYNFHTNVoKsmhOeoLaphKfmNWqjxqfGp/je0++tmSoGQitUYqcNq0H2jTJLbS0EYO2ejYYz113gDTSchRCKvrcRgsS7HcdRDBWZkXRdVwWIq7pXtVFjWk5xEp1gEA+QIMHjw8cQrsCnzz4FDzge7D9AKUocV8cYGSMkboLEPbtk0J7Yik2z6W3imnUNDbMB13ThGMQ+k8B0FEe0bppMgbuTcoKj/AgPygeYZsRsWnu/GkJl+fXSGRVcO6SFItk9LbuFXtDDtrGNttNWHyP3wlL8UO694jhWe04ZhElWspy3g8EAjuOg2WyiKIqleu/VTLxkUq/uL7t1V42hDAD1jLQsCw7DcClYLqtSlYmNs7GyE54UlLwqzVLpVYzzMZ7Mnqi/T/LJ2nWMgcFjHliD4dnTZ/hXt/6VOnY9uJWBP2MMo9FIlc2s6sbJ63JdQKcn4uq6VkF3r9dDt9tV67Xsa93xQK4TaZoqXYE8z9V36f2oW5PKvpPl0n/1V38FxpjSvZDxRavVUmyZqqpwcnICy7IUCKXHNbKtHqeeKJXzQsYno9EIm5ub6nP0+EW/riXY4vs+giA405xA/q7HZHoi9R//8R9x7dq1xTgztqRjId+jl6TIubYUH8+PX1+T5RjLvpXfnaYpJpMJms3m0rq+Gk8BxJKRMd7GxsYp0GQdQLgKcKdpiuPj46V7ijwvPWEt522v1zs1/z+rvTDQIG1/zp8/r4QfV0sPZBAsJ+iqgua6uqK6qmFVFrzUU4JUOmWnzdrKpqXRaCBwAzx+8pg2X/MA0QgNIACeDZ4hBpVwJCxBxCNMjAmSOkEUalRrB0Bn3gG1CbdyERoh3dwrqmXtmET/a5ZN0p6AC6/24GAxuXRUR9awyePWL2jZV3p/rU5QHXyQP5xzJUijLzb6d+t9KhcB+Rp9kdLreaRlin5B6sDE6uSSi4aOPOrHKdFY/SYsj0suiDqCKZFPfaLrF+LqTUYuqPLGpdcxrb5PUqvkpkCem/w7A0NgknBWXManFgLTNLGzsYOj+AhHx0eLeVrXKFDA7bho9Bt4cPAApVUqCuu0mmKKKRKWIOMZMk62gTnys3UINBBiSYdCMikE1jpq6O+XAIWkwMqSA0MYVKe/pmSjYAVKlMhYplw/5PdXooLga4IBAVWGoXQoBAVWgglwi6MyKpSM/OszRkKMaxsDMSpWxBiNWiv1qG14wkODNwikqNtopk1su7TRmQwnKptr1zaJVhqA8AWMjoEnkyd4cPwAmZVRuQefu3vwAnDm4nlGRowq1CqrtI7p8ZPnP4HByJkhsBdWi55JmT3Xonppy7AWQVFdqczeKB1hmAwxTM8uQ1iXHS8FOUmsVXMXODPzaMAgIUiDGBNSDFI6kkhXEsMgDZFCFNir9nA3uqvKLz6LRaGopnMK+Y3mDbVhbdktBGZAwfTceUOKWUqBwHE+xsHkAB8NPqJNnV2i4sQgONMCkhGjTZZgSOZQLcgFoxQlCROW41PvP6sZWDhDuAaNocOonMQ1XdIwQE36EMVc/LbOlNPGWW4Qn9Uk+CTnEwQginmZliGWavT1ZoKADcYYrReCPkcK4MnrV/8eCaacqUOxpkkQxf3ERcADtO02+m4fW+4W+m4fLZOyei2rhS1vCzvtHfS8HkIvPBX4rlvXhRDEoon3sB/v4yg7wt/+8G/x6PgRCqtAY7OBV/uvwjRMVRev1+pHVXQmld8xHCrXMWmecxCbSK5v03KKYT5EVhGDLKkIpFjXFGNoDrrJrGbTaqJttxXleifYwYa/gd3eLgIjgCtcNPwGnj59in6/j3d+/g62r2zjB2/9AOeuncO3f/Rt+B0fdx7dQc5zxZqorAqRGyHzM5QmWTYLQ+D98n38+5/++6Vj87mvrkPJZtKPtWGS0GM/7J+6LuW4LJ2rlsm0TRueTZR5ed+Te5SkTfO90+moTbYUqpZjP56OURgFPn36KeACHz/6GMIRGMQDPD1+ilk5w+PDx8iQkVMFSJ+i4hU9mmdnyCxGQIUBg8awLjDIBmodKOpC2QOfNaayr3T6vCxRaDtUAtL1uoo506t71H+8dWpz7lkefNtHx+wg93K4rotRMEK/38dPs5/i6tWreK94D9vb23j8+DEePXqE0WiEe/fuISkIXEmQoLIrCEfADE189be+SmyzuRXptJjiefJ8qQTorPOzDVvNg57Tw5XwCp2bRXa8EhSV+0HJ2srrXLFg9kf7KqCe5tPPLE1oOXRNdL0uPOah63VhFiZaTguucFFMCrhwscf2kHgJyqJU+lUAVN19o9FYChj1AAqAOl7JjtCDQb2t7gvlc3IvrQeSnFMpYs/oLe2dZfbedd2l8uSlLDDIDUXXU5J99sGnH+Dnn/wcFy9dVEGivIbksUvGtCzVlk50MhCW57VOm0EHLmTgJ4TAYDBAGIaqj+Q1Lb9Dxmf6ejwejxEEwdLeXO7f9detZsAluCBLwbe3t5fKaCSYJI+12WyqOEC6iOgxjuybVc0DKWKpi/5LYUmdiSGbnvDUGRo6uLB6L9Kf02Mw+ei6Lt555x1sb28rdw7Zl1KfRJ97cRxjNBotgZKrTITVv+lxnEyuZlm2NJb6ca4yg6SIqGVZyn1oFZBYTfDrzTAMRFGEO3fuoNVqKYaF7D+9T+SxymM4qyRltf1SjAZ5UDLLpdfsrNaD6eiHfN9q04NVmTXXUSq9TkhScoqigMEMysjBB2qgw8nfvJ/0T9F+zp8/j7IsMRgO4LZcUoDPBpgUEwhPIGaUmROeQAwSzkudFPf5fcSIF+CE1p+2ZcOpHQIgaheWacGpHIQG1bs7lYPIJIpkXudoOa1TopPrJtpq38mLdtVaSf+/vijIDQEApbSqK7yu63v5f32BWWUQyM/X7W1kW63jWl2gdMaGvojKY9f/r/9NnqNe46XXc+kAhD735IUsF7rPmnvyGGXTb1S6y4gCSgRHE03smDvIi5wyDDVTdkbSP1leD7LOL8mJsjpMhzACg6jpPAdccg4onRIzMSMmj4jJhkqkKNkZkeQKS0JALHQg5PNnMSjm75cARc1q0pOQGhSwUNYlMSj0z2BQIAIH0cyZYBBcUCaKnV36YMFSZRhSYLMWpDMgnUgqViktipzl5Kig88sNAO35Z2fz/2vNEAaVWzEXYR7C4hYqr4JTOQiKAO2kDasi27hLm5dwafMSHt55iJ5PQVJZE4vlwfEDHGaHQBMo3RKJkeD133gdg3SAcTrGJJsgKiKM0hEOqgOqzf2c7LvMYvuWD8d04JkepsMpWkFLWRy6JpUiyPEsqoJKHAoqoZhVZ4A2WIAU8jgEyHElr3LE1XqV9bOOU1L1Jd3dYXS8nkmbVZcvjrMWNZ5GT3F7dFvRic/aBAdmQDTTeZBmFibcwgWfUGa33+jjD3/rD+GaLjjjdC4MlMmfl0Pp+hPStm1aTomufYYgnMUsJTIphSylUGYlKpR1SZlAsT5LdVYzmYmAByS0yByEbrhwhJgzFkpBeh0yqx+XRM8vRUnB8qpoqThbK6IEXXtMMKrLlgjkHHQ4K8PmME0wEAvtGXn+EqiQTZanFGWBaTnFfraPO9M7n9sfioHDab40rSY23A1sepvY9rax5c+BijlboeN0cLlxGa7p4uhbRzA/ofvU7/3e7+H3X/79xfGIBZ1bOh2kdaqCMFmTLynmqqY7HSs3DPncrDzbucGAQfOcz4VPDUtdV0mVIE5iPI4ek7XuHGw6a657JgGkHbcDXnDsDHcQjSNc3buKI3aEHbYDp3AQpiE6cQf5KIcLF1ZtYTKeULBjWzBsA7d+5Ra+/GtfVsc/q8jdI0WKWT1Tgoj7yT4m5YJhcBZQGJqhAiMaVoNKTjhltqULSc/voet3l/QCXO4uJUv04EBPQHDGEbohLjUvIQxDuEMXrVYLg8EAYzFGFEX46PFHEELg8ePHmM1mMAxDibmZlgk4wObFTXzjj76xGD/p5FKTGOy0nFJGej62URmR28QZ+iCy5XWOcUHzgiVMAX+loJKvs0R3TWYqoVdZz9/1qI9Ck0oTNhubsCsbu9UuDuoDdKsuuMPRaDQUFVol5gSHVVhAttAs8BMff7r1p2r/Ivdh+p4qyzLABIG26UiNvxTKlOV2cR0jqiM8j58v1spieuY66RiOct3YcDdwtXmVQBmnoa4Hk5uLTHpNZXizdIZUpDiaHeFweEiaQfPxUoBgAODL2jws5xaupYWH9x+if9RfYtJJMKhlt5YEINtGWwH5cu7ptHP5nL5m6PvF1Uzvuv/rwbz+uXpjYHSMVgO7/u7S574Sv4LyByVe/uLLKkAGsBSXyKy3Pr5xHC8dqy4Cqccz+n4aWOxfPc9TzGmdNSw/Tz8f2T+NRkOxn/U9uB6TyRhiNTsu97a6/aZkRTDGlDWkLiraaDTQbDbVuUvgQMZ88lj1oBuASorqf5Pxob4/l02PYyRYpQfEOqikx0HrmrSo7fV6S5+lAyVynIUQ2NjYwMWLF0/FHuviDb0/9fVUgg36+/XSETkm8jjk3+T1oIMKq+e2Lt6Rc+Dw8BA7Ozun+lmPn3Sh0nVx5VnthYEGuRiuojM6giR/JCigXwz6pNebXkqgUzT198pJLE9UBX58YXGjuz7oHWOaJtWoMgOOcNAoGzBzE820CQ+eOtam2VyqkXJdl4IrXqJ2akzrKbkpiBgnyQlSI1UlHjM+w7F1jIxnC8E+2Xogwb/EgdNy4AkS9/FqoixvlpswKxOlW8ITHjkK1C4cOEqoRQck9EVj9aL8rMHXhSZXaVg6SiqbPlHX2Y+uXjDrwBM5b1bHXl9E9OOT80ch7tpz+mKiI8Krn6mzJeQc1Es95LGuItX6AixfL9VY9QydbdtLN5BVVFSyRZYWdZADiyEMuKWrjs+Dh6qq0ORkERRaIWVmigKTyQTTGW2Ya6dGYRbKUjJlKYQrwHyG1CINlEk5QWkRg2KtE8Nq00AKOV8VUPFZLAr5OgZV4sEEU8GhzK7qTYovpqDrUFqFVpyAjrNKUnjNF64KgqMua9qIMiCvcgI+UCqQIuNUfz4WY1rZusDSoczP+U28SaKZF+anK2htsF0b3OHgOSd2U+3Ayz1848o30PE66LidU4+O6aCoCozSEfZme3g8foxnk2fYm+3hYHaA4/gYJ8kJRukIk2yCWU7WiDM+wzR7sRIBmXmXwnySpWBxKqmQLJb5gBJbpSbl9Gk5PdPSjbqEqeAWIKp+JqjOWkk0vIB7rQEDnHEVqEmbS9ckhXyPE/uDgaEUJQb5AAN3gNSljOan5qd486M3T30uZ5yo/VaD6M/zAGnD3lB1yA2zQSAWDDg2scpKUSKuqN5e6VFIkThNLC6rs7VZOwPkvOEYFKhDYAFSzIVaS1EiLVK6Hl7QUtqAsWQDymuO6XAKlEQn77a7aDVbyCpy8oirWLlPFCgU60jXz5DjzxhbAg4EBFKRQpfakOO9yoDQmwlTWWgq0EfMnV7myvwSqNDBrVJQBioqIxynx7g/vf9CfcJ6DPgdYjR9ZHyEP3/rz9Gze+g7ffTsHs4H59GzewhYoGrfu7yLvt0/tQmX+4I4jlXZon6/K0WJg/EBDN9Q9P+4Jgvnab4oh5Bic5JFIQPeM9kUnNgUtkGaNqKiUqOszDAdU5D3yd4nmPEZ8mlOuhyrTQBmQQK5MhDbM/bweO+xYik0rAa2rC1VFy7r700sVMvzIscoGal5n/Nc1a0nIlHPT4oJjqtjPCgeqNr1s1g6tmEj5CFCM0TX7SqKfdtpo+t3leOOJzz0G31UUYVz1jmkRYoWWqfs4PS9wtI9uKbSETu2cdG+COYskhxZlqnst56l1LWqirJQ4Owko7HLWKZ0KKIqQlzFCrSRQbEELWbVepCmFCUu+hfxxd4X1frxbPYMHw0+wigdYZSNTjPPfjGfGx87cAT9mC0T9c1a6VEgIbF0u7bhGz5+fvJz9Bt9pQVg1ubSXqooChiVAbdysWPuoFf3AA7Udo3KW2R+XdddKhWQ9fGFKDCIByjMQoHDOkg3KSYKvHkWPcNsMlNZ+7NK4lzuqrKLjtXB1eZV2LUNozRgwsTxwTEGJwOVAWacQRgCtVHD7/pIygSHxaEC1KfF2UwKyeYJTZqLsuyrYS/0ipTQo0Xg2ZazBcd01ma09SYTqPLv+uNq3LOaGJSf53mkJj2bzdQeUz7K+Enua/XAUGeDy2PRAQd9/7+6T5djLPeVcg8tP0vfR68mFPVgWw+O5e+rZQuySWBDlnzorAO9xEWer2Qay3hG9rNkXOjjoscx6xKMq7py8pj1Y5evXxWqlH2rswj0cdYbY0yBhFJzZLXP9CSxHD95PutiJjnOUjNCPyY9wSv7fRUQWe0nnXmun4/eP+sAMz2mAQhQefLkydLn6Ulneewy1l7HKPms9sJAg6Sj61lteaHov8umI2+rF/Vq0Cc/czWLLv+vB9erF5D+ebraqo4Gyvov/ThWJ4J8nTx2xpiiMnGDoy3a6jNPohMYhqEcMKTokGVZMLhBgjahgZSlOJgewO26SFiCk+wECSMHhZk5wxE7wt3yLinot05PdGfqwPEc+MJHIAJYLikdCyYUcyL1UhJQqwnE0NVqdcaJ7Is0TZEkyVJ/SoAFWNR+6RNcV3ZdFbFax1TQmwSJdKRY12DQLwJ90dXnhz5eq0CLvhBJOs9qfZQ8Dv39q/V8Onglv0sCGfpFqy90ej/omwG9VEY2WaMmXysFanSAQ5+bRVFA1IJEswoTKBY3k7omi5kO78DnPhhj2DvYw87ODlzXxaf3PgUsEDuAk1tFbubwuz5ynuP5+LkSaY2NGImRkFAm/4zs/BqtCV2P4kWadP/QywQMMV+4mTgFOFRGtbAaZQZgzhdQCFTGGmHNeTNhwqxNGKWhHEDoFIh5wSzSXsjrXOlfJIz0OWCCMjAC6n3/+lv/+szztLlNGyy3gw1/Q224O24H2+E2Xum/shag+J//3/8zfuPXfgPgoJIKV2BYD3GSn2BQDDDIB2RvWVLNaFySuFpWZZiVs7Pr3VeapMJbzFI/MkNlG7ai00pdiaIulENDVEafCVqtMilKUSpnhM/TOQBACuyadoYMbh3DUYG4Y5D1qGmYEBBIqgSjYqTcF6bl9Oz6fGMuisYbygqtwzs4b51H2CAhxabVhGd7BJgZC9vOWMQqOyidDGSJ1KycfSaLQjI/LMNSYplCUCBe1URXjst4Ueur6Z+M6hEwWt9dchyVXeV8HC22yL5LplCGTIEUuYaC6ACDBIfkGEqQokRJ2hm/RJMAirTQNNhibkjmiMw4nwIpmABMWh9GGGEUjXA3uvuZ38fAlHOKb/hE4ba72HQ20bW7aHOyGmxwqtf3mU8Ub9NDy2zBho0NvgEjME7dL+QmT24cJWDNGENUREiQKIAiqiICfh2Bk5gAxVk5w8H4ABnLcFQcESNCxLT+nyHsy2sOXnOgpv4orAKFXeA5f47Dk0NVFnDWXLeZrdT1Qx6qeR+aZOsZmvTcFt+CCxLZlpoGJqdgttlsokJFwEtF4qAyYz4tpziakUZDbhLV/vHsMT4YfqBq2c9iePjcpzEwyP689Es4tYPqXIVqVsGubSAGRCKUy4KXeoiTGAYzFKNT3i9XN9RS2E4GMo7loNfuwTRNjEYjxTDUyymBRVAymUzQaCz0G2Y5BdeJSJCxjBiGSHG9cx1f6HxBBe6+76ugLo5jMIvh/vP74CHH+3ffh9/z8cGnH8Bu2fjw/ocYZ2McF8eYsAliN6Yx7hYorVKVV/70k58u9Z1ruMuBtUkisIqZMg+qO26Hyj38RZkPE0yBbTJoNGFit7Wr9tqybn6Vni0zzZIZyxgDdzgKs1BlgLNyhv3xPjKW4dngGVKkZHFpVNiP9xWYOzWnKLfXryeH+SFaoPnZsTu4aF1EyENi0M3ZYVI4sxYLweGsopLVST7BYXqoBB6n5WmQ4t995d/hcuPyqeBK7ulkH8g9n2KY+P7aDLR8XA10GVtk8mezmdJikHGK6kcNKJABrCwTkJ8tf1Y1GvQk7mqWfzUeYowtsRF05rlkH+glDnq/6P2zLmkoS8WlA6A8FsdxlsqV9b3+bDZTbIbV/pe/r9vvS8aTBIJW9+TyeFbZHvI85PqwmjjUx3NdkwlnubboThoy6Nbfr8fC8nc9ZpD7d/l+fax1kEGOu3yPnhTVY2CdFSLHUWfA68nOdclhHSRoNptI03Rpjq7G7HqspQNvL9JeGGiwbVtpJei1KfLk9A6VHSlv1PrFuXqyZVmqALEsS0UhkuI7wALVsW0bWZYtDbAcJB3Nk49y8snFQy4k8nf9ZqUPpjx++R59EugXs142oAMbAQ/QRBOiFuARx9Wtq+SxnY2XvtO2bfT7fezt72FvsIfczJEalLHOrRzN7SYpwlsFUpZiwiZI7ASxEyPn+dpMsN2gso6ABfAElZfIfwECcIsDBSlIVxU5BsiJsw6d0tkmMiDW+06n/OgLpP6Z+oWtPy+pXqsZCr3GSL9oddqOvlitAlyyvk5HAXWkWKdqye9cZVjox7Q6h3XRIn1O68eug29CCFVSIcEsnXmi26LqAI/8PHkcukqvfoOQc1yep2VSgOMxD6jpM3nNsct34TgOnmRPTt10GWPo9/sQhsAvPvkFwn6IwiwwqynLMYgHZCfHM8QGOXpIPYqSl2fbbq5pCiB4wdcre1JQQCqp40xQhnn1OihRojRKMJOp18mgpmb1og5+ZbpzwcEKBuSgzc38ZVevXqUAukwQFzGSMlG2kXmV4zA6xGF0iI9PPoZpmEqsS26M1iqvt4F/e+ffUoaGN1TWXgYKoRliy9+icqy5crjHPLVhN2EiyRMUJgXFw3yIo/yIhPnqed1oNVEZvLSe6wuAnG9eFCDi4Ev2kYpZYVhwTAcmJ0tWISiDm1YpWe4VM6T12SAFA2Uv578Q2DPPpk6r6YsBFfPPMWEujnFeKiFLPVjNgJrU3Y/KIzwVTynwq2LE9Xo3AelyoGwPzRAN3sCGtaFqk9t2G77pk1hrXaO70QUYVCZcalFM8oXd4rigmt60TNfPCUHrssfJg1vZgWplHpWoKLDDi2tRSGFIyTSxjblGRz0H/OaHUqFCLnICmuoEGdYDKRx8YY0o9SJQKxbNL9NMZoKVDKIUMGoDgRfAczwqx6prFFVBri9iDlTMdTxq1FQeVOaYYoqD/IBYSi/QFxLM8rhH4o92G1veFjbdTWw4G+jYHUWRb9kttMwWHO4AAJzSQb/ZX7pvOY6DXq+nbM1M08TR0RG63S7u3LmD8+fP4zvf+Q5e+y9ew998928QboT42Yc/o1KOudOLsAVyniOpqWa/tEivwLDJrSiu4jOzvFKYtEKlNB+EEKo8rhTEcDrrmg95uOTm0bSay1aAZgN9u4/tcBsdr4PzvfOkY2AHsG0bYRhSwCBy7I32kCDBg70HqOyKNBo8geeD5xhmQ0yyCfbZPmbmDFEzQtYiV6h194L/+O5/JMDEWmSrm3ZTrY/yGpXaXr5BYJJpmHjw4AG2trbQ7/dhmiaiKEKWZUt7Ej1JoN/jAyuAa7gq2JZJJL2GfvU+LYSAKUz0nB42WhsowxJXzl1B66CFyxcv4/LBZUSIcP/Zfezv7yNNU6VozzkHsxmYz/BH/+yPAA8K4JF6DTJoj0WMZ9kzTGYL5sFZ7kCe4SlAQndMCHhAApo2sXpbLq1nsgwktMKlPZ7cm9iGjY2QyqFk0DedTtFqtbC/v492u40333wTt27dwrNnz1BVFSaTCX7605/i/uP7eLj/EPCAyq5Q28TSvPTSJWzvbmOUj5SL2H62j2k0VSVyn6VJEfIQN8Ob+N+//L8jiiIwxpAhUyKtg3iAHX9nSYdEjyv0RM86Bq0+L2TT+0Z/ToI6MiGrM6xWs95yj7jqLmFZ1lIyVTFR5slSff+pJwlX99Hy/PR6fvmcPFa579TZucDynlA+riZ8HcdBEARLMZNkLQghEIbh0h5Vvk/ug/VYQS+P1o9LHpMOdumsD3ktr7Lg5XP62OnWuDorYd1Y6kCLBEbiOIbjLGKlJEmWAno9MSljYtnfsg/kMa2WZUsQcJU9oAM9q8eqJ5L12EVPoK6O52qcpJ+rFASVQIWeoNbj5VUW+aoF6VnthYEGudhKEEB+kY4C6guvRN30AH01kNUnoQ4M6Be2Pomkcq0e1Mr3SXtIfXDkRb6qK6AHzXIAdcRwlf2g03AkMKJrH+gXlES95OslQCIdG1YnuBACBjPQtJuqTxgYLGHhenAdn+59qtTAx+Mx0jQlBoVlIhEJnLaD0i6Rm2QrGtUROSdYJRKWYGyMcWAcIEFCmhPSy1lrVttSjAinoseO2UGAABa34MPHrJphJEYk3Fl54KBxkTSpoiiWFlF5YekAgn5B67V1+tjoQADnXCln6wu/vlDJ+SPBHr2EZ51QibyoZcCvj8nqAquLD8ljXxVyWX2P/lp9futZA/nZ+o1I9okO1OmftUov0+e/fJSZOXk8qwCFfK1c7PQmX+uYDrzaQ0/0YAoTVV0hSiOcDE/URkuek7yxWJaFmy/fRMnJ+zoGBXEJo0yedCiYYUZ2l4JU2pN6rr59RpZvcTKLxxorDhOf0QQTahPGMAcOBIlaKqcKrVWsIstFEypABYAnkycoqrPrfx3uILRJdMvmthLtE4JqWdMyRVzEmBUzlPViYS5QYFyNyV+emeAZV24Esm74rODCYhbVVs9rWlUZgUVBwRXzConTwYXPfFilBbM00XE78G0fSUEBToqUAmAxxqgaYVgMMa7GGOUjVeub1inSOsW0nL4wkwKYO5BIrYd5gCsz3mmcYjKeoCxKMIPB9V3YoY24oCzxmTa0888FFtn5EpTlSqDRvl+wlIGDL5ge3FblEq6x0IqYllOc5CdIK5qza10OPqYHqUUhtQiadhPb/jZear+k1NybdhMWI7vHR48f4bvf+y7iIoZwBK5/6TqcpqPq7Ze0KOoMmVgf/NuGTeV2jJ9iE8hgU4of/jIsJNk/NieQQgJOJiOxT2nbm4sFyHQW2GAyU9lcMrZgUWRVhtqqyUUGOYbZcPmNK3GUBAtkKZEUypTHI9fJrMqQllTaUrFFyUchChRVgVk1wxGOgAT4PNxGMYMMC4EZILQoA9t3+zgXnsOl7iVsepvw4aMf9lElFdzSVfsgyyJgrm210bf76KU9hFmIPM9Vpo8xhtmMtFjkvfOLX/wi/uAP/gBVVWEwG6jSjsIsMC2ISXASnSjni9IsSb9hDmJISvxZY87AUIiC9g1ZgsP8EACUjocEd9Y1i1kkCOuS5kbH7VBphdcFLzi2W9twTAfnwnPYyXfgBA4QA/ef3wcTDAcHBxgMBmAGwySb0PXrAsxn4CHHa7/2GpV91ORyE9URhvkQD/OHC82NM0o9ODia+82FGOY8yPaZr+wgJVjBMoYL9gUFqtiWfSpIWs2O6gkQfX8oA4aiKJZs4OT+TwbDwPI9nZUMfulj29hGL+ypbLjMrts2uWpNp1NMJhOUZQnXddFsNiEssSgFqeZ2pUiUm4JkH8yyGfbjfeWwcFaJghTMbtkt5cjStJvoul1shBukVeG0sOFvwKosnM/PI5tlMAMTYSNc0geTezGHO7Bzm1yTUq72Z7dmt/CnV/9UBTD6/q2qKmRZhjiPMcpGak4XvEAiEjwbPMPTk6fYMreUe0WSJJjNZrCFjU1nE+eMc+R0AiztGfU9pxxnGeSvahLI13xWk3NAJmR1i0V9r6bvO/Xj0PeS0tGvqipEUbSUiJUJOunuILUSANKwkeLxq3NM71v9u/QYTo6Xfq7rwAe5ngVBoOa1PK88zzGZTBTbR/ZDFEUqCShjBnnNrO7/DcOA7/twXRdxHCuLSn0/v5oo05/Tj1nu91fPeRU80t+j/7iuq6w09e+RYrg6W15PgOqxrh4PSPBCjocOTOnMcvk98j0SwNZjEmABAuoAwWoSVf//ur8JIRTbQwelVsGK1VhuFdj6rPbCQINEYCSCr1+wcpLqJ6IfgD65dURYpwTpJ64HqqsTQg6m3ikS6dKFSfRgLMuyJcqNPiH14xBCKIsj/XWrlHcJJugTQafu6E1nVei2mfoAyj7QAY9VlVgdAQXmG5/CRLNsUkZPWEuMDRkUMkZ0rqoi4a9ZMcMgGyDnOUqblNpH+Qi5mStGxZRPMWBk15RYCWWMZwCai/OyaxuucBEgQCtqgZscvMlhl/S8W7sIWACXueAVp6BpfnyyH/X+chxH9YFOT5LnIgN1ScvSLygdbZPUMn3uyab38yqKKueDHojrFCf5Pp3NIJuO9q5uTOTf9SbngWR0yL7QFw0533Q0WP6/KIql45fHrV8XOtoowS795qazdHSARr5Xvynr36Ofk36eTDCi7rKQ5prOWmDLi5bs2729PSo14rXys5c6FFEdgfkMwhFIeEJZ+mqC0iZHi1ycQUNWB4jl79c22mfZPqpmzOncc8u9qqqoxMDkRH1fARyyKkOWZOApJ3HHOV2+FvVnAhQcnOwe58EbZ3MUX9LN55nItE5PbQgLUWBUjjCrZpSdZobqDxlMnVVPbsEi3/W593rAA5UlDHmIK84VeA6psAc8QL/RVzaIDndQsILU/22iWZ+kJzhID3CSnWCQDTDMhhgXY0xzqn1OygTTYoqiXinNCRb/nWF2yu5TlXzMmRQWs1T2VtcaKAT1cVxSMHJWhg/Akh6FzPhWoiItgzPMYc5qFqikwzIs+JZPmhTcVYJlURlhlI/IfaSkMoZZsSbou07AllVaGKQDdFlXZXE3nU00rAYCTgw113DJGcClrP/J8AQHowNEdUQaLTxXdfhRHalMaFREa4NNpRkx16IQtYCo50Kxc7CrEpUS4nzRxsBo3ObgjQSYTGaS5fW8zCOrMxylR8hFjspaP26ccdJpYcuiv5WoiAEwZzh8JoAilsECxeoQBixuqWMSECq4loCEvJbk73EV4yg7wgM8eKG+MJkJLjiCHwVggqE1aCHfzOEVHuzUBlwgQACnIr0XsyDLZs458jxXextLWDgXkFXPwcEBnJmDG50baF1sKYu52WyG8XiM0WiEdruNfr+P2WyGg+MD1FYNr+uBeQwpSxVQMUgGyI0cs3KGUTpSAaqko1dltTYgLUSBYTZEVEY4iA/IZUYIVYIlxX5Ptc7cYjm0wM+TNTXLGFjGYJc2HEFC2yEjtknDbKDrd7ERbMA3faXFxTmH5Vg4mh3hzsM7OIqOUNtky1zbNcb5GClLYQQGxtkYj9PHS+UgS5lybSg97i2cKebgbdtpYyPYWLJ97Ad9ZYvaMBtoe20Ai025DNzkPVvPlMt5LPeHMskwmUwQhmStmqYpOp0ObNvG8+fPMRwOYds2ut0uOp0OyrLEZDLBweMDHBwcoNVqIQxDNHkTT548wZXmFWxubmLn6g6CIFABTRRFCMMQnudhGA/x9OQpaXkYudKtUJodSBQz61n0DNMDsrCdZOvFcw0YaP5szpSwW3BqB4VVoOyXiHkMqyRHKbuicX6UPcJ+sk8m2NxTgZsEGfI8hwGDtFpa52HbNo6Pj/Hs2TNsG9v445f/GN1uF1VVIU1TFRjGcYwoilQwqCcE9f6XYyPjA110T9+n63vD1WBN/5scTwky6X/XASlgmQ0sLQqzLMP+/j6SJFH7Xc/zEIahAhEsy4LnecjzXJ1jGIZwXRftdhtZlmE6napyA7n/Ww225Z5PP87VhNvqHlMPMH3fx2QyUeckY0PPW4yj1CKQ45AkiWIGSEMBPfnmui4ODw/x/PlznDt3DltbW+h0OphMJoii6BTAIGMJPUmtB/v6nnb1nNZdj/JY9T21BFT0v68yD+SPnGt6/OA4DlzXXTI6kKwN+Vrd6UTGfkmSKA2MVQBCP075ev1vq+O6CrLp/SDHQmfiyOd18FQmM+UxnxUPrWsvDDRIGpksXVideHowpF9wOrigT2DZEatUH/m4mhWWE3b15PT6SYlIyqaLkuhCLKtUEPm77DwJnsgAVx9UfdGS3ysXBB0okEi3vljIc5ef53keGGMIw/AULU8iZquZZ12DAVimBOlBsr64KRoOGHzDR13VlBGqiWkxm82WAlXbttFsNsE5x3Q2hd204W/4OJgeUIYaMSKDbkKpQRT6CZ8g8um5BMmCzi7v5T3AEXOLPcuG5VtK+NKtXTTNJlmDGUQjt+q5r7h20etIm5yTqywY/QKRC4LUAJFzizG2hATrF+K/q/8dilkBp+HADamW1YNHmRAEqOwKbuFixmewub0Q4cNpJVYdLNL/JhFKiUDKWkDJiFnVK5GAi5y/8oajf6f+fZLupd8QdRBCoqL6YqkvIvJ75DUjrzkJ8qybX/KGrS/osunHod8oyrIEq+ZlNPN/DTTU+DWrJkIRKkG30WiE7e1tNMIGZjPSKZCgRGZkSESCO+wOpsYUdV0jL3MSiURGGhSsQMGKM8UnV8EJGZOeVYu/2ipRISkpwyYzywYz4JouCQjWy2yAChVm9YwCoNpUQIOqlT9DAZ2DKztGKdjHwJSLQCEKGMJAUiVrN4M1q5EhQ1URDZ+BEVVdkB1qXudnBusWs1QQ3HaJbquo1k4Lr/ivLNGuZTaxZbdgMYsEGvMxvvX338IPf/pDjLIRKq/C1vUthNshKcLPN7lxFSOpyUrylwlygYXFpywZkEwT6SagNGdQKg/6FKcBHb2tOkIUIIAjqX/545PHZtQGyqQEairdMWEqIcg9saccDuIqPnM+eAa5PDTRhJM7MDIDPOfYMDfwavNV9No9DJ4NMJvMYHELW1tb8HwPYTtEaZUUSBQTRdUeZWTHOikmyES2pPGgN8dwyC7VsNX9UAIAZV2iEAXVUp8Bciw6Y/lXWZohwQDJipGuKBIMkCBcLs6wMwQBFLoArWmaKGsa80TMQYryc0AKzNk5zELH6uDlxst0rTK6hmujRs1rTFKyBEyqBFGxcEGQNrVZTuvISXkCNLQPP7fmC+caMd/D9/B//OL/UDoUTunAzmxseVu40ruC1E/x8PAhnt97joOHB0jGCWzLxoULF9Dv9zEcDvHpp5/i448/RhAECMMQvu+j2Wzi1VdfxdcufQ2dTmcJ8AbIqz6KIgXapyLFMBkiYxmJECNVLhApIwbFJJ9gmAwxLaY4iU8QVRGG6XDt+lmwAoVVgHEaU4SL60ravH5w8MHaMQ1NAiACHsDIDDiVg7bbxlZ3i8oBUg/bLbJBfvzxY/TNPr7y8lcQeIHKFFuWRdfYcA+PDh8h5zmJRooY04pEmCVYNytmOMqO8N7oPSUQehYToGGRXoJVWeg96IFlDL1BD9N8ioAHmHRJtLm2avjMR1EX4BmVpwFQAWqapmi1WoiiCLdv30YYhrh69SoYY3jy5Al+8IMfYG9vD7u7uzg4OEAYhqpEBAB+/OMfo65rbG9vK/vRGzdu4ObNm8oCL0kShFaIV8+9iuFwqAI2uecNggCNRmNp39BsNukezoBZMcPz4XNUVoWPH38MIzDw8aOPwXyGT599itIqcTQ9wsSYYOyNEW1HqiRItrfxNv6vH/9fNLZS+HderiYBcCk6auQG6riGmZu4fv46+mEfo3KE+CBGt0Hgy3A4VGCD7/uIogij0WiplEHf98h5L/fNq6xN4HRGGlim38vP0IMvvd5dvh5Y1qTTA9nxeIwnT54gz3NVipVldI8eDAY4OTmBEAKdTgf3799X4EK321X7NilaaBhkmVlVFcbjsdJRkAk6mcjTwYbVjLVsq8lT/TWNRgOHh4cEBs1jHwmCyL2iTCDKvknTFKPRCK1WS323fP3jx4+VTefGxgaqqsLh4aFi7jSbTUynU0ynU3WeOliiCxnKRz1+05Nsq0kzPX7QgQcZK0ynU8VI0ffpevI3y7IlYUzTNNFqtRRokCSJAo1c110qBwegEvjy86RLR1EUS8CRPmarOhurSU79HHUGzSpgJtkmemyxOifWsS1WiQBntRcGGnq9HhzHUdQ+PXsuD1w+yuysHgDKg9UPVB6s/igDllWNBnnDk3VBstP051ep4zJYX0WsgMVCobMFZAAon9PrUeTr9SBLHocURpE3MV3HQB/cVeGV1WBS7yN9sdODQT3gXhXwOWsR1RG9VRBHDxhXkV7DoEDJZz76Rh/Il+1NGCO2ROiFmEwmCvSoRY1EJEhZCrNl4mB6gFE+Qu3UpO5dTTERE8ysGU74CTKeITfO0JyobUK7hYeABcpS1GdkFcgdjiYnkKJmNRhfHs91Wh+MkZ3QdDpV81W2i95FRDzCqB5hKIZ4juck4ImUsvSSqdmmBy44XMuFEzhwOg6c2oFTOXBrEnGyTAtmbsIDlaW4woXIlq2A9MVfnyNyHuglPjp4pS+y+pxqt9tLCLscazlf9HIU/XqRoIwO+OnsE70sSL8Rr0P6V0HEz5p7+rzTS2pWbwa6sJBE8gHArVyqz2Vd3PXuYmgNEYkImXkGzVyQcFqd1rBBlHBDGKirGnEcIy9yMJuhNmsUvEDYDREV0Zmq2+uarKt/kaay8nOKshT2M2ES6LAielmhok1wFSm7UG5w5Yhwypp03jg4PNNTwbd0UyjKQrENVh0N9CaDYyEE4ipGnuQ4TA8JgK0LpdFwFoPDNmxFZa6SCtF2BKRz1XXTxkXnIkJ/LnSm1WL7hg+zNpWLRAIqDTuJT3D/4D72ZntIeYrKrZDzXCnOKzp/NXthzQd1rnM6vmRUWIyy3hxa5ltQsHsW60S2VYACIOFMJbjoLl4blRE+j3DDxEII0TEcMMFQJAX2hns0b2wTggukeQoxEshGGc2HlvwS+mEHDK5wiRY9t+pr2k3cbNxEd7MLRziIT2K0vJZyteAmB+MMcU3lUbEg4cAEVJYha63H+RhJsR7oMmDAN304Bm3qZ5MZqpqcbCzXAgygrMm94rOAn3VNzlEuCKyQ4FKRzwW9YIKbXJXBrGuS5SHLMQAAAkqH4vb0thrzs0DIhtnAlr8FV7joeT3kkxyb3U3sP9+H53l4vv8cwhQEJImESkfMEoVRoDZqJY5bshKTivRW9ov9+QQAsX+eaV/YgrIQNEBq/6IQCGYBWJvBet3CbnMXLdYCpkB6mOLokyP0n/dx7dw1vHT5JWxubqrkS7vdVjXghmHA5z5Mm9b/drOtghvf99X9RgZYjuPg4OAAzWYTH330Ebr9Lu48vINJMcHJ7AS3H9xGZmTYG+5hmA5R2zUJFxs5uSdYpFHBfEblddocqFBhXI4xLse07mG+14oFqlm1sFk+0PrmBMA/kP5Jw2qg43SIrcAbyj3DqR1KKBg+LjuX4TmUkW+7bQRmAN/30e12KcCoK7ofGBlG2YhEQLMRuU+k5Pbx8PAhaqvGQXyA4+gYxzhGWqVIN1Jg4/R8YTWDXdl4e/I2ep/20HJaKB+VsCsbFzYuoGN38J13v4Onnz5FOkxRRzV++1d/Gy9ffhn/5J/8E6Rpir//+7/HhQsX8ODBA1y/fh2tVgv9fh8PHz7EwcEBptMp3nnnHbTbbVy5cgW7u7tqz7axsYEsy1RQLgUSG43GqcAMALjB0fEIUAmCAJ20g83NTVzPruPy5ct4r34P58+fx7179/DJ7BM8O3mGjz4iW1PDNKhExmMINgL83h//Hq0ZcxbcOF+AzZNqgofjhyS+KuJF6diny/3Hwcmq1QyxEWwoBl7TJitST3jgBYcHD02HbJbl32WpjGyr+3z53OpeW39e9o+kt8dxrP6u771lcJnnuQJxPv74Y9i2jcuXLwMA3nnnHbz55pt4+vQpbty4ga9+9asYDAaI4xhFUeCv/uqvcP36dWxsbOCDDz7AwcEBrl27hldeeQUbGxvKmaWqKmxtbWEwGGA4HKrYRgIOeqJIH+PVAHI1OJW/B0GAJEkUI0Tfq0rWrUz2SgBCAmKWZcGyLPi+j9FoRCVUjOHatWsAgAcPHoAxhvPnz6OuawyHQ7TbbQU4jMdjFYvqegcy2aYDQXrsprOZV8dnNdMv98SNRgOTyWRpv7uuzFkylRuNBsKQYqLj42P0ej1sb28rTRYpFJqmKabTKV599VWcnJxgNBqh0+mcSqBaloVms7nk3KHHlbrbhdTI+CwAYHVfLX/3PA/T6RS9Xo+uKS2+1D979Tp5kfbCQMN0OoVt22pSySYDH13ERE46OWhnXZSrwclqxlVOSLnolWWJwWCw1MmSLnRycrKkXgoArVZrif0gj3U1iFu9yGSAJAM/vVNl8LoKmugL0Kq/q6S46WiXzCA7joPhcLhWcVaWcehMCXncOiCxCmroIIp+EemfoQM8AJbUYHXaHwBF4ZSWPfpxSsRyNpshjuOlcbcMCzvhDsIyRJqn8LinbKokQis/v9VuIUVKwpcG1YOnRooYMQqbrB1znmNkjBAbMWJGgoTCWaFibwFWZZFFaEWbh5bVQshC2NyG5VoYJkPwgoIsp3Lgli4MQef8u+x3YZs2joZHCgkGKEgojRJhP0RqpHg2fEYbIZaoTGCMGJmRYWbNkPJUbZ7WNauyVImJDxL7c4WLRt2AyUzkdk610LkJn/kwagMWrKXFUM49neWSpikGg8HSdSDnvO/7EEKc+rt8jU6LkmO8urDoDAYdrFgFrYBliuAq2qov2vo56TcIHYCU14D8mwQ59cUWAH6j/A38V/3/CiejE9x/eB+FWVBZEM/VY7ARwAgMPE2eonIqlLwkho6RIG2nSgFctkE6gGmY2Aw20XE7aDgNBFZAQbtWKlFWJaI8IuG/bIxJOkGUU+nALxMwLb1Wv1+Ild/nIorSKUB3cBBCnBLorEAUeGAuIGeYKmgu6/LMjLkBgyj7cxHIpRr7uqbscJWcCcQ4nJhMDndgGRYqQUBJ7MWo3AqCC4wwwo+f//jMenATpsrqBkYAIzdgFvTcZnsT5zrnkA5TPLv3DF9ufBkvX35Zve7jjz5GUiV46csv4cLNC5S1T0c4SU5wkp/gODnGcXyMYT6koLlOVBD5n1NSwRlfCGdyewmokMyTvM6RlimiPPr8MqCVJphAjhx5lZNwJg3SknvFvNNONWlFy0oGlAAzSFB1mA0xzseAAQWgJGVy5nhImznJWmm7bez4O3ilQ4yWtt1GaIU0X8AViJVVmRLHnOQTHEfHePvgbcQiRmVVMJmJuI7P/F7JojBhkpiovCY41OenxRwUXh03E/TcmiXZYIZy9DCNeekC40rwUjmyVCnG5fhMEVHf9OGZHgxB985ZSffFuIzx4OQBIiNCnuUQnTUATE1z2s6IWu7VHi5uXURgBRgNRmg0GvAbPg5ODrB3QqKLlVWhNEuUvIThGqoUKEcOWMAIIwKyXOBQHFJ/BQCuAP+If6Qvfk4/BgyYhqnsaD3moeN0EIoQPaeHDu9g093EVVzFdmsbTbOJgAVLGTYAp353TAchC9HyWmiXbSRVApOZCEYBDg4OlP5AmqaqZtlxHPzZn/0ZOp0OaqtGbdcYxAPcfXoX+6N9WA0LdsvGIBrgkyefKMtGt+2idmrkPMc4HaNkizUtExmyPEO37CLshIjrGHujPSU6eJaWBWecGFwOuQs1rAZadgsdt0MCijxEx+vgQvsCrlXXsNXcwigc4aVLL+HeJ/ewvb2Nt956C7Zt45333sH+aB/TaorKqjApJqisCsIRqN0a/Qt9+IGPJ0dPqATEqfHR0Uck+mhUwBUaOwD4OX4O7AH2HpWrogn0qz7EhkC/0YeIBfgJxyyZwfRN3Lp+C5vNTbi1i6PZEca3x7h65Sra7TaKooDv+7BtGycnJyppdhbVWv9dtz+X92QZSMracT1ZZsAALzhc7qI5a+L15uvq+/SEyPHxMZ48eQK7ZWN7exuGYeAnP/sJ3nz3Tbz+xuvYvrqNyqrw6PARbj+4jePoGJVVIeABsirD1Jji48nHSuvmLFDQNuyFNea8VKZpN7HT2kHASauiYTfQsoiZJ9l7TaupmCh6QOY4DiaTicqo68k++dz29jaOjo7w/PlzXLx4EVtbW/j5z3+OH/zgB7h37x4YY/jd3/1dvP7662g2m4iiCA8ePECSJHBdF1evXlX/HwwG6Pf7eO+999BqtXD58mW0Wi10u13EcYxOpwPXdXFwcKCuT2Ah5reaENL3YJ/VGo0G0jRFHMcqoSdLveSckHNK/khNL3nc+/v7aDab2N3dxWAwwLvvvoter4dz586hKAp88sknSNMU165dg+d5KuMu474oiig5NA+C5Z5THo9kvOtjpO9Z17VVMKnT6eDg4OBUjLhaStBsNtHtdjGbzXD//n24rotLly5hMpnghz/8IY6OjnDp0iVsb28jCALs7+8jCAJ8//vfx507d3Dr1i3keY4nT57g0qVLuH6dQDtZamKapkq0y3iXsYWr4lngkGyr56uv1wAQhiHiOF6KHeVrpAai/t7VefNZ7YWBBrmQpGmKMAyXqED6l+qLkPxdLhz6oiV/10sZ9Oyp3gGSkiOz0LqAiBACrVYLaZouCa3I75QiGqsBkAxiJMVFHqt+8ekBvE7B12mieinHapAvz2VVA0C+Xv5Nf042CbCchUzpCKueqdf7TP6+7mahX0z6OQILJEv/LokC6sivzp5Yx2zRA9NVdsvqpGWg7JqTOWiwBlp5a4nmKBkjUpBUCIEsJ6u/2q2RGilOkhNMCqrjT42UmBJmjhEbYY/tITZjpGYKkc8DMG1jLjMaYR0izEMwh8ExHVXa4dYuPOGRZV4V4Hx+Hi2HUoRJmixR5eQi22g0kFc5WX+ZC5vJ1EhVVlYGwGOMsc/2yZbOilFvno5ujNqAXdmwSgsBI/E/n/mwYUO0BMbFGHZtI2YxXDFnfdQOUC0zFiQbQKdhyb5eRXv1OaC/Xo6bPg/096yOuX5d6ECfTsfSm5490D9XXzfW3SjkjRwAZTRrE17uLaHcFxsX0XSauHtwF57nodVqQQiBJEnwfO85ojyCcGnjl/Mc//r/9a8pKI1P6FH///xxlI7Wnkdoh7jQuoCO10HTaZKiuenicO+QauIF1cMXKDCKKTuWG2Q1WopymV3wecHovNzjFKixClBgOaMuKekmo0zHqthmjVplsKUdpiwZqnBas2JxOAwe95TtocEM5cSRgq6FyiCgIUeO1T2+a1DAY2FOnRcMZVZiVI/AOAN3OQ5xiA+nHyIZz4VFN+dv1jKaxiZdN+ZjEzvTHfSCnqr7bpgNXGlewWsbr6FhNcBzjjqq4TEPDauB0dGIssxOTdfu/F8sYkzrKU6yE8zqGWIWkwd9MVO6FJNi8mJMipWxkVoUEtipigpVTusgMxnSMkVWZihRkoPKWfd7vRRIPsVIPBMmAJPKP+IqPiW4eNbnmcxUJTulKJUGxF66h3pM1ppplSIp17MZTGYuNut2C4ERwKoshFEIu7LxtZ2v4cLGBaV/wcBQViUqViGtU+yN9nAcHSMRCUq3RGmVyhUnqRKUZrm2P5hgMEoDrJq7TswF22zXViCF1Pn4LI2Pdc1iZDutrEbnWgVS10FUgqwSi2w94ChApVM1gd8Vr5CZGZjHcKe8gzzLUboliryg7DwA9LQ+LU04lYNdfxctuwUWMyTDBC2/hcANcLB3gHavDafhYDAbkBWhkaJyKlihReUnda5KSvI6p7kL4GHy8PTxrkhTGMxQ88LjHhpOA6ERYqexAzZjuJHcQDWqsBvuopiRkJ9XUSmpvK/owLG+9stg4tHdRxiPx7i5cRO/+5XfxWw2w49+9COMPxmjETfQQAObm5v45je/iZs3b8K2bfzlX/4l3v/ofZRmCathwQxNpCzFH3zpD9D3+jAMA5ubmwhDEuXkFkcG0gjaG+5hf7xPAtuahem0Il2LvXgPH48+xrgYY5SN1oOsP6WyptbdFnhOZQHlRonar8FSYjCIWBBgmvjooIM/Ov9HeH7vOf5k609w8+ZN/PCHP8RPf/pTujezErVTo7Zq7N7YxaWXLmFSTPD05ClG6QixSWy8GZthwAcYO2NMjAmyixkEE3j3wbtLh8fA4D/20bKIuSGdKVzhguccXa+L3WgXHa9DrhRuGzvVDjGf3KYKHmW22jRNlbGWCTxpE6kLg+v7YxmYAgvNqkePHuHp06fgnOPKlSuwLAs/+tGP8NZbb6EoCjSbTbx+6XWUWYmru1fBPmUIRYiMZ6jLGr20hwe3H+CVV17BuXPnsLm5SftGg5gz05IcLZ6Pnis7Wik4Kv//JHqCT+NPlXvQWfc4z/QWrh7zn0P/EEW7gHgslJNHYAS0vzRDXDt3Dc+ePsN0PMUXv/hFPH36FH/1V3+FR48eqRjjtddew1e/+lUYhoHvfOc7ePbsmerbV155Bf1+H+PxGIeHh4jjGDdv3sT169eRJAmOj48xHA5VCUUURfB9Hzs7Ozg4OFDlGRIQ0AXwdZaDfh2u7gVlpl+P36TugGEYCMMQOzs7KtCX+zWp6/Do0SO0Wi1cuXIF+/v7+Ou//mvcvXsXr732Gs6fPw/OOfb29nD37l08fPgQaZri6dOnuHLlClqtFrIsU/Ot0aBS2iRJFANLT1TpAfG6YHv13FYBiUajgSRJ1N6+qsiaXp5bWZZoNBrY2NjAp59+iv39fdy4cQNJkuBv//Zv8d577yGOY/zpn/4prly5ovR0+v0+LMvCgwcP8PWvfx22bSuNnbIs8fOf/xyffPIJrl69ikuXLqnye9M0sbOzg/F4rEQ25fHK2Hb1fNaBAqvnKbUwdJkBPUG4Gi+vakd8VnthoEEPHGUmUx9MPZjW6dV6J+j/128wMiiW2f4CBY6CIzDO0EILtVGj4VJRo+M4qgMMw1DqpquWhsCyVYyeidXZCfI7JV1Fr8vSqexCCDw0HiKyIoRGiC66sGsbRVksRJA0qrn8v1yAVzOwq4vu6gTQ+1B/nTwm+Rr5XhmQy8VdR910Fsoqg2T1+1ePRTI49DGTz+ughhx3nR2iq6TKz5X9o4NPq+CF7tAghMCb7pskgCZIK8EWVE5hwYLNbDRYA3VVw49JHVZ+hpwD7TZRPYuiQFVXaG+38WTwBMN0qDQmUpYiMRLAA1KWYmgMkfBE/U1t1uUedGOuOSHIRs92bFUaYRXEqGhbbXDBYZnk3NHMmxCJQBAEmM1maLXmYEpSwfd9lGVJdWizKfaO9zDKRygsCnLkceQ8R8ISMJ+hRIlDdojYUpCHAAABAABJREFUipG0ErxdvU2b7P7ytWvXpIXRSBrw4UP0xQJAEfQTsIA29zCRilRlq+VYy3GT471qy6TflFY3i/I5ef3r8wbA0liv3hjke3XNE5kxWaX8SYaNPtd0tpF8nVwv9Dq3I3aEb/e+DeELWAW5sNglgTpJmeBC8wJe234NPa+Hnt9Dx+0oJgNAVO9ROjoFQJx6TE7wbPoMz+JnZN21Wl9uUmYxYAHc3EUd1TAKA7ziYDUDqxk818Pu7i7AgVE8wtPDp7CaFmq3Jrq5US+zMj6LFYGFKOKpv60JVFdLQiRAASy0JfTPjasYcbUAKKSTQyEKqtU9Azxx2VygkHGUVUlBmqgBA6hMcjkoc42BIaeMmL+XOfAtH3lEzCDDMDBOxhiyIcYzspj0mz5y5IjK6MxsFweHb/hLFnqytKPttLHj7GAj2MCF/oUlTQrpLBHXsapbH+VErZ6WJKh2kp3go8cf4fHRY2Q8Q8UrGL6BHGRzuETtlyRCud89484tnT5QLqxJRU33OW5x1Iw0PPIqJ5Dil2BSgK2UfPySTbI8KlFRfX8ZQ9QCk3CCKqhQ8xpPJk9QTdYH+g4c+IxYLT2/B7M0kR1kqE4qtLM2+kUfvKTyli984QvotDrY3NnEk+dP8Nb7b5EgqVmgu9vF4eAQYSuE6ZrKyeEsRo9k8yjRVUGAjSwzkiDFWeDKmd0pGP3UTJXX1GJeEupaSgOkYMWZdqi8pmvK8iyMqzGOp8fEvvAL0h9J5+yJGgtnjRaB1kpnxW4vWXnazAYqIPRD1ILWlOFsiF/c+wUyOwMPOWnjlFROJsum8jrHtJjiMCXnip8Pfw4A+NvB3y4fd4uOHa8A7GVGNqsVg1EYsEoLbbONKxtX8Hf530H8VCAsQ9zcuombl24iHsb4y7/8S9y9e5f6cL7mc87xx3/8x7h8+TIMg9TcpR7BnTt38IUvfAH/+I//iH/xT/8FNjc31f3s+PgYjx49ws7ODgkp+k20WAvnd8+DX+Sqxp9zjp2dHaXOrgcbeU5aQON8jP3xPqIqwidPPwHzGB4fPUZplbj3/B5SlpK1pztD6hJ7s+DL1p5/9+jv6B5wYsD+BxusYLC+YMEqLTiCSjM3G5to3Wihtmo0RRPXvesIwxAdt4Od9g5YzTCdTjGbzfCDH/wAzGC4dOMSvvFffgMFL3AcHyunklk5w3F0TDpHLMMoJzHQQTzA5GiC5NH68iLTMNFxOmg5xOzwmId+2AfPObb3t5GNMlwoL2A0GaGua0ycCTIrQx3XcPiipFne813XxbNnz/D8+XN4nqeC5jfffBPvv/8+JpOJylq/8cYbiKII3W4Xh4eHuHv3rqLS13WN2WyGP/iDP0Cv18P+/j7effddlQG/dOkSNkKq///VnV9VGgjSPUEyaoQQuHz5sqrJl8DxtJguMbJkmdikoMdpMcXAGGAcjPF8/zlm1ex0CeX79OBzH+5zF8W0APc4rBsWnMohZ7eXPOzv78NxHFiWhS996UsKHNjY2FBxzPHxMTzPQ6PRgOd5cBxHAQCHh4dIkgStVkvpsniehydPniyxS6UendzT6fs3fQ+3Cj40Gg31fKtFSTfpRqG70XHOEQSBKiVoNBp49dVXEccx/vqv/xpvv/02GGPodDrodDq4dOkS6rpWopC9Xg/tdht7e3uYTCawLAs3btxQwIVt2/A8T4k26k4dEvRaTaYurcNnxD9yfko3Eyk0vxp3yXKVDz/8EOPxGF/84hdx+/ZtfO9731MuMb/2a7+GX/mVX1HAwj/8wz/A931cuHAB3W4X586dQxRFmE6n2NnZwW//9m+DMbLRPDg4wHvvvYft7W3s7u4q4dB+vw/f9xU7XI/xVs0K5L5a3yvr/5fjKZkLOjikywHIJsc0DMO168Op9eKFXgWoxVuqU+oBqB7AT8UUH/sfY6PeQCACNFkTjbqBAIGq8wSW6+Xlxl+6IwysAb67+d3FlzcBGzZ84cNsmRQgVZRh7lt9dMsuMp7BAVnzebVHIlJzoEEG3xJ502nisg5N2g3qWViJ3EjE9S3/LeyZe0v9whpske0WHtzaRWiECBip/LasFqIiwsSYwObzc4CpgILVCaAPpJ711xcGvVZf0pDkgrTKHpGLhy4mKIVaLMtSE2v1ApSTTVfk1dkPq6+Vx7WahV7VApD/l8c5NabYb++jbbXhC/LBbpkthEYIx6Sg8VnxDBMxQYx5tkluuuc2naYw4QkPlm2pIFGOiQ8fXaOLECEsZsEzPGwYG+ihB7/2l+i1hmFgo7kBy7Lw5OjJYl4zgZwR8yDcDDHKR9if7kO4AjFiynogwsSc4IgfIfVTZEa2dhPvCiqVCLYI4dY1J3xBStvMZMjdHFZuoZE34M6LuOWcqKoKbbQRsEAJtJ6cnOClV17C/ngfT06eIOfEnMiMDBmnLI3lWGQRxocYWAOkLEVqpAsNALnPfpno957w4AgCUczAVKUeTu3AKiyYhQkBgabVRMQi+PAVZVi/YcmmA2166YReiqXPER001AE6nW2kz7vV0qBVIER+5+r1b5omXLjYzrexN9vDzJhh6k1RN2qUdon//i//+1PjyMDQdtvo+T0FPvS8lf/7PVzYvrD0u28RjeZv/uZvwDnHKBrhx+/9GONijK1LW6jdGh89+AifPPoEkSAbu8qukLs5SqtEaZc4No/xJHuyOJh5Ft+sTbUGbvqbqNMayTgBClCwWVTwfA8XrlwA9ziiMqKa2II2SkmVLCuxv0AQWqNeHwSttFWAQtqNMsFOlXgAJD6XlqliWxgwyKFk7uRxliWqCSoJKcoCKUtJ4K2OqbykUSLiEYESJjDJFwKOnHEEZkD12NxXZR6iEsizueuQIGbHJJ/gUf4I6SRFVEVUN/zx6cOxDEsJZUptCv3xfHCe6NB7gEgEfObjT37/T3B15yo6jQ6+/cNvY1pM8dJrL+HZyTN89x++i2k5JRV3p0TlVFT6Y5dEvbZqoofX2XJW38ASEEMnvH6cJIvCNmwlMiogqP/qEnE2F6XUxFJftElxxNNfqh3XGY1jzlA0cpzkJziIDlBXBDyJLbEA1+bH9AE+oMB6TOCLfd6GWdAaZoQGrLGFL/W/hH7YR9cjZgsKIEsztBotBGGAoiowSAZKhX+QDPD05CmxezAXQazOtvPkjMNlLonBlhUs00KapWAGQ17ldE2wCpVBPwDUvW0dw0e1+XWjSh2suR2eqJQrTo0aOfK1wIcsnwHHomQGBJaWdUk6K+tKVzwCrYM4wPneeVy1r6LjdNAP+uRQYwUwYSLwAoxGIzCH4c6DO6j8CveP7iNikWKeFIwYW4IJAhxNAA6dd4QIz9Jnyy40+/MfAeAiwC4QQMMFh1Vb8A0fw2iI7Yfb2LA3sO1tw3ZsnKQn+NrvfA0X+hdwcHCAc+fOKUFzmZGP41iJ8e3u7qLVaimWm23bGA6HcBwH29vbSxpJUgMsTVM0m034to8GGkSNz7fRaDRwZB2h0Wjg/eh9WJaF9568h5OTExwfH8OyLBweHeLijYv4nW/+Dp6dPMPh7BBbl7fwwacf4O7eXRSc1v/SJvZOHuR4wB/g589/jrRKT4/RPrnhuMIFEiB4NUDP76E+V+P/efb/KIC04TbQBmXbm9ukceUyF91OV1Hc//Iv/xJXb1zF9S9cxygbKWbHOBsr0djj6BiTYoKj6RGeTp8SgHFElprZIw28bQL4LfqvURo0jwxyNPofPvwfYBYmAh5gp72DhtnA0ftHuPv+XWSjjHS6HA+BHeD87nm88cYbSgxwMBggCAIwxvDlL38Z+/v7ePnll1WpxaVLl7C7u4tHjx7h7t27aDQaKIpCsScsy0K/30dRFDg8JIBMT4oAUDGPTHaqy3DN/oQxhv/wH/4D3v3wXfzzf/7P0Wg0EJexKuu5/fA27j27h63LW3j3zru4/ZD0Siq7IseURorcz5GECTzXw507d9Dr9VTGvtVq4eTkBLPZDHfv3kWe5/j617+OS5cuqf2hbdsqEDw8PESapqqsPAgCXLhwAQ8fPlR7e8lG1pkA8vxWWamy1XWtMvO6/ar8v4wpPM/DbDbD0dER+v0+bt26haOjI3znO9/Bu+++i8lkgul0ikuXLuE3f/M38aUvfQlJkmA0GuHBgweo6xrXr1+HEAK///u/DyGEApfqusbu7i4uXLiAzc1Nde6+75Mt6rykQgIMRVGoUu/VMTyLOQ5AfV6WZSqWrGvSDdze3objOLh9+zbiOMaFCxfwrW99C7/4xS9UH2xvb+P69euI41jFZa+//jqCIECWZdjc3FRmB3fv3lV6Ddvb2/A8TwFLkuGxs7ODnZ0dAFDJyuPj46WqALm/1c9N33OvspYZI9HLOI6RJIkaa3md6O+XYFZZlnjw4AFef/31M/tOtl/KdUIekE7X10VFAGDGZ/iZ/zNkTFtk5vc6TxA1PZj/cywHRsMAMxg6dgct3oJXe2gXbfy3x/8tzJaJ1EhxFB/BalsYZAMMqgFSI8XEnuCAH+Bj/jHyKl8IXc2bVVsIRUge8k0LLd6iujFhgTUYWlYLbkVZ51a3hcAJlgJ0vVRCAhb/dfRfYzAaoLRpkzfMhximQ+Q2BQGJQS4M+8Y+YhYj9mNUrAJiLKlMW8KCV3tolk348GH6FCh7tacC0ZSlMEoDhVGAGcs2M/oC5ziOuuDl+OjlHDLzW1WVsoWSpQg6I0WnGekMBvmc7s+rZ4hXSyfk86v1SzpjRf/siTPBTzs/PSV4BwG4havcHnaNXfjw4QiHgBrBUOXzGkKTEY03I62ElKeYOlNknILs1U0cJoBhGfCa1N8SJPLgoVsRKJG5GdzahV0s7DrbdRu7xi42qg20ohbaVhtCCGVDI8+tLEswzmA3bCRGAh5yVG5FG9N6hoyTunWECCMxQsQiRCJCwhIaVxfA9vwHJJYnwROnokx7w2ygbbYRspA0HawcvboHIQQaeQMWt5bEJi3LwtU+1fXtDfbU/KlFjcqoUFgFOuc6mJZT3Hl8B27XJScHJJhWU0QswtAaKq2MnC9n4v8WlLVyQCUoHiP2iXTt8ODBNV0l7GlVFmbWjPq4tpXompy/+jwBTgtJriLPq2j7anmGrs6rz1P5/lbVwhuTN/Duu+/i6OgIpmliY2MDvV4Pf/Ef/wKDZPC5TIX7w/v46fOf4iQ+wSAZrBWDdE0Sh5PlL9WsQmiGuLhzEYZp4M5P7mDwdACncBAWIVnd5cSKcV0XrVYLL7/6Ml5+7WXce34Pe+M90hVgCSqvQm7miGsCv47KIwyMAeoW2YfWnPrk09GnwIiOx4ChauovO5dpE2o1YMPGk4dPcHx4jDIvwS2ORqeBxkYDJS8RVzR/Jzllg8+0sjujSXDh8zLAyoISFYGCQvuRU0B7LEGWvcxgxBYx6bWKtXHGfsJktOalZYpKUO20DK6lyOVZpQBdu4umPS+L4XM3EGPZRaSqK6rvn40RlzGm+RSTckIuJS8vPu/NozeBIwqsHcNBu9nGd+59B7OjGURDkHBmZcOvfARZgK7RxZevfhlfuvElbDY3cfDwAOPBGBnLKDiOB8gMWm/G2RiPDh9RiVXHptIxH4jrGFEZIS5jlZmOEJ06V+lOclYfcsYXGgdz1xUIKq+RQWxZl+tLEz4HtJDMAfVWC4v1fA3zZrVJYVdw4FH1CGWvxD9M/gHlmCxkzxJu9blPwoFWA0ZqoJyV2Gnv4KXgJfT8HkIzxLnuOXQaHVRlhTRLYTs29k/2UTs1PnnyCeACTwdPAQ94mj1FznJi9BjV6fvevDls7ujBbQLjMHeF0VgUpSiRIUNWZp8rICobA1OipmVVQtTkRiV1KKSd6do2H8tZNcOT6RM8N56r45KaI+vWAJOZCI2QNA54B72yB7uksoF0kMKpHZRZiSRKcPnKZVy/eR21U2NYD3GSn2BQDBTrJKsz6js2d/tATeU/iHE8OQZWzV8MAI/nPw3gf/lP/4sSeXUNV2mNdKwOeryHrdEWtrItbE+2cal1CXv39vDjf/gxLl++jLIscePGDVU6LO83EijX2XPAYq8sM/F6aapi9BYlHt55iMHNAW5dvYXfeel34Louvt74OsovLmyu5T5KJpNM00RapBgkdH2nSDHOqYxjUk7IorRBe42ojnD7+DYJLM5FhNeNkwEDAQ/QtJpAClSzCu47Lr40+xLOdc6h7bSJTeG0cCG8gJ7Xg2/4aDtt5HEOz/NwcHAA3/fx6NEj9LZ6+Mn7P8EgHuCjBx/hF5/8AtNqitIscfmVy3AbLmIRIy1SJCLBk+oJfrL/E0yLKV0Xt05PQY95+N7b30PDbFB5m9kAzgH9oA+rYyFpJXjWeoZoEMGFqxwrZPZ3NBphMpmoDHyr1VKuAnfu3MFXvvIVFbDKPQOwvFdYTYSs7oclxV3ud/stAjKSkwRbyRZeu/UaJpMJ7v7iLt6w3iBNEuZgZ2MHv/Vbv4UwDDEej/Hpp59iZ2cHly5dwjvvvKPAkOFwqEQEO50OXnvtNTUHbdtWwIIQAu12G3EcKz0Aub9P01S5IKyyq/Vya/3c9SbPU8ZJcl5LBrPU1Ds4OEAQBPjiF7+IOI7xve99D2+//TaSJFFMFt/38Wd/9md49dVXYZomhsMhHj9+DMMw8MUvfhGXL1/G5uamAjJ2dnawtbWF0WiE27dvY39/H1/5ylfgeZ4CGqSAonTVk9aSMkmlB92r5yWb7A/f91FVlbIMlolnCRA8fvwYw+EQ58+fx3e+8x384he/UAngS5cu4Rvf+Aa63S4mkwmGwyEODg6UDo08r3v37uGtt97CaDTCV77yFcXq0JPjly5dQhRFag40m01VPsIYU9prusaaPk/1+buOOSxZ1dPpVAEfug2naZro9XooyxJPnlCi6/8vjAYp5KdfaKvo1xX7Ct7gbxAlsZpghpl6jBBhKqbq8R67h8IqULVO39js2kbIQoQge7tNbJKPdHUObu7CLckK8fLGZdR1jccnjxEjRsISJJwCfrtjY5ANMMMMxzjG1JoisRMUzRW0vgas2IJfU2Dkg7LLMsPcMIhy7tgOTGGizWgzHuURDieHapDkjUVe0IPBABvnNsB8hodHDxEhUlT9zMgAG1Tzx8YETpjxgqafg26SHcpAuK4L13ZhBqZyYAiNkILP8ZzJMQ+YQxbCNWmiyDoiWXcjs7mSYh7HsaKN6QuGTr2Ri63OlgAWQaFku6y+Tk5QvQZM3jBlCcjl6jK+NP4SSlYiZjR+M0E3yIQlSAzqq8RIsM/26TlZzmCvzFGTarGdkpwfmkUTgRGgYTfgMAdVUcFgBtqtNiazCdU6G5nK7E/YBE+qJ4irGJl/mkrNBIOf+nCYA6trocEb8IQHUxCl1i4JEPCEh67bhWmY2Kg3wBMOES+YP3qJiVz4TdMEDCA3cvxI/AjPxXNUWUUbQblgGMSsiOwIJ+YJlVGAyj3gAX8b/y1d0eexACbmJQCe8PBR/hFsYaPwiiXdCR8+mmhi19hFJjLEkxjnwnOq3ETSv+ScYIwE5FKW4m7rLmbdGQKXGEsyqJMChSOMcIhDJCJBbMRUHw5QRvXqol/N2oQtqPzEqz04pYNAkA5Fi7dgViYSO0FLtNBGG6Uol+hcq1QwHRBdZQ/pDCy9yfmvKyQzRlodO40d7DR2Ts2Js1otakyyyZnAxJs/fxMH0wMUVoFDfoiHw4cYZkNU25UCmFQTJB7qw0fP62G/tY/3Bu8hTVLkcY7zG+fRM3rwBG28nMrB9HCKD9/7EHme49atW4Ssn9/Gra/eQsISjNKRymYOsyHZ0YkI02qKx9PHGGU0btlWtuwGQ1qScAyHspl+HzfsG8rO0rM8FWwyMBVgz4qZKh84jo5xMD1AKtJFFvpFs+NnvXal7ENALKjwTD4wlaFfBQ2kewQDAy+5Yt/J4OssQMQQBoqqoFKAKl4KrKVewbr32oZNImNlA9kkg1EZsJmNi7sXISpB1qiGAdMyEWcxgu2AVOuR0j20ikioEsB3B98F3lp8trSJC0368ZhH62DQwMv9l5U9qcfmyvoOWQV6jJiAk2KCaUnWhbNqhsKkuvq7T+/iw/sfwmpZiEDgaGmWELZQGeqkSk7pPXxWv+vjxwQjcdU5k0KOnQyqK1Gtf//nzJ0atSpRUrX0DC8UnCdVgqKm8S3zEsxieDp7ChGT2GqBArh/+n2ccbRsqnfvll2wimHH3ME5dg49t4fByQB2ZaOMaWMnQZzBcADhCLzxe2+Q9Wg2XgSI8kcb+1PfO7e+lcE0YxqIAAJ6ipocbs76jLVNzIEmQeddo1YAjwCBFYyxtSyMUpSI6ghFVkDUZF0pmEDVqFA25zoj8/YET/Cj4Y8AUFDZslrk4OFsKbG+ptlE02ySGw1onTGYgRo1JsUEx+UxDvNDnOQnBJJX0UKHQpTIRY5c5IjqiKxG9Vv9ycrBCwCX6JF/zOHed+GbPkIrRNfvYsPdwIa9gd3GLra8LWy5WzgXkF+pzCLLrKr8XX30PJCZTqf46KOP8Ku/+qs4OTlR911gsZeSpYOSvl1VxJDZcDcW9ziXK+HuJEmQZRl830eSJCozyTmHZVvIWa5cY+S8mhQEUAySAT48+JBAZD/Ch5MP8dPxTzHOCSRd1xzuEEjNG2g5LTjCQW+vh3JawoOHMR+jtmoYEwN2YmNyNME3/+ybuHb+Grqd7lJJNED2p0ezI6WbMMyGVN4qEkzyCWbVTK1TUzbF3cldzAbEPMLx6eOTgp6hGaJpNtGIaO/WOCSRz2d3n+HG4AZ6vR42NzeV9bgujKgHZvp+Q39OBuBFUSBJEnS7XZycnCCKIlW6EQQBgiDAP/2n/1TZHOoad8PhEABUsGmaJn77t39b2V3qSUbTNJXDhew7yXLN81xpFtR1rQQqHz58iO9///sIwxBf/vKXsbm5iVardSq5I+fo6vnJ54IgUEyGTqejYoEkSRTL5/LlywjDEFmW4ZNPPoFlWfjqV7+6VB7d6XTQ6/UwHA5VcvKVV17BtWvXVNmCjFdWNQm+8IUvKAvf3d1dNJvNpdf+7Gc/Uzodurafzs5eBy7oIJJM2qZpiizLYFkWer0e4jjG0dERBoMBrl69iidPniDLMrz00kvgnOPcuXP46le/ina7vTR2rutiNpthMpkgSRJEUYQnT57g3LlzeOONN9R+TcYF0lVC2styzjEajSCEUGweWQIkrYpXz0MHxPSEnH7uElzQGT1CCAXgVFWF58+fq9e2Wi3cu3dv7Xqw2n4pRoP08dTLJfSgU1qPVFWFclbCEhY683/6icsN3P+J/xMTMUHEImR8mWqeGzmGYogJJmAWw+PqMSqzQtVYyUqJOVWs5cKvfXiVB7dyEYgA22wbrbwFt3TRdbooRgWqhBS/a6/GuBwDAWC1LKRGipkxo8yykeC4OqbA10iQs/kNwoDKoljCgh/4sHbJGSBESJR4FqhMc8ISBAjQKTvopl10REdNZtM0caF7QaFcMiiqQZmXzvkO0TRZjMqpMCpGGBdjjIsxSpfo04/ZY2RGhtI4vWOyYCEA0fO92oNd2FRzXlAdWI/3EBohuMlRWiVEvNBVWLXNBJb1NvQaJRns6bXxctHURUPk/yWzQgrS2LaNPMtxfHwM13Wxu7kLz/fU96hhFot6sqIoMEvoRpOwBGbTROVUmFQTHEwPEIkIuZUjM8kB4jF/TBlf6d+cQWXEbDHXfhA+QhGizdsIECCf5DAq8l83GCmPwwD8lo9BMsC4GqPiFU6ME0ROhNQ5u1yC1xwOHLhwESAg8AoN+MKHU80tMUsqR+A5R26S+FdmEdiS83zBytCaIQwCwCoHrGBo+S2gBKpirnIsA36zxMgYYVANEIsYSTNZe5xeQqADLgFNi9g20j6PcaYAHB8+qaPnNjbCDcRWjJPqBAkSxIjXZuksYSl2Q4IEOXKVmasZzfkCBSJTy6TqmUoLQAB8R3yHKLUhWYs6wlm4d9Q+QoTYKDaQ2zlmrZnSWZBMEIc5S9mo1cVXLuZSeEln6vwyzWAG1T+7bVzDtVN//7+H//eSXdD+/j4iEQEcRI23S+XpXlr0/8Iq1HOPZ48xrIeY+lP8ePrj0xkqB7B+zUJohPiEf0LrEmvj+7/4PkIeKjHR0Ahx1b+KV7xX0HE7pFZeCXz44Yf41o+/hZ1zO/C7Po5mRziYHkB4Ao1+AxdeugCraWGYDUl7IBvh8ewxZdXy0doNKQND0yZbuUbVgDf1wHOyJbt14xZcy4XneOAGrSmpIIBilI8wqeY1sBUF9Hmd/3JCmcBnB7raaxRAIZb+sPa7pPsDKig7RN3J47O+Ly1TCqAdOo/IiDBNpioYWtccw0GDU+DVslrwTI+y3/NSB844mEEAT1mXKFEiKROMyzGex89VXfZZ1o5Kk4IH8JkPF64SNJuMJth0NsFGDOEkhAdPiWr+iz/7Fzh/7jy5rRQTzMqZqluWdczTgrQpBtmARNaKMSYZCWjCIJZLJrKlvmZg2DQ3iXUhojP7k8//SZFSOda19u8/p0k9Ea/0wEoGZsx9zjkBv4KLJWcD2SpRYZgNYTITo+kIqIEHJw9QiAJ1XKNsl2vtnAHAKAz89cFfwyxMWKUFFy485iEUIbpVF3Zlg2VM2eBKxmNhFKgtErAtTHJqKsyCxGUNesyMbD2LQlAJIhccXFA/CkFCo7WoFauoZjVKo1RikS/SJEBRiWoJnABIK6JGvQQ2yJbUCbI0w7E4VsCTgCBRWL6+/wxhLO6plQNHOGiXbQLcCxu84LAKYrYajOy7LcdC6ZQYszEmfILIiBCzGAUvkLNcMU8qo0JURojKCEfpER5MH5z6/nXnzhnNTZvZ4B0OIzBgdA24hYu6V6Me1nh3+i6e/n+fgs84QidUQZzcAxVFgSzL1H1K7rFkECkDEz24kAHs6mtlltJxnKXgWTJeW0YLv5L/CkajEV555RX8qz/5V4syAtdSgomDZKDce46jY0yLKQ4mB6qU4uH0odJ/mDkz1F9Ynnf/Y/w/Ap+QU8Z/l/53p2j6ssTBtuke7nIXjaqBc+Y55YomFfcBoKjIojljxMTMDdKzSkSCWJDzS1ZliNMYUzbFXrVH0r4sRcUr9Ed9/N3f/R3Onz+PmzdvIgxDhGG4BDJ8nvidDAhl/8tgcWODSnJPTk7w7NkzZFmGo6MjNJvNpYBX2otKZgAAxWCRwplSX0pmxWW/SQa2TOrJpCJAYJcMdt977z0cHx/j+PgYaZqi3++rUhM5l3Q9g1VmqX5cMmhO01S9dnNzU8WBelb+6tWrKvGoOybI85fPSZdBWcIjg1wZpMvPlDbt58+fV/pmktJfVRXu37+PBw8eoNFoKIcMadeqn8c6lrZ+HUn9BznXbNvGdDpFmqbwPA+9Xg9ZlqHdbuNrX/uaGh/P8xQIxDmH53kU4+zuqvkhr8WXX35ZsZWk9aUsbZGxYRRFaiwlI2R/fx9FUeBb3/oWDMPAG2+8sSTMr8fnqzHdalmFPEY5F6WlZhzHePbsGRqNBlzXRRAEcBwH0+lUsXc+r70w0AAQoqNTiWRnyUznwcEBoihCo9FYGkhdj0E+X9c1vnb8NVUv54c+aqdGjBixEQM+wAKGGWYY5AMYgYGT9AQzkHVgwReshIIVKKwCMzGDIQyVHfhZ+bNly68tEDItONV9Mcr+N1gDLdZCq2rhKr+KTWsTgRnAFoQC53WOYTbE/nQfTwZPMBMzmC2TqMPlBKVbYuAOsG/tI+MZCmN+bLvz7y0BtsFIN0JYyuGgm3bhwoXlWmhVLfilj0AECESAO+UdfN/9PoEXCPDMegZucpiWSeJ+qYsr1hX0zB6aogmrJFV2KZYmM/8xo/6MnAjH9jFmYrYsbugCaFO/OBVl46XGhFM7+Gj7I3iRB1vYcD1XBeSNmpSet8QWnNxBWZVKUFCNMa/xP0X/E8yOiYAR6GEyE5ZtQZgCZm6CjRl6Xg/b57axEW4gMAPKiguhAjF5w5U3x6qq6KKdunjy5AnYkMGzPNRRDcxI1IRzDm5xdNodhEEIgxv4++Lv8b7/PjxOm0ajNpR9mRACUzbFtJoiQ4bES9ZaU7JyrqQPFyFCNEQD3bwLI6XAaOAMUICE7kqDbMckWyNGjAEG2ofhTMV4VjOYpQm7stHKW/CEB7sif3jUgGM7sGwLzGB08yxj2LAxwQQzPqMSh9Xjn2d97ZqYA7awYQqTvOVBN42yKjGtp1TmwehzUitF3lpjwTfPtHslMWl6dQ/n6nOwagsWrAV1vK4XJRqsgG/4SI0UkSCAMTPXC/EZgvzgpe5DVVdgJrEpSkFK9LJfVc25oDESvgAur/lQMReVS3ywSwwNNNDgDTi1A6NtILoewUgN5EZO350ZyKscNrfXfNh/fpOqzZxzZd8kgQ2dRSRvblKDRUe35Q01iiPMCqohn9V0fcegwEwKEI5yUtfez8labVbNFp7kWmNgCIwAZmECrwEtqwWecVRGBSsgsMiObASzAN/4wjeohMduw+GOOi+AgmgpmjXKiT0hGRTPh8/x3vF7OKlPkHopYjfGj+IfYVbN1gaEFiyl2t1CCzvYIeBOuLAFXROiJsG7mpFOQW7kmAqaxzEjpljOchQoKND6JfUFzmRRaH8rRYnVOFgPkPQmSxTmJ0hg3hyA/yzNi7zMSbBNjLGPffW5tVGT1amx/r1mRUGrXdkIKwpYzdoEqxfroKjn5SyMss4VrzBkQ+wb++SCYpUomgXKTgmskHv+/Cd/DpOZaNgNJSy4qk2x4+/gpfZLS1oVs6MZ/v2//fdI4xQ84PjmP/smeMCxP9rH7Ye3cTA6wPWT61QeaHKUrFTOH+N8jFlJjIvKqlDwgrQqHAJXpb1wZmQ09sZiz+CVHl59/CpSO0XukJ5NbuWonTnoyeinZCVlytkEkOW9y5Iyapw54zANk1gZgsMxHYhSwLEcFHkBx3YwzaZgJkNURNSnrDw1t2qrRmqlYBaBCbEZq41vJaoFK2z1GARTwr9OTcG2K1y0ypYqEbSFDaM0CMybu8DkdY7KrNDcbMIIDEzLKWX7oxMMsyESkZCOhLm+pMIyLPimT2CXQWBXVVUQTCDJEwhDICtJNFKVL73I9ccWtqOS5VLVFVqTFjbqDeVGYzBaS5vtJipQIikuKVs/q2eY1TMMqyGJ755hN+0bPhq8QUygIoQ5NGFGJljKYKTEVvz1138dv/P136Gx5hymY2KYDXHv+B6eJc/wdPqUynuTAabFFElJmjdyzDIx106Z240CUPo6+9jHJ/hEHY8U2bWYBYc5EKmAWc33UcKDV3kErNchGmUDgQhgluaCWYJlMWQ90ed5HrrdLq5fv66CjyRJ8Pbbb6sa7TzP4fs+er3eUsmhZVjo+33smDtAmwIYGcBWVYUoihRd3fM83L9/H2VZ4p1338Fb776Fo+kRJcesEjzgKO0SzaCJ+8V99Ho9pZUWRRGeP3++lNhijKkSCJ0CL89XBrbHx8fKWUu+xjRNbIfbKoAtyxL7+/uYzWaKhZC4iaKgP3r0CFevXkWj0VgCAlaZk/ojsLhfy2BQZsQ//PBDvPPOOypZKxN0sv/k8UqAQf8+Pbusx1A6m1MHmWR/STZGWZZIkgR1XcNxHGRZhjRNYVkWJpMJ3nvvPbzyyiunSkL0OSOfl8cmk6W+76uyYSkK+cEHH+Dhw4c4Pj5esl5cTU5LQATAUkmQPCepw6GXc8gxlTGn3C9VVaWEIeu6huu6iOMYo9EIP/jBD3Dx4kXcunULm5ubS325er76eOrf5fskNO/7PobDIba2tvD06VNYloXj42MMBgM1dyU4IMdClnLIvpMMcL00X58PuhabnAMyWSsBpjiO1XFOJhPcv39f/a3VaqHZbC6dg950looOKMljjeMYrVYLx8fH6HQ6aLVaqkQ/yzI1zw3DwLlz59Ytp6faL106ISdNIQrKwLGFUOT58+cVneesemrdytK2bRwdHdHB14CRGMQMqH10rS5aooWyLPG/Ov8rqrqCy6lkopN34FakSt4JO8gzQuukEnRukHBfbucYl2OkRrrI+jOgYhUSg5DOAQaLm54MVrTKChMmMSZMF37bR9AK4Bakam7WJtpHbTh7DjatTar3CxuwPAspT/FO/A6SDSoFmFQTZIxAiIQnGLIhntfPaQSWNWboMAuaYJnIMBIjJRqV2qk6j+d4vniDLu4l5orjNSkV+yAA4zzOk31jRToZjuEgTmIcTg6RmRlqt0ZhE2iTGRmGbAgDBqI6wpRN188W2VfzUh0mGGzYRIMX1EcAVLZ76A3pJtvEmZt3m9H7XcOFV3kIsgBmbpJI5Dwb27bbsH0b29e2KaNtu8iyDPfv31eL9Fvbb+FR+EiVBwhDwBEODGGgYhUKgzZ6BShzsZQhmV+bpiBgxxIUPM9MsrJLzARjjCkb44rFxmEdo0FwCopqGw4ccncQ8xsDm9sbMjqGbP6vYAWNBYpFvfQZWVUppGXURL+2YaNdtonJUtsq6G+EJIQUJRHRu1lJ32lkmPEZjutjCtrXlF3xipMIZG0tAhRhKDYLOFHGxxiTIN08q7BufJVmQ+7CT+asDlBZktxQlhXpAnCHo+Y11ZgXY4ADCUsQYX1204ABCwS6VeXcCYeJRRDGQD0sMsAFXf9zWjBsQDRP16A7/x8Hnumh5baw4W1gM9hEP+ifEn5cfWw5raW1b+k455upO3fu4MmTJ0oxG1jWwVHdJhZKwsCizlfeZPW6PB0MruuawBQsg79VRTXOwhWoHNIPyThlQGUpUWGRmnxqpEhbKUq7RGGSEOPfj/4e/9u3/jf1eS53iYZvt9F2SMle/3/TbqLv9HGjeQOpkQKHwNNPn4KBYXt7G//yX/5LFGWBjx9+jHc/fhf7433Ubk1CaBZ9ryw7G4kRZW0tCn4rs1orbiiDLle46NZdEgJMDRSTArxYZMANGDBdE17HQ2ZmSA0CaiblhMoDeInSKBf6BKvaEJ/RPteFYP55KgO/CjKsloQYYnEvm5cbSAbFZ7WKkeBeyUuizM+/s2afDVA4woGRUUmaN6O12K5tIKegI/ACXNi9gMtXLoObHEVFc2ZaTjHJJzhOj3F/cl8xG6LytPYDfm0OrFYm7u7fJWeh2kar14LX8DC9NoXlW8SesLrwmY/h3hAf/uxDvNJ5BU23CcskZ5EwDHHz5k1VvggsNFxKUSKpEzw9for/9OZ/wobYwEZjY2mjfvniZbUZraoKP/7xj/H48WNljZfxDMNyCN7lKLwCrXMtWG1L2R5Oc3LTSKoE05TYKeoeWYLmqcCSvgSryX3CEAYlJSoCGDg4XNtF1++CM640Q6KUAMTCLJbGXTChdIkMGGotZSBwthLVmcwO13DRqBtoZXQNt/wWymkJP/HhMQ9tpw0zocC3v9HHrS/egoAgwIznOIlPMEyHGCZDRCLC0fQIUR0hKRIkZbLe0UMARmUo1wlRzzf6BiVMLjmXcK57jpIncwDh2cEzFEGBfZfcHeJaY06NFv+1mKW0NVpWC9vYxuD5ABc3L6IVtIj/wjhgzEWW55+fVik+ff4pEjNB7uYoegUSJKhZjQ/iD/Bvv/tvF99hWOS8YLfQdbtoWk3caN1A2KU9SsjJ1pBnHCIRKIsS77//PuIyxvPhcyR2gjEfI+IR6oDWOtu0cWP7BhKRYFaS0GhSJoh5DGEJHOFo7fjJxsAWAIXhwDVcBJwcc0Ieom210bN7QAhkYYYdfwdtq42333obVVUhDEMlvuc4Dq5du7YUgOmMZgBLwb4etJimuaRxYHKT9E5EE3mSw8gMsIgCyN/8zd/EzZs30Ww2lauHdJqQYIhsW1tb6PV66nqVTR7H8fExfvSjH+H69evKha2ua/i+j/Pnzyv9hefPn+PZs2dgjKksuRR9vH79Ol5//XV0u91TJRKrQdq6vwdBACGEsjzPsgw3b97E5uYmTk5OltjAACVw33zzTfi+r6jwso/1z9ZdAPTvk/d8+dNoNHDtGrEopU7Cm2++iTRNlV6V7/swTRPnzp3Dzs6OyqTLPYUe1K+etzw+WWI6nU4VmDGbzXD79m1kWYYgCOB5nnLDEEIgiiLcvn1bjZlce8+fP49+v78AVDXnPlmGfu/evaXybsYYrl27tpT1f/jwIZ4+fQrf91Vyxvd9VcoiP3e1bOKsMZZNOsVJkOjhw4d48803VSmHYRgKUGCMStLlWOvXhg4E6eO7ev3o+z/TNJWTh5x30+kU0+lUjYOc4ycnJ3j48CEuXLig+lj/jnXnqe8LJfNDioQahoEPP/wQjx8/xng8RpIkChiRc+83fuM3TvXXavulgIYkSdSBPcET/Hnzz8l1oSJ7vJbZokA2D0lfgYVK56DFW/CFv4S2MsbQaDSWngMWjgdCkD3KG/EbsLs29uN9RIzE/gbWAJmZ4XZ9G6VVLmUZmKBj8nIPQR4giAIMN4YQEBQ4wlY0QQPGUqBXsIIyHzJLNf+XgMABGFje1F7U/j8fQ0MYREG0OdzcRVAF2Mg30M7baGQNNMsmQiNEZVZUb8sjTM0pYitGzGOkZoqUE6WrYAVtLM/ySte/W9sAVyBF61SkGGN89qbYhrK7MsWiTt7JHDSrJs7V5+BnPvyK6PKlKFHbNTIzozpdK0JiJcjMTAkFFkaBjGWYGtN5hyz3z7rMuN5yRpuXGWaLGaqLxcr3z73ggbnivu3AvemqOv+a1dhNd4mWOxeDM4RBdlwsXwagtGYIA5awluYIEwwlK8ErokCWoIz6i9SLV6yivuKfAxho75e6BVZtqeDeqA0YlYGRN0JmZac+R+ompGaKiVTF0l9TgcYi0N4mGExhwqkdtEQL2/U28nGOdtDGlE/xyHykzqdgJGDGjPlBG8RaKbC84ZXnxMHhzv/ZIHu4JSV7m453WA+J7cCLUyKT8hxsQWBPS7TQYR0YqQGrtsAFJybP/Bi5xWG5FsbZmIRazVz9ZGx9aQt9xVwUTUA5kUggCAxIygTJLMH+bB84okybZGxUolqbhTaYgZbTQtfroh/00ff7CojYe7YHIzMQtAMUdYHyqIRTO6SA7zVUbd6TJ0/w8OFDMMbUxkku8PIGp2cv9AyITpsFFtorQpAt1ObmptqQWZaFLMvwi1/8Ant7e+Cc4+joSFH7qqpCEARotpq4cP0CXv+N1+F0HAou5kyFUUbMiVFO3uOPZ49VWUVSrYBOVwBchnIC+MXHv4BTOVTXfsNFs6SseM/voet2ERoh7MrGvffv4efv/BzdbldtOE3XhNkwsXV5C6VdIqoi5aQRI6Y63poC3z1jD7EXo7TKtRRyk5kqs2mOTTQjYovxnCM0Qgq6DRue62Fnewe7l3ZhBAZG5YiE69KBOudZMaMa/6pYL4D4Iu2sdXs+3wX7/HIQYA5QzDPh0slDbdQ/4+05chimgYIX4A5lXWZsRkAlCHz5qPgIWjIWABTo1LJb2HQ3caN5g2w/rSY5ejAL3OA4OT7B93/wfeRFDu5yvPTll5AbOQmcigQTMcH96j6iEWkTLLUv04MBAw1rXn9tNdEYNxDOQvV9UpOiaTfR9boYPBzA5S5+7dd/DS9dfkmVC0qas+yXPM8xm83gui42Nzfx5S9/eSnh4jgO+v0+zp07pyins9kMvu9jf38fzWYT9+7dQ7PbxE/e/wncLRff/dl3IRoCD08eIuUkDhzVESqLEgmlUaKyKlVKELMYg2iAU40vrEylS4j64bZyD5Flf7WYu4YUBNhGZbTkMJPWKdI8xVF+pNgZQghKgsz3E6rNALw5PwzG0bAbZI1pt9CxO9gINtBr9rDb3cV4f4zru9ex/3Qf3VYXDx4+QJzEmEQTjNMxTuIT5EaOGDEylkG4xEiprApP8ASfDj5dPu/5Ps+tXWLJmDsIrRC+5cMyyJ6ZGxwQC7vdUpSIcqLv3xP3MB1NMSkm6x1QQEKclm3RWj0X/AzMAKEXwjbsBaPBNlHUBaKURCoH8QD78b5aA9aCan0SKjc7piqV5AmHNyPx5J7Xwx9d+CNc6F2gPbVNjLIffe9H+Gd/+s/AOUdcxNiL97AX7+F5/Bwn+QmO02P1vdNyiqiMkJQJRtUIR8XRmdatejOu0trmmqRDEfAArZMWej/soeN0sB1sY7e5iwuNC7jQuoANdwNNp4k8z1UWH1gEv9LSXZZdy4ysZKiapqkc015++WXYtg3XJQeVDz74AL/+67+O119/feneFgSBymKvUsMNw8BsNsNrr72G119/XZXzyu+U7zFNE8+ePYPruvjSl76EW7duqcQnAGxsbKjAWAKOwLLrwmoco2f/pUWmLP2QsdO1a9ewu7urSgNkH9y7dw+7u7v45je/uVSCXJblEitF9q0EHPQsOYCldUyWIFRVhadPn8J1XVy+fFllrPOcxDuluKlsMvhcBRhkgKz/3TAMBEGA4+PjpTXxq1/9qmJqyPIHGZz+5Cc/UfoL8pxM00S73V5yONBBlbqu8dZbb+HGjRvY2tpaYrm0Wi01r46Pj3H79m0VmEuhxEajga9//evY2NhQ56kH/vr8WgVU5Lm2220cHh4S83k6BeccX/nKV5DnudJNkGwhxhh+8pOfLOlFyDHUA3+d4bHK+tcfwzDEhQsX0GpR4n02m+HHP/6x0taTx9But/HVr34VOzs7SyyQs5Jd+n5RHlMYhnj69CniOMbBwQH29vZw//59VUqxs7Oj5rdkML1Ie2Gg4aOPPsJ4PFZ0iXazjW+m38RhfIjCLsBCqpsbYYRneEYUfd2faH6fkpRzX/iwXboZlkUJIzGoBrygWrp6WGMwHKAqK/hTH1vRFvp5Xy0UaZpSJq+uqE7WylE6JZVdhCBLPzPFpJxgxEcwBNGhC16sz7SuNBnombUJXnNyq6goQJIU8NIkWrzyI5+PZ82I/lrwAilSjDBaLuGQbZ5JNYRBAWxtw61cuImL3XIXfuSjW3fx/JPn+JUv/Qpm9QzTeophNYRoCwyqAQq3QGzEyEyiC5ecAmCVnf+sjar295qTHVaOHDNzRoH9Z9CFjXoOplQcTunAqzzs1DtwIgdttOHGLkIW0uZmTmFEAOROjtgkQGUGonkXRoGKn63Afeo8VjfGDFSmYJRL2f8l2vIZ2gleQXRSq6Kg1YS5VEdasxolo8+WVNx14AQTbAmc0Dc6tVGTSvY8e/i54MQ8gChQLJUI/VJ9Iv/+OVlXwYQC12aYEYjWAZ7i6dLnSEaN/l2flUmVGbkaNTE0UBD1dR6gFCDl8FVBTwhy2TArAgRtw8aIj6jvnbkavgCYq9Warat1dgHTIrq4UzpopS1Vmxs2QtRujZPJCQzbgLAFDvnh8tiIs+cNQGMLQTch0zCVEKYUApTZ6WFKzjT3hvdgMEOJJJaVpr7PoCi0AAkFhjxEw2+Qknuzxma4iZ3WDrmwMMpQNa2mEoX1DI8yl3OaqM58kBsPPYiS9ZWe56kb9WAwUDV/WZah0+lgc3NT1YHato0gCHDjxg28tP2SytyocViTCZAtLVOMshGG6RAfPfwI3/7ht7E33kPlVAg2Auye28UwH+KkIgG3aU0+8PVsZU1oA8bvGGjwBnp+D02TVMWbVhNje4yO20HH6eC8c14FP02ziYAHiKMY/+bf/Btsb2/jm98kiv6kIGGxWTWjrHROOjjH8TF+dvgz5G6OxEwgPMomLmVQAeARPTSshgqs23YbF8OL6veWRcfhmZ4C2pIqwdHsCN9+89vYm+6hcAqwgMFpO4irGGmdLqjm69ovW/qhNeXA8fn4xMJ+EDS3pQYFAOWGsK5FBTmRDNPhUrBb1AWyOlsG5qSEiQCOZ8doWk2EPETLbqFv9xHyEBc2LsA3fHgW9eG777yLLMvw+n/xOrKKlPWn5RTTYqq0IJ6lz0gwrphiVs6WD/AS8BdP/wL8GUfDaqhSjpbdQmAGSmzwk+YnMD0TO1/cwXRjCt/w4QqXMuVzxtK6LKB+nQVOgL7bx9XeVQysAbp+F7cf3UZVVZjNZjg8PFSU4fF4rIBAP/Bx7ZVr+I0/+v9R9+cxkl15fh/6OXe/sUdkZmVW1k5WkU2yuXS3erqnPa2Zp3mSRh48a7xIsA3ZsIEHGIL/9X/+w/97gf8wYFiAN0gPg+eR5fFYT5rNM+qe6e7pnd1kkyyySNaee8Yecfd73x8nzskTkZHFao1sSaeQyKzMWO5y7o3z+/6+yy9xlB2xN9vjaH7E/aP7PB0+pbZRk3r5RVGpiucLTTMXQwEJoR3i2R6+5ePb8svFxRUujnAYDUdSs99ty1SnTDJSZqUsrEGCrMNEAmtqaDPPx1LuVu0vtuVg8QALaIJdt3HaDk7uYMWWXAPOffypT92q8+bLb/LWK29BIUFby7b40Y9/xPbVbdymyziX53aSS+BgkAykH0gm/UDOpWfYYM0kKLXtb9P0mtScGoEdyM/+xb359PSUWT5js7FJgfTH2k/2mY/nWq6zbjjC0ak9t1u3qVuSNapMnau8kkVZmTONJFiXkZGGKbEf0/f6PHAf8IOf/eDca9u2zX/7e//t0j1Ggzt+l9c6r+n/KzZZ023iO2cdmrzKOUlO2JvvcRAd8GT8hOPomGE65NHpIybZBBEK5vmcYTrkuDwmnz7bZ0adb9dy8W2fmlPDx6fltXTCVMtqMQ2njHojoiTCyzy5rja68K1WiyzL9HWjpB1KU29Zlu7EK6o+oEEHdR02Gg02Nze5fPmyfozS9kdRRJZlZFlGGIbcvn2bX/zFX2Rra0t3b1e7vKufb6aGf5V6r7ZHFWLz+VzfDxTQofZH0eaFEPR6Pd544w02NzfPvbZZgK5S+01JgqLsK6r/bDbTcYpbW1t86Utf4ktf+hK+73N6espkMtHAitr+1e63Ocy/m4yKel1G2aroRyEE4/GYer3O9vb2kiF8GIZcv36dr3zlKxrcEEJoFryKPzdNQdX/nzx5wiuvvEKj0VgyklRgDKCbIW+88QZvvvmm9ndQ88uUJ6yO1fOpvqvj2263efLkCZ7n0ev1ADTQ0+v1aLfbzGYz7Rvx6aef8vnPf17/X18rhgzIPJbKIF+9rwnq+L5PEAT6GL333nvYts3Xv/517SFi27ae++p6WTVKV/tn7r+53wCtVovZbKbNHsMwpNvtasBIsSrUXPvpT3967liuG88NNNy/f584jjXdV6FZRVHIhefLL1Oza0s7kxYp00qmTcwtGYc2KRepE0wYMODUPWWyMSH3z3ftRSFwYgc7tmmIhqSFJQ5BEmAnCyS4DGlaTcppSVVWvPLyK3ScjrzgLJv9030++eQTvvSlLxEEASeDEw4mB7gdl3E5pggKZpaMGcxrOamXSk0vsQYmlsZKt1pvayWkXri0pfYdmW9dUMiFUDGTEVtkZwXggr5aCMk+SMWii19bKfRehHerd4FFAbcwF/Rzn2bVZCfboRf16JYSdW7ZkkoaEfFk8IT92T5lu2RQDUiDVKc6KCmH3ia9M2smwGr33F4YVzoZsR9LzbA4OOuW95afp/T2KuO5QYPtfJtaWqOe1dkNdhEjid6dzE+ogkqDWFmYsT/bx27ZMo1iYSaYW+f1rfr8iBXa8hpworAWEpoF8GTSkC8EJyobP/UJRICd2YROSJFKyqPjOAhbUFQFWZWRVAlRFUmgwl5PHVWSDJELykKi6oqNkrMAsi4qeE2w6OctPKo1Pxvz8plDN0IvXoCoHPfzT724OBGlwC5tyrzURVZURFRFhW1JCUVlV+fP7er+LPYhd3JyR7KRqFYeYyG9SVZewyosRC5ZHo6QoNPW5S3iLJau/3ksO9QLGvJnmUMtHZOqJCsybEsuxN1Fe66iWtJdp2VKv+zTz/oQgl2zGdkj7hX3JKNIFWkGXioQmibcqTq6uDXlDL2gp6UM6veiEHqB1+/3tVN5r9fjrbfeYmtrayl7WrEfzA+ndQCD6dZdVRWe5XEpvMRWsEXhF7wzfIfyqaQh3nHv8Ddf/JtLi0ff9wlrIW7DZZgOdbzoP/jdf4DdsHnh8y8wzsbScDAbsT/bZzSQfhDr8uVV17t4uWCzvsm7T96Vkg5XRnv2gh5tv81WuEU36OJmLsGfBnQbXXZ3d3njjTdknnYmi9pxPmaYDJkUEwhlepBicgySAQfzA+4O72qmx7qOoiMc3JoLFri5S7ts81r7Nb1NTadJXdRp+S3CIKQqK6IiYm+2xzd++A2al5vYLZt+2l9iUChd+PMwHZ5rKPYE1cXRhysjr3LyQl57KhVAGP/WDoH0DimlbO9J/ETT/vMTGTO6tE8+/OOf/WNsbC3PUef0cnBZ+0Q0HclQ8W0fS1jcv3+foix44aUX5DnMpexhlI4YJbLL/iB/wDgbM2wOiYn51qNvyYhEY1hYNFwZ29jx5fvX7TobtQ1EIthubZMME67kV+jHfap5xaAaUKNGFEc4tnNuoWca1VLBTneHZtwkyAJuODegBXkt5969e3SDLi+88MLZ4TPotlmR0c/7nGQn7Ef7DLIBJ+mJNFbNx7rrPS/mTDPp6r82XcVmKTJSIHAtVxaxtk9gB4ROSOjItBkbm5pXI0sygjDgpH+C8ASH40NSK2WcjaXR6eLzt/AKEi85a8SU+o34MPqQ3/rxb52fJo8FNavGhr9xxlZxWzRo0KhJVotf+jKyHCmhy4uc08EpG1c2JKi4ACdm5YzBfMA0n2rAMS5jcOFe/97S+yp52OXagkXh1QgcCVL4ri8LJUsQJzFZlTFPJXtkGA2ZFTOmxZS8vrgPrMhl7dzGzV06aYed7o40eLXPWClpmjKZTdhp7lCKkkk8YW+6xzgf6+jPdaNu1/XxaTkt/blQE9KY+Yp9hVdqr/CXwr/E8aNjXt5+mdvXbuvOrOd52J7NKBtxMD/gKD3iODnmcHbIwfSAUSavl2kxZZJMmGZTTvITHs4fLs+lEHhp8aXGgrn5p9af8nf+4O/gC5+G05CJOGXIKw9f4avZV9mob0hmR3WWqKGAAdX5Ngtu1WlVGn6A+XyuQbyqqtja2lpy9DeliOoaVDR5BQIC5wo2U+agOu21Wk0X++r3CmhQKRCj0Uie90Xh6fs+w+FQM7zNa9nsTptFsGKLKHaD2g4VUZgkCXme02w2lyQZqslgpjCY+2UyN0yAwWRTqMfUajWdbmHbNpPJRHtoJEmijRFBFtNbW1v6dyYjU70XLEeTq+29dOkSGxsbersUeKPeu6oqWq0WL7zwAq+//jrdblcDF+q8mOdXvc86w8vVtU1RFPR6PW14qQCaoiiI43gJ5FHM0xdeeIGtra1zayWz2aNkLOZQ76e2Gc7SRRQYsbm5yS/90i+xvb2N7/tL8169rrmfq/tkHkPzuCsgTM1R5YmnmEcqsjOKIt2cunz5+ZLYnhtouHnzJvfv39dGG6PRiPF4zNWrV9ne3iYMw6XJWlUVVmXRKKVswrYWumFR6Yvk6dFTTk5O+OSTT3A9l37Up6pXtC+3cbvuWeqDmyJagrk/Z+bOOHKOzrvwV9LM8CPxEXWkoWKdOuKyYGyPeVx7TNfpkvkZwShg19kliiKJovs+g8GAXq/H1taWPgFZnvHw4CH9tC91mNWEuZiT+in9ok/qpwyzIbknDaoSS5r9zFnpetmc0xArYELF+tnV4kKwK11Ep1V6Lv6tpNR09ak95ZTTM3+AlSEQ2G0bp+lIB/EsoEePzrTDFf8K84M5NzZvcPLohO3dbe4f3mfzxiYPBg+wehaDakBRL7Q54Lyck9s5WZWdL0Y/Qw5RWmeMiRkzud02y5KIcPG8ulzEWfWFnEN4lKKkTZvdapdG0qCbdHGGDjubOwyiAX7HZ5AOIISH/YdUrYpROSLzMq2zTkX6TNbEORpyZRTGi/0rxGJxRAKO3E5F5yxZH9UnSkFYhjiZ1ClWaUXohiSxpJbZjk1cxKRVimM5JCTaO+L8RoKVW0vMCZASn1JId3Ac1mrWL5RsPC9IcRFr4ucYzwIItP7cWfP3Z0hMlJFeVS1AiKo6z+hZB6CsASdKpwRHdm+VA34ySjQ7ZXUIBDW3RsOT9F3f9qmoyMpMgxNJnpAUiWY65OXPkWSxYJNQnRmECcQ5U7iKShfeT6OnuEIuUFVxeFECgm/5spPrtvFLn+FLQxpWg532DolI6Ew6dBJZ9Hb9Ll23i4OjPyRNtH7p0Brdn1XvCUXp1CCEQZk0pR9U0iNgQ2zQC3tct69z3D7GcRx+/aVfX2sQlec5cS6ZbIqdoIr9k/kJP777Y1q1FmVV8mDy4CwZIRufP78vSI+WelGn90FPxlHaZ4v2pi074deD67wUvqT12k23KY+98Xk4y2Y6oUMZZB5Pj/n9P/19TmYnFH5B6Zd8OvuUSTF5ZvEQWiFBJ8Cf+lx2LrPhb3CjcUPH/6liu+k0sS35uTLJJxzGhxwnxwyzoQQn0iGjfHTGoKgu6IY/5zVuAgqWWETdVhJEeyZAYYD3JdLMUxmVqmQF9frr5nBBwSAdSNf72aH+vaLNX2Se6P3Y0+dTFWNNp8nl+mX5s91ksDfg6adP6TQ7bPQ2uHXzFlEp/Z0iImbFjCqomOZTRumI/rzPk/kTTmenzPZmTLIJPF684THyvjwEcVMaN7qFi9gVeIX8uZpXOJkjDWupEcYhYiz0vGq5LaxK6mY3NjbY3d1dih1Wi3fXdtm2t7kcXub11utLxZC6VlSRoxaWgPSAKGYcpUf84KMf8M6Dd0i8BK/nUfgFk3wirTirmCiPmGQTsjJ7poGpIhnahfRFqYkaIpOgcpVWGtytskp6J1g2tmPT3mjjNB2iQh7nWS7lMxUVoQj5hd4vaMBgb74nTRjzCRHRhef87zf+PhsdWbCsGgqqouDTh5/yD//wHzJnTutSiztv3iG1UmIR6xhI9dWP+wzigQap1n1GWFjUrBpdq0sVyf0tkxK7sinygiIrJPhsC9xAymAKq2BQDnQs8DSfUlHxa91f4zdu/IYuSNS5z4qMaSEBI3VMhulQG/8qKdlJesL9+X39/6xaWWPcB/u+vQRS98IeHa9DN+iyWdtko7bB7uYu7d02O+0drNji2uY1To9Odee32Wzy8f2PmRZT9sf7/Nn7fyYZFMkxWZBR+NJ7J3dznJpDUiRMqymH6aE+ht+efpv//vC/15sWWAENu0FN1PBLX5qWW3U265va/NQrPXpBj+vFdWqiRstp4TuSkacM9BTr7+7du/zkJz/h61//Oi+88IKWeliWTAwwP6NMpoFZfK8aMwKa0aAKfZOerlgGStuv5tze3h7f+973+OVf/uUlWj+gNfhKwri6Peoz2CyAlRGlSmO4e/cu9+/f5/Of/7xuEAN63qv9Xtf1NgEO0+NApRQoibtiDqjtUKai6hgo88gf/OAH7Ozs0Ol0lt5TGWKugjyqXuz3++ekoePxWJ+PNE25desW4/GYVqu1BBip4l0lsZhMNHOs/l/dH9rttvbcUNecKvLLUkaSqka8asacnp5qbwX1OMdxNBPCZMKZ26O22fz/bDbT6ybLstb6h5hBCyYjxwRQVoGl1f1V0iR1zuAscVLNM8UiUSyK5xnPDTSoSEKlx7Isi263y9WrVzUyaF5spu5l9aSqm+R8PtcRHrWwhuu4ku5y6OH2XUl7jqXb5Y0bN5jNZvoDcpbNSJyEyIrIvIzYicm8jGAzkNpHa8qhOGQUjshv5tzlrtywLfllVbJYcwvpaO7UHHpuj81qk41yg47oEIoQKrjsXsZLPVzXZTKe0Gw2OT09ZXt7mwcPHnDjxg0eP37Mxs4GjwePCTYCEifBbttMygknyQlH0ZEu2jM70z4GiZWcN/tbs7CzsCTFv3SxC0lVK6pCmxpmVabp+WbHWUkKYmKwFwaSG/BDfgi3Fy/eke9t9RYOz55NUzQJkoCr1VVu57fp2T1mRzOudK5gFzaDyQCn7XA4OSQNUo7zY/JGzml6SlkrmRUzMleyJdbFf50bq8DEAlDJOdv2KVOe8lTO2jpnrIlFd0B4C+1xTxCKEDEXbOQbtEdtboY3yZ5mXN+4TpRFWHWLoBcQWZGMCxw9YmbNKIKCcTkmszNZ8K9jTaghOLewWOcyX1mV7Kr7MK2miGCR+10vJbghKgmyrLyOioP0Ko8qrYgmEdEs0shiZVXSbd6R5zyzs7WO6CAXeS4uVVZRFRXCEliONMVMSWWHf50PyJ+HNfE84znZE3qs4hTWGo36s8CJEu25YRY4CmTShn/q+YJn6lsrKmbZjFl2VhQq/XtVVReCE75YdAPtENdyoYKsyqR/SJnqn9WCuaiK56a7l1VJWqUSrBPS7NAVsuOxCl4lZcJxcsxpcoojHKpWxRFHfFp+yrcOv7WWui8QsuvuLxs+dvzOEnV3lVERWNLoSzlvqwWVQuuVphXQXRnllq0MLx3H4f3336fVanHjxo2llI40TTk9PV0qHLrdLvFBzDbbbJVb+GOfX3rjl7RZoHpNYQkSkWgQoB/1+f7Pvs/eYE92W0Nwug4jZ8ST6Imk6RcTSSFfkZFbwtKshLXHxpeMk5u1m1yZXCF4HODlHi+/+DL/+i/969qYrKTUIMi0nDKv5rob/f2ffZ9BPJCSjuqYT/JPZIGRj9fOV5Uj33Jauoi+WrvKa+5rS1Tsrt+lbtfxLI+8yPk/v/d/8u6jd0n8hKmY0tiRsqNZJYu+pEw0QKEMBxcXxs83igUwbtsapCkpn+1tsQJQpFWqWVQ2tr4X29jnXudztc/x+a3PyyjRIpXsszziYfKQ0VgCVJoiv7N4UgJ8KAueltvSX5cal9iobXCrdYtXW6+y09ohHsZc37zOydMTLl+6zL1799i5vsM3vvsNnKbD3Qd3JRNmYZpZeIX0kWknFO6iCHNyfsbP+Ad3/8HStltYODccPt/+PP/OpX9nLaPIBBXMYRbU5oJbPX48HuNXPpu1TU7iE0Qs6IZd/sKdv6Aj3trtNr1eTy+YHcchyRNOZifsD/dJg5SfPvgpaZBy98ldMi/jyekTcjdnGA0lkGQnlE5J5V88UQ44gIXNkyMcfMunQQOnctiobbCX7tH1utyp32Ej2KDrdAnLkIbdIHRCGs0GaZ5yMDqQjJ98ys7WzlJePCx3hwFm4xl16lxuXeZrX/ya7sIq+rICKFT0YhRFhGHIyekJePDJ3ickVsLj08fExHz85GMSK+F0fsrT2VMZUWxN5Jq1lpE5mfwMX4y96ZnJd2iHkqGR+Gw0NvjB+Afc+/CelPo4LQ1AKfnYRmuDm85Nmd7FWfy4KpjUPdeypBnxPJdykHtP7/Gdt7+D3bTZvb1L5mRybhYSUPlw+KG+N06yydrzVbNr9IIeISGbtU1ELAEyv/CZulO8yqM5aBKUAW4mE3ACK+DO7Tv8rb/1t7QDPxbce3KP3/3G7/LmV9+UHjiJZOLMihmn0SmDaMCkmLBf7fPpXJrNamB2AqZnZmAHGhxW12xQBdy37+MFHtkw43B4KBNH7Ab1qk5gB3purHbCzd+rn9V8MuUgqtNuFtOtVouyLOl2u/qzx7Isvvvd7/Laa69pHwFzbq4m/ZlForoGFXioHq+k5cqM8pNPPqHf77OxscHrr7+uO/HKR6AoCpIk0fcGsyhV4IKKPzV9I9RntzJDVOCGmneNRkNHMSrw5oMPPqDdbtPtdnVRHUURT5480YX6zs4OVVXpsAAFYqj9VcdaNSnUcTk6OuL999/nrbfe4vXXX19qiKj7nDrmq2ab5rldLchN5oZ5/SdJwmg00owVNUcsy+JP/uRPtC+HOo7qmKntybLzzcTVe7brunp9pAr8siz5zne+w6uvvrrkPaF8hsz72UX7uA5oMeeCEJLt6vu+rtXH4zHT6XTJS+R5xnMDDUIInS2vFoPdblfTas0PK/P/ajGnTqx6LTVx1cmez+faTETRj9TrqBxVhbpNp1NNDRJCYFs2deTC8Qtf+AKdTkei9QhGYsST8gkDBgzKAafZqfQ0WBgXRnbETMygbqQ4mDTry/JnZSJpB9IUzBEOW+4WZU9q0It6gZu4tGnjDl2u967TpUslKvrzPo8ePeL69escHh7SbDYZTUZUtYrMzRjmQ6paxWlyit/1OUlOSEMpo8jcheuxMvqzY+1crYvaNRp1GxsPCUyQguu4JIXswidlQmmXy1FTYhENRwLegpXhwkMenrEOVJJJBaIhpK7Rd6TfRuLRK3psTba4XFwmH+Vs1bZwEodWrUVURDhth9iKmVgTRtaIQTlgXI4Z5kMZbVrNJTDCM4p7zrbh/K8q7a4+YQJNpCligIyOaq08t1yYYOJg1Sz8SkpRruRX6MZd/LHPtfo1psMpQTvgyeAJnd0Oh5NDnK7DUXxEERb00z6lX5LYCSnp+sWxoh8vgAXNfqhYWjCb+zJf/AOkl0EP6EqzzJm1pttZgogFTipNJD0hjcGqQmaPu57LLJ5JiqxXSZmHWDj2rwO3Fp0mUQgo0XGahSjOjBX/WYMPq5IOs+g3v6973kWPWZX9XJAjf9HrO5YjjcYsR3ZsFUpeFVLiURZLHduS8pmFVkVFXEkZxjAfLt52RbZjbK8aoRUSWAGBHUgvEdAynaw6Ayh00Ufx3FT3kvKsu1VJILai0o7sq9uvCrGj6EhqsoWMZlSF21r2hO3T8To4qUN8O6bcKfEKj7yVE+/H1KjRDbo07IYu3nzbx3d9/aH39OlTQJoWqcUayA/nyWTC06dPNS213W5z/fp1ptMp+/v7FEWh9YZbW1tLekkFll/lKlVVMRqN+OgffcSWu8Xhg0N2d3f5pRclXVF9Fgkh8GoeQS/QLIRhciafGKUjBqn8/mDyQJtljlODPXETHcP6vfJ7/B/v/B8aEGi7bZpOk836Jr2gR82qsdPY4QXxAo/2H/FX7/xVfuP/8Ru4jrvUwZhl0gxzVs7OGBRKIrBgeIyyEU/Tp7w/e59ROmKSry8eXFz8qz4iFvSCHhvtDd35b7ttQhGyVd/i5qWbdLwOoRuSFin7kTSrO4wOOY6P6cd9ThMZlzjNpjLTvpQsn5ISbENC9DwgxeIxrnCpO3UN2tWcmoyMFq6+z2ZVRlzGzPKZ7g7fnd/l7sO7Sy+p5EdNp8m12jXqdp3+fp8qq2jVW1zdvSq7YqVkfaSVjCgdpSOezJ8wiAcMkzUmgAsgqtav4doujaRB0SwICaX/U+rSjJvyWpjkhCwipu06X/3qV3nzy29yPD3mcHQou/eDPd7+4G2+uPtFRqPRErXbpFmbRaZZLJnFjypM1GJfLerTNNVGX6+88grb29s6CtD8Ugt5R8j4QDdx2d7e5mpyld3dXX4U/4gbN27wne98hzt37vDOO+9QlDLjPssyTgYn2ghyWk4pGgVlvYQGdK528Ls+p5H0TJjnMrK3EAWDyUCDEM8aSurhWz41u8ZvH/w2DadB223Tdbts+pv80vYvcadxR3cs4zjG8zzefPNNNjc3zxV35rW2ZLZWQcNtsOls0mq1aEzkPera+Jo2CP1o8hGTyYSDgwPdKRyPx9ieZJ5uXN3gK7/yFYJOwMHogGkxZZgO+Wj6ETWvRpzH3IvvLXmPrAOzbWFLc1RnYYbqyntK02lqYOKvbP8VnMyhUTXYLre5klzhl7/6y9y+fXupo6w8f5Te3XIs+pGUa83KGY9OHlH4BZ/sfULu5jw4ekDmZuxN93iSPZHsj9qY8kYJN1bOTyn4bvVdfv+7v0/TbmppRzkriboRfbtPK29xObzMS/ZLNOyGBDP8cEk6UVWSKTjNp9qjY1bNpKdGMZUeHvGA0/kpo3jEw+Qhh61DSr/k3cm7sEZq7ls+3aArt8k/A83b/pnvjgJ4FEjbdJt4nmxKqi60ArZM+YFlWbp5q5h4165dW2rMqusZzlgFZsfbLPbVY1UBq/woVNc/yzK+9rWv8corr+gCHc609uo4Kl8D9V4afJvN6Pf7+r5h27ZueAGaZq+SENQ+qoJcfT85OeHmzZvcvHlTAzIgO+kqPlEIof2f1Ge8+p0CCgD9XT1HxXUKIeh0OlqmYDYj1l3H6titHnMTwFDsBZWmYZp5tlotms2m9raybZvRaMT29ra0FKjVluIqTeBJsQNMYGeVaaCuQdNz5Pvf/z4PHz7k8uXL7O7u6tc1Ez7UWAVOVgEy8xgo8FSBM3me47quBnSUZYI672oefdZ4bqBBIVJwpi1Rk1QhROYBUifSPMHquSZ9VsWxnJyc6LgU9Xj1oWhG5SgzkydPnmikTL33OvlGR3Roi7a8iPKUvf09fZH2ej0ODg7Y2d3hk6efcPXlq2R+xpAh/arPsBpyND8idSVtLrdzIi+SUY8uPOYxXIaf8JOzSMDFIt3FJcgDatRwfAdxWfBAPMBpOHSKDmVW0pw0CeyA2ljmFbeGLa7aV+n3+3Q6HfI85+rVq1LvJBL2x/scTg+JbJnyEDvStTrxEkbliNRLie1YUlUpiIiIrAiCBcXcki76lb1GU11Z2LlNza5RJAW2a5OVGZUjWRFL0VgLva4ySYyRmsYDpEfDT/gJdM3JcxY7GBLSqBq0abPBBhvxBvkw58bGDebHc3a7uxwdHtHYaPDw5CEb1zbIahmDasCIEaNyxLgcy32rIkqn/KcGJsDwEnCQ2eyMeOIu/DEUMNFdPL+7KAh9qa93AocGDYIk4Lp9nav2VbasLdpVm0IU9JM++6N9BsmA2laN/ck+tOA0OaWqSTpz7uRk4hmGj4vjrb+r/VgDTmBBFVRkvmSSzKv5MsPC4rwpaQUiE3iZjMW0C1t2F0uBYzskaSKLViHjZRVrRgixFuBSPhwOji6cS0ptAHlRvJren59X0rHOa2L1+X9OcCIv859P7oA0dPQcTxtFqntjXuUyAjBPZTTq4o3OAQxrtjEqI6IyQrGCLwQnOPt7aIe6EFOO6TlS754UUtKRlqkESyg0q+V5UhKKqiAuYmxha+aEEDJe7RwlF0iKhJP4BLuyKWuLWFgBA2fA23tvr+3EC4SmtdetOoPWgM5mBxz44d4PdVHe8Tu0/BYvfvFF6kLur6KmdjodXnrppaWOBiwbfqmFn1oQHB0dEUURvu/Tbrf5i3/xL7Kzs7NksJRlGYETsB1us2vvamrmakdoFXwvyoJRMuLB0QP+7t//uzw+fUzpl+zc2uHK7hXNlhjnY57ET5iN5YI5Lg3viTvwe/we/8Xv/ReaUt9227pr1/Zk4kLbkd+v+Fdo1VvaHNOzPL1gFUL6IExzqV0/jSQgMCtn/O43fpfMlUaV9XZdssrmC5PFfCL19gAfnG2aLZap1+rn12qvaT+Djiu/73+yz+/8f3+HKq7wOz6//Nd/mayWST+BfMggGzDIBtITIx6SlIkG1CpRkVUZw2zIOpXZRcMRjgQkrODMYd+pazNfhLzmkzRhak2hARN/wt2ju9oEcXWo4u1a7RrdsIvIBM2wSTSNaNQa9Pt9Wq0WT/aeYDs2ffpEdkQcxkS1iNzJKezz19zvTn+X1rdauttaEzW8wiPxEopawT+Z/RN5fI1CqBvIgsd1zmLfVhmmav6bpmoff/yxpuTOZjM8z6Pb7XL9+nWOjo70+s/UOKvFp6krVovZNE2XWEtmsaT01aIU+LmPndtYsYU9lgv4l156if/oV/8jer0ejx49kv5aJyd8/PHHOI7DX/61v8wkn0g5UjLUvigH8wNOohMGyYB+3JfF+ALYOklP1gKv/9PD/2npHuMVHlknYy/Zw3rHYru1zaXmJbYaW2zWNmVRGXTpOb0lbbaWrSxiEl3X1RF75trYLDQ0GFRZVJOKV7de5ddf/3Vs2+bg4ADf9+n3+7x39B63Lt3iy1/+sm7QASCQANpCLjFKRpxGp4zTM9Bzkk806PkgeqAB0V/o/AJXwivM53MajQb1ep2dnR0ODw/Z2Ng418FX905b2GwEG/T8nkwuCO6wsbHBPfseV65c4cMPP2R3d5ef/exntFotTk5O+NGPf8Rh/5B7T+5RBqVk7Hg5pV9y45UbbGxtcDo/ZZyN+WTyiQRkvTHfvPfNtddb3akvscPMe03LbdH1u3T8Dlc7V+VnQ9mCCiaTCY1Gg9FoxB/90R/xi1/4RXqbPXIn5zQ6lXMqlve+WTmj8ApOZ6casD2YH2jgdpyN126bb/vY122c1OHdD9/V9+NXm6/y16//dWq12lKhf3R0xEsvvbQEAJjXqPk5spqAoa5p8/95nks/oUXqg23bfP7zn+e1117D87ylwlbVcgpIUp+BptFkkiQURUGr1dLbpGQCaptMKUaSJDSbTd25r9frGriM45h2u60by+ZQtZ76bFVghZIimO+trh2VdKJAO4AvfOELOqLVbHDDmXmo2QhfLbzXAQ6KzaS2Sb22kp3MZjOyLNPRkP1+n0ajQZ7nWt6hXmu1VjalEmr/zXuF+ll5k1RVRb/f56tf/Sp37tyhKIolHy11P193fM3XNR+vvit5SxRF9Ho9Xb+r46iMwdX5Vufis8ZzAw0KFVPaH3WgTQ2SqeswhxlXqUaappreoxApNTEBTf0wkTblGDufyy6viVAJIQ1VlBHLOtSqqip6vR5BEDCbzWg2m1RVRT2sc7V3lW1rW9LLFyckjmPm8ZyG02A8GlOr1RiNRtRaNWme5OX0iz4Td8LEmjAWYxInYc5cRjZVc8aM5YI6gEc8Oitery5OciWZAT4+zlWHptXErtl07S6dqsO0mLJRbdCyWrTyFp2wg+/7+vhkWUbohMxmM+0eXYiCuZgzSAcM86FciNWhn/bJvZxBNiB2Y2bVjFjEustbuiUjRpLaj6W7TLr7bgy7kqaXLq50HxeyiMqFBDlKsSiiFFtikT6QkDAUQ2l2aSNlD8oY6Qay8G0KWbC2XeqiTrfq0qXLNtvcrG7CHLzMIx/l7G7ucnJyQmOjwf5kn/pWnXtH9/AueTwaPUK0BKNiRBVURKVc0K3bn3PjAmBC0euXZC9dgw1TGs93gA1ZLDmVg92yqVk1HBy2i222T7Z5ofUCV+tXqVMnqiLmzKVZp5UwZcqYMcNyKE2lsiGZnVG5FZXzjKLUKJ4rqmeDEwIqryJx5blh4Xtgnjtd6Brug1Zh0RRNQiuUTuW4S3pq1U3PRU5CIs1V15hDwlm0qlVZVOXCiddazEm7lMXvBR4NawGCn+fcliwdL/O4/HlGWqb6Q/B5hkDgWR6OcM6SKbKcOI1xPGkymlfLgN+F4MRiVFTMiznzYtkzZp28x3gSTukQWiGtoKVlHcoTQnVxsyqTAEwlv56XKq8kIDoidbE9olp/wCsqZoWM4utXfbIwY+pN2dvfI69ykjJZux+e5Z0V30YR3rAbNKyGXvypRaqd2tIws9NjNpsxGo0QQnDr1i1efvllvRAwDZzUosAsHC7SuZoLQiEE3aBLFmS0J21mJ7Jz/IXdL/CXL/9l/dphGMr0gVqNw8ND0jLl8elj+lGf3/mD3+FX/1+/ittydaSocv/fT/e5O7urF8Pr9POBHSwt1Lt+V3oDlD5Xe1fxCk/+ftplu7nN3/yLf5OffPcn/Opf+lUN1MdxzDybUwYldsOWxcHCnFMVf8oD48Hkgd7OcTpePmeLGG67sHn/8H0NhrS9Npu1TW73btN22+STnM3aJg2rwR//oz/mL3z+L/Bv/8a/zf5on4fjh+zN9zSD4iQ64TQ51ds0z+bSJ2UxX/M8l2yxz7pEVcMmhV1rl5vdm9ScGjW7psGKjc4GwpZJIk/7T4mIOIwOOZ2cym78dCbjXY+R0rYSbZrsFA52ZlOLa7i5S5VW2KUtnfxtlzdefwPHdZjNZbLEPJ3Tz/scBAf8w6f/kPHD8VJMpRrK/LQXSm+RsArPeVGoY3zn6h26fpdRPOKSc2mJkjwYDDg5OeHBgwe8/vrrGjxY7c6ZIJvpAj+fz3UBYF4/prZdXUdqHai6n8rAzqT8h2HIhx9+SKveIjqMaEQNmqLJNfcan29+Xq8lVAzb0dGRXi9aliX9WxYgmVW3SOyEP3v7zwg3Qk6jU/pz6blwUp3ww+MfMsknJKNkLcAkENKA1F+AO4skHDd32W5tU80qtpNtojSiIzr6c10xxdRQ9xZl6Keo0qrIUx3qk5MTfN/n/v37mt1rjl7Q44s3v8h4PObJkydUdoWon92XlKb++vXrmlGSZZleS87ncz799FMdl2cWjyaAZDYO9bEw2DFm110Vtq7jElgBXuRBdFZc1Wo1fuNrv8HXv/B19vf3dbE7m834xje+wb//H/z7zKs5h+PDM8+dBStrnI1J7IR+3OdodsRHo4/0Y1ajS7/1V75FHMX6eCuvhPv375NlGTdu3CDwA0QgyINcA0Tb29t6/qj9VKMopWeJuu/qiOdkyB//2R9zPD2mt9NjVsy4P7tPw2nQaDSWgKkoiphOp7oL/uqrr2omkdk0Vd1mVWeZUnXzsycIAobDIY8fP9ZGfqpQHo/HpGmqt0HNMdX4Nb0b1PurOk/5DKjzpuj5KmVDMSiU0eTOzo6uydS1rZrVWZZx8+ZNPd/NYnoVBFHbofZPDdUY+PDDD3UXP89zvvSlLy3FqprggQJh1FxdbYyvm89q+9S9J4oiNjY29LYp+YcCPdS8UUMxK1ZlCqvvp1ggJiNNPcZxHN1UVz/fuXOHbrerAVz1eJVmYl675hxaV5+bjRAldxmPx3S73SVvCSGEBslMv5PnGT+XR4Oip5gfHqbOyJzw5oavo6soAAHOAIMkSWi1WvrEKXqOSeVQaI06mOo13I7Lxi9s0Hf6pElK3arrokW9flEU2vFUxeko7U2z2dQTVB3cyWRCEATkeU6j0dCmIEIItvwt4jimU3XwLR9RLj50yzPjl6qSxVoucgbpgMiJGNpDhtWQiTVhZs1kwoWISYRkKIwYQX0hWTBHCTQXzIDKxXM8apVc8NTLOqEdssUWG8kGXdElqAJaUQsxEVxvXseZL5Cy+QJFyxbGbHlK5mTSeNOOmAtZ6EZWxIwZMzFjypSpmBKJSNPOC1EQCckqEGLRWxWLDvYaaroCJhwcDTwok7BSlOfYEiracc6cY1NwZ3MG1mwg2RJNeUz8hk9btKlfqnPJusRL4Ut06GBnNl3RhRk8aD/g97zfQ1RCJndUPja2lB1YQvte5It/hSieG5hYKiyMhXRFJX00LBl3SgOOOIJdeJd3z72UEAIbGbXp4xMS4iQOtf0adt+mHtUJIpk/Pa/mZG5G7ueIuiD3c2I/JvVSyloJIeS2ZJ5c6MHwLHDiglFaJWPGTJjozrra17Jab4rpVNKU1MfHw8OpHMqspMgKPSfSIiXKI/BkaoT2sFg9RqXAyi1sIQ0xq0qaHpZWeeY3sW5/4Tyg8DzgxP/Fo6IiKRdgj9l086XR5zoiiI2NZ3nLso4Fw0HJOrIy00kVJnviwiHkfJkwYZJMjF8/Iy0EQd2t03Aa2n1eIM707wZzQm1TJSr96XORcZsaRVVQVqXcBrtCRQKbjJB1z4mKSMbvZUP5oajiFRfbsm4/Go40GHNSh+TFhA/iD3ht8honj0/43PXP0fW7hITadNEubP2Zpj6f1FhdWKjFgPosTJJEdyfV85RmU33+KSlhVVU4ONzo3kBMBI2TBr+89cu8/PLLPH36VHc6zM8e27bZ3tlmlIy4f3ifw/GhdqkfpSPGhSy4lPfD4fRQMhlOz+ILuSO//dYPfgthCf6bb/83ukhVXfZe0ONK94oELfw2L7Re0ABOL+hJg1Rj0awYHcN0yLff/ja//Xu/TeZkuG2Xz33hc8RCmnkOkgF7yR7jY7m9S2kiN+B3Jr/Df/b/+c/0djRtKXto2nL7btu3uX7tOrvdXdJhSjEppOTDa+NbPtNSms9NnSlls+Tu07scTg+ZWTNNvx7HYzk/Ldgv9qVnx+ow0ihssbgmhUvdrVO361ypXaGclWw2N+kf9ml5LSaDiUx8KTLG8ZjSL8mdXJpNBym5m1N4BU/2npx/P8AKLEIRcrN5E6eQwKAnPGwhQQo195qdJnEWczo+lekAufT6mOSTM9aSQRu378u4z4bdwCos6MDb994mrELe33ufmqhxKb/E5fQyG+EGHb/DZn0Tt3K1pKgsy3NNJ3N+m4WEuaBWzSYlY1LXldmQqqpKG5BNJhPG47F+rgIj1DotDEPm8znD4XCpoLCwaJZNbm7fZGdnB/9Tn+u716W/S0sWod94/xuSBeVWfO1rX6OyKxIrIRIR/ajPKB3J2O6FAeM4G3MyO+FgfsDx7Jjp8ZR+3D/P7NoEsSFwrsroZTeXBqDKs6ASFfY9m67fZXw0lv4Mhc8gGvBy72UAfd9YLcBUM87sCKu/qwJGXYeqUBwOh0ugx2AwWGrWqfdRsmZ1DNV3ta6GM3aGeQ9T513VCGqoe1UURcRxrP+/t7ente/j8Vj6DEUW7shlS2yxxZb8XA+gcAtee+01giDgpz/9qfYdqKqKuIyZFlJ6vHl1E8/xmBUzBoOBTqCYTCY8fPiQRqPB1atXdaGsZN2mrl/NWfOebglL3ufcNjcaN5Y609V3K9579B7/4a/+h5qlbYIwIJu3k8lEd71VofnRRx8tGe6p47yzs8OtW7d48uQJjx8/1sdYzX/btnnrrbewbXspyjPLMkajEf1+nxs3bmgqv+rCu66rpQqr+2kWvq7rLp1TBYqFYaj9KNQ2mdKtLMsYDAZMp1Nc1+Xx48cURcFwOFxKAlHvHwQBt2/fZjab8ejRIw2ImJ4Dt27dolar6c+9JEm0f8J7773H66+/Tq/XW/o8VPcn8xyu+7w2gQ84Y341m02GwyE7Ozu6Vux2u3pem6CgSqS4deuW9kMxP//Vcdra2tLHwmRKqGtWSUo++uijpfq61Wqxv7+v01NMk1ATtDBBBvPvF4EOIGUs4/FY3yMcx6HRaGigazKZ6DljgnDPGs8NNCjqi2nkZd7YzBNkMh5W/2ainXDmkKomcxRJsztFcUrTVNMOoyhiNptp+sqtW7e4desWaZpyuHHIt3a/xbf4ljywlSAoA+p2nbAICYpAxhLOA2pVjSZNnMRBzARu5VKv1fUNTiFTClk06UYK2VI0mbIstcGLOqGKTmNSkbI0o11v07W6Sx+ieZ4TBIFGcVvtFl7LY1JNOM6OGVpDps6UQ++QY/9YMgOELEgm1eSsQFpnAuiB3bKlXr/yqJU1vNSjUTVolS0aaQN37HIpvIRTOripy6a7Sa/saQRL7WOSJIS1kJSUH23/iLEzJrZk0kYmMlngPWMoYELJKFRhWopyvcdEZeNUDpnIlorYqqrOuu1wxpZYHJMxY/CR5p/qmKhUjhpa2mJXtu7cq8K4QBprpqxJp6jAw8OvfCkLqM62v8AAJjCAiWcVr9VZZ/nsV2eFoHq9mJiRNZLgygJgOeFEv4b5elZlYRc2TubgJi5e7NGcNAlnIeJEsNPcYe9oj9iKyb0capD5GZEdUdQKylpJ7ssFbumUF7MmONs33VVXxfozaticnHE11sCEQIArkx6WTBjNUYCf+FiZhcikZCXwAubTOQjwfE/6JIizJJAyKNeDDAWIQmCV1tnxFiyDE/+cAYel8RkASEFxJqd4zuEJT7pwVxIU9VyPspAF+zyay6LKhsqupBZdfDY4UVExzaZMs+V8+QuZE4t5Ymc2Tu7QDJvU/ToWlk5PyZFshTiLJZMDCTZgIZkxnwGkF1VBlEfExFrSAXIxsTbNBXlfKsqCeTWXoESYMRET9o/2SeyE7+x/Z630R7EnVqNDFVtA09kXBXo36FITtSWPIiGkB5KKlTO7IFVVafqlooiqDleSJDx+/HgJaICz4qPRaNBr9TjNTinigo1KOu5XboUTSgZhu93m9u3bvPfeezx9+pROp8MsmZHYCb/5279J7uXcfPUmn+5/ystfeFk77w/TIXvJHh/NP2J6IlMX1s2T0A6Xjo1Jcx4lI+k8nzj0pj1+pfMr7HZ3qdt1HFsucFTk2x9+4w8hhFk140++/yfcefMOV+5c4e7DBXtjAZw8zZ7yYfEhk2LC7GC2/pwJTwMTHa/Dld4VKGDb36Zu1em2u3T9Lu9+/10aVoM37ryBm7t86YtfklF/yQGH8SGH0SETMeE0OeVkfkI/6jMv54xjqaFPioTH8WO5DQPOGBIr0kKdnlMI7NyWrKJZyJ2rd9jubuOWLlVcyQSmTHZif+GNX2CUjHhyIs1JT4oTnTKg0hkYLu+3K1wadoNdf5eaXcMXPr1Wj9ALGfaHeK4n7+hWxVF8xMyesZfuUbgFP9n7CZN8svZ4upZL15f+Kpv1TbzC49LBJeJ5zM39m0zFlMP5IfvePm7uIlqCbHy2DjILCeXDNR5LFqkqcNVC//Of//xS48osbNTzzQ6pKnxXf6/eWxVQ9XpdN9JarRaO47C5uSmvNdujHbRxHIdbtVt6e9R16jgOo9GIVqvF3t4eGxsb3L17l952jx/87AdUYcUnTz/h032ZKnM0OSJzMhI7YV7NSWspkRfxU/en/OjdH53ryNOCv/Pp36H9pC3jwRfgmgLYGnaDltfik/AT6ladWTwjJKRu1WlakuZhFlqqiadYU8oI0Pd9tra2dCGi1qeq+FHH0TSVVGwWE7BQz1eMlaqqls6DKlijKGI4HGp2w3g81h3qjY0NOp2OrjnU65pAR5ZlhGFIGIZ6DhRFgVM5NIU8P69uv6qbhiCjLhU9XPm5qdc2m6Rm8aZ+ZxZoq4wD9X81l9S9WxVo6hwobb8pDY+iiNdee02zANS9XG2bAu/UfFVyHMVsUdubpqmW7qhapqqk55D6bFHFsPKQaLfbS54KqtYxi14T8FDnUHW4a7WarhNGo5FOm3jppZf0656enmqmUlEUuqaK4/hcsa9AllXA0fzbbDbTzWIFmMznc1zXZW9vjytXrtDpdJaYDOr11DlaBx6Z59KsZW3bptPpMBgMtJREff6quec4DoPBgKqqmM1mTCYTzRja29s7x2oQQtDrSUf7vb09DVSY96mdnR3dfI/jWBf2WZbx/vvv8+abb9LtdpfqatMPYvVr9fo091P9f2Njg729PX2cVgESxVQzfXw+azw30GBmrqrJo+QPCpUzby5qsq6ieOoGP51OSZKEJEn0xFT0IRWxoTQ9ykU1SZKliJGrV6/y+uuvS4YDBbfKW/w9/p6clKKSGkgrxnZshC+o6hUFxbnC1qkc6lVd5jBXDcIypCVa1KnTcTrSZyF25JdwaDab+mRHUUSj0WA+n+sbmeu6Oo++qs4ibRQAoRgUaZoyn89pNptMp1M6HSmL8H2fS+ISV+Ir+rimaUpUSMZB5mYyLaGcMC7HzB0p0ZgyJRaSoq4Kt0IUFFZBXMW6CF8alxbfDR8FD4+GaMiY0KJOs2qyaW/ilA7BJOBvuH+DWljTN888l5TqOZKdMWPGuBjLzhAjxtWYmZgxq2ZERNIwUTxbA16IYr1O/KICvlJ/FkvF8FIRvPhdSUkpZESolgNcQMMXLEwvkQaLdimR6bKSJmO5lZNa6/fHKR2cwsEVLqIUcnuEZHwo1kQhzs/HtUNJGqo1Bbll7JddknkZUV3ulwYlgPd4b0lGoRa2IhPYqY2TOISnId7Uwx26hNMQV7gUtnREv3TrElVYMbfmRE4ETYjtWJ53jHl30VgHTqjfX1RQW8icdU+yGCKkR0oVLJgOFwEKU7lgt3PpOWGVUpYBYNmWTuyo3IrSLSn89edBVIK6X8e1znS2RVlIJlCZPZeXwZ9r/DNmW6RVSpoZ3XyTEWyClYv3FUhwx7XcJfaEYzuSOVEWUiay+MrKTOugLwQnFq9duAWFKwGik+Rk/WONIYQ8p91al1bQIo9zLGFRVIXcr2rBmKgyLd1R/55H2qGAG83OWrAnsiK78H5lC1t37MfpmGk25cnsifbiiMtYmvCuGTWrRvVGhZVYeLlHWpMu/abXwm68y3Zzm+P0GCuxpJ9Avc729jaXLsmbt2mqtdplVJ+7aoGmHgNnNFX1e/UZnKYp5HCtd41e1mOrs0Wr3+L1K6/zlVtf0a+hOpGtVktq+y0h6cOxdIgfpkMd/TfMhgzjodbUKylFP+qTvX5WVH3n0+8AC+r/gubfC3t0/S5ZnNEpO1xqXqLoFFzausSt5i0arQY1ajTtJla1fEO4cvUKfsvn/Qfv8+T0CZNC+l8oH4xJLqObh8mQw+mhZDLkkzM/jM7ihQ7ltdD8xplhnUoWudSU2/HWxlv4hc/l9mXiQcz1reuM9kdcv3ydd3/2Ln7X59s/+7Y0qB49YVSNmFZTptWUwit0GlXhSsB7bs35weQH600Pu/Cj936EJaSZsWd5+MIntEJ23V3qfp2m2+TGzg18y2d8MpaxtIvIz1kxY1pOdUf+4ewhJ/MTZsUKMLMARuxcppbsBru0vBZNv0ngBPK+4HjyOiwLkiyhtEopnRkOOJmf8K3732KSTaiGi88uV24/XSnBc3MXJ3WwUnkd+KVP3arz37373/Ha9DWIoGE1uNK7QuImXO5eXipizWJhtShRw3yMWbyp34VhqI3O9/b2uH79OkmSsLOzs/Q49bNZ3KlCZFVOYts2da9OixaXmpeoNWv4+z6T+YTOUUcXBMPhUAOH7Xabv/23/zZbV7Z4cPiAeTUnsRO++f1vcvXOVXI355O9T7T84yg74tP4U6b5lGk5JX96nh1mYdFwGprps7u/i1d6WkJ2Kb2Em7tMtiY8zh+z2dhkmk1pB5IVooAY1aGG8874JitLAQtwFvVoggtmcaM8KNTvd3Z26PV6JEnCnTt38DwP3/c1dd80llfFt6l5N1kt6v9mDGij0dD702q1uHLlCpcvX9bn0dwP8/XV65pzafUYmMWcKj7VHFT7a7JMgiCg2+3Sbrc5OjrSSQuqObnaYTePqTnHTB8C1eAsy5IgkClPilGxvb1Nu93WNYi5L+acVvts1m7mMTDZDur9lSmtAvZOT0+Zz+f4vo/neXoeNZtNtra2qNfr9Pt9/XyzOFbXlQlcqfdVUZ8KtFLgiTJwHY1GGpxU0gzFyjGP/zq21er/zTlVVVJ2/+DBg6V7T5IkS7ICNQd6vd4SoGWeP3NOZVlGEARabqFqW8UmUk14ZUY5GAwQQmjTySzL9HFXn+WmweSqR4W5f+Z5NEev19P+G+ocqOaG7/taQjIcDpfusc8azw00fP3rX9foG5zpU02pgdpoE6U2kT5FRxJCsLu7q11WsyzTkRmrCyZlrqGQWIVIhWG4hBpaWNwWt/lP8v+EUTFiLuaMizGJmzC35M8zZswsWfDOxEx3rXORMxKyILaEJU0TqZaLiEB++ZVPvarTpEndkbSWBg3qQZ2maMriPK0TCknbM40t1c1DafBU58o0GFm9GStE1C5tKQepZNa0qARZLm9IdrnsSVFWpWQZBCWjfEQkIil/KKXmf+7IeKUZM2Ji2eFTzAAMtgRIqYIaNjoKU+SS3u8Jj4BAAzWtskWzbNIu2ty2b9OhI7vSvk9Fpc1D3MAltVNG+Uh2npgxEiNt+KhAIrV9zxWRiSxu9IWkqouLKPQGOHGRXKASC9NLcmIrXn7uGmBCgzWVJ7/sRbaxkJKMVKRkZKTiPHVb+15ULra1WAwtIuMUKJGL/PmBidKgu5vbugAmKhbGoB4U9eJCDwX1Eo94JD1FSsmSaYomTdFkl1169NgVuzQrCcKlIpVyHBExLadMqgnDashMzJgzJyIiEcn67vIFAERly3O05B+xbthQ1asl/wDlI4JgeU6rUYIdSzaIyAWBLRfRVmXxxotvyGuqyKROOpszSkYMosFSrKU+vMIidEItZygreQ7TbGFi9zxVrxr/Akg6UiSIMCufD722hbwvhE6IZ0twwhY2FjLJoj/sE2URhVXIYt4Rz5RBmNuSOznH6THH6fEzH6uGhUVgBYRWiC98CfohJHuplHGwSZkwS2Y6qUMXWuvmycooqoJpNj2LEhULozxK8jJfu0+OcKjZNazKkibDC5r8g+oBD08ekpYpcRmvNa5THWn7RZt7f3KPltOinJZL3c26VZeFt9NkJ9nBD3392WIuXtVCR31Oq+5lvV6n2WxSq9VwHIe33nqLb3zjG3z5y1/Wn++q02UWDY7tSPaG3z2nATYLPXNh86d/+qf8j3/vfyR3cxqXGvzKX/sVUjuVMZ7FhFkpmRXTYspJdcKD6AHT6ZRRbcQff/DHSyaUAL7wtQ9Hw2qwc7rDTnsH5mCnMjGqbtW55F2i6TSpizo73R3eevMt7t69SxzHMi7MgXE25rf+f7/F9Zev89GTj7jxyg2twVYSlCezJ9yd3NX67HXnzHvfoyZqtPqyM1WjBq7cnnbcJhyHhGWIX/pE/Qg7tWk5LWphjdfffJ1f/zd/naeTp3w6+JST9IRHg0d8tPcRG9c3pJ/A9EQzm8aFjDfV8260ft4KpGmrK1yaRZOG12DH3WG7t03DbdDyWxw+PqTu1bFKi+s3rjOcDJlmUzJHAvTjXJoPKkf/dWauyiviknOJlt+ijEtpZltAGqeyi7dgFWZkpF5K4iQMvAGPTx7zm8e/eX7jH0L9x3UZRWjJ701HslPqVp222+Ya19gd7xIPY8r47PoQ1VmxAWfrTBMoUDrzqqqWClw1f02psCqUVCGuihlTPqJYrkoWoq4dswg1r6WTkxNu3brFbn1Xd6/ntTlfvfZVtra2uBfc0zRxszhzHIerL1zldH7KOx+/Q3/elwyXcso0n8p1bykLiKfzpzKJZpGWo+ftCPie/NEWtmRkKQPXhRfFRrhB25e/7wU9rMTiWn6NLM5oxA1dEygfAQWiqNphtWhWxrvquKvi0WRSqC6yWV+Yxe9F8m2zm6uKcMUG2N7epizLJSM99RyznlkFFJ411P1OGaeaHXu1DWouhWGoAYdWq6W3Xz3XLExVnWQWjybrwZR5qLmk0gFc1+XmzZvAGYMcJOCmNPcmEL1ahJtggwmmqPdutVo6llKlBQohtFQiTVNqNQmQA7poVoCI+Tmh9lsxEFYb1Wo71DFR26wkBGVZ8vLLL2vjSrPZbe6Tut4/a5jFdrfb5e7du0uMCMUiCYJgiRXVbDa1oa6SOqnzbso3lDeGqnVNUBPQ8d6u62qDRsdxOD4+5vbt21ryb/obqmtvHQPH/L46FCNISVJM/0VVeysrA9/3tf/E84znBho6nc4SlWjV4GIdCrWKAqobj7oxmKYZKtfcdARXEgalFfE8T1+4KpP15OREv77jOORpjp/7OIXDhrtxrnBXmqGKSpbUjjTdK4JC+hFUU+bWXHoViCkTJsRWrDtaiUhIREK/6mtQouQ8zVxUQlLXKkldC/NQghDUqVd1almNIA9o0qRK5YeEosaoC1rRnhQyaNID1U1LfYjBmTaqKApc28VObLzcW7qgLMuSXgkLgGM2mxGEAVEpjQgTRwIzc+ZMyglTe8rUmTKuxvLvJOTIYlfR++fM6Yv+YseRhaw5s2xJ63dw8APpOdAQDdplm2bVpEWLDTa4kd8gqAKtwVYXcp7nOK5DLGIyL+NB+oDj+JjKr4itmGk1JXZiGf9JTCays+jOi4YBMpgShiVZxjOed+7nxb5rsEYky49b81klKqFTGoIqwMfHKi2dDpKTk5JK/w7WuLuWIFKByGWEl+M48nllTmlJ34vKvqDrv+a1qDjH/oCzbVdGmKUopX+GmHPI4cXHZgG8OJWj/SZqVY2e6NGlS7fo4o5d4tMYL/AYizF71R79UR8csENbJmj4Gbknc+ULp1i/PyuSDg1EVGfbvvT31WFB4RcUXoGoBCmpnBcCvvHgG+eprMi5oxZdLb9F6Ib4ti/NHMWZJ0Ccx8zTOeNkTD/uM02na1/LszxEJXSxmubpsn/JvySjqAqiKiJKL5B0OJz75FF+E57w8G2f0AlxbZcszzToNoknzNM5VmCRFhL8+CxwoqRkXs6ZlxdoCdfglxYWvpBynYbXII9zup2uBifiItbsCQVm6SjR58CQ8ipnkktvk8qpdLxsXuXk2XpA1REOoR3KNA0sMpFxND9iv9wnSiOZIlImJNUKe2LhH1C367IosxpnhZndpCZqXM4v85H/EY9PH0sZQf0KdaeuP2fLsmRzc1NTUVXXxWwkwNmCWK0F1jUf1P/VOiJNU2kEG1u05i1e9l7WJs2q2Gg0GtLV/t49wjBkc3OT3/7ff5uv/erXsBs233/3+/STPtNiquPsVLd+mA3ZO97jeHbMOBufpWSsnO/Oex3qQhatvUc9uoGUTuz5e8SnMZ1Oh6vBVT5X/xw1avTCHqErC4JLly7JSDgqHuw/oAxK3r//PrmX8+DwAaVf8vDoIbEV8yR5wrScMvNnzL05SSsh373gnOcOf2b9Gf/4O/+YltOiLuq0vTYeHrvVLn/t5l+j6TTpP+0TEtJyWnLeWhYFBfNyzvbtbR6OHvL2J2/LeNF8wLgYS3lFKU0qozximAxJy5R78b2Vybr4/uHi3C0ACt/2qbnSEHO3tsurvVdpu20pxWj0iAYRm71N9g72aLQaPD1+Su7mHCQHDKshczFnHsyJazGVtV6iF1Yh1xrXCJCRvqETksUZvufTbDSZzWfEWUxWZhynxzyOHjMrZDEdP13vhl6zanr+bx5vcqlxiXgQc628RlAF0qR1npHGKRv1DUbZiIbT0MWcAgvUetMsdtVjlGGmKhwUoGB2281OtFlYqW6luV5VazxVaJsFi9lRrKqK0A652rzKrDbjmnPt3DVXVRVf+tKX2N/f1zKFIAg4nZzyrR9/C7/j093t0o/7zKs5s2KmmT/DdMiD8QN+evJTzUxaB6w5wqH9fhu/9Nnsb+KkDqVdkndzRi+OsBILv/RxM5eaqFFlFcfDY7Y6W0tFp+l9YxbWZiFsgpeqYbfaoTVp5WrdrGQipiTAlDeYAIUJNpjAqtq21d9blqWLRwWQqKHS+hSDRhWhqsbRl92i663mlQlEmQku5n21qirNSjML7KqqtMGfqq+U/NsEg4ClGky9njoG5jkwGQjtdpvhcKjBJFWzqW6/6RuiojPNuWwWw+qzJc9zfYxUTaTOs6rh1HWhZAuu69JqtZYiOs1C2SywV1l95mur46DOl/pZvbZ5bJQ8XtWvqhBXx1EBWWo7V6U5CjBU6SAK9FTHWl3r6rWUjLDZbGrWinnvMFnmat6tSh8uGuocKz9CUxZiSjIU80tJlp5n/FxmkGqDzYJ21f3WXFyYO6Y23HQ0NpkP5hecnUiTBqJ8EcIwJM9zkiRhPp8zm830zUNlmSrpgvq9klys0riKotATxSkcmmVTfxBoWpYlyIQ03IusSJomWnNZBHkShZ9bc+a2lDUUlqRhz5GAxXF1jHAXqNIaLbooBG7mEvgBztwhLEJaVgs/9QmLED/38TP5syjPLlh1nBXiqya1uujUvsZxrPNlFfKlbiaKZaJuOLZt4wqXNm3quVxsqomnIqlm85mMJ/JzyqAkdWX3em7NmdgTZs6MyImkf4OVaalCuvg3YcJRdXTWXVbYlIteqFuVhVM6eJUsPBp5g3pRpz6r82H4IeP2WbSQX/qElewKdYqO9Oaw6viVDwXkRU5hFSQiIXcW51BEUmYiUsn+eJbh4zNAiecGJtaAEpVYmF4ii3b1+4texxa2jGArHNzCxc5sZhPZZbZ9G8uzKOyCyqoonPNSAIHAQzImRHUWjahkKrmVfzZAs9hGUYklmnJFtWToqYaKD02R0pqBGJz90UZTaPUxEuhEFvU7q5JgRViGBIlkz/iFjESjWtxIqSisQnbbLBn/mtqppCKviY07B0yon8Vi0WAAE0W5HvmuqKQxWDKSJmyWrY9pVmRrwQlAmtFVvnat9x0f35Vdlfl8juVaFKLgaHCE8AXzcn7O/0ANV8i0D9uyQUCWZxII/HlYE/8CDO03QSTNMC9KA7BBErAko8q1XHzLJxAyvlPFbHquh+VYTKMps2x2Jq2onhGxuhglJVEVgYNkcXhwOj995nNMQ8yaU9PbUlQFSZEQ5zFxGZMWKXmZn22HwZp4lilmXsmM+Hkxl/4wQmabV1W1tniWh8qm4TUI7VDKiSpBXMZEZcRhekhWZaRVSjyIye8b7y19xmTH+5bL7538Hs3dJj/58Ce03TYNq0HHl4V5y2uxlW5xzbkmfSmCDq7lngMZzEWeCVCohSygF2nqsepzylxQ6waE59MLevRaPSbNCamf6i6OuU65c+cOW1tb3L17l9PTU5IiYVJMJNNqAUgUfkF9o87Hex9roOL++D4/zX/KCSfEk5hqUsGKuie0Q9pum17YY6O2Qdfv4hc+l5qXsBNbmtc1ttgIN7ieXOdS4xIfnXxEnknzt9FoxGw243R4ShVU5L48x2Ugo19FTdDebbPV3KIf9dmb7/Hh5EMZWehM+IPv/MG5c+4IR2v2m3aTa/NrtJwWeZoTiIA7/h06fkczPtpem1986xdp1Bp85zvfodVryfsNU775k29yMD+gfrlO7MQczyVYE1URcRUzz+cMkyEPJw/X32+Uj+XR2Xx0kPGIXuURZiFhHOLm8rOsjEtpEmw5eK7H9pVtbu3e4mh8JGVJxZR+1meezplNZArHulGzalwOL9PyW5LtmUtwxBZnRWFBQVqkPJxKyciPH/14Oa4VYCq3XSB0fKnpv7JR29C/6wU9aqLGtfwa/ahPkAREcbTUVTSZvWbho4pCVSwoM7k0TbXmXhWGyiNMyXFVgWKanqsix7we1PWkCmO1vlVGm6Ed0ipbXAmvcHvjtmbehmGo1+tmJ1ddY5N0wv5on8RK2OvvkTop957cI3dz7h/cR/iCvWiPgRgwrU2Z3pySu+fjyL/5g29KA1VbMlIadgMv9/jDb/4hAQH5OKdu1+XcthrULMnY2og32C63lxgiqmhX26wKebPRaFmWli+rwlAVpOa+qqJaDZM5bIKsJlMM0LR9pWFXxbx6D1V0Kg2/6kyre6HZlF0tFNW5MwEZNZ9UUa4K2yiKdME4n8+1rCFJEoIg0PWCei9zn9T+qo66KorNuNaylN4ae3t7NJtNzUBX22t2xdW8NH0a1GNNZpHyIWq1WvozQW2L2Z1XhbZKQlQdd1XTqKHqGvUa6lpR8+Wirv8qs0P5BCo5gwJF1LlblVOYsh3z3JvnTAEX5ueeeoxZo6prXH0plr/aj9V5q+ptdezWAXCrgJ36UgCcuvcoMEidNxP4WJVlXDSeG2j4+OOPlw7Gaha5OVZplarjrjSdyoBG7ax5wNX/M5HxB7U/kEVjUadW1hi6Q069U5pZk3bapl7UeW/jPUZbI2plTfor5CF+JgvzjtMhLELs0qY5b5LPc01FUzeROI6J41jrmUwq1ng8lokTdanrGo/HTA+m2IXNhrfB1atXl7RM6oY2TSQrwmk7UsZhzUndlJlYFOBuIr/bCaUtu86pnZIGKTTkB9see2uLWLd08XKPIAtoFA38zMeNXezIpuf15H6lFo7l4Hu+Pk+reis4SwxRk0jR19TFqmJAlQREIZb1mqRAFVmBVUg0Tml64Owmq35OsoRZOSPzpJxjXEojyTRMGVkjZs6M2I5JrZTcynXXPLVSUidlypTT6nStVAEkyyQlZcwY4QhdsFZU0hhwZYhS4Be+THLIa3i5R506biFp1YrGGVexdjdOrITUkduXC2lY90xgoroAmLjI9FBv3NnzV39fIOUXuiPsI2PSLvCscCpH6nYJqYu6ZEwsih9lYJmQEFVSxrDOb8CtXOlmvqiIlKRIgxOfkRig9wkuNgdcedzq7zRIZadM3emyGeYFx1IxRVq0pPcKDQICDbKUlSzyMjJJKa1kAoyK4nwuecpiX/JSMnue165hlI4kOJDbOikCJBNgyaVcoD0UAiGNbWtWDVfIc6LAIgT4oS+TQKIxk3TyzC6+Z3l6fiIWWlQWdL5/zlKN5x3aMLXM15thrmloKnDCt3x8IcGJbqOLLWyGp0MazYa891AymA6IKskUKO3yM6UdFxliftawc1uaYpYOm51NSSetStIqPUvqqDIJUCxSetTIy89I66CQBRpTvf8aXKRYL+tAJhgEdoBv+0ziCaVVkvkZe8kej+JHxIUETVZjU9VQGffa9NFv03EX/1+YQqpi7bQ4JbMyKRtbaNRNWrqKclO64rIsqdfrei0BZ4WFyvg29anqs8jzPGq1Gg2rQa/qLXWu6vU6d+7c4V3rXd2oqNWkB9Ef/dEfgQVf/8tfp/CKs9jOhbRjlI6IrViaMUYnnMxOGB9L5tLaZBMhCL0Qr+vhNlzs1JZxzYWHX/iISOCXPrWsRmvW4g3e4G+8/jcYng6ZTKSccTwe87P3fsa/9//+9xjEAx4ePZTxeovYv1Em5QyTYsI8n/Nk8kR7ZcyKNfKnD6HltQjKgPbjtvbGmCdzynnJDju8sfkGXi4lIDutHW5t36LuynVArVYjFzmH40OenD5h5sz48cc/pqgX3H1yl9zP2R/tk1opo3gkmw3WhCIo4BkNsbvc5ZsffVMCisLlxdqLvOm8STfo8pU3voJTOBR5oe9bSZFIk9JkSETEKB3Rj/r0o76UuqRS7qLn7cqh8IVPz+nxlv8W3U+7bFzb4MrtK0zyCRGS8akSWw6iAz4cfailNBdFjLaetPBLn43xBk7qUDkVRacAH0QsG0yJm+BmLlgQWqHWdCdJwmg0wrZtnj59SqvV4saNG+zu7i6tu03Zh6LB37x5U+vYzSJYXVeqUMnzXOvrDw8PybKM27dvnytMzGJRFYSWZdESLdymLAZvuDdotVrcy++xubnJB+UH3Lp1i/fff5/9/X0OTg945513SNIEK7QofQmoVWHFV3/lq3htj0cnj5iVM+bVnOPsmA8HHzKIB/SjvgThVkHij8C3fVpOSzJlnTNzzKbbpO20ee/T97jUuMR4OCYkxE5sNhubxHFMv9/XBdsqo9qUO6/S99X3VWmY+m4Wa+ocqL+r+0tZymQ71TU/PDzkypUrBEGg/d3M1zS74soo3yxOTbBKgRiqKdlut/ngA6kzOzw81El6yufH9F4wu+4mI1zVcWZRXhQFnU5nqX4zu/mKmeD7vq6nwjDU5qOqyalebxVYUTT9VaBF3deDIGA0GunrQXlwmOdSbZM6T6ruMYvzdSwZk/UA6BpxlWVighy2LRM/xuMxjUZDgwLmHIEzHxOV3mjKO8w5pz4DFaNdpVmoY7MO8FLPU+9zkY/CajNAnXfbtrViQI0syzQopRrxinn1POO5gQblDKuQVpXzragt5oWp/qZOqkJ71AGfTqdL+lAT7VMnL3ETptemnDgnxG5M5mTPXATHxPSrPsKThea6gs4qLIIiIMxDakWNWrUwDQlc6lUdJ3Zo223CMqTKKt09MbUzKvrHZEiY9CkhhFzI4hOmIRvFBkVR6Ili0ogcxyEjY5SPSJyEmZjJFAArInZkRGHsxcROTGInFLY0icq8jJk345TT9Z1ZZPfKKz2p/az7hInc5yCXHeGwCKnZ8v9OKaeBOn8m+qi+giDQyK+it5nolomMmpNRCEl/rWVy4ea7PnEck8UZAVIHWVYljn1GG3QDV2v7lbdE4iWMrTH9qs/cnmsqfWmVOgLxM7u4xp9TKyXzMmJXViSn4lSaNFrrqfl2buMXPvWsjpd5eLmHV3rSZNBedNOFNPPK7IzcyXWkZWmVFwMTBqiggQlF9xfPCUys7JvqzCtpy4wZJ9WapIqlp0i2g4rTrKYVZVxiC5tOrwM2kp5Nohdd60AGB0d6TLD4gFowHVSR9FkmoKvb9EzpwzOOi9r3iIgBg+U/rp5fYXTIcenSJSgCaot//8pX/hWp668KkjzhZH7CyfyE4/kxw3jINJ1eaPh30SiRXirPSzyIq5g0TxkLGfmlX2cBiq0rrF2knj+0QnzLx7M8GmED3/OlC7Rjad+PuIw5mZ6Q2es9JMyOIEiWx0XF6r+oYwmcWJjAHgwP5B8tzhUe6veq+A7tULJQRKC9HhzbwXZssjIjKiLJOsjnxEUsJUyfwZ4onILCkf4oj7JHXBCIoYd5nW62NvGER5ZKA860lKyNpJQAhZZ0/ByGpTk5k1J6I4hsIe0QMMpGlNl6sEVHOTp1LR2yhU1FxUlywkF0QFzERLk8PueYPr8qwd+3y7f5s7f/jJbb0mkQTVvGUW73txntjwgJ2exvcvfJXX55/stcunSJW7dunVtwAfqzJ45jbfq2WjyZXU614FV6WMWYrKoK5nC9e50bjRtLksWyLOl2u5o9ORwOqdfrPH78GK/u8cn+J+RuzoePPiT3ct5/8D6zcsYgHTDJpTdRVI8YOTJ9Y3Wd84f8If/VP/qvCO1QsxRELHBrLgc/PtCJJm2vzZXmFV71XpUGmkGPptvUC1OlQy9FKZkBUV8WyemQcTbmNDrl/Qfv47Qc+vM+x/ExB8UB89qct4/epjhaA0JbrmaxdP2uPF9Ok0uNS0R5xIv1FwlqAS9ff5njh8fc3L7Jww8ekkQJs9mMh48eMokn5G5OLGIG+YC8npOHOUWtwO/61LZqTLMp42TMUXbE/eQ+8Tzm737j767dHpVq0vW7OgnmSv3Kks9Aw23gCAfLlvfR7/zwO2xd3+JgeMA4G+P0HXabu9gTm1/Z/hU9j5Te3Vwfqc7mPJtzMD6AEN79+F2shsU7H79D2A259/QeVmjxNH7KQAyYhBPiWkxqp2tlI78f/T7/+T/6z2m5LWqiRttrM02nvNR/iW//+Nu0vNbSeVfGpDWnpsE4VRiq+W4WEqq5lqYpN27cYDQacXJyQrfb1YWPKlhMbwrz90vHffE+6liY8hBVxCwVfcLCzmwZLZ7Z2HOb16rXePPGm+y5e9qz7cMnH/If/7v/MZYljeeLsmCSTRglElAbpkMp6ViAPf2ozzCVgNtJesLHc8lQGu2tT8OxK5s3Jm/wi/Evav8AUyZgdpNXmdqrx8Bs4FWV9EdQRac6FmrdrDwLhBD6XDx8+JCXXnqJPM/Z3d1lZ2dHvz8sU/kBXn755aWCX30pJovneYzHYx1t+bOf/YwPP/xwiWEAaLBCMQTMfVW1mAlCrLLLqqqi2WzqOE3FUrAsGV16fHzMrVu3iONYmwceHh7ywgsvcPnyZV588UV9/MxjqmTyGxsbbGxsLJ0T9XjFVleM9iRJ8H2fBw8e0Gq19D151UdglWWkfmdKFkyAR/09CALNWFCJM+p1lE9DURQ6bUIxzF944QWuX7+ur001lEwhyzLtKaG2w9wGs3YeDAbMZjMcx+Hk5ERfYybQpV7HtDpYZcWY+2d+dqqfFdtnlVBgzjXFrnie8dxAg0pGUB0ApXHyPI96vX4OuVF0GzjTPCkUShWpitqlLmaV1lCWJb7l86t7v4rjOMxmM2zXpgxLskAW4nNrztSeyo64PdPFeGZn5028Fp3P0i6Z23Pm3pzT6lR3d4DzoERm4cYuTiTTJtzElZ1E38NLPUjh6NMj/NLXRbK5byZdB85QQHVhK/TLRKCqvKJMS7zKQ2TS4+F6+zo7OztSC+NA6qaMCulWnfkZsS2BiNiNiVwp60ishNItSeyExE6kJGER7bhuX0UhsDNb7mPs4s5lNKI9sbHmlozEFA2CPNC0WPVhUhTFkmGSEGIJLVQ3HfV/dTEqBkWaphxdOuJ7m9+TxfuiiA+rUMoDSpuxPcabejgzh51oh2YqPS9CX7ZDhCso/ILcz6EORVAwEROG9pCJPWFuzXUMZyGktKCi+myzN4OZgIDczintktiN5dyxpFFkZq3R7y/iVZtlkyALCMpA7s+C4p6LnMF0wDSb4tQdnJqjZRyFVVws5VDAhLpfqe/Kk+DniWg0GAHa9G8hbaGBZEsAR+JoOU6wkjRYH1/qZwmkrEEE2vCvQBawSjIxY7YWZLAqCw+DMVHJaDUzNeB59kd39/kMScsFr2V6jkRES3PjnR++s/Y5lrBkN8Vv0Q27ksod9ujVevi2j0DIojaP5OI9OuZ0fsooHjFJJz93ka7BieccGZm8V5RTHSlrpZb0ESkSqmzl/RdeKjVRY6O2IQFTSxaOFgs/mkqCRmmZMs/nTLIJ43S8tqC2hY0tzuQkCpx4FhPlX7ihALsyZ17OOc2eLaGQTxE4lkPoSGCi4TSoO3VqTg3bsnWM5uHpIcejY+09YvnWOdbC6tDeQsic+efbBUHNkXr6ulsnsANsbPIql7KOMibOY2bpTJth/jyGmFmVyYI1HS8ZYlZVtWxMaAxXuDS9JlVSEU9irNIi8ANCOyQtUg7zQ55ET2TSUjFfZk8cA9fgf/kn/wt1p77ElGi5LW1g1/badD1ZjHWDrk6LaLpN3dxQi6UkSXTawNbWFqPRiOPjY7a3t5nNZktFhFm8mfRlczFXFAW+5dO1urQbbYQvM9HbTpvpdEp/0Gc8HhNFkc4ltyyLeTSHEJymAyFcuXOFO2/eISLiSf8Jp/NT9ot9Roz48cmPNViwDui0hb1E9dffVdTo4nhcq1/jTu0Om3ub/NUv/1Wmoynz+Zy3336bLMu4desWl65e4jQ6ZZxLNmLu5JI9kA21N0Y/7rM33OOHJz+UCRZPF8jdAstTdj6e7RE0AuzbNlYsUyfc3KWaVbi5S21eoxE3eH3jdf6NL/8bbNW3iEeyMP7ud7/LV77yFbo7Xc3iUIXmMB1qU071/ZPxJ7ooHafjtfdPF5dOJOdL3arjuz73N+7TclsMHw9p2k26YZft5jYbtQ0pmfA6SyxR3/K50pCRevaxzeXLl9k83OTVl17lh+Mf8uKLL/LOO+8wiAc8ePKA8XhMmqWUTsnp/JTEShB1gdN0uPnKTXZe2GGUjjidnzJMhkzDKd8ffZ8/Ov0jRulo7X54lnd2bn0JtmjQxZPgUzfo0g26iFRwmpxyhSt4nke32+XmzZtLRQSgO6dqfasaiaYxo8l+UKCCKgRNg03zGlot6BQNfnt7W8eaqiJdAYACoYEV9V6mbFsVhSaAWFVyLTBKRnzrR9+isdVgf7jP8eyYJ6dPuHr1qgYMzbU7nCX1mPRxc5vNfV4tXtUxU/Jv83lm8257e5vJZMJ8Pmd3d/cM2GSZLW6CAQoQUICX+XdVU00mE1qtlmaL7+7u6vtMt9vVTWLF8F49nqsd8nXzQn1vtVoSbFvINtS2qWMA0oNP7bvy21OglgkgqPdVzV0T5DC7/koaoOZbu91mMpkwHo+ZTCYMBgO2t7eXgBV1X1aAiDqWZn2yuu/mfFLsCpU0qM6DAqnb7bZuxlZVxcOHD9na2iJJEur1umbxr55TlZixvb29BOStSh3UZ8R0Ku/Pjx490nG0q9eAqs/M6039vPq65nPVHO50Ovp8qnNWljItstFo4DgO/X7/udcgzw00qFxjtTEKGPB9fynrVG2oqfdRjzcvFoW2qA658hRQNxYTzVGTyZrLnfYzadRVK2tc4tKS30CWycjB2InJ/ZwsyLBaFqIrmDgTpmIqafoL/fZFC97SLUnchKSx+ABXa6XVbmgJVmJJQGLxZcc2TrTY3vjs/yJfUFeNfVPHYPWGof4+m82IokgyRArpWO3lHt28i4iFvqmrm93x8TEnJydUVqXlGrmfI5qCIihI/ZQiLEjDlDyQbueVV5HbOXmQE7Wj5f1dBSUygZ3IBYIXe7hzF3u6ACkSF2tuYY9s7FQWGCZDAiT9qNlssrm5qal74TjkFV4hdVIJIlVzxuGYNEyJnOiZGntRCqxCIuRWJuOxgnmAH/v4sc9GtMG1+TX81CdP5cVSufLYRCKiDEtyLyeuxySNhCyUjJHckb4OlSVZBZ/ZGVTbs/AusCo5b5Mq0VTR0pKMCd3JCJZfwi5s/NynUTRwcxcrlZGMJ90TydywqmVA4Rnn6dy2LT6zlK+CQBpOltYzJCDP+L06HgkJI0bPLkgWPgtu6eKVHl7h4eYLSUa1SCNQfhVWRuIkxHYs2SVrmDp2ZWNXtt73UshOaymewRxZs00mlfxCs8hnFMVlVRLl0kzteP6cKQjCIrAD2k6bunPmmN4JOkTTSC4+KZmnc06iE2qbNaklT6ck5c/HmoA14MRnANClKGWue5TKpAiVorAwtlx3DVhYbPgbNFzpT+BbPq7tatBJPTcpE0bRiP6sT2Ila5lDKr1B3Sf/ubMm/ilAkYpKxmyWGbN8xjHPmBst9Dx2cKTfhB1IrwerRs2u0QgaVGXFPJpTiIKD/gGVX1G4BVERSVnFBQW92p5ZPpPbEj/fPJWb1uLL8ZeZjCe8/NrL2KHNfrzPUXLEMJfFXFREpFVKURU/F0snqzL6SV+Cg3V5DCIRMZqOninraLgNQhGSxzmXepd0tGJZlUyzKafJqQRN8hnjdLyW0m4L+8xF3+/q71EWsXW4xbXimoy09hzmjTkIaLQbSxF4qkNpaq7NRatamJnGfarDNR6P9QLOXPgJIRlcVizNSL3U41Z2i39191+l1WoxHo8py5LDw0MePXrEr/3ar+nP1SiPtEFfP+4ziAe6ED+ZnciCPB3y6eRTBifyb+N0fO44/6e/859Ss2UX3akcAivg8ugym9kmNWrUrTrbzW12wh2uNa6xEW6w3dpmq7mlc9UbjQZ3797l6vWrfPvH36a53eSnH/0Ut+Vy99FdTuen9GMpZ5iWU2IvZlKbkHZTci/XXf7v833+hz/4H+S5Fw4tt4WPz2/97LfYebxzVkgrRkfrCq/5r9H1pZFn02lK5sJiLVpWJbN8Jgv46JRJPmGUjXj7g7dx2640D02HHI2PeJI8Icoi/vTBnzLN18uhak5tqZBve20uNS5RTAuujK5wOjjl+OCYx/PH+LHPKJdzW7N3xcJ7KJGJI17m0SyafMX6Cn/tlb+mma+WZfHBBx/wwgsvsLOzQ5ImDOOhTD5KBhpIGaVSMtOPJVtllIw4mB+cMVfSNUXBJxDYAS23xX9947+myIql1AxTQ79aoJhFkdkNNwtXVeiZjAZANxfNxATl2q8K1NPTU10MrxZLJj18VeJg6vHVdm+5W9xq3+La9jWyntTwf/zxx1rya3aAHcfR+nRVBK4WaibAoX5v/qzYLuuMPdX+Oo5Ds9nUkgfbtvF9Xxfzq8W+qo1UEX+Rp4JlWfp1VI3W6/W4evUqW1tbWgKvJCPq5ziOtaRjtQA3i33z+Cqvi6qSaSn1el1T9T3PIwgC7cOjWNG7u7tL8nUTdFqVBJn3SBOUUu9fq9Wo1WqaQS+E4NVXX2Vra0snPZj3aJBRjY1GYwkoM6X+5rFU81Uda8Wg6PV6SwCTSkip1Wpa/re7u0uv11uai+q6MEErdY5V3Wu+twJH1Dnp9Xp6XqkkjzRNNRhn1tkXSSbMfVz9ndqfXq/H3t6evqYVoKKSTMIw5NKlS+eug4vGzyWdMPNAq6rSb7iKdJlgg9oQBSKoAw1nTAeFMilGhEm/UgdPXbDq5JqTU71vURQ8ufGEqlMRFiFu4hJkAa20xcZog7AIsQpJwwrDkCRNmOQT7LbN3JqzP9mXtPxmThKc+ShkdkbprCDIagFqQRmWpKGkrS4VfqtFUi5wUkeyBiJZmDuJo5kETuIgYsHDrzzESRzqVZ0gC8CCJEjwc2kI6aUejnCoyjOUTR2P+XxOURTUajXm8znj8VjSpqce1tw6h8wWRUFeSVPHzM9IXFnklWEpDbZqGamfknqpjGFzCmnm0+AsDnHdYrxC0uJSW+5j5OLNPbzII7ZiGptSN7/BBlZucS29hl3IyTwcDul0Olqb9emjT+mXfZJGwsSeMHWmlPVSbpOXy9xxN6MKZArFmPH6grGSwIQoBCITWImFFVnYkY07d3GOHeqTOt7Uw41ltKFiH5S1kjIoJXgVZiTNhLwlKZ6FX1C5lfTbsKSM4jPpygYzQRRCf+Wl7GiKSjImCregyisqf83qvZLghCgEVVmh/CWEK7SkRDMcFkBASfnZxZNKoDCSKIQQ8rUsLnQJfxYwUYpSar3JmK3lqBuvwwKYyCUw4eQOXiEjXu3S1tuUC3nuE0emxxTOGvZDhTQEKw3Gw8IDRH1/HmmKJSzZnVeLkrL4TFr8ulFWJfNcproM8+HZH0ZrHhwCs0UKgu3rYr7hSP1p3alLhlEloxRn2eycFvnC7vgzzlclKrIqW/aLeNY+UTJIBkyyiVzYGxGPqgheGg4yTjSxCQjYam3hOz4OzplnhSWZSvN0zjSV3gez4iyS2Byqiy5YdJafIyrzuYbyS/m/aeRVTl7kzIs5/bR/9ofVueEi7xuJZE54tkfbadNwGrS8Fk23KeNVhSOZSnnKJJcxduNszDyfk+SJNKR8BkNmzJg/Cv4IAvj+8feBBVtDOHiWR2iH7AQ7bAQb7DR26Hpdmt7ZexcUTJIJB9EBB9EBx9Ex43TMLJ8RF/EZwGek2jzrmsrJGWZDRowQlmA4GsrrkerCKFFXSDp93a0T2jIVxrVdPUfzMudofsTj6WMGswHzyZzZ49m51/ovv/FfSsM8pyVZE8p/wmtzuX2ZzfomG+EGVmJJ6UAa4aSOpsuq9ZFpSrcqGVWLXSVPNRfVSnLped5Sh1l1L4UQNGnSdJtcda5Kr6fFgnE8Hp/rPFWVPGbjdIzf9TmaHvH2B2/Tu9qTIEU65O7DuwzTIcN8yN5oT4ITFyR3KPCm5bbYCDfwco/Lh5dJhgnXxXXmxZzdapcuXWppjZfESxw+OWQ6mmrpymw2w7ItbN/Gbtp87ouf461ffItIRJzMTiRDYf8TqqAiyRPem7ynt2mcre+s1ZwaLUeer7bb1ueu5bTYbm1zuX2Z3XCXVy6/wmZ9k47XYXI84cGDB3iex4svvkiSyTWgkgxO8skSS2KYDhnEAwbxgP3jfY6nx0wOJtKvRRGgfrD4boF7w8XNJIvDKzyIwU3lZ13DbuAmLuFeyGZ9k7ol/U6w0D4lk/GEeBbjVR6Xqkvs2DtYdQvqMgFFCMF4PF5y9K8qeW2N0zFW3eJgfMDdh3fxWh6jfMQ0nVLlZ9F7qiA2GcrAUuFhmogrAz71fMUCUkWVYgWY7Af12up56v/Ka8Us8vr9/tLzzSLv8uXLDIdDYD3FW10zSq4QhqGO8VMsbVP+YRadpumiWSyuFqTqu9om5dGgwEUzetCslUyjzyiKtOG6ioFfTfcxEwFMjwZ9HdoyblJFTqptUWDNYDDQx8G875gG8avAiHm8zf1Ux0kxmg8PD9nd3dVmiapB7Ps+vu9r6YGSj3Q6HSmjXtzbVotslaiQJMnSXFDnWBXa6tzVajUtlVP7pwAIJaFQ3jvqfVYZHObP6/a31WppKZoCAJSfndputR2qCa+ics1YS/OYrjIYzLlvXotKXtFsNul0OnpeqNc3FQWqpjZBGROw+SywodPpcO/evSWm+uoxUyDZ84znBhparRbz+VxPbiHEUoqEMmeq1+tsbm5qDYvacJNqb2o7iqJgPp8vHfx6vb6UbauQUZOqqA6miWRWVUUVVAzrQ47cI1IvPdc1cwpH0u6LkKAI8FOfoAjwUo9slNHIG/SGPcIyhFLmmA4GA+IspqpVWC0Lq2WRhxKMSMKE2I1JvITMlZ3wc526xaK+cioyJyOrZbJ4q8QZxXt10V/CrJIFme5kG0NUQgMPQRbgphK4yINcutinPgjwaz6BF+C5y+7E5qJGR+lgUcRygqZpyuOrj2kmTfy5L6nxZYCTSJNMv+4TWRFTpoiGIAsypo70UkjdVKZvuDIuMG0YC5PFsbjL3aV9cQsXP/el8d/VFK+U5lh+5mPdsKhndRp5g9qsxvZom/qojogF49GY6XS6dAFYrkUSSjZK0kjIazlpkJL7ObmXU3olpVdShAX01hx7Y1sVAGBlC9ZELNkrZOAdeYSTkG7aJcgDAisAAU7dYevmFlWtYiqmDK0hE3fC2BozFTLTunRLnbhROdVnLrKp0M7xdmljFTKVwy5s2aUuAUt2tqiB8IWMtlwZohTYha07x+p9FRtAgxOwxFJ4ZuG2mMuKuaAKesU0uBCYYM2xX/zfBCbwuXgoYKKU5ope6eEWLk658IoobSmrWFxrmZuRuqmWFa2Vc5SSkSIquTG1eo2slIZ8WfF8xfc/K2ACFikIRURURJwmn03dhzNwIrRD6m6dptuUIIXboMgK9g/2aW+0JSMlGTFOxkzyCbk47wj+vNuYlukZ+PjZG0jhyRjMg/QAK1t84CPp9mtBDgENq0G31tV+AK7t4oiFjlAxJ4pEyjpSWVyvS/9Q8x+kCefPZVD6z3L8OYCMVebEyWoswpohENL13/J0SkYnkCkEo+MRV7evYtlSwrE/2GcQSS8BEQiSMpHvV2VkRcasmHGSnnB/fh/6z35Px1qAE07IdrgtXfuDDfpP+xzcP8AqLLZ6W7zx5htEecRJcsJJdsIgGzArZsyLuWb0KIDiedgTWZVxkpxwmkippDrncPF5d3Do+B2Z1pHIBsFmd3MJQMvKjKfzp9yb3CM6jhinY+LivFGKhSWBnyqkNWzhpA540sCusAoJCs9KgkrK69Ikxc3Oih6lHVfrHLWAVOZrukA3CrDVwqcsS46OjpZM19RjbNvmzpU7XPGvYDUtvvDCF/Ti9NuTbzOfz7l9+7YGLPI8J2gGFF6hzSdnlYxAPJ2fcjw7ZlpM2Rvs8fHoYw6nh3xz/E0m+eSs6FYM4s9LoNzNZOFtJRZO5kgzTGqEhFxLrnGpcYmd7g4tt8VessfLN1/m8uXLTCYTvR5VRfQ4l+kUiZ0Qi5jHJ4/ZH+8zzaeM8zHH82M+HX8qzTzzyRlL7P7yOatbMg5283STltOiG0hp3GZ9k24g/She7b4qpXJBj7pVx65kR/7w8JCtrS2+/d1vs/vCLt/8wTe5dP0S3//Z9xkmQ/ZH+1LuYiXkbs7MnRGFkfTocDN+VvyM//XP/tdzc6nxUYNe0KNhN6hbde1l0nbbEmC0m1yPZbpJPskJKmkgLDjrorvC5Ur9CleCKzSHTV544QU9t9599129FlwttsyOs1mAAto8XHk0KMmEKujVvFyNWVcyDJBUefV8s1GpaoxPP/10KT4X0GyHnZ0diqJgMBisLRi73a4uhhW7Qn03gT+1P6uUc3Nf1e9XARMTDFCAoO/7urYxGQHqPUz/BWWoruqhwWCgj4HJglDF+r1795hMJuekAbZt88UvflG/XxBI2qwZsTgcDpnNZvi+r4tWdV8Jw/BcoWvut1nLqf1VMozpdKrrNnVeTTm1Yrq02219LxqPx1ouYh5nIQTXrl2jLEsGg8G5JrUCmBToanrDqW0wu+1mCt8qY8Q81ybIZLJXTD8KZWS5zsNCnWOzLlbz+/T0dGkeqW3s9XoIITg+Pj5nqgnS4FIxGarqzIhVSRrMlA8FaCjwT50LBVxfBDCYx14BWmZdbjKS1DXtOA6NRuPc660bzw00RFGkD6K6uadpql1H1QRQk7fZbC5NSHOnTOMlFeGjaB6NRkNfpOpEmNQR82Co36lJkOc5rx++jjhaHPAiJ3dzEk926atGRVmTXenUS4n8iNPwlJmYSS+DlVg9P/dxUxdrJjvffibpjGEREkYh3WmXWlmjnJWUxeKGZAkKu5ARe4uiOw1SYj8mDmLyMJfUfC+XkoCLFrKWUdyp7rIBSFSiInalP4PlW1AtbgSX14ASpcBLPfxcbn+QLTT1eYATO4iZwM98apXMNLYsC7/jM+wOia2YzDsvMRGVdEx2Ege/kLQ/L/aoT+oEhQQ+7FT6TghLyBxlP2cmZsReTFbPqBqVNGQUUsqSOmdFikoDIETSi9eNBUPBzmyp81yALUEc4EYuYRbSTJu4xy7MwUkdkvhM16VzsO2SyI+IahF5Iyev5yR+QuYvzpMjWQtpLaXqLB/fESMOlAh1RT7hVq425GxUDS5nl2klLR7/6DFiIggtibZOchm3Fm6FNK81mfoyZ10Zgea2nCuVkEV77shr5cKiTrElcgmUWIWFXcoi3M5sKe3AkjGKdqEXPImTrJ+PmTRS1YWzYgRYZyacCkx4LuM5BUwgi3oFAmiGwTo21mcBE3Yp59hnpWAY58gubcJMeoE4hYNTOjiVI/+GLeVHQhpmHc+OOZ4fM03P02g9y8N3fOm/gfwwVAXgcwMTpmRgUWz/eYYJTix1x9WwgeHyr4QQuLiEVkjNremUi9AOEZb0KckqmcYySSdMsgmzfPbczIdzY8F0iav4uQvuWTkjmSfanFIt8vIqv1BaUrdlDGPNqRHY0mfGwsKxHbBgNBnJ7r5das+JdftkpsgsyW3+PEPN5/+bmBNqbmnmRNLn0eyR/vt7h++df5ItC0LP8jSTpuFIyU/X79JwG1i2RVqk0pV/oZuf5lOiPDqL9DQBEfMyuiW/PeUpP3n8k7O3FTJtILADLoWXZMTgwodhcDDgcy98jtCVLv1JmdBP+hzGh5ymp3puqutoCZz4jJGTc5Kc6HMtEOyN9vTz1513z/LYCrao23UaXgOncnAtlzyVnbQojkDAtJoys2akdkrUi0itdO091y7kZ9qP0x/zv/3Z/0Y36NJypLmhiASzZEZzT7rrl9OSticZLYrVA8saX3NhDCwtTE0/KbPIU48zJSMAvuXTrrfZFbu6SFLxd1EUEQQBe3t77Ozs8O6773L16lU++fQT7IbND9//IfvDfUbpiMPJIaNsROZkJLY0Gc78jNiLOfVOuV/d5/d+/Hvnj80DW0sjdKHttrTvRMttcal5iWsb17girpB5GU2nqQ2KVXHZ6/Xobff49o+/TdALmDNnlI14fPyYRyePGGdjRCgYpSMeTh/y7vBdmeixRnICMu6040tDzK36FsW04EZ2g1ky49bkFlEV0bJbkmXTb2MlFn7lMxlP9LFvtVq89aW3+Nr/82tERJzOTxlnYz568hGlX2LVLfaH+5zOTzlNTrk/uy8TRhRocnd5mwRCMp3clmbjbN/fZiPcIB2l3MhvyHQGt81JfIJIBdeya9Tt+truvVmEqflgWZaOiVTyanOtrgoTs8g2afOqQ79a7ICsD+r1+lKxp+anAjVWO8Pmz8p/RTEbzG62Cbip563KEUwq/7rnrD7fZFsrHwIz1UJ1oNXaUzEWlImjmp+mCaVqtKoaR6W/qWNvFsWqVlplkKiO+3w+174MVVURRZGOZzS3c1UKbxaw5jFQ26MkF0EQ6HhHxUZQSRPqdc3EDMU2WGVRKCBGnWd1D1LMGTXn1H6Z4JMJ/KyCRKudeVOmb85tdWxXn9dqtXj48KGe0+qYmH4tqqGrpDcKxBmPx0vgrwkm12o1zWRT26DeQwFE6loyZRwqMdGsg1dr41V2hDmnzblrPq5ery/NK/O1TTn8KnBy0XhuoKHb7WqETjmlZlmmY1iUk2q73dYHxsyFNW8QqxQZtfHrNDvmhWseFPOmY060Vf2QUzqIkTRBsyyLnZ0drelRJ2E6nbJ/tM8gHUg/h6Yg8zOG3pAHlx4g/LMYOVXsmUOUQkdqBnmAn/nYkU1YhNSzOu2kjTuV8gjfkgCK7/uS1uqkxE5MZEfa1HEezIlcGYOp/AJK+wLphuDMOFDR3CuxlFZQWZVkX1QJVs3SVPhCnAc6rNIiyANqRY16Wqc2rknDptIlj3Mcy6HT6RDUA0bxiNiOyb2cNEyZNCeyU7ymYBWVwMs93NSV8pHUpTFrsC22cRIHJ3MIbXkzquyKKIsYl2NOihPm7pzIjrR8o7RLve+VvfCX4MxJfun4rBwzkQvsdNFFSV2CJMBNXMRcylpqkxreYGFKWXh49lmGL0BeyKK/3+gzboyle/SCLVF6JaVTai+GgoLYjhmLMYfKCasB/BVjGxffrdLCKiwZTZo60v/i2KU1a9ErelSjinwsF812w8breVhti6k/ZWSP5JxxpBFo5VXyyl6wJZSfwmcCE6vHrJRz2yotHBxEcSY9wEYba54bpZQrWNUCAFOpICtfyuvh3Las2z7kHBKl0Nuqr8WLgIl190ADqMstmVwSET1TSnD4VJ4713JpuA3psO/VafpN+eU2qXk1BIJZNmOUjGQqxeyYQTw493q2sM8K5YXcRZlfPi/AYHbkK6oLJRI/T3GsfTLKjHHyfCY/FhahHRI68ksZH/qWpL/mZS7TGLIps2zGIB5I6vU/JVNAd/E/K57BGLNiRhInEmAQEmTTppZFur6rXTnUhEyYaAZNPMs7M7YU8ninRUphFcwKqfte5wegjtFnAhP/VzMn/pyjqAoNXA2z4XM9xxY2riUTKRruothZOOa33Bau7fLO++/w5PQJhVdgt2ycusO8mOvUjLiSUZrr3vN797537ncWFq7l4lkeG/4G3bBLzZK+Rj4+rpAMGAtLxn8WYyZiIj9rZifEZayZR+p8VeKzAYq0TDmOjznmGGsmAUO9qFyV8ixWXaIUeJknPw8X7KsyLbHUP9uiGTYBOIqO+DT7VMcrJmXCb/7xb57b94bT0MV2y21x5fEV6qKOiAQNW/6tacv4yobdoC7q+vmmpwScgQxmI8jUb5trO7Ojqr7UWs51ZULLVn2LK/4VQjdkls6oj+ucnp7qRbqSgNi2TaPR4K0vvMW/9m/+a6R2yuHkkH7U54OHH1AGJUE34Gn/qfRayCY8mD1gNBw9U0YRWjK5o+VIJsBmfZOrvatko4wr1RU2ahv0gh6vdl5lN9rFzV1ev/O6ptYr+jdCAp7jTDIoVOLB0fSIaTHlaHJEbMXcH97n7ZO3OZ4f84ef/OEZENpZfMlJhpM5WkoRVAEPigfc//Q+3UCCKR2/w+XwMjutHb74uS+SjTOqdFk7DxDnMVbdIhIR957ckxKOfCLldAswYpJPeDp9yt3hXU5mJ0wOJucZX9+U127TaS4ZqXb8jmZ2dAL5c8NuSLPOfszN7Zu6CFFzSZn6rRYkZvdYAQ1qHq3WAkp6YIIEJqVbzTFVd6ii2KS2q8bSqvRaFdiqYFTbqa4DNTdNybHaBliOgjSHmscm60ABASY4oI6D4zhEUaS9Gszr0IxjVKwDM8XCLAbV8Ws2mwwGZ2sPMwnQ7EIrXwFV0KuxjmFgRlaq46G8LJSkQ0kKTMaVCTKp11HAlNov833NY6j+rvbTPHZKbq+YEqrgN8EL83wqQEcdI3M+mftp7r/5dwUEzufzMxb44jXUfVFth5qTSkYShiGtVmuJmaHmg7m/CjhTTAXLsrQUxPd9fY4U0GDOVXO/zPp5lX1j7vfqvqr/K5aKeZzUUIDU84IM8HMADXmeU6vVNB1ERVoqDc7Gxga2bWt5hYm6rV6EivqnTojSMZaldLUEtPHRKsJpXuzmjUpNRHXi1EXXbDZ1hIh6H+Wmad50GmEDmwX1eu4iIkFiJ3gzj37aJ/dz3YHPfPmltaVWJRkLbsykmsiipcdaoz1RyIWqVVpYuaTju6mLn/p4sYc/8WkdtegNegR5oIusnByv67H78i5VrWLChLEYM7EnTMSESTlhLubkdn6++DNBCaVxXi0uVWfYKpl7c+blHMuzqOqyIDTlG4+QHTC7sAnyAC+T/g/NrEkv60m5Q2HJfa0WZopOoSUVqZcS2zFTf8qBd0AcxLLwNI/TApiwYwkKeDOP2qBGWIU4iYNd2uRZju3YYCEZEbWULJSeEomTSENHkzUioHIrcvcMmBgzvrhbDli5pdMw/FQyN+xE+k7U4zqNYQMrkuBAx+1Q82v6evF9nxsv3IA2jLwRe9keR+UR/bwvTTpdaThZiEJ35PHRSQ1qqOOtzpuSAtilLZMsUhs/8Qn2A4qHBc7IwZ25eKmHQJDVMoq3CprXm6S+BLZSK5XeE2o+rCvWF34MhbPiOXEBiEMFbuXqRbMynqyQYEdGRibWG7BahaX3ywTQNCgBZ2khzzvKM0mH2g7lM/FPMxRLYZpNMU7PhcPCkgW4KxkCDa9B02vS8BpM+hNsy5YMgSKW8W4LY69ViYVA6EJX7UdRFc8Vn6gKXEUbV8DGhc+rOJMiLAqkZx1zkznBz+FVaeUWVmlRs2t0G10CEeAJSYPMKgkkJCSyM539OVgTi6G6+M/9eJEzZUpcxUyTqZQZVZINkFbrwQlf+LS8Fg23QeAE+LYvDTWRSR9FKSMoozxikk0YxIML416XEpH+JR1FJaWNcREzTIfrH+QBlxc/V0igVUhTzK4tC5mm22S7uU3TblJ36yRFwkdPPqK+WWeYDjmdn8pI0TLWpqNJKf2XDuPDz9xOCwvHcnBwtM9Ew20QVAHFrKDMSq7sXMESFkUlJQP9tM8gk74kS4kYcHZtfcbpq6yK1EtJ3RVpIeh74FF5dCY5QF6bTbtJt+xy9dJVfOFTZRWO5ZxJ1jiTEH0y/IRBNOA0OpUeJ2uu++CdgK7fxc1ddoe72n9iOpniJA7X+tfYrXZl2K+oITJBj548XysdYLUANtdhZkGnnOJN828tebSWYyObDQnkBkHAprtJVVVsjje5dOkSt2/f5uDgQBdnZvFZVAWlVyJqgk8PPuXRySNdcI+ykWQt5WP6aZ+nB085mh4x2Z+sNw49sLXHQ8fr0At7Z8W235HghN/jWvMad+p36AQd6U9x6TLf/OY3eeWVV3jnnXe4ffs23/z2N4mtmA8ff8jJ7ISIiNRJGSZDGWtagzIoOeGEb+5/U0pTkpXPgx/Jb77lL3lOqJ+3W9ts1beoogq7sNl1d3k5eFnKLNwmjuVw/fp1Wq0WP/nJT7h27RrzTDI5fvyBTDFpX24zTIbMypmMYC1n9OM+j6aPeHfwroyMTEZrQXFl2tl0moRITxMFoEzrU0ZXR5SzUktk/MKX65CVIk3NJSXJUIXwKsvC7CorlrQpzzZBAtUFNxMbzCJYPdcESlbTMsyxWpuYv1NpfIr2vwrMmUCJKgZVN1s91vxuFrTq/6vMJVUHqVQ3xapQ21Or1fQxUgkICkQLw3CpqFyNNVWPNQtVQPsvhGFImqbaX0MBJgr8WWVFqcQJ9ThzH0zgRBlkmmwYVReq757nLXX6HcchjmNdEJvHXD3OnEvr5EDr2ACqnlRRnqbfhJqryleuqipd/yqwKkkSzcgw56/6nclcMCUh6px5nncOaDAZEuvmiwkKPIuRo/ZPDSV1ieOYer2ut0tJ7U2PlucZzw009Ho9bW5hWTJioygKnRdrXgiwbGhhblxZlvoiVI83kTR1MKbTKU+ePFmrrVlFYtRJ63Q6+nXVzadery/RkpIk0VmnSlumULMwDKnVavrEhGXIS4OXOD091TcoNUnKqkSEgsZ2g9RLmVvzM2aCHZN6qUytcKV8QoMSC918KUotJ0hImIo1zsaL4s0qLU2l/Lj8mHpaJ8xC6mmdy/llbse38VKP2WRGURZkIuM0OaWsSx+CxEvIahlJkEjZgp+dL8LN9xRIk0vWgBJGkVbYBTNrxtyZI4LFC4n1AIuVWTqZwo5s3LFLs2hya+sWTaspEXrkubTrNtNqyml8yml0KgEKL2NWm9H3JeizLv1DaT39wqcTd6SUInOxMkvHH+aVlEIkbkIWSFDCTJlYfd3SKSXTJIjPjoWerOdPmZVZWlLipi4fBx/TpUuraFEv6rwYv8jVwVWSfsJGsIErXA4ODvA8T85vGyIvYmAPmAQTZsGMvJFThiWlX1I4BaVV6q/MzSBARlJuAa9I2YlmdyiWSylIRYpbufilz0a2Qats0cpbNPIGzapJVVZM0ykPJw9JmylxGDP35jIBwi1kCsdCKnEObFgsjDNWus0XgTgVWl7iV75MnygXB9+SRUom5Gul1ppO8eL5yhNC+0FwBkwsyY+eY4hSaEmH3k8DhPt5R8lZKkU/eoaI3RgWFnW7LplFdkCv0cMXvgQlbZuyKknKhHkx167p6xZ8rnCXgImyKp+ZTKCHkKCmQOhIzIvMFZUpIBU6ElF5UXxW97505OPHjBlHn82esLCwsal7depuHbdyCe0Q15KoflImEvDII63p//OOklIGvl7AVFgdSZXQT/tM86mOBC2RRp1pmZ5nnQigkPctD4+tzhae5WmgZ7HjYMM8mzPNp0yyyYUSkX9pwQljk0tK0iolLVLZHeYIImDdFFko1iwkOOHZnu7Utz2pW7/SvoJf+YwmI0l7X2j4x7k0pUzKhBwp60hJmadzTtPzPig/Pfjp0v+V14VruWz4GxJIdBsEIqDltyiSAt/xmc1meL4nUwIKaSA4LafLAJMpn/mMe01e5Qxy2ak8ODrAMj6wLgIFbWHTsBtc8a7QcBoEtvQTciwHG5t2p01Zlewf7RM4Af2kz8PpQ45mskOfPk3h6fJrCoRkp/gden5PJz+03TZb9S3ySc7N7CYn8xPKcUk/79MpOrqzCCwld6iFsFkAmkxWk7Fqek2srh9V8db1u2x2N2llLa5WUhNrgiJVVdFqtbhy5Qoffvgh3W4XPBilI+4f3uede+8wySc0LjUYZzIdY1JMmBZT7g3vSZPMi5IcgLpbp1bV2OhvYCc2l5JLzPIZTbvJ1J3KONvYopN18E48iKDu1KnX6nz5y1/m3/qr/xa+7xMnMZNswid7n9CP+mxc2+DB0QMORgeaqaAMgB/PH/O9/vcYpSPm+fn7n0CyWro/67JR38CKLS4fX6btSLbCZD6hRo0ddtgMNrnSuyITPBrdpW6wavDNiznH02MiEfHB/Q/wOh4fPf6IzMk4jU7pR31m5Yy9bE8yQOpj4tvx2jn+J9Wf8D//4/9ZXreunFfltGT3vV1undwiGkY4mUPTadJyWlK6ZclUEZMtACzJDMxO/SpTQMkHVGG+yrY2G5pmR9js9q/WIqsFsJrvqqZQLAnzOWaXe53U3Cy+1T6qbvYqo1sBL8ogXv1OdccVMKe2aRWkUwwQ0z9DAS7mNWiyBtTxU5p+BTSa26Q68+p4RlGkQQH1/ialXxmEqvrP7Nqr11THQu2reQ9R9wUTJFoFDlbjMleBhtXjq0z2zfOrHqcYFgq4MtklJjikwITVmlk9Tx2PdYwZ5eNhXoPr9k0Nk+ViAlPmWAWT1P8dx6Fer2vWiCkRUXMC+GcPNKgdU8W20oWomBLzAlzVbpgIiy7UjZ3WzARLJgl4iafzXpWRi0kVMi96dWGsIlIKEVLbZlkWnu/pA6XcQEEapqgPKLV/5o1ECKEZFkEQaDCi6TbZ8XbOELkS8jRfmuBZlkmJhL0AIxaJDqNipMGI2Ik1SJG6Bq3YYCGUdknmZcyZcyouMIQrkdF/uS31f4VPmIf4iU99Vmd3tIsXedTLOjY2RVUQWxIUyYOcyF74StRTEj8hciPmYk7hFOeNBQ0mRGUbxaf6m9p+tWluSWInpEGKaMkFsbAEe9beuQ8eN3clU0J4WJVFmEojLabSNyMsQ6pM6stETSDqi+hOR3aHCr8g9VKmrSmZK+MqVxkTVJzJJ+aSleFkjkzKWEQnur5L6ZakTqolIYmTaM+EdR+YOhY1TKCCkRjpAqByKuk50QVekI+3SxvnBUf7WijGRPH/5+5PeyVLsutAdJmd+Ryf/Y4xZ0RkZVYlswayWANZxab4RFKCBghCA0LjdX94+gP6Af0TGpAajQYe0R8a3Wg00ALx1ARFDRzEQSSLqlKSxaqsHCqHyJjjjj77mc3sfdhu5uZ+3W/crIFPeBa4iDu4n3NsOMdtr732WhMBr/TQn/ThP/cRqQhtp41G0EBd1xgOhzg7P0N7p42z4gzzYI5pOIXqKri7LtmZeoIYLo6CchQKFChYgRlmOMeWNWSVKbjSJe2C0kWn7qCNNpp1E1EZISkSJE6C2qkx4ANMwglm7gwzb4aUpUvGhA46NwATkkm6JjsVvm2zrWAEHj25YE0ox4ytYAI1r1GyEgUvCOy6MDl0f5jyCagVQMF8f8Wm7R85W2RA1I8u+Li8RIm5IBHYaT3F6ejldoQePIReiIAFCFiAyKUA3FmoeWrWRCoJnNgmWufAgZIK3Flk4C8TScQqS8AI7ekSrg0t4AEgAFEKoqJzEGsKG+jlG8ZFQpoN/lWaptEHPEDIQ/O/wwiwkQ7Zzw5TKufYpKb/aZsuMcBKHHlJ9OgAFSf3h9PylD5HF4yVbXaiAIjKHHZIe2UBTmjWhX5vAQJfJuXkR7JGtTrw0212oP0jNANO1CVm9YzsRHWcdbLtlAtRTOaj43XQjbrGMWI32UUraCGbZXh69hSDYoCwG5pseFovSztqUb9cpNUy2dFlEaEMicVVk9CwK1w4yoGsJTGPHAblK7htF4VTYFJONq4FCblSGrWpCSUwrsdkQ1yullNJSMCu7lqMm8McxCxGh5FoZytoIeABXO7Ss8YPyAlB1qgV6X08mjzCsBxiXJBGh/xw8SzUhLzHi2eVT8KX/IBDNARZO9Y+1FzBFz5CGaLBGzjHOcblGK7nrmys7Qy1HWTqwGW9tno9MFwPKPUmvOE2EPIQYSdEHdI+7nM3Pmeyk5oibrMuFFOYVlO8mJBuwtn8DMNiiAwZ3nv0HljM8LR8ivPyHEfqCLnIMe/OIXrWXL62uFZJotjfwXfw23/82+hHfSqd8DtwSgeRitAUTdxo3MBt/zYxW4IuAm+pltxqtdBsNvHxo4/x6OQRRuUI03pqgJK5nEMGEqVT4pPjT/Bs/gzvFO8Ye0wJifWtgc99dPzOiiWstoVt8Ab2W/tIqxR3kjvgAcf96/ehUoWqoMDrxYsXEELgnXfewXvvv4dpOYXwF25dIe2Jdm7u4LOf+yzZcpYECh6Xx/jg+AP8mxf/BuNivHF9hzxE76Me/qdv/k848A4ALJ1ddDCuf9YxhI4R7Cz5Jpr8evLUDugvYzfo1+vybFt80C4LX1+Hdrm5bVO4aS3b1p72ORmjcoZ2u23Oqfuug30tLKjPp9kPdpCpj20Hq/a1rl9jo9HA06dPwRhR/DVzyY7RNIt9Pp8bB0ObTaLHSmfxdTm+fW49b+v3vC7VsOdbAxX6Wm3jAO08aI+R/WxYL+fQfdGMhaIoDMChx1a7PKwDULbzhGZy6Pfp8+7s7KwAAvoa9NjbVqS62S6Nur/2zwBWLDPXKwE2gQz6+pUi4cvZbIb9/f2VcdLzuQ4OXdauDDSkaWoGUFNT9AXpBblNJEL/ze6cTeHQF3vun+N3b/wuDWJN1O+GaqChGohVjFjEJGQoIsQiRixjojfWS3TFrq2SUmI6nZrzfrD7AT7a/4jeV4VIVAKv8JCoBEEVQE0U4pq0CVzhQgoq5dA1SZr+l+c5XNc1N6iuTdIfQPYDRd+0Dd5AW7QJhJgvqTv2ZGVZhulsitqvqTzDr5DylFTyvQJVVEE0BFSgkLkbhKQWmWDhC8AHcuQYs/HmCVWLIFdQcB3UAdzMhTN1EIwC3OQ3ca1xDcWgwOSMFLVlLMFbHEE/wGnjFClS5MhRORVEIFC5l7tuaCq+DjyVUhtBicqtUPMa3ONACBOUbnJRcCsXYRUajQztKNLMmwhnIfWrcAEBlC6VbqQ8RelQeUXhFAYYmDVmNM5udVGHQzH4gjQmojwyuhJcckDQ9QlHoOQlVEAlNsITkJ40Qo6bmuCCQBJVEmhibQA36R9wQYwJv/LBM47T2SmBJIUL58gBe8jQP+rjzu4dZIMM45Mxbt64iVarBcd1SPTRKzDzZpj5M0z9KVI/Neup4pVhLlRuRZTOAJhhthS9XJ9bLNkFniTLrk7RQVInaIgGGnUDLdFCWIZERQ9nGIdjTL0pZs4MmZOh4hVqVlOwbz8ALdDtylocoE2bIx34irQ2PLkAJjTLignUrEbFSYhsoyjbokzF94h+qG0k9YZHKLHdPnJDM+ULGsH/MUEJ3SqQuO4UJCp2lRIGFy4SLzHZzTzN4TEPlazQSBooVYlMEDsgk9mF93PG4bEldU+PxWVgQSEX2i3+4hdqexZWl50EPCCbRqWQlRm4x1FKcv+4CjihafSTjSnxi02DE6wmYcxG0EDkRqamv5AFcpkTe2LDuGxqV2GRCCYwl3PrV5dH95OKwAO9pjVrpZLVVkHUpkNCjqETmnHl4AhjshE7Pj+G1/BIT2NhQfk31v6GiRhGFFPVSIsUZwU5dvww+yGwCdsbWuAEJ+ZE4iZo+230oz52kh00vAZQEaPv0dkjpEhxOj9FqlKyFF2wJ2rUqPnic9IDgc/bmqBnvstdRHxhzwkPjnCw392Hz3yISpg+zeu5EeSciQ0sSav/lzWhBKZqiimmOMmIVaIBim3PLYc5RoPjmn8NO8kOilmBXquH2XiGJElIWV0QY2VaT1F6JSbehEoqD8oVG/Fv1d/Cv/jX/wIMDE2fAu5Qhmj7bdw6uUWJFERGGFIzKtp+G9e8a9RPa7Ous5LAck9qu5npTfY6VX1l3KwNvRZ864U9JCzBneQOJsHE7A3fLd7FjRs38P777+PevXv4oz/6I4RBiHfefQfD2RA5yyECgfP0HBnL4DZdIAaae03sR/uYiik+mn9E9pnFELN6Bjy6OO6xE5vSiX7Ux05jB6EMCZhwaFx6bg+33dtoe23c2b+Dm3s38Zd/+Ze4e/eu+bz9wTs/wDgfY+fWDs7TcxKY5AVynmOQDwwAMK7GeDZ/hmExxLAYLtkTHywu6EP6L3ESNJ0mAhmgwRuoWhXSeymQ0v5e23tG8wi3i9v4b+/+t0i8xIzrxx9/jG63i8985jP4/g++j8cnj5GqFBkyAk3EHONqjLgfYz/eB5uylUz6euDkOI7RgtBBo53N1+tiPTDV77fLfGywQL/P/j0Ak/22SyIArGg2rAfvOm6wbS3XywbsOMNOatpJVB342iCbfn2WZQaIsPtoW/Da17btPrCTs41GA0VRrGTb9XUVRWESv/p+K8vSXKfdN90YYyZbvp69t5u+5izLDLCkA3D7nrbnWQfu6+CSPdfr7AA9/2EYgnNu7EX1Oe1YztajsMshtGCuHnPNtLEFPO31Za8t/Xd7jdk/6zFaX0frTi2bxtBu9jpOksTYxuqmwSkNtK6P07Z2ZaBB15HoE9n2lHpx6YVnX+y2ztlol/47yxm+Un8FNa+R8hSFV0AmEjN3hlP3FBmngMRujnQIgBAh/MKHm7vwCx9BFSCRCXYaO2iggVCGuI3bCKchCq/AnM2R+inmwRypk9IH/+HyuFxyRCKCkzngKVkaOpkDnnHIjDIo79x/By/4C0RVBL/wEYoQUR2Re0OhSENgQYfRC06rkOqFoxEvjXaVRQmVK7KnQwBf+abcZLo3xQ+u/cD0uyGoXwEj6nmZlZCCgqG8yqF8BelTeYbw1jLwbBHkcgF4IE2ANgAChfFIf6K9AiMK6NQOCSkWHtJmCumvbTgklQ740ocrXGO5qOvvFRQqpyJGgCdWNhXLhQKjCSFApQSmbGNxPLvVLq2VzM+gBTu1NeL6cTXDIxQETIQiRKNqoDvpkpOIiBBUATzhIWyHyHiGjBGrQ/+fglgpZVBimkyJMeFdBCagFiBISdkZv/bhKheylsjLHPNiDgTEgChZCRZRBksH2xubor6VXonSKcnCsr0QgWEC+Dy97OniH0CaII/KR+A5h1M45JxS+/AyD+6IRKh6ZQ878Q72GntEMx6N8eTJE4RJiNItMZETtG60wPscY3eMmTtD7ucGmNBaD5JL1A4BAeNgC8C16AdTDI5y4EoXvvSRVFQOFNUkoJqUCcbOGM+azzD35qjcBQCybvW6hXas+ELsETVSL12+BtgIKHBJzhye9Gh8pGccKL7881+m2voqw7QgG7ez7Azzao71poMRMAImbBE5I9r4KYIqTY02wnQ/oYisRm02jdYFAi5wnF2sa2dgxAhwQkRuBJ/7RlwRIP0KLfo4qy8GONriUIrFhuIlDBIJiXk9x3yRDjYOANXmQMeFi9iNEbAAoRfC4Q7KukRRkwiuLl14mSuKBifACaidVlO8THdSa2gEDo1P6ITw+WJjJWtik9QpOTBsYk1sWJcvm2cFdQEIsF0SNr1/JmYoZGHYD7q/9bxeloesTV2ECD589JIeAk7OKpopIxQBpLmk/v1Y4MRPmzXxE2gGnFg4dpyX58QAGF3+Pgck/hryEAEC8JwjZjFUpiBTaYT9aq8mBmEgSFzYlYBHgrmlpBIe+956dvps6zkZmCkn8Tl99gQ8QOAQ64ljyX6BD0zKCYb5kBxgNjR+IXtwsQklTPAJAJgu1uRssZ7143Jh6wwfVPJYe3BrF/E8hl/Rs9fjHq7vX8crd16B5xN1txAFBpMBUknsibP0DKNihEk12aw94YTkksISo7PQdEkIs+22sdvYxSviFczHcxyoA9zwbqDltVaCUzuZtilQ1DT09f0sALOns90DGGNwHXI4CusQrGQQY0HW3dMYcRzjM43P4L/7uf/O1NRLSRZ/g9EAh3cPcTI9wZPzJ2QrWk1J26eispxUpThJT4wQ8aSebC6t4x4SnqD3oGf0J1jGEMgAt49uoxOQg8d+tI+95h66AZXG+I5vgBRt88dchr989y/R3G/iOz/4DqJuhIcnD02JkrY8HbpDjHojlDslhL8qJP2f8Z/xv/+b/x0e94zwpAaVbj6/CTmTQEZgqRbzvO5cx6vBq/j6/a+j2+ji2fiZSYDqMbc1F+q6NvX0diY6TVOTqbYZMnbC0A6eNRBgB1l2VlyviUajAcaY2fMDWAkC7SBYB6XAsizCZhTon/VxtH2iHXTawbTWA1jP6ne7XfT7fXN+HTTawp06Y637a9tw6vvBDqwZI4HJPM+Nxp4dKOd5brQiNKtBOw/qa7ADZ/1+HU96nmfY53a23waQ7BIsPa82YKCvtyzLFQFKG9Cx52RdX9BmkHieh+l0amw6169jnRGinXh839/IMLAdSOw1YgNMWv/CBqb0e/U42te8zjzQv1sHUdbXsD32URThwYMHJoG/HuPrc1ylXRlo2NnZWaGs6QmxvTr1hdud0gNuC5/YSJUeSKUUsijDd258B1xxspGsIyQqQbfq4npxHXEdwxUuKc8vtAAyniF1UqQ8xdyZY9aeUXbWXd3QMcUQy9h8hVWIPbEHr/DQRBNuTZtg13OR8xypk2LO5jivzpEGKcpmicK/6KhwjvONWVUmGdyaAjm/IIFJZ+Yg8ElQ0Cs8eFMPDdbAfm/fUGrWkU79ARfHMQ7iA7Q/ahOd0qNrMdoQPMMsmVFpBludfC45XUPtgZccqGljzjijLF1SkOAfF5vtBRdMhNqpSVOitSVlyilwzrG5Dm+lLYADphhckLgjl5xq9UHU0VKWKFG+tHTDOHJo2j/YRqq+tnGceTOjKSGYuABKMMUQitAIXdpCkN2qS8BT4RL4VNHDZVpNMcgHZKMaK7CEwet4CLoBnJaD3MsxYzNkEWl4bCrlCGWIRCQIRABWMkAsN7dpnZKQog/wiGwptcXkxuBJLb+kK4EEqIIKmcrAHQ642Fj+wRWV3OAzgFu48CuitLqxiz1/Dz3ZQ0M0EGcE7gUygBYYrBltlHOeY6zGmDpTTJwJpu4Uc4cs3UqUqHm9dH1wauTIL88467nUQyYXtpjaulM45IghQKweRxggy7hSrGst2OuDLe0xK1RIg1Vg4rc/+O3V9QGGwAmwl+yRMFjUQz/qo+E3iJYPiUpUSKsU02KKs+wMp+kpBtlgq3r9tuDwR2E9vIxK/aM0BYVcbncA2NQ4SGcicAIDTMwmM5NVUq6CEznIVLbxWk05ABbiWdjOmKhRY1Iv1lC1zL5uY0wEnIQbEzdBlVZoNBuQSpI2QE5gSa1IWPcqgpuGObFF8X69MZCDCqsXzBvHRztpg4GhRm2YE7nMt/Z5fc1cBZywS0T0OrFtVddbhgwlSlR5ReDEgoVSyWprqUnMYzSDpnEf8ZylXogGXjKRYV7NMcknG92P/v+paUeZQhXEOtKMHg+brZs1S0xyRCxCP+ybbH3Ta4KVDGVa4t7te5gUExxPyCZyVI8wq2bIRY5SEbCW1umKsPDGZhFztCOOxz0DvsZejDiIqbQKBPTmKjdWsJsCWVNKdQlriToJEtj2SQzR7v9TPMW3H252FmkFFGxeC6/hjeYbaHpNRE5EQTwjh4E4iTGZTzCej6mEqJ7gKF9qG2QyA35w8ZK0kGGMGIc/OETTbaIX9kj3IdlBgzeMKOR+cx878Q5cZ5Wurzf+eqOvAyw7gDJjvlaqa2flbZaFEgrXO9fRclro1J0VqjewzELu7u7i5OQEg8EASimUKDEVUwJl6glkIKFChXcfvgsRiGWpQnGMSTXBnz36M0zr6cbpStzEuE90PCql2Il3oFKFO/IOzrNz3O3cxQ33BnbaNFbpkITj3z1+F2+//TbG4zHAACdyoCIFFSrs39nH1/8fX6fSuMV1vhi9wKSa4N3zd3E6P8WoGCFTG1hkj4CW30LTIbtXWyBTl57c4/cgpgK84KiSCi2vBSWX46azv3btPICVmnk7uLXbpnp+zrlhTGgNA3vO1yn2+lw6wNRJSH19mjavr2fdjtYO/LXunM7c2wHo8+fPkec5Go2GEewvyxK9Xm/FiU+vUzuOW8+S2wCOtsjUQWmapsaR0AYz7FIPXf+vS9f1OTZpZeR5vgJ06HtoPbuux0jPlQZZABhbUV2KD2Alll2/D9eZAvp6giDAdDo1en/6NTbD3QakNNBkX4ueD/uZYDuO2PE0gBURSBv8sBlYGqywtTNspogNpKy39d9JKZEkCbIsu3AcPebbjrWpXRlo0BezfmC7o+t/X0c7bBDCVgnVA9BTPfzK6a+QaJIckWhhVOPYPTYiiyvHU8xkoqM6QrNuYr/YpwCx9uGAFEuZy1C4JLiYMqKJD/wBnrnPkIbphcA8kAFiESMSEdzKRbtoI8kSNFkTkYjAJW1+a5cC79RJMXNmGKkRWQx6BSqnMh+gaZICPWzNqDrSQSADuKWLoAgQFcSQYClDLGK4uYu0SNFnffTRR0NSnaCuMdI+w+fn58hyqsk9z8/hdlzISFKfHbqu3MkxiSdI/XQZiNljKhkxN3IOmVO9qJCEPnOPk4jbItNiMhMrB7C+30Jrt19nLPWcl6QNF0EzU2S3KBxx8XxW7f2F9y5KN0xQrqy/rWfDmSLtCrcEcxhYSGKDm8QiNVjCFNXa8oKTU0buIYsysAZDz+mhK7q4JYnuyXMSQXz69CkGwwH2b+6DNcjlpPTIcSJzMtR+jdzJaRO2oRzFLV2oVAE5gBpQUkFxBeYywANYQP9Lh0Qk4VE/N1lKckU1+lr3gEcclUu1/W7fxUP3IX7If3gRUFMMoQoRyQiRjIiyKSOiRIoI1+vrCEv6XaxiBJKyaUIK5MhReiWBhTzF3J1j4kwwcSaY8zlyli9LKmAxGRYgI1wYN4utTW/aBTflFI6g2myXuaRF4CyEJ1lFlp3aGnbL2tXZ5Hye42S+pQjcahwcoRdiP9lHL+qhF/UgpxKxG5tNMfc48jo39+7R5AiVU12Znr+pfSpxQBvM+QkFfRISqUhXhRndxdc6eLRoHNxkXn3HJ8E6Rh+eWZVBuQqzaraxZMWBY3QipJKXlrUUsjB2hADAxmu08LX5j1iExEmQuAkiHtHYcgXmsBXGgnY+uErQr7gCfBjr2Vm5nepOl8SorEMwNMMmApc0L5RSKCUxCwpZbHXoWAeg7BKgy5qAMHPIrH9a7HK9pTJFlVeYcnJW0UBkpaqLdqKL5xqrGQUxYQcBIy0Al7uQij6jKlUhqzOkgsb5x9Kc+C+xbVgu0pGYqznm2RwvshcX/v4n7//Jys+GxcB9dFyyEN1v7iPiEVCQK4rDHAgpMJMzsjyUU2TIMK/nSKvUACL2+A6r4aWsHg4OhzvGVtR3iEERuiFERRvseTaH4AKTakKsiU3PGGmt0W2sPtBzZVRQQHrVxkEONw2ngbbbxq3gFnbiHVzfuY5smlFpShhBQSErMrw4e4FpMYXLXBzlR/hw9iGJiW4BVnzuoxPQmOvyDbd0cZAdIJtkuOZcwzE7RlM0MXSGkK40duHriSWb9q2DHMdxVhwTbIE8e8OvX885N8FtxCK0VAvX4mvgnKPRaODatWv4a/nX2N3dNcf/+OOPMRqNcO/ePXiBR+KzgvQdtIXnpJ5gKqaYVBMM8gGO02N8OP4Q59k5Jk8mxIxaW6oOHCQ8gdfwIN4UYBmDW7nk5iV8hCpEV3WxF+7hM63PGOvR85NzjEYjfOMb38Ann3yCBw8ekFU7L5Cz3FiLhr0Qwhd4cvaE7E7rKU6LU3w0/ciASvUH1pwtwCWXkYVxw2mgd9QzQpMaoGg6TfSiHo7DYyrN8cjq0+PeSkBu1tia8KDen+uSAnt+7Lm1s/9lWSIMQwNQ6Hm1a/UBGG28deBKswV0Kbf9N8YYhsMhTk9PDZChbRejKEIYhuZa7XW1jY1uaxNo4UANksznc/Pe9TIJHdxXVYVGo2H6YZcSAEtrTM/zjPOBvhbbUWTditG219XNHh89LnbMuimzb7M57LlrtVok8rvGKLEZMBos0HafK0YCcin+abMP7Lm2r0fPu12WYrNc9O/0GNvMBb3W1vuySXNjHTDrdrsr82ETCHR/rtquDDTMZrOVzq0PgL44u27D/t9eIPZ7bAEMRzq4VlxDL+9hPp+bm8AwH5hE5mTInAxzPjf/pw4FZufBOZ4mT1E4xeoHlQJZRYLq9H3lw5c+GqKB3XoXDdaAV3moZzVl+ZlA4RYYu2MMwyFkIlG51YXMtytdw5CI6gjNtImD8gBNNNFAA4EMUOQFKed7pWFJZH6G1EuROqQVUDkVUjel2bBR/Q0fxlyQkFRYh2gIcgvo+33EMkbpl+A1h0gFWS9OG+BzvrJwq6rCOzfewbNrzzbS/VnNDIiCBgCH2AwGYLCbBFjKSMSQ+fCcBWLHiSlQoDDlGZJvYEpcFZSwXq+YWoIM29qmPco6yLP+/9prFFcELGw7HlZfq0BjhIhen7FlgDjTfGTdx/biD4sylRfqBQXAtUNzW4bwpz6cpw6csQM5l2jHbYRBiNPTUxSygAwkEAMqVvQV0RciQMbyIgCkYAAOVjGUeQnXc+GHvumrdCQqRutcQREwASwDeQ30LEAJVxETxQGJZ+YgZs2Zc0bsn/X7cHGMQAbUT0F1pKFc6K6oGIflIV6RryCSEQJB5S0OcyCZNLWiEzGBiAQyN8PUmyJ16V7KndxoTKzPr3RoTDTIsqLzsN7UAnCDA0c6OOgfkMCi4wEKRGEuZpiWU+R1/lKbSQmJtEqRVilezC4GDOtNCzN2vA5uRjeRIEHbbSN2YoRuSECNEsbCb4opzrNzsiSrN1vYXXYuxigQvKz++ifettzrEhKZXOgfbApuNiTRNTjhchce8+C7y3rQXObIRb4x+OaMGwq5Ymor2wQAMkWOFmf12eLyt7NGtAd902+i6TXRcBvwHd9YkuYix7SY4mh4hJKX9Gy8wme2AgEKYAt7sCvE2hqc8BgFgdppQCqJUpaGibFt3jexJq4CXlWqQiUqQFjZbcBYRF7om6uQIkVd1nDYUuS1VlTWsemcPvPR8luIOJXy+Nyn+QQBE6UqIZjArJwhrVJU6uUA0N9osy9l0/3wKUE/3e9KVJiLOc7KMzyYP7j0Pbqkyec+ZerjHtpeGw2/gWxI92Cn3UEURsiqjLQftANDPUNe072lrX83sif0OhXmpPSM1ayeRfKA1fTZwkFgYRiH4D4nnRix+XntMnfJXFJb2H2g58pMzDATMxyVC62hCbBJdsgem8awQQCY18FnW5/FXrJHugNuAI+TsKViClVdoVY1JjkF4KNyhPP8HG/P38awGCIfL8AVASpH1ddfk9aTW7oEzqsIj6JHyD7IsNfcQ4M30PE7CGSAcTHGvJpfCAJttrBdymz6viHzqIMFACvBllHJB0fbaxNbY5Fd1eCHtkPUpROu6+L09BT9fh//6a/+E5LdBJ8cf0LuE+k5ng2fYVyO8fjsMY6LY0osNFNMfSo9FZ7Ae3gPv/vnv7tyjYmTIOEJDk8PEakIvOBouS1ynXAaSHiCSEW4G9/FZ25+Bnk7Rz2vV6jruvWv9XE6OzUCneNyjEE2wIfPPsRMzqAChUk1wSfpJ8YKVYsy493VdaFZHUYg01/9vuXTNY7VGGmQYpAOLmgF6KyzjoH0PGhms60jsJ5xt5nldnCu51gLI9ruD7ocgHOy0dSvCYIA7TZpx6VpCiEEgiBYceqwA1CtU2AH1a7rGpHUqqqws7OD8Xi8Uk5QFIUpH2CMGVBD98fu06Zxmc/nZt3a/9sOG3aA7bquYajYZSv6+ForYl1TcJ0hZP9ev77VamEwGGzU8bD7AZApgXbY0O4NunzCLsGqqgpRFBknC5tJovup+2Xfz1p4c32ubVDGtvnU97EeD3ss158R2mXStvLU7Cyb1XKVdmWgwbbp0J2x6S76Z7tD9o1l13isX+Q6vUMIYWpS1lGYpE4QlRG6qrsCYOR5bpROhRJkiRhWkLFE4RfInAyDYIAppiidEnNnTnRtXRYUAmgu+6uz1Jtq/Y0av6Thy1mOzM1QtStU/GJ2nitOGd8F8yISERrzBvzSJ+aEjCArifFkjIKT7WLhFkjdFGVYoggLVEEF4RMdPHVSpF6KAQarAfT11et0hAOv9BCUAeIqRliGcDIHB/IAnzv5HF2/AkpV4sn5E5zlZ6iCCm7bRcYz1EFNQWyoLq4UBTCxoBgFCpkgxglzGWXQ+cWsuf4wdYULVhMgwVwGN3CRy5zE+dhaoGgmBFdv65nSq5RxbPt5W2Zbbfib2vD3be+zG1vQ/R3KbE6iCYER15d/XyktUABqgJUMLGPgKQebMTgvHLA5g0oVPEUbgLqqIbkECxmCXgC/6yPshxiyIZSvkCO/UC+pz8FLDlYy+I4PhxNtVjJp2B05y5fg03qfFhtJRzhGi8GRjvliioLbGZ9h6AzJgcXZ7BbhCY/KNESAIKISlljFCGWIw+IQQUaARFDT/1xx1LxG4RCDJ3fI0WUOYkwMggGxkXiNyqmWDAZrPhQUiba5NR6NH22YyNXGweE5HiI3QuRFiNzIZKuEFMgq0i/IK8p4vwyYkEzitDjFafFy1wmHOQicAAlPqJzD76LlthA7sQnCFFPgIceL6Qs8OHmA0i2RMaKva5HGFdbIhmbELK2yBKl+cqKWP2rT4IS5jCs4LjlwTDmHqpWpf6wkZc43ARM6i6/PeZnK/6garZSYbAQmFmLxTJKVcj8hlfmYx/CYRxl9RiVJmcgwraaYlkSBrtlmwcf1psGJEuVy4/ySpoNPLjk8eATkeIHRCriMNaEDfbuvRpvkJU1CIpe5uQY91pxxYjasjXepSgzKAbGT+DLgFFJsBU84OBJOzJSAUbAY+AFcz8VwMgRcOq62pf2pghMbnpmX/v2n0HQZTCVpPkfT0cUXDS/+Slt7Rm6EPX+PHAi8hnnmyErCcz0MxgMIT+D55Dkqp8IwH1I5pBIrAs8CYllSsmgp0gvAIgMBwL5L964LKpUwn09SkrODmG9kHnBwArIYlVdKyI2v02MzraeY1lMc5UfAFJuFQteOH7sxGi4FwtfCa3g1eBX7jX1MTidw4ODk+ARZlqGsiDGZCko4yUAiDVI8ch7h8cPHlIlfu7Z/9sk/g8/9pdaE1zaBbcttYbexi9fz1/GLnV9codNvooTbgZydVdc19XYAooN3O1O8nsGvqgqxG6PLupgUE+x39nFenONeeA8lL/FB+gF++PEPcXZ2ZgIvx3HAPY7etR7+yf/rnyBDRsKXYoYXoxd4OniKdr+N56PneDF/gcflY8zFHFMxXY7NCwB/Qd+6zDVj0/GXDJNrZ9fQ8TsoZyVuBjexH+/jXuMerk+uw6993L97fznvOkCTpDcV9SJMatIwGVfkqKLFMUflCCfZCT4Yf0Asm3Jk7iUAwM8A/yH7D3D+1DGMCX1Ne00qvWw6dL0iFzg/PsdddheqUAhAzL71rLtNrbcDSg086N9pZ0A7EA7D0DAQOp0Oer0eWq2WAQBs61g72N30v5TS6A8kSQIAmE6n2N/fvxAnBkGAKIrMNWqgQWtNAFgBX3Tf7LVrg2M2c8Bmx9vr2i7/0D/roNt2zNiWGF+/Fn0d7XYbT58+NWUu9hjr89j3lB5TnfywNfk08yhNU8Pa0OUKutn3nDZi0POtS2lc1zVfdpnFprm053HT6/TvtDilvjZ7fmyQ8SrtykBDo9GAUrQh27TI9UWuIxya0rFOQVl/rU0Xm06nmM/nFxDXdUEWfW69CGxl00CRqF8iEziVA1UqqJSuX6NGQgrM1AwykTiqjnCOc6ond+dGCLBUi4yTlSGtHaotp85YnV0viVCOseLjkqNGjak3xdAbooiovGL9PV5FwIBXePByD13ZRTgmhwy/8pFFGZzEwYzPkPmZscTMVY4SJZWBLAJg4QoIVyCPc7K22lK6AQU4uw6cwgGbMDREA0magJ9xFOcFCSeC1FalS3ROlSigARRuARUriEhARkRDvwAygOpNAZi/S1dSFtGVJttsN1/6cEsXvCZXhzRJTfBKl0zUY8XURabEpj5afb0Kc2Jrs+dbbnjtpwEl7Pl42aZz/RweoDwFlazVkC/OdaGcQABlVcKrPcAF/NxHa95CWIU4e3IGXnFEYYThaIhmq4md6zuY1JSdaR22UAdUxpFzchlZLzcyAJxyTbChQPMjONmomvdsAyWUQ6KZ0qX7RnoGqGCKAQwoWYl5OMexe4zCLTauNU8SMKFZE6EkkMKvfLSrNh72Hl54jysIBHMknY/LZab72u1rmBZTzMoZZtVsdSOxaBIShShQiOLKlF4Ocm4IHNpQuJzEJ2tRY17OKehZ2OhdFuzYtdin5emKnd628wZ1gKbXxK3kFtpOG8PnQ+y39oESuH/rPuq6xlRMMRZjDKuhUflO6xSZzC7N/uvmYFFDyJYf/LZrx99EILWtCZANpc6UTvPNNcl209R0rR3BGINiCqUot1L5tTaBUlu0HhQxolKkSLOX1NIvmgcP++E+umEXgQiQuAll8y1hzlzmKBgxXs7Tc8zF/KVCmMtLUmaNG+vZKwhiusyF53jGbhMgC9RSkIPJxjVsPwe5/Wt1scxtQ5NKEhVfrgJhWh9gfcwlJGZyhqIiUUzGGFhFAEVe5xDlZr2bpttEwyP7Qy2CqhkXlapQyIJKOkSxlYHx0vZTvB8+VRnVFZpQlIXMRY5hOcTj2eOXvofXxFZw1eJZWztgBSPnK+EYUIFxBuUpNPebQEw2v2mdohAFiXGiNpvpqzatPeFy0p/wuGcAh6IukNc5MrlFKwauKQHSoNk2hx0JiVltCeLqW3psvai7+Fq+idykBLkwNOsm3ui+gcPmIVpui4BipXD84hivvf4afcbMRybQHZdjPMwekr7BixFuHd3Cr/z6r2B3dxfAalABwOx/dZCig7QgCBDH8Ur23KaiXxjTRQCkM9Ka6q4z3gBM1tqmcOvjmsxsBYQixCuNV0x2GgDGjTEeiUf4O//V38FkMsGzZ8/M+4QQyGWOaT2FihR4wvFs9AxHoyOyoa2nVOpSTfAwe4jvT76PYTEkcd8NJJ/kLEHLaxkgoOE0yLHDa+NGccNYjd5v3kenT8yFxEtWqOz62ubVHONyjB8+/iH+z//7/0T/Rh+3PnsLk2piBDJnYoaPxh9hcjbBsBgutX0+Xr2u2I0NY6IdkO7E3qM9fLH/RfzitV807HB7jvUY+r6/IkTpui663S4ODw/R6XRMIOn7/op45vqXTcO3Yy67xAGgwHcymRjWi51QjuMYzWYTeZ6bDLleG41GA2VZrsSTNrDiOA5ardZK0K/7rNewDnr12rVFHvU9oMEwu2Rfz5ndL90Xu6zBToq3Wi2kaXqBebAONGh2g2apBEGAZrNpHBy1hoVdttBsNo3GhC1aaVj/VrmMbTJg99G+Fh1TazbLelWCbuvPCACmZCVNU0RRtMI80fP1E2U05HWOj5KPENcxuk4XTdWEq1bFH+3Jt6kv68iQfo+9cBmjIII5JO6m36vFMWz0yZ5Ue+BsARBb/GS9zkijWnqyIkSIqgjhLMSN+oZZqAAwn88xmUzgei7cposqrDDzZpj4E0ydKWbujMQYHdrUCaxm4gWjLHWO/PIMu6JAx1jwcYYyLFEmJcZ8TJaJa2KIpmxDxNiX+4hEBEwBN3chUoGqrODGLqqArjl1U5R+SboRvDKCfAaUCMjXGC2gUNamef1aBWW6ecrhpz7CMgSfcSRTAkLy0xxe7eGNz70B4QrMQLoYo4o0Nwq3QOmXJCq5JVDkijaLwhGGxq+YWjoOvAQI4JLer8dcMWW+Nr73RwUlXnYt+hj2/3bbdI++DJTYZB16FfaGswB5sFiPoSVkemP1pWOM8VQ9hStc8JI2GrGMsVvuEgNHRfCkZwACySgrpHUtCrdAxjPkPEfGMirH2ABMOIrKLphixoFA201uza4vyjcc5Rj/dV/5CFQAX/oE6qmFOjinaxq5I7oWnpG7zFrjckGhX5SQaNFBnU1+9+zdC+9xmIN22EbTbyLyiDXA+UJ7os4JlFhQtrdlcyUk1UPXBS44Emqa7YVppEyiB8p4O5w2e6Ukh4VKVpeKJurzZjJDVmQ4KRYaEzHwcf0xwIFvPf3W6vgsShMSL8G1+Br6YR8djyiiTa+J0KGN5VzMcZqf4iw/w7CkTd28nht6tenXhqYz2Hrzb6iIV7DN/JtqZr7Up9MH8Ngic6dp/bJaBqNr42G7jGzrc8UqHOfHOM4vuoOsNwaG2I2xG+yi7ZIFXsNtIHDIQcLlLoI4wPn4HM8Hz8EbHIOCsnVpnV45+DW0/XoLfX7LtWFRpsQkQ+iHcFwHlahQqe02ndvG6KrMCYWlKKZ2iTEU2QV7Yu1CybFDrTl2LOxEt7E7Ep4gZCFiLyaBUsZQiQpZlZF7kCxXHHt+mkDD+lp6GfDw0xCU1UytmtXUVxfEJKUTXmgn8gRstnTPaPttRDyCJzzc2L2BkIUkCLyYs1KUFOTVJG6YqxxZldEzcfEc+TTaHnptuMw1DA6Hr4qabjoeA6N7ni3dWS5jToCRYHPhFShAgqHPj58Dm27v/7Q8R+RESNwETY8ED19vvY79aB93+nfwmx/8JiIZGb2BttdG4iZGpLDZbK4E+xpQAGC+1xnkTeXQOnOuv7dfoxnJ2vXADtg20a3tYN1OQOogSmet7ewtYwwN3sAOdhCGIfb29jBpTTDpTC7Q4XUW2nEcfO/t7yHshkYA8+2P3sZUTOG1PUyqCSb1BONyjNPiFB9XHxMw8GyzLofL3WXZxKJ0wlisem04pQPpSQRVgC+2v0hgJW8QW4xz7O/vI4oipGmKSlR4dv4MUzFFY7eB4+kxjsZHmNZTI5A5rsY4zU7xyewTOHDws97PGpFRYBksagFSz/NM4K+ZA5zzFfa37/srWX4dx9klB/a823NoB776+FpHRH9+6wy7LtUAlk4JuhxCSmkCd/sa7PWp36/XiF6rOrlsgwW+7xuQRa9H+z2b2Bn2+rQZDbbjhv67zu7rNbp+LBv4WXc/0WUp+nvdNKPSBvjWAQxbVFS/To+/nk/9s31s+x7T39vgn93sOJ1zjmaziSzLzHs0cGFbm16lXQloeDp5ij/p/cnK73zhI64p0I1r0iiIq9j8LhEJAhUACiuTpSdUD4heUKmb4jdv/CZRoPsB3MxFm7fRQguJTBDX5BSRKGIo6A97gCZ3yqbEYlAkyjHwB3i3/S6CkiwNozpCz++hjTbCPDQZS428zedzg9J4noeMZXjoP4Qbu2iggb7so1200S27YCm7sCCllJjNZ4i7MaqwwkiNMPEnGLIhARIOqfGXvKRs8xpVu3br5cbqMpbEwoLPkTQGOc9R8AIFK1B2yyXTQr9dMgRVgKAK0FANRCmNhZu7mDyfgFUMk+kEvMGRRzlEU0AkArzFUbolSlZSeYlW7ncAGUnISKLu10gVbSrPcLZyrQ/wgNwuxKI8JI/g5R7CWYh21UbX6yKsQ+zEO9jp7eBofESe2nJCYEhQ4Sw/ozHzS0ACpV9urPnXVHw9XnpDc8EKcb2pRY26YqtMiUWQeWVQ4hKmyJXev/76q4IS+nj6mPp9+tmxBZRY2WTqMpMNzIzarQGXAIlznK+eYxMrRjnwJdWZhiJEt+7ihriBSBIwobONUpGDQOmUBozIHQICMp6RvzjbzIrR86yYopIlJ7sUlNAZNE96aNZNBFUAp3JIVZzHcCV96FesMmBEzhflFizbzJjgHho+BWsAUNQFMpXR5rPOMa8ouF5vgROgGTQRezGqrKJNK6Psei5yo8Z/WRMQRkjzqhULHjzarLuU9c6LHI7rUHZw8XUZqGYDE2fFGT6cfnjp+fSmPHZi7IV76AU99PwekAEP33+I6XAKOEDcinHttWsYi7GpiU1Faqz8LgtyNCih6/n1depSjqsEnH8TTQc4G+dqw5hLSMN00awJre2wiRHxMncNBUU2ofUcR8UlBemLxsccTbeJfthHd9pF22mjwRvY6++RRgmWAXYuc0zFFMNqiGE5xKSYXC7MuuHatKMRALIyvUKimjMOrjhCn/RKalmjEqTJsGkM9Bjpc66DE2b90y+2XqsuQTHgBChB4sDZuN7mco4CBX1GLo4rFVkACyY26nLwijLbriItjW6nC9d3KUhesCWyOkMpSlrvP2LZ0ssAhB8FYNC2p8r6t7VdsWzEBrDm9ZKq9eDZ5doTupwsdmOEPETiJFTW4URk77m4t8I4xDgb43x+jhQpZvUMmViOr3k+AhuB301Ns10UlHlGAUAtahQoLo4Lg2E1MCyCftcxAqrb7msttHuhvG7L0DAwhE6I2InRi3uIEaMX9nDQOMBBeICgClBPahzyQ9z176LltNBm7ZUkn51p1gGhDi71PrgsS+MmoGv6ta2kHcjYgZRmPW8K+AAq53j+/Ln5uw6SpZRoNpvY399HVVU4Pz9fCZ50ABwEARqNBiCBtttGP+wTi+OYatxfuf6KOacGRzQ9vN/vw4kdnM3PMCyGFPSXYyqP098vyiaO0iMDDMzqGfAK8B18B//q+//KXFPsxGi5LfRCEoY2QBBL4Asfr0evI+EJXolfMeCFr3xzfXEco9Pp4IMPPjDZcWBZ3g4At2/fXrF9BCj+0loK60GiXVaxDvqslxLoedcggp4LDZrY81bXNYqiwHQ6RRRFmM/n8DwPVVVhOp0alsF4PDbXqtdTGIbY2dkxwXpd16aPOjAvy9Kw5rWVp81OsDP4tnOHDRzYgIpu65oadp+08KVtOWkDEzZzRx9LgwiTyQTT6XQlIc4YQxRF6Pf7KMsSo9FoxelBg30aLLPvQ7uUZFM/7POv33P2fbLpvlRKodPp4Pj42Pysz1PXtblPrtKuBDTc793Hf/3D/5pEF/0MmZchczPMnTkyL8PAGyCNyGbR/gBxpIOoWjAGqhBRFREosfg/rEMEOYERtVPjZ5/+LAm7ueQMcR6e45n3jCwlsXpcfbywChHVER51HqH0SjjCQVAFcIWLIigIQdfZe6t5NZUoJCpBVEVwm8QQaMgGYhFj5szw/ZvfX3kPUxS0R3WEoA6oLyIyx0laRGFtFA1yhij5itqqprCkGdnezdgMMzbD8+I5ikYBp+cg93MUXoHaqTc6MWhxRQCrH84b3BBcQXXxTDAIV2DMxxixESpeoeTlaiZbAU7hUL3/nCE4CXAjuoHh4yEiEaEaVrh/6z7ef/A+gr0AY3eM1s0WZnwGGUmULtlQwoEBJSQnAb/cy6keua0H8uIacyMXgQwQVRE66KDN22jmTbTmLSQqQTkoEYoQjaiByqswVVMq1YgEKn/BlPBK5C4FibmbX6xjtrLo9OMCVIC8msWaWrItoCjYlZCrwb712h8blNj0uqu85yWghIK6vDQDoMBoEajr95h7aAsAI0ACjRmypfaJvs4N72GKwZc+AhkgEAHiIsau2DVe6hCA67hmLVWswlzNSfTRq1C6pdFg2EQL1/NUM7qX5s7cOG9caBqUkC485SGQAfbrfQRVgH/86/8Y/bCPZtAEYwx5neMsJcvK0/QUp3P6fzKnOs715nIXDb+BxEtMJrlCZdgHpSoxr+cbg5XQCRHwwAjdMTDUiuwPC1FstRi0WwXKumZVZubqwnAx+1uqg3ZBZTDgMMG7zs5d1mxa9aAc4OOpxQfdX3wt2kejj2jcmYuQh2h7bfT8HvYSAigabgOu4+LJ0yfwIg9O08Hx/BiDYoBpSYJ0hSheqnuhz+HwJbNAMarn1+Ucf+OsiUuAnVKVL79HsXyG6f7Z9O7LBB71ey+cW0mMK8r2gQPP5DN6FmwhT3jMo4xq0MHd5l3shDto+S2ETkiZO8apvEAQMDEuxjjLz3CanWJYDCl4/JSZfB342Vmyy5oeH52dBha1+bK+lDWhxVGVWgUyDThBP1x+rahRy9owdvTYc8WXnx1Wk55E6VC5FAeHqAUgliUom+YsZCEaHgGfeg1oMFdxRW4oFbGKfprsoKuW5gBYMvHWPw9/QqyOK1t7Wo2Dk2aHEyBGDE966CU9RE6EkIekC+F7BmibVTOMypFhbpWC5k0oYsVtY7psbGwBuCksAQrugEm2FXTVAq/GAlgRMLLN9UZBmXKx8/J8+7UcAXhfXxaBE4mXoOE20Pbb6IU97Ea76Ht93GzdRKxidLwOunkXwhUmMLSz4zqDbQewNrXdBg9sIUJb8NAOmA0DyQoghRDIsmxF6E8fL8/zlTpzYJXibqbBYlvbjOvETRAkAa4nJJylM7rr4IlmB1RVhfF0jH/+//7n4A2Or//K10lA1SqdKN0SMzFb0XgYV2PUjzawJ5hrtB26YRe7yS54QcLw2iEj4QkavIGG08D1yXUj5KcDeF3Dr5OpmtVil77YJQc2a8Eem/UgXB8/jmMzx3ZJggYI8jzHbDZDt9tFv983bIt15rnNSNf6DmVZGotLAAa40mUJeh41+GSXMdg6CnYsts4+sBkw9vpaL78Iw9BcAwAT49lCjvbrdX/0GNvlB+sMfD1H6wz+dakAsy4WbhsaiLHHwgY+9Dq2waX1khv7HtD/dzodfPzxx+a+0eOnWUvrjIht7coaDYmfIFYxZRVLBpRYsQiRkjaiuZtj7pITxIzPkHkESKRBikEyQOZlKzRqphgSkRjWQlRF6BU9xFmMeEo/+7VPljZ+Ti4TborcywmQ8DOMkpHJ5AtHIHVSc+xtFEHB6FpLlBh4A6gGuRlsAiS8mnQWHLXcpBZegXkwR+mQiN3KB6SCUdKP6siUOARlgFjGCKMQHvOQzBKUpyXCoxDdqItbt26ZG1zTn/yGj8zJMFZjjPkYQ2eIKqroK6ggAnILWC+tACfqeI364mZo7cOc1QtrxppDMAGn5WDO5sjDHNV+haFDwdMpToHPA6xkCMoAhSgQpiHYMYNz5iAWBB4VgwKykGAthsOfOUTdrDHGmNYFUtReDelZ18wW18przN05zhQpu+Pw4rVCLUUuoypCUiWIZIRW2UKUkdBmLGNC58saKUsxUzPwJkcd1ii9EqVXovAKpGzhAsIzAl7WGpec1vuitEcq0pUQbIPN5Xpjy/XHsECEF+UBnwqUeBlgsXbOre3Tvn7RPwn50tIMJqiPF8oj9Ou3nEdBoXDIneIqwAQUlQz5wjf3V78kx5VABWCSLbNJC0u8wimQ8xwpSw1rInfyi+KXi/mteIUKBEoM2AAIgf/hW//DhUvxuY/ET9AO2ujHfdzv3ccv3/5l3GzfxH6yj3bQpg8TUWKQDVYAidP0FA/SB5jXc6oxXZscDo6QhWhHbaoHXwQoCqRxIIRYBqMXhps2hoETmKyrUAsRv0WN85bJMOOgsBDC1EHYFT5LNF1YZ3mVUgaceFlgo/tSihITMcGz/Bls7VPT5gARp+M/LvsAAQAASURBVIhOHTohEjfBYXCIftDHTrCDxEvQbrUxmU4wyRY1sWJCavnVDKlIkYv8ct0LBVPj73N/daOxyHL+RAQwX3JffZqm2Rx22/bZp3+nM/MALhX1vIxmX6kKg3KAQTnAg+nlWWaAgLOW10I7aOMwPMSz959Bzshx6s033kS30zVBci5z0lNwCgzLIc7zcxJbu8S+c9t1ChBIdhVgDqDxdLAAJhQBnvo+2nZuA8qqi3ocK5oTL5lrxZW5Ximk0QdwubuqcbJoucpRliV87psyEKVe4tgBn1xsnBBRQLaORVGAu9yU9mRVZtb6Txyc2AbOv6TpJIHDFxn0hUjoT+J+lJDGhUW349nl5Um2a0c36CLkIZpBk5h9bgQmF5t+KMwyAkVP5idkf86rFeal3h9ISNTi5dSeSi2CSMbNs56Dw+XuRh0hBoaAkwCq53jEkpIL29kt60RBGabdGc6uMIrLz4L4eYwAARrOwtWtXwM+UEc1/NKHW5B9bYwYjF8MYO0AjDGGLMsuZKSVUiZg1e4Htl6EBivsAEk3/X4dNNklA3Z5x6byb01dB7BS768DN9d1EYcxWk4LKlV4o/mGeb1mWWhbSVsc8OnTp3jt86/hbH6GT46XDhjjcmzKPeZqTu4p6RGGOQlnpnINUFtg+zGP0XnQMYwJd+7iv2n+N8Z60wZMdImDLShos8/1WOg+2wG94zhoNBqYTqdGg6Gua6MjAFB5gBaN1EG5Bj00AGLPjQYndGCvA3oNPtnASRiGBmDSa8EOsm3nCX3OsizNONjselsk1e6jnl8tYjoej9FqtTCbzYygpWZX2GX9+ne6nzYzwJSIWkCP/rsGL/R60roMNuigSzBms5mZQ/ve0Oex16dey3bJjf7dOujS6XSQpqkBRfTc2AyTq7RP5Tphd86m0eiF6jIXTdVEo2xcQHX0957vofZrpE6KKV9a082dOW3uvQHmzfmFWuqwDqkko04oeK9j9Ms+kpR+5nMO13eJdeFmyP0cZUgZ7pkzw5QRg6B0yR5MOgsRQoUlHXv9QavIUlMDEJpSX/HqAiARipDqxJVvyhoUyCZywAd4wV4g5emF9zm1Az7niGWMNEgRViH80jfMiZCFaOdtRNMIjWkDySgBY8wIuARBQA9gV6F52ETplzgpT1A2S1RJhbk3J20EXtAH2wZVf+UqEo5cZFSlokzLhcBRErWTVQzgC4vANlC1Ksg7EjNvzQdeAONyjAbIYSOaRfCGHoIqQM/rwS99JCrBbD7DLJihbJco4xKZn6EKKpO1ls7iw3jBlDAil8gxVFYGecPGxas9+DVZmSZ1gkhESPIEB8UBmmxBpc8cyEpi0Brgmf8MRU4bDckkUWUDhiqokDmZCYwv1LxKBle5RrRQl2/Y4pz2da38vChfWNGV0IDEts3Ypt9/GlDiZe0l4JR9LuUsAJRt518/psGX1gKDK2S1tBDrSqZq25iA2E+GNVEHaOZN+AVpOnjMqq9jS+ZDznPSmVjMt4yo/tdupSxR5RVG+QgPxw8v6fSy1KITdrAT7+CgcYDPNz+Pw/AQB8EB9jv78D0fCmSx9d0PvovCKdDcb+JoeoST2QlG1chkmtc31QyUcYl4ZGw4NSAqJAVHDiMq7qYNLAc37AWXE4Okktup6Po9NgtA172vzMdlbfEa28rwqgGDTaee1lMc4QiYXf4e7TIROzH6UR+70S4OmgcQqUCRFdjZ2cG8og3cg2cPUHs1ptUUklPwsRWgWTTNAvG4d0Fjolb1RteEn1T2dlu7Cj1+vV+b2A7293YZwo8CTOgSoZN8oQ2yB4A06/CseHaBORHxCN2gi5uNm7jTuINe2INbuZiNZnjt1ddQlzXG07ER97Tvk1E1wqSeLIXWNlznev/spoN9MJBV5yVNAwkOHCPqChATwc70v8HfwGw2g3AElQqxlD7PnA3OP2xZKvWyZjt2uMw1trUe9zaW3ZQoSS9CZQTEg6MWNaSQpmxgvTlw6DnjRQZU1OyZWtWmtKOW9eUA4zaA7Qr3gx7Lq4yJbi4oMGVq+ZzR1/fjAigKpLlSyxpZvSjjSy+/91jIyMqyiuDVHpzSAcsZXJBQZZIkuPfaPRLeLqnsaVpNcTY7g3AFlc4sgAENML7s+aSvNZekK8EFN6WMwPZ71oGD0FkVQBVKoJIVCrHZ2UU/n8f1GAoKx/Xipm4uvq5tvDj82z/9t4jdGImboOWTa4SaKfznv/rP2I/2kYucyjl4G02nidiPTeBnZ+xtjQY7I++6rhGq1GCCDoLXRQ+BJYCggzfbQtIGF+yfbeq8Pt98PjdBox3k2QGsDn6FEOhECweXMl7pgw4IkyRBt9vFxx9/jJOTEzovhBGbnNZTdG90MRdzvP3gbfCEY1JNMMgGmKop3n//fezv7+Pg4ABhGCIMQ3NNumxhXWBQx3m2JaMeD62lEYYhjo+PjWuCZnfYWnsaaNDzYMcytgaAHlN9jDiOL4AFttZAGIYYDAbwfd8E41VVIcsyI45pB+Lr2Xy7XGQbmGIzFHTJvVIKzWbzAghla5u4ros0TdHv940tLAATqGsQQQixovmwbjFaFIUZP/03PYZhGK6Iu9rH2aSNsl4OY/fRBtVardbKPOr7y14DV2lXBhpGo5GhoWik0J6c9XqfdbREN68kVCdkFJhLKXHSOEGTNxGWITkulB54yJF5GWZ8RuwFLzUlG6fhKVI3pUyo1TzhLcsz6ghxGaORN7Av9o2GBKsYMidD6qQogxKpm2LGiHmR+8TGyN3cWCxq20Ftd2lEBa3GFFEsK1aRuBMnKn7F1gAJRYCJV3uksFwBvutjPp+DJQwjd4TCK5A1L9aGO9KBX/rwcg9eQWUffunDzVw4mQO/8LGDHcSzGO7URZRGBnnrdrumfuno9AjvP30fEzkB73BkcYY8zlFFFXibgyVso/gkAKJQBxIIiJq4EuCtsyQqsjYUghgmWUh2mVW/gvIVnrAnK+PiFi7cwoVf+PALH8k0QViHCKsQXuGBzRm5UDQ4ilaBiTdBGZeo4oqsEd0CtVtDcLEicll5BFjMtRT/lqCUSfLw3mitKWlt+ZWPJE/Qrbtwa5dEByWgJIEgTuBAOILACLcgar+bb/wwdiVtRpVUBtSRXG7WVdi0GVMbNsrbwIBtYMUmYOCq+66rvm/TGtEAxRoleevx7L4qdbFPl2xQBbPKOWz7tC2gCFccnvTgSx+hID2Rf/iL/xD9qI/ADVbEwApRYFbMcDI/wdH8CC+mL3A6P8UgH6CWy41fJcnWbZgP8cnok+0Xi4W+gfLQdJs45IfoOl3cDG7iq52v4np0HS2nhcClusACBYFxXomng6c4mZ8YIbRRSfaKw3J4sSRDAQ2vgYbbQOiEqIoKqlaoqxpRGJGTgiyRyQwzMdtY0sEZX24+lbpUFG9FvE+trtVPRbfGMtD9tEGCgCCdCZnhvDrHw/QhYLOHbdMJTiwvBw5iFmPH2yEbObeFxEkQuiGquiIBNxTGdnJWzzCv59sdFi5elAG6PY8ACs0C+f+FZehVnAnWx30T8LDpGD+q60EmMxRZgUk1MWvNaHj85erxXOai43aIxh10cS28hlf2X4FbuijmxQrjplIVUpESVTmoMZMzPDp9hIyTvd62UqyXrT2jybGlvSPfAWLrF4qYc37tU9BZEDDqM7JvvP/qfTCXIVc5JsUEs3qGUT7CvJqTy9SGdXLVsg4FZTL4uuRC66voEoCVvkFgUk+Qy3zl3teB5zY7ycRNEDohgZ2LTFhZl6Z0RWf1t36O/ZitRn1lPRuAgmvNItO2mRqY2Mb8+TTPI8WIyVeyEsxjYAGDbCzE1LmC5BLvvXjvwvsCFqDv9XG9cR0RIrS8FiIngrvYxldq8Qz2gUk2wSgbkWOHSJGJzJSX6f5cRcdGgAAxCBjw6mVAo899A07oMp5KVAaI2sYGrWRlnDSepk/N7//i7b/Yen0e86jk7qM2fZ7JEG2XNMB2/V303T56bg/33HuIOtGFrK8Oku0ae7tEww741nUj1mMbACsBM2Pk5DEcDlco9Tro08GwDVrYmgLrCV19TjvI1gCIAwdNRjaZ8IDP7n0W3W4Xfz78c9y9e9fYIn7729/GW+O3UBQF6rrGG2+8Ac/zTKxgAyWbWAz22NggixDCaBfoAFophdmMMgBRFK2UMdiOJTqA1WOvAQzNXLD7aQMGwJL54LqusdrUxwMoINesAHvM7JIcPa82QLUp66+PoYGPqqqMwKZudpCux9HzPAM0aDFHPd+2UKXuhy73sQN6WyRTszhs9oxmkdjlF/ZatPu+Pq/2NduAAwDEcbwCmNh2lnbZx8valYGG9QVmo3L2wK0DDOvMhrIsV0RflFJ4+8bbOGsuaVlMMdJ2qFf1HTrzDvbyPQRFADZbPMDCCpmboQxLnOyeYNQYGU0AyUh4yf7g4pKbEg3NjIjLGO15Gw3RIO0IEeJF/AJ/fufPzfs0YKBtGiWTJqBVTKF0SMVaK95vAiQcRVnFkpWQrgR8IPMy1K0aObPE4xSI2VD7pPwvOAWkimrVy6hE1shQBiUJ9i3a23gbTDJ4hYewJu2KFm+hgQYSmSAoAwyrIeqqhjtwsSt3MX1EVqJJkqAsS4RhiN3dXRyfHqMOajgdB8m1BFmQoWyWRkOjdEsIj8o2Nm0MlKdQe1S2USuqUd3k+sAkA6tIdVy6ErmbI2tkkI5caj7YY1g7xv4zFjGaVRNRTmukyZo0bhXVLaZeiok3wTyYG+eNwiUQQDtvmKCXq4ubSz19HESp14CFHfiui1Mu1oi2U0zSBKEKIUsJJoixwDmHcoieLjwBGUrKoKvsoq4ESCRKrzsNShgXjgsDj4vMgE0MgquCEvpvVwUl1oGcy4CEy9gaa0CCYUxsOqba8v2281+yH5SQhrUy9Sj6/J//8/+89fUMDM2giW7YRS/q4eeu/RwOG4fYS/YQuAvLSsYgpEBRF0irFM9nz/GDhz/AsB7SRlCmhvau63vzOsfp+eWm7RwckRuh7bfR8TroeT0cBAf4bOOzuBZfw260i67fReRFkB7V3r/z8B28+/Bd3PrMLQyKAQb5AA+OH2DGyUFnWA43AgaxQ97wsUd0a62+rzetpSyR1imm1RSzaray6dYbUgcOlFxsybfY0tq17Pp99ob2J5GFvEpTUKhZTRlxTHBUvlxI0WGU/dsNd9HwGlQi4LcR8pAENevMCDOeT88xkiPD1irxck0GzZoAAM/xoBkrPylg4tOMqx10X5WFoptdCqTZc5c1bRn4slarmqxY6yme58/BGcdfDP9i1fXEahGP0PE62Ev2sJvs4o5/B69eexW78S4SN8HJ0QnSNMVsNsNnXv+MsRIcVSMMigH9XI0xLIcrQoX2GNkg2yYbYizYlYVToPCLJQixGK6Hpw83HteDh37UN5To2I2XwfxCRDCXOUpWYpSPMCkmSEW61XVBgwQvY24AMECPdmPgjBsWxzplX0JiWk8JnLBYEDWvjePGhabocz4JEnIi4Q6tc0nMibyi0rdtAqk/bhOLf4sOXKkFLIDneMsgQy01bYSi/e46e88wAfV5XxL4l6rEaXaKUTEiy004tDdZsAs2rXEGhqbbxG6wa+wbm34TvWYPZVlimk7h+A6JGNdzDKYDAvd4YcAJvTYulFFuu87F+tCN2w/6LU5bjnQQBzF8x6fPCRDbKi1T2idtAaRrVWMu5pjNZpc/vz4E8B/pW495JIrpxggRwq1cHNQH6Dgd9IM+9oN9XIuvoe3SvRVWocncA7gALtg/2wlXxhju3buHwWBgBDLtTLLOFOums/k6ltKxlR1o20wIO5BcP7fNwtB/t107oihCo9EAYwx5nq8E5LbIoQ3CaEBBB662uL/v+7h37x4ePnxozq+UwmAwMK4Ft27dWumHDq7Xs+k2o8Eu7bB1CPR46fdou0w7+NZ90Zatuun4Uwf9mvFgAwab5lk313Wxt7dn7Dk1UGADRfbxfN9HlpE+VhAEiKLIACb2nOs+a8aEPf/6mPr8dmlQEAQroqD2deuyIHscN/Vt2+89z0Oj0UBVVQiCYAWI0yDQVdqVgQabNmGzGeyL1DeETfPRF29TcHTTF/xPin8C5jHM+AxjNcZIEFgwDaaYBlMM2ABTNkXFVh+koQxJ32Gh8dDhHciSqK7Slai9i2JPklEwWzkVJpgYu6V15kEsYrRnbTiFA1e5CBxSKlZMGYp14RbIvAwVX1q3SSaJNqkswafF+jVCjhJg7mJitzgj5G6OwimWOhPsohMCUwxhGcKrPAQqgAsXdVGTr60DKh+JSjxxnqDgBfVxH8CXqY9HxRHc3IWaKgymA0QiwiylOmZZSzRZEwfBAdgxI7qnELjdvI2dnR2UZYkHDx4ADDjLzqAaCkE/wFANwfoMzo6DzM0gIgEVKAJWNgESXNHfF4GW7Sqw8npFAbcjHKIzhwVytthwuPWFkhAuOTEiFoBLWIfYyXYQ1ZEBXvzCBy85UkWlOyNvhDzOUUQFZpgRU8IhYc4V+zG2YVNuBc6SS3JQcDMgonVgKOYbwAFPeohkhJ16B17tQRYSqlRGGZtxRsDEQiA0d4jav1HhXRBV1JRwMHkxY2Rf86I/62O90j4tKLGpqbW/sQ0/v+yZta38ZAvj46XHW99EbmN/XHIcBYVJMcGkmODR+NFLTkgtciMECNByW7jXuIe9eA89v2dqeh8+fIgbN28gSAK8GL/A0ewIw2pI9ZlyjkIWJnDRgetzPL/0nBqUiBDBVz6moykOo0O82X0TO8c76IZdHD87xt//W38frueSPaWcYg7SVZkIsv6aqRmGJSlvn+anGJZD5OKiw0bsxGj6Tcpm8hCe42E8HGM8G1Mw4wBBI4AKyBZTW0ZqCjpNB2VYOfhWajDHggKsVoGJHwuMuMra2dCEEmY+TGnAZc0loNWtXLRCYkvoEhjOybJUOAKpSDGrZphXc2SSNixXqeEGFtlZtnRrkZAvtT+9Svu0gI/OhgIwVF8AVxpnW0TxZcCGKXdQ5hdbWyYzlEWJYT3Ex5OPIWqBv3zwlyhEcTGweRtouk30gh66fhddv4v7yX30wz7u7N7BbnMXAQ/AOCnop2WK8/QcZ9kZBtkAZ9kZzrIzjIqRubc2Po8tEHvj58xiDEpQ0HmaXQ5G6sbA4HMfPY9U7hteg9g5TohG0oCAwLPjZ/ASzwCGo3SEAhvGYtFWHBle0ipJ2jd88c+AMAoXRTEZIDyBuZxTpo4v6pq1nSiqjQBAyMMVQUzHIavUUpJI40tLOn6MZmyKr9oUqNRSWvT5BTgBDsNuWi9hqmS1UX9hW2NgBjCYVBN4oDIDNSKR0LRON46Hz3203BZaYQvdoItO1EHsxuDgxs46KzNzjHk9x7yiZ5/WAtLH3Xi/ru09hCMwrafGdUYDmdvudQ4On/vwGDkqhT45XJR1aVg629atUPRMnddzc/yPRx9vfK09HtrFpOk10fLJNWIn3MFetIfD+BA3khs4TA7Rj/rGSvRXf/VX0el0VhKwdnmCnWHWgbdmH9tl6fq9domHbf+pg219XJ3htgNfHcBGUYSdnR10u10URbGSDe/1egjD8EJJiD7/OrXezv5/7WtfM6yGPM/BGMN8PsdwOESn0zHUf3ssdLmATkLbwoV6rHTfdDbfBk+KojAgjv1eDapo5oYGaGyRRH0d6xau+tp0/zb9/Gu/9mt4/pz2XDZYYoMfej49z8NoNDJzZl+rPf82gANgpXSCMWbGQrMxoihaAXzs/mjWiO6zZlasa0fo8bJjdt0XPW6/8Au/sDIu+nq1tshV2pWBBj05wJKCohEmu5xCd0Qvdj2A+m+2gmkcx4jjGEmSUK2LamJP7NFNIeghbAtYZDLDWI4xwQQzTo4NE0bfn7lnJD4ZrtrDOdJBVEfwhW8EHQEYUb+SL5Treb4SuKROCh5xsJAZ1sJ6MBvIAC3RQlRSrR0XZLul6XU1r1E4hSn5MNaT3MrQKtCHpraPXJwfagMrYm1TpqCQexR06pp1GcsLwSxXHLGI4ZUeqlkFVShAAMxh4C6H3/JRdStM2RQiFBg4A/PeH+KH4CWHk5GWhF/4cDIHh91Dsp4clGgkDaTHKdRAoYsuWictoxab5zkJw4ga2esZnnxpWTJh7Aqtvm5iPei+S5fcLV4WCDPJ4EiHsogOjb1kVM6yznCBAoKa7D+Dkv6Ps5i+RAyv8JDIBC2nBS/wqLTFyzD2xpg6U0zdKVKeksCkUyyzNFbQeiFrsxbIVLxCxSrMnBkBUMFiQ73JW10trWX93CeXBHgosxKjwQhe4KHT61DGyKlpfbs5ClZcOBZX3HzpLLNgYvMcbAMl7L9tmIuV3192zPXfr7Mw1sGEq4ISm87xMrbFhuPozbGpS96yiXlZ3TcAZDWVcozqER7nj4Hxhhc9Xn7rMAcxjykzFe6i63XRcTuIvRjdVhcOdzBJJxjnY0zrKcbVGINygEk1QS4om2tAiUUJkf6QNK0GcAj8y/f/JQUlzEfiJGh7beyFe7iZ3MSd5h1ca13DTriDnXAH/bCP2I2R1imGxRCDfGAE+3TWd1gMiTlRDDBWY0waEwiHxm6GmdlY+sxHy2shcRM0vAYiL1pmg5XC6eAUUTvCvJ5jXIwxq2cmk2+LIDKs0lvXm12/rpQyG+L1bOPfVFOcAMSBGGAgBlRSd0nTQmsNn8pefOUjciI4jOxKM5EhFzlSSdZ3myjS64yR9abBHZOJ3cDa+rTtqtlQvfaMqCGWNO+rBrXAUjQQbMGm3NJXAbFaZ3oJMDGtp8hEhuPs2FDIpZIoHlzU7OGMo+N10HbbaDktRCpCp+5gt9xFUBPbDRUghcRsRiBSY6+BqB/h7Y/fRumVkKFE67CFqZhiVI42Wt8aoG3xXNJ9Xb8eu0xiUA0A+1C2xp+FGTKQbkDDbRA44TbQ9Jtohk1yE5E1ZuUMaZ0ikxlpnFSzS0uHViw5bbB5A7indTe44MbBxOUulFAbwbJc0rPO454BG7SrzDYRz4AH+IWDX4Cf+RCRwCAf0PMzpcx+heqnY5XLSBNoY6nklubAMRo8StBnogYnjNvFGpCin4+VqgxIefllLbRmGGl7zCoSsDxKj4xAa1ZnW5kxLb+Fg/AA3aBL7jMsBCQFhH7uYz6ZYzQaYTafoUZN+mChgIoUon6EuSTA4jJ9IN2vXOZk8wysABTb3udxDx7z4IHEMB3uQNYSeU2l0tvACYc52A/2kYkMs3KG8/z8Ss8il7l4o/UGDr1DsAHDfnMfvaiHttdGOyMR6ev8OjphB0ouy891cKoF4YFl8LieebcDTLukQDsi6GPqGM33ffT7fbLsXPzN933D1jg8PMRoNFopWdBghj7HejmB/r2UEnt7exiNRgCA2WyGg4MD7O/vY29vz4AowDImrKpqpdzBBjN0zGj3w2Z4ADB2lrp/Ok61xTrt69PxapIkiOMY4/F4pYRiWznB+s+cc1y7dg1FUSDLMuMmYetb6Nd6noc8zw1govUY9HH1vGogRgNEetx1SYXtWKHXhmZkrFcW6LFmjKHVahlNCS16qudAH0ODEfp6zP6ornH//n3UdY3hcIjJhPSO9Gt/4hoNvV5vxaMUIG9b3Ww0Rg+4/l7fOI1Gw6AqWjBDKWVsS2xERYMU9uS7zEVP9dBnfah6tZNGCVPVOMlOcF6eowiIjjh351Sn7WWYuTMKPC3QgClGWW8RwpMePOmBKYaiJBaA8kksUVsn6g/EghcoWAHHdQAfS9aB1RxBQEcv6yGoAhKm4wHKokRVV3ACB6ezUzgdB2VYovTLjcHw1oBJLYJSJi8GZHpsmMTcmRNosqhTN+KK9uEkg5/7UDOFQBF6O5vMSCU38CEDiSIoMOvOMGlMIG+u9VU68AoP57Nz8DkxCqphBb/w0XE6aBdtqP+g0N3tAgmMC4QuZyjcgpxAvHJj8MnkEvS5MBb2S7mi4H2dSrjhmFzShjpnOfIgB0KistbOxU2AW7sIq9BoR4RVCK/0oEKFJE1wIA6QlOSeIsXiQcEFiqhAGqTIgoyCfrdAwQtit/DltW2yO7uwAWMg+zNeg7uLDxgwiFhA7pCd6NQqOOeKI6gDdIoOfEnAhKOcZdC8ABdqTqBE4ZAI4vqHNQMzGRgdpAgmLrBszDVfMuZb/2b/numXrzKNNr5H71sXGioXjn3VwHEb8IHt9agXD0EH0UHyZYJ5zDrRpVlaJTAVU0zFFM+LNYBgA5GBgSFyIjTcBvbDffSCHvYb++iEHZwdnyGtUrR325gWU0zKCR6fPUbBC8zrOZRDm3idoRvUA3ySfYJvD7+9tR8BD5B4CTp+B7vhLm4mN3G3dRdv9t/ETrCDftBHx+vg937n9/Dnf/HnGE6HQAx85W99BXuv7BFTAmT7NRVTzNSMNvsLoMLQ5kfL83rMQ9frInETxE5MauqOZyzoZikFQGmdYi7mmAvSTrhq/brum2EE6E2B+skwAhYn+NRNgUQ3B8Xg5S9eNIeREGbAA7LpW7gTSEVsvlzmmOZTKt/AsnyHLpEZS8ht2WCPe1Qrvsg817KmenFRfWoNDgVF7JYrDq/LXAoe+EVxwk06A9uavl85W9y3W2rxr5rFl0piUFLQqsEtDWzVvCa752Dx4jZ9DkUyQr/uo2gWYHOGRtbANzrfwEHrADd7N+HWLlzHxenZKT589CHe/OqbOJoe4ZOTTzAux5jUEyOAOa7o503zpa8HWDJTNs2tgkIFEvUb15vQ0A3jyDiaPtWKax2YwAkoEy4l8ionQKIgPZNUpiixRRNKjyXkBaBAMyPW15e21uXgVMoAYkUFPNhogVvIAn/64k+R8AR7Yg8dn2xab+EW3NLFjd4NNJwGmkET8ynpYvgtH+N6jKP5ETI3w1RMMS7GmJQTzCvK6F9p3W36PFtn+tl9g6Bn4dXxNoQgNpnL3JWyDsklHOagrEryF7IEa2vUV2ZMAQTWhG6IwA0QcGKUpFWKeTmHkGRzrJ/FtVeT+Ouu1WVJNtcJS3C7cRttr42m30TohBifj/ELr/wCDncP8c6Dd3A6P6WSJTmmMilB2jg5y1GIi4AfDSn127BhaDBX25aPX5/55vmyH++jH/SxG+xix99B7MUI3ABCCcyqGabllNxxCgLbJ9UEYMB3j7+LqZiimlRL9vPa9TW9Jtp+G37t4/ofXsdusgtfEKuk7bXR9tukE7QoxzssD03wrWMvna3XAaK2e1wv7eh0Ouj3+6akwKbuP3jwAGEYot/vr2T5bXtDzrlhQdjBMmMM77zzDr797W9jf38fh4eHJhBuNBoGSNCBrtaNaLVaJh7cBKTYOhV2YK5BkKqqkCTJitimrfsAwATr9vV/8MEH8H0fjUbjgp6BPSb6/TbYIYTAX//1X+Pdd9/F5z73ORweHpox0owLuz8aNNCCmzq+1cCBXQ4BwFiT2kKgdsmHPe92hYHNYFFKIQgCzOdznJ+fk6vQgtmg+6Lfp0s1bEBJKWWEK588eYLZbIZXXnllZZx/4qUT0+nUCF/ogdGL2whZrdFeNFVHD0iapob+ogdsPp8T2mnZnWyqddE1RNtqlhhjyL0cv7v7u4gaEaJyqe2wN9uDXyxVfpUi/YDMI/ChCAoDRGRuhok7Ie2E5urD1hc+eqJnPJY9eJC1JGEwRtoLuZuTZoRTGkrYzJlh5s+MfsOF4KxHrhVRFaE9apPjBI+g6gXrwaHgN3PoGlOPtAaEK0xQZhgSEhczvgpGcdkwJzasD8UVyqgEQhJQSpFCdi8KFDLByK1h6gAZyB4THFWxoPW5AIsYynYJcVNABnLFGumZfIagChDW5LAR1RE6WYfcAMqAhCGxeFAEAilPMSgGEJEgBwCPxrkKSAjvQjnCy6j/1u+N+8j6+9ZZEovxKx0CgmbBjBxInMqc/wib67i55PCUB1/4COoA/byPsAzRqBvwxLKus1IVHrUe4bxpqdTZGw8rEy/Zho3whg2KxMKXnS2cGtSSnbMuOAosaiXrGIEI4AoXrlpmgAGYUqPKqVDyEhnPyO5zrWkHDg4qH9KMko0A0ibWgt2nlzEQuH7pGiix6T1yoRWwyNRuPe+P2V4GTHwa6jkHNzaE+tgbXQysY6eCMtonxQk5Mqzbptsl5QzwFD3PdsIdtF3a1CROYmqAS1UiR45ZPTM6DLnIUUmqBc+LHOfFOT6efoz/dPqftvfl62Sj61QOzuU57pzfwWFItNPXmq9hJ9zB9c517DZ2zSYjLVL8zh/+Dr72t7+GYT7E2w/eNu4CE0HgxERMcFaeUYnHBpcBl7noul00PSrnaEUtRF6EwekAcRzDCzxkIsOkmOBkfIKKV8iRL+2BrzBVDhw43DFOGhLSBN6XlnP8iKUaV21CUWZ4U0Z8W3MZAZIOHAIRnIWSt6xQqGIla63p3Nq1xA7atwVcASObPaUU8ionVoqDT82e0IH/VbK1wGrgoEEFXe+fV/mn0rswJQALrZJN4MRVRfckl5izOdIiBWL6/DxhJ3jw9MHG6/Hh4+AvKXOcIEHH6+B24zY+737elHZ0gy5u37yNwXCAR0ePqGRDTDGuCYQY12NMxRQZyzDIB/ii/0X8j3/3f0RaklbL7731e3g4fog0SjHE0AAnGqBK66VVrOmHkpiUE0zKzU4fF5pDn62+9OFUDnjF4dQOHOkg8AJ0+13ELWJN5RXpCEyKCW62buLn9n4Ox9NjY686LseYVlSSIiFRiGXWXbN0NDhh34u1qjEWY0wmE7jchcsWGUUlIV/Ii2DZCR2v7bfRC3voBl3ca91Dx++g5bXIEYHHcBRRp/M6JyeUkoLj8+Ick2pihBpzma+W+FrPAv1MAUiAuOW1cK1xDVlJ5RC5IBeJQhQbrVtz5BtL27Y1BoYQIWIvhstcuNxF4iU4SA4AABXonLqUa1bNkFYpxuX4pWws+xw+8+HBg898QABVWYELjlKUeFG/wKP5I2Q1PY+/gW/g7976u7gzv4PJZLKSddaB4xe+8AWUosTbH72NYTEk0LqeYlSNMCpHmMs5EANPRk9wMjtBipQACrl5bDQ4USrSXnswf4AH85fb9zrMQezGaHgNtP02Yj/Gz+z/DPzCx/GHx2h7bXzxzS+i3WpTAi/yUaDA6fwU03qKT44+AVMMjyaPcJ6eG0vLC8+QP6ZyjgZvIOEJmk4TCU+QsAQJT3CQH+Bz6nM4nZ3i+PQY17rX0At78IOlMKIGBxzHQV3XyPMcOzs7uHXr1oWk8nrpgO3GYAebX/7yl3H9+nX82Z/9Gb797W/j85//PI6Pj5HnOTqdDgCsiDNq68T18gUd9Npuh+ulIPp/HeADuPB6W0DS1nqQUqLf72NnZ2clyW3302YxmHVhMQdee+01xHGMt99+G6enp/jKV75iYlW7P+ulEPZ42oBGmqYX4l9b1NEWsLRjXw3SaNDCPqYQAufn52g0Grh+/TqCIFgZ521zbLMjlFJ49OgRTk9PkWUZ7t27h06nYyw5r9KYumKRxT/9p/8UAKmHagBhvXbHrh+yJ6uua4OwaYqIZjHogdPUHT1w68InwKrYybrWg+M4mGGGH/R+gMInevuczy9YSjrKQSxi85XUCRqygVjGiOsYiSDxF1UrPHz2ECf5CcK9EMFOgDIskbkU6M/4DJlDGWr7+FxyI2LpiQU7QjIoocAchprXqHhljnWB0q43tRvAAk94BJ6UEeIihlu4CBBA1tKwGgpeIHVTVHGFLM5IYGq9XRKE2VnWrWUM1nG2aiqANBX80oeXkYZEgADpLEUjaVCg5CmqH/TJOaL0ywt99msCH5zMob6LCEEZQE0VmmgiVjE85aFyKtRBTc4WQYXCI0G/3M0xd8hJpHTLi/aeL8u+b+v/JbR/DipH0EG0BgU2AiJbWBZO7cAVLhzhkEOFZh84AllgleFcJUDZcp5N82//bsVpRYNUa82pHaICi4Wtq7DuWU6uD8IRpGmy0Lu4cAzpLMUugSUosYFWulKa8KMEZ9vmdNtcKI44jCEkPbtquV2Y6r+E5sAxQo3AEpR4GTjxqc/DyOqu7bfNBttlZI1Z1AXm5Ry5yjGtaDNdiIIyilcQ/9PNYx5CN0TTacLNXbx5+03sx/sI0gCHwSH2gj10vS4aTsN82O7s7CBMQrz38D3DiNCghN50juuxcRc4mh4hVRfrlDk4GryBttdGw2kgdmO4cIkx4XgoRLHM1IkUqSTmRC42u8xsatq1BnIhsuzwJR36J8Wa+BtqDAwud+FzfyV7Wsv6QjCqW8ADQACiFuZ+XmF5Wc1lZP3nMAe1qA0g8NO8F7XoogcPSZAYFpKmoleK6v8rWV1prkz5lbXJ+3H0RHpBDw23QXsrRdTveT3fqN3TdJvouB3KjrptU9axE+3g7sFddL0ujj85xje++A00vSYc7uCtt95CmqbY3d1FkiTwPA+7u7sIwxDT6dTs6Z48eYKoHUGGEh+dfYRzcY6z6gyn+SnOy0V2t5wQg0GkKEV5QTvrQnsJ+MbBETkRmm4Tba+Nnt9DP+ij5bbgMx9ccURhhKquUNalKcMYlkPD9hjVlHXeFGzu+/u4Hl4nMdiFRWquNgelHvMMEKygtn5GMDA0XHqmdLwOEidBkxP74+iTI+w0d3Cwd4D5fI55Osetu7cgIAigKIY4nh4jd3KMytFWAVIXLlp+C7Ebm2BesyU100iDE7qs7sdtPvcR8QiJlxhNnkbQQMACAnxkYRgNWs8hrdNPVQql3SUiHqHhNMjm0m2j7/Wx4+3gZ+7+DHbiHaSnKWLESJzECDHrpOft27dxdnaGBw8e4P79+/A8D2eDM3z7+99G66CFyqswrsYEQogZRuWIGKJiitOM9Ij0Ot7U7LK9T3NPa0ZY4ibwhY/95j4xEJ0Ouk4X+8G+YQVBEbPF7/h4NnyGT44+wbiiksmZmGEmZ+b/TeDrL7d/GX9f/H1cu0YeozrzX1UVoijC66+/vhJ4A8s4blNsBywDVJ1Z55zjvffew1//9V8jyzK8ePECnU4Hv/7rv44wDI2rwvn5OZIkMfMyn89XYkkdC96/fx9lWeKDDz4wgo+2oOT+/j6uX7+OH/zgB4jjGK1WC67r4qOPPsJ0OsX+/r7J5Ovr7XQ66HQ6K6yHTWDDuqaBHhu7/48ePcLDhw+NXaiWAwjD0JQvfPDBB3jzzTfBOcfx8TGm06k5pmYyJEmC+/fvYzAY4MmTJ+b3WtjSdV3cunULUko8evQIh4eHpsTm448/XmEq2KwQ3dcwDFfKJdZLTO2/6f7ra9TzoQGHnZ0dMMbQ6/Xw1a9+9aVr/MqMhk8++YQQON83NTW62UiRXnB6YAAY5EODDEVRYDaboSxLQ1vRk6g7awMJNiVoHWnS9TlKKWRZBr/2cWPvBnZ3dwkN8hxiGWjgYeGakDokADjwB5g78wtq/5GI4O17cFMXiUwQliGcUwde5uEGbsDLPDiC1HFlLMkq00tRhQQiaLBjEhA7YiXAVaQL0KgaSCYJZCrRCBoIvRBZlmGWzhC0Ajgtx9htlrxE5Vao3ArTaLo10HWlC68kQKI371HNpQLV7PNFJnqRlb+gHaC1I6zrvPCBv/jdCsCwxa5IOhJ5lCMPic7PwKB6CkM2XHmdIyhYbaZN+BXR+7lcCL8saLAlL5E1M4z9MQq3uAAYePXCaUNESGSCBhq45l5DLGJgAsixRM/vwfd8DHLyE854hsIrkDs5Kr9C7ufIvZxEIHlBFNf1tgUEWmEbLDL4F963oXG52HxaHt+SSchArm4YLwE2POUBNVCnNTzXQxAFJFq6UGsWTCzr2C9jB/DVvhiA4ZIm3IV9KbLlmsAWrQ0FeBUxOzzhgUty07CFMgUnHY3SKS/SDBmMDemKJ/qm4ESvU6yVU1xWrrGhSSYxK1+ueM/AEDiBoevWkuinlbpaEPKTahfE8C5pmnYuawnXcVHXNVzfXan53XbtQpFv96Sa4Mn8ycbXrLcAAVjNiAVVMUR+hF63B7gkpparnJTyF8FbpYg+OK3oQ/nxo8dbj+3AQeAEaHpN9MM+mqyJntcjBfHoGj7X/Bz2wj20vTYc7uDg4ACMMfzbf/tv8bmf+RzchouJmODp4Cn+4C/+ALVfI+gEKN3SCGGeVWeYZERLXwcTODjafhv74T7aQRsNr2Es3rR+ggYmZvUM57NzDNMhKl5BuVbJ3ZZ50xRwpiigdzjRMoUSBpj4qVhiXpFt8aMI1VWyomytA7IJhiIG1AYwSvffdyh4UkohDmJkdYZROdq4Tj3mERNDLtw5PuUYadHFEiXmxebgwm4+I1u/0A2XOgELnYlKVCa40+v7Km2dNWGDE1r7ZFtzmWsAGhcuMkG19SfFiQEmMpFBfGwFxY8Wa9lrm2CuW3bxa/u/hq/2v2pEzZ49e2Y2+3/wB3+AMAzxjW98AyffPcH9+/fx9YOvQyREV67rGvP53IiXCSEwn89RshIZy3BWneGsPsN5dY4Pjj7AWIyhIoUMGTJF17xu5SkhTUnUUfFyRxhgka3nIRq8gZbbwnX3Ot6M3kTMYioxXNgXM4ehz/r4Wv9raHpNkyEs6gKlU+IsO8OkmuAsPUMGKp2Yqznmco5xNcbz0XNUXoWc5UhVas6voDCtp5jWUxzlR4b5pKBQhRVkJVdL4X5I/0U8ImFGt4UWa+HV6FU0G5TBduEaAEwoKmUsRIGZmBHTa1FGo7/ftP4TnpA1JA/xxfYXcc2/hrPyDMN6wWJZMF9SsWCwrIFE2mXiquU1dp8SN4HMJOq0xhs33kB71kYd1iiCAuflOQb5AClS5IpKbqZiipNqg9Dui4u/0uBE4iRouk0cPDhA22kjmAZ4LXwN/bCPSEUIkgB92Uc7bEP60ggmauA6iqKVYFRCYlJNMMgGGJZkW61FXocFfT/IBzgvzkmbaKEptKlxcAgpMJMzw8R7dP7oIgNxrQU8QNtro4suEkaA1S3nFloBlVrsJDt489U38fTBUwgh0Nht0HNyrMAHSzaDFmFUSiGOY8M21zGXvl91321mwPrf9WsAKqt/5513AJBFog6IkyQxyWXXdQ3LXScK7OPago9SSvR6PVMCYQfDOukthMBkMkG73V6xnrTFNXU5QxAEK9e7KZsPLBPam4Jv/Rrf9zGZTIyQpl3Sol/r+z7SNEW32zUsDN1HfU06Ia+dJNY1GrQgpNa+09UEOvGf57l5xpp7wPPQbrdNLG6Xruj+rAMrm/qq33P9+nVkWXYBbHlZuzKj4Vd/9VcNHURTWLYJbuiLs6kv+mFdVZURpFBKYX9/H81m0xzbrjWyJ1n/bLMm1s91cnKCsiyR5zna7fbKpGsqjh5M+xhSSShfLUsp/AJzZ44Zm6EMS4hEoPALVP7q5sARDsIyJBHBIoBf+PBzH37hIyxD+LkPr6AFXrs1yqBEHuSooxpuz8UUU4zkCIVfQCQCIlhFwT3pLW0+6wiBCuAox1BTa1UTq8LPkXopcp5fDM42bBS55AhUgEiSLoUvfPjKJ3sqVqFgCzaAk5uA+1JK65ZssA48dbB34Rjb3reFSq8BiaAMwFMOXnGy9NLqqlygYgt2RFAic7ILYIEjyR5TgxJRHcEvfYRVaMQgwzoEzzmiRoSwF2IsxhiLMVKWUvmKk5HewkKIs3IrAqpetiH/USnSi2HTATZjCxcSyI1juqIPsGF8tfOAsZGyAAm9GV8R7Lrkmn7UPmsRUCNitgmYkNSXCyyUxftt2yyBi2UZTFGNvRaAldjgMGOda8Wy9KdIZQdg1LId7ixrt3Um7KdMpf9RmgfPACkr1pZSoFSlCTI/tXDaJevIYx5ZkPEQqIBOqwMpJdKS6MaFIHE7W+H8Ko2Dw3d8YikULg7bhzhoHOAgOkBTNPHi3RfYxS6++YVvotvpGmqplBLtdhue76FgBTKe4Tw7x3lxjmG+FL0cFrThPM/PzeZzU2CZ8ARqpsAyRpo4XoxG0MD+7j64x0mIzhEmizav55gWU/r9SzL5DIzKA7TKv52d0OKKVyn2/i9sLer+gAG3GrdwM76JRCUmk83AzPVyn8ONXDwdPsXx9NhogIyqkXE5sZvWo7CtNy8bZ6aI8eA4DmpJa3Dl2fmS5jPSzfCZT4wuCURhRMCIWtxXigCKSl1N78K2hwWWiYOrMCc0mOOCbCt1IMwYw/3gPr62+zXs+/u4Ed1AIhJ0Wh34vo8/+ZM/wWAwwGc+8xl8+OGH+MVf/EV0Oh2T/NH2a41Gw+wBdSZVb5R934cQAr/1W78F3/fR7Xaxv7+PH/7wh/jSl75EFnkOh/IVno2eAU3g8eQxXqQvMFETDKuhmd95TXospSo/FbC0bUxCHqLtt9F0m+h6XXRdKk3phT20fbJA3Il30PE68IWP733ne/jKz38F7XYbzGF4PnwOGUo8Hz7HXM0xqSc4mZ6gdEvS3qmneHD0AHM1R8YyAic23HMOc8z6BGAA4U3zGvIQLbdlrrETdJCwBJ7yEHqk21BVFbjLMZ6PwT2OUTHCN/vfxM+3fh7Pnj3Dz/zMz2AymaDZbCLPc0gp0Ww2cT4+h/CXukGjcoTT9BRH2RF++PyHlNBTKVJF7iWFLFbmwWdUAr1JUJKBGZZcx++gHJXgOUc/6sOrPFTTCo2oASUV5vM5BcYBJftO81Pauys6f44cNasvlHZ60lvZJx/Wh/gHJ/8AnuehLEskSYJvfvOb6HQ62NvbQxRFJjNtB112MnSlD1YyVH+VVYl/8Rv/Au988g5YzPD6z74OxMDZ/AwvJi8gA9LXGosxblQ38EXvixC+wIPjB3C6DiZsgqmiEsGMZShZiVbdQkM1kLIUpVNiJmconGLFne+L1Rfxt9K/haIocOPGDeR5jqOjo5X4iTGGL33pS7h79y5effXVC+KP66UqdsynA/n1TPh7772Hf/7P/znyPMc3v/lNNBoNfPazn8VoNMJgMDDHSdMU165dM3oSWZYhiiIAwHA4hJTS6DuMRiPz3ACoFCEIAriui/F4jOl0Cs45Op0OsiyD67ooyxLj8XglW9/r9fDrv/7r9FluOXfo/uhY0Z5ru2/2sTjnePr0KX7jN34D+/v7+OpXv4r9/X00Gg28ePECR0dHBkTQlqI6/tVuGfq4VVWh2WyirmtMJhMjqO84jrHSjKIInufh8ePHuHHjhgFydfxrx9xCCIRhiC9/+cuG/bCtPMSOt+3+6nnXr8myDP/yX/5L3L17Fzs7O9jd3cU3vvGNjfeB3a7MaGi32ys1IXqSbasOO6jXHyi2AqieIJse02q1DAqjRSY2UVh0h22gwQYL/ij+I0wOJgjLEHIk0Wq3iI4kE4QypNp0a+HYN48RklygSVJIzMYzHB8fo65rhGFISFJM5RPxQQy362LKppixGebuHPNgjokzMQ4HZsIWQpNRHVG5RhWDzziiLEIra6HxpAExEri2ew1plSJzM7AWM+yIIiiozCIiLYkyKFcCJSYZBchzEvzjKceoP4LySMBS8IsBmGQL+0UnAzy9mnCxVEN68GsfrapFgEoVQKUKrueiRAmWMBR+gdwj146Sl8s6fLastzUfhOvnWOgPcMVN5mdb8AyQ3kXqpEiDFGisHcdqrnQRiAD9qo9YxHBKB6gXFF2mqITBBwqvwGl0isxZaGrYh5TM2GOGVWgAiKiO0Mpb4Kcc6VGKBAnu3LoD7nL6APBKYkm4OeZ8jrk7JwFSJ0PGqFSm5i8BJdaDL7act6s0nRl04Roan3kvA2WKkJHQnQVKbNJa4JLDEx5c4S5tWhfOFFoMcuW61vulf16bex3MX6qrwC2tg7WyCcU2OD+slcFoVodmK2nga2PwxJalIpotoW1B15vehJtg5EdQJdfU64374PX1vGAe2LXgP0qG9sdpFUjc7ypMdZ+RFVjgkDiYw0lQUUqJ8WyMrCIAUHG11d4XoDEaV2OMF7Ycx6PjS8/LwCij7IQmWJJKUp3tIutWK9JLyMWydvlkfILvjb+3PBDtc/AbH/wGtCBk6FBmrBf1sBfvYSfYwfXGddxq3sKt5Ba+vvd1NP3mBZRfg9rTamrAh0FOgMSHzz/Ed975Dk6yEzgtB6qjcMJP8PHs442U5obbQMIS7Ma7OGgdUHkVCPSpJNH4M0laDJnKqHSlmBotjcsCTR1ku5zGDYosNKVaCI0tmDJXWW+2O8uPxOa5BNwwgbwCHkwf4MH08rppLVYa8hAJT9B3+3glfAUtRnaiGuyr6xpPXjxBp9dBgYKsXReB36gaYSqmF/quGAGETDFAAq7jkibOhlp5YAkums86JZHJDDM1W47TGjvfZS72nX3sxDuIECH2YnIZyktEzQhpmaJAYejomcyM8Okm8cNNzXauAEifSb9XX9db9Vt4a/7WhbH1OJWGBt0A/omPqB3hW4+/hcPhIQ7iA/TdPl6NXsUr115B02uuMGHtxI+mMPf7fXzve9/DjRs3cPPmTfzwhz/EL//yL2Nvb89kKT3h4fbt22g0GhiPxyuaXXEc4/T0FFEUURDiu5jVM5ylZ3h4+hDP5s8wZmM8mzzDnM9xNj+jYDkbEXi5FhQrkNtZlmdbNZjWGwND/N0YLa+FTtBB1+tiLyEL467fRSfuYC/eQ8tt4VrnGvpRH3/87/8YvV4PR0dH+MM//kNkKsPhvUN89Ze/isqvUPACMzlD6ZQ4mZ0Y8c+z9IzEOovxyudhLnPkZY7z6tzc0xpk1Jox683nPr43/R5aTgtO4eAVvIKG0yAQJaDAf8fZQStuoRf0cBgeGhtHnaV+55138P3vfx937tzB66+/jj/+4z/G3/sHfw//+t//a3zmS5/Bew/fQ9tv4x/+0j9ELWsM8yH+3X/8d3g2fIbSKbF3Zw8qUgas/Wj2EWbxDC/ECypbbi8ZIgiX67fJmvB9H72wh1bWwivRK8SYYAkSJ8H58Tnu3L6D58+e43X3dcR+DN7gmCvSiJufkWDeZDJBFEUGGLPXKICV79eZ1euBuA00+PDxlZ/5Cj555xOUoxKsQcFsMA9wz7mHL37xi+Cco9frIc9z1HWNfr+P6CjCzeQmJpMJ3njjDZyfE9UhSRIIIdBsNvHWW2/h85/9PI6OjtDv9/HRo4+QIkXvRg/jkzHuXr+L8XiMe/fu4YMPPsDt27fx8OFDYz2pKfk3b95ccUNYr+O3k7RaS0C/bj2uunHjBj7zmc/g/fffR7/fx4sXL/D7v//7BBguHBu05p7runjy5Anu3LmD58+f4/XXX8fp6SkxMRoNDAYD3Lp1C7PZDHfu3MH5+Tl2dnYwGo2MU0NVVbh9+zbeffddXL9+Hc+ePUOr1YLv+zg/P0dVVZjP58jzHAcHB5hMJmi1Wst71urbpp+BZTmCzQgAgG63i729PTx69MjExLPZDIMBsc2iKILv+0ajot1uoygKjEYjvP766/joo4+wt7eHJ0+e4PDw0IAjYRgiTVO89tprePz4MXq9Ho6Pj1EUBdI0xbNnz3D79m189NFHuHfvHh4/foyyLDEYDFDXNbIsQ6fTMddnx9d6Xdprd52lsF4mwzlHEATwfR9vv/027t69e2VGw5WBBnshaZXKdTXNdZaBfbG6E7a2g6aIAMBkMjGMBk0lWV/sthqoLpcAFrUlsUQapRi0B8gPc3zCP1leu+JIREJfkjQZErH6fyQiyHpJJ6rrmhDpBYqkqStCCnScDjqcsmtlUa6gP1IRMjl35phiihmfYe5Qycbcn+M0PMWsPUPJF5uR1+m/T+pPEJQB/Iw0CbzcQ4d30JANdIsuWmULvvABBuTOIoh15hR461KQxhzT5hSVXy0FDhfNFz58sSxLgIJxG6icikQN16w0K16hCirMgy20UUWBqFu78DMf3aqLIAsQZGTfxThD7dTgPY4qrnBanCLjGVSoIB1pgs2X2T/q3zHJzCbtQtbZKvPQZSLayk8HDpsYEn7tI8kSdLIO3JKsIpmieWYeI0tNv8QwHpJjhGdparxB5/wr+VdogNZQE000FGl+XMM10v4oYxLPVLShrUWN2q0xVWSNmTkZZt4MU2dKoBWbI0WKii/m8WWgxBp4o5TaCBqsNx2o6g2mEQy1jic5sQAkl0bMVDFlfrfeuCBgwlGOARIklwbIWGG2bAMlNrVtoIT93g2MBnqpWgG+Nr5/0dcLbcNaFIo+jLWgnFJqq4WaFsgDllZknzb4+jR+9Q4coutjyeLQ5xVYCCX9lDPUpSpR1iX5oq83jqXa/oJ944MsGn3mE9PAj0mvQJEq/Cwn4THhCJSyXLGztJuC+tSih45yELiBcS2oRGUACV06VKOmulcxw3F5jPfG720/3qKEI3ETtALyoN8JdnCYHOIwPsSd5h3cbd7FL+z/Ah7Xj1H/UY333nvPqF9fu3YNn/vc57BzfQcqUvC7Pk5mJ5iICQbFAO8/fh/KJxr3eXZu9Cc21ZnHTowmb+IgOkDCErS8Fnz4lOmRJbKasr6FKnA0PAICEo8rBWXSpZLLcqoL08iNcJ62otRzIBW5Pwn5KSwpJTaXpV21bQEnFJQBlUZshGfls+3HCAGk9NwIGM1hL+rh1fhV9IM++lEfnbADDo40S/H2D95G3IhRqYrYbiqDDCWmkrK8+RpqUKkKtaqN64fDHUBiq3aKthkclSOUDtGbZS7N3In5xWe8z320vTZ2o130oz6abhOJn6CYFQjCADVqZHVGuiIVlfHouvmszq7EDjLAhNbhYDVq1JgyEmJ8MnkCbLj1ARII9ZmP0A0RuzFafgsdv4N+2MdeuIdRPcK4MUbMY8CnsfF9H++99x5arRZ2d3fxve99D/fv38ef/MmfoN/vgzGGLMvw8z9PmXjHcTAajXD79m3aJwqFKIrQ7rTx2fizuHfvHoqiwIsXL9Dv9/Htb38bIha4e/cuvvvd7+Lv/L2/g9/8nd9E/2YfpVfi8fgx3L6Lk+zE1OqPihGm5dTYP66DE5qB9CJbcPpPLx1SAkmPQzS8BvIv53ALFyfOCQYPBvjyG1/G/HSOm72b6Pk9jIoR/vbX/zZ+8NYPcOf1OwAo+7t/ax//7k/+Hd74+TdQ8ALH02PM5MzQ+k9np3h6/hSlWxpwwi7PLGWJ0/wUZziDy1w8OX4Co0myha3mMhftoI1uQCwPZPTMPjg/wOtPX8cH0w/QPmnjwfwB7ql76ID2tDopuRPt4IZ/A7udXfi+j9evv44bN26Yvf+f/dmfoaoqpGmKr33ta/j44ccIOgFG5ciUrDw6fQS3Scyl0/kpjuQRntZPUaLEpJ4gFSnd28cwEY9TOmhNSbiz6TTBr3MUSYHUT0nM8zjGl9pfwunwFDvFDu73768kJ+3YRMc/+ssOvm0A4v79+9jZ2YGUEr/4i7+I733ve8iyDIwxPH78GNeuXUOv10Oapojj2IA3jx49wptvvom7dwkwEEKg2+2i3W7j6OjIaBK89tpr8H2fst17N/CFz30B4+tjFEVB67/dxsHBARqNBmazGU5PT3F2doaDgwOMRiOEYYhHjx6tlABsy+pXVWVit02xX6fTwec+9zkcHx/j8PAQaZri/PwccRxjOByiKArcunUL169fR5qm+MIXvoDd3V0EQYDXXnsNnU7HlKzkeY5r166h2+2i2Wyi0Wjg1VdfxdnZGaSUCMMQd+7cMWz2N954w7hqzGYzuK6Lt956C5xzw47IssyAHjq2WwdRdIC9id1gx5/NZhOvv/46xuMxdnZ28OTJE0wmkxXHiNdffx03b96k+3R/H8+fP8err76KW7duwXEcI0x59+5dnJ6eGmHI+XyOz3/+80Zj4fz8HHt7ezg5OUGWZbhz5w583zclDRpUybIMvu9jZ2cHH3/8Mb7yla+ssDBsoGG9XMVe2+v/u66LX/qlX8Jv//Zv48WLF/jggw/wj//xP7784YYrAg1SSZwVZ4gE0fr0Ce0by/Y8BbDCXrA7pr9/1HmEXtxDG21EZQQhBYqiMA4Wmg5iIyZ6EayXa0RRhG+OvonT01MEQYAPgg8QRAF814di5BSQOznmLgXmp/4p5s58RXWfKYZIREYk0os8uE0XSZ2giSZaaCGRCT7sfogftn+IBhpkbwjSQ4hrEpOM6gixjBHUAVqyhQ7vGPBEL+pGo2Gy889mz/B7vd+jm9cjDYYZm0EwgcfO45XN0zpgor8/kGSrGJQBirMCspZwYgdezyPWBZ8ZYGLGZ1RT72Vk1WkvBukikhHV0CvPqPNXIFuekpfGwUBvCKVDrgZlUGKGzXVoTDFjq+jPfPTqHsJ5iDiLEVYhGBiVfwQp0jAlrQQ/R+mUEI6gAJBRDf/WTZCdOV///SZAAiQ8lgVUBmEYEhuYHY5w4FQOvLkHb+bBzVw4tQPf8xEEAZrdJuADc8xx7pzjMX+MOZtfAFACFSCRCWIZGzAikQliEWOH7aDjdNBCCypXePH8BT788EMIKbBzbQfNgyYKv8DYGWPsjDH3aC1nPEPplihQLLPDV9moL9gkBrS5pAmIjQ4VF5qkwM3ehIIt6GZMbgzkXenS2rCcIAxTwta6+DSgxKKZfll6DRf+tvKGDce3zuMwZ6VWOqtfHtBKUNDlcsoWeyB/8k1Uek17BhZjtsVi77ImICDkJXNlP0/ATUZSbyh1Sc6noYD/yI0t/NBVjrxe2AbXADZZtCuACWbqbmM3NuCEvn4DClQzOqbKX0o5F0zQRlSsnmtb0wG2ZrToDKF+LgkI4/hxWrwkugDA7jPw2+TE4dUeIhnh9z75PRyeHOJO+w6aZROHjUOEIsRN9ybKj0vcuXMHSik0m018/OhjfOELX8CToyfgDY53Hr6Dnds7eD5+DulJPDh+gKAXYJgPceqdYiImKJwCxfogeyQ2nLAEO9hBgzeQD3KkwxRxEuPw+iGiVkQ6Ayo3WfRZPUNWZyjq4lJQgYPD4Y4BJjinMjAwoKxKyiRrN6YrAKumhEyzJl4CUn6appgitf46x/n0kmLpBGatOCDR3mbVRCfowJt4+ML9L+D08Sk6UQcHewf4wbs/wJuffxNu4GJezTEqSZx0mJMo6URMVj4zBAQySYFIXS2V1TUrZ715jETlABBwMMsM6JOWKekxbLgXtHr9LtvFzZ2bcGoH3UYXjnIwnU/hRR7OJ+fwGz5mJdlSaseBQhQXj7mlVM7hpDFSq5rWTjXDUXp0sS8HwB/iD/G//NX/ArwO/Ks/+FdU7skDtIM28jLHH3/rjzF8OsQr6Su4Fl+DOBW4/sZ1TIspYjc2AZ6U0pRvAEvld8YYzs/Psbu7i7OzM1OT/+TJE/iODzd3cegcot/tozPs4Jc++0uGdaFLPtI0Nd70laoMw+Cv3v8rPDh9gNatFs6rczw6fwSn6eDp+VOoQGFaLK1kdTPMiXKxF2kAQwzxUD3En77zp/QiS5rgv/+//3sqg3wWETOncnH7+DayeYbnT59jv7mPttfG7eZtfKH/BfSiHvzKx+/8f34HP//ln8fu7i7eeustfPNXvon/9f/6X/HNX/sm3nrvLfgtH6VX4p0H7+Dg7gEJUS50CDQjy3axqFWN8/wc5/k5fE7lt8pXeC97D3/4wz9EHdX493/x7wEX+N++/b8tlgdD+/9qoxf0iPFRdfHP7v4z3LlDrhJmzTCG09NT/NZv/Rba7TYePHiA4XCIXq8Hz/PQ7XYhaoHbzm14Iw/XymsmAPN93yQKz0fnyHiG7rUuxuUYj88eY6ZmxMJ1F7GBnKNwC2TXMrzwXuC92Xv4P/7q/wBA9/b7/8/3TTC9XroNLN3x1n9n5lcphGGIMAxRFAW63S5+7ud+Dv/xP/5HU/P+7rvv4sMPPzTlBJ7nGcbB97//fRwdHeG9995DlmW4fv06dnZ2cH5+btayLjnSTOzj42NwzvGd73wHURSZa57P52i32/B93wAa77//PsZjYg7+o3/0j3Dz5s0LQab+3nGcFZ0E3b/11zabTRPPffnLX8aTJ0+MteT777+PyWSC9957zwAHVVVhNBrh6OgIg8HA6BrUdY3T01OMx2N0Oh1yh3FdnJycoNvtmlIEKSWePXtmSuiFEAZo6HQ6qOsaw+EQWZbhW9/6FjzPwze/+U3cu3fPAArrjMT1MVifU/36breLOI7x1a9+Fbu7u/j93/99JEmCXq+Hjz76CB9++CE+/vhj7OzsoNvtGs3CwWBgtCq0DaUGC7SbxmAwQJZlRueCc475fI40TY3gZV3XmE6nphRDs78YYzg5OcHv/u7v4otf/CLu3bt3ofxDf62DDTbYYoNs3W4XN27cwJtvvonvfve7uEq7EtDwYvoCf/SzfwSmGAXVVYyojJCIBHFF5QBxHSMsqN5f1/WtK3QadAgKb+29taJA7/QdJCpBj/fQUi00VRMt1UJDNgAFlCixy3aRiAQRj0wpBABjkanLL9699S5Kb/kg55KT1kEVIcoiHFQHRIcXgQm2KlYZfYaz5AxZN4PqqQvUaVeQ9WLGyApTJMI4SdgfsHqs7K+ojhDXMZqzJXCRzBK8Mn4F0V6EzMkwkROUHjlSrH+AS0ikPEXBCgwwMJnllaDpgJwaEkEASZAH8HNiSYSjEPnjHPvVPmInBvMY6rBGFVaoomWZRhVWmIdzlCEBCCtjIEFaFJUPr/bgSKsG3pMESLglSrdE7dYmk1w5dA5EIJvF7toiW5RQeNIjYciqiSRLEOcxglEAPqRFnzdyTLwJRs4IoilQxRUxODxpAImXbjrXgQdYv7MAC50RF46AcAVZf3at960dR5cZ+LmPnXKHtB9kuCwNWYxD4RU49U6RuznyIIdwVufZ8RwEdwI4+w7ZIpXHJOKTcgQVgRX9qo9DcUi0dM5xdHSEk5MT9A/66N7sIoszZHGGNExRxDSvpVui9uqrbegva9vex4GKVRdVzzewU3xJ7BpPeUavwbAeXAoYtzEmXOGaIEXxJTCxTYBS/+7TMAl08AoArueiEtVGqzqjJ7L+fjCjeg3Qs+qybLy+PgFh6Pq6RnpbaYYL1wASuuzo0zStCG5d9NbGwOBzf8UWUAMmn1YjAcDq/XNFYExBmTKIibyCfZ4iG8VQhuj4HfCaI3Zi1HmNVquF6ZTquYUnCIyWObGhsL20SUJuZK5c1mzRt/W5VFxB+ALCF6hAmXF0gWMc46/x14CPpW2cANzrLiJBGivdrAve4fhg9gEc5eBnd38W+9k+7nXv4Q7u4Ks/+1X8wR/8AWaDGebzOb70pS+ZTFSNGipWqPwKHz3/CM9HzxHtRMhYhpP5CQqnwDAbYuyOMQgGeFQ9uiBQ9v+l7s9ibbvu9D70N2Y/52r32t3Zp+MheUSKKpGUVCpJtJ2y5ea6FNgwUEZZsGGgEMeGL/IQ5DmxHy7ghxhBYgMBLnxhXwQGjCROA5QB28l1ylVlq0oqlUQWKVKiRB42hzzdblc/+znHfRhrjD3X2ms3pFTlyjjYWPusPZsxxxyz+X//7/99nuUx8Afcbt9mEAwY+AM6bkcxQ+qCuIiN4vysULao02KqWCdlsgy2rb6JLO6/Fpb5dGzlaiJsxTyRQl6ZIdTUc/nUJR1rmnGiEDUZymbwKD6CAD5+8LFihGTAx0AHXv3gVbOuZr503A6b7iY79Q5iLnjli6/w5mtvcuP6DTzf4yfv/oQXv/Qib/z4DaJBxLgc82T6BCKUjeRirupSI2DpOj2PWWILG7uy6bgdrNoiqzOexE+YxTPs2CapEuIyphou5mwjjxA6IX2vz4a/QSQiur7SyBgPx2RlRtAOqO2a/eE+fsfncHJIZVdGDPO8+vbmuGq2mB7fWMbEWUwVVPz7/X8PLrw5fpNFZRX/+F/8Y7O+K1yC15RNY9tRVoN9t09XdLmb3mUv2qPKK/ppn952j43WBpubm1y7dg3btrlx4wa9Xo9er2ey0Lr+Wb/oNxXrAyeg5bfY8rYo2yWfCz7H8889T7vd5gc/+AGvvPIKv/7rv84rr7zChx9+SJZlPPP8M8r9Ih+RVzkvbLzAg5MH/Lf/7/+WcTEmGAR0djrcfeku957cY8qUOXMejR5R+zXjdKzAiYW7wJNDVdrxg3s/OH9cQ0HwdkD3vS5u5fK/ffd/o9qoSB4neJHHwBlwp38HK7L485/583TdLl2vi23Z5HmO4zh853vf4e6Ld5nLuRJEzBaCiPmI9568p8CJdIgMJY/Hj0lEsiSGKJGMshGjbIRrufiWz9/58O+w8XiDoA64+fAmPV8BEe/J9/jY/pj90T73f/c+1awi8pW44AsvvMBkMuFLX/oS7777Ls888wzvvfcerVaLJElotVp0Oh0ODg6Iooh7HyiKOT+CO1t3CILAiPg9evSIbrfLeDzG9322rm2RWimZnfHZlz97RvTQjOci8F4tJQCWMsiAAbvyPOfb3/42f/yP/3Fs26bVavH888/z+PFj6rrmjTfeMMCE1sObzWZ85jOfodVqcefOHQ4ODrh27RpHR0fcvn2bJElI05ROR5Un7e3t8fbbbxsnAi3IOB6PGQwGxvmv0+kszel+v2/cH/Qx6b/r4HM1+auPFU6NAKRULCIpJd/+9rf55je/yc2bN9U8ffKEz372s0ynU7rdLg8fPuTpp5/m3//7f8+dO3f4/ve/z3w+5+7du7zxxhsmOavdDqbTKRsbG9y7d4/Pf/7zvP7669y9e5eDgwM8z6PT6fDhhx/y4MEDXNdd0t5I0xTbtnn48CE7Ozvs7+8boKHZVo9R6zZoEUd9jvV4dDodRqMRr776Kl/+8pf5d//u3/Hcc88RRRH37983x+04Du+//z6PHz/m6aef5rXXXuOpp57i8ePHuK7LfD7n0aNHPHr0iL29PTzPM2CT67rs7++TJAk/+tGPePrppw1Q89prr5nj08dcliVPnjwx63300Uc888wzS3O3ee7WlQI1f9fnX5ejHBwc8MILL5x7r2m2KwEN/aDPLz74RRI3YebMFFXfixl5I2InXkLh7dqmVbaUVeTis10tShSqNlER4eHxVz/6q3gbHjN7xqge8Vr4GofeIRMmKojQDghreiikUKI2dUDPVn7vURLhVz677i5/6od/Csu1kG1J6qfEjtI+mDtz4iBm2B6e6bclFRgRlRFBFSDKUzvB0ikVKCKgtNWLqJACYS9O1Erm2sZW1kKuC66qiZ+iUOxcLL+kWpWFl3n0rB4ze6bqtgqf7ryLnyrhQ7dUAj7SksqBwlM6AFoLIHXT06BMQO4qh4qpnCr6YW+RGb4NvHS6Xw0aRYViFvTyHkEeEIyUMKKbqfKYzM6YiinjeqyEMf1MMQ78nLk/J/dzKnclWM5tWnGLvt0nEAEeyi95Fs+wfKVnkLiJEq+xCqR1as+ZORmToBFIXF98Ll4+RS0UoyD3aKdtonFEO2sTTSNuBbfo9Xok7YRDDhnaQ0bOiKm9cJkQmQJn9IvmBUHOhXaUC0bA6aRU7I7Mzsi8jOl53NEFoKJ1JAbJgCiP8HIPMnXRx9mChhvWpFHKrD+jvFZSeStBbkNHohgW5Mc5ru0StRUQ2Mt7eDMPv/SVqvvixqFtZgupymKSKCFpJ6S+YpKkfkrhKvDmQpbEOVoHa5kBK8vktmLHrGWgNFsJfuXjVI6xirWEZbQ2aqcmExm5k6/V9mgCYcbR4zxQotGHpuNGXp0fWApxWtuumRxSSkU//wQK/EIuQhYNoCwo6FLIc2nhpSwNU0MIsbb0ZXX7sDg2efn8N+suFsrrfK2A17qmQQld719RmbKET9UuAibOq+kXqODPyk4tySqULk2CerY0ni+2ZROIAHtuIzKBX/lstDfoh32l3Bw6lKJUFpnZWFl3ljF5nV+o03Gphse641n3PYqmPq2nIOA4OwYb3hm9A8C/fvdfq4U+VB/Wb1nKjrPr4rZcfnvy2+xGu2yxxY63w3X/Onu9PV4oXmA33aVbqZrVwirwXI/ffPs3+eCDD3juuef4j//if8y/+96/o7PbofRL7vzcHcbFWGlOLAQwH80fnVqK5qMzfXctlw1/g5utm4Zq3fE6xNOYN37wBoUoqKwFqCtyrMiidpWOQWEVSvtAcMo+WbncrcY/rbWiFpMGiKut+uLyIXn6aVmWAYjOA/GWQIsrXEs29hIAMMyGy84RLfjJOz+BNrw6WYASfXjz/TcRnsCPfSI7IvACbvVuER/F3L1xl5bVIpkmiqacxtiurbLsxcRYIx7MD4iJjYZEJRVT7bg4NuDmNJmq77M1ej1YRK6yDoy8CBuboi4YyRFHxRFJkTDLZurZNWyM10h9BCiBQr/yub19m9AO8S3fAHBZlfHk5AlxoVwGRCAYzUdGC0MiL2YbiQUoJZRYaFqnJGnCoTxcPn9Nh4J3T3/17nmIQvCP/sU/ooortkZb7HX28DKPl52Xudm+ycAbsBVtUc9rnrr21FJJsX4Rr6rKBAqO4/DkyROklBweHhp7vvl8Ti/q0a7aXG+pFxwhBDc6NxgUA8RQMBADoizib/zy3+C1yWv0+31u377NP//n/5y//bf/Nv/0n/5TvvQLX0IGku+88R1e+upL/Mbv/gY3n7vJrJop14P8hKP0yLgfjLOx0ZwAeLD/AIDvvfe9M+P53/2r/071C0HkRERORN/rIxLBs+Wz3OjcYNPbZCPYYDfa5bODz/I5Pkd6nFKJij////jz/Nqv/Rp/4S/8BX7727/N1q0tptWUdz5+h6d/7mmOk2OO4iNO0hMDTjxMHvLuw3cZZkPG+VgB819f7pfIBVZm8Vr2Go7n8Fsf/hYdu8P18XVGmyMFfvUiOp0OYSuk2C9wXVfpe7guvu9TVRX9fp/pdEqr1Tqj1r+7uYsQgs3NTZ7be+503ysZ7tWArJnRX11OgxJVVTEajQwIMJvN2NraAuDZZ5/l8ePHJkNuWRZpmtLtdvnCF75AHMemxEH/nJyckOc53W6Xo6Mj4jjmO9/5jhEOzPOcTkdpWGh9vaqqCMOQLFPPc8uy+OxnP8ve3h6+7y+JA2rx1qYIpgbemknkZma8qiparRZSSkajEVJKI84YRRFf/vKX+Zf/8l+yv7/PaDTi+9//vhFOPD4+JgxDptMph4eH2LbNH//jf5zRaESv1yOOY/I8J45jXn31VR48eMB4PGY8HhvB2apSDHnf9wmCgNlsZowI2u02Tz/9tGEGNsGS81gcumlQaRVM0ufq4ODAWFz6vk+WZWasHcdhMpkQxzFJklAUBScnJ+be0Ol0zHY2NjbodruUZUlRFEgpefz4MUVR8MEHHzCdTjk5OSFNU8OU8X2fyWRimAy6j7ocJU1TMxf1ZxM0WS0JWjePtaYGwHvvvccv/dIvnVl2XbsS0NDyWlyfXV+qSdIHIJFK6M5NiN2YmX2qSTDyRjyKHinRwUbfvdqjVbboiR6dukNURHwm+wyfF58nTVMeHz3G2/TIgoz9bJ8iLKichoWKWNhO2TkTMVFZHw/oLRbYO6XrB3WgdBnKNhvFBjfHN9l2tomqiEpWRkchdlTfUzdlZs2UNaOfnrGlDKqAQAa4wlUv+UJQ2zXzcq6o/paqhU5Q4n/6Bf+MA0Ct2BHkahuprRwjSrsk9VPorDkRi5pmp3bwao+gDNgqtmjNFbPEzmzlzyog2Aoow5JJNSG2lI5D6qQmWK3t2ggrnudSIKQgqAOiOqJbd/Fijw1rw7BDoiQimkXYtU1OrnQobKUxMLNnCtxZWIimbkriJRCdbl8zTTpph7AMcSpVkmALm5qawi5IHAVuFa6aA7WolR2cUyvmRXdZP+J7fM+Mk13buJVLWIZ0sg7X8+t00g5b5RZdughLMHNmTPwJJ84JQ3fI1JkqcUiRU1oNiupFDIDLXjBXXo5qq1ZlKE7O1D8fkBBSICqBXdgEcYA3VG4mXdGlHbQRjqCwChI74bH9mOpaxUn3hAP/4AzbIqgDoioyThthGRKUAa2yxXa1TVRHuHMXOZJMJhNjQRsEAbZjq/HYKEnbKXEYk/rKMjZzM2M3+qmYEqvAxGpzIHMaVO/zgrAarNzCKRyswsItXKzKMi+dtaipnZrKrSi8gsItzm5DntqNAgb8uuh4almbGtbLD1WYEooiKwzjQZdIFBTLNp0XBNYalLAtReHXWb/z1OWbwEnT7/vsgup+pJX99TFeNQOsx06LE65Z4Oz+pMCqFbVe1gtB2NWx/xlS489rFRXzeq7uUS2lV6CFKBljMqe6ecKj5ba4Fl5j4A/o+T26nhIZrKlJqoR5MTfB3jgfMy/myiLuIu2C80A9/bcrHq+UUpWQkIINJ8kJP0p+dP5uU6F0a2oPO7Oxb9i4my5W2+I3Dn+DY/sYGUs4hD/2H/0xtnpbS5TZ5ktKURUqgCjGKpjOT4wIpqZgH6aH/GT8EyVotzs+c1xCCtpWmw028EuflmhRxRVVXlGVFU7oUFDgRA7Tcqq0K0RGJpXg7lrNleb4ykU51MLNh/o0Q6W1a2pRX8hCOt2kMO4e2t5WCHEGoDD2s1dsDsqe03Vcpe1Tl5zUCpg4OFJc+rcfNjRDRsvr28ImdEICERAQ8FTnKSIZMT2c8vMv/jyvf/91PvvCZ5knc45Hx+zd2uPew3tYbYuj7IhhNiSuYwO2zIoZs2IGaaOUTJ7PLHGlSy/oITNJx+9gYZEWKUfJkbJ8LVPlplJMz45LotxutrwtnNwhsiJ2B7t8/OHHPHv3WR4+fogd2NR2zXA+xGup0o60So0bzUVNsyZ0t6WUlFbJ4+QxNTVP4ie8Fb8FwL96/V+tXd+3fUI7pO0q1sSGt4EVW3TDLidHJzwjnuEkOOHB/AFbN1QwqRkSzQyxFk3Xwuk6wPB9n/l8bsTs9DWm6+7TecpWuMVmtclLvZc49o758taX6Xa7SCkNdV1KSZ7n/PZv/zatdou9p/f4ze/+Ji/8/At86/vfYvvONo+GjxiXYzIv48PjDyGCcT5mVpzqeBymqhzsnY/fuXRsw/9NvdP94//fP8bJHXbTXXajXUQm6Cd9tsNt7rTuKAvVYIO202b/yT537txRMYaA/+M3/w/++//vf09qpZRuSemVZFZGHdTUQU0VVsxbc+Io5on9hOyp7GyC6IvgSJXY6bk96p+rCWXI+8H75NdzkkHCrJrhlz5xERMR8fmf/zz5NOepp54yc+O8pgPyJlDRzALrH9u2jXi+ps5rjYE4jk2g2RQ+9TylqXPjxg263S6dTofxeEwYhozHY9I0NedXB5k6M94sHQjDkMFgYACDMAyNcKAQAt/3eeGFF4xuni6Lb+oRnKdR0RyfJlNJl2tkWabYg/O5ESTUbhF63QcPlA3n0dERBwcHbG9vG/2KPM955513GI/H3Llzh+FwaHQsXn/9dXzf58mTJ8aZQWsl6Guo3W4bkEEIwfb2Nl/5ylcIw3CpVKcJFq4eY9MOtPnM08erQZ3pdGr+pvuhRRQtyyKOY548eUIURYZ98/DhQ+q6ZnNzkyAI8DzPACOO41AUyub7Rz/6Ef1+3wAxR0dHBsjxfV+VcXeUIHWapmxtbXHt2jVu3brFs88+y6NHj0yfV0simsCEbqvaHE2wrNfrsb+/bxxCLmtXFoPU1iJN7QU9eHVdqxuuFbIl1M1UZ01t20Y4wgSac2dO4iYch8fMgzkjZ0TsxqfiiC7QgbAKFRMiUTTxTW+Tg4cHdNtdok7Eo6NHuBsubMBJdcKEidqGpkkLqcQMrYKpmJ4KkPVPj6kJRoRlSLto08k7XI+vI0eSsAhptVvU7ZrUTZlaUxInMSyJqZgys2dKs6Dh3hBWoap3dVvUZU2RF1iuReVWzKoZhaMCitJS65WyJCU9m43UdPzaPaVRL2wIYydm5szOfSm1UOu5pSpF2Mg3cI4ctp1tdju7uLZLbS2EK605c+ZMmDATM2ZiRiKUe4a2czzmWIE5cGafbu0SyIB23aZTdWgXbTaqDa6X16mOK6aPprilq7zE3YzWtZayhvQzYykauzFpoDLqq+COm7l4iUdYhOSeyh5axcINAYfarlX5hF8bYcuKSn1vF8RerPq/MkZwyi5wK5cgD2jP2uyle/SSHu2kjVd5DDYH2F2bj5OPGYZDDjhg6kxVkG1lVFZ1vpaA/u6CLOVqn/TvUkgFqrg1RVQYccvHzZSM1h7wgRz80mfX3iWsF3ojtco85SInsRJiK2YaTNm39plbZ3UkvNpTVqqFj5M6hIX63cvV99vpNuEsxK7s04C8cbMqrdIIn86CGbGvfteARGmXqlSkOQ6fBphYzWJbUPs1ua/uIynp+ky3RGlJVCqodSoFTNi5jVVaWLV6wLqei3QlmZPh9l2Ok7O12pZQNokWFkVVrHUKWN61NDaQ2JzaC55z/BaWspJEebyLWi2YZinSVi/FZ8pUzmk6k6rPmQY3almfORe6hOMq2ELzZV02/l3Y1szzylb3tSWgZd3+pMCSlvpZPHtKWZ7PvDmP7XDeMpcF+Y2Wy5w8zxnmQz6YfXDJTlTQFDkRd9p32A638UqPB/cekM0zxfpYZPMJwOpY5E7OrJhRO/WF5RznHdOl2gWrx8eCsWeX6l6/ALsPOeT3p79/+uz04J/+n/8URzi0XCUyueFvsB1usxfusRvtGsbCZrDJjdYNXtp6ichRKHMzsBJCWaH9o//PPzL2bFWgwMDBzQHBIGAmZxzmh4iu4IQT8l7OvJ4vM5jcReZVRPRln47dwa985idzRC1I4gQv9IjLGDu0yUSG9FQpW2mXyhJu3dvQGtaEa7lGp8MSlrJ4LXID8lVUl14HNvYSoFfVlWExNc9ZSQmW+vtl51IglFWmUKAdKNZCVmbMpKKuP5ksnBMCeOvdt6AHbzx643Qj91XfollEZEfsuDvs9fYMhb5MSlzLVRZyRUJe56RVykl2wv5csSbm9dywJgpRcJQdAQro0u4Hj8eP146Ph0fbbeMLn17Yo0gL2lGbzMmoZMWj9BGz7oxXj15lLMfUSeP5tWDnd9wOe8EePbdHx+sQOZEBf4paMWPSKmWSTChEwbyYk1XKQrQW9cWsiYUQJiyed3XJpFblDx/PP15eeAYsTFH+63/xX6t1PlbOOJEd0f1QCWEOvAE74Q7XWtfYCXYoniqI85h2q83Lv/Ayvu/z2c9+1tjrfulLX8KyLH7+538e13Xp9Xp87nOfQwjB008/TRiGJrBqBgJVVfHSSy9RVRU7Wzt8/aWvc3vvNre+rFigWnhvMBjw7rvv8txzzzGbzbBtmyAI+OjgI4KNgPcfv48MJbN6xkl6wlFyxEFywFF6xOH8UNmMFsrtJpYxk6kK5n6S/QQW5J3/5Tv/y9r561s+3Ve7dNwOG/4GbuYyeX5COSyxUgtisMaWYoDGKrlw+9Zttre3eemll3jjB29Qu8pKtHetx96ze3x0+BHBZsDh7JDWdosn6ROSIOG+vE/cjXm7fptya/EsvqY+/q8f/F/Y2PQf9uk6XbZ/tM2Gv0HPVaDIIBjQc3vKWWRRQrTb2aXltNR7eqPMQgdqnucRBIEJnnXAdnR0ZNwujo6OzHq6Lj9JEiaTiQksmwKSWvNBgwFpmppAtdVqsb+/j5TSWD/atkpGNuOzo6Mjdnd3KYrCgCHNDHbTbaFZy78uGG8uo+PDJEkoy5J2u236NxwODdjQarUYjUa0Wi3j8qfLFDzPoyxLjo+PjYCltrqMooiqqoxeitYwaNpf1nXN4eEh7bYSIPU8j8PDQ771rW/x4osvEkWRiWlX9f9gGTBqMjZWSyu0s0Qcx0ulFtPp1JToaFdDPQe0q8bh4SGtVsuwTzQIUVWVKYvR+gu6hKLb7RLHMY7jYNu2ATC63S62bRsGhQYstN5Dcz6uWpU2gRa9XBNY0fcQy7LY3t7m0aNHRhPisnZloGFzc9NMzlX1Sk2zaU44LX4opYQKwjrET3x6soeUkoM7Bxy6p2JZtrRpizZ2blOnNVEYUdQFaZDyMHjIe857VM83Lt62oFW3GDBgs9rkWnINN3FxaofJaILjOtR+TeYpJdnUTxWg4eTmZXYJjHCnHIQLtZ1N4JYefZbAiG7VZbva5m5xlyiP6Nt96qrmBx/8AKtvMbgz4OH0IXN7Tu3VDMWQWTRToAIYFe+gCrAzG5lKJeIUhFSyohAFuZ2rB6tVGTo+cEoFXaGIGzCiVsETAqStatdjN2bmLQCJPnyoebWLY7Oxleo7IW3a7LDD8/J5NuoNWrKFbdnUolZUvPyYwi8YM2YiJsxQgERhqbr8qT3lsfv4FNQRwBbwGRSDo3RwMoee6BEVEb28x06+o+j9mU9YhggEuZ0zt+ekbkpsx0zEhMzLKMOSJEwo7OL8gEQDCAtGgyc9AivAkY6haBaiICenEOrFIrcVu2Duz9cCEppW6rQdAhkQ5AGdWYen8qewc5v91j4d2SGSEdTq5S7xFHgy9+ekthK2PMOQWG2NTNuFmcs1gRo2EEIqUh6yRll9ZVxc6dLLe/iVT1RHqgQDVZ6TkSnwKVAuJomTnM7fRbNrWzEkytCwJCKptFqiOmKv3FN2oEmIlVrmvqFbLnPmYs7cmjO1p4ydMYmvgMjUTSkcFQCss5e8csB13hjbGF2M3MlP5+u6JsFKlBBZ6IZ0/I560QgHtL02vuNT1iVJkTDOxhzFi5et/KyGgCUsY7l4JnvXoGzrc9+0ykuLhnCrs7Ls4tOSCpiwUbaHmj5eowJVo59xGUtAM4KsU5q3vnbOaAxwVnfgwrbmOC/ry5n9ierURte+eF82NqISBpRYsmNd3fd5YODPiDFRUCi19+mYe9N76suNxc8agMSRyiHomnsNOZaIuQLGi7TAsR2kkIhAkKHAsGExJLfyU2DiEtBm9fiuBBCJBSMGpR0yzseM8/HZIGtNc4RDx+3Q83ts+pvshirDmQ0zHm4/hBjsxCZMQ67V17g2vMbz289TVRWHySFf/OwXeeutt7h76y6//m9/nVufuYXVtrj7hbv88P0f4m14PJk84cHwAYVXMMyHjMOxKmcbrMl0Am7p0qpbyLmk5/XIZzmRH1EXNTdv36Q9aCvhw3JmdCYm+YS4jJfH64I5YwvbiK66jtJO0PeAslaOMkaP6QLQETBgYS3V+1aTSSCRZDIzYOplTd8nXNs91bAQGPBAu8Z8cLAGRHu0/F/fViUdvuVzvX2dvt+n63YJREAv6CkXJ0sF5kmVMC/nHGfHHKVHyqoxn6j7HTknhYpGH+ePFUskF4Zlsto84dFyWkaLIXRC44JSy5ppPuU4PTbncFJM1s7zjtNhy9uia3fxha+cb4RFKUvyUpWMpVWqQIo6VQKodXap5au+Vpogrx7f4/wcodEO8CX1679+9K8Rj4SyandadN9QTgk7395h4AzYDXcZzAa0rTajgxFRO2JWzIjsCNtWugrNIMn3fcOY2NnZMSUEOmDR1Ppbt24tCcrVdc3exh5BELB1c2spGNWBiF5Xi7o7jsMkmSgdimzEpFTOOZNCATNPkiccJoemdGJWKKHRg+SAg6ShfPks5wPGEo6rY9zK5fve96k+V+HmLlWvonALnESJTffnfaJxxN3WXd5+6212thVrABQAMM/nWG2LBycPKN2SP/mNP8kHTz6gs9khszNyO2eYDXkwe6BEXLPRaTleo9nCVnogfl9ZmfoKhOh7ffp+n3da73DSP2Fmzbg3usf+dJ8wUGDQYDAwQasOurXQoQ7Yfd83gNODBw+Ioog0TQ2woYNELaSvz//R0RHz+ZxWq8V8PjeODHrdXq/HZDKh1WoZ1oF+X9NAhg6CmwyHdXX9Oi6MokiN7UJDL01TptMpUkparRa2bZttz2YzEwxnWYbneaRpShiGzOdzwjCkLEvu37+PEILpdMqtW7eYz+e0223DcKjr2oA42q2krmtTsgQQxzHf/e53efPNN/mVX/mVpeR5cz6v023Qy+m/6fcK13UJgoDRaGQAoslkQqfTodPpkCSJKR3RTAtdVqGPdz6fU5YlURSRJIlxqMiyjCzLKIqCyWRCmqamD/rY9DZ1idB4PDaA097eHsPhkM3NzTNzZB0rRR/Tanyvz7VlWdy6dYu3337biOte1q4MNIRhuFSbo0U/mh1tUjKagYXuuO6kZVn82eM/i0wkdafmpDhhZs+QXcmD7AFjsVDWd+bGKxfAKRwiIrzao0gK1Se75sg6UsJ33VjdjBY1/XZtK62IosWgHHAzvUlURKcq90IaK8q5O2fmqhKK1E2XXkJLu2Rmz5i5Mw455D3eWx4cCVbPwi99tu1tPMejX/S5K+6qerVhxa3rt0jchDfvv0nVqoiuRTxMH3KYHlL2So79YyUo2Tw5ucrW6zpzpz4VwatERWmX5HZObSltA90X01ZLNUpH6SXYyiazlsq6MCdnJEacaLh5zQ3dtm28wFO+xFIpkz/Hc2zWm3RQAmBSSOZyzlROmTBhtPg3lVMSkRjq26rtV3McdbmDX/hKRDMNcKcu/UlfaXyUEfksV/aYHUnSShh3xoxaI8qgNJoPFQpQKKwFE+CSoMqplSOAIxRVtaJCWqo2tKBQFl5WSSpSddVEZ0GbdfsQtcCrVMlDVCktDK/wsGpLgRyW0ttInESpHofJpdu80ndy+dPUGi/cHwoKYidmbWuwPWxp41Ue7axNUCkBVa/ycCxVr55bOZmdMYpGquTEXtEgkZZxcwnKwLAswipU57dU2iDPxM8ol5iVm5rt2VRhpRw36rGyjXWnTOyJKs8RiaJSrpY4XIVBcpUmTkUTsyxjlI2UfduFq6jMTNtr0/E7DMIBm+EmkRcZ/Ya33nmLWTkjszJTcnWmvxcdgzz7qV/Um31fOy906UWlgAhRCnXd2S62Yxs2kBRKfPEyIEEgTHZXN612f267ShAsTyn5Kzs8d/kl9pBUVHUzDpeAEkIqQLEuF7aO4hzL00/bLmJXrP5NLLLZHjwoHqhyjnD576tNCIGTO3hzjzAO6Tt9iklBJ+pQFiV71/c4GB3QHrR5NHqE6Amm5VSVE+ls7kWtAUh80lIaUMy9YT5kmA/5cPrh0jLy2bPbs6RFmIZERITtkO88/g6lW/L+/H0ebj4kiiK6Qjk73S5v89WbX+XevXskXfVCfnBwwG/9/m9h2zb7B/v0d/sczA7oXOtwlBzR3mkr56UIjuIjCkeJIE/DKamVcj++Dyu3yI7bYTvYpu/3aTttlS2vBVmSEYURpVCB9DAekpIyylVAopX6k+Jq1quucI2goy51KutSgRMamLjgFOjgdlUEU5dBaeZFhdJGuKwJlJ2vIxwCe/EOIRXTopCqTGpaTBnKIU/GT660PZ3Z7zk9bke3adttvNojHafsDnZVcLGwDU6qRAWquSrHmRQT0loF/nmRMyyGZttLpaArzcKi7bRp220iO+J6cJ2vbX3N6IoczY94OHzIJJyQWRnjcsysnJ2Z7wJBy26xHWzTslp0vI7So0HN8+l8Sm3XqgS3Tsmq7FQI84JSNFsoS1NVxSoUU2/x/ImzhpPN6OLxdYRjMu49Z/Hp9nALl+32NlvhFhuBCoC7jmIkhVbI8fGxcTXQwZnOxHqeZwIenUjUNf46Q+v7PicnJxwdHfHcc89RFAVVWnGje4M9sYcbuWeU7nVQY9s28/mc/aN9oq2IcaFAzPcevce/+Z1/QyIUmzixE6b1lMqrkL4ED3I/V0mi3mIAtuGEE97lXZU4BNiFf8O/gf9IJTbd2sWrPdxCsX693CMKItzM5ejxEd6Jx8vXX+blz7zMM9efUc/YRUzjOA5pkXKSnhjnmGk95SRRJWKjbGTm1Lvjd83vE28CCz2+33jnN2AHlXicBnTudVSy1bYZhAMiGWEXNmVc8mz9LOPjMUVScJgcstvdZXd315Qm6PHTAbYW7QuCYCkei+PYCD/meY7v+9i2TZqmPHr0iJs3b5IkCe12eyl+03NgNbZb1Tho/l/PEV3CobPw7Xabk5MTyrI0wJfWGKiqiiiKDCtBa2r4vs9sNjPaBjs7O0ZvQZdAbG9vM5lMCAIVNGoxyvl8bspFNGCiWSKz2WwJiFvVaNDH2Uysr5ZPaDfBKIoMgAAYoEiXxnS7XcNe0N9rW2vd1yiKDBtEX3u9Xg/HcTg6OqKqKqO1ocVCNTgRxzFxHBtWShiGpGnK0dEROzs7DAaDta6Nq+WPzfO6jvFQ17VxKzk5aegLXdCEvKj4qNH+5t/8m0Z1V9d+Ne0xdEd0h1etKVdPlkbuwjA0g7O3t8dbb73FyckJX/jCF/jRj3/Ek/gJtz9/myfJEx4nj2ldazGUQ8ZyTBEVpwE2gFTUcb/yVYZ/QU80AZ2dnbF09CrPiFVqFw17biMTiawlMlTB7NSbqmyrs7BdFJdTGZfaIkNlZ0os83pwneTjhPlHc273brPX2uPRx49w+g55mCtRviDl8eZjFaBb5fp6cf2CLG1Vk14qWrHneNSiVtl7Kz/jbLA2iNElF0KVJDg4JkDVAXdBcb4dolQ1pR4ekYxoo0op3KmLdWKRHCQ40qE/6KtxdRKmzpSpOyX1UjJXifppcchz20Lfwq3UAyLIA1p5i+v+dQblgB1vh7BWlqlW2+KDkw949+Bd2AXrmsXj+DGxiKn9mspaU1t6AXouUNRt40qgSxwWtoyfKEO75lzalTqPol6I+wlpXlhqUa8P0Fb7e0H/LwxawQBZ6muDUlzc5DIo4dc+Xu0pEUfhqIy6KMmF0lVJnITUSs/0OagCbGkrlkUZ0aNHu2jTt/qEZUg5KlWGAucMdS0vc1JHzaHUTck89TkPVKlW6ikF6cqqzo6VOfhzxuVnlNG+qIlK1fuSKvFLT3rYwiYIA5IiwQos4lI5zuRWvj6Ltm4e6FMoVtwxPuExOcLBszx8yydwAjxLUbRraiPyqGujL9SraIAAS3PtIseQy87D6jm86Jye05czy18yPtqmUWdOq7o630bzIoDhor5dFZS4bDusX96WNk7l0BItnNRhO9pmp7VDGZcIhBKfqgs2rm2wcX2Ddx6/Qx3WjPOxup7r/MqAw5X72WzrwJSGWOpqc4RDy2oRViHb4TYiFkyeTPAKj/goZuANSE9SbvRvMD+Yc2PnBmVRsr29zYcffsitW7f44IMPuHbtGo7r8J/+P/9TEitRGddyzKScsD/bNwKOJ9mJydKepCeKTbDSQjuk76pSjkAEhE6ohBQX9qhFWRBXMZN8wjAZkoqURCZrQTpTqrbm2AXK+tWxHFOO0ZyXV3HnMGUBjfnVBCY+SWveLzzhKWtL1Et6JZXDSlZn5FV+WkJ2SbOFjS982m5bgQVWm47XoeN2iNwIRzoUpWI7FrJgXs0ZFSNOchUMzorZ2ms0spXIodZY6Lk9WqLFte41Faw7ShMpjmNsRzE8NZNnXIzVPhZZeW1ZOinOMtoEgp7XU9u3WrS8Fq7tIpGEhPTDvgocCyVYPctm3N+/T+VVzOSMaTVdex48y8MRjnkfudu+a9hoEklZKfArrVLiKmZaTtfOVUc4BnTo+316bo++18evffpen9tbt2mJFnZm03E67HX3GLQGhs4vpaTdblOWpcmaa+Ch1WqpkkTXNXoF0+nUZOiFEIbWLqU0wZUObofDoQlIHz58yN/9u3/XfLe3t8fd5+7S2e5w/+g+pVsyTIe4PZfMzSjCgikq2SVDybyaU1rl2XeBC5rW42i7as71vT6b4SY7wQ670S474Q5b4ZaaQwsGQ8ft4NjOUvD6P/7P/yO/89rvIFqCl7/2Mj+49wPGxZjSLREtwZw582pO4RbExCQiURopa9653dpVsU6pPoM6wCkcQqlYpF2nSzktFVArQ0JCKDDnQNuAZllGq9Xi5s2bRrvg9u3bJkCPosiwBDTDRQMPqwwKbX2pM/X/8B/+Qx48eMDXv/51RqMRDx48MMKUulxiNpuxv79vWBzaMQEwjIZut8tsNjM6E/1+n6qquHfvHlEUMRqNGAwGHB0d4fu+iSs1MOB5Hv1+n36/z8HBAdPplCRJ+LN/9s/yF/7CX8BxnKX4tamVApgE+bpSCr2Pf/7P/zmvvvoqv/Irv8J3v/td8jxnc3OT8Xhs2CRaJ0EzLTqdjilzybLMuIBo1sK1a9dMf46Pj00phi6p0eBGGIYMh0NTMqHLMrRY5LPPPsuzzz7LL/zCL5zRXFgFGnT83iyL0OWOuu3v7/NP/sk/4ebNm/y9v/f3Lr1+rsRoqGXNx5OP8TIPaoXyaB/VdQqVTS/OJpVGD5hG31qtljkIUAhQkiRm+TzNCdKAO9Yd7BMb79Djxd6LHBwccHR0xIsvvogTOTyOH/No9oi5M2dUj8iCjMRLFH3diU+zhaiAKKoUK8KVLpZUN+PEShR9O0woNhovyosAqFW26JQdrpfX2bA2aJUtvNKj1+3xePyYe+N7iC1B2Sk5qU5MlrIZrJWipAxLMpQ9Jc8Az8ATFhmAz6iMvlcpPYJu3uW5yXMMGNCqWgR5gCUsht6QsT9m7IyVC4gdG3p+7iqding1FbOgEbuNfwaBR7EMEpEo9Jw1PuuN06wzf5oBIKpTi68KpRgei5gjcaQC48Hi5+5pX0CVfNi1rRDl3Kc769KJO7TTNl7sUdc1H13/iNiPwUbZHlqlsZtM3ZTEX/hOwzLTZHG8nvRwt1wIoS/63K5v4+/7cABfvPVFnrr+FL/z6u/w5q03sTYshZaXU2XNuRrUi1Pq9mr28UzTx7hQQFdfLf6dB9QIqJxFnfbqIuexJaSHK1zSJKWSFbZng3Nag39hP9cEIRe5HJh15HKmUopTN4fCPtWSOK+JWonOuZWrfko1H6WUHPQOmLtzTjjhAQ+WV1xkJrQNqld7BJUqaQqLkCiLaKUtBvkAb+JhlYr+37xHVbLi9euvqxcHz8eyLQqpmC8xSsxUg11/GACDbtKWiukTcJbx46PGvZmRr5RzjFUou0+ZS0I/pJY1XuSRk5NKpf5d2dX5c850AFMzroXwmgFGKUvKqiSuYi6ThdBBj+d4hHZI6IQKcKprjk+OKepC2Zja8hRAO4+l02DkWFimhn0taCJPmTuXHeuZ/TQ/L2gWCycCKRUL4DKOur5e6gUoyQJQsdbsr3k9XtSX89Zb97dV4KXRKqHuNRkZtOCAA34Y//D0+t9Ty33Ih7CvAr121SYSETcGN9jyt8wLdeAEZuzn5ZxhOuQwPeQoPWKUj4jLmLRKzxdWvOK1dtE8LmXJuBozZsyT5IkKtHbVeMvb0txP3kEJ2Vm1hV/5tGhh9S0+cj6id6PHoX3IbnuXbz34FtvRNl27y3axzbP+s1RRhdN1zIu3ztidnJwwmo+IiRFtFTCM8pEJRI+SI2IRcxgfMo/nTKupupZWmodH3+nTd/tEToQrT1l2lmUhHEFSKEvDaTllXIyNWGMu8yWHHP18Py+DbgtbAYacJn8qVDmHeX6c0zSLSQNuTQvXGgUm5HW+9hjXNW1zGDohgRXgOR6WPNWvyOpMlStUGcf5MQfZweUbXWw3dFTZwbXwGn2/T8fr0LJbyvGiUjaChSxIq5RZMeMwO+Qn6U8YHg0ZZaMzY2cJy9hlDoIBg2DAtegan+l8hkE4UAGn38O1XGSlnvfzfM4wHzIpJhwnxxzOD5nVMyblRLF80uGSBaQZZyHwUo+u0+VW5xa9oEdoh4haUKQqwLBdm7IumWdzJtlEzY18MTfWzTHLY8vdUmCN1yZyI8WekRZlVeK4DlmZcZAc8HH8MeNMgWzp+2eZqI5wFCtiwZjYiraUnofdZTPaZDPcxK98dsSO0m2xN4isyASUnueZjLm2MdRZbZ2h19l5HTDqTPZspsYrDENmkxlCCrbEFlVScc2+RocO89GcbXfbACFyJE0JQpZnlI4Sl6z8iuP4WLmyeDnhbsi4GlMGyu0rJWVWLjQp0qMz47Cu6XmiSyh6bk+BHZ+V/JVn/wpb1hZpnTKNp/T7fZ7afsrYLs9mM5PtHk/G9K/1jTbGvcf3eDJ+wrScYrUtppUCUWI3pggLEhKyzUzdK68t98mubUIZKkH73MXOVWKn5/XI3Zyfr37eZP11sJ3nOVVVLZXh5HluMus6CF1lrQPcvn2bz3zmM9y+fZvJZGJESnd3d9ne3ubhw4dsbGyY0pAoihgMBhweHiKEoNVq4fu+KaPY2NgwrPrZbMbu7i5xHLO9vW2EEHVJQLvdRgjlHKJL/2/dusWNGzdM0P7ss8+SpilRFJ0RflyX+W8yAnRsq0GWGzdusLOzY87bcDjk6OjIxLo6eNe6GkIIXFfp3cxmynFwPB4TBAF1XdNqtYjj2AA+mgmhbWa1ywRgGCH6XDSFLjc2NiiKgl6vZwCiJrCij6/JMmr+rv+u54MGFD3P46OPPrrStXAlRsPDyUNu/oObIFEv9IUqR9BlCVGh7PRaRQu7ss3Jap40fWKEEEyiCcNwyPXWdXr0cBOXttdmMBjw+uuvU1UVn//85/n+979PWZZ87Wtf40c/+hGz2YyXX36Z+/fvMx6Peemllwyl5vDwkO/e+C5jOSbMQ2PdGBYhrnQVwu/WpF5qxAeNCKGbLr3AOJWDl3vqE3UjlEhKUVI4ygmhEg29CCnwU5+e6LHpbiImgk1nk5vdm0weTqiKiv6NPvfj+zzMHpK2UrIgY1pNFQiy7qXzvCZVJsqvfeXcUfQYlAPaeRtrbiEmQvV5YDEKRuxX+0ycCbIjyb2cVKRkZBfSZJtghIU69kIWpoTg0pfrRT8FQgEatY0oBWWuSieELZTNmDBRxLnbWGrrsnKFfXqubE9lHGxVo6zLHXKZnxvMaAq5Uzm07TYt2aI4KYiyiLuDuziZgysVMn5QHjD35owZE9uxCUjPRcavmKVrLmssCpvLXjUrq1ezxFqrR6uycHCwLVvV21MZNsaF7IWrZojXrdcAJZb6dFXGxSLzrQEbS6iSEyNYeMnYOKjz50tfPWDrgIfOQyOqum59p3LwSg+3UnP3C5//gkGzy7pkls+Uf3gyZJJNrnY9rDtW3X5WgMYl7AxRqTIJDw8KcGzFNtFOLpZrUVnVpRaUZ9TaG/8+bZ8tLNpeWwEnC6V9/Wiap3Nsz0a68hSk0AH7edtcBdHOKSG5cJmftp13TV8yZ4UUS+4HnwhYuGKfLlz/0zAw1jRHOLScllLk93tsBVsm29f22riWSy1r3v3gXd74yRvKMjnIKd2Syq0orXI9sPBp+3ceu0Oezmnbss8t+/EsT2Uyg00VZPoDBv6ADX9DlUbJiI6tMr2bwSY+vrGz09RhXW/sOA5xHjPKRxzGqk49FjEn6QmTasK4HHOSnnAYHzLMVLnJukDUszz6rqr/9oVPy28pWrijslF5kZPJTLEmigmjdMS8OgsEW1jKBQfF0DlP2FZnz3W2XLMmSnn5e4EtbCwsBW5Yp2UdRq9CKleNy7ajyzgCOzAi5IEdmDITDZgkZaIy+QtNhavcpwUCz/aI7Ii226brKWHCtq0EKiM3UvPIUmUwlVA6JaNsxDBX9PikOlse41uKFaBLFXquCkAH4YDtaJuIiJ7bIwxCZcfswMH8gP/1X/2vTIoJ3Wtd+tf7zOoZ40LNjUkxWXsubWErkUJXAQCRE6lSHKFKcWzbVuLFtRIwjkvFchjmC0vJNePUtttsBBt0nA6e8NSP46lnSFWbEpeSknk5Z5IrbYZ1Y2ELWzFIXJX91/eEQTAgslQpzW5n15R27LZ3CYSiw2uBu7//9/8+h4eHbG9v87f+1t8ytoA6iNLBrg5Km5oDgBEOXM3qFkXBbDaj0+mYwE2LFurE6bycczRXjizTcspJesIwH3I7us3N7s1TgHGh5TDMhuZHl1r8N1/7b3hx40UjlFgUhbEplFIa/Qgd8DV1E7SWQVVVOI5jkr7N4D/PFcg3zpUFse7HOFegkWbenKQnBrD8yuAr/Jef/y/xfd8AQHo/uk9BEGBZFkVRGM2PZqynBRib+iC6T7oUpznm2iVjNYjX5flpmlIUBWmamnIKrdegQQ7NfNHjohPVmoH/4x//mOPjY+q6Znd3l6qqODg4YDwe89f+2l8z80af/ybQoLevwYs8z3Fdd0lIUduFNgWO4zg280uXN+hj1/9vlgzp8dX9b46T1oDQQoz6+yzLzN+aRg0a7CnL0gB4nufR7XaNOGwTDNLzbFVvQV8/zWV0f6qq4t133yXLMr75zW+eucZX25UYDRvhBl9/+HVlXekoPYPYjzluHRM7y7Qer1LWla2yRZRHtMoW7aqtyhPKFm3aPOg+4LX+a7wmXlvsQNnv9UUf+YwkKiISL+Hx5mOCLGBmz8iKzNiE6AnaRHDquqaX9oiJSVspI290BkDQdopRqUTrdpIdwmmIn/nYUvmjpiJlwoS5PScLMrIoU5R+d/nlO6xDgkrVGJapEsARkeCQQ2btGT9yfqRuXgu9CFcqwUendhhUA+5Wdzn+4JjJRxNuPXeLUTBCFsplIHdy5c7hqbp9k5FcvKRWojKWlYfBIWfaAozwKg8ncQimAc+kz3BdXGfD3mDD2cCXPrEVM2LEPvscc8xQDtWxMyclXWZFrLycCYQpk3CkevGoUJTIktKUWOTkKhNro5TM19DWrVqp/dvSNi99taipxMLO0jo/qKjcisRZfphpvYjVvnuVR8tukcc5spZ4gQc2xGVMbdWMrbFimmyrdd7nfWgtNrEoT3EqR6HAuU9n1mHqT5m2p7iFys47tWNeRCpRUVuqPEOXvqwNkBpdvTQYX1netAb74jzssLaVNkNFpVgWixf2dX3Sx4vEABFSLKvtXgmUWEdzXtc9o893Wo6ityGFXKa8XhTPygUFuD5Vzi1EoeqlrdGlyvI6KCzsgtIqEVLwzvE71LImKRMm2WStZkHohHT9riorsFVZgRaJnOUzZvls+QXugkzzhe2iIOuSLLh01P1FMyfM9prrLnVREFgBoRuazGVFRVmVpi5bZz/P361YDgDqFS0KTdGmPqUbL64f01rLxyGEuvf4+KqMqVLZVf2QT/KEQi4ACacxd1bZPavn4IJA1BKnLxafSPxSXJyFX923cQXhnHtFs3+aKaH3c9G9ZRVMueo8ugowcU4rZanqrIsxD+M1ArXNdkMdixY2blUtwiKkLdrKohhVchD6IWmeMk7H1K2a4+JYlUjVCzq0XAMe6mO9oO9Gs6BePrcCQcftGGaObynl2KPsiMfJY5IqMSJ2q82zPBVEuSqo2gq2iIjYbm3Td/tshVtsRVtstjd5pv8M/bBvXuqbCvE6KKqomEtVDqDBh+PkWAWI5Zjj5FjZieYqqBkX4zN9coTDpr+pAlA7InACAz5LFJCaV8rtZFpNGWfjJZp9Xufk5KZMw2Qwa2vtfUAgTJmVZjwUsqAqq0ttcy0sw5pwLMdcg1JKYymclAlH8ujC7TSBiciJCJ2Q0A7xbR8t1qiD7nkxJ65i5oVipMj55deuhYVv+7ScFrvhLm2nTdfvGg0PHeAXdUFapsRlzLAY8n78PqN9FZQ27yn/2d3/jF/9zK/yjP0Mb87e5OjoiC/ufJE//3N//oyNXkVFaqWMizFHcyVEHBNznByfMiayIYfZoQoui/VAiC1seq7Syuj5PSJXCXvKUuLY6qEpbEFapkzTKcN6yDSeMikna1kTrnDpe31utm7SclqKpSI8M2ccx6GsS7JS6R49mD0wwfm6/mnmxL/6M/8KJEZDoNvt0m63TaZc20Gacy+ECYo1ExsaNrYrwvZ1XTMej03GNk1Tsx0992zbph/2lywPAVN3v7r8ap27XkcH6nmem4BRB91FUZjAUt8LmvoAzUBUU9w1IKLjpK7oct26boJcvW5TyFOXT1iWZZgLehu6/7pPOmDV46ddITSlXgfY+vxqSr9eXrtQNMehGbSvK7XXpR0aaJFSGnaABn+0RkJZlksOGaAAi6eeesqII+rtaLBCAzvrzlWz5EUDEXrs9DnR517bgupz1O12z2VEaBZE00JUj78+5qYGRhOAaAITGuBwXdcAbHquN/U0msfV1NdYFRBd1aVognT678bgAbh7964Zl8valRgNR/ERn/t/fY4oj4jyiHbZpiu7dKsuQRkoESV3zsyeKZcDZ6bEEx0FTDQV6y1pGU2ECCXMKCuJ67nUds3h9JDcz1WQL5b1F4I8YNPdpB7WBHnAZ3Y+Q5AF2DOb4qggEhGz6Qzf99Xguzalr+z2ZtaMua36qC0V586c2F4GSuzaJsgD5YJQhLRqBZT4lY+oBEEUYLUsxvWYqTUlCzKOiiPmzpzarpePkzZ2pkQcW0GLOIlJyxQrVNZlsXUxrdCqLNzSNdTwIA9waoda1uROTuIlqi7dU77hFwXkZ5pceGwT0qXLJptc4xpb1hZduvwP9f9AS7aIRKQyoPXCA9yqSKViRWQiu9ReT8gFq6GyKZICy1b0z8pSQfhaEb9zAlhNubeqRS2pDkItlZ2+8vGvvNSvy/47lUNohybw0sr9JQvAYF1N+cq2z3vx9yoPt1ZURb3tSigworQvsbG76Gq9DJg47wV8zbIWCwvBumGJqMGfNQJ5RrMCLmdIXKVv5/VPf3/VbPRl2V+9rXWU8wuaDqA928OzPQI7IHADfMfHtdSDPykTptmUYTqkrM9mSC2p9FBsoaivUqiM/ZXdAtY1fSwXjesfYLOwCOzA1IprfRddemFeqM+Zi6JuaP5wyTy6amZ/Abza0lbsqoX+iZACYQtlLWhJcw2ae9KnHDtNI6/lGvvQxrFeqV10DTQ/r8CUWGqfhCXxM2I4/Kybzo5rRf6+p0oOOk6HjtchtEJksXgxFRXH2TGP4kc8SZ4wzIYkZfKz1ZlAlev5tm8CWd/ycSxH6RIsAuOkTtYCE67lsuFtGJ2AndYOPadnrEJ7bo+daEfZNlodNqINc63EcYzv+0YIzrIU0HmSnpBaKaNixMHswOgJHCVHjAqV1dSZ+HXZbJ0d77gd4yphyVPQragLsiojqRLlyFGdZXhpsEFb7IICofJ6vdaMja1K3uQp6KZLOS4DFLQlsGZP6Bd8KVV5n3bOOVdPZbEdRzhKj8YK1L3dCgjtEFcobTLLtsiqjKzKmFUz4iomkxlZfbmwpj5Gz/IUG4OAyIoIhAJC9vw99sQe916/R3wU42QON7du8me+/mfY3Nw0gn86u6oz2lp8TghhsvJaF0Fn6hc7p3ALJuWEuZwrcc2FM8Th/FCJUgtVmjMpJ0yrKWm9pmwCh47ToW21Ca3QaHI4lgrm8yJH2EKV5lYp82rOrFIA1rrz3rJb9JwebbtNINS4e5ZnSoBkKfmq81WOPzpm/HhsLKpffPFFfumXfol2u72U4TdzdHFN6OC0CSw0g2SdwdU1883Mvc5oN4MunS3WwZsOrNeBC01go7k/KZUlpdaf0IGqzqwDJrhc1QjQYIUGBnSgrYNMva5OxOosug6I9b41S0EzKzSosWpzqDP2ht3ZCNRBBaZpmhpARztB6D7ooLRJzdf7agbtzWsWFBiis/daALG5PXO/a7AcmucqjmO+/e1v47ou7XYb3/fZ399nOp3yy7/8y2Yb+pgmk4nRSXAch52dHXMum3NB91nPEQ10lWV5hpmxzg5U/96ci1mWEUWnNtBN1k2zLEWfk+Y516wOwGgoNueL3qZmnKwrk1gtEVknfKm3p4/thRdeOHMtr7Yrl0589e98ldiLif2YzM2WMiO6TCHKGqUUWUiUR/RED2yYWlMSf1GyoD+9hKk1JbaXA+6gDNiwNkgPU0IRst3f5vDwENu1aW222J/tk/kZmZ8tlTBYtaX2W0aGVRHWIbEV0y/6bKQbdEqlDqwvopqazMmY23NiN2YqpozqEYmbkPmZUrl1kmUwQtoGLNmwN8iPcpjAnd072JbNZDbB7bnkfs6j+SO1rSBjwmSZYVFa2Il6kItKIGupHAGsgsIpKOxiOahbFzxIVK16GeAlHnZuk4UZwhVKAFOsMCI+RdO0de1a0Gw2NpFUD0kPD4F6sBSyMFoPS5Z6+jjWvMw6lYNd2yo7Qq20GKxPACAstmOj7Bsd6eDYjrJ/oUQ44tRi8gr9AUzGQ321Rl9BLkQ+5XKNqg7KLwqA9XZNPfyazKeoVJZPb0cKibSuGMivbq55nJ8k0Fm3vAYkFmCEFoMUcnHTQulp5Ha+ft3VMf8EANGlWdvV75vtpwnGFs1YPspPVi7gWA6+7RPYAclECaN2gg51ubBXsqRSpw9gVs3WlzfJ0wfxpeN6VZDnKsv+FO0iETtYZLIbQQEW5jq6cLvy9PoRQiwzDS6bC1cBJrBVJs72cG1X7cc6zcrqQCOXnz5YFY1/lwVSP007cx+7ZD+XnbP/OzZNiw/t0NDiB8GAntcjsiKlNWEFyErNvWk55XHymP1kn5PshGmhAq5PxGi5QvOECtA0MGGjyjdqUasgvk7WZo0d4dBzlLhg11buAi3RUvXxwSYtWvTcHgNf6QZYhbX0AqyDCoDpdIplW4zzMdNqyrSecjA/YH+2j9N1SO2UYTZkVs+YyZn6rM+KK2o3hpalstgOzmmphTwVlZ4Xc2b1bK0DlY2tAlahwMqqVu8vhSzOvdZslE4E8vTecZXryWJRTtjQmbAstU+tNWHKDM+7fyGM3oUvFDjRdlWw7Fu+6VdZq4A7ljHzeq7EfWV2bpnKUlu812injrbTpuf26Nk9tvwtNp1NttwtOlaHrtNldjCj43YMNT1NU+bzOY7jMJ/PjW2eDnA01fvJkye02212d3dNANPtdsmqjKPkiGk5VYxbqXRG9Oc4HzOXcwNeTKrJ2uNyhavENe22AXFELXBtBc7r8db6HkmVMKtnxPXZ+W/VlhKfrnx6To+93h5PbT3FbmeXrdaWKWvqu0q4seN0CL3TDDtwJojTzgxBECwFbk1AQodMGrhoAg2aHaD3oYN8nX1vMpY0O0KzJlqtlgnydICp99ksBWnuHxSTATDXczPYbgIcWmBQswx0IKtZCk2LxCZgoeeIZl3AabZbr6uDT83O0MelgRfAjEHTnVCLS66yS5qZ+CRJjHiidsnQ/WuWMmgAQP/keW769nu/93vmfG1sbPDw4UOSJOGXf/mXl9gRVVXxne98h9/93d9lOBzyjW98g1deecUAOnqc9Vg2WQbNc6QBgOYyTaCpyeJo9lkzHXS/9TnW29HzQZ8D4Ixooz5PTYCoWU6hWRXNMozmvG5CAqu/NxkNev4+//zzZ67N1XZl14lvfvObZgeFLEi9VAEPbszcm5+WVHhK+6D5IhcUAWEeGkZEq2jRp8/11nW6dZcyL3EGDofFIT/Z/wn2hk3ZLvl48jFVuyL10yVAwa5sojxiJ9jBzV1ELshSVf+TlimFU5AHuQIjvGz5pVKqF1SnVq4FQRHQKlq0szbdrEt33oXJQlV8QYfxAo/MyZjZM+q20nlollcc5Uek3rKKviMdOrKDEzu0qzbXO9eZPphSpiXXdq8hLcmP7/+YPMhp7bUU4OIsQJxG8yufoArwalWiAMqPvbCV40ZmrfiDnxN0WdIiIqJFC18oymCFErlJSMjJL2UnXNT0A1u/MK9uS0iBlSkbSbOcVRuxutJaEV+8JHjUlOGlIP2TZCEXD24PjzqtEbXAj3xFb6xTxU5Z3d4l2UVLLmf/14EStrSX7LfOLadYWa+5bWA9iFEtmAiWdaoWflWA6dMwJdate874aEDCrm0DJolaKFYKUgFrTrHECjqz/ctAgp8FWHHRcXyKtiSa+UmCNqkADbtWL91WZRE5EVZlUeUV1OrFtbJPbW4L6wJHmJVtr3Tyj1y7LNDVoozAlQP05jpXATI+SV9dyzWBomspWrmsJbPZDMd1KFClJnmdL4kTf9L9aAr6ZWJ9P23TwMSn1t74I9jEykS/ynE5wjFAQGRFBDKgY3fo+31CK8SRjqnbH8eK5XhcHTMqlRZCKtOrBZKfoFlYuCjxRD3fhFD2l1mtsuqJXE8/14Fox+oQEdEWbQa+stKLUPXxXunRc3qITDmPPP3004xGI0NbDoKA3d1dgiAgIWEmlW7A/mzfiGBOyompXR+XCrwYl+O12hctu0XLbhE5EYEd4Nqq1KCsVBlHVi8YE+X6zLpvKfDWFe4p0C2UjXBap2tZBpq1YF7aZX2la0ozJhzLMcBEM8GgLUgrWV24LQ1MaKZEIAJVomOF5HHORx9+BAJqp0a6ErttU3lKaDurs4utgxvH6FquYk0IxZroOl26VpeBrQQst71t+l4fr/SY7c94/NFjnnrqKW7fvm2CKy3MOJlMjBWfptHrAFPbJqZpSrvdVjZ/dcZJcsK0njLKFmUbJEyrqbJ7LE41DLRuwDp9IN/y+U+e+U8oXy159cevUnolpa9EGguvoHRLCq+gcAsqX32nhc1XW8tu0bE7dO2uEa3sOl3DGOrYiqExCJT+il3aJujVWWAduMFpwNikmDfF8pt1+LocoskC0AGypsDrbRdFcUZzoJkJ1/vR6zUDwCblv/mdFsTUAbPWj9HB8boMt142z3MjmKjHo6lJoOeBPkbA9E2v02Ra6Hmi12kKTXqet6RR0QRQdEDfBD80wwEw/dXAimYdfPDBB6bcaHNzkw8++ADbtvlTf+pPmbIDPbYfffQRb775JuPxmOeee47nn3+eVqtlxkv3SRsX6GtB/77KImjOlSarpgmwNNkOzfOnWQ5FUdButw0wrM9XE+DQc7BZOrG6rG5NMKOpN6HnUXP8Ndimz03z3OsxvKxdGWj463/9r5+hYABmojVbURXEbkzsxQaAmLsKjJi5szO6DkEVsCE2iPKI8qjkeus6G2KDB2894JnNZ7i+dZ3X33mdcC8k2A145+AdZFdiD2xOqhMmYkJmny2zCLMQJ3VIxylhKwQPSqekdEsl5OecI+S3ACPsUinjh0VImIW0sza3/Ftcs67hpYoyvbW1xauvvkqcxLzwlRd4Ej/hKD/C2/IYyzGP48fkQU4e5kzldOm4RakUhTfsDdzEVSKWZWBo6LWoKdxCuWi4ykFj7swVvd6cQEFYhniVh1UsssuWpWqxhbL0y52VrLLE0JpXAxMfnw4dQkKzTIYSk8qtnEIUp7WYnzQrXikU2hULVNSqzwp+SRQYUTkIubCOE5WxeDwT+JuBWL9fXfOPxGRKP2nAaklLMSQWZT5VddofI+Z5yTaW+iTV+W2egzPZ68ZyVwYMFutoH26dHS6sNS+68jQjrIP9tWN7GWV9tV2VMXBBGYNd20ghTaZaM2kuPXd6G1fpw3/obP8FLJpPuz8dgGjaqW/5+EJlBoWlAMC0Tpd+sjr7v30g2czWX6aU/0mO1Wpc2FcRqNPtqgGsb/t4lqcE9mzHWB7WtcpkZ/WCMXGJOOdF/TcvM3+AoAT80WZAfNLz3lxv9bxeJUPuChfP8kwA3XN7hCLElz5OrVhMgRuAgMP0kMP8kFGtavXn5fx8hsxVwdR1fbJUn1xc4xCiM8dpnZJUyZl9OsKhY3foOapUY6e9w4a3QVu0aYs2XbfLVqB0Jrp2F1/6JjiVUoFs2klM0/g/PvyYo/iIyq8Uk1TOjabEnDnTempAiXExJpdn534gVFY/EMoOWTNUq6pSotULNuW8nq8tkQjEaRmEBh+1GGVe56Sk59qLrgM4L5sTLi43ghsAlJUSvKyEGvuCQrE8ZHHhdowmllCMnK6nNCBCERJa6lgk0tzXJ9lEATOkqtS1zq4mhrl4fwisgNAKadttBUrVEX2rz+3ubW60b9CxO2wEG/iVj+/4pl4cWBLP04GeDrD133TwqGvTNZCRS+WcNi2nHMaHHM4PGaZDevMeB68qW0It/qcDO12bDqfxiGVb7N7eZePGBk7XoXAV+DCpVYnIpJqoMo56yqxW5RzrWBM2tgEfunaXjt0xPy3RUt876vu+26dttVWJUENIUdfQ6yBSsyB0ANfUPSiKwgS9TX2IpiifXlZrKqyWHsBy9lkHhOYcr8RreZ6bMhAdTGuQQ+szrJ7LZvDZDPqbWf1Vuj+cBr265CdJkiWQRu9LlzAkSUKe52xsbBiWjR4Pfe61E4MWnYRTdoaeI/v7+7zzzjsm+z8ej7lz5w7PPffcEstrOBxy//59Tk5ODJiwubnJCy+8wMbGhlk/yzJGoxFpmmJZFrdu3TrDQNHsDn3ceh+agaLnhx5bx3FMyYMuv2iyQTRzpsmM0CBTXddL51EDPBrg0nOmyWJonq8m8LCONdMsp9ElWvoa/Kt/9a+euW5W25WBhr/21/7aGYSm2YFmW6WwNBEa7aNcRRVlu+SkPmFiTSjbJWPGnNQnTO3lgNyrPcJc1YB1RReGYIUWz0bP0p118SfKeWIkR8ydOXmYc5AfkAUZRasgcROKcLn22S1dVe6RRwRloOiKVq2sGVk4CngFpVOeS4G3pIWPj0gFfuZzq32LYB7Qnrd5fut5rLnFw/sP6fV63L59m9d+/zUSO2H3uV2Oi2Pe+OAN6EG4E3JSnZD5GbmfL5dXVJZhhAS5+nRL9xSMsGsyOyNxEnPsmb/McnCkQ5s2Y8ZXy3jCpcFyRERbtvGkB+JUlCiVqRGENO2qmeYG28SubWM9WlkVhVMsMzc4tce0pCq10MKL54IR5/VFsyT0y7KmpV9w/Gu3VSuQxK1dU2qi9SM+MSCh/74AS4x/vFhfvrHUpysABlp4zcHBtV3KqlTAjyjXgh6nK67pIxf8fd0yly3fXO+i81WrrJQtbepSgVCWbanjkOX5tolXaX/QQMMfgWYLxZbQQneBrTQmbLGwrJIVWaVAxqRM/kBq2v9DNRtbXU8/oyD8ykCDXLyccfpcvEwUD04zoJ6tFN9d2zXAhFZ816UcV8l2ruv/HyaD4dMG/39UW7MEplnadNn80pl1nd2OrAiv8ugHfdpuWwXUVkA6U7XPtV3z4ehDIxQ9r+ZkdXah5sCnaTa2cSqwpa20iiz1nM+lcrJYvS86wjEZ4r7bpyVatGhhpzbXutcY+AO80lNuC/6Ant+jrmuGwyFvv/22oXFrQb3ZfMbJ9ARakNkZc5T1cGqrBMpMqgAxc5RYdyKSJT0wcyy1jVM66tm8YD1YlmXeW0pRUgjFEF33nPSkskK3azUOtqWAcK3bVFjFuWwyC6Un5Vs+SFUaUMhi/fmqwckdnNJR+k2L55/t2UZDRv+sZUyuNCGFKh+tHVViUPv4+AQE2OWizNEVlLayXE9kQipSSluNRynOcX1Zsx9PeLRcBax1HVXGM/AG7Pg7bDlbbDgbbLW26Lt9rNwijVOTJZ5Op7z77ru8887CdnYRDGVZxmw2M5R8fV01xRF1EK0z4s0gTQgltvjUU0/xjW98gyiKcF3XqPE3s8FCqMTeMBtylBwxzsfK+SU5YVQohs6knCg9i2pidC3OY+j03J4BIAb+wLhq9Nye+enaXeW+4ffOWA7q42gyG/I8x7IssiwzLg56vHTwqgPtJgBg27YJPJvxmA4iNUjUFBRMkmRJP6ApTqgD3qbmQtNtohnENt0SmsF20xlEX+/6vGgg4s033+S1115jNBrx5/7cn+P55583QbPW4RNCCVACJuOfpqkBaTRok2UZeZ7z3nvv8YMf/IAvfelLfO5znzPHMJvNuH//Pm+88QZJkphjnE6n5HlOp9PhlVde4atf/aoBUf/tv/237O/vE4bhUrCttXK0XoXuhx4frcGgz60+H62WUr3WZSK66bFL09SMs2YX6NKT5nlsAnFNBo4GEzS4o0GvpvNHkxGir7VVEEmfJw1ifeMb37jsFnE11wl9IvTgr9ZpNKk9zdqg1Tof/fPO4B2IYMPaICxCNtNN7vh3iMcx4/GYd++8yzu8w8yaKVqwlZMHOUg4FIew0OZ4l3ehh/rR2dzKxcs9xFDgHXpEb0f0hj3kTFJ4BbIn6T/VR3YleZiTBqlyz3DjJaaAVVtERcRGukG7bhPUqnZTulIF9iiGQeYoHYc4iBmKoenPb/Ab4IHVU9nwtmjDZ6CVtcAFP/ZpPWyxc7LD8+J5jo6O1EUpK1InpWpVzO05qZ+qMhUnJmklDHvDs5oRta0AiNSlN+7Ro0fbayNLpc4cdSMKt2BmzRgv/sViGb3VAk+GUnxRcCUgXvw7s1zj/7ZU+g2tukWZl0zmE+zQpnYVk2EJFFgEyFrxf20ZRwOMsGsbt1Yik5oZUDmKWliKZZDD0PYX1mVnNCP05oU8DW4vAxn0783lLJSgowZZzgMkJLi1YkjoUpNa1MtCdI31JPIMyLLURGNsEEs1vucBA9JagCBU6qXRbmxrZXmnVi8rWqujFgsnDVGuFYZc38U1Gc+rgBLrxlCPiy1VGRHFaf9Xt70YE6uysEqlDu7ZHlWpjr2yK0pK8/K2OvZ/FJsWOgMupehe1CpZkVTKAk7ml79QanX10A6NtZxtnfalkAVZmRGXCphYl428SvvDyI5XVBcDYCvN5pQiuq5U49y+rt4jRCMzfoX960BFoKjxRV0wlevF1FbXcyyVAXWEqpPX14LOoOYyN7aEf9glEn8Q+7KwTK2+Pl++8HkheoFSlqQyNW4KsVT18VexZbxKWxo/yfLnmtYEJqSUZFVGWqUMGSrGYMH5LD1HiSK7lYsvfHooxoRbucpSs7JVmaVUJXyJrd5VxtWYVKTkIl8qRW1uW7dKKPp/KlPd4VNHmjXPRz32eZ2bspFaqkA8qzLSozVigsKhJVowByqwSxtn7hBWIX7l4xUeTu4QHoW4hYtTOlRlpXQKrIh20Wan3jHvm1W1AEHsjNI/pdMbqn1QkbuKyVp4hfrOOTsOVmVhV7Z6t6hP9alyqRy0jGisc3be+MKnZbcMy8CxlE6U1qdIi5RZPqOwi7MZdAvVJ7cyiSSJYiXW1vpnuVMq9qd5l5C1YUjqxEtmZSRecqVnmqhUoscpHcWULTzsQs01G5uqrqgclTipvZrSVQLWpVsyF3OGxfDqjK6FnoWMJcTgbXhEVcTAGhDmoXJzyzOcXFlUN11H9DnXAav+rpnJ15nxhw8f8u6773Lr1i0TmDbp4s1yAMuy6DpddoIdcGBezans6swyel9xHRtdilk9M4ycaalYObNixnvZe4Y1sdaOFKXTpvVWOo5iEbWttnK2I6IlWriFiiPcwsURjilT0E4kzWBSB436/00nCR24NksP4FR0Ms9zkiQx2XudeW9m55sgxGqiWY+pBiC0WKLeTxzHJkjVgbLWNNBBsgZWtLvgkydPKMvSBOO2bRutCQ3AnJycLJVv6ONpaibUdc3R0RGPHz82OhxCKD2H1157zWxDMzvyPCfLMobDIc899xwPHz7Esiyj46HZKD/60Y8Iw5AwDI0gpLbm1NvTDAy9T72MPjedTkeV/y9AkqbWhz6mLMsMSOa6LmEYLoFKeqw1yKPBKw3KaTZK89zr+dM8b/r3ZqmGjv113zWocZV2ZUbDX/krf8X83qxBaV6ATUGPdXVC+vP1269z3D8+4/jglz5+4rPj78AI8sOc5288z2w843h4zNatLYbOkIPqgLyTkzrpuUHjmSZPf4QUOKVDkAf0kh4b6QaDbEBLtjhsHTIrZtR2jQwlaZCSeIlyq1ixUbSlsntzcPClquF0K8U2sF310E3qRJUvkK8Xd0MFwk7p4FcLp4usxUa5Qbfo0pVdOrKDLxp0G1mR2IrBMLdUScrUmjKux0bAMnWWrT1d6dKhQ48eXbp0RAcPzwTpOYrJMWFiwIiM5XIUXfIgkReKI60d+2ZbOV92ZeNlHk7pKKDDqhVoYOdIV64PNi/ZlyUtHKks8EhAlIJuq0tt1QzTIbmfn33J+DTZ+Z+mVOCy7clFNqZe1I8hTZB/ppTiqmOk2QANIT0El4NLK+uuK+WwKgu3ctXLWaPk4VyHkQv6Zb5u2FxetN6Fy1ylLe4NllT2el596gxy9zN3mRdzRUfNpsRFrBTTL7B1/KPQtCq/BgN07XApy08saGdo5AtQ66qBqStcPNszteS2ZZtgPa9y0lJRfX8aMcU/cplxzULStatcMod/Rs1q/DNsiYVi/2UZbw1QOsIxWWwj4EdpRPw+0X3/j3DTwnotp0XH7dBze4qK7irm5F64x4OHD3g8ecxhesjcmpM6qQHIzbP8DwOQXGG26XukZrdd5R6pwXm7UhbNTqGCSVe62KUKqB3hGLA89VIyNyNzslNR6nWg9ac8fqORUAvqvDZ2y9KS68skUewCt3BV4Jm7eIWHW7gGkBCxIKgC7NTGzV06XkcF44tson4v1QGAbdtkVQYREKkgP3cUdb9wFRih/6+/K9317kGaeRK4gXFJqGWtSjEWjgvrst9dp8tni8+SvKbeE/U+zP688vTHL9drGNWqDxoI0oLR69yLRCHU+a9OgUe9fG2dghOfRIDbF+rdt+/36TpdtqItem4PFxfP9cilKs8ZJ2Nm5YxpoWxT59WctE6V09JVykJroQTUS1uBH4s54KWeKj3OVOmxlVpqnshTK81ut8tf/st/GcdxjF3iqijeqsK+zvjrTHQzi6/vrzoIb4IPzd91M8wJR5CQcBQfMcyVuKpO2mnGhC4bmlSKRbHu3u3j07ZUCZMGJlpWi47VoUXLlLzoMo+20zb3c92a2XEdAGsBzKbbQTPW0+vppoUHdRDfHCP9nWZgAEsgQvP/TcFMLYb53nvvEccxWZYZG86XXnqJMAwJgmDJGtOyLEajEePxmCRJ6Ha7bG1tGUBFB+tHR0f8/u//Ptvb27z88stLjI/33nuPH/7wh7iua8okjo+PmU6nzOdzvvGNb7C3twfA48ePefDggWEuZFlGEAS88MILbG9v47quKWXQ80WXNOj7kdbmAMWC0HOyqS2hx7j5o9kIrusacUx9XprzUs85zVbQ29YxuwYSmuOox3JV36F5Xeg+6O9+9Vd/9ZKL9xMADX/9r/91cwBN9kITXWzWDOmfJiLS/Fu/38f2bI6LYxIvQfQF7x6+y0RMsDdtjoojYi9eyuTqEgpn7nA9us6ms4kTOxRzRUEZxkOSIGEezpmFM/Igp3TWiAxela4tF0FwpfbbLboM3AEODmVVEkeKxTCuxgrZtc6xJpTgoR6KgQxouS2yJGM0HuF2XKQvyazsQmtDSyqF3aAKiMqIbtWln/fpFB3CIsRNXPKZQvY8z8MPfSb1hNiJia5FxK4CEaZiykRMmDBhxmwZjMA1QESPHhHKAxqpUEPpSRISA0RMmCyVSFhYCryQFlXj35Vq6/XYN9tKIK1fsoxmwbpShNX1LtiPQFELRSrwpc+gNaCcliRxgttyKaOSKVNyK1+7/qX7+iRB8EV9XwnC1wX5ohRQgOu4WI7y7K7EOeUaV+lPc78qDWvWu/B8rgEMzrAxauUw4lbuKdAhTp06KvscC8TF9k0piTx1gKhktX5OXOUYP8XLsmu5tNwWG+EGW8EWnaCDa7sUVcEwHTJMhoyzsQIlqj/65Qa+OBVUs4RFIQslxibzS1XXV5tmJKzS8a8q2Kgz0rZQVGVJg7myZl+aFfQzaz9FAHXu9XXJvaA5VmrxP9j5opX2tSMALGx26wUYRXlhHwRKad8VrmFM2NiKGbQIsMq6/KkEhn+a9rNmxdhSMegCGdCiRUd06Nt9bvRvMOgNkFIyzsccpAcMSyWAOCknJHWi7sXnAT3r5ppc87dPMh8bCRUtmqy3YYCJi1h7i22Y0sTSwinU/fr65nW6QZeW28K1XZUJc2Feznk0e8QwHyo7xCr9dNfkedde4/mvXY70MVVWtVZr60X/Rf7zrf986UW6qiru3bvH7/3e77G3t8dkMqEsS8IwxPd9kkQlk5rq+vrl2vM8hC2U6GBQEYuYyq9w+y5212ZSTjhOjpmLObGMmVQT5vX8zBx8RjzDX7r2lxhmQz48/JDpT6bYH57W8JtDbgR6OrAoKAwjgwhSK6VwFSCSWimVX1H5FaWvwInCXW+XrC1+zb40OLHaKvWstio1F8IwpON2cC2XOXPm1ZxEJp9I8NTCIhRKoNJObcpJSVRH7G3vEbmRYp3Kgg8ff8g4HSM9SeVVSF9SuzXSkVcDQmo1hzU40ff7bLe26YgOz/rP8nLwMm2rje+d2j02s77NOKZJP2/aFep5oc+XDiybNHM4pfBrpoHOXGvnBu3OoMGOLMtMRnteKmePYT5kWk4VKFFNjOuHLiHSTjCxXOPQgUXbahtXmrbdpkVLlUBbbb7S+grb4fYZ+0p9LDrrXhTFUt90oKxFApvZ8qaegP5OSsl8Pl8SyXQcxwTalmXx4x//mIODA1MOo61ZtRbD17/+dT73uc+ZchpdevHkyRO+//3vM51O+cVf/EU6nY45j/q4Hj9+zLe//W12d3f5/Oc/b8AIKSVJkvDRRx8ZscZ+v0+e5+zv75MkCV/5ylcoioL79+/z+PFjI65ZliXz+dxoMvylv/SX2NnZWRoD7fCi+9IsB9Ljp8ezKQzanBOrjBX9qben51ZzrjVLb5qMiqIoiKJo6Tw39Tj0/VLbrEopzflZBR3+xt/4G5de81cunWhOGn3S9M5WVStX0ZHVZfRgi1rQKTtsWptsyk3sfXWRv7T5Et/+3rfJ8ozPf+3z/P77v89QDrn+wnXuz+4zZswj7xE/sX5C3stVuQILe8s8JEgDuvMu7VGbqFA1j8OTIb2NngqR3TFFryBtpeRuvnzTWqG5Vk5F4iQkfsIJJ3zIh8sDIxeU4tLnunsdMVZBa6/f42B2wKyeUbdqUkuVP8ytOcccQwtVe6hZA4sHqVUuaHulyj5gqUCtdEoKR7l9jPwRj3h05hzpunuv8mhXbdppm818k57s8XT9NF3ZVZoSGhgqc2IrZiIUADGWSjV7ypQDccBETJjJmXpQqusAV7oKiJA9bnKTkFDVcLIICETBjBlTFKAxZbo0pk610DBYvFSX4ooZIv2ShFz/IrZYRkihykAadpxnAuNGSUIh1AM7J1d97aB+Gs9cp3bwao/YitVDW6CELaV1Sp88L8A4Dzj4JC+Our8X1EtKWwXZBYViqZyzrEDg4iLLRei3qP0892W3CSxcAQzR+zDrrWsW6rw71XJ27oqAlF5O18nqba7rkyVV4FpXtbrGLKGOmeryl+0LWlEXSkk7G/EBH5y7nCVUuUHH67ARbrAdbdPzezx58oRZPiO1UmbVjHl1gQjcH0LLZEZWXu4Bb2Mb+zpXuIYende5ouPXuSpF+YTAhBZaA0wG/qqMEYESMNPU4SV9mMYy9qK+RlvfXbDBT9aa18Uqy+iKgeInLV1Y0pO5wvZXm2YpZPLic67Pi4sCFGxxynSoqZVLD6k6X5cElho8clCghD5fFZWZMz8rwOiTzr3meuvW1QBoSsqI0ekfRoufRtP2jC2nxQ3vBn2rz6a3yY3ODTpeh5PjE15981XDQCx8ReOvvXq59O6ce9qFIBaYeSiR6rlw0fNGT59anOoCNLZTWRWlV5IFSr9gVI/gbByzWFzV6gdWQN/rE9mREsEUPpETEbohSIizmLROeTR+xHF2TOEWyl3BXnP/bz6HFs+ItVn9Ne2H2Q/5Lx7/F6rMQrRplS02nU3iaYx31+PGL9xg9Hsj7mzd4antp+gGXVMfrV/IAUO317XVTdE5TatuizadQYeqqoiiiFu3blGWJaPJSNlZMucoOWJ/uk/f7fMnbv4JPvzwQw5fOyQ9TtUzcZFh1vuEZfq0DkzcwsVNXLzEW0reTadTbNsmCILTd3BLkJObkhFdQqKZExqQMMwNZ5Gc081GJcAW76e5yKmrmuv1dbacLXqB0iLoel1TsgnQ6rao3ZphNeTJ/AkHyYGyiC2nzMoZczlnLufqvXILTjjhgXywXDK025hbpcAqLMVeyBeslsrFKi1kreaN8IWyRHcqKlfpelWOum5zL2ff2mc/3wfgO9l3+GeTf4ZAKEaA21PlC/pT28Y66rugDhgEyi4zsiIEwoyz7/tLLO8mqyHLMmPL2Mwk6yAuSRJs216ytsyyzAAQ61gE68oV9HOgLEuSLDGglwYgJuXEgJ+zesa4HPMge6DYE+WYV26+wm5v1wT8eZ5TFAW+7+P7/hJlPo7jJUvFpo6DZiPkeW70NXTwbFnKlaSpB6CD6Xa7zWAwIAxDRqMR7777LlVV0e12sW2b8Xhsxuvnfu7nuHnz5hKDIs9zhsMhURQZ3Zfr16+ztbVFFEWmtOq3fuu3SNOUe/fu0W63eeWVV5ZEM0FpGBRFwdbWFuPxmI2NDVqtFl/4whcoy5L333+f4XBoBE6bQXkQBLz44ov0+/0ldoFmiOhgv65rJpOJAW7qujZgU3NsmiULOtBvxuJNvRK9ntYhgVM2hL636G2kabqkWdJkdjS1N3Rrlk2YW0PD2eKydmWgYTKZLN3UVifZKtKySpXR31uWxePOY0q3pF22FWugoWaqt1FVFa7j0pZtopMIr/T42nNfo/VBiziO+YVf+AWqumKYDPl48jGxG3NSn5BHOTN7xrw15zg4VsivbhJ1o4pdWkWLreMtWmWLVtVSD1kLEith6kyZhBPiMCZ1Uwq7uPAFsnZrEhI+4iPoN5brnC7vSAc/92lVLTaDTdJhytHoiGgzYtaakVu5KRmo3POzH0afoHKNTaBE0VtzK6fyF8AICYfRIR/ID/i++P7SNlxcQhQyvckme+wxYMBz1nN0hVKPtq2FlY+smMgJB+kBeZAzFmNG9YgJE444YiqmzMTsbJmG7DCoB9yub+PVHsOjIaPRiMHWgNqrlbaFlzJ35st1/hK80lOuE7Wy4KytU4cHg7qf8+IkRSOI1OdqTRO1MP7WlWxQBfW5bWy/FOVZkSlbBUQXvs9eAJpc+t1FGdDzXhwXrhpLx39mVZUdFpYwIJmFtfYFX3CaETMsktXrQPev8f3SzecCUMJs8wrLnsmOrxPEXAPymKDSPmf7jfV1pkwzOGpRGwrqpw2AalmTVilpknKYHPLOyTvnLqsZQa5wCUVI22oT2iGylsTECpCo52u95/+wWkWlKJ3V5NJlIzti091kw9/AFjZFXRg3hbRSJRN5pQAKLax2laaV33UmHomh+J8XYOp7JJxlD6wsqO4NthKLrOQVAJOrXOefhEl0hXamHONnvP3mfjQzTWfKz2um/GKhju9aCpzQY13UipWSy5xUXp7x1nX/+lOIU1tPDU78LI7vKqCEBmibc2eJsddoFRVxHRPnMYf54ekfThoL3WCJeeDh0bE7uIVLup9ij22CPFDjZwvKoKSICiUY7SlafWUr96NzSyfWPMvM96cHpr6y5bJuyQXAhBDCMGJsYSPrRV08CnxKZMIkn1w+ri4IW+nnGBHEQpXfOVIJQrdbbTa3N8GFuIyVI0U1Jq7jSzVgamrm9Zx5PeeQxXkoUO9oAn7n/u+cBrMni/uv8AitUFkeOhtsOptsupuEXsiGvUHX7jIIBmwGm7TsFrZls7Ozg5SSbrdrgk4hlBBh4AX40ud6eJ2f2/45xuMxeZ6Tpikff/wxx8fHS9Z/puSpQVluivk1k3iajh2GIbdv36YoCvr9vlGu189hXd/dpMjrTG8QBIRhaIJKIQRFVTAtldaADkwL99Q+MrGU1egTnvBe8R7TdI3t6LFi/W34G2z4G3TsDs91n6NjdwhlCBm8/trr5JkC1y3Xwm/5HCfHJHZCFSmbyspX78PSURpcVajAvqV70TkJA7tUbORO3WHgDri9dZuN1oYSObVO1f2lkBSyYF7OmZQTPo4/5s3iTcaFKvVYbRaWASX08RmRR69H3+2b7yMR0bE7RHZkYiTdhBAGtGoG6jr7vVqOAadCmU1nBzhlvxiBxSpgw90wbAS9DCh2RRAExHFsBAb1Mnp+6BKTPM/NvGg6FKwmj5t9chxnicYfBIGp52+KXOpsue/7Zh66rsvnPvc53nzzTeq6ptfr0e126XQ6DIdDE/g3QZ2joyPeeecd3n//fRO0v/766/zkJz/hi1/8Il/96lcJw5CqqgxgoYUhsywz+7Usi+3tbR4/fmy0L7TDAqiMfxAEfPnLX+bDDz8kyzKiKGIwGJAkCbPZjDAMzX0AMEDKaslN85oGlrQX9HLNc9xkPDTvE3pb+p7RLInQ86XJqtH71CyQ5j1Bl3U0NU70Ms1KhtWyoKu0KwMNzY6tomlwOombNzg4HRj9XVqm/M5Tv2P+7tYuXdmlT5/yRkmv7iEQDKMhXdlVrwGNE9WkwuR5rm4k8w5d0SWaKCrIO3vvkLQSeic9vFxZOuVZThAFzMoZRViQdlMm4YTUS5duUl7hqVqvPGB3ukun7tCqWjilg2d7iLZgJEecOCdMggljoQSWzojRNZtYBKtBSUqqGA1bwJYSVVQDqOrxvcLDKz38ykdIQeks0GYrp7AKYwnZFK5cPhEoZWRpY0ulLGzZlqKlL/4VFBSiYCImPOQhP+AHZzbjSkUT7dBhU26yYW1wnes8y7N0rS4hp+hbKZUQ0FiOjcaDZjQM7SETe8Ls+gxuYLJBdqVepDqzDl5+Cizol5XSLplFM0pPiSM1aX2iFkrToVKZMS2mWLtqPSMGeUHGWlqLwOOiF3UdiC/cL3TfLsxSXpZxWn3Ju+rLYbNdhWp7QdNMBjgngF4FAi4LpCQmODeshIuCrEtAoHP7vBpcrfZFNjKU8oL+rzvXAlUbvDoea/ooEASOEkMUQlDVlRHqK+uLKecXtZqajIxMKkX1w/qQdbGUyTJbLp7wVObQCbGFTSUrZsWMSTFZ1lj5D9DiKiauYj5OP75wOQuLDh261kI7xlZONqlUYERSJ2QyI5WpyXwDV9aZECv/AHMtnzlXQgVdJeWl89OAdHW9dE2ZpgNJ/WD+WYEA58zfn2k77z52wX40mFOihBcvM0JwUHoQHh6BUNeTIxwQyiI7rVMymVFQkMns0vOty26aOhWaeaHLQT5t0zpGi/+ods5YODhKk8RWZUh1rajgRV0s2xg2mAf6usdBgRA3znRgsYpQZRzSJaojoiKiU3foVl2CQtk95rZKtmgGZWIlZCKjEKqc7kLm2AXn10PpvWjGmhYSrqhUVvmcplksjuUYYCLPVGmNFAtNJr9SzmArbZ993kvfo4mtOsIhsAIG7oDIjpTTwaKcw8KirErSMuVkesK8njOrFKU8l/mFz7OaWt1zqpRhNeR+dv/8wdBjsrCbDEVImzbXomv0ZI87m3fYae+oMhu7w663y0AOTAAxm8341re+xXg8XtIKWKU+A0YZfjWY7HQ6BEHAzZs3+drXvnbGSq/JKtaBqgZC5vO5yfR2u90llwEtUqff5fM8x3EcWq2WWe4nP/mJCViiKKISldEYSERCMAjInExR/rMhx/ExJ+kJH8w+YFyMGeUj6qfPvntYhYWd2YqxMHMJT5TLml0qdzHXcVWSxFbvcIWlykaKoKDwF+KfixJkw0ZGsZE/OP4AThaMuTX3EgtLsRu8Hlv+Fnfbd+m4HaXzVUPgBeYZm5UZlahIasUeeCd5h1E+YpSPiKs1NpnCpu/16bt9BUZ4fXru6eeGv0HfUxoXERF90V8q3wCWgIpV3bvVUvbVYFMvq+eaDvBXrUibwpC6ZKRZPrSqy9dMMjfr9rWbhKboN4PgMAxxHIc0TQ3I0Ol0jIhip9Phy1/+MnEcM5/PuXHjBoPBgKOjI0ajEb7vm31VVcUbb7zBj3/8Y1PSoYG80WjE0dERt27d4u2336aua1PeoFkPv/Zrv8bNmzd5+umnuXXrFr1ej/39fTPOOvDW4BzAnTt3eOmll3j33XdxXZfbt2/jeR7Hx8fmnDXZBnqsdCK9qY2gWQbNEgnNLtAskGYZ16ogabPMRy+vGSeagdKcG7o1z2HT4aK5XHNuNdvqdq7Srgw0rNqjNC8AfSCantGc7M2OSSnx8Pjl934ZZ9NhLMYM5ZA8ypmICSftEx76D/mB/QP4itrvt+W38X5BBf9DZ8j02pRW2eKR/QhbKnVlPVjz+VzZI1UBjuUw788ZhkMqv3FTqcFJHIIsoDPssFPsGEV9xIJC7GSkQcq0MyX106WMu1u7qiyhbLPHHjtHO1QnFbc2btHv9Hly8oS6XVP1K+7N7zFzZ+oGeJlopYDKViKPiZ+sX0YqAMErPYI6wK1dqKGwClKRUliF0aQwWahztiOkyko4qGOXcmGf1AQjKJgy5ZF4ZEonmttwcPDwaNOmX/Xp130260326j2eq5+jZbUI/IA8z/md3/0dHk8fs/3sNkVYkHgJqaccNZIoIXETMnfZXkrU60UHJZLcVsCLEGqZVfEoW9pEdUQoQ9JZirAFfuQrq746UbV+zW2fE7xqEKN53OtewE3NKIsHfTPYvmpAoK/feuX/cFqysbr8ZaDEatOsjYuCBr3MJ9jmUiC1hlkAa7Z5RQDgzLrn9X3RD6kQogv7q68BDSKd69AhVflDE+iQSGP3eJWmAyjLspZAiU8LSJhsaq2yO5NqAhck9ywsc617lmcAvYKChORnq2/wKVpNzYQJk3rBkrggnvTxGVgD5e0ulIaMlFIJ78pEWbTJlISEguLTOSoswFohFfXYsqy15Rbmu/Pm2sp80s4+6+7LNqokwQBk5wEhze1exHr6ads6oPATrX66gfPGXpdvJCSM5ZiLpqGNTShCIicisiK6fpfADvDEQmyuSonLmFk5I6kSsjpTInOX9FGXcxjxTqQJRM59fl4y1iUlZV1eeDxmU5VykAidECdxIIbcy8m9nMqtTp9rmnmAKjcsKUnsZLmMY03TYE5kR7TtNgN3wKazyRc3v4iNTVZnDMsh++k+w3LISa7s/ObVXJVByVPgNCdfBrXPaTa2cltY3PO0s0YplQNFJSsl8ryu6Xvuwu3Bsz08xzNsFi1mG1eK4WXaJQQrW9rKFjxXYoib7U26YZeTgxMGGwMm8wlhNySVSrgxljEZl9uG5jInL3PGjAG4N753YX+MpbAISV5KEE8LxERgz22VcCkcxe7IHLzCQ6TC1KbrzLfv+1y7do27d++ysbFBu93m8PCQoihMNrIoClNrrpX7tSK9lMqe78aNG+b/mtpdliVZlhkGM6jkXhRFjEYj6ro2ivvNvzuOw8AZsGFv8PTTTxvrPb2czmDP53OklPxP//P/xPfe+B5EsPv0Lp95+TMMsyHffee7TKsptV+rEo92Qeor7YnSXaNhJpVVvVu4+IVPN+3iVz4uLoPegDu37xhmWilLkiphVswYlSNO8hPG5diwMWpqU0rwMH6o2ESLeafP9WpzhKNKR5wuW/YWz3afpe20CaxAAeaL5IcuC8uqjGkxZRSPuF/eN7aZWb2cFPhnn/1ntIO2iaV0nX0zY93M6sOysCKcujE03Rz0HGm1WsYqshkAN2M6DTJoxwb9vd5uk1EBp0lmHSzroF6LQWp2gGbYNMtBPM8z39m2TafTMX/r9/uGDTGfz3n06JEBRLS+it7X7q6iKN2/f9+UB3z/+9/nmWee4c0332Q6nRoQoKoq3n//ffr9Pj/4wQ8oy9KUdei/N8sIDg8PzRjdvHmTo6MjptMpvV7PgCQnJyccHh4uuUs02R7NcdYil003CX3N6uWbLJJVNoMebw1E6LFrln5lWbZUoqJLMHTyftUVslmuo9squ0a35jFd1j6xRkOTLqMHUu9Ud7pJ72murw/Glz79sk8/73OzusnAHZCmKW+//TZb21sM9gb89lu/TXAtwN/1ee/4PepOzWPxmJObJ5RuyZu8CRvq5S0chER5hLvn0i7bBGnAZ0efJcxCrJnFNJny0egj0iDF3/GxNiyqTkUe5Rz5RyTeil1kaeNnPq2sxdZsC6/0kKUkCiPc0CUhYW7PeWg/ZLg9pLxW8iN+pMYhshRVC2XRszHZ4O72XeaP5syHc3b2drB6Fm89eot99nH2HBI3UeUZl4lWClUrmlgKpT2vWbWlVPNLZX/l2q4BI3Rm44zA2gXZGVcqQUhNIde01VKon5iYA/vg7MpSvXR4gQe/ANbEAg+6eZetdIv2pK0epqgbaS1qUidVDh+uAiLm9pw0SMmjXNWzOoXRrZBIIwq4OmaVqJgKpTeh7U8RLI2xI5X6tkgFLbtFFEWMp2Plp+1Dbq9xCjkPkFhdbt35WwQvwFkA5TxgYl3T2S0pDGulyBfn0V/Z1rr+XMS4WMc6uAiUuCzYOW+/q9THdX9vbnfduucxG67Sr0UmccnmbV1/Fsu59qkGwLoARtsQ1vJsYJtV2RK74CKnBJ2NVd25INj8BK2mVnW65Gct1dbsfyHFaPpaUJyre6D7+YfVMjIO60PF9rigCQRtq03XVhRXHx9ZSGzLphQls0LpYkyrqdIYWLXatU4ZMVcFYjRgi1wAbwtwUGeQL9pOhRJhXGVerDYHB6u2qKsaYQlD3b8UNP1p2s96e2c2v3zM6+Z8RaWApCJRjMALyDrabaXjdOh6Xfpun7bdJnIihBDEdcy8mKt68WLGvJyTVMmV2EiGLcHpS7m2mL3SdbByj5G2pLSV6DARECqAXVQCJ3WwC5VY6Dgdtnvb7G7s4js+daXKAk7KE47LY1VOUMVnrlMD5pQJx+WxydL/+ujXl5bTpSGe8NT7Wd2nbbXp0GHL26LttHEdl9zKmVUznsyekNgJUzllXs/JyEwJlAZBM7JLWS3Upz9a7NG2bISjEgi5yC91pbGxFbvLVlau+v6Z5RnCEqpkS2bkbg6uuofMmas51IVH9SMIYVW31BMeLaGsKgMR4EoX3/JBQlEWWK5FWimNnYSEjOxS1kwlK8X0IlYlHP1LxkcuwKjSxikcelaPm52bdDodHvmPiN1YlXl4G4QyVPM8jAw9XGdkm+UZlmWxsbHB7u7uUl29ZjLkeU4QBGZZLV5oWRZRFNHr9ZjP5wbc8DzPMDF0pvT4+Hip7ltvW9e2TydTNtubhGHIr37jV9nb21OAx/cLfvjDHwKnAY5heCBxOg5EUAUVtV+bco46qCn9kszJmAWKCfux9THfe/y9tfNFlzrseDt0na5JBtjYSscJVeqV1RllXfJf3f2vKEXJtJwacGBcjJc+h7kSgX2QPjCaCOucRgIrMPoPt8PbdN0uLbuFL3zF/rEcHsqHfM7+nMlQf/e732U8Hi+VLqzS11d183QA6XkeX/jCF7hx4wZxHJtldXC/WrbT/EyShDRN6fV6xk5Sn+dmVhzOZsCbwIP+e7fbVdfBAtzQczOOY9rtNmEYcnR0xFtvvcWtW7dotVpGLFMHwd/97neNHoHe7u7uLlVVsbu7S6/XY2tri0ePHrG1tUUQBDz11FN861vfMtl+Dahp9sLt27cB6Ha7fPTRR4zHYzPnHz9+bIAzfSw7Ozt0u12m0ymz2YxOp2MCd93n5hisAg1NNsi9e/d48803DXthVfOiyVTRxyuEMADDSy+9xPXr188YMQghjK5M04xBl7Y096O3q1kRq+UZTaCiyWr5mTMa5vO5qbNZ1VxoImFNQGFVAFIIQe7kxG0lqudJD0tYBmkD8D0fUmhNW9zeuE1v3KP6ccXOzg537tzhtddeo7PV4dbnb/H+8H1OqhNGjIjdmHlrzpF/pB4oiyakwM99nKmDHEn82qdbd+mNenSqDu26rZB9N2PuzBkzZu7OVS1kmDMLZ8ydOZmdLW0zqiP69NkebWPFFruDXYQQzOYznMihCAoOggOe9J7wE+cncAe4o9ZtyzZiU+COXfZme3iJh5gIWqiav2Az4En+hBP3hHl7TuqnKuhdZzG1ptWWEp9MnVS9wKxpFhYBAT4+3oKukJOTkpKRmZdiTYW9KEttYxsxNsBkgyoqBYyIxIgsvs/7y9tZZJbtylb2prlPkAZEaUQ7btM/7hPkAWGgaqyyMqNu1RRhoZgRC0Ai8RLqds3cUVTR1cBUSGG0CPSLfymU/zMup8BNZ3k9u17QPkslYilzFUCUttJtyJxsuWzmIlqttSYoO4+ivNrWZPmlOK07J1yzzlXAhauCEuf16aJ1z1v/PBbGeX26yro/TbsElJBI8uryeuBa1ggEjqXq0texFrS2gAYk1m7nEzAMLgItPk3T+7+qW4DOBku53vJWAxc/S6G/qzSJVJ7m9ZSHxcPzF7QVMNuSLdzMxUotinlBVSjHlp29Hbyux6xSglpxHZ85jmaAfKVrQrd65e8a3LjgfJaU69lNa/bpC/+UsbbQrtH35k8FYJ3HSFpzz/sk224yhS5bT5cNafaHPo7mOampSWtVdnNSnFywNbU9RyhNiU13kw13gy1vi77XxxUuhSyI65hJsQgoignzaq4YE/JyVpLuq54jZVWer6mggU1LIm1J7ql7TkLCWIx5wAMWifOl/ru4BHbAlrNFJBTjIxCBKkmxAoQU1EKBjcNqyEl1ojQk6lNgQpeG5DJnxmy5bzlrGVN2aePjE9kRG/YGbavNTrBDS7SUfpRtMS+Vpsy0VAr5o2LEtJySlumpLtLiLXSpPPGCtiRMqgVkhQKAkzpZYmGYTVksAfSu5RrxbN9RFnESZR2e17nREsllfsq0aoy5RK4dk8AKCAgU48YKoADHUsF+nMQIV5DUCeNcAUPSOcvYXH0eSUeJgevS2/16n1fHr54/PkLNh7bTZsPdYNvfpm/32XK36Dt9unaXZzvPMqpGdJ2uYp84jlH518Gnzmrr4Me2bXzfN7T04XBosrLb29smwJJSGmG7w8NDbty4wcOHDw0T4vj4mNFohBCCVqtFp9MxcYUW3tOtGdT6vs/GxgYnJycUs2IpKLt+/TrPbD9j+q0z0djKmSMRCXPmxpkj2ow4yU44SU8YF2MO8gNT+rDOQeNXfv9XVOlDo/yh5yhNhuvhdT7vf56IiJ7bYxAM6DgdbGGTVIkSYSwmnGQnjLKRsa0c52NGxYhhMeSD5ANG+YhJMaGm5tf9X+d//xP/uzkHDx48YDAY8Mf+2B9bypTrDPYqPV4H4bq8RQfxQRAs6SeY8rJGsNn8PggCo2PQFKzUy+j+NUGPZrlA05Zxtc5f/0gpiaLIiE6Ox2OOjo7Y3Nxka2tLzemFSKEWcNR6A7ofb7/9No8ePaLX67Gzs2OAsmvXrpkyjm9+85v8g3/wDwzzvq5rbt68yde//nXu3btHr9djMBgQBAEHBwdMp1P29vY4OTnhhRdeoN1um2OMooh+v8+TJ0+YTqeGDaQ1LcIwPMMu0oF609Wjrms++ugjhBC88sorBqDQ50KPWZO90gSZbNvmqaeeMuvpOHoV4JFSOV/ouaEZRrpcRl9HzXnTBDb0fvWy+ntznV3Srgw0tFqtpcnSPPAmjabpPdtEWPQN7EnvCa/eWNwkJbTqFhtiA7/jU92uyDoZUR6RhAmO7xilXd/3jS1K222zww5VWrGT7DCbzQxiKoRAeIIiKoi9mNRPmTkzpvaUaX/KUfuIx/7jpZeksAyJiohW0cKLPYIsoD/psznfpCu7ONIhlzlFVJBHOWO5ACOCjEfOI5KthMfBY5XVVgCS0jiwAvpJn73WHuPDMVVeMdgcUNs1H00/IgkS7vfvk2wvB8ZBFRDlEX7qs5Vu0Zl3iPKIqIqwpU1hFUztKeNwzNgbM/WmpE6qbAGvGITV1Ma/97wmEAaIsKUSRytRD7tCFubFSL/AXhTUiloopwadrbWksVSSllS6E27JPJgbF5Hm+iboL5SoZpgrFks7aTMYDmjFLW7fvE0URWRlxrge89bGWyrgmE+RtsTv+UyKCRMxUWBUo5+WVPTyqlpkCBf9q4QCSzJvATQF64/NkpbSjKhtKFUWQrqnbiFL9dtXyLZfuV20rYu2sy6QvygjetU+XQZMNNs6UOIypsPqti9jUVzWLtrWRaDRuZuTZxgPTTBAv8zq75eC1JV2IWNg8dWVRDH/gJpErs3YNJsGLnSAeF4w6QrXAIFLgcIfQqutWmU4A9SPvv8IlMtQov8rCCz1At91uvS8Hr7tU+YlJ6MT7MBmVqp68JmcnZbc6fPRYDQBn8irvtnMfLrkWsvIllyNzmsenrE2FVJZha46iCwdx5p9XdZf1YXzO3HV823Khq6oz9FkIMApMNH8V0ilnTDP5zzJn8D8/O3ZwsYTnlGq3/a3GVgDem4PWUmmySLjWU+IrZhpMVXlPHVKQaGcFc4/uKX7nyWsS8EnAxBUOZPL6gdYPNMtn8AK2HF3aNtt2k6byIqInAjfURaPs3SmZo+dMSpVcBRXyyBbRaXeIKqYo+oIgB+mP7xw/4EVYFUW3tzDzm3s1IZElTYIV7C5t0nYD8lRzIlYxqQyJa9zMw8L1Pm6ypTRZWOykMhKqrlgLzS+yCnsgkQmFzIvbGxj4epZntKFKhVDoKIyjLWSUgFcpIyS0VIfYHGPz82XBvywSxsypU/g1EqrSutgYIEVWHhtj5SUpLq81K2QBUWpBB0fp49Zm2t6b3mMfMs35TV9q8+GvcFOuGPmttYTGFQD6qRmNpsxGo1I05TxeMx4PGZra4vbt2+bwC5JEra2tvB9n7IsuXXrFlmW8dFHH1EUBTs7O/zpP/2n6XQ6JqjRgnQ6UamDGsdx+NKXvsTLL7/Mb/7mb/LBBx8Y1XwhBC+//DJf+Yqqt24mQXW80gyo9H6awovNoFiDA+N8rJwyKiWMqbUlRvmIJ8kTfpz/mFE+YlyM116fHafDhrdhBCJ7bo+O02Ez2OR26za9/qmYZNfp0nE7SCTjbMy8mBs6vtbo0Jn5ZuzVDOqb7HLLOrWi7Pf7SxT3JuOhyYKAZX0PUNp8upxAj+0qO6YpRtgss9Ax32rWu8lS0eUBGsTQ51prFEgpmUwmRFFEWZb4vs/u7u4SANUsZciyzGhDaAeN559/nt/4jd/gF3/xF3n66ae5d++eYUf4vs/3vvc9Hj16xPPPP09d17zwwgvs7u7y1ltvkWUZr7zyCi+++OJSUt22bba3t7l37x4HBwfYtk2SJIblo+Nl7eigx0THyXoMyrKk1WoRBAEvvPDCuUyBZrCv/6bHYHU+NJeHU/aILl/RZTir574JODTnSpP10BxzfS6v0q4MNGhLy2b9SLNDq+jJat2Qrve5Ed/gqY+fou7WnNQnTO0pZbtk39pnvDPmQ/9DdYPdhdfl64RliNNzeOQ8olN3SG4kRBsRh9Uh0jod+DRNSdNUqbjmNn7l40mPuq7ZqrfI85yDgwN832ewPaBqV6R+ShZmxF5M4idMvSlxPybzsyWKvV/4REVET/bo0yfKI/aqPbbzbT56+yPI4Qtf/AIH8wMezh/ib/vEXsyD6QOyMOOJeMLJ4ITczZUzBcBNpRXRqTpszDfwKk89EKUEW+kuxHbMk+gJ77vvL+lEOJVDq2jRKlu08zZb8y2cmdKd8Csfv+MTOzEn7glpL2Xuz5laiiJ8huJ/QZNIUlIldnlOEGsolwswQmdDM7KlzI1W71+iqq+2WgXt2umgtuvT7J1Y+GXbFVmQqZeqNcGyI5UVpV/7jJ2xOt5mtt8Hv/IZ1ANlU1Qomuqhf0guclWTzal45hIYoX3FpWVACG2fqft2zkCesioqJS6pHQ5qocQ9K3cFJKoXAcW67NdFTIVP034WQfmn2eanBSUuWm913fOysBftR3//MwrYLwoSPknwtcReOA8oas7XRoB1lUzhp21XDSQv+vtVfdgdTl0MPomF5idq55x3iSSpE5I8UW4CTYzW5pR6LRaghAwQuUAmkmJaIAolzOuGLl7LYy7nFF6B9M6yQHxL0WjhtL5Yj1ETZNAUfmMF+inmrA5UL2ta18dklLEVq0qWprxmXU37Jz1HP8uSHO1S0dj4hU3PL0uoe7OUcqkcqpKLEo464bg45v34/XO3pTUgfOHTd/uERcjkwQQv84iqiOeffZ6bz9xkP93nuDjmhx/8kHk9p/ZrpKtYDVcZAy18iTy9zi6652i2x6gcXbptUCBgaIXsuru0nBaRUM9NT3gIKciKDMuzmFdzKq9SgVk5JanPlnemtbJyXJtMEDBkuFQWIxAEIqBn92hbbTa8DYp5QStqkVUZJ5MT4iJG+EKVO5KTyYxSlgbkzFElE7gr+1vzjuxKFx8f3/HNdWVYQLJmVs1OgdB1j3u5KN2xPaOHE7iKVVIWJbZnkxYps3xmEkOVU4GjgBsNoAi5ePbrPjb2JRBEdqR+nAhPKGawEOq+mJWZuk+VCUmVXMpOq6nV8os5fZ/76g/rybDqnU+6uAMXp3TwS5+wDukd97hT3+Gb/W/yXOc5k8nVGd6trS1Go5EJfF544QW2t7cZDoem3OLJkydnMt51XfPlL3+ZP/kn/yRSSgaDAR9++CF/8S/+RTY3NwFFeV+tGW/GIU0xwmbJgP5bM16JhBrXvWDP6AsEQbCUvdWJ1LpW82NaTBXokI+ZVBMDQDR/3pu+x6gYMSknzMuzaKYtbANIdN2uASnaos37m+/zSD7i+tF1IyDZtZVdfbM1Sys03b8Z5Gotg2bw2Dyu1fgNWLI/1MtoAGG1PELrLmhAwozRYnktNqqz6asAh/50XZeiKHj33XcRQpigvtVqLQFImhmwvb2NZVns7++b8ov333+f+/fvGxvJt99+m1u3bvHOO++Yvh4fH3Pv3j3u3btHGIYGlBkMBszncz744AN+7ud+zjAT9DFrPYT9/X0ODw85PDzk+Pj4/8/en8VKkqTnmfBj5rvHHmfJtTKrstau6uqVYjebpFgSRS0UuAikCAoc8JcwAnRDgOSFMNAAuhoMRreD/0Y/MDeaGYgaYgRiRBGESFFqimzWiGSzq3qp6tqzKreTZ4k9wne3/8LCPDzixMk8WVXdbEqyxMHJExHubm5u7mHf+73f+yKlZDgcVoyKOgMEqJLx9fP2fb8qXzLggfms2bbuDFIvvTDgwSYjpT5PN0tbTJ82SyHq13UTiNiUQthkVjysnRtoqCugbrNVqb+2raNGzEMIoZ0csgb76T4Ae8Eeb998m8FgwNPPPc07R+/wweQDLjx7gYP4gOP8mMXOgvvWfRbPLXhLvKU7dVGzEYI0wI98/NinkTV0SUTexE99bf+otHLuYrGg1WrRaXZ0X5eUQNNfI1aTlzl5kFN2SmIvZuEuSPyEpJXwrv0us3C2Cti/CHZm8y3rW1qcynG55FyilbW4PrrOznSHZ/xn+NqffY2kTHjsk49xlB7x9fe/TuRGyCckJ+FJFajWM99WYeEnPv2oj5drgZuKBisLUjvlMDhk0V6QX1plFmUpCbOQIA3o0+fp8ml69OjSpWNpV48JE4444j73OeZYl5+wODdl2rQqoyLS+ounAiFR6CDbElbV/1N+2AK9uLLPyP5vfHbb35VuhLXY/jmF1qkgY4bOOpai3E57Ntml5WulLDXgs1wIIE5nlEUpsHNbn6+UKLliRSixtGg6y36yRu2USmqmxHKlkYuc0l4PJmS5VL03i+lNSvV5s9vnzdw/ahBzXubBRwElHnaODxjnBzIlPgSb4TztowRRW8tu9E63tvMACwIdMFjCquwiC6UXvA9jK5zZr4cc71G3qbfzOAcY9oRQKxebR2J+fFRWiNLPhZhYa6Z46Frs5X5TUs2gWH5WFHrh3nAatNwWgRXgCB0VJWXColgwzaenmB4CoTV4lEORazvovMircretwKeqCdc+om2rEhu6Psv9bWvG/tCzvJUNIlq0s25pun34HnFuKKog61R7xOtYza86s+oBzcau7huFqp7FJtg3GgnzbHm9r662fY/3MDEdoMv2cjQgNZG4icvV9lUuNi5yObzMU9efYlpOK2DiJDlhnI6ZZlMW+YKkfLgzB6wzqc5TrpKpjKzItOjsWcsD8/UfaYG8HXeHi+IiLadF6IRkcUa/2yeOYj648wEnkxPCfkgiEqb5lNIttzp3KZQGdoqIQTHgg2yZqJlrxkfplVSx1nI6SSVxcgeZaqtMWSzXoZYuZzN22d7Uo3RLvI6H8hWzYkZSJnwx/CKJSDTlvdDslLma8z9Y/wO7/i5FUTCLZroMRQw4zA4ZM2YmZ8yFpufLQLLIdXnKJKlZfRoQxa5OUK8limUiAwsxXVr3CsAFv+VrNoOKqjGZF3PmxRxSKl0KM/+2gX0CQShDAqmfLa7lVpoyhr2UlNrZJ1UpL1gvaHcMpdlZxk7UWAgLIVhYC83QqcW6X8++zm//6W+zF+zRttq0nTYXmhdgAdfVddzc5f3sfeTjks6THRIrwbXcNXs9E4TWs7/f+MY3aLVaPPfcc5Ud4eXLl+n3+2ulA5vZ2HogVQ90AQ4PDys6+Wb9vAmgjJieKQWpf3YzKPdzn0AEXJaXwUWDX6yywEbs0PM8sjKrWBKGMTEtNFhhNCAGiwE3pzeZZBNG7RGFKPj3r65rq/jS12Ucji7lqMo7PP275/bYDXZpO23s1EYtFI501vZhhAbr41UPIM2YGZvHzVKLepCaJAmtVmsN2DH7NG1bCUX9GhpGjGVZVQmC4ziEYciXvvSltQDeHNfoOEwmE373d3+XyWTCzs4On/rUp7h48SJxHPPrv/7ra+UPeZ5z6dIl7t69i+d5/OEf/iE/+qM/iuM4/MZv/AZZltFsNinLkul0usZ8EULQarV46qmncF2XT3/607zyyiv8lb/yV7Asi+FwWM0VpdSa5EAd+DHjYcbOMETqAX+dbVNnlBg2iBHXXCwWawKbdZABNLOhDhBt3jebLIq6GKX5jClhqZfYnKedG2gwdSjbKBP1ztaFSurUJ3Nyx/4xmZPhCKc60TRNK8VPRzq08hbXy+s8Wz5L916XyWTCM9YzHB4ecv/4Plc+cQW6VCDE1Joyc2cMWoN1u0q1LItIQ8I0xL/mowpNoav0GZRVDbrRirAKjSw2RAOrsFALRTkraadtXNcliiNUUzGzZnzrzrdIw5Tm1SaHHHLSOOFd712duV/W+7vKxf2kS5AGDP0hTuFAAq2TFu39NovW4hTIYPqf2prep4SqfLPr73upR7gIsRYWVmHhSq0wXIiCVKYM/AG37FssxCr9JpSgJVp06NCjxzWu8SIv0l3+8/CYMeNQHHKgDjgqjxgwYCEXJCo5vWjfbFtAgMoH2XwJbgkYTabfLNQLWZwWyNwWBGy+Zij4Z/VN6ExXSqoXN2cEoxZaaNEqlwq/y4WjstTpa2U2k4rM3ViIb+6/BJlLrMKqFv1KLsEXWVbgxJmWqcr8Wi4SFTrjYXHqjnZLvaAoiiX7wtqw6KyPy8PaozAazvOZs5gHjwocnNXOC1Kd9fpZ841ltlJY1Vw9S3PhdJc+xqD8YwBBFErb6j2kWw66FMyVmnllAjuzQDU2d+c5nu76IwAOjxj4V+UpD5lHJuNcqmWGp6bf8qBWF+zcyqqoP382n0vrHdW/rFV9/DAZPqDLAlvZkOkyssAKCJwAC4t5Nsd2bLAhUqfp1b70CWWIZ3m6TEVowDortY3kNJtuZZWYQLpSTt9kCJzRTICd5A+3WLWwsKRVgROGsWJYHA8FvIS+5gbQMBn+oiy2KsV/nG3TBtUV+v5IVbodGCyXuiWWdVpfQgIuKFdRNAoiIt5a/iMCvr3albHydIWr2QbuBTpWhz1nj113l57VQyKJhM5Sn6T6Z5JrjYm4jKvA8TztUbRgSlXqAIopJ/kJItLXojjQdoCEQIjWggCwdbKmSbMC2gyzJEt12WEmMl1Okc+06KWVn5qHEokvtMWnUqrSbDBWiMpSmjm4/I6MG9ptYMoUmUlaVot9f5+RPaIhGly2L/MUT5FNMkIRku5p21Bf+VyQF3jSfhLHdmg2mwghePfdd6uA4vOf/zz9fp8/+IM/4Pr16wymA964+wbtx9u8efwm/+Gr/4HwQkjmZRwtjhikA6zAorRKVKzI/ZzC1c8wAzCABhUaVoOG3UAWkjzR7ibm+VVQkIqUTGhHIfPMUSjmpQYnLJbrDpNAEafBiW8V30IWekzadps9a4+W3aIlW7S9NnvBHiGaVVEJwaMYR2MWLCi8gqP5EYN4wJ35He7P7vMHkz9gnI51IukJ+I/v/cfqurUcfZz8mZx5b46TOdriMnOwEq3f5dxxuPSJS3qtbL5HhFiLMbYJJdYzt0Znbj7XwJ/Zdptwfd1asF4OYK6x+Sxo1wQTmNWPv1nGYfpZFAUtWlyVV9dq+WFFiTeBfVmWfOWPvsK33/42P/OLP8OsnDGIB7qcSS1WJR3ZiKP0iLdnbzPKNLti27P6kn+J//3z//up2My02WxWiQjWM++bAEEdlKgHrSbjb8oTTFBcvx6biehN1oUpiXnppZd48803ef3117l69Sp7e3vVtQNt1wpU7IObN28ym83o9XpkWcbv//7vr4memjJ7M8ZGhLHf7wPwB3/wB3S73WpOTyYTfuu3fouf/umfZmdnB6XUmo7CT//0T3N4eMiv/dqv4fs+zz//vC7LStO1kon6ONQlBeolLMa6c7N8wTB16mwRI/Rqzt/MQdPqIMVmicQmqLDtGtf/3pzTdSeM87ZzAw2+759CB81Jm05tioKYwTEXuSgKvvbM15iHeqK4pabbd0SH4MmAdtTGCzySRoKHHnAjANNoNBBCELj6S9VObfpJn5OTEwb2gDvuHbzYo1E2sKVN4erSiLkzJ/Ii5u6c48YxkVPTQ1gCEaYMIYgDRCDwI5+e7BHIoFJfr1uvCAR+5uPGLjt3d2g0Gnx6/9PcvnObNE15/InHuTu+y+3pbURPoDqKd6fvkvgJt6xbjNoj8h9YfcFbpaWBkIWPUzhYhQWFPmYmMjI7Iw9zEpGsAw0CMjvTX06+qsCITWeFjuqwr/YJCDTdEaFFj0TKiBHv8z4TJmsLiZCQrurSoUNf9bnBDbpllyAJCFVISsqR0IyIoTVkJEfM5IxYxNsD2W1gAOt/l6JcKxE5ta4RGiSpSivERhZfbQQwZ9HLzW91xnvLZpgHudgihmm2UytmgdmPYUmcma2Xy884D1iw11ggslxS4IVeMJWypJBFVb6B5Mw7ORWpXiTWa4M3xsVTnvapFssa+SUrZKvl48fJkDjPfh/GPvhzAiVMIPTAbZfNYkURNFmnjxWU2HKffNwtI9PaEw+YshKJJ3Stb8NuEMoQRzqVevei0Bm+pFxRmx/azLmZjz4qA+aBu1bbhW63NAMuGFDpPAGaJbR7Rxqnq347rD+DHqEptLUhrg5wE5KVvWFYfWi5a4ErNX3bk95Kg0GIyh5wlI5OBeKe9GjbbRp2A9/yV6CSWFKy84hFsWCcj7cCSwbEgFXZwsPmu3EpSB/k0crqvKpn7fLwtm1rd4FlZvY8zRJWNT5SSCghV7lmXGwDCc7ZHgRsmO8m27J1FnfJ8ihKHYBvvR/KVZBjKPKFKtbKaeblnOP8ePtBa/NBKqntsZVLW7ZpqiZd0WXf2+epK0+RJRnTdMpxfMxRcsRczJkzZ1EuNDBBeq4SJyPGafp/Vr+EWJYpFvp7JpUpMzHT5SOU5KWe42uaNwKwNfDplR5OoZlAFBAtIp0VtCRpmWK5lrbRDHQp6mazCqsqPwjsoLLjTMuUhVqQlql2JymnZCKDg/XtJZKGbNB1u7SdNlZiaQeD0uO1d17j6vAqB8kBs8mMkBAfny/sfoFnrWdZTBf8le/7KwRBwDvvvMP/9Rv/F2EY8rM/+7P82r/5NQAcz+ELL32BZz77DNNiyqSYkLkZo3TEIBnw7v13uRPfIZYxKlTMytmpeSuRFZPBk14F5hVFgbSl1p8qdKnFNJ+eElUdF2MW5YJjcVzN13JSngkA2sKm43TYCXa0a4nd4snGkzxpPckTF57Azm3efuNtdno7XLh4gVKVLPKFFkVMh3x7+m1tfd6Myd2czMkqJuebvMlvvf5bWsD3BySvvvaq1jhYlhh0nM7qb0frInQ9ndXvOt1KlLPuDhDHcVUSYACAtfHbEqTVM9EmGKuL/5nt6gH4JgPcfMa8p5Sq+mICREOVL4oCS1pYpcWVxpUKBKizEKrbo7a/UpVaiHXJnDiYHDBMhmRFVsVTdTZCXQ+jTuE3zZRPmHOqZ7brsZ8plzAslToDoh4Abwu2TclHEATM53Ns2+batWt85Stf4YUXXiDLMo6OjvA8j263q+dpWfLVr36VDz74AKUUn/vc55hOp7z55ptIKTk6OqIoikrnz5yj0cCwLIvxeFxZyA4GA5RSPPPMMwC8++67/NZv/RY/9mM/xpUrVyp9j5OTEyzL4sqVK7zwwgvMZrNqHhhLyU0gbPM6mRaG4SkGjpkD9bE218qwODzPoygKoijSsgG1cpQ6U6T+Y9omK2ETiDCABqxAjDqJYBsL4qx2bqBhUyiifkKb9Tnmxq0LQxqg4Qff+kEajzWI/Zjb2W1ea73GgTyoUO5XeRWe0Me0lEXYCmmpFm/wBt2nuuxkO6R+iohFdTGHzSFvXn1zLQi3CosgCfATnzAJ6U/6XEmv4Gc+trAprKIqi1i4C6bOlDv9O+R7tYWnAi/3CJOQZtakJ3p0VIdQhPRFHzdzK+0KU8dUliVSSLzMYzfZpZf0CGch7jtazOWpp57i9p3bvPLtV1BdRf+JvgZCnDkLZ8HMmxG5kbZxXDZRCrzIoznT5SBu6WKXdpXRzq2cyIlIvVQ7VMjVYicXOSNGlfuEcYKotwYNLnKRBg0N8CyRigyNit8X9xmLsd5uqXfgKpfO8l+7bHOFK7TLNu2iTUu1kEgOOeSAA27FtzhIDshbOaVbbhet3Ja13KTLLwPttS/T+nbLzJYpPYAtSvCl3kZIsZ1ObY67CVKclaE0AMk2cKW+fb6kRi7tKJHLTIJVrKzpNvatLA0cnZkpXvZLKIHKFJRoz3FLkJd5Nc4V6LJt7Sy0aNypJ0GtL6IQOIWjQTC1Kn3JZa4zS3LLavJhz5/zghFnATzb3nvQPj9uYOIRWkFxLhr22jyr9+c71K+Pu5WUmua8tNI7q9nCJrACLai4XBAGVlAJcc2KWaVOv8gW+rlzXoCBR2RLnLNVbiAP26W5J4WonHdOCcjW+6q08r0rXQJHBzs5uRaYW9aabz3GQ8ZCoTTTpEzOdB6q+oDWXnAttwKikyIhKRLSImVezleB47LZwmbX3qVltwitUGtKYFWU77RMiYuYeTGvqMDb3Dps7FVmtVZ28KDzqkrPls+sguLMgNYAE5awKhZOqbTgaFRoSn79s9uOLZHaTcDyK3CipNRsniKpxvlh800JBdY5NTGWoLljOTjC0UESemxzpefHtrlhHKDqZVAV4LPMXKekKzYB6HKIm6fHzRJW5VzRt/rsODvsWrvYiU2/0ccqLebFnMzOGKQDpkwrmv2iXBAX8dnOHKJ2La2Vu9UiP0OcWlEFrmSgcoVruxQUTKYTLRZpFZStktLdXiplZVYlbl0kBZawCPwAiSRTGYmVEOcxs3y2Dqgs2U8N2aiCdle42vJYLcuCKZmWUwbpgHkx5+WbL7N4d3kutUeh/B1J024iHhO8fPAybaeNVJKDFw9wMoffn/w+k8sTncUvPN59511++Pt+mGu+tt/b39/HcRwWiwVfj7/Oy6+9TJ7n/PIv/zKO62gBw3jAcXTMMBkyLaacxCecRCc6y51rK8ZJMmGYDE/NIVNi4Utt52ljY0stzoeArNRuHIlKmGSTU2uTXOV6/BZZtY5kDEmekB3VwImT1bg4wqnsHkur1Gv3SYCbu7iFq/VALJcbT9zg+Ree59Vvvso7t97hM1/6DJmT6Ux+fMTb07crPYRtJURtp12VFrQt/f+W1Vo5SdREL1tWq2Jr1BkRm8GZoa5viiia9zbLyc3/67HTNs07s60JVk1QbH42afx1VnnFyBBSgy+yh+M4zILZGqujniSuB8MmkDa/DZhSD3a31f1vMtvr72+yJjbBkfpYGXHI0WiEZVksFgtmsxl3794FtLPCrVu38DyPp59+mtFoxHvvvcfP/dzP8e677zKZTConB1MaY2JRw0gwgbMJpKMoWrN19X2fVqtFs9nk2rVrvPjii/zxH/8xTz75JO12mzfffJPFYsHFixd5+umniaKIb33rW/zlv/yXuXTp0tq5bytRMP83AI+U2sbUzKdNdkL9uppWd1gxc2hzvm5jn2wrY9kmAmmuxbY5v4059KB2bqChPqE2FTS31dyYzzuOg++vVlpCCLzCQ0aSK9EVPj/8vBbGcuaMxIgj64iRO9IZcjFjLrWQ4V2WXsdG2M8Fu7Rxmg5BGrB3b49Wvno4lHbJwtYgwiSYcM+5R2atB+/1soq92R4iE0y9KbmjA2IEJE5CYicMGXKb2+uZcgXuJZdW3uJ9933EviBIAmxpk4qUvNDBXq7yCkmNooj5bI5aKFpWi92j3eqBUZ8smcwYi7EWqnQ1EJGFGdPGlMRP1uj5Qgm8RDsxXJhcoFk2cZULBXQ6HS3UJOdMxZQpU+2aIebVAmDOnIhIi3txWmTNFjYtWrTLNm65XGiVOquXipQPxAdMxZRErAJWS1m0lC7PkImkcdige9TlUnAJP/FxM5d5MGfUGDG0hlqI012Qezp4XQssTJB8XjBCqPWFRn07uZrPpoZ77ThLQUop5aq2u36MbZnVev/qfar30VkupB8kxllqgMwqLJzSqeiNJqDPZb6uVL/8rYSq6iSrrKB5jtefA2qZmVV6np1ihNQ/Xx9PS5Faqd73GecoCoFd2Di5g13YWjBz6SaS2Zku/zmLVfGo7axtHwQ8bDtHHvD+n0dw/wjBNNQcXMT5ROO+V1qucvIiZ17MuZfcO/NzFhaWsvBSDzEXeLHH8089T6fZQUnFpJhwGB8yzIbM8pkONtSHEIj8OK/38ho+tA/Lt5XSoF0kNEBzVrOFjYsGmNNZisr1DryWV9HKS+tDWFai+5qh6/DnD7JcYBmAYmErmziPKcqCsRhTqpKkTNZo3qDnaMfucD24TttqE1ohrnQrDQrDDFrkC521XfrPz8vT/ajYDMvmLTxKUWIHNspSFVNm89wql4qHXhK1dhwD5JeUVfBs3jegxLYMciADQiuk43dQqcJzPUpVcjw9JikT8NAgzoPKOpZzyFgsPoz8Y4ABR2pgwoANpVoyA1RyCiwy/XWlu8rAqpKiLCr3lxzNfhkw4FZ+a7Xh6HQfLCwcHBp2g4vuRZqiSd/ta12DecFTjz/FPJ/ztde+xkzNEE3BIBlwb3SPwi4onfJsAUyB1gtItfWmsAWo5Xdf6/TH7VJ/F5WLkk7YYTFZ0AybOjFAqddnMmdhL4iINPCzEZu6wqXhNAitULPSlkzKoiyYllPSPCUutXbCZp8dHC6Fl3Byh7bXxpMe6SKl3+1TFiXvj9+HRJ9TrGLGV8bkds5vyt+Ez6/28yqv8m+//m9pWA3aVpt+oF0gmrJJPs056ZxgpzZ/cPQH9P0+fb9Pz+9xvXUd13Yr17bNOmvQwdcsn3E4O+RofsTd8V2m5bT6+zg6Zq7mLJTWiJkW061z1pc+DauhwQnp0Pf6fObSZ7g/vc/R/IiZmnE4P2RaTLeCYwJBnMdkRUZapBRdnXwxWhqmTPWbfJN/8+a/ARfk45KTkxP6QZ+u02XH2+FG80YlpOhLvxLILNXK+nSca0vJQTTgvfl7DJMh42ys9S42miMc2k670j4wwHjbaldOEk3ZpKVaPGs9WwXim1nsuvhiPVhbG4NaqcRmoGmo/lmWrQn/1SnuJvCrv2fo9fVMeJIkawngzex3PbA0LAWzrdmmDlZsY4DUE8uG2r/JYqgHw3W7ThNoNxoNoihCKUWz2cTzPC5evEi32+V3fud3eOutt/jiF7/I7/7u76KU4u/9vb/HZDKh2+2yu7vLn/zJn3Dp0iVu3bpVxVeGMWLEPessDsNCMMF/p9NhNBrRaDRot9tMJhO++MUv8pu/+ZtMp1Oeeuopfvu3f5sXXniBS5cuce3aNb7xjW+glGI8Hlfjd1YQb8bDOCV6nsd0OqUsyzX2gGmbAIG5XmVZrrlPxHF8aj7UtRbM9alfj22sHfOaSZ6fxYwwc/Zh7dxAw3g8XqPTbKuvMQNrLmZRFJUlZd3z1Nh6VBdg6crUclt0ZKf6jBko5Snm7pyhHDJwBoycERN7wsSeEHkRkR9BG+5zf9VhBU7p4Bc+zbzJxdlFWkkLRzkrFoAdMbWmzMM5R+0jInt9keRmLk7uYOUWkR+tAmDTBKROyolzwok60WUSKP5E/Ak8hnaWQGeorC9YOiusHKKdCPGcIExDxnJMK23RS3u08zaNsqEDfqXopT3NlFjocYzjuEJPS7ukaBZkjYw0SFm4C7JGxqwx48g7InbitbFosdJkeIInaCutWGsLu7KpmjBhrMZMxIQxusbS0HVHjBgzRlpL0SF7feUTqIA9tUeoQlzlVguxlJSBM2D29Ixj/5i3ebvqk5/5hFmIF3m0F21693u4C5cw1ZoTiZ0waU1IuglJMyHyIzIvI3fydQDgYW1bNn8zGDD/lbVMyya4YQLBcvkjNn6W+zELEqUj+9P9OIvNIZfOGs4DaMRKl9o4pYOvfJzSYXYyIy9zSqfEbtmUXqmpnltYElVmdlt51QYoUSlfbwFVjE2fGUNlKTIrWwFgm+e5HBer0PeBVVoVK6ewCjIrI7dyrcvxUYM+c+zzABLbAKI/b9DhnMd8FCG/jzvL/yg12x+2FWiB3NRKwdeA6B9Gf1jZTZpmoUUHd5wdem6PntOj7/ZpONpiapSNOEwOOU6PGaWj02r49efDx3Wtt4FZ2/4+61mw0UzQh2AtsEpY10AwNnWmdMW39II7UxnzfK5/ivm5hQNPn5aqss8oHmgLqE9PMC/mpGXKsTiuSjei4nRw1rJa9N0+zwTPaI0k2dB0b7GyQitlyTSf8trN15iqKamdnskQMOPgWz6uWLEQ0iKtWAhZeVqUsnr+P2AMzpr7JaWuhS/nHGVHGthN5QpktsBU3QgEDbtBx+7QtJsc3z0mj/SbwhYUdkGj39BrlTzSOihnAGkGsMmLXNfmP6QJRGXX6FounuUh0OutJNNsmk3QyDSjnwDrLJRCaAZPnMcrRlM9fvv6Wgew5vo7oBAFMpKEs5Abeze42LpIy24xHU5594N3ae23aO21uDu8iwoV03zKYDHQ5RBnaCzlMid3dZnRoTqEnZomxCZIjtCuX5lNx9fXot/u4zqurp0vdNlYlEdMEu0oEIntpS6GMdH0mniWR5IlDLNhtf1bJ29pwKex3GA5bW3Xxk1dvMTjwn+6oMsqQ5BNyXOffQ7lK2IRsygXzBYzDooDDcr1xqRWyp9+9U9PnVPLaVUOBT2/V2Xz+36ftt2ugImW3WJH7tB1uuRpTuImGoSxFlWQa9u2psHbijlzYismEjpwn5ZLx4V8zKycca15jf/x8/8ji8WC6XRKr9fj7bff5tq1a8yzOV/+4y9ze3ibi09eZJgOtZVkNuYkPuFmdJOj+IjcPVsLy8GBQs+9aTZlls0qllJSJkRFtPXZZoQTDWNhR+5wvXGdjtMhlGGl72IAt1SlWoi3mDIrZ7p/s5uaEVJMKtDkk41P8j95/9Ma27tOL6+7DGwG9PXMvwGFhBBEkb73TMDebrfJ85wkSQjDsCphqAeV9X2b9xeLRZWlN4L45jibme96MrkoilO2lvUsfBWbbQS/JjDNsqzaz+a+YV0bYzNZXZYl7Xa7cshoNps8+eSTSCnZ3d3lM5/5DP1+v4oTr1y5AsDNmzcZj8c8/vjjgAZspJQEQVBZfQJ0u12yLCMIgsq1AeDKlSuMRiMuXrwIaK2KGzduMBgMeO+99+h0Ojz55JN88MEHTCYTXnrpJZ544okKFLl8+XLFoqizVOpaCXWWA1CVbniex2g02mq0UNfBqI+hCfLNZ5IkIYqiCiAycweoynDqoE69X/UyHXN8IzBpSjLMtdq8Zudpj6TRUK8VMW0bHcjYl5gJl6apfmAvL67nedW+TKujcaYGxZw86AnZKlu0Rbva7p3uOxy1jpg6UxaOFqCpt0xmZDJj6k65x0bmTGmNiKAIaGZN9hZ7eBOPeBJrldleQN7ImcopM0fXvs3FfC1Tbin9RQkaWT9VliBWKtYFBYVdaCXyZYZ7vPy32USpBQjdzMVPfYI0oBE3aM6bhIuwErHM8xxiSCcp8/m8EjVpNBqUsiRyIsLLIQt3wViMGQsNInzABxpEqJ1Lg0YlBvkYj/Fp8Wnaqq0zaElK4RaMyhFTMWUmZ1XfZ2KmKdMiqhY4pta/+kIPgQKchYOXe7i5q10VhNYOmHtzBs0ByeV1W1E7tXEXLl7sEcYh7UG7shkNs5BCFAyDIUN3SNyOKVslC2dBIpLTugoPCyY2WQnbPlcDI85sBsAwn60DEmafpQ7UjX3lQxXxN/pUiIJCLucSwOXV23WxTauwaIgGQRlQTAssZdFsNYmJGWdjUifVAp3bqOkPysoK1p4Bp5qhj6saIos+z9zJyZ0z6tuX28hSYhc2nvLwlK4rLVWpgworJRbxdueOhwEM9c/xgM9ugkvf7fYdOObHDQqcd3/1DPRHstd8wL1bUGj9h2LBYXr4wL7YwqZttbWomd2mbbdpWk1m8xk3D24i25IsyBjn41MOPBWDRNUCzbOeEaaddy6ed+5u27a2jQmgh9nwzM0EAs/y6Nk9OramLIdWiBRSB6plVJU7zPM5qUo/FCgBVGyC84gyzooZURRxEB9UC5lcnbbM9KWvRbMWku68y4v7L3Jt95oWERTLrFRZEBcxk2LCMB1WP/N8zigbndIaMIJ0oQzxbb86vtEXiYuYtEy3AhMPakajomq1Z4tCMctnzHIdAFuBhXQ0G7Nw9DlPi2kF5jSshq47d7u07BbxOOb44BjLtrj+1HUiIibZpHKhiIqItNyuN6FQlZvEtmzuZjNMFoclnX5JqTfMxoKCOfOtxzLlF4EXIIVkvphTqAJhCzKRkQc5NDQb7xX1SpV4AnTCBvRrFjiJg4oVbuGy6+9Sjkvaoq31p2yXvMy5df8W3QtdDieHuF2X3MuJVEQmMpR9GiRSLBl7VsqCBWQgBmcDs1JIGqJBy27RtJqEltaiyVMNvikUQSOgtEoO00MipfUPIhWdej6YMRUILNfCdm2Gzw8RkcDNXFp2i+ut61zfva7p/CKsbG9PTk74oz/6I9I85b//pf9eB/7pUhgwHXESnVQ/URlxc3aTUaJFAsfZ6XUn6DnWki1adouO09HJP1uXFLTdNn1PgxM3mjfwlU/H6eBYDkmSEMcx7Xa7qv83ongmIEzTFFe4XAou0aXLp65+ai2IWiwWfG32NX7j3/3GKqi1CgqvoAxKwt2QL/7VL3Lz6CavfvAqV5+9Shlo4dFRNqrm/Kn5J10CK6gYF1Ee6ZI8VZAWmpWyKBZb7+tABrTsVsVo2A/29bjYLS08Ki1aTosjeUTH6eAVXhUHGTbBJnW9npwNw3BtLVWnpRsGgtElMADEzs7OKV2DeiAKrAEFpqxbSkmz2awsO00JhjlufXtYAQfAKdDB/K4H02absiwrcKOuVbGZGTcafJsUfCkld+7cqeJI13X5S3/pL/Hyyy9XjIxer8fR0RE/9mM/VukwGGbAYrGogAghdNmJ2f+NGzeYTqdYloXrujz2mH7AfPDBB+zv72NZFnEc8+yzz/LpT3+ayWSCUoqTkxPG4zHXrl3jc5/7HK+99hp3797l4OCAk5MTZrMZn/70p9diWwNgmDE2yfY6OGQ+5zgOURStjY/ZflP7w1xLc30MoGLbNo1Go7Lh3NY2QYv65wxAZa6bZVmVHkh9Tpjf35HSCYNo1rUa6hPTnPQ29CWOY5IkqR44vV5v7SasIzB1hKWOytTpPAbt25vsEc5DsixjMBjQ2enQuNIgDVIiPyLyIhbedt0D0OJDqZUydsfcadyBfu1NBU7mEGQBwTzgUnqJxqKBp7RIZWqlLBxd1mBEJxfOYi0IFUpglyvtcHpsAAEAAElEQVTroVwt69nPWkwaOu2Scp5bOQt/sf3zS5q9kzt4iYcTOTTiBnEec4ELNPIGnaLDlfIKbqmpOHWl0FKUTJlWTIURI0ZqxIgRd9VdxoxXme9AMxY6QmsydOlyRV2hK7RYpFu6pKSM1ZipmOr9FCOO8iNm1ozYjnXGO1xao50RNMhM4kQOVmphZzYovVBN3IRFa0F6JV2j31uFhR/7eJFHI2+wv9inJ3tYMwsv8njysSf56p2vckfdwbposfAXHKfHlcDQKcHI6sKdcX02rtMDP7vplrHxXhWsbAYaZp/LEg6BWIkyPgjg2MK+KOyCCRMm1kTPbQX3xZL1s2TmCSUIVICbuoRliF/42MomyRId2NspiZ2QyKQSoHxoQLRJH992vZegQgW4LIEIhaaSF7LQpTjbzpOlQ0muacpu6ZJPc+zcpt/qkzgJJ8UJsRVv1wPZ7Mu29z7uDPd/pe28QZlErrKk2+r0P4brUKKzVGmRMikm3MnurH9gZ3moXFQK9qEV6kW33UEWksFwQKISZuWMzM8022L9IJrRtLwXlVCr8qRz3DMPbNsYVh+iKRRxERMXMcfJGSKCLAEJ6dF1tKDanquV5z3LI89zZpm2vhukAyb5pApsPyygVLElVH76mVlrcRkTWzE09TPkcHKImqhTx7WFrfvt7bHj7fB8+3l2vB12vV1aTgvb0sFykiUczg4Z5+PqXAzFep7PGWen1dstrCrYsOXSmUMKOp4ulzDlLKNIgxoJiRaqfMA1K5yiAhhqg4IUGiBTSjHNpkRFxAEH5GiF/tzKef/k/bXNfOnTd/v03T4du6MF8oIOQgmmi2mlhTItpxqcyDU4cZbt6BqTxbQHXGZr+c/GrhxSQjtEIEhIKEVJUmib6c3vNQcHmUs828O2bJI0wXZtHRTmMcrV66MFC+jBwaZKYxuOOYa27qMvfWQisac2PadHNszoN/pc2b9Cq9Xi/bvvczw7xu/4zIoZmZWR2inK0WDMpvBrSclUTZlm07PtPmu6DK7UgEG/6HOhdYG21+burbv4rk/oh0znU4bTIfNkTuNig6O9I1I71YwMCa9NXlsDX1pWi5bVwk5tsgsZXuHhveOx19hjx9+h43a40bjBZzqfoUEDF5dGo4HrugwGAxzHoVC6bMLoOBhth0E84CQ6qbQ2jvIj3onfYZyNmWST089loGk3tb6Bq+dZ1+uy39ynbbeRseTx/HHSecpF6yI74Q7SkWQTPXCb2dtGo3EqSBVKIGPJhewCP3n9J/nq8VeJPoj4mb/0M3zmM5+pti1LPadGmRbLHKVaYNIAEeZ3/fV5fhpkc4W7YkLJpWsXBYNswGF6qIWN8wXzfL71XgmtUJdZLMsrqpKLJbBtWBVtu81FLtLze5WGTH1MjKCiUqqyFTQZcLOWr1PqN9kFsNLWM9vWmQSe552K4TZL5A1DwZQ42La9xqSoZ8DNvjZLROrxoAE3zPubWgSmGTtRE4T3ej1+8Ad/kA8++ID79+9z9epVPvvZz1YmBZZl0Ww2GY1GfOpTn+Kb3/wmX/jCF/i93/s90jQlTVM+85nP8P777/OFL3yBe/fuce/ePa5cucInPvEJXn/9dV588UXiOObtt9/mc5/7HL7vc/ny5YrN//zzz+t7pyj4whe+wGg04t133yXPcz772c+yt7dXMQYMi78OLNVLHEyfzRh4nsdwOFzTPtwEp8z/N61f678dx1mL8zaBrnrZxKZQaf149flV31f9vUdp5wYazM63DURdMKROHzInYdAjz/NwHKfyMq3bfNRrdTZFR+oDXfcbnc1mxHFMURQ0m032ensEKoAFiGg1+BVTQqwEIOfOnLk7Z+7MmXkz5s78VKY1czQVfNKYrJdloBc4XuERZiHtrM2F2QX8ua/teKRFLOMKiFg4C2b2jIW7WC85UNrmsBIuNK4LhuhxVrbM0OzFkiXhx9DVX67v14251TKbgE9TNWnRYocddtUuF7lIX/Tpqu7q4/UJjWLGjEE54KQ4YWbPGJZDpnLKW+ItxoxX7A2pnQtaosWYMXZh4+YuQRZwaXaJ4n5Bcpywv7eP4zsUXkHqp8xtfQ0iJyK2YwqnIHGSFa1wM7Nfgowkdmav3DkEzIM5E3fCXeuupt4vT8lSFsFjAV7ssW/vc628RvduFy/2+NS1TxES8sr7rzBpTZg9NuM9670HsxrOE4CqJU29ZMVUeBCFX6y2W2uSlfDlWcSCUgfqZbGcU4ZG+oDF7KmSBxSR1KDcmPGZoIAsJV7u4Wc+XuJpnRUlKWVJKpdghJNohxRro7TljED/odoCBoxAVP0WUjumlOjyoSlTfd5LkPDErPCW95CltKCZzCRWatGyWjTtJnESkwrd75k10wHjWdf6v7XveDuvA4UQogIkHkRhf+RWm/f1uv44jxnkg9XnNoQdRbms588UMpeITDvFCHS2tvRKVLABaOYaJC3TcnVOtsDyLO0owxJY37xvzL39XSrnUSgd1KcxJ+nJquxto0kkjnQIZchF7yI79g77vg40FEq7VGRjjtIjhtmwsld8qG3lg1oNzDyrzCFXOUfJEUfJUTVnzHmt70rQtJq65Mbps+vs8rT/NDvtnSpQd4WLKnWgP1MzJsWEaanV3Ef5UmSvmGhqdTbZ7AqBFdAXfbzS41LvUmUVK4TQFqNFzNsfvE2stNq+shXKVpXQcKXVAOvlKltWcMZVapgOmWSTNXvSqIjWGRboWvSe1+O6d13Xo9tdunaXltMiyzPGU03PT10N0A2SAcN0WJVzbLsHjRBqSgpyycw4S5NUob/blSR0QmQhyeKMsBmSJRktr6UFCLNEu0hsnm8hCOxACy0jNLvTlpVNdCITlKfAW5bXtmHAgLfLt2GMXm80VvuThXbPaQsdFO54O+w2d2m4DU1RLhIG0wHzXDtyRGWky2WW7J9NYCItU05S/b10Z7QEN53lecfo76qefnmsxkhff1cFE+3w9exjz9LyW9hSC6fmeU5WZpzkJxyIA4bukN87+D0m+YRFcVpM0xFOBQCEhGvCh22rzX5zn67b5XLvMk3ZxC99PMerLAZNttvxHM3AWYr1HowPNACRT5hkuqRknI25Pb/Nt6ffZhANtMXi29tLTPpf6WuBRkeXMjREgyzJOLh6gJXoJJopX/ZLn2azuRYnzOfzKuNu4g7P8rhgXWDf2z/FHqizBUxMMI30PTyIB4zzMcN0yCgbMcknK1Ai0a+NslHFPqo3V7i80H2Bv3f972nRzXTEJJtUJSEH8QFvzN5gkulx2vZd17SbVZlL11uJU7bsFh23wyydMWwP+fbJt/H6HhfaF7QLyAYlvx6T1YEAE5/VWeOGMV4PIM3fsAIM6mKRhpluAtp6AAx6bpprZP6uj3+dEb8Z7NZjPfO5KIoq4KnZbPLJT36S559//pTWQFEU7O/vc/nyZRzH4amnnuJb3/oWrVaLF198kVarxTPPPMOf/umf8txzz/HlL3+Zw8NDBoMBrVaLsiwJgoBWq8Xdu3cZjUZcvnyZLMtotVp88pOfPDUmnU6Hz3zmM8Bpc4TNzP8moFN/zZSHLBaLU2U29XGqMyE2AZy6K+Imy+Asm0rTj/px6uNZnxPbQLDzlk0ACHVO7sOP/diPVehTnV6xeRL1mhCDvJmByLIM3/er+htYFw8xJ1L/v7lZ6gNRFAVhGDKfzxkOh+R5Tpqm7O3trdWTbO7PoHubaI9Bzu4d3eMoPSJrZqi2IvIjZt6MOIxJg3Q92/CQgFOWEjdzq7KHxrxBsAgQc0ESJQzTIf4Fv9JYiP2YxE9IvPXyAZlre0OgUvs/JeBXb/VF6DlovEJp5W8fnwYNWrToqR57co8L6gL9so9IBL7nE8dxhayWqmQhFrqAQmhGxDHHvMd7LNSCXJ5tG1fV6qcOfuoTpiGNtIGTOVWNXC5zUiutrEljTy/ANrUhHtaE0pRPwdJnWqxfw6AMaBZNelZP00DjXAtkhS7zbM5c6p/ESs6m6m++dsZYm/frgfMpV4zztvNk3NUyU6wEqlwxEbbaVpp9GqBkue8zKeLm8xuAhFVYFcjkpz5Wqh/AhSzIbJ0pSh3NlChksS5u+aB9P+AczW+hxMparyxX2eSz+l/b3lIrEU6v9HALt/Jj/8SnPsFRdMS96T2G0Wml7o/cvkuB41/49ojj9DBdig+tW7H5jD0vA2qp7WLcZ/J0OY8cdCC0wYJqCU3Nno/mKKW0YNRiSiEKhCeQgbbi21bH/L3YBAJHOPjS14G93WPH26Fv9wnKgFKWDPMhx9kxJ9kJo1zraZyn7OK71Tzh0XE69N0+e/4eF7wLXPAvsBfscaVzhT1/j8nBBK/hUQYlf/zaHxPuhixY8Oo7r3ISnxDuhZXv/SgbbaV725lNUzS53L1Mz+sR2lpEUyhR1aLfObrD/cl9CrfAbbl6rMpHL3Mx4pdSaAvXB5WtSCQ9r6ftMUUTL/Noiib7jX2szMIWNs1ek4VY8Nb9t7g5uEnZ0KWVxqryQfoXD23m62j5vU6mdUkCJ6DttymKgsFIs44INGv11P1ZauDHwSFaRDrbW+a6bEWUZ9pZP0iXxpc+vtJrqAYNru1do9voEs0icnJdxhNPKJ2SeTFnuBiyKBakpNvLAB9hPKzCYifYYdffZcffoR/0CawACqpyqFJosOpoclSJrRoHiq2Br9WswIiOs2QquF1tH+n1CAmRkWQ33K3scE3w4/s++/s60B+OhpRuyZ3hHS0oWU65M7zD4VSLQy5YicBOMu2EEYlo63h4eHTdLm7hEg9iLncv8/SVpzVTYOko0XE6lQZFQza0k88yoK4HaEmSVOv+zUAb1gNKQ9+XUpKrnEGsgbZpMWWQDBhnYzpuhx/d+9E1sca1ubPcV1EWRGpVmhYLXd41SkfVGEzySeWeMc7GTPPp1nn3i4/9Iv/dY//d2mv1eMsE9iaTbkrYoyg6JVBYb5tZ7zzP10ocNoPkeuBsAtR6Zr8OPNQBD/OdVg/STZnHeDzmq1/9Kp/97GfZ2dnZqglg/q4ntE18N51O+eY3vwnA448/TrfbpSxL/vAP/5DPfvazvPLKK3z729+m2Wzyd//u362sO6WUTCYTJpMJ165dqxgd5nj1mLEetNc1GczfdaDLjGF9zOos/YODA959912+//u/v4qx6gCAiYE3XzdxtnEjqbMdNgkA5j6ol2TUwShznDp4V9dWNGNsxsNc0x//8R/fOo/q7dxAw0/8xE+snUR9oOonVL/4poP1E/B9v/Iw7Xa7DAYDjo6OqhvCsB7MA8somxrqiZkIvu8zHo+ZTCbVifd6PV27WbuJ6jeB67prE2GN7bC82LPZrKp1MQNp+tLsNplZMy0g6cyZOTNGlhamnNkzLRZp2kMCQZEL7MTGj3xaaYvmvEkjaRDmIRKpM622LvnImzlze07kRSR+smbjKQqBzJfoln9OX/raIvmUgvYZQZ+NrpkPCWnRokuXXXbZU3vss09QBtXCfTwe82df+zNmxYw0TJkHc6bBlLJbkjdzMn8p6Fh3Idi2YC91+YoTO7gLFzdxcXNX0zGlXhxkMiP1U9IwJQ1SMjvTdPkHnT9bjqVqC4nae3ZpExQBQRLgLlxkLHFxoYRhOORk/2RtH1V7SGD7sM+Ysgn9pzo7IH9QO++xWJUwAKfdNjb3uTFvKkDinP0TSjtUeLmHn/t4qYdTOqAgt3JiO2YcjLVdmXHpeNh4njfYYxlgVlUdD2FebDTP8mg4Dbp+VwuHOU18R6e54zzmZHHCweyAcbLdYuuhffxzAhzM9dTd+BiZAh+1nWcOP6B9JBHMcqnLYMC3pSVt9ZV53v486nVVq+DPEhaUWvisEMUpUMHLPS41L7Hj7dCwGrjSBQGp0vaSURExSkZMCp3tzFX+vXNtz2jGQcGXPg27QdfuVmUAXaeLyhRvffAWN0c3yYIMq2dpgbritKvCn1ezsGg4DfzC51LrEpcbl8mOMty5y4987kfwUo/Huo/R9bossgXH0TH/26/9b5zEJxR+QemXlEHJ1WevaterpcbEOBuf0pcQSugg0O1VP6EM8SxPaweUORkZcRkziAYMkkGV/TZOHY9a7iJq/0DfX9v24ZYuXatL3+tzqXWJntPDzXSgGE0jonnE699+nSIskDsSZ89hoiaIpiBWsWZ0qAeUmz6sLRlDhjGhMkXTbdIO2kTTiCzOtBW0LCCE1EpPayZBZW9qSliUUCS5LofZ1HIxre5csu09t3SxU5uu2yUgIJkmlJnWKsjJUY5COYqwF4IHURlpJhAf/h7+jP8ZPtf/HPvOPn7q07JbyFIyjsYsWJC5GbNSl0TNyhnTQrtNTApdXmOeI5vNEU5VItBzezStZlU20BANGmKlL9K22ohY8Hu/+3t88Ytf5Pr161WJ9euvv87/82/+H1KpS0eUr8hczQxzOy5XnrlCLGKOo2NyJyeRCZN8wrSYbp1/vvRpWVqPx5TANa2mdouwWprhsdQ8MaCKb/unmAB1hnY9tqh/blPnYJPhXS8tr4MadYG++msASZKQZiknixOOF/qcFyyYllOu+9e54d3YqpNQp7ib942oZ70swny+fg6bjgMmaK4ncGGlF2D243lexXiox2B1xoN5Pcuy6u/6eJVlWSWh6wnm+tibc9rsiznvTR0I434hhGA0GtHv9/mzP/sz/uiP/gjXdflH/+gfrQlb1hn1m5n7zetUb+azJpDfnAObcbOZP2maVuO4KZK5WaKw7TpvMhTMddqsCjCv1eNp029zzDqAtO386qwUU9rzMz/zM1vHY63f5wUazM42qRvb0K0606GOBmVZRrPZrKgwlmXx+uuv88YbbwArOk+dAmJ+27bNU089xbPPPsvh4WE1IZVS1YUKw3CNtWD6YvpoJpt5rT4Z8zzn7t27JEmC7/tr+zID6vt+dYMYAKTOsihlqTUbglhbUrqa0j91pyzsBYW9wYjYyAbX/7YKS1PUI49e3kMdKuJ7MY28QTtoM2NG3szJGzlpIyUJEuIwJguyU4DHtgB6rZ21EK4teM0+qvrpLYG6hfap9gqPclJiT238hY87cinvlez7+/iev3qAeIo0SDWg4i3HrRlpwMDPtPJyvS+G9r+x2BalwMs9gjzAz3xSN+UkOFn7zCoxX8tKfBgwYGOsjA5H3ZJSKE1TNbWedWDoofuvv/+A/kmWTBcFeZFr+qXkwToOZx1ns53B0qiDEWvA1LbPL+cDikrw8tS8eUgAJku9GPNKT19fAmzLJi5iZtlMM1xc7bBxVhZq6zk/ClOCc35+o9nSJrADvah12zTdJqETIoQgyiNuHd1iXIxPOyA8Sh+/y63KfC6fB2Xt33esfZfGQlBbQDzi8SwsXQ4hrNXzRep5XwWFH/c5mK4uS0kMk0ep06UEoRWy6+2y7+2z6++y6+3ScBrYpU2Sa5X2cTbWZQbL8oaoiEjK7bT474lWYzJ5lkfTadK221XmtWN3cKVLqlLm+Zzj9Jij+IhBOtgqfvhxu7I8SnNwcHFxjhx693r4kU9YhDRFk5/52z/DE088Ua1ViqIgKiMOpgd85ZWvcG9yD6tt8eSnnqxq0015g6Gyb96fjnAqy76eqwVBgzJAploLolAFOTkJCQu1YKx0eYhxKzHioOceq9pcNX9vW0NYpYUd2+z5ewRlwNXuVbpOlz/+j3+MUzg0G03arTZhM+Tu9C704bg4ZpgNsdoWhV18pCC8vpmxgZal1ICR5RO6Ia7lYkmLKNcCj8N0eAr4AX1NrdKqHMQUSutUldlKfHmjGTvrs4AVV7g0raYWs7VaeNLTNqZSkpWZLi8ptY1pVEQVMJGq7YKga8dG4giHQAY07WZV0rDr7nIhuMDl8DIX/Au0rTY7rR0KVXAwPdDikvlY6zxkuvTgODpmGA+rrP2snG1lsbiFy264S9/v03W1VaSMJN/6k2/BAuxEl+DaqU2gAhpOg7////n7NJvNNSFDE3fM8hlzpTVV5uW8+r9xjZjkk8qmc1pMmebTrf3ypU/b0XoKLWslBNlxO1o819EMiralhYTbdhurtKqMtWn1ANUADcaNYTM7Xw8q66+bYN8IONaz2/Ukbj0grQeg5sckcs1PXZhRSlnFNfWSCOBUUF0vt9i03DR6CVEUVcxy05+6GORZbABzXAM01M+1HvDXk8XmvbqmwOY1MKUJ9fF99dVX+c3f/E3KsuSf/JN/shaIb26/2f96DGn6WS9vqAMkm3Oh3ofNkggzP+rsjzpAZF6rg0h1xoX57Ga/6raedUZCfZ6Z+6kOImzOxzrgU2c1/PW//td5WDs30PDTP/3TAGsnXUdoNpGTOspmOm6QpTpd5hvf+Abvv/9+NdnrF9AADuYCPfPMMzz11FMsFguyLOP4+Li6UJ7nEQTBWl2ZaeaC1FGe+gAagcmTkxPyPMf3/YpNYS6e53n4vk9ZlpXVjLES2ZwY5vj1OiWlFMejY946fIuxGEMXVE9hXbTImhmJl5wfiFBaONGNXb0omYe04hZ+7HO1cZVABpxkJ9g7NqqjmMgJEzFhIieMxZi5mJ8KlvVuz1hoPwSIsLBWiuzohe6a60T980pU9Ho31Y4S3twjnIeEk5Bm1oRSW7dkImOkRsydOaqjNBsizEgbKVkjo/DXx8sqtde1EFpA8ZRVotIU+UoTgy2fMedbDc72866ynPBgq83lMe3CxikdnMLRAqGFXshkIiN2YxI30XZdj5j5fNhxzbUxGdFTi5yH7GPr3w9iANTGRf/5gDmlVgs6w6I4NZbnAF0sZWHlFk7m0HK0QFsy19RIQkjtlLmak1nZiiFR3/+fY1AvELrOv5SQaYCx5bXod/okKmGaTfUCX20RxvwebQaMsIW9qg9XRRXAfM8Gr+doFSDxqPepefYpC1EIXMutqImZyrTWSXG6vvvjbmcF1BLJjrPDnrfHvr/Pvr+v7ULtHu2gTcNrUJQF9xb3uBfd43Z0m7uLu5ykJ8zy2Yei7H+3m4WFK91V0ODooMERDkmu3TrmxZxRPqrOq94M0BbIQNfifwgmwCM3pYEBz/K0Av4yO9u1uuy4O1z0LnI5uMzl8DI9t4eDs8bSVEIxL+Yr8CEbkdgJ9+f3tasIc44WRxWNfl6eBmF86euacbtD3+tX4qAdp0PDalCqksFkwDgZExFxZ3qH29Pb2klCLAUcZY3x8CiA8BnPf1lKfOXTc3vImSQ6jLi2c43Lnctc7V/l/p37jEdjkiyhtdPi9uw2RVOLI5fNkkEyIBXpo9lkn9Hq7CMHB096WIVFHueaAVEqSqusHJNyefoet7CwlY1UkiJfCvBZGpwo5ek5VrGd0PfyWeV8oQy1i4TVqgAty9LaVnEek5GRKO2qEquYlPRc5S1GKNa3fJpOs2Iy9J0+HdHBXbj4sY9faEbBdDolszMWYsGCBXM1587wDtc/cZ1IRpUWwkl0olk99unji3LF3DHCim27XTnnGJHMptRsip1gpyoFqgdL9aDUiL1mTrZesrB05jBgnfmZZJOtY+1JrxLENC4d5hnT83qrvtVKUgI7qPoEq3iqnvyswMUoWktums+b8o96ctUEgvU6+3qgafYD63aGht5fBwA2A/A6uGM+Y4LdOmOiPtb1/dfjL7O/+r4M28Ec07gXbuoLbOpE1K/xZiuKonJCMft45513+Jf/8l8yn8/5p//0n24tV9jUrKjHuCYor4+F6VednbDJgKmPX/1czLhtimWauWDer5em1JPc5jMmZjXbp2nKdDolDMNqvOrgjYnV69ewfm3qrQ6W1BkNf/Wv/tWt47627XmBhr/21/7aKS/NzclX79gmwparnHE4Ztfa1Sq5rlchS3fv3l2bYObH2HSYi/r8889XXqdpmjIcDquBabfbOI5TOWOYPtRRnE0ksQ6CRFHE8fFxBYYYRM4cPwiC6mJ3Oh3efPNNvvGNb6zdyJvHNP93XZdnn30W27Z58803uXfvHlJKLly4QLvdXt3YriTxEh1Etwsm9oRpMCUJExbWAuXWLtVDAkQ3d2mWTXYtXd6wo3a0Y4Tq4uMzF3MmcsKIkQYhxES7TwjtQlEPyIQSFQ3wzMXCtv4odF1yoRcGElmVAJSy3F4OsFyQy0Ji5zZiLpAziTf3cCcu9rGNO3GRpUQ4grJVojoK2ZPkzVwzI/yYJEhI3HVdBTu3dZCvVhnZ3MrJrNMBvgEChNIWlBVt+RFAGBN0VyUJ28oAlpfUKjUYYaUWdmpjZzZWtsyQuorUTUm8hNzVNM9HKVU4q50CmDb79rBzrPV/62sPY1dsbvsAJoWlrBWAtFnTeh72wRKIcpUWR5WFxC7tigqbkLAQC235+aDSm+9EOyfYYRaXrnSrbJYQSzG5PCYh+fj1I76DzZyPjY0jtYiuUjrrlyn982FZBn/erQrozwIlHnDNTUDsWR6e9LRlI4J5NNfXuUy0Vo21ffvvRPOlz5f6X+KKf4Wnm09zo3mDntOr5qD5Ds3sbGUnmQ25E9/hg+gDDpIDBtlgBUp8lDr9Lc2AW8DHwrJxhUsggyqDGVjamrEoC/7mhb/JxeAifa+vhdqKGbeT23z13a/y2r3XSMOUcTFmoRakVkqikq3B5an2MYCeAoEjtQ5GaIVV7Xrf6bPr7bIjdzQw4V+m7/XxbK9aYEqpNQqmxbRiRwySAUeLI2ZqVmlKjNLRShByi75EQICd2IQqZK+xR3KS0LJa+LmPW7hkCx3cSl+yf32fP37nj5lbc8qg1OWfDhSyWLcGfdi4PACQNqxDv9Ci2E3VpCM6jO6M6FgdvvR9X+L2B7f58pe/jHAE/cf60INxOUZ0BAu50C4Q1scELNUSNBYWMtesCaEEKlNkiX6eK0eRe/k6s3PZJBJXutX9p5SqbGS3fQdYWCvG1ZL1tO0elEgNZLldGk6DQAbY0q7AjKTQjJao0BaRcRFX5TcPa45w8IRHKEMCNGh2uX2ZS41LWkTW2iEf5/zOr/8O8+lc21r6JZmboQJFsBPwxAtPEMu4coYxYpTjfHupomEcGFDRgBKG9dS224SEXOle4WrjKrAuUl9nZ5u4wYjbTvIJw1iXM51EJ2suLlW/lqDFNtaLK93KjaIS51wyKczfn2x/kj13rwqsN/tSZwnUARQDVNT16WC9xMA8t00clKZp5fZQj2nqCVnzmgl46+/Vt6kH6mYf5lgm0K03s72J+0zsZZgW9X4IIZhOp1WJex2AqJd6mGPmeY7rumsM9Xv37vGv/tW/Yjgc8iu/8itVAtl8ZhPMMceuB/hmPM35ApVeYP16bLI3KgZ8jcmwWdawWf5Q70ud0bJZGmNiT9OnNE2JoqiKQ+v93WTOpGm6ZshQJwpsJu7r/f7RH/3RU9dzs50baPipn/qpUzVIdSpJHUWvD5yZXFN/yp9+4U8BXffeyls0sybRnYjoboQYCayJhbtwUfl6XYu5IJ/73Ofo9/sMh0OKomA+n5OFGSIXdL2utquq2bkYS876xKtf7PpNtlgsODzUHuxhGK4BDaYFQUCe5zSbTd544w3efPPNrTSo+mtmeL/4xS/ieR537tzh5s2bSCm5du0aQgiOjo5oNBoV6FD3L03TFMdxeOutt8jJcXddYj8m8iPUriLrZOTNnNRP1xWZH7BoEUoQEtJWbfqqzz779FSPLl16oodbusQyZizGDMoBIzViZs04yU+Y23NGYkQq0rVjWctV75o13cMy53WqopJab0LpcoCScoXmb9LzTTa8kIhUYMc2XuLRyBqEi5BgErCT7WBLm8iJKm2Lhbcg8RNiPyb2YmI3XivDsLMlVa/QYIQqdYa9lKUWMnRPC6/JQgu7gWY2lJQaUDhvkF0DIxTqgU4VUkmcUisw+7mPl3nMjmdM5hPwQIUK0RLIhiSX+dmij/XjP2gBpzZ+m/YgRsPD9lX/e9t13bbfc7AatrazAryHvC9LqYGf0sYubJ558hksYVGUBZN0wuH8kHEyXtuszuj5XsjYG0aBJZcLTKVZBXmpLeq+45nYj6uZoSz1QjV0QxzhgICiLCrKcKay74lx/243gcATHq50tZOBcLUAXJmzKHUNfqKS7+j1FghsYeMLn4bV0H7z7j6Xvctcda5y2btM3+3TtJo6WF8u5BOVMCkmnMQnnKgTDtID7iX3uJfcY5hrd4qPG5Sol//USwE/ytyxsGjYDZqqiRd79O0+Xu5hRRZ9r085Lel7ffzS53hwzNyf88wPPMNRrktVTtIThumQg9EBqUy3f+d9yHN92HkZYCKQ2n6y5bTouVqkc8fZ4VJwiR25w3N7z7Ef7mNLvcYy5apxEXMSn/DB8QfcndzVIEWu7QOP5kdMlaatp07KtJieYuwIBFZiYcWWFs8uAlqyxZc+8yVGd0a8/O9f5mLnIkEY0L3Y5Y27b5A1MkRfMJdzYhlTuiXCFatnwHkZE7oDp14XpcDKLFqiRZs25bBkz9vjyQtP8sSFJ3Atl16vh0IxSke8cfcNTvITjpIjBtmAWMbMihkJHwMLbfk9WTEmhFOBzKEM8aRHSUmmMqIiYl7MmZWnnREEQpdzCKua+4Zhtu3+coRTMdIADWKcYV3rCle7RtgNPOHhoAXkkJCXOYnSbCHzcxYYUu1v4eLNPezUxsm084SXe3zqqU/x+cc/zzOdZ6qgux4IzvJZVSJhnCMqjYklGFEJT+YTFuVKa6JhNfgXn/gXa7Txema4nnSsU8hBxyjz+byKPUyraxrMshknixMiEVVAxLScrpdz5NO1Pmcq41cu/wrf3/x+0jRds/00yVNTQl6nuZtMs3kfWNsG1rPwhjmglNaiqydJzfvGIrLT6ZwKljez76bV40UhRCX0WI8VTQbfxEmbrhT17LvJ1tfBlPoYb7LnTRm+Ob4pGRFCazX83u/9HoPBgJ/6qZ+qjATq+67vc5tmgonVzHmaa7EpG1CfM5tjasahvm9zPMdxKuCiro9YBwVMf+vmC+azRiDSVAaY+WnGsQ4WmfOsM1I2nUM2x8Cc38/93M+dcSev2rmBhr/zd/7OGuVlUwDSDJDpyCZqlpOzCBfIHcnCWzBzZ4zFmOPymCzMVtkZBXImsSYW1thCTiRyJHHnLp+/8Xm6XpdooWlE4/GYV158hWF/iJVb+JGPn/iEiXYxaGZNOqpDu2xjl/Za32AFOADclDcZDUf4sU/P6uE7/tqkzrKsYjp0u12+9a1v8c4771Som9mP8Vk1k8dM7h/8wR/Etm2Ojo544403CMOQGzdukCQJh4eH9Pt9Op1ONcmMx625MV9//fXq7/l8TrPZpN/vr9Rw8xw3dOk93mPhLhiUA/KdnLk/ZyzHLKyFrmNnNc71Uoz6F65UkgYNDTwUPXbKHXbkDu7C5aJ/ER+fmLhiQRjnieq3GLOQi/VjFcvfdU/58y6kDDugFIhi6diw7LOyNC1x6/6WQaNTOJUTQpAEtLM2e2qPftYHC21zas+ZWBOmcsrMmlVCnHXaokgFzsLBTm3cwkUqqSmPrq2pkXZK4mibx82seEWPR/ezFDWtiwcF0BXuUAOyHkL3lIXWNvAzn6AIsItlRsLKiZ1YW5XJ9LQzyFlg0HnAiOX/T+mBfNjF8lmAwMNYEOfZ31nbP8L5mwW6Z3sEdoBv+3i2h0BoL+94dAqMcKSDFLqMJS+/d0oIDL200l5Q3wXthe9QMwCLIxwc6WCjLeEylZGW6Xckm/4XqdnCJpABvvSxlV3pSJj67u80YGOEHj3hVTTivtXn6e7TXAmucMm7xK6/S8/tVdRiw9oZpkM+mH/AB/MPeHf0LqNyxGF2WOkufNzX1ogOV6CEKitr3Y8yRj6a8m9KVIxOwp98+U9IThIdcKuAZtjkR37yR5h5M+7Gd7kf32eQLRXv8ynTbKrZLR/xPpU1VPxhoIuxMvWkR2AFtGxdyuGl2g5y196lT5+rwVXyQc7777/PY489xvXr10nTlFk24/7sPk7PYVpM+ca73+DVt18l93JEU2ghzKDEalmMs/EpYUZZSuzERs0VbbtNkybltORS5xLMIJ/k/Mjnf4Sd7g7v3nqXu9O7DBgwFEOSICGSEYmlSwVykT9cbPgcY+cKlx8Of5h9a58+fZ7sPUnP7rGYLYhVrNmj+YRxoTUMxmrMSI2YKB38DtKBBpg2y/r0BflQfbPQDiAODo5ycHCqZ6NSihwtEJqQMOd0uQxonQlb2np+iHWwoWW1+OkLP01R6pK4tEhZqEUVxFfikWe4WoQypGk1CaygAkfLsiRKI8bHY4qyoLS0hXXhFBSudlv7tPo0v3T1l7AsrYlgMt6ba3vzWj0QrQMBUkqmCx3cz9SMWMU84z+zVkpQD94391svB9gUyTexTz1wrNfi1+nr5jjbArhFvqDdbNP0m/qa1oLwulZA/e/NzLvprwnoN8u7B4MBk8nk1DnWNfI2/1/fRz2ArgfC9fc3y+hNAGv6ZsojXFcDeEbrob5tfZzqQM9Z/Z7NZlV/658Zj8ccHh6S5zm//du/zfHxMS+99BI3btxYy+SbfW27/qbEYTP2rcfHBiQxZSpm+3pcl2UZV69eJQiCU0wJc33qSfJNTcA6m6Q+Pub/SmmHkXv37lV9ql+T+jjWmQzms2YMTH83mRlmHz/7sz/Lw9ojuU7UgQPTmToyVu/oJiKolMJxnCpTL6VkPp/zyiuvMJqMKBoFRbug7JQUnYKyXVK09WsqqGWdC5sw0bX8zsyBfIm4OJLCKXSm2tc/9SDRyRzCJCRIgup3EOvA0499Xn7+ZWbtJRKswE1c/ZkkIMxCmnmTZt5k391nx97hm698k/fee6+acPWJaMbCNCklP/RDPwTAwcEBb731Fr1ejxs3bjCfz4njmE6nQxAEawAF6AdIHMd885vfXAMhdnd3Tz1cgiBgf38fgPl8ztWrV2k2m1UfcnLuWHf4141/vX5xz5Mxrr1nKYuGahCqEIWiR49e2WMn32E/38eZO9y6e4sJE94bvMfUmlK2S2RfotpaADL1N9gBRhXaZPU/TEZHrQJ6WeqyB1OiUWkxbAkkLaXrI/1iKcJVNLUl6aJBS7WYzLW2xcJZkIZpxSiJvXhN6NEqLBpZgyANCPMQO7c1uFCCtCSFXRBZEZEdEdsxsROvC3cux0GUy3umzm44azxqYMRaycI5mALGgtUtXOzSBgW50KUkmb0UFd2w3PtYgIPN/pyH1fCgPmy+9igMh237f9RtHtCMUJ9j6cDXtfSiKkkTkiI5pb0gSg1gCLlkIHwPlkKcJ1P6sbaPgVK+rRkBNEfq7J1Q+ovUMCTOUpN/5HYe9tCfU6vbGW5VbRdabV+KVRbUlLZ8N+amJSw86dG0m/ScHhe8C1zyL9FRHZ7oPEHf7bPj79CyW1AuF3hlwd34LrfiW9xN7nI/uc/95D7HyTGjTIsaZmX2sQFplU7RUo8EqGj/HxWUsLFpOS36dl+zDOydSiPBL3yudK7QcTo4lsM0nXInvsOd6A4H8QEn2UmlvTDLZsRl/KHP2UHfJ9XittSAS6G2U/BNE0pU19DHJ5QhbbvNXrBHR3RgDK9++VW8iUff6eM6Lp1Oh3/wD/4B/8s/+18QoS5duHjjIguxIPdygt2A24PbJHbCcXyMbEpyPycRpxkEdrFkKSa2dsZaCB7beYzLncu8/erbXGheYD/c59U/fhU/9HnixScYWkMWwYLb89vkQa4FNV2t51Raj3Y9JToZYcoGAhHQlm26sssF+wJ7zh7jm2PssY1KFU+++CRfe/NrfPO9b6JChWoq9m/s85OP/yRCCgbpgJPyhLvJXSbFhJmaMS60+GFURB/pmVWVbAnNmHCli2vr7ysDMizyBbN8tlVLxrCZOk6HrqOdRtp2m9AONbtO6KAlzTSjYVGugIlKA+EMnRAUeKXHbmOXnrOy2uz7fXpuj5bd0tabbo+u12XH39Fg6kam1zzrTO26iVksyyKKIj744IOKBe37/qnyAlixAzbLu8179QCxziCo78f8bSj9sIoZ6kJ7ly9fXgt+N6nvm2UD9Yx0fT8mYbkZzC4WC27dunVKDLAOstQZ4ibw3HThMOCJGdt6bFLXFaiXfxjgxYxhEAT0+/21BG8dKNqMNevgRb0f9THeViJgwJV//a//NcfHx3z/938/n/70p0+BSUa3YZPNYjQz6rFtfVzr5RvbSi3MONu2zZUrV9YS3pvXdhPg2Dxefc7UAZz65yeTCcPhsEqI10GCOohQ16moAx31sd8c8zzP+Ymf+InT9+tGOzfQ8NJLL50qndgckLqGg2lVR1VJ4Ac0m83qhpjNZrzyyitMJhOAiupS30dRFBROgbPvcOPzN5jICVNH6xZM7SmxV6O/K/BTX9tEpqGmL5Z64HJyEich8rQbROTWvHoVOImDvbB1Hf+SMqakIrdzUi8lduO1haId2zAEMRHIqcSe2oixQIwF1szCKlfUJd/3+aEf+iHyPOfOnTu8//777O7u8sQTT1R1VEZbwlB96g/F6XTK66+/Xk2SRqNRoX710pBGo8Hu7i5FUbBYLLh+/TqtVuuUE0esYqZiykzOGKsxMzljgmYnTOSEmZitl0Ysx0jPmC2TY0ugJ9CijyISMAZratFJO6gDRSNrcLVzlcRPePVTr5I6W7zgtwWP9fVMnRnxoLYlSDE1/1apaxZNuUMhiu1sAcWaiKWXe/ixj7fwaJZNmqIJFkROxMJbMLfnzJ05C2eh9R+WzSotwlyDZI1cz1EncSDT2e6szBjlI10T6mnnjczLSL1UI/pb+lTdvvWxeFjJwOZnlvsyehRbr8W2/W67Ro9a9rCtbXsiPew4Z+3nIeUWW4PmP4+A0PRHCZ1FrVFWNxfyjtAUWBQUFH8hLAu/061en/+d2r8tbFzp6pplpbRt4DLg/ovI/jhvs7CwpV2Jz5Wq3EqjFghc6a6V0RkR0O/G+AiEppRbIV2ny0XvIvvePhf9i1z0LtK22vScpYih3SDPc4bJkPdG7zF0htyL73GYHnKcHTNIB0zzqQ7ettRXf5Q+1kEJU75RqhJVLBeSH8bKeLnvhmzQslr07B477o4W57M6lVNBQzS02HKeMlRaQ2OkRozKEaNc083nxZxYxR/qmWKCakcu7YqzHGEJhNSlDQ8EJtRqH77lk0wTvNLDzVz2G/tE9yMuBhfZt/f55OVP8uknP827b7zLyckJX/va1+j0O/zI3/oRbh7d5Dd+5zfoXO7w1KefYpSOuD+/X9mDiqYgczNS0lNdaMgGTuZgx0tNB6ldHopxQctq8cknPsknrn2CS91LeL7H7eFt7s7u8u70XW7Nb3EQHTAXc106oZYOHZzToaP2nSRynTBxSocLrQvaNcbdZ8/e46J/kV1L620YK0YTsEkpyWTGAm2ZehKfcJgeMiyHen1XTJiUE2bFrHKk+CjNFrYu2bK0bpCNZg6XlJWmg7FR3WyucCvhxJbVoim07eTbX3+b2XCm13sCVKno7/Z5/NnHaV1oVU4XRqhxlI62aoW40q0AiZ63Aid6rv7b6JcYYKTrdkkWCffu3aPRaOhrUUueAmyGSyZeqWe/61nhOqiwyfw2IoDmtfo+lFJ0u116vd7aa6ZtBqD1JPDme5tgRP11IQRxHHN4eMhisVgTFnRdd+142wAP814dxDDntcm2qGfH62UTruuilKLf71fx4eb+TZ83M/H1Zv6uB8P1z9bHJo5j/vk//+fcunWLF154gR/+4R8GWBNdNH/X4ydz/Hrpgonh6vuvJ9zrJSwmiSylpN1u0+12T10/E+dtgiTbAJT6XKuPiZlfZm5Op1MGgwFxHK8xQ+r9rut4mHPYFMg02xnQQgjx8QINv/qrv7o2YKZORAhBkiSnJpD5O01TkiTh9v5tbj99m1beohE36Kouzszh1qu3SA9SrFSjXXUUsC7i6Ps+L774YnWBHcchjmPSPGVhL8hama7FbyQkQcLCX7DwFmT2apFgFzaNtEEj0Vlnp3CwlEVJySybMZMzsqZ2NUjd1QNYKEGQBTTKBj4+VmExGAyI8gjlKcpGSdlcF+cSC4GcSKyphbNweGb/GZy5Q3QvYnF3QWiHPPbYY9WYbtbNmJvdiF6+9dZbKKUq3QkA13Ur2k1ZlnQ6HXZ2dijLkizLeOKJJ2g0Gmu1TfUJY24Cy7KYz+eMx2P29/exLItc5EzUhGExZC7nzK05J/kJE1cLR87FXH9RnycAPIt+XgrsQn+hB3mAkzk4mUOe5BSiIBUpMzGjbJSaUuk+RHNgyWhAsJ0V8aBAmNPvCSU0GKFq1p7yDB2FJbhilzZO4eDnPmEe4ic+TqGzGdKSRHbE3Jkzs3V5RmrV5lkptKXpwsNLvIp1E2YhTuygCoVoCqyuVbEi7i/uM1ETyrBENZVm/2zpl/7vln6fNZYbnxOlqMAI0IvhU2rYDxjLs/b70G3MZx4CGGzd78NKPs4JVggh1sbweyWoX9P22OiThaUX+2iw4uMMlv6iNptlzbGgosF/J66l0SywpVacz/OcXOVIW/4XC0jU2SEGCBNoEeikTNbqoU0zQTewer4+4Hps1QH6CM1iWcIhm+w4OzqQc/ZoF232wj16jg5KOm6HuIwZl2NuTW9x1b9KRMRCLBjmQw6zQ+4n9xmkA4aZtvVbFIsPz/ionZovfM24kVoQVAh9v2cq08ySczgEnNUcHBqyQdfq0nN67Hq7dK0ubauNmin22/u0whZu4BKVEfeSexzEBxwmh5VTxayYERfxh+qDhRaCzaNcMxGX96bjOghbME/n2ib2QcCL0vuRhcQTHh23g1u4nLx/wo3dG1zwLjB8b4g6UTy+/zjRMEIg+MVf/EV+/Td+ncP5IQux4MozVzhaHHF/ep+T5ITMybA7NoVfaFDCOV3aIJG07BZdp0tLtgjKAL/wudC8oMtfyoCu06UpmvjKB2DGjIVaMBAD7pf3OS6OuTe/RyxiJulElwvUyyrP0Uw5koOzsmZcsiZ6skff6rPn7LErd9kNdgndsMpg7u3tcTA84N3Dd1mwYFpOOS6OOU6POc60dWgkI+b5nEWx+MjABGhwwhUurnB1Uq+mGZGpjEW22CrIbAu7cjup2AuudpdoWI0KBK7KPIq0AiJGqdYNGSXa3WKUjrbenw27sQI+ZJOW1aqAiLrlZUtqB4lQhDiWs6brsE1McFMA0WSDN1kBBnBoNBrs7Oys7WuTjbBJYzefMe/Xg9QHBbD1hOZwOKzEDE3ga2IREyCb17fpPZgA13XdKp7bFHqsj4VSqiqZaLVaW60hzd91Nr05Vn1czHnUf9d/NlkD/+Jf/AvefvttLl++zE/+5E9WgpsGPKrHYq7rroFFJvY1nzPjVGd7mHhrsxzHcRy63W7FNgfWSlvqrAyznzzPKxDEjL05Th3EqDNRNq9vlmUkSVK5Npp9mettrqVJeNd1Hcy8rJdvmGv58z//8zysnRto+OVf/uU11E0tSyHqk9901FzYPM9JkkRn8tUd3vffZ+pMmbtzFv6CxFqhnCIW2FMbOZbIoUSOJc7MQY4kaqLodro8/fTTRFFUBdeLxQKltJBjHZQwN4VlWZRuSRzEWggwTIgC7Uqw8BeaDWGCRgVupO0iG2mDIAtwS21BJm1NzYv9mLmr3RrqASIZyKlEzAQylZAvL66ttFNEG8p2ufZFZSWWptiny3KONNRZ8sjDT3zcUmsvxHHMYDDg5s2bABW9BcD3fcIwrCZXq9Vib2+vurmvX79e2XRulrKYCWjqiJIkYTab0ev11m6QOI4rZHM+n1fsizAMKUXJDM2KmIopw3LIyBlxUp4wYkTmZNvtI+ttM/jckiWXqdQZhsTHT31ELqDQYELh6YVAGZZkTqYtDOUZgES5/FGsGBGbpQH1vmy+zhnvn/ezJmNTWhp8yD28zNMZlNKGHJIsIXVT0jAlCRIyN1vb3k99mnkTP/axZhbpYcrs9gwxFthzm8uXLnP12auUzZLYjjUg4cS6ZMOKtDimFRFb8Xr960ctGVC69MUqV/ahCu3WUVgfwkL0YZ97RNDozG02t32UMo6/4E0idQZKCvLytCiYg3aCKFX5sSww/yI0k021hIViyVpYBnPfKYBJIlcZbqX+wupjnKfZ6AyoJ7SjhmFACCHW6NSxik9t64hVzXipyjPLN1zhamCwBih91PE0bI1ABHRt7eJwJbjCY+FjlaZEz+3pwNJuVlndcTZmmGkXjuP0mLvRXQ6iA45TXcYxyTSDYFvG95H7h1uVAVXWh2pZj68+WrmLRBJaIT2npynxtNlxdtgNdtkP9ykWBVmSYbmWZkuUQ27Pb3N3fpcpU2IZM1fapvfUfXQOUFiW+lnlCu04ZQm9CDbPriiPKCzjpfBg0MNCl3LIXCJiQaACru1co2/3SY9Srrev01EdLnYu8tjOY5SZXtPeHdzV4FIyJHVSaMBCaObAMNPWjNNySiQjJvnk1Hna2NpqUrYqW8aO3cHLPTp2h+nBFCu2aIomN1+/yWQ6we/7PPf55/B2Pe4X9zkqjjgpTjSYpRZERXRu14fN62kLW7tA2CGBCthxd7jcuMyuu0tf9mmXbbq2dmdo+k263S6O6xCpiEE84N70Hvdn97mf3tegRK6dZqbFlFk20yVK5wS4TyVDNq+Z0M9lV2pwQghRaR0lpdbc2NxWICoWQ9dZllp4/QqkCJ2wYhflhRZJnmWzCpQ4iU4YJsMKrJjm063HMM4WBpSoO0l0XQ3cGfCi63YJLV0+Xs8km8y+odo3m821pGtd3HCTHWECQLNP8zpwKhg2zGeldP2+0Z4DneU38UJZahchs486a90Et/Ws/ya7oB5DmL7XWQpmG9P3LMvIsow4jiv3wCiK1rYxAbFJNjcajarkxQTiRVGs6dYlSbJ2HCNI6bouv/M7v8OdO3cIw5Bf/MVfrMQZO53OGrPBxE5GdNOUy5vSFzPWZtzM+Zt91M/VBO1pmlbsdfN/c62yLKvERRuNRiXE2Wq1Kj0L4wCyyZ4BWCwWeJ5XASZm3piylXrZD+iS+zogZuZLHdgwIEq9JMj8/O2//be33rNr98l5gYZf+IVfqC6mmUB1dKyOstTRK/P/JEmqC2FZFs1mk5sHN/naza+RNBMdiPdKslZG2VkyBEzLwZ25dMoO/sKnq7q0shbqRHHRv8jezh5RFHFyclINtrmxNm+8tckitF5AFEa8/dzbGiCQAmzInVxbiC2bUzg0sgbdsksQB9x/5z7xVNNQSrtEtRV5K6+0JYzwLoBMJG3VRswEKlZYytJjGdgoX5F4uqSjniG2c5sgCXAXLuWwZH5nrtkRcwcv8hCJIAzCyiHDTJidnZ1q0l6/fh3P807VU9XpNZu1PQZ8MJNtNptVD6L5fF4hkOZhVf8xlKnRaMTBwQEAh0eHvH/8PuHFkPa1NrPGjKgREYfa9SG3zuGOcB69gULipR5e7BEWIR4ek5MJ02SK8IW2SgpKVKBQngKb084Qasv/zwNEbG5r2ocJoJfviVJg5RZO6uDkDnZha3EyYelsj5gzs2YUYS2IVyAXEi/ydC0vXa0tUjQJ01CXaqDrbPMy1+rYzJioCZmfkTgJU6YkbsLCWZC4CamdnhLkeuSAvfa6pazK1cHYhxaiILOyswGJjwowPKifj7LNf6WtEoncyCQLhA4apX4+ZKUWXPzYtA2+R5tAVIKKFpoRl5Zppab+cWXczzr29yK75uNuPj4Nu0EoQ1pui17Qw7c1RbtUJZN0wiDWDIJBMtgaaDlSMyuUUls1GYxFYJVRpaiEqz9qM5TyptWk52pticv+ZfacPXbcHXpej46lAxFXuuRFzv/6//tfuTe9p7+nWgrVVew9vcfcmq+0FvLZ1qDqkfuHrQM3S4sam7VcXuZrc/nDHsfFJRQhoQrxC58da4fHuo+xG+wiMkGRF5SFtip+7/A9jtIjjrNjirAgdVNcx+W6vM64HDNnTixiYqWp/g/qk2HJeGj3A1tpnSQpa0wmUZDLnJSUpFiKaT7gO8AIPvrS1zoThHTtLj1LC3ru2XsEuWYvXOpcYq+zR0nJ/cl9bg9vk7s5c+ZMCi3+aEo4ZmrGpJjocsktjB8rt2hZLQ3sWG1N73e6BGVASMiF5gWe2HuCHX+HhmhwNDjivfvvkTqptlyd3+ZYHTNkyLjQx1uUC9IyJefRr61hoDg4tOwWu8Eue94ePbvHnrvH1eZVLgQXqmDbszxKVTLNpwzTIXfHdzmOjznKjiqXjlE2YlbMmJdzXcpRPhzQbtttXOlSlNrOMy7jrdaWElnZA0uxcjMzLKuzRCq7jgZXGqJBx+4QipD4JOZT/U/xqSufIrESJvmEQTJgki11MtIxo0yDEaNsVNlcRuXpkg5b2NUYbZZ2+KVP226zG+zil34lturgEAQBSZIQRRGu65KmKZ7n0Wg0SJJkLSYzGetNlniWZezu7gIQRRHT6ZRms0kURTiOQ7PZrMraf+M3foNGo8H169d57LHHqsDexHH1ILReWrDJeqiXTZhg3rZtfF+zfHzfpyxLkiSpqP2+7zMajQjDsAp6Z7NZFZybUouiKKrS+zAMK2cM44oDWoBRKS0QWZbajSEMQ7785S/z2muvIaXkV37lV/izP/szvva1r3H58mVeeOEFLly4sMYuMMcF1sCbOhvFMCBMSbsBA+pxsQn4je3kdDrFsqwqXk7TlMFggOd5dLvdqmKg3W4zn88roMOAUrZtV9d7k01hYrskSap+g2bDLxYLfv/3fx/P8/jEJz7BxYsXq3PcZDsYRofZZ308/tbf+lsPvWfPDTT843/8jyt0qs5sqJdJ1CeXubBxHFeTw2TLTSnEzZs3eeWVV06pZiqladmqqyg6WhDSueDg7DvM3BlJkFTsAKEEbdWmnbe1y0TRoVt2aRdtWnkLRzkVMlNXCjXlBZZlUTol/8n/T5zkJyRhQhqkKGs1LFZh4ZROJZyVkZHKdBWEltopQ461Q4Y1tZCxROUKJAQ7Ae3rbSZywliOyRv5WpmFm7qaxZC4OIWzEkW0FImVMLfmJGGyBl6IVGtBeJGm2Dtzh3bZZs/Zw57ZhCLk8euP4zjOKUrNNsBh82YyE386nVbeuuZhVEfr6jeceSCMx2OOj48RQnB4eMjBwQGdTofLly8DVO4d1UPHsYnRrJOxP+bIPmIaTkm8hMzOtG3nWXoMWwJRgQDFab2FEkQksOYWciYRC10KUFraHqv0Sqy2hdWySEi0S8dZx6wf97yUfVO5oGrBglAPp0meFVwrINU/IhaQaIBCSqnPp1FqIKIGltiJjRfrObNr7dK3+sippJE1aOZNSDRr5tXwVd688iZu7uJmWizSUtZKgXoJDmRWRmppj/FTgMSD2gPKbIQS2ja0tLSWxjK7XMiC3MrPvi5mv5yx7/P06cNu+9/a1iaRBCLQGURktdBLVbo1a/1fUnOEg1u6qERRpAW9nZ4+7yImJf2OlW7819IkEl/69LweO/4OO96Orrn2OoRWSKlKRulIC+hF2kLyJDlhkp3ONhv6ObD1uhjtAalkxTgpKT8WpovRlrByCzu1kQvJvrfP5cZlXnzsRR7vPM6l9iV2g126fheBYDAbaJvdYsyt8S1uL25znB9XTIlxPta18R+TtamHDthsYeuSLLEqyTLioB/2OAINqtuJTcfqcMG/wMXwos6k06RjL6nrVpuWbIENJ8kJg2TAUXbEUXHEoBwwzIdMyykLtSBGgxIPAo0EQrMkcLXoqfSr8h8pJEWp7R8TlRCVUQV0POw8DYAVWAFNu0nH7Whgwu5xKbjEBe8CHbvDxdZFOk6H6eGUJ59+kq987Sv8yWt/wtv33ia2Ysb5GNmUXHv+GnM118Froa0b4/L0s7NpNek6XRos6f9CZ9D3wj12/V1dZuDv4GYud9+5y8v/78tce+4ajz33GJN8wrAccpwfcy++x1F6xCgfaWCrjD9UiU7lzCQ9XZLgdui7ffb8PS4EF7jg6etsmAa7jV0cy+FodsQ/+//+M+5N7uHv+rSvt7H6FlOmjPMxAItiUQX2m83GJrD0d44j9bpdSs1aKERBUiZERbQV1BAIDU5ILY6tUOSFBt/2Fnu8ULzAM1ef4bPPflY/a9wObbeNJVfxkGEplKVmXZ0sTiqhy2mprSsH8UC/lk0YpsOqrGOQDMjK00C9J7yKNRGiBVVDEVZARVAGNGjQtHS5R6ACHMvBdV2SJKnc8EyQbQJuE9iboNckkheLBf/5P/9nQMdtX/rSl3jxxRfXyq/regGGTQCsAQ1KqQoEyfO8KqmwLKsCGj744AO+/e1vMxqNqhjC8zziOF5jAZjY08SYYRiSZVkVDNdLDOoJZgO2mGtjYtaDgwNOTk6YTqf8wA/8AKPRiJOTE9rtNt/3fd/H5z73OVzXrdgDdTDBnG89Hjb7rWsV1gUlTV8ODw956623ODk5YbFYVGBFfTwNEyMMw+rcwzBcY7SYGK7ucug4DpPJpIrRjF2pYY2YczGMh7fffpssy/jkJz/JSy+9VFmomr6YEgrDtqmfs+nHD/zADzz8WXBeoOFXf/VXVxvVRCjM5KoHrkabYTabVSKH9clnJsMbb7zBq6++egoNMq1eS3TlyhWuXbtGHMc0mg3owHF5TORHREHEWI6Zuroso17fFeQBrbylQYi8Q1d16dOnXbTxSk9ngZfB8q1btxgOhziuQ+EXWuMhyCjaBUmQkPgJC3fBRE50SYBpOYhM0/mR6HIJuzbIqaCTd3DnLulhihgLOl6HVrNFLvLKKWPhLYj9mMRL1rLUzsIhP8mxF7am+xWaFio8gWgIimZB1sgonXUHhLZq65+yTafs0EFbfXZFlybNSnBusxlUMk1T5vM5vu9XSqtxHNPtdtdEbOo0GsdxGAwGnJycUBQFw+GwmiO9Xg/HcdYmbR1cMojobDarVIGHw+Hqhm1bJP2EZDdh3tHgS+7nFHYtmD5PRrxAgw5K6PpPZ30TRzkEWYCXaSEqSiitUmc/7JLMzkjtlMIqzi7T+LAZ8yWAINSGHaYsH2preda+KFjN0Qytzt5b1rJt1L9ahUWQB1iptdaHUmrV7dRJt4t3luDmrmZfLEGJklJvZy0BApmfXUpzFpjyoPMtqY5llZbWjVgeMxf5mcKeVXsUTYdqk5ra8X+BQaJc/jtPzfzH1UytfGAHmhaLqEQuk0J7r/+XaklpWCGhDPEsTzMkhF6gRkVEVETfXeeRc94Hf5GayWx23A573h6XwktcDa+y7+/TdbsUFAyT5WI/G3ESn3CcHHMUHTHOxluDOsMs2RZ02st/FpYu71MrSv/HcT+Z8wlkoDPdskvf7bPr7LJn77Fn79GRHXpuD8/yyMqMSTlhWky14GV2yHFxzCAbMCq0AOSiXHxs95kBgBzh4FgOSZyghEI6sgImPsqc9oWvA3i7Q9/rs+fusefu0bE7tGSL/cZ+9TtdpNwb3eN3/uh3GKohe8/tMVIjBsWAqdL2hpGKdDD9AGtXAwb50ie0QhpOg9AKtT2j4xLHmmWyKDTIMcm0Tsd52SeucLUFZSqxUxsv81AThRd7fN8z30dbtmnIBn23T8tqsYgXTIoJkYjIXC0ifRKfsGChdSBYsBD6Z878lL6EUAInc/AKj5Zs4eUeQRnoDL7SQup+4WMnNn2vTyfoYFkWt49vM0gGfPaHPkvjYoPbs9vcm9/jfnKfk/SEYTxkls9IiuRD6QIZ+1SVKFSsqjVYQzW4FF7iUniJlmhVAbejHBKVMFdzZmrGTM2YqzmRjJipGZGM9FhIPQ6nRM7RLEtPebi4WMrSCUKWwbTUTKdUaDvwbdoRlrAqYUnzuxKZdLu6jMLV5RR7jT32Gnt4ysOSq0y3CUYHgwEvf/VlvvXetzicHZK7OWWgrdNTOyWWMQsWRCJizlyXzxBtTfL4ytesIkIaNAjQTJiW1KKwT8on6Ykei8WCmzdvMpvNqlIKA0wIIfjRH/1Rnn322bWgtR6IAlWgDawFwGZNbwLU+rkapvt0OuW1114jSRLSNK3c8uqxpYkTzH5MwnOTsV5/ra6LYJopB3/llVe4ffs2RVFwfHzM3t5eBUg8//zz/PAP//CaKOWmhp5haJhSBljFTQZoMiCD0XgwJR+z2Yzj42Mmkwn37t2j3+9z4cIFsixbG0NzLAPSeJ63BvTUmecmCQxUYJIZozRNuXPnDq+99lrlCGKuQ1EU9Pt9fv7nf74qwzeghNEIrJf1mP3XdR/+xt/4Gw+9r88NNPzSL/1S1bn6hawPvNEUeO2117h79y5JktBut7lw4QJXr16l1WoBVNYgr7/+Ot/+9rfX9lWv+6kP6tNPP82NGzeq99vt9toNYVAzIQWxFTMSI0ZypAEIZ1r9pPbqQWMXNo24QZiE+Auf7H6GGAk6RUcHW9KqrCR936fZbOJ5Hu9/8D4vv/KyttBslxQtbcdZtkvK1rJ0YiXYWgVEqGW2xFarwFiBn/iEcYgf+XhzT+tDCL2DSEYc5UdMxATVVaiOogxrgiiFwFk4uJFLs2jiS58yK7FsC7/lE9kRUzllZs1I5KoO1FY2LdWiozprPwaYaKomeZYTRRG+71eUrbIsabVapwRX6kjX8fEx4/GY6XTKYrGoJnu73abX61U3rUH/6jdolmWV+u1kMmE0GlXin81ms7qZNkGrXq+HG7jMvBn3/HtMO1OO1bEGbTxWwM85hBAtZVU/KMhlvuYeYT4nS6kzMYUWgKxn+3MrX1lEPiig/TAL+2Vphcw1a0aVOqBW1hI0sR5xnw8p46i/JkqBU+hyDi/3qv9bhQYmClmQOboMI7ETEic5/eWsdGmQVegyCgSUYun8YRUrwc1HBR4ecB6ykFhKC9QZ7YiqZOdR9Rwe0qTQNfcmg1+U373MtWT1pfqdqvM3Hu2WsKp6+Vzl56qP/ijNEdoe1NShm/FNS02BfpBl4V/0kgNb2HiWRyhDTfdfLqiiMmKRL7YGw/+tPVozIp6hDNnxdrjgXeCp5lPsxDvcf/s+T9x4guaFJoNkwEzNOIk1S+IoPWKYahHIaTHdSuM+q9UF8cqyJMmSFRi7PQ/wyM1Gzx3joNBxOvRsLRK47+5z0b5IG50ttSyLzMkYJAPuxfe4l9zjbnSXw+iQg+iAxErIbQ0YnxsseAhTTCIh1d+7Qgks28J2bQ24LdkSH/Z+lUg8PFhoZ7EL4QUe6zxGs2hytXcV5vDE/hP0vT6XOpeIsoij+IjD/JBb81vcj7X+wHF6zEItVhl+dXaGvyrfkB6e0paWoR3SkA0CO8ASltZ+Iauy24VTMM/nOst+ju8dC0szMdDBZJMmbaGTSB3RoSmb9LyeDshVSFEWWtSROQuhRR/nas77x+8zSAbEVgxNiETErJydGm+pJEER4Jc+fuFzo3ODv3/h76/RtYG1ZKPjaD2HcaZFQyflhIPkgEExYJBrBsqkmFRAz4cp5zDj7QqXQAaVXkLX7tK3+1pvwtE2my27hYenAzMVMUyG1XiM0hGTYsL7J+9zND8itdPtaxc00BVaIYFcsiaEZsAIIShKbf9rSjrm5Xxr+UTdIaZpaeeygICu16UpmzREA7/06TgdrvSv6PIZt43neFXy1qzB54s502yqzyGfaD2J5fWd5BMmmS7ZMeUd42zMLJ/xP3/6f+bHrvwYX/nKV3j55Zcr6j5QudrZts3Vq1e5evXqmv1hXTuiLiwPrP0288H8v17SHsdxxV5IkoT9/X0uXrx4CtDYZF4bwULD1q6zsevOFptl4aYfJycn/If/8B8qcOXo6IgXX3yRfr+P4zg0Gg2uXLmy5vxntB2EEJXtaZ0BXo+DzefMZ8x+RqMRvu/jui6u69Jut9nb2zsF3tTvIXM96kKjppmxMfoWJvlrxqq+3z/8wz/kG9/4RlVWYtgVhkFy48YN2u12dRyj71DXZKiXfZjzy7KMf/gP/+HD79HzAg0vvfQStm2zv79Pp9NZEwapo1t/+qd/yjvvvLOGRrmuy5UrV/i+7/u+qp4kyzJee+01bt26VZVSAFUGvO4vawRMWq0WN27coNvt0u12KwqIoc/UUR8z8Y3gRUXztwsm9oQBA0ZixNgaM7EmjC1t82ge8rKUhElII23QKTq0shY9euyIHQbvDvj6175e7bsOvBiRGuVpUEB1FL0nesieZGpNGYsxRbNA+bVaZ5M5VsvMdS1bbOUW1tiiOCoQI4EcS9zERShBd7dLcDEgbaSaEeFop43cXi0A7NKmXWjwwGgXVMGBzJnLOVNrylRqwabquMqiqZq08hZdugRpgDvXJR5Xm1dpqmZVSlIXElFKcf/+/cq39c6dO0RRRKfTYX9/v6JOmQlrbhwzeetAw3Q6ZTweV+KeYRieso0xdKxer1fVc5Vlied5fP3rX+fk5GQ1T6Uib+dk+xn5pZy8l6+uhc3ZQWe91RgHwHZ3i43Py1JiFVYVjKPQSLksyO18rUznkej7DwuGsxWLQeTLD9qacaMc9WiAxHk1KNRKFNItXLxMC176uY9Xejhq6YQgChJLgxGxFRPbMYmTrLnEmCYLqeuIhc6052rJVjCCntv6+gCWiRGrLClPL+bLR7i2H7FVNnfLZ0apvjsigMbbHbQ95nfimAKBg1P5sUux/MJaCkvmKt9qL/dxNVvorLKUK5FFQ/V+0PmK2j/4+BwOvlvNEtZK0X1pw1mqUgumfUjLwv/W1pvJujbsBj23x563x5XgCtcb17niX6Flt6oSy3E65iQ+YZSNtENFfMhxcqyDgmXW+1ziiEoDBms2xEtdiUcCZc9R2mehS0Xt3MbNXfzCp1E26NChU3a47F/mx7/w43TDLrN8xp3xHd45eYfDYsmUKAaaMVBMSURClEenMuqn2vK5K9TKv71ku16GROKyLgpoSrLOHayeMQ5GW6Jlt2jRomt12bV2tYvDsiyhQYP9cB9RCmbljDvxHe4kdzhIDpjKqdZPWuohRCp6IChhxtuT+rtRJAIRC4pZgUw1e9USGnhpXWhhd2ym5ZRZMWORn98FQiIJ7GUph6NLGHa8HfJBztF7Rxo8uHiDv/nDf5Ne0CPLMv7vf/t/czA7oAxKrJbFpJxU39me8PilK79UJX9MUApUwnlm3WVo44babgJVk7g08UMcx0wWE/7dH/47/uRbf6KtvYMU1VaopiIPc0RDoAJFKlLNwHkEUO/UmFseLUeXl3SdLjveDuPbY9zEpet02Q13IdWlvdKS5LbWtSqDkmk5rXRTxvm40t/YJuoaSj2fDAPGEU5lA2ru4TRPNQNGxUyLKfNivrXfbUezI3puj66nmRNNq1lpPHTdrnZCWZZUdNwOruWuMc6VUqRFiuM4eLbHyy+/zHw+r4Leug5DXVdgk3lsyqdhZYVYD/rrwEVdUNC8bwAHE9wGQcD+/j6gNQXMfKm3uk5APbCuAxJmPtVBkDowMBwOeffdd5lMJvz2b/82Jycn/MIv/ALPPPPMmt7CJtCwqXtQn7umryaO2XTHMK/Vyyl836fb7eJ5XqUfsVnCvpnQrZ+zGQ8Te9UBmXoFgRCCN998kyzLqmR//TyByjzBHMckcCvtnpouRP280jQ9l0aD/dBPLNvJyQllWTIajfjUpz6F53lrgSLA3bt3+eCDD6ra+yAIKmQoDMPq4pgOGlVTM+gGhBiPx2vikpZlMZlMqp8vfelLFW2j/vAyg1C/yIYKYgbUVS672S677FbHMPu5d3SPk+KEslsSh7qUYebOOAgOeNt5e/VluQfyea3HYHQZ5EhiT22ssQUJEKHrLU8kjzmP0Z11GQ6HvP322xRFwWNPP4a752pLziCpgALz/8zRAVdhFxT9AtroL0aLiv41Lac4sYM/92kumuzGuzSzJm7m4ns+wW7A3NWigRNrwoF9wEROdI37svnKp1N2uFZeo5k3cdRSI0IoUlImTDi2jxkHY6LmCpmVStIsm7SKFs1i/XdsxZRiZYtjgAVzLeo3qHlw1edR/aEIqweHEfKsI3v1G8z8vYn6mWZhIUYCe2gj3lwdo3r4WfD45x+n+VyTW9ktJv6EuTOvzqcKOsVSHfkB2gmylFWwAlBYOtP/wEXe5vrovKKTZzUbDShwdn+lkji5o5kJS7vXQhakVkpmZeTyIWKdW/pWipLSKsmsjLk7P3NbUQqc0sEtXNzUpZ/0CdIAkQnKvMR1XJAgbKG1IOyU2I4ZZSMiGaHCLQts4ywiNn5Y/X7goles+iYLfQ2DZsA8nX/sAXlBoRdK57imRplfqY9e0nAeVwNTQgFUi6FHaQr9/EhVeuaiyRzHkQ6BpcUVHelo5hcFSZFUquqPenwTdJx3M3OvnkWH39ZMJtps80B3g+9SSUKhdLYuInrguVto7RMDxhgg5pQrwH/tuiVbrluJBm6SNGGQDnhn9s4Dd+EIh0AGWtjP7nPZvcz3d7+fnuzRkA0c4ZDlGTdv3eTVN1/V4KsXk/gJmZ9htS0WLMhERiYeTkc3NH8zN5XQYFNBQSEeMCmW51mwdAqSrJiZG2Pwf3zj/1gdCwcPXYffslt0rA5Pu0/Tps1z+89x95t3aSQNPv+5zzNnzv3sPgfZAfeie9yJ7vD28dtkZJSuZrQVoniotkIptKijJayqBFQqqdl9Wz7vCpci089aIQXCFlVJS72ZZ9YoG+kXzhrugb6HjB1i22nTD/pcta/q0hWrU7mQ7Pg7BAQssgW349vcSe5wP9NMiZP0hEmhHSRm2UwLZDu5Xu9ttAMOsCINSoR2yJXGFS54F3iu+xyu1MknaUmSImGcjRnnY46j40q0cJ7NuZ/f5358f33HF/X1/SP+iP/z5f+zetlpOwTdgL7fp+/16bt9brg32PF36Ht9Ju6EUpbsNfZoiqYWf6xR2dc012p19fUAx6wF6/X9T+8/zRt//AYykqihwrpvVTbtL730Ej/+4z9exQVZmTEv5wzigWbhzO5pJspC25QexxrUm+UzojyqyjkKChbFgkWxWB8PCQTVZFjNgWzpfCF9WsnSXcLusB/u80nvk1WpRGAF2JZNVmhAe1bMGGdjBsmAUTbSP8ZqMxtt1YloO20ecx+j63dpuk086XG9cZ3Hwse02OTSrnOcjflg9kHljDFOx1vXBUYnpOf21ko8dvwdXrryUqWZduHChSoBaNqmnlsdJHBddy2AN5+vB75mLW60+nzfr8ADw06u68eZOWHmy3w+X1unm8/XQar6+t9sZ8r366BBURS0Wi1c160c+wyAYvq2yRow25tjmmB/UyqgDhDUQbe6nkIdxKlvW9doMIL7Zjzq18KAAaaZ/ZpShzrYY96XUlbn2Wq1KoeJzbi4DiqYa1PvZ505sm37h7VzAw07OzvEcbzVRsVMjNu3b1fgAVC5IQwGA9I0raxLjDXlZDKpBscwEpIkqcQ2zKDWrUA8z6PVaq2Jb5iBgVWmu07v2EY3MRfeTKA8z7GURTtt01w0cXO3YkeUpV5EZkHGxJrw+v3XubO4o4UqLxSkz6ZrpRJiISrwwZpYHDWPUFIRT2JKpfsbiIBG1KAxa6xNNHPeKSlZmDGWY+7M7jB35qi2FsdUHYUKdEY3CzOyIGOmlnS3WobWKR1aRYu+6rNX7vFU/hQd1cFTHgUFM2vGWI4ZizFjOea+fV+XaNTqvRplg07Z4Up6hZZqEchAB3ISMpkxkRMmzoTb3m3mchlQ9AEFXuLBCMRYEJURqUxppA3CPNQ6E+XqIWMmcX3iGj0Hc61MPVSdqmUQQsNqSZKkEjSpzwFg7Xe9fqtewtFNuzw5f5L+Ub86Tp7rEpJMZWTdjEEwYOSPmHmzChSqKHbLwLaU5dmL9BrLQSixqokXWgeh7j6yud3W/59xjLXf2zLzarlodnSJw6nPm1ZqwVInd3BzVyt2O5riqYSq+nxuDYnlMZRQpJauPcSDAYMzx0yUAq/wCFWIPbWxj23s2IYMVKYqGr3lWBR2QemVqIaiCApUY3m/bD7tzjiWEkqXNy3bNF0XnJJCB8e+7VdK+Eop0jJlGk+JitNUybVzqVH4z9sK1gEJUwLynWrntVg02f9t5QgGqNjmWFE/TlIm57L3M0r5nvCqGvC8zJkXcyIVnUuo7axm+vYoY7pJHRdnTf5SB0Ge42H84j8Oy8WP0gzIlZKeDUgsh0Io/awWCITUQd4pscSlYKv5fqu3jzxXP2R5Wa0DH759DABLpjKyImNSTLiT3oHTBgO6WcBzIHOJndm4qUuYhFxdXOWl73uJvten63cp87IChibZpLLMPEqOKrHLWT4jKqKHApkG6DMWq4YZIFLBrr2rAzMbYhVvpbdXgCIp03zKQX6wfoAJ4C9/3lkJJYZWSNNu0nJ1+WYxKgizkOvt67xw6QU+fePTdPtd7iX3eG/6HvciLVA4yLTTyCybsSgWRGX0wKy2AdQq0FQpLbwpLDIyIrWd1m7o8OY7chsoUVBoano+gQc/8gEt6Ne0mrTslrYJdfo8336eT1z6BJc7l3Ezl+K44LWvvsa///1/TxRE2pGtW9J9vIu747KQC0bJiGk25e5Cl7S8NnmNSTY5dbzQCrWdo9vj8ebj9BwdaBonBiEEaZ7y9de+zjAe4vQcZEfqLH06ZppOmagJk2jCzejmQ8/PEZrl03Y0+2PP29MCrUu3jLbdrqwm23Ybt9TuZWYtZts2URRVdHoTRJmMehAEdDodkiRZo4rb0uaivMjlxmU+4X+Coqdr2k0W2qzvTGnDKB5VQMxJesJBcsBJdsIgG3Dz6CapnWqwtozWvpcKVTAv5syLOQfJwdYx2Gy2sFcAnN2h7/f5ZOeTlXaDL30NCMqlXafImWbapWNS6rKHe4t7PNN6hi/5X9Ln3bDX4hiTLE3ShFk+Y1pMq3kZiUiXrRQTZsWMaTrlvcV7TPMps3LGfrDP09bTtNttsiyrEoL1tlkmUI+pNpN1m2vtTcZDvXSirt1nGMt1pjzAzZs3KwCgHteZ0px6Zt70Kc/zim1u+mJKFZIkqeK6nZ2dygpzNBptjRPrca4pM6izMzaBFjMW28CR+pg4jlOdr+mjEIKjo6OKDVQfV2CtbAFWSdU6WGH6Z+6hnZ2dKq7s9/tMJpPKhaN+feqAhnmt7jK5WaJSTxKfp50baDCepr7vc3h4WN38pswBYDKZoJSi3W5jWVZVYuG6boWkmMly//79asIYBkTdEsScXJqmZFlWBZRXrlw5NbHqE3ATzalPxHo5R50WYyj79X3W6V5SSmQp8QufMA05ev+I4VvD1QUvC1SgKLslZaek6BT6d7cgfzznzfBN3uRNeB7ED2sQ4s3yTc0+mLv4C63N4CWrsg9XuNgzGyI4fueYMiorsRKlFGEnpHu9i2orsmZG1siI/Zi5OyfytEBQJjMGcsBADVaLPZOxVYJQhXRUh51yhyeKJ7RtaNnCKi0iGTFixFjo0pKBNeCec28FJiz30SpbtMs217PrNPIGdmkzHU9JsoREJhxnx2TtjFFnxFFwtFq4KfBSjzANCdJA/166Z0ihLRqNpoPneXieRxRF1QNhm3evAY7q/q+G9lO/8es6IGau1KlRpiSjTtPK85wszXByh71yj51yh7IsCYJgBZZYinE4ZtQYMW/MmftzIicisRNKa3kj14CIM4UkAUqwMq3/YJWWFnBUitIqKe0aIHEGgLD2+6xjCFZMCrHls2Z7CamXknopczVf335bWwIpstALNYGoSoKUOEN/Yds+a/1XUv3/mfuzGNmy9L4P/e15xxwZOZyTZz419TywSZFs2hIp0dZg+wqGHgzjCn6yBVi4sJ/8cgE/6NGwIPjBNiwBugYfbFiWBVxJkCiKJi1KTcocqpvNZnVVV9WZhzw5xhx7Hu7Dim/lisjIc7KK3cJdiUBmRuzYe417r+///b//R2KrFGfsAFuoNIo2Kxlc9GliCyu2sBc2zqGjMoykFrdv3KbT6pAkiRaOXNgLEj8h9RUtNHNeHydb1UoYNC1TJulk4zGO5eA7PqEb0vSWQn+W8sDERcw4Gr8xVdvrylW+51pKkM7CeiN99/OW1+kdXMWQ1ilbOadAXwZyFBTMyzlz5q89pxgKgR3Q8lqEthKzXZQLTU/9SZRLx8RWfRFX8UpYxqbiVIZRhForGqS5CsPpMwB9bzx2+bkAz6+bc6axtw4syN/CyrksREgA1wvX+TzG/o+DgXHVcLEfV5Fng1+R+RlZK2POnGOO+d5H39v4Fc/yaHtt+n6fnWCHd7vvstfYYzvcZsvfouk0FYBQKS2Tk+xEay6cpqeM0pEK4SjWhAtdOOBA1Wk5tK7l0nJaNOwGLbel1fltS4ViZpXKqBKVkab1r4MAFRVJpUCLYT5Ub3bQHvxHPOJfFP8CPlH/26gwPA+PsFbpJVt1izvcoVt3FZOyaOPhsWDBmDEzd8aECQt7QUREYifklmLn1Y6i3V+YfsvnlF3bWp/JdAI4OJu1mljeb5aZKqqq0sDE+jxO65S0SDkrzniSPDn/4OD8z213m71wj+lXpjAHP/fZKXf4D+7+B3zj7W9wd+cugaMU+U9OTjg+PlZe4rpgkqsY/GE6ZFJOiIg0xX+ymPCyeMmkVAb2ilBjA6zQIqxC9tI99tv7fKX/FUYvR3zprS9RxiWddgfXdZW2RJUzLaa8WrziOD7W2RMkE8Q4H/OMZyv9s+neYWPTdJv03GWqR0eJlKdWytHdI9zUxUmU0CIu/PzP/Tw//dM/zWw24+OPP6bf7694ik3qvjgm14EGMcqd2mHAgGveNX7p1i9pp9a/+lf/im9+85tkWUar1QJgmk5VaESuUpMepoccJ8ecZWcMsyGTQmkeLMrFebrUZZFxmeQTXvACLibJuFA8y6Pv99lr7jEIB7zdeZtJNuHvfvJ32Qq3uLl1U6fH7Lgd2l6blt+i1VJh3npKLw1aM+RB9ry9Xk87eB88eKD304vF4oJxadLw5bxwbmyue+3lPbGpZP9tUvkFCJLviAabjJ+M2WQy4fbt21qMUK5tCkKazm9TsFDsPjHQ+33FKpdQbEBf++TkRNu4pi1pGt2mPWiGGUgRMUcTcDEdo/JbNOsajYbuT9OmHQwGdDqdjWEUcl7pP7PvTYaJZVlsbW1p5r9kzxAQxgyJkLFet4kFoDDfk2PlPbHd31Q+E9Ag1I4kSXTKQ2E05HmuxQLDMGQwGOh4fAmbEHBhNpvx7NkzfbxUXpQ/P/30U7IsW8mFalkW3W6XnZ0diqLQaphmw+VvM5bEjAEykSdZCNPplNPTU/0dmYQaATWo+OvKo7Jg6rrGWlj4qU95UOLW55PYdV2+8PUvUHZLXkYvOa1OqfoV+bWcw94h6a3zDBNWZRHGIY24QTNp4i98nKlD1s1UjL/taH0Lq7BoRk2lHDw/F8WczWZM9iZk1zIG7QGOq9T/S1sxGMbOmJk9U6q19oIFCw6cgwteI7d2aVcqXehuvctuvMt2uk2n7lBRsXAXmgkxtaecOqc88h4pnQcVBqSo51MLe2rTO+rRt/q4lauzPWReRhqkxEHMqD0i9uOVOvipTzNXAES7aNMu23hbHtZCgSQtv6UXkiB4JkMGzmlWUswQGxNhNMEoUWYVwEKQXlnYslhFwdZcoNmrjEatRH1kXsqcKuyCaXvKrDtj0VwQhRFJoKiSlbMGOlhQeiWlb2zU1j17JRCplJVWZlFmJZZrYXkWlVtRe/VqBpTLAIn18jpD5Ipxv28EUozvWpW1Enus++Ky61u8XiithNpVLIbKNzZ9LnwSfnLhvE7pEJRKS6IVtZTIZe4p1onl8Ff/n3+VcTLmeHHMyeKEV/NXnManjOMx83yz0VvWJXERExcxo2S08RhJrxbYgVIdX9KdK5ZARp2S8PmNYh0+cEmxaktt4pcp3VzHJScnKqN/Y1keCoo3isqJgr/DMs1ppYQ8VwAJY0xrarI6IyszZuXM/GAjU8aqlbCqii5SrLDSLj+7qOplxQTMXgPMgLpuWSsxwNcxX2wUGG3Z58cUdXG1lJk/4TCIy66/zspZLxWVXhPy/zogYWOvpKH8ibJCrthPwgYABUL+WMRGr8jiyOtcp8d7PH/8xuN9y6ftKmBi4A/4cvvLilLtdGlbba5tX8O2bP7xP/3HvPfN95RoXzVilI90OsFFsWCYD1/LJHAtlWKw6TYJrRAyqPKKXq8Hlro3JYUCJcbx+NIsQQIG53VOZC2pIOYxaxmjrNrCqR28WoVzhFXIoB5gz21aVYtO0SEoA0qnVIJ59oIkSMiCjMRJSJ1Uxf/bS4PxkjFwcPBspT8TuAG+rTIWVFWlxXHjImZWb7YsBQDWbVzO47PijDP7DO6g58BjHvP+y/fhpfquaIO0rTbVrOL+tftsB9tKe6Gh0kS+G7zLTmOHntdTQK7hcKvrmriKdarMYTrk/Y/e58HhA+7dvMfCWnCanvKUp3z8/GMm+UVKfmiH/NzWz/HfvP3f6H1wVVdEVcS0mnIanep0nNNyyigfnQtAFsq7nlQJ82KudD6Sl+d9UzlUX6ouZLb6zew36fxWh7bdhgh2o106doeu21UCkF5f6xS0LaVbIGxDODdEzVSDYRiys7PDbDbTxprs+V6+fEm/36cuarpWl67V5XZwm68HX6funGfJWzcmCwqdYnaaTznJTzhKjxSwlysBTA1MVOmFZ3Re55ykJ8yKGU/nTzUIn5c5+TinPrx4b2m5LbbCLa3V0HE6/Jfv/Zd0/I7eg5oGvdhXkr4wSRJ83+fw8JC9vb0LITBSTMNXvOdiowHaCBfnr8kshlUjVYxcU2dAQqtlTvm+v6ILaIYnmDZanucMh0NdDwEhLMui3+/j+/5KeEQURXz00Ue6T05PT7VD27T5pL5y7XWwRY4x3xNAxQy3MM9R1zVxHGswROwK0ZZrNptsb29roMYEaaTtz58/13aFXGt7e5tms6mjClzXJU1TzfIWIGQ2mxEEwcqaWNfPkP/FjtrEZBFb6CrlykBDFKmb/OnpKUVRcPv27ZWBHo1GukGSY3SxWGhEzfd9DVa8evWK6fSc6mVZSgik3W5r9EVACOlk27Zpt9uMx2OdwUAWj0weswPMm4v5nvxdliVPnz7lu9/9LlEU0W63uXHjBnt7eyvolfnb9JDLuQV0kNgzE0mTOrTcFlZskZ/lDIoByfOEnVc7avLXBWkzJQ5jFv6CqBGRNBLOts5IbiTqZvungBqcudIYqIc1JNAIFSBhZSqeXK4XtSMO9w555D9aGUOv8GgWTbbKLW5XtwktRdvChsIqiO2YY/eY1FIP27EzZuyMecpT9UA32Ah+4dPIG3SSDr20x7v5u7TzNkERqIeMM2NqTzkpTijaBfPtOaPWSKWiXBancGimCki4Fl0jSAKs1KIqlXGY+CpOdd6YM+wMycLVtIpu6hIkihWx7WzTKTt4kUcra+EV3gorQeaG/DapUOZClhtDp9PRFKsoioiiSKf5lMXZbDY1mGaimSbSuH7z7BU9ytNSL1ITZZwXc0bNEckgIetniqnSyFUYgLO2CatRxnYLilZx/h6sHlehPft2ZOvNi+VZNPtNaq8mszNyO1faHZcZ+Osb39fto68ALqz866xtzNdZFpu+K59vOsZZvjw5dP2C6mXXKnzFr3zcyqWsSiI7Yh7OyVs5uZ9T2RX/9b/4r89PbTnsNHe41r7GF3e+yLXWNXaaO3i2pzaW6YxRMmIYD/VrkkxIyouAQU2tDO2qYMEbdAyWooq+5SuaulWTFAlpnV5ZEGyl/ShPdeEoMOKymH7f9gmdkJbTInRCXMslrVIWxYJFvvgThStctRTLH5Nhs6mYaQVhSXlGxXtfZjTUtpp7mnH0miJq8hL7fqWUhVdlD6xeaPnVy89bUSlmhoQ3vAaU8O1lZoOlmGpZlVcHJf4NF1kTl5UKZcg5lqNfVV1dMHolJAAUIPGTTBEqdXpTcS0Xz/LO505dXi4A+xMCg7I6Y5gPGeZDHi0eXTxA3tqFf/nyXyrGhNtWaSSDXb7Y+SJbwRZtu03baePbPiUlw8WQYaYEIM/yMyblhLiOmedzRuVIedBteDF7cbGplhLDtotlFqfKZXewS5EVTKYTLMeidmoqT4lsZ3V2aerE2qopLOOeJmy3/iUdUp+zQtpem+v+dbaDbXbCHZU2M9ylqAvm+ZxJNuEkPuHl9CWTYsKiWpCSMs7GFNXlIpQOSzDXuIdL+IZoaKRFqs5ZG5pGJvvTChg0BoROqNkTeZEzYUISJUSziGk2vRDzb2HR8xVlfyfcYRAMlMbC8v+dxg73+vfID3JaT1r8W9a/xU/91E9hWRb/5J/8E375z/wyT5494Wh6RHOvybg41xgY+IOVPW/oh4RVyI69w93m3RWHjux/THH2oiqYlTNG+Uinlz1LzpgdzXj0jx5R2AWpm9Ld71KFFTt3dnj3m+9yMD7gZfWS9qDNMBnyNHvKaDZiUVx8hoZOyFawpTQmwoH6WzQnwgE7jR2Ozo4Iq5BttgnCQHvaJYx7PbzXjPsXg3vFC2y5KtWsv3s+zQwj1HRW2rbSQ5OMNZNiwjAfqnSWdqbCO+IzzuIzjmZHTEt13Po9IyoiiqhgnIzxHMV8Hp+OGXgD3r35rmJ0Jhbfeudb/OKXf3FlDxwEAZPJhGvXrjEYDGg2mytiiiZwYNpk0g4xdk0gQMANs1/EeWfOCTHGxU6Ul9m3i8WCdrvNdDqlqhRYGQQBR0dHmhlgWRbf//73uXfvnmbUV1VFq9XSuhBiU3a7XXzf58MPPySOY9rtNnEca2a+/DbZCqahv/6ZsCrWndnrdqcJ+JgghJxDGCdpmlJVFY8fP9a2dqPR0Ok4b926xWg0YjqdaqaC2CytVotHjx4RBIHOcHFycsLOzg6TyUSzKOQ6Uh+TuWICaOshH2Y/SHuuUq4MNIj4o+u6OiuASbcZj8c6zaAABnEcE0URrVZrhWIuHSuD4DgOW1tbtFrKQ93v93UKTImhEW2GPM+Zz+c6baJ0mkxqScsii0iQS5OtUFUVo9GI733ve8znc83AkDy20k65tiwSWXwihGIigvv7+9i2zYsXL/S1BSCRPkvTVBuXeqBw8VOfVtxiUA1WBrcoC6aoGMyiW1B0C5JmQnG9YN6f80nwie5PP/dpJA2CRUAYh9z95C5hEjI5nZDWKUVzmWGhUzNtTRk2h+TNnDK4mHpw3aD0auXdrSsVF1o7tboRehmT5pISZhSrtPBzHy/yqA4rvBceu8922bK28PBIwkRpG7Ryik5BHMSc9E6I9qIVIMHLPDpFh27WpTFr0J/28WqPslAb/MiNVKaNMOJF8ILIjah2DSHQLypGhTNzsGc29tTGnbs4U/U/CXosZBzlhicLUJBQYfLI2DebzZWQC5kv5g10nVoFF9V5zbkCcHx8zMuXL0leJitUsTAMsdoWJ8EJk+aERXPBzJ1RNAolXrTJ+7oEI+pWTdlaekpFKNFmRfU/KAI6RYewCLFzmzzNqZ2a0lGpOgu3IPdVus4raTG8jnZ8Fcp2zUXAwQQWatD2iPWa9m86t1CUWYpWXqb4VQMl9Jt9tSlp7bDd3FaiV3VFUiQ8Gj3i/YP3OV4cb2QudIMut3u32W3u0gt7+rsfPfiI1FKZABbVgoRklUVgGKgVlaLdlq/RMVgeb9cqzEvo91rYbR0ksri41jeUrMrIqmxjDLCqnnWeW35Jq7Zqi6zOiMtYeW0+Dxiy+WKvLRqQuMqpKmXcWJXa6ONwLpR3yXXeZASvVnWpXVEbINrnNB61qJ/xs6lul5Wsyq6U4UOAFDHQxYC/ql7Hv8mi2Qz15raL9gecZ1mpqS+0Q1JaynpJ64tr7HU6JJ+lFPWb2TsAVqG88jZq34OtvJxF9W8eHMrrXBmC+YiHKNHL14FaLq4Sn3N77Pv7dIIO1byCBdzZu0Oz1SSqIhbFglk54yQ64cnJE1I3pXRLskZGYieKKWZzASBwahWS1nEUZTywAtpBW2VYQWmEJGWizp8q9sVr75uWCsGTNj6Pnr+xT3zLV2FZToudQBnvA29Aly5VXpGREVWR8u4XU2bFjFk5IyojpuVUZU3aNIbL8A0PjzIpVUrmSok/7u/s0262lXFXl8yymcpkYo04XZyuNcmi43UILMWUawRKoPB0ccrLyUsKVMafSTpZXQ/78L8c/C9snSkdhbIq+c4PvkPbUkDTteIag2DAu513tfaDW7t6Hy+vTR5cODdMTBr5NtvcLm9TVSrrRJIkjMIRfyf4OwA0ygbtSRt34XKvc4+/evevMuqOeFw+5i/+0l/UMe2NRoP+Tp9Xk1ccjA8YZ2PKsORwesjzs+eM8zGpk3IYHfL9o++rcI9iemEcbGw6ZyprQ8fusBUrtk/X7Wpdhb7Xp+t0tf6E3C83ebbXnZtimIljSmyoRtBg394HlD0j2TyCQAEfSZLw/Plzqqrip771U/zab/0ax4tj/L7P9bev8+GTD3l0+Ih5PScchHpd/TD5Ib/+ya9rAOr+4j7/6kv/agUoEceu2FmNRmPFNqvrmiiKyLJM6+NJ+wSUERtJbLJ1kXZpv+yRzc9kr22yIOQ8nufpa8dxrIERx3GYz+crTuHvfOc7fPe732V/f59ut0sQBPzCL/yCrqv5Eg2QPM9pNpuaFS1af3otGfPYHNP18TWZHuaefh2sMAEFOc4Mw5AQcctSDH4TsBFR/bquSdOUf/AP/gFpqu5t4vjc39/XNmoURXQ6HebzOfv7++R5TqvV0jZMGIa6zy9rsznWZjtNsOEq5TNlnRClzsViwWw202hKFEU6jn0ymZBlmZ5Qvu/T6/VWKEamIAgoEEPoPFVVMZ/PLwycoDMmQCHMAkmFUtc18/l8pQPWY1vkb8mhalkW+/v77O3taSBgOp1qdoZMajl/mqb4vs+9e/eYzWY8evRIT9BWq8W9e/c0yCJUnTAMmc1mzGYzHbLR6XTY3d1d6WNTxESQ4ugsonHSoHhZaBHNPM+xbIu7X7yLf90nb+dMOhNO26dMe1POts8oPSNmNrfwZh7+widYBLQOW/pvL/coGgV5Q6USyhoZ1sCCASwclWM4t/NzL+JlXvPKwqosRQ+2a9IgJQ1TJQwJLFjwhCeKmVE4eKlHmCgF3xvzG7SjNs2kSVInTKwJSZhQdArqXs3CXzBqjnjoPTwXqqyhWTZp521aeYtrs2t0yo6iadYwjaY8OH1AEiRU3YrsbkbVqVZmvJWqsA4BIpyZw+nuKY1GA2fmEE9jHFvRyhaLhY6LAjQ6KBQtWZQCNAiwZQIOcoOR983FKlSw69evU1UVL1++XAnJkPCct523YQqjpyOePXvGdDplMBgwX8yZ23OsWxbWDYu0m1J0CqpmBQHnhripZ2AYmqmbkjopBIp+ui4sCkozohE18BIPJ1VxsxaWont7pdIF8XNyN79o3G4q63sta8Pfr9NycDkHI5YCpesGtZVbylO//Nz27c11M+tiXtuBcTpmnI55NNngAVwWz/YYhAOuta6x196jF/Ro+S08WwkWCtvhcH7I0eKIw/LwQvYLu7YJCfEqT4uRVVRabO21YRTLOldWdbmYqLTTQqdZBbVezf4wM068ycisqS/GXF+o2jLPudWgaTXxSo/QDykpSeqECKX+fRVByB9XkfSyVyligGOp58lVwkq0UXo1raTXlqsa+SJ8J9cXb/lVDVOTYfOmInNEhwzwby49q5SrtEsAgsvqVVNf9I6vAXByHQEkXge+CJvmddd8U6ndc0ArqzboCSyLi0vDbhA4gU4fW9QFWaU8/kVd/ETG43X9XlAor3cx5mnyVKcKJoB6UsNk9XgHB6/p4cQOjaSBl3n4uc/96/cJvICHzx7S7Ddp77RpbDeYl3OlK5FOOUlOVEz8bHMbLSw8x6Pn93Arl0FzQMNTQGjgBQzHQ0azkQov9FH6LRuYZ+vFDMs6zA7fGHfvoLIVNOwGO/YOXadLz+4R2ApEWuQLxpFKdzpJJnhdj6k1pQgLcjsnsRIm8eSC4KQAaP1AhaSSKQHwuzfv4tQOR6+OqOqKTqNDTMxpdMq8mm/MutR229RpTSfs4NQOi3hBUiX88fCPyeqMlJS4uqh4Gdqhyq7h9+m7fR2SMwgG5ykX3b42ziWMw9yPm9palmVpxXs4p+4HQaANUnF6ZlnG97//fc7Ozuj3+/ylv/SXqMYV0aOIwAr4s3/2zzIcDvmND3+Dqqr45V/+ZbrdLr/6q79K4RR86+e+xf79fR6+esgoHXE0P+Lj5x+zsBZUQcVpfMqiWvAqeaX1LzYxaVpOi57fW+kDSZnZ83r0/B49p0fLbrHtbNNyW9rAXKfWS5+Ik9TUPJDse1VZ4eYu/aLPdfc6377xbb5if4VPy09xXZef+ZmfIcsy/vk//+ekacov/dIv8Qd/9Ad8+PRDfv5P/fyKsxPQ2f6EPSDFdNYKu8D0cJtGMKwKH5rFNFDleLHL5HP53roooed5LBaLlb2y2JFic2ZZRrPZ1CEge3t73L17V8+pdXHGqlLZT0SIVACTOI61o1GOM7M8mPXaVMw6bnqtAxQmMGP2gQArAlbI2Mj/oh8Sx7EerzAMmc/nSt9v6RwtioJms0mSJLpNrutqu9Tsx/UiNs16/aWe5jhepVwZaBDxDQlTGI1GNBoNjcwJXVwUYcVwEoaBDLgcLwiXIDziLTYpS3Jdy7K0zsP6QJqNh4uxM+sLA2A+n+t0nSLMMZ1OFbK4pJZUVaUZGXJeuaZkNZA2yg0A0MKFgpKJoEyaprq+gmz6vq9/mwarTNjpdMrh4SF1XdNoNOh2uxwdHSkwogYndejNelhzi3SUEn8ppgiLlc2tlVvYmU3plcSDmGgvovINGnEFwSLQwIM/92k9b/G16mvsuDtQwMdPPuYsPyMNUxXL2EjImhlpOyULM0q/VDHxnIMAF8oyTzao+P2klZC0E8aMec65F8GqLJzcwU98mlmT3WKXW4tbbKVbtOoWqZ8SBRGRHzG1p8zcGTN/xlHziNiNV85jb6n0o9bEIjgIcKYOJKp+dVBDD8pOSdkpyW5lVN2K9733eZ/3YQ/cyqWVtWgVLVp5iyAOaOUtdv1dOqWKDbQtWzNb5IYl82VTqM06/cqklMkD5vj4mLOzMz0vzFQzAqqZLB7RGLFrG/dTF/+pT1AGev4XRYHt2mStjOp6RbFbUA0qvD2PzM/IrXzF2DTBHHPDXXqKRUJrzatlGqmVTVAEil2TBnilpwCoGnI3J/ESEl9l6vjM7Ih1IMIMn9h0Dhu1iZRz1MvY+3XGTqVSe1rluU5EaZXkTn6e5u0NJa9ylWIrGfLR2UeXHufg0A7aXAuv0fW650wAp0HDbWihyUWxUBv2fEyUb94ASzpIieFMquSix3S970xa/iXtMo2oTcKFDs45FX9p3LzO0681E+qMiVgaGxzsnuXRdJUafdtr4zkKpInySCvp/yQp8JcVzWT4/68ogwtFp0t9Q3FwcG0lwinjl1evSb27oeg5coU+sYyf17EyfhLls1xnXTAZoGf3cGyHuIo3GlrrpaTUgI9TOzp8Z704ODqU5fMCNAUFs2rGrHq9pevg0LAbOmOLiDeWlOTkmi31Ovr/5y1mBqtNpaRU4ZQdiOpIhbRh88p+pfrkzvLADHil2tIP++yEO3yh8QW2G9tsBVsETqDCUMuU09kphVswihUgMc2nTOIJo9noYoiLf35+x3LU/dhr0fTU79AJ9X0uLmJG0Yi4vJiR4E1tXFQLFtWCU04vP9BfvuplirzUxcs9WlaLd+++SzNs6hS0i1wxNk5np1R1xbgYk6NYiC8Pl3oHcn8fq0xJTu0QEhIQsD/Yx85tyklJz+9x7do1Pvr0I3rtntJP8BMm6YRpOd04f1tui6bTxLM8XNslKlR6zkeLR2RVdilw3HE7bPlbOvWnGOc9r0fbauNnPm7mqvSj2bmRa7KGxVgSnTbTIJc9lDBOzdBtceQJ49q1Xbpul9uN29zv3CdqRtxZ3KEsS+7du8dkMqHT6ayIGSZVotNVTgrFihCxRxHCfLp4yg/GP2Ccq7Sa68WzPJUS1e3Sc3samBC2hIR67LZ2ecd5B985p/JHUaRFyMUxa/aJGN++7+P7PrPZjLIsafttelWP6+71FSFGOBe+F50DM3QCzoEDyQqyrnsnNprJ7hWmgexvzbBiE2gQO0rEHlutls7GIA5mMZTlvCYjXIAQ+T6g7SWTObyuywYqREJCMRaLBa9evdL7c6nnugilaV+aITUmIGMmFJDPTfaCGSbiui7Xr19fYVCLJpwJjoidLOPU6/VWUmi+88473L59W7NG5vO5BihMtlEcx4xGI/09U0dD2rSuZSL9LG1alwa4SvlMQIOkBmk0Gszn85Wcn1LxdruNbdukaaqF9SRdh3ScaVQLXUQoIwcHByRJcgE9kTy65oCLISXvmeqo8pkUk+GwWCz0IgVFV5/NZjSbTW7cuLEywOtghrk4giCg1+uRZRmTyYS6Vhk3pC6gRDRPT0+ZTCYroh8CIkhGDpk0ZuzO2dkZ4/FY97N4yWXA5VyTyYSjx0e0vtOitmqa15qkYUrsx1SdiqpbUXdr6m5N1b0Yq5z5GbmXM9+aU3vKAPmUT6GGRtEgaCggwpt5+HOfrckW/tzHLV1NDS69ktiLSfwEf8cna2YsggVJIyEPc4pAKT7rsikOf0mDLbyCwi+IrGj1wVwr6mRYhVrYaZAP+EL0BbbTbazKYmpPmVgTTotTHo8fk7dyymsl+bs5dWhcP0eFUExVClL3uYszc/jme9+k2+2S2AkzZ8bcmTN355w0T5h35xTOuaHj1i6dsqOUr8s23bKrs3B0yg5hGeLY52ihuWDXKVUynnme67zGZmiO3IRlDWyKjZKblxkXpmPHyhp/5lNNKvyPVerWn/mZn6Hf7ytgMLBZeAvG7TFDb8jEnTBxJkRWpFS6Nxmq5vgt/67sitiPlbBny6yc8WdtEdQB7bJNs2wSFiF+4UMNC3vBs+4zxUawDLXqTUbQJlbCZToSlwESFuSOytCyIiy2dlw36LLX3GOrsUXDa+BYDmmRMktnDOMh43RMXMRvjNUuKZmkEyZMeJW+eu2xvuXTdJvshDv0/B5dv0vbadN0mzScBo7tkJYp03zKWXrGKB2pvOGSA95sR41mSGxq32Vlk+GhDdpLnjM2tsp4YSv6d1VX2sP6upLXud60XZYuTtgRoa2MJqHEZ3WmYqY3UN8vadjnDmX4TKVChzB9nuuZIRiyHv6kxmBJSVm9GZCQcRQgy0xt2LeVhzKuYt3vlzE9Pk+dxej3LE89X/4NhAxsOv+kmnC+ZBStPbRDul6XLX+LjqvE1tIqZVEuVHq5fEZSKf2U1wlj1nWNhXWeRrGqLrLIcPAcxW4qq5K8zi/eYzYBsWvXmldz5tXrs7XYlk1oh7S8Fl7lUSUVoRdiuzaVVZFbOVEREZcxeZX/+NkSRkgbsJFlVlJylpxxlpzxcPJQZ70o65KsvJjFx8ZmEA7Y8/fY7+0zCAcqvKPZ5uWrl7w4fkFvt0ft1Zwtzpjnc2XAJ6dvnG+epeZC02uqdJ1Om7pUDJm0UgyApExUau+rho4twycqt6LyVFhfRMTJ6GTz8TaKyWB5NOoGQRFw+9ptfHyOjo+orIpGt0Ht1AxnQ+IqZmbNmAyXYRM2KgTxJdAEYvXc2ba3aZZNdvId3NxV7QyaXN+7zk5vB8/xOIvPOI1OlcFdTpnlMx0asl4adoPACvAsj7zIOSlPOIqOdF9FZbQyn77hf4MwDbUNkec5z549007MLMsIw1CzSsUGEOq7GLViwOV5TpqmDAYDTcM3nZvyajabHB4e8qMf/YiiKLh165YWCZQ9W0jIDW6wb+/jNl1tmK3bJlVVkZUZ80qlnBznirUyLadKi6JU/XUcHfNw/lCnpDT74e/90t/jC70v6DoAen8IaNtKQgGk3fKe/C8hD9JvpnHpOA5pmvL7v//7hGG4onFggjimR94EGqStsteUPe76HlRC1uUcZqhCmqYsFgu2trbodrta90BCJ8z6iGNOvPwAjUaDRqOhwyuAlc/XQwDE7ozjmK2tLU5OTjSgIbppwIW2mHak+d4m29TsK1i1T+VYaZccKyKNEjYj5xKxe2G8N5tNPeaWZfHzP//zBEGgQ0tGo5EGk0ymyKeffqod6qbtvG7rCjBisjoE5DAZH6Yd87pyZaDBDAeQxTubzTQoIBVJkkR3moALonIpjW61WivGc6PR0HHvR0dH+vtS2u32Ss7R9c/NwTepSOuLQzrUBEikzqZhZ9JZ1icJnOczzbJsJaYnSRIdJiJ1EbZGs9lcWeBBEGh6jMTkuK6rQYcsyzg6OmI6nWqBTNu2uXHjho6hkiwTx8fHpGmK53n0e33cwqWX9CCBYlisTIyiLCicgqyZUbQKirbSbqg61Tko0To37mIvJu7E0Ab2WaXeJ+BOXdypiz/zscc2/aLPrcUtOIbxaKwXlR/4tHZbOqQiDRQQEjUiklClFszdfHWjtcEbW1KycBYsnAXH/vHKHBUV/bAMCZKAclHivHAIjgLsIxvLtxTQ0oeyW1K0C6puRX4zp/xSCR78Nr8NgF/5dAqVOqtX9Li+uI63UN6FZrNJ5mda8HLmzDjyjngYPiS1zw0dp3Y0ENGp1KuVtzQQ0apauI6r0UtBPGUuydoSpFNuZgJSSRiPCSzI2hTkV8ZeinnTMWMlfcsnrEK2xlt0TjsXkNzCLVgEC4bhkEkwYRbMmLtzEiehsIs3AwH1qnc8sRMSO2Hkjc7HesM5nNpRAFRdK8rnhlSWV7I/NtXvsu9tOHaaTpmmUxitsTmM0nAbXGtf42bnJoNwQNNvUlYli2zBaXTKweyAcTImLt8MSMCSoptnCji4XCtSG99Nr0nX63K7eZte0KNaVDx/+Jx3775LYAdYnkXu5RxlRzot16JcfG6D4bK0ZRWV2li/xpa1sbVX3ULFHWfVm9N91tRv1qtYlsAKdM54ocjrDe0a8LXJ83pZ+z5T+QyhExbn6fTWf7Au985rkUrLUAdfesqvlD1kE0BnGeO4VreKinE1ZlyNl020lXfS7dNyWjTtphIORVFti6pQcfPVQm+mRbvj8iqptm4U/TPqK0CMyQ74SQESNbUKYqoypumUF+lFYUOpk2/79LweW+4WO+EO2/Y2vuuvABLjfMy8nOtUkJso7SVKTFjauIlhhAVUik1m1Ra2owTm3ihUulaqulLe+nKJ8tmoNbxhCjmWYkk07AZtt01ohVz3r+sUtNNiyiSbEJWRAkc2tO2N5Q172KIuKMrXg5cVFafJKTY2J2cneLYySiuUxk4VVLAmP9OgwaAe0LJb9Bo99gZ7irlYZczTOVmdMYpGRIXSmpgnc2pqdr1dsirbKNZnY9OxOzTtJoEdaCZaXuVkZUZ33qVVtHh19Ao3dLXGQNEsKMOSOtysD2PVKlw1rxVYHtkRo+HymSrPSvPZsbxFOJZDx+ng5A4tWtzaucXZ0Rmhr8KFm+0mTyZPiOtYPeM9JcxYjSr+QvkX+Otv/XX8bV/vYU1H2aJcMM7Gaq2XUy0gKca2MAGmxZRJMdkITPzxz/4xXuphx4rZ0bbbPPGe0HzSZMvf4mX1kgezB8zqGXmZk2WZNsZMA9cUHyyKgna7rQ22PM81WCHGqzhRXdfVTkexXTbFskub14Xz5POm26RRN9iz9lZ0GkzbxDTaa2p1b8jGxHbMndYdHbLgeR5Zlq0wNfI81/aEabgL81r2gRLvL8a0KWAo+8W9vT1u376tbRexVcwwCzleT6dLwiRMY1WMaZkrpoEu9XZdV3vpzeubGeSkbwUEkGPFoDYzK8B5Wvr1cAwZ6263S7vd1jIAYrsKE9/Un5A6iySAnMtkMJjAjDCJzfkgDkP5XPb1UqSfBeiQCIEsy1S2nuVnwmjo9XqcnZ3pPhBgIAxDzfYpimJlfu/u7urQI3O81ueutEfqazox1/vkKuXKQIM5aeq61syFZrOpWQtJkuh8tFIRoSutL1KZ6OtZI65fv66RSymS2sREUNYbuY7MyHvr1xVhv/WFLseJt3g9dko6V44RhNSMs5HzCygi9RCUyUS5JB2lTC5JZSKIYxAE+vw7Ozv0ej0t5CETtSxLJpMJ0+mUTqdDu91W6XjqVTESmdTmZJFFUY5LitPzG31VVZSWesClYUrWzJR+QzNT4o0tBUzgACEUYUGxU6j4cRvGjHnCExW6MLMVEDF0aadt3t15l71qj2pU8fgHj7Fii+1wm5s3b9LtdqnqilE2YmbPyBs5ZVuFNcz9OZEfETsxmZNt3BQDWkV/bs+Ze3P4Muolx9VgZRZWpLQZ7JEKNfBOPOyRTR3U9O71uPONO+TNnIkzYWpNedp4yqKzWBGabFZNxWAoOwyKAXeKO/TqHs26SWVVLOyFBiKm9pQT94RHziOS1jkF3q5tzYDoVooN0at6+KVP7udYmRIqkznjOA5lWTIejzk8PLwgwmSuU3PumYwJk1EhQJjEt8kcv3nzptYmMdeQBquSgiqqdBhHVVckdsLEnXDqnTLyR0y9KZEXkTqpDld406bXqc7zl9eoGPqKapUJs14qoFShMnW5ZFk4xut1hp7Jxlh/D+Mza/2tzfWJi5gn4yc8GT+59JKO5bDd2KZDh2v+Nbb8LRzLIcojhvmQ0/SUcTEmruIrGwra+M5SRtlo9cPr8DxdhiZFatx9S2WS6LgdbgQ3VGo7p03bbRPYAXZtMymV+vVZfsasmjEv5qTVao7wN9VNdB42ebQrqgsK6caJgeUcXeoAmEr9ZnmdMN1VAQlqVrMBLC0rOaeZZWK9fWaGi8+ih7C5GlfTf5A6W5YysiuqK4V2iEfed33qqsa1XbIyIy1Taqs+B1s27B1eV7eKimE+ZJSPdPpJy7I2Akdtp83AH3CneYftYJuO26FhN1R6VXsJTCxzz49LlXrvLFNsnbiIL4BEMreuAtxtMtJ/EqBETU1apaRVyjgf8zh+fOmxruUSWiG73i75aY47c3ErF9uxcTyHG2/foGqq5+IoUcr6cRFfDJGxz5kAbwqfcXE1UwWb89CZiw25nCVRlxqUEG2WT6JPLhzn4BBaigHScTsqU4WlsqDkVc48n/P85Ll6TngllWOkNfwxMo4qlEjkhRC0DW2M65icnEW94CQ64Vn+jAKVjnNT37bdNl2vS9fp0rE6dJyOCiuwPa2ZIqyupEo0EDMuxkzKCfNyzsvGMtxBUoMXttJByjya8yZf7H+Ra71rbHe3aYZNsjSjrEt++KMfcvedu7yavuLB0QOm9RSraTHLZxvBHR0aWJfMihlYas/28uwllmOp1NBRrRhlwWo/eXi0qhYfJx/zt17+LQbBgCZNeu4yDMDrsRVs0XN73GrcouE29J5TwqRNY0z2JbNsxjAZ8mr6ikkxYVJM+NV/+ass6gWFX5AHObNwpkQiH/0dDT7+z7/2P+vqNWYN/sd/9D/SdbvU85qteIvf+eB32G3u8ih/RJMmnWmH3XSXyqr0Ht50ughV3rZt7t+/z97e3soe3bQXzD2/GIayL4dVEMKkrZtG+LqBJ78bNLhmX8N1XZpeU19DmOKAdkaadpRZHzEQ5/P5BQ0D08sv42Omvlw3sM26rocBmOKO5r5TjhOQQDJGSJ/IXlT63AwpML3oruuuOK+lfTKH5HygvPzz+XxFA9DsW9PZXNc1rVYL27YZDAZacNNMi5nnub6GnMtMNiDtXHdqC7glYyZ7aDlO2ifOQzmPMHXENjRDJQQokLqss/xPTk50/4n9KUkIwjAkjmMNvLTb7ZV+WLk/rNnUAq7IZ+tOy6uWKwMNZoyIhFCYKVqE8iEDJO9LxgcZiPl8zng81p/HccyrV6+4fv06QRAwGAwoy5InT55QVUodU85tDuZ6MSk8ZqetHy83DhOYkIkv+hImmiXnkXNLPJEsYDOOybLOhTrM+B2T9bBOZTFjYqQ+WZaxt7enRVA6nQ69Xg/P8zRCJYtdFla73WZra2ulLnAef2X2kdyEzD41j9HHJpYSTJysUmyquiJ1U5IgIQkS6MNof8S8v6Rm1iixtV5J2StJb6UsWHBkHenP+TJYUwt7ZPN89Jy79V3e232P/XifzqiDnaisEO7YPfe6+77yfAYZWZgpZoOrXhN3wtyZEzuxYkasTA40LbMOa+qwptqq4J76OCbWQMS8nDOqRvTrPoNiwHvJe/QWPZyFw7yeU3ZKql7F3J0zdRSb4ZX3irk9P/ey1Rbtqq10HMouO8UOb5Vv0Sk7NOumSpflzpnZ54yIU+eUR94jYnsZcrCjztPIGjSzJq2ipb6fNTk6PqLu1TgLRxnZG6hM5o1Yxk3mhnwm6TnXwbR1VN58IK2DVes3e61DUUGdLNkQVcHMmnFanXLsHHPmnDF1VbtTO1XihZZS/b/MmJFwC6/0KBYFWZopDQZ3mfbMryFkM7BQowEJCvW367lqPjjKwLrU4/ZjpteXdclJdMIJJzyKLheXDK2Qvttn291m19ul63SprIpRPuI0V+ER80p5165sLFkGKLFMp/am4ls+gRXQsTvccJeghNvGszyt7D+vVOq3aTUlrmNdpzcxJSwsrRNQ1oZ2hgCHtdLKeJ3hJG0Xz/amrAJiYF4qCGjXl2ceWfuezXn4iYAL0g4RCjSLXdrURY3v+SqzRX2FlJhXKcuxXHm+vWGuao98kUnDVFkTiLVqS6fik1R6dV0r7+syteCmvhQw4nVhNfNyThRHHCQHuHMlrpjX+QXmQmAH7AQ7XGtd4yuDr6g4bqfHwScHkMG/9xf/PZ4/fc7p8JRFvaC93+Zl9JIfPPsBC2tBaqfkdb5CWf+8ff5jYbZcUoq6YF4vwxp6qJdRHk8fY80sPNtTqfrCLe6794lPYkVzx2cymlBZFU7HIXIiyrCkCiriOt7oLS4wsl+8BpOwUFmxJIWoZVmXjv1lpaRUYq95xGF+uPmgLvr5a1c2bubil0pnyMdnf2+f3d1dJvMJhVswSSeMs7FKr1tmV9Il+UzFWs1gE+ev1+ZIy5QpU6Ii4rA+pEbpnqRVemkYgRjn19xrtOwW05Mp4+GYLM24d/cez149I6+U5kLnWodT+5SH44dMT6fnjBMAB/71439Nx+3gWi6tqsVXdr/CXnuPRz96hFM73Ny/yf7+Po+fPObg5IDCLti+t82r2StejF6wqBeUTnmB1WWpm4xmVOXk5FbOLJvx5OSJAlFqLgVYRYug7/XZ8reUMKLbo+epdJs9r8dOY0fpFDT69Onre82jk0c6HFk0FwaDAf/pf/qfgg+//ju/zk/92z/Fxy8+5sMnH5I4Cdf3r3ManfJo/ogn6RM+evKRyjYhYPOHyxfKmO9/0ufW6S26TpdBOKBttWlZLc7SM+azOW8139LZJlzrPFWj7EdNj79oE2wS0RONAtN7LHvu9b2V6fE37+2y95e9WRiGwHk2B7E9xKtv27Z24IrBLGEoZqiHtMVkEpifre8p9dywrJX2mvtG06gX+0YYJKYNIjp4zWZzRdfAdI6aISEmC8PsOyndbpeTk5MVjT+T7Wv2seu6NJtNPM9jNBppW8pko5upI4XFbrZJ+me9TSaAtO7sNuttMlykSLiIMNTN85ihIBIZIHa5ZGEUmznLMtrtNvP5nFarpW12SZYg/b2elnPdkW+Ca+vvmeP8pvKZgQZB70T8UEQzZFBlgggqI6EWoOKJTk5OVtgKskCOjo7Y3t7WYi1CZZF4JNMYXm/supFsdsJ6ZwiSZBrYsgAlH6u01/xtLiRhGghFRwAHEe5bL+usCtMTLXU1DX8BViSN6KtXryiKgp2dHa5fv64ZGdPpVC+S2WzGYDBYYTzIuc0ii9hMvyl1Mg3NTX2q5wE2Hh7NuAkxtPIW9aQma2SkQcrCXXCSnzDzZ1TdirKpNj56tllAC5V2cV9tRD5a/oDanHu5RxAHNOdN2lGb/WqfrXILr1TCfUES0Hf6er7JQojjmCRPiNyIvJEzs2cqe4YXMfNnzOyZEswMuFhsJRY4W/4857kKGdlBb4ScUlENt+otrpXXeDd+l61qi+nBlA+ffsjEnuBsO2y9tUXeyhm6Q546TxWAIJepbQVCVF06RYfdYpd3snfo131atJgmU46SIxWaECYsvAWLcMGxd6wEL2/IRAJrYel0nfbEVtkz5g72xCZIAqxqNSZMRFnlRipUQ/PGY65nc+6vhyXJ8SbCe9ma8yqPLWuLd3n3nIGwXAMFBTNnxtgec2wfc2qfMrSGRE5EYiXKA2HVJJYKt9iYE71c9sXcwV7YWNlSbd6p1Nxrol4BVE5FYV0S7lErZoVdn9OUw1ao6Jk/iU3tJSWpE5I84TQ/5eP4443HuJZLx+3Q83rst/bph32oISszhumQk/iEYTr8bGDEWhERx1k145jj1x7r4hLYAT23p1NdBnZARUVe5ipOvJxr2rxsyC8tBnAnYOFl4nKX6QC8UR+gXvu9ScfDKJcZ2Je1o3JUHHRu5SuZK8wS2iGhrTaOtVWTFukbBTZ1+YxAmLAwXgesmCECb7IpLSx8fJpOE9/ywVJzJikT8jrfCKpIhorXjX1apRzEBxynxzQd5dXLqkzdR0P4J7/1T3R7OnaHm8c32fa32a63edd9lzuDO3z7a9/mh7/7Q7zSw/d8fuHP/gLvf/g+T46fMGJEtVVxnB5zFB3pkKa8yi/U9yetDXFpkTlPvZJm9jnPFTi09H7r38vv2LVNgwbb7jY3t24S1iHPHjxjp7dDt9klqRJiO2ZWzIisiFk5Iy42GNNiXAoIdEk3CNBmClyWdbk57OWyIvoMdkXlKu//ggVYcBgfwrOL13Rtl5bXout36Qd9Wl5LZ+Co6oooVwKF02zKKB6p+biu9fFjAJLzOicvrtZWYXlFZURWZZxxRlmXJH5Cei2ldmpe8Qpun39nWA4ZZMowf3vrbXbaO4qZ5Pg8evCI27duU1Ly4MkD4ipmns15dfqKZ9YzlUHhuELfun11P9053aHv99m2ttmJd7i7e5c6q5kMJ3iOx527d4jSiOFsqPU9skCFhUR1tJFdZoZ9Sb+cZqecZqfYC/s8I059SRgIFm1HpUaNvxRTRzVe6uFmLk2a2Ds2H8QfsFVu4Ts+77TeYWdnh+ZzZaj+5a/9ZQB++7d/myAI+MpXvsL29jb/8J/9Q06jU7r7XW6+e5PvfPc7vBi/gCa0u22O5kd8OPqQs/iMUTZSIpYPYZnNFVCslb7X1wKWg2CgM0u0aNF1u+y19nQGDkl5CRdTAZohreshF6ZmhPSVFLGjiqLQjFMxVs2sfLKHE2a0iEOKVoV4xMV4FXutKArG4/GKk1LqtO6QFPBgPUuD6bi1LEvrYEgmBekH07lrWZbOANHtdlcYFCZQIft808kle08JD5Gyvi81wYaqqvA8T4eTyPgcHBwwHo+18Po6WCA6hRIWIcWsL6w65U02gxwrwI44fHd2djSYISwVCQUSNoeZClXK9va2PlcURbofBBSxLIvFYkGr1eL4+JhOp0Oe51pofh2MWbf5xC6V1KfmHJBxviqr4cpAg2SYkBgh0SLI81wDCjLxTMFCyzqPcZpMJiwWixUEx5xwp6enWmBRQgdMdoA58OuImSww6SDpMPN4GUzRSpAiMU737t27QO0yj5F6LBYLkiTRi1aEL030x0SAzLJukK3XUb734YcfamRuPp+TJAkHBwe6L9M05ejoSMdcyQ1if3+fIAhWkLl1b7bZl2bfXoZKSr3NBSN9Iu2v4opyWFJkBX7tc9u+reeEpOSsvIqiUVC2SspuSdbJKLpKJ8HqWiobhqM256mjtBymWwrQesCDZUWUse8VHs2syVa2xU68w266S7tUlCBK6NAhyAJu2jfVe7ECiL773e9yeHhIaZf4Oz79O33qTk3ezGELkmZC3shJ/XQ1TeCy2aVXMl3+POUpNJaf94EvglVYBIVCMfarfd5N3qVX9miUDWI31qEUU0e9DuwDPvY/Vsrby+J2ltku8hbtos1etkcn7tCv+zCD93/4PqN6RNkptcBn2S3Jb+bU7XolJMAEIpyZgzNzsCYWISGVff5QM5HkTeFHlzEezJu+idavo7jm3+sPUad26OU9+lafO9Ud6lrppqRpynw+Z57PqXoV/nWfI+uIB5MHzLwZVas6ZzE4UHdqis4lxksBTuLQmDZoxgq8CuoAJ3Dwez5Te8rcmZO4CamTKoFISynxZ+lmir9ne8rju2xzUSkK8lVo3OcdwpU3u+te+6IudKzr83hz/nfxSvq1UvMetAc4tqPj78Xwf53hedUiqREX1YI3nc7CIrADRa8tLNJFimVbWoVeZ0LhcnDhQnldCMzlFbny8X8Sz7YY15tKUiWkVapYEUvBzvWNeFAH2IUSDSyqgtpegmhyj7pie9+UrhQLdd9Y6j4Ar2Vg1NSkXDFEhWUGBBq4tqsFQguKjV7RGsWimFSXM28qKsWkmcQ8sZ4oALGE/CTnV/6vXzk/MIXeP+/Rtbo06yZ9p8+XG1/mp2/+NANvQLNscr19nU+++wmL0YKojti+vU0e5BzlRxwmhxwkBxzGh0xKpW2S1RlF/ePP1KDL68b0EpAUS6W4lSwHR8dLFmEPXvACotWvuJZLy28xCAa0vTZNp0k37LKYLphMJ9i+TWZnzIoZCxar3nTQoTKg1n9VvT58yGT/yPMkr/LL1/gl90eZG3mWM82mvJhv1ssw6+k5Hh23g1d6dLwOgRMwPhvT7rSJEpXG2w5t5vlcZ/D5cY9tRaUymKyr3VpcZOPVyuliVRZ5mTO1piRRwpPoiRKZLGJiK+b3Xv6eOt5Tr+fD5yo8z+qwW++y19nj9rXbnJ2cMTwdUlPz3o33iIuYTyefMnbGfJR8xLSaEnUisOD3T39fV8O1XZpVk0ba4G7jLje3brLV2MLHJ01S6krtObIqI6kSpvmU01yJRM7KGVEVbbz/rd9Pa2pm5YxZOaNdtVXK0WZE4RaUfsmnzqf85h//pj7+v/21/1YBnIVPixb/7Dv/jO3GNvPpnK1giwfPHnAvucfL7CV2YbNVbvHLt38Z95HLs9kzdjo7/MVf/Is6k8NoNKKqKv7P3/o/qVs17b22YusVE0bZ6DzLRDbho+gjHeaxKbtGy2nprBJdp0vXVaE1batN11VCsubnkllC942lKOu9Xm+FNSqOWYmzNxkVsq8SI9j3fdI0JUkSbdiaLHQxUiW0O4oiRqORzpQn11w3mE1mgGgBmMeaez8Jq69rxewW3THTbouiSNuJIvxpXlf0CsTQlmuYTt2iKDRYIc5vAQRM4EQY7WZGRGFUjMfjFW0yc28s1zQdduuOWrOP1gGiTbaeeU7TcBeNBQmBkDEW0U4BEvr9vg6HEC0/aWez2dRpSyXFpTgZ5RiZZybQsO5IXLdlTXvyJ8Jo6PV6Oi5cYn2kYRI7lGWZRqjkZds2165dI45jzs7OVpQw1xslkzJNU00FEQVSWKP1rxkrJgVkHR00j5P3pZgdVZalnvSmGuj6sc1mUw9sXdcrFBvTgDdpNWa915kXUmSwT05OdOiI1Ff69cmTJ1y7dk33t9mm6XSK53ncuHFD50ldv8Y68GJe17xZbaLIrLMf5D2Tji83IAF1zAlcJzVWZOGeuQy6A32cMESqSulDBDsBt75yi7Sdcsops8ZMiVcGBaVTqpdbkoQJQ4Y8FOjZoF96uUeQBrQXbfrjPjuLHazIOhcCzaFX9Pjz7/55bty4wdOnT3n68VOKouDGjRsqPCabE/sxnxx+wnFyTNku8fY8/H1fZfWw49UNkgW1V5P4Ca+WPxqIQHkS3NolrEO6VZftfJvweUj53RI3c+nd6NG62VJMiHrIorVg0pnwMHiodA4AroN9W6XstCe2yppx4OB95Kk0Vgsbu2NTtkvsga10NdoFda8m2U8UEGHDjBm/wq/QrJpaG6JbK4ZFp+poxkVgn4dEmfPUXJMmwLeJGbO+jtbL+pyUm+58PufFixf6ftBqtbh79y7l05Jnz56p422LKlRAS9EtyAc55UCFt9TtWjFXLMCFsl0yb8+ZM7/gnXcqR/VF2eFmcZO9fI9+2ccrPf6r/+q/4sX0BR+dfsTD4UOeTJ7wavaK0+iUcTK+kjfLwVF1rdc2XKZOxJuo77Xhnb/k2HXRvppaU14JYJJNNn7Xxsa3fa3f4FouVa3AiLiISerkx7rpljj2JFvGTDdef7x4auty+Qyw681PL2v93x8f7f1zn+cKz2LNirjkEikpuGvsDnv14ICAreYWZa4YbrVTg6cMwNdqP6zPvaUX/UqMCtCABPBGjYmSkjlzxcYywlouKy4uru1qL3lZlZrKfV79pcef7LV9PckmzJhpzY0/evBHFFVxgVnh4dFzeuxP9tkJdtgOttlqbnGvdw8/89lytxj4A3aaO/iuirNNy5TD5JDn0XNepa84ShQ4cZKeMM7HCpiosp989gyTmXOFUtQqFGGSbgBzLC4AhoEV6LSPoR2q9I+2TV7mxHlMVEXMC2Wom0VSqup7U11RVK+ZlybgtdT8kHvnVfqv6TaxsFTazloBWWl5DogdF8v7f4BKnSlGfnJeX8n602/0dbrdqlTPpqRMGM6HxKW6N/5YsnBsWIeVq4DBcT1WY/GaR41TO7i1Szfo4tkeESpj1Dye8/jgMdNkSuwpgOOPHv2Rvobt2zTyBi1adJIO3aDL9e3rLKYLqlJ5k23fBhccz+HJ7Ak/GP+ASaHEPtdL01a6DVveFvea9+i6XRp2A9/2V0Ll8jrXGh/DXIkTz0sVDjjvrWZIcXKHIA+41rtGr9EjmkbsbO1gWzZnZ2cqRKeIGE/HvEpeEcUR/8fo/1it2BP4L/4//wVtu01QBvSOe/yj3/xHKrzD7+PnPn2/z7PyGYN8wB3nDvcb99Wz0PDW6+FZ7n/jImZanotbSljjOB8zq2aMszFPk6dal2MTMNF0mvS9vg4tuRHe4K/d+Wsr+yLZnwvTWxjYUq9+v6/6arkPD8OQra0tLRK5u7urw65NjQzJdGZZlg7RXne4yj5esg6sgx9SB+08Wn7eaDSYzWZkWaYp/eKgFdtBGBUS+m3aLXJ+Ux9QjjFtGDGoAa3RIJ+tC1CaYpKSgKDX62m2wqaQY1Nkc92Lb7IB5BizXuY+WPpOgB+z/6R9kmhhb29PSwnYtpIhEAc/wGAw0PVZLBYrIMrbb7+tgRUhCZgpRE0miuzhTXtO2mX+bQIu68e+qXym9JbmhJOGi9da8nfKpDFZC1VVcXBwoLMrrFNHzQGUxgNaEdb0uJqGMLBCYZFiUjzkvDJ5iqLQgipyHgFOHj16RLfb1eKTm4pMegFMJEvFuuaBqdRq3qA2Gf1mqaqK4XC4AiKYBv5oNKLf76+gaybYcnp6qpV110EDc+KY9TARq3WAYb1+67Qg81ybxkeoU4JKSuhKr9ej2WxqFFX0KGzLxpt77Mx3CIuQW+mtlXSntq3EwlI/JQoiZu0Z086UuBGTBRmFW5wzIhop0/6Ug5sHy4kB/BmgACu1mDPnu53vcswxe3f3+FLnS7x4+oI8zzVlyZ7ZeM88OtMO/X6fnfkOe/M99vf3cVyHqIz4oyd/xCeHnxDsBQR7AWkrJe/mRL7ykuiYc2tp9Nk5M2fGS+8lfAH1Akb1CKu0lDbGzMI+sdk62OJntn6GrtWlcAtOi1MenD0g8iPFYriWU3VW06JZcwtn5tBIGjQXTbyZh/1CgRJ2ZFM2Svxdn7vfuEvkR1q08tA9ZO7PV/QKGlWDdqlyMPfonYd8lB16dQ+nOqfXrbMhzGLOF/PmtAmIE5BK5rDjOLTbbc7OzrSokZzHsiyIoWN3aNOmHJWc/O7JuVaEDamfUnQKwhsh4d2QtJOSNlOKUM0VrGXIjDVj5s444IAf8kNdx7//d/8+bb/Nfnufu727fGnnS/zHX/mPeW/7Pe7179HxOxwuDnkxfcGL6QuejJ/wcPiQp5OnvJy95GRxwiJfrNLQ1w0Ca+2zywyFK8Thv1FQsOaCQVvZFUmVkJAwLacbvwbnKQ9d29UGZl7lmib/WcqVDC6jnyr7kqwjnB+z8j3rDdcQYLJeUjTF2/8ZGSbqVG8I0fiMRbJxiIFt3kN03ddKSspRdKS+59cb299wGnS8DnVR43gOi3hBUiWUVqnW/eegkl913Df115v6TNJAOpaDa7kKbLIUiLbxurXBaNxwbpPRkZebrbacnNPylLPxmRYi1eNgFMm2sR1sM/AGDHz12va3+Ub/G/w5/8+xHWyzHWzj2+dU1bIuGWdjnkfPeb54zqvkFQfRAR88/YDIjih8BaiLds2Vy48hDOBNRURnr1qc0sHDoxN2NPOhrEud/lHm9tt/8LZKZRdEKhtUu9JptwlV6FFRvZlhIKlC0zLdGOYWOiFepdJSurjEaYzt2SR1ogE5/VOXpHnKKB+99poChoRWSEBAaIf4jr/SXvH2J1VCVmcX5+5nAJwvK6VVUlolJ+kJyBDVKF2itW2ybdk0nSZ2qRgpvuNj1RaxE5OScjg6JK1TMjs7DzOtgIW6N/W9PvuNfdpOW4MIvuMrDZtKrU1JW/kkecI4HzPOxxfCaSws7dm/Ed5gO9im7bR5+KOHTEdTpRFR11iuhRM47G/vUzolJ5wwnA2Z5JPzc54t24ZNx+nwVvgWO+0donEEOTT8Brdu3uLk7ITRZISFxdHiiE+yTxgmQ4bp8HzOLGAZyUvbafPff/W/53rr+gXPdhAEtOwWO+xccJhscqDUtdoDDpOhysiRjxmlI6aVAipG6Uhl5qgnms1gOmo8zyPPc7a3t3nnnXe0HQZw48YNoijSNlOz2eTmzZs61l+M1qIodIY9QLMJxOsthqnpDBYjU97X82jNCbzuhJT/xeAWQXuTdSEilOKYNNm1da3CO8STL/VdmfdLZoXJABAnuGkLmo5UYTI0Gg1OTk50mve6rlc0Ac1Uoebe1hxj8zry9yZHrcn+FZaDMBJEc0PGYjabEccxo9FIHy8ObUnPuru7q+2xKIq081+ABxnjbre7kq7Ttu2VNm5yBJp2nl6rG5z2l9nJ6+XKQMNkMtEnFiTGdV0tnCgCj8AK1aWua4bDoUZcpBEmKgbnE1TSnLTbbbrd7grdQ9KgiGEvhr4sADHwzckuA2sCE1GkUFgzrkkmuKBCZtYGE7QQBFBihgShk4kjN5P1Bbhu9K8b5FKyLGM6nV4w9qUNi8VCj4UUc2JLVoLBYLACKqzf+MyyvmjW62nWRfr2MoPSBC1EyVVSr0hclKCXvV5PL575fM7p6Smnp6daxEZukHC+QCxLxQSHVUg/7VMnNdXxagxZXauNaOqnnPXOmPamLJoLIjei9EtwFfNgYk34o+UPLrCrXlZt4RSOSq80timcAvfMZcvZoqZmsVhwcnKi0eX8RU7zqIl37DEYDHBdl9u3b9Pr9dRWOlQsh7kzV1oE7piRM2LiTFQcqgMSo1q7tfLEt6HcLzle/qiBXtbtmoMXewSLAP+Vj/eRh71QN/WiWZC3c8puibfrEd2OlK6DjOtSYLKVtziwDuhVPfazfb5Yf5Fe3aNRN1hYixWhypmtDPATZ6m7YQARQRWcgw+VEr/sVAqE6NU9vHIVOTXBufW1Zc41Qehd12U+n+N5Hq1WiyiKVkBM+a5JM/Q8j8ViwXg8VuJHiwpraJE+TXH+yKHX6enj7759l6pTMffmLIIFk2DC1FftjuyI0lL3rHk259Php3w6/JTfePwbq+sHi27QVUBEXwER/85b/w5f2PkC9/r3uN6+TlIkHMwONBjxN//O3yQLM0bViLk7V2CIV+h5IOMt3ryyvijW9ZnK+levKBhsepyFJZHVGVn55rzw9vIHrkDZv0p50ybcBG82MBvUIWvx2dZa3db6ycbGszxl5C6FyrI6u5qoY33+0vGuV8xM8UYtkCVzS7y+tV2rlxjvl4yvmb7QLpYx1WtZXXx85cG1PcVsqJSwXVb9yUIFPgsYY867sjbiwS9pl/Z81ypUKHACGl6DWTKjoCAj+8zzbxO4YBbJtiFZF8y5vl5CO2TL3WLbU6BE31XpQAfegHfdd7kZ3yT7w4w6qfFcT28sv/Xz3+JP/6U/TWRHPJ8+5zA95JOjT/jBkx8wKkYkjgIHN4ISfwKjdUNnfD4QylHrJMmXwP0lffTwTylWolu4BHlAUAaERch2sc17g/f42a/+LNvhNg23QU1NUiacRCe8nL/kYHHAUXTEMBkySSfM8tnmLBqgMk+QMCtn6g0HZUCvtc3Gpuk2afttWl6Lhqsyo8RZTFqnzPIZUR4RF7FmIxV1QUSkzvda6RkltuqhAA/f8snTnCRKqOqKRrvBIl9QWMV5Fo7PO46XfK+qK+bF/PwYWZbB5u84lYNXLwVJG1uEbohjOyRFwrSYkpYqbessvyS1p9vhRnhDZZlxGgROoO+pZVWSlYo5d5Ke8KR8wnHvmHgrvlCXV4tXDIoBvuVzO7jNz+z8DMODIR4e9+7co9Pu8OTpE2q7xmt50IAH4wcM6yFRGvH+w/fP70EZcKzA10E44L32e3T9LtPTKf1Wn36nj4Vi0szqGW3aSnST86wCYpO8zuAy7ZCqqvAsj4bb4JZ160JcP6BZ0iYQIOeQcIjZbMb3v/99bNum0WjQarX40Y9+xGw2o9/v83M/93NYlsWnn37K7du3uX//Ps+ePSNNU7761a9qEMA04iU0XoCKTbaLaUu9bv8v+3WxEz3PI45jFouFBgRsW+mEtVqtFa0wubbJDk+SRGswCJDQaDS0fSg6FRLObwr1i00obZU+FmcWoDMrRFFEp9NZaa+5R5XvStlkT5l9YLbDdV1tr8r7UjdzDgijROaAhMqY7P6yLOl0OpolEsexDrGX+k2n0xVHvJAB5NrrAIy2nYxwDnMOr9uw633xunJloEHAA+mw+Xyu0R6TsmGmODE1DKSCm6ga5gAWRaFjeRqNxgVjUzpOhBcFATJpRCa6JnlVBUU7OzvTaJdMUJkQMllNesg62lMUBfP5XCNHZvYNk/pixrJI28wXrDIVpH8ki4R8X+poooRJkqyIecj5ZQLEcawFY+A888T6QlmfZCaAsA46mNeR+poLcB3lqutah8FI/wRBQL/f1yBSXdd6YbTbbVqtFtevX9cIXp7nTKdTFovFSlqZdQbGeh+DergFScCN5AY3jm6Q5zmPHz/GcRy2t7e5dfsWb339LV64L3haPeWII6bWlMiKKKyCwlcvOsBt5S38mI/1A9kpHdzcxU987G2VKrM8LRnPxzSSBkdHR1rFNgxDwjCkQ4ee3eOeew/LUiEj3/ve9zgbnmG1LRrXGnTvdXmePyftpFT9CrtrU7jnBmht1RRBQREUxL21B7FQTStFM3QKh+vT63TzLn6haPFlUDJ35yy8hRaqTJ1zD5VTO3TKjs6Y0Sk63Mpv0Vl0aBdt/Non8zLNgpjZM+bunLk756n3lFk408Y5gF/5KhTDCMkQYKJbdQnq4AJ9WsY4DEPu3bvHD37wA5IkYTabYdtKbddco7JW5IYcBIEG69ZTLs3ncxaLBe12m1u3btH0mzz+0WPm8zlbW1t869a32Nra0oZhbuX85//v/5zHo8c8GD3gg+MP+OT0E56On3K0ONKGl9CPf3T2I/75w3++0h7bsukFPfY7+9zv3+eLO18kSAJu5bcoTgsWJ4qieufeHR6fPmZmzQj3Qrbvb9O52SELMx6dPuLx2WNSL12hzEssvW0rAbS6voTRIJvJz7hh/ZN46T8TuLCJ8i2G+jpwsKEdVm3p718mCrnxmm/oj4pKeXHrzV5cbTTYHi5LsBlFC0/LVK0F681ef6HzW5al6eGX1nnZzhUNmQ3H6blh2TplrKQz3Dg2y/7IyLTxfFkJrEB5hy1PU+GzOvsTgxHnVfls39ehJyhGQlzGjMvxheMktMjDox20ldHjBhSl0lcZx2MyVGaNz7pWXjfXkyrhVfaKV9mry0/wi6iMOLlLkAY08gYn1Qnz53PuDO7Qd/v8ub0/x5/f+fP85qPf5OjoCNd1+fDDDymrErttE3sxvRs97n/tPh8+/5Av/Kkv8PD0IR88/YDUW2rPfB49lh8TYPGm+0HhFhROQVRHWLViwDydP+Wf/e4/2/jdftBnO9xmO9zmi4MvstPYISxDbu7cVOwXaizb4vGLxyR2wrga82zyjLPkjHE2VukuNwA0FcoQ18b4a4pruTSchppLqHXh2z4WFkmZsCiVtkVURuQosdG8VuOgQw9stKhnTHzOIlguAw+VBabpNZWRbymjdR7NVVigVbLIFz+ejDbLYmEp/QUU46+kJCFhHI3f2B+hHdJ0mhpU8C3VH0VdMMqUMOe8UPoH66FLFssUzIsQt3BVqs/KoeE3uH37NmEj5Gx8RpZnPJ095ZhjFiz4nWe/s1qRGbTcFi1ahIRc867x7s13qZOas6MzPMfj3ffeJU5jSqvkbH7GrJgxs2acpCfMk/MQoF87/jV92o7bYcvb0loLMv+2/C367jLDhquybbTd9sr+Y1OqTCnrRrwIA0rmPjNsfW9vT59H2LcCTprecslgILaKeLoHg4FmEVdVpe0n8aCb2g/i3IXzMAQz9GHdAWnaBNIeMYbTNNX2iegkmJoKwh4QoURpv4R3iO0hII+cX0IE5G9hUEj7TLvBLAIwSFsODg50JjbT1jEZ9eth/+ax5niKrbruNF4/R6/Xo9Pp6PdNG8/3fW1HS6RAmqYURUGz2SQIAiaTCVVVMZvN9Jg2Gg09fiIkKeETwpg3Q1Gknut75nU7cD2s4k1h0VKuDDSYAyYijYIoieCfTA5zkuR5rlUrTdVTQXNMpoAIkxweHupJIcqgJuJiIi9ZlmkxF3MQpQhQIRNFQA8ZAJN90e/3uXnzphagtKxzvQETIDF/y+CboSWmKqwMimmcy/XEa2+yA0RDwDSizQUibTD1G8zBN6+13m/rE8dEUU1Wh3ms9MG6BobZP+axUo+yLLWxJ33farW0x1/qKfNBjHJTmGQ2mzEcDjVyZ7Z5HWWUOpg3cBNQkZvZ1tYW7XabMFCG/1eqr/CF4gv6Zu37PrPZjAcvHmDv2CSthEP3kGP7mLE1Vukx3ZrSVToRaZjCFjpVprqgermFi1/4BGlAmIQ0oyaduENz0STMQ6pUqfFaWDixQzgM2fK2GD8e4yUKbf3GN77Bp+99qtpU2lRORe7kZG6mhAvdlMzJlLd7aWQVjtqsLeoFJ+2T1YW8pIp7pUejbLCb7aq0mVUTB4fKqoiciLk758A9YB7OVfiHrKfKUwBEuQQPyg634lv0qh7too2HR2RFOn3n3JsrJoc944X3glljpgTbjPO1q/b5OZdgRK/u0asUI2Q2m2lVXVHilfg0E4iT4+A8E4z5IDBR3MViwY9+9COyLOPBgwf6/vXy5Uu+/e1va7AhtEK+fu3rfP3a11kvdV1zGp3yePyYx6PHPBw95IPjD/j07FOeTp5yGp1qI2yUjBglIz48+ZB/+uk/hTvLk9xczpXc5VP3U8qtksa8QdNq8q77Lv/+O/8+X7z3RT744AP+9m/8bRzX4c6X7lC0Ctwtl6P4iMRLqNoVz6fPmVkzjqKj1TjQGhUTXytNi9paGrF/AuNhI0PgT1Iu88ZuAhg0pmBpWvtV2B6upWLEzVR9f9JSU1/O8DCZKcuYb4lTr1BZF/JKhZxcxqi45KJXAo5q6vO1ZlKzzSrWlnpVS6HRssL2bAVIvMYtKylSX1fEOHItFwvFyinqgpz89dlGXlc2AVIrDXrT12tNjU/S5PIDrSVYbQeEVkjTbSrjrqzouT31/SrRmgRRfTFW/XMVGw0kL1hwyinf++H3Lh7ngHtT6f3QRGVoypqEaUjbbnO7cZsyL/kb3/4bDI+H/K3f/lu0Wi1++qd/mriMsbs2k3ICbfC3fR6fPOYkOuFwcciwGHIwPWCaTzenPOTHuO4vKcLOgdeDdIt8QVmVTNIJT6ZPqKlJ85T04WaB0n6gMgXshXvcD+8zO5rRa/bY3d4ljmKiNGIWzcjISOyExEtIvZRxNt4YOiPPlnkxZ1bM3tguz/IUSGeHhJZiM/iWTx7lzE5mzJnjDlwSKyG3c81myMnJq5xFujgPi5BiLqVaAdu+7WNXNnZl4zs+7VabPM+ZRUtHgItmZq0UyzzV5cwos0gKVGGSlnXJvFQZhi4rN+2bfLPzTbp2l9AOsWubKq9wPZcojfjhJz9klI4og5LMU9nM4jDmZHZCPjPu28slHBJyI7jBdnubIipwcWn4DXq9HmfDM6aLKfNyzoejDxnFI4bVkLzK+c0f/uZKvXpeDx+fb1vf5j/5wn9CYReMM6W1MMpGSotBQh6yEZNkwvPFc/3eOhjmWq4ODem5Pfpun67T1UBEz+3RsTt0nS7bDcXYEXtIdA3E2BaPtDhUZP+epqk22mX/Iw5hERCUUIHhcKgZ2Ob+WAzuyWSitR5kH2/qL5jOSXHertsppk4XnGcmDMNQ2zuyZxNgRIxh8eCbzPVGo6FBFylyXZMlYJ7fDGlftxXkt4SJiI0iIozCrpD9o4R3mN5+WBVUNA1006Y1r71+fbGj1veqpj0nAJB8xwSjgiDQuhSAFt2XNKemgGS73V7RaZC+M8EYKVJ3+dsEWNad1FctnxloMJGyqqpIkkQrxEtlzJCIuq51mIG8ZKJJMSeufOb7vhY3lAabXnfTKJVzyN/muU3kq9FosLe3x2Qy0QMr9QuCQKGly8lmdrjZyZZlaeqPeOvXkSHxqq5TTEyqjPxtTs4oinSeUzPMZB11iuOYOL6Yjmp9MW2a3PL3+iQxQYr1hXnZQjEBkk3IlqmxsQ4SSf8XRcHx8TGnp6crqVABjeSu59pdB0s2MTVMYExKp9MhDEOSJNELUfpzPp+vMHK2W9u07BZ1VHM7u02WZTx79kzNlXZA2kiJwohX2SuiRkS5U1J1K6pwqZdgQeEVFF5B1Ny8CbUqC34OrMjCizzyOCeaR+RlTnlSUiQFs9mMuT0nDmIyPyNzLwqe+YVPUAT4hX8eT+yUZHZGYRfkTq6BCCylSp7aqdpAMb5YsRrcWuUy76d9WmWLoAzwKo/arsmdnIWz4FnwjJm7GkoRlqEGIdpFm07R4V5+j3beplOpON3ESlRIhjPT4SQzZ8aBd8DMma0AG3bXxtq2dNYMd+5S5iVfuv4lrjeuE+QBZaHSDh0fH3N4eEgURXptmnN0HX2ez+eMx2N9nKSSffz4sUaYzXvZhfGzLHZbu+y2dvnZmz974fOyKjmYHawAET88+SGfnn7KR68+onDOmSqFV3DKKezBaG/EAQd8t/4u/9Nv/0+4v+PStJvUX6vpFB1O3VPu2/f5euvr3Ehu8Nb1t7h37x4vX77km9/8JuPxmMeHj4nciF/5//4KT86ekAYpZUtlWom9mClTdX2jaEN8uVl8nTHxWSjwn9soWTcqrfWP30zF17oAFqvG7SVfs2qVDcO2lh6cutyYHvSzGlwlJXEdcxU5A9dycS1Xn19nMhGQZR18Oa/U6t9XqFptLUEae3m8e7nX2cbWtOG+21eGNxVppRgfaZWufFeMo9cVB0dlb7E8PVeKutAZKTYVCwvf9qFe7i+4mJLSwtJpDj/v/DMzBIzKkX7/RX4xw4GHpzI2eF1aTovACvBdFfcunutFsWCez4mqiKx+Q+jRm9g2FhRWoYQ1ezDvzTnjDGr41PqUf/niX0IPfuXv/opa12/ZhHXIb578JtvuNrer2+wH+7zrvcvP3/l5/tz2n6NpK82k/f19/uAP/oDf/f3fpb3bxu7aPDx6yI33bnCWnXGWqtdpcqpCR9Ihi3KxVj3FphHxTsv+DPeBzwCA5pWaY7N81cjveB12G7v0/B5bzS3qpCb0FLMwSRPm8ZyUlNPglGfFM5LTRKVzBZW9YVnCMuR68zpv99+mG3RpuA01rliUVcnZ5IykVIDTyfxEif2tIQEWlgbbQLFbFtVitT8c4DpQgZd7uKmLF3t4uYdTOmz3trlx7QbdTpdWp0VURAyjIU9PnypdBTtlnil9pYrqXIxzua6H8+H5dWBFL8jCwqkdQjekSApCXxlsURxpYLC26/M9xFopuCioellxcKirmuP6mOF0qEPxLqzfGxZO6uCmLk7i4E992rM2X73/Vb789pc5Oz7Dxub+W/d5/w/fZ1Es6Pa7OF2Hh8lDxvmYo/iI2cJI3VqCbHcCAgbWgL3eHp1AhUNUhdIIOxueERPzKHnEdmOb3eYut+xbql6GB93c+5dlie3YRFWkQQf5LQKRk2LCcXbMp8WnWvhyvTQdJaR5M7zJ33j3b+j3heUthrW5hxd7RcAJ004TZ684P8U5q/t5uR8SQ3p7e1s7T01W9WUOy03sBbPI+8I+h1W7Yd3GkrrKdaqq0voRZp1lny6AiQALEloi/WA6GU2bSAAEEcsU9rzYGmKvmX0t5xKGtalzIOczHbMyViZQY9bBtInNvpKXOMWFoSGMFjMcotVqaZv67OxMX0fq4vs+SZLQ6XRI01TLBUhWCpPtYdrNsKq/IQ57c9xM2+pN5cpAg+kRtG2lCC8NkQ43c4Oag5wkiTbqxOg3DW45Xt5fp+psapSJsmwq5qQX/Qah0zQaDRaLxUpIgXTizs4OZVkSx7FGmky6jiBnW1tbvHjxQi9aiSkXURFzcZrgiImGmSCJaCtIXc1Fuf6/iSLK2Jj/y4Qx03iuh5+YcTjmxDfRSfPc60DCOutBjHSTGdFsNjUi67ouw+GQKIo0aFDXNaPRiOPjY83ScByHTqejF2mSJHocTBBivR7m3+YNUYqorU6nU81YqWsV3rFYLEjTVKdUFWqSZVkrC3xnZwfXdZnNZviJjzf2WBwsCNJAj6/ru+RNpZHQuNmg7tfEzVi9wnglHrqmhgDqsCbdTklJmTBZqffvlr+LW6q41c60QxiHKlyjtMnSZRxaCFbHoggKRY/1c1IvVSEXK50FTuEoAccl+6G0l6Jj5jJabmILuyDyIk5YY0WgDDK3dGmmTRp5A7/yCeqA2lJsj4Wz4Kh5ROREKwJ2rbKlwyc6ZYdu0WUn36Gdt2kUDRX3WSfMXAVCfHL8CafFKUW7IL+Wk76TMg/Ps0Y4tUO7aNPMm8SNmLSRYk9tyrOSalhRFzV1VV+4icv/pvqvjOFsNiNJkpX18HmKYzvc7t3mdu82f+bun1n57D/6j/4jlTouzBjWQ07LU9xdl+P6mKSVsLAXSkWfZRrLcgotlS3kgAN+r/o9/t6LvweA+8Cl+7RLz+7xxfkXue5c51p9jXf677Az3SF+EVPXKnTpy1/+Mo7jMJ1O2b21y/sfv8/tr9xm/wv7nGbKcDjJTjiOjznNTplVqxt4G1sbcFcJi7iScbFuVNVo2nRVLQ1smZ+bhuM1Xm4NRmyqhuHlN5kRSZ281ki3sZUn0vYVC4iKvM5JqmRzOEK9fDBf0dCSeO8NjflsLBTjWImNt7Au14l4A0uiotJzUqv2X1ICO1Cp5yxfg1d5nZOWSutB6PslKpNEwuXsAhEgdSyHsiixbAvHdkiL9FJvtxh3dV3rsZW6m8d4tqfmc1VpAcDPU3JyJuVEsQTeUDzLY8ffoef2aLttAiugLmseP31MjkoDXTkVuasyxUj6z43lDWAcLAE2R4XETIspD4uH/H6yTF/4EviefFX1R8tTVHPXddmL9rjr3KWICr5WfY2v9L5Cz+1RZMrzKI6gs+kZ42JM7MRMygnDfMhZesYHT5TApT/wVf8UkwtjZpcqm4zWGjFAcV0+4214ls+Y5TOV0vP1UUD4ls9OsEPXVV7l4eEQq1RA1fb2Np1eh8qumOdzrQVxGp9uZEy0nBZb9hYNu0FgBzqEQhhMIpA4K2cqDbBZlgB/GIRKV8TKdJrfeTDnQfKAKqlYfxw3qya7jV26aRc3c7m9d5ub127y/OlzlU7ec7l29xrHk2MeHT0itVOqQAGEAigXVqEMXw91/8vYLLxbK92g0FEsn8AKVKimE+DYDjU1cRUrlk8ZkVSr2YpKVCiZ3A8uKzW1ctS4BXbTJq5jZvaMw/yQX/vReRiDjK1rufSHfW6WN7EtmxvuDXaaO3ztra8xPZpy+OoQx3L4qW/9FLPFjO/+8LvEdkyv12NezTmJTlQWqXzMolrwSfUJv/HoXIsptEOVncLr0/cVK2Y9feVOY4d+2Oe2e5v3nPdUO9Y82uJ09TxPpe7NVepMDUgUShzSsiytOWXaTkKlF+PdDP02Q6UB7RQVOr3JHlh3uggQ4LqudpKaod2mDWZqCsj11kNU1+0Xc2++HsptZl8wnc5iF1mWpbMB9no9iqLQoeODwUB79YMg0EDDen3EbpV2yv/tdlu/LyG3ZhZBMbAFcDFDSdadnFJ/EWyU/03b1rSv1veX5pi4rkscxysaIHJ9ych47do1+v2+nluiZSZAg9RVdDGOj48ZDAb6PDJ/1vfGgAbR5LrSb5v20FcpVwYa5ISyMZf4EUAbhELREZTJ9FybDZBjTJHAdrutvYxiTJupMgVdkrqYxvZ6Y03qivxvghq9Xk9TjOT7lmUxmUz0JJZBEJ2BNE1XFpqJuklbheokfbIOkJiggWm4i/E/Go0uLEip3/q1pF/NNpj1G4/HKwtIUEoBf0z1WTPdyno/rrMQ1mPMpG83fUfUXDudjgaghsMhnufpG6ZQucxFNp/PVya/Kaoi/WWijOZ1zfdN5FTKYrGg0WhosEUQXhnzJEl49uzZyk1d5mu3211BldeFR2XMgyjAii0G+YBOp3POxLAtMi8jCiLiRszEmTD35hS9grSZKjaEnhQourttUXgFuZ8zby0R8HVjoFKshrAIaWQNtmfbNJMmXqbixqlhnsyJrIjczykbJXmYk/u5YkkEG4TSlsaeptivGYK1VZO7ObmbMw83UyStytLiXn7h41c+WEog7MQ94bn/nMQ9NzDs2qaRNQgiFWoyqAcwB++phzf2sGMlfkcIg/sDbn/tNrEfM7bGjKoRs/aM6kZF3TCMqBKYqJc1sbDGlv67XJRkhVIrlphBefA8fvyY3d1dtre3N7btT1ocx6EuavaDfeqDGkbQPm1zLb+mNUxuvXWLb/ziNzhKj/jOB9/h9z/9feaNOVEYEXuxZpIUdcEwGzJkyONnj1cv9CWw3rNwM5cgCzhwD+hlPVp+CytSc/VueZef7/68fsABGkjGhdP0VIEQhRK+G5ZDzvIzTrITzvKzjcbVZ2IyrD+rLM7DOy5j51UQuiGe7ZElGXmRY7kWtVN/pswRUnRdr/C9ioqkTlR892uKV3nYpU2Zlgx6A7CVNzOu4gubbAFwgM1skteFCtTn53Ash7JeguNG370RFFqud2pwHVet07q8Epi0qaRVqsN3zGwTm87n2z5Nu4lrL50QlVKsz+tcpw3M6ky1U9q0AYdxcDR1XPoyr/NLw2psy8axVTYLeU5ERXQhZMSzVAiICNeJOOVr4+FfA9jkdc5pptaU1LumphpcnLc2NrvhLnutPTpeh5bfIs1Snr98TpRHlHbJvJhT2IVKhSiikJ8RkFMfL9OEpktAxoHnxXO+O/ouePCPP/rH+ljRJgmdkLbTpu/22ba3uRHeYN/f50utL7HX2WPnBzukacp/+O3/UF9jUS0YZkNGxYiT+IT3P36fJydPqFoVZaieTamXrjDbYJntwO8TuiFlrQQm0ypV4UefN21oDZmVcRAfcMABzJb94y5/L58dUppuk+3GNl/b+Rq7zV2quCJwAjxbxcKXlcowkdc583K+kvJwk2HdtJs07SZWYZEsVIauRrtBitJ3Kd2S0isv3AdbbkvF/+dqHDp+hzPOSJyEh/FDPn3xKcNsqBiQNfBk+UVfgRnb7jbvdN6hmBbUWU2d1dy7c49PP/mUdq/N8fSYzMpIHRWaWTiFnlslJYtycYHFcllxcVXK8drDLlQ4kmM55FlO7dQ4TeVciKv4HISyUEBHbdwzXnNvrqhUus1xpMHdOqn51eGvrrCq/rf/+3/T/d63+9xP7/P3/x9/n/l8rkXWf+f3foeIiP7NPot6ocYvW4ZKFBMm+YSX8Ut+mP9QpbLcoOPhWZ7SbPD6GqDoeT06ToetYIvtYJt+0GfL2+JG8wbvOu9qg9vc60qR/bfv+5r1LKHoYpsIY8FkSpuUf9FDkExw5t7YNJzXjWfTYJZj4ZxZYNbbBAvWzy2sA/M767YDnLPn4dzgFWdjWZa6veLpH4/H2oEsYIHo8K0DHqbNI98RzcFWq8VwONRtMIEWE2wxw8bXwQbTJlgXejS/L45vqYfYL/J/s9nUKTtFD1FsZQmbsSxLi/7bts1wONTtE2euZVnEcczOzg5RFLGzs6PrJsW0R82we6mTqddh2tyfhdXwmUInTLFDQHfAplh/05NuImemB9wcGPEcy2IR4KKqKra2tlbib9aRNtMYNdPBrMfMyG8ZBFOV1HEcoiji8PCQIAg0auV5Ho1Gg2azqQVNFouFFm00O1y84wI2mAO5iZZi0lZEyMPsu02G8npYinkjEFqNfE8mq5n2ZH181heE1M28mayjblI3+X+dVSA3BtFd6Ha7OvZJ+m5ra0v3mUn1kpuIoK/mNU1kchO6to4em9+TIgilXEdAJLlpj0YjXr16Rb/f1/NCQjhkbokmx2Kx0Oldpd8FwBGU12SyVEWFV3l04g7dSZe9eg/f99lOtrFthdpPnAljxpxUJ4ytMd6eR9JMVP7r5TAIm4BKGWSVXZF5GZmfMW1OOeLowkbXqizszMaJHJpJk3AeEswC6nFNmIW0nJYSdHMzlaWiDVWzUiwJPyVrZBShCgV5bdymcd+p7ZrczlWKMvlsbbNglRZ+6eNmLqRQV7XKetGacdQ8orxegkQl5OBMHeyprSieZcggHXAnvoP3I4/0k6XwqFeTNTPKdkndq6EP9KHeranfqaGtTldS8kn1CV7sYU0sgjhgFs2IphHH0TGnr075qvfV1zT28xeJzZtMJrx69Yr79+/T7/f5wz/8Q6Ioot1uc23rGl/d+yrvJO8wjsYcfnhIv9/nxo0b3Lt3j6Af8Huf/h53v3GXsTXmyfgJI2fEw+FDzvIzvfGunVoBS2HO3JpDC9iC3+a34dvwW/Vv0fmjDgNvwHX/OreCW9zz7nEjuMFeuMeuu8uuu6vrLQ8mWWN5mTOtppzlZwwLBUKMyhGn2SlH6RFnxRnTcnolY/fSzeR6iMDSaE+qRD3FNjzJXFxCVwmztbwWVaHWZ1zE5NZSiO0yo+zHUHI7V2vFg+PyeGPYhG/5tNyWEk2zlVcwr3OyOmOcjnWGCF1qzmnPZt2XXsKqXn64YY1KOIFjOZplkRWZNsRFZDQnv5TRYWPj4GiGRlVfkmZypcqvD3HJqoyiKnQKSwnJ2JjOsFZClM2gSZql5wa/dZ4GtKTUxsamsRXhTRuboixI6/TSuSmZUyws7NrWgMl6uIaFRdNu4lkeNur5klc5dmBrpsRl11gxrlitd0XFUXLEaXKK6yz7plahb3W4vP5yu2FVFn7u07E77LR2mIwmvPfee8zjOU8PnlLaJfhK2Dgnf60Gx5uK1iYpFEviIF2mkF6XKbih+ubvffD3CO2Qtttmy9tiL9jjVniLm62bfDX4KtXDiobT0Ar0vu+DB1WjYuvOFj/7Z3+W0+SUo/iIk+iEk/iE4+iY4/iYk+jkwjzpeB0GwYDQDamsisliQlzFZLWaa69lrrzmfhAVEdEs4vns+Rv7yLd9lf7U3+ad5js0LCUY6dmeWsO1Cv1Iy5ST6QkvihdEdcSsmJF6inWwXhp2g5bfOhebrNU9cD6fM7WmlG6p2AhLrMitXVpWixtbNwitkLOjM+zaZm9rj2bY5NXkFXPmRE7EwekB065KKSnClLAMJysDwiwkJKTjd7h57SYNv0GcxCzSBWmtmBrzcs6iVClzBVgpUHuGjAwcxcpTlVtewByKJWMiIKCe1tjVuZCt67sE3YDCKZjnc7I6473ee7TsFqNkxKJWoptJmWxkhNnYdPwOLa8FhbrOKBnxP3z/f6BjdwjKgL7XJ3ZjwiLkXnBPe8vXPfAmk6Ci4iw5Y14pcUsBJsbZedjEYXLIj2Y/YpJPmBYX00e7lnsOSLg9+n6fQTBg0Bjwn335P6Plt/S+WMKmxRYQQXUzU4XsO0VvwBT/MzMyrBuUk8lkJdWiPOtNG0rOL/1iggxSL9OwN8XtzesBeq9sUvPrutZ1EPtL2hXHsdYikHNI+0XMX+xHsc/MY802u65Lq9UC0KnTX7x4wdHR0YoH3+wf2f9L3c0+MW1REywxbRbpH6mvOHi73a62ecSRXxSFBkLESSxOW3F2b29v6/POZjPm8/lKqHldq9Bw0fYQG/rVq1cr2TXMcTSZF1JXk3Gybu9dpVwZaBDjSsQmLMui2+3qvPYSFy0XF2BiU5y0OVgyGWezGa7r8tZbb2FZFq9evWKxWHB6espsNmN3d5fd3d2NIhWb6mp2xLqnWwbT1GmQekgb4jjWxq9lnVOKRMNBDFVBDeVcplEP58DLeiiCDKK0RRbPpvaYoIg5qc0idCeZ3HIzWQcW1m8wm663vkjM+puLdP3mY/6O45jt7W0dliCLqNls6oUgRrn0hQAlZgiOZVm0220tYrIJAd3EqlifHzIHhXJl5pWV76dpysuXLzWYFIahTs3j+74GnIRaJZQ0c76boJIox4pOxXqdZZwODw+payV62G632W3ssssus9mMzmmHRqNBZVfM3BlREBEFEXN/zsybEYcqJMPcpHq5h1d5mopaWEqnoQxLyrBUD/xN3q0KnMxRYSFzj3bZphk1CV+GWDOLZtlky9+itErG5ZiFtcDtuVTdiqSVEPkReZhTBRWlV56zITCutW43ODWpk5L6qQIANtHoSwWS2JmtDIleyWJ3wYeND89DUd4GfhnsiY09samGFYxQDIanwFhlCqlrJeZp9S3oQ3g9pHe3x7AckvUy4hsxRUPNjQMO+KD+gN/5736He/173O3f5V5v+bt/j3v9e9zu3iZwAz5r8TxvJSSo1+uR57mea71ej0ajoQEw8bZIBhPf92laTW5Zt/gLt/6CerhuJXz5y1/mk08+IYojnpw94e//+t/nyeQJeSun6lU41xxm3ozESrShWlIyrtXG6FH2CNYcNIEV0HE7bHvbXPevcye8w9uNt9n39um7fUJfbUCvNa7pNScbBf2QritG6YhJPWFUjRjlI47TY16lr/jB8x+wsBYkdrKi97EyF95A6xfDUj8ILbXBFW/TMBtePN5abqKtALdy8SufKqm4ee0m1DAtlPBXVEWfL5NCff7btu2NBndWZ2R5xigfXXoaF6W87lc+dmpTFcqzWDu1otkH1YW4cPHsqz/Pf1ZSRZoMAaO+lmVdSE0qxrDOVvGarrCMH/nOm/pOQJLX0altlNFhWYri7zgq3rukJLXSi6AMii0g7cA6z7gBb0ghWp8/YyXk43X18iwVglGWJUVZ4HgOWZEpGrpRAiug7bRp2A39TBVjc5pMKe1SAT5rc72kXHVWbAoTsmoyL2PCRLEdgpLvn3yfOI/Jw4sMmq7Tpef32OvtQamekVmpwlrGizGzXIkivglIelPRTIlqCUokB/xw9kOjU4A/zzkzp1ZZBvzSp0GDe817+Cc+7/Xe45dv/jJ7jT0G4QDfUQhLVVdMsgkn8TkAcZKccJqcKsA4PSXPcpWasrion9CqW+y2dskixYzKyUmshMIqKK1y9Z70GUpWZRynxxynrw8zgqVYbMOHBXSjLs26yeRogm/5OLXDzZs3uXf/HrZv47d8RqligxxkB8yqGYtisZLtaaUeZBzHx1SVEnourIIn4yeY8kyWbbFtb7Odbqu+xSeZJZSpAu0s2wIHarcmsiIejh8SESmRzLX5YWHRtJoM3AFtp42Pj4dHkam1F+cK6E3rlAQFlFRupUNmSkqVJrTLRWZlyQow8WDygIbVoFk32fa3+cLtL2BFFkQKlLn31j3ieczjJ4/Jy5zb928zq2Z88OgD1W/2gr/9x3+bUbJ6/3Vx+ZvF3+TOHaXavO7UMr28tm3j5A7b7jZ7zp5iYgRGSmNjj1iWip0yL+dKWLKYMqtmjDOl6SAgxUl8woP5A6bFlL/2pb+m95ZitwB6Ly3saonLF/aAabAWRaEzt4n9Jvtt0wiO43jF0DeduGIXmA7cTfvfLMu0w3Hdk2863sSRK9+Ta5nOU7M9Zli9OPZMzQIz016e51r8UIAIc+8t7JAwDLEsi+PjY/b393X9zDpIWdeWMAU15bymjQGreoHyPbNfZY6Is1L6SFgWN27cWNEQC4JAAwSO42jGrdnv8lv6QjI5gtqHiLC+KbBv2sdmpsd1Jsc602aT/bipfKbQCWEYmBoKIrwhAMQ6vcYMd5DfgjKZyIllqawEYsiFYcjp6SlnZ2d6EZmT1jzXupfbnNTrHWGGboj6qqA2ZhyOidwIeimsi5OTE71Y2+02nU6Ho6MjPUjmoJptN89nTmSJx5FjTONY0KxNlBbznEIVku+b4Qby3jqiaH5uAjLmmG+aSOvgwroeRVmWOswgTVPdBlDiJWmabswyYfaZvNfpdOj3+5rRYqKLZuyUeVPfFFYiYI2ZIkduOHIjns1m+L7P9evXGY/HWjPDslRsmKTelP4WgRqzr815I6KTpjLsen9Ke2R+FUWhY7BkHtV1jVVadMsu3bSr+1j0QcqqpO7WpK2UmTtjWA8peyVRGLFwFiuif07q4CQOTuEoYCFwFOvAV7HBZVgq8KIfM62mG8Mm3EwJVYVpiDN3aFdt+sM+vUmPMAvxU59Oo0Ne5IyyEYvOgmK7oOgVJGFC4icUQUHlbaC4m149+VOyfDTLzQBJjaJSF1CHNWWjpL5VQ8gqfXxeqc3VSIVO2BOb/CinPCrxDj12BypUIs5jFs6CyI9o7Df4q/+vv8qT8ROejJ/wW09+i5fTl9pwsrDY7+wrIKJ3DkDI33d6d2h4DdaLrEd5sLdaLU5OTlZATQHb6rrWdEmZYxK+I/S7JElW1lMjbBCkAf1pn71Xe7iuS7vd5tt732Z0NKKyKrJGxh8++UO+/O0vk7UyDrNDDrNDjtKjlfjhtE7J8ozT/JSPo4/XhkuJJ/bcHtv+NvvBPnfDu3yh8wXuNO/Qc3uqTbbLIBhwzbu2Qv2rqor//Y//dwW6VIoqPCpHxF7McXLMq+QVWTcjby51R4Jic+zwcp6+LmzCwVEpKCVmv1Le/ITknC7dhvFivPI90WQI7ZCG3aDjdOi6XRp2g7iMGRZDxsWYqIrI6/wi1Vfq9YYinnMstKcMlh5Bd+kR9Ll0DbScFv2wTx3VTE4mBF5Aq9uiclSoR0REbF1kSKyHudScZ2Z4U319yyewA81EqOtaZ5cQLYbXgQyXiWpaRuPkswoVDlJSksSXh604qBjy0FWZfQICAjeg3W1zOjtlmk9VCsEN39NgkPVmJoZZv7Q2shwsKd/mPLVqS6f5A5hncwWS2CUxS8q4p0+In/v0nB7X+tdoNpR3KiszkkqlFxzFI5U1wGRDLF9anM89F4pbLxYWWZUxzsZk0wwLi6IqWOSLjYCNX/lsNbZouk0821OgUJWTVgrgmefzzw9IrLFPKutco2LBgtPFKe//8fsb2+DaLqETKgZDOOB66zr3u/f54uCL/PT1n2bL3+LW1i3OXpzhuR6DvQHPzp7xBz/6A2jDb/zubzCtp1zbvsaHiw+ZMFFGsJ1eABhaboud5g4dv0PgqE38ZDFhls20F//zhnCUlMR+DD7nc7OvG8pLXvL7p0pXw7EcBsGAnUDpfOwVe3zrC9/iwR8/IIsyrl+7zje+/g0+efgJLw5fkNopezf2OJ4f8+DVA2JiEjtZAfVqq+Y0O8X2babVVD1L2hV562KaV6/06Gd97nTu8I32NwirZfYMx8dzPRWOUmVK/LRSBvUknyjR52pG6l4E7ezCxk1d3NzFKR31PC+XIMNSsNZturhNF6ftMMkmmom0qBcsWHCSnfDJo09WNYTWErY4nzj0/J7KdkWb+8373L92n47boUorbBQFfTpW7BB53pqGF6DDjcX4NVMtyp7UFO2TvbTsH7f8LQbWQHvOhZFsOhV1iLrjrxjzYm+JQSvXEttDHJ7tdlunhjQdeHEcr+yBpd62bdPr9bTzWNpt2mmy7zT3/+b+3XQGrhvrptCj7HPkMwlVluuYYpTSZ6JZIOc102JK3drt9oojUdol+yKps7RJ9uej0UhrcwF6/2UCKnKuddti3WYybSmTYW72t+mIlnlhHic2X5Zlup2iVycvGS85NooirS3mui6dTofZbKYBB0DPAwGdZN6Ze03pH3Eom/W9KoNhvVwZaLh79y7Pnz/XxnZVVVpkQugdprdcKqVp40aYg0b24EIjZFI1Gg1u3rypqTBmZ5lGrWmgmqAFnCOKplG9bvyLZ1GOMVVAZTKaoh1FUTAej3Ue0/39/ZWUMpJpQYwFmeSCSkkMjTmpoijS6ftMw19CSi4Da+QmUNf1ip6FeNDNxSV9ZII05oJYXyxmv8EqsmXG6yRJovPXmuMp80FultKnonUwHA5XbpSyKNdvTvK3AFNmPaRNclOUIlQps21y85E4NZPeZDJxZIy3trbo9/tMJhMmk4kGlDzP02No3mTlJr5ef/muqVsic8u8phkHtk6zMteQ2c/Sv47taLHIHXa4XdzGGyukuKxKEjvhKD9i5s1Y+AuSZkLeyUnaCXFwvqm0cxs3dgmLkCpRD93Kqqj8irJZkvkZtVPr9GtxHWPtWJxapxeo2k6qmBFBEtAsmtSTmu6oyyAa4EYurapFERU0t5pM7Amn9SlpPyVuxeStnKJZKK0FOe86dZ61/z31WhH+W2Nr4AMDYGsJXvglpVWqUJMKokXEcXqMt/CoziqK0wL/yOev/8xfZ7+zrz3EWZnxYvpCgw9Px095MlF//+vn/5rn0+fnFHbgWuvaCiPiXv8eLxovKOMSO7Y1xdHUuJEHr6D3gso7jrPCdBCGg1AeZc1L1p71NSn30YbfwEs99mZ7/Lvb/y6DwUBTD23bJikTjrNjDtNDDpNDDtNDXqWvOEgOOEqPtGp+TU1SJaRZylF2xIfzD9eGxqJhN+h6XXb9XW41bvF2522+1PsSb3XfwskcTessy5KQkF7aY7fe5b53n0W2oJ7VDJ8Omc1m2LbNvffu8c0//U2uvXuND55/wO89+j3O6jMm1kQxbcrFRlr4BePZNubI0lvmliotmjz4c3IdypCWKZNywmF+eOHc0lbP8hS92W6Rj3KIIJknvPWVtxiXymslFO518OG1KS4NdsSFtbB8LaoFi2gJEO3CggVDa3h+HOj89hIvPDmdYGMzT+YUVkFqpVhdi0X95vjrq2pVgJHq0laAhIWlwi7qUjEG1jJWXMVYE9bEOiCwMYa8YEUU0MLCd3x6fo/QUYZSw20wHU2xLVvpfXg143zMOB9vrM+ma28s9fk6WRG9XLtnduYdwkWIw3IP4oHVsbS3fpiusnLc2qVX9ahmldbjqeuadq+N1/I4nZ/SudZhGA9ZZAtlOFvnfVTWJUmZMIsuScu4ZMW4uFi1hVM5WJVFWqaKrl/MLwoaolgbDaeBh0cSq3hwv+UrIcTqYgYmq7ZwU1dpTDjVpdkN9PHGh2bWiYPogA+GH1z6PQeHwA0ICGjSZL+zT17nXPOu8Qu7v8C37G8xP5oTnUT4lk9mZUROROZl3P3qXdy+y+HikNP4lJP4hKPoiJP0hEk+WbmOjU3H7qjsI0s9gqJSmVRkvaR1evEedcUQrrIuOUlOOEnOVSHf/+H7aj61gQX8g//7H+hzhlXIrdNb7Ia7bJfbtGnz3rX3+Pp7X+fTjz/l6NURcRLT3+nz+PAxmZtRhiXhdsjT06ekbkpkRRp4yZ2cE044mZ3oMKxNrCXP8ug4Hfpun4E74L5zn7uDu/T8HlZhES9iJpMJv/e93yNzMuXsCCqqRqW0o7yMwi8o/TUAK1Xn7tJlr7dHGZVQQOiF7O7ssogXzBdzSqukDmrGidJXkGxKso6OOOLx6WO+M/yO2h+uhVv8w7N/iDN06Lgd+l6fgT+g7yqtBRGB7Ht9thvbDIIBPa9HyDkTYN3ANG0W+bvVaq0Yn+ve43W7RewJM0Wlya4w9weAdtDJ3rfZbBIEgXagmTaFsKIty1rxapsGt+xH5Lfp/FvXeNtkj4k+hKkJZzqKzXPI/3Kd9fBv02ks+55Op7PCADCzFgLawSz1lRAD2VvL3n4wGOh6yB7dFGU0z2nWd90RLOc07RM5TsAWMzTF7A9xPm9vbyvnUaNBWZb0er2VtsneUexLqePu7q5OaykafZtAIjNsZd2JvG6PrNuVmxzRm8qVgQbP8xgMBrTbbQ4ODnTWBlCxIaYxvD7BTHTJNPqlyII0c4DKYO7s7BDHsUZb5Lzm5Fg3lNc9+GYHCXVIQh8Wi4XeYG9tbWmj2exkoSrbts3JyYlWQN3f3+f27ds8e/ZsxQiP41hPFDNEZL3+8r9kuFhnFphI3Lqnf2dnB9/3efny5QqVqdls8qUvfUnfAMwJtM6KMG8e5sLYdNwmCpSINiZJomOp5BjpY2AFhQRot9vM53P9fQFMTGrUugG+TuUx+2Jdi8OMETPZKhIGIbQpATDM2Kvt7W1c1+Xo6Ig0Tdna2sLzPJrNpjbCJMWNycZYp5DBeRy+5AU2b47r89ZETDcxccybkxkfaM4r6Uu5cdR1jW3ZhGVId9KlWTT1mDQaDWazGY1+g7E1Ju/kZO2Mhb+g7JUk/YSscZ5K0ypVCs5G2sBObOxSPdTyMocQ4iBW2TaWm4IyUB6apJ0wrafU1+uVjBsAVmoRJAFe7OFGLn7i0znsYM0simFBOSmp3IqyV2IPbMpeCVuQtlLKntpE6LIUz1SdyKoBKe/56HjmC7aBDWW7ZN5U6eK4CbjKWLv1393Csz1u927zztY73N+6z/3+fe5v3edre1/jL3/hL7PdOI+VK6qCl9OX50DE5Kn++/2D93k2eUZxrYBr6tJu5vKJ9Qn2dRu37bLtbNNv9LluX2eWnaPsnU6HVqtFo9HQ6sr9fl+vzVarpedFEARMJpOVeem6Lmma6tAlua+aD1ChXLq1y767z561x9fDr6/McVBG7VF2xKv4Fcf5MSf5CSfFCa/SV5wWp9porFEq5HEac5ge8sezP4Zjs9tt3OsuYR3SzJtsVVu07TY3uIEzcjh8dkgcx4RhyHSqPG0f//HH3L95n7/yi3+F/qjPp9/9lPKk5HbntgZ6d6/vcv/r92nfbPN08ZRPJ5/ycPSQp6OnzK35qvCYFAcKp1AxxBucszY2LVp0vA6+5Ss2RJmQ1EqULqsz9SozJZDZRr324I/iP1o5l2u5dO0uHVexIzpuB9uyWdRKKG9ezomKSIshXpjPZnkNMKHvlcs3RNdimC+NVsHvjegfp3ZoO23aTpuu06XltgidENdyKeuSqIw4y84Y5sONhuamolNdvoHYIWCNABKOre7feZWTlZkyBixp4uVGvoVKgWmzFHBEPSOaTpO0SpX+Q10zz+cMk+HF1Ke5esl5AjsgdEK8ytMZR0QgMisyFtWCs1ylxbuUvbJJW8Mos9aMWWt2fgzwMnqJG7v0/B7v9d7jVvcWXafLiycvWEQLarvmLD0j9VOVdSdIObFP1LWaMJwNGfgDduodWmWLG90bdP0uoa+0CwqnwG7YPJ085Tg+ZppNSctU613oTCMOHOQHsCGyRUJHPMsjsAMCKyCwldFSVIUKv6jKjSCDi4tdLwHuPCQoAtpWm9BW8612aryOx+13bzMtp5wmp4zSEbNcpS1Mq3QF1L1sLghLJyoiIiJGjHg5eqmfB7/+vV8//0JHzQEHB79WQqU3X93knfwdbndu82/f/Le50b3BTmOH+CzGLVwWxYKHRw85y85UasN6ylmqMnGM8hHzes64GF8wZn18Ok4Hv/RZzJZ70VaoNGTySIVHuZXSZLCsjQDlGxpPQsKD6QMeTB9o1swfnPwB/+vJ/6r+WYL0pOD2XBo0aNdt3rbf5lZxi/uD+7RoqVCEO/fwPZ+ammmu2ng4OWRSThjlKiRunI+Zl3PyOlcCwoUBkG3K9P01cHIHJ1MOk0beoDftYY9twjzEyzyKtKDb7bJ7Z5dv//K3OZod8dGzj9jd2eXBwQOG5ZBxOeZkeMIwGZ73UXo+Bxo02Aq36AQdknmCXdm0W22araYKcyoSJumEWT5jXs51qJmAjU+iJzpNqYAW68WxHLpul77fZ9vbXslU0fN6bHlbKmTJ6ykwitYFxjJwwTA193biJX/rrbe0E8u2bTqdDl/96lc12ADKfnrvvffwPI9Wq8WXv/xlbV+ZhrpQ6uM4XtlbSl1MO0Tqa9oxlxnf655wM1xc9t1mOIVpqJu2hvxvZoQwwRFhWghwI3tkE9xYP39VVdq2k7aIzt66Q9YMc1h3ypr1k+OlP6SfxQY29/5ip5gsFrOvqqqi0Wgwn8+JooiiKOj1ekwmE+10ETtQHLXT6XTFJjo7O9OM+UajofXoBJAQh5UwY9fHy2SLyPvm2JvOz9eVKwMNZ2dnK4J34iE2QyBMJVEzBEGMM3NwTCNYGpCmKaPRiN3d3RVjUyjvZgeYk9w0kteN6vXPhcEgn8sEunXrFu+++642koX2X5alpqTUdc3BwYHejEv6GdOoF9RQJr/002Vtl0kq7TNjhEwWiNlPlmVplsB62yXGe/1mAKwsZvNcm8IyTIBmUx3Eyyr/LxZqwyltl3khQIs5LratBCJFl0NS8MhYrF9n3ZNvtnm9z6Sszw3LUkwPs09k/ppGvtR5Z2dHL2jf9/UNrCgKreEg80fABWnjOiNGxEXX451Mw808h1nv9dAS8wZgUu3W0UhzfNf7y/zMLV3CeUgv7mGfKnCs2+0ynU5ptBtEvtKDqPoVU3dK2S2J+yqNpAYOavBjn9asRSNtUM0rGn5D9a1VUTUrYl8xFdIg1Z68OqhJvZSsmWFtK4pk5axR3zOw5zb2zMaaWVhHFu7cpZW22GpuYTs2uZszsScqfWi/puwuRSBNNkSC8mpaKEqzi/p8HZRwOKc8G8yIvMp5NHrEo9EjRRGnXtngNtwGt7q3eHvrbd7dfpe3tt7ifv8+P7X/U/yVL/0VOv8/2v40xpYsPc9Dn5gj9jzlnGc+p07XXMVmN1tkk2qxSVGkJAoUDV6DogEZ9jVw7x//NGD/MGBAggwYtmXQFkAIxjVk0aA1WRQ1tHpid1PF7q6urqmr6sxTzpl7nmLHfH+sXJFrR+ZpVt8rr0LiZO2MHbHmWN/7vd/7OWfqWkma8Df+X3+D4+AYvaXzZPCEhbcgLIWEl0MOOOD97H1+/+7vw12omlXq1+vUr9WZmBO6Vpf2pE1La7HZ2sznsIzHlN4KuWdJLYjRaMS9e/dwXTffx8Iw5Ic//CE3b94U7ThVbpbz93l7gZEZbLLJprcJ3tl6MwyDOImZZJMcfDgOjzmJTzgKjjgIDximw7wvUlJCPRSp94wxh9qhEKsEAcS8IFKyuqkLQ7AnNuVZmUPtkOlCCB8NBgPCMKRer7O2toZpmly+fJkXN19kpbPCz23/XP6y/v73v8+1a9d4/Pgx//if/GNKqyW239jmR90fMbJFFpisljFJJufSVaakTBBp855XynqZptGkbJTpHfeEcazHVFYqgl6dLEgQoQXDZMgwGVKUVygWGyGW6qYu0TwSOgP2qQ6KKcRgz+mhyDX2aWncKmanJUyTKdNkyiHPZ2/Yui3isE0BSpSNcp4JJIgD+nGfbthlHI/zDBSfpsRZTJzEFwMJBQPdwBAikobYX6M0yrNBqM80MTF1cymrhMwQACIdZ8NukCwSkalHA9MxhaGchXmYgarxcGGfaLZgSGBjpRZGapCFGbPRTIRbGhqJlRC7MZETnYVLqG0rsFZiYnpBj17Q497o3tkfTjVtyloZOxTrYlvfZr20Ts2qUXbK3H79No+6j3j3wbsM4gFPg6eMFiOm2XSpbx3DYdVb5TP1z3Cpfol0lDI8GFIv16lX6zx4+oDVK6uMtTFHwZEwnuOpAG0yIagZZAHTtCjwcvrvaZcZmpEDNa7u4uAQ+AETbSLC6qwFPbNHZC1nC3n72ds4hsOKt0LH7XCreYuO16Hjdmi7bSzDYpEsmCdzBnOhX9AP+/T8HqNwxCSYECTBxWljLx5MEc6g+fj49IY9Phh+8NzLdfQ8pWvDbNCyWrTMFi94L9CutambwrDM4izPSDGIB/SCHuN0zIl/wtP0KaEeMmYsUpsqp/MwC6kbdTbsDTZrmzlbYjaecdw/5urlq+z0dkQIlyZCpObR/Pxc/TMckLEugNYJEw6GB+DB+1MFJFUII6Zm4hkilGzVWWXD3eCzrc9SM2pUEGBRlmWkSZqzCoyqwdH8iIP5AcN4SG/RYxyLMIXEFvpRuVjklULlMgGCfuv9b9EutUVazdBhw9ngcniZbW+bX//Sr5OMEz65/wmBEbBxY4NBOOBPfvgn9IO+SFXqpTzwHzBKRxwEB0z96YWsLFcXITklo4SjO1jaWWrEOBWhYdNkKkKHEjHvkyzJAZcnPDljbz0HmLA0K0+X2bAaIlPFqRBk0xag+6+98GtiT2I5pWSv1+PBgwdkWcb6+jqapnHvntgfXnjhBVZXVwnDkDt37uTv9XfffRfP89jY2MDzvJxqL1kDo9GIk5MT6vX6ktEp7T7JPJT94Pv+EjtCtTdUJ6Z0YqhnfBVQkM44eaaVYIh0gkiDWIZGwNmZWT4vjmPK5XJuZ6gikWrIi3QwZlmWZ3GAM12+p0+f0mq1lkAD1cYo2lGqrSv/X3UWqhkw1O+qoIMUprzILp7P50wmE+bzOZcvX87rLOtfqVTodrukqcg2qNo4x8fHlMtlDENkVJNOppOTE6HzVSotgUhq38rnq7abymYvgko/rnxqoEF68VWgQTZIxnxIA0WGAaiDIYta8aJHVtO0HFFTDayiaIn8rioYqE5IObnUBSIRmdlslqeRlNd4nlA77nZFuimVVq8adr7v56JsUkhjMBicm0hwJm4otRyKlH+1fdVqldu3b9Pr9djd3V3qF3Uyq4iampWjaGSqhvlFXnS1rqoxqk5u9R5FD3uapnm4h2VZ1Go1arVaPgZywUpEroiSZVmWa3FISo8qqKluMMUNQu2X5xnWxWfJ9srNKsuE8OLDhw+ZTqc5KioXm6Sy12q1HBmWwI6cGzIsRkUiVfqVHCvJkFDrodZdzh0JqKhzSd3oiqDBnwUEPW/tFfuleA/1b3qm485czJGJN/WojqtUKhXBviGDGgy0AVpLY+bMCMoB08oUf8UX+gunxQosrKlFbVjDnJhCrPI0HZhVtZiZMwIvIHRDAi9Y+i4mpOWUzM3IVjIBAliw0BeMTvOOaYmGPtFhDNpUQz/QSccpWSCu1yyNrJShNTWyRgZNlg/4AUItPQbN0MjMTPzd5izNmVIuOjD4sc/9/n3u9+/zbx7+m3N/90yP9co6N1s3ebHzIjvuDp7m4fZcVh+vsr2+Ta/Xw7ZtqrUqje0GNz57A3fd5d1H7/KdD7/DoXbIU+8pC0cIlQEwhPJxmRVrhasHV9ksb1IKStzIbvA0fMo0mRKEAZYpwFAZOmSaZq7m3G63l9avCgiqwrrFF4xc1zL0Ac5olpW0wgYbS4eAHFDVoZ/0GWQDPtj5gB8+/iFze85QGzI2xwSGYpRqkJgJM20GazBbnzFgwO/xe/zeH/yeyIDw5wT1+qn2lFVjlW1rGxeXz+ifyd8TMmxkfX2djY0N7ty5g4ZGXa/zGecz6DMdbS72xL/+5//6EsVwkAx4MHvAE/8JT+dPOYwOGcQDZslM6DwoBtssnQkvf4TwjJ7OnXkoXHkODh27w7q7TtWqkmYpfuwzT4Vi+ygaMYtnS/cMERll5syF7shFoUGnpWSU0AKN+XSObujYVVt4rrP4x8fQq0bupyjSkA/SYNljWSjSAOsYHSqmEEEs6SUczSHMxEF9kAyYJBOCNPixKSNNzYQELN3CsRziJM7FLYM4+LHtizmNlUUIXRYNsCANGEUjEfePCPvTQ50gCy4UqJQZBHIPZ5YSJRGJLsCLWTY7M2odYPVcB6InOvpCx0xMzNhEizS0RIB4uqGjWRpew0MraQzCAZP4YoBr5s6YuQL4PtAOzgTzFsC3Rb/psXhOw26wpW3lgoqtUotXbr9Cb9jjyD9ib7zHsX/M08lTTqwT8T7qI+ZyHxpWg47T4VrlGg29wVppjRVnBSuzSEJB550kEw6DQ/b8Pe4e3WWuiXkbpIIp4Wc+furn+zcGYk8u9I+WapiZiaVbtMttKk4F2xAskq7fZW+6xzAQxqoEjGSxdVsAEV6HVzqvUKFCw2pwbeUaaZBy98FdjJLBvZ17TNIJ5bUyB+MDQkOAnn7iL6VF/LNKSiqYW6lPL+rx0H/4Y683MXE1l5JWomYI8cf6oo5z4NC0mtzYvMGHH36IYYoQmtblFpdfusw4HRMbMQf+Ad1Fl+6iS2RG/EeX/yPevPomw+GQRqPBjRs3GI1G3H10l1E8onWlxcnihO/e/S67s13mxpxmvckV/QpPuk84XBwy1IZ0F10WmgBDn8e8kSXOYibxhEk8YT/Y573xe8+9VkPDzVw+F3yOjtbh8/rnMRYGs70Zn/zoE+IkJtZiyqtlNm5ucDg5FGEbJZHxKnRDMi8jtmIW5oKd+Q6ZlvF45/HZQ6bwP/3R/wSc6cdUu1UadoMszihTpq23+dz257g1vkVpVuL2pdv8hb/wFxhMBxyMD3h68pST+QnvfPIOvu5j1SzGkRAFniSTXCD4otSepmZS1s8yCMm9gUz0VZgJjZVZIrJ0aGj8zc2/yTgVGhajaMTebI9PRp8wjIf5ev/lG79MWT9jPqiO2/l8vuS9l2dr9b0nbQCZ5h7EWdWyrDzsW4IFUiOsUhGpuYpnUOkJB849Q7VV1N/le1QNS5fPl89Qz8jy3CE/l4a1qjMhWQKyDrIN1Wo1D8lW00eqZxZ5DpbPkTaKdNbs7e3xUz/1U3l/Fm2nIsAh75vP9dN+VIXu1TO2yjJV7TD5rxpWIu0BqeUlHaGyDjIzhazbaDRaEs6U7GbpQOz1enkfSbF71Z6S46q2VbZB7bfn2RjPKz9RekspXCLpO/JB1Wo1R8CkyN9FRk0RBVGvkYdYGdKgCuHJ56gTWI3VUUVFZF3lj/TaqRoJcnHKNnU6nVx0Q9azGHYhQyKkFoJMM6LqBkikTAq3qciVOkhFpoH8XB5un5dhQo3DGgwGS+lVVCRMRRaLBmsR5FGfo/5b9IKrdVEXUblcXooVU8dFxgapRQU1ZF/I+DFVu0IFW9TFpd5HnTdyvNX6q+Ee6ucy7GN9fZ3j4+O8znKc5XfVFDdyk5SfN5vNXJwlCIIlVotc3BLAUkMdikwWdQ6oopnqeimOnfqZiqJeBOiofVnsM3WcL+r34lxR16+Ghhd5JJOEaljN14VpmgxHQ9ymy8SaENdiokrE2ByzqC/wW74Q9DstRmzgzBycuUPt6BSICCwGJ0LsjKoIaUiqQvAyq2VkdsEQSRHgRAOyZgbXECDBKTMhI4MEsnEGE9AeaWQzIbCpaZpgZnig13TSWirUro2ze2/Xtlkvr1N36xiawSya0Zv36Pt9hsGQMLlYcE0tfuzzePiYx8PHfPXRV8XBWm6F14WnxogMrIVFmza3yrd40XuR15qvsdHYoPeoh+/7rK6u8oUvfIHYjXn/yfsYbYPqpSr3T+4z1+Z89+i77M/2WewsYAVYAf0LOtbcop7Vmbtz4m7MpeolFocLKnaFq1ev5nNPpcpJRWg5Z4r7iwrOqWJCcv3LfUx6K6RYlWEYlCnzgvcCzr5DcBhQrVbzEIl5NCcshTwaPGJ3uktWz9DaGoNsQFAOzsJfOE0NaEBcjjnggAPtgPd5n3/Z+5fwbeExqtjCyFi1V6lFNT5vf57D8SFxEi8JeWnacpyqbN+Ks0LH7vAzjZ9ZEv8yDJGONyVlb7HHg9kDHs0e8cR/wkFwQH/RF1kElJjzAKFlcRQeXThPTExaVou10hob5Q1cXI56R+wc7xA5EdNsKry9prIGsrO5NE/m4q1eF2r8EQJYvcgIt7DIktP1T7oUw/9py/PEHGXJDTB8ukn3ufcxMHB0kZGhYlYo62Uc08GwBWNhEk7o+3268y4REX50XrBQ3sfComyXBZi5EAd6zdTAhMlCZFK4qCx5NXUuDPOwNRtP93IRTBnaEaYhC4QRcc6LnIl9jphc7BMNMl1kDVk4ix/f73PwDI8Vd4WaU6NklJj1Z8SzGNu0GY6GxFpMZmUEZkDiJkTGmYhfnAkB1VAXYNV+ts8d/w74wADYO22ypmPrNlW7iqu5bKQbNM0m7Uqb0dGIF2+9iOM6jKIRR/4RD+cPeXv0dh7/LoulWbTsFm2rTTWu0vSb/Pkbf56O06FpNbE0IRg4iSYc+Ae8+/Rd7p7cJSydxuRbiWDp6JkIuSESBtFFtHtOw210C9dwKVklSmYJz/BwLRdDMxguhuwtBCgxeDw4G+cxQgcj02jOm3iax2fan2GtskbHE0yJii0Mrmk4ZZ7OOZ4d59kt+os+Q3/IPJr/xKlCY2Km2ZRpNuU4PY0lWwNWYV/b5yM+gl9Q5k9icLd/l5pZY0Pf4Gr5Kj/d+GnMucn+s302Sht883vfZLw/Zmtli1u3btHv9/nwhx+iaRo/+9LP8nrrdQ6/cchWtMXVq1f59V/5db73ve/xzv13qNVq/MZv/Ab/9J/+U46Ojrj94m1Wr67yh9/4Q7SKxsJcYDZMxslYMDHCHifhCaN4xA3vBp7uCeM4ERkwglRoUMh9ISPD13y+Pf32ckdsnv4ggCU9EwCBYRskdoLpm9hjm8rTCmvWGuVFmXalLZjEJYsXP/ciRtXgo/2PGOpDapdqHEwP2BnsMM/mLJIFT6ZPctDo/YP3+T8P/s+z5z8A85EptGusEmW9TN2qE8cxa+4aV7wrfK7xObbcLepmHZ3Ts20aCYHLRAhcjuMxw3CYi18Oo6EAJWKRSWISTy5kQv3B0R9QN+u57sNlWwgo18waNauGa7k8mz0j0AO8zMtDHNQzsRry6LrukrCgTGcoM6C5rpszIIsebPl+r9frOQsXzjOL1TNskVpftEfk31Q2gCrWLt+zavYJ+T6WIIIEBBaLxTl7Sp7NZT/I97J02hWdj0XbVIpkVyqVXLOh3+/ndVLrKI179Z5Fp4v8mxTrVu0S2c9qyIRqX8CZnoNkysvwWDlmMuxbginSeXL37l00TWM4HOa2gXTkyvusr68zn8+X+tS27aX6F9uk2kRFm0D9zp9VPjXQIDtCGlEy7kV2jvRiS8GOiwzmovFa/Jv8TrlcplwuLxmiSZIQBEEOcEgAQRZ1Uv+450hjXnamXFSqISepQerCyrIszwYg+0F68uSPzBwgRTvUiS3vo9JwVFbDYrHI42suNOwU41L2ia4L1VHLsnLFepX2LEsRaVSNfNUQL16v9p1q0MpxUBFDubCLOWZlf6rXyWeqWgJyYUkAQ62TmrZGFhUpfV4b1H6X95RjdHh4yNHRUY4USgqRYRi8/PLLuR5DEdmT9yuXy/mmPRgMCIJgydheXV2l2Wzm/azWW+1X9f5SN0L2tyom8oPk5gABAABJREFUqm6Q6lpRNSjUsbtoXcnNRaLQckNTN291zsl5ptZX/V29Xm7yIA5+TuKQTlMBHFgW0+mUer0uDoy2SFHZpy/YELZgQwy2Bku6ECQiXSV90IYaxp6BOTYxZga1co2VaytM9AmHi0MiLyKtpaRVwX5YykwQQ67BVoasmoEJmZctGWxJmojUjgen10fiEPpTv/FTTIMpJ7MTdie79P0zD27NqXG7fZvV8io1u4ZlWPiRT9fvcjw75nB6yCy6II5dXWLZqTibLQQ3Z8x4xjO+/sHX4ZStq/2Uhp7qOJnDd6ffZWW+QkWr8IXKF/iLl/8is8qMl19+meFwyMnJCYER8D/+f/5HHg8eMzNnJNUEva2zq+8y2ZjwifGJ0KIAvvb4a6zYK6zaq6zaq3TMDm2jzUZpg0uVS9TNOqaxDICphw11v1D3O/milaFcKvAlr02SZCnns67rmJlJJangT3yiZ2JPWV9f5/79+wB4LY//4D/9D6hervKtD77FWx+/JQREqzFTa7qkFp9kCeNwzDAY8hjh/frqR18VzIC/BG9nb/PPh/8cs2XiBR4dOszvzHmh9AJr9hq2ZS+1S65FyWiT76KW0eLz7ud503gTatDtdvnX3/rX9Ho9vJLH3/hP/waH4SEP/Ac8Ch9xFB8xiAd5pgp5II+J6UU9eqMeH48UYc3TsSIVoq2uL7K7NJ0mzUoTwzCorlc5nB2yM9phlon4/R9nAEVEeX9rqYaWasLAMy4GDWSsu1o+bWiGFG58XthBgojhn6dzjqPnpwPUdBGe0PE6WLGFozlYhqCP9id9Bv6AgIBpNCXMFLbJaVYaWRdbs6m7dWFUGy7EoBs6R/0jYj0mICAkPMdmkBocP66YmEIQ1CxjY5P6KdPJVKQK1AELEjshsiMi/byqv5aKta5nIp2v7dgkCBE7VQAwDzEqsCWM1BDhFLHNtc1rOKnDzqMdtEyj3qyjWRqBFggPbTJmkYlsCWmWCs0RmdFDh2fZM8H2KsE7e++ctVEzsTWbilnhZukmFa0iRBZtwVZJs5RpPOWh9pBJecI/3P2H+OkyOFTWy9T1Otki4/IHl3FjES8ts0xlRgYVAQD/7F/8WSbahCfjJzweP+ZgdkBv0WMaTVnEiyVRyB9Xcs0N08HTPZJ5gpEY1L06YRgyXoyZxBN+cPSD/P7F77fcFqvlVa7WruKWXbzUo27UBZCUaSKN5mn/yvCoaSKEMxfp4tMzi5SlkhgJo0x4vXd6O8vfceBffuNfit/roIUa/+3/8d9S0ktgQpUqD959wKXaJe5p9zBTk3gWszfdI9GSJSPP8zwajQZvvPYGruuybW7TqXRYX1+n0WgspR6XnvVut3sObF4ysjTwM5+n/af8wbf/gKAaMDbGTLUpkRMJHSYjITPOsrDgIX6UcsTRWd+cjoUxNPCmHmZqUs7K3Frc4lrtGleDq2yVtvjZF36W21u3+dpXvkZv0qN6uUrlaoWvvfM1dqe7+JYPZegv+kzCCcNgyJPZEzIt40fBj/j6s68v1UFHgHGe4Ql9HatG226zYq+wYq/wsvMyq+6qAArMGlp2qn2UxCxY5KDENJ0yjIaMIpHOchSLsb3v32ccCaAiZ+koiZ7KZplSVmLtm2s07AYLf0FFq7B/Z5/rw+scTY+oW3V816ex2sgBftXoVrM/qCwDySpWsxLAcpY8OK+BJ+9TdFDK78tzocoMKDpRi+xJ9V0r7QrJqpfXqSH70rBX7QyVlSjtIekwUc+7UvtKas3N53O63S7Xr19fAhfUflAdpvK+KutctZeKIIzKFi3eT+0Tx3Hy7CHqepNtle1qNpv5d1RGg+rMDoIgDx2X9VLD/tV+L4IO8pymAiJqGz9N+dRAg3yojAOu1+vMZiLuUKZLkd5u1ZAtIjrqYVM9lMqOSpKE8Xi8BDCohlSR1VA0vuTnaueoz5SpYdI0pVKp0Gw28wwJqvGkPkfVpih64lXkUKZtlEax2gfFQVJRPiCPdVLbUa1W0XWdyWSS10tONk3TaLfbbG1tMZlMcqBBzeSgTnB1I5DhCuqiVMdJ1lel7Muxk4BPGIb5Bub7PvP5PJ/wruvm7cuyLNdrkOMii8xhK2lQpVKJyWSS97Osi2pE/zhvu2x3ESiRRTIZFotFzjKQTJbjY3G4LZVKbG9vM51Oc+CmSJFS+6YonCPHVoIOxb8VN2JZd1XxX9ZNzUJQVPSVL/PipqeCY7IfZf+q1DsVSZYvFnlf13VzsUv5mQqKqSBGMcxJruuiB3wJcIt1vKlHNslo+I0cpATwQx8a0E27zJ05c3dOVI1Ir6YkjUTErgLTbEov6OHMHJKDBHpgPbHQRzpZXyhIp7WUrJoJvYYa4t864qd81pdkgA96qJNGqQibcABbgBF/ePcPl/p+pbRCp9ShYlcwdSGQdzg95BP/E45mR3loha7pXKpd4rP1z7JSWqFiVzB0gzAJ+fqffp25NWemz4itghAdnDM+Mi0jMRLm2pzH0WNhNNvwjcNv8LcP/zYAxg+EV7hslmlZLWbNGekixR7ZeCOPW+ktVrwVjg+OWbu8xr2Te1gdi+s/dZ2j4EikB5vf49+F/y6POQXhoVyxV1hz1lhz1lixV9jwNli1V2kbbapaFVM3l8LbJAgs55+q9CxLqVTKKZ2qp0a+BOW+pYrx2rZNu9TmtZXXuLl1E/eRS9JN2LQ32XQ3cV2XwA4Eu6WecRgc0o273Du5l2fQWOpXMkIjJCgHzMozulqXO6M7SFa3zCLRNJusWCts2ptc865xi1us2Wt5G4uhI3L9hWFIrSbEwTqlDq80XllaI5L+mBopx+ExXbrc9e/yYPKAw8Uhg2DAPJ4LPRAN0CF1UnzHx68K+vkTngiqvPRMJzrMwJpZlLUyty7dIgkTDNvAbtk8HT6lH/eZJ0JJPtQEuKdlF++bOnpOBzY0Q2SKSCMW6eLCsIKLyqfKzgB5aENGdiEokWkileThvKAdUfR2Z2LszFQYxCWrRL0ihLQW2YKQkFEw4tgvgBoa+Ro0NIO6VadiVqhaVSpmBS3WCJOQIAvwU1+k8EumBFmwBBZN0gmT8NToNRDZbtSSgp4KoV4PDyu2RJrJUMNGiFhqpkbmZbTbbSbphO6ieyFoKUEijVOdGz1lxoyZOWNwPBAXnUrE7KQ7S5ogOjpNr0nNrFG365SNMlWvyrA/ZDAciBSZVkhv3iM0RbpSqTESZzHzcM5xqPRhoXqaK7zUZa1M025S1spUzIqgliMU4HfSHbIoQ9OXQzdN3cQObMrzMj/X/rk8Dl1938l33iyacTA9YJJN2B3ucjA54Nn8GY+Gjzjyj+jOuwJ8SoXeRhieagGcavQcL07bMFSngkjd65ouJbNExargWZ4QBTUtIfo732O4EIaiumfKUtbL1I06bavNdfM6daNORa/k4T+u63LvyT0W2oK98R5jxiLrgh6y0BbnMpNcWDIxV2XfAYzDsQhN0eGAA+49vicHHErABP673//vAMGmcycu//Mf/s8YCwM7tXlw9wEblQ0emY9YT9fRNR3HcGi7bRzLyc8OMvORyobTdT3XN5OC6GWtzCXnEs1nTa5cuYLjODx58mTpzCsZxq12C72kc2/vHnN3jl/1mZfmpNWU2I4xKoYIJ0tDEj1hkoh1NsgG7B7snvXLGFSJGQ0N46FBaaeEmZgiZave5LXGa1yuXmbT3aQclll31nnrO2/RuNogqkfs+/scLg45Do7ph31G4YhpPOVgccAz/9mPHRqpY1E2ytTMGi27JfQsvA06doft0nbOZmg6TUz9zFEXEoqMJmUYx2OOpmIe39u9h1kS7JKj9IjHPObt+28z+0RZfPeBPxUhRK7pUjuscek7l4inMVZk8eSTJ1zvX6eUlagaVYzAwNM9se8q5758iinnbBUwkP8WbQT1LCo/L9p7qg2k2lfy/9Vzt8yYoNZF2l9qmLXqwZfOcPUsrJ5HpX0k7TXpPJDhJKoxL4usZ9FJp+pZyWfJcBRpwxSdoSowIp8jmSTS0TibzWg0Gks2nSqAGccxnU4nf85wOMw1HaSAfRzHuQik7DsZRi4BJ5VhXWSpqOf64jgX7ZLnroNPdRXC6JW5Ow3DoFqt4rou/X6fxWKRCwOqA6kOZrFkWUatVmM+n+dK+LKzBoPBOWq5nDDF7BNFL6z6ufyeWlT9Bdu280ErZodQPViyyHh9SZcfjUZL8VC2befMBJmLVg7ERfWREy3LMqbT6RI9yXEcNjY2ljIcyO/L58kNXv69eFhX+7ro4c7V5ZVUeuqkUesrwQJpSMhwiWIeXLng5fOTJGGxWOQLWZ0XcizkwimGFqjG6kVoqXptcQMstlt+bzabLSnxw7KoYpYJQdIf/OAH+QYmw4IajUYeLqOyStTUprKkaZoDR2oIUBG9LIYrlMvlc6KnaixYcTNT2ys3DPU5KjtE04Suh9TVkAChYRh5Gh25ecgQFgnAqf2s3k8FonRdz0ELlVqu1l/9V90nll5YqYY7d6mP62hjDTuy88NImqWk5RS9o1PaKmGsGPiez3x1TnwzJnIVo2cGDEAbaGT9DO1Qw7hjiN/nwnOrNTSoQ1bLMNqGACOqIjxjKXwCqNpVOqUONaeGbdhoiDRvPb9Hb95jFIyW5l7NrlFzapi6ye5klweDB4wWozNDoSLykTszh5XZCu1Sm1F3RJZmtNfaZHZG0kjYn+8zCAZLnq4lMUrFNpQZAebJnJPgRIRnKLHPd09dI9qGhpmamBWTWlwjG2Ssuqu86rxKzatR02u4hktqpAyToRBzjE6EwT65x7+L/l1+uANBV29bbVbMFTpWhxVrhQYNNkubdKwObauNoRlLAII6b+X6V9eRaZq8+eab+TwcjUbUajXK5TKf/exnWV9fJ8tE9p3XX389XzuGYeBpHqWkxKtbrxKGIZcuXeLOnTs4jsPX//jrvPQzL/G//rP/lU8OP6F2uYa5YnLgHzAxJiysZVGwNEuZJTMmyYSnwVOKxdZsymaZhtlgzV5j293munedUlrK14+kMKrzX+6VqhepVW3hui5/1furRFFErVZjOp3y1ltv8Sdv/QlJOeFu9y5dq0vUiQiqAZETCZq8ImqaGiL8J6pFDBnydvS2+FsEHInxqlpVWnqLZJLgaR7JLCHwA8ySSXWzSjfp0gt7IqSCNBeytHSLDAHkFbVKci++LtZHlEaEaZhrOXwatXyZHeDPKhfpLBRLlEVEWoSPzygacTA4KD4MULzchkMSJiKNrSZEMSfRRBz2L47UAMQc6BgdynqZdJaipzpaqjGbizSriZUQ6iGBFZDZGZmeCcBIT1kgspZgcKGOA4jMERWrIsC9zipVo0p/r08wCjB1k5OTEyIitLLGwliglTURZsP0TFBR2ScMjKVMDD2/R4/eUt9mZIJRkAEh+UmxYlQE+GIIvQ3HcLB0iyRNGPkjZszEHpTOc/p8oieMExHbDpwXP63C/q/to2WCyWElFm7iUo7L1OM6Ha3D+/33oQqdUgdLPwvHlO+fil3hWu0a5XIZvyl0vqQGjeu6HB4eMh6PuXL1CkfjIybZhKe9p/yLb/4Ljv1jWrdaHCwOOPKPGAZDptGUKBVpV4MwYBSO+HFFO82W4uin6TONksjUgpmDAP2oz85ih3E6zrMaqGNteRb6QscZO1ytX+Xk6QlGaKCnOpe3L7O5uYnhGJgVk+6iy5F/xMn8hEEwINbi5ew06hy64DNd0/Own4SEGTMejR/ll3z06KOz60fkwCuIMJ6KWaHpNGk7bZFm0lkVKR5PDeaKVqGqV7EMKz+/WZbF1tYWP//zP8/jx49pNBpYlkWv18vHB2A8GpP0E5ypgz22aRw3ckdUp9PhS1/6Emma8v777/NX/spfYXNzk6986ytMkgm3P3+bnWCHt+69RT/tszAXzDMhEBpmIQmC4SbLYXjIJw8+OT+gOmg7GsaukYflVI0qdbvO7cptNrwNNr1NNtwNqlaVOI05mB6wH+wLcDvo0ot6jCMx1seBSBVdBOKKRQqK1swaTatJx+nw12/+db506UtEUUQQBHzkf8TVq1fpdDp85StfQdM0Pve5z3H91nW+9fa3OJoekbopXsfjWfcZ3/3gu0RGRMksscMOg2zAJ08+YXR/dC5ji4bG3zb+9lKqbNV4lraHPCuqzlf1XCvPx1l2FtquZoxYembhXKzrep6iU97XMIxcH09eJx1tMuuCtIFkRjUZIg+cs0Nl/WXohG2fnTOjKOLx48c0m+LwVGQzqraVanirdSs6Iy+yVYtAjcyaIfvbsiwmk0ke0iGfK5kI8jrJNJL3kHaXpmmUSqXc5pHAgLSr+v1+/vwie/8ip6L8u9r2InjyvPKpgYaVlRXm83meHUBOIjWLQ7HC0rstjR6Vum+aZo7USF0GtdJFpKQYalAEMdQFUfSuqkU+W1JyZPywrLsKOKhGcZqmNBoNGo3GkhdXdnar1co/k5NAfk9FrFTDUrITJDgh+05mPahUKiwWiyU0T5Y0Tel2u/kCkYtdFYz8cZ59uRFItoHsY7mRyO+rCrESnGm32wRBsKR27zhOzqZQkbrJZJJfK8EK1dCXfS6NkFKptATo1Ov1nEUjx0L2n3p4vwiEKLZ5PB6fo4HJeSzng6q5IVPdHB0d5eBauVzOqZ3z+TxP0Sn7XN4rDEPm8zn1en3Jo69uqrKucixms1mulCuBq1KptDS/VU+vnOsSxZXXqVoSsu3lcjl/kUsAIIqiHCSUc0J6n9U0OOqzZDvlfVSgRgrrLBYLSqVSbkTKPL/yM1knuSZkUZ8xm83yjV+ufS3TRNiEUaOe1qmP66RpyoMHD5jOpkJnoaMTVQSNPmkkQrPhqgAQcgMmAAaQDTK0gQZ9SB+laAMNbaQJPQctQ6/pUId/+Ef/kKfDpzwbPePZ+Fn+uwouWLqVx/aW7TK2IdbCIlkwDsZMgsk5b2SiJSy8BZEW0TW6RBuCRr3LLlqmsRFscKt6iyzO6O/18WyPSrXCytoKR/MjjsNjpuaUXthbOrSamsh7H6exMGrUkom2RWZEZAoj7Gh+9Nz4Z0/zRK5ws03LavFS6SWalsiqgCZSefqJzzSdchKdsBfu8d7sPZHe8dR20dHp2AKAkOEZq84qL45e5GBxgGZqpFG6tD7iOOZ73/seBwcH+R4zHo8Zj8e8/fbb/Oqv/iqe53H//n2+/vWv5+FK8p3yyiuvLO1Dcs6XnBLr7jqrs1V6uz0+u/ZZXq6/zL2je4xGIy5dv8SV166wN9tjb74nDo1Jl37a5yg4WvJaSmN3HAuq62P/8dKBnD8H2uc1HqWPePjooQAivG1ulm/ycu1l1spr+TqSa0ruE3Kd5AAvOuW0TLPfRBtqVPerucdjOBzyyquv8F/+rf+S93be47///f+eJ/MnxM1YgGinHsB83nGaso0hp2nfl9Jb4p+KVlodGnYjNyplbP00ntKLRYy2mmVgkS5I41Rch9AGCNJgaW4aiNSZruZi6IbQkciiM4YE0bIBViynhtOnTvF3kaF17pIzYcvnZX6QRUfH1V3KRpmSXsLVXSzNypke88qcWTpjHI/xS8vohJZoWAtLiOMmgm1Rcko4tkOcxvSmPWJbAKaJLXQK0ISIYhAE9IKeSFEoS+3038rpvylYmUVZL9M22lzlKl94+QtYscWzx89I0xSv4qEZGn7mM82mgnnBhOOZSG15EZNE4/RAqZFnI5F9kZ9tLhDIdTQHJ3IwFgYb7Q2qXlWwGLKIWTJjGosUrjKmP9MzkX3ATFiwYMiQvVOazre/+2347tkYWIZFySzRcIQg5eXqZW7WbvLq+qus2Cu07FZ+jgAF5NZ0mk6TNXuNq6WrdMtdnvWe8Vu3f4utra0lUbo4i+n5PbqLLidzoctwMD/g0fARzybPOJof0Z8LZlCSJWIeJwLg6SU9nlek4KjUwzATEyM1CBZBHso5ZcqsNSN2YmInFtl4JoifLriGS8tuUTNreBOPz734OUbHI2bjGbqm8/rrrzOejfno/kfMmGG1LYbxkOPpMYEmQL+iE+7HgXcGp+dAMvzEx098AWb/GUVDEwCkLij/6VbKu5+8y6q3ylprjfXaOpduXWJ7ts1gd0A0E+/8w8NDRqNRPhbS5phMJrlAnnS2WZaFi4uRGvx0+6f5pcYvcXt4myzLuHTpEjdu3OCb3/wmx8fHdDodfuVXfoVvfe9bvHfvPUI7ZO3lNR5PHvNk9ESwSuIxfb8vQLIsyds7YPBjAUfZhzKsyDM8qnqVy6XLdKwOLatFyShha3YupHkcHdMNuwzjYS4yKbNWPPUFsP2Nk2+w+t4qbbdNy2xhLkw2401uTm/yLH5G3aizOdnkSnKFFXcFJ3Ro1Bq8dPMles0erfstHMfhF7/wi7z//vs8ffqUL/7sF7l67Sr7A+HIeHj4kFk244d3f4g21/Lzn7Sp4DyjuMhQlteo71sVQFD/rhre6ufyd3l2lN+TGg0SHFDZ7sXQDGkvqBkx1HO+LNKYr9VqWJaVO8sdx+H+/fs5mKM6P6T9WAQYVANdrYvq9FWdi+pZXr2PyhKQTmSpiZemImy9UqnkbZfRBTJ1pYwIqNXEy0GSAeS9JfiqimqqYA4s67XJeqjjexHQ8meVTw00SIO8Uqkwm82Yz+e5YSKpKqpxLr010vO0s7OTU6Q1TcSCVavVnM6uAgTqAKhomTSGijE2aoOLA6uCARKBk52XZRnD4ZBarba0qIoLSr2/HBC1s3Vdz5kN6nOLoRPFNsl+7ff7SyEn5XKZZrOZ30MCDaohK0GKfr+/lKlAjeVRafKyXirtSfZpEZRQNxZZL5VRIq9RDUE5gSUgIPt6sVjkoRXyb2rMtnyBSGM4TdMcvZO5f4shJ6rRDed1D1TPoRyLNE1zg1qNF5d9VRxjWVS0cTAY5GEsa2tr9Pv9PERC9qN8ZpIkTKfTPE2OCoLJuDlVzGc+n+faHhJUkgI+6nwrzivZfnVzUMEtdWxVllDxBSDjwVThVDlmxbgwuXmHYZgzI2SdZSjNcDjMNz45vvJe6rip7ZDjW6TQFWlvtVot917n6zsVTAV9X4dA0JLVM1NmZcK73wKakDZSaEL2oginyA3yBCGQdgpEMICKXeHXb/8615rXcE03v+doMeLZ6BlPRwJ4eDp8mgMR93r32J/sLxlNK6UV1iprtLwW9z+6j+d4BGGAH/ukdkrohEKhHWH87M/3OZwfoqOTrCVn3sm+SHlYp86N8g3+QucvUDErbKxucHR8hJ/4PO4/5t7RPcbGmMiLzii4Gnn8d5KJmHE1jVyx+JmPH/ochAd5nP1FNHgNjabZpGk3uVm+SdWsYmtCIT5D0N1n8Yz9cJ/3p+/Tj/pwyjrVPI2yU6ZcKVNNqpTcEpWkQl/vk3gJ5bhMp93JkXk5X5MkYTKZEIZhDnRKkVapvSNjENU1LkPc1LUt94mSVeJy6TIb1gavlV4jy7IcSAUYBSNOohOOo2OOw2MOFgccLg7zsIyIM1aNlgkqe2iGPPIfCSX60XKf2bpN1ajSsltsuBtcr13njZU3uO5cZ9VczffZi/YmgOPjY7FXmhZr5TVeb7zO1u4WvQ+EoWPbNr/1W7/FysoKG1c3cLYcvnnnmwydIR8ffszuZBff8Im05XSCAYFo43P0EnR0SlqJdXOdslGmbJQFGKFbInVnPKQX985l5dA1YcxECHAhyRL81BfCr0rxdEE37pQE2GHpFoEfsH+4T7VVJXVTjsZHzLP5ue8WJuanAhvy8mdcm5LmWhJ/VtFTHTM1MRIDLRYZJaTuQmzHBGbAyB7l4WAynEtLNZzIoUKFmlXj6sZVaqWaMOwNjeF8yP39+/SjPqERno2dLnQ3hgzzFLIffvjhcqX6sls0PDxadoubazd5vfk6Lacl9pVKnZ1nO+zt7+G4DlbZ4vHJY4yGwdSYMggGTCKRJeTHYUJBFhBYAZgi64oWnYL7Wby8f2jixwxM7MjGwcHiVIjZ0LAqFrqnM4tn+LFPkAT5zyAY8Hj8mO8efvfCOhiaIUQiTUERv/bJNa5Ur/Bi80Wu165zEp0shf9IgwTEWmvbbdp2m9u120vvUslSevr0KZPJhIkvQja6iy77o30mTOgnfY6iI3pxj1E6EiyP0xAbOf9nCwV8dsmBvzFjEeKXAYkQg6w6VREWV2lhGzZGJsRoj/VjPuh9wJF/xFgfk2op3/nwO6cdIEIjOosOa+U1Kn4FJxH6FK+//Drj8Zhnz56RaRnrl9fZ7e7ybPgMqjAIz4Qzv2B8gVqrloOqg2ggMuSkF4TynIY/wQXr5RQUu8MdwZRR9WE9MD2hb+KsOASbAdpUw/IttJkmwm6ocGd0hxVvhTiL8/ORPIuqulXqvi/PhNI51nAbrBgrOKbDr1z9ldzo6vf7WJbFV7/6VarVKtvb22iaRqRHjOMxg2jA4eKQvcUee4s9kb73FCBYpEIrJMxCkVEnnXLCCY+CRzyvaGgYGLnuw4a7Qd0SYpBlo4yFRaVUQTM1ptGUnt9jJ9zhvaP3GO2NzoCht0F7WxNhGHqdjtfh+pEI1TlJTijPytSOa4ySEWEmnGBJnFC36qxWVqlHdSGKuFPJBQJlv6lC5+o5U36uOgjU8AjVGavab6qzV15X/C4sG/HyzK6G2Ur2gVyr8oxt23aumeb7fq6/oOoMqLakYRh0u12Ojo5y5+t4PMZ13RzskNfmOmQFZ6EKnKjtlX8vOthgOcxSbbfK5pYOWhW0kWEVKjPy8uXLeWpvWW9pZ0ubSjr71FSi0h5SHdSqjSfrqQI0Rdv405SfSKNBdrY85Kl0bfUaWTlJyS+iPLqu557qWq3G8fHxUjyIBClUb7ecnOpkVQ0N+X3pxVWNTInamKZJu92m0WgsedZVRkMRoVKLClioBq26+FSDsmjwFn9PkoThcJiLQErP3crKylLbXNfNdTDkPeTzJHVfVS1V9QFUcU7ZBjWc4yLDUwV71PFUvdBy8UiEUZ2IcoHEcZyji3KByEVgGEZuqMoULY7j5KwCKV5SrHsRdFLHpkjNLwJN0qgoZheRxqws6iKSG62cG2maMplMKJfLOZtB9le9Xl8KgVGBDTXkR25m8n5pmuYsAhVIkuNbpCvJ9slQJnX+y7+rKK9EQiVrRBpZqjaDvLbRaAhhtX5/KS5e13Vms9nSmpJMpuFwmNdFgo7qBikp5BKhVUEWOYcMw8iZDxKIKuo/aJpGuVxmdXV1iTVRqVTyTC/qGl4C0iIN7UQjOz4dWxWE0DNogNY6TX/ZgqyZkV3L4E34q//HXxX3Q2OrtsXN1k1uNG+In9YNbrZu8vOXf566W1/aL8IkZG+8dyEYMbWmHBvHJOWzeacnOmW/TMtpUTErXL90nSAIePL0Cf1Jn9iJRRpQU6RgO+GEk8EJ3xt8T9xAGu6nRoRu6LhDl8Zug0pS4fLWZaJYZC6IKzGH/iFaS2PEaCm/vK3b2JotDsRplIsV/rg4+4yMftxnlIxyunCWCYCh6CWzNItVe5W6U2c2nKFlp/GGxHStLgtnwUJfQCe/OVZgYdw0sOc2kRHxR4d/xGZpk93FLkESsOqt5vveYrHI15A8oEj2mtyP5AFFZgmSc3Y6nTKdTpeohhJ80zQRcrLOOuvWOqZniiwo2VmM4yAacBQc8f373+fe8T2OwiPsVRutpdGLe0t9YWjCoz+IBvSiHvdm9/hW71ucalbmMeJO6mBcMaglNZIoARucwCEYB3lomtxT5ZpS95udnR0R3x5qvNJ8BafjcOPGDX7wgx/wp/f/FNM0GY/HfHL/EypXKlz92asM7AH7wT7dqMsoHjFLZkshDSmpUM2PpzxPb9LEzA/MZb2c56PXNZ15MmcYDenG3SWgQEMTDArDwzZsFsmCndkOs3jGOByTllIh1LoQYEfdqHPZusylziWCccB8PMcxHdrrbfqzPruDXXzNJzIi/OwnS1n4k5SLsm+k+qn+hckyY6RYMpFKWI919FTHSA10dEItZMSIu5O7zAYzhuFw+ZmmRikt4UwdQbnHQU90Sm4Jq2Qxi2Zs3NjgeHrM4fiQSIsES+K0nnPmzMM5uzu7F1QKEc4RgTbUcA2XTthhs7bJjfINKlqFil6hZJWwTZs4iekPhBd4nI7pJcK4Phgf4OOTmuk5mvZS0SF2YhI7YZEt8v5MtVRkRinIH9TtOi23RdWq4lme2GvigEW6YBpOmUUi9WyQCIBzFs+E0Te7wOhrwv/yb/8XQZE3XWpWjU6pw1Z5i1vNW7zUeolr9WusV9ZpuS0s0zr3vvUsj7bRpmbX2Kxu5muxeEAHka53GItUkoNowDAecuAf8HjwmH7aJ7IjYlOEnKAB5ul3GDKMh+wN95brb8PB+OB0yAzc1KVdaVMySsxHcywsVkuruLbLHnsMGeKnPj/8+IcESZDPQZ6cfl932TK3uFa9RjAM0COdS/VLXGlcYcVboe226bgdalYNTdN41n3GIByI7AqnAqP9sC9EDqNR7qEfx0J4tFhUXZZc34QJtBE/p0uqqwlU4iM+Eg6Ba/CPvv+PqNk1dE2nolV45/13uNK+wmgyom7U2R5sow00fM0nSZP8/VB0ZPi+n9s18jxbKpVyoWLTNFnRVsScVAxI1bZQz5pBFoh+WPQ5Do7Zme+wP9/nKDiiH/WZRBPmyZwwDYXeSRozT+f0ot6PZU3o6Kzb66xaq7y58ibT7hRHc1htrLK2vsZx/5jepEegBTwaPeJwdshReERMzO//ye/n9/F+4PH2rbexTGvpvKxS6+WP6uhTjV01pEC1H4pn9KKjTbW5is5Z1fmk6h7J95v8f/m+Vc/ycjwajQalUonXXnstT+WohmeqTAx5ppTXqJkVa7XakmNbtZOKzln1rCnfvSoLv/g9td3yGtVulI4N13Vzx6vqJJWOPcm0/st/+S9z+fJlsixbCr9WM5TMZjM8z8tDNKT9Uhw/1XZS61gEk34SsOEnAhpkB8oYETkZivR1dYPVdT33jMriOA71ej3/vVKp5DoNsCyUKA0ieS/VUFY7RjWM4zjO0TYVBJCLpgiOyHvJNshDpYyflUWdSOpiUO9XXFiqgaqGZchJJQ1sufkZhpGnS5QTVa2jNOYkO0C9JssyJpNJbtCrdVU9Yur4qPFW6jVFT5r8jvRuq20qIpvNZpNms0mapoxGo6U4Lul5lOMpQwTUHL/Sk69qN6gLW51z6gapptJUF3AQBPR6vSXPvNpedUEVQRjXddnY2ODx48c5sLZYLDg6OsrDHCRwdvnyZfb29uj1evk8lkCR7DM5XhJYkAj7RZtPkUatvhzVDUH1yBY3e/mZHCtN05jP53l2FGmgj8djNE3LGUsSBLNtO9emkMCCDHEBIWJafDGp80cycTRNy+lsctxl/VT2jayn+lJSx0giszJNj6ZpeSz7ZDI5t16KyLMKQsh+yrJMaDcMtDMPXXYKCpLxrP+MB/0HPBw85GH/IQ8HD3nv8D3+ySf/hOFimD+v7bVz4EEFIm53bvMLV35haVP+zd/8TUbjEcNgyMZnNpiaU3bGO8SVmMpWhYk+4btH36W36AkjxRF1Kqdltp1trMDCweHK5hUBUBkZgRHwdPCUQTxgHI3JyhmUYcKEE07yrAt6pgvjNTa4alzlF5q/wIq1QqvUIskSenGP4+CYo+CIw8UhR8HRUp56mdpP0zSSLBGx2KdARZIlwiB9jqdTQ4xHkAZMogm+5pPpGYEWEOgKdV1eH2vCAEMnKQlq+tya83c+/DvCqKsBfw3uhncpx2U+cj7Ca3gsxgumx1Ou1K9w3byeHzBlqJllWfi+z+HhIdeuXcv3pWfPnnH16tX85a56NGRR56q632iaRt2oU3WrPD1+iv/ER9/TuXr1Kr/zO7+DZmgMkgHHoWAKnEQnZ+yI6JhBPFjqJ6mHMM2mJJ7I0sILwAuww46Yo4mOndh8y/kW//kf/+es6qu0P9/mS9e+REkXOkGrq6tsbm7SaDSW9k4JbMo14poum8Ymf+3yX8PzvLzdvu+L/yfjODrm/uw+j/xH7C32OAqOhBBePD3HLIgRzIZhPOR5xdEdWlbrjBVheNi6TUzMLJkxikccL46XqPmu5tIutSEUYEamZSILwbzHmDFBHIBiOzuZw4q1wqvNVwn7IXZqi7Su164yj+f05j0ORgdCXG1yJLJN/FkhHIXy3GuVUIx8HypeexoKkppKjL26DhTyhK7pWJolxB9P0/OGZggVWOgLEiMhNmPxfQd2j3bREdlq6lGdjtuhVWqJ9J+WxTSc4jU8utMuo1DsR/NkfmaAntbX1312w12OB8eCYp+lQqfjAj2NslamZtSoUGE1WiUZJ1xfuy4E8Uq1fG75ic9xcMwn+58wiAYkbiJSW5rpmZH9nDKLhGaIqZlCUwOEpkISnOtf13BpOiJ9ZdWrkqRJHvI1nA3xY5/MELoj43DMOByzO9vlvZP34Mn5Z8s0oGWrLDQrjDrrzjpr+hqXvEsCcNBqeJa35B2UehGe51ENqnS0DoktzroTc8J3P/ou/X6f7e1tTNPk7R+8LULcnIgbr99g9foqo3REXI1FzP/8kH4gjNZEO41F1xISLWFvLpVhxT9P+ucbYmUWNbNGNTpNZ7q6wWQ0YTQe0bJbhGnISXrCOBvz4fBDFv1lkEBHhKHUzTotu0XTbtK0mrScFlcrV2lYDZpWk4bVoJSVSKKEew/vUd+sE5oh40SkfhzFI5FRZXbCIBwwNwQIOQgGF8+DjJxZFxDkIRxHHPHw8CHaoZId54SlrA3uoUv7nwsBYyuwqEU1nD2Hn1v7uSXGp+u6S0CC3OuL59C8L5TzKAiwvqbV2PRE7k71fCYZvxLkkOtQskVOFifs+/vs+2fAxDSdMo2mBEnAYXjIPvvc9ZWGHZ/+KMU1XDzTY8vcEueG9W3iIGYynlCulvmg+wFtt03DbmDaZyxbwzBYLBa5gS/PTPIMqzpRZZtVcKH4uWprqQCE+lNkEkmHkxSWV225YjikaoOkaUq9LtgZ169fp1KpLNVPjpsMkwByG6xUKuWOzvl8zmAwoFarnXMYF8+5xfO2qrWnOuZUJ1jRgSbb0W63c+e8tIHUd7TqUJfOEtl+qashnysdvZubm2SZ0IAD2N/fZ21tjdFolGfykDZJsS3SaWyaJkEQ5M9S7a5PUz410KAanLIhRUE6+bl6naRgq5NI0lplKZfLDAaD/HrVc6+CGOoiVetVNCDU2PSikSqvkf8v7yefVYxHKna6rIuKWKuTRq1H0cuv1kllU7RardxbLI0sKdonJ4wqHibV2qUau4oESsNTnQxwBigUDfTiBljsJxWEkNeq2Q7k5iSfU6lU6HQ6RFFEo9Gg3+8vhauoqWmkUakCDSrIJK9T2yH7UF3oKqgjNx8gB8MODg7Y29s7h76pXnWVuaE+s1wu02q16Ha7DAaD3GCWmhqaJjJTbG9vU68LzYDxeJwDCcWUpdLIlvVV2RTyfhJYUNk8Re0NFbiSc7KokqtuiPIFItFMdU5JkEZdu6ZpMpkIwT9JV8uyjPF4nIuByr6eTqdLdC1p1MnNU25Wsn9lPJy6uck6yvAKNVRInY/z+Zyjo6O8fyR9XrZDTWmkzmEVRCv+rViKANRWbYut2hZ//uqfP3dt3+/zsP/wDIg4BSO++fibHEzPxOfKVpnrzes5CPGg8YAszTASgy1zi/FwTLonXpJfvP5FWq0Wv/zLv8zH9z/md//B77Iz3sFoGTirDqZlshfscawd86ODHy0ZYSW9xHppncasQTyKWYwWaJmG5Vp4Kx6TbIJv+gRmgF/2+VH8I3508qOlNlmaRckoUTfrrJgrvNp4lbbZpm4KvZFe1KMbdjmJT+hGXY7D46U0iqYm6K/SIIkywYpISESWh1Pl91E8OjtEqu+rTBiH2TxDD3UMTYCR83Au0pFanEvbGNohsRkz1aakpZT3+u/xD/70H5z1i1GiZbRoak0uPb3E3uoec39O/VadeSJAq93dXV555ZUcJC3OBbme5DxX17S6dsIwpNfr5Sy06XQqvmc7rGfrrLvr5w6mIIyEqTFlak151H/EyBhx7/geD7oP6Kd9YVDKkgqKvaZphHbILrvsPDhNe1c+/clEqkMndqh+XOVW5xafDz9PZVyhtWjljA31nW5ZVs5ggrP9UL4/Nt1Ntrwtvmx8eQnUl+0YJkM+Hn/Mo/kjns2fcRgc0gt6TJPpuTAKINdG6Ed9nldcw6VpNnEyweLY6GxQKVc4HhyLvPWLcS5oC8IIWXFXKOklomkkwlOsKkEScBAfCA2FaEz6IyX8ET0XtpN6JNdXr4uQjSRgGk8ZhSMOJgeMUkEZl6lJn6sXoYZhaD8GjIBlYyq74DOEt1mq0kdEpEYq7ll6zrMBx3BERpgkZm7MOeCAQ/+QZCFCVjKyPJxHQ6NhN7hRv4Ed2CTThLJTBhOOB8d0vA6ddodFtsgNomE8XBKGBZhnc6IkYsSIxE5I2gm77BIn8ZnOwGlxNRev6qENNLyJh5u41PQaXubhaKLuhm3w0mdfIvZiDueHHM+P6Qd9RsEoD6d4HltCR4SIDcOhCGeI+5AJbZkgCUTYhH7WXxqaEDO0KziGOKNGidAPCWIRriGZEotkkYtofiDzEF/wfFuzha6HVqZttVl31mllLZppk1V9lbpWJw7j3MDd3Nzk4cOHVCtVIQYXGFSOK3z+pc9TKpW4efNmnoZvOp3yz/7ZP+M/+X/+J7z98du8e/9dFtaCF3/6RXbGO3z/3vcFY82LxVoJxjk4JFOCjrUxBgY73R2SNCFxEp70niy1w8Njy9uiZtUo22Vcw81FLv3QJ4gDns2f8VH0kQipSJZDKjQ0qmZViEUeCPChaTcFQGE16WgdXjNfo0yZy63LVLwKf/iHf8iffPdPSFyRCpYylFfKvPz5l4mdmAcHD8hKGRNtwuH4kDnz/B1THAPJxluwOANhABL44fd/yHZlm7YjNBDWKmtMsymdeYem38wdbvK8VHR4FsX+pB1RdPQUz62q89VIDFq6AGuumFdIS2e2S61Wy41FmZr9wdMHvPIzr/DDuz/kYHzAzJrhOz67o132J/uMYxHOMQkn1BzhmX88ecwwHDKIB8T9mD/+yh8v9VPdrlPKSvy89/N8cfrFfD6qZ8jiefKiM1TRuaPaR9JZKf+VTFtpU6nOYKk95nlebmssFgva7fY5fbC1NaF3JA3u8XjMaDTKr5FjpetnqelVp6vjOEtAQBAEPHr0iBs3buSh67I+qhNcZeIvhfKe2k2yFAEGOW8k20CGWl+7dg0gZyKUSiUWi0UuEim/12q1lkJBpL0h7VHZLtM0uXr1at733W6X/f39HEAZjUa5PVBkYMl6yPvLs3lRZP7TlJ8IaJCDqmotXMQukJWQlMx+v79kTFSr1aWKlsvlpcWpdqi6GIvIllzUqvEun6EiaxchkuoGoNKAgHxBqd7UYnhF0WiU1xX7QAUeljr+dHJJ1LBSqZwDaWSbpKK6uiBrtdpSO1Wjsjhu8p7qwlD7X6XZys/ld9UDsfxdAg0qs0ECH1L8UPaVpDNLL3gR8VMRMpVJomaqUBdxEQlUF4ekE6mUrizLcl0FlXKlgjOyqP0gx1fqdzQajVxMUR1XXddZXV2l2WzieR6mabK7u8twOCSKImazGevr60vhEnLRSqNYgg4y/OBo44jB5gA7tDEXJk7kYEfidzu0xeeJubRpqe2RnkqVbibHXz6jWq3m7VBZObLdEvSRQIocQxmyoIpPArkoE5CL1NXr9aWwk8lkkrNx1LUsASKpxxEEwYWpO23bzhWCF4vFUijHRXvRReCf2l9qUUG4n6S0vBatrRaf2/rcub/NozmPBo8ECHHKhHg4eMg/vfNPeXzpMdllUYf3s/fx6h5226ZFiyzOuJXe4srgioinG5q0x23aRpubKzd5Ze0VHk4fUq/V+Zk/9zM8OHxAUkk49A/5aPcj5tact0/eZlQeMW/MSW3Fy5BquKFLc9HE9m2qWpWr21fBQhxEkzH9uM84GXMQHPBscT59l46Op3tUjSpNs8n1ynXaVpuqXgVNiAIOk2GeqeIkOlkCQ0zNpKSXsAzBKkj1VMTrn3rl0CDQAhHPezpMM2ZLKUktzaJpN9F9HW0hNB4sw8J2bMIkRHM0ptqUrt/NM3HMkzm77PJh8CFsAluI1JCA0TYwqyY/sH/A1v4W69Y62+42V9wrrNqrVMzKEnVTBaCLIPRwOKTb7ebZgAaDAYPBgI2NjXwfVQFwORc9y6NTFiLAt7RbbG9v8+zZM765802ePHkCDnzw7ANG+oiJOSGuxNAAmpDUEmLt7F0pUx5mZPiWz1ybczQ54k8+/BMA/pun/40QpdswRcx2wyN1U0Iv5KPpR9wo3aBklvK9QN2n5DtFHsDk/q9pGq7u8tPln+azpc/m7ZQsJk3TmMQT7s7u8mTxhL1wj6PwiH7cZxovp4jM24FGmIZ0g9Ngbhe60+45Kr1ruDg42ImNq7msVFaYLWZMmTLKRjyZPCGbnN3bw2O7sS083boHidjL9w73mGdz9iZ7vBO8syR2CcIwbhgNNp1NmmaTttWmbtQxNIMwDZkmU4aR0KfYH+8zzaZkdrak3fHcorIZLsAkEs40prTnuftTMDMTAwMycB2XKImIdUHFDzKFKq8UHZGFYJEs2JnskCYpCQlpmBKFEZmT8Sh9JDzEINJhGnWul67Tslp4umCimLpJFAsDdhpOeXrylFEyQqtqjLPxOQZEnMX4mk9cjgnLIVN9yol+QmIse7K/8/g7eXrFjtfhau0qHbdD22uz4q3QdJukcUqkR3RnXY5nx/SiHodTkSJ2uBgyi0RGjB8HSpi6SZAEJEGCoYvsHGmWEiQB8/i8LodrCGFQW7NJSUmyhDAJidKIGJECdJEtWCSn4pbxHh/4zwElronQmW9q38S95mKumugTka55NprxSvAKl7xL+ZlFBdVd22XFXeGydZlOp8Mv3/plwjDkXzz5F+i6zuc/93muXbvGW2+9xcnwBL2qc+nFSzw6fsRbH7zFTJvhrXgcz4+5t38PajCMhnmokZ/67Pl7HPgHmLqZ98tF6W0d3RF7plHB1V1sTcyLLM0E4y1L2Pf3eTB9wDgen1tj7ArA3GgYJH8uQfd1zMDESzyczKFNm89e+yx74R7X167zU6/+FN/5zndEprbLG9hNm+/96Hscz47xdR+v7fGk+4Td/i4jRviGz8AXGhMpKX7ic390n/vcz+dBRoaNzXeG36HttWlYDermaRpKu0nTEewNGbah7v9Fe6Po+ACWnCnSWagaqBLAlaw66VQyDIN2u82jR4/Yrmyz8cbGEghuWWdZPpIkybN5yL1aGuQym84sm/H4+LHQi4lG3N25y0q8guu655i40lGovgNU+0v+/7l5rTAC1B/J7lHvV7RDJOAg/53P57lQvnTKJUmS2wvyHSXPkEWmhe/7uREP5OdICZqr35lOpzkLQAIMKpigAhhybFV7Qt6vaE+ZprnkaJe2j9Q0W1pLjpM7KuSPvE4+T3X4ynrJ++zt7TEYDPK/j0YjRqMRjiNS60rbUmXvSwBJHT/VsakysC9y1F1UPjXQoIYLFA161fBXKzGfz3ODS05WiSapDAI5OWSj1HgaODOIi4MgPysyEqSRok5+aZAWheZU8EA1WovMhiKLQvaJ/P+cgq14uOTgyN8vmpTF0ADVuy3bLtWTVaNK/VEpX+pEU9kBF6GO6r/yWhWVVb3p6mJRgQZZz8lkwmw2YzKZ5BuBVFCW95NGpmyL/EzGGMvFLCnOal+oBmIRSSsaj2o/S8aI7/tLmU/Utso5ogIRQF63JEmo1Wq5Voban5LNIIUsDcOg0+nkNP5ut5vrHqgbrl8XXoBsksEM0lhoHWRZRlgLSaOUqTclaSUiLrmwUrVEw1yYSz/GwsDwDZiCPtcpZ2WcSMTtyg1PrkW5KalzWZ0TWZYtZVhRQ1zU72malgMKst89z6NcLueUtDRN89h4GVKhAj5wphScZVku3KOOvTqvJaAk9Ssk6vo8dFWtrzrn1XlYBLCK1/3/UkpWiVdWX+GV1VfO/e2v/cZfY3eyy8ydsfLCCnvzPcbmmF6tx1eir/BHB3/E//DP/wcAnFccPN+jkTYYuEKQNCKiYlfQ0WmZLa6uXyUMQz4Tf4a1tTV+9xu/y9HREU+fPkXzNEobJVZvrjLSRszMGVNzysAYMG6MuTO/s2TgVfUqHavDbfs2HauDg0OkRcziGaNExNwOkyH9uM9hdMgnnE8PZmomFb1C02ryauVVOmaHkl4S+5Um0vr1wz4P5g+YMBEU2dOiZzqe5kEMUSziyuWPFI6Lsojj4Fh4I6W4fMZZ6rxYHHjXSmuse+vYoY0WaSKFYZTwdPcpw2BIeb3MVJsKUUErZDfdZXdyPmZdR8fRHcpGmabVZMVZYd1Z57J3mSveFbacLUqGcC1LsLVareasoEajsQQEF+ek3H9AzPnJZHIubZet2ThDB47BWlh5+EqpVOK3/h+/xd/8f/9Nvv3Bt/nd3/9dTqITAi8gradE5YiwFC4ZbrZuo6UaCQlzc87UnMJl6NHj3fvv5m12dZeKUaFltFiz1tg0NrlZuskl5xIlo5TTKi/aR+V+p2ZFcnF5o/wGb1bePLfONE1jFs945D/iUfCI3WCX9da60DqZ7fFk+ITj8JhUU4Cz0/juIAlYsMjnwU53Rw5cXizdwskcPM3DM0RqxmE4ZDfYpR/0c+E7EtBNnRVzhSv2FWp2jZJewsRkESxISAgJ6SU97i3u0Y/6SxonGhoNs4FnebSSFi+svEDH6VAySxiaQZzEjJMxJ8EJJ+EJvbDHMBIpFVXR1h9XMsEfP8uOIa/XyQ1cQIAEZOcEXz3No2SWsDSLF1ZfYB7M6Qd9xsGYeTIXGUR+TGaPWTwToQ/hsWBrZFmu5aIW27GxFhZrrHHdvI6VWMyGM0xNvJdt16Y/7XPv5B6JlxC5kdBq0JbPejoiXCFKI7p+l67fFWFbsdBgKGpv6JqeawlsV7d5pf0KpbTEjbUblPQSOjqLbMGD3Qd8/ORjQjfEalr0F33G4Zh5PCcIi3k4l4ulW+iaTpiGor4ZQm8mSy7UptHQ8DRPhPsg0hMnyn+pJsJnhgwFw6t2+rMlvv9f9f8r6IPxyMA2BKB2zbtGVs343+/97+zc22F4MORnV36WaTylbJV54403sCyLlZUVkiTh2rVrbCw2REhoZ4OXai/xhcYXcr20o6Mj/rf3/jd++5d+m2q1yg8//iHv3RfZGTpXOwxjIQTZD/u5voSBwX+2/p+JUIhkxCgZiTCAeMgoHuX/jpJlLSAQ74mW2coz0VhY2KZNGIYczg6ZJTNiL2ZRXzDxJhzYB4KBJxNeHEP5TpmO06HltugEHVbcFRpWg9WNVS45l2jZLb60/iVWvVU8Q7wspIE/i2f0Fj32R/scjg/xdZ9hPOTOszscTAT76XH8WGSOSibngDIdXYBulgAh6kadulUXwIRVp2mJ0JKaWaNmCL0aeZZRjWzVoaLqJKgedHlWkVpmUrhSnsGl2B8Ig3o8HvPRRx/lzrx2u02z2RTOo8yhrbVZs9Z48+U38zPeXfsuw+EwZ6MC587eavidymYGLnzHqQxbYEkXTL7fZBvk2VCCBcVzahiGefY4IE8xH0UR8/mc0WhEr9fLRaGHw2EuVA/inDsajQjDkEqlwng85unTpzn7ULVt9/f3l+wFlcGhiinKdsgi+0OyFWQbVeeZrItspzyXy3pIVqFlWQwGg5wxLM/QavsNw2BlZYUwDHNNPmnnzudzHj16xHg8Jo5j1tfXuXz5MklylnY7iqI8nF21WYtOV7V9FwFDP678RIwGVdQtyzJKpRLdbndJFFI14GUMu/xupVJhY2Mjp8KoBq36fZWuo3qm1QFVjX91ocrJoCIuKr2+GMIg7ycneFHb4XlGiVoX9Z4qA0DeS22H/JuqsQBnhp6qMyCvK6J88lnyeyooIeuj0rRUcOJ5fVgEWuRzVS0Dicapz5Z1kZky5vN57vlXY+Yla0VdyGpbJAjged5Syk0V6JD9XwScZP3VPi6OvQQ5il5uFZwojrlEklWP+xJFV9Py+o5Go7yu9XqdUqnEdDplsVjQ7XaX2AWaprF7a5f+yhld2IgNzOAUMJgbWDML60SoLWfTDC0U68qwDIyKQVpKid1YHM5KEX7NJ11LSb10mYYOaKGG7uvoc32JFWEFllD6Pv3diRxMzoQ9VXQ9y7LcoC+yf1SNDtmX7XZ7afwA1tbWcoaTHG8ZlyaBJvn5dDrNDRT5o7Im5FxSYyhVfY3i3FDXgbrnXGTsqN9Xw1P+fRYt0ygFJVzf5fPa57mzc4csy7h69Sq3b9/m0kuXWHgLvnf/e3zj/W8wMSb4VZ/v6d/jG8ffEDc5gfrX66xaq7xw/AJr9hqlRYkXtBcYZ2PQBIBjaiYb+gYvmC+ws7PD9vY2w+MhBwcH/PZv/zZu2WUQD/KUhSeRCInoJT0+mH3AcXi8FH9vazYr9gqX3cu0zBauJmiIfuozTkScqWRFDBdDHi8eX9gHktmwoq1gzYTmRJZkIhOGkXE4PCRxEtLyafy60ndNt4mJyWQ6EUaGAYmeLGdOSAOeTZ/xbCpYGTKuF8iFJqfZlAoVNtINkl7CS5dfolProKUafixSAPbjPv2kv5R+7P78/vkxRcPSLFzNJf5CjBM6hJdDyn6ZPz78Y9ZKa1T1KmXKOLqzBI7puk6lUlnySsh1pqb6UsVsZcmyDMd2aNpNblduU3taIxud7e2GYaAbOr/8m7/Mi3/uRT7c/ZCwHPLOo3fYm+4xtafMtGWqs6MJ0cZcsyM65s7ijvjjUPwjgYiaKcINNuwNLruXuVW5xWXncp6hRTLWVNBevqtVNlscx5SMEq9WX+XN5pvYts2lS8KDG0URDx484Ktf+yq/+pu/SlJJ+MY732CQDVg4CwIv4KP9jwTAgmIgZqKeaKdUcYQYKinovfPp/LRMo6SVKJtlXNNlmk7pz/vM4hmTeLJkSFeMCqvOKtdK12hYDTzDE2kAEallp/GUYTLkzuwOvUFPhAopxdVd2k6bjt3hunedR+8/woot9Eykb7Q8i6u3rxKUAqGZ4h8xCM4yAZxOuvNgA2e/Pw8s8DMfPxLvst5+j6pVpeW0uFm/yeX6ZYyBQXqYUvbKeA2PDx99iN7S0Rs6/agvxP+CEYtsQZiGz31OqIWEXsiT9AlPs6eiTrVUrNOYnJmir+viHRQ6NBYipt/GxtJFqOjLr7yMU3IYBkJE8Wh+xPH8+EKQwTVcKlYF13TxY59nk2c8jB8yj+YsdhfnmQkmeLHHVrjFRmWD17zXWCmt0HE7eKaHoRnMIpGOs+f3OPaPOZodMQyGDBdDpvE0nxee5uFn59X8LM3CNU5ToZ6+oFPSPK1rmqUUwSVHczC1M2deoiU0dBFPH2URfuzzxH+CX/L54OMPhBG8AV85+Qr/9T/6r7F1WxjfboeVJyvUrTrrlXURAkOHzkGHhtmgXW1TsYWHWHpLJXOxpJfw5h4vrr7I9db13IsrHQu6rjOZTPLzrFzX0qmhhh3ous40ngqAIhqKn3jIIBycgRHxSABv8ZBFqyAcmYEVWTTdJmuNNbIgI4szmo0mINb30fSIh8OHjIIRw2h4Lu2qozs0rWbOjGlaTap6lfXKOm2nzZXKFb5Q/QJf1L7IyeEJWZblnn0AH58ZMxb6ghkzJslE1D0SOhODcMDObIdRNMoZE0tTTTMFIHEKTDSsBjVDgBAVvULNqOXviJbTolFqAOQeb8uyCMMwT18ombIyPbphGLkWVpZlXL58OQc11PBYOU6zmdj3JbAgHWZSyFuOs0zXroYiyLOUak+o9o/KAJSfXaSNJu0gafDKOkgAQV536dIlDMPIz+DyXSLr6DgO5XKZarVKu93OsxlKp6Vq30kHmOd5XLp0iddee41/82/+zdJ7qdfr5U5PFXRRAQfVjlVt1DRN8/OqXL9qAgJ5L00Tmnby+8fHx3l/SEZEkiTs7e2dY4uo9or8jnp/CUjcvHkzv16O39OnTwnDkEajkX9fMqFlOLS8p5xTari36tT+NOVTn6Llgd40zRwomE6neZ5PlTKiMgUkwCCzPchBUjtEeiiBc7E/Kq2maCzLTUy9XjWSVENYrZPKWpCdWQQRZFEPRyp4UTTqZXuKYIZa5yJNXwVlVKBFDqJ6n2J95Oeq8a+2XR5Y1UNpsX3yICpDONS+VRks8rtJktDtdvF9nyRJ8uwYQRDkNJxyucxsNiMMwzz+Sr2frJ8KskjwSvaLTN1S7Gf5vSJwopaLxuCiOSTpavJax3HylK2ynlJcRYJQ0puv3keGg8j4KDk+7XY7z+M7HA5pt9u5CJau61z74BrbzjaxGxPaoYgxd2MiOyL0QqJGRGSLn3OxuqGRgwSWL/7V+7pgMiwE/VJHRzM1YkfcP3Zi0lJKWkpZNBaCLeGcF1oyQuOMJRGYGL5gSViByP8uQzmsyELLzhvpcgykgaRS7hzHYW1tLe8TyUaoVCr5mIMQfNQ0jdFolM8PXRcq0MX4eDkuaZrm+4gKLBTXzEXAZXGOqYCJFK39913W19d59dVX+cEPfsDBwQHb29vcvHkTz/Oo1+tcX7mObdt0tjpsHG3kxhkAHhwGh9ASlPmHg4ecBCd8/+j7QhhrF7gN+k0da2rhzB0mTJiZM+JyTEWrEERBHv9omzbr5jqb+uYS4ChRbk3TGEUjjgMhYHgcHHO0OOIkOuHp4indSKT4kkVDo2k1uendFAaY7qFlGjEx00QcNPtxn3E8ZqyPRSq3kgIEyDnpATG4kUt9UmcxXmBrNhvrG1xavUR30uXh4CGxEZ+lCEQYixWzcnZ4TgL8yGeRLs55XEMtpE+fvtmHFdjxd6BwvvU0T9DlrU1xSLc7NI0muiZS7o3Tca5XMYgHjMIRi9KCeWUOLRH28fenf3+J7m9pFmW9TN2s0zbadKwOq8Eqa4s1rqXX0DWddX9dUI1Px16GKcByqJxkG0m2UKPR4NKlSxwcHOQHF9MwaZttfm7751j1V7ly5Qpf2/kaD3oPBKVyOuK9J+/RudXh5k/fZJANOIqOhHBleMw4Phtfye7Q0EhJ6UZdDsNDPpp/tNRvOjqe4QmtD3uFLW+LK+4Vbrg32HK38GxvCWiXjgw551RvlnxXkiHuV11hWp1imiarq6vcunWL73znO5ycCNr9K198hR8++CFvffwWM2uG2TbpZ30eDx4zyQTLRBoBpiY8zEkmgKoZIkuBEQvQoBj3raFRMkq4usssmTGdC/2JWTJbopKbmknH7rDqrHK9fJ2206ZiVrANGzKRlWYUjTgJTjj2j+l2ugSOEHaV5UfDH9GYN1jz1nih/gKX6peo6BUe3n3IsD/EsAyiLGJhLPBNHxrQi3qMgmVQQ0dx3BSMnyiN6Ad9+kGfB+MHfPf4NFWkCUQI73EF3NBlZbxC22xzzbxGxayw6q1S02vUzBokgmJ/tDjiOD6mG3XZG+0xSSdoJY1QCwXr4YJ0uqmVEliBUOvnbK7JveC7D0WdZPhE226zEqzwovciL117SWhJZGDZFn7o05/1GaUjDqeHnPgn9KIe43C89ExDM6iYFbRQAISGbjBcDDmZn7A4XjCNhKZBcc8oW2XabpuNygafaXyGclambJaZT+esa8JgdS2Xaq0qBCaTsWCLJGMORgf0gp6grTNjls4uTM9qIzL/6Jm+JNo6skbEwVl60Hk6B1OAv+WsjLbQqHt12vV2rueRktKddXkWPeO7R99lGAxZpOeFHet2nZpeI1wLufvJXdbKa5iBydAYkugJ8TSm43ZYrazm3lAZTimdEPLcLT216lk2yzI6lQ6r+mpuBKkOQNWhkSQJ3/jON/iDf/kHZKUMraJR26xhN20a9QaWY3EQHNBP+uz0dxhH43N9WDbKNOwGJaMkQnsMmyw9dVgZOr1Fj53pDuNQgOMq2+KffPGfcP369dxDLEGV4XBII23khrtKn5ceazgNwyVjHAnBy0EgMm8MggH9oJ8DLd2wy4PogWA0JdNzc02GCDbePWNI1Mwat8u3WTleyZ8n++7g4GDJbpJnft/3c5aDfLdLQ7bX6+XvEdXxKOP+9/b2GI1GrK+v50axevZWU4iq76eiw0e1weDMkaqGZ0jnnAy/Ve06CUbI78oxUd998tlS00DWTfaPPPvJs6kU4S+KPkuj/6233uLFF1/MRRrl2Kq2p6xP0Xmp2o0qyCDPqRJ8kxoMtm2fY9fLtqmO0yJDRLIS1LWnOuekTSPXp8re8H0/D6sAuHv3Lu+99x6XLl3i6tWr1Ov1JYZ+UZ+hCH48r/xEQINsXJKIdDDSM1n0ssuJ6Hke6+vrOcAgkZGiUS4RKvXgpB4wVG+IajSq2ShkZ6jhHPIauZmpn8trVQ95EQCR3ysOrCzq5CqGehQXe9Gwkc+SE+oiCk7xWWo/FKksar+ofaIiXUVAQ508xXrK+xYZItPpdEngcWVlJf+epLwvFos8Bk3WXb2PRBnhLI5JTnbp2SrqBsi2qYCPOmZqv6ihHhIIUdM71ut1TNPMEVqJcsp6y3kjlYflOEkNBtXTrqq/qmE51Wo1/1sQBHS7XVZWVs7SzSQmRmCgT8/PjaXfdUicJAckIifKwYPACgSboekLMME+n2fODM0cJHBCB9M3sUeCwWBHNlp2Kl5Ekt83siMBfpRCgkZA5EQkdoFFkom858ZCABPyGcbCwPRPwzjmBsbCwA7t/IW9urqKaZr0er38ZSLRdxV8c12XxWKRxzFKloOq36GCRXJeqX1XZCyo81gFFORcl+tGXUfPW4///5bhcMjHH3/MYrGg1Wpx79497ty5w5UrV7h8+TK3b9/GMAz29vb49re/nXsb3njjDVq1Fv4Tn9dvvs6lS5fo1Xq88cYb3L17l72jPcbGmH/8jX/MzmyHoT4UKuW1Y546T0lrKX/Kn6I1NbzAY3dnl3V7nQ1ngw13gxVjhVVrFc/08nmdpil6pLNtbrNtbAvNhyr5mBiGQUwsaOBxj37Sp5f06EZd+kGfx/FjenFviXZaMkps29voC52SWWIxW5BqIud6aIVMjakIlTBhYQqvaeZm+JrPUB/y8c7HpzcCUvAiDy/1qNpV9EynXqpjOIaI/Z8HZ8J3p3PXSiwIRQx7qqVERCIV4QXD7Wc+fuxzEB+g+2fCYmopG0LobcvawnxisjhekM5S0jgl0zNuvHaD2rUaR+ERvVB4t4fJkGEy5CnC06uPTj3sD05v+r74x2k5uDUXJ3TwXZ94GKPPdQzfwI5sSBGgZBwtpTaW3iHpoalUKkvUShn2IKmyJb/EjewGf7H1F/PvSwPCT32OgiOOgiP25nsch8ccheL/D4PDpUO6pVnYutjfkyzhKDxiP9jn/cn7S32mo1MySjSsBh2rw5YrgIjr7nU2nc08ZFAWuZ7l4ViyIVTweWtrizRNud28jd7QqXVEyJtMdfad73yHMAkxmyaV7QqH/iH7/j5PBk84XBxy5+gOM2Mm4u1P56uOECSVbczImCUi64GlWaAJrYFztHBM/NhnJ9nh2fwZQRqcA7sqRoWO2cFZOKx9vEbnsINW0sgqGUkpoXW1xbXXrjHTZxzOD3n35F0OpsLAQsFAzcyknJW5bl3n9fXX6XgdymYZHZ04iUn1lGk45cQ/4WB2wP5sn+P58ZJega7pmJo4U8VpvAxIaLBgwU6ww06wc/b5efsOW7OpGlUaegMv9XBHLi81XmKrvkUlrVAyS5DBJw8/4ePDj5nZQshuYS1InITEToiNeCmcSpYgCdif7bM/2xcfTOEbP/rGuesMDFpei7bbZru6zedWP0c5LbPR3iBNUgGS6Sl7J3t88PgDZsywdZvuosuJf0KcLo9lxarQcBqUrBKu6Yp+TWN2J7vMo7kIKwtH56j1pmaKmH5bCCC6iUs7bbOlbdGyWzTKDVzNxdANPvr4IwbBgNalFrEj0jzuj/Y5mZ8QWiGplxJr8bm9RzJaoiwCEyb6RDC9iFgki3PzEqBu1alZNTzdI/ZjSKBkltB0jUVzwVFyxJPBE0bRiHllztd6X+NU91L0h1Gh5Yj+rWpVIezoLGeeqFChqldxdXfpvKoaLhJQVNmI0qCseTWMsYG9sNkubfOrr/4qN27cyKn36UaaZ/JISBiEAwahMOT7YT/PajGMT416v8swHjKOx+fANsl0qJgVPN3j9+7+HnWjnmfNqGgVHn/4mHAQcvvybW7dukWj0VhiVcpziMoorlt1GnaDbWc7P3OoDhEgPyOGccgoFMyIYTxkzhy9ojNOxyJVaDziIDjg48nHhFnIlze+nAO0cs+X52l5Zi3WT9axWq3mNpFkBUgbpOi9fvr0KR988EEeJnzz5k3W19fJsmwpO548K6l2hMpYU89qqlNKNa7V855k10iBYnk/lUGr7v2qXVn8TK2Dyho8Pj7OGclyLNI0zc+eH374IYeHh2xvb/Paa68thbVIW0N18sKZY6w4p2UbJSggwY40FWHTi8WCyWTCeDymUqksZX4ajUYcHh7mfVdkqxczXch1Ja+Ttkqr1VrSWjg+PmZ3dzdvd5Ik9Pt9Tk5O2Nvb480336TT6eR2tdSzUIGeT1M+NdAgOzNJEmazGb1eL58I6oFdDkC1WmV1dTVPEyY7V3a6OtFUeoxqEKgGeBFBkYaJOlmLi0nTzlgP8t5qPKlqcKvsB3XySqO3aHzICaxOIvkdOalVo0alFKkMCNVAVgGbIsVIfr8IWoRhuKQDodZTtkllA6j9JQ3vYlpIWSdVyE+OmW3bOX233+/nsfhATneXY6oa7Z7n5dlHpHdbBTpkukWZeUCdUyqYIPtHRehUloaKJqoHajn2ci5KCpqsizpnLjJA5eYh2y/7QxVmkXWVf5PGsq7rjMdjLMtaitWWCLIKMhXHyMDIWQUOztL4qiCVpmlkeiYMDifKQQn5/5EdETohs9pMAAfWeeDAjmycyMGJHZzQoRbUsKcizEIP9FxcLtZiIitiYS4IrIDQCYmqEX7HJ3KiJZq7qCgC2AhtypRxYxcrsFgEC7SZRjyMCcYBdmhjJIYQjUqSJTRWglxy8ywCAup6UsdN9qvneTndUDIqLkLi1f0gy7I8xv7fd/F9P6dCbm5u8vSpMDZVZlAcx4xGI6IownEcPM9jZWUlP1zJg45kgPi+Lw7Boc7GeIPwaYg7EvS91dVV1jfXOZgfoLU0DoIDJtaEwA74yPqIb42+teRZa+gNVq1V8WOusmatse6s09E7VM3qEvskTVNszeaydZntdDt/gYJy0EgTJtmEXtyjG3fxbZ/7x/d5Mn1CL+sxckaE+tnztUzDmlnogaDn6+jMwzmGa2DUDJFnXRnfRE9YaAtCQhISdqY7xJPluPkKFRpug3AaEgYhcRLjlT1m6UyI9SkgQ0kv4Rkic0acxSzSBX7qLx1QVTXzWTJjlsx4xjO0TY1sI1vSB3iXd6mOq6w4K9yq3GLVWaVm1jA41R1JQgI94MA/oJf2OJgfME0EBSLQAwJLxKtm25n40bPc892ly98K/xZ/53//OzSsBuWfL9MwRWq5q9ZVYQCYTaqXqtzr31t6H6vgv1xT9Xo934Pl3uvpHtdK17hevk5UjZbWYJqmjJMxh4tDDvwDDsPDHJQ4DA45CU9Qi6SEgzDQDxYH7C52eW/y3tJ1Bga3arfYqmyx4W3AAI7cI/bjfW63b+N5Xr4nSw8rkLOjHMcROgAKa6rZFDTr1dVVbl2/lafglenO/v7f//tcuXqFu7t3Wb21ysSY0E/7IrtKcMyKvsLnK59nrI05iU4YZkPBZglF9pVFduYpDrMQPdMxMPKY/IuybpxEJ2LuXYegGuCGLl7kUVlUeNV8ld+4/Btcv36dLMtyNfS3vvcW++N9jhfH+KbPVJ9CDRrlBvuTfd49epfD+eFymAXQclqsl9d5qfkSv3jpF6k7dUzNZOEvGEwGQgNh3uXZ8BmjbMRMny3pJcjwIDn3kywRqTmVdRFmIb24JzIylAAPHgePz+Lq5b0qGsZlIwennbFDI2lQN+qUs7IQ/9NF2rlXv/Aq1e0qTydP2Zvt8WzwjCcnT4SQpB6fYwUkJJz4J5z4J9wZ3Dn7w4NCHdCwsSlR4rp+nZ9e+2m2q9uslgTYmofBhFP6UZ+j6ZEI25gdc+wfMwmX3w8mJlWjSsk4Df3QTrWLEhjEA2bhDD/zBZMhLDAZVsFJHJp6k5beoqSVSKYJlUFFvP9jBzM0MZJTQN5K8/d76qUkTkJoh2ReJkIoTc6FDoBYV1ESMUpHTJgQJzFhHJJMkwvBHVdzqZpVwQrAZjqaYugGVmbh4zNhwqPZI5GZJR6d1+nQbBpWg4bZyMUUa7oIF5CgRMNsiLMBLrYlnDHyfS8dNe+88w7b29uiDQVavoHBqrvKqru6dKaSrAPVcI7iiHk2Z5yOOZ4dC92hcMA4Hedsg/uT+wyjIaNEAY9K4keLNNwfiewov7nxm/zS2i9d6CxUjUHV+SjPrudi9jVDhAzpNpfKl3KW7XA45OjoCNd1WV9fxyyJ8/7e3l4OuqhnYxn2IEvRLtA0EdauntH7fRHG6zgODx8+5MGDB0sMX2kPVKvV/NysMj3UdsCy40Z1AKlGugRd0jTNz+GlUgnf9+n1epTL5dy51Ol08jOFFEFVny21u9Q6yXqr4T4yLFue7Wq1Gu12m08++WQplG8ymWCaJtvb23zpS1+iVCrlmSpUgEG1V4vtLtomkrmthuPK+0mdMzV82PO8nK34r//1v+bq1au0Wq1zTtiiTS3ro4pRxnGcO/7L5TJJkjAajfjggw+IoigXvZTXvfjii2xubub2mLy3FOEshlD/WeVTAw2q53cymeT0edko2elSeKZWq+WNVZG+5wn8qc+Rqe/k4KiGmTwQqRNJ9c6rKIuK5hW9m0XPsXzGRWgZnKUalH8rGvHFDpebm7r41GvV+8jFrIIaxY1Lba8KvqiIoGxzEV0rAh1FhE01elVQ4qJFJFNWSgTu4OCAF154YUnLIIqinJ4jDXzpvZZFevYvaq/cENTPZNtUEEoFd4qMEnWDVEUJ5aJLUyG4IjNDyA1A3fzl5iU/l/VW+1DGxUnPoaxnmgpKvxRQlFQ7WT91zajzs/hCUOeEOi9VFkd+H3TshY0TnIUXqOtz6b6IMI0lpoQb56DE1JnSL/eJ7IjEvACUONV1kABCaVjCCsXvRmScHUTNJA8BSbyE2IsZO2OCijCeUr0QqpRo4r4LC20uQAhtJjQmsklGFAjPrZd62Jqdf0/NZCHbqBZpdMhrZciXuo6WmnjBvPr3WeQ8ky9B9WUuP5fpUeW+VBTd0zQtz4Ms4zJN02QwGBCGYQ5mSKGkslfmpneT+XzOWrpG97DLz1/9eTY2NrAsi5k24zA85Dg65ig64jA85DA65H3//aUUdmWjnLMg1uy1M0aEs0HdqAuBPl1f2nMAalmNS/olsiyj0+nw/cH3uTe8h2VZIg2snXEcHJPVM/ame+zP9gncgKyZMXfnBLVgyXgvG2X0hY6NWJNRIjJXhHq4nPLTKFEza2RhJqjISUBgCoBsmk2XAIayLmjQlmahoYm0nGlCkC4Lw7m6m8fkJ5lIFSjpyJIariVnrCR0mCQTJvMJj+aPhG4AyzH0GhpVvcpWZYs3629yuX2Z6WDKw/sPSUmJdaHQP7fnzPTZmfghwhtdtao4ptCVmWkzRumIj2cfMxqMCNMQlAQilmYJsdiGg+Gcso7MiMiMKD0qCW+r2aCqVTGzMw+QCt7Ld4Y8+LS1Nm2tzSvOK+jeWVhcmqUiJWrcFSEZ4RHH0THduEs37tJPl1NbepqIi5d749PpU37U/xHdRZekk/DVd74KiHRsW+UttofbXDq+RMfrsF3eZt1d52R0wtraGhsbG0vepTfeeCNfP6rwmPx7FEUkccLl1mVutW4tHT6BHFyX+j3FQ7aPCB049AXY0o26HAVHHAciBCWIzuaRhoat2zi6g27oTMoTTqonYr89nZNv8zZ/71t/j853OzS0Btkow/RN9JlOOS5TSYVQ58u1l/nFz/4iV69ezc8wYRjy9OgpY8b0oz4nCxH21F10OQlOePfgXY4WQvdBNQ4tzRLx4kmN9WhdCEfaJRq1BoZpCCN+csJMmzFMhR5Lns3itNjYGBikSSoAFj07C0E5bVumZcROTOzEBPWAGTP6WZ8dds4xi76y+xWaJyK2ftVdZYMNanGNul7n137u17i6elWkfHU09if77A52OYlPhIDo4lhkofB7TOPpkqZDRkZw+t87J+/wzsk7PK+UzBI1p0bLbXGpeomf2fwZGnoDJ3Wo2lVOeif0xj3mzIUgYnYqjhgKr3rRAC9pJSp6Jc9aMR1NSeOUslUmTmOOk2MGjQHz5vw8oxAwolNdp+CUURhZ2DMbt+tihRYtp8XnXvocVy9fJdETxsmYaSbAgGE0zD3+g3DA0eSIeXY+qwYIMHASTZhl4vwWG4Ihk8QJ2Xy5TTo6dbNOSS8JccfTkBSANEvph30OggOheZJMzqUpNjComTWqWpUgC5j/3BzmMF1MmQUzHv/RY660r3Br8xZe6tFqtHL9LzXsGpadk6rxCaf7vFFmq7GVr18ZJtBYa+A4Dt/5znd4+uwpk3AiHDWWCHHNShmdqx1CK6RhN5bsG9WIhuXQ3aJRrr7/YVl3SjqqgiBgdXWVa9euYdt2bnxKA1Rtm/TmA+fOf6oQuuwfCXRFUZSn0JRe/OvXr7O5ubnEGrVtm+3t7XzfK95PPZur9orcj1Q2u3rul/0kz9ayj9bW1mi1WvmZV3VIq0We3y4Kb5eO5yzLqFQqOeNaPnNjY4Nyuczu7i6VSoVut3vGRqnXeeONN/J07mma5mAAnOm6qQ7vfB0UWB7SDpLtUItk6crxlCKhQRDk97p+/TqXL18+Y/OczhPVMa7aCapdKW1L1TkgHb63bwt2jud5+XlTzim5niRTXc47ldXx7x1okIMmU3WpE1oNLSiVSlQqlbwz5OeqR14W+d2i8J/8XDXCVCO7aGSqB/Xi5C0aWUUjWgUq5HPl9er3ZP1UsER9TtFgUCeg/K76r2yHZCNItKvI9lCNIPX+xYFWAYjnGUdFVFX9XLatyAApAkkSbJBCK+qGKiezDFVQjSK1fio7QhpH6sKQhpVscxzH+YanGpLqOKhiRECO6spNVtZRrYdE8SRCqqbvAZZUb2UflUolJpPJ0ovjIgZKkiSUy+XcQFQ3KLUO8pBenJPPQymL80EdP3W9qHNNBSNkMTDQFzpe6J0D3Yov6sRICMzTEAovESEbCkgxL8+JmqeeFaOwZlItByPc2MWLPWp+DSd2MHwDLdIwdIM0S4mNmNASISGBFeDbPou1BYmXkJXOPMSj08TveqTnKUDTSUrUj0gnKUwhm2S5kCYzMUckLU/OhyKoI/8tMoD+7yhy7qqqxJL5I+dVkiQCFFhby/Mex3HMYrFYmjcSCJMsEAl+aZqWi5Curq7mlPOtrS0ePHhAp9PhhRdeIIoikZrWrLPJ5jlWjWmaTOMpB4sDDgLhsT4IDjhYHHBncIdu1M3bZWs2G+5GHoqx4W6wYW+w7qyzYq0s7d0yVEmuSzMxacUtKkGF+qyO89DB931WV0VM8En/hEsvXeJzv/Q5tIbGj3Z+xA8e/IC4HDPWxsyM2VKKR0u3aNgNTEwh6HYaez91zsQSDAxKSYlSViKex2ysbmBYIlXhLBYsBckskMXVXFzdzT3VcRYLQ14peqKjxRpZkqHpGrqnL6U4zMhywcU4jYkQiv3jdJyL6ZoDU+gGNBV2TqbhJi61pMYt6xYv3ngRx3BIs5R5OGcaTekFPXanuxz7x2f10XQ6rlBmr1pV4kXMqC/EyhbWgoExYL49Z9/Z592Td5fbq4uUjlJVvWk1hXfSqFPVq7TsVv6ZpS+Ltcp9es1Y46p39cI1FcSB0HlYHIpwjPCIw8UhvbTHoX9IPzgDIvRM55XGK/yHN/9DDheH7M/32Z3u8tX+VznwD5aMyLpVZ6O0wVZ5iw13g63KFuvuOqv2KtuVbdqVdn4AlodYecCXHia5L8h1appmDh7L95bappJeolPu8FL20pIGlOwTPxZAxP58n8PFIcfBMYe+YIJMp9MlkEHe79fWfw1d1zlZnHCoHXLgHTCoD5Zo+gYGv/+j36dzv8OKs0LH6tCyWtS1OuuldTp2h2vuNbBBq53tN7ouwnX6Yf9MKyLs0g279KO+ACgioXEQjJbBhIpeoWW1uO3dpqbXSIMUS7fyNbFIFhwvjhmlIyIvWlqbIASQ9URHy04Px5EhwgSt+JyOwzyZM5/P2ZvvnX1oAxr8X3/6f+UfWbpF3RZ09Y3qBuveOl/c+iJr7hraVOOVa69gpyIV5TgZ8/DoIX/y3p9wtDiifa0tmCnz4zz7hApYzuM583jO4eyQj/n43DxWx8LGpmJWaNkt/uNL/zF2bFNySownY+I0JjTCPGvDKB3RD/tMrSkze8ZBdgByO3HEXuL4DsyBAIzUQEtPz7kapFpKaIUsyiL0JLKj/F35VvoWPBGhNVJ8sG7WaZgNGlaDbXubRr2B03C49+49omnECy+8IDSdvJS5Nuf9B+/TW/RYGAsSNxEGtxWyMM5r3sh0kXEWMz0VpYmzmCiLLkyFWdJLZ2yJU00KTdPQdA3qMEyGJGsJiZMwsAY85jFv83beP/VRnabfpGW38lCOlt2iYTVoOS3WymtC9NFu4hpnDEm5ttXz62w2o1Kp0Ol0mE6nhGHISy++xM2bNymVSkvGmoxdl4419aymGnSyqPZK8XxfBOSlF1rqUaVpSqVSoVQqLYXtyjDjItipnhHlv/K8IcHhYgiy6jS7fv067XZbsCdMMxf1l85mWV/VCSyL3CPVuqjPVx2q0phV2XNq/4VhyHQ6zVOxz+fzc4x1IDfc5bkIOKdnpGla/l6VJYoiDg4OaDQa3Llzh8lksgSADIdD/tW/+ld87nOf48033zzX1ovsS7VvLhr35zlw5e/SGatpglUhDfw0TfnBD37AZz7zmZyBLc9nRZF79Z2jnv1l+Im8r64LNuDDhw955ZVXcjZJsX6qFkaRSfRpy08kqX58fEy/318yytWJqmki9kft8IsGRn6uMgTkpFdF4eTLUHaY6omXB/HiYpYeF/kMeTiXhiSQGxxFsEGWIjKnGugXTRB5qCoa1PIaVWRFRYNUI77IkJCGrPS6qHWRE0YW2b4iiKMCE+o4qUwJ1asu+7RIAStuYMU+WCwWSwYTsDQ2kv6mMjHk3yS1rTjO6lwpbuLyX1WLQbZLfqZuGDJHrDreaj1V7QUJGsiDp5o5BMjTVMqXlQQR1D5X+6lSqVxIsVKLigqq7BR1s1CZKWo7dF3P2RqyDipwJ+9ZBCrUflDnnPx/tR5apOFGLt7Cg2WNsaX5lpGRmRmBHZyJXFpCiDL2YijDxJzQNbsXshn0RAAHkhnhBi7pcUo6TvNDVpIkaIZGdaVKabVEWhIiYjNvRnLpNBVoiXMesRP/hP6ijxmYaFONsB+K+85EKtBskpHOUiEEmC6znv7vKKonVY6tYRhLObOzLMvF/Wq1GpPJhJOTk3zuSUEnCe7Jl/dgMFhae1JvpAiIqbRLaTQV556M469aVWp2jdvcXtpnkiQhJuYoOOIgPGBvvpcDEt8ffp+j4CiPEdbRWbFXWLfXuVq/ynwxJ9Ij6kkdL/UoZ+V8f/yFX/gFSqUS8/k8X2Mvvvgiv/M7v5MDnX/50l9m/sacSqWSt2+STDj0D9mb7XHn4A7DbMiDkwechCeMGC2DBhkYmdCXmDAh8iIG/oB0cTYvq3qVa/Y16kYdT/POYvLTmFk6Y5gMGWSDczG/mSY8t5lxGuZQOJAbGJiayEevaSIlnmo4WppF2SyTxRlBHIiUhbowwHzTx8fnJD3hkwefCDBCub+pmXTcDm+036DltnBNF0MTzItpMOVocsSRf8TQGIp1cnoKcEKHtWSNS9VLVC1x+NfRBX08EzHj42TMXf8ug3jAOD4vlFcxREpTyYhoWAKQqFBhTVs7AyXsRm5gaprGprHJur2e729pKkKC2u02i3TB/nyfb33wLXanu9zq3OIvbf2lfC8Lw5AwDGm2mnT9LjvTHXYmO3kox8H8gG+Pvs3Bk2UgombW2PA2WHfXWffWuVW5xZe//GVqtVoO/B8dHbG/v8/ly5epVCoEQZCDfuqPXK9FMFw9VIPIiHClfIUr5StLDpTFYsHf/bt/l9F0ROAEeOsenZsdvFWPX2n8CqW0RGm1lIctarqGr/kM0oHIGBP1GCQDTgKRLvPR/BEni5Ml0T8p0tqxBRix6q7ScTq0rTZtq82Ku8Jl5zIVp5KDL7JdX/3aV/nw7odQh6yS4XZc2tfajLIRg3jAQXhAN+kyjpbnhO7oOKEjshw4HSpWBT3VmYwn7O7tkugJqZ0S6REvv/1y7rU1XZPQCfOfhb0Q2VHsGb7pE5gBgRGcm39RGtFddOkuujwYF+IkAN4964uyVaZqVjFjEw+P69Z1Pr/1eTYrm6x6q6yWVnEMh0k84XB6SD/si5CJ+TG7k12hdTHZp+t3mSdnbICEBB+h63ISn3D3wd1z1TAxqRgVGmaDUlxio7JBNaoSDkKub10XDKVqlcfPHrN/sk9kRwyiAYEVEJfF+zR1C5kqMqHJ5E5doc2UWEIYOhNnoPWNdarVKot0wUF0wCfTT+gH/TO21sbpmKU6jaRBO25TM2p09S6ddoeb6zdZLa/moQ4rpRUa5QZ7gz1O5idMsymTdMIoHuXZGKS2wDAaXhhWsUgXZEnGPJ0L4dU0EcBtFoprG8rFicg6UTErrDZXsXUbQxNgb5AEPJk+4ePRx0yiCZP4fMhjyRBMLakn0bJbOfgi9yrd1wm0gJJe4urVq9RqNVZXV6nX67mHuWg7FB2ExXNe8fysOoiKDIRi0TRtyUEi6yDfkxJ4KJ7NVdBCZTOomSfUOsr3rPRs/+mf/imvv/56nrFA1lsVnlfP80UHqVp/1bkpr1WZ1/J7MvRBOkxM06RUKglHx1S8t4uGuhRzlGdvyYqQOl5qvSQwI89d8lwUhiH7+/sMh8M8DE/uyU+ePKHZbPLSSy8tASjqObsIKEswRJ6n1PGRgo/q2VxlQLuum59nLMta0kRotVo0m82lzIiSsSrvJR1P8tnqPt5qtfL/l309n8+ZzWaMRiNs287ZHtJ2Uh298nd1/nxawOFTAw3FtHRFFEfTRBx0MUWXXIjyMC0nrJxQKo1E0hSf9+KWg6guSvW+8jvqIMoJICe5ijSpwIBq9MtJVPRmX/S7uljUmG/VcFONRHWSFdEnObBqHVUUTC2yf+WEVkUvi0CIOuFVeo3sc5VepMYIqRNK3chUCpOMeVJpRJJuo9Zf1ehQ+1waVcVxvcg4VsEC9UAqJ7sKXqmLXj5H3eSLwjCwnBO4OK/UOqkG2vPqq46XGs+lzmsVXJKfy+vl/WWbLvq+usEV11nxRVcEsFSDUp2rapuK91BfGvKeRbBPghLZNKOqV/N7y/SnUlU4zVJGwQhf99FrugAmrFNdidPwjXllTtgMSdzkXMpOP/SxAotSWsKNXepBnexpRtALyGYZJECGMPKcDL0mUrOlZZEWNL2eQhnwWDLwkjSBOWhzDW2moU01/ouv/hesVdZYr6yzXllnrSx+b3mtC4GjT1PUlExRFNFqtajVanlqJgmQ1ut1ZrMZruvmLxrDMHLNiYODg/weElQ7OTlZEnfSdT1/cUt6ntREKeqLqGMN5C9suDjcLcsybMPmkneJLWeL15zX8uvjOCaKI0EjD0UoxlF0xEl8wofDD9nJdojWz/Z/L/aoxlVWjBXiWszT+lNKdolKVKFslanX61Sr1fz9Ua1WabVaS+ukRInVyiqvrbzGG84b1Ov1vN6TyYT+uM/f+/2/x52DO8SVmOpWlagcMUgHTPQJPsup6fzM5ygWKvpxFi9RxA0MVq1VbpRusGKtCKMlM/nhOz9k5s+YMydwApJKgrPiLOlKxMTM07kALuBiz2DokyUiS0CiL3u6rUykeXMtFzJYxAum8ZR5MifOYg79Qw79Q5Fu8RTIKIIRK/YK5axMPI8xMnGdYRscBUc8DB4yjIdLAErDbLBqr7LpbPJT9Z+iY3Vwcam6QrNjnsyFsnooFNb7UZ9H80cMosE5VojMP6+yI1pOixVvhYbZEEbAvM44GlPWy9T0Gr9y9Vfy/WR3d3fp3QBCI0jTNJpxk6bW5PXS6+iVs3dnmgkv9sHigKOF0JA4Do85Do55MHnA4/FjfubGz+SU4l6vx7/9t/+WxWLB48eP2dzcpN1uE4ZhfmAsHmTV/UDWrfhel3unSms9OTlhOp0SBzFO4qA/E+kef67zcySjhGeDZ3nGnjRNaTabrJZXWWV16Z2ovv9832caT+mG3TzE4SQQ7IRu2OWd/jv0oh6zZDm1aVkrU6MmdBMCB8agT3Xsqk0pKFGal9jWt/ntX/ltyuVynpbu6OhIpEMNeuxP9jkJTrh/eJ/H/ceU18sssgVHwRHDZCgyZFw5e6YRGnzwxQ9wQ1cwjJISXuThRi6VRYXOrCNYcLryrs9SsBFAhLUgdEICOyB0hWZQ4AR0F12Gwfk0h4BIWRnJ/Jpw78G9cxoOALZuU7WrtNwWK6UVtipbXGtc48tXvyzCWSYZG9UNdg522B3u4us+g3jAJJ1w7B8TuzGH80P6UZ9hKDI+xMS5GCwZ3B3dJXMy0vWU99P3xYOHQE38mJGJ7uswA3Nk4hw62ImNnuoiJbAGmqGRGimpLd5xgSdAiciNyMyM/Xg/18lwdIeW1eJG+QZ1q46VWOw/2ydchDQbTVr1FoZtMItnpPWUp/pTPh59jD84n7qzalSF9oJ1JnrZclpcr1ynZtTOwEa9QpiFBEYg0qOeAhDD6DQ9ZNgX2RnCgQjzKOwZGBBrMTN9ljNbkiwhTMNzIpwaGlWzStWs4houruHmmVeSLGF/vs+j6amuRFhIQ3lfAL1e6mHtWayP1rnUukRNr1E367noZctuUTNquJmL55wXrn3e/6vnt4uuU2nx0jje3NzEdV0mk0nO1pU2ShzHOeVfghIXMbPlfin3EKlBoAKrWZblegSlUonhcJjfU4ZAy32vuJ+pZ8hiCMtFmQnVLAsylFS2WQIG6+vrBEGwVA/DMKjVajx69Ijvfe97ZFmWs48Xi0Xu+AvDkHK5nGdFkc9ZX1/ny1/+MpVKhcFggG3bvPbaazx48IDNzc1cGyMIAsIw5MaNG3nfqbaRaojL/pTjBcup0WUfyd/V94TqAFdTl0ogIE1TGo0GT548yUM8ZClKEahnfPm5dFbJ64MgyBnp6+vrPH78mNlstiS+Kdsr6yR/1DGT4e+fpvxEQINcGMXDqezEer2eT0TVKFEnmTqZioYkkMeqqAdZOQmL6T9U77FqcMrvSSOsKEQp/y6/U2QCqBuBSlFWB1Q1tOQLvkhheV6og/yu/H8VGVNp3bKO8toioiTRJ/WAISet2g7VeIYzw7foHVeBCmmAFzcUGfutjnERhBgMBkuZLGQ7Zb+ri05+Lj2zajoatc3qtaqBK+eHbIscj4tKcZyLoIAKVEjARN5TXifHRDXiiuUi5o76rOJcVBeryqBRvyPbp/5NNSZln6kggPoSk/2igh4qWKau6SIAV2Q4FIEQ+ftFRT5DisgEQZCHnqRBSsko4STOhd+dz+f0ej38hU/mZjQvNfF1XxhApRStqpE2U6JGxKw2w3/FF16e4pAsIJklpLMUbabBERgzA23+/+Xtz2MtSc/7MPhXe9XZ93PX3nu6Z6ZnIWc4JEWToiSKlGxHtkTFdgzDRhIHsJEggAEHBgJkQZx/bMNAEnyIHRmOjUg2FVmWJVo0JXG4D7fZ157u6f3u9+xbnVN7fX9UP+99zntPU0N89FeNxr33LFXv8rzP8ns2BXqoo5grZowXMVInRZpLoZU1qCUVQS3Av33/3+JodgQ3XFbIDdVAu9AWwAMHIcTfDwGKollcWqO/9Jf+Er7//e9jPp/DMAz8nb/zd1AqlZb6XReLRfzar/2a4FFEj/SfaqHU6/Wl7i0UYSQrGFwJieOsc9BwOBRnTBaEnHdwxUgGreTaJLLHo67VUbWquGJm0RBUuO/Fr7+IjtuBa7oYqkNEhQhTc4qxPsbvPPgdjMojUVnfiA3U0zq+/63v40zxDNbtdVyqXcLZ4lnUjBp0TT+lfA2HQ9y8eVOg+K7rYn9/H+WojNasBcM38InLn8DB/gHy+Tzu3buHv/xX/jIWWuYp7kdZ54xumLWu7ARZWLmbZHQQI8ZReIRBlIX2h2mYKawPDSjVU6FNNBgTA8+3n8eVtStwVAdamrUknGCCcTLOwrW9rB3gMMxC4uM0zorwaRD1HPgVKREm4QRu5CJOsxoSHEgo61lVeUu1REFLN3IxiSZYJFloczfsoosuNPNhilkaZRXsH0a4q6mKqlpF2SjD0R2oShZmfxgc4oP5B2Ks/Jktq4W21caavYarhatom21U1SrySR6arWEYDjGOMwOj52fzHYUjHPqHeN99H6NodCrfX1f0E6/jw/8EUFD0RNWool1ow1bsJU8bKa1pmgIJUNbKqBQqeKL4hJD/i8VC8FfXdeG6LsIwxP7+Pnq9nuiSMx6PcfnyZZRKpSVFmstRei795PxR5uucDx8dHWE6nS7x3sPDQ7z22mu4cuWKoF+KqKOUKPKmAssts1U1S2Oto44L2gUxTvrJDRE/8XEwO8Abd96AVtUwikd4+WYWjTQzZ/A2PSzUZQPTVmx8881vomlnaRpb5S1YvoVclAEEvXs9nGuegzW3YO6aeLr6NI53jtFoNPCJT3wCb994G1/93lcxTsZQKyqG0RBJIUHkRJjlZhhYA/iGv5RCoaQPI+siB07oIBflBChRSAqouBUU3SJyeg7WxMLly5fx8//Jz6NSraA762bzq2jYHe3ieH6MQTjAg+ED3OrcwjgZw1NOpwLoio4kTTD0huh7fdwa3Tp1FsX4oMBSLOS1PIpallJU02p4ovwEPr/+eWwWNlFAAaEbYre3i9AKcTA5wJu338QMWdvfSTrBQl8gyWWFHul8RUYEGAAKWdQGUgBqBqSf6pQTZx2hjMCA5VoojAowQgO6osOxHNQbdTRaDahWBg6OozEOg0McFY/glT3sYEd0E1GgwE5sWL6FbWcbZ1tnkdNzMJUsmkDTNLgLF0GU1bsZhSPcn9/HOBqvjHiyVXspgoDAiS1zC0/mnkQeeTiJg6bTxO7dXfz2v/9thMbDTltGAKNioLhWROt8awmoGIWjpTo6KdKM18WLDGx92Do5TuOlFDa6/pcn/xdcrV/FrcNbmCbTrFtS9x4ezB7A0AzcnWaA6Tganyo8+njucfyvF/9Xca65nOa8iOuFdMm6ohj/Q+M5SRLRJt2yLHieh36/D9M0hed6Npvhn/7Tf4pisYherydars9mM7TbbXQ6Hdh21vljOByi1Wqh1+uhXC7jb/7Nv4lWqwVVVTEej5EkCS5evIhvfetbOHfunHBsACd6IY8W5no8d2DJOgRv08jXhbe25PougQVkExEPJwBisVjg+PgYr7zyiigaSXOl9FPf91GtVjEYDJYKK5Iz2zRNlMtlKIqCs2fPZq2+TRPtdhv7+/tCb+BFhHmKPM2T6+yrnDGcJ8s2E++cqGma2Ccy+Cn9OgxDvPvuu3j33Xfx2c9+Vuh7fP24TULPJF2S18mgopZU+PFHP/oRSqUS2u22GA93JtN6qWqWzkJjI7vsw1w/UXtL+eY8X4g2jhuR3AiSjW++AURcdNB4vr0MDnDhzg0hUqw5sdJ7/Jl8PDIgQvflIAgPa6bv0cWNTDksiM+fDiD3THNUUDYMucJAz5ZBAT4mGgsHZeSaB/wgyPuyan25QkSgA+0LdY+g5/Bc1jQ96SJCc+Sv0fy4IUQHgtabhxdxI5zTCAdgKDKGh6Gv8i7ROnFAhcANrqxxQ4kzB85o+Z7T2PjvcroMp2VeW4LTMJ+jDEpwwIKvAc2NAy+ygsvXgANjHIDiBuwqYcH34lFjlKNI6NmmaSKXy4nvUz0BqluxSgmn+0RRlIWXewqqYRXnK+dx584dDAYDce6bzSaq1WrWLjLwkNop8u08IjuCCxdpPgWKQJpLkRZSoA4khSzFIkK0VFQPC0BxFWiBhryShzW28Dc/+jexVlhDySxB1zLG7sc+evMejmZHOJod4dg9xjudd/C12ddwNDtCEC8rI47uLEVFuGMXw/IQlmNBqSiYVWfIOTk4uSw8PwgCGIaxFD3D95LOI3WoCMMQuVxOpFURkEDrubOzg0ajgTAM0e/3UavVhBe12WwuKUb8Wfy8yAg9hXJS1xmie/oO508E2iqKIqIqnrr2FJIkC/+7ceMGMAGeeuopbG5uotPp4Bvf+wbG2hhRMULxbBELZ4FBMMA7O+/gaHEklFhDMbBur2Mrv5UZVtULOFM4g4bWgGEZoggUgaCkWHmeh1dffVWEjZbLZZiGibJTxrqyvgSSEmCtqip8+OgGXXSDLjpBJ/vpZz8P5geZl1IBEjtBYicIGyG+nXwb3zz4plgPDRrqVh0b1ga27C08VnsMLaOFdWcdJkxMwgm6fheDaCBy5gnwGEUjxIhPhcXzaxJNMI/nImWC50dr0LKIAaOcAR8Pw4+9yMPeYA+u5gJGlvs9TLPe72qQpVDInkMDhqiybygGwijE/fA+rk+uYxwtt/vLq3nU9TqaRhNNo4mG3sAV/QrqVh25MAcHmTKoWAqOpkfYGe7AVdys3SlmmGMON3Zx17+bFdmLxqeKyVmKJWpJUJV7AiTof83McrltzcZwOMTh4SHOnDkDRVHw8ssvo9vtYjQaiagfSq0rFApYX18XwByX2xyQ5Yo0V9KIjrjiTp+9evUqKpWK6IikqiquXbuGn/3Zn8Xa2prI1xbtkR+m+vGICtljyM8ffYYD9/RsUzGx5WxBq2vY2tpCHMcovV1Cb9jDmTNnsgKaGjAIM1rsR31M0gm6XkaPe94e3py8ia7XPfEMFwBtrsHJO1AvqXh1/CryRh5ntbPAGPA0DyZMVEYVlOISjGNDpI0RH1NUJSsUafnwDA+e6cEzPCyMrBXmcf4YC2OBUFs2HM3ERD7Oo3BYwL/8vX+Jj17+KM5Wz0L3dDxlP4WLmxezqJlqDffv38eXv/xlRFGEX/qzv4TqdhWHs0Mcz7PuEkfuEY7mR+L3g9kBJsFyrrcKFbqqA2lWj2AQDdCP+rjv3wcAvDh6EfKlQ4ejOcireeTqORiegeKiCHVPxYXkAj7z7Gfw5Jkn8cZbb+C1m68hySXYH+9jmk4zsMEOgTwQOREiO0JssYgnDYhy2WtKXslACQVZqqIG3MZtYHgyFhs28koeBa+AalhF2S5jrbEGx3KQpAl2D3Yx82cwigbuTzMQYRgNBU+p63X8o41/tJT2bBgGwjjEKMiKTo6iUQakxBNhyLuhixvejQyUiCen2nCqqQrtUw+7kvg6tIWGPPJop208YT+BhtNAw25AmSu4dvEafuP//g1cv38dRsXA4fQQhVYhA0OtGGpRxTgewygYgAaERrjkkPgf3/sfBaBZNrJ0imK+iOBBgHV7HZ+59JmMr+hlmMj0U1/JIjO0RFvSY8T41eV0iqX9Z5G0/H2StaTb0GdJtzdNE5VKRegF3W4WojIajZZkN9fBSUcnfYHq6JHzh57hOI4w8nVdx87ODq5evbpU6F0GHbje9yiPPY2Dop9le4ffb5V9Q2uhKIrQNcbjsdB7HMeB67oijSKOYxQKBUynU8znc8FTC4UCPM8TAA5/lm3bME0Tvu+jUqksOfGoAwW3W+Wo4FW2HNfLSS/iYASfJ0Wb8lp0cRyLOlYEPN24cQOf/exnl+raycUlac3588lm47YYfX86nWI0GqHdbi/ZLdz2ojGTfUVAt0zXj7o+NNBA7dbCMBShN3IbQh7+wkNoZA8pTZhQGQAiX4bnJ9Om8gNMk5VBBRnN4QvOQQd5M7jRxZ9Fxic3Ch+1qKvABzqYMorJFXBuaMvgi8w06D0e3v/j1klOkeBrJBuiq9BW2Qgmo5tCaDiD4vUZFovFEhhF8/V9f+lg0Vz4vvA0Cp7uwdeS7kFzlOcuAzbyfHlEjEwfHJyhvScmwveErxt/Fv+bF7jhRjQHLogJ8CKRq4AL+pv2hRuEMhi16pzxs8P/8zXj9CdHWMhj4OOiNaXXOWhF/ynPjhBYHn7H0z34vehZPKdO0zRRrIlqxeRyOTQaDfT7/Qz4TBUoCwW5aQ76Qod34J0ynmm/NFODVbXQvtDGwfQAc2We1XgoAEbdQFSJ4Bd9/MPv/UMMvSGfMhQoaOQaAjxo59u4fOYy2vkswqFgFYSR50c+Om4nAyXcIxzPjnE/vI99ex+L3ALfC7+H3/wPvynuXTAKqFt11K06WrkWmk4TDbuR/XSyn2uFNTRzTYHEx3FWfPT4+BhJkoVQEk1YloVKpbJU72B3dxc/8zM/g7W1NWH8cLqg9eZGEr846k5pIHy/kiRZAtp4Gpqqqtjc3MTm5iYWiwXG4zGm0yk8z8P58+dRrVZFv+d+v49CoYDLly+LNkyqqsKLPHTDLg4WB7g9vI39+T46YQeve6/jj/p/JJRgFSrqeh0to4Xt/DZaH29BN3U8YzwD/8jHq99/VXT22NzcxHw+X2qVyAFNosucmsNZ5ywuFC4syQ5VVXH79m38y9/8l5gpM4yVMeb6HKgAl5+/jCAX4Dg8xiAaIEqjrBOB38FbkyxUmnv/cloOTbOJdXsdF4sXcaV2BS0zazfasBoYB2N0vA66flcAEYfeIY6DY/TDPibR5JEF2GLE6AZdDIJBBh6k8Ynn2AYQAebkYVeZxEDOyMG0TJi2CTWnYhAPMkPj4b9BPMAgHkB7mN8kgxGmYiKv5mFrNmIlxn6wj7v+3azqPDMuHNURAETLbKHdbmPb2sbF+kVsFbZQs2tLhjIAuImLYTBE3+9jEAzQ9/voB/3sNa+PXX8Xb07fxMAfnKqjUTSKKKklNJ0m2uM2yloZd9O7KLfLcBoOHms9hvOt81ll+iSTV67rLtUVklPvuJOEPFRE87LsAE74JOXMcsBb0zQUi0VxlrjyyAt6cZ2IaFbOpSXZuirNkD57dHQETdOEIXJ8fIy9vT1UKhVcuXIFa9oa1uw1oauRnCZP4FHnCHeO7mAYD/HB0Qd4MHyAzqKD48Ux/IKPoBJgV9nFt3a+lW3ACwASwAotaDMNpp91TCokBRSSQhaxEOVQXBRRmVfE+nCni6JkrW0XxgK+6QsQYqEvMNfn2Pf2cfvGbcyUWZY+8TArQVM0tHIt1IwaQj1EXs1jvDvGNfUa1vPruFy9jM9sfQYFs7AkOxRFgZ/4OHaPsT/N2my+cesNzPU5HgwfoLvoYpyMMYyz8/GF6hfwc9s/h735HjpRBwezA+xP9oUHvht3s/OiASgAuArcwR28ErwC3AaUnAL1GRVGbAALIJkmMH0T+kSH03Ggj3XoUx0WLBglI2vVGA6R5rPIvDgXA3kgzmWgQ5JLkFpSnYTUgx/60E0dqqlipI3wwH0Af+pnZ8ZC9j/MUiMregUb5kZWx0VzYCs2vjr9KqrBwygjM2vPWTEqaBttrKvrS7yUR+ZynWIaZfUdFtoCo3CE3eEuvv7Dr8PXfcR2jLASomf10Ml18P3d7y/NQb2twmybQBHIJbms7pKaQp2oWeqN0sD0cIqG00AyTZDGKSprFRxNj6CVNHzq858C8kDf66O36GEUjjDwB+hVejhQD/DtB98+xUdtNSugXNJLeHHyYta2Uy+hoBSyFAuzhoqZpY2U9BIMzRDnlYAJTlfcuUbGPhl1aXpSC4IiElU161BAvKZQKGA+nwvZz43rIAhEiiUZtFzvo0hlciQCEO23iZ/Q8+nnKucQ17m5zsD1d5oX8TWuMwInujMvcB0EgQAIdF1HPp8XBXuBkzSDIAhEFAbZGcTTyKFJtJgkiahNwNNYS6XSkuN6MpkIYJcMfLpkpwx34Mk6Fc2ZA8LcLqF1pnvati3aexqGgYODA5FWIetj5JRbpeNzvmmapih0Sk4echZxMJrbBSR76Heefs7X4sddP1F7Sxo477tNFxmZFM5PxK9p2lK/VvnSNE20MKnVaqIABh0Eera8CRxRos+QsOXjko1M/ho39rgXjnvfZK8F99jR/biXmBt4dCi4MsC/yw1DmgN5zrihTYeDtwrlc14FHHDvM18LsfEPDw5fP84wZOOTrtlstmRwc2bh+77IKaJ5EaOSDXqu8BCaJwNCMmjC50v356kvHBygsa0CIbjxQO9zEENeSyroJacA8TXi7ThX7Q89m4NxfGzcSF+FFvN94jTOUV86a6vALrpkQI7vC09BoefLHRr4GvO5cvrm68yVUqI3XhsgTVMBQskKOKUCUAEkIEvhojw8x3GwubkpigXx88sBLT7PJWM61qC6KnKTHOw9G/7Yz7womoZGs4EzZ85A13V8/etfXwILjt3j7OfsWPy9M97BKwev4Gh2hIkvebwUFa18S6RVtAtt1Bt13Nq7hYJSwOOXH8fjVx9HihTzcI7j2XHmvUqmGAZD3J1khd2G/jLYAWT5saTcFVCAP/AR5AOkSQpvy4O+0GHCxHwxh7fwUCgUMB6PEcex4LccBKCzyKMXOGBK54RojIQ0F04U6UB0SlWjOa0NBgOEYSj27fz589B1XbTkVBQF1WoVlUoFmqYJuTIej4VAtRUb59PzuFi+CJQZ70tjDOMhOmEnM77DYxyFR7g1v4Xv+N9ZKtymP6ZDX9fhLBzsznfxyvdfwaXaJTyx9gTONs8uFZXyfX9JFtDFZcR8PkccxIhncdZtZWGiVCrh1z75a2hVWyeALBYYJJmBPkgGOHQPsTPfwVjNetvP4zkeLB7gweIBfjj84TI9Paxv0DAa2LA3cC53DlcKV/BzzZ9Dy2jB1jJv1zyZYxhnKSD9oI9D7xD3Jvdwf3wfnu5hFs8yIELGz3UgKGZ57gAwVlgRNz9rP7lpbaJpNVHQCln+80N+ME/m6Ad9HAfHGAQDxIgRpAGCOIASK1n0RJqeAiMsxYKlWvBSD7vhLu74dzAfzbPx3c8+42gONnIbWHMyg3fdWRfFHM8VzuE567kl+UHKW5pmdS7GwRg9L0vX6MyzNo/74/0sbN0b4oPFBzgsHsJTPTFX7Gb/y0YZNaOGPPKoGlXUray6fd2qi/DvWlpDSc+inni9Js4jZXnM35PB/nv37qHf72Nra2upqBfXkXg7bqJF3j1JBg0JJOHABJ3dTqeDZrOJNM0KoVEKVi6XW2pNTY6DfD4v6DkIAiRRAju0ccY4g7NbZ3HTvYn7B/dx/fp11Go1fOELX8jyov0h7vXv4fvvfh+u6gIlYIIJonyEcWmMnt071VLZDE04oSP+X+1fRSHK0kjUWEUxLaIUlsT4aJ1IryiWiqifqePCMxfQD/rYn+5jggnu9+/j7cHb2FV3cXvnNqZ3l4sI/tfP/Nf471/47wVdaZqGnJHD2dJZnCmewbPVZ/G0+jSKxSJu3bolcr7n8znUnIpioYjLm5dFpGYYhtjd3cVgMBCe09/53d/BneM7MKoGulEX5pqJ9tU2RvEIB5ODLDJIj7PzWAaChy0XxrwycwooiZJ1uvFSqAsVylyBOlWhHWvQhzqMkQFl+tAwKiADHXIpknz2027asOpWFilhRBiGw9PddBQVQRpgkkwwC2ZQoCBIArwcv3wq5Qk4Mcap2wXVOBAdbMwsNausl7M6GE5N6Adjc4yd3Z2llKI0TXH16lX82b/4Z7FQFwitEA96D5DmUrz4gxex09+B3bAxMkeY5+YI6kGWdgIs1QTRIx25JAfMgXyaR2lawhnjDEpqCVvFLZTUrDPWH37rD9EoNfDX/vpfwyyZZUWl/cFSkcuBP4ALFw8WDzAMslQwHm1GV0EriHWoWTVR66Gsl8XPkl5CWcuiJujixcZzuRx6vZ7ofFMqlbBYLGDbWXQWOW6Kxaxujuu6Ii+/UqlkgM7DOg9k55DBTfobtTi8cOHCklyna5XtRxfpt8Bqxxl35HH9mhvF3PEkR0KmaSr0SXJ+U90CivLQNE1EeGiaJiIfut0uWq3Wku7u+z4KhQIqlQocx0Gv18OdO3fgOI4AHgaDgdBTuP3FDXqiT+4U5jYi/S7Plb7LgQyqjwFARJ0QsP3MM8+INSdHPY/i5jUkSP+Wo8dVVUWpVEIQBPA8D1tbW3AcR8yRgwtcZ6b9kJsRfJjrQwMNpmkKrxNfSBm9ITSNLwZX9ElZo0ETylIsZjnM8/lcEBBtKiF6ZDyTEUHfJwNpVVsVPka+0Bx54uiNrBDzixtHcmg890bIRiT/XQYKeFcJ+kleEH6gH2W002tkCMuAATfM6Tv8oPE50edlzz03sAlIoPQG3/cxHo+haZogXD5/2VDmB4tCQF3XFWFh1LpFVhj5eGjN5f3lDEymA84QOPDAPZWccchGNF93+TOrAAE+br6X3FCTQSb5/pwm/jS65sALPZuDcZy5yXTNPy/Thyws+H+OUvMzwvePkHXf95e8bLT2hKzy+RIwSUKS34u8eeVyGdvb29C0LEeRqvNyAImPexVwRkJPFnwARGQOGdyWbmG7vI3t8vaptZOvRbhYCUbQz9uD23h/531My1NESoSvd74OPOxEqEFDSSshn+bRzDWxXlzHhr2BRqWBvJqHEimwHRtQgJk/Q2fRwSydwVVcdBddHFlHmK5NsVhbAJdPxvRW+pbouW76Juy2jT9J/gQbvQ1RfZty3S3ldMVmzrNkoUX0RUoDN7KArKiebdtCWPL3aX2pWJEsT+hc0N7z9BtK3SPaIBpPkgSFtAAncWANLFzSs/ZkXuLh1oNbGPpDdKIO7gzuYGbMEBQCeEUP08IUO/YOXsNrgAvYro222caauYamnnXLWLPWsr/NJpL4dN5lsVgUig3JSsp/pGgMRVFQSkpopS1xPkipoUKgMICu382q288PsTffw333PvbcPfT8HiZBFo58e3Eb3xl+Z4n+bNVGxaqg7bRxNn8Wj5UfwxPtJ/CLzi9CW2g4PDjE009nRTt/+/d+Gz947wcob5VhNA14ZS/rIBD0MI7GcBP3VEjzIl3gvn8fD/wHUKCcihQo6SWsW+t4svgkamYNpmJCV/QsPSP2RBpIx+9gEGSRBn7qw498qFChKupKMCJJE/QWPQy8Ad5J38EiXixFbFiqhbbVRstqoWW1sGatoW23BRhR1spYU9awldtCYmd7t6/sQ1EUXLx4EZ7n4Utf+lJW0NDr48rzV1DZrGAYDTHwB9n5mh2hF/Zw17uLYTRcAq2ADASiHPSqUUUz1xTpGjXjpNJ91agir+UFLR8fHwv6Jtno+z4ODw/RaDSwtrYmCnKTMk609t5774m6SIvFQpwRAnCjKBIF0Yjf2raNyWSCer2OX/u1X0OapiIHPE1TVKtVxHGMw8NDocBy79V8Phf93klH4J8j8Hc6nSJJEnieJ85p2chSk5rDJopeEflRHp1ORwCcuVwOsICFuUBohwjsrMijZ2XpE/1cH3Fv2cvG5TjpPDy1dzKeILwdIhpFuHjxIq5tXcPZs2fR7Xbx1cOvYjQa4cmLT+LTP/9pHM+PRUeJ88XzAsyR9QXiYeSddhxH7E+SJChqRdiKvVRjKk1TOI4jQrXTNIWKDCgphkWknRQfP/dx/NLVX0IYhnjzzTfxw1d/CNu2sbOzg26/izSXIrADKE0FUSlCkA8Q2VkKRWRkbSnjYny6VpE4SADiLDpB9VUoCwWqq0Kf6zhnn8M59Ry+cPULONs4i3kyxx+/9Md48/ab0Coarn38GsbxGINwIOqr9LxeltIhXbZqw1ItJEhEHYU4zYBH+ewCJwUcq0ZWUDKPPHau7CAaRzAjE3Cz6Je5Pcd0NkXJKqHiVHBp8xIKhQIGXxsgfTczRLexLTqYQAMCI8AwGMKFi8pWBeNoDN/w4WkZTb3vvo+XZy9j4A+W60p8NCu8++LrL6JqVFHWykudKs7Z5/BR56N4tvWsOL9xHGMRL7LOG+EIs2Qmum6MouznJJzgwDsQhTBPpY085CNlvYySVsJ/fuk/x+fLn0eSJMjn87BtG/l8XtR4MgwDOzs7OHv2rGgZT7p4LpdbMiI9zxNRpWR/AFlUQ6PRQKfTwWOPPYZisYh+v5+RjKSDr3LgcOeT7Mgi23CVDsodSdyeIycUyc1cLifSSSeTiXCSkM1JY6CuCtRtjnigDHbQeiRJImpjLRYL7O7uCmNaVVVRiJJHa3A7jngOXdyO4LoLB5ZpHWgtuO7r+75wtFPhR03TcO3aNayvr4tn0OfJZpN/cjuCPgecOIdovk899RSCIBAppgQw09iiKBJ7ZxiGiA7hTs0/7frQQAOF3NLNaTL0OuXOzOdzkavFF5D+pkWnTfc8D5PJRHjAkiTBYDAQG1Or1Za8slyZJEIgAUabKKNq/Hv0eW6c8/dl0IEuOQde9gZwFGnVRc/lqQ+0cRTBwA042XjmQADNnxttPFqCXuNzlIETHnYprxNH2ug1fjiLxSIKhQImkwny+bzYd07M9Kw4jhEEwdKcXdcVOWH5fB5pmooCXITqKYoiiJ3vPc1R3n9+iLnRTEyB15GQARy6OAopgxGcPuizqxA/fl8Z2JBpgTMfOR+K5s9zgTkTl4EOGVTge8Cfy8fEn8/PJweK+O90T15wlINV8jwVRRH0QYooX3PiH9SphiIyjo+PRcFRbkCSt5s6LpAAIQWX5k5j4egy31/+Go2Fcs7ovTiORdjaT3o5hoNzlXM4Vzn3yM/8g3/wD9BoNFBpVYR3dZpMRR7rKBphHI9xZ3JHKHRyESpd0TPF52E+ek2r4VzpHMpaGXZqYzwY4/ad2xiNRpgnc0R2lKWFVA0klQRvx2/jpf5Lp+6rQRP57VR0r6JXUFJL4mdJK2VeH1hLPIsLO36RETMYDHB4eChCroMgEN2KOGgqxqItF7zjwBOBF5yvc/S+3+8jCAKcO3cO5XIZP/zhD3H9vetZvmoQ4cnCkyjmilACBebUhOmbqG/UUTlfQS/u4dA/xJ67h37Sx133LvqjvjCqdUU/AR6sNWxYG2iZLeT0HAzbEDSpKAparZZIGeLnlGiQCkBZloVarSbAeEd3sG6uIyqfeCmEjIsj9L0+jrwjHHvHuOvexQP3AQ79Q/T9PnpeD0eLI7w1eCvzyj+8FCiwYKF2UEPLasGxHPgNH5vmJqwdC1/87BdFS17iayFCjOIRDr1D3J/fx76/j07UQT/IUjTm0XypVsIkmmASTXDTvSmKsPGrpJWw4WSdK9pWG0WjCEvNnAnzeI6e38Oxd4x+3M9SPB4q/37iw08yMILSkvjlJz6O/CP0gz7en76PIAmWjBld0dE0mmiZLZwpncHZylnogY6aWoN738V6cR3z+Ry5XA5r+TVcKV5BTa9BNVWkToowH2Kkj8SeaZqGECHG8VicWcpFJ4Pi3vQeXg9fX3l+DcUQZyuexBhfGmdF/HwDTpy1e33v4D20t9tot9uiWriqZiHTh4eHuHTpEn77t38bu7u7WF9fx2AwEDR/584d1Ot1mKaJw8NDnDt3TlSn39zcxNHRERqNBr74xS+KyFLyJlOh2el0isFggHw+L3iuoijCUKazSaHH5FkkeiYe/9RTT2F7e1vwZd/3YVkWgiA41ZEqTVOYiQkrsKBGKrSFtiSnyNGhaCeyj+QpcCJfCfTksrzT6aDb7aJUKuHixYsol8viLEZRhKKddSm4ULlwylEm8yYuI4n3yGHhpNcQX5I9ogCwvr6OJ598Em+//fYp/ZjrCbquI41TaHMNeS8PwzWWdD4Ki+71etn91RSxHSOpJUiqCZJygqSYICkkWdFjK0VciLOCu2oWKfHqw3+/+8HvAh9AFHxUL6goW2W4Ixd1s4621cZz1edwsXwRtaSGUXeEyIwwwwyTZIJJMsEwHIoIolE0wjAaZp0+pG4SCrJ0NFu1YSgG4iTrXHIUHWFSmSCoPew89VCduYVb+IPdP8jOj/GwqKRZxaQ5wfFjx1DmCtZKa8Asa09sBAa0qYaG2oA+0nEmdwYHBwcitSCOY/ztn/vb2N7exne++x3Ut+pIcgne+OANvHPvHZTWS1BLqgALbnu3xfmOEOEJ+wlczl+Goiiicj8AFJQCSlopS4sxlnVNMvRon+dJVpRz4A+yjhvpLOMpDwGaNDqxLahAoGVZAlQg5ySdSTIYuVFJYfKUGqCqqoge1HUd0+kU29vbKBQKePzxx4XBSbROZ49omdsE/BzKOrHgd1KKMJ0X/j1+Nki3o/NFtWtI36DaX3T2KNKVdApN0zCfz0WryEqlIuZMAAaBGYVCAdvb27h7966odwVkLSEJVKXXeAoHtw+4/cd1Y+JdZLDLUde0Njwdj9aA7q3rOmq1mrCxSNei/wQYcbuI/ySAmfNKArXr9TqOjo7E2GjteVozpbAQfyMg/KcONFAFTEKmuPFPirnv+3BdVyC2JHD4f9loAoBGoyEWnQiLPPjk6aGFl41xGUniBra8mfR9rpxy1IcfDiIajmBzw4t+8giAVfkq9D36HI2b5zzxvqiy4cjnywmUh8zLBEbz450b5DQJPmce3s8BG0LCaByWZWFtbU28R3lSlNuVy+WWBDM3LPmBBLIUjPl8vrTupJzz0Hx5/fne8L3g6RgcZJABCPq+vFbU0oxe5yALF/oczeTgkOzl5WCQjG7KaRjyfWXAhK8lB9NkTw43RrhyRGvClRx+drlA4IVi5LFwuuNpEnz+3FjXtJPcOP6fz4cq/3LALpfLCaZHnmF+Xkg5IMCKA4s0H6rUTGPigJ0sAHkvaporRU5QTtxP+9ra2gIAeBMPFbWCqvWwqnFh+dwDJ4LZg4dp8rA/eTg6qeAfjzAMhtgNdjGMhstF+C5mP5RIgbbQTsL5vRIqZgUvVF7ARn0jC2lPUqRqCl/xMUknGEdjjOMx7s3vZcX34tOeF1MxBdBRUrN2hRQWW1bLohgf3KwAWi6Xw+bmpmhNyTsMATh1rjgItsqbyBV9DqDRmX7++efRarWytX7oVb18+TLa7TaeeuopACfhmUQf9Pw4joXhCWTV3ntxD0f+EQ79Q/Hzzemb+KPeH52szZ8BdFeHOlahjlS8FryG3os9fP75z+Oj5z+KvJEX8+UyKY5jHB8fC+CZlCIuG/j5Kqkl1Eo1XKtcw2fCzwhFhT47j+foeB3sLHZwd3YX99x72JnuoLvoouf3cOAdZINYB17H60Ae+H8++H/gaA5KWpaasW6s40LuAi7lL+Gacw1P55+G7/vI5XJLEU1+lBXIvO/dxz33HnYXu+jHfYyiEWbxDEEaCJBmEk8wmU1wY3bj1LlQoSKv5dHQGrhYvohfOvtLmO3NUHbK0FQNE38Cp+3gR+//CHcHd+HbPlw9KxgJAEESIEAAJT0pgkcGSpRG2b4tjvC++z7SozSr4M+ebW/byPk5WAsLR+MjnMEZtK0sKqKklpYUb0XJugxsmBvYUrcEXcrGKPG6QAkwCAYYBIMs5DrMfvaDPrppF8e1YwRmgMiKsm4CAN7Em/gX7/8L5G7lUDWqKKklUb8FM+Cadg0P7AeYl+YYYwzTMUXkAqUd2baNZrOJMAxF1wriu1SBnHgy9asfjUZiLq7rIk1ToegTqCuD0Dw6hz6vKIow6HihWkppIF2RKt5zWUnrJ//O+TS/PM9DGIaiQrw8RoqoU9Ws+v4bb7xxElXA5sWNHs6DOJ/iwAcZEHycSZKlk5CyzvUqAjDIgLh27RriOIZlWXAcR8gtMmboeVypp3txAIOvR5IkQALocx3KQkG8s1yojqfGpWkKRVewfW0bpYslzJ05wkIIq2VhHI1xMDqAb/jop310xp1TwCEAmDBR1+vC0098v620cdm5LGi3pJUQJ7Eo5joIBhgnWZtrAiQmSQbaDaIBtt7agn436/wRmzGUgoI0l+LpTz6NtUtrWKgLDPwBBt4AE32C2dYMsR1jYA5OjdGIDKieijvpHShNBQU1i3rTPA3f7n0bV52r6KZd5OY5PL31NJJSgsceewyqqoqIO76GSZJgHs/hxZ5YUzpvsj4iy3OZjkoooWSWcLZwdqlWHZ0XDnTJupmiKEIXB06K6XNwkM6jaZqwbVvojyQrDcMQZ5+KspKBzscqOwFl+4tf3C7hejTRP+l8nueJlpp0D9mO4HowzZ2clbzbGhUdJ/lPDg0AwhiP41hEfpFzgwxuWhv6bqvVwq1bt8QYuN36qLlxvsHPKoBTa0a0xGv0cMcwt18oQoN4B0Vu0HNlvsl1J7ooDYn4E0/l5nb5qjp7XE8hHeXDdp740EADobKEFPGwCVooGiSFwjiOc8qQ5EYgCTyuRPIer3yiqw4q3whabDLaecqAbASRUSkbg5ygCbnhwo3eo/dpvrzAiDwuImR6jw4BFyD0WVIQ+JzlSAmZgGit5BQIHkLPkTc+b+4hX8UQ5RQGWlMiNuo3O51ORRgToYv8MMoHiy4ZfaP14IoaN46JPuTP0wHhjE42gPlcOCLL780Zibz/HPTgl+z955/htM/vQWPgwAOtEQkEvhd87+X5cRqiNeCo5SrQgTNxui+nQ26s0f35PPnzZJSe9o7C+Qj9pc/LYV0U7kt7y8dE9yTBR6/TPiVJIqJgOC8CTlJ8ZEWRM256nSKqOJ+hcOOfpIXPT3JRihCtAaUC0E/q4UwALk8ZIMHazDfRVttLIMoHH3yAyXQCN85SKVzFRWzHiJ0YKACxHUOv6ogrMR7gAd5L30MyXFbYbcUWymJZK+Ny7nLWD12vwFIsqEq21lEaYRbPRPTFKBrhjn8HIzfz7MoKaU7NjKW6XceatoZaVEPDbqBu1UWoecPJ/jZ1c4lmOD/jYBUH1+g1ej0IAgwGA2H4OI6DdjvzDg8GA7iuK7wEtH6c360CyDe0DWxam0vvp2kKP/DxT37rn+Du6C6CQoCROoKf8xE2QwzKA/StPl4fvQ68kbWBXLfWRRrGdn4b2/ltnCmewYX6BXQ6HfHMfD4Py7Iwm81ERA/JWporKR30N9GISD+MFHzu/Odw5YUrmM1muHHjBkqlElRdxb/+yr/GHfcOJvkJrE0LQT7AOB5nhTaDA7yNt4HRyR5q0JBTcyhpJdT1egZE5C/grH4WLaOF53PP4znnOVFIjBQ+RVGQKAl2g108CB5gL9jLCldG/aUUjQQJpvEU03iKe917eLH7sGL/wzGoUFEal6B6KryRh0baQHvaxuJwgY21DSzSBabqFFpVwzgdw3d8JIUEruEiNjKvaKqlCNIAOnToip4Vw0RWx2FuzjE35lDyCr48+jLiEet6BAVlrYym0cSGvYF1ex0tqyVSM2paDaV86RSv4WBSK22dkudRFKHb7eL//Mr/mX1WVaA4Cp74+BPYfnwbLlwstAUOJgfYG+3BjVzse/sYBkN8+Z0vI30qBZ4C7uJutkeBBju2oXu6SCFadBao6BWYgYk88qjkK0hGiSgqmaYper2eiC4jQ8A0sxojpJcRP6aONhygo3nS55rNpjC6yHumqioWi8UpmSKD7jIgzGUf/y7n+8RLOT+k9SceSvndNCZN01CtVpEkifgMNyD4nOg1Gif9JPCG6J2K79FrvJMIl0XEP4rFouhRT4YGPZdHcxH/JwcJ5z9kvMmGBe2FbAjJOoWaqihFJZwPzkOLM0D6lz72S9ja2sK3v/1twT+r1SoCBDgKj3AUHeE4PMZxcAwncuDHfgaEhyPs+rsYR2NMk9MpFXk1L4AI0SFGqeC8dR55K4+m00TdqsOKLPyr7/8r7Gq7UFMVSqBAG2vQZho2hhv4fPXzcBwHvu8jDEO8fvw6fu8Pfw+NRgO5Ug7deRfFtSIW6gKxHcPTPAyDIcyqCdd0MXEm6Ggd+LqP63vXgb2HAxwD2j0NRbWIklZCUc2KkVJhx7KRjbmklVAxKlldlocgA7WWlGmGOz85zcu6GRVq5PonT2+gOkZcZ1QURZzJIAjEmSuXy0IOUJR5tVpdKhpInRpM04TruoJuSffgthrJWt4hTj6zsjOH67X0k9cRoDmTPOP6GtE7rZ3saKV5e54n+FKpVBKtO2mNc7kc+v0+isWikI80T6qnoiiKqMtAxns+n8fa2lrWzexhGiSlash2JTfGuc0h83r6vOyUJv6TJAnm87mI8OS6tmmaIrWNy3euX8t6OdliPOqXIj0o2pzsRw5wEBgj8yQ5wIDzmh93fWiggZBWyqHjRMWZMxEFHTzZ8OXMUT5sNFluqNKE6H0yYOjwCUapnhSM5JsoG9EyMs2fz6MguLB7lLCh3/k4+CHk3+f3lT1xRDgcDOAXR5vksXCjiX7ySAIZYSMDlAtMPh5aO24Ey+tBFxlGBCjJYBAJSg7Q0DMJrOH7zBmNrFjw7/M15R4+mrPMBPi9iUb4ehATo3Xm9UFkY14GM+g9DhxxoEw22Dk98fkTo+L0wRkx0YgMCBBd8PHw1+hvHpnCxyXTJn8uX1cOztA9ZeCDgEjyzKhqVoyo0+mIav4U6kaCjGiVV73lQAQXOnyMcRyLMGECG+Tvc1qT6UdRMhCwXC6j2WxisVgI5ktIraIoS/mDP83rN3/zN5d4IY+yonHTmpKHg5QQqgq/vr6O9fV1EXZ3584dfPvb30axWIQDBx/d/ig2NjbEGbQsC4ZhoFarCUEcxRFGwQhzZS6iFsbxGJPkJKLhxuJG5mla0Rc9p+YyL5aWhbBu57aFEmYqZtZ2UcmiAWbpDLN0hnE8Rs/t4ebgZpYqEk1OrU9Jz4CNmplVL6+ZNZHnXjWroiBf2SxDU7RTfMcwDMzn8yXlbm9vD6PRCK+//rooUMmVBuKVPAyRiuBxmpJBTbqePvc0bv/ebVSLVWiTrHZIHMcwLRPnnziPz/7qZzHAAJ2og07Uwb67jzfGb2B8dFLULaflUFfqojZE22zjhUsv4PWvv47XvvEaioUiNE1Dv99Hu91GGIbo9XoiFD4MQ2xtbWF3dxeO46BcLmN/fx+/8iu/gmvXrsG2baHE5PN5aBMNtV4NlmvhsncZv/IrvyJ4YJIkGMQD3A3u4r53HwfBAbpRF+NonI0/3Mfbi7cBtn26oguaaJrNzNtvbOGcfQ5b+S08XXoazyjPLPFwIT8VYBgNcX9xH7cmt9BFF92omxXuiybwE1/ke8MEcB7YwU724GeAB3gAJIAWa3BCB4ZrwJ7aqPQrMPsmNoubWCgLHC2O8NzPPYfiVhGH80M8GDzAUXCEUTyCl3oZGKFkNSI0aFCgIEKEFClG8QijeIRb3i2oUE/VpyjrZbTsFjacDWw721nxSnsNG7kNtOwWTNU8BeDLwLymaDAiA7uv7eKTFz+J7e1t5PN5+L6PDz74QLS7bDab8AIP/93/9N/heH4MtagisiOM4zFyzVzWEtL2MNSG8C/52Df3lwl2G7AiC3/1u38VNbMGv+fjYvsi6k4dB/YBoq0IhbgAq2qJNAoyYlzXXfKWygZTmqbY2NgQ54/ODK8oT4ovN6TkcyXrJPI553KO+CQ35Og/OXB4Ot3a2houXryI8XiM/f39pXxnbiysAua5/kYgAqVG8QKy3BCg+5B+TPciRb/RaCzJHR5qzkOwS6WSAKhojFwnkH9fZRjwuRDYQfehWlvU8o6AFIpAU1UVa9qakCu+76PT6SylBYtUyCTEOMwiFwZ+Fr0wS2eYJlPRJnPP38MoGmEanwYljE8YwDVA93RongZtocEKLbxjvINCryAiJdZL68jn86I7gemZqCgVlL1yNv9Ztvdr/hpafguz2Ux0M1BUBV/4lS+gfaGNV66/glE4Qr6Vx4PeA5EWNUyGeDB7ICK05MtW7aWClxU9AyAIoKdaCzWrhopVgaGdRPvwiDoeQcB1Yb62RMvEP5IkEXoX7WOlUsHdu3eXohpIFlB6UxAEmEwmgg+7risi6Eiv4ucQgChAyx09MuAlnx2aC6dlfj45GMjBfdLDqIXlaDSCbdtCtwSAarUqdEs6P/l8HoVCAWEYiq49cpolgTW2bePg4ADT6VS0EObjo3kdHh6iUCggTVNMJpMloJOAIUVRRFc+bmdwXZQABTn6kC56rdVqCd66WCyW5kyRCGSn0Hdkm5X2jSIQKEqDg1T0XWrkQLooXTzyOk1TUTeNnrfKXl11/UTtLQkB4Ua0bBSRkcU9TzRImgAJG25AE3HxVmmyYUavraqEz3/KoAEvCkTvc6SXG3hkNHLmTEga/8kBBT4HbtBxRk/vyyCILLw48CAb2zLax8dIB5guGYygv8m4k418fvGweH6gZIVcXjeuLNF8V/3Nw+TpPvx1+g4Pl+JgBQeGOELP6YZ/n39HNqZpbJym0vSkeixnBvJ+cJCGDFK+1nR/bvjTHpByQq9xZsRpH8CpYlL0O99r+i6/JykzSZIsCTRurNP36Xs0fhofzx0jI4x7hPg60vu0N5qmCc8Xhe1R+hUht1xJ4p40Pl8OavD1pAggmrescNHFlVG+d8ViUaD+hmGIgrT8LK0yKH8a18///M+LsGHK36e1JjCV6t9wpZQDWaZpijlTmL+maTh37hzCMMQnPvEJrK+vC8FI/Ifuo+s6EAA1q4a20T6lpMpKfaqkmESTLHohPMlFH4ZZusYoHmFvuodhOFypNBa1IqpmBhA07AbOOedQs7JK/bZuwzRMkYs/9LN7970+en4Pd8d30ff7cGN36Z4qVFGcq6pXUdJK2Cxvop1vI4gCFJUihg+GaDgNdLodsZ4E3k0mmZXM0wBIYSCjiheC4iHQnO+kaYp2uw3btlEqlYSXRFEU6JqOVqGFZxpZxWjONxRFgRu6GKQDTI0pjoIj3Di+gW7cxSuLV9AZdfDPO/8ccAD8EmAvbNhzG9pEw1ydw/Gy9mau56JYLIquLMViEUBG+81mUwArqqoKmlNVVRQMI3CBoj+ATKFpmk1s5DfwaeXTgk/QfBfeAiOMcNu7jXuLe9jz99DxOxiGQxz4B9jxHhbWlPYrr+dR0StoWS1s2Vu4kLuAdWsdDaOBhtVAy27h+fLzAkx75513RF5yd96Fa7j4jd//DVzvXYe5bsLLefB0D4mRACoQqzFmxgzIAWgCRzgCgCw6IwHUWEV33sXZ7lnUUMNl/TI+l/scnm0/iy/96y9hqkzhrDl4/BOPZ91L/A46i6yDSc/viar/BDLwtp7jaIzxbIxbs1srz31ey6Nu1bFmrWE7t41tZxstswVlqiBECBWq0LMUJWutRnnYiqKIdWg2mxl/iA3YkQ2jZ2ByNyvu2M63UYtq6PV6omOEruso18sYhSNM0ymUooLYiWFUDVy8dDFrBRrt4mh4hHFvDM/2gCxLEr9//PvQoKE4LmYgolpCLsnhYngR64V1NHNN1M06dC+rIeDAEbyrVCqhXC4vhWlzecP1J362OL8jHkT0vAo0pou8gHKdLlkPS5IsLP6pp57CD37wAyRJIiLY6DNcRq4CPOj+NG6SscTH6f04zvLlqSAyRU54nif4997enjB+er0ebty4IcL26Z60flQYlELdaUykq5AeSmtIP1fpk7I8IQCD5AnXnUmPkHU0Wbei+ydJAsRAWS2jYBSwoWwI8Jy82LQ+cRzDCz0M/axg4wwZIP2jd3+E+/37WVSeHcMv+XDzLr5vfh/fP1xucenAQfSrUZZCFJgoKAX04z78vo+G04Du6XASB7qlY9FdwLIskc5jxAa2c9uYF+c4Pj7GR9sfRUfpIE2zLg3UxQEA/MiHm7oZaB6NMQpGmGEGFy5GQRbNd3d+V9RYkGuzKFCy7hIP5VZJK6Fm17J6LQ9rTlTMLAWloBSQ1/NinFQ7RY78pjaW3W4XqqqKmgKz2UzUJCD+PpvNRFT6aDQS55H0YNq7w8NDQZPcwcT1MV5DjOiM67r0HtEI0RYBVByMI9lCz6BI1lKpJFoyAlnnKaLDUqmEGzduoFwuYzQaiRoN5LildsRAlq5tmiaiKMJoNIKqqkspXdyZXigUMBgM8ODBAyRJgm63iwsXLiCOY9FSlJ9/omsCL/h6ES/ka8zBFNleJFApTbP27VSbhwACHnnFQQbiQbz2B3/PdV1xP16EPU0zUJhS3Ahw4To9fx4f64fVjz800EDGDg+RX/Ugbnjy97iyT8YW/zxtGk8DoI3iG8ENC/oMD00jpVDXdWH00AavMsz5/WnMq/LUOWHJRhnNhwp5kKefp39wYcov+i4R0aPWk6NrPIRo1XscaeICjxuApGRyEIEEKyd07t3lxjUX9Fwo0+HhoAc31jldyETL91QIKulzsvCXQSYu+FaFDNJn6T60/rKwlEEg+Xkc8OKvyfeX94CHU62aE9ETf5a8f/R9Mg648sZBO9lY5D2HOcBDDIqMXTpLnOnzOdE6yaHadG6I0bquK4CGfD4PTdOEAk35f9TfmdOMDAysUlANw0Cr1RLFeg4PD08pVxyJpb/5mnCDk/aJPv/jgLifxvWpT31KPIuvJ42f81AKqZPPDykQRIskSD3Pw2w2wze/+U187nOfE4KDLn7OuCLA6ZVoUAaOc2oOZmxizVhDrMZIjASRES3dJ0kS+FEWRjuOxyKKYZpMMcMMnu6hs+jg7dnbmCZTzNPlyv1IATu24SQOzMCEGZrIJ3k4MwdmYsLW7YyuVAUwgVk6A/LAgX6Au9pdvD9/H+N4DC9ebjGmPK3ADEwYvoGSVsKtzq2sPaFeFV4oIzBgBRaM1BAF7wAsGRAy6MABBwAiwoZe07SsxzkvpsfliWVZaBttUQ/kVnRL5NzuHuyidqGGf/x//2O88eANOJsOomKEWXOGQWGAVEvFmpmeCX2ioxAUYMwMFKMiVEWFf3iimPi+L7qAyHmYVN2bDAriL1xu0GeTJIFt2dg2tnG2dFbQMvdgu6GLQ/8Qd9w7uDW7hQP/AJ2gg1E4wp63h11vF6+Nl4EIBQpyWg4VvYLN/CY+0vkIMAbOpeewkd/AVmULju7g4uQiOq91BFCoqipq9RoiPUJcjDE35xjpI7g5F2ElxMJYIDKy2geJmqCPPvrD/jLd9QA8DWiphhJKiIcx6nod54xz+GjpoyhXykhGCZI0wVSdYpyOMU7HGGGEUZLllPfjPgbhYKk4JpCBESlSuLELd+5iZ76Dl4cvL33Get5Ceb8My7fghA7yUR6vvfsaarUa8vm8UGRrtZpIAeD7QWAthUFzfce2bYSLEFWzCtM1YcOGMlPQRBP/zbX/BgDwf732f+FTn/oULly4gK9/9+vYeGwDL73xEvJr+ayuRjLBNJ1imk7RU3q417mH4d7wVPcAUzFRNaooKkUYGwaSQoIH3gPMD+do2A3kklyWHpb4UGJlSWfh8prriLIsW6Vb0nnj8oPLFDoDiqIs1ZGQ60LRRWPiNSlkmcHPD9eNSCcFTjrk8G4AxE8oFLvf76PRaIgiyPfv38fly5eXCsjS2kwmk1MRszQGGqfMl7huw/VDeo3alpPxQfKbUgh5Xj+tEU+boXnyZxNgxh1GPM2YyzzSIXJxDnZiY91cR76cx2QwweTlibifoiioVCp4/MnH8ZE/85GM9xeAaTrF7aPb+PZ734ZW07AwFpjn5/B1H0E7wC0sA3/62azoqhmayCU5dPtdvHHnDSTzBGEcouAWUFAL0GIN9+/fx/b2tggzpzUsakWUlBK2sS1eT7TltVFVFQECTOIsQnCaZmlhBFJM4gnG4Rg73k72fjQ9FSX1ZPFJ/G9P/2/CgKa9yufzaDQawmkymUxQKBREKhAADAYDrK+vi9d0Xcfu7i7a7bYAxIn2qY5LsVhEFEV46qmnRP0sHsbPZT0/s1wH5eeB0xTXxYDlmgXc+CbAy3Vd0WUsCAKhUxJwXiqVYJomCoWCkAMARMtPDoRYliUiw8j2sW1bRHdWKpUlJ6uiKNjf3xf84vHHH1/So2jenF/I/Ec+m3zuq3gat0tID6XoDIo0os9we4tHjvPzR7YlRW6Sfc1bpiqKglqtJmiHopY4v+D8kM//w14/EdBAi0gTIybMc8o486OFp8nSInOggk92lUFBG05GDOUaycKJj4sb/qsUdBoXJwQOOtBziRGuSsUgg46HoNDh4Pn3HAQgRY/GxYmSj40LCSIkrvjTd+WxEHHyNBWaN/8sPYdQcvqbAwkkVAjl5geQg0ncuOMIHV8rbmRzAn7UvFd5E+gZwImCT2shAwic6RHt0iUzB3pNjlygvZcBAbrI+86ZJxfenN44A+Tz4TTF94XPg8bHlRw+bn4WyaDk9EMIKgdV+F5z5kbhU3xcxMA4aMaVBA4oaZomCoJSsR1FUZYUM5onrd2q3FUOqMjAj8yYPc/DcDgUAIbMg+T94POW94zGJ5/b/xjXYLBcrIrTPa2lrAzyCCxVVQU4lCSJUFZnsxn29vaQpik+9alPid7tfO3pnAInFZQ5oCgDxfL5Ih4nA31EIwS45pHHGtYEwk73brVa8DwPh4eHUFUVZs7ErYNb2LyyiT948Q/w0lsvQStp0CoaFuoCc2uOfr4Pv+wj1pc7DSipAjMyYYUWDM9AKSnhhdoLOFM/g9iNoasP23ylCf7kW3+C0AzhGz6SUoLD5BDvT9/HMBqe6mBgKiaqbhUVrSI6bDTshmgBytM6DMUQwpx7PGjN6WzSWnEwkIfN0uvkqczlcrh47iIMzUB9WEfhegFbky2Yponj42OYlonyVhlBIYCf9zFWx+infcxqMwTrAfb1h6Hy14Dr6XX8m+/8G1TiCsyZicuNyzivn0dkRTDMbPxPPvnkqegpmZfIoB1XPriHhPhDA1mUwkfVjy6dzSiOMIyH6EU97Pq72Av3cBgeoh/1MYkn2I/3se/vY2exg86ig2T/hGeWjBLUMyo8y0N/3ofhGljLrWURARMVWlfDWm4NRbcoepJPp1NomgZP8aDVNDz1Z56CvWbjpZsvYcffgW/7mMZThAgRKzGGGOLF4Ysru2aYMFHRKqgqVdT0GqpqFef0c3gu/xwqagVNq4kUKcYYZ+BD2Ec/yv4PogF6UQ+jeHTKoPALPrpXulkhyIei5g28gS/d+RKau01s5DZQQgkX44s4Uz6DptVEVc1y5lVVxebmpmhZRmlW1BGMAGH627ZtuK4L13XF+eYpaJVcBWcKZ3AldwXr+XWxn5QWR9FpmqYh1ENM4gluH93G7nAXC22R/X18G67iom/0sbAWuH58HanC1vLnAC3UYAQGFFeB4Wcgn5M4yCU5mIEJO7JhRRasyIKWakuGiMznORjGearsWQWyNItGowHDMER6FA+t5jxPBu/pfHCwlss5+k9nxvd9TCYTkV5QrVaXzkiaprh8+TL+6I/+CAcHB1AUBb/+679+ylgjvnx0dCRCwblRIuuSNDb6yXUlrnMTr6FCesTHPM8T9KFpGm7fvo319fVThibxLPIop+lJS9UgCASdKIoi2hPydFAaG496WSwWuHPnjmjfSCH6ZChZhoW6WUdTbSJn5VAsFnHZvYy3fvQWLMvC+vo6Go0G4jjGeDZG1+2islUB8oCv+xgnWVSeVbPgGz4+CD/AO/ffydKyAPyLN/4F/vEz/xitWQuf+9znUK1WT+mAdHH9ntMnB5/5d+Xf+R7GaYxxMMbAy4rGDoNhVnB2YwPD4VCkvFHECRXfp3TKM2fOIEkSFAoFLBYL5PN5tNtt0dKSeEOj0ViqiZWmKba3t4XtUqvVRMSHDCgR7XN5wGmBnxH6DO09d6JxvYbfl5+t6XQqaIk+R+tVKBRQqVREwXDqSOZ5HlqtFkajEXzfR71ex3A4FDyPUglo74g+6bnz+Vx05yGHwd7ennCKcRrgNpGcZsDXi89vlT0q/016Ftf3TNPEdDoVqRT0fOJ7dDYEwMUK3JMNzZ2NNGcCqSaTiSjeTtGynIdwXZzbMx/m+tBAA7CsXPD8HVI2ePgpGUbEPDjqLOem08RlIwKAUGrlEG4aAx1qWhRu1NCikDLHFW35oHADmZ5Lm8wRXHqP7su9VpzQ6Hs8/5AbT3SfVQePHzY+T25YcwORM2wSSpw5cGOQ3ieGTmOSjXf+fA7g0Pc5mCOUxxWeAW6o82fzfZCFs2xg8zXhxg6tqbxefF7y/nJAhtMcBya4scf3hK5VigU/uFwQ077wlCCiG66Y09i5MsAvzvA58+H3JoOO0zk/E7w1DtGmfI5lWufgFp8/pyd+0XsEIPDaFfSTn1UZROB7Qp+X/6bnUrG/6XR6ah6r1pKvjawYyAJCjjz6aV+kbHL6kwUTsFy5mT5DaTq7u7uigJNlWbh7964Yt+u6GA6HosglfZ9+0n9KZaFnUcQYj8KR6Z9HYHEAhCsNXCDx0EIy/Kj1VpIkyFt5bJe2caV0BS9PX0bhZgG5XA7tdhuz2QxnzpxBt9vNlItqAZWtClzFxY3dG4hzMfSyjjSfGXYza4aXhi9h2BnCT5brayiXFRiBATMwUVJLuFC4gKpeRVEtwlIs6KoOpFkI/DyeYxgOxf/b3m28Nn8Nk2hyykCkIpe6r2PwZwZwVRdu0UU4DKG4CvRUR2iHiNMYKpYBR84n0zQVdU7K5bIwCGlfgKxlp6IoIu0HHuCEDrSJhvwkj5qXtSgsloqYhlOgBozUEc5/9DzUnIqbnZs4CA/wjc43gA6AZ7I+8c7CQc/rYXtnGw2tgabWRFPLDFkq/kl8jkAubiBwUJeDD0Q3JJMEf9ANtI022mjjMeuxJX4ZRRFCNYRRN/Cxcx9Dt9/FXJujE3TQ8TvYm+7hT370J9jX9uFv+JjlZxgYJ8CdmqiwAxvWwoLlWcgFOdhFG6W0BMuzYM0sfDT/UTx17inUb9dx5/AOfvmXfxnNZhO/94e/h8q5CrphF77toxN0cBQc4Tg4Ri/qYZ7MESBAJ+6ggw70KONvcgSDCRM1o4a6WUfdqKNhNvB04Wk0zAYqagUNq4EkTTCMhziaH+HB8AG+9+734FkefMdH5EQIrABQsrade94eDrwDaIqGbw+/vRxF8DOAGqow5gbMuQk7sFFICrA8C9NwitRPYQUW8mZegMCmaQolnC5uIPK9pbPO0/046F10imgbbdgzG615SxSU3Av3sLOzgzfeeANxHOOX/+wvo3Gmgb7fx05/B9989ZtYaAuEVla/JXZiTMtTDO0hIvN0EV4jNGCFFqzQgh3ZJ/9jG07swIkcOLEDK7GgqcuOJJmnRVEkxsllM/Etnq4m87Mfd6mqivl8jtFoJAxA13Wxv7+Pg4MDaJomurbkcjlhfFFrW9u20el0hDHJn8f1RvKuczlNF4Ge9LsMfMhAcpIkArDnDhQqlEupVufPn0etVhN6J72nKIrwmJOht7u7m0VqtdtLEZLEG/iac8ccXSS7Pc/Dv/t3/w7vv/8+7t27J2j0ypUrKJfLQu7HcYx8Pksx2NraEqkOuq4j6AY4WzuLvJJHMssM1Fu3buGMcwatODNIP/7xj+Mzn/wM4jTGe/feg9NycCZ/Bvl6HsViUdTd4HrFowAd2YDme0e/c9m/pCtDEbUezuO84IvUsjFJEiETSA9I01REOLmui9lshlwuh/E4q/1DhrNhGBiPx+Ls9/t9sYaO44gIU03TRBoBGag8ik+OpuG8XnZI8fny9eM6DbdTyD70PA9f+cpX8NZbb6FWq+HatWt4/PHHBUBC8rJYLIp6JkTHqpp1wOv1elAUBePxeAkMNwwD/X5/ySi/efMm3nrrLWFgU3qKqmaphjdv3sQ//+f/HM899xyuXLki5kROPl4zME1Paj+R/coBAb7/ZB/zM0r0nCQJ2u2TlFbaZ9oXeh7JzMViseTUpL3hqQ80Llpveqbrujg+PsZwOESlUkG5XF7iKZzuaT4yXf+460MDDaRUcsZHh4mHtvP6BkSY8iGj+3DDnB9Qeo8IgybJERYysGjBianwv4EToIIWliPEfEwkSOXxyEYvAQw8zYEjsXxDZEMSgJgb/5xsCNAYSJDwvEEexswBBDnigAsVPlc+btnI50CDDBQQ0ZIA4wYkZxb8sPDnyAJbZtJ87iSIOMPm4ADRFV10Pxk44Psrry/tOac7epacfsJ/p+fJOfR8DvJnuYLC9yZJTqINyOPEW87QfsnrIwMy1LWBg3B87vR9HqHBL9nApXvwtAyKaJIFC/9uv9+H67qo1+ui/Z2sPNDzCfzgxj8P/5INfg6OUH4ZobC8BaDMADktyvxrMpkIxrqqDsIqwOendRE4IgtgGiOtFeeftIeapqHRaGA+n6NUKglPR7/fR5qmIvw2TU8iwPi55PTKQQK6NzcoVgFtnPYILKL/HOjk54H2kgODpACQJ4YEPMkUKgA1Go0wm82Qppmn5Vz5HO7du4fKUQVBEODixYtw3EwxsiwL/+Wf+y9hWRZee+c1uIqLntfDQlvgq9/9KmInhq/7KG4U8WDxAG8Eb2AUjk6FfxuKcVLESy3hvHle5NTamr3ceSPKOm/cm91DbMUY5AbwGz4SO1vvGWb4LfwWfuvl30JRy/Lc6X9JLaFm1lC36tiqbOFM7QzSXIp8IS8KpHJwyfd9mKaJWq0mlMH5fC72mcI/FShI3CTLAUYJf7705/HRax/Fj370I3znte/gV//yryItp/hn//afYfOpTfzwgx8iKkV4ffE6elFvqf5A02iiZbTQNtpo6k2smWvYNDfRNtvQNX2JP3P5z+U2B12JdkhhIWOFcoipzVkjlxX3isMYNbOGVrEFvarDr/o4/t1jRK9FqNfrUDUViZkgLsZQKgp824dneZiqU0ydKQb1AQJzOU/6Rwc/wvZ0GwiBMBci7sa4mF7EzJzhgnEBa+oaatWa4IMkWw77hzicH2KUjjC35hgmWVRGL+qhG3VFlX2qzD+IBrir3EWSJgjSYCk6QoOGupEBEbk4hzRJkevnUAtqcEIHZbWMP//n/zz0qo6pOsUgHuBgdoBhktWNOHQPMQgygCUxEvhlH37Rxyydoaf0RItMQdORAcu3YLgGqloVel3HLJ3hR8c/QtNsIkiy0FwK7Seew/k0L0hM4cvASZ92nvvbbDbxxhtvIAxDrK+v48z2mSzFRzVhaRbePXhX6GfdbhealnW5cBwHmqkhtmIEZoDQChFaWSRSaIUIzAALY4FRbgTf9BFpy6CEkiqwogyMcGIHdmTDDE0UHv6zYxs1tYZ3b78rojNUNWslK3sLuYFCRgqtC9cVid4vXryI+XyOarWKXC6Hg4MDhGGITqcj8ufJaCX+TXyY5D95XeWoNl3XBTDMU5ro4rJElnU0TtkzTfeI4xi7u7sIwxDNZhOqquLOnTvI5/O4cuUKHjx4IIot0lkl44la0JNuoGkafv/3fx/z+Ry/8Au/gM9//vNLhjcH9vnrXIeksReLRfz1v/7XMRgM8Pf+3t/DYrFAuVxGvV6H4ziilo4obqtpok4NFb2dz+eo1+ui8CG3BShig8afs3PYLG9iq7kl5OdkMhE0wWWf/JMbeT/Omcl1O0q15tG+xDPTNBW0OZlMstSSXG5J9yZ5UK1WYRiG8OaTbK3VMh5GdT3SNBUpcu12W9wrCAIBNJCNMxwOUSwWxbmUdViZP3AdgWxBfk64A4zrlvQeP1tBEKDZbGIymWA8HuPevXvwfR9/4S/8hVOt6Gn/qcYKOTIcx8FsNkMURbBtW9AmRdrQc0ulEh48eIDXXntNRE5QS2u653A4xIsvvghd1/HJT35ypROGz4/0KSqaS6AFdzDSd9M0K7KYz+fF92TgimiIHDXEa7kut1gsUK1WlyIkOS1xe5FsSrJ7K5UKzpw5AwCisCjnazQW+pt0TDoXf9r1oYEGGeElpVg21ugznHlQ+C5HUGQjgoiPDhwZmpw50UUoDR1cOTVjlXeTI20cveaMjRaUG670N58j5fXJDJszEPlZJKR5NAcPhQawxHAACKbOx0hjod+JufGxcEKle/IIB2688HtzQURG3ypGIQsD+h4VWuE0Ia8nIZb8dTooMpjA78t7+q4Cux4F2sgXGfW8lSAPF15FH7JXgQQtHXYu1MlI4kKTG6/8Po86+Nxw43THaYwLMFo7oh3OEFaBChyIkPdJ9nbwe5Fw4DRH/13XRb/fx3w+RxRFotsB3YN4BT1HBr84T5AFOP0ehqFoS0joPBWzo7lyBVn2ssrr6vs+RqORyNGT5y2DNj/Ni6o6rzLSuRAko52vA/Egx3HgOA6m06nYf84LSehSKKwcNseBUNp7AgPk59KacF4hn1UZvJB5KnACdJKRXKlURApIEASiz7OmadjZ2VnyuGiahjNnzuCJJ57Aa6+9JkJwKdqnWCyKwndJkuDBrQdYW1vDM+1nUCgUcKN7A5cvX8bOzg4+/+TnRWXnOI4xi2ZZEctkilE8yiqi09/RCHfju1mdifR0Dq2pmCirZaipCi3UYA9sWAML/tAHYsAyLDz7kWdx7uK5k3agyRj9qI+70V2MZ2MskgXAmgKoUFGzaqL15/0L9+EWXDiJAzVUMclPsPAXsDUbtmNDU0/yNClEkrpDcSWH2hGnfopzuXM4uziLL25/Ea3bLfzMpZ/J+Ddi9MIeOlEHx8Exjvwj9JIebng38O3g26K4mQIFVb2aRSY8BCEaagNNPYuGcBRn6UwRLyfa4LyM0x3xARk05/neJE+pSFaxWETZL8MYZKHw1PbMsixYloXpYgq1omKmz+CZHh574TEk+QTvDt9Fr9DDb3V+C9FxBBgADgBLsVAf1FHX6qiqVdS1LCqhnJZRNItYxzo0RYOdt5foP1GzTh2DZIBu2M1SJuJ+9nvcxzAaCrCBikcu4kxpC9czg1rU3QDwg84PUB1U0bJbaDttlJUyruSv4Je3fxnr+XUUlAL+4T/6h7jbuYvSVgme5SHOx0gKCabaFAtzgcTK6DXUQ4R6CNjAGGOk1RSpmuLVV17NHnYJ+PfTf4/1m+uwFha2726jP+zjqnMVa84aSighF+WgKdlera2tCT5DMpUXQkzTrEBqt9sVcpJC82XdRm7lZqQGVE+F4RlLMp0D9/R3rMQCgPANH77hIzACeLqH0AoxsSaiYGisnsjcL+1/CeahmdWSyBkohAW89cO3sF3fRjvXRjvfRivXQivfQjvfRk7PrTSs6CfVOKpUKtje3kYcxzg+Phb0z+UcyWy6DznDSC7waDH6rq7rIr2B0mDIOcaddrIOwPUsPl4uU/j+ABC5/EmS4OrVq6LDjTxuTdNEtwIg03/u3r2L4+NjRFGEr371q7h27Rqq1eqp88x1AG48yzp5kmSh67x+271791Aul8X7JDNs24bneajX60Im5vN5AFlBe/L8WpYlgAhqt0r3ojWm6I7xeCx0Ok57sn5GaYjUWUt2shJ/4/KTr4esx9N3OGBA5ydJElEsUVVV0WmBvNrHx8doNBpifxzHQafTwfr6upCzJHe5cU37pKoqzpw5I36ndMhVOhKNl9OrrDfxz3H7RNZRaS3y+Tx+8Rd/EQcHBwJ4azQaQt6Tzk6GdRzH2NzcxHQ6FcB7p9MRDjICw6hIM60hjbfb7Qoatm0bxWJRdOagMUdRhOvXr6Pf74sUIjpv8rrw/aQ5Hh8f4+tf/zpUVcUnPvEJUdCX6IyiPPklR3+rqipqOdF7PN0oiiKhz9JYuO7OQT5u19CZns/nYkyybcCdnAcHB+h2u7h27Ro+zPWhgQZ+wGhiMtjAlWU+SM5cV4W604Lwg8s929wTx40x/hotJo2VFFpOxHTvVe9x5JozCBqDzBg4s+AoP42Jb6CsvMtzJMWKC2luHNE4uICjOdC9+fe4ocuNeGIY5DWi7xFT5HtI45P3EThdz4EuUmp59WYZVePte+g7dMneb9qzOI6FQsmLFsqIKd9j+s9pk4+f5k4gGEUS8GevWnd+ycayoigC5JKNef6TfpdRYe5F5+DMKsBAXkP+DHnfZIOdxs5D2rnSz8+CvA4cdKI1JoWf5k5nlgN+fP5kDNP3CR0l41kG0+heR0dH6PV6qFQq2NzcxHw+X0oN4CADCU7bttHr9U6tP60FzYPzBFlQ/Me46PzxNaYxcoCGzg/nd3ReoyhCv9/HeDwWXQaIZijqgdOGvPernk/vcwFJY6F14jTw46LEOA3S+aDuI2QIVyoVERJKBdNUNStUO5lMoGkann32Wezv72M6naLb7S7l63qeh36/j+l0ilKphHq9LsY8nU6Ry+Vw5coVUQjqZ37mZ3Dnzh3Bo+g+VaOKil0RYyfPRxzHS/mKqqZiEmbVxKkV6DTJCuTdnN6Eq7uYl+cI10IERgCowAILfAvfAnqAozii5VlJK+GMeQZVq4qqWUUpX4JlWRiOh1ANFb7qo+f1cDw/huu4WFxYwM25SPUUu9g9oZlEgRVY0HwNhpeFlxdQAFzACi2ocxWdqIOJNxEF3qiiPClhnD40aGjpLbT0Fp7OPb2kkMVJjHE8xlF4hCM/Syk4Do9x37uPl8OXlwp75pU8mlpTpGK0zTY2c5to621U9IqgK6IPWU5wfhPHsRgncALMlkolAVJVq1W4riu8mTS/fD6P2WwGy7VQMApY9Bf4ReMXsVnfxDfe+gb29vbwn/3V/wyhGeJrL38NVsvC/dF9zJUsfeZWcguvpK9ggcXJmkNBKS2hjDIqSgUVVFBWyqhrddT1OmpaDc20mfFCJQHMjC96oYeu38VEnWRFJDHCMB1ib7qH+g/rCLshYjNGnI9h1A20L7eRW8shsiP0/B5uB7fR6/Xg3WOFTp8D1IWKgTtAPs6jrJRhTSwUR0XMj+YoakU0m00gDxzODzFVpojyEdSKijAfInACzONs39zUxW33NjRouDm4iaSc4JX5K8D8ZN4Vo4K23Ub7eht1o4713DrqRj2Llkge0p/xMH1ibw+z2Ux09OFgNpd1lmUtRfLR61wvlEFZel9TNFiRBSVWoHgntaJkHc12bMAE5uociZNg48oGdvo7mKZZtMhCW+Ddwbt4qfMS+l4fUbocKZE38mjn2mjmmmjn22g6TTRzTdiRjbXCGlpOC/k0j4pRgaVbwqlC3nbiNRSmzvU2Kk5JgA15ljk/JUCA9BSKBOI8+lF6AOfHMggBZF5MkqW8/lqxWMRisRBGG9WfofM6Ho8FvySv8d27d0U493g8xvvvv4+PfexjYl9ofLL+yQESMnaAzKu8tbWFv/E3/gb+2T/7Z3BdF6+88gqiKMLFixdF20HSbZMkEQWRKfRc13WRs08AAwAcHR1B13XRsQkAWq3WUnj49va2iBjjejy3XWg9kyQRKYFcf+B7IevUsm4q31Pmkfxc0BrlchkINhgMRAcB8vJTqhSdKVrTQqEgOlKRHsxlK8lqmV64PUf3XGUbyXoHp2OZB8j6sqIoeO+99wAAly5dgqqqeOyxxwBAnB3SPRqNBjY3NwVN9ft9UWuMbEEC7+g13/eXahbwcxXHMWzbXuq+QKkRZGBvbm4u6dd8vkmSiHaZJGd1Xccf//Ef40c/+hGiKMLrr7+OL37xizhz5gzK5TKOjo5QKBSQz+fx3nvvidQjKsjpOI4AF5MkESkyXH5HUYRerydqVfAIZOpUQfYh8RtuF9Ln0jQV9ESXrP8rioLpdCrq2/xp109co4ETCzF8mdHJBCgfMuBEYaaJkqFBCjRdtFCEZlKIk2ykc+WbvMq8UCEpLnLeGyd4Ph4yGkn54X1YaR240cyL2/H78cNJr61C9OizZDzJRjcHXGj9OHOmcckh4Hwv6Hl06Kj/KzElGQSgZxNj4fOg+XJipHnxXru0Z/J4OD3R9Sjgib/HvSEcPZUPOwd++CWjjrR3tEacplcZ6PL1KGa6yqhdtbZ8nPRs/nn+U2bysiEoP1sGXWjO3BiV0Vj+LB4pQhf3OtL9SXmiFkqj0UgYAbzIJvd+c2Cm0+kINLdcLotK26RIcQOEPBIAhLeb/pZRX/JYDwYDIWT4uGVARRYcq4zwn9bFeRBdq/acPivzUAJSjo+PoSiKECyUO91oNHDhwgV4nncqf1BRlCVBzAEj4KTzDxcqwHK/dq5wES/noAOnQa5w8ddLpZKoGs1bvhF9aJomwgOpvRXNh8uCNE2XqvLT90jIE0+u1WooFou4dOkSLly4IPgSN35IRnBPiKZpJ50/oCCv5JEzctgwNoRCHkURintFXL9+Ha1WC6qq4pXXXkFkRHCaDj71hU+hfqaOSTIRAMU4GuMwPMTEnWAaT08VHSzqRdSsGspaGbqrw+ybsBMbmq+hmC9mymEUwos86GU9Cy03Q7glF0NziHAjFOHz7/bfBb6b1UbQPqbhfvc+1v11DOoDRHsR9o19qCNVFL4sKkXYui3OKZdZTuTgvHIe2/o2YiUGrBM5MY2m6MVZNMQQQ/SSLK3gg+ADTOYTYJTNzYQpIiAaWgN1pY5KWkE5zgz3vJNHvV4XYAH3OnIvLxWgo37qpABRZfJisYjxeIwwDNHv90XtD/o+eT81VYMe62gGTTxefBy5+zlsb28LnSRJEhgFAxNMsDvZxeEiS5+YKBMMkyH24r2swGOUgMo1FLUiWmihaTYzg9xZR8vMWnrW9BqKShE5Jyfo//Xi6/jqV7+K8XiMsl7Gs+efxXPPPCdy+dM0FYBZpEcYJSPsTfbwT/7VP8H+bB9200acj9G3+vDrPsIzIfB0NpYbuAEzMmEuTKgzFTWjBntho+gW8euf/3UUtAJee/01bF/ZxkydYW+yh93JLub6PEsL8bvwUx8pUlG35M7sTqYLJNFSlI8CBRW9grpWh7fmIXVS5KM8Xpm9gopaQTEtwrIzbyzpOnSeueOI82jiVbK84xfXGaIowuXLl7G7u3uiQ2o6NGhZCklgobhTxCX/kviuaZpopS3kijm88LkX4NQcdBdddOfd7Oeii57XQ2feQXfexZ3hHRzPj9Ff9FefXbMGJ3ZQMSpInARu24UTZ60/17AGLdZQSkpQlZM2s8R7iN7ludLa0JrRa+Td5ul4MsjAZZos8yiVkQwMy7Lwwgsv4NKlS4iiCOvr68KAJgOUogfIyUAGP+nrFEl3584dfOELXxA6IenRnKdwfZ6DEEAWKVIsFkXBQyr898orr+DmzZv42Mc+ho997GOiUOnu7q44M5ZliXz8IAhQKBSEfKDokUqlImogcPCHX/KartJd6b+s/8mgPckY/hlZJ6HP8X2j51qWJaIX6UxQuiPp9GQcz2Yz0TkCyCKcp9OpqAFCMpVqRsn6AAeHaAycFuUaXNyY56/Lzi8ZVKS50XPTNMX169dx+/Zt7O3tIZfLodlsYnNzU5yTJMkiH6hwJTk5CVwplUrwPA+DwUA4jGl+VOiUAKRyuXwqZZT0ArKLKOWCwDXSUWQ9Sdd1VCoVKErWycK2bYzHY0ynU1QqFRSLRcxmMxFZUywW0W63Ua/XcefOHfze7/2ecKiMRiP8xb/4F5eiDOjs0++037addeOiIqhkJxF4ROMlPWk+n8PzPMznc9RqNaiqKqKSt7e3hd5Jth6loNBeua6LO3fu4MNcHxpo4EyKmDIpabQRFFoje/yJ8Gii8kHmn6UD9CiDj4iRQl9WhZrTWGmM/ADT82WDlhsgnPlyI4lHAsjKKX/Gqt85csmfJzOsQqGAOM56pRLoIEcOyPtBayh/RjacZGOShBtd3LPNPycDSauUAHmu8vdlQIKEJY/wkJ/3KCbF6YDT5KpxyhffL75ufE9WCQBOV7LQlt9bNW+ZvvgacmH7qHFzYfOo+a1aR1nZoPnKey2DDVwRWWVw83OlKIroRzybzYS32PM8obisAjv4PtKZHg6HME0TxWJxKXyLmPZ0OhUh0aT83L17d+nMVioVNBoNOI4j2mvKc6XXVkXF/Gl78dO4CBzheZ3y2nChzfeP1r3RaAhvIeWsUn2M2WyG+Xy+lDMthzXK0UwEGPDn8IgK+WzT90kIyeAC5/M0Zv4aCXnOSwlooHaQlDdN/LxQKAivGs2jXC6L8E9SqshrnySJ8NSRkKSIKJI3/CftPd9/+TOkDMlyg4wm8qopqQJlrsAaWTifnMfjzuPi/rQmBMqmSgqzasJVXNw6uAVXceHrPvpBH8fuMe6YdxBsBwgKAWIzRg+9JXoyQgNGYMDwDBT8AtJuCtVXYcOGqZn4+Mc/jnK1jFfffhU7/R3Uy3W4gYuO3cFXOl/BuDDGt46/tXRPR3FQUAooKRnwUFSLyMU5FFBAxaigiCJyaQ555KHiIeivalhT1rBpbIozRhGBsRpjpIzQCTvoRB300z66URfvRO+gH/YR42F7VWj4+5W/L+QTdbAJggD1el145+g9AKIQ3Hw+R5qmSz3BSU/I5/MolUpCoaRw3Ha7jXK5LGpfLBYLPP3004JOSqUSyuWyCNWez+cYDAYCDBVFr9MYXa+LbpD9P/aPcbg4xIF7gPfm7+Ebg29gEZ9ERZiqiZbdwpq9hvXcOpqbTVz+Ty/jB3/0A3ixBzNnCmPNNE20Wi0kSSLOwIaygUulS/jy8Zfh3fGwubkplO3NzU3sdfYQ2AEKGwV0vA6MmoHADjBKRug7fXglD6EZ4pXrrzwkIiB/P4+W00LbbsPUTVy0LuKTpU+iolZgKxnwpBd09KM+ukEXvaCHbtDF0eIIXb+LCFEGRkRDjKIRlKICFIBETfDBwQdi7ipUGB8zYMwN5KIc0lEKZaJAn+soIQsNtmMbSnJaD1xlzNF54p+TwWXOR5MkWUpDIF5H9WD6vT6e2XwGdaeOJxpPCD7MeR/xq+F4iEk0wdHsCMfzY3TcDnpeD32/jw8OPsA4GqOn9TBqjRDqIV7Fq4APoAMoHSUD97QSwkYINa8iLIb4971/jzO1M3ASB1N7CsQQcpE74uh80e8kTwhwkGXYKmOYjFI6R+R8unjxomifSAas7EwiPki1DF588UW8++67KBQKUNWsBsKrr76K69ev49lnn116pnzJegIfJ42Bp06laSrSNuh1DrYrShYKTjyCwJjJZCIKKBPwQLUOeBcsop1HyQI+Rk4b9BkZYJcdS6tsDvosf5/+8zXgEaFpmgo+tLa2hsViAdd10Wg0snP0sIAg8UZFyYoJU6Qw7wwCnDiB5fHQmLgTdBUwIs/xUfOT9572y/d97O/vi5oh3W4XURTh2rVrSykscRyLNp0EGlHrV9I1ZrMZbNvGF77wBRSLRezs7OAHP/gBFEUR9T0UJWvvSCmruVxO0AJ15qMzFwQBjo6OxJy4IU6gdJqmAkSl77///vuYzWaiC0ij0cCVK1dEmk2hUBAFPclBUiqVcHR0hMPDQzz77LNCl6PoVVoL0n1I/vHWvdTmk+pVhGGIfD6PXC4n9Ks7d+6g1WoJ3Wx3d1d0WUnTzFFnmqaon0U6/fr6+qnuaY+6PjTQQGgkERZn2pQr/CgkkBMmfZc+xw8sJ7pVhhJ9ngxxCn/hm74KROBpAZwBrPovRwjQfIkAyPNK8wVOF4iRGQpX0LmxKEcLyEKBlGG+3jID4+vJP6soyinFmBv2slHD14vPhf+UU054qgE3lmQjlA4j0YLs9ZS/J8+R04+MmHKmzP+mtZXnwQ0eHp7LQQJZwHFD9FGgxCpjlv/ODfZVQBzNUwYj+JzkveFnUB7PqnkAWDIm+WdkOpP3hf8tG2I0HypKRPS2ak1X7QcpSpQOQwo+8QYuyEnAdLvdrOo+G1c+n0ez2US5XD51Xvjc+Px4Nw8ZuFulzP60Lg6o8nNFYyNvO48S4NEkpFyQ0CFF49lnn8WDBw8E8LAq6knmQbR+nDcAJ6kRFFbI66/QGnFDkgx5OWoMOKFtDv6SsCfDcbFYiDxDKtJVKpWQJAnOnDmDer2OZrMJ0zTx+OOPo9froVqt4vLly7hx4wYcx8EnPvEJABDyIU1POiTxqtoEaHA+yGu1BEEgiisBmYJBCi3RCs2V9tPzPKRpKvJhaY7kQRuPx1AUZSnXOUkSkS70ROsJtI02ypUMLCHwrtfr4e/9f/6eSBsK4gCbj21CKSpwFRf7432oRRVmzUSSSzDX5/BbPgIrQGpm9HRzdBMYAUpBgeVYQApU4gpqUQ0X7YsIFgFa1RaK+YyeFF1BkAYYx2OMohFG4QgPwgcYxkNM48zwoUuBgpJeQsWooGZkLT8bVgNVsypqTFTNKupmHc/knoGundTooctduNib7qETdrC32MPF3MUlnslpiM4IKdamaWIymaBUKgnFcmtrC/P5XBRyow4Ltm2LCKcoinDmzBlRYKtQKODpp59Gr9cTspJSLygvlmiY/qbXVFWFpmhYc9aw5qwtyWCag67rGAdjdPwODtwDHC2OsDvdRcfv4IPhB3gpeAnDcAh8LLvvy3gZzo6DulZH227joncRTaOJc+E5bOY3sVXcghEZwrM4mUzEWMMwhK3acEIHhX4BZZSh+7qIZCKPtGZp+Mv/1V/GIBzgjTtvwDM9uJqLQTTAA/8B3py/iWmyHG1jqzYaZgMtu4WG2cCTpSfxs42fRS7OYdwZZ0VMbQUdr4Mfvv9DDKIBFtYCVtPCMB4iefjPt334po95OkdaS7PaFApwgINsXRMFlm/BDmzYvg3Lt+AEDuzAhhM4yEU5GJEBTV1OkyC6un///hKNcRkOAE888QT29vbw3nvvCVllGAbq9To8zxOh28Sr6czncjlcu3ZNyK00TtF0mqiZNVwqXBJ8LU1TfLv7bdRqNXS7Xdy4cQMxYqxdWsMknmCuzhFYASbxBDPMcDO8Cdd0sdhc4Gvh1xB0Hha9ewpiPXRfh+EZ2U/fgO7pWWtf34DhG8AMiHsxUn+14Up/y/KGPlMulxHHMWq1GtbW1oScIR2MA8P0N3m1X3zxRXz3u98VZ5WckPP5HL/1W78F27Zx9erVU/oh3zNZ1+Nj5zUpSH+jQqLXr18X4GCtVhPRfqqqCmPOtm10u10AEDw3n8+j0+mI88wdMDQmrkfy/2RrrNKPyKnK5alsl3BZLn+GLllnIp7EnboEmlEb236/D8dxlvL/iQ8SP1PVrCUpRcEQOMPXgOt3MujBxyfTEV83/n1um8m6Gf+u4zi4desWXNdFmqbCgSXrpTzicTgcCh2l0WhgOByK6MdisYjRaIRqtSrA4iAI8L3vfU+AzI1GA4pyUpCYxkp0QmtvGAaGw+GSbKI1kEsIjMdjoYNYloWPfOQjmM1m6Ha7ePzxx9FsNsXa0X62Wi2xD81mE7VaDb1ebwnkU5TM6QZApMVQK1OKhCIbdTabLc2hWq0ijuMl/ZpHS9RqNXieJ3Q6y7Kws7ODS5cuCX2LCrKura3h8PAQH+b60EADN5K4AkwhFESgsjLAjSr50MmK/apn8sgAeo0Ilw4RZ1r8fqsMR35gZSbLCZ9/V0Zfeb/pR81HNkxXMSv+3p9mLMr35gxqlWddZog8VYUbn/y7f5qBuYoJckNVNu55sUUegkpz4EJlFbNeZUTLCCp9fhWyKjNKniogK4Gr1n+Vsb/q91VGurxvsnEmX/KZkZn5j7v4XDkAIUeOyFEv/Hl0cfBplUBZNX85b5HQXU6jMt3TMzVNE8xPXpdVESI0NwqTvn//PpIkCxtbX18X6RqyYP5x54uvIR/rKpr/aV3yOhOvo3NDhYloDQilBiByB4kHkkdKURRsb2/j3r17uHTp0tJ35Llw0IWHonNAg4wxyhmmSBUeGUEIN3nBuHIh172gz5NAp0gDKlhJ0TCKoohCVoZh4OzZsyJK5c6dO7h9+zY+9rGP4Y033sDVq1cxn89FuF+z2RRG/3w+x2w2E0JfUbKCSaPRCHt7ewIYAU5S32juhN6TQA7DUAANHGCWwVQqzjkYDJZ4VqVSEYW2hGGqLfdYV1VVROwYhiHalBHQkySJ8F7k4zwM14Du6pjtz1AoFLC2tgbLsuC6rvDaOUUH+XYen/6lT2OuzfHDd34Ip+kgcRJM0ylm+RleX7yOmTXDYrYAZic0qikaqmYVNbOGmlnDk7knUTWqKGpF6IoOFSriJEaCBPN4jlE0Qt/voxN08IH7AYbhUOT+C7qDiqpZRdXIQIiqkdWnyCU5lNUy1ovruGpdRW/cQ0EviFQqollOq8AJOGVZFkajEdL0JCyWFGiKCHBd91RLXVVVBYiUy+Xg+z4Gg4Go7B2GIVzXFZ4ioiFSIPl9ZJnP5THxySiKsGZm+fyxGcOzPEFnYRhi/3gfX/nuV/DB8QcI8yGSYoLSdgme4+F7ve+h43cQ3jupfZPTcsDzgHJZQT7KIxfmoM00FOICjNRAzawhDmMRUkzV1HO5HObzOczExKaziccaj+Gqc1V4Nrl3M0aMftBHL+jhYHqAA/cAx4tj7E/3sTvfxZvjN9EP+ojTh+iTm3VtqepVJE4CZaqgMCjg5y/8PMpqGaaSRYp+86VvZvUichFm2gyBEyDMhYjsCFCB9lEbRmjAt30srAVGxRF8y1/qpqHGWStTO8hAFSd4+D90YHomGklDREXIPPf27du4d+8ebt++LcKLqbbHN7/5zVOGIhnVtVoNZ86cEeHRJGtlp1GSJKJOTLFYxPr6OtI0xVphDeVZ1j7OTEzM3Cy8/WsffA2LxQL9fh+/+Iu/iI1zG7ixdwNv3X4LM8wQWiFcxUVohYisKItIaZ4uIAoACAFlrkB1VSiuAsVVoM5V8RrmgLbQoM01qMlJ4ULi4QREkfEmy0POx9I0xauvvorXX399yftLzrkoirCzs4Pf+I3fwN/6W38Ljz322COBDs6P+Rmis09GE+2Fqqo4OjrCeDzG888/L/LdXddFkpyklHA5VqvVxDxJztJ4ZcOf60ayLrBKR+N6H9f1ZH2WviMb8Y+6twxScL2F1iKXy2E0GokoLjJAyZlCThzqTmVZFnzfh+u6S7qhfH+uE/ExkQ7N/5YNcD5X+iytNdd76KJ6SL1eT9Ad6XMvvvgifuEXfgGFQgH9fh+vvPIKBoMBHjx4AAAidYZAJqpRoes63nnnHYzHY9y+fVvIhzfffBPPP/+8oIfNzU1EUSSi3izLEmmQvBaYrus4OjoSoAh3npAOoSiK0GlM08SZM2dw7tw5BEGA3d1dIXMKhQIURVnq6lGpVAQgNBgMBI3wtAVux1D7VdM0RScRognSo4CTSA3Sw2isFCFKa0gpUCQHisWiiGiidKl8Pi8KLX+Y60MDDXIYrGygEbHKzGOVsc+JkCb844yvVQf6UcYdZ0z8+dyw4X/Lhro8L65IyPOWvbo0HnoOZyAyE5EPNQlCfrD5PFcZYfx9YDniYJUXU15nMiweZUzLBiJ/NmcONHbZ20eCi9q08TFzwIg/m49ZBhhkYEqmL7o3H6/M8Oni9MNfkw0jvl+rhKJM0zKtxnF8KopAHgN/nry2q+ic5sUvvrfcM0vgnzwmeZyPOmP8ubKhysfwqPXn95Vpht+Xh+txgSSvGVVWphwzWlfySMoIOffo84sbw/Ja0rlYBX78tC56PudP9J+UAjIuaSzkJaG1J+HH85sLhcJSD2du3MvPjeNYCCjaM1p7onkSMJPJZKmdMN9nqpNBocj0ff6TK+IABHBBLcoo75BA3Ol0ijRNUa/Xsb+/LwqMTSYTPPvss7h//z6Gw6Eo5vXuu+9iZ2cHpVIJH//4x0X16Y2NDSTJSds9AMIzVigUlsBxCj9UFGXp3HAZRQqvopzkxRLN/ehHPxKGAo0fyJSnS5cu4cqVK6dAZZrzYrHAcDgU9EHrRv28ae/b7TZ838d4PEa73RaFQJvNJmazmYjyc103yztVDQSdAFeLV2HbNgrVAq5cvCJojIqCqaqKAAFcuBlgEPQx9IcYBAP0gz4G/gB3vbsYBkMMo+GpdqC6oqOslZFHHlW9ikv6JRTNInJKDrZhQ0kVzL05oAHzeI5JMsHEneCD5AOMk6xmRYTl4ns6dAFKVPQsWqLZbaLpNHFQPkC6lcKDh3yah2M5InJlfX0dQOadXSwWGI/HqNVqYv+IRqm2jKJkFcHJA7q1tSUUTUVRhAeQwqzpLBJ/4CGzfE9J2SPap3NG51BVT9q8EV1cunQJw+4Q87051ImKlt3CFy5/ARcuXMi8j1FWZPTYO0Y37OJu/y7+3df/XVZWMj9Cv9BHbDH+mSowPROmZ8IJHOTDPMyFiWJchBqr2CxsimfzsOQlA0/RsF3cxnn9POLGSW54r9fDeDzO2rJpCnb6O3h35124mouFscChe4i3O29j6kyxKC3w/47+X5EeAwDK4woMz4DpmdBcDU7PQcWvwPSz9pfW1II1t4CEFUfWVERWBN/2EdgBAjvIfncCTOwJuvkuAisrwgoAv/DuL6CQFJaMgDTNgGpqx+z7voisItCUImVXRenO53O89NJL+PSnP70EVnLAl67JZIJ6vS6AUkrhobQgMlQojYzScz3Pw7g7xvTBFFbHghqe1CDjkX4AstapaoDQzDpvDMMhPN0D8kCSS5DkEyQbCaJchDSXirUR++ArGC/GMHwD38l9B6Zv4kA7QHGniLpVR1kro2E3ULNr0JWTGmBEK6PRCC+99JKYN6+5Q6HpQFbZ/0tf+hL+7t/9uwLwkvUtmhPpTRy44Ua1opzUUKMzR0XwaA9I9xwMBgiCQEQ2UKcF13Wxvr6OOI5Fm2wCMmSdQNZruO636j2uz3Hdl9buUca4rHvS6/LvvOsdfWdtbU3IJJLHtVpNePFd10WxWESr1RIGObUh5/JtFSDCx8DHIY+fxkO6xKP0aTm6gT5DY+FdVOg+u7u7ePXVV/GZz3wG3/jGN/CDH/wAlUoFk8kEa2trgp+rqoq1tTXEcYybN29iOp3CNE3s7OyIVLQoylrJ/v7v/z5eeOEFtFot/Pqv//rS/Oms0UXvGYaB/f19wR/iOMaDBw/QbrfxqU99SqwJ8ROiUwK7KD2P6kgAmY4xGo3wjW98A6qqisgqwzAwmUyWCqYTD+FF/ynqiNfLIscQ3xuu29KciIfRHvD0wiiK0G63oaqqAGQIYOWtVv+06ycCGriRzb3DnLjkyIdHGRf8O1wJ5hcXDqSE0/3oJ1fWZYOe30c2ovlhkn/ysdB85Lw42VCQ5yB/f9XY5PnK0RurjEx+EOiicZDCIz+DmPWq8fLn0GuyEciJcNUYOGhAOYJcIaf7cHBllfEqMxxOT/Q9OijyftA45Hvwi+/zKmYu0zBnvPIacYNBXm/y+iqKsqSI8O8/iv5khszXna+5DB7IwpozIdn4ozXktMDvwefG902mhVXrxb3d9JOfW3m9+LrQeHhBME6npOxTb2ReEZkYML8/V+RXPVdRssJxtVpN9CentaV5P2q8/79eq4A2vtZEP7QWtCdU3JAbz3Eci24NhmEIzzvth5xyQd8hzzkZpyRYOfhCz10sFsKzzgHKJEnQ7/cBLEcF8Krh/DtJkohWqJQ3yvN/yYPW6XSgKIpIATk+PsZwOEQul8NgMECv1xP8YDQaiZxIDnQ2m01cvnwZqpoVByUPxK/92q+Jyu/cS07rz88L/ac14eePK4yk6NJekdFL+zmbzU6lytAe8oKmZHRQMbU0PWl5pyjKkqd+MpnAtm3RY51qCnEPOVX7VxRFeCnJC0EK9s2bN2HbNmq1WpYHrhRRVjPlFDqQaikiY1lh8VIPo2iESTzBOBlnHRqiLFd9kkywE+5gEk8wikZLxiWQtQOtGBVU9SqadhOX9EtwYgdFvYg0TmEaJlKk0EwNbpIBH4NggDuLO3h18iqG4RDx4zHw+Mk91VCFERiwIxtWZMEMTGgLDYqrwIkdqKYKda7CCi1R1KparS7lmNP+8FRQilIhQ1TXdRGeSvKB80w6s3QWFovFEpjFeS7nT2QYpGkqzriqZuHN3/rWtzCdTvHJT34SaZoib+TRymWV8aelKd65/Q4ODw9FSOvGuQ0szAXGGCPIBZgoE0T5CGEuxGH5EJ7pLRmbL37vRaw7WeeI7eI2NvIbWM+tYyO/ga3CFpq5kxBf4imcXyVJAkMzUFJK2Fa3USxkHrapMkXxqIjBYIDj42P8rb/9t+CpHvphH4fuIb7z5ncwVaYInRC+5WNRWWDoDJHqjOcmgOZpMBYG9LkOY2Gc/D4woM01lLwStJQBAioQ2VHW5lNLkORO5BkvJkytdGkelmWh3W4LWuD6CtE+AUj3799HGIZYX1/H9vY2NE0TfJTzXCpMura2tiTfqJYM5/fb29uoVCq4e/cuPM/D3t4eDg8Pl7zuq2SGAgVaoEELNFipBb/nA/NlWStkoArAAdJ8ijSfIsklSPMptLKWFZQt+BgVRtgz9/C9174H+aqYFTTsBppOE61cCzWzhsHOAHfNuzCrJhbRAlZoQQmXjWlVzepGvPfee7h79y6uXbu2Uoelz8q6D/FV3gGOzh0VvXNdVwAbBLgTL6SCsYqiiHNfr9dRKpWQz+eXujZxni7rY9xg42PlhtujHDD0U9a7uF4l6zzyd0i2yHZFmqb4K3/lryy1MeTjkj9LfAo4AYbkdIxH6elc9+ZjpJ/890fpmkTHsnOaXqOWpNwZpKoqfvjDH+K5557Dm2++ieFwiHq9jmq1KmpV5fN5EblCtX3y+Tzy+Tx+7ud+Dt/4xjcwHo8BZM6Ow8NDXLp0SdAEReJqmibqXMn6Ndk3nK6vX7+Omzdv4oUXXoCiKIIXkF4yGo1ElFwul0MURZhOp2i32+L+L730Eq5fv44wDGGaJvr9Pq5cuYJyuYxer4e1tbUl+3eVjko8hWwQSo/h9rPneSiXy2IPaK1pj6h7DO1fHMdwXVecM04rMq0+6vqJUyceRXw0CXkh5M9z5Yw+B6zudc+fJxOifKBlopW/xxdy1XuPev9Rhh8HUFZ5qVYxJ+DEiOLrsMqg5vPi95JD9AjhlT25fPx87PLv8mfoJ3/GSoGFEybAQ/R5ZVd+cQST75dszNJ79CxOU7R+fKwyeMK9SNy4kecpr/ej5riK1lcxSEIsaQ1kWllltPOf/HxxQ0f+3CqGzf9WFGVpDzjARPfgtT+4oON/cxqQaZkr1XxN6G8uWMjwoc/IZ1amsUcJYr43pVJpqWLveDwW54ILSWK4fB58jEmSiKrE3DCWQbn/GBeNSf7JQ8P5utF8kiQRhgwVjHUcR0QcAJlRu7u7K/opkwFPPJpQeA5W8XZqpKhQWJ2iZOF5BCCQ4saFL+U10j3lftDkQSCggUdjcKFIQGWj0RDABqfrSqUiKjhTMUyq7EygBSmTjz/+ODY2NkSF8eeee05UfSZFbBUQzs8rGfjcuyEryP1+H2+99Zaobk59v+kczOdzfOUrX8H9+/fx1FNPCVCMF7qk7gfkeeeeUaIP2jeKQCDFe3NzUxg25KmrVqtLxayIj1KrMKK3YrEIwzBg2zbq9bqYn6z4rlJAOe/nRjMBGaSMTKMpxvEYfa8vQAPqXDAKR7g9v41BMMAknix1LwAAR3VEVEPLauFq8SpqZg3f/ONvYjLMCn/Zjg2n6CCxk8yrawVwCy7C+sMWo9Ix1iMd/3P3f8aat4aG3UBZK6NqVrFeWEcz10TJL6GhZB5cBcutFIkuaC9kvky/8x7sRCekyPI1pjUikIHACc6nfN9Hp9MRQBL9p/araXoSOeg4DhIvge7qaCgNKGMF7agtzpnjOIAKaGUNE3WCuBDjmZ99BqNkhN3pLl4bvIY/3P9DzKOTlBdDNbCR38BmYTMDIHIbWHPWkAtzqKpVtLSWkLmy/kdF1mazGRQoqFk15JFHw2zg1t4tlOdloayHYQhN1xAbMWbaDKETIrRDxPkYoZP9dNsuolyExFimE83TMvBhbggwwlgYeOA/wFq6hrJSFoDswcEBisUiqtXqUmcyft5lec2NDDJAd3Z24Hkezp49ewqwJP5K90ySRBTnLZfLS/KG6CMMQ4zHY1HrJU1TEdFEvFYGeem7xNtlvY7TZ5Ik0BUd6SIFFoA2PCmg6DgO1tbW0Gq1YBgGGo0G/upf+6sIrRA9r4fuoou+10ff74vze+Ae4O3e2zhYHMC/fAKGZIMCdF+HttCgezo0TwOmAGbAl+98Gf66j7pVR92qo6AXxBhlXVR2+lBaAM2RLlXN0s4uXboEx3HEPIhvEs8k+aBpJ12t6BxSC0G6n6yH8LWl9eeAMslmPj7uoOXf5bKTz5fTHZ+7/F2unwOZfKW0QXqf821KDePv0X3Jyy/bT7Juycf1KGcksFyEVdZRZR1y1RyBrJYAd/JS7YQbN25gb29PgG9U9JKcDd1uVziNyJFB8psKPHKglLz+inK6GDZ1sqDvcxuG0wedfarhR5GCtKbUWStNU0wmE6ErUXFG4vEffPCBcFaQrjUajWCaJm7fvo1yuYxisSjWb5WdJdsPtOd8brZtL+nE8n2IjomH0e+k+83nc9EilgC6P+36iYAGTgwykCAbBHzisqFCv9O96P7ASZ0H+Tv0LHpPzg2SCZgTPt1HNijlA8I/z40NmQHy+fNxcCRWNtDkZ3Iv748zrOQxAstpLDRWWkO+J1xh5ko1DwnnhjAfOx+bfF+OkvJwHZmhyMxQXj/5+fxvvrfy/GXDk/5zJs/3jAS6zNTkcfK/5d9lwc2NQ/pPeVYy7fLnrhoDpx3+niyUuFLP954bAKuENWeIj6JJfk85d1w+g0RDdMmADr9WfZ8rEfK55OPj75ECoCiK8EaToUTeeN7Bge8NGcB0b+IxZDwOBgOEYYhcLidyz/7/ATSQN4bTEtEwL95K60vGPQDRSpRHf1CV4eeffx4bGxuiHzgJCEoNALBklPMIDvl80prTOq46m5yX0ec5ih7HsQAZgiAQfeXJQ0xGhud5QhkkRWE8HqPT6Yj8+/l8Lgr2dbtdaJqGra0tvPrqq6LTCI2HkHka23A4FHmRq7oH0TpSdIhMi5yWaF3jOMaXv/xlvPnmm0KZpfx9mv90OsXbb78Nz/Pw6U9/Wng0iG7pmbQeRAc88oGMVFpbUmjIaCGliooVUxoLfZ+UG8rN57yZlAZZgeF8h+7FjUl+RrihI/NVPdSxYW1g0zxpTcafRdXCi6Ui5ulcREoMggEm6QSTeIK+30ff62NvvodxNMbo6kgACFNMAQBaqEH3dZiBCdu1UR6VocwVGDCgQkUSJzBsA9CBJ55/AqEVou/3ccO9kT0rnCydUQUKKmYFTaeJip79bDpNlPUy1vJrqNuZodRSWqhYFaRJurQWwImsXsWTudHIP0fGLIEScRwvpfTQT1pDXdcxn89x9uxZuK6LnZ0dUdwsSbI2wzs7OyJHO/AC1J06YjcG+sBfO/vXUKlU0Ov1RFePcTDGkXeEw/khjuZHOPKOcOwd4874Dr578F10Fp2ltaqZNdS1OkooYSPeQDtoo5AUgHVk9RMmjsjtJTBe0zShzJODQNM06IkOb+hBhw4rsZbOHl2pmSLKRYhyEeJcnEVsOCEiJ8K8Mc9ef5hCchd3ASBra+lbUNYV5OIcRvksn308G0Of69BTXfBZ2jsuCzkvoPFT8VoO4tKeRVGEYrGIWq2GOI4xnU6FUUPF+4BMHr333nuioFuapjg+Pkaj0RCh1yTLCLAFTgojE5+hzxB/5eOmz3O6JIcEdV6J41gUfPM8D9ffvY7nnnsOeSOPx8uPL+no3HD5gz/4A3ztW19D3+vjcHYIs2pCLalYaAsERoDIjuAXfUSNbK9+J/0d/M53f0eMy1TNDHSw62jYDTSsBmpWDXWrLurDNHNNNKxG1tJ3Rbt00j+ef/55fPKTn1xKMeQdhrh8I/5Fr5NOQGdYDu/nRrYM+HCet8qByPW0Vfsi34fLHjmFk16j8fFizaQTcICeO90orYuidkzTFN0XyGNN9MENUW7nyOPnn+WfEWeV6RN8DfgayvohFWHm70VRhPF4jA8++EBEgBHQQ2MiHTwMQxwcHIi5lUol7O3tLUX6EgBz+/ZtcW4IJFcURUQiEL1RIVHbtpHP54U8Ho1G2N/fx8WLFwV9Ul0fAAL0p0gFinoi3k20SSmSQFan4cyZM3j88cfx3nvvQdd1vPrqq/j4xz8u6ljQutB36VxSXQh6j57D6Zd4CNEHjYnWnNfi4qBomqaihgOBGB/m+tBAwyqikQ0Y2RiRDV2uzHGCJgKRDxM3Xjn6yw0xLsSJwGVlid7jB09mVJwR0cHhhiufPze2+aGSUTo+Rr5uslLO14kfWv73o4xQ4HQ3CNmryxkfHy8PaaZxcWWGz082yDnyJyvkfA8etVerjHduxHAmJ6dR8Hvx79BPmcbodW5IAct1NDh98VBzvuachmXUmu4tI4WcLuTzwD/P7yXvlbxGfO14NAn/nAwMcnqTLy7cZFqU508XBxZIMZPXiN9PPhN8jqvOKr9knkNzI4OPkHx53WifTNMURqyiZB7XarWK4+NjYfi4riuYq+xp+o9xzefzlUoiefqJ2fO15XOheZDCS16oJElEz3KZlwAn6SRUlZrWdNU60zOJDmifuIIrrzXtAxnHpAiRQh4EAUqlkhgzPz9U50fTNJFXaVmWKORYrVZFiDJ5EMrlMrrdLgqFAvL5vFDeNU3DpUuXxLg1TRPKAbV3omgNngZIc6M5008OOHLBOx6PRbsrUiYI2CE5QfQ/nU7hui7q9boQ9LTuvMgZrT+tLXlEfd8XrV1JyFOtC1JiisWi6DPv+z7y+byoRcH5Iq86TfMlz5YMPPF95bKTfudrR/vNDWHegYbOEwFlPLpDmWYgYgstNNUmzpvnTxkMlmVhPp/jf/if/gdMoglKGyUgD7iKC6WowNd9oAikuRTD/BBBM0BsL6duAMBR/wjNfBNNu4nHyo+hlWuhYlbg6A5MPdNFgjiAF3sYeAMczg5xsDjAO6N30Pf6cCN36X66oqNm1TJDyW4IEEL8t7PuG+vFdeS1/FLoKSmCtO9UG4LLb1IUeSQOKdS0pgRWUSg4eeuCIBAdTCgNhIAqagHK761pGqp2FTWnhscrjwueSHQRRRGCOMBb99/C4fwQx/4xjhZH2Jvu4WhxhFenr6I36J3U8dgGtA0Nbw7exFawhYpSQSktwW/5sLsZ4EfpQ5xOeJ42rY34HypQ5gq0RIOhGkv8SshWLUHkRLBbNuJCjCSfwLM8zLU5poUpruevwzd8oH2yjy9HL8MJHVi+BWthwQosmAtzKUrCTEwR6bKxsYHPfOYzouYA1Z2J4xh7e3u4dOkSHnvsMdy8eRPj8ViE69dqNVHzQ9M0XL9+HdPpVORkTyYT1Gq1UzKYwsLT9CSNg4NTURSJvHv+PVlfIlBT0zQ8//zzuHLlCr7//e9jMpmItX/jjTegKIowoOg+nPdblpUVnEs0lJIS3LELy7NQnpdF5BrRoOd5CMIAn/38Z/Ff/Lf/BY7dY/S8Hgb+AD2vh56f/f7e8L0MWPRZgdGHl9E2oH5aFV03jMCAFViwYxuvTF7BYrZAw2mgbbfFXMkQpzPH9U4CF9I0FWeC5CzxL1mPJp5O+jM5Azh/I5rm9gTtAeexxP/lNA3Sn+kn18l5lCZ/HhnHRBcku+m73C7iOiMVA+RGO9Ell+E0VnnscktQWi8C0vnZJB2H5B1PL+Aykwoc8khLatM5Ho8FT6MivVQkOUkSDIdDLBYLoSdtbGwgnymZvWYAAH8jSURBVM9jOp1ifX0d+XweL7/8spgb0SjpBTQHctwQ+NDv9wVoQLWtgJMisb1eD3/wB3+As2fPotlsAshqtFBxRzqbRAuu6+LmzZtC7yyXyzg8PISqqrh69Sq2t7fR7/fx3HPP4Y033sD777+Pvb09PP300yKyYTgcYjqd4syZM0InuHXrloiQPDw8xPnz53F0dIQkSUSXpTAMcffuXaE3EmhzcHAgCjDn83nMZjMcHR1hY2ND7Fmv1xNAz97eHj7M9aGBBm4ccyLjxiP/3CpjgRt49BodBtmbxg+fDArQJTNR4HRtiEd5XfhBXeWJ4QeSz4ff91Fr8uPWhY+Hz40bG7RG8jjkNaXPrDIe+fPki55LoZh0L9lI43tADIsMCnk+8vrxMfH9k4EH2dCU15+vL42Hf55/js+VG97yOvMx0e/yJdPbqr3kc+ZjpPdk45rTyCoQjn+O7il7kPmzuCDja/eoefB1kJ9D148DBGSBQc/mc+Lrz9eBf27Vs+Q14J/hrxP9cTCA2lzJkTp08VZPlHvN0Xw6/xQpIfOd/xgXrR0JaWDZ+0n/SeBzLyCdVYq8oNc++9nPLuXeEY2QAspphOZNSiWPCCFFktafwuZ4ZWS5TzunCQ4Oc68gD60n8IHuS4auaZqoVCqI4xiO44hQSGrbt7m5iUqlIhQDVVVFjiVP7ygWiyIPml67cuWKCKHkng3gBIzg3hvaJ5n2iQdpmobj42Ps7u4KhZv6SlOUBq1LEASipzuPZuAABnnTaL1IYSQDjC5SWjiN0z5SeDiFR5IRyQ0NqiZPwAKQKUQEouTz+SUgle5P9MT5KK2RDJCQZ5iUMFkBptBSChUl3j6fz4VizMfNw5xVVUUSJYhGEYrlIha9BbSZJsJtNU1Do9HA4eEh0jRFvpRHaIRwmk7WUtAM8PGf/zjSfIruoovuvIvb09voeb2VUQ1VqypAhKdrT6OslVHP12EqJnRNR5Jm7Ron/gRDf4i+38edyR287L2MnteDnyyHlZuqmYERDISgfPe6VccudjHVpyigAD0+Ke7FQXc6Z/ysU7QKFb7rdDpQVVX0Y6/X68Lj1Wg0oOs6Lly4INaYaGWV3OLGpaIoMDUTT6w9gcfTE083dRigs/xvvvpv0PE7WdtSv4O0nKK8VsaRd4Q3F2/iqctP4XzhvOgQc+/ePWHEua4rWnHS+ZT1CllvkC9VVYEREHVOzpWWaiikhZOoJEuHWlaxMBfwLR+hE0Itq1AqCtyci361n4ER7PZaouEjw4/ghfgFkdtNZ/fdd98VxsrR0REsyxKdLQg8HQ6Hp85Lr9eD4ziiZbOiKOh0OiISjM4TkFWGp9Q5XhRwlayS9VH6DN2XZOBjjz0GRVHwu7/7uwKkfOyxxwRo4vs+hsMhbNsWHXHiOBZRZPQZ8paT3KFoNjEGKJj2pqilNTSLTaC4HB255CRSgHk6F2kbo2iE//Cd/4Bbh7cQ2REiJ4JX9TCyRwjNEP97538HOllnm/+j+X/AtrL6WLPZTJwJGhNFeBEN0f6Rx5nkEslEkiPE/xVFWeqoxg1wDnDwmjs0P1ojDnxw8Ij2iPRton3TNE8VlOVecDqDs9lM7Dd53Yn3ch2Cnx/iJTwiRz579Br9TYYnj3TkTk/ylNO9KZqR6Jvokfg91xEp+olaXJKDYD6fi+hAVc26SfEWjwQwEM+k8zQej4XuR5FDVKPl4OAAb775JhqNBv7cn/tzopYH160BCMCet0Qnvkl1LjqdDvb29vD888/j7NmzmE6n8H1/qYU3yW+Kxtzb28NsNkO9XhfgC90/CAJ0Oh0888wzGA6HePvttzEcDvGrv/qrUBQFR0dHKJVKODg4wEc+8hEMh0OMRiOUSiXcuHED9+7dQ61WE+kOvu/j7Nmz2N/fF2MqFovY2trCzZs3kcvlRPvNjY0NvPrqqyiXyxgMBnj66adhGAZu3bqFfD7/oVtbAj8B0CAbZVxp5cabLKRkAuJESPfgnhHgRCHlz5QBh0cZAdwQ438Dy4o8R+BWAQar5s+VatnDKj+fnrHEOKX7c8OJhw/SxcfFFV0OyqzaF2J28hrK4+Cfo4vGwgEbuX2LfC8+LxkA+NOAFxl44PPjyg3958WA6Pv0Pf46X3dZSeFryeciAxiy8Uv3JOYqG1fynGSwhH9GNuJlgEA2sPln+f1l8EE+I/x1mZbk9edjlfeWrxkXvjR3UkblfV5Fn/wZfE3l9eZKL6cHMhS5MUyCftWeOI4jFAseelypVISSQHQuA26rFNifxkXRB3x96W+Zb3ADg5QiXrhRUTJ0vNfroVDI8l2pvRc35sjQpdB5Ev7c2NO0zGB77LHHsLW1hVqtJtIMOCjCAUauLHCeTvtBn+e8hecdk/eHCi/FcYyjoyPRDo7Oh+M4OH/+vFAQKCwyTVPs7++j3W6LPeR0yHkij47iZ4XTIff+8zWnz3OAYDqdCjojBZ2nTvAQR1JMSfHlspOqtHPvFSmKpDg6joNGoyF6eeu6LsJC0zTFYDAQ60Jt3GazmcilpAKSdNYMwxBhmAQ6UMEqPnZ+Dvh+cECY1pAUSfqd8yK6BynVfF15TQoq4iZ78ijnejKZiIKJlKuby+VQr9cxHA6haRpc1xXnvl6v4+joCPbQRtEoIkkSfLH9RRHSrqqq6GkeJiEGwQCDYIDj2TG6i27meV300PN6OHAP8NbiLQyCwamoBhUqanZNAAjnGudQt+som2VoeAh8I0UQB1jEi8yA8vt4f/Q+vnv0XQz8AaI0yozaT2T31GINdmTjlegVfPf73xX3bjpN1K06imoRaTGFaqgi0mVjYwO6ruPpp58WBSIpJYLOarFYFHsth87y/SZeQ0YtPQPIANxCoSBogcAq+ruoZJ0tjIUB+9BGYVzA3/+v/r4AIn733/4u7o7uLvEDOnc8akiW4bIOyj8jy0ZOQwR8crCZDK8oimDGJmpmDe1SGzk7B0M3oKYqkjDBLJ1hps0w1+eYqlOcbZ3FGSPzDh4cHAi+9dZbb+Gpp54SUQX7+/t48OABFEXB5uamqD0zGAwwmUwEjzw+Pl7ysMpAJAea+bkjPkK8m0BlWacl3ien0VE73jfffBP9fh+lUmnJu0sAEPEoWj+SBdPpFHfu3MFoNBJdb6rVqgD1aQ94xOH6+jreeecd4eGlc831PtpDVVXhLTzEkxhO5ODT+qfxTPmZE3kEFVqoQUkUJE4CX/cxDseYu3PM/7/tvUmPZEeWnn3udY/wMeack0lmMrNIijV0l7qrqyBAArTQ4oOk36D/pZ022vRCKzUakCCgC+qWAFVXFVpFFdlFMpnMOWMOH2Nwv9/C+Vo8/qZ5kpSydm6JhIdfv9eu2bEzvOfYMbPBcA6TaOmKHNOI1zfVFl2kHzWb7dhNOEjLDejXEAdy3Ggv+/3+HF5jhlvE/LICTaww0B9xmd2ivqgeThRovw+2kZMXooH0sWRFcq/rzOzTM8oA4alVantVVWkiU3xMTKu6XEYpv+y/nuMyohcvXiT9/+DBg5nOWVuLp0+fpsDCn/3Zn8UvfvGLlA0onLG5uRl/93d/F2dnZ/Hpp5/GxcVF7O7uxpUrV+Lf/Jt/k3wNYpiyLBPPErtp0qaqqnjvvffi0aNH8X/+z/+Jq1evxvb2dtqzSbTSuBXFLPtAx3OS96UPm81mwhjb29vx5MmT+P3vf58yHqqqip/+9Kfxt3/7t/Hy5ct0hPFPf/rT+O1vfxv37t2Lqqpia2srPvroo/jlL38Z9Xo9Hj58GO+99150Op34/PPPoyxnx8Q+ePAgLdP40Y9+FCcnJ/GLX/wiPv3003j27Fk67eInP/lJ/PKXv3yNbxeV7xxocMfenTsps5yj5o6hBk1FTCUiE5iQgd1J93blshAIMnWNTOzt8yi5rpPhGOWig6NnKSBsrzuMpAcjjG/qqzv5fk3vpdHNvc9nLmTspWxII/2ucfU26TuVsc8+cCzJI/xbCoXRU/1O0OH9V394H9/D+/gbx4iOnj/nhk/FA0EcM9GAxdtCZ5rAyNuo73IuuT7S+8C2sw1+nTT3PjjvEwDQGWLJ8S7b53Lv8kEauMEhb2tDH+dN3cMdkclHTo+iKNJMOceN4+Hj9zaLdvHVuGps6eDpu1KgqSNkqBRA0dGIcpK558F0OksnfPjwYRweHsbKykqsr6+nGQwtHREdFbW+efNm3L17Nz744IO4cuVKbG5uJt6jrhbdGGSgfdB9Gj8Z7Ij5YKv6UFVVCoYQuO3t7cVvfvObZOjr9dnZ4dowTUEotUczopo5Es0Y1FWaumd8RFweyTkajeY2v5RTVhRF9Pv9FKTSTtIMUqgtBJ9y7GgrRD86WnIMBBYjZrOZr169SrRUdoToVpZlrK2tJR5Q8EAyIxBNWVcbfKaO11T0LtFGOleBlul0Gs1mM92jPpNurFu0liwwgEm7ygCnlolEzGZjdAqEjgYsyzKuX78eBwcHaQOtWq0W29vbERGJHsrsoO2qF/W43rweN1o34k55J1Z2Vub0gAB3vV6P8WScAhB7p3uxP9qP3fFu7I33Yn+8H4/6j+LX+7+OvfFejCeXs/MR80stdpo78eHmh7G1shXtlXb8w6//IT799NNYbazGams1ohnRudaJ3lkvvjr5KvZO9+Lw9DCq+EZv/fns//N4Hhu1jVgr1mLldCWutq/G9sp21Lq1uF2/He1JO84OzuLu+d24dfNWTCaT2N/fT2uZxTcaO268p5k4Oafn5+fx6tWruH//fqyvr6cd2snjOtVG8rW7uxt///d/H//jf/yPePHiRVr/TF0r/aWz28U7jjuUxcbMJAVByI9yAogfFGBTevDm5maSP9oX8d/q6mqsTdeSvpQ8F0UR4/E4vvrqq3j06NHcOutXr17FaDSa20unVqvFq1ev4osvvoitra145513UubR+++/n4LAP/zhD5Nzpp3zJXvS0wpmMhAnfaFAvPrJ/81mM81Ei06NRiP29/djOp3GD37wg0RzBaap37l3kOiptihw46n0zC69evVqPHjwII6OjqIsy1Q/bYR4wrHQZDJJp0RkcUtVxPpkPXZiJy3D8yUJGuO554rLgLf0i7KwdJIR7yWmYnHcrTHzPSVoG9lHPicZJB8yw4fv9qC1xsvb5zZGupxBEAYXiN9yfRd2kbPvPhaXPknGib9YP4PPaqf2kVA9h4eH6Vjan/zkJ/G73/0udnd303h++eWX8dOf/jRhg5WVldjY2IhXr15FvT47Lejq1atp6ab6o/onk0mapScO1X9l6jBzpSiKtFRQ2RRra2uxv7+fNqpWMF+6R7Z/dXU1vvjii3j48GE8ePAgbVwpvan6T09P4+XLl4nHiqKI//Sf/lNcu3Yt7ty5kzKhhsPh3KljCp5rw3RuDi6a379/Pz7//PO0DFdLLV++fJnor+ylp0+fxo0bN+L58+fx5ZdfxmAwSCd4fFv5Xns00EHS4JBJfe0OmdUFyZk+5xS6Y6SyyGnm7BEZhFFLto3vJOPQCXXmp3KIuAQevMbIuwcM2DbvyyLHlHT031yJUhG4Qsw5T4w0yhjyHd5O9sPb6opSdKOyd9pT8ag9pDmDOq6gvQ3eRm+fB5dIP3fqdQ9TwZyHGShwXsrxMuvP8XaOP3Sds/nkRe8r+c7pwft9doj3sK+UBU/tViHvsz/8nX3X/QxKMWjwpvaQRynjHpDQe/QOz1Rw/UQ6qj3er7dZfve7372WhqfZYIGTiBmI0GyX9hhQf1ZWVlKAQlF6zSIIKPZ6vWi322lzIjnlMmQCrZpRV5pfVVVpY7lf/epX0el04saNG/H+++/H7du348aNGwnU01HTuMipd97WbLUAq2xIr9eLx48fp+DB1tZWtFqtODo6iohIkXQtIxAgUn83NjYSiCYQYLAl4nI9Ja/Lnuk3zsBQN/q+FKKxigC3xkR8ScAtx0Lv4NrhnO0pyzLNflXVbCZMm1G12+04Pj5OoK5er8fNmzfTxpni5UajkZxDyg9nQHWvdI0CNx5QEg2oDwTquF5Zep+8IdpIr2pmR+/p9/vR7/dje3s7rWvVexU4k0z8q3/1r2J1dTVu376d7vG1s9PpNJ3AoeU6kps//OEPcXx8nGh/fHyc+H44HCZ5uX79eloPe3p6Gv1+PxqNRmxvb8d0Oo2V85W4VdyK99fej2nnkme0VEZ7IpxWp/Fq+CqOJ8dxdHEUu6PdODw7jFFtFIdnh/FJ75M4Oj+Kw/PDOG+eR/zppa4op2V0oxvbh9uxVqzFx7WPo9vpRm1ai2IyOxqzKqoo6kWcFqfRj36crJzE789+H73TXoz6l87cTBgjOk870ak60Y1urH+6PjvStL4R3ehGe9qO1bPVuNG9Eesr61ErZ+nGrVYrHjx4EBsbG9Hv9+PLL7+Mvb29+LM/+7Po9XppNlVjJfnX7Pbq6mrs7e2lzActaZPeIe9r5lw6XOOmAJGAMbOsePINg2eqQzq2qi5nK6UTaO+FGSkP0mFcDqWxVj+VGv773/8++v1+ChT0+/2o1WrptIujo6NYW1tLwZSzs7O4cuVKjEaj2N3dTWuotT+NgheHh4dpv4GIWcCs2+3GlStXkv7jzDcnJBwn6pPXJAu8h7aReNJlnMfDcu8VybbuU5YaM9ocr/Bdjs8Y4PR+0J+Q/NOGcyz5Xk6k5JYO5JbSkTZ6V26PJ27cSbtEjMKidlH/O51kt9RmPUc9K/p5xgaD/zl66Lrzhu7TO/Sd9XsGXM5fYeCavpTeoeyWiIgbN27El19+mXhqOBwmrHD37t24du1a/Jf/8l/SErGrV68mu95ut5M+0ubOjx8/TgHQv/3bv026XHuoKICtQIXsPpcZyYkX7eTI7+zspBNjJHv7+/txdnYW169fT/UIFwlvaDlGRKQMjbK8PN5Te9ccHR3F9vZ2/Pmf/3l88cUX8eTJk3j16lWMx+PY29uL9fX1dNrK3t5e1Ov1uH37dvR6vbhz504KkFy5ciVu374d7XY77fe0trYW165dSxMFOzs7sbOzE++9916irfZ5+PDDD+Phw4dxcnKSMgW/S/nep07QiWCkksLBAICXnANOwfH3qVBA9d2dRtbnbeDzDCh4cSXiwsB+sK3sm9ej9zIos+j9uYgf66MyWdR2p5uDWG4CKQOzKFDgSodto+HxvjPVylOZdZ1jp7p4PWdwIuadRo7zov7rGut2o+SBAYEIARwaQm+z82XOAObo6uPo0W31X04Jo5w5w8M6OD45WfCZSrZDf9PASjHK0XU51Huc/3nd+dkBhopnCLE+d350fVHgKSf/Pk5eB4NdHph7W0VrXTX7FHG54a14TmU8HidDp3bLaIlOOqZTPCIDtLe3F0VRxP7+fhwfHyf6ae0jZxrG43F6l95Rq9Xi5OQkms1m7O7uxh/+8Ie0mea7774b9+/fj9u3b6djEgkIVYricjdozfrL0ZBToGCH2vL+++/Hs2fPoixnSwFk2G7cuBE7Ozvx2WefxXg8jsePH8f9+/fTzKWCLxrXer2egjaUEQZDJGMCOTm94BvTCVQq80IBB9HUg5pa5iAHlHuDcF0u5V600V4WcqKVDq826eSVopilWl6/fj0FX4bDYdoUU3qZnxGX6ahlOduQU/cyeC+aytmnrGk2SgEkzcyI9pqFHQwGqb8KeCmzR/xydnaWjtqTwyVaKACnLA6lpNI5FNDX2It/la6vmd2ynKWKagZTM6vcHyIi0jIN0pcOK22SeGw8HifZ5H3iq7VyLZrnzbhXv5eyP07PT1P7Ts5O4nhyHIMYzIIG05M4XTmNwfns+5fxZfS/+TeNaYSw/jRiZbISnZgFEbaKrbhT3Yl20Y6VWIla8U1WZhlxERcxKkbRj34cVAfxuHocvbNenMU3qbBlRAxny0G60Y21ci3Wz9dj55PZ0o121Y7D8WFsT7Zj+uU0mpNmtIpWCoyNx+O4fft2RERsbGzEnTt3oiiKODw8jBs3bqQND/f29uay0DSrpo1dORvPIAJtl89+u62R48AAxCKMIF7nruxvwkUMaFBX8IQYbn67uroa165dS9k1tOuTySRu3LiRNnONiOTcnJ6eRrPZTPuQSJ8wsMC2EnvncEUuyK975XBLpqQHHJPpc3V1Ne7cuROvXr1K40bcp3pFVzqqZ2dnc0HAHK05no4Pve0+cy49rbFahOFJuxzedX9jEf94u6RnT09PUyAsF0RRvzwwwHaqXzl/xO2ubHiOV1mXaESdl+tLzmfy76QN38cx8w052U+1RWOpfZZWV1djf38/Li4uYnt7O+2vcHh4GK1WK+3nosDaxcVFHB0dxWAwmNt3hnua3bx5M/7kT/4krl69GkdHR6m9T548ib/7u7+LX/ziFwmbSRZlz4hNq6pKeChidrT41tZW2lz8s88+i+FwmI6p5LItZY0pENLv9xMG5ISSsN9gMIh2ux0ffPBB4usXL17EX/3VX8Xt27dTu/b29tLSysPDwxiPx+nErUajkU7eOjk5SfQ4Pz+PVquVTiMT3+rY8KtXr6b9rba3t2NtbS16vd7cBqJvKt9r6YRKzqnLOft+T+77d3HK+LsLpt7FlPmcIxlx6ThRoDzylzNUOYFyB4iOndONfXyTk5u7P9deB4p8P+nI9oixRQMaXioyb+uiMXCjlnu3jzNnEPm87iVIdKPmdOOzuTFyfso5w/6cB04IYHycfFz8nXRqaNTJW94HN6BVdZnuzTRSFbab4IH1O60dGOu7L4Xgc1JqXLKQM7xu+L+NH/Tdja/ziMueCo1lznh7Xf4O/c1x8Pb7mL+tMhqN4uTkZK7NRTGfQXN2dhbn5+dpBkwzhUxt16wewWBZlvHw4cO0Tl+7BAtAyiDLMJHXxUOqnzM8SpOs1WpxcHAQjx49iv/1v/5XSgO+e/du3Lp1K7a2ttLmfRHzp1VopkuOs4r+Pjs7i36/HxGzNeC3bt2KwWCQshUELK5cuRKDwSA2NzejKGbHxQl4v3jxIs2WVlWVHNyiKOaCddPpNG3QpjbJ6VbAggBODu54PE5pg19//XWsr6+nfnY6neSMyjFS3zudTvzN3/xNqlPAY2NjI65evRplWaaMB9VXr9dja2sr/vW//tdp+YpmIZQ+rlRjZohopljZG7/5zW9SwEJ8ollkLj3RjIqcf6ZFK0AtvdRsNqPdbr/m5IhvuCxH+2+Qv/SM+lyrzY7fFNBhwEubgYpHuBkpl5mIV/VdwQ/dryAAnZIcRhD9tXmnTvAgyHQgziCodBMDSnqP2qegBOVtMBhEMS1iq9iKjelGTCaTGV8Xl5MBBPDDahgn09lRoOeN8+hFLwUhBjGIp+XT9HcV1SyAIJmLRnS/+XejuBE/KH4Q7aId9aoeZ6ezdtdWanEe5zEshtGrevFo9Ch+N/xd9KMfF92ZU/Afn/7HiIhYKVZio7YR68V6rJVrsVHbiE7VifpKPZrRjG7RjWbRjHpVj7VqLdFJOpAZKfpUn7n0gfulaDyIh4izdA+xkAfjfRwj4rVsANoBx4oMTqoNvs8YnTUB+i+//PI1vvP7Vaf0vM6tp81iJq/opF34VdxW5yYs1AfaTGaEsG2OL9977714/vx5yqxi9pf0hwKy4nW1g4Em4goWxy8M7Dhe9Gcd17JOYjg9nxtzlkV4mDqLtKqqam6vhxw2y2Gd3Lg5Dbwdbxoj0kLtzeHQiPlsUw9wOAbztnkwj7/7tRwu1/WLi4u4du1aHB4eJrmZTqfxk5/8JFqtVvzmN79JGWPPnz9PMnV6ehqj0ShlXXKci2K2BOKf//N/Huvr67G2thZ/+Zd/mfp3cHAQ//k//+e4evVqfPzxx8lHIn9I31DGlYGg5Rlywvf29qLX68U//uM/pmVUdOJv3bqVAiM3btxIyxSot4SbhOG2t7djfX09Pvjgg/j0009jMpmkY8C1/0Kj0Yh+vx9PnjyJhw8fxpdffhm3b9+O0WiUjs4dDAYpy1HLLobDYeofl9MpY2kymcTW1lZ0u900Ht+lfO+lEy5kYkp3gMjwEfNHBrowuOPlguPKgYqSIDbn/FBgBHwiLtc/qZBgrkC85K57CpKDCyrCRU6Z/602Oi1zUXkqDv4m5hDzcE2ft4HtyykLHzfRTb+xz14XDbI7xnRy/Z3kGx9T77/e6c/k+ILtp1GVUfcxyhkBtpnt8Dbqf87g8XdG4wXsy7Kc22mYwEzFr3nQR23MBXqoRHP9UHs8MEEeIR1Ic+8f2+jv87oYfBAdeI9mRVz+c/UTRLosOS19bL6rIv2+ZXd3NwFDrheU8xgxmym6ceNGPHjwIB2NWFVVSjlXG313f43/zs5Ocnp9LbpoQudR4I1BC429jCpnnRQJHwwG8fz58/jf//t/x/r6ely/fj3u378fN2/ejM3NzbQGWmMgfaAlHHq31tpr88nT09MYDodpJ+Q7d+7E8+fP0xpGHQV6cXERJycncePGjZhOpyk9WSn94vuIS9mQfLFfCuBwxkGzGefn5/HixYt4+fJlHB8fx9HRUVoDfvPmzSRHzN7SGvJXr17FixcvYjgcpv0VmArebrdje3s7yTrBmzbbo2Efj8cxGo0SDbXJGZ0VBqQZgNIu3KSH62utFWehbCn4oN3vxSeqg9k44jFmCvgMI4E6gZ0+tcGp2kenU5/8m/VyxkXvoE3U2PK9Kgz6cUkN36nfeQa9+k17LLkTz0mfqk7NtlHulDLvPKG2nZ+fx0q5ElvTrViv1qNRNRJP6Lg4OeuTahKD6SAOTg/irHEWo3IU/aKfAgiDahC71W70ox/DGEZoCL9Rf81opqDEremt6JbdaEYz6lGP6cU0avVaTKtpnF2cRX/aj0ExiFfxKnrRmwU5at/o28bs/+raanSudaI1bUV72o7O9PLvbnSjNWlFu2pHp+pErZjfSI/7Mkh3EIM6NiI/cI+BHD/SjohfFskBbZz0mMbGbYfbfjrblFfazlzKfg6PyD461pEDT9q5s0Q7Trz2JufS6aF3KKip/mqjYS53K8tybl8O2iPRjXLlfc7hbKebY48czUgLXmMAhjjVsSYxr+MtBhvFr5Rd0s7xE/vDIFJuMpUykBsT51UFe9gfpynfLR4kTnQ/yfslmvJ3lpxc8n18v/hTywaUnaa9ov7kT/4kdnZ20rLPwWCQ9uVxuqporJTpMBgMEq4Src/Pz+Prr7+Ojz/+eG45ETNtKc8Rs72CiqJISyeUBaegsk7ZEu9HXE423bx5M77++uu4d+9e4hWdANHv9+Pg4CAdJ3l4eBjr6+tpw1jp94uLixgMBknWlL16dnYWR0dH8ezZs/jss8+i3+/HP/7jP8b7778fX331VXS73ZTZt7OzE3t7e2lPqPF4HKurq2k/FdFGp7f4XilvKt9r6QQZheA99z33fA7k0zmk4VgEHHLC6gqF1wlicg4WnWCvi0qQdXrUUv0jWFE7c4o6p/h03QMmrhj1ftLZ20iHPuIyHZRgxZUVC4Wf407FSmOq+wl0XQl5Cq6Meo5HOCZsD/vqkWjdJ2DgIIF1uIHnumEFGXLj48YnV29urMhzuaixl+n0cmaRM8N8B9uX4yMfN+dxp0GuH3LGeCSR826ORm4APUjgbXM94oCJspgDIqR5TkeQrt7GHF3V5pwRf1tFRy8ysCTwFxHJSZDM6LoMjOigzAA5qVwqUK/PjrLTrKz66bNNusY1ljJSBEl6TvVwdrwoZs7WYDBISyxarVZsb2/H/fv348MPP4xbt26lYKfWM9PR2tnZiY2NjTRzf3Z2ltYeV1WVjHm32507JUGpfTdv3oxPPvkkLi4uUvqx0gSVmquxLYoiLWHQTLo2c9KylslkkjZ31NFQOq5KswvkSQZRyKda5uLyQJ2gFHruL9DpdObo3uv15vSydJfGl2OjT/GIwNXBwUFWT8k51eyJ213dTx6NuFyDrHt8nbfuZYaBBy0ZyGQdfJ/jAT3HPUIITtVuT8cWWKL+ZtYOdYPqlzzqftaldjDgolkiZljoXulTBoZEH7VXbYy4BLC5SRGeGCIZEf3ceaymVaxerMbW+VY0ysZc39mPoihiGtM4vjiO0/ppjOvjGJbD6E17s89v/ikoMSpGr6HIVnEZJLhT3YnWtBWNqjFbunExjaIsoiqrOIuzGBSDGJbDeFp7GoNiMKvP1Hdz2kz1taezz9bkm6BE2Y1WtKJ10Ypu2Y2V2srcGLo80rklBvQgtgqdMH4mWiHA5s4S7SvHTXQnlnMHTPU41uR9dMj0nZlCzjd8NwvX2xPHOb0cz7CvZTnbVPLJkydz9SobiO1WEIxjQl1EG+6ODDGyvqutbtcdixD/sD5+9zFUP3J094kbjo9jF/KDj4fTQO1wXEQckMuA8eL+A/kuh+UW0YS6i4F5tU908D28qOdzgUC+n/JEObl9+/ZrR3aORqPY2tqKa9eupXrKsoynT5+mk5mU9RYxvzedB9g/+eSTODw8fC1Y9OrVq7nMAmIgBQ/Yj/X19SjLMk0A0P5o6RdP5eG4MFOu0WjEy5cv4/e//32aVNjf30/LUzUBwM2vlfEnPtUmvrJBnU4nVldXUwDh8PAwBoNBvHr1Kh49ehSHh4fx4sWLeOedd+Lzzz+PlZWVePHiRZydnUW3242nT5/GzZs3YzgcxsnJSdTr9djb24tr1669pksWle+V0eDAgwLnxsqZS8T1hvF+VzbutOeEXYPFQkXoTgevv0lIcwo5J4zqOwFWTmm6UOs5MSwNlmjt9PM+OW2dgTkr7Pd/W71UJu7AUZDc4feAlDs0VVXNpeC7kiMvOQjysciB4JxyzbVbbZUT0G63o9VqzdGKtCEtCWIIiL1d/E8wQD5wWmkWQLtf54yCG2O2jzzJ5zjjSpo6EGGbtWY4179cIbD6NjnT7x6I8+fURudllycHR7m2sX/q/5vGgt/fdlFqK3kxYj7KL+Mgg0nnpCguU8Z1r5zhk5OTtMSB52y7jtbfco60rpwzXdwkke3VNY4jwZ4M3osXL+Lhw4fxm9/8Jv7lv/yXrxknLUXY39+Pn/3sZ3Hjxo3Y3Nycc6T19+PHj6Pb7c4dxaco/mQyOxLz8ePHr4EeAh7pam0mx/X+6guPnRNw0DpirefXSSDkJ9GBTmNZztZe+lpG3TeZTOL69esJHJAflKmgDeAE3AkWFwFzl1kGiMU/1BdFUaQN8nwGzuXWZVfX6bBRD2kMuFSPuplL5sR/ru/8/axD/WBAXYUOgf5TZxCEqj8MhjOIkMMULksR846+5ETvEy9S97q9IGDkWLHoGR735jiGekUyrus5XZBs2rSKbtWNzWoziou8AyzQPS1mQYlhMYzxyjjtK5H+Ff14WX8Zg2IQp8VpxCobGNGOdgoeXJ1cjea0GavVapRRRlQRk2oSUYs4i7MYlsMYlIPYLXZjsPpNfaRJVUQ7ZnVpn4pu1Y1OdNIml+2yHd2iG42qkfrkOp7Y1XlMNHObRgeJNKY8OlYhL9Fp5b1sB2V3ETZ2x9rvYV+9DzmHWtdz7/P+TKezpRG6X4EwyXRZzvZFUbDNcQr5ijpE7aac+tIJ0ljtkdySZo4bHCfnJvAc07jPskiWfdxcXzo2c16kruM7c/zHv8kfOX5wpz+Hy/SpZZmiKQPhrEN6XEW/aywZmCB9Wdh3PvfOO+/E48ePU7bAyclJ3L59e24JQr/fj4uLi/jggw+Sw76/v5/oT90aMQt2tVqtePLkSVrWx/cfHR29likUcRnc9fHWhKCy/YSbO53OXHCGm2TLXvV6vdja2oqimE2ifPHFF3OY5eTkJP7Fv/gXsbm5GQ8fPpzD58rslI3R5JCOmz08PIxer5eWUjSbzbRkola7PKJ0b28vrl+/HicnJ/HixYs4ODiIw8PD2NnZiS+++CLKsoyvv/46+v1+rK+vx5MnT+Lu3bsp4/PbyncONPi6tGSUqnmn0IVMz9Dgu1JkEMKFQJ8+A0klFPH6+iI3HmwPr7FPupYzDnzGU5gWCar3PwdUuAbOlYLPzruSVN1c45ZTVv6MAzXWpe8SMgfRBIh6RkXjpDZwXa+ArPosR4ig2aPlrJ+/5cYyB7a87xxnzQyXZZmikc4zDB75OOQcdBbykf52ZebGS+lWcoIcgIq2nAkjKPJx98AL5Zbfc0BLEVEPkOTABeshj7B4eh4BvgMhlz9mmdB5Ed3cGOjZnA5w48/f+E6+649RKLfc9V+boenoQv1XP+lMkR9UR6vVisPDw/RbrVZLM/TsF0GtaOG7lQus0UEVb/CIMB41p++NRiPtNt9ut2NjYyN2d3djd3c39VVBFAU5tKbR6UQ90ev1Ev8K3Ktf4lfRkTrD9a8yHmi0HQSpL1VVpSyGXq8Xx8fHcXJyMrccRdkGtVotut1uon1RFGnDSOqvsiwTQIiItEEm+U/90yyK2p4Dl6KDsrKoz2SzqDs8MFSW5dwyE/0neKRu13O6h8sZGNTkM7QdvE/PEQhTrtlP0oD6w+2j6hFN3AmkTmbd3ga3G7Tleo52zbPodA9lmHTwJZzSC56VwDp1n+5lAIX3uswyc48y5DzF4CNxljtUZVlGURWxUWxEd9KNlXJljhfVBn2/iIu0VGNUG8WwHMa4No5BMYhBMYjj8jie157HoBzEeTEfmCuqYpbVEJ3YqDbi1uRWtGO2yaWCElFEnFfncVbOMiWOiqO0R4XXV1blZTDim3+dohNrsRadavbZrWbXV6YrUcT8kjLa1hxeyeEYjnvOVtHe5PCE4xnHwI5LyBPEH24XI+ZlJWL+NDWXX8eEKjppQzYkt4T18PAw9vf345133nlNJiVzzGhybOo0Jj1c3ztP55ai5N6f+91Lbjy9rbQ5EfOBHMdmlHUfT8q37Ir65MFj1UFdk+uH0y6XuSO9yvZQf+fGQ/foOZ/5z40L38ci3mu1WukY47Is04SI7PPZ2VkcHBzEe++9F81mM+EhbuLrmFf4X3spKINRNNcEDcdWuIfZkWp/s9mM1dXVhGXkxEdECi7I/mopnCYNtMmjliVomZ7u17JNngyhZbK1Wm0u0KC9Fr7++us0caojuPf39+O9995L9kUbUWr/Be2NExFpuWqn04nRaBRPnjxJuGdzczPtx+J+0KLyvTaDXKQE+bsrRN3PgeF9nIl1MMj7qERkYKkAPTqqOvQ+f36RU5Jzfqrqcp0VhUWCoOfYTjLtImeO7yEd/brTXSCARo8OIfvi9GCdVKwR+RM6vA1UHBGvH2mje+UgVdXlMVakdc6Z41hFxBz4Z3uZZk6FyjRu1eeGV6mmFxcXafM4V3KuWGn0fGad7SOtHDj4OKuOBMK+AYzc8ZV0dv70uthOByA5g0xAnBs/OWu5AB/r88g5ZUxyQMOUAyuL+uJt8kCljxfb5Mt4vCzibzeKHpB4W4Xr9+hUiWZKN9URb65PGLiNmMmKnHD9tr+/n04R4IaF4nmBQe7MrHdrFl5GSRk/nU4nynK2pEeb+Ilvu91uVFWVMjFkSOU0HRwcpLRy6lHpM56iQb1PJ4m8xxRN1kX9nrNLvjQvB4rI26J9o9GY29RL79fyBR1VJd2kfih9UvTXNc0qSCeJ/1zfs6/Os5QHLt3js6o/54TQjglgaQdqZTiQztQbcrAZJOcn26jnCbQp55z5zAUZiqKYC6jrGc5W+Zg7HVSIC+hEq37uGSQ+YH9EUwVnaBtdd+le9c0Bunbdp1yr/+yb2zXdKxnTspdF9pjBKjotHAfdR4e60WjMyZDbLbWBeISFur9Zzk6m2JpuzTBVUY+Yzjswasd5cT7bRyJm+0gMy2HaU2IQgzgoD+JJ8WS2KWUxzxdlVaashu3pdtyp7kSjasRKtRK18hvbGlVcFBcxjlmg41XxKvrFbNPMSTmv8+tRT4GIbtGd1V3MsiW6xSwY0Y52dIpOrBTzpzLlaCH+X2R/3PaTp3MZTV53ogPGVnomd5+3S2Ocax/tPXlN9kd2pShms7PvvPNO/PrXv55zih8+fBi3bt2ac/6ke2UTXfZpix0vOr5gkJd94KQen5Gd45hQj/E9knkGUXP0kB6TXXP9lGu/ThARLvWsDced0+k0nRLFcXJ66X79J62Jc6QX1DfpJ8dgmljI8R8xd043iA45f43BbvIAMw5WVlZia2trzuYcHBzEcDhMWQEKRmgi0ZeFys7t7u7G3t5e2qSRuGp9fT2NmwIXGk8tn6zVanOTYNvb2/H8+fO5U47q9Xq8fPkyLU2cTCbx7rvvxtOnT9P7er1eWirKQINOnLm4uIjPP/88fvzjH6dxajabMRqNYm1tLeGmVquVAga7u7vx85//PB2ZyaOwtaRUOK3RaESv10v3Pnr0KO0tJdppI+mLi4vY2NiI/f396Ha76bSmbyvfOdDgis8Z3x10OoAsBBCsl8om52CRIakIufOng8WcsneGpxCxTRGvAwe1gyCUAQy219vM3xY5pbnCdhJIy2EgANJ/0seDCRwD0Uu/S3j5ncBdQufKhfSvqsuzpuU0exscEPI3dyAIBNl+vl+F4JlgUkUKo6qq5MA5vzrfeh0OovmbBxZ4L8GdA1y9042X6vHxZXtJGwdrArm566SPg18BEz6Tu4/9dGCf42s3WA5ocjLI5SOqIweEWH+u5Prixo7t0m85HfI2is5WV9v0X5v5TCaT2NnZSYpeRpsyqCi8xkpr8C4uLtIxaNrFX0X9OT09jfF4HP1+PxlOGVadhrCzs5NOc1CWjfQOo/ICUwp0MJ1bbafzpkwC6hbJh+7LBdR0Xf0VHXymSnLhzqzG1cGm2sDTPOjkTafT1H/dQ12jWRPRXiCEzqPaxnZGxBzoc73KdvN/DqBJLlzGNFZcvqC2+Qk/zJYhb7pton7wQr3jvO26mDqI390eqF4GmV2/u40oisvTcjwLjW1SXUohJYBmm13vR7ye8ca/p9PL4zwlN/xNY6XMG9lMztpRJ/Ed+i9e4xIL0ZK85Xy6SI+zftFSAQzHROQ12S4uuXK9SnuosSH+8sDXaqxGo2rERrWRxWN6z7SazvZ6iEEKFGjZxrAYxqAYxG65O8uaiEFMCgsiVPWU2XC9uh7dopsyJYqiiIvzizifnsekmMRp7TSG5TCeVc9mJ3kUg6iKefvQqBqpvk7ViXYxy8LQ8pC1Yi3WirVoTVuxUl7yhQdp1GfpT/E+lwM4DimKYu60Dgb4iK1Jby6nUb08MpN8paLx5VHXr169SmvQVdf169fj3r17sbe3F48fP051PHv2LKpqllZOXOE8yU93BnmvMKN4h20gvzJIlvgn46SzuHwwpZ/XHQcx8MAxU3G/QTpAgf6czs3JlYrzBIvjc9lfFgU7ubeM7qXDLZsvurm95p5iorECMyr0K7y90iVsO5fjNJvNuHLlylzGjE7kot1Xm9lWYaV6vR7b29sxGo2i3+/PyYt8qvX19Wi1WlFVl6dWlWUZ7XY7Dg4OotvtJp4TrvjBD34Qz549i+fPnycM0O1241e/+lX0er0USLh161YcHR3FaDSKWq2WAtxPnjxJezVpfHRKy97eXhwcHMTp6Wk8efIkRqNRwsaj0Sj29/cTBrty5UqMx+P45JNP4vHjx/Hhhx+m48kfPXoUJycn8fLly3RsuDIVnj17FsPhMI6Pj2NtbS2qqkobbAsXajl1RCR5/y7lewcayOAO/ik0+nSHYVGd+k5jpDqccXPvpQLlQNEw+yyJO5WM9uWUvkpOIbqiZNu+S13sq9Pa6c1+0Nh4WmwOHLAdnnLFFFMHxV5fRMyBcdJYRoog0ttPGjvIcT55E+BzxURDRNAkI8Uj9dyZ4W7ILEzL1TtygIq857xNQ0+HQTTUzFgu0OCGV0CevJWTLZdJ1sVj1ygHHvXnO50Pc/TW+C7SFfpbIMDHin2lg+q8l5Mj6gCOgQMlb4s+c0E7p+vbKuJHyYvvd6Ci44ZUBAQ4u6Ox1OzDZDI71lB0EFjQDssKDsihXF1djU6nE91uN5rNZnQ6nbkZFUW0tQGjjlmTrMhhyul/9Se3GSXlniCSgTfqaXegHHy5flH/XdZcDpneTUDKegQ8uD+D+qjAyqJZoUWBwFwQVfTgc9ybg2CQmRgaAzoBzCYhXznwpg4lUKcd028eoNF11zPUKaIx+Z7jkcvEUDtpnymPdKLIgwT3KhwXyjnlio4V3+/6QXUzvZv0kMz4sijKitNXtOHMHwFyznmYTi+PznSM5MvuiqJIdTcajdf4n8+z7+Qjz8bwsYqIORDqvztOEE9Q75F3yEMaQ+keBjTKKKMe9egUnbgW1+aCkHSKarVaRBExqkYxiEH0opcCEYMYRK+abXb5oniRjgOdVtM5hLxSrcw2oKy6cStmSzca00YU0yKi+qaPxTfZGMUohsUwdsvdGBbDGK28PvPXqlqXAYnaLCDRrbqXe0xEJ1pVK1pFK2rlbAxarVYWZyo7Kmd3HeOqUB8QF1HONBOrsdd/TthdXFzEkydP5lKpy7KMO3fuRLPZjI8++igODg7STK9O3/F9zbxfzkNpzC0A4liC/KL7WBezAyPiNfyYw1S8RpvLNvi7XD+wLmZ481kfP/bf+8Pf+em45U11Or2YKa6i78IEPj76T0wxnV6ePsQNqnkCUU4fO5aOiDg4OJjL3m61WmnzW2Vb9vv9aLfbrwU01tbWotFoxOnpabx69SrW19eTPtMkjAI7Gpd6vR6ff/55TCaTucw+BlSV1UFeUkbFeDyOK1euzE3CyA788Ic/jJ2dnbhx40YKvpXl5bHYCixEzBx5ZaSWZRmHh4exsrISGxsb0el0Yjwex/r6eqysrKQMDD0/HA7j2bNnsbe3F7/97W9jc3MzXrx4EScnJzEajeLhw4dpA8m1tbUYj8dx+/btODo6iuFwGLdu3YqvvvoqZa5W1Wxy9ujoKGU9npycpMDIt5XvtRlkzmhGvO7c55iUjpcLpzs1qjN3P99FJs05XVKIbhAJ9DjTpmcWORg5ZbhIQaptDn69n7l+LZoJc7rrHv+dfeF3OigOzAW0faaKacmqT3UQwEkIWRdpQaDn45y7V+3SPWyzXyfNSHsaG+4azr47P+lv/3SD4crenW3yhitlPiPF5SDUgw2kPWcoaLT4XucDGhTe584VDYTzuBtb76/64WOa0wveLo03wa/zFJ24HA/pGX56cM37nuuL3/PHKFeuXJlro2Y2GRTTeFOulXJPeriTrNQ4ZS1oPaIcB+2ZoL0bZFw1G6b0PTro4/E4rTksiiKtFWRQh+3l7AJlVuPgwFdjyv0FXAdKNzFoISec7VDdfs3lmXW7Tiag1XuGw2E6qYI6UONBEJazZd533at0WdGD+l/fJQtvoqn6Inq6U5GTx1zWGIMN1Je5gGvEZSCEzodmz0kD8RJ1nN7LoJK3yeWe/O59YkCGtog0Ey21vM+XgKhwHMlrZXmZtcP2OZ9x4y8Pvrk+nk6nKY01F3hy2su+cWkD9QEBv94xGAzS7J1sBgMDbJfud2zmtoX0YmBAz5CXOAZK5fdMHvZBdfL9flytnnN7zHem79MqVqvVaNVasT3dTvey/mTDqmn0J/0Y18dxfHGcTsfg52F5GIPaIEb10WuZDavVatr/4Wp1NdqTdjSiEbW4XH40KSZxXp7HsBhGv+qnkzzGMb6sqBFpk8u0l8Q3gYi1WIv2tB1rK2vRqreiG92oV/U5GnACgZhXMiqeV6aWeEXBzLIsE49prHxT0aIoot/vz00KXLlyJdbX16Moirh27Vp8+OGH8dvf/jYiZvrixYsXcePGjTldxOC5YwsfW2JB9c+fdf1O3vSJH8cmjiUdGzDgqpLLgGYQ1/GHfs/pRe+v7uXzVVXN0TyHVZjt5zJDjCY9mes/+YaZD3ong8jkIck3l0Rq+cB0Ok3ZD/V6fe5IZAYqtP+C3lOWZbx8+TLtO7Czs5Pa9fz587RJZL1ej+Pj49jd3Y3V1dW0v9Lz58+jVqvF9vZ2NBqNWFtbi5OTkxRoU1+Hw2E6gYp+BH0PjZn6w2D1rVu34g9/+MPc0hFliPZ6vfjoo49ib28vjo+PoyhmR3j+xV/8RTx69CjRQXsvaK+rP/zhD3H16tW0DPaLL76IJ0+eRLvdjqKYnZTx9ddfxyeffBJHR0fR7Xaj0WjE119/HScnJ/HBBx/EvXv34uDgIB49ehSnp6fx7rvvpkkjZTncunUrtra2YjAYxIsXL6LX66Vrh4eH8fLly4iYnZzGDfTfVL5XRoPKIiPga8ec+V1Y+XdOCPXdAQUF3evVdyoc/S1GjXj9uCoaJz3nKZe5/vgzrNNnY2ggaUzdeXWgwd/J6FT++t0NPNtOOvoMhYSbhifXhojLo1UIVEkPgSCOl/pLBe1j6cLsz7uzyza7UZFyoFPOvngkWe9l25g14LMspDv5wo0aac+/BXBzPOZ8QUPj4+p85zxHvtA4u7yoXzw+iXQXv7FPLHSMaLBIV+fTHM28MOWODpfLIWnngJ/tYQCEBpglV8cfo0wmk+S0EjCoDdQB3J/AAyjiUwYVBoNBorf2TtAsQKPRmAtSMRAnuT86Oko0oCOf01e5QB0dReoHlzeOi95BvaHZSOdnBhacZjn7Qx3NeiLm1+LyHu+fZvYUtOFxhq4n/L2UHfZF+kkBHpcHASbqffaB6ecCf7SLvmyNfaVOWzSeekZ94pIXjoGAFJ370WiUMnWoUzSLnUt19jGWo84MuhyuUFBM54p72rfjBfFcVVVzjqvLFd9Je+I8xHHzoB+XN5Gv/G+NtzLbyLPkKcqJrnMCKBfgU/vZBq5f1qwgHbCIyw3O/DqPcKOsRcTccknNkKqfDFp4IIj0yI0XAyIMRHt9bA/th79H1zmeDHhVVRVrF2tRr9djMBjM9PT0clxWVlaiuTILDk5jGr1JLw5OD1IQ4rR+mja5HJSDOIiD6Fez5Rxzpbo8vrNbdeNqXI1m1YxGNKKoiiiiiCqqmMQkTuM0RuUo9mM/vi6/jkEM4rz+DU77xjRzk0v970Y32kU71spZYKJ9Njvpo1k2o5pc7vvgepA6lZhfNOBSK62JF//ev39/zg68//778cUXX8RgMIharRYvX758Td9y/Pm342vK2iIfgLKVSJ25x7FDDsc7ds7xvWjDYGXOb/F69d2XORHf6x7pItZLzOX4irLk+nVR4JP3UM9qjFUv9xTy/nBJHvWG60kGJDh5wOVkJycnaUmoeE7ZBJppL8syne4g/lP20+bmZrRarbi4mB15ff369RiPx3FwcBBff/112qOBePD8/DyuX78eZ2dncXh4OLdxdMTsxLCjo6PUNwbgNjY2Yjgcxq9+9as4OjqK8Xicsl/+yT/5J1FVVTx8+DA+/vjj6HQ66YQwBSSePn2aTppaWVmJDz/8MD799NMoiiIODg5iZ2cnXrx4kej2D//wD+lkiP39/Xj58mUcHR1FvV5Pp3HJRr/zzjspq6goinj27FncuXMnZaK9evUqZWPs7+9Ho9GIR48epYCLPpXpoCzX71K+c6CBkXsyJpnJGcmVBj/5t551JefCw+sUZtXh76BS5IxQrriz6n12gdU1zxDw33Pgzf/mkgVuOumzK/7OiHnHkTM+VD45x32RM0sAq8L2c32yrz3leJOeBE45WumeRc5szphQEYtWjAoXxfxxZ05PB4wC6ryXbdZ3GoRvC0A5XZldo0CXR5n1LqcpaZhztmkQKEMcE+8LC4MylC2OGd+Rc054P50Zl2n21Z0w8gGdb9JEvKX6XS7ZLjfgpD+dAtLXdcYfo/R6vYiI5LSq/9x8yU8PUD+4o7IyDaTwm81mbGxspKwEpimW5Wy9pEB7VVXJWPjJCE7LlZWVuY2aSGPWz+fE83TgVKinGMwQ6CDgUqFzwffoGlNx3YlRO3MBQD7jfMvglpZNcA8K6lHSjjLC91FXsy9y1LncgTqLTpXLMfulOkh36gy1zXWUHGkGTtQ/Bn/cGZf9l5xp7BTQ0jhwvwTaLHdkIi5nxnSvijuT1OFlOVs/686n3qFPtUW/c/00l3V4wMAzdfR+pbK7fXGbLPs0Ho9jNBrN2VkeKys51DPNZjOl+1KW9bsHSKbTaZycnLxG0+l0msaEtCuKIgUHyMcK9DCLiLKi9ct6RtkVWpssniYuU3uVTSJ6kd+YtcO2cymEeFlLRHyzOT3DADixGCdZyGOy/2VZpn1q1NeLi4vo9XoJvFMHRMz2erhaXY1qUs31Ww5RvV6PKCKqoopBzAIQvaoXo3IUJ9VJ2luiV/Vir9qLfvRjFK8vt2hHO9ZiLXaKnXgv3otG1Yh6VY9aUYuiKuJiehHncR7nxflsf4piN74qvopBbcH+FAhIKECxFmvRmrSiO+mmayuxMjd+4nHpDjliZ2dncfPmzbhx48acHLRarbh9+3Z89tlnUVVV7O/vx2g0SntlUX5yetPxzLdhMMdQLpe67jYmh1/0HvF/Ltio4pldtI20K8QYbndZcviNuCc34eV8T3/B9YLbBn+vY3Xq65zPI4whO6T+sU2SH+IeDzzI+X/58uXcEYplWaaJFGVo3bp1K+FpLaGYTqcJF3Ez+o2Njdjc3IxHjx7F48ePYzKZpBO+aEf+7b/9t3OnViiDQkFynjwhHVRVVfz85z+Pfr8fn376aZycnKQA3JUrV2JnZydqtdlpFK1WK7a3t9MSoohIR2dHzIIZ3W43NjY2YmdnJ/b29pI+UTC90WjE3t5etFqt2NjYiJOTk7Q/g/ZTUCBZG1YfHh6mrBQFUnTUp/qlDA/hvtFolCbFIi71YW7vkUXlewUaxEjOlGISKXA6VD6T45Fqd2TIuO5UOYDNGV2CMUYXczMnBCLeLt3jgs538H4aejfkrkAZAdP6NwqlO2EEPv5u/Z2LbPL+XOSSRsP7x7ZqfKvq8nxYd0ioiFSXA2t/f+4+ghnez3464GYdGle+x9vpvMi+ktcITsiLXh/byIAEaUp+86wBFrbRFT3pssgwECjrOjN5dI0GQ2vUGIhz4Mz6c45gTr4WKaGcgSYPS5d4sIx08UAW63b6UQ58PNz4iy4ebHvb5fDwcE53qb+cDdTaPhllfdeyhbKcrVfUxo2afRQdqF8iLoNpzF6QLqrVanMz0KKxaET6Lwoa0CGNmD+RRk4CHSPqTB/HiNeD0nIKcnzFcXUdRJ1KHmUf6WSIVrqnKIqUMaKgiL/PeZ/tyuki3V+r1WJtbW1udkbXWV8OjJKn9R4BANGaOotp+nTcBJKoDylnbDdlh2tXc3pSjrzawX08yKsMXulUEs3u690KqlGWOctUFEWacZMc8aQVgVudHOL2UBlGBL8+Ti4XjUYjrXOlDBBMT6eXGz1SBuiAREQ6EjbnoIjnuBfI6elpSj3WPQpQaSMv8gU3eiXd9TxnpvSclk54sFPOvtqrOpvNZhwfH78WWCRWY/20w7JR5HXHAo51VLf4SX8zmEL5Vlv1Xb9xrCgTxD0MHlDO2M8cVlNwiaeTFEURzWhGo2qk+txG12q1uIiL6E/7s+M+V89TtkSvmu0vcVgepv0kTuM0ooiIb+Acl1tcLa7Gu9N3o1W1UlAiImISkzivzmNcjWNYDuN58XwWBIn8Jpftqp0yJDpVJ9pVO9bL9VirrcVR6yjOW+exerYaP/vZz2JtbS3Jgnjs3r178fnnnycHrtfrRbvdTv2VfmFxvEXZ9L8X+RZ61rGEj5vzqH7Td18akdOJHmRwe+N/U1+yv9ShObzoWI39ZJv0N/WJ6s35TE6vRbTO/a53ElP4ePAZyaScYdarZRPc20FBBtUlR1r6vtvtxsnJSbTb7RSI0J4DKysr6SQtZXb+6Ec/il/+8pfZvm9ubiZaMfBPfTAej1NQkgHser0e/+yf/bO4detW/Nf/+l9TYEQY7cqVKxExC4p89tlnURRF2kRS46R9J/7n//yfc5neOnVDkyobGxvx6aefxnQ6jR//+Mdx7969+G//7b+lvb2azWYa56dPn8bz58/jwYMHCe99+eWXsba2Fv/0n/7TuHbtWjrtQuXq1atJf62srMxlf0gmvkv5zoEGMaaYVNc0QD5DQ6FSoyLmo9eMEvId+lvPOVCiQo6YF3S9W59vMm5U8DJg7BP/VptzAk4lRQWjthFQEJAXRZEiSexnDnC7A0qjSxp42zzyKAVABeeKMweaBRJ9zNh/GVmvy3kipyBJb76HY7gIsLtBc77Rf4/k5oJGOSfGDZHa5kZRfCQeIZ3YNr+ftGA7HeyShn6N9XAMfOwj5gMqmkHn6Q6L+E1FThnplTPwTp9F9GWb2W4GNHLPkodzesd57U1t9Xpzcvi2C42ootkCuHQYxTtaq6dNG+WM0WEQ+Fc/6fQwmMRsB91LuijwwdkQ51HPQuC4O+8wMKRCXURaM0DhS308KMT7FvEKl9/ITtEZ0TXXaWyvZkfoVOZkhOCRNsv5U3XIWafjRIeHy2bI77mgCh0jBs8FxLx/vvSC/z0IXRQzJ038Jl0mB520EihUPxhgEDDT+m0CY/1OOqoOLVlhexik53irbYPBINkHBVMFmtyWqJ2+maEyK3i/+P3s7Cz29/dfo7v6RfkSUPNNJxU8EA5Qe9xek48lywqYUH8vKtzkjePnMsNgk55xB070UiaG6EydQLkQL6qtnn3gM2MaMwYAad91r8AyP/0e8ldVVUnHyimkg8DZYcqA44SqmjlCXD7FILnzdMT85st0SOmcUU+Nx+MoiiIaRSNWpitRjuaPYfUxPK9mGQzD2jBG5ShOV07TsaDDchj7xf4sUFEOsseBtmMWRLg6vRrvVe9FM5pRi1qUUUYUEdNqGhfFRZwWp9GPfuyVe7P9KYpvsi7+v8v6/kP1H2KtWEvHgXajGxvlRnTvdGP149XofdWL6ck0BqNB2pyUvOiBYbdRtEkqrj/0t+ogbs1hV/53PuQz3IPAMSD1JzPTXPfn5CmHubzfOYzkNFtkm3jNC20W6ZXDUfrOPudsNW2U4ynHkqSX6iyKIm082Ov1Eq7RPnPaOyEi5k7T0fuktzVekvv9/f3Y2dmJiNlxlJ5doaCrTgWrqiq9U4Ft6WRlWvBUjtFolPThz3/+8zg8PIy//uu/jlevXsXZ2Vl0Op30fLfbTXRQEF0Zch999FE8f/48Hj16NGdPHj9+HHfu3Inr16+nNhwcHESr1Yrr169Ht9uNsiznlreL3rKXm5ubKePo2bNncXh4GL/+9a/jhz/8Ydy9ezfZZvIsg+QXFxfRbrfn9tX4tvKdAw00EO6gskEuSJwlUiHo5EDqfr2L72AdBLRU2t6unAPDe+gssT2snwqP7/e+E9ApdZUKjhF7tsWLCyMVgPeDbWYgyGeW+JwHCXLgnAzqwRspV09J12+85so750zkwBGdR1d25DPvu9Mkd29OYbKduXd7cGVRmxaNJx2JHI3pFOR4LzeOboxybcv1kbwvoMVoco52/p00JIj0dufGndfZFx+H3JIH1u1KVPfk1gSy7lx7OG4cS5eVt1l0BBKBpgyXeFnpu/qvtfxMTVZqrkA5wQ1n33n2NWWYYIA0JhBnG3MBTcqOou6Uu5x8UP51LwMXb5I3jbPq9qAB20ae5Sy52pILMjDYLT44ODiY2z1ahbLIZ/0eFt4/nU4TyCjLcm42lXum6F59kn6aDRdg0zsZTPKMsl6vNxdkJ43dVnG2fDq9nKEXz3EjS41txOsb8LIcHR2l94nvPSOHY6e+qX2kja4vArYK2Cg4l3NKRFO1XZlDuk91c/0xATIDtbl++ycdeoG2nP4lD1Em3YlZRGcVBtQce+RApT5pt3hNx4HmMhV1n1Jxdb677vFsVGYeUJ54YgqxleyVlr8p+6uqLne+V2Eqe8TMsVHaM/Wayw6DG7Q1kjtmfmk8pW8VgHHMyDFSYIsBKgaVXce6UyseFXZurDSiPqnH1nRrlrFwNp8BQp4dT8cxiNkmlv3ox2n9NEa1UfSrWfbEbrEbo9oohuXwteUWZVXO9nmYtuNGdSPKYRkHLw/itH8ajdVGvHf3vSjqRUzKSYyLcRzXjuNZ8Sz60/4s6+JnMfsfEX9Z/eXspI2i+9p+Ep3pLGNirViLzqQTjWhEVPOZD47vZPMWYSXxAnWI6O62zfWPxlhjroCh5J1BLtojZki5biNveF+8/TmdxeuiieNi7ifkz+oZ6gTV4RnZlE1vu/578NLvy8mErmkPBQVe1bZbt24lmVLdn3/+eWxvbydctLu7G0+ePIlerxd3796NK1euxHA4TPzAk3MeP34cL1++jOPj46SXNNnGz1/96lfRbDZjc3MzVldXY39/PwUaFIzWsZvc50dLgra2tuKDDz6Iu3fvRq1WS/tNRFzuPfb8+fP0/na7PbdUTf0VbyrQMhwO42/+5m/i448/jnfffTfa7XY0Go24du1aWvagOnUSjYpo0Ov14ubNm/Hxxx/H1tZWPHr0KPb39+O///f/Hru7u/Gnf/qnyRZr/DXJos/BYDAXSP+28n+9GaQbOF1XWZR94I6FR+tzoIzOY8RlBJxMLmb1d+k5Oi/+HhoUKhcaYDpTXre+Ox1k+GToZCB9Fo/v8/pcYD2Ywsj2ImCfc8hohPTJjZv06Y6F9z0XPfV++Hjyv9OOClH/pQx9rH3Zh7fd30mQqHtobPRub78HpAT0XHmTXu4oOHjgrr3cG4JjtyhAx7Hle2mECa6cRtPpNCkyKRTvR87gkf+dn3I85u93Q+tBAQEAGnbytgMIFeojAQafwXD5pGwQVLJ/DuTfZtnf35/ro8CLUrubzWa02+2U5idnRPdzN2MB2+l0mpyjXCoy04G5zE2/U/49yEqjw0/KEvVIDrSpXfydTqLeSXDDscjNEjEgRceE+tH5iQCRbef9/C/nWvSjHOX0M9vAejgWlFk6HOfn53NHmtL+kW/5Ls4uOB9zWYTrcs0mit7cA0A7edOBFr0140NQLb5yXsiBUt0jEMVdx2nbXaarqpo7ak+0kHNBx0/8NJ3ONqYUWJJDStviYFt/r62tvZbBkQPramPE5TGZHnDl+Dug515Hi3SoO1jMZMrhBB8HjS/74EF18jUDLwzG6XMymcSVK1de4w+lFO/v76f+8Yx7BjkX6RK3J9JvKqQdHRPvu9t66UDXdWyb6KN2EKxzBs8D0bKp5D/q1lz/xPu8rk/aJ9ZXFPNHI+YcXD2n/njwtV1vR6tqRVl8s35+UkYxff2Y3aIs4izOLje1jNnGlsqUOFs9i2fFsxjdGsWkNYlhbRiHcZier1f12XKLaSfuVHeiVbXiZO8kdl/uRkwi3n/wfqxvrcd5nMcwhnFcHceT6ZPoRS8u6vNYvlbV4sr0Svy70383h119fx+Ng4KLGifHTdRzyjRSZozvDeUBJeJByrVjD860k698GY7PDPu7ctiWeEl/O+Zh4F9jTz3pQWzH3tKrjvEd85FH1T/1jX6gT7i5Paqqy0whbbxcVVVcvXo1er1eGk8dAxkRcePGjTg+Po5+vx/Xrl2LBw8eRMRsw9r19fVkS7Xk++nTp3F8fByDwSDee++9NGZ37tyJL7/8MqpqtmTsq6++iojZnlfin5OTk6jX63Hv3r1ot9txcXERh4eHce3atRgMBvHs2bMYDofpSMsvvvgiVlZW4qOPPorz8/N4+fJlHBwcJOzW6/Xi3r17cf/+/fjRj34U//7f//sYDAYxHo/j2bNnUavV4t13300BlZOTkzg4OIjj4+P49a9/HV999VXcvHkzfvjDH8bq6mrs7u5Gv9+P1dXVuHr1auItBUa0t0O3201ZF1tbW2lTyuFwGLVaLe0b4faF+lvj9V3x8fc63pJCo+Lgn41yoyxBcKeG9dCYsV6VRQEJCoIbDQkKU3JpYPyaOycSNgLjHB1odBhZlVF1mvm73NFbRAv1hQJOJaS6fcy83QRbubaSXlRqythgH1zpeLucOflfTtEierIuGgx3tkkT7yffTSdS9fg6OAd8rCcXRBN4UTt0NA35lU6DBx583NkG/03tp5NGGntqas4gTKeXm4Ox+HjxmvNiru4c3T1Q5EEj0lU7x3NM+W7dy43/+F6BN28D2+qgd5H80Xl620WzzbVaLQUWmMEgA9HpdFLategjZ13p/HSqOH65PmgsKOccO8nDIt5xPUa59vfousZRvMaAgNsJgSAHz+6QUKcppZ4ZG7zfnbhms5nWOS7KrKBe5FpQ9lnt57MOQklz3iMn7vT0NB0XRboq+EjQxnPJz87OEp8LlHkQQnSQM0u5Iv1yjh6zIbzv4jnuN+DjR/7x/3yOY5abVSQ/Mf1f407+YhsFlMV3CgAIdCpo5We6q//Ui3wn+Zz90axTLsjkeo5t5rFqjq+INcRXFxcXr+3l4PpebdN10oH87f2mzFDu9AxtltbrcmmP6qnVZptkSi61Llq/87Ql2jBOFLk950y02skjVNVmXSeGke6U7BCLst8cX44/s1wc5+XkX86QxlW87badtoy6gjLv8kw5YwCIY6Z6RAP2U59sK3W764VmNdtLYmu6NXt2OgvWrK+vx0qxEl8dfhV///d/H4PhIH7w4x/EzQc3Y1DOAhPDYhj96Ke9JV6WL6N3tRej66OIMuJ38buIb1TpSrWSMhp2JjvRmDSiVW/NAiCTaUzPphHDiM93P4/pdJpmvxm0WllZiY2NjZSe3mg00qalOR6fTCbJASVf88hI8iPtCW2ANt4Tv4sHyT+iOY+xJg8o6FuWlyfBECtJV3Ipj+ggXCB7KVkVL/E65VH6jzpXz/lEmn73mW31V+/heykb4k/R2ydbRePpdJqcYekuBh48O6koZhM0H330Uayursbnn38eX331VQyHw5QBWhRFcqw3Nzej2+0mO/bgwYO4uLiItbW16Ha7UVVVyh4tyzJhs2azmY5wZabbxsZGwtK3b99OfLG6uhrPnz+P1dXVuHv37pwMSufv7OxEt9uN1dXVePz4cdy8eTMePnwYk8lsOeL9+/ej0+mkAGZZlmnzb/HH0dFRdDqdqNfrsba2Ftvb23H37t0k+9oIkpiDukW6SvpK18T/xGHUNzkf7E3le2U0EJS4k0TlTWWnhoowZBQyGq/JMErgFqX86TlnVhIn4vV1vnrGjYvaTCDBOtU+1qNrBDsOkvnMIhBPMMF3LmoDmYEzlBwPHwevW+3VsUS5iLgzZW7cHUDzWQexubHzPjpPqA8yKuxfzhjQ6fI69Cxp/SZh8T5LYTrYcBoI1DMwQqDJ+wkeKGPsN3mW4+dANMczbL8cVe3ES/pxTDwI5+Phjj/1gwMqHwfnXwZI+BzHy4uPCdtI/svx0iKZlMHk2Pgsz9sqW1tbyflrt9vJEZI8TyaT6Pf7cXh4OUNE8MzZBvbV9S9nL6hDXZcuArU+xtSNHvHmO3J6lPcL+LDNrVYrHbMXcQmmc4CEGQACX3Re2C7yo8DSixcv5niMjiwzP6Qftdu86KCSc5BzjrvaS2dFmQFra2tztFMAghlRSpMWCGR7BYyn01k6drPZTCmjApSLdDaLUn3VTp2OIJ3hAXOBS81qk8YelFQ9Aj0+k8sx5Sf5PKerImIuWLCyspLWuQrU8SQn1428TlvIXchzwUYCaj9BgWNOejg/8PhF8ioL7ZjW+TOLhGOZk9eISEEoOgbUeaQBZYXLOVxn6h7RWv3nsh/uzM7U57Is5+hLe0A5Yt/1nC/zy+n4XEovZStHJwU56Jy5TvT30n5yKYbqJB0F+KXbiRWEFwjoiS9y7XedL96jM0B+zNk835iU/yPitcCP3lWWZVr29+GHH8bdu3fj008/nWXj9RrRKltxtbia6MpxWVlZiU8/+zRenLyI2notrt+/HoNyEGcrZ3FaP42z1bMYrYxib2VvtpSjNohYjfhF/Rfxp6M/jZPmSdJtyriKiLR/DJ1njRkzG0RrBiPJ09PpLAOKS8V0L+0KsT5nf6XX9V7xLXmKciiHV4EJ4UbpSmWQifcVHNe7xT/SNRxvyZ5sJLMY5cA7FqUt5f40zMBlQJc+n2NS+W/qv05UoO4SjxNn65PBdsmYb0ZfFEWycQoMnJ+fJ72kfa2og/Vu0UF9vHv3bgqg+ORHVVXR6XTSprMM0ulebUJJfjo7O5sLQFO/nZ2dxfHxcVpqeu3atYiItOn0aDSK6XQa3W43arVatNvtaLVacfPmzYiIuaW0apveqz6IFzUuDKIRJ/K6xpa8Jl5hFih97G8r32uPBhU6ADlAQYNaq9ViZ2cnRX5OT09jNBqlFBI1mIBeg8Uzmd14imEYEc8BBwd8iwwmwYoTjxFEOtIEjgINEkh3dCLmI8YcZG+b/+0gRXXlAgE0CHQ+3QHxiKwbbo5tDrSTL9RnXyag9rI+0jQXSVWdXocMNmngACPXB97rkTrey7Hy31lID/aPY+nAm4DQZxn4vANa8iHBHPvOurzNubYpYuzLFjxw4PzJfjCARgDmwNF5JQeOyKtylHJBDHd+ua+LywtnEEgL1zO5cSSvs/9vu+zs7KTsBWZgyPh59N75xUGt+iMngim11BkCJQKaTj/dRxox+6IoirkZYQIYtfX09DT6/X5Kc3fZZ0BNnz67Qt3A/pGfZDxFH54bLd6ggeWMrq57YMxnjOR8aj0m61Y7fXmcy4TzoWglXidI5HhL1qqqSg6zZnF9Jpx9kcMh54br1gnc9L1er6dgF48tbDabc+vNCbBpT8RHXi/502fdVK/GguDPaUcnQrzGwDh5ryguj39tNBpzJwPk9BXHxde0+j1uK3TdN3jM8Sr5lDrFA4VOM8q7QCtxD+t2p91BuzvPuodtYZDMJ3nIQ0VRpDXH0jkMZnS73SQXbvd5jXyk9qjdPhY+DsRA5FF3Jsi3jgvYNvWTS0y8TRx/8pVniXCMdZIKdRCDVHIqlbnm73OZrarqtSCA2kvd5MEl3qt6Xb+SFqSJ0208Hsfp6Wk6YnBrayuOj4+TLhc/0MnScqyf/8XP00Z3O8c7cb1+PWFQZfglfolpTBqTWK2vRudaJ3Z2duayaLRJrN5xdnYWo9Eojbl4hHpeziX1DdP6NWaUWdGTE13kZQUneQ+zmj2wJ7l3/tMSPf3tekV1cyKWY6hMt8lkktL+FcTwGWn1jfzMY7+1NEmfqoPZDGo37xV9pFNVX6PRSHIoftO7JafCJcS7tCXiL/I/6/TlkDn7J7qyHrWfQRzqTwVelM3gQRhNDrAvCiD4RrAqOWy+vb2d6tc1Br263W46VUh0V9BIYy/9xQAbx0r8x6XkWlLoy2mEJYjT2ea3Hmhg1N4NIhvG77qmFKbpdJrOd9/b20vPcx2p0jwj5pUi3+sGJ1dckHTNgaTucQeQAIYKgsaNBlbfWa87CRJWMa7TjsbUaSgF4dHDiNeP9SFtaKzIeFLsBKh6l6fXiHZ0XAgIHYB5O7yvEZfR90VtZsRVxor8xbZ4wMLLorFkmwnQCPhz48i2On38XVTkrNNpQvovctJz4Mh5jc/wfVKgTFXOBXMc9OYcJJdB0sKBoweQnE/pwEj5U1mrbneKv22McnKk5z0gx+I89ccKNPCIIvVPIIOOsPpCfcM+6Hc6BE4PrU0XKKPMO5jmjEZZlmmPCDdYMmwOVFxHuZ3QjAR1l+iglEDxJo/co4OrfmmGkGDdbQXH2mmpomucCaW+0cy+iur0GX13OKmjSQP1Wf2kXdVnzrnTulTyP/WwnHF38nMyLD2v+7R5FmnHvQNIf/GHjwcDMbR5+tudHf1N+6uMBM5Uchab9ox9FICkU8ClFuJt2mmWi4uLuZMLGAyjzWf7KSfkDdojx0TM6BB/u65noRwp2OSZPm6nKat0VhwLiW4K5LgNk17y+lX36elpnJycJJlWkVw4LSh3Lp+0Rc4rOZxEfqM+oW7jcz4JsggTeMnZV94nHuB4ucxJB/NdupcOjNZIc8kl6+aYqS+5YAGxkNtyyn/OjjuP+z25bBqdkLGxsfFa4Jn9jZjpGemWDz744I1BnaIool7Uo5gWUZ/Wo6hfLofw8dJSQu36r30a5EwS+/PIZ73TM6ykO+Twa6mQxoZ6dzqdptn6RqMRp6enaVylp5ldQNvmgWk58Qpc+ISBbzRKZ1C4QdePj48Tv3AfIAUT2H89r7Esy8tjGzk5URTFnE5fWVlJQV3ZIznWCnTpfb5Rq2xWrlBPqD25iQjuPyDeUQBGZXV1dS5QQh4l/ekbOp6Vndd10ZzypHeLT6QXOfHI4Ibq0NiI16TrGYwSP0nf6J3K8nDdpfZSzqkHSAP1mzxPWVe/KPNuE7+tfK+lEw7+ORBUZCwXFxfx8uXL2NvbSw3XzBPBM0GKBEzGlHW60XQlquLG0Y2YAxU9w0FRuzSoEhYCez3nwNBnAtxR43tzAQoXSM7SuvLmWJAevK70SaYf8T6CpxywcAOQAzgOYpzBSX8HAxJ68pmCU5xBd/qRTvruYP9N/OB1+Rg5X+hvKnTNRuSEPedk810Owr/LjJoHughA+J087IEs3uNlET/4GPvvDsB4jePsNJLiZfqWxtzHjG3mO+jQ+fiJNrrfx9bHJAe43nYR4IqYT1GNuKSDB+JkqAQCBEbUNwYo1BeBHb1La/FoYAlUCLrl3KqtMvTc5ErASXVJxyySGaYPk778TqPGoucUfRdNqGvUBudFB7L670En9p/XNRbUsWy/t1V1e5YS3yOaCJi6LSXgZT89e4IZSgJ5bAN1jO5vt9tz46z7BNbLskzrf3WdNKX+cx3Me1S3QA/HSLxCW6l2cAkDHUq3jeJbORpKqaYj6rrE7bVsjdpBsKX7KYvsl9t30SNno/QbnRrX7d5Ojbf206GcqU6lsVPPe2DIg8r6fTKZxPr6elRVlZbr+GaungXFIO+9e/diY2Mj0VT16/v+/n7Sb+Rj7787TxxnlzW1W3XmaObyzzp89po0ydkrl3fiC/GD2yf/T/xG/eN4jhMC7Bt1LNvHAIq3nZMZ5PmIyDr47G/uGuWTulHjSueL48E26V4/htkdF+8rN3ekfdbvkg1NZHL/BuIEBXyYyeDBgIuLi+h0OqluZaRQ3s/OzlIgVk6g2kW6M3DEDUh5NK1sK4OH5IGIS4eedOfzcoLl2KsttOmSA7WJWRHqt/qjdtL+KNiiJQIaewUBKA+rq6sps0SONHmLfdXz0i0KJKnIAW82m2mjRQVBaPsYAIqY3whZe8SQBuQfvo/4mtiIwRXxlcaEe2qMx+MUQOA79bwCA6PRKC0VZcCINoLYjUFSjRffQTtPPMAsYdGFskk/gsEHldXV1ZTJQXn1+95UvnOggQLr0WcVOuXsSK/XS8TmEgUCYk8F5MDomjtuvO4AxJ0oV4xe3DGhEfBZIxpUFXfIqHjd8IlWVPIE8lQKDMjQENLQugOm9giUF0UxN1vj4JP99QAA782NuYobINLCncBF9UkwZIhotBaNr4SDBjzXLw/cqA5eJ3jkO3zsvD25oJKezz3r9fBegl+2X+/xuh0IsV71T8qQM0/6zUEFAcWb2q/fGGX2unOghfQWcBUgcJDCMVF7ck6N+NazAFyfsE6nL3nHx/CPUTqdTvT7/bnzjvVuGibqAdEpB7bogLgB4b0cdwY45FTqOdd7dF6k1/Rf9UoW6fQIYPGsapdJd9Scl1w2XK/LsDJooJK7X/aGuo4BGs4eCFT6nhhsI51uvUM0yGVZ6B5uZkiZo/xQT6sOATPN9pMunHmibtTv7XY7quoyizDi8ihHAU/2T+Av4tI5cdl2+SSA5AaWKtTdDCSQtwiWSR8PGNG51jIkPh9xuZSNfKL/0i9MQZbddPAvOihDSODVnSnqcBUGPVwGq6pKZ5fn8I36yZkw2fXz8/M4PDyco7/XncMSHMeDg4O59tPJ4UwgeUrOktpCueY79TzrZv849i4j+t1nD8l7bsNcX1AWZa9yQemcfcrZNL+Xv7szxfGXPDhuoM1zG0e+1+/CoHw3g7ss1P2uF9kPTiCyHc7b1J1u40VT0pbyxfeqb3Ro3c7yPsdjlC39rmUZDF5Sp8oGHR8fJ5vbaDQS3tTaezqtkiVu7Bkx05ebm5tJh0iXtlqtlEWh8ZbMKlih9xLjc5zoQyl7QfqLM+rKwCqKIu2FQr9M7SePqJ9My6cNU/sUUGAWRrPZTA70aDRK9JXs+1gKd06n8xkkeo/6ySxr8oTaJ1mYTGZLQagPyrJM7RQdVZfa5UFjBYSUUeZ4n6cf1Wq1GI/HiX81rgrE0ydTWz2wJR3tuoDBMD6vMWFgxPU362HQQHRRv5kx54EUtYHtoe5R32QbadOlC75rkCHie+7R4A42hd0HmoqZRCdjU2mxIySIOp1zQFwB+XW2nWCMxoT9EAFprPh+76+/J9e+nGKMuBRMN6wRMRe5pQGhoqFCkXIXM8qwioEIonw83OlnPzkGOSPkoI90ZL0ce76HgIfPSNgZRXQAkXM4/W8CPipcHwvyziIjT7qzLR5IckXggIc09LHMgVbe54CAEVDd60E0KT8Fmug8Mqil56Vccnzp4+jjyXbndIU7IRob53MpOT2Xo4/zgLdT9bijRppSPlkos4ve8f9aXr58+VpQgaCF409jSaPKwBNnFkQ/jeV0On90LZ+jY+m8LOAhGnD5yvn5+dxRmhHzgToCAv3NQAbrJR98m26hs+kziXq/2wLKn/7zWX5XUTum0+nc+d6uL9kvtdd5nPymNo3H4/QMN8liHd52Oe5MoScdvM/6rjqn01mKr94nQEZHjkEmzggJFLI+8Yx4w8EW+cmDTIvAD/mDNnIymaTZSvKFCteFM3DgvKP352ymfmNbnV+1tEMZIREzID4YDOZkgXKlviiYRxmUM3F8fPyaTFDfqz4GG9gXAVeOtdsFAmnSg46q9rWoqss11RcXF3O792s2mu8XLQh0NS79fj/1x+UnhxWogyRfnDFWIQ/xWfYt9y53+ElL6n31zfGl05Vyy6CAaO5YjnxIPZfDVuyL95+F72Udfj9ppD64U+p0IQ35XO5T71jULr/n24IhpF8uaK5nRqPR3EyyO7763+l04vz8PPGkxloBBy4Rl96XruVpA/qUk1+r1VIAUjPNtA3iYfIEA/3SLXJ+2RbJvfQIZ9PlTDIgQbzAI1erqkqO83Q6nctUVJu1x4XepfrH4/EcX6kejYeWfKoocEPsJtpIr1DW3W6JPnSAtfSTssp9IRgooc3RdZ7eoL+ZAZjzXcmPCj5TBzM4WBTzJ4NozCT7tK3qN4MN1E+kL9vDsXS8Q950/OqyR72jtsrOcFxko7xuYsbvUr73qRNsLJUMgQ0NOQEA73XFFTEfhVMnCJjdCKkd/Dt3j95L4OOgxhWc6mO7PYjhgMkNpjs3DqQJQL0+N0SkubeLoFfCqfQovoOGhGPH8SFDO/Dlu3z82ZZclD7XBwIiPse6+W43MGwfS07I3GC6g+2Om7+bTjmVB/vt7c4ZTgc0ufZ7Ch0L36X/b0oFnUwuj1AjaKKRcKefNMoBjBwgy/XVlZnThrPe5Hd+sj61iWDdHRTyovdJ/VzEkx5k+mMFGSIijo6OIuJyJ261hyBJv0uWJaecZWZfBeZ1LZfhoTFRxN4DU1VVzUXgeVJAxHyQjCCKwMn5Ozc+HD+Ov37T/Q6wKStMt1SRwWTwgIEJ53kaffKL6vXAhGd5CFzoGu2XO+XOm7Q9kgfvM8Gl9mDQGBEUULcT8OjoKtfxAttuv+Rksp85fuIzpA0DDLJFtFUMBihzRIEB8rhm6zg76BvliQdy9oKOrdsv2l/9VqvNdvWOuAzeczNTBV0E7quqeg2Yq716juND3qfciW7j8Tim08sTRZzPVL/Gh8+LRtId4isGFTQudEIY0JEMMlCpMdIabK4P5/gr+5C8wL1mtLeI7nebkRsj1xXcIM4xmMsYsShpJ77Xc9Sbjl9z9sDlUnzqe7uQvq7HNP4sbLNoruKY1p2PnF50XfAmPEU7SOxOeoi/9DydMX+n8ywdftLC6ep0Ip+wrY5HqPsjImUu5Gin0we475DqPD8/j16vF8PhMO3Jw8CedBV1FPWh9gHQGMmJ17u5ka+cSx3hqKC9Z7+pPxoDLQ2Tsz6dTtN7uWGgbLH0sAc3J5NJCpZyQpK2kX/70k4GuJnV6EFM9UN6Oxdg4B4ywjk64lzBAW4Cr3v0nfsiedCOR4Zq/PSeyWSSstMZxFG9ahdPKGFQXd9Ff/GLsi5UL/W1dLsHUGiPiNGZfSV+U1CZ+FRjwAC57qctVj36Td9dvlwupdfpn7Gu71K+c6CBBobATAOTU7TqtCsIdkbfydz8nc6y7lW9ej/XDKut3ICD7/VPRtlzG+W5YvPBYH1ql0e56NRSkXo/aCwokGJwKl6OgYBPvV5PoJKgxg0w20oHYpFTm6O9GyOOt8+a54qu0xjmjCbbqjbk7vVx9jHhs6Sv8wivuROrwh3P2Q/KAwETr7FvpI3GjMrC/+f65ulUbIui1LmxXeQEESQR4PFettdp7jTm76QxQQ3XmTH9iwrQwVIOBJJPvb8e9GPxcVK9/tzbLFeuXElGmLpKBs7lQ+CEBkfggQBFzrc7rE5zrsXWOKkep4HoIGfCae5gcpHM6xnqBw8y0IFh4E/PUS5pmMWPBFPkh1yfcqCb+pp0zdWT0+PUT7rf9fsiXecAryguN3jMBYUW6WuNkWbV+v1+eg+PXlMdbKN0B0HFoqA42xxxOangfEdA4r9pCYhAnXSr9JZv0qb6mbl3cXGRMjWoRxUsUhBDfeXMofpJR1bOtYrbOvIJQTjlVO8j7ypbwAOw5D3Rn3ZZPKAUZtHaszvUV42NAC83guO7pHsiLtftMlBA3nfdoHYPBoP0XraZWQ5VVc2t8XX9S971+tVe0pp4aJF+Zn20y+qLdoinvNDuqAjEUx4onwyQ5fhe9CDvasz46fLFflFf5DA3n3eMSN2Yw7PkP9ZFPnF87TSiT8DxYVDE9SL7xvd5ADY3nk4TvjuHF/TceDxOgSEG9Ktq5rBKtk5PT+P4+Dj6/f5re5KQF9zWSL61RJX6WUcR6ppwjgJ10nPkEfVD9rLb7SZdyiwEBlSle/QuBQiZ6XF6ejq3t4H0KJ1f8R83iWVQQn2gzY2Iud+0rwSDGJ7VqGdoI2TrOEaiF5efKONC2RMap8lkMndN/fEJOdkJ2ihOkKtP2m+Cv7s+UMYabQ7HkdkPzs8aRwZQqP9Yn+hNH5O8Ih5gNqL6Tx1Cne34Te2SzVHQir9NJrOlLDldnivfOdCghlBQqMgo6ASLbjhYlyuSHIj1+8nYvmma7mF0jwPA2T6uX4yYX0PqjpmDVG+3fuPvNLqikRh1kcImE+qTAJYKdTKZJFDjzgpp/W1td+PItuo9buh0D+lFXqDSY916XsLI/S44zvybCm7RvTnjy3r026I25+7N0Y0K2IMrTkOnrdflhsh/Zz0OOthOygmfkbJlFo/4jwGwXH26xiCDfvf2Esi4sfdACnlfRkDyyn4Q5OfGxq/lQIW+ix8dUDl9XT/9sQvBCGdANWsoQ6uZQTpEDszUHxWmZRMIuVPJQucm4tLQ0tCT/2Vcncc5DpR1f7/LuT5lJPku6nLxEMeV46f6/TfV47KVs0ncDI/9cWfN66Dsud7wd4rvTk9PE1iS3ZLz4jPSOd512otviuJypoyzuHrWdarAjgNpPsfvaqeK5JvvIAgjjRfxqjIK/IQU8ZHoSB3GIBk3vZJjKp4SD8gZ4ASD6MqZJv33gIQCIrKRXB8tMCm+099aD7yyspKCKVqyIhp68EEORMRlYJtpvi6nTlc958Fy8aZoTIDOIIloT7qSnznO9Xo9bt++PWdXKG/n5+fx4sWLucDzm7CU2159ur2KmF9e67hT4+46j5lKnCHV3xxrjb1naep3tlV84O3O6bpcv/U3sYk/Q7l3nc3+8n7Xf+QX8oT3KUfvXN9y+EbffULCS04f+/2sj797AMX1ea4eLQHwIIielYzu7OzE5ubmHK2cvhERg8Eg6QE5/6pP2U1yjGXblJUg+RQW5kw5sS2D6NR/nK3W/dIVzHjTb9x3hjhDfWLggTPx+k1BUuk8LVsQTVkvcZWCeqSjZE57LCigQVsluZOu1L4Fp6en6YQq6VXpHg/8Sia1bI2balLHkl9Vj4IyxFjUJ6KHaKT6ZQckW8qgcJysMZxOp2kppa6xXQwGkGYMGDi/E/8oM4J4l0vRPHAknaelLsInjsN874k3le+1dEKfagyNowMeN3AEH1SQOefRB1Z/k5Du1BB88jkqSzpa+u7RnIhL48U1Va483eiwDd5GB5fugNNwExjnBlG0lbBzQyrWQRqwDa74cyDF++pj7GNCASIzUujVbs1AMc2TvOU087F3PsgZE5+Z4PgRXPsY5eqLuBRARil97HK083YTmBEA8B5e0985fiAfuQyxrTlDzGecLj4WTnf2I0erXCDG2+l1uRPrACk35ryWy4BwPZDjEwfMuQDdm0DS/0vRGlEHR3qnK31vt4wh6RQRc/T0MSDNKbsRr8+m5/S9j4PPdHHc9DeBF6PwtAN8J+ugsSZdIuYDwywEKuybPkkHH39mbHDGXP0ggKIuF93ZRvIU+8W2CSjRljB4Q5DJ4IXLpWbjWE/EZTCaAJKfdKi41IJ8IFDIGWq1k3x3dnY2FyDiGKvv1PeeQltV1dxyIvKnjjBjmxWE4cwWnULSjlkEolmr1ZoL4Ak4c31vWZZpLwYBOtUt8CeQS4BdVVXacZ26UoE/jq366ksiuHaXG67xKLPRaJRmpSiLdDCYxqu6uPxmOp3OpQcz00XjyOygsixT1pWuHx4exsbGxtyYS05FK/U9Z6so+x4wcPxFWrgNcp1NGdEneUS8pkAKAwvUh3xO9VJnk4/5PrbBbZvrTNbFQFoOE5DPRCd9up5hof1dhGvpODnWZV+pt2kDqA9z73G65NrG5+kwuQ3S7zn6L7KrfC9trO5VEFKyLV2jwJsCwrRtkj3hWjn5shnKuJLeE3bXkcJql56TbuJMNpdeiF5c5ka5oWxQDukEExuIr4fD4dx4ydlWlgYDyXRemV1Jm05dxyXdPKlB9+tUPLVTwQEFIKqqiuFwmPS+gg0XFxcpQM1sArVJ76AuU2aY6O+6hDIrOpO3iWOqqkrHWVLP6pP8xcAE9STtqPpN3MP7VSf1oGy8B1toS6griGP0u55TsER0EH3YL9lMBaq/S/nOgQYCHXXclSc754BW1xzAksBkWge1esZnP0lAKmXWyRRYGo2cYnKl7wzIgcoFM0gX0kD3uULVe12Zs890HAT8fImEClP62ZacU8OAj9NUwpmjAWmbA/H6mzRmChqPNnLgzz7RiWc7vA/OazmgwZl9GmbSnmOi71L43MXcQQLblTOgbFcOBLjDQqOfA1IEfqRDjlcdrHBMvA3+npz8Ub687S6/TiN35F3J83e1VTThe8kXzj8EQiqUbw+wkMasI9f/t1lk8D0bi/1XNJn8xUwQyillx51fOobOwz4e1Jc5vnQngrwqPeVLDiQDes71ndsP6kQHi5zBdbo5/3B9P0GvnCsCTcrpZDJLc+/3+3F8fDwXbGD7qXO8fpcB9Y3vkuOo1FlmNfjSPzmlXGakd0iGRqNRAjMCuRo/6owcX1fVbEmBZqf4n4Eu2hf+rvGlbeU99Xo9rZlVmyMipYxqZspTiBmckIPoOlFj4zM31EvsA/c0YCCF2RWcieJSJNYvXvAglO6jLld7mEqsfpPfqcPcAarX62lfjRz+4TOLMA0DfwxQSK+I1zh2TLMlLpAuHQwG8eTJk7nd9Ckf4vPBYJDNwJBOcd2d+1R7NI6O4yhn5CXKHZc6iL9Ia/+b11wfURc5FpDc8TfHKf5fz7hNZf+p76h7nO6LsDgDLfpOGrgtoo5kfeQ/FeIs19Fu37w+lyt9evDJ3+v2gNdpxziu1Kukm65z7x/tn1IURdpIl/q40WjE5uZmuk7H/+zsLO37MBwOU2C51+ultlHPRETaWLZWq6Vg4mg0mtNRkhm1X3ibNOLSAzqT1IVyKpVRwckLBRSLopgLLqq9VXWZgSB8wfHTfZJ/Yi3KpNrreygo67MoimQ71D+1i0HCiJjb9JyZCww+c88IYQEuI3W7qkkN7R0h+hEnleVsTwgtEROtGaxoNptzelr2RwFutYVYT3KugIvspGyeZFc2jIEC7vFQFEXqp+sP8VGz2ZwLUlCv6B5fxun7dywq33vpRE5R6jcH+Dnl7MrWlZHu0WDQeFLI3AkhCCARyLxUwKqDfWAbVbf6wULiU2Fr0NiOnDOr+vVsToGrX+pDWZZzTL7I4LE/ueiV2ujv4fcc6CEP8JNjpXc6b1Dx+TjzkyCSTow7EHR+qKhoSNzZ8va4ISYdxWO63zchcz5nPwi49S534r3fPoZuDKnMyPtsu4wAQbWDzxwNFrWFdTqveTDIecRBhQMYOmz6pGL1/i+6xt/IC3w3HeNcXQ5gFgGet10YZGAQUfKp7zQ2KpRhtlV9yWX0cDZC93IsaJD0jNdLx8RppKi3ywKBEOtnoe1wnvGAm95FfUb5olOq+xfZFuqqi4uLBKYU4OEmW6SDt43AxPvEa8zc0fNK16YMaF0knYyISCn3BJr6JPDQu8kjegfbozGlXSEdaQeoK905IV30X+COy0FUn9Z2EtTr3Ho6Kr7hpZaZOF9yN3GfbVcf1WbRSbabacfcOV201LOehSbw506KaKzr+pvgT8/Rxqg4CCSvi4cYyNEspTIdOBvu4J/9UFsYdGOf1A4POrL/bFu32037ziyStdFoNLdRn9frekV1OH/REZGjQTli1gvtvGMn/ua8r7IIb+XaynsIwKn7qHPUP14TLVyXauzVJuoQ6h9iINpG6kDpSP89R4/cddoRFrV/UVAgRzO1Jxc0EZbWeOdwdA4z8X3sq/fJacS+8RnyoMZQYyQ5kByx3bq/0WjEyspKdLvdOD09TdlO4hHVw0lUZjop0MHMLOmuer0enU4nptPZRoTUDbpPulS6WJkCkhHRljPZVVWl7Cdi2k6nk+5dXV1NmZnU28R3ok2j0ZgLaipDo9VqJb0rx588zHoY+CAeUht9aZn0hXhIE5zKKOE46ze1LWd7qd+VvaKi+nR8tJ5VhopoSjn39mkc6PNIH0dE2mhS/NJut1OfOQlBPcE9KDhJQdyj+hXMUYaE5GI8HqcN5UkvBjK+rXyvQAOF2GcWckWdolFxx1zFAxTuMLANul+GmECV6Twkvislgga1lUZe1/jp7SHYFQOKLg7MCP4i5qPWuTXpGvCqmh3JI0b3zJLcczmj6DR3A0lASdDvRpLOkYRD/WG9YuKqutx1lmOo9+hvGkL1jWO2KLjAQt7w66yH/OighrTnhltej9PX286S4yfW5YbYCw2m10/nTGDLr7/JmX4TWMqBIv89d1310GhIPjyVj0WKy8H7Ilqpz3x/rh3ueJMGDnAJOgis33ahw6QUZm93Dhwxyk6Z0xgT5HiQyAM+HsjwACllhDRxWY64nAHy4B2dBJ999AArn6HedLmhPlN/pY9cZtVmBnH0LPtCfceAndL2GXBgWzyIktOj7igQWCh4Q55nycnuItpxdocbBVLHCtiobjqMfh/pTBCr53kKhutStVlASGBS/RBQ46wPz0QnkCdPcKyKokhraAVw1V8CLJ8tz00wMEVX72EQgg43QWtEJAdDbWo2m3Nnx8uhz21QrcCW87/arBku0U6zUgzsCef4EXOqN5eFQX3iNFHbaXe5Ozx5Ue87ODhIfMTZQTotwjPUA87X1HEMIIguxJCSdeEudzJyNsEDlI4FOQ7UF8QOXg/tI4N+Lg/k/5xt5fUcJhIdKNuuMxbZchWfpWUdOTxA+8g62Vfe6/ewLTne11hSRvkM9VUO4/q1RW11G/Km4rZnEU09Rd15yetsNBrRaDSi2WzG1tbWXD1633g8TllLmumWA6hMZr1T7dPeENxXicdky0Y5j7vcaAyYVacAgXQ58aX0hgLV8mP0LGVf+k/9UDvlsGoZhLKiRBfnS7630WjEcDicW+JeFEVywGVfIiIFNmQjiT1d76ltsknSwcpAIw2LYpblIv2roBKDKvydepZHc1LutRRDMuan/mg8JJsaN2JntZXZHbT19JPVZ24Uzc00xRvSxWxzTsfkyvc6dYIDQ7CSm+2gEWKhA6lneJ0KgdE5dz7YFil2EZpLC3IA2R0kKqScMXZHi88xgsv+6H71zX/zv1m3lAQjUEzJUTupJHJ0Yxu8/TnlqHEl3Xy8aFwIzFiHxsMDDLn2eF0sDnZ8fPz9pIMbPY6/A3/Wp/559gjfQz7lu7zd3p+cIXeaRFzuDcL2esqbrpP25AEZ6Yj5ZS7fxaH1dvJ7zmDnAF3EJcCm06X7fFaaxs/TTfU7QRr75wZ+UWCFvzmf5Bxqpk++7TIej1/juVxKL8FpURRptp0grCguUxu9nxHzIE6Gl7PFup+8IRoLjDACz99kvDj2BMmcpXE9oaK+OOjWmNdqlztmU9+67mZ/NXbKVNBsuNsxFvIXaUCD7AEB8rf+i06uL+QceiCHgFsggbQkPZjCThvF2RCNL+VJzzoQpv0WnRWoUPtcZ6voqEfpJoJbjh95g/KkNE/Rg2BZmXz+rNsE8pb6IxtG2utdkitmZKyurqZjyprNZnQ6ncQ7RXE5e8eZR2YCacZKYE4zc+qTbLnu56wZ9Z6ccdFD4J7LK4i9RCe1UfRg4EJjoZkx8p9kS0EB6SHqRAbyfHd3ltPT07h69eocjcVTHLPj4+M5+6YxYCCSGEa86LLnckNa5jAar3vA1fWP949yLJlnxgIdBwbSOf6sy3UW2+3v8t+8HtGKOkP98f7xt5ztpXxK/oj3ctid7RXf+Ky67vX2kU45LEcaOf7iOIqfc31y3MIAL/ERC/vICVXSiPUzSK3n6dxLZofDYZIN6ZR2ux3dbje9s9PpxJUrV+YCfMPhMAVUy3KWkcDNAyMiZQvpPulAZlA1m81YX1+fw1PCIc1mMwaDwZyjen5+nvYLkN3RsjJiCbZFuk2bZLZarbkNOKVnRffBYJAc3LOzs7kjPKuqmsODWtoXEdFut6PX66XTIFZXV1OgWo417aKWbSlbUCeQrK6uxsnJyVywXWMl3hqNRkk/M5CvPolOp6encwEMLkchlqBunEwmCc/pfdp8kYEW0ZN9UlaXaOd4mHsKRUT0er2sD0B9IP7Wkc28XzxKXK9Mh28rReVoa1mWZVmWZVmWZVmWZVmWZVmWZVmWZVmW5f+yvL5AbVmWZVmWZVmWZVmWZVmWZVmWZVmWZVmW5f+yLAMNy7Isy7Isy7Isy7Isy7Isy7Isy7Isy/LWyjLQsCzLsizLsizLsizLsizLsizLsizLsixvrSwDDcuyLMuyLMuyLMuyLMuyLMuyLMuyLMvy1soy0LAsy7Isy7Isy7Isy7Isy7Isy7Isy7Isb60sAw3LsizLsizLsizLsizLsizLsizLsizL8tbKMtCwLMuyLMuyLMuyLMuyLMuyLMuyLMuyLG+tLAMNy7Isy7Isy7Isy7Isy7Isy7Isy7Isy/LWyjLQsCzLsizLsizLsizLsizLsizLsizLsixvrfz/2ayh7eJK0U8AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ], + "source": [ + "plot_images([img0, img1], ['Image 1 - point matches', 'Image 2 - point matches'], pad=0.5)\n", + "plot_matches(matched_kps0, matched_kps1, 'green', lw=1, ps=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "Kve9xdngdpC_" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/third_party/GlueStick/requirements.txt b/third_party/GlueStick/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6ccf01735a036ad91060ac884bbc94da275dd487 --- /dev/null +++ b/third_party/GlueStick/requirements.txt @@ -0,0 +1,12 @@ +numpy +matplotlib +scipy +scikit_learn +seaborn +omegaconf==2.2.* +opencv-python==4.7.0.* +torch>=1.12 +torchvision>=0.13 +setuptools +tqdm +git+https://github.com/iago-suarez/pytlsd.git@37ac583 diff --git a/third_party/GlueStick/resources/demo_seq1.gif b/third_party/GlueStick/resources/demo_seq1.gif new file mode 100644 index 0000000000000000000000000000000000000000..c758b0f8df3cae51a45d0c94ca6a9fad03f3011d --- /dev/null +++ b/third_party/GlueStick/resources/demo_seq1.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:917f9243daaffb896909582dfb9889e7b8638e230cc7466e6e6829b5a112cecb +size 22805528 diff --git a/third_party/GlueStick/resources/img1.jpg b/third_party/GlueStick/resources/img1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..cb81115885913737e5260e4a9d04ffaf15cb741b --- /dev/null +++ b/third_party/GlueStick/resources/img1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c8f829bcdb249e851488be4b3e9cd87c58713c5dc54a2d1333c82ad4f17b7048 +size 1209431 diff --git a/third_party/GlueStick/resources/img2.jpg b/third_party/GlueStick/resources/img2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..1ac6ef6b3504288cc7d53808030e04443d92c395 --- /dev/null +++ b/third_party/GlueStick/resources/img2.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b91f870167f67ad8e3a0e57bdcd9a9062d8cea41e9c60685e6135941823d327 +size 1184304 diff --git a/third_party/GlueStick/resources/weights/checkpoint_GlueStick_MD.tar b/third_party/GlueStick/resources/weights/checkpoint_GlueStick_MD.tar new file mode 100644 index 0000000000000000000000000000000000000000..eb6071fbd839f205b49a79042ea3bebef54605e0 --- /dev/null +++ b/third_party/GlueStick/resources/weights/checkpoint_GlueStick_MD.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5a7013a1987978c23d71f338f3673d80f9090c4791a955c2f9e1ed4f4dbb3207 +size 112588421 diff --git a/third_party/GlueStick/resources/weights/superpoint_v1.pth b/third_party/GlueStick/resources/weights/superpoint_v1.pth new file mode 100644 index 0000000000000000000000000000000000000000..7648726e3a3dfa2581e86bfa9c5a2a05cfb9bf74 --- /dev/null +++ b/third_party/GlueStick/resources/weights/superpoint_v1.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52b6708629640ca883673b5d5c097c4ddad37d8048b33f09c8ca0d69db12c40e +size 5206086 diff --git a/third_party/GlueStick/setup.py b/third_party/GlueStick/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..f0caa063e99cf6d7784fe7d54af08dbb66811627 --- /dev/null +++ b/third_party/GlueStick/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup(name='gluestick', version="0.0", packages=['gluestick']) diff --git a/third_party/SGMNet/.gitignore b/third_party/SGMNet/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..7e99e367f8443d86e5e8825b9fda39dfbb39630d --- /dev/null +++ b/third_party/SGMNet/.gitignore @@ -0,0 +1 @@ +*.pyc \ No newline at end of file diff --git a/third_party/SGMNet/LICENSE b/third_party/SGMNet/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..944d16f2d01f3550dd7061bfbc1dc2f73b77cfbb --- /dev/null +++ b/third_party/SGMNet/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Hongkai Chen + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/third_party/SGMNet/README.md b/third_party/SGMNet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c01115fb33623295fb74314ad33cb340af70509d --- /dev/null +++ b/third_party/SGMNet/README.md @@ -0,0 +1,295 @@ +# SGMNet Implementation + +![Framework](assets/teaser.png) + +PyTorch implementation of SGMNet for ICCV'21 paper ["Learning to Match Features with Seeded Graph Matching Network"](https://arxiv.org/abs/2108.08771), by Hongkai Chen, Zixin Luo, Jiahui Zhang, Lei Zhou, Xuyang Bai, Zeyu Hu, Chiew-Lan Tai, Long Quan. + +This work focuses on keypoint-based image matching problem. We mitigate the qudratic complexity issue for typical GNN-based matching by leveraging a restrited set of pre-matched seeds. + +This repo contains training, evaluation and basic demo sripts used in our paper. As baseline, it also includes **our implementation** for [SuperGlue](https://arxiv.org/abs/1911.11763). If you find this project useful, please cite: + +``` +@article{chen2021sgmnet, + title={Learning to Match Features with Seeded Graph Matching Network}, + author={Chen, Hongkai and Luo, Zixin and Zhang, Jiahui and Zhou, Lei and Bai, Xuyang and Hu, Zeyu and Tai, Chiew-Lan and Quan, Long}, + journal={International Conference on Computer Vision (ICCV)}, + year={2021} +} +``` + +Part of the code is borrowed or ported from + +[SuperPoint](https://github.com/magicleap/SuperPointPretrainedNetwork), for SuperPoint implementation, + +[SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork), for SuperGlue implementation and exact auc computation, + +[OANet](https://github.com/zjhthu/OANet), for training scheme, + +[PointCN](https://github.com/vcg-uvic/learned-correspondence-release), for implementaion of PointCN block and geometric transformations, + +[FM-Bench](https://github.com/JiawangBian/FM-Bench), for evaluation of fundamental matrix estimation. + + +Please also cite these works if you find the corresponding code useful. + + +## Requirements + +We use PyTorch 1.6, later version should also be compatible. Please refer to [requirements.txt](requirements.txt) for other dependencies. + +If you are using conda, you may configure the environment as: + +```bash +conda create --name sgmnet python=3.7 -y && \ +pip install -r requirements.txt && \ +conda activate sgmnet +``` + +## Get started + +Clone the repo: +```bash +git clone https://github.com/vdvchen/SGMNet.git && \ +``` +download model weights from [here](https://drive.google.com/file/d/1Ca0WmKSSt2G6P7m8YAOlSAHEFar_TAWb/view?usp=sharing) + +extract weights by +```bash +tar -xvf weights.tar.gz +``` + +A quick demo for image matching can be called by: + +```bash +cd demo && python demo.py --config_path configs/sgm_config.yaml +``` +The resutls will be saved as **match.png** in demo folder. You may configure the matcher in corresponding yaml file. + + +## Evaluation + + +We demonstrate evaluation process with RootSIFT and SGMNet. Evaluation with other features/matchers can be conducted by configuring the corresponding yaml files. + +### 1. YFCC Evaluation + +Refer to [OANet](https://github.com/zjhthu/OANet) repo to download raw YFCC100M dataset + + +**Data Generation** + +1. Configure **datadump/configs/yfcc_root.yaml** for the following entries + + **rawdata_dir**: path for yfcc rawdata + **feature_dump_dir**: dump path for extracted features + **dataset_dump_dir**: dump path for generated dataset + **extractor**: configuration for keypoint extractor (2k RootSIFT by default) + +2. Generate data by + ```bash + cd datadump + python dump.py --config_path configs/yfcc_root.yaml + ``` + An h5py data file will be generated under **dataset_dump_dir**, e.g. **yfcc_root_2000.hdf5** + +**Evaluation**: + +1. Configure **evaluation/configs/eval/yfcc_eval_sgm.yaml** for the following entries + + **reader.rawdata_dir**: path for yfcc_rawdata + **reader.dataset_dir**: path for generated h5py dataset file + **matcher**: configuration for sgmnet (we use the default setting) + +2. To run evaluation, + ```bash + cd evaluation + python evaluate.py --config_path configs/eval/yfcc_eval_sgm.yaml + ``` + +For 2k RootSIFT matching, similar results as below should be obtained, +```bash +auc th: [5 10 15 20 25 30] +approx auc: [0.634 0.729 0.783 0.818 0.843 0.861] +exact auc: [0.355 0.552 0.655 0.719 0.762 0.793] +mean match score: 17.06 +mean precision: 86.08 +``` + +### 2. ScanNet Evaluation + +Download processed [ScanNet evaluation data](https://drive.google.com/file/d/14s-Ce8Vq7XedzKon8MZSB_Mz_iC6oFPy/view?usp=sharing). + + +**Data Generation** + +1. Configure **datadump/configs/scannet_root.yaml** for the following entries + + **rawdata_dir**: path for ScanNet raw data + **feature_dump_dir**: dump path for extracted features + **dataset_dump_dir**: dump path for generated dataset + **extractor**: configuration for keypoint extractor (2k RootSIFT by default) + +2. Generate data by + ```bash + cd datadump + python dump.py --config_path configs/scannet_root.yaml + ``` + An h5py data file will be generated under **dataset_dump_dir**, e.g. **scannet_root_2000.hdf5** + +**Evaluation**: + +1. Configure **evaluation/configs/eval/scannet_eval_sgm.yaml** for the following entries + + **reader.rawdata_dir**: path for ScanNet evaluation data + **reader.dataset_dir**: path for generated h5py dataset file + **matcher**: configuration for sgmnet (we use the default setting) + +2. To run evaluation, + ```bash + cd evaluation + python evaluate.py --config_path configs/eval/scannet_eval_sgm.yaml + ``` + +For 2k RootSIFT matching, similar results as below should be obtained, +```bash +auc th: [5 10 15 20 25 30] +approx auc: [0.322 0.427 0.493 0.541 0.577 0.606] +exact auc: [0.125 0.283 0.383 0.452 0.503 0.541] +mean match score: 8.79 +mean precision: 45.54 +``` + +### 3. FM-Bench Evaluation + +Refer to [FM-Bench](https://github.com/JiawangBian/FM-Bench) repo to download raw FM-Bench dataset + +**Data Generation** + +1. Configure **datadump/configs/fmbench_root.yaml** for the following entries + + **rawdata_dir**: path for fmbench raw data + **feature_dump_dir**: dump path for extracted features + **dataset_dump_dir**: dump path for generated dataset + **extractor**: configuration for keypoint extractor (4k RootSIFT by default) + +2. Generate data by + ```bash + cd datadump + python dump.py --config_path configs/fmbench_root.yaml + ``` + An h5py data file will be generated under **dataset_dump_dir**, e.g. **fmbench_root_4000.hdf5** + +**Evaluation**: + +1. Configure **evaluation/configs/eval/fm_eval_sgm.yaml** for the following entries + + **reader.rawdata_dir**: path for fmbench raw data + **reader.dataset_dir**: path for generated h5py dataset file + **matcher**: configuration for sgmnet (we use the default setting) + +2. To run evaluation, + ```bash + cd evaluation + python evaluate.py --config_path configs/eval/fm_eval_sgm.yaml + ``` + +For 4k RootSIFT matching, similar results as below should be obtained, +```bash +CPC results: +F_recall: 0.617 +precision: 0.7489 +precision_post: 0.8399 +num_corr: 663.838 +num_corr_post: 284.455 + +KITTI results: +F_recall: 0.911 +precision: 0.9035133886251774 +precision_post: 0.9837278538989989 +num_corr: 1670.548 +num_corr_post: 1121.902 + +TUM results: +F_recall: 0.666 +precision: 0.6520260208250837 +precision_post: 0.731507123852191 +num_corr: 1650.579 +num_corr_post: 941.846 + +Tanks_and_Temples results: +F_recall: 0.855 +precision: 0.7452896681043316 +precision_post: 0.8020184635328004 +num_corr: 946.571 +num_corr_post: 466.865 +``` + +### 4. Run time and memory Evaluation + +We provide a script to test run time and memory consumption, for a quick start, run + +```bash +cd evaluation +python eval_cost.py --matcher_name SGM --config_path configs/cost/sgm_cost.yaml --num_kpt=4000 +``` +You may configure the matcher in corresponding yaml files. + + +## Visualization + +For visualization of matching results on different dataset, add **--vis_folder** argument on evaluation command, e.g. + +```bash +cd evaluation +python evaluate.py --config_path configs/eval/***.yaml --vis_folder visualization +``` + + +## Training + +We train both SGMNet and SuperGlue on [GL3D](https://github.com/lzx551402/GL3D) dataset. The training data is pre-generated in an offline manner, which yields about 400k pairs in total. + +To generate training/validation dataset + +1. Download [GL3D](https://github.com/lzx551402/GL3D) rawdata + +2. Configure **datadump/configs/gl3d.yaml**. Some important entries are + + **rawdata_dir**: path for GL3D raw data + **feature_dump_dir**: path for extracted features + **dataset_dump_dir**: path for generated dataset + **pairs_per_seq**: number of pairs sampled for each sequence + **angle_th**: angle threshold for sampled pairs + **overlap_th**: common track threshold for sampled pairs + **extractor**: configuration for keypoint extractor + +3. dump dataset by +```bash +cd datadump +python dump.py --config_path configs/gl3d.yaml +``` + +Two parts of data will be generated. (1) Extracted features and keypoints will be placed under **feature_dump_dir** (2) Pairwise dataset will be placed under **dataset_dump_dir**. + +4. After data generation, configure **train/train_sgm.sh** for necessary entries, including + **rawdata_path**: path for GL3D raw data + **desc_path**: path for extracted features + **dataset_path**: path for generated dataset + **desc_suffix**: suffix for keypoint files, _root_1000.hdf5 for 1k RootSIFT by default. + **log_base**: log directory for training + +5. run SGMNet training scripts by +```bash +bash train_sgm.sh +``` + +our training scripts support multi-gpu training, which can be enabled by configure **train/train_sgm.sh** for these entries + + **CUDA_VISIBLE_DEVICES**: id of gpus to be used + **nproc_per_node**: number of gpus to be used + +run SuperGlue training scripts by + +```bash +bash train_sg.sh +``` diff --git a/third_party/SGMNet/assets/scannet_eval_list.txt b/third_party/SGMNet/assets/scannet_eval_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c3338fac3c3ae0a2837c819dc0ee21ed8bc2012 --- /dev/null +++ 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+from . import extractors +from . import matchers +from .load_component import load_component \ No newline at end of file diff --git a/third_party/SGMNet/components/evaluators.py b/third_party/SGMNet/components/evaluators.py new file mode 100644 index 0000000000000000000000000000000000000000..59bf0bd7ce3dd085dc86072fc41bad24b9805991 --- /dev/null +++ b/third_party/SGMNet/components/evaluators.py @@ -0,0 +1,127 @@ +import numpy as np +import sys +import os +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + +from utils import evaluation_utils,metrics,fm_utils +import cv2 + +class auc_eval: + def __init__(self,config): + self.config=config + self.err_r,self.err_t,self.err=[],[],[] + self.ms=[] + self.precision=[] + + def run(self,info): + E,r_gt,t_gt=info['e'],info['r_gt'],info['t_gt'] + K1,K2,img1,img2=info['K1'],info['K2'],info['img1'],info['img2'] + corr1,corr2=info['corr1'],info['corr2'] + corr1,corr2=evaluation_utils.normalize_intrinsic(corr1,K1),evaluation_utils.normalize_intrinsic(corr2,K2) + size1,size2=max(img1.shape),max(img2.shape) + scale1,scale2=self.config['rescale']/size1,self.config['rescale']/size2 + #ransac + ransac_th=4./((K1[0,0]+K1[1,1])*scale1+(K2[0,0]+K2[1,1])*scale2) + R_hat,t_hat,E_hat=self.estimate(corr1,corr2,ransac_th) + #get pose error + err_r, err_t=metrics.evaluate_R_t(r_gt,t_gt,R_hat,t_hat) + err=max(err_r,err_t) + + if len(corr1)>1: + inlier_mask=metrics.compute_epi_inlier(corr1,corr2,E,self.config['inlier_th']) + precision=inlier_mask.mean() + ms=inlier_mask.sum()/len(info['x1']) + else: + ms=precision=0 + + return {'err_r':err_r,'err_t':err_t,'err':err,'ms':ms,'precision':precision} + + def res_inqueue(self,res): + self.err_r.append(res['err_r']),self.err_t.append(res['err_t']),self.err.append(res['err']) + self.ms.append(res['ms']),self.precision.append(res['precision']) + + def estimate(self,corr1,corr2,th): + num_inlier = -1 + if corr1.shape[0] >= 5: + E, mask_new = cv2.findEssentialMat(corr1, corr2,method=cv2.RANSAC, threshold=th,prob=1-1e-5) + if E is None: + E=[np.eye(3)] + for _E in np.split(E, len(E) / 3): + _num_inlier, _R, _t, _ = cv2.recoverPose(_E, corr1, corr2,np.eye(3), 1e9,mask=mask_new) + if _num_inlier > num_inlier: + num_inlier = _num_inlier + R = _R + t = _t + E = _E + else: + E,R,t=np.eye(3),np.eye(3),np.zeros(3) + return R,t,E + + def parse(self): + ths = np.arange(7) * 5 + approx_auc=metrics.approx_pose_auc(self.err,ths) + exact_auc=metrics.pose_auc(self.err,ths) + mean_pre,mean_ms=np.mean(np.asarray(self.precision)),np.mean(np.asarray(self.ms)) + + print('auc th: ',ths[1:]) + print('approx auc: ',approx_auc) + print('exact auc: ', exact_auc) + print('mean match score: ',mean_ms*100) + print('mean precision: ',mean_pre*100) + + + +class FMbench_eval: + + def __init__(self,config): + self.config=config + self.pre,self.pre_post,self.sgd=[],[],[] + self.num_corr,self.num_corr_post=[],[] + + def run(self,info): + corr1,corr2=info['corr1'],info['corr2'] + F=info['f'] + img1,img2=info['img1'],info['img2'] + + if len(corr1)>1: + pre_bf=fm_utils.compute_inlier_rate(corr1,corr2,np.flip(img1.shape[:2]),np.flip(img2.shape[:2]),F,th=self.config['inlier_th']).mean() + F_hat,mask_F=cv2.findFundamentalMat(corr1,corr2,method=cv2.FM_RANSAC,ransacReprojThreshold=1,confidence=1-1e-5) + if F_hat is None: + F_hat=np.ones([3,3]) + mask_F=np.ones([len(corr1)]).astype(bool) + else: + mask_F=mask_F.squeeze().astype(bool) + F_hat=F_hat[:3] + pre_af=fm_utils.compute_inlier_rate(corr1[mask_F],corr2[mask_F],np.flip(img1.shape[:2]),np.flip(img2.shape[:2]),F,th=self.config['inlier_th']).mean() + num_corr_af=mask_F.sum() + num_corr=len(corr1) + sgd=fm_utils.compute_SGD(F,F_hat,np.flip(img1.shape[:2]),np.flip(img2.shape[:2])) + else: + pre_bf,pre_af,sgd=0,0,1e8 + num_corr,num_corr_af=0,0 + return {'pre':pre_bf,'pre_post':pre_af,'sgd':sgd,'num_corr':num_corr,'num_corr_post':num_corr_af} + + + def res_inqueue(self,res): + self.pre.append(res['pre']),self.pre_post.append(res['pre_post']),self.sgd.append(res['sgd']) + self.num_corr.append(res['num_corr']),self.num_corr_post.append(res['num_corr_post']) + + def parse(self): + for seq_index in range(len(self.config['seq'])): + seq=self.config['seq'][seq_index] + offset=seq_index*1000 + pre=np.asarray(self.pre)[offset:offset+1000].mean() + pre_post=np.asarray(self.pre_post)[offset:offset+1000].mean() + num_corr=np.asarray(self.num_corr)[offset:offset+1000].mean() + num_corr_post=np.asarray(self.num_corr_post)[offset:offset+1000].mean() + f_recall=(np.asarray(self.sgd)[offset:offset+1000]self.p_th,index[:,0],index2.squeeze(0) + mask_mc=index2[index] == torch.arange(len(p)).cuda() + mask=mask_th&mask_mc + index1,index2=torch.nonzero(mask).squeeze(1),index[mask] + return index1,index2 + + +class NN_Matcher(object): + + def __init__(self,config): + config=namedtuple('config',config.keys())(*config.values()) + self.mutual_check=config.mutual_check + self.ratio_th=config.ratio_th + + def run(self,test_data): + desc1,desc2,x1,x2=test_data['desc1'],test_data['desc2'],test_data['x1'],test_data['x2'] + desc_mat=np.sqrt(abs((desc1**2).sum(-1)[:,np.newaxis]+(desc2**2).sum(-1)[np.newaxis]-2*desc1@desc2.T)) + nn_index=np.argpartition(desc_mat,kth=(1,2),axis=-1) + dis_value12=np.take_along_axis(desc_mat,nn_index, axis=-1) + ratio_score=dis_value12[:,0]/dis_value12[:,1] + nn_index1=nn_index[:,0] + nn_index2=np.argmin(desc_mat,axis=0) + mask_ratio,mask_mutual=ratio_scoreself.config['angle_th'][0],angle_listself.config['overlap_th'][0],overlap_scoreself.config['min_corr'] and len(incorr_index1)>self.config['min_incorr'] and len(incorr_index2)>self.config['min_incorr']: + info['corr'].append(corr_index),info['incorr1'].append(incorr_index1),info['incorr2'].append(incorr_index2) + info['dR'].append(dR),info['dt'].append(dt),info['K1'].append(K1),info['K2'].append(K2),info['img_path1'].append(img_path1),info['img_path2'].append(img_path2) + info['fea_path1'].append(fea_path1),info['fea_path2'].append(fea_path2),info['size1'].append(size1),info['size2'].append(size2) + sample_number+=1 + if sample_number==sample_target: + break + info['pair_num']=sample_number + #dump info + self.dump_info(seq,info) + + + def collect_meta(self): + print('collecting meta info...') + dump_path,seq_list=[],[] + if self.config['dump_train']: + dump_path.append(os.path.join(self.config['dataset_dump_dir'],'train')) + seq_list.append(self.train_list) + if self.config['dump_valid']: + dump_path.append(os.path.join(self.config['dataset_dump_dir'],'valid')) + seq_list.append(self.valid_list) + for pth,seqs in zip(dump_path,seq_list): + if not os.path.exists(pth): + os.mkdir(pth) + pair_num_list,total_pair=[],0 + for seq_index in range(len(seqs)): + seq=seqs[seq_index] + pair_num=np.loadtxt(os.path.join(self.config['dataset_dump_dir'],seq,'pair_num.txt'),dtype=int) + pair_num_list.append(str(pair_num)) + total_pair+=pair_num + pair_num_list=np.stack([np.asarray(seqs,dtype=str),np.asarray(pair_num_list,dtype=str)],axis=1) + pair_num_list=np.concatenate([np.asarray([['total',str(total_pair)]]),pair_num_list],axis=0) + np.savetxt(os.path.join(pth,'pair_num.txt'),pair_num_list,fmt='%s') + + def format_dump_data(self): + print('Formatting data...') + iteration_num=len(self.seq_list)//self.config['num_process'] + if len(self.seq_list)%self.config['num_process']!=0: + iteration_num+=1 + pool=Pool(self.config['num_process']) + for index in trange(iteration_num): + indices=range(index*self.config['num_process'],min((index+1)*self.config['num_process'],len(self.seq_list))) + pool.map(self.format_seq,indices) + pool.close() + pool.join() + + self.collect_meta() \ No newline at end of file diff --git a/third_party/SGMNet/datadump/dumper/scannet.py b/third_party/SGMNet/datadump/dumper/scannet.py new file mode 100644 index 0000000000000000000000000000000000000000..2556f727fcc9b4c621e44d9ee5cb4e99cb19b7e8 --- /dev/null +++ b/third_party/SGMNet/datadump/dumper/scannet.py @@ -0,0 +1,72 @@ +import os +import glob +import pickle +from posixpath import basename +import numpy as np +import h5py +from .base_dumper import BaseDumper + +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) +sys.path.insert(0, ROOT_DIR) +import utils + +class scannet(BaseDumper): + def get_seqs(self): + self.pair_list=np.loadtxt('../assets/scannet_eval_list.txt',dtype=str) + self.seq_list=np.unique(np.asarray([path.split('/')[0] for path in self.pair_list[:,0]],dtype=str)) + self.dump_seq,self.img_seq=[],[] + for seq in self.seq_list: + dump_dir=os.path.join(self.config['feature_dump_dir'],seq) + cur_img_seq=glob.glob(os.path.join(os.path.join(self.config['rawdata_dir'],seq,'img','*.jpg'))) + cur_dump_seq=[os.path.join(dump_dir,path.split('/')[-1])+'_'+self.config['extractor']['name']+'_'+str(self.config['extractor']['num_kpt'])\ + +'.hdf5' for path in cur_img_seq] + self.img_seq+=cur_img_seq + self.dump_seq+=cur_dump_seq + + def format_dump_folder(self): + if not os.path.exists(self.config['feature_dump_dir']): + os.mkdir(self.config['feature_dump_dir']) + for seq in self.seq_list: + seq_dir=os.path.join(self.config['feature_dump_dir'],seq) + if not os.path.exists(seq_dir): + os.mkdir(seq_dir) + + def format_dump_data(self): + print('Formatting data...') + self.data={'K1':[],'K2':[],'R':[],'T':[],'e':[],'f':[],'fea_path1':[],'fea_path2':[],'img_path1':[],'img_path2':[]} + + for pair in self.pair_list: + img_path1,img_path2=pair[0],pair[1] + seq=img_path1.split('/')[0] + index1,index2=int(img_path1.split('/')[-1][:-4]),int(img_path2.split('/')[-1][:-4]) + ex1,ex2=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'extrinsic',str(index1)+'.txt'),dtype=float),\ + np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'extrinsic',str(index2)+'.txt'),dtype=float) + K1,K2=np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'intrinsic',str(index1)+'.txt'),dtype=float),\ + np.loadtxt(os.path.join(self.config['rawdata_dir'],seq,'intrinsic',str(index2)+'.txt'),dtype=float) + + + relative_extrinsic=np.matmul(np.linalg.inv(ex2),ex1) + dR,dt=relative_extrinsic[:3,:3],relative_extrinsic[:3,3] + dt /= np.sqrt(np.sum(dt**2)) + + e_gt_unnorm = np.reshape(np.matmul( + np.reshape(utils.evaluation_utils.np_skew_symmetric(dt.astype('float64').reshape(1, 3)), (3, 3)), + np.reshape(dR.astype('float64'), (3, 3))), (3, 3)) + e_gt = e_gt_unnorm / np.linalg.norm(e_gt_unnorm) + f_gt_unnorm=np.linalg.inv(K2.T)@e_gt@np.linalg.inv(K1) + f_gt = f_gt_unnorm / np.linalg.norm(f_gt_unnorm) + + self.data['K1'].append(K1),self.data['K2'].append(K2) + self.data['R'].append(dR),self.data['T'].append(dt) + self.data['e'].append(e_gt),self.data['f'].append(f_gt) + + dump_seq_dir=os.path.join(self.config['feature_dump_dir'],seq) + fea_path1,fea_path2=os.path.join(dump_seq_dir,img_path1.split('/')[-1]+'_'+self.config['extractor']['name'] + +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5'),\ + os.path.join(dump_seq_dir,img_path2.split('/')[-1]+'_'+self.config['extractor']['name'] + +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5') + self.data['img_path1'].append(img_path1),self.data['img_path2'].append(img_path2) + self.data['fea_path1'].append(fea_path1),self.data['fea_path2'].append(fea_path2) + + self.form_standard_dataset() diff --git a/third_party/SGMNet/datadump/dumper/yfcc.py b/third_party/SGMNet/datadump/dumper/yfcc.py new file mode 100644 index 0000000000000000000000000000000000000000..0c52e4324bba3e5ed424fe58af7a94fd3132b1e5 --- /dev/null +++ b/third_party/SGMNet/datadump/dumper/yfcc.py @@ -0,0 +1,87 @@ +import os +import glob +import pickle +import numpy as np +import h5py +from .base_dumper import BaseDumper + +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../")) +sys.path.insert(0, ROOT_DIR) +import utils + +class yfcc(BaseDumper): + + def get_seqs(self): + data_dir=os.path.join(self.config['rawdata_dir'],'yfcc100m') + for seq in self.config['data_seq']: + for split in self.config['data_split']: + split_dir=os.path.join(data_dir,seq,split) + dump_dir=os.path.join(self.config['feature_dump_dir'],seq,split) + cur_img_seq=glob.glob(os.path.join(split_dir,'images','*.jpg')) + cur_dump_seq=[os.path.join(dump_dir,path.split('/')[-1])+'_'+self.config['extractor']['name']+'_'+str(self.config['extractor']['num_kpt'])\ + +'.hdf5' for path in cur_img_seq] + self.img_seq+=cur_img_seq + self.dump_seq+=cur_dump_seq + + def format_dump_folder(self): + if not os.path.exists(self.config['feature_dump_dir']): + os.mkdir(self.config['feature_dump_dir']) + for seq in self.config['data_seq']: + seq_dir=os.path.join(self.config['feature_dump_dir'],seq) + if not os.path.exists(seq_dir): + os.mkdir(seq_dir) + for split in self.config['data_split']: + split_dir=os.path.join(seq_dir,split) + if not os.path.exists(split_dir): + os.mkdir(split_dir) + + def format_dump_data(self): + print('Formatting data...') + pair_path=os.path.join(self.config['rawdata_dir'],'pairs') + self.data={'K1':[],'K2':[],'R':[],'T':[],'e':[],'f':[],'fea_path1':[],'fea_path2':[],'img_path1':[],'img_path2':[]} + + for seq in self.config['data_seq']: + pair_name=os.path.join(pair_path,seq+'-te-1000-pairs.pkl') + with open(pair_name, 'rb') as f: + pairs=pickle.load(f) + + #generate id list + seq_dir=os.path.join(self.config['rawdata_dir'],'yfcc100m',seq,'test') + name_list=np.loadtxt(os.path.join(seq_dir,'images.txt'),dtype=str) + cam_name_list=np.loadtxt(os.path.join(seq_dir,'calibration.txt'),dtype=str) + + for cur_pair in pairs: + index1,index2=cur_pair[0],cur_pair[1] + cam1,cam2=h5py.File(os.path.join(seq_dir,cam_name_list[index1]),'r'),h5py.File(os.path.join(seq_dir,cam_name_list[index2]),'r') + K1,K2=cam1['K'][()],cam2['K'][()] + [w1,h1],[w2,h2]=cam1['imsize'][()][0],cam2['imsize'][()][0] + cx1,cy1,cx2,cy2 = (w1 - 1.0) * 0.5,(h1 - 1.0) * 0.5, (w2 - 1.0) * 0.5,(h2 - 1.0) * 0.5 + K1[0,2],K1[1,2],K2[0,2],K2[1,2]=cx1,cy1,cx2,cy2 + + R1,R2,t1,t2=cam1['R'][()],cam2['R'][()],cam1['T'][()].reshape([3,1]),cam2['T'][()].reshape([3,1]) + dR = np.dot(R2, R1.T) + dt = t2 - np.dot(dR, t1) + dt /= np.sqrt(np.sum(dt**2)) + + e_gt_unnorm = np.reshape(np.matmul( + np.reshape(utils.evaluation_utils.np_skew_symmetric(dt.astype('float64').reshape(1, 3)), (3, 3)), + np.reshape(dR.astype('float64'), (3, 3))), (3, 3)) + e_gt = e_gt_unnorm / np.linalg.norm(e_gt_unnorm) + f_gt_unnorm=np.linalg.inv(K2.T)@e_gt@np.linalg.inv(K1) + f_gt = f_gt_unnorm / np.linalg.norm(f_gt_unnorm) + + self.data['K1'].append(K1),self.data['K2'].append(K2) + self.data['R'].append(dR),self.data['T'].append(dt) + self.data['e'].append(e_gt),self.data['f'].append(f_gt) + + img_path1,img_path2=os.path.join('yfcc100m',seq,'test',name_list[index1]),os.path.join('yfcc100m',seq,'test',name_list[index2]) + dump_seq_dir=os.path.join(self.config['feature_dump_dir'],seq,'test') + fea_path1,fea_path2=os.path.join(dump_seq_dir,name_list[index1].split('/')[-1]+'_'+self.config['extractor']['name'] + +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5'),\ + os.path.join(dump_seq_dir,name_list[index2].split('/')[-1]+'_'+self.config['extractor']['name'] + +'_'+str(self.config['extractor']['num_kpt'])+'.hdf5') + self.data['img_path1'].append(img_path1),self.data['img_path2'].append(img_path2) + self.data['fea_path1'].append(fea_path1),self.data['fea_path2'].append(fea_path2) + + self.form_standard_dataset() diff --git a/third_party/SGMNet/demo/configs/nn_config.yaml b/third_party/SGMNet/demo/configs/nn_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a87bfafce0cb7f8ab64e59311923d309aabcfab9 --- /dev/null +++ b/third_party/SGMNet/demo/configs/nn_config.yaml @@ -0,0 +1,10 @@ +extractor: + name: root + num_kpt: 4000 + resize: [-1] + det_th: 0.00001 + +matcher: + name: NN + ratio_th: 0.9 + mutual_check: True \ No newline at end of file diff --git a/third_party/SGMNet/demo/configs/sgm_config.yaml b/third_party/SGMNet/demo/configs/sgm_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..91de752010daa54ef0b508ef79d2dc4ac23945ec --- /dev/null +++ b/third_party/SGMNet/demo/configs/sgm_config.yaml @@ -0,0 +1,21 @@ +extractor: + name: root + num_kpt: 4000 + resize: [-1] + det_th: 0.00001 + +matcher: + name: SGM + model_dir: ../weights/sgm/root + seed_top_k: [256,256] + seed_radius_coe: 0.01 + net_channels: 128 + layer_num: 9 + head: 4 + seedlayer: [0,6] + use_mc_seeding: True + use_score_encoding: False + conf_bar: [1.11,0.1] + sink_iter: [10,100] + detach_iter: 1000000 + p_th: 0.2 diff --git a/third_party/SGMNet/demo/demo.py b/third_party/SGMNet/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..cbe277e26d09121f5517854a7ea014b0797a2bde --- /dev/null +++ b/third_party/SGMNet/demo/demo.py @@ -0,0 +1,45 @@ +import cv2 +import yaml +import numpy as np +import os +import sys + +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) +from components import load_component +from utils import evaluation_utils + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('--config_path', type=str, default='configs/sgm_config.yaml', + help='number of processes.') +parser.add_argument('--img1_path', type=str, default='demo_1.jpg', + help='number of processes.') +parser.add_argument('--img2_path', type=str, default='demo_2.jpg', + help='number of processes.') + + +args = parser.parse_args() + +if __name__=='__main__': + with open(args.config_path, 'r') as f: + demo_config = yaml.load(f) + + extractor=load_component('extractor',demo_config['extractor']['name'],demo_config['extractor']) + + img1,img2=cv2.imread(args.img1_path),cv2.imread(args.img2_path) + size1,size2=np.flip(np.asarray(img1.shape[:2])),np.flip(np.asarray(img2.shape[:2])) + kpt1,desc1=extractor.run(args.img1_path) + kpt2,desc2=extractor.run(args.img2_path) + + matcher=load_component('matcher',demo_config['matcher']['name'],demo_config['matcher']) + test_data={'x1':kpt1,'x2':kpt2,'desc1':desc1,'desc2':desc2,'size1':size1,'size2':size2} + corr1,corr2= matcher.run(test_data) + + #draw points + dis_points_1 = evaluation_utils.draw_points(img1, kpt1) + dis_points_2 = evaluation_utils.draw_points(img2, kpt2) + + #visualize match + display=evaluation_utils.draw_match(dis_points_1,dis_points_2,corr1,corr2) + cv2.imwrite('match.png',display) diff --git a/third_party/SGMNet/demo/demo_1.jpg b/third_party/SGMNet/demo/demo_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..187c36e942d7d8fa4d1b09661fa3b9ddd01939ee --- /dev/null +++ b/third_party/SGMNet/demo/demo_1.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f52b8feb635d19473200d6bc89e37a07a0728bfd37a6a63dd0915f111b86b51 +size 296810 diff --git a/third_party/SGMNet/demo/demo_2.jpg b/third_party/SGMNet/demo/demo_2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..513cbeb46369b086886e6271b928d6a17d5075cc --- /dev/null +++ b/third_party/SGMNet/demo/demo_2.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c2cab0e68625150ca0aa1fa7d0c54675ed7e3e1f7125a820215aa2a5d7f3e6f +size 227732 diff --git a/third_party/SGMNet/evaluation/configs/cost/sg_cost.yaml b/third_party/SGMNet/evaluation/configs/cost/sg_cost.yaml new file mode 100644 index 0000000000000000000000000000000000000000..05ea5ddc7bce8ad94d3ef3ec350363b5cc846ed8 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/cost/sg_cost.yaml @@ -0,0 +1,4 @@ +net_channels: 128 +layer_num: 9 +head: 4 +use_score_encoding: True \ No newline at end of file diff --git a/third_party/SGMNet/evaluation/configs/cost/sgm_cost.yaml b/third_party/SGMNet/evaluation/configs/cost/sgm_cost.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2f43193fb63fb26d50a8c3abd3cf53c43734dbca --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/cost/sgm_cost.yaml @@ -0,0 +1,11 @@ +seed_top_k: [256,256] +seed_radius_coe: 0.01 +net_channels: 128 +layer_num: 9 +head: 4 +seedlayer: [0,6] +use_mc_seeding: True +use_score_encoding: False +conf_bar: [1,0] +sink_iter: [10,10] +detach_iter: 1000000 \ No newline at end of file diff --git a/third_party/SGMNet/evaluation/configs/eval/fm_eval_nn.yaml b/third_party/SGMNet/evaluation/configs/eval/fm_eval_nn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d467a814559a27938f010dbf79a8e208551b2b5 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/fm_eval_nn.yaml @@ -0,0 +1,18 @@ +reader: + name: standard + rawdata_dir: FM-Bench/Dataset + dataset_dir: test_fmbench_root/fmbench_root_4000.hdf5 + num_kpt: 4000 + +matcher: + name: NN + mutual_check: False + ratio_th: 0.8 + +evaluator: + name: FM + seq: ['CPC','KITTI','TUM','Tanks_and_Temples'] + num_pair: 4000 + inlier_th: 0.003 + sgd_inlier_th: 0.05 + diff --git a/third_party/SGMNet/evaluation/configs/eval/fm_eval_sg.yaml b/third_party/SGMNet/evaluation/configs/eval/fm_eval_sg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ec22b1340d62fad20f22584ddbded30fcc59d1c9 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/fm_eval_sg.yaml @@ -0,0 +1,22 @@ +reader: + name: standard + rawdata_dir: FM-Bench/Dataset + dataset_dir: test_fmbench_root/fmbench_root_4000.hdf5 + num_kpt: 4000 + +matcher: + name: SG + model_dir: ../weights/sg/root + net_channels: 128 + layer_num: 9 + head: 4 + use_score_encoding: True + sink_iter: [100] + p_th: 0.2 + +evaluator: + name: FM + seq: ['CPC','KITTI','TUM','Tanks_and_Temples'] + num_pair: 4000 + inlier_th: 0.003 + sgd_inlier_th: 0.05 diff --git a/third_party/SGMNet/evaluation/configs/eval/fm_eval_sgm.yaml b/third_party/SGMNet/evaluation/configs/eval/fm_eval_sgm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cd23165c95451cd44063a2b6cccea21c68fb6fa0 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/fm_eval_sgm.yaml @@ -0,0 +1,28 @@ +reader: + name: standard + rawdata_dir: FM-Bench/Dataset + dataset_dir: test_fmbench_root/fmbench_root_4000.hdf5 + num_kpt: 4000 + +matcher: + name: SGM + model_dir: ../weights/sgm/root + seed_top_k: [256,256] + seed_radius_coe: 0.01 + net_channels: 128 + layer_num: 9 + head: 4 + seedlayer: [0,6] + use_mc_seeding: True + use_score_encoding: False + conf_bar: [1.11,0.1] #set to [1,0.1] for sp + sink_iter: [10,100] + detach_iter: 1000000 + p_th: 0.2 + +evaluator: + name: FM + seq: ['CPC','KITTI','TUM','Tanks_and_Temples'] + num_pair: 4000 + inlier_th: 0.003 + sgd_inlier_th: 0.05 diff --git a/third_party/SGMNet/evaluation/configs/eval/scannet_eval_nn.yaml b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_nn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..51ad5402b6266b60a365181371be8a5e64751d2f --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_nn.yaml @@ -0,0 +1,17 @@ +reader: + name: standard + rawdata_dir: scannet_eval + dataset_dir: scannet_test_root/scannet_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: NN + mutual_check: False + ratio_th: 0.8 + +evaluator: + name: AUC + rescale: 640 + num_pair: 1500 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sg.yaml b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d0ef70cfa07b1471816cc7905d6a632599d134c --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sg.yaml @@ -0,0 +1,22 @@ +reader: + name: standard + rawdata_dir: scannet_eval + dataset_dir: scannet_test_root/scannet_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: SG + model_dir: ../weights/sg/root + net_channels: 128 + layer_num: 9 + head: 4 + use_score_encoding: True + sink_iter: [100] + p_th: 0.2 + +evaluator: + name: AUC + rescale: 640 + num_pair: 1500 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sgm.yaml b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sgm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e524845a514e6d8d50f97bced5c9beeaed26ebe5 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/scannet_eval_sgm.yaml @@ -0,0 +1,28 @@ +reader: + name: standard + rawdata_dir: scannet_eval + dataset_dir: scannet_test_root/scannet_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: SGM + model_dir: ../weights/sgm/root + seed_top_k: [128,128] + seed_radius_coe: 0.01 + net_channels: 128 + layer_num: 9 + head: 4 + seedlayer: [0,6] + use_mc_seeding: True + use_score_encoding: False + conf_bar: [1.11,0.1] + sink_iter: [10,100] + detach_iter: 1000000 + p_th: 0.2 + +evaluator: + name: AUC + rescale: 640 + num_pair: 1500 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_nn.yaml b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_nn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ecd1eef2cff9b93f3665a9cf4af6bc9f68339f0 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_nn.yaml @@ -0,0 +1,17 @@ +reader: + name: standard + rawdata_dir: yfcc_rawdata + dataset_dir: yfcc_test_root/yfcc_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: NN + mutual_check: False + ratio_th: 0.8 + +evaluator: + name: AUC + rescale: 1600 + num_pair: 4000 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sg.yaml b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..beb2b93639160448dd955cd576e5a19a936b08f1 --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sg.yaml @@ -0,0 +1,22 @@ +reader: + name: standard + rawdata_dir: yfcc_rawdata + dataset_dir: yfcc_test_root/yfcc_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: SG + model_dir: ../weights/sg/root + net_channels: 128 + layer_num: 9 + head: 4 + use_score_encoding: True + sink_iter: [100] + p_th: 0.2 + +evaluator: + name: AUC + rescale: 1600 + num_pair: 4000 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sgm.yaml b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sgm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6c9aee8a8aa786ff209a5afadf0469f62ef2a50f --- /dev/null +++ b/third_party/SGMNet/evaluation/configs/eval/yfcc_eval_sgm.yaml @@ -0,0 +1,28 @@ +reader: + name: standard + rawdata_dir: yfcc_rawdata + dataset_dir: yfcc_test_root/yfcc_root_2000.hdf5 + num_kpt: 2000 + +matcher: + name: SGM + model_dir: ../weights/sgm/root + seed_top_k: [128,128] + seed_radius_coe: 0.01 + net_channels: 128 + layer_num: 9 + head: 4 + seedlayer: [0,6] + use_mc_seeding: True + use_score_encoding: False + conf_bar: [1.11,0.1] #set to [1,0.1] for sp + sink_iter: [10,100] + detach_iter: 1000000 + p_th: 0.2 + +evaluator: + name: AUC + rescale: 1600 + num_pair: 4000 + inlier_th: 0.005 + diff --git a/third_party/SGMNet/evaluation/eval_cost.py b/third_party/SGMNet/evaluation/eval_cost.py new file mode 100644 index 0000000000000000000000000000000000000000..dd3f88abc93290c96ed3d7fa8624c3534e006911 --- /dev/null +++ b/third_party/SGMNet/evaluation/eval_cost.py @@ -0,0 +1,60 @@ +import torch +import yaml +import time +from collections import OrderedDict,namedtuple +import os +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + +from sgmnet import matcher as SGM_Model +from superglue import matcher as SG_Model + + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument('--matcher_name', type=str, default='SGM', + help='number of processes.') +parser.add_argument('--config_path', type=str, default='configs/cost/sgm_cost.yaml', + help='number of processes.') +parser.add_argument('--num_kpt', type=int, default=4000, + help='keypoint number, default:100') +parser.add_argument('--iter_num', type=int, default=100, + help='keypoint number, default:100') + + +def test_cost(test_data,model): + with torch.no_grad(): + #warm up call + _=model(test_data) + torch.cuda.synchronize() + a=time.time() + for _ in range(int(args.iter_num)): + _=model(test_data) + torch.cuda.synchronize() + b=time.time() + print('Average time per run(ms): ',(b-a)/args.iter_num*1e3) + print('Peak memory(MB): ',torch.cuda.max_memory_allocated()/1e6) + + +if __name__=='__main__': + torch.backends.cudnn.benchmark=False + args = parser.parse_args() + with open(args.config_path, 'r') as f: + model_config = yaml.load(f) + model_config=namedtuple('model_config',model_config.keys())(*model_config.values()) + + if args.matcher_name=='SGM': + model = SGM_Model(model_config) + elif args.matcher_name=='SG': + model = SG_Model(model_config) + model.cuda(),model.eval() + + test_data = { + 'x1':torch.rand(1,args.num_kpt,2).cuda()-0.5, + 'x2':torch.rand(1,args.num_kpt,2).cuda()-0.5, + 'desc1': torch.rand(1,args.num_kpt,128).cuda(), + 'desc2': torch.rand(1,args.num_kpt,128).cuda() + } + + test_cost(test_data,model) diff --git a/third_party/SGMNet/evaluation/evaluate.py b/third_party/SGMNet/evaluation/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..dd5229375caa03b2763bf37a266fb76e80f8e25e --- /dev/null +++ b/third_party/SGMNet/evaluation/evaluate.py @@ -0,0 +1,117 @@ +import os +from torch.multiprocessing import Process,Manager,set_start_method,Pool +import functools +import argparse +import yaml +import numpy as np +import sys +import cv2 +from tqdm import trange +set_start_method('spawn',force=True) + + +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + +from components import load_component +from utils import evaluation_utils,metrics + +parser = argparse.ArgumentParser(description='dump eval data.') +parser.add_argument('--config_path', type=str, default='configs/eval/scannet_eval_sgm.yaml') +parser.add_argument('--num_process_match', type=int, default=4) +parser.add_argument('--num_process_eval', type=int, default=4) +parser.add_argument('--vis_folder',type=str,default=None) +args=parser.parse_args() + +def feed_match(info,matcher): + x1,x2,desc1,desc2,size1,size2=info['x1'],info['x2'],info['desc1'],info['desc2'],info['img1'].shape[:2],info['img2'].shape[:2] + test_data = {'x1': x1,'x2': x2,'desc1': desc1,'desc2': desc2,'size1':np.flip(np.asarray(size1)),'size2':np.flip(np.asarray(size2)) } + corr1,corr2=matcher.run(test_data) + return [corr1,corr2] + + +def reader_handler(config,read_que): + reader=load_component('reader',config['name'],config) + for index in range(len(reader)): + index+=0 + info=reader.run(index) + read_que.put(info) + read_que.put('over') + + +def match_handler(config,read_que,match_que): + matcher=load_component('matcher',config['name'],config) + match_func=functools.partial(feed_match,matcher=matcher) + pool = Pool(args.num_process_match) + cache=[] + while True: + item=read_que.get() + #clear cache + if item=='over': + if len(cache)!=0: + results=pool.map(match_func,cache) + for cur_item,cur_result in zip(cache,results): + cur_item['corr1'],cur_item['corr2']=cur_result[0],cur_result[1] + match_que.put(cur_item) + match_que.put('over') + break + cache.append(item) + #print(len(cache)) + if len(cache)==args.num_process_match: + #matching in parallel + results=pool.map(match_func,cache) + for cur_item,cur_result in zip(cache,results): + cur_item['corr1'],cur_item['corr2']=cur_result[0],cur_result[1] + match_que.put(cur_item) + cache=[] + pool.close() + pool.join() + + +def evaluate_handler(config,match_que): + evaluator=load_component('evaluator',config['name'],config) + pool = Pool(args.num_process_eval) + cache=[] + for _ in trange(config['num_pair']): + item=match_que.get() + if item=='over': + if len(cache)!=0: + results=pool.map(evaluator.run,cache) + for cur_res in results: + evaluator.res_inqueue(cur_res) + break + cache.append(item) + if len(cache)==args.num_process_eval: + results=pool.map(evaluator.run,cache) + for cur_res in results: + evaluator.res_inqueue(cur_res) + cache=[] + if args.vis_folder is not None: + #dump visualization + corr1_norm,corr2_norm=evaluation_utils.normalize_intrinsic(item['corr1'],item['K1']),\ + evaluation_utils.normalize_intrinsic(item['corr2'],item['K2']) + inlier_mask=metrics.compute_epi_inlier(corr1_norm,corr2_norm,item['e'],config['inlier_th']) + display=evaluation_utils.draw_match(item['img1'],item['img2'],item['corr1'],item['corr2'],inlier_mask) + cv2.imwrite(os.path.join(args.vis_folder,str(item['index'])+'.png'),display) + evaluator.parse() + + +if __name__=='__main__': + with open(args.config_path, 'r') as f: + config = yaml.load(f) + if args.vis_folder is not None and not os.path.exists(args.vis_folder): + os.mkdir(args.vis_folder) + + read_que,match_que,estimate_que=Manager().Queue(maxsize=100),Manager().Queue(maxsize=100),Manager().Queue(maxsize=100) + + read_process=Process(target=reader_handler,args=(config['reader'],read_que)) + match_process=Process(target=match_handler,args=(config['matcher'],read_que,match_que)) + evaluate_process=Process(target=evaluate_handler,args=(config['evaluator'],match_que)) + + read_process.start() + match_process.start() + evaluate_process.start() + + read_process.join() + match_process.join() + evaluate_process.join() \ No newline at end of file diff --git a/third_party/SGMNet/requirements.txt b/third_party/SGMNet/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a47c9a51a87a3eb4ab3ce80201c328bcd0cd75d --- /dev/null +++ b/third_party/SGMNet/requirements.txt @@ -0,0 +1,6 @@ +numpy +pyyaml==5.1 +h5py +tensorboardX +opencv-contrib-python==4.5.2.52 +tqdm \ No newline at end of file diff --git a/third_party/SGMNet/sgmnet/__init__.py b/third_party/SGMNet/sgmnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..828543beceebb10d05fd9d5fdfcc4b1c91e5af6b --- /dev/null +++ b/third_party/SGMNet/sgmnet/__init__.py @@ -0,0 +1 @@ +from .match_model import matcher \ No newline at end of file diff --git a/third_party/SGMNet/sgmnet/match_model.py b/third_party/SGMNet/sgmnet/match_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1e55fa5d042b010f8d9a99e006002563a3961ae7 --- /dev/null +++ b/third_party/SGMNet/sgmnet/match_model.py @@ -0,0 +1,222 @@ +import torch +import torch.nn as nn + +eps=1e-8 + +def sinkhorn(M,r,c,iteration): + p = torch.softmax(M, dim=-1) + u = torch.ones_like(r) + v = torch.ones_like(c) + for _ in range(iteration): + u = r / ((p * v.unsqueeze(-2)).sum(-1) + eps) + v = c / ((p * u.unsqueeze(-1)).sum(-2) + eps) + p = p * u.unsqueeze(-1) * v.unsqueeze(-2) + return p + +def sink_algorithm(M,dustbin,iteration): + M = torch.cat([M, dustbin.expand([M.shape[0], M.shape[1], 1])], dim=-1) + M = torch.cat([M, dustbin.expand([M.shape[0], 1, M.shape[2]])], dim=-2) + r = torch.ones([M.shape[0], M.shape[1] - 1],device='cuda') + r = torch.cat([r, torch.ones([M.shape[0], 1],device='cuda') * M.shape[1]], dim=-1) + c = torch.ones([M.shape[0], M.shape[2] - 1],device='cuda') + c = torch.cat([c, torch.ones([M.shape[0], 1],device='cuda') * M.shape[2]], dim=-1) + p=sinkhorn(M,r,c,iteration) + return p + + +def seeding(nn_index1,nn_index2,x1,x2,topk,match_score,confbar,nms_radius,use_mc=True,test=False): + + #apply mutual check before nms + if use_mc: + mask_not_mutual=nn_index2.gather(dim=-1,index=nn_index1)!=torch.arange(nn_index1.shape[1],device='cuda') + match_score[mask_not_mutual]=-1 + #NMS + pos_dismat1=((x1.norm(p=2,dim=-1)**2).unsqueeze_(-1)+(x1.norm(p=2,dim=-1)**2).unsqueeze_(-2)-2*(x1@x1.transpose(1,2))).abs_().sqrt_() + x2=x2.gather(index=nn_index1.unsqueeze(-1).expand(-1,-1,2),dim=1) + pos_dismat2=((x2.norm(p=2,dim=-1)**2).unsqueeze_(-1)+(x2.norm(p=2,dim=-1)**2).unsqueeze_(-2)-2*(x2@x2.transpose(1,2))).abs_().sqrt_() + radius1, radius2 = nms_radius * pos_dismat1.mean(dim=(1,2),keepdim=True), nms_radius * pos_dismat2.mean(dim=(1,2),keepdim=True) + nms_mask = (pos_dismat1 >= radius1) & (pos_dismat2 >= radius2) + mask_not_local_max=(match_score.unsqueeze(-1)>=match_score.unsqueeze(-2))|nms_mask + mask_not_local_max=~(mask_not_local_max.min(dim=-1).values) + match_score[mask_not_local_max] = -1 + + #confidence bar + match_score[match_score0 + if test: + topk=min(mask_survive.sum(dim=1)[0]+2,topk) + _,topindex = torch.topk(match_score,topk,dim=-1)#b*k + seed_index1,seed_index2=topindex,nn_index1.gather(index=topindex,dim=-1) + return seed_index1,seed_index2 + + + +class PointCN(nn.Module): + def __init__(self, channels,out_channels): + nn.Module.__init__(self) + self.shot_cut = nn.Conv1d(channels, out_channels, kernel_size=1) + self.conv = nn.Sequential( + nn.InstanceNorm1d(channels, eps=1e-3), + nn.SyncBatchNorm(channels), + nn.ReLU(), + nn.Conv1d(channels, channels, kernel_size=1), + nn.InstanceNorm1d(channels, eps=1e-3), + nn.SyncBatchNorm(channels), + nn.ReLU(), + nn.Conv1d(channels, out_channels, kernel_size=1) + ) + + def forward(self, x): + return self.conv(x) + self.shot_cut(x) + + +class attention_propagantion(nn.Module): + + def __init__(self,channel,head): + nn.Module.__init__(self) + self.head=head + self.head_dim=channel//head + self.query_filter,self.key_filter,self.value_filter=nn.Conv1d(channel,channel,kernel_size=1),nn.Conv1d(channel,channel,kernel_size=1),\ + nn.Conv1d(channel,channel,kernel_size=1) + self.mh_filter=nn.Conv1d(channel,channel,kernel_size=1) + self.cat_filter=nn.Sequential(nn.Conv1d(2*channel,2*channel, kernel_size=1), nn.SyncBatchNorm(2*channel), nn.ReLU(), + nn.Conv1d(2*channel, channel, kernel_size=1)) + + def forward(self,desc1,desc2,weight_v=None): + #desc1(q) attend to desc2(k,v) + batch_size=desc1.shape[0] + query,key,value=self.query_filter(desc1).view(batch_size,self.head,self.head_dim,-1),self.key_filter(desc2).view(batch_size,self.head,self.head_dim,-1),\ + self.value_filter(desc2).view(batch_size,self.head,self.head_dim,-1) + if weight_v is not None: + value=value*weight_v.view(batch_size,1,1,-1) + score=torch.softmax(torch.einsum('bhdn,bhdm->bhnm',query,key)/ self.head_dim ** 0.5,dim=-1) + add_value=torch.einsum('bhnm,bhdm->bhdn',score,value).reshape(batch_size,self.head_dim*self.head,-1) + add_value=self.mh_filter(add_value) + desc1_new=desc1+self.cat_filter(torch.cat([desc1,add_value],dim=1)) + return desc1_new + + +class hybrid_block(nn.Module): + def __init__(self,channel,head): + nn.Module.__init__(self) + self.head=head + self.channel=channel + self.attention_block_down = attention_propagantion(channel, head) + self.cluster_filter=nn.Sequential(nn.Conv1d(2*channel,2*channel, kernel_size=1), nn.SyncBatchNorm(2*channel), nn.ReLU(), + nn.Conv1d(2*channel, 2*channel, kernel_size=1)) + self.cross_filter=attention_propagantion(channel,head) + self.confidence_filter=PointCN(2*channel,1) + self.attention_block_self=attention_propagantion(channel,head) + self.attention_block_up=attention_propagantion(channel,head) + + def forward(self,desc1,desc2,seed_index1,seed_index2): + cluster1, cluster2 = desc1.gather(dim=-1, index=seed_index1.unsqueeze(1).expand(-1, self.channel, -1)), \ + desc2.gather(dim=-1, index=seed_index2.unsqueeze(1).expand(-1, self.channel, -1)) + + #pooling + cluster1, cluster2 = self.attention_block_down(cluster1, desc1), self.attention_block_down(cluster2, desc2) + concate_cluster=self.cluster_filter(torch.cat([cluster1,cluster2],dim=1)) + #filtering + cluster1,cluster2=self.cross_filter(concate_cluster[:,:self.channel],concate_cluster[:,self.channel:]),\ + self.cross_filter(concate_cluster[:,self.channel:],concate_cluster[:,:self.channel]) + cluster1,cluster2=self.attention_block_self(cluster1,cluster1),self.attention_block_self(cluster2,cluster2) + #unpooling + seed_weight=self.confidence_filter(torch.cat([cluster1,cluster2],dim=1)) + seed_weight=torch.sigmoid(seed_weight).squeeze(1) + desc1_new,desc2_new=self.attention_block_up(desc1,cluster1,seed_weight),self.attention_block_up(desc2,cluster2,seed_weight) + return desc1_new,desc2_new,seed_weight + + + +class matcher(nn.Module): + def __init__(self,config): + nn.Module.__init__(self) + self.seed_top_k=config.seed_top_k + self.conf_bar=config.conf_bar + self.seed_radius_coe=config.seed_radius_coe + self.use_score_encoding=config.use_score_encoding + self.detach_iter=config.detach_iter + self.seedlayer=config.seedlayer + self.layer_num=config.layer_num + self.sink_iter=config.sink_iter + + self.position_encoder = nn.Sequential(nn.Conv1d(3, 32, kernel_size=1) if config.use_score_encoding else nn.Conv1d(2, 32, kernel_size=1), + nn.SyncBatchNorm(32),nn.ReLU(), + nn.Conv1d(32, 64, kernel_size=1), nn.SyncBatchNorm(64),nn.ReLU(), + nn.Conv1d(64, 128, kernel_size=1), nn.SyncBatchNorm(128),nn.ReLU(), + nn.Conv1d(128, 256, kernel_size=1), nn.SyncBatchNorm(256),nn.ReLU(), + nn.Conv1d(256, config.net_channels, kernel_size=1)) + + + self.hybrid_block=nn.Sequential(*[hybrid_block(config.net_channels, config.head) for _ in range(config.layer_num)]) + self.final_project = nn.Conv1d(config.net_channels, config.net_channels, kernel_size=1) + self.dustbin=nn.Parameter(torch.tensor(1.5,dtype=torch.float32)) + + #if reseeding + if len(config.seedlayer)!=1: + self.mid_dustbin=nn.ParameterDict({str(i):nn.Parameter(torch.tensor(2,dtype=torch.float32)) for i in config.seedlayer[1:]}) + self.mid_final_project = nn.Conv1d(config.net_channels, config.net_channels, kernel_size=1) + + def forward(self,data,test_mode=True): + x1, x2, desc1, desc2 = data['x1'][:,:,:2], data['x2'][:,:,:2], data['desc1'], data['desc2'] + desc1, desc2 = torch.nn.functional.normalize(desc1,dim=-1), torch.nn.functional.normalize(desc2,dim=-1) + if test_mode: + encode_x1,encode_x2=data['x1'],data['x2'] + else: + encode_x1,encode_x2=data['aug_x1'], data['aug_x2'] + + #preparation + desc_dismat=(2-2*torch.matmul(desc1,desc2.transpose(1,2))).sqrt_() + values,nn_index=torch.topk(desc_dismat,k=2,largest=False,dim=-1,sorted=True) + nn_index2=torch.min(desc_dismat,dim=1).indices.squeeze(1) + inverse_ratio_score,nn_index1=values[:,:,1]/values[:,:,0],nn_index[:,:,0]#get inverse score + + #initial seeding + seed_index1,seed_index2=seeding(nn_index1,nn_index2,x1,x2,self.seed_top_k[0],inverse_ratio_score,self.conf_bar[0],\ + self.seed_radius_coe,test=test_mode) + + #position encoding + desc1,desc2=desc1.transpose(1,2),desc2.transpose(1,2) + if not self.use_score_encoding: + encode_x1,encode_x2=encode_x1[:,:,:2],encode_x2[:,:,:2] + encode_x1,encode_x2=encode_x1.transpose(1,2),encode_x2.transpose(1,2) + x1_pos_embedding, x2_pos_embedding = self.position_encoder(encode_x1), self.position_encoder(encode_x2) + aug_desc1, aug_desc2 = x1_pos_embedding + desc1, x2_pos_embedding + desc2 + + seed_weight_tower,mid_p_tower,seed_index_tower,nn_index_tower=[],[],[],[] + seed_index_tower.append(torch.stack([seed_index1, seed_index2],dim=-1)) + nn_index_tower.append(nn_index1) + + seed_para_index=0 + for i in range(self.layer_num): + #mid seeding + if i in self.seedlayer and i!= 0: + seed_para_index+=1 + aug_desc1,aug_desc2=self.mid_final_project(aug_desc1),self.mid_final_project(aug_desc2) + M=torch.matmul(aug_desc1.transpose(1,2),aug_desc2) + p=sink_algorithm(M,self.mid_dustbin[str(i)],self.sink_iter[seed_para_index-1]) + mid_p_tower.append(p) + #rematching with p + values,nn_index=torch.topk(p[:,:-1,:-1],k=1,dim=-1) + nn_index2=torch.max(p[:,:-1,:-1],dim=1).indices.squeeze(1) + p_match_score,nn_index1=values[:,:,0],nn_index[:,:,0] + #reseeding + seed_index1, seed_index2 = seeding(nn_index1,nn_index2,x1,x2,self.seed_top_k[seed_para_index],p_match_score,\ + self.conf_bar[seed_para_index],self.seed_radius_coe,test=test_mode) + seed_index_tower.append(torch.stack([seed_index1, seed_index2],dim=-1)), nn_index_tower.append(nn_index1) + if not test_mode and data['step']bhnm',query1,key1)/self.head_dim**0.5,dim=-1),\ + torch.softmax(torch.einsum('bdhn,bdhm->bhnm',query2,key2)/self.head_dim**0.5,dim=-1) + add_value1, add_value2 = torch.einsum('bhnm,bdhm->bdhn', score1, value1), torch.einsum('bhnm,bdhm->bdhn',score2, value2) + else: + score1,score2 = torch.softmax(torch.einsum('bdhn,bdhm->bhnm', query1, key2) / self.head_dim ** 0.5,dim=-1), \ + torch.softmax(torch.einsum('bdhn,bdhm->bhnm', query2, key1) / self.head_dim ** 0.5, dim=-1) + add_value1, add_value2 =torch.einsum('bhnm,bdhm->bdhn',score1,value2),torch.einsum('bhnm,bdhm->bdhn',score2,value1) + add_value1,add_value2=self.mh_filter(add_value1.contiguous().view(batch_size,self.head*self.head_dim,n)),self.mh_filter(add_value2.contiguous().view(batch_size,self.head*self.head_dim,m)) + fea11, fea22 = torch.cat([fea1, add_value1], dim=1), torch.cat([fea2, add_value2], dim=1) + fea1, fea2 = fea1+self.attention_filter(fea11), fea2+self.attention_filter(fea22) + + return fea1,fea2 + + +class matcher(nn.Module): + def __init__(self, config): + nn.Module.__init__(self) + self.use_score_encoding=config.use_score_encoding + self.layer_num=config.layer_num + self.sink_iter=config.sink_iter + self.position_encoder = nn.Sequential(nn.Conv1d(3, 32, kernel_size=1) if config.use_score_encoding else nn.Conv1d(2, 32, kernel_size=1), + nn.SyncBatchNorm(32), nn.ReLU(), + nn.Conv1d(32, 64, kernel_size=1), nn.SyncBatchNorm(64),nn.ReLU(), + nn.Conv1d(64, 128, kernel_size=1), nn.SyncBatchNorm(128), nn.ReLU(), + nn.Conv1d(128, 256, kernel_size=1), nn.SyncBatchNorm(256), nn.ReLU(), + nn.Conv1d(256, config.net_channels, kernel_size=1)) + + self.dustbin=nn.Parameter(torch.tensor(1,dtype=torch.float32,device='cuda')) + self.self_attention_block=nn.Sequential(*[attention_block(config.net_channels,config.head,'self') for _ in range(config.layer_num)]) + self.cross_attention_block=nn.Sequential(*[attention_block(config.net_channels,config.head,'cross') for _ in range(config.layer_num)]) + self.final_project=nn.Conv1d(config.net_channels, config.net_channels, kernel_size=1) + + def forward(self,data,test_mode=True): + desc1, desc2 = data['desc1'], data['desc2'] + desc1, desc2 = torch.nn.functional.normalize(desc1,dim=-1), torch.nn.functional.normalize(desc2,dim=-1) + desc1,desc2=desc1.transpose(1,2),desc2.transpose(1,2) + if test_mode: + encode_x1,encode_x2=data['x1'],data['x2'] + else: + encode_x1,encode_x2=data['aug_x1'], data['aug_x2'] + if not self.use_score_encoding: + encode_x1,encode_x2=encode_x1[:,:,:2],encode_x2[:,:,:2] + + encode_x1,encode_x2=encode_x1.transpose(1,2),encode_x2.transpose(1,2) + + x1_pos_embedding, x2_pos_embedding = self.position_encoder(encode_x1), self.position_encoder(encode_x2) + aug_desc1, aug_desc2 = x1_pos_embedding + desc1, x2_pos_embedding+desc2 + for i in range(self.layer_num): + aug_desc1,aug_desc2=self.self_attention_block[i](aug_desc1,aug_desc2) + aug_desc1,aug_desc2=self.cross_attention_block[i](aug_desc1,aug_desc2) + + aug_desc1,aug_desc2=self.final_project(aug_desc1),self.final_project(aug_desc2) + desc_mat = torch.matmul(aug_desc1.transpose(1, 2), aug_desc2) + p = sink_algorithm(desc_mat, self.dustbin,self.sink_iter[0]) + return {'p':p} + + diff --git a/third_party/SGMNet/superpoint/__init__.py b/third_party/SGMNet/superpoint/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..111c8882a7bc7512c6191ca86a0e71c3b1404233 --- /dev/null +++ b/third_party/SGMNet/superpoint/__init__.py @@ -0,0 +1 @@ +from .superpoint import SuperPoint \ No newline at end of file diff --git a/third_party/SGMNet/superpoint/superpoint.py b/third_party/SGMNet/superpoint/superpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..d4e3ce481409264a3188270ad01aa62b1614377f --- /dev/null +++ b/third_party/SGMNet/superpoint/superpoint.py @@ -0,0 +1,140 @@ +import torch +from torch import nn + + +def simple_nms(scores, nms_radius): + assert(nms_radius >= 0) + + def max_pool(x): + return torch.nn.functional.max_pool2d( + x, kernel_size=nms_radius*2+1, stride=1, padding=nms_radius) + + zeros = torch.zeros_like(scores) + max_mask = scores == max_pool(scores) + for _ in range(2): + supp_mask = max_pool(max_mask.float()) > 0 + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == max_pool(supp_scores) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +def remove_borders(keypoints, scores, b, h, w): + mask_h = (keypoints[:, 0] >= b) & (keypoints[:, 0] < (h - b)) + mask_w = (keypoints[:, 1] >= b) & (keypoints[:, 1] < (w - b)) + mask = mask_h & mask_w + return keypoints[mask], scores[mask] + + +def top_k_keypoints(keypoints, scores, k): + if k >= len(keypoints): + return keypoints, scores + scores, indices = torch.topk(scores, k, dim=0) + return keypoints[indices], scores + + +def sample_descriptors(keypoints, descriptors, s): + b, c, h, w = descriptors.shape + keypoints = keypoints - s / 2 + 0.5 + keypoints /= torch.tensor([(w*s - s/2 - 0.5), (h*s - s/2 - 0.5)], + ).to(keypoints)[None] + keypoints = keypoints*2 - 1 # normalize to (-1, 1) + args = {'align_corners': True} if int(torch.__version__[2]) > 2 else {} + descriptors = torch.nn.functional.grid_sample( + descriptors, keypoints.view(b, 1, -1, 2), mode='bilinear', **args) + descriptors = torch.nn.functional.normalize( + descriptors.reshape(b, c, -1), p=2, dim=1) + return descriptors + + +class SuperPoint(nn.Module): + + def __init__(self, config): + super().__init__() + self.config = {**config} + + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256 + + self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1) + self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1) + self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1) + self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1) + self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1) + self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1) + self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1) + self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1) + + self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0) + + self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convDb = nn.Conv2d( + c5, self.config['descriptor_dim'], + kernel_size=1, stride=1, padding=0) + + self.load_state_dict(torch.load(config['model_path'])) + + mk = self.config['max_keypoints'] + if mk == 0 or mk < -1: + raise ValueError('\"max_keypoints\" must be positive or \"-1\"') + + print('Loaded SuperPoint model') + + def forward(self, data): + # Shared Encoder + x = self.relu(self.conv1a(data)) + x = self.relu(self.conv1b(x)) + x = self.pool(x) + x = self.relu(self.conv2a(x)) + x = self.relu(self.conv2b(x)) + x = self.pool(x) + x = self.relu(self.conv3a(x)) + x = self.relu(self.conv3b(x)) + x = self.pool(x) + x = self.relu(self.conv4a(x)) + x = self.relu(self.conv4b(x)) + # Compute the dense keypoint scores + cPa = self.relu(self.convPa(x)) + scores = self.convPb(cPa) + scores = torch.nn.functional.softmax(scores, 1)[:, :-1] + b, c, h, w = scores.shape + scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8) + scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h*8, w*8) + scores = simple_nms(scores, self.config['nms_radius']) + + # Extract keypoints + keypoints = [ + torch.nonzero(s > self.config['detection_threshold']) + for s in scores] + scores = [s[tuple(k.t())] for s, k in zip(scores, keypoints)] + + # Discard keypoints near the image borders + keypoints, scores = list(zip(*[ + remove_borders(k, s, self.config['remove_borders'], h*8, w*8) + for k, s in zip(keypoints, scores)])) + + # Keep the k keypoints with highest score + if self.config['max_keypoints'] >= 0: + keypoints, scores = list(zip(*[ + top_k_keypoints(k, s, self.config['max_keypoints']) + for k, s in zip(keypoints, scores)])) + + # Convert (h, w) to (x, y) + keypoints = [torch.flip(k, [1]).float() for k in keypoints] + + # Compute the dense descriptors + cDa = self.relu(self.convDa(x)) + descriptors = self.convDb(cDa) + descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1) + + # Extract descriptors + descriptors = [sample_descriptors(k[None], d[None], 8)[0] + for k, d in zip(keypoints, descriptors)] + + return { + 'keypoints': keypoints, + 'scores': scores, + 'descriptors': descriptors, + } diff --git a/third_party/SGMNet/train/config.py b/third_party/SGMNet/train/config.py new file mode 100644 index 0000000000000000000000000000000000000000..31c4c1c6deef3d6dd568897f4202d96456586376 --- /dev/null +++ b/third_party/SGMNet/train/config.py @@ -0,0 +1,126 @@ +import argparse + +def str2bool(v): + return v.lower() in ("true", "1") + + +arg_lists = [] +parser = argparse.ArgumentParser() + + +def add_argument_group(name): + arg = parser.add_argument_group(name) + arg_lists.append(arg) + return arg + + +# ----------------------------------------------------------------------------- +# Network +net_arg = add_argument_group("Network") +net_arg.add_argument( + "--model_name", type=str,default='SGM', help="" + "model for training") +net_arg.add_argument( + "--config_path", type=str,default='configs/sgm.yaml', help="" + "config path for model") + +# ----------------------------------------------------------------------------- +# Data +data_arg = add_argument_group("Data") +data_arg.add_argument( + "--rawdata_path", type=str, default='rawdata', help="" + "path for rawdata") +data_arg.add_argument( + "--dataset_path", type=str, default='dataset', help="" + "path for dataset") +data_arg.add_argument( + "--desc_path", type=str, default='desc', help="" + "path for descriptor(kpt) dir") +data_arg.add_argument( + "--num_kpt", type=int, default=1000, help="" + "number of kpt for training") +data_arg.add_argument( + "--input_normalize", type=str, default='img', help="" + "normalize type for input kpt, img or intrinsic") +data_arg.add_argument( + "--data_aug", type=str2bool, default=True, help="" + "apply kpt coordinate homography augmentation") +data_arg.add_argument( + "--desc_suffix", type=str, default='suffix', help="" + "desc file suffix") + + +# ----------------------------------------------------------------------------- +# Loss +loss_arg = add_argument_group("loss") +loss_arg.add_argument( + "--momentum", type=float, default=0.9, help="" + "momentum") +loss_arg.add_argument( + "--seed_loss_weight", type=float, default=250, help="" + "confidence loss weight for sgm") +loss_arg.add_argument( + "--mid_loss_weight", type=float, default=1, help="" + "midseeding loss weight for sgm") +loss_arg.add_argument( + "--inlier_th", type=float, default=5e-3, help="" + "inlier threshold for epipolar distance (for sgm and visualization)") + + +# ----------------------------------------------------------------------------- +# Training +train_arg = add_argument_group("Train") +train_arg.add_argument( + "--train_lr", type=float, default=1e-4, help="" + "learning rate") +train_arg.add_argument( + "--train_batch_size", type=int, default=16, help="" + "batch size") +train_arg.add_argument( + "--gpu_id", type=str,default='0', help='id(s) for CUDA_VISIBLE_DEVICES') +train_arg.add_argument( + "--train_iter", type=int, default=1000000, help="" + "training iterations to perform") +train_arg.add_argument( + "--log_base", type=str, default="./log/", help="" + "log path") +train_arg.add_argument( + "--val_intv", type=int, default=20000, help="" + "validation interval") +train_arg.add_argument( + "--save_intv", type=int, default=1000, help="" + "summary interval") +train_arg.add_argument( + "--log_intv", type=int, default=100, help="" + "log interval") +train_arg.add_argument( + "--decay_rate", type=float, default=0.999996, help="" + "lr decay rate") +train_arg.add_argument( + "--decay_iter", type=float, default=300000, help="" + "lr decay iter") +train_arg.add_argument( + "--local_rank", type=int, default=0, help="" + "local rank for ddp") +train_arg.add_argument( + "--train_vis_folder", type=str, default='.', help="" + "visualization folder during training") + +# ----------------------------------------------------------------------------- +# Visualization +vis_arg = add_argument_group('Visualization') +vis_arg.add_argument( + "--tqdm_width", type=int, default=79, help="" + "width of the tqdm bar" +) + +def get_config(): + config, unparsed = parser.parse_known_args() + return config, unparsed + + +def print_usage(): + parser.print_usage() + +# +# config.py ends here \ No newline at end of file diff --git a/third_party/SGMNet/train/configs/sg.yaml b/third_party/SGMNet/train/configs/sg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb03f39f9d8445b1e345d8f8f6ac17eb6d981bc1 --- /dev/null +++ b/third_party/SGMNet/train/configs/sg.yaml @@ -0,0 +1,5 @@ +net_channels: 128 +layer_num: 9 +head: 4 +use_score_encoding: True +p_th: 0.2 \ No newline at end of file diff --git a/third_party/SGMNet/train/configs/sgm.yaml b/third_party/SGMNet/train/configs/sgm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d674adf562a8932192a0a3bb1a993cf90d28e989 --- /dev/null +++ b/third_party/SGMNet/train/configs/sgm.yaml @@ -0,0 +1,12 @@ +seed_top_k: [128,128] +seed_radius_coe: 0.01 +net_channels: 128 +layer_num: 9 +head: 4 +seedlayer: [0,6] +use_mc_seeding: True +use_score_encoding: False +conf_bar: [1,0.1] +sink_iter: [10,100] +detach_iter: 140000 +p_th: 0.2 \ No newline at end of file diff --git a/third_party/SGMNet/train/dataset.py b/third_party/SGMNet/train/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d07a84e9588b755a86119363f08860187d1668c0 --- /dev/null +++ b/third_party/SGMNet/train/dataset.py @@ -0,0 +1,143 @@ +import numpy as np +import torch +import torch.utils.data as data +import cv2 +import os +import h5py +import random + +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../")) +sys.path.insert(0, ROOT_DIR) + +from utils import train_utils,evaluation_utils + +torch.multiprocessing.set_sharing_strategy('file_system') + + +class Offline_Dataset(data.Dataset): + def __init__(self,config,mode): + assert mode=='train' or mode=='valid' + + self.config = config + self.mode = mode + metadir=os.path.join(config.dataset_path,'valid') if mode=='valid' else os.path.join(config.dataset_path,'train') + + pair_num_list=np.loadtxt(os.path.join(metadir,'pair_num.txt'),dtype=str) + self.total_pairs=int(pair_num_list[0,1]) + self.pair_seq_list,self.accu_pair_num=train_utils.parse_pair_seq(pair_num_list) + + + def collate_fn(self, batch): + batch_size, num_pts = len(batch), batch[0]['x1'].shape[0] + + data = {} + dtype=['x1','x2','kpt1','kpt2','desc1','desc2','num_corr','num_incorr1','num_incorr2','e_gt','pscore1','pscore2','img_path1','img_path2'] + for key in dtype: + data[key]=[] + for sample in batch: + for key in dtype: + data[key].append(sample[key]) + + for key in ['x1', 'x2','kpt1','kpt2', 'desc1', 'desc2','e_gt','pscore1','pscore2']: + data[key] = torch.from_numpy(np.stack(data[key])).float() + for key in ['num_corr', 'num_incorr1', 'num_incorr2']: + data[key] = torch.from_numpy(np.stack(data[key])).int() + + # kpt augmentation with random homography + if (self.mode == 'train' and self.config.data_aug): + homo_mat = torch.from_numpy(train_utils.get_rnd_homography(batch_size)).unsqueeze(1) + aug_seed=random.random() + if aug_seed<0.5: + x1_homo = torch.cat([data['x1'], torch.ones([batch_size, num_pts, 1])], dim=-1).unsqueeze(-1) + x1_homo = torch.matmul(homo_mat.float(), x1_homo.float()).squeeze(-1) + data['aug_x1'] = x1_homo[:, :, :2] / x1_homo[:, :, 2].unsqueeze(-1) + data['aug_x2']=data['x2'] + else: + x2_homo = torch.cat([data['x2'], torch.ones([batch_size, num_pts, 1])], dim=-1).unsqueeze(-1) + x2_homo = torch.matmul(homo_mat.float(), x2_homo.float()).squeeze(-1) + data['aug_x2'] = x2_homo[:, :, :2] / x2_homo[:, :, 2].unsqueeze(-1) + data['aug_x1']=data['x1'] + else: + data['aug_x1'],data['aug_x2']=data['x1'],data['x2'] + return data + + + def __getitem__(self, index): + seq=self.pair_seq_list[index] + index_within_seq=index-self.accu_pair_num[seq] + + with h5py.File(os.path.join(self.config.dataset_path,seq,'info.h5py'),'r') as data: + R,t = data['dR'][str(index_within_seq)][()], data['dt'][str(index_within_seq)][()] + egt = np.reshape(np.matmul(np.reshape(evaluation_utils.np_skew_symmetric(t.astype('float64').reshape(1, 3)), (3, 3)),np.reshape(R.astype('float64'), (3, 3))), (3, 3)) + egt = egt / np.linalg.norm(egt) + K1, K2 = data['K1'][str(index_within_seq)][()],data['K2'][str(index_within_seq)][()] + size1,size2=data['size1'][str(index_within_seq)][()],data['size2'][str(index_within_seq)][()] + + img_path1,img_path2=data['img_path1'][str(index_within_seq)][()][0].decode(),data['img_path2'][str(index_within_seq)][()][0].decode() + img_name1,img_name2=img_path1.split('/')[-1],img_path2.split('/')[-1] + img_path1,img_path2=os.path.join(self.config.rawdata_path,img_path1),os.path.join(self.config.rawdata_path,img_path2) + fea_path1,fea_path2=os.path.join(self.config.desc_path,seq,img_name1+self.config.desc_suffix),\ + os.path.join(self.config.desc_path,seq,img_name2+self.config.desc_suffix) + with h5py.File(fea_path1,'r') as fea1, h5py.File(fea_path2,'r') as fea2: + desc1,kpt1,pscore1=fea1['descriptors'][()],fea1['keypoints'][()][:,:2],fea1['keypoints'][()][:,2] + desc2,kpt2,pscore2=fea2['descriptors'][()],fea2['keypoints'][()][:,:2],fea2['keypoints'][()][:,2] + kpt1,kpt2,desc1,desc2=kpt1[:self.config.num_kpt],kpt2[:self.config.num_kpt],desc1[:self.config.num_kpt],desc2[:self.config.num_kpt] + + # normalize kpt + if self.config.input_normalize=='intrinsic': + x1, x2 = np.concatenate([kpt1, np.ones([kpt1.shape[0], 1])], axis=-1), np.concatenate( + [kpt2, np.ones([kpt2.shape[0], 1])], axis=-1) + x1, x2 = np.matmul(np.linalg.inv(K1), x1.T).T[:, :2], np.matmul(np.linalg.inv(K2), x2.T).T[:, :2] + elif self.config.input_normalize=='img' : + x1,x2=(kpt1-size1/2)/size1,(kpt2-size2/2)/size2 + S1_inv,S2_inv=np.asarray([[size1[0],0,0.5*size1[0]],[0,size1[1],0.5*size1[1]],[0,0,1]]),\ + np.asarray([[size2[0],0,0.5*size2[0]],[0,size2[1],0.5*size2[1]],[0,0,1]]) + M1,M2=np.matmul(np.linalg.inv(K1),S1_inv),np.matmul(np.linalg.inv(K2),S2_inv) + egt=np.matmul(np.matmul(M2.transpose(),egt),M1) + egt = egt / np.linalg.norm(egt) + else: + raise NotImplementedError + + corr=data['corr'][str(index_within_seq)][()] + incorr1,incorr2=data['incorr1'][str(index_within_seq)][()],data['incorr2'][str(index_within_seq)][()] + + #permute kpt + valid_corr=corr[corr.max(axis=-1)= cur_kpt1): + sub_idx1 =np.random.choice(len(invalid_index1), cur_kpt1,replace=False) + if (invalid_index2.shape[0] < cur_kpt2): + sub_idx2 = np.concatenate([np.arange(len(invalid_index2)),np.random.randint(len(invalid_index2),size=cur_kpt2-len(invalid_index2))]) + if (invalid_index2.shape[0] >= cur_kpt2): + sub_idx2 = np.random.choice(len(invalid_index2), cur_kpt2,replace=False) + + per_idx1,per_idx2=np.concatenate([valid_corr[:,0],valid_incorr1,invalid_index1[sub_idx1]]),\ + np.concatenate([valid_corr[:,1],valid_incorr2,invalid_index2[sub_idx2]]) + + pscore1,pscore2=pscore1[per_idx1][:,np.newaxis],pscore2[per_idx2][:,np.newaxis] + x1,x2=x1[per_idx1][:,:2],x2[per_idx2][:,:2] + desc1,desc2=desc1[per_idx1],desc2[per_idx2] + kpt1,kpt2=kpt1[per_idx1],kpt2[per_idx2] + + return {'x1': x1, 'x2': x2, 'kpt1':kpt1,'kpt2':kpt2,'desc1': desc1, 'desc2': desc2, 'num_corr': num_corr, 'num_incorr1': num_incorr1,'num_incorr2': num_incorr2,'e_gt':egt,\ + 'pscore1':pscore1,'pscore2':pscore2,'img_path1':img_path1,'img_path2':img_path2} + + def __len__(self): + return self.total_pairs + + diff --git a/third_party/SGMNet/train/loss.py b/third_party/SGMNet/train/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..fad4234fc5827321c31e72c08ad4a3466bad1c30 --- /dev/null +++ b/third_party/SGMNet/train/loss.py @@ -0,0 +1,125 @@ +import torch +import numpy as np + + +def batch_episym(x1, x2, F): + batch_size, num_pts = x1.shape[0], x1.shape[1] + x1 = torch.cat([x1, x1.new_ones(batch_size, num_pts,1)], dim=-1).reshape(batch_size, num_pts,3,1) + x2 = torch.cat([x2, x2.new_ones(batch_size, num_pts,1)], dim=-1).reshape(batch_size, num_pts,3,1) + F = F.reshape(-1,1,3,3).repeat(1,num_pts,1,1) + x2Fx1 = torch.matmul(x2.transpose(2,3), torch.matmul(F, x1)).reshape(batch_size,num_pts) + Fx1 = torch.matmul(F,x1).reshape(batch_size,num_pts,3) + Ftx2 = torch.matmul(F.transpose(2,3),x2).reshape(batch_size,num_pts,3) + ys = (x2Fx1**2 * ( + 1.0 / (Fx1[:, :, 0]**2 + Fx1[:, :, 1]**2 + 1e-15) + + 1.0 / (Ftx2[:, :, 0]**2 + Ftx2[:, :, 1]**2 + 1e-15))).sqrt() + return ys + + +def CELoss(seed_x1,seed_x2,e,confidence,inlier_th,batch_mask=1): + #seed_x: b*k*2 + ys=batch_episym(seed_x1,seed_x2,e) + mask_pos,mask_neg=(ys<=inlier_th).float(),(ys>inlier_th).float() + num_pos,num_neg=torch.relu(torch.sum(mask_pos, dim=1) - 1.0) + 1.0,torch.relu(torch.sum(mask_neg, dim=1) - 1.0) + 1.0 + loss_pos,loss_neg=-torch.log(abs(confidence) + 1e-8)*mask_pos,-torch.log(abs(1-confidence)+1e-8)*mask_neg + classif_loss = torch.mean(loss_pos * 0.5 / num_pos.unsqueeze(-1) + loss_neg * 0.5 / num_neg.unsqueeze(-1),dim=-1) + classif_loss =classif_loss*batch_mask + classif_loss=classif_loss.mean() + precision = torch.mean( + torch.sum((confidence > 0.5).type(confidence.type()) * mask_pos, dim=1) / + (torch.sum((confidence > 0.5).type(confidence.type()), dim=1)+1e-8) + ) + recall = torch.mean( + torch.sum((confidence > 0.5).type(confidence.type()) * mask_pos, dim=1) / + num_pos + ) + return classif_loss,precision,recall + + +def CorrLoss(desc_mat,batch_num_corr,batch_num_incorr1,batch_num_incorr2): + total_loss_corr,total_loss_incorr=0,0 + total_acc_corr,total_acc_incorr=0,0 + batch_size = desc_mat.shape[0] + log_p=torch.log(abs(desc_mat)+1e-8) + + for i in range(batch_size): + cur_log_p=log_p[i] + num_corr=batch_num_corr[i] + num_incorr1,num_incorr2=batch_num_incorr1[i],batch_num_incorr2[i] + + #loss and acc + loss_corr = -torch.diag(cur_log_p)[:num_corr].mean() + loss_incorr=(-cur_log_p[num_corr:num_corr+num_incorr1,-1].mean()-cur_log_p[-1,num_corr:num_corr+num_incorr2].mean())/2 + + value_row, row_index = torch.max(desc_mat[i,:-1,:-1], dim=-1) + value_col, col_index = torch.max(desc_mat[i,:-1,:-1], dim=-2) + acc_incorr=((value_row[num_corr:num_corr+num_incorr1]<0.2).float().mean()+ + (value_col[num_corr:num_corr+num_incorr2]<0.2).float().mean())/2 + + acc_row_mask = row_index[:num_corr] == torch.arange(num_corr).cuda() + acc_col_mask = col_index[:num_corr] == torch.arange(num_corr).cuda() + acc = (acc_col_mask & acc_row_mask).float().mean() + + total_loss_corr+=loss_corr + total_loss_incorr+=loss_incorr + total_acc_corr += acc + total_acc_incorr+=acc_incorr + + total_acc_corr/=batch_size + total_acc_incorr/=batch_size + total_loss_corr/=batch_size + total_loss_incorr/=batch_size + return total_loss_corr,total_loss_incorr,total_acc_corr,total_acc_incorr + + +class SGMLoss: + def __init__(self,config,model_config): + self.config=config + self.model_config=model_config + + def run(self,data,result): + loss_corr,loss_incorr,acc_corr,acc_incorr=CorrLoss(result['p'],data['num_corr'],data['num_incorr1'],data['num_incorr2']) + loss_mid_corr_tower,loss_mid_incorr_tower,acc_mid_tower=[],[],[] + + #mid loss + for i in range(len(result['mid_p'])): + mid_p=result['mid_p'][i] + loss_mid_corr,loss_mid_incorr,mid_acc_corr,mid_acc_incorr=CorrLoss(mid_p,data['num_corr'],data['num_incorr1'],data['num_incorr2']) + loss_mid_corr_tower.append(loss_mid_corr),loss_mid_incorr_tower.append(loss_mid_incorr),acc_mid_tower.append(mid_acc_corr) + if len(result['mid_p']) != 0: + loss_mid_corr_tower,loss_mid_incorr_tower, acc_mid_tower = torch.stack(loss_mid_corr_tower), torch.stack(loss_mid_incorr_tower), torch.stack(acc_mid_tower) + else: + loss_mid_corr_tower,loss_mid_incorr_tower, acc_mid_tower= torch.zeros(1).cuda(), torch.zeros(1).cuda(),torch.zeros(1).cuda() + + #seed confidence loss + classif_loss_tower,classif_precision_tower,classif_recall_tower=[],[],[] + for layer in range(len(result['seed_conf'])): + confidence=result['seed_conf'][layer] + seed_index=result['seed_index'][(np.asarray(self.model_config.seedlayer)<=layer).nonzero()[0][-1]] + seed_x1,seed_x2=data['x1'].gather(dim=1, index=seed_index[:,:,0,None].expand(-1, -1,2)),\ + data['x2'].gather(dim=1, index=seed_index[:,:,1,None].expand(-1, -1,2)) + classif_loss,classif_precision,classif_recall=CELoss(seed_x1,seed_x2,data['e_gt'],confidence,self.config.inlier_th) + classif_loss_tower.append(classif_loss), classif_precision_tower.append(classif_precision), classif_recall_tower.append(classif_recall) + classif_loss, classif_precision_tower, classif_recall_tower=torch.stack(classif_loss_tower).mean(),torch.stack(classif_precision_tower), \ + torch.stack(classif_recall_tower) + + + classif_loss*=self.config.seed_loss_weight + loss_mid_corr_tower*=self.config.mid_loss_weight + loss_mid_incorr_tower*=self.config.mid_loss_weight + total_loss=loss_corr+loss_incorr+classif_loss+loss_mid_corr_tower.sum()+loss_mid_incorr_tower.sum() + + return {'loss_corr':loss_corr,'loss_incorr':loss_incorr,'acc_corr':acc_corr,'acc_incorr':acc_incorr,'loss_seed_conf':classif_loss, + 'pre_seed_conf':classif_precision_tower,'recall_seed_conf':classif_recall_tower,'loss_corr_mid':loss_mid_corr_tower, + 'loss_incorr_mid':loss_mid_incorr_tower,'mid_acc_corr':acc_mid_tower,'total_loss':total_loss} + +class SGLoss: + def __init__(self,config,model_config): + self.config=config + self.model_config=model_config + + def run(self,data,result): + loss_corr,loss_incorr,acc_corr,acc_incorr=CorrLoss(result['p'],data['num_corr'],data['num_incorr1'],data['num_incorr2']) + total_loss=loss_corr+loss_incorr + return {'loss_corr':loss_corr,'loss_incorr':loss_incorr,'acc_corr':acc_corr,'acc_incorr':acc_incorr,'total_loss':total_loss} + \ No newline at end of file diff --git a/third_party/SGMNet/train/main.py b/third_party/SGMNet/train/main.py new file mode 100644 index 0000000000000000000000000000000000000000..9d4c8fff432a3b2d58c82b9e5f2897a4e702b2dd --- /dev/null +++ b/third_party/SGMNet/train/main.py @@ -0,0 +1,61 @@ +import torch.utils.data +from dataset import Offline_Dataset +import yaml +from sgmnet.match_model import matcher as SGM_Model +from superglue.match_model import matcher as SG_Model +import torch.distributed as dist +import torch +import os +from collections import namedtuple +from train import train +from config import get_config, print_usage + + +def main(config,model_config): + """The main function.""" + # Initialize network + if config.model_name=='SGM': + model = SGM_Model(model_config) + elif config.model_name=='SG': + model= SG_Model(model_config) + else: + raise NotImplementedError + + #initialize ddp + torch.cuda.set_device(config.local_rank) + device = torch.device(f'cuda:{config.local_rank}') + model.to(device) + dist.init_process_group(backend='nccl',init_method='env://') + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.local_rank]) + + if config.local_rank==0: + os.system('nvidia-smi') + + #initialize dataset + train_dataset = Offline_Dataset(config,'train') + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,shuffle=True) + train_loader=torch.utils.data.DataLoader(train_dataset, batch_size=config.train_batch_size//torch.distributed.get_world_size(), + num_workers=8//dist.get_world_size(), pin_memory=False,sampler=train_sampler,collate_fn=train_dataset.collate_fn) + + valid_dataset = Offline_Dataset(config,'valid') + valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset,shuffle=False) + valid_loader=torch.utils.data.DataLoader(valid_dataset, batch_size=config.train_batch_size, + num_workers=8//dist.get_world_size(), pin_memory=False,collate_fn=valid_dataset.collate_fn,sampler=valid_sampler) + + if config.local_rank==0: + print('start training .....') + train(model,train_loader, valid_loader, config,model_config) + +if __name__ == "__main__": + # ---------------------------------------- + # Parse configuration + config, unparsed = get_config() + with open(config.config_path, 'r') as f: + model_config = yaml.load(f) + model_config=namedtuple('model_config',model_config.keys())(*model_config.values()) + # If we have unparsed arguments, print usage and exit + if len(unparsed) > 0: + print_usage() + exit(1) + + main(config,model_config) diff --git a/third_party/SGMNet/train/train.py b/third_party/SGMNet/train/train.py new file mode 100644 index 0000000000000000000000000000000000000000..31e848e1d2e5f028d4ff3abaf0cc446be7d89c65 --- /dev/null +++ b/third_party/SGMNet/train/train.py @@ -0,0 +1,160 @@ +import torch +import torch.optim as optim +from tqdm import trange +import os +from tensorboardX import SummaryWriter +import numpy as np +import cv2 +from loss import SGMLoss,SGLoss +from valid import valid,dump_train_vis + +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + + +from utils import train_utils + +def train_step(optimizer, model, match_loss, data,step,pre_avg_loss): + data['step']=step + result=model(data,test_mode=False) + loss_res=match_loss.run(data,result) + + optimizer.zero_grad() + loss_res['total_loss'].backward() + #apply reduce on all record tensor + for key in loss_res.keys(): + loss_res[key]=train_utils.reduce_tensor(loss_res[key],'mean') + + if loss_res['total_loss']<7*pre_avg_loss or step<200 or pre_avg_loss==0: + optimizer.step() + unusual_loss=False + else: + optimizer.zero_grad() + unusual_loss=True + return loss_res,unusual_loss + + +def train(model, train_loader, valid_loader, config,model_config): + model.train() + optimizer = optim.Adam(model.parameters(), lr=config.train_lr) + + if config.model_name=='SGM': + match_loss = SGMLoss(config,model_config) + elif config.model_name=='SG': + match_loss= SGLoss(config,model_config) + else: + raise NotImplementedError + + checkpoint_path = os.path.join(config.log_base, 'checkpoint.pth') + config.resume = os.path.isfile(checkpoint_path) + if config.resume: + if config.local_rank==0: + print('==> Resuming from checkpoint..') + checkpoint = torch.load(checkpoint_path,map_location='cuda:{}'.format(config.local_rank)) + model.load_state_dict(checkpoint['state_dict']) + best_acc = checkpoint['best_acc'] + start_step = checkpoint['step'] + optimizer.load_state_dict(checkpoint['optimizer']) + else: + best_acc = -1 + start_step = 0 + train_loader_iter = iter(train_loader) + + if config.local_rank==0: + writer=SummaryWriter(os.path.join(config.log_base,'log_file')) + + train_loader.sampler.set_epoch(start_step*config.train_batch_size//len(train_loader.dataset)) + pre_avg_loss=0 + + progress_bar=trange(start_step, config.train_iter,ncols=config.tqdm_width) if config.local_rank==0 else range(start_step, config.train_iter) + for step in progress_bar: + try: + train_data = next(train_loader_iter) + except StopIteration: + if config.local_rank==0: + print('epoch: ',step*config.train_batch_size//len(train_loader.dataset)) + train_loader.sampler.set_epoch(step*config.train_batch_size//len(train_loader.dataset)) + train_loader_iter = iter(train_loader) + train_data = next(train_loader_iter) + + train_data = train_utils.tocuda(train_data) + lr=min(config.train_lr*config.decay_rate**(step-config.decay_iter),config.train_lr) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + # run training + loss_res,unusual_loss = train_step(optimizer, model, match_loss, train_data,step-start_step,pre_avg_loss) + if (step-start_step)<=200: + pre_avg_loss=loss_res['total_loss'].data + if (step-start_step)>200 and not unusual_loss: + pre_avg_loss=pre_avg_loss.data*0.9+loss_res['total_loss'].data*0.1 + if unusual_loss and config.local_rank==0: + print('unusual loss! pre_avg_loss: ',pre_avg_loss,'cur_loss: ',loss_res['total_loss'].data) + #log + if config.local_rank==0 and step%config.log_intv==0 and not unusual_loss: + writer.add_scalar('TotalLoss',loss_res['total_loss'],step) + writer.add_scalar('CorrLoss',loss_res['loss_corr'],step) + writer.add_scalar('InCorrLoss', loss_res['loss_incorr'], step) + writer.add_scalar('dustbin', model.module.dustbin, step) + + if config.model_name=='SGM': + writer.add_scalar('SeedConfLoss', loss_res['loss_seed_conf'], step) + writer.add_scalar('MidCorrLoss', loss_res['loss_corr_mid'].sum(), step) + writer.add_scalar('MidInCorrLoss', loss_res['loss_incorr_mid'].sum(), step) + + + # valid ans save + b_save = ((step + 1) % config.save_intv) == 0 + b_validate = ((step + 1) % config.val_intv) == 0 + if b_validate: + total_loss,acc_corr,acc_incorr,seed_precision_tower,seed_recall_tower,acc_mid=valid(valid_loader, model, match_loss, config,model_config) + if config.local_rank==0: + writer.add_scalar('ValidAcc', acc_corr, step) + writer.add_scalar('ValidLoss', total_loss, step) + + if config.model_name=='SGM': + for i in range(len(seed_recall_tower)): + writer.add_scalar('seed_conf_pre_%d'%i,seed_precision_tower[i],step) + writer.add_scalar('seed_conf_recall_%d' % i, seed_precision_tower[i], step) + for i in range(len(acc_mid)): + writer.add_scalar('acc_mid%d'%i,acc_mid[i],step) + print('acc_corr: ',acc_corr.data,'acc_incorr: ',acc_incorr.data,'seed_conf_pre: ',seed_precision_tower.mean().data, + 'seed_conf_recall: ',seed_recall_tower.mean().data,'acc_mid: ',acc_mid.mean().data) + else: + print('acc_corr: ',acc_corr.data,'acc_incorr: ',acc_incorr.data) + + #saving best + if acc_corr > best_acc: + print("Saving best model with va_res = {}".format(acc_corr)) + best_acc = acc_corr + save_dict={'step': step + 1, + 'state_dict': model.state_dict(), + 'best_acc': best_acc, + 'optimizer' : optimizer.state_dict()} + save_dict.update(save_dict) + torch.save(save_dict, os.path.join(config.log_base, 'model_best.pth')) + + if b_save: + if config.local_rank==0: + save_dict={'step': step + 1, + 'state_dict': model.state_dict(), + 'best_acc': best_acc, + 'optimizer' : optimizer.state_dict()} + torch.save(save_dict, checkpoint_path) + + #draw match results + model.eval() + with torch.no_grad(): + if config.local_rank==0: + if not os.path.exists(os.path.join(config.train_vis_folder,'train_vis')): + os.mkdir(os.path.join(config.train_vis_folder,'train_vis')) + if not os.path.exists(os.path.join(config.train_vis_folder,'train_vis',config.log_base)): + os.mkdir(os.path.join(config.train_vis_folder,'train_vis',config.log_base)) + os.mkdir(os.path.join(config.train_vis_folder,'train_vis',config.log_base,str(step))) + res=model(train_data) + dump_train_vis(res,train_data,step,config) + model.train() + + if config.local_rank==0: + writer.close() diff --git a/third_party/SGMNet/train/train_sg.sh b/third_party/SGMNet/train/train_sg.sh new file mode 100644 index 0000000000000000000000000000000000000000..a6ba093dfcaad6005520b65a068c60d7e93b03f8 --- /dev/null +++ b/third_party/SGMNet/train/train_sg.sh @@ -0,0 +1,10 @@ +OMP_NUM_THREADS=2 CUDA_VISIBLE_DEVICES='0' python -m torch.distributed.launch --nproc_per_node=1 --master_port 23003 main.py \ +--model_name=SG \ +--config_path=configs/sg.yaml \ +--rawdata_path=rawdata \ +--desc_path=desc_path \ +--desc_suffix=_root_1000.hdf5 \ +--dataset_path=dataset_path \ +--log_base=log_root_1k_sg \ +--num_kpt=1000 \ +--train_iter=900000 \ No newline at end of file diff --git a/third_party/SGMNet/train/train_sgm.sh b/third_party/SGMNet/train/train_sgm.sh new file mode 100644 index 0000000000000000000000000000000000000000..f82704e04746ec3353ae2e39f727b55fc072043b --- /dev/null +++ b/third_party/SGMNet/train/train_sgm.sh @@ -0,0 +1,10 @@ +OMP_NUM_THREADS=2 CUDA_VISIBLE_DEVICES='0' python -m torch.distributed.launch --nproc_per_node=1 --master_port 23003 main.py \ +--model_name=SGM \ +--config_path=configs/sgm.yaml \ +--rawdata_path=rawdata \ +--desc_path=desc_path \ +--desc_suffix=_root_1000.hdf5 \ +--dataset_path=dataset_path \ +--log_base=log_root_1k_sgm \ +--num_kpt=1000 \ +--train_iter=900000 \ No newline at end of file diff --git a/third_party/SGMNet/train/valid.py b/third_party/SGMNet/train/valid.py new file mode 100644 index 0000000000000000000000000000000000000000..443694d85104730cd50aeb342326ce593dc5684d --- /dev/null +++ b/third_party/SGMNet/train/valid.py @@ -0,0 +1,77 @@ +import torch +import numpy as np +import cv2 +import os +from loss import batch_episym +from tqdm import tqdm + +import sys +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, ROOT_DIR) + +from utils import evaluation_utils,train_utils + + +def valid(valid_loader, model,match_loss, config,model_config): + model.eval() + loader_iter = iter(valid_loader) + num_pair = 0 + total_loss,total_acc_corr,total_acc_incorr=0,0,0 + total_precision,total_recall=torch.zeros(model_config.layer_num ,device='cuda'),\ + torch.zeros(model_config.layer_num ,device='cuda') + total_acc_mid=torch.zeros(len(model_config.seedlayer)-1,device='cuda') + + with torch.no_grad(): + if config.local_rank==0: + loader_iter=tqdm(loader_iter) + print('validating...') + for test_data in loader_iter: + num_pair+= 1 + test_data = train_utils.tocuda(test_data) + res= model(test_data) + loss_res=match_loss.run(test_data,res) + + total_acc_corr+=loss_res['acc_corr'] + total_acc_incorr+=loss_res['acc_incorr'] + total_loss+=loss_res['total_loss'] + + if config.model_name=='SGM': + total_acc_mid+=loss_res['mid_acc_corr'] + total_precision,total_recall=total_precision+loss_res['pre_seed_conf'],total_recall+loss_res['recall_seed_conf'] + + total_acc_corr/=num_pair + total_acc_incorr /= num_pair + total_precision/=num_pair + total_recall/=num_pair + total_acc_mid/=num_pair + + #apply tensor reduction + total_loss,total_acc_corr,total_acc_incorr,total_precision,total_recall,total_acc_mid=train_utils.reduce_tensor(total_loss,'sum'),\ + train_utils.reduce_tensor(total_acc_corr,'mean'),train_utils.reduce_tensor(total_acc_incorr,'mean'),\ + train_utils.reduce_tensor(total_precision,'mean'),train_utils.reduce_tensor(total_recall,'mean'),train_utils.reduce_tensor(total_acc_mid,'mean') + model.train() + return total_loss,total_acc_corr,total_acc_incorr,total_precision,total_recall,total_acc_mid + + + +def dump_train_vis(res,data,step,config): + #batch matching + p=res['p'][:,:-1,:-1] + score,index1=torch.max(p,dim=-1) + _,index2=torch.max(p,dim=-2) + mask_th=score>0.2 + mask_mc=index2.gather(index=index1,dim=1) == torch.arange(len(p[0])).cuda()[None] + mask_p=mask_th&mask_mc#B*N + + corr1,corr2=data['x1'],data['x2'].gather(index=index1[:,:,None].expand(-1,-1,2),dim=1) + corr1_kpt,corr2_kpt=data['kpt1'],data['kpt2'].gather(index=index1[:,:,None].expand(-1,-1,2),dim=1) + epi_dis=batch_episym(corr1,corr2,data['e_gt']) + mask_inlier=epi_dis0,i0,j 0, + depth_top_right > 0 + ), + np.logical_and( + depth_down_left > 0, + depth_down_left > 0 + ) + ) + ids=ids[valid_depth] + depth_top_left,depth_top_right,depth_down_left,depth_down_right=depth_top_left[valid_depth],depth_top_right[valid_depth],\ + depth_down_left[valid_depth],depth_down_right[valid_depth] + + i,j,i_top_left,j_top_left=i[valid_depth],j[valid_depth],i_top_left[valid_depth],j_top_left[valid_depth] + + # Interpolation + dist_i_top_left = i - i_top_left.astype(np.float32) + dist_j_top_left = j - j_top_left.astype(np.float32) + w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left) + w_top_right = (1 - dist_i_top_left) * dist_j_top_left + w_bottom_left = dist_i_top_left * (1 - dist_j_top_left) + w_bottom_right = dist_i_top_left * dist_j_top_left + + interpolated_depth = ( + w_top_left * depth_top_left + + w_top_right * depth_top_right+ + w_bottom_left * depth_down_left + + w_bottom_right * depth_down_right + ) + return [interpolated_depth, ids] + + +def reprojection(depth_map,kpt,dR,dt,K1_img2depth,K1,K2): + #warp kpt from img1 to img2 + def swap_axis(data): + return np.stack([data[:, 1], data[:, 0]], axis=-1) + + kp_depth = unnorm_kp(K1_img2depth,kpt) + uv_depth = swap_axis(kp_depth) + z,valid_idx = interpolate_depth(uv_depth, depth_map) + + norm_kp=norm_kpt(K1,kpt) + norm_kp_valid = np.concatenate([norm_kp[valid_idx, :], np.ones((len(valid_idx), 1))], axis=-1) + xyz_valid = norm_kp_valid * z.reshape(-1, 1) + xyz2 = np.matmul(xyz_valid, dR.T) + dt.reshape(1, 3) + xy2 = xyz2[:, :2] / xyz2[:, 2:] + kp2, valid = np.ones(kpt.shape) * 1e5, np.zeros(kpt.shape[0]) + kp2[valid_idx] = unnorm_kp(K2,xy2) + valid[valid_idx] = 1 + return kp2, valid.astype(bool) + +def reprojection_2s(kp1, kp2,depth1, depth2, K1, K2, dR, dt, size1,size2): + #size:H*W + depth_size1,depth_size2 = [depth1.shape[0], depth1.shape[1]], [depth2.shape[0], depth2.shape[1]] + scale_1= [float(depth_size1[0]) / size1[0], float(depth_size1[1]) / size1[1], 1] + scale_2= [float(depth_size2[0]) / size2[0], float(depth_size2[1]) / size2[1], 1] + K1_img2depth, K2_img2depth = np.diag(np.asarray(scale_1)), np.diag(np.asarray(scale_2)) + kp1_2_proj, valid1_2 = reprojection(depth1, kp1, dR, dt, K1_img2depth,K1,K2) + kp2_1_proj, valid2_1 = reprojection(depth2, kp2, dR.T, -np.matmul(dR.T, dt), K2_img2depth,K2,K1) + return [kp1_2_proj,kp2_1_proj],[valid1_2,valid2_1] + +def make_corr(kp1,kp2,desc1,desc2,depth1,depth2,K1,K2,dR,dt,size1,size2,corr_th,incorr_th,check_desc=False): + #make reprojection + [kp1_2,kp2_1],[valid1_2,valid2_1]=reprojection_2s(kp1,kp2,depth1,depth2,K1,K2,dR,dt,size1,size2) + num_pts1, num_pts2 = kp1.shape[0], kp2.shape[0] + #reprojection error + dis_mat1=np.sqrt(abs((kp1 ** 2).sum(1,keepdims=True) + (kp2_1 ** 2).sum(1,keepdims=False)[np.newaxis] - 2 * np.matmul(kp1, kp2_1.T))) + dis_mat2 =np.sqrt(abs((kp2 ** 2).sum(1,keepdims=True) + (kp1_2 ** 2).sum(1,keepdims=False)[np.newaxis] - 2 * np.matmul(kp2,kp1_2.T))) + repro_error = np.maximum(dis_mat1,dis_mat2.T) #n1*n2 + + # find corr index + nn_sort1 = np.argmin(repro_error, axis=1) + nn_sort2 = np.argmin(repro_error, axis=0) + mask_mutual = nn_sort2[nn_sort1] == np.arange(kp1.shape[0]) + mask_inlier=np.take_along_axis(repro_error,indices=nn_sort1[:,np.newaxis],axis=-1).squeeze(1)1,mask_samepos2.sum(-1)>1) + duplicated_index=np.nonzero(duplicated_mask)[0] + + unique_corr_index=corr_index[~duplicated_mask] + clean_duplicated_corr=[] + for index in duplicated_index: + cur_desc1, cur_desc2 = desc1[mask_samepos1[index]], desc2[mask_samepos2[index]] + cur_desc_mat = np.matmul(cur_desc1, cur_desc2.T) + cur_max_index =[np.argmax(cur_desc_mat)//cur_desc_mat.shape[1],np.argmax(cur_desc_mat)%cur_desc_mat.shape[1]] + clean_duplicated_corr.append(np.stack([np.arange(num_pts1)[mask_samepos1[index]][cur_max_index[0]], + np.arange(num_pts2)[mask_samepos2[index]][cur_max_index[1]]])) + + clean_corr_index=unique_corr_index + if len(clean_duplicated_corr)!=0: + clean_duplicated_corr=np.stack(clean_duplicated_corr,axis=0) + clean_corr_index=np.concatenate([clean_corr_index,clean_duplicated_corr],axis=0) + else: + clean_corr_index=corr_index + # find incorr + mask_incorr1 = np.min(dis_mat2.T[valid1_2], axis=-1) > incorr_th + mask_incorr2 = np.min(dis_mat1.T[valid2_1], axis=-1) > incorr_th + incorr_index1, incorr_index2 = np.arange(num_pts1)[valid1_2][mask_incorr1.squeeze()], \ + np.arange(num_pts2)[valid2_1][mask_incorr2.squeeze()] + + return clean_corr_index,incorr_index1,incorr_index2 + diff --git a/third_party/SGMNet/utils/evaluation_utils.py b/third_party/SGMNet/utils/evaluation_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..82c4715a192d3c361c849896b035cd91ee56dc42 --- /dev/null +++ b/third_party/SGMNet/utils/evaluation_utils.py @@ -0,0 +1,58 @@ +import numpy as np +import h5py +import cv2 + +def normalize_intrinsic(x,K): + #print(x,K) + return (x-K[:2,2])/np.diag(K)[:2] + +def normalize_size(x,size,scale=1): + size=size.reshape([1,2]) + norm_fac=size.max() + return (x-size/2+0.5)/(norm_fac*scale) + +def np_skew_symmetric(v): + zero = np.zeros_like(v[:, 0]) + M = np.stack([ + zero, -v[:, 2], v[:, 1], + v[:, 2], zero, -v[:, 0], + -v[:, 1], v[:, 0], zero, + ], axis=1) + return M + +def draw_points(img,points,color=(0,255,0),radius=3): + dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])] + for i in range(points.shape[0]): + cv2.circle(img, dp[i],radius=radius,color=color) + return img + + +def draw_match(img1, img2, corr1, corr2,inlier=[True],color=None,radius1=1,radius2=1,resize=None): + if resize is not None: + scale1,scale2=[img1.shape[1]/resize[0],img1.shape[0]/resize[1]],[img2.shape[1]/resize[0],img2.shape[0]/resize[1]] + img1,img2=cv2.resize(img1, resize, interpolation=cv2.INTER_AREA),cv2.resize(img2, resize, interpolation=cv2.INTER_AREA) + corr1,corr2=corr1/np.asarray(scale1)[np.newaxis],corr2/np.asarray(scale2)[np.newaxis] + corr1_key = [cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])] + corr2_key = [cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])] + + assert len(corr1) == len(corr2) + + draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))] + if color is None: + color = [(0, 255, 0) if cur_inlier else (0,0,255) for cur_inlier in inlier] + if len(color)==1: + display = cv2.drawMatches(img1, corr1_key, img2, corr2_key, draw_matches, None, + matchColor=color[0], + singlePointColor=color[0], + flags=4 + ) + else: + height,width=max(img1.shape[0],img2.shape[0]),img1.shape[1]+img2.shape[1] + display=np.zeros([height,width,3],np.uint8) + display[:img1.shape[0],:img1.shape[1]]=img1 + display[:img2.shape[0],img1.shape[1]:]=img2 + for i in range(len(corr1)): + left_x,left_y,right_x,right_y=int(corr1[i][0]),int(corr1[i][1]),int(corr2[i][0]+img1.shape[1]),int(corr2[i][1]) + cur_color=(int(color[i][0]),int(color[i][1]),int(color[i][2])) + cv2.line(display, (left_x,left_y), (right_x,right_y),cur_color,1,lineType=cv2.LINE_AA) + return display \ No newline at end of file diff --git a/third_party/SGMNet/utils/fm_utils.py b/third_party/SGMNet/utils/fm_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f9cbbeefe5d6b59c1ae1fa26cdaa42146ad22a74 --- /dev/null +++ b/third_party/SGMNet/utils/fm_utils.py @@ -0,0 +1,95 @@ +import numpy as np + + +def line_to_border(line,size): + #line:(a,b,c), ax+by+c=0 + #size:(W,H) + H,W=size[1],size[0] + a,b,c=line[0],line[1],line[2] + epsa=1e-8 if a>=0 else -1e-8 + epsb=1e-8 if b>=0 else -1e-8 + intersection_list=[] + + y_left=-c/(b+epsb) + y_right=(-c-a*(W-1))/(b+epsb) + x_top=-c/(a+epsa) + x_down=(-c-b*(H-1))/(a+epsa) + + if y_left>=0 and y_left<=H-1: + intersection_list.append([0,y_left]) + if y_right>=0 and y_right<=H-1: + intersection_list.append([W-1,y_right]) + if x_top>=0 and x_top<=W-1: + intersection_list.append([x_top,0]) + if x_down>=0 and x_down<=W-1: + intersection_list.append([x_down,H-1]) + if len(intersection_list)!=2: + return None + intersection_list=np.asarray(intersection_list) + return intersection_list + +def find_point_in_line(end_point): + x_span,y_span=end_point[1,0]-end_point[0,0],end_point[1,1]-end_point[0,1] + mv=np.random.uniform() + point=np.asarray([end_point[0,0]+x_span*mv,end_point[0,1]+y_span*mv]) + return point + +def epi_line(point,F): + homo=np.concatenate([point,np.ones([len(point),1])],axis=-1) + epi=np.matmul(homo,F.T) + return epi + +def dis_point_to_line(line,point): + homo=np.concatenate([point,np.ones([len(point),1])],axis=-1) + dis=line*homo + dis=dis.sum(axis=-1)/(np.linalg.norm(line[:,:2],axis=-1)+1e-8) + return abs(dis) + +def SGD_oneiter(F1,F2,size1,size2): + H1,W1=size1[1],size1[0] + factor1 = 1 / np.linalg.norm(size1) + factor2 = 1 / np.linalg.norm(size2) + p0=np.asarray([(W1-1)*np.random.uniform(),(H1-1)*np.random.uniform()]) + epi1=epi_line(p0[np.newaxis],F1)[0] + border_point1=line_to_border(epi1,size2) + if border_point1 is None: + return -1 + + p1=find_point_in_line(border_point1) + epi2=epi_line(p0[np.newaxis],F2) + d1=dis_point_to_line(epi2,p1[np.newaxis])[0]*factor2 + epi3=epi_line(p1[np.newaxis],F2.T) + d2=dis_point_to_line(epi3,p0[np.newaxis])[0]*factor1 + return (d1+d2)/2 + +def compute_SGD(F1,F2,size1,size2): + np.random.seed(1234) + N=1000 + max_iter=N*10 + count,sgd=0,0 + for i in range(max_iter): + d1=SGD_oneiter(F1,F2,size1,size2) + if d1<0: + continue + d2=SGD_oneiter(F2,F1,size1,size2) + if d2<0: + continue + count+=1 + sgd+=(d1+d2)/2 + if count==N: + break + if count==0: + return 1 + else: + return sgd/count + +def compute_inlier_rate(x1,x2,size1,size2,F_gt,th=0.003): + t1,t2=np.linalg.norm(size1)*th,np.linalg.norm(size2)*th + epi1,epi2=epi_line(x1,F_gt),epi_line(x2,F_gt.T) + dis1,dis2=dis_point_to_line(epi1,x2),dis_point_to_line(epi2,x1) + mask_inlier=np.logical_and(dis1`_ + +:Organization: + Laboratory for Fluorescence Dynamics, University of California, Irvine + +:Version: 2015.07.18 + +Requirements +------------ +* `CPython 2.7 or 3.4 `_ +* `Numpy 1.9 `_ +* `Transformations.c 2015.07.18 `_ + (recommended for speedup of some functions) + +Notes +----- +The API is not stable yet and is expected to change between revisions. + +This Python code is not optimized for speed. Refer to the transformations.c +module for a faster implementation of some functions. + +Documentation in HTML format can be generated with epydoc. + +Matrices (M) can be inverted using numpy.linalg.inv(M), be concatenated using +numpy.dot(M0, M1), or transform homogeneous coordinate arrays (v) using +numpy.dot(M, v) for shape (4, \*) column vectors, respectively +numpy.dot(v, M.T) for shape (\*, 4) row vectors ("array of points"). + +This module follows the "column vectors on the right" and "row major storage" +(C contiguous) conventions. The translation components are in the right column +of the transformation matrix, i.e. M[:3, 3]. +The transpose of the transformation matrices may have to be used to interface +with other graphics systems, e.g. with OpenGL's glMultMatrixd(). See also [16]. + +Calculations are carried out with numpy.float64 precision. + +Vector, point, quaternion, and matrix function arguments are expected to be +"array like", i.e. tuple, list, or numpy arrays. + +Return types are numpy arrays unless specified otherwise. + +Angles are in radians unless specified otherwise. + +Quaternions w+ix+jy+kz are represented as [w, x, y, z]. + +A triple of Euler angles can be applied/interpreted in 24 ways, which can +be specified using a 4 character string or encoded 4-tuple: + + *Axes 4-string*: e.g. 'sxyz' or 'ryxy' + + - first character : rotations are applied to 's'tatic or 'r'otating frame + - remaining characters : successive rotation axis 'x', 'y', or 'z' + + *Axes 4-tuple*: e.g. (0, 0, 0, 0) or (1, 1, 1, 1) + + - inner axis: code of axis ('x':0, 'y':1, 'z':2) of rightmost matrix. + - parity : even (0) if inner axis 'x' is followed by 'y', 'y' is followed + by 'z', or 'z' is followed by 'x'. Otherwise odd (1). + - repetition : first and last axis are same (1) or different (0). + - frame : rotations are applied to static (0) or rotating (1) frame. + +Other Python packages and modules for 3D transformations and quaternions: + +* `Transforms3d `_ + includes most code of this module. +* `Blender.mathutils `_ +* `numpy-dtypes `_ + +References +---------- +(1) Matrices and transformations. Ronald Goldman. + In "Graphics Gems I", pp 472-475. Morgan Kaufmann, 1990. +(2) More matrices and transformations: shear and pseudo-perspective. + Ronald Goldman. In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(3) Decomposing a matrix into simple transformations. Spencer Thomas. + In "Graphics Gems II", pp 320-323. Morgan Kaufmann, 1991. +(4) Recovering the data from the transformation matrix. Ronald Goldman. + In "Graphics Gems II", pp 324-331. Morgan Kaufmann, 1991. +(5) Euler angle conversion. Ken Shoemake. + In "Graphics Gems IV", pp 222-229. Morgan Kaufmann, 1994. +(6) Arcball rotation control. Ken Shoemake. + In "Graphics Gems IV", pp 175-192. Morgan Kaufmann, 1994. +(7) Representing attitude: Euler angles, unit quaternions, and rotation + vectors. James Diebel. 2006. +(8) A discussion of the solution for the best rotation to relate two sets + of vectors. W Kabsch. Acta Cryst. 1978. A34, 827-828. +(9) Closed-form solution of absolute orientation using unit quaternions. + BKP Horn. J Opt Soc Am A. 1987. 4(4):629-642. +(10) Quaternions. Ken Shoemake. + http://www.sfu.ca/~jwa3/cmpt461/files/quatut.pdf +(11) From quaternion to matrix and back. JMP van Waveren. 2005. + http://www.intel.com/cd/ids/developer/asmo-na/eng/293748.htm +(12) Uniform random rotations. Ken Shoemake. + In "Graphics Gems III", pp 124-132. Morgan Kaufmann, 1992. +(13) Quaternion in molecular modeling. CFF Karney. + J Mol Graph Mod, 25(5):595-604 +(14) New method for extracting the quaternion from a rotation matrix. + Itzhack Y Bar-Itzhack, J Guid Contr Dynam. 2000. 23(6): 1085-1087. +(15) Multiple View Geometry in Computer Vision. Hartley and Zissermann. + Cambridge University Press; 2nd Ed. 2004. Chapter 4, Algorithm 4.7, p 130. +(16) Column Vectors vs. Row Vectors. + http://steve.hollasch.net/cgindex/math/matrix/column-vec.html + +Examples +-------- +>>> alpha, beta, gamma = 0.123, -1.234, 2.345 +>>> origin, xaxis, yaxis, zaxis = [0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1] +>>> I = identity_matrix() +>>> Rx = rotation_matrix(alpha, xaxis) +>>> Ry = rotation_matrix(beta, yaxis) +>>> Rz = rotation_matrix(gamma, zaxis) +>>> R = concatenate_matrices(Rx, Ry, Rz) +>>> euler = euler_from_matrix(R, 'rxyz') +>>> numpy.allclose([alpha, beta, gamma], euler) +True +>>> Re = euler_matrix(alpha, beta, gamma, 'rxyz') +>>> is_same_transform(R, Re) +True +>>> al, be, ga = euler_from_matrix(Re, 'rxyz') +>>> is_same_transform(Re, euler_matrix(al, be, ga, 'rxyz')) +True +>>> qx = quaternion_about_axis(alpha, xaxis) +>>> qy = quaternion_about_axis(beta, yaxis) +>>> qz = quaternion_about_axis(gamma, zaxis) +>>> q = quaternion_multiply(qx, qy) +>>> q = quaternion_multiply(q, qz) +>>> Rq = quaternion_matrix(q) +>>> is_same_transform(R, Rq) +True +>>> S = scale_matrix(1.23, origin) +>>> T = translation_matrix([1, 2, 3]) +>>> Z = shear_matrix(beta, xaxis, origin, zaxis) +>>> R = random_rotation_matrix(numpy.random.rand(3)) +>>> M = concatenate_matrices(T, R, Z, S) +>>> scale, shear, angles, trans, persp = decompose_matrix(M) +>>> numpy.allclose(scale, 1.23) +True +>>> numpy.allclose(trans, [1, 2, 3]) +True +>>> numpy.allclose(shear, [0, math.tan(beta), 0]) +True +>>> is_same_transform(R, euler_matrix(axes='sxyz', *angles)) +True +>>> M1 = compose_matrix(scale, shear, angles, trans, persp) +>>> is_same_transform(M, M1) +True +>>> v0, v1 = random_vector(3), random_vector(3) +>>> M = rotation_matrix(angle_between_vectors(v0, v1), vector_product(v0, v1)) +>>> v2 = numpy.dot(v0, M[:3,:3].T) +>>> numpy.allclose(unit_vector(v1), unit_vector(v2)) +True + +""" + +from __future__ import division, print_function + +import math + +import numpy + +__version__ = '2015.07.18' +__docformat__ = 'restructuredtext en' +__all__ = () + + +def identity_matrix(): + """Return 4x4 identity/unit matrix. + + >>> I = identity_matrix() + >>> numpy.allclose(I, numpy.dot(I, I)) + True + >>> numpy.sum(I), numpy.trace(I) + (4.0, 4.0) + >>> numpy.allclose(I, numpy.identity(4)) + True + + """ + return numpy.identity(4) + + +def translation_matrix(direction): + """Return matrix to translate by direction vector. + + >>> v = numpy.random.random(3) - 0.5 + >>> numpy.allclose(v, translation_matrix(v)[:3, 3]) + True + + """ + M = numpy.identity(4) + M[:3, 3] = direction[:3] + return M + + +def translation_from_matrix(matrix): + """Return translation vector from translation matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = translation_from_matrix(translation_matrix(v0)) + >>> numpy.allclose(v0, v1) + True + + """ + return numpy.array(matrix, copy=False)[:3, 3].copy() + + +def reflection_matrix(point, normal): + """Return matrix to mirror at plane defined by point and normal vector. + + >>> v0 = numpy.random.random(4) - 0.5 + >>> v0[3] = 1. + >>> v1 = numpy.random.random(3) - 0.5 + >>> R = reflection_matrix(v0, v1) + >>> numpy.allclose(2, numpy.trace(R)) + True + >>> numpy.allclose(v0, numpy.dot(R, v0)) + True + >>> v2 = v0.copy() + >>> v2[:3] += v1 + >>> v3 = v0.copy() + >>> v2[:3] -= v1 + >>> numpy.allclose(v2, numpy.dot(R, v3)) + True + + """ + normal = unit_vector(normal[:3]) + M = numpy.identity(4) + M[:3, :3] -= 2.0 * numpy.outer(normal, normal) + M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal + return M + + +def reflection_from_matrix(matrix): + """Return mirror plane point and normal vector from reflection matrix. + + >>> v0 = numpy.random.random(3) - 0.5 + >>> v1 = numpy.random.random(3) - 0.5 + >>> M0 = reflection_matrix(v0, v1) + >>> point, normal = reflection_from_matrix(M0) + >>> M1 = reflection_matrix(point, normal) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + # normal: unit eigenvector corresponding to eigenvalue -1 + w, V = numpy.linalg.eig(M[:3, :3]) + i = numpy.where(abs(numpy.real(w) + 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue -1") + normal = numpy.real(V[:, i[0]]).squeeze() + # point: any unit eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return point, normal + + +def rotation_matrix(angle, direction, point=None): + """Return matrix to rotate about axis defined by point and direction. + + >>> R = rotation_matrix(math.pi/2, [0, 0, 1], [1, 0, 0]) + >>> numpy.allclose(numpy.dot(R, [0, 0, 0, 1]), [1, -1, 0, 1]) + True + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(angle-2*math.pi, direc, point) + >>> is_same_transform(R0, R1) + True + >>> R0 = rotation_matrix(angle, direc, point) + >>> R1 = rotation_matrix(-angle, -direc, point) + >>> is_same_transform(R0, R1) + True + >>> I = numpy.identity(4, numpy.float64) + >>> numpy.allclose(I, rotation_matrix(math.pi*2, direc)) + True + >>> numpy.allclose(2, numpy.trace(rotation_matrix(math.pi/2, + ... direc, point))) + True + + """ + sina = math.sin(angle) + cosa = math.cos(angle) + direction = unit_vector(direction[:3]) + # rotation matrix around unit vector + R = numpy.diag([cosa, cosa, cosa]) + R += numpy.outer(direction, direction) * (1.0 - cosa) + direction *= sina + R += numpy.array([[ 0.0, -direction[2], direction[1]], + [ direction[2], 0.0, -direction[0]], + [-direction[1], direction[0], 0.0]]) + M = numpy.identity(4) + M[:3, :3] = R + if point is not None: + # rotation not around origin + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + M[:3, 3] = point - numpy.dot(R, point) + return M + + +def rotation_from_matrix(matrix): + """Return rotation angle and axis from rotation matrix. + + >>> angle = (random.random() - 0.5) * (2*math.pi) + >>> direc = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> R0 = rotation_matrix(angle, direc, point) + >>> angle, direc, point = rotation_from_matrix(R0) + >>> R1 = rotation_matrix(angle, direc, point) + >>> is_same_transform(R0, R1) + True + + """ + R = numpy.array(matrix, dtype=numpy.float64, copy=False) + R33 = R[:3, :3] + # direction: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, W = numpy.linalg.eig(R33.T) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + direction = numpy.real(W[:, i[-1]]).squeeze() + # point: unit eigenvector of R33 corresponding to eigenvalue of 1 + w, Q = numpy.linalg.eig(R) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no unit eigenvector corresponding to eigenvalue 1") + point = numpy.real(Q[:, i[-1]]).squeeze() + point /= point[3] + # rotation angle depending on direction + cosa = (numpy.trace(R33) - 1.0) / 2.0 + if abs(direction[2]) > 1e-8: + sina = (R[1, 0] + (cosa-1.0)*direction[0]*direction[1]) / direction[2] + elif abs(direction[1]) > 1e-8: + sina = (R[0, 2] + (cosa-1.0)*direction[0]*direction[2]) / direction[1] + else: + sina = (R[2, 1] + (cosa-1.0)*direction[1]*direction[2]) / direction[0] + angle = math.atan2(sina, cosa) + return angle, direction, point + + +def scale_matrix(factor, origin=None, direction=None): + """Return matrix to scale by factor around origin in direction. + + Use factor -1 for point symmetry. + + >>> v = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v[3] = 1 + >>> S = scale_matrix(-1.234) + >>> numpy.allclose(numpy.dot(S, v)[:3], -1.234*v[:3]) + True + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S = scale_matrix(factor, origin) + >>> S = scale_matrix(factor, origin, direct) + + """ + if direction is None: + # uniform scaling + M = numpy.diag([factor, factor, factor, 1.0]) + if origin is not None: + M[:3, 3] = origin[:3] + M[:3, 3] *= 1.0 - factor + else: + # nonuniform scaling + direction = unit_vector(direction[:3]) + factor = 1.0 - factor + M = numpy.identity(4) + M[:3, :3] -= factor * numpy.outer(direction, direction) + if origin is not None: + M[:3, 3] = (factor * numpy.dot(origin[:3], direction)) * direction + return M + + +def scale_from_matrix(matrix): + """Return scaling factor, origin and direction from scaling matrix. + + >>> factor = random.random() * 10 - 5 + >>> origin = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> S0 = scale_matrix(factor, origin) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + >>> S0 = scale_matrix(factor, origin, direct) + >>> factor, origin, direction = scale_from_matrix(S0) + >>> S1 = scale_matrix(factor, origin, direction) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + factor = numpy.trace(M33) - 2.0 + try: + # direction: unit eigenvector corresponding to eigenvalue factor + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - factor) < 1e-8)[0][0] + direction = numpy.real(V[:, i]).squeeze() + direction /= vector_norm(direction) + except IndexError: + # uniform scaling + factor = (factor + 2.0) / 3.0 + direction = None + # origin: any eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + origin = numpy.real(V[:, i[-1]]).squeeze() + origin /= origin[3] + return factor, origin, direction + + +def projection_matrix(point, normal, direction=None, + perspective=None, pseudo=False): + """Return matrix to project onto plane defined by point and normal. + + Using either perspective point, projection direction, or none of both. + + If pseudo is True, perspective projections will preserve relative depth + such that Perspective = dot(Orthogonal, PseudoPerspective). + + >>> P = projection_matrix([0, 0, 0], [1, 0, 0]) + >>> numpy.allclose(P[1:, 1:], numpy.identity(4)[1:, 1:]) + True + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> P1 = projection_matrix(point, normal, direction=direct) + >>> P2 = projection_matrix(point, normal, perspective=persp) + >>> P3 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> is_same_transform(P2, numpy.dot(P0, P3)) + True + >>> P = projection_matrix([3, 0, 0], [1, 1, 0], [1, 0, 0]) + >>> v0 = (numpy.random.rand(4, 5) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(P, v0) + >>> numpy.allclose(v1[1], v0[1]) + True + >>> numpy.allclose(v1[0], 3-v1[1]) + True + + """ + M = numpy.identity(4) + point = numpy.array(point[:3], dtype=numpy.float64, copy=False) + normal = unit_vector(normal[:3]) + if perspective is not None: + # perspective projection + perspective = numpy.array(perspective[:3], dtype=numpy.float64, + copy=False) + M[0, 0] = M[1, 1] = M[2, 2] = numpy.dot(perspective-point, normal) + M[:3, :3] -= numpy.outer(perspective, normal) + if pseudo: + # preserve relative depth + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * (perspective+normal) + else: + M[:3, 3] = numpy.dot(point, normal) * perspective + M[3, :3] = -normal + M[3, 3] = numpy.dot(perspective, normal) + elif direction is not None: + # parallel projection + direction = numpy.array(direction[:3], dtype=numpy.float64, copy=False) + scale = numpy.dot(direction, normal) + M[:3, :3] -= numpy.outer(direction, normal) / scale + M[:3, 3] = direction * (numpy.dot(point, normal) / scale) + else: + # orthogonal projection + M[:3, :3] -= numpy.outer(normal, normal) + M[:3, 3] = numpy.dot(point, normal) * normal + return M + + +def projection_from_matrix(matrix, pseudo=False): + """Return projection plane and perspective point from projection matrix. + + Return values are same as arguments for projection_matrix function: + point, normal, direction, perspective, and pseudo. + + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.random.random(3) - 0.5 + >>> direct = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(3) - 0.5 + >>> P0 = projection_matrix(point, normal) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, direct) + >>> result = projection_from_matrix(P0) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=False) + >>> result = projection_from_matrix(P0, pseudo=False) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + >>> P0 = projection_matrix(point, normal, perspective=persp, pseudo=True) + >>> result = projection_from_matrix(P0, pseudo=True) + >>> P1 = projection_matrix(*result) + >>> is_same_transform(P0, P1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not pseudo and len(i): + # point: any eigenvector corresponding to eigenvalue 1 + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + # direction: unit eigenvector corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 0") + direction = numpy.real(V[:, i[0]]).squeeze() + direction /= vector_norm(direction) + # normal: unit eigenvector of M33.T corresponding to eigenvalue 0 + w, V = numpy.linalg.eig(M33.T) + i = numpy.where(abs(numpy.real(w)) < 1e-8)[0] + if len(i): + # parallel projection + normal = numpy.real(V[:, i[0]]).squeeze() + normal /= vector_norm(normal) + return point, normal, direction, None, False + else: + # orthogonal projection, where normal equals direction vector + return point, direction, None, None, False + else: + # perspective projection + i = numpy.where(abs(numpy.real(w)) > 1e-8)[0] + if not len(i): + raise ValueError( + "no eigenvector not corresponding to eigenvalue 0") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + normal = - M[3, :3] + perspective = M[:3, 3] / numpy.dot(point[:3], normal) + if pseudo: + perspective -= normal + return point, normal, None, perspective, pseudo + + +def clip_matrix(left, right, bottom, top, near, far, perspective=False): + """Return matrix to obtain normalized device coordinates from frustum. + + The frustum bounds are axis-aligned along x (left, right), + y (bottom, top) and z (near, far). + + Normalized device coordinates are in range [-1, 1] if coordinates are + inside the frustum. + + If perspective is True the frustum is a truncated pyramid with the + perspective point at origin and direction along z axis, otherwise an + orthographic canonical view volume (a box). + + Homogeneous coordinates transformed by the perspective clip matrix + need to be dehomogenized (divided by w coordinate). + + >>> frustum = numpy.random.rand(6) + >>> frustum[1] += frustum[0] + >>> frustum[3] += frustum[2] + >>> frustum[5] += frustum[4] + >>> M = clip_matrix(perspective=False, *frustum) + >>> numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + array([-1., -1., -1., 1.]) + >>> numpy.dot(M, [frustum[1], frustum[3], frustum[5], 1]) + array([ 1., 1., 1., 1.]) + >>> M = clip_matrix(perspective=True, *frustum) + >>> v = numpy.dot(M, [frustum[0], frustum[2], frustum[4], 1]) + >>> v / v[3] + array([-1., -1., -1., 1.]) + >>> v = numpy.dot(M, [frustum[1], frustum[3], frustum[4], 1]) + >>> v / v[3] + array([ 1., 1., -1., 1.]) + + """ + if left >= right or bottom >= top or near >= far: + raise ValueError("invalid frustum") + if perspective: + if near <= _EPS: + raise ValueError("invalid frustum: near <= 0") + t = 2.0 * near + M = [[t/(left-right), 0.0, (right+left)/(right-left), 0.0], + [0.0, t/(bottom-top), (top+bottom)/(top-bottom), 0.0], + [0.0, 0.0, (far+near)/(near-far), t*far/(far-near)], + [0.0, 0.0, -1.0, 0.0]] + else: + M = [[2.0/(right-left), 0.0, 0.0, (right+left)/(left-right)], + [0.0, 2.0/(top-bottom), 0.0, (top+bottom)/(bottom-top)], + [0.0, 0.0, 2.0/(far-near), (far+near)/(near-far)], + [0.0, 0.0, 0.0, 1.0]] + return numpy.array(M) + + +def shear_matrix(angle, direction, point, normal): + """Return matrix to shear by angle along direction vector on shear plane. + + The shear plane is defined by a point and normal vector. The direction + vector must be orthogonal to the plane's normal vector. + + A point P is transformed by the shear matrix into P" such that + the vector P-P" is parallel to the direction vector and its extent is + given by the angle of P-P'-P", where P' is the orthogonal projection + of P onto the shear plane. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S = shear_matrix(angle, direct, point, normal) + >>> numpy.allclose(1, numpy.linalg.det(S)) + True + + """ + normal = unit_vector(normal[:3]) + direction = unit_vector(direction[:3]) + if abs(numpy.dot(normal, direction)) > 1e-6: + raise ValueError("direction and normal vectors are not orthogonal") + angle = math.tan(angle) + M = numpy.identity(4) + M[:3, :3] += angle * numpy.outer(direction, normal) + M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction + return M + + +def shear_from_matrix(matrix): + """Return shear angle, direction and plane from shear matrix. + + >>> angle = (random.random() - 0.5) * 4*math.pi + >>> direct = numpy.random.random(3) - 0.5 + >>> point = numpy.random.random(3) - 0.5 + >>> normal = numpy.cross(direct, numpy.random.random(3)) + >>> S0 = shear_matrix(angle, direct, point, normal) + >>> angle, direct, point, normal = shear_from_matrix(S0) + >>> S1 = shear_matrix(angle, direct, point, normal) + >>> is_same_transform(S0, S1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False) + M33 = M[:3, :3] + # normal: cross independent eigenvectors corresponding to the eigenvalue 1 + w, V = numpy.linalg.eig(M33) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-4)[0] + if len(i) < 2: + raise ValueError("no two linear independent eigenvectors found %s" % w) + V = numpy.real(V[:, i]).squeeze().T + lenorm = -1.0 + for i0, i1 in ((0, 1), (0, 2), (1, 2)): + n = numpy.cross(V[i0], V[i1]) + w = vector_norm(n) + if w > lenorm: + lenorm = w + normal = n + normal /= lenorm + # direction and angle + direction = numpy.dot(M33 - numpy.identity(3), normal) + angle = vector_norm(direction) + direction /= angle + angle = math.atan(angle) + # point: eigenvector corresponding to eigenvalue 1 + w, V = numpy.linalg.eig(M) + i = numpy.where(abs(numpy.real(w) - 1.0) < 1e-8)[0] + if not len(i): + raise ValueError("no eigenvector corresponding to eigenvalue 1") + point = numpy.real(V[:, i[-1]]).squeeze() + point /= point[3] + return angle, direction, point, normal + + +def decompose_matrix(matrix): + """Return sequence of transformations from transformation matrix. + + matrix : array_like + Non-degenerative homogeneous transformation matrix + + Return tuple of: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + Raise ValueError if matrix is of wrong type or degenerative. + + >>> T0 = translation_matrix([1, 2, 3]) + >>> scale, shear, angles, trans, persp = decompose_matrix(T0) + >>> T1 = translation_matrix(trans) + >>> numpy.allclose(T0, T1) + True + >>> S = scale_matrix(0.123) + >>> scale, shear, angles, trans, persp = decompose_matrix(S) + >>> scale[0] + 0.123 + >>> R0 = euler_matrix(1, 2, 3) + >>> scale, shear, angles, trans, persp = decompose_matrix(R0) + >>> R1 = euler_matrix(*angles) + >>> numpy.allclose(R0, R1) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=True).T + if abs(M[3, 3]) < _EPS: + raise ValueError("M[3, 3] is zero") + M /= M[3, 3] + P = M.copy() + P[:, 3] = 0.0, 0.0, 0.0, 1.0 + if not numpy.linalg.det(P): + raise ValueError("matrix is singular") + + scale = numpy.zeros((3, )) + shear = [0.0, 0.0, 0.0] + angles = [0.0, 0.0, 0.0] + + if any(abs(M[:3, 3]) > _EPS): + perspective = numpy.dot(M[:, 3], numpy.linalg.inv(P.T)) + M[:, 3] = 0.0, 0.0, 0.0, 1.0 + else: + perspective = numpy.array([0.0, 0.0, 0.0, 1.0]) + + translate = M[3, :3].copy() + M[3, :3] = 0.0 + + row = M[:3, :3].copy() + scale[0] = vector_norm(row[0]) + row[0] /= scale[0] + shear[0] = numpy.dot(row[0], row[1]) + row[1] -= row[0] * shear[0] + scale[1] = vector_norm(row[1]) + row[1] /= scale[1] + shear[0] /= scale[1] + shear[1] = numpy.dot(row[0], row[2]) + row[2] -= row[0] * shear[1] + shear[2] = numpy.dot(row[1], row[2]) + row[2] -= row[1] * shear[2] + scale[2] = vector_norm(row[2]) + row[2] /= scale[2] + shear[1:] /= scale[2] + + if numpy.dot(row[0], numpy.cross(row[1], row[2])) < 0: + numpy.negative(scale, scale) + numpy.negative(row, row) + + angles[1] = math.asin(-row[0, 2]) + if math.cos(angles[1]): + angles[0] = math.atan2(row[1, 2], row[2, 2]) + angles[2] = math.atan2(row[0, 1], row[0, 0]) + else: + #angles[0] = math.atan2(row[1, 0], row[1, 1]) + angles[0] = math.atan2(-row[2, 1], row[1, 1]) + angles[2] = 0.0 + + return scale, shear, angles, translate, perspective + + +def compose_matrix(scale=None, shear=None, angles=None, translate=None, + perspective=None): + """Return transformation matrix from sequence of transformations. + + This is the inverse of the decompose_matrix function. + + Sequence of transformations: + scale : vector of 3 scaling factors + shear : list of shear factors for x-y, x-z, y-z axes + angles : list of Euler angles about static x, y, z axes + translate : translation vector along x, y, z axes + perspective : perspective partition of matrix + + >>> scale = numpy.random.random(3) - 0.5 + >>> shear = numpy.random.random(3) - 0.5 + >>> angles = (numpy.random.random(3) - 0.5) * (2*math.pi) + >>> trans = numpy.random.random(3) - 0.5 + >>> persp = numpy.random.random(4) - 0.5 + >>> M0 = compose_matrix(scale, shear, angles, trans, persp) + >>> result = decompose_matrix(M0) + >>> M1 = compose_matrix(*result) + >>> is_same_transform(M0, M1) + True + + """ + M = numpy.identity(4) + if perspective is not None: + P = numpy.identity(4) + P[3, :] = perspective[:4] + M = numpy.dot(M, P) + if translate is not None: + T = numpy.identity(4) + T[:3, 3] = translate[:3] + M = numpy.dot(M, T) + if angles is not None: + R = euler_matrix(angles[0], angles[1], angles[2], 'sxyz') + M = numpy.dot(M, R) + if shear is not None: + Z = numpy.identity(4) + Z[1, 2] = shear[2] + Z[0, 2] = shear[1] + Z[0, 1] = shear[0] + M = numpy.dot(M, Z) + if scale is not None: + S = numpy.identity(4) + S[0, 0] = scale[0] + S[1, 1] = scale[1] + S[2, 2] = scale[2] + M = numpy.dot(M, S) + M /= M[3, 3] + return M + + +def orthogonalization_matrix(lengths, angles): + """Return orthogonalization matrix for crystallographic cell coordinates. + + Angles are expected in degrees. + + The de-orthogonalization matrix is the inverse. + + >>> O = orthogonalization_matrix([10, 10, 10], [90, 90, 90]) + >>> numpy.allclose(O[:3, :3], numpy.identity(3, float) * 10) + True + >>> O = orthogonalization_matrix([9.8, 12.0, 15.5], [87.2, 80.7, 69.7]) + >>> numpy.allclose(numpy.sum(O), 43.063229) + True + + """ + a, b, c = lengths + angles = numpy.radians(angles) + sina, sinb, _ = numpy.sin(angles) + cosa, cosb, cosg = numpy.cos(angles) + co = (cosa * cosb - cosg) / (sina * sinb) + return numpy.array([ + [ a*sinb*math.sqrt(1.0-co*co), 0.0, 0.0, 0.0], + [-a*sinb*co, b*sina, 0.0, 0.0], + [ a*cosb, b*cosa, c, 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def affine_matrix_from_points(v0, v1, shear=True, scale=True, usesvd=True): + """Return affine transform matrix to register two point sets. + + v0 and v1 are shape (ndims, \*) arrays of at least ndims non-homogeneous + coordinates, where ndims is the dimensionality of the coordinate space. + + If shear is False, a similarity transformation matrix is returned. + If also scale is False, a rigid/Euclidean transformation matrix + is returned. + + By default the algorithm by Hartley and Zissermann [15] is used. + If usesvd is True, similarity and Euclidean transformation matrices + are calculated by minimizing the weighted sum of squared deviations + (RMSD) according to the algorithm by Kabsch [8]. + Otherwise, and if ndims is 3, the quaternion based algorithm by Horn [9] + is used, which is slower when using this Python implementation. + + The returned matrix performs rotation, translation and uniform scaling + (if specified). + + >>> v0 = [[0, 1031, 1031, 0], [0, 0, 1600, 1600]] + >>> v1 = [[675, 826, 826, 677], [55, 52, 281, 277]] + >>> affine_matrix_from_points(v0, v1) + array([[ 0.14549, 0.00062, 675.50008], + [ 0.00048, 0.14094, 53.24971], + [ 0. , 0. , 1. ]]) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> S = scale_matrix(random.random()) + >>> M = concatenate_matrices(T, R, S) + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-8, 300).reshape(3, -1) + >>> M = affine_matrix_from_points(v0[:3], v1[:3]) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + + More examples in superimposition_matrix() + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=True) + v1 = numpy.array(v1, dtype=numpy.float64, copy=True) + + ndims = v0.shape[0] + if ndims < 2 or v0.shape[1] < ndims or v0.shape != v1.shape: + raise ValueError("input arrays are of wrong shape or type") + + # move centroids to origin + t0 = -numpy.mean(v0, axis=1) + M0 = numpy.identity(ndims+1) + M0[:ndims, ndims] = t0 + v0 += t0.reshape(ndims, 1) + t1 = -numpy.mean(v1, axis=1) + M1 = numpy.identity(ndims+1) + M1[:ndims, ndims] = t1 + v1 += t1.reshape(ndims, 1) + + if shear: + # Affine transformation + A = numpy.concatenate((v0, v1), axis=0) + u, s, vh = numpy.linalg.svd(A.T) + vh = vh[:ndims].T + B = vh[:ndims] + C = vh[ndims:2*ndims] + t = numpy.dot(C, numpy.linalg.pinv(B)) + t = numpy.concatenate((t, numpy.zeros((ndims, 1))), axis=1) + M = numpy.vstack((t, ((0.0,)*ndims) + (1.0,))) + elif usesvd or ndims != 3: + # Rigid transformation via SVD of covariance matrix + u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T)) + # rotation matrix from SVD orthonormal bases + R = numpy.dot(u, vh) + if numpy.linalg.det(R) < 0.0: + # R does not constitute right handed system + R -= numpy.outer(u[:, ndims-1], vh[ndims-1, :]*2.0) + s[-1] *= -1.0 + # homogeneous transformation matrix + M = numpy.identity(ndims+1) + M[:ndims, :ndims] = R + else: + # Rigid transformation matrix via quaternion + # compute symmetric matrix N + xx, yy, zz = numpy.sum(v0 * v1, axis=1) + xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1) + xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1) + N = [[xx+yy+zz, 0.0, 0.0, 0.0], + [yz-zy, xx-yy-zz, 0.0, 0.0], + [zx-xz, xy+yx, yy-xx-zz, 0.0], + [xy-yx, zx+xz, yz+zy, zz-xx-yy]] + # quaternion: eigenvector corresponding to most positive eigenvalue + w, V = numpy.linalg.eigh(N) + q = V[:, numpy.argmax(w)] + q /= vector_norm(q) # unit quaternion + # homogeneous transformation matrix + M = quaternion_matrix(q) + + if scale and not shear: + # Affine transformation; scale is ratio of RMS deviations from centroid + v0 *= v0 + v1 *= v1 + M[:ndims, :ndims] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0)) + + # move centroids back + M = numpy.dot(numpy.linalg.inv(M1), numpy.dot(M, M0)) + M /= M[ndims, ndims] + return M + + +def superimposition_matrix(v0, v1, scale=False, usesvd=True): + """Return matrix to transform given 3D point set into second point set. + + v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 points. + + The parameters scale and usesvd are explained in the more general + affine_matrix_from_points function. + + The returned matrix is a similarity or Euclidean transformation matrix. + This function has a fast C implementation in transformations.c. + + >>> v0 = numpy.random.rand(3, 10) + >>> M = superimposition_matrix(v0, v0) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> R = random_rotation_matrix(numpy.random.random(3)) + >>> v0 = [[1,0,0], [0,1,0], [0,0,1], [1,1,1]] + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20 + >>> v0[3] = 1 + >>> v1 = numpy.dot(R, v0) + >>> M = superimposition_matrix(v0, v1) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> S = scale_matrix(random.random()) + >>> T = translation_matrix(numpy.random.random(3)-0.5) + >>> M = concatenate_matrices(T, R, S) + >>> v1 = numpy.dot(M, v0) + >>> v0[:3] += numpy.random.normal(0, 1e-9, 300).reshape(3, -1) + >>> M = superimposition_matrix(v0, v1, scale=True) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v0)) + True + >>> v = numpy.empty((4, 100, 3)) + >>> v[:, :, 0] = v0 + >>> M = superimposition_matrix(v0, v1, scale=True, usesvd=False) + >>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0])) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3] + v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3] + return affine_matrix_from_points(v0, v1, shear=False, + scale=scale, usesvd=usesvd) + + +def euler_matrix(ai, aj, ak, axes='sxyz'): + """Return homogeneous rotation matrix from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> R = euler_matrix(1, 2, 3, 'syxz') + >>> numpy.allclose(numpy.sum(R[0]), -1.34786452) + True + >>> R = euler_matrix(1, 2, 3, (0, 1, 0, 1)) + >>> numpy.allclose(numpy.sum(R[0]), -0.383436184) + True + >>> ai, aj, ak = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + >>> for axes in _TUPLE2AXES.keys(): + ... R = euler_matrix(ai, aj, ak, axes) + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + if frame: + ai, ak = ak, ai + if parity: + ai, aj, ak = -ai, -aj, -ak + + si, sj, sk = math.sin(ai), math.sin(aj), math.sin(ak) + ci, cj, ck = math.cos(ai), math.cos(aj), math.cos(ak) + cc, cs = ci*ck, ci*sk + sc, ss = si*ck, si*sk + + M = numpy.identity(4) + if repetition: + M[i, i] = cj + M[i, j] = sj*si + M[i, k] = sj*ci + M[j, i] = sj*sk + M[j, j] = -cj*ss+cc + M[j, k] = -cj*cs-sc + M[k, i] = -sj*ck + M[k, j] = cj*sc+cs + M[k, k] = cj*cc-ss + else: + M[i, i] = cj*ck + M[i, j] = sj*sc-cs + M[i, k] = sj*cc+ss + M[j, i] = cj*sk + M[j, j] = sj*ss+cc + M[j, k] = sj*cs-sc + M[k, i] = -sj + M[k, j] = cj*si + M[k, k] = cj*ci + return M + + +def euler_from_matrix(matrix, axes='sxyz'): + """Return Euler angles from rotation matrix for specified axis sequence. + + axes : One of 24 axis sequences as string or encoded tuple + + Note that many Euler angle triplets can describe one matrix. + + >>> R0 = euler_matrix(1, 2, 3, 'syxz') + >>> al, be, ga = euler_from_matrix(R0, 'syxz') + >>> R1 = euler_matrix(al, be, ga, 'syxz') + >>> numpy.allclose(R0, R1) + True + >>> angles = (4*math.pi) * (numpy.random.random(3) - 0.5) + >>> for axes in _AXES2TUPLE.keys(): + ... R0 = euler_matrix(axes=axes, *angles) + ... R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes)) + ... if not numpy.allclose(R0, R1): print(axes, "failed") + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + j = _NEXT_AXIS[i+parity] + k = _NEXT_AXIS[i-parity+1] + + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3] + if repetition: + sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k]) + if sy > _EPS: + ax = math.atan2( M[i, j], M[i, k]) + ay = math.atan2( sy, M[i, i]) + az = math.atan2( M[j, i], -M[k, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2( sy, M[i, i]) + az = 0.0 + else: + cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i]) + if cy > _EPS: + ax = math.atan2( M[k, j], M[k, k]) + ay = math.atan2(-M[k, i], cy) + az = math.atan2( M[j, i], M[i, i]) + else: + ax = math.atan2(-M[j, k], M[j, j]) + ay = math.atan2(-M[k, i], cy) + az = 0.0 + + if parity: + ax, ay, az = -ax, -ay, -az + if frame: + ax, az = az, ax + return ax, ay, az + + +def euler_from_quaternion(quaternion, axes='sxyz'): + """Return Euler angles from quaternion for specified axis sequence. + + >>> angles = euler_from_quaternion([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(angles, [0.123, 0, 0]) + True + + """ + return euler_from_matrix(quaternion_matrix(quaternion), axes) + + +def quaternion_from_euler(ai, aj, ak, axes='sxyz'): + """Return quaternion from Euler angles and axis sequence. + + ai, aj, ak : Euler's roll, pitch and yaw angles + axes : One of 24 axis sequences as string or encoded tuple + + >>> q = quaternion_from_euler(1, 2, 3, 'ryxz') + >>> numpy.allclose(q, [0.435953, 0.310622, -0.718287, 0.444435]) + True + + """ + try: + firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()] + except (AttributeError, KeyError): + _TUPLE2AXES[axes] # validation + firstaxis, parity, repetition, frame = axes + + i = firstaxis + 1 + j = _NEXT_AXIS[i+parity-1] + 1 + k = _NEXT_AXIS[i-parity] + 1 + + if frame: + ai, ak = ak, ai + if parity: + aj = -aj + + ai /= 2.0 + aj /= 2.0 + ak /= 2.0 + ci = math.cos(ai) + si = math.sin(ai) + cj = math.cos(aj) + sj = math.sin(aj) + ck = math.cos(ak) + sk = math.sin(ak) + cc = ci*ck + cs = ci*sk + sc = si*ck + ss = si*sk + + q = numpy.empty((4, )) + if repetition: + q[0] = cj*(cc - ss) + q[i] = cj*(cs + sc) + q[j] = sj*(cc + ss) + q[k] = sj*(cs - sc) + else: + q[0] = cj*cc + sj*ss + q[i] = cj*sc - sj*cs + q[j] = cj*ss + sj*cc + q[k] = cj*cs - sj*sc + if parity: + q[j] *= -1.0 + + return q + + +def quaternion_about_axis(angle, axis): + """Return quaternion for rotation about axis. + + >>> q = quaternion_about_axis(0.123, [1, 0, 0]) + >>> numpy.allclose(q, [0.99810947, 0.06146124, 0, 0]) + True + + """ + q = numpy.array([0.0, axis[0], axis[1], axis[2]]) + qlen = vector_norm(q) + if qlen > _EPS: + q *= math.sin(angle/2.0) / qlen + q[0] = math.cos(angle/2.0) + return q + + +def quaternion_matrix(quaternion): + """Return homogeneous rotation matrix from quaternion. + + >>> M = quaternion_matrix([0.99810947, 0.06146124, 0, 0]) + >>> numpy.allclose(M, rotation_matrix(0.123, [1, 0, 0])) + True + >>> M = quaternion_matrix([1, 0, 0, 0]) + >>> numpy.allclose(M, numpy.identity(4)) + True + >>> M = quaternion_matrix([0, 1, 0, 0]) + >>> numpy.allclose(M, numpy.diag([1, -1, -1, 1])) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + n = numpy.dot(q, q) + if n < _EPS: + return numpy.identity(4) + q *= math.sqrt(2.0 / n) + q = numpy.outer(q, q) + return numpy.array([ + [1.0-q[2, 2]-q[3, 3], q[1, 2]-q[3, 0], q[1, 3]+q[2, 0], 0.0], + [ q[1, 2]+q[3, 0], 1.0-q[1, 1]-q[3, 3], q[2, 3]-q[1, 0], 0.0], + [ q[1, 3]-q[2, 0], q[2, 3]+q[1, 0], 1.0-q[1, 1]-q[2, 2], 0.0], + [ 0.0, 0.0, 0.0, 1.0]]) + + +def quaternion_from_matrix(matrix, isprecise=False): + """Return quaternion from rotation matrix. + + If isprecise is True, the input matrix is assumed to be a precise rotation + matrix and a faster algorithm is used. + + >>> q = quaternion_from_matrix(numpy.identity(4), True) + >>> numpy.allclose(q, [1, 0, 0, 0]) + True + >>> q = quaternion_from_matrix(numpy.diag([1, -1, -1, 1])) + >>> numpy.allclose(q, [0, 1, 0, 0]) or numpy.allclose(q, [0, -1, 0, 0]) + True + >>> R = rotation_matrix(0.123, (1, 2, 3)) + >>> q = quaternion_from_matrix(R, True) + >>> numpy.allclose(q, [0.9981095, 0.0164262, 0.0328524, 0.0492786]) + True + >>> R = [[-0.545, 0.797, 0.260, 0], [0.733, 0.603, -0.313, 0], + ... [-0.407, 0.021, -0.913, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.19069, 0.43736, 0.87485, -0.083611]) + True + >>> R = [[0.395, 0.362, 0.843, 0], [-0.626, 0.796, -0.056, 0], + ... [-0.677, -0.498, 0.529, 0], [0, 0, 0, 1]] + >>> q = quaternion_from_matrix(R) + >>> numpy.allclose(q, [0.82336615, -0.13610694, 0.46344705, -0.29792603]) + True + >>> R = random_rotation_matrix() + >>> q = quaternion_from_matrix(R) + >>> is_same_transform(R, quaternion_matrix(q)) + True + >>> R = euler_matrix(0.0, 0.0, numpy.pi/2.0) + >>> numpy.allclose(quaternion_from_matrix(R, isprecise=False), + ... quaternion_from_matrix(R, isprecise=True)) + True + + """ + M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:4, :4] + if isprecise: + q = numpy.empty((4, )) + t = numpy.trace(M) + if t > M[3, 3]: + q[0] = t + q[3] = M[1, 0] - M[0, 1] + q[2] = M[0, 2] - M[2, 0] + q[1] = M[2, 1] - M[1, 2] + else: + i, j, k = 1, 2, 3 + if M[1, 1] > M[0, 0]: + i, j, k = 2, 3, 1 + if M[2, 2] > M[i, i]: + i, j, k = 3, 1, 2 + t = M[i, i] - (M[j, j] + M[k, k]) + M[3, 3] + q[i] = t + q[j] = M[i, j] + M[j, i] + q[k] = M[k, i] + M[i, k] + q[3] = M[k, j] - M[j, k] + q *= 0.5 / math.sqrt(t * M[3, 3]) + else: + m00 = M[0, 0] + m01 = M[0, 1] + m02 = M[0, 2] + m10 = M[1, 0] + m11 = M[1, 1] + m12 = M[1, 2] + m20 = M[2, 0] + m21 = M[2, 1] + m22 = M[2, 2] + # symmetric matrix K + K = numpy.array([[m00-m11-m22, 0.0, 0.0, 0.0], + [m01+m10, m11-m00-m22, 0.0, 0.0], + [m02+m20, m12+m21, m22-m00-m11, 0.0], + [m21-m12, m02-m20, m10-m01, m00+m11+m22]]) + K /= 3.0 + # quaternion is eigenvector of K that corresponds to largest eigenvalue + w, V = numpy.linalg.eigh(K) + q = V[[3, 0, 1, 2], numpy.argmax(w)] + if q[0] < 0.0: + numpy.negative(q, q) + return q + + +def quaternion_multiply(quaternion1, quaternion0): + """Return multiplication of two quaternions. + + >>> q = quaternion_multiply([4, 1, -2, 3], [8, -5, 6, 7]) + >>> numpy.allclose(q, [28, -44, -14, 48]) + True + + """ + w0, x0, y0, z0 = quaternion0 + w1, x1, y1, z1 = quaternion1 + return numpy.array([-x1*x0 - y1*y0 - z1*z0 + w1*w0, + x1*w0 + y1*z0 - z1*y0 + w1*x0, + -x1*z0 + y1*w0 + z1*x0 + w1*y0, + x1*y0 - y1*x0 + z1*w0 + w1*z0], dtype=numpy.float64) + + +def quaternion_conjugate(quaternion): + """Return conjugate of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_conjugate(q0) + >>> q1[0] == q0[0] and all(q1[1:] == -q0[1:]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q + + +def quaternion_inverse(quaternion): + """Return inverse of quaternion. + + >>> q0 = random_quaternion() + >>> q1 = quaternion_inverse(q0) + >>> numpy.allclose(quaternion_multiply(q0, q1), [1, 0, 0, 0]) + True + + """ + q = numpy.array(quaternion, dtype=numpy.float64, copy=True) + numpy.negative(q[1:], q[1:]) + return q / numpy.dot(q, q) + + +def quaternion_real(quaternion): + """Return real part of quaternion. + + >>> quaternion_real([3, 0, 1, 2]) + 3.0 + + """ + return float(quaternion[0]) + + +def quaternion_imag(quaternion): + """Return imaginary part of quaternion. + + >>> quaternion_imag([3, 0, 1, 2]) + array([ 0., 1., 2.]) + + """ + return numpy.array(quaternion[1:4], dtype=numpy.float64, copy=True) + + +def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True): + """Return spherical linear interpolation between two quaternions. + + >>> q0 = random_quaternion() + >>> q1 = random_quaternion() + >>> q = quaternion_slerp(q0, q1, 0) + >>> numpy.allclose(q, q0) + True + >>> q = quaternion_slerp(q0, q1, 1, 1) + >>> numpy.allclose(q, q1) + True + >>> q = quaternion_slerp(q0, q1, 0.5) + >>> angle = math.acos(numpy.dot(q0, q)) + >>> numpy.allclose(2, math.acos(numpy.dot(q0, q1)) / angle) or \ + numpy.allclose(2, math.acos(-numpy.dot(q0, q1)) / angle) + True + + """ + q0 = unit_vector(quat0[:4]) + q1 = unit_vector(quat1[:4]) + if fraction == 0.0: + return q0 + elif fraction == 1.0: + return q1 + d = numpy.dot(q0, q1) + if abs(abs(d) - 1.0) < _EPS: + return q0 + if shortestpath and d < 0.0: + # invert rotation + d = -d + numpy.negative(q1, q1) + angle = math.acos(d) + spin * math.pi + if abs(angle) < _EPS: + return q0 + isin = 1.0 / math.sin(angle) + q0 *= math.sin((1.0 - fraction) * angle) * isin + q1 *= math.sin(fraction * angle) * isin + q0 += q1 + return q0 + + +def random_quaternion(rand=None): + """Return uniform random unit quaternion. + + rand: array like or None + Three independent random variables that are uniformly distributed + between 0 and 1. + + >>> q = random_quaternion() + >>> numpy.allclose(1, vector_norm(q)) + True + >>> q = random_quaternion(numpy.random.random(3)) + >>> len(q.shape), q.shape[0]==4 + (1, True) + + """ + if rand is None: + rand = numpy.random.rand(3) + else: + assert len(rand) == 3 + r1 = numpy.sqrt(1.0 - rand[0]) + r2 = numpy.sqrt(rand[0]) + pi2 = math.pi * 2.0 + t1 = pi2 * rand[1] + t2 = pi2 * rand[2] + return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1, + numpy.cos(t1)*r1, numpy.sin(t2)*r2]) + + +def random_rotation_matrix(rand=None): + """Return uniform random rotation matrix. + + rand: array like + Three independent random variables that are uniformly distributed + between 0 and 1 for each returned quaternion. + + >>> R = random_rotation_matrix() + >>> numpy.allclose(numpy.dot(R.T, R), numpy.identity(4)) + True + + """ + return quaternion_matrix(random_quaternion(rand)) + + +class Arcball(object): + """Virtual Trackball Control. + + >>> ball = Arcball() + >>> ball = Arcball(initial=numpy.identity(4)) + >>> ball.place([320, 320], 320) + >>> ball.down([500, 250]) + >>> ball.drag([475, 275]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 3.90583455) + True + >>> ball = Arcball(initial=[1, 0, 0, 0]) + >>> ball.place([320, 320], 320) + >>> ball.setaxes([1, 1, 0], [-1, 1, 0]) + >>> ball.constrain = True + >>> ball.down([400, 200]) + >>> ball.drag([200, 400]) + >>> R = ball.matrix() + >>> numpy.allclose(numpy.sum(R), 0.2055924) + True + >>> ball.next() + + """ + def __init__(self, initial=None): + """Initialize virtual trackball control. + + initial : quaternion or rotation matrix + + """ + self._axis = None + self._axes = None + self._radius = 1.0 + self._center = [0.0, 0.0] + self._vdown = numpy.array([0.0, 0.0, 1.0]) + self._constrain = False + if initial is None: + self._qdown = numpy.array([1.0, 0.0, 0.0, 0.0]) + else: + initial = numpy.array(initial, dtype=numpy.float64) + if initial.shape == (4, 4): + self._qdown = quaternion_from_matrix(initial) + elif initial.shape == (4, ): + initial /= vector_norm(initial) + self._qdown = initial + else: + raise ValueError("initial not a quaternion or matrix") + self._qnow = self._qpre = self._qdown + + def place(self, center, radius): + """Place Arcball, e.g. when window size changes. + + center : sequence[2] + Window coordinates of trackball center. + radius : float + Radius of trackball in window coordinates. + + """ + self._radius = float(radius) + self._center[0] = center[0] + self._center[1] = center[1] + + def setaxes(self, *axes): + """Set axes to constrain rotations.""" + if axes is None: + self._axes = None + else: + self._axes = [unit_vector(axis) for axis in axes] + + @property + def constrain(self): + """Return state of constrain to axis mode.""" + return self._constrain + + @constrain.setter + def constrain(self, value): + """Set state of constrain to axis mode.""" + self._constrain = bool(value) + + def down(self, point): + """Set initial cursor window coordinates and pick constrain-axis.""" + self._vdown = arcball_map_to_sphere(point, self._center, self._radius) + self._qdown = self._qpre = self._qnow + if self._constrain and self._axes is not None: + self._axis = arcball_nearest_axis(self._vdown, self._axes) + self._vdown = arcball_constrain_to_axis(self._vdown, self._axis) + else: + self._axis = None + + def drag(self, point): + """Update current cursor window coordinates.""" + vnow = arcball_map_to_sphere(point, self._center, self._radius) + if self._axis is not None: + vnow = arcball_constrain_to_axis(vnow, self._axis) + self._qpre = self._qnow + t = numpy.cross(self._vdown, vnow) + if numpy.dot(t, t) < _EPS: + self._qnow = self._qdown + else: + q = [numpy.dot(self._vdown, vnow), t[0], t[1], t[2]] + self._qnow = quaternion_multiply(q, self._qdown) + + def next(self, acceleration=0.0): + """Continue rotation in direction of last drag.""" + q = quaternion_slerp(self._qpre, self._qnow, 2.0+acceleration, False) + self._qpre, self._qnow = self._qnow, q + + def matrix(self): + """Return homogeneous rotation matrix.""" + return quaternion_matrix(self._qnow) + + +def arcball_map_to_sphere(point, center, radius): + """Return unit sphere coordinates from window coordinates.""" + v0 = (point[0] - center[0]) / radius + v1 = (center[1] - point[1]) / radius + n = v0*v0 + v1*v1 + if n > 1.0: + # position outside of sphere + n = math.sqrt(n) + return numpy.array([v0/n, v1/n, 0.0]) + else: + return numpy.array([v0, v1, math.sqrt(1.0 - n)]) + + +def arcball_constrain_to_axis(point, axis): + """Return sphere point perpendicular to axis.""" + v = numpy.array(point, dtype=numpy.float64, copy=True) + a = numpy.array(axis, dtype=numpy.float64, copy=True) + v -= a * numpy.dot(a, v) # on plane + n = vector_norm(v) + if n > _EPS: + if v[2] < 0.0: + numpy.negative(v, v) + v /= n + return v + if a[2] == 1.0: + return numpy.array([1.0, 0.0, 0.0]) + return unit_vector([-a[1], a[0], 0.0]) + + +def arcball_nearest_axis(point, axes): + """Return axis, which arc is nearest to point.""" + point = numpy.array(point, dtype=numpy.float64, copy=False) + nearest = None + mx = -1.0 + for axis in axes: + t = numpy.dot(arcball_constrain_to_axis(point, axis), point) + if t > mx: + nearest = axis + mx = t + return nearest + + +# epsilon for testing whether a number is close to zero +_EPS = numpy.finfo(float).eps * 4.0 + +# axis sequences for Euler angles +_NEXT_AXIS = [1, 2, 0, 1] + +# map axes strings to/from tuples of inner axis, parity, repetition, frame +_AXES2TUPLE = { + 'sxyz': (0, 0, 0, 0), 'sxyx': (0, 0, 1, 0), 'sxzy': (0, 1, 0, 0), + 'sxzx': (0, 1, 1, 0), 'syzx': (1, 0, 0, 0), 'syzy': (1, 0, 1, 0), + 'syxz': (1, 1, 0, 0), 'syxy': (1, 1, 1, 0), 'szxy': (2, 0, 0, 0), + 'szxz': (2, 0, 1, 0), 'szyx': (2, 1, 0, 0), 'szyz': (2, 1, 1, 0), + 'rzyx': (0, 0, 0, 1), 'rxyx': (0, 0, 1, 1), 'ryzx': (0, 1, 0, 1), + 'rxzx': (0, 1, 1, 1), 'rxzy': (1, 0, 0, 1), 'ryzy': (1, 0, 1, 1), + 'rzxy': (1, 1, 0, 1), 'ryxy': (1, 1, 1, 1), 'ryxz': (2, 0, 0, 1), + 'rzxz': (2, 0, 1, 1), 'rxyz': (2, 1, 0, 1), 'rzyz': (2, 1, 1, 1)} + +_TUPLE2AXES = dict((v, k) for k, v in _AXES2TUPLE.items()) + + +def vector_norm(data, axis=None, out=None): + """Return length, i.e. Euclidean norm, of ndarray along axis. + + >>> v = numpy.random.random(3) + >>> n = vector_norm(v) + >>> numpy.allclose(n, numpy.linalg.norm(v)) + True + >>> v = numpy.random.rand(6, 5, 3) + >>> n = vector_norm(v, axis=-1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2))) + True + >>> n = vector_norm(v, axis=1) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> v = numpy.random.rand(5, 4, 3) + >>> n = numpy.empty((5, 3)) + >>> vector_norm(v, axis=1, out=n) + >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1))) + True + >>> vector_norm([]) + 0.0 + >>> vector_norm([1]) + 1.0 + + """ + data = numpy.array(data, dtype=numpy.float64, copy=True) + if out is None: + if data.ndim == 1: + return math.sqrt(numpy.dot(data, data)) + data *= data + out = numpy.atleast_1d(numpy.sum(data, axis=axis)) + numpy.sqrt(out, out) + return out + else: + data *= data + numpy.sum(data, axis=axis, out=out) + numpy.sqrt(out, out) + + +def unit_vector(data, axis=None, out=None): + """Return ndarray normalized by length, i.e. Euclidean norm, along axis. + + >>> v0 = numpy.random.random(3) + >>> v1 = unit_vector(v0) + >>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0)) + True + >>> v0 = numpy.random.rand(5, 4, 3) + >>> v1 = unit_vector(v0, axis=-1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2) + >>> numpy.allclose(v1, v2) + True + >>> v1 = unit_vector(v0, axis=1) + >>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1) + >>> numpy.allclose(v1, v2) + True + >>> v1 = numpy.empty((5, 4, 3)) + >>> unit_vector(v0, axis=1, out=v1) + >>> numpy.allclose(v1, v2) + True + >>> list(unit_vector([])) + [] + >>> list(unit_vector([1])) + [1.0] + + """ + if out is None: + data = numpy.array(data, dtype=numpy.float64, copy=True) + if data.ndim == 1: + data /= math.sqrt(numpy.dot(data, data)) + return data + else: + if out is not data: + out[:] = numpy.array(data, copy=False) + data = out + length = numpy.atleast_1d(numpy.sum(data*data, axis)) + numpy.sqrt(length, length) + if axis is not None: + length = numpy.expand_dims(length, axis) + data /= length + if out is None: + return data + + +def random_vector(size): + """Return array of random doubles in the half-open interval [0.0, 1.0). + + >>> v = random_vector(10000) + >>> numpy.all(v >= 0) and numpy.all(v < 1) + True + >>> v0 = random_vector(10) + >>> v1 = random_vector(10) + >>> numpy.any(v0 == v1) + False + + """ + return numpy.random.random(size) + + +def vector_product(v0, v1, axis=0): + """Return vector perpendicular to vectors. + + >>> v = vector_product([2, 0, 0], [0, 3, 0]) + >>> numpy.allclose(v, [0, 0, 6]) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> v = vector_product(v0, v1) + >>> numpy.allclose(v, [[0, 0, 0, 0], [0, 0, 6, 6], [0, -6, 0, -6]]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> v = vector_product(v0, v1, axis=1) + >>> numpy.allclose(v, [[0, 0, 6], [0, -6, 0], [6, 0, 0], [0, -6, 6]]) + True + + """ + return numpy.cross(v0, v1, axis=axis) + + +def angle_between_vectors(v0, v1, directed=True, axis=0): + """Return angle between vectors. + + If directed is False, the input vectors are interpreted as undirected axes, + i.e. the maximum angle is pi/2. + + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3]) + >>> numpy.allclose(a, math.pi) + True + >>> a = angle_between_vectors([1, -2, 3], [-1, 2, -3], directed=False) + >>> numpy.allclose(a, 0) + True + >>> v0 = [[2, 0, 0, 2], [0, 2, 0, 2], [0, 0, 2, 2]] + >>> v1 = [[3], [0], [0]] + >>> a = angle_between_vectors(v0, v1) + >>> numpy.allclose(a, [0, 1.5708, 1.5708, 0.95532]) + True + >>> v0 = [[2, 0, 0], [2, 0, 0], [0, 2, 0], [2, 0, 0]] + >>> v1 = [[0, 3, 0], [0, 0, 3], [0, 0, 3], [3, 3, 3]] + >>> a = angle_between_vectors(v0, v1, axis=1) + >>> numpy.allclose(a, [1.5708, 1.5708, 1.5708, 0.95532]) + True + + """ + v0 = numpy.array(v0, dtype=numpy.float64, copy=False) + v1 = numpy.array(v1, dtype=numpy.float64, copy=False) + dot = numpy.sum(v0 * v1, axis=axis) + dot /= vector_norm(v0, axis=axis) * vector_norm(v1, axis=axis) + return numpy.arccos(dot if directed else numpy.fabs(dot)) + + +def inverse_matrix(matrix): + """Return inverse of square transformation matrix. + + >>> M0 = random_rotation_matrix() + >>> M1 = inverse_matrix(M0.T) + >>> numpy.allclose(M1, numpy.linalg.inv(M0.T)) + True + >>> for size in range(1, 7): + ... M0 = numpy.random.rand(size, size) + ... M1 = inverse_matrix(M0) + ... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print(size) + + """ + return numpy.linalg.inv(matrix) + + +def concatenate_matrices(*matrices): + """Return concatenation of series of transformation matrices. + + >>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5 + >>> numpy.allclose(M, concatenate_matrices(M)) + True + >>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T)) + True + + """ + M = numpy.identity(4) + for i in matrices: + M = numpy.dot(M, i) + return M + + +def is_same_transform(matrix0, matrix1): + """Return True if two matrices perform same transformation. + + >>> is_same_transform(numpy.identity(4), numpy.identity(4)) + True + >>> is_same_transform(numpy.identity(4), random_rotation_matrix()) + False + + """ + matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True) + matrix0 /= matrix0[3, 3] + matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True) + matrix1 /= matrix1[3, 3] + return numpy.allclose(matrix0, matrix1) + + +def _import_module(name, package=None, warn=True, prefix='_py_', ignore='_'): + """Try import all public attributes from module into global namespace. + + Existing attributes with name clashes are renamed with prefix. + Attributes starting with underscore are ignored by default. + + Return True on successful import. + + """ + import warnings + from importlib import import_module + try: + if not package: + module = import_module(name) + else: + module = import_module('.' + name, package=package) + except ImportError: + if warn: + #warnings.warn("failed to import module %s" % name) + pass + else: + for attr in dir(module): + if ignore and attr.startswith(ignore): + continue + if prefix: + if attr in globals(): + globals()[prefix + attr] = globals()[attr] + elif warn: + warnings.warn("no Python implementation of " + attr) + globals()[attr] = getattr(module, attr) + return True + + +_import_module('_transformations') + +if __name__ == "__main__": + import doctest + import random # used in doctests + numpy.set_printoptions(suppress=True, precision=5) + doctest.testmod() + diff --git a/third_party/SGMNet/weights.tar.gz b/third_party/SGMNet/weights.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..0746e75e6d05e700705fbb97df3cae86d913c9a4 --- /dev/null +++ b/third_party/SGMNet/weights.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5300c2e7fbf783bcf95f8480266849095645e13e72af3c7eb721e82b9143db3d +size 205965102 diff --git a/third_party/SGMNet/weights/sg/root/model_best.pth b/third_party/SGMNet/weights/sg/root/model_best.pth new file mode 100644 index 0000000000000000000000000000000000000000..98e13d45f4b8b32877883bb57915e091d99b852c --- /dev/null +++ b/third_party/SGMNet/weights/sg/root/model_best.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b38d22d1031fd0104be122fb0b63bb6887ff74bea7eceef951c7205d5f40993 +size 12428635 diff --git 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@@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/third_party/SOLD2/LICENSE b/third_party/SOLD2/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..a78ff590248398498242d1eba03791ad0288bdf2 --- /dev/null +++ b/third_party/SOLD2/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Rémi Pautrat + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/third_party/SOLD2/README.md b/third_party/SOLD2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..69713c07084d26ab689532c29293d056bc84f655 --- /dev/null +++ b/third_party/SOLD2/README.md @@ -0,0 +1,216 @@ +# SOLD² - Self-supervised Occlusion-aware Line Description and Detection + +This repository contains the implementation of the paper: [SOLD² : Self-supervised Occlusion-aware Line Description and Detection](https://arxiv.org/abs/2104.03362), J-T. Lin*, R. Pautrat*, V. Larsson, M. Oswald and M. Pollefeys (Oral at CVPR 2021). + +SOLD² is a deep line segment detector and descriptor that can be trained without hand-labelled line segments and that can robustly match lines even in the presence of occlusion. + +## Demos + +Matching in the presence of occlusion: +![demo_occlusion](assets/videos/demo_occlusion.gif) + +Matching with a moving camera: +![demo_moving_camera](assets/videos/demo_moving_camera.gif) + +## Usage + +### Using from kornia + +SOLD² is integrated into [kornia](https://github.com/kornia/kornia) library since version 0.6.7. + + ``` + pip install kornia==0.6.7 + ``` + + Then you can import it as + ```python3 + from kornia.feature import SOLD2 + ``` + + See tutorial on using SOLD² from kornia [here](https://kornia-tutorials.readthedocs.io/en/latest/line_detection_and_matching_sold2.html). + +### Installation + +We recommend using this code in a Python environment (e.g. venv or conda). The following script installs the necessary requirements with pip: +```bash +pip install -r requirements.txt +``` + +Set your dataset and experiment paths (where you will store your datasets and checkpoints of your experiments) by modifying the file `config/project_config.py`. Both variables `DATASET_ROOT` and `EXP_PATH` have to be set. + +Install the Python package: +```bash +pip install -e . +``` + +You can download the version of the [Wireframe dataset](https://github.com/huangkuns/wireframe) that we used during our training and testing [here](https://www.polybox.ethz.ch/index.php/s/IfdEf7RoHol7jeg). This repository also includes some files to train on the [Holicity dataset](https://holicity.io/) to add more outdoor images, but note that we did not extensively test this dataset and the original paper was based on the Wireframe dataset only. + +### Training your own model + +All training parameters are located in configuration files in the folder `config`. Training SOLD² from scratch requires several steps, some of which taking several days, depending on the size of your dataset. + +
+Step 1: Train on a synthetic dataset + +The following command will create the synthetic dataset and start training the model on it: +```bash +python -m sold2.experiment --mode train --dataset_config sold2/config/synthetic_dataset.yaml --model_config sold2/config/train_detector.yaml --exp_name sold2_synth +``` +
+ +
+Step 2: Export the raw pseudo ground truth on the Wireframe dataset with homography adaptation + +Note that this step can take one to several days depending on your machine and on the size of the dataset. You can set the batch size to the maximum capacity that your GPU can handle. Prior to this step, make sure that the dataset config file `config/wireframe_dataset.yaml` has the lines `gt_source_train` and `gt_source_test` commented and you should also disable the photometric and homographic augmentations. +```bash +python -m sold2.experiment --exp_name wireframe_train --mode export --resume_path --model_config sold2/config/train_detector.yaml --dataset_config sold2/config/wireframe_dataset.yaml --checkpoint_name --export_dataset_mode train --export_batch_size 4 +``` + +You can similarly perform the same for the test set: +```bash +python -m sold2.experiment --exp_name wireframe_test --mode export --resume_path --model_config sold2/config/train_detector.yaml --dataset_config sold2/config/wireframe_dataset.yaml --checkpoint_name --export_dataset_mode test --export_batch_size 4 +``` +
+ +
+ Step3: Compute the ground truth line segments from the raw data + +```bash +python -m sold2.postprocess.convert_homography_results sold2/config/export_line_features.yaml +``` + +We recommend testing the results on a few samples of your dataset to check the quality of the output, and modifying the hyperparameters if need be. Using a `detect_thresh=0.5` and `inlier_thresh=0.99` proved to be successful for the Wireframe dataset in our case for example. +
+ +
+ Step 4: Train the detector on the Wireframe dataset + +We found it easier to pretrain the detector alone first, before fine-tuning it with the descriptor part. +Uncomment the lines 'gt_source_train' and 'gt_source_test' in `config/wireframe_dataset.yaml` and fill them with the path to the h5 file generated in the previous step. +```bash +python -m sold2.experiment --mode train --dataset_config sold2/config/wireframe_dataset.yaml --model_config sold2/config/train_detector.yaml --exp_name sold2_wireframe +``` + +Alternatively, you can also fine-tune the already trained synthetic model: +```bash +python -m sold2.experiment --mode train --dataset_config sold2/config/wireframe_dataset.yaml --model_config sold2/config/train_detector.yaml --exp_name sold2_wireframe --pretrained --pretrained_path --checkpoint_name +``` + +Lastly, you can resume a training that was stopped: +```bash +python -m sold2.experiment --mode train --dataset_config sold2/config/wireframe_dataset.yaml --model_config sold2/config/train_detector.yaml --exp_name sold2_wireframe --resume --resume_path --checkpoint_name +``` +
+ +
+ Step 5: Train the full pipeline on the Wireframe dataset + +You first need to modify the field 'return_type' in `config/wireframe_dataset.yaml` to 'paired_desc'. The following command will then train the full model (detector + descriptor) on the Wireframe dataset: +```bash +python -m sold2.experiment --mode train --dataset_config sold2/config/wireframe_dataset.yaml --model_config sold2/config/train_full_pipeline.yaml --exp_name sold2_full_wireframe --pretrained --pretrained_path --checkpoint_name +``` +
+ + +### Pretrained models + +We provide the checkpoints of two pretrained models: +- [sold2_synthetic.tar](https://www.polybox.ethz.ch/index.php/s/Lu8jWo7nMKal9yb): SOLD² detector trained on the synthetic dataset only. +- [sold2_wireframe.tar](https://www.polybox.ethz.ch/index.php/s/blOrW89gqSLoHOk): full version of SOLD² trained on the Wireframe dataset. + +Note that you do not need to untar the models, you can directly used them as they are. + + +### How to use it + +We provide a [notebook](notebooks/match_lines.ipynb) showing how to use the trained model of SOLD². Additionally, you can use the model to export line features (segments and descriptor maps) as follows: +```bash +python -m sold2.export_line_features --img_list --output_folder --checkpoint_path +``` + +You can tune some of the line detection parameters in `config/export_line_features.yaml`, in particular the 'detect_thresh' and 'inlier_thresh' to adapt them to your trained model and type of images. As the line detection can be sensitive to the image resolution, we recommend using it with images in the range 300~800 px per side. + + + +## Results + +Comparison of repeatability and localization error to the state of the art on the [Wireframe dataset](https://github.com/huangkuns/wireframe) for an error threshold of 5 pixels in structural and orthogonal distances: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Structural distanceOrthogonal distance
Rep-5Loc-5Rep-5Loc-5
LCNN0.4342.5890.5701.725
HAWP0.4512.6250.5371.725
DeepHough0.4192.5760.6181.720
TP-LSD TP5120.5632.4670.7461.450
LSD0.3582.0790.7070.825
Ours with NMS0.5571.9950.8011.119
Ours0.6162.0190.9140.816
+ +Matching precision-recall curves on the [Wireframe](https://github.com/huangkuns/wireframe) and [ETH3D](https://www.eth3d.net/) datasets: +![pred_lines_pr_curve](assets/results/pred_lines_pr_curve.png) + +## Bibtex + +If you use this code in your project, please consider citing the following paper: +```bibtex +@InProceedings{Pautrat_Lin_2021_CVPR, + author = {Pautrat*, Rémi and Lin*, Juan-Ting and Larsson, Viktor and Oswald, Martin R. and Pollefeys, Marc}, + title = {SOLD2: Self-supervised Occlusion-aware Line Description and Detection}, + booktitle = {Computer Vision and Pattern Recognition (CVPR)}, + year = {2021}, +} +``` diff --git a/third_party/SOLD2/assets/images/terrace0.JPG b/third_party/SOLD2/assets/images/terrace0.JPG new file mode 100644 index 0000000000000000000000000000000000000000..e3f688c4d14b490da30b57cd1312b144588efe32 --- /dev/null +++ b/third_party/SOLD2/assets/images/terrace0.JPG @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/notebooks/match_lines.ipynb b/third_party/SOLD2/notebooks/match_lines.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f10d98da893d69ea97ab41c53f36796c53ccda40 --- /dev/null +++ b/third_party/SOLD2/notebooks/match_lines.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import cv2\n", + "import torch\n", + "\n", + "from sold2.model.line_matcher import LineMatcher\n", + "from sold2.misc.visualize_util import plot_images, plot_lines, plot_line_matches, plot_color_line_matches, plot_keypoints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matching from scratch given pairs of images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\t--------Initializing model----------\n", + "\t [Debug] Adding w_junc with value 0.000000 to model\n", + "\t [Debug] Adding w_heatmap with value 0.000000 to model\n", + "\t [Debug] Adding w_desc with value 0.000000 to model\n", + "\tModel architecture: simple\n", + "\tBackbone: lcnn\n", + "\tJunction decoder: superpoint_decoder\n", + "\tHeatmap decoder: pixel_shuffle\n", + "\t-------------------------------------\n", + "[Debug] detect_thresh: 0.25\n", + "[Debug] num_samples: 64\n", + "[Debug] sampling_method: local_max\n", + "[Debug] inlier_thresh: 0.9\n", + "[Debug] use_candidate_suppression: True\n", + "[Debug] nms_dist_tolerance: 3.0\n", + "[Debug] use_heatmap_refinement: True\n", + "[Debug] heatmap_refine_cfg: {'mode': 'local', 'ratio': 0.2, 'valid_thresh': 0.001, 'num_blocks': 20, 'overlap_ratio': 0.5}\n" + ] + } + ], + "source": [ + "ckpt_path = '../pretrained_models/sold2_wireframe.tar'\n", + "device = 'cuda'\n", + "mode = 'dynamic' # 'dynamic' or 'static'\n", + "\n", + "# Initialize the line matcher\n", + "config = {\n", + " 'model_cfg': {\n", + " 'model_name': \"lcnn_simple\",\n", + " 'model_architecture': \"simple\",\n", + " # Backbone related config\n", + " 'backbone': \"lcnn\",\n", + " 'backbone_cfg': {\n", + " 'input_channel': 1, # Use RGB images or grayscale images.\n", + " 'depth': 4,\n", + " 'num_stacks': 2,\n", + " 'num_blocks': 1,\n", + " 'num_classes': 5\n", + " },\n", + " # Junction decoder related config\n", + " 'junction_decoder': \"superpoint_decoder\",\n", + " 'junc_decoder_cfg': {},\n", + " # Heatmap decoder related config\n", + " 'heatmap_decoder': \"pixel_shuffle\",\n", + " 'heatmap_decoder_cfg': {},\n", + " # Descriptor decoder related config\n", + " 'descriptor_decoder': \"superpoint_descriptor\",\n", + " 'descriptor_decoder_cfg': {},\n", + " # Shared configurations\n", + " 'grid_size': 8,\n", + " 'keep_border_valid': True,\n", + " # Threshold of junction detection\n", + " 'detection_thresh': 0.0153846, # 1/65\n", + " 'max_num_junctions': 300,\n", + " # Threshold of heatmap detection\n", + " 'prob_thresh': 0.5,\n", + " # Weighting related parameters\n", + " 'weighting_policy': mode,\n", + " # [Heatmap loss]\n", + " 'w_heatmap': 0.,\n", + " 'w_heatmap_class': 1,\n", + " 'heatmap_loss_func': \"cross_entropy\",\n", + " 'heatmap_loss_cfg': {\n", + " 'policy': mode\n", + " },\n", + " # [Heatmap consistency loss]\n", + " # [Junction loss]\n", + " 'w_junc': 0.,\n", + " 'junction_loss_func': \"superpoint\",\n", + " 'junction_loss_cfg': {\n", + " 'policy': mode\n", + " },\n", + " # [Descriptor loss]\n", + " 'w_desc': 0.,\n", + " 'descriptor_loss_func': \"regular_sampling\",\n", + " 'descriptor_loss_cfg': {\n", + " 'dist_threshold': 8,\n", + " 'grid_size': 4,\n", + " 'margin': 1,\n", + " 'policy': mode\n", + " },\n", + " },\n", + " 'line_detector_cfg': {\n", + " 'detect_thresh': 0.25, # depending on your images, you might need to tune this parameter\n", + " 'num_samples': 64,\n", + " 'sampling_method': \"local_max\",\n", + " 'inlier_thresh': 0.9,\n", + " \"use_candidate_suppression\": True,\n", + " \"nms_dist_tolerance\": 3.,\n", + " \"use_heatmap_refinement\": True,\n", + " \"heatmap_refine_cfg\": {\n", + " \"mode\": \"local\",\n", + " \"ratio\": 0.2,\n", + " \"valid_thresh\": 1e-3,\n", + " \"num_blocks\": 20,\n", + " \"overlap_ratio\": 0.5\n", + " }\n", + " },\n", + " 'multiscale': False,\n", + " 'line_matcher_cfg': {\n", + " 'cross_check': True,\n", + " 'num_samples': 5,\n", + " 'min_dist_pts': 8,\n", + " 'top_k_candidates': 10,\n", + " 'grid_size': 4\n", + " }\n", + "}\n", + "\n", + "line_matcher = LineMatcher(\n", + " config[\"model_cfg\"], ckpt_path, device, config[\"line_detector_cfg\"],\n", + " config[\"line_matcher_cfg\"], config[\"multiscale\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read and pre-process the images\n", + "scale_factor = 1 # we recommend resizing the images to a resolution in the range 400~800 pixels\n", + "img1 = '../assets/images/terrace0.JPG'\n", + "img1 = cv2.imread(img1, 0)\n", + "img1 = cv2.resize(img1, (img1.shape[1] // scale_factor, img1.shape[0] // scale_factor),\n", + " interpolation = cv2.INTER_AREA)\n", + "img1 = (img1 / 255.).astype(float)\n", + "torch_img1 = torch.tensor(img1, dtype=torch.float)[None, None]\n", + "img2 = '../assets/images/terrace1.JPG'\n", + "img2 = cv2.imread(img2, 0)\n", + "img2 = cv2.resize(img2, (img2.shape[1] // scale_factor, img2.shape[0] // scale_factor),\n", + " interpolation = cv2.INTER_AREA)\n", + "img2 = (img2 / 255.).astype(float)\n", + "torch_img2 = torch.tensor(img2, dtype=torch.float)[None, None]\n", + "\n", + "# Match the lines\n", + "outputs = line_matcher([torch_img1, torch_img2])\n", + "line_seg1 = outputs[\"line_segments\"][0]\n", + "line_seg2 = outputs[\"line_segments\"][1]\n", + "matches = outputs[\"matches\"]\n", + "\n", + "valid_matches = matches != -1\n", + "match_indices = matches[valid_matches]\n", + "matched_lines1 = line_seg1[valid_matches][:, :, ::-1]\n", + "matched_lines2 = line_seg2[match_indices][:, :, ::-1]\n", + "\n", + "# Plot the matches\n", + "plot_images([img1, img2], ['Image 1 - detected lines', 'Image 2 - detected lines'])\n", + "plot_lines([line_seg1[:, :, ::-1], line_seg2[:, :, ::-1]], ps=3, lw=2)\n", + "plot_images([img1, img2], ['Image 1 - matched lines', 'Image 2 - matched lines'])\n", + "plot_color_line_matches([matched_lines1, matched_lines2], lw=2)" + ] + } + ], + "metadata": { + "file_extension": ".py", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb b/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5ca610dc697b5be20d321e2b21215601452029c5 --- /dev/null +++ b/third_party/SOLD2/notebooks/visualize_exported_dataset.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import yaml\n", + "\n", + "from sold2.dataset.wireframe_dataset import WireframeDataset\n", + "from sold2.dataset.holicity_dataset import HolicityDataset\n", + "from sold2.dataset.merge_dataset import MergeDataset\n", + "from sold2.misc.visualize_util import plot_junctions, plot_line_segments\n", + "from sold2.misc.visualize_util import plot_images, plot_keypoints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the exported ground truth on the Wireframe dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info] Initializing wireframe dataset...\n", + "\t Found filename cache wireframe_test_cache.pkl at /home/remi/Documents/datasets/wireframe\n", + "\t Load filename cache...\n", + "[Info] Successfully initialized dataset\n", + "\t Name: wireframe\n", + "\t Mode: test\n", + "\t Gt: /home/remi/Documents/datasets/export_datasets/wireframe_test_adaptation_iter0_epoch043_ce1_detect_0.25_inlier_0.75_local_max_v1.5_refine-v2.h5\n", + "\t Counts: 462\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "# Initialize the wireframe dataset\n", + "with open(\"../sold2/config/wireframe_dataset.yaml\", \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + "config['return_type'] = 'paired_desc'\n", + "\n", + "wireframe_dataset = WireframeDataset(mode=\"test\", config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read in one datapoint\n", + "index = 4\n", + "data1 = wireframe_dataset[index]\n", + "\n", + "# Reference data\n", + "ref_img = data1['ref_image'].numpy().squeeze()\n", + "ref_junc = data1['ref_junctions'].numpy()\n", + "ref_line_map = data1['ref_line_map'].numpy()\n", + "ref_line_points = data1['ref_line_points'].numpy()\n", + "\n", + "# Target data\n", + "target_img = data1['target_image'].numpy().squeeze()\n", + "target_junc = data1['target_junctions'].numpy()\n", + "target_line_map = data1['target_line_map'].numpy()\n", + "target_line_points = data1['target_line_points'].numpy()\n", + "\n", + "# Draw the points and lines\n", + "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n", + "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n", + "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n", + "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n", + "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wi+V0cRynOo86kfDnVCeTSRobG8m/NM+m3k3knJPL/TpQfFaRtmNtONnqVFI5hgjnQr7A0vcvZdfVu5j2f9OoOJXAuF+Es0TAAr4gl+/lR6avil54JFjBbXli0gp8Bbg8+HF+Ms/RQ0eZUZpBKBTi2FnHuOMv7yDTkuHWP7uVXDjH/JvnB6xV0WiUYrEI4L94Elaukx6EQiH/xZN54NFolP7+fsLhMCMjI0xMTLBnzx4OHz782NaHxWKxWCwWi8XyFCSZTNLc3ExjYyNNTU2kUini8TixWIxYLEY8HicSiVSniu6J0vvdXn7yyp9QCVdYdN8iLvvxZTjZ6hhelgvWU0M9z8MrenR/vRsv5PkaQNBTSPX8bMkTValUKBaLFItVL7dohkcaVm4Ft+WJRxi4GThLfeZB+/Z2zv3PcykdLzHsDDPRN8HGV28k25oFoJAosP4l64lORpl1zyxfbOv53OFwmHw+7ydYKxQKuK5LNBqlVCrheR6NjY0Ui0UqlQqFQsF/edvb2+no6KCnp4d8Ps+6desYHBy087wtFovFYrFYLJaHQQRuKpWira2N5uZmWltbaWpq8udERyKRwIpD4hSTMbx8PnvLbF7+tZdz98V386yfPYtoJko5VPa90JpyuVydj+15hMPhU4pr8VjL9FMt2MVhJ6JbkqxZD7flyctXgWXBjxp3NHLBuy8gH89z38fu49xfncuyoWUcvu4wB19xkFKqhFN06P55N+GfhjnsHfZf3kQi4b9ojuMQi8UCL5FYr0qlEuFwmEwm48/9luQJgswdb2ho4JJLLiEUCrFhwwYOHz5MNpv1w1gsFovFYrFYLJanE7KOtfzE43FaWlpoaWkJiGvxDodCoUBIN+CP0cV7LL9lLB+Px2lrbaPrRBfzvzOfQrZA2amNv3WYeLlc9o8tywXLSkYi3k8ltuU72bdcLvuCWxx2kUjkEdWL80i9c47jWDee5bHhucAPgXj1354tPSx57xIqyQqbX7+ZoxcfxS269H+/n5GFIzTvaebAcw7Q+8te5n15nj9Hu1Qq4TiOn3lQXnyZnyHPfjgc9l9ISagmL5DneUSj0cA8DRHV8p0kZNixYwejo6McPnzYJlmzWB4FPM97ZKZji+UkdqxisVgsZx6JIpVwb/k7mUzS0NBAY2MjDQ0NAXGtxSsQ+Ey8xabHWH8mAj0Wi/nebsnDJON4mRYqHul8Ps/ExATj4+O+Y0wEthxfyqQNAOVy2XfISRlc1yUWi9HY2EhlfoVUNEXbaBtf+MIXfu9YxXq4LU8s/gT4HL7YnvPQHC6+7mJKHSVueu1NHF1zFIBKpMLOP6+ume24DouuXUTXHV0cfsZhZt4800+Cpl+oSqVCJpPxsxxWKhXi8bj/4snLm8vlfAuYiG2ovoxisZPvc7lqwoZYLMayZcsolUocPHiQ8fFx9uzZQzqdfkyrz2KxWCwWi8VieTSJRqOkUilSqRSNjY0kk0ni8XhgnrVONmaKZxHCgohlU2DrZGS/T3zL2F57sU1EOEv+Jp3DqZ7YFgGvxbsg24w2j7LlNVuIEuXy/7287nlNrOC2PHF4NvB5YHrto+O9xyl2FmnPtLNo/yJfcGuGVg6Rb82z/9n7yTZnSTkpWm9qJZvLEg6FAxYrqIWUVCoVstmsb7VKJBLAyZfYdbjn7+5h9X+txilVX0gR47J+t4Sdy/IBItxnz55NoVBg9uzZFItFfvvb3/pJ2ywWi8VisVgslicq8XicpqYmmpqaAp5qmUcdjUZ9z7X2UOtxtplQzMzobYpZwfSG688Bf9ku+UwLbfOY4ukeuHyA/IE8qTtTvpA2EyXrc9YT234StWiJBz7yAJlZ1ZWPfvG2XzyiOrUh5ZYnDE7YIfbJGIW/L1AJVwgVQlx8/cWcfevZHO84zrVvv5Zy5PfPkXaLLud+7Fy2vnIr57/7fEInQoHkZzKHu1gsUi6XicfjeJ7nL2TfPqud2665jW3nbKPxcCOXvf8ywqM1cZ1MJv2XtFAoEIlE/Dkl8hKL5zsej5PNZjlx4gQPPvggIyMjZLPZM12VFstTAhtSbjld7FjFYrFYTo1MsRThGolEaG5uprm52Z9nLWJUz3PWEaOm99n8zPQeQ20etvZem+tgC6bglu8jkQikIBKNEC/FfQea9mCLl71cLlMoFti5eCe3vfk2qMCiv11E06amKecXx1w4HPaXDc7n8/4xdZ1t/eJWJs6eqK7/fRKP3z9WsYLb8oShra2NtWvXsv+N+9n2jG2s/NVKzr3hXI7MPcKv/u5XFBOn7yVu3d7Kgh8toPH+RirHKxRbi1RaK6QOpPwXTsJRurq6GC4Pc++L7+X4y477x+jc2smqL64iPhAnFAqRacrgNXm0DLYANY95NBoFCMz/Fk94sVgkFApRLBa56667yOVyDA0N2SRrFsvDYAW35XSxYxWLxfK0YBWwGcicehOZ7ywe6VAoRCqV8pffamxs9KdfiqjUAluHYGsRLNsI2lOsEw5rka29yFqYy+96IejyW8S21+hxz1X34EQdLrn+kmpmcpWDSZehWCyyfdl2bv27W31x7BQdFr1tES0bWgLnEsEtqxoVCgXfUTfF4x6D7Z/dztjZYwBMf2g6R5cftXO4LU8OYrEYZ511Fq2trXR8v4PGwUYW37GYSqhCuiWN5/5hY6gTC09w9zvvpufWHhJDCcY7xsm352nd3EqoGGLBtxb4lrx1F6xjxm9n4M4IrqmXi+YYc8YIl8KUkiXuf939FFoKnPeF80gMJPyGplgsBsLLdZI1PSf8oosuIpfLcejQIcbGxtixY8cp555YLBaLxWKxWCw+lwD/DdwIvBnwqgmAE4kEDQ0NJBIJP4lZIpEgkUgQi8UCa0drL68I6nprSsu2ZpIxqIlyjfYG6+zf9cLEBX0MLb71sTzX4/arb2frRVsBKIQKPPeHz8X13MBxpJw7LtrBnS+/M+iJdjyKXUU/ebI4yCT7ufyts5qb5QpVQiz72DJ2vm0nkXKE8756Hvzvw9yrk1jBbXnccRyHSy+9lK6urqp1quIx/3fz8UIeuVyOuffOJZwNc8ObbzB2fOTnGLhkIPD/2NIxnLJDoaHAws8vZN8r97H3T/aSmZWh7Z42Bi8e9LdN7k8S3xynGCty7z/ey/EVVe/3XW+/i8s+cBmRTIR8Pk82myWZTPoe7Wg06nu94/E4lUqFaDRKoVAgmUwyf/58stksPT09jI+Ps3HjRuvxtlgsFovFYrHUZwVVgTev+pNsTbLsP5b586sjkUhgjjUEvcWCFr+PxJOt5zTrxGM6QbF4iiV61JzLbf6vt/l9a1rf/Kqb2bV6l///5vM2U46Wef43nu9fjz5G+9523JILsdoxFnxkAV2/66K1s5XW1lYKhQIjIyMBoV4vlF3/7zgO4eEwCz67gGg4ipudaqSohw0ptzyuuK7LqlWrWLBggR+WLS+vpO53HIdQOMTu+bsZWzbGeOc4y69fzq/e/isKqT9y+a0yhDNhyvEyXsSDcnUOeCWurG1Fh1n/PYuJFRMMXzgM6t1q3dPKle+6EoBKY4Xfvvu3rH3/WuLZ6vJjk5OTpFIpP5THdV3y+Xxg6TGohqFns1n27dvHgw8+SD6ft4nWLE97bEi55XSxYxWLxfJkRkSe/ITDYVpaWmhvb6ehq4F7vnoPxRm18aFbcJnzvTnM/+Z8XwhHIpFAYjGNXuPaFNT+MZVANkW4DtuWbeUzGefKdjpkXHu864WY15vHLWUKhUIUm4t8/Z+/jheqfhZPx3n1v7ya+Hh8yhrepVKJm//8ZrZfsN3fHiCxP8F5rzyPkBMKiGwJJ5dQdIlK1UYGmeOtjQwSVfDLX/7SzuG2PHEJhULMnTuXZcuW+Wv4lctlP+FBsVj0M4FHIlUvsjzkkUiEie4Jfv2mXzPeNf6YlHfuZ+ey7/X7AmJ87SfW0nZHG/muPBvetoETi0/QeLiRC//lQhL7q1nPZc1veVEltFyus1Ao+Nclnw8MDPDAAw8wOTnJ+Ph43UyOFstTHSu4LaeLHatYLJYnCzLHWuZZh0IhGhsbaWtro62tjdbWVn8cDFURO9w9zK1/fyuTXZNQgb7r+1j6+aW+uJWxpqA9yOLJNudYCzpJmul91mixrcPSTcxQ9XpLfmlBL8etl9Ts2OJj/OwtP/OnmP7VZ/+K6YemBwS85GQqFovkcjlufMONHF5zGBxI7kqy5G1LiA5Hp9wDLbhLpZKfVNlM6iZ1K5+Hw2FisRg33HCDFdyWJy49PT2sWrWKWCzmz30Wy5wsNi+YS3vJy3i8/zh3vOoOpu+Yjud47Lh8x2N6DW7OZeY3ZpI+J83wucP+52072zjvS+cR3xn3LY2SjEEnpdDWxUqlQiwWw3Ec39hw7NgxDh48yODgIEeOHHlMr81iebyxgttyutixisVieSIiYlqW2ZI51vJ/Q0NDYExohjYDvpg8POcwG964gWkbprHkf5fgOjVBK9trj7SEecu0RTN5mexTL5O4YOrFUyU/M78/VYi56dU+1brXruty4OwD3PrKW8k35P3vVt6xkit+egVOuXadIrgLhQKTk5OM5ka593X3Mjlzkv5P9pPak5oiovWPXrNbC3kZt8t2+lri8Ti/+c1vbNI0yxOTxsZGzjnnHFKplO/BlkQFpVLJtzQBU6xXlUrFf4Fbt7dy6ZcvpfFoI6VKiWgpSt7Lc2TFESZnTJ7x63AqDh0HOyjNChoIYhMx3LFgWE+pVPJDymU9Q6g1UDLfRowNuVyO9vZ2Ojs7GRsbY3R0lF27dnHgwAHr8bZYLBaLxWJ5AhIKhWhpaaGtrY3Ozk4aGxt9b6j2ZkNNyGrBWS+8W4vZju0drPr3VdVISg8qXiWwjQ7ZlimN4tnW39ebP63ne+uyaIEsDjBzf+2ZluuR6aFyDDN5Wr155fq4ruvSPNpMuBQmT01wtw+34zBV9AfqMesx+7OzyTRlqnXlBj3q+rp0WcwM5qaHXmuSQuGRTW21Hm7LY04kEuEFL3hBwPMrD66IUAg+5MVi0V9mS6+VJ9YzeaEL4UJ1Dka8yE8/+VOKiSKhQohyrFw/03kZnLKDF/3DHu/wRJhL/uoSaIAH/uEBhlcO07SniQvfeyHueDX8JNQUws1WryWVSlEsFslms5RKJRKJhB9K7jiOXydipRMDQzweD8zr3rJlC1u2bAmEvFgsTzWsh9tyutixisViOdOYHtq2tja6urro7Oykubk5kLdHtpdxq4zv5HOoJS2Tv+slOYOah1vmGJsh2ILpXZZt6wneel5oc462zm6u52yboeFS5npZx/Xa3qZRQULczTBynbwt35HnG+/6BqVIict+dRln33k2TrEm7qXslUqFfD5POp1mbGyMiYkJf+xseuVNA0KxWPT1hr7Xuixy/+QehsNh1q1bZ0PKLU8sYrEYF154IZ2dnf4LosVzOBz2Gwbz5RMPcb0kDkDgeI7jMNE4wY5n7WDFdStY/6r17Lp8VyB5AmXo+kUXzfc3s/vvd1NsDiYpa9zdSLlUJrPwYRY5NN8KB1IHUpz7gXNpONZAenGae952D6s/sJrm481+yHi5XCbTmSGaiRLJREgkEpTLZVzXJZfLEY/H/TW8E4mE/4JLYy2N2bZt29i3bx9jY2Pkcrk/6t5YLE80rOC2nC52rGKxWB4tXNf1V5yRZbVSqRTTp09n2rRptLe3E4/HAXwRpkWljPfkby0OTfEKU8W2bCf/e55HoVAgl8v5x5Vj+MtnKcGq19iWY8h3DxcGrsW0lMP0BGvxbmpJPUavF4pubitiXHvmtWffdV1OzDpBspRk56qdXHDTBYE6lzqVn3w+z5GWIxS2FshPVJ1VIvr1tetyitjWqwXVMw7oegyFQkSjUW699VYruC1PHKLRKGeddRbz5s0LJEcAAp5rbbXTyxpA7SEX8Wl6uOVFkWW5QqEQpVKJXC7Hg699kN3P3+0fa9bPZrHki0uoVCrc/677Gby0thRY+8Z2ln96ORk3w/pvrj/ta215qIXpt01n75/vpdBaoGlbE0v/fSlth9oIh8PkenJsfMNGmgeaWfnNlbhF15/f7TgOsVjMv/ZCoUAsFqNYLPrzSPL5vJ9grVQqsXv3bgYHBxkYGGB0dPS0y2uxPBGxgttyutixisVi+UNwXZeGhgaamppobGz0l9Zqba0uIdXU1OSLaxGypVIpIEa1ptJzimV8ZwrseuHb9bzW8t3OtTvp/k03xWIxcF4trrUnW4t1KbdgCuZ687ZPNe9afybb1SuLOV9an0sbAsywdjOk+/Dyw9z8ipu5+JcXs3zj8kCkgPxo59xAzwC3veY2On7ewYxvzPA1g86hpMskoeGFQoFSqRTwyNczLmhRHgqFuOuuu+wcbssTA9d16e3tpa+vzxfBMqdEW7F04yJh1jKvWYe7yIMvL7W8JMlkMpDQQNa9LpVKLP7KYlq3tXJozSEajzey6NpFuFGXw2cfZmzxWKC8pWSJglNg/5v2/0HXO7p8lNHlo/7/44vG2fL2LZz9ybNx0y73v+l+RpaMcHzZcYqxIqu+sCoQVl4qlSgUCv66iul02v9eQoqy2SyJRALHcVi0aBH9/f0MDg6STqe57777GBkZ+cNulsVisVgsFstTFMdxaGlpobOzk46ODtra2nBdl0QiQTweJ5FIEIlEpnh6TVFteoFN8a0Fpyl6xblULzTbFLTlcplNL9jEpqs2Mdo6yoJrF/hTMPVccNObrdfLFkyvty6D3q9eJnL5vl5YtvwvxzTrSPaVz8ylxurhui6Hlh7itmtuI9Oc4earb6YQLnD2urOnnFtE94neE9z5qjsZnznOxOsmKEQL9H65N1Au89zm9erwd7OsEnKur/GRYAW35cwRohpyXYHW1lbOOussX0yKVVCSpQF+SIfMbYZguIckE3Ndl/7+fkZGRhgbG/NfDDl2uVz2j5vNZv0w82K+SNfvuujY0AF5KOfKVJwKrZtbad7TTK4zB061vHM3zqW90E7hlwUG19Y833jQdHcTPR/roeyW2fWNXVSaKoHvE1sS9P1TH0fefoTxK2pLlo3NH+Puj96NU3bIzDwZpu7AgUsOQAku+MoFzJkzh2PHjlEsFnFdl2w2i+M4NDU1Afhh5qlUym8EpM5c16Wrq4tyuUx3dze5XI67776bQ4cOnXbDYLFYLBaLxfJkxPSYtrS00NvbS09PD9OmTfMT14bDYd/rqT2XOmKynve5XtItLbZNr7EpSGUcLHO7zTWt5bzFYhHP8dj+nO08dPVDlGNldr50J5RgwQ8W4Hg1R5QuS73EZNpgoKNFJRGx6f02RadpMNAe9HrZz03vvDkd1BS6cj5dhpGeEW551S1km7MAFBIF7nj+HSQyCRY9uCjgeHMch2wyyy/+7hdkmqtjbC/scfiaw7hZl55v9UypG51IzkxiZ9aleNRFv+hw/keCFdyWM0MD8ElgEzR/r5lLL73Uf7kk1FsaE53BMBKJEIvFfEtVX18fmUyGEydOBF7WHTt2+I1kLpfzw61FxMt8jWw2G0iIUCqViGfj1fCSyMmQl7THyg+v5N4P3cvoWaPM/9F8uq/tJjcnx8b3bQxcVvxEnCv+5QomWya55533UGkMCtn4tjgLX7mQRFeCY7FjU6pl0YcWETke4YEvPEB+eh48aNzSSP+n+hl2h0mn00QiET+cPBqN4roumUwGx3H8+dy6gZBtxWDheR7JZJLm5mae85znAPCrX/2K8fFx0un0lCXXLBaLxWKxWJ6MSNZvGS8lEgl6e3vp6uqip6eHWCwWEFFQE1oisrVolShJUzCaIlTPoZYlpfRcbDNLtzhIdMi4bGuKefnf8zyGFgyx+fmbKceqorYSrbD3qr207Wyj4/4Ofx9BynYqoWx6qMXoYIZz6zozo0u1t9c0QmjRLPvXE/+mQcIMQ3ddl6bjTSy4awEPPOcBf/+uXV1M3zzdDxOXOi6XywxPG/bFtpDckaTrW12nNITodcdNwa/vTz2v9ul4uq3gtjz6xIGPAG+q/jtj6Qy8mzxfYEuotDSOumEwrUX791dDusUSKC+LNA7iCRbvtszDkAYToFAoBOY7A75F0c9G6MF5Hz6PQ39xiAXfXcDQ2BD7z9lPJRz0Xveu68XFpXBugXxXvuoRP0nLPS10vbULp+Jw6MJDnLjwRKBakvclyR/ME86G6VjXwcCLBmi/u52F71tIZjLjz0WXBGpSH9KZiAEhHA6Tz+eJx+O+oUGym2tLnzZkXHnlleTzeXbt2sXx48c5ePCgn7XRYrFYLBaL5YmM4zgkk0l/jnU8HiccDtPZ2ekvoSrT7E4lFoG6oliLq3rrQZvhx/W83jpplykyzWOaGb21mJVITRkDTt81nfO/cj53v/Jusm1ZYidirPjyCqY9OA1c/PGf9rya5ze9znJu7ek2w871Neu52rps5rzsetnQ5Xt9PPm+npjV240nxjk091DgOTgx/QTDM4Zp3N/ofyaiecf5O4LPTNGh7Y42Qs5U77kpts17ZBoYtJdeP0ePFCu4LY8+/wH8de3fna/fSaQ5Qv8P+gGouBW2vXQbC7+/0M8orhOgAeTzeT/MxVyvWhoVOPWcFdlOhHk+n/c94JlMxreMacLlMDO+PIPRyijhcJi5X59LpBRh9+uqidZ6v9nLgh8uoEKFQr4QyFA+/b7prPzySmiAY9OOMfKc4Pzppk1NzPvMPGKjMba/azsjLzj5vVcV/268mp1cMk/qdbq18UAskZ7nMTY2RnNzs9/ASeOnk8iJQcN1q0uSrVixgnK5zP79+5mYmGDTpk1kMg+Thd1isVgsFovlMaahoYHp06czffp0mpqaCIfDJJNJGhsbaWpq8qcemqLXdOBoAaX/1uJQe7hPhU7oW09UmyHI+nvtUQcCYtP08NabM92zoYfV+dWse8M6zv7C2Uy/ZzoA8XichoYGSqUSmUwmIAhNwW+GU9erGynHqZK76esyz2OWW3vMTa+y/s7cX5cndSzFuV88l/VvWM/owlHiI3FW/fcqph2YRjlWDuzveR5rr1tLZbLC9udsB6DvP/ro/kE3bqh2Tfp+Sd2Yof96G10HlUplSqK8R4oV3JZHn28CrwVOPrdu0aXrrq5qWA8et77jVoaWDlGmzLIfLfMTU0AwiYQIZGngRIjqRGgiSvP5fF0vuQhRSTwmL1UkEqFQKPgvkAh9LebL5TI93+mpzrVpLNH79V7G8+Nkz8vy0N88RL4171/y6JxR0tPSNB5vZPu/bSczS4lYDxIHEqQOpNj2oW2MXFET48PnD/PgJx9k5VtWkkgm2P5325n1tVl4J2rh9xJOLl5u160tUyHXIeWVOUlibJDfoVDIr7NIJMKCBQvI5/P09PQwOjrKPffcw+Tk5KP+KFgsFovFYrHUQwRLKpWip6eHWbNm0dfX54u+eDzuRyhK+LN4Wet5psUhIRGM2gurx3YSHahFohxLH1t7dvUcaD3fVy/zZYo202sr1yFjTe0t1fPHRZBrr+v0+6Zz6QcuJXUwRYXq5/l8fooo1nOLT+Vd1vVUz3EldWCKZG2cqCdedfnlWNrjXs/rbRo69P+e55E6kGL5R5dz78fvZeWHV9JwvIFMqmpcSCQSvg4AKEQKDJwzAMCcf5vDtB9P88+hM5Wb88hNL3s9o4f5/enM3wa7LJjlDOA4Do1XN5L7eo5QMcTVH7uayPEImWiGO99wJ4fPOewnJ1vxrRUs+vUiEqEE2Ww2EC6uM5mbwlt7fwuFQmAuh7woeqkA3UhLQyxCtFQq+ectlUp+I+2HzoRPWudKJxsxx2Pgzwc49NpDVOIV3LzL4u8tZt4P5xENRzm+4Di3fPSWamV40Lqhlf4391NuLLP1P7eSm1dbLzs6FOW8151HZaLCwdcdZODlA4TTYS5844WU99e81GJo0Ak+xJAg4URSN65b9ZYnk0k/7L6hoSFw/boDks8BfvGLX5DJZPx7YbE8nnh2WTDLaWLHKhbLEwsZp8g0wlgsRm9vL7NmzWLWrFnE4/Epok/GKfU8sFo8y7Z6X9NjbY5l9NhQe3VNUa2FqxxDO2XMkGsd1i0eUBHmkmwLpiYbM73cck166SmZLimOIrlGvY8+tjYEmF7pep5nXX/aSCHXp69Zi2XzfpwqFL3eNZrHNfcFyFQyPPicB8kfyzPtp9MI56vTUVOpFKlUikpPhdbJVhzHYbxxnO+953vkG6vOMKfksOi1i2jc1ujfi0gkMiU5m6y/bd5XXS65B6IXBPn73nvvtcuCWR57ent7uazxMvZ9dR8NRxuIj8QJRUMcWX6E0dmjtXnPLhy64BBz1s8hcqI6B3t0ziiNI40ksolAg6HX2RZBDLV5J+FwOLC97BOJRKhUKn62b1Nsep7nLzkm88QBP5lbKBTC9U5a5KI1gdp7bS9e2GPgLwfoubaHad+eRj6Wx0t6DC0Z8uvCKTtc/MmLGV84zgN/+0BAbAMs/shi4oU4B/7mAAMvr1rlSo0l7vnEPSz/2HIadjVQpszwOcPM2TWHyclJf756Mpn0X36ZtyTXIWtFSvI4uTbP8wJLXXhedem0YrFILBbjpS99KUNDQ2zdupWxsTEGBgbO7MNisVgsFovlKUE8HqexsdGfY+26Lq2trXR3d9Pb21sVSUZYtulFBnxni6BFkJmczBRpOgmZKcS1eK4n/MwEWloQ1wurluNpEa+vT4ch6/GXvnadXE1nRtdlrCf2dPi36YGWY8k5tEdbi169T70o0XqfST1pz7Ect56nv54HXY4t9V0vlLvslNn6rK1s/tPNAJQKJbp/2I1bqiafO7T0EPe+8V6e/b/PpmtvF1sv3koxUctN5IU9jv3lMVo/2Oo7q0wPu846bt5/c1vz+9MNK7eC2/KoMmPGDM477zxc16V/Yz/FYpFIMgJA98Zu1pbXcvtf306+IU/nrk5W//dq4sfj5Mt5RueOsvFvN9Iw0MDqL6zG8RzfY6sTnkWjUaBmMTMTVpjWTFNkQ62hyuVyfmMgwluOG41G8bxqojed+Vwa4Zlfm0n0WJTOX3aSc3JMTk5y/C+Os+/P9/nn9lyPHS/aQffd3TgNwZey5dYW3L0u6WyayWgwnLscLjMZmiTlpTj4qoMcfelRWr/Zivu9mvVSEqhJwyqWO2lYdEeRzWaJx+OB+d3i9Zf6kdD09vZ2LrjgAgqFAnv37mVwcJC9e/faJGsWi8VisViAqrieNm0aM2bMoKmpCcdxSKVStLS00NraSkNDAxAU0pLUVos8LZp1xCFM9cLqaEcdAq7z18h3WixJIjIdHam9rloc63Ge9pKbodSmOBbMMG7xrJohyYLeXydQkzJK2WVb+V4LSP2dnFuLTH2NZr3qujTFsa5Dc3yttzXFv1yLvlf1xub1wtTlsw1Xb+CB59eyk+97yz68lMfsa2dz6IJDbPvbbRRaCtz0ipu47NrLOPen51IeK3P/NfcDMO0n05j92dlTPPWm4Nb3Wf8t907Xq34GTaPI78MKbsujRkdHB+eee66/dJW8eJIlHGDaPdN4RvoZ3PHaO7jkfy4hfjhO2SlTmF5g/VvXM9k9yejsUcqxMms/uRaovajhSBi82vqF4oGWl0AEsk5okM/nfe+ubrAkiZqs1y1iU5bYkuOIyJds5jIHWhqItp+1QQg/TKp1fSv7/mKfXyeO59C1qYv2Y+2c97nzuPPddzI5c5KOzR2c9/XzcEoOI/ERxlaO+fu4OZdl71lG8/5m9r1mH/teto9KtMIdf3oHSweW0r2xG6g1DOL1dxyHw887TOeRTroOdgXWlgT8hBqJRMK/BrEi63qsVKrrls+ePZtkMsmcOXNYsmQJBw8eZMOGDWf2IbJYLBaLxfKEIhqN+mHgM2bM8FeakdBeLfxkrGV6sgFfAOqEZVrcaWcB4I/1ZOwViVQdOGYItCmItZiUcZI4b/QccBPzM729lFFvZ3qc9XXr7eVzc8Ud2UbKKMfRDqITC09wvPM4s347y6+vcDgcCHc3PfqnCtGWvyWEWoS73kaPDeUemWLexAwVr1dXUgfa6y7fm+LccRxm3D8jILidikPDXQ0MnjPI7jfupthSdQKNTRvjpr+8icv+/TKOzz3Ogi8tYHTmKLO+MItQLoTnTF0HXM4NtTn9+vq0oUKXT4ttKW+956gedg635VEhlUpx/vnn09vbO2Vuig4zgZPzriMFnEz1pStR4qef+CkTXRP+8Zyyw5zfzuHcL5+L4ziMdIyw6W82sfZf18IEvmCOx+MUCgV/fouEW+u5L/l83m/gpbGWpclyuZzfsEhjrOd3S7b0eg1oKBTyvb5+JnXXYWzpGNv+dRuO57DojYtoP9hOpCnCjjfu4OBzDoJbnVuy7AfLmP/j+fzmI79hfN54rTIr0H5TO63rWtn7tr2Uk7VrcfMuZ7/+bFqOtHDk2UdIJ9P0fKeHWCjG0DOG2PQPm3ArLlf80xW0DbYRi8UolUp+yHksFuPEiRO+J1zuj177WwwWkUiEXC7n1104HCaTybB//37uu+8+f/1zi+VM4dk53JbTxI5VLJbTQ8Y+OjFZT08P/f39zJo1i6amJj/HjWwDU8O5tbfaDMMWdAi26anVIqZUKjEyMuKHqCcS1Tw/OtJQexbFMy6fm6Hg+tgaLaC0V1x7ufU1meHsMHXOr4neXo+FdRlkX20oADjRc4JfvP8XeK7Hqs+toueuHqgEj2t6vc0wcO1B1vdcC15dd6fyfJ/qGuvNI9dz17UnWNetHFe8zObc9uHGYX7xuV+AA6GJEPNfN5/ErgQ0wYF3H2D4yuFAOUL5EOVImZZ7Wuj/QD+hEyH/udZ1o8smTjnz2TC99WL00WH9WpivW7fu945VrOC2/NGEw2FWrlzJ0qVLq5nIvWDqfMmurUOKKpUKuVyOeDxeFcsdBW56202cmH0CPJhz1xzWfG4NlUqF4XnD/O49v6OULNF9RzdLv7iUVDrle5+lodHzvAH/e6iK41wuRzQaJZfLTWlAdQciDawIcZkHrhsUHaYuljEJe49GowytGcLJOrQ/2I7ruoyeO8rOd+6k0FXwj9E40Mg5nziH2GiMX3/z1/7nPRt7WPAPC5jsnGTHe3YwtqLm/QaY/x/zKUwU2P+e6hrli/9vMeHhMA/940O1682GuOADF9CyrYWGhgY/wkAMAxI+L9euBbg5N0fPpdKd0t69e9m5cyejo6OMjo7+EU+QxVIfK7gtp4sdq1gspyYcDtPY2EgqlQrMse7p6WHmzJk0NDQExgCm11Z7OOsJMzNJlhYy8re5lGk9z6npuTU9wvLbDP3WHnQZA5peZj1+M8eNJlpwS7nkmFL2evvWE5U6nFzqRjzeMjbT4vPYvGPc8A83UI7Vjr/6X1fTc3tP3bLKsmkyzpWEwvo+ai+vaeTQHnGNbKMF6KmSn+n52+azo0O5H85bDnCs7xi/eftv/ARocz8wl47rO/A8j/FV4+x9314KMwp19wVov7Gduf8+l9h4LOCt1tEFxWLRF9xSPkGcbvqe6FWU5J7JMe+44w6bNM1y5lmxYgXLli0DaknMzNAdqGWOrFSqWcIl1Nx1XZomm7j4yxdz56vupPVgK2u+uQbCMLB4gPWvXU8pWX0hDl94GK/sseI/V+DknIAQ1pZRCYvWXm8dRqTD3XVDHo1GfSuWbhzkuNp4oBtQ2UY86G13tVWvO1Ktk8SdCeZ+ei573rWHQkeB+N44C/9tIS0DLWy9ZmugPs/78nlk5mTY+fqdU8Q2wM437wz8v/U1W6ds47kex9uO4467ZDIZYrEY0WjUX2JDOlS9hJgsrabDvGSOuFyjNkTMnTuXBQsWMDAwwKFDhzh06BBHjx49rWfHYrFYLBbLo084HKazs5Np06bR3NzsR7nNmDGD9vZ2UqnUFOGoRZe5iks9D632WGoxIuMGczUYUyxrQS0RdtozLeMQCM6Z1oKtXmi26dHVAliLbX0O7YAxvdn6HPK9duqYAtv0VOvy6WNqD6qMUaXOJnsm8dzgMSb7Jut6iqWuAbLZbKAeTc+1+SPI+O5UIeP6bzN6wbyXGnPuuH4GTO84wJF5R7j9Fbf7YhvgxOUnaLu7jfBYmNaNrbifcdn1jl0UO+pHWRbaC5RSJSKjkSnREPVC2Ot58PX43jS06ND0UxkNTKzgtvxRLF26lBUrVpBOp3Ech8bGRr/h1fN4otEolUrFF3XxeNz3hkujNuP4DC7/2uWEh8J4+eoLkTqeIpqNkibtnzOxL0ElUyHsVK1Le162h+6bu4kPxf0GQxp813XJ5/N+6LM0kiL0daOuM6DXC/FxHMcPZZcfKbss1yDnhlqyBagK+ekbphN/X5wtH9zC3PfMxdvhcd8/3cfBFx8M1Oldr7uLRR9aRPRgFFY/gpvgwZp/XsPgtEH2vWZf9dzFEEvvXcpEbMIP/Za59SLARYRLZyoNh2nRlXoLhUK+p1zubTweZ+7cuXR2dtLf38/w8DB79+5l9+7dp/kkWSwWi8Vi+UNwXZfu7m7mzJlDT0+PLwbEm51MJoFadBvUz9qthZN2UJhh3KZ3UkSr69aS0YpIN5e3Mud2m55iLXx1fhnZByAWq3ous9lsICRci1hzDKPRSdS0t1nqQX5rcWZ6aPX/pviV687nq6JRnDU6y7Uuo+M4RCIRYrGY/33/rf24aZdb3nQLAIu+sYj5P5tf9/xyjWbYuL63ch5TSOrIRjOSU98ffY26zmXfep5s/b8+56kMFK7rMjhvkLEZQWfTictOkP98HmekWp7m25uZf2I+W7+4FS8SvLepLSnmfmwu8SNxQuHaMrq6TDpEXDsKtRdc6q1eJIU2eJgGg1NhQ8otfzC9vb1ceOGF/vxgwPcsmw1XPp8nmUz6IjWTyVTnb5dKgYZTHnx5ccPhMOlomhs/dSPZtiz9P+5n8XWLKUwUIAT7nruPnX+zk/BkmItffzHeSK2Rn5iYoFgs0tLSEkiIINkipVxSbimP9uKGw2F/HrSEwkvHoq260iDpBrpeOE2pVMLtcAmPV+ePlxpLbP/Odj80JpQNMe8v5hHbHcNJOex/735Gnz1KKBPyG5X5b55PuBhm6xe2ggvLP7CcaQ9M4+hFR3noPSfDyj3ovq+biz57EaFiiCF3iA1v28C5Hz6XcKFqyZV1vSORCMlk0rd879+/368b8YZLtIJka5d76ziOb2jQHWa5XGb9+vV+dvN6c6cslt+HDSm3nC52rGJ5qiGiRg/+Z8yYQX9/P3PmzMFxqquqRKNREolELaeMEg3igNAhy2aCMjNcXG8jn+uIRd3n1/O6apGitYYWLToU+eGEuB5fAX7kYj1PrVlGsy5zueqqMqlUys9do7+XY2jjg+nZN6/HFJISySljTu1h1t5/vZ8WhfJTLBfZfdZuhrqHWPT9RYTKNWGsxfvvMwDU89Lq+2UaUH6fWDbDz00vbz2vbz2Dhek5d12XrJvlllffwoFzD1RzHhUcZv3LLDp+1gHlYNkmz5tk2xe3+ccIj4U567VnMfjMQRr2NdC5rhOXmgFFO5YKhYK/BLCZS0CXVydQNssq13D77bfbOdyWM0N7ezuXXHIJ06dP9xsWEaLyAIulTkRZpVIhlUoxMTHhC229TINOXKB/u65LpaHCfVffx4qvrKg++C7su3IfD7zhAX9d7/jROCvfuZLYvprnXESzHMvsgAqFgt+Q6/AiafylcZBOR0SnbCNJ1/QLqrfV64NLmH1jYyPHjh2rdSjNHlu/tJVKQ4XlH1tO4oEEk5OT/vIZBz96kEX/vIiRNSO4rkvP/T04jsORxUeotFaI/yxOtCvKQ597iMzcjH+PIpkIZ193NrMOzOLnb/s52cYs7Xe0s+LzKwgNh/zr06HmkUjEt1IDftK4UChELBYLWEmlzrTBQdb01nO/N2/ezL59+xgZGfGzwVssjwQruC2nix2rWJ7MuK7rZ/6WiLLm5mb6+/vp6+ujoaFhisetWCz6xnEZi5hh2FoEmxFs+ljag20KWQkNN5Nt6VDyet5QLbq12DIFqva0aiOAmfVcC3XZpp7n3RSZpnjWYzM91hPvtHaa6OPoz7RnVsaN2ouq52ubx9DGCr2vrpNSqeTPM9ZeWFPQmnWr67FewjRdR1Imffx6Zakn2B9OyOvj6/P/Ps9wsVhkcnKS2992O0Mrh+j5rx6mXTttyjFwYcd/72BiZS3hMkbrv+w9y+i4q8MX1No5JPPcdWSsfk/082J6t6GWL+CRzuG2gtty2iQSCS655BL6+vr8hlq/PJlMhkql4oeMixiXkGQRaWaDpUW2ni8knYMO+SmHymx840YOP/OwX67wWJgFn13A9Fum++HhgszNFuuuvChiIdXiWM5VqVR80atDxXUDpkPT9XVqK7JkPRdxK+WS+sjlchTnFMl352m7p80vi1yvNAgi2PUcbEkGFwqFmOydZMc7dpBekcYtuCz78jI6D3Wy4W83MDpz1K+L2etms+jzi3BGHMZ7xyk6RVr2tuC61aXNRHzLGo7FYtHPTip1If+Lp9/zPLafv50F6xfgem4gwkHamO3btzM8PMz+/fuZmFANpMVyCqzgtpwudqxiebIQCoVobW2lo6OD1tZWXNclGo3S2dnJjBkz/HnXegykxZpe2ghqXmKZxqfz1EBNsOp5s/WmkAmm+JCxnin4tYCW8RvUhLApQvXxTdEm5dPosaE+ny6bFr7mtlLXUqZTzd3V59PneDjxqYW7WX69nSlM5TM9dtZlEZEn0xXrHVsLZCmDKWbN+6zrXdeh+Zm+Hl1PppDW5dLlqbeN5lTn97zqsr0TExOMTI6w/9n7mfH9GQHxq89HG+x9716OX3i8/nkKDov+ZRHdN3f7Y1ap11wuNyW7vem9l3F4vfrT65zfdtttNmma5dHFcRwuv/xy+vv7fY+0eH0lxEceaN+D69Uy+pnhG+Id15kaRaTqDkCLb8/zqBQrrPjKCkKVEAefdRBKMP9D82m/t52yU/YtldJJyDlgqjXRdV3mz5/P4OAg6XQ6MMdJhKeIZrNsZhiP2bhLPWgxr7eR7xMHEyQPJfHw/DqROT9SZpkDrzs0x3H8tcSbDzez6NOL2Pq+rcz+/mym3zyd9Mw03mSw0UtMJGhNtZJtybL57zdTckos/9ByIgcivsCXMPJEIuGfW69ZrudlRaNRNl28ibtfdDeDcwZ55g+eCVQjFqTeABYuXIjjOPT39zM6OsqmTZsYHg4u62CxWCwWy1MN13Xp7Oxk7ty59Pb2+gbzVCpFQ0MDqVTKN7Q7jkMikSCdruWu0WJKIgO1+NDbmA4LQcY7njc14Zf29JpzfPWYw486VNvocshYRwtsPfbRYlrGQLp8cg4ply6Hvn6NHivKtub8cx2eLtegDRTakKH/N40Bur50mU3Bquva9IKb12iKcn2MesLXjCDQz4D2zOvzmMeVfcwIADPS0/TQa5Fsoq9Vzl3Ps67rw/xfylAoFChlSkz/3nQq3tRVcmRcnSgmOOt/zmJzdDNHVh2ZUian4pAYSQQcRFJPWkSbxzfrRpf1VHX/+7AebssjxnEcLrzwQlasWAHUXlqxvkmotRa30lCLANbLFAjSkOdyOf8lkrkvruv662jLZ/Lyl8tlyqkym9+ymRk/mEFyQxLXcf3EbLrxF0OANKSRSMRPpuZ5HqlUKnAt2uut1/bWVlxtWczlciQSiUBHJi+hdH6O43DWWWexc+dOJicn/YRlUk8Soh0Oh0mn0/7xJFpAh6lrI4XMD5K/i61FYhMxjh4+SmtrK840h/WfXU92RpYZ189g+f8sp6m5iV99+ldkOqoh6NGhKBe98SLc0epxiqEikXLEP2YikQCq2S/b29spukUiVAX4vgv3se4V6yjGijhlh6XrlnLJdy+hkCnguA5EoZKtheKLQSGfz5PNZrn11ls5ceLEKZflsDx9sR5uy+lixyqWxwMtphzHoa2tjSVLlrB48WI/YalMzdKRYTJm0vN5zcRaksz14RJuyW9T5JgCTUepSZlkTCZlMKf0aRFlei/reT7luHp6nSkwtYAxBT5M9Ujr77Twk2OaolAbFHSYux6/mf+fyrOtjR36nHpbMyRfGzz0WBRqCdvkXminjZ8Hxy1TKVYo5ounXHZMe7lNL7QYJup5kuuFg+t7qevNFNz6/uhzyvFPZVTQ9amPbT5D5XKZydwkY6NjTI5N+s+pNgKIZzkSiZBIJRi5cIR733yvv5qRX56Cw5q3rKF5T3MgsVuxWPSTCZtRBrp+TEdfvXsg5bn11lttSLnl0SEUCrFkyRLOO+884vF4oAGXOT1aZEvDIWHbpuVVN3I6g7g8wOIJL5fLRKNRVq1axa233uqHUenGLZfPUSwUp1gTJRREXp5sNusnTNOJwLToHh8fB2oZN8VbL4m/JExe6kSuX8SyhK7LtpJBU7zF0tDquVB6rpUcTydlM63D0imKccBxHGbPns3AwIB/7qamJv/vUqmEl/DY/rbtLPrEIsJumC0f2sKxi4/589/xYMZ9M7joExcxlhpj/bvX0//5flo2t+B5tdD5WCyGO8Nl3RvXsfh3i5ksTXL3W+6uHQeITkY5/5vnM++eeQyvHObu59zNM//nmSRGa95yCVWXNc7T6TQbNmxgaGiI4eHhuo2b5emHFdyW08WOVSxnmkQiQTKZ9I3IiUSCBQsW0N/fT2tra8C4bApZnexLG9q1aJTPAH98oQUo1Az5On+O6UnVQlci9KRMOpIPgl46z/N8kW8KWT0u0V5VU5Dr85qOCC3AdDSkaTDQYx9Bi1gzpF7vK9vI33IcmR8s0QVm+LuMXbX3U9dNPaEv+5jRBabHW67XNFLo367rkkvkuPPld9K5uZO+m/vwSlM94A/nKdb3SZ9fn8cU23IMfW/qGRj0s2QacvR1nur4Zt3p6/I8j3w0z70vvpfyrjId3+nAy9fOIUYceb5jsRijF4yy7r3rAmNQYdF/LWLmT2bCSR2un8VcLjfl2ZNnSt4TnclcP986o7v83HzzzVZwW/54HMdh1qxZXHLJJTQ1NfmfS4izdCA6YyTgz9cG/O/kwa4X5i2WXsdx/H21Z1k6CD0/Oz8/T/pEGmen43thfZHpBUN5YGqjo7NvxmIxMpmMXx4d7lSpVPwQbz03XDCtetrKqxsjbSgQy5i82NrKK8Idatk4pcHRmcL1EmiyjW4UQ6EQ8Xjcn68Sj8er1vRIiE3v2MTws6sh3dNuncbaz6+l0lXhntfew9EVRwllQiz+yGKmrZ/md0ql9hLb/nYbRy6eGroDEMlGWHXdKpbcuYR9Z+/j5tfdTDlaZvZDszn/G+fTOtkaaJDlGj3P86cT3H333UxMTDAwMOBnkbc8PbGC23K62LGK5dGkubmZ9vZ2mpqafAP5jBkz6O3tpb293e9/zbGF/Nbh2PKZfG5GzQFThIs5b1qLcX38euJQC1C9jTkG0mJCf6ZFqBxPn0MbErTIrDfO0p51+d4Up3oaoK4XU9CZXlVzfq3eRos6wfTOC6Zw1fdFylDP2y6/9TU83PnM8aAW0MV4kXUvW8f2S7aDByu/tJL+3/Rjoq/PNDyYgluXx6xHHa2p96kXDq6Pr8+j0fUj38lY1zTGmMevRCvc/eK72XLlFgD6PtdH17e7cJ2g91nyHyVTSR5864PsvWTvlPoR5n9hPrN/MpuQW8tOnsvlyOfzgXtm1oG8e+Js0/pFfssYOxQK8dvf/tbO4bb88TQ3N3P++eeTSqV8r6w8uOL5BPwXShKNSUNtrm2tRad4kEV4ynbinS0UCn4INdTmModCISanTbLlrVsoThaZ8445hMfDfsOjvd3aCy2fy3l1uLdYoiE4d0Y+l2uPxWJTLJVyTt1ZmdY7qQdtVdMdjlhVw+Gwf91QE/tyLAk/kzrSllUR4/l83r8+Lf4Bfy72nE/NIZQJQQL6/7Of0egoW/56C8eXVZNPlJNldv3jLhr+r4EZd8wg62V58K0PMrz6FPOuK3D+/57P/Pvms3vtbu56+V2Uo9WBwb7l+yi+usgV/3kFkWyEwysP0+l10rSjya+vbDZLPp9nzZo15PN5jh49yvDwMBs3bgzMs7FYLBaL5dGmpaWF3t5eurq6iMViOI5DY2Mjra2tNDU1TfECynhED8RNJ0I9byJMne+qxwzaO6s9aXrgr0W36ek0vdLac6sTwOpymJ5lM8RaCzw9rtLf63pJJpO4rsvk5KR/btlfttPCXv7X3zuOEwi/r1ceXUf6u3oGEHMcUc/ja4pQsw61gUHGwlALgdfh+NrZo58XqWNdH5VKBc/xuPUVt7J31UkB6cBDr3kIL+Wx8OcLT3lcfa36uvTx63mc63mxH+651Z/rc9e7D+a9NuvfNKK4rssd19zBtotqS3wdeOMByokyff/XFzieDisvRx5+KmLcixOLxgKOKdOIYhq0pEw60kFft/mumQaPU2EFt+VhiUQiPOc5z6G9vX1KhwEExLEO1xBvsDQE+kWThAg6kZjs09DQwOjoKLlczm8QxOst21UqFbLhLBs+uYFcdw6AXf+9i5WvXkkxXfQFajab9cPfZW1weUm0xz0cDpPJZBgfH6exsTHQsOss5LK/iFyZB97c3ExbWxuHDx/2LbkinMVLboaQAX5UgO5Y4/E4juOQyWT8F1m8vPplLxaL/rXppSckI2k8HvfrTkLPk8kk+XzeD5VvcVuI/k+UfClPOBeGKDTd2MTxJcc5uWwhDSMNZOZmGM+P88CLH2B4cX2x7VQcLv7sxXTf0005VuZI3xFyjTn1oMCsu2YRLUUZXzzOLa+4hagT5SWffgmxY9WBjcxtk3D8uXPn0tfXx6xZsxgaGmLDhg2k0+m6z6HFYrFYLA+HFgDNzc3Mnz+f+fPnE4/H/X4xFov5YlH3zyIkTGGnvZU6/FvGCUDdBGeyj/RneglR6dfF2K9Dw01Prjbem44CPV7R1y/HNucGy1hJxnEiOs0Ea2ZiMS0ahWw263+mw9u1U0ML9HoCX59D6sk8r/aca2eHWefmWNQ8vvmcaPGv61F7Rc195LcOp9dlNp044vjx68KDBb9bwL5z9+G51euNpCPMvGvmFGGvRa3+rY0N8lm9NaR1ubVBQteZPpdpwKgXNq/viRlCLt+ZZZNnqVQqMfemuWy7cJsfHu6WXbpu7AqsKy+OpVA0xLq3r+Pw+bWVii788oWwG+746B3gwNLPL6Xnxh4qXoVypYxXDhptzOs33z+5Pn1+U6Tr6Nbfhw0pt5ySWCzG85//fGbOnOk3CjI3CfAtl5FIBKh1Hqb1b3Jy0t9PhKuIaBHfMr9Zi2oIWsR02NA9n7iHkZUjgTnILbe20P/2fr+RkI5Kz7+Q8soLJUJcwkJ0EgVtvczlquJRwrvrzdHSnYl45PWcbj3fQ8SwCP5cLud3+JKhXBpJnfVdRwJI4yUe73g8PsXrLXOkdZ1Go1Hf4BGJRPylyorFImWvzJFrjnDodYeoxCvVNQ09cDyn2gGo9jM+EAcXys1lLv7WxczfOJ+iV+SBCx9gw19s8EV7OBdm9Y9Ws+imRRydcZRfffBXeCHP/+7FH3wxLSMtVCoVxtrGaBppIhaNEQqFyGQyvnGjUqkwNDTEHXfcQS6XC2RwtTw18WxIueU0sWMVSzQaJR6P+2OTRCLB7NmzWb58OS0tLb6AkGgvMwwbCPTXUBuMa8+gHqhLhJ8etJveX9NLanrMZKxiiiqoiXYt3kzPtg7NNr252jCvx2imSNUJa/X3MFWwR6PRgEgD/DFFNpsNOCDM8uqkW1r4a0+iHueYQt8UbPp+md5wM5Re6lafRwt6Hcauy6iFmmkYqGcoMEWdeU4tdP06cODgsoPc8tpbCGfDPOufnkU0Ew2UQws9fW/qPX/6/PpZ1sYObRjQz6T5zAqmwNROI9Prq58P00ts3vuh2UNc/+Hra4I753LOK84hcTThlzMcDhOPx9n157vY/pLtfhQlQMNgA2v/di3H+45z4pwTzP3+XCKFCIOXDTJy9giLvrSI8mg1pNz0cosGkOelXhZzfX1ezMNr9kiMVTOg33DDDTak3PKHEYvFWLNmDd3d3YHkW+Z6zHo+tbb4am+3CEHJHi7rSOfzeV9kZzKZKeE7hUIh4InW4eor37OSBz74gB/e3HxzMwvftxDCBBp58ZwWi8XAHG1pbPQSX7oDkkZCOjl5GbXY1UlLTtXgi8dZGyJ0wxGJRPzM7SKsy+Wy74kWA4TucIDA+uAi2PX8J7MDlpB07WmXxlUaF8maPuf7cxi/aJzRlaPVhs8Bj+AYNnVfisXvW0y0IcrkhZO0/q6V0cgoAy8dYMOfbAhsO337dBb8egG4sOOZO3zLLUA5Umb7hdu54JcXMLRgiBtfcyPnX3s+8zbN84W2XEs4HKazs5Orr76akZERtmzZwujoKMeOHbNeb4vFYnkakkgk6OjooKmpye+Xp02bxpw5c5g2bZq/nRbL8lsnNtUebJgqKrSA0gNzGa9oz7Cgvc6nEpdaxNYTUSJMdLZmXT6zLPpvLSK10BERUS6XSafTRKNR35kg5zVFqhkK7XleYHwj5RkbGyOfz9PY2BgYM+lwai3koGZMMEPKzWuS6zbLpP+u57U3j6k9/rp+TA+vKTLrnVePtep51/X2ppFGjh+Yy41D70O9rP7Gapp3NhPLxqoeWiP/jz6uNgzoZ8MUyrou9H3U913fH8H0YOv7obOsm/Usx9fU20Y+23nlzoBTpxKucPS5R+n7cp9viJIx7/IfL8eJO2x54Ra8kEfb/jbW/PsaShMlYvtjTL5okt3X7CZ1OMXWt2wFB5ycw6z/moWXDd5rPb+8HrpeACpuhYFrBphYOcHif1tM41DjKfcNHMd6uC0mkpH8oosuoqGhgXQ67TdQMv9HXlAtACWEWoQm1DzCsrSXzCFyHMf/zHWrS3npfVy3OvdHe3Z1YrByuYzX6bHt77bhpT36PtOHO15rELVYE1Esc69FZGvLnXR2sVgsUF4znEsMDjr8y/R2x+NxQqEQExMT/prWpqVWZyqVfUVgy3UC/rGy2ax/Tml0pMy6U3IcJzDfXSzveu6WaSkW40csFmNi7gRDLx5i6KIh8h31E5Y1bWhiwWcWENkX8Z+JUChELB5j20u3ceCvD/jbRrIRLv/y5bTd2cau5+xixt0z+Pl//txvVFf/eDULfriA4UXD3PWauxidMUo0HWXtd9eyeP3iwL2TiACoza+ZnJzk8OHDnDhxgi1btjwKT7/liYT1cFtOFztWeeoinuru7m6/v0ylUkyfPp3W1taAENF9oyD5UWQMI2MLbdiX3zrrt3jrtOjRxwX8KCztgdQeSaj1WxAMc5bv9DhAG8MhGMptJmEzhalpcNfjfNNjJ5+ZdaXHGrocuo5NwaTDws0ymB5eXQbTS6zLZm5vCmrTq6z3Nc9pbmt+r78zk5+dSiudyoMr+5jXrMdqpiFEC/BCoRAQraZY1s4pKYcplPX9OlX5zHLJ5+b/5rFkGy24zevQoddmXZjvD0C6lGb9y9az9wXVOeyz/msWPd/swXWqy9PGYjHi8TixWMw/75art3BgzQEu/srFxLfGOZ47zn1vv4+Ri0bq3q/pv5jOnI/NoVIOLtkmekHqX09R0DiOw8G/PcjhvzoMDrTe28pZnz6Lm6+zWcotfwBdXV284AUvIJlM+h5WHS5uZqaUhkMn8pIOTMK3S6VSwJMr+8hx9RJR8kyK9VQ869LJyAtcKpU4ETmB67kkMgk/66DuMKHWMJhWbG3V1A2VDuuSBkMMAo7j+EuDyRwnCcnWc7xlfrWUV+rEDB8HAnVXLpeJx+NTQsgdpzY/W+pfhwIJEkWg61JbanX0AdTC1fP5PJXpFbb81xZyM3NM4eTbn9yVZP7b59Mw0hCwcsq9rkQqHPmzI+x+7W4AXvjPL6R1aytbL93KfX92H8njScb7xsGBldeuZOmNS0lPT3Pj224k3VkLEY9NxFj1lVUs3rSYbHOWB698kLU/XOsnhatXp+Pj4+zdu5fNmzdPmbNkeXJiBbfldLFjlacGkUiExYsXs2TJElKplG88TqVS/ook2gBrClhTXOmoNy1EdTisKbC0sJbf+pyCPpYeg9TzMEp/L/2XFucQjLTTSWr1/tqAr8/nuA5eJRjG7nkeQ0NDNDQ0+GMLfR16HCfb6xVa5HMR+3rcoqP59P66fNoLa9abLjsEPb36c1MASt0BU4wPen9TSJueYC2uxVmhnRN6nGsaR/QzYdahvj75/lQi2RTcMh7WUyHlXDK+1NduGga0oNc/so1pENHohHpmQjstnuu9W/Xut1kX5n3Uz0W5XCaTyXCifIKtf7mV6ECUrh90Ea6Efa+2niriuA4hN4SbcMl35kkeSZLL5fj1e37N8NJTJPYFFr5zIS23tATqS44v91zG4vr+yHXu/3/7OfqnR/EitXvTsLeBiTkTVnBbTo+WlhZe/vKX09DQQKVSCYQq64dPI0IQai+ZzN/RL6T2aDuOQ1dXF9lslr17904RkeLtlsZfz/t2XRcPj0JzgcrRWlhLJpPxhbt8phOliUgHfOFmNrjiUdWWcP19Op0mFov51yoebh0KJceUzlTEt17iSq5DksVJhIAO05Hrl4EG1DzsIpIjkYgfWSCJ1HK5XGAtb22J7OnpIZ/PMzIy4h8HIN+a5/5v3k+pOShUnbxDaCRE3yf6OPaqY8z7f/MIFUIB4S6WwVAoxNq1a7ntrtvY96p9TNs4jdZtrYw/b5x7/vYevHDw2Tn3v89l4e0LiUai7L1sL7f/6e2UY2WcssP8m+ez6purcDocvvfB71FIFFj282WsuXEN4XI4cC91OL7U8759+7j//vspFAp1l3GzPDmwgttyutixyhMfWe1DJyOaP38+K1eupKOjwx9r6JwxMhbQfbbprdWeay2QZH+NbBOJRALJSet5pnXoqT6uaczW/b426tcbN8l16Bwtsr0OLxeHgxjRzTGCHGdg9gAbLtzAc77/HKL5qH9eGZ9odHl0eXUko65D3d+K+NZCSxvfdV3o8mqxqcWfeS0A4+PjTE5O0tbW5ifg1VPmAkYGZ6rn3xRK+rr1Pvo50vvL1D7zvmlPtnbo1BPk4nwyRboeA5v3QsafIrjN/cWZIv+fSjjrcWMymaSpqYlMJsPk5KQ/ttb3TR/LTGqn77M+vn4nTAOHWe8a04Alv2V99PHxcXKVHJSryXjlHkUiEd+7TRI2vGoD/Rv66dvWRzhUi0aZaJngR+/7EfnG+hGaiX0Jzv6rswm74cDqP3K9Mk1WRLfcM18jRCts+sImJhdNggOR8QgXvOcCbvncLVZwWx45zc3NvPCFLySVSgWsPjqcSHsOpYEWi5x0XPK/vNiyv4RXy5zudDodmBOuBXA8Hg90gOIdl8b6yOoj7H3jXha9fxENuxuIRqP+2nqCeJIlY7e2KOuGVnfcugGSsss5xVsvGdTlOqA2b1yHYMk5RPCZlj9JTqLnYUej0YDwljDqeh5+vZ9u8EzhrjtUqUvAT3jiui4H3nyAgZcNgBiLPWi8rZHE5gQdn+/w67FcLlOZU6GcKeMM1BpCObZ0jjJIKDWW2PypzaRXTE1w1rGng0s+dQlN+SZSy1J8/7nfZ3DxIAt/u5CLrruI0b5Rbnz9jYxPH/f3OftHZ7P65tWUMiW/4xlYPEDvjl5Kxdp0A5lSsGvXLvbt28fIyAijo6On/U5YHl+s4LacLnas8sQiFovR1tZGU1OT/9n06dNZuHAhM2bMAGqiUfpD7Y3Wwk/6eQgmopI+91TiVtCiSPf1+lhSHulvdX+q+2F9PC2KtFjWYyQtasxy6u8PzTlE94FunFJQxEJt4K+vNRQKsX/Bfq77q+uohCqsuHcFl//ychLZRMDrKL9FKEJt3FKvnkxvrOkZ16JZrl97W7WgK5VKTE5OEo/HSSaT/nnMOpcfM0xattPGECmL9PmyvVlG+W2G3ct2pnCV8Ys5z1uOoSMitJHFFMe6HvRnZnRCYD88Di04RPv97VMEvzY6mM+eHrNqQS9jRHl2ZR9d1/rZMiM6zCgIfU7zuddl1dGh5n3QaMNUNptlfHycdDoduFdyHRJO7ja7PPinD7L92dV1yp/9n89mzuY5/jWWy2WG+4a59fW3EklHGJ41TCVSu84Fn17AvFvm0dbaRigUYnx83E/OK8+W1JUIch1xEApVveqbPr2J/PQ8K/59BZ1bOvnZz35mk6ZZHhmNjY1cdNFFdHR0BEIp5CHTnZ/2nsrLK6JZ76sbQrHMiQdSXkTZTzdCOoO5bmzl58iVR9j+d9ur60S/axezPzab5PakL7QkXFs3FrqBNjNx6kbbbDREsMo1QHVetW74xfIsx9ZzwGOxWMC6LOFKuvGFmrDWDSJMndelQ5fi8bifDVTq3pwzo+dzQzUaQRoNfR9n/+dsvLTH4ddWl1jo/HEnfZ/uI1KJEJ8WJ5PJUCgUyE3PcfDtB4mVYsz/xHyckeCSGWKMkPNH01EWfGQBe9+1l9FzRv16aH2oldX/t5rGXCOTLZPccOUNDC4ZxCk7rPjKCsrRMhPJCUqRoMd9vHmcbD5L1KlGBey+cDfrXr6ONT9fw+KbF/vlkEFJf38//f39DA4OMjg4yL59+xgcHPxjXxeLxWKxnIJ4PM6FF14IQCqVoqenh7a2tilCSv+tB+jaMC7bSORbvRBwPVbQAtIUtfJbtqlXnlMJCS2WTC+t9nTr/l1HV8kxde4VQcqxe8FufnX1r1i+cTmX3HjJFCO7LLXV2NjojzcemP8AP3/uz6mEqsd78LwHKTtlnvuj5/rRYHK+U4V162s3DRja06/HKHq6m75G09spgqm5uXnKuWXMpecB6/urBaYWYNqAYAp8fd/0s6DHJeazdSrxaop97V3WRqJ6Ilg/L9qho8eyepwHsOXKLdz/gvs5+6tn03tb7xTBXU+4msYRfW3i+NCYxzSNRfo+6JwF2qN/quut996c6l0y74GZEVy2laX6otEobsRl4zUb2f2M3ScvAG5+zc2UvlOif12/f+/aD7Rz4Vcv5NCyQ5zoPUGF2nF3vHUHlXiF6ddNn/JOy3n18oClUomJiYmAPnCLLks+uoTsvCytD7Y+YiVtBbeFaDTKWWedxaxZswJhRSK49dwGbWWUTNrigZV9ZR89p1seaJlnbc490Q26hECZ1l2AwWcMsvMNOyknq2WcWDDBzg/sZNFbFhE+EvaPIQJaW5klO7rn1ZYyk+11pnVp6AEymcyUOVS6odeZxOVYcn1iYJCOTsLP9XJfLS0ttLW1sXXr1sByZWaDLgMOHSZfKBR8a79uxMVTLtcjZdXie968eezbt8+PTMhlcvR9o4+wFybfmWf2l2YTcSOUvVroWNbJsv9f95NbkiNNmq0f38qiNyyCSq3O8/l8YJBQbi4z/OJhFn9mMQ+97yHavtXG8Vccp++DfYwfG6fYWuS+99/H0Nyh6nW4Hve8/h4u+p+LaNvQxmUTl3HDe26gHC2z6PZFrPrJKkLFEIVygd1rd3Pvy+4ln8pz11V34TkeS29eGqg7qbe2tjbmzp3LvHnzGBkZ4f777+fIkSN//MtjsVgslgDPfOYzOeecc6aEYteblmaKBPlMfksbLsZsqJ8MSwsbgGPHjjF9+vTAGEN7rrVHUIsZUzDp/kTP7dUC29xfX4cWGfK3NsJL3zwwd4Drr76eiZYJ1l26Di/i8eybnk0ikSAejwO16Xt6GtcSbwk3VW4iQ+bkAWHR+CJSsRTFXHHKnF0tRE0PpIwt6tWFrj8ZX2nBrT3hetyi61HOpceKOprBrD9zfCjnNL3gur71PZPtzOdLECeNdnzI+XR4uunZ1h5ifVw9NdH0hJvPgRgtpKybnrWJjS/aSCle4r5X3Idbdpl518wpxgV9nfq50/VgGhbqefTN57lehIh5H+u9J3qMqZ8tM8zefC+02NYGCdP4IlrCdV3woGNHR01wA6FiiNYDrVMiMKbtnEY4G2br5Vshpm56CPa8Zg9lp0zvd3sD1yHPhETojo2NBepcv+uxkRjJDUkInzqhnskjW63b8pTFcapzp84++2x/WSjdkEjyDD3fxc9KfTJ5mLxY+Xze9y5mMpmAt1M3WpVKhfHxcf8F0Z5pebEkO7c0xtL4td/aTuvGVsRg5eQder7dw+iWUX8eNODPY3Ych+7ublpaWqaEdEP1ZZcwdMepJkSTzkTWwzYtoRIiJY1LPp/3604+12tmaoOEiHHxwo+OjnLw4MFAGL90wLrhcRwnEHKvIw8qlYovpvWcdZ0RXjwD0hgdOHDAL4vUSTlTZsbXZtD3mT7Kw9U54slkkvHxcUqlEof+7xC5xbWEauMrxtn6ya1+XU9OTgaMM06bw4avbODgnx7kwOUHWPj3C5l12ywW/fUiIgcijI6OctcH72JozpB6IOHABQe475r7CDWH2PDnGyhHyszYNIOV164kMlmdsz66eJR7/uIef55OMVHk7hfczb6l+yi0FvjFu39BubWMF6s2hJFIhEwmQ29vL3PmzOGZz3wm11xzDUuWLCEWi9UNq7NYLBbL6eE4DitWrPAFhYgK/b30RbrP1z+yHdQG0CKGtGdQxKdpQPc8j87OzrohyRD0dmvxa4oyx6nmbdHHkOVMs9msv42UKxQKcfTo0YDo1MfS4kPvN9E8wU+u+QnjLdXpU5VQhXvPv5cHL3uQRCLhjw1SqRTJZJJYLOZf8/SR6bz7h++mKdtEuBzm5etezhU7r2BG5ww6OjqIx+P+GEyHy4rX0DTqQ20coZ0NOqxeL9mq76cer8i91fdXX7P2yOp7AgSeCxnLaEeQGbWoy1nvOdNzs2VcphO06WdNxlrDw8N+NKaUW4SYTmimxau+v/UMOaaXV67h4KqD3P/C+ynFqwalfHOeDa/ZwNCsoSmOJ30/JITa9NDqc+j9zevWdWLeF33PpR71/lIW/UyZIl4w328ZK2vHmn6XdV1KOxGJRIiEIrQMtQTqI1QO0TTaVLcdaT3YyvPf83zCk8q37EHjrkZ6buzBi9XKrI0lkjNKR65KlnSZ9qmNArr9eTjsHO6nOXPnzuXqq6/2X5ZMJsPo6ChdXV3kcjkKhYIfQq1FHdSsz1B7yWWOkyT1EiGdyWQCYedQE9p6voyIUVkbUwtCCXMqFos89N6HGFk9Qt9/9dHxnQ6/YUwkEv75pXFoaWmhtbWVAwcOBOZr62vS1tlyuey/WJVKxQ/DzufzNDQ0UCgU/CXGRPyaluFoNBoIbdaWeSkf1ELltPVeytLT00OhUODQoUOBJde0ZV2H3mkjgF5CzMwYLwYA3UHp+eOSjE0MKlL3WbJs/uJm0our87EbNjYw+9WzCbth/5xyv0tzS2z92FYyczL+OXo+10P3d7oppos0NTVVrzcGD/zHA6SXnZzj7UH3jd2s+fEaNvz1Bg6cXVtibP6t81nxzRXE0jFisRi71uxi/TXryTfmiWQjnP/T8+ne2c3P3/Rz0q3V46347QpW/WwVTrpaP3Lf5J5IPTz44IMMDg4yNjbmRw1YHn88O4fbcprYscrjSyQS4R3veEdgrrBMizLXedbzoXW/pPsr6dtkG9nf9HBrb6OOntOiQ/pQU1DIeXRUnOmZNc+pB+g6Gk0G4TpM1hT7+kfCVwfmDvCdF3yHE00nCJVDXLbxMl607kW+kJDj6Dwx2oA/mhrltrm38bz7nxcob6FQIJ1O++tjT7RNECvHSEwm/Clxpqf0VAJMyq+p59nX9SRlN+tdH1uLQNNLrctherz1vdfb6PqSMpjnNu+rjsTU32tBaE47lHPVK488yzopm65L81o3XrqRe194L6VEifh4nPO+ch49d/VgYpZXP4daaJsGDn0tem63bG+GvZtGCG0YOZX3Whtc9Dtp1pH2asv1H+s5RnxXnGKmGJj6GI1G/ehUovDdr3wXL6SmfhZCnHPjOaz65aop77wYxo60HeH+V99Pem6a5P4kK963gv2v2k+IEP3f7MfNuL7I1qsyaUFtet4lYbGMzb///e/bOdyWUzNz5kye+cxnBrzXsViMadOm+YJQL+slYlfP3YlEIn4YsTSoet6SCFXzJdXWajPcplAoBLKI68RtnucxfMUw/f/aT/KCJJ0/7gQHPzGaWCHlPJVKhRMnTjAxMVG3MdKJ4cykErIOoraYSai4bC+WLnnx5Np1A6czi+tOQc//1vOv5f/9+/dPsRjqkHQ5nljXpJPRnaPcIzEkaAGuG3/t2ZfjSJK7SqVCKpWiMF5g3j/NY//79kMa5nxsDrFkzL/mXC5HpVIhMzvDwXceDIhtgMy8DPlKnng07s87H3n+CAvetYBDHzjE8fOO0/n9Tnr/vZfNz94cENsAOy/ZyYzfzqB7ezflcpn+df04jsPd19zNqp+son1/Oze++kZfbAM8+IwHKTklLvjeBYS8kJ99HmrZNSuVCqtXr6ZQKLB//36OHz/Orl27/O0sFovF8sg477zzpghjCM6d1uJYf24a2M2En3rMoAWGHrzXm7Mq/ahZJl0GXQ45lt7eFA+6DOZ11gsD1qHLruv6Bm7phxYMLuDlN72c655xHedvOZ/n3v1ccKbOF9Z1p8Vj43gjz7v/eYFIQs/zfLESCoU44Bzg18//NYl0gmf+8JnEvbh/PC2KtTNFhz2botn0TpuRYqZXXx9Xe47reYK1F9sUtGZ96+dAl0V7xc1IBi3q63nHtffVFOz6c/NZk/11Bn49fpQ6kFw/Ek0583szSY+l2flnO7n4+xfT+2AvGScTuE5dX2aWdPO6TNEo5TXfR9MYYL4b5rH1PZJj+FGNxj7mtub9kvo4vPAwt73yNmbfMpsF31ng33tz391X7MYz7KnJ8STn/vxcf562XE+lUmHP6j203t5KdGeUJW9ewuDlg7Td18aev97D4RdX8xWVQ2X6v9CPV5n6/sqzpp9DHXVhPje/Dyu4n6Z0dHSwdu1af4krLcgAfz1CyUwtL5YIPtNTrcN0RMBKAyOWqnqNlDQM4vU1X2TxgMvxBi8eZOff7qTh8gbmv20+Fa/ih5KHw2E6OjqIxWIcPHgwcD2e55FIJAIiSnc0OqS9XC6TTqf9dcihJs5EhGuPsxxHN+gyN1o3eLrTl320IJa6kfoS67eEg+tkcmJtk+3kHmhrsW6IPc+joaGB5uZmDh8+7J9Lr41eqVQCy7BMTEz4oe7Hjx+vLuNyJMzMj83EKTiEx8O4kWAmVmbA/g/vJ7MkKLa7bu5i3pfnUXbKlJ3qfT78isMMvGqA6TOnM/ufZzNt3jSa72uu1gHBRlWQZSHEGNP5604uOHYBM3fMJDMjQyQ9NbRny+VbKMfKXPh/F/rPdTaeZeuztrLyxyv9enAch7lz59LX10d3dze5XI7Nmzdz4sSJ3/M2WSwWiwVg5cqV/hxjqAlNLQJlLCFtLwSTdwF+f6fHAtpLrAf/ui/X4l0P/GHqvFI5jhk9pvtP/Z25BJLp2dbn0MJP/pbQVIn2M71+8/fM55WFV9I32Odfm74++UyLAblm7XmtN/4Kt4b58bN+zP6e/QDk4jle+pWXBryk2kOoBZkWFzL+MOtMC319frM+tXip50k1haIpegRTdOv6knOZz51gili5N/q+6fGajlSQ77QBaPNFm5m5YyZNg00B8aqn2InIlshRGXtp48zcX86l9VArnbs6yVQygbFePeOOWUf63mmhr+tMX795TaaBRNelri/9zOj7az735nOiRbs8X8P9w9zxyjuYnDbJ5pdsphwus+ibi3zHmT5+45FGHJza+NCDi667KFCH8vfe1Xu5+5q7aVnZwuJ3L6ZcLtN6Qyv737+f4y887tfJwasP4qZcLr/2ck6cOMHIyMiUOjHrTDvCRHs8EmxI+dOQVCrFFVdcwfz58/2HSYcYyxyVcrnsZ+nTHZ5ehkp7TD3P87eFaoepk2jl83nfSy6eXfGO6rnG0rGBWs/QqzB23hibPrSJcqoMHjSva2beP8zDLdY80NJ5a6+0zLXS3ulYLOZnEteCXhoIPWdIGgkptzSSet62hMsVCgUaGxsDRgaph1gsVneOlEQA6IzmpiU5Eon4XmHZTocsyfGkjBKJEAqF/HlHYumWgYysD67n4OhkLNLJym/tORALq1xjuVyGEGz+zmZys1VItgfNdzaz5MNLaIu0VdcdT0TZ87w9bH3FViqxCk7RYdp3pjH7v2YTC1XnUxcjRXa/aTdHX3i0mmmiDHO/PZeFP1xIe0O7/2xI/ch65sWGIje89wbGesbg5FgmnAvz/H96Ps1jzdX7Fy3zgw/8gHRbmrN/cjbLr1+OVwp2VIVCwV+Lfnx8nN/85jdMTk5O8Z5YziyeDSm3nCZ2rPL44TgOb3rTm+jq6vLFqu5HIOhZ054xLa4h2AdKf3oqsaYN93qQriO2ZIqY9H+ClFMLFr2Ukun504Z8EUJyPi2GZOBfKBRIJBIkEomAEd5cvlN71vR4S0e+mWJGi24xBpjXJGX6+Is+zr5p+2o3y4MFDy3gRd98UUBYmWJS6lEbCRzHCQgiGUu1tbURi8UYGRnxj6M9jrqepNza+aA94Pqe1PN0y3Wa/0sZ9X0wI/+0kNbHNUWj7K+NAIFs3Y7HjrN3cOtf3UokH+Gaj19DMpMMOHJkXe1CoTAlD4E+v/yYK+zUE6qm0UpvZ4roh/stxzM9uvo50oYHXS9mnZlC1Lwu/b/sl2/J86P3/CgQlRjKh1j+3eXM++m8wHMgHFt8jFvedQsAz/vi8+jd1uvXZaVSoeJVGFg2wC1vvIVioggVaL2rlf539FPJVij1lNh03SYqyZPPWSbE5f/vciJHI+RyOXK53BTvtTZeSFSsns/tui7f+c537DrcliDhcJgLLriAc889129URYxJiIsIU50YAmoWyGw2G3i5tUjW69fpkGw5jzSe8oJoMactjRMTEwHLXqg1xPp/X096du3FdDMuXf/VRc93qvNcpFN3XZd0Ou0LQsDvrEWU6rAw6cylM9TJ18y5Y7oedYcvol3KobcXL7V0iNlsNtCISAcu5xSPth5kSP3INUkHpxNmiPCWxlO81aVSKRCKLvPL9WBCz52TfcXrKx5lqQcxAGijgkwDKHeV2fwfm8nPyoMHretbOfe95/qGglKpxPjccTa+fyPZaVm/jiIjEeZ+ZC7t69v984Xmhdjw4Q1M9k7S/EAzSz66hMixqncgmUySSCQCnZMMAkYXjHLDB2/wU0Je9YGr6DjUQbFYZKxljFvffiujfaNVQe7BqmtXseTmJYQqId/YJGs+yj2WxHq33norIyMjjI+PT+nsLY8+VnBbThc7Vnn8WLRoES984Qtpamqa4onTXkft9dICwvTUmQN+qGXB1ttCUDhAbclR6ffEMC7tthZRIhKkH9TjFtPDaAofGft4nhdItCRli0ajvrFdrkeXTZ9LjMdmfYiRQAtzM0mXGNS1EUNvkwln+NSLP8WR1urqHD37e7jmi9dAJWgE0fcLCJRNX78+j45eNEU0TF0fu17fqQ3e9cohx9LlMdH3xvy+VCoxOjqK67o0NTVNGdNpkSzHqif4ZczkuA47Vu7g16/+tW/cT44neclnX0LyUNIX2TKmMM9lPrtyTn0P9TXo90LfW7P+9f/62Lrscg/0d+Z91efX9arLU+9dMA1U5rb6s2g0yuiyUX75il8y0TSBU3ZYdvsyzv3quf7a2DpSQDhy9hHCqTBzH5gbOK/neeQSOX76Tz9ltHvU395Nu3T/Zzcd13ZUx839Zbb92zZcx+Xsd51NYiDhG0Z0ngkttF3XpTitSLQcJZ6J+/O65ed73/ueFdyWIOeddx4XXHCBbx2DWsdTLpfJZrN+YikRxiKutPgQi6e5jIL8bwpFLdhEsIuIkUZaHnRtPQZ8b226J81Db3uI9FlpnJLDrP+aRftX2/350aY1TYe869AWeYlyuZzvEZYf13X95c7EGi6Nh+6sxdOtLfhQsyxLpyPnFWODiGBpSETga++7GCpEGMtxpAy68zWtpLJdsVj057XrwY5phZf95JolaZocWxsscrmcn0hNGnWpB7kmAM6GAx88QHl7mQUfWYBbcAPJL8LhMLtfspttr9kGQGwsxpIvLqHp+ibfIlyeX2bnP+5kbMWYX862O9qY/2/ziR6L+qF5IroTiQQAA8sHuPMNd5Jvyvv7LfvFMs767llEQ1HWv3w9m5+9OfBO9Nzbw5Vfv5JwNhyYWiGdlzzX8pyOj4+zfft2CoUCO3futML7DGIFt+V0sWOVx4+rrrqKNWvW+P2Nnv8LwTW3zXBl6ZfMhGeCDhHX2+v+rJ4XvZ4gkf5Yb6OFtYmU2fQ46qVGZcAu/WY0GvXFgllOKb+e/iVRhWZkHdTyo+iM5XKdgoSqa8O8nF/q/ljTMb5y6Vdwx1xe+L0XEs/Wct/I8UyBq++HxhQ6eh/526xLLWD1toI8F6bg08YX81hisND76e90ucz/TYOQFtv6OvU5fc+74/Hrl/+abedv878PZ8Os/NpK5tw0J1AW/byaHnN93HqitF496e/0vdFGmlPtp98ts3z6e/MzXW/1tjefbV2vAUOFUwtJj0ajNDU1cWjlIX76nJ+yeMti1ly3hhMnTpBOp6dEasjzLON1XXatWUa7Rrn11bdyfO5xnKJD7+d7af9Gu//Oh8NhMmdliIajtGxv8d8//X5L+aXM+Y48O9+6szpe/fISEqWEL7pd1+W6666zSdMsNZYuXcratWsDSbQg+BIlk8m6VmIRQvJw60ZcOh45nhbb8rCKiBGvqfnyyfm1x9bsSMtumfb723ETLtN/Op2OH3ZQpBh44XTGTfHaSyOrjQHy0moPrbzcOtxbX5PudCVbuTYq6EGF1Ik0AtqoEIvF6Onp4ciRI/7SY7qcIryl7iuVCi0tLVXv8Hh12RDTmqivUa7f7LikjBJ+LRnlxWAh90AEpvZCVCoVcrlc4HmQMgoysAhvCTPzAzNxDjp4WQ/PrS2lUigUyM/JM3DpgL9f/HicvnV9eM2enzl8cMVgQGxDNTS8kq8EPPfj4+N4XnV+fmNjI/su3RcQ2wCbnruJ+d+ez9GZRzmwLJiIrf++flZ8ZQX5E3lKoZJ/bLlu/VzL501NTaxYsYJYLEZvby9DQ0Ns27bNZje3WCxPa7RnSwzUegALtZBiUwSbA119PO01le/1IN/sC83vBR1erI8rfZ3pmdMiXLbTYwKZVib9riwxKSLVDM2WsYqehidjCLkO3VfL9UajUf+6dWSgrgvttNCCRMYzruvSNtLGy379MnLHcsSzcf+cgh5L1Su/FoSmF9Y0gMj4Qt97uaaGhgai0ShjY2NTRLnUtXk+PR7UAvNU0YgyTtWRFHpsaXp45XymONYeTimfRIOu+N8V5IZz7Hv+PvDg3C+cS++6Xipe0DCkBb9+zuV/nZPHTNZmGhl0Pchn5rOjMT3Xugz1BLVpIKj3uXkPBF0u/V7rZ12XW8afKw6soOGGBjp3djLqjQaMYGYkh9xzHdov74Q8A42HGln936u54/V30PndTlp+0ELZqZ27UqnQ8FBD9fmIVtj9+t3M/eJc/zjyLPj3PFlh27u3MXrOaPX/xgqrPr2K08UK7qcJvb29nHfeeSSTSV8wmUtwhEIhf56weHsdxwnMuc7lcv7LKqLV8zx/frZ0Ph0dHZTLZY4dO+a/hDpsXOYtQ81yLY0b1DzFUO1sMqkMD3zqASrxCn2f7aPxZ43g1pb2EtEo4rZcLvvXkEgkAi+7XId4RbWXWDonWQ5NryMq64rLfGqpk3g8HgirlxByEdOmeHacahIyHR2g60I6VW0JlVAXLe6lcc7n8/68cTmmziQPBNb1lvrWFkIpp+605JmQwUEikfAbIblG3Tj5idOAyr0nIyNcz/cOF4tFMuEM2z+5nUJPLXndRO8E2162jQXfWVAV+zOL7HjNjuADXIHo5iiRkQhOotboSlRGsVjkxIkT9P5rL0c/c5RsT7Ya5lWGsz90NkecI9z/D/eTa62J4s7tnZz9P2eTyqRw4y4FCpS8Ei61pDtSdj14CIWq2fwLhQK9vb10d3fT399POp3m17/+dd2BnsVisTyV6e7upq+vb4ro1YN3ncNE+k+ZIqWndWmxB1Pnnurj6nGD7GfmG9HCU4tE7UGVcmlvqXYaSD+uDfZybL1SiG77taCSMUkmk/H7cS2u9LXKZzq8XI5hCrZ6YdQ6VN1cC731aCujo6OUK+VAfQo6+kDGEXI/TSdIvQgCfRx9H7UHslKpkMlkAs+JHNPsP02xJdethbo5ftTiVY6n763/DIVORgucPJTUt9xjKXu5XCaXy/lRCP5a3BWPld9ZiZfy6Luvj+4N3XhuMLeAaSySz7XxwIz0MJ1Rui5krCX1pevo4QSydp6Yx9blMd8RU1BPqUNlkNKfaQOHvk797moj2Zx9c5jITfiRIjpaRYdu62dAX7OMWeV+tRxqYe3H1pI7kCPtpANGF9m/RInNH93M6HmjlJ0yff/RN+W+eJ7Hps9sYmLZhF8PRy88ykY2subf10y5Rw+HDSl/GtDZ2cmzn/1s2tra/A5POg5p9PTnIhZFlMl8bF9MVWpLRonI1VZE/Z1uwES86zkt2vqlBbsOI8nNyLHhSxsoNZ/sRCuw4E0LaLq3KWCBlRdSI6JXOjeoNaqSOE13RlBbTkJbi6WssgyaNDx63WrpFLVXWxp8WXZLRLsWxLr+ZT1suR49J0wamHA47Ceu040HTVB0i4RP1Kx/ZrRCOByms7OT48eP+x2H67q+gNQNb7lc9i3ruo7l+qRepJ6hFt0gDamEuYlwLZfLTC6eZOdndlLqKEEFOm/rZPW/roZydf9kMsnBsw+y/g3rKTYVcYoOM34yg9n/PptIKBJoxMVYIJETpVKJHDk2fWMT6d40Xf/bxaxrZ/HgFx4ksyCYOT2cCbPy2ytZctsShucO88v3/pJ5d83j/OvOZzQ2SudoZ6DudSci0RpCMpn0jR8DAwNs2bKF8fFx0uk0lj8cz4aUW04TO1Z5fDjnnHN4yUteEvDKyUBcouq0gJH+VLbTA2K9v+QfkX5Fz2OWbSAoFAF/2psWPUBAfGtvtS6bnEdvI+eIxWJ+f67PJ2UWMS1llvLKuEP6SrNMcjxtWBfHhhxPzqETpEn5tMdPyqoNxLLP5OSkv0zqw3lFZWxyqnrX16/rWsZNerlVbRzR16ojBmSMZho9pA/Wz4QWmOaUBH0/Be1UEYpOkQcveZB8U57zbzyfSDES2Fdfx+TkJGNjYwFDTkBQuoCHv7SU3EMZz4TDYTKZjD/G0h58vTKMfo7lHDJelOuT58aMBtUOD6kPMWLFYjEikWpSMG3o0PWoxag+t/ZUyz0zveV6H0HKVu97ue7GxkZaWlqIx+MUi0WGhoYYHBxkfHx8yjgyGo0Gpn+a6GezXC4zHh8nPZQmfTTtO2UCho4Wj4MfOMjYFWN+Pp+eb/TQ85Ue3Fww6VuxschDX3mIXFfVYdO8t5lnvOMZhKhpjmuvvdaGlD/dSSaTrFmzhs7OzkC4rFiRtMdTC2cRMtJwiGCSl08sSdFolEymtlagbtTkxRCRLeeVl0UaCRFv0hlJmeR4xy85TjmhGksXRl44QurulC+Y9dxoHSImidykw5LGW8/BlsZEN6baWmZ2IHrOuGyjOwTdCJph2dKQ6nrQVkLHcfyM5mbYmZRByqcbGC/hcfgNhyl2Fen7VB/eYC2kSwYNco6BgQG/rCLupaOTDlAbSBzHIZFI+HPetTdAPPx6brnuLORY2srbsLWBWR+YxYH3HKDpgSbmfnguI84IsViMRCJBOp0mtitG094mhs8axs24NN3cRNgN+14RqGVGFe+IDCpSTorum7vZ+YqdHHntEcKEWfK2Jez88E7GzqmGqbs5lzlfm0P39d0cuuAQt735NryQx86LdhIqhth/9n7O/8b5zLxnpv/sSAhhIpEgm836AwwdBhYKhZg5cyazZs1iZGSEzZs3k06nOXLkiPV6WyyWpyzS32lDvI5E0gZLQScw1cfRwlsbvrXo0IZ8QfpYfR4tKqU/1WG2MtYRZ4EcF2rjGdlOR6rpnDC63HrOs0yj0mMsbXiQcmpRqscTug+V42phqQWmvj5T3Or619GFD+cZleNoo4UWLFI/WuToY4oANJdM0mMEM4RXGxikXPpazP3lPpiGAPlO/jdFuuM6bLp8E3e+6E4AwqEwa361hkqu6nnP5/P+NDgtrOsJVM/z/FVOTGEv22vHiS6TKUjNcuu61Pe3nmDWx6r3TmknklyPaZTQxitd3/r6ZZxerwymYNdRnNprL2MpGTsCvoFK3iER2JVKhdHuUUYXjTJ7w2xiXmxKXgEpu9TNZMskd//F3YQOhuj+XDdOLji9ACC9IE1ufs5PeIcDA68YwPEcZv7vTCjWrj2eiXPWO89i67u3Es1GWf2vq3G9auK808F6uJ/COI7DM57xDJYvX+57GsXjZops8dhpoaVfIOkkdIZrsUSK8NKJSMTiKS+mIB5eLeZEOMmzKB5usWxXKhUGrhpg51t2ggPTvj+NmZ+biZMOJrqQzlBbnfVcKhH82msr34dCITo7Ozl06FDAWy5iThoDSfIwOTkJ1OYQ6bo0LZT6HdPfawOBbCv3Qjqeep5jc0k1x3HY/c7dDL94GICm25uY8645OJO1jsYsj5xTkr7UCwnSmdXlGHJvTAOCfCeGDDmP2XnqAdnYqjGatzYTyVcHUpKUze122fb+bQwtH/LrLXo4St8n+mi/p91vsCXsX84nDfTe1+1l95/urjWkHvRe18u0H01j+9u2M7lmkvn/Mp+eX/UwdskYW/9uK7mOqfOvYxMx+u7tw8Nj+Q+W0zDeQDweJx6Pk81m/eyzg4ODgeU5pM4l2/rk5CSHDx/m6NGj7Nmz55G9vBbAergtp48dqzz2pFIpXvKSl7B06dKAmNSCKSBQvGDY6amQPhJOvU4zBI27OrJMvtMOgXriXjiVYJHosHoh02ZknTgYJOJJn1MLV31uPf6RY9XzuJuRZdowoeu1Xp8r+6fT6cDyrXJ/tNdV9tXbSFl0328Ktnp6QtelvpeyjxzfDF3WEQz1DAL6udLf3X/R/Sy6dxHRTDRwLHk2XNflrivv4q4r78JzT5bXg2U3LeOsr5wVGAvpa9fGHtNIYJZDyl1PrJrPm75n+kejPfb63TpVvZjPhsmp7pV5v8xj19unXjnMz/UxxZmVSqVoamoilUr547mxsbHqdIeTY+1YLMZgYpDr/+p6hhcOM+vOWTzza88kHokHzqHHnIVYgd/+zW8ZWFHNE9T54076Pto3xbjkui65NTm2v387hc6CuiDo/m43cz43J2AAC4VC5BflSZVSNJ9o9u+JHPfb3/727x2ruL9vA8uTlwsvvJBFixYFRKi2Ioqwk5DreLz2EGvhNDk5yeDg4BRRLZZP+RGRLJ2iFtJiyZQf2UZEPtQaYzm3Dnef8bMZzP3UXDp/2knfF/uIl+KBUBvxpsdiMf8Y8p1OiDJ37ly6urqmvEiO4zA8POzXh+5AZTmRRCLhh+HL9UnD3NTURDKZ9K9L5lzrjkl+REjrAYJYHnX5gUBGRh2yJZbBSCTC3vfvZfiqYb+84xeNs/vzu2tzjiMh9nxmD547NQmK3G/TuqmfGdPoItck85wgaNWWzl0EsJ7rJZ1TuVym+Z5m3EzVQy116nkezqhD47pGkLbdg+jBKImtCf8chUKBZDJZFf+OF1jS7cjaI8EXwYFjlx4jeiRKak8KgGNXHqNUKRF9IErsSKx2LkW+Mc/Oy3ey6/Jd/Padv6XgFMjn84yPj5Mr5Ehn0mQyGf/+hEIhEomEL8qlvnp7e1m0aBFr167l6quvZuXKlY/o/bVYLJYnA6lUisWLFwcG5dLmay+kFinSx+rxg/Q7ekqZbGsKBz22EOOu9uJpkaMFgynutJDXQlD6OT1Wkr49n88H5vRKOXREoEydk/7OTOAqTg0t3kzvuHwv4y5Z5UOiubTHXde79szq/l6XSXumdd8vaJFdLBY5duwY6XQ6MO6S7QQ9ptD3UYeWS3l1fegxiNShXIs5XtHe8YAzw6vwwJoHuON5d/CDN/wAz/H8yDct6iYmJui8pRO3pOSPB923dAcMQKaINIVlvXLJs6LnG+vnRhxQOtrB9CrXE4amAJa6rTevWR9Tj7f082mKfD3mO5XYNu+TrntdHjmG1Id2hMk7X6lUqFDhO3/+HZxQLWy8oaGBzs5OZsyYwYwZM2jqauKXf/9LhhdWx7f71+7nN2/4DYVSbfqjGXlx/Vuu98U2wPEXHmff+/b57yrUxtVtD7Vx7rvOxS2oZ8GBIy89wp4375ly7Ka9TSQGE/yhWA/3UxDXdVm2bBlXXHGFL9I6OjoAOHr0qP/gS4Mv1k4Rebqxy2Qyvldbh0IBAS+4dK7a0qQ7AGlIJcGZ6fmWc+pQa2kcYrEY43PG2fF3O5j9D7OJjkb9bKDyElUqtSRgruv6c5x1aLkcU2fa1iJQl0V7yYvFIslkMpAsTToibcSoVKpLeaVSqepc5clJ3xutX1wdgiznl+10XUojJ52wng+mw7fy5NnyrS3k5lS9tJHhCEv+cgmhYyGKySL7P7qf8YvHiW+NM/sNs0mkqw2GbrAmJydpaGjwQ9kluiCXy/niXjz+Wozr8Ckx1MhzoJdWMztePa1AW6DlPITg4OsOcujPDtG4q5Fz33YuTtHx17UUo1GxrchD//EQK/9xJa2Z1mooWCTPvV+9l9x05bX2wCk4eGEPQtX/2+9rZ8VHV1CZrLDhcxuYXDBJKB3CC3lUEsbSMB40H23mef/8PLySx2/e+hvWfHENDYca/PnviUQiMPgTY4M8ezqDfKlUYvPmzezYsYN8Ph+YD26p4VkPt+U0sWOVx56ZM2fy1re+1R/M6xBuU7CYg30IJheDoJjW/Ywk9xRBJ+MSLd4hKBS0oNW5Y2QcJMc2xZY2AOjxjY5iEsOyFpF6DrZsr6dimSJHjyHkmrXBXcqlp5NJ2URAm1EApnFDjpvNZv17ZkYDmNubnkp97+SeSb8t4yxBjxPluLK9NuLr65Dt6j0zut4EGU9UKhXKXpltK7dx0ytvwgt51fm4B3t42ddfRnismvNmfHzc72cdx2Fi+gQ3fvBGPNfjss9cRvvOdryKESp+ClGnjTZm+bTRRP/o+yTby2899tHjUV0PptddO4W0jjMNIXIMbeDQ59COEx19oa9F3kHTWKWPL99po5uus8B9a61w/Zuv59isYyzev5hX/fpVJPKJKQazj7/64wy3DNeiFQG36LLiNytY+7O1AeOLvGsTTRP89P0/JddSHf+5GZfFf7qY2JGYf03iHBQnXa4vx+2fvJ1CcwE8aNzWyIo3rSBUrq1OJBpJjAhi/JJn9pvf/ObvHatYwf0Uw3EcZs+ezTOe8QxaWloCD6QWKo5TDeEtl8vE4/FAo6LnPetOzbQkyzrausHRczvkBZWGRn4E3RGJCNNWS2mkh5cOs+VTW6ikKrTc0sLcz8wlOZr0BaB0JlpI6/oQQavFom48dJi0zNXV6Izcruv6Yl/Pg5drk+QOZlibDkvX+0v9SePouq6/frZch5xXi3SdIMV1XfLJPNs+tY1iQ5HFH1lMcleSbEOWvW/ey4nnnfCvpfGuRuZ8Yg7xwbg/QJB60r+1kUI+04MG2cbs4HWHIfWonyNtRZVzy72QwZS2Ch94wwHmfWseoVItUYcM6jKzMux47w7Si9MkDiZY+IGFpHakqnkDuoo88P4HmFw0CR50resi25JldPGoX/7oSJR5n51Hxy0dHL30KDs+tIOe7/QQHYqy/7X7p4jucDbM/JvnM947zsDyAWKTMa747BW0b2v374kO+xekTsQQZA5a9u/fz549exgeHvanKliqWMFtOV3sWOWxxXEcXvCCF3DxxRcDtXm1WhhL/6ANxaaAMo3X0q+4rsvk5CTxeDwg5B4uPNkU9np8Y36vjet6/CBRS7Kt9lCaIbIypqrnXZT8NVqIyJxxU5QKej1tcz9dTvGy6230fdH9jE48p48n4zDdL2vxXa+fh+p4IJvN0tDQ4NeTPq82mOhymOXTz4l+HvT1FOIFRqaNMOPADP+zwBSCVJkfvOoHHJp7yP8+OhHl3OvOZd5t83zngbmc1PCCYQoNBbrv6w5426V8+tk0w8jN59fcT1+r/F/PmKBFYL0QeTl2PYNNPaOE/JaxltTVlIiASmXKdqYxTI+fI5FIYGUa8x6ZHmxzLC/bZzuzrHvVOg4tr92rc7afw9W3XE0qnQoc87tXfpf1y9cHBHdyOMlln7uMnsM9gYTI8lMsFjk6/Sh3vulOJmdVx1Px/XHmvWceqV0p38AgyQ/lnTq24Bgb/99GokejLHvXMtx80OuvBbeOapTr+ta3vmWTpj3daGlpYe3atTQ2NtbtuLS1TYsE6ZBMz688xJLNGoLza+T/eh2ZNB66w5FGRTzsutPS4eby2dCaIXa+ZSeVVPV8o5eNssfdw8KPL6Q0VvNES0ch3mfZH2pZ1XVDaFpSdZi0Lqtcn3SQ0mnpNb3lZRSPrtSV69bmvEtDJS+sPp/UkZRFL7EGNcGmrY56kFAqlYhMRlj6qaWU28vMSs9i0B2k6BahOfh8eCkPktVj5lvyTKyeoP2Gdr9zlA5SWzy1x12+0/dQDDYyGNEJXrQxRBphvZyKII2azt4eCoWY+YWZlCNlKk7FbxhDoRC53hx73rmH9OJqToJsb5Yd79jBvE/MI7EtQWwgxoKPL2DnO3bS8lALZ113FoWZBTa8YQPDy4YJZ8Kc/T9n03ZPG/teso9dr98FDoydPcaCf15AOB0mMzvDwMtPhiZVYM51czix8gRHlx8FIN+Q5/a/vp01X1nDtAen+denB4W6w9MGIS26+/r66OvrY2BggMnJSXbt2sXQUG3+usVisTxRcV2XCy+80O//zUG/bCPTx0xvqhnRJW1nNpv1p+YkEgm/T9SJM2Gq8NYeVUGLEtMYoPshOY7eRk8R02JJxi8yTtKCQ8pVLwpMxgXicZR60v2m1J92XJhjHS2g9XhLyqJzvZj9kRxff2dGF+i+Ss6jhWE0GvUFjz6mbK/vs1y7bKuFmek11hGBjuOAC7+7+nccnnWYK797JTP3z/TL4j8/Ew7P+Ooz+M2f/IaBFQO4RZfVX1/N7HWz8Qjmm9Fe57btbdV75QXvkfn8akwjjn5uTcOHKUj19+a42fRqm8Jdl+VUYlu/e/o7fV/l3Hqaht5OC3rZT5LFmuPTeuc2j6OPHwqFcFJOMAkykIlkyHpZ4uV4oIxX/eoqQpkQd51/l79tOVQmE88EnIJTypCHSKbm9MjNyrHnvXuY94l5NO1p8sWyzsvQ/FAzCz+zkPDuMF7Go+LU2g8x1OhIhLrn/T3YOdxPISKRCM9//vPp6uoKCGioWZ21tUYsNbqB01ZdybA5OjrqN2x63pFu9BzH8YW1iCZdLkFCRnSDrC1FUGss8vk8qb0poqPRwHze5s3NOLna8hhyLbFYbIo41cJazl+pVPxGQ8SxvOTiodahZjoUR9en2XiK91uH4Ui4sOM4/ndybm091NkadWOqralSdxKZoAWc67rED8dJPZRibGwMx3FIjiTp/3Q/jRur86ETBxIs/NhCUgdSOBGHXf+6iwNvPcDwJcMBa7+E2+iM8bpDh1oYkohguW8yGNCDDd1RS93J86WnL4hlXRBDh9S5XpqO4xDaFgo8F7FDMbyDnl9XiT0JVvzLCgozCgyNDpHflOesfz+L5v3NrPn4GmbfN5vhFw2z67W7qMSr93dy4STbPrCNjvUdzPq/Wcz4VtWivugTi+j9US/RB9SzCExMm+Cu19zF6NxR/33ad/E+ji04xvj4eOA9lHn7ut7kGsPhMD09PSxbtoxLL72U5z73uUybNu2U77rFYrE8UdD9rhk+LP2AbON5U5NpjoyMTEnQqcWc9EkyBtGGTe3tM4WAGL7le92v6z5VG9h1/yuZqrWwMj2F+prNSD4diirTzSSCTY9PtJfR7G9F4EofKcu2ajGsI9Vkez1fW/ohXTdaLOlpT/KdnFuPG8XwINuYXmEZ80j/X0+QmeNQN+QGhI/ez/M8rv+L69m8ejMnZpzghmtuYLx7HKiuUjI2NsbQ0BBDQ0N4+z0u+NoFdO7s5JLPXcKcu+f4x9DjTT2O0stF6fGPKZzN+tLb6+dAo/+XujJ/zOfX/JH3QJKymvh1qAxHD/fsSz2bRpJ6167HaePj4wwODjIxMeE7j8zpo/r518+a+Qx0Dnfygu+/gI7BDvBgzuE5/Olv/5T2dPsUYwYluPSmSznv9vOq0wJLDme//2ya1zX776bpcS+Xy7jHXRL7EoHxYeJggvhQLUpGPwsSvdK0sYnocDRgONJRGvXqTr+7vw8bUv4UIRwO84IXvID+/n7/4ZNGXwb49V4QeahSqRSxWIzh4eHAPGeohR2Nj4/7oUcy11o3ZvLQakuvDhmfPn06iUSCAwcO+CFi+mGX/WSt6M7OTjZu3AhJuOdL95DrytH99W5mfmUmIa/m5ZX9k8lkQOhr76L2vOqGQTd0IoL08lbSAEmnLNcj89ClrsS7ruefS+eu95dyyQusvbYS8q47Wm3l1McTQ4X8r5ce0SFTjuNQjBTZ8u9bOOsdZ+FmXCpNFe7/t/tJz0uDA07eYdE/LiJ+RxyvclKwtpSJZWIU8zUr/vj4OKlUKuChlg46HA6TzWYD4XVSD1JGsQzK8ymDgWQy6dejHFfuYywW858fwJ9HH41GcRMu29+xneOXH6ftvjYWvXMRTr42UHIbXA7+w0GOPPcIsb0xFrx6AfFcnFhbjNZYK17FI9ma5M433smhNYfAhVAuxOJPLWb6HdM5tvgYm/55E17M45y3nUNqQwov5HHgjQc4+OKDeGEPt+Cy4JsLWHL9EiLhCIdXHeaO/3cHTsXhuR95LtOOTPMz42sji9SDPBv6fusBQCaTYcOGDQwMDPgGracTng0pt5wmdqzy2HL++efz0pe+1M9loccfWjzotktHmcmPDrPWolh7W7WDAGoDbPG46oE/1OZAS/8i2+oxip7qBLX+X84jbbXpvNB9nmyvr6leJJeesmZ64OV6tWiW72SMocdwpoFaxg5msla9rrUeC2kvqD6njsCS66kXcVfPg6rvu+mk0IJQ+sJcMsd33/hd/uQ//4R4Ju5vJ+W95Xm3sOGiDVTCtbpqHGrkRe9+EWRr0QrpdBrP80ilUlRiFZy8A15wbq8YPhzH8Zf98jzPX6PaFHumx1nff/ncjP7T91HqQupADDy63kxDlT6OKeZMZ8Spnp1TfWYaAATzc9OYoMujtzXfb33v6p1f/o/H4zQ1NRFrj/H1a77OG374BsKF8JRrknc7l8sxeGKQ3179Wxq/0UjT3ibi8TipVIpkMuk7LKRM6XSa8fFxJgoTbH3HVoYuH6L17lYWv3cxkUotAbGeh12pVMhms+RyOX+cpetBws+1p1sbowC+9rWv2TncTwei0SirVq1izZo1gZdTGvd6ScoqlUogNOtUlmhpvOUz8QjrbV3X9a1Nso0510mEdb2wGW3tkwZMh2GdmH+C2KEYA38+QM/newKCTR56Eey689CNpxaukuFaJ/WSfUKhkJ9wTTprXT8iAPV8eLnGjo4OhoaGyOVy/jWKZV7C0nT96sZRBgDSQcp9k85Bd6aO4/hJzXRIuxbd0uHrdUCTySSFQoHD1xxm/6uDc5QbH2yk/2/6iVQipGem2fPPe+j5rx6ab2kOlFEPcMQAIOWWhHg6FE3fa72/XGcikfDLrg0J8hzL86cTiujBGcCOf9zBef99HuVSmdHR0ep+iRIDrx9g4GW1bJUN9zXQ974+5jCHUqlUjaBIVecMPfjeBxlcPshZ3zyLOTfNYeicIW5+x821uUNlWPEPK+h4sIOJ3gk2f2Qzuek5Zn17Fn1f7SO9NA1z4N633+vvEyqEeP6/PZ/OnZ2USiUOzjpI74FeIuGaoWrevHkcOnTINygUCgX/2vQgLpvNct9993HixAmGhoZ8Q8dTHSu4LaeLHas8tvzTP/0TnZ2dwFQvnxbfYlSUtk0LDy1U5ThanEr/BzWxLn/LsWQfHYKtBYwWPXIOXQ7d5op3XY8b5JiS88X0VmqDus5fIvVgOgDke31s0zOo99Gfm0JOjqPnGet7oMthjrukL5c+WfpbXef6nDB1uVX5DPD7ViBgfNfPg+M4nOg4wS9f8UuO9x6n40gHV33jKjpHOv17ImOmX734V+y4ZAc40DzQzBX/cQWNRxunCH4TbZgQg4uOkpAxh0Q0ms+ulFPfA31P5Rz6GawX/WCWSX9+qroxz63RY3w5v65/s060EadeHen3Qx9XPytyfXL+ekJej7friXYZLyeTSZqbm2loaAhEMOpnTZ7lYrFILpfj+PHjHD16lNHRUX+VnmQySTKZ9FcPkvqfnJxkcnKSbDZLuVxm9zt3M/9T8wNRvfJbBLc8s6JRvJMORBmXpc9N07Wzy39W5H3RxqRvfOMbdg73U51QKMSSJUtYunRp4MWHmiVWPGbywOks0fJg6zAh7VHU1k6zc5Pz6GQK4rHVYlWHbWmRrzswbVGUB7pUKnHinBNsf/t2pv96OrO/NJtcOeeLEnOesVyfvAjaU6g7+lAoFLBiiUCUc4tQ1clDZEAgHYlco66zwcFBvwxiWdUDAAkdl7ArOa9YznQnajbu0sDpc0qnL9uHQtUlMLLZbOA76fTE09pzbQ9ezmPfW/aBCy13tND/yX4iToRsf5YD7zxAbn6OPR/dQ+/Heum8vjPwzMmARK9prkPcdeNuDnbkeZFrKJVKgbXAzfnfEtouFs2JiYlAJ++6Lgv+ZQHDxeFgeFfSITsjq4tNsbFIviXPwOYB37qZy+WIRqMs+9Qy2i5pY9Ydsyh7ZYa6jDnUDpQWl5icnGTr27aS6c0QSodoH2mHZ8Cm120i1xVcy9tzPI60HaGl2MK+c/ex/lXrWfGTFSz/3XL/md23b59/LWNjYyQSiSlGBnkWVq5cSSQSYdeuXZw4cYL9+/eTTqexWCyWxwtp68WArccJeqxgikBzHAHB6C9zmpnuF/VngvS5OnOwKZhNYa2XNpV+WrxZOjRcrtP0YmvDvI6Y09drGgLkM10PWsBrg7bObG2KILkuqTct5KU80vfr7bSgkuvS4ztt2JbzaDGt75E2pOtz6+Pq+yXbjc8Y5zcv/w3He48DMNQ1xI0vv5ErrruCxkON/lQ8z/NY/X+rcXMuRxcf5YL/u4DGo43+cU2BK9eo76s2aEj5tPPJNLzo+6OfHb2v3scUyOac/Xri1PzOPI6mnuPIvF7TC24+X1JXZrSBeY5TCel6xzzVOc26lu/1+yJrbGsjgTYaaWOQLMGn547L2FreD3FQSX3oezzvn+fhhmoOLV1m/azG43E/ElHnZDj8zMPsfONOvK969N/WH5gSYR7n92EF95Oc7u5uLr300kDIuAgUeWBE2Ih11/M8vzGTF0C81LrRFW+v9kzq+dkiaicnJ/0XtVgsMm3aNEZGRvwlnKQcOqO3Fr9iBBCBK4IjvTzNtrdvI9+d58BfHqAYLtLzHz1AzXssjb/OEC0vozQK0nFpj7i21kmYiM6mrq3jsr9YQ835K9JZagu3JEURUSrby7XJ97qjFIEJQaOD9mLrxkxnjJR58Locsq14UXVW9Zk/mYmbdRl61hC9H+8lPBym0FJg54d2klmQAcBLeAz8wwC40P7zdiqxCoffdJiez/T416YbTDm3NJTasCPWcymrvm9SD4Kub7l/5XJ1SRPdUWqru5xTMspHT0SZ8+k5lENlxi4YIzIcYenHlxLfG6cYKfrRBtFotDrn7HiZ1p+0MhoeJR6Ps/BXCwkXw9z3uvsAqonRHpjGze+6mcm51cyX5VSZLa/fQrQQJdcaFNsAKz6zgpn3zOTIFUdYf816ck05Nr5sI5VIhcW/XOzfL6nHRKK6XJteCkaeDZku4XkeS5YsIZ/P09/fz+TkJLfffvvTxuNtsVieOCxdupRUKjVFyNXzqOo2XvcV5lQpLVi1SKg3yAf8iDLxWkn/LsfWv2GqJ07aWAlZT6VSU5ZQejjvpSlAtcNDthHhK2MMLdLlPFpoyP/1BGA9I4JeSkmP66SOtfARo8GpQr3lMx0yLGXTTg75rcsox04kEtx78b3MfXAujUONgSmKjuNAGZrHmxmgFoHmHnVJH0xTGg06OhzP4azvnsW8afNoO9wWqCPtTdXPnVyPdiZpISY/eilZ836Y9zhg0FfPhF9+tzYHX9eL+b2+F3Is/VPvXuj99LuknwHtGDKFrqCfU/N8Zrl0ObTDRJep3vOux4D6PNrDrOtbv48654B8JuH/Om+BRLRKuQbnD1LuLNNzV48/1pfjyLOnhbKuF61FdHRmOBxm4MoBdr5uJ8WmIptes4lIPML8W+dPqQ8ruJ8GNDU18aIXvYhUKgVUX+iJiYlAg25mlNYNqbm8l/yvPaZ6qS55ybXnGGpLXEhncuzYMf+BjMVi/nnFuy3H0i+YFvqlUolQT4gtH95CobO6bqIX9jj6J0dxj7vM+PaMKZ2gfkHlOnQ2wXA47BsG9FwuvYamzh4unaMZOpXL5QIhJcVikXw+73uvzQGGWMol3Fp3qvF43DdSOE41FD+fz/vWNhFRun50Fm/JEipJ1ETUyhJo0vDt2bPHfxb8SIaKR+9Nvcy4fQaV0QqhcIjKWIXe7/Sy4x078KIeVKB5WzPdd3VTbCyy5X+3kJuVw624dH2+i5gT9ASYDb/ppdcds35GtYUd8NcK1WH1YlzQ90d773VHKuGAoaMhFn5gIVv/dSvLPrwMBqBYKvpRBUAgMkPehVwuRzqdZvpPp7OMZcSIMed3c2huambpb5dyT9891TllFSiEC2RSmSnv5qJPLKLtpjaO9h9l419tJN9UfcdK8RL/n733jo/rrNLHn3vv9Bl1yZIsyXLvvSUuiROnOj0hBELvdalLXUogsPQlwC516QQSSCC9J6THcey4F7nbsmyrl9H0cu/vj/HzzrmvxiF8F3YJvzmfjz6SZm556znnOe3ddsU21MRqMGnjpMIcewF4AA+KOe4cF65v6eXgvmxqakIul0NTUxMGBwexefNmDA8Pl8F3mcpUpv8VWrRoEUKhkEsB1w3aOiCTYIRyVUbyyO8kSJJyWYIPVjCnzqIXCpNtkBFhlJcSZMlilvyebWebJBA2TVM5FaQBge+S90pAx6g+KTdlJKLUZ6SDQ/4Gip5nqfxLIEJdgXqc7qGXXnTOnW4skTob4M5L1g3ghmHANmzsWr4Lz6x7BhvWbsDbv/V2BFIB1S7btuEZ8mD5z5cj+s4ouuZ2Yfye8Vj9i9XwpDzIG3l1HZ9vpSw09jWiZlxNIT93dNTVX90zyzniM3SgJY0O0iHF+yUIl8+m/iXHTBqPOJdy/uS+kLqKNNDI9aT3i785h1LfknVxSoHsUv2Wa1N/r9xXOig/HVCXv/U1yPXCvSm/k3tS/riMEaaBrD875nO5X0ZaRrDxYxvhWA7O7D0T4Q1hV1qJ/KHuTiebXrxR4pqB+QPoeGcHshUFfSobyWLr67Yi0h9B8+5m17i8XCrncL9CKRwO46qrrkJ9fT1GR0cRiUQUoNUBsgTSVMYpKAh8uJkBuBYrUKwgLT3gFEz0OsriJEBxQ/BvPlfmccvQKXrYmbNRW1uL/un92P253Ug3pmHkDDTe3oiWb7WUDF2Xwlh6jSVTku/lNdKbSCas/06n0wqQsVgcn2uapgLhBIASIEugy/dmMhkX2OQPGRMNIAo4Wu4CcclZSYT3hxHwFUJgEomEywAhQ+u4JmgUILPMZDLw+/2qj7JQXO/VvTj07kOo2lmFGR+dgdz4HDpu6MDo3NFCfrIDtPysBeN/Mx5OwkEikUB2Yhb+QT+86WIIuGEYSCaTqq+0bJaq+sq22LaNcDismKkE8FJ4SCEjlSX29cwzz8Tzzz+vDB0kqQzqgjWRSKi54PMymQyCwSBqamoQCATg9XrRcVEHNl+1Gc17mjHvoXl4/F2PI1HnBt0TfjQBLb9vQVWwCj2rerDrX3YhU5WBlbIw+47ZmHP3HEQiEeQ8OWw+fzOydVks/9NymDHTFTmgC6ZwOKyiU1KplMtCT4PQ008/jUQigWg06rLAvxLJKedwl+mvpLKu8r9Hb3jDG7B48eIxwFiePgHABex0z6sEHiTyMwlA5XUSVOkgVf5IHUbqPzR0U2ZSxtbW1sIwDMTjcVc+p1TMddCf9+QxVDeEht4G1/uUoR42jtcfR9PJJpduJUGENBDIfFQp16XOIAGN1H90Y4CUdfI98j5JOhjUQ+75jFhtDL6MD4FEwD3mhoPdS3fjkesfUbVMKkYqcO1ProX/kN91DBnf88Q7n8Can6xxhfDrYF4C5FJ9kWBeriOuOUY+sKaNnuKYyWRUSDHHgb/lvAeDQfh8PsTjcVd6oQskivll6qIetq8DV9kHfc7YBuqI3FsyaoDXSH1In1uOmfz+dCBarinZL3196AYG3qc7QoBCKmEkEkFNTQ3C4fCYlFBpKOPnB1oO4M4L78TSG5citzendB/2Nz03jW0/2FZwEAGAAyz+1GIEngmoPiZmJ1B9sBpej3cM4Kb+JA0R1LPz+TyOnH8Ee962B7lIDt64FwtuWYApj04ZY4gwTbOcw/3PSuFwGKtXr0ZdXR08Hg+qqqpcjIECAsCYzcWNT4YTDAZdIVVchFz8BGJ8pvRaSm+yPJMSgAr5kFYruVFYkEBuLgoSr9eLRCKByPYIZtw0A3s/shf1j9Wj7T/bYFvu86vlOygcdIumrHAthbQE67rVXN4vQXlFRYXqK4ERGaVetV0KBpKsxE3m7/V6kUqlEAgE4Pf7lWIgq4uS0QwvH8bBzxzExN9MxNxn56rcFoJWvpeVv2WIPOeNnnddUeHaaL67GQ4cjH90PLLIIjY+hkxtplhAzADiM+PI+rLwJDxIT0/jxBdPoGJbBRpvalRCi6BeCiMZyUDPgDz+hUYbzrEM7ZOUy+VUVVwZwcFnvPjii+oZ7H8pqy/fVVlZiUwmo+4HoKIEcrkc+vv7VYGOyfdMht/rx/THpyMVTeHMX56JDW/ZgHhdMZ86MTcB7/1e5FN5ND/XDASAXe/chYl/mIip909FLBtD38V9GGgYwPZLtp8aHODM285E0BNUDJ9EI5eMzGCVVa5rj8eDcDiMdevWYWhoCAcPHsTQ0BCOHTtWcgzLVKYylen/lZqamjBu3DgF9Cj/gCLYk6HTjLzi51IW6N5CXZEH3GGv5NsSfANQEXXSs0gerkcLSXAHAPvm7cOSviXIR/PKGZF1stg5dycaexphOAbqTtbBNE301fcha2XReLIRz5/1PPYs2IN1t61DrDaGqR1TYaSKxtwXV7yIDWdvwLo/rEPrvlZX36TRQRr+2Xb+LasjA26wyf91gKZ7XOV4y3GVOgC/l8/V2xitieLRax9FxUgF1t69Fv6svwgyLRtdU7qKugKAtCeN/ZX7MTkxeQwIzOVyWP3D1cjaxagsORb8m7qJDgClM0KOh75mpP4j9TvDKERhMjpR3ieNI2wH0/KoA0rSjRl8n5wT/UefQ70Pcryoo0pnmAz71/VXOX/8TI9+0PVy3auu9+F0hgh9LE5n6LcsC/0N/RitGsWEgQmucdGNZrum7cKdV96JdDCNTR/YhKnfmIrAoYBq9/CyYRz+3OEi2D5F/Qv60fpMKxzHQe9ZvTj88cOY+aOZmPDshJJ8ReIJqW86joMJD0+AYRnY8+Y9mHfrPEz78zRYXncuuHzWX6Iy4H6Fkc/nw4IFCzBlyhQF3vx+vwLQsqgWAASDQQVsTLNQ7VhueC5eafmVG1ieoU0rFK+X9wFQlSDJIHktgbteDEtacOlF5sYLBoNwHAcNmxpgfNlAaEcIhlkE/gRZ+lEZkgFRAaClUeYh6TljulFBWlulQkGSQJ3ecn7OMaVHVwJe3dLJKt0AVOVMyRSlN2Bw0SA6P9WJ7LgsDr73IHK+HFr+0KIEMauRy37LyAV6+blW2F+uGRlO1nhHIzL5QhXPui11sL5kYc839iBfmUf1s9WY8LUJCMaDSLWkcOLfTyA5O4nkvCTsoI1J35jkyqGXTCmbzSorsYy84LxL8E0AzHElyGbYPdeb9K5w3mTVczJUerslY+WY8BxYWZ2VFmyulUQigUQiAb/fj8Y/NCIdKKzZ9l3t8P23D49+6FHkgjk0PtcIf58fhteAnSh4nRsfbYTdbaN+az3yoTyOX3YcO1+1E9lIcf/sOXcPsoEs1vx8DSzLwvbLt2PWY7PgSxbrBrCInOM4ao84jqPGjeNeXV2NxYsXI5PJYHR0FP39/diwYYMyPpWpTGUq0/+Epk6dira2ttN676Tso+JP+aRHklE2SIM9MBa48G9pWNc/o4wD3EdU8W9+R8MwAOyaswsPrXsIe0/sxbW3X6ue+cjlj2D3vN2oHqwGAFzzm2tg5A3cf939yJk5VPdX4+C8g3AsBw9e+yASkQT2zd6HS393KWzbxvqz12P9BeuR8+Xw0DUP4eI/XIyWQy0uGQzANUYSDOrGcF2x53UyXFx6v9lX6aCQYyn1Jfl8PkvqR7ZtIx1I44HXPYATU04U5imYxTW3XqMi8zKZDJb+dilysRz2n7cfsIGz/vsstG1rg2O4C+VJr6Ie0s62yvUhAZ4ERaWOR9N1OCnrpRFD6mhSX5H/y7mRhg3ZDr6XbWJ7ZKEuHezpuqe+nnX9VL9fGox0b7XUqfT38fl6+oD8Ldsggbn+Hb+X61YaOaTBwDRNJKoSuOfKe+D4HLztgbehbrBuzJgAwO4Zu3H/xfcjHSzoYNF5Uez7t32Y+5m5qIxXIhgMoveyXmTGFXVEUrq+oB/2runF4Q8fRrYmi473dyBYFcSExycoLCKNOFJv1/s56ZFJqByuxPjt4+GYbg+/PmZ/icoh5a8gMgwDc+bMwQUXXKAEF8+BJOPgkVZ+vx+pVMoVvstzrwlG/H6/EkDyyCouSJ2xyKqAMvyFXkYCRpmbTDAnD6knYKIgMU0T0WjUBUBtx0bf0j4kWhKov6Uedm5snhdDy/nOhQsXoqOjQ7VHMnQKJemRZqg3hbvX61VVvgmm0+m06wgBgjCC73Q6rcKh0+m08iw7jqMqksdiMVUQi/dyLDgHDBXSrY38LDM5g+0/2I58taiaGrcw6duT0PBAg9r0FFzSeCG9AbJ4HgtxyTUiPQ8yPN62bcRb4zj4mYOY8bEZqMpXIZlPYstvtiDVViwYZmQNNPyhAW03tRXWQFMWx997HO03tsPIFwU9w7+lksV5ITOkV1/2gcaYbDaLZDIJx3HUEW88jo7XA8VQxkAg4DK4yPxovp/V9tl3rjHOjTwqhhU2uWYCgQBS01J4+rqnUbejDvuv3o9AXwBrP7oW2WhWPccxHPSf34+OD3cgF3SDXzNjYs1H16BppAl7L9mLba/ahor+Clz2+cvgtb3Ke0PATcOKYRiquKHH41EGOMuy1LFnmUwGsVgMhw4dwqZNm14xZ3o75ZDyMv2VVNZV/nfo3HPPxate9SoXH6U8kfqDlL9AMZ2NvFgWQ5JADChGZOl54VLJl6SDER2ISx3AcRzYjo2j04/iT6/9E9KBNAzHwMyOmbjq9qvw4MUPYtvibbCtYnsi0QjgALGq2GnHxbANTN8+HW2H2vDkpU8i6y8aVSMjEbz+h69H9XC1C0yxvbLODcdOgjcJaOT4clwoy+VJINK5wDGhYVl6TQ3DgA0bexfsRcaTwfzN82FCHMfm2Lj5gzejp62n2FkbmLppKlb9YJUrdDgXyOHZ1z+L9qfb0bK7BQaKa4NtpcFfAkvdKSABJPtKPUDqTKXAD/Ud6scAlPzk2uMJKVwj+pjq41vKmy7XmQRspa6T0QucU31e5fN0cMd+yes4brItOvjl2te/l21h++TzJAiV81GqLTrAls83TRNWpYVbP3srorVRAEBlvBKf+PUn4I8X6wDxfQlvArdffTsOTD9QiJbIARO/OxGt97Yi5CvoeyOeEWz9+lbEZ8RdERXIA6GuEDJVGeSqizqWL+bD4m8vRuPmRni9XpVmIB021EmJdch7eK0cA90A9nJCysuA+xVE7e3teOMb34hcrnA8EXMRuBlkBW1uKBaB4rWWZalQWQIIeoClBVRWiQbgAqb0TPIegl4yQIJUClEKERbD4kakIMhmswokkKH1LOjB9q9vBwxgyremoO7OOph20frHtjAUiMdscQykUCbj5fl7bBdBGt8NFMEwUMxD57hJAwIZE58vC6nNmDEDBw4ccDFtaUGT7ydJsClD32zbhmEa6D+3H/u/vF9db2QMNP++GRN/PBGmUTR8SFBIJivTB6RA4/jJuZVWUc4RrcOGYcAxHNi5Yp2A3OQcdn93NzJNGcABqp6pwtSPToWdt5GdlMXuX++GHbLReEcjWv+rFcao2xDDsZMAUv+hUNZz0iV4DgaDGB0ddXkDqHyYZvGIOJmLJ9eHjKrg9RwH+VtXHOXcVdRUYOiaITx1/VMq173iSAUWfmYh/H2FVIF0axob/n0DUk1jq5qv+rdVaNjfgMMXHMa2d25Tz6jtrMWF/3khwsOnctsDDlKRFIL9xeiIMcJNpGfIiAuOSVdXF9avX49EIvEPfbRYGXCX6a+lsq7y96dIJIJrr70WixcvdoECAiHdkCpDTPVUmVQqpeSdfq3ka+TB8n+gyMdl7jOvZdskgJOKcyKQwM/f9XMM1g2qd3rTXtT112G4dhip4Fg+/XLIl/bh3EfOxVDtEDadsQm2ZSMYC+KKO67A5D2TXWG90oMrva2n081LGRn08ZBAXo4Dx0gCuuJgA/vn7Mddb7oLAHDxzRdj7s658FoFsJpKpTDqG8XvP/J7jDYWipbVH6zHJV++BJbhrqli2zYcOLDM4mkruhdU6ik66R5Y9ku2WQc8lHu8lkVofT6figyjnmpZlir2C2AM8JJyXwJOCS6lHsHPdcBfyhige0d1gwL/l15r6vX6s9hOaQiQ3m05xrIAoG6AKdVGfV3qgFr2Wzrg+BwZYeD1evHch5/DwUUHi+DYAWYcmYF3/PEdY9ZONptFNBbFrW++Fd1TutH6i1Y0/7rZNT+O48D0mtjzqz0F0H3qmTXba7D444txfN1x7H/XfuTDeVhJC1NvnorJd06Gx/KoCEtpyOMzqQ/LqGEJ0OW1kn7729/+RV1lbFZ9mf4hqampCevWrVOHxVdVVSEQKBQGYOI/C47Ri0xLM601snI5mU0qlVIWPwnSZJgxPWdkgDKHhUCP11mWpTy+ABQY5nulR5dMQoaG53I5nDjjBHb8+47C6jSAgx8/iJ6re1xg0e/3o7W11bX5JBCShgePx6O87hIYs92y0AjHjQyE9+nAi5uQBgcZJrdr1y6XBV4XpoFAQAF4mTeczWYRj8ddIVYA0H9JP/bfWATbcICWW1ow8UcTYZmWmk+e5y1DmySY5bogeb1e1NbWKiYiDSFcAxw7VcEdpjLe+Hw++I/6MfPGmQgcDqD2sVpM++Q0OLaD1OIUDnzvAOywDRhAzzU9OPauY7D9Bet7ZnYG2bZsyXZy7fE3DTtSYBJMkkHW1NSMCREyjGJagWmaYzz6fAfHi0w2n88jlUq5wtK5fqVXWyot+Xwew8lh7J6025XrPjppFLs+uQvJ1oI3PtwdxoKvLUCkMzJmj3cv74ZdaaNnSY/rGbGKGA41HirMnQVsvXQrnnjXExhpGlFtlzl+5A8yZF8v+jNp0iS85jWvwdq1a7FgwQKMHz/+5TGiMpWpTP+/p/r6eixdunSMt0ca2aUiLwGUNAjqnmgZ5SW90roRWtYnoQ4gi2FRXvC+YDCIUCikZCQ9m+F0GK+77XUYf6LA/8ycidWPrcZbv/9WnH3/2fClfK5+T944GbO3zVb/z9g9A/O3zUfVUBUaTzQCAKychTWPrcGyDctwwf0XYOmzSxGKhXDRPRdh0u5JLuAoAY702Eq9SwIZwF1hWnp8SdIQLOfBtm1EI1GcaDuhvpfz07GoA3e/8e6C7DGAB9/wIHYs3oGhoSH09vbi5MmTSHYlcd53z0PdoTq0bGvBBd+6AJbhPo6UlM8VK7JLQ7juFZWyiZ9JsCu/4zqS+qhcK1wvUv/SgaAEqbJIqgTX0pEigau8Xq49CU51kkaV05EcEz5PX+PyWo6FTDmUwF0+UwJxqRvK7+Uek4Yy2Xc5B6WeIdvL50hd8fLfXY752+er58w9MBdvveutY56l2pTNY9XXVqH91+2FAr3O2JB4r+nFgo8vQOu9rag4WIFx68dh6eeXwmt5MeHBCZj2y2nwxDyY9rtpmHxHAWwTPMu9UcpgIo0WkvQx0cf9paicw/0KoPr6epxzzjkIBoMYGBhQTIohzqZpoqmpSXn4aMmi8KmurgZQtEhJa5n0eknADRQ92dKiKBmPbdvKoywrDNJbKCuby3B0yaTkeXq817KtMWNgZIv5qbJtyWRSbR4KWaB4Frasvi2ZuLS0MlyYn8vxkaHEbKu0ospiGxxLeS2AMQXCCIL0sHpaXoGi5bL76m50vr/TbRqzgbZftrnysphKQG8B38t30mvLInk0hJw4cUIZPWSImcwDJ6OTFkuOn8fjQcX2Ckz92lR4Dnhg5k04hoNcKgc757Zc9762F1l/Fk2/bkLnpzthxk1M+dQU+OI+l4VXGjdk2LT0okjhkclk0N3d7fIMyHs4FtFoVI2HFCqMfpDeEBqzAKgICI4H76XFs6qqCsPDw3CyDmbdNAsd2Q70nd1X7HgOsLPFtV63vw7zvjcPWz+xFclxSXXZwSsPwgk6WPjDhdjyji3oObMHZtrEwv9ciMYXGxH3xLHnHXvQsa4DMIDn3/k8zvnhOQgOFus0sI0yHYDGFul1Ykhne3s72tvbMTo6iuPHj+PgwYM4cuTISyoHZSpTmcoEFL2KNO5LcCG9R9LILmW9lDmUnSRpCOczAPeZ1hLASXku3yV5uazRQj1mXN84XHnvlfjjlX/E4ucXY8H6BcjZOcxbPw9WwsJzlz2HiUcmwjIsnHX/WfCaXgSyAWS8GVz04EXw5/zoauuCP+nHHVffgWXPLcPSzUtheQrG/PMfOx+TOydj8p7JcAx31XXZv1JKuwQuEnTJNDEdiMj7gKIekLJSeODVDyBWGcNlt12GppNNrtBzKz9W74oPxdVJNHxW9clqrPzFSvhjfgTSRf2HhnH2hfOk91c6IPhuafjmGtL7zedIZ4TudZakA2/5Tqln6WHe0jgkdSCpO5LkuMg5kz98pgRv8vNS/0sdWQfRco/ofZfPKQXy9fEvZQCRbdDbIaMnJFiV7y0FUAmOL3vwMgScADK+DK58/EpYOfc8SANANpuFk3Yw4ZYJyJpZ1ztdz417Met7sxCdHoW32wsn4cCxCvxn8v2TER4Oo2l9k0uH1nU5uZdUZGkJ4w5Jd+y8XH2pHFL+D06hUAgrV67EnDlzil5GbeHbtq3ChROJhMu7K73VBHQEhN3d3QiFQqisrFTPkuHU0gvLc4vlc7nYeJyXBKtcuLJ4FkE5FzRBjn6IvW3bGFk0gk1f3wQAmPSVSWh6uAl2phgWQ0sj72XIkARP9LTzc+bdyjxwbjS98Iu+Lzj2vJeCIJ8vFlOTTFoWP5NjAhSFkWSm8m+2w7IsJBoS2P6D7a7iELM+PwuVj1bCa3nV+MtcKM4TSQL6QCCgUgqoBAFwFd7jGDPHHSjmlpEI6pLJJPx+P3w+n1ICGKLsTHKw5WdbkI+caksemP3p2TjwoQPItBT6E+gMILQ/hGmfmwbYpwSXBzDtwlrgOEoBKa26UljKPaGHhNPgQMWQn9MYJIWPjGCQwp9553wfDRlMqYhGo4W8/Tob2z61DYMLB1F1uApLPrcE6AZMrwkn58C0TBx+02EcevUh2D63UcLIG5j42ETMv30+nv/I85hx8wzU7q6FbdvY/bbdOHjJQTje4tqs6qrCVV+8Ch7H41o3LAhkGIV887q6OvT39yvDF41tbDvHK5lMIpPJYMeOHdi6dev/OfB2yiHlZforqayr/H3JNE1ceumluOiii1zKuQTBAFzRYxK0SCUXAOLxOGKxmOtkBr24Iw3V+pFirGshQQT5n8yP1fPMpa7Bdo7WjsI8UeDRwClwAgeJxgQi2UghdSlVMG6mA2k4loNwKqza6DgORmpGEB4Mw3RMwAQeuvQhrHxyJSpHK13Gax10SlkmHQLSScJxS6VSKiRW8mcHDg7NOoRUKIVZm2bBgKjBY9j43Xt/hxPtBe925XAl3v7ztyM8FEYikShEOuayODz7MB79wKMAgLU/WYsJGycANpRjh+MtnQbS0ML3yf7Jfp/uOxLnhjJaGm7k+OhpXZxH3kNnD9cCdQnWOgkGg2OOpeLz2K5SIdwS8EndQPei6x5vqftJgMrvdKCqe02lvqYbaXQvrGwHQSP/l861UhEH+jP0+/V0D11vKgXgeXpKdXV14cjXcBZ5J49gMui6nuOWy+WQSCTQ39+P3t5eDA0NuXRP2W/qaUCx6DB1G0azSD3XsiwVsSmNFJxv6kfEJ3w+dWg5PvyfxoFbbrmlnMP9Siav14szzzwTS5cuBTDWmsXPZIgD83glqJTMiec2A2MZBhknN69cgDIPm2ADgAphB6AWOo+pkkySHjWZE2yapmLk0huXzWaRm5BDcnwSfdV9qL+9fkyOkG7RDQQC6nkE2/Q4E6TxvRQYkolJJkQARu82STIUegulpVzmINPKzkgE3bPPewKBAGKxmGIafK9hGMhX5rHvM/swtGrIVRQi2BnE0rctRdAKoqGhAcePH0cikXBVUeeYctynTZsGx3HQ3d3tAuYysgFwV1Gn8JPMV/ZdChV5niHnwXEc5MflsfPnO5H35jH7c7Nx+J2HEZsfcxe5cIC6h+ow9dtTkanOYM/X9mDqe6bC2+9FKBQCUDyTXAJGzjvfSXAuBSX7IXP9ZS6TtOIyykE+m/2VRhlaPfk391gkElEpGqbXxNYvb8Wqb6yCk3Yw6B/E5m9sxqKvLsLAmQPoeHPHaRN66nbX4YyvnYFgJugyMjmmg+dvfB798/oBA/AP+bHmi2swbmCcK8qDwkYaz6RXSO47eT4px45rJ51OY9u2bdi3bx/i8biravz/FpUBd5n+WirrKn9f8vv9uOmmm9T/lB26MVr3jJGk7KA8TiaTyOVyiMfjymBNRZayXVfqKXcI+sj3qFsw9U03ZEsln+3jTywWU7Kb90jFPR6PKzAvI4p0L1/aSuPp857G86ufhy/tw7t/8G5UDVW5AGYpY7tU/jmWvEaOm4z84vVdM7vw+7f+HgBw5a1XYuaumTBOsc8Hrn4A25Zsg2MWt0bdsTpcdeNVMFDUYwzTwJElR5AxM5i8fjJMoygLOAZMS+NYk6QHmnOlgzqCGQmOpUGd4yfHQgJS6WSSBhw+j7KaYyLfz/XAtWIYxeKoEszLZ8r5kvMr28r7pS4tnyWfLe+Tepg0DOnjwLGV18tIEamzkOTaliHwsg96O+W9fIfUB3U9WRoJ5Lzze+4Rr9eLYDCIyspKRCIR15jLseNzWKOqr68PfX19yoko2yajN1nsNplMKr3T5/OpY3ZhAfHmOHxxHyKJCAKBgEtfpUNMFo/mHpP85XQRKOxDGXC/wmnx4sW48MILXaGgEkxwIyWTyUI+UjisvJcAlHdX5qFIYcK5l1YiArT+cf0IZoMID4VdijY9YFywEkDyuQTlFJy0IOubU7aT93g8HgwsHUDHJzow76vzMG7POIyOjioQTwDMTcD28rgm6en1eDwuYMENLsOLOD4E5roXkwyAY817mDMr+03QRwajwJdZPAJFCsnTGTl4/cDyARz+xGFkmovjH94ZxpwvzoG/x+8yUEimRYFWShhIwMg55JqQQkBWK5eAVOb76xZUyZAlM4tPjSPZnET149XI+/PY/639iJ05tsrruD+Nw9DqIWTHZRHZFsGMr8+A97AXo2tGUbe+Tq0detMl86dlVM6v9JJw7GVfZT9onOG6ojIon8n3mGYxH5z7xrZt1NTUKOWRffd6vUi1p7Dtg9swOGtwTJ9LUWAogEU/WwR/wo/qPdXwO36lfJp+Exs+vQEjjSNY/IPFqN1RqyyxPp8PlZWVrmgDAKpfcn6kF0FaqmnQ4Brg2O3YsQOdnZ3o7e3FyMjIy+rH34LKgLtMfy2VdZW/L+mAmzJR8l56mvi9lE26DJQpaczDJj8H3IWlGN4pjYVShslinBLQSQMzUNSjpCHV4/EgFouptktQInN3dYO/NLwbhoE00nhm7TN45uxn1BjVDNbgVbe9Cs3Hm8d4E6XMlO2WQFPK966JXWjuaYYnVfT2H5x1EHe88Y5iRXUHuOq2qzBn+xwFJO655h7sX7kfMICmfU04/wfnwzfqc8k3mTolxzabzbrq/hDsSFAqAZyuT+j945hK/amUx5mkA0X9HfqzZRV8yj6pQ+r6q4xG1EGs1GV0oCrnUPZLn1/dky/BqtShJNjVgb4+BtJRcrp28X9d95W6uCR9HPkZSRqndJIGAdlHn8+HSCSCqqoqhMNhl07JNnA+c7mcOsq0r68PAwMDCkTL/Sy926ZZSKmkU4C6i9/vh9/vR++Fvdj+se0Y9+I4LP7pYtREa8bMP0G3jLKhYYb8RkYSlDI2vJwq5eUc7n9QmjVrFtasWaNAHSuNS9BGSxELe1FIEQhwsXORcnPKPCYJVriI+mv78efX/xmhZAgX/OoClWchQ1FlGLgUQNwIkonq1kMJAPlchngMnDuAjvd1IFObwb5/24fIjyOwHi4CIEmpVMp1XBfbVl1djYGBAdi2rfpfiqnzmZLhyjxxMhXpBeSYkTlIIA5ACSipWJABSI+5ZDay0jyfAQC1G2rRv6sfA80DAIDw9jCmfHUKfN0+5O38GGElzzKXQlI/q9pxHHVmJg0HnCNpNZUChnNkGIXzqKUglYYKCeL4d/hAGBWHKpAzc7ByFqbcOAWdn+zE0Joh13z2XtOr/o4tiOHAJw8geCCI/gv7MfnHk9F4d6MSitLiyDngfLAtSgE6FfWgW8sBKOOJVMDktVIJ4VowDEMZweSeGBgozBPTL0zTRLopjc3v3YyRWaVBqjfqxdTfTEXf6j70L+iHJ+nB4h8vBvLAhvduwPjN47H818uLEQi2hcXfXYzh1mGM2zMOjuUogxUV1HA4rNYZ14BusNP3gzwOTZ71zTFevHgxFi5ciOPHj6Onpwfbtm3D4ODLMyCUqUxl+ueh5cuXu/g/gQ2NxdJYB5QGSzpopbyicZ/epWAwWBLUSt2Bz9XDbWX7dJKKu2wj4JblQDFnvFRf5LMUUDGBjN8dDWSbNjKeYhFOKW/kMyTJ8aGyf3jSYTx49YNoO9KGi267CKZzyvNZ4bijxgD0ZfowNDSkdL1VN6+CP+3HUNMQVv56JbxRLxyMLZqlj5H8X8oN6biQ8kXX1UrNv9R31BhpMlp6dfX8fim/qefKaDNpROCzJZjW88D15+rrQgf18p5SAFd/pgTQ+hzLdaAbEKReJsdO6jenA8+nexfHQ7ZRAmAdVMo1IKMySo2dDtipB1IP1dspdVL+5HI5pFIpVbhWRptwHKTTqFRxOWKa7qu7cfC9hcrovUt7sdW7Fat+uApWvHRONteQHHPp6S81R7qB4qWoXKX8H5Da2tqwevVq+P1+xSQIyiQodJximEw+n1fhULrVmABBB7wSUDrOqbN6rRgeeM8D6J3ciyNzjuDud9+NTDajwBTBJ3/Tqiy9aRSWtC4CcC1YWqcB94YbWjaEPe/bg0xtQViN1o1i/dvXY3DKoOs6ClH2M5lMoqKiQgFHWqm5WXm9tJzKnGyOFcdCt37LzwhQZR9lKE0wGFQhWLyfm5PvnDZtGhoaGmCaJhKJhAKKUgHI23kMXDCAkWVFoBY+GkbFsYrC96escY7jKCufPFddgicyMBbVM01TKTIcP8nQ+XypHNGKyHUgiYBPMlkJWP1+v3pWJpOB2W1iwtcmYOobpsLT5wEcoOHpBtQ/Xw8IvjWyYATdr+pGriKHQ+8+hK0/2or4rLhL+ZLKEavQcl1JCyvHVxZyMU0T4XDYlYMvhYssICfD9bnGGxoaxnhD5JhZlgXviBeNRxuL/XKAuvV1WPaBZQh3hrHwMwsx4Z4JmP+d+ag8XIlVX10FX9SHze/ZjGRDEofOP4SNb98IwxThk0MWGnY1qP0vDVaJRAI9PT0qj56CR18TPLscKFZn5/UVFRUuIS69GK2trZg7dy6uvPJKXHXVVWhtbX0pVlamMpXpn4wuuOCCkh5f6hFAkZdKRVbnk/r3UnEm6UqvBELksTICiyTfWQrM0HDKNvBaHWjJ98j2S+AmgREA+Gwf1v55LZa9sAxwAE/Wg+t+fR1aD7UqnUOCBr5HGerhri5NOdQ3vg/3v/p+DNcPY8eSHXjw+geVbJ30/CRc8NMLCnLGAc797bmYu36u0i2qq6sRQADL71mOs391NiK9ERc4lvqH1OckqGYkg148FoALsMtxkXOoA1z2jToQAZMEOHyennN7OsNHKBRCXV2d63QZ6neyfWwz+y91BOovch2WAvDsmz5+0lAgQaDUf2Wkhnym3i/5rJcCdXwX3yf1/1Lrv9QelN/J3GX5DjlPOukRdPyth2SXMirI9dLb3outK7eO4QPSWKDAuZ3Dpo9tQiqdcoWDO46D7ku6cegdh2AHim3tXtCNJz7+BGCOTQeQ+EA3NOjjLMPPOc4vh8oh5f9gxIrk7e3t8Hg8yGazauGz2vjg4KACXGREeiiMtDhKBm+apgq70MNkHMfBHz/5R/RM6Ck2yAambJmCc354jtoQLEhBZi8rkPP9DA2jQKFCL6+j942M3PSZOPzWwzh8zWE4XgdG2sD474/H+NvGK0su+yAZIvvL8HVpvWbbAPcRZwyNTiQSqKysdOVfyzGRQoLfmWax2ricBxk6T0HHIiP8Lp/PjzlSSo6TYRiwvBZOLj6Jji93uApkWaMWJn91Muoer1NzIXO/mcNMb7phFPLPJDNmW6QBJ5vNIp1Oq3Oo2Q6GJjM0mp5ltldGR8hQPXlElQRsFGS0XFqWBdQBh248hFmfngWv14stX9uC4dnD8A35kIvkkA8WmS4AmGkTKz6wAqGTITj1DjwjHjiJorLi8/mUYUBamjkGrDHgOI46F54gnICUhcQ4PjSghMNhl/IHFK3yjKSQRiY+07Zs7HznTnRd3IXa3bVY+aWVSEfTyHlzQPKUwavSwYYvb8Csn8zC1k9uRapBnP9qAzPumoE5N89RZ9azbcytYztt21Znq1dUVKgCITQM6GePlsqJt6zimfPcZzK6RgompnXccccd6OnpcQnJvwU55ZDyMv2VVNZV/n5kGAa+/vWvo7KyUslLyjbJg3gtUJRNvAYo5i1TdwGA4eFhJBIJpfP4/X6EQiEl46ThXN7H51FGyffrQEXqBVJX4s/o6Kh6dywWUzydz5byjMTnyHfZXhspXwoPX/gw1jy5BtUD1eodMrqQ93BcUv4UbnvDbVh35zrU9dcpPSIbyOInH/gJojVR9V4ra2HBIwuw7O5lhfsN4MDiA0gEEpj19CyYTkG2JxIJF8ALBoMAMKaWjQSRHL9MJqPS9UzTVKl/4XDYZYSQOoAErXKMpeeZ11EP1OdBtuN0ehifTXnMtcEivvSqMpycziipB8r1qHt9S30n2yGBoryPMlKCSrkf5LW8XuruEujpYyqfJfef1HPkc+VcSPAtgbZcw/ycQLLUvEiSbeL/8sfv9xci7hp9qPPXwQuvqy3cr3z/ieoT+PE7f4y8kcecm+ag9oFa5DNF/UPqaU61g41f2ojhqcOoebIG7Te0A6PC0+51cOCmAxhdMara5x314oIvXICanhrXXtTbQawhjQW6oYfEe2+99dZyDvcriYLBIM477zzMmjXLBVwlwwkGgyWLVukhM1zM9PgR4DD0mIwzEomoYzN8Ph+ygSxufsPN6JrQBQCYumsqXv27V8M0Ch5O3st35fN5Fe7O9phmoVI1GYG+0WWeBOAOb3EcB/vevg9dV3Sh+VfNaP1Vq7qGIACAyyspGYvcOPI6gkrbLlR0lwXV6HGnUiCfyefIEDkJLnXLoB7SIkNppNdXKgZ8FwVAJpDBs/c9OyZErO6OOkz40gR4PB6EQiGVny6FGcOpHcdxKSASaEmLr6x2KgWwNNLQAizvJ/FefscxlkKTXn96CgjoaPSor69Hb29vodBFKIB9792HeT+dh651Xdj1pl3IRrKucbASFibeNhEH33wQk343CTUdNajfUO+K5tCtrbTOSmONHH9WfqWCRaWOAoXgtFTBHAJZuUYYdZBOp5UxZ/d7dmPqf02FiWL+WzabRawqht3v343+Vf3wxDyYcOcEHHrDIVefW19sxbJfLINz3HGFwNt28QgwPQfOsixVqIRzJvPmqEgyGgQoWnYty1JREDx6j/sdKIagS6Xx9ttvx4kTJ/C3pDLgLtNfS2Vd5e9H06ZNw7vf/W5UVla6jHFSEdXBC3mRDoal8ZcpOYlEAolEAo5TSJWrqalxAVypZ/DdEvRIuS1TvnS9SAIbKSdorOV3BKwtLS0q5F3qOfIkDyUTPDaeXf0suhu7se6+dYjEIi49zTEcdE7qxMTDE11gJV4ZxwOXP4COmR3wp/x47a9ei9ZjrapfXZEu3P3GuzHUMgTYwNw/z8Wq21ep+9k/XS/kGMTjcfj9fiUPdMBN2SiBImUDT3xJJpPw+XxKNvBdUreRzgQd4Ml1IVPBpA4n5YwE9RLcSYeLdDrl83k1RzKtivJNptjJtSE9nLpXVxpF5JrWvcfyWrZdrjEdmPM+/T38XDdc6KC4lFFD3ss9oD+z1LqXfZB916mU173U3udaCIfDcCY4ePg1D2NJ5xKs3rEasN0eed7f2dqJn7/650j70+p9078yHY0PNcI0ikYIy7Jgt9rY9aFd6FtcPH617t46tHy7BdZwgR+l2lM4+pWjSM4uHr067c5pmPvHuQjki4UYqQ/rdYFklK6cNznWXIf5fP5lFU0r53D/g5BpmliyZAkmTJjgOlNXMk3HcZQFluCB35P5A0Ww5/F4MBIeQd+kPrRvaXcxMYaUVldXu4588ua9uPZP1+Luy+5GOBbGRXdfBMd2kHNyrpAjggUCWAkAuNnIpAgGJAMgMCxlJWz/YTs8Rz2o/1M9DNMdAqMzFv6mNY6AThZeIUOVR4hIAS6fI0PgyYikF1AyUylM9IJbtMJRIEtgIsGsBKHsR9eru8asD0+/B9WPV6s2cOylkJPvloBXMkHDKIYmc4zIcOg5JciS1mjpyaenmJ5Tzo0srCIVolgs5mofhR7XHYtwpVIp5HI5zPjBDHjDXrQ92IZ8Ko+dH9jp8vTnQ3kcfPNBAMDh1x3GkewRzPzRTDTf2azmj0YUKnkEpTJMnKBRhj/Jc9z1dSZz4YFiiBXnkQqkrnRyr87773lII62iOvL5PFADHPjgAfQv7y+stUhuDNgGgGQoiYSVQMgIufojvd3SOMA5YHqFDLcnj6BiIoWwjACJxWKuz1KplOo31y/HJpPJYGhoCGUqU5n+eWnt2rUIh8NKnsjILMCdG03+R3klo9Mod2S6EXmKx+NRDgHyJeob5G86b2UbpN4kj3UEoHiu5NG8RwJPCSK8Xi9qa2tVNJSU2/IaaYR+/KzH8eyaUwZzB7j8rsvhT/lVP5458xk8f9bzuOS+SzBrV8G5kvAncP8l92PvzL0AgHQgjbuvuxsX/vFCNO9qLsjZEybOvflcPPnGJ9GyrQXL71gOmO5QZGlMoKxmP0OhkEtvk4BJB3I6wOCz2QfOtbxffk6ScpT/S/1Lv0bqTrKNsh16u/R38plSVuteS/k+6p+yoKoOurkupE6gg2I5PlKmyjko1Q/5mdTb9Hfxb2l0kI4BUqm5lc/QQbV0EpW6RgeYeiSAfJZ8n2maiFfF8ew1z+LIlCM4Ovko0p40znnhHNdzOXZ9oT7Yhjs0O9mSHDN/Xq8XqeoUchXuowOztVnYARsWThlyanKwQ+7nxWviyBpZ+GzfmPXDsdCNPKX2g/65XM8vReUc7n8QWrFiBebOneuaQMnIaMllsSsJauS1ctF4Qh7c+/Z78edX/Rldc7uUZbK6uhqNjY2ora1VTEFWJG5INOCyuy/DOXefA4wWvJay8JTMtZGhFpKRSZAngbZuHSOzI4g/dtUx5Kbl0HhXoys8l+2TISAyXJxh2rTaEuxKZmSaRc8i20iBSpLMx3EKnk/OAcEYvZf83DAMVyiuBH0k9lGGWktLPa3J+XweJy8/Oca77RnwIPJUxCXU6HXUveoE9Bwj6QXgNaZpqsgGgmYZ2i4twVRsdK8w/6cFmcoSx5nRE8FgEKFQSB3vkkqlkEwm1XiGQiF1LaMjCPYmPD4BZ3z2DEz+5WSVn6aT43Ww7237cOyqY7BtG3s/sReGx61AMBKDRgF5vjd/OD+A2xDDfrKvHAs9tJ6KJdehfDc/I+hV69b2o/64O3e9al8Vzvj0GajaWwUAqD5WjRU/WYFwVxgv/suLyOWLaRkyWkGej8qjL4CCZ0OCYV7DeZUGOhmlIfP1ZCV8aQAk+f1+BcjLVKYy/XOSPCpHKtapVAq9vcWil9JzxZQdqTc4joNIJALbtlWFYZkiJoG7dDqQFyUSCXV2t9QppOJbCmxIwEVeJ50VEojLSCbpaOBzSnmEH7noEaw/a72S33vm7MEfrv+D4tVPrX4KT573JOLhOO5fdz8OzDpQiFgzQmjsaXTJAe+IF56jHpdhtP5QPc758TlYet9SeCyPq4+cH7/fr4y60lkhU9lokPb5fK4ot6qqKtTW1rp0NKkzEQyXAoPSuAGUrtVD3U0Hv/J9OjiW+pqcG3m/BEzURfUoNN1TKXUvjt1fOp9b9kPq3XKtymcz11euM7lW9XfofdK9rrqDQDqGqAPItE3OkXymjC6U7dD3UClQWSpqQRoX2O98Po+8lcdD73kIh2ceLnxnOHjozIfw6PJHXWHc3Pdzd8zFlb+9Uul5k342Ce2/aQccd2i9x+NB7fFaLP+v5YiciAAAIpsjaPtSG6wTRadT1Y4qTPvMNFjRwvoZ98Q4TPv+NDiD7lx0uW7kHEj9URpR5LhIw8bLoXJI+f8xGYaBWbNmYe3ata5QH4I4euJYLp/hnSx8xA0mGY3X60XKm8It778F/c2FM3u9aS/e+tu3YsrAFGQzRUux9FIyNxsoLnCCKbk5KAAonGiRJkNKJBKu47Vs23ZZiJlTROaTy+WQRx7dF3Sj44MdMLMmFr5hIby9XpcVTIbDykJjEvyTOci8ahlizDGXz6LlnHnMFFjSGsx2cKxl3q88Lou57T6fT/WZYyDzqDimjFYAikJ8pH0EW3+5VQltz4AHc6+bC2PIXdCCG50Kj86EpQFAt5xyrilwZb/kkRq2bav+Eahx7QDFCvMy9EYKVAJ/tltXnORa5xyk02nAD3S+vxMtm1pQt6MOyVwSe6/fiwmdE7Dl7VuQaEyM2Utm2oSZMpGL5OCJe2DAwKSfT0LTk03wjnrV2uba4Zn1BMM8Y5XjyHmjJ0b2I51Oq70mGTVQDLOXHmgqKtI7nslk0NDQgJnzZ+K3s36LA5cdQKgnhLM/ejZ8aR8y/gye+/JzWPG5FYh4I9j4no04euZR1O+rx5qvrYGVstDZ2YnGxkaEw2G1hoDisShAIS+bBpFIJILKykq1jynsAoGA6h+r/tu2rc5Bl8f78T18LnnPTTfd9LIFz8slpxxSXqa/ksq6yt+Hmpub8ba3vQ3t7e1jPGuUYTKii3KASr70PksHQjwed3nrKAOam5uVDNPllmEUorBCoZDKJ6cuJAGIBBqUOZS7EmTwWhbxIkCV8ks+FyhGj7FdlmUh7U/jh+/8IYZqiwbOd/3Xu9DQ3YAtS7fgocseQt5T9IZ7M1687vuvQ+XhSmSQwQuXv4Cta7eicqASr/nGa2AlCsp/KpVyOS5kUTD+Bgq6Dc/HTiQSLp2nlCe/FFHfojzWT7KgTCPpHk/qALoThu0HxhbV0x0wuhGFz9J1Pa4pOl+oh9Gbn8lkXOdvy4gK2S7dI8zP2VbOsQSbsr2yLfqY8+9S48U+6hGQuiFFgjp5ryS+Wx87HTRL0M7nyDmT18o26hEhxCXBYFAZeNgOrpPogijues9dyPqygAO09Lbgvbe+F9500RDDPqdSKYxER7CjbQcOjz+Mib+cCCPrNsQQ9xBfRK0o1t+wHlPePQU4larN9UH9zG6x0fHxDsz+zGxYKUsZVljfBijWM5DGNVk4TjoW5bxyzWWzWfzhD38o53D/I5NhGJg4cSLWrl2L6upqxfCBorCQOVKZTAbRaBShUMi1KAC3wAOA9Reux8bzNiLvLTL39uPtePtv3g7LtpRgpKACChY+5mmzDfwB3BY/MjgZTsvFR8Gmh/HqbVXMFA4OrzyMjn/rKILMIQ/mfGgOwgeL4WsEayR5BJZkRgxr5vnkfr9fAQgKWvaRm5NMhJufVl+GZRGcSa80+6fPqRRQ7DfbyfAz+RmtyKZpIjopii3f2oJcVTFcZvLnJqPmwRoYMFSxNhpgGLVg2zYikQji8bhLUEhLqy78CAjp2aRQ0j0FNEiQscqcX+nN57hR6aFCIg0A0ppKxsXrOb4juREcfO1BnHzzSQDAis+vQPWL1WpNGdUGnv3ys4hOKRaQOS2dspbO+eAc1HbXIm2k4e/xu471ku3mfHF9cF7IYPUCL1xT0vjBH2lg4bNoGJFh/RyX3e/ajZm/mQkjYbjSRuwKG7vftBuHLiyGmjdvbsYZPz0DZk/RO8O9JRUiKTz5WWVlpYomkMKW7aECQ4Mf/5feb6mMco6/+c1vlgF3mf7Pqayr/H1o3bp1uPzyy11glfSX9r2MEqIxN5VKIZFIuIz5kieRx5BPk9+SX0nAy+t0IC3vkW3RAQ0pk8mo40YBd/i07k01TRMnmk8gbaTRcqRFKeX7p+/HLa+7Reky19x6DebvnY+d03bi/svuR6KyaCyu6anBJT++BDV9NUpWP3vts1h611IFSmigN4zCkZwEHZL/SqK84fjI/hNwS6Ow7p2UxmbmbFNnIljhfEuPtfR2ynGSjheOKa/RPbES0EoPsg44abRhu6nHSqMAUHQYUU5LoC6BZCl6qevkeLL/AFw6RCmDAkka3kuBa33NlgLRelvkXuAcyvfJ/SDHkfdK/UW2V3dmca/K+dB1S8uyVDTt8YXHcddld6F2pBbvueU9sBz3PmKfMpkMBgYG0NfXh2g06irurEdkcG4TiQTi8fiYSuHkBZxzzpPjOGr/SP1GetoJ1Kn/yyhN27YRj8cVTpK53y8HcJdzuP8PqaamBmeeeSZqampcDIQAVm54Asna2lrXxiUIkSHhjuPgzIfOhNf24ul1TwMGMPPATFz30HXwGT44ZnHzkVnI0G6CAukFk8yN7wCK4cjSci2BqsyPoWLOBU7Al8llEJ8bd4VROz4H6RlpRA5F1IZnyCpBmrR8MxKAbWI78/m88jQbRrGSud/vVxWrpWAyTVN5W2ktlRuRZ3wTUBH4SkumPN9aFlSRBcZ0yyY944feccgFtgHg8GcPo/qhahiOuwAdmUU2m0UkEkFzczMOHjzoyrWWbdCBuG4A0RUYtov3SC8FjRoyvIqKAZ/P9SAZq2yLDCfjuocJdL2rCyevOan6v/FTGzHvu/PQ9ExT4d5hB8u+sQxHzzsKOMDRdUcx5dEpGJoyhJ4FosI+UFhTBrD3K3tRs7EG+Uge1Tur0fTbJtSEajA6OopUKuUyCHCvsVou+6IfAcY+5/N5pQDJ+aFBhj/0rEiLubTYz/3p3EIbPMUQstraWhz3HMdo46irW8naJBLVCUR6IsqSzzFkFXYWWNQNAtFoFPl8HpFIRM2DPhc0rHCe5fhw/5EvMCqiTGUq0/9/SNcF9AKa5HFAsSq5NMDKEHLpEafMoB4iea4EvdLTJBV33YMpDYXy+aX6I/UwXi/7Y8PGpkWbsGzLMhyYfAB3XHoH0v40LrvrMszrmIcd83bgnsvvcekyd736Ltj32Zj85GSsSa/Bn6/7M9LhNOqO12HtLWtR01fjes/K21YW+mwZirdz3BhZpEeOAUU5LseD/ednks/rxgYdyMm6NJT9MkqLz5ZgTAeSlIdS75FtlL85xhwL3WMqAZVuHJD3S48k+yXlMv+XjoCXAr6nI30N8lp9TLjOpf7F/ujg9nRzo+8B6fziu2VhOH2M5TN4v/xc6jNy3OXYybWj73X5HKlTeL1ezOucB8/jHkw4NgFG3kDeKUaIyHZSl9aLKksnhj43+rhJXYU/bKcMIdfXltSP5NhTn8rlcsqZJQ0O8v0vh8qA+/+IvF4vzjvvPDQ3N6vJYqEQGTJMi61c8LSqyDwdaeWj13LVc6sQtsLYP3k/LnvwMviiPhjeoiVOeq7z+bw6BkNuIoaN6pZKbtp8Pq+qioZCIRiGgcrKSlRVVeHgwYNqE8ozxdleWcBs4o8mwsyZOPqqAoiafuN0VD1VBdsoAmEKZTJ+6ZXVvd+O4yiABMDFgDmWBKT8nKG09ExKAOY4jiruxY1JxsCicVKpAIrAlvPHkGVZ6IrvtywL3Wu6EZ021mvb8rUWmCh64VOplCsvzjRNjIyMIBqNjgkf14WcZCgE+dJCK8EULeqVlZXw+/2KIerzz74S6BGsMl+az2W/CdIIYgnqcrkcbMdG4FjA1X8jZ8A8VjC4VFZWIhvMouu8Lsz8zUxks1nUbatD9eZqNLc0I/ORDIbmji3elavMoe+8QkXLweWDiLfHseSmJS6jB/+WioccCxYTo7Bk6BpQNHzxOrlO2H9ZXEeuVY6n3B809oyOjiKcD2PBDxfgxQ+9iKFZQwieDGLhdxaiprsGGSfjMhZ5vV4EAgGMjo6qfcs1Qg82rbQAEIlEXN+xsKA0KHFOWcFXpmrE43F4vV5XZEWZylSmfy6qrKxEa2urMkZSVwHcgJakAyx5koLkteR7VLSl3gMUgTrlPb8nn5Y8h5Fc+r06MOdvylNpIA6Hw2NOhgGAg1MPwnIs7J6xG9sWbMP+afvRM64H0aqCvH7w0gfh8/pQO1ILQw/KsQHrSAE4T9k8Bf6UH49d9xjO/dW5qO+qVwVppcIvAabeX7ZLgg8JdviZ7L+uB0gZJyPO9PGkXKATQYJFCQLlffLZ0mkkQaJcM7rRROqIcu7kZ7LvlHGUVdKBJL2a1JdlO+RzdF1ajiGvlXPDe6ShnWtP7oNS75LjLWW0rCWgy9NSwFwaVfQChvq6l9EO+lrgM3QDkzQIyNQK2XY+nw42tkVW+V54aGFBlzLcZ5vLtsn8c/lTylvPdssfjqt0+pXSgfku9kN3NujrSw+119vBsXs5VAbc/wdkmiYuvfRSTJ482bWYZbEGCgNOvsw78ng8YzxPBCy83+/3w+/xY9XWVViyYwlCmRBsp2gFk9W7mR/Ee0mSscpK2/IIIVloie8fHR11HVklwTaPEJNhHpZlIWgHMf3X0+EEHFQ/W42q56tg+Ap5WsFg0MX0pHWJeew6sCBI4HhLz5xhFEN2ufF0DzTDynRFgUYJ2X9pOZSbluPBsBbZDl6fzWZhOzZGzhjBvo/tQz7sPsfYSBuofrEa+VwetlE8hgUohmZJBi7ngv3XLazScMJQK3o0Od+WZanwdY49x51gmgDUcZwCmA476PhWBxb/22KMC43DwMCA6j+fm81mkUwm1ZqgBZGGCNjArGdnIVIRwYtvehFG1sCyf1mGyv5KeMIexPIxbPjGBiSaEnBSDtp+14aKjRWABdgHbcz5zBxs+c4WJJuTsAOnsU4bQM/aHmywN2DGV2fAZ/qQ9WXhiRcro3L8aLSRqRVSOZJCXipxEpRyr2YyGZV7x3UovRPyb44Pnx/uDmPFV1bgmRufwRk3nIHAcAAZT9Grzeu59oPBoCv6hPMvFQl698eNG6fmmcYcuW9oeJECXBr8TNNEV9fYyvplKlOZ/jmosbER8+fPh23bKlVL6i0yvUp60MhLyRulkspcYxLDf+V1MvqGoJy5lzJyiDILgEp/kYZUwJ3HLIF4Pp/H6Oho4egrx0bOm4MXp6KC4KCruQu3v+p2AEDGl4Ft2eiY0eEan3gkjrsuugtrf7IWRtYATh0IYWUtvO77r0P1oWrk8gWeP37XeLz6W69GIBoAjKIHr5RiLw3c0oEgU5uksi91Nh3ASl2SYyudLhJgUfZIh0QpT6AEn1IHkv1gW/lOqUNS79JPDCnVfpJu3NG9lrxXFg+Tz9XfIQGxPl5cw1IHkvqevE8+X+oL0vGiG0v4N++jzD1d2kMp4EfiXOn9I5UChTpolvtWf7f05Os6rwTccl7JC6gLy3UojQD5fF4d3ZtOp12pajLEm+/j+qT+yWfLfU3gLR1Ksn9ss1VlYeuXtmLFF1fAcor8iP3QjW9yXepGkb9EZcD9v0xerxcLFy5Ea2ura4FzIdHKw4VKgAoUPODJZBK5XA6VlZUKUMnQ1HA4rPKPbdtGNpGFx/DA8LtDqQzDUB4rCRj4nQzp4IKV1jQZQio9zLwuFouNYeYEBFy8PNsxl8vBCBrAeGDpL5YiNhpDzpNTOUO0SNFDSoEZCoXU3zLHS7fUyXwTXisrvXNeCNLlMxiOTgAqNyoZBYWFzNtxnOJ5yQyzlp5h2eZMMIO97907BmwDwJQvTUGoJ4S0nVb3er1epYDwffybxgE9hF9WWGX4EauW8n4yKq5B6S3QLcWZTEb9nUqlkGnJYNd/7EKyNYnNX9yM1k+1IpKMuM7lZvuZX6UXnuM4x0fiqLi5AtPy01C9vhpVA1Xw+X0YrR7Fxk9vRKw1BhjAvjfug5k20XJHC+ycjYqKCmTTWSx56xJkJ2Sx69O7AAOITYyhoqcCI+0jxYE1gYELBtB1rAs1j9Rgz017MPPzM+Ed8MI6XjRgSUHIceLa5T7guEhDDQ1kXEM0uiQSCSVAeD/XjVyH0sAFnBKAQwZWf2A1HNuBYxWNVxxfRlrQU8T9xWvS6TQikYjyngMFha6/vx/19fUu4wGVIGkcSyaTqrgaCyXyHeUjwcpUpn9eoncZcIMF3YgNuOtFSBBH4v2MejJNU0WWUe5QV9ABE2WTrCgtQYgOGkiUfZTb0nBvGAac8Q56Ij3Ih/K4++y7cf2t16NmtAadTZ34+Tt+Dsd8acXan/Rj3uPz8MLVLyAdLp4jfOZ9Z6Kys1L1h7w4OBpUR57KPGBpyKBuQ5kOQJ2GogOXUsBKfi51Lwl22H/KOGmQZb6sLBDK+eWYcixZcZ6F2qTMlylNesRCKWcT50XOGfsl9SupM+t6NN93ujB7OU6loiV0Q41cs6Zpqqi/0dFRV5/ls3Vvuuyv7mmWRg6+Uwd6OsnncY9IUMznkOSa4Tt10kF2KaCpg1CpW8t7OPfUqWWetf6Ov2QgkfOe8+YQq43BODz2HPJSxgsa8mSoOrFCsimJ3d/ZjVRLCi985gUs/8FyhEfDY8ZD7g32WXrXpeHwpagMuP8XybIszJw5E4sWLVKMoaqqSh2Jwc0mKycDxXL/pmm6jnAAivnK9Ioxv4ckLW46AyG4ksxLtoOLlmFFstAGwQSv57NkZWuCTQoTbg6GrSoroQl0XNuBkbNGsObXa2A+ZyqQKIudkbmw/QxrkxZOMmxpyZYWRhl6w/byueyzDJ2RRg2OmQz5p1GEY0EQKYWMLNYhi81RCHpzXkz57hTsummXa70EOgIwDhhqbuUzOCZSkLOdHAvJwKSlj/3P5/OoqKiAz+fD6GghR1gaTiRg5/qTHnEVtTAlhYOfOohkW8E4EV0exbF/O4bp350Of69fjeXA6gHUbKqBP+9XVURZdZae1Ugkohhr7UO1BcBXdaoYzTX7MTpD5DIbQMc7O2B7bEz/03REJ0XRM7MHLY+1wDxmYvkHl8MwDBy85iBmvDADm/9lM3oWF3O8fd0+OHCw/+v7kWnOYPt/b0flpkpM/vpkBLuDOLnqJGoernEZMQhAZYidVPK4P6VSID0tMkWEniKOO9ck104wGFTrnPc4jgNYcFWG53cM66ZBg2uSc24YhbNYU6mUOnaNRqSBgQH4fD5VSIR7mu+0LMuVOsE9wr3T39//1zHDMpWpTK8IsiwLU6ZMUbJGB9T8jPJVkpTXupeP/IM8k3qBLNwodQx6rGRKEtsnAYwO7qTxXf52HAdb5m7BpGOTcOfFd+LA1AOq3XdceQcuv+dy/PHVfywJtmt6a9C+tx37FuxD1p/FWfechcBwADtX7nTlb3fP6Eb+hTw8WY+rHfr48nOOrQSO1EkkCNNzaKWMkQCEfeVYS0BXam50EEzZzzbpZ5tTfjGtUK4Jfb5LedH1OZN909uuAyppLJDpBGyjzEGXVbR155EOxPX2yLZzTUsDsz7mpQwa+v9yfkoZqOTcyX7Kd7L91NX0dpYCzxKQl1onpX7zXvlu+R593VlWoc4AsYhudOD7qT/ato2j448im866jP56HxzHgQ0be67eg575PZjxjRmw9hSBLnV9/vDdcuxln+IT4+i8oROp1kJtqN5Fvdjyji1Y+oulCnTLaFp97OVzXy6VAff/Io0fPx5Lly5FOBx2CQKCKLkBKEToSWRIhR4SzUlntWoyQMmoSLo1kIteCjS52CVIkGHrkply49FIwPcQwBI8AMXK5vKYLsMw0PG2Dhy88iBgAg+/5mHM6piFQFfA5SXUPe4yvIREEMJ268XbCJZlm6WwZvVtAhYpzFjFWhYR4/PkPDI8WmeaBG2GYSivoAr9qnBw/E3Hx6yX0Ash+Hf5AV/RIkeQynFmW7iGpKVcr6rOtcE+AEA0GlUGAa4nPk8vuMYwYj1Pzm/7YWXcFr5gPggvvAiECky3b00fDr//MIY3D2P2V2crxYogDwCCwSAikYgyIvH5HLtJGybh+JLjGGkZcb0LQ0CqNYWtH9yK6NQohhcNY9nXlyGXLkQyTLp9EsyAiTnfm4P8+/LoX94Pb9yLafdOw6FzDyHZXjy+Lbo0ikOfOwRfvw9DS4ZgB23U3FGj1h33ovRKcG2y2J5upQaKxiv5nSzIpu8dPSyTQiwWiyGRSCAcDrvWvrTcyiPBdE/68PCwMuBwP3KuZWV2KrUE3gy5pOFN9z4MDg6OWb9lKlOZXvnk9/tx3nnnAYBKP5OGZ8DtVdXBNWUXAZ30BAJwySzppWMEG5+np1NJ3kWAJfUXwB3uyzZ0tXTh2aXPwnEc7J26F+O7x+Nw+2FXm45OPoq7r74bq59bjXsvudcFokPREC76/UUYf2Q8Ju+ejKw3iyk7piCTyWBtfC0efO+DyAaymLRjEtbesRaBZAC26QYokt9z/KShgJ/R0E3HA3UIXl/KqAC4U5Qk6SBMfiZlLlOhOGaybbpxQL5f7xs/0z3oHAf5LOWAgTtsWddHuWY4RhJIy3VIkqCU4yWdClI/lPfrxiOSDoJ1wCU9n3Ju5ZhIea2DZx2IlwLEUr+XuqQ+x3KOSo2N7JP0Pst9K+eYz+R18l7On346iwTk+vsdx0HXuC7ce9W98PR7sOCGBS4dSQJoj8eD7W/djgOXHABMYM/H9mDaZ6fB0+VxOQd9Pp/CDXIuxhik0gbMtHuOvWkvLHts0cRSnnkZqVFqr5Wil5fpXab/MUUiEVxyySWoqqpSii5DxPVFmslklNebEyrzaR3HcVXi9vv96sgmXcBIQJ1MJpU3MZFIuCyA9LiyCBnDiaQ1UhY5k/kaMt+CoFVaaglUpYDkBtn7hr04fOlhtRJHp49i+ze2wwm6c3JkOJvjOMrrx3wPEp8rN5xhGCrvS+bVECBL8E9rKQD1DGmlkyA9m80iGo0iEAggFAq5jiHg96UKeXDzMgcaCaDhwQbXegl2BNH480Y4TqFSeCwWU+uGbSZAchwH2VwWeaMI1GTFR2npY185H9KQAhRzxrgOyWAYwsfxYai9bdvwdfkw44szED4UBhyg9tlaTPneFATjBQ9t/4J+dHy4A5m6DHrO68Huz+6G4SmGLcuxl+d+ch6BgufX2mlh8b8thm/kVBVOG5jzvTkY99Q4PHfjc4hOLRSw6TmzB0/84Anse/s+GJ6ioaIqWoXF31uM6o5qnPW5s9D6YCtqt9YCWqp3dH4U/Wv7ka/Ko+ujXei4qwO7b9sNT6VHtZHjzvnl2uSYyH0nIwW4nziH/EzyAK7LeDyOWCxWCH1KJlUUAA1CbANJX9s0PsXjccUvmCtPAcr3Ok7xHFt6ymW6RjgchmUVqvvzfikYpRGnTGUq0z8PSVAr8yLJI3SFHCjyIH4mQ3t1RZggSxqQqSNIEK6HsupAkTJEApe8WdCh8sjjV1f9CgMNA/jNNb/BttnbsH3OdqT96TFgm9Q5oRMvLHsBr7v9dTBsA0bewKt+/iq89nuvxfgj42GaJuadnIcZe2YU+mTY2LtiL8778XkYf3A8zrvlPIQGC0U1KTcl0GJ7pbGcOpMEe9RDpJdWAgDpLZRjKEGfDuoAuEJsJYCU48j76PCRYEy2U3oS9XWjR31JfU2uFwnIS3lzJUhWzgqhj1JvlTqQbsQgSRDGvsj1lMlkMDQ0pDz3cvyCwSCamprQ2trqOoJTB3ey/XKN6gYEPXJDjoNudJD3Sd2B48dnyDQ1SaXAr942Cc7l5/qP/E6CYo/HA0/Ag5tfdzMcb3HcdKOSbdsYDA/ilutvQf+4fnTP7sZzX3kOtjnWwOM4Dna+ficOXXSoiBVmjWL3d3bD9o41FLBv3HPSIMCfcHcYs26chdDhEOAALRtbsOQ3SxBKhsakJsgfOc9lD/c/IAWDQVxxxRWorKx0MVRubGl5YxgGwY4E1xIwSg83QR4ZTTKZVMo1GRRDu+np4uek0wk+hhFls1ml/EuArVvuyDgCgYACCQBchdMIQgzDwMxbZmJ00ii6l3UDBuAb8GHhlxfCm/Mi42SUsSGVSqn7A4GAGi+CVsuyVDiuBI1kSmyzDE2XFirJMGQEAJ/Pezku/L6yslKBYPkcAh2gWP2cgJ2WY6DgFc0hB7iLW8J2bGTtLHyOT40vC7YFg0E1ngxnH54/jMEPDKLt423wjxQAWT6fLzxfgEIaRbjWpLJEocVxk3kpzIMBipEEslp6aDSEhW9fiI4vd2D+F+bDdEwYloFcdQ77P74f2cpCegRMoH9FP7qv68b0+6YjlUohHo/DarCQN/PwJXwqt5zrndXXg8Eg6pP1uPZz1+K+j9+HKX+egtmbZuORLz2CRH3xbFMYQGx8DLFXxRDIB1D5SCUCJwvFyrInslj8wcWorapFJpfB/F/Mh7fCi6PnH4VjjWWcdoWNdEUacICdt+zE7PfNhnWi6H2WSgqPFzOMQrVbCYA55lIpIEhl3jfnQjJxepWlR5rvZQgmc7mZ/sB2SKVEGlj4XPKAVCqFcLgQQpXJZFSdiKqqKsVTWIRNWrNlZESZylSmf06aNm2aS5aSj0nZL9NtKF9lmDP/lmHEOniiTJZOA/5NuSoNx6XAAo36o9WjGPWP4tbLbsX1d12PP6/8M3ZP2409U/f8xXxsUuVwJV73u9ehaqQKV9lXIZfKoW1nG+AApqeg9UejBSNvzIzh6euexr4z9uHovKN47U2vRTAWBEw3YJRgRgJejhv7QflKnS2bzSqjfiAQUA4ZFiHlaRMSGEqvpwQ7vEYWVZX6H1MZ+R3bzh/peNHBoJ7HL50y1CU5X9KDL3VPGvTz+bwy7sq26JFf/Ft6ZHUjuARbuqdWesC5rpieqb+PumBfX58LdPH5ci3yHj6XpIPV03mydeDL50jjk+615XXsp3Ra6Z5aHSjKeeT75d9yD8s5lHvWNE2kK9J44M0PoHNqJ256w01415/ehapY1ZixMQwDv3/N7zFaWUwTHJ08io4PdmDmt2e61q5lWZj3+3mIToyie9EprNDnw/RPTYeVs2CY7hx7riPqTFKn4v7yeDzwjfiw8v0rsfOGnTjzP86E1/IqPVAaE2S7OYbSgfZydSDj5aJzwzDKZ778P1A4HMZZZ52FmTNnupRpvWQ9FzEBn6zWyWtyuRxisZhShPP5PHw+H8LhsKvoBBkiwSoZO49tkkxAD6FhGwG3ZzmZTLoYsVT+2R+Gm/n9fjQ3N+PEiRMqV1S3RKdSKSWkfQEfXvjoC+hv78fM78xEzaYa1Q49fJogTA+hISOQ+T8k3UDAfrEdZByy6jb/dxxH5b2WCvXlmNOSls/nEQ6H1Tzwej1kSQqZk1ecxL6P7nO12Rww0fTVJlTeV+kC/hwDaVAZXjWMzm93wvE7qHy0Eu3faoe3zzum8qf0ZBKwyaJ8nDvdSi6t4oxYkB5bzhMFKsEhv09MTWD7x7ZjdPIojGzByDL5D5MV2MtV5XD4A4eBLDDzpzMRzoZVRILjOCq/nKH4oVDINfZpI41nP/IsepZqZ3CTbGDpT5fCPGiiakuVMnxwHKqqqrD1LVsRGx9Doj6BmgM16DqzC/ngWEt8ZGcE4z8zHpGuCMwKEwNrB9B0vAmhgyEkEgk1x9XV1RgdHVVMmXPOiBS5H+S65LqTio6sqcB5kkJet7CTL3D8KXBk8RJGY9BgJ1M/5DOqq6td9QrIdwj2gQJvuOWWW9Q8/S3JcfQzdspUppemsq7yt6VvfOMbqKmpQTqddlWclhE5uiwuBX500pV/HXRI/iiN9Lr3yzAM7J+wHxO7J8Kb8eJE1Qnctu42HGs59lf3dfaO2cj4MxipHsGVd16JpmNNiofKisiy3WlPGs+sewZbzt6ivqvtqcXFv70YjV2NLpAidQD2IZVKuWrzsE800lMfDIVCCiRzfBlpJoGp1Msk4KKslrJFEvVQOkkk6A+FQohEIrAsSxXElYYT+UwJQtkOCf5077SUY7yHwFbKNznmJDpTWOCT6V65XE71gyHG0iDBdrHN+rqS75Kf6+tT6sxSx+P3UleVoFQ+Q++bHC/9Wnm/BJGyDzLaQUZ7yLGXbeS75ZicjvR9LufXsizk6nJ44XUv4OCyg+qeKcem4NqHrkXtYK2rH47jIGpFccs1t6BzWicAYPz94zHzP2a6nFcuQ5UHWP/h9RhuH8a0r09D5eZKV18ZwUfDUCKRcDnreB3XCtcGHWHch6WiA+QYco1x7C3Lwq9+9au/qKuUPdx/R/L7/Vi6dCnmzp0LoLjYyRTkWXD8jgCHXmW5iblYONkbL9+I8586X4E7Cax0xiDzpuVZgZK5hEIheDwexONxtSGZC0RGDwATJkzAkSNHlFeXirzf71eetK6uLti2rayEjuOo6wGoRW3bNvLZPBb9YBG6W7pR8WIFYLhzeMjACWD5uW5tN00TTU1NiEajGB4edlUVBwrMWR5XJi3qZCAyH4weYvaRobaScUqPv8fjQSgUQktLC3p7e5WBIxgMupgTlYdcLoeeN/TgyDuPuNaNkTYw7jPjEHosBNu0XSBfFgUBgNF1ozj+seNw/IXnR8+P4mjgKFr+tQUhI+Qy8hCAK0UhnXatKcncZaiZHBc5NlKYy+rajKJgn717vJj5rZnY86k9mPzQZLTf0w7TU7Bu2x4b+z62Dz2rCmA5H8xjxX+tUEoGAFWsS3pPqAR5vV54Mh4s/cFS7HjHDnSu7By7EU1g07s2IXgyiDn/OQe1L9bC6/Wq8HWv14uFv1yIbH0WqcYUQttDqNteh80f3OzK3QMAwzIQqYrA1+fD/o/sx8CVA+jd1YvJX5yMcDyslIShoSHk83nU19fD5/NhaGjINQ8yHE9XTPSxJy/g/xQWMgdbGr3kuufnLqF4yjhCD7tuEWfudj6fx8jICOrq6hAMBl2KIQ1VlmWpmhRlKlOZ/vlIylvpZSb/+Uvhq6dT7vk35Yr+PglyTve9YRjY3b4bfzz/j5hxbAbO33A+/njxH/8qsF3XXYdFTy+Cbds4Y98ZSHgTiNZFMf74eNhwe9Ql0JDgRZ4vDAAOHJU2xToYwNh8XtM0lQG5FNiSPFyCRnmtBFBA0Wmjk17HhiR1LanbUG9k26PRqAvEybmU86iDSKCgt1RWViKdTiMajbr0Wdkfkg42ZR8oO2kEkUenyehE6tK6rJVt1QEtdTk1jxoAlfpyKfAtDU26B10H7vKHz5GgThqU5P2U+XIf6WNYqn2SpD7K39IJpBu0pJGEfdKf5zgOTMscgypt2MgjP+a5tm3Dk/Dg/FvOx8NXPwxrv4UpP5sypt2S9zi2gwX/uQD9Lf2IbI+M2XccOzoY9dpYEjPISAH+r89LqbUsx13e/3KorCX9ncg0TUyfPh2LFy9WIaRyM8n8Esl4GCLqOIXQaVouqWhXVlbCH/Dj8Tc9js3nbcajVz0Kj9edgymLJtHKQ8EYDAYBFMOC5UKkEk2FmuBUeukymQy6uroUGGUOC98nq1cT7BEQy6rOCjj4vNj08U1ACmja3aRAv7SYMm9VhnQTcEirYyaTweDgIKLRqGqvtCzTKCANE1VVVZgzZ46rwJhklPl8HsPDwy6wTcspifNLkDI8PKxCwEzTRCwWQzqdVp5NJWwsE/0r++H4tNCejIHqp6pdOT0EtVwL7L/xnAGzxwT4CBtoeL4BISNU8P6m00hn0jj4zYMwvabKwSWDCQQCmDVrFhobG0taU2ncYcSFrNBumiYcwy0kpFLGMbYsC+M6x2HhDQvRem+risZwHAc7v7wTPSuLnumTZ53Ehn/dAACor68HUCjyls0X1nYgEFDhzSMjI8poEI6Hsfzm5Wje2lzIyS5hqE02J7HjozsQnxFHIpLAgY8cQDgSVmvK6rVQsasChmFgwlMTsPZjazHz9pmFZzlA4GQA8788H9UnqnHoC4cwcHnhnPH4nDj2/cc+xP3xggKQLR45F4/HXYoKDTP6vpeWfo4frbRci1IY0sMg0z4AjKnlIPP+yB90hYs8h/MsIyMcx8Hw8DCi0ahSAhKJhCqkCBQUsVJ5eWUqU5le2TRlyhQ4TiHKiPKRukwul8PIyAi2b9+O/v5+peDK+iHS2J/P55HL53DL6lswZA0VjpMUBTJ1z10+n4dhFmufwAAOtB3Ac7Oeg+0UeFFncyduu+g2DFcN44XZL+An1/wER1uOujtxin/L/w3bwCW3X4KqwSpc+5trAQtoHGyE3/YjFA2hrattDCihHJahw6ZpIuVJ4ehM9ztjVTGcaD3h0iX4LF2Z1x0qvI48nlEFEviPjo4ilUopWU6gqBsvdABMGaADkFLeURoKKFtkkTwJ0Cj/JaiVMo3geHR0VB0xJvtKfVhGCsq2SdknwZf0fuqRFjQu6MWzCNL5PvZFfh+LxVTIvgS+cizlu+R6lf2QEWdSh5bf87n6NTI9QzcElALTsm3SCcX79Pfp/Sg1rqWcAmoLif7KtkdiEay7Zx0mH5gMOMC4/nG4/r7rMW5o3GnnLtwfxrIfLcOM382AlXWnCMh+0unmDDqIbIq4QtupK3OuZcSp3l+px+rFl/WxKmUU0fkBP385VA4p/ztRa2srXv/61yuGKUNtyCCBIqgjI2B4gwQtMpQ77Unj6Suexs5VOwGzIDhWPL8C5z5+LqyM5fJi0YvORci/aWVsaGhQXl/pNaRXWh5txe/4P88eJHgio+FC5QaRIdXS+pjL5ZAL5rD1PVvRtboL4eNhLP/YcgRjBW9wLBZTgI2bngCE4UayEIc0DEjvnwQ5EtxI7za9qQT17AM3Jg0PLPLFv0sxwmQyiYqKCrUBCbQlAwiFCoVUOq/rxKE3H4IdKApIq99C+6vb4e8pgHNpvHAcR7WTnsZcLgcrZGH3b3cjMz6D2m/Xou43dfBahSJn6UAah288jNiaGEJ7Q5j14Vlw+ouFOmicYT4354d/s5I4x1la69MtaRz6xiEs+PgCWEOWi6FKoW1Zlqu4CIV2JpNBOpjG+p+sR6q+YMQI9YVw/ofPR9gJq/cl5iWw9bKtWPKdJfAlfKisrFRtpPAMhUKFQnoeG56IB/fdeB8c00EqkkIu6M6vMbIGDMcoHCd273TM/PNMhAfCMB1T7RGudduwsfPNO3FiyQmc8d4z4E15ceQNR3DgugOw/W4vgrfXi3lXz8OJd5xARU8Fau+rRa46h+BwUO0ljgPDkTi2VCh4KgGFAI0mPEaN64FKid/vV/UZmFIhhUAul0MwGHTNaTKZdJ17Tm+3LFYHFNJhZDh7OBxGdXW1S5HKZrM4cuQIHnroIRXe+LckpxxSXqa/ksq6yt+OPvvZz2LatGlIp9MFfp1OIxgMKlDd2dmJAwcOIJfLoaGhAU1NTcrQLA27uVwOeX8ej5z1CJ5a/BRCqRA+fvPHUZ2sdkUuKbDm2Dg68SiemPMEXvvwa+FL+3C06Sh+eP0P4cDB6x98PWZ1zcLXX/t1jFQVT62wchb8WT8SwUJNDytvYcXmFbBsC88seQYVsQpM3DsR5959Ljx5DzJWBgfmH8D9194PwzHw1p+8FY3Hi8Zn8jqCVMB9RKht28jbeeyfsh8PvfkhZEIZGLaBGdtnYN3v1gG5sR5QoHhiDHUcHSgztNs0TRVOLvUxku60CYVCMAxDpfLpgFsaTXRvuwSj1KGy2aw6KlJ6b/WoBZI8FUY3LJTCGtSNZfoa14Lu5afOTMeDdAZwHKnfMQecKW6cK/ZVHzfdMCL1WN2zKR0SP2rB1AABAABJREFUvIe6mhzv04FG3iP7Kq+VoFRGFJKkMYLrRhow9D7q9+nXyLk5nedaf04p7zqj3WpqahCqDuE3r/0N3vKnt6gj8TiuHAMWch4YGMDg4KAycsjxkQYVrhcWfia+kNfIY3wTiYSrWLN8JiNFGVLONSXHXh8LaZTiPMsaUP/93//9F3WVMuD+O1BDQwOuuuoq1NbWKgYkCyoRQMvjtvRNRzAljyDK5/M40X4CD7/uYYw0FoVMfW89rr79arT2t7qsWAy3IQiVgFfmlhB06xWQ5aYiyOVzCdJlxWIyTP5PRZ79YniS4zjIRXLY/JrNOHxBsUJoza4aLPzuQgS6AqrtXPh8tmRsAFwgW3rP2UduVHr2lGdYeMXZV24g3kOwLJmwDO2VDF6OGdsrGZwMh5chRVu/sxUjC4tz2fKuFlQ/Vw2gwMhqa2tVlIIUAARf7HM6lMbo9aMY94txatyy1Vmc/MRJjFxWfH7Dlga0fakNRmdxbpMzk/Ae98KKFkPz2T6Or6x86ff70d3ejX3f2IdcfQ7Vm6sx/ZvTYR4ppgtIMMg1Ib3jck3ULKnBo+9+FHbWxprvrkFjthGxWAzZbBb9C/rx5CeehO210fpUK+b/Yj6q0lWunHaOB4Enj8ezLAu7Vu3C5tdsRjZcNFqVopU/WYkJT0yAY7vPLOVvaZDK5/M48LYDOPSqQ65Caw1PNsB3wIfjby8c8Tb5e5Nx4uoTmP656ag6VOUyIMnj/qThivMuQ56494Gi9VoXqLLNVODIVxjZEo/HXSHl9H4zV5ARD7ZtK35F4cQ9UVFRoXIJWSxw586dWL9+vdpLf0sqA+4y/bVU1lX+dnTDDTdg9uzZCsTE4/GCITedRjKZREdHB44fP674jc/nQ1VVFSKRCCoqKpQ8tX02njvvOTy56kn17HED4/D6e16Ppt4mAMVwXcuy0DGpA7+85pewTRtLty/FzCMzcesltyLnOWU8dYBr77oW406Ow23X3Ia+xj4YeQOr169G25E23LXuLrSMtKBxoBFXPHkFbNvG/Wffj7VPrIWdKnqvNs7eiPuvu1+1yZPx4LpfX4f2Q+0uGSiVdvJn8mTmiO5ZuAfPXfccJu6fiMtuvawkYJPgUdcVJFijY4HvZ9qO9LxRZki9qhQY57s5xhJoSmDMOQaK+duO46ioQ/0dpbyush/UJXgfv3up9lF3oN4hASGfJ3U6jpOM8qJhiDoInykdLnJuZJvl/5L0turXSWOMrvvpejfXj9QfdUCre7hLAXk5tqVI1w3kffp6PJ33Xu87nyeNKexvIBBAJBJBZWUlIpGIyyHG50jPfzabRTwex8DAAIaGhlTkqIxskBEY1BdTqZQC3BwrOsuoD2cyGRVBI+eIeiH5kjxyWXrepT4mx6QUduJc/fSnPy3ncP9vU3V1NVatWqWswECxKp6+uQG4wB8ZigwtoZea5yI2H2nG+X84H4+8/hFEa6Oo66vDpXdeivqueqSN4tE83AhUqHUrJ4mbUV/YZArSo0uwIQEjjzXjJmbbdW8+GTutjllvFtkKNwjK+XNI+9Lw5DyuXB1pBWab2Q+OH4t/kRnLHGLJvCR4MYxi8TTZZmkUkbnDOrOiVZht4qaVhWUMw1DM37Zt5eHM5/MYXDGI5Hi3V3Dk2hFEtkZgjBav5zvokeb9uVwO8fPiqDxcCU+XBw0/b0DeLlpCs1Z2DNBMmAmkPWkY6cJcpOemceKzJxA6GEL7je2wbMu1dmQxLYK39PI0jn3gGHL1hXYNLx7GgX89gKn/PhX+Ab9rbKRS4TjFPH4WQDMMAxV9FVj8X4uRT+fh7fEi5itUw+9Z2YONb9mojn3oOrsL3qAXK/97JTLRjDICcI/wfWSY1dXVOGPbGTAzJqLjozi56CQmPTMJHed1ID4u7hqX59/6PPKBPCY9MEnNm5x3n8+HZDKp0gKm/3I6jLSBA288AACYcP8EOBkHx95WzB089MFDAICDnz6I6d+cjqFFQ5j4p4mAXVQsuI4Z3cKx476R4ee6EYkAW1rGpXHKtm0F6IPBoLL6E2jLtTxz5kx0dHSo53KPEHx7PB5VHTeXy6G2tlgAhUf0lalMZfrnoba2NnUcIPc3PUGUazSyqaindBrd3d3w+/2IRCKIRCIIBALIB/LoNXpdz08jjd5sLyKxiKtg2J75e3DvxffCNgvv3DR/EzbN3zSmfUPBIcw6OQuX33k57rn6HszeMRtnPXEWkskkzk+ej3mj81BlVyld5rJnLiuchx0uFgA1WrRQWcNBLBxTskkCFWlc172xwy3DiMQjWPuntZi2d1pJ8Ks7DqS+oQNz8n7dISDlkjTC6uCUbZQgVfZD1/skkJBOBqmz6mBdeuxLAXs5VnKN6KBbgkapD+ugVT5PgmagKE/lUazSSSD1EL5XB8uUu5J0UK2/l8Q2SQNGqWfooe/6+zlO8rNShgzZj5cy7Mj5kWtPf16pa+W86vOty3uOL4uWSbyjt0f2nZGoenqoBPKlsJBuHJB7ifqUrE4udSQZRi7vk0BcXwdyfomD5Fzq3vDTURlw/w3J5/Nh4cKFmDp1qvosm80qzx4ZglwAspy8DCGRnmIuZKAg8CYenYhX3/xq/P4Nv8f5PzofzYlmBIIBWJaFqqoqDAwMKJDHhU4roHyuXGhcPDJ0hW1jyI4MBQHcG5uVueWRXPKeQCDg2izeIS+W/mopbL+NEwtPIDAQwOJvLkbgaADpbFp5APkeyVCB4pFdDIXmBqBgAdwWtUAgoI484uaQOdFklHpYraymTgBK0EtLmmTsbBeVEYbV8RnSSOA/4Id3xIvMuFOeQQeo2FkBI13MQzpy5IjLqssx9Xg8SKxIoPvz3RgcHcTUN01FPl6sdA8A3m4vmr/QjJORk0gsTcB72IuGf22AfeLU8WgTHHR9owuZiRmkZqRgh2xM++Q0xdgsq3Dcgp0vnkGeyWSQ3p4GugA0o1BUzAECHQH4kj41Rul0Ggc+egCzfzEbnrTHxUDz+bwrd6+npwcVg4XcaZhQDC18MAxv3ItkVVK9p66jDvlkXhlgOO+soMuCYUDxyJbpz0+H43cQ2xCD/7AfFc9X4OmvP418oLjWba+NzdduhmEYmPX4LJV3SLBNRYRj6/F4MOPOGfDaXkTro5j808mIzYrh2JVji/XEZ8TRcUMH0g1pJKclMe+b85SCyXUhBRPXZmVlJeLxuPpchppzfbDvAMaczy1/+vv71bohuKdF2XEcHDt2TIV48VnkG9xv/J7H3FVUVKjzSss53GUq0z8XLV26FOPHj3fVhZEFV5PJJBKJhNIhGF3DiKxEIoFoNIrKykqEw2G0vdCGPSv3AAC8aS8u/9nlaOxtRNpKu4o91h6phTflBYJQfP+C2y9AMB7E3W+5G3CAC++7EEs3LYXjOGg53oJrf38tagZrlG41ff90mEET2UDWpRNIA7tpmli3ax1CVgi3nXEbAODyX16OyR2T4XiKKXFA8SQJ3eAJAJnqDB542wPIerO48tdXwm/7YXiKwEGm9VGvkICBRmOCRFks13EKtXwk4JbAUwc11Bcpp2QkoIwWlF5N8m6OiWwD9RfZft5DQ4aUWxJYcax1b7Xef92QwPfIqE/pUZQgS6Yysr+yf3LMpSMLcEeLSYCpG7Cpj8jvJFgjcS75OdeP7Bvfy7bqhhKpJ/GZEqDqQJNUylDAd0gHEz+T4LkU6UYgvV2yzewfw/epJ0mPvjQmyHlizRg5LxKXSN1/96t2o+7eOqDTvfb1EHA5/tLYozsVZdQldXg9lFxvV6moDmmg+EtULpr2NyKv14szzjgD8+fPV8zG6/Wq8EtOjAxZMAzDtThpmWOoQygUUvmX3CSZTKaQL3WyAW/7j7ehrrtOCcR4PI4TJ06ocAsyI+aEA0UwQ4bMsvlAgSHFYjHs378fIyMjcBwH0WhUhUwxlEMKLeYTZ7NZFfZExk2mxCOeeIYkKTAcwNn/eTYadjdg9ftWw3/ErwwU9K6zL9KSThBA4SKZDzc3Nys3E40GHHsJngC4Nhs3nGmaCAaDChwxdySVSingTmswQSRBvdfrVdXJydgZDmMYBvLePI6/6jjik4ue1upbqhH6VaiQY2wUQ94pJDlPlmUhPieOzu93wm62kZqWwr7b9sGsc4cge71e+Pv9aH1XK0JbQ5j2ummI9EQKhUFSMRz44QFkJp4C+wYwfNYwjnz8iMptyU7JYv939sMOF9ZSKpUqzPNIGLM/ORvh3WEgB0x7eBrWPLoGuZFCiFfSSeLwhw/j5OUn8ewPn0XcG3cxbKnEyLwbKXxM00QkGcHaz69FqD8EM2tiye+XYMZjM+A1ioYSFueJRCIIBoPq3PGRkRGMjo4WQXjSQU1PDfx+P1oGWvCar7wG/kQRoMIGmvc3Y8GLCwpjbwAD8wew5d1b4ASKbeKacxwHSAMT/zgRC362AOFsGHVb6rDsc8tgJSx4Yp5C8bZTlGxLwg7Y6Dm/B7s/vhs7P7gTfXP7YFomslYWOX9OrWuuteHhYQwODqp0ApljR+XWsgpn3tOY46oOLwrcMFfJcRwsXbpUKXf8fHh4eIyFnEYGKl2JRCEvMp1OY2RkBD09PYjFYkgmk2Ms/mUqU5le2SQLlergirxIRoxRTkuP1ejoKDo7O7HH3IM/v/fPxYfbgNVjIR6Pq1zLY8eOYXR0FNYhC1d9/yqYtgkra+HsP5yNti1tePRVjwIAzlx/JhZvWQwvvAqkjxsYB7/hH+PpoqFAly0qHDuRxYL1C3DW+rNw1c+vQvuedhcYlR5Y/s3+OY6DEe8IfvvJ32KwcRCjtaO49X23orem12XspI4inRqnc7ronlcdIEhjK6/n97rnrpReJIuF6UCO1zOdj+/U51gCc+kJl/2U7ZagRu+PBEtsD/sgIxN1D6sE+XqVfII/6r1yPmVbS0V1SsAuI+f4XF7PZ+l91eUgQRp/qJfr4F8fDx0wy5RNeQ3/LzU++hzL7/XicdKDezqQqq8TSfIz9lF/tj62UW8Uf/j0H5AMJF2h5hxXgmB4gX1X7MOea/dg/Q/XI12THjNXbJs+1xwfqevI0HHqorLNutFFN8Do6533vxwq53D/jWj27Nm45JJLFCOXRy5JZiAZYS6XQ2VlJbxeL2KxmLIWy3BQoOipLWWp5UTLnFYZpgEUPeckWZyEllRZRIBt50ZnNUygWOWSRgH+zecSLFChZz/ZZ+bZEhx4PIVjyGThJ4bBypAQUiwWU4BZCiv2RfaBxgbJRGSlcwnk+T83p2SIfI4sxCatnBx/CisaDQyjEHZHCzUZpu230fn6Thx9k1ZNFUDz25tR+WylykU2DHdVdL/fj5yTQ8eLHWOqm1c8WYGJH5hYEBTIIjsnC9+2oteZDElZPCcYOPyfh5GangIcIHJPBBM+PaGQvzU/hQM/PgA7ZKP6gWpM+s4kmAOmAm+O48DxODj64aNo+3pbcd36bRx/23GcePMJ1a7KvZWY8+U58B0rFgO02214Mh74h/2ufUJBkqhOYMu7t6B5VzNan2tF1yVdWHLfErUvOAfxeFx54xlRwDOik8kkQqEQWltbceTIEbXe/H4/gsEgTrScwHPrnkNPaw+ajzZjxVdWqHV1cu5J3P+h+wEDmHHfDMy9bS58GZ/yDDNsiYqo4xQKCQYCAXSu7ES4L4wDlx3AwLIBBAYDiLXFxjING1j+1eUYmj6ERDiBGT+bgWA66PKgcJ/IAoByrTL0nPuVc0OvilQEpGGIhfz4fBktwH3F/Sgr+0teRiPSpk2bcPLkybH9+xuQU87hLtNfSWVd5X9OkUgEb37zm3H22WfDtm0MDw+rIzJzuULF6b1796K3t1dF4QDFNBfKbtah2f7V7RhaMaSeb+QMTLt/GlbesVLJJPLnwfGDePgDDyNWH0Pr/lYsfWwpnrj6CQw3Dqv7z3v4PKzcsBJGruiJSyQSSg+izCVwk+k65GO5XA4pO4Unlz+JnpoenHP3OfBH/YpXMvVG9whKIP3QuoewZdUWOGZxyU3dNRVX/fIql/evlEJOvszryO8p76mfsCaJbDt5PlA8gUUCD9lOCQ7lO6XnMZ/Pq3Qv0zRVmpA88lFGRurgQxoKSgF6+V79HmmE140j+jOoV0nvKIEUnUKyiJpM3eIz5LvZD2moke2Q48kxlKCOv6U+LcGenBs5ZrpeqzuKpGdaxw8SEEuPrJxT2Wf9Hvk+3eiirxf9O93gwN+BQABVVVUIjg8iV5tDw2DDaUGobds4UXUCv3/N7zHQMICqPVWY+9W5CJwIuIw2lmXBG/DiyKVHsOUtW9T9vm4fZn18FsKHwi7swrGjo451p3gNDT/SaCG92zrIprFH7hEaKoBi1f5yDvf/Mk2ZMgWrVq1S/+tgTebgys0uk/Vl+Al/pBUTKBbHoBdL5lOTEfJZ9D4TGBBY8xpuPJ7xS2JVP8BtdZMgncIoEomoI4/4TirqevvZRgI2AOhu6YbhGKg4UKE2JwGDZOzyf3qNJYiWm0UaETgX7AOt79yUOvNnO2UIDvslx156OnmdtM7Kezm/7LdhGEjUJkqC7dDGECLdEbWJpTeVAiWTycD0mKi9tRYDbxpQ95pxE7UP1qp29L+zH7HrYhj/+fEIrw+rdSLHpTHTiPDXw9j1kV0I7A6g9oZaZHIZJFcncfyG47BDhfEdXjeMk/6TaPhcA6yEpebRyTqY8I0JgFHsb96XR2J8wtWvbDiLZF0SvmOFuc2Mz6DjAx3wJXyYfdNsVKFKGVxM00SuOoft79yOnuU96FnWA9tvY+7tc5H1ZF25fsFgUEV9sJJrIpFQzLSiogKmaeLYsWNqD3Ad5PN51B6sxTU/uQZ7l+3FzI6Z8E0onLW+cfJGbHrHJvAM7r2X7oXjc7DwFwuRS+aUkYlAtbGxEaOjo2rft68vFNypuKkC3Rd2o/pYNTa/dzOi7VH3hJvAC595Qf3rWA5mfHcG7CYbqfoUxu0ep9Yc16VcF/xMGooYEcHwf6B4/ry0xpumifr6egwNDSlFVfIhoBhWLnkS3y/5ysu17papTGV6ZdDUqVOxfPlyAFC6BKOFKBNjsZgq0KnXOtHB4LSvTcMLdxV5nWEbCBwq1ISQBunu1m48+9ZnEasvGCi7pnWha1rXmPYNNA4AXsDIFxXkcDisIs9kpI8ELolEAsFgUHkvnzz/STy39rlTjQLO/t3ZyrAq62dIkjL0rD+eBSfhYMtFBUAw58U5uPCOC11ODh0IyXbpIF46HaTOIUkCPI6bHuZKotzXAbb0BrKtNNhSh5MeYvabzgiZQlfKKyq9jy/lIZXeWqkz6x566eBgu7nmpN7HuWbbdYDIZ/HduiFAjo18rwSYsg0SmLGvcuzZRl6j3yN1Td0YUgpI61EOssq7lNN6P07XNx1kSx2xlFwv9ZkCrAEDD1/0MAYaBnDtg9eioa/Bteb4vt7GXty97m4MNBT015FZI9j9kd2YfdNsRHojLs+6DRvDLcPuNvhtpFpSCB0MqTGV60WOlZxvkhxDOfZ6pIKeJldqnetj/peoDLj/h9TW1oZzzz0XFRUVClxL5ZVKsfTyknFks1kMDg4qi6VuCeWG5ebjbyrXMr9SblACwmQy6Vo8MpyDlh4ZziSVarmICfakJZLndAPuYxqAoqLOTcx3SK98rCmGTf+yCY7t4Mx/PxO+kz4XmKCQoUVJ9o9toCWTG4xAHhhbpETeRwale6xlBXO9SBvbLqs3y+91i6UUhDIk6aVyXcMvhuE76kPezruMKrS0K8XGMDHue+PgpBwMvmsQcIDmjzaj4rkKOIaDvg/1Yfgtw3B8Dk5+4STaPtOGyLaIa20AhXQCY4eBCTdMgHnChAMH3qAXzoADY9TNQLK7ssjEM/DkPGpdHP/ccbR9tQ2wUUx9GLIx4VsTYPgMDK0ZghW1MOWzUxA5GEEeeaQ9aez53B6MzCxUTs8EMlj5pZUKxGbtLDZ+bCN6Z58qsGMAO6/dCcNvYMHdC5ShJBAIYHR0VDFKHodi27byNNNAwyrdpmm6jpULBALw+XxoebwFqAB6W3vR2dyJKV1TsM3ehiyKhqiG7gaYhgl/qOABZ8VvuQ6l8cpxHIQ8IUx5fAoAYNn3lmH9J9dj1r2z8OLbXiw5/ycuPIFUfQq5ihxy4RzCJ8NY8u0l8KSKhjrHKVY4pfGA0Qvc4+Q3XPeMuJCGPuZeU2GmIsV1LgE855uhhlS2ZDvKVKYy/fMQU6KAIgCizOMpJfL0Bl151//f/8n9rufbPht73rEH/pAfs56dhUAggEAggLA/jOpsNYZQ9Ia339eO/gX9iLcW0q8WbV+EdY+tQ8AOIG8VAYsENzpYAYoAl/z/sYsfw6ZVxWJsHUs6kPalce3N18IyiyeeyIg36QElj112/zJ48h4kxyVx7v3nwpv1wrTcY0L9jW3Vgaf8jvyVwFYHLOTjOkCQuqMEibyulPdYjpUEhkBRX5JOHflsmZstw7/l57pjSLZFjqXuTZXhvrK98tnSSSVDtmW/dNAunydJn49S9+gAWc+Tl+2VOfnUh/Vc5ZdaC9Jzro+NNGbxt3xuqVz1UtEIMjpV9lH2QzeYyDHh31wb9732PhxYcAAwgFsvuxVvve2tCEVDat/xOcFEEOFY2DUOgd4ArJjl0qNN04SZMzH313Ph5B0cufgIkANmfX4WqrdVAx53RfdSxicZQi4Lokk+IT3epcZDN5Tous/LBdtAOYf7f0T19fU4//zz0dzcrPIoHcdRDB2AK+xULk55lpxkcPpRYRKg6wuD38mCbFxw9KZKwC6BuczBksCB+Zh8P0OzJVMjqOAmloXSJLhlGLpcuJZlIRFK4OEvPIyRthFE26N48htPIldZODePSr20FDKHlO+XDFgPP2coMcPKLMtyGQek0YOWOWkp5pwxLF7m8egRB5wvxylY+5nrroqWiSMH+NxsNgvnuIOJ35roWkvBzUFU/bLKVVyNY8yweI5hJpOBN+tF04+bUPPrGox/x3iEnwwjk8mg5zU96H9jvwo3z7RmcPSbRxGviiuB5PV64UQcDA8PIxqNwrPXA0/co54dOBrAxLdMhOekB8gBNT+uQcPPGuB3CuHa3rAXx288jv4r+3HgpwdgVVhqLXu9XgSGA5h0wyRUbqrErDfMQrgjrPq+87s7MTKjeEzZ4JJBbPzUxqIykXcw7U/TigPjFM7mnnTvJJdSwTUnlYF8vnB8Cq3bsl4A55d1BAiaR0dHkUwm0ZnvxG/f9Vs8fvXjGG4cxrVfv7ZQBC5nYsWvV2DmszMRChTOwubzybiTySRyuZyrYIgscujz+VB9tBrnffI8THt8Gpb/cjnMnAlP0gNPQtg8jcJ4RKdHkWhJoG9pH5769lPI+kvzEHpAuF+ocMq6AlyzXIO81+/3q/XKPcI5TKVSiEaj6ug/rneG2+uCqJQCU6YylemVSZZlob6+XtXHIFCQQHBwcFDxYvIIymH+Tf1l7w17MXTm0Jj3ZCNZbH/9diTPTqK+vh7V1dWoy9Yhkoioaxo3NsIwDSSailFT1SPV8CV9LicGMPbYTuo3yWRStZ3Ogw1rNmDzis2wrSJI9Wa8uPiJixEMBF1KOeBW7AG3l8xJOlj55EpcePeFCMQCrnfL8dDBMZ8l03dktCFQzKOXRg32WwfZMj8bKIa8cmyk11mCB+mJraqqQmVlpdJBydvlnEqvItute1+pG+meZt4nwbB0eEgAI98tx0y+R3oyJfCVfdUBqHwmP/d6vairq8PEiRMRiURc4y3HVBoW5Bhwnvls6pd+vx/hcFjJThmZpoeM622Suj/XBnVT6uU6HiDRaaXnzXMds8CZnv8u5Tv7oEcgyLbx57m3PoeD8w+qqMATjSfw39f/NwzL3Yd8Po/wSBiX3nIpxh8YD9hAwzMNmP696bCGi05KAlqv14tAOoA5P5uDlj+3YOG/LET19mp1jc/nUz+cFzrSJF6Quoo0cHDdRSKRwvnhp2punQ5U62PAdfdSjjRJ5Rzu/0cKh8NYu3Yt5syZo6oyS/Aq/6Y3VVqMuEm5gckspbdKbjx6llh2n98xV1MuKAKroaEhWJaFUCjk8ubKhSTnn6CW7QeKTIAAR1q9GZ4lLZsS4JNJ+Hw+BWyz2Sye/sDT6FzeqTYnHKD5qWYs+NoCVwg428G2kMHI/GzTLB57lsvlXAXqbLuYnyrD6nkNGR+jEmQYrgxhlyFEFFDc8Bw/CsFAIOBipNzczP3NZrNIjUth8582F/sPwBwx0fCNBlT8oUIxahZY0624uXk5hA+EYRmWyvdixINt2+j/dD+G3jAEeAHPSQ8mfnYiPM95lLc8MymDI98/grZPtSG0I6Tmi+NIBp6vzKP3Pb1o/FpjsYpshYPBjwyi//p+VUG26pkqTPjSBPgGfa5+61EC6XQa3lovdv5oJxJTCwpU5bZKrLlhDZx8YXzjc+J46qtPFc+3doC1X1qLxo5GtV8Y1igZI99B5p7JZBCJRNTfZMBS8eH1xyqP4YkbnygeU+cAF/3sIlR3VuPgnIOY++BctU/ZHwpbhnQBQDweV15ueovJzFntHgB8fh+2X7gdTc83wUyYePbGZ5GzcggMBDAwfcB1tjccoOJIBZZ8aQmqh6qRd/IYbh9G7dFal5GIv2XlffIHCgTue8lfOA5cs/Sa82+eLkCDnoxe4Zpev349BgcHxzLJvwE55RzuMv2VVNZV/mdUU1ODr3zlK8jlcuoUkGw2q/SIbDaLrVu3oq+vT0XS0aBHIz15CmXxru/twuBCN4/wpXw456FzsGzjsoL8DuTw5LlPYsOqDS7dAIBLVtaM1OC1t70WjccbC1+dUnx5UgWPMiO/P50H/t6L78XmZZvhWA6qhqvwqttehZZjLa5QbwmSpVJNnQgoRIpVVFS42iKj66Sjw+/3K3kk9QdeIwvY8iQI6WGn/NLlGO+Xz2SbKSNlH6Q3m3V6CM7j8ThM00QgEFCfcT51z7TunZXeRfmbbStloDUMQ6Uo0thLou6je+epC8loQxq+ZcVpHSCzLXxvKYArv5N6uL5+5LNI0tCtg1S5LqQDSm+HfJf8W14njT8cd7n2pCFCGgdkX+Ua0ME+f+vRIvr4SVD+6IcfxfEFxwEDqO2pxfvveD8qY5VKB2E70uk0otEo+vr78NQ7n8Ksf5/lMqSUArtSd4lGo+p4Ujr2pFOL0TdST+fz9QJ+LE5dVVWl0maYoshxlQYPzqvM3yb97Gc/K+dw/z3INE0sXboUc+fOdVkVJXPgRFMpB4qAUVo29XAULkx6Nel1IslnkuFwQUtm4fV6UV9frwqS8XN9U0lgS6OAtLJKiyQ3pxRgIyMjqK6uVp9LwC+973yG1+vFqh+sgpEycHRNIY+57bE2zP3uXJiWuxgI3y9BvC60pIVWFs9gf3gEEq2tMoSczyRw43sJWvQx19eAFIAyBF4aVigsCZ5N00T/Zf0uBcJIGqj/Vj0qb6tEKFwIwaFXQbYTAKJnRtH3tT40/KgBFbdUuPJ7yQCbvtkEM29i+NJhNHy+Ad71XjgozHV8ahzdX+5GdkIWnf/Vick3Tkbk6Yir0Jsa7xETjV9rVIYEwzCQrEgWKquz/QaQbkoj3ZSGd8DrEkS5XA520EZidQK1T9YWBGHSg1n/Ngv7/20/PHEPpn15GuKZuBqrF9//ohtwAuie0436XfUucE0ASMVKRpfwOoZ6A8XaADTWcO68Xi9G543C9on5NYA94/dg4X0L0bazDblwTnnGZdFAvpNAOhQKqUcwKoOAXypu6VQaM++ZqdbDOT8+B3FfHON2j8O2N2/Dzgt2utqSqcggNS0F7xYvDp97GHtfvRcLv7cQdZvrXEKEe4JrkWtICgbJA7iGOY7sCwUdlT6uCT5T8rBYLDbGK1OmMpXplUsEk9JbxIgxKqSUsdRBALjkJyPKKPfP/PqZ2PXxXYgGoxh3cBwOn3sYZz9yNpa8sAR5pxAVlg1l0dvc65KNkeMRjN86Hp2rO5GqTqFmsAZXP3A1WnpaCmdqC8O5bANlggQMMiTaMAycd+d5yKfyOLLwCK647wq0drUibxf7Q11GL7RKohFZ6kZ8Pvm+lFmUP7qzRDcIUHbJcHK+W+pSbIM0/uvATYJV6in6dfJ7qbfJmkPSQCvbxH7pYFHqcNIZJPsq25/JZJTRlvJLGoTZTpI0pkhAWgqs8V167SOOj9SHdYOA7vzhnPFeeRoNC7jqY6UDfDlmUncpBfzluOnrhO2TspztlMZ4/Z2kUgBcf6+8T86rbCN17RX/sQLPv/t5JFoSOP935yMdTSMeirui69iubDaLfC6PuV+bC8d0pxDItrAdMn2U+gbnWY+eYHs459SROJc6vmCkX6nIiVLrT34nx+HlUBlw/z/QokWLsHjxYhfDkpZGhhVLKxcnlEKMlhkyZqB4jq6shMe8Yhm2w8lmpWGGr8bjcZdXlwCfjET3YsmcZ5lzIr3C/J/Cg0XSWDlTKueyKro891sP7TEcA4t+uQhm2oRhG5jxyxlAHrBR9FryWhkepPcBGJsrTWOF3NixWAzhcNiVa65b7Xg/hbduWeW8cOPyPXLzcx2k02k1b2SGjHI48r4j6LrOXQTGTJqo/kM1srlCyFAwGFTecApJ27aRXp3G4A2DyDfn0fOJHjghB42/a3TNL4VQ/ffqEXk6gvCmMPL2KSvtFKD7q91IzSpY0XO1ORz55BG05drgf7xYnZV95XrgbwDwn/Sj5YstOPGVE0gsSsBz3IOGTzTAu98L22urnGJarQ/dcAijC0dhmzYqH6osrLsuH6Z+fSo8aQ88ox7kPYX2HbviGJL1yTH7bc69c8ZYZKWSxz3GtU/BwJQCni2tW7pp2Z/y5ynwJrx4+v1PAwYw876ZmPrrqTBRLMgWi8WUcJWnB9C6Ko0vUgGSSqkUDNzPXq8XFYcqUOOpgRkwsfzO5TBTJrZfvl2tjalfmIrQnhB2v2E3Oq7vQD6Qx7b3bcPC/1yI5o5mxW9kFIluhJD7imtK1kbQ889puNCFk7QmU1EqA+4ylemfhyZNmqT4B41uNFgCQDQaRSwWGwNI9Wg0yT8qUYllv1iGbSu2Ydb+WZh9fDamHpwKx3KUXPaEPEgGi/w/MBjA0v9civFd4zGvcx4efsPDuObOazChawJsw3bxIb5XN5aTdC+ebdswYCDvz+OK26/AlBNT4JjuM6nlsyljyN9lCht1IamQS55Pkh5IjhHfJSPneL9MC5TgVjpzTgfAJZjTvfylQATlhzxdR3/26QC9Dp4Z7UnnRynAKOWKNNLzfxmhIL8rBRBlIVupi+vgVD5bN5Dwt+5YkTqn1DvkmEjniNQxZHSf3ke+T7ZRv17+6OtIAmN9LiQ4lnOjO/XkMzgu8nsdYMs1KN9LI5xt25j7o7lwxjvwd/sxHBwuhJCfSvOTepoeHSExkhwvOhSTyaRyluj90Q0lch70OZd6o3RWyKOOdaOaxB9yP5QyPvwlKgPuv5JmzJiBJUuWKMuvzCOQlbWpzHJiQ6GQOrJAWuVKVXuUTEpa8WRFUOkxzuVyatPzvSSdCcgjveTikQw3lUqhr68P4XAY4XBYec4AoKKiQhVjY58TiYTrHdwIBPWO4yhGzn4F00HM/dXcQp9SFkxvcUzYPwJ3aczQARPbTeYuIwo4PrTUSiASCARcjJKMG3AzJBpIHMdRG5NjQVBCZs826mG9UgCPLB4BStSYovEil8spZcZlEZycRee/dyLXcOoYtLCDvvf3wRq2UH13tcs44TgOMrEMvM95AV8xZMvpcVD5cGXhGDALgA34t/rh3ehVoT7+oB+D7xlEaHMI/heK56Kr+TMc4CjQ9L4mdP2qC41va4S/x4+cVfQe2LYNw2dg71f2YuTcEcAEDn7iIKYOTkXV5kJFcs+hU4UFUXiv6TMxPHMY+bA7F2blN1fCSluwHbdRRa5bGdbDseT6I0BklAHXhCIP8NRbnsLcW+bizJvORHRJFEvuXQJP2IM4Cp53Fhjz+/0YHR0FAJXzwz7LtSBDAKXxiGuIxhr+L41iZtrEkgeXYMYzM/DMG5/B3JvnwjxgovOiTuy7bh/ygcL4JJuS2PzRzVj56ZWo6K1A5/JO5CI5ND7SCMsoWnGZQ8e6EVxjTKGgdTeVSrnWuMyRlx5yXXFi3YUylalM/xx05ZVXKsBEh0A4HFbAM5lMKl5KeSMjjSgzbNsGPMCeG/ag7U9t6G/qx+ErDuO4fRzv/PE7UVdXh1AoVFB2AwY+c9VnMFBdPHkjU5FB16outN7RiqmdUzHlV1NQHasGvEUgxDby/0gk4gJnEhyTbNtG1sniztffiQOzDwBBYMJdE2A5RR4nPYvSmEoZqxsWpKznu2TqH9tBOUC9kfqFBGi8Ry8CK0GQlCvSc8e26eBCBwV8h0wB4OfyeVLfku/lZ9JjyHHjuFCnksZf6X0tBVR0ECoNB0AhbSAWi6ncaHksk5RL0uGjh1/TOaQb8XUgq4+XDgbZPul9lf2Q7+T8Sp2O46dfr+vxHLtSoK7U+EkHmNRj5TOkd1jq3KWMEKXWjWwr9YdsNgtPunDUayKQUOvacRxUVFS41jvrKkkjiWyPHJNSAF03SlA3lONWaj51Q4Q0AHAtS92SRXdt21Z1bfS1UAbcfwcyDAPt7e1Ys2YNampqXOBPt2iRCevhyzIfxnEK+TrJZFJZf7hBWBRBes/YBmnBA6Byb/x+v4uhk7jAucFI3JTMzaVgpRGgoaHB5a2XxTfo8dZBITef9ATT6k3h4TgO0tk0kuOTCHeF4YcfWatoIZPWPgp1KTikV04yccmU2EYKY46HtFLx+BDDKIa9p1Iplbck87U4l7TkEVBLAUPGSQHA0GOG+zPcOHw8jNFpo8VyhQ4QOBhQa4R55NIIkMvl4D/qR/OXm3HiiyeQr87DyBio+0Mdqu+pVuF+juOoqAcCP4bq5HI5OFkH4342DrbfxsBbBhB+OozmjzSrcHNv2Iue63ow9MEhwAEmvnEifJuKxWksr4X+df1InZVC3Rfq0HZZG3weH/JmYawIyv1+P/pe04foiqjqZ74yjyOfOoIZb54Bf9yv5oZ5wcPnDGNgeVHZAoBgfxCjW0YxMjKiCpKx+jjnnQKc4Y66NT+RSIyZo0wmg5GREVS2VuL5q57HoZWHcGzxMVzz1WvQ8McGZOwM0nZaGWr8fj9CoRAGBgbUOjp58iSGh4dRWVmpCttw7xDY0qjDtQsUQx2lkKACppSXNFCRqsAF37qg8H8TUPliJdIPpXHk8iOwvTa8US/m/3g+QidD6F3Riw3/uqEwaO8CVv3rKlTurVR7l+tfViLPZDKqMjD3Kz0THCuuRwrjUCikzuIlX+EeL1OZyvTPQePGjYNhGC6jGw2PADA0NKT2PHmKdCIApwBmRQ6HP3QY/Wv68buzf1cIFTeAHHL48Yd+jC/c9wUEBwo1YU5ETuAdd78DP7zih4hWRwEHqNtRB2vEQsfrOzD+ufGoidfAQVHhJ/+UXi/AnSoneR/7kg1n8eC6B7Fv7j7AAHYs2IFAPoDzHj4PfvhdwEiCHvJBCd6ZqqZ7mSVI0D2KMoKQAElel8/n4ff7VaiyXg2e76G8kEZo/pYeSV6vh93zb+pKsi16GLAEwJRVcm3I+aeM5vhLGaH3QQdH0rghn8vreRqJTG2QRgsZdamPq+y3DsIlkJR6vASB0mnFZ5CkbkmSeoh8ttSbpe4sdVq2Sf6tr3H+1gGxfLYEp/I+6td8r3RcyHmQ8yGNPXwm9XHiB5neapqmSqUEoIxhMuWTz+R88G99jUlsoBsNJA7gWpDjX8oowT7IUHYZySCNV/xeRjLK9SPn/C9RGXC/TKqrq8OaNWvQ0NCgJkEHlkBRAEkhpAsGmUskNzOV4VAo5FoQuicacBeNkOHm3AS8T1by5Gajt4vEs4slYy7FxCQz4saQC5PAh1Y/yVwoNLxeL46efRS7X7Mbi25apCoO0iBBIC8Zg2VZroJeZBC8j4yX1ZlZPIMgSJ5nybGkAJVnc3Ms5BFSMvRXMkYKKqDISOW46ZY0noPc8HQDes7qgWMWPq94ogITPjkBpuX2FlCI8z2O7aD2yVqY/2Gi61+7UHtbLcZ/fzzyRt4Vvs61yfQEwJ0vlMvlUP0f1XDSDmp/VIt8rgDKbMdG/+v7MfSpU9VkDaDzJ51o+0Qbgn8uCP7RV4+i7/N9gAEYKQMNX2tAPl7MV5LjXfWzKmSdLAY+Ujgv1b/Dj8lfnAzvqBfZfGFceSxXNpuFtc9CsCuI0aqCBzl8IowF31+Aungd4CkyVzJwgr/h4WEV4cE1qQt7rh/uKdM04a/xY/2l69GxpgMAkA1m8dAHHsIlt16Cmr01yrNLK6xpmqitrVVrjf0dGiqMVyAQUAoSf0pZzw3DwFDDEBzTQU13jTLmSIHD9/kNv1Jyw6EwFv1uESzHwtELjmLez+aheUMzjq48iu2f2O7Kqd/wpQ1Y/I3FaNjcAI/Hg55ze1D9YLVa+9xT0rDD/cHvpaWce80wDLU/uO6l0lKmMpXplU2TJk2CaZoKNCUSCZX6ZhiFkx0Y7ROPF47pknoO+UE2mMXhdx9Gz0U9hQdrDqCMmcEjgUdwTvc52Ne+D3dcdAdWPbYKbUfbsKt6F1qea0GkM4K9b9wLoAC0Lnr2IvCkRvInyj1Zr0Ma6Pk/ZWvan8Yj5z+C7Qu3FxtjAF1NXRiuGEZDssEluyXQkp/JwmeAuzK21KX4LJnXS+OlLLQrdS0a9Um61081WwAPkgQllCdSP6POpgN1vf0SCOmgj8+UbZBjJcGh7lVku07n8dX7Id8hvZU0CshisYA4E1roXhJ0yc+lM4bv5BqWzimpT0jwL++X60QfG9lu+Z1cn9Igz+9lTR0d/OogUr5HNySUApzyB4ALJ8i1qwN7CcTZXt1RJj3+xCn8jjocPdzSQVKqfV2rulD7aO0Y77sE03J/60YgGeXAyF49n1sCftlvxyk4bGKxmCsqQi84LdfQX6Iy4H4Z5PP5sGrVKowbN25M+A9Q3CRy0ggcZcgNwTCZmrTC6QBNhqWSiQDuIyTkkRNsp9wIktHrQJFeWF1ASA+yaZrIWTnsumwXZv9ptiu/WzIdCgy2icxbWoXped173l5su34bcsEcNr93M+Z8ew6qdlTBNE1V9IwCh89l3wha2U4pGAh0+QwCJn1cCXDYRwmkOV8yLIXC1XEchEIhZeSgEOO98vgvjj/HaGRkBOFwGIOXDuLA+w7A8Qom73GUMkLBYxgGmpqakMvl0N/fr9phmiaq76lGfjCP6mcKOd+8niSZNIkMhevHtm3U/FcNPF4P4DslUBzA47jZgeM4yKUK1w+/eRh9H+lTbY1eF0U+lEfzvzbDMt3FU8iYan9aCyNhIPraKBr+rQE4CCSNpPLaU7ClGlPY94l9SM0p5Jb7B/1Y+P2FqNpaBcd0C2vDKFbsp2VepnewHdLwxLUkmfELb3oB+1e7z4aFDeTSObXmRmtGcXzacbT+uVXNLavW870Myx4dHUUmk8Hw8DD8fj98Pp8yCsj8pUxDBs++5Vk4hoMLf3UhKgYr1P5ju5mq4TgFD3NvVS8GJw5i3OPjMPfWuajfX4/xm8bD8BkI+ULQKRfJYfu/bMec789BenIaHa/uwMzITISPhOHr96HqcJXLcsvCglKg27aN6LQo9l+4H9P/NB2V3ZUqioa8heupTGUq0z8HXXnllaioqFAG7mw2i8rKSsV7yeekHsC/pXfJcAxYpXKnTtH5956PhesXYtu0bXjkskcQq4jhoaseUt/bXht7X7NX/f/UiqeQ9WZx2SOXKT1G5lbbdvEEFQDK+C+NAvl8HpZpwWO45dy4nnG44p4r0NjfCBvuPE1dCddBDfkm3ylBrgSjEsicTn+UDhF5BKN8biklXwewBDr8Tge+sk3Sm8uTNDiOOhgt9c6XAtkSnEnjrO6xlGOrO6F0x47U22SUA9cdPfWc71J9lm1leySQln3WAbI0Suuyr5TRQDfc6ABYri99XehGCf258v9ScyoNTy9lwJDzVmq+X+o7oFhzivqwjAYNhYqn4DiOg1xdDnvP2IvGuxrHhHCzn3z+vov2YedrdqK9th0tP29R7+OakMCX75RRvnx2KQ+/XGuSh5RagzqIL+XZLmUAKUVlwP0yaOXKlZg0aZLLGsJNSg8QULQSEbhJjzDzmQko9YUmF6u0QBLYMS8KKOYjkClnMhkVni7BoAQgBIAyzEYKBn4mc5Rsw8aTn34SA1MGYBomFj2wCAaMMeCGP9KyxfALMr5EIoHOczux7bUFsA0AybYkdnxmB5Z9fBlCJ0IKUEuLuew3+0EPMFA0LAQCASSTSZelm0dsEAxLoM2xlaE07HcqlVLvCIVCrjxVGg5kW3K5HOLxuCqeYlmWK/S5oqICMIGRuSPIVol8Vxuoe6QOZqaYq88+ddZ3Ir4qjpqf1MDyW0jHixEJlU9UImfn1DEtjCbQ86T4WwJwafWTwDeZTCL0ixBq07UY/PxgIaT87RPh2eIpnIO6MgEn6LbkJS5MwPJasBzLZWUGirnVDX9qQPCxIDzHPch53HnuqVQKVtjC3u/vRbqt2L9sRRYnF55E1dYqNVdUbKQyMjw8DABjCoKR0cu9ysJhXBtTnpriAty+hA9X/vBK1IzUwAybiDtx3P+v9yMdScOT9mDi5olqbROgejweFVURj8cVM2eOYywWQzAYVOGBodoQ7vzAnRhuLLT7rg/chTd9+00w40WvsyxwBgD5ijwe+uhDyAazWD6wHFXrq9D0QhM8voKS2/B0A1ZnVuPpTz7tSlMInwxjePYwjlxxBPlgHnuu2wNPygMza+Lcz54L83gxVI8RADJnMFoTxQufegHJhiSGFgzhnI+e4wpr1A1TZSpTmV75VFtbq2Q55SG9iPl8HqOjo0r+yeg7GqVJnpQHE380EbbPRvfF3WM83NuWbENgZwCPvfoxxKviY9pxctlJGI6hQsgNx8CynctgGAai0Sgsq3DcKdtAfUh6umQUH1CQfYFsAGsfWYs00ti+ZDvC8TCu/931qBqucoWrA2MjEKkvAEXnitTzeF08HodhGApsSBAheaf0ZsqCrTTgc/xJOoiX90pQK3O4pRGEAEfqbPKZMqKObaROJu+VOkWpMGQ5bno+uQSDsi8S3OrgUDqNuM6YxiUdYKWqkEtdUgdFMjKUz5cAje/mj/482UYJ8EpFFPA++a7T9Vnuo5cC2xJfSGOBbvSQz5ef0zkj3ynbw/fLd+vzK4/RkrWTZP42+5A1snj4Qw8jVhXDzL6ZaHiwYcw8OY4D27FxcO1B7Lp+F3LBHA6/9jDy2TzaftMGHhOoG3T01JLTGQkkeJaAu9Q4S2MO9TM6eeTY/zU6UBlwvwRZloUzzjgDy5cvV8BKhgsDbq+1nEQJQHUPmwR7DBmVIcScQGm1k0dXkamweJnMmyZxQciFJ4GytDICxUISBPfpcBobPrQBvbMKx3Rsv3Y7zJSJWX+eBa+nGPqdqE/A1+uD4RhqIVII0MOtNrNhF4puCXKsIlNgThMZpr4JCLpYPA6A2gAE5rqAAQrGDgIKetLZd5mXzWdxrmWleD6bRhMWm6KnlcJXRiYoIR3yoPOqTpy49ISr7+0/aUf70+0wqgqVPTmmyalJHP7FYcAEctEcBl49gInvmwjzZNGqns/nVTV06WFgW2hs4NxyTchUBypVXKO5XA6RX0VghA2ENodgvmjCsE6FG/d4gBxcHMNz3APTMGGZlmu+uIaYI+RP+mH5iuvfMAzlQcm0ZZBucStGDhzYhttaTAWFZyXKtSv7QK92Op12nSFtmqb6DHB788MjYbzpe2+Cd8gLmECsIoZbP3ArRmtHAQN48r1PIvSDEMbvGI9sJqvabxiGWg+hUEj1b2RkRK1hgu94PI7NH9yM4XHD6r0j9SO46y134bqfXucSxrFYDFVVVYhWRHHLR29BojIBGMAzn3wG5/z7ORi/b7wak0g4At8WH8781pnY9aZdiNfGUXOoBjPvmImNH92IfLAw9/lAXhVce/imh7HmY2vgGfXAHCmuW/Yp15LDk995Ut2bbEri8e8/jgs/cyFCQyFXCgP5V5nKVKZXNlVWViIQCCgZRq8xDWvJZBLDw8OwLAuJRMJVHVqmt1Hv8Ca9WPzDxdhZvRP9k/qRaEqod/W29GLX2l2Yf8d8bLp+E7J+d+HFmqEaXHfzdbj5rTcDJvCm378J406OA8xCO8nzmRYm04akjqTn8VqWBcM2sPSJpUiEErj6nqvhj/lheooRRlKBlkCNOgBlFwEViX/T2Esib9c9yrJd1C30cHLqUBLA6u2SAEMa2SVIkCDPNItHydq2rYq7UZ+h7ig9uuyH/C1DcvV8dNk2kmyXnCN9rHVvfimQC8C17lhbRLZJzoF8L9/FZ0snGfVz/q+DN6nbSVDK5+vFktluaZyRUaqcDz6H48u2yv7ooFHeI8EtP5e6t270oO6rRxPIvkqMoEeTEi8wNFwaL+R6yWQyiEajSFWlsOnfNyHWHAMMYOe/7sScxByMe3rcGOA7PH0YHa/vUI4522+j69VdCO8Po+75Olc/pJNTzoU+9/rn+vdyH8t1KA1NMrWW37OfujHndFROwDsNWZaFmTNnYtGiRWojAW7m6fP5XMdjcYFJZZwTyTBTVgEmYOZCoeIqqxYDUJ5ITrAMn2WuN72x3MinO7hdWoJ4H4GbBL25XA7dU7sx0jZStEybQOeyTox4RlT+Ut+kPjz2ucdwcuXJMRY1tkMu3vbH2jHnljnwpE6F5x4PYuFXF6Kip8IlAPTQbMk45EZg/6Twk9ZZzqO0xsmwdwJsekz5fsuyEAwGXcoHx9bn86G9vV15S5nTKzcix5me0GRLEgffc3DMbjMdE07eUYAtEokA5wKdN3cCXgAWcOKTJ5CenMahXxxCbE7MxWQ4X1xfDJ+WwFuuG649abHm30BBoQgHw6j7aR38m/yKaQNA0xeaUHl7pbIwRrZF0HRdE1LxlFrv8lmJWQk49Y7yAHMNkiFz/I//9PiYcQmeDGLqj6eOeWYgEIDf71ehb9yLFD7sI5+tH23Fueyc2YkHPv6Aet8Ft1yAwFBBYA80DOCut96FaF1UrX3HcrB7zW4YlqHGeGj5EHpX9qr9Iz3osr/xeFxdM+9z81D/fL16b/Vz1Vj9tdWIRqPq9AK/34+qqioYhoFDKw4hHUq72nFo3SF4fV6XALdMCy0vtOCyj16G2ffMxjlfPgfNu5qx6KeL4B8q5vKTcqEcHvvBY3jxwy8iVh+DYRk4uai4hw+dfwh5n7sQWiaSQdc5XaqIGvkXj1wrU5nK9Mqmiy++GM3NzcpYS4MuASCjdsjjpE4kFVkZ/Rf0BXHOj87BeR8/D+N2jVPvmrpxKi68+UIs27IMqx9cDV/Kh7nb52J813i0drXiLb98Cxr6GnDdH67Da+55Ddp62lxH9/Bd/FsWypJgR4IawzCQtJJ49NxH8fOP/Ryt/a0IZAKugq7yXn4GlA6rle+UwF4nKuuAO5ycehsjBCg3ZIQTdTXqVnyOBAMkCcIkINA9tgTHUneS4ySN57JP8rlsjzwqU3pxZXskSJSAmbot5ZjUT3R9ju01TRPhcBjV1dVqzH0+HxobG1VhWkk6MCo1dlJnlfMn50qCYD5XpmKy3/JUHH0NSDAv75dREnKMuH6pf1IH5djLlEbZV8633j7dUMBrZZSn/F6Og8zD5juloUA6q6j/8iedTuPw/MNI1iSLeMICei7vgRW0XIYN0zRRd6AOi36wCIGBU86REQ/av9eOqmeqXO2Wa1RGIpQCzHJs5LhL/VLfv/o4cI3Ivknjycuhsof7NNTS0oLly5e7chAkmAqFQmhoaAAA9PX1jbFscQNx0uRmIaOVFf7kQqcgY042AZ3cWNLqKRm4/JueWnmfbduuEGTJeLmB8vk8Wje3wvyhiU0f2YRcOIf6XfWY//35CI2EYHgN9E/px4vveRHxcXFseOcGLPQtxKTHJ7nAoN52y7Iw86GZ8Ka92H3tbsz7r3mo3VGrgAYZaj5fPO5DWmdlzoYEnLZtKyMGNxLzraW3WwoCuVHp8SajpMGC39GDnEql0Nvbi/7+fhdg53iS+BwKtshoBE2PNqH7/G7XGhtYPYCaJ2pg9Rb65Pf74avzwTDHWsuyzVkc/9JxTLhxAsLbC0e0xGfEYdaYiGyMuPoKFIUKx43t4Tjxhx56mfsiBQ0BlmEY8PR7UPOrGqRmpdB0YxMwDFX8jUzZ6/XCXmCj/4v9CHQFUPfhOljZYuihHCfTNFH1n1UY+MpAkRHbwOTfT1bXMqQtl8sVwt5DIWVcksyUCgv3BPeCtMZbloXDCw/j2eufdQHKbau2oelQE0L5EDJWBnnLzTynb5yOVb9fBY/hgek1cXDxQTz72meR8WawMLgQ0x6fpoQOAHWKAT383FP5bB7zvj0P+96/Dzknh4nfnogTsROFcPNQSIWosxq7J+mB4RTXwsznZmLZLctgGiZgFRVEy7KQSqXg8Xiw4I4FhbWAHNrWt8HKW3jxXS+i5dkWHLrkkKtf/Uv6seMDO1BxsgJdq7ow6yez0PpEK6b/ejrsmI2Dbz1YuNAB5t85H/60X80F57EMuMtUpn8O4nm50WhUyQGZysPUKvJYCeIoX/TcYcrHoB3Eip+swMa3bUTNSA1W/mmlkp/LnluGykwlFuxagP7GfngMD6pj1bAtG/8fe/8dZtdVnQHj7zm33ztzp480KqPeiyVZslXc5IqNDQQIJZBGSyGhB0IJJYFQQi8BQjUlhG4bcDfGHcmWLcnqvbfpM7eXc35/XL37vmfPNTjf88v34WT288wzM/eessvaa613tT337NxxOpV6/tQTq44KYHzuqg8ft19zO5668CkAwG8u/Q1KkRKuu+e6gJeS71JdxnGcwIkXBMZ8hwI7BbKqM6oext/UdxQ0aP42+6ON36lRQYGh9snWLzl3/Jx/Uxdt5CHmvHBdbT1UnTq6Ro3mQpsCQNU30uk0isUicrmcoS2+gzqO6imMIOMJM/b7VCfWz9WraRtT1Otq0xSvsedDf9tgTddSwTVQj0DUyFcbKKuOrv3SNdM5sWlf54H91v1jr4mOR//WfceoTC0ka2MZ/p506yQk/AT2/OMeIARMeWxK7czupI8TG06g985eM7+u62LaU9PgfMXBlr/dgtmfm432e9rHeaV1n9pj1blQMK8gW+lc8Zreq/Ope0f3tj23v6tNAO4GLZVKYePGjWhvbzdeMwABpun7PgYHB8dZ7aiQ0nIE1Auo8RkaHq4MVfN3FAxq7jYXn15mfseKfxRifJ5Wb6ZSrsIkHA4bL5yCUACY9MQkXPLRS/Dk65/Eqs+tQrIvCTfqIjspi81v3ozs5FruVSVZwfZXb4ef9zHz0ZkBBsqwrmq1Cs/34Doupt0zDamdKaQOpVD16jnwNmOht52GBwouVo9W4E3Go+NwHCdwPrfmTHGuWFnadV0Tdk7hzb6zfwy9YggNQ9hSqVSAGdDjSWNNKVnCyOKRIJH5QPpAGk25JrPhS6USWh5owcrRlXj8k4/Xr3Vq1ydOJhA9XpuL8uQyTn76JJyog0h/BLNePwso1Yu1qYFBrZNMQ9AKn9lsFplMBqlUCr7vm0rzGg3R964+DL1iCMl7kuh4RwdCfSGUvXo1fD6z0FnAqU+fQrm3jPLiMipfqWDW384ax/zIpHNX5QJzMvf9c9HxSAeqoWAVftI0j6SJx+NmznkNaUZz+bQg2NG5R/HInzyCXEvwnTMOzkCoHELVr2Ly6cm46bs34Ud/+yNk01nM2zEPV/7iSqSQgp/wsX/2fjzyikeQT9eA5tOvehoxxDD3kblmf7shFz6CR7QZYVT0seTrS5DJZeAOu3Cjrsl/isVipup63wv7sOe6PahE60aU03NOoxQpIRFOBJScUCiE5uZm8x4N7+p+tBtrT6xFy2ALnJCDg9cerNMUgP6V/ehfWSvMt/v1uxEuhnHu4nNY8vUlSMaSePqVT2Ptd9bi4LqDGJk2Aq/sYebdM0210QnAPdEm2nO/pVIpE6qtoFlT6QYHBwHAyD89KlQBBPkxAZCRk/0pbLh5A5rRjFg1hkg0YuTtip0r4DgOJvdPHqfQ2vnDQF1XYF9pmKeuAwSLQVarVfz8hT/HtuXb6oN2gN9e9Fv4ro/r775+nJGWhnby0mg0imw2GzD4cvzqBGH/9HnqbVZABMCExKt3ThvfbxeEUpCsgIHrYJ+iAtRBJiPwCO6BehSmGi4UdFPGK7jmszSqwTYQqAdR15HP1hQGz/NMPRQ1YKhDQb2bfK4NQtn4uRrobRDF/qgnv9H88287ZZBzoOH+OlalYX3e7wK39rwr/fAddrP3oPbZNhro2G2Q3GjMttHGdszY/+sahcNhRKNRtDzYgpSbwvHrjmP5fyxHfCyOBz78AMamjyFUDWH6fdMDz2t/rB0rj61EbE8MVac6bl14ndKqeve5LxoZerSPAAL0p9cprdugu9Gzf1+bANxWi0ajuOmmmzBlypTAZiIAAWoLpUdpkdH4fr1YGEOvKJQ0TFrDhzRcg0xXhQjfRwbJhdZzt3kNLYAKnNUiTAHAsFheSy+m7/uoeBVU2ipIZmset+ShJDb+40b4OR8ealat6Kkolv1sGR5/zeOoxqtwqg6mbJqCaY9NCwgBv9nH5rduxoJvLkDr2VbsfeFepEZT6L2/F81HmuGhNr+FQsEAMO2bAggybcdxjHFBjRe6UTkurg0FJL3mDDlXhl0ul016gDI8GksoVBhCxe+KxSJGR0dNEQ/OJ/Pz/ZCPR770CKqpoNe05aEWdH+8GyWnhNbWVgP4RyIj2PXGXbjgXy5AdmEWpZYSjl91HO072nHhJy+EH/YxMG0A+36wD17z+YJ8k0vY99N9WPCnC+AMBAt7Mbc9Foshl8sFrLccOwDjYS2Xy8jn65XEQ8kQzvz1GQz/6TAQArI3ZBEaDSH5b0k4FccYhRzHQdWv4uS3TqLUez6v1wHyG/I49YFTmPyByYhGo8hkMrXr0w5O/utJ5K+shxn1frkX3Y92144qC9WL7lEJIKBWxup5nhkrAb3SIPdUpi2DB1/3IPItdYAYKodw2a8vw8rHViJfzKMcL6MaraLlXAte85nX4JaX3YLLvnwZYtEYIokIBpoH8OtX/9qAbQAopUrY8sot6OzvRMeBDjhxBztu3IHEyQRmPzYbTmk8/boZF4WhAuDWjSE00F1wwQXYeXAnRqaOoJSs50dHChGs/8V6NI01wQk55gxtGt6UTyQSCbP3XddF7GwN+K/67iqs/K+VuO+f7sPQrKFxhYzKrWVsfddWeCEPw4uHcfn7L0fvA73Y/NbN6JtXq1C/+c82wxvzMPXBqaZY40SbaBPtud2WL1+O1atXG2BCGaCAQIuysqmHj/yHv5lOpPpM03ATYvEYoonoOK+1ggTKcOWbKsMVBLB/tpfK9hK+8K4X4kjvEYy0nTd++0BXfxeuuu+qQEioAhRVqFXfU8VbvZfqhWRjhJmCb3UgADBgnkYOvluvVY8y5YUNdNhP1f8U7NleVuoKlKFA3UnSKGSduiv/V31Cr9V5Yh8VRBod0QJ8qsMqONb32CHAjBil/qVgmvOha6X6j86VDdrt59hgTOeF9yg96v36Y+v9CuR07bWpUcAG4doHzr0917aBwQbYSu96r64nr9O+2A5Hvtv2JkejUYTcELoe7kLLQy2oJCt44MMPYGhRTQ/Z8fc7EClGMPnRybViieff03SwCSW/NG7OFQDboFtTERR42/8rbnsm4K7vU0dWI6PMs2kTOdzSEokELr/8ckydOhXNzc1GoQVqTJM5vZx4MkD+6KZJpVLGU5jNZpHP5xGJRExxB4I3eqQTiYT5m7nhtOqxQBc3KT1iujlsgtL+KYC0wySKxaI5a7hareLERSfw4PsfxNn2s8hkMrVjNfIIMAW/6mPS3ZOw5DtLEM6GMe3+aVj91dUI+/W5yTfn8fjrHseZlWfw8L89jIMvOIg9r9qDLX+zBYcuOWQInOOIRqPm7Gg9G5t5PSp4XNcNAF8KLQoTtfgSFGvujBpGeD8FCYUj545GD64/Q2lotW9qakJzc7NRSAqFghECsVgMqaYUvFjQGukUHKS2p+BWa97NgYEBFAoF5KbmsO/z+5CbncOBvziAnvt7sPyLyzH9julY+/61CFdrfet7XR+8lDzTAUpTSzj0xUOozK6N04QCzqyg1F6PhiB9qeBhNXbSBOc7EokgvyaP4TcOw5zwEgIKlxeQXZQ19ECPh1/1Mf3PpyP29Pm8YR9ovqMZXe/rMiGJQM3zkFmbQWlVKcCBBq8dBLqDFnk97sGEtp83DHBt+/r6AsxfoyvoyW8ebsaa768x7wqVQ1h972osu30ZqsUqIokInrriKdz36vtQ7CgikU/glTe/Eu1N7UilUnBdFz3FHtz0g5uQHkzXecZoApd8/xJ0HuxE2Sljz417sPulu/Hkm5/E0fVHxwk/0lIqlTKeI443n89j6+6t2P2C3Tj64qPmHeHRMC741gWY8tspCIfCAUWI91Ox5Z5ShcdE1JQdhPIhXPn+K9G1pwupcylM2TIFoYIUDIl4tcJxUzP4zZt+g0de/gj6FtaPg/PDPg7ecBB+uu5lmWgTbaI9txt5CPks5QX5CsPJbWCgBlCgDkopZ/hsGrmpDDPCDIAxCKvHnIBMU8rsYz41hUp1A/4oKHBdF+lQGq/92msx+cRkAEDviV684VtvQKQSCdxDvcCOEqM8oa6ggM4uTEZlXr2eqjfqnKvSr/nbNkjj/XaVZAUf6kSgrNRnqZdXi8FSX1EwrjnCtteQ77RrxZAuOCY2W1dVwK/NBqN8pr3eNLTbofq6/tofez75DK0FYxsXbIOOAjEbgOnffJc21d8bGRF4P+dT6UeBtJ4jbTtOlL50jfS5Sh86J3qvGnEa9ZXjYQqJ7TTQkHjSiClGXKqgOFrEoTWHMDpN6uREfBx+8WH4qbpeoQXZ7L6qrq77X9dL113XSe9XYK77qFHUjq6LRnX8d9qEh/t8i0ajWLFiBRYsWADP8zA6OmrAsQIzEqRNvLpIrDbJ5zqOYwoMMYSHgA4I5hrZFk1tuhk0hFari5N5AggcqcV38TpVxJkHfezaY9jxhh3wYh62v3k7Lvj8BWg63hSwRBJMuq6LOXfOgVtwMe3+afBD9SPAyrEydr5hJ05dUqvKXY1XsePVO8w4tr9xOwqhAtr2tsGreEjvr1cdDYVCgUJkGvrOebOrchNgMJSYG0I3vTJ4Cu50Oo2WlhYcP348wMAohDgeDdElw0ylUgBgjh6hgYSCge8/dtMxk+fMFhmKYMrNUwL500MdQ9j/5v3IzMsAALK9WWx/63Ys/9xyLPnyEhMNEA6HseLbK9AcbsauG3cFnltNVuF3+kicrFWWzU/O49SHTiE0EsLk905GOFMvUGKHxJGZB6y4SR8jL7ZC4QGERkIIZUMBZmyE4Wmg51096PvXPri7XEz610kB5Y3vio5G4WZcoFYGAc27m7H404uR6E+gjHJgTzD0jXSqDDQcDmPSpElm/zE8T3MPfd9HJV3B4bWHzRhi+RguvvNi845Nz9uEzddtrgkAD7jxlhsRyUQChdcKhQKmHZyGK394Je59xb0oJ8q45tZrMH3HdJTDZWy6YRN2vmCneccTf/sEyoky5t0zL7DfAYzzTkejtdz9A392AIdeGsy1bjrWhI5bO9AX60MqlUJTU5PZj6qgMRqDEQGOUz9uhkaWUqkEp+hg/efXY2T6CHp29ODAtQew40U70L2/G0fX1IF+9oLxx/V07+jGmq+tQaKQQCaUGff9RJtoE+251aLRKKZOnRrQbfg5ecjw8LDhrZQdTFOzFU+C6Xg8HuB5rL+h+ZOUqfReq1JLA7d6/VQuUP7ofWxqeFXDcFOxCTf84AYcvfAoVj69EpFypK70+/XwXxs8U08CanogebD2x46Os72F9jPVkKCykfKGgMYO8bYdKKonqsHA/swGlFxnzpPtibfvsUGygj19F9dU79P32t5W3qepk3yO/b194g9pTU/IUX1V+91oTXQOGgFsPsf2gNp0qMYV27jA5+uc8DrtB+lex29fy34pMLRBJmmITcdg04T+r2uj+EYNBVwj6rg2/fH9tmGGfeB9HT/tgFNycOgfD8GP+Ji0bRJWfXUVkpUk/HC9ZpPOm90vXRMds/ZH+YTSAj+3DSg6l7ZRrRHN6lo/mzYBuFGbuDlz5mDFihUmvJpeTADGy6pAWT2wtvWKi0JmybN4y+WyKbzBZ9GD18hqReCuIJfMhuCaoMK2nmkIOwBzjIcCLlaXdhwHx689jl1/uct4Y4cXDePJdz6Jde9bB2e0rrgDdYtuuVzGzPtnGuHAuUQJSOxNAJc8w4R7QCQTwdP/8DTgAyveswLxM7Wc3EQigWg0arz6at3kvPG3AmOgVsBJGYAyMQ1VZyg6C3FRsNsMhpuSlneCGd2gGr5OcEfLfqVSwdFrjo6PI3HqR5yRvqqjVSROJZBZVQcxib4EkplkwNPg+z6qXi1Eev6P5mPfy/bVaGUsirWfXQvnoIOcm4PT7ODYp48hv6gW/lxuKmPW62YFGCDBJOfJDvHyoh7Gbhwbt3yhkyFEB6NwQrXnJBIJM/ZQKAT/oI/uf+xGZCiCkBdCLBkLeEeKC4o4++GzKM+oe0eLnUXkWnKIloKhhkA9x0ut85ofx/QKx3HMWnK9AKDiVXD/392Ps4vOmvcVkgU8cuMjWHvrWjz2wsewdeNWo3gdWH4AP2v6Gf7kG39SXzJhtrP2z8KLvvMipNpTmHFiBvqifXj0JY9i92W7A2Haruei+2i9Mq8bc/HAOx9A9/ZuzL9lvuk3w/lcx0XT9ibgpTLXhRBWfGuFMdSxEBuNgYwKIV1yXoB6uJx6oQm8E9kEWva2oOJUMPeeueg40oFUfwolt4TTF54et+Zs7bvakTqTQtUPVnSdaBNtoj03W2trK6688koAMLKcOgTlK8+Wpo6hR1dRxtIASt1EC4rSoGuDbfIsAObYRt6redkKcPhOdXCoXOA4FGRUKhVUqhX856v/E1OOTcE1j15T67wzPlRVn2+PEcA4/Urfq/eoZ9BW1ClzyZtpuGABV86nGsEVIKjHW/vL96nRQN9p5uK80UTfpcBIvX06HjofdDzqJFKDuBoQFMDbQB+oe8TVI62gh/OvXs1nAra6ljb4YlOwpGNV4Kxrq0BNaUPBnL5fx8n7uXfs/qjxRVNT1QCiINEep9KTDdq1KS3o/Oh9dv2GRvNnz7u9TxXHECtwbukg9DwPrbe2Yk5uDs69+hzmfmIuvAEPpWjdqEeDkz5bvfXEQRrpoOuq96gR4JmMAbaRwqYFG9T/PwHcEyHlALq7u3HllVeaYwX0SChlLp5Xq4hIMMhFZ0gyJ17zbHzfN8Bdw60IIgkEgTrx8nkMwVKQr1UBGVarCjZDswhQ+F0kEkE+nzcgi97AilfBsYuP4ek3PI1Kk1S5LrqY96N5CI2EAgo7UA/n0f819DdUDWHRHYuw8IcL4VQchDNhc5wUUAtL3fPGPchOzyLbm8XmL21GMV3Ly87n8yjGiohEg0YOWtS16BuNF2RONCjQQKKAFggyDN7PiqwAAvPJnCACFD6bgtaMNVQ/woJ50BRKWtirPnggNBYywoOCMTIWwexPz0b7A+1AFWh/qh2LP7EYhaO1CpyFQqF2bdLHYx97DMfXHkemJ4NrXnMNurZ14ap3XIW2Y21oa2vD5MmTsff7e5FfWM81zl+cx/CPh7F48WJDZ67rohquohwqB6IjaDAop8aHC8e2x9D19i5UT1VRKBSM4cIWDKGDIVQHqgHDSaVSAdLAma+fCYBtVIGOhzrQsrUFrusinorjxNUncOolp+C7vjGMZDIZUxlTjyI7c+aMYeJcP1Uk0AL0ze0LrEHzQDNW3rkSALDmnjVoHmw2NBotRrHx1o3wq8HjQjhn4XAY3ce6kdqawpkzZ1AqlbDql6uQGkqZV4QLYbzgn1+ASccm1bwjsTLu/ed7cW75Oex6+S4cveEoWjpaAgYcpIF9b9wXmO+1712L1kOtASXLcRyMjIwgm80GKgpz/8diMSSTSaRSKfMZDVlKr8bwBwfp3Wk4Zx1c8qVL0L27Gxs/sXHc2nfv6MbSXy2F4zhGMZxoE22iPbcbU+BYiFQjumiYz+VyRm4qyAPqXiPl/0yRoyzVUFcNi7aBN5+p0W0KHjRSjwowlWAFy2oID4VC8BIebv7Lm3Fg7gE8cvkjeHjdw6g6VXO9ynby2Wg0GsgN5jjtkFn2UYuM2uBFFXg9slQNAhqqr41jsMEwAbvOj+ob2k97fsjDOV6d50gkgkKhgMHBQfN8PlvDpu3P1aBCPTeXy5lTfBQ02qBW55HzQD2TEZw6P9Q3OY+2p5fzzXttLzD/VkcV58w+PpXzze/Uo640QN2A96mhQEPX+Wwb4GkfVZ9WoK/P4lqrMYP/k9Y4PhuQ2v3WtW0EWPV4QOrJCoa5f3jMLo+pVfzE8ei72+5vw/w3zYdz7LzzsVLGyQtOYterdqHoF817yFuIi9SpYhuWSFOksUbfNfJS24YH9YorrTaKFtBrflf7P+/hbm9vx/Of/3wDtqnc07JIglLi0jwLDdkE6mDUZowECSRMWoU0H5WhtxQ2ypQ0X8JxHKRSKWQymYCgcxzHAH8FQhpyzTFSmJ5bdw5P/eNTAc9cZCyCxd9ejOmPTYcbdeGFPVMExSbWWCyG9vZ2nDlzxuRKO44DL+9hznfnoBKqYNpt0/DE+55AZn7Ne5vek8bowtH62NJl7PurfVj6iaUY6xnDzg/vxOJvL4Z70kXToaaABblUKpmiZJxbhueqRRVAoLCcWl+V0eg60TuqDF43FhUIIGh95bp5nodkMmny7od6hlCJ1Y0YtRuBxa9dPE75IC0tft9iHPyng1jw0QXwnXoVU8/zMNY8hj1v34OhxUMAgFOXnkJkLIKLPnERwtkwQpF6UbyWMy3ITQtW467uqWL37t2oVmsVZsvRMkb+agTVeBWTvjAJbrZO05FIBKfefyrY9yoQ3xqHk3PguLVoBDVGkamqxZV/03hUzVbR/u12nHv3OfPM7lu7sfCLC2vCxfFw7LJj2PPOPQCAkB9C9y3dcDzHCCrStrGUtraaari5XA6JRAJNTbWj0s61n8ND73qolpt8vnUe68SLP/dihAoh+I6PSD6CV3ziFbjlrbcgl8zhhT95Iab2T8VYbgxDQ0OYNGlSIOIBqCtLFCrxYhx//JE/xq1vvRXFVBFXfv1KdPV1IdwcxmByEI/9xWMYmlNbNy/qYctrtiCEELpu6zLHmhx+/WEUO4uBKd//qv3o+WhPgHZpOOPcjo6OGmGXTCaN4OTRg2qc0GgQ8hk9CcHxHFz7sWvxg6/9INAPt+Ki81AnopUo3Eg9hH2iTbSJ9txuPT09ho8DtX1NY7HjOBgeHjYebgXGanBTgEEjH+UunQCa/qK/+Tf5EfUVzYtu5LWj0VqBn+0Bd10Xo6lR/Oz6n+FI75HafeEq7rryLqAIXLzlYkTciNGrKD8pB/k8BfpMJ1P5xr4pCNOILPJZnTfKdeqa6iTQcQHBEHDNrbaj+BREKsgD6sfNcqz8jCBbdZ1kMhmIFmvkxbO96/Y11IdUT9C54vrrOiv44dxR9mrUA9/PCtgaDmz3TwGrHUWq+jPHop+zD/YcqA6soN32nOq1qidxnOrF1jE905zzcwXKOp9qBFPaUECu66Pv0XmxgbfdiEXstFjOm3qT2S81gPD9TtWBW3FRDtf2/ND6ITz5gSdrfSi6mPqDqfDz9doMasxQ8Kt9VAPAM82h7kMF7Gpk0ahdO1LDpvkJwP0sWmtrKzZu3Ii2tjazGemhtAsa2RuVwJIWGxKWMlX9YWEQEimfrUUHqtWaR5Cf03JHK4/mkLOYgAoF9h9AwPqnOc9sJLaBBQMBsO0WXcz7+jxMvXcqvJBnhBqfQeEB1ITC6OiomQf2RQXFwpsXolqtYuWHV2LX23fBH/Gx8DMLcegNh3Dm+tq51F23dGHOp+dgZNYIDrzrAPKz8tjyoS1IHkti6aeXIr2zdi6jAlN68ZPJJKZOnYpMJoO+vr7A3LLfbBo6FYvFjMCzwQjzvTiubDZrvNca+s/1SCaTAWDJ9xx71TEUu4MAqvNXnTWv/3nBQSBKJaVarWLZp5ahhJJhTiZvpruCUmcQ5OSn5xHtiiLuxwNh/6u/uBq/+PYvzHW9t/Zi9hdmIx/L144tq5Zx4nUnMPKaWo52uVJG98e64Xr1s9173t2Dg48eNM9o+04buj7WBT8UPA5DwaBWTiWTIjBzHAcjrxvBwDsG6gPwgNi2umHk7AvPYv9b9xua3Pm3O1FEEbNvm20ELt/Dv9XIxMIvlUoFY7PGsPkNm5GZFMw1XvtfaxEuhlGpVnB67ml0lbrQNtSGF3znBRiaPISe/T2oOBW0traiqanJeP91flWQGa9QwcF1X78Oo92jmHasVtcAAArdBRTarGreDjAwdwBTYlMQCtWiSNJ703Bvc3H8j44DAKY9Ng0rv7jS0KTtraBhj3SjoaChUMgc2ZVMJk1+N4UdDRc0wlWr9fPoD244CC8k3msfWPKrJVj+4+XwUfemj47WjWYTbaJNtOdec10Xr3zlKw0/r1aryOfzaG1tNXx9ZGTEAGneQx3AVjypYySTyYDHXNOC2FR5Vt5GI7SCCjon9B3VajVwrKeCIDX4jjWPYSxtpUY5wOlpp+Fv9XG+PIoZo+1t1N80ZPJvOy9XvWJ8TigUCuhv7B/1FM4hIwEoV6h7Kgi0DRYKdAhCbPmr/aAcY98bebBDoZDJ11cwpnOhP6oT81oFajp3/F7nS8dlgyf23wZa1DtUH7PXyvb88zpdJ13rRvOqfddnsQ969rztLdXPdL60b7bDQuezEeDVebJTAfQ5SjtcZ+4pDalW0KproX3VfqhDyp4vpW2lJ8UHatzguPi8/qv7cfjdh43ud/g1h+HEHMz69ix41WCBQJ1PnaNnMhKosZD6kfZXjRfqHbefp3//PqNEo/Z/NqQ8mUxizZo16O3tNco7mWJHRwfmzJmDRKJ23i3PVQZgKogzvEKFAxkavdm8j15vBWr8IZjmD89EZvgFNzRzFehF5v9A4zP3dOPwGhITz8/14aPSVMG8H82rTYoPXPCZC9B7d68B/8qgeS/fTevo4ODguLB5Em+5XMbQ0BDCJ8JY9JlFWPD5BXD7Xcz83ExM/ulkTPvZNCz8+kJUOirY8949GFlSL9KV681h59t2YnTWqNlYDFXhWhSLRZw6dQrDw8PmPt3YGo2g4Tye56GpqSlwxjpBCcEv6aStrW2coKVFmkdv0QDCSILRS0YxuHxwHN31/KwHMTdm+hUKhTB37lyEQiEMDw+bEGxlJASYHYc7sPxTyxE/EwcAtO1sw7IvLAOOwViAWYPgqdc/Zd45+0ezsei7ixBya2Hvra2tOPtPZzHy2vpcj/zpCM58+Ix5VygcwrkPngv0vfPmzoCCwDO9NZSMgkSLa6glOn4iHnim4ztoGWwxBpKms02B9AM4wKHXHcKRPzkSiDgYnTmKIxuOBELVtIBPtVrF0OwhDM0dCrxv3kPz0Ha6DdVqFX29fXjgTx/A7a+6HV7aQ+tQK2bummn4QKFQGKcUmSJssgc1IqJtsA29u3vNPiwUCmjf047Lvn4ZkkNJ04/Fv1mMNT9Yg3QijXQ6jTPXn8H+v9uPUlcJi360CFMemIJFX1qEUC5k6FkVGoY7+r6Pc+fOGbphpIzdT829TKVSaG5uDlSrZ4jcno17sOnlm4yxgG35PcvNmKgYZrPji6pNtIk20Z5bbcqUKUZma6Eupl1lMjWDpXpO1YhPvqR8kDVrqBfpaSONjOHkQxoqrgZCym/lxepJoyzVNECOqedUD278yY1oGWgx71uxdQVuuOsGRL2oeb7ebwN48l9NHbSBiO3QYJ91TApw9HsCShvAq4FCjRI2WLNBkgITG1BrTSKgFgmo4ecazWWDaTX8ah/UcWTPze8DQ41AG9dAjTycP8pD7Y+CJjukm/SoERYK/JQe1UhAfdEu5sfvbNDGvqo+r+HJCrJtLGCPkePknlL60vs19Fm/4w9PRkqlUmbNbXDO8fJ+XQudK3s83LMKPPmsRuuge1j7SwCc7EvC8YK0ke5LIxqJmmdxvemsUq+57ivbwKHrxT7aP2p4+F30rP/b4/l97f8k4A6Hw1i2bBkWLlwYsGpwQUdGRnDmzJmAIsqFKJVKyOfzgRwSVuRUwJFIJExVTgI1HiXFDW4TLPug+SPKzHVD814lDgXhFJhUwMnISaRO3MFT//IUTl15CmM9Y+i9rRcrPrICXQ90BTYB79WxqnfWDh+JRqMm/50MKtobxY4P70CiP4H4YA1wuWMuZn91NqZ8cQowBiQzSUy9fSqcSnDDZXuz2PbhbXA76kciVCNVPP6px4EkjAc1m80acNfc3Gw8nVOnTsXMmTNNhIEKtlwuF4hcUOuxhvYACBRW4/pwTuPxeODIEgCYtGcSOg50mPudioPeL/ai6XCToQdu2hMnTiAajaKlpcUIPgpBDckqFotI7Etg7dvWovlgM1Z9aBVCR0MmlDqbzSJXyeHxdzyOE+tPGEth8+FmVDP1ojaO46B4QRH2OcyFFQWT3nDqo6cwdlXQK3D0K0dRLBXNPDNHWBUK0iLzeEhLsVgM5XAZfW+s51I7RQcr37QSXTu7jNLR8ngLLnzHhXBKLB0LxAZimParaahWqxgbG8O56Dk88qFHsO2vtuHkqpOoelVjkaegyvRksP3l2wP9d6oO2o62IZFPoHlpM+548x0YnTyKMzPP4Oa/uRkFFIyQaiREuPdUUJEPaP4fj7SgchYKhdB1rAvP//DzEclHMO+383DRTy5CvBhHJBrBwBUDOPzWw6ikKzi7/ixGZoxg5bdWIpVNGVpwYy6iyaiJiOB8R6NRdHZ2mnBM9ocGP/ZVCyaqkmQUmpCLg+sOYtNLN6GcrOfXuxUXGz+/EZF8xBh0uK485m2iTbSJ9txsra2tBsCQb6TTaaOE8mhQx3FMUVJVUhUgUndJJBIGOFCWUjaSF9neW8oOGo4pq9RYrqGolDE2SLQ9c/y/51wPXvnFVyKRS2DJ7iW46e6bkCqmAiCUz27kHQNqMoCh8vzfdqbYIIzykLyXqT+qc5ZKpUAROvZjaGgIIyMj40JobcBD4Kb1fLSpzkl9jsYV9oHgTcPQ1bus0QWcI5UllPPUjThvuo6aFmADML1H86PpwKLxm433cl7suVPAx88U8Nqy3Z5PBdvqESWtaWSfPr8RKFaQZgM/O8rDNvpof/TdXEMNSVf9U41jdkj5M3mviVva29vR1tYWiFa095TtGVcsY//oHFNnUSMPdZzug924/F2XI1QKwa24WP2F1eh9qDdgJNJaSmpkaQR6dR+rcUXHq3xMDSM2kG/0o6BcjW2/q/2fBNzz5883RdI46coMHccxXmCGkwIwi03CoZVXz3rWomThcP3IIv5oVUrbWkRCIkPWqtu29cjOnVAQT2IgITIvPZfL1f5O5vH0O59G/5p++GEfZy45A9/10b6lHfAQ8OhrSAiFL/M/1WJG4iwWixgbGzPjK/QW8NjXHkPfuj7seNsOoB1IpVI1QFaJoTXSWgP2hQp6bu3B7K/Nhpuvk2X0XBQL37cQ4bHaXBfaCtj6sa0YXjqMhz/9MAqTCmYuyJBZWMv3fezduxcHDhww39MooYzPtpAD9UqkCjB0XTSkjutDxuC6LnJuDqVwPfy7694uzPzJTLQkWgIGGQJ5Cst0Oo22tjbzPq41GVUkEkF0IIpL33wpEvlEIP+pVCph/4b9OLvibGBn73/1fuTTeVNwDwDW/P0aNB9sNtckDyex+FWLjeez82udgXWIHIlg0qsnIRaNIR6PIxQKIZfLGVDHPUF6yGazBhwCQLY1i+M3H0dpXskA/enfnI6mQ00olUpGSQOAlu0tWPpPSxEZiiB5JInVr1mN4qkiRkZGkOvNYfM3NqPUUkIlWcET//QERtaOmDUql8soloooOSWs/tpqxEZqkQpu2cXyu5dj8a8XI5PJ4HvP+x4KTfUw76GOIfz6Bb8265pKpdDb2xtgqGp59n0/UJOBdKRFbFQZ8H0f6ZE0/uxNf4aN396IcLG2f4qTi3jkLY/AS55XGlzg1EWncOSGI+b+SqKC7X+6HTuft9PkoqvSpwqChkTm83lks1lks1mzH5RnkF6bm5uRm5TDphs2BWoORLNRbPjWBszZMQeOX1u04eFhDA0N1UL2x8ZXsJ9oE22iPXfam970JhPp4rouMpkMksmkkanDw8OGz5F/2GHXlFHkV7FYzES8kdfoKQqqnNqOBd5DnQNAQKlmvwhi+V4avBt56nzfx2j7KKLFKN7zmffgFT97BeKVeEB2UxdjnxSwaUqRfQSZeupV6acORvlge9LYCNxYR0OBO1OaFNDaoEZTH9XLbXt92QcNa2c/tEgYG6/VObVBqeqtfA/vVWCua833qJ5l91nBNOeeOgedP1x39Xo2MpJr//m3DbaU5hTo0pii71OAaxc107lTD7WCXfVI2zTRyJCi49N72Gcb1Oo8k6aIWzjftrdaPbkaEddoLjXCQI0EfB9/NKJAQblt1OH4WGyte6Abz//E87HmB2sw59E5cKqO2Wd2pIFGKXD/qS7P5+r4yStsQ4r+aH94ne5N21Cj8/f72v+5HO5Zs2bhiiuuQKFQCGwCMkfdHFoITYmElkLbWkvC5+LmcjlUq1UTduz7vvGG8dxmtdp5Xi1kmx7zUKh2ziVQL6JRqVRMftXQUC1clgsfj8cRi8WQz+cDYF7vr1arKE0tITc9V/dwOsDYnDEUW4sI5+pGBaCeq86/SfCVSq16KauRKgOmYMwuyWLH23egnK6B99NXnoZTdDD/K/OB86nNBL8EnjN+MqNmWZ+RweiqUcz75DwkdySR9bJwe10cevMhDF8wDADIzMhg29u3YcGnFiB6OGrWj+tRrVaN95kCXBmAerBp6SVwVQalDI6pBxRgBHoa5lLpqGDPa/agf02/eX6uN4fc7Bwix+qVHpVhATDegSlTphjjCJkt/1bGwc90nWbePRNlv4wDf30AftxH8/5mLPvMMjRnm1GJ1AUAysDK96zEjnftQMWtYOG/LkQynoQX9TCwYAAn33cSXlOdmSV/m0S0XA+/A2COcCmVSoYZcT44/+FwGKWeEk7/02nkL6hXTQeAsZVjyN+aR9gLB0IHAaB9UzvmfW4eEk8ngFz9GLWBiwfgRcWi7QDHVh5D2+Y2RCM1AXPkiiPY9Se7sPoLq7H8G8ux7bXbsOD+BVh560q4rotUKoXn//vzce+f34ujFxwFACx9aCku/8nl8ML1KJbh4WEzz5ovxX3LomTkHUoHute5F43SeP5IrUqlAm/EQ/eT3Th3YT18PzmQRNuBWl0JJ+5g58t2Yu/z9tboMexh6S1LUSwUA9EatpVbhTRQL9DDPaChXul0GlWnikkHJ2GsqwaiI/kILvyvCzH7kdmouvUjatrb2w29MUd8ok20ifbcbM3NzYYvkAezPkq5XEYmkwl4kCjbFajahctSqVTAqE3vLeWnRtsAQeOhgh2VyQrUqeAq0CUAYN/4PN/3MdA9gJ9e/1NM3j8ZL9z8QhO2St5sg0g1ICuf1Hv4LhtE67UKatl4Hw2gyq/1Gn6u19jP15NF+C7VfXRcmmJE/Y7zzWYXL2OEo32d3RcFeir/bOOCDeIV3GqzATIBOulMDTAKgjgumwb0WbZRR40COhZdv0Zg6pmep+/k/Cg20HnXedR1ZdO9wO9Uf1Wdwp53XqM6gQJUzpXd50qlEji5h3hHdWMF0DpO1VftcZG32B5mdTKSjjsPd6LjUAfGKmPGqGGDYw0nV2MNr1NDhR2KTh2o0ZrZc9vIqNHI4NHIw96o/Z8C3D09Pbj88suRTCbHhbaQENVyA9QtqRryoyErJAY2zfEAEKhYrsxCmTpQP4dRN5RuFhJXKBRCOl0rIsaiRexHuVxGR0cHfN/H4ODguP6SoTbvbcbSzy7F0+95GrmeHJr3N2PxZxYjdSplNpVaPBW0q5CilQ+o5wep1aw0WoJXsgROzkGpUIJTdkwIugGB598x4yczUAqXMDh3EM3bmuG45yuBlj14Wet5pVqVQ/VwEvgp4wWAOXPmYGRkBCMjI2aNubkYnqbeyUaMg2HejuOYcH2Cd8dxEEqG8OjfPIqzq84G+hmuhhGqhAJGmmq1ikOvPIQZP5+BJqdmyc7lcti3b19AoSANqiGE79O14D1z7pqDeCWOQy8/hOWfX46mQ02oenUvufHMDoex8LML4bkeYn0xOG6NLhNIIOyHUULdQz/yihFUEhX0vru3zpzCPvrf2I/Oz3YGrM8M3WefcrNyyF4WzPft+G0HFn5+IdycCz9UP49UBWzPw7Xq3GW/xnRTqRR6b+1FrBLD1r/fCgCY8l9TsOA/F8BBjb73X7sfO161A5VEBU/+9ZO48CsXYt1X12Hqjqmook4PbsbFVT+5Cg86D6KlvwXr7lgXUFxUeKtVmMqoWjVpENHf9FqQThoJt3AkjMevfhyzPzEbpTeXMLx+GJFcBBu+vgHdu7rhNDl49C8exaErD5l52/PyPUAcWP7D5cbwp3yDe5d0qjUBSDdz587F0aNHjbJ8uu807vub+3BoWf09vuPDT9Y9UxSayusm2kSbaM/d1tnZaXgVDYT0IlWrteJpY2Nj5hpt6ilVgx+9aiz+akeSqZODNXKAOoizvZz8TBV8ygi9j436Ce8baR7Bz2/8OU5MPYET008gnA7jpntuCjybz6Ec1eNI79p4F9b/cn0gWsv3fZzoPYHNqzfjsl9fhvS59DjPPcfIsSi4am5uNoXoNO2ITY3pnLN0Ol07MvV85IDqqfpenTfl+SrDKNu4XmpEUUChwNAOS2Y/uTbqqW4ExhrRjc6nPlPvZSNdNJLJunY2OLXXReeLY7PHrEBMDSx8turljeZfn91oHHZrdI++y36f/VwFtDqHOi7bkKT3a//sseiaKsDVOVIAzf+5XrYXnd81CqOnV9l1XZNG0Eh/afTeRmtpj6PRuFS3t+e8kVGm0Rz9d9r/Ge2pra0N69evR1tbW8CzzEWmUmx7Nu1Nxckn42C4Znt7uxEEBH4UYBRmQG1TMR9FQx3s0AatfqjFExzHwdmzZ41gVOu079cKKPE9GrpSLpfhRT3s+NcdWPnBleg60oXV71qNJ/71Caz6wCqEzoZQQSXgraYyr1ZmFYbNzc1mA2qYGYFfy9EWrPzgSmz+980oN5XRc0cP5nxrDlAAPMczgN2ODACASDmCrp1dQLjOLJJjSSz50hJsT2/H4IWDSO1LYe4H5yKRT8AJ1RmkhpOogD916pTpHzeohm3z/Rq1wOgEXstcKYbMUKhyLF7Sw9mVQbANH0gcTyBxPGGO+qp4FRx++WEcfuVhnFt3DuvfsR6xUMy8Q61xDIEplUqBPCyuMxnutGnTEIvFsG/fPvT8ugcd2zvQPNyMUKKes65hS+VyGbGTNUMFQsLw9wHhoTAwQ8ZQATr+q6Pu6Y04OP7V48itygEu0P7pdlTLVdM/MtB8Ko+zH7DmwwOSh5KInomi6gSLkHCtyLDj8bgJeeS4p9wzBa7n4tyMc5j/n/MRKUdQ8ko4tO4Q9r1yHyqJ2t7OTc5h81s244ZP34BYOFYPDUQFIT+EaH8UG3+wEcgDYScMzw8akPTYlnA4bM6ipbJAunAcxxgyuBaq9GjYOZ/puz4efOODOLHsBHoqPVj8ycXY8eEd2PC9Deg+1Q0/VKPTJQ8sweErDpsiZqFSCHPvn1sXZpEqHnnbI7j0k5cCFZhiRSqoisUiYrEYcrkcotEoDhw4YMYZS8Zw55vvxImFJwL0GhuLYfqm6WasHC8jc7RI4USbaBPtudeuu+46c0KL49RytPVEg2w2a+rOMHWI/IBGWyqr/M2oQDVO+/74vGzKCWC84twIpNoeKC2OxsbnGpAQ8vD1l38dfR3n64Y4wOZVmxHyQrjhvhsAwIBOO8rRg4dfXfsrbFm5BWfazuAF336B8YwPdQzhxy//MTLNGRyfeRyv/8LrES6ej2Ryq/j+33wf7X3tmHpsKtqH2jHrwCwAwPaV25GsJLFg34KajAi7KFVLiCQicFwHFacC13Ph4HwklFuF4zsIoXYihIKqqluFWw1WrFYAYc+ZGoPpoFHArWvBe4C6PmXrHGqAUeCs4KQRSCHYtz2MNsDR/pDOqLdTvtreTNt4o44KBen6Xhts8371SKtu22ien6nZ62Bfz+fZz7INDuq8UMDM1ihKgM/5XWBU95wd8cC+2UYGGzzbY9L3KZDlOHSP69/U16nXsAgx55/PqlaryEzP4MjGI+j9996Al1/XU40Y+r7ftVZ8t2I/nctGRjp7zX5fc571hY7z34Pyf0AtmUziqquuwpIlS8yE6mKSSQP1iadyroyd+a8avqFWTAVyuVzOPF+93QSYWnSNoFwBP4Uc30EGSYbBM7EJzmkAYJ9JPHxPoaOALZ/Ygty0HLo3deOCz16A0EgIvuvDK9fDxzkWErkedg/UwShD3wEEBAHnMBqNopguYsu7tmBw2SCaDzbjgvdegPC5cMC4wWrgFKJK9LqR1GpeRRU7PrwDi96zCKjUz+NMJBJm7njEEb/jxtDq8rQuJxIJE77PNVQBoWvL8XHMtIByk97+xduR65ZwfQCJIwms+rNVCDk1xp9sSeL0C09jx+t21HKtfaB1ZytW/MsKRIZqkQWpVArVahUtLS3G2sd3cq74TgV7XH+bOSoDKhaL5n49SspxHFSSFRz8m4M4/fzTZgzhoTB639mL5sdr4YfldBnH/+U4sldka9d4QOcXO9H17S74eT9gHHJcB4WrCjj5lZO1h1WBSfdOwvyPzkfYDR7pwXkPh8OBvcZ0A/5UKhX48BFPxAGvrhSUyiXs+stdOPqCo/AjdXaVOp7Cje+7EaFKCOWOMu5/6/245PuXYNrZaWYttVo9C8fF4/FABAvnTIuRcf+rEsAQezWacIzxeByFeAEPv/phHL34aG3+fGDGj2dg2S3LEC/HA8IhFAphbMEY7nnTPQj7YVz/r9ejebAZpVIJY81j+PX7fo2xnjFM3zYdF335IkTH6h4m0oEqMnwm1/zJlz6J3dfvDpxTnj6Xxos++CKkkDJzo4Y90tB3v/td/E833/ef3XkbE22inW/PZV3l/832hje8AVdccYWp2dLf34+WlhYDsA4dOoR9+/YhmUxieHg44C2kzgHAGCWr1SomT54ckLfRaNQU/rRDR8mD1JCv79BCXRrBUygUEAqFEI/HzffKf6n3RCIRnEqfwtdf8fXasWA+MOfIHLz2x6+FX2l8nJnneShHynjg8gfw0LqHDH+es2UObvrVTSi3l/HVv/4qKpHzqYA+kBpN4ZWffyUSTgI//9Of40TvCfOdAwcv+8bLUGgq4LaX3wYAcKsuXvbpl+H4suPIlrM4uuooNv56I3726p9hzcNrsPyp5Sg2FfG9v/weVmxagUt+dQkipQjG2sfQMtiCbFsW//X6/8KN374R0bEomseaMdo1iqaRJox0jpj5ahtow1hyDOnhtOHfnKPTkdPIh/NIpVJI5BNIDNd0JwUZqh+rzgzUQbYayTWS0vaIc71VR7ZBIME1ZSYdU6y0DaBWGDaXQyhUO3FF+2EDbvVi2uCW36uX3AZY/E5lOZ9DHaERkLX1LW0K5LWvjTz42k/bsKTvU0eVPZ9qJNNohEbzz3vUSKB9ZNSLXcDOBs9aE4DpeVpDRueE9JJMJpFKpZBMJg2+YK0knori+z6GJg/h3n+7F17Iw8zvzMTU70+Fn6unQGi+NfmNbZjhXKjuyblSL746QdlvO0ydcw4An//853+vrvK/3sMdCoWwZs0aLFq0CEAwHxtAQBnWMB4q0cogCJSVcOgBBYJ5uQSraq0jMTA8yd7UGjqhSrIKA70WCB6XoQBcx5qfnseut+2q5W0DOLf2HHa8bgcW/McCOP1BKxc3AP8m8FFBS0BNsELipCWxVCoh25LFgb87YI7GGps7hl1v34V5n5qHxLlEIAyWY7AZnAKWUqmE7u5utLe348iRI1j5/pXwXA+hRAidnZ0YHh4OnCnp+77ZvBQ0BOS6htw0zLdVI4mGxHA9OEZdL4K1M4vOoJKsBKt/e0DXY12IhOoF5jKhDE5edLJe2MwB8j15ZFZk0PNYD4rFIvL5vPHssogNw+89zwvkTdNSrVZhXU/13tNYQ8FBpcZxHJSjZRz6y0M4fePpwB5KbU8hcjhi5ja/OB8ofgYX6H9TP7yKh/SX0nB8sTjGfOTX1nN9w/kwlnxiCcpeGaVKyXgX6PFwnFoOYDgcrh1dJ3uEe854jH0Hni9ncobCWPC1BaiUKzjxshOACyS3JbH0I0uRK+RQnVbF1tdtRf+sftz+9ttx/Teux4z9M8YZlUKh2hFt3d3dgSgGphBo4x7x/XoFW90/mtbA2goHVx3E0bVH5SHA0ZcdxZydcxDfVT82jWvTcbgDl37zUjQVm9A82AzXdZGfnsdjf/4YxqbUcq6PrzgO75UeLvrBRYiNxgJFaTgGNu65SqWCnq09OHjZQRRaa7Uiug524cr/uLKWrx+pnwIAwNAjgEDKykSbaBPtudWam5vR0tJidATqJPRQ53I5k7KmzgEq0eol1arS5DWU7wosbI8beRSjZ/gsIFg7RfmV6h/q/dJ3qEe3s78TL/7Ri3HrC25Fz0gPXvmzV8Kv1sGXelt530jzCA7NPBSocTM4YxBD04ZwcP5BVEPiYHCAbEsWP/3TnyI5lMTJGScD3/nw8cPX/zAw91W3ih+86weBz3765z8FAGy+fDM2X77ZfP7U+qcQrobR2teKxy99HGvvW4ut67diqGsI3/2H72LawWlYsnkJHr3uUVzw2wvw8A0P1270gcvuuAy7V+zG8/7zeWg73oZ9S/Zh/s756JvUh3v/5F70Tat5/qfvn45rfnwNBnsGMWvnLDi+g9O9p9HX3ofFTy3GaOsoSrESWk+2NvRgqmfb1mN1LSlHCJ5JGzaA1PUkffi+bxxW6vwgfRD82p/bfbNBrHpGtfF7RkAqPTaiN/6vYFW/t0Gz9k/1aPUa838CyEbPst9hz6F6h7XQr15nGwF4jY1/GvVR57SR0cIeK9+j1/LvM8vPYPbx2Qj7YYMvABj+cmrOKTz41w8a58CRvzgC13Ex/TvTjdNQsRl1Re2rAmU7GkONHfb9diNd2fP9+9r/esC9bNkyLF++3Gz2RlYjVeoVQAEwHh5dMPVEK7HT68yFoAWXwkSBHhVzoA767fxIKskkBgWD7CctbQpQeS37NXDBAIaWDgWe7YU8eL6HeKR+nBmZJceom1EBPt/JqtmaS8ox5PwcKgiCE8+thdKqJ12fDdS9/Y0IemBgACMjI8Z6RS+eGic435VKxRz7RS+6enw5j8xTS6fTgXNCuS4aBqOWd51rrrUX9mAftTX1a1PR+5+9qPr1wlPxQhwL/20h9r51LwZXDyKUD2H5F5ej7dE2hJIhtLW1GW/79ou3Y/oT0xGp1AvPUDHSCAgNq2K/bCtetVpFV1cXPM/DwMCAYS7GE+o6tTFYzQ/5cMN1IZt4KIEpH5iCE/92AtX2+tq5CRduyEXYqZ/pefqfTmPk5SN1mo5VcfSPj6Ln+z3jaIDGIrvyOZUs7hMqcnbagO/Xwqd7PtcDb8zDyEUjmPEvM+Afr+WRb3/DdvQtrykZpUQJ9/7JvbjiR1dgzu45ht7JByZNmgTXdZFO17wDIyMjAYOaHfLGtA8VRno2ve7H9r3t6NjVgYHFA2Zeeh7tQfJUPfwyQFeeh+k7p9f4T+R8BEzEhRMOElssFUMkGjH1C46sOIL2c+1InkuaedR5PbfkHDa9ZpMB2+372rH2G2uR7EvCjdS99kA9Tz0cDmP7Zdvhfu//5AEXE22i/a9oixcvxrx588weJ2+jYl6pVJDNZg3ApkzR/F8AAblD+UnjtXq7qLjzOfweCDoSVJ9SZZ8y2wZKqryrJ4r8tlwuY8qhKbjp1pswIzcDoWqwMK7KDn4+aWQSXvTLF+FnN/0Mp3tOo2mwCdf86Br0nu1F7/FeuHkXD177YGA+S9VaXZr/ifb4pY+bv+98xZ2B707MOYETc2oedQO2AcABHryh1se7X3Y3uk90Y+/KvTj55En0T+s3YBsAjs87jnv++B70TenD8oeWY+rTU/HAyx/A4KRBnJl/Bpm2DIqxIq757jXYs2YP1t2xDr7vY+slWzHcNYzLf3a5mf/fXvdbLP7tYmy+djMWPr4QPQd7ACCgh9lAVfVcpQHVZfi3elLZqAcoSFQApOBVdWkbtNoeeDYbH/A9ug9swGVjC32W/Tf7ps4i2xjQyDtv6+eNALeO295bvM42FGif9XoaPKjrA0HQqo4q9abrOtpgnTzi7LqzNf3s6T5c8eMrArVj+HwvNF6/dmLnjW9OcE1/V7Pp7JnmptFc6jj0WjuS4Zna/2rAPX/+fFx22WWB8FsgWCyBYIMLTwVaJ1Q9hEB9gyrTp8BhOA2FEMEpBYyG/gLBIygIyCnYlHkQ7PN/Mi82DTvXsJzKnAqOvOJIcGJ8oG1HG9x+F9XQ+IqbSoQaHqbEr0ceNMrhaRpuQsvBFgysq4GKpgNNWPDxBYj3xRGKhgLh9Jpjw/XR6s9a9EONC5zf/v5+M4eu65oz0Wk5HxkZQSKRQCqVCgAOFnih1VTn3nEcE8qiUQpqpVNacl0XU56egss/fjnu+dA9gAPM+tYsTPnxFOPx1VykxJkEln56KbZ+cCvW/OcaNG1tQh41T3A4HEZXdxf2r96PnX+yE0eedwRXvOsKVIvBozCYf8X1J62yn5w3LeQ1MjISiGTgWjqOg2qqiuE1wwFSSe1IYeZHZsI9W6uuTW90+vE0el/fi8M/PAyEgM6vdKL7G90oekV4Tm2vVbwKRm8YDTzPLbloeaAFlUrFFFfz/doRVhSc2Ww2cOQc95xtXOFcUVlk6FLYCaPn5h60/LwFyb5kLZJgDOje1o2+C/pMmGCyL4n0/nTAas715p6ll4e0RsOaRkZwD2iOPecegPEI0bIfPxbHvA/NQ+7jOeRn5NG1rQurv7UazblmlJxSYA8zVNO2zjefaMbGr23E7W+7HaNdo5i+czou+dEliOQj8EIeTs49iSde+wSixShu+NANiGbr3mkAGJ4yjEff8ChynfWztDM9GQxNHkLziWazz5LJpFFmwpEwdqzbgU03bUJ4QRj4ZW0eJ9pEm2jPrcbwTRp2CZIpy3K5HAqFAqLRKEZHR8cVK1KnA2U3i9GqB1udFYzw4zNs2aqpfaq8a4qS3VRfsb1wdCA4joN5Z+bV+JhT11EULNFDSkV/8rnJeNmPXobv/Ol38IJvvACdZzvhNtWet+6RdSiXynjshscAB0iMJrDxCxsxdGoI2z+4HWOL5LjE87IG/vm//z9IkDnbexZne2t1VHZu2NnwmuPzjgMAtly1BbtW70KmMwMA2HHxDnPNra+/FZn2DAqpArpOduHRGx5FOVZGMVrExu9vxKYXbMLWK7Zi74q9GJo8hKMLjyIxlsD1370eO9fuxPpfrodXretfnu+ZfHXPP18bqVqB79WBM+WgnjGuRzbZIJF6vtbhUT2edMd7bADIz22ATtrwPM+EVCsItqMggcYeX90fGoWoWILvt3EIr1OjgZ3OaRsSGn1mGwlsXZZNdUTiC0b52WPj3tajSRntqpGnCrRd10UoHMLQ8iFs/autKLYVseuSXUAIuOwHlwXSNT3PQ+f2Tqz/5/W4/+P3ww/5mPH9GZjxwxlw/ZojQvujY1HDn+rLjYwNz7RutlFPP//vtP+VOdyO46CnpwcbN25EV1eX2QAkFHqGbU+rTqp6k4GgF5abppHnlO9IJpPm6JxsNmsWHagp6QyXZeN7NNRcmQfP+eb7TFitUy/ExGs8r14MIxQJ4eSVJ7Hjr3egmqjCqTiYdMskzPv3eXCq9WqYGhKuuVNaDZvjVk+j3W/HcVCqlnB642nsfsdu+OHzY/SAab+ahjlfmYNQoW680MY5nDRpEjo7O3Ho0CHDVFiwqqmpyVzLtWOfWKmc0QzM/WE+EJUC5ozY+ddk0spkKbTJNBg2zLkivYTDYZTby7jtLbdh1tdnYXjeMGZ9axa8crDSPJlnNBpFPB5HNVxFNVc/VqqtrQ2p5hROXHgC976+lqsCH2g50oINH9yA6GjUzFcqlUI0GjVHiJE2SY82I+U4otGoAYek66pXxeavbUZmVkYWBGi/sx0z3z/TeCWYP0fBU1xQRPbFWfR8rgfVUv24K7QDJ79yEoWVhYCScdEfX4T4ubih0fnz5+PYsWPIZDKGnhiWqELGtgaT7tQKWiwWzTE2HCf3UnNzMzq6O7DvZfuw48YdaD3Zihd+/IWI+JGA99aulcB9xr1FOiHv0JQDFhji/HCe2fdwJIzTS09jz8V7MPNjM4EKsPeTe3Hxhy5GIpQwRhQqniqkmIfEvU9FNu/kcc8b78Hzvvg8uJXaOp+dcRa/eOcvTLRCfCSOG95zA6KDUdO/glPAvuv2YeeLd6ISr8CpOJh/z3ys+eEaOF69EJwRRiHg2IZjuOdP7qkVcPMBPAbgRgDB4Jn/vzZ/Iod7ov0323NJV/n/ormui+uvvx6veMUrkMvl4Pu1U02SySTi8ThKpRIOHTqEAwcOIJlMor+/P1BclAZJAAG9oaOjIyAf4/E4EomEOaM3kUgYfqLGYhotmfKlxkq+k9fxFAzyXsp/BWfk3ZVKxeSn8zxrjl89pjouvp9G4Eq4guJI0RTvpGw5cvwITvadxM737sT8j89HePR8lFjcwVNfegrJE0m07GzBwdcfRGIggQ1v3IDTV57GrtfvQjVeReJcApV4TWZXkhXERmIodBbGrVU0F0U0F0W2PQvf/cMga8dzAB+mkCc8IFQJwQt74/von/8u5OGiOy/CqntXIVwJw4t7ePSGR9F1tAsdZzrwwEsewDXfvwYPvuRBrL5nNVp2tWBoaAitra2mUGcGGRRKBcSKMaO/KehRpwlQp02grhfxc36mDiSN6LI90apLMeVPK3bzOyAYWt7IM6+gnP3iM1SntI3sqpPo8zXdLjD1DTze9m/2QfVdG5TTIMf8bc6THvfHfhHYcp8UCgWUSqVxKYG8p9JZweYvbQ7QfrgYxppfrMEF910QmJd8Po+R0RGcajuF49cex8wvzkQimggcTcz+KKjnO+kQVdzA8TFdRcem86xrovOsIPwLX/jC/80c7o6ODlx22WWYMmWKmTR6wQCMY7Zq4VAGryG3ujEUwJN50yvLcFh6tXzfN5U/qZBzUdUr7nme8dCpVYv9tRP07ZAI9cxyHMzrnXzXZJSiJez/8/3oursLcz43B3CAaCyo3LOYFueLVivNW1XvvW5Keu4BoBAv4NSNp+pgG6jl+a7oR9fMLrTsagkwExaaIpgZGhpCNps1QtP3a2cF08KmG4VzomkArIzKSoehUAiJRMJsKIJCKgJaaIXj1/AXRh8MDw+jra3NnJetVtOh7iH85rW/wcjUEex7/T4s//ByREO1UBc7/FjD+PxS3WAQCoUwNjaGscoYHl/7eA1sA7Uc78l5ZK7PYNFvFmFsbMwwszOTziDtphEZq3lCY7GY8VpwXTWagFXQaZigAcp1Xcy4dQZ2vqVuAXfzLlp/3GrmkHPPOYxEInD2OUh8PIGSH6TbzMYMSrNKAbA97fA0xIvx+r2Og7179wbClpWWCDQ1FUO9JgT/pFcyVt2/XGegpiisvG0l3KiLC26/AG7FBUIwBdKUFjUMTA0Y8XjcXKNWeJ1H7ns14pRKJRxbegyPvOsRwAXKg2XM/+Z8XPSeixCJBsPhtAYDAT/XktXqgVq+ZMpJ4QVffEF9jzrA0zc8HUgNKCaL2HvFXlxwywW1OQ47OHT9IfQv6Me8X8/DgSsOYNaDs7Di5hXww7XUAs6BOeYnWsC2tdvqSpYDYD6AmwB8BxNtok2050ibNGkSrrnmmoARlXsdqMnT/v7+QMQU+SH1AjXGa8SY6kNamZw8kf838g7ZoIN9UbkABE90UV1O/wbG12rJ5XJGFmg/+ZtNxxyuhJGr5gLRYNVqFcVcEcgDi96zKPDucCmMla9bacbpNrmYee9MhCthzLl/DtyUi4EFA1jxyRXoX9SPilvB4IpBzL59Nna9fhcc10Hf4j70HO6BW3Uxe8tszH58Nu565V04tfIU0k+lkVmSQamzhPSWNIpTi2ja24Th1cNo2t2EkYvOp2/5QPq3aeTm5dC0tQmZlRlUOioID4XR/FQzMssyKHfV5Ez8ZBx+2EdxUhGhbAjJ40mMLRQvvdXGgWoXqEarjS92gGqk9t2mGzbB8Rwsv385nrjqCTx1xVOBS2/+wM0AgKMLj+KGL92Anp09hoaK0SKefP6TyCOPtT9bi5AXPNVH9WXqxjYQt4E2P7dlttKBDUzptCBN8fk2jlDdWPVIBbDabE/3M41Nx6TX2MCQ12pEp1kSS3fWvnGM9pwp4FcnpPZXr+P3+nxdExpDEtkE1n58Lba8dQvGpozBrbhYcc8KXHDfBQGdyDg1PR/NR5sx6/Ozak4fr2D0fz7T5jvUwWx8pLRhG1gUWzUy6DTiT8+m/a8D3NFoFOvWrcP06dMBIABgSXhUnmmFpfWEHibNKeFCE5ypVUstI6pgZzIZZDIZNDc3B8JL1Fus51zzOUDQw03iJtDkMwg4CNIABEAGBSnDP3zfR8/PeoAM0H13N8KxusCjwFGGQyKqVqsGvLGfnFPXrRUhY795fTgcRnO1Gcv+bRl2vXkXBlbVQspjp2OY/6n5aHq6CVXUQ/Q1d4w/GobP+SoWi4Eca+0vx6HrwnBYgi7HcYxlnEyCVkpWu9RK1ZxrjpsheBSsGuqS6c7gwVc/iP5Z/QCA3JQcdrxtBxZ/ejE6jnQELOhcT51zGmJoPXVKDpZ9Yhn2vGkPzq09B6fqYOV3VmLx1sUIR8Jobm5GuVzGuZ5zePI1TyJ1PIVV/74KTkWOnbIs/9pftUamUinjBRhrCQpZ3/VRbCuiGc1G0eCccBwMs1KvguM46PxlJzKvyCDXVgtZbn+8Hcu/vRzFXBEFv2CEmVaU5D5SgwcQNDjp+2gQ4rxy/+ZyOQPUNbyJz17x0xW1cTj1Ogt2qkc0GjWhY2r1rlZr58dGo1EjNDk3fB9TEdTId2zDMWx5zRZTKG/gpQM4mDqItd9Yi7AfNu9UwahpLIy24dpqf9RiGw6Fa56C4oPYffFuAMBFv7wIC3+1EH64xp+euPEJbH/R9lpRn6qPJbcsweI7FiMUDRlAz3exOZ6DKQemIO7FcXTBUSAP4E0AgrV/JtpEm2h/4C0Wi6GrqwuFQsHIJgJkhoFms1nDk1W2KnBWeUu9xD5eSo3jqm8oGAAQAPs2YACCNWRU7utvNhuUUEaQvzbyUPFardtCI7PqF67rmvOwFVwpT9afuT+dW+Pdkdr8zr9rPiq/qiAUCWHq/qkIhUKYtmsafN/HRf92ESKRCPqv6cfsrbMRqtT6UfEqWPTZRYitiKHznk4MbhhEYVoBHXd3IDMng7bNbei7oQ/tj7Wj/6aaoSQSjqD33l6cmn8KTXc3YfCqQRx71zHM+OQMtN/TjuErhlGYVlv/9KE0/IiPfe/ah96v9aJldwv2/8N+jC0Zw/zvzEdhRgGFVAGZaRnkenJoOtWE1LkUzq6ohamn+lKYf898HN5wGMMzhn8n7Y2mRvHoCx/Fjst2POM1XtjDfX9xHy77r8uweO9iVFHFIy9+BLs27KrRVNzF1T+8GtDadRbQJO2pTG8ESPV7OpL0mY10YgW5fK4+n89RA4AZmxcsUqg1i3QcjQChPlONPDY41PHZcpzNptdnmjfVd+3n8x4CXQW19n60+8n3hEIhdB7pxLpvrsNjr38MSx5bgpV3r4SPYDQKnZiFQiEQ7cvveASziewNhcb1k/NhGxw0KlT1T9tIoPPG59jP+33tfx3gXr9+PebOnRuw7KhFhgREItFQES6cetyAeg4miYqghj+6YX3fN14wDYVlU0+04ziGiID6hmX/KABJ8Hz+yMgIHMdBS0uLERJkCHw2+85CbqVSCVPumQI4dQtxsVg0xKTEBtSNDRoiq8KRc6WMg0aEcrmMyIkIlnx2Cba9ZxsyMzJY/v7liO+NjwvVSCQSBnDZIWLshwIRBWJasI3X2x5qrXbIuSJ4V2DEyISmpiYzZ75fP3KLYFOtegRjhe4Czi08F1jnzNwMirOL8A8H88k0n0VDjHRspVIJ4TNhLPrCIpSjZfTe2YuORzowkBpAKpWC67rItmXx0BsfwkjPCIZmDaGcKOPij1xsxqm5clwzzhOZPKMuwuEwKtUKRlbVi5vBB+b+w1wkH0ka+mMf6eXQ87Fd18WiRYvQ39+Pc+fOYeTPRlCYV6Pr9K40Fn92MYpna4ySx7Dp/lJljpEGpAX1QpBGlA7Yr0gkglmzZuH06dM4e/asobNoNIp0Oh14PudBI1coEFifgDTPtaeSlc/nA0YC7t1IJGLoiMYbGo5OX3ga5VTQqn3uunPAt4FqsWoMZPF4PCDYdR3b29vhOA5Onz4dECjcg4YmMy7W/WQdqqii63gXFv16EfxwbWxbXroFO67ZYSIPTq08hWJTEYvurHtqpk+fjqGhIXP2q+M6uPeN96J/Rj+W/HoJcuEc+t7cB/wKE22iTbTnWGN4tUZa6YkeY2Njhj8zlUt1E/XekW8xp5a6koZzAnVdSnUvPpORWbbhnLoYP9PcUUYbqe6kyjLld6lUMo4Pygzbg873UAap0Z4GeZUPjAhQA6jK8VAohLH5YygtL2HufXMDoEodL5wndQpEIhEs3rYY5UpdD61Wq6hkK+i4uwM+fLQ93GaMHNHHo/Dho+v2Lvi+j+6bu+sOl3AJ6WNpePDQ8esOJM4kkNyVBAC0PdBmdFTO79J3LUXT/iY4cLDgwwtQbC9i2vFpqDZXUXAKcDtdbPqnTbjoExchmoti8zs2Y2TGCDZ8cgPajrZh8hOT8eA/Poilty7FptdtGkd3i+5bhAtvuRBnF57FjkvrMmhc84FoPorO051IJBK45aW3YNeKXebrA+sOwEt5eP43nj9OByddKiDl2ihAbXSdeb1co9eR9u0IWaUD3Rv6LNVpGQ1C7yvBHq/Xfti0rftWr7F1yXFTahmC9B59jg2ogXqkCMemjgC9V3U66k28txEO4n4Mh8OYcnAKnvfV56G9r/0ZDQDUn+mQ0/VRfUnXWR107J/OJe/XfcB1UZzWyMBiz+2zaf9rALfrulizZg2WL18e8PySCLkoVOY1TJOLpTkJqngrI1dgoAJC80P4bGXszBtnH/RaeizVI8bz60jAfCePi3Jd1+S96vcE9KV4CY9/7HGsfPdKYKAeCkYrtG4+BTcMn9ZiUHw2PesqkCj0aGhQy1DkXAQr3rECvusjkoughOCxSmooYN90LoFgoQn2laBUhSWfQ0sXQQ8BGoWmAk81rjQ1NRlP5fDwsCmy5jiO8ZQTEOn6+46PSnOwGjsAuEUXfsEPMAcNdeE8eZ4XOFNdLXrRvihWfWgV/JwPP+KbswnhAr/5598g03M+39oBzl14Dk+/8Wms/OrKwBz7vo9qUxXIANFwNOD55XznQjls+vgmjCwWwO0AJ//2JBb+dqHxDpRKpUBIP5kU12vfvn3wHA+jfzSK0289DT963ghSDCM0FDKGKs65hlWppVAr2ypzpLEiHo8bYxHXvVqtIpPJGJCYTCYN+GU0BukhkUggm80aIJ3P540BBqiHkCsTJn1yT9OIpQKW/5Ne1Fi28t9XYrhjGCMLRmpGr1wIl37k0poXI+SY8WrxIp0bGtr0SB5VVG3FIllIYuMPNqKcL6NarkfUrLpjFY4vO47BqYOAA0TyEVz0rYvgVB2E4zXh39fXh0KhUNvb4QLuftPdOL2wdi77tuu24aJvX4TR+0dRRPEZuPFEm2gT7Q+xRSIRvPGNb0SlUsGxY8fQ2dmJTCaD7u5uw6v6+vowNjaGVCpleC6N1QACOgN1llgsZvQbVVbJd2ydSPUv6mN2+Koqz+SzLIjqOI4B26qTkW8zinBsbCzQdzuaT0E93w3Uo/hoILbBCsEqUDe8G/k928PWz20FQkCynMS0R6ch7NYjw1RPU30QMSDaGq0XqTwvq5kHq44JzTlXkMPnUofTsSV3JceBB9U70gfS8HwPPnxEj0cROxGD0+IgNBxCvBpHPBPH1W+5GpF8TU++5EOXwHd9xAox+K6PphNNuPYfrkXci2PO1jlwXRf3/t29uOjHF2H3xt24+KcXI1QOYdauWbjh6zfg7j+7G27VhVt1UUqUEMvHUIwXER+L48p/vBLVYhVDbUNY+59rcXjOYeTTtaKy8VwcV/74ynFAWNdeDRo6N6qzq9FeQaTKbtKX7cVU/V/XgM1+L5+jNKD7h9crqLO97fpb/9aIXcUB9nV2/2yAroYE/Y77ijqHzok6+BoZJuy50h/lEdFoFKlzNX3bD42v7M7IAO2jGj3YRwXMuqf5PO0Tx2FHDjQC2Oq81b3DuWwURdCo/a8A3KFQCPPmzcOqVasC5+Y2EhZcXIJrtVYSEKknnEBDrTy254meL7Wikunx2RoSznt5LftJazFQDy/l2b2qhHOzkjGTmHldZVoF296yDaPzR7Hp85uw7L3L0Hy82YyJwMEOSVHCLhQKSCQSaGlpQTKZxNmzZwNF33RO2TiOarWKXDKH0vIS0vvSCA+HEYlFArlbCpS4VjqHumm5KWmcaGlpwenTpwOh/NzAvI/MjPOoAIpzqbn59PTSUp/P5zEyMoKOjg6jNDBygbnD8XgcGSeDLddsCcxDNB/F8h8ux9QnpqIULQUYLddP88ZJl2T6gbMfM+fTDbx6WF2lUsGKt63A1o9sRWZuBvBrx0qt/856+HHfzEu5XEZpaglPvPkJ9P6qFz2/7glYzfmz+692Y2hJsPJValcKC9+4EJ5fzyNmIcBMJmP2BGmZc1pIFzDwZwMGbAPA6KxR9G3oQ/e93YHQI9ICx6wGKlbIpXHAVjbU+EMvMvOqNc0hkUigubkZxWIxEKGiCiANNDSyURBo9IQe7cc11Err+XxNGeCRXKRJXjt6ZhSz/2w29n91P8rTy1jy2SXoPtANhGHm0g4bz2az6OjoCBigtGo5owzUuMQ5oUEi4kTgxJx6MUUvij/62B/htrfchlxbDpd8/RJMOj0JFbcS6DP3+lM3PYXTS+rnslcjVezauAulRAmoFzifaBNtoj0HmuM4iMfjOHHiBM6cOYOWlhZj2Cf/HBgYCISOA8HTOajTqD7S1NRk+JDmTqpSrGdoK4BRuW0r9KqP0RNPPkdeSVkJ1FPb6A1ramoyz2dfbbmj0VOMJARgHAlaH6ZQKJiIOL3e6D1Lctj6ya3wErX+bH7rZrhhF3M2z4FXDR6lxJ9IJAI/7mPXi3YhPD2MS2+7FKFqzSA+0jmCTD4TMMhyflRn42eqW+rnquMpONL1VYMC9WENq/c8D5FcpJaK5PsIF87Dh/NY03VdJCoJxGIxJJ0kvKqHGz95I3zfR9e3usxawwN6n+zF5dHL0XaqDS1nW/DkjU9iw60bsPm6zZh//3zEwjHEUjXZ6uQc3PTPN+HuN90NRIAbv3YjEqMJVL26DqtONeoXSg8KthuBKXXqsJ9cU36mHlv+b9Or0hbfb4NxBXf6md6r/yvYVrDH71V/UR2A47L3q/ZNn6UGBb6Pc2Abd2ynC+dc51Dfr+Ccf5NH8Ef7w8Z9bx9JpnOiDjutRK40zvfZ82tjDV6nOrheo8+zC7U9m/acB9yO42DKlClYs2aNqZBpEwOJxs4voIeWzJmMRS1HBEe6ITUUCKiHWAMYt3C6IYF6CJNuWrXs8m+Gq7LPGopsM1NlqPnuPJ7+q6fRv6iWT1yYWsCed+/Bok8vQtOBpgCo0GrQfD+ZdmdnJxzHQSaTQTQaRSwWCwAsvpsgn5sdAJwmB0f//ijOXXcOk++djAWfWYBKqV68SkOodG6BuvDlOpLQ6dnr7+9HNpsNAHfOt/0/x6qbh+uuFlCGRtNIQqBGa7oW5yLIYfRDKpTCdd+8Dvf/yf04sfAE3IqLdT9bhwWPLUC5vWy8wrrmao3juFlNnYyF88t1Yr857mh/FIs/uhh73r4HicMJzPvcPAxFhswxTp7nYbRtFE/9zVMYXDSIoXlD8BIeZt490wgKx3GQmZ3B8Lzhcftq6uemIlKMoOTUz2CNRqOIRqPmfFYdk7HAV8KIHo2iuPB8gZt8CAu/shCd93aiWq2a4mSqNHFOdJ+oEYb7wW6kIaVhZcqk23g8Hsj/J/2RaZJeOO98nnou2DelVxqd+C41yqmwHpgzgEwyg/DuMBa8dwGqF1cx6clJKEQKaGlpGafwMay8ra3N9KdYLJqIC/K0SqWCpqYmowQq7yCN8VoqudVqFdVsFVd/9WoMTR/ClH1TzJEamjYBACNTRtC/oD8w5927uzHvE/Pw+NjjKKGEiTbRJtpzpzF3u1QqmSMYyTey2axJudIoGhuUUZbycxqpR0dHzbPUu0jZyVopqqSrXkXFXiN1VE/SPG/KTr1edSnP85DL5Yx3ms/T4prk6wrI9W/yTdW78vk8crmc0cdUbvm+j9yMXL2w5Pk2OmsU2FTXl+hcMbLC97DzZTux96a9tbF6VVz986sx2jGK+19yP6rHq5j7ybnmDHFdB3W4aFNgpF5f9lX7rMBO9WMWzKRsVz1VAZrqXLyGfVI5pGAMAOZvmm/eecltl8CHj9V3rK7p4W4wPzbRn8CGr25ApCmC9Nk0nHBdFrMP2j8dp01PtqHC1kFUz3IcB2fnnEU0G0XiSCIAPm2Aqs028Ojn9n220UDXUoGc3V+9T/ebglrVRxTQUz/ReVG64LvUq6zrqn21DQcKjhsZF6h38Xg3jU6hbsZ9rftEI2J1rHwe6VXBtdK/HjVsOzdsWla+Yhs8FGfoHno27TkPuFOpFC6//HL09PQEQqVpSQVgrEAKwqhck5EoIwTq3kSCMfUmEwhwsdRrSKuH5gUpwOH3VOaV+bNIkzaCCgX4xirq13NOjYI/6iNyJhJ4Rnw0jkQ2Ye5RYafzpV5vgkzf9zEwMGD6wkIrtGYDMDlcvl/Lmdr9nt3oX19T1M9cfQaVZAXL3rcsIFyVsXDTEFjzM86T5nfbkQM6h9xUHFs8HjfWaAJrFeqkDy1iRkCo+ekM283lcshmsyiXy0gkEnWr+7kkLvnuJbj/tfdj6YNLsfjJxUAM5l7P80wUBN+lAlsZGoAA6GFhMhVW9Lw2HWnCwn9biMRQAigBJb9Uz4duCeGJdzyBwfmDAAA/7GPXX+yCE3Uw+47Zph+xsRhiw8Ez3SffMRnNh5sDxqZqtYpz52p56mR+XDNj8HAdHP/kcWTXZ82z/JCP3LRcwMqoe0OFIVBT3mbOnImjR48aT3oqlQp4m8mIFZRrZXM2Fbi2wOffNJyogcxx6ikLyvTVaKdWWO4Vja5h33LTc9j65q3wih4WvG0B0mNpND/eDA91kM9n6v5ubm42+4lpEraCUS6XMTw8HAjnZJ9J56ysT37D/RQdjSKxLWHGokXgEokEhmPD+M3rfoOBGQNB2tg7GbFjsWctYCbaRJtofzjtBS94gdEpKpXasaUdHR3mOMW+vj4jzzVtK5BGJTyUIdcKftWgzWK06klupNzye/WCqWKrhkjVuZRvqnMFqBfE5N8AjJ6ioEvBDq+lbCBgp8wrFArmjHKVAbx/yr1T0BZuw6NvfxRwgMU/XYzlv1weeAfBAfXCba/ZhoPPO2ieteviXci2Z1FMFHFm+hlgDlCMFbHk3UsC4NBW+G1wpqBX36+AQ5/D5wIwa6YeUwUW6rWMRCJobW0FABPmn8lkzJooGGRTedrI86hgnZ91nqzldLtht+HYuP5Ko6rj8jsbkHL9lB75MzR1CA//5cMIFUO44uNXAOeCHli9X+mH0RwKevm/PZe6bjY96TPV8KPX2XqNzqO9pkofjYwtNvjW97HftrNEdTmdF3tu1IBir5v9Pupz6txSvGV71fXHnjulBT7H7qf9zEZrYl9n0+yzae7vv+QPt4XDYVx77bXo6uoKbFLb+0Qmp5PLyec52byOCjyBsYJa9T4CwWR+EgkBoyrpFChUsCkQGPap4Qk8n1mZcyNQpmE+rNrnui6ShSSWfHUJJj0wCfCAll0tWPqRpUgMJAIWYg3jIIBvbm423kCdP6AuvFKpVCB0g+CQ+bK7ProL/WuDXrH+tf3Y+d6dZo55P8ehVZ6r1arxCtPQ4bq1M881z4ubnuvD6zlGACbMl2CI72a/OTaGHetc8DMqHLyupaXFeHkzmYxhGu3D7XjJN16CJduXGCGhhon29na0traitbUV8XgcqVQKbW1t6OrqQldXl4m04HvoqabCoFZNpYXkkSQi2WDOcy6XQ+ZsBtO+Ow1O6Tzj8IHmY83ovqvbhFRHo1Ekh5NY9tllaDrSBHhA26/bMPkjk4FBIJfLBehZmQ2NEKOjo3XlJ+0gtzoYZxzOhtH7096AsqXV4jUNgfvy4MGDKBQKxjtNAQbUQwC5JxiZ4rquOZuT+4jpB5qmQPrSOg/K9GmZ1eeQT5DOuE6NhJoqMX63j4c++hCyM7LIz89j57d3wksHq/KWKiUU3IIxCpGmeSye8gdVfhhtQIMgaSMSiSCZTGLhwoWIx+Po7OwMGNS4722Byz1hUh3aK0Gw7QNTt07F0juWBs4Wn2gTbaI9d1pPT4+RtTQgNzU1GUVzbGzMGP01zYXAQfUjyqJkMmmMgpFIxOgv5MnKf4E62FJvM/WywcFBDAwMBGQoeSKNgurIYFMlmZ5o6jIK3LSRT9sKe6NUL/JgNaKrHKJuUywWEb0jiqXvX4p5t87DstuWIebHTH91TLxv2S3LEMvUjd6RcgSnZ5yuge3zrf/ifux6366AzCHP1lRIoF5oVnVQO9WK76cewDFSLtH5oDozn6NGXz5ndHQUuVxd9ivI0rDhaDRqaILrWqlUjEymUUOdSFpHiboGIzFs76saE2zjuw3IdO20XhN18Hx7Hre/83aM9IxgcOYg7vyXO1FJjq/Xo43vsQ3ziknsXGilrUaAWWlTPbmNgJ+CSTb2x+4nf9vv0gherrcaK4B63jj7zzVRcM7nqNdZDSyVRAV3vf8ulKPlQH+YvsgCwUDwJACOVcenRZBt4w6vicViaGpqQiqVGncijL1+jYxMjUC43vNs2nPWwx2NRnHxxRdjypQphrA58XYeAxeByjIXNRarMTmdVCrkBJ0Mr1Lmyu9IaLTGsPoyLXdUXllMie/lPbxWgTtQB+vMEwLqzJWh1XyOemCZb+5nfSz60CLgPcCyf1uGkBMyZ+sSuPBMS82dyOfzgQ3NsHbOI8dCxkxGRcYcjUbhtXrjzDjtT7dj6UeXwoMXCFPzPM9YnDmH6q2mNx2oVySvVus5t2rJ1vB9NjIkhmvzOq4jN77OKQUA6UGFGZkFN24ul0OulMNA2wCmDE5BdCgKPxyMhrCNJQzXz+VyKBaLKBaLSKfTcJxa4ZnR0VFDW6rwaGM/OUZVMKLRKFKpFEZHR5H+TRrzovNw6E2HkDqewob3boCL2tnT5XK55sVoLWH3G3YjMyODyFAEs745C6FSCHAR8PYz9FBD7KLRqCm2U6lUcPJdJ4Nnr/tA094mRLIRhOJ1ZkuvPfuveXC6z9RSaf9QwWtpacHg4CBGR0cDCgaFJgE714Q0R2XDDgM3536e3x9a6E3pQsGxHi2YSCRM+sUjb3gEpeZ62HW5o4zDbzuMlZ9eafbU6YtP4/DzD2Pdl9ahKdtk9lS5XMbAwAASiYQxApHWde+zb+qxGBkZMcWCSMMEycpjQqEQCoVCwIgBAAO9A/j5O38eoLnEWALP//LzUSqVMDo62lCwT7SJNtH+cFtzc7MBR5lMxtQ44ekKLDpJBZIAkfwDCJ7hy+KViUQCmUwmYEgnIKfcVu+QykVbWeVJDKpQ0wCqhlDKSPVo2ikx+txG3jvySjW2q7dQjdi+7xteyVQlNXSSD3d0dCCdTmP60ekoHywjGqlHp1Ges/AqjQfRTBQ3vvdG/Ob9v0E5VMYff/+P0VZswyff8UnT/1AuhIX/stAcZcn3Oo6D4vQiwufCcL3xocacdwDjAA+N3+3t7ejv7zfHg3L+NM1Sn8e/1SlFAKbgnXOsUYWkDep8tvdTa/mQ7tRQ0AgA8bcdHUq5qICRrdEc8X+GOt/2p7ehmKoXBs2157DtL7dh5RdWBuhKwZnSnRpYdD+xr+pQ498aXq2/Se9qVNJoOhsA2wYHBeO8p9E88B41PNkGD3VsqvONRiX+qIFN18pxHBSmFLDpvZswNn0Md77lTlz37euQGqoDbNXJisUiCoXCOP1eaUqfzfuVBvS52khPigcatUb8S/v4bNtz0sMdjUaxbNkyzJs3LzAB9ECTGEl8JFxaX23lXsE2rbcaykmhRNCr7yLQpaAhs9DwBzskmO+mMl8ulw241ER89olMQMfK7zQMor293Sj/vudj4YcXwoU7TugRTGueg+ZcUDjk83nDdPlO/q/GADLXswvPIt+eH7deSz+yFL5XB55879isMQz2DBpGqXm6aoXlnJGpUHAp47XzO5TpMAJAQ32UAZFR6HFXGsLruvWcbg1ddxwHB288iIff/jBOzz1taIbeUApXAleOh8eS0drG+Y/FYujo6EBXVxc6OzvR1tZmjmtRS7wyNdKK0jXpFgA67+zErC/NwtJ3L0UukwtEFAxiEE/+6ZM4tfYU4ADl9jJ2f3A3xhaNmefTEEEjA4UpaUTBbfPjzYDwnp4He3DBP18QMOpwbfmbtMPxKWNWIcK15lrSs0tjj+bnxGIxJBIJNDU1mWdoNVBeTxomM6cxShmoRoHofrMFkwo6gu7l/7Ic7Xe1m2dNu3MaVnxmhdnjp64+hcff/jj6lvZh82s2Y7RpFPs27DMCioYBNjuMn00NThSCOg5VQKlMcC8S0PPaU4tP4Y6/vQNeRBbSB2Y/MNvs00wmMwG4J9pEe461q666ypzGkclk0NTUhKamJpw7dw5DQ0PIZDLI5XJG3lAX0IgYVfiLxWIgB9MG0KrokvdoBJCtID+TYZVyWcNHbYMz+8lnU9/T52qUI1tLS4vh8TbQUSWd/B8ARkZGAryVz45EIhi6agjdvd1Ip9MIh8KmfzRWqz6p+kpnuRMv/dFLcdPPbkL36W5sWRYsxOpHfAxcPTDOG5+ZlcGef9qD4689Dt8df0QU+6ZAWOVXPp/HyZMnTYE49aJyHrimOv/q1aQeoOCZzQZavl+L3lIDjnp/qQfRCKz6pwJ5jlNpSOmGNKoh6VxLpW39m2MrFAoYHBzEVV+6CjMfn2neN/feubjoqxcFdAI1KPDH1gfU0BCLxcbVlaE+oDqdPSYdj+4ddTzZxqzfBQrVCGF7yXUf2HOn4F/3jA2ydf1tEDs2bQxPvulJjPWOAQ5wes5p/PoVv8ZY21jDdeQ+U6cl95t6+p/pvWooUENDIBVX+ITOD+dV9439Yxs3fld7znm4XdfF3LlzsXLlSqNY2kqoDbIBBDYBN7KGNCn44mfKvJUpk5hJZPoOoE6YeiYvwSSZUywWMyFSutjqhVJFn+Oj15nv4ffhcBiDg4PGW6sbmOHmBE8EeFokSgmKVm0AgcJV7COfS2BRrdaOZPIGPLjlIOFPvXkqKgMVeOVa/1OpFOLxOLLdWex55x74no8V/7wCkTMRw7zJhDgOO/RdraDsVyOwbls+qRwQuKggpzAOhULo7u7G0NCQCb1m0w3veR6euvEpPPG8J+CFPTz6F4/i6u9cjbYDbcaQEo1GkUgkAlESXCsFVUA9VJr5wKVSCW1tbYZWh4eHDZDXOgEEiqRLHn/FaI5SqYRJd0yC7/soOfUIDyPIBoIM0i25SJQSJmqCY1G6JO0R2BeLRQy9bAgn//5kgKMUW4uIxCJwy/VjWuipUBCtBcf4bF6vSo8dDUILeWdnp6HnarWKVCplDBUEonw+11+FB4VgLBbDwMCAUZJIN+QbNIyRlvQ79rdcLqO/vx+O4yA/nMf0T0yHU3KQrCax4LsL4FU8OGEHx553DNtftd1EBJxZcwaPJR/D4OxBlDpLWPGrFYEwcjs6Q4UJ55P0o2HmjPjR49zUKBgK1fIsh4eHUVpXwoOvehC5tmBawIW3XYglv1hiIn80rWaiTbSJ9txo0WgU/f39GB0dRWtrK1zXxdmzZzF16lQAMEAoHo8HImL0ZAYCE/IRRo9RVttAQ436fIbqWOptVY+V7VGirFcjM3meggU6MhSY8X22l5Pv4vvViKw6j+qBQ0NDpricNtd1cWbjGRx89UH0HenDpV+5dNx7+TM2Nmb0Cl4Tj8fRPtBek/GOj+RoMrh4PhAbiQWMH+WpZRx63yFkF2aRXZxFNVbFnM/MMXJCxwME08I4vzpv1AMdxwkUktU1svVUrg090rbXW9dO51ObAkc6qChT9fn6HJWD6tzStVSatWmP71WdRjEAUDvadd331sGtuAgPhLHkh0vge42L1Ck4VYBn06Z+p7Sj82L3Q+dc5460QF2P82a3RqBX+93of+2P3q9zZztIABh9VGme1/F3tBxFpGjVmSrEEa6EA3OoOEYNbDoXyieeCfja865/21GLakyy54af2UDeXpff1Z5zHu6Ojg5cfvnlaG1tRSKRMKCVE6eLQ2FBAjALfj4kiEozmad6vdUrrODUzgsnKFFrCRmXAnEulOZHKxEqQbMfNggmc6AHLxQK1RTl855VVs8kwWi4hw3C2X/f9wMefIJeerHtzUowDMDkWhcKBYTDYYytG0OhuxBYr77r+lCK1ARgKpVCpVLBqD+Kxz76GMbmjyGzMIMnPvMEKvGKeVc+n8fQ0BCqbjUwP2RaCjyAoOVNPd1cJ643vZG2h5LrRGF44sSJQBibCn2uw/aN2/HkNU/CC9f6MDx5GHe+/k5Uuipoa2szYWpjY2MGcKtl0zYmEPjH43FzHb2v8XgcPT09mDp1qgmR1qb5WgSKjQBsJpMxxV4cx0Gxs4j+q+r59tGBKBb/02JEDkUCxij2TQvJqfEGISCzOINqc114OVUHvb/sRXmsZgxg3hbnWelJGV1TU1MgrYH7Egh6TPjZ0NAQ+vr6TP42lZdUKmWuoyCicNbj5PhTKBQwPDwcAPY0XOixf+qpsT0u2WzWzFU2m62FII5FsPCrCzH/5vmIFmth9OVyGZMenYTUuRQgfPrcknOoJCrY8YId2LpxK/J+Hnf9w11wEg4QRsAropZrRuRoH7nP1QJuCzHORaVSgTPXwa9f92uMdY3VO+QBK361AktuXwK/5Af2/kSbaBPtudO49/v6+kwqycmTJ9HZ2YnW1lYUCgX09/cbxV2PMdW0G+os5CmJRMIYZZVfawSa8iqNUqOMUZBC/SuZTI7z3tlRPfxc+bh6u20PowK3RroiUD/yVJ0O1Itso4IB0a6D0fWj2PeWfSi1lbD7gt245y/ugR+q94vGYPabHmWtFWJkj+Ni6dal2PjtjXCqDpyyg+V/vxzpJ9J1z3q4im2f3IbMwsz5BQbOvfAcjv3dsYBMjcViCMVCOPDBA3jqG0+h4tYNCuqk0vnn2PS0FDb2UfVp1RHVmMKm/3N9dP10DflM1e30uaoHNgL16mRSZ5b2X3/bBh9+x76k8ims/d5aLPvJMvhZP2DYV7BH77UdOaqgU3UQ9QpzztWpp2NmnxTU6hrE43FTuJD3qRfYBpC2p9zWwVTHsulD11v7p/vbNlopDbmui9RACmu+sAbtB9oBH5ixewau+ulVaM43B6Ihiam0D0pnsVgM6XQaHR0dpraUjkXXUsds0wL79bsME/b7Gz372bTnFOBOp9O4/vrrTZEPIFgAzK5AxwWwPdJk+PQYqqdcQ8MpRDRknPfqRrGtsCocfL9WxCqfzwesddVqFblcLhCKoUAbgOkfn6+bmWG1zc11IuWckAFwQ7EPerQYry8WixgdHQ1US1brsxoY9Ieh0gRJqVQKc2+di+YDzWb+owNRLPzwQiRyiUAfdrx3B/Kd9dDzQncBe963x1g3U00pFC4p4OlPPY1KRyXg4VZhB9Q3MpmPClPOBw0S9FBS0CsIJwhRi6mCEjvsZNXDq7D4ycVwqrX3J8eSuOb71yA2FDMWRy1QNTQ0ZPKkCODYZyoXasRh0Taewzw6OopIJILOzk60t7cjnU6jqakpUNRFx0oaJEBkiDUNSZFEBE9/52lU2uqW+lJ7CfvfuN/sCc6HFsLhPBkw3hRG31/3YfCFg4G9uvjmxZj5xEw0p2qGB3pPqLypAqJFesrlMiZNmmTolN+rRVit/Mlk0jw3mUyayBEVBErDBKV6JAVz1zRnnvNIWqPnmIJNBTf7n0gkzBqMjY2ZeYzmo0j5KRMuF41GER+L46p3X4XWI63j+Fw1VsW2P9uG//zaf+L0otP45he+iUde8whKyZLJ06dBgnxMBTDTUxg6ykgOx6mlV+RyOWPwo2e+LdeGRQ8tMn1wyg7m3z4fC7+/EJVMxaQNPJMVeaJNtIn2h9vWr1+PdevWobe31/AHyphSqWRqcyhwIY+gMc9WoMPhsPGGq9eUvEYNrCqvyZsI0NVYzv9ZNZ3OC/I2m/faHm+NMlRDKHmeeuzZyKfVM0w5wWdTNmmorfGStVWw70376gZnFzi+4jgOXH0goDdQJukxpaFQCPnePMLRcEA38ys+Jt09CQu+sADL374c6X1p480sl8vwCz4WvWsRomfPF4n1gdbNrZj5lZkB547b6uLIu46g/9p+ZOdlsf1L21Hprp+QQ5lIPcFJOCj3lAP6tcpQlZ8aIq26NsdBOlAvu+rKCnaBYGEsW6/T3H3qVeqIsb2xlOPq/SY96DqyT41AGr8PZ8Nw88EK3KqHUgegXsd+K05QYKb0oE4ABZa2EYmeY/t5/LsRCGZkm0b0BdbaCZ5MpI4rNQzo9/pO5QUK9u11sec5FAohlU/hpn+9CbOfno1Xfu+V6Ax3jjv2T3Vw+/18B+v4tLe3o7m52WC1RukTNq0oH7ND1G0dx3b8qdFH6fX3tecM4E6n07jyyisNANGwBW7IpqYmY2ViOCYXyPM8EwKtG9IOlwHqm5wbVsN2qeySOdsFBZRJE/hQUSXjN6DH8iorSCAYUDDOTU0vpe/743Ko2HRTkah0TCQUFtmihVW9YwACYI73Mw8JqG0OKvzD84dRaqsXiWre24ymvibjaWd/ln9gObof7jbXTXlkCi786IVmrgcvGcT+z+zHyMoR7H3LXhQ7imZelGGxb/l83uTjGiAo+VpqLOGGVKHAzUQDAhmzPo+AjEKvUq7gqh9fhWWPLUNqNIUrf3Ileg/0IhFPGODFvsZiMSSTSUSjURSLRQM+1cLN9+pZgqTvZDKJ9vb2cUamdDqN1tZWtLW1mTGrlVQVCKAeulUqleBVPXQ/Wl8DAHDzLlq3tAZoRAUC9w9BYyKRQNfCLuTX5QHhN6mzKbQcbTHKEiNRlOGr15kV5EnvZ86cMXtI14vCm0V/RkdHTdVT1htobm5Gc3NzIH+PSpnSjed55oQCCjLSPveiemEYOq88h4qH7gka0vgMrRtBwwo9QiE/hCv+9QpM2jEJrXtbERuS49kc1Ljz+d/71+3HEy95ArvX7obne4be29raEA6Hkc/nTS66vkOFA+mdBgvSRNkr46mrn8KWm+p5g5F8BN1buhEOhU2BIIbx2wrrRJtoE+0Pu5Entra2Ynh4GAMDA8YA29fXZ4670pQtIFhsjPKHQJk8Xb1kqtzyfwXBQN0DrkZUGjt5rw3UbP7dSLFXvqfedkaM2d5U3kvdSsPobcBAnpfP58elJDl9DpZ+YCnSh9IAALfsYsUtK7Dk/iWBMalDgH+fmXsGt7/5duxetdsYIcLhMPYu34tCvoDun3ej+alm0wd1GsUPxzH/Q/MRPxZH26/bMP8f58Or1AFRMVTEgb84gJPXn6zJEQcYXTiKve/cC2+6Z3QNk+rlVXDsZcew6/27MDJlBEC9Pg+vob5iPO3iMNLogUahukofOre6rqr7GOOCZfzgM5WGbHCn+qHtada+2GDXBmSqB+nztN/287QPqkuqsUb7qDpbo/7bxiqgfmytynj9XudJ5/yZvP/ab9vIot54e8yN1lVp/ZnWIBwOI+yGccPXbkClVAngDTbiLtIBcRLxHHXBw4cP48SJExgdHTV7i3StAJzNpj0F9+oFVx7zTHTLfjxbneg5ER+YTCZx0UUXYebMmQHiAxAolkSCsRkAEDw3sFAoGGWY95Ho6ZnUBaA32XEc4/WiQk9BQQbPTUDQTg8SLcVaBZKCh2deKvHTaqwW53w+H7D2Uanne9TKBNTPayYD4/to+aKFmmO1Ny7nwhaaKpA4R2MLxrD77buRn1r3XA+sH8Cu8C4seN8CNPvNdSBX8rHsi8uwr7wP8IBFX18Ep1ozCpy8/CQO/v1B+NHaWvZf2o8diR1Y8sEliFSDxaBI8HY1US2+oWCO1j7bmMJn2gLFNqYoeOf/l/78UkzfNh2z9s6Chxrz1rBxPofhxixYpZubRiL1qnI9FRwRsNFLyXxi13XR2tpqgB4QrEJvjyEUCsHxHMz95Fz4oz7OveAc4AMzPzETk+6eBDdct5BynpVG+Fk4HEZqOIWeu3swtHQIQC3PbPWXV6NzZye8cG3P5fN55PN5E/atQJb7TxmaMkelWTscqFqt4uzZs4F8OCqBjDJQ5YZjIs2oR5sCQQUf9xNz1TVsn/SjIdosxEZvON+rwkT3led5SGQTWP755Rjzx1CYVMCut+3C3G/ORd/6PvSt7wvwwR2X7ICz3sFwyzBW/3I19l+6H8tPLUcsX/fUa6qK8j7lSa7rmtoLAPDUHz2FmBc8ix0A4tF44Agc13UD/GuiTbSJ9offKOdDoXoRT9ZjGBqq8W11SjA6h0ZH8kTKSMoAGuLIu8lDW1pajPGvkcebIIdFOPmZglwauRsBa76LfVRjrAIk/c13KKDhczhG6mqZTMakG/I+lat8r+pCrUdbcdl3LsNjf/MYFt+zGDPvngk/0hgIsb8DCwbw2GseQ7Yzi9uffzty4RzWbF6DhzY8hAc3PIjZmdno+l6X6a+CWbbUkynM/shsxA/GgTLgOaJ7hqqolBoAAQ9wECx8BQD7X7Mfx15+DHCBLX+7Bcs/thzpwbS5TfVT20llGxMoqxkpxz7ZwFf1SHVA0LhgwFk4HNALgMbnHyvoVeOMDeyVHlT3UDre9MJNWPWTVeMcWWqo0ffrGtu6tOpgdn/13XqvGvdtvUSPbFOPrz7D9n6TxnXMnAd9BmmMdKbYyu6jvZ8aGVLsPczrSSf5fB65XG6coUOjBHiP6mvqXLPD5nm9zrnOiUYfNDIENfJya99tOnu27Q8ecIfDYaxcuRJLly4FUD/OiMBZQw7obdIQJz1aKpfLjQtbIKHxGQq8dXOoB7VSqQSsJgpI1dLlOPUjBubNm4fjx4+jv7/fKOoEZKrEKnHRkKBAWgUMCZJ9IYhhH9VTbQsrIMhw1LDA+3UTO45jogYagZGWvha0HGxBZmamvnge0PFgB0LFECpuPQ8sGo0ilo1h0dcWwXVcRPNRVJwa+Gnb2oZYXwyV1orxmg6sHsC2j23DmreuqQPG83NRqBTghlyE3Ppc6oZgETLOpeZ2UUAzdJ5j4TrH4/FAlALnk3NSrVYR9sOYtWcW4NTmKJFImPkheKbwVoskPbOlUsmcd80oDa6XMlutUwDUPBapVMqcpcpjwUgH9MjyuCwafNSoFM6EMfvLs+HFPXQ92oW2e9sApx6Oxz2m+4peXI5nX2gfDv7ZQbPeL/rCi7AQC3EoecgodfzhetDAwP2pVdW1ngKvYzoGxx6Px5HP5wP7h3lM7KvtVWF/dQ+pdZi0CdRzxWOxmOExXHe+17bsArVQ8kwmY3L2GT2iNEEeQ2/xyMgIvHMeUkghdTiFVW9ZBeeAg9ZHWlH5UAVDy4YC0QO+6+Pp65/G0dVHkW3N4oncE2g924rrv3x94CQDGm8YkUODHmnddV1UvAqeetlT2Hn1TkzZPwUX3XYRNt+0GY7n4MbP3Ijmw81wIvXQMsdxTPrJRJtoE+250aZPn45rrrsGP7nkJ7jw/gsxNmMMp9edxvxN85HP5dHe3Y6D+w8GwrdV/6GsBOr8NJVKobm5OVBYkvoOdS71RNu8UoGV8mRt5OEK2G1PnAI+6gYcB1CPirO958rTVB+iLqTRgeR3rIGiAI7vDIfD6O3rxfTvTEfpQAnVUDUwJvViVqtVjHaO4uE3PoxsRxYAUIqV8Jtrf4OnLn4Kw23DKEfL2P+n+1HOlzH151ONfFI9k17Kpq1Ntb44QaeDW3Ex7T+mwYt6OPeicwCA9O40Fn5iIcKjYZT9uq60/8/34/hLjpuY15HFI9jykS244i1XwC254+iB8+/7fiAak3OsdNSIBny/dkJGIpFANpsN0BzXSouBPROwtAEs5avSKvtkA0FdH6ULDx4eftXD2LduH0aaRnDhFy+EVxl/DrTdH6VLfaYaNex+6XWNAKzSpV7XyHjEseg8q97UaL7subPfzc90L+tzVHdqZITQ/uv8cJ3V8aU6EvU6PVXH1on5bH6uur3Oh23sYf91XtVgpPreM/1wTnSMz6b9QQNux3Ewe/ZsrFxZO/eOwNIGmcw/Yq6iAh2dCB7HxGuomKsXTb3jXESCUMdxDGDXqr8qdNTrRSCdyWSwZcsW0x8q4+qFVMAP1Aldw+f5PvXS8RoyKwXnfKYWAiEBE/ClUikMDw8HKh6ywrOd60IApYChXC7Djbo4ftlxnLziZGD9Zn5/JmbePROheN3Tq2H0kbGaZ7rq1AuZJYYTuOCvLsBT33wK+Vk1b3msP4ZZ75iFYrFYH6MLnF1zFtvfsx0LvrkAc+6ag7BXrzROsAggcB6z/gAwIJbj19A2bjpNKWA/CRgZBsbP+ExdD8dxAoUtCoUC8vk8RkZGTP4TPb9DQ0MIhUJoaWkJrKuGyIdCIQwNDaG5uRktLS1obm5Gd3c3RkdHDXCPRqPm2BeGWxuBIkAzko1g3ofnwa/4BmxTcCoI5vg0OiA3NYed390JP1ZnsKP9o9h3bh+A+pFZDB9XjzIrk7NxjvleRi0QTDPCQ8OLqtWqmVf+zXv1bHV7vVUZA4KnHKhCo2vN/nGtWCiQvIZjZd2FRCJhzldvaWlBJBLB4OCgCdmkoYQGKAqZxIkEEK/lja1+92ps/tRmFDoKKHbVzwOtRqsYmlrzTJVSJYx0juCu192Fy759GZwxB4XuApoHm83eJQ2Rb5TLZfhRH0/f9DSevu5pwAVOLDpRO+v11wsx78F5aD3Riirq/JD0zyKAE22iTbTnSEsAd264E/csvAf3zb+vxvtdH1NapmD6tun4wOs+gPbN7VjwrQVAAhjFKGKnY+OAtqbq5XI5w9OBesix8nwFwdqoA6lh2i4Imc/nA9cCweMNY7FYoCAudRfKWnUO2DqVgi5bb7IjFgEYo/jY2JjRCcaBmxSANqB1qBVnyvWUKNtzSD7fNtyGi350ER7+84dRTpbhVl1EShH0TapHNXlJD4f+/hDix+No29QWAG6cV/1NOc1+eZ4HjAKzPzUbfsJHflYeK9+8En7JR7aaNfPoOA5m3TwLg3MHMbx6GHCA6HAUF3zsArilYJokx6HGCOp16oEE6oZrNRaoHqt59SzQy3XWddAIKzWyPBMA5rzzWvVa2tfZALwaq2LTCzZhz4Y9gAsc3nAYXtbDspuXIVqq07RGhKoxRQG8rWtqnxU4c71soG6PRa/Td5NWOb/aB6VRXTc+l00L+dqOJb5fPctcazvCz9af7J9GudJq/OCccG+rvkea0nGop1t/28Yd3RukRY3UI30pzdnroftZmxrtfl/7gwbc06dPxzXXXGMUfyDonWWoFD8nAbDAk4YZ8PtqtWqYNcODqFTbXja+hxZSZbTA+MqJ7IcKGXqT1PtqV0kG6sdCqVWQYcYKeIC6xZdjU0uOfsbf6rVWBlGpVNDS0gLHcQzQU/DB8RIwsW/sTyhUq8bsdXg4u/EsIHI1NhhD86FmOL6DqlffhOy/gkmCFq5FxItg5ZtWYtdHdqGcLGPJR5cglosFcjhGnzeKbe/eBjjAnr+qFVyb88s5qFbqR7VpvirpheNSRsUNxTBbDfkl4+fnjFiwN7WGojNFgEYfrjHXn2HuykQcxzEWX54DydQFVqVXwdPV1RUoQBYOh02e99jYGDKZjDniJZFIYHR01KQkVCoVOCEHAxcOoOXRFoTdMCqoA1EN+9aCgqRbvvfsS8+a0P/aRAL7r9uP5V9fHqgY2ciQRc825540yhw8mxHrZ8y3A2pCZvbs2aYAIVAzrNnzpesMwIB0es8J9rlneB/3M8fBPtJ4RoUhFAohk8kYYxpzBsPhMIaGhmrGjfNgO5PJYGRkxDyXPIL7ktE0YSeMtW9Zi8yUDLb94zZk5kv0iDYHOLzyMEK5EKbtmoZNf7wJG7+6ET37ewIGIc5DJBHBU9c9hW0v3hZ4xsCMAVxy8yVoP9qOUrVkBCl5RrFYNPxook20ifYcaBHg8MsP4/CywwCAaqiuZP58w8+BDbW/z15yFmcvOQsACI+EMeezcwAAbU+1IdQfGlekigZsKvkKtghItNaN6hUKgl3XNVFlGrrKyCbex2v5bFb55ufqnWZEEb8Dxoesq5fKcerHX5K3KzDL5XImWoygXsF6NVbFnlfswdH5R3HJjy8JGBzU8UEZ4jgOIuEIZj0xC36zj9+++LdYsm0Jljy5BHe8/A70ddZBd/JoEtHBaEBXDABEifBUIMA+ep4Hv+Jj/ofn1+YJ9chLoO448jwPC9+6EIc+eQhjM8ew9PNL0X60HY4bnDP1bAJoWKRUjQBKG7qG6sm2wRx1TJWLQF2XViCtY1X5rrSqn6u+TH1Xgerg5EGcWHCiXt3KBQaWDGB06ig6DnWY8dgeU32Pjll1cb1Pm40F9HOOxf7sd3mGtT9s2l/tE69VJxH1GpuutI+qr6ghSefBHifHUphUAFqA5FAywBN0fyogVlpRnPe7Cp3ZIFh1QO2vfvdMXvRGc2g77+y1e6b2Bwu4J02ahKuvvhrJZNIQgg5WLZO21c9mogpQuZG1qBoXmM0GUsD4g9B5j3rl2EjASkgEMbwfGG+R0kIGBHwsumRXyLSPHDMWTdStSwzJqVQqxvungAIADh06ZCIENL9ZN6cCJs1jdRwHqVQKZZQRzgVJySk7CBWCTJb3aESAjkmZQSwbw+J/W4xSUwnNR5vhhWsAKJFI4MTzTuDAGw8EQm13vXYX/ISP3u/1BsanHkS2bDaLlpYWA6qUoWuuru/748KZOE8MrVZmx3e0tLQgGo1iYGDA9IWWeI2oYHE13kfaZFXxSqViitkwOoNeBXrtKdy1ojZDqxkCV61WTQVH0uvOF+3E0T86ijlfnIPJ904OKANcG/aNc8l5IR3N+eocnHvZOWNoWXjbQqz8yUqEkvWQMPX+qxVUaZxGAwCmj+oFUZpVgwnn/siRI5g0aVJACdS9ROOaHr3CAnDMeyZt82/b4qrPVuMYrxsdHQ2skW2E41yy4Bvpkt+rsYN0xed3Dnbiqh9chd1du3H4FYcx8wczMbJ4BOc2ngvsuQMbDuDAhgMAgAdf8yAu+c4lGO4exrzH5iFcqBejccMuSslS4N74SBzrbl6Hnl09qPpVw8PIDzgHam2eaBNtov1ht1AkhNkXzcZ+7H/W91RaKtj7gb0AgM4HOhE9F0XyRBJTbpkS4IdAMFxTwRR5hu25Uw+RRs/ZyjIBAD2nQJ1PajVo8ni+i+k8jRRgGziovkF5U6lUjM5J/YTylzKK1yaTSVS9Kvb/3X6cftFpAECuksP6L69HxI8EPH6aBqcexBVbVqAj3oE5T81BLBTDi+54EX5y408w1DaExJEE5n58LlL7U/D88eeNK6C355fjL84tojiliI4nO8w1On59puu7WPG5FRiZN4K2p9rgRuoh5LYs428aWQh6FPDZa/tMgMgGXNpowLavbQRwbZBnA0Ttg4JufW/bsTZs+PYGPPiaBzHaM4qms0248KsXou1gG9xQ0IDS6F0KGFlgtlwum8LCOn57/u050mc3Ar3PBMR1b9j32HOi+pVGotgGAK4X8ZWu9TONR50dfF+5tYwtr9kCJ+ng6u9ejWRf/YQe7Tt1LQXBtqGDPwTfOh57nvS5CpDtcWnfGxly9HvbwPBs2h8k4E6n01i7dq0JySTT5eDoXaJ3SENVdDLVQ2wzYWUQXFgSHReRoBnAuDMJCRK04IYCSC6kWvxoTeMCUblmGKx6LG0CZOOzAAQEhRoNXNc1eTX0qlFJJhjh+JR5cRzMheB4OG56tpkXWq3Wjqvymjz0r+kP9LPQXcDQ/CETCkVLtm3J5JgIShV4RPuiKJ0swYNnwJDneWh7ug2hQgheUja856B9czui0agpihcOh9G3tA+VSRXMfnS2WUutWMo5UVCkBhMtpEZa5PnjXAOCRF4zMjJiUgaUwbuuG/Dk6j0AjBCncKeRhNZ+PQ+aHgHSF0O01VDQ0tKCdDodOBLO8zzsfcVeHH7hYXixWshazIuh4zcdJhwaCB6pZ4dM0XO/80M7A+cczHl8Dryyh3C0Tj9cN11rzqfv+wbw8j2kU+ak07DEI92oUJFGNEVBBYDuO80BJMgfGxszn/MZVBb1WaSLbDbbMFyOdMt0h0QiEUh7sZUJVrnVudV5YZ9VqSwUCog8EcHM0kykn0wjsT+BzrZOVONVDKwdOL8BAtsPma4MHvrLh1BKlHB09VFc94nrTD+qxSpW/2I1QqEQtl+9HU7VwZWfvBItB1vgx+o55mpwIw+dKJo20Sbac6e5ZReLv78Ynu/h4PMO1r94lnV++i+vyfVQJoSz153F3K/ORWJ3Ak+/6WlsuHkDHDdYV0b5LFsjANxIoVWjNnmPfdpEpVKrTEyjMvmTHuuoOlajv4EgCFTngupCdNpQfqlcYBTSkfcewdnnnzXjOrrqKIpvLeKGT91g5Il6xMjfqUelUims3LsSI+UROGEH049Mx7VfvBa3/dVtWPy+xUgcTZi1UkM155D9pS6g81dtr2L/x/YDSSD2wRjSe9JG11EAxr5FIhHEc3GEt4RRrpYDMs5OwwLqJ16oQ0tBj+11VJlKfbgROGK/2GyArQZxXdNGTcEWaYX/s3ZOMpkMhMRPOjwJV33hKtz9trtxyScuQfJI0uTHK90q/XB8du0jRhhS9tv385n2s2zgSJphH7nOGmGifVKgq0YCG0Sz6TPp+KEuRv2v0fpozZ1nMpqYdQj5ePg9D2N47jAA4Jd//Uu85OMvgVsMepKr1WqgQr09V+yn9of6vBq2SH+2cU4jeanL2Gug9zUyJthz+Wyb82xvchznv//0/wctmUzi8ssvx8KFCwOFK+jtI4i0idwOeSKz1vxKHuujz1UAQ+VbQ4gZukQlmYtNkEzQxIXTcGkNN9XQFfaPQsIG+Owv79FQagoX5gOrFVkNAPzNpsTDfihA57gZmqvGAL5HPcKpVAqTJk3CmTNnkClmcPT5R7Hnr/eY97U+0Ypl714GFOvnTjLEjLng7DPBCtdZix8Q2HJuCXDKLWU8+Z0nUWorITIWwYo3r0D8QNxEBHi+h8yiDB796KOAA1z86YsxdctUuE5dINDDzHkhI2eRM9vYQ8+2Wgg5dxSEFIZ6hnojowvXQMdWLpcDlbyZ6x0Oh02uFfNaCOqbm5tNioQt0IzgFY/p/tX7cd9L70MpUZ/X6FAUF77zQsQPxgPPIY3oWnAMR990FCdffBJ+uM4WUmdTeP47no+YEzNzwkKFfD/noVAomBx7NbaQ3iicNIeQuegKjpPJJFpaWoy3n8/gETcqCHS+uUcUhFPB0lQHZdLKcBWMnjlzxhgIeNROPB434XYc+/DwsMl9VIWBBiw1RDUqJsQ9yP4XnSLCqTAe+4/HUG4pIzoWRbGlCD9isWof6NnVgyu/dCXC8TB+8bZf4KYv3IRoJopHXvYI5tw1B22n2lApV8YJJ/JRhnfu2LEDO3fuHMe3/6ea7/vPvgToRJto+H9PV3kutEQigauuugoVt4Kdf78TC29ZiGKyiEff/yjgA6FiqHbs5rPcZW7RBTzAj/kIlUJY/YPVWPDIAsRCMSAFxKIxNLlNRk5oahd1MMonVloGENBlisWiMTjSgAnUjdYaIUceXqlUkMlkzPWNeLX+Tx5Pp4ymy6TTafP8kZERnD59GmNjYxgYGDD9MEAvCez4wQ4UptRkWXQ0iue9+3loy7YZfk35QX3H8PomF5N7JiNWiSGTySCZTKJcLuPw4cM4PnC8VmzWqg9kG5U1YoDX+b6PUksJO/5rByrtFbNuq16/CsmjyYCjgXNXTBaRdtJIJ2sGeqCel686NdeK60sPtBbsVOO87cigYVmN3DReMFWPxUe5/toPe8yKA9g3BefqMNK1U6Oxgk2OsVqtIoccyiP1I0P5uQ3EtE9qrOG4Vdbr+/i99v2Znqn95/hsw4uuv45Z559rovq2fS9T9pg+oWul+pAWPuT1CnwVAAPAlg9twbnV5+q8xgd6DvbgRZ9+UUCfKhQKyGazpl6M9tEOJ2fKpYaWN/pR/Y/4hQX7aHix58wG7jpHXB/dj1/5yld+Lxf9g/Jwh0IhLFmyBHPmzAkQJwleC1/Zm1onlAsN1I/YYq5mMpk0REWCZegrr6W3UC2HzO/VDaUAmt4pMisSHPtph0fo+Nhn3RRAvaAXmbUCeG5GvkMZuzJ4BTnqwWff7eMvmL/EeyuVSgCAsr9jY2MYGRmBG3Jx4qoTAbANAF6rB7/XR9OpJsPUCdjYuKa2VYneQYJfMi32IRqNAhXg4n+4GHtevwezfjYLnWc74aTrgHRgyQC2fmyrAR+PvfMxXPzxizF9y3QjaNViphuZa6FrqHSWTqdRKBRw6tQpdHZ2mrlWpsjraZDRcH6+Wy2RCpJZlIrnZBKQcn5isRi6u7uRy+WQyWTGnRGvfaHxgrS3ZPsS5FvzeOTaR1COlZEYSGDZF5fVjhUROhxdMorU7hTCTt26ybVIJBJY+b2VGLp0CNmeWpXV1mOtuOSTlyCKYJV7FTZqDaXRSqNDWEyMSpOCc2W4pJFEImGUOQWxXFPSLs9X5We2UNZzuDkHFGjMYyeNqHcAAAYGBuA4DpqamuA4TiAqwXEcYzChUqHKAg1btmBUyz8NOlTWSA/VahVRPwon42DV367C4PMGMfeWudj9ut04fNNh+KFgbv1A7wB2PG8HTi09hcFpg7jlbbdg7Y/XYsktSxDti6Ls1fP1Gc3D8afT6YD1fqJNtIn23GiLFy+u8Zyqg2WfWVaTfdUwVnxiBZySg44dHdj8z5sxuGrwWT3Pi9UVzEq8gt/+5W8RCUUw76l5ePKFTyIUDeGq+69CrBwLAAhNQyEvpUKvPJV6EdN++Df1Bo0Ko96l0VvqIACCYbV8F3m9HcXo+745epOymuk/NAAo4PB9H+FyGOvfsh7bP74dpVgJG76wAamhlDkSjAZVlV+u6yIfzuPpG59GS1cLNty+AbFobb7OdZ5D5mQG4VIYPuphsCrT2VfV5VRe+L6P039+GpUWqUUU83D0dUex5ANLzBpwngqTCtj37n3ofawXqftSAOoy0NZ9+J2urQJN2+tueyg1+oH6rB1hyOcpmG9kbFCd0Dauj6Nbb3wouq4H3x3QVcqNgaoNuFXvoG6i7+G6aESizkkjY5CuAemoEfi2aZs4SNMddb3tsGnFB3y3RiQqgLYdTbqf9f02nVKXWv+x9fjtB3+LM8vOAAB6DvXgpi/cNG6tFCvp/KiOrlG4Shd2n/g87Q/rMahBQNfLBtq6jkojNh08m/YHBbgXL16MNWvWGMJ0XdeE69r5zao0KzHoxqSHls+jZ1iP9FJGbxMz+6AKMFAXGPbmIIjXPqiFS+8joagFkx5zAoREIgHHccYdf8bxK/jwfT9QoZrj5X0KbtTqx3ExZJcVPtlf/iZI5BjphT120zHs+asg2AaASqKCSkcFzun6MRu0XmpVbs/zxuWQk+D1mDMA4xh44ngCK9+/sjanbp1Jh0IhOPOdcVb7kZ4R9Dq9Zu2r1Sry3Xnsu2IfZm+ejcl9k8cVUuN86SYPhUJIp9MB7zvnVK18pDNex/4z1Nw2yHC+VSnQMXEfsD+pVMr0MZ/PGzrR48xUeeBnS+9eCrfo4rfX/RYbf7IR7QfbUeishWuPjY1h4MIB7P+H/ei5uwezvj3LMGEaKhzHwfH1x1Fqqo2r43AH1n5zLVqHWg39cW0pCNVLztoKGoXBvU7jGBv3D5/HfRKLxYxHWQUTUK80y/lUI5YtwDQMnz+cXxox2EfuVS04w9CnpqYmkyfPvUlaqlarGBsbM8YWKnBajV37r8JY+0da4f4nT0yNpND0oyZ4YQ8Lv74QbtHF6XWnER2OYmj5ENyCi2k/nYbTa06jb3atIM/IpBHc9Xd3YcoTU7Dua+uQKqQM36MiyjkaGRmZCCefaBPtOdh42gV5DPlh1xNdNRkTdrH4nxfj2PXHTATavlfuGx8l8zvaw69+GH1z+mqVnQFUw1W88K4XwnWCYZ2UbeSf+p3yNyBY6FZ1L7vxcxojVUewdTb7bxoWqSOVy2WkUinzXMpVNRYrUKFMa6m04PL/uBxj8TG0Havl+qohWWW87/vwHA9PvOwJHLi6Vm8jW83iRXe9CP1d/bjz+XfCW+5h1sdmAfnxucbqzdQaG/ZYp39uOtyci9Ovr+WWT71rKuZ9fp75nv3Ltedw4B8OYPTCUexYuQOVWAXTfzw94NXUtCfKJgVwNviyAbqudaPQdNtZYevijZ5jgzGbPnS+9YfPtes6EXQqLqC+xP9t+axjVuDW6Dp7LNpf+3nEKzqWRiDdBuA2PlEAbuOjRvtJASX7Ye8bXXc+m89TkMpreJ3rugi7YVz+5cvx+Gsehxf3sPGHGxGpRky4vs6HepDtsSuNqAfdng97buyx67NtHVL1xUa40NYpdQ/8rvYHA7hnz56N9evXm2Ic4XDYFLbK5XLjLEv8n55TegMVwCpATKfTZhOqx4tKdyQSMZZIbj4lWCUq3gMEqy4yPEmFh/0dgMCYWDiJz1JLMMNb2VcKKjIMNSyEQsGK2JVKJZB3rtYaDa3Q83lTqZQBANp3EptayzgfqT0pwEOgQnl4LIwVH1uBtsNt8J3xFk6CBfXYAwgU/tIcUo5JwazjOAHLsRo6XNfFlDumoJqpYts7a5WYF3x1AabdOg1ZN1uvMt7s4qE3PYSB2QM4feFpXPOpa9BebDfAjvnCajHn2vF/TW8gQOOacG5ZlVwNGEoXaqDh/Vp0jwoB6YUMWZ+vc5rNZk1uEvPVFTCHQiFc8NgFmHx6MqYcm4JKR+27TCaD3Moc9v/1fhQnF3H0lUdRDpUx88szzRhDoRDObTiH3X+1G+XmGn21H29H14kueG4dUFKx4n5QIaHeUvVgq/Kgxg7OGfddqVRCIpFANBo11cYbnbGtAJl0omup9Rc0HUBTM2g84Dv5jHK5jLGxMRQKBSSTyYAw0qJ7lUrFhMdz/hzHMakU9NKThjW1RQv1qAKntEI647MjoQgW/HgBJj00CdFSFE/+45OY9/15aNrahAM9B4AlCLRTq0/hweSDuOHTN8Av15UXjldDNvn5RJtoE+0PvzVS+G1nQLlcRqwYw4zvz0AymazpAdtT8EM+HDjY94Z9yMx5htMRzjff9Q3YBoCtq7fCi3t4+S9eHgAPtifTBjMqG/id8n5bH+G9/F7TividAmPV2xoBGjvUl6HmPL6Kz7I9auFwGIlTCURKEVT9ek5xo3c6joOH//JhHNxQz6ffduE2jHaMIpfM4eyks8AUoBApYNF7Fo0zHqhnV59J+cBx+L6PqTdPRQQRFKYUMOOrM+Dka04Io9NGPOz58B5klpxfXxfY9+p98F0fC25bYJ6vc2x7/dSYoOuqujPpz36G53nmtBKumeqtdiEsXe9GnzcCaHqtDeQ07Fk/37duH9LH0mje1xx4vq6F6nO2QUVpVMGaNgV+St/sl+oQ9vN0rdnYB5uu9X1207XQ+23PMceme4MYRPtqv0Npk/ck8gls+PEGhGIhNI00BXLj1Umofddnk7aorymtKW2onvdM66CGCf6v86a8x+6LvXbPtj07WP4/3Hp6erB27VoD+DSEs6OjwxRPA8afQ8eBa06Ofg4gsJAKWviO5uZmA0pKpZJhAhreRKJjuK8eIUbF2maGvE498Ax/0qIivl8vHsUiC+wjf+gVZoiN5szYYbUUWDxOikckUXnW/G8WV+O9HLNGAXBsGhZLy2/TjiaseNeK2toUXERGI1j/xvVoP9JuPLJKkMzP4rwROHKOtegEN5Ad4kqApc9m6BYjGBzHwbRHpmHVp1Zh2feXYcUjK5CI1MDZ8PAwBsuDuOPDd2BgVi0va2j6EG770G0YwlAgf4xgWb28XEvmoLG/vIZjoCWO47Ir2mvEgn1UGIGY49SKBKZSKZPHzfsYcsX58H3f0AkLgUyaNAmxWMzkgZMGfc9Hx/4Osyc8z0Ops4RH3/IoipNrNO5HfJx+yWmc+5NzNTDoVTG0agg73rkDpbY68Dqy+giOrzuOdDodiCihh5tAlXSvwkPXUAEr+6wgG4AxLMXjcRP+ByAwN8qQub+oQCkYV16Rz+fND4WOLUTtM8C5n0KhWrE7zWVjGD+VCn2m53lIJBKIx+MYGxsLnPGu6QascsrnKS8jnXA+uAc8z8OFSy7E1L6piB2P4aJ3XoTuzd2IZWOY/4X56H6gu2Ygk9a3qA/3vuneoJGwrZ4WwP1A5XOiTbSJ9offFixYYAy4CoTskxQ0aqVYLKLlyRa0b2lH59ZOrHrrKlz62ksRzUQRzUSx/MPLseFVGxDKnC9cWQjjhn+5AbFsPTUtXohj4+0bzXsBBACZKro0LmoEHaPgVLZrhXCNKuT3Gp6uYEh5rhobNLJKDZiq3NMRQ10DgAHhAeDXUo9EorywC9+qgX7Nj9cE5itSiODwzMM1sA3UUoA2DGDPP+8JGIHtk07UCWR7Z33fR9SPovcHvZj72bmIjAZr0ZTLZYQqIcz+2my4+fM6qw80HWvC9F9ND9CRDcxsOaupVtRH+S5gfCVzNn5GXZfv0mezv/yO41MwaL/H7rfKT/3OxhC+4+PIyiN45FWP4K533oXR7tGADqcOHdVZ9Jmqt9qRtI10dQXa+jz2XZ+tY+e4+JlGwOpn/JxN50qL3qmBi/qRDXzVaMJ+aIE129Ot0bxKO01jTUgNpsy7FLwzh1sxgg1qlX8QGzHq0cZ2Oj7b6Kjg2f5b36NrpToq9T2brn9X+/+8aFp7ezuuuuoqTJs2zQyWCfEkFAI926OlVidOJje7TUAcp3oVFQBx4ZWZKUOh4s+cTmWEdn42gSt/CIzIiG3PpW4OKuxk2JVKxRR7U8+gHgvGdxBwKdhTIuNzuSnUS+r7vgF9zHcnmFPgo1YrABiaN4RNn9wEP+Jj2i+nYf435yOcrVeF5qYhUXLMOmdK0ECdWVA54H2MZOC4dX3UI64CVPvq+76pcL/jVTuw9/l74YUFfXjAgocXYO031xrA5/s+sk1ZVN0q0qPpQKiwbeUlffFvLRLHZtOPGmq4JhoqxjFyDfmdKhP8YSGyarVqiq+5rotsNotsNot0Oo1wuFbxXIEc6aVcLuPEzBO45y/uQaY9A7fqYul9SzH3q3MxMjKCjJPBprs3BUL1w4UwVv1iFZbeuTQg2AgySY8EqIVCwTDoeDxuGDvnQBUjjpcGMEaxMJw+nU4HDEwcA8Eq3xMOhwPh9rrfdA4JqrmmrC7KyAqlOxbRcRwHLS0t6OjoQDKZNMXFwuEwRkdHMTw8HMhnJ49SBq2CiQoeP9fQfL5f87gdxzFzSiME9zn3DVMY+J5t79mG7KQsxhaMAQBad7Ti6g9fjRBqhpKBOQP47dt+iw0f3YD2U+2mdsbIyAgeffRR9PcHTyT4n2z+RNG0ifbfbP9Tuspzra1atQrd3d0A6nxLdSXy13w+D9d1TTh1NBpFOp3G2NgYMpkM4vE4uru7jd7V39+P4Z5hHH77Yay5ew2m7ZsGb4aHn7/+54ADvOoHr0L7YLvpB6OsVPmm3kPdi98zpSgej48LDweCRdBUL2C9DuoCjcAK9QLKG03xKhQKJsqR7zl79ixyuRxOnTqF0dHRgCIOnI+qW+Xh6Q89jSs+eQWaDjWZfvF7Pos6FKOYMp0Z3Pf2+wAXuPZL1+LBVz2IMwvO1PtcdjDpB5PQ+6XecZEA7DvHpDqn6qB6Djo/1+fwWQOXDODwuw4jeTqJi955EaJu1FxrgyagrkPzxy6q1cgzTd1APye4UoMLDRqUvzTe87kcs9JBIyDId6r3uZGXV+fj8PLDuPtv7jb6TSgfwqXvvRQth1oCax8wTLtBT36jZyvNKC5Rw5E9Fr1W9W4FruMMBn492pP7Rw01Nl5SAwX3NnUJu88KonUdmVZnF1/Ta0OhkNFN6BCz54drlMvlDN9hdCS/Z59ZJI24Sp2QauDgXNjRE6pfNcIeapTQObKNk/Y6//u///sfdtG0aDSKCy+8EFOnTjVKor3YtjWC4JMFuOjdIXBTKyOfqYyITFk3pb2oVHpVgbdBDpVwMgESGC1yalHj5qAnNxKJBPJ7gHp+KAEClX/d1GQaJFj1kitz0UZlWwUB54gEqoA+FouZcyi5yXWjalGSsyvO4um3PG3yvU7ceALlchmLv7YYCacefquWNv1hfzVs2+4/gYNtCQeCOSYqyPW9BGm2ZXbFD1f8/9h77zDLjupafJ1zc+rbuSd1Tw4azUgzGmmUcwIhIRFskzEYMBiwjU0w2BiQjUkOmGCMMdkgkowQQYASAgmkkUaakSZoco7d0+nmcM75/XG16q5T3TLivWej93td39dfd997QoVdu/Zae9cuuJ6LrS/YahTsop8swrnfPDccrt/h4cHfeRCT/iSe+73nIlfIhcZO+0cBp+4f4tjpouc4jpn8CuBJMujCoYsqDQoqYZUVMnx8F8EjPeQ0ZkqlUiiMWkPzZ+2ehcu+cRl+9pKfYcXDK3D53ZejOb9F+tyz5p4pczg3nMPKH61ENB4NRVGwHrY3gGPG7Q+cizp3lXzgM9k/XEw6OjpMIkMF2wryacxpWLb2G/uY15MY4Xd8NuvG8a5UKpicnDQGGheSdDptwuPp2WbdGTbHvAycd2yXEm21Wi0ks5Qp13XNPWR2KVtqPHJM+TnnPa8944NnoNpbxRN/9gRiXgxLP7QUI+MjSCaTGD93HJv/eDMq3RVseNsGXPCFCxDf097mwPfPlJkyU569hcai5kWhjldwQB2XTqeRzWaRzWbNesCEYVyPSH4CQM/JHgzdPISuri7E4jHER+J47tefCzfqInMsAyfphPSoEvfqndM1n+uaFjXi1ShmoU7SSEIF26VSyUQ/qQ4H2vpUvc+0SRiJx6gmBa5crybOmcCev9yDem8d97/lfpz1qbPQs6snZIfwnZVKxWz9chwH+bE8Lv/C5UAUyB/P4+p/uRo/f/XPceCsA0AAzPnaHMz5lzkmcZquh3a/2J97nofx68cx5/45QLg7Q8CUP70P9CL52ST6t/Qj5rSjHxTUq83FQrBtA14F2eqZtLeH6Tja9p+ufxqJZjs4bDlRIsYmuu1ie8mPLTkWciYEkQCTKybRfaDb1Ne2i+01Vutl1xFo2/k2CcLP1H5VEt72vtrRDXo/f0i+q6da62RjK835ZBNkvNe2fdWene56toX2ih2FoH2hUTDaF0oOPF0IuF6vc1Vzf6mDUwkEG3Cz6GfTkTrT3fPrym8VcJ977rlYvXq1ERiCI04y9VRSyWnYtCpRdh6ZI3sCqtAytNQeVB2MWCwWCpPgNUA7wZqGqasQAjDv4H0UOC4+OoA26FbBYDspjJrBWeukxX4+BcZmRplkTb1+QHifqQ2OVbiTE0lEmuFQnfx4HlEnfC4jCQzWWbNOqrDy/QqCdHLZAIwywv5lPSknvu+bIz6KxaJhwTgGK7+zEk7ZwZZXbMGau9Zg5XdXolKuoIJW0q5cPoe7X303Dpx2AADw066f4rpPXoe4Hw8p2CBoh3Kzngr2FLxpqBzrTjlhCBsVBa/T0GWbfFElo2Ol/aMh+7lczmRZ53EIjuOE5tjCJxci+ZUkBg4MoOm0+jqZTGJhsBBPor1Xz/EcXHjrhVPkkbJjj60SCM1m0zDwNMY0HF8NNUYlsG3KkmpSMZV5gmolLyiH7FfWm/JOr0AQtFl69qPu99aj81KpFBKJBCqVivEGEWxTnunJ1718XKjt8Hvf900eCwAmaSTbk0wmjW6h3OtCrFEjSgSxvWxLejSNMz51BlzPRbQURT2o4+jyo9j5xp2odreM6onBCfzqdb/CBR+/ANgGs91mpsyUmfLsLgMDA8hkMiHdoGCEOj+TyZjfJGDr9bo5Lof6iJ5w5gaJx+NIJBKhEzIGTwy2yONoOwM111w75JohoBqFB7STNhKA2UatghQFL1z3lZyl/rUBHvUmw9dVP7KQDLdDyPm7eFYRe9+1F/WBFklbmFfAxjdvxLkfPxddB7vMmsJ1n+1U8rz3QG/rmQgQL8Wx5nNrUHlFBfHNccy9ZS6cyPRAh2uJHSbNsT510ykc+ZMjKF1YwvIPLg+1zbZnWb+598+dAprUPrbJEvU20zZUgti27dT2scdBo+zs+qkdyfroWq/gTe0Ltb1t+eLzbLC77rvrEKlFsOmGTQCANZ9cg6H7h6Yk9eJYcD3VOcZ32n9PVwe+V+tkg2f7WZwX+ix73Hy/lV2/UCiEQvVtp5za881YE9tfth0L/3Vh6HMdC90qoLb6dGBeAbkNlBWf2bJG20jJkOmAvI2fbAJA8YrKBYkYO9piuvrrM1X2WM/piIdfV34rgNtxHJxxxhk488wzQy5/oM10EXyPj4+bz9lwKnigfaSXesho0FOpU9lzwrPDGd5DZUHloR42G2TqfayX67aOtxoYGMDw8HAo2REnQKFQCHkT2UbdK64LEhWreqDtuujiopONyb40yZpNRqjwaCiGZtBmX/F7hm9RUBPbEzjvHefhZ5//Gfx4S6g7d3XCr/uoO/UQQaFgnqCKbbazQWpbuGhzcWD9NLuzAhv13LONyWTSLJoEXMzGvu4X67By80o4pxzE/BjyvXnDbP/0TT/F4RWHjWweWXwE33/r9/Gij7/IvIMeanonKSMEsNoeLixA+3gT9r967vmsZrNpwprZjyyaiZzjwcUzmUyiVCpNSQRIbwBDqzOZDBqNBkqlkslQT8U6e+/sVt+i7ZmftXdWeB4HDmbtn4VYvB32z2sjkYjZDx0ErXB3Lq4svJbzl0qQ4U3TKTf2lS0zqtBJqjDUSQEr9Uaj0TAGn3ptlcxT44+/i8UiarUa4vE4Lr/8cuzYscO0gR4MGqzsZxbKixIN6tHX7R1qEPD5BPnZbBaNRgPFYjHkEVJPP4kJ1oHjroRl4mjL8+NEWvOhd28vhjcN49g1xwC3RagseHgBOk91Ap3AgQMHjF6bKTNlpjx7C/O06LoAtAESdTWjoqhfaZByTaORTS9vOp02W7tIPtLeYEi6vY0ICB+Tys/r9XpIP9oG7HQgRW05eo55D+uogIv9wKKGPv+284horgpujbKBU2Z7Bp0Pd+LE7BNGV85/ZD56hnvgRMIAQvfD0u5TQ96smScjWPx3i9GYaCBoBAgQBj0KdhWU8Fme72H8qnEc/vPD8DM+Tl51EoEfYMmHl8Dxw9sQ2adOxEEk1XawkKS3AZMCG60LZYxrjkZ8KpDU97IPGVGmcqqyYhMNakvxOsqEPaZcv+32Klhlu4yjre7itNtOQwUV5HblMOf+OSFAq+1XsM/6qK1igzTtR42wtW16lTW91waI2hdKQmndpgO1Sm4YezIBPPB3D2BiaAKO52DRlxfBr4ftLnsOaf21P20SiMWP+vjRn/4I13/pesSr8SnRDqwP8ZCSC4qL9J18n02c6LPZj9qf092nRbHJdOSEPebPFGwDvwXA7bouVqxYgUsvvdSE+tj7ijlxe3t7kUgkMDExYQxJPaCc4Zz8PwiCkKeMyk4XG92PTFDPAdYOVY8ajWPez++DIDDGurKOXJiAqXH/KjjK+qpSpmHteZ7xKBFs6MJFQMDv2Yf0stlHVAVBYLKjE5Swn7Ru7EcFkvxMx8vzPFQSFRP2BAAbPrgBF7z7AuQ25cxYkIBw3dbe12w2a0L7VS7oyeR2gWQyiTlz5iAWi2Hv3r1m+4B6dXUR595kNRpKpRIKhYKpP9tuJl/DRebUU0eBOK3kWQMDA8hms8h8PoP/fNd/YniodZRS7nAON3z6hinnamroEseAgLlWq5mQbvaHGgmULTUySBqwLSrz7DOCaQJKm5jg+NrKnnt7xsbG4DiO8XjXajWMjY0hGo2acG2dPxP9E/jmW78ZCrnyIz5+9vKf4ZovXWPkXwFzvV433mCbNaXXw3XbXmWdG5x79ErHYjHkcjnjUValC7S3V+i2FM6v6RZvLnqqXElukABQjzTJMe4nj8ViePjhh5FOp012X53LDCfXRZ37jThvFVyrXlEjgCHilGff981+/GaziXK5bMaY1zBiRU9FoGzFYjEUCq292/F4PBSOGa1FcdrHTkMQCXDykpNY/KPFWP7N5XCiT3lh5ESFmTJTZsqzs9jeIDWWFZQoAKKuV+NdbQug7bUmsOYWpXg8jlwuZ+wjJb25vlUqFUSj7WSYJAFIMvJEC67RXEfYBtZX26Lb/dQho7qQul71IdtLPVytVs1aqs/m9iuti1lv6lGs/be1eDL/JA5eeBDL716OM75zBqKRKLxI2+DX9YlrSrlcNhECQRAADnBy8CRGvVEERwO4fvtYUK5d6v1V8K+2Q2OggSNvOQI/8xSojACjF43i1HNOYeCOAWOrGplwgZFrRlC8rIiVn1iJeLOdqFf73ZYprl3qSKB9bEcVKDDRyAXaLPazpovOtNdpfqeOG9sBNh3gV5tBn6/3pZoprPn6GtNX0Xj7aE+1pdgm1s0Gt4onVH7VFuP1rJsN5uz5Z9eD75iuz+05zzmgJE0kEkGlq4KH3/YwxheNAw6w7yX7kAySWHjrQvjlcLSBRliSZLFPUlKnpYng6/Vx/x/dj6Mrj+KWd9+CF376hegY7giBWcoOf1T2tN+nCwHXPlD9od53jpXaQvxcx1/fqU5OLdPJj33N05X/ccA9d+5cnHvuuSHPnRq/VDDNZhN79+41na2Guu3RohFLJUXh0HBTACGvs4asa1gLB07DRnTy8jcNZA3V0czZjuOEQtdtBaEMnO7F1klKTx4AA4JUENQA5vMjkUjIc6cef8dxQqCVCyRBLuvC/m82m+jp6UEsFsPY2FiISabwH7vwWOhIMADYv2Y/TnvkNNOfGp7b2dlp6svJxf4qFoumL5il8MCBA+adWj+CWhZGHthefC7wGuKsCoOLBZ8diURw/PhxI2vXfvxa3P2au4E0cP6/nA9v0kM9VQ+FxTuOY4ARZZN9TFKJybQUgNGAUQKEfcXvKIM2a6nfq1Lhdcry2VEk3Keve3Kj0Sg6OzuNLARBYEiRY3OP4c6X3YlKrn0+NgCsfHglLv7ixXAi7aPP2K+1Ws2EILLPdaHU+lHWOUf5fl5H7zFJGA2713mvClYVKskjvstO8EG54Rhy/rMfKauFQsF4c3juNj00HJtCoWCichiFMN08Y/vVGNRsrRxzjcKh3DebTYyNjRm9ogasGi36fNYhn8+jXC5PMRL42/d9nP7R05E7lsOK76xAPVo30QAzZabMlGd/yWQy6O7uDhGGnNtcH7j+kBTUk0uoY4CWPVIqlRAEQWjrmRrJ2Ww25A2nDlKdTHtB1wM7MpHruW28qiGvtqDqRduzbbdFo4l4D+0Y32/nxQmCAMVcEQeTB+Hvb+tr1oN1i0ajSCVTOPffzkXHqQ6c8b0zEDhByKZRIkBBn7672Wzi4PkH8Ys3/AKpvSks+ttFSO9Ih+wjvde2k9U2jZ+IY8FfLMCh9x1CZVkFbsXF/M/MR/+P+uEH4RBd3/dx4oYT2PPOPYADRGoRrPr3VXCr4VxFLLaXX0GnttMeP37OE19IiFPu1EmgdpjmtVF5sokklQ11ktmA1Lb91PZQr7fneQj8APQhqX3KsWMdtD/1f+0j+/n2NdP1lS1ver9dfyWUtG+03joX1DYMggBjc8dQ6im1HSkOML56HLGfxRBBxMwdbZNdb9bdJk8cx0Gto4atr9qKo2cdBQAUugu449V34PL/uBzdh8N74+msmI5g4vM1SZ+2z5YNBdWqq2gHab/ZOYKmCzd/usJ3KFj/r8r/KODOZDJYt24dMpmMyYSt4aT2UQqaRVxZNzbOcZxQVm4FIbyWnV+r1TA5OYlMJoN8Pm/ep0pXjXSdZApcdN8wryU4I8jXEHCd2FyU6AFT4eGzqPx5fzabDQm9gjlORF3Q6LHW7+lBJFiwgYmGm+tC6/ut4794rBPHolKpmFDzBd9cAJSBnW/eCQBY8LUFGPziIPzAD+33VNBIhW4zfhwvDbtXUkaVsk4qjr0qFR1/oA2IVFkw7F77jYSFmUCTwPlfOh9+3Mfs2mxEsm2WmjLBRFh8P5lbu81KuNDjSUODigAIgz2CUfaRei1d1zUkjBofbLvez9JoNFAul6dERqjCttlRt+HC8cJhM2f84gxc+IMLoed6Upb2D+xHIVJAx686jMxQhjWkiYaSfg60z4c3i00PcPR5R3Hmz84MJYnjs20Pucq4Murlchm5XM60VUkIrZMaHPyhN6darZo95Ky7Zs7XSBclTFh0ftvfs96q8BmlQh2WTCanbFfgvdw2wfvsxcnzPBw8eDBENgLAxMSEOUaOJMGC/1iARmTqcWUzZabMlGd3oZ2i28OUWObaoiCXaxHXKc0ToYmxqJ90PavX6xgZGUF3dzcymYzRNUA48lAjixiZpXk2qAdZN133+CwS7a7rohar4aELHsKFv7hwCuBgUVJewQyv0ySUQRCgEWvgBy/6AYZTw1h2dBkij0amtTlYX9dxsfq21XDctgOF7bDrTdKCYxQEAXZcsAMbX7IRQTRAeVkZe9+zF4v+ahHi++IhUAlMb5cqwRAEATp2dmDJh5dg1827MP+L89Hz4x6zB1kB6pHfOYJDf3jIAK2jVx2Fn/Gx/mPrEUEk1Fa+m21ToKPgyvae2uDTJh6m81SqTaa2/nRg234O+2U6AMw22OBX7RAloVhv21bWek7nFbXHReuuNi3vmy6awO437X/anHq9DS55Df9Xx5B9be+jvVj1T6vw2F89hmauiZ6NPVj9udVITibhJtp4RQGwkk5K4NBuVRmJIIJ4MxwZ5zZdOI2wo4i4ids5OQbsYxtgq26wQbfOO9VF/Ez7mHa6Anu91sYm9o9th/268j8GuCORCC6//HIMDg6aytp7bdk57FA7QRA7Qw1VKk072yQFjowJFbzjOOYzo2SfCm+hAHmeZzyjVPB8n05G1ofP1mzHntc6mkkXHJ3cTGhCYVaw4zgtTzSNbbKCysYosOIEJvgqFotmMVPPPQVEFy1OFO7l0hB2tm14eDjEErFuZLQHfzCIwA/gzHGw5LYlKHklNP3mtGdYK6ikEcBn69njNBY4Nkx+RlDhuq4BOkpC8DeVgJ7pqYnNdDLSiKBs+H4rUUwymWz1SyWOxmQDzUjTeEGZG4AgjGHFlF89ekAnN+tEBcb+qFarxqPMuaCsux7tYDPerD+AUFSFynQodFiUMX84RsoQ+37rvOiuY11IjaaAee35PGv/LCSbSbhpNzSWY11j+Plbfw7P8bBudB2y27Ih7z7npy5k3HKgpIoB2zHgsX96DLVZNeRTeZz5qzPhe+G98bFYzBwlQmOS7+Fz6BnnPOP4qVHKvtLIEe5DLxaLqFQqJgmQ7sOm3PEcb27p0EXR9pirrLMt3ErBcVAPkEYQ8Bk6h9XDxP+BdpZ/O9KEeoTnvat82ttmGH0xMTGBmTJTZsqzuyQSCXR2dprEjYwWow6wI8NyuZxJgMZkmkA7URb1NfUtrysUCsjlcsjlcohEIigWi8Y+YJQd0NIjtIXUCUAilLYB9SG3gtFWUpKcOrwRNPDl3/8yTvaeRMyJ4aINF8HF1Hwmqr/UZgLaIJW5SxpBA199zVdxdN5RwAG2/O0WrHzDSiSPPaXP3QA7P7YTQTTA8j9fjmK5iK58V4goNuvWU0UBKwkKri8H1xzExpduRD3b9qKXV5Sx6+O7sPIVK+EW2x5Sm/BQUMY28b2Z7Rmc/ienIzWSgo/wvmDel/9pHkdfchR+0m+R5p6D+T+ZDxdPv79Vw835bttxwrVM68O1zU6kqiCZ66jaHnbblUCn80gBkUZzKUDU93GM1G4GgCAa4Md/9GNc8tFLpjj3tC4sakvrtYoplKSwPcMKNhXQ2e+w+4r3qJzze62TkgH6Dj5XiYqex3twxlvPwPZ3bse898xrOUPTjkmOqGSc53vY+YKd6N7cjezW7LR5pPiOZrOJ2HgMa/9jLZAF9p+zH/nhPK793LVIT6QRoB3ZUq1WzRFjdvSj1luxIuXRlkudG7b+UNxpzwm7/+z2aGH/s3/t75+u/I+cwx2LxXDJJZdg7dq1U4w6AGYC6aRSEKVhzgwvtZk1ZUTU0wi0OouHqXd2diKRSJjje8jgKNtKw1eFmmCNe314D/tPlSlBFetDAaEhTRDJdlA52GFVypSRtVblxP4iqOR1fFe1Wg0BS3oN6d1XA1yZKqBNamiYD8eJQAYAqrOriB+LI/ADpDNpLF64GIcPH8bY2Fhokef1BISRSPsMPfV+ss22glJiRkGR7tlgmzhOJA90glGe7MVCJy4XZ3oHNfELlWkQBMazTSBGucnn82bvMYmYbDZr2DuVc443t1hwgqsi07kwHYHBEG56XhWE84d7fdXYUEOBQE/rVa1W4bouHrriITz0/PD529nRLG76h5vQX+03MlKcVcSX/+zLaMafqrvn4II/vQAdezpC0Qk2CUGPtZJBAFDrrOGJjz6B0tKnQp584Mr/uBJDvxgCvDBjbS/MjCCh3HIc2T41DFT2GDbJeeU4DsrlMsbGxkwfZzIZLFu2DABw/PhxI1fVatUYigqMudDr/FJlTr1AIK+LJ4kBBeo6HzhX2Z/UIQrg0+l0yOCjMUwwrmQaPVSVSsU8l6D8iSeewL59+/A/WYKZc7hnym9Y/ndslffSE1wAAQAASURBVP/bSyKRwAtf+EJ0d3eb9YY6iWCan0ciEQwNDaGnpwdA23HgeR4mJyeN/iFgZXJN6hLf99Hd3Y2uri6kUinzrmQyiZ6eHnR0dBgdwi1MatsBU0/SoJ2lzgBdL6LRKIrpIr5805dxcO7BFlj0Hdzw0xtwzuZzEPHDDhz1dilhTdtgYmICuVwOsVgMP3juD/DoukfhR9qgJ7kzidNeehq8rIeD7z2I8SvHW194wOLvLMbaH61FR6wDtXgNftRHI95AfDgON2gnnaXXVu2+SCSCwAnw6PMfxZbnboEXe8rOGo1i6Z8vRWZLxtSBa77avLSH9X/adkzeqWARCG+HbDabqCfq2HbLNjSzTaz5xBrMeXgOYtG2/UAbXb3MrLuG3trAiO/WzzRKlbaoJotlvhc6o9Qu5phN501mUXBpAyH2E21klY8gCFDKlvDjN/8Yw/OH0be1D+v/fj0iY5FQexTEqc08XR0o8yp7eo/eq3aQtpn3aXttIG2TGjZm0GdOVw/2AxPnlmoleLX2FsdEIoFUKmWcFYgDe67eg02v3gQncLD+reuR3JYMtUHBL5+Ty+WQzqVx1xvvwrVfvhZONQyAPa+V46FQKJhtezZZoXa9fe62yqGSgtzSyGupe1R27TFR+Z2ORLDBtY7jZz7zmV9rq/y3e7hjsRjWrFmDVatWhSai7qNkRypbo0kvlHUgI6qAGJgaWmInyIjFYgbUUPHr0V00iAmmpmNJaFQTzNvKRkMpCMDYJmV0baaN7+HA6r4dm+2zFyzWXYE4+4aGNg1rPaqKYWMUZPazCpSG5xPQEJA0m02MnzaOze/ajGVfXIaeO3swPjqOreWtxlPruq7ZR679wveQBFEPnJ1UhZ8rgFDSRrcduK5rkpyQUWfd+ZvtV6DOwrZrNALbrIpfw43L5bJRTFxUC4UCkslkyGiwj3Li53yXZkqlIikUCsa7b7Pa7CMFYuxj9fRSoaphQ1nSxZpjQ6MLaBlv46lxjMwfCYHtnpM9uOKrVyA9ksakP2me8ctlv0Qz0k48E7gBjl1/DN2f7g4RP5QtjgH7XMGi7/sonF9oHbvCd7vAzrN3Yu5DcxGvtxIeMomZPodGB2Vc9UwQBIbcU6JFZYHjxq0v9BClUinzc+LECTPGJDR4hAwNLD5fvcp8T7PZNDqBfa+LtOok7Sfd+kDDiR5+3kODWEkGJS1JxNkGDQ1rnTMcF4aozpSZMlOevSUajWJwcBCu6yKbzZo5T5KW+nJiYgJjY2MoFAro6uoC0HZ8qH7UY7Fc10VHRwd830e5XDaJ0KhnT506hWKxiCAIcOrUKSxbtswcKcYEtapvdM8m1wbqLOpOO+O57/vYuXgnRjtHzboQuAG2nLYFa55cg2g1GookUkcCdS2PN6Ruo7157e3Xot6s4/HzHwccIPtIFvPfMR/1TB3H//w4xq8al44G9rxkDxKxBJb/YDm2PX8bKj0VTMydwGk/OQ3L7lsWsluVNFXgc873z4Hnedj6/K2IDcew8CML0bG9A4EbTtrE/lCQpXYI8NTpJvMa8NIe8kfzyGQyKJVKoefQTvQ8D9FKFGe98ywUzihg4FcDCNx2ZIDtiNETb+xtkPb1tIvVUaD1pp2qP3YYN9dBJcfV1lasYAMnrTffrzmKlGif7J7EL17xCwwvaCXGHV41jEff+CjO/NyZSI4mQ/3G+jwdMFb7UJ1irINew2faNqjdHgXtQNgRY4NBlRPth+lsXP3e2BRBBIHbzjHAcaHtc/j6w9j8+5tb9yHAIx97BKvfvxqdj3SG+oZtiEQiJnI26kRx/eevb/VdJByeTltYbVL7R/tGgT3tPe0jOhXi8bgJT1cnlNrISrzo/bxmOpCt/aj22jMp/62A23VdLF68GGvWrDHhzqpggbCXSsN92Vh7EPlcBcwEG8D02f5YbM+fgnx2rM3SUKmpB51Kx4RZWM9QVg+AWZAoCARYfD4VIH8IJqnMmNGSbY3FYpiYmAgZ2dpu9i8XSn7GPtGFj1599g/7WUE/26JtG1sxhi1v3YLKQAVb3roFS6JLMOdHcww4UY8djX9VQKyLsoFko5igSQE/F18lHVRpabt1Hy3HUcePz9PQIU5elUvd+6F/az+SWKlUKkaJZ7NZeJ5nQvPoPWa9OcHZJ0B7r5kSH7oQsd/Zfl5LEkrvUePFjoawQ2k4t3SMTIh42sO9v3cv9p6+N9T2uVvnonNHJ6q1qjG8PM/D4L8NojHRwO7X7AYAnHbHaVh9y2o0Y20Dqh6t48BzD2DJ7UtCYVeqXJvNJtLpNBY9sAi5aA4b/ngD/JiPBZsX4OJvXIxkM2lC5di//HtiYsLIkE3SsM9SqVRoW4mCWc5Rjjm9Qjyrloqc38XjcZMJX2VFZYlySuMxCAKTJ0CNEZVVlQMyvJzTfIfOc17LOaR6U2VL+0rzUdAgYV/ZRCMZ45kyU2bKs7swTwOdDDRKE4mEIY/z+Ty6u7tx6tQpc2rGvvX7kHgsAXfMDdkWtDl0u48a/gBQLpdNSDmAUB4b1WW8x15b6d1mRA9/aKNQBwLAiidW4MG1D6KYLQIAlu5diuf/6PmIl+Lw4E1rw7DOzINBXcl1gjr43FvOxamDpzCxcAKDfzuI2HgM9Y46vGh4/WfZ9sJtmByYxOGL2seHbnjlBjTjTXQc7UBmMoND6w7hzNvPhONNPVoLAFZ8fQVqu2uIHo0i98scAoT3s+vab9toavf5PT72v2c/gmSAVR9ZhXwxP8XeYluBlk2XG8khd3cOQWTqcVM6PrR3lAixPblqa9rPmg6U2x5rXmfbBAruOabqgFCbSIEcAOMxp/2scuc4TusEjqgVEBODAZ42mNf+e7q2cGz1Om2Tfb+CcbWDprPdFUzbhDnXfB0DtZG0LdpHdHRodK0SG0xkO9mYDLXRcRwk862tl/uu24c5d82BW2nbNHbIt7ZpunlJcGyDX22XXRQD2LLuuq4h/Cir+l61i+12KbkzHehWIoo24TMp/62AO51OY+HChWZ/kLJDyvKw4cqWqZeG3h8No+VvBa7sKHYOJ6eGQLMO9AzpflZORh0UDQEG2uGfQDirnSo/VVh8FskBTTSg2ZZpzCrrQiJBPboE36OjoyG2jO8kE6yeRJsJI6hgqAXHQI//0joy9Kxer+PIkSOY6J3AY29/DOWBFqD0Mh72/OEeROoRzLp7lmF0h4eHzUKv4eush70vNRKJmJA1ggbWzff9kHedfa9jF4m0skez3brgUkFT8eh+aXvxp7xqXQma+R7KmeO0jtYisA2CdiIWLuYkXBhOxv3evD+RSKCrqwsnT54051VT1rPZbGjy8z7WXeWDY00CRSMYVGHoHOH/msfAJGKrepi/fX4bcAdA9+FuLPzPhTh27Jgxuti/Xt3DvG/OQ+AF8Lt9rPrBKiTdJJrJ1nW1eg2/eu+vML50HFEnigW3L0Dgtz3w9DjE43Ekk0nkcjl0Pd6Fvs/14dGrHsWlt1yKbDELP9ImbthG9k8ulwPQ3opC76+GzeuCoLqGIJhyw0zrnIuaA4DHq3HuUmZJtvA4OI4TPd+6NYQywT2EJE847rNnzzZZ+tWrTXKH84PGD3UijVfqUxJCPKceaG/F4GLK5/Ne9TTROCWBNFNmykx5dpaFCxfi1KlTANq5bJhokfYDAUs2m0U6nUatXsP+0/Zjwys2IHp9FKf//unwS+291iQteSQjSXXqMYYCK6mtx2AyUZjaR6q/uAarx5lrEPd1Um9GohFsPH8jjs09BgCYfXg2rv/u9eisdALO9KBFn896JBIJc4woAHPmdWGkgNmfnY2uZBcixyJwIy5iEzHM/vBseCkPhcsKoWgvADh84eHQ/37Mx6YXbUKsEkO0EUW5q4yJvgnM2jgLO5+/E1d99ipkR7KmX+v1Onq/29uybfz2yTrU3+qYAML7wY1tGgV2fXIXyitaOnrTBzdh7RvWAvWpSbUMCIuFT7BQj7T2oYJbTRaqXmaCKgU2rLsCqOkAIsdY36ngW+1tTZDK9uiz7UJ70baRWa/siSwu+fwl+OlbforRwVF0b+/Gmf9yJlJjqVCyOcq1kgw2CLTbqv2hxd4eYINkfa4CY/3Mfr+CftsJyHfaQFblajovLtvAkPJ535wHx3ew9w0te/DiD12Mvp192P2C3djz8j04ccUJnPv2c1v3uW1bR+1sxXt2u2lDaxsVUNvOIiVa2CbtI8qOtl37SOWFn+vzFcRrXRSoaz6mZ1L+2wB3JpPBddddFzKA6a0F2oCIxr6yYDQcdaKzQzTUgeyVTgR776uyQpwABEGFQsEsSKrMtL66j0W90KoQ7HAIZZUo+ARFBDZkdOj1Uk8w0GKINTETWSACgCAIDFOrgIUAgwpZBYl9wzqlUilEIq1kJ47jIJPJmHOTdb96qVTCnj17zL3Jo0ks/+pyPPHmJ9BMNeF4DgbuG0D/vf1G+OLxOPL5fIihUsZIw5IULLCeBDEKxAhw2A6CHFWi7GvtB4a06YTl+aEcV/aR4zihhZj143cEMmpwlEolAO290BwXkigEcppkTbNlB0GA4eFhY4BwrzALk+dVq1VMTk6iv78/pDC17lQCrJ+t3BgGSGKFxAWBmcpMBBF0BB2mHtFaFM95/3NQHa+iUq+Y91IGXdeFU3ew4NsL4PkehpvDmD17NtLpNIrRIu5/y/0YOb0Vnr71NVvhF30M3T1knm/qknDh9DmI+y0jccGOBViwbwGcugPfac0tzZlgR4bQ0KMssR+4qJD15Dt1IaDhUKvVUCwWzbn1lBXuaaLM0rvPec2FgXqO48Hwcd0qw3FVhc15UCqVcODAAWQyGTOeJMTYT5xL2j62nSSDLjbUmQBCID0Wi6GjowPpdBrHjx8PbcchAOczZ8pMmSnP3tLZ2Wk81iTTuC7xaCb+MJfD8Kph3PPWe+BHfdQ6atjyjS147geei+BEEEq4ph4o6hqC44mJCbMW+b5v9nTTY63kMHWeOjbs9ZvXK8lebVTxxOon8KOrfmT2WUeDKBJeqx3jmXHkG3k04g0kSomQ0cx3RaPtEzAajQbS6TS8uIefXfEzdB7oBLYCKAKJQgJNNM26Fh2PYujPh3DgXw6gNlRDkAoQuAGW/ulS7Hv/PtTmh3VjM9VEM9UmpPddsA/7z9uPIBrg1vffihvffSOSlSRKHSXUhlvRAM1EE0EyQKKWmAKEaD/QPlbCIhKJYM8/7EF5eZsQrcyvYMvHtmD1m1dPkRHf99HsbuKRzzyCs99zNlJjqVB0qe2NJNHL92mOI6BteyhBwJLNZpFIJExOAF1nFaCq3a92tQIg1p3v5G8bMNLWng5E6lYs4zAZz+G6m6/DT9/2U1z4NxciqAUIEPZO2ySAbf+rQ5FjZ0dR6hzQYttw2l51ioQIlqeK7ZRSO0KvtQE671EHIq+zvfrxeBxz585FIpFA/z39cDMuuh/rRnZvFgevOojHXvsYgmiA8ZXjuPO2OzHvrnk4/Uunww3C52arw5R1pT1vRzVMR0awXbTv9dm2bLAd2o+qX2yyxJZb1UlKCGhd+C619X9d+W8B3NlsFpdddhkGBgZMQ9hIepYV6DDUieBIJ4pOHIZIUWGSHVMlxGttdk5/2JEERsqE6ECpgateUJ1EauxTCao3TUE0FQ2Fhka6zbxoXfk5wS/fq2QChVaTdCnAthcbPg9AaD8zDXnNdE2PZ0iZ+sDQ/UPwcz62vnwrBn4+gDP+5Qx4rgcvEt4DynHUffeqwFlozNOTyHYpk8X+oiJjew1L/dSYkQWld13Dh9kfBCw6LuwjevTUG8CxBNoKUhcJ/q+MJGWa3k3u/yXhUq1WTSZ+gnkdU3rIKSO8n/XT9/E+yqMdcs8FgW3i2CuBwP6j7Dx51pP48e/82IxR4AY4ufAk0g+nQ4st+4jyQtms1+soFApwHAfHLjqGiaGJ9r67SIBj1x3DwK8GkGi2x88LPBy85iDGrxnHFV+9ArnJXGtultuLr2Ybt/fGkS3X5GJUijQSSQhRRpnbQRO5lEolQ26l0+lQNm+C5kKhgNHR0ZBsc2woT0yox7mjulDPJVUdwDZ6nmf24LGvOaaalNGOwmHkD+cL71W9pFnvSTKRjCFYZ7/Zi/JMmSkz5dlXuKbpmqdJFwuFQkhvRyIRxFNxbLhkA/zoU/PbAerddQw/bxhrfrrGgGzP81AsFo1uYy4b6hzNlQHAEN56UgltPt3GA4QT49reKLWPmqnmlKRmp7pPYffy3ege7cYtL7oF6x9aj0MLDuH5dzwfXcWuEOAAwkc3xmIx+HEfD1z8AH55wS+BC4AV+1Yg9s0YEIRzpTiOg4gXweI3Lm7p3/PqQA7Ibsli+WuWY/c/7Yaf89HsbKLZ3Qbaprit9RMAmskm7virOzDvkXnYee1ODH1iCP239ePIa46g0d3A/H+eD6fkoHpmFdnN2SleVa636uhZ+s6l2P2PuzF+4TgAIL8pj5VvX2lsJ7UrK/Mq2HvzXpQXlvHw3z+Msz50FtJH0+Z73UNvgy8FmupVBcLRmbSnJyYmzLpJOaAs8DpGeuk2LL6Tn6t9b7/H9l7ankjbtlZ5MNfVHVx282UtG8+Zar/q+mpjBduTr2S4ekP5PAXO9trOfmSxyQ+dY+wfvmM60EqZ17HjuNI+oK6w38tSKpWwd+9eE6k67/PzUKvVcCJ3Atsu3tYOyXcAP+Hj4PMOItFIYNWtq1BJVDAxawKzjs0ybUulUkin06jX6xgfH0e5XDZ1sQmS6dqjv7XOofF0wh5pdXzqOOg79Fn2+Csm4/W2x/2ZlP/jgDuVSmH9+vVYsmSJmZj0+BCwEFyxE+PxOPr7+1GtVnHs2LFQOC+AKd5QCrWCXBbtaO1QZX9s1kUHicDB9nTbrAgXGAX7fDbroIqdXjMa63wPj0ugYqfxrnungDa4m84ApgeK7wemgnL9f7pkcRRKDSclWcC+VKFqNpsY/MEgUAT67uqDH0wVar3XZuJYb0YY8J3KgNL4JxOm4e+6cOsEI1hV+SLjGQRByCPKa4xHNxJOTGaHTLHflaVjXyjY4bWUewXx7BeCafXY0xOv8s1+IYD0/XZGf91Txe9sGdA+9zzPhCN3d3cbpUvGMAgCc159MplEsacYmoeBE6DYWUQaaVMvvsc+Bo3yX6/XUSwWkf1BFuub6/HL1/8SzWQTfdv6cNa/noV0Iw030/by7nnRHuz4/R2AC/zspT/Dtd+8FsnhdgSKvR9HvdmO42BycnKKQcB99pxjCtCVSGLdk8mkWQToDWL0CBf/ZrNpokFsnaM/dqZ7OyJDt28kk0lUKhVDjtikBuWb46R5CvSIMI6DerT5HceJIJ3XqRyRiFR5ngknnykz5dld5s6dC6C9HumWKmAqIUzP3LKbl2HXH+/CyNUjQABc+dMrcdYDZ6ERbxj9yq0vamdMTEwgCFrZz5mIkaQr1zomE+UaoSAOaIf86rYaLQq88808LvnlJfj6734dABCtR3Ht969FupbGd278DgodBdx99d0AgNtxO2687UZkK9mQR1gdCbFYDPdedS8evOBB874db9+B2cFsdH+jO2TYq2HtOA7ym/MtIJkA3JKLRe9bBKfPQXNuE7v/dDf67u/D0euPPu1YlXvK2HntTgDAwbceRGVRBcPXtxJ3BdEA0ckoxi8cx4IPLUCtv4buu7oRrURD4xpaPwIHS96/BPvfsR9eysPSf1iKSD1iIsJYqnOr2P+e/SivaunzytwKtvz5Fpz16bPQvb8bQNujq2Nggxy1fRVAso/0O8qLEvvqHVbb2fYg69Go2l5d67T8V8BH5d8GT/b/Wg99Nq/RfCzT1UHtAT7LdgbafUtbzSYQ/iuyW9dtygZxDceADijFNArW7efZdoeCdL23MdzAgr9agAPvOYDCeYXQc3bdtAt+h48gHmB8wTgu+colGDg4YJwI5XLZeIZJ6vE336myRVtP5UZlS6/T/leSReeOOmK0T9h2m7yxSRsb/P9WAHc0GsWqVauwfPnykCHLY3QOHDhgjifSDN6VSgUjIyMhoEEwoVmV2TAKFBur3pogCEJJwAh4aXRzwhPAqIeIwFgnhbIjOsBciFQ4VHnw2DEmDiNAVG8rr6dBr0CagqPPtEE9WSGeGU0B5vNjsRi6uroMk+T7/hTvsbaLQJfKhHXR6AMFq9FoFHPungPP91rHczw1+Wmk6xmbQCvcbeHChdizZ4/xfNogkf9riDDbyvozc6qCEg3f4XjyXvY/ASaVH/uMY0P2nnWggcA2cVyUHABgFBrJE10Q+Zkm26KM8bqOjg4DoJjlNZPJGC+n4zgmqyoVD/tHyZ5EIoF8Po/JyUlMTk6ad3GMCfQJ/rlg0KvtOK3wQG7TOLT4UGh+X/vFa9HxQAeqTtX0m+79sj2qysJXKhVEb4ti9eHVOPCWA7j8q5cjOZFELV4zc3z37+zG7hftBp6yuY6cdgTf/4Pv48UffzHiXtzMebaXHlmSeUHQOn6GewYJiBWoUl40woGfMXrmyJEj5gzbdDptImt4n+M4ZkuIzbbrUSdKoFCWHKcVqdDV1YUjR46EFDmJN7bRnof8m/M0nU6becBICT4jlUqFjGDeRyOG46fySNDOPuHc1b36M2WmzJRnZ+E2JV3XFTRwPiugCYIAzqiDBf+8AH7cx+qjq7F261qzBUuJPOo06pCOjg40m01MTraTKXHdBWDsDs/zzJoCTM0rA7R1MJ0x1OFAGwAWU0Xcdfld5vNYM4aesR58+/e+jYnuiVBf7Fy6E1//3a/jxv+8ETtW78BFD1yE2593O675wTWh58/fNx8Pnv+gibxymg5yW3Ihp4kNMtkXuv4mjySRm8yha7QL6fekkTyUhB/4iB6O4uQLTmL2l2bjwLsOtN4TILwP3IEB2wAweu2o+Xv/e/bDS3sYuXEEbtNF151d6P1Wb8jApy0RL8ax8BMLgRiQGE2g6bVtZEPyjjpI7kuiuLZo6pI6kUJ8OD4FDHKc1AHCNclei9Qpw88UvClRrnY2ccB/Bd4VvGpYL8dDwZVNxlN+bADM9ikoU4xhRzHaQEvnmL6Tnym5oN5ilSXWQYsNoLX+dt/Y80e/1zbq9yyMcrOdd67rIp/Po6enB5FIBGNjYyFyXiPnfN9H5GAEgzcPYv9HniJxRK73XLHH/H3Pa+7Bc/75Oegt9Bobm4SferfZBrW7aeNRJ/HHJnV0bG35USer6j+bNNE62OBd+5d6jmNlj+PTlf9j53A7joOFCxfimmuuMYmrWNFoNIp0Om0SDNkMg3pWOZB24h67cLJrJyj7RIEHYDyoutdius7UUFUFXQRKKni8Tie2hlUp2GQ4Ks/AVG+iZhJn/RjGrd5n9frrBFFiww7HV+N+eHg4tM+gXC6HAAGZMBV81kkL+0CfQWAVibTPUKzVaqHwbvUMcyFTJcoxUS8w25BMJo2nm3JBYkHPd6SiIeHCPVrqHbaBMmVHgSnbQznVsCZOXH7P0GRV6nov28JFiPJJgBMErT3guieFfRsEgdl3l0wmTUI5vl/7iZ9ls1lEo1GMj4+bPiHI1kgA9UJQcZAQqlQquOvVd2HX2btMGBwAdB/pxnPf9Vwz7tPtW1FiQhdA9nk0FkU1UkUmaCXVyeVyZg7Xu+v40bt/hMJAiy2NVWO44bM3YPau2YaQ0j5mUQ8328e2sdissm674PiS4Dh48KDJTJ7NZpHNZo0cc46MjIwYb7rqAl24KYfsa8ohjUoeI8cxVdngM7iFQMePc0jHUz36lHOSjZyj1COu2zo+r6uryxCcmjFe5yL7eWxsDJs3b54y3v/dJZg5h3um/Ibl19kq/38sjuPgzDPPNPqXayN1g3oGuaaxFE8vYvjFw7jg1gswOzUbbtBKepZOp43dQ8JQSVXqpI6ODpw8edJERzEajXaM67aO6czlciHQpkenptPpkOGrugwAHNfBJ173CZzoO9Fus+9g9bbVmHVyFu6+5O6pmcQDIF6Lw4t6iDViqCVq6B3uxas/92qk4ilEmhH4DR9PLnkS3/2d78Kv+Vj+8uWIHYxNAdbU8TT+1WDXrUdq09ScGvymDzftIt6MY/jFwzjwqgNY/zfr8fB7H0a9M7xGPaNxrjsY+tsh9P20D0FTQGZngEwjg1i0nWCY9bYjA72YhwPvO4CRK0bQu6UX5/3deYjUImZt6evrM8eb2u3W48EUAKuX0V5r1RagU4PbALm1jgSy2uWUY10P+TwlqtX+VwcO66f38plAK8R/5zk7UUgVsPgHi1EvtZ17akOzTrTBdTtkyLaxbFvaIWr32kST2ov8XMkK2nXqeNM+tgGmXRTjAAjNf57AQltJCYlsNmuSAfPMetr19EqHcEjah5f38OQ3nkQQDxCpRtDINQDZrp45nsH177ge6VjaOI14/jYj+xTTsa7UI8RE/FEQrUXnrXqutV/V1p5OtrTfVLamGwO+/1Of+tSvtVX+j3m4BwYGcNlll5lJow3xfR/FYjs8VRlOKgQgnFhAjXUauwQcnPhqiGqHAW3BUmFTz7C991uZC9tYVw84Jx4HXYE966zMn4YiUyl3d3cjCALs378/FKasoJT9SGPYZq5U4bM93IOqodGVSgXj4+OmrVwwqfyYgVANehIdFDAFdwzzNiBJM4hGIqF2e55n9i+z/kqgsI4MryagJiBRcMt7Sc4wAzSfQ0CmzJgqPCVCSqUSOjs7QxNIgboaIwqGFNwoQCNpwgVruvtYh87OTgRBgLGxMXOdZjNnxAL3elcqFXO2uJ77bNeTMjI2Nhaqi+M4Jjyaho/rumYM+bmG4sfjcZx1/1nYd+Y+NBKtz/oP9+OGT96ARqRhSAmdWwR3LCSbNHTa8zx4TQ9xL46qVzVzpa+vD5P5STzwBw8YsA0Apz18GvqO9JnnZTIZE5KkZJyOCYueTMC+1WR0akgpG1ypVMx8zmaz6OjoMIQF5btQKEwhPyh3GrpOwodyNp3O4f1sH//XbQg0UJmsTcPYVc7s/Ay60NNgprGQy+XM93pOu85LXaRmEqbNlJny7C2Dg4Po7+8PRd6obUTdoODRdV1U11ex5RNbgAiwLboNfT/tQ6wcCxG+9pGu1IPU96VSCclkEolEwpzSUCgUDOkXibQSs/q+b5LoJhIJA7a59ivApl1lDOMAeN3XXod//91/x4k5LdC9fNdy3PTtm1p2SKSOB85/AAPDAzg896ms4Q5QT7baTTA+PDCMb7/y28iUMph3aB7WP7geS7YtweXfuhyH7zqM+KE4ArSBGzB99mP+rUBHbR3P8+DXnzL2fReRRASDtw1i6HtDcBwH5/3xedj4gY0oLSwh80QGpdNLgAskDiQQPxpH4ZzCtNZ5EA9w4OYDcOCg64ddrTV8WQN7/nEPlv/9cnQc6UDDbSDY397/bG83SgQJrP3YWmxtbMXaz6w16wLHgafLcK3iOqCOJq5fJB90LVXiWe0VRn3quwCEnCb6/OlAohbaIUB4n7KC2OnGj8/dec5O3Pv797bmR6WOhT9YCMeb6mHmM+hc4fOVZFDsonu4aVuz2N5q24Gnzi7iCQV10wFruw5KVtj3UCc00MDY4jHEH28fZ6XtqVQqRp4Vx2jOHK036gCGgdPfeDqq66oYvGMQ2/5iG45deQxwgNzBHM77m/PQKDdQTVTbTkenhrEFY4htiYXmFeWD7VFvN9uh9dbxnW6+qiyo7aR9aHvMpwPa9nOVWHom5f8I4O7t7cWVV16J7u7u0ETi3zbLwL85eOwMoJ1hWsEmMwVzgNU7qo3lc2gwK9uroFiNXYImBb7sTLJwvEe9d/oeHSiCUA2tpuHM6w8fPmwMch18TdKljCSNbfVIsl1kmuy94cpAcsFk//JahsvqmGlYqbKFbG8kEkFkdgRHh45i4KEB9PT0hMLMudgzjJ/hvvbnGgGgE8xxHLMYU3kpM0UlRhCuwIQyxX4mgOGzWf+Ojg6zoDApFYGRTQ4om6nfs71smxJHqjD5HGVJCZxVweiixonuum4oSRrbSNIBCB/3wPsJnHkdQTfDjJXds/Mp1Ot1HF5+GPe+7F4DtufsnYOrv3k1cvUcyskyqtWqidSgrKscs/0a/aDjxc/q9TrGxsaQSCRQnFtENVcN6ZWJ3glUYhXk3XwIVOupBlTGGh7NHz1LlrLHvmXdOG6UhZGRETSbTXNsnoJ4kn6Tk5MmaRDrQj1AeeR4cP7SM64kIXUdtxzQq1wul9HR0REyXijHOgc0oobfK+hWcpL7LVlfJopj2DnrrHOZ8ykajc6cwT1TZsqzuAwMDGDWrFkYGxvDxMTElLUMCB+VEwQBRi8axYH3HjBeqO1XbIcHDxd9+yLkEjk0m60TStLpNCqVCiYmJkIgmGu5eqOpA2OxmFkneI+S+FoXJfzVFlQvs+M4iE3EcMMtN+COF92BvkIfbvjBDQBaNsvl912OdDWNlU+uxHef913sWbJnSh+xHJh/AACw7bRtaLgNLNu0DM09TeSezKHiV8x71ThnUUCj4c26TUwJSups2h9cK2JHY1j+weU4sv4Iem/txYlXnEDhogKGbh5CdnMWR/7oCMavHUfsRAyls0tT2lBcVkT++3nUV9Zx4L0HUB+sY/vfbEfno51oxpsY/JtBuMdcswZon1Pfn/aPp8FNt0+HseVEbUbas/ze9iwqgLSBto6hDZrUc6sgyMYRNvhVG0v7mN/bzw+BQwDbLt6G+19yv/n/8Vc/jmakiSXfWRJqn9pouoWUddJ28zp9p9aHfamOgOmeo3PU7mOVQ6Ad3ccx0naz6JxiO3a8eAeOXnsUSz68BB2PdEzbv9OBULVfFPPwvtzBHHqO9wAusOKjK+CVPJRWlLDy4yuROJ6AF2l5zGkzb33JVhw85yCWfngpMpsy0EK7zQazrBf1kM5JrQ//V7D+dBEB2l57TNRhp3pruuf8uvK/Dbjz+TwuuOACdHZ2tvaRPAUaaahpyCIQ3ktA4MGQBQ2rVTCunameX3qjbLCiXk1V2twDUCqVQuELumAoyGQ9CcjpxbM9nsDU0BISAxR+9UrZe1kYcso6ElCqx5fXM0ybQB5oTbpUKmWMaYISXkvvb7VaDTFUfI6SDcrIcfKrgmwEDTz0pw+hMKsAp+Fg9ubZoT5WsMfxpvKhQrD3SvN6NRJIdrCvlFXjeCkY4hgoqOK+Z1X2BKQEvqlUKgRWfL+1t1v70FamlHEFftMpOSU92JbR0dEQGFP5plzF43HjQfV932SNZmbHUqkEz/PMEVWUEc3qTkBOYEgAp4QMyQe+v9Fo4OiCo7j3pfdisqe9L6+UK6GZaZNYvb29SKVSOHDgAAqFQmhhoOyr50IJKw2zptyfPHkSfUEfLv73i3HPn96DUk8Jc3fOxeXfvBy5Qs7IioI+lUv2xcDAAIaHh40+4feUKd1/Td1gZMWt4sEXPIi5n5iLSCRiPDZqqAAw0QKqlPl81knHXAkU3QvNecY5Rz1Gco16k2PPdjIawtY3ut2FfU6ihbLGezjX6J3goq2h+SpHvu/PJE2bKTPlWVxqtRoKhYLRXST0dS3n+kIdkTqZQqQegYf2upU8kEStXEPUbx8dOTk5ibGxMYyMjJh3ceuSOkjUC8ootFgsZrYgqRGroelq5ALtCEgFCgBQSpWwfdV2XHfrdej2uxGpR+BE2h7F9Q+uh+u6eN73n4fbbrwNQw8O4YGXPBDaFhUqDnDf5fdh2/JtmHQnEb86DpSABe9YAKAdpab2HqOXbKJTyQI9RUfbps6ler2O2JYYZm+cDQCY9clZ6PphFzI7MoADzPrsLOTvzSNSiODAhw6gcnp77ev/Qj8G/m0AtTk17P/AflSXtNa7ZkcTI5e1xqjxoQYWv3Ex3HoYbNltsoGdbc8qeOH6xX6wgYj2g9piPDqTmED7g2SMHoHLopGv6rSbzu62QbjiDBucO46DruNdcAIndPxXem86RDzr/QqGbVCndqzaCkouqQ2sQF6vs0GkPlN/s04KHolPtH+0b9Qe3fmandh/034E0QB73rUHy25eho5tHU9LINhg1O5XoO10VLlq1BqY98/zUOuvwTngoJgsmnF2XRfbX78d+67fhyASYOdf7ETySBIr3r8C0XLUPEtPLeL71ami2+u0b5QMU2eF7eiaro/5PHs75HRY7zct/1uAO5lM4pxzzsGCBQuMIDGkkoNhA1kFGTTw6vW6yWZpJ0DTWH0C92g0iqGhIXieh2PHjpmJq/sl+G6CPBq2tlBrYiy+l+wsAaDNiqgxrICLgk6wSHCrYS9cCAnYqtWqAXCqlJUMSKfTxhPFfVW2ICnQVWFgn9FDysXQVr4M1SUQ13aSaIh3xPGLv/wFRpeNAg6w6S82IfKeCPJP5gHAKE721XTMHidDJBIxbVE2le3nOOqizHM9FTgo88i/lWhR5Q60Fwu+TxOnqTeZ/aTEjXpm2Sa22Y66sOtCEkHlX5UbZZfGCGWEdajX65iYmEAkEjHkSrFYbGUAz2bNZyRQNBGaJrrh2DO8i3XxfR/RWBSl3hIme9tgGwAWbluI7kPdocWRHm72C70h3L+n4Vd6vJkmrTPgMh7F7it2ozPZiRf+/Qvx0z/6KW740g2ITEYQOIF5vu595gJG+fZ9H8eOHTPkkR3domPB9pJs8RM+vvPH38FY3xgapQZWfHOFCaWkLnMcB4VCwYRaqodZ66I5Djg3KXv21gvqPxovlMtIJGKOjOPc5uJCPae5A/RoP8oix9/WPwDMdgzWwV5I+Z3KJ8+anykzZaY8u0o+nzf7LTs6OpBKpVAoFELAVYlPoLWejj1nDI3u8Ikwu5+3Gwt+sgAxN2Zsulqthv3794fIZ+Z/od5SspFrNHUWdZXruvCjPm576W248qdXInk8aeqiRW0xft9MNPG5138OhUwBaSeNORvmhOwLrZe3x8Pam9fCm/BwzqZzcOiqQ1j3nXV48F0PYmTRSCixkx/1cWKoFaJemVcBfGD/J/dj8bsWw6/48JIenHJ4P60SknaUox0RRdtCw14BoJloollun/LhVB2ktqcQOE/ZGl4E6a2tPeGL3rQIzc4mdn19F7q+14X+f+1HpBGBc8JBz3d7cPRPjyKIhQFA6cwS9vzrHqx8w8oQ6RtEAhx88UHkRnKYs2FOqF4EM1wnXdeFl/QAf2rSLdpt+rlt89kOFSUn9HuNxFCwqODdHmeuT9rXWni/PkPvmbVrFm782I24/c9vR4AAZ3/4bPRv7A9hA1sGdauWYhS78DMbBCoOsm0E29mnwFDbrM+zwaACbK279u3em/Zi3w37zFFe1blVPPm3T+Ks15+F+Fh8CoGi754Od+jfJN8AtJPhVV3Ex+OooWZwWjwex8FXHsT+5+xHEHmqHkNVVIeq2Py5zTj7DWfDrU09ao3OQ7bJ/tE+INZSJ54NzFm0HfyffUCbSftzuv55puV/GXBHIhGsWrUKy5YtM4YkQYJ2gJ6drBOUHUHjVQdO91DTUPZ93xiKnufh0KFDxgC1QxyoFDUTMzuGBi8XAU1wwHpruK4CAwoMBYB10b1KNPA1CRw9uhxAGtbqveVZ5Dw3E0AoKoA/rKcdnsV20vAmoGD9KCSaZVInJr2dmunY3ge7+fmbMb5w3CxYzUwTW9+6Fef+yblwG622VpdUkT2Uhd8IhwOxr/g+2yPHflEWi7KjSUrYRrv+nJAkSnTMOYG4v41to9dUmS+2mWOjiTI47pQ3elyZLMV13SnAjv3HMVAA7Xle6AgmfQaVRLlcRqFQMIn3CKa4X45tZ12UIda+pyzqPnc1UnzfRzlXxkPXPhSa5/FyHPkjebgNF/FU3NSbYJPzIhaLGQ+87UVXz64uZJFIC1CfuPYEtvzRFgDANV+5Bq/4xCsQ+AH8WKtfGII0MTFh9gAq2cFx4jirwmQbKUO8l3piomMCP37tjzE6u0Ui7XvZPuRiOay9ey0iT8VaUha5/YDjkEwmzXcaHk7dxnGhUUNZ5/s5j6kParUaEomEkXueY0vZY+SLRm1QrpmXQPuZfc+6Ulbq9ToqlYp5NveJKztNedEInJkyU2bKs6/wdAoAKBQKiMfjSCQShkBX20bXpKHPDKE2UMOpq08BDpAdzuKij1yExmQD47Vx1Go1pNNpjI6OGmKORROp6Rrjui6KxaJ5H0F5LBZDtDeKDS/agB1n7MDeFXvx0k+8FL3He0OeT6Adtko9OZIbwddf+nWM58cBB7jz6jsR9+NY9/A6s+eWxOPIyAiGh4dRr7TWp9yjOSx9aCkmMYkr33sl7vvgfZgYmECkGUGxozilL+ECExdN4ODNB9H/zX7sfv9uLPizBYgOR+E6LhITiRDwUIBCXavrjIZQU6/X19Sx/a+2Y/FbFyO6L9xurv9aIhMRRCYiOP2C081nvuPDhYuBbwwgSAc4/trj8FNtMJHclcSCNyww6wUA+K6P0ReP4sBbWyH18b+JY/6m+aFxVQ/iqXmncP/b78c1/3QNkqfax7YpWOT16g1UEpl/02GkSXTpkOKzpgyF1IvrYSTSTrimdi+v57jwe5V32/vbvacbV/7TlRiLjmHWo7PguNMff2bXzwbUdui8Ami+j4BUCQd9htZNI830vXY4ur6TdbDxgP2chbctRLGviIPPPYggGiB2MoZl71+GyKmWLabP0D6ms8SOVOH42+3T8WUdaec3Gg3M+sIslPvKOPrco+ZUGgCoDFaw6Z834Yybz0BsOBaSAb7X9uxrseXo6UgZG6dqX2pRm1LbYffTMy3/y4B78eLFOOuss0LglHsbe3p6QmynAhFWnpXVicl9zjZLyAGnQqJwKVjmxFVAyfcxZLNUKpkQBRq2Clqp5HmPFltoWDdlzij49mSgAKqXnwsYkzhxYG0B5nPYdr6XRYEs+8n2avHoIPVc8zkES47jmJBkfs+JT3LitG+eBjSArb+3FXCB3k29WPmxlUAN8OFj/IxxbHvHNgzdNoQlP1wCACFPm90HSkAosxoEgdm3z2fwfv6vhAD7uFwuh/pPx5QyoAqZmRH5LNujPl1f6STjmOu52Opt548Cfob5c3FWb6zKtNab2V0plxxzvpfP8H3feK7ZBzz+hIBJtyvYcykxnsDVn7sa973qPgwvGEakHsG5t52L0x84HYi1gG8qlUImkzERHnoefTqdDhEoSjxp4hZNmnLweQex8092GhLnzlfeCf+bPs545AwDZjnuXV1dIYVLg7CnpwenTp2actSZziOVH8ofAJwaOIVyhxxn4QDVNVW4G1y4pbbXmd57m/G1WWC2T70dHE+NYOCCxDlpG106p/kMGi26CLDvu7u7jUwrMaShV1wASTZVq1UT9qmeAJVH1XczZabMlGdf4drGtUzXNKDtbeK6YMjJAFj014uAClBfV8f5Xzwfc8bnALOB0dFRswYXCoWQ7UFgzUI9aOez0LUxSAfYd9M+HDqvddRkI9HAd1/9XVzzjWuw/MTyEDBU8AEAJ+eeRCVdCenofUP7sOaxNYj57f3fk5OTGBkZMSeaaD6XIAgwcnIEz/30c3Fi/Qlk6hnc8fw7pgfdDjBy1QhGrmqFZ+/7xD5kNmeAABj88CDcsXb9bK8ti5L4qssnL5jE7r/ejWZXE/s/uB+DHxhEcmf7CDS1XfWZanPo967rYtYXZsH1XRx75TFkt2bRjDcx76/mwa26CKJtAHf8947j6NuOmn587D2PIfKpCOY/MD9E4Hqeh+OnH8eGP9qASk8F9735Plz+lcvRd7DPtEOBt9pMtv1Le4z/c22kTcf/FXjrs23wpqHsujbRxtR71E4HMAUQep6H7k3dyNQyIdBnAynta/1fQa5933T9oPNmum0JbIf2A++366PvVNlRZ5NNGFBuVn5mJfyKjxOXnsDCjy5E9rFs67x2N3y8rv5NXGGDbZusUafg0/0fiUQQj8Wx5jNrkIllsOuaXe3Od4BGdwO1pTXkx/OhUHJt+3QEoo371Hmndpptj9l4bbofHRtbP00H1J+u/C8B7kWLFuHSSy81+3g4YTo6OpDP543RPV0juK9Ww3XpdVPvoQIJ21Bl46fr6CBo7422mSYdPGVq1Cus7JLuwySQ0jqyzo1GI3QuND2YZIT0vZqZUxOOaMItjQbgtZ7nmb7TMC16dAGYUC5VQjrRdUwAhPaC64KsiyrD3llWfHcFos0oDp55ECv/eSUyYxl4EQ/jK8ax/W3bUZldwa4/2AUn42DJt5dg30v2Ye735iJVT5l+B1qh3DpxbaCqnmtOelXWCt51HBlyaydc0DEDYMLsdR+2KnR9P4vKJcEw60yFZBsmlHkFPQROunAqccN3EZDRO6sMKNDeB8w9yaw/k86VSiVUq1Wk02kAwPj4ODo7OxGLxfCrF/wKF//oYgRe+yg113XRd7wPl3/9ctz5mjux+vursfKxlYDb9oIXi0WzbzsejyOfz6PZbKJcLpv5yrmgMq0ySCAei8XQWe+col9i421SgPOAfUCGlH1UKBSMjNheGFWo3OdOOaGcD2wawLrhdbj/nfejmWli6PEhXPidC5GqpBCJtcaMx1Yo+Uc9QLnUsHElMXROU5dQNnTBpkwpY6uh5JQNGtfaPu7jnzNnDo4cORLay0+ArXkaqCuUnFEvkUbqUM5mykyZKc++Eo/HMXv2bOOoUOKdEUf8jA4N25Bf/KnFSK1LoftAN4JUa83L5/MYHh42OSvUqKSuoj7XLSjUkXotAHg1Dxi16o448sgbHa0RkuqQqcQr8CJh0q+Srpj9t1wTxsbGTJQb12NGyHGdLB4s4sz4ma31+zsxfO/F38Oqjavw0OXhyC4tjYEGxq8Zb70r7WHxxxZj5MIR9NzSY+quNq8CH6AdbTS2fgz737kfza6WXi2fXsbB9x3EgnctQPRgOHO3DTK4hqo9DbQTk8766iykdqUQ3xNHw20gdjQW8tg2m03ERsN63AkcxCfiZgx57fDKYWx8/UZUelpRc6NzR3HfK+/D1V+8Gp3HOkOyo/aRDcDUPgNaBLnaWEoG8928T4E3+5Z2HdAOu1YnigJuBYdql+nntvdUv9c12gaVeo/arDbgs/tESRS1/eggU2KCv20nm23P/leAUeuptr3jOFj074vQeX8nchtz05INtNc0hNsG2za+mA4Y6/jyJx6PI51Oo6OjAxd+70LM3TsXe87Zg0PrDiFSiWDNP65BzxM9cFwH2163DWu+usb0D5+nYeLTtVXlR/ta66t1mq5v1Ta3Qbzep8TGryu/MeCeM2cOrrjiChO+qAl9gLYHjsYtO8GeDAydpkBRcTBbNBOiqWLn5NH9IJzACoS5Z1TD2e2JGwSBOfZndHTUfMZrCbyVofF9P5ToTAdFFTwFmwpJJ5qd1ZMAgGHJNvjic5jlmH3IPlMFrQnk7ImjY2Xv4aahb4e+U8B1L3qj3kDvLb3ovL0T2UoWPnyUu8rY/FebUetr7bH14z52/t5OHLz6IKrdVRy97CjWv2k9nIYT2gdDzyPQCssmS0/gZgs5yRSOjxIXumdDvXl8jioP9QbYbaThQhCl7C3Dp3Vx1SPSWJQA4G9+rgp0ugWV48t6cO8yQZN6bSkr3HvOvd+VSgWxWAzJZBLNZhMTExMAWsdc+Y6P+190P7Zfvh2jC0Zx/aevB57axsd+7j3Sixs+cQOiI1H4rh8KlaYccV4kEgkTmsx9vpzDrB/D9kkQcPyi0Sjmb5mP/KfyuOuP7oIDB9d/5nrMemIW6pF6qK26NzqZTJoQet/3MTExEVL6lF+dd+l0GplMxswZzoHx8XHM2jkL6/9sPXb9+S5c8G8XIFKLIIi1AGelUsHY2NiUzOisC5PvkQTgO32/lfleiTvKLvuTcqueAQ1RJ5lnG7iUN8oX9e2JEyemkIlKcumxb3wf+4n6jDJjR1LMlJkyU559JRKJIJ/Pm7nMEG4SlJlMK/MvSV5GwHA99SIeUvUUOp/shJ/wQ3bY4OAgDh8+POUkCyBs3NskM+uiHsVIPYKhLw8hiAU4/OLDSFVS+J3P/g46y53wIGHPAkioa5c+uRQb1m1AZU7Ly+36LtY8vAaxZszoxsnJSZOcjZFrQdDeEsbnlcoleBEPju9gcOcgXvUvr0JHrQNnbDgDP3nBTzA8Zxiv+NQrsOXsLXjwigenJFwrXFTAtmXb0Mw14VQddH+vG0EsQKTZbquCNyUj8tvzyD2Zw6lZrRB+eED+7jyix6MhG0e9s7bTQPtZgUDgB8jen23pbj825WizIAjQ/ZNuJCIJ7Hpvy6N48fsuRs/OHlNvjmN+Tx49T/ag2F8EXMDxHcx/Yj5yI7kp3kK1SdjfNlAl6atH19J200gMXds0OoHrqcqcktm0fRQA8lra9bTnzNhEvGm3/6ldpbjFlkn2KcdIx13tdpUJ7Tf+ryCYn9v/qw1vg3r7e17DsWH92VaCerfpouPRjlA+Az5P26xtUtvHrpPeq1su2D+2Fz8SaW0HTPpJLNm4BH2b+lBL1bDisyuQ3Z2F53rY8pYtOHLlEVRmV3DxJy6eYmfb9eZvdcbZXmydMzbQ1rFSebPfo+Njg+9fV5xniswdxwk6OztxxRVXYMGCBVPCf+x4ffUAAjBhPspy8Ho1POn5rNfrJqEYjUENP+ag0nAnIFEFwMHXPdNU0Bzwvr4+VKtVjI2NmcmpHh0F7sr66OCQUVUB5P80mjlIlUoFvt8+qoFgm22iV5xKSr1Thi2WkGACZZ0kXHTVK6ceMn6mC6SCXR1Pjg3Hj+POzJOxWKwVgnXWCB5/x+NodIaTsLSkFMjtyuHMvzoTmclMSGkoIUHvtb3fmXVUjyfrQllxnHZSMlXaCsQUjBHQuG47ZwDHSSMMuE9XJ5kCThoyHCvf941HW0ENx0XZVxvYsz0cC8oP6+K6bshLq7JtzVUjF5xPvu+j6lTx+PWPY8eLd7QUbQAMbh7ExV+4GMlSMuRFJikWiURCHnnKB9tH2SOI5P5g9qdu++CYMXy/PruOe/7yHlz98asxNn8M2WgWi7cuhtdsE1cMwde99Lq4c+xIsilhpnVVIor9Wa1WMTExgUajgYGBAYxPjiMZT4aSwZ06dQoTExOGNOBYa7g7x0JlzPdb54tnMhns3bs3FOWiJw9w3isJxwR6CsLVg87xVq85o184dpQf5gigd0sXXT5Tk6hRP5KQ8rzWmboPPfTQbyW0PAiC3ywryUz5f744jvO/lkL2/8LS1dWFtZevRc2vIV1KG72cSqWMjdNsNjE6Omr0IXNRlHvK2PGJHVh982oMjAyYHDLUB81m0yRLA2BOPNAcEtRZLGqAci1W72lnVyd2/ekuXPPANYiOtXR5Op1GX18fSqWSsY/UuG82m6h7dXzpTV/CRN8ErrnzGqzduBYRt62zjh07hhMnThjSt6enBydOnEC5XA5FiBUvLGLyDyZx43duRGKytReYbfDhw3EdeHUPcIB7n3cvNl2wCR0THRjrHZva+T6w9G+X4uBrD2LBny9A5GQEsbHwKTYsBqC4wI6P7sDk+kn0faUPsz49C0yUrYQqMPXM9OnCkNlHtNM0hJ7EC9AGval0CievP4nO8U7M2T4nBCBDgNIBHvqTh3Do7EM4/een48JbL4TrTN2/bQMbPotrJOWGEZqNRgOdnZ1mTWK+AQVErK8S5uVyGbVaDZlMxkTsKchSm5Y2HqMzHccxOUw8z0O1t4qfvO0nOO+T5yG9K23GiM+jw4NyqP2s6zTQjlhTz7j2o46XHQWhdhHtQSOLfjhihPXis2z5UjnT5/LZWm/ahnakqA2iib08zwttS+H3Oq9pq9Impl3GdrNEIq1TFDo6OtDd3W3ktl6vw426cAIHRRTxyI2PYOdzdxo7deGDC3HB1y5AqpYyOk5JBrZDAT/QTlSrdpnKurZBn6XPmU6/2WPrui4+85nP/Fpb5RkD7mQyGVx00UVYsWKFUeRaWWWhFBgRRFBwOID8zE6YoRmNCSzVaA6CwBjgPPJIWSp2iBqVCmQ4CArAWU8qNbuw43WPs7KR7HwFFfxcAbEay3wOiQk9AkEXKwX8CvKV4FAv69jYGFzXRS6XM21WIaE3lHXlMzkBgbYHXFlW9RyT8VYW0nEcHL3oKHa+ZSc6N3Si1lHD5AXtbNfxsTgW//NiDNw3YJSfgk8u5Ow7+2g3BUya+IvypFnalTXlxFKyhfLJfiehoJOQ2wSi0ag5pokEB/vIlk0FggqSqeTUy8s5o0cz8Xr+zfrwWrbHVuoa0qykFu9hnWuzarjvD+/D8NJhMy65Ezmc/8XzMW/3PBw4+wDmPzw/RErZioXt1eQw9mJH2eGedbLPnOuZTAaFhQX84rW/wOiCUXSMdeB5X38eep7sCYFNBciqRLUvbOBL2VVDg/LFueQ4DoJUgCf7nkT+wTx6enpQLBaRTqfNYu66LjKZDPbv349Tp05Neaf2Mce8o6MDnZ2dGBkZMYCV/aHjq4SQPpNeAB1D1p/ecSXwdOFRIo/z0l5A+TnrTR2iR3Wwz6PR9lGFJ0+exBNPPBFaeP6nygzgnim/afl/CXDf+IobsfElG1FL1nD6505Hrtz2QpKoU5Kc6/zk3EnsetcuTK6aRGI0gTUfXYO5u+aatS4ajWJsbAwnTrQyeKse1jXUXht0XaINpiC8v78fvb29Rs/GYjFjfDebTZw6dSqks4H2+nXKP4UDFx3AeY+cZ9pFgvvw4cMYHx83W5vo9VSbcPTSUey8eSeCWICVW1biituuQKYYPvtXw3objQbuv+Z+nLnxTPzg934ABw5GZo2glp4+4id/dx4d93eg9ye9cGpTj2YyOt3xcOy1xzDrs7OmBdcstmHPYoM5DUdWopl2LZ8fi8XMUaLMrWQD7kwmg1Qq1dpuhABP/M4TOP9H55t1ZDoAqOOuTo1oNIq9Z+zFnI1zAK+d54kyxnVN66K2qTqDJicn0Wg0QoBb7QKtHz+rVqsol8smD47v+xjpGcGvXvsrnFx+EvGJOM79p3PR90TflLbRplQswT7XyA32P+vC8dGiNqyOtY0htC/1fx1rmxzRa+31WT3rxCzETbQz1D5Qm1NDt7kN0k6cajuzqG9oVzCxbyKRQLFYNI6YTCaDfD6PfD4fiv6ko2O4dxh3vPQOnBw6ad7VeaQTl3zlEszaPyv0bo3847vV/qZzR+eMziedl2rPKq7TsVcZsctnP/vZX2uruL/uApa1a9di5cqVSKfT5rgaZQ20oQQ3yowQuKiw0rOjgquGuioMerzVaNROZwepV1G9a+xc/tZ9nEA4bl9DiXVgOLhckHQQdMFhoYdP2Re73qy77anWCRyJREzoLn94ZJEa3gwF41nlQPhYNhUke2LH43FkMhmTZMtWnkEQmMVaGUT2c7PZRNedXVjyoSVY9MFFWPDBBej8eWfrWTUXp//j6Zhz/xzTVr6XQEI9cGyT9qmSENMxW/xbi048HTPXdY0HwHXdEKvPPqGXgMqdSkf3x6pRQxmt1WohkoP10v3e2r/8TiMcdMFU8klZS207+45KtFAoYHh42NSPBEfieALr/209une0Emwlx5M473PnoW9LHzZetxE/f9XP8cSlT7Q9IW4JT1zzRIjo0PfoOBJ0KilFJjOfzxujKpFIoDiniF+99lcYXdDa1DfZNYm7XnIXTi45aYCyGkycf47jhJIiaki1kmCaTA9AiF1nn97zgnvwwGsewKG1h8zeP92X7futJDzUUUp0cNHSMHKN1NAICpI6HGPd/kCgy6IKnTLO5/B7Egs0RKhjWT/uc9Nr+Fz7WMRUKmWiLlhXJbkoezMh5TNlpjwLiwvse9c+HH7eYQxfOYytb9uKqlMNJRmlziCR6XkeKn0V7HznTkyuahHite4anviTJzC8etjoCo1U0vWIBqz9Q/1D3cY1icS15vVg1AzXL+pdhoQD7SNJFQBl6hms37A+ROoCCL1LbQW+z3EcDD93GHv+Yo85Pmvbqm2448V3oOpUjb3JtgKt9TmdTuOa+69B6kQKV9xyBa7+xtW4+ttXw/Gmt6snrpzAofcdwqG3H4LruiheUkR5WXmK/Rk0Asz67KwpXjdew7bpWq92Jce1PlDH+I3joT5in/AztZcJPNQGne7ZxvaAg7O/d7a5hveqDTlFJJ+yter1Oh4941H89Hd/il/d8Ctjw0ajURMdaa852maVJZuMsEkKew3lGkxbjP0wmhrFL1/7S5xc3gJy9XwdG9+0EcNrhk1fqBNOI3Sn6zN1Aur3ai8oCNe+sx0ZNnhW8Kdtm67wudPZhrY9wecoCTCdrT0d+LQBttphurWYdkmtVkOlUjHZ5XU7Lv+2MVUsFsPA6ACu/PqV6DnUA6B1esKFX7wQfXv6Qn35dISF4qHpSCy73Xb7td32HFTnmmI4m2R5uvKM93CvWLEC0WjUeJc1gQEFX1krggkVUP6vTIhOFvXe0vBmByvzqKEz7BwKvjn/TYRCQyzppWWdVGhUSNW4pQeRYbMMU2JdWUegnbHTBt98nzJOygBrYhK7jxRosI7qxeLEdl3XsIeO45iwVQAhIddM1QqcAYQUoCEoXAf7LtgH75SH2Rtnh0JjXNc1iUoAoOuXXQiCAPGTcQx9cAjNXBOLvrAI+S15NIN2Bm4Kr+O0QldK/SUceP4BrPyPlQDCx2gxrF4XE+17m2nluCkZwJwBuvgEQWAy1ytgokzZY8A+0fAazgkdY9afkSAKfsgSOo4TOt6CY6J7pdUbbh83xfsAGE8kAVwymQx5zqlkms0mOo914vLPXo47334nzv+H89F9vBtP3PgEtl6/FV7cw6YXb0K8GceKh1bgJ3/+E0zMmwDqwGl3nwbXcUMGEceP2w3YDySadPsHx9nzPExkJnByQZu9RADk9+eRO5wzIDsWi6FcLmN4eBizZs2awsaz/WpkcEFn/TQUnkx6rVHDnS+7EzvP2YnADfD4Wx5H16e60LGvI6RvqtUqhoeHjdGZSCQM8LQNUNalXq8b8K4Lj8qDjqfqI5t0VD1AstEmOnUPmrLwrJcelUa9YZNVunhoxnQSQY7jmK0pM2WmzJRnUfk68PjKx82/w+uHsfEDG3H+X50fWsN5qoTnechmszhZOInOjZ0orCqYkM3EngRi22JoBA1jj2i0jTpGqJ/V9qGeU3JPAYTruqYe3OoEtPRisVgMJXGk/jHhuoGH23/3djzntueYZ+p2Mp6WofVUw9pxHGQeyiA6EkWzo9lqsw8kbk/g+IHj6Mp3Gf2pZC3fk8lkEJ9o2b2dI514MV6MDWdtQKKSwM5z2idssIzcOILy6nLrjPMqsOy1y+CedOH5Xqi/qOfZJhvEKpkNtD2pvu8jSAbY92/74HV7SNQS6Ly3MzQGSvJrRJQNvhS40FZmdno7sTH7R+vLtYf2XBAEcFwHR88/igdf/CDqmTqeuPIJRGIRXHT7RWZdZ+4TGyzbf09nS9N25fcKZjn2JGAI9D3PQ2Q8goEnBjC8bNjIffZIFvm9efMs9p2up1oPrZ/2Jd+rQNp+pg2qdW6w7xUDaNF13wbs2g9qw6rTjXXU/C7TlemcSSqrvNcmQGg/U9YoM0zcSzmh45DEHW0OnQe+76P7cDcu/8Tl+PHbf4wrPnYFuk91w425IcDLMbdJpsAN4ARtfDEdKOfnCs4V/+k2Zv3cJqymG/P/qjxjwN3Z2YkgCDA5ORlS5nb2YP4muKPwK7im91D3ArHwOzVYlamkEKghr0kY1LtI5ctn0cAF2sdVUUnokRasBwdUgRyP0SHI4Du0w9UTy8FgqKetoIaGhuD7Pg4ePGjAmO+3j3fiO9WItlkw9kEQBIZNVuDNcaLyJXgjaaJ/ExQaIO97OHrOUTzy5kdaC/MHEuh4pANes73/HmhHIGi4bXI8iRVvXgHXd1FBxQg428h+rs2p4ZFPPwIv7iERJLDk1iVwGu0+JwHDPlEFpuQBw1/YBgIyfq7bBng+Mo88YQg+66TAx1a+HHP2G/cZs78Z9quASEE05YOAkM/l9ZQVKjqVW/atgjS2nZ5cvlPHVMmZ3FgON/zlDfCqHvafsR9PXvckvHhLaTXSDfzy1b/Er175K/hRv7WX6/ceQqQewZJfLAmBXs4NDemnrCqBpVsURueN4mfv+VlIt/Qc6sFl/34Z0ARGJ0bR09NjDDSOBY00lX01Jjiu7JP+/n7UajWMjIyY/q9UKth8zWbsWbvHJMOpddbwyzf9Ei/+8IvhjYf7m/Xn+/lORkNMp2j1HsoMx1T1COcy5VINPJUH6gkmfOO+N+pXzg9V/GoYU6bVyONiwTmkCYbsLRMaWj5TZspMefaUJX+7BEcvPIryvDIAIHYyhrP+7iwzhxnFVSgUQvZMR6wDy7+xHH7Sx9EXHUX/9n6c+eEzUZ4oo95dN4ShJqq05/90uTUUNADh0114gsrExAQ8zzPkIcOFJyYmkMlkQqAhEomgHCnjx8//Mbav3Y4ji47gtZ97LXKFXEjn6Vqof3PNaTab6JjswI0334g7PnwHSp0lLPr0IvT/uB/DzjDgA7lc65k88YH6mzYAdW6z2cSCbQsw6/FZcB0XftLH7tW7w6A7ApSXls2/T976JNa+YS2OXnYUmAR6bukBvLBnWe0A9p3aG2yL4zgIBgLs+uIu1OfVAQfY/Xe7sfjPFiPz8wzgh4G62pvNZvuYTn2HkhRBEKAar8KNuMg0MyFAy/XDBsm2k+lU9ync+4J7Uc8+Zc9GfWw5fwt6j/Zi6L4hUxcWtZ+5ZikA0v3UWmib2V5o3/dDCUK59gdBgLU/WItIJoInrnoCXTu6cN4Hz0MUUQROMEVupmujjpVeNx0A03Ej+Lfnk23PsD8Ud9g2rw38WW/dmjudE4okhGIIBdBsg+57tvuez1eySPudji7b0273kd0O/s0tq/V6HelCGjf+xY2IBlG4kXAGdD5T5ScIAhQ7i/jh236I5/3D89BR7Ajhjek89RwffYYCeNqfSjZM13fPtDxjwE3vEStnh1RrZ2h4gRqSyphQoGzDUQdKJw1BDL/jswEYpkRZOnamDZz0t51si4KlbSTQV/aQSkw9+93d3Wg0GigUCqE2MrRC7+Ng1ut17N27F+l0OpSYzfdb4VW8zlbE7CcVAJsto8eRE4ULkyoCDSfl+3Ufteu6OHj2QTzy9kfMgvLg+x/EmX99JnoeamW3HF4zjPzGvBljnfBBECASRND02ok8VHH5vo/SyhK2fGALvGSrf3e+bCeCRoCl/7kUQaPNlOlkpkxw4jGzsud5oXOF2V/sk3K5bLy/rF9XV1eIEFJPdxC0w5MBGMBjM7qUXVVKqix5Dz2nGu6ixzBRlikHDDvmAqUTnMozCFr78gmc2FY+h6F8lHsThu1FUG1UsWjrIuAbwMaXbEQ9W0dqNIW+HX04cfoJ1DpaHt0gEmDHuTsw/5H5SJQTZuwOnX4I/Y/1I/DbizbBmS7g7Mtms4kdz92BICLMrO9gcNsgIkEETtRBLpcDAHR3d2NsbMzINME226FRGdQ3HG/f97F///4QGcj+XHXHKow1xrD9pu0IogHyh/O46otXIV1OA/G23OZyOfT39+PIkSMhw07nmdkPLsQO9aSdaCiVSoXAL8PqOjo6QvOTCwENEiUv8/m8SfLGd9J4tQ1Nk5hOMsOqgcVCY5g5DPgsGmeU35kyU2bKs6sM5Acw9M4hPPbex1Bza1jw7gWoDleRybSAEvM66HGhJGKDZoAl/7oEqUQK591+XivrdrOt52zvq+oWrvV2hJntUVMwp2S067rmaEl6unXNpp6sRCu476r78MS6JwAAk52T+MbLvoGbbr0JvcO9oS1tdoJOFrahu7sbCSRw5fuvxI6zd2DWHbPQ9Jpo+k2MjY0ZzxvXYxbd/wm093hHG1FEI1Fc94XrcNfL78JEzwSOLDoy7Th5WQ+PfP2Rdp38AMn9SXT8sh1VZQM11p39q4b92LVjaPY02yA/Cpz8nZOYf/98OP7U/dTsd/UO6rgpYPYyHja/ZDOCWIBzbz0XsXrb46/roA2adP3qPNmJa756De5+yd2Y7J1EpBLBWbedhRUPr0DFaUWMMXpC22//ng6ATgeutZ9oo9GZoVF3rusCPnDWt89Co9HA4i8vRtAI0EDY2aakjz0evI7rK2VP66ntsMeXf6sNrP2p8qt9q3YG66j1VTxgv99+nv7WcbQBsNoL7EvmmeERb3ZbbBCqDji+i31nExD8YXQNbXIez2oTLizEeKfmncJdf3AXJmZN4Cd/8hNc+cUr0Xu8d8p80DbaUQcqk9ORIXax9c2vK88YcFOhKZAgsCM4U+8Mv+Nkp+Frg3R6U3kPBV1BNNAOK9fB5AKi4bZUiDpJ6FVUIEDwYQurLh7qOaSCUIWs4UfqQY9EIiiXywY0k7nVeulzisViCBQQLOtA6tEc/JvX64KoSlA96rZys4kNBaZKSBB0hUpfq+1HLjyCJ9/8JGbfNhvz/n1ee1zSHoavHEbvrb1T2CCOG/u8kqzAj4T3Xtc76mh6TTh+2Dun7dWztunVpVKlB5KAiEqKwJR10gRb9oRTYoJ/k8BQb6cmgCPoIfiiYaIKnMBLFTOvV+DIOjP5Fp+hyS6UvSQoUtJAiRTer/txSawsvm8xgmqAx175GM790rmY++hc7D1rLza8eQO8hIc5W+dg3efWwT/lo4IKEokE9ly4Bw+95CGc/sPTMWvHLHi+h/49/aE5bm8FAIB1/74OTsnB7ufuBgCsv2M9zvrhWQicIFTnEydOmDYyGmE6w0c9tUroqLGhslGtVrHwSwvRLDRx/JrjOP/z56P7SDf8SFt/cIyZwJHgU4lCLgY6xhxL3YaQzWZDOo1zTTOt2gSFehKo31QvaBt5TUdHB6rVqkkuQ/lmvfiZ6mPKAhc4khnaf2p8zpSZMlOePcVxHMTH4lj2oWUoooj04TSqQdXoBG4fo07RnA9Ai7Q+4z/OgJNprfn9/f1GZ8ZiMcTj8VDiS/UUKnHOulCH8TfQ0m/Hf/c4UnemEHfaThE6J4IgMCQlbRGus82giXKiHGqzF/VQi7UcGL+87Jc4/97zQ7YbdTXrxMzWI1eMwN3lwj3mYvH3F6MWbTtBKpUKKpWKWX/trY50uvCZGpXUbDRx7S3XYrJ3EjtX7MQTFz2BVY+uQrGjiE0XbJp23I698xgi4xEMfmwQnT/qNHaa9qfac7ZB3/sfvXDKDo689wjgAvk78pjzoTlwmmGQpQDt5AtPYu49cxF121sZdY1xHAeIAI+88hHsu3Rf6xmJAFd89Qq4Qdhzq3aH7fnk2jG4axCXfO0S3POqezD/C/Mx8IsBVAeqxo6iXNEms23Dp3MU8TP2kdaLRHG5XEa9XjfEE9c52sHVahUrv7oSTb+JwJ3qMFSMo44bGxwyAq9QKBjCWvvI7hstSiDYNrK2U+12HVu1qViUsLfJEM4N3YKnY6+glHXS69V+pSNDI1rt0HMdJz6f79PoZ5UZtksxH/WY2mbTjdOpuafws5f9DOOzxwEAp+adws9e8TNc+R9XovtEd6gedr8oCcWxUZyqpI7dVwrWn0l5xoBbPafK/Ggj1H2vk0A9eHqdGpd8BtkPFT59ngIIgh2CEF0IFKRoCAWv4yKiqe95jS00CoSVYOBzHMcxiwfbS6/RxMQEDh06hMHBwVDYMicA36NsLQ1rHumk33GAlYBgPThONJRVWNWYJxgCEPIW0iOqjPSiny5CtBHFY29+DABwxkfOwMAvBnDynJPY8eYdaHQ1cOjlhxBEAyz8/EI06g08+dEnUV5WRhRRzPrhrJAxr1sBXNdF6hcpLP3Tpdj+hZbHcdY3ZmHwK4OIulGz55syoN5tghcFK1wY7W0FVBy2wmRfs066P1lBeBAEBtST5bPBET2NiUQCpVIpBGbodadi4jwiWGOd+X62SSNCVEFR0WnoC+XEjhKxFwF+r+Ct2Wxi8BeD6Drahe6DrWyxmTsyWH1iNQ6+/iDO/tez0THegZrXSkKyf/1+PPaSx1DP1vHEC57A7ondCIIAV//D1UgeSpp2Uolpu9AETvvaaXAjLvLFPFbeuRKNZlt2p/OUa2SDyjANJOoXXdj4fjLcnGuFQgGVSgXLvr8MS7ctRf+pfiAaJloikQgmJiZw4sQJs2WA0R9qkGrYG+WMBh+9ShoSr9dx/ilRpwnP6G1RnTQ+Pm50WyKRwMDAAI4fP94ar0wGXV1dIZ2kXio+115kSEToWNEI5faA32RBmSkzZab895ehoSETHePudpFqpACnpQuLxSKi0Sj2/vFeLP7aYmRqGVSr1ZAHLhJpRZ499OaHcNlXL4MTtPdGc7sLyWE1iOlBZFI1/vA7XWPdiItjLzqGg284iMKFBZz912cjEomEDPVisWh0fC6Xw8jICCYnJxGNRjErPguX3345GpEGdqzagXQpjRd+/YXoOtmFO55/Bx4/+3GU82Ws/+p6Y++pUU79N3LuCHa+eieS40lc8s5LTMgqMPUMZQV8GoqtW7q4ntZqNZP5OzYRwzkPnIMFOxag42QH3GzLNs6P59F/oB/f+sNvhcLOvU4Ph99+GKkghew9Wex63y4MvXsoZOgDUxPBsnTd2gWn7mDiqgnM+cgcRMfb9WN72I7hFw3j6JuOYuyqMVzw1xeEbGv1mD74lgdx8LyD5h271+9GM9nEdf92XWjN0Pppf9DZRJtt3pPzcOXfX4lgS4BSuYRTp06hs7PTPKNcLiMajRoQp3aPAiob/Nrjq39rRCntCMobiR3NwWNvHVBbSWVJARnrMz4+PsVhp1jCDkPXsVE7XtunDkGu4bb3mvNLn6tAlO+3MZvOf60f7+U8ZNJlJjHk+3k0G5/LOUKyTOVC+0nnF+0QzWFl11MjhHVbLJ+pfc72ZMYz6DrRhZGhEbM/v+d4D3Kl3BRSYzrZssfelu3pohcUCz/T8oyPBfvLv/zLQFlSRfie55lzYMmosjLpdBrd3d2oVCoolUqhSUWmRI1RsrI2mNSwYt7PCdRsNg0gYt04mDQodfIA7dBsmxDgoLBDKVCagZ3XELRzorEtQRCY0EyeQZhIJEJeLQoVvUtA2+NKL5gytex39VrxOWTtGo2GOdObbeT4aHgoBU0nsxIKBOEG0Do+Dj/vMLxTHnru7EF5Thmb/nkT6t3tUFO37GLoc0MYvXgUk2snAQdw6g5O+/Bp6PhJBxq1RkhZcywp4I3lDQy/ZBjLPrEMXrW96DHUSxUWAEO2kCBQgKfjSHBkKzmydVTQvJ77xe2s4vTWVqtVA0QoX/psBTUKHKlkVJlSxrgvl7LFNmpItCoflumYN22rvSAxSQm9HLrNg/NASQs/8OGkHUTrUdNHpaUl3POue1DvnBpmHK1E8bx3Pg+58Zz5TMkEJeYiqQhikRhSsVSIeNK5wfaReNI5qlEEnN/8mwsJ+499Nzw8jJGREQNQecSIEii+395ffeTIEdM3fDbnHnUUF3e9n7pGk9tRRrk/n3qMfaTgOB6Po6urC4lEAvv27UMymTTJ29g+Pb6HdaBXqlgsGmOCOok6iDKrckqyjrpMj3xsNBrYsGEDxsfHp4z3/0QJZo4Fmym/YXH+HzgWbNWqVRgcHAQAEw1EQrgZaeLw6w7j+CuOIzYewzmvPQeJYsIYq47joBap4ck/eRInrj6BzkOduPqDVyNZTYYipNSw5rpie6ei0SiKxaJJhFWtVjE+Po66V8fByw9i3zv3tTKDB0Dvhl6c/fdnI+WljC7lu/r6+jB79mw0m00cP34cExMTWLhwISKRCMbKY7j9Nbfjpu/ehA6nA/ddeR9+fvbPW8mRfAdn3nMmFn9pMSZPTk4hKUdOH8HOf94JP+EDAZA+lMaaP1oDd6LtzWs0Gpg1axY6OjpCSZxInHJN0G1h8XjcJFzVezTpqZNw4MJF0AxwbMExfOcPvoN60jpaqe4CDcBP+XArLvq+2Ifu73YjeioK+NOHmrdvbnn8ner0e8HdmIvxa8dx6AOHEMSfGoPHe3H+x85HopYw+t8QKh0Bfvj3P0S1q5UAN1lI4uUffDmSxWTotQpUAJhtAWpbURaYNKtUKqHZbKKzsxOZTMZsqers7DR2yXRgzXFanlTa98lk0hBB6lGl3VAqlTAxMWEcVo1GA5VKBbFYDNmuLPyUj2AsCOVgskkXyrbtbVX7k9fZAFr/14hbFsUQCuz0t11soMe5rg4VBZB6vak3fBQjRQSjbRlVUoHXxeNx5PN5zJo1C5OTkzh+/Lg5UUDlT7Ga5o/S5M+UCeIjPX87m80ae1YxQaVSQblcDm0JUHtEwbf2Q7PZRDWo4q4/vAuHVx/Gok2LcNWXr0IsaOcsUKKJ/2sfPx3wnm4sFGNyTJ/JOdzP+FgwGrFUQjTWafRVKhUMD7fO9+UxS2Qds9ksurq6jLFIoKkhmgqQi8UiisUiqtVq6Jglvo/gh0zu4OAgEomEMRzZmSqI6jnjvgsOKNA+WkJZC2U4dc+oXW96pIA2yKURnEwmkc1mkU6nDTvM/Q98Hw1l/mayE+17XqfMkIaVKIhmH+nn7A8mJbCTjiigZzsIOqNOFHO/Nxez752NiBNB8mASp33wNCROtAx4t+Zi0dcXIVlPtpKFPCV2QTzA/hfuRy1bQ2lNKdQ/GiURiUSQP5THGZ8+A9Gg1T4mL2HdarUaisWi8ZZ2dnZiyZIlJvmeEhGaGEQjCaLRqFH8juOYUCBNEkGQQTaPsspnkFxSryf7mQpWwZVGKnDhUBkMgsAsSJQ9Kie+j0qNbSkUCoZdpUxQ5lXZMhJA52ilUjEZ5W25oYJh/8SiMbhV19QhEokgtjWGtf+yFsnR8CIMAM1kE5tu2hTa21cqlUw/qQJLOAlEgkhoPzL7VKNcOG81XJ/XEUgq+2iTJ57noVavYXv/doyNjaFWqyGRSCCTyZg6aTgT602yx04cRCVNmbAXIfV0q67j8RjsZ+1vkkXUOQAwPDyMEydOGIOP9WVkD/uF+zRpsJCBBhDyHOi2GhJv7E8uiKlUKsQc04DRBDczZabMlN9uUXK1VCqFPkt2JnH01Udx7NXHEEQC1Hvq2PTPm1BbVDM6o5qoYsfrduDEtScAFxifP46fv+3nKPWVQrpU1xcFFfp3s9lEKpVCb28vBgcHsWTJEqxbtw4rLlqBymsq5hguOMD4snEcOetIaPsT653JZJBIJJBKpZDL5ZBIJIwOdOoOXvKVlyBTzmB0cBTblm8zSS8DN8CmqzZhy41bEMu110Lf9+G7Po69/lgLbD9Vh2p/FceefywEoDQ/CAl4rtUEhrT3qAtVb7Iwaoy2r9Nw4Nd9eE0Pc/bPwfW3XI/F2xcjVou174n78DM+4AJ+xseJt5zA9ru2o3hlccoWyineNB+I1MOAieuZ67rwcz6GXz7cAttPtX9y0SSOnnc0BC5p6yWqCVzz3mvQdaAL3Ue68cK/fyGSxWRojeOYM/KLthJtBo34A4B0Oh06frJSqSCTyaC3t9ckH+Zz2Z8anckxYb8rsFV7hWQHHVzEGsyQncllsO+qffjZm36GUk8pBEaV3Fd72HZGav+zj9VOV8eePkffpePDZ9K20e136nBU25LPVvCpW3ZpG6mt4kZcHL/iOB794KNwl7XwmB6/qnPe91vHyu7evRtHjx419sV0Dg/eR3lQb7jqJBZG5umcYb9xLmqEiXrCaeNTF6kjhH0aD+K49tPXYsUDK3DN569BLIhNAeq81wbUqttU1vX7EIHxFKnE8aIe/nXlGQNuGmjqBSU48TwP3d3dmDt3rjEK2VGVSgX79u3D0aNHDQhJpVIhMGULKjtTw6TZObZAnTp1Crt27UKxWDQLBCeBMjgq6Grg0xilx4jgRgfK9uirMJB9UXBGoQFgFhH1erJNeqY29y1xAmnWayUBVNnoJCARAoQZMc0c7/t+6F3qidMs5mqIc5FRsOO6Lno392LVv6xC8lQSy7+yHMtvW45FP1uEVZ9f1WJtAXQ80oEl71uCEy86gb0f3IvSxaXQcQCu62LssjE0u9vgn+2gcZDL5TBrVuuwe4Iseh/37t1r6mOTJfzRsOz+/n4sWrTIKHI9lovjYfcL68v+0OzvVKo6kTVBmrJ9fC7HhNnkGbqXSCRCx2SQsFGvOJ+RzWYNEKM8KOlDo0EVi8oMZYMAjR5TXXx0/rGdkUgr+de8R+dhzb+vQWIigUU/XmR0xLI7l2Hdv60zxgqVEcmt/ZfuRy3WWmCLxSImJycNA0750sVaw7b1c12AdF5xLig54vs+tl2+Dff80T04cN4B00/8IcFEwKkGG0kLgm/u6dY+VSaX8qZROhx7LhQ2q8r2cN6TFNRQOBoTqpPYPgX5CqK5lSGTyYQiaFhnDQPjOylHGn6ui8pMmSkz5bdf8vk8Ojs7jd3B4vs+oskoKrMroeu9pIdyV9noRT/hozGrEbqmnqmjnm/pCVunqZ7R72kLESioruhudOP5334+5h6YCwCIVqM44/NnIP+jvCFgdfscdavjOCaap7e3FwAwMTGB0dFROI6D0yZPw6sfeDV6C72h+u/4nR3Y+sqtOPy8w2a9alQaWPGBFRjaONS6KACWfGkJ5t8yP2RH0dHBtuoaaP/Y9hHtXa7NfKYCRzpghh4fwk2fvwkX/vBCpAtpXPiTCzFwcGDqADvAgb89gMJNhVB4tdrBHG9d+2xQ5I67GHr3ELIbswCASC2CM790Jhb8bIG5Tm1MAOiY6MBF/34RLv3Spegc6Zzi7WM91LtXr9dRLpdD4IzX67rqOK3EtaOjoyZClX2q8qXtIxbQKLPpgBLrwMg9OjhImG9/znb88mW/xLHVx7Dh9zegnC+bNdO2O3Q8FfgqrtG+1ogP3m9H6CpAte0AFns7hI6vbeOoDUJZ1Hqzn4IgwN6r9mLDGzdgbNkYNr95M7Co5bDq6OgwY6P4xpZ9m2RQJ4XaZrYnXu1exUt6ygqfs2/xPozkR0J2vP483Tv0e/bJpbdcGmq/Anlbt4WmnSVX+r/KvoJvlsnJySnjOV35jfZws4FUwOxIesw0TFINVwoyO9NmhwjgGeatip2dyHfyc/UkESzR4FVjmx2liwQFbLr6UGFQEDjBGDLFSaf9wUFVDxUVOd9LbxmvsYUymUyGhIoMiipbmwFTb5Wy0d3d3eju7sbx48cxOjo6ZXEBpoZCq9df2R62kyBRgefsR2ej4+87kN2ebSWgCAIM3TuEZCOJJ1/wJM78zJnYd9M+HHvJMQSxAHvesQfxv4sj82grmcXY+WPY//b9SB1N4fK/vRyl8VJovFmnZDKJzs5OTExMGAVPRRaLxTBnzhzkcjns3LlzipGgbRgZGTHg2fd9zJ07F319fdizZ4+JpiCYY+GzdB5wDDUqQMeJckQ5AGCMDBtwacK/6ZjBarUa8t6r99UGm5QRtoGA2d4PxO+4nwloh+gr4Ke8sM26HWHOQ3MQH40jfygPp+7AD3yc9u3T4HjtaBJGD0QiEey+YDc2v3IzDpxzABd95CKjD/ibfad9SX1CJp3tjsVioWPzNKqEx7xls1mkUilsvnwzHr7hYTSSDex68y6k42n0be4zusv2LCujrEBZFzaOna10KR9sh4JY13VDCfNsXUJ9yegTygHHWxcMhqSrXkskEujv70ehUEChUIDjOBgfHzcLUaVSMcSf47S3vjiOY2SJ81/3knPOz5SZMlOeHaWjowP5fN4Qc1pO7j2Jef84D07EwannnIJbcXHa+05DfmfenAMdHYli6T8tRTPaxMT6CSSHkzj742djeO0w4tU48sfzoWdybdIQWdW7thFKndRzsgfX/+f1uPVlt+KS71+Cvt19OJg7aJwjJDPphAmCVrRXuVw2OSlisZjZCtTV1YV4PI4Vx1bgD+/+Q/zDdf+AarwdrXXgpgOIFCOoe3X0fqsXyWQSc9JzMPTVIdSdOjrv78TcH85FM2gfkakGOICQV1LXdDsCi8S6elq5bqhjRCPnCO7W3L8GfQf6MHh4EEs3L8Vtr7kN433joT73Uz4O/MkBDAaDKM8ro+8rfXAmwom9+E4bKKjtkTiYwMIPLsTeD+3Fyu+sxOCjg2avP4sNQnoO97S2WcXD4xmLxZDP5020Fm0DOhoYvVmZVcH+Nfux6s5VRmZSqZTJbVMsFpHL5UyERq1WCzmcQqSBgFetI9c1JXto7+jWqXg8jn0v2YftL9puIi9PnH0CG965ARf85QWIeOF+UMxhyzTXZV3z+bfazPqdYgMlC9TW0/fa9gTnmpJeKpcqx3ovZWDfdfuw/RWt/EgAMLxqGA/92UO45iPXIF1KmzHTtrF/1YNs94viPyBMELB9tAPVQ81tftpHR4eO4o4X3IFEMYHn/ePzgFrbjtI2cX7RLrSx138Fpm3iSPtIiz2O9nhqfZT0KBaLU541XXnGe7jf8Y53BDSeyYAA7dDjzs5ONBoNTExMhMCoetc4iDQWFRAEQWBCJWns0+ijVxNoH9Nge/foydRrVPFp4iYqee10BVp8HxcZVSo0nLmfl0a4CqyCIrZTDWYOqipjVRK2V5p7yLkgUYi1HQxxZeZEhofS6NdkJ0oYKJvLOrEPtM/4XraP3j/ug+ZzGo0GYokYmqkmjlx5BDt+f4c58gsA4qfiWPdH61Dtr2LzRzbDT7f2VvXs78FlH7gMk0cnQ+QF3xOLxQzzrfJE72wymTRtpSzYk4fAhvel02nkcjnDjFIOqFybzaaRSa2PKntlJfVvm8wBEArlV1nk8+yQdAVpuodXx4ALOd+hzCz7igBQveb2XHRdN5TdmkSakmFKnrGfkskkak6rXW7dNR4Lgvpao4bj64/j8Xc9Di/hAQHQ/2g/1n90PSK1Nmmhc4b7tFgnjqHmh9A2sE2NRgOjo6OYnJxEPp/H5NWTeORNj6CRbntz4hNxXP6RyzH72Gzkcjmk02mzR0kNSN/3MT4+jsnJSXhe69x26gj2u+21VrDN+Vur1UxUBOep67rGYGOonYbNqdeduoVywjHjtYx04N+e5xn512gLJjZSfa/kmk2oMupnz5492L59+28trDyY2cM9U37D4vz/fA/3nDlzcNpppwGAIdWom4zNlfKx70P7sOQzS5A61I7cAdpOgmpnFVv/bisWvW0RGlc2sOttuxBpRHDTX96EXDFnAJXt2VNymL/Vo0d9wnWzlqohWooi8Ft2xL59+zA2NoZ0Oo1sNovBwUEMDg4akvDQoUOYO3cuuru7DSAYHR1Fb28vZs+eDaCla49njuPmF9yMWjx8kopTc7Dqk6uwYuMK9Az1oHKignFvHJXRChzPMcQi+yKbzSKXyxm9z3WdIbDNZtOABaDtPIlGWwmmlFRnP3AbnOp4Pts29OvZOr78ji+jnqijkXhqrQqAvo19yOzO4MCLDiA2HMPKl66EV/HgN334CR+RUgQIwh7FUD88ZVcmEgnEemPojHQi6oY92hw7tUPtqDoFWfybfaSfA8BYbAxf+8uvoZlo4qJvX4QzHzkTMTeGcrmM4eFhjI+PIwgCzJo1C+l0GhMTEwiCAP39/SGHBH/7vm9C+yORiLFvuU6RxKjVaiiXy3Bd1+wRLxaLiGajePxdj+PoeUdDfXPxX1+Mrse74HttG009ogq4WR+OoZICum5OV/R5ah+qI0fBN59pEwy2M0bBo00esbiui2hnFPf/xf04sfxEi2ipR3D+P52POZvmGFmgM8X3fXgxD4+9/jHMu20ektuSIWJe7VzbxlQvs4JbAu1UKoWOjg50dXWZLbNBEGCkbwRffuOXUUu25nDuZA7Xv/d6RCvhUHKVPe1Hzil1XOn2RZ3n2i/2PFE7U/ud3+vfrAvlplQqYdOmTXjkkUf+z+3h5qAwDJbGHwWdk8E2NqvVqjGq1atNsEhFTo8hQQE7Tr26Otk1kRrBJA12NU4ZVkJDmF5ABft8BhUmB4eAlV4eCgm9a0y2pF597r9lpj8NY+B+boZQMXxd28lr7XBRJqFQZaDANAgCjI2NmX2qBB56nBj7X/dcUHi4MOheXyoF9ezppPb91p4cCjvDwb2GB7fgYtEPF2Hhjxaa4yqSI0ms/ehadFQ6sOcte1pgGwAcYGzOGDZdtmmKwvO81j5S7nEmYOU1ZLu410oBGSc9Q7YJdNiGSqWCkZERc6+GYut4UwYoL6pQdI85s59S5nif7j+396+oIrMBJOtL0oHzhuNFmeEiqWeQsy6UQc4Xe0sGk/bRgADa20cow57X3reu72efJpFEzIsZ+aKsBkEAv8PH7lftboHtp8Z6dPkoDl520LyLSXeAFlFRLpcxOTlpwK4SIMoqBkErU+jo6OiUUPRqtYr49+MY+soQ3Eqrf+MTcaz70jr0HeozxJ4SZuwf3WPId3Nu6PuVLGB/VqtVFAoFo/ecDgeFgYJ5l03eMFdFEASGoKM+0bpw/DSKSBlkJTB1vvIZnE8E0jw6j+9jfdVg1H1ZM2WmzJTffqF9cfLkSQwPD6OrqwtAO2GX8TT1+Fj2t8sQ2xcz39Mo9jwPte4adr57JwZuHUDx/CKefOeT8OIe6pk6br/5dozNGQt5b3O5nHm2hm9qRB+Lkpa+7yNabIFtoJUIdPHixejr6zPro2aoVvJVwU0qlUJnZ6d5r+d56J3oxdt/8HYsPboUmWrGvD9IBDj6qqMYvnQYX//zr+P48uOox+uIu+3EpLRtaKtpaDWfz9wq1Kdqk6gu5zqgZD0Q3hJFu0XXEer0WCGG1/3163DT525CdiwLBMC87fOwcP9C7P+9/QhiAepz6tj5hZ0Y/othjLxhBLtv3Y3qqqppy3RgW0FHopqAE7RtIQ0jJqEysXQCjtveMsm2qteSdj3/1/eMzhrFt979LdSyNXgxD/e99D5sXbcVDa9h7DA6gkhykyS27VSOD2VI7WheF5Lnp5KgKY4AgNHzRzG6cjTUN5kjGaRGUnDQJkq0neq1NjJlAV72n+0JtrcYEBMAU/fgqy1lbxez36nh10poaQSC3s85Gq/H8YJPvACDewaRLCVxwZcuwMDDAygVSigWiybCwHEcBNkAW1+9FQevOIhf/dOvUFhZmLLFVvuGIeKUE7UV1NFEXJZOp0Mnn3i+hx/f8GMDtgGg2FPEYzc9ZvrPdiQpmFZHBTGnvZdcCUElVdiWeDyOXC6H/v5+dHd3hyJH1XnFcVTChO8vFAoYGxvDMynPGHCz0ymcCgh938eRI0dw8uRJwx4SSKrwEdTZ5xpOJ3TaoVRU/f39GBoaCoWP85kKtNkhBIIqmGQln441IQhlB3NiUxGrEp2OZVLgyu8YBcBBpgePnkCG09jAQicoCQNll3SvZSqVwvz589HT0xNKUmELZhAE5r1AO7QcgDGyNYTXcdpZ5Hm91lF/NDSYv1f/x2os++4yJEeSWP3p1ejb1gfUgVXvXYWuDS1jAT6w6KuLsOQbS0JtBGAIGfXU6vfariBo7UlmRnzKFwEn2876KUjT/lZAynfR28r3sa2250CZr0gkYsAslaQqJLZFJzMZvUajgcnJSZNAUA0N/s15QIXDevGZmt2bbdf66vs0KoALGOeCzk1VXARq3FLCMdKs8qlKCus+vA5d21pj7dZdLPvCMsz+3uxQtkzNZ8CoFiaHIyuuRiX1TFdXFzo6OgzRwbEkUTL7a7Ox8PMLEZuIYfm/Lkf/vf0G/Pq+j4mJCXieZ+rM/qGBoJ4d9inBuiZU07Br7mVzky52/P4ObH7bZpQXlE0fBUHrDM9UKhXKCUBdofLDulBvUm+pTlEDV+uh5JXqKp0v8XgcfX19pm4E7Tz2hyTfTJkpM+W3X6LRqPHGOo6DkZGRKd6Y+rw69r5nL/a/eT+QDnvnAKDZ28Tut+3G+Hnj2PvXe7HvA/tCR1ZVO6rY+IKNId3CPYq2TgmCwGxHUr1FfaXrC/VPPB7HwoUL0d/fb3QgSb7JyUmjd6mzqGfVbuTzBocH8fYfvB0veuBFSNRbUYLzTszD6i2r8dArH8Jk/yR+8u6fYPPrN6Oab+fEUINaI5XYT7ZhrSBOHUBq3CtZCoTP0OZ7aTdrYmHq7YEdA7jiW1fgrLvOwtVfvhqji0ZD41JeXMbxFx7HiTeeQGNOA/s/vh+li0soXlKE1x3e9sPx4Hqi5DGJXdbRcRwcWXsE977tXuy6bFdo3DUnEv8nsa996fs+Ts4+iUZMcgM4wMHBgyhXyyFc4LquyUTNvDUaZci+tYG3YgZ1Btn7u2m7RCIRLHpsEdbfsh7xUsth0bG/A+s+vg7pY+mQbQNMBXBKNKgdzmtte0gJCl2LFXyqvaxjZWMgXmOTAZRXO/KAcqnvNIA1AG768k249PZLsfzh5Wg2myYbuIlciwNPvOwJ7H7ubsABgmiAzR/YjLHzx0JYSOvMOvFz/cxuowJgQxY4Ll70Hy/C8i3LzXWn//h0rPvaOtMf1CGKCdVW17+1L1gvmwjkM9TBUiq1yAfOb8qdPldlT0sQBCiVSs/4FJdnvIfbZn80fFvBmHpNWKEQm/dUB7LRCtoJJpQRUuXOI5n0GB92jnrutGNYXypGG0gTVCmTps+hx1A/I/iisHJR4fN5hq6GOBPk2uyZAjsKGd+jikYVNuudTqcxODgYGnDHcULgkl5+9quyivTga1g5wUcqlQqFWeh4sg908ivjo2Egruti5XdWovvRbuQ35dEIWgypc9TBwo8sRPMvmpi9YTYW/GBBaMwJBuk5pxLVbMvKOus9Whcde/7PMdFwapIt7Ft6fIMgCJEsJCIAGJCqfcL7NeSXY80+4ZEH2l9UoKwv5YnXMNsm68DQKr5LF3q2iQuRypFtWKgi0SgUNdBUrijvnG9qSOlCqHojtjeG0z9+Ojb/xWYMfmMQ8+6bB8cNewpoZLHPNd9Bo9Ew+5Jd10U6nTZGGrdiFAqFUDSGGoZD3x1Cx8EOdD/ajSKKpo8JejkWSqTxWdMZmPSMsO85LuVy2RAHjuNgy1u34NAVhwAH2Py2zVj/d+uRHE2a9/T39+PEiRMhAiuZTJq5yPcyfJ46UJlsZW35v+oi1R9KwNEAqtVq6O3tRVdXF0ZGRhCJRMw2EdXzM2WmzJRnR+Gc1rUPeIpczvvY/zf7UTqzhAIK8LM+Vt28yugHJ+Fgx3t3YOysp/fIJItJnHXnWaE1iWuNGrRc/1gfBbHZbNZ4Q+01hmvL/PnzTdJQAIYwz+fz5ghVkv/ZbDaU9JHXsz/O23ke0l4a/3nuf2Ld/nW475z7UMm0k8cdPe8oaskaznnfOYj60dBaT92v+l3XENaDNhVtAF0/WQ99hp1nw75ObQ+u80NbhjBn0xy4rotLvn0JXLjYedbOacep2d/Ekb86AjhAfH8cC9+8EHgKP6l8KOnBbVdcW13XxbEzj2HDqzag0lnBQ7/zEJykgzPuO8O8Rz2rXPNosxCsBkGAFY+tQKKewH+++j8BFzjj/jOw/nvr4dU846BIp9NmPSe5kkgkQsd0aT+prKltrMCXdja3YpF4p52w4tEVSFQSeOClD+CsT56F/J68ITKmA/VmrsjfLDreGvnAuihO0r7he5RI0PYpIa7/04mo67heo3jHrjv7LRqNIl6JY+XDKzHWHDM2bSwWM7luEAXyE+HcDVE/ip5mDzKZjLFJ9J2KHyhnNk5knXUrsEbWJqoJXHXbVfADHx37O7Dq+6vgxNrtVCeWDexZl1q+hu3rt2PNnWtCBNN042LXjfOO9qbiMZVDHRuVUcoyIzR/XXnGgFs92lQ807npOSkImmlM6nX0bCnw0YGk4rW/Gx8fR7FYDO2n4fVAOxEEwY6ej6ieTHa0eikZiqKeSW2PMliaAToSaR1hFY/HzaBRMPWILXrGFLCpJ1u9fWooE0Rq2FKtVkMul0MkEsGxY8eMolJQZDPBuqjQi63P1ElNDyI93mQ1NTke99OqN1jHkOdKO46DGGIY3DuISqxiwP/kskmMXDGCZe9fhnQ1jSbaR0npIsWJRTngmCkDZoMi1lEXZ/Xus54aIqJyoqSQhrzRwCATGwSBCc2drm6UcT3Si/eRgQVg5IbywjoxkR4BLo98Yr2UfFCFzrHmu1RBcR5w7xnlhDJBwKanCNjhc1z8KB/cR6xKiXOX87jzWCcuuvkiYASoNWuhcaMxxRBDhj1rVAhDhcrlMkqlEnp6esw5m/Sycyxo6BjvQdND7qEcgmgQkv9CoWDOhqSnmXrNnsO65UPnlIZ5k1BJJBLY+qatOHzpYbOwjy8dxy//5pe4+m1Xw/Edk2k/GouiGWkiaAQhnUQZ1qgijqFGaDSbTWPc6n5wXktCQo0VJdyCIMDJkycBtDKXaptmwPZMmSnPrsLM3SwhQ90Bdv3rLlRWtIHmyKUj2P7u7Vj54ZWt6+sB5nxzTghwxw/F0f/1fhx5+xG4nos1b1qDlJPCIy9+BPMPz8ecHXNCoEMdJgr4gbb3iOsW1yp6M3V/sJL1nueZ7Wm25xdon/VsE8lqh6zavQrzj85H0kviUM8hPJp9NOQhPnXmKTx888NY/aerTX4LZrcmgat2nhr4bBt/0yaz11odE/sZukZzTVVbgTrXcZ7KnF6N49JbLkU5XsZ5952H777hu/BiYZ1cn9OyReqz6tj7+b1Y+ualOPaaY8j9PIfUjhSSTnJKOLDatKfmnMKDf/ggqp0t266RbOCh6x9CupTG0o1LQ+2mLcEIro6ODlNXruULty3E8//l+XjynCdx4e0XIuEnUIq2j2LNZrOIRCKho9dSqZSJBGQEIhA+ion/E4Dq9q9yuRzKd0LHn2bDHtwyiKvedxXcEy78YKqNab/P9mSqjPP/6QAv+8LeGmgTFiwK+mizqBxyvGwyR+8HwlEXvIf2g+aP0ShIRlnwyNGzHzgbQTTAYzc+Brfm4qJ3XYTcRA5u3A3ZQtO1RfuQskDswpOaOP8VzAJAYiyBi790MbySB9Rbx5hxXihuZAnhRLeG777juyjny4h7cZz5wJmhven2OOn9GiVgt0uLvs+WjWq1irGxsdCY/lflGQNuNQIVKKsxqF6RIAhM6LbNUhB000PG+zUER8NfOZH4bGVKWCd2Rjqdhu+39vTq9xpKazNTrIeCJhuw6OAomOL3muGb7ySjqCwKr9P9svyObeN7bSXJfvc8D+Pj4+a4LJ51yAQUeh2fqQuT7Z2cbgIrUOWiqVECJCbYPj3Ki2EamUwmFA1A7/nkskk88c9PAC4Qa8Yw/5b5cH3XTGhl8OmlJwOq7JyCWWVHlSDh+FJWK5VKCLynUilUKhXj0WY/USEpI1cul0NgVPtMvb4qJxxXjosSAZqpmopXDRj2GUEtmUb2MceXQFE9vJVKxRgpNuvI0D09mkGzg+u4U5ZU6fMztpMLHPuNpI/rusjn82aPNU629/OQONJwIbaBe8+pY7RfOW+4x5tEFsF2R0cHRkZGQkSfEh58bqFQMN6Kcrls9vGl02mk02lD1hHoqxJmPyjwJiHFPln5mZUozy5j+KxhwAHSx9M456/PQa1cM2Neb9RxbN0x7Hv+Pqz+6Gokx5NmrGhUaNi4gmBlyWkwUk9QJoKg7RFPp9PmmTaz3mw2USqVkM1mzVzLZDJTjI6ZMlNmym+vOI6Dzs5OAO01To3BwA+w6I2LsPsru1EbaoVHdmzpwPK/W46G1/LQulEXXodFpEWAgY0DyH8tj5X7VsL3fGy4ZAN237AbD+NhvPjTL8bQvqGQx4iAkzaFrmHxeNycrsE13I7c07Wo2WyaCKZZs2YZjzeAUFSV2gXqJAHa4c75Sh6u6+L1d70en3U/i00LNxnQ3XuiF7/31d/D8Z7jOHnyJCbnTCI4PjWrtIaua53ZZjts1TbS7bqyj/hMXW+5llNfs120pRLVBG74zA2Ix+L43X/8XXz/Dd9HxIugmC+iY6QDY7OfIk4coLSmhE33t9p74nUnEB2LYvWbVqOr2BUC9wStjuOg91gv1n5pLTb+wUbUc3VEGhGcec+ZWProUjOeStozAoptY/9HIhGTB2TWE7MwZ9scQ/Zq5B8AZLNZs05PTEwYW58yQ9BNmeL6r2BNj/NiH5OQB2Ai5nzfx0h+BPHhOOKn4qj7dSPDNgizw7ltR5QN0JSAIUZgezViUh1/atcrWNcoB7Vj2e/6XH0n7V59Hm0cAl0bxHNcmGzYyEYzguW3LsdEfQId3+pA4XgB9UTdRCHY4fOcv7RjNds528X+VEJCCQjzeTGGZqVp5oKds8h+juM4KHQX8L03fg+TvZOAA9z/u/cj6kWx+uHViGBqFn7W6emKfjfdPfputqFWq2FsbOwZOyeeMeAGwiHO9ETREGcF9EgZNQ5VmHSiKkAFwmBLG0FFSOZKEwTRQ8hn9PX1maRhdlgPJwKBtXou+T6+g+/VyamM7HRhVuoJ0/bxOjvknoqW71UWS8PWCXri8TgGBgZCdSHbWC6XTQjXdJ5V9dDZ9bOFmtdwYaCC43iXSiUDUNg2VQjqSebYN5tNjJ0zhk3v3ATOh4O/fxDRaBTLv7kcbuCGvMAqLxpWS8Wrya3YPg3Rstul2aI9r5WMjROcMkkwreNNuVeCQmXeHiMlY3i/yj2vUwCkyVRUXhR883N+x2c5TvvMSTKKjuOYfexKYvF5vM/eDqHGgbaZsqp19jzPEFysC+/h8/r6+kxiMPaJPlsjONifun+PR7/xGhIwSmBR3/B9PN9b+5jJMDjv+/r6kEwmTb1IHHHvckdHh5mvJCd0YaesaD+SqXccB/FYHOv/bj0ee/tjKM4pYs0n1yB3Kgffbc/HE5ecwKN/9iiCaIDtb9mO0z51GlITKUM8UR9Sjtkn8Xgc+XwejuOY8EDqQ5VZXbg10Q2JL8/zjJHEsVWGXhPkzJSZMlOeXUWBniEtJ+NY/PbF2P/+/UiMJrD85uXwmu110Yk7GD5/OPQcL+vBu9DDeT8/D4lEAvdecy92//5u8/33Xv89XPe167DsyWUhHagkq9alUCiY5Fhqx1G3KzBhvRmtxcRaQHvrSywWM+uMEg3q/ABgCFy+9/V3vh7fuuBbONZ9DHCBF/znCxBNRuHMdnBq2SnsePMODPzHAPpv7Ue9XjekqzpbNIqOYeWmL4UAZvu55rCNtOPUIaBbKXUs9beCmmSitX2p53APrv761UhWkth/+n4seWAJ7n7V3Ti67KkM3A5CHv1mdxO7PrwLHZ/qQPZw1qzJPJmF0WeLHlkEJ+3gkZc9glX3rsI5Pz4HgRPAD9o2O4lvrpfqxKI9wDWDEQpqy+pWsUQiYexVHscaj8dDuVU4ptz2ZUcHqsOB6zQTOjMrdjKZxPjQOO571X2Y8/AcLLpl0RTAzDqqvcn3aNv1M8UTQBu7cLyVWOF99rzR8aeNzGuUEKfNq2BQAbYtN2yXXkuAmEgk0NnZaTLv61YD2j6NegODnx1EoVBAxauY3DjcKqd5dpT4ojzo6UaIACcvOYklm5YY25eRJYqL1Mbhc7SPFKPomB2Zf6S1bYRd4wAHVx3EysdWItJsO4RsjKNzjv1mRzAo8WKDb8WnhUIBpVIpdM1/VZ4x4FYPmHrZqGTsTtT9v7Z3uFqt4uTJk8jlcshms+YZFDpbgPlMDa3mOzVsieHePBONg6cCrQwpFQCfTUCig63AWMOIFHjzebb3SME+601BpwJ7OjYNaCe10pAgThKCwmaziWKxiF27doWUhHpBFeRTORE0RKNRc6QWM2prHXRxA9qgkmG+9O5RMaRSbcDA/mCG7ng8DrfuwvGtxAOlAM1GE4EXXrSCIAgxXSprynZxLJUJVZbRJkV0jDgufIftDQba5IGG7SgpoUYF2Vmb5FBZIHhTjyjliO8lANK+UFaZhIrpQ6kH+5rvpwFBZcU9xhq5oB5stlEjR4D2fjoaDBx3lVtdvCYmJgzo137TKAvOX7KuGjWjJJuGIOr9Gl1TLpexdevWdtbNIAjpDdVFnueZqIxisQjP85DJZEz7yFqS0eeYEtjSa8N5pnOT9YkiinWfW4dSfwmd+zqBaDuxyYErDuDJ1z1pzsc8fsFxeEkPa25eg2jQJis1OoPjyG0Z2WzWLHKsi8oo56su+GogU7fYEQtKbj3TUKmZMlNmyn9vYTg556naIfw8CAIkdiew6IOLkJhMIFqJwnHbpHOkGcHijy2GW3Ex/LxhwAMW/M0CzN0+F8kFrSiZ5sTUOR9rtrf32IaoGpv0MlarVXR0dBggTd3IMl2YLEETo7NIYHL7nK5jCr7VRlHAE3gBXvrLl+J45ji8wMNsdzYq3RUcHzqOzTdsRnV2FQf/9CC8lIe5X58bImZ1vVb7gP1MO8vWrfo/r6W9pt5IBVssCgaoy7VdpVIJvZt7W4kuD/UhCAJcfsvluOdl92DulrlAHXjk9x4JjVtxYRGb37oZ537qXOQP5U10pMpQNBrFkgeWIF1NY+G2hQjc6c8rph1Ecp/rMwBD3tZqNUxOTiIej5v1ifYFyfJKpWL2D586dQrFYhFdXV1TbGDf99vRcU8Vrp+akZq2H9fhTCbTiqQcmMTPX/VzjCwYwcjQCMpOGcu+uCyEVezf2l7FMyw2wTXdmsu/7ftpM/Izffd03nWdX2qHTTf3pnseZVjliPva1ak0HW6jnUX7uNFomHHj0a2aeJr2IBP+ep6Hra/fiqNXHkXiGwmseWyNsTts/KS4idhCsYzOF3XeLnpoESKlCH76xp/Cj/pY8sgSXHzrxXCrLnzXD9VNsZ+2l98RC+q2UHVosn7a3/V6HadOnfqNnBLPGHATILJCNMpc1w15ztSgZWV1bygZQ+7lUECsSkaZSg1XohFIo1+ZFhYCPFXM7CTbA6nHmPG9AMy5xyoAQDvU02axCD4VMBqQ6bbPEybzy8FWbzvQPqdXQ4AATLuvie8B2uCW1zO0l4b50qVLcejQIRw/fjy0UBH00cOqZ0VzYtvPpadYQ+R1/HRhZb+zdG/pxto/W4sNn92AIBJg0RcWYfa3ZqNULZm98DbAY1HPsJI/XKx1kTQGRqSd7ZxhMcVi0Ux6gk+NJqDi0FAoBd1su7aLClO9vxxjeuN1ItsEAhcjXWA7OzuNd1iVAZWhto/zih5LACEPLOcNAaEaD5o/gAuwkgZqFPFdvu9jZGQE+Xx+iodD5xcXW6B1tMzSpUvx2GOPmfnLdxA0qpdB+5L1oAFDtp3tU7kA2uHpGvaoIeaO4+DUqVOh9vGsbXtuUQ5U36jBp+SdMuKu6yJWiCFZTMJDe7xisRgGtw7iwOgBNLKNFkPrA3PvnotEkDBGYzNo4pGbH8G5HzwXTtCOivF93xg3QdDep62yZSe4o5zVajVjVFar1VB0AMeNsqDjM1Nmykz57ZZ8vpXUSI1yNV4dx4Ef+CidXUJzeRPpW9Nm/yXvazQa8BZ4GD9vHACw+J2L0ferPqTntbfizbp9Fnzfx54/2QMEwHM++hwsKCzAxvM2onukGwv3LAxFRNmkseM4KBQKAFq6amxszHgdeQYvtxuqfUMSn2srDVk99cLWR7Y3T+0v1qm/0N+6zgVqvTXcetOtGO9otT9IBDj6uqOI1WLo+06fOfOZyaRsRwWJbq4p1KHUu+rw4LrENgJTHRist67p9hpv2hlB6PMgCNA53InnfPk5iE3G4DU9RCNRPPjCB0PnD40vGMcv3vULXP2eq5EstLzlgRvAdVw4aIOlZTuWIZoIH8VLO5TjzfGjvcQ92Ll8Dg+/7GEs/uFixA7GDLBn2+gdTaVSmJycRLPZSlpKckajCymrtJsmJyeRyWRM5B7Xct26RvubR14iA/zkT36Cyf7JpzoZ2Pv8vXBqDpbdsiwEtOh51QhIBWMcN7WbeI3KnYI5fq5gjbYHbQclEqYD67QVNCG02lk6NgrKeR8jA21PukYVshAD8fQV2+POZ+u71GGkdoQbdbH1DVtx8DkHEUQDbHz5RvTEe3D69tNDJJbaZYpfNHpCyQ8lIViXoS1DuO6T12HzNZtx8TcuRrLctrV0nikpok5DjgkjaCYmJkwUB69TokDxZLPZxPj4eCi7/q8rjoKG/6q8//3vD2q1Gvbv349IJILZs2cbEKLGrXqIOXH5o2wfG0oB0WRinNz8mwJte2cJ/hhKwg7lZFV2hAqCAqjhyBQ21o/7IQlYFfTbDJQyfSp8BKM0hvWsXQX99CwTTNHDzAWIz+Tz9WgqVc5koAhYyRTqkU/q0dT7qMR0nzYXPI0m4DsjkUgobEiJAH7H6zmBOf6sy6m5p1B4bgHLvroMXrOdiEzZLN3Tzd/03FJGOA4ajg2Es+OrAmNbmQxO38t90uPj4yZUmZONcqGRA0ou2cqWdVSlxrlCGbSPIWAdOT+Ghobg+z6OHz9u5EknP/9niJAaBlpHfsakd0re8H9li+n95buUjOAYKCBTkkQVtXpJOZaxWAyVSiW09UHHmPOWHmTNAaHKmUSZhjYCbaadY8bnKalGA8g+CoxtVG+M6i/qFz1aTuVQ504sFsPs2bPNWZecu5RV13VR9au4/5P3o9ZXw/J/W465P5kLv/kUM9vn4JF3PoJTq0+hc1cnzrn5HMTGWjqA5ATJLcq1RgBRFjUckv1L4M1+0bqr7I6Pj2P79u2GfPxtlCAIZjaSz5TfqDiO88yMmv+LiuM4WLZsmdFZtkEJtNaR6llV7PjcDsABFnxwAebcOQfw2gZ9cX4Rj3/pcQSxp7xbNQfrXrsOK9wVSCaTOHr0aGs/tevg0O8eQseWDnTt6kLkVRHc8/J7AAAv/9zLMXffXABtfQ20I6hKpZIJsxwfH0e1WjU6VENYNYlaOp1GX1+f0WVctwFg9uzZyGazxsZQ3QtM7+VTu9H+fOuCrfjKlV9BOVWG4ztYcv8SrPjHFZgYmzDrJgCTVIpRe+l02tgJfB/br0Y8x0t/890EFAqgtA0KkLgfORqN4kD3Adz/ivvxgi+8AKlCygBOBVAA4PkeNl67ETvX78R4/3gIePfs7MGV770StWwND/7xg1j9/dXo29cHP+ujq95lbEwbWDxdWxuNBorFIgrNArbcuAXbX7Ad0XoUL/3IS5E7kQsdLek47TPAaX8lk0kUCgWMjo6io6PD5Blhv7PvlHihXTo5OYlyuRyyMaLRKDo6OkzY9JHFR/D9t3//qQYAA48M4NwPnQu/MXWLqG4LtMEvr6MNovepE0L/V6eK4zjIZDJIJpPGgWLLJN+ldqQSWUq+TOeht4F9JpNBLpczCdHs61lX2lb1eh3j4+MYGRlBoVAwtoq2xZZzyqA6QuLxOE4+5yS2v2k7mpm27ZubyOFVX3gVuka7QoCVSe+IeyjvNllg11vJhwCt3BRRJ7y9w+5PnW+0X/U7jcy1Pd16P+3vY8eO4cEHH8SxY8f4/a+1VZ6xhzsIWp7SJUuWmDMZCQxsY5aDT0WgAIEGKwdSATIbpxNdjUHb62sbuRoWqQKiYEa96ipwmixLgS6frwCd4J/h2Cqc6iFTjxHBDhWKJhJhHXgvwRUzSZOcICgmq2oG8SlgSOKD/cEFin3MxYTfcyIq00fFwz4g2GIiKc/zDODQPVPqiVQmj0CAfcj3J3cmkdmTQT1ZN8ynRgkACCUT4X1A+3gtVWjKdiozx36zQ5VUlkqlkmnj4sWL4TgOJicnQxEdZP/UY2wTGmw/wTxlREkiBeAkmSiX7CP25549e0KZIalo6KHUfcXaF1oX1oOeeZIAKsNdXV3mrGuSUfZ+LG47YN+xX1X5K4hWo1DbTeOLhgx1hxo63DcUi8VCCwbfqZ4EDZ23QSP7TE9FYJ8VCgWTZVX3VbG4rotKpWLaynFkZlUaiwyfY3QG38P52dnZacLqaaTQ8Eo4CZz/7vNx7JJjmH/nfFTrLY99ubOMbW/YhtEzRgEA48vGsfnPNuP0T56O1MmUkZX/j733jrPrqs6Gn3N7nz6jkWbUu2RL7gXb2BTTMZAAoSWk8JJCeAOhJKQX0kMSUggJkAQIMQYMMSbGmAA2uMqWrd5HI2k0mn5n5vZ2zvfH9bPvc/Zc57MT0/LO1m9+M7r3lL3XXnut9ay19tr5i/PoHO+EU2htD1CHlRpPNEjYB2OgNVq1KGgIqqNUM0yW23Jbbt+flslkfJFSdfobeXBdHiN/NmLqo4z+xihCsRAGvzxoZOvkqybNNhYA8MIe5n9kHql7UkYWNhoNBLwAVt+2Go1GA+OvHMfxt7SOpvq3n/o3vORjL8HQ/iEjH2lsUz+xb47TzKJjCjCN+1AoZHSB53mYnp5GsVhEV1cXEomE0bsKvnTMQEu+sVEHKhBXO4x6auuprfiRwI/gizd+ETtO7MAL73khJvomEEDzvHEW3C0Wi8a2YxVzVr5WO0mzIO1AAK9ROcs+Ud/akWzOA7ORzm48iy+/9cuoxqv46hu+ipu/eDMycxnfSSIKGK+850pcec+VuOfH78GxK44BAIaPDeP6v7gepe4SHnvLY5jcNYnp7dO4+D8uxtTmKTzv1uehe77b6FXdpqc2Iu0o6tKegR7sv34/jrzkCACgHq3jS+/8El7+qZej90TvkkiiBmGIKcrlsqm5ArRsFqUHG3WS6nq1dZidmOhIYOriKXPf4AODuOLProDj+Y/UpB2oe851jdlAVm128qACYY0Cqx1EG4D1p5Suaitp03doUEF1vPaFzcZAdlRe+wzA2LZcp0BrX7q+UyPnxHOaaVCpVJpHFX9zGIkVCex97V7UojV0TnXiJV94CTpnO1GJVTC1YgpDo0Nm7mx7lTan2vI2WOa4OZ6AF4AT+K+PXCNNbLyiz1QHi77DdowBwMLCwjMORjyjc7gjkQi6uroQiUSQy+V8i4GTSqNTARCbGufKCP9VFNsG3xx0O0Yn2OAzlKjqCVIBR2+lClDdxwD4i0XZBq1G0DW9m8KZY1CAooucjYKXEVCNTtJg5v08o1JTkAhI1DtMsKaAQxcxmYlAngBanRT80bRUVu9W4UwvJheKMqjntY6Ry+VypnAG+YFAk84QBW36LApiOj90UbF/ClDVU2svGFUm7Fuj0cDIyIhvy4Lyoxo85FmCJ1v5qfOH1xB8EtyQF5jKzzkj/6gi173hmi5HI0fXF5/PdahzpTTQrAfOm9KUSigcDmN4eBj5fB4XLlzwpZxxntRzr3uO1bnBPms/WSWT86ag3XVdU72W2QcsrsL1zDVQKpVMXzXln+NiMRXSmw4ebvHQsZCf1ItNmmk0vNFomBoUnBugtb97enrajJ/zo5kiAJAqpbD6jtUIhoKGNrFkDKGEXzTXnTqcUMswmN4yjf2/uB89oz246M8vQgCtgjx8F3mD76di04g8x8e55/g4d8sp5cttuX3/GwG37WAFWvZQPVL3Fc0CgEbMX/Bz/d+sR7gaxrk3nQMADP3zEHbcuQP1nrqvhos6cN2Y36D34CHfyBvHrP4QkGr2WywWMyBaGx24lKWU46rHXbdZoLVdvRLqdQ1W2L/b2X+O4+Dyk5cj5sSw5eQWeB2eSXmfnJzE7Oys2cZVq9UwPz9v6nskk0kjxwEsybCiHce/2/WXARPP80xUV4M2tDUdx8Ho9lF883XfRDXetHlGt47ia6/5Gl76mZciUUwYnaZzQJvlps/ehEglgmJXETd94SYEE0Hc/5b7MXbJWJMWYRdP3PIEAOBbb/wWbv70zTh85WFc/rXLDc00KKP0pH1Vr9Xhxf1Ase7WkS1lkSqnALT0YSgUMvyRzWaRz+dNVHpmZsY42elooL2g/K4OdH7vus2M0EQiYSLo33zBN/H48x83fdr2yW2AC8Dx20ntgBz/1uCeBg80aGODX73XnvvFxUXfWNrhJ+2POo7+K2Cu76YtpZmUeo3tLNBgHI/lU9tb79e1ZYNeNjrWNty1AcFyEPtfsx/Pu/V5WHl2JeqROr760q9ibPUYXvLFl2Dl8ZW+dHJ1gOjfSg87q0VpzWvsOVUaAi28qEE82wmh77CdMOSD2dnZp33+NtvTBtwUPNx/AfhTfjVSpYuFxpx6lBSUKbjl/xkNVoZWo1gHDsBE/DTlVCN53OBPBtHUYlZI1IlRcKWTrEBUJ0snQ/dg09BX0MFomB60zu9skKk/9AaTLhSEmoLO7+iFWr9+PUqlEs6dO2eiX0w7pZJTLyEVAZUj91OFQiF48HDmbWew6q5VCM62shFIY9KMtOX+HgXqpEk2m0VnZ6cvFRmASRVWQU+60zOuypz9VgXIBcj+EJzSSaDAy04HIkhlUZF0Or0kI4CN71DnC/fLUsHo2qHCIR+qc0m9xro2FMRqNJzvVUcIr2dfyHP8jtsFuF2C2zD4fr5LI/+kb61Ww5kzZ3wZEwrYyEt0oqg3n/RlvzhXVKQcM+c+GAyiWCyiVCohl8uZwitKbz1BwHEcFItF8y5+p9sm+F6eH8q+1GrN4+tisdgS54AaVOpcUGeerjcF0sqTCuB1fjnnXBe1Ws04HwJTAez+293Y8949yG7LIn0qjW1/vA2B2QCqoSryw3nse+8+lPpLKA4XUQ1XcenvX4pwqKloNUuCa47rWXlODV07s0fnbbktt+X2/W22QdjO8O74zw6s/cW1OP3R0wCAdR9ah8E7Bn0GadgJY/hfhgEAjWIDA58cQPembkQiESwsLBjHt7bVd65GT6YHD//4wwCAnR/YicHzgwgkA6YWD7fNMDJcLBZ9+pxymzYIbRTVBxwXx0s9Nj4+jnq9joGBgbayzbbD1MHNH7XdeO+ukV2ou3U0Ak3HaSwWM+OZn583xTS5r5tBlaGhIczOzi4J4ug7aReo44K6k/IegMmi0vEq0FxxYQXS2TRynbmmM8UDhk8NI1LxF3IlTTXIFK6GccNdN8CNuUjmkqgFalh/fD3O7D6zxDEztmUMX/z5L2KxZxFuyMVVd10FOEAoHAKeZDOOUfkx6AZxzVevgeu5ePzGxxGsB/Hqj74asZMxFMoFY3/T7gBgipAyq4Hp1rRxqJtojyngoh7Toql8ZiqVQjgcxkOvewiHbzzsG+Nj730MN77/RpNOrk5oXR/Kgwr+bJCp92jgkN/p95z/dvyh17VzAviiuFaQ0m58byASwLff8W287FMv860JPkOfSZu1HZ5hUztGgyE6fl2Li4uLaDQa6PuPPrxg5AUYKgwhlAjh39/w7zi68yjgAF9+7Zfxmk+8BomTCR99dE3ZjirSkvOu99lOFLXzdB61z/ZYFJTb8tV2wOTzeeTz+We0fxt4hlXKFaCogCR41IFrxE+LTHBB6R5SZQKNxCqw9LzWnkxleqanUJhrtFmBmx1pI8jWBUfwqsCDfdOotE4EJ55eJQJjPoMRf42SccHpvllNR+EYFSCTBozuEdjF43FfcTE1+k+fPm2EW6PRQK1eQ627hshc64xhzm08HjdzyUqhQPOsPi/sYfyN4zj/lvOYeO0EnvO258AZd3zzo6CXDGo7SQiYBgcHzb18J+lrLyQCOnpHSUMWViAf2tXMFewpGCPo1jkmjYvFIgKBAHp7ew39tSI8+YrpZdxDxLmMx+MmAquAVccOwLc/TZUs54SfeZ5nQKICQnUwaXqMOrMUYPIe8j6j63qEAyPdSiedV73W9nTymY1Gw0SOtVCiplCpYCddSWsabTR+enp6ALTS0OlN5Fwrb2lqOSPYdraB7tHhPeqcoTxR54OmkjcaDeTzeVP0MRaLGeOAfMo55tnYwWDQFDcLBALG8aXCe926dYhEIjh58qSZs9h0DNf+xrV46HcfwtW/fTUa+QacsAM37GLv7+xFqf/JVCYHmLp8CiffdhI7P7nTrI2yU0YhWEC8FjfriU45ZjRotXrep7UubENkuS235fa9b4zcsan8pS1Evdf5WCc2/d9NqG2pYdUdq5optIHWGm94DSzuWsTY68YAD+g73IdYtSnHaA/wmbVkDU7FQSacQc9dPZivzCN2JobeJ3oxH543Mpt9DAaDSKVS6O7uNtvHaI9RzzN9dXZ21oBx7o+mjNJsQsrs2dlZ1Ot1dHV1+fS+Gsmkg25n02COnTWoIJlBi56eHoRCIUxOTmJ6ehoLCwtG99frzRNh9u3bZ7bY0SFM+1gNfA3wAGgLuID2QNbznqz3shjCSz70Enzp/V/C/OA8rvrWVbji3isQcAOA44/wqT7mc51FB7FSDK7jIhaO4bLHL4MX8fCdl30HkVIExUzRAFOe6b3n5j0I18I4dfEpvOLWVyA1lTJ6lTTX8YQqIVx1+1UoBUq45FuXYEVuBSrxZv0f7uXnFjJ1sqdSKczPzyMSiSCRSCCbzZoK9QwG6DxqdJup6dTFzDzwPA+X3345Lmy6gNk1s4ADhBfDuPRDlwINfyEztbts/lB+4g/nRcEh77PBMnlA58V2mKmNZfOygv52QUb7er6n0dnAPb97DxZWL+CeyD146RdeinCtVTeBfSP/u26rjhSLhSm4V/tUMy75LM3ktIGvW3fRc6EHgc4A7n3hvTix7YThtVxnDrf/xO143W+/bom8sx0edtRaA306fnUCKBaxAwmauWfPpToibMeCNgLuZ9qedtG0D3zgAx47TcYngdUbAbSO8FKgq4NUUMD3K9DS3Hp9Lg1YjcpQUehi0aimAjn1HvJ6O+2aQEGPPAoGg+b/7DsVDYUQx6sRe/abk8e0a/VEaiQe8J/RSGbQQmeklXpE9V475YfPqdfrOLP5DB59+6O44o+vQPdIt6/YHBuVrtlPHfQw9aYpnPmFM+aa6GQUF/3mRUifSJsFSFrS8cHxqjdKvdPca0OgAvi3GQDwKUN1SNgpsbzW9jASLCndGOFWgKQ8Y6eDaR94Hcerc8wxaH/pUGC/Kci0QCDfZS96deRo1gQFAJ1MjuOYOVPnlB05oGeYe2Tq9bov4qtV2O2MAR03+c0uWkbnkkZ6OUYaWuRh8rVuX+Bz6/W6Mb5UwbOCONcOf6gsOH7eQ74k2Od9Klf0/5pSzyNt1InDtauREp0zBeYqF/m3ZiKQd9kP7R8dH8yEIb9wrUajUdSH69jzq3uQ25ADPGDoK0PY8Tc7WtHsUB2n3ngKxb4itv/jdoTnw76MGDpUVC5ryiadDufOncOhQ4fw/WzectG05fYMm/O/rGja4OAgVqxYsWSbXruIjOphzciibM9el8XRPz5qDF/HdXDzX92MoUNDOHfunDnhpd5Xx+lfPo3MSAYX33UxcrM5Y2fFYjGk02lfZhAAX9Sa9pI6i2lfsa+UbxqIYCp3OBxGoVAwcjUYbG6RWbVqFQYGBnxOXnVu2w5s0kj1MGlBWqouoFwsFAqYnZ3FhQsXMDMz4wMkBMPJZBKJRMJU0Ka9qcBD3626j2O2AQ3HUSgUTJX0XC4HJID9r9yP5979XHONAgR1umhgSLM+FTw+8vxHsPmhzbjnx+/BYtci3KCLfNdSABHPx/Gqj70KA2cGlticCkypZ+l8oH3BCD7Pb2Z2Ied6bm4Onuehp6cH8/PzyGaziMfj6OjoMA5tzisLbHGPveu6iMfjyGQypnq6AeaxGj7zkc8gMZnArr/Yha6DXcaWUbrZPGA7TBSXaNPryGcKDJ/KCaKBRbV3lR9sQGzfr9Fb5bPyijKe+MUnMHPxjOnnrkd24Yav3IBYqZVhYJxvT2KnM4Nn0NjbQH4678MWOlbyNLGMVjNX/ib/U0Z0dXWZ4MTXX/l17L1mLxAA+i704eZ/vBmxszGf/cotB6QNx0c7ReWIOth0TSndOb/6Oe1MG8Tb86BzqM11XRw4cAB79uzxpZQ/HVvlaUe4aajRMNXCBvZgbQ+e7dWzQYUyqXqT1JPBgaqxSmJo5UsyhgpRTQtWUGB7nTg2FZoK8Nl/gnmNVKonk4zARuGuQtVecLqHp16vG4+2RgoVvJKO9oJs973neTh39Tk88tZHUElX8Pi7Hself3cpkvuSvggp+6Vpr3W3jnJ/2ccLXthDraeG4EjLM8do4OhLRrHmG2tQy7WimnQacMwKDDXiyf5qZWWOh8qM9OGi0VRYjUbr/HPRct5JUxVcpJldqVC9eOQRvVZBMh0eGo1X3lZAa/M8/9+u9gHXgQJ45T/lVwDGMKJwpJHEeeA2AT0/nSCL/K7Kk1H3dutB9wkxPZv8yP1YrMqpkQb2i2BVBbXuk+Y61agGv+O+I9KGmQpKT86Zve7sNUo+YHSc82AbTJ2dnb4IvqZmK4+qc45jo4KyU+IJtsnLnDdNq+Mz6vU6wmNh7PqLXTjwzgPoOdiDdR9dh4bbWkfH33YcZ25pOsicsINdH96Fer5ujB6Cf9v4U0+9Zjwst+W23L5/TZ2V6hxWsMbTWtyki5kXz6D/zn4AS/elnn73aV+qrRfw8PAbHkbfe/tMJo6bdjH63lFkn5tF9sYsnJSDlR9e6dMJ1EG0F9SpXqlUjL4BWhlTtq2hTkfVC+wz93Or4RyPxw3g5r0KWG2Qojab2kM2AGKj0z0ajWJgYMCA6ampZgEuntxCe4Zj4XuZUaj2oD6b46SOUv1GOa92JbNH424c1991va/quOol0kdtHwVkShvXdXHF16+A53l46b++FNMD06gH6vj6G7+OUtpfBKqUKuGuN96FF3z2BVh1apXPZm03Bxy7Oub11Bryg+M4JkOA+7mp+7kPmDZUqVQydKCu5RwlEgmTXaE21cEXHwQArHh0BfqO9qHhtGrY2E4WjsF2XrUD2hy32n1s6oBQ/mr3TDvqqjTkO3Rd2e+3AaLneagmqqgl/AG0YrKIRrgBlPz3sh/jW8fx7dd/G53f6cSav1yzZKz6Dg366FqywXEwGDTzrsWhX/DlFyBSi+D05tN40edehPRUGoVGwRcYVKcQ16LdF9d1MbNmBm7AxYozK3wywp4fnYOncsLZc6xz1W7ey+Uycrncf+v0lqcNuBXI0CBWwaYLSo1OjZjZBi6PDSCB6fHkswmi1XNJI1ajpRRsNLbZPwWhfEdXVxdWrVqFkZERk8LKMVFA8HmhUMgUi5qdnfUtegXzysS+lArXn1LNpnTjwtJMAQp1KldbqCqD0NvDhaCLlMWjTl90Go+/5XFU0k0DOjeUw6PveBS7f2s3QieaAm7yFZNIH08jeji6pBDAqr9bBQcOJn50AmgAW96/BV0nu9BwGmZxBYNBnH7xaRx981FMXjKJXb+5y/SH4Ij9UoWjgkkNCtJCiziRB8hT3BekUTpNeyH/Aa1FZhfU4jXsKwG3CgDOMeeIDgMFz1Qg7ZSpzp2msqiQovNAMyt0rzbHxi0FvF+PZVBa8X7SLBwOY9OmTTh06BCKxaL5THlMvaWkmTpK1MNJXlehxK0IHR0dxvlBY7HRaJgigurJJ2jm+Zl0YrB+gG7J4LrjfOj+du1To9EwhQXprGEfbBlB+cE5UAWiwJOVMyuVCpLJJBYWFkxfuPY4N7xfU7Z4tAxlAde47v9W2Un6qKebBdoqlQoypzPY/aHdSMwmgGDTcA4EAtj/s/sx9tIx84zz151HLV7DFb91xRLHHBsNZY6DSuWZFgRZbsttuT27LRwOo6Ojw8gTlbe2zgwEAxj58xGUtpcQRBDdX+4G0AKqnudh8wc348BfHTDAzWk4uOqjVyGXyzXlHzwc/+PjWLzsyfOLHeDMj5xB1ati+C+HjYxQZyidi7R7KE9ZeVpBsBrTGqWiPgKadlwul/OBKAYd0um0z8ZSO0IdrBrMYVNDnHpRn6//59hSqRTWrVtnCqrNzc0ZpzUdr9VqFel02uiPZDK5RCezKUBSsE86UGdSTmvqrO2QsAFPOxDB39TdClgDgQDi83Gsnm8eP/qyj70M97zlHmx6YhMeff6jxjETW4ghMh5BqVQyWQz6fHWmKBhnvR3NNKTtxS1u1PmVSsVEtnlCDG0bHi3GRn7jMWNsfMcjr3oEB28+CDjA+NXjGHhoAN1PdPt0vdJQ7XMF0rZdpDzUbg7Vpuc82NkMNt/ZGEF5j/9vBya1H/y760wXrv27a3Hfr96HQl8Ba06uwQu+9AIkF5M+/uPPzPAMvvHGb2CxZxGLtyw29/j/2fol76IMUWzGz9qBUuI98ooGPK77xnW46PGL0HmhE3k377OhlI/VqaCOrUAggIWuBXzzp74JL+DhpR95KXrnepc402z+tx1smrnAeWLTObXlLdCsTs5TfZ5pe9op5e95z3s8Lna7Ap4uOGVgLjb17NiCktEzLYalxrUShX2tVCo4e/Ysurq60NfXZyZUU9ZJZAJzLnDXdZFIJMxZwO2OXeJ5yxwbx0GBSCOazEFBQ2Wj3hkKCtKM46HCIrMBrZRwXsf/kz5MvdA0G71PaaUKLlfNYc+b9+D0807DC3oIVAPY9oltGL5zGK7r4vy153H4vYfhNBzseusuBEb8KWvBYBCBeAAj7xlBz7/1IHIoYlK8AoEAIvEI5l48h4PvPAg34gIu0PdwH3b8/g44JQf1Rh3Tl05j4coFDH9kGKi0FADHqmnWtgdQnTW6QJlurXOgWxnaRfs1TdheWGo0qDOETg2CT86lHRHWOdG5IXDU+bedVe08xIwo6LEoVMBaKV73DvNvNUoYKSUP8Rl2pFX38CrN1MNJw8hW+lQsBKca0VVHHdeOOjF0ewoBtq4jrWOgDpB6vW6ALz2qAEzqNemj53dqNJ1rVHlOr6ccURqrQ4c04ec0RAGYtE6VhaSHzrcaP6R5rVZDPB43spcyh3QNBoO+M0j1efVEHd/5+++g3NeUO8F8EDe88wbEJmLGoei6LqYvnsbMc2aw6R83wS366U0Z+8QTT2BmppWe9v1o3nJK+XJ7hs35X5RSnkwmsWXLFl8Gmm2EA4CX9DD6l6PIX5lv7u2tOFj/a+ux4pEVCAdbx/iEwiHM75jHoT87hAACeN4fPA9dJ7uQzWab5++ma9j773t9lclj52LY9ZO7ECw15QxTqZnyDLTkoA1qNUChtiZ1rdp2lJXFYtFXOJfPKW8sY/oXpvET//kTiHtxn95XfaS0UV2mEWA21be8Xu05Ntd1MTs7i3Pnzpkq5uVy2WQF8AhL7idmGi1tEjtQoraVOu1dt3kyRygUghN18NDzHsL+G/fjLR95C3pnen0A3LY9ORaOi02vJX2oh5X+nuehkWwgWAvi26/4NvZdtw89Ez14/V+/Hk6hZUszuszxleollL0ywpVWETy1a+y518xHx2nWwllYWEAq1axqPjs7Cy/oYfzV4whVQui7sw+1cs3HS+l0Gt3d3SZaTtoeuuEQ9rx2D+qxVhZsdC6KG953A6KTTftAgzd28Et5SLP5bKBLvKDAWO9V/tH+6dzYDpd2YJHfa0DOvo7Xci98ZCCCr/7CV/G6j78OoUoLf+l7i5EiPvW+TyHf2dpG4FQcrP7kagx/cti3pjXzDoDBTrrWFDDTMdbd3Y1MJmMy/9RerNfrvmKEBOg2tlSg3Gg0kE/lcetv3IpK8sl6Q6UI3vTBNyG5kDRzyvnQfulnKgNsOvJ7ez5UxoyMjOCBBx5ANpu173v2Usp1T44aerpvwmYEghA9o1GZTD2c9BYy6qbEU/DAdN0tW7b4oo0aneJ7NBLJNFrXbVWh1n4RgNDDqCnl6s1lI+iwAYl6b7lo2R/umaXhHAgEzP5sOhyUOTheglPP80zlbxt82B5dgoRUKoV4II4rP3ElGk4DY88Zw4ZbN2D1HasN2D706819mh487P+n/dj1S7sQPvDkecUX5ZA+kEaoEcL6D65vCqmwa4BBMBhEMVzEyVedbIJtAAgA2e1ZTF43ib6v9mH2ilkc/dPmvrG4G8fQp4bglFupJzQGSGPNiOB3CtCAlmJU5a5F4Ogc4fzawJzzzygxU+HogCHQU8GaSCR879Q9Jeo5JP3JD0yz1uvo2eW16qziewkauX+ZDgrXdc1eYXWAMcWFNOKeM02DZxRd91xRsWgVa92rQ56lY0GdUqSHbn1QpU7eVieUrgudLzqoCI6pQPQ+dUrQGcF79HtN1eN7qJy5xtTYogzRrBxNC9f7yFcAjDNAwbzyjWZB0KFB41llg8owOiE4T8CTSrJY9NVJ4PojDRuNBoKFIK59x7XY8wdNo2PH7+6Ad9pDNVg1789ensUjv/tI0zAvOth06yYEyq2sDAAmgrPcltty+/422jV2VgrQAqQzL5tBcXurAJYX9XDhbRcwcGQAtQU5S9n1kDyVxPA/DWNwahCb5jfhQu1C83SYNWUc/aOj/mPAPGDX23chXAnDc/z9oGxn1pKmRVPuBQIBX2aTAmsFodQV1MXUnZTPue05PPAHD8CNuPh8/fN49f2vRqqW8tFJZTRbINA8WzsQCBhdoo5WzSBUZyhprlsPe3t7kUgkcP78eVy4cMEUSGVVZlZmZ9ZWKpVCR0eHz25Um5I6Qm0S9ieUCGHvjXvx6EsfBQB88p2fxBs++gYMnBswNKG+tZs+k7yioEH7QcBjggCLDpyQgxu/dCO8oIfrvngdgvUgAqFWsVZmZ5XLZYSiIRy64RDOrT+HG2+7EYH5gCmExn3V7IOe0KLBJo5hcXERHR0dSGaSOHDtARx5a/Ns74tKF2HV11ahUW/yVDweN2nkagc3Gg2s++o65NI5HH7ZYbjhZvHRS/7iEsSmYggE/fpeeZCfMTvCdkionUleIz/bYFL5R0Gz7XSxQbgd2FDQZ6dWK9BnHxmwyDQyePPfv7nZF6/lTOJvAIiVY3jFP7wCd771TuT6c0ADGPzKoAHbiuM4FtKZY1TnAu+hTabAWWnGfpDGyou2k8h2TDiOg4de/RAqidZWt2qsiu/c8h288J9fuGSudLzKh0+Vpm8DcV7PPtDmWlxc/G8VTAOeAeDmy9XzQ8BMYaveHwWF7LAazU/lkdOiYypM1Nuiz1CvEwUk79d0W04AJ4Qgmqmi/F6VggIaZXj1bJFZdIEQdChI1zRpjpkTby9EFhtTsK+pyJwD9aTx/nZF4HjtJR+5BMmTSQx+fhD1QLNvC1sW/HMc8lDcVETfsT5M3zSNk+86iTV/twYXP3oxYrGYOYdZ99HHF+LY8JsbMPKrIyhcVECgFMD6v12Pnrt6MH3zNI6/57gxBEbeNIKqU8X6f1hv0os4Hl246o3Sha2Czign2SvLMev/SWMuOgWDKqx0YSvQVmeRLkwVkjof6gjRaC2dIPxNEMbILNcIn0c+UZDJgjLkH/6tR2MpLyl91UGgWRJ8NsdGOgUCTSVLZwSdFLrGFVByzWrVfM4HlS0BPN9hp89xbRYKBV9ldY2ekM/t1CvOO3lewTrHSnrQmcC1TzCqQt/mSZ4MwPFq1onKAwCm/319fSgUCsbhx0i3yhHbACSId13XRMk5XvKAepzV2eN5HkLzIez84E5UOiroHu1GINaKYFy4/gIO/1LryJRTrz0FL+Rh6ye2IuD50xKX93Avt+X2/WuO46Cnp8dnJ/FzwK8bez/fC8dzcP795+FFPHQ80YENf7gBzoJ/C4kX8nDm7WdQ3FnE2r9Zi/Pnz8NxmnvAJ350AqU1/n2JA18fQNSNwgm2L85GecoMKhY9s20uyioFlprNRxBGPcM+O46DmctncOCdB4xD/6HtD8HzPLz+269HpNE6UUMbn+k4Djo6OszzNKKp1/IzrZUCtPQw+51MJrFu3Tqk02lMTU2Z6wuFgrEpCoUCKpUKisUicrmcKRylupM6SMEMdSUAPPL8R/Dgix40faxFavjim7+IF932IgwfGzaf67Y1W0eqja1bn9Tm0RNP+EPnyE1fuKmp60Kez35VB/qeG/fgwVseBBwg4kTwvNueh45gB0qlEhYWFkx/yBOpVMo4jNXWCYfDWFxcRKlUwqlbTuHIG46YMXLuB784iHA4jEwmY4IYxCGsQRMOh3HVnVch4kVw+PrDuPgjF6NrXxfqnv+YUA2UqQ1kgzR1mitPqjNDt/61A46A/8gwDcwpyNNoLPuo4FCxDp9p28/EI/p+5QFtXWe6cO3HrsX9b70fXfd1Yd1H1gGBpUch63oiT9k1dZSPCfy1zpf2U21Q2oIMOKrcUL7meJ//r89HuBLGwRuae/S3P7gdN372Rh9NlIZ8tz5Pt5woDfUevVexSKlUwvz8vI9Pnkl7RoBbwagymYJmfqbGIH8rE5EoNGA1wqdGukaV7GcEg0F0dXWhu7sbk5OTJoJMIvK5fI7u2SVBaQgTPKnnQwGPCgc1kjlm3s9Irb2ICP41RVkzBBS8sDojFQBTcNkXLRLHPmpklmdpalpto9FA0Aliw39sQBGt46zW/OMaoAiM/WTzmJBL/vwS9N7XiwsvuoCRnx9BvbOOM+88g+Qnk9j0jU2GhpoNEAqFkDmbwdY/2Yojv3cEK/9mJbrv70bNqyE4GgRa2BwAEB+No1Fv+PqvClCP71LhpcA5EAgY7yzHr2m7BDeAP4KswJvCkgte51kXI+eO/dLCXOr14w+Ph+NYlJ/UMUTwrUdnKEjXMaug1VoC5px0z38sH3mN4I28yGeq55QCmWOyHRqxWGwJH7qui0aggdH/O4otf7vF8Fg7IcbnaQaD9sGO+tKw4ZxopoLKHY6Lz6UBqGOx59KmL+UD361Al1F/dXLx/zqv+h7yMtMtM5kMQqFQM03O82f42Pyua1WNIOVb0kr7TTqrHOqa7II34QFBf0ph/EIcTt1vnMbPxFEulREMtByn5LPlttyW2/enEXDzb65hG4ADTTnW+8VeODkHcz85hw1/tAGJ8QQCwVbBTQA4+r6jmH7xNOAAj7/rcVz9B1cjNhXD/CXzmHvOnO+ZK+5egc0f3YyoG0Uj2DKOqfcVrNXrzarUapTbMleDMkArkkc9ZO9XJriLz8YRLPsLkCUvJBFw/Sd3qLHO56tTU2moMpj38R7+toEO+xaPxzE4OIiOjg5MTk6a83gpv6n76SRltBdo2RyFQgGdnZ2+rYqkayQSQeeFziX8EK6GkVpI+fqietoGC3yfjrEdAOL3BJgKhmgPkB4amdzzsj147AWPGeftsd3HUIqV8Iq/e4Wpd0Knreu6JgWf20DVLqPuXlhYgHPUysz1mjqKx9fq6R0aNKEN+PgrHse6u9ch8VAC3Ue74QWXVqVnn7ie7Gi0rjXlDdXPer3NZ+oEsgG62ii8VgMeCsR1vdhYS/GYjVt0Xm1gr2Cy70gfdv/pbgQOB3zv41wD/gi02mpKG+DJDMKdDmZ3zmLo0SFjy2lTp45uheX82Vkw6qDwPA9ezcNVt1+FQC0AL+zh6i9fjaAXhBNcurde14DtYHkqWuua8bHgk9cUCgVks9nvPuBW8KMLkQPQ6K1GujQKpdE+oLWvmYYtQQIFlQJaGsIEqpyAubk5c0SXelPVMWAXbqDRrvu3+X4luHqH+H8VWmqgq9HO91K4al+0qceHNKaAptJhpE6N73ZeIPZFgbpG+ynwqPg45ka5gf6P9aMWrqHrcBeSDyaRvTiLI+84glrHk2d7p+o4/JbDcMYd9D7U63OaqBJNnElg1zt3IbgQhBtojil9JI2Lf+Zi7PuXffCCHjb86QasuGeF8ZgrGLQFky28FNyyCEyhUEB3d7fhGU1rsxcVHRFAq3K18iNpox5N9kW3CNjOGc6h7Yjh9Rpx52d61JvjOKYiq6afE0Rq4RXyip2qbDsTgNbRbtovrh/bm8g50PRuGg/KY1TK9XAd+/9iP/Jb8ggGgtj49xvRKDd89GJVbPI6+0+HnCorFd7sI40Uglvey33ibDpmyhSOiY4Cx3F864h90DWuKd2O42Dz5s2YmprC3NyczynH8eiz1KDUORkdHTVzyvvoZCAfMiWea13nWMdH/tY9T+RLKkjlS/ZZ5Vf3SDeuesdVeOAfHoAX9rDjozuw+purUQvUDO1J63aKZ7ktt+X2vWkqJ+0sJP1N/dOoN9DxtQ70Pt6LaCEKBP3HEJ18/0lM3zxtANLiukXc+8f34rLfvgyP/uqjqKVaFY47H+rEuj9fh7AbRjDsz7bSbUO2XKfMUMeoGryq12ivAH6npToUG40GMmczeMHvvgBf+9DXUI6Xse7f1iHxpQRmt89ixYoV8MIePNeD0/Cffa2gR50F+q52Rrr2E2iBcMp5ytx0Oo14PI5UKoWxsTFMTU0ZW5R1OAiqCTypeynn24FgAFi/dz1uLN+I+37mPrghF4nFBH7sIz+GWK4V2dW+qaODOlrtKeoc1Xd2JJYZB6SXZlfyudRrnudh97d34+gVR7HYswg4QLAWxPp/Xo/Z6VlzfSaTMRlykUgExWIRs7OzJnODz47FYojH45ifn0f6/jR2vX8X9v/+fjieg8s+cBm6DnYhkW4ewUbgp87vcDgML+Bh/837se+l+3Ds6mN44btfCM/x22j6t9JR515tUQXIvFeBL5tNS82YVP5WUK7Xq+NE58x2mNi6nnNCets2nc3b2phRkjySRKlcagvOdfwaBOC8qYM+n8pj7+/shRtxMZQewpaDW1Cr1ny0a4chNXuAdrkGIJSPA4EAgqUgLv/S5QiGgojWo4DjDyK1a/aYdN3rdxrcsoE6awYtLi7+t22jZ3QONzvEpoaqEp8Gt3rH2HEOVgtIkYF1vycJbt9DMKnEAVpgyQxMmJpGJPdOM+2JfVMhA8DHzLYXiiCaANteiGq081k0+icnJxGPx7FixQpTOVlTUtlPVQw66XZ6B9OvdAHyez1jm4qNqTe8Vr3SBG+O00wvm37JNE78wgk0Mg0Ei0Fs+JcNWPultQBgCoYwNYifqSOD3qpGo3mERnFjEYXLC1j1xVUIOM10VY0SE3gEg0EMDAwgFAqZMxpJR0295qJkQTJGCZVmFHykjypQdTqwD8oLHJcueCpOvp8GgX2ElKZLM4ODwlT3nmsxGXXiKB+xL9y7q1FtCj/SWp1G/DwQCJhCadqvZLJVuZL9VwHOcdmCvV6vo9RZwrEPHEP2smzTePOA4c8MY82n1sDLe2ZeNDpOmml2i65F0p88redQa+SfNOT6KxQKpqJtPB4385rL5ZBIJIxxaO/v59wpXShfKLt4tir30KuCVocB+bherxtHkCpOKkM9b5KfJ5NJU4VVM0fU4Ug+VtBuV3u10+s1ZZMOPC2IlxvOIXtlFpvu3GSuV5mYy+XwwAMP4PvdvOWiacvtGTbnf0nRtJUrV2LNmjW+fcSMgqrzn3/H43GUSiUjE9QB67ouzrz9DC684QK8SIs8yRNJwAUKWwrmM6fqYOVnV2LDxzaY59oGaDAYRDqdRiAQME5ZRinV5uN9sVjM53hmo1FNB7RGCvWa/v5+DO4axLcu+xYuufUSnDt3DvV6HZuv2IwDbzqAjrkO3LTvJkTQqskDtJy52hfbwNb38IeNNpHapLyWcrnRaCCfz2NsbMycZU5Z3tXVZXQQ97ZyfJTNtBGobyORSHMfeLmEkeeO4Pj1x3HLx25BrBgzc6rRWQVitj2rznTVU/yezmzS3XYoa4SanzN7MhAIYLGxiM/+0mdRSVfw/H9+Prr2dpmz3EOhEJLJJGKxmMEExWKxecpGJoNUKuUbR7VaxezsrDlFZfr6aaAIDDw+YM47V73teV4rTT/o4cQLT2DPm/YYmyRzNoOr/+BqxCfjvgxRHQ/n2AbSpJcGuXiv8rft5Fb+18/5nc1X2pd2vKjgj3yotohmIWYyGWQyGcNPfI9G5XVci4uLmJubQy6X8xWBtsEo7SXX9Z+/TV4IhUKorK9g74f3op55MqjoAa/69Kuw7ci2JcCV2y34Tj1diPytwNzOnFT6aVan9tkG3zof/9WcaLOdD6VSCYcOHcLBgwfbHgn2dGyVpx3hbuc9oWGoBqECCSWMCgRlAAUoJC7vU6a20xeA1qLQAhQklKat2gCC3+vzKaRogKtQU88eAQA9nUCrGJOCMWVWCqiBgQFDP927qsxDYaLMYi82Te21004pyDX1hwweCARMoQ8KPo5dBXe1WkXHv3dgDdbgzC+cwfC/DGPwC4OoB1vFUVKplI9+juNgcdciwmfDSOfSviOQYrEYgqeDiB2PoRZs7eflfmR7DqhguadHQaimJ5O2Ck45/yosVdkkk0lkMhksLCygXC4bBUWaa8SegJB8QHqxPxoNVOcMeROAD8Soc4bXqCLjGDRtT4/J0uOveJ0tUBn91MiyplqHQiETOda0MT7PREsaDSNU6FjhM/Ir882z2cmiDpDbkkM1UUU43wTC9FSSJpQJdvYJBa6mI6rDhj90yBAYqiGlY6UBaEej9UffYzv3AJhUSQBm24Jd+E0j1DQkeZ/NB5wjzUKhYZVOp33nlHMeKVPsvnmeh8HBQQAw1XLtPYe2wcn0Ty22lhnLoON8B2rwHynId3J8y225LbfvTxseHvY5G9X+siPKrusaGUo5qI7NQCCAtf+wFo7r4PxbzwMO0PFwB7b+wVak02k89s7HkL0yC3jA0K1DWPOPa+A5rZM7NGhCPUzdqhlGQCttWh3jlCeUb1pYktFK1cFsfHcymUSqmMLL7nsZqr1NQ/383HncftntGNvy5DGIQeDmx272ZQYpiGKzgQz1kK2fNMqtdq864zmWdDqNtWvXIhwOY3p62jiCZ2ZmjOO4UCggHo8jk8kYgE1ZrcV7zbs8YOcjO3HpE5c2++q0ov+qk0kn9lMd2PxMf/g57UTb1uRvAjnqK3UAG5u72MCL/+7FmF83j8HDgyg0CohEIkgmk2g0mtvlGO1nvRpGvOnIUSd2LBYzgK7v231Nx3K0tS1B7TGgVauoHqvj7PqzPpukkq5gbs0cVoyv8I2NtOH8Kp00wMZ+8TsFyAoAFeAp0NVr+Q51mutzFejyHr1G+U37y2fReaM8bzuWlG5aS8hOh9c1QtuLNrJiIT7X8zxkL8q2iiY/Sf873ngH6p+rY8cTO3zPVexnB7rscep86HiIWZRepIXep/xvOxNsOttzouOjbT0/P/8/so2eNuBmlEejNhpFUcBLI1rBpXpi1SOhBFNGUk+qen5s41k9UMps6sUjw6jwtR0AAHxKQgWvgmAytyoZPkuLq2k6t6auqwFteyt1b7AqBQXEqhRsrySfqYCG1ykYU9ry3e2e1fulXoSnw+h5uAdusAWK6Rjg2IPBIOqX1XH+fecRvBDE5vdu9i1iTatvt9jYRy662dlZU+kyFostASSkTb1eR0dHBzo6OjA9PW1SjJTWqkjJg0zn0v6Qx9QLz+JfVKq2l5P3xuNxH3+wgIcufKUtx6xg0nVdo2h0z506FdRZpPPN9/GZyn/K5/aPrmPyvkZT+GymRFOZpx5LYfMfbsbhDx5GrbuG9KNprP2jtYhMR1Cr13zKRtOuOe92Wh/QqvbtOI4xJpRurBVAY0MFosoSpkmx/wr0GeHl5zao5XOi0Sh27dqFxx57zDhMdB3SWFInFWmnmS+2EuN2GdKjVqthdHTUZ3RpZotW8OczXdfFwsKCiRgpD9t8z//raRDaJ869Kin2u50Hd7ktt+X2vWtqq1D2UReoPFU5r/qPulJruQx/fBiNXAOla0rY8KcbEJoNIdqI4tp/uRaPxR5D9KEoBj816JPVAHwF0tSWo7NTs6sA+OxCZveoTcVnMVuOdoUazrodkNuB+Fkmk8Hdb7wbY9eOmXfecfkdKDklvOyhlxka8FnaSEvKQW7N0QACG+/VKBt1gOonRnPXrVuHnp4eTExMYHR0FNls1hTn5Ekf+XzeB46YvRaJRMyZ65p1Zo9DM7P0O7WD1T5U54w9Hh2nzT/8sfUf9RD1Sud8Jzr2dqBUaWIEVicPBoMmg4vbwKhnSqUSYrGYL8qtfWUfotEoEokEAJhou+IK0j7eiOPqT16Nvc5enLvmHALVAC778GXo2duDhtvScbZtbdPDBnmA34mt9Na1126udC408GNfa4NB/UwBH9/ZLhtBt81SdtjyQd/NTEm1N7Qf+l5tOi4dx4p/X4FINYJDv3LIOD3coIt7XnkP6uE6LnnsEt8zbOyj2IX0AuBbk7yW19vbItW+5ndKj3YA23ZE6Jq2QXe5XEYul1uyNfiZtKcNuGdnZ7GwsIDVq1cjnU6bQbBxEsnMGuFSIEBhocTlD5kGaO3bJNBwXReFQsF3fJYyswJzNViViUlg25OqnkYlsL1o+L0aqQqsOWY+j32hMNf0HY1cqlOA33FBKNDkmBV4aToz+8NoKOlHQ79QKKBQKKBcLqNYLCKZbJ1dZwMc9it5bxJuxB9h47wbAbjOw4HfO4BqdxVYDRz7h2O47BcvQ6DhF3BUOKSves3ZVx2Xpq2rJ1b7UK/XkclkUKvVMDk56YuS63UENNVqFRcuXDDCTxWpDb74Pi4w9k3noFKp4MKFC+js7DSRYz3OSYWLZn4onzKCyqrXXC8EiUArSqHrRtO96Xwifev15jmHsVjMGFzKjxR2tlDRNcH+Uzmq4s4cyuCSX7wEB37rANb+ylpgFqhFaz5hZe/TJl+pQFQnCfun+9aVXpq1oQXV6PVmFJr00WPOGGHW7/R8c23VahWHDh0yConPtOtR0KgkrxFsk180K6ZaraJUKiEejy8xgNTQII/aDjM1EEqlks+g5fzxHtKC7yctySvkRfIV79EtG9yLuNyW23L73reenh4j36h3VMaVy2U0gg14FQ94Unw50ZYzWE9oUfDWqDQwdNsQ8FUgPB8GAs3P07NpbPqdTShNloAG4DqtdGXKTu7BpSys1+smgkldamdvUbdqkMDzPLOljPrKjiYCLRDJCt+0r2ZnZxGNRnHj4zdi5KoRuMEndUI5jKv3Xw0AZs8w+6JjUTuCukhtBdWNqhsoy9UW1C1J1J/d3d2IxWIIhUI4c+YMZmZmTHZlPB5HPB5HLpcz80OZzEyvzs5OwAEubL+A3IYcLr33Ujiu31HK/ihQotNawZltZ9lOYo2S24CUY9ItnLRR+X6gqZdZEC2ZTBodR5tLM9gIuufn55HP532OFi/oYf/P78fKT61EeCxs+CkWiyEYDJptkgT6jtMMnsRiMYTDYcysm8HkRZMAgKt+8yr0HO7x8aLuYVewxWvUvtC5V+eEBjR4nV6v9ygesSPfNsjWudLgoPJfOyDIgJ46cLTPNv7hd7SvaWuwD6SrOpU4Xv7YjgaOY+V9KxEPxvHYLz8GL9iUSx3ZDmw8tNHHc/zR4I9NX7vPdmBB361zzL7RbuN3amfxGWpb6vxxHnReXLd5nDQLIv5329MG3L29vejq6jLpMWQiBQc2GLAjLWQmAL5FQ4HXDvCSoWq1GmZmZhAIBLBy5UojFGlUMkLIdynAV0LzGDCmMSnw0jHocVoqtICWQCbjep5nUm8IILhvhZOl+3VVwJMZ+H5d6Ao0+bmmM5B2HLvSi+nt+kNvcjgcNp5UpbvSifPWaDSMcuT8aWo+HODwbx5ugm0AcIDipiJGfnEE2/52m+EDLmwCaBXu6l3mOAnOgVZkkHRU2hSLRZw8edIHhAl+2kVxCfLsVCr12itv0/NM2vA5THlOJpNmb7emXJHP2S/OC3mKwJpGFHlBU/EJ3LW2gZ3yRmeTeqHJT4FAwJzdHA6Hkc/nfY4p0py847quiWiTB9iH+fl5c/Yl13f4TBhb3rgFpXzJrGn2gTUK2Fe+t1qtGkdKu73nWmBM17KOW7dC6FpQBU3HldISaEX+NS1K30O5wxMPtMgcrzOOiTBQ66shOhH1Zftov9hn7mW31xl5hDxhj5X3qPygQqERzGi0gnLSXNPzKe/UoAJgMnO0MOBySvlyW27fv7ZixQokEgmz/lVfBQIBeCs9HP+r4xh67xDiJ+Oo99Ux+tejqA3UsPWtW5v7Vlc34MU8BE429W11sIp6rI7EaAJOtqm7Y7EY0uk0stksUAJCDX+RNr5btxipjlV7hXJHQZk6Km2wQhuF15mxiUHvui7i8biR357nobu7GwAQn43jJz7xE7jtx26DU3Gw4207MDU4hY5NHUbXpFIp038FO7QVCODYf83cU1nKpgCAoIQ04diCweb+9vXr16OjowNnzpzB+fPnDWAkQKQNTLuCgZJ8Po/qlVV8893fBBwgUo9g54M74db89qfdT5039pM6TYE4dQKPq9TM0HaRRx2/XT/A8zxTpZ3AmPYF9+VHo1HE43FDZ2YYLiwsIJ/PI5VKoegU8cQtT+DETScwcu0Irvn5axAcCZrMCm6xoy2aSCRMjZVgMIjJTZP46nu/CjyZDLvnN/fgue9+LtKTaTPnqj9JOxtoc25pU6lNyvupVzVbU3mY19MeIV+1A8M2YNf1oJF2Bd7qUOE7OAbyH+mmfbTXswYzFZQqfys+UZtE+0+eiQQj2HJgC9KfTuP+192PjvkOvPEv34gQQnADrfo7WodC32EDefZD20L/AjpnOn18r7yu6/Sp1q/OGdtTObHYGo0G5ubm/seZf08bcHOPCV/O/RZM37SZSydaJ0xBoUZZSBwFsyQCIz+rV682E0VhpZXObS8KJ5fG5uLiInp7ew2g0PeQwfVvToAe4cX/s3FPCoGTpl49lYcK8HvUCKTsBaFHVfH97JOm6hIYqWBltJSGfLFY9NGUY2UEXJWh3XdNYePnVDDBYBBb37kVJ3//JBavWGzuAfuPIWz5my0IhVtHXqi3WRvBO50d+j2VptKCQkIFitKH9GIKmzblS81IYOOecioNvg9oCTnymgpQCn3Sm3RmpJD73XVsKmw0Y0Gj7o1GwwA1fs+oq10pVtOlKQCp6PhM8ojtbeU8KE/weyqfUChksiL4vGq1imq5laHBsbOAnq5pHZ+uAwpfHp3CfV02MGbaIZ9Dbz75h2NSJUrjQuWByiDth4JYAnryoDq7OCfBUBCjrxzFhZsvYOsfbUXqVMpXl0GdYaQD08DVk6qpltpXoLVPSemgGTXkBcB/tBh5h+NMpVKIx+O4cOGC2X6g8pkOLqXF/9STu9yW23L77zfbGFdHeHlNGSPvHUF5QxmnP3Yagx8exPyL51Ha3jQGR/5yBIOfG8T468dRGaxg629tRWg8hJO/dhKlDSVs+o1N6L+/H47jIP/CPEL3Ns+Rpk6cuG4Cfff2+Yx6teWoI2j7USdS1lHGqDzSApx29AlYWsVZM/MSiYRPpqucWn1+NV51x6vQGGvAjbg4deoUKpUKNm3ahJMXn8TFpy5GwGnZdLZ9YwN9O8KpNplG/NpFhgF/XZVwOIz+/n7E43Ekk0lcuHABi4uLKBaLBhTRgayO3rHdYzj1gVMGPH7ztd+EE3Kw81s7DZ3VOQz4q2wrAFf7kn1kYMqOwJIeqjt0bjQIQ8c5bWMW0ON4OHcMbGk6uuc1t+FVKpVmEMgr4+CPHsSxlx5rPj/WwGN/+Bgu/uOLkTmZAQCfk5301Cy4E9ecMPQCgEakgbGrx7DpC5vM2PT9NshWvlJ68nqlaTv6KEDWzxQc8z3qzLLfp8+wnT/aFFCOXzOOyN4IsNiyRdXZrs/n35ptqbapjdM0i1iDXTZdTEAuGMLFj1+McCaMrQe3Iuy0at/wGbbzS2mjNLedEhe2XMA3fuobuPZz12Lzvs1LaGLThc/X34rrdPxsHBt/+Kx8Po/5+XmT1fvfbU8bcNsRIWVYFUwEMRoNpgDVVEs1voHWOdIaeVYQSMFOz5J6FCmMyWi2N4oMwcINapyrgAX8aTIqhLU/el0wGPQd8cDn2AW1FIgrkAkEAsZQ172t6rVROimwVC8RGZTRKT1HmILNcVpp3QryqBDZdKzqLOG4dCE6jgN32sWa31+Dsx84i9jRGIb/eRg1z7/H1QZbdFwQmCgfcWykH5WF7oMi/XRxkQf4Tk0x1nHpYtNFpfNWqVRMGpumEvM5HAfpo9/zeXRK8b26mFUI6TMUSOvz6GCy98GxP/y/ClICOr5bFZQ6xvgMpZ+mJfLzQCBgBI6mF5H27B+rrKp3lFkCuh9P5UY8Hsf27duRz+ebZ3E6rX3P2lfOpxp4Ov/qdNCmjhbOB8eufK7OJVVWKt8cx8GZN57ByTedhBfycOw9x7Djj3cgeS7pkzvkWc4F+0y+obOQ2zw6Ozt9BQXZTzr0GNGm84rRGTqjdK3yXmYG6Z5vfs/1x+gRnaCu66JYLGK5Lbfl9r1vmUzGbE9SsOB5Hiq9FRx/73Es7loEADS6Gxj77THf/cUNRZz6lVPm/yfeewKRyQiKW5tr+vSvnkb6n9JAGNj/4/sxuHIQqz6yCgAw9hNjGPuxMXi9HlbdsaopL5IeRn5xBJ33d6Ln2z0+8Go7BBXYqB7SYIDW49B93gp4eE84HDY6VOW/2hVbRrY0s7AG5gEA4+PjOHnjSex53h6c6z6Hl+95uXmuAmJ1zurz7Agk+6d6m02v4fPVhuR8bty4EZ2dnTh9+jSmp6fN+1VvA0D2+Vmce985eDF/BK5cKS8B2Go/2P1RGto2rud5Ro9rhFHtXbUn1ZmrIJMpyalUylcMjfcoiFenN4NTDNg98vpHcOpFLX4FAA8ePKe15Y42BFP1STPS/vJPXo5INYIjLz4CANj6ia1Yf8d6wMESPtR5V9ooELYBmeKKdkC13Wfsmw36OEe6XtrVN1CHW7t5DQQCGH3xKI78+BEU7ixgx+07lhRjDofDvkwA0s3e3sj3qP2sdi7tL103yvekEQNWlz5y6ZMLzh/xV7tRMUE7ZwX/dl0X5zadw31vug/FriK+/eZvAylg556dS/AJeVSfp7ay0tKeA7XpFbsQcD8b2+yeNuAmwZQYHIB+x2imnVZJQ5X32N4zwA/i7SiiDdbUU8X3qVC0gVYgEDCpPPSyaP8puDRdmEqFniLdh0TBoiCGCkH7rEBGBZntAVODmaCF+7H5Do0E815ND9EiX0DrDGJ6izlW2zNHg11Tq/g9o7aaeaDAgd7twJkAVv/WagTzQbgNFyW3ZEArKySzf6Sbgk4KY130BNg2/yjfKO2UDynEFFhoUx4kbfk5eYg8YTuQeL06dNQ5pM4MRphJO/ZVtwCoA0qBpC56rait3mpVjKSR8ndnZyeGhoZw9uxZQwfSmvfocVXqXCD/c350j7LuESR/8ntuaeC9Nu8bJ0+9hjN/eQZD7xqC53mYmZkx9FHwx3u4znTtc0ycf91jpO9Ug6GdU0MdTGooaF8Ibs/82BmMvH4EXqg5B7mtOez7vX14zi89B+FCq+o+jQXNPtH3kv85B8Visbl/Dy3wq/TlGCh7+DxWZed2Gc49gbo6p3Rfucpo2/D4n3pyl9tyW27/vdbZ2Wnqq2g2kuu6CC2G0PlYJxYvXjTHH8FD8++lAR8AQK23hlpvy6Fe66jh4E8fhOd4qKVqOPf6c2h4DQSKAZx/y3m4MRejbx9FyA1h1ddWYf9f7MfizkVkr8siXAwj/WgzTXfi+ROIl+MY2Dtg9Jnnec1zu2stZyid6zwvl9FfwH9sF4MpQKtIWzDY3CIE+NPN2Xh/OBxu1hZygPM3nsfDr3oY9Vgd37j8G0AAePEDLwbQ0nEK8FXX03ag/Lb3RetcqB1lO+TtYqs8pYY2zfnz530OUMrijkMdmJqeQr2nbub3Of/2HGy/fzvqXitrjD9qw6hNpDqI12gGlQIdYGmRTwUwfDbnTp+t+4dpL3K++C7Sglmymhb+0FsewsiNIz7eDZQDuOLXr0B6PG3O0U4kEuZ+DRIYEBmro5gpYuPdGxEbjWH4P4aX2NcacFHbVcGyOjHU0a8AUW0vjfq2A9o2eFZ+IZ9p1hrfD8DXV3t+4QDnnncOR37iCOrJOo6++igcONjxpR0IOi2nUq1WMyf9hCNhHH/xcQycHkDH0Q6z9Y41cOyx2ZmX7fiM39GesG1ZG8ya/sOP/ZRP1fHhOA5mBmbwzZ/4JgrdTcBbSVTwnVu+g2glik37N/nsZl2bGpCzA1RqF9pzbq8Bx3GwuLj4vQXcuVzOl6JAIpF4KkAp0MiwCsK5X9MeHPevKOB1Xdfs9dQ0V2VYElABF0EJIzeM7vKH3jhNbaIxymcp4Ob3BCoExHw3PXB8L410BZOMTOvzFIAQ0Os+dCogLSzFZ6mgUOOb+1wUHNveI85Nu2ic67q+6JrNzHof6WBA99yTjg60ouEqsLgoSV+N1HO8pKeCXc/zTKEo0o7zqDzG5+teaQWQTN3SYnpUqHyuOkmoHLRfTJdSYc/+qaeSi5m00wJd5DUFgpzbQKBZXI9HYKnHsV1WCdeJpupwThuNBnK5HBYWFpBOpzE7O2vWJu/TVHKm79XrrUJkAIyzRFO3Cfy4p18dOlQg6hWlk4rRilqyhtHfG0XuhhxGbxvFjnfvMOewk3/JV7rWOPd8J/mM71eHifIwnT1cT0xT1PHwHgp8AmzOUTweR7lcxsp/W4n5LfOYec4MEABCiyHs+NAOePMeKm7FGIt6P+UM+ZLzyz53d3ebyL+OWenAPimNuA4oA8ibGklQWUD+4LpggRv14NvRpuW23Jbb967ZugVoyadwPYzVn1wNL+Zh7EfH0HGkA7vetwujrx/FuTedgxt1EawEEagFUEs1QXagEkCgHEC9o1Vdt5JuOdS8iIfzbzrf/M+TSXyNVAPH3n0Mx//vcXjhJ/V5dw2H/+Iwtv3cNtR76jjxayfgwIHzPgcdj3U05XJXDfv/ZD+2/852pCdaacYIAePPG0d+XR7r/2m90VfUtZTp6vgEms7d6elphMNhZDIZI580K4fviMVimFs1h31v3od6+snaF6E67r3oXgxOD+KSk5f4zhAG/CmnKvtUl6nDXW1eO5tKgZtuPVMbo6+vD5lMBt3d3Th9+jRmZ2d99lR0NoptP7MNh//lMCrDFVz0uYuw4T83oPbkP4631FdCx0KH0f3KM1psV0/IUTuHY7ADEXbQww5E8BoWHY1Go8bBTietHvFpO71pn7F/V3zmCkysmcDChoXmfvW5CK545xVIzCTghFqZa6SnDfIcx0G5u4w7f+dOVJIVrL97PdZ8aw0ct3mMGm06td8JuDTDjZ/RNuJ37ewaxR5qlyu4JM1tZ4zyHOCvB2UDazvQoG1h0wIO/8Rh1JNPnsYUdXHsZceQPpPG8GPDhk7G4eXVcPLak9j7+r1wPAcv+PUXIHMqY9L+c105RCZap8PwvTwTXe11lUuO08zUY1BNt4Yqzyg2I135TBvQs5GmiTMJ7P7Sbjz8xqYTLVAPYNsj27B+/3rTFw2yakBGHUuaValzojhIbXRek8/nMTc396wAbufpGla/+Zu/aS60U2h1MRDkKFhTMAS0wCYNYAXMtqLhxFCIMDWVRjaJrN5GJbxGngOB5n7G/v5+5HI5LC4u+sCkTjwXmkaINb0TgEmd0argKkx0AXLSbYFP+mjVco5Do9e6ADR6y+/Zh1qthmKxaPZsKwOqMLWVju6VthlX+6rRWgW4ysgKzG2FxD6z6AXnUt9BcNZoNMxC5v+1P+okYZ8JZNXBohFfBVlcYMo3GqmmgqAg0RRrCiEVLqqcKpWKmXPlB/WsEuySNgROpK8aIVq5Xj21HKPWUrA9fFQ0/FvT25VWahxoxEL3jdMppDyla0fBtq5R0hhopkHO/tossi/LGhnSuacTm/9sMxJTCZ9jjmtKFSfHCjSdKKx2ymwC5WUaHIy4M6qvWSQAjFdet69wfmwh7bounICDI79zBPPb57H5rzaj7/4+IwsoK5SvmUJH2UJ6kIaq/Ckv2xlGWrRG+Zbrinytxokqa9spqVk72pdvf/vbS5T896N5nvcUcbvlttzaN8dxfmi9RdFoFJs3b0ZfX59xFHK92+tx9GdHseGfN8CrNoc78pMjGH/tONbdug6pyRQOvf0Q6uk6Vv/raqRPpHHil06g0tcERYkLCSAIFPtbW0cyJzMoDZRQSz/Dgol1YPuvbEf4VBijHxjFwhULCGfD2P5r29FxqAMAMPnSSRx7f3OP7trPrMW6T6+DU/FnGFH3q94CWnqenzO1OB6PIxaLoaOjwxxF1Wg0cHLTSdzxkjuw0LmAYD6ISz9/KV5y+iXo6+tb4rTm8xUsa9YUAJ/sV7Cl0U0222bie/T/dJjOzs7i/PnzmJubMzLYOEp7wyi+tYjdd+1eshVoZtMMvvlz38Q1t1+Drtku9JzuMbVi7G1QLFKr41C71P5b+UyBt+qGWq2GQqHgy5ArFosmcs1K5JwvBTuaRuy6bhPMVObw+O89DrffxbY/2oauE10+214LE9Nu5FgW1y7i3p+9F/Mr5w19t3xpCzZ/ZjNQbtHbBrPkAw3s2LpWsZHqSOUNBdhMl9ZsQ9rJ2mwb1n6PRmNteuk101dP4+DPH0Slr4LgYhBr/24thr46ZOw0zdQ9c/MZPPb2x8x7QqUQrvmDa9B7qBcTGyew9/17se2PtqH3id4ltnC1WvXVi1HbmgGiVCqF7u5udHR0+Cqms9+06UulkrEt2Uc7a1fHqMGkIy84gsde9Ri2PbgN13/x+iV0te0lpTOfZTs3dP507nWMMzMzeOCBBzA6Oor/qj0dW+UZ7eFWr5AyKwBfYTEFT/bROpoixQGp14ggltEcOy1BvRAkjIIHfb+m+zI66TgOstmsDyhwchX4kiHUQNVxsXGPrC2YNFVBC0awX6ShPku9d+wP79FjMbS6JxuFNcv90xvDRadGti1cCNDYdwoLzjE9pZquqt8r/TRKqwKThj+vJ6BSwMzGuaUXlbTUtHKlNbMgbI8ilbdmG2gEk/Ov6bv8jlFdzhWfpRFi5QWdYwWH5FXdm0swpHUROH4CavI8I5QqlDXSbB91we95v6Yl8xkqfGxjTrMj2s0LeVSdOfyOApnfMVJLEGw88KiiHPUX5SoHyyh4BUTqrcguHWy6lmKxmCkkx37q97bCYpSX2SL21hDep7IG8EcvtFiacTI0PGz5oy2Y2z2Hru90wQu2Ks6rIrIzfWwDx84AaZfqaDvHlHd0zaocUqcmZY7KI+UV5XsWW1xuy225fe9bIpFAb2+vz1i31z7lw7qPrjPHejmOg3WfWIfURAoDXx2A4zjYXNqMWlcNK760ovkMFyitLWHs1WPY+eGdcMIOnnjXE6h0VdBxogO7/noXsoNZHHzPQXhhD90PdKPzsU6Mvm0Ubqwp5we+PIDYWAxnfvZMKw3YAXLrcpj/0XnkrsgBAGpdNRz7lWNY8wdrUN5cxtlfPGvGOPrGUdRjdWz7+21GtqldAfgjhpRjlJ88bQOA0f2ZTAarV69GMpnE+mPr8bLGy3DHLXdg9627kbo9hYOJg9i5c6c5bs1+j+pBBYhqeKv9q/qYjXNlR4kV5PF3OBzGihUrkMlkkM1mUSqVfFsAI5EI0l9PI5pq2j/lchmlUgnnN53Hwz/1MApdBXz9p7+O7vFuPPdTz8Xg2UEfj6guse0HOxrIz7SvdvBEaUY7s1QqLdEjdCwzeMBWr9dRLBaRzWbhOI6x64rFIrySh21/sA2N4QaSh5KoB+o+e5mOhFwu5wtSBAIBnLrqlA9sA0AxXkTdqyME/zZE1aOkjUaqyYe0C9VO12t5v22D69YvDTraNqv9TJvH9FqdH9sJ1fdQH7bVtuHQuw9h1YdWIXVnCvOY94Ft/p0NZ3199RwP1UwVM7tmsP9n96PSU8GRDxzB7n/cjVUPrzL9U4c+bVd+R5uDdWV0a68GKWijcA3TBuE4tNkOK+3DRd+6CIlKAlse27IkCNKOxrZjQL/Te2za++jkeVhcXMTi4uKS7/477WkDbhLKTqug8cYBAa3Fq3s4NXpsL2DbQOTztKkwU8GoXhQbhANLz06s1+vIZrMGzGi0kFGySqWCQqHgAyr2ZASDrb1FeoQY0Fp47bw2XIiM7pKZCebI0PRUksm5eDh2RnwpjJnSw+vVIKcA0SgeAYUWkuOCULChz1FPFRea47SOuNJ55DNoNHD/DYsxUdiSBtFo1Oyx576gZDLp28tFBw5prICdkUUCVE1LV/7k/3UvP3lFU25V4aoDQkEM38fxKPAjb6rBoHxJ3qPHWAUxARTQjHhoupYqbfI6+YRrjJFWjZSWSiVftJn8qE4CAlwW3dP3qmOC/2c/ucbZf9KYaX98FzMWMAv0/kYvplJTqFxVQfxcHNv+cBti4zEEQv51z3XJdygvcL3qOlG6kj68NpFI+GSX8gOdcfyMBgR5TI81IY3cRRcd93bAC3hmHTLSQm+/67rNvYWAqepKfmdWijoKbacc+ZFOG65f5WstnkaaaVqcOs1oCKoi16wEW5Yut+W23L63TZ3CQCulljrU1jUKFga+OmBkZu99vc2UUacpY/oe7oPzqIO+vX3oOteFYDCIy377MjzxK09g95/uRmo8hcSJBJySg9HXjGLjn29EfDaO1NkU9v/JfvTf1Y/1H12PYD6IaCGK4798HACw5Xe2oOehHjgdDnJX5cze4+h0FMnzSUTzUTh1x6SmwwO6H+n2gRjKIY6Xn9mGNeA/vos6nIVh165di87OTmw9vRWpT6cQPx7HdPc0pqamsH//fmzbtg39/f1LAg5s6jBX0FMqlUwGFXUf0MruVOc+4Afk6kC3ARf3JrMoU7lSxn2vuw/XfOkauLVWECidTiO3NocHfvIBFPpbqa1zK+fwnz/5n3jZX78MXXNdvoit9kMdCppFoH1Rm1z7yvEykFMoFHyObGbFMcrdaDSMUySRSJgx0o6an5/H7OwsnICDkz9/EkOfGEJqPoVQPoQKKsY+4zFqrtvczlWv15HL5YzNNrt9FiPXjPgXjweseHgFAqWASSnXcSuAVDtBbTauL45daaF8Yl4pAFGvV15S4K1rl+8l36v+bRdAUZ5yXRe9D/di+y9vR/hgGI1A62gwzUIOBALo/5d+uNVmbQYAuPT3LkUoH8Kjv/YoSv3N0w0qXRXse9s+RKtR9O/rN2Mj9tCCy7pGx24ew6qFVVgxuwLBUNC3zYOYgr81wPhUGE8Dc4rvAGDzI5vhBPwZCLYcbDdP7YC54lD9XH9Xq1XMz88jl8vh2WhPO6X8/e9/v6eD0Ug0FzAHRyDGvRwK3NSboGBEQZ1GJVWZMNJEIJFMJs05wYD//G7HcUzKKA1SBQb8Xj1dBFeaok1wbDMBGVEBrXoTFSDzes/zzN5NvlvTktpFwRzHMd5E0oTGMdNky+UyisUiSqWSAd0K4mzwp4tGU10JnHVh0HCn00QjisqkGknlbwBmvARGtsNGPYoE2uoI4fjV88xjp1iQTQGSFrhSAKVpvJqOryBYo9UK2KhoeX+5XDZ9ZkEszp1Gn/lMni2tApbvT6fTGBgYwKlTp8wz220bIN3tbQH6PvWI8nPOOemi60NBpeu6Pg8meYOKleuCnnjygL1/TFPXOQcapddMgEakgXOfOIeL3nsRItWIL6uAAJHrw+ZjygOmlLuua7YfBAIBc8xNJpMxfKf8z35x7Jrxwv7amStAK2JOucUzxbV6vsournPeq043zqE6eWyvK5s6MNl3BcvkF85RMplEMNg6extoFc3Rgo+8lw4Zz/MwMTGB/fv3/0CAbm85pXy5PcPm/BCnlMfjcWzduhVdXV0A/DaNBhq4bu3aGpQpatyrDKPu4O9AIACkgECxZSC7ngs36sLNPwkMPBeNRANhNwyn+iQIQAPjrxxHaDGEwfsG0ag14EQcnHz7SYy/ahzJE0lc9IsXIVRt2gP5/jz2f3w/3JCL7b+7Hd0PdyPo+O08PTJRgYgazfybukRtiVqtho6ODmzYsAG9vb1GJxcKBUxPTyMajSKbzWLDhg0+0G073BVsUwYymst0abUf1HmvgNe2v2zgxPs5Z7laDnddfxceu/YxpOZTePNfvBmxYqyly8MBPHHdE/jWi7+FRrgFXAAgPZXG6377dQh7LXqqbav2JPtpR02VFuqMr1arKBQKyOfz5v5MJoNUKoVQqHnKBsEx080ZGIlGo0gmk6aSued5yOVymCvM4ciPHcG5159DZC6C637+OsSKMeP41qAB0Ar4kccjkQgaTgPHfuYYLrz+gun/hts3YPunt8Np+EGYHUSxwZ46vHXuFfja86nXsqkjXGlrP4N/q+Pb1rfkcc0OtcE40/m1zpM+l+8OhUIIRoMYe+sY0gfS6Hq0C67j4vxrz+Ps287Ci3pwag7Wfmkttn9mO0Jea21p1Jqt0WjAhYvz15zHo+98FAEvgFv+4BZUVlQwcdEEnnvXcxH1WlkOdCjR9tBU8nZRfsomzgu3LjADU8eo+Ir/twNL2uyAgtrE+gzHaRZL27t3L44cOeKjabv2dGyVpw24f/VXf9UAbp0EXcA0RAEs8WZQoAIw6Qc0Wu0oD+A//oqCiYuPxi0Ac9yNDR44kYA/4q4p6ep1UlBGbwzvV6D5JGF9fdTFaUf6OfmaIcB7VSmqMGYkVyOouneXeyEIPEulEvL5vKEln6nKmO8iICL9OU8axWW/SQc2pqkoaOc42SgcuNDq9boplkXlSpDApl4l/h0OhxGPx42CU4HD/mkVZgJEOoE4Tl1AupCBFsBWpc+m6fEaOVdQxmfYkXGCGU2fYbTXzkCo1+tYvXq1SbdS0G4rZj7Tdnoo3+jcc52pk0qjmeql9rxWYTpeT/CaTqexbt06nDlzBtls1nduKEE1x0PniNJRjQryMK8JhULIZDIGQHIN6l5o5T0CRs5DvV43tRhCoZBJ++baSCQSbdeormmCdeVfu2l/KLd0mwAzRdSgouNGHSd8NuchmUyadUtZR2VkK3TP84yDkePnXOk64niY+cF51zWuRhnXEWXm6dOnceLEiWXAvdx+KNsPM+AGmvbR2rVrMTw87AN2CoJUr9nGNZuCLdWdtAfUUap2HP9WB60dZW4XUWIfT73zFNb+7VoE6gFfPxc3L6I8XEbvPb2+AIca23yvPtu2MxQIUQ7q56lUChs3bkRPT4/RdQTd5883i8Pt3r0bK1asMLSxjXbKTrVn9DsF5TbN9Tfg30Ko86UZi7VQDd+49hv4+pVfN/cNjA3g5Z95OTqnOn3O2Eef/ygefOGDqEeacr/zbCdu+sub0LXQzFrIDeeQmE8gVo4ZGa9byozDu+HfX6x2DMfIiLUWVwVgbC3WDCqVSsbxTP3LgBud0plMBrFYDCWvhMdufgxHXnvEvDt1OoWLfu8ixM/EDaDWQBxP4tCMzEpXBfvfth8T102Y56z89krs/MediM5Hl4BZXUc2P6kjSsFcO9CtdpeNCcjryr9qN9rX8BntHOz6ne2053qm01+DjtofrnPOF9CsA1Uut7b0TbxtAlNvnULv7b1Y8+E1ZkuA4jnao7RlHMfB2HPG8NAvPYR27eKvXIzdd+5G3IsbO417wRkoo41ir2fu8w6Hw+jp6fEdu0zQTdlk20ekgdJB+cCeKzvzwH7m5OQk9uzZg/Pnz///2kNPx1Z5xudwt/PCcAFrurfNoAoKNIqlxjjgL4RFQOh5nmEWLhoCCQIaXkvhqM9VQ9cutEYhQ4bQ/qpRay9SGr3qndEoHsdO41wZVxeX3qtRVTWedSwUHJpKRQalAFUPmx71RSCn6T16/A9px4UVi8V8qcl8poIKbarAVbnH43Hf3lkVDpwz0k2FI8fFeaCzIRgMmuwGjcyqUKWS4feqJF3XNVW4CbZ1G4BGhu055jW6yIPB1pnVuo9HnS1qXNRqNbM/PRgM4sKFCz5+UJ5TgaAOIyojOgM0jYh0JJjiNgGb9loAjU4eO7LueR6y2awv6k7aqfdRszVs/uW643wpXbkm6EQjjzDlmmNUg0PBMnmBCkS9+pwLdQ6pI4gglHyiKdlq5AUCAV+VefaRIFwdDzq2SqViDBD+HQqFfFtQqAzpQFJZqIaA53lGAZVKJczPzxujhPKItFKPuPKN7eTjMzl+0pkZHMttuS23732rVqs4fvw4qtUqtm7d6ovMqUGoAEH1keoM2mYK1CnrFAiovFA9qrYS0NJrzB60AXswGMTmv93cfH/Yf7xr+lganSc74QaWOovVXqBc0rOi+Vv7pt/pWMrlMk6dap7r3NPTg2Cwmdo8MDAAAJiYmMChQ4fgui5Wr14NwF+XhzSizKbOoJ3GH46BekCBg2YaqCy1HQTG0RBqYKZjxs8H8SpKXSX0zvUa2ex5Hq6+92rE3Bhm+mZwbvAcrvjoFYheiKIcKCO/Lo8H3/wguse7ccOtNyDUaGWU8Yc6ywZlpCPp6jiOcWDzh9mDpJnyokYlWUjLdV0TTJidnUVHRweqvVUf2AaA/Lo8zr3gHNZ/dL3PIUBQWavVkEwmkU6nzbatbH8WjT5/pL/R10CkNwJnwb8twLa5dez8v/JVu7VkB320KZjmvTYIt4Mk7Jf9LNvhb88J/6YNo9iJ77b7qGuZ80SeH/zYICK5CPq/0EwjZ3YCbUo7DTwUCmH2VbM49HOH8FRt/8v2o9goYvsntiMcChs7Stewbp1Ueui2vImJCROsU/vFdpTpOO350jlp59iw7yX9qtUqFhYWsLi4+KzZQ08bcAOtqAwNeU3DsJmW+yVpFKshR+FsA6N2ngfbk0RGUS8G4PcW8loKF1287bymCtrt+wkoNHqo9xKMMhVaq5Ar4A8EAlhYWDDAQ4GRLmL+xOPxJX1WBwRTyXO5nE8xKSCjMQ/AeCKTyaQZBwGA7i3VKD/BHItUcX61gjTfqwBLBSYXFnnAHhsFtUbrSE86QZTuBD/xeNzwEZ9DGun1Wt2b4EcVRCqV8kWVeZ/jOD4wosYIAZP2RyMR5C87RViNFVVW9qIH/BkQfK+CYo1cqjFG+rNIGB1hTAXn+8h7zAhQ5xAj+LrOaIBwrSmA1UwI8jWVpEb6dX7U26hp13ZmgTq2bGOF/eXcKS0pzG3QqsopEGimntOo0j3uCtr5Tq1arvsp2TSrh2uLcjKdTuPiiy/GkSNHsLCwYGhQLBZNH8mf6hjiXNE5NjU1ZeQv6a/7BlUOM2uA12l/SVt1VDHbYfkM7uW23L7/bXR0FI1GA+vXr/dFKNXotI3hdo33qdOeTuBqtWq2ALYDAtRh7YxUyqmnCh5QbtmAmdfaslazoDyvWZWax21ynDZIUb3KRpB38uRJADBAOx6Po7u7G67rYnp6GkeOHEG1WsW6det8tGF/eBY60DqhRZ0E/M7Wh7QNFAzY9ygdHcdBspbEq7/1aniOh32b9yFWjuEtd74FmbMZVL3qEmB2yXcuQSVSQb4rj2QhiVw8h5nIDO77P/dhYfUCptdOoxqt4kUff5GxA6g/VQ9o0EHHp/3juGw9D7SyQTUoxTnkM3n05PT0NLLzWTzxvieW8GhyJImV/7ES0WgUnZ2dZk++zjdpy9Y93Y3rPnEdvvUL30J2dRaZUxns+utdWFlfCawApqenfdsUbP5jU/taecq2F5SH9Vp7zdg2nf0eBd42QFS9b68Ru6/2M+x1oeO2sZrd197behEIBYwdRD5hhqrNv9VHq0AdQGTJME1LHE2gXC6j6lRNhJ1Zk8qHOhfsO08iAFrH0Gnf2BfFnu14GFh6qlY7J1i7uXJdF4uLi8+qPfS0ATcHpCCEi4wMwsGygILtVdLFQmM5m83CdV309/f7vB28R4G3KhcFUDSudc+ngmcFzQQUGjll3/g+AhE+Q/dJKvNqBLVSqZjIMd+vSqdSqZhKd+vXr0cwGDRn3KnXiaBd+8wfLn4KOU0rJ+gjKFCnBos+JRIJn4AkUNW5c13Xt6ebgpbvtSOFHH88HkdfXx/y+byv0rUqctuzayts7QMBBRco9+sqjXTeXbdVWZz9Jo10PhWAsC98vqZ620pTowLKU57n+ap1UiCoQCGItAEV3893kuf5mfIR+ZZ7m+hVZ9SYAJf7d5kSrqCVNGZWBK/hdYwyc0+QRsqBZgXdQqFgFCHpxfXQzihS4cimQpKeTKC5T45zrlEFyhSOn6CQTrx6vW6OifE8z1dsJRaLmbkk/9C44pEyGqkm36XT6SV005oQFP7cc4YU0Ci0Cudw/kmPUqmEffv2mXQtlXGUWQTO9tpTJwJT68jfjUbDRxOVYbqNgPKJ8pLpf+R9NTg13Wy5Lbfl9v1rY2NjyOfz2LJlizmHmvKAIFA/o0ylzUCHqh0RUhBu2ypqBNu6G/AHHfR6O9Cg/dEMKPbdBq8KXCjHKf/pAOb3lMUqnxX0AUChUMCpU6cQCATQ0dGBQCBgClg6joOJiQkcPnwY1WrVODV0jPytIFrppk5zdcyr3WsDAVv3qx3VUe7Am/7zTSjFSnjdf74Ofbk+1Luae9C1SjvHG3WiSM4mgRQQiUdw+7tux8JA05kLBxi9ZBTfesu3kJpPYWB0AEMHhsz4lPYA0NHRYVKNdf7VpidN2tk31DntxkjnRa1Ww1x2DsP/PIyJv2ylgUenorjiPVfAm/NQDzR1WiqVMnaKBh+YSVar1RCMBZGcTeLG374R3/j1b+CqD1yFUD6EidCEj3/tpryrTWlrN3Ws27aMAtd2YFs/V6eUrknaURo80KYgUZ3q9ljIU5rFYEeWNdjJvmjQj99pgEYd9YFAAF0nu7DrZ3Zh/8f3wwt6uOpvrkLmVAZf/7Ovwwt52Pahbej/Zj/gAR78mR+K6ej40z6qfKv31HHPj9+DN932JuSn88a2awesld7qhGiXfaDyRm1v/Wk0Gma74rPVnvYe7l//9V/3aExqxJgdVdDNAWk6uHpoeS0LLFQqFaTTaVNcigahGqAKfIEW4FaARDCiQBrwR3fYRxYW032uCjIY9VZwrkyhk6IASSOCepaxgraenh6kUimcO3fOgDVNf9Ux00Dm71KphFKphGKxiIWFBd8xCaQLmUyVAftEIKICRD1OjuOgr68Pa9asweTkJC5cuGDmSitB03jnvt9wOIxt27ah0WgYz7yOQQWBXUGc2wU4JwTVpJnu63Ucx0QkKXwJyumpp+CgkiqXyyZ1mEJNo+J2NNlOqQ6Hw75FTqDP/9MhoXymIJNCFIAxIrQv6umj8KFXmDThfLFfWlxEvaC6/41OBFYP5TvovLDTpAneqUDpqCH/DQwMYOPGjXjwwQeRy+WW8BDfa0e1+TeBKNDakhGLxZDJZHx7zTXbQ5UH4C/8RZ7kfupYLOaL6NMJk0wm4bquAZK8jmCUz9X58jzP7Ovm+zXjhF5X13VR6C/g1J+cwsZPbMTA4wNmLJRJXOPq1NP5Xlxc9BULVKVIZwvnmfytGSnqYFPFyTWjRe+eSiFzzLVaDQ899NCzVpXzf9q85T3cy+0ZNueHfA93uxYKhbB9+3b09fWZzxTkcr1TbtifK1hSO4wGsIJhygg1TG39owCYn6njmM5ZbQoK+NvWuwoqKMs5Duol1a9qL1G3aDS6VqshnU5j69atyGQyBnSUSiXMzc1hfHzcODTWrVtn7Bq7PwoE7IxHm86kiaaVa+DBHjfHCjyp3zwX8LBEjlerVaPXJoYmsOrCKtO3UCiE2fgsbv3pWzHbNwt4wMr9KzE0MoRHXvUIAODlH3k5Vu1f5Uv/1qZ6QAE0dQr7QJvZdV0UCgUDirnvV8GQOoNc18VIxwju/f170Yi15m3XB3ah95Feo8+oX2kPKD3N1sSIh9OvOY3opiguve1SZE9nfbVIVJdzbMp7tB3ZTwXo9rW6Lmxeth3rOl77vfac63poh8Ns55jah4oFbGeA2gS8nv3kHD5VFJ22PXlcz+DmtbRdI5EIKrsqcLe6WH/fejQaDcysnMHM1hlsuHuD4R3aQjyJiM/XNaPrhOt+bnAO//Ge/0A5Xcamg5vw/M8/H5H5iC+oqmO0aavzofRWR5muTfaF9164cAEPPvggJicnl8xNu/Z0bJWnHeGmsah7ODQV3PawKANzEan3i+Cps7NzCSNy4Jwo9eRompJ6OGiE8hm254lMR2Jr+jAnkMBPPUFaUIuTqh48fqaGsHqvNF2ZgHB+fh7ZbNa36Ml0tqdZ04G8J72+8/PzPmBKcAnApH+kUimfAFOhznRsFh/QlFTP80wZfAXsCgo5ftKNwO3kyZPmuZr6Qf7hWGKxGIrFokk5ZoVkTRlxHP9xY1QSBFucK4JHpsbzfkY8OV6tcK8KRZUa55Q04ZgVnJPmbHQokb/YH+UzjVarMuCz1cCh0BscHMTU1JRxaJDmnEOOX8G1Ch06NrSugmYnEBSyP1qhnB5pglvO/8zMDCYmJswcEhxzL5GCSX0W+8nrdC2q8A8EAgYMM+pNEEhe4r5+lStA6xx6lSXqAFRnoQpjlRGqRLmWa7Wa2VPEtUDngeM4KK4s4uy7z6K4pYiDv3MQjT9tYOj+IWO8kMft56pzhtEadS5p5Ir0Z5Sf/9fn0PmijiEAxonpOM6SI+zYH4JyrRy63JbbcvvBafV6Hfv378eOHTuwatUqH4Bmo5wD/OndtkEK+A1Lyk11LvN5bCpX7aADP2N/PM9DOp02WY62Y5b3qu2o8tceG2U75ZWdFk1npo5F+8/08k2bNiGVSgFo6s/u7m4AwLlz53Dq1ClUq1Vs2rTJZPjRDrRpQB2mdqVGA+2IGsegkWEFafp8z/MQcALw4PmcDLFYzOiG/Wv34yuv+gqe97XnYdfeXUZXdOY78ZrPvQZ3vupOdE10AWeBR179iHn+XT9zFy6/83IMnh3E8MjwEoeK2gMKgJRnOIfU+3R6U3+ofaXzwOcVdhXghfzgcuGSBXQ/1O3bmkfbxbafSevjrz+OkTc1jwQrlorY8Q874FWWRix1POpEoM3Da54KZOuYle80m0+fqfrYdrDYn/FenXvls6e6z854VXuWtrNiFgabdJ3TfrJ5W9+lNpyNgzjWvtN9SE2nEAo37Yae8z3oHutGMOK3U7SvGhTQIKbagbMbZ/HNt3wT5UwzSHLiohPwKh5u+vxNiBaiZm50LSmAt50VttPC/kznF2jiioWFBZN1+Wy1Z5RSDvjPcNaq2PY+QhW0JIBGJRUwE4iqN0oXG5syNg1JRu7sftpCXKM/nKh2E88Fr0JTHQgUNGrM830KOHifekJV+XG8+n+OUfvI1mg0zzYkACPYte9Np9O++SEjATDFy9gfAhaCGb6blQ9dt7XPWwE2F6oyL68jcCCz69/qGU4kEr79xwqedJ7U+aApMPa9ekwSPXKJRMJ41fhuFeAUKhpBZJ8ZgVXHSyDQTMfWlGEFXwT88XjcV8ROPWrkUTqdlP9JXxoIjuPg5CtPYuDrAwjNhgwwI2+pE4AgX50IXV1dGBsbM8CM9ONc6P5hm795xBafRccI6aICjXOl64XP0RQi3qd9pEzQLRzqtLMBNHmRfddqqep95RpglFr3mfG5zKQhDW1lqTynkXeOu5ap4egHjmJhezONz425OP6O40AQWP2d1ebZlGX6HjU4VSGqbNDMFNJGZY+deq90Uhpq9EkjKWzkfXUYLrflttx+8NqxY8dQqVSwevVqn4Goadxql/A7tYUAvzPSjkjy/+3AgIIFNjv44TgOZmZmljgwbePathNtw9neoqQ/QKsYJOA3vinLFADNz8/j2LFj2LhxIzo7O40+7O7uRqVSwczMDMbGxuA4DrZv3+47blYBKPtj2zXMhNRMKe23RrtV96ljXt/DcVBO084ZvWgUd990N4qpIu55yT1ohBq4bM9l5t7+8X688kuvRGo+hVM7TuEwDpt5akQaePg1D6NjsgMv+JcXoH+03xftbgde1H7VbDxm2dFW0FM61C7g2KmTN9y9AY/96GNwQ80533rbVgx/ahgNp2F0oGZ0EiQyAy+TyWD/m/fj9MtPt9bEDcdQ8ArY/ee7fcBZ14PqW9K+3bGjtt2t68IG1/pduznn/e3WDX/rtku9TtcKn6Nj0P7pfbRZNHtT7T7tK8dsOyL0Pfp+tclIQw2skY9JA+UZG+TStm4nlzzPQ6QYQbjaslEAIJFLINhYer68Ntte0mtsPKqVnwk5AAEAAElEQVROSFt+BQIB5HI5s8Xw2WpPG3BrdEUXJwWCGtmO4xgQwXvJCJqmqxFQGns6gSr8bcayK/3y80QigUwmg0KhYNJeVfACrbRUetFouCpjsimo4kIA4PMy2WBChScXtgJF9STamQP6fBrVtVoN+Xwei4uLvj20BBMKDMLhsEmh1VRY0k+PzQoEAqaSpC580lMrWAMtoM132fNH5RMIBMwxZeQPLbahTKz0UiDKsVMYqOeaQJX8wSMGCAZd1zVnkieTSQNoNP2a/ECwSiBNPld66kLUyDF5nvtqNU2aEWCdawXvFE7KE6RHIBBAtV7F2EvHcPzNxzH68lHc8As3wKm0FHIikQDQ2s/PMalBs3r1asTjcRw7dszwKoE26cm1wPGR1pVKxUR27e/ZZ3t8yiucPyphdWKRVlxXiURiiVLk3HK9cn3Q2RYKtc5lJG9S7vA6dQzp3mvtY6lUMg4SLTRH/lb5pkqz0WggsBhA/7/3Y3HzYtNr7wGp0RR6Hu7xKTd1BOhJCCoLdewqJ0kDriVdC+rAUkOj3brXtDDSTiMSXLfFYvFZ3bO03Jbbcnv2Wr1ex8jICBYWFrB582ZTo4KyhnID8AdJgJZ+UMcs9ZgCTMog2giAv2iURp75txrOlLe2E0CNYwUsTwVgKHMjkciSwArHYzsX+U41/nlfPp/HyMgINm7ciHQ6jWCwecLFypUrEYlEMD09jdHRUVSrVWzfvt0EdShH1bmhoEKdsWzaLx0v5a1mbSrNlT5qD3ueh3N95/DZF34W+UQeAFCJV/CNF34DiXwC249uN7q/f6J5xviOvTvQqDXwlVd/BW6wRbuFgQV85ee+gtf86WuQmc4sAXUcG520DKxxTtUe18iqDRB5L4+FrTk13Peu+1CPNvlx/RfXY8vtWxAIB1CNV831AExwgTzImiNVVDF50SS8oD8Svfbf1i4JlOk8KA8TP6jTQx1ANgbQcdlA1KYbv38q+5jPUsCn9ygQt5+ttnAFFR++oO2lkW/NrOT3urZsftV5tJ0I7BP51MYAyhcK/u31TDvDdr7Z85O5kMHNH74Zd/zqHcj15LD1/q24/IuXAzWgjrpvrvS99lZWnUOlvcpIdXLwO2YhP9v1bJ424L7tttuwceNGrF27FslkErFYzLfYgFaqjYIaCnMOjoJcvQ1ahKnRaBiDWqPh6hEB/BFlBfYATDo1I3K8loYoPWm6p1Sr/rL4Evtpe2BUwCp4V8VHsKFKS88v5gQrY7CfjPYR8JbLZVM4Q6OVykC2IHQcxxfdU+HFxn4wyqgRsHg8boQej+LSPurC03RtAhSNjlLBc77VkUKaNRoNA5wJyrUSPoExwYt6ie0xKjAuFovmGYw+s4+BQHOvdGdnJ+r1ZnESRrw5z3qcmR7/oVFrNWDIy5o1QOHO/rmua5w8ykPG8RFyMPbcMRz6hUOAA5QGSvjOh7+Dq3/zakQutFLp+VtBG9dAqVTCnj17fHNH0KWGF/lP1wnPP2efOEdqXBC8a7EuHbO9v4+OEV07WtyM9AsGg+Z5HKPyDz3rfBfXCudBZQa/V3pRwVLwk78pL3i6AveqkbdZN4BriGB3+OvDqEfrOPOzZ5A6nsKuX97VTAkMtyLSlBuu6yKZTJrIgPKSyjh1Pqgy1vkgf1JmqhGgzjZV7CyKx/XAYoeUX9VqFYVCYTnCvdyW2w9wc90nKz5ns9i1axc6OjoA+EGvHSkFWqnZvJaONs/zfJV4A4GASbdtl1KtBjPfRbnE7zXSpjaU/bdty6htYEecFoYWkBxNLrFh1OahPUJ5yf7wmvn5eYyOjmLdunWmCF00GkVvb+ts8DNnzqBSqWDbtm3GllRns71djPpd+63ymDYA+6rBH6VFO2DPMQSDQQxND+GW+27B7TfejlKshHAtjOv3XI+dJ3ai1vBvT3QcBw4cXLzvYtRiNTxx6ROYXDEJL9B8djldxj0/eQ9u+cNbfDRSHaJj1W2k6rRWXaPjUSdIvV7HQmABj771UYxdOgY8aU7Xo3U0gg3Eo3ETpKHdxww7PpN22MlbTiK/Ku9bD4nxBBKzCdNn0lh5RLdFcm5Ul3OsGuCzQVs7nlQwrk2BJunLpn+3o5n9POWpQCCA+e3z2P8z+7Hl/VsQngz7TiNRftK54f/Vxte1w37Z2Q42aFdnAevO6DZQXssfxWhcL3okodo9ane4rovoQhSv/q1X48G3PIjnfua5cND+SGJ9hwJ4O1Cka9jmBfabNJmbmzNFrp/N9rQB96lTp3D27Fn09fVh5cqV2LRpE/r6+pBMJk3UTsERB8CFqgSxPRMqZBWQ0VjnszR9SH8r4xQKBZw5c8ZMLu/lsxU8qJCsVCqYnp4GAPT19SGRSCyZSE62Fgeh4a0AhEYwAQ4PelcBqnTg3xQyfX19aDQaOHPmDHK5HHK5HBYWFtoWb+Mi0UgpP1Ohw+iu7mNWAKELVJkxGAyafbW6IAhEFNhQcXMu7BQUGxTb0dVgMIjh4WFUq1WcOHHCp6AIkFgki5+R/gRRQMvzrgKYgr9QKCzZr0qlGwqFkM/nTZ8ajWYVbM3WUOXKfqtHUEGpOiWCwaDx1NuOHHvfbQ01jF8xbhQTHKCarmJ66zRWjq/0gVhdI8pbKvDV88m+aKSd1+neaDombAVsv4ONnlZN0dJsAYI63UbBPVvqbNO0ajqsdH6VZ7nW1IlAHnBd15cpoYqE14RCIVO5X4W18o2uDV0HHFOlUsHA5wbgBT0M3jmIgBPwFSlTZdNoNFAoFHxK0HEc81zSjOuX3lVd66rgVemTxyl3dI7se0gvOhZ0jdqKb7ktt+X2g9nq9ea+7u3bt5s0aduBz3oplBGUoyrXbYe/2lz8XGW92k5qS6ncoL5n04wyBSE2OFV7iM9wHAezN87i6DuOYtufbEPvo71LHAf2vZqxp45fOttPnDiB9evXo6urC0AzwNDV1WWun56ehud52LZtGzo7O824lU7UW5pKrc5TeyxKc32O7aS2ZTZ1T7VaxRXHrkAj0sC/X/vvuGnvTXjREy9CJVFBNdQqqGZnPl3xyBW4/OHLcffL78aeK/cADjC4bxA3/uONcBsuGliauq8gSPtDm4B6mZmDmumgcwA064ic3X0Wp69ppYEDQG5zDpXeCjrnOo1dR/3H+VNw5nkeeo73IJqLohgtmuf0P9IPVJcCSLXb1QljO080Sqx2mfKyAjk+W3EOP1PaA/DZaXy/Rq+12Q53fTb5YPLySTz+3sdRT9Rx8tdOYu0froU33spQU6cX+2djBZs2eo32UW1GfR7pRftb+VyDCOqE0fvbOQYpH9T+cBwH4WoYN/3zTXACS+tQKIbU+dH1pmCa71UnoF6n/JPL5VAsFvFst2d0DnetVsP4+DgmJiZw6tQp9Pf3Y/369RgaGkJfX5/vTF/7N9ACYMq4KpSBpRFU3qfEpvFuR7PUo6YRbBWGmkJOA50/jPIQfDBdi+mo6skhQzLCqPvQ7ZRQLnjbUaDRatKKwN/zmhXA8/m8KWBmFzcD/EeDqCLUpp5oFRTsg+1Z5RxQeKhXyQYt6jRRAKygD4AP3GoKjDpZqtUqzp4965tvdXrQ0cH32+m+9gJUAat8QDoT6Lqui3g8bsAZ3xeJRFAul81ca6RWFy3pRoCn9Ob7WEVbnSOcL51/13URqodw0V9fhKO1o7hw0wU4dQcX/cVF6H+o30cHOk5UUKnweaoj/NTJxXsBmCqjBMI6Nt1nzOeo0aPOHlUoNpC0PZRq/Om6UY+zXqM8rYZhrVYzEXR1sNiOMuVx3aOnW1R4T7lc9p2EwIwLrarPsay8bWXz/sjSNENmHyjdAoGmR59GHsdCr7HrukaeMmrDcfN68hr7wPWkBqDKOMo3paHt8W6Xurncltty+8FstVoNR48exdDQEFauXLkkIqVOVq592zZhU2eoGv5qIKu+0OeoIxbAkv8Hg0F0dXVhfn7efG9nLun1mokz/vxxHP0/R1HrqOHou49iy4e3oPv+bvNcjssGRionte/ZbBZAM4jEQmp0urMGjuM0o1yHDh3Czp07kU6nl9CL79SmDgQFik/lvFCd3A6o8xqedOF5Hq4+cDXSxTR2jOxAIBAwzvFgMIhSqeSbW33GTV+5CeFyGJP9k7j2tmsRyAdQ91r296OvehRXfuVKBL2ladmktQ3GOSbVm9Rd1MHFdBHHnn8Mdht4ZACZ8xl4Mb/jnn1WpwEz22qFGryGfw6CtSAc+PdQK71t8MXPiS84FuUfdSgRi6gjAPBnaygO0fvI53aAT3GR9kl5iI3rbOo5Uzj884dRTzTnbPHKRZz+jdPY+CsbEcj5j2TTQJPaZ/Z71S7RH81CtvmXNoYNxNV244+CWQW/tnOE8xwItLYbkmbtHBN8huIEG3PoO237tx3d+exSqYTFxcVnvWAa8AwBN5vrushmsyY9p7OzExs3bsT27duxcuVKnzHOfaAs6qQMSkbWPYfKEASZBCWMOOqi4Pc2oZXRtbgGDVeCHxVu8XgcsVjMV/iC4IzPrdfrmJiYQL1eb3t2OI1w3dtA0M5+5fN5pNNpk7Zre4s52QsLCyayTa8SjWIVpnrMFt9rL15lNlVm7LfSkAa7eoOAVrq9OhXUc8i/+TwCYtJPnSKhUMjwhPIDo3qkNZ/HPafspzpv7AUKtAAiFzD7rMaE67rmLPNisWjATiQSQTweNz+VSsWk99dqzbOuqeAU3NjOH1X0vF9T6GxwQ6HgOA5SpRR2fWwXGqkGVn1+FXr39QJOa5uGPWYVippZYappF4u+NHFuF1CBqWtSASn7zTnUcehWikKhAMdpbWXQdUQ6aAYF6aSZDnTYqLGn/Ms5JU/RSNKq5lQuKkc8zzNZGHSmKP/RQUFnA9/P36SHPoe0Is35HZs6o9RxwfGSlvoOrhd7zdKpwLnjuq/X62avocoAyj++i05IyiullTpgloumLbfl9sPVKpUKRkdHsbi4iC1bthgHbjAYRLFY9EXd1G6gPaZprFz7Ci5Ujmk0lk0LVqpNAPgBTzabhed5ZguP/lC/qx0HADNXzhiwDQCV/gqOvusoLpq9CB3HO3xyVI1pOsZVF9jydGFhAUePHsWmTZvQ0dEBz2vWX9GTc+bm5rB//35s3boV/f39Rmaqo5x0U3uANh9bOBz2bVtU+5J0UmBiO0TsAMRFpy+C6/mPTUskEsbWrrpV3Ppjt+L1//p6OO6Tp1S4YVx373UoB8uIN+IodTSPl224DTz4qgdx+IWHMXrJKNYdWIdr77zW8Ab1Rnd3N/L5/JKsU9pzAEyQyHEcJJPJpn7PhzG8dxgTGyeAAAAP6HqiCyu+tMJUstdoOevfFAoFBALNQsDkjfDpMCK5CEoDTTC0/kvrsem2TXDqTvM4NWlKY7Vj1SZp50giP+l3mq2m82TrSj7HjmLbINjO1uU1ts2jn3Ue7ETifAKV7koz+9EFeu7tQbjUqiDOd+p7yXMajFAesx0F7KcG1dhYvI62pWIN/a1BHrWL212r82SDcf5t096mv2b0KtBXGvPedqCb31MuZLPZ74od9N8C3GwUnpOTk5icnMTjjz+OLVu2YOvWrejr6zPHMdneEQVN/KGw5h4ORpjJwGqU6nnLfK6mU6lRHQwGzd5cm9HVILcjwzSwadTqpMdiMZTLZQM2NB2F0bBCoYBsNotQKISuri4f+OaG/NWrV/uYmiCACqhUKhmhrRFM0kWrRKqiUQcF6ccxEjTW63UDKDV6ppFTO1WXRr9WOCYtdYGTTloAg3ThwnfdZpqUOjMUvNvpKvaREfos5QGCOgAmK4EAyY6oE/x4noeVK1dixYoVOHnyJPL5vDl6jOBbC2rR21oqlUyFThZOYyRUwSb5WwUK54xjoXOJ76hWqwhUArji96+AV/fQQOtz8ogWAlMHBOfe9lQyYqrOB/K27jGzBZV6MhUkcl54D+dH0621Crg6Zfg7Ho8bZ4Duzad8aDQaPoCrBYB0HUejUbPvWnlYq89zrOoAJL9oKjrnzs6o0Ei37bjSwmdqHPE63kd6hMNhdHZ2mjXFueCWDKUt32OnbynP2wqaNNesHab8K2+ygBplBh1Dy225LbcfntZoNDA9PY2FhQVceuml5thHO81cgw+Ui9QnKk80OAIsraqscob3UOaqcQy0tvfYxjJ1FytcK0hxnGaWYceDHRj64hDOvOEM3KiLQDGAtZ9ai8SRBGpuzQdwqdc5NhZCVbCj2wEZ3BgZGcHmzZuRTqfheZ7ZTki6zc7O4oknnsDOnTsxODhobD7SyZaX6pBQh4XaQOyT3ku9QVmszgHOpdpzNjABmoGkQqqAj7/64xgfGMdnfvwzePVnX41YoVlzKVqPIlwNwwk39UEgEcD919+Pgy8+CASA7GAW2YEsnIqDS752CSJuxGSGLi4uIhqNmkABQXI4HEYmk0E0GsXCwgIKhYL5nUwmEYlEsOHLG5Cr5XD8dceRPpHGRb98EQJOAPVg3ZcKn0wmTdFf0oJBsHK0jH0/vg8LGxZafOl6cBsuQk7IN7ekJeewnY1KumnAwga5bEpnGzyzaaaFglflAwV/ijm0T9pvTQEPF8K46gNX4YE/eQD59XkM/dMQBr8wiKATRCDsd85oYEd5VNemHRzSday2qvaRTe1zfqdrhrKCPNIugKL/53s0Eq+t3bXap3bX8zob6/F6XZvauIX3u9H+R4DbbsViEY8//jiOHDmCoaEhbNq0CWvXrkVvb68x8ghONeKtAjkQCJh94cqkPA7L9k4xxZN7lWiQawRQwUgg0NrTqQXPAPgqp5NZFMzwGel0um2KkabNEhizaTryli1bUC6XfQzK/jYazX2ejHITcNB4BloeNzXClaE5bvZDx0lgB7Siclp0gYra9rLa2QmAP71cgQX/JrDiolPwotUnNf1Wz0bkXOh+caW39k+BpNKa/WQjoNDUdwCYmpoyBgP3EzGlmAYBBZJ6+Ah6ldeooNQDruBLBbY6RWKxmAGOpHG94i+6pcaS4zjm+LZMJtNUSk/+X3lWwTB5kfNOoEkHEnmG7+H1aqRoipQKXGDp1gFbaWj9AM6PFmJRg9BWTjp+AmuuI/Vc6polyLW9ouRvGn0EwnToaGSY9OaPjoG8yqPNABgD1q5rocKf60OBOmmiqeo2fVXZaESaBh15V+eRa9pO6yctVKZo5Gq5Lbfl9sPVqtUqDhw4YCpxM7KqAFlBH+B3XmpkG1ialq3yiHJHAw02uHGc5okaWu3XBjeUnQRyqlscz8HwPw2jgQbOv/Y81v7LWgx+abApvxy/E533tDP6NTKvIMjzmhWJz549i3Xr1hnnru7pdhwH2WwWR44cgeM4GBgY8MlJlaccv9b2AWAczJoVpvrTzvRj4z1PBc45Xn43m57F557/OYyvGAcAnN5wGne98i688M4XIp1P+9Kog8EgGgMNjF081ow8m4cDj73iMaAB7L5rN+qVlt7gHFFPa0ZgJpMxttLExARyuRxKpRJyyGFueA5DnxxC1a1i+N+GEYvETMBFTxppNBoolUo++4DvnRyexMy6mVZtGwCzu2ax6qurkDqX8oEs1W3aFHiRhnawjmuB9LVBtj5HbUsNvLUD7TbQ19/8XkGg3k9eCbgB7P7l3Rh99SiGPj3UvA9LC4DZDjTtkw0ySTPdf61ZDIrFlMeVlrZDgrzf7sd22JHe/LFrHOn79HO1Ne31YdNA50WBu9qmQFN+5vP578r+beBZBtxs5XIZJ0+exNmzZ9Hb24t169ZhzZo16OvrQ09PjxE2ytxq0KtiIGHsfZw0Drmflp43pmPyez5LgTQnnB4pNVh1Mvmden4A/2JRTw+VE9AEBIlEwpcmr6AnGo0aQaLGO//mvdy7Q4BH4Gx72JR5yPCa2sXx24pJ6a5eSx0TP1cPNmmk71eFpqBc6cYxMr1ZlblG5u3n0tnA9/Je9oegwla2fJ/uMabDhSm3QFMhnj9/3tfHer2OfD6PTCaDZDLp41k9u1gBLgBTeM326pHWClipnFXxquAgT+kRdhwr+aBer6NYLC7JtFCQTr6jkFKHgOu6JpqqXledK03NU15RIGivH50Hzi/frY4cjlcdP+Q1OjVoNKqjjLxK2pNHuUbIT5qxoeBdI8bkWRus6rxpVIN8pMaHKhBjMMrzldc1C4HrjxF4LYDCudUK/nqdevUdp5kxoIUJlfaa4sg+qaFoZ64st+W23H74WqFQwPHjxzE4OIiVK1f67AS1EdT5pgYt4LfDgFYwAfDXkVAbgXJT7Qt1EPK9fAYbHep6jdoAruti9cdXI34ujsFvDMKDX5/aoIa/9XM1ttUJzZ9CoYCpqSkMDg4a/UqdTzpls1kcOHAAlUoFQ0NDvgihyndN6W/3XttBwL6ozWQDKD5L77Ej3YFAAIgAjZDfaepFPSAEH035jo7ZDrz89pfjztfciQtDF3z3VaNVuF6ryBfHp1mfNnCKx+MYGBhAMBjE2NgYZuZmsO8n92Fu2xx2/vVObPnsFlTrVUSTUSSTSTiOY07GoL1FulLf0gauF+tw6q13pU+nsfMvdyI+GocXWFq9Wm0T5Vu1YW0Qps+wwbBer9/xe86PZpfqeuB1auvr3KtdZQNydcjEvTg23LoBZa8VbOB3+i5dB8pLyo9PFcTiWEk3bRxju8CIjoF9sgG+Xms7MGybsp1jgLwPwBfUUCeB/duWB/acc4zMTLZriz1b7bsCuNmq1SrGx8cxOTmJffv2YWBgADt27MCOHTuQSqWMt0yFlhKThHAcx+yvLRQKJu2SoKNQKJiFCrSYk8a5ekxsYUjCKnByXdcY/ypIOdl8Dn/T6OWxO6pA7D3eZBqt1G1Hb7mXnFF7nr2tzMsiXyr8OF6gdVwUx0awAcDsVeUZiQpIOZZQKGSOZ+OYy+WyiaDqYiDgUFqRxqyuqd4t24OlSpkRb/ZbG71wtnJWIdVOCdrPoLNDAbrntfYXa9STRyUx6ul5nuFFBVU6H/TcMqXd9jaqA0HTtdW76MGDg1bWAK/V+WR6MDMGSA8FohpZ5RYE0lr397luqwp3O76nEiGNyuWy7/36Hs5vT08P1q5di6NHj2JhYcHnDGnnleS7WbNA17I2121mxNBhQp5XQ4T/Z/o40/ypKAhmbeefCn51xOl17JvezzWn8608x/VIWioYV6NJDTMt6qb74xmxZ1+4lkk/RgYox7R+gG1883PderDcltty++FupVIJZ8+eRT6fx8aNG312i+pou45HO3mcTCZRKBTM5woCFRR7noe+vj4Ui0Wfgzyfzy9x8D0V2Ff5yr6ce8M59O7pbYJtATt2tJDGumY0stn36BiBZpDo7NmzKBaLWLdunTm+MRqNmj3doVAIMzMzOHjwIFzXxdDQEKanp9HV1WWOyVU7UwGCDSLUUQvAF/Vm9Fm3MvK56lxVRzrQ1Ed92T68+Z4346Mv/yjGe8ax4cIGvPHeNyLSiCAXzJm+8LSMeDyOFTMr8CO3/Qg+/ZOfxnznPABg1927sOvLu9CotDIEqBPVVlT9385p8vh7Hsf4deNAANj/y/txzQevQfSY3/ZUHaq043xWKhUU+grY9759KA22ClmV+koorC0gczKzxEGjc642h/KGfm9fo3zJxmw0ta2VHzWwZj+jHbhVmtoOFxtcso+001auXImFhQXMzs4auqu9otFfDZbZY7ZBOu1Ke31oEIB2OH8UaKs9TWzELaWa1WHTzAbWasu3u05tPR2DTSvbzmoHvNU5UiqVMD8/77vm2WzO032w4zjPSg8CgQD6+/uxefNmbNu2DT09PcboJ7MATQIzimgXz+Ki0EknUCGg44Sr8FbDloCJz9PItHo9FKyqwFMwYwNI/q3PITOw341Gw0TtlGE1egU0GaJYLCKXy6FQKJi+uG4zjZgCiYqTwlGZWkEbF5RG+Dgefk5Aw2gqn8VKx/w/nQu833WbBeF4hre9v1ifTzoRhBI8ab+5/5QLXD2B3OOue39JL438keb8jt9r5NNxHMMHmsbE+dfUe41uMtIaCoXM+c2qcLknl5VDldYq4FQJNxoNlIfLOPjug7j0ty6Fs+BP36cA0r3vGu1m38PhMBKJhI/3WfANgM85REBIYc5IKnmQ4JY1Dfj+XC6HRCKBVCpl3svrObbNmzdjcXERExMTS4Sq5zX3y2UyGcRiMaxevRqBQADHjx/3CULde65Kic40vUYdWtzDV68394kxs4HzowqSaY0Uurq9hO+09wuSplx33ApAEGw7WkhTNT7pLNHPOU4tfqdGF3mAz+dcMhVPawhodIK8z7WoEXfO6ZEjR8zxiD8IzfO8pdVNltty+y/as2Wr/G9pHR0d2L59u7E7NK1ZbSLAn0KuBqzeS9ms6ZiUZ4B/fyRlpR6nSZ2pzkX+1nsrjQqmbpnC6LtG4dQdXPP2axAc9RcjVbtMn8XvtS4I+6YAx95m47ou+vv7sXbtWrMlkLpxYWEBU1NTmJmZQSgUwvbt2zE0NGTsGNoDKqM1eKTfKRjju9XBrUC7nXPEHg/nwtgsoSo++qqP4hf+/RcQ8ZpbncrlsrEhgWY0j0WNAaARaWCuZw6P7n4UN95xIxbnFpHL5UxhTtrG5XLZ2IfpdBr9/f0mc5U24Pj4OL52y9dw/HnHfenqgUoAN7/lZniLrRM0eGwdi6xxHASKlUoF+VoeF153ASNvH2nyXMPB0DeHcPHfXgzU/FmDnHsF77bTg+9Q4GaDYZ0/oIkNBgYGEAqFMD4+buxCNtupooEUtXvVPre/4+f6w34w049rLxaLGceFOnZoG3G+9EQnNg1KakYrg4U2aFXwHIvFkEwmfQE5joH91O2r3IKp9LZppu/jOrCDF0ontc00GKJ4zZ5jdaDYwJvrfHR0FHv27EE+7z/r/em0p2OrfFcj3O2a67qYmJjAxMQEDhw4gHXr1mHjxo3o6+sz5x2SWe2JJ5MB/vQYElI9PSq4VCgr+NPqmoycZzIZ3/Fm7I9OII1r3X/JqJwuMvYT8B9tRueAeoX0Or6PQjwejyOdTmN+fr61LyaXw9zcnKlMyegrhYDuPXUcxyw+XsvjrvhOLlRVYlzMeryUfdSaAnW+a2FhAb29vSZiagsCoBWZVk+nghOCWo2s8nrOCYvHKWhVUKupx+rtJjgBYMbGauQE/gRtvMZOn6OTg/1SoKXGSSqV8kUZlT/pjNBq4PPb5vHEB59APV3HwfcdxLa/3oZkNrnEuaFgk7xPYcz3EGRyDJwv0k/nkbTUdUKDhZkWKgyj0ajZm6cCX50b4XAYIyMjvgiGzguvIbg8ceIEenp6DL01Oq1edNvTqoYKaURnjRaqUf6w1xodLeqM020K6sFX5x6N0nq9Di/sYXHbInqP9vrWB5+hzkSOgbxOxxKNOyo3Bdc6Vk3x53j5Ts41v+NWBBov8XjcfEfAb49xuS235fa/oy0sLODYsWNYs2aNWfsKeIEWaG4nIzVYoXaQbfwCLUc15VSlUvEZ5foubbTJTD+CDqZumcLp955uPjfk4ZEPP4Jdv7YLmSMZI/sUlLbrv/bbjhyqQ1I/m5ycBACsX7/e2AeNRgMdHR3mWbOzszh06BAqlQo2bNjgs/nUjuLY1L4C/BlU7K/qRV4DtAITNhhVp4bKesdxEKwE8Y7PvQMAUEPN2ImBQAClUgmVSgXJZBKlWAn5rjxWTK1AoB5A34U+vHj8xUAIiPY1bZnFxUXz7KpTxcS6CfQc6/FFuLVf1WoVM6kZLAws+PeGA+g/0w/U4dNTvD8ajZqCaZrlmEgmcO66cwZsA0A4H8bqu1bDqTsIBP3785WnlY8VGNrOf/K5gnPbQV6rNY9FVieFzcP6Ls240GBRu7VEXuVvtZlJZ+13o9EwFd61L+qMUfzE5/I7tT20b6S9PkfBKt9nBzIVy6jNT9tII+S8TwNEvLaGGqY3TGPFqRU+QKzOCqW3/l+faztPbODNz5U+jUYDi4uLvqNTn+32PQfc2rLZLLLZLI4fP46+vj5s3rwZa9asQX9/v8/YVeVATwyjrQTNgP+cb16vwF29tsoUZGSNqDKlGMASJuIPDVUFAXYqiRrFqmTYZ75PmV/7ynFEIhH09PQgnU6jXC5jenoaFy5cgOd5BtRR+DHNnEDWzgBgVIzP13HzGRwjATzv18VDoEbABACZTMZ3jzoPbM+vKmrSxlakFDZM0VYe0NQjFRLsIwU6hZRGQwn89T3kOfISx8CxMiqg9CHQ4TPq9brZb8t3sqgIAONg0CwMCrm5XXM49K5DqKebymbqqil4DQ/b/nQbwrlWOrt6SLXYnuu6JsoZDod9UW/lefKICh77aC41nJRvbEcY6c95Uf5Wfi6Xy4amlUrFB3ZV4OdyOaO0uQbVsQb404BsJ5EKVzZNe9M1qAqMfKiA2zZmlI8UbLOd/amzmLl5BvgzoP/x/iW04XriGFTpkcdtOcO+aR80MkVjk9skKFOU77km7OtUedZqtSWpoMttuS23/x1tbm4OtVoNK1aswMDAQFsjnP9Xu4MyRp3ONmAhILR1uMogBTT6DJXpCu7hAZWVFd8Y3KCLYncRKTfl08/AUgcyn8n+qTNRAYRm+Sk9pqenEYlEMDw87Mt2SqfThg7ZbBYnT56E53lYv369r/CmHQBQe0gDQtpHdQboXADw2bkcr01j/U7pDTQrL1MHJhIJhMNhlOol3PPiezC1YgqvvOOVWDW1yvSFz+no6EA8HkepVEKpVMITr3oCo1eO4sqPX4l1J9f50pdJ12w0i/tfcz8mL5r09Wv94fW48bM3wuv0sOAsGJuO2WW0pxkRNTZL0MOR/3PE96xqRxUnfuQELvndS8x47QiqgiulfTtdqnxo2xs2WNOCWu3Ash2cUV3Ne2y7V21Ke15tYKq8avdPgxF0EujWBOUR/mZ/7PVpOyBsLKN0VPoo//BaOzujXeaB67p44pYncOrqU7juU9dh7bG1vn7z+fZ82M4TmweUntonG9CXy2Vks9nvatDh+wq42fL5vElD6ejowOrVq7F161Zs2rTJTL6mi9PrE41GTaRWjUdlbmV49SDazJpKpUxqrC5S/uazqVxs7xEAX9VqnVzbqFcwOzo6ilgshv7+fgOa+ex6vY5yuYxIJGKKwzGdI5VKobu7G8Vi0RwVRa8gQSH3TzMaRmBKQKYAnYUpOP5Go2EqdCuoY3qughhmBOh54bq49W9djAr+OS/sr3rDNAVfAZXygn0cFPf5qyCyi5NRMBFs2CksWviLSlfP0mRjvyngSEvuwwfgA9iaJcC+5LbkcPjdh1EebFVzhQdEjkZQX6yjVmqBfd32QL5nGj+BvgI7AAaI24JT+ZQOCq2oqkYMDRSgVdWc59lTYCtIVkeNOpJ45nkymfQZP/p8WxGwtZs/jRzzO/K/GgI2mKXDgg4tvk95zTZeeK2mItbrdZx+x2mM/8g4vJCH4798HOEPhtF3os8oCFVaTPciX/AdPH+UvEK6kbe5lrgWbZ7nmrANO/Krris+h84pAvFlwL3cltv/zpbL5VAul1EqlbB27VqfbLUNVDoLKV/VzqEDj3JRZbsatu2AoP29DRyNrGx4WPNPaxBoBDD2ljEAwEW/fRE6H+v0yXEFCKq7VQbquLRRH3d2dprUaTXgJycn4bou1q5da3RuNBpFJpMB0LQP5ubmcOrUKdRqNWzevNmXKcYx23pHHeZqn2qmlvZZf3xOiSebOi/UccxnkK66ZzwSieDTr/w09m/eDzjAF17zBbzpU29CZj7jex71VSgUwv0/ej+OXH8EXtDDnrftQeYfM+ie6/aBx3q9jvnoPM7tPLeE/9aeWosV9RVwVjro7e01e5C5D7lWqyGXy6FWq5kgUqPRQL1RxzUfvwYP/J8HTIXyyHwEWz+ztS3/tHOqt5sHu4hou2fpFk+1IRTw2Q5+fYZGdBU8Kp/ZwNEG8Rr4sPGLrikFs8QoWptFeU95kO9X20Ob2lkMZJltCI2lW1hJJ7U12Gd7bvT3I699BAeedwBeyMP9P34/4p+IY+XoStMHpYnN4zovCrjt+SA9lX68hlt3v5uA+3u+h/vpNE7UunXrsHv3bmzcuNFnoCrIAlrMq5WJOUFasVfTUh3HMZEgjczZC0Oj7KyqqBWA7ZRoXYScWI3kKgCo1+vmKLNMJmP2Fv9XVSBpHFMJFotFU1itVCr5Kq8T8NXrzTMU+VxGahnl5k+j0TDHd3heMypJYavAgzRlpNiO8lLoaZq7ehNtkEPhp8qSTaOJFJDsjwIkAhQu9lqtZgpMaR/ouOAWAhZ0UAGsqbw8ZonAkvux1XlDwey6rboDjFYmk0nzTipsjpHgtFwuI+/mcfTHj+LUy08BAJy6g1WfX4XVH10Nt9xKv2ZqGPmClfk5D+QZOmsIqDh3Ghkm/9IZxX05AHz95JzRscPnsJAc15uuA/IQr9WMA/JgIpEwPwoo1XFEXlJFSWDPawgmKcvUUUMeoiOHfLiwsGDWB3lJ5QnpQo876cdnkufr9TrGXjuGkZ8agRtrKZPITARXvP0KhOeae5jIP/SO871qZHHN0bFCXua7+R37qHu7lQeVlpq5wPu0PgTH4rou5ubmsH//fp8h8v1u3vIe7uX2DNv30lb5YWyO42BwcBBr165dIg9Y+FHtGHUuU29RpqvjDmjpZjuDjTYXm0b26PRWUNloNOAFPNTTdYy9dQw93+5B3+E+eG4rWsg+sCkgVbstHo+bfcK0j2hTqJPANsKVVqtXrzbFKXlkaKVSwfz8PObm5lCpVLBq1Sps377dV0BNwZCOW4GIZtXR/iQN7HlTJ3Q7Jwj/VpCjkUXed9uNt+HBnQ/CDbR0Q/dsN37ur38OAbcF8Dgve27ag4de9BDqkZZuSGaT+Om/+2msia8x+qpcLuP02dPYe8lePP7Wx9EIN+C4DnY8vgM3334zYogZh3O1WjVbI+fn540dQprEYjFz/G61UcXhyw7j4bc+jGAliBt+7gbEF+JLIpw2XdQW0aaBH926yPupd5U3FYOojtX32O9TJwCxBu0VzpcCfXV82c/UbYO8xnbg0K7XIBWDQbYTjMEfzp3ad8prxFus78Oz1ds5N3QPOAAT2FNngMobOgX2PX8fHnvVY2hEWzyfmE/g9X/yeiQWE0to285hZzvVdLuITUuN4NMWPnr0KB5//HGzheKZtqdjq/xARLjtRmIcP34cp06dwsDAADZu3Ij169ejq6sL6XTaMK6CUiWwFstgJEijN7ZnV5mRwoZ/83PerwuFi5BVxPlMLXYE+NPdG42GYWwypB2R1PR0FQRcQHwXq2gmEgkDvgF/oSvP80yBA97HBc/v6b1ixDsWi/nSktkfjkMVBselAMLzPFPYi15SVSIcLxcuBQRpyihioVDwpfkrYGcfjCdUik8p7clP7COdNeQR8gznknNLo0E9kLzG9iqSpuoNLZfLWFxcNO8inzCya4RDGDj9wtMGbKMBrLpjFbZ8bAsaaKAWrBljguNXnqXRwrGTjpoarX1s5921z4zWPfPKI+QpzhF5UQWX0kIdLbYy4zPVUOKa0H3FLMBHJ4+9Lrmm6FBR/tbIO43JYDCInp6eJZXkyWfqLKIxaUek1eM89LkhVPuqOPfqc/BCHuKTcVz8RxcjvhhHw2llq7DQjNaPUAcJq93Oz8+b+eU8ULGqbFA6qOzTquWlUgmhUMjss2f2gTpfKNuKxeJ31bu73Jbbcvv+N8/zMD4+jnq9jlWrViGRSPjsETrkqVd1axjvVwc34I9AaURRI82adm7LIDWeHae5L3fihROYeskUtv3RNoQnw3CD/mg1ZbQ2BQCU33Sa6rYmG3zYIE2jcTwudGhoyNhrqnc9r7mn+/z58wCArVu3IpVKGXtHjX3NGFOnA3UswRidBUov0lxlvfab+llt23ZbtxqNBq4/dD32bdyHfKJVHOr6+66H47YAjDpwd929C4vxRRy46QC8oIfkdBIv/qcXo7PaCSfRsnsrlUrz/O5HdiM1mMIDL3wAW49txSu++Ao03AYQgLEhQqEQ0um0AdYa7OE1i4uLSCQScFIO+kb7sOtfd6HjoQ5Es1HU3ZaTXTPjyAdq29sRUtt21aaBHLUBaU/YPMLPbP7XNaIYglvqbKeM9tsOWmjmmgZ7dP2oc0ztrXbOHpsetn1mj4/9UQyhNNb7FcPQ3tJsSZUn7PeOu3egkC7g8M2H4YU8pGZSeP7Hn4/YfMzUAWi3blXW8MeWCbyXP/YzSN9sNmswy3er/UACbm2NRgPj4+O4cOECDh48iFWrVmHDhg1YvXo1+vv7fZFjG/xoNM8Ga3q9DcxshiIAYX8Av9Lh+9sBCzs9iAuOQIWeIxXKFIoKEG0ww/ExVZjCi0VR+J5SqYRyuYxAIGDeo94713UNqI3H4z7gy+vL5bJvrMqwqlT5XgXoCoZ5v6amULCyqaAPBpvnkcfjcdMHG9ySFhqJZ6PXThc2n68CWtOD1evFFHLOv/JKO8Wm4CUQaFbdpMeP1xPkERhGIhEc/7HjOPzaw6bfTsNB6njKF+nVegHc70ujg3vP7TQh0oJjocOA/Waf1PPI8RGgqZLWcXDOSTeOkzSlx53P5Vok8FVlV6/X0dfXh3Q6jQsXLpix6TxyXjTVm2Mmr3DcWsdAsyzIm+2KtpEH+FuLIDIThe9SGlNxrf+79XDKDiZeMIGtf7kV3ce64UT8+xX5ty1z2GfXddHd3Y1SqWRorY4OnUf2kwCa/VNHIzMoksmkyXDhetZoCkG68vByW27L7X93m5qaMlGtnp6ep7R1VN7x/7asUDmnznfVjaqTAH+2H7+njB2/ZRynfukUEASOv+s4Nv/5ZoSy/r3Cqh/VoFa9BcBnRKvtoiDUDsDoe5LJJObm5uB5nol0a0CEz5idncW5c+eQy+Vw+eWXI5PJtE0BZz9ID7Wf9O92tNM50LGz77Ydqe/T4qxjXWOoB/2ZTGcHzmJrfSvceivVVytxX/nZK1HP13HmmjO48mNXoudsD8LDrWM5OSee5yEej+O5jz4X6VAaV+y9Am7Y9Y1Fg0iO4yCTySCRSJjsAQBmi2elVsGDL38QY5vGcOlHLkV8PA4Xrm/saqu3ywJoN9dKc9ve0P8rX6r9z2fYgNsGnjbfA61UbJ0rXqPZGaFQCPVYHdOXTKPnnp4l42R/7bnWsWhQSdeNpobrWHQM7D9pTZtCwbPOqToGnoouOmZdb1d87goE60GMXDuC6z51HXpP9KIeqBubh80eu8os24Gi86CfUYbxuYuLi9/1gmnADwHgZvM8D3Nzc8hmsxgZGUFPTw82b96MDRs2YHBw0Ae8KeS0qrMCTZ0EjUqSaRg1VS+s7pO0GYDglMxspzmph1Ej7griyOgEL+y39pVMrP0liNCoezDY3A/a2dmJcrmM+fl5Y8STBlzMgB/QaESTYKVYLMLzPHR2dvoibto3jbKqALD3negiZAo7wT77onTgAk8kEr40e1bNJo1YDVXT2Bi1YzovBQEBC+dfhTFpTGCrXmYVXOrFs4U6/yZd1BvJ7AaCnHg8jviZuI/XHc9BZjpj+kGvpioI5R3SShWNGgKMdGvqmgJrHvWgPKhzSr4ivRRMc8zq4FLw3o4XWCVfvaeFQsH0S9eWepU11VH7pL/VuaIZHMqbtsLlfFHYsg/kP/ISx6DH12kq++p/WY2eh3uQ2JdANVpdsteewJnPIZjm/C0sLCCXyy3hQwX+nueZI8cCgVZVdjUi9fnBYBCZTAb9/f0YGRkxaXwAkE6nfWnpy225Lbf/txptg2KxiJUrVy75Xp3zAIzcp2yiTFMAAfgzwAD/sVj8XHWDGvoXXncBZ37mDPAkFpi7dg6Hk4dx6a9cikbZH9FT+adyXUGrfua6LlKpFEKhEBYWFpZcqzqB/aLMnpiYQCgUwpo1a4ydScd5b28vwuEwpqenMTc3hwMHDmDr1q3o6upaAoiURvoefZ8NvvmdXQBLv7OzB6jzgsGgCVhQH11y+BKECiH882v+GXCAK++/Etd+9VpUy1Uf3dQmcF0XWz67BT0P9GDF2AqEu1tZceoEqdfrpvDZ1XuvXuKcV11DO50BmHA4jFgshkKhYHT3d97wHRy+/jDgAHt+bg8u++BliI3FltDTBpq2Xcd323NBm8AGwDpu+x61xewosL5fn2c3fa7Ng4oxnnjPE5jbModtwW0Y/NagL+ChWR4MvjDYQPuCfAMszQQgr6id1G7cnFt7XGoL2zTg9wyEcWx25F3t2N1f3o01h9ZgcHQQCLYCehogpE1oB4MUk9iAvx1mUUfH4uKiwTnfzfZDA7jZPM9DoVBAoVDA+Pg4Hn74YWzduhXbt2/H6tWrDQNSIHKyAPgmiIojHo+bBafpto7TSk+1vUTtmFH3DgH+Ss80/OlR0kWgHiYApvCVpmZz/ykjdwqaOUb+aCo7jfzOzk5TXZPRbv2bz9WjzjSimslkDD3Yb/VqsnAdhSyP1yIIY1+4YDTCSYDPyplUoIzclctl5PN5I5DpsIhGoyiVSkbY8X1M16HgZiaBXXGefBGPx317chRYauSU9zJKqNfyei5we/Hr+MkH9B5XKhUk70rikvoleOKXn4DjOrjy/16J1KkUXGfpMRUalQTgKw7Hps4Ptmg0amhBoULArxFR8jgVJ2lFTzfPfrSjCnrcm2aWUClQ6ZP+WoeA92SzWbhu87xTVnYnb3JdscCeOonaKQa7qi7BNHlTHWQK9BnZ5trS7Si6Xx1onTDAzyuVChL7EuY78p3OoS3QdT61PgXpTlnANaFbShi1Zx84Pu5b5DqbmJjwGafpdNrIQRo7tpd/uS235fb/RqtUKjh79izC4TD6+/t99oztjKPso9yiPKZ+oG2kkV01bAnS6Qy0gXogEMBlhy7DwswCZodnAQdwqg5W/+tq1At1hENhnzxVAKG6mttoFBTwb+oAZo4paFCnAPWHOlvHxsYQCASwatUq8z5uv0un0wCaOnBychKNRgM7duxAV1fXEtBpO3ttAM3x8G8bFLKpXaDX8jqN2GlUEAA2Hd+EN/7rG3Fo0yHc8LUbEKgGUEMr29B2ZjQaDbglFz0neuBGWxFYX3OAcEfYFwVVJ0I7Z7dNf6B12s3Xb/46jl571BRLW1yziId+9yHc8LM3wKksLVRGcGcDZr7TdFPsBjvNX+0BG1Tr/Rrc03co9uA7OE8aNFA+sKOyjuMAUeDRX3kUk1dMAgHg8DsPI91IY3DfIDzXXw3ddq6fP38es7OzxhaKJWIYv2wcxcEi5rfPY+vfb0UgG4ATcRCotfqroNumIW1CDbbYNoOuQR2bgnHKFNo1pJ/neQg2glhxeoWZbwXotOu5NvXd2k87c1P7Y2ch896ZmRkUCgV8t9sPZNG0/06LRqNYu3YtduzYgS1btpijmThRZCICM0aYdYES/HGR6N5OwF/oQgtUkBl5j4JgTi4BI9DaI64OAD6LBr0KLO7/5L005DXKZadCqHBVwMl+l8tlEyWmEa+Vzunt0ag9BScrpXOhUIFxPBr1Y3/ZVDhxbyyLt2k0mAuHz9XiXqRTu0XGiG06nTbnUKvC4NnMpCuVrr03l/Olc87xsP/kLS38YntxbYVYq9WQz+cRj8cNCG40GkgkEph++TTCI2F0H+tGtdKqek6gq04a8hL7reCRvMqCfORJKiRmFYRCzUKAbNxCwHXCAnF0GOnzteaALfjVYcP1pYqJ/Y3H4+ZMSwp6FcYUlnq97bkm+OeaI+9zDhZ7F4FJIFRp7UnX/ijQpodYZQaAJWuUfEca6hmZBLxaY0Lpr+NXsMz1Tj7RrRZ8n2bq2MYwn08wzmq5Ojd8Lp/J+4Cm0X38+HGcO7e0uuz3s3nLRdOW2zNsP+i2yg9yGxoawooVK4wcBFryTx3HlD3UKepkVnmjgQpg6dE+vMa2EbyAh4P/eBDlwTK2/tVW9N3bZ+wP1TNq1CtY1ucquGNTOa7PYx/t6/V9oVAIQ0NDWLlypaETdVahUMDU1BSy2Szy+TxSqRQuvfRS9Pb2mudo9pG+W2mlAE9ppb/bOZuVhoB/b71mJjDgwmAJ7V79oW3C/uXzeSwsLCAUCiGTyWBwcBArV640eqtareJQ6hC+8rqv4C23vgX9uX6jqxWw2Q5zHRf7xr5me7K49advxULXAgAgPhHHpb9+KTLjGTMupZMCd7U97bo/tpNC6alA3AadNu/Z4N7mOx2bOn20j3q98urZW87i+E8cRyPRqquSHkvj5t+9GbFizNBSnw/A2BKVSsUEOSYvn8Qjv/GIeU6oGMLWT2/FwuYF7PzYTgQXWw4ytb05bwBMYd1MJoPi+iI6xjvg1T3f3Cnt+MNxKfhV0KuAm/aQ4jXbmaG1E/i54gp9rtqKvF9ljed5KJVKePjhh3Hs2LH/UdDh6dgqP3QR7qdqlUoFx44dw4kTJzA8PIw1a9Zg7dq1GBwcNMc42EKVkWMFb5reQOOU1wN+Q1iLYanXRVO3VVkpkzCypMyj79MK4QR17CsBOftCA1uNet0H4nmt6sfhcBjd3c2jHCYnJ80+b42SuW4z7ZdVz9UzxN+lUsmMm/0k0NbMAn6uzO66zWJOuVzOVD7UtBelBe9hxI4Fu/RvKhGN7K5duxYzMzOYmpry7em1lZR6ylR4kw7qlWNTZa1RfPaZAKrdPABAIpHwOW3C4TBqgzUkzyaRPtY655NjoVNCHRlUyOrZtT35dCaoIiYt7cwL3kvlyHklz2kkQlOLVJip4uC95GXXbUW1SatqtYpUKmUKiSkYVMcV0DQcBgYG4LouJiYmfNFrFvnzvFaNBs/zkBvO4eT7TyKzL4Phjw6jXm05yWg4KZjVdagebnuLBemraf7KF2qI0hDRc+9JN3UgkEd0zFqgj3OvXuF2joFEIoEVK1Zgfn4esVgMuVzORJ9sp4euR1Wsy225Lbf/N9vY2BhKpRJWrVpl9tBSVlIXKPAA4JNJNmilXFPbRkGL6lUFSI7rYMf7dmDh2gV0f6MbruPfF8t3AC15rpFUG1Db71WHOJ/BMbSzBxRI1Go1nD9/HqFQCIODg75013g8jt7eXvOOfD6PAwcOYPPmzRgYGPA9S52eNkBrB0RVJ6mzVfW72gA2nVR/HFl/BCuOrYCD1nU2eNSAj62faUvqPB5ffxyff+nnUUwUcftrbsdrvvIa9Ez2mH6p/tE+tQOkrutiom8Cd7/+bgO2AWD9F9cjM57x3adObNWFam/THmvnNOK7FdgpXW0njtoJ7IdmftjzqvQlPfm5OkHUnvI8D2vuWAMXLo7/zHF4YQ+ZIxlc8leXoD5VRzlU9tmQminLeWK/Jp47gUff8ShkqlFP1nHw7Qeb99aD2PnxnQiXw75xapCKlc6r1SrG1o5h7y/uxda7tmLtnWvheR6mrp5C7xO9CNdb2bU2Xyv92zkqVB4oFmtnw9rrVflGnUztHGc6p47jmIzp/wnYfrrtfw3gZnNdF2fOnMH58+dx4MABDA4OYuvWrRgaGjJ7FmnIkok0xVsNURq5mqbuuq7PEFZDWRevCnwFKnx+PB43XiRVAIA/oqb9UQBEJcZnEizz/byXjM/FAwCzs7MGhAIwZf6B5mJlpXMKKUa+OV4KX6ZjEXDT+UABol5Wvp+0CAaD6OjoMNH7RCKBQCCATCaD4eFhjI6OGrBMGsRiMXMMloItPf6LdBkZGTH0YF/osQ2Hw6bwCefUdV3Mz8+bY7coDFhwhXyjnn1V6OpUUcWkAE75R40LL+1h/7v3o5KpYPef7UbqZMoYOVoIrl0EVMG8gmDOD/eZUTgzkqsOEo5Lj8JKJBKIxWJYXFxEvd48e52gke/n36Q9i51xvAoG9RgtOo0I9ClQlQd1bXFdT01NmWPLFHDSCKKDIhgMothdxPFfPY789jwWdyyiFq1h8IODZl2o8KfzjSnc5CtmB3ANMctAU+q5BoLBIIaHh+E4Ds6cOePjW9dtHemlSojPUR6kgmPUnOua7yUdyZOkLWUAeZG/mVppGxOkL/tOGiy35bbc/t9us7OzKJVKGB4eRldXl09n28EBbnmhoWvbPnYKul7DppFrtrM/fxbrP74eA3cPwMNSkKwGs9ba4HVqhOtn+k7bPrMdp+wPgxTz8/NGt1UqFYyNjSEUCqG/v988NxQKIZVKmfunp6cxOzuLAwcOoNFoYOXKlb7thgoK1BalLiANaY+qHWHTRCO9pAW/U9vg4PqD+PzzPo9Vm1bh1be+2vRd7Rl1TtDpTLuLvMDsQQA4vOYwvnDTF1BMNG3HsyvP4vMv+zze8MU3oGO+w9gaOgc6P4D/GKdsZxZ3/9jdmBya9PHmuRecQ/dD3YhdiPnsC7VvbVCn79N55b0KnJUWCuCVFvoOm/5qz+h8Kn31fdS/tO3sOR76whDqC3WMv2Yca39/LWqjNcwGZpcUCKYdrsdwmYDPrAv8F1hy9IWjqMVruOSPL/Hxkc4P3zW3eQ5Hf+4oCgMFPP7Gx1EKlhA/H8fBnz6I3gO9uOxDl/kKGNtzYINrdcDodZxTdezoll/a+u1S+vX5Onc6B/r54uKiqWvz3W7/6wA3W73eLPM+Pz+PkZERdHV1YfPmzdi5cydWrFiBYDBoKnprlMeOXnOi+DkNfqBVxZF7TGnw63dcuIwk0ijmMwmeeR8Vh6aR66JlBEyjyVqUQA133kvwRhBhV/ymIgwEAgZUBQIBU/mY+2FZXV0rV6piYAq2Am2Ol++2ryX4zOfziEQi6O7uxvDwMObn55HNZn3gge9Sgalp4hr9p2AjAHccx+yB5b3q7bTpqPvZ+V5mJSi99HvSmZHjRqNhzipXhc+xhMNhhOIhPPChB5Db0Fzwj/7Oo7jm/16DRDbhO5NSBRWFMo9/Ih+kUimfYrQFngobFVpKTyryaDSKTZs24eDBg5iZmQEAk6KvWx4UsHEuqDhYA4HvoAAlXxAoc361IJumRPMdunVCjShVjvF4HHWnjr1/shfF4abyhwNM3TIFVIBVH1plnskK/9yDpylaGsFWPlcHFq/hOpqammquTzRw5B1HsPVzW5GYS5jsBI5fPew6D+VyGZFIBJlMxpfqp4aQZhvYCpxz+P+x995hdl3lufi7T2/Ti2YkzUgaaUayiiVbwjbYxgbTEmoghISEQAqp8EvCDZBy4aaHEm5yExICCYGQQCAkhBLAQAAb3HCRrN7LSDOaGU2f08ve+/fH8bvOu9ccBwi4AOd7nnlm5py9115tf9/3fm1NT08HohTUIKTvnxqN1LjUoha16AebCoUCzp49i9HRUXR2dprPbcWYvFrzHxVUUc9QcGQbpNUT6MLF+C+PY/rHppHflsfVb74aqAW9r+rFIp/T1BleR14Zj8eNMbgZCLO9liqvCDbz+XzA2A7UoyrHx8cRjUbR29tr+GsoVD+hRA26KysrOHLkCDzPw9DQ0CqdjWOhLqMeYZU77L8CDHXW6JxrVAFB/rm15/DRZ38U+WQey9uX4f64ix/56I+YaDh6TFXn0bWqVqtIJpPGAcH+bJjagHVX1mGpbamed+852Hl6JzqKHYF14prY4ErXxvM8pJZT2HxkM66svQI/9Mh+8oD+e/sRn2/UVgKCEQ52m7rOCmSpg9kGGeoyOgeqf9sAVJ+puhPboy6kYJ19aeaQYTscW61WQ/enupH4agLOjIOcn2s6ZuqDbW1tJmqQ6YvdD3Zj75v34sF3PgiEAN/xEXJD8CKP7Jsa0P/RfiyvLMNzG0fVsc/UzSq9FRx961GU++t6ghfzcPLlJ+u5/+1VTN48CYSBG959Q2CsGkGsYNeAbcfHkecdQf/5fqw7ty6wZpxToIGN2C/qiwqyVR/nO2TvNd07lUoFy8vL/+Ozt79d+r7J4f5Wqb29HVu3bsWOHTuwfv16E9JMhVNDzKmwqjeZLwYZUSgUQiqVguu6phK4bjBuGK0SqC8c0CjPr55wO2yXjAJohHyzDful53PZZ81jVqaqYci2IHWcesGyfD5vPPF8gRmuqso+gRT7qmBMPbBsi/2wQWA8HkdPTw/y+TyKxWIALOoLaEcpcB74MqqVj6CO866Fp9g3AnauaalUCoS4s3+ai6/PVA+m7hfbQEDwTXCz/537sbhnMRDu03mqEzf/5s0BwEcDDMfPMfI50WgU69evR3t7O06ePGmOH1PGrYYQji2TyZhoC4JOFgPj/LGKLeeMwpzGGXqHgcY5k+ol5z5RxcDz6uF3LODFvpJRcuycX+4V7leulXoENDJhac0SDrzzACr9FcAHuh7swrY3bEPIDxmPPZ+VTCZNNAefQY8732WtpMq1VxBt6jMkXJx81UlMvGwC4WIYN/zyDehZ6jHKFAWQAmFGd/BIFN/3A/tbBR/3MZ+nR3ypZ0aNf2og07Y4d45TD6u6++67n3Sg22/lcLfo26TvF13lyUKbNm1Cb29voNiZ7e37ZiBK+Q//t4GjG3Nx6ZWXMPEzE/Wzd32g594ebP3TrQgtNU5zobxQZZv9sPUEtq06jvYFaCjfzfrINhUwAnXHSzKZxMLCAqLRKDZv3ozu7u4AiPb9eirfzMwMrly5glKphEwmg7GxMaxfv75pv1UPU7DQTMfhD9fFBvC20b2SrOAdP/YOzHXOySIBVx28Cs/79PMQzoZN3RbqitQvWPPH9+unZKxbty5QpNhxHFTcCt7/0vfj3NA53HL/LXj2Xc+G5zY8mJonrtGOxtjiBnOuc4kcvvgTX8T5HecRqoYw8qkRjH5wFG416Mluts90btXwAARPO2k2v2qY5m81wui+s5+nbXBMmhZnP5fj17GwHTowqHs1G5tGzUWjUXR0dKCvr89Egy4vL6NQKMBxHCzvW0ZoNITZzbO4+jNX43Nv+BzKkTI2/+/NSN2XAvzmRgqS53tYfuoyxv9wHG6XC9SA2JUYKmsbkXHhUhh7PrEHO7+408yDPV/6f8kt4eiNR/HAqx4AALzoHS+qVyi39q/2Q9tSA5i9D7ifbD6hBo3l5WUcPHgQJ06cwHdK34qu8gMHuEmpVArDw8PYsmULNm7caDYpgQhBBIEYlXBdePWsaU41Q0l4jQ3iFZTaQoekjIAgSMG1FmJSocAXkKT3KfBRJqceYLsdbs5CoYB8Po9yuYzZ2Vlj4SQwVUuhfb6f/cKxGjkZOIV4JBJBb28vkskkLl26FKhCzjbIAO3wZqAhoBQIKWDjNZ5XP3ucoIr9UuBCpksvNYG7Xsf9oWvNPcC51KryvJ4Ad25kDkfedKThiX2Env2aZyO9nA4wJ41q4DzbYc0qxAAY0E0BQyVF953neUgkEkgkEoE+axv8oddec5+j0WjAgMF5UEbHsSvQ4zy3tbUFKnGrAYd7h89JJpPIZrMB5YL7hn1WQ8vUyBRO/9ZppC+kcc3br0ElX0E+nw/sBbZB0Mt55dywPfVG873k2ExeesTFqZ8+hQs/esHMX3I6iT3v2IPOE52mfXqFKMR7e3sRj8dx+fLlgHFIjWTcA6pEqhVao0d076tXRuee889ohaWlJdxxxx2rhPoTTS3A3aJvl77fdJUnmhzHQV9fHwYHB5FK1U9hUE8gr1H+osBRZRWwGhgDjzgZ1tZw6vdPIburEd6ZvJzE1ndtRcdDHaucAvbzKWf4nfJRVcpVPvHUDXVw2A4Rkh2yrDpfJpPBxo0bTdqi8d759VN15ufnTSG1cDiMsevGENsbw8jkiJkj22DBcdkGCht82EYBBeM24JjpnMGHn/dhXBpoFMfsudKD5338eRg4PxAonMux1Wo1ZLNZcwJMOp3G0NAQ1qxZE4gmdBwHNaeGL1//ZTz7nmcb2aUOC+ox7LvKUzV45BI5fPkFX8aJfXUglJnI4Bm/8gyjg1BuN9Ohm+0TjVyz94/9W2Wmep2bzTkdbNqmjonf6VpwbygW0P3G8VBHZrSbvacVR7AAbltbGzo6OhAKhUydpEqlYo7WZfpotVrFZPskzifOI/PFjNFt7XWw3yHHcbD47EVMvGkCnV/uRN+/9eHCH1xAcVsRTtXBzo/vxO7P717lENS5Z3sA8PAtD+PeV9xr5i9SjuDZ73s2ho8OB66z/9b55tzY/dY11f/12qmpKTz44IOYnp7Gd0rfiq7yfRtS/s2oUCjgxIkTOH/+PHp7e7Fx40aT683jrWxAxwJWDG3lwtJbS9CjIJyCgJuVL6DNvOk9bWZ5U8BOUsbFF06JfWPYMQtXsQCXVuqmkq8FzthfgiHmAjOXl56wdDptxk9mqBYkZYQaBqZVmdUrz/xxtdaqZ5ZMhsXwLly4YKyynDMydoJdDZ9lqkAqlWpYfh/x6CoIVfCiLy2FulouyfwJDFOplAGp9BzzfjJS13XhhJy6JV9o8+c2I15sFNjivlMGy7nmvBDga7iWGinIlNlnMkF6bFWoxGIxs8e5xhqloOuouf18BttRIa/95PrY66vh6NxrOt5YLIY1a9YgFAphaWnJtE0BpmHbZPQdRzow+o5RpMfTiCKKaLq+D7LZbCDywzZScZxMAeH6MjpC51KBtx/2zdmx+h6GIiEzV21tbYFCf6FQCHNzc8bDzXXh3PBdV6Oeero5rxq1ou8254j3qEFB0yZUCLWoRS1qEcn3fczOzqJYLGLLli3IZDJIp9NYWVkJyD4geJwm0Bww2kZV6heRKxFsfttmnHnzGeSuziG6EMXoO0fRfahRNE35O+UWeZfKahakpMPBVtwVXPE7TYdTXU7lHg0LAIx8pEyZmpoKpCKqDtbV1WXmM1vM4j+f/59wd7t42e0vw+bJzaYd+zm2J5if295vyiHKey3MpWPtX+zHSz//Unzshz6G6YFpdCx04Hn//jysu7gOVa8akDnsj+p3AIzeoHKD8xQOhw3YVh2J/dD5VaeGeqKrThVffNkXcXrXaXNvqbOEC8+8gLW3rw3Mhw2g+JxyuRyI8tLrdO4UbNugTPeMeqcJ8vgd944+o5luqHuf99pg2zYc8B41pthAWPtAQz7b4N/U6/jO1Wo1JM4n0D/fj7yfXwVmbaOFzm33f3UjXA2j/Z52hN0wtrx9C86+5SxG/msEW/9rK/xYY7+pgUPbN/jACaZDAPWQd3tddW/r/9TT+A6oYcTut64t5yCbzT5u+dvADzDgJpXLZUxOTmJ6ehqHDh3C0NAQdu7ciZGREXR01PNP1EpFZsdzfCkANKdarVY2YLOZKoAAWKW3TJkmK5DzWoJttRwTNKrXVXOQ7T5xHBQy7CP77/u+UeQ5NhYnYygzGRtze+m5zmazyOVygXlW8KdjpUCiIOFLwL4o4yHD53EHW7duDYAJzc9W4aQMi8CHL2ooFDKgisyJc8wxaiqBWnRtRkjQuXHjRniehzNnzgSqZnPNqtUqQuEQFrcvotwdDOGdvnYaez6zB/GV+KqwOeYxcy+RuMaaa+Q4DQs8n2sKij1yXBjDydVCrvnwnB96+QuFAorFYmD+VCnhHuJntkGIqRuqtOha6V4gIFYvwcTEhFkTNYjp3tUcNABoP1g/oaCSqBhliF4YCvpQKGRCs9kfNWDR0sz+0zigxjLf94E0cOW6K0H+0lHGwlUL2HB4g+knx6Xz1ky5I9kFErlH+bcN0Nl/7Zu2wagHNZA82ULJW9SiFj15iKDy+PHj2Lp1q+Ffdi41EKyYrIBMo/KUNGIqdSGFrb+3Fcf+7zFs//3tyJzPBJRyG8irU4SOCcrYVCpl6nQoUOH1vu8bPYUOD/aZMp+kAIvyXMfiOA6Wl5cxMzODkZERI1PVcdHT0wOEgP2v34/Z62cBB/jI8z+CX/jEL6B/tj9glACaF3ZTQE29hfOv+qZtrFd9oX+qHy/78Mvw4dd8GD/2jz+Gnrke1PxaQCbp+rFYp3pUaaxWPVIdTzovagzgXuDnNkD1PA+O72DLw1sagNsHYtkYuvd3B9q110r1RTqP7Kg/PsMuuKVrpfod69DwmZxPTZHT9VIdn/3je8L77L2o74k6ITQNkHtf5433AY2aU6rn02mhfac+V6vVjC5sO8XUIKXOMNVFu77eVV/3WBiZCxn0/HEPurJdCIeC1dp1nGqw4O8ND2zAN37sG48sDPD8v3o+Bs8OwkOwEFozAwPb5DvR7Dv9rfsGaADux7NY7A884Ca5rotsNotjx47h5MmTWLNmDXbu3ImdO3eira3NeET5MioD0pAG3awa6qzgXAECgYvmoqoCDiDATNg+z3CmoKDSrGBGKZ1OB/rJtvWlION0XdcUGVFASeBFr7Z6vhlaHg7Xq4+nUimT/80cIACmajLBDVBnFjQqcCxqvWQ0gRa2KpVKuHDhgjnHWb2mKqDYJoVjoVAwAJ3eZ1Ua2C8Ft9qWvty8RoWG4zg4ffp0YBxqIQUAP+xj8rmTOP3zp+te0Ucomoti7/v3IrYSM2Ow94xaaTWagnOrEQZcT7ZDoNXR0RHwMthHVmlkAMO1CXJ5VByAQDE4tT4S6OtZrgCMoYXXKIBlmPXGjRtRLpcxPT0dCD2zrfbFYtEUPVRBz0ritie4WCyaIoX0fuhaMnpDwTbfWxvQcuzsjwHnK8COv9iBh9/6MKptVcADeh/uxaZPbYIHL+DFtoswqgFCDQFcT663nRqhngVVsjgW7lEaxNTrzagXhlW2qEUtatF/R6VSCYcPH8bmzZvR2dlpgI2mY9mKLvWV9evXY3x8PADO1WngOA5qqRri+Tj2/vxehGsN/mt7uHivRnSRyK+z2eyqyD/qCup8UDChgFeBm0apKYCgrsC+TE5OmrFqXzgHZ37uDOb3zZuaLSvtK/ib5/4N3vyhNyMVTpmxqEFYZb6eJ63ArpnexHYUSHG8XUtdeO2fvxZ+2UfZKwf0VzX+Um9QWZpKpQLGfc4rf1NOsc9cK+0n/9eibBplGKvGTNuO6+Dm198M5AAnFASc9GJTFitgpMNAx81x6PP0Rw36lL/2XKs+rnvFdq4pAOd9/E5ltxogOIc67yQTCSKFbhVA0pnAZ9Ojre8adSU9g13b0n1gOzQ4BrbPeQiHw0heSSKWbhyTrO0qduF+dhwHK20r+OTvfdK0/dy/ei56T/bCCwXTOtWIYI9ZHTvN+q9jV32czojl5eVH9Yo/FtQC3E3IdV1cvnwZly9fxkMPPYQNGzZg27ZtGBgYQHd3dyAn1q5oyU1ohxHTg8QibUAwnInARsM9Gb6qISmO45gXiUCXn9MQQLDBglUAAtXC+VxuZgInjp1MVY8hIvDV88M9zzOWTjK9SqWCUqlkgB09ahr2wfnj8wuFAsrlsql+SWat41RPux7txtxizqEKWAUrBFUEGnwpmR7AeeD88xlaKExfWs4L50FzmEOhkJkrGkTYruM4KHYXcezXj63ad5s/vxm9+3uRq+YCFknOma0YELgpU49EIiaMTo0QZMBktnrMGa2hrFuggpTP4n5mioEyVGW8uk95v1pLuafVms9+1mo1zM7OmmNVlJFy/6jA5PvCd4LPcN16AUO+G5oKkkwmTc46vfW+76+q7K6hX+qdV+XJdV0jtPjOrz29Ft5fejjyS0fQdbwLe9+21/SLR9EFQPojRI8+hQzTQDT1gvOvigz7znec80/Dlq1gAAgobuRbnOsWtahFLfrvyPM8nDt3Dv39/Vi3bp0xfKpMJx9LJpNGFp47d87wrmY8qZwq4/wvnUcUUYz83QhQa9Sq4XMpV8jX7DQ8De1Vj14zYzzQAIAktgEgkKbDcSloYJ+AYKTWxYsXTRi58uparYZbP38rvISH/U/dDzhA5lQGo789ilPJU9iyZQtSqdQqg4T2XWWnjkPlqQIkEueAOp3neQi5IePZtj2F1J30BB414NtGeo5dj61UA4w6euw10nQn3/dx9uqz+PSrP90Ym+NjYecCuu/rDqyzzj/b1H2hwEz3nDoqbGCmhhhbD9I5Vv2H3+v7wc/Yhu2t1UgN20Ci66f3se8cjzqH+J6ovmX/zz1YqVQCdXn4fO2fGhBso4QCaM6Z/ZndX84DnzO3fg5f/sUvo5JueJfnRucwfGYYqAQjSnSOtR3bCWX2ixg41Dlm78VcLofl5eWmDsrHilqA+5vQ/Pw85ufncfr0afT392NsbAwbN25Ef3+/WUD1zNoeUnqK+UJRmaYnjhubIJU51wztBRrWGQJBfUY4HDYvENBQpkulEorFYgBQ20xIrUb8X72F+nw7/Fz7pAWn0um08SaSYbMPAIwhQfN1OR41GGj+LAs+lMtlU6iNwJ9kV4wm+GY/KpUKstmsERbJZBIAkMvlUC6XTVu6JmrF1KrQ6kWm51AtqTROUFDxM2MAWA5j7RfX4vJzLpv+t11uw+DBQSSTSfT19WFubg6FQsH0i3uDYdxqyVTvq+5HAn+1lHMMnFc945CglABX9y292m1tbWbP0fhBizYNFxTGzK1nX9gvNVYkEglks1mzd1id3igFoWCOE//mb1UGuIe4Fyh4Xbdxtjzniu8fUGfkl37qEtZ9aN0qAamCiEKdSgv3qCpj1WoVfXf14arqVeg92Buw8vI6pnuoIY7vHqMCCKRDoRB6e+vtLCwsmL2lBgb1NqjFV73eWs2c+0eVMM5Pi1rUohZ9M3JdF9PT0yiXyxgZGVmVKwsgwO9tRZ5kgHc8hAu/cQGzz50FAHgJD9vetQ1etSGH2T75moIj/Z/X2eCG1/FaBf3aN7ZBuaJkgygFInym67qYmppCOp02Tg961j3PwzM/9UyEi2Gc23QOO/9qJ/w5HxPOBHzfx8jICNrb2wPeaY5Bx8nn231nHxX4kIpOEffuuRe7794dkKcAzMkiOj8KmDhu6k4K5mxArf1WfVHXW3+r4wAAsmkrp9YBih3FwP322JvtL/W26jorCLMBosp7BXS6/+z1VuJ8UtdT/cQ2UNgecFvHUB3E1vW4P3Sfqn5vp0Rov0896xS6P929ysNuG0/sd9k29rC/1E0Z1q57S4G3OhjLqTLccBDoFtIF+I6PcCgceLaOwWwJAfu67+zrdTy6j2u1GlZWVkzk7eNFLcD9LRKT6ycmJtDW1obNmzdj27ZtGB4eBhB8KclogMbLTu+tLTj4YulLpwDCZqj8m0q/PpsvtRZZo4JOzyI9m/qCNmNGBGFAI69bra4ETL7vo6+vD+FwGNPT08azTuBQKpWMEOI96knmXBGc02upucqVSgXpdBqdnZ0GdOdyOQMM6c1jezRA0BtIwJjP5xGJ1KuE9/T0oLe3F8ePHw8wKT6fDE2ZMkESwSfniB5aAk32Sy1q3A+e58FZcdB7b68B3JFcBNf+ybXomupCOVQ253ROTEwYps3fysxYCIOgiaTWU/u85UgkgjVr1sDzPGSzWWMsUQMI2+beIYMvl8uIRqPIZDImhYDecs6fWoDZBz6bbXPulBFz7VnQT63IKliaGY08z0N2KIvZPbNY89E1gcrwNPbQ8KCF2wiYz/7yWUy/dBrugIuxvxwz49XoFY3M4P18P9VSzv3Qc1cPfPgoOaXA0Smcl0QigauuugoLCwu4ePGiuZfGiFAohM7OTlSrVRSLRaOEOY5jUjp0T7FdgmmuiQJ9XqN7kuv6eJ1D2aIWtej7g3zfx8LCAsrlMrZu3Woi98gjXddFLpczEUCUG3akkuM4OP7W45i/Zd60Pf3caXgxDzv+aMcqZR1YXftDPZxqKFdQDqz2+qkx1wbTauy1nR92BCMAE+UXiURQKBSwsLCAtWvXmjGaKLKqi6fc/hRsa98GZ8XBQvsClpaWcOHCBZTLZezatQvd3d3GeKq6peomHC/7rfKJ/TTj8T188MUfxPjAOKrlKvbct8fIAI124o+uoYJh6o+UISrzdV9oPxS824ZsBXD87uLWi8GN5gDze+Yx+PlBc73tRdZnqsHATr3SueP3lMv2HtH7bNBrj499eTQDj+rYthFCdXuS7YXnXuX1BMAaTUtqBqT59/Gbj+PASw+gfWc7tv7a1lWeZ3tebH1N+29jFAW29trqPDiOg8ETg3jmXz4Tn/vfn4MbdbH1a1ux79/3wSk5pmaDzpuOTcfIedcISAXmNkjXMaysrDzuqXQtwP1tEkMx5ufnTZG1HTt2YPPmzcZrZltPw+FwoIq0gg0qzgsLC8jlchgYGDD54grgFPxoHqZuIoZeK3h2XTcQ3sy/6c0EEPCQaZ/J7JVRKdDgfQSp9JopI2hvbzd5v5w7BSdkKhoaryFYaqQYGhpCPp/HxYsX0dXVFRAWyiDZd83BZog7x3Hp0iVcuXIFjuOYY7oABIA/gacKZb7YLJqnQEmFQDQaRSKRMOPiOErlEhY3LOLIm4+YPRXyQ2jLtpn5PXbs2CqGDDRyoMPhMHK5XMCCq5ZVjpkMiHtOjT8bN240x1ARlHIe1dtPxsZCX/yMhg4aRwgUdU3V6MN9yHx97i1ey3BrHYMqRToOAlj2vTZUw/1/dT+8iIfKQgWdn+yE4zkBgaDKiDEGJBxM/twkLr/8MhABpl48hZgTw8jfjcDNN6yvetQYx61GEO6DaDRqjDzchww9Z1QL39dyuYzTp0+bYzo4x4zcUMs4j+PTvG+dK76DGuamChHXQc/+ptffdV20t7c/roVDWtSiFn3/UD6fx9GjR7FhwwZ0d3cb3kSwQ/nJ84DJkynLqtUqRt4xguXdy6h11XWb2GIM29+9PZDupoo9ECzMxO9VwdYUJyBYaEt/69/qNLGBpHrQKYsoE6gXMW3L8zzMzs6ivb3dGJEpZwEg4SUQmYuglmmErS8sLGBqagqe52HTDZvQ096DWDX2qN48zgf5OXVMgmDjOEnU8IEf/gBObTgFOMCXnv8lhAthjO0fg+M3QsFJHDflOmUIn69GfD0dhH2kHkM9VQuZKlBSowbH8aUXfQnnd5w3Oe4AEF+MY9tfbTPXqe6g66JrpbqKAmjHccwaUYfQPdUM/KrTQAGg7h97D6lX205p0Gt1z/K5xAVa9K2Zs4J7n7qmnbOt1zgRB6f3nsbdP343vIiH+WvncfRdRzH626MI51dXUee86Xjsd8j1XVQyFaT9dKBvarjQ+VTDi+/76LnUgxe99UU49COHcONHb0TYDaYJ6Frqc/m3gmvOj+p5NvhXXXR5eRkLCwuPu97TAtz/Q/J9H8ViEadOncKpU6ewfv16bNmyBZs2bcLAwAC6urrM4itwscNLuFGo9KrnWvOH+aNHAOl3ypRtq5KGzzIcmOHmtMgyTIjtKulLzLGQoUWjURQKBSwtLQVeUtd1Tah2NBpFf3+9CifP1w6Hwya0WYGZClGCNABYWFjAgw8+aJ4Xj8cDhdaSyaQxKDAknSBFGYBaA+08VioABEmFQgGVSsX8r4xPBVA6nTbrQibOPiiD8X0f0VgUB//6ILxEg6lUE1WcfuFp7PzoTkQiETMv6k3X0G8FV9of2xusRheCPnpMl5eX0dnZafYUw9VVQeJ+5TxlMhmsW7cOV65cwezsrAHnvu8HqmLbCoIaWRgmzb2ne47eWfsoF91znBPutZXRFRz+s8Nwk3WAfPGtF+G5Hno/02vOoUyn02b9NbIiuzGL6adPN7hgGJi+YRpr7lqDrqNdAaWLXvfe3l5EIhFcunTJtMm+0vDEMHOujx3KzfEyNUI96OqRpgKXSqXQ39+P+fl5LC0tBbzZfDaVCAAB0K/rot4fAOjq6jIpL493aFWLWtSi7x8ql8s4d+4cCoUCBgcHDd+zQa7KF6ARKeQsOdjx2h049Y5TCCOM3W/ZjUg2glAkFJBrbEvrmyi/VK+e6kEADB/k3/xOQbZ6+LTPJAJa3ks5pjqTyr2lpSWsWbPGGGbn1s2hc64ToWJDZre3txt+vbS0hLO9Z/Gp138Ka5fX4pX/+Ur0LPQYOal6nPaV/bTHEwqF8PDmh3FxzUUDYt2Ii8/+xGdRq9Uw9uAY4AfTCdmGDUhpUFbPv0ZZkvT5CiI1Ess2aHD8t/77rSjGizi57yTgAG3n27Dv9/YhVogFnFi6zpS1Cm51/RSU8X8Fw6rrqK6upDoRn63j5Pzb4FCNS/xM51cBvM6ZeqkVANvAl31LJBJGh7fnNBQKoZwq49CzDsGLPLKXHSC3NYeFZyxgzefXBPrUbE5sfQwA5p85j6mfnsK+P9uH9EoQdFPfoCPFAH+nEU3q+z7ap9txy/tuqd/rBA0Q+t7az2bfdE30e3sv6lz6vo+lpSXkcrlV6/xYUwtwf5doYmICU1NTOHLkCAYGBrBr1y6sWbMGXV1dhknZhQD0JYzH4xgaGgowOgW2BFDqpWIb9PQBq6263PwKYu0XnuBYw1QI4tgGrUcETeVyOZDHqyFXaokEYPIlUqkU2tvbTV4TjxTT3FZa91jwiaFoPJKKR4ZxjghKKIDZNueUOdc0Zqi3WT3G7Kd6Z3neuBa04lySNLyGxgwyXYIZUjgcxsXnXYQfCb7k0UIUO/5lB3wEc0647pxrBdUKyDkHBHvKsJQhqdGA4M4OsdPCXcr4ub4zMzNmnvRMasepF9rTPHzuAxoN1EihfVahRMHCteXaaFi27vfKhsqq+axtq8H7tGcK0BDoquCIRCJoP9WOzW/bjHO/fQ7loTIS0wmMvWsM8QfiOPPSM1j7H2vhoGEoiMfjmJmZMUWCSGrwAoK5YzrnGn3A95L8gAY8DannM6rVKhYWFsy86rqyHT2mRQsj2sqt7zeiFbjerDXRoha1qEX/U6rVapiYmEC1WsWmTZtWKcH8nzJXU9Zcx0X2KVlse9s2RJ0oMvMZuHADEXv6oyBLjZiUI7bRV0GX8lZtW0Ecr7UVcgXytgdVv+MzFhcX0d3djUQigcn1k/jCy76ADec24Jn/+UzzzHA4jPb2doTDYcxdM4eTv3wSXtLDRHICH3/ux/Gjt/8oehd7zfW291R1Bs6teuOfcuwpgAv827P/DdVow5M9t3YOYxgL1Dxh+hVlvxJlvNZkoTfblkdqEGC7WnVbdVTVVX3fx20fvg2hYghTg1O4+i+vRvxKHJ7fuFZBrYJdBWZqLLENELYBx153XT+9v5kXV/ek7hG7bTUW6PcKKqmL69w38yzbYFtBcbM++L6PRC6BZ/3Ds/DVV34VM2MzCJVC2PiXG9Hz2R74oWAOOYCmoFvnceb5Mzj7v87Cj/k48mtH8LQPPg1tS23mmbVazRSuzWQygahJzj/ff3svqGHhm5Hue10TBdj22nieh8XFxSfEydAC3N9Fcl0Xc3NzmJubw4ULF9DT04OxsTFs3boVw8PDgUJH9ktOwUIQxDBTfeEJcBVYacVtzWMicNXqhXyZgIZHUV8ketQ1F0lfFIIx5mBrdWm2yXkAEAB/9DzzBQ6Hw6ZatBY2K5VKxjMej8eRSqWwYcMGXLx4EZOTk0agKIAkeGff1ArLatQs3qLXa2VxMmP2VQEJ16wZQ9S5ZxtcO2WCXMf2C+1wPAc+HmGIPrDvPfsMmFdrvR471czry/W2K7naDItrzXVh1IEKfF6v1nutCk8hlM/nA9XPaZDgevq+b46r4zM5NwTfXD9axzU8XeeU/eV4dXy8Z+COAYSWQzj8jsOAA/S9vw8D/zBQfz5cs2c5/wS+BOGdD3di9C2jOPWOU7jq965C56lOnH/1eVx85UXkN+Sx9f9tDQh1FiSkcUj3vRoDPM+DG3Nx+OcOY+ff7zTP53vFvcr5VaMI9wz3WiKRMHPK+WXBO66n7hVew/HqfOk+4Lxq2GaLWtSiFn0nNDMzg2q1iuHh4VVVt1UZViPrmbeewcJNCxj5wAiGPzkM12lEA1HuqtFSQY6CO9ujaId+KzhV/UuBMuWfgnoF7eyT/q3AXUOIy+UyFhcXEd8Vx+de/jksrFnA7MAsqrEqfugTP2TmJpFIYGZ0Bod+8hDKvQ3j5/mh8/jIiz6C137stUiW6oVeFUjYgFMBGvscDofxlJNPAVaAf3nFvwAOcN3nr8PeL+2F7zXGoaBF5YSOjcWxmgEaG7DaRgG2o/PDdpmS5nke/JqPff+6D1PhKYRPh+H5wXx1HZ+CKZ0LfT77qcYX3VPNvOE2gLWNPfbzbINQM8Cuz9N77bmh7mSDXRvcq1zX90rnh2PyPA9tl9tw3Xuvw9de/zWse986dNzRAT8ULMKn74LOk+qdMy+awYVfvgA/Vm//yvYr+PqvfB3PetuzkCwnAzo460fZ7yn/tw0XGgWhUR32OquepOulDkrdoyQ6xVZWVlAsFvF4UwtwP0aUy+WQy+UwOTmJb3zjG9i5cyf27NmDDRs2GEWYIKOrqwuhUAhTU1Nmc+uLxrxOerGAhjfWfiG0ArQyN76YVPj1pWIILEOw6Zkl6GXotm7gUKiRd8v7bEufhkHzHjJyMkTNA1bATM+37/u4cOGC8f6x37zH9loTADqOY/KFk8nkqsJqGuKkwISgiHNDbyDnpFKpmHBsDdfVI6rUM2sfH5YZzwBBozk6LnQYQM15JND3fd/Ms22w4DjI3BS02R5OVgRnGwrsFXTZQpPAlHOgFnYFitxfKuh4HjfnSL0LBJ0ULlwjeuxtKzPvV0swDQWZezPY8rotWLppCQPvHUCt2KgzwDlUQR2LxUyYezQaRdupNuz4yR2IFWO4+MqLGH/VOPyYj5kXziCGGEb+dgSoBEMFtS3dR0aJTHi47//eh/xQHiE3hK3/uBV+pfFeqsKghdxUCbH3EKM9WLyOfeH/AAL94H7l+nJOWMOB8/94Fw5pUYta9P1NzI/cuHEjMplMgOepd9uP+Tj1W6cw96w5IAycfe1ZxCtx9H6uF6gFi1NqTRZtR/UfO3xVgacN2oGGV1WjglSP0Qg4oBHyq8BBwYIaLsmHF8uLuONn70C2+5Hq2w5w+JrDSOQTuOXztxjZNjwzjK1HtmL/zfvhhx9pu+Kg/wP9yC5kke5PB57HZyogUXmpn3ueh83HN+MlH3gJJjdP4oY7bgBcoOo2gK6mqtEBQiMunRd6IgvnxzZSKGgnUe9To4DKeHUK1Go1hKthtK+0I+fnjL6gBnzVR3XubSMEZR/D5Skn1RFih5rbUQO61gpqFdDrOB7NA8910bpE9t5SnUU/55j1O17PdEDFAnoN579arSK+GMee39iD2nwNNa8Rgao6h21EUX3RDbnIbcqh1hY8RnTH7TsQzoexnF9GLBYzRaJVr9S9ZYNqPpP6FJ9rG2ZI9jxR79J1VaeYzkU+n0cul3tCnAwtwP0YE8Op77nnHuzfvx8jIyPYtm0bNm/ejL6+PiSTSSwtLQU8jpoLyk2oLzCvU/Csm4tCiEo1sDoHRY8mIoVCjaPAuNEJYAnWtfo2iUDLZvAUJOoh5YvBY890jGyfnm3mG7MSNgUvwal6eRW0sL+spE2gxyqqPI+Z4bQMbdYjvyhoeC/nI5VKIZ1Or7J4s2I3wWcikTBGEmUEp37hFLxo8EU/8AsHcMs7bglU/OT4WAyDVdbJFNmuhpqrl5PrrgKQc6+AnZ+xrwRhFLYUVtx3dkh9KBRCOp02YJlt8h56/Lkv2B73p+4VAOZYPI2k0CJ2NBARqHqeB7fqInVXCum70/V+RhtzoPneZPr8WVlZQTqdrgP3xRBWRlcw+ZJJY7n1Iz6mb55G5u4Muu7rMkKPe4vjViNOrVZDbbCGg791ELmNOcABzv3IOfgFH2OfGINfaSglGgkSjUYDIfB2hAXTLOjZ5h5RQUyhyneFSpe+00DDssy90Aonb1GLWvTdplwuh5MnT2J4eBj9/f0BpwAjcRaesYDlpy0Dj+AOL+7h7I+eReLeBGKXg/qLyjFVwu30J9awoKygLCJAVweAygYaJ4Eg+KJh1/bo2p5RNRArIHQLLnZ/djfuetVd9UH6wLaD23DzZ2+G7zT6WMvXcP3Hr0c1VMXhGw8jUoxg5G9GEP9UHIfbD8O52sHAwIB5ngJV20itQJE6oeM7GD0xis3HNtd1EqeRgw4Ej6VS0K1RUgSs1PXsUHEb/AII6FTatoYt6/9zG+eQPp42+iLXXeWaAjA1TpO4vkCjaJvquza4tXVjfq6/dZ51DhSc6l6114LzqB5YjRa0oxKBoPdcn20bF5oZmHRs7FO1WoWz7MCreav2SLO2FOQCQHZ3Fgu3LAQ+S11JIXMlg3g0jkRXw6ljO590TpTYD03p1EgNBeW23s11sdde97JiHM/zTP72E0EtwP04UqlUwrFjx3DmzBkMDg5iy5YtGB0dRV9fH1KpVNMKyEAwT0GtkQo8fb9eBXR2dhbhcBjpdNoo27rJCURsCxuFEytIc/MT9LJf9BrrS6TgnS8ZwRmZlFqcaCTgfQSOGn5Mqx2vYWgKgb/neSacHkAgdEX7xfOHCSwVrOjxSRqiT5BGEK/CQ4W+5sJSINFjyBx0FrKgda/nWA8u3XbJ5M0AQN/hPgOmVVFQARcOh03usIJl7ZOOndexj5ybUCiEZDKJdDptPOfNGCPXiUyL4TcMydHjq9hPAlo1HumYeC29slwjgkrd32roUMapQsl164XtWH9AhYNa23X9+S6pUYJjSZ1OYextYzj9xtMoDhURX4pj+99uR9eBLmMkoUJHwM+5o6HFdV0sDS6h3FVuVFp1gNxYDtVYFbFaI93A9kz8d4KbP9ybOqecH7anoYC24YW8gp9XKhUUCoVVQqxFLWpRi75TqlarOHv2LGq1GgYHBw2PI9Do+0of/KiPs79+Fm7GReZsBle96yq0LbQBiWCYqx2Fpcq5fqZyQL1qeo9eazsr1EPI62ncVbnGtpnOpvJKweTFF13EiZ86Yfrk+A7Wn11v2lePre/7uO2TtyFZTiI2FUPfN/qwnF7GysoKDh48iHK5jPXr1wfAvg3UHs3jTblgz5WG7lLv1ONAVSfR00T4PJXxNqDid7YuADQMv5p+dm7XOXz9VV/Hnn/eg77b+5rKRa6BAmbb4MDPm+ltlKMalaf7phmgJ2n7qtvaIFfnxm5D54NzqfNke3/tMWjb1GG55rY31wazGuHYrF/2Gmq6oeM46Hq4C5v/bDPOvvEsKn0VpK+ksffv9qL9eDtKTsnsZVsfs/eXvru6h3R+dKzshzpNdIzKJ9RAoXNH/X5lZQX5fB5PBLUA9xNAlUoF4+PjmJiYwMMPP4x169Zh69at2LZtW+BYDW4YBYgE2gy5BmAKLTHktFgsGi8lXzQ9/5qASK29AAIAlKT52mSOhULBePoAGJClL6t6R9lPrd7NdpUpKVBU7x+t4RQE9CZHo1Hk83kTVq0gS+dOi3sxeoAvqOZ7U5hEo1FzlFmpVDJAUY+ToHGABgACH2V8tVoNuVzOtD/5w5M4/prjq4p8JeYTZl9QsHH9VclQo4aCW51fIChQNe9fAShDuJXRcS21UJzuj2ZnxjOnSz2m7Bv3LHO+6AHWMarVVwWknsOuHlwFtpprrwDUBtdqCPI8z/RHvcME4JlDGYz98RiO/8Fx7PmrPRg+OYxKW8UYCNhHeuK5bhTaoVAI3Q93I3Mhg+LaRo7QujvXIZlLIpFKBOZBBQ4jMlRp49hsgaOGtr6+PsTjcVy6dMkY2RRoqzLgOPVq8+FwGEtLSwCAQqGAFrWoRS16rOjixYsol8tYt25d4FipcDiM3s/3wl/2MfEbE9jxrh3oONsBP9xIt1HApUZhG3Ta4EflPD/XVC1+Tv1CQ90pM+iESKfTmJ+fN98BQS8p/7cBi+u6cC8GPXDwgbZsvcCU1sjROiY3f/FmVKtVFHoKpt3l5WUcPXoUtVoNGzZsCAAh1QvUCMH+fPWmr+KqL16FhJ8wgEU9hpQ5lGN6Wg51Gzu6sZmhX+dB10yfyc9VLgHAhT0XcPcr7kapvYSHfvohbK1uxcB/DgTGZYNC27OtxhSV+7oXONeqI7BPqm83A8z6bOo39rMUMPI+NQ6YbSBzb/db+2r3kc/lflHA3cwAouuj0Qb6HJts44iub/c93Yj+URQn33oS1//19Vh3fh0QbUTY0QGitXlsYwCfzevYvj0fOucco+qDbM/WH+255ryUy2WsrKyY9/zxphbgfgLJdV3Mz89jYWEBJ0+exL333ovdu3djz549gdAhblDd0ATAQBAIdXV1oaury4Bp9VxyA1LZD4fDBjgQBCjx2alUyrRHIcfwaYKnWCxmftgntXSxL0AwFIuMGGhUyCbDJ5OlUIrFYubcx1QqhUKhgGQyGTj+i55uW+gQdAON40EUdHEsnueZXCW2x7nhcWbJZBLxeNx4egEYIKjjJ0MBANdxsbB5AdVMMF82VAth6OgQwslGlXO2yX41CxFXr62GOHGcKhSptFDRYdSChmbbOdWcDwJbx6mfV57P5wMKTTIZLOTCfcN5YwV5ZbAE+vS6k2EyIsFxGvnfWkleK41zHDSUqPWW3gc+v5kFW0E5BRhTDnrP9GLvr+5FciEJ9MAYJ/L5PCruI8fOOVED/NPptDniKxKJYPL5k1jYEwy7Ov6zx9F1vAuxpeB8sFKmVtrXIiMaLkWhw1QICp+lpSX09vYimUwaQ5HudRoIaKCqVCrYsWMHLl++jMnJydaRYC1qUYseU/J9HzMzMyiVStiwYYM5qhGoy7S2r7Zhz7k9SC2n4IeCoErljSrjajynjkTwTHmgupMaZVVJ14gg/mY7AEyqmToSbI+pAgESdYKB+wbQ/afd+MbvfAOhWgg/8YGfwPpz6+GH/AD40GMhmRrU1tYWcEzk83kcPnkYn3r9p/CrX/hVZEIZI880AtB498LAV2/4Ku684U4c2HIAL/6jF6Mz2RmYW5X1XCvNjedcMbLRBnG2R51tck4453ofdQ2O/fLQZdzx03egnKqnN1UzVZz42ROILETQf18jHUHX3wZXmsZmAzx+rutoG2z0et0PNvC0nUR2lAX3jkZ4qp5mA0POOx1auq94vfZLvdtMh+T9nOtASoProlKtoOSVAvVgNMLAbl+fY3ujAaDrYBee9utPQ3epG+FMOKBbqlOQBitGBfq+D4SBcCiMkNfQabVtjoVrZj8bCB7x2syQ08z4wPeHJx09EdQC3E8C8v16rvTExAQmJiZw7733YseOHdi1axfWrl1ryuqrV8wOFyX40Nxb5nuqF48eTIIYWsf4ovi+HziaiOBFhQxBJb2nBOLKLNgvAiJbyPHcar5EfGnYHw2xUmCv1mgWv2Lfy+UyCoWC8WByrrRfttWcIedkXolEwgg/AkNWNFQLr1rQKaw4n/zNcTqOg4lnTmDieROr1v7mP7kZWAIqbn0u+XwFvhR86hnlvtF1JWPkuLkX6KlWsM57+DefSfAJBAvz6RjVmMCCXVRS1IOtlkgFuzTiMGJDra78jGeD02BABltaX0LRKSJ2Imb2CL3srusin8/D8zxjmNEceu5hW1lSRYcMOrlQD99fWVlBV1cXMpkM4h1xHHrWIaz4K9j04U1IuImAMYlRFgOfGUBuOIfJF0/Cj/iILcew8//tRPRyFJVIHbCrZ4NrTIMVFSy+08lkEp7nmXPhbQWnXC7j4sWLZi1ZHE/nPp/Pm/nMZrN44IEHAnujRS1qUYseS/L9xvm3O3bsQCaTaRwh6YTgTDuoRqoB4KpAhjJIo8zUcE85ptfa3lGSFtCyI4ZsgEP9ijJYnwkEjxpTUMC/w5Ew3Ez92hd88gUYPj9sjrlSQ4AWzdQ2E4kEuru76zKhq4pDbzyEpW1L+Iv+v8Av/PsvoCffY+S2Gg/ckIt7rr4Hn7vxc4ADLKxdwGd/67N4/t89H23ZtoBXVQ307IcCMzV42wBTx61Ap1lb6sE1+p3jYXH9ogHbpIE7B9B3X1/gOc0AmK45T7ihQ0IdDuo11T2ma2fvAy0+rNfYHmwF+nZ05qOFcatOrA4BfscxNUv1Y/Qj9SzbaWQ7qy5vv4wDzzuArb+3FU6+8Xwt/Ms+cIy6ltpnYot0Po14Kh74jtiBaaDlchm5XM4cq+rHfBx73jFU01Vc+5/XIlqNBvaN/f7pe95szdUA1ixtQte9UqlgaWnJRPY9EdQC3E9CWlhYwNe//nUcOHAAGzZsMMeK9fb2BsJHFBiSqZBxAzB5uvRkMqSCnylTssN6tZojPa4sfqV54ArKueltxsE+quVRLbsscqZMgt+rZVvvVaClLxrP9qanj3Oh55rrS63MlpWr2Y9SqWQ8jwQ/7e3tZqwATM62MmmdD9/3Uc1UMb97vpHT+wj1nulFajEVmBeCy3Q6bQAYC5NoiBePNqDnOZFImOfqHGruNr2jepSEMjMCPzJp9UArI+f1Gk6ugkEFi1rE2UZnZyf6+/sxOzsbmCe2wevUixsOh7G0dQknfvsEyr1lbPrdTei7ry+QThGJRJBMJs26q2LGcWm+eDMrqipABPCu66KzuxNnX3EWR156pL5vfA8jHxhBxI8ErNHRaBTRSBTb37cdYT+MqdumsPN9OzF8ZBhuvGHlpnEkHo+bNaORQa3u7BPfUb5fFDI0gtgeBtvo5fu+iR5gnQaOseXhblGLWvR4Ua1Ww+HDhzE2Noa2trYAYLM9e8ov1Qivod820FHvJtsheKJTQa9nO/p8fbYaQJsBLvJY1Z/Umzv13Cmcev0pAMD5jecxcnQEqUrKAHkAAX3LBheuW68FU+4q49yPnMPSdUsAgNmeWfzTD/0TXn77yzG4MhgYeygUQrW7iv+49T8aE+8AVzZcwcFbDuKGT94Q0LHsuaJsZFs8wlWj7XQe7Tm1ATnHYetfvu+jGqri/M7zgT0SXYqi+3A3HDjw/Ea6lgJB2wMcDjeOJrU967q+7It6ym2Die0g4B6hvqDeYXV+qS6n1+t+USCp+4bRmfaaqIeZRH1H942Cdo45FArh/DXnccfP3QEv6uHkG05iwzs2ID4X/6Z9soG2/q0h7fY7Q/2EfeQ7V66WceIFJ3D45YfNc6/71HUI+8Fz7O19yPHwHjuCgZ8rhlFswXaLxSKWlpYCtZ4eb2oB7icx5XI5HD16FGfPnkVPTw82btyIkZERDA0NGS8uASE9j8DqYwSolKv1VBkPwQnQCGNVAKXePzJcz/NMODHzyW2Pu/6tOeRkWOpJNZZuAfIAzIsNBKtGEihQGLLatFo47fByvnz2MQh8PtvSULRKpYJ4PI7Ozk4ADUFCRheJREyVZzJNFfy1Wg3VchVuKZjH1X2+G0/9h6cicyWDit8IISbx2WRu9E6qt54hwqVSCd3d3WacBNNcL1swakE2m6Gp4NDnMDJCjznTgnw0eugcqiGETNh1XeRyOWMBtYWcHk2igmply0q9mNlwPS96/HfGEXpXCP139Zs54/7gsRSsZcB2VDhy3e00AyXOYa1Ww4GXHcDZl5w13028cgJ+0sfWv966quotBd3mv9uMzgOdWHd4HaLJqAHXJK2QT2VEzyPV91Zzu1U4cR7ZXzWQcR0I5DlHHCeF2hNxHmWLWtSiH1zyPA9nzpzBmjVr0NPTY4z5NnCzjbX6GakZ8LPDz9WYbEf/8Do1KrNdyrpMJoOlpaVVnkjyegX+JNd1MfVjU7jw2gvG2H5432GUo2W8+KMvRgiNXGK9X71zCjCS8STSsUYoPgBcHLqIf3vev+FVn3sVugvdAcAVK8Rwy4FbcOc1d5rru2a6MHZoLODVtI3PCmo5f9Qz1eOpxgHKLV0fvV+j3NQI7rouHM/BjR+6EfhxYPwp43CqDnb8+Q703dtnIgGaAVmS/bcCRTWcaJ/sdda9ZBs89DrbCcTxPpoRQEFqs/5on9Wbq3oRSXWXZtEGOje899R1p3Dvy+81RV8Xn74IL+ph7HfHECkF0y9INhBXDzxDxPv6+ozhXudGjQN0KFC/2f+y/Tj6wqPmOYefdxi1aA1P/ZenGlxgG7T4t76fmjKh77fq9NSbqP94Xj19b3l5eRX/eDypBbi/B6hUKmFychIzMzM4dOgQ1q9fj7GxMYyMjJjNrCX1yRi7u7vR3d2NK1eurLLqkDmod7tarSKfzwdCzekV1Txx3qOeaxJBFQEa0AjfIpOKRCImpFgZvfaNFkTmNKsXE2iAU75QmodML2YmkzFF3nK5XCC/RJkpw4/1eAsFjqxoqZZQ9STT2KEAk2sRjUaR78xj/rp5M77EYgLXv/N6xGfjqDm1ALPlOhSLRRNKrMJMjSDM8aLhQcNpVKiooGAePNvVNeXcaiQAQammJagBRJmXGmXU2si5jUQiJswon8+vCodiGwwF5z4od5Zx/I+OozzYCDmr9dVw4U0XkC6l0X2427RBgWT3NZVKmdoHfI4aH1TI2V5i3/ex5vY1OP+S8/Bijwgjz8HVD16NSDpiKl7aBo8oouh/oB+hVCiwp9lmOBw2Rfn4GaMl1MPAeeV3WkSQ/xP0qzJCAcQICd2/aqV/IgVQi1rUoh9MqtVqmJqaQi6Xw8jIyCqvqM2Dba+XDfxI+hn5ofLTZoBKea2G8JLvZrPZACh5NE9rAJzBw+K1i/CSQf667b5tcKsuwtFGYSkFvewHZRf7mcwm8YxPPAPleBkXrrpQB/EeUPl0BafvP41dY7vQ0dFh9Bh/xcezvvYsFP0i7r/mfiQKCbz4vS9G+3w7an4tMBdqNFe9UD2Vtty3ATb/1rXiXKqnUR0BfGZiKYF979+HUqyE4Y8Oo3N/J4AGUNW5VhmneqMddaCA1Qalule04C/vVU+9GoJsr28zYzhJo8t0HfVvfk95bO8n/Zx6suqduo81rJzPWH9yPdJLaZQyJbNf+r7Sh2gtaiIHuE7NwrE5r+phr7pVlMoltLW1IR6Pm3pOql80M7psvnszTvzQCbixR5wwnoOhLw2ZAshacV2dOYzy0/fVnqNm86+gnAWQs9nsE6rvOMqo/tsLHad1bsyTiBynfibj2NhYoLo5N9zMzAwSiQR27twJAAZsKtBQ5sKNyQrg6kGjtYjeW4IUBYdkokAjD4jPVIsUXxB6pPm/ni+sjJXV1j2vXoCER2PRi8exqeGAXj8C/VCoHh5eKBRMcSsCzlKpZAwDtPrauV92Lg77pwxCrWlqlUME+NK7voTcUOPcP6fmYOT2Eex6/64AQ+WzaRSIxWLo7OwMFCshYNQzxdVjr0W4NN/N8zysrKzA9+uF9dwhF23zbabvDDfmnlDQzf+1mJd6IQAE0hoYXaD5WzRcsB3mGqvxhnOux3xRMM/tm8PJt5xEreMRD38xhM3/uBkb/2MjatWGwFNGzc9UoeLvfD6PYrForLB6PcdLAw5D+rwRD1/7w6/BD/u45W23oP1Mu+mv6zYKtNm576FQyBS/4XvAfa0Cg8Jb14794fxohIEWieG6eJ5nojxouaYRTAWY5mzdc889T1ovt+/7zje/qkUtalBLV/neo1gsht27dwfS34wMRRAUk1ReKTDT75gmpvwfCIbIAg0jM4uh6jP0egV7zYAfZR0ATP3UFM7/4nlAHJqZlQxe9bevQupKyugW7J+mvamnWSO0arUaEAE++UufxOSmSWz6xCasfc9aVMtVDA8PY+vWrejo6DD9q1QqyJfy+PSLP42nf+bpiC3HArLA/juXyxmjOOVvPB7HmjVrsH79enPMKfUn9ZJzjjl/WhOGOpv9zHK5XNcVYjUUnALK02VUihV4brBitQJR9Xbyf9vLzH5Qd1PjBWWrgnXV4VQeaxQEr1OvvbbJsWoUmo5Vn8X+sA3qfKp/ci9oPSGmxSWTSbS1tZnjhG0AqsaQQrWAT/+fTyPXl8Pwu4fR/x/9CCEY+aH9o06meMJxHPgRH7UNNVz+8ctYe2ItNj2wCYlYwlzXLLWNf/M5K/0r+OL//iIQAm56203oONMB+I0TktTRp+8n29E+6nxSj1aPv95XLpdx/PhxHDhw4DE7Euxb0VVagPt7nEKhELq6urBx40Zs3boVfX196OnpMV5X5rhSiacFly88816TySSy2Syy2awBdQSZCm4ViPCFoOeNir0WJVMPNBDMCSegJ1MnU1FrJBmPgnYyIrVGq8HAtlDrS8jqiQyPZqE1giYyO2XKGoKv46LHmIyGIFjDgmq1GgpdBdz/pvsxt2UO8IGR20dw7T9ca9rSfGO2ReDc0dGBjo6OVVXTydw5pxoWzzVyHCdQZZ2gunRtCff+2r248WM3YuPDG83z7XFzfqgMlMtl+L5viuDZTFWFiJ6brsYIrcDOdnSdKPRDoRBSqVRgfHO3zWH8DeNw21wMvHcAQ/88hHg8jlQqZZQUtUKzPV2LWq2Grq4uMxauOfcY76PRhoCcZ5DOjs7CTboYOj5kFDqtpM55Y7sUwMlkcpVXhc8keOaeBRoGE8478/kpPBgB4HmeKR7IOeA+KBaLqFaraG9vN+8Mrflc02KxiAceeKAFuFv0fUMtXeV7k+LxOLZs2YJMJhMAPQACMt32cOuP7eGzFW9Ne3o0UpnEZ9rGWPJnAAHeb/SbNcDkGycxcVOwUOqL/vVFuGr/VQBgdBV1FlAm6Lg5DsoMz/PghT3cddtd2PpPW7G0tISVlRUAwNq1a7FlyxZ0dHTAcRwUCgVTSFSBtdadUQOx67pYXl4OHP2aSCSwdu1aDA4OmloxnBeCbk2r4hzpb66HhvhyrhZyCzj+o8exMLyAHX+9A6HJxvzzOuoR9l7geqmnWXUTXRsFcuxzM5lsP0O923we/9dxq86q1ygY1n1rPMaPzCGdQbru1F8I0qmHZDIZZDIZ44yxwajqlsViEbPeLE497RTW/PMa0wf2SQGx9p/gFwCckIPpl03j3G+ce+RlAp7yV0/Blvu2BOZX50lJ98Ps6Cz8tI/Bw4PG88x3iniAerfq3va7bGNX+/lqBKCec/z48ccsh/tb0VVaIeXf4+R5Hubn5zE/P4+TJ09i7dq1Jtx8cHAwYKkiM+TLoXk06mmjp45MR0OtgWDBKYJ5WuZ4tJdaeimc1PunXlOCCgJvggn2mUBDmYJagJXBJRIJA7xtZkiQRyHCPheLReRyORN2rn23GZN6djkGZQwqzOPxOHp6erAQW0CkXH/Vxj45hu0f2R4QABoez/95XJsd6t6sT2rsAIJhVirQ4/E4ZkZncOgXD6HQU8Cdr74TxWgRG+7cYMAar1WGTSao1ndViPi55mST2bE/anFlNXh9HvcGc4RUaFBId9zegQ3lDah11NDxbx3IVrOmQF4qlUI6nQ4IXFWUCOAp/OzCca7rmmr0driWRlb0nOypew1CeaRSKbOf1HvPZ6hB5PzTz2P46DBiSw2gzPlTQ4WGF9L4Q6Gq3hy7gA+VF17v+745zozHhFFpofGGUQlPZIhVi1rUohYB9YKnp0+fxsDAAAYGBgzvVpCtnlT9rVFAzbzEQDDFJh6PGyBqgzTlzUAwnJ1tqA4EBHPNgfoxoHk/6Elbd3Yd+sb7DEDSquoE35T/Gpmm8tY4GhDG07/4dJQ6SmZs2WwWExMTKBaLKPxyAc889sxVgFE9tlo4k3oZ5YIW46IOZoNH2/Chn/GZKrfUuK1A/Nirj+HYs48BDlD7lRp2vm0nwstBnVXXU9fMBlcKalVHUQDO+dT6NvbY7H2mf2vkhb239FnN+qp6kOrYOi+kR/MYU5bb52+TdC2oPzrLDtZ9dB2qfjUwJr23mR7AtiZ+cgIXf+GifAHs/8X98Nt8XPXVqwKGIW1H22b/B84N1Pd6NGRSHNVJYYN+Hae+Y/Yz9B3l5+xLLpfD0tJSwHn4RFALcH8fUS6Xw+nTp3Hx4kW0tbVhbGwMO3fuxObNmwNeSQUi1Wo1YNHkDxkwgRo9aFTeKRg014heQ9uSTCEHwIQbAwgAB1pLKQgYRqMhwuoBppBg4SmgYUUjQ02l6pVAmTOsYCsej6OtrQ2OU6/yTVBFAwCPMlDGSg85588Of2EoPucjFAohmori87/0eUyvnQYATF87jbGPjCGGmAFJZCxA8KgvDdnSkCi1SodCIXM+uD5XATKFR3FDEQfecADFNXVvZiVdwQM//gBqyzWsuWuN2UeaVx0KhTD+zHEkJhLoOtqFWCQWEAwqWJRRco5odWboWDgcNgVY9H5lqKlUKqBosS3HcdDz9TrgrXn1dslA6a1Np9PmHHU+zxZABN61Ws28FzT0aNoB54Jj5d7wfd9ERfT29qKzs9NEh6hXnWOavGkSx159DOfnz+O5b30u3EpDsFDgaooGP0skEoGoBUYOcK9zDvWeUqkUqKugxjDOAY0K3Ee2AGtRi1rUoieCKpUKJicnUSwWMTIyEvDMkqdSvthA2E6bIakRmTKDMkjDnml8tAGM7UAAGoBSi3JqXwodBeTHgoB7sW8RCx0L6JzpDIAI1TPUgK5GcLZLowJ1pHQ6HYjgy+VyOPysw5h4xgQujV3Ci9/3YqOXUBdSIEZ95tEAvuM4mNo2BfcaF2vOrFnlzWWf9Yefce4fDYABwN2vuBvHbzluisrN7pvF/j/Yj+vfcD1Czuqjx6grKqDXedPr7fWy107/13VvZkiwjT/qVNDr9TmcQ3XM2MYijS5QAw4QPHbOztduBkDtdeE9Grmp19iOJbv/pMs/fRkTr54AgsfMI1wJo/9w/6rna1Shzov9GYmgm3Oleq+mhuoc6D5QvmDPL/fa/Pw8stnsE67rtAD39xn5fj0ft1QqYW5uDg899BBGR0dx7bXXYvv27QacslgTmbiCBABGGBmL6iNAST2puvEJWGmpUoGiOSKhUONoLbVGl0olE16igJtggkIpmUwaYcnzhunNJhhSiyxBDcE3r6NntKOjA5lMxgA09iEWi6FYLAb6Y4+Fn3FcHIcCng+97EOYHpw267MyvIJ73nIPnvo7jcqMGt6vAo9jsa2kauzgT6VSMXnxXBv2i+13XunEtn/fhkM/dwhu3IXjOhj42gAyX8kgX60rB2pkQQi4eMNFHPmVI3B8B9f/r+uRmE0gsZIA0CiQR8ZHzzAFCa+xC6ZwblQJ4rN5Hw0admE3zj3DvZUJM7ybeWZ6jB0NOsydtg0cFJqJRALpdBrVahWFQsGsBfcB14d9m5mZge/XvckM3zKKHHxc2XsFh37zEPyoj4W2BXzhj7+A57z9OQhdaawv15DzpOfD0nPBUD5939QbwjnUdAOOl6SCmwaFYrHY8nC3qEUtetKQ67qYnZ1FPp/H9u3bjQxULy/llIJEBULqpdTirHQy5HL1mioa1kv5QhCl+pDySD5TvdPqTQ+FQsiczqDvU32Y/IXJ+nNqIex8cCc2nN4AoCG/KJ9V7qn3VQ3aHI/mfQN1A3UoFIIX8jDxgron0o/4OL35NP7px/4Ju9+2G/5KI6VKQamGVrOwqeM4JmJseWwZ9/zuPXDCDto+24adZ3cGPM8qE6kfqU6ga6EOHd5702duwtnrztaLegGIrkSx85074Xu+AXhqjGjmjVWgzev0f1vHsAGi3qPPsovIqjPn0SIHbAeRgnLKaergnH+m2jUDpFx7RuTpUbF22Lk9V5x3BbFqhLD3MvuqbQDAun9dh+W9y1jat2QMI/HlOJ73puchuZKEF2rsTfXYN5tz1f90Hfk5nR/UG9lnzj3ngDq2HTFrp2BwHCsrK0+K409bgPv7mAi+Dx8+jGPHjmHDhg3Yvn07tm3bhvb2dsNw4vF4wMLEF0RfIm58gjG+pHx59OXgi6PhtcpkFDiSeQGNomX8jF7EZDIZYOYMPSfzpneSZ2+rlU1BDKut88WtVquYn59HOp1GMpk0OdgMa2ahtWKxGGAYWu1cBa0WAuPno58dxdTOKfjhhgAY+Y+RABNX4wTnmoxVmRgQzFvSPH2uG/uoRhEtqjF65yjcpItjrziGTXdvwq5/2IWcm4PrN46g4piXf3gZh3+7fmaiDx/3/tW9SF1M4Zo/vQZdE12mTZ3rcrlsFAK1TnIPaLi2GhdUUDO8jvnn9I6rENOwaDWsMNKAfWPYdyqVMlXfAZjCbLwvFoshn88bxSuRSJj0CCpDCvrVu7y4uIhoNIr29nbTdigUQjlUxvgrxuFHH1l7B8j2ZXHmhjMYu33MtKehYxyrKol837ieAALA2fM8s1fYz2QyiXXr1mF8fNy8x3x31CvAeW1Ri1rUoicTFQoFHD9+HENDQ4E6HbYXVRVs23hIL5l69uhgABAAupQt+plt7LW9aAr8A9FQmQrKQ41TNfou9+Gmz95Ul3t+8PQQO7yZ/aZ8V3DHfvN6ystIJILkcBIzL5iBH2nIm5l9Mzj0S4dw1d9dhdKlRsFYfS7HQTlM2Zvbl8P+P91v5NcHX/hBvPqzr8bVp64OzIcN2DT6TdPcFARyzuaH5+GFG2u28y93om26DS4aIEs9s/xbgRjnXtdJwa4Nqvm/6qq61jbg1N/q/ND/bU8219DWcylzbWOR9plkG4+oJ1CXtR1mapTRva9gXvvMqEjuIb1fDU2hcgg7f3Mnjr/tOHJjOXSd6cLVH70aqWwK0VjU9FfXWz3MOgf80ffT9kirPk1jFJ1h5XLZ1LBhdKKunTof+Gyev83iyE8ktQD3Dwi5rotz587hwoULeOihhzA0NITNmzdj3bp16O/vDzAy26pLQGhbmrixKaDs4lG2wFJFX3OJgDqA0GOwNFeVx3ipQKzVaia3ldcSdFCoqCDjc8PhMBKJhOk3i6jxaAJl1O3t7WbsZFKxWCxwDBcZjIJ7FcYP/fRDAbANBzj6y0fxzAeeacZSLBZN8TXezyJWzGGh0kDLH4E6gAAoo8Cwjxoh2Eomk9j15V1IuSls/tpmVCNVMxa2RTCb7c+u2keF4QKO/MYR7P6r3ei82NnIEXIaZ2i7rhsI808mkxgcHEQ2mzUhbQSsBORsh2PmntWjvLiOtMRrRADQsO6S6TqOY4w2ZNisSK6ef7bBiAmNDgCA9vZ2pFIpTE1NBRQMrgc9Mul0OnDkWsJP4Jp3XoPDv3oYV66/AsdzsO9j+zB2xxjKXuPIPO5vrqOCbuZgq5ee3gHubX03uffm5uYCBRDVs0Bhxj3b8nC3qEUtejJSPp/H+Pg4+vr6sGbNmoBXUb16lIcKuBTUNANtzf5Wo6U6AzSUFQh6IG2Z4Sd8XPpflzD/3MZRoLnOHM7tOIdNRzaZtng/n6WgiPKajgG2DSAAvtR50J5vx3P/5bn4rx/7L0xtmjLPnn7WNNIdaWz4ww2oTdcC0V0KOFW21Wo1zLbNwkfQGDvbMRsYv+355xxxPJoOZdPl7ZfxhR/5AirJRhGryVsn0bm/E9F8NKC3qWyjfqbRDnwmP1OyHRA69zqGZmBQIxlUlyTpWuo9qkPr/iBphCcBse6tZj/6bDvaw9anqSNMXjsJHAD8ldVh9HY7zb433mkHaDvVhg1f3oChB4fqunc0vGrO7PvZFxq6Ojs7EY/Hkc1mjUNOI1bUkKHOEOqI1AfpXNMq9BoVouu2vLyMlZWVpnvw8aYW4P4BI8/zMDU1hZmZGRw/fhydnZ0m3Ly/vz9gBbYtxrairkyFG98u/mEr9QR0tqWYhdooTAja+IJp6BKfCTQKsrFfCuSAYC60nQetoSsacsP+qLDWYwu0gjStbuyPMhCbyQIAfGDvX+01gkhzrXVcFHqu65oIBA1B0zAaAjK7cJttHdWQ7813bA54lflMVTTW/vNaeAUPF1930YQSAcDy2DL2/+Z+7HnzHkRWIgFjin3eerlcRqlUMiHRCwsLAQbLOdK1ARoeXFaP59hpMOGYuA95nAsjGDh+zlOhUIDrusjn86YqPyMfdOxq8QUaVvCuri6kUiksLi6a+dFoDD67ra3NGHRc14WbcZFbXw9f9B0fs5tnseUrW8zeUiMW7wEawn/Tpk0olUqYmJgwRiXONfcH51n3mxqJdF/RyKNV7Fse7ha1qEVPVioWi7h8+TIqlQrWrl0biPhSIEJeqHzZBkQK3kgKCAnebUAMoCmA5/PVU+lGXMw/pwG2ASBeiqN3rjeQV8t2+AymJWlNDupCCropM2hEBRrpXX1X+vDcf3kuPv2zn8bCwMIjgwbCD4eBSuM4UXWK2MVl+dng7YNIVBM49YenAAAvuuNFuOngTYH54LXqqeScqqebY6XcLBaLcM47iGVjQC9MP9vOtcEprz5HXdfPNraovqXeWV7PfcLfzbyv3ANqgNCUNu4v2+hig3Xtsz7DNkaoM0odNzbIVi82ddBm3m3OOfsCAJfGLuHBn3sQkdkIdvzyDqCKVbqj3mfPn87x2TeexexzZxH7RAzhA8Gwc40K0PZ0fgmWl5eX0dHRYU5tYfQo50Cfb0crMOqT7wNPGOL+JzBXJ1mtVsPKyopJIXmiqQW4f0DJ8zxT6Glqagp33303RkdHsWPHDoyMjAQqHKtwoKJun69N0hwKAAa46jEUNjC2LaBaJVMZCkETmTzz0Am4mENOIEvwp33XZ7K/BPusVp7P580LTCscQW+tVkMmk4Hr1qubU3jQ81goFOpGhkgNB3/5IHKDwRc9Voih+0x3YI7UWk3rLS2U9HayqjcNGPQqGyH/iIVfLcoEqHqutQo+hueoQYICIxqNIoooBv91ELVIDVdeeQW1thoQAkLlEAY+NIDKxQrKtXKAUXPd2K4TdlCMFnH+/HmTBkBGqADX3jtllFH1GxUlFWTq2IFgPQIyf1bqVosz565YLBrwzXMs9X5VckKhkClGSKFH4bC4uIienh7j1S6Xy8hms4hEIkilUsgjj2/8/jdQ7HvkyC0HOHfDOUQXo9j14cYZ7Hymehr4jp48eRLpdNqsqR1tsrCwgK6urkDuFNtVpccemxrPWtSiFrXoyUy1Wg0zMzMoFotYv379qhoc/JvyU/leM0M9SQGXekJVV7H/VnChBtBQKIRyvIz9f78/YKCGD2QWM2ifaUc4Wr+WjgC2S5lGnqygX/m0ekbVqKA6Ve9iL17yzpfgo7/7URTbi9j+8e0Y/dIo3JiLmNM4zozj0cKaNEYw2m/g7gF0/WUXOnd14oaHbgAAFMt1PamtrS0A0tVby8/Yno6J8j85lcQz/+iZ+Nwffw6F3gI2/NsGbPiXDXCqDjwEQZsaQBSE0+hApwzlqOpFCrLtSAYFdepg0r3U7HPVefhs7gHb4aFro3u12d7Vcer9NCZxjyjgpd5APd3zPcxvnMdXf+2rqCVqQBdw8P0HsfPndyKUb4R12wYRzmfAWRN1Mf4b47jygitAGDj98tNIeAls/8L2QN/VuEUdw3jHZQ09z8PS0lIAS+hY2I6mHdpt0HFEfML6T9Vq1QBuOgCBejHpJ8uxpy3A3SLUajXMzc1hbm4O+/fvx+joKHbu3ImNGzeio6MjUP0aCIabNLPwqsUQgLE8aUVMvphs17aCKqMG6i9bOp0O5AuzOJxtveRLylxiPiMUChkDAsfteZ7JB9E8GIIox6kfs8Q8cQKrcDiMhYUFpNNpE+pWKpXM8WIXr7mI2afMBio7ZiYzuO3/3Ya4F0fRK64KoVHLO40GKshodVUQyc84Ll6v3moCNDWQ0GjB9eG1GhEQDocRRRQb/mkDtnx0C47+wVEs7V3Cpr/fhN7P9QZCtxXIaSj48jOXceVnr+Cq378KkZm6oGA1ePXWq4W8Eqng/CvOoxKrYM271wAFmPVUhcn3fVS2VRA6HTJh/gBMxEEzbzDbIJjOZrPGEJBKpYzBhkUAtdCI7i/HcbBmzZqA0GK4ei6Xg+d5yKQzuO0dt+HON9yJ7GAW8IDhrw9j5z/vhOfXPfzpdBqlUsk8g/3T/V8oFFZZ5unh6O/vN/tblQsKJtYboFWcgJzr/2QIs2pRi1rUom9Gvu9jeXkZADA4OBio0K0AW0Gg6hA2YG4Gqng95ZmmmfG3tg8Ej+Gc+JkJVAaC5/wOnR7CS97zElS9qpETtgNC+bGCRaABKvlsjQJs5vTwPA+RXAQvfMsLcfjZh3HVJ66q6wvRUOA+A9DEIAE0QoApPzcf3IyR7AiiiajRh3zfN8dxqrxSry/lJPUs2/MJANn+LG590604+iNHMfaBsfp9TvA620vNtgEEItoU1CqA1b+1FhH1D5W1qh+p3H80PS0cDpsoSR2frgmvZxvsn66xFlvT+xWE616zvejsp+M48BwPd//o3XWwDQAOUFpXwuRPTmL9e9cH5kf7Y5PjOMjtzWHx6YtGj/WjPs7ddg7rDq1D9+XuQDt2lIGOj+NlCqHuE3udeD2Bt71nbGeZ6rc0GjHcPJ/PY3l5OVA89okk51v1cDiO03KF/ABRPB7H4OAgtm7dim3btmHNmjVoa2sLhGDb1ict9KQgigxdw1j4OT3RGl4LBHNL2L5arumdtQWHWsB4/BeFrFpyyWj1fMlyuYyFhYV6PlR7u/Fw02pLIAY0jouKRqPGEFAsFk2/LvRcwJ2vuhNLG5cAAJ0XO7HvffvQd66vbgmXMHSGptsWdY45nU6bglwATF8BGIWARgBlQJxngnX7PHN66B2nUWiMHlSCQPXAFr0iLj/nMgY/PWj2gFaDp/BnsZcrz7uCS2+5BD/mo/OhTmz/8+1ITifNevJHw6Fd38WFX72ASy+/BADo+dcerHvnOsQQW7WHCtcUMP6WcXR8sQP97+5HKBTC8jOW0f5wO5zFRn6b4zgoJUrIPiWL/rv6V+1V7rXOzk6kUilEIhEDcnVfUdhx31EY0HjAdsncu7u7kU6ncXnDZdz9mrvRc7gHe/9xL+A18srUwELFStdIlUMVGmqgUGGln7MtHiWmCgkjHw4cOIDZ2dlvlz08buT7fpO8jBa16NGppat8/1Mmk8HatWuN0bMZ8OZv5bEk8mobcCgAVU+qDcbUwcD7KCcv/NwFXHxVPQ1r9OFR3Pax25AoJQJAVr3TlGu295Tt8jmsi0J+TlIdjLpAqVQyx7R6nhcwyAN1nePss85i/efW1yuCo5H3y3bi8TjS6TT6+vqwceNGoxuwTY1U4/2apkZdS6/ndZVKBZNbJnHPz96D9V9Zj43/tDGQy2zrfxyfHbpsR3HZxcjs+dF9oA4PG1zrfc3+VkOOAkPbSaSkQJTynBGMbEflPvUB9jMWi6GtrQ1tbW1mDzXrj+d5yMazuOs1d+HyUy4DPrD+veux9gNrVxkWdN24fzXVz3EcLN26hLO/eRbVnioyExnse+8+rDm9xujYtsFHQbielKJpj7rPdX9wDfUdsD3cuob8Ww0Y3Mee52F2dhZHjhzB/HwwxeOxoG9FV2l5uFvUlMrlMi5cuIDJyUns378fQ0ND2LlzJ7Zu3YpUKmXAFX/ITLRYFxBkOlrURK2KWhRLveS2dUsZNgGkhiizkAJfXAodDWVmNXbHqReo6uvrQywWw9TUlAlN1nxt5vHk83njwSboJqBXz3M0GkVtUw13v/huLA0umf5X0hXU2utMgCHoFPrmGKlHvLjKeNlXCluGzfi+j3w+byyGasBQxkNvLkOnaZlmyDeZLK3uWviNbZrnOlEMf34YNadxbJvdVz577sVzmHz9JPxYnVku7V3Csd89hr1v2YvQSsg8h+vKkPyLb7qImZfOmHmbf/k8kAJG/3g0oKCsbFrBhf99AeWNZZR+tgQkgY6HOzD55kksnl3E6K+PglFpvuNj/E/GURgrIIwwOu7oMOMmo+c6RiIRDA4OIhwO4+zZswFvAj0edtVwDddnZXYAJry8/2w/bnzvjQidCcHxHTihRng3gbsKDQpSKiYM1df8KyoWqkRxfvr7+5HNZo3RRK3nzY6baVGLWtSi7yXK5XLmBIahoSHDNxWAqW6iXksFdEDQmGmDduWvWrdGPdMKin3fR2F9wYSUr3StwHWCHk4FVAom9LnaJmWA67omjFsj+7Q/tueShlzWwyGdfcVZnHzpSRTWFTD2vrGA40LlT2dnJ3p6eowMUieKgmkbHHEc/M3wX94/u2EW977mXmTXZHHi5SdQckrY/P7Npu9qNLbnhetEuavyk/3XVEQFw9pX23Osc6d9aAbAeb/KXoJVdSqpjFU9WT3UtqdXjQr6XB6JpeHSzQwBnucBc8C2d29D+ZfLyDyYwcBHB4zeofnS7Lu+E/b70X9PP9JvS+Pom45i71/sRd/FPoSj4QAw1v2vfzfbm9q2zpWuker+thGjmUed7XHtWdeGaaJPFmoB7hb9t1StVjE7O4u5uTkcPXoU/f392LVrF0ZHR7F27VoTRkvgwKIcyvBtJqLVlTVvt1QqmYIhKiDpieYLC8CAHwUQ9tl9tVoNi4uLxjpMb3FHR4dhjFeuXEE0GjXW4FqthkKhgEQiYQAqveSRSMQUeSiXy8jlciiXy+jq6jLeUQBIlBO47uR1uL3/9vqRFx7Qf7wffcf7jKAi4KxUKua4LLWsA41wJRaKIKDTomSscg3UFYLl5WUzV+qlpmAi2WCL32tolIavk1GrhbK9vR3VahXZbNaMiR7v9i+2I/FDCeSvzQMO4FQdDHx0AJW5CqLhaIDpq6Dof38/Zp83Cy/ziBAqOeh5d4/xOPu+j3BPGOf+3zlUBx7J8Y4AV378CuZ+ZA5uxsVy3zJOvecUdvzmDsAHjr7jKFb2rQAOcP73zmPzGzaj81CnUXxCoRDcPhff+F/fQP+RfoznxrFx/8aA4sOiNJz/UDoEv1TP1XfLQQVKx8XIg85znfAd3xx3otZxOxRcvRFcY74DXA9dV/aJP4lEAu3t7ZiYmAgoUdw/3Cs8W7xFLWpRi77XqFwu4/z583BdF+vWrQtE+pAouzQCyTb4K3DW+xU8KIADgjneCviOvfUY5m9teNNmNszg31/373jl216JcCh4BjXlBfUTfbbjNI5IpRxxHAe5XG6VM0JltBN1cN8P34c1D6xBz4keI2epm5VrZZx63imcePkJeDEP4y8ah1fzsPkfNgPu6lofMzMzyGQy6OzsXAXMqC/xes6hzq3Kd85hsa2IL7/+yyh01Qte+REf4y8ZR3g5jE3/vsm0o4BKn6G6iK4T9Rbtp+u65lQTzivpm3m1+b/qnPa1OkbdH3Y7dgSm7lU1/Gv7+hwaTVjol+PVe9W77zgOYldiGP29Ubg5F467uqCq7huuj+rpjuPAj/hwky7Ov+I8nvZ7T0PXTNeq6uTNDPcKgvW3/bk+j+3oaUe6XjrnGgGhYFujFqrV6pNOx2kB7hZ9S0Smf+nSJVy6dAmdnZ3YuXMn9uzZg97e3lXFQ/SlUoGkXlH+ry8MQTfP2qNFj95bepPJdNTDTSZAIOu6LtLpNPL5vCmUVavVkM/nkclkjKdSBRiZ9MrKijmLmYAnlUohlUqhWCyavmazWcO4GHIf8kJ4+v1PR87P4Z4b78HgQ4N42nueVmcCftWMyT4OgiE3ZDqJRMLkSylT1HnVUCAeWcbjoNg+x9Xe3m6Yj1olCcpotFBGrOFu9OyzvY6ODvT39+Phhx9eBRpjxRi2/8p2nHjfCRQ3FzH6vlGsvWctym4ZpWrJMEQN+w6Hw0guJLHzp3bi5LtPwg/7GHn9COIzceMxcBwHWAY2v2kzzr7zLKprqoALxC/FURopPXIRkN2exeRPTMJxHeR25sz9bsbFpd+6hL5f7kPSq4e354fyuP8v7ke1rYrp66br173DxcD9A4EoAM7ZSvcK7n/L/djykS248IILuPpPr0ZyMRkIv1dPAdDIuaZxR0PxFNjb68Lvuf6aSqBrr9XWx8fHAwYtG6BzfQuFwqrwxBa1qEUt+l4hz/Nw4cIFlEolDAwMIJPJBAzWCgYUmKlhVL2bCnLVo6p8XXm1GsGra6oorysD1ilJL3rviwC/AcK6u7uxvLwcaIfGarsYLcGi9o0FoxKJhElhC4VCKDtlPHjrg3jgmQ8AzwR++E9/GN3Hu418dxwH+dE8zr7wLLzYI+OK+ph+zjTi03EMfn4Q0WrU6Emqm6l8B7BKtvx3tUAop4wesRjFM977DHzlF76CYncRjutg7X+txcZ/2wgnFARjatjQPmjUln6unmP2k30kadsKFHVv6FhtYE1wawNv6m3NwLsaKVhx3i62pnuLbTDfWaNJdV7tArL8bWrWlKJwPAc+gpEP9ti0fWPYSPi49FOXcOnV9fS+e0buwTN+/xnonO0MzIm22+w90/2iAFr7osYn9p/ON+qIGrGiHnHbYMbnVatVrKysPKmi+FqAu0X/I1paWsJdd92FQ4cOYePGjRgbG8Pw8DDWrFljlH+CNbsquL6cGvYTCoVMaK8t+NQqTIGjnm22ZVsQGS7MSoYUkCsrK8Y7T2BPIBwOh1EsFk21cvUqKjhua2sz/S8UCigWi4jH48bTve8/96EwX8DGf9sIF26A8auF07bakTn19vZiYGAAExMTWFxcDOTtME+aAIzgm0clkOF1dHRgeHjYMB4VkKVSKfBctsO1owDh+nFuuS7T09OYmZlZBZy5vq7r4qrfvArLP7yMdZ9ftwrAsy01ajiOg+R0EqNvqVdVTY+nEY43cps5f5ETEYz8yQguvPkC2u5pw8A/DmD898eRvTYLuMDaf1iL/g/0m+dN/NIEEAbaDrZhyx9ugVNwUPSLKF5dxIk3n0C1vVERHQDu/V/3Yvef70b/f/WbsGzHcVDZWMHBXzmI7IYsDvz2AQDA8V8/jp1/vRPObLAyqXqXfd+HH/Jx4foLGLxj0MwPi7alUilz9NiVK1dWAX3d+3pyAME2lTM+n0VDGAXCexjFwH30ZBJGLWpRi1r0P6Hp6WkUCgUMDQ2hs7PTfK4ATEEfiVFOttGThn2tVaMGZVX4Cf6ym7OodFsF0w4OwckGj4CanZ0N5I9rCDc/I9DmdRq9R55toq1CISAEPHjrg7j3OfeaZ3/p17+Em993MwYeGDDX9o734oa/uwHf+NlvIL8mj/hSHENfGMKVH7qCclcZmz64yYyRc0NHgIIlzc9W0MM5UUMGP9P82shMBJ0TnSh2FzH0mSFse882+E4wrNgGtmpAsfUlDdtW2aZzrOuuYdz8X/UgfmcDSY0Ws6+1vbH6v/aVUWm6lrqvdJ9WKhVTsNfOgaauZuuV/J+eYrtPtvfdJs/z4IQdTLx6Apdedcl8Xuou4b7fuA/X/9316L7QHYjks9tVzznH/2jzpJ9r2pyute4F6o3ck+q4Yj8YqbqysvJNx/t4Ugtwt+g7opWVFRw6dAinT59Gd3c3xsbGMDIygqGhIZNvRGupkoZW0ZLFH3rLyVg0J1w90mRavJ9CicwYgLEaK7AgWOXLyrBsepMZ+pTP5wE0wKe+7BSECvAdxzEhLPF4HAdvPIiNd25EPBI3/VJPsh5touchs3/z8/Orwrc4byzmprnQKqgcxzG5677vo6urC7lczoBj2wpLYcA1AYJFtiiw2D7vKRaLhhmqEOD6OAUHG27fgHC0kQfN+eXcaQ46n9t9rrsO+MPVVcdgcB66H+hG7E9iiDwcQdyN46o/vwrH33wcnV/sxOC/DgKheh/Xf3g9ouUo5p43h9F3jCI2GQMij6xrzgOaOXl9oDZXMykEoVAIbr+Lo796FEtXLQUuvXLdFTwcfRi7/89uJGqJgLLBVAUAOPjKgzh38zlsD2+vz8kjYX5afKSjowPLy8vGa6GGCVUOtUK8CjMKqlwuh2QyadaE96sg/GaeiRa1qEUt+l6hlZUVnDt3DkNDQ+jr6zNykPJCwYECbBtU8ZpkMmk8knqvXse/AcBNuPAjjbaGHx7GjR++EVgCatGauTcajZpTNWhUpcdac7Nt4KonerDoqvHCe0CikAjMh+M7iBajAV3L9330H+rH9e+5Hvf8f/dgy8e24NKzLmF5dBnLW5bhJTyM/PVIANSpfqPRbxqlBSAgXzQyQHUT13VRSpZwz2vvwez2erHOaqYaCKfWa9XIod5fDRO27+NnNml9IfWG670692xH9TIAAT1FdTq7TfXOq05q67NqPGDUgMp2tq1OB15vn/LCQmw2YG1mMLIBseoS5193HtOvmF41h6FaCDE3Fhg779d5V2MHSee4mTGDz9figBq5ynapQ1I/1UrzbL9arWJxcRGlUmnVGJ5IagHuFn1XqFgsYnJyEjMzM3jwwQexYcMG7N69G9u3bzcWOgJJWltpsdMX33Vd4+UmkAaCljFWdlRAXqvVkE6nA8U5TM5QsYhisWieScDMvNhKpYJcLodCoYBoNBo4V7tcLiMajZrq1cpkEolEwAJJL2KpXMLhPYfx9R/+OuJPi+MFv/sCoNYInWGbepwEgRcAA8RqtRpWVlaMl5LebN/3jceeTF6LdXAeE4kEIpEIVlZWDIPSPG3NSyMjpwFDmZiukwJ2rpsdcUABwtx3PTtalRagwUA1RI33qBee+8M2WHQd7kKpWkJndyc2Zjai/R3tyI3nEAo3mHS1WkXfJ/qw5u41iM3FUAs1wGr6XBrb37QdB//+IKpdVeARGbD7TbvRebATkXjjmDRnwUHPV3qwtHUpGDboAoNfGISX91BwC2bvmue7VZz4+RMYf/Y4/IiPo68+ilA+hKG76kYpKk9LS0tYWloyY9V8a36mkRgE8wxR41owPNEubMj3iHPcOhasRS1q0fcTFYtFnDt3zhiZWVMEQEAZ1yM3gYanW0N7V1ZWAjnKQAN0qSwLhUJY2rOE8284j1pXAwhe2XwF2cEsUoupVeHjCiBCoZCp9cFoPJWXQOPYKMpZyiRGvdVqNWz/2nbUvBruesldgAc854+fg85LnQiFQ6ZdGmp7j/filj+8BV9/09dRGKznUiMETL5kEqFKCCPvHwmAMTV4a3SVFrzVH85hM0/yl970JSxsWjDzNPWMKcAFdr5rZ0Cn4P10TOjcUT/gWmjdHwV1vE8jF0iqC9lAl3qGpkByLnQebEOEjtOOmFDPtZ3yR2JkhYLseDweKJhGndc2uHM+vHAjD1qBrRqYFMDbXuhQKIR1n16HmZfOmKK38IH0bBpP//OnI72SBkLB4/B4n47fRCNKhIRGCejY7bViTQM7ms+uwWB7+9lmrVbD0tKS0Y2eLNQ6FqxFjxmFQiGsXbsWu3btwp49e9DZ2WkKgAFBC6HmnvBFomDUkBIyG60GraCcgonguVqtIpfLBQpLkdHxRY5EIia8nO2yKnkoVD83uqOjw+QsR6NRLCwsBPpNAVkoFfDw6MO4/advr4MyH8hMZ3DzW29GcqGeM8y+MXxdjx2jN1IZfjgcNrntKuhVeKjFmd8lk8mA0kFjRD6fR6FQMAxZhaYKDQojGigAmGgBzvPCwgJisZgxSCjIo1FEK8OT0dKjzz5QqBJgJxKJQPE7DZ/zvMYZ2xw3AHOPnnGqglsL+umxXr7vo5Kq4OIbLqL7SDciMxH0PNizivGHw2FEohGc/rHTOPfj5xCfjaPSXcHoe0cxdPuQydPTn3A4jKlbp3Dy/zuJWltDGUvMJvD0P3o6uma6DHCmR5zPdRzHgHHur1gshkQiETBGUBnQ8HGOj8aOSCRijn7jOl68eBGnTp367rzsjxH5rWPBWvRtUktXaREArFu3DmvWrEEikQgAAXp8bRAFrD46SiOLgEcvqhWJRjD5o5MY//lxeCkPkXIE133mOuz68i74nh+I4lNwQeBP0MioJAINBUfsi+pBaiSoVCoolos48JwD6HuoDx3nOxBy6v2mAV0rlUciEcwMzOCet9yDcncZ8IHeb/Ri1//eBcevg8iOjg5zLKwdqqzGenrdOS5+z36p13oxvYhP/tUnTT/aT7Tj+t+4HrBsv6oHPRrQVQO+OgKox6h+qd+pgV/DsYFG9KV6uRUg2o4Dfq/ebW2Xa6fh5Dy6TesI6H7SvZJKpdDW1oaOjg6zl6kzarue52EhtYAvvOEL2Pn7OxE60TgNhn1XQKr91bGbCNJNFRx810E4IQexbAy3/s6tSHpJo7fqXLLPnHPqbKpf2/WB1BjAcWgb1WrVpE3q+2gfb8p71Yi2sLCAb3zjG5icnFz1zj5W9K3oKi0Pd4seM/I8DxMTE5icnMTDDz+MTZs2YcuWLRgcHERvb28gJxqov0wKtDRfWD2fCkD4YvOabDZrQKXv+2hvbzcvnB7vZXsFe3p60NPTYzziWt2QbdOq3N/fb/qtxTt834cX93D7q283RbrgALnBHI78xBHse/c+Y6XjGNUDqV5ltRKr9ZWecAIz+2guMuJ4PI5kMhkQ8Iwu4LiU2VNI2oKEvylA7TXo6ekxc6QhZbS8VyoVVCoVFAoFhMNhZDIZ46XVftDyzjZo7Ghra8PAwADGx8eRz+cNY9f8Jwo2ChebIWv6AUEoK37z2cWpIrb80RYTzhQKN87c5pq5rgvP9bDlo1vgOA4GPzuIK7dewfrPrYfn1wWLbZBwXReDdwzCz/g49fOnUEvXkJ5KY/e7dyN1MYWSXzL9pOdevSiMglDwzL3L6yjYGEqmhhzODavc8l47xaNFLWpRi76faHJyEsViEevXrzd6AGUM0ADTNtmhrhq1pfcCjZBjt+ai/yP9CMVDuPiqi7j29mux80s74SNYGJO8WVPMFHAxpLxZ9Bnlrx2GrGHSju9g52d21vUit4KaFzxyVcOxK5UKOsc7se8v9uHArxxA5lQG17z9Gnih4JGX6hVVzyiN9ex/NBo1skiJegB1nkvXNvKCux/sxp4/2mMqpCsY0zWiXNM51x/KM3WCqMdadS3Ou0YokFTnUZCqfaI+pvc3A9p6r/032+EY2baGi+v8s980uNhGI8dxsNi/iDtfdSeW1y3j/j+9H2NvGUNmfyYQyaF90c9sXdtxHCTGE9j5RzsRTUax5sSauk4XCQXWxjZ2sE8aMq8/ahzQPWwbNYwhSwxS3D/UydgOgbrug5WVFRQKhccNbH+r1NK6WvSYk+/7mJqawszMDI4cOYL+/n7s3r0bGzduRH9/vwGgAAKAA2gwTfVUqpVLwQkAE0LNsHDf9433lWCFQESBezabRTKZNEydXlu+1JVKBUtLSygUClhcXEQqlaofs/HIy08AV66WYVNiMYHRu0YDZ3Xr0WiPxhRUGKh3lx5ctSaSGfNveoA114UM31SwfCQCgDn2GpZOQ4T2r5lgIfP3/cYxZPQKq5BPpVKBXHoNg9LQMXpoOR5Wq+/s7GyEdj+yH2gUIRBVizYBv3oPKMx4D8OzgHrlWBUEXE81MHDfRCIRjH5sFLVaDUP/MQQfjTPQY7GYyWtnv3zfh+NJgRXPQcSJrJoDNbpw/SlEqNxwvjlnWlxQBbpaizkm7geGqtuKUYta1KIWfT/RwsICyuUyNm3ahM7OzoBcAoJ5uerdtj2ozUCG8mnP87B46yKGDg5hyBvC1oNb4fmrz4zWdC7b66geOjW2qsOB1ys4Yp/t8G79W1Oc+GzK2b7Dfbjm3dcgfjwOv9YIn3dCDk6+8iS2P7w94MElqWOA/xsDhIROs1+e5+HwDx/GoZcfMm1s+PgGhAuN0GZ7DSgTNRVR9Qpeq84b9YDbc6D1YNRQwXWy5eg3I+236kbcF+yfzruCddXN2AfdM4xo01OAOD7V17JdWdz5qjsxs2UGAFDtqeLMb5/Bpj/dhI79HYF1I+n+tsfBn+7j3XU9KRaslK5GF5K+NwBWhflz3DpejoN/q86m/aR+qsDbjlzQOV9aWnrS5W8DLcDdoseRPK+eF7WysoKJiQl0dnZibGwMV199NbZs2WKEhgIoEkE5i1hpQTAWO6M3mB5MHtkFIAC47CJSnlcPQS+VSkgmk/A8z+SDE0gS3DH8lx5Ex3HM86u1Kj75mk8GxuxUHdz8hzejb6YPNdTDnQks1cpHgUImRmbVjEkrs1FPLwEtc34YxsOogVwuZ0Ku1ZNORQBoWCpVgHO++CwVUsr8eD/vVYFC77wdmuU4jvF2s101stRqNZw5c8aMXxUVziFBqAp2Vaw0V8k23mhekIbmVatVk/5A0M0+675hX13XRaFQQDKZNGtPQTLzzBmc/PmTqKXr/cuty+GBX38AN7z1BqQvpc1+4By4roulpSWk02kTpcC5VSWHygjrE1BYaxiZWpQ5T8lk0uy3FrWoRS36fqZ8Po9Tp05h06ZN6OrqCoDaZgq7AiUF5CTb+woA2b1ZnH/TeUxWJvHiP36x+V4NxKFQPVUon88bI7d6zimr9HnUW4BgOLXydV5fdasBns/nqnxmm729vcjn85ibm4Pruug90lufk8gj1zrA4TcfxpUbr+CrQ1/FS+9+aQAwadg1P7ON6VpsznVdHPuhYzj6I0cDheVOvP4E9r1uH6LZhs6n/Wf7do0a27hsA3WVd/qd5s83IzXa655Qg4fuAe2LesfZLwXubFN1CH2mPk/3QzQaDdT4UW83KZ6NY/DUIKY3T9ejK30geTGJ9Ll00/5qX1XftD/TH3039DptV50+XDcNE7eNGWqo4BzZfeTnoVDI6LR8Ds/a5jxxjVuAu0UtEiqVSuZYqXvuuQe7du3C1Vdfbaqb08MINCyDvu8HvI3KJNTDmUgkDIPm9WxHPeO+76NQKAQAqzIfrSBNgF0ul01eOJlEpVLBirOCL73mSxgfG2+EkwO47bdvQ8elDrhhN5AXRGak1kQyehUsasWzQ4ntcDj2PZFImDlkv9kOGZI+xwagtKxrsQrbwq0hZJwD9W67rou2traAlZvMkjk5mgek88Cwbv6vlmgCYq4tc944f5yrcrlscrbVasx5Y2ESLS6i19Hww+dppXD2Wc/CZs41C9lp2GAZ5YCSAQBuzEU4VhdGPH6OezAWi6Grq8tcy37aQFv7oaFXaql2XTeQu05F087na1GLWtSi71eqVCo4deoUhoeHTVQd+bntZVNPKfmphlWrVxcActtzOPH/TsCP+qiiik/80Sfw0j94KdrybQaYkhfTCaDGYn7PaC4FH75fL/7Gwq8AAmCGfy93LOMzv/gZPOcdz0EqlwqAFTvf1vd9XLlyBW1tbYjH4yb9jjKsFCnh6C8exdTTp4AQcM++e5BBBs+49xkIVRoeSDUQqLGYpH1wXRebPrMJkxsnMX39NBACItkIrvrLqxAvxOH6wZxpG/ypYULbtR0zeo8aO5Rsry7vUVBN2d8sPFz1DE2Fs0Gj6qT2ntJCqHqN7eVlRJ1GRWo9ALM/SyHs+rddKKCAU88+hbbDbdjypi2IeBGEwqsruDfzeNtH1elPM3DdbKzUq6ijqbGpWQSBjl0dBtpXe16px9OpVKlUUCqVTNri8vLyk+78bVILcLfoCSUCsgMHDuDo0aNYt24dtmzZgh07dqCrqwvJZDLwwvEFJsAgEUjWajVTTKqtrQ3JZBKJRMIAGr6gdtgWvcsMEScDIROKx+PmJ5fLoVQqGU/68vIyLmy8gKmBqQDY7jnbg2Q+GQipUSZMpqMMRg0KQNDiaofbaFiVAlOCM86temQ5JgJDRhNw/vQe9Q5zbvWIEPaHoc7KoIG6UYWGEy0SR2WD/VZvA9th/yjUWICNYf/MdeN4E4mEOcZFK8tyfdkvzfe2Q544p9wrXA+gYYzRqrAEvhqZoJZ13jPytRGEOkM4/pPHUUvWkJxJYve7d6P9UjtcuAHvtKY+UDHTUHjbA0BjiV11l2NjTQIajzgHnNMWtahFLfpBIN/3cfHiRdRqNWzevDlgaLVDepmGRLnbLHeb8vTEX9fBNqmSruD4zcex97N7AxFjmqKlhm8FM9QrNKd2cXHRFB4lf6eMdhwHU+un8Nmf+Sxy3Tl89XVfxdP+/mlIX04HQJ7KUn7OEzEoIxlyXdpRwvLuZXMShx/ycXjkMK45eQ26p7uNLmEbdzlOPbtcDdRe2cOu/7ML1d+qIrs7i21/vQ29B3sBB3AiQXBuhwjzb51/rqnKQX5uh18r2NP+qsGf7elnaqyw+8Pn6Fh1jZtF2FE/scGvbWBgCLW2zz5p8THuCd/34VU9bHvfNhRzRQz9zRBcrxEZoXvWjn7TPW8DbH7Pa9T7r5/ZkQN2ZIEarOx+6D6x14ptcj3VO87PGHrPQnSLi4vI5XJ4MlILcLfoSUOVSgXnz5/H+Pg4jhw5gvXr12NsbAzDw8Po6+tbdV4z/yegpFAMh+tHKaRSKWQyGeOJpNdTj+PS845rtRqWl5cRj8cN8+TZ1ZqvrNa8aDSKfD6P3rt7cU34Gjz0uodQTVfRf7ofT/vg09BeaEcRdcs0zwQvlUoBgUrGRKudhmqRqapnXAG4Wng1BJoCmX3msWK0mrJNzhkZuHreqWjYxgL+aFEuFYSaD87cMU0BsAUkq26zfyQNAWPYPp/FudR9wEJrjuOYqvNasZykBg/NNycA1RAo9pX9c5x61VCCVq6JChuC+Xg8buZoy+e2IFqL4ujLj2LHu3eg66Eu1KK1gLXWPlOTx99pWCD3nxpVNCRfjTgKzvUIEo6tBbhb1KIW/SCR79frybiui7Vr1xr+b4crA8HwVjVKq9F85hUzAbANANFyFHs/u9cYqHlkqBpp2a7neaYP6uEDYGqMqBxV0AgAk+sn8ZVXfgW57jrAuDJ2Bfe+5l5c/7fXIzWbMvJYAbKOj99rf9acWIOnfehpuPvn70a+K4/eqV688HMvRN9yHzzHw1z3HK6kr2DjyY2r5laBPWUWjfWUZ5v/eDOyN2TRf38/nPBq77Q997bn29aHqNPYfWnmENDvOX6urQ3I7THZ+hH7qevBftpOIjWW6Jh4DQ3m+jn3iupK2h/+z/mm0WTT32wyup/qi7YOqW1pexxDuauMub1z2HDvhoDxoJmBwAbPzbz8Orc6Jnsu1Zig99hgXedJnTXVatVEgzzZqAW4W/SkI8/zTJG148ePo7+/Hzt27MDVV1+Nvr4+AKsLgwCN0KlUKhXwMDYDlrSKqRc7m82aUHF6ZUOhEIrFomHa7e3tpv1kMmlCfgCg7+4+3Fq5Fff/7P246e9uQsdcB5xIQwDRe6pWX/X2quWYgF6t4Mo4eZ+G1jNEOJPJIB6Pm2sJTrVSqLZRKBSwtLQUKM5hM3clZb4E4rYHnc/nUVQMi1LrN9dreHgYHR0dOHLkiPEWMxKBz9LzF6mQkAEzZ5159mrMYM48QX2tVjOGCQ0ZZL85P4lEwoyVYFXrCOhc2MKEQp65Vwx5Gv7iMLrOdiF2tJEuwX2r6879zD6rMqbCSYUPQTXHq0KZ81ypVAIh/5zHFrWoRS36QSHP8zA9PY3l5WVs3LgRnZ2dq0AXsDp02/bYOY6Djoc7cMm9BDAoygee8XfPMClF1DvWrVuHy5cvA0AAjFG+aCg5DaE2qFTeTWCRnk0jNZfC/MC8yd3tvNSJSDZidBY+k0ZYe3yUfQrIh08No/8D/fj0az6N53/o+UhOJ7Hcvoxwbxj//NJ/RilWwsuKL8Pa8bUG7FEH4N9azJXpeCd//STWvmst+u7qW2WoUIO6DT5VNnJNbM8054WRXbbHVA0WnH99nkYPUFdTwEq9ketv7ykbFGp0A6/h55pKQMeGOnMotzUNkp9p3+39pJ5z7ltNaVB9gXoV+6PPOfq7R1EcLqLcV0bCTWDo4aFVc6VgWddAdUH9ToGybQDRNdd11vtt4G3r/3x39BixJxu1zuFu0fcEhcP1I6V27tyJ3bt3Y9OmTQYwk2H5fuP8PwWqFDbc66x2rcy7Wq1ifn4euVzOeIXZHhlMOBxGW1tbIDe5VCpheXkZuVwOkUgEmbYM4l1xuCtuoPCW5vaqUNLQeGXsarlU5mIzdhZxo1ceqANuhuLHYjFTCG5lZcWcSa4VO6kY0EChIUZaHVKZpgoGoHE0VSwWMwCYgpgMnSHfasDgnKRSKSwvL5uIAw2rVoESyFsKBY/LcBzHgPRYLBYIDU+lUsYAoFZdjpMCiXPNAm/FYtEYPxjqTuHIz9TgQODMvqqSlk6nzdzTs0/DhFqkdT75GfeNhqyx7zQGuK6L4eFhRCIREzpJzwjnjcaDlZUV3H///fhW+f8TRX7rHO4WfZvU0lVa9K1SJBLB0NCQMeRr6KoCJRJ5vYKJ0lAJxz94HIgAt/7NrVh/dD1i0ZgxvPI5lLVaf4Nyh55w6hW+7xv5osVLKVcp/yqVCgp+Abf/+u24suUKNt+1Gdd98Dqg0tBzVFZrVJbWQtFUsVgshvb2drS3t6MSryBWrkdQFZNFfPhNH0a2IwsAiFaieNVfvwrdl7sDJ79wbIxYq9VqWCot4fBPHcbEiycQn4rj2l+8FplqZpWHUwGpelvVsK26gw38CMxUpio4V/1B9SrbG6z3qtOGsljBu0b5NQOG7Bf1QD2SVnUptsGfZDKJ9vZ2dHR0mP6FQvUTX1KplMnrd10XpVIJ2WwWi4uLRidU3ZJj0qhE1vtJJBJmX7txF8d+/RimnzFtUgpClRBuefstWHd6HSLh1ceHkWg80v2pe0vJjs5Uwwj/19QCtt+sDd1zk5OTeOihhzA7O9vkbX9s6VvRVVoe7hZ9T5DrulheXsbdd9+NQ4cOYXR0FNu3b8e6devQ09ODtrY24+UDGmHB9PrZjJMMSBlCZ2cnEomEYSQKlhXQLy8vB7y3juOgu7vbWETDpTBqfh1QE7ABDcupeh5JKhgV1LKf6iGmtRtAwCsLwITSE2STCQINSyjvs5mdhuxTcFBgav4vP7PHEovFjOLAeWHuGa+jt12t0eVy2RSjU+apyooKVs5DpVJZBVgZdaBeeAokIFiETo0IvN824NCowzGrBZn52zSc6F5VYU0gz9oC/GGIOo0LTDXgPeVyGel0OrAfgUbOlFrouacvXbqEnp4es7aMklClzQb3LWpRi1r0g0i1Wg0XL15EpVLBwMBAAPio3AEQkBcKCpMTSez57T2IjkbR+2AvapEa4DeOOFUjLbA69BpAQAbZ4evKpzUqjvw/WoviuW9/Lu79qXvx1H98al1Ge+6qqC3KIz2RhET5wmgw5gkna0mUqvXjPu+79T7kMo3c2Gqsijt+6A689P0vDegSStVqFUWniNM/cRoTL50AAJTXl3HsXcdw9duvRvJy41QPO/yY41XPJ9CICLO9ony+gjbOkQJh1WPUE6zeaM6TRglSP9I5Y38IxHmfzrd6vTlO2ztrGww0FVD3pOM4JsKOug+dOWb88LH4lEUkl5Nov9Bu9h31Lo6bzhQ+z+l0cOJVJzB923RgDb2Yh0MvP4T+P+lH2A+v6rPdd9v7bDsf1LDRzMBFXUbb11x6OxJFjUnZbBbZbBZPVmoB7hZ9z1E2m8X+/ftx7NgxDAwMYMuWLbjqqqswPDxsrHVkegSiygDU88liC9lsFuVyGYlEwpy/nc1mjSc4nU4bAM6KiJo3kkwmkUqljAWT3mUCdjIWgiqSbdEjcGX/7XMjyczUSkrA5jiO6Tst6WRUvJ6h10DQcs929H+1htN7zb5q+BHHoOFc/CFgpUVd77fDsjhfaiBgP9QgwGs5BjVe2MxdUwl0vgcGBhCNRnHx4sVAH9TIocDZZu7qIdAUABWyDCvks+m912v0GWp9BmCOBNOq8XZ4mVqbOadXrlwJeLY5zzQ0VCqVVv52i1rUohahLiMuX76McrmM4eFhA8gYzmtHftme1XA4jN7zvWifbwcQTA2LRqMIJ8I49pRjGPvamNFNNNRb76FsscPO1WFAsKngL+JEcP0HrkfVrTYFv5Sj9mkWCrqoGzG1TL3Jruvi+n+/Hk7ZwYEXHAAA7Dy4E8/71PNWgV0FRq7rIoccFnsXA3NezVRR7CkiMZkwn9lpUurF5lwp+OS4bE9ys0g8bV+j29SrroCce4DPsnUl+3k614/WbxpxbJ2P19GQwH5ybXSfcr8VCgVTGMx1XeTacpganULnXZ249OJLOPUrp5AeT2PHn+9A56lO01fuZZ1zOqVK6RJyPauLjQ0+OIin/P1TgApQizT2kM4vHVl2RIeth6mTxv6xw911zjl2nXPby16pVEwE55OVWoC7Rd+zVCqVcOHCBUxOTuLhhx/G8PAwdu3ahdHRUeNJVauZArxMJoNUKmXCb1jYZGVlBcViEe3t7Qb8EkyzKnm1WsXS0hJWVlYClb3ve/Z92HX3LlQvV03oLkPClZHb3msVpCpoNPSHRc9oiVam1Cycmc8gg1cvOOdBw+55v1pUaZhQoUIBwOerJZrMVgUDDRZsU9fEFlYUfAoqVUjqWqpSwjljWDXHwrw4VQj4/9LSEgYGBpBOp5HP5wNjCYVCBhzbVdnVCKCFyPhMevQrlYo5H5v30vPPPcTP0+k0BgcHcenSJaysrASUH7W0a0ibnuGqQsq2LHNNbVD+ZBZKLWpRi1r0eJLv+5ibm0OlUsH69etNhJh6oG1gRtmkp6CQB2uk3dde+zVc2HEBXsjDtju2BYzDtrdbeTT7pVFuCpZt4EiZ28wLyPY1Wk0BGI0L/E697Pze8zzs+c89cMoOFtYuYO8n9tZ1p3gjtxyAAV6UkYVwAaX1jTORIysR7PyTneg82wkn1IgAezSDhm3UV6N7MpnEyspKQL9rppuo7NZ1VOM8jdS6xrbRXdukwYL/sw/NDN2qo2lNAK3forog59zWYQiQdV/UwjXc8St3INedQ+Y5GSxsXwDCQH4kj2NvPIY9f7AHyYvJwHi5NgpY/Ys+NrxtA9y3uFjZuwIA6D/Uj73/sBep5ZQ5Xoz95PzoPtS9qHOp/bWNKapTcl45b7r+Gn2g7y0/KxaLWF5eflJH7rVyuFv0fUP0Mq5duxZXX301tm3bFggxplcUANrb2zE0NISpqSksLi6aXGwWu6K3kmc961ngnlfPh15cXESxWETVr+LE807g6E8cRTKbxAt/64XwlutMR0PAlXGQIWuYsc1Q+Lke3UUBoICNgpoGAeb4sLI3vfIM3S4Wi4H7mgE2FmAj6KMw5d+8nvnysVjMhOPTkp5Op1d5qCnsmOvTLP9Jw8oJDNWqqpZNtXjbhgRl5AraCcypxLCPNrinQGXoFtdEPfhUVHgfc+nZb86jhidG01Ec/ZmjGLxjEIMXBlGr1dDe3m6Ehm3pV0s4x0KvN3PmtQK9bb1nO7VazeTRnzt3DkePHv2uv4PfbfJbOdwt+jappau06DuhRCKBoaEhdHZ2BhR6NWb6vg8kgTPvPINr/uIa9Pv9q4yyfszHfb94H8avH4cf8hEuh/HUf3wqtnxjCyKhRvVptqlAXKPTKCf1LGbXdRGOhXHshmOolqrYeMdGeLVgOLSmLNlRW3wmZTnT0FKplEkN02dz7LVaDYVaAVVUgXwd9La1tSGdTpuaNjyeqVwuI1fM4Wvv+RqKw41Co47rYN3t67D9L7cHvMA6H/+dZ1/BZ2dnJ1KpFKanpwN6iXpHqZMoAFSgyefbnn/Opaa/2d5p1e10nrSfuo5aqFT1Ll2P3IYcpl81jeRSEmPjYxg8MQi35gbkuam7E6vizj+8E0ublgJH0irFp+O47jXXAYWgMUSdFuxzKBSC0+ng6F8cxXV/ch3SfhrJajJgiLEjB9hv/c31VMODHXWgkX66Vhrlp++U6jJ6H9fl0qVLeOCBB7C4GIymeLzoW9FVWoC7Rd+31NPTg82bN2N0dBRr1641XmuCFzKCUqmElZUVk5vd3t6Orq4uY2nWqs8AUCgUMDs7i2w2i5Jbwtlnn8X+V+83VULbL7fj6e98OjIzGSM8CQDVsmgLDyB4pqFt6QWCFnQyfLYVj8eRTCaRyWSQSCQChVpKpVLdOPBIrrDmo5FhMaQbQEAwaMgQhYfmKKm1lv3R0HzmHOnxJxwDjQdkpOqhVysqBYTmaXPOeByYbdW2rcXNLN/8m0oIDS4AGiGBoggwH49COJVKmbbovQ6Hw4F8bBZy830fXtLDpVddwpmXnwF84MbfvhHdx7uNYUUVG4a1qUdbFQLuqUgkUi9wU6lgbm7OzD/b0XAs0tGjRzE+Pv6dvF6PC7UAd4u+XWrpKi36TikSiWDdunXo7e01n2lObTFTxPk3nsfiMxcRW4nh1t+/FR0THQZoAMD4TeN44FUPoJKRaCIfuOk9N2H0oVHEojE0I/J5GtptI73neai5NZy+8TS++lNfBQDc+Pc3YuiOIcBrGOy1UCzlLT3WaqB2nHqtFabNMS2PMqtUKpk8YcokAnoWhk0kEqbeSLlcRqFQMDpVtiOLk+8+ifKmMuADvff14trfu9Y8g7Jcj8BSL6oawPmjIJVzzqgCW6eygbK2wb8VACroU72PxHY11Uz7o3qVHRGheo22Qz1idngWd73jLviRBgt79tufje6HuwOFfjlnJ37yBM699By8WPOq3LFLMWx5wxZkLmZMH+0oSPabDiauJyM37PBwjtceG50v1Lfs9Do+m/PKz9XYoaBajSaqA2t0B59dLpdx+vRpHDx40KRMPt70regqoW92QYta9L1K8/PzuP/++/GZz3wG//mf/4n9+/fj0qVL5oWkAGK1aIb3ptNpc143ryuXy4HjLkKhet52uiuN3DW5hnXRAcqpMubXzxvBREbE33yuFi+h0NDzqgmsFPRpTjAtiEBDCDQLR1OQrwXgbGupWnJpZLC98tpftbiSWPlSq5TzHhWEFFQDAwNYu3ZtwOOuQpHMm+vhefVjzOjRVa84sLqQhxIVJqABaDWUTq2xBLh6lJoaOBgFwTmi8OCYufY0hIRCIYRjYZz7qXM482Nn6vslBNz/v+/H1N4pEyrGeWI/KPCoBDAFQsdDxayjoyNwXJx6D1TBYjstalGLWtSi1cRialNTU0in0+ju7kZXVxc6OjqQXJvE5TdexuJti4ADVDoquPc37sXcyFwAUGy6ZxP2fnQvIkXJ3HSAu3/pbpy+6bThz+oZVc+uRjVpmpnneTh207E62HYeafPn7sbZZ5xFJBIx4fAqC1WmU47ZwMU+gsoGRdQVgEZROcepFz1dXFzE5OSkqZKtnlRn0sGm39mE1IkU+r7ch11/uMvIeLbD8TIyTMFws/ByNbjTmEBS3UHBtupZvE6/VxCqIFANFzo3vJ/32t5a3/fhRl1M3zQdAPkEttQ7+Xf56WU89H8eqh+X9si6wgG++v99FReuvWAiBwqFgqlQvuHvN2DDRzYAj3Qvc3cGff/cB7hA/Hwcw28dRuRUxEQcUMej7qCeaK616kSaZqDjs/VG7lkbnOt6cQ82K6CmIF31Fb1OwbjuARqBmFrwZKZWDneLvu9pZWUFKysruHDhArq6ujA8PIzR0VGMjIyYc7Fd10UymURHRwcymUzAsqxhMGQMtP51xbrwoi+8CF+OfBkHdhxAqBrCze+9GV37uwwDI7gh8FIGRIbn+/WjqLZs2YIrV67g8uXLAcYCNELFbCuvWlIBGIZuh4RpCLdWPNf8Z/soKdtaS9KCYGppJLPmtWoFpqArl8uGsZbLZbS1tQXOGKWnmEy9r68P3d3duHjxInK5nGHQDH3j2dMcsy20NTSdn1OBoMBXQcj5jUajgTMdCW7pfdCQfVVwKNQ456YomltFdK5RsA8AQl4I6WLaVDxnmHqzddUceY6BALxYLAYs9aVSKSA8OZfcv60c7ha1qEUtenTyfd/I4dHR0cBJKIlcInBtuBJGtNgAtJQrW762BfFiHHe87o6GUd4HUsupVaA4Ho8bPt4shFkNpqnl1Kr+ZlYyBpRQdtngM5FImIgrtsljPBUwqTdZPZR2GhjlL2Us66EAMLLTcRykT6ex5U+2oH2lHaFKCK4TjLqyvecKim3ATF3DNijb12v/eZ1Zr3DjHGy7KKqOWR0Btqdb27S96/x95A1HsHDtApyog7VfWxsA/eodLpfLKE2WgGZi2QVqkzVzBBhlOUHuug+ug5t1sbB3Aev+YB1i0zEkH0oiPhtH6kgKPhp6LB0nCp5tb7U9z5rzz7lTIK2OD71XvdS2LmRHH2hUp+pmagwh2Wvluq6JqNDIzCcjtULKW/QDR/Qkb9q0Cbt378bmzZtNiFQmk0FbW5sBlPSGM1RYw3H47kSjUVRTVfzzD/8zdvzHDqQPpQNHgfAIKwV6ZJahUChgnR0aGoLv+5icnAwwHwVbalFUJkXPciqVQiaTgeu6yOfzAOoMK5fLmXOotdiH7ekmc9MQcAogClt6URmC5HmeCaNmaBrnmjnkbI+AOx6PG4bJ8HKCR/aBecobNmzA9PQ0FhYWzPO7urpwww034MSJE5idnQ3MLY0G2n8Kc0YuqOW+2Rprrjxzpnk9P9MQfFajp3dfFYparQZEgbs+eBfKaxreZcd1sPELG7Hzb3eaNhg9wTnUfH3uR64394UqIbrftPCKyfuqVnH33XebUP8nM7VCylv07VJLV2nRd5Mcx0F7ezu2bt2KNWvW1GV2vIqjP38U47eNIzWfwm2/cxviS/GAEZy5vXCAS9dcwh2/egccOHjJ374EvUd64dbcAKjIZOrnUmez2QAgsgGj69aP/bq44yL+63X/BQB45v99JgYOD8BzvYBHNhRaXSDTlmFMRaO3VcGSyhTqHQTyCm548gVlj6a8EdiyvgyN+8280Ap4dbz2Z7xWZXUzQMz7OW7Np6bMVK+traPZxmpdLwWodgi0H/Zx5M1HMHPLDBACIrkI9r1rHwYPDcJBMLxci5cVegv4+t98HW7iESeL62DXq3chdSFl1kKfy7/dsItStATMB40OOrd2nxU405nAmjD8oeFDIwBtnUrP3gZg9A3b2KPP1pN6uA6aRqDe7GaRAzo+z/Nw8eJF7N+/H0tLS033wONB34qu0gLcLfqBpkgkgvXr12NsbAxjY2Po6OhoGprbrFIliYyiWC5idqae261eVYJqCjigISyABnjltQShGurDcCDex3t5DRml5lP5vo9cLmdCkVigTa2O6km2+8E8YPVwa6g0SStpkhnTas4CbWSoBKO0wnMMFILlctmEcQP1Aja2RZvAMRQKob29HZFIBMvLy6bf7DOfqWHdrJqu60+ArgYOGiVoLKAiwrULheq5/TyLPR6PB0LCeWRYoKBKxMe9f30vchsbR2+EqiGMfGYEOz60IzDHVGCYT6eCSIWdHf7GdVVhxXnX/2+//faAgvBkpRbgbtG3Sy1dpUWPBcXjcezduxdr166tF8f0XNz3i/fhmr+/BpFyoyimegJ5agUc4NxTzqHdacfI8RHDo9VTyd8K8EgKsAwIrlVx4doLgAusf2g94Dd0CY2g06JrKsOojzC0mWdNAw2dhuBZw4cViHPMWpWcKXj0pNfCNdT6a8jMZgIgjkBP5ZWCwFAohN7eXlQqFSwtLQXAMcdA2a0OCAXFdiQgSQGdtqUeb/XC8nteY4M+/SwcDmPiJRM49ZpTcFONEOfU5RRufNONiC3FTL/tVLVQKITqQBWXXnIJsYUY0g+kkTiWCHh6qZtohB+jKDUqUuW7bcBRbzSjGhi5ycJ5doi4Am0dM9ede0ZTE2wdheurbfJHdTw1gthRHvqOcR6PHTuGY8eOPaFOhBbgblGLvkVyHAe9vb0YGRnBtm3bsGbNGnR0dARypNXCRyaveUwrKytYWFhAPp83TE+PYWKYsu1xVSJI1NwtAIFnap4KhR0LZ/H4smQyCQAm54cFTvhcVjClYYHPswuCqcBmP/g9c6oBmP/1HHQKLi3Spl5u3qMFTwhwqXCoAKYw4xFpVE60oItWSqf1mM/lWGkgYJ80n41jVMuqHvGiofQMw6MhQ63B+j/HUavVkO/M49AbD2FxzyLgAyP/PoJd/7wrYEDRddDq5roX1evBPtreevvYMhXGX/ziF78Lb81jTy3A3aJvl1q6SoseS7ruuuuwYcOGwGkXjHaijFVPHkEs0CiQpYBEwRL1A61nAjQ/9kvzixWAqBfaBqhq8OfzEomEiaiiDFFAqkZwO8JPvZIKznkiiud7uPzTl7H4jEVsfftWdFzsCIQ0U+4ScKmco9Ohvb0dhULBGLHZfwV86tFvFl6un+t3zYCz3q+kegGpWWg72xh/2ThOvfoUvJiHzKkMrnrHVUifTQdOh1GgSa8zdRP7GjW4EHBTd6J+x3HYUZj83A4f132qDhvqkbxP94zOKedAIxm0Jk4zL7VGZur4/zsHAMekfSHl83k8+OCDuHDhwhOaw/2t6CqtHO4WtQh1Zjo7O4u5uTmcPHkSfX19GB0dxejoKPr7+wE0BBaZC0FdNBpFpVIxXmQKEFp9WfhKhWsikTDMUnNb1DoLwFxDLyqZmjJoOx9Gw86B4NmVNBwQLPN6DXknI61UKgFvMI0DCrrj8TiAhkWcYdDsA8PGbYCvIWDsm62skOzwK/ZVBSaNDvZxFwpY1SKuVb81r90+y1vD0DjXKrh4H8Gxrg2Vl1KpZOYpNBXC2J+N4eQbT6Lr4S5s+dgWVFAJKA/sG+eRe4P7jKH8nDP2g33lXlShpGFyT/Y8pxa1qEUterLSQw89hOXlZWzdujVwSgVljXqsm+UJl8tl420+ft1xrDmxBsmZZIBfsw2VZzbA5fWUgY9mcFX5R7lNeUJZptdpm3y+ym7by2t7iYEGGLv02kuYes0UEAbOvPkMdr19F1KXg/nnCrQ0lJuGBx7zxOfaXlAdt5KOReW5zjHv1zHob9tLrOCRc6e6lo594yc2Ajlg/EfGsfltmxE6FkLey68y8PMe28Fifwc0CqXq97omtkfY9syrEyOga8WB8Z8ax5aPbTEAmsYf3Q9sVwGyrWvqPuZ4lLStZlEGzZ6nXn1GKtKJlc1mkcvlnvQF04AW4G5RiwLk+z6WlpawtLSEixcv4v7778e2bduwdetWbNq0yRTKotCiQCoUCsjn84GiVxQEBFGak1SpVEy4sVqsCdooWBmiBTSKrtFCqhZutq/h3I8WWqYATYEjgb0WTtGzPIFgNXRlrCSGirMwF4UclQWGrbFfnHMWK9OQ9nA0XM93chuV4R2nccxYMpk0wJOGDA1Xp5LDKAD9W9dHlRWtAq8Cluum11cqlUCYHoVhoVAwc0GjSygUMv3NTGew++27Ec1GARdw4ZrIAIYEUuFSa79taGEfNHTd931TGIf7SqMYarUaVlZWvqN3pEUtalGLflDJdV2cPn0a2WwW+/btQzKZNAAhHo+vMgjbaUCVSgXlShkT+ybwtR/5GhL5BF72By9DuByMiCIAVgDE59f8Gny3AXgJNrROCQujAg25rUVBVY4o6LaBjpLWXbEBne2BnPqFKUy/chp4BHOtXLWCA398ADe+7kaEikGQr/oF9QA7PBxoGCIUtKoeYbelY1DZbXuvqcvpGtv3AQ29p1lItF5LmTvw2QEkv55E6GIIZbe8qs+qg6i813QDlfm6LvZ62euopOti//ghH0ffdRS57TlEwhGM/tuomX/qnLahg/uzWU0jnUO7QJu9n2iw0H7aRh/+zWjGTCaD7u76MWnz8/NYXl7+nqhHA7SOBWtRix6VyuUy5ubmcPfdd+MjH/kIPvaxj+H+++/H8vKyOX+SXkd6g/UIJ/VAM4/acRwD2plfbVsONayIHnC2CSBwfIJdNIShQcrUeSQE29ACJ/RKp1KpVVW3bYs3+8hrbOOA5hNRKDHHOZ1OG6+6ndND4ameZDft4uCvHcTiLYtIpOo5ZgSvrBCvRcb0GC+GpLO/Ggqn4Nm2TFPIce1oOKlWqygUCsazTMHIomVcl2KxaArT6bFcruuiWCwaz0YkEkFsLgY3X+9TMpkMeLFTqZSJZtA2CPCZ1869wLnmXmMBOu4nLcoXCoVaR4K1qEUtatF3QJ7n4fLly/jyl79sKotTvlImqicYgJGRoXAI50bP4bOv/izKqTKWe5fx8f/zcRQ6CkamUQ5S3mj0VqmzhE/+4Sex3LUcAIIEc9Qb2BcFg1r8i7KHnkz1Zmr/FXBpDRT2x85BZr83fngjOh/qNEdWRZei2PX2XfDzDYCoHlB9FvvLsWkYMj/nfNtedRtY28YFsw7iAFFHiUYkqGe4Wbh9M4832/Q8D3CBxHTCtMv5ZX/saDkF8fy/meeacl/D6flbc6+b7Vv1crvtLk686wRW9qzAi3s49apTmHjZBMKJRoSGrg+f4ziO0WV0rRRQc798K/nkarhQ77nqNly7XC6H2dlZeJ6H3t5e1Gq1FuBuUYu+X8j365WzDx8+jI985CP40Ic+hK9//esYHx834I55ysrkyfjYBr8jGFNBp0dT6b0Ediy+wmcw14a5V2R+/E2majNf3sPiJWpJpbeWHlYyOBVSfJZaYkOhUOBMSb2+GUDn/NCYQEFG8I924Oxrz2LqOVO47433YfJpk2YumoWB8X/HcZDfk0e0pxEart5/23KuuUdcI/6okYNF6ZhfzvGwLVU2aPxQLwDXt1qtolgsmnAo3sf8q0qlYv7WcH/ez/B0XsvcddvIo4KW6xQKhczeaIWUt6hFLWrRd075fB6f//znMT09HQAJBEkEuBo55Yd8HL/1OPzwI/LLAbK9WdzzE/cAaABG21vq+z7meufwxV/5IpbXLuO/fve/ML95PgCEGO2kegjliIYrUx5Eo1F0dHQE5Kl6E5sBOuonCppUbppCcH4Y239rO3q+1oP4TBw737ETnac6EXIashdAQD9R4KpAU0GiXvffOQg0hNz2dgMI6DfqFVaAaOsbagzQ5yoY5rVaTExBoz6LfdAxa+ShOidsb7rqYNpf7Q8/07HrCTq5a3IobCo0jqwLARNPnUCtoxYw4Ntt2IYMNbyQdLw6P/Y7Yht22JYeaWsbPlzXxcLCAs6fP4/FxcWAU+HJTC3A3aIWfRvkeR7OnTuHr3zlK/j4xz+Oz3zmMzh8+DDm5uYMGFMLablcRqFQwPz8vDk2SsNstCiYerSVgWm1UzJAPS+bedPKtHit3aYCUHpNKTi0Mqp6aG1gSkFNoczPbSu5Cht+RgZsGx/YXyfs4MgvHsH4D4/XJ9wBDvzqAZx5xhkTms+2dC7D4TBye3M4/qbjOP260whFVp+DrYKJ4H/uujnkh/OrhCnXzWb6GgKnQpwCQi3fvJfXawqBChkV2mqd51jL5bIBybzWDvEnCFcLvEYRqLW6dQZ3i1rUohZ9d6hareKBBx7AyZMnzbGNCizIdw0odB3c9P6bMHLPSKCd+fXzmN4xHZAtpHA4jGxfFne9+i7MjswCAPJ9edz3C/dhfnQ+4F1VgENgTRBOWcjvK5UKisVioKaKDb61gJsCRU3Zsr2pBkyHItj69q0Y+7MxdNzbsQr42gCR8ko93QTxKocVUCpI1XlXEG57w22grOBVAbHtvdbIOBtE6pyrQV+NLdS9FLCrp18j5tiuesOpK6oOodfYQF3XW+eJz+/8Wie2/NkWhHP15/ce7cV177kOiaWGV97WS1T30PS8Wq1mIgFt8K3zbRsZ9HuuhRpXVP+098Dy8rLJ8f9eoBbgblGL/gdUrVYxPT2Nhx56CF/4whdw55134tixY1hYWAhYkBl6w2O6CAAZFqzMVT3Deh6mXqM5va7rmpB0ZYR6RIfNwNWzrEKVDDkej5uQbc0LZ5tAMPeb5zWyHdtaS8bZ7Pgtu3prJBKBAwd99/eZMDQACBVDSN7fyJNT4cvQ+OyGLI6+6ShKQyVMP3cah950CJFoxOSsaUgTDQ1zY3M4+YaTOPIHR1BKl1bNr1b9BB4B6aFgiJ5dcE0FhVrt2WeNRlCruYYPcu0AmKgB+2gXFZ4acsd2+JmGJPKaVkh5i1rUohZ996hUKuH48eM4cOCAMWjaoIHGcsdxEM/Hcd1Hr8PwQ8OmjZX+Fdzx03dgceNiQDeg3Evmkui92NuQjT7QNtOGtittgcrQNiClHFBArQZoerIJTlW+Ue6o7KfuocZmNTQrQPJ9H4lyAn37+wAEC2w182RT37EBNe/V9DA1SmhbOkZtQ50Lthebclg95iTVHxQY22HS9rh0vrUtph7a8l9D9dV4r/OrBgn9TD361An4o1GPtpffcRx039uN3b+7G23jbdj37n1om2oLGG8495o3r9ESnlevE8O0RCU10Gifda10jjRST+dJDSi6b5eWlpDL5fC9Qi3A3aIWfQfkeR4KhQKmp6dx8OBB3Hnnndi/fz8uXbqEbDZrAHcymUQymQwcJdIs/CmVSgXyeRWE2dZgFYgKqNimetE1D0ytqgxXYzizWuNNns8jjFY93ryWzJPHfaklUpkl0ACeVDpsAR0KheDWXPTd0Yedf7oT4WIY0fkorvuZ65CaSAWiAVR4VKNV7P+D/SitqXsX4ADTT5vGiZ86EZg39fJnN2fx8J89jHJfGYXhAr7x/m8gn86bkLz29nbjxTeW2Ahw+YWXce7l5+BFvIBRRHPn1dKsXgFbEeP82+1QGeI9+jnHTMON4ziBKAHOjSpK/M3PWdStRS1qUYta9N2hWq2Gixcv4stf/rIpoGqDXwCmlksyl0TnlU6Eag01PNedw+2/eDuqyWoACAJAtBTFvo/uw+jXRuG4DgaPDuLW99yKZC4ZAKAKYIFGiDQBDCPmaPBWuU15o+CxGRBWmUIQruPUvrNfdiFTNUir/qJpWs2ivmxDhv08PkM9qUwLs0PBAQSMFKpD8TsFtByLfq7Xsk+PZljg33bRWe2nDd7tiAA1oOjc6BrxOs6XzpsCZv7dfbwbN73hJmTmMoF7NNVR9U3eq/pmJBIx6YqqB/GZiUQCPT09yGQygfloZjRRY4bOg/5fLpexvLxsokq+F6hVpbxFLfouke/7yOfzOHXqFC5cuID+/n6sX78e3d3dBpCRUXheI4ebXlsWN6P3l98ToGohLNvLCawuaqHM2/f9Vecq0tOtDE+ZtuY7k0EXCgUTnsacbjsHXK3Vvu+bM7M1jM0G8Zp/7Lke1nxlDWqpGtLfSCO02Kggztx1FXyoADvesAPH//A4ipuLgAes/dxajLx/ZFXlUfbp9CtPw4s3mHq1vYqJl09g6we3mvHG43EUi8V6obZUApeeewknX38SABD2w9j0qU1wfGdV4RCugfHai6BQYOz7PpACiluK6DzTiVKptEoZYltqqOEasQibHgGjBWx03PrMloe7RS1qUYseG8pms7jzzjtx7bXXore3NwCgwuF6kdBisYh4PI69H9+LWriG4886Dj/ko3u6G7f97W2IFqOAE/SOAoBTc/CU9z0FKAPXfvDaegVwJ+hBp+wBgkdp0ZisdWKAIKhjOLBG1ikpwKTuQJmlBWDLa+p/J2eTq/QToCHvFTgryGREHPvNPhG027JW27BT2HQcWu/ENoTYYeQK4tWYbdbCAsJq7NB+2/OnukGtVkMtVUN5QxltZ9tMv1QnI2kEI8E221Rvv+2QsD/TOTaOATgIV8JwkkFArs/XI1NtQ4iug66pGlbK5TIikQg6OjqQSCSQzWaNLqL91LnSuVdixEI2m1313ZOZWoC7RS16DKhSqWBiYgIzMzPo7OxEZ2cnent70dvbG6hAbTN5ZbhkQhROyshtrygZZywWM2HWCkrplWZFbPVmq1VXGSSBGi2qjuNgZWUFtVoNHR0d5jMCvlKpZI5IoRcdQAD0UiCRUaZSqUDoto55/WfX19tIwuQG0UBBRk+hnRhPYPRPR3Hmt86g54EebPn7LQGrOvO/Oe/b/ngbzvzaGcz80AwAYMM/bcCWj25BuVYO5Hzz99kXnMWp154y4zj58ydRi9Yw/KFhk0PP6uzqEdA+ci5NOFoIOPMzZ7CwbwHXvudapA+kAyH3GmalgkfXil4JCiCupSoWzea2RS1qUYta9NhQLpfD/v37MTY2hk2bNgUAJUEgj++6/qPXI1KNYOLaCTz7o89G71wvqk51lQxRr+2+D+4zRnGCaHqxKUMoaykPSJQLlHMkgjD1ctseZAVaGnnHe13XRaW7gtNvOA3P97D9z7YjvthIOeNzqM8oWNN2bbCm/eYc2l5QbUfHznv12fzMBqH6DHUSaPsKQvV/jVpU3U6fw2eY2jKxMM6//jxyO3MYe8cYkkeSq4wPnA+V/azXYhtXbOJ+0bXUuVSHijpLbE+zrafaYfnUs2xwzO/peGDRV9Vfdc0UeHP97M/5XuTz+e+5I05bgLtFLXoMqVqtYnZ2FvPz85icnERPTw+GhobQ0dFhhCDDsclktLCGCjQARniSuZPx8W+CLjIrHpsVDodRKBQMKKfABYKChQDetjwzvKizsxPhcBipVAoAAsdy2UeRqIKhzDwSiaC9vR1A0GrKcCUCcM3/5rMIMinQ2HYkEkH32W5c9UdXITGZAHyg5taMVZ/h4cZyXvOw6a83wYOHjskOrP/X9XC9hneYBgsK39ThFBzfgS/J5W1H24zyxArkzGvnXGruG4E55+XUr5/C5edfBkLA/tftx94/3ovIiQhKpZJRorg3dP25dmyfnn+gYZyxlRsC8VaF8ha1qEUteuwpl8vhyJEjWFpawjXXXBOIHnMcxxiBw+Ew9nx6D8YeGkP/XD+i8WhAzjXzTvKHKVp67jZlHQExgICRPhQKIZ/PG/mish4Inmyh0XJsy/b8qnfViTk49HuHsLx9GQBw+A8O4/o3Xg+/Ejyrmu3zf9UXFKyqwVpD15uBy2beVv2tHnVNz1JAq/nR2q4NtNXwzzXREG+NELAdBFrE9PRbT2PuuXOAA5z43RPY/sbtiF6MrvL4Um/R5/CH/Vaw+2hrqsel6rg0opLGIDXkPJoH2jZusH826Nacfzp/mkUa2PPI59jjrlQqWFxc/J45DozkfKvueMdxvnf89i1q0ZOUyODb2towODiI9evXI5PJmBBizaEGGqCNDIdMix5qhhSn02lz1jWZWjweR6lUwsLCgsnR1qOkgIbgIHBzHMccGUYA7Lqu+YyAkuCNIJNh3mTolf+/vTeNkus6r0P3rbmqq0d0NxoAATQmAsJEgOAgkjJlyZEiydKS35MdJ4qtxIqcOI7lYdmOYj0/+z0P8vBsS7ISyc56sZeHJT0PSqRQljVY1AQRAEnMQGOeGkMD6G501zzf+34U96l9TxdoMhIoNHj2Wr3QXXXvPeeeKtzv7O/b3/fV6yZnXZ0JlMxrgRdW4SbRpbQ6lUqFemOqXJpzJsEkGI3X9wGY/C0aklisTWxr0RoifgQJJMw1dE304V/eXMahjxwCPOCBX34Aw0eH0ah3WmYwMsAND4BQnhzXLx6P4+SPn8T5d5xvSwJfQKwUw+v+5euQqCQW5GyrwbF7qaojg2NpD3HeE9CWO37rW99aNJXKgyDw/vGjHBw6cHsVh7sJnudhZGQEr33taw2JUXVaNpsNtQpNp9NIJBOY2DCBm8tv4sG/fxCRVrjVlBbtop2icq4eqyPmxxA0OvVR6ERnQdRkMmlslRJD/qvEVsfUY2jTtYjp3t/Yi9kHZjttpgJg6OAQHvqlh0JOd3UgayT/dlFpnmvLyQklj7dThum4SiDVmUHoPFT+bisV9H6i0SgQAWqxGvxch1TaxJx7n4s/fRHX33UdQbzzuEpeS2Lnu3fCq4fno3sAqhj5HeCP5kyb+SCcxqZBBNaPSSQSpuCZvefQ4m622gGASWHQej265rrWnJM6RfR7pveon4OOxz1jPp/H4cOHcf78+bsmiPBS9iouwu3g8AqCBmxubg5zc3OYnJzE0qVLcd9996Gvr8889LQVBhD2+qpMuNlsmnPosWaLqHQ6jRUrVqBarWJ+ft48vOhtZPRUC4upcdIfkjz+kPzSI8p8Yj7ki8Ui6vW66fEJtA1UT0+P8VByLPX68x6azaZxKqizQT3fjLIzd0y9rUr+6QywPaaJRALRVhStoIVItB2l55hq7Ok0yJ7MYtsvb4Pf46N/fz/8aHuDpIXNeE40GjX3aEu1IpEImqlmiGwDQDPThBfxQmSbc9Fq6/q5sAKpLcvnHKgg4FqwdZ2Dg4ODw51HEAS4efMmdu/eja1bt2JkZMQQYN/3Ua1WTScKysEvbL+Az7/38wCAaDOKrV/aimgz3NKLijIlXP6Ij30/sg9LDi/B/U/fD/idKLFWqqYTnaSRNph2Xh27AAzR1dorHFsJ1rpPrsPs9jDhXvOXaxbYLY242oTaJr8aLWZaWbdIKu9Lr6+Rbbu2iUa87eg4j+e6dSsQZxN/3/dx83tu4tI7L+H+X78fkcnIAvLI6/u+jzUfW4PmYBPTb54GPCB9KY2NP78RqAEtvyP31jkFQafdqkaHGQChze8W6ddoeBAEaGQbaA41kZhNmPftoq105uhacd/ZjeTrmIrbFbrj9fRv3XvpZ8t7Z8CpWCwuuvQ4R7gdHL6LKBaLKBaLmJqaMnneg4OD6O3tDVX1VLk283Pt1k9AONdqZmYGpVIJjUbDXIcRcKDjnVSjTXKphksfwOl02kRwOR96y9XbOTIyYo4hIaYxoMSbJDCZTIYKqWkxNtsTzbnyfnnvfBBrDrRK0tV5ocXgAIQ2PYxI831bht3/fH/buETCEWZemxEDnqP3pJU+lzy3BNcfvY76YCfSvHr3apRmS4glO5sx3Syo1I3fAd3I0CjR+HEttDq9RikcHBwcHF4Z3Lp1C4cOHcLmzZsxPj4eiuZpmtCph05h94/uNqR13zv3wY/42PHUDgAdh65NporxIg798CFcfvgyLj90GX7Cx+a/3xyKVmqureZDcw5MQ9MIto7ZjXST4LVaLcxtmuuQ7Rcwt2kO/Yf7Q1FPjTarE50E3o5SE+p017nbEWwldSpn7ja25jnzfZ0P9y72vaujIBKJ4PpbrmPiZyYQxAOc+fkzGP+tccSvxUOEVW2v7/vY8KENiNajKGwuYN1vr0PicqIdAJA6LXqP6iDR6Lqdq63rZzsGgiAA0sDkT02idl8NWz+2FZlbmdD8bGeGvWdQAm+rNrqpDVTRoJ+XRq/ttbff5/UYsCoWi4tuL+MIt4PDXYByuYxyuYzp6Wmk02kMDw9j6dKlWLJkCYCw4VGPr/bMVg820PZOzs7OolqthiqK2g87LZ7C3+lBBdpybP6Q6KmcSXPOCT6A2bJBo84kvBrZJjGnceQ90bDQoGj+E6/BNQHCeWdqQEmElcDyPc6pXq+jt7fX5GCrE4PGhdFmldCrkVPjTKJNQ8m1azQa6P2HXmy+tRmHf/cwgniA+/7hPmz7i23w+r0FygbOkXJ09dprH3agY9i4aWJRHYJOEQcHBweHVxaUwlarVWzfvj1E3ID287v3Vi88S53ae7MXLb+FaKQji9LIdaPVwLM/+yxu7GwXAYUHHPvnx4A0sP1/bg85kLvtE5TUktSoMst25hJ2JHz8r8cRFAOc/emzAID7P3E/ln16GQIszINWImUTYpsk2hJl3QNpFPkfy7+2I+g6lh0tJ3QP0u19ALjytis4/2/PG3n4/KPzOPtrZ3H/T98Pv+iHAhe6jhE/gvWfWI/ysjIypzNoRppmT6Gkmefzs7Fl3rYTRu+Nx+g+78Qvn8DsG2cBAEc/cBSv+9DrkK6nQ0oDe6+oe7Zua9Btb2kfZ+9ju8F2aOj+iq+VSqVFkxancITbweEuQqPRQKPRQLFYNEXWhoaG0Nvbi3Q6DQChaKrmMWtRlCAIUK/XUa1WO62tXsiJVkm3yo8ZFWWeNQ0pSVo6nTYkWqPhSqAbjUZIWq0kVQt9kZhq/287R4gPWFbk1AJyKh/jWtjGIZFIYMmSJWg0GsjlciFjQoLP3pGUspOY81jem5Jv24Cw3RvvjdfS6LN9rUajgd79vdj2k9sw9aNT2PhfNqJV6xSy08+Exof57SqR5/u6ieL68z4oNw+Cdts6R7gdHBwcvjuoVCo4evQoSqUSdu3a1SFfL5DFJSeW4Pt/4/vxuf/zcwgiAd70yTdh/MA4GqkGWpWOwktJWdSPYvOnN2N66zT8uA8EQCaXwcavbgxFGzVP2o7SAuFeyLQTrKYOhAmTFjM15K7l477P3Ycg0nZy3/d37d95bdt+kjzaDnKtFG6TL0W3VDHuceyxNBptO6htGXO3qLDeu71vWvGtFbjx1hvIvSYHeIDX8DD2yTHEajF4MS/U8YXXI4GOVqPIns3CR3huSlrVaa9EWx0M9n6Or9mdSk5/8DRmXz9r7i23MYdv/to38ZYPvAVRREPRZc2xt1V7qiK014zfI0091Jxye6/XzaFifyf5Wq1WQz6fN/uaxQRXNM3BYRGgr68Po6OjWLJkienTnEwmze/9/f3o7e01+byVSsVEzRllVpnQ7TyVfLDqg5+ENJlMhq5PA2YbIkrHgXDhEl6PD0rmqedy7YqmGqVnxUweo5FpJZBq9Di+3teKFSswPDyM06dPo1gshry9JPNq2LLZLHzfR6VSCTkOdL6UvNNhYBsW9YarN57rwmg6o/h+4CMei4cqxqph1vwxnscNEDckmjLAivS810qlAt9vV2ufmJjA5OTkoiHdgSua5vAy4fYqDosFq1atwo4dOzA4OBgqnOr7PmY2zKCwpoAH9j6AqXVTePp/fxpv/tibkZpOGfUUbRLQtqvXH7iOZ3/yWSSKCbzzN9+JRCNhHL1UqWk6mC3Zpi2jI18d3RrhpKNdbbmd9wsPiHgdxZlKtLtJxfV3lW/TVncrpsU562u2A0PRzb5yLrpvUFJ4u4g4r2MKi3kB9vzeHpRXljH+0XGMfH4kRJh1DiTPNglVZaHuabg3orNea7YEQQA/5aM+UEfqesoEX24Hz/Nw8PcPYm7HHACg51oP3vgrb0S2nDWRbduxwM9cvxuRSMTsCfUYvV/dt/E7y79tgm6nCtxu7aampnDw4EHcvHnztvf43cBL2as4wu3gsEjgeR56enowMDCA5cuXG5KdTqcxODho2n0xelutVtuVuKXnNv+/p1IpAG0pu0qPaACAjiFLJBJIpVJIpVLwvLbkuVqtGvJrR2JpIPShrRJrjsWejCTvPI/F0Eg2gQ5Jt4uAkXBr2xLP80wOeL1eN3PRQh+8vpJVfeizKitJNz3LjGCrQ4C50jyOEWcdwy7motFurRaq0il+BlQr8DNkxXiNcKiXmCScmyzf983nfODAAUxNTX37X8ZXCI5wO7xcuL2Kw2LC6Ogodu7ciZUrV4acu3ymX9t1DU+/92k00g0sO7kMj//Z4+i51mOc4rTDtO2TD09i4NwA+uf6kclkjA058+gZbDm45bayZLWxWphL85Y1IkmlHO1Sq9XC1e+9ipEvj8BDWOKs0VclWxrdVrLMczTtjMfaDn4tvAaEW5kCCM1XiSDnp+RaVQC2PNveI3B8lWpXkhVMPTGFpU8tva1iT9PmtHuKHm+nGigR5r7GENi4h4vvu4j89jxe8wevQfZyNuTo7xZdbqVamPjgBJpjTTz08Yew5NoSs6dRxwbno58h58v8fVUv2k4P/dzUWaPkXO9TVQn6fdHv6blz53D06FEUCoWX8t/rFcNL2atE/rEDHBwc7g4EQYBisYgrV67g6NGjOHr0KC5duoRCoWDIKgBjgO22EfSG+76PoaEhbN++HT09PQA6xq1SqSAI2oUwkskkMpkMenp6Qi2t9GHJSC+Nh+bZqJEgSdbKqmoEeTzJYaFQQKVSCcmXSFwZ4aXXlwZIvfFcL96bRsJtoqobCZ6TTqeNMeR1NYdKz1MyrgbDzi3jOqihAmDah6jR0UI2Kt/ifdMwcr6UWnFeNIiU//MY2+Pv4ODg4PDdw82bN7Fnzx6cOnVqAbm8uPMivvHub6CRbpPhqU1T2P1ju9EYbBh7CoTzZ1fsXYH0jbSxufF4HMfefgzf+pffwv637A9FQLU2SjweN61D1Y7bEmBtIarjXn7nZZx8/0mcf995EwFXJR3tt8K+X77GHzqklZRzDlpA1SZwAEJtTW35MV/TQIT+3M4JwDlxXrakOlqIYuxzYwv2E7qedpFbnq/7Il6X+zoe32q14Kd8XHvvNXOdC++/gCvvvoL8tjxO/uJJ+Ms7BXWZPqftQT3PQ7QaxcaPbMTWj25F36W+Fy1mRuKuzpdQZD8IQk4Ugp+r1g5QJw/H4O88Tr8P/K7yflqtFvL5vFFtLja4HG4Hh0WISqWCSqWCmZkZnDlzBhs2bMD69esxMDBgDGKlUgGArkTx6tWrpkUUc8E1emddXgAANepJREFUP4ikMJ1OI5lMhrzf+jBXObUaJo02a3ETmxDSAPHhru2v+MClk4APaBpKNWJqHOyIejweDxFPfcgz/5reeq4HNxmNRsP8zWg+r6EefK4d0MmdTqfTqNfrJgJubxii0SjS6bT5vHic5tdzTK45nQu251jzu2xvs+/7JjfdEW4HBweHuwv5fB579+5FqVTCAw88YJ7hw8eH0Xe1D5WBSjs32Pcw/tw4Irk2EWFkm6ouJZCe56HRauC51z+HA285gGaiieff/DxiQQwPf/1hAB2Sp3aGdshWWSkZpEIrFouh1qjh0hsu4dS/OoVWpoXJd00CAbD2T9YCPkK2XKOcHMOOUCtx1iiz7gHUIa1RWVV+qTSb7/Ne6MC3I9kacU8kEqjVamZtNPLNv7vJ1nVMdYjoezyfZFXJpx3NB4BIMoJYEEPdr+PAhw+gtK6EeCSO+mAd195xzVSGz23O4dnffBZP/tSTiNQjC9adjnvf95HJZxCvxNGMNUOkXh0OXBOutb5v7790D8jPUo+3X9cIuN67fv76+Xqeh3w+j2KxuCjztwEX4XZwWNRoNpvI5/PYv38/PvOZz+BLX/oSzpw5Y/J2NdcHQMioTk1NoVAoGMLs+z7S6TTS6bSJHAOdwhws2kW5Msl3EASo1Wool8uGDPNY9VzqAzkIAhOVTiaTRq6uxpF540rCdYNQqVRM4TPNt1byGolEMDg4iM2bNyOVSnXyy9Ax7CrrJsnl/Jn3zUrfNFia605wHpFIxPSJVJUBkU6nkclkzBpzLfX+ucYqO08mk6bPOp0nWrCGx9ZqNbMWNK5MLdAWZw4ODg4OdwcajQYOHDiAvXv3msKl8UIcb/jtN2Dk9AiitSgee+oxPLzvYSQiCcwNzi2IHlL9RbXZzKYZHHnzETQT7ed+M9HE7u/fjbObzoYiqACME1jVWgTtuNqoRqPRrneyvoJz//ocWpkXCFkiwLW3X8Ps62YXyI1JomgTOb6qv1SRBSxUqPE9m/TxdTuqzPnzWjaxJeHTPQkVY1qt25aIk2QS6qCgLWZdFnUWaEsvXo+Res7F8zw0h5uIZCPIbc/h4O8cRHVdFUf+4AgKrynAT/o492PnkL2VxeCpQeCFjyo5k8SDv/YggnrYiaDyes0fj8fjpkaPrQjUOjSMMqtaQdePex9VVNrrZH+e3daMc1Q1Jv+NRCJG/bhY4XK4HRzuMcTjcQwNDZkK59ls1pA4Na5K9ugBXb58OZLJJHK5HMrlsiHD9PIyyqoR8VKpFJKOkUjS667eWj5UK5WKqZjNnGQSdM4HgCHirVYLpVIpVDFVc9E4F/UU87hYLIaVK1eiWCxiZmYGQDgni/PT6utAx3tuS9k1h1xJOtAx6FpohkbXjkbTOUBjy42SFknjmjG/yvM8lMtls1EhuVbJv46tJHxubg6HDx9eVAYrcDncDi8Tbq/isNixevVqPPjgg6aIp5/ycfKtJ7HrC7uQTqdxeudp7H77brzuE6/D2PmxBRFF/f3SQ5fw7I88i8pgxby+4xs78MTfPYFIJRIqjgYg1LqTNkpVbCrp5hg3dtzAsf9wDJWxCmKFGDb88Qas+OIKAGGSqzZaI5caJWUEmXsGlUbbEWle33aik9zR4aypa0rIeR6Pp+20CapGt9VRbxNHlanrtW0pvcrku6G+vI7TP3cafWf7MPmuSfjJ7sq0oaNDeOgPHsKRf38EheUFbPvoNgweHzTjqnNB14v7jWg0ilQqZfYotrNC709l+CqN19Q2fkbcd9ifi34PdD5KvHWdbYfJ2bNn8dxzz5l9z92El7JXcZJyB4d7DI1GAzdu3MD09DR6enowODiIoaEhjI6OGpLqeZ6pMElvNUl0X18fisWiaQNGI0iyCGBBdFVlWir5Uqg8nNdSA0ayCHQMUjeyq/IkNaxK6iORiCnoUq/Xcfr0adMWjTJvysZU0qYbAr2u5pLZ3nYep3IzbdlG8q8GjQbYSMZeIMsq/1c5mHqPueHhPFSyxc+GjgiVvvE9BwcHB4e7F5cuXTLy8uXLlyNSj2DrZ7eiFWvh8COHsfcH96KeqmPP+/bgiT95AqNnRwGE7SDtzfj+ccRbcXztJ76GZqptXw89eQiNVAP/5FP/ZAGRpS1TSbZGaTUqbFRwfgSe/4I9DACvGW7tZBNrm3BpxNQmfXYq2O1InM7Vnq8qzPQ9PScIgpDdpqpNSaTO/Xa2lHZaU8F4vC1f1zx07hHqfXWc/oXTuLXrFm49cuu235Hhg8PY/vHtSM4mseMPdyC3IoeB4wNmLXlPNmnV8RmoYBcVPfZ2+zj7murI0L2GLSHXcdUBotfSz5H/ct2LxSLm5uYWZf9twknKHRzuUfi+j0KhgMuXL2NiYgIHDhzAuXPnUCgUQoSMhjUWi2FmZgYXL15EtVpFMplENps17a+KxWKIxNbrdRPtViKsVUt5Xa3OrfJmjd5yPkoS+bCtVqvwvHZedCqVQjKZDL3P8dVA0GBrZJ7RaVZIJ/lmXnQmkzFReSAseaKTgX/z+vF4HKlUKuTRpvGh0dXXeT6AEDmvVCpmHlqwTSMNtVrNjKlF2myHgxpcu4+qg4ODg8PdjZmZGTzzzDM4e/asedafffQs9v5vbbINAPnleXzz330ThaUFY/9ow9T5vPrYasQa4fja8YeO40vv/lIoAs1xtNip2lZbEt1qtZBbncPhnz2M8vIyAKDZ18SZnziD6cenF0ROlVBpRFvJoe1M1xxfICxvJ1TKrBW/uxUio03WImBK+hiE4H3bHVd0r9Jtz6H53hoQiEajiMVjqG2s4eq7ryIS7bRq4zXjqTgOfuggbu3qQrQDYPt/3Y7HP/g4+s70YduHtyE9lQYAxPNxDJ8YNofqfXVTP/A1uyjdgiFFEs511c+In5PW7NE10r91DTX6reumY+rv3IMWCoVFHTRwhNvB4R4HieLMzAwmJiawf/9+nDp1Cjdu3AiRaIKyavUM0zAAMASZEWI+KO0HrHpwSc4jsQjqQ/VQxW2N6pJ0ct6a26RSJf4kEolQP1ISzHo9PIZKxOLxOHp7e839aNRZPbactxrsIAhMm7Vum4RUKhWKEHAu/F2rtXOuvH51oIpqvYr5+XnjYef6Us7FiubMbde8dt3AMBVAnQfdpG0ODg4ODncnKpUK9u7diyNHjqBWq2HlMyuxes9qeK02eYlX49j1P3YhPZUOEaNVq1aZ7iJ0tv6z3/hnyOQy5tqDs4N441+9cYFqi4XCgI7NsB3aSmQz5zNY+8m1iFRecFI3PIx9cQwD3xowDmJgYfVwJfG2sgxA6D11nCv51kipkmCer2RdFXpAe09j22DOk2um0nlVmPFvrp3dgsyWWnueB3/Ex75P7cP+T+zHufedw9QPTCGWjplOMLFYDFFE8ciHH0Ei196bIABSMylEahFs+m+bsOKpFRg6OoQn/uMTSM+mQ7JxnY9dpI331sg2UI909jR2lF9hf27djlXSrc5+Hm/ndOsYdlBA93q2k6DZbKJUKiGXy2Exw+VwOzi8SpFIJDA8PIyRkRFks1n09fWZAhpsC0a5OY2w/ZAmUaa8nA9Vrf7Nh2ij0cDlxy/jxA+dwI7f2YGByYEFnkz1DNPosYd0t7wgLcCiuUXqNGBxF56vY1KaDaCrMeV5zCXn9RqNBpLJZEhWx4i9Rvo5D8rhtZgb0PHsVu+v4vAHD2P9X67H6FdHzfqTQNOLzNw6ys94DZXJqZyNx7RaLUxOTuLYsWOLqnDaS8mLcnBQuL2Kw70Gz/MwPj6OrVu3ore3F8//m+dx8dGLeOypx7DtW9tQLpdDEW3aLNo3EtRby2/hyz/2ZaQaKbzrz9+FZCGJarUaIpbVajUU3S2XywsqaWt0uJatoTxUxtzOOZx/z3ks//xyrPv4ugXk2c6n1tfVJpOodiNvhF2sTO2ukk91EFA2zfQs/s1r25FbO6qu49mEk8fwHCWXkUgElfEKjn/gOPL350Of69ZPbMX434/DQ1gCfmvjLRz8mYNI3Urhgd99AFfedAXr/2r9Aom+7aTQtdL7CIIAlcEKTv7USfSe78WGv92ATCxj6ufo90bvl+NohBsI9wXX75dNmO18bf1cbSeLPSb3d0BbGXnmzBkcPHjwrq1Q/lL2Ko5wOzi8yhGPx9HT04OlS5di+fLl6OvrQ29vb6g6OKPPSnyVbAKddlgkverR9jwPF77vAo78myNoJVvoP9WPbR/dhr5LfebhmkgksHLlSnieh3PnzhmPdKVSQaPRMNFsLVpGcknomMyJpnxd5ep6HOXmvB6JMsdnlEClZGrwlfQD4VwzGjDt1c0587j6pjrOfOAM8hvziFaj2PxfN2PVl1aZyD2vxQg+15Zk3vO8UKswLWajEY5z587h2LFji0qS5Qi3w8uF26s43IuIRCIYGRnB9u3bMTw2jMknJrHx2Y1Ip9s9t1lVW4menUoUBAGmVk0hW8tiND9qWosd3XIUYxNjSOVT5lpBECCTyaDVapluJkq4gyBo55T/zGEUVxSx6fc2oXR/Ccu+tAxeEJajawSd81DnvUazNTKtfyvp18KgABZcu5sN1q4raie5f9C52uAeSKPXen11dBO0x+XlZRz5uSOY3zq/4Lrr/2Y97v/z+wF/YZR5dtsseq71IDGdCJ1jE1Pde/B9Oxe6nqnj+M8fx/Trptst2z69Fjv/v51GKaffMR2DrzFYYDtK9H7t6L5Nqm05+4uRcp1Lq9VCLpfDsWPHcP78+btWpfdS9iquaJqDw6scjUYD8/PzKBQKuHLlCpYsWYLVq1dj1apVIe8l5dTRaNS0tQI6D0zNwaHsGWg/MC+84QKOvucoWsn2wzK3MYeD/+kgdv3iLmSLWXieZ1qVZTKZUPQcgCHFmtND+ZtdGVMdASr3sgvDaFsvlcXzWiT26gHmJoLOCM1T97xOSzI1wJyTXck8EomgNdLCxK9MoDLeriDbSrVw8r0nEQ2i2LhnIzZs2IArV65gfn4+5JVntID3p5sTGm71SHczpA4ODg4OiwO+7+PGjRvYs2cPtm/fjtXN1WjEGqZlpDpzldTQcUvCuvTiUgBANdruynHpwUvY/QO70Tvdi7f/ztuNvWu1Wsjn8yF7pSq2AAEOfvAgbj5yE/CA4798HDt/YSfgA/A6NpXFWPU+tNgnYe81NGfcPpaOd7VrStTs6LiSZCAcIae91+4kOie1saoii0QiqI3VMPWmKaz75LqQQ14JZ7qURt+1PsxvmTf9shEAG/9qIzZ8ekN7XgjntgPA8LF2TnYQCUeB7Zx2O8rMvZMh4h5w6P86hPkH5l+4KeDCD1xANBPFI598xMzbJum6PqruU7m87WTgHHVeOleV9Gsk3v4MdY0Z4CgUCiHHwmKEy+F2cHAAANN66/Lly3jmmWfwla98BSdPnsT8/LzpR82Ha7lcRqVSCXkxa7UacrmcKe7FvtwAsPqZ1Vh2chlesCuI1qLY8MkNSOaSoSh6Pp/HjRs3TM63ttjg2FrBnDngttcUgJkHANPDmlFvrSBue+EZSc9kMshms0aiznlq9FvboTGiQOOlkW8eQ5LP/OpMMYPVf7YakeoLj2IfGDo0hNGnRzE/P4/JyUkzPtA2RFx73gMl9yThLASnhoyyfwcHBweHxYtisYhnn30Wp06dQr1eN9FoFu5kyhPJTTabNYXUtHCYH/g4u/osnvrhp1DpreDmmpv47C99FvVUPRT11LZgauuOvf8Yph+aNiSysrqCQ79/CC107F40GjXpZUrE7IisVkS3ZdwsmGXPQyXNdt6v/TuVYbTNNrlTUk9ngHZn4fqq877aV8Xej+/F+Xefx+UfuoxEJoFUKhXqNhKNRpEoJ7Dlj7Zg+TeXAy0gMZ/A+H8fx9pPrUW00Sm8xnuwO6Ao7EixytCVzNKBEI1GEfgBNn54I6LFDjHumenBrv++yxBnvW97/YOg05FGv0PqENE6N7wP2/Gh3wmVpNtE3ybd3Bdyn7OY4STlDg4Ot4XnechmsxgaGsKqVauQTqdDRcj0Ie37PiqViinWQmOQTCbR29uL3t5ePPWvnsLFtRex/S+3Y91X15kospJrjksjyzHo3WXeUbFYDM3Blo/RELA4CttrMXJOUsqcLpWiqRGnd5tj+b4fKpzG12nkNO8I6Bh0RqdJ/HmfrVYLV992FRd+4gKGDg5h+69vN9fV3txavMWWx2v0n9fl5oJreerUKVy6dOnOfVnuAJyk3OHlwu1VHF4NiMVi2LBhAzZt2oSBgQH09PQAaOdg5/N5pNNpY4u1/RNrmgSJAJ/92c/i5rqboeuu270OT/7Jk6YbiRb8JDmjQ/nABw7gxpM3AA/Instixy/tQPRmuM6KqsPs6LbaKZWsG7IokU4l6Uow1SnPMXmMFmezK6/bEWKV4gOdVDglpbTvuVU5HPnQEdSWdvpBb/9/t2PdF9chEnSiv0rYAeDI+49gy0e2hIio7SxQwquOCu6xtJJ8MplET0+PSb1jkVfeF8dvNpvIbc5h4oMTSJVSeMNvvgGZZiZUE0cJsM5Br6HpdRpsITQXXCXvWthV39eAhO0EMXL4eh0TExM4efIkqtXqS/q/8d2Ak5Q7ODh8WwiCAIVCAYVCAdPT0+jv78fQ0BCGh4fR29sLIGzoKCPn65S7cUPwtk++Dc+sfgYr/2ElEIMh7jRqAIyHHuhEs3l9la3H43FDyrVPNdAp6sGq3hqF13xs9SjzWJVBsTAaEM7V1vwtlZlzY8PrcnNjR+CVQCcSCaz+0mokY0mMPT0Wkmgpada1osxKJX6cBwu88Vga6Lu12IiDg4ODw8tDs9nEqVOnUCqVsGPHDsTjcdM2k/YAeCEa+wJR0ZZXrXIL3/eJ78Pu9+zG5e2XzXVvrbqF6dXTGL44bOyMypbVnm75nS2IVWIori5i84c3Iz2fRjMSrkLOOdjklbaaJJy2ylad8V401xrokHnO0T7HjvpqepjaeDuHWOeqBdBoQz3PQ21lDX4yLG/OrcrBh4+gFS4Eq8R0+8e2w4vdPnqtc1DSqUXQtFc426UuXboUyWQSuVzOOEmU9APAwIkBbPnoFgzODSJRTSCS6OzbNFKuxFuvoWtpOye6zd2Wi+ve6XbKBJ6vn3O9Xkc+n78n9i9OUu7g4PCSUC6XMTU1hdOnT+PQoUM4deoUisViSNKkxi4Wi2FwcBAjIyPo6elpP4grAdZ+fS2AsFFh5DeVSiGTyZhIsuZfaTS3VCoZA0uvPY05o9d8v9FoLMhn1kIgzPeOx+Mh762dj60GNJlMmiruBI2SvdnhPNLpNNLpdGizQfm653lY9Q+rkPATZi21aB3nodFyldfREKshpDc6lUqZazg4ODg43BvwfR+Tk5PYvXs3Ll++jHq9jmQyief++XOI9ITbdFJVRUe453lIziZx35H7QtecWzWH3e/bjcJYwTiR1f6G5OHRtr3Z/PubkTmfCTm8VZ4MhPOTlXwzagvAdPQAwr2kSX7tXGzdQ3Qjb7xP22Fg7z10PJ2vTShpg4e/OYwdH9ph2rOt+cIabP2zrQjqHWLZ7bNSB7ydZ27LuW2JtZJVdjGJxWKo1WqYmpoy+facu73GADB6dBTZ6ay5nh3d1z1ON1m77nd0nioTV8L+YkoCW8pvk3B+LrlczhTtW+xwEW4HB4eXhUajYR6Cly9fxvDwMMbGxjA0NGQelJFIBOl0Glu2bIHneSiVSpifnzdF12yDyYer77fbgfX395uos3qjFSrRppyK5JJEW0k2o8/0UqtBUakTJe6Uj9F5oN5mGh7tv+n7PsrlshmPJJkknq8rIdc+4gBCEXweD8BE2pU0a1ERzZdTOT/Xj84FBwcHB4d7C7du3cI3vvENVJoVzP/CPI68/ggmN07iB3/vB5FoJhYQ5UgkgkQigXK5jLXfWIvptdM4+9qzJgQ3v2Ie5YEyeq71hGyz2q56vI59H92H8soyYvMxrP3TtfCanVaaiURiAYlNJBJG9mzPifMCEFJ5KRil5nzseinqnAc6dl1zxJXo8Tgl5Hyd9l/zk2nno9Eoho4O4bEPPIbL77iMB/7iAURrUTS9ZmgsO6pvy+QJjQCrpF1tuc5fiTX3GqwPo9Fl7q3Uya+Sbr0uoVFxnb+ScX2Nc7adCDbh1oCE3ouer/Pk36VS6a6Wkr8cuBxuBweHbxue52FgYACjo6NYunQpBgcHMTw8jPvuuw/pdBrNZhPFYhEzMzOm+AUJYRAEJi+cUdtkMmkILIki/7WrntLosoKm5nSp8SHh1hZa3Ui0th3jeEB7s2DL5xmFpmy7Wq0ukF8xh05bfHF8LdKm79FQagV2zYmyvc+M8GtOFu+rVquhWCzi4MGDKJVK35kP/BXCS8mLcnBQuL2Kw6sSKQAfAPCraBcyC4BlF5bhrX/+VmTn2lFNjfLSoV2tVhGJRPCZ3/oMcstynesFwFt/660YmhgyDmwW5CwNlXDwPx3E/KZ5M9baP1uL1X+9GpF6h2Da49GOaYspwiaGQDiiqtJsJYsEHex0jitZVce+LW9WJ3e3CDnQSSdLJBLmGK1lo3ZbSbo69rUNmT13jaCzIKtNzHnvui63i/RzPN0LUcGXSCRM8TMtPGcHILpF6jXQoJ8HPx91ePA9BjBU/fBikXJd71arhQMHDuDUqVN3fcDgpexVXITbwcHh20YQBJibm8P8/DympqYwNjaGjRs3mkIuJMUq59Zz+TBmhU/1JPN85i6xarfmcpEUB0G7migAU0VdPcu2BJsEnGMQKluzjSrnxXM1Wp1MJhcYBjoD1AiRZHPzUavVkEqlzP1R0t5qtYzEvlwuG0NZr9dDEXlCjT7nzGPudoPl4ODg4PC/iCyAh9FpPeUBpb4SZodnkZnNmMNIrBgRZa2R8W+M4/APHQ4lmk4/MI2RUyPGLtGGlVaWUBushcbKbcyhkWggXo13JcTdIstK6mxHMu26LTXWY5X00aHeTaqt19W6KGrPbbLPa2u0m85+m6h2C1zqXOyotB21VsKsqjldRx1DU9x0PFsSTkUCnQ28F23tZcO+L43wa3Sbx2pEX+epgQUNpqjjQaPp6gDhuVRS3it7F0e4HRwcvmMIggD5fB7FYhHXr1/HsWPHsHXrVqxatQqZTMbIotXgaSEXOwKs+Tx8jw/vQqGAarVqvM4qcSJx5znxeNxcXwubAQhFl+mN7enpMcZENwJsj8F7VVk3ZV12FFpJvxoxNe6UTKVSKbPRaLVaSCQSGBwcxNjYGC5cuGAk67oJ0bW3c6Ion7eJuYODg4PDPYQZAP8ewB8BeBsQK8Tw8H9+GGtya9AIGiG7pQSTzuNtn9+GRCOB537kOQDA4089jp1f2YlytGwilHR6+1kfrUTHab5k7xLc/5H7EcvF0PLD1cyVdCp0Ht16bes5SvzsaKySwNup20heVa4MdKLN3WTk9rjdpNU6FyXyOp8XU6cp0eZ4hE1Iu0nLbUm2Xlevo/fMvYjeu65lN+hx/FfvTauQ63G6FrYzQR0KnC+dGtxTFYvFRafKezE4wu3g4PAdh+/7yOfzyOfzuHr1KgYGBjA+Po6lS5eahzrzjXm85mTZhkXJIvPDotEo5ubmQiRYPan68GeEmeRaoa9xvHq9btqJAeFWI5yDerBtj7ntxfb9dn9sepcZxeaGJ5vNhoxvvV43VeEZ3dZq42roKG3jpoj3TfkY59utkIuDg4ODwz2CywDeA+DvgOZ7mzg2ewxDTw5h2bJlIVJkE0PP84AWsPHpjUAUqEfr2PjV9u9M76oFNQQIMP/gPA7/h8No9LajjtkzWWz8fzYiOZcEvLBtBDqEWFtXquyZ5EqPVRvKY7pFXZXwarsxXpvXBDpRca4Ba8BoxFZrrOj+wR5Pf1KpFBKJBEql0gKngTrX7f0HbTjft89VIq1V3+01tKFrqPelkWO9lh6n89a142uco5Jl2/lgOwN4L7baQM+zI/msOTM/P2/av94LcDncDg4Orxji8ThGRkYwNjaGsbExpFIpQxxJEiORCGq1WqjvI729aoQajQZKpVKnr6gYF0If+NFoFKlUKkTIbZk4c54bjUaovZYaK0rQ1fCo3KybxIrGhR79ZDJpisrwWBoZtlbrVnSN0jluXnhNlaHruNzIXLt2Dfv27btzH+wdQuByuB1eJtxexeFVjxfyqgEgm83iySefxKpVqwDAOGlpO1qtlrF5rVYLkegL5CdoK66SySSKfUV8/t9+Hpv/ZjP2/8h+FJd1SFCkEsGaP12DVX/bvr7aaC0ISnupRNeWFPM4HsvfGfVUR7MdQSWUDGqrMPvadm45EM6NVmKrTgCmg2n19r6+PmSzWUxPT6NSqSyIkOtc9fq2A99WBNgE1VbH6X11Cw74vo/SihKyN7JIxVPGCc/9h6r4FL7vh1qX8rO056b7J95HN0m/3rP5inZR6On7hUIBx48fx4kTJ7p8we8+vJS9iiPcDg4Orzji8TgGBwcxOjqKoaEh9PX1mZ7d3ASQSJMMR6NRYwSYx12r1VAul+F57bZbAIwESY0VjWA2mw0RVkacKSVnXveLRbaZk81NC6/BvHIaKZJwFk0DYMhxN489DTodDIRG8EnWI5GIqfZKss1NiBJ2bk4uXryIo0eP3tHP9E7AEW6Hlwu3V3FwCCMajeKhhx7Cpk2bjOpJU6tot2hTlFA17mtg77/ei8lNk4hVYtjyt1tw+YnLuLX2FrymhzV/ugYr/mLFAlmzkmYltlrADAjXcOHfSiRVnaUEWP/WqDdf1+va0XC17zo3JYB2epY6EVgwldcGOoq9UqkUiubrj0r51ebzM+K49nEa0eY+Q0l8t0g0AMxvnsfR/3gU6z61Dmu/vhapVMrsL+g44Jgk+/xb69MwOm8Tbr0nwo58K8fsFk1X4q1zn5mZwaFDh3D58mUsBryUvYqTlDs4OLziaDQauHnzJmZnZ5HJZLBkyRKMjo5ixYoVyGQyIUKsVS7VM08JmVZPVRmbGixuKhid5jFKsOv1OprNpiGs8Xgc9XrdFCijYdBzSL7tvtz2BkIJM0l4IpEISfx088O50XixvRcj2JlMxhRZ43rwPlmtXT3mtVrtlfpoHRwcHBzuIrRaLTz33HOYn5/Hzp070d/fH1J60V7S2U2bVUqVsOdH9+DGphsAgGa6idPffxrrv7YefszH+JfHMfI3I6hH6iGyqrJudS5rxNSuc6IErZs6zJYq21JxPVfzn23nuV7XJtlAWBJPcqvrw9dpx20by7+DoFOLRfcjqpbTyDnHtiPGXEe7vo0WirXvKwgC5F6Tw8TPTaCyrIITP3kCkb4INn15kzlfybSebxN4nZc9nq1E0OO0JVw31aE9Zx3T930Ui0XkcjncS3CE28HB4bsGFj8rFou4du0aLl26hPHxcQwNDRlPfLFYRLlcRl9fH+LxOJLJpCHiaojUOAJYQL6TyeQC6ZVdcESvQU8zDaIWeFEjzwh2pVJZ4FVXWbpuOlReZ/fgVlk4jRjl5zTGdALwPvQ6zNfSzdO9UuXTwcHBweHlw/d9nDlzBsViEY8//jiGh4cBINSiS1OkPM9DrBzD2L4x3HjNjXb18gDonenFxq9sxNqvr0VyJolivIhqtWqiv7aDmSSTfyvZte0v7SnTs5R82sW/7CiqRl+1oJfaZPu4biRf09CAjqqN0Vx19muxL85RW5PqfOhE17nY98HxlBBzD6DXuV09Gl6vNFzC0f/jKKpL28VYW6kWJt49gUyQwcZvbQwRebtXuu2A0Pl1i153I8z2uvFzVrWCOhf0+tzfFItFVCqVLt/kxQtHuB0cHL7rYIT5xo0buHnzJjKZDJYvX4777rsPqVQq1JaDBFLJMw01/9ZibCSilF7TcPm+j1KpZPKxkslkSCLVrW+mnetFcsuCcPv370culzPGVUm5et85N/5wc6Fzb7Va6OnpQSKRQLFYNM4ARtjVuWBL9mq1mjmu0Wjcc4bLwcHBweHlwfd9XL16FV/4whfwxje+EWNjY4jH44Z0qw3yfR/xII71X1yPZqqJEz94AkPXhvCOD78D1VgV0UIUfsM3rTBLpZKxT0C4SjWhpI3Ei5FiJb7sKqJk3CZ7RLcItk2iKZ1W8m7nkCtJt2vC6L6BDn+9Rx5rE0tbLs1UNL2m3hed7XoOwdfoPNc14z3zuJ7ZHqz/+Hqc/MWTaGab8FoeVu1ZhTXfXAMvGs5ptwMQSuoV9jj23O2ofLe88G6fo6bjce2azSZyuVyofey9AEe4HRwc7ioEQYBSqYQzZ87gypUrGB0dxejoqDHk6kVnETTmntkGU40+H+wk7Swa0mg0jBFT8qubD0rWtS+4SsDPnTuHYrGIRCKBRCKBRqNhvP6ccywWQ71eh+d5psq65j+pkaYBi0ajeM1rXoPTp09jbm7O5F+pXJ7rAsBsVJj/Ti842445ODg4OLy6USgU8LnPfQ6PP/441qxZEyrgqUXIgiBAFFFs/sxmeDEPO7+6E/XeOvb88B6kplLY/NebEavGjB2mndH8bDsyqkSLBdyUzN1OXs65Eep0tsker8XjOKduxVc1d5lQQm5fl04F+/oazeXrGhXnMfxX5ez6up3bbUe/E4mESbNT0q1rGPEiWL53OfDHwKkfP4UVe1Zg13/bhWgyGiL7qtTjfeseiZ9dNweAKhVu95oer/fJMZXwq0qxXC4jn88vOH+xwxFuBweHuxaVSgWXLl3C1NQUent7sWzZMoyOjmJwcBCe55nCHpoj1Ww2TUSXhl2JrU3cKd+ikaUhU+8qC6Np/hbQMSD1eh1XrlwxrcT0WBLfRCJhrq/jAUA+n0cymVxQtT2Xy2FiYsIUfeM8aCxVrqWGnpI83ruTlDs4ODg4EK1WC/v27cP8/Dy2bNmCbDYbes92AG/+9GbUe+vY+y/24tLOSwCAeryOB//kQcTjcaRSKeP4tiXk3XKuuxFezXHWCKlNKjVv2I60dpOL29HqbiRdHQ1KQm2nPdeHewo7Smz/2Pej0WySWXXg2/NQRwjH0nZm/Fsj1Dx25VdWIh2ksfL5lSFlge6HXqwHuq6zzqvbMQRf057m9hoBCN2DXWunUCiYNmv3EhzhdnBwuOtRr9cxOzuL+fl5XLhwAaOjoxgbG0N/f7/x9qpBYpTaNsyUbDEKrXlnzWYTqVQKAAxhpdRdC6HYeVkAQkYT6BB9zwu36mKEmgYbaG8yUqmUuaY6EGKxmGl9xnVgP28AIWkb27pQHq9ec1c0zcHBwcFBUa/XceLECRQKBTz22GPo6ekxdrRarS4oSPa1n/sabm65ac4/+6azaEVbePCPHkQikUA2m0WhUECr1TLOZ+3WYdvJbkSZhNGWMAMInWe339TULyAsEecYarNt2DnJ9tjqvFcngp2DrnsNjejahUx5TLciapwrz6WTX6PJdo66pqvpvS/75jLEUjHE4rEF+yJeh+fY8nC9d3VAhKLpXYqskUDr58ZjuS66T9Pvgu/7KBQK92SQwBFuBweHRYNWq4VSqYQLFy5gcnIS/f39GBoaQjabRSaTCUWvKZNjNFq9raxITvKthlrbhilp5WuaS8XWGior479avKzRaKBWq3WV7rEdGXPXKHHXImraCo3R7r6+vpDXmPdRq9Xg+74pOscxHBwcHBwcFM1mExcvXsStW7fwpje9CX19fYhEIqH2kkDbfu786E589fe+inpfu9tH73QvHv3UowhiQYgkFwoF1Ot1o+pSskvypfnUsVgs1GVEI+LAwjZeJJSEHstxtG6KLYVWqJSZznB7fJvoqiNdneuci/2a3ivvQVty6T1o/jTnwdQwXQeNstuOC1vCr/Oyo81cd82l5tgaFNDz7N/t69opfUrw+dnrOut9tFot5HK5e5Jwuz7cDg4Oix6ZTAZDQ0MYGxtDOp0OSae6ycT4sFdZt0rEtH0YEK5oTqNMotxNSqc9sdVDzVxuEuhkMmnmwsrjKmsniWYv7lgsZtqFcRw1/tzg0FhFIhGUSiV8+ctffkU+h+80AteH2+Flwu1VHBz+15BKpfDa174WK1euDNk5LViaX5fHvp/dh3g5jn/6kX+KVDll3m82m6jX6yiXyyiVSm372htBeWUZ2dNZY4tVxQUgRD5pL0nStOBZNwKtknCea0fm7TGVmBKqLtNrATC2OB6Pm6JnGsXVnHVbym7Pn7+rc96+Hx3bltzb8nU7Ck/nOlPKuEfh3LX6u6al2RJzXk9l+DbRtiXxSrTt+ek87Qi4fhbXr1/H888/j+np6dt+T+9GvJS9iotwOzg4LHqUy2VUKhXMzs6it7cXg4OD6OnpMYYG6HhsNS/KNpR2Ow+7IIgaP83N0hZc9NhqzhZfo+SbPbRJtrX6uHqTdaOjxVfsoiRaSE4la41GA8Vi8U4vv4ODg4PDIke1WsWzzz6LfD6PDRs2GOUVi3XV63X0nevDAx97AKn5FIL5AEi3beGFRy5g5NAIYvMxpNNpAEClWsHZ953F/IPz2PQHm9B3tC9E7uxULyBcJEwjuYx+asEzjeCqbSY57DYWr2uTPnWmd4sUayRWU9X0fCWwPMaeP50IHMcu8sr9hB5jS+FVqs77saP43FNw36B7BpWf635BfyKRSChlzQ5c2HsVXeNuxyt0v2J/hrlc7p5NgXOE28HB4Z5AEASoVCqoVCq4desWUqkUBgYGMDIygr6+PmPEACwwlDTkvI59DA1UrVYz+Wk0vlo1nHlW6gXnmIy2qwFn1XIaXDoHVIbHOVFm3m3jwI2RysFI5B3hdnBwcHB4KSiXyzh27Bjm5ubwyCOPLJBBNxoNDE0MtauSx6uIRCKYemwKz//o8+h7Qx9e/+uvRxxtezTxMxO4+tarQAQ48QsnsO1XtyFzMbOAjNrRa9tWKiFXmbaSTCXCdv6zOrzt3GvaWXXI63xIxG01sDoGeC116HcjoTxPofJ3tfdaVE3P5VjqfNAIuK6LLW1XZZzOmefZ56rjQtfEVgF0i7TbAQc9lnPWteEYuVzunu2qEvnHD3FwcHBYXGg2mygWi7h69SqOHDmCkydPYn5+3uRH0wjwIa8Gj9I4jS5HIhHE43FDbHkMfwAgmUwik8mEvMKUqamXmu+rFI4ycZJ1rZAej8eRTqeN1FwNoeY98d5qtZoxfmxp5uDg4ODg8FJQr9dx8eJFfOUrXzEOW021IglttpqY3DCJ3T++G9X+Km5uvomn/++n4ad9HP/x47j2lmuGZZRXlrH/D/ejkezYX9YsUdky0CGE2mFESaFKxgm1t3aBMpVaM31LiaJWzNbItp1nznXQNbCjwN2k1XqeTb75u1ZB57jcfxDcMzC1TEksx4hEI2j1duTzdoSboENei7vpezov7dKiRJlro9FzPY/Xsp0jmsPNY5n+xvz/exGOcDs4ONyzoBG+ceMGjh49isOHD2NyctLkXzOficZOSTCNn5J05l3T0GkBtHq9jlqtZgyUbYB5nI5hy8Xo1Wa+GCPpqVQKyWTSeL7tCqgqCWMvcOZr3YvFRxwcHBwc7hyCIMDMzAy+9rWvYWpqKlS4k1HoSE8Ex99zHM3UC0U5PSC3OocLb7mAnls9iDTCFCN1NQUv6KjBmGKlTnDt7qGpX3aBMI1Wqw0Hwm27eF3aepJHlZsr0SaU8GvusxJ9JZ1Ap8Cpkm4gLAFXKbYd/eZYKo3X82z5PCXjOpf85jye++PnUFlTCa297ajXHGydO4/l/kbXj3Pj2tjOBzvqbZNxGxyD4xSLRZTL5Rf7Wi5qOMLt4ODwqkCr1cLc3BwuXLiAiYkJXLx4EYVCAcPDw1i1apXJoVajSyPD6uCMHieTSaTTaSSTyZCBbDabpj2XStdIlIFwa5N0Om0i2nqcSseazaaRWHEs9d7z2GQyiXg8jmazGcqBulfzoRwcHBwc7izy+TwOHjyICxcuGBJFUhWtRfHo7z6KkSMjAACv5eHB//Egtn1xGzb93SZs/aut8FptUjZ0cAhbfmULorWFEVE6qO1orcqs1S7bFcBVkkzYlcaVJJN087p2ay4lunQCUIVmF/ui84B/k0ASvEe7bozmP+v8NeJu3yOPU5Kq9zX98DQO/eohVMYqOPKBIyhsKCw4T/cW6qjwPM8EF+x9i7YMs9fUzuHW63EsLUjbDb7vo9FoIJfLoVQq3e6ruOjhcrgdHBxeVQiCAIVCAcViETdv3sT8/Dw2bNgQakuipJYGViVldq4Zq4prn0u7uieAUCVyACbKTiMHdKLcKrkCEPK0U1quxptknISb165Uwp5uBwcHBweHl4pCoYDjx4+jXC5jy5YtoRZSyetJ7PjEDux//36s/vpqbNq3CV6kTbzu//z9iNaiuPDEBWz92FbEZ+No+I0FecDAQqm1OpRpz+x8bzvSrOSPr6uTulv+t9poJf123jMjyo1GI0TmScLpKNdiZzYZ5T3xGJu86+92ITNVyQEd2T3vZ+ahGZz46ROoD7bl2Pm1eRx4/wF8zx9+DxLTia4ODL0+9xMk8xrR1wruOgeFXaBOc8/t43V9eGypVLqn87cBR7gdHBxepQiCdpG1c+fO4dKlS0gkEhgeHsbw8LApssZNBY1sMpk0r9Mw0IhSaq4FQgCYXDXNzeYmQiuck/CTMKdSqRBxVy8xSbf211bPejKZNHN1EW4HBwcHh28HtVoNp0+fRqFQwLZt25DJZExUNDOVwaO//SiihSgq8QoymYxRjK3/xnqs2LsCwXSARqxh8oZtZ7RNloFOkTAAIae2rQSzj1fpNc+zyS/VYxq1t2XeKommfU4kEqE0La0Bw9fpCNf7sqPBdgtRoEPebWeCOuR5j3YOev+xfgwcHsCN77sBRNpqg/E94+i91RuKMmskndfTwILuNTRf25bI2ySaZF7VC7ZjQv+1VYC1Wg35fL6r9PxegSPcDg4Or2rQoDUaDZRKJUxNTZme3tlsFgBCFU7VQNGgV6tV9Pb2mtxpm3SrAeOGgGOqjMzzPGQymdAGgO+zjzevx3FZ2ZyGTWXwlLg7ODg4ODh8O/B9H1evXkWtVsOWLVvQ399v7Flsvu1QrraqJl0qmUyi0WggW82inqwbpzWwMGdYyTDQ6epBQqudN0i8eQ07Us7r0E7yb41wa863RnntXG7OJRKJGOKrOeCMAOt4GiEGOiRTi7PqOLqv0Pnx+vF4POSsp9Ndo+TJehIPfuRBHO05iqldU9j4uY3Y8tSW9jwiXoica8RaI+vq3CdsJwShagT915byayE7O+rN+261Wsjn8/d8RxVHuB0cHBwE9Xod169fx8zMDHp7ezEwMIAlS5YgkUiEKnvSOLG4GdCRg9O40PhWq9VQ3hjPY/EzNf4AkM1mjfddDTQJNPPJuvUl5UaEcvJ72WPs4ODg4PDKYmZmBocPH8aGDRswOjoachjTEUwJttqlZDKJarVqCoDeLjcYCBcao42zC5VqNNWOCmthMZVDK+lWUq8RbiXIShKBTmRWW4VpjrV9HW2Fxfdpu9UJ362yucrJdX2UdKucPRlN4nv/+Htx6V9cwvhnx+F74Ui27dTQa9kFY+1iZ3ZU3ZbAcx3UyaDRcd0b6Xp6nodqtYp8Pn9PF0wDHOF2cHBw6Ipms4m5uTnk83lMT0+jt7cX/f39GBkZCUnb6vW6qWZKUH5GTzgLqSUSiZDh1bxtev8plwM6OWjcmKRSKTQaDdTrdZObVqvVQvnj3KBovpaDg4ODg8N3CvPz8zh27BjWrFmDlStXGvtER3Kj0TA2SokaK537vo9arRaSIWt1bjqsSSyBcNTbrpHSjViyGwnHJFSxZhNDex60p5ynFhOjzddouw11FNjRYt0HcE62RN5u2aXnazSaaWnVchW7vrgLWNLOvde0M9uxwd9V1s71UdWB7TRQKInX9+1IvEbj9Vh+D3K53D2/X3GE28HBweFF0Gq1UCwWUSqVMD09jZs3b2Lp0qUYGBgwhBqA8eYDCOWXsU1Xt3wzGjYapXq9boyRknUadL7PsSKRCFKpVKjKq+aFl0qlBQbbwcHBwcHh20WlUsGpU6dQLpexYcMGU8uEZLlUKiGbzYbaZNJu0SaylomSbpJElWiTbJNkNhqNkJOb59Le0d6SdNMm2pHublFfzoc/dnqYOsE5DqPNei92OzA7gs3xb5fDrUVRuynVdE21W8rs7KzpoKJE33b2a262rhv3ELo+hH6WfN9eV1s1YEvn7bWpVqsoFAr3vBrPu9dv0MHBwcHBwcHBwcHBwcHhuwHXh9vBwcHBwcHBwcHBwcHB4Q7AEW4HBwcHBwcHBwcHBwcHhzsAR7gdHBwcHBwcHBwcHBwcHO4AHOF2cHBwcHBwcHBwcHBwcLgDcITbwcHBwcHBwcHBwcHBweEOwBFuBwcHBwcHBwcHBwcHB4c7AEe4HRwcHBwcHBwcHBwcHBzuABzhdnBwcHBwcHBwcHBwcHC4A3CE28HBwcHBwcHBwcHBwcHhDuD/B9LNSYLW3ufDAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Draw the line points for training\n", + "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n", + "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the exported ground truth on the Holicity dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info] Initializing Holicity dataset...\n", + "\t Found filename cache holicity_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/Holicity\n", + "\t Load filename cache...\n", + "[Info] Successfully initialized dataset\n", + "\t Name: Holicity\n", + "\t Mode: test\n", + "\t Gt: holicity_test_homograpy-export_512x512_v1.5_detect_0.25_inlier_0.9_local_max_refine-v2.h5\n", + "\t Counts: 520\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "# Initialize the Holicity dataset\n", + "with open(\"../sold2/config/holicity_dataset.yaml\", \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + "\n", + "holicity_dataset = HolicityDataset(mode=\"test\", config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read in one datapoint\n", + "index = 2\n", + "data1 = holicity_dataset[index]\n", + "\n", + "# Reference data\n", + "ref_img = data1['ref_image'].numpy().squeeze()\n", + "ref_junc = data1['ref_junctions'].numpy()\n", + "ref_line_map = data1['ref_line_map'].numpy()\n", + "ref_line_points = data1['ref_line_points'].numpy()\n", + "\n", + "# Target data\n", + "target_img = data1['target_image'].numpy().squeeze()\n", + "target_junc = data1['target_junctions'].numpy()\n", + "target_line_map = data1['target_line_map'].numpy()\n", + "target_line_points = data1['target_line_points'].numpy()\n", + "\n", + "# Draw the points and lines\n", + "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n", + "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n", + "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n", + "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n", + "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dxnrd2yziWgS2VBvXe3DrIl4PTxfcbjdwSJwLetE2ed/lcikDgAh127aZnp7mhz/8Ibfccgt33XUX1Wr1+bhNBoPBoDCC+2WOZVm85S1v4T//8z9xu93Mzs6SSCSwLEtZoaVlh0xk8n2wbRu/398yMbrdbqampti0aRMHDhxg165dXH/99Sb/22AwPC8YwW14ppi1yrPH4/GwZcsWRkZGqNfrhxU9cwpevVq5CG5dWOuCu10uti7gdU+2vCbo6xL9PfGA6+MRz/e+ffu45ZZbuPrqq7nnnntMqLnBYHjeMIL7ZY7f7+eJJ56gWq3i8/no7+/H5/ORz+ep1WokEgkVEiaTop4rValUVG5WuVzG4/GoqqL1ep1SqcS+ffsol8ts3ryZr3zlKyr/W8/FMhgMhmeDEdyGZ4pZqzx7jj/+eG677Tb8fj+1Wq1lzodDgrtdsTQx3EvLL9le1hPOcHFdROvi2vm+LtCdv+s/ZVy6N71SqTA1NcVdd93F3//937Nz505Tl8ZgMDznGMH9Mufkk0/mnnvuoVqtqrYaYkWWMPJGo6Gs0PrkWSgUCAQCPPzwwyxbtoyuri4Vfi4TnNfrBZqTXLlcVuFku3bt4p//+Z956KGHKJVKzM3NUSqVXuC7YTAYjjWM4DY8U8xa5dnzx3/8x/zt3/6tWhvAofZfegE0p0Hd6cmWdDX9GCKs9XB0Pdxc//1IbcWkaJvuRYfW3t+yDdCSR16v1/nBD37Av/zLvzAxMWFyvA0Gw3OGEdwvc375y19y1llnHdaqQwqMSEi5WKF1K7Me/iWTpojtSqVCIBBQlu9arYbP51PH0C3OTz31FD/60Y/4zW9+wyOPPMLs7OyR23csAvzA9ufzrhgMhmMFI7gNzxSzVnl2eDwevv/973PhhRe29MeWtYBukBf0dDN9ez1P21nsTLzfuqiWY8k2euVy/VzOcHQR9rpIl/fFUOA819zcHN/73ve46aab2LRpk+njbTAYfmeM4H4Zs2HDBj7ykY/wzne+8zALshQaqdfrqrAJoCYwZ+/LUqnUMnnJpCcVzXVLs0y01WpVncvn87Fnzx7+9m//lk2bNhEOh9m/fz/1ep1UKtUccDfwHSAIvBvYe9RulcFgeJFiBLfhmWLWKs+OpUuX8qMf/YgVK1ao15w52/Ka3sJLBLdze92LDa1i29kZxZl/rffg1g34glP4y2t6BfV26F713bt387Of/YwbbriB++67z/TxNhgMz5qns1Y5PHHG8JLgD/7gDzjzzDNb2n7pOVb6hCY/Jf9JD+2S/G+pYi6TpBxPvOROq7e0B5FjAgQCAbxeL7//+7/PyMgIxx13HBdffDFLli+Bm4CLgXOAn9AU3gaDwWAwGJ531q5dSzweb5njASVg9ZxqqQyui3BobTUq20r+tt4FpVKptPTZdtZ9aVcFXcc5HhHzHo+nxQsu4eX6Ncl5lixZwvvf/36+8Y1v8JWvfIVly5Y9L/fVYDAYwAjulyTHH388J598MmNjYy15TdLvstFo4PV6CYVCajK0LAu/368EcrFYbMmFKhaLVKtV9Vq5XG4JMdcLlZRKJdxuNz6fT/XmlIJs9XqdX/3qVwwODmLbNul0muS3k3CSdgFrIXxXmGAw2LaYisILxJ7XW2kwGAwGw0say7JYt26dKqQqIrbRaFCtVtVrevVxQPXOlmg28RKLAd9ZXVze8/v9qriaHhIuTgHJuXbmbOueaxmf3opMz9cWYS2Gf72gmm5MGBkZ4W1vext33nknX/7ylxkcHCQQCDz/N91gMLysMIL7JYbb7eY1r3kNr3jFK4BDE121WlVW5WKxSKlUOswTXavVWqzFlmWpCuZ+vx+v14vP58Pj8RAMBvF6vVSrVQKBQIulWvegizjfsWMH27ZtY2RkhPXr15NMJuno6CCXy3Hq509lbOeYuobe7b380Y1/xLve9S42bNjAqlWrWLRoUesk6AY+CvwnMHSUbq7BYDAYDC8xenp6WLVqlRLK0BS0UnSsVqu1CGLn7+Jd1uu66GJYbx2mh3UDqjWpLuThkNdbRHc7T7d4zeUcep9vQT+n5Ji36wMeDAZ517vexb333stf/uVfcvLJJ6suLQaDwfC74vmfNzEcSwwPD6tCaXpYmFQUl7zrWq2mRHOpVFJVzGu1WkuFUfGK670xxYqth6LLefTQLZfLhc/nI5fLsWPHDubn5+nt7aVcLlOr1Thw4ACpVIpQKMS53zyXA2cdoGu4i1f98FW4vW4GBweJRCIEg0Gmp6cZHx+nVquRTCZ5+KKHqX2uBhZQB/4AmD/ad9tgMBgMhmObwcFBFi9e3JJ7LfO8RKbpOdv6dvraQLbTQ8x1r7MuiHWPtXN7ibzTz+n0luueeKcgd7Yca5dTLuPRHQQA0WiUj3/847z+9a/npptu4uabb+bee+81rU4NBsPvhBHcLzGGhoY488wzWyYtfTKybZtgMHhYf0yZFPWQq3q9fpiFVyYoaTXmcrkoFovK+6y3BBGh7vf7GRgYYHR0lGq1yv3330+hUKBUKjEyMoLH46E4UWT4H4bZcMoGOsodlPwl5WWfmJggHA6zYsUKuru7uemVN9E4u9EU2wBvBBLAq2mKb4PBYDAYDE+L/v5+hoaGWsS2rAXEe+3Mt9ZbcukCWuZ/8VrrRcycVcn10HTBWWTNWUhNH4fz/M48c92L7swZ18PY9fHJ9kuWLOHDH/4wF110EXfffTdXXnklTz755PNx+w0Gw8sAE1L+EiIYDPLpT38an8+nJphSqaRysDyepn3Ftm08Hg/lcplisQigJtVCoaBCyHXxrBcgkVAzv9+Px+MhFAq1TIQixqVVmMfjYePGjZx00kmqZ3e5XCYej5PJZJienqZcLTNz/AxPrnqSutXMN7/nnntIpVKEw2FSqRR+v59YLMYZvzmDxEICDkaX+So+Ln/ocn7v0t9j0aJF+P1+fD7fb8//NhgMBoPhZU4wGOSkk06is7OzJbdZL46qoxdBA1q82pLH7fV6VUVyZ6i4bOPMtdbDvtuFhMu5dcEuoe56hXPd467jzEN3escltN0p7BcvXszll1/OT3/6U7785S/T29uLz+d7bm6+wWB42WDagr2EWLlyJY888khLCy+pJK5bcfVq5FK4xLabvblFWMs+fr+/JS9bJshKpaIKowFks1mi0aiaTAOBQEtxkkwmw759+7jlllvYtWsX4+Pj7N27l0QiQTaXJfGhBHd98C4ALrj+Ak5+6GRoHJrcA4EAk5OTdHV14fF4mEpNcc0nryEbynLpTy6l6+EupqenqdfrRCIRisUiTz75JBMTE5TLZfL5PKVS6QX4VAwGw7PFtAUzPFPMWuWZ0dPTw7/+679y/vnnqyKncKjbiMz/YrCHQ5Fuzm4nIlxF7Iog1tuI6WHjzjBv+VsX6c68b2cvbv01fY0jldR1nD27ndXUBb1Ym+wn/4rFIt/+9re55ppr2LFjh+njbTAYntZaxQjulxA//OEPueyyy5RlVp8sZfLRq3kCVCoVfD6fmkyk8Imer1Qul5XHu1qtUiqV8Hg8Ki9cjicecQlH83g8VKtVarWaOkaj0WDHjh384z/+I9u3b6dWq7Hvgn0k/y55KMHBhrOvP5uN924kEAgQi8VIJBJMTEyoHPJMJkPPK3rY07mHgU0DHDhwgEAgQC6Xw+12E41GKZVKuFwuZmdnSafTTE1NMTU1RT6fJ5fLHZ0PxWAwPGuM4DY8U8xa5ZmxYsUKfvazn9Hb23uYV1vysuv1ekuEnBjqdfHqFK26+NUrljurnMsx9aKtsr8urvW1art1q1PES0i7vKcbBfRjyH66+Bb0wmp6i1WXy8XevXu5/vrr+eUvf8n999//7D8Ag8FwzPN01iomh/slwtjYGKeffjrlclmFSznzrmTSE++3Hr4lod8imGVy1bcRsS0CXQ/zKpfLhMNhNWHL/tLDW9qHeTweenp6mJ+fp7OzU4nyJMmW6+nKdqkCabZtUywWlUV9fn6e+fl5VhZW0lXs4gAHCAaDFAoFAoEApVKJSqWivOwul4uuri6WLVvGE088oXqKJ5NJ9uzZw8zMzNH6mAwGg8FgeNGwZs0a4vG48kbDIQEs6WNSt8XtdqvK5U4R7QwDl5+6p7hd/ra8LiHdcjznsZxIq1PZRi/Qprc1k+M7Rbv+/pHGe6Tx1+t1hoaG+MhHPqJyvL/zne+YHG+DwXBEjOB+ifDnf/7ndHV1tfS1hGaodzAYxO12k81mCYfD1Go1FRIuHmOxYBeLRbxer2ohBs18rnQ6DTQreGYyGWWNlvNFo1HlURdvNqD2i0QiattCocCpp57K9u3b8Xq9lB8rY33M4okvPYHL7eI1V72G1ftXQxBGR0dVSHggEFD9wTs7O9XEb1kWgUCASqVCKBRSE67H4yGdTuP3+0kkEszMzBCNRnG5XPT09NDT08N5553H/v37+eEPf0g8HieZbAp/3aBgMBgMBsNLDZfLxbnnnquM2SJCpdipXojM5/Op9qEidOGQKBWBrh+jXX61U+jq1c7FsC/n088hHuZ2DgXbtpVBQBf+QMs++vHgkBDXI/p0o8CRCrLp41myZAkjIyNceOGF/OIXv+Dzn/882Wz2sNx3g8Hw8saElL8EGBoa4vvf/z7r169X1mi9H6bf71feX/E069ZkKZLmzH/S/9YnQbGE+3w+8vm8EuzhcBi/368mZJk8pfWYnP8zn/kMu3fvJplMks/n2bdvH9FolOwZWU4890TeVH4Tmzdvpq+vj46ODqanp/H7/QQCAVKpFLZts7CwwIoVK6jX66TTaZYuXcrU1BTz883eYJlMhnA4jNfrZXp6mvPPP5+f//znuFwu5ubmKJVKDA8PMzo6Sjab5Qc/+AGnnXYaXV1d1Ot1Nm/ezMTEBIVCgUKhYPK0DIYXABNSbnimmLXK0ycQCHD33XezbNmylj7W+vyvi1E9H1sXqiJo9Qg6fR9oFarOv3VBC4fC0MUpcCT0fZxOgHaVx52h63qYuFyHM+Rcftc95HqoufNelUolrrrqKv77v/+b8fFxs3YwGF4GmJDylwmve93rWLduHaFQCMuyqNVqVKtV6vU6ExMTLFu2jGAw2JLbJOJX90broeS2bascaMuyVCsxvTcmNCuc5vN5YrEYLpeLJ554giVLluD3+1vC1MvlMtlslq6uLvx+P9PT06rKqNfrpVarMVQcwrvdy87qzhZPvExsBw4cIBKJUCqViMfjAKoa+fT0NLVaDZ/PRzKZxOVysWfPHhYtWkS1WuX6669XhoVoNEqhUCCZTBIKhSgWi1QqFQD6+vooFoucffbZ2LbN7OwsU1NTTE9PUyqVOHDgAJlM5ih/wgaDwWAwPLesXr2a0dHRw4S0XthM76+tC1RdLMt7Ymh3FiCDQ55mSW+TVLN2edXQWind6bWWbeXYutBu17tbP48zl1t3LOjb6sfQt9fr4OiVzgWfz8cf/uEfcvHFF3Pttddyzz338MADD/xOn5PBYDj2MYL7GKe/v59zzjlHhVJDa/EQfeKUkC8RwFJ5VBfiAKVSqcV6GwwG1Xvlcrmlb7fP51PF0PSQLhHKEvYtr6dSKTweD729vUxNTWFZFj6fD98iH7v+ehcH4geo/kOVZdYyQqGQCvHOZrN4vV6y2SzlcplkMsnY2JjynkuuWSaToVwuMz8/TyqVUvfJtm1SqRSWZREKhajVakQiEXp7e9m2dBuV8QqVSoVisUgymSQQCKh88+HhYXw+H9lslu3bt6vq7Pfeey/79u0zoecGg8FgOOY488wzVfFTiWLTvb/yt95bWw8Db9dnW/f+6t5pEduyv55rDYc86bq32Pm7XgRN39Z5Xvlb7+Oth487vedOz7gzP1yf448kyAX5e3BwkI9+9KNccskl3HXXXVx77bU88cQTz8GnZjAYjkVMo+JjnDVr1nDRRRe1tOHQe2gPDAyovGrxejcaDXK5nAotr1arKqdb+mtKf+1AINCSS+Xz+ahWq+p4N9xwA41GA6/XSygUYvXq1cpbLN5wyfHu6elRIdput5tCocD8/DyesIdd/76L1NoUUyNT/OrTvyLVSKn9SqWSsqJLj/Fisdic+LBp0AzjKhaL+Hw+ZUyQAnA+n49AIEAkEiGfz+P1ehkaGiIUDrG/fz83vPkGMt/K4FvZDJG3bZtQKITP51M57XLvVq5cybp161ixYgX9/f2mH6fBYDAYjknOPfdcgBbxK0Z4PZJN1hMyp0tamYR+i6B1im+9r7XufdYN/XqtGH0sTkeAvKeHs+v7OyuKO8PD9XHrrzkNCXIcyVd3jl03Auhecd2YoFdxHxsb461vfStf+cpX+OxnP0tnZ2dbj73BYHhpYwT3MUwoFOKSSy5RlmOxSustPLxeL41GgwMHDrB3715lwQ0EAsr7LCLW4/Go/GuZLDweD4VCQeViS6sNmbxe+9rXqnB1meDC4TDpdJpSqaTyv+Vnb28vXq+Xzs5ORkdH6ejoYOpfpqgsqajrKvYV+fUnfk29XleCVzzTbrebSCRCuVwmV8vx05N/yu2vuJ26u1lIrVwu09HRQWdnJ0NDQ7jdblKpFHNzcxQKBS666CIWL17M8PAw+WV5rvqDq8iFcjAE1/2f6ygPlVuqtkvOuhgUxLgQjUY599xzVW45QFdXF4ODg0QiEfx+P36//7B8NoPBYDAYXmiWLFnC8uXL1dyte5pFNMIhb7LeXtTZ31rP/XaGccvcrxv8nX2u2/W91kW8jENEsN4XWz+nHFvOI/vp2+kV0nWPt76P7t3XjQZybnnPWeemWq0e5vWWCL++vj7e/OY38/Of/5yPfexjDA4OGoO9wfAywoSUH8P09/fz/ve/X+VXy4NdemuXy2UCgQC2bTM0NKS2kYe87t0W0SwiWyaoSqWC1+tVr/l8PnK5nCrOVqvVVBESEcb6sdxud0tlU6/Xywc+8AGuuOIKNekt+pNFHPjnAyy8agGA0S2jvObfX0PZUyYajZLP54GmVdzv97N//356Bnr4xYm/4KY1N8GapgW+81udzM/N4/F4yGazzM7OUi43BXRHRwd9fX08+eSTNBoNkskkBz5zANulFWrx1Hn4hIc5YecJVKtVFhYW6OnpwbIscrkc8XicQqFAvV5nfn6eeDzesrgol8sEg0EGBgYIhUL4/X4VEl8ul5mdnVXh+gaDwWAwvFBIkVVnzrOISKnpoudoiwEawO/3KzEqXmlBDzkHVDcUOY8IV/Ga27ZNpVJpEcJ66LruPZb35aezvZju9Racc64zZ1w3DuhiWxfVeih8u/xwPQdez3nXrwOaUYLve9/7uOiii7j++uu59957eeyxx8y6wGB4iWME9zHMhz/8YQKBgMqfljxtefBL4TJd+Hq9XsrlMvV6XeVZSw62eLb1npuS5yQTj3idOzs7cblcKhRdzh0IBJQXXKze5XJZ5X57vV527dqlxuvz+Vi7Yi3u/+XGylt0jnRywlUn4LJdBKNBJe6ln7fb7SYUCnHdqdcxefKkuhc3n3MzfVv7sD93aNIqlUo0Gg26u7tVy7NkMkk8Hqerq4t3HHgH1+68luuXXQ9A91e62bhtI7lqDsuyiEQiFAoF4vF4iyW7XC6TSCTUAkIPx7NtW11bNBplxYoVxONxnnrqKYaHh5WBYnx83PT/NhgMBsMLwoknnkgsFlNCV5/n9Xxpj8fT0nNbBLjkOYt4FuGsFzrTq3/r+dTtwrqBlgri8r4ueNsVQXNWJNfFutPjLji90M5+3frY2+Vw69s6W4jqYl9/TzcEWJbF4OAgH/jABzj//PO58847+cUvfsGWLVue3YdpMBhe9BjBfQzz9re/XYltvTCZ5BW5XK6WUHBAFU6TsCo9z1nfVg8dFy9xo9EgEAgQjUbVecTKbVmWErh68TQJBRcPeK1WIxqNsnz5cvbt26eOG61HWfGNFSxetph+T39LK7LZ2VmGh4cJBAIsLCwQiURY8dgKJu1JkDm0BtPfmIaDGlYKmw0ODpJ8ZxL7EZvOPZ10dXVx1llnsWTJEgYGBrhixxVMTE7w4E0PctLWk/CN+lT4eKVSoVarUS6X1d9yXyV6QO4xHMpZtyyL0dFR8vk8xWJR9QgfGxsjEomQyWRYunQpXq+X+++/n1wux/T09NH74hgMBoPhZUtXVxfLli1Tc5gYj0X8Shi3GI/FkKyHncu8L6lpQMv6Q7bT87HFGaC3DhUvtbwvx9EFr/5TF9vyejuBrYt7p9jWf3eKdH08OjL/iwND3tevVz+XjEe/p3KvdQPBokWLeNvb3sY555zDfffdx1VXXcXs7Owz/kwNBsOLGyO4j1H+6q/+is7OzpZWGDLxiTc3FAqpCqSVSgWPx6MKigWDQUqlEh6Ph2AwSK1W45vf/CZXXHGFslTL5OlyufD7/cpLLYXVCoVCS+643++nWq2qnGYZl3i4q9UqPp+P1atX85a3vIV//ud/JpvN8tRTT5HNZol74pRTZTy9zVZipVKJaDTKsmXLqFQqLCwsqJZn54TPYcPVG/j6679OMVeEswHNOOxyuajaVQ5cfIDip4okG0mGPzrMu895N0NDQ+RyOcrlMgv7Flh+93L2PryX6lCVfD6vLPeBQIADBw7Q3d3dElofDAbxeDzMzc0RCoXIZrPqPkjkQDKZJBgMks1mKZVK5HI5CoWCigJwu93E43HOOOMMbNsmGGx685988kl27NihWqLJQshgMBgMhueCpUuXsnTpUpXupbfKknBoCSeX0HLxdMOhftVSVEwEqF4oTbYD1HayrpC1hS6I9fBt3Uuse4Z1Iez0bDtztHUB7txWR+ZYPQfdKcqBlu4sTo+/01uu30eJHpBtnAYOOcbAwACve93ruOCCC7jmmmv4wQ9+QDabVULfYDAc2xjBfQzS1dXFhRdeSKPRIJPJqOJcMqlVq1UluoPBIIDqhy2F0SQfGg4VLHnHO95BoVAgGo225ClJGzEJH69UKoRCITVBiaiWyt4y0YjYB4hEIsobXKlUOOGEE7jgggvYvn07Dz30EJ64h9mPz1LoL9BzQw8ddKhryOfzhMNhlX9uWRbJhSRjk2O88xfv5Cff/wmTT05iuQ5N1g27Ae+C4leKANjYPPyvD3PgNweIzB0supbLEQgEWH3Rah77ymNM/PsEI4+NYNs2mUxGWeLFUCCTtlQyl3x2mUzT6TRdXV3KEz86OorH46Gzs5NkMqny5fRQ9HK5zODgoAph7+vrY+PGjVQqFZ566il27NihQs+deWkGg8FgMDxTxsbGGB0dVUZyEb+611m8srrXWvfeyk/xdh8p/1oXvhINpgti4Uh51M6wbWdoeTsx3S6Mu12OtP6e7nl2zrV6oTU4vLe4fp3OEHPnNQEqxc85BokYuPzyyzn//PNVjve2bdvM/G8wHOMYwX0MctlllzE6OorL5WqpcqlPTmKZFk+p3g9bwqR1ESn51NKCS0SmHiIlAtg5cblcLsLhMHCoIqeEmHu9XvWeVE+vVCokEgne8Y53kEwm2fTgJj6d+DQzb2oKyzJlXnvda4n4IqTTaYLBIIVCgZGRESYmJgCUt3nj3Eb2pfcx455R7bsajQZul5vKcKtluGE1mPPPEZ9u5mT39/dT6C3w3RXfZXtsO9bHLSI/irD6Z6sBlLCWe1QsFlXvcSnMpluufT4f8Xgct9tNOBxWvbt7enrweDzKEFKv14lEIng8HsLhMLVajUKhgN/vV9Vfh4aG6Ovro6+vjxtvvJFQKMS6devI5XLs37/f5H8bDAaD4Rkj7TsjkQhwqEWXXuhL9zbrIeG6mBbBKHOWLm51j7R+nHa9reX4epst2c4pXkW46p5hZw0VXYA7j+F8T0c3DLQT8M4e5HLO/6kautPL7swfd4bhy3V0dHTwrne9i3POOYd77rmHO+64gyeffPJpfsoGg+HFhhHcxxixWIyNGzfS29uLbduUy2VcLheBQECJTQlhktxhmUjEUyziWoS1TIri+ZZ9RFyKeK7VaoRCIdX+wunF1idNr9eLy+Vi37599PX1EQqFVDgVNMVsIpGgu7ubz6/4PLOBQzlLO07ZwU9CP+GML55Bd3c31WqV/v5+Go2G6qUt/bQ9Hg/RaFRVT5WJq16v4/m/Hhq1Bo2/bU6IH7/j46zcs5J9B/YxODhIPVDn8yd9nh2xHQDYbpuH3vgQ1XqV0+49jXg8TiaTUR5rv99PPp+nu7ub+fl50ul0y4Rbq9UoFotEIhG1eMlms+zZs4dQKERXVxelUol4PI7P1+z5ncvlSCQShMNh1WIsn89TrVbVPbNtm87OTsbGxvB6vWzYsIH9+/dTKBTYvXs34+PjxvptMBgMhv+ReDzO8ccf3yL8pECqzFt6P21dRDvru0h4tISU6+LSmVutp6jporedd13fzunNdnqPnV52PaTcKW51IazjzL3WOVJRNqfXXX9fjAi61xwOha87w9CdY9CPPTo6ysjICKeffjqbNm3iRz/6kan5YjAcgxjBfYxx6qmncskllxxmFYVmuwmZAPVe3BLGbVnN1lVwqCWYhIjL75LTpRdLyWaz7N27l5/85Cd85CMfYXh4WIVSA0rsS36yeNO9Xq8S24CqZhoOh1URsoWFBT4f/Ty3+29nxmp6bX15Hyd9+yRSqRTd3d2qEvv4+Dh+vx+Xy0UsFmN+fp7+/n5KpZK6TvHg1+t1/FU/0SujBMeCvDb4WlbmVhKJREgkEszPz7NodBFvffCt/MNp/0DVXwUbErsSjPx0hFq8RiqVUuH1kUhE3ZPp6Wni8TixWEyF8QMqXF9+tyyLrq4uli9fzubNm1s+o6mpKeX9lyrsMu5wOEw2m2ViYoJcLgedkEqnWFhYYGxsjHw+T39/P9PT05x22mnq89qyZQsTExPqOM7vh8FgMBhe3nR0dHDccce1DbsWY73uNZY0KF046/nacgw9dFoEuDMUXU85EwO8RH1JfRmgZRu9kJouvqE1B1oKturjd4pawRniLdegv6dHDOrndwp459ic98EZNu+8J+0837pwl22Hh4fp7+/n1a9+NTfddBP/9V//perCGAyGFz9GcB9D+P1+1q9fTyQSoVKpqP7Ztm1TKpUolUpKyIqwLZVKLb0wZRIVUSZ52fJ7OBxWbUJEBHq9XlasWMGnPvUpyuUy2WyWYDCoPNm2bSvRXq1WiUajapIREaqHjWUyGQCCwSDBYJC4K87PF37OBfULKNaKnP3Fs/HN+wh3hVmyZAkLCwsUi81cbDEQ5HI5enp6qNVqdHd3q8lNxmxZzerosUCMjds2snJsJdFolHg8zuzsLJZlUSlXGN03ymsmXsPPX/9zArsCvP6rryfXaBY4C4VCzM/P09XVpfLP5B6WSiX8fn9LT3Op+q5Xbhcv9oknnqiKoOVyOXp7eykUCuTzebLZbMv9iUajuFwuBgYG2BneSf2qOq6PuggFQ6TTaWXAKBQKxGIxRkdHVeh6vV4nl8sxMzPDjh07qFQqqlp6u4WHwWAwGF4eWJbF2rVr6evrOyzUWYSv7tHV24PqXmo41JXD6XWW8zjFpV5ETDzSumdcT8+S84vXW6/2LUYBwSlg9bBvZ/E1PSwcaDmnoIfVyznldRmT7uH+beHrzrE4vdj69s7zOY0Ksm8oFOKyyy7jwgsv5Oqrr+bee+9l//79RngbDC9yjOA+hujq6uI973mPmnTC4XCLZToUCqlK4hLOJIXSZBKRauEiXL1eL7OzsxQKBQYGBpTHViZIOYbeQ1MP25Jz69513VIsIen5fF69L+HmQqlUwjfr49QfnMq+2X1EkhECsQCWZbFp0yYCgQAdHR2EQiFyuZzKhRajQzabBVAh9vJ7KpWitqzGzW+5mdnILH+y+0+w7EO56tBcNHT/upvORzpZunMppWBJhd7LdZfLZfx+P6FQqCWU3VnYRYwVYpAQ40M2m1Ve6UQioQqyQdOIIl77QqHQvBcH7+Wevj3s/OROaj01Mt/OsP/r+1myZQmpVIpQKMTIyAihUIhwOMz09DRut5vu7m58Ph8jIyOsXLmSUqnEwsICyWSSVCrFzMwM8/PzJgTdYDAYXma43W7OOuuswzzU+nwgIrTde+Jx1oWhnjuti3Wnh7mdoG/XplTOowtZ2UbWJc5caqdQdeZV6+OR1+X87cLFZXsdOa4IW6eRoV2oub6dM8TemZvuPGa73/VrCIVCXHHFFZxzzjnce++93H333Wzbtg2DwfDixAjuY4jXve51DA4OKrHndrspFostbSakj7a0/ZJwcqlKLgXILMtSRboCgYDyRFcqlRahLMeTyU4824IUA9PDy2VSlPBur9eLZVkq9NyyLFUkTAR8JBIhtD0EWyHVlyISieDz+QgEAsRiMYrFohLAqVQKr9erxhoOh9VkLXlctm3TubqThSsXsI+3ud2+nXq4zp898GeUSiXVriuVSjXDstbUqO5qVlAVD3Y4HOaUU05h586dlMvlFk9xV1cXhUKBYDCIz+dT7/t8PtX/W+5bo9FQrcRSqVRL2FsulyMWi+H1evF6vfT29pLJZJjqmOJnv/cz0j1pAErxEg+89wGsb1oMPDaA3+/Htm38fj+zs7MMDg6qquuNRoNSqaQWL0NDQ6xevZq5uTkAUqkU9XqdzZs3s2fPnqP19TUYDAbDC4jb7eb0008HDkW8OQWm7t3VBej/VMhM9nUKV6AlTU3fVi+UJmlQ0n5LzgXtq4/Lz3b51E6PtRP9NVnb6KJd1hL6/XEaHtpdvzNMX79ep8fd6bl23lfdoNDunspxh4aGuOyyy9i4cSObN2/m+uuvV8VlDQbDiwcjuI8RvF4vf/3Xf628n263m0KhoFp/2bbdkkMMUCwWVRVsr9erBKFMnMVikS1btrBhwwYVRl4oFJRQLpVKJBIJ4FAfTX1ykjFYlqUKl+mFUcSTro/N5XKp0HfdymzbNoFAAJfLRTAYJJFIKM+y3uZM2qD19fVRLBaxLKvpyT4YIq/6fjZqpK5NYa84OEFZcFfvXfjW+fi9yd8jGo2Sz+eJJqLseu0uZt88S/rtaZZ+dimVuWZIvsfjYdu2bcqiLyI3EAioiVDupXiX5+bmyOVyzM3N0dvbSy6Xw7Is4vE4ExMTqghcOBymXq8T6g7xg4/9gNd96XV4a14ef/xx8vk8XQNddN7USfo9afCA1bDofrSbgZ0DLCwsYFkWY2NjVCoVFdIfiUSYmpoiGAyq8dZqNbLZLNPT0/j9foaHh2k0GnR0dJBIJFi9ejX1ep1du3axf/9+tQAyHnCDwWB4abF27VpGR0cPE9p68U+nZ1rvfe2sug2HhLgIQDGEtwv9lpxtPS9cF/4y/+jjE5y500f659zXKXSdnm7ntu3ErYxfji9rIGdIuvOn/K737dadAu3GJ685Q8r1bZ3GEdu2WbRoEUNDQ5x11lncdtttfP/731edVQwGwwuPEdzHCJdffrmqci150+l0mng8roSsz+dTIkuKmlmWpUR6IBBQ4lu82MPDwyq3GJoPdJkkY7GYCuMWj7nkiOuiW0S09MqWXG7xkMsxy+UypVKJQCBALpfD4/G0tNcS0RyNRtU1lctl5VUPhUIUi0UCgQB79uxhZGQEgGg0qrz1hUKB888/n0cffZTSm0tkrstQX1YHG1ZNrOL3b/99ntr1FNVqleWrlnPz0M3cd/x9YEGpp8T1n7mec/7hHFxTzWsS4RoMBpW4rVarlMtlOjo6WL9+PfV6nUWLFvHggw+2GBzS6TTZbJZYLEaj0VDtwQCy2SyFngK//MgvmRuZ49o/upZX/sMrsVNN8VzcVWTNxBrcUTe737ybsYfGuPCGC2mEGgyuHSSdTjMzM0N3dzelUolisajumRR5KxQKDA0Nkc1mufHGG1m0aJHyyBeLRaLRKB0dHTQaDUZHR+ns7CSXy/HEE0+wfft2yuUyxWKRQqFw2OLDYDAYDMcWZ5999mE5xXBIyMncJSK6XVsrMUBLpJbMOxJ5p4tJMeTr+dpSV0aEt23bKnVND08/kujXQ9d1D7fTM6+HvOvbitCXbZytyvRQ8t8mcCW1rFgsHnaP9OvQOZI3++kYDdoZA/Trkjz8WCzG61//ei644AKuueYa7rrrLiYnJ1UPdIPB8MJgBPcxQCAQ4EMf+hDQnGxEdHZ2dqoJo1qtKhFbq9WYm5tTYgoO9cBuNBpUKhUVzi2C3babuVnieRZhL+3AoPnQz2QyKpxbRKfsKyHWfr9fbW9ZVkvBLumVXSgUiEQiqkhYsVikr6+PLVu2ADA5OcmiRYsolUrU63U6OjpIp9MqhB0gk8nQ29vbMkafz8ett95KrVYjXogTfHeQ8r+W2di7kStuv4JGo6FajBVrRR7qeAhkfrWgFq2RXp6mM9mJ2+0mk8ng8/nUvdWrlUto+eTkJOPj4ypsvFqtqoJyXr+XyVdN0vlIJ9VqlVKpRDAYZC4+x6/f/mvmFjXDvOfG5njwQw9ywtdPoLC5wNjYGB0dHazYtIJ7gvew4roV+DqbIfbi+ZdQdlnUuFwuvF6vMkxIWzPJH9+zZw8+n49oNIrb7aazsxNALYDm5+dxuVysXbuWxYsXq/z1nTt3kkqlmJubY3b2UPs2g8FgMBwbuFwuzjjjDDV/Cc76LLpHVq8irudRC7qBXhd0zmg4Oab+vl4NXAzVzjBqQcbTzissx5K1kNOg0E646mODVq+3Mxdcr1ejvydz75HEsIxL7xijj1n+OVuU6e+38/Tr90j/bPV9obluvOKKKzj33HO54447uO+++9i+ffth5zIYDEcHI7iPAc477zxGRkZUQTTxskrxr2AwqEK4xdo8MjKixJ9urZYJoFAo4PP5lHiWSVjEmlTfLhQKh7Uak3xsOWe9Xleh3xJWDihxb1kW6XRahTq73W7lZZcQdhHlUuxNrrPRaBAIBMhkMti2TSgUYnp6mmAwqLz35XJZ3RN9IrYsC/sRm3X/so73vOk9dEQ7KJfLZDKZ5mRaaPCu37yL5FSS7Ru3Y9UsTvn6KYw9NQYelHddDBR6mJwUndMrpEqVdBHqjUaDA396gIU3LGD9l8Xx9xxPJpMhkUiQq+Woplstzt6KF7tss3r1auLxOKVSiXK5zIprVhAMBlWOdjQapVwuE41GmZubY2Zmhr6+PhXyLp+nRAhs2rSJ448/ns2bN7Nu3Trl5ZYFTm9vr7KO53I5yuWyqoS+bNkyhoaGKBQKZLNZZmdnqdVqbN++nT179pjQc4PBYDgGGBkZYdWqVUpUi4B25jCLyAaUJ9spJPX6LvKa7nXV1xqyJpF/7Yqd6QKynVhvV9hM/yfbiQD2er3qup0iVz+H/rtT4OpGAef+clxdMOtea30//T7rnnj9POJ5l+1/W7i53GPnWseJjGt4eJi3ve1tnHLKKWzevJmbbrqJ/fv3/9bvisFgeO4xgvtFjmVZvPrVr6a3t1dNXiJKy+UylmWRzWbx+/2qqJgUJAOU6BLBHAgEKBQKAEp027atxJpU2M7n89i23cwnPhjK7nK5VCsyaLb18nq9LcXKpL2YFErL5/NKNMOhSUyEvYTCS765FP3q7Oxs6ckpYe2NRoPu7m5mZ2fp6upSrczEQq6TzWaJxCIcOPEAWypbOLl2ssr5fvTRR7niiisIZUOc8O8nkKqniF8VZ5VvFaVGs1K5x+OhVCqpSALx/APk83lqtVqz9VgsRiqVolQq0dXVRT6fJxgJMvlnk8y+ZRbba/PYFY9RnC+y5K4lhKthagdqnPbN07gncQ/zq+bpfryb0791Ogl/ojnuSITJyUk8AQ/RUFQZKSKRCPV6nbm5OTWB9/b2Ak1PRS6XY9++fbzhDW9gfHycu+66i2w2y/3330+1WiWVStHb26uq2vf19ZHNZqnVmn3HM5mMEuRSdV0MG11dXXg8HuLxuBLh5XKZvXv3snXrVsrl8mGhcgaDwWB44dmwYQOxWKwllFvmWKAlpFuM3no/bT1cWs/Tlue93uHEWRytWq2q9DKn19vpgZZ9nOd0btMutFp+OvPAj/QT2hdVEzGrV1F3FkHT57p2gredh13Plde92Po1tPOWO4X1bxPuYtRwXs/IyAiLFi3izDPP5Ne//jU/+MEPyOVyZr42GI4SRnC/yDnttNNUJepQKKQ8kR6PR1mMxdMtIeASFi6WaQmzktBzEW1SHE2fqETIiwc5kUgwPz9PJBJRedLpdJpYLKY83iLCRexLQTCZtGVsIrBdLhe5XA6v10uj0SCbzartRMiOj48rT3IgEFB537FYTHnLM5kMsViM+fl5yuVyS39MgGA8SOUDFdIfTvPP/DOf+8XnWJ1dTSKR4NRTT1U9uzP7MtTfWCcyFCG/Pq9acxUKBfr7+1VOvOSX5/N5Ojo6AIhEIriiLmy/TW2qpjzx8+fMM//aeWzvwcVIsMaOd+/Av9lPN9309fXhslyc8ekzuP+v7ud1//Y6aIDX71UT9eC6QW5+282s+9U6YvfE1LVJJIHP51OLgmQySU9PD5VKhUQiwdDQEKVSSeWNz8/Ps3jxYoaHhymVSszPz7NixQoltmVBIfdR7yUuxhPxmsv9aTQaDAwMsH79ei699FIWFha45ZZbmJmZoVAoqAgGg8FgMLxwWJbFiSeeqDy/eo6xLlbFaC3Gc6e31dmSS2+rJfO5zL96DrWIdGfbL11sSg65c9y6B1poN3bZXsauo3vv9VByXdjqhoIj5X+LoNU96/qY9J9yXuc91O+Nfh36ceT1drnpsq2s4eQz0cPp5Rzyt0QMWpZFd3c3l156KW984xv53ve+x+23387c3JzJ8TYYnmeM4H4R4/P5eOUrX8nb3vY2FYpdr9cpl8uq6JmeV1Uul1WosIRX6cXNREx5PB4lKPVCKHp18VKphN/vV0XDstms8lSL19yyLOX5npmZodFoEA6HyWazSpDJJK1binO5XEuBFpkgOzs7VU/q3t5e5W2XcPNoNEqhUCCdTlOtVlm7di1+v59YLKa85LKgqDfq2B+zKX62CICNzedf9Xn+6J4/Iry1OcapqSmmpqaaE9E5FvmdeSU+a7UanZ2dytDhcrlYWFhQxoxAIECxWKRMGf4WGAA+BDFixONxujd30/3tbp5835NUw1VCEyFG/vcIw5lhXN0uFY4XDUfZ+H824hn0UK6W8Xq91Go1puwpHrv0MXav3s2eFXu40Hsha59aS6PRUIJfJlipJF+pVFQ19GQySbVapb+/n1gsRiaTYenSpUQiEZLJJPF4nGKxSC6XIxwOk8/nVYRELpdTvchnZ2dJJBJ4vV6SySSRSER9p6rVKrOzsyrqwLIsTjrpJLq6ulSu9+OPP04ul+PAgQMq2sFgMBgMR4+uri7Wr1+vItmcIcu6MJM1hhj3Bae4leJpQjvPrAhUWZc4t3cWOHN6cnVDgB7xpu+vj08/hv67LkLbhag7x6Of+0gh3rpxod21O8V7uwJqgh6Crh9PF9Dtwsj1lDZ9zE7jgv63bOP1ennve9/LRRddxC9+8Qs2bdrE1q1bMRgMzw9GcL+IGRwc5IorrmhpdyUTmFTklvBsvSiaVPysVCpKbEuva6k8LfvIA1jEuXilvV6vysuuVquqv7a06JLCXel0Gr/frwqXWVazv7eEpENTCJZKJTUBSfi75AtLWLt4y+v1OlNTU3R0dKjiY8lkUvUSlxDuZDJJR0dHy6JAn9TdHnfL/bSxqdVrKlRezp3emCbzhxkajzWwv3+o+mi9XieTyRAIBKjX64yMjKhrlHDqOy+/k/lz55uF1/4f9Hyhhw67A5fLxfDPh3EVXGx7/zZO+/Zp9GR6iA5FVdE4KRgXDAapVqv4/X4KhQJdQ13c+M4bGT9+vDlut82v3v4ruAHWP7xehW5LOzjbtuns7CSVSpHL5fD7/UxOTlKv14nFYlSrVTo7OxkdHSWZTFIul+nr66NUKimhLG3YotGoMpIAhMNhFhYWgKa1f3p6WrWAk1xy+SwTiQSJRALbtlX0QCKRYN++fQwODuJ2u8lms6oIm97CzmAwGAzPD2NjY/T39x+x8JnuQXV6cfW1h+Rli5Fe1g96IVGnWNT/6SJVF8i6GJX1ji4odXHvzP2W13Qhrl+bfu52AlbfTr8v+n2S/Z1rMaeg1u+dc3/9HuvXL+jRAs5ztLunzj7qulGhXcqA4DRMDA4O8s53vpMzzzyThx9+mJ/97Gfs3r37sP0MBsPvhhHcL1Isy2L9+vWsXLmSWq3WIsj279/P4OCgEjXT09NYlqUEKqBEs3iXJfcYUF5U6dEtIl1ClWWbarWqcqSTySTBYLBZ/Tsep16vMzExQU9PD7VaTbXygkO5XHrlUHldrk0vEDI7O0tPTw/VapV0Ok2xWKRSqajWZoFAQFUAl8mmXq+ryuwyucViMUZGRnjssceax/6Ki1gtRuZvMgB84L8/gHezF1fYRUdHR/P+HF9n88bNVDuqJIeT3Jm4k1P+5RR8Ph+VSkW1QYvH43j9XnK5HKVSiWq1ysPvf5htZ207VOX8fNg6spUTP3gi0UgUj8fD0geX0vWZLgayA4RiIbUw6e/vVxXfZTERCASYnJzEX/Oz7NFljL9ivHlsG0L5EKM7R6lWqy1i2+/3Ew6HSafT6vWBgQEymQyRSIRAIMC+ffuIRCIqaiEYDBIIBFSOdqFQUOK5s7NTRS0kk0lVlM7lctHX10e5XGZ6epr+/n4syyIWi6kJXIrtpVIpyuWyMtb4fD4GBgYYHBxkZmaGWCxGZ2ezavumTZsYHz9oWLBNLpnBYDA81yxfvpyBgYEWAS1eT0EEocyzTgEr3l6Zu6Ugq+551sPHobWtmJ6TrQtHp0daz+vWQ9flePqY9P3beYXb/e0UynJeZ764cxt9W31s8lq7nGqnQNeNC+1el9d08a3fJz0/u931ylhEbDuNCPrn7rzWsbExFi9ezGmnnca9997L97//fVKp1GHXbzAYnh1GcL9I8Xg8fO5zn1MPYhHXoVCIsbExNaFJ0S7JfZZ9RSRJsRKZDOQ4kocLzQJgUmm8WCzi9/sJhUIsLCxg27YK4ZZiaclkkmg0SjweVxO0nter53JLOLhMIDIBS+6wy+VSHvJQKEQkEiEUCtFoNIhEIqoPtBgbJiYmCAQCKsdYBJ3L5SKTybB161Y1wdQKNRr/1CARSXBJ8BJGSiN0rehiamqK8fFxBlcO8i9n/AvFSDPsHAsOnHSAJy57gnX/vU6F03s7vOxs7OQ37/8Nr/ruq4jsi+D1etnwHxuYGp0iPZYGwJ/0s+iTi4jH4gwMDLCwsNA0BEzGWKg072WxWKSjo4O5uTkleiX8ul6v093dzdTUFIvvWsx5wfP49Rt/TSgd4jWfeQ2+gI9SraRafqVSKSKRiMqvn5+fp6+vj507dyov98jICEuWLFETsrRxk8iBUqmkcvnL5TJ79uxRkRISpi9CXKrDZ7NZ9Rk1Gg3S6TRTU1PEYjEikQi2bavw+0ajQV9fX0uv0tWrV5PJZAiHw1xw0QVko1kG6gPs2LGDhx56SBkBisXi0fnPZjAYDC9RQqEQxx13HLFYrEUYQmsutXiJZc0gaWbOMGwR5Holcl0o6u9J5JwcW8S0HpWm98WWujP6mHSvfDvvdTvBqYtwPRTbKfad/9qJdV38Os8LHHZM2UZ+OtufOe+bHKudMcH5GTkNB/o1y/7ONmPtQtJt224pYqenHkpxtcsuu4xvfetb/PznP1cpagaD4dljBPeLlBNPPJGRkRFKpZJ6OAKq2rh4mfP5vAodl/dFMHm9XtWmq1arKa+x3+/H6/XicrlaBLHP51Mid25uTk0MhUJBecKlDRlAIpEgk8lQKBRUTrOEf8vDXXLNJSxbvMOAspbLpAzNyumFQoF4PE4+n1d9pXO5HNAUjDIGyUWXVmVi0dWtzm63m5FHR1j6yqVKbHo8HgYGBojYET7x80/wDxv+gfTiNDRg6U1LWfafy6h6mmO0wzYPvf4htl20DYCbPnUTG/9uI0smlmDXbM75m3O476/uIx/L84ovvoKEO4HP52P37t243W6Vaz04OEij0WDz5s0q97xWq1EoFPB6vZTL5ZbcfI/Lw9htY6RraVY+vJJKpkKqlMLr9TI0NKTucSQSYf/+/aqX+a5duwiFQixatEhVqk+lUrhcLhYtWqQ+a/lc/X6/Ml7Mzs6Sz+fVdysUCqmq5iMjI2zbtk0VkstkmlEDExMTBINBYrEY3d3dTExMqB7r8j2Viueyj4SjAzy5+kkeettDvOXHb+HE2Imcd955BINBbr31Vq699lrl/XcWwTEYDAbD/0xHRwfLli1TqWZ6YTRorbbtcrlU8TOgRfDKmkKM/SKsda+0zOli8NdFuxxfvKt6wVbn8USg6+HjumB1ikfZv91P/dzO7Z3i3hl67swB19GPr4fgO4/tFN36WHThrVcwl3vv9KDr43eGrOvjkrXQkZD7q38eevi5OGc++MEP8qY3vYlrr72W+++/n+3bt5tINIPhWWIE94uUv/7rv1ZVxeXhrQtVPfdZHpSScy0ebvkdUMXHZB/damtZVkv+t1RAlwd6IpFQE6ku4MU7LaHdQIt4lvFKrrmIfPGoStVSZ46SWNnD4bAKa5eJoVar4fV6yefzyiAghdacIXLFYhHPiR6e+rOnuCFwA39w5x8QcUVaFgKJ6QRr/3ktD374QXru6eGkX57E3hV7CQfC+Db7eOKKJ9jz6j3qmNVolUc/+Si1L9cI3hUkHo/zyq++kungNKEnQtTddRKJBKFQiEwmo8LRZSI9+eST1bUWCgVisRjZbFZ5FAKBAMlkUuXMr7ttXdNAsHKAfD6vog1CoRAej4dIJKL27+vrU9EMuVyOrq4u0uk00eihvPFyuczCwgI9PT0qIkHy1Wu1GoFAANu26erqUvnlgUCAcrmsWrtVKhVVdE8MGD6fj2QyqVIXpBd5d3e3+qzK5TLd3d1K0G9as4k7XnsHlUCFq19zNed9/zxOrJxIb2+v6g3e1dVFMBikv78fn8/H/v372b17t5n0DQaD4WnQ1dXFmjVrDos0E0T0yVysezz1quS6t9sZVi3RdSIuZY7TxbYIfV3w6wJSz93WX4fDi53JWJ253Pr2Tq90u+1kW327dj91nNcvYz+Sx/1IQly2k89CL4Dm3N7pURdnwpGuyxkKL2s2cU7oY3eO1cnAwAAf+chHuOCCC3jggQe48cYb2bFjx2HbGQyG344R3C9CTjnlFNatW9fycCyXy+qhKwXTRCwBSjCJJVVEZalUUm2+JCw7FoupImcinD0ej9pfQtH9fr86n2VZLW28ZOKW9lTValVN2jIu8eBKZfR0Oq0mSj0EDWgpYibFu9LpZqi2VDmVUPVSqaRyh23bJpPJtFQxFTHtX+yn9J8lWAWP2o9yZfBKPvHzT5DNZpmZmWHRokUA9B/op/PDnfhmfCTPS/LIJx/B7XLjnneTWpVq/XBs8M35iI5HKdVKTQ+wr5eR+giNUEP1Cpe+1uFwWN1fMU6I0SOdTqvc60qlQrlcVi3PAoEApVKJUrlEzBdjenpaefOlNVmhUGB+fp6Ojg58Ph979uxh6dKlJBIJksmk8lZ3dXWp1muzs7Nks1nC4XCL5ziXy+HxeAgEAnR2dqrPS1qiidcil8tRLBYZHBxUPbklt9/n8zEyMsLMzAwDAwMqVF5C6GOxGIlEgpmZGbZt2MYdFzfFNsD84Dy3vOsWTrz9RKqFZr9wWeStWbOGgYEB/H4/w8PDnHzyyTQaDZ588kl27NjRUp/AYDAYDE1cLherV69mcHCwpZiXFFkFWjybupCUf+IZh8PDm/UwY3lPQpWhtaK4GMxlDM4e37p32Zlbrnt/neJbjq//lN/b/a3/c3qmnR5pp9dZ95bLds52pE7BrZ9bNwQ4Q7/bbeu8t0cyBOifie5J1/eV8eufbbt7rW+r53qvWrWKlStXcvbZZ3P33XfzH//xH8zNzbW9BoPBcDhGcL/IcLlcvPe97yUWi+F2uwmFQirsWLzd+XxeVRl3Fi2Rtl35fF5Vvw6FQiqfGg6FGLvdbhKJBPl8XuV25/N5Fe4s5ysUCi0CORAIkMlkVKi3FF8TfD4fuVxO5YIBZLNZNSGUy2WVByZj8vl86loDgQDVapV8Pk8gEGgpxibHLRaLLF68mGw2S0dHh2phVqlUmpORBeUby7Dq4KAs2DK0ha9v+DoX7b6IwcFB5ubmVJuxyv4KdrfNLz7/C+qhg+HLfY4Px4boniinffY0ivNF6vW6qvYdDocJBoMq/N7j8dDf3w+g7lOxWKRarRKLNXtqDw4OkkqlsG2bSCTCwMCAqvpeq9Uoe8rc+7F7OeWeU6jcXVGh2NK2ze12093dTTqdxuv1smzZMkqlEgsLC6rwmRhm5L7WajUV6SCTaS6Xo7e3V1WwTyaTuN1uduzYgW3bLF++XBVpE693NptV3y0Zb39/P9PT0yxbtoxKpcKSJUvw+XyMj4/TaDSYmppSho61T61l66at7D1zL7jBV/PxyeIn6S31Mj09rcIWJV9fDDvDw8OkUikWFha44IILOPPMMzlw4AD79+9nz549qpaBCUE3GAwvd7xeL6effvphLaF08ecMU3YWIXP2d9YFpkSriYFdF4vyU9YReti0HiauF1OVc0hbsiOJamdUnD5eXezrgtJZmEy2aYfTe94uv1rG5wwX/21ecWeovO7ldnr05f46Pw/9eE4xrV+fM1ReF/q6wUEfo4zP4/GodaVuZHC73SxZsoTR0VF+7/d+j29/+9v88Ic/VOsBg8FwZIzgfpGxdu1aVq1apcSjy+UiFAopESHCRzzPyWSSUCikPOASJgy05Fyn02k8Hg/z8/MMDQ21CN58Pk+1WlViWryusVhMTaKAEnA+n6/F+10ul5UAlIeunnsrRdokrF0MAiKSpV2Zy+UiHA6rAmzi2Y5EIqRSKeVJF097pVIhkUgwOTmpKqbqE6jvIh+VayvYJ9pgw/LNyznnP8+h4Wt66ZevXs5keJJgNkjjXxpMrpls/TBs6H6wm2K8iCfkwb3Xzdr/tZa6u9luSxYx8XgcQIV2S4hepVKhr6+PZDJJo3GosnipVFKF5iS0fnZ2ljVr1tDZ2Um9XmeuPsfDlz7MnlP2sO+kfVxYvRBrk6XEcjAYZHZ2lpmZGUKhEMVikQMHDhAMBolGo6roWTAYpFwuMz8/TyQSobOzk66urhYPRblcZnx8XBlXPB4PwWCQlStXcuDAAZW/v337dsbGxvB6vXR1deH1elWOtxhIEomE6uM6OTlJLBYjFApRKpXYvXs3fr+f++67j5mZGbzf9OL+khvvZV4u33w54bvC5L15FUYukRHyHZdrAxgaGsLtdhOLxQgEAixevJhXvepVTE9Ps23bNqampshms+q77QyxMxgMhpc6fr+fM844o2UOdwpTEaW6N1oXtLog18OR9fdkH/1v+V0X47Im0AWtnNsZBq4fy3l8aXXq9PjqIlS2199v5/X+bZ5jpyddrt05nzi92+3209P0nO+1m5+OFJrebszOz1RPsdONDrrQd16vvCeRD7K/bmyQz1Ai9j760Y/y9re/nf/4j//gzjvvZMeOHcbYbTAcASO4X0S4XC5e+cpXMjY2RrlcVjnOgLISCxK2HIlEVGi3ZVlKOOuWS3mA+nw+pqam6O3tVR5EEdlSEEUepnoFS6FSqRAMBltyqSuViirCJu3BJM/YmS+UzWYpl8uqWmo+n1e54/l8Xk3G8jMSiRCNRqlUKsrgkEgkaDQayksuRb+kx7MeAuaecxP4wwCNbzRY61nLa25+DeG+sOoffvMJN3PrkltJrE+wcNzCYZ/H8luWs+7f15EZyuCOuXFtctHgUBV28c6LIUJysCV/W0LDJQ9e8s0B1cdc8p+j0ajKcy42ivz6Tb9m56k7m5+1p8FtH7iNs8JnMbR9iFKppM4n+fEiPi3LIplMMjg4yPz8PJZlqWgEGWsul8PtdlMul1W7sGAwiM/nIxaLNXPfPR5mZmZaoiZOOOEElWteKpWo1+v09/dTLBab4e+lEnv27FHpEOFwmH379pFMJtm1a5cyzCxbtkz17fZ92Mea3Bo2VDeQzCXp7e3Ftm127NihFgASOSAt4vx+vyoUKGNrNBoMDAzgdrtVxIXkvI+PjxONRpmYmGB8fNz0/zYYDC8LlixZwtKlS1X4M7QWS9MLZ4lxXMSsLl5t+1CxTWfdFafw1r3kuhDVf9erkbfzcLcT8/pPPWdaR/ck655f/ThH6tctOEW6/rvztXa54s7j6O+1834faV8xLBxpW90IonuvnSHjRzIA6MfR0xDbva8f10lXVxef/OQnee1rX8tdd93FDTfcwLZt2454nQbDyxUjuF9EDA4OcvHFF6uiZSKqhFKppCauUCikQrukKJUzREgKlYkgr1arrF+/nmq1qsKLvV6vEo6BQIBCoaCqkIvXWS/qIYXPxJMr3mp5X3KBfT6fqoQtD2oJixdjAqAs4DJOsX6LKJc+kLFYjEKhwMLCgvpbzzmHwwul1Go1eBw2fGUDG8c20tPVo8Z/3UnXccf6O2h4G+Q784d9FqNXj3LSLScRiAQIzAQo7ClQsg9VRxfPvAhpmZiSySS2bavrk/xnyY+WEO94PK6iAbxer6oon81myeaydE90s5Odajy+mo/u9KECZFIoTnLn5T5KxfFSqUQikWjue/Czr9VqZDIZAoEA3d3dFAoFAMLhsKoKb1kW4XAYaOZeS0RBR0eHEuqFQoEDBw7g9/tZsWJFS/G38fFxyuUys7OzFItFMpkMxeKh8PtAIKDG2Wg0qNt1skuzlB9thhAWCgVCoRC7du1SeeNLlixhcnKSSqXCwsICo6OjHDhwgCVLlijDkFRYl7Z2Uuk+FAoRjUbp6+tj2bJlJJNJFhYW2L9/P+Pj48YabzAYXrK86lWvAloLo8o/efbpQlgPI3bm8uo54IL+t+4J15+ruoCV+d3tdqsIPqc3Xfe+Oj3pzpZaRxKCek0XoZ2gd3p6nWHb7Tzr+uvtcqX1UHHnvXSewxnu7Tx3u/xxfTzOUPd2nnM5ljhenPfXeT5dqDsrl+viW16Tz3Pt2rWsW7eO8847j7vuuourrrqKyUlH1KDB8DLGCO4XEUNDQ2zcuFGJwkQioSzL+Xyenp4elb8rIdzJZJKpqSlWrFihJgOpNJ3NZlVlab3aqORVywM4Ho+r3GfpvSwCsFqtUq1WcblcSvTK31K8TcYi5wVaRLhtN8OoA4GA6tOsTyxS0Mu2m7nMuVxOiT/xiIuIlXO4XC4WFhaUyLIsS4Xel8tloCm4w4kwWzZuYXXHajyZ5jluP+527nnlPTS8jjAuGzqe6uDE/3UinoIHK2wxl52jt7dXeXH1MDmfz0c+n1fjK5fL6jOQ+yxeWCluZlmW8nLLBCo574lEgnA4TDqdZuUvV5Kr5Njy+1vwl/xc/sXLiZaiZF3NgnXbtm1jdHRUtfZKJBLKCNHX18e+ffuwvBaWz4KD7aw7OzuZn58nGo1yoHiA2//4dl771dfi9XqVkUVy/6PRaEuu/4EDB6hWq6xYsYKtW7cyMjKCbdvs3LmTnTt3sn//fnK5nDKapNNpLMsiEAgwMDCgWs0VCgUqlQrVapWBZQMUflBg+8rt3Nh3IxfeeqH67OS8Ho+HhYUFOjo6aDQaqh1aR0cH8/PzKqVBvrcSURAKhZT3ZGxsjGQySXd3N4lEgs7OTgYGBjjvvPMolUrs2LGDxx9/XBlwjAg3GAzHOpZlce6556o52FnzRRdtTm+1iFkRxhKxJoJXF5zt9pUwchmH7vnWQ53biVRdqAIt49X/PpIw1oV/u7BzfbxO4dtuG11gOqMHnTnh7c7lvEdyvt8mymWbdseV3G59nO3yveXa9fsinmz9nrfzWst1yXGdgt7pqZfzejweVq9ezfLly7n88su56qqruOqqq8jlcmZeNbzsMYL7RYLf7+cTn/iECtMulUoqXFpEqfQ0Fq+2z+ejp6dHFVDz+XxUKhXS6TShUEh5mQEl4jOZjKqWLeFj8vCWPtDy4Ha5XFQqFVXRWh7WUt0cmg/gaDSqvKrFYhG3262KsOkFRfQwdHlA12o1lWMeDodVH2/xwEuotd/vV8aDXC5HqVSip6dH3Tun1dXr9eKL+Sh9rET1Q1Wu4ioC1wdYc2ANJ91/Ek8Vn+Lhsx/G9tp4Z7zYXpvIgQhn/vWZ1CrNgnFWpCmOo9EoiUSCrVu3sm/fPvx+P729vUrsibVdjyYQY4R4+SVn27Ztent72b17tzI0ZDIZvF4v09PTVKtV4vE42WyWsf8eo+avcfLDJ9OYb5CyUxQKBbLZLEuWLCGfz6ue2PV6nR07dhAOh5uGErvKvgv2MblokrEvjTHgHWBhYQHbttkf2s+dn76TfHeea664hhO+dALF8aZnWDzAHR0dPP7448TjcXp6eujt7SWZTPLwww+ze/duHnvsMaanp1WOeL1eZ+3atUxNTQGowitS6VzC4OWzzHXk2PXVXbAasOD2tbdTnC2y8daNLO5drIq2yHfL4/GQSqXo6uoilUoRCATIZrN0dnaysLBAPB7HspoF4CRFIhKJqIJry5YtY+vWrViWpYoOWlaz7Vh3dzennHIKmUyG2dlZtmzZQrlcJpvNksvlfmsYoMFgMLwYGRwcZP369S352brHVI8oc3q3dc8oHDLOO0OTdW+1rBH0fUWcS5i6E6dHVa/+LfvqYly2/W1h3/o2zhZk+ra6WNavVcblFKbtROaR3nfmkrfzHutjlvPrBntn9IAulp3i/Uj54XJ9uqfamRagf67Oc8t49Pvk9Irr6y553ev1EggE+NM//VM+8IEP8MUvfpHbbruNPXv2mOJqhpctRnC/SBgbG+O8885TlmUJvZWe1N3d3SrHSgpiuVwu1WpLLxomhdPuvvtuzjrrrJYcaEDlTotAF4Gq58bqYlzyrSQHFw5NwHqYGjS95VIgRTy8gDp/o9EsdiYiSoS4GBbk/JOTkwwMDBAIBFRhNwmXluJrImTlvkBrrnv1z6tU/+zQw/3fX/PvvPH6N1L9ryrRG6IsmlrE9EXTrPzySohD9wPdFHIF5dmfnJwkHo8zPT1NT08Pxx9/PJlMhvHxcQYHB1WYdKPRLMKWyWTo6uoimUzi9/tVOzaXy6XCqmOxGLOzsyoSwefzMTMzo4wrEsYtho61V6+l6qlie2xV7Vwq2Nu2zYOJB4nmohSfKCojh2VZ7L5sN49d/hhYUC1WGfjvAayCRXFVkU3v30S+pxmBMH/SPHe9/S46P91JOBtWk2xXV5cKIxcRGg6H2bZtG8lkUkUkeDwe9V3YvXu3+lsMM41Gg4WFBQKBALFYjP7+fjo7O+l8bydbz9p66D+ABfe96j5Oeuok7JKt2qDJAqNSqeDxeKhUKkQiEeWJFy/4zMyMSnPw+/3E43GSySSxWIx0Oq3aoUn6goT4ywJvfHycSCTC+vXrWbZsGQsLC8zPz7Nnzx4VpbB58+a2xW0MBoPhxcapp57aEkasiyy9SJn+GhzeHxtQUW06urDWveT667Im0fd3erGdRgD9+PLT6ZHV08hkv3YC0nk++Vv2abe/Ljr17fQQe/13/ZjOcznPp4/NuW07j7fT0ODEGRYvwvpIIezOa5Nt9eJqci79n/M4RypYp28vx4tEInzuc5/jrW99KzfffDM33XQTTzzxxGHXYjC81DGC+0XCX/3VX6kHlExO8nDU+1qK8JD2UtB84OniWURHf3+/Erji9RPvs3hnJZdWjuF2u8nn8y3nrFaramySh53P55XYkXHq4WYy0Xq9XlXgTHK2JORMPMONRrNgWyaTwbIsent7lUCX9lZ6WJSMX+5RIBBQ7UnUREiD2kdbi2OVvWVuHryZlftXEgwGWfqfS4n9JkbfU33K2OD2uZVolPNJrvPU1BSZTEa1/pJoA6m8Hg6H1f2t1+vE43FyuRzQDOeWa5dCc4VCgUgkosKlxajx2GOPEYlE1OQpudgzMzM0Gg3V+qu8ocxjH3qMSCrChs9sIGA3Re0DlzzA1ku3wsE598CrD3BHxx2c/oXTidkxgvVg632ZKDO9d5rB0KCKlqhWq6rKtxhjJHRbrw0AKMPJwsICIyMjqhic9BJfs2YNixcv5oEHHsDtdjM+Po7/Gj/BDUGKxxfVOM7efDbxbJz5zHxLuKPcX7fbrT6nXC5HT08P6XRa/T9YWFhQofH1ep1MJqO+m1u2bGFsbIxQKIRtH2p1J/8HgsGg+r+TSqXIZDKqv3k4HKajo4Pe3l5KpRKTk5PGUm8wGF7UvPKVr1SGbF1EO/+JYVS2c4ZMi2gXr7NT2Ip4l7/19/X6JmIU1z23TsEv59YLW+qiUo7nFO96qzJdpAq6KGyHvq8zTNxpfJDX5Fi611mO1S43Xa5XP2c7YdxOzDsNCs6xO3O55afzvuiRgO1wevb1cenXqn8megV0p7dbH+OaNWtYs2YNl1xyCXfddRf/9m//xt69e484FoPhpYYR3C8CFi1axAknnKCqgItnWMSOZVlKoIbDYfUwk4Jkfr+fUqmkhHqtViMWi7F06VLcbjfxeFwJaslByufzSggHg0HVoqrRaPbNDIfDSqhLlWsRtOIhlPxuyQmXfeWhXK/XVUE2mVD1fuLyezKZZGJigk2bNqlxjYyMqIlc7sGuXbuIxWIEg0EikQi1Ro2HNjxEPVnH5W61yvu9fnqu6GHqB1PY3mZbMM+0h5G/PnTcYqbI0PYh/EG/6ikt3tRIJKKuPxqNkslkeOKJJ8jn82rh4fV6VesrEcIi/uS6JAdZxHitViOVShGPx/F6veTzeRVqHgwGqdVqTE1N0d3dTd9wHw+/92ECXw+wylrVsjBJD6R5+C8fphwtUxoqcd8X7uP0Pz2d+fl5lvx8CXsu2EOx82AV9ZqLE24+ga7OLrwFL5f8+yVc/eGrWRhYgB8BfwIdvg6KxaJq8SWTfH9/P5ZlUSqV2L9/P6lUSo1DQvklRO24445jYWGBRYsWMTw8zOLFi9m7dy/79u3j8ccfZ2FhgdnZWQqFAuG5MLwOrNst7CU2p247lfNvOx8vXnbN7VLfa/muhcNhFU4v9Qwk9cGymu3S0um0iuaYmZnBsizVLq+7u1sVipM6AfF4nEajoXLVJQdfFn5iXMjn8/T29rJmzRrq9TqveMUrmJmZIZVKkc1mefTRR8lms0f5qWEwGAzt6enpYe3atcqrDa2CSOYRXXjL63CoC4ozn9sZaq7vo4dyS+Savr3Tw+vs2+30KutCU7bVBWM7MdtuXM7tdPGp4wyp1rfVaeexFnSRKhEDTjGuh3fL+Zyecuc+YrCQz1Pe1wvZ6eN3RhyI8Nar0Lfz6Ovh5LKGk+gIZwE1/Xr1Yx7JKy9/r1u3jtWrV3PppZfy7W9/myuvvJJcLmeixwwveYzgfhHw/ve/n0Qiofoni0dVhLR4SsUrKg++UChENpvFsg612XC73Xi9XkqlEl6vVwkL6UUsYdjS3kmvbi4PykQioQqRlctllcMtvaVFiEg7rlqtprzggBLSMtnm83ls+1A/Tsm/Bdi5cye33norU1NTLF269FD16oOitlxuVq8eGhpicHBQ9ZzGBfMXzHPbG28DwPqphXVDs8K2bdssWbKExbXF7P/Ufh7/1ON4ch5WvmMl3b5uSsGSyvGtVqvNomW9OXITOULuEIlEQuVVyyQQjUZZsmSJykHu6upSHle5NpfLRTabVceF5kQ0OjpKuVxWHuJarcb09DS2bTM0NEQ4HFbFwvL5PGeccQZzxTmeesdTTL5uEutCi+h7o6yx1xAIBJicmWTH53ZQjjb3wYLMaIY9H9rDqT86FZfLxev/6vXc+Dc3Ug/XOf/b59O7rxfb05zAyxNlNrx/A7e/73bsd9i4XW48IY8SspOTk0rESqpCPp8nm82qnGiPx0N3d7cyiECzBsBZZ53Fnj172L9/P4888gi5XI6+vj7Vsi0Wi6kCPAlXgsrFFeq31jnv++cR6gjhC/uUN0aMGrZtqwr9/f397Nixg2q1SjqdJh6P09vby/79+1VUQqlUIhqNqsVEPB5naGhItTgrFot0d3ermgN61dvZ2Vn1f0/qCYhxBFAGrYGBAZYuXUqhUKBcLrNp06bn7wFhMBgMz4AVK1aoNolOEeoUyCKs5DWZz0U0iRFSxBegjNbOY+vvy762bas0Mqfo12vB6N025DVddOqpbjJHtAs318Wy7q3Wc9nlfTm283ztvOP6vk6xrgtMmUt+m6Btt2+7a5ZzOUPH23m6dRGse7z18G9nsTXnd0EfkxhZ9LEc6dz6e3qUhPwt91qvc9Pf389f/MVf8PGPf5zPfe5z3HrrrYyPj5vIMcNLFiO4X2CGh4dZv379Yd5sCdWu1+st1cYlfFcmH+lTLeJXQrhFsIin2imWZUKVfGU9z1Y82VL5WUSM/IRD/Zyl4napVFITpVijxQMu1aPlAV4ul9W4k8kkY2Nj9PX1tYSLyeQsec7pdFrlA+XzeXadtYv733W/Cpu2f2Tjf7+fVY+vUgaFhfkFwvNhRv/vKPG9ccKEVf663Gu3201hWYFHP/QoA/cM8MrbXonf5yebzSoPdz6fJxKJEAwGyeVyjIyMEAgEVOsv3eARjUaBZqstKfw1OTmpelxLVXPJVZfPUTyx5XKZVCHFjnfvYPcbdzevzW+z7Uvb6L2yl7H9YwS8Ad70zTfxk7f+hKl1U2DD4p8u5rQbT6NSragw6fO/dD6p4RT9W/qxXJaaCPv7+8nlcoTfG6boKqrvlNfrJZvNquJkYgCSvtz9/f10dHQoo4/X66Wvr490Ok0+n2ffvn385je/IZ1O4/F4CIVCqphZV1cXtVqNZDKJz+dThfJSb07RkehgYc0CPdM9qq2XfMckP76vr49EIsH09DTRaFR930qlEtu2bcO2bTo7O/H5fAAq+qBWq5FOp1sWZJOTkwwODqrX+vr6mJ+fZ2ZmhoGBATo6Oti9e7eKwhCPOKDqCMzOzmJZlqonICH3BoPB8EKzfPlyuru7W8SRzKsy5wCHiThdNOleUxG8+u+64JY1iS60xcPdLjzc6fmEQ2JQr8mii1HdQaBfkyDrJafw1gWz08uuj8Hp6XeKY31fpzdWrr9dTreO8+92Xv12Y9JFq9O73O5+tsun1j3X+jW2e08fix5G7gxRd94X/T1Zb+i/69vLuSORCF/4whfYsmULV199NTfffDNbtmzBYHipcXjZSMNR5aKLLuL4449vmQDFs6oXNBPRLBOm3+8nl8uxZ8+elglQrwwu/a71nNtqtapCgfVe2iL0peK0LgRF6IZCIRXyLpO3eIj1B7FeBVNylgOBgNperOgej4dYLEY4HKZUKjE9PU2j0VBCPhwOk0wmyWazLblkkUiEWq41P9uyLF6x+hUqpFuEWqPRoOvXXcRmYmpcUlTO5XJRHCny4IcfJL0szVNXPMWDb3iQp556CjiUR1YqlZiamiKVShEOh1Vushgg9Dz4XC6Hz+fDsixSqRSNRkPlYFtW0wMvRfFcrmav9Xw+r4p6NRoNatUavbHelutz4cLT8KjvhGfBw+n/cTqDjw2y9rq1nH7D6QSDQZWrX61WqW+t03NXj+rVXSwWse1m/vKePXuU6JdQdsmPlmJkUgm/o6ODoaEhQqEQc3Nz6rOYn5/nySefVFZpKZgnk6gUXZNc6vn5edLpNNAUrtbHLfgyJGNJfnzBj9nRvwOfz8fu3btVm7mOjg56enpUtXCPx8Pg4CDhcFiNV4RutVqlr6+PSCQCQLFYVAYlSVUQw5JUkJdFmt/vZ+XKldTrdeXhFyOYeM5dLhepVIq5uTnl5Rfve7sKvAaDwXC0CYVCrF69mnA43PK6LqD0CDdo9XYLIrrkOSmvtRPbsp3M8RLhJsZ3EeG6s0CvQq7/rgs3Ed+6yHWOSX9PF/5H8lbr/+R87fLbna85j9HumNBaTE4Xs/rfzmvU72k7L7IucNsZTPT74Lx23bvuPKbcSzGOOP/p+fntvPPt0hL08ehpA0cyVMi/tWvX8tnPfpavf/3r/P3f/z1jY2MYDC8lzCrxBaSvr4+zzjqLYDCowqVrtRrFYlEJiUajWSRMqpbDIYtjKBSir6+PQCDQkhstglqqPOvWznA4rFpHiTfQtm16enoIBAIqZFiKsIlQk9BeyeXRRaP02BbjgIQiy0O7s7MTaK1oLnnL4sXO5/NkMplm7+yDRaoAOjo6Wvbbv38/fr+fzls6OeOrZ2A1LKyGxal/dypDm4YIBoMEg0HC4bDK2ZW2VFLVPZVKUS6X8XZ4eeh/P0RyabJ5H1w2Wy7ewr5371Mh+eLJBZSolF7khUJBhYjL+KSY3MzMDDMzM0qgSis2aeEl4heaE18oFMLr9eL1eumMdrL+xvWcdddZYINVtFj8B4vp2taFZVnE43FKpRLh2TBnX3U2HVd2QPVQ3ni1WlU5UXIdIhDFWy+V0EVIirj2+/3EYjG6urpUFIMYSNLpNJOTk8o4UCwWKRaL5HI5VfROvovFYlHlOWcyGSYnJ+ns7OSMM85g3fp1lK4okf6zNBxcE852zPLdi77L1spWduzY0bwPBwvNSX2CvXv3kslkiMfjqiCgz+dTNQBs22bv3r2qeFs8Hqezs1MVRZN7Lt9zWYCIQUKuz7ZtlZcfj8dVekUq1WzLJv8/bdsmmUwqMW8wGAwvNL29vaxateowgQXtC1tJNJqsE/QwYtle91ZK5JaeT+3cRt9PGYC1sHU45J3WC7GJKHMeW8SfHEcXqTJWXcw5vdXt8oOd++vjc3rJ9XO2Qz+fXvC2Hc7jtxOqzjE7DSG6kHcaP5xeaj0Uvt0x9OPrn7nzPrUzQjgjDuQzl2PpRfD0iAn9O+D8rDds2MAHP/hBbrzxRj7zmc+QSCSOeC8NhmMJI7hfQF7xildw6aWXYts26XRaeUclH1i8eB6Ph0gkorzR8r7kN0tos3hSbbuZNy0PbLFCSl5qPt9sCSUToQhkQAltyZWSSs2SKw6HQr9kH/kp+a1+v1/tL97wcrmsvNuRSASfz0epVCKVSrF//34AJYwl33lmZkYdV8KY6/U68/PzdHd24/pvFxdffTGXfOsSBjYP0NXZ1dJqrFgskkgk8A/78cabfSHD4TD9/f3Na8jDys+txJtsCmps6H6wm9H/HFXRASKipbCWGBkSiQSxWAyXy0UoFKJaraqw8GQySSQSoaurKZDls11YWFA91ovFYktRGSkEJ9ENrrKLDddtYNmNy7DWWXj3eJXwF9GYyWQo7C4QdoVJp9NKzMtnKt8b+dvtdlMqldizZw979+5V+e5SUV0iIObm5ti3bx+VSoVEIkE+n1dVuaVon3jCA4EAkUhEiVKpFC593js7O5VgDofDbN26lf09+5n9+CyNyKEFhb/o55JbL2G4Mkwmk1EGjZGREYLBIIVCQVWjn56eplwuE4lEVI2BUChEKpXC6/USj8d57LHHlMFDvtOdnZ0qJUGqqEejURYWFrAsi927dzM4OKjSB8QI4na7CQaDqo7A0NAQgUBAfW4SVWAwGAwvNH19fSxfvvywAmO6cHKKY1lnAIcJI6eoknlLjitCVES4XrBLF6Gyr95jW+qgOD3nTo+w7rnVPel6u1P9+E7x7Qx7ljHpodpy7fo/OZ8uQp2ecP08uvjVr0H3TuuGgCNFFujnce5zpLB8p3HE6WV3jtP5vdDP7cS2D+XhO++t7nF3jlfGI+vAI90j+ae3phsZGeHP//zP2bNnDx/4wAdYtGiRchIZDMciRnC/QAQCAS6++GLlJZUK2bq3VMRjoVAgl8up0NdSqUQymSQUCqmc6Eqloqy/1WpVCSHLstRDSoSQPLBFLEgxMwlDl5BovbKlHKtUKrWEoafT6ZaQXRGpeosy8bzLA1faf8lxRcCGQiHlfRfhZNs2kUiE3t5e1YNb7omFxbqH1rFm2xrshk02m8XtdreEH+c6c2z/q+1MvnuSmqumBGggEAAbBsYHOOFfTiA8F6bnjh5O/vzJeCyPmrir1SpdXV3KQ1qv1+nq6iKbzarPampqShlKwuGwCg0Ph8NKJAeDwRbDRygUAlD3TkR5oVBQ56qUKlh/YuGb8CmvsXwXKpUK8XicQCBAZ2en6vedSCRIJBJ4vV5Vfb5arRIMBimXyypPX96TaAYRj41Gg46ODjo6OtQ5xavu8/nUvZXvh/45u1wulZvt9XoZGhoiHo+zdu1aRkdH6ezspLe3l/iOOMGPBbH2Nyf3QCHAq296NQP3DGBZljLu1Go19u3bp0S1RGLE43EWFhaIRCIMDg6qXvFimJGIgtHRUaLRKF6vVxmjAGUIkRZvkjOeSCRIpVLk83lVzV/C5KWKfS6XY35+XoWrZ7PZFmOUIP+f2+F83elxCgaDKqrFWPYNBsPTxe12s2rVKgYHB9Vr7cRWO++pM4xYnut6uLfMeU5vtRjv9eOKUNXFn6AXPdNFnH5sOFQPRs8jln/OCCUR8Po59ZBmfbztXpPX9YJwTmGrb6eLRhmP7t1tJ2Z1A4KOjFP/TJzRAu1Cs3UBq/90inTn90DGrZ/POWbneZz58rqXWyrYy/dFPn89MkD/HPWwd92Yohebk++J3+/nn/7pn/jpT3/KRz7yEY477jgzLxqOSUzRtBeIRCLBW97yFpVXHAqFlIdULLdSoEy2qVQqqpCVCG3xvskDTYSC5E7rxdfK5bKyRIqnNRQKEYvFyGazLZZqGYucX6oxA8pIIKJSKkhLYbZkMklnZ6eqTl4sFlUlbl3cSwE3224WG5Pw8mw2Sy6Xa1ayTiRwu90Ui0UlRJzWbblG27aZm5sDmhWzGx0N9vzlHuZfOc/cK+couUqs/OZKSqWSOqbb7Wb4sWHC/xbG+6CXeq2u8n6j0Si5XE5N4mJ0kLZeeqicjE+MDen1aayyxcD4gIoIkJ8SySBGlUAgQDweb4a5H/So+v1+ZcQIhUJ0dHQoT4D0pZbPqlgsEgqFlLFD8tSlGriEkouBY+/evcpDrbfegmaEg4hWiUoQY4GE50tkBaCOMTg4yOTkJMuXL6dYLJLP59Wix+v1kkqluOuuuw599jt9BGoBqt+o8uZ73syiBxYR6Yio+yCCW4Rro9FQ4epi7JDK5/Idkcl5YmKCaDRKMBhs+b6JZ7tSqdDR0aGMP5JqcMYZZ/Cb3/yGXC5HtVolEomQTqfVtc7MzKh7IGH4UgfBuZh5JgsCZzifGBDC4TDVapVEIqGeBxLKbjAYDE78fj8nnXTSYa/rXlpnKLHuwW4nMJ1eSNlO/6lvq4tFCQ/XPZdOL+mRwqV176z+ty5aJcXNeV7ntcu5hXYi1BnuLO/rY3Cii0bndQu6CNfvkz5WZxi/8/xO0a4fW783so/cG/01HTm/85ztjBDyejuPfLuQcuf5ZI0m91EcObqgd35uen0BOdby5cv5/Oc/zxve8Abuvvtu/u3f/o3du3djMBwrGMH9AvGBD3xAFavK5/P4/X7V+1kmCAkdn5+fV4tw6b0tnkTxgonYlQepiALxQIoYgaY1VSqbi1gUS6SEeotXXESb5FaLaBdxL+JXrN9SRA1a+22KuHS73So8XoSh2+1mfn6e3t5ebLsZXl8ul0kkEio/fG5ujq6uLlXhGlCCRI4jbassy6JSrfDY/32M3HHN6tJYMPHmCepWncVfXIzf76e/v5+ZmZmm6H5imEwlQ5lyS5iZCGLxiIpwTafTxBNxqqEqT773SVZ9aZWKDigtK/HQHz+E1bA48c9PpK/Rp7ylck8l73l4eFhFL+zdu5dQKMTMzAyrV69WnmfJbx8dHVVefkAZLWq1Grlcjlqths/nU23cyuWyyjmXgnjynQPUd0AmUMnXHhkZUaH9EjY/MTFBd3c38/PzeDwe+vr6GB4eZmxsjEgkwhNPPEEqlVI58noOuR5GKIuAYDCI904vF151ISd4TmCiPAE0C+KJccnn83HOOeeQz+d5+OGHVZrAgQMHCIVCpNNparUaiUSCsbExFSUQiURUJXrJtx8dHVX/98T4VCqViEQiqsL4rl278Hq9xGIx9X9QPlOpM6D/37Btm5UrV+LxePjhD3/Y8v9b0jbaoS9GZLEoiNGpUCioVJGOjg6SySSDg4PMz8+r/xvT09P/02PGYDC8jPD7/WzYsAFARf04xbHuHdaRdYSO/K17HHWcnlDZRw8f1kWoLvBlOzmuXuFcF2LicRXDqdNwIL/rXlR9zPL7/ySenfdC39eJLmr1EG+n+NTFsn7vnOLYeV/bGRnkfjv319+X84uAb3ds5/76/WoXrt7uc9MrxuufsX6P23no241ZjwzQvyci1OV7LPufcsopnHTSSVxyySVce+21fPWrX2VhYaHNp2gwvLgwgvsFIBgM8ta3vpVKpaIeSrJ4FjEgubMul0sVcJIK141GQxUyE5ElRbAknziRSCgvtXhMG40GyWSSRCKhclBlv46ODizLUiHNMg5AebCz2awKq5WcZTm/iHI9VFoemlKh3LIsJcBkf+mLvHLlSu6++26VTy6iVLyz0WhUhSxLfvqJJ55IoVBQorRWqzE/P6+MC6v/bjUPf+1havEa2BDaG2LJ15fQsJuT1uzsrLoWKWoWDofVQqBQKNBwNRgvjuPLNlukZfIZAgMBfBEf9/7NvaTWpGh4G0Q9UZZ/aznFjiL3fvFeGv7mOe77xn287i9eR2W8afgIh8PE43Hq9TpTU1MqX14qbgcCAXX9MtmEQiElMKXtmITyi5Dt6+tTHnEJv85kMgCqAF00GqVer7OwsKAqh8v3K5/PMzw8zKJFi9izZw9+v5/h4WEVBbB48WIKhQLLli1T/V2r1So7duxQ45G+8NLGTqINoGm4kJ7l8rfVbXHdW65j3Z3r6Cx0EgqFOHDggPLi+v1+7rjjDpV2IIUE6/U6PT09pFIpFR0ihpdSqcTg4KDKA5fUi9nZWbZt24ZlWRx33HHKACRjB5ienlb/h4rFItFoVLVIE4OWhM9J9MkjjzzCU089dVgu4G9DX+zIglb3cEidBq/Xq1I4oBkVk0wmVUG7oaEhvF4v4+PjyhtvvN8Gw8uXpUuXsnLlShVZpHss24Xs6qHI8jwSY708z/RwYaeXWxdievsvXVTpzzhn/m87ga97jXUBJ2sMOZa+j6ALPBGG+nXqHmG5Rl10thOa7UStCEN9G30757F1MatfE7T3gDs9zUcS2vq16MdzesrbfS66F9x5v9qNQRfzzu+BHsLezuvtNE7o59DH0O66nfvJd+G4445jxYoVfOITn+Av//Ivue6661hYWGj5HhoMLyZMDvcLwAc/+EFisZiaEG3bZmRkBEAttMVzLJXKRbDo+THSy1oqY0vBLfFmygNKhE65XOaaa65R7bgECUsuFosqnLtWqymhr082ImxcLhfRaJREIqEe+FLUTB54jUZDtTQTcS/iMhwOKyHT2dnJjh07mJmZUaJGwrfFU6i3ZpJFw969e1WYem9vL9FolGg0Sk9PT1Ok7XBx3J8fR3A8SPTRKK/4yCsIuUOqaFsul8PlcqkQbTE4yLncHjf7zt/HA59+gMnQJPlCnspbKmz94lZ2/OUOFjYs0Ag0wA1bLtjC1rdsZdMbNtHwadb+QJ37z72fZDLJwsKC8hZPT08r4TgwMEAwGCQej6vrE2+4FOTSFz0i0sPhsAqzT6VS6jMqFouqbzk0Q9jFsKB/nlKYz7ab/dgjkQgLCwst4nh4eJh169axePFiBgcH2bdvHw8++CD33Xef6rktHmIpjudyuVTYvtfrZfny5axdu1YtmFwuF5XRCnPfniO3JMffXfJ3bA9vVwaWer2uvh/RaJSpqSmGhoaAZjqDIN97yUPPZrMqh35mZoZ0Os3AwABdXV1kMhmGhoaUAcfj8TA7O0smk1HF1vTFiBSHk/uZyWRIJpMEAgHK5TL5fF61LWu3sPltOBcr8v9ZFptSA0Dek9B+qdNw6qmnks1miUQiNBrNtm4nnHCCimAxGAwvT84++2wl9pwCWV7TRbJTKEpEl0SeiXFfF1vyrJT5RMSkM7dajquLXjjk0dS9sDJWEWcS3eNE1h7i8ZbrkePrOcPtwq114aobHZz/4FCYs1MgynXqIdsyb7cbq35+3auvH1PG5DyX7Os0lOjv68doh3Mb3RCg52vr0RD656mnzgEt6Xx6RKZzHnTmdOvjcW6r31fdOOG8t07DhThXvvjFL3LHHXfwwQ9+kOOOO66tV99geKExHu6jTHd3NxdeeKEKUxWBIl5Nv9+vQmql0JUeciPh4qFQSLVhkirh4XCYSqWiQoolRFweUl6vl3e9611qovR4PCr0V6pISriy5KjqXj09X1p/WEsOsQguj8ejcq2l0JtMThICXalUVO5wsViks7OToaEhJUwbjUZL9VJoeiK7urqUEUJfGIiFVkLO5V76nvSx/J+WE54I4y658Uf8LVVW68N1UoMpFqIL+G73MRwaVoJz4rIJdr57J7bbZu+n9uJ7zMfsFbPYXlu1EtN5KvcUHX/Rge/PfVT+sLlgiP6/KMPfGiZXyymP6sDAAD6fj9nZWXV/pSCaCOBkMqlEnoR3W5alIgrEkCHGjHw+r4wwcMhwI5EOElpdLpdbPM+ShiDttvSiZ6FQiMnJSbZu3UqpVKK7u5uJiQnl4Zb7LdES0i7L6/WqiIFarcbMzIwKcQ+Hw1SHqhS+WKDxyoOfa3eaWy6/hbff9nbCT4XV90SuN5PJ4PV6lYFGziXv5fN5wuGwqmQfCoUYGBhQ92xhYUHloEtxPllEyoLBsixisZgKZZfcdklTkO+f5KJJmoG+KHk26F4bMXwsW7aMnTt3qoWJVJyXYnlPPPGEGr8Y4ebm5kxrMoPhZc65557bIqgFmcOdxj7nXC4iRy9WJoZ8eHpiSY6tez6d4la2AdpuA4eHs+vHl2vSPdqy5hF0D7ATp5jV92n3LG93jCOFazu9uLqBW96XY+oi21lwTTdA6Os/J85ztbvH7e6ts7Wb3D9n/rTcd+e9lRRBvT6AGDtkXaYbCpzGgnbpVO1C2uW69ePo0Q/C6OgoX/jCF9i0aRO/+tWv+Na3vsWuXbsOu18GwwuFEdxHmYsuuojFixfj9XpVVW8RsiJaA4EAyWRSLfqlFZUssvWHrxROkwnVsqyWUHJ5IEoYDqBEUbVaVWHp0hdbvJV63q0UdNMFvIRcA6oat+5BlfGKoBOhLftLKHkwGKS3t5dMJkMoFFJh0eVyWVUdF1HR09NDOp2mUCgQjUaVgJT+1lLBfWFhgWXLlpHNZpuRBJubLbXS5XTLIsMT9/DQ/3qISrxCw9vA+1ov8T+O4/F42Hf5Pna+vSm2AVInpeDwWjQK94/d+P7aR3oiTd8X+pivzTPUMUT/j/sJR8MqF10qpIt4qtfrqhhWJBIhFAoxNjZGX1+fEnZ6lU+ZFKUgmIjKYDDIqlWruPfee1V+uHzWEj7udrtJJpPMzMwow4kesq73CJ+amlIeZOnLLe3OgsEgqVRKiepisai+yxL+DajrTCaTNBoNRkdHGRgYYNH6RdxTvIfdHCp40l3sJlFMsGX7FuXhsG2bUqnEokWLsCyLrq4u1f7Mtm3VjiyXyzE4OMj09DRdXV0q3Nvv96tq4tKHXSqvS8syKewnHmTxbku1e/Fgl8tlZcyQ4zUaDVKplMot/G1ehnaI8cz52vz8PJZlqbSCYrGo/g9YlkU0GlX/j6TPezabfcbnNxgMLx0GBwc57rjj1N/OUGddrLQLnXamxegirVartbSldIZqy/nkPHJMXVTJvCtrAl1Yypyhe7qdIdPtwp2dXnNdnOti0+nJd3q42wnTdpFL+nn0Ymz6s9d5LOc91j8PHf0+6Z+ffg1yPuc1Os/pvH9Oz7n+WevviTNCjDP65ynI+WXu0iM1dQGvf9/06vH6tepRDiLi230++rXr7euc1+NyudiwYQMbNmzgggsu4MYbb+RrX/sa8/Pzh32WBsPRxsRdHEVisRgbN26ko6NDCS0RtHCoQIiE3Xq9XizLUiHE8XicaDRKKBRSBZ+kynij0SCXy6mCYyLSxHMtDzEpQCGTnuR2Ay39t91uN4FAgGAwSDKZJJ1O43a7lUCRMerF1SzLUj24JSwdDuXcAEoMiSfRsiyVWyuh3m63m1Qqpaqyi8dfBGoikcDlcjE7O0s0GiUcDpPP59m0aZMS6Hv27KFWq7GQX+DJ9zzJvjX78Af9Kje9Eqzwm2/8huyyLOXeMtWOKoUNBZ648gnGLxhn99t20wi2Wrq9eS8cnCM9ZQ+umgt/zo/vFz5CHw1RnWxWNncX3CT+PkHflX10Bjrp7OxUixWv10smkyEYDDI4OEgikaC3t5euri7y+bzyxk5PT5PNZlV1eJmsRIBLKoEYSHK5HFu3bm1JVZDiWxIuLu3IxDDR3d2tFkQzMzPkcjl1Tpkkg8EgmUyGubk59u7dq84n0QUS+iffTen9HQwGGRsb45xzzuG0006js7OTcrnMAw88wI3fu5GZt8/g+aEH6jCyc4TTv3o63jkv+/btU4YGMfosX75chdb7fD7S6TQ+n4/h4WESiQQdHR0Ui0WVliHfabG4S8E9SRWQHttyrdIaT4w18/PzeL1eNm/ezMzMjPo/kUgklMc5FAoRj8dZsWKFikp4puiLznPPPZfh4WFs2+bAgQMqjE+MGmI8kP9jEgVRLBbp6ek5YgimwWB4eXDKKaeoVCwJw5W5X37qIkUXNGLMk2eS7p2U/WVNoYeSA2puk2PBIREnz3A5n56jLcfWPe/6WPVj6kZn2VcXdM5z6Oj3wyn09XM4n+HtBJ9+Ll346VFO7eYCZ5628z45x6rfdxGlToNGu/G1O/eRIhKc90H/LPTQfP0zk9ByWds5+6DrRgU5jzPc3vkZ6tu0u6Z2nwlwmAHGuf/69ev5kz/5Ex555BHe//7309HR0bKPwXC0MYL7KHLCCSdw0UUXEQ6HVbsmqcoMMD8/r7xsIqIBZR3UvYkimqXNkTz8e3t7lTdZ/ukCRiyY0vtbxJNlWYRCIRWKLP2wZdtIJKIeauKRlWJt+gNYiqTJ9enjFyEPhyyV4XCY1atX43a7GRwcZPHixeo6JY9cQq1jsRjhcFi1EIvFYiwsLLDVvZV0Nc3y5cupVqukUqlmcTZPjV1v3cXkFZM8+YUn2bViFy6Xi1AoxJOfeZLicBH0uciC3FiOerTOqqtX4S4eejjHpmJc8n8uYfjJYfxZP2d+90zW3rqWt/312+j/g37KqXJL3lgtUaPgL6jFiVRnh+ZEk8vlmJubI5fLqZBxPXQsHo+r+ybV6iX8WLybIqx9Pp+qIC5VtaHVaiwh/tL+ze12k8lklEVZT3GQUPBCoaDCtAOBABs3blRV6EOhED09PUSjUYaGhli6dCnxeJwTTjiBvr4+Ojs7mZmZ4a677uLRRx/Ftm3Gx8dVaHej0KDz452Erg7x6V9+moQvobzMkpueSCSIx+MUi0XGx8fZtWsXuVyOzs5Okskk27ZtI5VKUSgUyGQyZDIZVek/mUyya9culZ8/Pj6uFnRSLK+np4eOjg58Pp+qSdDb26uMOfPz8wQCgZYCgxLOn8lkyOVyLCwsqJSJZ4Mshm677TbGx8cJh8PAoTw58WxLNEcikWDXrl1q8SXXIAXyDAbDy5Ozzz67xQvt9G4DKo3LWWRL/sn+lmXh9XrV9npbJ2cIsoh1Xfzq3lNdkOrpabKe0MWwnp/rFHuyzmgnvPVx/LZw6Hbh807Bqo9XQuv18+j3QBfGuqDXOZJ41EW60yPt3NcptPVxOj9v/f12XnonzmPJ56PnX8s24gQSw8tvCxt3RhPI+tPpcXdGSzg/X+fx9G3ldadhSfb3+Xx0dnbyxS9+kfvvv593vetdLFu2zAhvwwuCCSk/Svj9fk477TS6u7vVxBMIBFQFcMmZlYedPMykLzOgRJkIagnblv7EgKoqHQ6HmZuba7Zf0gpCSUVnmewkNFlyiSVfSxe94l2U30WEFYtFJSZlktWFve7x1vNLJTxNrMSSwy3eRRF4uVyO2dlZPB4P8XicfD6vxi+FtaYXTfObd/4G94ib0647DRcu5dWc/eQsE7/fbDeFBRNfnCD8j2FG7hppX1SjDqPfGGXpr5c2Ba3t5ZF3PELHTAdnfudMBlIDnHfleTy16imWPryUkTtHwIWKIpB75B5xk/5MGm/My9A3h3CVm5XA5b7XajV13bKPRDrofdXle+D1eltek3tbr9dVJetQKKQqiZdKJTo6OpiZmVEeYIkOkONJXrDk7Mv3QcS6WLrlPcuymJ+fZ2hoiJ6eHmUMyWQy7Nu3j61bt6oICb3NnPQcX1hYUPdJDAalUonGfQ0yQ5kWY4S0I5PzykQqrfM8Ho+qRp9IJAgGg9i2zcLCgioWp4voRqPB3r17VR50V1cXMzMzBINBZmZmGBgYUJEAco7Z2VmWLl3a7Kl+sIp5KBSiVquRSqWUgUJC1Z8tzgWxFO3TvU3d3d0sW7aMhx9+mO7ubqanp9X/00KhQE9Pz7POITcYDMc+iUSCtWvXHhbSreMMQdbFkFNIitByhpvrxR114a2LH10YtvOo68Zj2UfWOE5hrIc+S0FNGbcu9HUvvS7gnN5pOFQtXY7vDO/WvaVOI4PzWd8uNFw/ny4SneJaP5bz89LvlfOYgvM9+d0p3nUjhn69zvxp51ykH1Pek6Jv4kyRdEinocTpvda/R+0MHUdqY6ZXuG/nqdejAfRjyva6wO/v7+fLX/4ymzZt4tZbb+W//uu/TI634ahiPNxHiY6ODj74wQ/idruZmGiKQP0hIl5kQLXGgkMhO05rZjweJxwOEwqFsCxL5WXrFkKpei2LdxEiEsL+3e9+V4lqeV1EkUxeEr6ay+VUqK7uoZcQW7GE6w9cOZaMS0SfeNTFOytFtqRSuogx8fI2Gg0mJydxuZrF5bLZbDPXtWueO99zJ3NDc0y/fZpH//BRVUjL4/XAU62fgVWzcO1t5p53fa0LV6b16x/64xDrfr2OkZERQqEQa29fy6u+/CpOu/I04lviTXGVc9F1UzP8W1qoSR6tZVngh9mvzFK/pM70GdPc/4n7qTaqKt9dtw43Gg11HyuVCqVSSU1eEq4t90smEsmr1tuHiWjOZDIqTF+J/4OGESlmJ15wMZTI7/Id1EV5d3c3q1atoru7m0AgoML0i8UiCwsL7Nq1i+3btxMKhejs7GxZUEiUg7QPE2ODTIKWZVH5ZIXy/ynzw40/VIajyclJGo1m+zrxsIuXPpFI0NnZyfj4OF1dXarImxhugBYDlRR3k1oA/f39xONxFVUgiwVpMweoez0xMcGKFStIJBIqFF/qFsj3vFKptNQpeLboC5xisQigrqejo0MZZ/SwST3qRdIvDAbDy5PVq1fT3d3d4jGGVu+1M8dZF6S6ENLrwejrCV1wQ2sVbl3cyXPKKeh0T6fTGynzAxzKGZdziEde5iwZv2wr84Ne7Evek231sTiNBc7tnWOX13Svqy7wdHQh3W47pzh2nk8Xt/r9aieW9c/FeQz9fd3IIq/pn5tzHzmO8zvi/B6IANfnpXZja2eMcUYv6N9Dp1FBxirH1A01zs9INwbpxhl9/40bN/IXf/EXXHXVVXz2s5+lr68Pg+FoYDzcR4nXv/71Suh2dnZiWZbKlRWPtd5v0Ov1srCwQG9vr8rh9fl8yrsskxagipzZtq1yuGRy0ntxyrbi1X7ta19LMBhUorhcLqs8URFoUmhLhLWcR3pp27ZNMBhUnnpoFlETj6ZMprpIlCJtUoTN6/Wye/duXC6XKtxFCJU3Ds2HqxSqikajuCNurv/U9RR6D7aJcsHuV+1mYfcC/s/6adgNku9orSTeCDWovKPCydefTG9vL/5f+/nUxZ+iRg3roxb179apn1dndnZWjW3RY4uo1+sqxD8QCChxI72mRaR2dHSQuyFHZcMh8bPwigXu+/R9rPmzNcpwEgqFWLt2LbfffjuNRoN8Pk8sFlOLnHA4rArl6d5OPVxQBJhUspfQ43w+rwrYSQExEaESsi0edvnMxNDj8/lUXnggECCbzfLkk0+q71p/fz/lcplkMqkWXeFwmNnZWRWJoFu/Ja9f0gFSqVRz4rMa5N6Xo/HnDQjAw6c+TNAX5LIHL1OTZDweZ9GiRQSDQWZnZwmHw6rXdzqdpq+vj1AopKIvCoUCvb29AOo7tLCwoCIiJIKiWq2qauMej4c1a9YwNzfH/Pw8PT09+Hw+MpmM+j+qe1EqlQp+v19VLT9w4AAdHR0MDw+zd+9estnss3o2yPGdVnkxzulREFK9Xu93PzMzc1jeosFgePlw/PHH09PTo/4+UnizHlIt87J4m2XtoUc3ybwBh1pzwuH9oPUcbz28WveYy++yztH30d+Tc0uknJxHjiPrCr1Ypax35Jgi3vRjOj3IuiB+Oq/rHnFduOronmVdoMvPdsd1XrdzO3lPL7Cpe6JlnmpnONEjGPTP1il09TWj/ppEUtq2rdYVegSBOGFkO/l8nJ+/83vijCzQPeAyXt1woc+P+j3Xj68bHPTvlaxHZIyyzQknnMC6deu4/PLL+dKXvsT3vvc91bXHYHg+MB7uo0AoFOIv/uIvlFiTolXBYFC18rJtWxVwkt7CTzzxhHqwS9/gm2++WeVay0PU5/O1TIYiYuWf/rDy+/3Kcyo52CKMdC+7hOTatq3OJ/2OxeMXDAaJRCLqQWZZlgp9tm27pRe4eGzlYShebbknvb29yvjgeYWHHdfvgFXNXsv5fB6Xy8Xk5CQHDhygUChQy9Y44dMnEJxuendpANdA7mM55ufnmZ2ZxT7Phm0HPwQb1kyt4dN7P83atWspl8v0Znr5m5v/hrN/ejZL9i3B8lpKTIm3Vx7QtVpNtUmTya1cLjM+Pt6ycOl/Tz/uLYfyg3yP+TjzH89UXgMRiHfeeSf5fF7135bJToqipVIplWvt9/vVd0VyfLu6upR3N5fLqRBxQHnPxVOdy+VUNXufz0csFmN4eFhN4LK/FAuTKAER8DLJ7d27l2AwqCZumQhLpRK5XE6Fb8v3odFotgJbvHgx2Wy26Q1uVCi/q0zjn5piG6DhafDo2ke5w3+H+r6Fw2EWL17MokWLWr576XSanp4eJicnSSaTHDhwgMnJSfW5SYV+QLWHk4k3EonQ0dGhipy5XC7S6Wbl+q6uLrLZLKlUirm5OQKBALFYTP2fkG4BXq+Xzs5O4vE4g4ODqoihhOI/G/TFpBhDvF4vg4OD6v+nhLyL0SCbzare6vr/M4PB8PIiGAyydu1aNXfrbS+BFgEm85CkKelG/na5z/Js0nO85fkv/0QgynNLBKDMBTpOD7DMSbph2RmK7RSKUkNGT+M6ksjVx6cbH/QoMKdnWBfduie+nXdZ9hOc90wXgrK/UxDr19kO/Ri6V1h/v110gvN4+r1y3jc99Nvp7ZbXpOCuLtDL5XKzXo5W3Vzek/Ho45TrFGOJnkut3w89SkMPM9e92M5jOqM79DpC+vWLAUkiPoeGhvi7v/s7Nm/ezFvf+lYWL17csp/B8FxhBPdR4M1vfjO9vb1qQV0qlVr6F0r4rRQpg+aD9YwzzmgJLU8kErzpTW9SPZvL5TKpVEoVhpI84UKhoAQioMSAPilJyyH9waIv3OU9Eeu5XI5UKoXL1SyaJqHf8k8etKVSSVUil7AhvRCbPvnovcflPhRPLpL6TorKYIXH/uYximuKalKWNmky/u6Zbk77xml0zXYx9MshfO/xtUyskUwE7+97sR62WLdtHR+59SMUC81xBwIBctkcneOdLJlYQvqbaWqfrFEql1QROhGfLpdLVYkXES5h8XIt0BS6pWSJjnd2EN4UJrEpwbJPLIMaSpTJQ97r9apCdNPT07hcLtVaS7zZ8n2QcH4J/87n86qXtliZp6enD1toScV3mdQklDqTyTA5OamKkskE5ff71YTq9/vVOAOBgLpOvb2W1ADo7e1VuftSIR+aC8ElS5aoNm6Dg4OEE2GsV1stxeqC2SAX/+xiVu9erb6Xfr+fvXv3smfPHvx+P/l8nvn5efL5PLOzs6rGQKVSoVAokM1mW/pkS8SAx+NRhcXkOnt6elqMPYVCgcWLFzM1NdWykDtw4IDKC8/n88r6vW/fPvL5PNlsVkUUyD16tuieCfl/s2rVKuURkPuqG7f0/9NGcBsML08GBgZYvHixErm6J1CfD3UxCLSIIv35oYs1p5dUN6bq4cS611NEn/PYemqcLl71tDn9Nd1DKqJLX7PIc0/mVafXV4Sf07Ot76u/rgttfR51hmbL8XUR6BTdznuprxV0Qavv7zQa6DiNJs5t9M8bDuVE6wXs9BQAZ0i+eND18HBdnOvCXP+c9Urt+vdBR793ulh3picIzigCp7FE9/br99I5J+rec93Drxs+9M+9o6ODr3zlK3zzm9/kox/9KEuWLDnsczAYfheMGed5xuVy8da3vrXlP7aImVqtpry+8nCTBb9Manq/bHm46FWoRVTJJCf5vBJuXKlUlFCTB79YF8vlcktBNWgKbQln1tsSiXip1+tEo1Hy+bx6QEt4soSnS1E0EQmWZR12nXIPoBk6Ho1GmV0xy4637KDc3SywVlpWYssnt7DoU4voqHeoquq6kF8zv4ZF1y5i639vZbY+q96DpgD2POFh9RdXs2FwAyxGiSwJw5+OT3P9OdczPzwPn4at127luO8cpz4HqXo9OztLIpFQbbW8Xi/xeJzHH39cWfTlOrvSXfT/XT8d0Q4GegbweDwqLD+dTqsK8FIxXMKeq9WqqlqfyWTIZrNUKhUVsh8Oh3G5XKpKe6VSoVqtks/nW0LQi8UiXV1dyvsZCoVUGLx4d2Vi9Hq9RKNRlf+dyWTUhBcKhajX6yoqIx6P4/f7VbE1j8dDX1+fyuFPJBIARKNR0uk02WyWV7ziFWzevFmNtVat4f+EH7toU7+8jrvm5g3XvIHjJ4+nFjjUDk++d5lMhlgsxooVK9i7dy/T09P09/fTaDRrAoRCIdWmS+6JeNpFvIdCIZW7Ho1GicfjeL1elY/tcjWrmEu1cTF0iKdbjCR+v1+ldMj3J5/P09nZqc7fbrH0/7P352GWnWW5MH6vPc9D7V3z3NVz0t1JpzMPTUKCyHQIICIcJA44oSB8Inyg4kEUPAdBzk+PggzKOaIgKLMGhRCSAIHMU3en5655V+15Htfvj537qWe/vTuCwnc07Oe66qqqvdd617ve+X7uZ+gn/a7VB4ROp4N//ud/FsWO1vJTWcf+Jos/kIEM5EdPJicnsW3bth5ABZyfx9kEjhoI6/81Q8vPmD2FJuYmADLBO4G/CZo04NKizY1Ns3X9HO6zJAP6gTEN0lknE6T2+04DbrOumlTQoJzAU78Hf19IKWoy+LreJjDV/5v+8hr09ytH97kJNC8EUs13Zxn6ftM8XUew556rWXMNflmuycLzcyp0zCjiJpPN99LnGL6PHntaAaDv02OQ44n3XXXVVbjiiivw4z/+47jnnnvwoQ99CGtraxjIQP69MgDcP2T5sR/7MczMzAig4oSvVCrCtGrfXi5EBD96U/T7/SiVSsJ4MWURTcMJaAmMuRgzujJZZYJyAmL6perI5J1OB9VqVVjdWCwmDDiBOIEHF1QGfNJmvFxENTNMn2OClrm5ORQKBUTPRhF6KITMTZmu7YUNzByZwWhpFK6wS0x6qYSgKXb9njpGAiOwLEtM9MPhcDf4XLuJ03tO45qJa+BsbPkxVyoVlFDCx179MaSj6W5nOYBTLzwFV8uFy75wGQCITzv9oQnEtVKBbVAsFreCXD1aRcfbgXvMLf3n8/nE95bKDvYBQSxNzmkRwDbmdc1mN9f32toafD4fgsEgYrGYAEH6ZafTaWEDdPA0/k2zfu33DXQBOX2ttSaaSpLx8XEkk0mJ3p3P57G2tibByarVqjDChUIBjz/+OGKxGNbW1rb8u7IODL1jCMXhIm78zo2Yyc3APeLG2bNnJeK6ZVkoFApIJpNwu91YXl6W963X6ygWi+h0OmLeHQwGZY6Uy2Vks1mMjY1JnATGFWA/JZNJCTBHUMtnOhwOyXWfzWbleyowGFiNwQOLxeL3DLQpJguiRR8ieTAii0O/RprQU4HHoHwDGchAfnTE6XRix44dmJiYANDL+GmQRNH+sxqE6nXeBIMEpWQVqeTT1+tYKya72E8BoMvWCmuTNTUVBmYwN1NpoIFaP0Cu241yIQZbl9GPqTbBLq83o6Sb7WK2L6/V5tr8jPea9THBP9vHfI4OMqfBumlGb9aF+x0ttwhuaTFHhTbrrX22tb++qQzRZ1TdLhr8mgohfQ7RbaSVNBro8z5dJ3PM8D7twqXb0ul04sorr8ShQ4fw8pe/HH/2Z3+Gv/qrv0K5XP6+9/qBDIQyMCn/IYrH48ELXvACDA8PCzhmRGr6LXHSE/hQS10ul4W9tixL0j8RJFELyCji3HD0Qub3+yWYlvbdAiDMdiQSkbzFjApNUOp2u8VvudPpiH+5Xqzp46vzB5MNJCMLbPkWEaiS8WY5LpcLpdUSZt81i6GvDMFqWth31z4852vPQTKYRCgUQrlcFrPfSCTSNV8P1vDdt34XxxaOod1pC6gsl8uodWqwf9VG8S1FfPDVH8STsSeRTqexsrKCWCyGqfgUXvOl1yBU7Pq+wQZGvjuCbf9nmwBrWh1ks1kEg0Hxp2ZqLZrtO51OxGIxUZ64R91wDG0BJPo58b2Zx5l++zTV1+CKWttIJCJtyA1wYmJC+qJQKCCbzUq/0K+ePr6dTge1Wk18jjle+B3HEAB5XjgchsPhwPbt27Fr1y5Eo1EAwCOPPILbb78dd955Jx588EHce++9ePzxx1EoFCTQHscrGWpaJvDH6XTCzthI/mwSe9N7pR6rq6uo1+vY2NhAKpWSDZJm7KlUCuVyGWNjY8JMc3NdXV1FKpWS91haWsLGxoZEID979qxYaQSDQRQKBWQyGQSDQfGVbrVaWFpaQqFQwMzMDHw+n0QwZd/Ytt1jbcB5cskll4gi6/uVhYUFHDhwQP7XG7pWkPBA4Pf7kUwmMTU1hWQyibGxsfMOcAMZyECe+RIMBnHllVf2sJfaj1ULLXVMUKx9u03AagYg41mFz9GgT0cJJ/Ciz6wGVCZoBHpzP2vwSNcl/Te/06CTgUG1EkDnGzcVACZQ77d+mkDbBKz6/6cTEzj3e1Y/hQV/9NqvFQhsL22Kr99Fvyf7Q58VzSjz+n4dLFQrPliuNtPWZ1jzHpNlZr88HYOt20dbEeizJ7/je2hFzAMPPIBsNnteP2lWnMoCMts6UCmZe8aNmZubw7vf/W4cO3YMP/ETP4HJycnz6j+QgXwvMmC4f4hy2WWX4Yorrugx++Sm4HQ6BXzpQBeNRgNOZzePNtNENRoNFAoF8SlmZGouZFw8WJ42yXU4HJILm9fQDJcgnwsXzXmo7avVagLY+TyCQAJ7Mt0aeLIs7aPO1FQEZVpzSvYVACzbwsSbJ2C9zsLC7QvY8G2gUqmIRjIej4upcy1cw7de9C2c2XMGeA/gWnHB90Wf+Li73+pG491dU2obNj7wwg/geR97HubW53D//ffj2c9+NqZT03jBZ16AL73gS2je1cTeP90Lb2TLtJj1jcViYkkQj8cFgGmFSDAY7Ppxh2tIvTGFZDCJoT8fQqgV6vH3ImCjdUE2m0U4HJZ2Brqm71RmEHgyYNuOHTtw5MgRyXFtWRaSyaSAs0KhIOMiHA6LX302m0WxWBQLBioDOB5pWREOh9FoNLC6uooTJ06I6X86nRZfdG60TqdTwDgPQnRBsKyuCf1DDz3UAxzpItFqtLBjxw5JBcbx6/f74ff7MTo6iqGhIZk7Ohr+8PCwWI0UCgU5PDDY3OzsrBzCarUaRkdHhaXxeDySA31paQmTk5MS3I1R/u+9916pB+cBfdapKGM+bs4rv9//b2KaT5w4IYfJYDCIcDjcE3yI4y6ZTIpCi32ezWZx8cUX4/Tp09/3cwcykIH855ZAIIDLLrusB7ABvWBEiwl8+wE1/R0BGfdDDWq1j68GhQSBVL4S4JlsKkG7dgPTSgMN1nTUaQ0o+UOgpBX8fF+tfDABuBm1W7cDRSscTECpfcdNltwEh/3A9oUYeLM/tKJCK0p0HfX76nFgKlf4uTarJ/jUlgR0J9Pvrutm5kUHtqLI85oLxRHgM3Q/aAbatMTQrLVuN7PtHA4HDh06JOcobUbOd+L/jB2kFUZ6/On+bbVaiEQi+NCHPoQHH3wQf/M3f4OvfOUrOHPmTN++G8hA+skAcP+QxOPx4Morr8TMzIyAG+2bQn9rAhDtk0kGVC9oXBAJeMl2EkBwQSHYIZDis7jI6HRQeiHzeDzIZrM99QO6JtVkBvk/FzqamJvvZNu2pEiiuTrfi77rOjK60+lEKBRCIpHA4uJiF0z8cRL27u5iSkCn8zq7Ai589+e/i6XLl7oN7gJaf9pCPVyH9bFuUK5WpzdCqsPhwOjEKBZaCzh58qRsOMMPDGPyvkmsf3EdrdkuM0yftVKphEgkIqbZZEJppWD6z3pCHqT/exr1Z9exjGW0fW3s/YO90jaMtE3TZaYAs21bfp87dw5TU1NiWs6IshwHJ0+elE2Nlgc0dWZ/EZjR/FhHmOcY8nq9KJVKsqEwMjlBNfuaz6fPOYCeCOixWAw+nw+FQgEve9nL8MUvflEUMHos8BDGZ9duq2G1viqbI9lvHRyHqeGoJOJYiMViaDQaSKfTAoipiHE4HIjFYtJP+XweiURC+pDgmMqUpaUlBINBWFbXb51zKZFIAICMUwZpY9sCED/7RqOBZDKJdDp9wfWg3W6LGXo8HkepVJJgiU6nE5VKBfPz8xgbG+sG9XvKAgKAKBU4DqicaTabOHr06MDMbSAD+RGU+fl5zM3NAehlB7nf8nMNeDTAJoDRTC9BCq/XoJpBXDXo1EBMu7KZZr+6Lv3Wq34AUrPhJng0QWQ/JtoE6PqdTACpxTRxpuJbAzItZn31u5qm4qbvtP5tlqfBn/mOuu66fM2K62u0tYKpGNCAVitAzD7g9zqgLp+vFfe6zZ6uXgDOy8tu/m22ie4vvo8+YwPnA3heY1ph6POrBvm67jw3s/0OHTqESy65BC94wQtw77334sMf/jDW19cxkIH8azIwKf8hydjYGF7zmtfA4/GIDzXBMQ/ftVqtxw+GzBUXhUKhAADinwtAABHNdGlqTtBOQMhFjD7ewFbkcYIuAn6ag0ciEWHZLatrJk4mnODP4XD01R7SvJYR0ul/TDaVGkVqvfk5/WuZ+mt+fh6W08L6n6+j1WmJltXj8Uj6qVQqhWa1iW0PbAO4BtsA0kDnX57SrMMCPgD43u4DOoDVsfDaT78WC8cWxP+XOSRdLhdat7fQKXbBq9fr7ebpfkpZEI1GYVmW+NRvbGwIa0yWmSb6ax9cQ+GmgoyDtWet4ZHffgS1Wg25XA7lclkWdubEZluxr7iZsX2Zp5zicDhQrVYRiUQkANvY2Bjy+bzcx37VY4sbHqVWq2F4eBiWZSEcDve4OLRarZ7gealUSurDsca+ZiC4iYkJ3HXXXbJhsU1o/u7xeBCLxeAP+OH+BTdyb83hz1/658iX82KCblmWtH2pVEIwGMT09LQoFhj8TaciOXr0qIDPsbExicROIMx24NjnuKboGAiclx6PBxsbG8jlcjhz5gw2Njbg9/slZRn7nyDY5XJhenoagUAAoVAIl112GcbGxhCPx7Fr1y5cddVVeO5zn4s9e/Zg79692L17N/bu3Yvt27djcnISY2Nj2LNnD8LhMHbu3Il2u418Pn9efzWbTQQCAUSjUczOzmL79u3npd4ZyEAG8swXy7Jw/fXXi0uQBiQEDtxjNWNtAi4NMDTrqVljDRzpYsZrua5qE3ENUE0Ap5lrnnf4o12FtC80zw2sm34Hk1nX4NR8nr5e10dfr0HphUzHNZg3yzMDv/W7z6yHLl/XWyvz9f6t287sR/O9eb0Gm+wXDW5NpQsA2Vt027Nd+K76DEvLLPOdTCWHtlrQTLeunzlO9ZjR9eS1fD/NSpvuFbrePOvosW32A9tFtwPPbzfeeCN+/dd/HZ/73Ofwute9TtLyDWQgF5IB4P4hyf79+7Ft27Yekyz6HVM42XngByAgLBAIIJFIoN1uY2hoCKFQSBaner2OSqUioAOA+BTrSMvciAme6/X6eTm7yejZto1jx44JOMnn80in08KSEogQ4DHQV7vdRiaT6QGHtt3NDe12u4VV7HS6QdgIdHgvGcNQKNSNfu4rY+lTS8gdzuHut96NE5snBBwPDQ1JWxZyBUzfPY3LP3Q53HU3AqkAgtcGYS1ubQZ23Yb9fhue3/fg5X/6ckwuTgpjzHrHYjFYlgX/rB/lb5bRGO0GXCPL7fV6sba2hlqtJn2QTCZlk4hGo6jX6yiVStjc3ETkFyNwrm9tgt41L3a9a5dEAWd6KbalNmmjWTWZfJpA12o17Nq1S/ozl8uJqX+pVBJWnMxro9GQIHfU/LIfk8kkkskkgK7ZOoGjvtfj8UigMJr7k+nWmypjAvj9fuRyOZRKJWxsdCPF1+t1jIyMYG5uDl6vF1NTU93UNXYbledVcO63zsGO2FifWMffv+XvUQ/XxUScUdeHh4d7AvO1220J2JbJZCQH6MLCAjY3N+HxeLC2toZUKoV8Po9cLiebbq1Ww+LiIjqdjuSytu2uWWQ6nUapVAKw5ecYi8UQiUQkHR/T8AEQM/ahoSHZ3KmEuPbaa7F3714kEgns27cPs7OzGBsbQyAQQCQSQTQaFeWR0+lEJpNBp9NBOByW2APf/e53RRFERQ2fXywWsbq6ilqthkceeQSPP/44UqnUD3MpG8hABvIfUCzLwg033NBj1tuPOSYw0Sa6+n8CGA1wNdjgPgJsKe0JcDSg1i5KPMdooKtBlwZBmtXs5xeu2UZ9r7YY1O+h20GXYzKbFJOt7qeM4HUalPUD4+Z76PJ4DYG0Bua6DL6rGfTOZLt12/YDldpVkeXwudonWoNyEjW6vhqUaoUGLS21okV/zzqYQdT6gWYK93m+p1aWaGUMv2Nb8RzMsdfPAkCTQzyn8od11KCd9dGKJ1MJ4HK5sHv3brzzne/EI488gltvvRUjIyMDH++B9JUB4P4hiNPpxNve9raeSXvs2DExmaWPMaNSc0NhuipqkQEIOKRvDH1hCRwdjm5EZQ2ENjc3xRSVCxfBeKfTzS/MNFV8tsvlQiKRkOvIRnOx5QJFVt3n8wlI8/l8wnYCW6bDBIyss8fjwebmJkqlEpxOJ1KpFDY2NjAyMoL19XU8UXoCj73lMWR2ZAALyF+Tx+k3n0Yz3pRc1MlkUnxcy6Uy9nxzDw5/5TBe/N4Xw1F1nOc/5HK4MPKhEeAewO5s5fhkNPDFxUUUxgpdRn1XC9/+79/G0tiSfE+/cZryhkIh5PN5BAIB0bqTzQ2Hwwi1Qph59QyCx4IIPRHCJa+7BL6OT8A62eR4PA7LssSPqFqtYmVlRRbzZDIp4E+zqmzzYrGIcDgs44d9RlaYQJjm1Rw/9OXmOKWZvE6L4XA4RCkAdDeV8fFxFItFjI2N9VhtMAgZ0A3WFgwGEQgExJ95dXUVJ0+exOLiIlKpFIqtIvAr2HJmsYBCooBvjX0Lm5ubsG1bFEnavI0pvDKZjCgJqHRiILtisYjNzU1hyDOZDDY3N5HL5QBAAgRWq1VpA86ZSqUijPjq6ipyuZwAdmquS6WSmN3TtJ1m5gTSPITQlJ1xAJi7nn7hQ0NDYorPQwmDATLtGSOROxwO6WvGPtjc3JQAgZlM5ge7gA1kIAP5Dy8jIyO49NJLe84ZBE0ETgQbBBxAb25tigazJuDVaxQ/08DJBIVkqk0GV4M5E9SZdTJBMT/XAFK7HrEMDYx0HTWwejr2WV9jmpXz+aZioJ+iwGSrTeCu2133i75Hm7CbIFa7+bFvzTrp77RywGwDgkPNDOtMMyaYN+urRT+P12l3AN6jTc01i6/7xWxPDZp5nSa0TKWFNuXXbc+66WBrWrmhy9GB/Pr1Ef92u90YGRnBxz72MfzDP/wDXvOa12Bubq7nuoEMZAC4fwhyxRVXYNu2bQAgLG8+n+8Br1ozaNu2gOZwOCyLh/bBJujQWmX64nJx1OCLzwB6N9NisYhYLCa5irkJlstlJJNJAU0agHHzpL+x1+sVP3Ay13xParYff/xxSftFEFyv1zExMYHR0VG0222MjIxIvuhCoYClzBLS9V4fWOeQEw1HQxZtbgoejwehUAgulwtXf/dqJOoJATra9MjhcKB2Sw3WgoVUKoVMJiORpVutFjLRDG5/2e1Ibe+yhLWxGh7+9YdR3FOUQGQ6nRsDktFXmub37C/btuE660Lk1yPY/q7tiJajPdezXvl8Xhh0Aiumw/J6vaI0qVaraDabwmpWq1WJ2k0WPpvN9hw6ACCbzaJarcLhcKBSqSAQCGBoaAijo6PSb0xNZ463Tmcr13s0GoXL5RLmmsCP/epwOCQYG9NjFYtFySNer9eRSCS2fKNrTsz/zjy2Pd6dH46WAzd99iZcet+lwq5HIpGeSPlkqDc3N1EulyV2AOeS2+1GPB5Hp9NBIpGQscJo5IVCAcvLy6jVanLwI+Pv9/sxNjaGUCgkrgKhUEiUFcvLy1haWpJ2Zf5uHlKoxAIgViqWtcV4U1HFQ3C5XJb0e9zMfT4fisUistmsKGZs25Y0csPDw3C73T1/LywsYHZ2FjMzMz+4hWsgAxnIfxq57rrrejIjcE0kAOznY62vBXrZUBPkagBuBiQzWT/+D0CAOfckYAsomyC2H8DWzLV+Hw0WTYbZBPAaCGsQZQLjfuy2eQ3LNcEYxWxnzVpT9H0mCOz3TBM8mnU0r7Gs/sHpTFZWm4+bIJy/+WMCfh3Jm2Vo0NuvD0zgrOuk2063kTZb15YNvEa7J/xrY0m7Lup6mHNFM/gUPcZ1/AJdd83u87tLLrkE73//+/GBD3wAb3nLWzA8PIyBDAQYAO4firz97W+XQzeZ32c/+9k9ab7IYBOwad9YstVkEOv1+nkAneCBC4IG8bFYTPI9OxwOCXBFtlAHz+J3BCAEfdwwuXgzEFexWESlUhGmj+XQN50L8LZt2+RvRo6mJpDvatu2mEYHg0GMZEbw8n98Ocaz44ANBB8KYv4984jmo2LaxraiQoDt9E+v/Cc0O82ehb/dbqN6RRXpd6Vx+y/dDkfUgUgkIkx9vV5HrBVDfCne4wvuXfGieaL7rmbwDQYho6KCZu5sC6Brqu096oX/TDdIWiKRwNzcnNSXyguywKlUSkypGaWe7WRZXVadoD8cDksQN9u2RfFA6wLmCqdCRedJ73Q6kloO6ILyXC4n4HZ1dVXGXygUwtzcHBqNBorFIjKZDFqtFs6dO4fNzU2Mj49LH3LsuVwuFAoFyVXZ6XQwMzMjTGw2m+3WLR/Gc/7hOdi5uhPP+ehzsPCtBQmMRj9uBpADgHw+j06ng3K5LNH36TefSCSknWq1GlZXVyUHOccjTSJrtRqKxWJPG9A9olgsIhQKoVar9bhscJxRycFr6/U60uk08vk8FhYWMDw8jEgk0nPYokk4LTyKxSJOnz6NTCaDdDotaQA7nW4E9muvvRbxeFzmuU4TNzU1hVgshuHhYfGXn5ubw65du/oyDQMZyECe2XL99deLi5gGlgRLGkxoMVlCLWYZJjjUzCWF5xbuW1wnNUOq2WjWl8/vx8oCOO8e00dcm5OzTiQRTIbaZKFN4NwPHF8I1JttqdtRg0ddlv77QswtRde3nyJEs8Ua/PEZeiywD3T7UUmtzbVZPs8npgJA//Qzz9eAF+hNWcb6EigzBoAmRUxTbV0H3S9mv9u2LSTLhRQl2ldbKwF07m3T9F37bZvAnG1q5u7mnOMZ5sYbb8Qb3vAGfPKTn8Sv/MqvDHy8BzKIUv6DlsOHD2Nubk78YXW+Xpr7WlY3ly4XSZqdmuYqZNmOHj0Kl8uFHTt2yEE+FArB4/GgUqnIRHe73RJ5mWDc9Iex7a5/NhcMra3U/rKsB6NfZ7NZAfT0E6dfqV5I6vW6BJeqVCoIhUI9Gw7rR1M1Lr6xWAzbtm1D/ck6rvt/rsM//fo/4dK3XgpUgWasKSb3lmWJz3utVkNkLILPvvSzOH7pcVjfsOD9cS9am93FFZcBrc+3gACQtbP42Bs/hjf99ZvgbDglR3mgHcBNX7wJ+WYeZ284i8QTCVz9x1cjYAVQqBckRRb7icApHA6jXq9LLud2u5smKh6Po1qtIjgSRHQsilqxhnK5LHm8Cbq18iQYDKLRaGBkZAQ+nw+JRALFYrEnZ2o4HBazdubY9vv9cDqdknNydHQUfr8f+XwexWJR/JK9Xi/y+bz4JQeDQTGzpstCqVTC+Pg4ACAcDkvfOxwO5PN5eS+6FCwuLqJYLGJ4eBjFYlEUH2YKOPqdz83NIZPJdJUg1SqCuSB+7tM/hzPHziAY6VpOVL1VdBxdiwnmC69Wq3KoZJszjzuwdSizbRtDQ0Nixk2LgqmpKcnfHo/HBeiOj49L3u9KpYLTp0+LkoKWGvV6Hbt27cLm5iY2NzfhdDoRDoclYjpN08+dOyeRz3VgRKAb8HB8fBxerxeJRAKXXHIJAMh7LS8vI5FIiFn6+Pg4qtUqzp49i1gshk6ng2KxCMuy5PfExARWVlawsLAgoH8gAxnIj47EYjFce+21PX64BEImO9yPceT//dhYrWwnYKF7EwEFsGXRQ6V5rVY7LxiaBj46Dao+j2jLKg2YNGgmGDKVB5qdBLbSfPE99Hvzc+2iZ4JZ/WyeobT0K1uLqZzoB5Z1vU1wyN/6c7NM01zeZPFNgK/bp1/bsXytJNH3a5Btgm+SMSbY5hmR92hlihlYjWBYxxwyx6xuf9ZRA2dNiuh2JqlkKpnMs7F2wWDbkIzitaaSRrehtr7Ucy4YDOLAgQPYt28f3vCGN+A3f/M38Y1vfENIhIH8aMmAGvkBisvlwq233opYLAaXyyVmtGSpl5aWeqKUk2Wjr6wOlMXyKpUKtm3bhm3btsHlcgkIJ3tOFkwHYNMbBjdKoLsx+nw+AetceBjxmn65XMy8Xi8mJiZEI0rTcubkJvhjqiVu/LVaDZlMBqdPn4bP5xMNtd74LKubcotmPM1mE0eOHEE6nUbcHcfQi4YQsAJiKux0OrGysiJtVK/X0fA3cPstt+PIZUcAB2BfYqPx1w0455xwOB3AHwAIPNU5FlAIFnD73tth27ZoITudDqr5KmJvisHzvz048KYDcDa6uZuZ6oog2eFwSGR39h/N65mOrdPpwI7YOPPLZ/DQSx5Cup6WhZtsqd7ICcQajYaAumw2i1KpJJsIWfVAICDpogj8Nzc30Ww2xVRcB9IbGRmRVGQM7sa6cGPZsWMHrrnmGuzfvx+RSERAeigUEnaboJuKo1qtJlHl3W43RkdH5fpms4l8Pi/tu7q6inA4DI/Hg+HhYcTj8S6gnWx03QVqXbPxs52zyHwwg85rOihXylheXsbGxoakxtJMAg+XmUwGqVRKgP7u3buRz+fFXJsHQkb7TqVSEiV+eXkZm5ubEoiMvvSXXXaZjEsA2NjYgMvlwuTkpCg4/H4/3G43otEotm/fLmN87969mJ6exs6dOzE7O4u5uTnMzs5KX3HjLpVKCAQCmJyc7CofgkFUKhVks1npI86Z4eFhtFothMNh7NixA6Ojo9i2bZv4zfdjXQYykIE8s2X//v2IRqM9DKYGWiYzq4GF/oygWJuGA/2DX9G9TLt3aSDH72hdZJoNazZQs5omGNTnGdNn21RW9/O3vhAzzLapVqtYWlqSva4f6Nb+vVo0oNNtbJq5AzhP4cB27Kc4YJm6nvq3+Q5mX2tG2GxvzcA+XVtpc3MzerdpsWD2J+uigamp8Oi3V+lxp+tgWiPwTGP6WmsTeL67Zqq12TffQc8Vnj3NOaLPtzxv6H7i2ULnotdBA3k/Qb3f78f4+Dg++tGP4vOf/zxe+tKXYmZmZmCd9iMmA4b7Byh79uzBZZddJqBMm6t6PB7s3btX/J3JcBOE0XwcgGj6zLzEtVoNgUCgR5sWjUbP82vSG5a5OLI8puIiSCdoJtCmya7D4ZAAaQQxZO9pQk2QztRTzWYTw8PDElEa6OYrZvqQfD4vaah0CqyJiQkJalXz1HDyipOY+9acPH9+fr4LkJ/yY25H28hFcz190BnqoDXUAk4B1issOD7kQPtlbcAGrr/zehy+6zAs59YGQCsEr9cLx6840Llqi2GlppZKAQImguxoNIrR0VFks1npv7azjc23bKL88jLKKMPhd2D7n26H1bB6zJyZr5ybTSgUEvaZ/UxwzP5PJpNYX1+Xtg4EAigWiyiXy6hUKnA4HAIeGRCNqd6o6WW0bAbEO3LkCFqtFrZv345isShm0doMkGM5FApJkLD2rW247+r6M9Nqg0x4IpFA9XAV49lxOJa2NMWxWAzNZhPenV586tmfwmx0Fjev3Iyqu4r7XnofitNF4HKgM9mB/ZWtw1ahUBDFko4hEAqFxHw8n8/j7NmzACAb3ObmpmzCDocD5XJZ5off78fk5CRqtRr279+PkydPIpfL4YEHHkAoFOqxCgiFQpiamhK/9Ww2i0QigY2NDaytrcHj8UhcAprCV6tVORgw5zfzgWezWfh8Pun3arWKiy++WJQsDN7mcHR9yDmHq9UqEomEpI/rdDriOjKQgQzkR0cuvvhicY8C+oNGk/XUgFArwE0gpANQaVeqXC7XE6wVOD8fN/cLHWiK19FsF0Bf8PZ07LEJXIEtsNbvHS7EhgNdCy5a3plssFmG+Xc/1rrf9ea7mwC1X3lUZLCuGhib5ZkAsB8rrO8x66tN8FnehRhtrYThOUdnKjEVHBoU6+ebhIvuF329Pn8QtOqxQlBsKn/MfuJ5WgccpjJBK4N0n5h145mPSnDOHR05Xc5+CmjzuRS6OViWhX379uEDH/gA7rnnHnzjG9/AJz/5yUGmkR8RGQDuH5BYloVLLrkEu3btQigUEpNaLhw0tWWwMa/XK2avZKG1ho4LC82YmSqJJrU0Y9agXZvI0CxWm5fpQGjNZhNer1euobaXJslkkbnoMZWR3++X1FVcuKrVKkqlEoaGhmSRI3gn66o3FDKEBOOWZSEej2NjYwMOhwNerxfFjxSR3ZdFJBTB1D1TWF1dxezsrNwXiURQSVdw2YcvwyPhR5C6OAWcBZy3OWE9bqHdacPKWfD+phcNVwOHWodw8L6DcAS2olqm02n4/X4UCoVuu33QRvsjbWG3NSiqVCqyIDPIVbPZRDQa7TFZyr4/i/qtdRkXK7euAAFgxx/uQLlcFmVLoVAQEHr69GnJYb5t2zYJlKbTr2SzWQwPD0uEdLLN1Kqurq5idHRUxhNBqfbhJnAeGxvDysoKUqkU6vW6MOcOh0PM0x/76ceQ/F9JWJYl6cuY2sx6iYXyu8twPunEjt/cAXTQE4Xd+xwvjrz1CNZL67j2t65FKVUSs/BMI4MH3/YgcjtyWMc60q9Mo4EGFicXuw3mAPK/mUd6Oo2RL4zIuOF7VCoVjI6OCsu+traGcrksmy8Drq2ursLlcqHZbGLfvn04deoUpqenUS6X0Wq1UK1WMTk5iVwuh5GREdTrdaRSKUxNTaFeryMejyOTyWBkZAQAxDyefoqtVguVSgW5XA7JZBKxWAypVEoiynOux2IxeL1ehMNhpNNpBINBRCIR+Hw+pFIpmfOcfww8x9zvVKS4XC4MDQ2JtcXo6Cg6nQ5Onz7dlwUZyEAG8syUQCCA/fv3S7wVgjMCDw2iTPNjHT3cBDEaYJvsONfSjY0NTE1N9QRB0z+aNdXR0oH+JtPmd1zvLwSEdb35P7Bl2st7tIlwP5bblAt9Zt6rgZp+rqksMMt9urr0UzboewCc56Nslt2vrQgEWVey9ibLrfua8UZ47uDvfqm92M/9LBX6KTO0Oby28NTtrOvB7/U78rdWGPFa/S5U7rAOHMOmwkSPV+1eod+Hc4njnW1JIkG/o1aGsM+01QPP+T6fD4cPH8aVV16JW265Bbfffjv+6q/+Ss4BA3lmygBw/4BkfHwcL3nJS4SJJitL0ETGimbdTNtFRrher8shm8GaqN2juXa5XEYgEBAw22w2ZYEEuqCAKaEYYIzAGADK5bI8kwGoyIQy2nKn05G/uUgxMjejU3PBajQaYnZNn1v6BNOvhr6pLIPvxPzRZC1zuVyXsayk8fe/8PeoXFwBLOChX30IVtGCM9Vd8Ah+CdbjxThe8JEX4Atv+AIy12bg3HSigy1fHitlIfnmJEYOjKAQL6Be6vqkb25uIhKJYHh4GE1HEyu/v4L6DXU8sv8RHPh/D4jG0uv1IpfLyULd6XSwubmJ4eFhiarNhdbtdiP4ziDqt9SBcHdcuMtu7PvkPmSrWUSjUQSDQVFoAF0/uHQ6jfHxcQFxk5OTSKfTOH36tPRHMplEPp+XIHW0SGBatkKhIGOjXq/DcljYbG4i5ooh5oqJNQL7sRPtAH8HdF605UfncDhgeSys/PIKVl++isx1Gey7bR9isRiGhobQaDbQuraFu958F+yQjc5wB8f/4jhufM+NaOQbCAQDyIxn8OjvPYpWsIXaSA13/OEdOPS6Q2hUuvV69E8eRXF7UebNyYmTsIoWrJoF22cDNhBYCmD2H2eRTnfN8anUsW1b8lVbVje92vbt2zE6Oop8Pg+n0ymmjLRQIDu+bds2JJNJrK2todPpYHZ2FktLS4hGozJG4/G4WAjMzs6iUCgIi0RzecYeOH36NAqFAtbW1sRyg6zz0NAQms0mzp0718Mmud1uMbfP5/PiYkIlGtlyMuz02Z6fn0ehUJB4CUtLSwC6G3o63RvVfyADGcgzWyYnJ7GwsCBACOhlCQkW+L8GGZrhNkGRBuBAL6h0OBwYHR2VIKEUgmOa5ZoAksCHZxENQFgnmuxqgKLZcA06dR01cNSiQbv+MRnmfiy1bgtdnmZRdZ20655uMxPUsVxTeaAVB0/HSJtg2wTAunzu52wjHWTMbCfdzrxOuxb0nA3U8zR4NsGyro/JPpvlapcBs836jWUNqPW5y1TiaGWEVkaZFp96XuixScU2mel2uy1kkna75DmbZ2UdwI0An+WakeGJB6688kpceuml+Jmf+Rm8853vxB133CHn44E8s2TgQPADkj179uDmm2+WhZ65tunfy0lPpphRj8l+l0olWSR5uGdKqmaziWAwKCnDOIG9Xi+CwaCYf/Ngr/2LCM7JANKfOxKJiCKATHqr1ZIFhL7dlUoFkUhEFkqaytIcnD6ooVAITqdTmFrLskQDyGdwoWPe5Hg8LubIo6OjcLlcOHLdEaQWUsBTe0Pb38bxnzuOirObwiqXy8G2bSwuLorvrL/px8/82c9guDPcEw2d2s/yehnNHU24/W5MTk6KCbDH40HZVcb9r7wf5248B7iA/IE8jr3zGHK+HKrVqrDAFK/Xi5GREVl0x8bGpN1zuRxcqy7EfywOz1kPfGd8uOIXr0BrqSUmzVROMPhcs9nE7OysmEqTvW2324jFYsKGLy8vS5osy+qmOMMw0Bx5KnhcJCIRym3bxsaBDXzzS9/Eg7/6ICqBivgd1Wo1ZEYyKH6jCFwPBG4PYHj/MHw+H5KTSUTeFcHKz6zAdtuozdRw9M+PwrngRDQahX/Ij4d++iHYoac2AgvIL+Rx9JajCAaDqNfr+M7PfAetYEu+L42XcPJVJyXI3hVvuwLh02Fpz8QTCey5Zg/2vnMvAoUAJpcncehXDiHmiCEWi2FhYQFDQ0PYsWMHtm/fjvHxcczPz+Piiy/GwYMHsXPnTvj9flx66aWYnp6W9FmRSASJRALz8/OIRCKIx+MAgKGhIYkHQNBOs3TONx4qA4EA8vk8IpGIWJqUSiWxNtmxYweArtaaCicqhIrFIqLRqPjUN5tNiTafyWR65h4Z90wmg7Nnz4ofOjMBOJ1ObG5uyjPIwDM3+mBjHshAfnRkbm4OMzMzPSy2Fp4BNBjXAIeKdhOQ6z2aljymDzX3OgpBhPbvZhk8k2jGzwS62u+Y1/J6/Tew5SNu1oXvYJqxmyymZsMvxDibQMyM4q1FtxX/vxBY1yyvqeToVwcTnOvyNcDvB+z5jrrdeV8/33j2GYFiv3GkfbhZL21BwTOh7i+T9TbfWbexbhfzPv25qaQwAbp2Z9Dm7pqZNttcu2FyXGlXR7aldu/kM7VSyCxX/88ytKLI5XKJ9WAwGMTCwgI+/vGP46677sKLXvQiTE1Nndd+A/nPLQOG+wcgHo8Hr3vd62RhaLfb4qNLgEvml1G/vV6vMFs0bx4ZGRFWtd1uywGd+Zjpm2r6SVHDLEG7jIXL4dhKVeByuSSXMc1hmN6JgJlAnf6oZEXJvAMQxtHcPJiujOy+DnahNe00M9eb7cjICG546AY4HA586yXfQsfVQeKJBC7/88vhiDgkuBzBLv3hAeDRg4/C+3de7NmzB0888YQssO12G61rW/jKL30Fhx87jPEHxuWeVquFdqSN1cSqAHxYQC6aw6Z/E5PZSQCQyOq8R5uc0we8UCiIf3vrWAtDbxpCxBuBc9kJ22WLGbzOkU5FiW13c1dTi0rtamFHN5f1lVNXyjigmbtr2IXvvOI7qLqruPRDl6KZ6iplWq0WFq9cxPG3HAcsYPXGVTxoPYhdf7oLzrITlR0VPPxrD6M+1lUi5K/Mw/pdC7H3xWB3bCxPLPe0RSPaQHW2ivrROvwuP3a/bTee+I0nULq8BLSBnZ/YiZlPzyBVT8HhcGDP/7sHJ3/rJDau2gBsYOfnd2LX3+yCFejGG/D5fLjmvdfgwV97EP6GH9d/5HqEbwijudHEti9uw9zpOeTGu0qVoaEhhMNhOBwObGxsiL94IBBAJpORvOScU1rJs7S01PUlr1YBAOfOncPIyIiYbF1zzTX47ne/C4fDgaWlJVEcUUFEJppRzBlhXluGZLNZXHnllWg0Gjh37lzXcuCpzTQajYrfYzablcB17XYbExMTspkPDw+LFYvT6UQ8HkckEpFc4FS+VatVnDlzBhMTE3IgYG7ygQxkID8a4nK5sHPnTllD+jGNQC+rqoGVZvt0MClgy2/YvEcDWa5bGuBoAMhzhL6H4EWDV13XfvXQZwvNevMZ+nvWhWcdiukv3o+NpZgm6RcC03xePybVLFMztGa/mNfoMvRnOl6P/k4z+/0Y7n510f1kBs+j6POjmb5Nl8uxx7bTbgBauaGDmem+5zjhc7RPuanU0XXTz9T9QEtPncpWv7O2UOTfZv34P1l/bSqvAbluT51rXrt3crzrfmK5xAQkQfhcum3Mzs7iox/9KL7+9a/jjjvuwN/93d9hfX39vPYYyH8+GQDuH4Bs374dhw8f7tnMaEJNcGdZW7m36dsEQJjPAwcO9KQh0DkACYTJBtPMVpuq0JyF9+jUWfSZJqvGDZELFKVWq4lvSiaTEZ/YUqkkiwHNbAiYfT6fKBIACCvIFF6M0K79h3w+n7QD28DhcKBUKqHZbOKyey7Dkw8+idptNex53x5ES1EsF5YxMTEhvsTJZBKRSAR+vx8P3/Aw7n3hvYi2o8DbDD+oqyzUPlCDPWvjzsk74Qg6cN2d1wHoLnjxfBy3fOoWfO7WzyGzOwPfqg8737MT0VNR1FxdBUM0GoXT2c0TvbGxIYsmmXy+MwBRMrjvccM74oVv3Cfm3E5nlymemZlBJpPBqWtPIXdDDof+5BBa2S2W07ZtFCeKWHz9ItobbRT+sIDmYpd5DQQCKFVKeOA3HkDqim6gjUfjj+Ly370cjVoD6zeu4+QvnUTHv7XQn33WWZRdZUz/z2mcesMp5Lf1ppHyFD2o5qsIOALY9kfbYMHC5nWbcJad2PWuXYg9HkPBUeheXAIO/vlBHIkewczdM7D/l43k3qQoYJLuJLZ9dhu+Hf02Ek8mcPFXLoZ3oqtkYHq5WCaGKz5yBeyijag7Cn/Qj/X1dex6cBfq9TparRZisRiArQjj7XYbo6OjaLVaSKfTopDixkYT+0AgIIHNOG84D9bX16XvcrkcIpGIpHjjYYBzqN1uo1wuIxKJSCA/Mjm1Wg3hcBipVEp817mxMuhdPp+XNF9UwDFSutfrlSB5nJvFYlGUCTQzp0n7+vo64vE4ms0mcrmcpBqkQmAgAxnIj4aEw2EcOHCgBwjyIK+jKZssqAYf/Zg4/b0Gl5qVNsEc920NMjRwNkGzBrU8A2m/b9OHls/X5tP6twk6NZjWzzdBUj+LAPMeimlKborJYmoxGWETAJvlXKh8U0lh/m8CWC3m+5jvqttGM8j8XFssaACqz426Xixbuy7o8nmNHqd8jtl/ZrnafF+PU5ZnKkpse4vRByBnXz0O+U68XperTd5JcrFPdfn6vM7rtMKK78J7SRhppZT+nO1200034fDhw7jllltw55134iMf+cggBeh/chnYK/wA5Nd+7dcAQECmbXcjEw8NDckkJZjkQZ5sKCMnA1smKjQ9L5fLwjSHw2E59NNHmuwoFxGaWDPIVKvVkoN+vV6XZ/l8PsTjcdi2Lf7khUJBfLRrtRri8Tji8TgCgYBEiLZtWwAJIyU3Gg2kUilhG8vlco8mnSCFwIdpmPieqVQKq6urcs3ExARarRaGPj+E0Z8dhe/slt94rVbD2tqaRNz2+Dx47OrH8O3/8m00/U1svmwTZ994FnA95Y80CTT/tgl751OHB1cbd159J7516FsIh8PSft5zXuz77X3wnfBh9GWjmNmYEeUGF1PWOxqNSlt3Oh20O21MLUwBgDCgbq8bFbuC079+GpUdFdz1W3eh3qmLGf/Zc2exevkqnviVJ7B4xSLu+L074Al3fY9rtRrK/jLu+J07UJgvwL7Cxnf/+LvwxDzIZrM4s3wGX3/L15G6fCuq5ebFm/jWf/sWSqUSciM5tGKtnvFpNS0M/cUQvOte+D7vAxg8swNE/ymKiT+aQBzx7kFpGZj6nSnEHo7hqjddhfhjXbP/YDAoLgSBlQCuf9/12HPXHuzauUvag6bcoWwIN37sRhz82kHMT81L1H2Ox0QigR2dHZiyprqR/O02Nl+4iXPXnsPy6jJKpZJECbcsC2tra/D7/ZIejD/FYtcX3Ol0YnFxUVhpp9OJWCwGy7IQiUSEJU8kEhgbG4PP58OxY8dk3sXjcczOziKRSEiQs5mZGdkgqTQLhUIIBAKisIrFYhKEzuv1ymaby+XENz8YDEr0fZ/PB7fbLdHIw+GwtA3TCEYiEQngZttdywf6bJdKJYyMjKBWq6FYLEr6s4EMZCA/GhKJRHDZZZcB2AJ7POBrllGbzpp+xSaQYfRmDWo0YOBvxgvhmsXn6OBatL7RwIbgmq41ZAq1ua5mWvk5gY7+4flKAylNTvCcwXMRWVr9o0UztaZywLQKMO/TQFB/RjHBrfYPNtnift+bz9WuguZn/FuzyxT2halYMQONaUCpLRhMFhzAeUoVrTTRLDTbQUcU78eqaxac95jvRzEBszl+OCbIOPN6BizTfafL1Ey2Hg+6z/W7sP30ONZKLa0MY320woDjmfUnKca+t6xusOJrr70Wb3nLW/DVr34VL3nJSyRY4kD+88kAcP87ZXZ2FjfddBP8fj8ACIijySfNQd1ut0RI5iLAjS4UCp23WKXTaZm4XNzcbjfuuecefPGLX0S1Wu1hiPUGS59xMmcEjdosmibmlUpFFhIdFZILChk1LjRktOmD/pd/+ZcS5drr9Qqby8WNSgYuJPq9CewXFhbgdrsxMTGBVCqFZDKJQDiAzfomNjY2sL6+Do/Hg+XlZcnLHQwGsRZfw7GfOIaW9ymA6QHwKqD9/K5WHCuA6+ddwFPWOFbbwvw35jH8yWEsLS3B4XAgnU5302qdrcDeZ8O17pII5HznjY0NUVg00cSqfxUbGxsol8tYmV/Bd9/3XQR3BrtjwAF0XtFBcbWI0s+UcPf/vBu5y3M48Z4TSHVSKBaLOLPrDB5/1+NoB9tdE/bZHO783TtRiVaw7FvGV97/FdTiNemL5mQTx36zCxDPvuIsshdlt8y+AXjOerDrjbvQ6XSw/W+2Y+xzY7Ba3Qs8WQ8u/s2LMfTkEDzwYNdnd2H6r6fhqDuQuCeBA+85gPnEPMbHxxEMdt8h6Uzi5j+4GaGzIcmhzUBes7OzmJ6eRrASRLPa7PFrIhCt1+uI23G0K91811ToUGPcbDYlOFmtUcO98/fiH1/2j/jUf/kU6j9ex/ETxyX/99LSklgXtFotBAIByQHOZ9H0nJYTVABsbnbHTzQaRSKRgNvdTWM2NjaGfD4v5uJUvpAdr9VqSKfTCIfDiEajEqzQ7XZjfn4ePp8Pfr+/q3wIBCRo2tDQEKrVqgQ/BLoHhnw+L+bl8Xgc27dvRzabFZY6n88jFAphYmICgUAAR48eFWUW15NoNIpoNIrNzU2USiVks1l514EMZCA/GjI1NYU9e/b0AL1+zKcGjRpsakAHQICXZhw7nY4EatXrmC6fMS00SOZ1GqBrtyl9HwGIBmW6jjzTaGCiwZj+0YCLz9PKBgIe3VZ8V36mwa/+TtfRBH4UDRa1m58uRz+nX5/pttXvpZlf3acmu63rofvDBJYUrXww2V7d19oc23x2PyCs/ex5HfsagPS/2Qa6TqaSg+NHW1NoJYdWwpsKJw1cNROuwS/rotl7vitJIlqVEpBTqaPHNs3D2TamMobP4P1a0aCVHabShGeTPXv24OMf/zjuvfde3HzzzRgfHx/s///JZNBb/0559atfLaxTu90W1olAmybdtm2LDy/Ns4vFouRwJnPc6XTER5hCYFCv13HVVVfhhS98IRwOh0Tr5oLGCRsIBGQxoO+pw+EQ31OygAQGDG5C81SHwyFpzZjnlyCZ/ieMyPya17wGwFbwlE6nI+CKGyXBERe8U2dP4Z6xe6Qs+sVmMhkkEgnU6jVkX51F/W/raB9oI5lMwuPxYHZ2FqVSCRsbGwCAufIcrv7zqxFZiwAArKwF11tcwN8/tZjbgP3PNhy/6gBWgUvuuQTP/odnY2J8AgcPHhRLBCoMYG+ZsFEjX7SKWHnRCtZHuqj9/mvvx0O/+xCy81kUDxdxx1vuQOvSFlofamH+pnm4X+tG9gPZ7syyIL/Xr1zHgz/7II4+6yi+9dZv9c48C2iONVHYWcD6wXW0fb2sZfizYSz8zgIcDgem/nIKs38zKyx1+NEw9r9lP6KObgT0cCiM/X++H/O3z8Oz6cFFf3IRhh8ZFn/8ZqOJ7X+5HTv+bgcOvecQwqEwMpkM1tfXEYvFtmIINNvi/8xNgf7GmUwGrVZLAqXRmoLpuSKRiIwZmmNzvI6Pj4t1hNvtxpnDZ/DNX/lmt60s4Eu3fQnB13bNskulkpiNM6f2vn374HA4hLUGgI2NDcRiMfFhZ/24UdZqNVEmMHVdIBCA3+8Xs25uqEzpR5NwZhfQqeCCwWA3yFwyiUQiIfc0Go2uwiKZFIa8Wq1iaWlJrESCwSCWlpYkjR7nDtsrl8tJfIBsNotwuBtgLp1OC4MTj8eFwe93mBrIQAbyzBOHw4HDhw+fBxQ1oNZAU4MQDSi0cE/mj2aTgS1QZZr8akCmTcoJqLQywOl0SuwRlqUDsunrNDPeb23Tn+no52ZZvPZC720y/frzfqbtJoNrti3rq8GkVgaYjKQG5Pr5F3pvfmcCev1uJsDTrK1+jwsBXd1WZhtqX33dLrodNDNrvr9uJ17PM2U/U3F9n2aNzX7VbaZZeVPxoKWfFYDuDx14l0ojYMuSQrsK6PtoWdqP4dd/U/Qc0UodDcR5jY7ZND8/j09/+tP44Ac/iNtuuw1jY2N9x8tA/uPJwIf73yELCwu46aabeiYwNxf6kOr0WOFwWBhf+rTSPJpm016vF4uLi/j7v/97vO51r5N7A4FAj6YW2DKt4eZI7SBNtni9uRn5/X4BJFzk2u02KpVKD4PIA74OpqY101QocEHi39pkh3UjkGu1WvjiDV/EkcuPoH5/HTvu2SGRvhn9+cHnPYhz151Dx9HBybeexPCfDsN9vGtu4/f7xSc8n8/D+y0vDvsO4+u/8HVMvW8Kxz9+XNqCpvnWly3su3gfhh8Zxuj+UWmTYrGI8fFxFK4p4NiLjqG11oLvj7qKjmKxiEAogKNvOIrUjSlEj0axvryOpcNLsF02Hvm1R1CL1YRdL19bxso7V1DYU+hhn7V4HV6sn1s/b0N1VpzY/4H9SBxNoPFIAyP+Edz/q/cDFrDrjl0Y+cgIEqMJlEolOBwO7PzUTgSsAM5cfgb7/uc+jLXGgBB68rTv//h+jNw3gtEHR1F2lGUsptNpeL1eHPzyQdR93WBj+XxeFDNkim3bRjAYRLPZ7PErnpycxMbGBoLB4HnB/MiK8BDG/i4WixgbG0O1WsXm5qa4L2SzWWwsbfS0hQUL8UBcLEaY6s62bYyMjOAzn/kMxsbGRJHldHZz1BeLRfHJpikZ3SH8fr9sXvl8voedicViaDabKJfLiMfjYja/sbGBkZGRnoBpnU4HGxsbknqMijCfz4fh4WGUSiVhpRllfGFhQTTmlUoFm5ubcLvdSCaTmJiYAACcPHkS6+vrSCQS8Pv9Mj/5DLqT0K+d68b6+nqPVn0gAxnIM1ccDgduvPHG88CTBiAaBGrwQ9Gsnsnq9mNgNbDQwEaDJe6nGnjxrEBArOvHdyHQ0Pfo//spC/T7muAS6GXi9Tvybw0+9ftoAGmCMRN0689MsKfbWdfR7IN+DLW+jvuobhMTKD4ds90PYOtxpIFdv/fs987aekH3x4WUCryfgcx0nc021GPVNBnXbWmy1BpQ9+tDM7Vcv7GulUdaoaHHpGa3zXGon6MDALJt9HNNdwLdl+ZYZaBl1kFHkaeP9zXXXIPnP//5uPvuu/GRj3wEhULhvD4YyH8cGQDuf4c85znPwcGDB8UMlSwZ2T9gazGhuS2wld5CL5Isw+PxoN1u46abbhKW2Aw4ZprTcMNjmikG89L5f7UZOCOI85mczFwcmIeb33HzJADn4qODgel0YsxnzFzkNPVyeVz4yMUfwQM7HkDb2cYnDn0CL1x5IbY/sh3xWBznzp3D4isXcc9V96Dj6L5jdU8V9779Xlzz+mtQr9fRbrel/Fwuh3A4jJGjI7jtY7fh7i/fLW3MtvN4PGi0Gjh66VEce8kx7Pj4DkRD3WjRsXgMS2NL+OJLv4hisAh0gOXpZfje6oM/4Mfj73wc6eu6eY7zu/PI794KWJGdz/YOhg4w/+A8js4eRdVXFdBttS3YDhuBrwQw9/45uIouuH/fjbvfcXf3e9vCLX94C1yPulBpVTA6OgrvP3pxsHEQqUtSmP3zWfjdXQCWSCQk93bkqxGMfX0M7bNt+CZ8stDTOqJWq2H+2Dza3m6/h0IhbG5uYmRkpCfASKlUEqUQASzZapbTaDTQaDQwNDSESqWCUCjUDQz3FHBstVrw+XyIRCLI5/NIpVIYGhqCx+NBqVSCZVkol8u46KKLcPz4cQGs2WwWk3dNYu3cGpb/aBk2bDznz56DyDcicMa742h9fR2NRqOrAAkEMDIyIjm2E4mEgGial09OTmJtbU3eMRKJIBqNYmlpCWNjY8LCmAoCzqtarSYsfK1WE/aecRRSqVSP+whTeiWTSbhcLuTzeaTTaQmC1m63Rdng8XgwNDTUA+bZ5kwNGA6HxUogEAjg1KlTMie5Dng8HuTzeUklOJCBDOSZL4lEApdddlkPIDGZNg1A9HW8lsDFNCnW5Zkgm//znMCzBc8Bmt3l3yZw4flFgzczyJc2rdUgSIMV7aeuzyYXAo+6jUxQqMvuBwT7KSDMupvAtB9Y16bX5vdmvXktAbH5/k/3jnovMJlmfZ0Gl/p7DSSptDbfUZevXRTMnNNm2+oxxM9N4K7FBLWsK5+nmV9ezzpqgomix6fZr2ZdNUjWYJ9m5DqbjLYgIMFERRPL0P1PKxJzfrCsfooC3U/aNRQA/H4/fvzHfxzXX389Xvayl+H9738/vvCFLwyCqf4HlQHg/jfK2NgYbrihm8Iql8shFAoJE8YAW16vVyYi2UICVS4+p06dgmVZmJubE7/nmZkZlMtlAFuMualxI3tbqVR6fKRrtRoCgUCP2ROZbq/Xi3K5LJOX7JzeYDVgZf0LhYJERq5Wq8IIau0jwT7NkLUPKiOaf3noy7hz251oO7vPqHgr+NyNn8NrzrwGj9/9eDci+F9EMZWcwpnLzgAOwJV14cAfH0DCmUCukRPGf2RkBNlsFm63u2uy37YxOjIKoFfTaYdstH6vheoLuiD407/0abzi069ApBnBt6vfxkM//xA6zqcWRAdQvLmIM79/Bq6MC5krMhdkqz15D9reNtq+Npx1J+Y/Po+5f5pD9HNR3PsX96IdaiNQDOCaP7oGD73iIVz7l9ei5q+h5W7B/7gf1//h9ciFc1i4awHNchOBYAAOhwPBYBDFYhHb7t2GoX8eAmwA/u7CzfbtdDrwwYd4LQ470V3kC4UCYrGYpCrTKT04rmKxGNLptLgV+P1+cTUgOwxA6sHP2aeNRqPHKoJ/A92AXh6PB0tLS6LksayuXxOZ7pWVFRljlUpFxteNmRux+TebKG2WELo7hEQyISDd5/MhGAxKSjHLsrC5uSlm3Ol0Wsy46/U6bNvGzMyMpKdjBHnNsjidTkmv5/F4cO7cOQwNDaFYLEqKsVarhXA4jFwuJ5sezdWnp6dRq9WwsLAAn8+HkydPymY5MjICAEilUuLfDUCUGrlcToB5oVBAIBCQvOxOp1PcJRqNBoaHh5HNZuHz+bCxsSG+7DRtP3fu3GBjHchAfkTkhhtukLODVtabjDVFM78UE1TpNZFrs8kSA+eDVhILGgTyvEHlP63xWKYGNnSr08CHTKgJzPSZR5sw85m8X//WbKjJaveTfsxxP+Zas5b9gHS/8k3QqwORmSbaZr/pcs2y9bnNZJJ1G5jvp99DP6vT6fRk1dH10yCZv3UdeY1O6dVvXLJPtAk7P9cgn+URYJKw0uOVZA7Br2nNabanHvetVkusMfsx4xxnOjCaPmezvYGtiPscoyS42P5aEcJ3Yr0JvDUJR9JDKxm0yybbn+cSh6PrCnfJJZfgYx/7GFZWVvDzP//zePLJJ7GxsdEzTgfyf1cGgPvfKLt378bhw4fhcrmELdMTm6mGdJ4/TkQAwnjt2LEDnU5HmEOyyoxWrH03GLDs3LlziEQiiMfj3bzPyjyck4tMKJlppg3TGjbLsgRMaXMaXkONGoG6zndMnyyn0ylRl7lYceGg6S4XyRsXb0TFW8GnL/k06u46wuthHPzTg1h9eFWAlMPhwBV/dAU6r+9gdfsqFt63AOc3nVixVoQ9dzqdCAaD2Nzc7LbH1Dl87We/huFzw3B/zb0VETJgo/DmAvAzW/22uLCIL734S3jBP70AxZ8uCpMuYgG1fTXM/LcZhN1hnH7pafG3Hn5gGJ6WB5ntGRz40AEUp4p48qVP4uIvX4z5f5rvKiMKTVz3u9chty+HfffvQ7FYxHX//Trxf7csCy6nC0P3DiFux1FrdIOjUYkRDAYRDoe7qdgsF8KRcI9mlb67BG4AJJgdAV0sFhPFBKNZJxIJeDweJBIJWaQZvIObCn2GCcS5oDN3tbasKBQKwrrW63XEYjE4nU4MDw+LkqjZbCISiUj9aP5MYMz0WOVyGRN3dPNLd+Jds22CVQYU5ObtdDrFr7tUKokfNpURjJpP/+toNCrP29jYkDkYj8eFuWcedY5X+kk3m91UbHT5oOKIgc6YA5zBAsmQ5/N5qTMPnZlMRjZGfWCu1WpIJBLI5/NyDfOH27YtkdkdDgdSqZQoVfL5vKSoG8hABvLMl5tvvrnHjFcDKqC/aa552DZZbQ1ueY8pJoOufWQB9AAP/RnBBcvoxxJrRlcDNr3X8LcGfxp8XchEXptNc88zAaxZrlYs9AO5WvS1+rMLAfx+rL35nf6+3726fJ0urR9za5oz62sdjq2o2uaZz3wXiu4vs1zud2Z/8Fn6edqasp/ovjDbQjPApkLJND9ne/F/rSBgsDOzz9iGZtA1WqHq4HumEgLoDRBnjlPdT3oO6bzcnDc8o9NFkG3LPtK4QreRw+HA1NQUvvSlL+Gee+7Bxz/+cdxxxx1YW1u7YHsP5P87GQDuf4N4vV688pWvhNfrFdNpPfipJSTLRm0WD/7aLJsm3uFwGJZlSV5egixqupjeS2sxCaC9Xq+kw9CsJQApnz68NEPl5OWmRDZaa7cJxgFIYDWdbsE0M+dzCcb4bC62lmXhRadfhEA7gP+z8H9w+V9cjoW1BdRjdYyOjmJxcbGr0XS6cPmHLse3vd/GzJkZNN1NAWwMpFWr1bo5inev4/6fuh/VWBXld5ThzDvR+cunNoVhAG84v//ObZzDvQ/ci8kvTyK/mEfqv26l2PKkPNj9R7sxdHIIgbUA/G0/nnjFE5j5zgwu/6vLUUlVUDhYQOjOECZDk4gWopj8+iSKlW40+FgsBt+GD+3PtJGJZEQxwcVXbz6dTkcYS8uyEAwG0Wg0hMXQ/uocYzStpoaXAIybBKPhU4tMZtjv92N1dRWjo6OSYqtYLGJoaAi1Wg2FQkHANgABln6/X6LUFwoF6UeOHTLhjLrNcc+xTfPtXC4nrg608nC5XMhX8zj+wuPY/0/7JbYB2We+AxU9zEEZi8WQzWaRTCYBQOphWRZKpRLm5uawubmJeDwuuawZzHB4eBjj4+NIpVJwOBwyb2j+nkwmJSp5oVBAsVjsCXrIKOSFQgGbm5sYHh4WE/x8Pi/j1LIsmQt8BwZKLBaLOHTokMxH7XfudrvF1J0ae7qZ5HI5CdDYbDYH/tsDGciPiDAdmAk+NEig6D1cM78aTPVjL3mPBvMa2GjwQbNZsnG6DNYB2GLi+ByTkdaudlp4remDawJFzVpq8KHfA9hSAOgfs03M5z+d6HLM9tUg0ATBJqA1+838TPeLVnro3+Z3JqNrSj+rB/0cE9hr5Q3/1tdpMcebCQo1m94PiOoy9Ltp5pl11r7cvJb1NBUhuixz3OsxpoE5n8NxqM9trIvuC91PHPeaiNNWGrpuGpibyixiCVMhwM9M1wZtPXHDDTfgyiuvxL/8y7/gnnvuwUc+8hEhTwbyf0cGgPvfID6fD7feems3mrPSNNFEhew1mW+HwyEghCw1JxCZaYJi+nHze4/Hg1qthlqtJr6r09PTaLfb8Pl8YuZNEESAS9aSJjlchLQWjamFeC3rR7Nzgjey4wQC1WpVIjtzAdI5v/md3uyoFLAsC89efDaGTgxh8cgilrJL8Hq9PVYCzWYTzVITlbsqKO0pSURTPoMpmbLjWTzw+gdQHekqBuy4jdZ/byHuiGP47mEMTw1j/cPrOPFzJ7qm4Tbge8iH5FuTKNVK8Pl8WPjfC3D73Fh+6TIcLQcOveMQvMe8CIVDcDqd2Pm5nRh7eAxDxSE40054mh5E747C5e0qHOJfjqPt7WpsL730Ujz55JPyHj6fD7lcDpFIBIVCQXKn53I52HY3KEY0GpUFnf73jPKtg+sxGIZOs0L/6lgshkql0pPf2eFwSMorssiBQEAC8NF3m+PZtm0BtoVCQZhjml5xbFAJFIlEUKlUephfKnsKhYLkkGcsA1FGPJWuq9VqodFs4K4334XURSm02i1c/KWLJUgZ22dzc1PGRzQaFZeJQqGATCYDl8uFvXv34syZM8JI1+t1CTxWLBYlrzfb+OTJkygUCqItLhQKmJiYwLFjxzAyMiKxAcjmuN1unDhxAolEAvV6vZsr/an2XFxcxMjIiMxzKqoKhQLm5uaQTCZl/gYCAWSz2Z4NlX7e7ItKpSJrRyaTQTgclnlDQM6AdcxsMJCBDOSZLQcOHMDo6Oh5DC3FZBs1COV+oQ/qpsmyLoOfaxCtfU51HUzQo1l3DdK0GXGn0xHrPRPgA1uMuWZw9TsTwLBOT8fy9gN0JlA3r9XgBdhiMPkuev3Wbd2vTfs9718Dqvoes7/NfjHfjXXS95iiFf+6rXTK2n4KBQK8C0UevxAbreus9zpdtwspCDRA1+NX+25r0KnbQgNpDZy1D/SF6szPecbVrDIVTrrvKVQ+8Uez+Rqsa0WTVk7oMWey9LrvtFLFtAbR9/j9fjz3uc/F4cOH8fznPx8f+9jH8JnPfGZwdvi/JIO0YP8Geetb3yosK4EpwS4PxozgTfaYTBWZawJQHe2Ti53WhJEFjUajqFQqAmSZl7hQKPQsJE6nU/x8ae4rIPYpMM56kk2PRqNSP60AIDAk2ObiR1NiLpwE+KVSSVhMsnIE6TSNbTab8Hl8GF4fxtDQEHbu3InZ2VlUq1XMz89jenoarVZLokEz1RLN7J1Op4Ay/1k/tn9mO5z1pxbcloWZb83g4jMXY35+Hs6WE8MfH8bIn4/AaljwHvdixy/ugDvVNdHJ5XKwShYm/3gS+67dh+e+6rnwH/f3KC5ivhjGF8fhzrilHwg8ga3DSKfTwX333Yf19W76sNHRUcmfSAsG+uqybQhOb7jhBmHC6d/LQ1LaTuML7/wCOmMdCVRG02P6azOXOvuGOdoZcZ55xIEuoFteXobP5xNFSrlcFhcGAtFSqSSpsEqlEtbX14VRdrvdCIVCPcHDtO/X0NCQRNJmyrdOp4NSqYR0Oo2xsTF4E17c99b7sHLpClreFh54yQM4evNROLxbJpNML8ex7vP5EIvFEA6HMTQ0hEKhANu28cQTTyCfz6PRaKBUKqFUKqFWq6FarQpAXVhYkIBr9DOv1WqixEkkEgJkM5mMsPtzc3MAuhkJ0uk03G436vU6JiYmEI/HEQgEpL0nJyfF19vv92NjYwPj4+NyCMjn8wgEAt3I+IWCpBjjwTEYDIrSjkqSUKibC93r9YqpeS6XE8Z8IAMZyDNfDh06JCk6NRjj3s99CNgCGEzvZR7ENYBkUEgKy9LKcq7v/GG5BNH8mzExeA8/0/EzWBemWyKAM83FqSjwer3iimUyqwTdBDW00OMZqtlsyvlDl2uWYTK+GnRpM3S2PT/XJtRmoC1T8aDLNcG9CUxN8KefqwG4/q5fH+rydZ1ZjvYRpoLbfAdgCxxqywLdnrrdTNBsKg5MRpb11u+mwbE+H+t3Ma0BzHI5LrV1JT/T78j34Nxhv+n5ZCo7qGji8zjOaPWhFR6cGwDk3M36a7N13sO+I3DnmZltod+Hn2nrjn7KHoejm0b1uuuuw5/92Z/hvvvuw1VXXYV4PH7e9QP54coAcH+fMjIygttuuw21Wk3MYpkaSW86NBHnhscNKJ1OS/ogbjQ0Edagl6ww/UY4oTXDCQCBQEA2JgZaY4A2midzw+MzmDNYBzXh5gdA6hqNRmWD5SJB0E9/WS5EDI7FgFM0Xem3KFYqFUSjUfFVPn36tPjDZjIZAF0TOi6E9IulEoNKBZfDhbl/msNl/3AZ3DU3dn9jN7Z/YDs69Y4oIprVJib/YhJjnxjDtldtQ6fSEeAeiUS6C1XbgYgjgspGF8jT77hSqYivtNPpxOrqKqrVKrxeL8LhsIwB3lMulyWvN/1+aarNA0Cr1UIoFMLU1JT45nzxi18EADlc+Hy+bqTrkSIe+b1HUNhWwJ1/cCfa+9rCgKZSKWGrG42GsMoEe/TpLhaLMv6oAKHJOn2rmXbK7/dLXWkVwYOMzhnd6XSQTqd7NN60tNjc3BTzbb7rqVOnkM1mRXHj9XqxevEqVmdXJSid7bRx9OqjyPvzAoT1/PJ4PCgWi1L+2NgY6vU6lpeXxXSdgXro4+/xeIQVX1paEvPx0dFRSdFH3+319XXMzc2hWCxKpHBu8NVqVUzaA4GAKJS4wVELznZjzu+JiQkcPXq0O1ZdLkSjUYn54PF4EAgEcPr0adi2jZWVFZRKJVSrVYRCIVk7tJVMMBiUPqN1wUAGMpBntvh8Plx66aWS3lADHZPZ1mCyH2trAkIdZwXoHzlaAzV+r0Eyy9dsm75WAzETkLBcnmsouk58Hn/rHOH6GpPZpbudBmoaYPRjmk3AawJjk/llPcx302WZTK4GdboPzd+6HPM5JojVQNIcFxeqt2kGzfu0a6RWrLBNTYbVfF+zzS7ExLIMYMuaQY8T3qOVF3rsMwYNz4k6iB+BOt9HW3jq8vgsntO14oRnH5NBJwlFEov5uoEtE3DNpOu5oNuo33tRqcHsJzpYmmb7G42GvLe2vOAzNGGnlVkulwvbtm3DV7/6VXzqU5/Ci170okEe7/8PZQC4v0/5pV/6JWEjCXR9Pp98rxcVasnI9DHwGDcXp9Mp/qFkFYEtf2lgSwPIa/g9gQ7Za4JyPpOTlgs8D+2MYE2/Vda53W6L4kBrFflcAgzWnewtsBVhVJuNkakjQGYQCIfD0cN4hsNhAb5kCmu1GkqlkiwcLpdLgCMBqWVZsple9JWLcP0/XI9nf+7ZArxoPsyFc/aDswjYASmT19BnmItVsVgUwMeNg5YMBJ2Mjk2FBFOUEfBxIaRShWnYaJpNxrhcLqPT6SAUCiGfz6NcLgs47kx38OjrHkVmb1cBUUqW8K1f/BaKu4rSnqFQSMYIF1wGX6vX6z0++NyY2PeMKk4mmD787DeXyyWm0/x+ZWVFQDPNnKkgyufz0q7sl5GREXi9XmHXvV4vcrkcVlZWMHHvBK7+xNXwVLpjcOT0CA79ySF0zm5tJrlcDj6fTywKOI9SqZTMo9HRUYRCIWzbtk1YdZfLhWw2K4Cd46VQKMCyLNlgMpkMvF4vksmk1JPAmOOPPuxutxsjIyM9CjC2K33ws9ksUqkUfD6fgGa6iXAsBoNBUYbQWsHr9WJ6ehq7d+/Gnj174HQ6JdXZyZMnpQ85pqkMGTDcAxnIM1/m5uYwOzvbw6jyYH0h0GOyjPpwD2yBaG2WrcEO0AtidVn6eVyHuH/quDAUE7gAW4E+dTYNTQr0Y51N5T3PIWbwL5MFNkGw2VYsv9/nmsk1294E3fq9TeWHvt58t37tapbfj9lmffp9pgF5PxCuRTP0Zv200kW/i7YM0AyxyXqzbfqx37rftQk3zxdaoayBsu5rHR9HC1l5cyxqQKrnju4r8/21gom/taUA60VSxVQw6EBqnG+cM1oxoME4FR4AzlOm8Byt25Z90K8sip4flmXhmmuuwUc/+lG8733vw6/+6q+KxeJAfngyANzfhySTSTzrWc8SoMFJTrCpTbU6nY6YV/l8PgQCAfh8PoyPj8vnvIa+M0y9xUmjtWIEM5y4jGZOky2Xy4VAICAm4TTH5iTlvfSF5oTudDrCjAJbQdbIrtOci4sg35HgHYAsesxbrDXSkUhEACs3VzL0Y2NjAl6Wl5fF3JZtxJRO9CkmuKGVgE4zdeC7B9BqtKQ/2G585plfPwNXzCWmbCZD7/F44I/6ceQNR1AoFMSkx7IsicDN+3jv5uamACYN9NnnBKPsv0wm03PwoWk+/fFjsRhisRja7TbC7TDCq+Ge8RfMBeFY7Y6PYrGITCaDTCYjYJ8KACoQbNsWsyEG86KihaaEVAQ5HA4Bzo1GQ66nMoaxBXQAPipoGLCMY4VKKG4YuVxO3CvoWtButzHx7QkcfNdBDG0M4dbP3oq54hxqtRqmp6dFqbW5uSn+TJ1OR3zCT5w4IXnA9+zZg6WlpR7TrWQyKVYmDocDExMTcLvdyGaz4mc/NDQEy+oGWmN6ruHhYcRiMYlrUCqVRFlB824qasLhMGKxGFqtFiqVijAqrDuVW0zdNz09LX1Olnp1dVXGbC6XQz6fF4XK6dOnRUFTKpVQKBSQSqVQKpV6DkgDGchAnrkyPz+PycnJ81hLrm0aIJh+zby+H7jU+7Q24dVB0CjmvQQVOmUTzxXcB1h+v2fps4N2pdMmuMAWoOAZwwQQGgyZAMoEkdqUmucos5102aYiQu/d5n0mwO33fz/lRT8wbAJmExRqMqefAkGTPvxbs+MakGmwqPuoX3vqPjGVBwSPuv793qefIkDXkeXwb9PcWz9bg2bWV7+zVuCY86VfX+i66H7RViWahOLzOPbNdjRZdbaXnhMmWGYcH84lbU2g20b3n/lss494nVYCcCz4/X7ceuuteMc73oHPfOYzuO2224SIG8gPXgZB074PufXWW3HgwAFhzjhoudCQhSVw1v6rOk1Gp9ORQznBtm3bAmhpOq6ZS048msxqs3IuvF6vV0y+gS1QTCaTm5aeyAwuxWfoyOMABBATnLvdbgEFvKdYLAro18oCDbTJWrKcSDyCh44/hNHEKGZmZjAxMYHV1VXEYjHx2yZgIWtZq9VQLBYl/3GxWOwB406nE+VyGe12W1JGwQOkfi2FtZeuIXdNDnt/ei9czW57+3w+xONxZDIZ2CEb33jvN1CaKyEYCGLXn+2CF91gXQwwVqlUelJVNZtNTExMIJfLSfAymvNT8UHzd5o4h8NhCQRGbW4ikYBt2zh79izC4XBX2dAM4Kq/uwruITeOX34cQ8eHcOX7rgTKgD/gx9TUlIDGcrmMQqEggbloUl6r1bC4uIhEIoFcLodAICCpwdbW1lAsFmWcMdidZXWjpZP5Jogku5xMJkVZxDgGzFmdSqVkbA8NDYn5+NTUFHw+H+r1utzPfOPjx8ax8LsLqJVrYkVRKBQk8NmpU6dw/fXX49SpU6hWq+Jr7nA4EIvF4PF4cPToUWQyGQG/NIsvl8toNpuSG5vfh0Ih8ddmmrVarSaB4tbW1lCv18UHnoqk4eFhCWhH5QPnmMPhEN/6RqOB9fV1AdiJRALlchnFYlGAOVOpMQgbGfVGo4FarYZ2uy3WAgCwsbGBoaEhMa0vlUoDk/KBDOQZLi6XC3v27MH4+Ph5gFD/1sGWTAbTZBdNMMnDPNd13qPFBLs8Q2ggoQOlmiwbTYZ1AFQTAGqAxXI0AOR92uwXwHkggvVi2Wa9NfAwwd+FmFgT6PFz1l0DI7ONTRaa+4Y26e7Hevd7B7NMs5/Md9DX9xsXur58pnan0m0GbJlKm/WhpYMJNHVfsU70B9fP13EIWA6ZXV2mqWQA0JP3Wo8VrXBh3+hga+xX/dtkpnWANX0OZxk6Wj/rrQOq6bbT1qM8i5tnf17H57OdWDbfXyun2C4aE+io/FrBoPub72pZ3TTGN954I6688kq8/vWvxy//8i/jiSeeQLFYPG98DeTfLgOG+3uUaDSKyy67rAds0fyZmwcP7DTb5bU0v7VtWyIQl0qlHjCttYBMDcRJRdZMa23NYCHcLLXfCAGWXiz5PDKUZNx4X71eR6VSQb1eF39ZrdXTSgEusgygxQBejA7NuuuNvVKpoIMOvjb/NXzl576CSqyC48ePAwDGx8cRjUbRarUwPDwsrLHX68XIyAh8Ph+mp6eFjXU4HOKLSxaRJsLJZBK218aZnzqDcz95DnABtekanvxfT6IyVhEWu1AoIBfO4dHffxSlhRLgBE7efBJHXnUEFUdFzL6pTCHrSQUD00Ylk0lUKt3rGT2efsi2bQuzzCjkZBLog+9yuSRgXKVS6b5PuY0Df3QAk1+dxKE3HkIz2xQFR6VSgdvtFuaZ7aRNu1qtFoLBIEKhkJhgdzpd/+vh4WGJQE/XgEqlgnQ6LYw5FQQOh0NyS7Odg8GgmNY3Gg1xDaD//uLiIjY3N4WNZeCzYDAo7gOxWAzVfVVYhS47vrGxgW3btqFarWJ0dFRANdueTLPX60UsFhPTedvuRtx3Op04d+4cKpUKstlsT/DA5eVlsfqgpcTIyEhP3IJqtYp8Po9ms4lTp07JM5lZgHOM5bKdR0ZGxIzd4/EgGAxieHgYgUAAnU4HQ0NDkuKNzDmVY8PDwxJJnszP+vo66vW6KDVWV1fhcDjEUobxDgYykIE8syUej2P//v095rdAb2oo7ueaOQPOz+2sTa81GOM9mi02gSg/M1lUXmcGdOKPtv4j8AbOz9Otzzo0Fac1Gu+jIl8zhprl1s/VzKfJrurP9buwPO1PbDK1/ZhfDZZNRYgGh3wO2VFTgWL2y4XeQYvJ1pv9diFLKP0sLSaQN9lgglM+l+QOiZF+ZelxqEkqWk1SUcNyuUdq4EngyLMS91DLsiQwnrZ00GOjXzvxs35Mth5rWhGjy9VjhGOP72/GOSDI5lmFY4Htx3min8V5o91CTYWCNh9nO2lrAD1G+H4XmvcsIxgMYvfu3bjjjjvw5S9/GTfddJMQFgP598sAcH+PsnfvXjz/+c8XcwsCHKYs4mca+HJh0QwzJyIXAJr3cvLxM22qQ1NcbRJVKpUEzNN8lYAeAKrVKorFojB0rIu5aZCd1SZHjIocCAQkAjYP+Zyw9LHhu9RqNQAQH1e9YGuNqNvtxhfmvoAPH/owTu05ha+++KsIbwsjkUggn89jeXkZLpcLq6urslDk83msr69LcDCyhPF4XCI36yAV9IV3hV2wL7YlMBcAtKItVMeqknO63W7Dvd2NZlz5w1pAebqMot0F0/rdQqFQj+aVDDajs+v0aQRJXHR139BsnyC40+lGjGW9Go0GVldXkU6nsesPd8Hv9YsZMkE8lTLcvLTJtdPpRDgcRiAQwMbGhlgE6MBzNBsnaCfLTeuEer0uftvNZlNM+FutFtLptLDAAMT0e3p6GuVyGYuLi+IPX6vVkE6nxSSf8+DI3iP4+v/zdZx81kkBo6dOnUIkEpENwuPxoFwuIx6PSxRwpsQKhULyP+uxf/9+AdLDw8PSVvQJT6fTMv4jkQiCwaBYJVCx4nK5cPDgQSQSCViWhUQigWQyKanI2OY8ALDNx8bGxOzc5/OJZYS2QOD4oZn4wsJCj+LK4XBg7969CAQC4iKSSCQkowDzgOsNdSADGcgzU+LxOPbu3dsXnF2ITdViAmaTvdX3m5/zO5NVNIGfw+EQxT3/p0uVjhRNIG0qD3j24H6qSQXN0PVjnjUrqUGEWU8NZsz36AegTcDc7z7ddrqNzev0Nfr3v9Yf/Fuz6/qzpxNTWWDWy1QSmMBYt6dZZ+3PrN0eSUJp02v+aNNwEjzaN9kE8Vo5o5XtvI5glOXxXKKBO9/Dtu2e1Hj9gg5qZZVWFJj9ot9Bm8JrpYBuLw2wWR/dPvo9tAKKMYD0b37HtmFddD/x3fXztdsA68KYMqyTOXYA4LLLLsOnP/1pvP/978drX/vagY/3D0AGgPt7EK/Xix/7sR9DMpkUZpWTgKYkHMA0E+aEZGAQzTwT2HBCMCKhXvwZZAvYCpwGoCfHMxcAml4TdLE+2ueDi6PX6xWNWSAQEDNwAi+yeZzg2icc6E5iAjQCQ82AM6ADF2b6ChOgf2735/C3+/9WQPADcw/gk//lk7CdtoAf7Z+uNzwqD5giyrZtyYccCoUE1PJ9PWUPJt8zidA/d4OLOYtOzP/WPEL3dk2hM5lMty/u92Pb726Da7PbR4kHEtj7J3vhzXqlLWnmSx91RvRmm5L5rNfrAtoY8IvjgYs6FRIMpsUNSweVi0aj8Hq9iEQiorDhWNQB7/SCSdCey+WQzWZF49lsNrGxsYFQKATbtpHL5WQDYN1zuZxErOXmRN9mXkczcSp2gsEgGo2GKDwKhYIA4WAwCK/Xi6WlJXQ6nR4rjWAwiPVr1nHXT9yFWriG+3/qfhx51hF5rmVZWF1dxeLiogQwS6fT4jIwPj4uTC/Lpmk/x//GxgYKhQIcDodESc/lcnj44Ydx8uRJnDlzBkCviRzZbCoYGMHc4/FgYmJCIp7TzNuyLKysrKDT6eY+j0ajYuWRzWaRz+eFeefBk8oip9OJbDaL4eFh8W8/ceIE6vU6SqUSQqGQHE7Z37ZtIxKJCOAfyEAG8swVy7IwMzOD7du3A9g62APng2R9iNffaxPtftf3M63Wey7XLr3PmCwrsMV86r2IZxszoJVZNoEAz04mA62Bgqlc0CywycT2e99+bLcJTPWzTUBsAlMTlGoxlRT92stsE/NeEyjr32wbE9T3s1DoJ0/XDrxXv7/J6PJvbULO84bJyHIs8DyqxyTPsBps6n7X5JVpEaqBq+4/3U96bmhFgjln9LlVA2dzLPQD7eZYN824eS/3frNuJgDXmYR4LtRWIvqddT36jV1znOp31mNQtyfbORAI4MUvfjF+//d/H3/913+N2267rcfMfiDfnwxa7nuQoaEhvOlNb+oBnRQe2HmQBrZAUb8Ni+YyNMOlWTEnhj5I8yBOzRLNiDnhstmsmCRzASBTSPYPAMrlMizLEv9rHQG93W6jXC5LKiRey/rH4/Een1ICHKZDoFn9yZMnJdVVrVYTEzdzEb/xxI34xrZvYCW8AliAq+3CL6R+AdMT0wh4Ajh27BhGRkZw+PBhnDp3CnBAgnL5fD4xm6Zps2VZsL024ADKhS6zyjRrzWYTnqoHO/5gB47Hj2P7/9iOoc0htAJb5vCVSgU+nw+R4xEsvHIBy+9fxsLbFmDXusx4LBbD0tKSmFYHAgFks9mePiXQ1qbH6XQawWAQiURCgCJNjsLhbjA0BidzuVzY3NzE0NCQBBXb3NxELBbD8vIywuEwisWipB4Dunm+AUhdnE4nQqGQRJGn5QIAYRui0ajkEtdKDY5JAJLGjOZ9MzMz4rM/OjoqObyz2axYEtCyg2B/cnISfr8f1WoVIyMj8u623Q3iafehtgABAABJREFUdnb4LP75Zf+MaqgbRb0RaOCBlz0Aa81C+KthYeK1K0ar1UI+n8fGxgb2798v/uLc7FKpFGzbxvr6ulgJEHBzXhSLRcRiMZw+fVr8z8PhMLZt24Z0Oo1ms4l8Po96vS5zudFo4P7778fY2BjW1tYQiURw7NgxUbb5/X489thjAojJeNNnfXl5WUzpyQJpd4zHHntMwD0j3q+trQEAhoeHsbm5Kebo2rKBbT+QgQzkmSlOpxNXXnmlZEExgaVm2zTwMQ/bmlHWZwyCCtlH1YGd6xvQe0jn8wmqCE6472k/Xh1QzQT8rDcVz+12W9ZQ7eOq663NaIHewFD6el1n3s/rNfjXDGA/kKLb0ixXm2pzj6HVE6/TZVFMVtt8hn4uQU8/ppnP7VdPE2SZ7WC+j66zrmO/67XfsG5jDdw08KZyXteZzzSZWFqHkrgywSPPWxdSDOgI+RdSGlC0EtsEo7pt9b0koXRdSPKQhOMZn4y8fr6pkNLP1OcczgEqozh/aMmqA7ZpJQXHiz7PmQoTYGuumAoRzcLr8W1ZXavXW265BTfccANcLhc+/OEPYyDfv1hPpwHrudCyvrcLn4Hy+te/Hu9973sBoGcT0qC60WgIG8w2pZ9lNBoVlpRmzCYLzAM6sBUEolAoIBaLAdgK2FCr1WQRIyOtP+dmp81PdD1pOqwjUXMh4X2c9HqD1AsoFxKCNqaOInjj83QgCADI5/PdP7zAO378Hcj5cnjtva/FweWDYjY+OjqKsbEx5Kt5vGPjHahP1TH+P8fhqXmkTekf2/K2kJvK4cSrTmDfd/ZhxyM7cOzYMWQyGYRCITG7t20bzR1NeM544IKrh3nXua9brRbsoI1yoAzfqk8AMwO9lUolAdVcYKkEYI5m9l29Xkc0GhV3AW5C+XwewWBQImizn1KpFDwejwBUnce80+lgZGREgra5XN38zNlsFrZti7mxzsvt9/uRy+WQSCS2fMLbbbEEINBn8C+tfbYsS8A9lSsE53Qj0C4ObrcbsVgM6XRa/qeJdy6XE1PoSCSCVCqFWDyGe+buwSM/9wg6sU43ENx7/bjxOzdKGiwqf8bGxnpyxFOhxE0lGo0inU7LnKRihgHbOJ/4jrZti8KDmw0DqTkcXV/1cDiMRqMhacm4sWll0/r6uswpKoRo9QJ03QbIwpPZB7obPZ9v27aYx/PZp0+f7ste00wTAEKhEDKZzI8Ey23b9vl02kAG8jTyTDmr+Hw+fPazn8UNN9wAAOcBIy0aFJkMGNB7wO9ndq4P/QTgJuBmHbhv6fOGNvnVuYhZnq6j6VNqnj/6+ZxqJrDf+cRsE5ar/Vz19f2AGetmAmJ9jf7pZ3quFRZ8ZxPw9QPQpnsA29Z8Jw3eTDDO79lX2txbiwa9Gphx36/X6z1nVA3IeIbUZWnFC+8hCNXWkRxT9L3WQFKDxX7sP8vTZ2yCfwJtbWWhU+tq5lYrEkgacTz06yt+p9+VY0Sfl3RsAQ1weaY3xwnL1L7gLN9UxmhXDf6vx4z5rlSI8d10v+hxpN9X96/+Xo85h6MbGPaNb3wjPvnJT2IgvfK9nFUGDPe/IoFAAL/xG78hzJRebIEtDRIjGmq2mdGHo9EoAPQEJKEGmJtCtVqVsgkmyHwD6GHUdN14WOcCZOZTBtATZdwM9KYBNBcPPsuyLHz729/GFVdcIRpB3q8Drdm2LaCCwE5r3AlqVlZWEAqFkEgk8Bt3/AaOjxzHweWD4pe8Y8cOab+vX/Z1nNt1rlt+x8a+v9oHV8slYL5UK+Hkfz2Jsz9xFgDwtUu/BuuvLbSf6IJKtlUsFkN6TxpH3nQEo58cxcKXFqQvGBwM6LL9tWYNmZ/LIHtxFgf+5ABcJ7dyVjMwnI5OH4/HhbkdHh6WYHjcOIrFIhqNBuLxuJgnx2IxUa6wDev1ukQbZ7Rq7RdXb9XxxIEnsPP+nfB4PDhx4gRGR0cF0APAw/sfxsydM7Ih5XI5LC8vi3LloYsfwtw9cxKAjPeFw2GUSiUMDw8LMKav9enTpzE9PS2R2dfX12URZ/A39vXJkycxPDzck5OVPvcjIyNYW1tDLBbb0lz/tYXkZhIbb9mA+71u+P6XD991fRcOh0P8nWkZMDExIQoMjmeadmcyGak3x3ssFkMqlUI0GkU2m0UkEkGhUBALDSoZCKzJTDCPOrAVA4FR38nSOxwOZLNZcaughQo3YLLyZGx4EGXQOFpnBINBVKtVhEIh2Rw59vW6Qul0unEcAIjyYCADGcgzV5LJJC699NLzTGT1gd78DujP9urrzMO/NqHVP/o8oJ9FNlH7kZrmv9o0VZ9rCIw0iKdoZo1ghp+zbA2ICX50e5hA2QSMpuKgXzv1q5duN5Z5IRZc94HZ3hqkU0wmvF955nVmOfp9zf7X9/f7W5fBIKcXul/7b+sI3fosqUGsZq05Nnhm5R6p363VavX4+fMZDM5Gl0FeC2xZmZIMoCUEgJ7I+yZzqyN562eZ53vt9qfv1WVR6cQxRpDLuuhyOKfMMajHs2aftTKM92g3xX6gne+mlWv9lFN6TdGxllgfUxH00EMP4Wtf+9p5Y2Mg35sMAPe/Ii9/+csRj8cB9Kbc4OSoVCoSCImaQQLv4eHhnvzHPHD3S6FFkKPNc/WA54FeRyO1bVuACZk3vQiYB3amtiKjS1Nb3mvbtjB8zWazx+/UsiwJgEWgoTcwTnxtYkZNG9BdFBcWFuS6eDmOaxavQQcdAZbcRD6x+xP43I7PSd1TP5HCE7EnsPvdu+Wzo687ipUXrmy9q8PG11/ydUwuTyLyiQgCgQAymQxKe0pYevMS6uN1LP3aEhxBB8b+akzMtJvNJoaGhgAAJ197Esu3LgMW8PAbHsa+d+6DK9dduAqFgphiMzBarVaTPOMMgsbFi764ug0YdM7n82F5eRnFYhGJREIY6XQ6jfn5eQF/QNcd4L6fvg/pH0/DEXFg7p/n4HA4sLi4KCz9k696EmdefAatRAtzn56TKO90Hzj1X05h6cVLsD5nYe9fdwNylUolYVqXl5dx8mdOYv7/zCPo6gYQq1arwgy7XC5Uq1VROBSLRWHXebhyOBxIp9OoVCoYGhpCqVRCMBjE5OQkOp2OsLOBQEBM8z3/xwPcBjje74Az4BTmneOIiqjFxUVRytBCQ5vwUdEEAJFIBG63G8vLy1hcXJRNmBuUuaFwXpA558ZCxQA3IpqOEXzr1HtUKJmiN1Oy//yfFgyFQkGuXVhYEEuFfgceCp89kIEM5JkrV199NcLh8HkHXxMsmSDcZKg0YDYP+RpwUPqBXvNZJgOmAXg/EKrPBLquev/Q4EIzkZqRBdBz5tDvwGf2AyAU/a4m02yaXveTC7HfGnxfqC80O8r30mK+Bz8zAZcJnEy2Uredydya9e0nGoTp600GlgCWba6JFu5RnU7nvGBbegxqM/x+ygjdxwB6iCLgfIsECkkrTfpok3MtpnWHZqtNKw+tfNF9quuo54C+h3NAnzGooNBzXM8TXTdTiaNdL0xljVZssUzzWj0OtJJFz0M9JzqdDrLZLDY2NvqOm4H86zIA3E8jlmXhla98Jfx+f48JCE2pLcsSE13LssTXkpoi27Z7/LMJJgheCaq4GBDI0heE/rgE6gxSpVMV8Zp8Pi9MWqfTkfzb2nRKpxOiKTR9cQnAvV6vsHmlUgl79+6VtiCLyPro4HBMO0WfaO1XZllbKRO4cPO9CDa138nzVp6H23fdjoqjy+g5O05c8o1L8OJXvBiFQgF//6y/x+olqz3Rx2EDWAUqn6ggdy7XBb3JOprvbKIz/pSG09PBuVefA0pA82+b8Pv9qFQqXTb4jctdAP9UmentaXz7976NS19zKWKBGJxOJ5aWlhCLxZDJZIQdPXv2LKanpzEyMoJTp05JdahUodaTi6jf78fi4iJcLhfGx8dRr9dx9dVX47HHHoPD4ZAI5sViEYFQAEd+4QjWX7wO22Xj/lfcj+JKEQecB/D4Y48jX8xj8dWLOPPSM+h4O3jsZY+hVW5h25e3IZVKYXl1GblX5PDIrY+g5WvhzEvOwG7ZuPgzF6NSqXSjq9cqOPuzZ7H5sk1kLspg9+t2o5jtpswqlUqYmJiQ+kciEZw+fRqRSETGNefCiRMnkEwmZfwxKjoj3lOTzc34xKkTyL0zB3vORvtTbbRua8Hu2MJI0xSdyg26UHBs0qxda91brRaazSZyuZwEt9Mbsj4omIdUuhnw8KgPLQTTjDhubkLmIc58HsvXddFzg0oYug7QXFyb2mmm3OVybblnDGQgA3lGyuHDhwGcD/J4eNdgit/xt2bQNGjS5fUDdkAvW63vA7aUlCzbZMUo2ixd+17re/X63K9eGqBooKTBhgYFer01n6efS4Ut38dsV5MB1G2i20m/twmCNRvJ//XfJtjW728qFsz+0woKs2zzGlN5YdbZBOuaWdV1JCDTMUj67X+mWbK5z+m6E2iy7bXSxtxH9R6ozciBLXCt92vt+qD7n/Un8cV2oIWqZVk9ftJaka4BMetNQoJlaEKA768BsgbnPAsx9Z12p9DzgLiAZeh5z/fQZxvTUoT9qcekbhPeZwJsc/40Gg1J4TuQf5sMfLifRl74whfive99L+bn53sYaTLJerLx/3a7Lf7AWttHEEJfaKfTKdeRpaaQbfN6vQI26vW65FPmxCW452LFycFJSeaPwJzMmdPpFNOhbDYrWkgdkI0pPUqlkgSwol8wgB7TIUap5rO4SBCokWEMBoNiFssFVG98BKS2bSPrz+Jtz3obmo4m3v7A2zG/Or+1wHrd+J2rfwePJh8VgBxZjCB4fRClTLe+rVYL7qAbvjf7UHxHUdrW94APF73jInjS3TbN5/NIJpNw+9y47xP3oTZRk2v3/fQ+eJ7ovkex2C1jbGxMFA18R2ArUF6tVsPw8LBoAVutloDXTCYjihkqN9i/R48exdDQkPhODw0NIf3cNI6/4TjsqAo0s+LE6KtG4T7tRvhlYRx52xG0h7YCwTg3nLjoty9C5ksZhA6HcOoDp9BINLa+zzkx9445WF+20HK24Px/nDj5cye7+QpswHmHE8FfCGI2MIuVlRWMj4+LP3QwGMTZs2fFz1xvhuVyGTMzMzhz5oyMa7fbDb/f38NuNBoNOMIOPPnKJ4E3QZ6LTwL+N/kRtaOYm5uTtpyZmUE6nUY4HEY+n8fExAQ2Nzelbb1eL9LpNFwulzDrnBvhcFgAPBl71oPsf7PZxPDwMNLptFgZkNmnJYjOnb2xsYGhoSFsbGxI3ASWF41GBTzraORUOJkWIZFIBGtra2KhEovFxGw/l8thaGgI+XxeNnFao+jx+EwXe+DDPZDvU54JZxWv14uHHnoIMzMzPZ9roK0BF0WDun5sJ695OhbXDKrWTzS7Z4LEC/lO96u3eb1Oq2kCZ81C6rWU+4sGQiY40e+v60OhBSKVt/o7E/jqMjUbrIGvqbzo1yf9lB792Md/rS94r3mdWZ4GgSaY1Swz66cVz3rMaEWMCeZ1W3FfM/Nma0UM79FxhmzbljOSdhlgn7FcrQTQ7DW/55jQ5t8EvKYSQLc1r+H5TL+fnhsakGsmu9PpyPmH5BrvJXDl/aw/y9D/83yszdP5fvybxI4ZzM4MnKb7SVuT6nFm9qVuEwBYXV3Fddddh5WVLcvSgWzJ93JWGTDcF5BgMIjnPe95mJ6elonASUMtmN5sOEDJxPEwb9s2vF4vHn30Uezdu1c0bjxEcyGi2SoP7JoRJeAhENeaOwDClFMT5nA4UCqVUKlU4Pf7ZeLati3BzQiEmZqIixZNo9vtNorFYs/EpVkxzXf5W2+kVDyQLeQCwIjgbEd9D83udVkTmMBbv/1WpP1pTJyZALxb/jjNehNvu/tteG3stehs6yB5MokDf3sAjzsfR7qe3tpYwzacL+oNPGLNWQjdHMLcg3Oi6HC73SheVoQj0qt19r7Eiz3OPdhY28DMzEwP+AIgDLnD0fXrnZycRDqdRjKZRCQSEc1+Pp/H5OQkkskkQqEQlpeXMTMzI7mrT58+jWQyKcHg4vE4isUiJr8+iZqnhuXXL6MdaCO8GsbQW4cw3ZrGpn8TgbsCmH3fLM688Qw6iQ58Gz5Mv2ca1a9VMTs7i+V7lrH3fXtx7A3HUB2pwpFxYPqPp+H4Rwe8Pi/KkTLOXnR2KzmgBdg7bLivcWP99nXEYjEBhDSHB4Ann3yyx8KC0cCZlktrSw8cOICHH35YFvlmswnffh9cz3ah5WjJc3EJUFuooXp3FWtrazI2jhw5Ihso83T7fN2AdtzA4vE4Op2OpI2jOTjN0KvVKqanp1EqldBqtVAul5FIJMRqhD7UjMrOg5pWXllWNwBKMBhEoVCQPNoMWKJzs1tWN/VZu92W1G7ValU+46bqcDgwMTEhY/D48eOyOTcaDaRSKViWJRYjDM62vr7+A1jhBjKQgfxHlUOHDiESifQFZqbbmAmsKOZn2lRWs14EDQRGGiTq5/N/s06axeRZiHsfzzQaVOo1kOulZW356ZpBoUgUaDBggkYN1jSwBHrBrwba/F+vxxpom+3IPU2DSu4RZnv3A9EmQ3qhe/S9Zn2e7hlmP/E7Ppftoc9fus3092wb/Ty2kwnotauWLtNk2PmZtnjQqWR1edVqtccMnHs0SQ6tJNBkE3+02xXHnOlDbQJR1on/m77bHM+sP8eCBtq6bFPho8vWJvgsk/eYbhkarJvKK61AoELDtJDQUc1ZD1NJp8f2hSwsGo3GAGz/O2UAuC8gBw8exHOe8xwZiASyeqEgkCT44ETQvqAc2MyfSxN0asdMv2yCQL2JcQLTHFybi5FdZv7qTqdris4UQjTLZbqucDgs5t+tVgvz8/My8cvlco/Wjgw2gJ53J1OpFweHo+uXS99zHQCK/3Oh0iYy2iSNdXW5XCgUCpivz2MuNwdnsDfYXLvdRqfRwew7Z5EbzmF/dT883m7aqzNnzmxtUpuA6xddcL3HhdZNLThKDky9cwqt+1o4bh+XhSwUCmEjsYFmq9c3tlgrolAooN3u+uqfOnUK27Ztg8fjQSAQwObmJqLRqJg202c7m82i2WwiGAxiY2MDo6OjkjN6ZWUFTmc3YBvzLe7Zswff/OY3kU6nRbFCgDX+2XFUUhXkfiOHqXdOIXQ0BMvXDdjm9Xox/+15ON7twLm3n8Mlf3oJho8PYzG4iGAwiG3btsF1nwv4I+CJ33wCl3z4Eri+6kJ0exQrKytYCCwg+sdRPOl6EpUrKnAVXJh99yx8D/gQ3hZGuVzG/Py8mNJzvNN1IBaLYWNjA3v27EE2m5UAYNwAPR4PpqamUCwWJSgcA8g1/kcDT7z5CeBSwL/hx4GPHoDbciP8vLBsVDoAIedYp9M1u9am58lkUjTHnU6nJ0Wbx+ORiPWxWAxerxe1Wg3RaBTr6+sYHx+XDb1cLmN6ehrpdFpiAJCtpgsEzcoBiNUGxzLHQbVaxZEjRySAIIO65fN5Ua5pMzKa3vv9foyPj4vioFwuy/ygH30gEJD5M5CBDOSZKVdddVWPUr4fmNJATh/qTeaKB3VtVgr0Mp4mKOb9+ln6sK9Zap5HtOjvWR7PNPyc5Whwoa2hdFkafOoy9Xfmu/GZJlOnn0kxFQl8jtnW/dpLg0Wzj8y/zbr3u8esaz/pB7JNuZASRreT7uN+ANusF8cjQZxlWbI3moocXY5W9mjLUM3iamWM7kedi1qDeA1++4FTniE0+OZ5msSZPqub48LcY1lPnt+1f7cGyZqV5z080+so63rc63R6OruPrg8VAOa7MJOKbkfOBxOL6HYyxwT7TgNtPQbuv//+vuNsIN+7DAB3H3G73bj00ksxPT19nnZJM3Uej0dMr3UwBw5cbcYyNjYmDDIBLg/dnGwul0uCS3FicnEjy603nkgkItHIGcyMeZxt25Yo5kCXnQbQkxbs9ttvx3Of+9yeRZCgBdhKfQZA/K0JZKhtZE5gmszqjYsmL1qbyPpqTSl9X2gir0EbAAHhVDqQaQ5VQ6jcV4G9t/usSqWC3bu7gdUIfLwlL5K/n8S58XOYfM8kxk6PdXN2l8twOp2IRqNwOBwYemAIV/zWFfjmB74J22Fj99/uxuwnZwXkO51OTE5OSmCvUqnUY1nA9lhZWRFAXi6XEQgEUCwWhaVlHm+/349gMIijR4+i2WxiZmYGzWazJw4AAIyMjMBzvwfxP46jdLwEK2JheHhY+rNYLCJxdwKjbx7FXG0OrUAL8XgcLpcLmUwGk5OTmHhsApPvmYTjmAMlbwlTU1MAulYcvqoP3v/uxaN/+Ch2/+Fu7CrtQn42j2q1iqGhIbTbbczMzCASich4sCxL8pI7nU6Ew2EEg0FJd8Y+83q9cDgcmJycxNzcHMrlMnK5XJfx3ahiNbeKQrOAG99zI+wjNobnh4Upz+fz2LlzJyyry+iurq5ibGwMV1xxBWq1muRGv/LKK1EoFHrywnLebWxsIBqNwrIsJJNJbGxsoF6vY3R0FKdOnUI4HMb8/DzK5TK2bduGRx55RIDv2NiY+NIXi0XJrb22toapqSn4fD7k83mZoz6fD6lUSubH8PAwJicnsb6+LnNuY2MD2WwWV199NdbX1yV93fDwsERbHx4exsmTJ2XeHD9+HBMTE5ienhZXg6985Ss4e/bsD37hG8hABvJ/XTweD6666ipJrcg9SJuhatCrxQRZJmA2TZ31AVzv35pZNYGgBhGmIh6AACRdFwImve+bZuEECDy/aD9wKu913XWdNFOuP+f7sUz9Xb/3uxBI5Tvw+bqN9JlHt6NuKw2ONWjUYtavnzm6vl+/04X6SD+D5yrNZOv7tWWaBre6fH0tz3GmFYP2r+eY4xlPK21YT54TtdKEeznPyyyLf2sLT/aLBqn6DMl7eU7vx+JqVw0CWRJL2nWQ9eP5jOJwbKXm1e2k60jzdr4Ty+f32kpO9y3flfNDW+DxTEzLU2IKcz7o57P9dP30taZCic9617vehYH8+2QAuPtIMpnEa17zGgHWtVoNwWBQBme1WoVlWTJZyEiSUSPTaVmWBDoDuuw1NxSmDKKJsm3bwkQTLNNP1uFwSOCpWq3Wky6Meb1rtZosRmRTbduW+7g5ZjIZmejPec5zegCubXdzFPP9tEbS6XSK6a5t21hbW0OlUsHo6KikPGo0GmLGy/p0Oh3J7ciga+FwWIJfmRrAfhsFNagul0uAn23bCMaDsDpdk/xcLof5+XkA3QWCfr3tdhvRehSet3rgWfegaTWlTQiU6AvsPenFzW+7GaeuPIXtn9yORrWBmqMm/cp3YYopMpwulwupVEryUcdiMZTLZWxsbMDv92N+fh5DQ0M4ceIEfD6fBBSbmJjAwsICNjY2sGPHDqTTaWkfMpmVSgUHLz2IlTMraPm2gvOtr6/D5XIhEAggl8thdGMU7iG3AFpqoGkC7TzuRCQWQTweR6PRkPYJBAKI5WN4ybtfgkaxgehQFF6vF4VCAdVqFalUCmNjY/D7/fB4PMjn89JPTIlFs2mOTwaVK5fLqNfrGBsbE2WL3+9HMBHE6ktXkb0hC2fHiZgrhpyVk34Ph8MYGRmRDdeyLBw6dAhHjx7F2toaCoUCOp0OTp8+LS4RpVIJHo8HxWIRHo9HWOhkMolqtYpsNotsNiuKlvX1dfh8PmQyGWxubmJkZAStVgt333039u3bh/vuuw+tVguJRALNZlPYfNu2kUqlJL95OBxGLpdDsVjE9u3bsb6+Loqc+fl5GVu2baNYLGJmZgZjY2PI5XIYHh5GPB5HJBJBNBpFPB7H5uYmQqEQfD4fZmZm4PF4kEgkAADT09Not9uibBnIQAbyzJOFhQVMT0/3mI73Y4zNQzEPzSYY470EUdoM1gTt/UArPzfBsgbbuj4EYTzQ0+VGs6D8Tpu16wO/Lp8gUbvGmaIVBxp4mUBb199kvtlGJiPIH+0j24/J1W1l9hs/N5nffn+bZVDYJqZJu/lu+n4NqE2/Xt0OwJZFo/6O//Mz07KB7aJBPEkktg/PWnq8EkSyPLpfmt/xHp37m2dnbbrO8zfHPi0qdf9rJlyDen7XT3HEsanrrkkyWqmyDN1WptJKj0NtkUe3NH7GtmV/aYUCz0M6DZoOrMx+1hYBHBta4cF+02uF7mMTgLfbbZw+ffq8MTmQ70/626v8iMuhQ4dw4MABGYiMIEw/EDKaDodDQIRewDkhCPjcbrccoinaP4V/EwiTVdYLKlkyh8Mhwb4ajQbK5bIEdtA+4KwTF6Vms4l8Pi8AMRgMIhaLweVyIRwOy+TS7Cqfx4VDB4IIBoNIJpPweDyyMfr9fiQSCTGb9Xq94p9Nf1av1yumt51OR96D3zscDgm0pc3N+Q7MmwwXUPj5ArL/vyxSvhTK5TJSqZQs3LZti89rdUcVp/7kFMovKaN4SVH6p16vo16vI51O49y5cygVS3AuOjHz7Rl0mh2Jxh0OhxEOhyWH9sjIiGwAfDe2K/2E3W43EokELMvC0tISHn/8cVk4aaKcy3Wjqe/cuVP84+PxOIaGhsQnB4CAeZqZp1IpJJNJbNu2DfF4HNPT05LuLRaLIZ/PIx6PY2JiQszUp6amBKg1m03Mzc0JqLdtG6nFlKTvIHsfCAQwMzMDt9stc4DjTEeXZ755jhe2Ty6XkzarVCoolUpoooknXvAE7n/e/YATaLvb+OybPovq3qpstLFYrGcO+Xw+UW7QooJm2LZti+l2OByG2+0W/3nOAdY7Go1Kvmu6PnAuc7xPT09LjmzLsjA6Oop4PI5AIIBgMIjNzU0JesIxHAqFxLzd5XIJmF9eXhY//Uql0mOOb1kWstmsKARoGpbL5bBjxw4ZQ3wXjjdaLgxkIAN5Zsru3bsxPj7e9zsN+C4EsDTQNAEWASl/no7FNJlZzSbzvMJ9yTQN5+cmMKWilgd+1lmz3fxMxw3R70oAbpqL6/OSfqYG42Zbmu1q/m0qIPq1icky6+/N8gmmTIa3n6JDt4upHNEssfl+/VwMdJmmD7kGZjrwHEX3C/d4xjzR7WGOJ97Hc3O9Xpfzm75GW0NoEA5sjTWagdM6U3/P99IKHT0HNODmO/O5lUpFUr3qNmY7MPWqLpMkFOOtAFupOgnAWb75v3m+b7e7wVB1Nht9zmKQZPYNz8psC9ZFg349x/Q6oK1ENNA25585tjudDk6cONEzDgbyb5MBw22Iw+HAO97xjvMmH4V+xgQn2iSFm5mpSeSiQOZZa0oJyHmtDvFPk3BGWNaaRC6QBPEsl2XRPFmbkjDCMXMJEySxTC6IDO7E51BD2W63US6XhdnkAkENIwGd3mQJbhYXF7F9+3ZRDlArR/C5vLyM4eFhRCIRYfr5XqaJu9PpxCd3fhL37boPsIAn3/gkdv7BToSL4R4z/Hq9DuwHjr35GMrTZRz5zSPwrHiAPwIS9yUEIMfjcQSDQdQ7dTz6s48iv5DHwrsXMLM2I6bgwJbmlZpFKhbi8TjOnj0r44UWD2wHv9+PVquFiYkJaT+WSZDFugSDQaTTaWHV/X4/fD6fKCDa7TZmZ2d7AoS53W4BhZZlYWpqSuoSi8Uk8juBKzdKalaHh4dFI03z6VarJX7XDBLWbDaxsbEh5uPtdhuhUEjGCjdC1ml0dBSJRALFYlGUL/laHpVIpWfO2S4bdsyGa3PLdYKKDG4yLCMWi6FQKGBkZEQUVsViUVKCcc6wr2h6TpcFj8eDsbExbNu2DZ1OR9qWEdCp9CoUClhYWEC5XEYmk5F2pP81ALHuWF5eFnMyHbWUJuwMmKZN88vlsiiu2H7VahUbGxvIZDJwOp0oFoty8A6FQuh0OjJ+BjKQgTzzxOVyYdeuXRgaGjqPFdNigu5+jCYPy2agK5N509fyvGI+W9+nQY4ug4d4DWxN4M+69PPT5hmD5wZdR37G8vheGrhpgMEyNbgz68rnafbebFe+D4kHsx9MZYLuH7P+pvLhQn1rglZ9Xz9FgPl+pqKF9/Vj2zXY12ctbVKulRisk2ZB9RjSbW+2iQbTpoUBf7O/eEbku2jFRD+Azj30QooClm3b9nlm9fq3rg+V3gS1fDaVTSSA9LjnmGN9NHOtg7BpJYl+Jz1/TEWJOceo5OB5gEw055Y5H7SJvclim/1J4b3vec97enzhB/JvkwHgNuTmm2/Gnj17ZLDysMzBbVmWmBMTNBFgabDXz4xLR4TkZOJnZJapoaK/NJ/HiUVzGoISAD2Lkz7we71elEoleZbWglHjGIlEetJzkUHXwSa42PE9GQRKB8LQZkBer1dMwAkc6dvLz4Auaz86OgrbthGNRgXAckNmPSqVCiKRiADd/733f+PzC5+XlGCpQynUfr+GQ284hGK+uBVMY6SNE+86gerclgluY6KBE285Ac9/88D6toVQOITUjSk40g6cfd5ZpJ+VBizg2G8fQ+x3YxguDEuKKbYB25bKjkajgUgkgkqlgmAwKEHrMpkMbNsWEEszeyo++J6M3k5fHJfLhWKxKKbYtm1jeXkZ4XBYNKs08afi5/jx42i1uv7bTmc3DVsoFOpJOefxdAPLtVot5PN5bG5uIh6PyzjgmM5ms2i1WohEImi32xgaGupxG+BzNzY2kEwmxa+dOaptu2tdwCB8tVoNyWQStVoNPsuHPX+1B7VqDeeefw7OlhPP+ZPnwHrIQtldRigUEoVLs9nE0NCQuELQ/SEWi6Hd7kbRj8fjiMViyGazyOVyYm0Qj8dFScFxT9BaqVSQyWSkH5iqjsqF8fFxhEIh2HY3qn8kEoHP58O5c+eQy+UQDofRarWwvt6N5E4Xj0AggNXVVTQaDdRqNWSzWdGC83+HoxuV3+VyYWpqCtlsFo8//jjq9Tq2b98uCob5+XmsrKwgl8thfHxc2mFtbe0Hut4NZCAD+Y8jQ0NDuOiii3piogC9oEqbvvJcoRXx+j59LfcS7bcJ9II64HzQog/5WsmumVcNArivmddrAEzQYwIIDQC5drMsKvbNtEq63jyzaABvglbzb7N9NcDWosHyhUCurr8JRAlUeY8GUBeqiwmM+J4moNff6ef2q6O+To8LLeb/Jsuuz4uarddAj4oMbRFhMusmq6qfx3Oc2S/6enN8mgoTff4myaPfX7czsGVarn3FdQBjXqNZZF0Pnm15Ltbl8B79XF7LOcIy9DzR88Y0o+f9ehzp+cl7tZLE7EtT2WCOF8uycPTo0fPGxEC+fxmYlCtxOp145zvf2ROkgSbQwJb5MBcQAKJ51aH3NQDlgCWQJVgjqO50OvjSl76EWq0mAIHm6uZiqHP52rYtZjBkrwl0aQ6byWR6NqdgMCipRvx+v4AbTuZIJCLvyUBf3Bhp2ttqtcQXnemKmB9Ys9JkHGkCw+jUZBm5mbJ9COJoRk4TfB1IjkqFV595NWYqM938zQBcZRcOfvAgSvkSotGo+FG75lw9YJtSH6nj8Xc8jk6yg/TlaTzypkfw0O89hPThtID42nQN9/zPe9DxdrD54k08/vzHEUlEelKYRSIRuDwunLzoJO5/xf2AD4hGo5K3nKA8FAvBinUXXQZMsyxLUkLRD9rn86FeryMajWJ4eFjaq1QqIRQKAYD4gNMsuVgsiv+6x+PBxsYGHnroIQnO1mg0kMvlBAyXy2WxEmC0a61l5SJbKBSwubkp44xjbmhoCK1WS9LOFYtF5PN5lEolAazcZKPRKM6ePQuv1ytMsdfrRdKTxEvveSn2P7wfB3/uIOZz80gmkz1+SYFAAGNjY9jc3MTKykq3HUMhUcoQ0OuxWyqVJAI5xw59oDmPQqEQKpUKJicnMTo6CofDIab5Z86cQbVaRaVSweWXX47x8XE0m03cf//9OHbsGCqVCkZGRhCNRtHpdLpWEU8FN6T5t2VZCAQCqFQqSCQSmJ6e7o6nWk2Y8MnJSVx66aXYuXMnyuWyBFCjtcXU1JSkLbMsC+fOnUM+n5c5OZCBDOSZKSMjI9i7d6/8z8O4Xpt51uC6A2ydTUwAoIkADdxMkEbwoJ9rMrc8a/Awr81m+T0P9tpNR7N9PN9oM2ENXHgG0iy2zrms/WQJHoAtYMqyKZrNZPuZ5vQm8KAQ1PRj/nTfaBZSn9t0W/dTaJhMZ786aEWEBvtmUDLdfiZD2q8MPUb0c3W5ejyYdTctCfRzdT8Q8GnFia6/OT7JJut25zjR78nnsA/0+/JZ2qyaddBKGJ7RdcYQfk5T8mazKedYnt/ZbhxbWglBi1WOQU1I6XM7f5hZSJ+/9P/axF+TZuwjHexY9x3bjtfrOaEVLeYYBnr92W3bxsrKyiBuzA9IBgy3khtuuAHRaFS0qJxUejGiSQkHuAbfnPhM7URQTQDNCaq1iw6HA7fccguAXvMrgksCfE5k5rfWOQW5wHChYt05abkZ+/1+Ydw4oZhrmybYXEA0E12tVnvAGcEffV9ous76M50RNwD6mHCzZQAysvRURrAO9AsGIAsTFw6fzwerauF933wf3nzZm3GmeQb73rcPwyvDyIS7vq0ulwuhcAiP/PIjffs5sBLAwm8voHGggSf+4AkB2aa0/C0cufwIjvzskW6/wouZz8+g2djKM756aBVfetWXAAuwKhb2fXkfhoaGJIVTs9XE6ZtO49Tlp3D444cx7hmXBSydTndBlN+Jtck1jC2NYXR0FMvLywgEAhIkIxQKiaVFrVaTPjt79qwE6AuHwxgbG0O9XkcikcDq6qoATTKx6+vrcLvdEv2WKeJo1k8QTrBKk3GtVXU4HCgUCrBtW5h8+nPl83kxTy8WiwAghy+C9UQigfX1dbQrbTzrY89Cxs5I/3LsRaPdtGVUFnDTrtfryGazPdYX7XYb6XRarD8412htcPr0aUxOTiIajeLJJ5/EyMgIVlZW0Ol0sGvXLuTz+W5ft7rp4Rgr4dy5c5LyjZHHnU4nSqUSxsfHsbm5Kebg3HCXl5dh292MAdPT08jn86jVati/fz/S6TTC4TDi8bjM8UqlgjNnzsjBhCnKxsbGxMKjUCiIgiaXyw1ycA9kIM9QsSwLk5OTmJ+f72GqgPNTRfGwzD1XH/xNgKnL1/dp8KPNjTVb2+l0ZC/XAE77bfMsocEtyzfd6zRI1mchTXLo+032UiseTKaff7MM7bOrGW0NFk0/cs28moywZmfZD/1AaT/R1+r21N/p99dist+6rvoeEzzrNtPlmGbtT6ecYf+bjL+22NQAT9eJrDKv52fatFm3vwmkue+zTjxLc4/n2VeXo+tovm+73RZrNM2e83yslUj6GZpR1u5cbFetMLJtG6VSqWec6/FmjiWWQxad12vWmqBfRyBn/XlOY5uyD/tZWujI7hRiHD7XVJzYto1PfOITWFxcPG9cDuT7lwHD/ZS4XC684AUvwNjYWI//LUGIuahpk3FtgsLBTsDEFFZut1sYXB1wBIAwvgBEm8bgXwxKxucx4jgAMWnnhqv9qWu1Gvx+v0xKatbIAvJeBrjSG7dOyWXbtixSBFZaI05mXTP+5uKt/Z11oAf6/HLxoMkycxDTL5Y+s2zrer0Ou2rjp/7+p5B4ewLOO7fyF7L81HoKB/7gABL/0o3wDBvAnwCecx7sef8ezKZmYV16AaRNcQBH3nBE/v3OT30Hp152SgDkAxc/gM++/LMC2B980YN44EUPoNXeyhv52E2P4Vu3fQvr+9Zx96vuRtaflbHBBfLsT5/F/W++HxsXd31+k8kkksmksLHnzp1DIVDA0Z1HUSgUAEAAss/nw+7du9FqtZDL5WScsf1rtZqYXEejUWFfaTZOc3AGr2MEcOa2djq7wcc2Nzel7TnG6BMOdBfrSCQiTLXT2c01PjIyglKp1LO5ERj7fD7EYjExvQ8EAmIiDwDZbFZ8t227a6bOWAGlUkmsH2q1GiKRbgT2ZrMpFil+vx9+v19S5XFs0SWCCiNG/m82m5IOjIHJVldXEQqFEAqFcPbsWVE4hUIhmcsMHkcfbb/fj0KhgFgshoWFBfEx54amGRWm1KMShOk9GMWfB9lqtYpcLofHH3/86cfsQAYykP+U4na7ccUVV8gaaJrTAr2mtPrMoYGXebjXIFaDUNOc1wQ9fKY+vJvCc4ze/03Qo//WgFczyLyPynkNljT413UjMcByTDChGUjdPhdioTXY0H/rdzVBjPmj31m3Zz8gbT5ff6bf1zSjNvuvX7/oZ5omxBwHWsGgy9dWCea40L81yaIVFHoM6D2PZ2MSRHSfIlOrz2+6fnrs8BxrlqPnhG47DVh5fjFBM5lslqffmWNMjzP93nTN1KSXBsza9FtbkWhFCOvI8li2Zui1WbvJSJvtw3u0BYppKaOZbT1fzbHscDhw+vRpOVMN5N8nA8D9lFx00UW47LLLztNM6QANZLR5CKbGCehdMAlweX273cb73/9+AL2pKrjA8D6CXcuyevJlE2iyXnrya1NuHQyLIIXPJ8jhxkiGXvtvA11/dAByLSex1gzqSODaxJ4abf5NgM/FgIoHBo/jO5gboxk4g+/k8/kk97nP54Nr3QXnV5zi2xsIBMScO5/PA1lg34f3IXlnEq63uYC3Au2fbGPk8RFcfvnleOXiK/Gib7xI3v25n32umKlfSOLn4rCsbjqxJw4+gZanN4DV0ZuPotPp+gU/+mOP4olXPSGzbOWSFXz1F7+KtmvLWuHR//ooHrjlAdSn6njglx/AudFzaDQayGazEqGy4+zgm7/+TTzwcw8gc3lGomjrQGfNcBOPvewxAcSBQKDH4sL22Ljv1fedl2ouFotheHgYwWCw52BFE6J2u5tyjvEEarWaWCMwgBmvYSRuh8MhEes5jwhQs9ksgsEggG409HA4LLmyW62WmE2TWW80GlJOrVZDoVBAsVgUqwuHw4FEIiHzhYodpsZIJBIC0icnJxGLxRAMBmVcxeNxjI+Pi9KLZuoXX3wxgsGgfEffSsuyJL1YLBZDp9MN2kYFCRUf2polEonA7XajXC6j3W6jVCqJooLKsbm5OSSTSYTDYXlvzqW1tTXUajVRBgxkIAN55onX68W1117bF+xpJpGigSf/1wdrfnYh5tVkVE22UgPZC5Wjzw7832T8+Dn/1plNtLmsVvhrcMjnc13X5uUAet6f9/LdWG8N7vW79msHE3RfCFjrPtBtZwL6fgDcBPz8Xp/zTLaR1/TrV/O99Fghw6v7inU028BUtPRjRE3yiecJDfT0e2kQy+u1Wb92LdDWDLzXBLIkbvoBdB29W5tks77mmNN5vvlDcMt6aeUDr+VnwJalhn5/npPNutGaz/xcm7FfaN7wffQ1bFsN+KnE0MoQ3Q7mGOC79RtD6XQam5ubGMgPRgYm5ehOxMsvvxxXXHEF8vl8T95qYAt4e71emWyc9ATfXCjoU6E1fdVqFT/90z+NSqUiZt1kxZj6Qg9202RGT3LLsiTXsF4Y6f9KsEPfEAYrK5VKALbSi2nT20gkIoxyLBYTk2UCX7/fj1Kp1M2h/FReZ9u2xXe7WCxKCib6LrOugUAA0WhUojLz/Wq1moB/vqdt22JmHggEBNiZCwXfuWN1UL6tjHwoj+S3k5Lvm6nJ4vE4SqUS9vzhHjz52JNI19PwPOJB5IURDA8Pw+v14seO/xg8IQ9GOiO4LH8Z2u9u41/e9C+wLAtXvuNK+ODDN/7bN2BbNq74gyswvzqPsV1d3+Kb/+JmfPrtn0Yp0W1bV92FF77nhbA73Q1lM76JlrsXkG+Ob6Jlt+CyXXjkxx7ByR8/CdvVXVDLY2Xc9aa7cPXrrsa4s2u2XHfWcf8f34/ijiJgAd/5je/A+m0L44vjMi7T9TS++affRH2ojna1jdlPzqK02PVnB4DVwioe+pOHUJwpwmk5sedje+C3/HI4cblc8Pl9yAxn8PDLH8bNf3MzrI1uGq2m3US1WZXYAD6fTzaAjY0NJBIJ6VOXyyXzhynXOG6ZJ5t+3fSf5gaUyWQkBVaz2RQgPzIygkajIWPF6XQimUwin88Lk8+AdmS80+l0D9j1er3w+/146KGH4HA4kEwmJdjaiRMnJKL65uamMPSPP/44ZmdnJap4q9US323btpHL5cS1IpPJoNVqYWlpCU6nU1gqj8eDYDCIo0ePIpPJiPVMp9MRZQo35pMnT0qwt0KhgEKhIOb0BPkDk/KBDOSZK4FAAFdccYX8T1ClD9f92GGTVeT/tBzToK0fmCNI0Gwky+M9F1IC8DeBg64Xz0hm/TRDx3v0tU6nUxSUJtPHOpFI0EykBpomKOc791MEaIKF7XShezTYNFlmk3XUIFZ/9nTCe7RZu+5n3f8sS5NC7A9tgcky2JYcVxrc6vL1mNHvyv4yXRZ47uU1PLNpM2yTtNJgmOVps2p+TwUMy+CZhxZhPG/r87iuH8cX+4ZxkGg9xncwQTXPJlpZY85BzdDrtqBrpv6c7p56PPKMbQJj08Wg1WpJHAPdjzzzaDNynunYfvxe5y3ne2oLGj0f9Dj81re+hXvuuedpx+xAvncZAG4A4+Pj+Mmf/ElYliUpkbhgMcWR9mHmhCCg0Jo6vRBy0rBMDnA9obhwcAKTreb3NP3O5XKSiotm6mQTuegwaBoX0nA4jHw+3+PDQjNg3k+TXL/fD6/XK39rjWKn05Fo1gzIxrIIhGiqa27Q9Hstl8sIBoOyMHOT1QcALpwAxKS2XC6LcoEB1xwOB2rNGtafv47cJTnkkIP/j/yIfyUuSgSga5LcbDbRqrZQzVfROdiB9biFxcXFrTRTgRhe9ESX5S6WiwjfH8bo60cxMTqBqVNTaLfbuO5/XIdmqImd53ai2qji2LFjiEajcOQd+Inf+wl8/s2fR9VXxeEPHkZiKYH11DqcTieu+atr0Pa1sXj9Ytfs/ASAFwAP1B7A2NgYpv92GilvCvlX5gEX4FhyYObtM9h8YhPO0e7ieOQnj6A0WxKz9VaghaO/ehTjbx/vsrhzbdz71ntRHakCFnD8NccxNzaH9nu7m08lUcFjb3wMhfkCYAFP3PIE7KKNhU8twNnYMgtPz6Rx++/cjo67g2AniIv+90VweB24e8/dKC4UceAvD2DMM4ZqtQqXy4VkMol6s47itiKa32lK7AOyvLRI2JjdQLgWlojpDMYWi8WQn8xjtNQF3hMTE5KPMpfL9bgbTExMyLyo1+sIBoMSFZ1jYn19XeZTLBYTk/NisYhUKiW5zicmJjAxMQHb7qZJKxaLSCQSmJubE+UTI8Rz3lSrVXi9Xhw9ehSjo6MIhUKwLEv8u5nKLZVKYW5uDpFIBKdOnUIul0On08HU1BRmZmYQCoXEvH9zcxPRaBRjY2NIJBIYHR1FLpdDOp0WpVkwGITX68XExARisZjkGx/IQAbyzJNbbrlFzgP9QB/Qm07JBFb9gCPPBhR9yAZ6XcC0L6cJuPSeri2h9JmH+7lm17VpsAZ6GtiZZry81+/3yxkLQE/eY15Ha0INrPhOmqFlm2kfcNbXZGVNNlgDad0XGhRpppBtwv/7sci6TK0QMZUGWjTwNa81yzLL16DbVBb0A++mQkY/nwoQAkieQ1m+Vt5ohQvL0s/l2ZllsA7aRYFWXRrE8nqSW9ryiwC1XzA/06VC9x9BKfuYVqI68r1WfmlFkJ5rBNLmnOXY06bk/K3dKPSY4TuyXXQ/sL302O83B4gZCOx12lqOMT1u9JzJZDJIp9MYyA9GBoAbwM6dO3HllVfKIKUWDNiKjG2a9zAImPZN0doyrZnUQRkIlqlBA7YiCOo8k7yeg59sGrVY2iyd/icejwflclmYeKab0trFYrEoIMvhcIh/LZl2TkhtRkYtnsfjETMe+pVWq1U4HI4eZQTTWzGqOTdPrZnX2j3to02NHBlx7T/D8p1OJ74w+wV88KIPChB94I0P4GDgIKb/eVoYUtbBtm24XuVC57910HpDC/WhOjqzHdTSNTE9dji6qafcbjcc5xxolbbSQUwd7QJvy28hn88jmUzC4XAglUph2BrGCz72AiyHlxF6qBuNPBqNSvTwS99/KXyWD8eHjgO/AOAYYM1ZEhtg4j0TQBOoPq+KxO8k0LmnA9tno1KpoFwuY+rDUxgbGcOD/+VBwAKGHxzG9R+9HtFAFA1XA2tTa6gFaluB3yzgQf+DuMh7EQCgOddEI9Ho+f7IS4/A5XNh+p+m0Wl2sLhjEXfedic6nu6Yf/jqh1EqlhCvx3H0hUe7488XgvNPnGgXtg4TG7du4J6b78Gh+iH4jvkQDofFXLrdbiP17BTufMmduPKjVyL6ZBSRSATZbBZerxeP+x/HQ696CDse3YE9f7cHXo+3JzhfLBZDo9HAqatOYfvKdrRKLZln1WpVNiQdk6BarYpWXFuQhMNhFAoFBINBzM7OYn19Hc1mU4ITjY+PIxqNIpfL9WjCOZ9arZb4tns8HuTzeeRyOTkMWpaF0dFRTExM4Pjx4zKnLatrjcJYBBMTEwC6QQhZ/tzcnCgR1tbW5B1CoZCw5EA3IBvXooEMZCDPPHnWs551QUANnB88ywRX/F+zl2b2FF2WPpibpsP6WRqk6WebbK0J3MyyTZCogQUV/LyWbB7dxbQZsAngNHDTbGq/9jHrrckPlqvNeE2wyc90fbTockwQo/tUv4PZHv1Muc1+6VdnE2zxev0+BGFmkDHdhv3aj/drsGi2p6lkMAEn+4d7o2aV9bsQfOryyJQznhIDA+tzobYKtSxLzhIAegA8AMkKxM/IVmtSTLPwZv9oBY7uS00UmEoc/tY+5P2UPLpN9TzSlg5a8WNaMWgTeZ6H2O66v/uNFV1uvV7HuXPn+o7Dgfzb5EcecDscDhw+fLjH91hr5mgmTZCozcy15ooTnmbQ1WpVmDDN3OrJRrCnNY5kwunHSV9xTkhtlqL9yWu1Gr7+9a/j2muvRbvdFjDACMc0ywH+/+y9d7hdV3UtPvY+vbfbi+q9ara6ZMkVy7g7NtUlmMRgagjF4GBIAk4gIYZHh/DoPKptYmLADVuucpdlNUuW1XV1ez29l71/f2yNdeY5ln+PJOQF7Lu+T9/VPXefXdZae605xhxzTigvcz6fb8huyPhtli2TXnlmDedCxXNRPs4+IyHAxBaSNSfryMWBWcmdTqcCGuVyGU6nU8XwctEj8CcIjhaiLxvLymA9YRZl98lkEmMXjCF9QxoIA+WvlbEzvhPpSBofeegjKBfrGd27u7vxQuYFJG5OIIkk+r7fBwxCgUg5NtlsFi0tLdA0DS3JFjgHnaj6q+qe6Zn3eXxY/ZPVGD82jszTGdVnBO1erxdtX2tD9fEqbE/bYJhGQxIOu92O1Q+sRmokhcl1k1j67aUI28LQdGtMe3f0wvhfBp7/zPMwXAbmbZ2HZT9eBl3X4ff7ETgYgOcHHjx6w6MoBoqqr4K5IDTzRKx2AtCqjcbTkQuPNPx+4MwDSNQSWPW5VVZ5q/cXcfzy46i6q9jxvh2wf88O/1Y/YrGYFd9+2j4895bnUAqUsO2d2+D8pRM9z/TAMAxk52TxwkdeQG5+Drvn7UYOOay6dRXsdqvc1/HjxxEOhzFy8Qi2vmkrki8mcdZPzlIe63w+D13XMTo6quKraTDYbDYcOnSoIdM6vS4ejwe5XE69A/TW+3w+bN26FaZpqpAJyt9HRkYQi8XUe+jz+TA1NQVNs/ouHA4romx8fBymaWJychJOp1OVp2NitpmZGWSzWTXPJSvOZHIkqwConAUjIyOKIDtZeMVsm22z7U+7+Xw+nH322QAaQY30+jUDHPlTehWbPZTNBjs/A+rgvNkD2SyJbQbXvK400CVokteVnjQCgOZYWgnCuF7zu7JiS7M3WgIigpNmhV2zZL4ZWDeDZJnIjc8h/9/cB/zZ3F+vRFQ0H9N8TumtlNdpVh7I63PsJCCUnzePV/M+0kwGyO+fjOCQ/Sv/JueVHM9mJQbHW461jEeWfSMVGnL+yPuR98vna3aCycRj0it8Mg82z8t9Wh4v5yjtZx7He2weB2nP8Z6k517OT4JjPguJEP7j88uxku8hvyfl6RIzyLnIPjzZ2jAyMoKf/OQnmG1/uPaaT5rW19eHv/7rv1Zgmh4lLj5ygssMmgTDBLuc0PyO3+9Xiwy90AAaXkC5KPFvTArCe5EvlUw0QUBOgz0QCGD9+vXqhSaBIIG+3Bi9Xq96VjKB/F3TNAXY6fkmyCQY1DRLKu/1euHz+RrkZlLCImN2ZNkxHidj1DVNU2XA5MJuGIZK3MWF7tzkufjU85+CVtWgVTSc+rFT0Xe4T8nig8EgqtUqZtbP4PiNx1ENn5AXtZkwlhg41H4It1x2CzR33QvgiDlw/9/ej8KyAvLL8njg7x+ALWRTEmbGMJfLZYTDYXW/lNJXKhXkcjlMTU0BgKofnp/Iw3yuvnFw/FjS6/ILL4dvh08BcNM0FWCvVCooFUqI3hpF64dbEYqHFFHhdrvh9XrRvq8dr7v5dejY1YGV31kJf94Pn88Hp9OJQCCA9qPtOOuTZ8FWsUGraVj141Vo+VULpqemMTY2huq2Ks7+1NmwZ+1ADdA/rwMLAUyKF6UCZG/OYteuXRg4awBHrzuKqtvq01xrDk+9/yk8m3wW27dvxzOhZ7D1qq0oBSwCpxAq4Imrn8BW21bk9Bye/8zzyMy3yAfTZuLIpUfwwhtfQKlUQjQahcvjwpHVR/Dslc+iEqjgxfUv4ql3PAWby6aAcKlUQkdPB176m5dQ6bQ8IFNTUypJH2O8vV4vDHu9XB1Jm3A4jLGxMTUv6QE3DCsJ2vHjxzE5OalCD8bGxhRTTDmZ2+1Wdd9JEOm6rvIaMEka54eu6xgfH8fk5CS6u7tV9tRisYhkMolarYZAIIDOzk717jG+vK+vD8DLkxTNttk22/7028qVKxVxCKABKEgvLvfYZi9iM2gDGsuMyt+Bk2fIBtCQtOxkQKbZqJet+W/NYOBkIFfei81WTwLLuFOZBIvrqHQ+SAcJlYQSZNCOa45lb/4pvbvyn+yHVwK0QCPwl02SH9K7KPurGdzymnLM5LHNtpbcE04G7jmmBHY8l5RP835eyXPP1uxZlcSJVHJK0Evlonyu5mRl0tamLcXxow3KvbI5jhyASjxGW7pWq6kEr/Je5BxpTlpM21KCdYYz8jMZ0kVygPYqiXSJB2S+AjlPeH5+X5IgtHl5LAB1r3I9kH3ZPBckHuHz8R2RYy3HSq4phmEl/h0eHj7pPJht/7n2mvdwf+Yzn1EeaXqCCWbkZtVcT9Lr9apJLBlcxlxLVtHlcqmXiufiSyIXUMlK8V74UtZqNfXyyxhzfq9Sqagav5LNovyW8lq/349araYk15VKRXndbDabKpFEYMmFkvcaCATUS8+ET7xnuQlyYczlcg2MLRNrkcXjC+5wOJQXT2asZP9Sis/xyWay6DrShUX3LoKZNtFzqAdlm+WFjkQiSKVS8Hq9SMfSMAInBykjgRHc0nsLLv33S2G32/Hrd/wamVBG/T0Xy2HLe7Zg5T+sVPJ0GgWU+NPD7/P5kM1mUSgUEAwGEY/HlWLA6XSqpHUAVNw858Tdd9+tiAuOBeOQCOSmRqYsMN5m1XnmP5I03n1erPq7VTDsBkp6CXPnzgUARQj0Fnpx7Vevxd6FezH37rmolCswPaZSI+jDOs5631nYds42mP9sIp/LA0sBPAtoIQ09N/bAM+ZB2VtGZEsEE7dOIPXOFEynCXvKjsDfBRCbimEiPgH9pzoC0QASH0rA8BrQMzpc/+zCxK8nEHfE0af14ciXjyAfyQMGEHksgnk/m4eyvYypqSno83S8eN2LKHsseZZpM3Fg+QG0HGjB3EfnIp1OI6fnsOOKHTi47iD2r9yP6791PTACHD9+HIsXL1Zz77h5HPffdD/e8uu3IBC3MoAnk0mV2bylpQWDzkFk5mXQMWklwxsYGEBHRwcmJiaQdWeBKFAYsuTu9LDLxG61Wg2tra0olUqIx+PI5XJYvHixypofj8cxNTWF1tZW9d6mUilomhWiEI1GVRiIx+NRJcl4XHt7O6rVqpKmz7bZNtteXe2ss85SShkpjZbe1pMBHen5YjuZNPVkAL0Z+DaDr2ZPsPSONXtgJWCQ9lAzAJXhcEA9R470cDZ7qbnGymeSx/PYZjKSBCnvVcbWymdujiGXYFX2JxVT8tkksDkZgJa/Nycbk3///wO4zcdJObckGORYSGAo+0eCR/YLf5eVNeTfeM7m+2r+TM41CeAkcUQnEBWS/KzZ3qOdy7kkPdRAPXlbM7Dn9eS7wfxCsj+lnJ3/mt8pqa6Uc0k6i6T9LftYSs1lBvHm94bPKoGy9MTz2tLLT/KJ9y/7gf0m5eV852SiOD6nvF7z+z02NnbSOTnb/vPtNe3hXrhwITZs2KAShjHrMWXYEiQCdXZRslZ8IWV5Lb5kbARFEkRzcjeX2ToZYytfBqAej8INQDLB9JqlUimVCbxUKikpbS6XU5scACWFocHPz3g/ZAll8hIuLnyhKQfn88uYcq/X28DIMhkaE7ZJmbl8HpfLpTKs22w25WHmYuJwOOB0OOH5rQfOB53K42+aVqwsa55Hk1F4D3lPOv6nPHsKln55Kfbv34/x8XHM2Tan8QAT6N3ai2g0CofDgUgkouT2sgRWuWwBRYL9UCikSm253W4kEgnYltqAZdZpmZiOz2yz2dDa2oqFCxcqMkdK9XXdyqzNmo/sd7fbrRJ/2W12RMIRVU96dHRU9QlLiPmP+7HinhXwuK361KlUColEAgAQCoUQyAQQ/VwUpaJFFCAO6G/S4fuYD9rjlqIhGAwil81h7rfnovWnrbAlbOj9Si+WH1yOzs5O9PX1Yc2aNTjjqTPQd2sfHDkH+n7ch3mbrWRiAOB7yYfzfn4eQjMhuH7pQvCvgkin0qqGtm/KhzO+eQZ8A1b5MEfJgY2/3Yh5j81DPB6HzWfD9jdsx8ELDgIaUHFW8Itrf4Hy+jLWr1+PUChkJWmbW8ST738Sya4kbn3HrahtqteAd7vd8Pv9yC3N4bZrbsPtN9yOmTNnoGmayk5eDVex5917cPemu2GEDBTdRRybf0x5nzs7O5FvzSO9MI2BgQFMTk6iXC7D7XYrkD0yMoKxsTG4XC5MTExgdHRUJSAMhUIq+RvrgpMo8vv9qjIA39vh4eGGdWW2zbbZ9qff3G431q1bp1R2zO/RTLhLQClJeuDlib2a/w6cPEO2BH/NYFwa/VJ5J0GC9KpJgCCJgmbwJT14BFvN8bJSzcd/zYCW35EAkX+XIEZ+Ju+r2bsqAVvz58191Xw+SVycrI/ZTuZBbj6v/PxkoFaOa7Ok+2TPzHNJ+1TajPLZJanT/Az0xjaPVfNzcmwl6JRgn3uYdNxI5UPzvfJ33i8rfEgbWYJb3r+cu7yv5n5q7q9mkkc6luRz0WaVz0kQy/PK0ED5bOx79qWM95b9wb6VatjmecTjZT/xnkgq0KvNc8owz1dSOBiGgVtuuQWz7Q/bXtMe7ne84x1oaWlpiM2WLwxQXwy5+fEYmbSBUmO+GBIgN7PKfBG4UDOWBWisVygnv5Smc4OSZIBcDAimXS6XknDzWVgrmbGhTPDAbOBA3UMvMzby+3Jxl3EskumVCdfYZ36/H6lUqiFmhSWWGLPqdDqVB8/tdjc8C+VlUuZjt9vh8XjQ0dGBI0eOKBBeqVSwbds2uFwuBINBePZ7sPCWhTj4jwdRmlOC7SUbbMds2DR/Ey584UJUz6xiYmIChmHg3jfcW08uBgA6cPCag1i3f10Ds8lMlLlcTgFn3m80GsX+/ftht9vh9Xotj/g8F8x/Mq1zXwv1OWPX7XY7ZmZmkEgkVBiBzCqpafUEeJSZy7nocDgQi8VQq9WQSqWs5z4BqDOZjApPKJVKDfkIZKI9TTtRBkyU9wAAbb8G94Qb7qhbkS+pVAo+nw99P+lD+/F2BB4JoGarqfj58fFx6LqO6A+jmNk2A89WDwrFgprrLpcLSwaXIP+dPLZ9axsqtooiDwKBgJUsbqQHq/51FXbcuANLf7kUawfXImu3Mo/bbXYE3IGGd1nTNOimrtQquUgOj1/xOIa7LElUwVfAXZffhXXj6+B6yYUFCxZgJDqC+954HxKdFulw67m3YuPYRqx4aQWSuSSefv/TOHbKMQBAzpaDaZiY6p7CBeULEHguAHurHQ9f/TBKnhI25TYhNhVDJpNBMBiE3W5HMplEpaeC4RXDWLJjCcLhsBrvWq2Go0ePWv19moYZcwbOR51ob2+HplmhCoFAQOVOkAkLZ9tsm22vnjZ//nwsXLiwwWZoBrHNnikJqJvB0cmAn1TQAfWkjhLESvBBG0YCUJ6XhvsreWUlKDyZh50OCLmHSbDWbP+wyXuRXttmwCr36mYP7cmALZuUNDc/nwTd0tt+smds7vNmEN/8TPJ4eZ5mL3jz3yRBIL20PF7GATffC78rbdVm8Nk8t+Sc5HklcJVx+CQj+Dc5X082ttKGbH5+Gf8vfzbfBz3OUgF6MkJEzvVmEC4dSXwO/p33KPtJzmtZNUjeq3x/TdNskPafjChqfuebSZDm9UHa5XLu8pkYKiqfQXrFT0au1Go1bN++HbPtD9tes4B70aJFOOecc5Q03DRNVU+aIJAvD4EOJysXD8nk8gVl3K5cLOmxlgkT2Pji8JxMniSvIxds+QKmUiklOQagvKWshU0ZKgEWX0BKygnE6DXm4lssFtVza5ol585ms0qWzuN4Ppnpm4CR4IvggfdPKTlf+EAg0BDHQgk75dhcyAjKSQLk83lougbzWhPYBdQOWGCTMbWmaSIWi6FYLKJ3ohfxa+MY+fEIzMtMrF2xFm946xsQDodhBA10d3ejVCrhH3f+I27cdCMM24nFrKrhvG+ch0wmo+owc9wzmczLjBS73Y7x8XEAFiuZzWYR7gjj6X94GkbXieMeBPJvy6ts8VNTU6psHGOUwuGwIiB0XUcmm0HFrCiwTJIlm80iEAgoqTK92SQeWIaKC63P51P3RrkzF3duLhxLNgJk/r9YLCIQCFiyx3IVlV9VkHfnUSwWMTU1hZ6eHlQqFbS2tlrhBL/WkfKlGsIOuAFEd0URtofh9XqV1Ixj7Xa70XqkFRd/+WK05lqRrWVVzLu9Zsf8n85HvpLH/ov2w1Fy4NKvXQrvmBdG1FIf2E07OnZ24GjnUYvoMIHwWBido53Iu/NIO9P49XW/RiqWUs9aDBXxzNuegfeHXuw8byfGltUlVUdW1JPIPfAXDyC6OYr8dXnM9FolM+794L1Y8941cBVc6OjoAABE5kTw8xt/jqKniPB3w+ge6UZfXx/8fj+SySQSiQRKHSU8+u5HUdEqOGP6DIzsHEEoFEKxWFTv2cjoCOJXxHHk6BFg3++zus222Tbb/lTakiVLEI1GlfEtAZKULDcb5vw/gJcZ72wyvEwqzXhsMxDjz5MlUZIgSUp/pfEvjX2u+bL8Fp+JNkmzF06CGt4DgVQzKGO/NN8/vYYE0FI2DTTK2k8GaprBdjMxIfv7ZOCYf5cASnosT2bTydZs550M/DffH68hge/JMrbLczRfS/Zls5xaKhukPFxeVzqSpKdYOm1eiQCRc4geXl63WQHKa8ryVpzjtLGlvc2xps0qHVucy/LdkPOg2fvcXKJM3ht/53PIfpH3KPubRIFMDEgVJZ9VJkmT85N9JN9FuTbw2aSHvDnhmjyO42Kz2TAyMvKKhNps+8+31yTg1nUdF154IdavX98wWZlVnIsRGavbb78dF154IVpbW9XfKVMBgMnJSYyOjmLp0qXKa9wM2mWmcd6D/MmXmZOc4FPWF+RxgPVCh8NhAFYW7YceeggXXHBBQzkkXtM0TWXAE1xzQSUQp/SFXnTeBwG2pmnKe+1wOPDCCy9YZa26uhpe3AMHDqgET8yCToBFME3ZO/vS6XQ2LE70AkrVAGPC/X6/FXNs17Gjcwc2b9gM8xoT3k960b63HfF4HLquIxAIIB6PK8LAtd8F93o3ip4ijHEDtWQNtrZ6rIzL5ULbZBve89334AdX/QC6ruPUG05FxB5B0bCUAU6nU8nk0+k0vF4vZmZmEIlEGoA4y1s5nU5sfs9mpDvT9QFcCBz9ylFUr68iGAwqcoFJuDRNU6XdlBx8XgUDfz+A7uu7kc1mlYw8k8kgHo+ju7tbkRrt7e1wuVxIJpPw+/0NEiXGwZfLZUvmfiIuiYqCTCbTMAc5ZjyPpmlqLClvZyw5k8ANDQ2p/Ae6btXXHhgYgK7rCIVC8Hg8iEajKPvKGE4OI5lMKsUFCQBN0xAMBuH3++HP+RXZo+s60um05cW3BbH+l+tR0At43eOvA4aBGqzs8Axj2PDYBpQcJezatAvh/WGc/uXT1TlcWRfWfW8dnvrwUyj6LC+yrWzD8keWo21vGy4dvBT/9ul/QyZaj+lnywVzyH01B3TXP8vEMnjipicQuCiAHTt2IO1Po/ZUDbWWGqABT9zwBEY/OAr/o374/X7E43HMPWcuHvzig6h6LBncllu24OyPnq1CFFpaWuD2upFYk8Djb38c5ttM4AIAW15hYZtts222/Uk1XdexfPlyRKNW1Y2TeTxP9vkreUb5s9nzy/212WMH4GUgVoITaQ/xb5qmNVSA4PmagW0zSJTXlaExEpDIGF8ZU8tjeA0J4pqBPq8tY2ybY1W590lA+krgU34uf2/2ODZ76pvJjJOB65OFDDaDpldqUlEpCQTp1JASYTlOchzlMbzH5pAA2jemab4seZ0Eb3KMOO68lkx2JokfVvaRfcX51Uy0yO/yXHJ+0tZudiRwvOnYYAil7Gfapuxb9lezd1oSEhJ0S5tWglo57vJ77BPm0ZHOFJkgUTq4mj3ZEnRzLsqxku8NyS9+LkkTec+maeJf/uVfGpxJs+0P016TMdxtbW04++yzXyarki8WPayapuGaa65BNBpFMpnExMREQ8yEaZro6OjAqlWr4HQ6lZybycUIYJsXDb50coPiQmYYVgktLury5eELVSqVFHB1Op249NJLFUjjgkIwReDNBYfZj3kMZcfSYz8yMqLAq3wRuSAsWrQIXV1d6rxcEGKxmALajKMGoGTYXPj27dunkqBxoSM4TqfT6vkAS07rcrkaFtInOp7AlzZ+CaZuAnZg9xd2Y2jVEDwejyr/1MyEVq6sAL8DkpclUYqV1KLDfna5XChtK6HzY52Y/8n5yD6bRSFvgf9gMIhgMIiRkRFMTk6qOHmOLftlZmZGAelKpYLT/uU0dDzXUZ98vwMC1wQUIPf5fGpzoXc6GAxacdl2O3Krcrjr43ehekoVhR8V4F3uhcPhUPHU9IzIhDucFzMzMw1ziwm8CGA5n5hMj7HHkv3mQk0POgkll8ulksg5nU71/3w+j2AwiKGhIRw6dAjRaBQdHR1ob29Xi33ancbDb34Y+6/dD/igVBl+v18BeofDgUAgAIfDoWpgu1wuhEIhjI+PI5fLwe/x44wfn4HCwQJaWloQDAYVEZBMJlHIFnD2787G6kdWY+XHV8Ln8qlnCvgD6HqpC2f/7Gx4Eh7oVR1rH1iLFXevQKlQQmGygDd9/U2IHI6ge6gbC59bCHD/2QbgQgD/Vh/W3gO9mPdeK049EonA/24/jIihQhRMm4nDFx7GC3tfwM6dO3Hw4EH8rvd3qDrqhqdpM7F1w1YcPnwYBw8exJYtW/DCqS/gsfc/BtNmAg4AmwFc+l9Z/WbbbJttfyyttbUVS5YsaQAc0qgmcJLgrtlekaQqvyfl0TyWf5dJmP7/gDv3Ztn4uzTkpfFPo745Dl3aMrQDeB15n7RRpDdPAiOCCQJzec8S2DocDnX9QqGAgYGBukJKVAuRTfYF77fZPuQznkxqLb/bDOTk+eXPk4Hr5uP43PJZ2Q+yyb6SccbNRIIcaxmLLftcNhlnTKeI/Ezaw83PJe+zWeYtPby0K2jn8h6lfdz8rLQ9+Xd5j6ZZr9XNMWJYIkkG+fwE4vxMxnDL78oxISiXNmCz46z53ptLC0uvOD+Tzy3tdokfmvtbfia/K9cG2vJyrvDYZmJmVk7+39Nekx7u+fPnY9OmTWoyEpDyJWaWcsmQceJLqbVkiYGXZwKlTIRgVDKgchEEoOpP09vLlz2fz8PlcjXIY5o3OS6w9H5KSTa9pwTTPC9ZND4PSxtIRpgefoI4Po9h1Et0yFh2m82GlpYWvPDCC+jq6lLSY13XGxYMv9+P5cuXNywQkpCgpFpKYSizJ6NX9pZfNq5m0FTH0VggCCu8r4DaB2uACziw4gB+MfQLfHT7R+GpetSiVygUEI1GYft3G3SPjtaOVhiGoWpwM4N7qVRSsbbpdLqhbzs7OxsWZbfDjTXfXIP7j9xvSdU/DLgcrpexzF1dXUqFQHCZXpPG8U8eRyViLciTp0zimXc+gzO/eyYCCMDlss7DGHkC77GxMXg8HiXXZ99ns1m4XC54vV4rOZnP1yBvjptxVN5Rgfm9xoQsfA9oSDHBIOcg48pJ4vD5Ge5w6NAheDweRCIR2Pw2PPeO5zCzYgZYAdgMG6qfr6pYeJbyope8VquphHE2mw2BQED1O5OjUYrPuUzgz41tw90bsLe4VxFB4XAY4+PjKJVKiDwawYr4CpSjZSzbugzQoUB/7WgNG7+3Eb2dvchsz+DY4mMwLjGAdwE4Aujv0y1J/EIvrnjgCtg32RUZMnp8FBOPTeCui+8CNGDNnjXo39wP7a2aisUe+c0ItFM1bLtiGwBg7m/mIvPxDHZVdqFcLiObzWL4xaayHBqA4H9i0Ztts222/dG1jo4OLFiwQP0ugR5bs6fxZN7PZjB2MkdCM/Br/q48t/SQn4wIkES1PMcr/b/ZQ1m2lfEDxw/wnvx7ANTD4ZpBhfRCN98X955m4CIBDWAlBGXJx2YniwSZ0pvJa3Kflp7z5n6UHl3Z+L3/GwCX5AM/bx5POZZSui2vIb8vv0tbSHr8T9Z4vCQUaI9Ie4VjyD6XZS+lTSrtyWY7meeWc06ONZ08vJYsKcv+bvZ+S89u85yhHS/HST4vzyOJAnqgeV9S0SGvIUFrMzEmCQQ+o3yP5Bjys+akZnxe+Xlzf8o1g88rw2BlLiep3pBqVt7P4OCgIkBm2x+2veYAt9PpxDvf+U5Eo9GG+BJ6CLmIc1LKxA+hUEjVyuSxuVxOTWZKgjWtXkeQQEGen4sCwSr/Lhd3eq51XVcyWSa6kiSAXPR37tyJ9evXNyx48j4AKM8x78FmsynAxuyPLpcL0Wi04Rr8vvSeN0vK2Lq7u1XMtYyBkbXL5ebGcmBc/Px+vwLb7Af2L8trvf7o66FXdXxj5TcAAOu/vB7+h/0wHSay2SwMw0AqlUJvby8OXngQ4+8YB1z1ebCzdyc+7/k8bn7kZhi1+uJdKpXgutoFu80O53PW/cdiMRQKBUxOTiIUCiGZTKq+leXiGHtjt9tVXHY+n0c6mYb9JjvKlTIwCUSXR+H3+9VYlEolTE1NqVJjpmnC5/MhPBG26iD2QMUhe3Z7kBnMIBAOIBgMqnibcrmMaDSKRCKhMpXbbDakUikFhN1utwpzAKwFNhaLIZ1Oo2pUsetfdsHsNoECgJ9BLdosq8G5yDwFzH7u8/msRGW5HPx+PxKJBDTNCtHYv3+/UlkAwOT3JlE4q6DGoXZdDamOFEK3h1SVAG50TqdTZYNXNc1PJI/jnKcnJZfLwTRNhEIhVcuVZFWtVsPSpUuh6zomJydVngI+f/BYENk9WUxnpxWYb29vh67riI3FUJ2pYmxkDOavTeAnAE6Ec9uLdpz681PRMqcF7a3tMCKGIg5OPfVULHxxIXwOHw7MO4A/3/7n8JzqUUoKRZ6ldfRu70XGl8GbC29G9sasugeHw4HhsWGkHkvh9nNvty56JYDf/qeWvtk222bbH1nr7e1Ff38/gEZD+GTguhmYyeOlwd0MHJo9ufLc0uiXYFPaFs33QpuFCqlmAMRjCDZ4vPI8Vsq42n81nrM9Bw0a3lt8bwMYkLaBBOF8fnoWJejhZ1K5xfMwrI12GdV0JyMnmvuV12WTwPyVSAa25vM3HyNBdfM5mv8mP5dy42YiQwJOjo0cVynFloBOXltK+SV4l8/Pa53MicTjm695MvJGglT+3gxCOb7sy2ZFqHRqSaBOdSPfgea/Nc95Prucu9LhII+VHnA+iyQ0JDHC3zlHZXJESdY0Hy8VHFKOL98R2dfNc4j9IlUpchzYD825pW6//XZMTU1htv3h22sOcIfDYVx99dVqQsvSG5TNEhw888wzmDNnDrq7uxviZYB6fWO32418Pq88fpVKRTFRzUnUGKtCAKxp9WydplmXsXODIEClTNwwDJVIi/cQCATUQrNixYqGF1vGhtBTS082E1VNT08rAA1ASa7oyeUL6fV6kc1mG2KDeA161Ckbj8ViDeSFBFCMA+fz04vu9XrVIkVPLxPasU81TVMy9Wq1inMHz8XR4aPY9+w++J72WV5Yj11J6xl7bIvaYDhezkDvi+7DJ3o+gUt+dom1gBo1FM4s4NC1h6BBQ/FDRZwyeYoC+ZFIBIVCAV6vFwcOHEBHRwfsdjueffZZ9PX1weVyNdTR1nVdeZkxBeAEaZjNZuH1ehWLSLCYTqcVeDVNE8gCq29ejZf+90uYmTuD1jtasegXi9AaaYXNZrO80vG42gzYxx0dHZiamkIul0M0GkU+n1f9nk6nG2qsDwwMQI/q2PrxrcguzFrA/vsAcoD5axP5fB5DQ0PKoy/nYyaTUXLwSqWCtrY2OBwOZDIZxGIx+P1+HD16tAEI+2/24/hdx1EIWKDbMeZA1+e6kGnLwDRN9T74/X7kcjn4fD5Vs9rhcMDv92NqakptJMViEW63GyMjI5g/fz5yuZzqx0qlogixRCKBxYsXq1h8VhYYHR1VmdHD4TBiMSvTeDgcRjKZVKqA4eFhmHlTgW2+K22ONgRzQYyUR9Db26uY+EKhAJ/Ph7V712LTyCY44IDpNRV5RAIq4ArgrUffamVxN+3wtnlVKTCXy4UVK1agGC/CttWGX3zxF8Bd/6Xlb7bNttn2R9LcbjfWrl0Lr9cqW3kyz6MkF2WT+zXtkmZAIaXa3COoxmo20pvB0sl+8lj+lOeR/5fGuwROtVoNM5UZvCf4HjxqexTQgE+5PwWP4cE1pWvg0OqJXaVMuRlE0gEgwR77QII0qayTf5PexGYvKb8jAcnJWrOXWj4vgVQzcG8eV9nnryQ7b74HnrsZgMp+ph0lwwYIJBnmSFsyn88jFAohFAqp+2ieD3y2Zs+3fE75TBIUSzAsE9jJkAT2vSzf1Uy4pNNptLa2NgBhAMpZRXtc2vS0O6UqlPNQkkaSDJL9ymOb3xXeQ/M8lXNQEgd8pua4ao4Zx0omu5Phpa/k9ZakCs8vf8rxkP3Kc5TLZRw9ehTj4+NYu3atSra7b98+VR1ltv1h22sOcH/qU5+C3+9XLwkn38mYn1WrVikAwJ98weilYuIxAmZO7kqlgng8DqfTqUCxlGTXajV4vd4GlpL3QmaO8mheR8Z60Psta+xxQZMyYsMwlLyWLzZBLMEXQTwlyvzc7XYrIF0sFtX9sA9k/cR4PI729nYlay8UCiommf1HOTKvz2zUpVJJASB6VQlaSFgQyLBVq1XYbXZ03tWJPZv3QFtQX0CZMI2J6zqf7cTgmYNI94rkZQBanmpB5MMRPF5+3Ir3+jM7nr/ueUAHTJg4/O3DCH0qhAWHFyhgSXn5woULYbfbkc/nsWLFChiGgWQyqTKAcwy5YMoFkp5b0zSRSCQakuq5XC5FONjtdkTKEVx8y8W4Y/0dcN/iRm1BDR6PRy3qbW1tqNVqSCQSaoMaHBxUNcBrtRrC4TASiYQqGUbADQA+nw97T9uLVEeqXhLNBeBGQH9Ih5az7j2VsrJ50zvMUAVmMO/q6lKebW7g2WwWnZ2dGB8fV4aePW7HG7/xRjzwrgeQGkuh74Y+hOwhJYmPxWJq/vJ83ByBegxUoVBQHuxq1ZKkj4+PIxAIwOfzwefzqWz9o6OjmJmZQVtbG9xuN1paWjA4OAhN05Tnn7kD+P4Wi0Ukk0nEYjG4XC7MzMy8bC3hfXGzn56ehtvtVmDf4XDANEzoVR3JTBLz5s3D+Pi4IqGownC5XKgWqjBtpopZl6SWzWaD54ce2H5rQ82cLQs222bbq6F5PB6sW7euAVwRpJ3MEwc0yoRpO0gvYalUgmmaKiRHEuPS0yY9pfLavKbcr5q95M1glCABsAAKlVu1mlUqMpfLoVAooFarYXNsM3aFd6m9pqpV8WP3j3FJ7RJEK9GXScpP5uXn/Ujwzdb8PM1hUc1AVx5L26b5PLwujzmZpFd6+CURwHM1j5/8frOXV/axPD/PS7KBc0OCQtpABNc8nmNSKFhEN/dG7kFerxcul+tlqj35DFJp0Ezs8B4l+OT/peeWdotMuicBIseMthP7gU4UOsMk0G8eA46hlKgzOZnsVzpF5H3Juc1+lM8v57wkS2T8uATCPBd/SmKGx0lPdrNyQPYjx1eOCe9LkgzNc0euGfw9k8lg27Zt2LlzJ/bt24fHH38c3d3dOH78OF544QXMtv+e9poC3F1dXbjiiisaJmHzZCbLVqvVgY30bEtZl1zI+SJIuQnlS5zox48fRz6fxymnnKIYJslUM6EYz0MvMGWqzfFMksHj8ZVKRX1GT7CMIZeEwYEDB3DKKac0LM5SRswFhF5BAIrVkxtfIpFAMplEZ2enWmRkXxJ8ymQVXq+3YQHns3Bc+H2CUfZR89hEIhG1QAeDQRVrHY/H0dXVZTG5oSJq7kag0vVgF+Z9cR5cEZeKzd/5/p2NaQR1YP+H9mPuB+fCZrOpODDWi+Z9SSImHA4rSXt/fz8OHToEAHBscKBYLML/ol9lX6chwu8Hg0FVHk2C4lKmBP+n/PC1+VSWdhIRnCPRaFSNVywWQyKRaPB4ML6+WCwil8upRT6TyWDe/fPgMBzY+s6tMOwGcD+A91mS6WAkqFQSLpcLPp9PeeQJvvP5PJLJJLxeL3K53MukU4zJJ+j2H/fjjB+cgWfvfxaIA+H+MILBoCIj2B9erxd+v19tfDMzM7Db7SiVSgiFQla28RPx5OFwWJEy6XQaHo8HlYpVSo1KiXK5jGQyiSVLlmBgYEAZFzxvLBZDIBDAzMwMKpWKkuZPT0+j9L4S8EUAYhqZN5go7rOk6fl8Xs1pqjQki22z2XD48GGV1I+GDoG3LIvGdYWkWzabVeEes222zbZXR4vFYli7dq1at6V3kU16DKWxT8DRDJKr1SoGBwcRiUTQ1tYGoO795bnoLQNOHk8rgTavJ414ekhpa3A/yOfzCnBTrQfUlX4OhwNvyL8B3kEv/nneP6NgK2BVZhW+Xvw62l3tMO2NcdXS9pJ7mQwFlFJbadfxd2njMaxPPrMEidIObPZaSlB2Mu+2tG14j7Lv5M/m/mwG6M19LwkPCbJORhwUCgVkMhk1PvI5AKh9mCFvvH6zV7U5IVvzs0ivt5w3zf3zSnOoeRzYZ7JP5fVoe3G85fw42djw+pwrMoxRzi95bdkf0kMMQPWlnDMSYPOe5bsm74/P2awckP0nSS7Z5/Jdl9dik/Ofn/OzfD4PTbOqvvAYwzAwMzODhx9+GIVCAW1tbYjH4xgYGFAVl2bbf097TQHud77znQiHw8rQBuqJzSTbJtkyNrmI67qOTCajYm55LoJewHpBI5GIik8ulUqIRqPKu84NSV6HXmRN09TixwVDgnwCIKDu+eWmxNJKZEHj8TjS6TTa2trUC8tjFyxYoKTq0vsqPecE4EBdbi5jQQqFAiKRiGLUKTGnLL5YLDZsZlxcyFjyWFkeQS5WL730Ejo6OqxkZie8iTJ+TNM05D+Sh2+XD/Ypu9o4Fi5cqKT+wYNBmJ8z8cwtz6DmqSF2XwzLfroMTocTZbOsPKJ7HXtRQt1Q4Jiyb5g0jbXaOW5yLjkcDlXjOp/PW/Nrjon8B6265OW3ljE9Pa2AFLOSk21lTL3cLCKRCPx+PzKZjCqjNTMzo+TqlM5ns1kEg0FFVLS3t6NYLKp5AaCB0KHXwev1ouN3HdiQ24Bn1j0DvB/AIFDVq8qLTQJJvhs2m01Jr8my8p5oEFDWTYOHORDCB8IIT4SRK+dQLBaVpI0qAl3XVc4CPgPfq6mpKZx55plIJBKK9WYZNCor+B57vV44nU4VkxQMBpFOpzEzM6O8/7lcDg6HA6lUCvPnz1fqlWAwiFwuh/1X7EfhygKwFMB1JybGvwLl68t46e6XsObXa9SaEAgEkEqlFOlVLBbh8XgQCoVgGMbL6s2TjGKmVpID7EPOu+bygLNtts22P+22Zs0ahEKhBhAgSbpmYNPsBeU+KUGY1+tFb2/vy+JrgUaAeDIQwPuQ4AmAshGy2SwSiYQi/5rVW9yvdF1XVSt4Pel1vCB+AaJ6FF/p+ApuHrgZITOEcpdFIkvPIp0ZMiabfSETaDXLhpuTW0nP4MnIC/4kKONn0saQx8r/N9uK8h75d9nfzaQAv9PsJZZj3+wYYt9wz8jlckgkEup8NptNEeqUWTdfn33CPSqbzaqkvc2SZD6LBLfyc3m/sp8lccG50Xxu/pReWjlfXylUgf0mnUi0W2lrmKaJLbEtCJkhrE6sftn35b1J4kUCZv7eHNdOe4hztPm8csxor/JeJZkhlafNn0l1RvN7yfuQYJ33zSotHo8He/fuxcjIiFIOnnPOOQCAgwcPKlXk3LlzMT4+rhyAkiybbX/Y9poB3K2trTj33HMbamMD1kvrcrlgs9UzdssFTS6gcoLL2GlKXHlOSrN13YrhLRaLSpbt8/nUAkdQzRdLglUmipISbsMwlAyboJ3eXcbkSpaPXuVAIKDktwS7BBtMSsVsz9LLzGemTI3gwOv1qnhhv9+vym3Qm5/NZtXzyI1Tet4LhYJaRHRdV3FsZMdJPPT19al4Z5Yzoxe0UClg6m1TmFgygUQhgYtuuAj6qK5qeNP7HolEsCizCOFPhfHE257Ayh+vREegA6l8Sm0ApVIJF3/2Ytz1hbtQ9lmeZWfWiTNuOgO6rqsSXplMBsPDw1iwYIGVEC2dbjAACOQYM52wJbD1c1tRC1qAsbyljOwZWbSb7WqsZZw/CQd6aNmYrI7ALRQKqXGQDCgBKb2znI8kATRNUxsrpdDpdNoa8zs1+L/oR3bUilk3TSuWmuzo+Pg42tvb4fV6kUwmAUCNBzcgJjAzDAN+vx8tLS0NCW3oOZbl67jJMhs/k+0NDw9D13VFbMXjcVSrVfT29mJ4eFgRH+FwGOl0GsViUSkGZmZmkM/nEYlEsHPnTiUbn5iYQKlUQktLi0rsFwqF1JhlMhkFih1uBw6edxAHrj0A02EC18LycKcAvBcwHAYOvekQ/Lofy+9fruYoPfmlUglmwITNtDUoXbxeL6anp1EoFJTEP5Wy5uLU1BRcLhfC4XBDvobZmKrZNtteXe31r399g7Euq4k0ezTZaMSzSQDJ7/h8vpft4c0AsVneys8LhQKq1SoKhQLK5bIKL2N4nIw35fWkvJ17EI+jV5KkPUnYdYl1+D+5/wN32Y1MMYPR0VF0dnaq/ZgAhHaZ9NZKkkESC7Qv2KdS/dfsbSShS/Akf0owLvu+2cMo+1iCUN6LPF6O18kIFem5lWPM+wKgwHEul1NSfYJL2pw8B+eRnBtSeSZJCO67Mr5fzrdmgMf7Zf83e48l+cD7kaSLvKfmuUlgy/klx4P9Uq1WMRwaxu19t+Om/TfBWavPLTqkakYN20Pb8dm+z0KHjq/u+CqWFpcq25F2liSWCIybCRvpjaedw77i8RKgy4pCfBY5B6XDQhJq/I7ss+a/c55IgkL2Wy6Xw/PPP4+jR4/C4XAgm7WSsO7btw9+vx8jIyPKxqczbGBgALVaTTluZtt/X3vNAO4LLrgAS5cubQDQzYyaXOwkoyaPaY4FkUkTAOsFzWQyABqTTVBiyg2JpZlCoZCKr2GyJAmeeL+U0jIemwueTPBBOS8BtWlaZZZcLheeeuopnHLKKQrEM3kGF3PKxunFlfIt1rWWSR0ANIBmfld6rMkAlstlhEIh5UnkQkSARlaOcd68BqXbABS44vc0u4aHFjyEHy37EaABJWcJD3/xYbz+S6+H7aBNgTpmPE+n04gvjgMB4OB7DyKdSmPFQyuQnk6rmGFjyMCl/3gpHvzQgwCATV/ahLAZht1nRy6XQ7lcVom1JPNIZp5ATWYy3/mNnSgHRYmFKJDenEZlqUWodHR0qDEPhUIKDDNpHb3+AJT302azKUk1S5lxnhYKBYRCIYyOjiqZdSaTURnAgbqsTM4RXddx/PhxQIS5m6aV8Z1e61gshvHxcSVjl4n9SBLQ2GN/MUEciZ6uri4U3UUMuYbUGIdCIeWpB6A8KIFAQM0hKisYa53NZtHe3q4IGr/fj0AgoLzeIyMjCqy3tLSgVqs1xNin02krAzygwHF7e7syDP1+P9APHLn0SD3hng3AJQCqsGpiAzAdJg6cdQBdu7vgOeBRz2oYBgb6B/Dsnz+La267BvMq8xSwjsfjSurFuU+Wn5teMplUm3ehUMDExMT/bYmbbbNttv2JNLfbjdNPP/1l4IbENA16oNFr2uyhlZ5enqPZkye9bQRtvA49WjK8iWs61VqGYYVuyTA5EvWapqlqGbwHSYoThEgAbrPZsNu9GysLKwFAJRudnJxEe3t7g+NAetAlICRoIVHKvuBzy0SrEozLcDhJdsj8NCeL9eZxMgu6HDcJuHic/F0e2/y5tLV4HtpPDE9Kp9NK7eV0OhvId+m04Z4i7dFmbzP/xrEg0RIOh5V9K//OeybRQu8rx0mCU15f9jv7r3nu0oaSBAfnsrSv5TwHgIPhg7hpw02o2Crw1rx439H3IVgLNoD4Z33P4salN6pcAR9Y+wF8Y/c3sCK3Ql1TOoUkqSHHR75n0gHH4yR5QHJI2uTNz8xzyfdUkkfNhAeJIQm+a7V63ig51olEAlu3bsX+/fuVLarrOvL5vLJRp6enlUOC+ZNSqRRCoRCmpqYwOTmJ2fbf114TgDscDuOss85CLBZrkHPIl0UyvgQJMpM4vYVckMikSg+pZCN5Lnqfa7UagsEgstlsA7gmE1qtVhUbRTkWF11mYQSgXmg+h5TocJOTLxjvYd26dSpJBq/LF5LXIoiXDDgBM39KmYymaSpxWHNSEYJuHsuNwjAM9Tyytrb04LO/yLBTxi0lZrqmY39wfz3RF4CKs4Lp9mn0HOtRhkUwGEQwGMRLa17C1vdsheE0kFqUwjCGkS1nsfSOpQ0bNg4DG7+9EaZhIhQPAVo9w3wgEFDgp729XcnBaajwH8fRMAwseHQBXrj2hXpsuAlEfhVBzWHNk1QqBV3XVWkwn88Hr9eLSCSixrS9ux3la8vQflf3gNMT63K5UCqVMDo6qgArE7JJQiUUCiGRSMDhcKhkdiR1mG0/Go3i2LFjqj8Zty0TpHGe0/Cg8cYxZNw0PdjcSAjEiyjiqcufwkH7QXh3e+GPWzHpTB7n9XpVLHRLSwuy2Syq1apKAqdpGrq6urB9+3YsWbIE0WhUeYdlIrjW1laMjY2hVquhra0N6bTFJEQikYbkaATItVoNra2tmJmZgcvlQi6Xg3OfE6d96zRsefsWlBaVgBkA3wNwAaxSbSeaf9wPZ9bZECZwdM1RPH3906h4K7jnrffgjfe+EcHhYMN1S+USnlzyJNbvXq+MXM57mQSFddJn22ybba+OtmrVKpV1uZnUJ1CR+6z0lkn742SeVBLWJCMlwOa6DKDBFmB+DtM0VSJMkpTSo8pzkhBtb28/KYCSgI/gk+Dpd4Hf4SsLv4JPjH0CF05dqI7J5/PI5/MIBoPQ7Tp+Zv8Z3lZ4myJ3JdCW998sXQbqoYISkPL/0vaRwLv52GbnC59HjlEzwJVjKL8nP+cYyd8LhYLqW96frE5DJRyr4XDe0M7ivUuyRXpqqbKUEuhmzz9JCullZYge+1sqIJttQfmZdF7JaxBQS5uPTYLdV1IN7GzdiW+s+gYqNmsO39t1L0zTxNufezv0sq6qBt1vu7+x3zUT+8P7saqwSj0/bWhJoMj+433wfaH9y3ttVpA0y8XlnKKty/lJ8kAqU5rnpFS58KdUL7C/2Vejo6M4cOCAUpiSICORXyqV0NHRgVqtpoA3q8HI3Eez7b+vvSYA99KlS/GWt7ylQerBTaY5PoXyYQJtHmsYRkN2Yb6kTCDCBUECZQI1yaqWSiWVNCufzyvQRA8gF/tyuYx83or7lXHclHt5PB5FBORyObX4yrrVNptNeSElA0rSgF5zeX4ZDy5ZOL68LIslvXNSakYpPCXV8pxyAWEmcwAqOyb7mYw3vcfcbLghVKtVODQH/urAX8GtuXFfz33Qaho2fG0DOvZ2oGbUlJy/Wq1i32n7sPPanTCcjXKZA1cdgOkxsfK2lUqBYLPZkNJSGL96HJ4fedCWb4Pdbkd7e7vawOhppxEj45R9Pp/6u67raP1pKxbNLMLBGw4CAPq/04+ue7tQWlr3ajJ+GLBk0/F4XMm9Q6EQHr7yYcRXxhHpicC8x1JQcFzL5bLyXuu6rtQVra2tSKVSiMVicDgcyOVyaG1tVUYT5zPvU9d1RKPRhnwElNHL8iKMCZObOYknJixzOByqH1iii8859tExJDYmAA2o/GsF7R9tRzqdVu8Wn4XvFj0OklUHgP7+fuV959wgAcJNz+12IxKJQNM0dHR0YGZmRvU553NbW5siIKanpxXJQJVIwV2A8X8M4L2wYttfBLC2aYGZBCqpCvSSNV+HNgzh2aufRcVrbdTHu4/j3y7/N7z9l2+HO+5W8vD7LrgPO1bvQM6VwznPnqNkkyQPPB4PJrIT+NW6X2H629P/pTVwts222fbH08455xy1h0ugIYGL9MKxNYM/ksUMpyG4lnYJAQPXT6/Xq9ZIrrEyJIfqJP6jAo+A2ePxoKurC9FoFOFwWMmR2WQIGVC3X2w2Gx6IPICvdX8NKUcKX+j6Aoq1Iq6IX6Fsm6mpKWiahs+2fRa3O29H0kziPZn3AGiUeEvvNAEHAbC05YA6OJJAS4L2Zvku12H2sQSzBDJ8plcC5tLxIgGnJE/S6XRDRnfaXBxv2oOSRADwMqAvbTs+E8lwVhRhCGEoFGogc3kOVq2hTSz7g/0gP5cqTylv5rNJgqMZjMu5LceKNoX0rPN5lY1SisJpOBveJfuwHYnpBLIJy2ZMpVI48+iZKMaLeOLPngAA3HDgBlwxeQUMvdEObbZj5DsmVaQ2m005GTRNU+Q+7dFaraZy4tDmr1QqSKVS8Pv9Krxx/vz5CIfDqn8kcJYkhJzbsv/5HkvlR0Wr4CvtX8HCXy9EOp1WuaOG5g+h6q4i/HQYk5OTqFarSCaTGHzvINoG2hDzxdDW1ga/34/7L78fvZ/vxWz7722vesDtcrnwZ3/2Z0p2yxdXMnwyJoNNyrfY6OmlzJWMpJQT0aNNjy5Bb61WQyaTURssWSe58HHhZXM4HMo4Z2woWU8CLilhIZBmDCxlX9z46LmTknRuLpTWcCGR8hkCXbnJMKFVuVzG/fffj8suu0xtGKzXzc1AxvfS60qSoVk+JEEowTwl+XwGLs7D/mFsad8CADB1E/vevA+xfTGEfWFVEsxut2N0wSjKXiHrPtFM3cT03Ol6cjOYGOkcwc5P7kQtWMPUvCmc/d6z0eHpUJmxHQ4HOjs7X7b51WpWCZR4PI5YLIZYLIZkMomAP4Dwr8KYb86HI+BA3wN9MF0m7JpFeIRCIWQyGUQiEVSrVZX93DRN5Ct5HPjYAUyeMQnTZuLY+47BUXSg7YE2tRFwDF0ulyoxRRZcSpTo9c7n8yo+mIQN504ymVTzmXOZZd5oMBUKhYYyFpy/JJqkmoH5BrixzPyvGeTPzytVQm1NDcd+dAyR6yJq7mUyGaRSKVXujBJG1kInseHxeFQuBLvdjkgkoqRXTqcTBw8ehM/nw/T0tCKKWOKrtbUViUSiITN4oVBo2ACr1SpGukaw9SNbUSlVgD8HsPnExHk7gEcArARwDzD9jmk8WHgQDocDbW1tsL1kg6PfgdK5JUvZUAHmPzQftbEa8rU8bC4bNr9uM55f9zwMm4GHznoIHnhw+s7TUcrXExJN56bx8w/+HFOxKWg3aMA/oCFL+mybbbPtT69pmoZzzjnnZXWHJRBoBtU05AmkaGfIXBhA3UDneg2gQSLMNZY5Z7LZLPL5PDKZTEMMdLMnFrAMfbfbje7ubnR1dTXkquFeTvBBG4HNMAzs8O3A53s+j5TdIpdT9hS+1vs1tNXacFriNBiGgUwhg8/bP4/fOH+DqlbFLd5b4Kl58Lb822BD3T7jM8n4az53sxeRUl/pEZTPRztHflfaYXKMJNhtjt2VoXq0vaLRqAoLK5VKSslF24b3Q0AnnSLNIJf/aFM126a0QUicNCvMZFUV2nIcN84f6cFl/zTv9fyOBIYSMJ7M489zsV9luIQEvjI8Qnqg2eZl5uELT30BHzz3g8jZc7hk5yW4bP9lGBocavDgV7NVnPb0aSgbZfRH+7H44GKYC0yYWmMmejmH5DjL+Syz8hNEF4tFpFKpBkeU3+/HzMyMcmSkUilEo1FMTk4iGo3i0KFDsNlsaG1tbbA1+LzyPWt+75q92WpeOg18cPkHccB7AF2nd6F7Rze0pIbiwiJe/OcXAR1Ye/NaxPbHYOomBq4ZwPBVw4i/Lo7+f+zHyOQI9vz5HgyfPYwjtSPAowBebirPtj9Q0072Ypz0QE37/Q78I2s9PT3YvXs3wuFwg7eWLzPZPf5Oabff72/w7ElPNV88Gb+qaZqKgyYwIajxeDyqtI9MDkYpb61WQy6XU57A5gWACycAda9utxuZTAaapqFYLCpgz4W6VmvMLs5GT64EjGTA+Zw+n095svP5PHbu3ImlS5fC7XYrwEPJPa9HAMSYEV6XnksCPT4rmXbpZSfrRxkTNzP2Oz2sqVQKptvEJ8/7JIbDw+rZbGUblv52KRb/cjHK5TICgYDFxjrseOa6Z3Do7EMwbfVpHNoTwvmfPR/VimUklFeUsfmfNjfIv71xLy74lwvgn7Dmw9TUFNrb21GtVlUpq/HxcTXGg4ODcLlcCnC7XC61AHu8HricVhmyTCajnofeTM4nboAvLn8Rhz96GNVgVd2zd8KL0z59GsJTYbWJM9M2vfq1Wg3RaFT1I4mioaEhFafMucO4eZvNhqNHj+K5555T46dpGlpaWuD1elU5MMm4e71eVRIuEAgoOZrL5VK1pKXc0eV2oXhXEVMrpizQfRhY/sHlWORfpOTkdrsdPp8PyWQSuq5jdHRU9afNZsOhQ4fg8XiwYMECaJqmDAsmB+SzM17bNE1MT0+jtbUV8XhceezHxsZU8kHW8ub7aLPZMNg+iEdvebQ+FyoANgLYAWAugHsAhAE0kcLcQB1OB0q/LsE804Tjcw7gy7DAuM2G3Lk5xL8Uh9lan4v2UTve8MM3oGuqy0pEGMzi12/7NYZ7h62+MgH8LYCvAXgNJRE1TVP7vx8122Zbvf2x2yrLli3D7bffjoULFzYo5QieZVkt2hFc4wArZEaqfSRBSgky1zIpF56amsLIyAjS6XSDB1yCRvlPxk7rug6fz4eenh60traqtZP7B8G6DEdTa+EJJ0OhUMAvfL/A9/u/j6KrCFfZhXcNvAvvSL1D7R0PBR7C5+d/HklHUvVXb60Xt2dvx8LiQgXK6EFv9ho3e1EpA5ZAWibHlfJxAl2OCZ9DOi0kGKbakJ5kgmw6ZKSHWpZ8lMlkgZfHSgN1sCm97UBdys5QASZRo1KhGZwBltKrq6tLJT9lP2g2DVOxKXQmOhXgbW1tVTl8pCpRAn/aCzL2nECZtqoEv2yS3JBeXc5ROXfkM8v5xOMy/gx+HPkxLn/iclXus1KpKJubx3p9XnR3d2P+vPlqrkqgLR1MtIWZRJZSa46X3W5Xajz2t6bVc7DQFuG7bLPZGsIEbTYb8vk8LrvsMmU/SwWonLOcNzLUk0rWyclJa+zDRdx99d0YWDCgbITuH3Uj+lwUe/91r5XoFQAMYPH7FyPZn8TE30yoY0OHQggfDOP4ZcfrNsYvAfw1gFll+X+4/T62yqvew/3mN7+5IZ5ZMmmcyPxcsoGMlZVsKTczl8ulMvwRAEu5LUFt83d9Ph+AeiIILv5k0SR7RQDGF7FZ6s2XVXriuXhwA6RUVjK4QD2+STKd/D7LdVE6bbfbsXr1anWP9FzzWqZZrxnNhdHj8ajFgn3NhcztdiuAw0WL8dxyceezUuLLhY3ltrzw4qbHb8K3T/82Xmp9CTCA5Xcvx6JfLUKumAMAJTM2DRPdL3bjyBlHULPVN4DYkzHksjkl3X5+5fMNMeHQgJq3huTyJHzjFkNMuTRBKeOt4/E4pqamGsAcE2NxE3Q6nCphV6VSUTXEZdwOx07TNCzeuRi+n/nwwjtfQMVbQXAoiHXfXYfgVBC6rquENqlUCsFgEMlkUkmo4/G4IlUIYKPRqMqsTrDNTcftdlvlyeZUAS+AHdamyE06n8+rsSdJwnwEzJLKucQ5zXeN91StVHHBNy/Ajo/twKHKITje50AtU4O5pF6OjknjDMOKW+vt7cXIyIiKHe/q6mrIhi4JAI/Ho7LrJhIJFXPu9XpVOTACavW8J8gPEnI0/EbWjTTOBRuAywFkAXwfwKkAEgAuA3Bv/TBlWJVM4FeA/rAO7esaakZNyboctztghx2VL1WAVkA7osH9MTcefOxBBAIBK878rDSGzeH6PWgAXgfgB3hNAe7ZNttebW3u3LnI5XIYGBhQtgCBNvc9p9MJu92uYqvZ+HeZ6IwAnWuk3W5HMBhU5TpJcqfTaSVjll5KGS9qmvVcMFQoMXcGlVtcq5vztkh7x2az4bjjOPK2PBbnFyOXy2F6ehorZlbgiukrcPfGu3HJc5dg3ZF1SHQnEAqF4HK5cGHuQpSHyvjSnC8hbU+jr9aHr2a/ir5SHwyzURpOG452jPRMysSeMjeNXOMJvGhbybhcKS+mbSTl31IVJTNec9yAOvlKp4NU9En5uVSMSXuU4FqSMFRPytJs7HOCTCk35jllmVkes3X5Vjy84WH8+f1/joXDC9V1JeCVgJj2iRxzqTQgcSD7jY3nlOERDZ5aozH5G+9TqiR43nw+D1fZhStevALFUlGFQ7AMLJP2FYtFZNIZ1NpryvNMBwSVHvF4XPUTnUWs6OPz+ZQU3OfzIV/O49jqY1i0a5Gy/+mAy2QySCQSDRVbGN5BYiQQCCAej2P79u0q3JGVY1KplLLlOK84xiSNpKpR0zRkT8tiyjvVYCPYz7FDW6RBiEEADfC+y4tqqIoJbUJ9Vo6VkVuca7QxlgKYj1nA/d/UXtWA2+Vy4frrr1cblK7rKt6omb3j71NTUzhy5AhWrVqlNkBuWmS2WPaI8coEvlwI6ZWV55YAhCAym80qmTi94nxhKQHmAiYZPrkoM5soz+3xeNRi6fV6lfe7uXY3GTsADcSD9IhKD77MuikXSnrkJTPZnBRNetW56EuZOxdVySqT2JBsNe+TgMnhdqBsq+tfis6iIhkke22z2WDL2ywGT7RTe0+F0+9UJbP6ftEHj+bBrit31edQ2oW5j85FxbTIFi6UfDbTNBEIBDA8PKxk2QS0gUBAMZvcrMrlskpMQVDOPiQDK70S3Zu7kRpOYegjQ1j+teUID4RRqVVUjDvnMzPGs0wZiQ3KBmXmTG4s8rr5fB6VYAWV71UAP4BrAXN/PZsl62SbfSYKKwoI3RVqmBMME6BRQHUF5XSK1EmVcd4d56H4QhETL0wg25ptYHonJiawZMkSFT9eLBbR3t6ujA8AaiOkeoSl2DgmmqapBDOcf5TP81m5Kba3tyMUCinCKBwOI5PJYMN9G9AZ6sSWC62QBXwSwC9gMcBnnZgcEQDfhsUI342GOYePAsanDejf1NX7Xq1WVby5/isdZspE9VtV4Hqg+HRRrSnxeBylfSXYDttQ+1UNCAG4D8BfwUrcNttm22z7k2y6rivPdiqVUgBXhgIBUPaCjMMlwUnZcLFYbEim6nK50N3djba2NuUFJ0DjXh4KhTAzM9NQvpDeSZb5DIfDaG1tRSgUUmsovWs8njaAzLFBO0jXdSQcCXxm7mdQ1Iv42z1/C+24lfyxVqvhzBfORCgbwpKDS5BAQu3nBIqXJi6Ft+bFF+d+EZ+b+hxOd58O3VFPmHoyqTFtBAI27rkEefxus3xXglAeI2Xh3CsInqTtQmJBEiK8LylblnYNbSTpmZfSdILBbDarQD6Bl+xjXt9ut+PQuYfQ9UIXXJMudR7+jcSJLCFlmiaeWPsE7j/7flQcFdxx/h142yNvw9yhuUilUg3Hnwy0N8vGSXwAdW8956S0Z2Tfsy+kh/hkYyNtXZIIJJoYLmaaJqLRKFwul9pfbTYbZmZmUC6XVSUY5pUxTRPFchG/2fAbnP/g+Uo9wgo9exfvhWfQg47pDlX1xGaz4fnrnsexjcewZmYNOn7XoRxXANQ7CkCpYQmmNc1Kcjs4OAjDMDA2NtYQQlAsFtX8Z4k2KkB1XYfH42mQ79N26RzqRMcPO/D4Rx5HKVBC9/5ubLpjE6KpKOyDduy4dgcA4PQHTsfZT5yNfDCPh/SHsH/RfjhKDlx+5+UI5oO496p7MdY5BhwB8G4A2/+vy9hs+0+2VzXgvvbaa9HV1aUmMBc+xh9TRirlPOFwGG1tbSqrI2At5pQOSa8tgeLU1BR6e3uVJIwMIBdkgmJekzWvJRNL4JnL5dSmws2YgIreTybp8vl8Sl7EzZCJ2CRjbRiG8oZy8eAmxPsicONCTSmVYRiKtSXTCkAtUOxbAA2sH+9HgnYujpTxcrEigCVYpweTfcjFiGAml8shW8vis2d+FmO+MWuwdeDAxQegmRpW3LoCTqdTGRV2ux1z9s9B/3v7sf+HVmbzjT/eiPOK56GywZLTx+NxxLNxPHHOEw1zKBvNYvvV23HKT05BJpNRsdI0ULgoEpCm02lVI5vsp8vtwpFVRzATnEHLAy2KBHE4HCpZ19y5czE0NKSMDyYKcbvdCD0UwpyJOfBN+2DYDBXrzcySJFkoMyNYnZqaAoCG8iHSo03jqFwuwxP0YMvnt8DsPcFKPACYq02k4inkcjmLCQ5rML5vwPAZ0DIaok9GsWDBggZWPJfPATZAq2kqK3oikUCtZmW1dblcKA2V4Nxbz2/AfpI1RjOZDNrb2zExMaFYa4ZcMHM9jQD+v1gsIpPJqKz2NCgp9eJ4hUIhFItFJJNJBbYp8efmnclk0HNHD7qHuzFy/QjwPlgZyn+DOuAG4BhwwLPHAwRPxJdrJvQP6qj8g0Vc1G6soYYa9C/USRTTNKE7dBjvMoAWQLtag2uXC2bVbFij9Kd0lC8qo/a1GvAOAFN/mHVxts222fY/06LRKPr6+lQlBgLCarWKmZkZldQyk8mo9Vyqx7jWUrpK4tztdqOzsxO9vb3w+/2KtJcybyqRuGdRjWa32+H3+xEMBtUaHQgEGs4D4GWAT4a+SclxzV7D9Quvx4B7AABw44ob8aFtH4Kn6FG2wKlHT0VNq1fqGB4exoIFC+B0OlGpVHBW/Cz05/vRU+tBKpxqSIYp1Yi8NgE2VXMyuSf7WKr6gHqlFpIY+Xwek5OTcDqdqvII8+jwfLSjJKAGoGwlqVKTTp2aUYOhGdDMuuKNXvJisYhJ3yS2vW4b1v90PWqVunfbNE3ADjzy8Udw4TcuBCr156/Wqpg6dwrbrt4G16UuXPbJy+A23OpeI5GIUtHRq69pGrYt34bNZ25GxWHZPlPRKfzk4p/gQ3d8CP4pf4M0XXrO+WxSti69+0CjZ5tjTU+4lITL+dPs8Zdx3LJPeX5WXKEdRdsrm81ienpa5UYyTANjF4+hjDJel34d4vG4ldhMq+G2K2/Dkb4jqDlreNOWN6GYL8Ln92GofwgPXP4AbCUbzv34ubAlbMgVcjj0oUMYfN0gTLuJ5655Dj0He2D/jR1ulxtAXTXqdtd/j0Qi8Hg8Dc4OadMycTNJLSoHvV6vAuzM6UPnHm1lm82GXC4H7wEv3viVN+KRdz2Cy269DO6UG2WjjA3bNsDn9aHiqWDdI+tgwkSsEMMVv70C+avzeNOWN6Ftqg3VahXvuPMd+OabvonseVlg8L++xs22V26vWsDt8Xhw7bXXNsh5gMaskVxMKP0lU9fW1qYWGMYM0+MqZR2AxWa1tbWppE4EolwkyEYTULMsEz3hXIgYs8rFTG1eJ9hVlhHjgseSWQTIUmpCz59c8CkPJ7BlHDUAVS6MiyHBCaX1si/ISErpm2EYKjaWpIKUq3PDo5EBWItj8zjQMJDsKjdSLkhkmANmAJ948RO44bQb1JibDhPZniwQBWqp+sbH7JG1XTW4b3KjuLiIrrEuuHpcsMO6Vm9vLzqrnfjQIx/C1y/7OrLBLGACLXtasPrfVmMqNdUA5JrLnAWDQUWcTE5OWpKfE3NieuM0dv/dbgDA6txq+H7ngwZrzN1utyJOKH8juaLrOnK5HELBEHzTPkV40MjKZrNoaWl5mXQrGAyiWCwqdcbhw4cxf/58RY7QsHC73WhpabFUHR8+gkx3pv4C9QKue11wXuBEe3s7ZnwzSPwuAXQA0IDBrw1i5A0j0DfrqvSazWbD2PIx+P7Oh2WfX4aWVAsAqxTXoUOHYJom/CE/Jt2TagOV2ed13aqbnc1m4fP5MDAwgHK5jPb2dkxNTanScJqmYXp6WmX8P3bsGHRdx/z585XhOD4+rlh3v9+PqakpFItFTExMKMKkp6dHEV79/f0YGxtDNBq1VBIuB4rnFzHyxhGrPxbAYn6/17jOtLa14nVXvA72hDW/x+eM48G/fhAVv2XMwA3oH9HhO+yDfbNl9CS1JEr/WoLxZwagAcb7DeRzeeAzgAceReZUu6swbjXgusKF0tSsjny2zbY/9UZPHMk+Ep75fB4TExMqfIb2BfdEoO7xo1eZQLutrQ3z5s1DS0sLqtWqiunlXks7AgCCwSDsdjuSyaTat7l367qOQqGgCMpcLqdCzLjnSCk6bQACXBKfH+36KI67jqtnnnJP4d+v/Xdc9+PrGrx03PNJNmiahnnz5ik7aY45BzXTUgK43W6lmtK0enJXoB5/KyXOzbYE94JyuayekX0gVXHcO0nya5oGv9/fIDWnx5nAlC3pTEIv6nBVXA32jKmZ2L14N/bM2YNL7rsEtbiV0ZqKhUQsgXtvvhc1Zw1GxsDae9bCUbYAXMaTwVMffgoTSyZwz9/dg0u+dQna0Q6ny4mBNQN46E0PATpQcVfwwJcewF/d/leYa5urgB8TqPF+NE3D+hfXY6RtBM+ufBaGzYCn4MGVW65EW74NhrtR2i3nniRaOF+krco+pu0mbe5mwC6JD9qIctzkPDNNE0WziElzEj3ogaZZZD4rorASCud5pVKBAQNj547h0WsfhQYN+k90RF+MouwtY8ufb8GRRUcADXh6zdOYOT6Dnh/3ID4/jl1/uwumbgIe4Hdf/h36ru5D+YwyRjaNwLSfcBD5q5j4+ARWja6CZ8yj8hvQMUDyQL4vnCu0/W02G/x+vwqDIyESCoWUHUhyJ5fLob29XTmcSAZR6WceMvG2r74NTpsTNq9N2dlnPX8WdJsOOOskhjPtxHX/5zrkMjkUPNa8Lx0twVxqAun/0tI2236P9qoF3BdffDEWLVqk4jYodSGY4YsgY5a4uLAONwC1mbDWNEEMQSUBKoEXZeLynHzZqtWqksnyvFJGQwB3MpaQL6XMPMkNTp4XgPISyyRvBG8EXQTHBLSSfWR/kYkj4GUjAyeTXVA2To82iQ7JclLWLxdsetC5gfG+KK8j+OemypJNxWIRrqQLC2cW4kjsiLq3RFcC093TmFuaqwBZNpu1pN5XplH8jNVHd+JOeJ7yYNP+TQ2KhY6ZDpz/o/Nx75vuRct4CzZ+ZSMcPgf8fr+6PhlvEis+n68hnrmtrQ2GYcDv9+PwysPY9/f7VPKtnZ/cib5CHxY+t1AxtIwj9ng8DUlXGLPOEmpMCmaaJkZGRhCNRlEqlTA9Pa3qK5IgcrvdykPR39+vDAXG3y1cuFDNjQULFsBziwcD3gEMnTMEAOja04V5n54H81RL8lT5ywpS0RQM7QS4twH6u3XYHrUhk8lYcemXVoBfAjlHDum3pdF2SxtcKUtmzhjv3St2Y89le9ByvAXmoKm8LfQmeL1eZSDJOUkQLpObpFIplUW9q6tLySd1XW/IRcAEgx6PB36/X30uybF4PK5UAuVyGYlsArv6dtWTpmkAWmDFbIuWak0hvTSN9m3t0DQNnUOduPjOi/Hgmx5EJpyBL+/DpQ9fipV9K5Fpz2BychIDPQN48fUvYlKbtE6iA53XdsL7qBfTz0xbY7RGx8xXZmAuMFG6swRcC+D5//x6ONtm22z7n22apmHOnDlYsGCBKg2UzWaVHJWELfdSGuEybEfXdVUK0eVyIRKJoL29XSVhoodcAnZem4Cyvb0d4+PjKJVK8Pv9iEQiaq+nbcQQJe6LdAbwXBLQMlyGQOJfjv0L4oE4nu+zFqwLjQvxrfy3sHf+XgwNDTWEn/F8pmlaJYsGB9HT06PABEHU+Pg4Wlpa1P5FO4J2HPuKwBuoJ8WSsbRUAFJabLPZGhLqAlD1yAEoO4rkQnP8O8mPGe8Mfvm6XyI2EcOFT14Ie82u+mTPij2486I7AQ0ozBSw/o71cBYtL+X4/HE89t7HUHNZ9tWei/fALJvY9PgmIADsecseTCy14m5n5s/g2fc/i8vvuxzunBs7T93ZsD8V/UWMrhnFssPLADQCX9pwfMaL7rkImXQGh9YdwsUPXoxTh05FzbRCA6nkk5Jp2d+0GaW0nT/luNKe4zyU4yn7m9+leoL7v/rMruPevnvxdOvTePsjb0fLTIuy6zgmPAfJk+ybsnj0ukcBDTBh4t/+4t+wYO8C2FI2jLSONMQtT62cQm9PL1JXpiywfaKZXhPud7ix7u51OHTnIey4cgeq7ir8I36c9r3TEMgGUPPWFBkUDAYVTqANAkDZ7iz1GgwGG7z7oVAIqVRKgfBMJmPlKfJ61btHgol9Ta+3x+NBMBi0bDuvsx6ypuuw207E15s1dR7a7nbNruyg7du3o5gr/r7L2Gz7L7RXJeD2+/0499xz0dra2sAIA3X5ET230jjn5iIXXL78XOgla0e2jkCLmw9BExsXIb449AzLBQeox0NxoSLw5fdlco/meBgCEQmgCbzlIiezS/KZeA2Z0IL9xWtJtpybz8mYSYJG9qn0jhcKBVVKhAtqc2yTktOekNvz+5R+lUoljI+PW5mzx3z4wPMfwP9e979xJHYEnoQHG36wAW372lDUioqg8Pv9ePGSFzF53WRDf9++8Xbk9Twu3H2hup9arYbQrhCii6JY+dhK+F1+JQvK5/NIJBKKvQyFQkoazsWRsn2Oid1nb0y+BcDht5J6MP7f7XZjYmJCGRky9osbU0tLS4PBxXslE8r4YKoedF3H2NgYVq1aBbvdrmpMM1EIyRrOCafDiTXfWwM9p6MSrWDF91YgEA7ACJ4oX/Gshq5gF5555zOABnTc04Heb/WissgySIbPHUbyH5PAiRwnxTcXMeIcgf42HfbqiWRrH7Nj5LoRmG4ThX8ooCXXAkfGoWT0zPROg8/tdqtM+fSg0zvNGts0qoLBICYnJ1WyQRIg0mvhdDoRjUYV60+yjSVxmGnfMAx0t3Wj8uUKZt46g8zlGasc1wcAPAfLy70JcOad2HTrJiw4tAAlRwnBYBC5XA69u3txmXYZ7n7r3XjDA2/AisMrYNpMtLe3o6OjAyu1lTj1wVPx0wt/ilQwhd6pXlz10FUIbgxioGMAia4EHrz2QVSWnlhDFsFKlvZOADt/vzVwts222fbH1dxuN5YvX47Ozk4YhqHAnww/41qey1mJPxmLzf3C6/XC5XIhGAw21NPO5/PK88okTvRg+/1+eL1eFVLjdrvR2tqqDHC5/7IOtyxfKu0HoDHXipR18xzTk9N44xNvRGVDBXqLjptxMzraO+BeZyWvHRwcbLAnaGPVajVMT1uEY29vrwIstB3i8Tg6OjpgGAa+5/sers1dC2fFqe5LJhbjvfN5CI6ZBI5qRaBR+sw9l04USVgAdaeIvPe8K4/bNt2GF3tfBOYCOS2HM289E+VyGS+e8yKeuuwpZQPsO3sfCvYC3vTrN8Fpc6LUUYLNITNcAcEWK+Fd2pOG5mg0HkybCdgAu2nHm+97M+6r3Iddp+6CZmr4y61/iTOOnoEa6lnWmeRNOkxoh552+2noeKkD8w/PR62n1hBWyO/TVqR9KEG79GJLQM1/7HOgXtpKhjhI208CdRIotHFuX3w77lh8B0zNxLfXfhvzPjUP2qClIOR3+E7wmYv5RgCpaRoi7RFE90TR8f0ObL9hO/KxPKJHo9j4/Y1oL7aj42cd0DM6Dl9yGABw+p2nY+lDS6F5Nax4fAWcFSd2XL4Da7+zFj2DPbBFbcrepx3CfqAjhcQO5xkxA8ve0v6TWextNhtisZh6fr/fr1SoDAd0u91IJpPw+Xxwu90NCf1UHpkT744c00KhoPqjWCzC7/djYmKiYX7Mtv++9qoE3KeccgouuugiAFAeVwIqvsj0WtNAJ4gCoOREBDSyNADjewhsGXdBqYfMQE4vOL3PEuzKhYiLBF8OLvz08BKk8j5ZNxqAuid6zPmiUfYsJS5kv5oBvJSF8Zy8Jy4oPFbK1/k7fzJ+SioH6N1sjpcnQ0fvNjduysmAOjmSSqVUzcNAIIBYLAbA2vzm5ebhzG+eiZH3jGDt59YiPBmGzWNTIIv9OOeFOThSO4KaKGRcs9Vw79p7oVU1tP6iFYsXL7Y815cWET8rjmeWP4NLPn0JCoWCqmNJ+RJjvlKpFHp7e9ViZ5pWJu2xsTGLHDhj/GWAe+p1U+h/ql95/MfHx9W4Ma5L1cp2OvDSZ1/CnB/PAQpQngd6eT0ejyrJxdrjnCstLS0qgVsikUAikcDk5CTsdrvKU9DV1aW85/u378ey2jJU7VU4006YTlPN61AohIWPL0Q+ncfg4kF0fLUDIT2ESH8EhUIBbdk2PFd8Dlkzq0pMLNizAO1r2pFJZnD84uNIfiIJWEo3GMsNJL6TQOXDFRWLnkgkGrLYc1NhX+i6jnA43ED46LqOeDwOp9OJQCCgxsHv96tN3uv1IpVKKSLM7/crYsfhcGBmZkYpLWRCNXvODvPDJmAA+BmAO63nmvcP8zA9bxqX//ZyePZ4YA/bG2LMTNPE4oHFcH/bjb5yHwqVgsqIXiwWEQwGsXhiMT66+aP41oXfwl8/+tfwprxABGhtbUXNV0NST+Ix87F6uY4XABz7LyyKs222zbb/0ebxeLBw4ULlheY+D9Szg1NRBlh7OBNvMtaTslX+pBydYJvrD8H7ggUL0NbW1gCcWN2hXC5jZmZGeQV5H1w3gXr2aQANgFR5Ts0a/nb+3+KW47dAN3QMDg5iYGAApXgJm+7fBM2lYYdvB7r+rAutra1YvXo1arUahoeHlaqJ+xlBy8TEBBwOB7q7uxUpr+s6stksxsfHce/8e/GlwJfwO9fv8N0D30W1Ugc2VPnJWPNAIKDsNfYrbRRpp0m7C6hL1QEo2477M/tbt+n4wbt+gOMddQn982ueR7VWxRs3vxHuGTd2VHYg48yotfzssbMRDoZht9kRGA/gL2/7S3z7Xd9G1V7F6dtOx6bHN0Gv6ohkI3jj796IX3p/ieM9x9Ex0YE3/fZNCKQDqGk1+Ao+XLr5UhTsBZx+7HSsH1vfYN+Q0OH4sT9YQnNqago923pQcBeUDcD9nh5baZtx/nA8pLNFAmo519ik15q/08ZutkdpF+m6jl8u+yV+3fdrmCcq/U30TyDxzwmsfPdK5Vmm7U5Zt2masD1lg27q2P7XVgawdbesw5zdc2AGTEQPRRH8X0E8fuPjOO8758E36QN0wFayYeO9G2H32RE4GsD8x+fD1E0FTkO/CeGs3WfBN+RDzVvHBrRj2Q90fPGemAMhFAo1qFeZxygejyuHA98v6XijF5+qRYapMtkiCSk6/YgJGKrHcsWs1lSpVFTYI8NHZtv/m/aqq8PtcDjwgQ98AF/+8pcb6uFJ9o7yIgAq1lnKuLjgyE2HBr+UGAHWwsGYLE3TVPkkTmQuNNxQmfCA8l7pyZTZvMkOMgaJbG+tVq9jzXrZ/FwyjgTG3NT4NwISGY9D7963v/1tXHXVVYhEIg2bLO9JLgZSzsX7BuoEAr28QN1zz8VDZlanzJ+eSHqS6aElE0omHqh75nmuf3/833Hv396Li750EdyTlsESCoVUKaxqtYrHrn8Mx844VpdgAYABzN01F2+9462IBWNwe9w4vvQ4vnn+N2HYDKtW4UQI53/mfHiyHjVGum4lSYvH4/D7/fD7/YpdD4VCyGazylNbdVXxwFceQKHNGifHhAPnfeQ86Ol6SRPWdpSbvWma0CM6Xvr4S5g+axrhoTBO//TpmDkwo7wejB2ipIpglXIrGl0HDhxAKBRCT08PnnzySQX8Vq9ejXnz5iGVSmH//v1YsmQJUqmU8uIHg0HlUfd4PEgmkxifHIepm3Bqlrc4FothbGzMSrwW82DzlzajFClh7a1r0XVvF1wOF1atWoUnnn0Cu2/YjeGzhwEd0NIaWm9sxdLDS5HNWLLKtrY2tLe3q36NxWIYHh5Gf38/hoaGlGeoUqlgYmIClUoF4XAYpVIJgUAAiUQCkUhEKQ+Gh4dRq9XQ09OD8fFxlWCF5bk0zSp1Q3KG7xprxh8/fhz33XcfymYZqEJluV+zZg1WnrYSbq1evYAGysyMlUbc5/MhEokgk8mov/MdDIVC6p2r6BWUM2VFRHFO2N12/PTcn2Lbwm0wf2sC18C6h9dYM2frcM+2/2D7Y7VV5syZg2984xtKQUePID1hlJuWy2UMDAwgk8nA7/cjGo2qtZ3EL20SrhfValXJp10uF2KxGKLRqLoGs14TbNOmkGFdJDqldJj3J4l6wLKzkkYSn5n7GTwWeQxzMnNw0/03ITuQVflZJAnZ1taG888/Hz09PZiamsLTTz+N0dFRRapLryidA319fSo8q1KpIF/O4zfB3+Bfl/+rlXDNBFanV+Mzez4Df8Wvnp32hSQR+FOqAGgPsf/ZPzwuk8kooE9VmSQJeK/JcBJfvearyHqtnC0dUx348M8+DFvV6ueip4ivvPMrKDlLeOsDb8WpL52KWlWE8ZkG4pE4njz9SVzxwBXQjbqRYhgGTIeJH1/zY1x3+3XQK3VVIPsuW8rCaXNiTs8c9ay1Wk3VBZfkA205r9eLl156CTMzM3A4HOjs7EQ0GlVzIxgMwufzKWeKDMEkGJYeaqBOZDSPPX8nUU47h3OXv0uZPgF4GWV87szPYW/bXkADHGkH1n1qHYIHg0rdRycYE/7R7i9Xyxi8cBARI4K+F/qga7pyQpVKJaSKKRgFK1Ey+9LtdqOqVeFyuGBU6uEcVKLwvWB4IZ+fqrpYLIZaraZqsDMpnt/vV44QKhsJ1DkHGSpCG12RB7Z65n1iFMavs2+ptGWCY/6N7z1D9Zh4lveye/dubN68WSlqZtt/vv0+tsqrDnC3trbiwQcfxJIlSwDU6+JyMgKNMUjN9Qml0Svl0HypuJDJeCHWBJayK5ZtkpOboJULPmscE3wTOMkXkeCSnjfKjqXUm95jvsxk2LggsskkYpTSU2ZysmRtQD2ropSpyLJPZPnI8Ml7Ijtus9mUJIafcfHmtbg4GYaBdDoNwzBUluv29na18Mn7TKfTGPWO4hfn/QIDcwbgmfHgnK+dg87hTtXPBIqJlQk89jePoeqqoxZH3oE3v+vNMA3L+ChUC3jq757C2Klj9WNKDvT/pB/99/UrhlDGxbN0CiV8xWJRZZBluZAZzwye+NgTKFfL6PhQB+ZU56jFj7HalBIS+JX8JQx8cACTl9Zl8LFdMSz64iL4Jn1oaWmpA12PBy0tLWouFYtFxGIxNTenp6dRrVYxMDCgSIipqSksW7YMCxYsQDKZRKFQQCQSweTkJGq1GlpbW1WJEBoY2WxWbW5udz0bKpUEuVwOti4bRs8dxcpHVipm2Ol0Khn73pv24kj/EYQ+G0L75nYsXLhQGZaAlR+AcdnRaBRDQ0OYN28eBgYGLO/vibkyPj6Ozs5OlTQlEAhgbGwMnZ2daqPKZDKYnp7G4sWLcfjwYXR0dMDlcqkyYlRM8P+VSgUdHR3KeJ2ZmcH999+vvBxsq1atwmmnnaaen5n+aQjb7Xa0traqOc01hBmBafxw8+N6Q/k815VqtYofrPgB9p639z+5Gv7pt1nAPdv+o+2P1Va56KKLcNNNNzXYGAQpuq6r2tmapiEej6v1iXsenQS0Qeglpz0gPWf06DIDsryOlATzM6CeERqoe0MlQS/BUcaRwde6v4a7W+9Wzzf36Fxc/pvLEUlG1H7N6xmGgblz52LTpk0qhvypp57CzMxMQ5JUXofe/a6uLpWQNGVP4aMLP4q9wfp6GKlEcOPwjbgsdVlDyB1tIAI+ru/M50KAxH4leKKNRjUe12JpD3L8pIz6eOtx/OzCn8Gf9+P6O6+Hu+JWa7uu60jEEjjScwTrX1ivxolj0OxdJxiV9hjHh/chiYpqtYrp6WksWrRIJcxiWALPSbuU/cqkpAMDA8pB0dXV1eAxbmlpUc4qfp9jSeDH1uzV5j3K67LfpCxdNv5dKkqr1SpgA245/Rbst+/H8n9djti2mJrfvF/aJLK0HnMUaJqmwu2o1gSg8jTJecJ3hzYc1Rc8hnOGORjcbrdSEfCZOS9kqGkgEFCVB3hPDJEMh8MYGxtTHnrOY0ny0GFIxSJtDebGod0hCRHa1sytwPXA5/MhW85iuGcYR/7PETz00EO//yI2216x/T62yqtOUn7eeedhyZIlDYsvWSnp2eVnMm5CTlbJ5MqF1jRNlVlQypIo0QKg2FPptWz2KvN+uAlIKTA3HsrdgXrskIzNIHjlhkLPOZ+HLyRQX6z5XaAuayfwlguhPBevTa+cjIEvl8tqsyLYJnPO78t4bPYPF5BCoaDKQFGevnPnTqxfv14REgS3jEEmqBy1jeLuC+/G4ByrlkEhVsCz73sW67+zHrHDMUVamKaJhDsBA/WNDgDedOhNWL1qNaamppDP5+GHH5t+uAlbrt6CkY0jgAGsvnU15j8yH5pHa/DSk7CQieyAurIAsDah6elpeNwe+D/oRzwTx/iucRRCBSxYsEDVx67VrFJXTOhVq9Xgm+ODGW20G2dWzSCxOgHbb23KaGB2Tm4kzHru8/kUeCYYZrI81nClKoOEFLPS0ihhnzNUgPIkyfpSjUDQXR4qo/3WdiTdSbWhUWXicDiw6dZNiPXFkHk4A7vLup/e3l4lJSMrTxa6tbVVvWvsZ4/Ho+ZWPB5HPB5X5b2YJ4AZfDVNU6w3QyRcLpclg29rg8vlwtTUlCIbWBGAZff8fj+SyaQaAyYE5PjSI861gUwyPVZ8N6hsYaMMTCZYkQYck/NsvHUj9uK1C7hn22x7NTRN03DKKaeoBGkk02OxGFpaWuDz+ZSKiyWdSPJnMhmlmKN6jAQfARlVNEAduAB1MCE9cVxvmoE6DXvaKyQFJDnONaxqr1oyadHKzjLKrrLa5wkAaIONjo7i2Wefxemnn462tjasX78ee/bsUSFV0klA4r1arWLOnDlWHHrFi7/d/7f4Uv+XsDOyE+6aG39z/G9wQeICmPZ6yUV5n3yOQqGARCKhwBOVYHxmAA3J0ZptK+l9l+dnX3eNduHKB69EOB2Gq+xCzaifQ9d1RONRRONR9X15LvYRUI+FluQG/8njZL/a7XYEg0FFkPNZ5Pdon0jPP5VglJFLbzafj/fK4xiWKWPw5U8JXuXn8ru0Nfl32ScyF4A837u3vBu/PP5LhHaGUKqVGp69UqkoYorOMfYRHRL5fL4h8zxzNdH2MQxDZXanUoSOLKoFaSfzHvnO0YZj3wNQjhfpkJOl6ujVLpfLSCaTyt7m/dlsNmVrAVCAnXaCHGPOM/bjU6c9hXXPrINu6IqU83q9KhlbtVrFY5c8hh39OxB8LPh7rV+z7Q/TXlWAW9M0/N3f/V2Dp7W5lIXMBsrYBXkM0MjGyUWXpbX4ssgFUyY443UZV0vjWwIxm82mpFdcXGXsFEEmGTY+H5+NLyvZO0rFeD98CSn3PnbsGGKxGEKhUEOsN+t8ylrOvB8uJgRzJB/4ogNQMSokL5hgTMb+ECjx3nRdRyaTQSqVUkDOMAxougYNVibXarWqJE3MKjo5Oak2YZvNhkR/AoMLGwsHpnpSiM+Jo/VoqyJGHA4Hlu1ehtpna9j1z7sAAF1f78LpztMBlyX1o0fUm/Ri7Y/XooAC4hviGF00ipZftaj+obHC+2Z9RM43LrwkSqLRKJLJJDLPZqDXdBiwFj9KAwm6crkcEomEShBW21+Db6sPUxumGmLAB64eQMveFuSPWVks6Ykul8tKtkTQTGBKlpreWM531qvk4s7kOoZhoKWlxSpJFgqpzLI+n08xqtzAstmsktQ7HA4EAgGEw2Fks1n1rpEAME0TuXgOc56ag6m2KcUmM/t4Op2G3W5XpbkKhQL8fj9yuZzyANFjLj0TgFVHPZVKIRQKqbnG99HtdiMYDMI0TXR2duLYsWMKuLPv4vF4w7hxk21OJuJ2uzF85TA6ZzrRMtWCQCCAYDCoPO3sZ0nE0QgIhUIvk4KxfyQRIomy0dHR/8qSONtm22z7I2i6rmP16tUKFMZiMbS3t6vkZYZhlf/J5XJq7eI6QcOdhj7jubkO0m4hUJOxn7WaVVYrk8mo5JD0ZhJwSkBGgCodFSTb5Zoazodxw8EbkO5LY0d4BzqLnbjphZuQTWRRMAoN4B2oA9PDhw9D13WsW7cOXq8XHR0dSCaTyhaS1UtIoAJAT08Pst4s7m2/FzcduAk3n3Iz3j/4fpyZPRM2R90LS8KaNgVD8EggExRJ+67ZGcImwVWzApL9zf4wTRPzhubBNE1Uqo3ye9paUpLNzyVgfSUwLj/nsfw+74GJszjm8plovxGgsq+CwSCi0SgmJyfVc/E73HOj0WiD3dZsq8pnJKEjyQjZj7SRXul5pH0rz1+tVuHIOLBg/wIk9AQAKNKcnmiZLZ/XZMjlzMyMAuVer1fZk9JzDUDtv/Qem6ap6tbTwSTVI6lUSpFcJMHC4bB6V5xOp7IpDMN42dzTdV0pKWgHsI+dTieSyaTCGfF4XB1P+8br9aq8PbzWIxsewWNnPIaR7hG8++F3q75lRQO73Y5fnfUrPLXyKRg2A+lPp4F9AJ76j6xms+0/215VgPviiy9W9Ya5eNPop+HLZEyMe5BJJfg7Fw6+8FI6QyDhcDhUkiW+PMxwzBhwbpqs8w3UswNSDsONkUyajM+SGxw9ZdITzQWMgBmoM58AXib1piSaQECCZqBxs2ViKSZx4bUJ4MiU0xvJczEOKhKJNCRtoByOYPzIkSOYN2+e6hOX14Un+55E1V/Fpj2bYJQsA0TWTibYIxDrHu/GxY9djPs33d8wD2reGipmBVNTUwqEFgoFRJ+LwnudF/lT85j8h0mU/tHyqHZ2dsJmsxKt+UI+7OrZhcTaBKABw6cPw/wbE2f//Gzkp/PKg0pvBDcmkiCynidDAbZv3w7TZcKoGUDBkjLZ7XbE43FEIhG1kDPsoFKpYGZmBpGfRNC+qB2TF03C1E3oJR3z/m0eHCMORFujCIVCKJVKDdLkQCCgPL4ca25wc+bMgc1mU0nCurq6GhQTo6OjmDdvnpLIU3lA2RTnOBPH0APsdDpVzLzb7VZECue3TCpCYD9//nwkEgk1L5LJJOx2O9rb21XMXDweV0n3uKEahqE8zpynixcvxszMDMLhMNrb25XRwI3t6NGjmJqawooVKzA4OKjewdHRUcRiMVVyLBaLwTRNFUt1pPcIMhdmgI8CKAKwAdmrs8h9OId7a/fisk9chog9ot55Sdzpuq4ULjRA6L0nw16tVnHvvffi+uuvRzptFcGkIUGiYXCwkVCabbNttv3ptUWLFqmwGSkbHRwcVEY1lTzMQkwgIEPVZHwmPWUAVDbufN7ao1KpFNLpNDKZDJLJpLrm/Pnz0d/fD5/Pp1Q/lOFKJR9Qz8fC34H6mlqr1RAxI/jSoS/hhqU34Iv7vwi3341DnYdw8OBBAFDJryRRb5om9u61yoMtX74cgUAAnZ2dGBoaQjZrxUBTxs5rT05OIqEl8M9//s/IODNwm258dc9XETSC0Jx1JSG92LlcDtFoFIFAQKmmCGzkfkjbS6oKCa5lPxBkSqBJR4xUCPDYnCcHs2rCV/Y1KJdom1WrVQz2DOL5Vc/jigeugLNWVyZIzy/tu+YmbUkex/5NpVINOQJkviFZIpc2rcwHQ5UdVVwcAxlSQLuZ98A5yXuXIQvsM7kfm6aJnCsHb9Hb6GBCBXf23YlYJoazhs9qcNJkPBncvP5mnP7U6TAMA8FgEE6nUyVQBeqJ23gvkrAgCc9G+5rvI50m/Ec1IKKArWhr2NfT+TQGLh6ArWLD8ueWq32fHnCWK1WKTh04vuI44v1xnLH5DPidfmSzWVVCjGFmkUhEjQ9tg46ODjUWHCP5DCQmnE4nKqjg0aWP4pE1j6Bmq+GlU1/CHd47cN0z10Ev1pMHPrT8ITy7wqq/DgDoBfBLAGsBTLxsqs22P3B71QBul8uFD33oQ8pryM9kPWwCW6Auc+UCKBlhxqxqmqbORW+xTJggy09ISTfjpiizlcyerusqQdXJZF9kKSlHlcCWi52MFyEbBkBtLNwE+HdmIuV5pCyIMd88ns/OhUQumlwAuRAz9pjPT1aP3mKy1JVKBYODgzBNU8WQLFy4UP0ODXhu6XO49exbAQC1Ug3LHlmG0aFR1ccEb7quIxKJwOv1IhKJYAmWYGtxKxLuhJoLR885iv4X+xEYtbJWEyTO75+Px1oeA7oBf49fAS+gzsYW2grYc+YemI4TRoYOTK+cxvCOYXQ916VqHvKeaFTk83klG4pGo8p4KJfLWHvOWmw5bwuqqAJ/b52WSgCWtOJcK5fLCAQClic/V8VZPzwLeyJ7cOiUQ5jzozlY+OhCZMoZZSiQuc3n8xgYGMCCBQtU6S/GYIXDYUXokOiZN29egzchHA6rhG8kd1imi15ZmQHTZrOhpaUF8XhcxZ1zXjqdTszMzMDr9apzsI8jkQgAKHa5ra0NuVwOlUoFyWQSc+bMUfNGMsR2ux2JRELdVywWQ1dXF7LZrEqWlsvlVKwWySLW5/Z4PEgkEggEAsjlcgiHw4hGo4hEIpiYmFClMVwuF9LpNAYXD2LzjZstdUEOwGcAvBXAD6y6nlVU8cC/PAD9qzp8By0Dh0mKisWiypju9XpRLBYRDofVxky1BABce+21qp44DSSn06nWmunp6T/cIjnbZtts+x9pZ5xxBmKxWINklMQ9bRKGB0lFHtd4kvkAkEqlUCqVMDExoQA2z1Or1VSMaHd3t7Jt2traEI1GVciLBJOy6ook8+mpa5ZPS5vAZbrwwwM/BDSg4qhg0aJFcLvd2L9/v9q7pUeWa2AikcCxY8dw+umnY86cOfD5fNi1a1dDEljaHMmuJH76xp8i6U4CAH4070ew1+w4d+e5qBaqSqGUSqWQz+cxd+5c5fFlUjCCKylZB6BsKgJb6d2XRAOf3zAMFPUiJkIT6JruagDcmqZhuHMY37vqe4gmo3jbb9+GlniLcsjwuoMLB/H9q74PUzfhKDlwwZYL4Cq5GoA9zydLcUklgvQsE2wyV0ozwSEBugyZ1DQN0WgU09PTKrkXHUymaSKVSqln5p7EknIy7r7ZU110FDHpmcSc9JyG+W4YBg5EDuCfVvwTPr3901iYXGjZvEYJdy24Cz9f8nN1nhV7Vljj2hPHVzZ9BdPeaTzx0Sdw2ndOgzvrVs9BW4xzVQJ8JlWjIySn51BsKyI0HFL3XS6Xke5KQy/qcFadypGWWJjA5g9sxnnfOg9zxuZY3y/mcPC8g9h21TZrDAwT/Y/3q/5hGFx7e7uyu15Y8AIeusqKkY54I7h0z6WYZ5sHAIqUb1Yk8Hx8X+g9J0EhCSFmQB/xjODOljtRs1nz2NRN7G3fi90tu7F6eLVyUmx4egOm9ClsWbvFAt1HALwds2D7/1F71QDujRs3YvHixQCggKOUbQD15GQEjATPv/71r3H11Vc3MMoyDptAQjJZzERumqZ60biwMBabDBSBLmAtlPPnz2+I4abXmJ4vAgzKy2QsMhcwoFFSJF9YspfcNKPRqEqqJZlTAny+4GwnS8BAD2qzTJ99zAW5VCrh+PHjWLBgAY4ft0plcLFgEgh+h5vLM2ufwW0bblPX//ez/h3Hxo5hzfCaBkbV6/UiGAyitbUVwWDQMhQcNgRLQQW4w8fDWPftddCP6UgUEqpOYTgSxp437IFxkbU5JJFEeiiN/v5+NW7VahX6MR1X5a/C7RfcjumeaTgyDqz64Sr07OhBpWaNVTKZVCCZDDbjp+X9apqGGmrYf91+lN5gJcSDA3B/x414PK6+w7nFeWqzWdm0CZzP+ck5GPGOwHG3A77VFmtOEoEZKw3DSrIxOTmppOCcb5RJsVRWpVJpKH8VCoVQqVaw95y9WP748oaajpxnlOuRiCGQ5Hgy+ydjyaimYGkSSt5ZTo0yahnf5HQ6GzzpJIkIhF0ul0oEQ0KHRBfLsFGBwg2eEsPJyUkEg0FMTEyoerSZTEZliOdx2WwWo2eO4tl3PluX8t8Iq5xZUyqmolHEvUfuRfShqNrcu7u7EYlE0NraqjxHHR0dqv6tLB/HZ2xra2soKSjDT2azh8622fan3ex2O84991y0tLQoAMO9lWsoQRJg7b/j4+OqykK1WlXEpsyZQoARDoexaNEitLa2oqWlRYEPJmz0eDyKWJdJ0CRJyusAdU+kBJ7yvgE0xIsDdZtL0zTMmzcPtVoNR48eVUQB7QgpjR8YGIBpmuju7ka5XFaeSO6njGvfv2A/ap5GT++W6S1wPOeAmTNV31HFRTswmUwiEAio9VQ+A/cOCaxN04ThMPDknCdx9uGzXybv1jQNjy17DFPRKRzoOoCrHrwKPSM9aj0/NOcQfnvZb1FylTDWPoY7Lr4D5/z0HHiHvIpcHz9jHE9e/iRM3brm1tO2wrAZuPjei2EzbQ0e4ubEbNIeYx9JRwgAlROIxArtTgn4ZQ4h2jB09Mh8QqzzTrWex+NBDjk81vMYNo1swpbuLdg4tRFuw61sXUM38MP+H2J/eD9u2HsDFuUWKfvg+djz+M6K72DGM4Mvrv4irtx8JVr2tWDPG/bgl8t+qcb2G6u/gY37NkJ7ScOLF7+IpDcJwHJ87HjPDpz+w9Phirsa4s1pcxuGgYnzJtC7uxfIQ5EIpm5i5zU7MbN4Bmf86AzEjsfgcDgw2TKJ597xHMKjYVzxwBUwiyaGFg7h0bc/inwkjy0f2IJL7rwEi44vwotnvIhtF29T9/nEtU/AHXFj055NAKBsK6ppty3bhjvPv1Md//DZD8Pus+Oixy9CuVRWoSLSeUK1RSqVUnOS4awSs/Ddpj2UTCYRvT+K8X8aR215Dd6CF294+A1YeGQhsnpWhaxlMhlsemATjLyBLXO3AO8D8Ox/bl2bbf/x9qoA3Jqm4YILLkBra6vyOPKnjAWhR1LTNJVAwel0YsmSJWrRopycQIIMI2W/0itNCQkX7OZsxgShANSEZzInerAIsrlI0jPHxYMSYz6DjJHRNK2BGABeHsfFkgDcULnRSvkYWTN+l15y3ruMaeffJBvH7xJU9fT0NMSGG4ahFm/Kwhl/Wy6XERoKQTtNgykQTWS4njQmFAphzpw5KlyAKoYjtiP4wYofYCg4pL5XCpRQi9VgG6wnwbPb7dh+1XbsOX9PfXCuBW7bexvO/+b56O7uRltbGzweD1wuFzrHOuE/5Md01zTO/MaZWDS4CCVXCalUqiHuhpkxCZg5Z7LZrPIc7HrfLoxdWs96jvcC473j6Ptsn9o8mY2d84dZuqempizP7FQC+mYdtm4rWZrT6cT4+DiKxSLmzZunZO1MrMGYZSYAk3M7HA6r+Hk+QyQSwa4/34VDZx2CrcWGhbctVF5tjheNtnw+j0AgoN4Ryb7m83nlzaYSQMbo0xDM5/OqfIaU+7W2tqrn59yQyUL8fj8ymYyS7HM+8jterxeJRAKFQkHJFEOhEIrFovKi8x3m2JF0Ym4At9uNaDwKu2FHGeX6uO0B8AsAeQA3wQLf1wDl35UxjnF12ODgIJxOZ0O8e0tLi5LR0yvf19eHrq4upT7he8i1S9bGnW2zbbb96bbe3l50dnYqkjMej6NQKCCZTMLhcKC1tRUdHR3q3U+n05ienlY2h2ma6O3tRSQSUUokJg6t1WoquzmBeKVSUVnOmbuCiR25HzaDaynDlUC02U5gIxkrk2RJB0NfXx8cDgcOHTqkSqUyHl3mdzl27BiGh4fR3d2NlpYWlbuC6iaHw4Fz8+ei5YUWfGLtJ1CxVbB8x3Kcc9850Is6aqiXeJ07dy76+vqU7eH3+zHpncTT7U/jiiNXoFwp43urv4f37Xyf2g9pvz09/2l0pjtxz7J7sLdjLwzNwBn7zlB9V6lU8OS6J7H5zM0oOSzy/PYLb8cZXzoD7uNuJBYl8OzFzyIZTqo+GpwziLuvuRvLb1oOd8ZtOUKOAFpJA3z1+dE+0w67zQ7UGj3XkgiQfS0/Z5+zSTuQhIkkZ5ibhECbdkM+n8cjb3kEVzx0hbIBa0YNt55zKzYNbkLKm8KagTX47jnfxaG2Q3ii5wkcCh3ClsQW3PTETQos/uy8n+GR3kcADfjiqV/ERd+6CMZRA+OLxvH0+55G3BMHAIz4RvCDDT/Agt8uQOJ7CeDr9f4wayYquyqwj9uhxTVgbv1v42vGkb8zD3/GrxLtMpmvy+XC8FnDeP7q53F4w2Fc8q1LlER+89WbcfDMg4AGPPGuJ3DlL66Eo+LAU+94CjO9M5jpn8Hmls3Y+ORGPHnlk8jGLDsmE81g81s2I/jbILpmuhreaw0a/AN+RYozfI4A2oABbAIgtvGtP9yK7G4rWS5zQkk1LG10Am9pp0gHiMQk/B3bgNCHQ0j9OIXzf3s+1hprodnroSIkcEqlEpbftRxbntoCPHeSBWu2/be1VwXgXrlyJTZt2qTkq5qm4Z577sGqVauwYMECAHUpkYxp5kRcvny5WpAIihkPQlYYgAJIXq9XeaYp++HmJ7Nj5nK5BrmXrD2taZoy8OlhJnvF/5O9AqBIAr50Un56/PhxpNNpLFmyBDMzMzh27BhWrFjRUL6rOTEcgIYYGvYFPdgElgTeMlmD3FxlSTHGq4fDYZXkgRswn4kbXDQaVYvAmvQa3PibG/H1N3wdBgxs/PxGLBxeCLfHjTVr1qClpUXVJyVr63a7ER4J43XHXofblt+Gml4DDKD7pW607WtTSSoo3Vv4u4V46ZyXUPFU1POb/2Li2RefVbFtS5Yswfy++di2chsGzxiEBg3dk92q3wKBQIMnl55bt9vaTIeGhhSzzD5cd/c63H/u/aj4T1w3A9T+tgbDZag5I5PrBYNBhMNhTExMqPkQ7AqqUACv1wuv16v6Nx6PI5FIKDkzE3zIecS5wiycNL7K5TIcXge2Xr0VL216CabNxL4370N8NI45/z4HqEAlWSkUCrCFbSozuWEYGBsbg6ZpmJiYQGtrK9ra2lTGTYJ+Zk71eDxqU04kEli6dCkGBweVt5wsezqdVjL7rq4uNSfT6bSqlUq2ngaIx+PBKaecoqRtExMTsNvt8Hg8imzq7u5WhkVXVxeOHz8OwzDQ2tqqYrAZR9WWbsOSty/B7tt2w3SaWPDNBfBu8yIejqP2nRpyXTl4H/Si8EwBWVu2wUtCrzqTrwHA1NQUNE3Drl27lPqEZdU6OjqwbNkyGIaBUCiEU045RZUClLGBs222zbY/zeZyufDwww8r1RJzT7S2tmLu3LkqmVEmk0EikUCpVEJbW5sqkRiNRpUcnMmU0uk0arUa5syZg0gkopRABOuUltOAlzW2pSyc+z9tG6mek3JhNv6d9gTBtvTE0hM3b9482Gw2HDp0qCG8DqhLuatVqwwUk3D6fD6U7WV85rTP4OZdN8OvWZU7vDu9+Ot9f417192Lc+84F3pBRzKUxJa3bEH70XYsNBbi+2d9H988+E3Ys9ban9ST+NiZH0PBXoCr4sLj3Y9jX2wfKnoF1z93PfSKjmKpiO3t2/Gj034Em2FDwVmAqZm4bcNtGHhhAB1PdqBUKuHIpiN46eyXUHXUnSoTsQlsvnEz1l+3Hv69frQ83oL0G9Iw9BNy9aoNpz15GnqiPWjtb1W5ctb/Yj2++p6vouwo4+KHL8a659fBqBgvW/Ol84RksbTZJDnCfq3pNaRqKdirdthhh6Eb0OwadIeOYksRt55+Kz74xAdhwMAXLvwCPv7Yx3Fg+QHcsfQOFHwFpOenccN9N8DUTXz9sq/jQOcBbJ23FYZmwLXahbQ7DVMz8Xzb8wCA59ufx0cXfxR9N/Vh4H0DGOoeUsqw48Hj+MX1v8DyK5fDPmCHZ4EH2hs1mDYTWk1D55ZOdCW70DnRiblfnIunb3gamqnhjJvOQMdYh2VbzEwgYSSAutmKJz/yJK78hysR0kNwuVwIBoOwO+w43HcYT1z7BCquCsbXjOOxmx7DNf92DR5+/cM4vOGwuq9kbxK3vfs2wACybdkTExvYt3Qfiqkiep7uQeKShIWOqoDjHgf2/mQvpkem0f1gN0a+OwKbbsPrP/96eA54EA/FlWOJoNbtdqN3tBd/+Y2/xE8+8hPUUEPkxgjcd7sxYhtpUClwjDmmklSR0nJZQUCNt/h/uVxG+fkyohdHEVwXRHKhpfCQXnLar2bZnAXb/wPtT74Ot81mw3ve8x587WtfU8CCni+CCnohCUDlpJVJFySYpewcADKZjLoWPcpkilgWiedgmQG+SIzHzuVyDbE2shwB/9GzzWvJF48eP5n4ghuYLEvFBVgmMpNsKV9smQiOXnN5Hnrn5P3w/imDI2kgZef8rFAoYHJyUm2sJB7ooWb8GpNiAcD+OfuRs+XQv7sfDocDvb29CjDS20fGmTG9ereOb6z5Bna07IB73I0Nn94A36hPxe/ze9uv34795+yHaatPYz2vY/E7F8Ox31IRhCIh5N+axws3vKAWZ1/ch4u/dDG8w144HA4kEpZ0nUnTmJmbUi4Zd6dpVrzxsH0Yj3/qcUAHtD/TEJ4Io7W1tSGxGfu1ra1NyQltNhv6LuvDi19+Ed6/8KJ1qBU9PT0K0Hu91j1NTEwgGAyqElcsV0GPAUkKlqCQNd5n1s1g2we2oRAuqH7RjmtY9ull6BjtUDHN5YVl7P7Cbqz4+xXonunG9PQ0pqenVUbyzs5O+Hw+TE1NoVQqIRKJqKzcgOXxzuVyiMfjyOfzOOWUUxRBQGl7Op1WScMAS1odiUSUcckEbceOHYPD4VDZ5avVKoaGhhCNRtXGx5jtaDSKiYkJGIaBWCym6nezRExnZ6fKTkrPf39/vxW73pFG9rws1jyyRknGHQ4HZmZmcPjwYVU6jGVEAoEABgYGVL1PmfWVMYV2u12RgpL0A6AIEo/Hg2XLlsHlcuHxxx9XhN9rsZmzdbhn23+w/THZKrqu413vehcuu+wyVZmCP6k+YpjS+Pi4SoIZDofh8XhUrgkSksw2rmlWlQmZLJXlwxie01xDW0qVScpKJ4GMa+a6JHPH8DoAGjI5k/yUHnAS68zfsn//fqXkoS0hbYhKxYr/ji6L4mvLv4bnW59HW6kNn9/+ediOWCFDrFyRz+cxEhnBPZ+8p4FAB4DebC9ufu5mVLUqPn36pzHtOZEDgzNCs/6/8qmVWHH7CozMHcEjf/NIQyUQtuhUFEs+sAS2kA0v/q8XEY/GG/4eSUTwttvfho6ZDkWc33PBPXhu7XNwVBzY9MAmLHt8GTRNU3J5jkUqksLuU3fj3KfObQgzkk0qDzgGdAaNto+ia6ILOb8VdhgsBFHWy9iycQs2n74ZV9x3BTYc3ICn1zyNZEsSQy1DGGyzEnCecewMzPhmcKDtQP1ifH4T6H2xF7aKDQOrBk7aL83NXrCj78d96LmzB3v/cS/GzxgHNMA/7sfrPv86+MZ8ak49d/1zOP6641j84GKs+MUKaKiHOB45/Qhcky74t9cTt9kddjz67kcx9XqrWktwOohLv3UpWmdalQNI13XkXXn84v2/QHZutn5jGWDprUtx6sOn4sH3PIjk+UlAA2wDNvR/uB/lZBnHf3Qctb4aYAKeRz2I/IWlrEz/YxrZt2fhuc2D0N+HGhxf5UvLOKXvFCw+uljZV7LKCseKCXsH2waxw9wB83tmQzI42Qi0+S41h37wHaHNTwxDHCK947FYDOeccw4ikYhyftATTnn//fffjwMHDmC2/eHa72Or/Ml7uFtbW3HVVVcpwEImkJOUpY/4YkqpNF8OTmxKYzm5uRAQvNIzyBeCtfoI4oHGWo48J2OyuQGRYeamRe8jzw2gIZEHX7paraY2LXquCdopq5EvIs/D56ZUiISBTGoir02voixNBlibLJM7cdOUiwefnWwc+4ZAojl+igCe35l/yJKMw2clFZuYmFCeZZZeoizdMAwk3Un88tRfYkfLDgBAsaOIPZ/cg/XfXQ/joKFIF5vNhvU/Xg+9ouPFC1+sTx4H4LjAgf5av1U+Ij2D1JpUwyZTcVcwPX8a/VP9sNvt8Pv9SKfTiMfjiEajSg7OBZJJvgiKAcA15oL+FzpMuwn9kI6ORR2oVquYmpqCYRiIRCJKEs2Yt97eXhSXFbHzEztRa6+h/LMyYv8cQ2VPRSkskkmr1nUoFFIGEhUTAJRxwji2SqWivCGAJcPufKETG362Ac/8xTMohUuwD9jhv9EP/ZAOvcNKBje1YArHbz6OUnsJL/7LizC+aMA54VReWpZFO3bmMTh+5VBMPjPLB4NBpeIguZNOpxEIBNTcNU0rOzhlVJz/VHkwfpvhF7FYDOVyuSE5XaVSQUtLC9LpNAqFAsbHx1VCHZJBcl6zDI9pWmV6mDMhmUxaZEIxjLaftmHSOQlN0xS4pgx0/vz5ipQaGhqC3+9Hb2+vSn6XyVjJ7UKhEEKhECYnJxGPx5VHP5fLoVQqqbnGTT0ej+PJJ5/8L6yKs222zbY/hhaLxfD6178e69atQzqdVjJp5sMg2KYn2+/3qzhtysftdjtmZmYwNjYGp9OJaDSqABxDl5LJJMrlssqNIauvkNCXNo0si8T1l00q2QCofZTgnOCJ4FE2KX9mSNz8+fNhGAaOHTumSGmZoA2wbLHB4iB+svAn2N62HQAw6Z7EZ5d+Fm8deCt8BZ8q7zTeN45H/uIRVLwvJyKHAkP46qqv4tSZU1Gw1UnkBuCoAYWFBTg7nSicUzgpqOyd7MVb7n0LWlZZpPWK+1bgVxf/ChMtVnaplqkWvOHeN6BjpqNBnn7hvRfCWXDCn/Zj6RNLUSwVVfiUtJWC8SDOfvxsmLr5MvBlmiaqzioO9x/Goj2LlO1K++ylhS/hzkvvxDlbz8FQ1xBsmg0X3nMhnt/4PB454xEAwF2X3oVMawaPnvboy57t6QVPv/yBRd8kQ0noNf33Att6VceqO1ZhwWMLUA1WcdpXTsPuym7E58Wx7rvr4B31QrfVvbmrv7savkkf1j60FmWtXu9a13V0PtxpSa0r1p44PDyMQqEAz3s9iHwpguppVWz44QboB3Wk7CnlbOI83vCFDdj54Z2IL4kDZcB3iw8T353AlD4F51NOeL/qRXlFGeGPh5HanoJhGAi/L4zUV1OwH7Uj9vEYaqZllwb/MQhtSkPwfweh2ev2rWmaCD4SRGA8gEprRcW+04HB8rwkSiqVCtyTbqwurMZu227lgKDTjKGvxAd8FtoVUoFCW18N1Yn5ZHfZUXhjAb5f+1QIH/PwMJ+N3+9XeCIWi81WP/kfan/ygHv16tXYuHGjSioANGbTlLGjnMhyk5FJQzg5m2NkCIq5wXGzIMCkB4oAVHrI+Tul6nyxKEMny9ws5ZKJIMgCs0k5GGXyXJD5TAAaPO/8PxlrJsEgOya9/fRuy7jk5mtS2ktAy4VHMrIOh0NJWHhuZmjmJs54dnrvm/tD13VVb5HfJavvhBNaUgNEaE18YRzbPrAN676wDq54owTr1FtPRS1Tw/637AcAbPzxRvQ+0gtDt2pMp9NpbLh1A8qpMsausOKuXRkXFjyzAOVaXSbM/mb5NxILBLiMk6ZXQtM0mM+diIdDTSUNYwxVsVhENBoFAJUttLqgitwXc6jNsxbJWnsNg383CM8XPQgdCak5kclkVP1sZulmDDkXc3pRaHiRtOA8n/P8HNjyNjz6l48C1wHl58s4bj9uxRiudWD05lEUF1gkVb4tj70f2ov+TD/sWy0SKBAIYOCiARx971G0LGzBaT85TRl9jMemjLJaraos7H6/H5OTkw3hCwybkACYMdiMHaeigOQE3zFmJCXg1jQNw8PDSKfT0DQNQ0NDKBaLKhaS9b+ZfI7XoGddxu+RFOH8ZtI7AOr/zHiuaZq6xtTUlDK4KpWKSvhH8q5YLKqYTq/Xq+bAbJtts+1Pv82bN08RswAaElLm83lMTk6iUCggEAgoGTm9Ug6HQ5W6YrZtkpLZbFbFaheLRYRCIQQCASXPlmS4jPmUWZ35GfdI2jWSLJZkvJSLSxmsrIwCvNyGsdls6O/vtxKg7d+PVCqlAKqKRQ248Ju3/AaH5x1u6D972Y6gHkR7V7uqutG/oB+7fLuQQeakfe6quHDBwAVYlFiEz532OZiaiY0vbMTBuQcRD8WxYGIB3v7E2xEKhND+QDu0goanz7VA6Ju3vBlPLn8S1zx4DXqzvdBC1t7ZPdSNTT/dhHuuuweXP305QjMhdA93K7tKxkqf99h5lvqxmlcxxIloAsfmHMP6PetV/xEsNsdr12o13PWGu3B8/nEkM0nM3TpX2ZuJMxN48qInkfVlcd9596lnjiOOwX4BojScFGz/35on7sHG72yEbup46iNPIdte9xivuHMFvHEvDlx4AMvvWo49f7YHC+5bgJ4He1DRLLvSY/NgzU/WINGSQOBgALqnbgvzGRf/ZjFyWk7ZzcxfQ7svGAyqPs1kMjBNE+HPhBE9MwrbMRumalPW3BD2tNvthmfIg6VfXooXPv4CCl8pwP0rN0ycSP5X0RC8OQhjoQH7Ljs0/cR47dMQ/ZsoMAxUMpUGYt7zdQ/ylXwDriB+YH1vOjwMw1D2HN8/JpilsyEWi6lz0aYxDAO2eTaULinB/1O/Cj2tVCooXlWEf58fviGfciTquo6hvxmC55NWObFgMIipz07BvNyEo9MB1w9dyg5lnhubzcqYzmeToaqz7f9t+5MG3DabDR/+8IcbgIVMIkCgyJhfgk2gDkZrtRq++c1v4vrrr1dxTnLzYEIDgnUyWUx+BkB51gErfjSbzWLOnDkqZpteYk5ynkeeSybAIGjm/cryXDIxipTHS/KAfUGwTGZMxq/zHpgdm98nyKe3VEl2TtQS13VdZTyVSRzofSa4Y//yfPybVAnQS08pPTcsZvsmwGcSqWw2q+p622w2BLQA/nLPX6IaqeL5zufVvIg8GYFz2omqUVWJw2ZmZuDz+bD010uhe3V4j3qxYNsCZItZVUvc6XTCmXfirLvOws45O3F41WFkY1lseesWrPnZGrjtljSHIQJOp1MRBvQeEAQTEHO+8TjTNNV3AcvLnM/n4XK5EAqF0NLSYsm0K1mMPzyO5DuSgA2AAQT2BODe64Y74laLOqXzCqhXq2oes8a03+9HpVLBvn37EAqFlKSfXvVcLgc8CAR+HkBqbwpVVJVc3r/fD9sDNiub5Yn76NjfgfBAGGkjDbfHjczlGRx+92HUfDWMXz6OPZ49OP2205FKpVRsIkM7wu1htIXaYJpWibDR0VG43W7EYjGVqTsQCKh7pkJlampKETuZTAZtbW3Ku04gTAM1Go3C6XRi69at6m/FYlFJuX0+Hw4dOoSenh4V1sCsn9PT0/B4POjo6FCkiWFYGeBbW1sxPT0Np9OJYDCIsbExRKNRJRF1OBxIJpNoaWlR5+3v71fjEggEEIlElApCqiVisRgCgQAAoKOjA5VKBUNDQw2x4LNtts22P63W29uLefPmqbWJirdkMqlCUqLRKMrlslovXS4XEomEAsAMu6KjoFwuI5fLqVA3htqwSYm3zFdDO0DaCFJO3pwI9WQx11Kl1gzoaYvwHmTuG7vdjt7eXmiaht27d6s9ErD2xjvffSeOzT9W7zgTmFOYgy8e/SLCnWHo3XqDzfalF76E96x9D1LOFDRTgwYNNa2G/mQ/PrbrYwhVQmibbMPfP/X3uCt6F8767Vk4O3w2fv72n+MDD34AjrgDU1NTcDvdePO+N8MZcmJOYg5W71+NlYdXIpQNwdTq8dPZbBbz0vPw/p+/H7FiDEbNgKm9vE43+4Z7jcfjQdFdxA//4ocoOUtwl904Zf8pMGrGy/rOMAyYdhO/ufo32L9sP0zdxJY/34LwY2E4H3ci159D4ZoCSsG6KoptcNEgtKqmsp/DtLzPhqMpD4gBLNy8ELm2HMZXj0Ov6DBtJkybCVvJhos/dzG8Exbxe/7nzsejn3gUyx5chlQohaV3L4VW1tC1qwuepAfth9qBSStunIqKUqkER8WBYDzY4PWlfci5RNUjbSfpxWefj4+PI5FIwDRN9Hh7EDoQgubS1Dk4x0jsa5oG11EX2q5vw8juEZS1sgoprFarqE5WgUkgX8kruxQA8CygGzqqlWrDe1ONVFH7Tg326+xwaNb96zYd8cvjePrTT+O0fz4N+qiuciQUCgUkQ0kcfMdBnP7z04EqGuzmOX1zcOBbB9Dzvh44shaotoftmLx/EmbEREgLwXmHEy6XC9nzsih/oYxkIYnYm2LQx3Q4PA5M3jKJyqUV2HvsaPlkC+KfjiP31hxgBxJ/n0Cv2Qv8Eirsk2FwMuzzmWeeeU2Hqf1Ptj9pwL106VKsW7dOyaRlkjCyR8ViEb/97W/x9re/vSFDJpknwzDwkY98RMVHyNrXtVpNydMJkLnoM2M1Y03oIWYWYrlZcTOTpcO42ciNjJsgr09vH5+LoI1AXkq+eB0pgZfJTLgRSLkLSQigXhOQrJwkH1hOjQsmF0QppeLz5vN5jI2Nob+/X9Vjbmtrg8vlUrHXx44dQy73/7H332FyVVfaN/w7lXN1dXVOkrrVygEFJAQIkXM0wRjbGHscMDg9nnEOYw/OxjbOaRwJNibYBJNERggQCOUcutWtzqlyrnO+P47WrlPC8z7zvGEM39X7urhEd1edsM8+e4X7XvdKM3fuXAKBgDLi0sPbbjdbUYyNjamEh1DrrCUBmqbxesvr7KndU7UuBs4ZYPmB5TgOOAiHw0o8S2juS25fQiFfoOwuKwqe1P3n83nGsmMksib1x3AYHL7gMI6Cg0X3LcKpm4G5ZDWnpqZUvVwymVTXaa1/E8q1zJHNZlOGRALiVCqlVMfb2tqYmJjA8wcPrrCL3kt7cTzqoOGLDXiiHnp7e6mvr8flcpHJZJicnKSmpoaJiQmi0agKAIU2KIFjW1ubEnUbGBhA13Wlap5IJKAi9o5hGGbv0ixEvxNFd+lk35XF+6AX2+dtjNhGzPXRqbP3qr2U/WU1X0fXHqWvp4/wI+EqHYTx8Divff812t/fjrvPTTKZJBQKKVG1QqHA2NiYolR6vV61HqPRKLFYjMbGRmpqalTtdyaTUYi2rFlRbY/FYgopFyp4oVBg586dNDc3MzAwQKFQUKJ85XKZpqYmABKJhAripUa7v79fBcW5XI6mpibVx1TajMn77XQ6GRoaUtl62S9E2FEQolKpRFdXF8FgkLGxMdUPPBwOq/1jekyP6fHWGz6fj0WLFuHz+RQDLJlMMjExwdTUFC0tLXR0dGC325WCua7rKiErdt6q2SI1mMPDwxiGQWdnp9rToSJqBhWkWRSRRW/GGtxYqapSYieJY6ioXcs+fLy+jZXqbPV5VABpVLRjbDYbdXV1zJ49m3379jGRn0Cv0ZkVmsUt227h802fp89vorSzsrO4Y/cdJhjgMNQ1SSIgkAjwg8d/wB8X/JFZY7OYWZrJw10P85lXPwNlyJZMltCMkRlcM3YNLo+L5kgz33joG2TTWaZSU/h8PiKRCE6nk2tfvVaBNKFCCDTUuTKZDBMTE2Y9faaWUrlSOmcNmMXHEsZebW0tsdoYv7nhNyQDSdDgz2/7M9f95Tpm75mNTauAMAIuvLLyFQ52H1SBczFQJPWdFMtvWo4j4WD0F6Psv3k/5WB1m7Tg0SAnfOUEXv3Gq+QacwSHgpz2rdN47ovPUXKWKHlKBMYDRA9FWf675WiaxsZ/28ipt53K0ZOOsuviXaz5/hpCiRA2l/nMa5I1XP6ly7HbTLHUcsn0aQLJACWjhGPMARo4PZWALh6PV/nYMgTEkTJK8U10XSfeEqd2pFYJssp68Xg86O062qhGqi1VYQsaBobToNhQVN1o5LiaphFIBSh3luFAtRiwzWejHC3jGnYpMMQIGxS+V8C9wY33Li8e1zE1+S4Y+usQRsTAY/dQd0sdJCF1YYrEbQmwwQs/eoHzP38+oeFjNr4pzmNfe4yyu4yj6GDpPUvRsse6C9UW2PjRjcTnxcndmaPjpg4CzQGO3n4UvU0HDYa/PcwMbQZGwWDo+0Ngg3KozOG/H2bhJxcyctkI8UvNksfsxVkOX3TYpP0fe+WNoMHwJ4ZZdnQZxqChfJ5SqUQ8Hlcs0b6+PpUYmx7/s+MtHXD/27/9m6JJCQVagkyv16sC5uuuu04ZA1FLFiMkxspa+3y8sZA6SwmkrNRXoV9Jtk7qxoW2JeiZ3+9XxlD6IssmIS2y5DqslCzZvKziJtb/t9ZaiVGzUuplWNuLyXXISyebklB7ZS6sQmlS8y1BjWT1crmc2gRFvVsMswSSPp+PbDarrlPaN/X29hKNRjEMA5/PV0V7n5iYUAZQaNti9IVG73A46Eh00Jho5HD9YXWvNTtqcMVdON1OJbQidByhuUuywZoBlfvLd+XJzMhUFpoGkwsnKT5TREtpCrWV74sohd/vVxlX2fxdLhdjY2PY5togC/oeXaltC5IvjpAkI0ZHR2ltbSUUDNHx1w5cuovRm0fR6yvq8ILyiqiZw+Ggvr6eQCCgrslq4DweD7FYTK016Wkuz99K+4eKaq2IfBkfNbCN2nB9x0XWm1X17KmtKWo/VsvkdyYpdZXQkhotP22h/tV6BscHmTFjBqVSiaGWIXZ8bgfZpixHfnqE1s+1kuxJ0t7ebmZ0j1HPvV6TKjU0NERNTY2ao0gkolTMpQZaRmNjo0LqrQZdnq3P5yOTyRCPx1XiZWJigqamJqampqirq1N9L0UtVyjeQl2vq6tjcHCQyclJdY0+n494PM7w8DDBYJB0Ok1dXZ2qqZcygWQySSaTIZfL0d7erpxoqfWXJFo+n6e5uVmVCYyMjPwf74nTY3pMjzfHCIVCzJo1S9VXT05OKpbWnDlzqKurUyUzVt0T2ROOr9cUu+pwOKitra3yEaAStAHK/xBxNGvgKwl/a1At/oScS9hwVsTaKgJpreu2fk78LGtXFGvC3+Fw0NHRQd7I82jro6Rnp5m/dT5RT5Tv7vguX5v/NezY+eqBr+LQHRhUxKDEt9vh2YF9yk5dsY7rN1yPpmn4fD4Wji2s8nny+bzJjNLsqotHMVtUSvAyh7IPW+vS5Z5yuRyJRILa2lplU+WedF1n9+zddB/sxq5XtHKEVed0Orn/3PtJBi3Udw12LtpJ194udT9WdHzliyspUGDD+RvQHTq2bTZmfmMmjripvN72dBuJYoLBjw7ifMiJbZ4Nu2bnpJ+ehGPEwfJvLWfgwgEWPriQ0ESItd9fSzwYJ9OeYc5zczCShgICTrn1FGx2GzNensHMV2aa922rCPsWCgU0NDDAYXegl3X1bCXxYvUdrYxMa3ecUChU1dpWgkBN05hcbZYAzr9rPp59Hnw7KyKqxdlF9G/ouJ5xMfmeSZJfT+La4UKzaRy59AhHTz/K7FtmEz4Srlp76VPS6F/RCX0mRPCZoJnoSicpfrYIp0PoMyHYC7awjdgXYpSuLVF6e4lafy3+e/3kFuYYvXUUvfYYWPC2JEbawLHPQeyWWCXAdRg8/bmnWfOjNbg1Nxtu3EDZY74He87dAyVYeNdCcoEcm9+zmZH5pj3Pz8sz/r1x5qfmM9I0Qkk7Vi5qg/qP1Jv185Zcuz1kp+0bbaT96UpdvQYzkzOZE5vDCy0vkHVkCR4NMvtbs6mdqsUesFd1R0qlUlWg1fT454y3bMC9YMECli5dWoW8ygYp/ayPFx4QQQGp77Sq/UFFhVyEpoR2IhuwFe21ZjeFMi3GTzZcoXHLJiXU4ePrnawZ7OMpW3LdVoQ6FosxOTlJV1eXMmIS4MmmL5sbUGVArO25AIW8WwXGBBEUZNZa7yu1JLLRWoXS5Fwyb36/KXRSV1enFJodDgfBYFDVskm/0ebmZubMmaOCTxF8iMViNDc3A9XKqKp+uljCRjUSGPAFiE3FsE3ZVDAsoloyv06nk0QiQV1dHVNTU9TU1KjMdMO+Btb85xo2fnQj6Uia+kP1nHX3Wdgn7GgerarsQJ6tZF+lfkZoPLlcjrgvTvq7xzbL66AwWqCxsbFqPqVuXhDa/fv3Y7PZmDdvHrNvn81gYVCdMxAIKLq/qF9LkJ/NZkmn08ycOZNMJqOSPZJ8ElQbUOwNySwLPRtQQa0IgJVKJQLfDlAbra0SwdN1Hd/rPox/M5j88SShb4TwP+8n3hQnk8nQ39+P7QQbBz95kFSHeez8jDwDXx2g4YcNpHvSCpUX6rasC1mTQheLx+M0NjYyPDxMNpulvt5stSLtLiKRCGNjY/j9fgYHB1W5hBx3cHBQCRhayyaam5uJRqP09PSoZJ1cj9VZra+vV+tTRI2kJV9tba3aV1KpFE1NTfT391NXV6fuSZI9EmDLO2K329X6NAyDmpqaKi2J6TE9psdbbwQCAWbNmkU6nWZ8fJxYLEYgEKCxsbFKh0LsvNhQqy6FNZiVQFnQb2HSiD2Sz0hyXPZtCaxlT5EACarbEB2fuD9+iK8kiUxrSdnxIq+S3D5eQFSO/cAZD7ClfQto8PvA7/nq3q/SVGjiswc/i8vhoq5QR4mK6KrY7V2uXdy28DbCs8Jce9+1hGwhFUxbg/5CocDExARg7tvSLlNK2g6vPIwj7cAx4VD7rNX3KpVKbDxhIy0vtuC2uVVNvdy3zWZj+6Lt/P2cv7N011LOe/Q8NddStrS3ay9DTUNVc3jilhM576nzcNldFPWimnsr0LLi6RUkh5LsWreL6OejZHdnOVA4QF1dHa2trTT+rZHx3eM4HnbgXerF7/GTzZsJ4tDeENFDUdPHcxQJ9gQJGAFsO8znYbgqukRWATxZV+Ibil8qdkkSKKJLc3yJggA1Ys+sfnXan2bnu3aCATPumkFkIgLA8EnDbH3/VvI1ebbetJVwX5i535qLb4+PUluJ8c+OU1pWorTSXL8HP3sQx/ccxJfGOXzdYQy7Qc8Xe1j4zYV4h0x/e2TVCP2f6KdcX2bqO1PYbrEReChA4T8KZD6cARtMfHuCWV+cRf9N/WQuOwasaDD1lSlq2mpYHVvNC+EXGGdcPbfFixbTNauLO7ij6nnmI3n6z+tn7vq5b3hf3A6z/NPpcGLTqt+nrlldfGTfR9ixewffWvItDM3g4omL+UDfByjnyvxa+zWPznwUDPj0kU9zycQl7LDv4JbuW+gN9tKR6+Dzhz9PfW89npc8bLpsE+946R30DfdheM1yPaGzi5hyPB6nv7+fycnJN1zr9PifGW9ZzuL73/9+1WNbEGtpOyUZMqhGgyUAko1ANgxrrdPxAbAYQ/mMVQVaMshyHnGkJXiSrLAg3bJJQSVDLPUnQhWTzd5Kn7ImB+x2u6oxFYq7ldItaLxkraX+3FpDbhWosFLLJOADqhx+qyqpGE7JZMr3pUeoZDZl3kKhkEqCyCYsm7fMfy6XU0JiLS0tRKNRRfWqqanB5TLrWoLBoOqtvX37dkZHR6kfq+c9f38PkYS5iTe/2syS3y3BmXAqoy/JjlwuRzqTZvfpu3nw6w8S98SVgRTxr1wuR3NzM229bZz0xZPwHfWx6D8W4T3sVUi90KEcDpOyvmzZMkUdF5qa1N0ZLoOe/+xBP0WHk4En4fNf/zwtLS20tbXR3NxMU1MT4UiY5uZmIpHIGxIaVjEdwzAIh8Pk83mmpqaIxWLqmQkiDCYlWlgGQoMOBAJ0dHSo81jr/uIpE+GV5yx/E2fK6/USDAYVnVveB3mWzk1OotdEaXq+Sb1vDQ0NlEolxl8dx3jRqLRmMcCz3YPriFkLLUbAqrgvyZlCoaCQ5pqaGtxutxIeEzE9SQ55vV4CgUBVq45gMIjP51NBsVDHJRkk1PSpqSmam5sVxd3tdhMIBJQDIu+VOLSS1HO73QopCYfDRCIRFdzLc4jFYuTzeZqampQTbJ1ja6cDodcLg2J6TI/p8dYbmqbR1dWlSo1sNhtNTU00NTXhdDqJx+PE43ElYibBzz/ScbEGgjIkWWoFA8R/kWBb/CKodGSx9vKVYF5soNhKK/ptLZmTa5PrkkDfmtw/XoBN/BYAm93GA20PcMOKG3i0/VGF1m2v286nTvgUDpeDmfmZtKRblJ9hpWv3lfr48uIvczhwmC2NW/j5FT8nEq22l2W9zHMdz/FCzQvEYjG8Xq/SnBG2275Z+/jr2X/lRxf+iKQ9qfwt5QsaOq8teo1HT3uUuz94N76wT7HscrkchWKBLW1bePjch8n4M7yy4hUeufARs+WU3Y7f72ekY4S/X/53EuFEZVEYMPPITJyZ6pJAuU/xXWOxGM0PNzP/0/Nx7Kjo+0xNTZFMJk379IibfC5PcWdRtd0SFt/xz9Pr9Vbp5fj9fkKhkHqWxwM71lJI+bsE2y6XC0/Ag9PtVH6A2+2m97Re0uvSvPTZlyhpFZHiglHguX9/jt4zeuk9s5dN/7GJrCvL4OxBtnxoC7loTs1BvCPOtn/fBrNhx3d3kFtW+RtApjnDzk/vpOfqHtXeNTUvxfZvbKfsLxNfEGf/p/aTbzBttV6nM/GVCQYfGST7gayKdgonFuj/VT9rj65F0yslGJ6ih1NGT2Fedh7vu/99BDIBMGDtgbX8y8F/4eKhi/m3V/8NTbo/GWbrs0V3LqLhcAPn3noujpwDDFjy1BKW/G0JIXeI2lwtZ959Jm3728CA2cnZfPngl+nSurgofREfffajnD55Ov868K+0+dpor2nnXw7+CxeOXMgtR27hitQVGIbB3Oxcvr3n27TmWvnJwZ+wJL8Eu93OVaWr+PmBn3Nx9GK6u7uVKnkkEnlDjDA0NKRAlenxPz/ekgh3d3c38+fPB+Dll19m7dq1CtmTzVWcWHG8ZQMXdMpqDATRFUEpa10yoDJ8UKFfCxosm6bQaeTvEiwIkiYBhATaEpAJiig1K4K6pVKpKuMlm6IEF7KpipG1JhXEkEoduPxNqO5WCpkos1sFUKwtygThFMTWSkeXY0mLI1GVlmDEbrczNTWljL3X661SW4cKfU2o9y6XS9F+JRiX+xcU2e12s2jRImpra0mn04R9YZz2Y3XsRZ1yoYzD7lAJCJWt1QscXnuYXR/YBXbY/MfNuD7swpkyv9vc3IzH4+HIkSN4PB466CD4r0FKmRK5oIl+e71epfIoNTFbt25VgZmoxzY3N5NOp9n+5e1kZ1hUp7vglpW3EPx6UAV49tV2DvzLAU7/8el0FjrVXMTjcWpqaojH41U9TEWLQOrIRVxM1F9FtE5QbREMc7vNummZ73g8TlNTExl/hqnHpshfk4cxc41LCxupEy+VSiqgl0SGtQ6rWCziHfSS0lPKOXE6TaOsT+gs+MUCRmaM0HdKH84HnYT+LYR3bkWx3ufzKY0CoWLLPEjJiJQd6LpOXV0dhw8fVorfkuQQtkk2m6Wrq4uBgQGcTifNzc1K1VdqCQcGBpRDmc1mVSY4l8sRi8Xo7Owkm83S1tam5lQSArFYDMMwqnpxyzsrKLwk0GTNyPll35iamlI0+Pb29qpayWkl0ekxPd66w263c8opp6gSH2G8yb4szCsBAWQPtSLLggzLfisMJStN3BqMyzHEZlpZdBI4WbVFJLgWlBVQ5xM7LwGY2+2uSgBa/QWr9oz1mqw07Xw5z2N1j/GDmT9A195Iaf3aga+BXmlJag2Qs9ksuqHzoas+RNqVVt/pa+rj16t+zYdf+7B5TgxebnyZX6z6BQDvmnoXp+ZPJZVKmW0aU0kOtx7mzrffiW7XSXvSfPPd3+RTd3+KYNJsUZkv5tk+Zzv3nncvuk0n35zntzf+lvf++b0EDLMbyJhvjCeueIKsz7Trul1n65KttE+2s2rbKgACEwHWPreWJ899kpK7hKPk4LSXTmPx7sXY7LYqn07TNHS7TtKXpDRVYmBgwPR14pqiv7vdbjo7O4lEIqrt5pYtW5SfIInf+vr6qpIqee5ix63K2cK4kgRyoVhgIjKBPWPHKBt4i6Z9drvdlGpKkIXxhnEe+cIjzH12LivuXUHAHuDA4gNs+tAmc73YDB7/xuOc8KkTMHSDrbduJdVeCfCyzVme+tFTNF7aiMPnIP+ZPPgq6yDfmGfzFzbT/ZVudn9vN6VIxV+0Z+x0/raT8bXjjK8ZBxs44g7mf2c+JKFYLqLbqteWy+ti3bPr2M52huYOgQb+tJ8bHr+BwNEA9ffVc+8l9+IuuvnkHz6Jw2tS4B2ag6/e+1X+eOIfuebZawhGg7jdbk4ePJmJZye4ffXteNNeLv7CxRQTRQhAZCLCFV+8gq0XbmXpHUtx2p0K8HPGnJz5nTNZ/4n1XPHnK3Bc5CBGDJ/u49SJUznzpTMJNgUpFE3xuaAe5H/t/l943B7SelqBZO5xN79K/AqtqDFhTJigXa6AZ8CDN+xlzpw5TE5OqtLWRCLxhtZj0+OfN95yAbfNZuPUU09l/vz5FItFGhsblRoiVOg+1joloe1aDYwErACHDh1SNFKoUIvEaAklV3pnWkXPxNjId6yqn7LRWVUYrecX1FmoSrJx6rpOKBRS9FeotDqT88jvrP9Z1S5lDqw0J0C9dPKztVZL5kkMtFDQ5VyAotRbae8ilCHzJAyCUqlEd3c3o6Ojyojm83m8Xi+NjY309/dXUbjGx8dpbGxUCovhcFhRwaTuWoI8qQs66jvKXSffxah/FICRk0fYkt7C8j8vJ95nClU1NDTg8XjYs2YPuz5Y6cNdDBXZcusWVt66EluPTT2nhoYGRT/Tc3qVqrQopEuyxIr6Dw0NKZQykUiQyWS45q5ruKtwF0MnmtQy7/NeuAyS2SSpVIrI1RFGvz6K7tV5/abXWXPHGgLxgEKUPR4P+2bvo/xEWbWNkjpsccSsAiVC+ZM1IehEe3s7Bw8exOFwEIvFVAZ8IjTBK+97hVhXDB4H3gb6C7pCDQTplWctbAHrWpfEg6i3FwoFhQiLA+h1eTnnjnN4euppsh/J4g/71b1IP25xJsVBKhaLKqkg60aSP0pV/phiuLzrkuiSAHvu3LlMTEwwPDxs1sUfWzdjY2MsWbJElSnYbDbljCSTSSYnJ/H7/Wpth0IhxsfHVSKpWCwyNDREXV0dhUKBqakpdQ3SXkyC8ZGREWpqatR8CutE6sDtdjtDQ0OKkSDO0fSYHtPjrTk8Hg8rV66s8ikkYSdBtTVolgBIStrE/thsptKw1+tV2i9WmjlUUGVRfJZA2+pnyLlk37cew8oMlADZ6uNYg3c5hkIwj4EKgs6LnyPfke89WP8g35393X84V7OHZ1MYLdA70UssFlMdOPL5vNr3x1eMk7dXq3Ov7VvLza/fjI55jpdnvMyPTvqRQs7veOcdRJ+NsuDwArMu2e3iD9f9Ad1+LCjTIOFPcNfZd3Hj3240bZ7N4NUTXq0Ebsf6Uh+Yf4ATt52IYRg0ZBt410Pv4i/n/4Xx2nGcRSdnbzyblVtWYmjm/CQ8CcI7w6wqruK1C1/j5M0nc9YLZ5lzSIWtIIj61lVb2bJkCyt/vFKthVAoxIwZMxgZGanSucnlcoRCIaWFk0qlFJpZLBaJdcZwDbkw4qYPYNUtKkVLjC0eo+VQC2MzxmjZ2gKGeR0Dywd45qPPUL+/HlvJRv3Oemb+ZSZG0GD7e7YT6Y2w67xd6E6dPefswVawERoK8dK/vFTVszs5M8mm/7UJ+2Y7mUjmDT3QS6ESE+dO4LrNhX2dnfKZ1QJeXeu7qD1Sy8KvL+TApw/gOugitSRF9++6aXy8kcbHG9n95d0kFiaY/YPZeDd7KTvKBLYF6P52Nwc/cZBiQxFfxsd5T5zHkuEldP6sk0c++AiJlgSXP3I5TaNNZG1ZFh1eROnpEjNHZlJj1JB35tVaDhfDXP/o9RS1CvXf5XSxYPMClvUso2tPF7a8qQsl73RNooYz7j4D3aErgVQpY7Xb7Zzz3XMYqBng1vZbqbHV8P7D78ftcjN0dIh0PK0AMNGDEh9O/H4JvKWri8QOUppSV1eHP+BnY3gjqydWq1p8j8fsbHP06NF/+A5Oj/+Z8ZYLuKPRKGeddZaiGre2tlYZCqlrtWZsBWEV1E3quuUlmTdvnqJdSMBqrX2SIYGWYRiqxRNUqOnWgNpK2xIjZ81MS92vtbYJqHLorcc5nlZuNaKSSLAqkMu9WOngEvRaEW2ZI/mM/Ct0Yfm8te5b0FYJHmRurEY9k8ko1DKZTKrMq8yz0JHkZ6Fryfet9WwipiUbTrFY5OjRo5TLZV5b+Rq9bb1Vz6nvnD7mPjUXvdesoa+pqSGVTlEKVT9PMFtapLU0ES1CPB5X4nYOh4NCqcCRdxzhhMdOUCir1ETL84pEIlUJAdngbDabSU92eTn51yezKbuJsr1M9JYopZYSqVSKsZPHGP7mMJhtxTm05BDZa7LM/dpcGr2NRCIR9qzaw7Yrt+GyuyjdV1LBtIiZiRCGsDJkLgVlzufz6npFsVvKG9KBNDveuYPhecPmBdQBvwM+BMXniup4opJvzY7LEKfKWssvvavBpJVns1k1N6vuXsUT+SdUHaLUjQmlvFgsKv2EmpoawOxzLXR+QfxFmVze6XA4zOTkpEL43W63ajfndDo54YQTGB4eVrV4jY2NBAIBxcjYtm2bqrcWByabzZJMJpVInSSXrHR1QYREVXxqakqpC8u9iGCgJEM0TVMJNaCqNVkmk6lqFTg9psf0eOuNOXPm0NHR8Qa6ruxxhmEwMTGBruskEgnFhBMmjCTumpqamDFjhvJpjg/SxWaLNoSw6cSOiq2Xz1mZcVZfR4Yk7qESyItPIMcQhpX4VfKzlYp8PAX+p50//S/navVLqzm07RCASjYI6ykSiTB08hC/X/57SnaL/Tbghu03qPt8uvNpfr/099XBHZB0mnolTqeTgD3ARdsu4oEVD6i/uwtuTtt9mvI3vIaXd61/F39Z9xd2dO3Aptu4+vmrOWHXCZQoqX28o7+Dtz32Nu656B7OePUMVr66Et04xnZ0Fnn4gocZ9Yxyyf2X0PxEM8t2LquqlRefFeClU1/imXOfwbAZbHj3BuYPzcc35FP+kzyLcDisfErDMGhpaVHsg2g0isfjYbxjnJdueInwcJiVP16JR/OoZ4IPXnvva/Sd2EfH9g6Gu4ZZ+NeF1P6+lskLJ9n1/l0YNoPReSZ4MbxwmKP6UYqtRVJLUvQtsfT5BnZdtIv/ahQdRZx3OgnuCZL8UbISaRhQ+81a3L9x46xxUvNADUdPOoruq6yVvC+P2+Omfls94f8MY7xmMHHiBO3PtVOymwFn97e7SS5LEn05iuaqgF/hZ8LM1eZy4NMHuGj9RSzesxjsEPKFOPMPZ1JaXKKrvws0c05SqRTzX5tPuVxmqDCEYRiMj49jt9tV8CtzLwwUt9tNQA+w45wdrL53NXYq+k3iLwu7UsRd/X6/iUZrBZ6/7nnls07lp/h05tM0NDQohp0IPhcKBSKRiCmm6+lhyDXE8tHlpFIplcB/MPogpydOx5/zK+Zf/zv72TF/B7Ofn82cV+YoYKCnp4exsbH/8plNj//vx1su4G5paeGiiy5SFCehKUvQISiTIFZioKSPpRUdFmddghj5VwyRZILFoEivQaGEyuZnpWcJDVqMnlyLGCN5aa2qpEI7kSBcjKFcn9yXNWgWejpUgh4r7VaQO6i0WJAg/R8ZxuONqzXTLRR4CUStKLpcH6CoTA6Hg6mpKaXcLaijbD6Cjvr9fhVgiJKz3LOV6i5Z7kAgwNjYGIcPH8bv9xMIBFg3uo6jfUfZ3b7bNLYGzPrLLJyHnZQx51V6J896YBZ6XmfL9aZYCymwX26nmC+yLbGNGTNmEAwGCQaDTE5OsvHDGxlcPYjm11h631JFT5eMpq7rTE5OVhQ9jzk4EuzKuWup5YQ/nEAmZ2bu7a3mBt0z0cORxBHyRl5de+GJAod3HabP6KN0VYnRa0Yp+ooU/7VIsiVJx4MdYEAwGKS/v1/VuYuTJiiwtIGR2vJYLKbWnASamkejsaeR4SXD6vz0A7vNtZFMJhWaLoZH1pPQB2UdidMmawPMHuPJZLJqDUs9eTKZJJFIKIVySdJYa8YkM2s9hwh+CPXcOv+ikC9oj1KmPfb+Sns4odXLe+JyudRaFOV3oWWJ0KHdbicajarrrK2tVQrs0hpOhNbAbEsoImvWZJrH42F0dFQlrayK67quVyUipsf0mB5vzTFv3jwGBwcpFouqVltaGEpgnU6nVbvMUChEfX09mqZRW1urBLKKxaKy3arGWK8IpDkcDhVsS5Jdgl8rtdsaqAkAID6FtTQOKjRya222lXYuAYX8Xv4V+yB+kqZpOJwOvj3r2+TslnpcySMeC44fWfsINw7cSDAZVIndQCBANBqlpqaG7KEsrtkuMyF8zE7duPNG/CWTPaBpGosmF+Euu8k78+ocH3r2Q6zsXQm2Y73ADQcX7boIBw7uW34fADc/djPdg92qvrdYLOLP+bn6mavJuXKcuuVUFh1aVKXoLvPYdbSLG+69gfqJ+sq9afDHq/7I4Vlm15QHrn+AD975QXXP4sPJcV489UU2nLFBtQGLL4mz42s7WPvptYRtYQYHB1Wpo4jkiU3u6OigsbERu91so5pryLHhoxtINiaJzYhR8BQ4+/tnm89fL/P8Z55neIGZXJfgeevbtuJZ7KHQUVCtPa33MnXd1H+9yI97jlV/mmXQfV43R1YfeaNS1EVwQ+kGJY66/+v72fi1jQDM/cNcZj0wi2zWpOu37WpjMjuJ91kvdkdlnTqyDuo31aNj+qHSQtTlcuF72ccJ/3kCsxKzKOhmUjuTyTDWM4a7x81mfbNat9JCVTSIAoGAYprlcjmi0SjFUlEl9QG2dW3j5YUvk/VnydZmOf3npytQQViedoed565+jjPWnwHxio/+5IefpG9hJXHx7MxncQQcfH7X503BOaedL876IrccuoWpySmTKenL8KmFnyJjy/DJwU/SPNJMKpXi+fbn+d2M3/G33N/49N8+TSqR4oV1L/DQgofI2/L87bS/8Q7ewcrelfh8PgWOTY9/3tD+uyiKpmn/dLjF6XTyq1/9ine84x3KsIgitBgNQVqF9iz1ShIQiZPb39/P2NiYQretNVTj4+OKogMVuoYIowmaaK2xFuRVAk5rFtrarkyCFa/Xq2i3QhFKJpMqYADUd6FaYVRoJvLz8bRvCeBLpZIKFuTvViq79dlb68GtKpZCf5XvSTAs12az2RStzDrkWHfeeSfd3d20tbWprKHT6SSZTHLgwAGy2aw6X3NnM/s/tJ8Tjp7A8tHlGOVKltwwDPbu3UsqlaK9vV3RdAKBAHktz3fP+y797f00PNnA3G/OxWVzKURUrsfj8WBz2dh+znb2nr0XTgWOmPcejUa55JJLTDGywhS7bt5F32l9YAOtrDH/T/OZ89AcsvEs3d3djI2NkUqlqK+vVwrUIyMjNDQ0KFTUZrMRDAYZHx9XaCtU0/KzZHnhRy+Qa8zR8ccO2u5sY3RwlPKSMr2/6KUcrRhBraTR8eMOZj4yk7mdc1WrmSOZI8yJzsFpdzI+Pk4ul8Pv9yvjPHPmTBXAChItAn844dXLX+XQxYdgO7AWgs4gdXV1TExMKBqjx+NRtCSZU4fDUYWqW2vFBF3v7u5m5cqVSrl7cHCQ9evX09TURGtrK8FgUNWjS91yXV0dhw4doqmpiYaGBnRdZ9++fRiGQXt7O319fTQ0NGAYhupp3dDQQDQaZXJyUql/OxwOjh49quh5w8PDqk2bMC4kMI/H46pvd6lUorGxkWuuuYZbb72Vmpoa1WKmqamJfD5PY2MjY2NjSq/A4/GoBNr4+DhHjx6lra1NvaOizO/xeBgcHFSBus1mo76+XinqJpNJlixZwo4dO6bbggGGYfwDd256TI//evyzfRWbzcaVV16puojIniYU6YaGBjo7O5k9e7ay/U6nUwl7itCVNdkvCXVr0lP8DWsALr6IFcG2tmQSu20VMbV+V3woOY/8a02yi+8gx5QyMqs4q81mI0uWn3b8lL82/7Wqbvvzr36eny75KXF3XP3Ol/Nxy/23EC1GlQjrkbYj3FlzJ2f++Uzsmp3ffOI3xHwx3rnnnVx86GJseoXlZxgGMWeMT1/4afL2PO/Z8B5WH1mtfAirf1OixMPLH6a7r5vZg7Ox2yrsPLGPdrudkqOErWRDL1U0ceRc4rPI85bn/Mer/sje2XurgtDG0UY+/tuPo5d1xW6QZ1HQCtxz/T30zu4FDRxpByd85QQa9zTicrk4evQofr8fd4cbX9KngBjpqCIlgE6Pk/V3racYsNTZlzW6nu1ixa9XsOnGTfSe1qsC+6qRBlvChl6vvwGC03QNT95D1put+r0j62Ddz9dRd6SOv/7HXymEK/6fXbdz5t4zueqVqzBcBp+55DPEQ+azDmQDfOXer+BLmaWCO4Z2MLhrkOG5w8SXxJl992z0QsVvrq+vZ3h4WKHFAiIYhqF8BytgVSqVyAfybP7ZZkKpEJf8+hICpYDy1V0uFwVfAa/upZwzfWcRSU35U7imzOBbEGSb18Zf5/yV5lIzZw2exfa67Xx1xVcp2o7Nsw5dr3Vx5p/OxEiZ8UagPsCG8zew/YztBJIBbvjhDQTzZleCSfskd37uTnKhY8zQsp0v3PkFnH1OUo4Ud95wJwONAyw9tJTz7z6fgr3Arz/2a5K+Y0yNkpMP//HDFFuL/PrsX5usDwMa+xvpfLaTV972Cnqg8ny6HGMAAQAASURBVK45J53M+cgcAocDHDx4UPkZ0+P//fHf8VXeUgh3a2srV111lcrUOBwO1U/ZuiFaaVMSFFjp1VL7HY1GgUogKi/s7bffzs0331wljmbtIy09egWdFmMoSLZ1g5e2HRK8izGTDVeCATm//F1oyVbDJwZSNher4bNu+kJft/YqhkoLMqvRsNaXy5zK9QoybhWEs7YvkrkTQTUJssQIGIbBNddcw5EjR1TgLiieIMHJZNJUfvfqPH7K4+zu2M3f2//OZzZ8hoW9C8lkMgwMDKBpGoFAgLq6OnUtSnkTD1f8+Ap+/K0fU4gWcM5wkjuQI5lMqkDJ6XTS1NREXV0dzu874b3mnAh1edmyZZx++ulMTk7ySs0rJE9MqsysYTfY/a7dzDg0g+j+qEKv6+rqVF9yl8ulVLmlH/Po6CipVErRizRNIxaL4fP5VPDlKXtY/bHVDFw7wNwH52IL23DZXEwcmGDe9+dx4JMHKERNY2Y4DCYvm6RuYx2Tk5Nma7P2BAc/fZC6v9URfCao6OKC5NpsNtLpNHa72YtU6IvCAIn6okS+GcE97Cb/yTzkIZVPKQqj1JIHAgGFasu7ILoI8vxlvUo9oCTEtmzZQltbGw6Hg7GxMbX+pF1bIpFQAaus45aWFsLhsKplKpVKSkytubkZp9PJ1NSUon6nUqZYm1Dm4/G4UrSXHqpCaxchIysDRu4nVhsjM5GheLTIj3/8Y/VOChqeSCSq3mW/308qlVJ0zlAoRCwWo6amhkgkwuHDh/F4PArlGh0dVfMmCLyIxTkcDtrb20mn028oZ5ke02N6vDXGzJkzee9730tdXR0+n4+GhgbF9pFuC9KFwzBM4UVhEwWDQWX/pVRNAjsr+8cabEvgKzYGUHuUsNIUMmhRIRebb63Jlr3OyqazlslZ2XrWGm0BBgB21e5icWIxL9W8xH0t91XNTWO8kXJPmXl183hl5isqMM26s2xct5GPHPgITqeT10Kv8alFn6KslSmfW+ZDvR/iRy//iIfbH+aS/ZeYiQS9ElwWCgW8eS9ffOKL7K7fzZoja1RZjxWNNwwDO3Yuf/1yk7VYLlAqVu7RWrtODspGuYqaL7bCGnQLvX60dpR4OF4VbLcOtvKeu9+DXq7ueiNlgvHGOJfdeRl/uvRPxGbEWPCzBTTsbiCbyzIyMoLdbic+N86uW3bR+blOCs8UVOlRNputqLifblB2VyPUnoSH1b9bjWE3OPk/T6bsK9N3Yl/V9bmzbmb/fjbhu8LEvh1j9ym7K383YMWeFazeupo/XfQn9EEdZ95JoivBstuX0bylGcMwWPuFtbz8qZexF+1kWjOs613HdZuuM3uoZ0pc/7PruePtd+AJe/jg+g8SzAYpU2Y4NMwjb3+Elj+00Px8M817mpXQq7Wc7XjRYbHF4ksKkFUsFsm0Ztj5uZ2km9KkSfPC+1/g4kcuxjVmMj4z0QwPXfoQi3oXseC5BQrw6Wvv448X/ZEbHryB5n7zOvJ6ns3rNvOXhX8B4OjwUba2bq0E2wA2OLTqEA0HGpizfg41zTVsPn8z29ZtAyAZTnL3++/mkjsvITIawZ12s+SxJWy6xhSZK9vL3HrBray+fTUHzz/I0Sazxnrr7K3sm7UPvayTd1W0C4qOIr+d/1u0gFYpsdBgIjqBdkSj5ZctDN88TMlTIhAP0PGtDgKHAySTyelg+00w3lIB90c+8hEV3AjqKsJi1tpfybrKZ2TTPT5LLBunBOwSKH74wx9Wwbo1Y2ylkstmLn+TgNIq2mbtg2mlsEvgLAGjtUe1XI+V0i1GTwyvHEtQUsk+y7+SiBDDKoG2HEOCbDEkVgEWOb5kyGWzk+9Zs7oyz/J3MNuVSB2K3Htzc7Pqi6xpmupXLPdsc9jYfPVm9p+737wADW5bdRsXTVzEvL3zVGsPa42MVXm+VCrRc0kP2CC2PMa2D2/DdZMLZ9xkPmQyGVKpFLpu9mO20p+7urq48MILmTNnDsVikWg0ygXGBQwMDfDC7Beq5uXwuYdZvH+x6m8YCATYcfYOuh7sUhQooQeWSiVF4xEny+FwKEX3aDSqanrrtDp8t/soG+YcKtXqrTo1v6vhtQ+/Rt6fJ7A3wJxb5+AacDHmGsO+zM6WG7eQ7kjz7AeeZXFpMbNfmg2g6uRlHQrqLoGdruuqzt7v9+P/kl8xLsTxkPsQVoOUcFiRAKtSucyp3+/H7XYTiUTUu1EqldixcgeTL00qBMfrNYvXRQ3d5/OpFnlCwZSguaGhgUAgoAwxoGrKs9ks4XBYUdQnJiaIRCL4fD5lZNLptApupd5aviPrsdBSYO8n9mJL2vDd6oMMqi5b6qtEPE/uU9bT1NSUorQFAgH1Ts+dO1fVgsdiMfUMxJEQNEXmzufzMTw8PC2aNj2mx1t0rFq1iqVLlyo7NTk5qUrFGhsbqzqhSOeCXC5HJBJR+7I1IJYh+7bsjcez7MSmW4NuoY/LsayggOzz1gBagkoBE6yJVKv9tTLPrL7Ji/Uv8t3O73LeyHkc9VQLNLWmW/nY7o8x3zuftYfX8tvQb7k/ej8A1w9fz4f6P4Tm0Xgy9CS3zr6VsmYec+tJW7mv7T4+uvOjXHvwWsr2ctX1y7/lcpn6yXpOmzgNgwp1XoYVkbeCFdZkgfhMVhXx45F9KzNQjjFaO8o959/DUEOl7/asvlnMPDITu17Z361MgcGmQR6+7GHq+uuY+7W5jC0Yo/G1RhxuR4WWf5bG3o/tpVBfYO9X9qINaWiPaVV+n81mg9lUU7sN6HygU+mv6LrOyb84GWfGyaHTD1H/eD3xk+Jc9tJlLE8tR7tMI/tMlnvS97DrPLM2++StJ3P5c5ejlTROu+M00lvTuFNuRk8aZdamWZQMc56CA0EWf28xPsNH6MIQFw9fTFmvlDOW+kqc8otTmLdiHm3JNnR0JuonuOeUexhqGmLkf42w0LuQGetnqGBaABl5BpIkF1/abreTbE+iR3RqdtRgs9lINaXY88k9JOZWWrHt6d6DcZ7Bmt+soaSVeOGcF+jt7OXArAMMxgbpuq+L0RNHefncl0kFUvzunN9x4q9PxPOYh4mPTfDykpfVsf56zl9Z9fQqDr16CC6rfuf7lvYxd+9cbHYb8dp41d9yzhwJX4IIETx+D6mm6rZcJUeJkcYR0u501e8blzXS+a1Ock05Np5jUu7nvzKfzts6cTY62X7zdg7PPYytbOOsR89i5sBM3G43h548xLNnPMvFj15Ml6sL5wVOenp62L17N9PjnzveMgF3U1MT5557rgoGhRJrNQgighAIBN4Q4ErwGwgE1O+FWivGQpA/qdM8XhDEaoSkptuaGZZrswaggAp6rErSEnBbUWG5DwlwZKOU71hRemvW1kopsxoJa227DGuNuBgO63GtwbbNZmNsbIzR0VG6urqUcbJmdSXAt26EVuq9JDgOHz7M3LlzqzLkUmdeLBSZE5/Dfvar63ToDrrSXYo6LAGMpmkKzRQE4JnVz/D4iscxjjEJJ1ZNwE8gdEmIfDKvqNypVKpKrMRms7F48WKamprUdcrfLtpzEXub9jIWqIhMdIx3YKeC4D5/0fPsOWsPB7oP0PJCC51PdypUWFgQuq4rsRN5zk6nE7/fr+iE0vpE1lg4HFbCMQ27GnB9z8XG926k+2vdNEw2kLAloAVe/+TrxNvNzb3sLbPn/Xso5oss2LIAQNHKt1y6hc4nOnGUHUo505qll7ph65D5kKB5ampKOSvy/sj6lTZ7clxBlz0ej5qH/jP7OXDJAezNdmrfXatU3oU9Mjk5qZwHUTaXv1sz2oZhKFRZfieZ78nJSUUVl/7dk5OTSoStvr6eUqmkWghan7fu1dnx5R0k55rUrZ2BnSz9t6UYRYPeq3qZ+dpM6hx1is4naLu1l3cmk1HK5IZhEIvFGB8fV73BpZ+4oOn5fJ62tjYAGhsbmZiYqKrBnB7TY3q89cby5ctV+c3Y2Bi6rhONRgkGg0oQsVgsMjExofQfRIfjeKacsH4EOMhms6qVk+wV1iBQPi8+iZXtBxXFcSs1XfZYa3247MXW7i/WxL7s01Yk/LXoa3xv1veYdE3yp/Y/vWFe2ovtnG4/HXuLeV0fG/gYGFCTq+G9w+9FQ+O50HPc1nUbcVd10NIV68IoVez28f3CxSeRa7fq0hz/d2s9tpURaf2dHENsDlQE5aygjc1mI+vPcucld1YF2wCxUIxNKzbR19bHDXfdQLlUSRTEa+I8eNWDjDWMMdIyQv1N9Sz77jJsdvM5eTwesouy7P7EbrJNx+jcbWD8zMD9QTeRHREaGhqw2Wz09vYS/3XcrGH/lZk0X/rbpXQ93qWeV6lUwpaxsegPi/A95CNyJMKMwzNYUVxB2WneUz6b54T7T8ChOQjWBbnwpQuxlW1kshnq9tXhiJtz3vZCGyV7xd/TdZ3w/jDRaJTlu5ejhyr+aDabJZFI0Fxqpm2oDS2gkXKn+NUZv6I/2m8+L5fOnvfuQUOj5dEW5bMPrRii1ldLz9IeFv5sYRWDI+lNsvUzW9E9Oiu+uQLvfi/2hB3PkAeWVK+7ww8fpvxsmdhdMca6j/lzGrx20Wv0zO4h1ZIy1dSBRH2CDe/ZQNPmJsr3leGcynGMosHArwboGu1irHGMxEmVwL5usg533o1RNDj1vlPJFXIcWHEAV87FpXdcSqQngs1rQy/orHlgDU67k22nmCh42VMm4Axw5YNXcu8N9zIVmaKjt4MLHryAmtYabC/b8Nq9jNaOcvKDJ+NpNpl69ffWk70my4IXFrDs4DKMGpM1uPTlpTQNN1F/qB6X19xvdu36rwXupsf/3HjLeHXvfve7aW1trerlLAGf0FLT6bQyGBJkyd81TVNOrmGYqp7SdkPaYgAKfZL+1RKsW5FuCdShIiAFlQ1ZlDYF4ZW6ZclKSg9OyWJa25pZa7ZjsZi6V7kWocYKTVm+YzUeYiyt2W75vQR8VvRcDIx8Rwy/1J52dHRUXZcgtfL/YtwkgBbk2hqYd3d3q6AuFAopA26z2bBpNhrWN3DOXefgKDnw5D189s+fZVZiVtUasCYspN7XZrOxdvtaQplQ1Wc9t3hITiTV8/Z4PKr22JpoGBsb48UXX+TJJ5/kqaeeYmpqimKxSFuija8/8XV8aR9aQWPV3atofbCVTDJDwSiw/ert7DxnJyVXiaGFQ2x57xZ6V/Vic9iUiI0kcKRFmMytYRhMTU0Ri8UYHBxUzyORSKDruhL1stvtxONxeA7W/ds6miab8Hg8BAIBQtkQ8/8+H1vh2CusYxrhFxuUQxaMBNl1yS52XLKDx775GAnNfEe8Xi/hcJi6ujrK5TIjIyOkUpWsqyRWpIWWtRWXOC1CkU4mk+r9CgQCOBwOJUQSi8VIZ9MMrBpg1827yPlzpNemGfvjGAVHgVgspmjqso6sybFcLsf4+LhyGIrFIj09PYDZys/n86k1Jf/v9/tpbGxU609Q7XQ6zejoKMPDwzgcDurq6pQq/uTkJBtv2UhyTlLNQeyEGNtv2c7w24bp/1A/L/3oJYZLw0p/IRaLqXdYkgCNjY04nU7i8TjpdJpcLsfY2BiHDh1S72GhUCAcDuPxeIhGo6qvqiRj/H6/ootOj+kxPd5ao6mpiQULFlAsFhkcHGR8fNxkEB1LsKbTaUZGRhgeHqZYLFJXV0dNTY3yL6xJb7EB4nNIYlZsp9XWA1UMNKgk0sVHEWaQ2HkR1BRbLvbYGqCrhKRFcM16Xvl7v7efb837FuPu8X84L7XFWm7tv1V9x+12EyqH+Nf+f+UDox/Ag4dX3a/ylblfYcJdob46dAcf2PUBLjhyAU6bU51fwA/xecQXsSYNjteokd9ZqckyhLko96ZpGnlnHl3TKXqLlB1lSnbT/yt4TX+v7Cnzm2t/Q8FZ4KStJ73hnqdqpkj70hyaeYjfXPcb8nbTFylpJW5/z+2MNVSCv/HTx9n94d0UCgWGh4cpl8t0JjqZ8dwMOMZgtuk2Fu9bzLu638XatWuZN28eZ555JqtXr8bv81P7aC0LvruAFXetYM7Tc9D06lJBu92Ov+An+nqURlsj82PzlfCZlAu6ii4ufOlCLtlwCe68u6q2X+bX7XbjcrlU0lx0dGR+BDTSNI2kLYkW1bCHK76iL+/jvG3nVc2Vf8hPdENU+RvjC8bZ+pmtPPOxZ+g7u4+N391I2Wuu+7w3z6ZfbiLVmSLTkuGlb71EsaVIIB8gMhBBK1WX0urv0Uk6kri/6MaetR+7YfAe9dL5hU7qn6tHKx9LuJRszH1lLqeGTuW09Gmce9u5OAoOnHkn7//V+7mi+QouXX4p7/7bu2nqa0LTNbpf7GbZn5cRImT6Amk/lz54KbP2zOKqr19FfX89Xq9XPYMwYU56+CS69naZ68hbZvMVm+md2cs1P7iG5v5mrr3jWlqKLXg8HkLuEKe8dArn/+18vJlKC7JIIsL7//Z+Vvesxukw5zufz5PNZGntb1XPNJlMsmPHjn/4Xk6P/9nxlkC4GxsbOfPMM1VPS3H8pV+zbAQS4IiIkWEYxONx5ZRLva1VRVw2BxFfk88KEmatmTIMQwVOUi8KKBVuawAqG7ogbdKHTzYvuQ+73a7EIATZdjgcuFwudu3aRXd3N/X19SoottLH5PqnpqZUgCTXIpl0awY7m80qMRYr0mw1sFaRN9kgPB6PcgasNHS5Xzmutf5GkgMy35OTk6oftqCeogjpcrkI+oKs2r0K/3N+uvu78cf8ZPSMmn+5F6g4DIK0xgKx6pYhQK4tBxq4nC6i0Shr165leHhYnVMy82NjY5xwwglqHg4cOEAkEiESiQBw/Q+u5281f6P277VkQiaNr6e2hwMnHcBwVAy67tZ59V9fRfuehvNpJ442ByWjRHAyqOjdkUhEta4SIa5YLMbk5CTlsqkw73A4SKfTVUkNj9tDSAuRcZhUQ7/fz9TUFL57fcwtzeXAew/QuLuRld9dSTASpFAokM6n2bxmM69e8SpokK3J8tCXHmL5V5bTmmqtauniXuyGMcDCaBLUOpfLqTUjQaAgw+Pj44RCIcU4AVSCRTQEnDVOUp9PoXuOCXnYoLCiQP6aPOVHzIBYkHgJOmX9SmJi5cqVvPrqq3g8Htra2pSjKsY5nU6rtS/15bJmpObf7/eTSCTU+zU+Pk5DQwOJRIK6ujpO/OyJvPr9V0l1H0s8vAjaoxr7v74fNCi6imz93VaWf245tqM21e4sGo1iGAaJzgTJl5PUhesIBAJqPTU2NioxJKfTSUtLC7putgISkT2p1QsEzForeQ+nx/SYHm+tIQKI/f39uN1uurq6VKtJeb+lxEkQykwmo2yrtc4XKp0/pMbb2jLQmtSGSjmX2GYJPq1BuYAC8jlJlgsjDiq0c2sZHlSQY2sts/zXbG/mwvELuaPljjdOigFLMkvwl/3YXDaFKGuahlf3otk0ekZ6+Pj5H1csNRlrh9dy+ZHL0Q2dUrlU5T/JNcl9yTVZS3Tk8+LjyP3IPMt/48FxXFkXY3VjuMou/Fk/d55/J8u2LuO5Nc8x9+Bc7Iad2T2z+esFf+XKR65k44qNHJx5kG996Fv/14tCg8GWQTafsJnlLy5Hs2mc8uwpPHzlw2p+Gl9sZN5P52Gz2xS7MTWVIvD9AJFYhPh74py852Su3ns1xVlFBt2DDA4OEgqFWLJkCUeOHMEwDLpe6DJ9T6cNj89TBU5J+1K3201TU5MqkwITaJKSKL/Dj71ox8BQ4EY+nyc9J43vsA87dgVW2d12xuaP0bu8l3g5zsojKxUgNdkwyU+v+iljgTFmHZlF7WO1ROIRyvYyUzXV6ufxOXH2fWQfC36xgImZE7z89ZerKPKxOTE2f2Ezi7+/mCPvOEKhtiLSVvKV2HPTHlb+x0pm3jeTfCBPzxU96E4d95ibpd9bSjgYxpF00PjNRnZ+Yif+CT/n3nIutpCNuQ/NZVPNJg6sPcCSZ5ew5uk14Dffg7reOjz3evBMegj0BMB+LHlRtnPGA2fw9+v+zurnVxNyhlSXFbvdTm48x7k/PJdwOIzT51T+k2gWjS4bZbB1sPKeO8u8vvZ1OvZ28N5fvNd8t22VbgbiT0lCQ4BHW9qGy212XBGhZ/H5JQYSVt30+OePt0TAfdZZZ7Fo0SKFWEkwK4JdEhhLAJvJZAgGK4GObMbWDLAEa2C+WGKspC5UKNHyOfmetO6yBvWywJPJpHL+hW4lgkk9PT3MmzcPqCCIEqRba8Ot1NmTTjpJIeNQUQyFSmCsaRojIyMEAgFVXyvDWgcOKAE0Qd2FamsNtsUgy88SlFnFVI4XWpN5lwy6dV7k+uvq6pThF1q7IPM2m41YLIbNZuOk104yA6d8Vp3DioBaqXZCMXp02aPEArHqRfNzCD0Qoi3apujwknQQYRqpnwsGg7S1tTE0NMSRI0cYGRkhmUzS2tpqKnT/xkN+Vl4lFZz7ncy/bT67P7abfLMFidRgbOEYhdkFUnNS6E6dxp2NdP6qE13X6enuYUZ8BskDSeLxuEpKyPoSpFhqu6214rquK/q5rDen00nbg2047A7mvjiXIkVFl/ZFfcS6Y1VGSw/osAD8u/yqDjAzK8PhDx0mvToNnwRK5vqU9Q+QSCTUepLfSd21sEasZQ2pVEpluL1ZL12f62Lo60NMzpuEPNR8s4b6jfX4an0qKSMaAZlMRrFLxFnau3evCsLdbjeHDh3C6/WSy+WYmJhQa0mCbUl0jY6OKgr9kSNHVMs8ofkXi0XVrzvsDHPK909h5yd2MrBnAD4Ek9+arJq/nC3HdrYze3g2ra2tlEpm27Dcqhybrt1E+xPtuH7pUnR6h8NBMpmkpaVFzamg7ZqmUV9fTzqdVvfe2NhIMpmsqomfHtNjerx1xqxZs5g5cyaNjY1Kt0MSl7FYTAlZSgswYSNZA0cJEIVCLsl+sVkSJFpttwTtIsh4PP1byuQGBgYAs72qJEutDDcr+8sarFtBAivCXC6XMWwGf+z4I3e33G1OggGXjV/GA/Vmv+sLpi7g3/v/vQq9B5Qv0N/fz/7D+1nRtoLXFr+m5jJQCLB6bLX6XEpLsa1xGycNnqTQaKhQxEtaiccWP4amaSztW0rTZBOGYTBQO8CO9h2s3rua7R3bWbNrDYZh8Gr3qyw9uJSp0BT3nHUP4USYbXO24c/6aZpo4nDbYQ63me29xqMmcv/8Sc8D8J/X/Wflof9vtIltZRtnrD+DE185kaJRZMuJW3ji0ieqPlOzs0aBM+FwuEqlvuEHDcyrn8eVfVdiaIZiT9XU1CiE+dRTT2VkZER155AyBGvCpFgsMhWcwrbMRpToG1lleome03vMPtWYyZ6cK8eeeXso1hZ58X0v0rm+kxOeOEEluLeeuZXXrn0NNOill+CLQZb+ZSkjDSNsuGwDY0ETxe+Z2cPtJ91O53c7mbxikh3L34i4Dp42iFNz4ol5/uE85qN5svVZFv9sMfaSnd5LewFof7GdE39xIg6f6bcvvHMhRt5g4IIB5v94PtHtUdx+k9np3eXF9VsXrX2tuG1u3F5TWHjtPWtpyDaw8tmVFO1FVbJot9uZ+cpMU+um1q+eyZ7OPTxy1SNkg1nWv2c9Z//5bCI9EVVeJmxPYdIKgCDsgAV7F4AbHr34UXJeE6xY/eJqOoodFG1F5T+JHxEKhapK7qyJM/ErJNEmn5HYYsOGDW/QaZoe/5zxpg+4a2trOeOMM2hsbKyq5XU6nW8QOxMU1NoSTIySOOTSBiCbzVYpgVvVLI+v0xFjZ3WErcGoFQn3+/1K/MntdqvAUwRR5HjyEkq22CpGIoGqUN2tAiFQ6eUo5+zs7FTXbFUVFfqrbKoi2CSiLNIaBCrZYGtmW/49PlsuCKeVSm6t3wZU4CQblM/nI51O43Q6Vf2siLtJPS5UVNKt9WdWVF/X9TeIxZ22+TQOtB0gEazU1Jxwxwm0rm7FKJnXlUgkVAJFqMT4oHRLicIBM9CdM2cO4XCYTCbD+Pg42WxW9RMXA+bzmUFi2642Qj8O8eIXX6TkMw3+zD/NpNBWYOD8AWWEk11JCo4C9c/Xs/u9uzmUOsSa/1iDo+wgHo8rurv0cJYkzMTEBA6Hg2AwSC6XU+dNpVJV6HQ2myXy5whao8bU1BS1tbXU1taSSqZYfedqyvkyh089jFbUWPzNxYR3hcmGzHrxYmORV256hVhXDLqBAPC+SuLF6XSqhJUE+pJUsWoMWPUF5P2R769Zs4aDBw+y+O7FPPPBZ0j/e5rmjc34OipovqzBTCajKOmC4tTX16t3V+ajpqbGTCoce8+am5vRNE21CYnFYgAKgRb6t9SjJxIJZs6cyYEDB2hsbCQUMrPTrYVWMt/OMPr0KMXRIvwvIA/caLZHOe2XpzH43CCHRw4zMjJiBuuneRh+xzCphhR7r91LY66R7tu7aW1tVe+IlJkMDw+rDgLCsnE6ncyePZt9+/apfeX47gLTY3pMjzf/8Pl8LFq0iObmZvL5PCMjI4oKPjk5qcQfvV6v2rtlf7AyysTPSaVSSttDgmErjdpKi5Yktjj64tfId5LJJAcPHmTnzp2USiXmzZtHd3e3Kr+x2vjjlb2tKLhcq6BmDoeDHy/8MQ+3PFw1FzlbjuuHryfmjPGJo5/AUXJgaBWRVanvHRgY4Pbm21k8uJiP936ce+vu5YHmB9AMjc/t/BwnDJ+Ajo7dYedHy3/EwehBdENnzeAalczY0rmF2nQtT3Q/wYudL4IGm2Zs4ubHb6ZIkd+d/jv66/rZ0baD/vp+Jo1J3Ak3609bz2tzXyPjztDb3KuuveAqMBX+v+g//b8Zlzx2CQ+d95DyAS5+4GKWbFmi7GTNVM0bvuMacJHLmZ1V6uvrVdkSmAzPdVvXYa+vCKAahqEE+ABVjiS6PR6Ph0QiUVUGUPaW2fm5nej1OkufXEpgMMBIaIQ9zXuY3TebDW/fwKE1h7A/bWf++vloNo1HbnyEvro+HAUH6YY0u96+C1vAxprH1/D6Ra+z5bwtVQmH59c8zx7bHvINeRKtiap77D+hn4lPTtD2TNt/OXfOESfzHpiHe9LN9vdsV7/3Tng58Scn4t1vovKL7liEq+QiG8my+PeLsRfsaDZNsVizzVnKrjLF2mJVuYHT6WTm6zNN39rpUGUbmqax6vlVuDwu9e5IT22JOcQPOzT7EOsvXE82aNbWj7SM8Pg7Hue8X53HzOJM8vm8+r6Uk8o5UqmUAnzW9K6haX0Tv7j4Fxg2g9dOfI3ZPbOJJqKqfNXr9SrBV2tZpyDlArIJSAOV8gG55t27d08n8N8k400fcM+ZM4dLL720SqTLGlwKxVYUlMXAWetI5GcJxiVwEGp4Pp9XRkrOIcibDAlCrMrkQhMRtFoo72L4JKvsdDoVRVkMplyfvCzWTUFqtCWTJQGz3LtkMOV4NpuNo0eP0t7ergL14+lTMl+SYZPrlPZU1vuUeZPvWVXgrTR0a82ZNeMu/1l/p+s6R48eJRAIEA6HFZU+GAwSi8VwOBzEUjG2n7qduQfm0n60XT0rr9eramWhIm4n89Y22sZNd97Ed67/DrpLZ+2ja8n9MkdtVy3Dw8Pq2TudTrxeLz6fD1fQRempElMLp9i7cy8LdplCYzU1Nfh8PmpraxkaGmJsbEwJ1ViDdZvNRltfG2d//GxGO0cZnzdOsbbI4JrB6oy3Dfov6Gdo3RDFUJF+o5+J2ybAgDkPzGHeU/PI5/KKYWFV2ZcEhzg31vZskUhElUuUSqWqpEU+nycUCpEfzLPu3nXoQZ15989D26HhC/hwuVzE03Ge/c6zpFqOUag14F2YtPKPVmrdDJvB1Hum8PR48D/tJxgIAqh+31bE/XjhvIaGBl5++WVqamoIDgRZ8bkVbHp0E45mB+FwWBkQoarL+ymK/5JYGR4eZsGCBezbt0+JkMk5m5ubVa/uUqmkkhfitObzedVTMxaL0d7erjL60WiUaDTK1NQU6XSampoa9H06+tFjxikOfAq0kMY1qWtom2gj9C5TK2BoaIhXjrzCru/uotBwTMPBaTD6rlH8BT8tL7So5IhhGNg8NkbvGqX7O92UR8uqdVwsFmPfvn0qiSD7y/SYHtPjrTXC4TCtra1MTk6qvVDaN4VCIZqbm6v8DfEbrAGu+CbS3UICbSsKLXtjoVBQYqw+nw/gDeKXYOpt7N27V9UGp9Nptm/fTiaToauri87Oziq2n+zfx5/bmsg3NIP1netpcjZxc/JmntKfIms3AxCP4eGjIx+ltlhLmTJ+3U/ZqCDS0vbwUO8hHmh5gMdPfJwXF7/Indvu5Kaem0jb0px++HSWTC5BN3QKFPjmqm/yWoOJpP7yxF8S2Bigs6+THdEd/Hr1r7HrdtKutLK9R+qO8PXLv46BQcJvBn4HWw8C8OSpT6LpGkVPkd0z/+8pN2u6hmZo6HYdDLOfsqEZXP3g1SzcuxBv2suR1iOseWkNgXigai5n7pvJpT++lAdvehCApV9eSvS1KPYaO5OTk0ozSOzH7NmzVbJYWHnSIcNut5PTcxRKBRVoFwoFxsfH8fq8HFl4hGRLkoUvLuTJLz9JvMMUo/vPy/+TS39wKQ+94yHyzjzPnvws6XAaw27w5JlPcmj7IabOnGK8a7yaJefU2XXBLo6cdIR0JE3ZWY2cOjIO5v5sLmNrx0jOTGLYK4xLW8nGgkcXUL+pnhpvDa9+6FU0XUN3mnM46++zmPuXudhKNtofaEe36WhZjSNnHWHZV5cRToXJ6ab/5cq7mPuXubj9bux5OzrHWvO6bWy+aTNDa4fADns/uJeQEaL15VY1/yKYLMlv8futTFIJZCXoleRSKpUiHoqTDCWr7nu8eZwHb36Qm356Ex7No7rRiM8p/om8WwL8hXaF4GLzGKMto/zufb/jpp/dRESLKB8sEAhUAYuASh5IMJ9Op80yhGPtB0WrScCq6fHmGG/qgNvlcnHRRRcRDJpOfiaTUeiPBFtC4ZB/ARVAWoMUWXxgGhVZxFbEVAIcySjJ9yUgcLlcyjAKQmUVJ5MX1BoYQXXvbQkiJdgVSogcWzYDKx07m80qqphkmq0Zb13XGRsbo6WlRc2PtY5cDJ2gldY6J0Hh5ToBlViQebPSksCkJlnr3SXxYK2NttLPwWQqzJo1C6fTWVV7L+eOZWPsOnMXPat6eGrVU3z0zx+l5UiLooFbld+FQmO321XCoDZZS8M5DRjvMrDdbSObyHLffffh8XhobW1VCrG5XI5SYwnjpwacAIZm8PCyh/Hg4Zwd5+DQzA3upZdeYubMmXR1ddHT02OKV1j6pUubrHApjGOjg7ZX2nA5XTwXeY6xE8cqRkoHbb9GccEx5RMNMrWmIubW925Fz+l0Pdul0A5ZH7KOw+GwqtEXUUBpJyMZTKkHF2q6z+dTiRVb2sbZPz2bfC5P1p9VAn+U4MQvncgrX3+FTFPGFE95AviY+efR0VHShTS2G20kP2kal5k3zaR+Vz26rtPR0aGeTTKZZGpqStUp5/OmMvzIyIjKvHq9Xtq0NjZrm6v0EaLRKIVCQXUFEMp1IpEgHA7j8/mYmpoikUjQ1tZGKpWivr5esTfkuW7dupVFixap9VlbW6tomdFoFE3TFDU+Ho8rx1LE4hwOBwMDA2zfvr3aQKVg9ldn03h+I4bDUDXnLpeLltYW1r24jt+e81sSgQQ23caCVxfQ9nAbU/oUIyMj5PN5/J1++r/bT3pZmmdve5bOGzsJhUKEQiFFKY/FYrS2tTLVNoUz9kaneXpMj+nx5h6RSITW1lYVRCcSCUqlEqFQiIaGBpWAFxssbCafz6fAAwm2rewu8S3Ensv+L34FVIRTrUG5JEHlM1JCY1XvtnaCsLYjlWQoVBh/ErCUKfNU21PcNvc2AL5z+Dv8ae+fuGn2TeiGzo/3/5iaZA3FcpF0Ok2sHGNiYoJEIqFq0fOlPC8vfJnnz33eFA1zjPP+Ze/na5u/xr9u/VeTMeW2E7PH+NH8H6lgGyDjyvDD7h+yYvsKnnn3M/8lpTvuj//D35dc//uEZiAVoGwvE0wEmYpM4Uv50Ms6/pyf8aZxlry+hKwvy57Fe/Bn/Jz33HnYDBuL9yxGL+ss3LWQxXvN/y/pJXSjumTA8YKDBUML0Fwavud9TOXM1pOBQIBQKMRY0xilTSVqampoaWlRifV4PE4ikSASieD1eom74tx15l3UP1lPywstKuHt9XoZXTbKxk+Y7aR23FBN4U570/zpc39Sc1d0V3pLl9wlBt83SC6Ye8PcamWNuRvnsuZPa9jwtg3sO3uf0rLxD/tZ87k1OCec1D5SiyPoYO/b9lJ2l3GlXJx0x0l0Huok58/h3eJl7IUxAgcDDJ00RHAoyKq7VqHZNXRNx67bWfDwAsrlMvV/qcfv8+PyuZQfr3zVrEHZqAj17rp8F33rKr3Gy74yL33iJS78xIWERkPq+9KZRfxKeQ+hooJvZXlav7Ps9WUkQ0meX/M8Jae5lgITAS7+5cXY8jbKlCsaNk4n0WhU6RyJ9o2U9N1/xf1VugXJYJJfffBXfPh7H1bHECBPknii/5DL5VRSRjSlXC6XKlM0DIMtW7YwNfV/n60xPf7fHW/qgLu2tpZ3vvOdyhgd3+Tebrer2k4xSm63m2w2q5BnMRIi/iTCTFb0V6jp1sBT0FnpXyzBjCBQsqAleJYXVBB2yU6LkVS0nmPOvJXCJS2WrH01rcIfElzKiy+BhLWWa9GiReoa5bNW9FnOZUXPJAkgmTRrXbs1CSGOgRhvCdKt2UGpIZFEgNyPoOq5XI5EIqHmQu7N4XBgYND79l56ru5R1/brK37NNQ9ew8LehYrWI/MiInRyrRLEuwfc2L5ro9RSUrWw8XicWCxGQ0MDDQ0NRKNR7MvsGC1GxZho8EDsAdxb3Myvn09zczOnnHJKFTOgUCioTVPmQ4IlwzDQ8zqGx2De5+dR+myJcncZ3aFTs7+GzMczOO90MrJ4pHqBa7D9w9vR3Brtf2tX6y0SiTA+Pk6iM4Hu0nHGnSpxoZDnY/9ms1n6+/tVIqC21my5lUqlVB9rqfOanJxUSuJut5twOYz3l15e+eArxDbGcL7Pic1rU0F5+oY0fLNyuX0/6iN6axTvI16lGZDP57EtsuEr+fAcMoPlVCpFoVAglUqp80sZh8yfGAqpcxKBMWtpgdvtVs9O3j2A4eFhtZZyuRx+v5+FCxdSLBaJx+NMTU0RDodVYknq2nVdZ3JyUjmsorUQj8epqakhlUopIUT1iDSNYCCo3g3ZbySJEOgNcOX6K7nv7PtYcmAJ79n/HgLvMftwj46OsuHABh4+/2H05ce6J3QUOPqdozR8voFYf4xgMEhtbS2BQIDYGTEO3HyA0CdD0MP0mB7T4y0ybDYbnZ2d1NbWKnQymUzS3NxMc3MzDodDBbTW5L7YNGHkSHeL40vaxK4Wi0VFNXe5XIRCoaoOKNY6bECV2ixcuBC/309fXx8AXV1ddHR0qISl2OTjWXVyTCvL6uHWh/n5gp8r+/nZzs/yxQNf5P1Pv590Lk1/bz+7EruqtF90XVe6Hz6fj/Vz1vP8Bc9X2eC4I87u0G6ack1qPo4EjtDr7q0K/Lonujnv/vPYc+ae/8tnctLBkyhR4rXZr1X9vma8BmfeyVjrGM6ik5XbV9Lf3E8gHeDQzEO4c24ueuQiMt4M3fu6eX3R69Ttq6NUKDEnNofnL3ietD/NnsXm+dP+NC+ufJErHrwCvVxBIA29Mo/yHA3DUEJY9a+Y5VLOsFP5jIFAgMGTBtl24zba/6Odzr5OwuEwNpuNdDqt9ImCwSAJV4L71t7Hzs6d8H5YVV5F1wtm/fXAmgE23Ljhv0xGNG9pZmzeGCXvG5MPwYEgJ//+ZEa7Rtl69VYMu0HToSaS0SRtG9tYde8qsMOqP63CKBrsu3gfkb4IK3++kpp0Da5ak6a97JFl2LCRi+ao7amlfUM7RYoUnAW2XLOFnjN64AxY/LvFzH9svtk73VbxU8XmOx2V0kp5LwQplrJFQaDrjtQRnAySjFYQ6KYdTXgLXhVgC5sUUP6sgB3i50KlRZysXyuifMGmC3A5XDy+xmxHu/DRhYSPhin4C4plIHpSdXV1jI+PMzw8zOLFiwkEAvT39+PxeLjsz5fxvVu+Vwm6Nch78wwsHqBtZ5vS77G+/8LaLRaLeDwexQiUe7IyBQ8cOPAGn2Z6/PPGmzrgvvbaa5W4iCx2CfQEwZPgwKqcCaggNZvNqgDQGhBaqbpScyEBshgsQbatwhKAekHlJTxeaEQo5fI7q5iBBAHiwFuDTwmS5Wer2riVTi4ZZ3nBrBRuQT6hglTLJiHDmoiwGnwJuq31W/L34wXbjq8rs9ZZyznkWFILHI1Gyefzil1QLpcrvUlz1UtRMzScRWfVfcucWvufG4ZBMplUSKrcfzabpaamhkQiQaFgtqAKBoOmAvgTDmYMz+DAdw6gB3Xs6+04Pubg2eSzHO44zOLFi+nu7qahoYF8TZ7+i/uZ1CcJZANEY1H1zOV6pB6vXC7jwoXz40788/zMmjML514nu8d2s/xXy3n9xtcZXjj8hnVuS9tUfY9Qy/MdeXZ+dCc+zcfJ3z+ZxJEEHo8HTdMUVUkCR1nz0uc5kUiogFWeT6FQIBgMouu6qq8H6J7qhl/B6MZRmGOuu1gsZiYUImn66a8895LOlg1bCO0M0dTUZNb/zXKw65O7MAyD5d9ejm23jebmZgzDYGhoSL1b5XKZ0dFRZYxEAC2bzapWd/IOBwIBAoGAUveWtSbHyWZN6qIkXERATdacvAfW916MrWGYfdIlkSbMkkKhUCX+JkNq7CWRJnuOqKmXSiXmHZjHlbkrmTsyF92nK8bKwoULCXeFmVw2yQY2VI5ZMEhPpckMZ5SewsS5Ewx9YohSsMTkNyZNOvvf3rBUpsf0mB5vwuFwOFi+fLmyCWC2v5Q9WYJPQaKtNlMABKGIWwNxK9XcmvCXRLig1uLPiK8k55BAoq7O7J4g9eWS5LMieBJcWDVa5FyyL3q9Xu6Zd081zVjT+U7Hdzhr91ksfHkhhzOHcblchMNhIpHIGxiET694mvUnrq86hkN38Kk9n2LF0AqKuplY1XWdlsEWLum9hDsvupN4XZxZU7P4wKYP0FLbQveL3WQmMry07iUA3v7627l72d2gwbrd67jilSvIF/PYM3ZeWfIKZz9/NhuXbOT020+n0d/IfVfdx7oN61i2YxnDjcP4s36OtBzBlXcxa/8sZUuWrV/G5OSkKRQWdLD2ubWsP3t91fPXShpaoRJYW/+FCntQavvFhoDpv4nQ7NhZY+z8wE6KNUWOfuEoCx5aQGmkxNTUFOPj48TjcVwuFxPxCZ55zzPsnLnTPIENXr/hdQyPQdcTXRhJQ7X8krH2T2vJ1ecYqhti1R9WMThrkJc/ZqqBdzzSQUt/C9uv2s7aP6ylpbeFpgNNOEtODp58kJN/fzKpmhTRnVGcLqfy7Vbct4JIX4T6iXpqBmsoOUpqbRcKBebeP1clbJLlJJqmseWTW+g7rU9d165378IRdDDv/nnqd7KWZf1JkluSDtLRRd4T8Xk793fiu9PHo+9/lLzP9N87n+vEn/Hj9rirVP6t7FQBqaxMEQnABQQTYEx8jYKropR+cM1Bmrc005hqrIoVisUizz77bNU7Lr52OBzG6XZy1tNn8eTZT6pjGWWDcrICBkrnH0kWyH4gyTBpsyvvLpjxj1z/9HjzjDdtwK1pGh/84AfV4hSESqik1kyPUGt1XVdKxLLZibMsVF0JWCWLJQ45oAyUINdWxBdQwaOmaYpSLAiuleZrRcjl5ZMNVs4rNRnWANf6wsj1C5IrvcFlbqAS+Fp/lmNb0XJrAG3NDlrrsR9//HG6urqUoreVFi4ortyLbG7WwF1+tiY+hJEAKMq/bJaCxvv9fuw2OzMemkGpWGLnDTuxGTY+dt/HCPWF0NEJBoOqHleuQ2g2wmSYik2RnZGF94L2W00F9IVCgVAoxLJlyxgfH6evrw+fz8dMz0wavtDA9pu2031bNzlHDnudncOHD9PT00N9fT3t3e1s+tUmEq4EiVKCeDHOpV++lPxoXm2guq4TCASqjKxt0ob2qkakFGHTpk1kMhk4DKt/tJpSqMTj//G4mVkuw5LvLaF7Szc5Lac2V3ejm61f3kq+MU+aNE//+9Os+JcVpCfSqvZH0zRVj97f319Vl19fX68EU5xOp1LstwrVSSu6ZDJJYChAiRLBGUEMw6Cpqck0QNt03F93c/CzZu2bdq6G9pJGwjD7efeM9KDdqZFrNVU2X/nWK5z+sdMJ2U0DMDg4qJIdhVKB4ZFhhoeHmTlzZlVdlLzTU1NTai4nJyfJ5XJmJj+RULXZpVKJ1tZWlUCJxWJKYKixsZF0Ok0gEFDvljAjxMCKcyOtu0RRvVAoKMr58UOSOsFgUKHzLpdLJQ0Mw6C7txuv36v2DckyR4wI171yHUmSbJu5jaapJj68+cPYzrORPDnJyMgI25u2s+1ft6EHjwX7LcAvMFu1vfi/2Sinx/SYHv/04XK5OO200wgEAsrWi308PpEulF9BtCXpJvbZGoiLTZXvOp1OQqGQOp4kaK3Bufgx8rPs/W63m8bGRvUZYawdf3wrtVxsvoALt829jSnXcRRVA4LJIAt6FhAMBgkEAsrGSM24nOOFpS/w95V/p+goVh3i65u/zpKJJar1F5iJ9YmJCQL9AW4u3Mzvrv0dX3r1S3hiHnqO9DA8PMwV5SuoqauhMdXImj1rWHR4ERgQSodwZB3YdTtXvXwVZ7x+BkavQevTrczSZuEtevngPR/EN2UKxzaNNKHrOvOn5pv2wlYBINLptLIZDocDe8HOOU+eg+7S2b5sO6FkiOvvvZ5gIggaVckFxRawaexYtgM9pqPt11QdL8DU1BROp5P46ji7b9xNMWjOTaGuwNNvf5rBLw4SfL1i63K5HPG740zNmKp6Bt6kl84dnTgcDlp3tHLmN87kqS89BRqs+tUq5m+fT8leYrZtNq64i+7Xu3H+2En/sn6W3bMMV85F875mfKM+yi5zDS3asIiZr8/EM+EhdDRkJpltJZUod2tuZr8820zOGKWq9SN6BeKfC/Cz6IVF9J/Sr+q7nTknXRu7lK9oTay7XC7VrUX8GeksAhWFequGTHR3FHvBDiZDnNff9ToNfQ00pZr+YV2zMOWOB+SAqoSZ/JzNZnns1MfYeMJGhUxPdE3w1Kee4p3feafSlbKyUI4cOcKBAwdoaWmhtrZWgRLlYpkTnj2BfD7PCxe8gF23c90vr2NmdibeWq96J8PhsHonxRe2itQKaGHtdNPb20t/fwUwmR7//PGmDbhvvPFGIpGIQpKsdcJWdWShqoqxkvonMUgSnFtpUkJRFqffarzkBbbWbefzeV5++WUGBga49NJLqxBWycBZRduEKi1B3/E13rLxSCBrpWHLtUuWWuq3JciyUsesAbogdXIuq4Kp3J81uLfOhcvl4pxzzlFGESq9rq2IuXxWPmc17NY+5FYqmvU8Pp9PPTdrr0AJxpc+uxRn2Mnp2dOpHazF7rZXOR66rqv1YM1IAuRm5Bj6xhC6Q8db9uL4sgMtrak6aKfTSVNTEw0NDUxMTDA2NkbrwVbOu+U8UqTI1GXUmgCYcE+w51t7KNWWKrVjRoa/feNvnP7Z03GNuNS9SjZTUORIJKJE9ObPn8+BAwdMlH7YAQNw4acvZP2/rif6hyj6fTq93l503Wz7lW/Mc98t95GPVNqNJVuS7P/3/Zx060lq3U1NTVFfX68CQZkfl8ulkOR0Ok1tba1Sw7YiI4J8S+9IazJJssnFYpGuF7uw/cSGt89LUAtSXlVmaGiI8fFx9Pt1Mi0ZdZ3ZpizP3PQMK760gmg0itfrZWRkhJwjx9OXP824bxxHj0OVIUgyKZFIEIvFlFMniTPDMAiHw4rOn06n8Xq99Pf34/V61bW7XC5qa2uVo2sVQJQ1m0gkTLG8Y7VOsVgMr9dLXV2dah0Wj7+x5k/2lEwmo1Bw0Q4Q0RprMqi+vl7tS5K4i9qj3PzMzfz4jB/zrgffhZEzCNeFaWpqYsaMGZxkP4knDz3Jg8EHKTqLkAK+Bmz8P942p8f0mB7/hCGK31Z7bk26WxPtVm0YoZBbS8CEeSMIswR9gmaJ7bcy36w2F97Yl1r2ewnMxf+ASscV+Z61/MzaAjUXyHHUf5SyVi3C1BRr4osPfJFMKUO6lK7yV2QI40rEv6yjLldHS6qlau6y2ayi4XZ0dDCzZSZrXlxDIVNgImV28Fi2bBl2u51rNl9j3mtZpznerO67rJnz4il48I346BvoI+gJ4moxQZbApJl0lX7T1k4xcu2iNRIOh6tYi96ilysevgLdrXPZ3y/DU6joBlmfPwA22DV/Fw9d/hAApwyeQsPWBlNXJZslm80SiATIz8qrYFtGxpdhR3EHsydn43P5VKu50I9CrP/eelKNpgZJcCTIZV++DEfRQVkzA7DO4U70b+ukW9PMfG4mBU9BtT/NFk3NmKYXm2h6qYlywRQcC4wHsDlsVUGmJ+tRtlQS1dJbXkACYXvpuq7+JsBIPp8nHA6rft41e2o4/wfn8+QHnsRVcnH51y/HHXdT9lQYHD6fD6/Xq5iN8v6ILynvgzBea2pqlO+/4eYNZMIVvyQXyfHQ1x7iyvdeiStlsj3EzxFwyuv1VoFXUElmiT9kLYs8b+N57J2/l9G6UfX5C/58AV68hGvCpi93LNmv6zoNDQ10dnaqGEHXdVWLnxxPsuTxJeSdeVYcXGEmMnS3SlTIPUppq/hp0u1EmLyJREL9rVQqMTQ0NB1wv8nGmzLgDofDXHPNNUqNWDZroErMwFpLLcamVCqpns5S+ykvkTjH8n3JoklgKItaarrl3MVikWXLlrFs2bIq8RB5maybrJxHhpWyIp8XVM2KnlsNo5U2YqWNSyAhhlPOJ/coG4b1GMIEABQ6bb0eqATkcq3WzPfxFHUrNV6ex86dO1m0aNEbDLwERHJOqxMh1yq13oVCAQqwbsM6ZnfPJmPLVAm+CC1Y5l9EttxuNz1tPdx+8e3oLvOaDl5/kJqJGpY+vJT4ZJxQKKSuv6WlBYfDwbZt28jn83R2dqrNPBQKEQwGzXYl5w+gN+jVNVAa5GpzvPreV1n6xaWKzWCz2ch2ZSmMFXDGnUxNTSkjEQwGaWpqIplMVkT2+jUWXbOIRCKBbjfXTiqVolwuM3X5FEVfseq8LRtaOPVnp+IL+dSzSyQSTIQmyIay6JNmgCp9PAUhlvUuRkJo0ZqmKbEcec6iki2bupRsuJ1u5j8z32zBVZNSz7etrQ3H1x3ssO9gYu0EAIGnAxQuK/BK/pVKP1ifxuCNg4yvG4d1UG+rx/O0R7EPBO3xeDxMTU0pAygJGqlVsrIqpDZKelQKgiOMA7kvqRcXI63rulLwlCx5Op0mmUxSoMDgkkHYZ3neteBZ5sGT8ah+45KUyWQy6l0XZ0Tm1jAM/H6/osTLs33v395rOpseVJ2mvA9nbDqDbCHL40sfp/yVMvzkf7tNTo/pMT3eJOOUU05R9l3sqtV+AVX2Wlg58nkr20z+E18nn8+rMiJBWeV41u9aE9ziJ8gxodJpReyxtexLaL+yX1oRWtywvXE7ulvnqPdo1X13D3Vz49M3Yi/ZVfAlrEK5VzCDl+bmZt4VexfJzUn+uuyvlJwlZqRm8Ok9n6ap3ESRogIpkskkw8PDBAIBZbPFZojwHFRK1wQZlzmxMs5SqRSDg4PqWMezDmTuxD7L73O5nNL3kDI4mRux+2//69vN72jVZYXyDHVdZ9eSXTxw1QPKpr/4uRdZ+d2VRJ6JkE6nzfagUYOxU8f+4dqqu7GO1fbVONIOdY/5fJ4zv3YmL338JTRDY/UPVkMWikZRoaCGYdC8uxn7Pju6w7zfgYEBlUAWOwWgOaoDOJlbAX+sPrfMk+gUidCfPG+rUGqxWOlpLboqhUIB38s+lhSWUD9VjzamkSll1NqUtSoBsXWN/leMNQn0C4UCDbsbGF48TNldSQzN2DGD+rp6bNgUUCblllLOZg24rXpIx+vn2Gw2jrQdIefOVT2nw92H6ZzoVPdtLZ3TNBP8yWaz+Hw+hdqnUinlh6x+eDVHTjjCM1c/w3UPX8ecoTmqPZzsAVNTU9hsNmKxmPLzbDYb4+PjVc+nXC7/QwBhevxzx5sy4D7jjDOYOXOmQuYkOyuLXQyZBKzSJ9nqWFszuaKaLAiaLEorPdpK/5ENAypG07qJCY1cEHerarbUVsjGeDzSfXx7K2tGTa5dNjepzbIaY3HQ5XvWIFvOA7whmAdUpk5+Zz22GAn5nWym1sDcauiholYej8er7kXmy/p5eZZyHXI+adtkrXOTZy1q4HLPolYvbd5sNhuZTIZMOaNUQGXMWTIH/7N+9GKlh6jNZqOvrw+v18vs2bOJRqOMjIwoNe3u7m5V49uxvoM6bx2vfuzVqtYWjpSDtofaiMfjKmGQn5Hn4I0HsSfsLPj6AjWnYtRqamqUMqWIxgijQTZUmY/mPzWTG88x/JVh0CD6tygLbl9AMptEL+lq07Y12thy4xYyZFjwrQWkh9NEImYrifHxcRwOh8qGW9ejBLPWZyrGIZlMUltbq2qQJZi01vaJOJ68K7WfrkX7iobdYafzh51kZmeUIm25XCb3tRyJd1eo2uNfGifcEqbt2TYaGxuVguaRjiMYTQYt8RZlVK3GPBAIoGkVpfFyuayo9PX19Wars3hcqXpKH3MrYiQMi1QqRV1dXVUCaOe1O0kuSkIZuB9wAj+HwgkF4t+J07enz6zhs+wXsrdIGz95z3Td7HRgRcZlrQeDQRWAS8/3YDCI0+nkom0XYfQYPPq9R//Brjg9psf0eLOOFStWVCmLH48YS9AiQY10pRCbCxU7bfUJJGEubD9x3uWz1gARKtRaQCGy4ttYy8Os/oFco/wnQY5855dzf8mLDS/y2SOf5fO9n+eLnV8k5Ugxf3g+73vlfYSyIUYnRxUDTa5bjltbW0traytut5uRkRFO2nISEWeEh+c+zMd3fZxZk7MoaSWFkEqwbbPZmDVrlmrrKPuqCFEJWig+ity7HEcC04mJCUXLlUS4lS1oBRjEfxLBVZlza4Au37PaRitDQT6jmIXOamEyA4O8La+SrsFgkDrqCP8gzNYbtxJfVQmUWre1ctIfTqI8WaZQLqhj5/N5XMMuVv1yFW6Xm2AmSCqbUkw1sfvC2BP/UFqcylqR0kYrO1LmxBrAWYEt8WOEqSm+tJVZJkrZMqdWUV7R9Jm326zZdrqcVWCK+FVW1p48X0nky/qUtW5lli56chF7L9lL1p1V87jqgVXoJR27s8IsFZ9CxH+FdaKQ/WOBvFUDSVr8lmwldK3a53ztwtc4e8vZSgQWKj6++FgCRllL/dxuNz6fj11Ld7H+kvVkfVnuOf8e1vx+De7n3ExMTKigW/yq46n0Ho+HVMp8/oFAgHg8rp7z9HjzjDddwO33+1m3bh0NDQ1vMB6CbMkmIAtXglp5+Ts6Oqpo1LIRWPtTy++F2ip0cglIxKBZ+1PKRicviWxSPp9PvUiySUktqQgsCO1D6pat2TQrknx8Dbe1rkWuQ+7dmukbHh4mn8/T0dFRRUWTDG9dXV1VUCABjWzEVtTfmjG3qprLdcr1yIa3bNkytTnKJm5F/uQ4gqpaFec1TaOtrY1EIqGovnX1dWBUEgRC2Zb+pVARyioUCnQe7uRdd7yLX3/g1+g2nXUb13HySycz1TLFlpEtAOQKOQ5+8yDdn+tGT+uKxu7xeIhGowohjsdNRNzj8dDyQgtn2M/g6Y88bWanDai/vp7iviJFTEeo4Cuw75v7yM8wKeDbv7Odmgtr0AxzXQwNDdHb28ucOXOUwRPEWZ6jXIcYKM+dHuiHyNUROn7SQTaXpegwVbULhQKG3eDFL79IvNM0zFu+toV1n1oHQCKRUEZW1qCmaSozKvVFVqMsiuHyzMTw+P1+dU5BYxwOB5FIhGg0Sm9vL522Tmx3muslMZmgpqaGUChELpcjl8sx9twYw/8yrLL7WkEjsjGi7t/lcnE0eJTNH92MDRudv+rEfrBSqhGJRPD5fKpHtbxPUvKhaZpS4ZT3UtrxiJMgrdOcTqe6D+lk4Ha7eeVdr7Bn3R6wAz/FFCz7BHARFLUiB//jIHNumoMzVpkzv99PKpVSmWu32004HFaMGYfDQSaTUYJ6cs1SflEulxkfHyccDqs9xWaz0fxM8//hjjk9psf0+GeOlpYWZsyYoX62Bl1AVYIym80qH8bKMLLaWWtZmQQ71tpS8QWEmi4BuOzhYletassyZE+U/VOOJUGC7F2apmGz2/hB9w94uPlhdE3nGzO+wZd2fYlPrv8kv139Wz6w4QPUpmsZnTJptcFgsIoBuK9jH96ZXh5rfIzPDH2Gw3sOMzAwQH19PZePXc6a3BoaY41VieBUKsXIyAiTk5PMnj2b2tpaZSPFlra2thIKhZT/YxUOterwSMlULBYjEokQCATUZ60BNxqUSxXNGgNDJUvr6uqUb/P86c+zYt8KolPRqgD7eEq/FbzRNI1lW5dRSBVY/05TaG3Vd1bR8moLRX+RWCxGOBw2fcWhHDO+OoMDtx6g++5uBq4dYMUvVuCMOSkYhaogDs2sJ/aPmfRtu8NexbiTQFGGlGXZ7XbC4bD6nfggsu6sJZzHswUkoWKlyxuGoUq15PdWhiiglLTFh3c6nfTN7OP5y5/nrL+eRctQi2qTdXyyXQAw6xoW4VJJpktCS96b19/9OvlgpSQP4MkPPMnV371aHXdiYoL9+/erbgLiq1rnQnxZYQTKu+j1eqmpqcF2qg38VLER77n6Ht72m7cprYVUKqWS8takh/jaPp/JWjy88DBPXvQkWZ+ZJJiMTLL+HeuJPhFFO6CRTCbV+pZrsv4rbFKhn0uZ7fR4cw3tv/tQNE37H3l6K1eu5I9//CNdXV3KuFjpy/IyWzNmsilI8CnUT8m2ivGRDUZUjiVzKVkuEfeQc8p/snnk83n1UlsDVSsaLo62IJiFQqGq5sJa0yUvYV9fH01NTWrTslLFrEi5BNKySUrgWi6XFY1ZlKjl+8PDwxiGoQJuMQyyYQlyqYzNsePKRmbNtss9ijGR48m8ys9WSpUEF7I57Ny5k0AgoJwIaZ906NAhs8arK8L977uf6x6/jsZEo6I4yTxbaXGCEsj6GK4b5pXFr3DO4+fgwKGov4PZQR543wNMzp8k/FqYzs92EigFVH1uuVwmFApx+PBhJiYm8Pv91NXV0dHRARp4ajxsPm8zgecCaJs0EvGEmXhohJHHRig3WGraDNByGs3PNnPiHSfy0pMvkc1mWbNmjdIDcDqdjI+PV4mCiCCY9Po+0n+EUCSE1+FVySBBevd/Zz8Tp0xUNnoDfBM+zv/y+ZQGTKMZDAax++2k9TTGpKEcKjGQhmHWyYm4x9TUlEJRZG5TqRS1tbXqufv9fpVwmZiYUDQ7wzAIhUIcPXpUPRtBwh1OB7nTcjxz4zMUk0UWXr+QLk8Xra2tJkrt7ufRbz+K7j72jucdXPipC2kttQKmUyBiaocPH6a5uZlkMqnqxKTlnPQC7+jowG43W7YFg0FF1w8GgyprL0qjNpuNA+cdYOu1Wym5LSjEUaABqAgOox3UcCx10N7UTnNzMw0NDYp6X1NTo2rOWltb1VwdOXLEpOIfQ7QlcSDXLrXesvcYhsELL7zAs88++/9kC/3/q2EYxn/R2GZ6TI9/PP6nfBUZl112GZ/73OdU3bKVwi02sVgsMjU1pXwP+b2VmQcVJFI+Y02MSiBkDayP3a/6V/wHq1K5BCZWDZzj6dfWgF/Q6bvm3cWdM++s6hMcSUX4zL2foYYaCskCY2Nj2Gw2M/iz1O7uqd3Dz676GWimink4Feb6H19Pi72FefPm4ff7VbAs85DL5ejv72dwcJD29nZmzZql5mZqaorh4WGi0ahCvOVaBT0U31BGMpmkp6eHaDRKbW0tyUASf9KPoRvqngcaBlh/1nqu/eu1uDIuJhon+MtZf+G8n55Hnb2OhoYGMqUMLyx/gafOfgpHycEnfvMJIrEIgAIXrKi5+DpW9LvvaB9bl2/FU/DQurGVYsFMxErby1KpxNjYGIZh4Al7CHvD2L12bMWKTo/4Pxl/hg3/sYHzf3g+3gmvum8JOgXM0PWKiLAkmIWlJmtLgCfxe6V7h6wXaeVm7cxj1UoR9Ff8PPFxBc2VZLTVJ5/qnOLeT96L7tCxlWy868fvInQ4pMoFrEKq0gIvHA5XsfGEaWYFq2QdbfyXjRw69ZCZQD82nEednPYvpzE5NsnExIR6bvJOyLsqoJ747tbnKMi8CLYVnAV6Hu6h1HqsF3cywA233kC4HFbK4RIPOJ1OkslkFTXeMAwF3KVyKZ455xm2n7Ed3aGjZTS0T2s4f+vErlXKKqXsUfYa8WETiQT19fWKGRqLxdi/f///s41tevwfjf+Or/KmQrgdDgerVq2is7OzKmizCiZYBdOsddHyd8mGSeZIEDFAqfhZkUUrrVoCXnkZ5D8RcjAMQx1DajCECiPHFYqOKJNbM4RyzcfTwPv7+4lGo1WbiDVTChVamNU4ysZkt9sV3dqKVGuaRlNTk9oE5T6tSRarCJrMubWG3DrH1oDaipYLzc16bHlGggDKHEkAZzVKzc3NHD16lBHXCA+d/RATLRPc9vbbuP6e65kxOEPNh5XaL5u+tW52Zmom0SeiZpDs8VAsFsnUZHj5+peZnDMJQHxVnL3/ay9t323DH/PjbfNSjBTJ95iIbltbGz6fj+HhYfbu3asCqqafNlFbW4vWrpGKpBgbG2PiAxPoNdW0IjQwvAaDFwyyU99JS18Lfbv7SNWmCNYEqZmoIRKJEAqFSCQSqsZJUNnm5mbK5TLtre1KMEMy0yKONv8L89n1zV1MnTylzpkL5dh7yl667+nG5XJx8MhBRm4YId4VZ9EvFpktzaJRFaROTEyoQFnXderr6xUrQmqarGrfmUxGJQMA6urqsNvtjI2NoWkaY2NjKrgUapeu63S0d2Dvs8PdsP3u7QTiATw1ldrr3ZfuVrX3ACVXid6repl1/yympqbQdbN/ayqUItueRStXtwER4xgMBvF4PMrhkHuQa7Y6AtJaZcaMGQQ3BiECWy7YQtlZhgPA9cD7gPejkhrGjwxK+RKHDx+mr68Pu91OY2Mj8+fPV8Y0FApRV1en3utoNEoymVSZeqnXEqaLXK+u6+qzY2P/uI5vekyP6fHmHIsWLQJQvocExRJoCA1abJW1rttKJ7cm2q3lXGLfrD/LZ44voYNKm9Djjy17pgQZMo5n07lcLmK+GH2hvqpgu3GqkeufvJ56vd4MsihQV1dHJBKhpqYGMH2SbfXb+Pnyn1O2VYLfWDDGgzc+yKe2fQpPwVOlLSM+l5R31dTUqN7l5bLZTrKnp4e8lie7NEttprYqQTAYGMSZc+JP+JW/2FPXQ3nATGj6/X6Gmob43YW/47JnL2NRzyIzqT3jCL+75ncUnUX+dv7fWLRrEfdfcj95T55n3vMMb3/u7dT4a9iydAtPrjDbNhVdRX7x7l9w/b3X0zbcpkAKKxpqTXyUSmZLr0QsQccTHWZC2+lgdMRkBfh8ppR2yVUiMT9BYFcAR9mBTbPh1J1gr06opJvTbLppE4mOBI9+7lHO+eU51PbWKn/U6/VWJWzEPorPZi0VEwDHuhYlESIdUGS9CuvNCgQJi02OLwloAcKsPqPP58PtdtPf2c+DH34Q3XFsbTp07rrpLi78yYU07W9SCehSqaRQWhEPEwE2secSkMvfxedb8sMllIwSR9YdAQ0CewKE3h1iR/8OFQTL+yp+vQANgNKv8fv9VfR7QPn+Uk4ZuTjC5G8miTZHedtdb8OX81G2lYnFYmoPEIDD7/czPj5OIBBQ6ucy7z6Xj7OfOBscsHP1Tlr+swX7I3ZoQonHZTIZJTxoZcLW1tYqPRtJxEmp3vR4c403VcAdDof50Ic+pNDh47O1svFYhbSgEqBa+2bLyyN/lw1RAmQxSlaatPXz1jpXMURimDRNY2BgQKFcYhyPf5HlHiT7KZSd46k6J510krqm4+locm+Aqp2xfl+uUZIIVoRc6CXWIF/XdZWEkGBbMpfyssq9y3kl4y3nsFLFxcBY51TOL2iuHEOErGSTtmb1qYOt79jKxHJTgCvry3LPpfdw5eNXMndgrno2ErxYqXXybIXyZM34Gn7jDeIWkc4IkdYI6QNpBj8+SLY9y9IfLWW8cxz/fX5SoylyuRyNjY2KkhaPxxkbG6O9vR2fz8fs2bNpvq+ZAc8Ae2/YWy2udmwcuugQjlEHzq852fuJvbgDbtb9fh16TFdCbtls1mzNdWwTDgQC7N27l4MHD6r2WbKZyn0Xi0VmfnEmzs86GT13FAxo/1k7HY93kDNMxH7wxkEOXnnQRBgMndDRECfcewIup0sJd0iwJ9lboT+Pjo4q6pRkwK2iMdYkl1AQJSklbdjESEigW/9UPR1THYTCIVVbaLfbWfXbVfh1PzvPNvuJnvjciax5YI1Zm3/MWSgFSmx6zyZSrhTNv2smvzNPKBRSRl0C7HQ6Tblcrnqfp6am1DWHw+Gqte/1ekkkEsy/dz7Dh4Y5etlR+ADwMnD2cQ/zNtBCGsbXK8KIfX19TExM0NHRoVDt3bt309HRwYwZMxTy4XQ6FV3OZrORSCTU+9HU1KSy/5K4mB7TY3q8NUY0GqWrq6vKFln9ClEit/o0UAmiBGm2JsPlM8f7OfI7AQGsdFr5vth92Z+hus5bfBWxI1JmJEG50+kk781zW/dtvBB9QR2jJd3CR3Z9hAWuBfjazSCxo6ND7b1CVQfYFNlUFajLKLvLFINF7DF7FXX5qZanmL13NuMj4zidTlpaWnA6nYwao2yKbGLmnpm4XC5evvJltrVt40ObP8TiscUADPoG+c2q3xDIBXjfC+/DlXWxv2E/d627i0hzhPe++l5inTH+tO5PTNRMcNcFd3H181fjSru496x7za4QwI4FO9ixYIe61t4lvfzN/TfOvedctrdvr74PW5mkL/kGBqbMs5VZIAkDEfm0ovmaptHY2IjNbmPP9XsYO2GMzm91UnuwtqqEUEYymuS1D77GxDzTR0rWJXnm+mc47Q+nETlklmlJuzlBnWU9WRFgWVPWRM7xLEfr+onFYlUostyzVUdJkG9hZMgxpOxPunaMe8bfsDYMzWDKO0UgZSqgC1oux7f6exL8i78tYI74feL7n/jrE/GUPIy3jtP1rS4O9hxUPrnYfmu3IGlVCzC2bIzAeAD/gMnoy2QyeL1e7B47A5cOUPu7WvXOFGNF3J9zE7kxQjlTEdWTJIS1pNThcCjGgWgTCQsXzHLaMx4+g3BfmPZX25mYN8Ho6Kiai2g0qhis1u/puk5tba1iUJZKJSYmJt7w/k2Pf/54UwXcZ599NvPmmUIK1uBSevkdTyOxGhLJ1ln7UlqDQAkcZByfDRajmEgkFDVKXnTZlOQ85XKZxsbGqn7gQhuHitGVz4uRlJdEehIKEihUV2udC1BliOW+oaLMac2iWmum5XyycUmAIxuVbDRWOpkYBzHeErRYa9WtwbR187ZuzjKP1kDMyiiQWlarcmOhUCBoCxLtizK0bEjVS4eyIerj9WqTlXPJf1YkVeY9l8uRTqdV5jgyFuGyBy/jz2//M8P1wzT1N3HRAxfhrHXylx//hfGTxkGDrZ/ZSrYhizHXoOM/Omhra1OUI+k5aRgGg4ODRCIRFVB2PtCJvWxnfNY4kXsiHPz6QUrRY1ljA9yb3WTuypBdbVIJn/roU5z9lbOxZW0qkB4YGFDXr+s6+MD2Mxven3jxuD1qPUiSwuFwoKd0mr7bRF7L07Snibq/1lFymWUPPTf1MHD5gEoCjJ48yqg+itagceafzzT7Yh8zjrJ2JicniUQiio4k1CWhQ4vBlDUofe+FIikofU1NjRIBk/doampKHV+cOvmeXtBZ+OeF6GWdsDvM0oeWYpRNVD8ajVIsFfn7J/7OyPwRAF74+Aus+rdVaq7q6+tJpVKKKj81NaWCfKFwa5qmriWZTKr1c/DgQbxeL16vl8BvAvAHYNuxhfyc+fystH37Bjs2h01lrIXm2dvbW6U1sWPHDrW+Z82aRXd3d5UwoMy9OEfyznq9XlWrNT2mx/R484+uri4aGxurytDEJqXTadUKyYoqSjAmAYIERNaaW2tgJAlRseuS4LYy545nqcmwIupW30CGFWl2OBw4nA4+teBTbAlvqdykAS2lFs5ynYXWUmGbHQ9qyL1dOH4h32j7RnX7MAOask0sjC2sSti+0PIC/zn/P2loaOAde9/BrBmmSFqZMresuIVB7yDXjF/DrtZdvDznZQybwS9W/oKPv/Bx6uJ1/ODUH9BX0wdA6owUVz5zJXecdQdj4TFGThvhl12/JO1PMxIx7UfOneP+U+5nxfMrcKacEKxcX+VmzJ/bR9vRUhoL/rwAGza2n70dzdC4/v7rmXF0BmWj8gysIIfc266lu3APuyntrfhnIqIpc1cqldh5804Gzh4AG/R8qYfILRHCY+Eqn8swDNLhNGOLqhOyoViIUCKkxOCkBM+qnWMFl8RHFjab+KryHfEFhR0qpZS6rhMIBEin0zgcjqo2WtY1JGCHMCzk+IpVtmEG9qydZz/+rJrndT9ZR+vWVrCjgmJ5NwTsEV0aQdZF6VsCcrG/Qn0v6AXGusfIBDKUGkpEIhGVDCgUChiawdA3h5jx7zNUa1pN04jPj3PkC0dwpB2s+vQqtPFKPfnBfz9IbHWMGa4ZtN3Tpt630dmj7D11L6MnjnLdz67DyBqqL7bUrwvoJu+5lVkrvrjNZiMcCnPSnpPI15nxTyabwWGvAARW4MXKdJEONZqmKX9oerz5xpumhtvhcLB582aVLbZmzoR2axiGypiFw2G1cQs6JEJPsulJoCyGQDYUq9ER+sXGjRuZNWsW9fX1KlsGKPqyBJHiZFuNnSQErCi8nN9K/7YGtxIEA1Wbi6idH58BlxdW5kU2YRFksM6RFYWUINpKWZNNWObBujEeT6WXJIF1PuU7YjQkmAYUoicJjlKppAIUMUTbt29X1HL57oEDB+gb7mPbtds4cP4BGqca+fjdH8en+9Tc5HI5is4i9156L+e+dC5dmS6FaloZAkJvkkC9VCqRdWS544Y7uOH2G/CUPDxw0QNsXroZw3bcsi5Dw0MNzPzBTMppU0m6VCoxPj6uaE65XI76+npaWlooFosEI0HsHjvOkpN9sX3svX8vJWeJ6JeiJC5JUFhbqArcWg628I5fvkOt2d7e3kqbqKDOxh9uJNeco/GeRub9fh72UiWzKwGb9GMtOopQBKdmGpPR0VFipRhHHzhKviNfhbxrukbXk12s/NNKMpMZampqKGgFdENnrH+M1tZWJiYmyOfzNDQ04PF4cLlcTE5OqnIKqftOJpOkUilCoRCxWEzRr2Rd1dXVKRGxQqHA0NAQgUCARCJBW1ubWvNOp5PR0VFqGmuojdSST5p1Y5OTk7jdbp65+RmOLD8CltatkUMRrvnuNUxMTBCJRCjrZeIr42xu2UzXL7uo8dYQi8VoampicHCQqakpamtrcbvd1NXVKWr+yMiIUqLdtm0bvb29VUvBttaG7XEbGhqX/PASgnuDxONxBgYGOHToUJVgEZgZ6kQiUaUOKrQygFmzZtHR0YHH4yEQCNDe3q6+L8mXr371q9Pqopbx36mLmh7Twzr+v/ZVrOPaa6/lE5/4hErwAoo+bmVwiT20IoNiv0W13Ol0qtIya5mcNcFtDait9F752RrYWwVOreir2HZJykvJkiPi4HOLPscNAzfwhTlfIO0wxSjb8+38ae+f8Ok+FSTI9VtrsOVahoeHefjIw/zy3b+k4CmAAR2ZDn6y6Sd4SmbiWjd0Xq17la+d8DUKdvMz3ePdfPWVr1Iul/nq2q9yMGIytJxFJ2V7Gd1WSezXpmtxlB2Mhip9kDVDoyZVw1SwQqe16Ta8OS95V56So4Smayzfu5wrH78Sw2Fw6/W3kggkOOulszjptZP4+bt/zkTNBGtfXcuZz5xJ74FeRkdHaZrRxOvXv86aPWtoH25/wzqwzqlm09g3Zx9/edtf0HSN0z9/OqEjIRW4jY2NKdSz56M99FzSU1VvbM/YueCDF+DMVBIwJU+JJ3/4JNn6ivK2VtaY/+R8Tr73ZLWuRONHnoWwzsSvlDUIlaS52CerTyy+rN1uZ3BwUHXTkHpuQZWtCDpQlXiWoF0+EwgEzPVjg96lvbx4/Yuc+ttTad/RTqlQUuK4drudVCqlrslaO51KpZQ+i/ia8jkBcsqBMo985RGSTUnQwJa10frOVhzbHHg9XvxNfvbctofUghR1T9Wx+EeLcRQcZGdmeemHL1H2mu+TZ9TDqTefStgT5pUbX+HomqNgM4Vf62+pp/6heiZnTTJy1wi6VwcDouNR3vfL95EbzSlFc9Hq8Xq9SqvF2rfcit4LaDaZnmTT5ZvwbvASfjmMy+kyhdpstqpjyn5gLRPZsmULr7322nTQ/T88/ju+ypsG4b7wwguZPXv2G6jGshBlo4jH42zatIkLL7xQUVwkcwSVDJsEp1aVcmvwKRtDuWyqhp966qmquX06nVablIgnSBAuG5og7FDdi/f4LKf8XgJmqPTLtCLJYhQl+2nNSkugfHwGXOpC5IW19iGW41sTDMcro0rwfXyQbg2orUH78TVj8oys/wrKLOcSUTDZ1GUurFlRqRt2Gk5O/euphCIh3rHtHbg1Nw63Q6k8l8Nl/n7a39k9fzf7u/fz/rvez5ziHGXohLorGVyZJ5/Ph0f38OHff9i8doedSx++lIwnw+55u6vp4HaInRwj9nSMpm1N6j5F5EvQhlwup+i/iUSCpqYmilqR2cHZBD4aYGTJCI4HHRi3GyTuT1A82XzOtldsnPiDEyk2Favqp202G1O1Uzz7wWfJtZsB18g7RnCVXHT+qROtUBGg8/v9lYxvxlSoTJfTjI+P4/P5aI400/HRDl6/9XXicyotRgybweDyQaa2TdG8u5m8I8/eq/eSIUPHtg6CfUFisZii+4kugGSNhTYttCWZY5/PRzgcJho11VsjkQhjY2OMj4+rNiByr16vV6l3yloIh8Pk4jnKHpONMTExoQLQeV+aR+prKSZWmBSpaE+UFZ9dQWR2RK3LbdFtbPrQJtBAy2gseXCJYibMmDFDPbtEIkEikVCqtW1tbQSDQbXPWOseASK7Iyz6ySL8Lj8NRxoINgRpaGhgzpw5rFmzhmQyydGjRzl69CipVIprrrmGn//854o6JvMkPbv37NnDjh07sNvthEIhGhsbqaurw+12M2PGDDXP02N6TI83//D7/cyePbsqYJGynFQqVYUCWzukAMruCwOstra2SiBNbLg1SLb+DFQ569ZE+T8q/bEm7q19hcHcv1PBFN+a8y22hrfyFd9X+PDRD3NX812ES2F+0/MbvIbX1CexUN6tjEMwu2L09/ezZ88enJNO3vnnd/L3a/5OtBzl+1u/j71UoUmnjTS3d91uBtsAGhyNHOXRxkcpaAWOho4qu1x0Fpk9MZvDkcPoNp3GRCMfee4jRBNRvnPed+it6wVgwdEFXL3xan5+7s9NRNuA1btXc9UTV/H8sud57OTHWHhoIW//+9vNeSnAx3//cZ5b9RxnbDgDm83Gx/74MR4/5XHOeeqcqjZLIXeIyx+/3KQwG5X5tfp8MvbP3c9dV9+lrv+pbz7FWV87i+CuoEKhhQnmH/Njz9kp+ytsgOBAEJtRyTDruo5Ld9H1YBc7/2XnsV9C59OdLPn9EiYLk1WMQrvdroJba1JG1mc6nVZ1ylb/zxp8C3tNdEVk7TQ0NFShtHa7vUoIUKjsiUQCl8ulrktKxCQp0Lmtk1mfnGX+3e2gZDeRd/GtUqmU8h3lvRFfVFiSVs2kZDKpPr/nsj2k69Jq/nWvTv/d/TRe10h5sMyhLxwiv9gsPx0/d5ztie3M+MUMBm4cUME2QD6a58DbD+Db7mNy6aRK+hsuA/1mndmp2Wz+yGYz2D62huOROK+e8iqnPHWK0m4IBoOqE0kqlcLv96vyAqGdi1hcJpMhb8+z6bxN/z/2/jtMsqs6+4Z/p3IOnXPP9ISeHDQzykI5oIQkQEgPSEICmWQwNtiAhbGMARuTZTIYkQVIQigL5SyNRjPSSJOnp6dzrK7qyrnq++No7drVwq/t57Wx+N7e16VrNFNVJ+yzz17rvte91uKVs16BM2HrjVvpeqWrrg2xBKBkPxDCz+FwcOTIkUWw/QYdbwjA7XA4eM973qNArb5Y9AIQ5XKZpqYmLrjggrpIsmwsugRdwKw40wKWBCjrm5BsnhK5luiogFiRuOgbkxhKQF23bAJybWIkdQZarkcHv3oOkGx4emRcrwy+kH0UIKT3+JNz6XlExWKRiYkJli5dquZDfiP3od+XbKhiUOQadYMugF4YVXEEhFyQfxP5vJ4npBfnqlQqdW0+Ln3iUkq2Eg6Pg1QqZUqQyXLfGfexc+1O8xy2ErdcdgtXPnwlvQd663pCCqGQy+UUGyvnkdQEq9XK2+98O3cX72amYYa8M89s8yzuuJv1P1xP6ZkSZXeZiYkJent7mT92Hu+QF9u0Ke8RaXKpVMLn8zE3N4fH4zGVDAer9M/0k+vMEQwGiX4sytwX50gX0lj+zMLDMw/T399PR0cHTU1NqnXXXMccRX99PYF4e5yitUgulVO9KaVIiDClovDo7e2lubmZWCxGMVFk7WfXMnzGMDOnzlBcVsQ2a2P5N5YTGggxX5xn73V7OXzOYQAOnX2I1O0pls4tVe+dRF0kB0sqk0uRMin0Jc9NjGwsFlNAs1AoEA6H68C2btydTifz8/N1RUm8Xi9gMvAN4QZO+vpJ7P7z3aRcKY79/rFYc1aVfnH4mMO89M6XlHE9esVRLC4LK36wQjHA0WiUQCBAV1cXwyuHyb+Sx5v3Eo/HiUajdaoNfdhsNpbuW2qC52JesdPC3nd1dbFkyRKVizg8PMzJJ59MLpdjenqamZkZVUlfJ9LKZbPqrhQ28Xg87NmzR+XRL47FsTje+KOpqYm+vj5lzyRSLZE6qIFisZG60kwAtthQvd/vH5KNC3iBmm+h52TL/qVHx3WwIn8Xv0auIefLcVPfTTzT8AwA8/Z5bm+7nYtiF3Hh3IX4qj6w1gN8uTar1UrFqHCL+xbWvrKWQ4cOkc1mCYVCbHVupf9wP8sKy/Bb/FTdtSiwr+zjht038I113+ClxpewVCxcc+AaLpy50Ax2WFz8dPNPqRpVNk9v5r0vvJf7l9/Pix0vct3z19E114VhGHzoyQ/xbyf8G+6Um6ufvBpnwcm7HnkXvzz9lywfW85bn3grVUuVU3adgrVkZdsr2+rmw5138+an30zFeC2HvWDn/EfOp1wts2/NPpp3N7OkaYkCj1DLrdfJDz3daqJ9om6dVC1Vot1R7DvtdSlYlUoFx4gDa7YGuJtebmL1V1Zjzdai1fl8nsHLB9lz5R51zNUPrWb9zetVgVgJxgjglVQl8Sn1IFEwGFTpcgsVm7JW5fdiw0WVJUXZxDeUyLmsa12FKgq2XC6nZOkWi1m4LZVKqXRFSXWQ44ofLXMqOc8SZBB1hRDugKqmbrfb2XrXVvwOPy++5cVaMMUCTWc1EdoXYr53njy1tmGOfgertq1i2TeXsf292xk50UxR2PDABrY8sIVgIMjR+45y/8X3k3flaRtv44I7LqDR0UjnTzu588I7Gd82DlV40+Nv4rhHj6NcLatuPOl0GovFogrPSlBR5kGqnjudThLJBC+85QVeOe21ugEGvPTJl3D+yMmql1bhdDpJpVJ0dXUxPT2tfFCZf2kPtjjemOMNAbhPPPFE1q9fr148HVjqAFLPNdIXlQAsAabyO73AiGyMxWKRoaEhuru7TaY1na6L+uqF1WTIMQWQyvEXRrT1CLaexyLfke+JU61L0OU7AkYkai/GfCGIlw1KyAW9WIWA5oUR7Ugkolpt6P8tlIPrgF7PA9Krxev5P/rzkucjxIPO6MsxwuGwim4K8Pf5fLhcLpLJpOp/KXOSy+XAZuZj68NesOOL+eqem0T8dQMkc+90OtWGZBgGDhycfvfpZINZcuS47V23ccltl9Aw28B0/zRTU1NYrVZm1syw7wP7cEVdHPM3x2Cv2mlqaqK9vZ18Ps/hw4dJJBKquJjP51NrccmSJXhnvYS/GmZweJDCbIF8Ic+hQ4c4cuQIPp+PNWvW0NraStvBNoLTQRItCXWP4YfCVCI1+b48F3kmgOrrrhNKlUoF64iVZT9ZBr+Dye9M0vTRJuxH7cwEZjj08UOMnzdem0wDdly8g0w1Q/+t/SpHSmRnetV9MYI2m02xt/Ic9WIhYujL5TJ+v1+x3IFAoM5I+/1+ZmdnSafT5HI5Ojo6FAjv6upiZmaGrn/pwhq00lhsJGVLMTY2ht/vxz/tx1LS9OZAeU+ZyGwEp91kvnO5HE1NTUwum+TJdz6J7TwbJ33mJNLTabVPSMFFfejrWV87IqVbunQpQ0ND6n6am5uVdOzAgQPs3buXqakpIpEI09PTar8QYkjWv8yLvL+LY3Esjjf+aGxspLe3V0W1Rdkl/oLYXl1lJzZebKcUcJQ9W7f1OqjVe3EvBHqyj8pv5N9lyDH0NDOxl06nE5vVRnepXiYdKAe4MHYhnYVODHvNR1lYfMswDD7T+hnu9dzLmU1ncvLQyXR1dREMBmlubmZp9rXWXq/lc+tgvzXbyof3fJgvb/gy5w6fy5kTZwIm6Dtv4DzcFTdP9z7Nn+36M8KZMJftvoxjjx5Lb6SXUsX0RcKxMFc9dRXWjBVX0UWVKkunl3LtI9fSEG2gUq75Xye+Ykqvy9VaO1e5Dz0IU6lU2LNiD4+c/wiN2xq5+pdX16kK9HmU3+rHOP2Z06mkKzxx3hMAnPxvJ9P9VDdz5bm6quETaybY9+F9FJpMgsa23Yb3U15mJmaYLE/icJgFThMfSxB9W7ROiTffaSrRSsVaa1xApSrq/rOAXyHHnU5nXRBKfFLx63Q1RFtbm6o/IjnEss7FhumtxUTNJj6upDsK2VwsFsm4Muz4ix2s3L6Svu19qmioHENfm7JmZL71QsQCNIUwkHtwOBzEOhZU6TYg05thzSNr8HzLw7OfeJZ0a5qm/U0cd/NxBLIBMuUMx99yPDaLjZa5FtbcuwarxfQfN+zdgDvn5vcX/J5L77yU9lg7JVcJa9HKmb89k8ccj7Fuah2bnt6kSAy5X11yL5F7AdnyWSgUMtWB1jBN0011l27HTr+1n9bWViXNl/dQyD3xzQcGBhSpszjeeON/PYfbMAw+//nP85GPfERFjnRjIjJuj8dTx+IuLEAC1Bmtu+66i40bN9Lb26uMXLFYZHh4mEqlwqFDh/D7/WzatAlA5cbqIF3+DrWG8xJZ1JlqnSSQDVleBv0apZCSXKNsHroMHGoMqlyXyEgkwimyGgH9wkrqIEY/hsyzFKLTwZoUmBIZsWwSIm/Vc3T0qLvck0TCdfCuGy6ZH5nTSsUsJjMwMKAAqzy/3bt3k8/nCXeFWdKyhHwur/os22w2bB4b9264l99v+z2urItP/fxTOBNOFRl0uVzMp02g5rTUwJ6+YYucSlcGKMPRUGLXA7s45phj1LUP+Ya446/uoOQrQRX8Q35O+etTcFVdBINBpqamVAuNmZkZfD4fQ0NDZLNZlSIRiURwu92Ew2GKxSJHjhwhEokQi8UUYdLS0oLlQxbGPjJWJ2uyzdpYe91anDNORaoIWSHKCilCJlU7Rbolm/PExAQHowfp9fTS2NiI1+tlx/gOUttTVIP1r7Uta2PDjzew9JGl5tyXcgxdNoR/3I/3BS+esimV9ng8KtUinTZz/aQXtRgBcUi6uswCI7Ozs1itVtUSQycphoaGaGlpUeBcpPNSsO3IkSMqj2tsbIyGhgaam5vJ5XIMGUM89e2nKFvKnH7b6dh+bsNtd6u+4dlslvn2eXZ8fQd5T94syDcS4Iy/PYPCvJkP//zzz9flcBs+g9U9qznuuOOw2+2KPRej7nQ6aWxsVHnrgUBASUO9Xq/KDy+VSiQSCYaGhpienmZoaIhkMqlyN/XolpAbi6M2/jN5UYtjcejjj5HDbbVaeetb38pf/uVfkk6nlW3TSXSxsXr9GLG9bre7rmCVnhMte5bs57oNXlgnRuytXJP+mR6s0AGiSqHxWPjY6o/xpYEv4bK4+Fr31/ht42/pKnTx88M/J0xYHVPOL/dSrVbJV/N8vuPz3NVwF2WjjK1o40MHP8Rbom/BaXMqGyQ9suU65fqEwE3akrgKLoyKUQemykaZnC2Hv+yvy3fVfShA+Yg6KJb5lvvV0wF1ab8e6ACoUuVwx2F+9rafkXeatqJvqI+rf3M1Llx18nFdFbVwro9OHOW5U58jNBdi6fNLsWJlfHyceDxOIBCguqzKs//8LIXGmhrCSBv4/85P4Fazh7PH4zFBaLfByJ0jlMOvAfy8hRM+dQJdE13qOwJWJaVS/C8pwqsD1UqlQtVdxVFxYDVqKslSqUS+mFctuyhCKV9S0Vmr1WxNatgNivYitoINj8OjyCaPx4PdYSfWF+PJjz3J2l+tZd3OdVSKFfV+FOwFHvzSg6Tb0tiyNk755ikEnwuSzZhKsGAwSLlcJh6P4/F4lO/ndDpVlxF9zeuklRQntFgsJBoT3PX5uyi7zDmzJ+1c+ulLscy8VsugzcJ9H7mPUz97Kq6si3A4TCJhBjosQQvlbJmwJ8zU1BThcJhwOMzwyDCVcAVjzlDBIZvNxtzcHPYmO2uXrsVpcSrfrLm5WaWVie8fjUZxu90qGCG+kviJNpsNi93CzrU7ue/i+zDKBtf/6/UEI8E60kzmRdIexP9+9NFH2bt37//NlrY4/l+O/4yv8r8e4V6/fr3KnxaQulCGvHCTE4dfNg952YT5NQyD8847ry4XWj7v7u6mWq3S1tYGULdxC6MmIEwWt0SfxIjp+TIyhB2U69Ej8tKkXqTdC3OhdSZyYTE1AVR6hFvuUY9gSyRYWEBdega16o8yxEA98sgjnHLKKWpj1r8PNUm7gPqF8rSFEnN5PhLllrkRZ0TydBwOh6ogLa0dWlpaeDHyIr9+96+56rmrWDG1Qs2xYRhYShaOvf9YRiIjLL9zOb41PooUFSgrWAs8ceoTlK1lzn7ubMr5GlMrUUW5BzG6stEBNNobOfnkk9WzqRpVnnvPcybYBjAg1ZVi4KoBNv1yE/F4nKamJhKJBFNTU6TTaQ4fPqycp3379qlIfTAYZG5uDrfbTXt7uzKC+XyeRCJhGrSvWbG4LZTfXwYHWKYstH6ileJgkULVlFrLWpCq4c5+J6XpEraiTcnnxbloamrCZjNz4CvTFQqdBeVsdRqdzJ09x9wtc1SWvWZg89BxRwfLHltmRgEsFUYvGmXX5bsAsBasnP6p0/EOetWzFPApbU4kUixrtFKpkEgkVI5ROp1WagbJSY9GozQ1NdUV3ItGo2p99Pb2Aqhe5c3NzdjtpspgZmYG+1E7F33uIg72HKThoQZyFrNSq9frJZ1OE2oI8dRHnjLBtjzHzhT7rthHz009VKtVmpuba4B7JfB7sH7GqtJJ7HY7wWCQeDxeJ+cUUkAcDomWC0svke/29nYcDgeTk5PMzMwwNzfH8PAwo6OjpFKpOgd5cSyOxfHGHg6Hg/7+fiWflSie7Hs6uBG7k0wmSafTNDU1vU69JyA2l8sRjUYZGxvDarXS3d1NQ0NDXUVwsdNiQ3RiXPwO/d+gvrCp0+kk7UvzheVfYFdgF+9a9y6+PfBtPj3+aSqWCp8Y/wRu3BiWWtBAB9tyf78K/4o7Gu5QkdeSvcRty2/j9MHT6cp21dWukevUU9nENwuUA5SqJcqV+vZnVqzYy/a6e9FBrgBMoM6nkqFL+OV8uoT5DwHnnC3HXWfdZYJtAANGO0d5/tjnOfnZk+v8nIUKQTlWNBolF8+x7vZ1pt/hsBGNRsnn8/j9flpbW8lFciz/5+Uc+sQhSk0lLAULy+5fxrqhdRjHGrWq4005nvvL5yiHaudY9utlNI43YlgM5ufnqVQqhEIh5bNG/BH8eT+2rLlmZP3JvOeb8jz1wafov6efjh0dtWi31cLkCZOMnDqCtWCl4dkGlj29jEqponzYTDHD4JmDDHxogA0/28Dy+5erdVgsFplfNc8jn30ELPDi+1/E9QMXrQ+3UswXqfRWePqvnybdahL0JXeJxz7+GKf/y+k07GhQRLxE4J1Op2pNKmtanqHcl0jUZW2K3Q3MBTjtU6fx7CeexVKxcOwnj+XQq4fU8WwjNrZcv4W0kSZvyyuS32KxUIqVlNy7ra1Nrf8Vy1eYvl1rbd3o/rIEcmRNShV18W8lkg2oIInkoEvgQXyejbs2kqwmCb8axp1w4/A5FAHh8XiYnJwEqCPk9KDd4nhjjv9VwG2z2TjxxBPZtm2bWiz6CyWLR49cSxELHfgKmJQNWIC2gGx5AcRoCAsIqM1CZ0R1ZlScaZfLpV58YUr1HGohB4Qg0POjFx5bNgpheEV2prcL0H+vH1dnd3V5ltVqVSBmoRGAWg6Y3JNcwxlnnKFYUvntwuj2H5LHCwEgUWJ9Y9QZfrlv+a0Ue5BnKfk51WqVREeCHVfuIL00zY/bf8zl913OuiPr1HxKNdWOb3RgeOujCKlsit+f/HueONGUcVVtVdrm2jhuz3EqiqxvzgL0S6USra2taqPXQaPNZuNdv3oXt198O4dXHIYqbHhwA23faSNOXN2Dw2Fuhg0NDXR0dNDS0sLs7Cyzs7PMzMyQSqVUazGpet/S0kJ7eztut5tDhw4xOTlpyqhucFJKl6i+u4r9L+3E742Tc+bqohfV6mttJza6GPzkIOln0iz/3nIlJRKDJQVSpOiJ9KCUKudHdx+l8c8bcV7nZOasGZpub8L9ZTej4VF8Ph/jF46z/137a2vIWeaZTzzDCd8/gcDhALFYjHA4rJ6PgHtpHWKxWAiFQqTT6bqq9dlsVjkuVqtVVX2fn59X70E4HFbFQWZnZ9U7K1XFnU4n27dvx+UylQYchGW7l1Gy1iRukss2MzPDhhs2MPDpASa3TJpVc2/t4bj7jyPfkFfEBwBbgH+D6pIqh796mIbvNNBzuEdF76XtSi6XI5FIqCiUSOv1VmSFQoFMJqN6b0q+v9/vZ9WqVaxZs4ZkMsnU1BTRaJRnnnlmsS3Y4lgcfwLDbrfT2dmp3nWd4NZl3zrxKMUuhYgTeyw2PJfLMTo6yoEDBzh69CgWi4Xu7m5WrFhBT08PwWCwjjTWU7x0IKifW49Iy3dSnhRf6/saz4aeBWDSOcnfLv1bbhy7kRvHb6xTGOrRd9m/i8Uih5OHear5qfqCo8DVk1fTlmyjYqm87hrFp1gYDNAj1HKNenuphcBY9lL594XKQv17OpiGWn0Z+Z0MpZzM2bjsJ5fxu0t+x9TKKYyKwVlPncWbdryJqqW+R/ofAjpCIGez2ToVg9SiEdXV3Nwc+XKewK8DzF8+T/NtzTT9rIl8uKYei0QiRLoipP3punlOrEmQeyCHLW9TAFOeT7orza537yJ4JMi6n67DVjG/k0gkzEKijVkOX3uY2VWzRJZH2PyNzfjv9uNyuYhcHGHn+3eq4mATx01g99npvrdbgfmD5x/kyPuOAPDK1a9Qtpbx3OQhnUoTPznO5Mcmax1FDHj6vU/jP+jH+VMnlcYKcVu8fs0YMLpmlMYXGxWxnc/nVZoV1PwZWT86wJYe2fJcheiqVqsER4IsuXMJ3pSXpfalrDh2hQpKyXFkLUirV7HbItuW4q2Su77weevnE9/R5/OpNSJpn/I9CWxJgEICPoJp9PW75dkt5m/tVVUjQhRzEvBwuVxqDxIif3G8ccf/KuBuaGjg7W9/++s2yYUvjh7t1plaMSgydJZXjxbpQAVq+S167rew0VDLGZG/Sx6KGFZ52fU8poUbvnxHzifgWTcAYhT1/3TWWzZSYYb14mZyHh0Q60ZI/l0MnJ4zKueSfFQZAvhlyDzIxqQzy7qkR58vMTDT09NEo1FWrlxZxwRLvk17ezvxeFwV5So1lbjr7LuYaTbbfORcOX539u+w2qysObRGzZ1EShduYveefi/PHPOMuvYntzyJo+Ag58xx+kunK+mOLvOT579v6T4cKQfLZ5crZ0Ykvt6sl4sfuJi7jLtYOrGUY54/huI6Uw49ODhINBpVa3nw0kHaHmhT1bmDwSC9vb0MDQ0pgzc3N6eAr8xnR0eHarlx+PBh+BJkn8hif8Ze18d8dnZWOV1RR5SZ982Q7k7DO8DwGnT9Q5ciP3SyJhAIKEAp5FM8Hjerrx4O0PK1FtyPu3E968LqrhFGxuzrFTLZ1iwvvOcFNn59I8ndSdVbe/jcYbr2dtHqaFUEkrwvhUKBdDqNy+Wivb1dqUakQrk4oLIWhBWGWs96qY46Pz9PS0uLigrL98S5E5ZcKqFbLGa/c0/Fw5Z/28IuYxfOV52suWMNWXuWWCyGz+czC5itAn4IbDTvNdeUY9f7dmH7ng1jwGBsbKyO4FtYp0BPGZGCMLJ35PN59T77/X71fvf397Ny5UpyuRwHDhxYBNyLY3H8CYzOzk6ampoUCNCrO8ufsv/JdyQlCOprsRiGQTKZZHh4mJGREfL5PIFAgFwux+HDhxkaGmLVqlVs3bqV5ubmOnAhvgxQB7jlHPL/QgzjgE+v/DTbQ9vr7sdVdeHF+7rUsEKhwPz8POl02izGWSwSj8c5Mn+ERCABHbVjbI5vZnNsc10QQI9sQ80Xk2sSYL1Q4i02T/wYXVUn97aQcNCBj0TPFz4D+TfdX5NhGAbpdBr7tJ1zbz+Xh9/1MNsObOPYncfW+XbyfOUYMioVs7K3EOJCelcqFUUUS67urHOW0X8YBSv4/sVH+WdlDpUO0dPTQ1tbG/Pz88zMzOCMOHG+zUmu3exc0rKjhTXfXIMr7cLqrPXOzufz5EI5nr72aeZWzDG9Ypo0aUJ/HQJes/s+GL9xnPRmM8JctVV5+T0vE5wMYg/Ymbtmrq79JoYJqvO2PP339LP/7fsZfNtg3brZe/leXJMuLH9vId+Qp1J4fYQ1NZgiF89h/MKAMeBOwP3amrl/M/2/7cflc9UpKETlmkql1HrUaxTJOpBIsa5gEMXJfMs842eOYyvb6J/sx5V1KXwh3VNkLcn7IYBaQL60QZOIt77GJKfd6XQSi8WUvRcfN5vN4vf7lfRc/G/xZZLJJOVyWUXAZQ1bLBay2axZRC2RYLY4y/S505zwxAnKb3e5XIrEkzai0Wh0EXC/wcf/KuBeu3Ytxx13nNrM9Lxj2cj0wlz65qk7sALEdZAK1G32ApTlGLLgdaZZj4zp+eRS1EQArLwgIvkWgCEGVN/Y9Ui8vinoUjM5htyLfr/yn2wyIvGSSKV+PmHO5IUU5lg3VLrhkPnWJV66dAdquWDCzOnPZyGTLIDdZrPR3NxMMBhU/y4VMd1ut8qPF2Abj8dxzjvZvH8zg42DZm/sKrRH2lk+tlw5KT6fT0UYJTdGNunjtx/PCxteoGivyfwLjgIPnPgAtqKN43cfT7lSxnAYlNO1iq+DrYPcfvHtGGWDq79/NY2xRtxuN5VKRV1rKB7ibXe+DVvehsVqoaG1gZaWFkKhEAcOHCCTy7Dv9H0cvOwgh049xOkfPZ2gNagY04aGBjKZDKOjo5TLZQKBADMzMyQSCfx+Px0dHQSDQYrFIl1Lu5j5/Ayui1yKCJI5EgbV7XUzd+ccpZU1qfv0m6cppAt0faFLqQbk9zabjbLNlN3rhENLSwvpdJpEIoHzSSe5fE5FaN1uN8G7g6zMreTQDYdqfUIr4H/JT2lXiY6ODtKZNBOnTrD33Xs5lD7E2//h7VQSZn5ea2srqVSK2dlZmpqalNRaDFQoFFLqA2HqJXdbjFuxWCSZTCqJvMjq/X6/WldSRbVcLjM/P08sFiMQCNDY2EgsFlPye8uchc3f2Iwla6FcLNPR26H6rHd2djLx6gTlu8uwHvN+K1B8vMjYXWMEe4LKuOqRKdlj9HmVyuRCDsk+Ic9eIvdi4HXQvjgWx+J4448tW7aotDaRgktgoFKpkEqlyOVyqiK0OPC6QkzspxCcqVSKRCKhiGWPx6M6NnR1ddHY2FgnZ9aj2QuVZ3oes6jWypYyf7HmL3jV/2rtRqrQm+3lnw79E7aojYnyBMPDw6RSKZLJpCrypBeDK5VK+Kw+rtx+JXe23cmunl30Zfv4/OHPEyqGqFpqZL8e1a5UKpQoYTEsGFVD7e9yXPEv9HPpcnbxS3TwroNnPSdWD1ToyjsdJOvgXcBONps17UGqk2t+dw3enJdK2Uwv04MiOklQpcrhvsNM+afovadXpfXJsScmJjAMA5/PR6FQIGfNceTHRyh2mr5K6aMlTtx3Io6cQ62lYDBIc2szgxcOkjimVkTVO+klnA1TtdZS5RwOB1jhsU8/RqLrte8aMH7WOFMfmcL64deiw5Yqla9X4CeA3Xz2lcMV5n86j2E1qF5ehX5qEegquOIu+nf043A46Hmwh8HzBykEa3nnJCH39RxG1qD6dBXLmyxUdlfABZTAfbmbwj0FMF6rR/SYBcebHeQfzLPmkTWsv3M9ifkEecP0D/1+P6lUSpHUxWKRUChUl4KgB7Mk0ivkt/ibJX+JZ//hWXKNJhi9+xN3885/fieWXH1xQvlzamqK2dlZ/H4/iURC+cyigiwUCjQ2NhIKhVQdp3A4zNDQkKoc3tjYSE9Pj6qVJASMrgbN5/Mqoq0XuJW1KKS9gHab18YDH3+ATEsGJ06Ofe5YrNVadyPBPOIbL1S2Lo431vhfK5pmsVh46KGHOOmkkzAMg/3797N8+XL1mbBPAn71yGy1apa/l/wF2YT1DVXyQxdKkmSDF4AtL50wkfqf4sTL3+X7Ig+WzVG/NomcCtDR87x1YkA3uFADtrJZL4w0y/cLhYICYVK0SmfY9b7eUJOwAwrULIzGy4YgwF7Ij4WR9D8EuPX/12Vh+m/l3PI8ZSMaHh5WObAej4fYfIw7Vt7BjvN20Dfdx7W3XItRrZEFEnXeuXMnB6oHWO9YT2NjI83NzVitVmJNMX545Q+JB2q9pwE8MQ/X3HYN+OG+k+7jqruuIpgNMtYxxk1X3GQCfMBWtPG+772PrlTX6+ZYL26hA+EyZR7reYxHr3zUZIdfK6x24udOpLnQrJ6txWKhtbWVkZERjh49isPh4ODBg+RyOfr7+01AGsyz+9O7mV8/T/BokDV/s4aBJwcU+JQK2Ha7nUJHgehvo1R7THLC+byTzqs78bq9dSRRqVRi1DLK7K9maXl3C+1JM5c4kUiowh/ZbLauT7bO+FqsFqJvjXLkA0cou8qE7gux7B+W4ff5cbqcTJ4wySuffkXdu3fOy1mfPQvvnFf1xbRaraqlhxRZE8BttVqZn59X8i1pI5ZMJnE6nUxNTam2J+3t7apaqtVqJR6Pq96WPp+P0dFRpQTYsmUL09PTZlu2aJRIJKIAu55aIPn7kUiEl19+2XQ2vgx8ELgLuMK8L8MwUxCWLl3K6tWr6ejooLu7W51b5nr58uXKYOttguT+fT6fchxFQl+tVpmfn+fmm29mamrqP9w7/780qotF0xbHf3H8d/sqf+D4fOlLX2L9+vV1smmJblostSKn4oc0NJg5qn9IuSd2d25ujsHBQebm5lSQoLm5mZ6eHlpaWtQeoqfD6Yo6Oa5eUBZQRaW+vfTb/KbtN5QsJXUvwfEgH/jBB6hka6ojSdMRdZQUdUx702SiGVITKTo6Oli9ejX+oJ9PLvsknzvyOYxizVcQRZX4DNVqlYw1w/eXfZ9l6WW8efzNlPP1cnfxGyQAIPckvp0OtMQGQy3lUHy3hSoD3bfSfR6d6CyXy0y6JikdLWHDRlNTkyJEZYhvIz6jXMuRJUf4yTt/QpUqx/7bsSx7chlG2dy24vE4kUgEi8VCY2MjVquVJ//qSWKnxOqAbfvL7Rz/j8crW9/Y2Ei0P8qzH3uWXDinrsEZd3LaD08jtCOknq203Eo1pXjsxscothehCjwOlnMsWKj5T6VSiep1VUr/UsIYNHC9yYWlYvqtbb1tzD04Rzacxaga2BN2TvzzEwnagirPmEa489N3kmvP4Rh34DrdhTvpxmJYFGFe7a8S/U6U3p/04r7f/EwUd+l0GrvDztJlS7Hb7JSLZWWL9ci1RHjlnZI0s0KhQCwWU/6CHv2WwJXdbud3N/2OTEOmbo79R/2c8uFTSCaTyoeQCLG8I7oCxe/3q24r8i7owTgpECuycZGd6xXZ/5D83O+v9WOX38n8CMDPZrMkvAluu+Y2pjunzfuowqbvb+KckXOIzkRV0EHe2zvuuIPp6en/yla2OP4bx3/GV/lfi3CvWbOGbdu2KQMRiUTo7+9XL46wd8I06fIoWex6XrcOriVyqkudZcPVZT664ZKXQSoRSx4z1PKUhUXTc2bk+Lq8XVjZbDbL7OwsoVBIXa9uIHWpu56/JPco+Sm6NFwiswKy9QiZEBWyCchLrF+bbAJiOEZHR2lublbMmhhI/XtynzqTrj8PuYeFki/5u27oZF4TiYRyOCRqaGCw6YFN2B12Lj16KUWjSLFUVNKzfD6Py+UiuiXKoT87xPK7llPaXmJycpKGhgYao4286553sX3tdvat2EfGk8E76+WEn5xApCXC7976O4r2Ine8+Q4ufehSdq7ZSVXzzcrWMoc2H2LJc0vq8uxlvQgwFGekUqlQcpSYOGGiLm8pG8oyvXqa0IshlR9fqVQYGBigWq2q3s379+/H7/czPj5OtiHL9J9PM79hHoB4X5y9f7OXJeUl8JqKa3R0VBXaqB6p4rnGQ+7bOexjdkLvC1EsFUlX0oqsMgwD17Eu8p/IU+2rEr0lSvgzYRqGGhQpJYqKTCaD1+tVjo6sSarQ80APFXuFSGOEpV9ZSgVzHbd3t3PgzQfq7j3dlOaJjzzB6T85ncpARRkteZ/T6TTlcplwOIzD4aChoUGpVRwOB6FQSPXozOVyuN1uWlpaiMfjqhq6FE9TKQxOC4dXHyY0G1IkQrVaJZlMKnm5VEifmpoiEAjg9/upVqu43W4l/WpubjYjTJ+qYpQNbJ+1YXgNNefZbJb9+/czMDBAd3c3a9eupb29Xa1ryacS0ksnuPR+45KrJrlhhmEwPDz8B1uTLY7FsTjeWENAsEg5AaUs0+21kLWSQgTmniz/L1Fdse2tra34/X4ikQhDQ0PYbDZ6enpobW1VdlOXQYs9lr1Qj3zreapiXz8y+hGwwC9bfwkGdA92c9aPzqJcMFVH0gUiEAjU2W2bzcasY5ZfHf8r7FN2rvVdy4qOFQQCAQwMPn/g84qoh9f7AuVymaK9yI+W/oi7Ou8CoFgtcuHQhXW+nfhzOmiWsTB6D6iI9sJouvgsuvLvD82N7sNMtE3wmwt+w6rHV/GmV9+kfL2FwFyPphuGwd7le7nlkluUH/HCe1+gWCnSc1+PIlzy+TzNzc3YbGZLreb3N5P/lzyZC80c5Z5dPZx000nkMNeTECueOQ8NAw1MbDP7erviLo758TH4n/eTLWSVvyo+c/FwkfaPtDP22TE8Ex54B1g8FjweDzabDa/XSyaToXpvlUpvBdutNrxdXkXUNIYaWf0Pq5ncNImlZMG7y0slWSHtSCubVRovsfYTazl80WE6bu3AH/Jja7KpgFOpVKIQL9B1VRfNzc14V3jVM7Xb7czMzJgR4lIVw2oo+7gwGCT+uKg2xU6KwlEk2uKLS4Vy8cu6X+zm4DkHawvIgEq4gu10Gyv2r1D+nawN8dllTUhXEnkXBJzrfruezma1WhUhICoVIdpDoRClUon5+Xnls+vtdwuFgvK9pF5UtVpluGeYZDBZIw0MSJ+dxnWHi6Xeperdd7vdzMzMMDs7+1/axxbHH3/8rwHuz3zmM3Vy7lNPPVVtnvJy6lHV6ms5EcISC4BcWBFcwJkcV/6uy9GBOjCpb8KgMYHVWjsxAV+ysSxkSvXz6lFtYf3EiMiLLdegA1QBdlLxEWqRbz2qqkusJBq+UKKqGwmRGAsQ041ZJpOpy+mWTVM/psybHhVfWOREhi6PgVq+t1yP3GMkElGbjzxbp9Psm9z7y14qm8xrVFUlXyMwDvQdYOeZOykECzx4+YOc5T6L5S8sV1HMJSzh0qFLaW9r5/HLH+cdj7yD8aZx7r74biU339u3l8I5BYK54OvWZaFo5qxJ70mZM4vFgt/vB1Bsr8ViwVax8fbfv537zruPnUt3Yi1bufyhy8k/n8fpNVtHTU5O1j2LlpYW5ubmWLNmDU6nk2g0yqHhQ+Tj9YDL6/XS3d1NV2sXk5OTGIbB5OSkip7wMtj/0g5HITGdUPJDFTXoNzjyl0dI9pl5wcXOIkN/N4T9n+zkHzcl56FQSIHB2dlZAoGASh8QyXY+n8f/Yz/5SJ5yuKzyrNPzadbetJbIxRHSb0+r665WTANoFAxaWlpIJpOMHjOK7aiN0ERIGRe9RoFEhaSyuMjE3G63kjWKEdPlg7Ozsxz96FFGThzhOM9xtD7aitvtVtVbPR4PPp+PtrY2AgGz0JvL5VLSwcbGRtLpNB0dHXi9XiKRCIlEAuvNVkLrzYJvqVSKTCajcvNzuRxHjhxhdHSUlpYWAoEAra2tbN26lWAwqOZHAL3sY3raiDDlct+ZTKZu31gci2NxvDHHunXrVFRKoq7ZbJZUKkVLSwsul4uGhoY65ZjYel31pUdMZa/weDwEg0FaW1splUpqr1yoFJP/F7CiK/30Iq4VZ4VfdP6Ca6avwTAMPjz2YSqJCs/Zn+PS+y+lI9hRRw6I7Fmix1arlWQ1yU/P+ikHlx6EZdDU18Tnxj5XB5DF5sv/iz8h1/WVlV/h/rb71bV/d8V3SVfTvG3wbXUKRJkPvUaO3L/s/fqcyXlkPuU4+jF1CbpOUsj5Jhsn+e35v2WmZYbIZREcjQ7OeOEMda26vyTHVaTAH8gEEkVAPB5nfn6+7tzpdJp0Ik3HP3RQ9pSxuq1su2UblrJFRaorlQoFZ4EXP/AiUxtMxZMlb2Hrd7fS80oPVXtVqSj14FQ+n6fyfIWGv2mgNd1KLmRKn30+H16vV63HcDiM60EXzpZaelQ2mzX7Q8dcLHtimVpfRbdpkyQNqlKpUB4u0/xsMw6vg1w1pyKzYtOlSGi1WjVro1Crt2QYBmV/mcNvPszqh1bX9QSXVEP9+YniU3CAXnBQxwqiSBX/e8stW3CX3bx8/st1z6ZaqdalsMl1Kmn+a0MCa/J+6+kJ+vVJEWBRvgqpIv6y1K6Zm5tTvr+oUV0ul9nKdn5eHVtPUdtyZAu2X9u4+5q7KdvL9B/s5y0PvgVbxkaZmupDT0NdHG/s8b8CuDdu3MimTZuUUZCNT5xqAZUCEmWDFSAqL5V8JvnBUCuSoTOaC1lPnVUTIK1vwAKywZS26BFDXQIlEWX9GvXzGoZBY2NjXURbpMnyG/3FFSAuhkV6j+uMnpxX8lZ1IyP3JC+tDrKF5da/m8/nWbbM3FxlDvVcdQEEiUSCaDTK0qVL1X3p7bXk+gWsy+/kMyW/1vKeRDIcCARUbos4G6lUilQmhcflUa0Q0uk0U71T/Obc35D0mgAy48nw0IUP4Sl66Hm1h1gsxt69e+no6OBEy4ms+tUq+kp99Ln62JPfw2h1VLGFh5ceft26tJatbDm0Ba/XW7f5yYaukwMigzcMg0A6wKUPX0ri3ARnvXgWy8eWM9U3pSqVZ7NZM1+5mOf5P3+eLd/ZgrPspLW1FZvNxubNmzGyBi9ve7nueqLdUaKNUdpGzRZ2kiNks9mYmppicHCQ5FNJZZDEWIvEqWAtkFxZX4Qr15tjNjCLI2cWAenp6aFSqajCYZWK2Sfd4XAoObdhGHXFOBwOB/F4nP379+NwOFgyvISx0Bjxs+IYowaWd1iIuqJK6j+ydISXrnsJI2tw0T9ehKtkFvyYmZmpaxHX2NioelRKLrfdbicajRIMBlVutwDxqlHl0IcPMXruKFVblZ3X7GTJ4BJCkyEFhGX9WixmdwNdMSMGVp51KBRS77NUrhd5WGtrKz09PczNzTE1NaVkXBMTE4yPj3P48GFeeeUVvF4vbW1tnHHGGbS1tanCbRJpF8mkvLu6nHQxwr04Fscbf2zatEnZOHl/i8Ui4XBY7VliC6HWrUPkpgtJa7HNoiqzWq1KdrqQPIcaMNV9GEABLxXZtVv5i7V/wUHvQWzYeOfMO0nGkhy36zj6Lf2029opuAqqkKf4J7rSDeC7b/kuRzuPqr8/1PYQFquFTx34lNofBShBfVFXMEHTqUOn8kDrAyoS7Cg7OGnqpDpVon5/4vfodk2/d5k/GXLNMs+63yf+1x8qnpbz5PjpZT8lGjaLn1ZsFZ485UmcOHnTzjfVqfhkvvUAz5oja7jqV1fx4//zYwC2/XAbPY/0qK4VVqvZPUbSqwSALwktof3OdgyLgSvvImvJqvTETDbD/q/tZ37VfO3c9grjJ46zZM8SFbgwjFpL1Xw+T65gRsgbDjXgb/BjuE3AJ/MnYFjv6CNzKsBNorK6wkzmVfweqbYukeVMJqP8at23FjJA1nG5XCZbyDL4i0HyS/I4DSfLHl6GAzOaHAqFmJ+fV2144/G4yqOWeRefWN4dqZ0gPoOQEPaqnb5f9ZFIJxh82yCWooWzvngW4ekwZaMGtnWgKspWnQhzOp2q/opgkkwmo+Y0m80qnzkSiSi/I5vNqtRTAL/fr5QIEhgB6joqyXXIvFerVXr39HLWl89i91t386afvwmv3YvdYVfvu9PpJJ/P88orr7xun1ocb7zxRwfcVquVK6+8UlXblBdUFp28bD6fr46B0p1kiQ5JFEmAurwEskHo4FBAKtSiyVLQQJgn2ZxlM5NNW5euiPGU/5eXTTY9kY/q1yDAvFQqEQgE6gCvHpWW38g161XJoRbNl3vWC4gIoNXnS4CiGBk5lw6KdaWAbKA6oyibm0ji5f51Gb5ct04AyHXohlcUArrUTa5Fimd1d3fz3NhzfON93+ADt34Aa0YzuAaULfVFISqWCljNjau7u5t4PE4ikTClW8Vm8oE8vqKPj/z6I3ztyq8x0Tzx767N997zXjqSHVhcFpVrLEZanqm0+xBWVDZ+e8zOe297Lw6rg3wxT0dHB83NzVSrVQYGBsg5cuz9s71MbJtgfvU8p99wOqVRc17Hx8dZaazE8ZiDnW/dSclhOhKeJz1M3D5B05Im4vE4S5YsobGxUeW+NzY2KsY3FouRzWYVMVCpVHA946Lxxkain4xS9VQhD60/a6Xl4RbKfrN9ibCshmEWd4vFYqRSKfWnw+GgqakJt9utDCvUGHuJEM9fP0/mOxnCfx7GVXIxY5uhWCySWJ1g++e2U7VVIQR3/ctdvOOf3oErYq4VAfL2JjtTqSnlEOiyK3n3YrGYIkNmZ2eZvmSayXMmzWMDWW+WwU8McvzHj1fycSliMjMzQ2dnJy6XC6/XW6dWaWpqMquazs/j9/tZvnw5c3NzSo1QKBTo7OwkmUzS0NDAqlWrsNlsHDx4kOnpacX+z83NMTc3x8jICDt37sTj8dDV1cWqVatUnr5E7mUORW6/GOFeHIvjjT+CwSBLliwB6ttxCcBKJpOqpoNOvAOKbJM9bWHXkYWqO11ZBrV2pQJCdeWYXIeS4XpyfHr1p9nt3w0G/Gv3v5KL5eg/2E85V2ZJ0xJKlJS/JSBAB77io1x7z7Xc9O6biDjNehc9mR4+uvejFMqFOtCmR9oWRpW3pLfwTzv/ic9v/Dz2sp2vPf01GnINqn2YPk969wcBl/o16VFuOb4cQ/5fP6b4f7oUXSn40g7Ov/N8bn37reS9eYyKQf+Rfk7adVKdrVsYaNGfS2hXiDMmzmDSO0nbQ2ZLtKmpKZX6JMRuIpFQNT/a2toIVoKkEilSBbNAndjXSqVCwaoVJnttFAyzzaSeQyxrYj48z6tffZXWK1vx41dBJ/FLpdWYBGD0aO5CHxJQvmwsFlPEh4BHQKkTxfctFArKv5bnJAEWRXqEywx+eZD0arPN2YvXvIi9aKf36V4cVociezKZDOl0Wr1L4usL0NWVsV6vV/nvAvLFZy2kCyy/eTkFa4GeO3uYG5wjakSVdLuxsVH5b9lsltbWViKRCOFwmGg0qj6XiP3g4KA6j9frZXx8nK6uLqLRqIpwi08rudqixHM4HCqNTZ6DPBPJKZf9QIqyCQHXdaSL0D+GMDwG6VBazb28d/l8nmeeqXXoWRxv3PFHB9zLly/n+OOPV0yS3jfaZrMxOTnJ0NAQZ599NlB7sQX0QK36uM706TkkMnTplh79k4UtG7HIZqWwkkTdSqWSKnIkYFYAsb7ZyEurR3vFoIpREAZYfqtviDorLUMYPV0Crxsl2XD0Qin6vSrJ82vAfWEetm585HxAXasEMEFyKBSqi04vlKvr1yrGTAyeyI10Jr5aNQtSSDsHna08GjrK49c+Trw5zk/e+hP+z/3/h7ZYGw6Hg76ZPi679zLuPPdOkv4k7oybs35/Fv0H+ylSY3wBJSeWayjlSnz41g/zqzN+xXTTNFNNry9O9YOLf8D77nsfyw4vU+x0LpdT1Wj1qLbcs1w3gN2wY1gNpUYQyX/fpj5u33Y7B7eZOUWZlgxP/tWTbP76ZjpLnUod0Huol6OzR5npNFujxS6METsSI/+vebx4FTCTXOAlS5YoIufAgQNKviUbdblYxv1DN27cZD6ewfY9G9UvV7F0WMiXTIJD7xUKJhMrPaflvdCZfSkuIoVGpIq4z+Wj+l6zcmc6nSaXy5FMJtl7wl4FiAEKrgIvb3mZbfdtM9n8TAZXu4s91+whG8/S8mCLWuPRaFTlRFqtVlKplClv9/uZn5+n855OkkaS/Vfsp+KsEJgOsPqLq/HMeig4CqpliDgzlUqFxsZG1cdbnI5QKKTAr2EYqiq6vKMej4e+vj4mJiYUiWW321mzZg1LlixRsnMhPUSCnkwm2b9/PwcOHMDhcNDa2qp+EwqFlNRPrx6/OBbH4njjjiVLlqjosx5JdTqd6j0WQKTbPD2apgNnPZIsdlDsh4BY8X/ENkM9KBVbI8e02Wy81PASw55hpeiqGBXub7iflkoLXYEuVXxKAIXuW0j0UwjHtrY2vrb/a9y48kY8BQ+ffeWz2Co2DEst0ivXKdeq+2RCJmxLbOMv9v0Fbck2WsutdeS5+Cbyez1aLvOngzedxLBYLMqPWAiq5XtyXzoINAyDYqFI48uNnGM5h8cve5xlI8t4x13vMM9hrc/51pUEOhivlCt07+umMdNIxWaef3x8nKmpKcLhMMFgkHw+r1qDtre3K/AFqFRK8V3y+Tzr/3w9h750iNgG06b3PNfDcTcdR5mykv/LOoksjbDzYzspNheJ/yhO91e7sQ7VgjF6wMnr9Sq7pkumJRotEXG9zZ0EGwQc6sAdampN+X8BjLoSslQqET0+Sr4rr9Zk1Vpl4JQBmp9pppguqjkRvzYYDJLNZolGo1SrVRoaGtTzkPvRFYgSoRa7brfbmZ+fp+vpLlpppXFVo+qDLtFnWRPyTgYCAQBVpFACfPKO64E9n8+nah/I7/1+P+l0WlU7P7ziMCsHVjI0NERnZydtbW0mzvFP4ig76Kh0YLVamZ6eNqP5bhcja0bo2tOF1WqlsbHRTIEbypJaksJj9ZBJZ5Tf5nA4VPrc4njjjz8q4DYMg61bt7Jp0yYlAdEjyTabjdWrV7N8+fLXATfZJHXQqBdW00Gw/j29gBGgNqmFBcHkJdcjtgsZO8kxkQIm8p9uBKQokjjsYij0XHQ9yiu/l+vQDbC+qQv40/OwxTjL34VNk8igfm2yucomKMfVW5Lpclv5nVyPfv26XEs3rMLK6dF5fS504xwIBIjH47hcLsXUDjcNc+859xJvMquMj7WOcdt5t/G2e99Ga7yVcrnM2oG1zEfnefiKhzn/ofPZtH8TVqe1Th3g9/sVS+rxeBRzHHaGufb31zLZM8nPzvgZE8H6aHfZWuYnZ/6Ey+2Xs27fOhXtl/xxqFXNl/sQgyRzLP8u0qtyuQw2KPsWtGtwgbfFS2WsogqBxCtxCpZ6ZrulvwWby0YhUeDgwYNEo1F8Pp/678j6IwQGA3R1deH1elVqQCQSIZ/PmyD1C3mMEYPKzyqk3WlKpRLLli0jlUrhcrkUsNaZfyEcAoEAhmEQi8Xw+/3MzMxQLpcJhUJ1nQAikQjJZJKOjg5V2bNUKtF6QyuxfIzEO8x2JcF/CeK9x0ukJWIWbXNZee6y55g61iRADI/Btu9tI+ANEI1GcTgcimGWwinVapW2tjbS6TRLbl2CrWDj0MWHOP7fjqdhsoGkJala/pXLZquwvr4+5ubmFEE3NzdHZ2enUle0tLQwPz9PtVpV1cVXr15tAvvXotsit3O73SQSCaUCsFqtdHd309fXRy6XY25uThnB2dlZEokEhUKBkZERRkdHcTrNdILe3l5Wr15Ne3s7iUSCxbE4Fscbe/T19REKhersGdRyPnWALLZRJ8kXpnXJb8Uf0X2YhVJr3Z4vBJNQX8/m7PmzsQ/buXHpjWStWZaNLOPyhy+nw9FhKo9eK+zk8/ledz3io4TDYbq6uszCT6kQHz/wcbxZL968Fyz1LVt1ckGfm4Ug/NTZU007bTHq7kn3AxfOix65lu/oIFv3XRbOtR6Akc91okRI1mMGjyH4+yCrR1fXkQc66aGTAXK9yWSSaDSqyHW5H7vdTlNTE36/XxHUUgy0ubmZSsVM3Zqfn1d+pajTAOwZO8fcdAz7PrwP54yTLT/fonxm8RMLhQKRFRF2XL+DbLPpnyQ3JDn08UP0f6Ef57xTybAFlOq+8ULCSHKw9cCUFNOV78o1SkcRWQP6fwsVAQLqg3cGqeaqDP/DMFVnlc5XOjnp5ydBHgxrrTWwz+dT99na2qr+LsEFkY3rqlBJCxXfU4JMiU0JXvngK8wcnuG0W04DUEETuVYhCWTNydqS4IGQGwJwxacVn1t8JwHBDQ0NOJ1OXjz2RR45/RHyv8/TX+hX8vRYIMYd592BJW/hnQ++E7vNrtbEMyc+wxMnPMG53nPZunereoeMNQYPXv4g4XyYd9z7DgrZAoFAALfbzVNPPfV/s5Utjv+F8UcF3E1NTVxyySXqpdYjolBb7NLUXTcoAoQBtbnrAFF/AXSWTI8cyfH0TctqtSomq7u7m0KhoCKvsmHrMhapIChGQl54+RNq1Qv1axIgrhtcqBkBYduUfJp6wyX/LsycGGg5lsyJzJH+mc4Yy6YkwFhn4PW5lWNI1XOR9erXo+eU68XF5BokCqAXizIMM69Iov5yjzabjbZCGy3zLYw1jqk2CG3TbfjSPnU9TqeTZfuWMfiBQdob2qk01wgLObcYEM8yDw+sfIA3P/lmRS6Uy2W6xru45s5r+O4l3+XsF8/mN6f/Rp3PW/DSG+nl4eMeZt3edXRkOtRz1tMSxGAJ6aGnKyzsa27P2rn40YspWArsXbkXf9rPxT+7mMlnJyl1lFSuffBAkJO/ejIPfuZBCr4CS7YvYcOtG3CucdLV1cWjjz5KOp2mWq2SSCRIHp/kwEcOYJ23cspfn0KYsKpIKi2zxJA4f+2kaJiFRYaHhxkfH8dutyvZm0jLy+WykpLLe2KzmW1SZL2INE4i/j6fT71rMzMzdcoOV9lF85easbvsuA65cPzEQcwSY2Zmhmq1SvynceZOrOWHHz7xMCVbiTf/9M3qfTMMg0rVbFujKolXq7S0tJjv1H0WrE9b8af9zKfNgnc6KSTSeyF34vG4cigSCbPYXCKRULlr8hyFFPL7/eodEhJF0l6kArHI4MXwyvx1dHSQTqeZnJxkbm4Om81GJpNhbGyMiYkJdu/eTTAYJJmsz7VfHItjcbyxhtvtpq+vT0nGxabpBaxE/bIQcOvAQ/YJXRGXyWQUsaj7NwtBpg4E9airnEuvvnzG/BkYuwy+3vN1rnzwSror3VSoKIAYCASUX6JLw91uN52dnap7CZh+QX+83/SF7PUEvdzTHyIE5O+6LyaBEN3HW6jc0+9ViG2oRdJ1Gb5EaOX6F/pYOkCWcxmGQSabIRqNKkC69vBaEzRaa4rIhX6UukfMvycSCUWQiI8gCqmWlhbVdmp6eppKpUJ7e7sqqCdqKPEbJRgk9+mf87P525upJCpU8hVy1pwiwiUd0jHqwDvtJdmVVD5Mw1ADtoTp2judTlUwTdRimUxGRYdlfgXU6m0rdf9XAlbpdBqLxaLSsuS+VUsw7dnpqZjyvfCDYTwlD5E/i3DKz07BE/WQsWSULF38D5fLRTweVz6ivCey5gR464E0id5XKmYQI9YV46WPvkS2Nctg1yAWr4Uzbz5T+YHSu1r816amJmKxWF2aZyAQUDnVEk2X+QgEAqrukMfjUWvT4/HwxJoneHTTo+QdeZ5681OU8iW27tzKWHSMuz5wF7OtZkXxH/l/xLu/926MisFTJz3F0296mqK9yIPnPYjTcNK/q59KuML919xPtCXKLLP8wvcL3nTjm6BqkgfPPvvsf8v+tjj+58cfFXAvXbqUt7zlLUBt0wPqgJfOikm+iuT76kZGCgzJhrEwoqwXupJNUge8QB07CjWw+Yc2er2omchs9ciubPy6dF2OrUfQFxZr0MGoXJM49HJ+Oa4wqAuVAXqkGWpgWComynHEGZA8Mv2e5JnolaNlPnTZuP6MhHFdyMRLawOLxVKnBpDosESIhV2vVCpmm6i0g3c99C4y1gyHeg+xYf8Gzn/wfAKuANhrPcYNw8D6qhXrGWZekcib5Vl4PB6mq9Pc+NYbyTqyOEtOTnrmJAKOgHKMlpeW84mffoJcJscZ/jN4dNujWMtWrvzllexYsYPfH/97Ht36KH/7878lnAkrYyXzImylzIFEXwV4y/OT9RosBLn691fzI9ePuPbha3EFXRTfXCQej3PkyBFlvBrLjVirVhoHGznphydhKVpIl03JcmtrK8PDw0xOTjLTN0PlxgoVZwUa4MlvPsnWD2zFY/cQi8VYv369KuoViUSoVqsqT7hSqahiXvv27WPJkiVUq7W+k0KI6K0r5P0MBAJK/iZSbeknDaj8f8nJz2azWAtWmv++menxafLksYasijSwv9+O5TELlabXSKaIjeNuPo5ipaiiAMllSV6+6GU2f32zqgSazWaZnJwkGAySTqTJHsxS7i+rNJB4PK5adUmrm3LZ7IcrrVp8Ph+JREIViJH7bmhowO12Mzo6qtaxVBuVd6KpqQmPx8Pc3ByJRIJsNquiQcJ6z87OsmLFCgYGBli5ciWxWExVmZf3Ip1OL+ZvL47F8ScwGhoaaGhoMHsJaznYQJ39FtUYUAdedEJYAIbkp+rRO12WrkcbBbSILRcAL0Wj5Jpkz87lcnQMdXD9Y9cTcoQo2s06FblcjlAopI4lIFvuo7mtGVerCwe1axBQrivdFkrHZSwMlug+StqSxopVRSDlnBKl1CP92CBjyeDIONS8GIZBxprBUrJQzdUCLdVqlXKlTM6bw5fzmb4fVdKuNO60W12HHD9ejfPTq3/KCTefQIurhYq7Qslawlv0YrXUghmVaoWMO4Mn4yHnymEv2BlqGuKFY1/g7FvPVgR41Vklb80zl5pj9PAoBXeBdle7KtRpt9tp7Gkk3BzGbreTdCWVzZcIt6RyWSwW8oE8uVQOy5gFu9VOidLrfC2n0wkxWPcP68j+Y5bE+gTLn1rOpl9tYi47p9ILxPcClO0U9Z48X4nSik+oio+9ptoQfxpQPpxh1AoZiz+cz+dVIEZXn8r6LJfLtB9oZ+WNK3HYHWQKGXV94rcKAV52lsFj9jIPBoOq7aa8P66Qi92X76b5QDOtz7fWRbrtYTsP/+PDlF2vKQsNGNg2QHYyy6YfblLvjfiq8XhcEV56BFveaUlpE/m5zWZTLX8LhQI+n49MJkMgEGBk5QjRy6KqFk/elef5i56nI9XBk2c9yWxLrX3XaOsov/k/v2HTS5t49k3Pqk46WXeWe8++F8eQgwff8SDRlqj6zWDXINMXTWN9t1X5EIvjT2P80QC3zWbjox/9qHopBXQJ2BaDouck+3y+OkZTlxHJZqJvQAIKdbCrM5SyOejRO9nkZfPTK3vrEhz9OiTqJyBDj+TKiyrGVD//5OSkqgpdZ1ioSa/0qJ7chwB1XWK1UPKktwfTKy2KY6/ni4mxlmi9zszrjLz8TjeMC5UJcg1Qy+3WSQud0ZcNXY4pTofNZiOVSjE1NUU2m+UdP30H9154L+986p2UjFobBjm+3Nfs7CwtLS1qLQhbedR/lF9c9gvSLnMjuu+E+yjnypy5+0wsWJSUzG118/jWx3l026Pm+rGV+cYHv6HWbN6a58uXf5mrf3M1yxLL1DOVe9FTECqVimqxJgypGCIxWjbDxvvveL+5qZfMjTUUCrFq1SpGRkaY8E7wyEceIRvI4kg7SDYnsR60Mj4+rljUU045hVBDiF+86xdMOifNCzUg15Dj0DmH8NzlUc/X7/ezdOlS1fZL5j+fzytiIp/Ps3//fhoaGpSETAyb5PdlMhmamprUGpDIiLSQSSQSDA8Pq+itxWL2/tR7TxsFg4ZggyLBrFYrwWAQI2dge7uNme/OUC6Waby2kSPGEVpaWiiXy6Q3p3nkU49QsVcwrjfY+sut2OK1+ZdWgV1dXeq6/H4/09PThMNhAFpbW1WRE+mLLmSQ3+9XoDqfz9PU1KSeq4B7mU+Hw0FjYyNHjx5lbm5O9eV0OByqfVk+nycYDDI7O6sKQ3o8HpLJJMGg2YbO6/XicDiYnp5W+54Y88WxOBbHG3NIEaTZ2VmCwWBdpFtsnJ6/LfYaUHmgYod18C12Vmy8nmcstlXAyEJS3W63k/QnwQkd+Q4FdhKJBIODg0xOThIOmvvg3NwcB9oOsGZ2jYoK65XVKxWzj/jzK57njvY7uOHADbSn25UPo0fldWJ+d2g3G+MbMaiBb7H7Q54hQrkQ7qKbefs831r5LXqzvbxj6B3YDFutqKtR5UDTAVbPrTaPW63w+9bf80zvM3zghQ/QmjUBVcwZ46dbfkrnbCfnvHpOXQre3r693H787Vz/wPU0zzRzqPsQvz7z11x1x1W0T7crhVo6kObOc+5kaMkQU5+a4uQdJxNpjDDbMMtpO05j65Gt6t4O9R3izrPu5MJHLuSZrc+w6tAq7jvzPirWCvm5POt+tg6rYWX/xfvJtmSJdkfx/MTD/JXzdH6xE2PcTO+KVqKkPpwi78+TP5xn+zu2c9wXj8N/xF9XrMxisRBfG+flv32Z4790PP59fuWPScBInoHY8Wq2ysr3r2T6hmlOvP1EssWsqp7d2tqK1WplcnJSrZ+Ghgblf8kzF6AvPqisU1mz8ryl/omezii+tQQfdN9AiHfdTwz4A9isttepJ/Uq545mB8+/5XmsBSvH3Xcc1kJNQZrP58ENB99+kEMXHuLQBYc4/svHs2TXEkUA5bN5GkYamF1ZA7dGxiCxI8ELL7xQl34guEN8aN0/1a/L6XSqQIX4+dJmVAqnxmIxHE86aPx8I9G/jVIMFnEmnZxxzxmsmlxF/639/OSdP2GwYxCAvsE+rv7t1TisDhKPJnjkjEcouAp4E14uvO9CViZX0vTDJm699lbGu8YBWPLKEnq/0QubYXZ2lkOHDtWlTiyON+4w5CX4D79oGP+5L/47Y/ny5ezYsUMVLZCXTI/mVqu1Bva6NBlqxTN02fVC+ZK8CAJ29Ki5Xr1QB4bC2glYE/AsnxcKBXbu3MnatWvViynGVIa8mPL/whhKNWthzI4ePcrKlSvrpFVy31CTQOn3XC7XqkzqUnz5/R+SWumRVd1g68ZaVwYIYNQj6kJm6CSCbNxOp1MBMN1Q6HOgqw6kb7QAF8MwK1FKf+pQKKQAqxAT4XC4rud4JGJWSA0EzNze+cw8e7bt4YKxC6hWazJuu93OwWUHuf2820n4anmxJ+w9gfPuPQ+34VYVIpPFJHeffTdPrHzi31237qybt97/VjYd3aTWqYxMJqOAoygy9DUQj8dpampSpImsGZE3iwEtFApMeaf41Vm/YqRvRB2/8Ugjm765CfeAG7/fr57PgQMH6FzfyUvve4mxE8agCpYbLLTc3KLy1/1+P52dnXWqhWKxSCQSUVFWyTeamppSbbk6OjoIh8OKEZf3KBgM0tnZydGjR5WR9nq9TE9PMzMzw/DwMHa7nZaWFmVs5bx6dXqZI/lMlCrGVoNitkjlpYqK+lTeXGHqs1OUWmrGZOnTSzn2R8dCBlWURoqoiXRO2GghPkSKPj8/Xycja2ho4OjRo3R2dqp9RyI9AriFCReSJRwOq9YumUxGgXK9sEowGCQWi+Hz+VQxRnFWJycnmZmZMSPzr1XBz+fzjI+P16lEFoc5qtWq8R9/a3Esjtr4f+ur/KFhtVo588wzueaaa1Rqit5FRWwm1CK88s7rUbOFRPv/09BBtg5i9d8XfAW+suor5O15bhi6gbZCG5lMhqNHjyqb6XQ6yWazPNv7LL+/4PdcvPNizj1yLoZh0NTUhMvlUmqz25pu4xt936BkKbEpuom/2vdXdBY669R+eo2YRxsf5WvLvsa7ht/FJSOXKNtvsVg44j7CV1d/le50N+858B6+t/Z7PNpqktuXH7mcK1+9UtnTO1bcwe+W/I7rtl/HuqPreHLTk/x444+pGlU2TGzgwjsvxF10c89b7mF773aowvm7zmf9raaSa+TkEe46/y7yjjw9Uz0sH1zOc5ufI+vO0jzRzEk3n4T1JSuEYe9H93Jg7YE/OOe2ko1LHrmEbbu38XL/y9xx7h3kXLl/9xn1Pd4HSRi8aPB1nzUeaKTnsz0kXk4wfsM4uXfWHycwFGDp55di3WVVvkT5rDLDNwyTbc7infKy+TubadzdWKdQlGcApsRbirG1tbXR1tbG3NwcY2Nj+P1+WltbicViHD7pMC2PtxAZieD1eut85ebmZlLnpQi+HMSdMu2fPEfxA6c3TcNBsI5aFUEzOztbt9ZdLhfDFw/T+bvOuv7ksv7lmPFr4qx8YKXKjZaghChH45k4+/98PyPnmr7Q6kdXs/nmzRTzZuqF1WZl/3X7OXxxrbWrNWflmB8dQ9+TfTUFSUuBZ695lrnj5qAMK76xgtY7WlXKqrxL4mOKTF6CKoINJDovuEGKywEqhU+v4yN1AaIXRHn84sc54TcnsOGVDYRCITMA1l7hF6f+AlJwzh3nEK6GCYVCxONxXjnhFe4/8X4uvv9iluxcourGxJvj3HnBnTimHZx313k4imbQ8NFHH2XPnj3/qf1kcfzPjv+Mr/JHi3Bff/31r+vPLPlDAhR1yc9CVkwYPvmdGCAZOqMsTrIeEdVBrS6lgnowKucXiYthGHR1damNB2oVMAXU6nJreRn9fr+6bjnnihUrKJVKyqkXlk2kMBJx1iPKY2NjFItF1S9bosISBV8IroW5FvZQZ/F0ObgYOh2o63I0PQ9Hj44Hg0H1LHTmVaKEeqReZwp15YKoCESSK9JjAXLSksvv96sN0Gq1KkNhtVrZ8ec72NO9h4YnGtiyfYsClpVKhY3jGwneF+Rf3/qvlK1lTthzApc+fymGpbY+SqUSlUyFsx48i3KpzNNrnsZSsnDpzZcytnaM7Sdux6gYXHnblXQNdFF0m4SN5PQIQNOLkejtzfSIvjDAslaE/AFUjpyn4KEl28IINcDtmHXgSrjqpF+lUsl0kjIu1n53LVihY6CD4sNFos4oo6OjFAoF5ufncTgcNDQ0mAy6rczRDx+l45861MY/NzfH5OSkKgSYyWQ4fPiwqp7t8XhU241yuUw6nVbrxeFwEAgEOHr0qLk2Pgqux11Yo1aVNy7tLqxWq2KCBfDqNQRKpRKNRxpNaZ2voFIEGAUjZUBLbR/pmOuglC1hlM1q4n6/n+fe+xzbvruNbDZLtVolFArhcrmw2+3E43E19xIVl3Ubj8cV+M5ms7S3t6v3QCqP+nw+QqEQ0agp6ZIKp8FgUK1NMciSdiAF0MLhMOPj43WOizwTIWlEqr4IthfH4njjDrfbzaZNm1T+ra4EA+pspfxd/2xhRFAn7MUu64S4HF9suvybTrZjgRvX3siu0C4APrHsE3ztpa9RTps+QjgcxuFw4HK5eKHnBR5a8xBZb5a7TriL1s5W3jn3TqWAMwyDXzX9iu+2fZeSxSQ4X254mX/c8I98/ZWv46v41PWJTX+m+Rm+sewbzDvm+eHSHzI7P8vmRzeTTqcpN5e55d23cNR/lP2B/Yy6RtnfsF/d821Lb2Nsdoz1P17P4SsO89SKpyhai/zwmB/SuqSVofYh1bP7lY5XmDpvCnvFznjv+GuTBvdvvJ+B0QH8R/y8etqrFBwmSTzSNsJIW82OznbM8vv/83u2HtlKYD6A66gL1v7h51yylbjn1HvYvXI3ky2T/49gG2DwtNcDbRlzq+ZI/12aarRK/sT86z5PLEmw96/3EromRPlomdLxJdJ/labSbD7jdFuane/fyaZ/3kR4IKyimLoiwefzceSSIzQ/1UzYCCtiXWxNpVJh7OwxRq8fZf6MeZqua1IKU7fbTWlZiVc+9grpvjSecQ/bPrENS9VS508mNyTZ/YHdWKIW1n9kvaoj1NjYCNTW68DlAwy9Y4jCygLH33x8naJD1vezlz/LyLkj5Hw51v56LYFAQAFWUZ8+86FnGD9lXM3T/tP3U7QX2fadbYBJNnj2eeDi2lwaZQPvoFfNkWEYeGNeNn53I7ucu1j25DI6n+jE3elWgTBReQoZJnnY8rn4AhL9l7QPeV+EKBD/X0+/s1gsGCMGS+9cSvBwkIK1oObBmDC49MFLKWfLuCounC7T93K73Zx68FTCY2GWTCzB8JvncLlcOOIOLrjjAiwpCwFrgIpRqcNPi+NPY/xRAHdbWxtvectbFHAQwCwvmBgRkcjIorXb7aTTaXw+n2KPJMIrwEvPc5KonQBmXc4lDr6e0yyOuC6R1kG8RPlEGipRXr1YhLBy8jKCKT3TJdnC3ok0Rc+5LpfLZLNZJXPXc6IKhQLd3d1q85MX3ePxqHnQ2UGJDOpDDLsezRdArhMRuoxNQLRElg8ePMjpp5+OYRgqV1k/vkQrgbo50qVCpVJJVa7W892lXZPkvurSJJHHS3VPm81mRqXfeTevLnuVqqXKfWfchy1vY/3u9QSDQQWMeid6uehzFzF8xTCXPXEZHsNDwVbrvZzP56lUK4w0j/DCihfMa7dW2HXeLt5957spOAsct/842ofayRVr1eFzuRyZYoaHT36YZbPLWDO0RgFWvQaARA90skGPVsicq9xtGrno8YuY889xtOcoXbNdvPuFdzPvmWc6OW2SSD47u6/cTe9TvXiHvYzsH+H0H5+OtWTFsdmUOw8NDXHvvfcSCARIJpOm/LEzyMgtI2SXZsmkMqz98VqsmC0nRHmgr4NoNMrc3BxOp5Oenh7a2sy2bJlMhng8rt4fl8uF1WGleEWRwkcKRLNRbKfacCVc6pnLetUVLRaLRUXBW1paiEajTE1NqZYe8XicarWK74CP9svamXhoglKoROjrISI/jeBuc+Pz+Qi0Btjx8R3MnDRDPBhnzafW4Lea/XBjsRihUEitt7m5OVX8LJfLKcDc2dmp2PlcLqcKpcm7KPK5QCDA7OysIoOsVivRaJS+vj5cLhcjIyNmpCCVwjAMRkZG8Hg8RCIRJT0/evSoOqYcr7Ozc1EOtjgWxxt8uFwuVqxYUUeeiU0WZxzqi45KutTCNDCob2cl/o9uf3U5q5DUegqb2+3mMxs/w67gLnWN+zz7+NjGj/GjAz+isbFR2Zq97r38YPkPSNvNqFzWnuVHS37EMscyzkycqa7r0vilPBx6mJ3enap10+HAYT626WP8dP9PAVQXjEPuQ3xx2RdJOExyMWfLcefGO0nsS7B6z2puvvxmYn6zpRUGHAgfwF12k7Wa1bQbC4382fSfcf9l9/P0CU9TtJq+SdwfJ+fOYa/YKVQLYICj5OC9R99LIBfg73v+nrzdBK/hdJirx66mWC6Sm8yxL7iPqlHFWrRiyVsoeopmNfWSlVNePYVN7ZuwtFpY8/wanEEnO07agaPoIOfUQHUVuqa6uPLeK7nzjDt5ZdUrVC3Vus8dOQclW4mNv97I8ueWc/fn7iYXfj0wN/IGvm/5cO5yMvHABNVAPTAyigZL7l9Cq7OV6uoq5XSZ0adHmXzHJFWr+d1se5aXPvMSZ/zVGXjjXhXYMAwDm9PG/pP2M3jNIMNXDtP9t91UYqYv29HRgd1pZ3jrMAf+/AAVV4XYiTHsv7Bz2rdPw5q3Umws8rsv/I6S/zWpdjjP9q9sZ+snt+IsmXYv2h5l+99up+QpQQvs+ckeLv38pdhzduWjGnaDfWfu48jbjlCxVxg/d5yX7S9z7C3HYiubPnewOchzFz7H8NnDVK1VBt8+iMtwsfbetVgK5vvidDrJ5/Ns/sVmpo+ZpuR7rS5MykHHTR1YnK/VBCpX6Nneg+9bPp67/jksZQtn/c1ZeKe9KughaXyhRIjNX9pMs9GMM+hU76qklol9TyQSSjYuKZpSLE0wgWAA+Q2g8ITsEeKPS0Cuc6SThDeBrWJjfn5eSfKrw1W8bi+GzfT3k8mkSnXsjHSSyqdUi1bBL/aInVQqRWNXI4ZhMD4+rtR2i+NPY/xRJOWf/vSn+au/+iu1UGVRStsmvWiDnjOigxdxgAUU62Bbz7vQnVdh1eQ3klst4Nowar135fhSoVyMqS4xl6imMGJ6jz4BmTqZIEZTLwonjJ+ALzGmgIrMQ01CL2yZHEuOr0vj5d90uZmeR66rAXQ5vdyDzrgL2NYLgkkEW5wJXb6tR7oXFkHR21jIPclGZLfbGRkZUdJbqeZptVpJJpNMT0+rnslyDpvNxo6VO/jtqb9V+dkAzXPNXH/r9TTnTMADKOZSqm/Ktcl8GIZBmjQ3vf0mxprH1LHseTsnP3oyxz91vPqeFEWzWq3YfXaeOOYJ7j3pXgDee8d72TC+oa7olYA8kRe53W4FrMWoyLWolAN/iduOv40d63aoaqPrDq7jskcuI3kkyXh8nP1v38+r574KVXjLV95CeF+t57VI13O5HPF4nHw+z+DgIDOuGbLfz1I9uaqOu+w3y1hx6wqqqarKWZb8o0KhwNGjR5mfn1cRY4fDQVtbG42NjVitVjweDw0NDaQyKfZv28/4P44rB80yZaHxikbCk2EqFbPvtbQ+EdnV9PS0ighLHmQkElHGTleyuFwuaIHxy8YJfSWkjKG3y8vYX4wxc+GMuq/GJxs56ecnkR3OKlAteYqxWEzJQOfn5+no6FCpC4FAgGw2y9DQEP39/SoFQldoyPUkk0llbD0eD7lcjsbGRiYnJxWRUKlUGBgYoL+/X8nSi8Uig4ODWK1W2tvbKRaLRKNROjs7GRwcZM+ePf9Xe+v/v49FSfni+K+O/wlJ+YYNG/jqV7+qbK2e1qadF6gBZ/lTl5Hrij39WAuJb538l31I7K7L5TLbNVkNPrjmg+wO7AagP9vPzYduxlVxqesQ/+GB0AN8of0LzNvm8ZQ9fGj2Q1wzd03ddQKUyiWuW3IduwImkF+WWsZ39nwHb8Vbd32zs7PcabuT2864jVwgh6vs4orDV/DWgbeSzWZ5aM9D3PnhO5lpmoEqnBw7mavHrubvVv4d3rKXb+75Jv6i2c/833r+jV/3/pqitUg4F+aDL36QdZPr+PvT/54p3xTv2fEeto6aedV7Q3v53qnfw51z8+FbP4wlVUtl+8lbfsLh3sNs+902On/bya6P7WJk0wgnPX4SJzxxgrp+yTG+7833cdoLp/Gjy35Ezpkj78jTMdnBe379HuVP/eLiX3Cw7yAdUx1EGiMEY0FO+/5p7F63m7V3rMVmszFvn+eJv3+CrCVLJVghMB0g1Zai+SvN2H5kxrRKK0pEfxzFWrVin7eTXplmyS+X0PvLXrUuxD849OFDHD33KFVrFfeMm+O+ehwNhxvqAjJOt5Pxc8Z58qonle31xDyc95Xz8Ayb6qxCoMB9H7+P3IoaGWBJWFh18ypWR1dz38fvI+vP1q1za95K/2/66fxZJxUq7PryLtULHIAihH8Wpu3Lbeqas8uyjH1zjFJvzfe2zdpo/0w7nsc8ABROKjD6hVFKrbXveGY8nHzTyYQHwnVBt2w2S7Ynywt/9wIOq4NzvnoOrblWUqkUmUymLlB05KwjNA400jjaWOdXi18ccUbY+Rc76X+ynxU7Vyifzm63EwqFVFqfBKH0Divy3opfIqSXSM4l6CDvs1RZl3fP6XTyYsuLPHX2U1z6i0vpKHQo5awUcbXb7SrokcvlKJVKqli09E0XjKHjD4vFwq5du3j88cf/H3atxfHHHG8ISXl7ezunnnoqwWCwToYrcg4Bflarta7qtRQEESMjL5BId4G65vULo9O6kdMNn0TUBYQKgIQa2Fwo4wIUMyabnvSOFsAsbLJchy7J1gupgAnIJCKtv6ALpfJyD1CTsQN1G7Qub9dlqfq9y3H0glVyT/KnLunXZUBQY+7knmT+hdCQY4s8TZfGLSRCRNYrzzmXyylQrDsvwqSLCkKUBE3xJvwZfx3gbp9ppxQvUbQUlUQ/n8+TTqfxer3qOYnTIlWpbRYb1919HT8/8+cM9prSMGfeSdtsm5p/+VOey/MnPc+9W+5V5/7JRT/hioevYOO+jWrDl+cuSgchDPTCdAJyZa2lXCliTTFlPDFgPjTPnrk9bGnbwktXv8Srm15Vnz3wgQc49rvH0vB0g1pbUqSko6MDv99Pb28vrza+yoFlB0gZKfXb4eAwbZU2Gp2NJBIJ9R42NTUxOTlJc3Mzfr+faDSqinlNT08TiURoamqioaGBfD7PVGSKxHWJ2jUDVXeV0roS5THzeUoLGiGALBYLzc3NKuorRkoqpOvvOrxWH2GiSuBLAYrlopJITpemSXem6+ar0F7A2mXFOWVGoSWaLcoSIffkOYm0PZ1O43a76egwiw5JD2+pGiuF4ACltIhEIqpViMxTIBBQ87Vs2TLy+TwzMzP09fVhGGYhIyGvkskk5XJZScsXx+JYHG/csXWr2RNXz/2EWqeThUT4QiCuE9piv3W7KHZUvqO3QtKVbOL8A9itdr46+FU+u+SzZK1Zbhy7Ea/hVRFZ/XjnJ86nUCnwpc4v8b7p9/Gu6LsoluvbLIFp07948Iv849J/JOlI8snDn8RZcFI2ail70tawfaKdKzxX8NvTfsuVQ1dy+cTlpKtp9u7di2POwYee+hC/POOXdGW7+NSRT2EtWvnYgY/RXmjHk/NQqpo28Ir9V1DJVbiv7z7e/+r72Ta3jbK1zCef+yQvN73McRPHUTFM32bV3Cre/dy7CUfCeMteckZO+TlX3nklT69+msa7Gylbylzy20t4JfIKxz1/HFhr9WakoOelj15KtVrlqruvIh6IE/VGWbd/Xd0zfNsdb+OFLS9w/I7j2bNqD22H20hOJNk8vJkCpt9on7ez5G+WkPQm8W7wsnLfSg6vO0zhlgJ5i6na9I36CN4YxIMH/5Cf6cun6b2jl6q1quy2SJe3/XQbroqLo1uPsuk7mwgeCFIxanWKisUi+07fx4GrDtTZ3pwlx7OZZwnvNIvklctl2kbbmPzcJPlj8hg5g8Z/aST/qzxPXfoUeWOBzL0CG27fwJrfryHtTVMoFDj2C8fy6l++ysRxE1CFnl/3EPpGCMNhKELHN+3D+Y9ORj8zSqYngzPuZNV3V+F71Ye11fQlK0cqhL4VYv9f7CfXmMM35eOY7x1D6HAIw1JfZ8ZisWA7YmPZPyyjwduAZcpC3BFXPphUMi+VSvQ91KcCdXpQK5FIUGot8cp1rxDbHGP7hu2UflhizbNr6jqPAOrvUodBL1Asz0Z8NalBBaiAiviu0q5XfNx9G/dx55l3UrQVufet93LBnRfgS9R8Xd3nz+fzyleRKDigwD2YaS0SUJHWbovjT2v8jwPus846i61bt9ZFnnW58MLIqix8eQF10ChOL6AMkD50KYfOgsn/y0YqIFvPrZLWDHJNOuAWsCbHlRdEl36JAX3ppZdYvny5arshn+lF3AS867nkC9uAQA0Qyxzoxcz0F1a+owNLOYfMqUSV5TMhNGQudSmcbKTCFsq16/nZC69RIsfigMhnQnIIEJZ5lwi4xWJRrRn0Vm4yvzLUPSTt2HL1yzY/nOe5J57j4jMuViCoXC4r5hCom3+J4BuGQdFWJO2pgfdUIMX9F9/PsZPHsjm2WTk40m7CPe2uO7dRMbBOWhUrKefXCRZdZSBrR8/3drvdOKIO1oyvYaB9QB17xfQK+pJ9OP1OgvPBuvNaShYcUYc6ZjKZVGA4m80Si8WIx+N0xbpov7mdhz7yEDl/Dvsjdjx/42EoOoSx1CAUClEul9n/7v0svXkpLS0tdHSYTOzk5CRDQ0Mq17hYLDIyMsLU1BSBQADDblDqqJdDV21Vyi3luhQMMR6iVhCWWp61x+NRTqthGFx00UU88sgjdeoQyakWws0z4SH8hTBD/zhEanUKDoHlvRbGcmN4vV4CgYCStUvrrlAopMgyp9NJR0eHAsJjY2Ns3LhR9eNemB4Qj8dZtmwZ2WxWkSn5fF6pFWRtSYsv6ekpv5XaBz09PebcGQbNzc24XC6V8704FsfieOMNwzDYuHGj+rsuD9f9F6DOR9GjZGL3F0ayZd/7944n/y7pVjoYMAyDcDXMp8Y+Rc7I0VnurDuHyNmF4L4ofhEt1RaOSx6nAL6Q9GKvyuUy/oqfvx7+a9KWNJ25TiV7BbNI1/DwMENDQ3R3d3Nm9UzW7VvHhsgGiuUiBw4cYHp6mlWrVrHStZLOg500FhqpZCtUqLBxxiSmK5YagAS49OClrIqtYkNkg5qTYDHIiSMnqnkVe7p2dK1JRFgrdSpDe9XO6kdXM1GaMOt42F0c+9yxGJba3Ms8yj1Xq1Wao820J9qV31C2aUGQqpXjXjgOw2Kw8pWVRCIRBXQEYGWzWewDdtqd7TTPNeMNeOl+oJuDpYPKf3S5XPj2+0y/z2al764+DJuhfDbxqzweDy6Xi7W/XkvoiRDtR9uxuWyqIKi0xfLOeqFKDXBXYPUXVhPcHqQcqJEjuZkcDV9v4PCNh+m7tY/wM2HsfXaMVwxi34ix4293qGP0f6ufroe7KDgLyjdzGA6O/emxvGB5AeceJytuX4G9214HUG02G/mpPK3/2sqOT+zg5JtPpnFfI0ZPLW3SbrdTOFSg9QetPP5nj7PlG1toPNRIxVJrBVapVAgGg8rf9e/142/wk7GYhVYld13WoaSBye91/9UVdvHoXzzK7BqzSnnVWmX3Vbtx+Bxsfm6zIrUkjVUCV+KPZjIZGhoaVFBI/A55XhJgk/dU5qJSMdt07Vy9k4dPeZiizfQ/R5ePcufb7uTKn1+JJ+tREXCJdDscDlW4UFfCiG/g9XqpVmtV4iX1b3H8aY3/UcDd0NDAaaedRiAQIJPJqGg01BfxksWlF0zT5d16rz9x4nVJufxW5Bjyubw8OmiC1/e6jsfjPPfcc3R2drJ06VLFUom8RRxkidbKJrpQFm4YBqtWrcLj8dQm+DUArMvF5RgCzGSz1SPQQh7obKsuydYJjIUAWOZQj3jLnOu5tfp/evRczqFHYsVISZGpcrnMY489xqZNm2hqaqqTmoszsVB2r88XmH24JZdW5sFut6s+yLKxiDICoDXeypW/uJLvXfc9EuEE217ZxnGPH8eEe4IdO3bQ3d2t+immM2m8YS+2ik3Jo2Ve5TpaM61sHdvKvc21qHXbUBtdE10cmThCb28vfr9fPZflzy/nHbyD2867jWq5yps/92ZyAznmWucIBAJYrBbGO8fZs3wPl+y8RLWEksJa+rMTMGq329kf3s+jax+te17b+7ez6k2rcI+6OfHlE6laqtxz8j1Yy1Y+fMuHsVQtDPuHmZqaorunm0PLD5HuSbPmwTVk0hlmZmZob28ncCjA277yNh685kFO+fUp7Cvt48jsEebm5nD5XJRvLJM+L83EyglO+8xp2LARi8Voa2tTFbil8qnH4yEUCuH3+5mYnCD/uTzcDTgwHYBxKPywQCZngmqRdevrfn5+XpE6EukGk5hIpVK8+uqrprOQy9WpT2RdSxV216SLzvd1MvD9Aezn26lEKhx1HiUcDitAm0qlcLlczM/Pqz1IFBVSybxsK2N32JUywuPxEI1GlWJDcqmk7oMoPiQiL884EAhgsViIxWKqb6jX61XRbLfbTTgcJpfLEYvFlNFd7KO5OBbHG3e0traq/G0hkRd2KxGHXGynbkd1nwFQbQfFVuvqLwHf4jOIKkoiXWKH9AJsbeW2OoCuE/e6dL1arXJ88niTcK7WilbqakDZZ1uLrZQKJaz2mtIon88zNjbG8PAwvb29rFmzBp/Px7HJY0kYCSZGJpiYmKCpqYm+PjPy2BPvMeeD+qJwujLParXidrjZFN2EYTVUFFOCEqKEEp9E97+k9kuhUCCbzTIzM4PL5VK2S5foWyyWuoJjOuAXH1SX98ucVqjw0vqXKKQLNDzQUFefxmo1O2JIAKhQKDA9Pc3s7KzyWaUomE5uCHCT+dZbuRaLRcq5MuH9YUq2kvLXvF4zh7tQKNDxcgf2z9rZfsN2AE78/Ik07G+gHCrXBVA8Hg/2OTuhG0LY43bsPtMHdTgc+F/24/wXJ4MbBln6s6U45hxU7BU1n7LGHbMOtn17G+VkmVK5hNvnVsEbicjm83mcg07O+cw55nGo1S/Se827Drg499Pn4og4sDqsSqVqsZiFS6VGTCKVIFPK4Eg58Hg8ihASgltSt6TQcLVaJZvNqqBZ3sgzt3yu7j0ueorMLJlh7oE5bNhUgEfep3K5TDKZVMeWIIqQNBIwEp9lfn5etQGVd1WOtW5oHbund5NeYqrwLGULW3ZswZV3EYlEFJgvl8vMzc1RLpfNujSvYSUJCMm9STuy2dlZGhsbiUQiHD58mMXxpzX+RwF3f38/F198sVo0kqetS5xFTq4XO9AjpPKiCSOlO6diIPTorDBBcgyJromzrEusJQrncrk488wzAVQEyzAMJT3SAaoYUr3StERMxRBInoXkC+n9BSVip0vQZYPL5XIK3Mk9y8YmG7oAc4lgy/WI4dQl9KVSif3797NhwwY1XwJuxbDohesEAOg57tVqta4vuIANp9PJWWedpQy5RJDl+HpxODFcej6K3LseIZTnIZ9JvovMlygRmsvNnP7h03n+6uc5+aGTaQg30LG1g927dzM9PU0gEKClpYXJYya555R7uOaha/BavGrDFiNgGAb5ap4Uqbp1W3FUaOpqwl12s3fvXlauXKmY14AvwAVzFxDeG2bynkms+6zMZ+bNaEM4zEDDAD+44gdUqeK1eDlz55l4PB6VyysgU+/xnE6n6cv3cdFzF/G7k39H1pHFUXBw9gtn0z/YTypvAsPz9p5HxVlh7d61BGIBvG1egsEgFouFgSUDPPvXz5rPOVvF+3MvXq9XFQrL7sly0sdPwu1xc8EFF/DYY48xPjdO7PoY+Q/mwYD4yjhP3/A0G764QVXtlmrly5cvx+v1ks/nVRX0XDZH4IUA2auy5L+ZhykwjjXXTM6RU8X8stmsWh8ul0u1ONPrHujv8NjYmCpwKEoYeYelqqmQFYFEAMtGC+VqmcaWRuUAVKtV5ubmiEQiJJNJDMOsai4twgRIZxuyPP/nz7P+N+spj5fVPpROp9X6F9l9PB6npaVFrdtSqaQqogv7nEwm6ezsVHub3J8Y0FgsplQxQjBks/V5dItjcSyON87YuHFjXUtIIRDFboqtFjut72Xyma4M00GdENk6SS3nEMJaolpiU6E+oi7Hl31FgKoO9uW65LxQA5fy7wLydSJB0oAkrWhycpJQKERfX5/qtnLYf5hPdH+CC5+5kE5vJ1u3blURS93PkusV0lk/p4BX8Q/EX9QDCHrnGLEfMhfVapWBgQHK5TKdnZ11nWDk+OJzyX86MSLzoafzVSoVSpUSr6x9hTsuugOA0+Ons+LlFZRLZhvQkZERRkZGFJHrcDiYnZ0lmUwCtRagLpdL1QXR/bRqtYrFaiGzNEP7XLsKFkigQ64VUMU8AawWKy0vt7DlS1uwGTZaXm2haq2lC0ItOOJwOHDmnZTtZeW7VSoVcpkcoadDrH98PQaGWWBOT+XS1oZRMKCMIjgMwyCVShEKhRSR7XA4yMayJFIJlY6VzWbriowVi0WYgGw5q5RrXq9XSaRtNhsWm4X4BXFib4rR+d1OPFkPHo+HfD6vIsLig8v9CRaQII2tbOPMG87kqb95ilxzLYd9dP0oHcd00LOrR0W2Za6cTic+nw/DMNSfIhuXWjyiflvYmcdisah5zefz5BN5rv35tfzbu/6NqfYpTnvgNFZtX4U76Ka9vV2R8WAWZRayQAqyVioVUznx2rtcKBTw+/0q4CDvzeL40xr/Y4Dbbrdz/vnnqwrj0WiUQqFAT09PHbsozrQsWp1l1cGrbMgej0cZPAFjAiB1cKeDWz0/STYRMMH13r172bBhQ93mLxUKdXZYN7JSVVyMRrFYVAWU9Ei0Thjo7Td0CZEe1ZeNVo/eL8ypFhCvG++FkXJdxt7a2lpn7IXJE5nzwoi2DP14Qo4IaQGojU5nqWW+dCMmxlXmS+6vUqm1ACsUCnWF2MTxkGent3pQ0rx0lcBHAhgX1Vqcbd68mYmJCWKxGDOnzfDby35LxVrhFyf+gksevoRAIaBYSJmjkcoIO4wddWs31hIj2ZakLduG3W5ndHRUsZJut5tAIMAJL53AkdwREj0Jpqen8Xg8vNr5Kvdeea9qZ3LXlrsoFAucs/0cqNScKkBJxPTidCftP4mipcg9x9/DuS+ey2m7TgMDBcwNw2DVbavwBX1UjapimO2X23nh/BeUNGzntTs5znUcrh+bcmX5ntftVUTD6tWrWduxlqevfJpB47XWJgbM+ec45DhER9Xsxy2VuSc3TbLauRpn2cnevXuJx+Pq/crfncfqslJ+tEw1V6VgFNQ7KJIsaa8hRgxqDoGwwnrREvm+GPhSqaRUCnpdgEwmQ6Vc2wfcbrfKh5Ooh5Ah8Xgct9ut5PHpxjQD7xsgtirG0594mtxXc7Q936acXHlWUtgtnU4rYycRbmnRJ2QYmI6Rvm7D4XBdFVIh8Px+vyKhFsfiWBxvzLF58+Y6cK1HQ8Xf+Pdk4ToAEj9GB396mpvsabo9FrJZbKrYbTm+nkInxP4fsukqX9wGj4Yf5ZzkOcqR1x13AecLlYTz8/MMDQ0Rj8fZtGkTra2tlMtlnvc/z+dWfY55xzyPXPcIH331o3hsHnUs/U/xhcSW6WSBAG39fmSO5frlWDppIPP5cvvLzD0/x9LOpXR0dKi5LRQKRJujWOwWOuY76p6N3KecR/ef5HyvbHqF317wW2VbH/uzx8h/P8+Sp5bgcrmUb9Pa2lrnz1QqFaqbqlgyFtxJt5JeL/TdqtUq06dOc+hDhzj228fS8WqHWkt6mp4QMLr/l8/maX/eBOmGo5ayJ+tOCt1CrdaAREutVqtSgsViMWWHC4UCmYwp4RYwqvtd4rtZLBZCoVBdYETvYCPPUq6hXC4zvXyambYZWu9pxWaxqWi0gGcJ/gyfPsyBD5j56XsLe9n44411PrCQ9/JbIaIkOCXDGDbYctMWdr1vF9muLI6kg2N+dgydOzrJFrKKmBfyXp6/APhKpUIqlaJYLCoCR9SW5XJZgeRw2MyZF8WczI3VauVdv3oX29dtZ+uOrcwlzc4vfr+fTCbD/Pw8oVBIdYSRtFbx94S0kvsVvwZgaGjoP964FscbbvyPAW6/388VV1xRx0bpzKvOpOkMsbzQYtjk5dYlXLIRCBDWN2dhQmVT1plUfbOVz2WTkWuTHFxdpq0ztQIK9fxmOa4M2Th0AC4gQ4DBwpxpuRa5zkwmo6Tp+jyIkZA5EIAqc6nnWns8nrrNQwycTkzI7/W8poUst04IyD3Idcl3xHEQ8CQbo2xiuoROji+yYanQKJtJHbOqOTSyscsGKQbLZrMpJrWlpYX9x+/niTc/QcVqzsuelXsoOou867fvqmsFZ7FYKJaLVB31RW3LljK5slkMQ3JvLRYLU1NTisTIZDK8eOaLnPLqKXR0dJh5woUELGinbM2ZDovNXouQyHoSQyHV5AFOeeUU/HE/G4c2YrGaxl+YYt3o6akKzorzde+fkTFUz2iXy0UgEFBF+kTuFoqH2PaDbaSuSDGzYQZLzIL3I14iD0WI2+NMT0/T0dHBzAkzHHz3QZr2NnHMV48hHA7jdptMbTabZXx8nPitr7XSctbSE8R5lGeeTqeVQyIgVpeFqfnXIi0iNbNYzFz3SCRCOBxWx5GcNnnvJbVDAK+QYdLWLJfLmcA9UOXghw6SWG/mSFWcFXZ/YDdFW5G+7Warr0wmo9agzWZTTLQ4GYlEAr/fTzKZVKSC1WpVqoOZmRnC4TDFYpFEIkE8HmfVqlVEIpE6pYB+74tjcSyON84IBoOsWLECQO0nQB0A0AGaDiQXBgLkPRfbqttCsdE68JPIoIBuIaLFR9JrwuiRY7kO3S8Ru/HFzi9yX/A+ctM5LoldAtT8C51k14nvTCbD4OAgc3NzrFy5ktbWViqVCi8EX+DLy7/MvGMegNGuUX7o/yF/t+fvaM42v25e5N71OdP9EV0NoAMnuS+ZG32eqtUqu3t2c9sptxFuC3PicyfWV3ZuznP7ebeDBa65/xpC8dDr0u90EmVhxNuaqxEmai5z5lwOnjRIbC5GsBhUgSPZ14srikS+GMGWttH8mWZsRVudHyY+5NDpQwy8f4BSoMSLf/YiW364hdadrerZin8jtlwFTlw2ht8zzPpb1yv/QFRW4l/L8xOVgoBw+VyUjOKHiXRbfBMBweJr6GS4btulw4z4Mw0NDWodik2OrYyx89qdJNoT9Hb2svWHW+sIFzAxw76z9rH/qv2K4Bg7c4yyr8w53z/HJBleK2AmPp/kces+hPjzDoeDpleb2PSvm3jpb15i8w8207enD6fbbD8q96sHqHTCQkAwoNS1wWBQrT9dGSrpDUK8K1VrtsKxzx0Lhnl/UEudk/ehublZtUKVgIjX6+Xo0aPKdxOyXvDUYleTP81h+Y+/8n83rrvuOnp7e9XG3dzcTGdnJ2BGlkVmKsBL8iR06bCwqzo4XQhwdSMhv5MFLzIXySFZGH11uVxKLiZ5QrIJCLDWQZJsdrocXG+tJSBADK1cs0Tp9WvXZfV6azHZBPX2VboBlxdaNj45hm7IdKk21EvvdXZeN2rCPJfLZfbs2cP4+HhdETr5jc5gyiYlc6n3WhYJtT7vch55btlsFr/fj8/nU+tBeg4KSSPXrisCKpUKeUeexA8SFCu1DVGM0+bpzYTTYTOnGLOw2fqX1mMv1yKokgu1xL6E45PHq+9Sha7pLtpn2tVmKptid3c30WiUX9/6a34U+hEPHf8Q337rt6kYFcLhMNvS2/jgbz6IpWIx23r9bB199/aRTqZrMn2HwQ/e8gOyhayqaC0kT7FS5Ddn/oaO8Q51rzKHohDo6OhQ9ysStf6j/Vz7y2sxKgZGxeDy31/OORPnKPmzqEImWyZ5+ZKXcbqdqg9lw3wDb/7lm2kaa+LSmy5l+fhyRfhEIhH2hPZw4GMHKLQUmDh1gu0f3U6hXCAYDOL1euno6GDlypV4vd66Hpa64yRrK5fLkU6nKZVKpFIpZRglX0zk1uVyWfWzlvdQ1q7T6VTFaqxWqwLFsj7FgZB9Rl/bqVSKfD5v/hnN473bC+Xac3dPu+k62KWcVynmJmBfSA/5rLGxkVAopORo8XicdDqt0lJyuZxyjCuVClNTU8zNzak5mp+fZ/fu3f+VbXVxLI7F8UccfX19quaDRL8kwiiqnIUSYR3ALoz46cBW7O1CACj+i/xdxkLiW5dY64BWB3UqOk6Ff+n6F25tuJW4Lc6X2r7Eg74HqVQrdWAWar5BuVwmV8rx8bUfJxKLsGLFCrq7u5VdWjK3hO54d83OVg22TG/Bl/bVqfxkr9YBtuyr4kMt9HPk+3pkV5eeyxwcbjnMz077GfO+eYZOGOKWt9xCsWL6DSV7iW+99VsMdw4z3D7Mty77Fnlbvg64yz3rUVz9/9cdXMflv74cKqYfcda/nkXf7j6GNw7z3Due49C3DmENm+AslUoxNDTEvHWeqZunyK/Jk96WZs8391CiVr9G1sjMCTMMfMAE2wDZ5iw7PriD7JqsAoMScRZi2mq1ghWe/btnOfSWQ+x5xx7SmbSqJ1KtmrnM8hsZ8v86gBTVmKjJJB2rXC7XyflFoSkkgfxd79iRTCaZnJysA+miApsLzvHcx54j0WF2NBk5e4Qd1+0gXzSjxdJLulKpsOLVFTjTzpo/VoHeB3tJzieVElKXVQuwl2uW4mPikzscDloOtnDCDSfQ/VJ3nR8tPmomkyGTyZBMJqlUzKJnUkhVCqNmMhmmpqaIxWLMzs6Sz+dVbR5514T0yGQyxONx4vE4LpeL2NoYz5/wPHZHrQVYuWx2ZrHZbKomjkSwJVLe09NDW1ubUqRKa1op6rY4/vTG/0gfbq/Xy759+2hrM9sr6RFLARd6sQSJfuoGRjZ9XX4jFcRlw5J+uXr0VzZqHaSLXFkYQnlJ8/m8MnbC6MkxBBzrbZ50IymgWi9eBqh8Ud1gVqu1/HWdfZTP9cqHYmDkM10ipt+fbngFlOoEhmw2+vMV46zL0xcyyjrJoX9PzqNLyuW+5Dxy/XKdAlAELOrPUSKF8XicyclJ7HY77e3t6nwjIyMEAgFVnVovdDfvnecrb/8KCX+CY/Ycw0UPXUTQCBKPx/H7zd6eFVuFr131NRKhBBc/cTHbdm/DZq1VcBeW0TAMisEiPz/v5xxccpCmSBPX/fI6/Am/yiOWPOhisUjZKPNY32M89vbHTLqqCkunlvK++9+HN+PFZrMxGBxkR/sONt61kXTSBGArV66ENvjepd9jommC1SOrufr3VxMomTlJJWeJ3x7zW57e8jSugouP//zjeGbMe29oaABqkRHJ7ZHnIQ7TwJIBpsPTbNuxDZ/HzEFKpVJMTEzwUvElnr7paaqWKsffcTxrH1lLZj6jGNRsMUshW8BhdxAIBJifn+fJPU8y8KsBKr1au7msQe9Pelnx2xUKDAcCAQUwh4aGGBgYUFF7AZfJZLLu3wS0yrspxUn0aqHyXso7KyqIxsZGBcIPHDig8rIBZZQCgQCjo6OkUilaWlrUOeqIrmKB0UtGKfx9AcuAhb4r+uju6Mbn85HL5ejp6VGAOpfLMT8/r56B5N4vX76cRCLB7OwsDQ1mr1SpcyAGVNIH4vE4lUqFiYkJxsfHVfG3wcHB/+zW+v+5UV3sw704/ovjv+Kr/Efjyiuv5JprrqlTRQnZLYohnYiGemJaCDfZw8Qe636NfE/2cafTWZfvK7Zej3rqyhs5hk7wi50XUHlH8A6+0PIFUtZavZKWQgvf3vdtOnOddb8FM5o/VZnihtU3sDe4l5WzK/nMy5+hy9mlbPvg4CA7du3g0Y8/yljfGG8bexvXDlxLuViuA9l6FFAH07qKTeZMV7HpQQRdEi3zlLAk+MLbvkAsVOsTbS/aOWf7OZy982y+f9H32bd0X62SdxX6h/u5/vbr1fPRpfwLSRMwfZ6x8TF2r96Nw+Jgyc4lTKyZ4NGPPUrVWoUquOZcnPrJU8kN5BgdHWX8N+Pkt2gttyrQ9kQbG/55gyLQAaZPmObVv3qVkq/mQzrmHZzwuRPwH/SrIJAo/zKZDNVwlT1/s4fZbbPmfVXgmNuPYfndy7GWrEp9pc+lpGXF43HzHK+BN0mvymQyqlaR+HJ6zSHdLxfALn6Ww+EgkUioCL+khYmqM5VN8cA/P0Cip74Thy1jY+tvtrLhuQ3qu6I+myvM8fQ3n6bgK7D121tpfa6VcCisyIRq1ayrIsBa1I3yPCWAJiq1KcsUL/7zi5z8xZNxT7gVZrDZbASDwbo1L0V+PR4zb1xyqqGWhiDrUgIgEs2W1l7iq3g8HmaWzPDDa39IlSpn33k2vQ/10tPRU1fhXvxaaZHb3t5OqVRSPb6PHDlCLBbD5/NRKpXYt28fd9xxxx/arhbH/+L4z/gq/yMR7quvvlr1yZMN1mKx1Mk+hBnS86tlE9YNiB7lWgjGRfKhy7DkWHokW48aS+EmvfCSGCj9HPrmq0fNBIzr1ygvn+Rf6CBWz1uWzUonAPRItER0dbCuGyZdkiSfyTWKIkDuR+5FN3j69QrwF4O3kOzQJf8yP7IZ61FqYUp1Vl+eq2yCsmlJBF+ev2EYNDY2qhwYObbkxuqVMuXZTjRO8IOLf0AiYLKlu9bv4r7T7iNjy9TVA6jkKlz30+u48LkLOWHPCTjsNaJAwKrX66UQKnDbm27j4JKDAESaIvz2rb8l1ZoilUoRDJrtuEROWPaUGT5uuPbmGBAJRhjoHlAOWOdMJxe8eAENoQaCwSDhcJhBY5Cbz7yZieYJMGB/735uO/024u44eSPPA8c9wNNbnwYDcs4c333rdxluGVZGT8+1E2MgzoKsmVVjqzhx14mUi+W6mgOZYzO8+OUXqdqqYIHn3/o8O07agdVuVaRHtVg1C6eAIhk2925m099tonWy9bUFCvav2Cl9vsTMzIwipeYa5pgNzVIul+nu7mbdunW0traqyt5i3OTdENmdEGDCLstaFQZXpNiyrvTqs9lsVlUJrVardbI5cTZ0Es8wDFpaWtQ6TaVSxKIxil8pwj+AcYZBIpZgZGSEyclJRYTJeyVOgRSRa2hoqCt20t7ejtPpJBgMqnoO8lxmZ2cZHh5mdnZW5UGGw2FVLGZxLI7F8cYbbreb5cuXKxChR7dlX5b9V7fFCyPbOqDTo9syxEew2+2Ew2H8fn9d+y+xmXo0Wyfc9c90dZFeR+KiuYt439j7cJVNJ74n18MXjn6B3lKv8oV0Nd+EdYJ/XvHP7A3tBQMOtRziO1u+w6zLjO5NTU0xMDBAOBDm71/4e94+/HauO3wdRrU+11fmRQfMerrawuCI7oPo86pHw+VPS8LCxd+/mNDREACWioUzXjyDM144g2KxyFW3X8W6I+vUHK0ZXMN773pvXVBhYfBC5kG+k8vlyGVzdD/dTcsTpmJs4PQBE2wDGFD0FZk4cUJdf/s17XieqXWqaXu8jdWfW62ev/g3TU830f/dfuwJE4C7Z91svmkz3v1e5U8Kqe5wmER4ZWuFTF+mRiJYYHTTKFl/VvnAsnakFonYvFAopNplZrNZVVdECB4BorryUsCjFMQVtWg6nVbrWq/NIrnGQkhbqhZOvvFkmvc3q/mw5C3039JP5z2dRKNRcrkckUiEubk5CoUCbd421nxkDcu/uZyGJxqwWW2vS+cQn1FAqsy9rGHxmSLNEV769Euke9I8ceMTzC6fxWKxKLAeiUSIRCKkUqm6dyaZTBKLxSiVSnXRfvHx5V4jkYgqJigFnSU1dWz1GD++6sdULBWqlioPXvog4xeM1/nv8m6L8s5isRCJRBgbG2P79u1MTEzg8XhoampSnz/11FP/wc61ON6o4789hzscDnPBBRcoUCtVocUwyQIT8KBX7RZ2V5eH6IyoHsnVwboAx4UMry4Z0iO2UAOVsuGIAdULL+iMlhxToqN6ZFuuXY/ii8MPqA1Lfi9GW/9TvxcBKDqQ1eVZYrAl2iz3ohstMbryuZ47rueeCQCX88rv9XvTWXz5vhhNyavRq4KKI6LL5eQZyLUKgCmXyyp/RZwO6U+oS8/gNRmUUaJi1CdKl+1lCqUCQWdQPcdKpULIGuLEnSditdXmNJ/P181XtDHK7jX1st6BzgEOtx/G86SHzs7O+mqRyQIX/e4i7rnwHo4sP4Kj6ODyRy9n49BGikaxTkUQCoUIBoPkcjmO2I6Qr+brzpMnT6FcwG13k6W+UnXVqFKxV+okSyK3FwZXnqGekiCGz2o125U4nU4cQQeGpZ58s/qtlMolLNYaSeV2u+uY1MbGRsKVMOnfpLn/8vsp/LBA+h/SjFfGicViZnXSPhej147icDrouaGH9nI74XBYyc+mp6eJRCLqvAtz+fX3p1AoqGuW9S9GfWHFVjE+sl5lPYpTkM1mFcssUW+9HUxjY6PK/+bLUDEq2NrNfSiXyzE2NqaKmgi5Ie94KpUiEAgQDofV+gyHw0QiEQWy0+m0ajEildpbWlqURHB+fl49x8WxOBbHG2+0trbS3t6u7NUfso9A3Wd6Fxaxj7ofArWAgNgisevi7+jKON1m69FuscF6zqnu9+jXmslkSCQSXDJ/CQF7gO80fYcbhm9gQ2KDqhOikwWFQoG53Bzzmfm6+yxZTNubSqU4ePAguVyO9evX09LQwtWHr6ZUreWPy16py9XlnuTaFkrNdZWdfE8+08kAGXNzcxSHipxvPZ9Hr3qULfu3cOZLZ5ptmCwWjKLBFQ9fwZ2lOylT5tLHL8VSsmBYjbrr0e2Q/PuR3iMYMYPiQJF4PF6r/VKqsOFfNxB9S5TkW5NQhQ03byB8W5i5+TkzaFKo0nFDBzM3zuBNeFn5/ZVYjFrwSECdxWKh48EOnBUn+/5sH8fffDwNrzRQspfUd4SAljlpe7mN4390PE9/4Gny/jxNB5vY8r0tMApp0uo+xE/O5/N1QRh5PnqARtR7uiJBT+GSzysVs1WWKMyE+JZ7knl0u90kk0kVaPKUPGz9zlZ2vHcHkfUR1v3bOpY/spwqVeVzi6rObrcTiUSoTFXojHYSaA4omyq+jtyPqGVFLZfL5dT9ut1u4o1xdr9/N4lVZnQ915xjz0f34P6Bm46jtdS8hcE3fc3J5xI8lPdYKozrak19/djtdgq2AlXqxTZ5W14RGTKHMn+Vipl2VqlUGB4eJhaLEYvFWL58eZ0SZrGryZ/u+G8H3Keeeirr16+vW8RifORl1sHoQlYWTGD2ve99j2uvvVaV55fv68ZHKgbKZyLN1uXoYtwE0Imx1CNhkisk4FIAji65lg1S2EndAAijJyBDgLIcTyQnOkiSiL/MwcIoukTu9E1ASAa9ersO5KHWT3Ih2NaZd11OJc9lodRdCAAx8HoVSTmXMM16ZFlXKMizkTwYIVsEEOukS3d3twLbAhgl+l+pmDUAZmZm6J7t5voHruerl32VpMdsvbFmcg22lA3DZ9TNhVST150UuTapDt461cqpL5/KE5ueUGt49aHVrD26FpZCJBJRBb5kbTWlmuiJ9HBk+RFsZRtrDq+hZJSULEjP/QdTftWf7afp4SZ+FPoR043TdBzs4Iw7zsDn8JFypTjYc7DuPUq6k0z3TrNuft3rohmS/iCyKbvdrgqC6ZVSfT4fhUKB3n29XB+/npuuvImqUeWCFy5g/UvrGSwOUigX8Pl9PHHtE5x6y6mKUJqdnaWpqcnMsz/k58xvnYl1zErl7RX27dvH/v37GRwfxHKXheIqk3lOfCmB54MeAs4APp+PdDpNf38/Pp8Pq9XKxMQEkUhEFQOU90OMvRTQkzWrFwSUtSprolgsYlgMSl8qUf58GaNQe/bCuM/OzhKLxcjn8/T19ZFMJlVetuRZy5C1Ku8KmM9+dHQUq9VKQ0MDjY2NKhIg+VnCjE9NTZFOp2lsbKyLyouKxe1243K56OnpIRAIUCgUGBkZUe1jFsfiWBxvrNHa2kpzc7PyOaAWkYUaYNTtquxTOgDX0+bkGPJ9sft62yg9gq2TznqkW0D6wvQ2UZ0JGI9EIoyPj+PxeFi6dCmXxi5l1dwqlmSXUKWq9l+o5Yhns1ky+zJc8MoFpK5IMdY2xprUGj458Elu6byF1TtXMz4+zurVq2lvb6+Tuet+h1yD/JvuX4ivIHOj+yQyR7qMXgerhUJBpaK5XC5WllbSe08vgViAYrl2L06nE2/Jy2VPXUahVMCdcWNY6luK6QpEOfdo0yi3XnArRtHgTXvfhK/gqwNgM8MzhD9n9sheenApHc90kCgmzIjua8/MMeug9wu9eC1ejLyB1VFTKoiqS47X9UwXjZONNEw2gA3VclI6YMjzl+tr2tXEsZ85lt0f283Wr23FM+vB7rGrOjgy1w0NDcoWiQ+nA0eZB2mFKSoO6T8tPqMEdgR4JhIJ5YNKz+qFKQB6G7tqtYpnwsPqL64m6U7SPdeNw+VQKlVdWi1pVtPT08QTcbVmpGCZkAVSkFgPYsncyrPMhXJE+mtkP0C8I858+zztg+2vq5ck600wB9T8aFGdSjDCMAwVZJK0R1nXMpcr9q3geuN6vv22bwNwyh2nsOqpVaSsKTweD1artU7dl0wmmZ2dxeVyKf9UgPjIyAhNTU2Mjo6+Lo11cfzpjP/WHG6v18tnP/tZPvKRj7xO3q0DV2Ha9J7NwlTpFa5FxqLndUqJfh3U6gyqHFeYLt34yCYjQ4yU3htaB4HyEkUiESWlEeMmpEE8Hlfsm1yDLqeCWkEvuRc9aqtHfOWFBZQRkO/KfMh92+12ksmkyi/WjZ5cg24E5T+ZU91ZkN/IOXXiQpexyWf6ueQ+ZJMRB0COoeeoLCRVxKiKlMjlcqliXrlcjoGBAWXQ5dkahsGcZY6vXvNVkj4TrFjLVq76yVWsm19HPpdXc6TnBevKA/0epjum+c5bvkPKU8ttc+VcXHL3JfTv7VfnlKrzLq+LF/tf5HcX/s6sgl6FvtE+rr3rWtx5t5pTcbQAReZUq1XS1TTff8v3ufCbF5JPmH0lly1fxsHug3z3zO+Sc+agCmsG1nDt/dfitXtfJ7sTAyk5R2Dmd8k5ddJI5FDlSpnJrkn29O/h4u0XU86bz+TI1BHuP/d+9p+wH9+sj4s+fxGBQq0qpjhgDoeD6elpmpqaiEajjI6OMnHzBLGtsbocOdczLtb95TrVnzqRSKgo8b59+4hGo2qeE4mEOof0+NbXoazVcrmMz+fD6/WqCrkjMyPYP2tn4qoJmIem05rwJMy1I7K5eDxONBolm80SCATo6ekhHA6TyWSYm5sjFosxV5yDAmxZu4XZ2Vm1P4TDYRoaGpS0LpVKqfkVkqqrq0s5KX6/n2g0qtapSOqWLFnCkSNHcDqdXHnllfzud79jamoKv99PtVrl0UcfrYvaLI76UV3M4V4c/8Xxn/FV/qNhtVq57LLLuP766+uiVno0SgeJOqEtNk4H3/KOL0wR06XqC4G27m/oAEA/rq40kz2zVCqRTqeZnp5meHgYv9/P2rVr8Xq9ddFsSWGT40qqzfbt25mbm2Pt2rUsWbWEj6//OJ979XP8uvfX/LLrlzizTj5288fY2rVVBVTkHvXjSbtDPZINvO57OoEg0VSodZiR38hnmUyGgYEBotEoPT09qsaJ/F7m1uv1KmWYfgyZSz2FUfzI2cZZbrr2Jko2E9TY03Yu+dtLcMVcSm328ssvk81mCbWECHqD5DI5hoeHlZpT1J3ybHUJsqwDiQy7XC7cbrfy7wKBAEDdNctzlfzicrlMsVQ0lX3xgiKh5XuyRkROLr+RoI+e/tjc3Kwqfct39d/KGtFT2NLptAKFUtBV3g3xSeV6RGEh/oq8K3IOaX9Vl0JWrbCvZx+Wt1vY9vNtGBmz8rmkkMnakDxn8Q8cDoeSujudTrL5LINnDrLnuj1UXBUsJQvLH1jOttu28f9j76/DLD2rtG/4d293KdfuLml3Tcc67oqEkDBAcJfg8I4AIww2MINLJkASJMSIG/F0p5O0pF2rurpct7vc7x+717WvXczzPYy9w/DVdRx1JF219y2XLDnXudaqD9ars6zvm9ltisWmLRQK6r304sBS40XeTerSSAHnYrHI8QXHGWge4KytZ0EJZVPkcjmCwaBaH5mvgYEBDh8+TDQaJRAI0NjYyLx586ivr+e+++7j2LFj/ynZNjf+e8YfY6v8l0a4Fy9ezBVXXKGMZkBRh+VwQrU1hF5sS8/V1aOqIqRkM+pCWY9GnXphtdn1apCznVmdJi2f0asH6hSmUqnSGkoOne78iaMuCk+Emk6jF0EO1Vxy+azu8M6OUOu57Tq6rSsu6QMpSkQEAaCihvIdERAiQMUJFPBACtgBNYaE7pDL+wvFWuZUnGthDAA1ik7yaUTxyLVFqOv0MXl+QYv1dZFo+o51O8i6smrdS9YSW8/ZysK7Fqp7yF6QHB+pSi7IbCKRwGKx0DnVyZuefBN3nXcXcX8cV9bFpc9dyurjq8mSVUJUgKK8O88r619RLccwYLxhnAM9B1izd01NJFzfm7LHHUUHH7vvY0y7p4lkIySTSY4dPYYv52NZzzJ2Lt6JrWjj7FfOxigYFCio+ZR1lgjwbMNNgCVZQx0Vz+fytA+30znaSclS2QfRYpRdb9jFwdUHAUg2J3ny409y1o/OojnerKLkYlRJPntXVxetra14f+XlHu89jCyr5K/xe8hdkeOQ4xC9vb2qZ7kwHNxuN/Pnz6/Zi6lUSp03ORd67rTse9lnAIlcgsQHEmRvOrUH6iFyXwTjfQa+Pp+SHRMTE9TV1akiaIcOHQKgqampEv0P5+HvgMMw8OMBSumKom5sbCQYDNLY2EhdXR0Wi4XJyUlVvVRSXsbHx1XkulAoMDY2RmNjI4ZhqLYfUigtFovxyiuvKOPFZrOpHLq5MTfmxp/WkIiwDhbrDpuAb6JDRTbpDjdU06jk+6LP5TN6gTTRu1Bba0Vk+Wz2mh7IELkpTKepqSlGR0dxu910dXWpwo3iHMiz6Toik8kwODhILBajubmZBQsW4Lf7+fb+b3PbvNu4vfP2yuc8GW696VY69nTQlej6A1BeZLXYH7qjKZ8V0Fvur9PK9WvJ/wtQXi6XicfjinkmLRjFppFrypqJvaNH2WXudPaU2FRPnfEURWs1glhwF9h/+X42/HID8XiciYkJJiYmKh0qPCF8Xh+lQqlm7cSe1Avf6etvt9txNjqJNETwj/pxu90qD1mcNLFhBHSRtVOOoaPCYnP4HApIEadcbG6pVC7rLHtA3zeSeiaRbvms2NlCKRcmp9VqpaGhoWb+ZF3L5TI+n0+tfzKZVCld2fos2VIW+8nq+6RSKRXIkOt7PB4mN09y8hMnwQBnwcnKO1cqRxoq9qRetE0CIrq9WigUKOQKtD3QRtaW5fhbjtP7Qi/nPHAOZXu1ALFeTV1sRmGPut1uFbCQuRObS9isot/1PaXbylarleVjy5l/bH4NuOPxeNRcSfqkrH93dzcdHR1MTExQLpcJBAIEg0FlO8+N/73jv8zhttvtnH322XR3d6uND9QIO1FUoiB0ISUOnAx9c8rndLqzHv0V4S1OpwgM+dHRZ6Gs6rRnvfe27izNjurqlGhxmKXPrnxW0DW5hwg/yVXRFagu6HVFLQJbDrfcS881EudTR41l3mTotDU98qlTm0R46NfVn0XPa9fRZpkXqRSv31MHUPQ8IVH0sges1kpv7VAoxIkTJ2oMC6FF61FzQUkv3XkppViJRy55BAxoe66Na7dei81SzYcxTZOiv8gzpz3D8hPLWTS2CKhW2dQruC8/tpx0Ks19V9+HFSu+tK8GdZd5dTgc2Ao2bnj4Bu659B6OzTuGvWDn2qeuZd2RdZSMKr1P2BUCHOhzK8/ocrnwer1MZaa494x76VtcqVZdtBf59dW/5s3PvpkV/SvUnhCKkcyj0LaEfiRKQ5B7QZ1lf+osCcMwcAVdpPypmnOctqSZYYZmmhV7wuv1KuUkdC6AyESEC2+5kK1v3VpRTF+wMOOsFEHp6+vD4/HQ2lrJ6XY4HLS2tjIwMIDH48HlclFfX09raytTU1M1eVB6D1iRHxLBT6fT5Mt5PIs8ZKmCLobbwNJsoXC4oGSGVGcVuRAKhYhGo8TjccLzwmQ/lYVrKt+f8k5h/3xlLtva2vB4PIpa5/V6FXVf3iWRSBCNRtUZiMViNRH6QqFAa2srMzMzOJ1OOjs7KRaL+P1+VcVcqsbOjbkxN/60hs/nY8mSJUru6cY2UPP/YoeIk6LbK1BbU0UH33Vqs+g++b7IMHEy9SixblvoRn6xWGR6epqBgQGmpqYIhULMnz9f9UXW9bY8hx7hm5yc5MiRIwSDQdatW6fos0WKDJlDNfNTsBaIO+NY01Z1zdnz8m+9m25n6fR8eX6djad/R+n0U3VBPB4PHR0dCrSXa0ja2+x8XH3+9SCMrJOs2ZseexOuvIuX1r4EwMoHV7Lu3nU4nBXHcGxsTMn5QqHA1NQUMzMzCgSX95Z9IzpYD/CUjBJH3neE6JIovu/5sB6p1HuRd9XZbGInWCwWYqEYRy86Su/2XkInK59Pp9NYLJX0RP0dpQOLBJ8kwPJvsTLkGQ3DqK75qSi4AEJ2u51gMKgqceuBDbmvRJ9F94rdGLVFee6G5yg4Cqz69ipc4y4VmfZ6vXi9XuLxOC6Xi8ObDrPnvXsUa+7wpYcpO8us+fGaCkPwlD0lkXi9npOso+wJsY977uvBmXKycOtC8r68ChoJy8Dr9aqK7eK8iw0eiVQq4EsPbJvNprqRCLNN0sLkeSTVz2qt5s0PLhykPFzGcbxiT0gdKOnkI8y9ZDKpzmlTU5M6I4VCgcnJybkUtP/l47/M4fb5fHzwgx+scbT1PGoRynI4xAkVZ02PXotDIIa3HIDZiO/27dspl8ts3LixxpERZ2N2pFbaH+hRYBH0sx1XURZ6JFiPCOvI42yauMViUcJH3k+uI++oo97yd1WYo1yt7Ky/s3xXhhxMqFbRlnfWnWb5nc400FFo3aHXkXWJOupGwL9FDxfGgSgLub44ezoVGipFNaQnM6AoTVI9U4S8CEFRXsFgUCn2s187mxNHTlC+oMxpD57G9Mw0we6gUjg2u43b3nAbfR19HFh6gHfc/w46Ih01DihUqPAul4uZ+TMUHAWy9iy/u/B32PN2lpxYouZV3tFqtRKaDvHGx97I7VffzlWvXkXXwS7sLrtCVnV6kjjb4qRKxD4QCCiF4wv7WJVbRZ/ZpxSNJ++hLdZWU7NA5hRQOUySiyxOsayB7AmdISHPJmvsSrm48skryV6YZU/3HrwpLzc9ehM+w0fJV2JqakpR4V684kUW3LMAd7ZaENBqteKIO9h428YKJayxQNQe5cSJE0xOThKPx5mZmSEUChEKhViwYIFKG8jlctTV1REKhejo6GBsbIwTJ06QSCQUiBWLxRTdTt47lUqRz+dp/EYj+VKe5HVJyILnLzxYD1spUzk7jY2Nqu04+fvcAAEAAElEQVSfXnfA5/NxzrnncMfb76CwvlopmI9BwVageHORY8eOUSgUqKurVJkXUEoUnxT1a2hoIB6PMz09rYwS6dHpdrsJh8NMT09TLBbxeDzEYjGy2SyhUIh8Pj+nPOfG3PgTHa2trbS1VQor6dHZfyuYIHpa9KTuhMjQI+C6I6RAXaOWPiwOvnxOrvF/omfn83kmJycrqT4jI9TV1bFs2TIaGhpU1FSeWX8WcbSi0aiin69Zs4ZgMFjV+1kLZ/zuDI6uOEr/ef04yg6+tPdLLEosUsU4dTtId2x1e0K3kfSghszVbEaYzgC0Wq1E7VHu6rmL3ld6aW1tpb6+vsZJF5tRl/dAjcOrO9dyHz1Cb5QMznjgDGZmZnDFXPTe3UuBAnZbxd4T4NXr9VIuVwpYzczMKDaeMPdEbwqALBHRcrnM3k/tZfS8UTBg1827OOcb52DMVNPw9D2l9p3fwvM3P0+0O8rY+jEu+9ZluMfcan6kLorYeGIfiL0nf5f9JPZWKpVSz+9yuairq1MOuDjfEvwRu10H8MXBFaBF0kRl/xbLRR7/wONMLp4EYPtnt3PJ312CJWtRRWz1fVJ3og5r3krRc4plYILrVReFfAGHzVFTKFjsKnGO9c484szK2s6smMHyUjXtQs6B2FDRaFSBJ6ZpKmaknEm91pO0IJW19ng8LFiwgLq6OqanpwmHw+oMZ7NZRttGuefye7BmrFz/o+txFyu533q0XPav7KtsNqvmtVSq9Ozet29fTQHaufG/b/yX5XC/5S1v4ZZbblF5lwCPPfYYqVSKq6++Wh3yQCBQo5B0GghUq5aLIwxVRaMLJKvVSiQSUXQMcVB1JxIqhyKRSPxB/pJsdol4SqQ2lUrVtFLQkWA5cPIsuVyOaDRKfX29OiRCkRGwQApC6MJJBKo4cXq0WwAJESKAuq8IPnGE9UJtOmKpv7vupOnGAFSVnvxOhJPkY+trI4JXR4Z14Qao+dQVqQxxBMvlssqlln8XCgVOnjyJz+cjFArhdrtJp9O8/PLLLFu2rKbv9MjICOVymaamJm777W0s2rCIDfM20N/fXymC1txMxpbhthtu42TnSeXAOvNOPvvLz9KSaVHUO3FEX1nxCveccw85R7WKuC/p432/eh+B0YBCPSXfSAyBnRM7WV63HErV/SE0MqFj65F8idrLXpU5KhaLlCwl7l9yP8+d+xy+pI/3f/f9NHua1VwIYiq1CWSPlUqVQiOCuupFx2T/iuAWZNo0TVpbW5menubYsWNY/VYeeM8DvOPJd+CMOpUynZiYoGgp8vyZz7P7yt04404u/sTFeHIehTQHg0Hi8XjNHgqHw5RKJZ599lmGh4fVGaqvr6exsVEZSoJuC73M4/Fw8uRJcrkcAwMDNftX9pzdbsftdmOaJlPJKTI/yGD5sgXfkE8ZHhJZFvaEoMmyjhlnhsNPHsb0aiKtDJaHLbiud6noi8zjwoULWbx4sapKLmdfn4NYLKaKsklhmZaWFg4cOIDdbmfZsmWq3oOg4jt37qS/v/+PEb//fzvMuRzuufHvHP83W+X/NiwWC29/+9t561vfqn4n9onIIT0FRvScVJMWxw8gS5Zv9n6TK4auYP7ofBwOBx6PRzlkpt3k+63fZ11qHeckz8FiWMhasnyu43N8dOKj9OR6lI4W20O3b4rFIlNTUxwdPcp3LvgO5/ziHBY5F7F69Wrq6+tr9LMOHOh1PhKJBMePHyeZTNLV1UVbW5uyfzKZDNu2bWNgYIBFqxZx/xvu590n3828zLwaOro4k/o8iX0nukcH9vXcc91uERtED0yUSiUS9gSfveKzxB1xznrqLK46eBUBV0C1mDQMg6g/yq8v/zUffuzD+PApO0+eS89Zl+vq0XeR7f39/Uwlp/A4PRjFii4sOUr09fWRmkrR1tamWHszMzNMTU1VmHx1Tga/OcjqW1fjGnQpRpmABxaLhT0f3MPQZUM1DXk9Ex6u/PSV2Io2VTjWMCppScViESNk8NCXHiLRlKjaM3Enl9x8Ce6Um2AwWFObR2xM2Y+6HSuOuPw9Go3idrsJhUKqJ7ff71dsOtM0VXEwWVfJo3c6nTidThKJhLK9A4FADfv0/k/fz1jPWE2dF/+In4s+epHS5YCyZWZmZpi2TbP1J1sx3SZrfryG9qfasWJVNWHEuRbbp1wu43K5FECQSCRUf3Cb38aBmw8wfPYw9f31XPCPFyiH9/jx40xOTqpr6CzEbDarUsVcLpeyu6CSbiJ2u1Da9XRRv99PKBSirq6OwPoAP37/j8k7Kj6RL+Hjwz/4MLaETdWtkVQAu92uGHHiS+g+x3PPPcezz1aL+86NP63xx9gq/yURbqfTySc+8YmayKxEqjdv3qyEgSBGYkSLgS4HVOjXIhR1ISLGsjgv5XJZ5VfqQlQimHq0T8/f1Z372XnmgmDKJhcnWRA/HZwQASYAghwaeSc5kOIU686unn+rR8/FwRWFo0fzxVGSQ6jPszyPDnaIgBclInMuikenEUFt5VRRVCdOnKCtrU0JAwEnZlO9dZBAp8LpNB9x+i0WC8lkUlXTlhwZoWCLU+J0OmlqalKUf3nHUChUQfyt4PqQi0NnHmLxI4tpaWlhYmKC/v5++q7qY6xJE/JAzp7j3rPv5V0Pvks5xvL8px04jQPzDvDa4mp7sLMfOZuG6QYypYxSri6Xi3Q6TTAYJNOe4cnrn6T8fJllx5epvBwRllJdVBxjQO0T2RfigFutVmyGjeX3LadvrI/LDl6GUTKI5CKqtVUymcThcHDkyBG6ursYWzBG93g3hmEo9FoUvJwRYZFIjpSg27lcjkgkgtPppLe3l0KhwIfu/VAFEPI61b4PNgbZuWUnO0/fCUAmnOHpv32ac//lXNqT7eTzeWZmZlRVT9lrAgL19vbS0NCg8t6mp6eZnp6u5L+FQgqFFuWeSCQol8vMmzePSCSijMtUKqXoX3Ku/PP9FGIFeHOlhVrRXfyD9l1i6OXzecLhsIooT948iemstck9z3ngzah51NMoDh8+TCaTYf78Ss/a1tZWVZgtHo8rwCMcDiuqWkNDA5s3b8bv9xOLxYhEImQyGRYtWqSoiMLwmBtzY2786QyLxcLmzZuVc6iD1LMDA1DVnbqeL5VKpKwpfrrgpzza8iiPNz/OV176CutS66rsKiPHrXW38rOmn/Ez82d87+T3WJFcwbdav8XvA7/nBd8L3HL8FtqH21WU2mKxKHkIldZYw8VhHrjsAY50HmHk0yN8befXCFlDNXaM2Fez84n3+fZR3FFkeHCYnp4eWlpa1Dtls1mOHz/OwMAAwWCQFb0r2Hx8c8VxtlWLz4pNo9PC9ei02CEil/VotNxnNlAvcwgwFhzjm2d/k5inkoLz/MXP0xpu5YJ9F+AwKzbXQHiAH7zhB6TdaX52/s94y4tvIZgJKodf7BCdwq4HHqaD0zhzTpiqsKjIQq5QaU1b8pTYcc0OxofH6fhpR029mmQyWanZ02Zn5sszJM5OsG3dNlZ8ZAWW3RaVl2yaJrmmHInmRI2zDbD565sxcgYWu6XGORe9vv2K7aTqUrX2jC/H4bce5uw7zlb7Tu4jYIrYIhIFFn0qa59Op/H5fEoXif0o+dGyhvJ9sXElGi56Uk/jFMdU1tp30gfd1Dy7/6RfXUcYhvF4XIEFrWYrPX/Rg3G5QfsT7ZTLZRqaGlT0We4nto6shfxbbC5fu489b97D8LnDlbPSO82T732S5v+nmfjBuKoto7MH5d9yDz3oFwgEVN2ZcrlcbSsKNalipmkSj8cZHh7m6DuOKmcbIOVN8cRZT3DuPecqBovValWOtg5AeDweBYgMDw8zPDz8xwuxufEnOf5LHO4LLriA7u7uGmqVw+HgmmuuUUJUinXIBoNqOyw9B1uUlx4xlOJk4gzr7cB0VFWGjgLrNCzdKRB01zRNdT1RHLqzq1Ok5NpQSweRv8lndcdbf2f9HXWHWwS4jnaJgpAD/+qrr7Jp0yY1b3IdHU2U59MpXHJNPZov8yXXEIBCHCXJzxkaGqKpqUk9h3xGFJWe4yxDHG5ZRz0aPzQ0hMvlUiCFACcul4twOEw0GsXr9SqqkJ6jJO8mf//9mt/zypmvgAHGhQY3Pn8jrdZWTpw4Qdf9XRglg6eveRrTWnnX9QfWc+NzN9a0e5O9daL5BBP1EzXv8Vrna/Qe7qXB2qDeUQysEc8Iv930W4aahvjV1b/iDU+8gdOOnKZABlnrdDqtWlQJRUj2ip5DLmuVzWZp/pdmXGe4KDvKysiSKp8NDQ0sXLiQ7au38+gZj/Kmp9/E0kNLVSsPMSzEwZ9dyEdf93K5rArH6ZEAvbih3WPnZPhkzbxkHVmGXEP4hn2KOpdMJlVagBhS5XKZlpYW/H4/XV1d9Pf3MzIyoiqHp1Ip1ed6/fr1jIyM4PV6lcMqxcei0SgNDQ0MDQ1VAR9bnrFPjVFsLMJ7gWOoFA7JZROqmGEYhMNhFQUBCH85TCaaIf2RNADOe5y0frkVWirfj0QqQIc4+Q6Hg+HhYQYHBwFobGykt7eXzs5OmpqalAEsRonT6SQUCqkou2EYqopqOp3GNE1FVZ8bc2Nu/GmNuro6urq6amSjnG3dCReDWU8lE1shb+b5Uc+PuL/jfgDKRpmvbPgKnzv+ObZEtmCxWvhpx0+5pfmWyk0N+GTHJzl38lweDT8KQM6S40NNH+Ky5y9j3uF5GIZBJBJRkbV0Os1UaYqXb3qZkaWVwpVJR5J/XPOPfP7451kZXVkT4YXaWi/bGrbxraXfYt2JdVxrXsvChQtrinNGIhH279+P1+vl7LPPVoW99Mg2UGM3yb91EF4AXJmz2cVp9Vxr3e6SMeIdIW1L16zRROsE3iEvRsZgf3A/d5xxB2l35TO7u3ZjNa285YW34Eq5aurS6MEduddUcIq7L7mbQDzAGbefoWwwALvbzq4bd9F/foWJFA1HWfLbJZTyJaLRKKlUilg5RubmDOalp/L7vSVe+8vXcH7YiXe7t8okrDfJ+Gt7KLfuasUZqdbiEbBcz4lf+POFZONZjtx4RDmuix9fzOl3no7dZVf1TcQ2lgiszLWA4GKDCOvSNE2i0SixWIxUKqUi3QJS6OCIFOiV30uqnVD4xXbI5/Oq4FihUGDtj9ZiZk2OX34cgK7nulj5vZXkijnVLlRYj7JPIpEIjhEHjXc14qx3KkaZ2F+AiqrrbYdjsVhNYMcMmKRaa0HtmU0zJD6ewPFRB450tbp6IBCoASwkdUDsqVgspnK9xc4Thq2AC4VCgWg0qjqalEol/O/2U/jHArk3VuyTjS9s5LxHzgNbNQAjDDwBK+T+NptNOeDxeJyTJ2ttsbnxv2/8px1uoV8JZVt3gvXoqdCwhZahf1/Pq0mn0+oQz6ZxyXVEaIjwFAEgTpQ4FHreiVxDryyuJmEWOivfffzxx1m0aBFdXV01AlC/pvxeqE1Cs9GdF4nMyZB76NQpHW0Vpw2qTlNjY6OKhM/Oj9Lp6PLuuvKX/+rOtChCPQpfLpdVhBzg7LPPVo6anlMlgi+Xy6nCUjqFWV9bXUh7vV4ANT+imEulEh5PhaZcKBRU7q3H41FrIWCAaZo8uvFRntj4hFI++5bu4+e+n/OO37yDBQsWkEwmqd9ej7Po5JE3PcKm/Zu44ukrcFlcSrHLOzudTgK5AO5clTYF0FZqY3JwkqnEFKtWrVL55VlPltsvv53BlorzVbAXuP/8+zFsBmtfW1v5nUb/F6fNYqkW+piN6MreyWQyNbk7Orgj9PTtm7Zz/2n3k3PkuPOcO3l94fVsHtusrim52mKQ6edNzotE36WGgigHiZALQuwquXjj82/EipVXlryCrWDj7Y+8nVA2RMQTUWCIrK2ss6ybVD+12+10dXWp4j3bt29XjnUikWDp0qXMzMwounkikcBqtTI9PU06naajowNAIdzxH8bJX3lqn94Brte7OHPJmWzdulW9ZzqdVsh3JBJRLb5kPzv/wUl6Kg2LwPu3Xsr5CkgYDofp6emp0BgTCfbt26cMCVk7AQ4OHjxId3c3nZ2d+P1+UqkUiUSi0pIkk2FgYABA9SRvaGhQskbvmTo35sbc+NMZa9euVXpRhuhV0W+6vhOdLDYIQDFfpCnSBB3V69rLdpqzzRXbxWJlfmZ+zX3Ngkn+QB5aqr9zFp00FZoU0N3S0qJsBq/XizVrJRgJMsKI+o674Maf8tfYOvpzW61Wttdv5zuLvkPUFeWFq16g61gXG6Ib1OcOBA7w0ImH6LH3sG7dOsLhcI2togcmhDkl766ni+mBBfkRe0hnO4o9Jd+Bah2g7gPdnLfjPH73kd9RdpQ5u+9s3r7/7ThLTky7SbPRjLfkrZnLukQdlrxFPd9sKrk8R9Ke5I4r72CotVIUbur6Kc7/l/NVe6cXb3qRY2dW2zBNvHmC3Q27WfS1Rap+itPixBw1yVB1pt1FN92eblxd1ahnabRE+W/LHP3WUfKNeZr2NrHmR2sIZ8Jky1kFGosTKfnNAEvuXYKr7GLPW/ew6N5FrPrdKgrFAmbZrJlrndkpRUOla47YpDogIkXQpN6MaZqqJomsp8/nUwEqmUOJoEtwRfa+3++viaRn4hk23L0Bv9VP0pFk0S2L8Nl9lK3VYsL6+ZHUxWRrkvwVeYLPBBXAJPo7m80qR9Q0TVXzR4rqyl70TntZ/p3lvHbza0RXRdXaWEes+Kw+bB6bslckXUxYAbNbm+XzeZVOqDMeGxoalB0j7VNlPtPpdCVC/Xk3lGGlbyVbtm0Bs7ZWlD63Qs0XH0BvJazb5nPjf+f4Tzvc5557LuvWrQNqc4Z1AWexWJRDLhtHBJ4IPxHK4iDoVRX1XGa5vggMnSauF1TQo7w6cqo76vKj99UTtKxcLnPmmWfi8/mUEyIFF4AaRSOKTRwl3bmFqkOvKxhBx3WnU3JTZS4l/7ZcLtPd3V2DtuvFxfR5FqcJUE6HONc6aiyCTY+EC6qn08Pl4Isw0XPGhckg19bp7EInl3ez2+3U19fX0PQlAqmDIfK+Xq+3BnktlUoqN+nCwxfy6tJXmQxOggHWvJULn78QC1VadywWo/XJVj524mM0OBpw5B2kLemqQXTqvSwWC30dfQw31tJ1Dq06xNqFa5l4YYJXX32V7u5uGhsb8Zf9nPfaedzReAclawlMaJ9oZ9mhZcpwKBQKivL3f9qbkh6h72l3yM3Q14aI7I7gn/TXGAYul4vdvbt5YNMDKtc84Utw98V303BvAwsiC2oYJkJr11MBRMEJy8Plcqk5FZaHILjynN6Ml8sfupy4Jc6V26+keaIZ9zw3jY2NjI6OcujQIZqamojn4jz3l89x2rdOU4j10NAQ4XAYwzBUEbFsNktDQwPHjx9ndHSUiYkJHnvsMUqlkvq8aZq0t7crY0BPIRn+h2Hyl2mKZyPkHs2x54I96qz19/fXAGFStCwYDKrK4vmGPHwUsIP1cSvGixXDQSh1pmnS2dnJvHnzOHToEAcPHlTovZzlqakpJicn2bVrFw6Hg9NPP53W1lZlFMg5sNvttLa2qpoPLpdL7YW5MTfmxp/WWL9+vdITun6SoQPmOjtIDzgYZYPLBy7HtJn8bPHPcJVd/Ozgz6jP1VM2K5/d3LeZ9wy/h1tOuwWraeXDv/gwnkkPGTPDS+e+hCfj4aZ/vQlX0gVWVOqNOKSlUgkzZbLlmS14mj3sOm0XTdkmvrLzK/jyPgpm1bmSdyiXy/S5+vjq4q8SdUYByNvz/LL3l4T2hrgmdQ3DnmE+vezTZBZm+FD4QywILcBhr3ZNEVtKQACx33TGlg60y+/E/tK/o9tLej0bnVo+MTFB6GSIm397M8+c8wzv2PkOPGUPxXLFTqmbqeMTT36CL13+JSZ9k5xz6Byu2XkN1qIVi63qzOm2lTzvj970I0aaq2DFydUnef4Dz3Phv15IPp+n9xe99K3ro+w55SClrfTc1qMYZXa7HUvJQvjWMImmBJNvmcQ542Tdp9dhi9mwBKr1eUqlEu5JNwu/tJDnP/k8F//4YhwFB85gJUgjFarFbpIIbi6Xw5q3sujRRSzesRhPrhKJLRtVB1p0k+44SztWmU/R86KbBBQul8v4/X6CwSCtra0qGq72mFlNQ5T1keuLYy12ijDyBMS3Wq2U4iXW3r8W0zCxuiq2dzqdJhaLqXWWFMNyuUzGm2Hw1kFMn4m/7Kf9uXZlCwnQJc8ngQgBw+U9ZT5CkRAr/2olr37rVTILMrjvdOP8ohOjbOAL+lRKwMzMjLIZCoWCSj8TR17mWG8b5vf7FchgmpX2XtPT06RSKaampmhoaGDBggWV730jz8IVC2lZ3kIulyOTySjnWlhvYpOLPyJ1DwzDYHR09L9Qws2N/6nxnyqa5nA4+MpXvsKHP/xh5TTognZ27qqeO6sfVl0pzHZItfvXfG92dBdqUWfdwdUNWxG0cm+J0ErUUxAleQ85XOKEQKVfpU4jk+INenEmPW9Iz9EW4S+5y+LE68CDQkRL1V7dujAQASrzIN8tlyutPVpaWmoEhJ4Lr0eqDcPg5MmTdHR0KAUqayfPJQJbz30aGRnB6XTWFBCTewkFR9ZBqEACLsgayRzJc5fLZYaHhzEMg0AgoCqUT05OqmfTqVKm1+QT7/4EGHDOT85h8auLWbhwIV6vl0QioYpaSXsmPUpstVqJxWKKDpTwJ7j1/Fs52nlU7ZPzt5/PBdsuoBArcOjQIUyzUmisq6uLfD7Pzo07ue/0+2gbaeOmO27CLJuqJ6jsFXGAhV6uV4mXCqGSqxUxIzx0zkO8vOpl7AU7b/vB2wgPhWvylTweD0+f9jS/P+P3FOwFvFkv1z1zHWsOr1H7Wc6XFA7RlZREzGX/6nnsEomXcyRUcVlzoTgJ8KKf4/5UP3ddfBcjG0ZwzbjY/IXN2PoqRUHEgZY0AbvdrvLHo9Eohw4dUi21BLDxeDx0dnaq+0jKQ6FQYHpmmvjdcVKbK1Qx61ErjosclEYr+1Po48lkkkAgoIrJJRIJBd7lFuWYun+qWjTNhMa3NRLYFlDtgKSwm44+j4yM8NprrzE2NlZTBFEQ6ba2NnXPxsZGBU7pkQJ5v+HhYR5//PG5SuX/l2HOFU2bG//O8W/ZKn/s8Hq9/OAHP6ClpUUxoERH6aC1Tk2WITpad+hsNhu3dd3GRWMX0VHoUMZ0KpXi+PHjnBg4we7Nu+md7qV5uJlIJEIul2P7G7dz9razCeQCQLV1lW4HSe5zd3c3y5Yt43srv8f7j70fW7aqd+SZhMlULBaZnJzkEdcj3HXhXWQDWexFO+dtP48tz2whuzjLV6/9KgXbqei+CT849gM2pzYrWabXfxG9Iqw0cYZE5svQdb4U79QZcJlMpiYvXnTo2NgY+/fvx+l0snbtWnx+HzZrNYABqJzasqvMHRvv4O0vvl1FXUWG6wwFHSBJu9J8783fY7x+HIDWw61c88/XYJYr65zJZHhh8gUGfziIw+5g9WdW4x51MzMzQyQSUXaYALoH33+QJb9agrfgVfcW57bQUMDv9tNsaVbRS9lLEizRWYhiSwljQAIk8llhCegOmtfrVXMoRdfEIZSIuayX3W4nGo2SyWTweDx4vV4VtdaDKnqetji6elBGnlf2pNjTsqbOoBNHr4PAWED9Xi9KFgqFSCaTZLNZJusmeekfXyJfl1f7b8O3NrD64GqSiWRN4Vd5F6i2EdYj/BKpzmazJLIJjn32GIv/cTGFfEHkBNFoVNmlcrYFJJczLnOhgwjpdFqBE6FQiKmpKZxOp+qqI3tHbGKLxcLChQvp7u6uKQLscrmIRqPk83laWlpwOBxEo1FVoFfAg29/+9tzEe4/8fHH2Cr/qQj30qVLOeecc+RmNZtTj0TPPuhiwIuDoH9XbymlKxlxQIWaq9OSxeHTn0MEqkTMi8Wicjr1z8h3dEfz34p2S8RSj/LqNHT9uiIQxdDWgQFxZPV3z+fzKsIscyDCQ88nFwUl15KoqSiUQqHA/v37qa+vV0JSp2RLFF2cVrvdTl9fH+3t7TXCxTCqVRr1d5K5kGJPHo9H0XskCqsLah1oEeUgCqFYLKpoqgg8n8+naMMSBQ6Hw4yNjdW0hzIMgxcWvaD24ciqEZp3NLN3716WLVumHCs9j0zmQaeTC8o4Fhgj4ovU7O3+hn5iRoxmfzOrVq3ixIkTHD9+HKfTicvl4vyj5/Na72u86aE3YbPasNhr8/1kz42MjNDU1KSimqKAZc8EAgESxQQPn/UwL69+GYCCo8Cdb7mTy++8nEVji2ryuy7acRFWq5Wn1j/FFU9fwarDqzCshjoTukEjSLWsg8yFKMRisaicYAGLZL/quccy5OzJebPZbGS8GX5/we8ZWVmJEmTrs+z63C42/XATnkEPY2Nj1NXVqX1QKpUU0OF0OtmwYQO5XI7h4WH6+/tJJBKk02mOHj2K2+1WrcNEcXncHpo/1czRvzpKsaVI4+casVvsxHwxZdDJf2OxmIqMNzc3q4q88TPimHbNJjcge34W93OVivAnTpzA6XRSV1en9l0+n2fBggUEAgH6+vqYmZkhmUwyNjamQJO2tjb279/PkSNHCAQChMNhlixZQiAQqDESxsbGGB0dVfM9N+bG3PjTGMuXL1eFOSWyJ3aAOJB6ZFZsCDHI9Rxcl8uFw+HgPaPvqdgwtsr5TyaTnDhxgr6+PlwuF+cdOo98Pq8M7XA4zFXPXVW5rq1qN+jRO3FQurq66O3txel08tEDH61EiksFZXvpdUPEsR0ZGSHQH+Di4sX8/qrfs2XnFi559RKytiwvul+kZJRq5uQJ+xNsKG5Q1xOnTKd/67RXPZVQbBfdyRUAU2wCAS51sFp0zPj4OBaLhQULFlRoxCY1Ml7sJMMwcJQdvGPbOyiZ1Ui8OD+ik+XZxEYNFAO89Xdv5ZcX/ZLySJkLfn4B5VJ1vsbGxrD0WVjw1wtobmimOdNMzIgxNTWlGIR6he/wF8PkQjmyZpZ0Oq2eLRfKMfCOARq8DQR+GcCesitHTABwyeeVXGyxl4+vOE7r/lZsZZtyrMV+lPfp29xH18tdOGwOtT91MKNcLjNy4Qhdz3ep1E6r1apsRQmKpFIpFQzS2VwylzLvYp/3ndVH79bemqCUblcbVoMDVx9g+sxpzrzlTOoH69U1s9ksFotFtcssFAoMzhuk4NBSrQyYWj8Fh6vrrq8j1NKz9cBCOBxWf3NanKz6p1WUrCXShbR6XrF1pR2XRMrFJpIhfoxQyJubmwGUQ6/XjvF4PMrOkdS+ZDKp7DA9VbJcLhMIBGqKDFssFvVdncEwN/73j/+ww22z2Vi/fj1LlixRzpsY8zrtSEfBRAjohQ5kSMRNkGSJcgttST6rO4YSXRKBDdUcbXEQgZpnECdr9vV0pSoHQ6K7FoulJmdEBJIIF0G1xBkHVIuCY8eO4XQ66ezsrFFAghiK8JLr69F9cVB0upouZGUe9Hc8/fTT1btI1P3fUj7yrmeeeWYNSgjV/uQ6WqlXOu3p6fkDyrgIBF0Yy7pKdFJaQOnvKc62tEuZnJxUz2G32/H5fNTV1TEzM0MwGMRut/PE6id4aNNDKof76GlHMYIGW769hUOHDtHR0YHf71dCU/aQDrjI+ubzeTqOdXB99np+fvXPSXqT9Az0cM0j11Cfq8fiqDz/vHnzqK+v5+jRowQCAQ6vOcxQeIgHz3+QGx65Qe0bfU+JQSF7SLWKOUUVEgMmnovjy1cdWwBLyYKr4FLz43a7cblcZDIZLtxxIU2jTSw6vohopkJR1q+vz62shTArpFCcGITyHHpVbqHGp9NplaIgZ1n2j3zWaXESIFArG4o2rNnaiqWydwSwGBsbq6lQ39XVRVNTE6Ojo8RiMUZHR1UxsnQ6rbocNDc3Y81a6fiHDmbsM/hH/ASbg0oeTExMqHMi510Am7a2tsoz/MiJ8RkD03HK6f5H8P7Iq541Eonw2muv0dbWRnNzs1LGdrudQCDApk2bME1TFRU6fvw4kUiEl19+GbfbjcVSqcSfSqWIRCLU19dTX19Pa2uragkmwNLcmBtz409nLF26FI/HUxMRFV0pMlDkKlAj2+Q8i0wT3Sm63jRNEokEQ0NDDA0NqahiqVRSUVq/31+TfqS3kbRaKxWYxSnq6Ohg0aJFBAKBmmfRHSPRx+Iw9PX1cejQIRobG7l48mIWvriQhX0LiWQixONx2n/dzqbJTbx000sAnP3c2Zy26zT62/uZN2+e6s6gR7AlAmuz2ZSO0utl6M6FfEf0mq6nZOjR7YmJCerr6xVgKvae/k6zbSK5tqyXzhjU50ecnMBwgPN+eR7ZoSz2rJ2iUVQdOA4cOIDH46FrrAvblI2ckWN6evoPdGUkEsE0Terr69W7SGpbqpiicEuB7IYsQwyx1beVC39wITajYn673W4ViNJtV4fDwdGVR9n11l0MHRjitH8+Tek1neV5cMtBdl+/m+jiKJt/tVnNswQ0rFYr+6/dz8GrD5JtzrLuoXXKyZU9rneNMU2T4+cep3l/M6FISO3/crmsotKZTIbD1x7m4OsPkm5Ns+w3y4BK1x4B/J1OJy+/8WWOXHUELPD8Tc9z1vfOIjgeVM8nayr2x7wn5+Er+9j7V3vBgK5HuljzmzWULdW0Az3YZLPZFIVd/l/yrIXZVigUsLvtHHnbEbp+1KXmVt93uj0vY2xsrCbtVWoW5XI5BebPnz9f+TVip2UyGYLBik2SyWRUEEQYMzLHOvNTbCMBrXSWwIEDB/5LZdzc+J8b/2GHOxQK8da3vlVtGBHwUKWg6rQf+b041XphMF14i5IRZ0miajqFW4/W6QdGj6oLQifXF+dHEEQ1AZpglgMt+bVyyMRZk3vL7z0eT41y0xWeIL3t7e01rbz0iLHMiV5EZPaziyBQDo6W464rEaHA6LQpccDkkOvzriPJch/J7ZF10JFqfY3lnnItQWN1+r3kzeq9IWXdpKiUvL+OXIqhIo5eqVRShbSEinPa0dN4duWzRGwRMCrO6Xl7z6OtvY0jh48wODjIwoULMSwGJ1eeZNwxzul7Tsdhq66DrKu05qo/VE/X/V2M/csYb3n0Lfizfiy26hoJhd7j9bCjbgePXPwIOU+OHYt3kC/nufGRG3E73GoOJOIvfSllTvW0gVQqpYTreVvPI2fmeGHTC9hSNtrf1k6+Pk+sKaZo0fL9YqFI79FevP6KIxiLxXC73cRiMUWFtFqtZPNZ9pyxh1AsRO+RXvUu0ivabrerQi2yXlJpXPa77FXZUwKWyXrno3mufPZKcrYcO5fspC5ax/seeR+uBhfDuWFOnjypjJsCBV74/Atc+6NrCYVCNT1UvV4vgUBAsSqcTieDg4MVGvn0NFChDhpGJeUgsShBblmO/C/yipYmaQcej4eBgYEKZfBUrl2xWGR8fJx8OU/ih4naHtwvQmQsAiHUO5bLZY4dO0Y6nWb+/Pk1DAWoGEnBYJC6ujo2bdrE6Ogou3btYmpqqiadRFqHTE1NsWvXLpqbm+nt7VU0v7kxN+bGn8Zwu90sWbJE6QSR0zo1WqcJ67pcdHY+n1e1MURuiiwtFAoMDAwwMjKidIOkgQnDzePxKBtJQGzd2Rdjvauri0WLFtHQ0KDkteh+PY1IZEwmk6m0zOzrw+124/f7cbvdrB5dTd5aAZ5HRkYYHx9n9aurmT9/PtP+aS7acxF2087IyAiJRIK2tjaamppU1wWr1UqePIZZtaF0naGDEGXKlChhoZYBpz+/YRhYnVbyyTyDg4O43W4WLVpU02pK2SYO1FzNdpZkbXSHW6fBm6ZJ3sxjliqVup1RJ2bWJEdOASETkQkwKrausPgkEutyuXC5XAqEkL0htGqxr9xuNyM/GSG7PquebXj9ME995Cku+OcLVFEzibLKGgOMLR9j+7u3k/PmGDxjkJJRYu3X15JJVOygXCHH8JZh+m/op+gucvDcg5XK4LetxWJW9pvFZuHw1Yc5cO0BSs4Se6/ci6VkYfHvFpOP52si6dlslkAwwJHVR9h7014OpQ9x0Scuwp6xq/NhmiaxRIwTV53gwBsr1zx09SFy6RxL715aY4Puu24fxy47ptqgRboiPPWZp7jsM5cp9gdU0jgEtGlubsb2vI3WD7RSfl2ZNb9agw8fhVJBpeXpZ8EwKvnfcl71AJ2ylyiy6+92EV0eJV/Is+hni7CY1dx6AcV0xzcYDNawSsRZlqg8VGypcDhMKpVSgS+n06lsV2FxClivM3VTqZSKhPv9fjKZjArcCVhRKpUIhUI888wz/6Vybm78z43/sMO9Zs0azj777BpqdalUUlEzHR3WW0mJItAj0Dq6CVX0E6hBNMVx1nOQoUpdEsUnlZr1qCOgUCOhOOsHU4xyEeByLYmiu91ustmsihrLc+rOh1xH2gJInozQhnSjXZSMINByTTmQcqj1ohS6UhEnXtBmmWuZf3k2PTotClKGngck15fnk0JlAi7I9SVCKvMj8ycRYxEqIjAKhYJyvCWfWwdmxEGtceJORTMFdCgWi3R0dBCPx4lEIviLfj71s0/xrbd+i7QrzfWPXs/yseVY663Yl9s5duwYW7duxX2Nm3uuuAcTE1fJxdoDazHK1TUTGrVpmpTaSwx+YpBoIMp9597HtY9cS8gM1ayx0+kk5U3x7BufJeepKEXTYnJo4SG2T25ny+4tlEol+vv7cblctLS0qMrcsndlTXO5nGrL5fF4sGFj0QOL2O3ezdWvXo27082+ffs4efIkS5YsUZFWAaFKpZKiKglIkc/nicfjlfNmM9izeg+/PO2XALwn8R5Wja9Shp3sT0HL5RmlVUcul1MGgCgTna4l/y0Wi3isHq5/6HpK1hKX/+Zy/HY/nqBHGZWpVIqhwhAv3vwi00umufNjd7Lp7zfBNKoOQCKRUDlkhUJBdQcYGRlhbGyMSCRSocWNjDDRO0HxtmLFGHKGMO4xiE1WerTqxV8mJyfV2VBRl5tNipcUa3uhfhtyp1UKmsn5a2pqUq3xZmZmWLFiBYZRodjLvInytVqtLFy4kJaWFgYGBjhw4ACRSEQVwUmlUqpC/+DgIMlkUp3LuTE35safxmhtbSUUCikgWRw2qKWz6oWoDMNQhr8YyiKPRZ+KzXHixAnFeKuvr1dAnNgUeoQLqrm9pVKJMccYmf4MlqKFxsZGWlpaCIVCNdRhPWosQQvR3xMTExw/fpxUKsWSJUtUActkMsmga5DESIL+/n46OjtwLHGwec/mCnBrt1KylBR4+Nprr1FXV8eKFSvo6OhgzDHGjR038rUjX6Mz1QnUMqDglL3jMHik6RGOuY7xnuPvwVlw1uhEcbzjtjjfWv4tOp7qwBq1smD+AlW4Vrdnxr3jfO2ir/G5Jz9HU6pJyWG5r75uYkOJjjMMg5gnxo+v/DFXPHwFZp+p0t28Xm8l59eZYN9X9lH6Rom6yTplK0iHCofDQSAQwO/3qyCI2C4SObZaK21Ml/1sGTs371T7LDAUYMu/bFHPKuslaWEWi4W0K80Lb32BnLdiZ2CBkTUjxFfHCfy0AtTkFuSIvDMCpxqsmDaTo5uPMvXQFOFHwhW7c2OGkQtGMJ2n5sNRYv9F+3HtdhF+LawqkDscDgyLwcCqAV795KtggYK7wKPffJQV719Bbk9O6bLUqhRTl03BqQYoZUeZ45cdZ/qhaVzPVmxcu90OL4HT5SRzSQYs4Il5uPCHFxK0BSlbyoo6r+/VfD5PPBbH97SP0K4Q6XAaV50Lv9+vAgWSeiA2tP59v9+vzqNhGNhb7Rz50BEiqyvBmeEbh3GVXPTc04MlZ1EOeyqVUsEEcZjD4bDaQ1KjJRKJqHQ6m81GKBRSARyRCxJc0u17n8+nOslIvnsul1PAlQR1hIGby+VUezUBYObG//7xH3K4DcPgc5/73B9QdXSKkCgicXp1B1F37vQcHEEhPR5PDfqkFyPR0WWdziX30VEkXSmKU6G3NpBnFudWDoooPb1yplxXKD1CYwGU8Q2o3GndudaBCP39RXDI+4nCmD0EWROwIp1OUywW8fl8ioqjO3N69fPZObwSwdfbp+k5MHLIBTSR38uzynxI5FqccQE8ZlO+9BQBeU8RmjI/Mlcyb4L2QrUirNDu0+lKr00fPt7923dzsu0kawbXYFgMhSgvWbKEgXUDPPLORzBP1c/59aW/JlvOct7x85TikL6LE80T/PLCXxINRgHYs3gPjoKDq565inqjXhk0xWIRT9bD2+57G7+57DeMtYxhK9q4dPulbH55MyVrZX3b29uVkSUCVBxXYW4IvV5ypKecU7x81cske5L8ZtlvuNpyNcvN5QwMDLBt2zaWLVuG1+utpCqUiuxduJdVx1Yp51koS9Ju68g5R7jrgrsU7f6W193Cux5/Fwv3L1T7Wehhpmni8XjUuYCqgalHKQR0MYxqjr+kLLgcLt775Hvpj/cTtUbVOevo6GDMPsYT5z/B9OJKpHqmZ4bXPv4ai/5pEeF8WCka2RM+n4/p6WnsdjuNjY1s2LCBF154gUQiQeT0CMU7inAKNzr56ZMUKZL/Wp7W1lasVitjY2OEQiGVBzU+Ps7ExESlcNo2A2PMwOw5BcTtAus7rJRnymTNrDKqZQ9KhH3v3r2EQiGVriDnRwBFoVT29vYyb948RkdHOXLkCKlUipGREZLJpELJpaifHpGZG3NjbvzPjvb2dlpbW4GqjtJpyTJETum0aUnL0SNlYnQnEgnGx8cZHh7GarXi9/spFArE43ESiYTSe3I/nQVlsViYaJzg3ivvZf6r8zn9+dNZuHAhnZ0V51YMc92WqQkkWEu8EHoB324f+XyehoYGVbwzl8uxb+k+fnPab9gS3cLi8mIiWyL87HU/451Pv5MlQ0tUBFSAfXF6Tpw4wWHbYf5pxT/R7+jng4s+yF8e+EsWjy9W9pce9Xts3mN8u/vblfkrWbhhzw2YmWphOavVSsQS4fb1t7O1eStcD5fmL2VTZBOxWEzZPoVCgZG2EX52wc+Y9E/yzQu+yQe3fZDumW5l8wntWGxFqC1MNh2Y5tfn/JrB5kFuefMtXJy6mNbXWpX9kw6kee7a5xhZOYJxi0HsWzFCO0OqurSwDES/yPoLo0oKshqGQXRjlCNfrvbPBpj//HwK6QIWoxrQEUqy2IjenJdVf72KnR/bSWFlAfJg/J1B9htZslRS0hgH4y8MzJ+YsAiIg/ULVrI/zzLtmK7o7YfAZbjIfiOL2WriirhYfctqGvY1YHPalI2YTqfxBX2cPOtkFYw2IOfOsattF85HnNVCx8NleC/wfaCTynPcbBC/K07SklRnwm6343yLk8xoBm/My9m3n03raCuRZETli6fTaYLBoAJfZI9NTEzgcXsUU1YYEHoNpNm2rg60COgx2TZJrCVWnX8DptZO0fRQE664qyaAJP6HzibUa0Op4IwGlAi9XWxysX3lDEoAIZvNEg6H8fv9qpCdAEFCQ5+ensZmsxEMBtV1Tp48OWcn/BmN/5DDvXHjRjZv3lwj5IXuoUdJdWEi0VGdiqzTmeVQiWDUD5HejkDQQJ0aLiiuFH+QgyttEqAajRbnSRxYObSAUkJ6ZF6eVQ6H5AzJu4jzIY6s0EeEFq070AIUyEHVq4Lr0Va9ArJE03QlLHRlQfZ0Z242C2B2RFnABJ36oj+bHHT5vKCFMrfSwkAEnSgp+a7cU1BTEVh6br2gyYACLnRgJZ1O09LSolDpQqHAkSNHWLRoEXV1dYot0J5rJ5aJMdI5QmNfo5p7m83G4esOY9pqC2M9ee6TnHnwTLVn5F3kXWvGqX/qRdeEct8508kbH3sjd155J0lvkgt3X0iGTA2gIsCM7Fk9L1/eCypUP9Nn8tuLf8uB+ZVcnZK9xGPXPcYlnktY++pauru7OXr0KC+88ALLli1j97W72bphK4UXC6x6aZVqgaEbXJlchtnDxFToqs/nIx6Pq/dOJpNKuQkdTt93OrLscrlUVVoBUmTtm5ubFVNErmO1VRkcMorFIl6fF1e6ArY4HA6mpqZobGwknU7j8VSUrdRCWLx4MblcjpGFIxyxHqFMVQmNjIwQKoXUmSwUCkxOTuLxePD7/TQ1NREMBjlRf4Khvx2qOtuVScFus1O2l2sK/iSTSaUopcYAVAzc+fPnK0q8yAsBUeTd582bR0NDAxaLhf3793Pw4EGllHXZNzfmxtz4nx82m4358+crR0mnkQtzTX6v2y0CdEutEAEa5TO5XI6RkRGGh4exWCw0NDRQKpWIRCo505L3PDslTHTumHeM+664j5H2EUZbRwm3hbk0eqnSKwKIyrPpTLtyucyPl/yYZxuf5fKxy+lKdTE5OcmJEydoa2uj79w+Hj/ncbLuLC+96yV6B3s5sOgAKU+K28+9nRueu4HFfYuJRCIkEgkaGxsJBALk83l2xXbxu+7fccR9BIBJxyR/v+Dved3+19F4qKqLAXZfvJsHFzyo5vPe+fcynZ3mgt9coBh2ZWuZh976EHvb91Y+ZIFn3vwMloctLHhygSoCOtQwxNObn2YiOAHASGCEn2z6CR/Y/gHaJtv+gK0nzpnMR9wR545z7+BIZ+W5C+4Cz7zlGc61ncv8vfPJ2/I89xfPMbhyEKhEjXe8bwelX5QI/C6g7Kzm5mYaGxuVnSf7RE+HLBQKFEtFTGpTh2z2ChPCLJsqx1fsMX3tHUccuD/ipvDdAvwYLD+oBpwUU/RlC3wEcrfksP2VDcdvHRhWQ/UIt1gsuO53UW+rJ/LNCN1f66blSAsWq0XZDWKXpeIp1v5oLUbGYOD8ATCBD0H5F2XVY1w+63jcQfmDZYo/KWL5gAXrQ1bKRpXRIXo499Ec2KD+eD2t+1opWAoKjJHzIN1nxOYQG1uKukqhMafTyfT0NB6PRwWFAGWfqxZqWgCvaW8Ta763hh2f3kE+lMe9w03D3zQwdWRKgWRer1edJ3k/nSkhdq5+1oCaYsc6I1fsZ2GQ6gBEKpVSdHVArb+8rwT60uk0brebZ599dq646p/RsPzfP/KH49Of/nRN4TPJe9BpzLqC0tFb3ZEV4SiHTTZ5NptV0XGobNxYLFaT1ywHQ8+x0CNx8jc9UidOgsViwefz1bRQEGdCotjiZInTrh9q3cEUJ8vj8ahrieMtTrPkZkkEXW8vIgpXAAWo5pHqdHxBmUXxyNzKnOjFLPT3F8UnB1ycB7keVMEIeXfJRYNaZFiADp2+JQJC6NFC1RZnWxxQmTsdXJG/66wD+YwUkSmVSng8HmKxGC+88IJyDMfHxyuR6ct+yU8u+gkjrpGatXvjvW/EVtDaypkG77z/nTWAjkQph+qHmA5N1+zx453HyXgyCtyQ/8radEe7CaaCZB1Zbj/3dkyqbfF0xFveFSpIv7yvrLvT6cRWsLH0yFKUXjbBF/PR3t9OOBxmzZo1rF69mlw+x90r7+bZ054l48pw/5n3s3vF7pp9I87fup3ruPSuS8GsvPt77n8Pa0+sVVGXVCqlnsU0TAxLNT9fj+7oaRhCkRIgKRKJKHBIL2aiFxoB8M34ePPv38yCkQVgQsdYB29/9u10G91q74jB6vP5iMViKiqdTCZVD8pQKMSy/mWc99XzoEzl56PA9yAei3Pw4EH6+/trch+lBUkoFKKr3EXzSHPNPFtesZA/XkHPJS9Pis0kEgkFGObzedXOa/fu3WzdupVMJoPf71eFj6Slj5wpccJPO+00rrrqKq677jo2btyoirDNjbkxN/40hs/nY8GCBTVGtshDHUAVOSjyTvSoyD3RWVAxyEdHRxkcHMQwDNW3NxKJMDo6qkBYsS0kwiYOeNaS5RfX/4LBjlPOn8XkyXVPckf3HYrCKp8XG0WlZtmtfH/V93l43sMkvAkeuPQBptdNs3DRQpYtW8bIGSM8fmHF2QaI1Ed4Zc0rpDyVDiTTvmluO+c2JuZP0NLSwrx581RBSI/HQ4etgwUDC2pkafNUM12ZLlpbW1Uk3ePxsLpvNfaiXc21YRos3blUFZ7yer1QhNDjoZrrOZIO2va34fP5SKfTDAwM4Bhy0DrcWvO5rukuwtFwTWRSbDGdBVkul3EX3Cw9Uatrg5EgrcOtlZQAnMx7ZR4KzzXBGXPie6XCupK0I2HiyTokEgn1N6Fp2+12Gnc1suqTq6BUudbq36xm/t3ziUaiTE9Pq3xwPfAj6WKJRILSqyVsr7dh/6kdg2phOEDZVPZtduyX27H+xqrsKhkS+Jq3bx7rP7We1p2tyl7W66ZIYMqZcbLwRwsJPBLAeIsBt1frJun0Z8MwCGwNUPeGOpyPOdUZkVx3u8OO8SUDPgtYYXTVKIc3HlY6XlIoJJ95cnKSWKySFiZ7OBwOEwqFlEMr8y7+g9gjoueloJkEAMRWbtrfxIbPb8A/6Oe0b5+G64RLzZOe1jU7FVOqicsekuCQ2LPC8Mzn84yNjXHy5EmGhobo6+tT9YakmJ7YCCIzstmsshcmJiaYmpoCoKGhQUXc5TN6Wuzc+N89/t0R7jVr1rB8+XKF8ohzoTvNQm2VHNDZP1DN2REBCahWYSKsJBor1FX9MOiRIj1fSI8om6apkD5xUiVSJYUt9CiuDiDo0W9xIPQWY9ICSy+6Fo/HayLNeoEzQDlkEhXWfy/Oik5JkeuLcNArVIrTJs8jcyCCRhxtnY6vv5sIKkGDRSmlUqmaiulWq5VoNKqin3JPYTSIAs7n8zWF0MTRFMWko44idHRQxDRN/H6/mkPZE+KY+v1+Tpw4QSaTYc2aNfQ5+vjF639ByVpZ7++85zt89vbP4kpWlFdrrJUP//DD/PQdP6VsLfOeB95D22Abps1UYIDM6eb+zRwaOcSu7l1q71z0ykX4x/0U3UXFKpD3LNgLPHT+QxzrPIZpmLy84mVsJRsXPXERdc46Naeyl2WuZT1lvn0+H6lUimwqy6bXNpEqpXjsnMdoSDTwvlvfh61so0iRVCpFV1cXkfMjvHjRixTtlTXNurI8fP7DNI430j7ZrhRQsViEMpx24DScTzhpzbeydGgp8XRc7UkVaXcXufvCu1nTt4ZFhxeponY6wiuKS/aiKOyTJ08ClbxpyWkSpSXrKwi1O+3mI/d8hB9e/UPeedc7sZVtZJoyikrV2NiIaZqMj4+Ta8pRPF7Z08FgkHw+j9frJZPJEAgEaDzSyIbPbGCgeQDX/S6m7dNkSxVaXDqdJpPJ0NHRgcPhIBaL0dzcXDFCMtDxNx3krXlmtszQ+HgjDd9r4GTxJDkjp86tnF29tsP4+DhjY2OqtZgAK01NTTQ1NSknXQxyodwLm6SlpYVsNsuZZ55JKBRi69atzMzM/HvF79yYG3Pjv2EEg0F6e3trItg600tktuhUkZOis0Uviw6WVocDAwMYRqW4UrFYJBKJqCrG4kiIHhYdLLLZ7/Lznhfew/cv/z4xewxMWBNdww0DN1CmmoonP6IzTdPkgY4HeLrtacqWiueY9qW589I7edt330aD0cDaE2sZ2TfCtg3bKFlLOPNOAtkAEW+EorWIo+DgmgPX0BvrJW9UmYfibIQsId68783k7XmeX/Y8y8aWcfO2myupPm5U4bFyuUwoHeKLd36Rr7zuKxSsBd7zxHtYEltCuaes7EaPx8MV6SvofLyTu8+7G0/Gw2d/9Vlsbhumy1TzZ7FYOH3X6dzRdAcvdb7E6YOn895d78VqsVJyllQgR7c39Tmym3bOeuUs4oU4z571LPUj9VzxzSswigZFW5FYLMbY18fwH/ST+lIKT8zDNV+6hpGhEaampsjnK6lLkt6WSCRUtFUYUmI3yZrUnajj4r++mJH1I6x4aAXYIO/KK6dM5lUcVmFWSWAmFAkRbAvW2F8SuEkkEmSzWYKRII1LGpVN63K5VCVyj8dDKBTCMmzBxFSMULFrZI6kOn9+NI/nvR6K0SJ1bXXq73o0X+yB6LGocqLFRs5msyQuSmB+xFT55QVvgR037KB5spnwQFgVwhUGp8fjUXVtdHtTghR6jrTYxmLbSmtaKdir08PlmQMnAlz+hctJx9KMl8dJp9OK5i22mnRmkTzwtrY2rFYrQ0NDNDY2ql7bwWCQ8fFx6uvrmZmZIRwOE41GicVitLe3E4lE1HNLQURJJdEp616vl1gspoA68Q9CoRDpdJqZmZm5/O0/s/HvcrhtNhvXXnstHR0dKoKr062kmIDu9On5T3IY9Ii4fEav7KcXWZPPCcIlvQ8lEiq5GmLwyz10Z1QizhKd0x11+RFnSp7NNE1FU5corh5JBNTBF8qtLjB1tFuEsSjlfD5Pf38/LS0tBINBoOqUi6CQa8523MRZ1em8IuDlWQQE0amr+lzrBe7ESdLTA+Sech+Jnsu764i/zJU42wK+iHEiilo3LPR8ep2+I/Mmcw5VkEL6H5bLZbZv386jP35UOdsARXuR59c+z7XPXgtUFEJjopF3/O4dpLwpWvtbsdlt6t31YmFTwSki7to+3KPNo6zwriCbzqqoeTwex+Px0BfsY2/zXpUfjgHH5h1jffN6QjMhNceyf/1+P1DNjxZgQz5jt9sxyyahn4doO9bGdTPX4bQ6MS3VdXQ4HKw+tpq4I86j5z5K1pXFNmJjw20b8Ex6yLgyNWcKwGJYOHvv2RUE3W7FFXapfZjL5Si6izy46UG2LdrGtoXbeDfvZumBpX9Q/0AUrpx5UXw9PT3qfcTZB9TaicMp+6WcLfO+376vcm1bxSjo7e2lo6ODiYkJstksExsn2P+h/Zz+rdPx7fepVI1MJqNy7gE6+zoJ7g5CL5S6SvT19RGNRlV/2mPHjtHf34/FUimkIuBGoVBgxZdX0PeRPhZ+fyHl+jIBb0C1IdMjGLJP5B0FACwUCtTV1TE5OcnRo0fp7u6mp6dHFVqRfG3DMFQLHzknTqeTQCCgQK25MTfmxv/8aGpqorW1tYZ5ooPpOvgm9o4enRadLXbDyMgIx48fx2q10tjYqIA6ierODhSIjNApw6FQiFX+VYSPhPlG7zdYmFzIX+/6ayhXmWe6fhdHI5vNsvnVzQxEB3hs42MUbUUaZxq54dEb6LR3kivnMMoGb9j2BhwuB1uXbOXql6/m/APnc++me3l2+bNcuetKzt57NgWjUGNPCWAuKVYXP3gx5OEv9vxFjX7T6e3lcpm6TB0ffuTDTAYnWTqylFK5pGqciIxPJBKs2bOGjCXD0mNLsWQslMySKq4l+a0hf4ibX72ZWwu3ctPumyib1Y4qQI3dowd4FAU5b2XFfSuIpWOsfXwtbpsbbJXvJRIJojNRWn7XQrmrzMLtC8mkMqrGiFR31x1isbsSiYSyc4T1EF8Zp2m4iWB/kKaTTRU2WKli6wYCAfVuep68zWbD4raQXJfEPlRpRRkMBpXzpRfKFWc1GAzW1F+Roqfl5jL2VjvOuLOmXpHYX2KDyT6XGjAumwvTWwE6ZP119qo4+6ZpEgqFqKurU/ZePp+nsKfA1LenGP/EOGV/GfeUm/U/WY/rkIu0mVZgg1QoF8apFE8VWrm8k7A9hfno8XhquvBIbSEdwCgUCkSj0Uowy+VgetU01t9bFUAgLdyEzi1AhR7QymazKr3R6/VSV1fH+Ph4ZW1PFUDLZrNkMhkMw2B8fFzZd1I/aWRkBMMwCAaDLFmyRDETotGoqpnkcrlUF5ZYLIbdbmfPnj1zoPyf2fh3Odw9PT2cffbZyniEagVOnWohKJ8eLYrH40pJ/Ft0Sl1QwR+2UNJpuvJ3cU51B12EvBj5Qk8WBE8cPJU7dIqCq9N0dAq1tEkShaZThsUxEUddDi7A9PS0omDJ+8l8uFwuwuEwdXV1ypGRFgF6AQ2dui1RxnQ6re4pgkbeWeZdFKJ8T3KG9cg2oHLIRUjqOcYShZeUAEH7xbEShSv3EEqbXF8Uh9De/k9UZfkR5oHT6cTj8ZBKpVQV1nK5jM/nUy0W9m3eR85di/wZJYPNd2/GrK+mAhiGQc9UD+akCc5qLp70nFTF3JxFCrbaitEpe4qiUcRhOJSyk9zizolObnz6Rn55wS+ZCE/QEG3gzb9/M+1j7eSpAiuyT8VRlH0oe0X2rNQjcLlc2BN2rEZ1HuUcCaBx2u7T8ODh/rPuZ9OvNpG6N8WrrldZtGgRnZ2dam3ljM7Ojxdgw+F2cNd5d/HS0pdOTSDcdv5tvM76Ojbu3Viz12VvyvflXEkkR2o1CD1OZIKwIPT8J4fDoRSRnG2/38/09DSTF05y8IMHKQQKvPzBl1n+7eXUH6xXtEm5hzjf5XKlZ3Y4HKa3txfTNBkbG1NMCAGojh07pnLBJbLU9vU2Sq4S4+PjhEIhent7SSQSxONxBgYG1L6VM6IDcaZpMjQ0VLPXRQYsWrSIYDCogDd5XwEl+vv7icVixOPxP1ruzo25MTf++4bVamXlypVK5ul2yuyos3x+NttMT1sbHBzkwIEDFXbY8ihRa5SWoRYikQqoK0WRVI0LzUGUSNira17lOvM6fKaPc1LnYO2zsnhmMYZpgFGtuSLfkWtI/Yrp6WnOmzgPa8bKi+te5Ponrmf+2HzKVFsUWSwWXvfi6+iY6GDDoQ3kyXPli1fSNN3EWX1nUaZaa0ScGH1eLBYLgUCAN738JnBUC8RCtcCm2B/lcpm2iTY6pjrAoErxdrsViFtXV4mmrn95fcWWcVlrqPvicEt7qnftfRempVrESnc4oZpHrztt4oy63W7Of/Z8MuUMZaMaXIhGo0AFLO58qLNC/y1nVTRWKpPrdUtKpZKyhYQubbPZiK6PcvDmg0y/Ns2W32zBLJs1qXWiN6AaaCmXyyQSCXbesJORTZXaJN69XhXRlvcQ+0yCTFLIrQbICcLElyawNFnwfs1LcDxYs+8FyJZ0B4BiQ5HhRcOUb69tLyfzKvtc9KOkSoqel31hsVhovq8ZS87C5CcnWfX9VXQf71Zrqp8zAeWlmJ/YB5IfPbvDj54+qgen5L6yv3VA6+AbDjJ49SDLysuw/saq7BRx0KUujZxti8Wi2HUyr1arVaWh6mBXIBCgpaVFBQPF5pPAhtitVqu10tI0kVD7WQeEpHe37CvZU3Pjz2f8u3K4V69ezZo1a2qcNN0ZnV0lUw6DjkgJIifCuVwu8+qrrzI1NVUTMdfzM/TomtvtVnQeqVg926EoFosKOZUcbBH+qtjEqdwWncIt6K38VxxQ/T2Eii7/lWvqaK5hGDz66KM1SkfPT7HZbLS1tdUcJh2hlfnUFbkeTZd/Q7XF1OziDCLMdSqOOHmSUw7Vwg4yL+Io6vnd+vwIUKHTjCSNQBwjuRdQo5zdbrcSquKs6UXZAOUMP/vssyofSN5TgJHW461Y87Wt5K5/6HoooqKLFouFYqm2Z6ogwuKQi7BrH2/nrQ+9lWAyCCY0vtTI2p+vxZf11SCPAgSYpsn8oflc9+vrCMVDfOjJD9E71quAB9n/krOfzWZrFKs419LP1G63ky/kGb9snMEPDXL32+6mUCwooSwOutCeVr62khtvv5Fz0+dy1llnEQ6HOXr0KHv37lU5PzabDbfbXdMDXj8DlGHBkQXo9VzsBTvdY901URZZe6fTycjISE3KhIBDgv7ra6jXGhBjRmdtyHoKy2LyjEkOvPcAhUDFqEi0J9h9825S81Iqx0xo+FarlWQySTgcJhAIqAJ8dXV1zJs3j82bN9PT06N+PzExQV9fHxMTE5RKJQKBAMe+fIxMJkMikWBkZETltPf09LB+/Xra29sV+q4bHGLcyh4TA6m/v5/+/n4ee+wxHnvsMXbt2oXVaqWhoUExWTwej8r1Etk1N+bG3PifHVarleXLl6vggN5DG6o2xc7wTl5oe6GmJoro4Yw9w9+3/j0jo5XINkB2QZY7Lr6DW86/hSP5SscCMc4bGxtVuyuRnS6Xi9bWVg5uOsgdq+7gM0s+Q6lcAao3TG7Amam1Y+Q5JB8WYGZmhkgkooz1M3adwU333UTPaI9y0kSGl8tlSoUSGw5tqEnJ23hwY409ogdSxMYQW0T0hAC9uv0nbACxlySIIc6h3E/WQGw/mQ/dnrLb7dQ31CtmkOhzfY1mp9rpDC1A1SlJJpPqOcQOyOfzPHPlMxybOUYoFFI56ADDw8NMTU1hmqZiJOrOnby75PE7HA5SS1Ic+OQBMq0ZTlx8gm3v2IaJqfpdz7ZvRb9ls1l2v3c3I68bwZxnEv96HHOzqWwvoKYQsaTcFYtFFVWV9qBHv3mU2LkxIssivPY3r5F3VxmoYlfKPi4UChh2g+c//TyjnxklfUm6JmCkU/TFXpSzIiloQtMWW8fhcOC+y83STy6lY0+Hsh2dTqeye6R7iBQsFptB7NZCoaB+J/teHHz5jvzE4/FKlfl0mlQqpdId+97WR9/1feSCOfa9Zx9Tp0/VsEHEXtKDhDolXfa+zJ2km4ldC9R09pE5Ep0vbIdoNKr2s1xP3kPsokgkQjqdplAokEwm/zNibW78CY4/2uFuaGjgjW98Iz6fr4bCAdWqhCI89L/J76RY12yBrBdjEEdMnHnd0BVHRXpH6sW/ZLOK86g78xKt1inVOi1bUDT9gEk1YT3nVt4HUI6lfE+PSIswuPrqq5VDAShFoedO6xWtxZnS0XXJifF4PEohifMhTo8AHeIIyBzJ/OjUKlFuOoVbRwblHhKtlvUTQSetzwRsEBRS5k7mRp5dcq/ld1BB8qQ3tQ6syHUBEs4E276/jcnipEJvxeH1+Xw4o06YVbixNFCivqFeFTZJuBJ8/13fZyI/UUPhE6dfz9/P5/M0RZp4z3ffQ8uBFt5875spDBfYt2+fek95N6vVqqKkXeku3ver9+E+7q5hIUgKglCK5YwIOGIYhspdjsfj5It5Di07xOOvf5xSoMSJeSe47Z23kbQkVaEOUTw2m41SsURPvEfN57JlywgGgxw7doxDhw4xPDxMLp9jZ8dO7j3zXnCijEk5C7FojLVH1/LmR96MLW/DlXDx6V98mrZ4mzoXej6ytPCQaLcoY/1cC/It8yDOtKydniMtZ1YQ5NUjq9l4ciOW8ilGSMHGGdvOoC3ZpqrkSqRfcgOLxaKia8o+C4VCtLS0sGjRIlauXMm8efMUCJTNZjk0fIh7P3Iv0xdOc+jrhyi6Kr1VT548ST6fx+12EwgE6OnpYcOGDfT29iqFKRR5ibQL+Fcul4lGo6rNjzjy99xzD7feeitbt25V1VUXLlxYQ2+cG3NjbvzPjlAoxKpVq1TESc631PkwTZO+xj6+tOFLfG3513gx8CK5fE7p0Zg1xhsWvoH7Wu7jW23fImNmKHeU+faN32YqOMVEaIKffvinNK1uwu/3k0ql8Pl8dHZ20tzcTHt7O8uWLWPVmlUMnzbMd1Z8h6QjyU7fTj607ENkHdkap1aGzjLKZDKkUinGx8cVGwrAho3W6VblkAmVWXek9EiwyGtxDHWbQa+WLPJXfi/OqzjBYhPI32bbaGIPxEtxfnHlL5jorFQdF9tQ/1zSkuQHN/wA5qOCNfI5oAYMmJ1KJzoq78rz1au+yqRrUjly4jzlyLH96u3sPX8v40+OE+gKqMJnkqcNKIBX7q8HWYSmbLPZKAfLvPLNV8g2V+wZ02Jy9Kyj7HjTDjXn8n4yh7FYjEwmw8GrDnLi3BOY1lOARFuJI39/hKwvq95N1kPsEZvNhtfrpb6+XrEsp78/TXpdFdRNd6V59auvKvtHIrxiFyasCR78mweZ7prGbDSJ/zCOcbGBxVqloMtecLvdBINBFcEVG0r2hLDQTNPE7XKTPppW6ySOcSqVIpFIEA6H1bNYrVa1j8VWkHkStprYbsJOlP3s9/sVQ0UCHXa7ncHzBul/XT9lR2W/FeoKjHxphGRHUu13/RzIO8heF5kgDrDYeXrgQKLTck/xZWR99FbCYsP7fD4ikQjJZFKx3QKBAF1dXTQ3NzM9Pc3Ro0f/aBk2N/53jD/a4V6yZAlXXnmlOqSCBsnB1/NAdCcYqjRtcX51J61YLLJ06VLq6urUhoVK1dDZkfJCoUAkEuHhhx9WjqteMEz/nI58inDNZDLEYrEaVFV6AMuhFgq5IF9SyE3eVXdO5TMSkQaUMJe2RjJkDuR74qhI1Fz+Xz/EYszraLPuPApKN5v6o0eYhVEgCluEhcyR/jtxlmSd5PuCAkuuiQg/mStZU3FGhDosQ2hHgiiK4JH1kX1gsVgYrRvlm6/7JmMtY3zzom8y6h0lk8kQj8eVUzd4zSDFQK3HvecNezCsBl6vl23JbXzrum8x1jrGr97yKyJ1kSryfMoBBhSFSIRudjhL29vaaA40093djcvl4sCBA2pvzM5/Gukc4Qc3/oCReSM1iLtESPQ2UwImiJLWEeqiu8izpz1LyXbKCTNgomWCQ6sPKaRfKNSmWSkuJ6wFifiuXbuWTZs2EY1G2b9/P9tbtnP7G2/niVVP8LtVvyNnyakCHgKGeN1eerb2sPaOtVzwVxcwc3hGCX8xPJ1OJ5H6CGlfWlHW9eiFsE508CoUCqn9JMpLlKUerZf5KBaLkIfXP/l6Nu/bjDPv5JIXL+Gc/efQNa+LpUuX0tDQQDweZ3KyYjCZdSbT9dPk83mi0aiibcsZEfrXihUr2LJlCwsWLMDV5aJ8a5ni2UWwQHxLnPEvjVMMV5zugYEBTp48yeTkJMlkUq3d8uXLWbJkiQIZvF6vKuQiayBV1gEikQiHDh1icnKSSCTCzp072b59O08++STHjh1Te2FuzI258T8/1q5dW+M86ZGrQqHAy56X+fj6j5O35ilYC/z1+r9mZ9tO7HY7/fZ+3tfzPkYdo5SNMtvO2MaOy3bw4utfJGevpj3lnXleueIVli1bRjgcJhgMsnDhQlasWEF3dzft7e0YDQaf6voUBcup9CYDjnmO8Vj4MQAVydaj62IzJZNJBRrqubnyTqKzxCYR+0wPRMgQe0bsAClEKcCr0JDlOeQ74lzPtgVEx4o9Jz9JR5KHL3mYA8sP8N0bv8uxtmN/wGqM++Lcd919nJx/kk+e80kO+w8r4Fl3rmc7wPL7crnMjGeGH5z7A443H+cb13+DsbYxFSzJmln2XLqHnZfuxLSalJpLHPzRQRIdCfXuQp8OhUKqA4uwuMrlsmJbJpNJMpkMkTURys7atElrxopz2FkTsZfn1W2QRfctouPeDoxiRfe4h9ys+MsVWCNWBQLJWom+kwJ1Uh+kvr6e1ptb8T/vV/evO1rHxs9uVAB6MplUTqVhGAxcMECyJVntV+2C8feOky1nlW6VNZYIuuhICQLJ3pJ9USwWia2NcfKek0x2TNYAOnoOvIDxeuRbZ2pKqoYEmgTotlgsChiS9ARJf5S5WvrKUpbfuxxrtvJMnikPXV/swj/sr2GqyPwDivEgdpLofJ/Pp1h2klcPKNtGt43FXhanW4Jh0m60UCjQ0NBQscNOFU+bmZlRNqOkvc6NP6/xRzvcH/rQh/7NXrq60BVBIBFmKXQhG1CoG3rOtUSiRSmUy2WmpqbYvn27ijSLQpCqghdeeOG/SVnW80H0olgSmQKUoBVlpLfSkkrbsunL5bKiEIsTJc8reSV6/pREBOW74tTOpksZhlGTHy6UHEEJxdkRRSIKRiL6AhDIoYZqBFXyUUQZybzIZ/SItXxW1kzeQdZKz9fSc9clcik/En2VoYMkMmfyHOLcy3v5/X5VqXG4fpjvrP0OQ94hAPob+7ntrNsYcgxhukz6tlTaLXTf0U3XrV2KDr22by3vffK9lPIlYl0xXrv5NVKtFcf6eMtxfn3ur4kGo+q+ArgIQivv7Al4OHHTCQ4uOkhTUxNLliyhUChw4MABxsbGKJVK+P1+TNNkf+t+fnHZL4j749x+xe0cmX9EratEsWczLPSzIWtitVohBjc9fhPzj8+v/K5k5Zrnr+G0PafV1EcAlEEjTq4oBcMwaG9v58ILL8R8q8nDNz6slOfvN/+eR854BKerWmVfzs7w8DANv20g8kqEVCrF4OCgouUHAgHiDXFu33I7d519FyVPSRUlkyKB+nkSI296erqG3SFnV86G7AOdBijpAtc8fg0XPn4hZ247E8Oo5JZJFGjBggU0NjZSsBfY8849HPjEAdKdaaxWqzKAoFJB3+VyEQwGVVSiu7ub9Reux99bNUIAaANruHJ+p6en6evrU47yzMwMLpeLuro66urqWLhwIeFwWMk8OdPJZFIZCwKSCb1OlKsg5ENDQ3OVR+fG3PgTGhs2bFC6Vgxdnfk1Fh6rFsg8NU76T2K1Wom4I6SttekhRq/Blye/zJsib1K/u2HqBv5m7G9ob29n3bp1tLa2UipVirUKffmO+jtqrmMv2/lw/4e5euxq5ZDMTmmxWq2kUinGxsZUmyHdkdZzqiVoMDsvVY92y7vrARNxesS+E4dELxgrnxcQVqfmyv11Vk+6lOa+LffxyqpXAChZS9xx9R0c6D6g9HHaneb+i+/nUO8hABKOBN9Y+w32hfcBKFtSz6eV38vv0r40d5xxB/s6Kt9J+9M8fMPDnOw8WYm8UyTaGK2Z97KnTKG54mxL6y5x9MRh1HN9JZorczHZNFm7H4oGy/51GQseXVCTFih6UA9alctlWr/div0bdjwjHlb8ywp8h3w10Vhx8HQbTxw+0UF2m52ev+qh47kOQjtCrP7qanxlX00QQJ7FMAy67+tm4a0LVTs079Neev+fXsysWcMY0MF2AZnlvroj2tTURPaSLKPfGiXXnGPvZ/aSXp1W4ILYi+l0WoEYOutR7Hev16vy/AWoFl9BUiOFii65/brNCrDswWWs+NUK3FE3G362gbpX6pQdL8Vrpa2YOPP5fJ7p6WlisRjRaFQVde3v72diosKaHBsbU8xPWRMBPWbniMven5ycVEw9nSnhcDgIh8NqPwwODjI3/vzGH1007fLLL/8DCoaeS6L/AOpACj1cfidOtxx02ZhSYAgqBZ9aW1trcj9FkEOl6IgoBhHoQuMQ411/TomoysGOx+M1LcukTZQ41lDNYZbfS1RY/5s4WBKxE8GnonbU9oHU84dFSIgC3bFjB263m0WLFiknSyKyIph1pSLzKJ+V50qlUmquRBjLZzKZjHLudZRcBwdE6MvvRfjp0UN5X7mGCF6JYEItgi4CRYwZnSWgI9GhfIhwJgyh6r6rS9RRZ63jtitu40DdAZqHmul+pptFDy7CVrBhXGTwuudfR72jnmJjkUwxQzAdZJRRdY3DnYe55dJbeNft78JZrNCqQ6EQqVRK7VGLxcJTb32KmcUzPJB8gMBTAdaMr2HDhg2Kql0oFFi+fDnHG45z21m3MeWv9E6cCczwmwt+w9sffzsLRhaoORIloef/ADWKU/Kv/BN+Vv7LSkbfMcrrj7+eVUdWgRUF6gilWyLoutKVlAaotOja6NnIa8ZrZKlGUudNzcNiWCia1ch0NBpVfa8nJyfV846Pj1fAnFY3Pzz/hww0DEBbpQ3Z++9/P7ZytSicUOglnULfQxIB1iMQQkHUwSSXy6WK1ZSLZTbt3ETRqLbEkyh6d3c36XSan73+Z0xtqMz9jk/vYM2n1+CwVGlbwkqRfSqKfJ1rHcWvF9nzN3vI9GSw7LBgvN/AO+YlZamcGzFaGxoaGB8fx+v1YrVWqtc6nU7WrFnD5OSkopEPDw9jGEZNKoq8s8ikXC7HwMAAq1atYsuWLTzxxBP/PyTt3Jgbc+P/q+H1elm+fHlNVFUYTVABSK8YvAKP6eFry78GwM2DN3P95PVYLBY2pTfx1RNf5X0L30fSmuTS+KV8fvzz1Jv1fGryUzgMB86Sk/dPvx+rYWVqaoojR44wMTFBe3s7PT09OJ1O/rnjn/lV069qnu2Lx7/IlvEtFIpVnSlyVGyQVCrF9PQ0MzMzNc61AN4Za4Znzn2Gq569CqAmOi2Ap66n9ciazpjTnS75vP5v0SnibAt4IT8ydCe+ZaIFllff156zE5wK1tB1myJNHOKQ+owv56MuXadsErmerJ3+fBaLBUvOQmOkETqq97FFbZQHK7aNpWhhyY+XMDI6QvLaJLasjXO/ey7NJ5sZNAbJZDLkcjnq6upq6OPyXsLW9Hq96t077+jEYTrof38/AKd//3Sanm7C4XTUFMqSd5R0J6kQHolEcP+Tm86DnTRMNpAoJ1RwS95NQI/ZNqDMvdPpxIybzP/WfIruItZJKylSeL1eFZiqyeUvlej8XSfWrJXDaw8T+kIIFy6ytmr6oNiSorfle9L2VbcTM+dk6PtEH4W6yl6KtEV46q1Pcd53zsM/6CcQCNSw5CTYpbM2JL9ZrxkjYIr8Wy/WBtVisdKmEyr26bzfzaOpv4nG440MuYaUv5FOp9WcSFRd6OCxWEwVyxO7JZFIKDs/Ho8r/0DmR2xKi8VCf3+/YrTmcjlCoRD9/f0YhkFvb686U5JiIimkx48f5/nnn/+/ia658b9w/NEOt54vLRtfj66KgBUB4HQ6SaVS6iAKLQfgJz/5CZdddhnNzc3KmQRIJBKqxYH0XRSnTygrUsVQz3fW85M9Ho+KUsvGF2cvnU6rwyjUWb2wlDjk0rtb6Ngi5KR1mAg0QamgCkCIs687nyKQhaYiFYwl1wQqBenkevIugq4B1NfXVxbs1LzPFlCiMPXiD7rDI1FAcXhFeEuekq4sAcVOEGEiz7R3717sdjuLFy9WQEogEKhRfBJh16PaOuo9O1ddqFqBVICPvvpR/m7T33Go8RAr+1dy7YvXctsFt7F3fqUN1/DnhgkSpH5/PU33NtG5rxN3rxuzsQKc1Kfquf6+6/nZ9T9joH1ARXkHGgf4yZt/wvtufZ+aP3mngrXA3RfezY5FO8CAuD/Ov176r3zswY+xNLmU5cuXq5YVExMTdOQ62HB8A4+vfpyypYylbGFj/0Zah1splqq92WXPCAIuZ0OoR3pKhmmahCNh1nx5DWvOXENdQx1Wq5V4PK5QZclfEqNI9o5ujAF0jHfwwX/9IN9993cpWUp0frGT4ZeHsXXaWLhwYU2FUdmn7e3tSvELmPW7d/+OiYYJJQMOzz/MrRffylseeIsyAAzDqGlXsmPHDs466yxV8EPl0J0yDnRUXeYhEomoc6xTyeRz8XhcVfZ95M2PMLZiTD1TdEGUl//xZc6/+fyaNJNMJoPb7cbj8ag+nYlEgu5iNw1fbGDb323jslsvY2thK+4ON0ePHlVGlHQCGB0dpbu7u4Y6J2taX1/PvHnzqKurY2hoSMlFl8ulHG9hgAigeN5557F48WJeeeUVhoaG/ljROzfmxtz4bxqLFy8mGAz+QdRRd1ysVitXRK/A2eckao9y3cR12IwqpXllYSV39d/Fl1u+zN+O/C3uUiXS5i15+djIx5iZnmFoZohYLKYKiNntdpqbm/F6vdzWfBu/bfotJaPqmLpKLlbFVykZIsChHtXMZrNMTk4yPj6uqOSiK0zTJGvL8v2bvs9MaAZLwcL5z52P3ahGRqGaAqdHukUv6zntMh+6vSG2gu5QixM/m1WnO96maZKIJ1jz/Bpy5Pj9lt/jyrp450/eiTfjpcgpVmPC5OpdV+Np8vB4z+O0plr50stfwlPwULZUAf/ZEXV9Lb0lL9fuuJakJcnWpVsJj4e56idX4Sl4KHkr+iIyFCH42SBFT5HT7jkN67CVSXNS1Qryer00NDRU8r1P6bpSqaSKlElEU5wvO3YW3LcAi8tCQ7SBnt095J15leMtrSL1faanBuRyOTw2D42DjZQdlSis3u1lNrtTd+Blf4kjWx4v47A5yJQzNTR8n8+nWJLST940TfIP5Zn+0TR+i5+Sv9oGD1BdSMQOkZQ8iRTLmtvtdjyveAhuDTJx5QRYK1H+nmd6CE4EwYJiAtrtdlX3xOv1KuDBNE1mZmYUjVyqlct6CyU+FAopnS/dYMT5lX1YLBYxyya2wzZypVxNzSOhfc+ubB4Oh1VkXebJbrfT0NAAoNi2cq1AIFDjbMuZqqurIxKJKDvP6XSycOFCoOJTJZNJ9bzii4TD4bmiqn+m4492uHWhKZEb2YT63+SgSzRVBLaex3DNNdfg9/tV/rKgO36/X6Fdck9xVORg6wZ5Pp/H4/HUUNtFCIjDKbkcgkxK5Et3UtLptHpW6dsXj8dpamqqUXJyYKamplRUVw600GeFIiMoogiAZDKpnPFyudLmSnfAJZI5m0I/NjbGvn37OPfcc2lpaVFCT3JWRPEWi8WavCIBGux2u6LL6M6P3prC4/Eox8lut1cQ1lPVwuXdhCmwYsUKtQckiidCW+Za6EEul0sJf+kLKeukU39isRj19fW4XC6mhqa48ac38sB1D3D9g9fz4rIXOdp6tErp88Dwe4bp+VIPjpiD7GQWs6eK9vr9fkrREjf+8EZ+/YFf099eQZnrhur46MMfxeGvFvgSxbF73m72LdiHaanSBnPOHL/d8ls++8BnsdvtdHZ2MjMzwwsvvMDSpUu5JHUJqVKKbau3cfq+07ni+SswDVMVOxHwRI9GCNghBdMaGhpq6glkmjLs/eRe1o2sw7q/tu6Bx+NRayhrLesgwIqsu8vlojHeyMfu+hhH647iG/BxOHqYHRM7yGaztLa2qjMnxXSkJYtEzwuFAuf/w/k88f88wXT7NAC9w73c9NhNGI6qUacbUXa7ndbWVqamphSLAFBRfkmjkD6besqFnGHZT4Ikezwe1Qu0UChw9d1XE3fH6VvYB0DjRCPzPjyPhpZKjncgEFDvGIlEFHgm0Xer1Yon5+H8T52Pw+9g/vz5qoBJLBZT9G+pNDw4OEgsFqO1tRWXy8X09LQ60319fbhcLrZs2cL09DS7du2qKfwohdWgYoSMjIywbNmyuZZgc2Nu/ImMZcuW1aSziTwT3W6z2RQwfsXkFeps63rMMAzaMm1879j3KJVKJAoJlVsq0b9gMMi8efOU7aTr0LdNvI1R2yh3NtxJySjRlG3ii4e+SGOxEbujyqbTad2ZTIbx8XGVoqI7y06nk1goxs+u+BlTdVNgwDOnP4Oz7OS8V8/DKGiVu41qFxJ5/9lOsw66z6aN65FuneUo3xF5KO+sg8yWkoUtL26hYC2w4dUNuFIuTKNaE8fpdNIcbOZjxz6G1Wnl3QfejaPsAGs1wCHX1G0xeWax3Zymk/N+fR6RSyNceN+FmHkTi7Wim4eGhjhw4ACkYMPfbiDcEKZgFlTFa8nTlVQi0Vl6WprYUTpjy2axseSuJfh8Psq2sgrqACoP3u/3KydR6tTEG+Ik+5LU+eoIBoM17Exdt0hwSxiZYuPqLNLy+jKh0ZBiw0n9HAm8iM0krStLpRK5dA5ryoq7ya3o3XqlcL04mgTWxHbVnX9n2UnPV3vIlXMkL0uy9MGlLL5/MTiqre+kmJ7YoNlspf2arH+5XFZAuwTLJNosvgKgggcCZIntLXOUzWZJrEiw86M7Oefr52CNV4Ea8Qn0tAB9v+rFicVm9Pl8jI+Pq8/k83n8fr9yksUPktz6kZERdV05owKMSFBPmA3hcJh9+/b9V4q4ufEnNP5dfbhFEOv5EXLAxDkwTZPp6WkCgYBy7iSSJUheY2Oj2sASOZc2VXI9qFZilOuIkpJ761ExMdhFcYrhrjuT8oxyIHRadDpdzQWdmJjg4MGDdHR01LStEpqs5C3rcyHRS71NhCgeee6BgQEWLFgAVGnYcghFSErkW4TNqlWrWLZsmaK66NFqub68l46CyjPoVHmhPwlYId8VZE/Q8b6+PlVNWUARfc6kSrTMt7y7jnQLSCLvp7dmk88J5Vecv5mZGbLZLHXhOj7y8kco1Ze4oO8CAO6/4H6K9iLzR+ez8acbcWQcWP0VQ0Z3TO12O3V1dUxNTXHdbdfx6PWPMpwcZuMPNpJrz+Gr93Gk4Qj+lJ/QTAiADX0bsDxn4a5z7yLrrNCwwy+Fefuet2Oz2rBYK+BFXV0dy5YtY2JiouIwtzTgWOaga6wLq9VaA2oI7UwEvsyzFB0ZGRmhoaFB7edoOMrua3aT6c7w0+U/5U3mm1h/aL3an7KHxLgQBapTtNPptCrsYRgGzaPN1A/WU1hcYNGiRWzfvp3Dhw8zOjrKokWLCIfDlMtlkqcn8U/5sRk25SSWy2U8OQ8X/eginv6LpymPlbnsicsYL47T2NhYY3TK2mazWRobG2vSGsrlsgKI9HMpqR+yFwR4EwNVp2brFG2LxcL1d17Pg697kFgwxrUPXktgQ0BF/IV6v3TpUiYmJlTkJxwOq/6XQtEPBAK0trZSLpc599xzOXLkiGrxJbTKRCKhDAEBqorFIjMzM7jdboWwS8Tb7/dz9OhR5VQ7nU4V8d6+fTt+v1/1450bc2Nu/M8Np9PJggULlAMB1TZf4syInSK6d7aDKc6SXtsCUFHElpaWGntAT93SActPDX4Ka9HKk3VP8tn+z7I6vRrDVnWydcpusVhkenqakydPqvon8iwSmR4NjBJ3xqtFsAwYah4iQwaP6VEBEz2FDKo0Z12n647CbEq0Tt+W99adeUC9szyfzs7KZrOc9cRZ6nryX5vNRkdHB01NTVitVj5++ONghaJZrSCtgwN6pFjsH3kmYW1d9NuLKrTfXEp9fmhoiJmZGVpaWggEAup5o9FopVq2y07hTQXy2ytOlbAsC4WCKsCqV9kOBoMMXDTAoucWKT2tAxZiP1qtlVo8Yq+Vy2USrQmOfPwIyVeSdP+8W72LpCXJulgsFvVvmS+x4WR/TF06xeTnJ/H8xEPo9yHVCk2qlIsNqFIPMhkMq8HYZWNYfloBI/Rn1aO/Mv/CdJiYmFBpXzrwYLVaaf5CM/Uj9azethq3z632u+TG6/WEPB4PgUBAOatOp1M9s/gCYjPIWRJ9HovF1BkQ+1iYbuObxnnpfS+R9+fZ9sFtdP1DF6mhlLKHLRaL6q2uMzNlz8vayb4S5ovMo3Qs8nq9TE1NKdkhn9eZqIlEgmg0SjgcVmxbscWTySSNjY28/PLL/0GJNjf+1Mcf7XCLkpidk6k7BBaLhZmZGXbv3s1ZZ52lch3k77pC0L8jv4eq4y5Ilh4tlgiu7jjrQlYcS8m7EYdWjHw5kGNjYzidTrxeL/F4XCkyERrhcJhNmzbVOO/iPEQiESVYoRJR1517QQD1+4szNDo6yoIFC9RhBtSziwCV9xXkT8AKEQZCXXI4HCqvVKex69edTXeXtZJiYTrlXkf0urq6FP1XHHtRzDoVW6c4y/OKANMBDonOynrLfST6a7fbGRwcVJXIZV7FiW57qI3T+0/nyBuO8PqHX0/YFmbIV6HxSq9lnboGlVzmXbt2cYXvCmZyM4wOj7JjbAdtV7Rx9wV34864efd978ZbqOTybzy8EXvazq3X3krbwTYW/XARu2O7cW120dLSovZmW1sbHo+HrfO38vTlT5P1ZLnn3HsoUmT13tXqmZLJJFartQbYkP/a7XZWr16tnMGit8htF92movFFW5H7zrsP025y2oHTaowpceRlHcRwknWQyIqeMiBI9KZNmxgeHmbnzp08+eSTLF26FLbA/pv2c2LiBG++7c0qIgyVPHzPsIfN/7qZSH+EkfIIoVBIMQnEqDzQeoBCsUDX8a5KasCpFAO9SJoYC5LPJGdK9qQg/fKOTU1NCrEXGr7ki1uLVq58/EqK4SJNiSaS1gr1T9Ypm83S19dHNBpV51/2v8gvOS9er1f1Wff5fLS3t5NKpRRyL7n7UjwvHA7T2Nio2Bpy1gEaGxsVABSPx4nFYsTjcSVHIpEIBw8erKmUPzfmxtz4nxktLS00NzfXOEWik8SZEL0HVYdTGHzJZJLp6WkMwyAQCOBwOFTxKPmsbrCLXBUZLqCe2BbvHXwvZ86cydrkWqwOaw01Wq5hGIaqHZHJZP4gh1WYaG372nhj6o3cdv1t5Fw5lhxewlVPXIUz4yRfqupw0bez87J1oFOvx6LbFeJw6zagfFd3TvTrQ7W9pq7X5LOiz+rr6xUgrTuseoRXBzJ0h16/n6Qs6c6oOJGFQoHjHzuO57OVwnWSyyvsq2w2y/iXx8lelSW6I8q8B+bRsLdBfVfygMUGy+VyHP+L4xy95ihGh8Ga360BqAlKSMtIsYvkufOhPAc+fYDo4iisgsH5g3T8oEOBBTJfemBL9L7Mo6SCzVw7w/inxikFS+x61y6W55bTuq1VgfHi6AorU2zVPe/ew8BZA9Sb9dgerdjoEvmFqoOt1yLSwShpYSpMzEKhgMflofupblxhV02tIwGu5bv5fF5R7U3TJJVKMX/+fHU2xdEWx1uAA92+kmCXsAzy+TwDKwZ49W2vkvdXWLPTPdNkP5PF93Yf9mG7Sn8UyrrsJbF//X6/YqfK/Ao7V561VCqpvSP7W5xrAQ8kml0sFpmamqKhoUHdR5xysfsnJ2uL7s2NP5/x745wQ5WKIY5zf39/hf7T3EwgEGDz5s01vbpFIAgFXASFHBR1OD0etamtVqs6DCKwJKonxroUa9JzN6VFgQjYaDSq/iaHae/evZxxxhmKTqPnWdrtdoWu6bkjQhc5dOgQTU1NtLe3k8lkVF9i+X9RCOJkyPMFAgHWrVunaGaNjY2qOrogYhJBE2Eq0XWJIEIl4iaCU6oi6kiyrI8INp3mLUXVhBqvC05AzUV9fb0COCRyK2sg9xbhFAgESCaTKpIpSlfWUqKVUBHY6VKaL2z4Al/d/1XcZgW5lEik0G9lXoRuHovFaHq6icXDiwk7K1WnFy5cSP9AP/FMpT1FfX29WmOhHMViMXL9ORa1L6J+RT0P7n6Qx65/jLQvDWH45+v/mY//4uP4bJUqoEuOLeHzP/o89rydUkeJY9ljPPHEE5x//vl0dHSouZ1YNsGzlz1L1l2JhifdSe479z4a8g007m1UPaNlf0nlWDEIdBqcaZrYsjZO33U6Ay0DlK1lMKFppollR5bVGHxQ7Qwg50HalujRCplHXaGZpkldXR1er5e6ujpmZmZ4fPhxRt8/Ss6fI9GQ4Ffv/xUX/93FGAVDne1isYhvyIfDdFAoViptHz16FK/Xy8qVK+nz93HLxbdgmiYfvO+DzJ+ZTzKRVO+mt7YT+SHGg1T6lz0je1be9+DBg7S0tBAKhRRLRd7fl/ZRTpbJW/JqLsWQ8/v9FWrnKcPG7XYzNjamznbRWsTn95FIJAgEAupZhDa4evVqnE4nbW1tjI+PE4lElMyampoiEongdDoVONfW1qYqlUvUOxwOUygUFP1sz5499Pb2snHjRp599tl/j9idG3Njbvw3jPnz56ucTNHbehFN0SXiUEp+6cREpa6Fz+ejrq5C/RWnS480il7WC65KTnjOmuMDnR/gn4/8My4qKVu2ko01iTVg1HZfEWBPcmVHRkaYmZlRDpgecdado96xXj50x4e456J7eMODb8CZdlKk+Aefn00Jl3fW053EsZDPyxzp0T/RbzrQK9cFiCfjvHj6iyyILaDraBcG1cJqAFa7lX2b9uG1etlY3Kh0h9hDs4FrnaIu91bReJeFcr6sWkkKACyBkbK1zOPvepzJ5ZM01DUQ+mkIr8eranEUKRL7eozUm1Jgg5nzZoivi7Pq46sInKgAyhK0KZfLpHNpTt54kpPXnsR0muy7ch9mwWTp75ZiFk1lg0ndFAEP8vk8Zco889fPkJhX6feNAePnj/Ny8WXav9iu9oDMpehHWRc9EBY5I8LYp8YoByufydfl2f+R/fiTfsKHwsq51gGRolHkwLsOcOLiE5g2k5lPzzDjmsH5K2dN+oAeAJN8atM0VZs7qLTEFBZkIpfg8NcO03NLT801ZI7lbIijqheAk6i27HGbzcbk5CTxeJz58+dTKpUUu0OeQ+6hB+sWjy9mYHCAY3XHwACjZNC1rYtkJEk6n1YBJ/EBpNWvBDCkqK34JeLkS8BDgLPJyUlVrV3muK6uTvk5Qt+Xlmpi10vqrTAeXnrppRp7b278eY0/ui2YIJ3iPEBVYO/Zs4dQKKQMXsn5BWpQH/1wyJBop/Td1p1D+bsYzXJP2fxyGHSFpNOfi8Wi6lMcCoWUUX7JJZcoQ1+QM3kmiT4JpUZoYuKQb9iwgWXLlqmolhxSHYGU4mlSdEMUueR0hEIhRd0VASN9FEW46s60IGlSUMFur/QAlsMtP4JeisKU9RLkT1A2QdVkvsUY0KOIAh7o/ch1ar8oPL2tlghIUQpivIgBEnfF+eL6L/Ja+DXev+H9jLvGmZiYUFWx5RrRaBS73c7Q0BCjo5Vq46VCCXe82n8xEA4QuTzCvk/vI70kTaKQUOi4AEKNjY2cPHmS8fFx5s2bR+yHsYqzfWpM1E3w3Ru/y7h9vCJMbXYaM400GA3U19ezZMkSli9fzrZt2zhy5Ih6n5UzK3n9a6/Hla8gxI68g0tfvpTewV6VHyX7UOoRiJLR0VhZI7NosuHYBi65/xIsCQvzx+bzsbs+RpiwEsbpdJrh4eEaB1uo6i6XSxX806naeoTBMGoLjXQv7ubkfSfJ+U+1qDJgfP44z37gWXUtuZ4oC2FsWK1WZmZmeHT8Ub5+/ddJuVKk3Wn+6fp/YqR5RDmxgmjr6LUUjpmNcgsQIawAwzBYsmSJapkj4JfO2hCjT/aEnLV8Pk9zczOrVq1i4cKFCmk2TZNMfYbH/uYxJron8Hq9FAoFYrEYqVSKcDhMNpvF7/fT0dHB8PAwa9eu5fTTT6erq6umJkUqlWJqaorh4WHGxsbI5XIq3y6bzRKPx3G5XExOTuJ0Ount7aW5uZnGxsY/VuTOjbkxN/6bhsViobOzU6WEQG23DdGl+XyeSCTC6OgoY2NjRKNR2tvbWb58OT09PbS1tSkbQx+SOiT6WI+4DlmGuKHnBnb4d/CJJZ8g6oqqZxL9r+tRsT/0tkR6pFNnmEl0Vmyi0EiIm35+E66Mqwb01K8vDjJUKeVSb0PsPbFJ5LrC5JPfi76Q55HrKluMIgfPO8jWq7fyy7f+koHuAfWuVqsV0zDZv2I/D7/+YX57zW/Z3bObUrmkbE2d1q4zpuSdBOC2WCykPCm+ceY32NG+g2QySSQSIZvNEgqF8Hq9pJ1pnrj+CfpW9WHaTCYvnGTve/eSd+WJRqMMDg4ydcEUqctSNSGpYrDIzh/vZFtmGzt37qSvr48jR46w5+AeXtr4EgPvHsB0ngIzHGWOXXKM6PKoembRc6JTVZqCabD8k8txj1ZsXExoONjAyu+sVPaVOHliR8g15feyz/zP+Wn4SQNG+lRqRMpG7y29GC8ZSre53W68Xi9+vx+bzcbY5jGGtwxj2k49u6fMibedINocVftKZ9Cl0+mad5menmZgYIChoSG1F2YsMwx+fZDEGQke/dqjxOsrTqbQ7w2j0vJTQG5hl4p9I3ax2K5SA6itrU2dNUlVk0CZvJvYQ6Zpkp/Os+FrG2h9tRVL1sKS3y6h87edBN1Benp6qKurU7aB2CbCVPH7/YqtokejZU/KvIuN5/f7OXHihGolPDw8zPDwMMFgUKWmSQs/PRAmvkWhUGDr1q1zDvef8fijHW7ZBLJBoNrP95prrlFttsQglw0qAlHPpxFnVaKsIpTF4Nbp4noOkUR8xfmXhvci4MWZTCaTigqj5w+J06gb63q+ufxNlJIUahDakM/nU9QSua5OZ9L7eUtxs1wupxwkmReoUrZ00ECnxsv/S8RSnl3e2zAMRW8XR1nmWRxOETxyX4fDofJs5PldLpei5wj1NhwOq3WW4mlyDbmuODkCLkg0XNBCQZx9Pl+l+qYvyde7vs4roVfAgBPuE/zlgr+k39FPIBCoyX3ft28f/f39TE5Oqtx6gPHxcaVwnpr3FL+69FfsXLyTr13/NZ5a9xQWW5UubBgGXq8Xj8dDNBplZmaGd9/zbpYcX1Ld1AaMNI9w5+V3MhmYVHMj+7Curo6Ojg6am5vZs2cPu3fvJp/Pk06l2bJ3C2987Y24c24u23YZZ+86m3KpamAIFV8MD721hVSY1xWngUH4rjDunW7e++h7KRVLNSkNFouFYDBIIBBQwluPTuiVRhOJhDLS9MJ9AmIZhoHNYmPV4VU1Z9yet9N1pKuGOibnRu4jFVttNhsn1p+oKTRXNsrs6tmFx+OpKSooRqycOT16DlUwThxmQeD1yJBeIEc3rmw2G9PT08pJ19HzUqlEMBhk5cqVrF69GrPL5NUPv0piYYKnP/M0xxYfU+knNptN5cBLpwKfz0djYyMdHR0sX76cZcuW0dbWVmOkp9Npjhw5wrFjxygUCkxMTChqXSwWUwaT0+kkFotx+PDhP1bkzo25MTf+m0YwGKS9vV05PbqzKN0oRkdHGRkZIRqNEgqF6OrqYvHixdTX1yudKPaIAMtir4isnQ2wDjgH+ELHFzjuOg4GvOx7mX/o+AdivlgNBR2okZ25XI6xsTFmZmb+oMCb6Bkx9PV7G4aBWaq2cNWjdaKbxJETe0MPWAhoL3IYqIlu6yllOuAtNpFEAJ9f/TwPXvJgJafcgF//xa/Z37NfUab3rt3L/dffr/7+lTVf4fctv1fyXo++iy6Sd9fBg6gjyo9X/ZidLTv59lnf5qUFLxEMBlUF7FKpRCQUYbhhuCa/PdIVYcpdAVBTqRT2O+04/8YJs+tbWsFxgwOv16vWxRKwYL/CXr0e4Il42PyLzbQda1M2mAxhZoptbBgGgXiAJX+5BPsBO62vtHLmV84kn6s65VCldOs0bJ1abpqVbjjz75xP7696safsrPzVSrqf7lY2rOxT3Q6f//J81tyxBnuqsm88Qx56v9yLs8/5B/pU7AqxLYXZKi227HY71iYrI58eYfyMcTAg1ZDiqQ88xWjjqALeBUCS/PREIqEo+jabjVAohNvtVgXmhOkoDrbMgeROCz0eKkxPCS5YrVYaGxo561tn0fPrHub/fD6ZdCU639fXx9TUFKVSibGxMUZHR4lGo8TjcdWDXdrtSYtemQOhmTc1NQHVOjY+n0+xeIVmL+mi0i0llUpx/PhxFSSDCngQDAaV8z43/jzHH+1w69QfqFYt12k84mTplCU5TEJBFgUkglqEPVR7NwsVXZxoEfh6YROJnum5I/IcVqtVAQDiVIrwFwdYBMlDDz2klIk4uXJIxCmW9xWnVJxvqFC89RxvodrrNFlxlCWqJ78T50KQaXGuIpEIzz//PF6vF6/XqxwUmVdxCMU5EAdfQAP98yKM5V7iSAmaJ/kyOoVNV/qSLyPOjfyIsBOKv57nJo630K3sdjtOuxOPzVO7qYpQzpfVtYSqJBXBXS6Xqg4tlKWZmRkeX/Y495xzT42C+9263/HA6Q+ovSX31VHFsC3MhQcv/IO9fXTeUe689E4mjUkFsAjq7/F4mDdvHuFwmF27drFz506lMM/fdz5n/OQMNm/bTKFQUJFUoerrNDiZd92Z1Wlx5XKZ6JYo2cVZ7j/z/poIuOxPj8dDLpdTSDCglI9uQOkOq+wVmV+JfjisDq594lo27d5UmQQTtty+hYVPLVTRDTmTw+uHcZ7mVJGPsbExyuUyS3+9lNV3rlbzuPaetZzxyBnMzMzUREH0Aj3yX32PejwelVahO/pyzsXI0N9HUj5mO+uyfnrk2+Vy4Z7n5rWPv8bY8kpLsYKnwLabtjGyaUQVbJQ6A1LEL5vN0tDQgGlWcgoXL17Mhg0bWL16tarALu+QzWY5ceIEfX19nDx5knQ6TTQaVUaNgHFTU1N/sP/mxtyYG//fjlAoxIIFC5SujkQijI+PMzw8TDweVy2Aurq6WLhwIcFgUMki0anijOosMT3iqkeP7XY7M64Zvtj5RXb4dtQ8i1E0oMQf5GOLTSFFNgcGBojH44p1J+CtRMF16rXuWOsMNP1HZKzoId1mEdtKZCtUWVr69fRI/uworID65XIZt+GuXQATjHwVrLDk/tAUdZmuGmakbmfqtp7o0qK1yHfXf5dtndsqn7OYPHL1I7y26TUikYhyUF0HXHR8sQNbX+W9gieDnHHrGTSMN6jnL5fLuH7hIvzZMGgBx44fdDD/X+crtlJDQwNN9ibav9SO7+mKTWjL2jj9J6fTur1V6RR5TnlWt9utQHmZv9xLOZo+38Sa76+hnCzXAB2yprrtIClWMu8SIAkGg6x4eAWbf7iZRU9WirdJ4CSdTivmpehQh8NB97PdLPrqIuxjdhZ+ZSF1++qqS6WttXxe1jWdTpNKpWhoaKC7uxur1crUxBSpSG2dknK+TDaRVYxLm82mbCZhRwgzTM6LrLMEhPT0s3Q6TTwep1wuK4BfzoDugwjobzWt9PymR+WuS/VzYQbIOZB9n0wmlV2czWaVTSMOudVq5eTJkxw6dIhMJsPRo0dJpVLU19dTKpUYHh4mHA7XMGaj0ahix0mamqRoAkxOTip7YW78eY4/OofbNE1FeZbNKQivDNmwOoVGnDzdkROnQwSzIJS686nnUokTBVVEU+4vjqxcT/JHxBGUgl3iaIjyEDrKFVdcoQSJUF5FSOp0Lol2S86yKEbJF5eCUKIc5N7Sb1BHM30+Xw09X+ZE0GWv18uqVavU7wS5lmvKPIuhL063OIJCHxeHUT43m54mzyQRO7kHoISivNeePXsolUqqjYooyrq6uhq0XQqK6GtutVppKbfw2aHPkrQl2RrYSkesg4+89BGazCZSxZRSICMjIypnHFAUJnmfYrHIkv1L2Hr6Vgr2gnK6TcPkieVPUC6UuWLrFcpYicfjeDwelZe/eGoxFx28iCeWPKG+a5gG60+uJ0SIgq1QE0UtlUqEw2HVPmpkZISDBw+yfv167HY7G8c3YvFXK+6LchW6ncyNzJe0gNBZB4ZhcKz1GM9e8iwlb4mXm1+mSJEbHroByhVDR5xRcaj1wjGydvp5dDqdqhhYqVSpMSDrKZ/zlXyc+/C5RAtRFu5fiP8pPwV7Ze3kftNLp9n6jq3sMnZx5r4z8SV86t5WrHT+tpNMLkOZMp0PdnLAeoD169fjdrvJZrO43C72N+3nhOcEl/Vfps6AKHBJ3RBgTWSDDuTJ2ZT3lc/InDY0NGAYldQLvQaC7tS7c25OHz2de3ruqbSYM8E36qPuYKXf+eTkJEuXLq2pHyG5+A6Hg1CoUuk1GAzi9/uVsZDP5xkYGFDATiqVIhqNkslklEGWz+cJhUI0NjZy9OjRf7+UnhtzY278lw4pctbf308ymaS7u1sB2GLA67RlPd9Zt4F0cFo39kXPKoq1UeZ9C97HMdex6kOYcFr8ND4z8BmCxSB5S77GvhHKsIAB4qzoAQ15NqjaRjrbR3dO5RnlsxJM0N9v9nvK/WanjQkoqgdhZOjPUyhUWmzNf3I+lxYv5dFrHsXA4LofX0fHyQ6lCxbtXkTAE+A3b/gNBgZf3vtlNk9tVn/X09Xk2vrvrVYrzrKTC4YuYHfLbiXjPTEPnXs7VeuqXC5XcZ6Ol2n8i0Yiv42w5WtbCEQDDA4NKgfJYrFU9sMTDpre2ETikgQUoOOuDkrOSqBIWrtaLBYKMwU6v9TJYN0gZ957Js39zSpfXkBmsRl1AFxsWInkNg024Xf5SZfTZDKZms40UqRWbGq5tx5MUfsTg/oX6kkVUzV7WMD4crmsgmDyvo4HHdQ9WYcHD0Wz+uzyXb3WkBR8zeVypFIpEokEwWCwovNdLtbdsY7+zn6GzxjGM+Fh8zc244/5KZiFmnUEVGRbzpWwIvSuOlITRewACUbJ+RP7UNLfZO+JHeZwOLA5bbjsLsUSzWQylEolAqEAJiYet0cF22R/50t5Nf+6ne3xeFRUXe4v4JfYzNI6TIrMig/i9/sJ14fxBXyquFs0GuWVV16Zaxn6Zz7+aIdbkCLZwPI7oSfLBtcjo3qRBajma8vByOVyqs2CCHu73V7jpMRiMeUcizMoSFM+nycYDKrv6/2iRekJDUVyvqVioETfhT5dLBZrClpI1UBFGzr17jrFWSJhIhjq6+spl8uqmb2OEEukTT4vPQNlPkXIAArRE0HkdrvV80hEUA52MpmsLOQph0UUpgjhRCJBfX09VquVRKJSlEMcKqHJiPDLZrOqCJwIWTEcenp6lKMmVHlZZ4k4irAUhoGAIULvN5IGb33lrSQuTnDz8zdjKVooOSqfn56e5vjx48zMzCjF4vV6a2hTIuwDUwG+cMcX+Ifr/4F4Xa2Asjls6r0lb1/afYy4RmhMNPKGF95Aopjg5eUvYylZuGr3VZz56plMt07zk/N+wrvvfjfOjFO9u9vtpr6+Hr/fj9PpZP/+/WQyGRacvYCffuKnvPfO99KQrlYvlf0k3xfDyDAMtY56m7YIEW678DZS3oqyNw2TPV17aF7bzEW7L1LnRtZoamqK5uZmtc5Wq1XRnUzTVGfL7/dXrnfKMNIVvMPhoLGxkejRKAv/diEN9Q34wpW2XFLde6Jrgoc//TCm1SRLlse+9RgXffoiApGAekdn2UnPnT2Vc5dPkzEyPPvss9TX17N06VL6Wvv48et/DEDL8y2s2LkCu8WulNXsfD8dGJNzIcUE9dwmicqk02kCgQCpVEoBVJLzLsaHoP5Nh5tImSl+f/rv6Zjq4AOPf4BYQ4zRwqi6r9vtJhKJUCgUGB0dJRaLsXTpUtU7HKCuro50Ok1nZ6cyBk6cOKEo5OVymenpaaanp3G5XHR2dtLY2Mjk5CT9/f1/rMidG3Njbvw3DLvdzsqVK/F4PLS3tyv9ALW1IUTGADWpLvCHLUkF4NNbFektRS2GhX8a+Cfe2f1OpuxTYMLaxFq+feTb2AwbFluV7SR61jRNotEoR44cUdExsbkymQzT09PU1dUpO0N0jgCNch1hQcn7CX1e5KnoJ7HlZqcN6k6jvLfI7P+Tky5D2YCGhVXbV1G0FWkYaaD9ZHsNm9HhcHBB7AIW7F+Ax+rhrMhZFM0iZbMK7svciF2iRzJN06RUKHHaydN4f/n93Lr2VpwzTq7/x+uxlWzKdjRNk0gkQjKZpNnVzOmfOB27YSeajKqoowRQnE4nHrcH50kn/n+t6FIstQW6ZI48Hg/t3nbW/tNa7IadQrFQE1zSU/jEzpyZmanaNzaTQlOB2GSMPXv2qHf2+/3U19eTzWZJlpKUXWWIoFiKshelgJsRNLB6rbhxq0CN2CViewhzTJiXomfL5TLuUTfFxmJNmqOsqe4ES0BGgPxkMonL5SKVSlWc7pMumm9uJv21NK+7+3V4nV7KDdXWoMKiE4dT9qGkysn8y5kQ3SvOrc6CExvLarWqyLVEzXO5HJlMhsz8DM+/+3nO/PKZ2KcqdndPz//L3nvH21WV6ePPPr332/vNTe5N7wkhBEKvQURAQMSG7TuM4hTbqKOjDjqjOAVRxxEEwTKCIiA1hAQIKSSkkXprbi/n3NP72Xv//jh51lknfL/zxe/4c4p3fT75JLlln73XXut93+d5n/dd8zCeHEfkryNw73YjsCcgnlnTNGAl0PvNXnTc1QF1SBXrWdd1FIIF2NI25JN5sYaTySQMXQbYJ+yClFJVFXlTHgVPAYXJsnpiOjmNkTUjmOycxMUnLy4fXTY1JXrAzI3/uUOh0fi/Db08qmpouODJtKXTacTjcQBAa2vrv2u02aFYNtQMVrl5ZPaNYFzOdgIQ4JPf57WZDaVhlhldZpjZaZFf5+YnsOOzJpNJ0eiCgT/rz/l9GjbOi6wAkDOabBRFgECjR8ctgzNmeenUZCNJyZbcmIpGkTIbGi9KZmkgSSDwDEDeG7/Gz6OBZXM3StQobZalzjLQ5HXkzP7IyAgGBwfhcrnE9aLRqKj9npiYwOnTp0XAQiNPJpLnNNfX14tu0IdSh7Dtw9sw0z4DRVdw6eFLcePrN4psZzQaxcGDB+F2u5E4J4Hnb30eH3rpQ1gWXoZSqYTvLf4e8sN5XH/0ehTXF/G9i7+HhD2BeSPzcONzNyKyN4Kuri5BKBiNRkSjUYTDYRwxHsGu/7UL6Y40aiO1eM9v34PmcLOYZ74fMs8EmHRkXO98X9GmKB689EFM1E7AoBpw6Z5Lcdnuy8Qa41rM5XLo7e1FU1MTvF5vVa2dnD0m8cHgh0GW3F8hn89jenoag4ODqK2tFUQTSbCX3v8SRi8arRgLVcHSx5di4S8XCoac70sO4IT06zLg2Y89i5K5JK7xjqfegVVvrBINW+hYyVzLgJsqGQaJ3JMMXMhQkz1mwKjrOmZmZtDe3l7VnIR74tm1z+LiAxfDqlkFaJ+ZmRHkHBueJZNJzJ8/HzabDalUChMTE6IzscFgwODgoNh/iUQC2WwWfX19oikKBxuwlErlLudv1+bODUDXdeX//lNzY25UhqIo/+4Gczqd+Na3voXly5dXBetyNlcu/5Gzq2f7eaASn8hlRAQ5snpN0zQc8RzBV9q+gpZ8C+4+eTcMxUoSgyQjgX4sHcNzxefg3+uvUq/J/W2ixShmW2fRMdZRVdYlK6zohxmPyD1W5Ax4KpVCIpGA1+sVSroz8yniEvlIMzl2I7ig5BwoA36S7bx3kgEyyFcUBa2trejp6RHJFRlkESTKYIT/Ziwjx0vxeBy/qvsV3C+6YZ21wu/3w2AwIJPJIBqNYteuXSiVSli8eDECgYDIRk5NTYlTZAhoGefJhC99Oslg1arCuMGIhVMLBdkiKwkAVPUKCQQqcm2TyYRoLIodS3dg6vIp1H+mHu4Rt/hcrh/dpGP0/aNIdCfQ/s12OMNOMQ/C17iBsTvH4GhwYMmPlkCJKEICfnZSjE15M5lyp+7prmmMPz+O0mzl+FjGPVqTBs2pwTXsEskyJm2sVitisRgymYyYSyZHstks6uvrhTqQJBQJEypCU6mU8PWspV6wYAFqamqq1CRybDg7OwsAQpXCrDcAEadz/kbbR/HCR19A1pNF6M0Q1v/rethGbciZcthx+Q6kPpACdGDRNxYBD5d/P7shi4nvTkBza3Bud6L2r2vhnC0ngJQFCia+NgHLcxbUPVwHXS0nOfQrdIx+eRRL714KdVt5DxQNRWQ/k8WYZwx1f1OHQDGAgZsHEP+zMk66dvu1WPLiEkyOT2L79u0YGBj4d23b3PivO95OrPK2M9w0ojLoJjNGR+FyucQB9DSmBJE0/vzDoPzsrwOoau5EQ5lOp+Hz+cpg58gRXHLJJVW1S/w53gv/pFIpAep4zzT+NptNGBzeqwxcmCVTFEWAVpkdLhaLCIfD6O3txapVq8TvElSTwaOTO7uBGpkxfvbZygGyeXJzOaBSS0VHzt+Vm07QWbrdbhQKBZw6dUqcIUzjVyyWj2KjRJ6sJWU5DALk+jRZ3svGWGQ5CcSpBOAzzM7OCtKCigc6bda6JhKJqmwnwTuf12KxoLOzE8lkEplMBkePHoWqqlj53ZXY97F9WDG2AlcevBLH6o7B6XOidbhVZHhPLTqFAzcdQMaVwYObH8T7974f68Prcfve27F//37s7N6JwxsPI2EvZ8v7W/rx2OWP4fzh80Vmk4oEs9kMdaGKo1cdRbqpnJGeDk7jF5f9Au/Z+h60J9pFAxACV1nGxW6b8vvWdR2tiVZc/LOL8curf4lLxi7B5YcvR0kpCYJDfs9dXV1Ip9PCsXA/cr65BuVu/kajsYqoIvnC8gk5u8Ls+Oafb8YbeAPHLjoGAFj3+DrMf3w+Cnqhak9zXzFLzTKA8Ez4LYytwWfA6Ogo2traBFEj16DTLshrMJvN4siRI+jp6RFrkEQFnS2fiYFXNBoV53LLNgUArn7j6ip1ABu/RCIRjIyMIB6PI5lMwuFwIJFICCBPFp+kn3ych9FoRG1tLYxGI5qbmzE1NSWa/qVSKaFEmRtzY2785w6eQMGsJ22JnNUjoJLJc6CSZDhbhk1fxZgIgLBNBJn5fB6LoovwmdJnMC8/D3aDHbqlki2mDQTKNao/6PgBngk9g6ujV2PpyaXiHkQ2ulTEs1c9i5meGVz71LVoG2gTdo2AVY6HAFTdK++dz8PGrHL2VgaOBGx8zrMJBzlmIdHPuIVZTLmfDtWCfr8foVBIxFgEV/xcOT6UEzEAqkrxCAaTySQWHl+ITD6DrCGLRCIBq9WKZDKJI0eOIJVKoampSfjVXC4nVIkkWmw2mzhilvchzz/flcFowPCfDyN/QR61P6lF45uNVfXxvH85DpRPfjGbzei7vg/j7xiHbtQR/bsoer7fg7pknfDhqqritetfw/j144ACjJpHsfH7G2FLVSTNJa2EnR/ciciWCAAgWUhi7T+vhdVgFWuRsmj6TQLY2NIYDn34ENR1KhZ/azHymUp8pwQU9H6hF6pbReffdMI96a6KRTNaBuHbwwg9FKpSUVCuPnbbGEJbQ0JazfiP5Y75fB4nbjyBZb9ZJnxs4pMJTD4wCY/HA5vNhqPXHsWK51fAY/cIMoTKDqpEZZk617GmaRieN4wdt+5A1lNOroWXhLH/o/vxoZc/hGc3PIvUsjN+WQFO3HUCrcZWWCYtmP70NDR3eS+mN6cR/tsw7J+xw+Aw4PQXTiO7PAssBZwhJ87dei4OzD+Avk/0oRQs4dAnDyEwHUDNgRoM/8UwEjeW48qMP4P8ZB7xLXGxfp88/0nES3H4v+vH+Pj4/8VyzY3/7uNtA25mOoEK43h2YzCDwYDGxkYAEGCSAb4AK1KmjvJXOhmCQI/HI7JXuq7jjTfegMPhEOcIr1q1CkAlc05DTXaVxoSbm86B4IX3LhtXdkyks5GbNMisMuuLQ6GQkIDU1tYKUCgTEzKAZlb3oYcewpo1a7BgwQJh7A0GA3w+H5LJpLgngiDKhgCIs6ppWJj1oyGTO7KbzWaMjIxgcnISl112GTo6OgTBQRaZrK3T6RSyZRpGZrz5ToBKs7pUKiWcBx20eOfGssKAwczExARUtdyluaGhQTQIsVqt4tgudtUm6GfDLq6LxsZGLF26FOPj4xgaGqqqV9cP6VjwpQVY07UGp2tO48HLH4TBZMCnMp/CLxf/Em16G/bdvg95X5ktj7qjeGDDA/Ds9GCRUq5Hjx6LAoMAfBB13b0tvbhm3jUoxUriXvjcdcU6tMZaMdE4Uf55HWiONiOUDSGZTIou7gw++G/uHb4fuUulqqrAPmDTyU24eNHFYv3wWTn4dZfLVRUoUoLPoIjrlpkK/p7c4I73QQDpcrkEweB2u2HOmbHuN+uQL+Xhn/Gje3s3VF0VoNpkMiHbnMXoxlHM+9k88V75+XU762D8vBFHvnUEAHDZk5fhwvELMR4cx/T0NNra2iqSNk0tN9I5s5ZIaJGlbm5uFgGVnFnhfpWzMTyGSy5poTM3Gst19JxbHgGiaRqCwSBsNhs6OjrEUSe0F8ViETU1NSgWy2du5nI5tLa2inXY1NSESCRSbqJTWwuPx4O6ujpMTExgdHRUBLlzY27Mjf/csXTpUqE4kmMUWRItg04Ogj05MywTnTLw49cINtmAyel0Ynlsefl75kqDTX6+pmkolor4545/xhOtT0A1qHjqiqcwdHgIbSfbEAgEkEqlMDI6gsGvDWL43GFAAX593a9x08M3wTfkqwLJJODP7iAuZzFlUuHs55DVjLzm2UCbsYrcrFTTNBxvPg69RsfCNxaKeaYdNJlMsFgtsNls6O7uRl1dnYj3eH+MTxh/sUSL70uWdNMORyIRDA4OCh9EP8meOZOTkzAYDKipqRHgl/6PvpTxlHysKZ9XPhYXCjD0xSGErwoDBmDH7TtwwT9dAH+vX9wbM8lApXyKn+dwOPDGZW/g4DUHoRvPHEu7PI19X9qH9/zTe2Avlc8h37ZlG4Y3DYvYJHZODHvr9+Kme26CUSm/x6dvexqRNRGxVmcvmsXBwEGc85VzxP3Lx2vynStLFey5cw/SoTTwLmCiaQKb79sM6OVz0R//7ONIdZdBaf8/9OPGb90IY6ocE6qaiuf/7HnEFsXg9/uxeOtikVjK5XM4et1RDN08hGh3FGvvXotgIChiZMbgu2/bjb7NfSjUFDD/nvmY+csZxN4dg9qpwv8dPyY+NoGBdwwg3hXHVQ9cBYfhTAdwg4KphVOIe+No29EGs8lcRWYYjOUyBd+YD65pF5J1SRGrLYotgj1jx6JTi7B/6X4xrzbNhuu918Nit+AnxZ9gTK90sk9uTGLgXwcAM5CbV45PYQDGbh/D9su2I+qJouQpr+1iSxHhb4aRiCSQ7cmKdzKzbgY4S3tj0A3YMLMBp+2nRdw7N/7njrctKdc0TWedqNvtrgqI5SCZBpkAjoaSoFZu/qSq5TOX5bN5AYjmIMwI0vCLDssS6BO1J3Z7layLdUgEPGxyJANWZncpfyHQldlddk8koOXxPqwPTqfTyGQyVceiMRvPaxEMRSIRcUQAa8+BasaUAJfzQyNpNptFlozgib9H8EawUwXiUGHaWfMus4GUtrMRBJ9BZqb5HMycDgwMIBAIiC6XlEbrXh13ttyJzw58FgsNCzE1NYVwOIx0Og23212Wjhcm4Ff8KOaK6O/vx/T0NAqFggiA+N5JxLChzbp163DkyBFs375dZBbZpVNRFDgXOvHUN59C0VqeU3PJjKKxCOsLVpiHzMh8IAPNrMFUMmHLG1uw5cQWWIxl8Nnb24v9b+7Hq199FYlFCWFkLQUL/vLBv4R71i2CCmaLVaOKn135MxzqOoSlA0tx61O3lmvSz5ALdNSKosDsMCOJJEwJk3gfNpsNsVgMqqqKoyK27t2KhfMWor2+XRAj7HNQKBSEakBWInA9cC1QtkcpH0G4XFahaeVGfEajEcORYQwMDqDZ1yzAuVzrVSqVADOQSWUwPTENr9cr9kupsYTn7nkOmllDz4970PZkG4xqJUAhkB5bNoZUWwqdj3di07mb4Pf7kc/n4fF4UCgWcLzlON6Y9wau3349HHCIMhWuOc4Zn481/mS45ZIVeS+k02nxfc4nn4nH8ZlMJlEuwuCOBOLo6Kg44358fByKoqChoUEAbrfbLWyVz+cTdXL8eVVV0dTUhGg0iqNHj2JiYqJKaj43/u9jTlI+N37X8e9KymuAL//pl3HxhRdXkXbMQlPNRrtBEj9mi6FWq61SEcnxB4Ekyb1SqXx00Pj4OMLhMFwuFzo7O+H1egWolJuSAZX60F8Gf4n7Ou5D3lzpWGybtWHz5zbDGy93S9//jv04vuU4dEvlUd2zbiy9ZSnqnHUwmUyYnZ3FxMQEcrkcamtrUVtbi2AwCJPJJEporFYrGhsb4fF4hG1kFlGWlZ9dlkf7THICQJUKYLxtHI/c8QigANc+fC2a32iG0WAUBCqUsvIsuiKKjw99HC2hFkE+yDEp4yA5080555ypqoq4OQ5X0YWp8SmcOHGiqo7dYDAglUrh2LFjeP3119HS0oLu7m6R8ODxT3x+qhxInAs/LvXVUVUVY+8bw/iHxsXZ2wDgCDtw49dvhD1nFyCfc8n/y+tjYnYCvd/qRXJDGRAaEga0fbINvsM+uJyucpJHy+DNH76JXHeu/DNRA9betRZNqXL/AU3TMJOdwdZ/2IpiY9m/WGNWnP8n50MJV96poiiiD43T6YRqUfHEN55Atq4CCpWigiXPL8G5T56Lp97/FMbWjVXOMtKB0EAIt917G/LWPJ65/RkMLRwClHKn/UsevwRLX18KVVNxYOMBvLLllTKRoAHt+9txyc8ugVMrx8mqRcWuK3fhwOYD5aNFNcBYMEI1q4ARgAYY8gboFr18DR2Y9/o8bP7pZmhJDRNNE3jhiy9AV3Rc9K8XoetIF6CV49mENYGXv/wybvzBjbDGrNDMGh77k8cw2TKJrle78M5d74QlY0FJK6FvaR+efNeTMBfM+OA/fBB1SvmIs7ySx33vuw8ToXJSxZ6x465f3AVrvRXf2PwN5Ow5QAcWTC3ApX2X4ofrfoiCpZIYseVtuPnfbsbTVz6N6dA0AMCZdeLLz3wZcXsc377o21A0BR995KNoiDZg27ZtePbZZ/+PZmtu/NcfbydWeduAO5vN6rt370ZtbS16enpE8E/ATSPFrLUso6JhprGmzJbfByp1r3RY/FvOojILLdelABBNkgh2GDTzegCqAm+5NpRMH4FrNpsVAN1sNsPpdAppOT9PDvjZUV0+XoCAkQ3PWPOiqqqQqTqdTgHydV1HKpWq6kBOuZXdbq+q7ea8qqoqWFJFUYTclc1TOOfMoJPlls/U5j0T5POzKEEj+6soinA8hUIBDzzwAD7wgQ+IOXa5XBjVR3F3893Y5t4Gt+rGd059B/7jfjG3NpsNE84JfHv5t3Hx8MUI/iqIiYkJoZrgfMsZbqfTic7OTtjtdrjdbrz66qsYHBwUgJLArlgs4uQnT2L0mtGqo8I4Ol7pgE23oW99Hy554xLcdPImEWg5HA4kk0ns2bMHe0J7cOArB6oOy/OkPbjjiTvQNNFU1USN//7Fpl/ghu03VNXBc54p+9uzeg8OdB7ArS/eimAiWEUgkRGOmqP47aW/RVOuCZe9fhmsekXKlkwmq6TbDO74/tjFnGQUz/jmvbJjuNyQcO/evehZ24On1j2FpJrEhsc3wJyuSP4pCztbUslGL6meFF777GvIBypB4cIfLETn050o5StH4AHA4OAgrFarIA7q6+tRU1ODpqYmjK0eww+v/iGgABceuRBX77oa1oJVBFdkrXnvcrM1GWyTtJEllawR43X4h+oOBm4MGOVaTFkymc/n8eabbwqZutPpRDgchsPhwOjoKAKBAHw+nzjWBIA4y7e7uxvxeBzFYhHHjh2bO4f7dxxzgHtu/K7j/wi4FwP4OfCp5KewJbdF2BTaA8YKtM/8s8u5C1/t+CruOX0PFmcWVyUVaItpV3laQTQRxWuG1+B43YFQKITu7m7U1tYKdZlc3y379Ewmg+HhYXw/+H28fvnr0K06PDMebPzXjag9WSv8xenTp3HwfQeRfH8SMAKBsQCuuP8K+Kf8VSU2zA7zuCtKx0lYp9NpYbN4GkOxWMT09DSi0Sjy+Ty8Xq+QP7O8j01XWa7HYw+tVivy5+Xx4p+/CM10JhOsA1se3IKaV2pEbBi+IIyn3lc+l/vm4Ztxx9gdsJQq2U+SrhwyKSHPv6IoGHWM4lsrvoXL+i5D0wtN5eZVZ2w5UCYHhoaGsHv3buRyOSxatAiNjY3Q9fKRqKOjo4jH44JokI/LktWTshqB72/irglM3TYFGAHPqAfn/vO5aIm2iHujok9uRBeLxUTyIpPJIJVJ4c2vv4nk/CRavtEC27M2rmPxd8FUwOgPR1EIFVD76VpYXreI7wkFZqOC8e+PQ7EpWPPNNbCdtsHtdguFHe+DyZpkMonT5tM4+pWjZTCvAZ5HPGj+RnN5PWsqRr87iuwlZUBufd2K1V9YDVvJhpFzRjDwqQGooUqXeuthK676/lXQLBqe/cizyC+qxAbOqBObf7EZ847Pg8FgwFBoCC9+4EUkG5Nve187Z51Y/YPVUAoKXvn8K9DMlfV1yQ8vQdeBLoy7xrHjIzsQnx+Hf8qPax66Bg3hBqTyKfzyol8iH89jydIluG7PdShGyjHnvmX70DraiuBMUCjgrFYrEmoC9225DylXCtc/fj06pzthMplwuOEwHr/qcTTEG/CR33wEal7FnmV78MR5TyBny8Gb8eLmV2/G6tOrEdfjuPeqe5G2p/G+re9Dy2QLzGYzTsw7AUvWgtbhVqRSKdxzzz2IRCL//gTMjf/S4+3EKr9Tl/KlS5eKjBkdh9wQBIBoFkUgJ0uuCBwJQIFqo0IjSdAgZ8wp+ZFBJYCqM4npYNgITA6gZadK4EzAQ/Avn/1HIMwmcKzHJfAgiOTvEKgSoMgAwO12Q9d1JJNJUd+byWRErbeu64L5phPm75IN5T3x8+VGcsxk8t80wAaDQRxzBkCADz6L3Mnx7EYZ/Dc/UwYy733ve8X82O12RLQI7m4qg20ASBqT+Hzr5/HJ5CfRPdINo9GIKfsU7l16L04GTuKU9xQ29W/CoplFIjjgeuEc+Hw+LFy4EKFQCCdOnMDx48fx5ptvClJEJnhUVUXPvT2w63b0XvvWY5cGNw2ie183Vv9qNS6ZuAR5Z14QQKy1bmxshN6ovwWw57QcBnIDaDG0vEVmqOs6tjy3BbpFF0oMriOu522rtuGJjU9AV3T8YvMvcPMLN8MVd4m1UygUkDfl8cTFT+DIwiM4iIPIG/K4bud1UFA5f5qkjhzoyf/m+0un01WdXwn8Sfgwa9PW2Yatl23FG8veKO8/i4LNP9+MYq4SCHKPyA0ISTZMm6ZRUqpl0vFAXMi45ffKejUC2UQigVgshmMrj+H1S14Xc/7S0pdQNBbxrhffBYupcuYt51JupMZsBPehXNfH4Ia/Q7khv5fL5cT+4OB+4p4sFouisVsul8OqVasQiUSQSCRQKBSEXeC1GbS63W4ROHMueH/cV3NjbsyNP/BYDOBHAJYA96n3Ifl6EvO2z4PL5RL+nwoyh8MBt9sNl8uFw12H8Q+d/4CoOYq/av4rfOrYp7Aqu0qQmIlEQqhcstksJicnEYvFsG3DNuxevRtX61djDdaIBlDyOc8su5LrjycnJ3Hw4EF4j3uxOLIYg9cO4ryHz0PHWAcUd7kBazweRzQaReO3GqEEFEyvncbmhzfDO+FFSasQ5kClTpsxBWMAAOJoJR6rKN9LKBSqIvrpe+SyJIJUmWA2Go0IB8PlY7mkMWwahj5dJkaHLx7GqXefEnb/560/R8lcwp29d4qEBVAtWZcHbbuu6xi1juK7y7+LXl8v+lf249KJS7Fi5woRMxH0jo+PI5lMoqmpCYFAACaTSTRRY6zJ2Imya9k/AJV6cTbbVRQFnT/oRMgVwtjGMZz3k/NQM1MDk80kEi1USfL/qqqKrLTVai13IS8F4fu+D9NLplHXX4dCW0HEu3KfnNDfhZBuTsM94UaxrlhFThgMhjJgvtsD1aoifzQPu69S1ma1WuFyuUSvI74rz7QH874+DwOfH4Bvvw+uv3Uhm8+KazZ/rhmzxVkU3UU0fKUByakk4loc9qfsaNPaMPSFIWhODbZ9NoQ+H8KbQ28CAEK9IYS/EUZ+aR6WpAXrHlqH1pOtMNrKfZVqM7W48GcXYvt7tyNRm4Bz2on259txetNppNpSME+b4X/Ej+ilURSXFGFL2rDxkY1oPNWIvk19b43TGnPIzeSw/+b9iM8r++ZoXRTP3/w8Lv3FpfCP+WEymDDz3hm8htdgdBjh/5wfJpiA3cCp0im43W6R9DKbzfD7/bj0x5ci4o+gabYJuqUcby0eXQzXay7Mm5kHi8ECs8eMiwcuhtPsxKPrHsV7dr0H6ybWARYgqAXx8Vc/jqg7iq5kF1x15YbN50TOKSesPCUkk8k5sP1HMt424DYYDPD7/aKOmQG0XFfDDByZO7K3craMQb8MbvnvdDotGFPKVuXmSazVYTYPAKLRqGiSwKwsP4NOhplH1uDIknegkiGXHSJBKYFgJpPBwYMH4XA4sHjxYgCVeiZmZrPZrKjXZlM23rPcbI3XByrM6ezsrDhmjM3WSqUSDh48CKvVihUrVohMHgEd2W6gunMmHSSNOlAGTmzWRScm13fJBAd/z263C3UBpVByvTeJBo/Zg+VYjm36NmEIfSUfGjINAICsMYu719yNfm8/AEA36th19S6oioqVL62syjQDgM/nw4oVK1BfX4/x8XEcPnwYsVhMvEsZLDNAcLvdWHN4DfY17cPW1Vvfsn7bptrg/o0bxZUVGT8AsV6sVitqH63Fxa6L8eJNL5ZlUpqCW395K1LPpBDuDsPr9QpHLzbQmTXOo9voVO12O7afsx3PrHlGBB8n20/i/mvuxyce/URVk5mfvOMnONF+QlzzxaUvIqfncMOOG6reCQMREiQkpoAKYUG5OWXVcl2ifLTGi7e+iDeWvCE+89S5p6DaVVz6w0ursr4yqSGXh8w7Og+uv3Vhzzf3AArQ8esOzP/5fNjMtqoSDPl+GexxH2T3ZKG/WwdsZ25CB1pHW2GAQfRYYFkK55z2gEEuf47kFAE27QqDNmbXuRdJyjHolQknTdPKcndJZs4GLo2NjZiamhK1+iStLBYLxsfH4ff7hd1pb28XARqPTJkbc2Nu/IFHLYCfAVha/m/emMePF/8YrVtbEXwoKPY8STICLu0KDb2X9SJuLgfwQ/Yh/O2Cv8X8v5wPW79NqHhISBKAxv88joHzB6CaVWy9bivWnlqLtnSbkKfLSjUAAhROTk7i+PHjOHXqFMxmM5bvWI55I/PQMNEAo6Uc/yQSCYyOjiKXy6GlpQX1T9Ujuz8L14gLaZTtC+2m2WwWJLacFeZnyyV4cn03fRxjJvoQZkfpR3w+XxW5SqLfu9OL3oO9iPxzGURsemwTFu1eBEtHGah7Ch4MaAMo4EwGWwcWRRfBbKoolmRym6BSTuqoqoq0KY27V9+NQW/5qEXNqOGlS1+CqqtY/vJycT+zs7OYmpqCruuora0VpYCFQkEQJrIiC6j0x6G/YInY2ckcj8eDJTuWID+Uh2vUBdVS/jxVLXfNJknMmItxGckPqrTUlArzXjNggpB8ywpSxl04DViaLVXZc/5csViEnjgzZ3VK1VqmKtDhcAiySFVVBAIBuGIu1N5bC/uUHYYug3huKizzD+eRUlPwmX0wd5iFckx9Q0Xg6wEc+/Ax9HyrB7a0Dcb6cqxsHbai7u46HP/749j4o41oHmxGLB8TcbzVakVTfxMu/t7FePbOZ7H2G2vhH/Kj9HgJvf/QC9N7TPCMe+B/2Y+he4ew4XsbUHu6FkarEcv2L4NbceOZ9z8DAFj+0HLM3zkfuluHfcwOzKts/VA6hHrU47e3/xYTKybE11+Z/wrMt5jh/xO/iJs9Ho8g200mE3w+H0ql8mlGb9rfFO/KYCifwrNT3SkSXYxLW1taMRYZw87anYJk8fl8qAnVYCo3VT6Tvq1NdGZ3Op2YnJz8f7dtc+O/1XjbgJtgj1kjWVpD8MiOw319fSLYBCAWKLO+mUxGZM5oWNgQjFk/HiTP2k1m1eXMGeuO6RBoKMxms+iQSak25Ukmk0kEvjKQJ8DgZuNxQjabTTTe2LBhg+g0zpocdmM0mUwiSOcxHMzE0ZAzK0+ZEmXRPF4hn88Lxp2AYtWqVeKoCn4u69WdTicSiYQAzrwmnRMNhAy05XppAmkZvMiyeM4zSRDWe59NYlgVKz4e/ThSagoP+B5ATbIGf7Pzb+BQHciUMrDAgpsP3Ixvn/dt5Ezl2pfasVqs2rsKRlN5/iORCFRVRTAYxIYNGxAIBAQp4PV6BZmRSqWE4+Iz2+12LF68GCFfCDOzM+X6bVOlVtZStGCDcQPCC8LIZDKoqakR75xZV5/PB7/XD+/LXhRKBezZsgefeOETaIw3YqhxCH19fZg3bx7MtWaY1IoTBSDkyVyfPP9y85HNONx5GEO1Q4ACWItWXPfqdcgn8zDay047EAjgnVvfiXtuuwd5WzmQ8Wf82LJ7i2DYrVaraPhFMEmAKHehpUyNTc2YeWBXeaCcgU2lUrhl3y041XUKKXs5q2PNWLH+p+vFPmO2h/cgE1QE997DXqz/s/UYv2QcXQ90QSkqSJaSQqptNBqRbEpi7K4xuL7qgtvgFn0CVFWFcdiINXeswd4H9kK36bj62auxemg1oAC5fE4QbzFDDM68s6rGjj0hmD2Wg1i51o9kCGXxnDsSWvw/S1bkXhLcH/xZr9eLTCaDxsZGNDQ0IBKJYGhoCMPDw+JYtVQqJWyYoiiIRqMCrMdisbdrbufG3Jgbv68RBvA1AA8AcADQgZpjNejc2wndUznGy+v1Crl3qVSCul2FY6UDiS0J6EYdhqIBtvttSO1PIaNlhL2hPcvn85h91yymrp0ScteYM4ZvLvom7jtyHxoyDW8Bufx/NptFOBzGxMQErFYrfD4f7HY7fNM+mKwmAQ4nJiYQi8VQX1+PYDAINaXCMeiAZqicqS2X8BGU8V5lhR8/n/bQ4XAIubOcSJFLoOSzxfl5BIRMxsSiMdTtq0PDNxoQXBLE0p3lut5MrtwsrEarwVce/wq+dtPXUDQU8ZmTn8EF0xcgr1cakjG+kIkCoAK+DQYD7EU73nvivfjm6m8ib8qXm5dONGPjiY1Q7aooiTt16hQmJibg8Xjg8XhEFnNqakp8DgGmfJIKiQuCW86FXNfucrmg5BUoRxRkDVm4XC7RXJM/y5I9+jMmWUrGEhK5BKx6+eiyWCwmyAwCcgBC0ce4kIQzs+ckfCiF5xqWS6UYGzBJYbVa4XA4RPmZ87SzTJIrlXJP+k5j2gin4oRiK793ZuhLpRJ8Az40/3Uz9JwOQ51BEDKKogBpoPXzrfAoHpjMJuFbWUY4MzMDb9aLW75+C/SYDsWvwP6GHZ7zPHCWyjXmjgEHFr5nIUK1ISi2SiKp7tU6nJ8+H7mGHJbtXgaL2QJDwYALH70QmlPD4MpBNJ1owvn3nw+D0YBNj25C/7x+FDxncEDChK57u2BpsAiyRVEUoVRjfM855Z5g8pBHog4NDQk8VCqVYD5oRtQcrUruKYoiCBw20WOfHJPJhKmpqd+vvZsb/2XH267hLhaLuixtljNVZH0IYB555BHccMMN4jxrGib+O5vNihogXoNSVVVVEY/HhSzU6XSKo8Z4frbcXI3sJEEkDRIzXayxpoSINdkAxLl9dLiqqlZleglkCDAIzkulEtxutwjICdBlY8xnYXaOYJGNsCgDpwOxWCyIx+OCbWctudPpRCqVEkwmDTFBPLOJAES2k9eg9Ots586vy6oBAFUZcRIdciM5uaaWNd+8JxqkL7q/iDWPr8H8hvni92iEdzfvxn2L7kNwKojrv3s9CvlCVVM8r9eLjRs3or29vXxe8rPP4ujRo8LhZjIZQZ5w3gm26+vrhSN5YfEL+M2q3yBnycGZceKGl26AP+bH/Vvux+2P3o4V+goAFbmzyWRCMpnEq6++KljNXC6H9vZ2NDWVa8IOHz6MRE8COz+1E3f85g40zjSKdcC5k/8WANUE3LPlHkR8Edz26m3oPtmNw4cPo729XewJk8mEXk8vfnbdz+BQHfj8i58HZiuBHBue0NjzncjrjqCRDpeGniQAf5ZOOxQKYdw4jn+84h9RQgnv+NE74Bp3icCAa1A0uTmz1kiMqaqKo0ePoq2tDRaLBdFoFIVCAR6PRwDOzKIMXv3aq1BtKmqfqcX878+Hp+iBrutVvQ/yrXlMrJxAy69a0NDQgEWLFonmLr3+Xjxw9QO44/E70BJtEXtOdvqy7eEe5DndbJjDc7BZoycHdPl8vooIoL3iXufaJ4nHzzQYDBgfH8fk5CTi8TjS6TTS6TSSyaTY062trSgWi/B6vfj+97+PaDT6OxvpP+YxV8M9N37X8X+q4XZ+yonC3xTQM9ODd/74neLr9NMElCSW6Sdf+8BrOL7iONa8sAZdv+iqig9IApLoVhQFJ285iTevfhOqWUUoF8JfHP0LrAivECCYv8sMbCaTwcjICA4cOIBoNCrsDD+fdi4SiaC/v7+cSWstH3tJMMXPBirHI9IPnJmTqp419Mv8Wf4u/YecjZcl3DKgZ1wlZ6Snp6dx6NAh+P1+9PT0VPX/oG1taWnBypUrMRYYw1HvUVwzdo24R6BCGhBky3ZZfgbe40sNL+H+ZfejfrIeN/3oJihQRAZ3dnYWr7zyCuLxONrb29HW1iaeaWZmBuFwWJDT7HfCGElWPwmQfCZxkfVkoRpVdJg64PP5xL2z+RyVbowxOIeMhXLGHI7dcAy6Wcfyx5ZDjanindNvkcQneGNMXCwWkcvlUCqVBHlAUphNgJ1Op0hcUekAQCSSDAaDAJVMYjCLTqDJJBkJKDZ/4zuhik7uhyIr7pgUIl6QFaXpdFp8jrwG9+7dK/YAr19TU4OurvK+8/l8sFqtgixhDMqEA5/1+Zufx8b7N0JTNUGaj1nGsOsLu6BpGtZ8ZQ2CsaAg7ZnM0DStKqnG2JZ9hYgFSqWSaJjMeIDqQrl0j+QI9wefl+sjn8/j1KlT/0GLNzf+K4zfaw03Fw03B1DtqLh5TSYT3v/+9wOAYNnkeiIaceDMAfNn5JalUknINB0OhwDjilLurEgjQ2cng0YGz5SUM8hmIC0eVgIqZJcIYgjeWU9NMMBr8V74LMwayjXg/H2Cc4JkSuiDwWBVfSdQAWixWExcnww0DRWfF6hIx2WAR6dKBpM/Q/aSGT5ZTkamUJ4XGlk6IFk6zq+zOzMl+HxmgvYvx76MpzNPi/XBWmWLxYLN4c0oHS+h6c0mTJunoakV0G+xWERjGVUtN/U6fvy4UAXIjfh4vxaLBc3NzaipqREgz2Aw4LJjl6GUKeGZc57BdS9dB0fOgYe2PISEJ4FfXP8LOHY6sCi8qIo0ojOVzy5NJBKw2+0IhUIwXGHAji07kPFm8PCWh3HL1lvQOtJaJe3n+yNAVhQFmWQGH3zqgxhoHsDCwYUoaSUsXry4KgNrNBrhLrnhnfUiVAihkCrApJmE8SeJw0yFzWYTpBTXPd8z3xnXN7t1E3ATlGcyGXg0Dy744QXIIw/3lBtmi1nMhyzVloM2dmoFgIULFyKVSpXLCjweBAIBnD59GgaDAdE1URy88yBUW3mdT185DQUKlv5gKZSMIj7HZrNhZXAlXnnkFaS1NEZGRlAsFlFXV4fM+Rk8fs3jiDlj+MlVP8FNL9yEtrE2MceyLZIDT6Ci9ADK0kcSRcxmcC3xZ+RminyPJBgpo+c+oB2yWq2ora1FXV0d0uk0Dh06VJVtp22UJZtzY27Mjf+csejVRWh8qRGXRy6Hrd0m/CjLyJjppU0mOFnw2gJsT27Hhb0XQluhVR0hqmmayKSyZvfKPVeip70HL/S8gE+e/CTWxtdCNVQaNNIO6LqOQcsgjs4eRXxfHOFwGPX19VVyZvryeDyOkZER6LqOmpqaqmZYcqZTjgsI8ghMCd759bMH70u2U7T9crKFg/6JAKNQKGBqagpmsxn19fVVxC0AAUp40kV3oRsLphegpFROmSCReXamW1Zayc9XLBbRvb8bl41fhpYDLchlKyqlUqmEaDSKVCqFUCiEhoYGcQQka+15TcZ1tP1M2vC5CSoLhQJyjhwmPzcJxaGg7UdtQBYixuH1ZCKccQbjgpJewsF3H0TfNX0AgEwpg/U/Ww+TwSTeJf0FY2pK9wn85BNlSApz/WqahkQiUdVXiHEUSXODwSDAJmNDOalFkM2yKa4Jh8MhvieXdnKv8BQgfh7fG+eTz8ZSQN4jSQoe66Uo5UbA6XQaoVAIJpNJKDVjsZj4t0wGsYxC0zRc9PBFMDsragUAKE2UcOH9F6JQKMCf9cMdcotSCe4Plhtks2W1gtfrBQBhI5jZdjqdIjnB8gTuVVkJwmdn8kImT4Dysblz449n/E6ScqByViQ3MWWr6XRaZGzJ7sjHURF0kj0k08PgngBKBs4Oh0MwlbxuoVAQzR/kTsI8+5lgl0ylzK7yd7jBZTk174VAlOCCBo/Gl8AFqMh54/G4ADlyYE2ygMdX5fN5kV0HINjHbDYrfu7s4DyXy4nNSzkSu6jTgQGocuacE96TwWAQqgK58ZQMiOUjpsji8lk5/2QPadj4TvmsPKOSRpv3xSYdALBhaAMMHgPUoIqxsTEhMVqxYoWojT948CD27NlTqSHK55FIJETNLX+nsbERHR0dwviSHbbZbDj/zfPRGG8E8sBPr/gpZj2zAIAZ3wwe2vQQGuIN+OCOD8KYrHR1D4VCogMr1Q8jIyM43XYaL7zrBWTc5Xc37Z/Gzy7+GW5/+nbMS82rWlO8D64tRVHgyXuwanCVeO90AHR8JUcJ//aOf8NwwzCGMYyCo4AP/uaDbwF+MsAk4cQ1CVSTKGxiQ8UGgTsZdwYF1uNWmDUzEIJgePluyXBz/3B90akwMOLnO51OOByOcmA3qkHJVANM17QLXocXyWxSgHmuFe69jo4OzM7Ooq+5D70X9iLjLM/5VHAKv7j0F3jvb9+LxnCjCDhoP4BKJoJOjaUlcuMZKlY4l3IGX97rclaA75Sfx2Cb6gjKEdetW4dSqYShoSHs379fZOGp2uF9zo25MTf+sMNoNKK5uRkXnb4ImqIhrVey2CQq6XtlmwcAVosVFx65UABC2jjGLWxIxUaLtbW1OHf8XGzQNmBZfBkMxsp51rIsOWKI4G8X/i1m581i/Z71aDO1wefziXiJ8cTAkgH07+9HKVzC/Pnz0djY+JajJ+l/aMv4u7Stch2xbLf5OyTqZUUbUOnGLSv+aGfZc4V+ZahrCKPOUTQXmkX8Jccy0y3TMC0y4fzS+eJ3gGqgfzbBe/bXCbDo+xKJBMLhMBaOLyy/N2NFds7jGKm8CgQC8Pv9AoTzBBY+E+vx6QOASg8TEguKScHI340gdW655GqvZy9ueeAWIFcpEZRl6iQcCCw1TcOxTx5D/0X9Ym2evuo0DE4D1ty3pioLSukySXTGm/KxqLxHWb7O8gPGbDKB4XQ6q472ZD8mj8cj9gljccYQBN78npxtZwwrg3aDwYCxxWMoFUuoO1wn/K98Ao0c6/MzKO1m9jiXy8HlckHXdYyMjKCurk6UXDLWYczPNcM5AconhTgcDng8HpFU8/X7ymVonnJsLiew+LmcD8q/GRM6HA4Eg8Fy2UQshlwuh/r6eqFaZSPkUCgk5ohxMIkUAnS32w2LxYLXX3/9P2DV5sZ/t/FWmvP/MAh2CoUC0ul0lVEBIDYsABGAMrClhJMBqmzAz+6kzN8n0JPrMlmvSQdBBwiUN5fcrMlkMsHr9cJut4u6DAINua7VbDaLjqSyHFZ2NHSyMgCXj9YAKswoQRbZO2ZMaUzIQpJNpAEjO84u0zxGidfm58pOFKg4O4I3/i1LsoxGY5VMbdu2bXj++ecBAJlMRrBvNFxsMCXXuM/MzAjQxg7NvC5VCZQXJRIJQVCwrp+MHuelvb29fDa3w4hHP/YoulaXJUN9fX147bXXcPLkSSHZAcq1xzxDtFQqYeXKlVi8eLEwyAThdAIOhwOr4quwIL0AS08vhaJVwN+kZxIHWg7ga1u+BqOr/AzsBZBMJkX2gDW5hsMG1J2qq9oP04Fp3Peu+xCxRwRIJRClI5dLG/g9AlWg4ijvvf5enG44La59tOMoHrziQZEZ6OzsrCI2yPYyKytL/7kWCBDpoEmsUNJItpzvkOTY2acPcH3RmXIfkJH2eDwiSJmcnBTBi+u0Cxs/vxHmWTOgAsrdCiY+NYGjB44K51YoFDA4NYjjx4+LfdHd3Q0A8Pf64d/rB86c/KGoCpadXIZgOCjsUDabRVpNo1AsiDIDzpFMRAFAJBKpanIm1+sxOCSRxvkiMcVglsw+f4+BzfT0tKjds9ls6Onpwe23347169fD6/WiVCrh+PHjItiYG3Njbvxhh91uF30fSKjJvU8I0hiT0HbT3rKPCf0qfSvBTD6fh9PpRHt7O2pra2G32LEkukTYOgBVoDihJvCRFR/BSc9JzIRm8MLnXoC51Sx6gDCOGOscw7b3bUP/t/pRd2EdGhoaBHiRiUHaYNo42mjGMIw9SGLLzZ6Y4VVVFclkUqgDCRgZt5HIZAaQvkXTNMw0z+DlO19G79/0wrqxIjPms6dCKTz1p0/hN1f+BgPdA9ChC3UAYx7GOQT8lN3KRDKvSYAZjUYRjZZrZl0ulwCcmUwGp0+fxtTUFGw2G3w+HzRNw9TUFKanp5FOp0VsI2fR+ZxyGR0zr8ViEf339CO1ISXW1eyiWTz20cdE2aNMylIxGAgEEAqFEAwGy6TPzotgyVvENUwFE8557hwhaSfgdjqdcLvdAhjy+wDEOwHKJ5PEYjGhYJD7COVyOaRSKSSTSaGsNBrLx7jlcjnRUC4WiyEajQq/zPIoquPo7xnv0/9yf9hsNoRCoXIfgHkxvHLnK9h11y7kl+Th9/tFTMlYi3GE3W6Hy+US8Sn3CU/3cLlcYj2nUimEw2FxykmxWEQymRTZaKfTWZVMIghXVVXMHfcOEyrEJ4FAAIFAAHV1dWhsbITX6xV9jUjW8BqhUAh1dXVobW0VqgWv1ysI9mAwiNraWvHevF4vfD4fACAej8Pv94s5ZK+cufHHMd424JY7NeZyOSSTSSGxZLZN7vJJYEFjyf9nMhlhxCldliUddAoOh0MY17M7RtLZEUAYjUb4/X5h/Fm/WSgUEA6H8cwzzwjjoOvlo754XTpLfl+umaHBkeXyul4+5svv98Pj8cBiscDtdldJwMhMMutLAMCv8XzHZDJZlWEnWJbZQ847jz+jrFuWxTKIkLPKgp0/0wyCWdxisYjOzk5ceOGFsNlscDqdApyxOZvVakUwGBRBhslkEoaPn3s228z3ZrPZ0NbWJkgPAhqyhUDZyHk8HtQurcWT73sSA+0DuLb9WuyJ7MGOHTswPj4uDB4BIgOJUqmE1tZWdHR0CJaUa0uumWIpQClZwjtffic2Ht8Ig1a93Kc8U/jq1V/FhGUCuq5j3rx5Qj3AGim73Y7RplH0z+vH2SNjz+C+dfdV1bfRSTDg4N/s0s/giM9ltVrxyac+idpIrbhu8EQQNz9xswCCAwMDVRJGZrwJGuW6e85vNpsVDkhWpfD3OGfxeByRSERkVQjCuZfovLnXqaSgk+W6Z5Aj13Z58h703NCD4MNBzLt/Hiy6BQMDAxgYGEA6nUaqKYXDPzuMmcYZQe5wLRViBXT+XSfqnqmDIW/AsheWYd1v18Fr81aa0/nTuO+6+3Cs/RiSqaQIvGw2m7h/stFyAMigjmSFXG7Bjv2sa2OZBuvlGHwUi0XE43F4PB40NjaKOSDxw9KU9vZ20YxpLsM9N+bGf84wGAwYHh7GK6+8glOnTiEQCGDRokXo7u5GW1sbGhoaEAqF4PF4hL8iACNByTiGhHwUUUxbpoU0l0dO0ecClVIVWUau6zq+3f5tTFkrzZLyrjyeuP4JYf8sFgumu6fx+KceR8FRgObTsPcHe5FdmBUZdqrf6O9IGvDegQqpS3/M5Ibcv0Qu+SNJzudl3S5Lapgp5LUVRUG0K4onvvgECs4CVI+K337ptxhpHAEABAIBxJpj+PkXfo68K4+cJYdPL/40djp3VvkSOQvLe2BsJ0v3KdelXdf1ci8cTdMwOzuLZDIJTdMQjUYxMjICk8mEzs5OBAIBMbc8NYPXJ3HB+EhWk3F+CZ4XfnYhbG/axHsLTYaw5d4tAvD5/X6huJuenhbdqElgl0ol6CM6bvvWbfCGvXDPuHHzt25GMBUU80yyhzEVfz+bzYpjKeU4h0CRPs5kMiEUCiEQCJSTGmfiBJmQUBRFqARJRlAJCUAckcWmxlzLMzMzVRJ8vjsSJCe8J/DYZx5D0VlE0VXEE194Aic8J0Sj5MHBQfT19WF4eFisJcrfOc8kg+x2uwCrjY2NqK+vR3t7O4LBoPDxbEjKWnSuTfriVColCIdCoXycJzvT5/N58f9YLIZ4PC5iPyogSMKnUinE43HRFJWlCTU1NYJQ4PuLRCLI5XKir00ymUQ4HIaul4+7NRqN5e7wLtfv28zNjf/i421LyglAuFkJCgkMuSnT6bQASGcfq8DNzMCWIIB1EQzqGXjTSbCrOCUkZJ5k6TeNBWXFbMJmsVhw9dVXiwZuNBAE9HLtLQGxXAfNwJ2ZLjJjBBckDGiAOGiUGMDTyfFzzm4oR3BqtVqFjF5m/OR6FblGHECVM5cHCQbKW8hgj4yMwGgsd2VNpVIiM8zGIXQ6DDTINspnQ7KjOp025wyAKAUgoSEzocwSxMwx/Pycn+N403EAwKRlEl/s+iLO9Z0Lj8cjridnHTOZDAKBAHp6esQRcgS6XEtyDZGQOCkGvHfne2GBBdsWb5MmCEhYExjwDsAVKbOs4XD5+C86PV3X0XqyFRse3oDd792NrDcrft211YUV/7ICE8snEAwGheORJeO6rou6JzmQ4bvM5XIoJou4/fHb8W9X/hv0aR0bvrsB+6L7sGzZMsHI8v3IzWP43HK9m6yMYF0T5zyZTIp74xpyOp0iCOPvMDAge0tlB98/f06u0yIo5fsWcvQw0PjtRtS21aKtrQ1Hjx5FMplEoiuB1F+nkG/I4+g3jqLrq10IHg7ixIkTVWTYvLvnwT5hR/tT7Rh0DCKfz6Ompga5mhwePf9R9Df2Y+iaIVybvRatu1vR0NDwltouOXhiAMn9wPmRSwEslvJZ3Vxf/Drrx9gXIhQKVdWwcb1x3jWt3DSmra0N09PTYp/OjbkxN/5wQ1EUdHR04IILLoDdbseiRYvQ0dEh/BUbXQEQCQWS5gyYCZ5o4zJKBi9e8iKS3iTetfVd6HJ1IRQKiWvQT/PftDNUQN364q0YbhvGqQvLDZN6jvfgnY+9EyW9cmLKoeZDVc+hG3RMLJtAy4stVXEPwSr/LSudKGWW7T5ttSzHlUEVwafsX5hxZUZaVjgeajoEHVLsoQCDiwbRPNYMi8WCqaVT0Ixa1ff31u7FOfFzhC8EUPWZ4pn1ylGyvM9ToVPwxr1IjicxNTUl7D0bmBYKBczMzCASicDpdKK5uRmBQACFQgGRSATT09NCrUf/IMuy+Q5p+xkHlUolaAUNrR9vReQfIjC5TLjiZ1fADjtgqSgYTCYTampqUFtbKwheWd1ptVqBSeDyBy9HXsvDMexAVq3IzlVVhcPhQCqVqioFpF+WEwyMBWZnZ6tKAxm30kfz/yzN4/MCEOCWfpBxixyzMwZpamoScQbvhT+v6zqiG6LVZ2QrQOr8FDxPeYRfpAKRJLRcUkYfzViZSQPOG8nzxsbGqoQeCXGWozJmzWazmJ6ehsfjEfdMzFIoFERyCYDozxKPx0V38lQqJRQvVMZGo1FomiaSjrJyFaguA6UEXq4RJ+nz0ksvibKMufHHMX6npmlAZePJ0m4yunL9CkGbLGHlhuP15PpNSnjlLJsse6VROFtOTqBNFlCuKydbKDN/AESmndISGny5HpagXAaxcpMruXEKM88AhNT3bPaYhpGbj8aV3+fGY4M2PrPRaEQymcQrr7yC9vZ2dHZ2iqO9SEww82yz2YTkiPdLB0ajYbFYsGzZMtFILRAIAICo5eG98vdlUJLNZrF9+3ZcffXVosO2XLvOd+t0OkUDGBIuxWIR6XS6QhjoRthLlXsFAOQAQ74CWunwmIX0er3o6uoSjLYsNyPIlmV/VAUQcF3++uVVgNtSsmDL4S0oWivZeKo3uC64pjoOdMBatOLVO17F2pNrMWoYRf0P6hGfiOOE9QQWLlyI5uZmZDIZ4dTb29sF08p3xXnieuH+cI26sPYHa+GHH16vFy/uexETExNYv349mpubq+r7WWtGMoLrhdcHKhJJkkrctyRQSACQ6Zbr/Fj3zfUrkwT8W64V5Ocyw07FRDKZxMzMDPx+P3S9XK/d3d2NWH0Mb/7Vm1C7yxnfQl0BfZ/ug+GbBuiHdfEegDI5t+CXC5DRy8qYsbExjCXG8Potr6O/taw6UI0qnr32WdxYeyPMp80iu+/1esVe43vksXp8fn6d987PZKDMOSaAphRVbhjDuWBwxHniXuKRPwx+5sbcmBt/uGGxWHD++edjy5YtwhcSSNGm8g/3LQPuWCyGTCaDdDpd6SWjlvDUDU/hxIoTAIDHHI9hxeEVACq2QAZf/BzGNtPT0+g91Ys1O9fApbig1qq44tkrYFEt0AxlO5xMJlHzTzWYHZ1F5BPl86w3P7EZ6/asQ1GvZH8ZZ8iAW+7FcTbJR5UT74v+h7/Lkhm5vI8ZQbm3h8lkEoq4xh81IhvOYvSTowCA1Y+uRveT3YgUIpidnUXHSAcC5gAeu/IxAMCtw7fifUPvA1Dxf5wfvgPZj/Hfmqahz9uH763+HtwpN646eBVUVRU9fRgDzMzMYGBgQBD0VDoVCgXMzs6K+JHxHDPcfH65FpnAlsDZZDKhVqnFml+tgdVjhT/lB0yVuI1kLFWSzKwyI8usKwC4j7vhNXih6ZXmtQSvciIIqPQToGKQKggZhBPg8d4tFgsymUxV3TeTI1SCsaaYcTj9HFUEpVIJLper6mgyACKeY2xAgnrZ48tgzpux5117AABrfrkGq15dJd4NANFZXe4OL+87rlO+F6/XW44bYjGRvCJo5pzOzs6KPcx9IXee57GcXCdy8ieTyQg5ONWnrHHP5/OYnJwUkvl4PI5oNAqv1yv2aalUbhrrdrvh8XgQjUaRSCRErEcVIpOWjFmnp6fnYoI/svG2ATezjrJUmQzw2UE5WSS5iyYNFwBxPJbMHjKbRuaNC1+W0tIYMaBnsEzwB0DIS2hQCVRowHhNbkS5ppYyE/m+5ToQWQLF7BUz3NxENNoEPJQxyaCIn8uujnJ9Lo0XGy25XC7Y7XasWrVKMJ0Ez3a7XTR0IJtNI0VGk+CD90fWkGxrIpGA0+kUTjSZTApnSrKCcjKn04m1a9eKuaVDovEGyqQFP5sSH9aqy41ZLGkLPnTiQyjYCtheux2+sA8X3nchnEknVFOFeOG7MJlMWL58OUKhkHgPDGpk1leuu5WzzQBw35X3Va3porGIp5c9jZwxB6fqRE9vD3w+n3D+NTU1MBgMIjs5f3A+Nj2xCe58ufZ8vHsch3KHRFCSK+VgMBpEqUEkEkEikUBDQwM8Hk9VYx6SLXxf+XweiV0J1C+oR317PS6//HJs3boVe/fuhcFogGOdAy83v4xLXrpEkA006Lyew+FAIpEQjT8YuHBO+DUy1CPOEey7ZR82Pr4RJoOpqhsp1yfnUV5bVLnwHE9mW7j/LBZLVc0YwTvvwZ/wo/VoKwYXD5aLWjTAedgJxylHVcM+zg/fP2vqS8kSap6qwcBHB6Ab9fK57qlaLB9bLphmPjftClABzVTUMLBk8MlnlDuL83OZQfF4PKJ+rb6+HtFoVEjEGEhxLdJOARVFzNyYG3PjDztsNhuWLVsmAAZBnSz1JnCRy90SiQSmpqYwOztbVdP7xI1P4OTSk+L6fc19+Gv3X+OHh34IAML2ELikUilomiYUZQMDAxgcHERNTQ2u3nM1VKMKQ9yARLocpOdyOYyOjiI5m8SyZ5ch0hqBX/Nj/cH1ZXuoVcAim2oReDBTSMJSruOWVXj0A3KWlD6Jz0rAKINONpTi52SzWeSzeSx5aQnqG+sBE7DkxSVQTApypbLtDQaDuDR9KTqPd6LP0Yeb+26G2WAW98R7kf25/DefI+aO4e82/R1i9hjgAaIfjuLm79ws/AoAAaqj0SiCwSBaWlpQLBYF4UlSVC71kwlSg6HSa0eUpZUqncJhLsutXREXTHETcsiJPkB813z/VEhSSUCfYHPboOZUIU+W+wGRqObRWABEXT+BHfvNkASQO8UTbPMd8m9+FuMvAmr6OSZguG4YRzOOJWjnujAajXC73SIp4/P5BAm0dtdaaIoGu82OFftWwGgyiqahVElwbmw2mzhK0+12i7XI5shmsxnxeBy9vb1VsdLMzAwcDodQWbLsk3FnqVRCbW0tnE6nUGeS+GAcRJUb1xjjeO5BNuClhJxrhskYZtF1XRf7kMkC4gOn0ylOICKZVVtbK4ifufHHNd424AYqASs3GaUvzApbrVZEo1Hs3LkT69evFxuSRpzd/FgbkkqlRLArN2QjgOZmYM0UUDljGqgYIjbMUBRFBMA0IAxymXnitZhpZgaVDB5Z29raWiFDZSaXG1SWvPOadFZshMAaDwJigmKCZYJG3g+NMY27LJHmuckkLPgO2FyFToIbmhlhMpJyFpp/8w8NJd8DCQkAggwhmGeTLMqFqSAAKkfAmUwm1NfXY9u2beLaZE6paOC7dmtufHLPJ1FYV8AXjn8Bo0tGsWPHDmEQ+SxutxvLli2D1+sVc897lptjUCVBWRJQqTNTVRUfeeIj+Pa7vy06luuKjqizfC7y9y//Pu5I3yEyBDS6csO3QqGAUDpUdjy5cs33+Pg4IpEIZjCDX13yK1yy7xIEE0G0O9vh9/tFzwISDnIWmI6eX9drdPRN9aGzsxN+vx9btmzBa6+9hqeHn8bJu09CM2qwqlZcsPcClFKlqiBJXgv8jGw2K9QJmlbpbaAoCmINMdx33X0oGUpwKS6seWENzEqFvSbY5fyRBOKzcG+cnUHnvy2W8pFtExMTokkg2Wtkgfk/ng/NqmH46mE4tjqgfEBBPBhHKBQSNZGFQgHFliJsEzaYUOkbYDPZEHg6gKXKUhx931H4wj7c9Zu74Pf4UVJLVWtCztbIRBqJPTkgJdAnq879QhBO+0PbwZovElpc/9w74XAYPp9PBENzY27MjT/88Hg8WLBggfCLJCm57wEIoELyNpPJIB6PC6AjE7fX/fo6zNbOYrxhHABQl63D1w9+HflCXtjgbDaLXbt2Yf/+/VAUBRdffDF6enrQ29uLU6dOwe12IxQKwaJbUMgUUFLKvjuTyWB4eBgjIyPw+/1oDDRi/rPzyxJcswbVWOlYLav2+DdLYqhIYxwhPyfBH8ETe27IDcMIIKiYIxChJJ2ydJ7Q0hBoQMuLLXDYHViyZgkaGhpw5MgRjI2NoaWlBSFfCFdOXVmeRxNEfMd5JXg8WxFJXwwF+Mamb5TB9pkxXT+NZ655Bhf94iIBdpPJJI4dO4ZMJoOOjg7RMbpYLGJ6elrEefJgvCVANSrxDOMhg8GAfE0eR+85irV/txa5dE5kUdmzBYCYO/l69DvJZBIJTwI7PrcDF/7jhQhlQ6LMj9liEsyBQED4YfaFodpQbkTLe6e/JqHE+mQ5k825ZtKKQBSo9pMARKM4AFVAm3EgP0teR+z3YjKZ0P14N0xmE5J6UsTjjBt8Pl9VaSgbjqVSKYyOjkLXdYTDYeFzqWDgvXBufT6fuC6l74qioKamBoqiiCw8M/9MUDGu5v2waTDLUWOxmFiPbrdbxCK8ltPpRDKZFElFxlhMHLpcLhHrJpNJ0TCNdodnb4+Ojv6upmxu/Dcfbxtw89gvWVLC4JSGmQxcW1sbvF6vMMqUZ9MZyMwSDQRZRxonSsgpFyf7CUBku8lEEjDLmT5mSPlzDMDpqJgFJ0iNx+OC9SLTBkBI2vn53DByrZTcEZQbl4H68PAwHA4H5s2bBwCCNSPoJIiWG62xURMZbLJj3Pi8n0wmUyWJ57yS0aS8nTXwZrNZyORZ58JnACAcER06P5Pfl2vRyL7KUiHOkaIomDdvnnD8PLdbrn/n/BtgwFePfRUWiwV+vx81NTWIRCLis4rFIubNm4empiaxltigi2QEgSGDC1kSTOdiMBhQr9bjo099FA9e9CDceTcGGgdQNJ+RZSs6Di4/iPn++ZiNzIqMZS6XE4oAi8UianroDJcuXYo3Jt7AU1c9henWafS29iIQD+COl+7AgsgCOByOKik93y/XMO8x5oxh9IujsE3ZUDxUxLFDx7Bs2TIEbwji5ZtfhmYqr8fnz3semq7hvB3nQS/p4j7OZtEZPMqEB9f30dBR3H/J/SgZy+/tjavfgMVswdpn1wKlytmnLJcAKlJJBiRyFkLuh3A2SNU0TUi0KCMjQbb0/qXQkhoa7mvAjLtcczc7Oys68WaWZ3DqS6fQ9uM2tLzUUgX+S6USAv8WwAIswML9C9Fv70dra6voeCo3a+ScyyUhcrkFbQjXvVxXyH1gMplEGQrJCwZilIuxTIA2YXh4GDabTagO5sbcmBt/+LFmzRrR/EluOAlABPUy2GbTJAInWVqsaRpMMOETT3wC/3bDvyFpSeIzb34G7pwbMEBk4Pr6+rBjxw6kUilceOGFaG5uxuTkJEZHR2G1WsVxQiyDoi1l1+1gMIj6+vqyHNZqg9NRaV4ly66ZpaUyjt+nrBxAlX/kZzFLySaxBLkyIKcdpM1mZ+lCoYCELYFx7ziU0wpqa2vLtlUzIZPO4JVXXgFQBkdtbW1obW0VQFLXquvF5fpzxlC0y1Vyf03H5176HP7h3H/A0YajAIDFuxfjnEfOgc1VaZI5NDSEyclJABAglYommeCm3aY/IEks+wy5XCrdnEbfZ/uQac9g7zf2IvhgEO1D7QJonp18YjaXz1koFJBoTuC1D72GZGMSWz+3FRf/8GLUnqwVGWegUnoIQJQuETzL2XBK1dn/h4kik8kkCG6qUvP5PNxut1hjjIkY07BHCWNlJn4YZzIBwudkzM9YlKSIwWAQ9dBy4kguqeMzAuXYOpFICDJf7oBO/0tw3NTUBFVVkclkRIxDgorxJ5uQsWSU656JNJnQoLJT0zSRSefveDweMR8yCcV5Y1zldrvF+mRtOksz2UQtEokIUp5zQEUkSYK58ccz3jbgJuvLzUJ2B6h03uT3VqxYIaTJ3LhyAzUZaMoSbG4YbgxK2GU5qny0Dg0M2Vw6DzljxWwV2TeCJRpbuTaLBAINsCxPpvMhS0VQZ7fbEY1GhSTbYDCIeVFVFX6/H3V1dcKAkxSgQ5Rlv5TtAKhiETlXzMzTUcnM9Nn1wXweBhRUGNAZ8x4IvJlxpQyWxlmW3DBAkBukMcvJ3+Ec0UiT5eNzyI6VGWwyxQaDAYsWLcLr+17H3qv2Yv3T67Fo0SLU1dWJYAeAyObzOWXJnKwCACrnaHLegxNB3Pj8jbCn7Oif349fXvJLQAHOO3Yertt5HfbW78XI0hGE3ix3rE2n00gkEvD7/VUScEqHFJeCfR/Zh+muabEuZ72zeHjzw7j1xVvRNNIk5ktem5RVaZqGrCmLJ655Aic6TwA9gL3GjgunLywTUE77Wxh5g6nscIpasapGkAGDnPmQpU/smGq2mKsbmwCIJWNl+b/NVdWgRi4h4f8JTvkMVIDwvTD44xpjgxQGMsViUTS0WfDQAmSNWTQ2NiIWi2FmZgaDg4NwXuRE/NNx5Bpz6P1UL0rWEtqfaReSNBJ1Xc90wWA1YCI2gWg0ira2NtTW1gonyj0vkzF8Njrcs7NcskqEzyg7ayo8WB5CUkZWhlDJQ5WLfD7o3Jgbc+MPNzZs2CDsBVABNTLAk+t0z25OSeBFn+JyuTCvbh4+1/85JMwJNMQaMBOZEVm4sbEx7Nq1C5qmYfXq1Vi2bBmeWPEEav6lBql4SqjnZDur6zqi0SiGhoaQy+XQ0tJS1f1Y/jmSiQRkQCUjSx9IJZ48ZLk4bRqzhXJNNwE2UOn2zJp2ACgZS9j9gd2YDc5ipbYSvrivqtZYURSUfCUMnDuANbE1QukIVGq2ZZk63wH9IQGc/H2DwQBb1oZbt92KH6//MZzDTmx6ZhNMbpMof+KJG5pW7h4tn5HM52bsxEwtfTmfV1EUIU/nPOZr8xj47ACSy8t9drKeLF68+UVc+uilaDjeIGLKcDgsgCxQTiwwE5quSWP/h/ZjdkFZXZfxZbD9tu1Y98N1qD9VL+JJAAKoAxBJIyZ1CIJJfhAgMx6nzyLg5dFiBKu8PmNmxsIABMikJFrXy83YmNShf6Ovk8sNrFarSFSV6ksYnjeM7r3dVT2FiAfk48mYNJmdnRUnHp2tNmPHb8azdrtdEGIWi0U0RGOpHBN4xCs8K5xA1+PxiHVQLBaRSqUEscCklDz3JE9YcsnGvG63W4DmmZkZIWdnzMXfo+pP7mMUi8V+Rys2N/4njLcNuBnoErRRIstFS5aVQTY3OYNegmqCSLJJ27dvRzKZxJVXXimuUSqVhHxDbjLBQJ8bX64rZdaJ9VL8XTKEzLYT1BIocIMymJZZ3mw2KwLvdDot5KFAJbMr18UAFXkSv9/Y2AgA4rB7SrUoV5JlLTRCDA5kA0sDRGKhWCzC4/GIYF/uggpUjnGTj1Lju2NGj+SALKUiAAcgwIJ8xrpciy+DLzo9ghVKzjs6OjA0NIRUKoX+/n709PSItcE5oFG02Wzo7OrEv17wrzjScQSuZhc2924G9IqxZl2cw+FANBqtcpjMSMjPRxIgFouJeeyIdiCfz2PVwVVwmV043ngcN+2/CSaTCc/9+XNIO9IIPhDE+on1aDI1IRaLiSNIZMdoNpvhVJxYP7EeJ+adqAKx475x3H/p/fjgTz6IJjRVZSdkWZuu67j/xvsx1DQkfnd3z24Uby7i3c++G4smFuEDP/0A7vvAfdAMGjzf8WD28VmULi1VlQcwYOI1CQi5puU9sXhmMT79wqfxpWu+BNWgYs3za7B8+3JYjVaxr7mOudZkWb1cVqLrOjwej5h77g3+npwlMZlMGBkZQWNjoziKT24i6PV64Xa7MWwcxuQ/TUJrPcOI2zUMfWQIdsUO+0t2QU4xq85rF4tF9PX14cSJE1i6dCna2tqq9hB7Tsg9FGgHuEcYsDAIkVU4cj0315jL5RIBgaz2cDqd6O7uFvVpcxnuuTE3/vDDbDbj/PPPf4vqibZRBtL0Wzxn+uwsq9FYPn6UZ+xa8hbUZGuQN5blzKdOnYKqqoLYvPzyy9GzsAe/WfsbPNn1JBo/3IjrHrpOZCBlVVAqlUJfXx/S6TRqa2vh9XoFeU3VXz6f/99KxGln+TOyjeKQYxs5m0/1jyw35r+ZsCCoA4BCqYCtf7YVEwsnAAU48PkDaPlOC9zhSvmgYlbw1J8/hXRNGstPLkdXpEvML9UCZ/slfrZM9MqJE/FnWMOm45ugxTXk8uVYhbEiFQQWiwWNjY1iXklS0AfLGW5ZFs13zHsFznQrn9Hh2udCfFm87ON1wD/lR2AsIGIup9Mp+rSI39N1kZFOaSnUnqhFeH5YXMM77oV9wC5iLMbSVGvKZYH0p3JSgWtaVpLKGV8mvei7qFZk1p8xI1Am1FkGyd49lFTzPth4zul0inhVJmtsNhtK5hJ+/fFfI+POwFQwofvNbkxPTyMYDIp4UlZnMv5j/Mb7p7rSZDKhtrYW9fX1VU1eS6WSUELSBxPQygkjEv2Mc2VFB0l3vivODfsUMF4kviCQ57nxNpsNXq8XiUQCiUSiSuZOFaacpPN4PEgkEhgdHcXRo0d/z5Zubvx3GG8bcHPTnV1LLXdb5nFQ+XxegDugUlsr/z6BY3d3t8gcykxzNBpFTU2N+D+NsiwFZfMI/pHP4COjRXm2DCLpcEql8tl9gUBAGHZmcWWDbDQaUVNTA6DSTIrNwejsKIchWJGlpuwUKTdz4/9lhyODFGba+LysfSbBQCPq8/mEpIhGkllquT5KZj+ZYaUx5bzIcwxUgw2Xy1XVaEqu2QUgWHjOS319PVKpFE6fPi1k5StXrqzK9NMAkiVMK2ncu+BevN74OnRFx2urXkOjrxFX7bsKSrYSGPAscs4X66DoXPg8/AwyjMyq890kk0l07OzAct9y5N15fO36ryHuKTvVV+58BbZ/smHZ5DIhByQbyoCJCoONpzaiYCzgsXWPoWAqrwNz1ox37nonFtgWIKtmxdqg9Ayo1N3d/NjN+O6Hvouks8yg24ZtOP+n58NYU14jbbE23PXIXdi7YC86jnRg18QuPPXUU1i5ciVaWlrK3XRdMSjxypF2fH+cC8rO2bm7JdGC2759G17ueRlLnlgiJJdkwfmO5cwu55jlIgxY6US57ijLpOyKEu9CoQCfzydsADPVMsA3Go2oz9XD8X0HBv9qEKpTBUqAf6sfgd8GULKURLkIUD6ai2w771dRFBw/flycfVlbW4sFCxZUBQ6cFzp19pMgQUfpJdcP95PL5RLP63a7BWPNd8nsjNVqFceUMNMxN+bG3PjDjuXLl1dJW+V4gf1T5MwxCUUCsawzC2PaCKVQDvTr6upQW1srfDKDdTbzJCix2WwINATw3LLn8Hj349AUDaeXn8bWO7bi+qevh7VoFfczXZpG32gfpqenUV9fj4aGBuH/mQHkPVNOy3uUiU+WWzHRIfcvoV08m0A4O8ssK7Fo02nHstksXrvlNUx2TwpyOVubxXOfeA7v/tq7YVSNyLvzePITTyLSHAEU4B+X/iPsh+y4YPaCKuk6gaJM0jLu4LzKZIimaQKI+rI+6GYdikURyYKZmRkMDQ0hkUjA7XYLEEoChYQoYzPGUbTbjMd4D/xMg8EAq2ZFx086YPKZMPKOETQMNuCS710CE0zIqtmqtcBrKYoiiBuPxwO7bsfyXy5H0VxE3yV9CB0LYf231sOgGlBSSkgkEsJ3M3ZmjEX5eG1tregFxHfNz2MjO/oj+tlotNyj5uyyTJIz8jthbMVYjzEW40DGUMyA+3w+EZNls1mczp7Gji/uQLo+DSjACx98AZYfWNBj64HdVgbJgUBAqPGAclbd5/PB7/cjEolUJVCYYU4kEiIuZ+d1kgryugUq/pfv3mKxIBgMitiYcYnX661KHFApqqrlOmwCZ84vzz+nskA+0cZsNosMOMkcVVURjUbh9/vF+dzymd+JROL/B2s3N/6rj7cNuOUaafmsPBoxbgDKnFhXTSCby+Xg9XrFRiBTFgqFREBOdspsNoszbmVpNR0Ea074tXg8LkABnZLcPZ2biCBMNq5msxknT57EggULxLMwCynLdWWA5XA4MDs7K0AJUAapbrdb3C+fk3MlZ7/k65MNY/MNAl86IzKQDBD4WQTQckOLWCwm5p+ORO6SSYaQLJ4s9+X9MvtP4/bGG29g7dq1ACAMK3+HgJkMIA0NZUGZTAbBYBCzs7Oibp01vwT9+XweBw8exLJlyzDkHsLhwGHoyplssKJjd3A3VrtXoyPVIZQAdAB0tgROnCeZWeb8n91UjqSKqqoYtYziV5f/CtPeiixcN+l4/q7noXxPwbqpdXA6nZiamkIkEhEN7PiZJoMJV/RdgaKpiDfdb6K/qR8rfr4CLRMtMDQbhAPgnMlrsFQqwa/58fHHP46Hr3wYxqQR9R+tx3HjcSiLyg1AjEYjmiPNaHi1AdpCDfU19dixYwcOHjwIAChuKuKx6x7DB5/9IJqGmkSQAkAwsVxXrIEaGhqCN+zFit0rYAqahBM8O0vCNcO9Icv9uH9IiDFo5c9OTU3B6XQKggqAmDMAQm6XSqVEVicej8NitqBxZyOUf1Yw+PFBWH5rgfdLXmT8GVj9lfVZ7CpiKjOFWlNtVfmFw+GocoSKooiO/wxYud4nJiZE51dZVslA3G63i2w3S1C4jngWvBy0M4NTLBZFd9K5Gu65MTf+c8b69euFXQIqx2LJ5TIsT2Lmj4H+jGsGD573IBb0L8DFRy5GXU0dgsGgkJjLZLhMytPnDigD2ObfBk05Y48VYLx5HKMto+g41QEASLgTePayZxEJRjB/ej4aGhrgdruFxBWoNEk9uxZW/p5criXbbblfjizV5RzIDeTY2JQ+g5/H0yg0TcPKf12JRD6BiSvLGe7agVpc8S9XwKAbYDAZ0LeuD6lQSgBy1aDiydYnsT6yHhat0ttGru09O6PN5+Cz8fvT09MYHx8XMQavQ0Uks4yhUEhIyRkfyNlcllgxhiIIJfHA2AyoZJF1Xcd5vz4Px/3Hsfm5zbB4LIKMJTFAYoQJHfok+jmb2YZzfnYOHBYHFv94MTRogLGS+ed58KpabojLuJh1wdPT08K/UF3Fkj2CVF5DLo9keZV8kk0gEBDSc7l/CWMnmYjg9Rh3yQ30gIr0PnplFIVAQbx7zajh4AUH0fwvzchlc+J9yf2XwuGwiOO4xtg9XW74yux9LBYTknbGkXyncu03UCEZmJBiPxXiCjmZJDel0zQNsVhMZMYJ/lkvz+y7xWIRx39RkcJnSyQSqK+vF2WyPLNblqHPjT++8bYBNzcdwQ2DUdZHyFJoWbIFQGS8w+GwCF5pMLlB6KSYMSWzS1ApNwGhg1NVVbBYlHbncjnx+ZRs0xnx9wCIjRiNRnH8+HG0t7eLDCSZLYIJ1r/QuDscjirWl8/A7DGN+NlsORt90YDT2NHgEtAnEgnBABLUk5kjYKATpVNSFAV+v1/MkSyh5xwAlawq2VO5IYRMODALL8u/5CymfG8yGKORo1Qnk8lgZGQEbW1t4j7IBHI98RnOKZ0DW78NX+/6OqZsU2iON+PDez6MjlSHWEcyK8970nVdqCs4mB2QVQSyQ5ePnMuZc8gZKr8rhgHY/sHtMD1hQs/rPYKdZRaBNUN01Jcfvhxr4mvwa9OvYf2VFYMdg3A6nXC5XDjlPYW8lkfLSItoviUHO23xNtzy4i2wxW3IL8vj2LFjOHjwIBYsWICFCxeKd0n2evPmzdi9ezd2+Hfg9NWnkXVm8eDFD+LGF29E12BZwic3M5FLDuLxOCYmJmA0GhEKhURNUjqdFhIxKhVkgClLF2W5oryGOMdUH5Bkk7ML3J9simg0GoX0j+uvVCqh/ul6KDkF5t+YkSqVJZdsJpRpzmDgLwZgjpnh+CeHuBcGCMxsxGIxTE9Po7W1VdR3c+5pV15d9CquTV8LU9YkOsCSpJHljMPDw4jFYmhpaRHf4/NzPpgh4jqjXZAzJ3NjbsyN//+HxWLBypUrhT2TJeW0QXKGWAbQEWsEPznvJzjZfBInm07C4rPgYzMfE2BbVmqxTE3uBl0oFJA7kcPK7Svx2odeQ7wrDnfWjdtevQ09kR7oAR0xxPDUpU+hd0EvsBCItcbgesElbCUAoa4BIGyTTF4DlTI2xj6y35Zl3PSF8lGOssLI5XIJAp2+U1YClEolzM7OouXuFrjgQnJ+Ehc8fAHsMTt0pexnVu9bjaA9iEevfBSaUcOG6Q248/idMBVNUIyV87blOeRzyll4grt+bz+yShbNQ82YnZ0VfoPvgb/be34vUq+l4PP50NbWBpvNhkw+g2NXHUPg/oC4NoEea44ZX1JiLxPUXBecD7fbjQueugCqpiKjZ0S9M5NHjOGY7SSoY4KCfnbjrzbCFKhkUwnamRhhbA1Uyiip2nS73QLo89pyiQB/lvEqYxX6RWZgNU0TcSbBrpwU4Kk/uVxOxNHcM1wXLIlgkmfF9hUw5o149bZXAQXo2t+FjT/fCKNmhNFU6aIuJz3q6uoEyJUTI4ypeE+qqorkg1zqQV/LjDHfndznZXZ2VsytqqqYnS03xWUzV6pS5GbHdrsdTqdTJAGZIJHl6el0WmAWfh7XtBwfyafe5HI5HDhw4A9jAOfGf7nxtgG30WjE5OQkTp8+jWXLlsFkMgkmisCK2SQCLwJzOgJZeilLMAlc5E0sNwPjBpUNCjcn5aEyuOWGldlcfjY3BoG/0WjEpk2bBNDgRuG/CeLk48FkcoGMIZlBXpNdJRmY09mbTCYcOXIEXq8XTU1N4hkBiKyzyWSqqu8mC8e6GzJuZG9lqQ3nkfU6srOVmW8AVYCPGU5Zig0A5557rnifvEeCBznzT8dAiREJFKfTidHRURiNRsybN084JY/HI5zC0qVLxTrqGunCZ8Ofxd3n3I2vHP8K3Ak3XG6XOK+R50nKBMrZpAGfg6wiUMl8U+LL92exWNA81YwPb/0w/vHaf0TMGata93l7Htuu3gY9o2P+oflVRpVrjOSRqqoI+ULYdGITDpgPYHR0FDabDa3nt+LhKx6GBg3v+fl74B5zC0MtBxh1Q3VlB9hZvt7AwACGhoZgMpnQ0dEh3iWfp+m9Tdj1jl0o+srZm1nfLH5x6S/wnl+/Bx3hDqG+4NomccOaemY0OI/cN5xLAG9pymOxWDA1NQW32y2AK38PqDQ8pHNmOQcJIzY65PoxGAyor69HLBYT0n8etZHL5dD0chNUjwq3rRxohMNhzKqzyD+UR6azvPf3evbi3M+dK67JZioMVq1WK6anpzE7O4umpiYsWbJEkC6vL30dP1v2M+xP7cff7PwbsfeYFQsEApiamhIKlJGREUxOTiIQCMBsNiObzYrgSHa2nHseMTI35sbc+MOOlpYWUf4il0cBqCJt5QamJpMJVrcV9266F6drTpcvpABPLnkSzhEn3j/4/rcQbXJZFlCOFcbHxzEwMID6TD3e99z78NPQT/HeZ9+L9ul2lFBCoVjA/Tffj4GmAfEZvdf0or61Hh84/AHE43HEYjFBSNKG0Hez+7RcHifHJ/TpMulKv06fI2fECTaoogNQddqF2WxGNBpFKpVCjb8GS55fgsKeAjxhD3RDddPXS6cuRdehLjzV9hT+19H/BX/OX0XuU20AoIq8luXjuq4j4ozg3nPvRUkp4baR20ScA1Sywrquo29DH07dcArF1UX4P+IXPuvkF09iat0UVKOKxh83ijOboyuiCLvCCG0NiZjA6/UK6S/nL5vNIl1K48QnT2DV91ZhcHAQLpcLHo+nKlnEHjoEqoyNGQOyBw5BOOeAySV+nkzuykmccDgMRVHEWdDMBKtq+Ugt/hznzmq1inOvuc7ZIJQ+0eVyiXidPpvSaTkZxDkm4SKrBJmplZNbXdu7YNbM6F3Wi7UPr4UlbUFBKVRlx2V1HIl57kuqTbg2qfRgR3BK74PBoFDKMplF7EEZP9euTICfLRXnezEaKw3ceAY6k1+M4RlXMBGhqioCgYBQGUSjUfE9xsUkLIhjfD4fZmZm/h+s2dz4nzDeNuAmaPb5fGIBycfkEIRxI5EVJauYz+cRCASErEXuHMmNQRaKkg+g0ryCsia507a8+eUMLetsabjkLLrMSCcSCXHmNxu9cdAIUJrELp1kB2XpDQEuZS1kPDkn/D3eEwN5mdkjE8faEl5frqd2OBzC4cjsHKXTcodxoLpenSCUAJ8gWXbGJEcoJ6Mcm45XDlbIkNN5kmmlM+fvqKqK5uZm9Pf3w+12i+MdEomEICsMBgPi8bj43QWZBbhvx32oc9YhYolUOSky0My2k+zhfWYyGWEYOb88EorsKAGfrKJwj7tx1wN34Z733YOCpYCctezADJoBXf1daDzWiFwuh7q6OvHZXKfymlBVFfX19eL9H5o+hB9f/2PkbWXn+v0Pfh8fu/djCCgBwVRz3dDZ5nI51NTUiNq0Q4cOwW63o7m5Wewnk8mEZbFlmByYxMuBl6EaVSAPrHhtBVpmWqBqqnDwAMRaT6fTQkrG5nUkbag8YKND+Sg3WcXAczS5v7mnGBhS9eJ2u4WcWpb1yXbB6/Wivr6+HAid6UQaj8eF7I8EnMPhwIIFC+D3+/Hmj99ErrOiSIgujuKNz7yB1X+3WoBgAl4GCqlUCg6HA319fTh+/DgWLFwAvAv44cofomQs4aD/IL5wzhfwudc/Bz/8Yp1HIhEA5aDKbrdjxYoVCIfDiMfjIlsiE1QMGiORCFwuV5VqZ27Mjbnxhxvd3d2itpW+C0AVoGDswv8Xi0VoqoYP7PsAvnnxN5G1ZAEd6Ex34pbTtwCodNqWr8uvpVIpTE1N4cSJE4hGo2htbUVNvgZ/8chfwFK0QFfKwCo6G0XLV1ow9PdD0Lxlm+GP+HHOk+egv9QvytEodyUwImBhAy2gYpvlZqSyKo02kM8JVPdcMZlMIkMrK+ZIIlssFgEoLBYL6urq4IYbWlSDbqiomiwWCwKBAELBEJrDzVgXXgeLahH3Isve5QwyUOkhw//PWmbxuSs/h4y1TKree/u9uKH/Brhn3SLLWNJK6O3pxY7bdkAza8B5wMgjI3B+yYnxT41jctMkYAAmPjKB9HQawV8EYVtnw4lvnoAOHQFzAN6dXljMlqpYjPcDH7Dnq3uQak7BbDJj+Y+Ww1Asx1MEbYVCAbnuHJ698Fls+sEmOLOVDuOsO06lU5haNoXhlcPY+OuNsJvsIn6Sj4l1Op0iNqZPtdvtouyQ74++kb6W11LVSi+bWCwm4nCSLvKZ8tPT09D1ctMwdvoGIOIoEvRUMDI+8/l8ImYmic46b67Jnn096NjbgVKqhHQpLQCz/L4Zs87OzopYhPEBY3yWLPJoVX6eyWTCxMSEUKMyiUaSXv594gSfzyeAMedKVhoytua9MJYJBoMitpmYmBCKRf5+NBoVOIDPyBJKxkAej0fU0s/1cvnjHm8bcLNJQigUEuCLzQMYqNN4U9ICQICDVColamwYbMuSKJOp3IF73759WLx4MZLJpABKckDPWkgaGYIFBtj8nizR4e/ILCwJBFVVRU0IAafBYBBNpCjhJjiiPE1mjGWwJH8u50A+8xooH1NC58IMnNzUi+QCry0/p6qqohb1bBkSASUdMQMCOmv+DVTqwGmg5XOK+VzZbFacvy5LwAjoeW3Kszmfcj2+wWAQwPbEiRNwuVxwu90iuJFleDLD7rV4hbEkQyuXBNBJ8T0yg8/3zLkBILIEvD+SAXKWwGg0wpww4zP//BkMdg/itTWvYaBhAItHFiOoBbH7ut1Y/JPF8CfKDDo7ksqqBKvVilgsJox6XV0d+m/vR95aAVwFSwE7r9qJlt+2VMmweb63XE/GeiyPx4O9e/dieHgYa9aswXj7OFrHW2E323H1q1ejZCxh19JdqHmwBtn7s5g5b0Y0AqTige+kv7+/6lzOsbExNDc3V4JNKXvDfXJ2QxKuRb4vrhuuS5IQPG6D74HPysw+mePJyUmkUilRu8Y1xoY9NptNMNh+vx/rPrsOh759CPEFcQCAcasRK/5xhQhm+XmUSgIQJQdcp3uO78FBz0FxFjkUYMA3gNc7XseWqS3iZAQGOSwF4fPRIXM+AAinn8/nMTAwgGAwCJfLhampqbdrZufG3Jgbv4dhNBrR3d0Nr9criGgZWJ/9bxnIFgoFtEy04E9f/VPcf+79aCo04Z6D98CgG6BqlfIsZrZpr3he8smTJ4VdDQQCZTWN5oBiUkSDx97eXkSOR7DoLxdh5O4RuLIuvOeB98CYNyJXzAn7yhpe+i/2kKFNl+MO+jPaZ/pPuf6b5UW0U5qmCVJCrvVmzFEsFpEuptHn74N6UkVjYyOsVqvIbtLmqQ4V2c4s6t318Hq95XhENQKmCsEhy/blxACvQ0Jd13X8es2vy2THmVE0F7Fryy5svG9jJbFhLWDPhXvKYBsAFCA/P4/+v+pHZn4GKL8e6GYd8RvjSI+lUfpuCSgnwPH6l16H+zY3bC+Uz012uVyil4ferqP3071ItZb9wMBFA/AoHpzz9DnQk7ooSYwsiODR//UoVLOKPbftwYW/vRDWlLWq1HGwexDb/nQboABqQsW5L5wLQ6YSfzAhc/ZJH4y55COvuNaYFKH/5TsjuRsMBqsy4ZSHy6WcjKeYLGGSRU7yMKFCCTpJccrm6RflE0CsRiucRieiWhlksrEpnwGAaBzL+2ffFRL9jBWYtWasRaxAv8s4QU56UbqtqqqIrTnXVAIYDAaRiZZLMXltJhSoyLPb7aJzORM6JKKYlKOClv2nuIdZTpBKpfDLX/7yP2zb5sZ/3/G2AffZ9bDclDzu6uyaIXljc0OxLpT1IrKhYVAdi8WqapJoiOkweD05o8igmg5TzgJy0zAjTENDgCbLP8ke8hl4TV6Hxof3R0dG0Mv5odHj53LDy3Vf3Ijc6KwnBSAaW8mZaQJXOavIDS5epsTOUmnAQTKE88dsP1lOyn1lWRozjLLxAyDIA9kw82sExPJRI6FQCBaLBbFYDMPDw5g3b14VuAQqAJpzRwMry6hkQ0vDzGcjoGRQQakS35XVaoXdbhfHezHwIBAj2aGqKpqONOFj4x/DzmU7MR2cxvMrny+/z2IOpodMaGhoEGuCzoyAkIQOFSDnPngutLSGU5efAgBsPLoR79r+LhjM1cfT0DlzvctN8RKJBFpbWzExMYEnlSex//L9uPzQ5di4dyN0Xce126+FN+JF84FmvGl+E9u3b8fmzZvR0tIigiaz2YxUKoWZmRlx3qVcjmCxWATo5Xrj81B2KZdnyOQKAOGsyVzz83w+nwgcuf9kUK4o5dpxNj6kCoT15ADg8/kEo280GmHRLFj996tx6M5DyA/noXxcwWnvafFeaCdoB4BKyQnvRYtoWP8P67H/4/sRXRaFWTXjoyc/issnLkdOzQkmnu+GhJ/dbkd7e7tQ1nD9896SySQ0TUN3dzfMZjNGRkYwNjb2ds3s3Jgbc+P3MEKhEFpbWwXBxyFne/lvAIJsp2TV7XZjc34zfH0+LEotglE3QtMrQFEmnelv0+k0RkdHEYlEUFNTg7q6OmHDgLLUPJFIYGBgQCi+lmAJlj+xHMFYEJaCBape6bUiNyRl3JXJZJDJZKqaU7J8ixk1kgZyjS1QUQsClZMr2BRSLr0i2GM8s/fSvTi45CCWK8vhmHEInwCU7b5u0PHKVa8gviSOrmNdqM3XVpWdyXJh+mfOvVyLzbnVdR23774dxoIRW5dsBQD0vNKDdT9ZB6ujYs/zyTzaPtOGo3cdhX6RDkPJgOUPL0fw10EMrxlG36f7oPpUKIcUGD9qBNZCgHAOdYmK/FN5kbFknKe4FORN1cqkeDCOgqmAbKLcWC+8Nox9790H1Vx+1v5N/fD4PFj/4/VAoRzDjJw3gldveVU0Ejt29TGYnWac82/nwKBWmt4ydmLiI5vNihiF4J7KTsZkZ/s2xrpUlBKEUv7t8XgEaCXhROBL303/SR/N+5Ozuh6PR5Dh9N8GgwHBYFCAfkVRxBnU9Luy4pIlZIxjGcPxHkgAyOpUxuaMVeTyCvpgoBw/5/N5+P3+qubDMmagT+f+kpuoMTaS55X3w5OJqCDgUcEOh0PEgHw3vFc2nHY4HAiHw/++4Zob/6PH79Q0Tc6SMujmGZH8nmzcZZaHG+l/17mbP2Oz2XDZZZcJMEwZKoNbWSLM35Ol3GSbyPYR8JIQ4AblvdJxEOiRPQMgjh7gZxGI8rlpyEgypNNpYQATiQT27t2L888/XzwLM9i8PhlBt9tdJaeSO34T8HLz0xgQPJ9dOysf4UCmWAbhdGiyPJzGmcZXBhqUF/H9ksHk9eSaKjnwKBaL8Pv9VcCKBMjIyAgaGhrQ1NQkukpSZSBLuviZQCVjzfnn8RGcV7lBCX+XMnPW6vK9yIEKm2XQAckscalUwkhwBLsW7xJ7oO+CPuSteTiPO2HoNGD1qdXCgfAaZEP5Hs1GM9b+ai2MJSNMThOuOXYNUASKarHKYQCVJn28b37f5XKhp6cHM6tnsO2mbSi5Snhi3RMoGAu49PVLYTKZsPnNzVDml9/9vn378MQTT2D16tU477zzAAAPb34YGx7YUN70Z2TbwWAQjY2NgpRgNpqORu6zQMKN80XSguQC2XA5C55KpRAMBsXzcb9Q1UDWmXZDLhHh+mL2v7a2VnQZzeVy8IQ9WHbvMiTHk0i5U5icnEQ8HkdLS4uQgbFJDde5xWIRQF7TNHhnvNjwow3Y/cnd6P51N1pMLVAWV0oi2OyR9i+ZTFatMbmXAJ9Pto2c67kjQObG3PjDjrq6OjQ3N4tAW/ZNJIvl7GA2m8Xw8DBGRkYAlI8Ts9vt2BTbVC6P04rCHtH20U4za93f34/e3l5YrVY0NTVVqfhUtdwga3x8HH19fVBVFZ2dnQgGg6gbqCvbDKUizea15VI5gmHZ9tDOyM9CAAtAAAsAomcHyQWquzg3jBH4TLlcDjuv3Yn95++HZtRw7FPH0PzjZlhPW6t6mbx4y4s4tvYYoAD3rLgHXz/8dTTmGsU8y7J7+f+M/xjvAJU4QtM0bNm9BenZNMK5MFb+eiXUrIqiuSieMZfLIXk4CdPHTSg9WEL3c91o2t+EorEI1/Mu1A/XY/Lrk/B+wgulT4FxyAij3YiJv5sAADR+uxG+n/tQqq+8VwHcBoDinxcx8cAE1BoVrUdbsfmXm+HOuuGpK9dw17vqcVw7jiwqmXjDCQPikTjMejk+sZ+2QykqgL2yNl19vUxeagAA4ydJREFULmTSGWhq5bllEpv9gqiyJAHPjCxjKZLc9N30qxwEegTRpVJJyJ0pJSfw5mfzPpjkkMsueB/MrpM0kYH5Cze+gEsevQTQymuP8myZeGIsbLPZYLVaRXImkUiIzzmb0Ge9OZWjfF+MUQls/X6/kLzLGW2n0ylKAAuFglDfyaQTn0Vu/gtAxN9ysoBlatyHVKLwuUh8sKZcJuPnxh/v+J0k5Qy0mTHjIHCUGTZm+Mi8ymfbstGDLNkmqyR/nRIOGmlKYuQst3wmH8E3r0NwRXDGeyWwlpsq8GfYbZSfRRmqz+dDPp8XBoCbDYBoFkaHZzAY0N7eXpEqn6mlkZ0njycgyCVLRlZSdmhyvTpZOL4DAgkaBQI9AJicnEQwGBTGlc9OEJ1OpwVglY9rItCX1QpynTKAKvaVX2OGmO+zu7tbdMPm/RUKBbz00ku45pprRNDCd0oZuiyD57PxuVhvRDDOr59NJPDaXLc0vvwajTrldJSAMUuey+Ww8ZmNODDvALL2skO1Fq24fvZ6fP+W7wMmwJgyYunoUhgNRkE8sREc58VutyMfy2P5o8uRSqcw2TiJlpYWYeyNRiMSyQTgAV5d+ypa4i1Y2LdQBFV8xpnADF6+7mWUXOW1XjQX8fyq5+FOuLHh1Abxjrq6uqAoCl555RUcPHgQVo8Vh+48hEMLD2H4Q8O47J8vE3shnU1DtZT3EY8u4fzI70vO5sv7EUAVISMrTbiOyajzeegIGfDKZSksYaATlu0OFQrpdBqBQACJRALesBc+qw/qgnJ5xMTEBI4cOYJgMIilS5eK/c5aSAYO/FqpVEJzrhnnfPEc6HEdrzlfw+TkJBYvXixqJ0kAqKqK4eFhqKqKBQsWQNd10VCF3UoptQPKzdLYKZ114HNjbsyNP8xoaGhAfX29AGdyrTYBHmWjcjbX5XIhEAigqalJ+FlZvkoilH+XSiWk9BQ+c/5ncO6L5wIFoLGxsUrxxj/hcBi9vb1IJpOYP3++uD/GMgTTzDDKiQzaQfoVgg9ZCivHZLKvZl1wNpsVBOfZcmXadH5GLpfD/gv2442Nb0Azlj8jVZvCcx95Djf//c2wxct1sq+88xWcWHVCZHBPO0/jL1b8BR7Y/QAshkrplpwxZLZQVgnSJ/O+dV2HMW/Ephc3YTY2C0VVoForYCyTyWB4eBjT09Mw6kY0fbwJdrMdMVtMvDfLaxbUbamDMqvA6DxzpvhWB+x/Y0fWn0XNv9XAZrdBcVTegawCUMYUzLtrHo5+6SiWfX0Z4vk4dE852xsOh2GNWnFZ72X41d/8CnlXHuueX4fVu1Yja8iK92GZsODWe27FQ59+CJpZwzkPn4PGbY0ooSR8BYndXC4n+p0Q0HGwKRlQjj0LhUJV3TUbyhYKBfj9fgHWI5FIlQ/m2dkEk+l0uqoclHEMATxJHpInTqdTnCgil6zBCjz37udwavUpzARmcPU/Xw2rZq1KGjGWLRaLIrYHKqf7iPKEM6QDEwDZbBYTExNobGwUOEPOoDOeKJVKQvLO5mfMvHO/AxDA3O12C3zCeFYujyV2YIweiUREzMxSPxJp8n4k0U8Azwz54cOHqxSpc+OPb7xtwM0MlJwllhkgykPkroNc+IVCQTT+IHtGZ8efk5lNuT6Un0XHA6BKSkwwxp/TdV3UrsiSXcpV+LlyxprPRekN74msMbtQalrlSA0AVQ3eOD+qqsLj8YguiXzGdDqNmZkZdHR0CGNBBkyulyL4JXvJzc97l48foEHgXMhZZqPRiGeeeQa33nprlaxYZpLJfsvGUL62nGmlI8rn87Db7eLn+NlcC3IQMzY2JrLIlNWTzczlckLyw3p9u90uaoVYY0MShdlNGlgAQrnA/1PmFIlEqqT4fF6n0yneH2X1cm2bLN1XFAWBQgB3PXAXfvTuH0Ezabh2/7W47/L7UDSVSZfvXvld3PnMnVh0elEVoPT7/fD5fJidnQVQzq5qWQ1ukxuxWAx1dXXifEZFUTC+fBz/cu2/iL324d9+GKvHVot3bTQa0ZptxW3bb8MjFz6CpDMJQ96A+U/Nx/wj84EQqt5tfX09br75Zjy3+zk8su4RlJaUymfA9ozjhQ+8gM0/3wyn6kT/+n6cXH8SlzxyCZRC+Z2zDpuOn3+MRiPcbrdovCLvezor7kv2TWDTMKByXAedGp8tk8m85dgUSrXYFMZsNiMWi4lzrdkplMFjqVRCR0cHrFYrIpEIMpkMTp06hdbWVrF3WabBtcB9lc/noc1qInDeuXMnxsfHsW7dOtTW1lZlAlq7WzFuHgcTGnV1deJkAAaRcg36qVOn4PF45pzs3Jgbf8DhcDiwbNkyUS7E+IGARc5YAUCfrw+LLIvEsUP0e/x5/g7tB1DpLD1pncTXln8Ng95BxD4fw/UPXw+fxSeISdrPoeAQJveUVTitra2YN2+ekOnKZTlMBsh9WQBUgRb5b94LP4/BPe+ZvlzuW8K4iuCGSjl+LgHNomcXYdQwisGrBgET4Jp14bIfXwZP1gPdXE6KXPnClVAaFRxYcABQgMZ0I75+4OswFA1QlUo5GtVQct28nFHmZ/K+8/k8IpEIiskiLCULdJMu5kZRyvXyk5OTUFUVbW1t6KzrFH5KVk0paQWKTRFKSSOMqHm6/J5hriR3SHBQDccYtNvVjRXfXwGlrnxvvEeC2EKsgKs/dzV6t/Si/aF2zGgzIh5iEqN4oogNd21AbF0MgccDKJqLMDvMIlnEteZ2uwUIl4kFqvQ4T06nU8jD8/k80uk0CoUCZmZmkE6nEQ6HkUgkxBGYfDYmfRKJhFj/BM1sOEvfRb/OsgZ+vtlsRjAYRDKZRC6XKx+f1eDEjst24OS6k+U4Y944XvjwC7jkF5fAlDIJTMB583q9Yj+azWYkk0n4/X7x3CxJ41oh+Z1KpUSWn3NBn6vrujjilH2YuDc8Hk+ZgDkTN/B9cy651+Q9Lyt3x8bGUCqVRA8r2g+elS7vN5IDJOtJaADA2NhYlU2ZG39843eSlJO1JUAhsKOjkKVWcldsgjrKX+SMLmtVgIojlBc+Qbp85JbcFZEBMaWidJhyYC83TpCfhRIZNg4DIGqF5doUoAJouVnlpkn8TF6XWT8aL6PRiFgshv7+fnR1dVXJXeksCa6ZYeRnyrJdzik/kzJ4Zpplx6HrOj7ykY8AgMgUy2UBMvvGueIcvfzyy7jgggtEFpD3KrPwfD6gIvk2GsvnKTNgkWtoHA4HZmdnxb2HQiEEAgFMTExUGWSej0mHwMZqMqnB9ST3FKCBTiQS4jkohabcx+VyVWUFqLzgmmXQwTkzGAywR+y48fEbYfab8ZuNvxFgu7yQgAcuegDf+cl3xP0fPHgQmzdvFhLzaDQq1gQDnNOnT6Orqws2mw0Huw/ip5f8VGQIAOCBKx5AYVsBq0+urpLdLx5ajOu3X4/HLnwMa3etheMBBw6aDqKzsxNdXZWztwky11+1HsOXD2NAqRw9k65NI+6KY2T5CF657RXoBh07btyBjQ9vhDahiX1JoEviBag+T5p1SnR6sjye51eyVpzrhA6fAZGiKOKzuD7ZkZUlAXT4tA+KorxFHcIAjg43HA4jlUrhxIkT8Hq9oocA9xvXLN8Hg05+fWJiAq+88gqCwSDOO++8suzebMLD7Q9jb2AvPn3y01iQXiBsCfeI/H+/34/u7m709/e/XRM7N+bG3Pg9DLvdjo6ODnEaw9nKLDleea3+Ndy39D78Sd+f4NKZS0XsQZtAO0W/B1SI/UnHJL7T8x30+noBANHWKLbfth2B5wIIRUMiE368/TgeveJR+Cf86Ix2orOzU2QseS2WZ9Hm0qbJ9peAhzEC/bD8b96jnLgAKnEUfTOBmJxd53Upg52enkbLt1tgN9oxfM4wLvjZBWgaaoKqVO7LZrPhrn134RHXIzjpOYm7jt6F+lQ9NFTPOZ+TIFuWsfMdcW5ZKzw9PV2lEpCTHmNjY+I0C8qNSeSHw2Hhy9l0k7Eh54J/5JiGg76ftcKJRAJ2ux1er1dkXlkyZ7Va4Sl50PhsI7LubFVszNhK0zQ0JZqwYNcClIKVUkg55pTPW2eGl4Qzs6iUXdvt9qqMN+MuAnGgHEexhwr7F8nJIpa90S8yxpZVmLqui94qTqcTyWRSqFnZNM9isWB4ahj9tv5KDKMAMW8MEWcEtphN3DNJ8lQqhUAgIIhsqkM5Xy6Xq6rT9/DwMDweDzo7OyvKgTO/y0xzsVhENptFNpsV4FrXyyefxONxsXdYGpbNZsXpKIwfiVcYz+ZyOVGrzWPJGOt4PB5RZgdUTnGiQpZN0kighMPhOeJ9brx9wE1wzaCVToIbgMZUbojGP3IATYBJQ8qAnAu3UCgIyTkNFw2U0WgUgIrsGEEZ75HXpOGWGzDIknUaxkgkIgwfjSw3v1wXIwMLOgkyZgSvzOIBlXpOSlQaGhpQV1f3lvuVQQ2ZRn4fgMgKywwju5YKOY/0eceOHYPT6cSCBQsEUCNTKDsYOmm+Pzn7J9ems1O1/E5nZmYQDAarHLyiKEJ+z/cjO+WamhoMDw8LozQ9PY2WlpYqeTsBNd+3qqpCrWC1WsW52gRpsgSMAIzzIGetqSSIxWKiCykbXHA++PM8Ai4QCGB2dhaqqqJ+oh7ulBtbdm/BfVffh5JJOldZAYwmo3BcnZ2dbyF2zm6aF4lE4Ha7MX35NB479zEUzRKIB6BAgTfjrZIzU32wemA1vJoXLb0tmOiewBtvvIFdu3ahVCqhp6dHMNaqqqIx14j3v/Z+/PjCH2OgZgC2MRsW3L0AI2tHcOzGY9AN5Xc3uGIQeUseF91zEQyaoeqMdLO5fHQJSSsGTjLRwYAoGo2KbvIE/TKZxN+jmoIkBx0d1wv3bC6XE+eaMtChPTEYDCKAICFFdUJLSwsKhQKGh4cxMTGBmZkZNDQ0wOfziRMJWI7B0hCZ+OOeHh8fx44dO9DS0oLdN+/Gb1t/C13R8fcL/x6fP/h5dJY6q5ozMkufTqcF6z4+Pv52TezcmBtz4/cw/H4/urq6qjLbBHhAJU7YWb8TP1zyQ8Stcdy74F6oJhVXTV0lfpZA5GwZNLOFyVQSpVAJCFU+25K1QEtpIh7o7ejF45c8jpQnheyfZ9HQ2QD3UbfwDfQJcrMqEvpyPwz+zbIoDlkZKJPmcsKCP8dr048zzpHlsLTV0WgUkUgEXq8Xi15YhNnBWTT2N0JTKo1eFaVcHhjyh/AnfX+CEdsIetI9UA3VtduMQc6eU35fzuLza/KxsbLqzmazYWpqCoODg8jn86irqxOAmzJqyuflE1wYz9CHyMQG54j3wPngv4PBYJWvS6VSmJ2dFX6HiZtUKiX6Bnm9XpGI4fphAogkuly+xTmV54NHghEMy6WPJCUYc+dyOZGo4Bzw3uLxOAwGg1ASykfcUubP+eFzcp5437JvNpvNog7carXCEDXgil9fgR3eHRhrH4M35sWWX2+Bpc+Coqko5NjySTMEyMyqE+wDEL4UKMfks7OzIkkFlMFtJpMR2e76+nqhiCsUCuJ56+vr0dDQIBJLzDDziE9FKTchZBzOPUeVg6qqYv4ZK/N4MllxwqQfCS5iIJI9qVS5xwwVj3Pjj3f8ToCbAJLOgRs+Ho8LiSwBEiU9Miukqqpo2sGFzAYGP/rRj/DhD39YbEZZ1s3aZnZRZgBOuSadllwjxPuTm3r975wMryHXL8nybn6WLDEny0wZDg2rXOPOf/MZCQz4uWzaIHcdJ+iUZWPMWKdSKZH9IwnAQJ/gEwCamprE10XjrjPAjXXccuMwAlw+t8ViwcaNGwUbJzs6g8GAWCyGH/3oR/jsZz9bJdEDIKRHdAw2mw3pdFrUyxgM5fO2Dx06hJaWFqxfv16wnmx0QeBDZ8js9tkMPGXmpVJJHPvGTKlM3rAWmM9HRpNNsciOnk1IsHO+DBBbelvwp7/8U/zTTf9UPvcaQMaSwQ8u/AHu2H4HDCUDQqGQIE4MhvL/ZZKI1w2Hw6jfX4/6hfXot0vscB749DOfRv1YPWCsHH9HObyqqlg4thAz6RkoioKLLroIY2NjeO211+B2u1FbWyucp81mQ0OiAVd9/yo89P6HEHpPCH1H+9A10gX7pXYUW4qAAhhKBqzcsRJuqxvFQhGRSEQEIXSwsvSN2XvOGTu78tg/rjMGhzILzGwwz70Mh8Ow2+1if7GejXuHe57ZBa4NBpbZbBZOp1M4XFmVsXDhQjQ0NOD06dMYGhpCMBhEe3u7qGFTdRV5LY9iodLUjc9CqRoAvHTpS+hr64OulAOCXmcvPrvys3jk8CM4VnsMJ0oncMPkDTBqRqHW4L309fW9fWs8N+bG3PgPj8WLF8PtdgOogGs5o1ooFHDYdRj3Lb0PKVu5TjZhTuC+zvvgzrixanJVVeaT/k1uZJZOpzF1dArNjzZj4i8nEFkRQcdoB2569iYYU0bo0DHuH8dPL/8pUq4zRww6VRy64RDqzfVYfGhxVSDPeIi2RybCeQ8EYgQWjA84mJmVr0P/fXb5HFCR79LH8fdpy202WzkTqVvQ0NcgfBQ/0+12o7W1tXzvqo6eVA8ACOAkA0qCZv5flvDy34xNstmsaFBJEpz3nEqlMD4+LjKgwWBQvNt0Oi3K0eg7CMr47uUTdRiPkHy12+2iDIrHQBFgy0mempoaAKjqnUPCn4Q9pcSM5Zh1ZTKDPpQAmTEDY0z2BEmn01VlmWw0xp8h8GaWnH1X6B+9Xq8AkFQLOp1O8XOMs5g0I7nO2JZqLcrQOR88SmtmZqac1R2xYcPfbcALn3sBl95zKUJKCNaaMrhnQ1wCZ2aA5cQbEyrMGsukSENDA7q7u8X6JREi13szocL4OhwOi1iPc+9wlDvsj42NCdUbyXVZnUsFLhUnqVQK8XgcgUBAgGn2EmKJI2XvLG9k3JPP5+F0OpHJZOYy3HPjd5OUA9VnGvP8ZQCimQBQOZ6KC85iscDn8wmQQ0PPjeN2u3HnnXcK6TEdCrOjZCyBSjMQZqcI7mQmij9LYEy2kuCcDoHglcY4k8mIYF/OvjGrRiKAkhICVM5HLpcTjaLkeikSBtzQrMlhhpYASa4rASqduuUadTpiAlPeHx2Xw+EQTKKmaZiamkJDQ0NVB3P5bGVKk2iUeJ8kKPg+6fT9fr8A2zRKNJx0MPx9GlZZtuZ0OtHd3Y3JyUmMj49jZmZGnDftcrmqMoWcGwYLBM18PrkhBd8x69M0rXxuPNlek8kkOsLL5QSBQACRSEQEKQToQEVdkEwmxX11RbvwsRc/hu9e9t3yO1J07G3bC9MGE96z7z0wpU1V8v9kMlkV5Mjy/+xYFh/9+UfxvVu+h4nQBGoTtXB83gFnhxMGr6HKEXAOzWYz4vE4jEYjAoEA3G43TCYTpqen8fTTT2PVqlVYvHgxLBYL4mocWUMW8d44Nn9iM3Sfjje9b2Lw9UGs/dhabHtiG3STjnMfPRcdxzpQKBUE8OU+4bukgoEEFI/BoAPVdR0NDQ1iLzLYYTDDWql0Ol1Va2i324VEjQ5ODtBIhsViMbFHeIa4fJ57PB4XTLNMrrhcLnR2dsLlcmFsbAx9fX1wOBwINYTQf1k/5r9zPixftGD02CgACIcpn1Xe9sM2pBvTGDtnDFAAd8yNLx35El53vI7Pd3weOnRYNSsuHLxQ2BSgHJjOHQMyN+bGH3asWLFC9GKRZbn0t6lUCpk9GXT2deLoB45CdaqwlWx498C7sXpqNXRUju0kOUvQVigUkEgkMDw8jN7eXjgKDnzwFx/Eb52/xW2/vg0GGKBYz5x00GvBoh8swr4P7YPm02AsGrFm9xosOrSoKtsO4C2ZVgJluVeKrMaSlWsysGZ2m76bdjyVSgm/JBPtAIRKh8Btenoas7OzCAaDormVnImVVWs+n68KOJHMl7PyMtEty8o5ZEUYEziM4fg9AsiJiQlMTk6iWCzC5XKhsbFRxFPhcBjRaFQkEBijMaMrZ/aZdSSRz8wks5B+vx/t7e0iaw5U1ATpdBrRaFTcIzt3EwjLx2tm6jOwzljhtDhFXEz/RiUe40tZgUl/KPtCEg/JZFIQFLJyM5PJCH8jJ6k8Hg+8Xq94lyScGUMy7uR7pP+S42g5JmSpIDP6fDeurAs3fPUGWEwWZPWseA5m1nkUGMkJro1kMolQKIRsNotEIlFFnOu6jpqaGng8HtFUlRl9r9cr+ruQtKKalvEdM+NMdiiKgvb2drhcLkHiM7ajdJ73RBKC88s9pOu6SCZy7zIhxmuw4R2TjHNge24AvwPgttls6OvrQzweR0dHB06cOIEVK1YIYEUDw3pVGl1me+n45AwVATgXsszWyudXE2iw3lqWS8n1vLIchpshm82Ks/IAiOyozFozSGdWmNdOpVJCNk8WlawbNxDlNrJ0jU6Q9yJLn+X6IDaH4BzJxxhxLmXJNg2vDEJ5b7wH3r/NZkM8HsfOnTtx0003CbAuHxMCQBhuGnOZJef/+XM0zDQ2BI7y/fLzCf55bw6HQzTOY/fISCSCbDaLUKiiyaNDloET68IJtNmci/X5dPIsW6BsSJZx87ryudulUgljY2Pi6yQyZPmdXOvLz3CH3WiaasJY3ZnzlRVgrHYMUW8UTcUm8V6DwaAoWWD2n++SzGw6mcZHfv0RHFhxAJefvBzKhspa4udS3k2Hzpos7i+n04lVq1bBZDJhdHQUiqJgwdIFeGbTM+i19GLt8bVwqS5YrVZ0d3djZGQEw2uHRcZ2rGMMPYd7YIqaBNnE4I2gGqgQaPIalGsk6aDYUIR7moQZz3tnsCjLJLnP+LnJZBJut1vMgSzVk+0LAwsGkHKG3Ov1IplMwuVywel0QlVVRCIRJBIJJO9IYvxD4ziAA2i8qBHdp7uBDEQ3c/aXsFgscDlcOPcfz8Vrudcw3TGNVT9ehTfWvYEHz31QzOF3ur+DrJrFVQNXVUnc5yTlc2Nu/GHHsmXL3lLuxExkJpNBf38/Thw/gZbjLWib34ZtF23DrUO34h1970BRK1ZJnQloSGhTstrX14dSqYSWlha4HC687/H3QTEowt/wc/LP5rGgtACDfzKI9fvWY9PWTVD1SgNYZtnkWOFs6bucyabvoI+ikorPS1AJQNhUGeTy+CaS1QBEsoE1sLFYDH6/H3V1dVVSY/pyTdPg9XpRW1srPhuo7otxdomaHA/y3mUgTvtN2y/HhPR7zCYy40wJtcvlQqlUquoiTXBJXyyXKvGZ3W43li5dKshcuWaf9dBer1co6QjKZFk6741KPZ5cYbFYEOuIYccHdqD79W6s2b4GZlO5HNBut8NgMAhQxuvLiQM+OwloJhLkOeY75hwXi8Wqo11NJpPIilO1R2KFiRe55IA9bThPstKUEmmDwYDJyUmhYKytra1at6PnjmLZyWUoForia7ye1+sV6ktZuSHL6c8uH7NYLPD7/WIeiCP4bplYIVBmLM13zPO6Gd9ZrVZEo1GYzZWGZtlsFoFAAED5dBHODW2AoihVPZe453gSCZUGTLSx9JEx+vDwME6cOPF7sW1z47/3+J2OBXO5XIJFYn0rUGb4uFFpRDlkSQ1QqZ+mkeBmpJFOJBJwOp3imAR5MxIQ03jw+rIEmxuCIJKbj7UrmlZuukFWkc5IBpgkBYaHh1FfXw+gUtvD32N9h/zzcj2yfEQZwah81AGNmWxgyUDLoIa/y3mgc+K1+AzsYE7ZOu/1/PPPr3JuNLIES3R+zP4SNMnAmUGE7HA591wDNDry87JpidyYjc5Nzurn83k0NjZicnJS3A+dA1UDuVxOdMaU1QK8J5PJJCQ+MmMu1+dT6ivL9gjSyX7KQRaBMhuFACizsRMh3LLtFjxy+SOYCEygId6AO3bfgaZwEx5d9SjesecdokkIs728HoAqFlnXdbjhxqYDm1CyVJrc0DFRhsb3wr0oBzmKUq6lW7JkCYaGhjA8PIwDHz2AN1a+ASjA7jt245J/uQTmnBkNDQ2Y3DKJk7efBM7wLoPrB7HVvhXn//P5sMAi1hiVJJwzOfhjtttoNAolwezsrJjjRCIBh8MBh8OBVColSDnuEa5zEivcK+z6zbkKh8OoqamBzWYTgVhdXV3VkR6apsHj8VSdvcnstt/vF2fO8ozuE9efwPidFSA8/q5x6E4dzV9qrsoScT2QqV/yvSWYqJtA8Y0iXk29CvUSVcwhAOhZXThr7uuJiYm3a2LnxtyYG7+HEQgEBGlJe8Qs1MDAAA4ePIhsNotzzjkHG3IbsPLoSqyfWI+SWqryXRwM9FW13ATzyJEjiEQicDgcogETSVD6TappFEXBmjfXYNGvFmHJ4BLoBl2Q7/RjtLdy7xHGBnINshyjyKCVPpDXlZVpjCOY2WY/E1kZRmBUKpUwMzODXC6H+vr6KqWOfL8WiwW1tbUiZmCcwmvIvyPfO+MGubZeJmwJkBjTyGo5ZnjHxsaQy+XQ0tKCzs5OkV2mcopgl/Ju1tHyWrwOEzFOp1PU/BKYWiwWQfZqmoZYLCbmihldNiejXDiZTAKA6LcTa4xh5/t2ItYaw96mvSiZS1j1VLlUgTEKwS5JBar2qGzku6PSjjELABELyfHa9PS0mCuqC3lajjz/jAPl9SQr2oDKCUCMlQnGWWrFUj+uQZvNhhMXnsCua3dhZu8MNj67URw1yu9TMcZ7MBgMcLlcYt+Ew2EBmHlPXMO8Hzask+up4/E4EolEVebZ6/Uil8uJcj4C8pmZGaRSKeTzedHFnco4RSk3ZM1kMlVnocsKl2QyKcoYAIjmaYzHSHiQ+AeAkZGRufrtuQHgdwDcQNmR0XiuWrVKMHNAhaUjSCB7x+6IBFxyXYss+yQAAyDkLmQlZSm3bPwBCOkIwbQsGafDopHLZrOiUzWNh5wtp0FWVRWJRAKDg4OC5ZTl1mS9yMaZzWbRhMvtdsPtdgt5DoCq3wEgsnoyW8p7ZZDA//Me6Xh5LdazyPIygkoaJs6L7OCYraRj4XuQJcucQ9khcE7NZnNV45az62lkYsBoNCIej6OxsRGapolzE+nww+GwcBh+vx/JZFLUDRGgcW0RNHEeGRTJQQZVCvI6AyCOinC5XIKBltlzEjhytoBOms9NqTDfR9NUEz619VP4+yv+Hp989pOoy9fh4fUPY8eCHZh2TeOWJ29Bb2+vCFzIzrN+iqCMPQ+41vmHc8Radb5vkkh858z8ms1m0aV734f24ciqI6LmbmrxFJ7982dx+z/eDk3VsHRkKQbSA0g4E+Wf0YCWHS2wGCyAVgmSJiYm0NbWJhyr1WoV65z7rlgsYmZmRhArfGe6rotnLZVKgrAwmUzCEXN+S6WSaFJHkoXrSG6gyPc2NTUl9g6vSakcgw0GblwngUAAPp8PmqYhMBrA0+rTKJkrze+MDxurAj2uZTZ/M5vNQBIIhoPQzBpc211Y/pnlOPjtgwCAOw/eiS3xLYDpjIrFbMBX1n4F6Xz6dzGxc2NuzI3/4ODelYEg65JHRkaQTCaxcOFC9PT0wGw2Y/34+qpaZhnwMFbhcUvHjh1Df38/6urqhJyZIJSgZHJyEidPnoSmaViyZAmam5thOGmAbtRFjwjWgNKH8Z5l0C2DaVmmTeDF7KOcFZel2nwW1vQCqKrz5c+ws3MqlUImk0EwGITP56sqJeN8WCwW1NXVCbBCYhao2GGSHDJJwJgEqD7CVAaBciKHwJhlebquI5lMiv4iPHqTqrRwOCyyqSxfIuHP+eMfxoJ1dXUYGxsT88Y5t1qt8Hq9QiLMum6CX16b9dXxeFwoLxVFgcFnwDMffwaJmkT52Yw6Dl5+EFpOw5qta8S7ph+Xyx/keeM7leNCkggej0cAZ/ai4bGdmUwGmUwGXq9XvGc+C0G9x+OB2WwW2V4qwORkgzj260zcxnXKWEtVy0fgptNp9G3sw65rd6FgL2DXxl0oFotY++RalIolEX9zvVBpKZMrzGazP5G8zqlu4DNks1lxJJysxKCCljGK2WyG1+sV60yeP8ZMLMmTGy3rui7IJJ/PJ1SgJD6IaRgDxeNxMfdUWHAO5b05N+bG2wbc0WhUSGJzuZzIKBEwsSEDQWswGBTZJtbPkiEmE0nmKhwOC3ZRlm4zU332WXcEU7LcmbIbOdNOholHGzBbSRZUzuTKcica6nPPPVdksQlcS6WSuHduYIvFIhhkGnUAVWd9y+whJSrcmATznA8eGUFALbOP8jPzGTj/BNClUgnxeFwYL1kBAFS6o8vZZMptZAUCUDlvlFlqOkDeLw0zr03Dyt9vaWnB1NSUkCABEOxwJBJBXV0dYrGYuG8aWbmuhsezMVtJp5fL5aq6lXNO5HfF+ysWi6ipqalSBsj13lwzfAZZyibXvJGQUFUVOA188adfRF7L48E1D2JHzw7oio7XO15HZlMGoW0hlErl8xuTySQSiYR432azGSMjI+Va63gcbW1twuFxfVMaLSsKdF0XLCqPneD98L3c9MpNiHXGMFI/AiiANWzFNf96DXSt/L4dUQcu+NMLsP2ftiPryWLjzzZi7chapI1paIpWJTdjgxAAVeUEXFMcstKC81kqlRAMBsW/zWYzEomE2GcyGTQxMSHWjaweoT1gmQDXK8mKfD6P+vp6lEol0QBR7jPBOjWv14tsNotCoYCj1x+Faql2ghN/NgHlOgW1tbVVDfVkmR3tGefGvteOy79zOfR5OmrfrMVU21T5XrwlfHnRl3HEewTYDeAqANNv19LOjbkxN/4jQwYx9CeRSAS7du36/9h77zA7z+pafH2n9zpnetGMZtRlaTSSJVmWXAQumGJMMNUGQy4kEJJAIJCE8rtAEgIJHUKPbcAEQjHuRZabbMuSLFm9jDSj6eXM6b1+vz+O1p73yOReyE0g5ezn8WPNzClfeb9377322mtjamoKq1evxmWXXVZHmaYxqVUpvuVyGWOGMUwPT+P8+fPwer1oa2sTESb66Eqlgmg0inPnziEajaK/vx+dnZ110zNICdY0TcBIAAK8q0kqfRLjAO69TL6YPKrAOM9BTdgZAzB+4HEwaapUan3HY2NjKBQKWLJkSR3gze/lHt7b2ysJGs9bpfLyfOi7CGbwOFXNGQAIm8NwZp2i/g2gLnHO5/OYt81j4rEJ5HI50XcpFArQnBrmM/OYm5uT3mAmgfx8XlMm/Cy8sOrJ19BvsceZQAvvh+rfAIgAGVlcvB75Qh6XfesyPP6+x1HwFGqA9rEuDO0eksow2Xj8XPYuU1NHbQtgzKW2KrK6zdGbY2Njwk6w2Wzo7OxEc3OzxAcqYMDYggKf6ppj9Z7Xl2CDyoakfzWbzUikEhgZGsEzb3lGpp6UTWUcuuwQeuO92DK9Re494/NKpTbCs7OzUz4zn89jdHRUzhVYBEHURJhgh9frhabVRvfOzMyIT/Z4PHA4HEgmk4hEIkIRL5fLCIVCaG1txblz5+qYo4VCQQCTdDotyTjBKa5bTavpDLHFz+l0SvzJmJ75RaFQwNzcHAwGA/bs2fPvtLM17L+6/doJ90c+8hFs2rQJ/f39CIVCWLJkiQT+VCsnsmSz2RCLxSSxIG1UpTyThs6kk4kNH4KLe5rUapZa8VYrt+pmQEebyWSkLzOZTNYlD2oVmcdmsVgwPz8Pm80mlXdW9kTd+ALiBkCOn4ABAHGEajWWmx1R44v7tSjYpiZZpEar1CpuQGpCro4HIz2H368mRTx2NXFXHb3aw672kV08uk2tujMw4DE//vjj2LlzZ53DBSB0IlVwKxqNShVe12vK9fF4XJw51xUpOyqVnIknk2dWhZlcU1SFYzhIuWIiz/9rWq1X3+VySVLHNcbNU6VF8ZisVmstecyWkGpPYWTpiPTzQgOObzgO3xt9yH0vh3W966RSOzIygp6enjq6PdFVrn9WrrkmmWhOWCaQM+YQjAfhcDhE7RsA5ufn5Zk5deoUbvv+bbjr5rswq82i71N9yCaz8LR65Lx9Nh9+76u/h1PLT6FtVxsy/owERrwunZ2d4jz47GUyGRGSY9AGoG4UBgVE2F+vUgLL5bIk0/zv4jXFkSps3SCAxOCB953PEdX9ubaJyPOZYcWFFLbr//l6PGx+GMOX1ubneg96Yb7ZLLNfeb05hoQBPAMyr9crtLF18+tgWDBgOj6N6alpeAY8eOr3nsI+377aWhgCcCeAPwBw/tfdbRvWsIb9W212dha5XE6op3Nzczh8+DDC4TC6u7tx+eWXo729XfYFJrU0Jt3c9/e37MfX1nwNaw6sQdAYRGdnp+wR9LtGY23yyPj4OMbGxuD3+9HZ2SkgMz+XgT5jIyayjBPUxFRNcrjPqvslAPGNTOj5XoKVTLbUaqoq0MbzZyGkqalJqMRMtAimulwutLS0iBiVCuKr7EK1OqvGDCqbjucz7ZnGd7Z/BxtPbkTPmZ46vRQeY3hJGE+9+ylUC1WYpkzo6emB3+9HtpzFsWuPYS40B99f+ZBZnYH2lIbp6Wnkh/LwjHnED+QuyaG8pAykAeMvarRktRrPgozb7UYwGJQJJaRe0/dUq1UkEgkAkEoyGZOMH2xWG/qm+1D8XhHPv+15tJxtwRXfuAK+gE9iVFLEGVeSJckYlFo1jC+ZDDOOZiWeFf729na0tbVJ7MNiiwrcUHmeAmYEggBIAm80GqU1jP6Z11B9HSvKFUsF5644J8k2ANjyNux8dCfanmvDSGVEri2nfmiaJjoBHA0WCoXqilKcwU3QhL3WjA15TIwVyFwlyF6tVtHS0iLCeUajEfPz8zJJhQCAmmuwYs2YEIAIM7PwQMaDOtOc7AwCUnw2qC0wOzv7G+9hDfvvab92wn3HHXfgRz/6EVpaWtDd3Y2+vj4MDQ1h1apVWL58Obxe70toRHyAmIyrPZxc6FykrDiqmzn/RnEDJpcMfClYoCoeq4m4OvaAya7BYJAEgk5J3Qi4sTGxo6NQk3pV3ZxBOYA6eg4/gxsIUI/68m9qoqGqfDOZVtXDVRq+mkwTeVWRQQIL/DudAn/P68LNR+2NBhZHSPAaqfQ03mN+Ns+tXC5j5cqVwnSoVmvCJPPz8yiXy4IoMwmm043FYpLklkqlunnZXDM8BhWhV/t8SDXiNVbVWMlQACCoJHumkskkTp06Ba/Xi76+PnFuPAcm6xSh4+errIeWaAvevuftuOPKOzDmHZPrHn9THKccp9B2ZxvaW9rR1NSEaDQq99Dj8WBycrKGevcVEQvGsDWyVc6PYIPFYsGMNoM7L78T1WoV737m3TCka/eJWgaqU7Pb7dCyGt666604Xz2PTDKD6elpuQccyRU+GsbqudVII43JyUmp+BBIU6vcdFpchwwQGWSoxnNzOBzIZDJ1VEJed36O2WzG2NiYVILIMCG6rCrMst/earUK+EGFc7UlgnuRGqgyIGKyfNntl8FetCPsDKP373tR9BeRt+cRDocFVCGKbjabZWQJ1xHnhxKgYoA6MjGCucRc/eZpBWBBwxrWsN+CaZqGpqYmCbKPHj0qYwE3b96MYDAoYJ5KPwcWK52MIZ7qfAq3b7gdWWsWR99/FFf+6Eq44i7xyfSFiVICD614COX7apW0tWvXoqmpqS755J6uaRoq1Qr2XL4HVz59ZR3wz+O4OJFlcq+2hLGyzJ8vBrkB1H2uuo8DEGr1/Pw8ZmdnBQRm7MRjoY8NBAJ1yt0qMEEQXKUl02czvqPxb9OOafzT5f+E883nMRYcw6XRS7HynpXiZ8rlMmJLY3j21meRbk0DXwYCrQG0vtAKt9uN/W/dj/HXjAMaUPnrCgpDBRS/WoThoAGZv84gfSwN/5/5URmqYPYjs8j2ZmFIG9Dj6IHpEZP4Dfqj2ZtmMXRiSGIZVlMZbwKQnnCVUUiQl5VPrqueIz3QbtfQeqYVRoNRGFaatjj/eXhoGF3jXbDOW4WuTGCE913VBlITbcaKBAMoxsbxWEyuWVmmT1f7khkP0pdzPfO8eZ6kc/NnrtFsNosrb78S+27Zh3Prz8FQMeCaX16DwZODqDoXq8hms1mKGoy3eU2p98I4XW2LILOQxS/26qtAF+8h17nJZJI+a94fq9UKv9+P6elpuQd8xtT2CsYUZnNN9JYaPBT4ZQxIhivZraoQ3txczf9bLBaEw+F/+0bWsP929hv1cBeLRUxMTGBiYgLPPvss7rnnHni9XjQ1NWHz5s3YuHEjtm3bhs7OTtnY2dvNPgk+QNzsmGBT5ELtN2LAzIeKaB4TeTUJ5OsZIBO55ObHpMlischmRgqYSvuuVCqCTKliFXQyavVc7a+qVCrYu3cv/H4/Nm/eXJf48zXqcfNYVaqTClbwNaQdEdnkdwOLSS5pyED9TGwm8gwqWKFXAQh+lzoejNdQ3ZC5wQCLMwbVmeBArbre19cnyTBpN5FIBE6nE263G06nU2ZlUrRM0zS5D+xJ5kbJXiXeRzof9mJzBITf739JmwDp+aQCp9NpBINBuba5XA7nz58XilEgEKhzsCrtjoEDk0Y6MofDgXg8jqbxJrzzoXfic6/8HFLOlDwzyVcncSB4ADd8/wZYLBb09/djbm4OMzMzMBqNCIVC6FjbgS++7IsoWorwP+9Hx/mOusCrbCzjH676B0wGa6OrvnTNl/CJ+z+BcrYsz5bNZkM8HofdbseyZcsQj8fhzXrh031IrElA0zQcOnQIK1eulOdj//79uOqqqwSN1TQNiURCZmwyKOUsU9LwKAjCNUHwgQ6OYBmfH657/o60N659Jrbq2iQ4xPXp8XhEFIZCOQRYyEKgI1fbNFRHzmtqMpnQZGrCW068BWMLYzgXPQdLm0UqChz7Mjw8jObmZgQCAQGfiGSTqcF1ajKZEI1G4df92PGjHchZc5gdmAWOA3g7gEUcpmENa9h/oDH5i0QiOHPmDE6ePAm3242tW7eiv79fwHr6KDVBBRaB8X2t+3DH4B1IW2vVrlxnDs/8/jPo+VEP7FG7+JFSqYQfvelHGGsdQ2+kF+tfXI/m5ua6hFz9bJPZhF+85hc4sfoEitYiXvnUKwVsVpPvH7z6B7jpxzdB0zQ8vP1hbDu2DYFMoG4/k4q4dkHgFLV/Q1+k5JLlc7F+DoXkIpEILBYLgsGg9DzzPWpM5Ha7hVGlxj4qoErfz/NW6dy8zkajEXlzHl952Vcw46+JSupGHQeuP4BioYiV966sgQOdVex65y6kWi74UxeQ/IskZr89i7Mrz2LimgnRKcldXQOe438SBxYA9ACFrgIS7QlUA1VUemqgR9VVxcT7JpCYT8D+s1o102q1In9LHrO3zSI+E8ebv/lmlIoloRozic7n89I3TjYgk2MmfSwa0Qf1n+6v+Ui9IrEU1+fYmjE8+4Zn4Uw7cePf3AitoEkllwkl9VPKtjIOv/0whr41JMAzgQKHwyG9xmR78d+MjRknMM6jIj2TdVbS1dgUqB9bR/+sJumZTAaejAdX/ewqlB1lrHpiFZaeXopz8+dkbBzjSvWzqctjs9kwMDAAo9EoFGwWEbi+GbeyNZSAO4sijPsIqlcqFYTDYQEZ2NoWiUQwPj4u948j09TvIPOWzwaF2XRdRzAYrGPkqlpAfF74HJEFsW/fvn/rNtaw/4b2GyXcqul6bV5iIpHA+Pg4Dh8+jO9+97vwer1Yu3YtNm/ejO3bt6O3txc+nw8ABFkDFnumAcimR2owUHvQGVQzeWdCplbZmFiTCsJEkYkygLrebT78pKxQvIrHwe/lZsoqZyaTEWdDyrZawdc0DS9/+csFmc1ms3C5XELtJg1ZfTj5nXSArNbzoeZ7gMUeWSYurOqyCk7Ks0rL4cbARPFiuj5/VlUh+RlMpC6msjGB5XVQkUI6VLVioFKEuTGqaHAymYTL5RKGAyvVrNir56wmYbymZDao302HrwIEPG8imJVKBRMTExgbG0MikYDL5cLZs2fR3Nwsn5fL5eB2u0VZne0RKtOAlc5CoQDfvA9/9aO/wt+85W+QtNcEU3wZH/7w0B/iWPIYKpUK2tra5HrHYjHk3Xl8+o2fRsZTSxY/ueOT+Hjm4+iOdss5fubyz2AyMCnP3ph3DF/Y+QV84KEPyD0olUoyNq1cLqO5uVmqyz6fD+3t7YjH45iYmJBnprO3Ezl7DubUYm8+HTATTHk2PUWUMov9XlarVcZ5UdBEFXdLp9MyboPOkPeDYmqkr9Eph8NhoYFy3XGd8bnnGmKSnc/n61pX6CAp0shqBKvWXBc+nw/pyTQy4xkZV8Igg8KHk5OT4rg7OzuFtq4CZPPz82hpaYHP50NzczOAWuXoxn+8Ed+/5fvIXJ4Bcr/BxtqwhjXs/8moHn7q1Cns378f1WoVK1euRGdnJwCIABJ9Bv2+CoYXi0V0HevCMsMyvLDlBcAMmIombHlqC0LhEHTDhV5qewl33XwXzveeBzRg9H2jWHv/WliPWQFFJoK+qmgq4t7r7sXRS45CN+jYs2kPbLoN1xy4BuZqbS8v28v43su/h2Pdx5B6ZwqrxlbhqS1PYe/QXnzwnz4IX9YnibOu68hUM3ho50M4vP4w3vyDN2PP5XvwyntfiSa9CRaTpW6msclkQgkl5JFHyVxCejaNcDgsVF2VJcZro2kaAoEAOjs76/7G5EkFLlRGnOqLVZ9pMBhgyptw6/O34stXfxk5Sw7Qga6JLuw4tAPV4GJMsv2R7Xjo9x5C1V4FKkBobwh9e/tgPmxGeE0Yqa5U3b2H68J/AGAASoOll6yPsqeM6Eej0J7RgBcBwxsNqPxlBTADs/2z+NG7f4Trvn4drCWr9OrTr9GHcCZ0qVQSjRmVGUE/kU6nEYvFUK1WMTMzIzPGDVcZcPgdh1ExV5B35/GTv/wJrvyrK+HNe+vEU202G+KmOB78+IPIBDIwVUwY+PYAKoUK3G633AMC5IyhUqmUaJGwlYpMBFVENpfLSQuYy+WqE78li4wtYPTBKitECl/zOq6+82o4sg4BCRg/8NpQS8Hn84m6OMGFVatWIZvNyntpKuBOZgrjl0QiIeuLMQXBDH4GCyU8H/WaqLR1s9kMn88nsbuaP5CZSYE0tqiqzwKLSGTB8d40ppQ0TLV/c8J9sanI0u7du7F79278wz/8A1asWIE1a9Zgw4YNGBoaQigUQldXl/TI8D/OV+Q4AyZcFGgj8scHgdVOVtEopkEaEFFi0jwYLDNpoxASaTJ8+Bicq7QZPrRMUIn0qbOQVVRQ0zRROSRqnUwm60YI0KGwd5doMntXWWkm+sbjZjDAjYVJ6cUbDRNm/szKPjcPNflQkxvVmdJx8rwIhlCYjJ+hUqCB+vmlTHQvBhgo0uZ2u0VUTEXXi8UiFhYW0NPTU9dCUKnUlDHZR8zkm1QjBhd0LnQSpFd5PB7E43Ekk0lMTU3h/PnzkixyRjNnMrKnXF1TAIQaxuOfnp6W7wrag/jI/R/B1676Gsww4w8e+gO0elqhr9Zx+vRppNNpNDU1yfp9bvtzyNqzcv1LxhJ+ue6X+JMn/0TQ2w898SF87qrP4VjLMQBA3/k+fOj5D8Fir63pZDIpKuDswyYgxHvT3NwMh8OB48ePo7OzE+MT4yi/vozntj+Hy793OXwxn6wVUvzplEq9JTz9rqex7OFl6N3TK8kphW34/PNZ5P3mWufzyvtPVgRbQujUKdCSyWTq9BzoXNkW4fV6BeU/c+YMli9fXvccejweAUK4fj0eT11vt8lkkj4tVsadTqcg3ADQ0dGBZDKJfD6P06dPo7e3V4J0rn+er8lUG3l32WWX4fnnn4ee02F5mQWZXEOlvGEN+21aNpvFmTNncOLECZTLZQwODmL9+vXCouPeyH2d+xMrtOVyGbFYDKeOnoLrX1wY+PAAxq4aw7bd27Bh7wboJh1VU803Huo4hIngYqVVN+k4sPEABk4PwFV21VV4jUYjxrrGMNI3Ij2vVUMVh5YewoqjK9AWaUPWlcUDlz+AYz21vX60YxSjHaMAgLwtj6+/9et476PvRXe0u5b4GXJ4ZPAR7N+wHwBw+ztuBwB88c++iOv2XoeXv/BymComKQoUK0W8sPkFnO45jYqxAtMvTQiOBCWJSKfTdVV2o9GIhXUL2OLZIq1yF1ewVfYQfaQKlksVHotVS+jAsulluPWxW3HX5XchMBbAzT+4uRYnGGr+JB6Po/T9EoKngwh/IAzTgyYE/jaAKccUHA4HNr1vE5792LMo+8uouqqoNlehxTUYZ4worywDVcD7tBfFliJyKxZRT21Gg/lPzagerkL36dDfpwNm/hGItccwsn4E/U/3Cw1b3qvQ+zkHnElsOBzGkSNHZHylquytsg0KlQL0v6v/zpQ3hQfaH4Dvmz4BgFtaWhC6PIQ9f7gHmaaaHxl5xQisVStW/3Q1XEaXAOzq8RmNiz3qTGhtNhumVk7BccYhfpLUarZFLiwsyHi7YrGImWUzaBlukc9ndZxMVVbMASDXnsPuW3dj5b6V2HJ8C7xer4iO8voZDAYEg0Fhi/p8vjpFccY7Kl2c143Vbz6f/Dtjgng8jlQqJerynKNONsjMzAycTidaW1sRDoeRSqXqtHp4j5xOpyibqwU1jv4jgM8Yk8esat0wOWf7YMMaRvt3S7h/lRWLRRw5cgRHjhzBXXfdhd7eXixZsgSrV69GX18fli1bht7eXgSDQdmY6ADppEjvVkcpMYkgtZX9q6xK8jP4e1ZXSQ0hGqjret1MbJUyzgSB/aN8ONVRCaxmA/WiIColnD2rHC2mUsyY8HPDIV1VdXhMgog087xIleGGdvFGyJ9JSVc3fIIGKnhwcQ8Wjc6VyZEqOMHPUKuMwKJT5XXkxs/ryYTKarUiGAxi06ZNsnnHYjFJuuPxOOLxOPr6+iTZZ/KtVsLVe80KPUdOqJVPn88nIhi8Hy6XC0uXLkU2m0UgEJA1wveQWcFAQmU0MBkkOJTJZESY521PvA12ox1tlTaYLWZ0dnbieP9xzB+chylukgRz2Z3LYNfsOPaGWoC1Y3QH3n7g7XK+hUIB1VIVf7z3j/GtDd+CyWjCTU/dhEgsgmAwWAfsMAlm9VkFPoi8rl69GnNzcxi5cQTH33YcMADPve05XHnnlbBH7ULZjkaj8Hq9SLWk8PRbnsZC7wIi74ygYCpg9dOrZU25XC6USrWRO3w2iVST5n0xcKMKFfGYGdyxQk0qONeCKtDIZ99sNmPFihWybtXzZdBQLBaFYcHn2+l0omwv4+jyo+h6rAsOh0Oq9VzHBF3IHGDrgdvtFmGhgq2Ayasm0XKiRZ6hI0eOSLuG2rfYsIY17LdjExMTOHbsGCKRCC655BJs2rRJAmXuRUB9hZb7EFlXZ8+exeTkJLxeL9Y8uAYj+RGs27dOvoN7m+GfDWg60ITpT05Dd+joGu3Cq+59FZxZJyrVSl0sAADdI9244d4bcM+N9yDlSSEUDuHGe29EcCaIgl5A0pZEzBz7V88t6o7iybVP4h3PvgPlahn3bb0Pu1fs/pWvfWjLQ4AdeO3zr5W98OGhh/H41Y8LQIA/Bux+Oy555pK6/llWc0euHsGhNx7CwIkBrEytrItfmJSoAD73cYKu9JUqK42/A4D+4/3YMb0DwSNBJItJSbwYD5ZKJZi+Z4J5zozKDyqYd89jyZIlMBqNiE/EEXhfAPm2PHLOHHKfz8HzKQ88Ex6E/zYMz/MedH21C7mlOZz/6HlkV2eBGOD4cwc8T3ugNV841z8tI/X5FHJbczCWjLjiF1dg7bG10AKaxFdqSyTZWvw/k7axsTFMTEwgWUqi+sYq8N3Fe6G2Feq6DrwFwNcBvB6ADuAvgcqXK4gggmg0CoPBgLGxMWgWDfm3LSb8ADBnnkNgIQCLd7FwxCSb/doGg0F8msvlwtkdZ7H/9ftx6S8uxYqnV0ihhffCaDTi3A3ncNnBy6DpGiY2TOCJ1z+BVbtX4dJdl6JareLk5SfR+0wvbAZb3bSaXCiHJ9/0JKZ6pzDdPY20nkb//f1wOp3SnsaqO8XSbDYbfD6fVJXZTkpWhApsUKiUE0gAyLUnC8DlciEQCMg5MR5h/N7R0QGgBsbNzs4K25YiaAaDQRiC6v1ibFIul6X4wthb7eE2GAwyio1x8QsvvCCFpoY1DPgPTrgvttHRUYyOjuLJJ58UNciWlhasW7cOmzdvxsqVK2VWLnskmOiymqxWq1Xqkko9VuksAOoeENLK2RvMh0oVbGB1W539DCyO4lL7lgDIa/idqlNisg4sVndV+hoTWm7m6vcw4WAFXqWfs9eFVHomG/weUm553fh7VfmUx8sEn+eivlZNzLkBqo7VaDRieHgY0WgU27dvB7CYpPM1VEZ1Op3w+XxyvqTcnzlzBr29vQgEAlKZJ91pdnYW/f39darrvH7s/c9kMlK9VIEKUr1isVoAk0qlUC7XxkdpmoYNGzYIQspxJ01NTQJ+cJ2oCS1pVgBEi4CosMoE6I3VKsFGsxHZbBbDfcN4ePPDMFxqwJUfvxIt3hYYjUb09vZi3Yl1GDw6iPPu87j18K3wGr2AEVI1r1QqsKfsuO3AbTCZTfA7/Dg9cVrmyfKYCMBwnZNazd8ZDLXRas/seAanrjgFXMBVplZP4eE/eBjX/831MOu19erz+VBylrDr3bsQ7a6NatHNOo68+QisDiuW3L+kTv2b94Prk9VvgmBqkKW2cPT09GB+fl4cFgBpo1CdHgMwl8tV13cFQKoyuVxOqPB+vx+JREICA7JA2H9/5+/diYnQBNYtrEPLwy3CYqGxUu7z+RCLxUSXIpPJYHZ2Fi63C+PfG8fx1cdhNpix8tDKumeYgF3DGtaw364dOXIEkUgEK1euxJYtW2REqVoJZGVWbc9inLCwsIDh4WHouo5QKASHzYGhg0OoGuop6HNzczh//jz85/xY07UGL77hRbzml69BMB6U6jmwuN9xgkbvcC9+74e/h5+96We46cc3oTPeCV2rgYbemBevvu/V+NnrfoaJzolaMgZIghyIB7BlzxZE41FomobOiU5g+eLfL7ZH1j6CsqmM1z/zejy4+UE8Pfh0/WsNwMk3nYQ76MbQI0N1yfKhjYdw4MYDKNqLuPOSO+E468Ar518p+zKvo9qfrSbUTJouvu4AhHk4MzOD0PFQ7fogL9e3XC5jfn4eZ8+erTHZ7tRgNVpFUyObzdZmcpc1aGc0IAt45jxoOd9S8xEf88A2a4PJYILznBPBPw3C4ragkqnAdsgGf8i/qORd1WD/lh3Hmo/hknsvwdLhpdCci3O71Qk0VJ1nKx99RiQSESG06o+rwGUX7sl38JJzhxnA3wK4H7WE+w8AfBcviR3L5TK0JzWY32pG6eESEADan2nH6u+vhsfmER+lJqcsJjFuBoDTl5/GsdcfQ9FVxPOvex65fA4d93cgn6+JhJpMJkTfHcX5m88jsTQB7wNenLr5FHLeHF581YswOAxwxp149tXPYnTNKG66/SY4HDXqeNlSxv2/fz/CXTVxMN2o48BrD8Dpc+LKA1fKNBqK1TIeI+OMDDe2hzGWuDjO4+ek02mpynNSC1tPCbwDqJsdzqQZWByJy0SZcYRazOKa5f7AOJwFQRaYGB9zIhMBDk66mZqaalS4G1Znv9WEm1atVqX/e2RkBPv378edd94Jm82GLVu2YNu2bdi8eTNWr14tcwvVCi2p45zDS+qqruvw+/1CD2PViyM8+BBzQyKtXd2gWMFVA3QeMxNxtYJLYS4ikolEAitXrpQkgccHLNKsKLal0rLo6Oj4gcVqKh9aOi5u8nzo6RDUpEZFVNXES9M0Sc4IaLDaSBSXCTPfw0ROdQZM2M1mM0KhkPRFM+Fl4myxWODz+bCwsAAA6Ovrk34X0rKz2axQyl0ul6hsqor0gUAAs7OztdmPF0ZEsQrtdDqxsLBQV+E3mWqz3zOZDIaHh9HR0VHXQgAAnZ2dqFQqeP7552XcCc+T95uBGQW1KNJVKpXgdrvlPHm92traZP0BQLlSxmjTKL54+RdRMBeAXmDX3+zC9Z++HkFLLRCs5Cq44tAVuM5zHQx5Awp6QSjLTqdTlMcDhQCKqSIKhgI6OjrwxBNPIBwOY3BwUFBWTdPqWA08Dq5tALj+7PUYXTeK8ZaawqupYMLWn2+F3WiHZtQWK/tlM9b/fD2e/MMnUbFWAB0InAuge3e3BB4qgEMtBbvdjmw2WxvdojhT9o1Rpd5isWDz5s245557xHmx94nUT3Ut6bouwj5cC6yW01nyOWO7AZ0r53EXTAXc9ba7MNY9BmjAvnfsw/bCdiw/vRzJeLJOU4Dggd/vF/bE2NgY4qU4zn7zLAobC4AG3POqe5CdzmL97HqYDCapikiA1bCGNey3ZmNjYwiFQlizZg0CgUBdH7EKBjO4JmOrUChgdnYWBw8eRCqVQk9PDwKBgOxfbIOqVquYnZ2VpHxgYAB9U30Y/KdB2Io26Fic7c39i8kZfZT/nB+v//TrYcwZMYMZCfw9Hg+CqSBu+/Ft+PYbvo1Xfv2VOLHxBPa+ei+seSt+77O/h3KhjJHiSM33n7XhirErsOfWPaiYK7DmrSjYC3It3Ek3Ln/0coQzYazbtQ7Pdz6PSGtkMenWAc+EBx0/70DWsNiGM716Gntv3Iuis8bSSZvT+Gb/N9FV7sJQckjiK2AxUQQWNVRojEkIZnC/BmrVxmSypnWiMgspQhaJRGSUp8PhQFdXF7q6umCxWBCPx+v6xo1GIwJnAnAFam1J9ulaTGix1663ecQMR742jtTld0lsw/9C2RC6v9ANU94E3bIowMUKss/nqxPJymQySKVSMBoXhYF1u47Sz0vA1aiB2V8GtKQGwy8M0PTaNag6qsDjANYBeGPtGmlnNWjQYLaYJbEDaj7b4DRA/0sd7e9vR/kDZQx9ZQjGghG6oSYKyJjG6XRKxZWCffl8HsmhJI69pZZsA0DRUcTBmw/i6N1HoT+uo6JVgD8Eqm+tQrfoGN48DMN6Q+04AVTMFbzwsheg6Roq5grG1o7h52/7Oa7+1tWwGWzwWr24/NnL8Ys3/ELWU9NkE1Y9tQqas8aAC4VCLymO8bpRuIyjzvjM8P/lclniEb/fj0AggFQqhVQqJbOuyXYkVT0YDErbIenmzB8ikYgk0vTpjJMINLH/ndeTxRa+n3PgqeXEZ11t7SRbtWENU+13knBfbNyIs9ksHnjgATzwwAMwGo1Yv3491q9fjw0bNmDFihXw+/0IhUKSTLDyys9Q0SQmkUwyWbklrYxUaiaNdApMZukA6CgupkOpzgZYnMXn8XgE8TUYDDIzmEkDnbZ6jCoizIqh6qjYt65SuNhLpYqcscrO7+f71WTMYFgUGePfyRZgsqoCDvwufi+Pk+hjtVpFU1OTXAN+Fu+J2WyGx+PBzMyMjIqqVCrSQ8PNKxKJIJFIiHIlha1mZ2cRiUTQ0tIiwh9sMzCZTAiFQiiVSlhYWHjJubLPasOGDXUVWAZcVLq87LLLAEBQStLN0+l03X0sl8t189pZ4SeAwfXBa2m325Ev5fHjzT+uJdsAoAGZ9gxm3jKD5p83i9hHOVeG0+tEXsvXjZoymUwCIvH8KpXaKK4lS5bg3LlzOHnyJFavXi30cpPJJMekAk2CGOeM+ON//mN87eavYc41h23f34bA0QAqpsURXzzXZaeXoXxHGfvevA/eES+u+sxVMJvMyFfyUn3mWmUvExU6N27ciCeeeELaBUht57rVNA0PPPCAJM4AxNmxnaRUqs1Tn5mZkWRepcjzPpONQtRbreoT3LHb7Tjw6gMYW74oGV61VnHsdcfQ9MkmGCoGARJ4XkTB2bLidrsR+WAEhcsWg9qSuYQH3/sgej7Wg3ZbO3Rdx8TERKPK3bCG/Q7MZrNhcHAQLS0tAnKTyaUC0yp9ulKpTSw5ffo0otEo2tvbZbQY93W2iKRSKYyMjCCRSKCnpwcDAwO1sUUlHZph0aeqoqaMU4SKXCzBAQfMNrPEMgCkv1zP63jTF94EXdex9bmtgB0YPDyIgDmAslZejBuqGlYcWIGKs4LxjeN49c9fjftvvh8xXwymjAmv+9rrMJ+cF3bOlR+8Eo9+7FHkAjnAAHgjXlz9v69GMVdEypKSa+Td58W2pm3Ye9NeZK1ZuMtu/NHYH9Ul27x+KmgBoO4aM4nmXsjrkMlkRI+D+j0ESAm6z83NiZBnZ2cnurq6pFKaTqfremwv1sTxeDzix0+fPi2Ac2dnJ1paWgQE0TRNetPNObOAvQDqgFzqolB8Uy24UAsk+5psDYRlR54dCH4hiNBsCJaEBSlvChOfmkBp6ALN+IK4m/6oDuO1RpieM8nxVKtVZO1ZlP62BP21Ouaun8O2v9wGY2FxVJlaoWURQBUsy2Qy8B3y4VLrpXj+dc+j5C4BMUD/iI7igxfGvi1HrcLOsZVGoJqoAlEAnQDygD6so7r2QmytAWNdY7g9dzvMPzQj9OYQpm6ekmfPUDFg4z9vhLlgRgYZSVIdDgfS6bRo2CSTSYltHQ6HxDxs21TvK1mcajzPyj4ZCCrAxXiZCvMEv0OhEFpaWiRX4N/UqUXt7e2IRqPQdR1ut1sKLQCktY3XGoAUzhhbsSXtxIkTUmRqWMNo/ykS7l9llUoFL7zwAl544QXceeed6OjoQF9fH1atWoUNGzagr68PnZ2daG1tlc2dDzUA+ZkPlSqKRqeiUrYvTsyj0Sg6Ojqkj+hiug97ZFS6Sn9/f22jvCACxc/mA8r3q/3IqvNX1R8vrqgDizMHgVoypm4E6nVT6S98H89VTR74MxN2GpNTJu8qVV6toAOQz1URarW6Thozr3GxWEQwGBQgg5sjk0Oz2Sx91nTgk5OT6OzshN1urxtzQTObzXC73UgkEnXHwO+js1SpYZqmyTgU9ftJH1d74gGImBbRd1LR6Sz4Xqq8UhRMr+h43+734Y4dd+BQ5yFAB159+NV4zdhrcMJ+AufPn0dPT4/cV4PBIFVgNZHlGuR1TSQSiMfjaG5uxujoKBwOB1auXFkXvKkVABUs0nUdZt2MW39xK063nkbgRACpakpAL64/sjRWHVqFKqoIPRtCLpuD0WWUNUZmBtdRKpUSBPvRRx+VY2EQxPVHpgDPs1AoCBDF+8sglwADK+AErggOsc2DLQR01rzP5XIZbrcb1nVWTHUuBge0S39+Kex5O7KV2nM7Pz8vc0fJViDo5PF44N/nR2RHBKXQ4jPT9ngbSskS8u689LE3KtwNa9hv33bs2IH+/n7ZkwHUValU2ir3ung8jlOnTmFsbAw+nw9NTU3CACPwBtSSbfZ3Nzc3o7e3F263W/wkwXwmRfT7KqWVPhNY9N08FsYpBJTZqzr00BAAIGlIyjHTh2qahpWPr8SWg1vg8Xhw6x234kzXGZinzTCnzdC1xfa2crKMZR9ehtjSGMwOM/qm+lAulOv2UwBwuVy4KXoTNoxuwLf6v4X3jL4HN8zfIMesFiSY6BKc4Pmovd0XX49IJIJYLFYnjJXL5ZDP52stWMPDQnd2Op11CtrhcFhGVfKaWSwWOJ1OYQl0d3fD6XTWJoFcSOAqlQqampoQDAblc/j5vL+ZTKauDY8FklQqJT8z3mD1lIK+5jvNMJ0zofT5EuAA2s+345qfXAPvoBcAMNs7i8RAAhFE6hesBvS/tR/2iF3A+oKxgOSHkqi+9UKl2VbB/o/sx8Z/3IiuM13iG1nQUFsdGfuw+NPxUAe8J7xY+P8WgMOA9p3FMXI4DeBtAL4NYO2Fw3lUg+FuAypfrcDwjwYY7jGg/M0ysAVABsAHAe0ODcVqEVNd9f5UN+gori/Cf8j/kmITwQSVtUaV92AwWKdDQ8vlckgmk7JuyICjbyZzjgzJi4WMySYlq5J6UBQ75TXkfjA5OVkHJKnPPosrZF+yhUAF9Ugpn5ubk37zhjWM9p824VatVCrh/PnzOH/+PJ544gn4/X40NzejubkZW7duxfr16zE4OIjOzs46GjQfODXRoFACHaQq0kGnc+7cOZw4cQJdXV2yiQGLNG0mcKoACx0LEWrOqCbdnc6IYmMXU5fVCjsTZJ673W6XDYHHQIEpVvPo0NSKvapUTiRUTUZ4TkyYVMo9q4wqXUytnNP4HrWXnNeF14qbG3tf3G63ODAmg7OzsyiVSggEAnViZxaLBR0dHUgkEkJNslqtdckVe62JvBOVJAqubuJMotlKoI5SU4Ve2CagJtU8Lr5OVVXn59MRcE0AgCVhwa3P3wrdqOOS+UvwstMvg81uw6pVq1CtVhGJROB2u9HS0gJgUTuAjiWZTNYdJ4OB7u5ueL1eRCIRPPHEE9B1HX19fbhnwz247uR1wBxEpV0FRLi+/AY/Lh2/VOacplIpuf8AhFptMBiw9NmltXYEa1aupwo2MTln9Z/PAp0qK9aqCKJaPeZap8gKj5uBmPp6sgzUZ5LPNJ9vslJ4DU0mEwoLBbjynBlTs969vQhNhuD1e6UFgVQzovC8nwwg2l9oBz4GnPrSKehWHUueXgLnJ504kj2CwcFBeDwexGKxRoW7YQ37HVh3d7e0DjEJ5l6i+mzu2ZlMBqOjoxgbG4PL5UJbW5v4MybQpI/Ozc2J4vGyZcvq2Eeqn2HipPpsCr8CqPOhfB33MIK5TBroR9RKseqHGddQoDWfz6PtRFuNzacvAuusHpbnymgON9emQjgtMJqNdcekaRpCoRB6enowGB1E9+luXLJwCaqGxeuoHrcak6j0fb6OvpavochmPB6XFiGj0SjgazKZxMLCgiTIXV1dAmrkcjnR5qBvsdlsIphlt9vR3t4uGj3pdFoKI263G21tbfB6vSgWi0gkErBarejs7ITVahVRLMYCTOBZNSWgz/5ztiGwWl4ul1H9ThUdtg4UP1zEdT+9DqFoCKiRptAz3YNrfngN7vn9e5DxLU6vWP/99Vi9ezUcGx2LrQ52A15sfxFP42l5nbViRVOlSa4p4y6uZfprtm4Bi0Uawx0G4M8BrKnRx3Fn7TMNBgOqz1eBtwP4JWpV7asB7V80GN9qBJ4Bqpuq0O7XoLt0aJ/UgJ8CVf1CxftvAa2oQf9MbY1e+aMrMbB3AFFzVLRVVDYgCxFUE2cbI4EWVTSVxQ273Y7W1tY6dgSLRGRC+v1+WT+M52OxGHK5HOLxuBRLTCaTzNfm7G3GC2xV5fPHwlMkEpHvO3v2rMTrsVhMxv8yDuQabVS3G/ar7L9Ewq0aE5RIJIKTJ0/iueeeg81mg9frxdDQEHbs2IEtW7ago6NDZksyGSOtlA6IjkLTNBk5ZjQa0dHRgdbWVkkU1L5qlbrOJJKK5ewLV/vEmGTH43F4PB7ZzOncWFlm4s+fWfEjugagLtliksLElpVV0rFVJ6gm8io6zveqVGWieKrQmyoQp/67UqngM5/5DD7wgQ9IYqYmXjQeH8ddMFml2BcVVPfs2YOVK1cKAulyucQZVyoVTE9Po6+vD0BtVjITUyZZBB14fbjB8jXs8eH3AZD7Qsq4StWnajo3awBCj2Yyz+vZ2tqKQqGAVCol4jjqCDZ33I3bHr0NPrMPZsNi20J/fz/GxsaEWsdklTQpfi8DGFa7W1pa0NTUhGKxiL6+PqTTaRw+dhh7rtiDp9Y/hQP9B/DBH34QtpJN1hWTW95TBpFWqxUDAwOIxWKIRqPCRuD3qRUam80mPVVEnPnZRK95/9hDRdCCa0OtpLjdblmr/N5yuQyHwyFoMo9bHTOi0saLxaKM8uOa4prhd1arVYz4RjDWv0gnB4DJdZM4e+9ZrIytlOettbVVnl1d10VpnawSXdfhP+rHG778Bhx61SFc9tPLcDRzFNMz08hms2hubsbMzExdi0vDGtaw346xbYV7M/0aE1pgMXlNlVP422v/FsvuXQaDwYCenh4B/fj8apqGfCGPyYVJjI6OQtd1LF++HG1tbXUtaGT9cG9jss+9hH5BBSzpf9WWJB47wWsCmwQbAdT5cYKZQE1AUj0GvrZSqY2PpIAoq8KqqCtb+3S9NrKJ7UVDiSFUjYtsAJVppZ6rSjVX4x+18spEl0KlFDrl92azWczPzwvYqYIRnDxBMFUtmtDfNDc3w+fzCVA9Pj4uvt7r9SIYDIqmR7lclpgEwEvEtAqFgrRHeTweGTdJVexIJIJUKoVsNotqtaZLZDKZ0H+kH31f6IO37IXBtsimqFar8I/60fOGHpz6zCkY7jCge6Abnbs7odt0mF1mGYlrMBhw2ZOXoaAXsO/KfbClbbjmr6+BKWYS7SL6QvpktcJqMpnEhxvtRhS+XwD8AIwAvgo4y07gXqCYLwJmoPrOKtByYb106ih/vwztFRrQC+i/1GuZgg3QP6HD+ogVek7RRPiyDniBXlcveo71wGQ3yfnyNWwhZNzhdDphs9mEpl8oFKQtUG0PsFgsCAaDwjCkMBmAuphPpZhzXRDcX1hYqD3DFya3sIWA60IdEcz1Wq3WdBoojGgymWTtqvoFfL64BzAuIlO2YQ1T7b9cwn2xMXlKJpOYmJjA3XffDavViuXLl+PSSy/F5s2bsXHjRthsNplVrDpfOhD285J+ygebDz83fSavdI7s+aRzVikoKsr9rW99C3/wB38An88njoebBVE/9t4ycaEz5Wfxd/w/qerpdFpEtqRPV0EDiSSq48O4OfGYgcWZ40xML6bmApCkiA709a9/vQQ4DAzUnlyeY2trKzRNw+TkJKrVKoLBoDg9JrHso+NcdbPZLD0yHR0dGB0dlV5eAgRqn7nX60Umk0EkEkE6ncaxY8ewfft2EREjYqpWSzlWjr11bBXgtadyNs+ZipR8Lc83mUxKcul2u2G32wVF5fe6tNqs05KxJA7R7/fDYrFgobSAcmsZzpRTAjNdr81/ZM8Z1x3RdN7XtrY2xNIxPL7mcRy56gigAbOeWXzlDV/Bex99LxwRhzgiVhIYUBD4YBDBdcLng681m80CVrlcLpkxWS6XkUql5DpEo1GkzCnAD1RLVbmvRN1ZaSGzgyg41686B5aoPUVPmEzzOqu0RYoaMdkGIN8rTjxdgbVkRcm6SAU3ZU3IxXKYmppCIBCQ59dgqCnAcrYn70NHRweeffZZVCoVWI9bcXPqZhw+f1gAgWg0img0ing83qCUN6xhvyPj/vmr2F9AzZ8uWBfw5W1fxvnQeUS/GMW1X7sWHrOnjk1mMBigGTQcHziOB695EB1HO7B6yWr09PTIfgzUt1bRd1Jvgnst21/on7mX87voP1k1Vavy9OscXUSAmy1rTFrVmANYnNBCgbJqtQqv1ytMLVZwVRBg7dq1WLt2rVw3tU1HpcMDqEty+Fq1XQ1YZMYRQGZixUox3x+Px7GwsICJiQnk83l4PB4sWbJExkhls1kkEom6a0pwolAowOfzob29XSbRzM/PIxKJCBjc19cnMSDPSW0H0DRN/A8TMrbEqdT4WCwm/ntgYADz8/M4efKk0JpDwRCCehAmq0mOhde3UCigOl5F6BUhaNCwZPUSBDuDsFqtokvCWMOm2dD6lVaEZkPY8dQOIA7kyzXml8fjkSSU64mFDAL9k5OTaGpqwvjOcaS2pmrJNgC4AdPnTbih5QaYi2a8cMMLOHbdsXr1eh+gP3PBf6m/XwUUHi7A+hYrjJNGVFCB9hYN+kd0jGIUm/55E5oO1qrwPp9P2JOcSEMjG4ExIBltjM/YRpDL5TA6OgqTyYREIiHFILfbDZ/Ph5aWFjgci7PF+WwQHLLb7di2bZuswWg0KgUDsh2ZPKfTaeRyOYyMjCAajSISidTRwrkGVMq5WiQjk4OvbVjDLrb/8gn3r7JCoSDzv7/3ve+ht7cXAwMDWLduHQYHB6X/m4nRr0o8VWEEFWVm0s1kTBVHuRglAxYT1Q9+8IPidPj5lUpFKn90Okx6uGEw+WFSqD7kfM3x48fR3d0ttCqDwYDZ2Vn4/X4RnqIzYMLDhJOJMx2k6ixVIQoV4WtqapJNvq+vT5Bh9f1M/vlvoogejwcnTpwQURN+Ph1dNpsVZ1Iul9HU1ISpqSl0dnaivb0dExMTkpAx0eemSYZBJpNBsVhET0+PJIherxcLCwvCAmBgA0CSftLQVbBC7fuuVCro6OhAKpUS58Z7yUSNdCc6a4Iv6txLXiMGf7ADP1n9E5S6S3j/qffDOGmUPm4KeNBhq9UPJp2tra0oO8q4b8N9i85RAxK2BCaCE1iTXCMtCLx2vAYXt1u0tbVB13XMz88LykwaP506hczK5TISiYTQ2jRNg+bTMP2Oadhb7PB/24/EaEKCTwIxKq2dfV1U82f/E1ADOZYvX44TJ04IwEQnTYoiK/bqPeDz0dHRUZeg9432wXafDfffcD/S7jTs83YMfWcI7ZF2ZIwZaUUgNY+fy6A4HA5jbm5O1svExASee+455HI5+P1+CRhYrWlYwxr2uzHu2WrlmS011WoVs+ZZ3L7pdgw3DwMAkkuS2P/e/Wi7rw1Nsaa6Ku6h1Ydw7033QjfqSP1dCi2PtMBustclkqovVZNkoOZfVDVj1beqxwgsJsisYDI2YCyi9oITUFB9ryrCygSEPa7ZbFaYYxeLt/Jz3W43mpub64AJNXHmOdM3qLGD6i8sFguKxWKd/g2TZIPBINVNFiy4/7Nn12q1oq2tDc3NzTIneWxsDJFIRI4VgPh2Cupy/8/n83V7dWtrKwKBgMRU6XRaYr9EIiHnyxiCCRmvu8o24H1hsSeXy4nuTCgUQigUkmurtpfpur5YIS1XEAgE0BRskoo0Yyr6ykwmg2wmi/V3rIelyQLdoEvcwr8znmQBIplMIhwOy3pKJBLQvqXBesyK3CdzgB1onmjGy374MgTLQWRCGSSWJV4yVs74tBE4ClT+V6U2xkweLMC9zI1Vt64CHgAObj1Y61m/8P5/uflf8Aq8ApvObILBYJB4iEUVXgeuQQDChOPzSfCe19lqtcLpdAqApbJD2MLJ9wGQ4pXP55O4nK9jgYqxEFCLzaampjAzM4NIJCIFD943tY3iYuYIYxi1MMXnrmENu9j+WybcqlWrVZw7dw7nzp3Dww8/jKamJnR0dKCnpwfr1q3D0NAQhoaGhJ5L0QU6CzoVoqB0KOzz5Hfouo7Dhw9D0zSsWLFCHkw6WlatmSgBNUdss9mEFkTnxd4kIsx0iKrwGZM2VquZFJAivGvXLrziFa+A1WrF5OQkxsfHsXnz5jrAQK1gq2JyasLNz+fPbrdb3sPNk8aNTt2ceJydnZ0y4iOdTiOZTErfGXun2F/D3ikqbfr9fgSDQXE20WgUHo8HiURCjo+Bhslkwpo1a5BMJpHJZHD8+HEMDAxIpZUjyAYGBsRpVauL8925oVcqFaGwEQnldzAp07TaiDUKeVSr1ToRtUqlIig8rweBByZmAPCdS7+D/b37AQCft34eH85+GA5jTQCE64KJJY+DAQADA5/BhxsfvhE/yf4EExsnYMgZMPjFQaxtXgubvbZO4/E4yuUyfD4fyuUyJicnRclVpbL39fWhUqmIqByDJlb7uS41TROxOpPJBLPVjBfe+wISWxJIIAHdpaP/z/pRSdecYTKZhMPhEOo8K05qMMlKOpFiBlgMaFjpJ3LOiryqg8BntaenB+Pj4/Js6LqOVWdWodXdiu9d8T3s/NFOmA6ZoGu6fB5BjlKphFAoVPc9ZDiwz3Dr1q248sorcd999yGRSEhlpOFsG9aw350x+FaTX2Cx/apcLiM2H0NiJlHrWb1gxqIRemGRxqxpGg4OHcSua3ZBN9b83Py2eTzW+hhu+ektMJTrKdMM6NXkjS0vTJDVdjPVmOhxv1H7n7l3AYsBPeMLJocE0mn011TR5hgt0mnZV8vPBGr6JsuXL0dzc7P49ouPQxW0Uqve/By+jwUBvpd7N5mDrOSShURfGY1GpU2PxQKqSGcyGRGjVBNpu92OpqYmtLe314nVUkCXyRd9DfuEeQ0IAqtxEbVDeHx8LeMbu90uc6zT6bRUXnt6ekSsDEDdNSQTE4AwohhDqowDgtvsPe7r64PH4xHhL/Y9E+BnUYHgcDKZlDFZ9GflL5Vhjpnh+WsPrv/Z9QhFQtAtOiwpC3b+eCceeeMjmO6brt3XPQaY3mNC9XQV5nNm5P8hL/fcnDPjytuvRE+4B8YtRrRsbsE9uEf+rkGDo+iQ+Fhdl6oGAYs3HPHKWJPsjkKhIK1iKnuTYARj3WQyKQUaxkl8fnkPyYDjfVV1EEqlEsbHx3H69Ok6UEz2BOU+quubsS3XzMWgXsMa9qvsv33CrZqu6wiHwwiHwzh8+DAefvhhuFwu+P1+DA0NYfPmzXjZy14mauPcBNWqFVE3Prw0JmhqhY3VO9Up87XsXeLGoFbE+VkqXYrIKh26impv2LABmqZJf5bZbMYrXvEKmWEeCATEwakUMVoul5MECFhE0XkMrEiyV5rXkhVFvv/iZBtYTM64kep6TaH0yJEjWL58uQicMDCi8/L5fELxHx4exrZt21Aul9Ha2orJyUlks1k0NTXJvaF4CgMlUokHBgZgt9sRj8cFVWfySIfK+d/c/NWWAbVvTJ35zsBNFQIBasJaFHRTAy/21gH1VYNvbP4Gnl3yrNyLw77D+Pi2j+PLz35ZNvSLKX0Mprh+OIu8XW/H1u9tRc6Wg/9v/cAMcHjVYaxatQputxvr1q3D6Ogo/H4/ZmdnZS2QkkXk3Ol0oqenB1arFbOzs6hUKnKPWfUlhZsgQ6VSweN/+DimN03LucytnUPuczmsfs9queZcU3R2ausCFUcdDod8ZiwWkzXPIMftdgv4pArgkbKeTqdhNtdGc3DdM4gxGo0YOD+AW07cgviLcZS0Reom1y9pbrFYDHa7Xar6ZC3kcjnkcjm0trZi/fr1OHjwIMLhMHw+H7q7uzE+Po5MZlEUp2ENa9hvz5jwqb2gKvskl8shN5pD/5P9SH44iej6KNom23DDT26AM+tECSVJxIz3G2FcawR6AWiAVtWw8cWNMJQMqFQXBdmA+vnBNPomNQbg/q32cPM/UmIZ0NOvEMRlVZXvpz8mSE8wn8cfj8dlPBITE7U1TVhOJg1uX626zQSH14zVafV9jG94viq4zv1dZXOVy2XMz89jZGRE/maz2UQhPJfLYXZ2FrOzs9B1XcTS6C+YiLNoobbOeTwetLe3y1jMfD6PsbExSVCtVit8Pp/ETDwHxlO8DzxvNe4jSECNH94L3ie2ARJMbm1trWu5430g4EztF6vVCr/fL+fPY+A1NZvNqKIKm7MW07Bvm21kKrOODEYWHOx2u2jQUOhN13W4fuHCTu9OtFnaoJk1uY9NsSbc9KOb8KN3/gjr/2Y9jj55FPnxPCqWCnw/96FtdRsqHRXMDMzgyp9eiaaJJhEEHjwziNx3c3j07Y9C0zTc/N2b0X66Ha4el2gEcM1Xq1URqmO85/F44Ha7Bcgm4EFmBuMAFr84R9vr9cozwOtls9mQy+WEqcbKebValdiPcRN1eag2TtDnYlNbM7jOL65yq4A813/DGvar7H9Uwq2aGuAvLCxgeHgYP/7xj2EwGLBhwwbs2LED27Ztw8DAAPx+P/x+vyRi3CxY/WTlknMimfDRoamVZDpVIpRMZlmZphMhqivCFxcQdLWiy/+YaBJZZRIFQP7t9XqlIqpW/Pi5TKBU2jOTZTohAC9xtKpQmYo8qxuTpmkIBAKYnZ2VqujCwgJmZmZqtKqmJqH9korGYMLv9+P8+fMoFApoamqCrutYs2YNjh8/jvXr10v/8MWBj6ZpQmFikkyKelNTk4hsJZNJUStntYDXg0qZZBLY7Xap5rL3W0Vh6RRCoRCy2awkZ3SCPC4qiZrNZrz34Hsx5Z3C2cBZAEBzrhkf2/sxQIckwLzOvNYq4EMWgMvlQnNzMzqnOrHuA+uQSWVQtVZx5MgRVKtVLFu2TFDg6elp5PN5dHZ21gEoXJc8ts7OTvj9fkkgLxayWbduHU6fPi3vX/WpVQh/IYx8/wVEfBRY9eFV8pmBQECES/gsqYwLVr8BSKtFPB4XZDsQCMhMbgZcFF3hTG6v1ytVFAJVuVyuNif3wlqoVCroqnRhNj8ra4dMk1QqJWuU9Ean0ykBj9pGYnaYEfPE0N3dLQr2pA02VEob1rDfjTGZoi9UtS+y2SxGRkZw8OBB6LqOm75xEx7/08dx8103Q6tqMJlNkvzOz89j+vA0mq9rRmx3DPmmPK69/1osO7xMFLuZMDO5Uitd9M8MxknnJo05Go2K/+axcm/k3s69hp+hVrPZrkSdDbWVjSBpNput63slSKomDZpNw4lrTyC+PI6ro1fXXUt+H1AvGKsmIGpCpAIFjF+q1dqo1EgkItdGrYCXy2Ukk0mcP38e6XQaXq9XwHQAiMVi0tfN76cfpPCZSldOJBKYmJiQ/T8QCIgIJ1Dr1VVbvLg2mHQRXKZvYAJL0NhisSAWi9Xo2hfupaZp6O7uRltbG1wuVx31nveO7LpMJiMxGUfP8T9pGdRLODR0CNPXTaP7gW44daeAxgCEhcXqdiwWw7lz59Da2gq/349EIiGgOkEFp92JUCEEs9Ms38N4EHHg7V94O8Znx2FP2FE01FgZDrMDq59eDafrAqW7XIXVZZVYQtd1VH9chXW/FVevvRrto+2wWCyIRCJ161YFq9mHD0DuC+NgFewxmUxoamqC1+sVUIHtCIy5uUbYDsi53pOTk/B4PFJAMRgMws4ko4F7BAtSjPXUKjZjd/U/FThTKeQXs1Ya1rCL7X9swv2rjLSV/fv3Y//+/fjCF76Anp4ebNiwARs2bMAll1yC9vZ2dHV1wefz1dGpScnlQ0j6keoIWa2+GF3lZkRjgqVS1Xhs6obAZEKlGXEDUunufA8dpIpYA8D58+eRSCSwefPml1BuuMGrYil0mirdjL9TEXe12s3zUAVbwuEwYrEYmpqaEAgEMDk5KUIZZBj4fD6k02mcOnUKq1atQnNzM0KhENxuN06ePInm5mb5Dva4q0GKSjWn4jkFx6iGres64vG49CIz8SeyTQfKe0PklDMeVXVzAjgEXhiAMLGkyA0VzZEC/mL3X+ALl30BWU8WHzj0AbSiFaVqSb6XVQ6uF1IVmUAyMHA6nbXzqehob29HPp9HJpPBgQMHYLfb0d3dLdR0rgH2Z3N9kMnAzwOAlpYWzM7OikPjNSVNMZlMwufzocvbhaXvWoqpz08hUUrA9HYTxmJj6O/vF+q6WjGm0J1aPeD9Y2DD/8xmcy0Anp6WY+ZrqXNAOhjF3lQVdoI4AOSZYw8+Reh4PABE5MVut8u6CYfDSKVStfNw2PHcuudwaPUhXHvkWrRl2mSE39zcXN3e0LCGNey3axeDkqyYzc/PY2xsTOjIHpcHN3//5pp/0hYD6mg0ipGREaTTafT39GPJN5ZgZssMho4PQTMvAsmqL1VBbAB1lWi1xYhJKIFs+k+CsWp7Fyt1KqWcfa1MAFXxMACSAORyuTrKMfd+9bh1Tcexq4/h8OsOAwCCk0HcOn4rzDDXxTMq6K/6cx4Tz41xh1q9J73bYrHA5/MJG437cjqdRiQSkbacYDAoNONMJiPnxz2ee7XT6URTU5P0brPyHY/HBWRwOBxoa2sTQINMO4qEsjDBsVRqqx0TNYLALFRUKjXVbcYA6XQaNptNAGreU7X1qVgsSvsak7+Wlha4XC6Jt6hRYjKZ8NTKp/DklU8CALw2L4Z+PgSUIfGeOoqKiXdXV5ckkxRUI7vBaDTC7XYLuKMCRWRtApD1xFiura0NHnetkGM2mgHjIv1d4tuqjiVPLoE77kbSUwOomcgbDAa0t7djampKmGP02bz/wWCwbuQaCxz0+4zXNE1DKpWSvnmO2C2VSpibmxNGKQDRQiIbjefK+8V1wESd8YHa/seYTY2bVWM8rLZmNKrbDfs/WSPh/j9YtVrF6OgoRkdHcffddwvNqaenBxs3bsSqVauwYcMGBIPBOqRL7QOmYyUlnBU2OmgmcAz6+WCrlWuV9kRkEIBQk/h7lT7Hv3MjVRN/NREuFApob2+XUUj8vZro8/3qe4lIEjxQE2wGOCrNjEGE2WyWCnc8Hsf09DR6enrke4kyhsPhuvFmTKZICe/s7MTBgwfhdDoRCASEtk06EfvhuKFys2cQoSKS3FTT6TSamprqqGdMaPlZTHTp4HVdl00eqI0PofIq7zmwSHkidcrv9yMcDuPcuXPwJXz4I/sfoeAvoDPaiUQxIaJmTAh5fRmokAXBY2OwwNFYFARk8DA8PIxsNosVK1bA6/WK8+K1JQtArVqkUinY7Xa0tbUJ4h8MBnHu3DkYjUaMj4/DYDCgra1NKNfWhBUr/34l0tU0EukEZhZmUC6X0dbWhmAwCJ/PJ8ku6X9qXzvphHNzc7Ku2M9HSiUdvdFolNF/DCSoAcDxfCoN0mw24+jyozjTcwbbfrJNUG6uLVL7+PxxvTIxpzo+AMy/Yx4vXPkCdJOO+15zH66MXom2RJsEpo0+7oY17Hdj9HlqUlGt1saJnjhxAvF4HK2trfD5fHUq3fSb6XQaZ8+exczMDJqamtDf3w9/1Y/2/e0iXAVAkgf6cu4XBEkZB/CzWeGLRqOS1AC1BFlNGHncqi+nv+XexOSPYCMBf/prJoMEAVndVvelarWKY793DMdec0x+972O7yGrZfEnE38ivbfq8atVPl5XNa5gwqGCDkw22YpE3RNqYkQiEYyNjSGVSsFms8Hv94vatsFgQCwWq2vrK5fLCAaDCAQCCIVCwobid1LUFICA+UzSGT/w80lZ9/l8AriTxszrzkIGCwH8PSnS0tLV3i5xh9obbjDURNzIpgMgdHIVPKHv2D20G3dvvFvuyYHtBxAvx7HxWxtRLBZljCnBBbZZqfRyMsbU72TFV2UtqC0AVa2Ko689CuOLRkmop989jaEDQxJzMF5VRd4Yh4ZCIaFucyoJ2Zt8La+F0+mUdstyuYyZmRlZt/TX9MWJRELG3rIKzuvKa80YyWg0SqsCgDpAiuJpTMKluo/FFoyLWyNUII3Pqhpv8/nkGmn4/Yb9n6yRcP+aVqlUMDc3h7m5ORw4cAAPPfQQ3G43vF4vBgcHsX37dmzfvh29vb2CcrOCyCqoqtjJJIEbHympAOpoZTQmX/xMbtCky0nP2YVNCVgUK1GFttTP5GZB2g1/Vr+DVXkijgDqKseqA1JpdUzk6LDU82GF12g0YmZmBidPnsTKlSvR09ODSCSCZDIpG5fNZkM4HEZ3dzcmJydRLpfR2dkpFOqRkREkEglRiSZiqybERHpZ7VTnTHNsWrlcxtTUFLxer1x/FRABao4cWJzzynvM++l2u5FMJuvOlRs0Ay8KgjFQ6urqgq7rCBVCMMwbkMsvzvsmBZJBk3qtdV2XCgr7tRi4qdV0BhWapmFqagrVahWDg4O1hBdV5LN5+Tw6MgYvpMObzWYEg0G43W5MTEwI4FCpVPDMM89g+/btUgUOBALQZ3X4LD6E+kPSd1UoFBCPx7Fp06aaw9Kr0nNNIRRWyjs7OxGPx2EymUSXgJUAViZ0XZfj4/HSeZrNZkG/+WxZrVacW3oOd191N9L2NMbeOobLPn6ZBKFM+tVAkr2FDHCsVitaW1sx/NphTL5tErqp5oRnu2fx4B88iJv//mY0mZvqevQb1rCG/fZNpU2XSiVEIhGcPHkSs7Oz8Pl80p6i+jCCtufPn8fU1BSMRiNaW1vFJ6j+UWWZMflQf2Y1jXsBwXBVJ0X1mdyrgMV+bP6NSaKu18YmErTlvq6eM/1CKpVCOp2WtiybzVZHOQdqQPOSfUtw4lUnUDVcEJaDAS+PvLwuwaAP5TW6mDGnaZrEMvT3wOL0DwpTkWbOqnE+n0cikcDk5CSmp2vaH4ypgEWqPMXPzGZzXatda2srWlpa6kCAaDSK+fl58d2c3ELWFCu+pLkTwCWTgAkzW8LY+82kNZ/Py1gwTdOQTCaRz+fR1dUlM76ZrKl95rlcTvqPPR4PQqGQxAiMV/j/9efX4/7196NivFAtrQLtT7ZL5dftdksRg614BoNBRlEy7mFimUgkUKlUZGSa2oJHOrfZYsY/3/zPmOibgDvjhvVvrUh+PYmFVy1gT9ce7HxwZx14xcQ3k8lgtnUW+dfnoR+rfXdTU5MA6bwOgUBAWhwACOOB7YS83lyf7JGnoLDX65WfuXZ5bdXnj1NdPB4PqtUqUqkUKpWKMAIIms/Pz8s6djgccs8YS6gaBBcXlfjsqu2XKtjVsIb9a9ZIuP+NlslkapvN7CzOnDmDn/70p7DZbFi1ahW2bt2Kq6++GqtWrZJkQqWXq0qfai+L6pSZCDBRBhbFzVSHxuo3NwI6XJU2dLGjVRNqJmdqVZzon9PplGRPFUTjJqRStFSny0CB8xABSM9ztVpFd3c39u3bJ4kse3Kq1arMQKeYCOdEv/jiixgcHMT4+Djm5uawatUq6V0Kh8PYunWrJPHcQEl3ZgLNmYoEQNTRIEQ3Wd0GFmeO8/wYMBCFpZGd0NbWJkg4KwxqlVkVwOFnOZ1OxONxxGIxoUGpFKZisQiv1yvXu1gsCtW5XC5jbGwMXV1d0kdMh38x4GCz2RCPxzE3N4fx8XF0XNaBz1z2Gbz/vvfDWlyce06HbDAYBB1nFd/lcqG/v18qFpVKBVdccUWdKBAAAUCcTif6+/thMpmQSqUwPz+P0dFR9CzvwbEbj8Gb8uLSY5eimC3KuqeyPNXRWeknDVwVIVJFdKh8SxVaVsTZlzfdN4273nwXqsbavZ0amMLeT+zFzm/thKFgkCoJry2wGFwWCgVB5I1GIzae2IjUQgqxrpisgaFHhmApWmCymxAKhf4fd5eGNaxh/1ajT1QTnVOnTmF8fBxutxudnZ1SiVN7OQuFAsLhMM6fP49KpYIVK1Zg6dKl0naksn8A1CUvam+xCqqrgDr3uIvbvBgfMDGkf2ElkXT4dDqNhYUFFAoFoUKrmi7FYhFllJHwJJBO1Giy6ixkmkpn9+k+fPqBT+Oz138WVVTx+VOfx9rCWlFl5/mqvd9qMYGfRYCcky3YUpXNZhGPx8WP83OoFA6gLvElsEr/zXFmKvjPnuqmpqa6vmb250ciEUk8g8FgXRsWE176T/qZVCoFj8cDAHV6JdFoFHa7XdhTpKjz2NkO2NPTg0AgIHEOYwKgFqdRzZxU8/7+fgwMDEhBhHFLpVKBo+TAH3/zj/HFW74I3aJj+1e3o2usC0XU/PvU1JQkjuxZ5hrO5XIiEOtyuWQOOWeaq8klq/6aR8MvXvkLnFlxBtCA5HuTwP8CYAVgAA5sOwBr2YornrkCJr12j/jds/5ZnPrTU4ARGH1gFCv3rJSCBGeuu91uiXlZaGBsMz8/L8AU14ZK0eb5AYuFIT5DrGpXKpW6qjf9drlcFp+dz+eFbZHNZuXcHQ4HlixZguHhYWE9spikFk3U51ul4quTjdQ54w1r2K+yRsL972B0OOl0Gvv27cO+ffvw1a9+Vfq/OXqsublZBEG4KQGLFG7+m5sJH3qVrk3HzQREdTjcyNQebJXidTEthpse6bfqsdAR8T1MPnl8pGWrPdEqIk9ggK9n3xJpUAx47Ha7jLHg5xuNRhEW4WzmSqWC5uZmlEolPPjgg+ju7obT6cTmzZsFraRKPB06r6PH45GNmtRwKok7HA5xAn6/H8lkUoIfIt+xWEx6jehI6biYjJvNZsTj8ToAhFVf9g3xPXT0PKaWlhbpVfN4PBJksWJN8IW9SKxee71erF27to7KpzISYrGYUPiJ+ubzeTxdeBoHthxAPpDHN6/5Jt7xzDvQlmmr678jJUsFcgwGA7xeL5YvX46RkREkk0lxeASB2IvF+26z2bBy5Uqk02mcOXMGR04dwcRtEwjfHK4d73d0DDwxAKvFKhRCg8EgAilut1uCMc7OtNlsMiecQSp7w0lBZ0XHYDCgVCnh4csflmS7ttCBhfYFnF1yFt3z3fJslctlGSfGPvJKpYJisQin01lrM1k7irQvXbcHnNpyCl37uqBHFoPPhjWsYb99Y3DPCuDExATOnDkDu90utF81eaT/XFhYwNmzZ5HJZLBkyRJJUNTeZbXKrbZY0Q+Q1cU9g79nkiA0VA2YWD2BpcNLEeuMwVA2wJKr7aEETNVWqoWlCygcLmBq5RRCh0PC5mGiDdT2/tErRnHgvQfgf9SPga8MwFAx1I04BRY1YtIr03jqfU9hxcQK/PWpv0bekMfyxHIUUJDzVLVL1BYsXoOLW86CwaCAzdVqFdPT0xgZGZEiAPdv+rFEIiHtYy0tLejp6RFWQTwex7lz50Rok34gGAyir69P9FvkfC70gvO4Ozo66kZ2qiOnmLRz8gV9MYF3xiqk6/O8yShk5Zjg8iWXXILW1lYZ68XYgJV1JnKapsHj8cDn89VdP8ZjpJhbz1ix83M7ke5Io+VwC7LlbB2DkCAG1xmryC6XCx6PB5qmYWZmRtYGheV4/DSr1YqxnjFMtE8szuI2ArAvPk+6QcfwqmFsOLMB1nmrXIvTodP4+nVfh26pfd6Dr3oQuVwOQ/uH0BSsxbic6c440efziVK5rteU6qPRKNxut8RWpVIJfr9fGAeRSESeK5WZQKFbVXuFFedIJFLHGCVTkdcslUrV6R7x+rPvnFo1XNtc//xMxtlshVPj64Y17F+zRsL9H2SVSgUjIyMYGRnBT3/6U7S3t6O7uxtdXV3YuHEj1q5di+XLl6Orq0sovarzUh0dE0cqKXPjZmKnCoOpPV/cJLgZqD3a3CRIy6WYF1BDEtXEHoBQdumYGGxQwEpV/VRHlvE1RILL5TKam5thsVik53phYQHd3d0wGAwypi0ajaJarQl3ud1uzM7Owmq1wuPx4LLLLpOqLxNWHocqjMXfEdxg37C6UQYCASwsLMBut4saprp50mERRdW0RQV6brpEy+lUOGaL99RorI0u4WsrldqYE27qvPasZPCYOf+TKty8dyaTqW7MBX9PB8bkU+0nI2tgtm8Wx//0OPJNNYT+TOsZ3Ln1Trxn/3tQjdWQcAZMpKcTXCEA4fF4ZN1OT09LRcNoNGKuPAf3G9zwPrZIzadC+dKlSxG7KYbwe8PynBx4+wHktBzWP7FeRFTUa0fghUEPj42AhqpAHAwGBb3mmiOT4BU/eQXcN7vxwqoXausja8LgdwfRub8TRrNRngcAoorPKhPPvVKpwOPxYOVzK2E32rHntj2omqroP9OPrXduha1kQ7aQlaCvYQ1r2G/fmNAlk0nMzs7i5MmTyOfz6OjokL5SJi70BzPWGRzrPgbTMROy78li4OyATP1Q/bOadKs9uo9f8ThcGRe2vri1zt+p/bJqZfvIlUdw4PoDmNk3g6mlUwgmg7jp3ptkT6NpmobRzlE8+ppH4Rn0YGZwBuZvmdG1t0v8EMUoj15xFAfffBAAEHt5DOet5xH4SgCF7CJDh7YwsIBnb3sW6WAan/V8Fu8feT92zu9EVV/02cBiAnPxuauUaTUWABZ9JgAkk0lJMgns02eVSiXMzMwgHo/DaDQKfZuK4VQTZy88RdBaWlpkYokKbiYSCWn78nq96Ovrk+tPijN9NFuI6EOZaKn3ilVXlbYMQCjcsVgMuVxORpuSMaHGSVRgn5qakpiqubkZbW1tAuZcDGSUSiXMz8+jG91AFEgb0nU91DSC8AQiWJUnMEyA3mg01ol/AottgQCwamIVTI+bcNc1dyHtSMN82Az7PXZk3plBpb2C5vlm3PTATWiNtsJgMUgiXPAWpBVB7rcriWgsinKpLHoypPIzYa1Wq/D5fDAajaKizziUMSOfUYL/jAXL5TKi0Wjd2mOhhnEdgXvGxLlcDqVSCbFYTMSOCYSpompq2wiAOnYj1zgLIQaDQQoUjcp2w35dayTcvyWbnp7G9PQ09u7diwcffBB+vx/Nzc1Yt24d1q1bh6uvvhpLly59iaNjksPfq0mzpmnIZDKSjNC4eagOnwgzq+CqgjM3GiawTOhVRWtVxEJFevlvbkZ0UkSl1cSbm6DL5arrxV1YWEC1WkUsFpMkLxAI1KqTF/qQWbHu7OxELBbD+Pi4UHcpvsFkmVXhXC6HfD4vCa3f70c8HpcggLRpi8UizodU5Xw+L8lxKBSS6+DxeOr68dmPzgrs6Ogoli9fDoPBIBu5KmxH4RUCA+pcb6BGZ2MFnNeLVWtVgI1Vb95H9hyTusdecYIpBB9ac60Yj48j0Z6oIdo60Jfug6vsQrFUrGNI0NGpPWH8HI4/aW5uxunTp2vrxWrG3LfnMNM/Az2vo+f5HmiaJqM/QqEQdpR24DE8trhYK4DhaYPQ7Xj8bNlgmwHvLSs6rAQwUTYYDPB4PLBYLEilUohGo/IVdrsd0VIUE60T8jtTwYTO5zuhGTWpbDPY47+5Jvj8pVIpud/LDyzHNVuuwXf838H1v7wepqwJqUqtP7AxEqxhDfvdGYG2e5fdi8yLGeRyOXR3d0u1jb6TCU5Wy+KXt/0SSWsSjlc7kF6WxlMnnsLrH3i9gKDsdaUgE9usyuUy7rv6Pjw39BzMZTM0aBh8fvAl4k+kjZdKJTyz+Rk8f83zKNvKeOGqGgA4q8+i7Cjjg7s/KH7b6/ViJjiDr639GmK+GGIttRaWY+8+hu2Xbsf22HaMj49j3759OH3NaRy58Qiq5sUEaH7HPA4FDuFdP3lXXavSjHsGv3zTL5EMJQEASXMSX+r9Eiy6BVdErpDkRU001P5dlUKvXkv6eIfDgWKxiGSy9vnUF2EyxAplPp+XUWEtLS1obm6WhHB+fh7Dw8MC/Ko+kOPV1D7efD4vGi+km1MjhKy7XC4Hh8MhyR4TUSZMbBuz2+0SOwCoa/GrVCoSH/Aet7S01LUAcs0AtXhiamoK09PTAsK0tLRItZ1MQFZbjUajtF+xqMBe9lKpJOPSuPYcDoew4hhnMBFnFd9kMglTkPEj4w8mrqvGVuG6r12He954D1r+rAXVU1X0jfZh5B9GcMu9t6Al2gKTzSTguslkwqaJTcj+JIvvvvm7AIDtj23Hhqc3wNZkE3FXMgvYR64CNEajEfPz89Kjr4IJNM5SZ6zLuJMgitVqldZHtoUR9FBbKxn/UbiPsQTvrdVqlXFljJMZWzC+UHWWCEIxVlLZoA1r2L9mjYT7d2DpdBrpdBoTExN48cUXRYBi9erV2LFjB7Zv346hoSFxVGpFlsktEzH2kalou5poP/TQQ1izZg08Ho9UonO5nPTGEqUDFunswOJoJVZr1V41JtSqQAywKE5FI9WYG9HCwgKy2SyamprqKqfcRBOJBCwWi1TAWeE1m82IRCKibt3e3o4TJ07U9Slzk2WSHI/HpfLPCmmlUoHf769zSBej0UQ9z5w5g9WrV8v1VnvKuAmr46zMZjPm5uawZ88ezM3NYefOnVJNYcKr9vLzGuZyOblOZAbE43GhSanIKpF/Coiw6s5gyGAwIKWnsOcv9uCVj78S5aOLPWRutxvVahVdpi74v+HHvX9yL8K9YVw1ehXeduptMBqMMLYYUTKUkC1l4TF66tgVRqMRLpdLKHkWi6VOeGgsNobv3fI9FDoLgAac/MhJWD5pQfMLzUKFczqdyD6exabUJhz62CFUihW4r3GjkCgg35uXYKOlpQVut1vWEJVGWdkn5YvoOB3kxMSErCVSywHAYDLgnj+5B9HAYhJe8BRw6A8PYes/bZVAi1UbfgcDTz5vvBbVahUupwubw5sx8q0RdHR2IGKqUdimpqYaFe6GNex3aCW9hEeWPoKfDf0MWANc+4FrEbKG6nREuF/GEced77kT8WAc0IBEKAEAOLruKLLJLLb+aCtcBhcAIBgMYsmSJSLQWTVX8cOBH+K5gedQNVRRMBZw/8vvx4blG/CG5BuQS+fq9i673Y5nmp7BvoF9KFsvajvRgOElw/jxdT/Gn5/885ofMuTwiY2fQNQerXtp3pvHdy79Dvz3+zFxfAKjo6NYtWcV5gbncG7ZOaEG2/N23Pr0rdKbzCS6u9SN645eh7uvuBtFYxEG3YDt0e3YFt8mfpJVfQB1MQE/hz5JVXZmAmyxWJDJZDAzMyOiYkx82C9NuvjCwgKsVis6OzsRCAREUZ3fT+CZgKymafD5fBJ3MKkbGxvDmTNn5HgoMsrEjAkwhdoIrtLPkobNWIZJHOOqXC5XV3FPpVJCW+7t7a2b8UxQmJ81NjaGRKK2rjwej1Tn6XPoT8mSOzV/ColUQtqpFhYWkEgk4PP5pJ+Z1xKogfS5XE7E1EjbJ+hfLpfR0dEh56gmtmoC6z3jRderumDMGZHW0+gId+DV33k1bGUbDBZDXYsi39t6vBW+m3wYeN8Atjy+BaV8CbplsVjEfm9Vh4Yq9blcDsFgUIogvOf8HsYMoVAIXq+3rl2DtHTeJ6fTiUKhgLGxMYlNWahhaxpjJYq7OhwOJBIJYTnQ9zMZZ4Wcwm3cPxgXcb0wFmwk3A37v1kj4f4dm9rvtXfvXuzduxef/exn0dfXh8suuwxbtmzBwMAAWltbZeMBIMkuUD+2g5sNUWdN0yTBZTVaTc4BiJMgfYobhyr6olJ9mDCqiShHVPHz1Mo8Udfm5mZEo1FBdrPZrKCws7Ozdcgt0WMmo6xG+/1+GRsyMTEhaDGdL0eA8bUqhUzXa2O+XC6XKLmy95w0bgBCI6YQl0oFZBWfmy9Bj1QqhbGxMbz44ouIRCLo7u5GX18f7HY7MpmM3DNeL/U6E93mebCyrKrak8VAWjwDC4qXaZqGvDuPe264BxPNE/j2wLdxw+dvQPBcEDabTY7baDTCUrHgxs/fiOfe8hyufeZalLpKMNlNyFQzuHvZ3Zgzz+FNB98EPamLoJ3a302QgOfQ19eHgx0HEWuLSbCnW3Wce8s5NB1rkiAulUrB6/HCe8YL67esmDwwieTZJHKunCDZdrsd09PTElwYjUaEQiEcP34cPp9PUHo6OVYp1DXNNcPnQtM13PpPt+Int/0E083TgA4MHBvAax96LQqBAjKZjDjii4M0Bjh03FwXiUQCmVQG1+24DmNjYwAgNEPe74Y1rGG/fdu9ZDdu33h77QcH8NTnn8Kluy7FJZVLZP/lc/yLDb9AJpBZ7F+9YLpBR2RjBCs9K3FF7grxRdxf8vk8jjmPYU/bnjpabcVUwe3+29F/rh++ok/2EiY/KyIr8ObSm3HXmruQM+eULwQGJwbxrn3vQrQalZjgfbH34atXfhXzwXl5qT1ix2XfvwxHnjmCRKKWmOViOWz99FZk/jSD2aFZBKIBvOWetyCYDKKCSt2e5vf7cVP4JgRGA7iz905cvXA1Pnzuw7XjR0V8harYrFa0AQg9mEmSymBjhZf0biY7apyQSqUwNTWFQqEAt9tdN4ozkUhgbGxMmFv0hV6vtzY7/QLLjJMj2DpA/+bxeBAIBOQc6A/o33n/VcGtdDotfoUiaWrrmMlkgsfjkVYFavaEQiG0t7fXxVME7wmqh8NhWXeBQAAABFSnn+I1nXBM4I533IG2TBuMo7XYpb29HR0dHRLPcS2y6MLrxqSPgEcsFpNe776+PmEC8h4xfgNqQMTkxCSQrq0Bg8EAg2aArWwT4Ve2s6ntjOH5MLyHvFj3o3WwtdlQMVSEvs11RACEhQ8Wm0KhkMRnqn4PP1+tQicSCbnnRqMRfr9fwA9S0im0G4lE4HK5JDYql8syc50xFnV51HWtgvVkCDImAVBHJ6fx+VCp6A1r2L9mjYT7P6mx//uuu+5CS0sLent7sWzZMgwODmLZsmUYGBhAT0+PJKnqRshEsFqt4uqrr67rg2J1UEUqibaS9kUnxyqiqrR9sWCKKvahUsu40dExUBwjmUxieHgYXq9Xeq6p6k2n63K5hGZUKBTQ2tqKRCKBo0ePwufzwePx4JJLLsGLL74oVV+3241AIID5+XnkcjmcPn0aq1evlvEQTJINBoOgoWp/Dvu5CBIEg0FJpomGEzhQkz6eczgcxpEjR6QHfe/evTCZTOjt7ZWNnorfPE9W7wmWuFwuoarF43EYDLWxJERb1f55BhaJRKKGwpry+McV/4hdwV0AgLKtjF3v2oWdP9qJnuEeeR+djMFgwLV3X4uUrUZf6+ruws/W/Qw/6/tZbU0YdHTEO/C6kddB02pqs6TUU3SIjAaLxYI3h98MFIHvrf8eqoYqjLuMaP+79hrVyqHL9We1vHVPKzqMHZhdO4uRkRGcPn0aHR0daGlpAVBLcElHJxpdLpeRyWTg9XrrVNVtNpuMwKFDpLPn97rSLrz90bfjh1f9EOazZmz/8XZoHk2cN3vHuba53qmZwPvEapXFYsHs7Cw8Ho88N5qmIZ1ON0TTGtaw36F9e++3gY2LP6eLaXz36e/iqvmrhBJLdtTqM6txdtVZvPj6F+uSbk/Og7ftext6ZnowYZyoS3JIk3Xrbrw99Xbcvu12hD01XYolk0vwtmfehmwyi1QlJVVMu90u/u7yw5cjNh/DA9sewJon1mBy5SSCsSBufvZmhE1hYTMBgGHKgFdFX4V7Xn0P+ib7cGzZMVz50yvhfM6J2flZYcfNzMwgn8/j8q9ejvCtYSwdX4qWqRaUUduLstksYrEYbDYb3G43/H4/3jr3VjjhxGvDr5V+Vfoo+kWC7qrvV5Nw/p4xBFBjurG/mfswq8WsXs7Pz0uVsqurS2jeul5TBmfCRp8L1GZq9/T0wOv1CmOAyTtHYvH81EolAOkjJyWYPk1Eyi60ChGwV30HR1nRT1PFnLohVLHn9eF56rqOeDyOhYUFpFIp6LouIm5kK6rFi0nPJL67+buY9E1i5kMzqPxjBZ1PdNbRmjkxA1hkHrhcLqlus7rONggAcj1UQTBqE/C/E8tPYO6hOQHSTSZT3Zx6Fnd4XdX2At4HMtAASOxIgbqLe+6z2SzGx8dhsVgQjUYFAGNroGrRaBTRaBTZbBaBQEAmoJTLZWSzWZn5bbPZRBSRzMKFhQWYTCbMz88L84z3jr5dbX8EUDevndVstcDEWIzrGsBLjrlhDftV1ki4/5NbtVrFzMwMZmZm8Nxzz+HnP/85mpqa0NTUhE2bNmFoaAibNm3CsmXLZFMmmq1S6ACI4Berefwbnbbas83NmRVz9gcRyWVCTufBz+QGy+SHvVcGg0FUvleuXImFhQWhI6t07nQ6LfRnUr+Iivb392N8fByrV6+GxWJBZ2en9AZxrmcwGAQAGQGzbt06aFpN9TWRSODAgQN4+ctfjkAggFKpJH1mTNKIOKu98qxYElEn+l0qleB2uxGJRDA2NoZwOAyv1wufz4dUKoXh4WEEAgF4vV4BIuiM6XiYvNKZqrS2QqGAZDIpDpbBTz6fh8fjESdut9vhsDnQnmoHgotrx1qwwp/2C2Agv7+A6JNyZbVa8Y2V38BjvYu91U/1PgVT1YS8PY83H3uz9MHz/hNNV4VmXjH+CriNbvwi8AsMPjCIwyOHMW+dRyAQkGAnHA5LYAQA/f39MJvNOHbsGM6fPw+j0Yjm5mYBhCiCps55TaVSohpOtX6CCQxqSdFjAGI0GuE478C1P74WmdEMjGWjOFwGR+zzUulqPFd1LjsTfZ/Ph2w2Kz3z5XIZExOLfeINa1jDfgf2EQAFAH8GoArgDcDEIxO4E3eKxgeTGLPZDIPFAOwFsB3AtwB8Ayj+ryJ2z+3GbuyWqqfT6ZTkTK3wrn58NfZ8Yg8C6QBu+MUN0KIaJquT0jfMxIcAXqFQgMVkwY79O7ByYSWMU0a0mlrhMrtEw4O+NpvNIjAcwKt+/Cq0Zlux6sVVaBtvQ6atBkjOzs6Kf2ptbUVvqBcb9m6oXQcD6qjhNpsNu9+wG0MjQ3LsN87eCGhAWS8LkEs/o/ZMM5lW93u11UZNzhcWFhAO1wAIFahmxTqRSGBkZET8dTAYFECCImMqVV3V7yCdXGUGcuQW/cqSJUuEJcbXEdRXEyeg5tMzmYzEOATDSY3P5/MC/lLktVKpiPZLT0+PsOTUajPjiGg0Kr7SbrfD7/fL39RWsJglhn/c9o8YC9TYUhVbBUffcRRGoxF9z/YJpZ7JHynznMPNaSQsHBiNRoTDYZjNZpnawe9lTziT5P0t+/Gzy36GUqiElltaUC7Wps7sfeNerBleAxMW2ZG8foxl2JvONkm2ggG1RD+dTtcpexMoCYVCSCaTyGazIoIGoC7pVcUNzWazgAbqRBsyF6j3w3VOAMnlcsFqtQqzQgUbWPRgbKNqLjAhZ2GFzzvXPY2frRawGtawf80aCfd/ISOdKZlMYmRkBAcPHhQq9KpVq7Bz507s3LkTK1asqENRVaVV9gKxH4ufq/ZCc5MhFUfttQZQ15/En+kIWCmkyIRKMyPqqGkaenp6EIlEMDc3B4vFIvOl6Uy4uZdKJbS3twuKzTmUfX19CAQCSCaTkrhzDNbCwoIIXDidTunXtlgs0pdNxJsOPZ1Ow2g0yrmptCmitclkEl1dXUgkErDZbFINKBaLmJqaEqdNB82kkD1L6XQahUIBgUAAqVRKvpsjLngdKNwifdmplKiAk6rPQIKAgDFvxG3F22CwG3B76HZYY1Zc+ueXwuqyIqknpX+fIm08LqPRiLm5OWx6YBOeX/I8krak3NeyoYx7B+6FTbPhplM3SXWXx0BnJSi3ZsBVU1dhcGwQ8d44bJttePLJJ3H+/Hn09/cjFApJ9cDhcEhP3NKlS+H3+3Ho0CGMjIxgcnISg4ODSJaS2PeX+9C+q12CRlK96GxZdaYTpHMkUs8ee13XkUql4FhwwKJb4PA5MDc3Jz37BoNBQIF0Oi3UfqL2ansG71thSQHfDH0Tr73ntdB1HW1tbXUaBg1rWMN+B5YB8FEADgA/BbB78U9Mvl5iXwXwbQBpAENAPpLHERyRP18Mwqr+T9M0lG8vY0FfwO252+X3VNVmUsCkUsZMTek4pB2C9uLi6EyKernd7roEvzfZC5vNhkA8gJnCDMLhMBKJhPheq9WK1tZWaZfisTIJNDqMeOzmx3B07VF8buBz+PuTfw9jZbGtST0/7p9MpNURperf1ISb72OPNPdU6tCoVOhsNit7LJXg6Qvo25hoMdFnX7LX65X9ntRlzk1nzy+BUh67GpMAqFMrZ3vbxYUGFcgls8vtdiMej2N6ehrxeBxutxvt7e11hQ6CFuz9DYfDwg5zuVwSq6hitbquw1f24doT1+K7W76LiqkC6EBwOIiegz3SBx2JRGA0GpFIJLCwsCCiYZxYEo/HAUCuqZqUulwuSZLZQgcAR31H8a0rv4W8OQ9cCsz88wxa3tGC6T+eRuqaFL686cv48D0fhqFokJhDpV5nPBlkf5qF9wdeOR7GkyyW8P4wwTUajWhvb4fP50NLSwtsNhsOHTokYAXjPhZ7PB4PQqEQ0um0iNxx1BoA6Z+n6G6xWMTk5CQ8Hg8cDgdcLhcCgQCy2Symp6eRTCYxOTkpIopcv5wdD0Cuq0oZZ5FFZXHymBvWsF/HGgn3f2FjBS+TyeCpp57CU089hU996lNYsmQJtmzZgm3btmHTpk3weDzweDwIBoPw+XzyXpVmDiz2kwOoqwQQ1aXghcFgkCQEgCD+3Cy5GVHVmQEGN8bm5mahbjGRGR0dRSAQQFdXlyR0p06dEhG0jo4ORKNRtLa2Ch28UqnA7XaLYBnRaTrdRCIBl8sFs9ksAlvNzc1S9QRqzom0I2AxQCGCzH4oVjnUyj8dbWtrK/r7+xGPx6XS2dzcjFWrVtUJcTCJphNR1cX5GiakDJx4j+LxOHw+nwQDrOCS2u10OqHpGq598lrs2bwHO761A3lDHvF4HC6XS4TyWAXmOkin0zVqe9KMr+/5Oj627WMYtY8urjFjGbu6d2Freiv65/ulMs2kk+qgmUxGesX9Bj+cHU686lWvwqpVq3D//ffj3LlzNRp8wINYWwzu+UVqHVWEzWYzDh8+jJGRERwPH0f6z9NIXJLAV3q/ghv+/gYE54ICErDaXi6XBS0nsMNZ6FyfvKdUz6UIj6pGr+uLavmslEQiEQFcCDwREEn0JfClDV9C2VRGbksOm3+2Gas8q6QVomENa9jv0PIA3vMbvL504T8A+BWah0ww/lULA1VUkUG9fgPFsn4TI+hM6rfL5YLT6RRhUIKcTF5ZNcxkMpifn5dqM5PosrOM5655DkcuOQJowGH/YXxyxSfxoXMfgie3KBbGFjAmHfwcAALI0ydx71X7genzs9msiKUSlPZ6vXJ8ExMTwtKiHgvFr6iBwWouW81aW1vR0dEhiTLbsaLRKCKRiPhJgqbq/apWq2htbRURspmZGYkrmIBms1lh+zFRL5VKQotmnBKPxzE7OyuK4a2trS9hkJFeHo/HpU+9UqmNJVXHvjFWMplMMMKIDSc24Nz8OTz+8sfhP+HHmo+uQc6aQ9W6KEBGZprJZILf75cqsaq/o1b1DQYDQqEQQqGQJPmMfTKVDL6/8fu1ZBsANCA/mMfYoTH5eappCl95xVfwriffhWAuKNfTaDRiJjCDF25/AWlLGo+bHscND9wAW9kmFWP6WbYWsH3QZDKhtbVVfo7FYtIy5nQ6ZUZ2tVpFJpOB2+2WiToEzjmrmxR/stg43pWxKkXReP+ohs8CRCqVgsfjgcvlEgCea79YLMLr9UocrGoNcHJKOp3GzMxMo8LdsF/LGgn3fzMrl8s4e/Yszp49ix/84Afo6OjAihUrsHLlSgwODmLp0qXo6elBS0tLXVVQpZ2TssMNXK2oss+YwjHqbEc6LyY4TEyJClIp1Gw2S8WaFLtAIFDXw0vaE2lnQ0NDolQ+MjJSN1eUs5rpCAgWjIyMYGJiAn19fUJt4lgItd+ciSiZAEy2WXVnZdhgMCASiQi9nqZpGpYvX45oNIqJiQlUq1VRI2UyDUAQfKqKUjCO15+gBsdbEWFnf3AsFpPeJQrCkb7GCv1J/0lE3VGc2noKg6lBZDIZQYYpJMLrr872TKfTSJ1N4X8H/zfuCd6DJ5ufRNgWRnOuGe86+C50znfC7rdLSwIAuf7q6DGVAmaxWLB8+XLouo7HH38c58+fR/lVZZy55gyu+dE16DjXIeJ1lUpt/NratWuhBTWceu8plHfWaPd5Tx6Pv/txXH775QicDUggmMlkpFrB4JLicwCEKcBgaNIxibg1jvaZdgl0KGyjVnfU6gipkKQv2mw2hNeGsfuW3Siba8d3ZMsRRMNRGB4zNBxvwxrWsP8nY7LH+c2/rlksFqnMqSykcncZ568/v9ijrgFPTz4N88/MWJlZKWPOWFHnSCfSeB0OhwhHAS+llatCdOxXZmWVgqLZbBalUglzc3OYmZmBrutobW1FS0uLiGoWCgXRYGEhwGazobm5GU1NTfD5fLLPc+zn9PR03bGwagksAq2slpLSTH0WMh3Y98330C+y6s6xVul0Wij/ZrMZoVCoblYzrwF9CfuLCTpQO4D3RrVKpYJcNgfDNwzoHunGwNMDKFfK0lfOPnG2KJRKJcTjcVSrVWk7U2nxbLHjdb5YYVvXddirdnzgyQ/gm1u+iRNtJ4Aq4Lndg1xPDqWdJVkrCUcCc545BLIBqVqf8Z/B1zZ9DWlrDWA+tfYULLoFL7v7ZTDnzeKLycxrb2+XWI8FIMZqRqMRyWRS1o3L5UKlUps0w/GxXCMulwttbW3CFlTnqwvApIxVpWAeALnuVEcn06JSqUiSzs9gUUItRpHlSJ0BxmyMaxvWsP+bNRLu/+Y2NTWFqakpPPbYY/B6vWhra0NnZyfWrFmD9evXY3BwEKtXr5ZEgaqdNDoRJtCkFJEizvcwgSXqrf6OyWkwGESlUkE6nYbVapXxVpqmIRgMoqOjA93d3fI+JqW5XA6BQACZTAYnTpzA+vXr62aCM2HkxlytVmV0GOldFEUhCkrxETofji9Rhb1Ii2dVlO9PJpMiUqIioRs2bJBRYRTvUAXGKMISi8WENq/2i6kVbybfTAadTifi8bggvUxuAUhCf9RyFN9d+V0suBbw7HXPomQu4YrdNYVdAgVMjtVEHqjR7FKpFFYkVuB9qffh8ujl+NTKT+Gjpz8Kz1kPFooLcsxqr5Sq3Mm/EUCgwFpnZyd27NiBnyz/CU7ddAq6Vcdjb3gML/v+y9A82iw0OwBoaWlB2V5GyVvCaZyWdWgum+Es1xJ8l8sFTdMwNjYmYAPpiQRTGISxVz5pS+Ke192DsqWM626/Dq4Jl6xRVo/YSsFEm8wIjm8T4Zm4AYZy/czQpkoTIuHGOLCGNaxhvxujr6I6uNhZAJMA7gCwBcAIgNuA3Yd243HtcUnS1P+YrKs/q5R6JuFOpxMOh0P8YSQSEQCdvbQUtyL9O5vNiv8nUE5Qu6urC4FAAL29vVi6dCkikQiOHz8uY544isvj8cg4KcYnLS0t8Hq9UhCwWq1SVaYvZbLK6mksVpttzsSfPdBk7BF44Oxwtou5XC6pbhMcp5EtEIlEBEy22WzCVuBrCEyzoJFKpZDP57Hs0WW16xnU6qbCEARmRZdAQrFYFNEwFglisZjEWRznSf/GvuNqtYpgNohbnr4F39n2HaT+KQXP9z1I+VJIfi6J3I4cXDkXbnviNvSH+6EZNTluZ9kJa+Wi9qkwYNRr40N5DNVqFStXrkRTU5OABbFYDAsLCzIuzOVyIZlMYmZmBj6fr07hvqurSyaFsCJN0IBFErYQsP2CivMc+aYCEbxXFDdtbm6WQgqLFwBkfbAyz2Sdwrt8Fqg50LCG/TrWSLj/B1kikUAikcCpU6fw1FNPSdVz7dq1MoLskksukd5mYFF0hRQhJo6kXbOHmJuV2tPF5E4VlmhtbRUkkUl7W1uboIejo6NSQWZSTHR2yZIl4pQ0TUMikZC+K1WFk/TfbDaL7u5uvPjii2hra4PJZJJKMBMqHoPL5aobFVIsFuX4KdLC983OzgoFnSNEisUigsEgrrrqKsTjcQSDwbrxXkajUYTa+FkAJMlnQMMknMfFnjUqq/O6ktpPBfTHjjyGb//BtxG21cRqqqYqDl51ENa8FUNPDqFSqeDs2bNYunSpfD/BBvZmk9LX2tqKofQQ/vHgP6K13IpcW04S03K1jEeXPIpquIqrs1fDAotQtoDF+aBMZlk9GL1hFCPrR6Cba6+LNcfwwG0P4B3ffAccMYcAOel0Gm64cdWuq2DwGHByy0mYp83oeV8PXK0ulFCSVoSmpia59vwdryt77tLpNPJaHne99y7Em+IAgLv/6G5c/5HrYdSNAgDxPpKNEY/HZb2QdVAqlWoBX9wM8y1mILT4bDmPOrEwvdBQKG9Ywxr2n8/OAHgNav3s16GWgGNRyO03rdKxEkhgWq10s4+ZezFjAjLWCG7OzMzA5XJJPy8Tw+npaXR0dCAUCgngXC6XMT4+DqCmVp5KpTA7OyuUfbfbja6urjolbgB101fIZuJxsFppMpkwOzsLn88n40oJ0PP4Y7GY9A6XSiU0Nzejs7MTNputTkgWgIjHTk1NCeW7tbUVwWBQjoXXjNeQwH25XK4DTNReaF4HthpEIhGk02nxgxR/I5WaDC/SqxmLEUDh5BRv0ot1n1qHk3tPooIKzNNmXPqlS3Gi5wTes+c9aM+0w2BavNe6rqMn14M/evCP8InrP4GUN4VlTy7Dlge2wOGuJcfJZBK5XA4Oh0OAedLiqVBfqVSkH54gjdVqlRa/eDyOYrEo518ulzE/XxuP53K55DoSgMlkMnUiv2q8R2BF0zQsLCxIxVr9bLZmkMnm9XqlrYHgBlvR2Aff8PcN+02skXD/DzXpRU0kMDU1hYceegiapmFgYABbt27Ftm3bMDQ0JHOvAYg4CwDpa+GoC3XWNQCZYc3eLVaaZ2Zm0NnZCafTiWXLlomSJgDp3SoUCvD5fAgGgzJWpVgswu12Y/ny5TJ/+/Dhw+jr64PX661TdtV1HV1dXTh79qxUnDOZDFpaWuB0OqX/mk6V/WPlcrmuDxuA9KobDAZROvX7/XUVf6K+7NMiUp5IJASUMJvNCAaDmJubq0Oc2b+m9psRLWcVu1AoYHZ2Fn19fSgUanOjM5kMQqGQXOsOSwc+duZj+MTyTyBmjUGraOjd24vBJwZhtVlFTXVmZgahUKhO5IVOlIqvLS0tMJlMaCu3wWa3Sa8VjMCull34u76/A/qAL1a/iK889xWsqKyoGylDx0xBGq/Xi3fm34nYXAz3t9+PqqEKU9SE1f+wGqacCSW9JNeMyu92ox1v3/N2/MD+Azj+zIEXnnoBUy1TGBoaEnoaK+6sSqj95KSbm0wm7L5lN+LBuKz9jCeDPe/dg5d/9uV1ird2u10UhUlrCwaDEiwajUY0NTXh1KtP1WaOK3bg1gPYvH9zg1LesIY17D+nzQNYC+DfQeNJ7RX+t1ixWMTY2BjGxsbkd2Rbqaw4/p7ilRaLRSrFkUhEWqTYw84ecCZS7Hu32Wwyh5oJIJNvqlmzKk+aMkFc+habzSaznZubmxEIBOoKBwAkCYzH44jH4wK2u91ueDweoauz51xiFrOOKeuUVNTpu0iBZmWbrXGTk5MSezCp5nVg8YOtAtSx4bESHCFTLpPJoDRdQiVXu5dWqxVdji7cePeNMJvM0E26ABO8F0ajEYWxAlp3tqL7G914w543AD5IUYYUbQIpapGFa4fxI2OrZDIpmkFkrJlMJoyPj4sqfFtbm8RuVHwHIPEH1yPvCeMa3jtee95Tqp4TpOH7c7kcEomEtEZQgJexEYtAe/bs+Tet/4b9z7RGwt0wMV3XcebMGZw5cwZ33HEH2tvbsXr1aqxfvx6rVq3CkiVL0NXVJZue2qvNfituqKRPMwEmuszk2OFwoLm5GcDiuDKTyYRYLIZwOIxQKCToLlDbAEllM5vNOHjwIK644grMzMxI1Z3iGqlUCslkEtFoFJqmiUhbsViUmcukqZOOzGpyIpGoE4Ohk8jn8zh9+jT6+vqg6zrS6TT8fj+y2Sx8Pl/dtVDnQBKgIJVKFThhRZ30JjqRY8eOwel0orm5WXrqZ2dnsWTJEnEYFFizWCxwOBxYsWIFsuNZvH7i9fjpVT/FpXOXov9f+jEbnkVHRwfMZjMCgYDQqUjlczgcMsfcYDCgs7MT+XweU1NTaGtrk7VhMpnwQMsD+Ouev5ZewJKxhI9u+ig+fvrjWFVeVSegQzCDx5pJZ/Duw++GVtbwROgJbP6XzSg+U8SIbwR+vx+dnZ3IZrNCH+O/X/3TV2OkZQTzXfOYmZnB4cOHYTab0dTUVMe4YI+b2j9H8OMVP3gFDCUDTmw6AQBYenwptnxpiwQbZGbwGQAgbAyj0YiFhQVZB7lcDsvuXgaTxYSjrz8K3aSja6YLwT8PImhSZrI1rGENa9h/NvtPLKisqj1f/G8WCABgYWHhJe+dnZ3FiRMn6n6nVrappcJCAYFaMulIh2efOF8DLLK22NPNcWBksP2q400mk8hkMjAYDDKH2uv1yvcy6eT77++/Hw9seABX5K5Ay2wLwuFwnUBqOp2WCjGBAgITaqGDx0hf6PV6EQgE6nR62BLGuEwdXUdRVYfDAQ2aAABqXMfPyeVyMOVMWP+l9ZjzzYkGQKVSgclkEoZZNBqFy+USID4YDEq7mdFoxLPPPguz2SwiaNVqVdoJrVYrgsEg/H6/VMK9Xq/cL2BxMgkr1Zx2Q+YgALnH/Bsp9QRIyMjgeapTY1RNF/aNk2HYUChv2G9ijYS7Yf+qTU9PY3p6Grt27YLH45Fk+5JLLsGGDRuwZs0aLF++XBIfOhMilOosQzoZq9WKZDIpY1JYCaZQmMFgkPnSJpNJhDROnTqFSy65BM3NzViyZAn2798vFexsNove3t66CmtraytKpRISiQRWrFiBWCyGo0ePorW1VcTT2JNNx6PrOoLBoDhKAKJ6Xi6X0dvbC7fbXadQzv5xAHXvoTPkRp7JZAQxVWdNMpFjYKBpmhw7nYjf75exFgBkRjSdC99bLBaxLb0Nfaf7sCa6BpntGTz99NOi1m6329HT04OZmRmMj4+LIwYgjp3o+MGDB+FyubBx40ahVtmMtpeskQXrAj438Dl8sPpBrI2trav8J5NJeDweQcUB4N1n340tiS0IGUM4tv4Y7r//frS3t8Pj8QjqzYoDRebWrl0Ll8uFPXv2YHZ2Fnv37sXq1avR29srx0x6GFALOmw2m1zrUqmEHf+yA6aiCWlLGlfefSUcdgcSxUQdtZ7Xtlqt4vLLL0cikahDzqPRqKDblzx4Ca65/Br82PFjvO2Zt+HRsUdRCBb+ox7FhjWsYQ1r2G9g6lQJzir/dYwJGIFxJlaqONuePXtw/PhxAItjVRnT2Gw2TE1NYX5+Hvl8XsTjLta8AWoxwM/X/hx3r7kbVUMVz//+89jy1S2wxC3CQCNlWqWf53K5uoouxWBZuGCPt9raxsSR1HVWw6eWT2Hy5CQwUTv/zPoMUhtT0HKa6OSo02yYzLNKnE6nRY+HCTm1a1hoiEQict4ulwvd3d0iCEcQgxVnfr6MTLvQ1814jerynEhCH55MJqXSTy0fMgzZa53L5STZZqWbsQnBERVQYJGHrY1sd+RknYY17DexRsLdsP+r6bou/d/Hjh3Dk08+CbfbDZfLhUsuuUT6v5cvXy6oInt01BFNRFw5g9pgMKC9vV02W6KGHPXBzVPTNHi9Xuzbtw87duyQxDEcDqOrqwvt7e2LtGcsCpJkMhlMTU1hYGAAJpMJK1aswLFjx9DZ2QmTyYRoNFrXy2Q2m0UMZmZmRmjmpH5TfIVoqa7r0kNNUIG0aLvdLgrtTNrz+Tx8Pp8ocLL3i8kuf+aMavYvkdJPh8G+bwYU5XJZZloGAgEsSS1B1ViFq8WF7du3495778XZs2exYsUKEaijmAuPjaqhw8PDWLNmjYzzWrFiBbxeL3RdxzVz16CcK+OTA5+Erl1AdnWgN9mLvkyf9KHzXjudTrmuvA5WqxWXxS9DNBjFunXr0NHRgXvuuQcnT54UER2T2QRzeZEex1nsW7duxZ49exAOh3HkyBGYzWb0Lu3Fve+6F9d/83qYUBs3wuCK4nAAYMwasf3+7ShUC7CkLKigIr1eBHXC4bBUIY4ePYqWlhap0pOFwLVqNptx3eh1aA+3I5QP4QnzE9In1rCGNaxhDfuvaUzG1FFfF9uxY8de8jsVhFcr2AaDAU8++ST2798viSTN8HED5lfNo2qoJXkLvQvY/We7seLNK2DOm4X6TfaY1WoVCrmqws3qstFoRDweRyqVkr5oVnlZeeZ7i8UipkJTuPPKO5HenEbP23uQz+YR/mIY97nuQ8+9PViaWSrHy5YtFlMWYgtYuGMBvb/ohcvmkmQagCikMwbimC8AkpgzJshms6KhQj/LpDmXy2F4eBj5fF6YcBRZm5+fRzKZhM1mE/o5BVgJLAAQhkA+nxeleNL06d/dbjdsNpvEkCaTCX19fXA4HDIRZWFhQVrVSINvWMN+E2sk3A37ja1YLCISiSASiWB8fBz3338/TCYTenp6sGXLFlx99dVYvnw5AoEAAoGA9E4R/Z2fn0cwGBRk0+PxiNIjRy7E43ERTfH5fJiamsLc3JzQy7q6ujA/P4+zZ89iy5YtUllnP3Y2m0UoFMLy5cslUfL5fDJ+guPM6AS4CauVToqjqKM9iLBenKBRMZPOjNVo0svY0zQ/P79YMb5AuSdS6/f7pRpMVFpVIXW73fB6vTJWhWg31UjL5bJUdika0tnZibe+9a34+c9/jrNnz6Knpwderxc9PT04f/48RkZGsGbNGqGWEyVuaWnBVVddJX30JpMJ5WIZNyRvgDap4buh72LGMoNNiU349MinUcgVYHAsCsewOgxAUGPStThLlAyAnTt34tlnn8XU1BQ6OjuQ35jHszufxU333QRLziIOvrW1Fddccw327t2LiYkJ7Dq4C9U/q6K0uoRf/Pkv8OpvvBrxeBxerxfhcFjuMcVkzGkz9KyOkqEkiTwdLcVVeG8TiYRQ1KiUT0YGf1fKluBL+pAr5+oobg1rWMMa1rD/Wfav9bSzbYxMuDr7IwBtAF4FwAAgDGRfl8Wh5w/9H79LZcQxiVbnT7PX22KxYGFhoa5qTCp9uiuNj1/7cVSMFcABnLv3XO3DzUAKKXzu5s/hoz/9KHqyPXVaL7quI2vL4vlPPo94cxw/bPshbvzOjei0dkp8Fo/Hkc1m0dfXh/b29rpqPEd9UbyMxQkqkJNtyELAkiVLkM1mkcvlMDMzA7fbjbm5OYTDYWmpczgc8Hg8UtnnyFnSz30+nwiskpVIIV+bzSaJtxrjLSwsCLPAbrdjyZIlooRuMBjw+OOP/+p72rCG/SvWSLgb9v9k3ICLxSKGh4cxPDyMH/zgB2hpacGaNWuwceNGbNiwAV1dXejs7JQKLulJTHqoksqZiM888wy2bt0q1On29nY89NBDyGazokba3d2NEydOIBaLSWJosVikb7tUKmHXrl246aabYLfbZWai0WiUHnHOemTFmO8Haigtq+BqpZbnzUo0q/qkMLtcLlEaZVVX7QsnpYl0Jva5J5NJobHxdwQLCCjoui4ostrXRYG2EfcIgpUgmvPN8lnBYBCDg4M4fvy4JPw+nw9dXV3wer2Yn5+viYdoObS+slVmUA4PD8Pv90vfUrVahc1qwyvnX4lXzr8SX+74Mn7/3O/DYrKgaq5ibGwMVqsVbW1tMsasXC4jFosJrS6fz0tPGK/V4OAgTCYTnnvuOZxbdQ6H3nMIMAIPVx/G9buuhzlprmMI7Ny5E/sn9uPx1z4O/aoaQDLXNYcH3/wgdty1A560R9RYSScj4m00GgW1pvI6BfnC4bAALVarVZw3EW0yIEgTJJ1tfHwcDoejgXg3rGENa1jDfn2rAHgtgB8D2AzgPQD2Avr/pdme9GvVLmZY5XI53HnnnXW/o4ia0+nE5F9MomJQAIL60eCoGCr4h/w/4K373iq+z+12w9hhxH1b78OxllqVP9wbxqO3PIobH7oRvoRPesDj8TiGh4dlfFylUkFbW5vMPG9tbYXb7capU6fk80njpv+uVquiWq6K6bW0tKC9vV1iLsYnZAZSS4dAP8XlOFpsYaE2UcTpdEpcR1V6dfwtE2rGMgBEgK3h7xv2m1oj4W7Yv7vpuo7Z2VnMzs5i165d8Pl8WLJkCbq7u7Fy5UrkcjlcccUVQpVqbm5GoVBAIpEQoatsNovh4WE0NzeLGJrT6UQmk4Hb7cbk5CT8fj+ampowOTkJr9cLp9MJo9EoVeHm5mYRL2Hlu6OjA9FoFGNjY4K8svput9uRSCSkt5kJNalh7C8muqxSklmRZzKtqnCSNsWkk4kwABHtIrrK97GHjIk6HQodEYA6SrqmaThpPIk7V9+JpmITPjH8CVg1q/x9xYoVMBqN2LdvH0ZGRtDd3Y2WlhbY7XZYLBaEF8I48ScnMLdmDsGRIDbGNwrtzO12C7ASj8cFXPhfZ/+X0NX8fj/OnDmD6elpBINBUWZlnxbPmUkvR4ZNTU2hWCxi6dKlGNs+ht2bdgO1S4MX1ryAiqWCG39+I/RK7R6lUqlaP/2yXoyuGsUoFvuoSsYSSijJvVLBCjpRgiMcWUJnS0VV6g5wligTdavVWjfnFFhURo3FYjCbzY15nA1rWMMa1rDfzHQA7wRwOYAH/2O/ij3M4XAYeC+ABIAPXfjjp1ETRP2rCz9/Dkj/VRrfrHxTROccDgf0Th2zoVnghsXPPT9zHvc9ch+8817xqWyt8/l8Qnnn79xut+ilsPWQRQbOUGerHavKTLbpk1UwncKp7F8nlT6Xy0mfeCwWg9FoFBFdFlsYTzmdTqTTaSm8sHWMCvdkzOm6jhMnTjQmkjTsN7ZGwt2w/3CLx+N48cUX8eKLL+Lhhx+GzWbDfffdh1AohKVLl+Laa6/FsmXLpKdH13UR0SoUCjJjmwqjrERaLBY0NzdjdHQUBw8exMqVK+uUI00mE+bn5zExMQGLxYJQKCQV8nPnziEajQrqSXE30ryZvDPJpKKlOt+TxwksjjTxeDyickkK18TEBDwej6huMsFOpVLweDzSm6WqvdMBse+KySKdAQEIUte1Zg1fXftVzPnmMIxh/IX1L/C1E18DAOlp7u/vRzKZxMTEhIwV8/l88Hq9eORNj+DUxlOAAfjMwGfw2bOfRSAQwOzsLJYuXQqLxYJMJiMJuMPhEHE8oJb8u1wuPPPMMzAajVi1ahW+tOpLuPXwrXK+AETJ3mw2S5L6k5/8BNdffz3W+9fjXv1e5PV8zfHrQM/JHpRyJdgcNiQSCWEFNBea8fKfvhz3vOkezPbPAscBvBPINGUQ6ArItedxmUwmSYiJdpO5wDVH5XYq6nNciNqTR1EafgYpej09PXjsscf+4x+mhjWsYQ1r2H8vS+E/PNl+iZUB/O8L/84A+LsL/64AsAH4ZO01Omqj0VgUwQyAd6NWlb8MwFEAtwFnx87WfTxbxjhGjFNnVL0bTdNkfBqTb4rF0bdy/BrHdrJSTqaZ1+uF3++X8bHpdBput1vG1RIs4CQWNf4AIDEVdXQA1CX7+XxeKuIUuP1NRPga1jBaI+Fu2G/V1I17dHQUBw4cwM9+9jN0dXVhYGAAq1atwtDQkIiDFAoFxGIxGRnBDdXn8yEejwuV/MSJE3C73ejt7YXH46kTQDMYDIhEImhqakIwGBQRtgMHDmDnzp2Ym5sTYQw1OWTyGwwGJdGj+Acp8KlUCjabTQS2stmsJNtU1by4ksoeJaPRKMk1+8krlYrQ0VnhVpFdAHX0p3w+j1w+h7/Y9heY883JdX7R8yI+2v1RfOjoh6TKa7VasXr1aphMJpw6dUqqvbu37cbZjWdrPWQA5qxz+PP+P8fXpr+G4kJRxnwBEKfFviYqvJfLZQQCAbhcLvzLff+C+Lo4TnacxIHAAXz5uS9Dj9co8y6XS5RjOQLk2LFjOHnyJG655Rb8Tepv8KFXfghJYxLBTwdRPFSE9dKaojwBFzrF5lIz3vHP78Dtb7odxtcZsTCxgOPNx2G322VeJu+V2+0WNdLm5mZMT09LP3axWEQ2m0V7eztyuZyg2pqmyb3g91LhnjNO6biprtqwhjWsYQ1r2H8JywD4ywv/Ll/4/6cu+vlX2RSAlwF4BMC1AH4Fu5otcaRi/yaWSCSkOECjUC0AAcGZmBsMBtG4IUuSlHGXy4WWlhY4HA4RWaMortlslgp7KpUS6jor6A6HA52dnQgEAvL7mZkZnDx58jc+p4Y1rJFwN+x3ZqwaVioVnD17FmfPnsWDDz6IQCCA/v5+DAwMoLe3FzMzM3jd616HQCAgNGoqc2cyGTQ1NaGpqUkQT1YtmdCuXr0ad9xxh/Qus0d5ZGRERDnUnmnSk6jgzX5fle7NvyeTSelDp7jX2NgYfD6fjLOg8AbpTolEom6+JcU+eNykn/N7qLapKr7b7Xbp+a5Wq/jIIx/B51/+eQz7hwEAl05fivcfej+MVqPMxORYsmw2i4GBARw/fhw+nw8ve+5lKLlK2L1mN3SDjr58H/5m5G/QZGnCoblDGBgYgMvlkhniVIDncTCpNZvNWLp2KXZfuxsnr6o5pHnHPP5i/V/g/Qfej/5qv5xPLpfD6dOn0dbWho6ODpw9exZPP/00li5div/v4f8Pu/y70DrXiucnnscT2SewZcsWoZURhMhms3BqTtx2+22IvSyGJ554AufPn8f999+PHTt2YOnSpUJjU+n3hw8fhs/nQzabletJNJwKtXa7HYFAQJBvm82GVCol78nn8zgcOAzveS8qlQqmpqYEqGlYwxrWsIY17L+EXZwP/7r5cR7Ajn/nY1GMbWy/yn6Vry0UCr9yRvv/yVSBWYLmLFBEIhGMjo5KOxwnxlDArWEN+02tkXA37D+dRaNR7Nu3D/v27RPBjbGxMZTLZcTjcdxyyy3QNK1udiPHQVHcjGMncrkcbDYblixZgsOHD2P9+vUIBoOwWq1YunQpzp8/D5vNJtTqTCYjSCorpKQXRaNR6eFlPzVpxQCkr9tkMiGdTsNiscBsNiMWi0nVnXPI+dmiAH6h0srKMX+nzgnP5/Oi6lkul4VurmkagoUgPnjog/j6+q+jKd2Et+1/G3LVnDgRosBmsxnNzc0IhUKwWq14/vnnYbFYcM1D1yA5m8Ts1ln81eRfYXV1NVKWFNra2hCJRGCz2WTWJpNXtWovPfJ9HbB32+vuZ9FaRMlfgjFuRCqVgt1e+/v69ethNpvxmte8Bo8//jhGR0fxxBNP4GrD1Xh9+vUoby0jGAzi0KFDOH78OHp7exEMBoXqRbaE2VwbIUaRvePHj+PFF1/EwsICBgcH4XQ6AUAq3Bz3RsfK0S2ZTEZ665nUBwIBuUdkWVSrVZy86iRuX3Y73hh9I1qHWxsV7oY1rGENa1jD/gsZ9XFUY4GDxhiTU1wa1rB/qzUS7ob9pzaKfOzatQtGoxHHjh3DI488guXLl2PFihUYHBxEIBCQ/h4qd1utVjQ1NSEcDiORSGBwcBAHDx7Eo48+ivXr18Pr9cLj8cDhcCAWi6FYLKKnp0fGdDHx8vl8SKfTQl0i7VvXdRFII7W6UCggl8vV0a5Zkc1kMpKksgpPYS/OJVeTcI6e4msAyPuYnDudTjz77LPo6empzajMd+IDJz4Ae8GOarkKzahJ1Z10fLPZjEsu+f/bu/fgvO7ywOPf817OOe9577q9sm6WdbElXyTHtzhOQtIEQwPuLEnpMFxCdml3YAgt02nLdGbbsjO7lD8KNLPM0tIFGgp0uiyBtGFJHMehmDiJiW+SE8d2EssyjiRbfqX3et73vNf9Q/n9IhcoTre2cfJ8ZjyjiV5L55z3jec857mN6eElruvy4osvsri4yNbzWxmxRhgLj1EPLpVtq3VXHR0dlxyzeuCghpGpByCtvlbu2n0XF7IXOLftHMFckE1/sYl4f5zaipouBVPTQwG2bdtGV1cXDz/8MPl8nsXFRTo7O3Ech02bNuHz+di3bx/pdJpNmzZh27a+Hp7ncfvtt7N//346OjoYHR0llUpx+PBhTpw4QblcZvyGcY7cf4Q7v3UnoVBIB+3qoYrK3qvp5aocbfmxql3tbW1tTLxtgv037adiV/j29m9z5/k7KTxbkAy3EEIIIYT4GRJwi+uG2o148eJFJicndf9sV1cXY2NjbN269ZKhZaovxzAMLly4wC233MITTzzBiy++yM6dO+ns7MQwDA4ePMji4iJtbW16EJkqdc/lcjoAV5Orq9Uqtm1TKpX0QDPVu+26LsVikWq1yooVK/QKMhVYqzJ3NfFalamrdWUqu+15HpFIhHK5fEnw7XmeDtAjkQgbNmzQ/dWu65KsJ5cyz05VB8Nq6qca+AVLDzLC4TDj4+PUajWOHTtGJVXh5Q0vs31mO43qUjl9Mpnk7NmzlwwyUcNF1BRv1YPebDaXHnQ02tj+pe3s9e8l/Hth5hvzHN50mDvuuAPDMC4ZXqLOf9WqVdx+++2cOHECx3F0BrrZbDI4OEgikeBb3/oWBw8epHdlL9wFcyvn2PSDTezbt09fo0AgQF9fHz6fj+eee46XZ1/mlQdfobK5QsPf4B3/5x06UFcDXQBdrq72lapAW/Xaq7L0Iz1HeHzH41SspafgBbvA47sep/U7rZLhFkIIIYQQP0MCbnFdUllHz/M4deoUp06d4qGHHqKtrY2xsTHWrFnDypUrSafTzMzMMDo6it/vZ+vWrTo4VsMyUqmU3mmp+otV8KWCv+UlxqVSSff6qOndan3XihUrdB+RCkJVCbb6nWow1/LeIfWgQGVYVRm4GvyhJmarPnCVVVcTQFXPuiqTVsesfo46plqtpgfAua5LOBxmbGyMw4HDfO+T36NhNog0I3x8+uNYuaXhYLFYTPfHq0DZNE1d4q4efLiui+d5tLa2EvPHaNzZwGqzCPWFePXVVzl58iTd3d06gFX93CpzHg6H9QqR5dNKQ6EQPT09fPjDH2bPnj0cSh3i5Q8tTUR1/A7D/ziMZVgUi0U9ZXxwcJC0mWb2vlnYuvSZObXtFFbV4uZHbsbxOXied8maM8MwdCZblZmrz4F6X3pne+k73cfJ0ZP6szhwbIDcqZxkuIUQQgghxM+QgFu8aTSbTebn59m7dy979+4lHA4Tj8cpl8ts3ryZdevWkUgkmJqa0sEroMuMLWspaFOZ0lKpRLlc1oGtCpxVcFwoFHRQC6/vxXYcRwdpaoVEMBgkFouRy+UuyTyrEnHV/728p0iVaqsecVXmrnrHq9Wq3mftuu4lqzUA/bUKkNXPUvusn3/+edatW8fLAy/zo7f/iIa5FDB+t/W7VKtV/uinf0StVNMBst/v/5nebXUd1bn+yx2b8XicSCRCLBZjcXGRbDbL9u3bl8rEW/exObuZuBVncXER13VpaWkhHo/rBwbqd9brdVKpFMEPB5m6e2ppbRjw6M2PknEz3HXgLgr5gi63j0QirLplFaduOMU88/ozMhOeoRgsEmvGdN+1elihrq86P7U/XH3fMAwyoQwXnUsHs5yPnsdtulfiIy2EEEIIIa5zEnCLN61isUixWOShhx7iBz/4AR0dHbS2thIOh7nppptYvXo1q1ev1tPCVUZ7+RC0iYkJBgYG6Ojo0NnhcDis92ir8nDXdfH5lvZqVSoVHMehVCrhOEuZVFU6vTybrUqWbdv+mbVjauibCpRVQK4Geqg94Oq/qwy9ygyrjPjyyd7qOGFprUZvby8+n49UM4WFdcm166p04cdPoi3BxYsXWVxcpKOjA8dxOHbsGH6/n76+Pj2xW+1NN02TWCyms/uALiMPBoPMz8/z8ssvM71pmr8f/3sGs4P8+bE/X/qdXV04jnNJqbcaZGbbNpZlcWvbrezx76G0bA9J62KrflChHjIYhkHylSTv+va7ePi+h8m2ZPE/66fxuw1e5EVCm5ZK9OPxOIFAgEwmQ71eJxqN6iFp6pjVoDTDMGg718Ztf3sbT3zsCXKdOXpmetjwNxvYP73/Sn2MhRBCCCHEdUwCbvGWUCqVmJ6eZnp6Wvdtm6bJihUr2LJlC93d3dx0002sXLlSD0Pz+/24rqt3fathWrCUZfb7/bqUWvX+ZrNZPbQtmUwyPT2tg0hVCq4CYMuyiMfjzM/P64yu6rdWAbfKhKtAXe34Vmu4YCn4VuuvYCnTH4/HKZVKuudcTTP3PE9PQlfBZtfFLr6Y+yL3bb6PQqDAfefu456pe6g0KzRCDZ3lDYVCFAoFXf69fIWayqqrYWPL943H43Fs29YPNJ4LP8cjtzxC2Spz0DrIH2/8Y/70uT/FcRx9bqosHsC2bcLhMLZtszazlk986xM88KEHaNCg7Y/bKBwpMDs8Sz6fp1Qq6d8XDoexZize+5fv5aHfeYi1f76WU2dOcSx/jHw+z7p16/Q1VOcSi8V0RYBpmgB6TZs6nv58P5/6/qf4q3v+io/+40e50LzAs/5nr96HWQghhBBCXDck4BZvOWrCeKlUIpvNcuLECQBaWlrYunUrGzduZHh4mI6ODjZt2kSz2aRarerAs1QqYVkWtm2TzWbJ5/NEIhF8Pp8eqKbKy+Px+CXl1irAU1nYSqWig2MV5Le0tHDhwgVd4qxKttVqL5UJV6XP6px8Ph+e5+lyc7UyKx6P68x2MBjUve9qbVmlUiFUCvH1Q1/nO33f4f75+6mH6uTzeZ15V6sxfD4fIyMjwFKWPJPJ6AnoavJ5s9kkFovpQW8q412r1SgHy+z+D7spW6+t4jDghcQL/NOqf+Le2Xv1tHZVGm7bNtFoVFcEBAIBbkneQurpFEf9RymdKXHolUMYDYNwOExHR4feUa6qCsy0yW/+99/kpexLrFu3jjNnznDu3DkAnZEPh8Pkcjm9Fk5VCKg+bvXAQT3oaK218uCRB3k2/SzlcllXNwghhBBCCLGcBNxCvGZhYYHdu3eze/du4vE4/f39DA8PMzQ0RFdXF93d3fT29upy50QiodeBqQFo58+fp6urSw8+a2lp0YO4VJY8nU4TiUTI5/O6ZHl5D3Eul9M/L5/P68wwoHu+1ZRzQAfdKkBUU8/V5G5APyxYPoFb9Uc7jrM0xKzq8PEzH8ewlyaRh0Ihvcd8amqKtrY23WeuppWrTLf6WYZh6LJ2FdAnEgk9dC6YD/LuL7+bH973Q+YG5/A1fLzr0Lt4z/n3YMQMfQ38fj+O4+hMvzpPy7IIBoMMLw7Tkm4he3OWsBPmwIEDOI5DLBbTgbl6IKEGya1cuZKWlhZqtRqlUonZ2VnOnz/Pli1bGB4evmRSuRoGt/yaBYNBvV+92WxSLpWxbZtCofAzuzuFEEIIIYQACbiF+Lmy2SwTExNMTExg2zatra2kUilGRkYYHBxkcHCQVatW6X5u13UJBAKcPXuWvr4+6vU64XBYB28qg62yxGowms/nIxqN6sy5WlmlyrFVttWyLL3/W/WcA7r33DRNPaW8UqlgGIbOjKufC+he5GazqcvYLcvSa8L8fr8eHKf6sFUZvCqhVz3mapicqgBQmW7Lsujv7yeRSOh1ZKo3OhKJEPbC3Pm/72TvvXvp3tvNmlNrSA+n9bA527ZpNBq6f7tcLuueeFU6r752HIeRkRHdb3/y5MmlvvRUikAgoPuv1Wtd12VkZIRwOMzp06c5f/48zz//PJZlMTAwwPS6aZrzTXpne7Ftm3K5jGEYWJaly+VbWlpIJBIYhqEfOKj3QwghhBBCiOUk4BbilyiXy7z66qu8+uqrTExMEAqFsG2brq4uNm7cyIYNG3RJ8ubNm3W/cj6fxzRNCoUCgO51Vruda7WaLg9X2VuVGVbl5irQCwaDlEolvQJMlZerwLzRaOihYUo4HNbBved5uhxaBY3pdFoHiz6fj9P103yn/zvc/+L9GOWlQW2qhzwSiXDmzBnGxsZYXFwkEonooB3Q5+N5Ho7jUK6WqVSXsr6e55FMJimXy1iWhed5pNIpdv2vXRhpA8/yOH36tN6prsrjA4EAtm3rhwrqQcPyiemq77utrY1kMsnu3buZmJhYagno7ODI3UdYsW8FXekuveJNPQjo7Ozk6NGjnD17ln379jHVOcXUB6agCh/75scov1LW1QSNRoNKpaJ79vP5/NLU9Ney6BJwCyGEEEKIn0cCbiHegHq9TqFQoFAocPHiRY4dO6YznYODg2zfvp3Vq1fr6eWO4xCNRvXk81wuh23buke4WCziui7JZFJP2lZBdjgc1hOzVdZblWiXSktTun0+H//8z//Mxo0bKZVKhEIhQqHQUtD7WoCtyp1VwOn3+/Xws0qlQqFQwO1y+Z0tv0PZVyZYC3LvqXtp9bfq7HQsFuPChQvMz8/rSeoqWFeT0tXgtJniDN/b9T2mJ6aJTEb0+S8fBtdoNAjMB3RmPpPJMDU1pdenOY6jg1nbtvXQt4WFBT20zXVdHMchHo9z/vx5+vv72bJlC4899hiZUgbrNyxevvNlfLf7eP/n3k9gNqDL7k3TpLW1lRtuuIG2tjaeKj3F8S8fh6WKdr740S/y4c9/mNbFVmq1GoVCQffGT05OUiwWGR4eJhQK6cF0QgghhBBC/EsScAvx/6HZbOps9tGjRzl69CiO49Df309/fz+Dg4N0dHTQ09ODaZpEIhGy2Sy5XI6uri58Pp8OXFWPtyrfVplxNTFbDUJT/dkqM93R0aGD2lAoRL1eJ5vN6oy6CthVNl0F/ypLfiJxggfWP0DZv1Tm/u2eb9P0mnzwpQ/S29Grh8A9/fTTbNy4UZeUq+npyzPzaS/N1/q/xg97fgh/DaXPl0g2k3qA2fJVaMAlu72z2SxHjhxhbGyMQCDAi7EXSS4kWRFcAXBJzzugg31Vcm7bNps3b8YX8PH1zq9T+49LWedGoMFD9z/Ezr/ZSdsLbXoavKpUGBgYYOaDM5zwndDvaz1Q5+jYUXY8tuOSqgHVa++6rn4vpH9bCCGEEEL8IhJwC/HvzHVdjh8/zvHjx7Esi/b2dtrb2+nu7mZgYEBP3lYZadW7rCZ8L18RpkrJ1aRsWMqS+3w+vfe6r69P92+rABbQ/dWA7mVevgZL7QmvG3UaNC45BzNi6iy5KqceGhrCdV1CodAlfeJ+v38piPfBA8MPsDu1e+mHGHDikyd45sfPsP3gdj18bPnObLVeTZ1ftVpd6pu/zebvNvwdndlOPn3i0zTKDV1Wrs5v+eo0lQmPRqOMrh3lzu13spvdr5+QAYFQAMdxdICshtkZhsFt37sNo2jw4rtfBMD+bzZ9E32UW8s6w65K+QOBAPF4HMMwcF1XAm4hhBBCCPELScAtxBXkeR7nzp3j3LlzTE5O4jgOpmmSTCZZs2YN27ZtY+PGjXradaPR0MPW/H4/0WiUQqGgg2MVaNfrddLpNPl8np6eHr3jW60GUwPPlk/dXp5hBnRWeN3iOv7s2T/jD2/7Qyr+CrumdvGh2Q8R8oV05t3n82HbNul0mra2NhzHoVgsAujd1W7B5e3+t7O7YzcsJdEJeAG6J7vx+/16EFs4HCafz1MoFPTxmaapz33WnuUrN3+FhfACZ5wz/In5J3z62U/rye2RSISFhQU9LV7tMg8EAhSLRWLhGO848g5qtRp7b94LDVj9u6vpifbgC/h0wK8qCQASoQQ3PnYjdeqce+4czQea7Df3s3HjRkZHR/VgvGQyqVeENZoN8vm8XrkmhBBCCCHEv2Som+9f+kLDuLwXCiEuiwr4WlpaWLduHUNDQwwNDdHb20skEtF93qqnW00zr9frl6y26urq0t9TmWL1teotrlQqrwfGrqsD/EgkoiemnzPP8egNj/KJ5z9BNBTVGWmVxa5Wqzz77LPs3LlTB/Gu65JIJPQqrvRCmn3JfXx25LM08g1u+93buHP8Tmzb1v3k/f39ZLNZpqenyWazegVZOBymYTT43H/6HBdbL+rr5G/4eeeZd/LR5z+KZVm69L5YLF6yV1z1U/t8PvL5PKVKie9u/S7FrxSZ/cEsLS0tjI6O6h541dPebDaJx+PUajXypTwDqwZ46NsPcfz4cRzH4YYbbmBoaIhoNIppmpw6dYq1O9fy1fd9lbv/x908+vVHmZqauiafoTe7ZrNpXOtjENcXuVcRQghxNV3OvYoE3EL8CgmHw3r399q1a2lpacFxHEKhEMFgUAenajK567pEIhHq9TqVSkX3VMNS73e9XtcBaDAY1APbVN+1+v9fDVBTe6djsZheyRWNRqnVakQiEZ566im2bdumV4WpcvflwfmRI0f4kvsl3Cdd1gfW87a3vU3//HK5zPj4OLVajXQ6zezsrP67qiw8F83xrd/8FtOpaWjCjS/cyO+/8Pv6Oniep0vubdtmcXERwzD0wwY1md0wDDKZDOVymT179vDCCy/Q0dHB8PAwfX19FAoFAoGAPu9oNIrneXR2dnL8+HFOnDjB9PQ0juOwevVqenp6GBgY4IR9gu/f9328Xo/oiSjWf7a4+NTFf+1tFf9GEnCLN0ruVYQQQlxNl3OvIiXlQvwKKRaLeviabdukUina2tro6elh5cqVDA0N0dXVhWEYegWXYRiX7Mdevi9brdZSe8ANw8C2bd3TvXw/tyo79zyPxcVFLMvCtm0dzBaLRer1OgsLC7S3t+vgVvUwq/LsarWK92UPo2HgDXm6lF1lp1Xpu3p9uVxmamqKer2O4zikainuffJevvG2b9Az18M797yTC+ELtLS0EA6HdcY9n89TqVSIRqP62FTmXT2QUA8pdu3ahWmaHD9+nGPHjmGaJrZt63Vmqkw/EAiQTqdJpVJEIhFCoRCvvPIKTz/9NGvXriU/lGfiYxN4bR4A+ZE8+c/k4SPAK1f/8yKEEEIIIX61ScAtxK+ocrnM9PQ009PTHD16lEgkQiwWI5VKMTg4yNq1axkaGtK9zT6fTwfH1WoVx3F0ULt8KrjruvrvqEFsao+3yp4Xi0VqtZruG1eTzVX/tCqvVllytbIsl8vRbDZ1wK76tqvVqi6hVxlt27ZZsWIF09PTrFixQk8OB2ifb+cDj32AqBslGAiSyWR49dVXSSQSetJ6IBAgHA5TLpf1g4VCoaD3eMPru88Btm/fTnt7O48//jjPPPMMW7Zs0Q8gTNPEsiyCwSDFYhHP82htbWVsbAyA559/ntOnTxOdjGLOmND22pvUBE4AkuAWQgghhBA/hwTcQlwH1KqvbDbLuXPnOHLkCIFAgGQyyfr169m8eTMbN27Ua8Esy6Jer+ss9PIMdjAY1K9RWXC17zudThOPx/H5fEQiET0YTQ0ri8fjXLhwgVWrVmFZll5dls/n9WA113VJp9MMDAywZs0aPWm9Xq/T09Ojd45nMhn9O5ZPDLdtm/n5ebpKXdTqNQgsrV87fPgwjUaDu+++m/n5ebyAR7PSpJxfWpOmMvyqnDwSiRAIBPQk887OTiKRCD09PXzzm9/k6NGjrFy5kpUrVxKOhikECzQXmrqfXO35Hh8fx3Ecjh8/zqsnXiX7chbGXntjHgV+D/CuycdCCCGEEEL8ipOAW4jrjCqbrtfrzM3NMTc3xxNPPKF7jdesWcPg4CDd3d0kk0kSiYSeDK6C7nq9TqlUolqt4vP5CIfDBINBTp06xU033UQoFKJcLuv+72QyST6fJxKJ6FJutd+7vb0dwzB0SXahUNC7u9VqMZXRjsViGIZBpVLRw+C6u7spFAqUy2VyuRywlIlXJe3z8/PUajXa29tJJpOk02lKTom/Xfe3tBfa+bUDv0bEWhr+ptaGGYahe7RV/7nP59MPH+644w7279/P5OQkmUyG1G+n+Ml7fsJdX7sLfgotLS2USiVdLj88PIzdarPv9n2U7im9/mYkgW7g9NX/HAghhBBCiF99EnAL8Sbhuq7u/zZNk+7ubnp6ehgcHKSvr4/Ozk5aWloIBoM0m01d8r08EN+2bZsuyY5GozpzrYLver1OJpNhbm6O7u5uarWaXtEVi8WYn59nenpa79b2+XyUSksBarFY1Ku8LMvCNE3S6bQu6a5Wq5imqbPr1WoVz/OIRCK6H7ter5PxMjx4w4PsXbEXgKyX5b2H3nvJjnFVWq9+1/L95sFgkB07dpBIJNi/fz/Tt0xz5L4jNMNNnvjAE2z7m22wgC6XN02TZrPJilUrSN2c4gxnXr/oLUAXEnALIYQQQoifSwJuId6EKpUKU1NTTE1N8cwzzxCLxUgkEnR0dLB+/XpGRkbo6+sDXs+Y+3w+HfCqLHEwGKRQKFCtVnX/tOM4NBoNqtWqzigHAgGdyVa939FolHQ6TSKR0APKEomELnVXe8YzmQzhcJhwOKy/F4vF9Fo01U996623YhgGX9z+RX7S/RN9rj9Y+wOqvir3TdynA23V3+267iXBvG3beiL68PAwZ24+w9GdR2mGlwYbzw3M8eOP/ZhbP3srXYEunbk3DINgLsht37kNwzSYWj9FMBuk9097Of2URNtCCCGEEOLnk4BbiDc5lYVeWFhgamqKw4cPEwwGicVirF+/nvXr1zM2NkYymdTl5uVyGdu29XRxn89HLpcjGAxiWZYOhk3T1JPNa7UalmXh9/t1b7da1VWr1QB0sKt6ypvNps6Eq2C40WhQLBaxLEtnqdXfr9frfODYB5hITeAFlhqnzbzJb730W7oMXpWq+/1+WltbqVQqev+4yt6rP++qvosz+TNMxCfAACow9uQYbc023Re+PFseuBDgjq/dwe5P7Ob+A/fzlcNfucrvphBCCCGEuJ5IwC3EW0iz2aRSqVCpVCgWi8zOzrJnzx4cx2F0dJTBwUHWrFlDZ2cn0WhU7+Q2DAPXdVlYWNBBscpGw9LO77Nnz7J69WqSyaSeDK4Gs/l8PpLJJIZh6DJtn89HtVrVU8dVybmarK4CcpU5V1nqXreXz+z/DJ/Z9BmyM1na3t9G/sN5kn1JnZU3TVP3hS+fjL58Mnu9XsdX9PHbD/82f/Frf8G5leeIfDbC3HfniK+Jk0ql9HC5Wq1GR0cHhmGQzWb51P/9FADpdPoavItCCCGEEOJ6IQG3EALXdTl06BCHDh3Csiy6urro6+ujq6uL3t5eBgYGdGl4vV7n/PnzlEolarWaLjOfnJykt7eXbCpL5G0RYqUYjuPoKeWRSEQH/GqYmQpoPc/D8zydofb7/bqPWw1YU+u/6vU6PXM93Pfj+3jsq48xf3aehx9+mF27djE0NKQDc/U7KpWKXlum+rvr9TqFQkGvN7vjf97BmTvOcP5758nmsrzyytJS7d7eXiqVCs1mE9d12bRpE/v379drx/L5/LV824QQQgghxK84CbiFEJfwPE/3f/v9fuLxOG1tbXR0dDA8PEwqldK91YlEgkqlQrlc5rbbbmPeN8/jH3yc3D056p+rE41GKRQKZDIZ6vU6pmlSLpepVCo6821ZFgDRaBTP8/SOcLVPXE01t22bYDCI53nU63U2zG3Av8rPRG6CyclJcrkcq1ev5p577tG95Kp0XPVyq8FvXV1dlMtlstkspVKJ4Z5hRk+P8tTQU5RKJWZnZzl58iSNRoP29nY9kf2ll17Ctm0SiQQzMzM6ky+EEEIIIcTPIwG3EOIXqtfruv/7pZde4uDBgwSDQaLRKOPj49x4442sW7duqew8ZPAHN/4BM84MAD/57E/Y+e2d1EpLQ9T2799PPB4nk8nQ1tZGNBrVATWA4zh6Ini1WsW2bQzDIBQKMT09jWmaOiteqVQwTZO1a9eSSCQol8tMTEwwMzPDunXr2LJlC47jUCqVKBaLuudclbDn83lisZj+eS0tLQQCAXbs2EGtViOdTvPVr35Vl803m01SqRSZTAbP82hra9NZcCGEEEIIIX4Ro9lsXt4LDePyXiiEeMswDINwOMzIyAgn/+tJ8nflwffaN5sweGaQ+755H5VKhX379tHV1cWGDRtYu3YtsVgM0zT1Oq9wOEyhUNA91sFgENd1KRaLep93vV4nm83iui7hcFgPRJufn+fJJ5/k6NGjNJtNPvnJT9LX16d7zYvFoh6AFgqFKJVKBINBGo0GgUAAy7KwLEuXmudyOXK5HHv37mV2dpbOzk42bNigX3vLLbdw4MABPv/5z1+jK//W0Gw2jWt9DOL6IvcqQgghrqbLuVfx/bIXCCHEL9JsNikUChw8eJD8b+Th71//nv9RPzs+u0MHzSpzPD4+TmtrK47jYNu2LhV3XVd/XS6XKRaLehJ6rVbT68vUhPVKpQJAtVqltbWVnTt3sn79egzD4Mtf/jL79u3TZeV+vx/TNDFNE4BQKESz2dR/isUi2WxWD3QLh8NEo1G2bdtGa2srCwsLnD59Gs/zMAyDTCbDT3/602txyYUQQgghxHVESsqFEP8+msDHgTyQAP4AHik9QjKZJBQKUSwW6e7upl6v6wnk6g8sla97nnfJHnAVKBeLRd33HQqFdLCterobjQbJZJKdO3dSLBY5fvw4e/bsIRQKsWPHDoLBIIFAQGe6q9UqPp+PcDhMqVTC7/dTrVb1BHafz0c0GmXVqlWEw2EOHDjA9PQ0hUKBoaEhXrj7BbJ/l71GF1oIIYQQQlwvpKRcCPHvKwYEgWUbs9Qua9M06e3tZcOGDYyPj7Nx40Y9wCwQCFAqlfSgtUAgoPdmu66LYRi6FFytNvP7/ZTLZd2jHQgEOH36NPv27WNycpJkMsmmTZvYtWuX3hfeaDRYWFggGo1iGAae5xGLxfRgNeCSr2u1GouLizzyyCO8cvoVjP9isPCxBQKHAni3etC4+pf4rUJKysUbJfcqQgghrqbLuVeRgFsIcdUZhqEzzOPj44yMjDA+Pk5fXx+JRIJms4lhGDQaDSzL0pPQi8UipmnSaDT0MDO/308+n8fv97O4uIhlWZw/f56nnnqKQ4cO0Wg0eO9738uOHTuIxWLk83kuXrxINBqlUqng8/l0GXuz2dQBfa1WIx6P62OdW5jjH1r+gUPvPwR+ljL6u4EPAIvX9nq+WUnALd4ouVcRQghxNUnALYS4boRCIfr7+xkdHWVkZITe3l6SySTRaJRgMIjf79d7tAOBAK7rUq/X8fv9LCws4DgOmUxGl6RfuHCBZ555hsOHD5PJZNi5cydvf/vbSSaT5HI5TNPUpem2bZPP52k2m8TjcVzXBdDl7pZlcbZ6lm+85xu81PbS6wd9Drgf+KerfLHeIiTgFm+U3KsIIYS4miTgFkJcl4LBIB0dHXR2dtLd3c3AwAD9/f2sWrWKSCSie61VwF0sFqnX60xPTxMKhQgEAgSDQS5evMj+/fs5cOAAhmFw88038+53v1tnyYvFIs1mUwfwlUpFl5oHAgE9MT0UClGv10m3pfnSDV/ipc6XIAPcC3z/Wl6pNzcJuMUbJfcqQgghriYJuIUQ1z21eiwcDtPe3s74+Dijo6OMjY3R1tbGwsIChmFQrVa5cOECtVqNhYUFUqkUAJVKhSNHjvDkk0/ieR5DQ0N86EMfwrZtKpUKnucRDAb17m/P8/A8D8dxdN95NBql0WhQq9U46zvLA3c+wMxvzMDT1/jivMlJwC3eKLlXEUIIcTVJwC2EeNNRa76i0Sijo6MMDQ2xfft2UqkUjUYDz/M4deoUgUCAzs5Oenp6mJmZ4emnn+aJJ54gk8lwxx13sHXrVtrb26lUKliWhW3bFAoFvX7MNE2dKV8+Db3RaHDkxBH++i//mlqtdq0vx5uaBNzijZJ7FSGEEFeTBNxCiLcE0zQZGhqiv7+fkZERkskkjUaD7u5ufD4ftm2zuLjIc889xw9/+ENc1+WGG27g13/910kmk1SrVer1OgCNRgPbtmk2m9RqNUKhEIZhUKlUqFarzMzMcPDgQXbv3q1LzsWVIQG3eKPkXkUIIcTVJAG3EOItx+fzkUqlaGtro6+vj76+PlKpFJ2dnfh8Pn784x+zf/9+XNdl48aN3HTTTbS0tOA4Ds1mU+/jNk2TWq2GbdvA0uA0wzBwHIcvfOEL/OhHP+Jy//0U/zYScIs3Su5VhBBCXE2Xc68SuBoHIoQQV0uj0WB2dpbZ2VleeOEFwuEwkUiERCLB4OAg3d3dDA8Pc/LkSSYnJ5mbm+MjH/kIwWCQWq2G53lEIhH8fj+NRoNKpYJpmno9mGVZpNNpCbaFEEIIIcQvJQG3EOJNq9FokM/nyefzzM7O6t5ulcFuNBq4rsuDDz7I+973PlauXInf78eyLL0LvFqtApDL5QgEAoTDYb1OTAghhBBCiH+NBNxCiLeMer2ue7VV0Oy6LseOHcO2bbZt20Z7ezuO42CaJo7j6AFqtm0TCoW4cOEC5XL5Wp6GEEIIIYS4TkjALYR4y6tWqzz99NM899xztLa2kkql6O7upre3l0QiQSqVor29HcuyOHPmjGS4hRBCCCHEZZGAWwghXlOtVpmbm2Nubo7JyUkikQjRaJRYLMbo6CgjIyPkcjk8z7vWhyqEEEIIIa4DMqVcCCEuQzAYxDRNfD4fxWJRVoJdBTKlXLxRcq8ihBDiapK1YEIIIa5bEnCLN0ruVYQQQlxNl3Ov4rsaByKEEEIIIYQQQrzVSMAthBBCCCGEEEJcARJwCyGEEEIIIYQQV8Bl93ALIYQQQgghhBDi8kmGWwghhBBCCCGEuAIk4BZCCCGEEEIIIa4ACbiFEEIIIYQQQogrQAJuIYQQQgghhBDiCpCAWwghhBBCCCGEuAIk4BZCCCGEEEIIIa4ACbiFEEIIIYQQQogrQAJuIYQQQgghhBDiCpCAWwghhBBCCCGEuAL+H8ZZQgkzysDAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Draw the line points for training\n", + "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n", + "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the exported ground truth on the merged dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info] Initializing wireframe dataset...\n", + "\t Found filename cache wireframe_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/wireframe\n", + "\t Load filename cache...\n", + "[Info] Successfully initialized dataset\n", + "\t Name: wireframe\n", + "\t Mode: test\n", + "\t Gt: wireframe_test_adaptation_iter0_epoch043_ce1_detect_0.25_inlier_0.75_local_max_v1.5_refine-v2.h5\n", + "\t Counts: 462\n", + "----------------------------------------\n", + "[Info] Initializing Holicity dataset...\n", + "\t Found filename cache holicity_test_cache.pkl at /home/remi/Documents/test_SOLD2_data/datasets/Holicity\n", + "\t Load filename cache...\n", + "[Info] Successfully initialized dataset\n", + "\t Name: Holicity\n", + "\t Mode: test\n", + "\t Gt: holicity_test_homograpy-export_512x512_v1.5_detect_0.25_inlier_0.9_local_max_refine-v2.h5\n", + "\t Counts: 520\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "# Initialize the merge dataset\n", + "with open(\"../sold2/config/merge_dataset.yaml\", \"r\") as f:\n", + " config = yaml.safe_load(f)\n", + "\n", + "merge_dataset = MergeDataset(mode=\"test\", config=config)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read in one datapoint\n", + "index = 0\n", + "data1 = merge_dataset[index]\n", + "\n", + "# Reference data\n", + "ref_img = data1['ref_image'].numpy().squeeze()\n", + "ref_junc = data1['ref_junctions'].numpy()\n", + "ref_line_map = data1['ref_line_map'].numpy()\n", + "ref_line_points = data1['ref_line_points'].numpy()\n", + "\n", + "# Target data\n", + "target_img = data1['target_image'].numpy().squeeze()\n", + "target_junc = data1['target_junctions'].numpy()\n", + "target_line_map = data1['target_line_map'].numpy()\n", + "target_line_points = data1['target_line_points'].numpy()\n", + "\n", + "# Draw the points and lines\n", + "ref_img_with_junc = plot_junctions(ref_img, ref_junc, junc_size=2)\n", + "ref_line_segments = plot_line_segments(ref_img, ref_junc, ref_line_map, junc_size=1)\n", + "target_img_with_junc = plot_junctions(target_img, target_junc, junc_size=2)\n", + "target_line_segments = plot_line_segments(target_img, target_junc, target_line_map, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_junc, ref_line_segments], ['Junctions', 'Line segments'])\n", + "plot_images([target_img_with_junc, target_line_segments], ['Warped junctions', 'Warped line segments'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Draw the line points for training\n", + "ref_img_with_line_points = plot_junctions(ref_img, ref_line_points, junc_size=1)\n", + "target_img_with_line_points = plot_junctions(target_img, target_line_points, junc_size=1)\n", + "\n", + "# Plot the images\n", + "plot_images([ref_img_with_line_points, target_img_with_line_points], ['Ref', 'Target'])" + ] + } + ], + "metadata": { + "file_extension": ".py", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/third_party/SOLD2/pretrained/sold2_wireframe.tar b/third_party/SOLD2/pretrained/sold2_wireframe.tar new file mode 100644 index 0000000000000000000000000000000000000000..8be8d3e78756932e82172ba104e95ea23b92dc97 --- /dev/null +++ b/third_party/SOLD2/pretrained/sold2_wireframe.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b8b0e712f743c03a6b1a33b194926e7e8ae5467c85de55b461f54777f24606f +size 146458141 diff --git a/third_party/SOLD2/requirements.txt b/third_party/SOLD2/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..421b52557bb98a7663f6bbf8ddca84b5000a0a0f --- /dev/null +++ b/third_party/SOLD2/requirements.txt @@ -0,0 +1,20 @@ +pyyaml +tqdm +attrdict +h5py +numpy +scipy +matplotlib +seaborn +brewer2mpl +torch +torchvision +tensorboard +tensorboardX +opencv-python==4.0.1.23 +opencv-contrib-python==4.0.1.23 +scikit-learn +scikit-image +kornia==0.3.0 +shapely +jupyter diff --git a/third_party/SOLD2/setup.py b/third_party/SOLD2/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..69f72fecdc54cf9b43a7fc55144470e83c5a862d --- /dev/null +++ b/third_party/SOLD2/setup.py @@ -0,0 +1,4 @@ +from setuptools import setup + + +setup(name='sold2', version="0.0", packages=['sold2']) diff --git a/third_party/SOLD2/sold2/config/__init__.py b/third_party/SOLD2/sold2/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/config/export_line_features.yaml b/third_party/SOLD2/sold2/config/export_line_features.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f19c7b6d684b7a826d6f2909b8c9f94528fdbf94 --- /dev/null +++ b/third_party/SOLD2/sold2/config/export_line_features.yaml @@ -0,0 +1,80 @@ +### [Model config] +model_cfg: + ### [Model parameters] + model_name: "lcnn_simple" + model_architecture: "simple" + # Backbone related config + backbone: "lcnn" + backbone_cfg: + input_channel: 1 # Use RGB images or grayscale images. + depth: 4 + num_stacks: 2 + num_blocks: 1 + num_classes: 5 + # Junction decoder related config + junction_decoder: "superpoint_decoder" + junc_decoder_cfg: + # Heatmap decoder related config + heatmap_decoder: "pixel_shuffle" + heatmap_decoder_cfg: + # Descriptor decoder related config + descriptor_decoder: "superpoint_descriptor" + descriptor_decoder_cfg: + # Shared configurations + grid_size: 8 + keep_border_valid: True + # Threshold of junction detection + detection_thresh: 0.0153846 # 1/65 + max_num_junctions: 300 + # Threshold of heatmap detection + prob_thresh: 0.5 + + ### [Loss parameters] + weighting_policy: "dynamic" + # [Heatmap loss] + w_heatmap: 0. + w_heatmap_class: 1 + heatmap_loss_func: "cross_entropy" + heatmap_loss_cfg: + policy: "dynamic" + # [Junction loss] + w_junc: 0. + junction_loss_func: "superpoint" + junction_loss_cfg: + policy: "dynamic" + # [Descriptor loss] + w_desc: 0. + descriptor_loss_func: "regular_sampling" + descriptor_loss_cfg: + dist_threshold: 8 + grid_size: 4 + margin: 1 + policy: "dynamic" + +### [Line detector config] +line_detector_cfg: + detect_thresh: 0.5 + num_samples: 64 + sampling_method: "local_max" + inlier_thresh: 0.99 + use_candidate_suppression: True + nms_dist_tolerance: 3. + use_heatmap_refinement: True + heatmap_refine_cfg: + mode: "local" + ratio: 0.2 + valid_thresh: 0.001 + num_blocks: 20 + overlap_ratio: 0.5 + use_junction_refinement: True + junction_refine_cfg: + num_perturbs: 9 + perturb_interval: 0.25 + +### [Line matcher config] +line_matcher_cfg: + cross_check: True + num_samples: 5 + min_dist_pts: 8 + top_k_candidates: 10 + grid_size: 4 \ No newline at end of file diff --git a/third_party/SOLD2/sold2/config/holicity_dataset.yaml b/third_party/SOLD2/sold2/config/holicity_dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..72e9380dbf496dc4b4d6430d58534e0663c85f0e --- /dev/null +++ b/third_party/SOLD2/sold2/config/holicity_dataset.yaml @@ -0,0 +1,76 @@ +### General dataset parameters +dataset_name: "holicity" +train_splits: ["2018-01"] # 5720 images +add_augmentation_to_all_splits: False +gray_scale: True +# Ground truth source ('official' or path to the exported h5 dataset.) +#gt_source_train: "" # Fill with your own export file +#gt_source_test: "" # Fill with your own export file +# Return type: (1) single (to train the detector only) +# or (2) paired_desc (to train the detector + descriptor) +return_type: "single" +random_seed: 0 + +### Descriptor training parameters +# Number of points extracted per line +max_num_samples: 10 +# Max number of training line points extracted in the whole image +max_pts: 1000 +# Min distance between two points on a line (in pixels) +min_dist_pts: 10 +# Small jittering of the sampled points during training +jittering: 0 + +### Data preprocessing configuration +preprocessing: + resize: [512, 512] + blur_size: 11 +augmentation: + random_scaling: + enable: True + range: [0.7, 1.5] + photometric: + enable: True + primitives: ['random_brightness', 'random_contrast', + 'additive_speckle_noise', 'additive_gaussian_noise', + 'additive_shade', 'motion_blur' ] + params: + random_brightness: {brightness: 0.2} + random_contrast: {contrast: [0.3, 1.5]} + additive_gaussian_noise: {stddev_range: [0, 10]} + additive_speckle_noise: {prob_range: [0, 0.0035]} + additive_shade: + transparency_range: [-0.5, 0.5] + kernel_size_range: [100, 150] + motion_blur: {max_kernel_size: 3} + random_order: True + homographic: + enable: True + params: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + patch_ratio: 0.85 + max_angle: 1.57 + allow_artifacts: true + valid_border_margin: 3 + +### Homography adaptation configuration +homography_adaptation: + num_iter: 100 + valid_border_margin: 3 + min_counts: 30 + homographies: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + allow_artifacts: true + patch_ratio: 0.85 \ No newline at end of file diff --git a/third_party/SOLD2/sold2/config/merge_dataset.yaml b/third_party/SOLD2/sold2/config/merge_dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f70465b71e507cbc9f258a8bbf45f41e435ee9b0 --- /dev/null +++ b/third_party/SOLD2/sold2/config/merge_dataset.yaml @@ -0,0 +1,54 @@ +dataset_name: "merge" +datasets: ["wireframe", "holicity"] +weights: [0.5, 0.5] +gt_source_train: ["", ""] # Fill with your own [wireframe, holicity] exported ground-truth +gt_source_test: ["", ""] # Fill with your own [wireframe, holicity] exported ground-truth +train_splits: ["", "2018-01"] +add_augmentation_to_all_splits: False +gray_scale: True +# Return type: (1) single (original version) (2) paired +return_type: "paired_desc" +# Number of points extracted per line +max_num_samples: 10 +# Max number of training line points extracted in the whole image +max_pts: 1000 +# Min distance between two points on a line (in pixels) +min_dist_pts: 10 +# Small jittering of the sampled points during training +jittering: 0 +# Random seed +random_seed: 0 +# Date preprocessing configuration. +preprocessing: + resize: [512, 512] + blur_size: 11 +augmentation: + photometric: + enable: True + primitives: [ + 'random_brightness', 'random_contrast', 'additive_speckle_noise', + 'additive_gaussian_noise', 'additive_shade', 'motion_blur' ] + params: + random_brightness: {brightness: 0.2} + random_contrast: {contrast: [0.3, 1.5]} + additive_gaussian_noise: {stddev_range: [0, 10]} + additive_speckle_noise: {prob_range: [0, 0.0035]} + additive_shade: + transparency_range: [-0.5, 0.5] + kernel_size_range: [100, 150] + motion_blur: {max_kernel_size: 3} + random_order: True + homographic: + enable: True + params: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + patch_ratio: 0.85 + max_angle: 1.57 + allow_artifacts: true + valid_border_margin: 3 diff --git a/third_party/SOLD2/sold2/config/project_config.py b/third_party/SOLD2/sold2/config/project_config.py new file mode 100644 index 0000000000000000000000000000000000000000..42ed00d1c1900e71568d1b06ff4f9d19a295232d --- /dev/null +++ b/third_party/SOLD2/sold2/config/project_config.py @@ -0,0 +1,41 @@ +""" +Project configurations. +""" +import os + + +class Config(object): + """ Datasets and experiments folders for the whole project. """ + ##################### + ## Dataset setting ## + ##################### + DATASET_ROOT = os.getenv("DATASET_ROOT", "./datasets/") # TODO: path to your datasets folder + if not os.path.exists(DATASET_ROOT): + os.makedirs(DATASET_ROOT) + + # Synthetic shape dataset + synthetic_dataroot = os.path.join(DATASET_ROOT, "synthetic_shapes") + synthetic_cache_path = os.path.join(DATASET_ROOT, "synthetic_shapes") + if not os.path.exists(synthetic_dataroot): + os.makedirs(synthetic_dataroot) + + # Exported predictions dataset + export_dataroot = os.path.join(DATASET_ROOT, "export_datasets") + export_cache_path = os.path.join(DATASET_ROOT, "export_datasets") + if not os.path.exists(export_dataroot): + os.makedirs(export_dataroot) + + # Wireframe dataset + wireframe_dataroot = os.path.join(DATASET_ROOT, "wireframe") + wireframe_cache_path = os.path.join(DATASET_ROOT, "wireframe") + + # Holicity dataset + holicity_dataroot = os.path.join(DATASET_ROOT, "Holicity") + holicity_cache_path = os.path.join(DATASET_ROOT, "Holicity") + + ######################## + ## Experiment Setting ## + ######################## + EXP_PATH = os.getenv("EXP_PATH", "./experiments/") # TODO: path to your experiments folder + if not os.path.exists(EXP_PATH): + os.makedirs(EXP_PATH) diff --git a/third_party/SOLD2/sold2/config/synthetic_dataset.yaml b/third_party/SOLD2/sold2/config/synthetic_dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d9fa44522b6c09500100dbc56a11bc8a24d56832 --- /dev/null +++ b/third_party/SOLD2/sold2/config/synthetic_dataset.yaml @@ -0,0 +1,48 @@ +### General dataset parameters +dataset_name: "synthetic_shape" +primitives: "all" +add_augmentation_to_all_splits: True +test_augmentation_seed: 200 +# Shape generation configuration +generation: + split_sizes: {'train': 20000, 'val': 2000, 'test': 400} + random_seed: 10 + image_size: [960, 1280] + min_len: 0.0985 + min_label_len: 0.099 + params: + generate_background: + min_kernel_size: 150 + max_kernel_size: 500 + min_rad_ratio: 0.02 + max_rad_ratio: 0.031 + draw_stripes: + transform_params: [0.1, 0.1] + draw_multiple_polygons: + kernel_boundaries: [50, 100] + +### Data preprocessing configuration. +preprocessing: + resize: [400, 400] + blur_size: 11 +augmentation: + photometric: + enable: True + primitives: 'all' + params: {} + random_order: True + homographic: + enable: True + params: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + patch_ratio: 0.8 + max_angle: 1.57 + allow_artifacts: true + translation_overflow: 0.05 + valid_border_margin: 0 diff --git a/third_party/SOLD2/sold2/config/train_detector.yaml b/third_party/SOLD2/sold2/config/train_detector.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c53c35a6464eb1c37a9ea71c939225f793543aec --- /dev/null +++ b/third_party/SOLD2/sold2/config/train_detector.yaml @@ -0,0 +1,51 @@ +### [Model parameters] +model_name: "lcnn_simple" +model_architecture: "simple" +# Backbone related config +backbone: "lcnn" +backbone_cfg: + input_channel: 1 # Use RGB images or grayscale images. + depth: 4 + num_stacks: 2 + num_blocks: 1 + num_classes: 5 +# Junction decoder related config +junction_decoder: "superpoint_decoder" +junc_decoder_cfg: +# Heatmap decoder related config +heatmap_decoder: "pixel_shuffle" +heatmap_decoder_cfg: +# Shared configurations +grid_size: 8 +keep_border_valid: True +# Threshold of junction detection +detection_thresh: 0.0153846 # 1/65 +# Threshold of heatmap detection +prob_thresh: 0.5 + +### [Loss parameters] +weighting_policy: "dynamic" +# [Heatmap loss] +w_heatmap: 0. +w_heatmap_class: 1 +heatmap_loss_func: "cross_entropy" +heatmap_loss_cfg: + policy: "dynamic" +# [Junction loss] +w_junc: 0. +junction_loss_func: "superpoint" +junction_loss_cfg: + policy: "dynamic" + +### [Training parameters] +learning_rate: 0.0005 +epochs: 200 +train: + batch_size: 6 + num_workers: 8 +test: + batch_size: 6 + num_workers: 8 +disp_freq: 100 +summary_freq: 200 +max_ckpt: 150 \ No newline at end of file diff --git a/third_party/SOLD2/sold2/config/train_full_pipeline.yaml b/third_party/SOLD2/sold2/config/train_full_pipeline.yaml new file mode 100644 index 0000000000000000000000000000000000000000..233d898f47110c14beabbe63ee82044d506cc15a --- /dev/null +++ b/third_party/SOLD2/sold2/config/train_full_pipeline.yaml @@ -0,0 +1,62 @@ +### [Model parameters] +model_name: "lcnn_simple" +model_architecture: "simple" +# Backbone related config +backbone: "lcnn" +backbone_cfg: + input_channel: 1 # Use RGB images or grayscale images. + depth: 4 + num_stacks: 2 + num_blocks: 1 + num_classes: 5 +# Junction decoder related config +junction_decoder: "superpoint_decoder" +junc_decoder_cfg: +# Heatmap decoder related config +heatmap_decoder: "pixel_shuffle" +heatmap_decoder_cfg: +# Descriptor decoder related config +descriptor_decoder: "superpoint_descriptor" +descriptor_decoder_cfg: +# Shared configurations +grid_size: 8 +keep_border_valid: True +# Threshold of junction detection +detection_thresh: 0.0153846 # 1/65 +# Threshold of heatmap detection +prob_thresh: 0.5 + +### [Loss parameters] +weighting_policy: "dynamic" +# [Heatmap loss] +w_heatmap: 0. +w_heatmap_class: 1 +heatmap_loss_func: "cross_entropy" +heatmap_loss_cfg: + policy: "dynamic" +# [Junction loss] +w_junc: 0. +junction_loss_func: "superpoint" +junction_loss_cfg: + policy: "dynamic" +# [Descriptor loss] +w_desc: 0. +descriptor_loss_func: "regular_sampling" +descriptor_loss_cfg: + dist_threshold: 8 + grid_size: 4 + margin: 1 + policy: "dynamic" + +### [Training parameters] +learning_rate: 0.0005 +epochs: 130 +train: + batch_size: 4 + num_workers: 8 +test: + batch_size: 4 + num_workers: 8 +disp_freq: 100 +summary_freq: 200 +max_ckpt: 130 \ No newline at end of file diff --git a/third_party/SOLD2/sold2/config/wireframe_dataset.yaml b/third_party/SOLD2/sold2/config/wireframe_dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..15abd3dbd6462dca21ac331a802b86a8ef050bff --- /dev/null +++ b/third_party/SOLD2/sold2/config/wireframe_dataset.yaml @@ -0,0 +1,75 @@ +### General dataset parameters +dataset_name: "wireframe" +add_augmentation_to_all_splits: False +gray_scale: True +# Ground truth source ('official' or path to the exported h5 dataset.) +# gt_source_train: "" # Fill with your own export file +# gt_source_test: "" # Fill with your own export file +# Return type: (1) single (to train the detector only) +# or (2) paired_desc (to train the detector + descriptor) +return_type: "single" +random_seed: 0 + +### Descriptor training parameters +# Number of points extracted per line +max_num_samples: 10 +# Max number of training line points extracted in the whole image +max_pts: 1000 +# Min distance between two points on a line (in pixels) +min_dist_pts: 10 +# Small jittering of the sampled points during training +jittering: 0 + +### Data preprocessing configuration +preprocessing: + resize: [512, 512] + blur_size: 11 +augmentation: + random_scaling: + enable: True + range: [0.7, 1.5] + photometric: + enable: True + primitives: ['random_brightness', 'random_contrast', + 'additive_speckle_noise', 'additive_gaussian_noise', + 'additive_shade', 'motion_blur' ] + params: + random_brightness: {brightness: 0.2} + random_contrast: {contrast: [0.3, 1.5]} + additive_gaussian_noise: {stddev_range: [0, 10]} + additive_speckle_noise: {prob_range: [0, 0.0035]} + additive_shade: + transparency_range: [-0.5, 0.5] + kernel_size_range: [100, 150] + motion_blur: {max_kernel_size: 3} + random_order: True + homographic: + enable: True + params: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + patch_ratio: 0.85 + max_angle: 1.57 + allow_artifacts: true + valid_border_margin: 3 + +### Homography adaptation configuration +homography_adaptation: + num_iter: 100 + valid_border_margin: 3 + min_counts: 30 + homographies: + translation: true + rotation: true + scaling: true + perspective: true + scaling_amplitude: 0.2 + perspective_amplitude_x: 0.2 + perspective_amplitude_y: 0.2 + allow_artifacts: true + patch_ratio: 0.85 \ No newline at end of file diff --git a/third_party/SOLD2/sold2/dataset/__init__.py b/third_party/SOLD2/sold2/dataset/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/dataset/dataset_util.py b/third_party/SOLD2/sold2/dataset/dataset_util.py new file mode 100644 index 0000000000000000000000000000000000000000..50439ef3e2958d82719da0f6d10f4a7d98322f9a --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/dataset_util.py @@ -0,0 +1,60 @@ +""" +The interface of initializing different datasets. +""" +from .synthetic_dataset import SyntheticShapes +from .wireframe_dataset import WireframeDataset +from .holicity_dataset import HolicityDataset +from .merge_dataset import MergeDataset + + +def get_dataset(mode="train", dataset_cfg=None): + """ Initialize different dataset based on a configuration. """ + # Check dataset config is given + if dataset_cfg is None: + raise ValueError("[Error] The dataset config is required!") + + # Synthetic dataset + if dataset_cfg["dataset_name"] == "synthetic_shape": + dataset = SyntheticShapes( + mode, dataset_cfg + ) + + # Get the collate_fn + from .synthetic_dataset import synthetic_collate_fn + collate_fn = synthetic_collate_fn + + # Wireframe dataset + elif dataset_cfg["dataset_name"] == "wireframe": + dataset = WireframeDataset( + mode, dataset_cfg + ) + + # Get the collate_fn + from .wireframe_dataset import wireframe_collate_fn + collate_fn = wireframe_collate_fn + + # Holicity dataset + elif dataset_cfg["dataset_name"] == "holicity": + dataset = HolicityDataset( + mode, dataset_cfg + ) + + # Get the collate_fn + from .holicity_dataset import holicity_collate_fn + collate_fn = holicity_collate_fn + + # Dataset merging several datasets in one + elif dataset_cfg["dataset_name"] == "merge": + dataset = MergeDataset( + mode, dataset_cfg + ) + + # Get the collate_fn + from .holicity_dataset import holicity_collate_fn + collate_fn = holicity_collate_fn + + else: + raise ValueError( + "[Error] The dataset '%s' is not supported" % dataset_cfg["dataset_name"]) + + return dataset, collate_fn diff --git a/third_party/SOLD2/sold2/dataset/holicity_dataset.py b/third_party/SOLD2/sold2/dataset/holicity_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e4437f37bda366983052de902a41467ca01412bd --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/holicity_dataset.py @@ -0,0 +1,797 @@ +""" +File to process and load the Holicity dataset. +""" +import os +import math +import copy +import PIL +import numpy as np +import h5py +import cv2 +import pickle +from skimage.io import imread +from skimage import color +import torch +import torch.utils.data.dataloader as torch_loader +from torch.utils.data import Dataset +from torchvision import transforms + +from ..config.project_config import Config as cfg +from .transforms import photometric_transforms as photoaug +from .transforms import homographic_transforms as homoaug +from .transforms.utils import random_scaling +from .synthetic_util import get_line_heatmap +from ..misc.geometry_utils import warp_points, mask_points +from ..misc.train_utils import parse_h5_data + + +def holicity_collate_fn(batch): + """ Customized collate_fn. """ + batch_keys = ["image", "junction_map", "valid_mask", "heatmap", + "heatmap_pos", "heatmap_neg", "homography", + "line_points", "line_indices"] + list_keys = ["junctions", "line_map", "line_map_pos", + "line_map_neg", "file_key"] + + outputs = {} + for data_key in batch[0].keys(): + batch_match = sum([_ in data_key for _ in batch_keys]) + list_match = sum([_ in data_key for _ in list_keys]) + # print(batch_match, list_match) + if batch_match > 0 and list_match == 0: + outputs[data_key] = torch_loader.default_collate( + [b[data_key] for b in batch]) + elif batch_match == 0 and list_match > 0: + outputs[data_key] = [b[data_key] for b in batch] + elif batch_match == 0 and list_match == 0: + continue + else: + raise ValueError( + "[Error] A key matches batch keys and list keys simultaneously.") + + return outputs + + +class HolicityDataset(Dataset): + def __init__(self, mode="train", config=None): + super(HolicityDataset, self).__init__() + if not mode in ["train", "test"]: + raise ValueError( + "[Error] Unknown mode for Holicity dataset. Only 'train' and 'test'.") + self.mode = mode + + if config is None: + self.config = self.get_default_config() + else: + self.config = config + # Also get the default config + self.default_config = self.get_default_config() + + # Get cache setting + self.dataset_name = self.get_dataset_name() + self.cache_name = self.get_cache_name() + self.cache_path = cfg.holicity_cache_path + + # Get the ground truth source if it exists + self.gt_source = None + if "gt_source_%s"%(self.mode) in self.config: + self.gt_source = self.config.get("gt_source_%s"%(self.mode)) + self.gt_source = os.path.join(cfg.export_dataroot, self.gt_source) + # Check the full path exists + if not os.path.exists(self.gt_source): + raise ValueError( + "[Error] The specified ground truth source does not exist.") + + # Get the filename dataset + print("[Info] Initializing Holicity dataset...") + self.filename_dataset, self.datapoints = self.construct_dataset() + + # Get dataset length + self.dataset_length = len(self.datapoints) + + # Print some info + print("[Info] Successfully initialized dataset") + print("\t Name: Holicity") + print("\t Mode: %s" %(self.mode)) + print("\t Gt: %s" %(self.config.get("gt_source_%s"%(self.mode), + "None"))) + print("\t Counts: %d" %(self.dataset_length)) + print("----------------------------------------") + + ####################################### + ## Dataset construction related APIs ## + ####################################### + def construct_dataset(self): + """ Construct the dataset (from scratch or from cache). """ + # Check if the filename cache exists + # If cache exists, load from cache + if self.check_dataset_cache(): + print("\t Found filename cache %s at %s"%(self.cache_name, + self.cache_path)) + print("\t Load filename cache...") + filename_dataset, datapoints = self.get_filename_dataset_from_cache() + # If not, initialize dataset from scratch + else: + print("\t Can't find filename cache ...") + print("\t Create filename dataset from scratch...") + filename_dataset, datapoints = self.get_filename_dataset() + print("\t Create filename dataset cache...") + self.create_filename_dataset_cache(filename_dataset, datapoints) + + return filename_dataset, datapoints + + def create_filename_dataset_cache(self, filename_dataset, datapoints): + """ Create filename dataset cache for faster initialization. """ + # Check cache path exists + if not os.path.exists(self.cache_path): + os.makedirs(self.cache_path) + + cache_file_path = os.path.join(self.cache_path, self.cache_name) + data = { + "filename_dataset": filename_dataset, + "datapoints": datapoints + } + with open(cache_file_path, "wb") as f: + pickle.dump(data, f, pickle.HIGHEST_PROTOCOL) + + def get_filename_dataset_from_cache(self): + """ Get filename dataset from cache. """ + # Load from pkl cache + cache_file_path = os.path.join(self.cache_path, self.cache_name) + with open(cache_file_path, "rb") as f: + data = pickle.load(f) + + return data["filename_dataset"], data["datapoints"] + + def get_filename_dataset(self): + """ Get the path to the dataset. """ + if self.mode == "train": + # Contains 5720 or 11872 images + dataset_path = [os.path.join(cfg.holicity_dataroot, p) + for p in self.config["train_splits"]] + else: + # Test mode - Contains 520 images + dataset_path = [os.path.join(cfg.holicity_dataroot, "2018-03")] + + # Get paths to all image files + image_paths = [] + for folder in dataset_path: + image_paths += [os.path.join(folder, img) + for img in os.listdir(folder) + if os.path.splitext(img)[-1] == ".jpg"] + image_paths = sorted(image_paths) + + # Verify all the images exist + for idx in range(len(image_paths)): + image_path = image_paths[idx] + if not (os.path.exists(image_path)): + raise ValueError( + "[Error] The image does not exist. %s"%(image_path)) + + # Construct the filename dataset + num_pad = int(math.ceil(math.log10(len(image_paths))) + 1) + filename_dataset = {} + for idx in range(len(image_paths)): + # Get the file key + key = self.get_padded_filename(num_pad, idx) + + filename_dataset[key] = {"image": image_paths[idx]} + + # Get the datapoints + datapoints = list(sorted(filename_dataset.keys())) + + return filename_dataset, datapoints + + def get_dataset_name(self): + """ Get dataset name from dataset config / default config. """ + dataset_name = self.config.get("dataset_name", + self.default_config["dataset_name"]) + dataset_name = dataset_name + "_%s" % self.mode + return dataset_name + + def get_cache_name(self): + """ Get cache name from dataset config / default config. """ + dataset_name = self.config.get("dataset_name", + self.default_config["dataset_name"]) + dataset_name = dataset_name + "_%s" % self.mode + # Compose cache name + cache_name = dataset_name + "_cache.pkl" + return cache_name + + def check_dataset_cache(self): + """ Check if dataset cache exists. """ + cache_file_path = os.path.join(self.cache_path, self.cache_name) + if os.path.exists(cache_file_path): + return True + else: + return False + + @staticmethod + def get_padded_filename(num_pad, idx): + """ Get the padded filename using adaptive padding. """ + file_len = len("%d" % (idx)) + filename = "0" * (num_pad - file_len) + "%d" % (idx) + return filename + + def get_default_config(self): + """ Get the default configuration. """ + return { + "dataset_name": "holicity", + "train_split": "2018-01", + "add_augmentation_to_all_splits": False, + "preprocessing": { + "resize": [512, 512], + "blur_size": 11 + }, + "augmentation":{ + "photometric":{ + "enable": False + }, + "homographic":{ + "enable": False + }, + }, + } + + ############################################ + ## Pytorch and preprocessing related APIs ## + ############################################ + @staticmethod + def get_data_from_path(data_path): + """ Get data from the information from filename dataset. """ + output = {} + + # Get image data + image_path = data_path["image"] + image = imread(image_path) + output["image"] = image + + return output + + @staticmethod + def convert_line_map(lcnn_line_map, num_junctions): + """ Convert the line_pos or line_neg + (represented by two junction indexes) to our line map. """ + # Initialize empty line map + line_map = np.zeros([num_junctions, num_junctions]) + + # Iterate through all the lines + for idx in range(lcnn_line_map.shape[0]): + index1 = lcnn_line_map[idx, 0] + index2 = lcnn_line_map[idx, 1] + + line_map[index1, index2] = 1 + line_map[index2, index1] = 1 + + return line_map + + @staticmethod + def junc_to_junc_map(junctions, image_size): + """ Convert junction points to junction maps. """ + junctions = np.round(junctions).astype(np.int) + # Clip the boundary by image size + junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1) + junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1) + + # Create junction map + junc_map = np.zeros([image_size[0], image_size[1]]) + junc_map[junctions[:, 0], junctions[:, 1]] = 1 + + return junc_map[..., None].astype(np.int) + + def parse_transforms(self, names, all_transforms): + """ Parse the transform. """ + trans = all_transforms if (names == 'all') \ + else (names if isinstance(names, list) else [names]) + assert set(trans) <= set(all_transforms) + return trans + + def get_photo_transform(self): + """ Get list of photometric transforms (according to the config). """ + # Get the photometric transform config + photo_config = self.config["augmentation"]["photometric"] + if not photo_config["enable"]: + raise ValueError( + "[Error] Photometric augmentation is not enabled.") + + # Parse photometric transforms + trans_lst = self.parse_transforms(photo_config["primitives"], + photoaug.available_augmentations) + trans_config_lst = [photo_config["params"].get(p, {}) + for p in trans_lst] + + # List of photometric augmentation + photometric_trans_lst = [ + getattr(photoaug, trans)(**conf) \ + for (trans, conf) in zip(trans_lst, trans_config_lst) + ] + + return photometric_trans_lst + + def get_homo_transform(self): + """ Get homographic transforms (according to the config). """ + # Get homographic transforms for image + homo_config = self.config["augmentation"]["homographic"]["params"] + if not self.config["augmentation"]["homographic"]["enable"]: + raise ValueError( + "[Error] Homographic augmentation is not enabled") + + # Parse the homographic transforms + image_shape = self.config["preprocessing"]["resize"] + + # Compute the min_label_len from config + try: + min_label_tmp = self.config["generation"]["min_label_len"] + except: + min_label_tmp = None + + # float label len => fraction + if isinstance(min_label_tmp, float): # Skip if not provided + min_label_len = min_label_tmp * min(image_shape) + # int label len => length in pixel + elif isinstance(min_label_tmp, int): + scale_ratio = (self.config["preprocessing"]["resize"] + / self.config["generation"]["image_size"][0]) + min_label_len = (self.config["generation"]["min_label_len"] + * scale_ratio) + # if none => no restriction + else: + min_label_len = 0 + + # Initialize the transform + homographic_trans = homoaug.homography_transform( + image_shape, homo_config, 0, min_label_len) + + return homographic_trans + + def get_line_points(self, junctions, line_map, H1=None, H2=None, + img_size=None, warp=False): + """ Sample evenly points along each line segments + and keep track of line idx. """ + if np.sum(line_map) == 0: + # No segment detected in the image + line_indices = np.zeros(self.config["max_pts"], dtype=int) + line_points = np.zeros((self.config["max_pts"], 2), dtype=float) + return line_points, line_indices + + # Extract all pairs of connected junctions + junc_indices = np.array( + [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i]) + line_segments = np.stack([junctions[junc_indices[:, 0]], + junctions[junc_indices[:, 1]]], axis=1) + # line_segments is (num_lines, 2, 2) + line_lengths = np.linalg.norm( + line_segments[:, 0] - line_segments[:, 1], axis=1) + + # Sample the points separated by at least min_dist_pts along each line + # The number of samples depends on the length of the line + num_samples = np.minimum(line_lengths // self.config["min_dist_pts"], + self.config["max_num_samples"]) + line_points = [] + line_indices = [] + cur_line_idx = 1 + for n in np.arange(2, self.config["max_num_samples"] + 1): + # Consider all lines where we can fit up to n points + cur_line_seg = line_segments[num_samples == n] + line_points_x = np.linspace(cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 0], + n, axis=-1).flatten() + line_points_y = np.linspace(cur_line_seg[:, 0, 1], + cur_line_seg[:, 1, 1], + n, axis=-1).flatten() + jitter = self.config.get("jittering", 0) + if jitter: + # Add a small random jittering of all points along the line + angles = np.arctan2( + cur_line_seg[:, 1, 0] - cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1]).repeat(n) + jitter_hyp = (np.random.rand(len(angles)) * 2 - 1) * jitter + line_points_x += jitter_hyp * np.sin(angles) + line_points_y += jitter_hyp * np.cos(angles) + line_points.append(np.stack([line_points_x, line_points_y], axis=-1)) + # Keep track of the line indices for each sampled point + num_cur_lines = len(cur_line_seg) + line_idx = np.arange(cur_line_idx, cur_line_idx + num_cur_lines) + line_indices.append(line_idx.repeat(n)) + cur_line_idx += num_cur_lines + line_points = np.concatenate(line_points, + axis=0)[:self.config["max_pts"]] + line_indices = np.concatenate(line_indices, + axis=0)[:self.config["max_pts"]] + + # Warp the points if need be, and filter unvalid ones + # If the other view is also warped + if warp and H2 is not None: + warp_points2 = warp_points(line_points, H2) + line_points = warp_points(line_points, H1) + mask = mask_points(line_points, img_size) + mask2 = mask_points(warp_points2, img_size) + mask = mask * mask2 + # If the other view is not warped + elif warp and H2 is None: + line_points = warp_points(line_points, H1) + mask = mask_points(line_points, img_size) + else: + if H1 is not None: + raise ValueError("[Error] Wrong combination of homographies.") + # Remove points that would be outside of img_size if warped by H + warped_points = warp_points(line_points, H1) + mask = mask_points(warped_points, img_size) + line_points = line_points[mask] + line_indices = line_indices[mask] + + # Pad the line points to a fixed length + # Index of 0 means padded line + line_indices = np.concatenate([line_indices, np.zeros( + self.config["max_pts"] - len(line_indices))], axis=0) + line_points = np.concatenate( + [line_points, + np.zeros((self.config["max_pts"] - len(line_points), 2), + dtype=float)], axis=0) + + return line_points, line_indices + + def export_preprocessing(self, data, numpy=False): + """ Preprocess the exported data. """ + # Fetch the corresponding entries + image = data["image"] + image_size = image.shape[:2] + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + image = photoaug.normalize_image()(image) + + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + if not numpy: + return {"image": to_tensor(image)} + else: + return {"image": image} + + def train_preprocessing_exported( + self, data, numpy=False, disable_homoaug=False, desc_training=False, + H1=None, H1_scale=None, H2=None, scale=1., h_crop=None, w_crop=None): + """ Train preprocessing for the exported labels. """ + data = copy.deepcopy(data) + # Fetch the corresponding entries + image = data["image"] + junctions = data["junctions"] + line_map = data["line_map"] + image_size = image.shape[:2] + + # Define the random crop for scaling if necessary + if h_crop is None or w_crop is None: + h_crop, w_crop = 0, 0 + if scale > 1: + H, W = self.config["preprocessing"]["resize"] + H_scale, W_scale = round(H * scale), round(W * scale) + if H_scale > H: + h_crop = np.random.randint(H_scale - H) + if W_scale > W: + w_crop = np.random.randint(W_scale - W) + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # # In HW format + # junctions = (junctions * np.array( + # self.config['preprocessing']['resize'], np.float) + # / np.array(size_old, np.float)) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + image_size = image.shape[:2] + heatmap = get_line_heatmap(junctions_xy, line_map, image_size) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Check if we need to apply augmentations + # In training mode => yes. + # In homography adaptation mode (export mode) => No + if self.config["augmentation"]["photometric"]["enable"]: + photo_trans_lst = self.get_photo_transform() + ### Image transform ### + np.random.shuffle(photo_trans_lst) + image_transform = transforms.Compose( + photo_trans_lst + [photoaug.normalize_image()]) + else: + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Perform the random scaling + if scale != 1.: + image, junctions, line_map, valid_mask = random_scaling( + image, junctions, line_map, scale, + h_crop=h_crop, w_crop=w_crop) + else: + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + # Initialize the empty output dict + outputs = {} + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + + # Check homographic augmentation + warp = (self.config["augmentation"]["homographic"]["enable"] + and disable_homoaug == False) + if warp: + homo_trans = self.get_homo_transform() + # Perform homographic transform + if H1 is None: + homo_outputs = homo_trans(image, junctions, line_map, + valid_mask=valid_mask) + else: + homo_outputs = homo_trans( + image, junctions, line_map, homo=H1, scale=H1_scale, + valid_mask=valid_mask) + homography_mat = homo_outputs["homo"] + + # Give the warp of the other view + if H1 is None: + H1 = homo_outputs["homo"] + + # Sample points along each line segments for the descriptor + if desc_training: + line_points, line_indices = self.get_line_points( + junctions, line_map, H1=H1, H2=H2, + img_size=image_size, warp=warp) + + # Record the warped results + if warp: + junctions = homo_outputs["junctions"] # Should be HW format + image = homo_outputs["warped_image"] + line_map = homo_outputs["line_map"] + valid_mask = homo_outputs["valid_mask"] # Same for pos and neg + heatmap = homo_outputs["warped_heatmap"] + + # Optionally put warping information first. + if not numpy: + outputs["homography_mat"] = to_tensor( + homography_mat).to(torch.float32)[0, ...] + else: + outputs["homography_mat"] = homography_mat.astype(np.float32) + + junction_map = self.junc_to_junc_map(junctions, image_size) + + if not numpy: + outputs.update({ + "image": to_tensor(image), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map": to_tensor(line_map).to(torch.int32)[0, ...], + "heatmap": to_tensor(heatmap).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + }) + if desc_training: + outputs.update({ + "line_points": to_tensor( + line_points).to(torch.float32)[0], + "line_indices": torch.tensor(line_indices, + dtype=torch.int) + }) + else: + outputs.update({ + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map": line_map.astype(np.int32), + "heatmap": heatmap.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + }) + if desc_training: + outputs.update({ + "line_points": line_points.astype(np.float32), + "line_indices": line_indices.astype(int) + }) + + return outputs + + def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.): + """ Train preprocessing for paired data for the exported labels + for descriptor training. """ + outputs = {} + + # Define the random crop for scaling if necessary + h_crop, w_crop = 0, 0 + if scale > 1: + H, W = self.config["preprocessing"]["resize"] + H_scale, W_scale = round(H * scale), round(W * scale) + if H_scale > H: + h_crop = np.random.randint(H_scale - H) + if W_scale > W: + w_crop = np.random.randint(W_scale - W) + + # Sample ref homography first + homo_config = self.config["augmentation"]["homographic"]["params"] + image_shape = self.config["preprocessing"]["resize"] + ref_H, ref_scale = homoaug.sample_homography(image_shape, + **homo_config) + + # Data for target view (All augmentation) + target_data = self.train_preprocessing_exported( + data, numpy=numpy, desc_training=True, H1=None, H2=ref_H, + scale=scale, h_crop=h_crop, w_crop=w_crop) + + # Data for reference view (No homographical augmentation) + ref_data = self.train_preprocessing_exported( + data, numpy=numpy, desc_training=True, H1=ref_H, + H1_scale=ref_scale, H2=target_data['homography_mat'].numpy(), + scale=scale, h_crop=h_crop, w_crop=w_crop) + + # Spread ref data + for key, val in ref_data.items(): + outputs["ref_" + key] = val + + # Spread target data + for key, val in target_data.items(): + outputs["target_" + key] = val + + return outputs + + def test_preprocessing_exported(self, data, numpy=False): + """ Test preprocessing for the exported labels. """ + data = copy.deepcopy(data) + # Fetch the corresponding entries + image = data["image"] + junctions = data["junctions"] + line_map = data["line_map"] + image_size = image.shape[:2] + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # # In HW format + # junctions = (junctions * np.array( + # self.config['preprocessing']['resize'], np.float) + # / np.array(size_old, np.float)) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Still need to normalize image + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + image_size = image.shape[:2] + heatmap = get_line_heatmap(junctions_xy, line_map, image_size) + + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + junction_map = self.junc_to_junc_map(junctions, image_size) + + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + if not numpy: + outputs = { + "image": to_tensor(image), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map": to_tensor(line_map).to(torch.int32)[0, ...], + "heatmap": to_tensor(heatmap).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + } + else: + outputs = { + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map": line_map.astype(np.int32), + "heatmap": heatmap.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + } + + return outputs + + def __len__(self): + return self.dataset_length + + def get_data_from_key(self, file_key): + """ Get data from file_key. """ + # Check key exists + if not file_key in self.filename_dataset.keys(): + raise ValueError( + "[Error] the specified key is not in the dataset.") + + # Get the data paths + data_path = self.filename_dataset[file_key] + # Read in the image and npz labels + data = self.get_data_from_path(data_path) + + # Perform transform and augmentation + if (self.mode == "train" + or self.config["add_augmentation_to_all_splits"]): + data = self.train_preprocessing(data, numpy=True) + else: + data = self.test_preprocessing(data, numpy=True) + + # Add file key to the output + data["file_key"] = file_key + + return data + + def __getitem__(self, idx): + """Return data + file_key: str, keys used to retrieve data from the filename dataset. + image: torch.float, C*H*W range 0~1, + junctions: torch.float, N*2, + junction_map: torch.int32, 1*H*W range 0 or 1, + line_map: torch.int32, N*N range 0 or 1, + heatmap: torch.int32, 1*H*W range 0 or 1, + valid_mask: torch.int32, 1*H*W range 0 or 1 + """ + # Get the corresponding datapoint and contents from filename dataset + file_key = self.datapoints[idx] + data_path = self.filename_dataset[file_key] + # Read in the image and npz labels + data = self.get_data_from_path(data_path) + + if self.gt_source: + with h5py.File(self.gt_source, "r") as f: + exported_label = parse_h5_data(f[file_key]) + + data["junctions"] = exported_label["junctions"] + data["line_map"] = exported_label["line_map"] + + # Perform transform and augmentation + return_type = self.config.get("return_type", "single") + if self.gt_source is None: + # For export only + data = self.export_preprocessing(data) + elif (self.mode == "train" + or self.config["add_augmentation_to_all_splits"]): + # Perform random scaling first + if self.config["augmentation"]["random_scaling"]["enable"]: + scale_range = self.config["augmentation"]["random_scaling"]["range"] + # Decide the scaling + scale = np.random.uniform(min(scale_range), max(scale_range)) + else: + scale = 1. + if self.mode == "train" and return_type == "paired_desc": + data = self.preprocessing_exported_paired_desc(data, + scale=scale) + else: + data = self.train_preprocessing_exported(data, scale=scale) + else: + if return_type == "paired_desc": + data = self.preprocessing_exported_paired_desc(data) + else: + data = self.test_preprocessing_exported(data) + + # Add file key to the output + data["file_key"] = file_key + + return data + diff --git a/third_party/SOLD2/sold2/dataset/merge_dataset.py b/third_party/SOLD2/sold2/dataset/merge_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..178d3822d56639a49a99f68e392330e388fa8fc3 --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/merge_dataset.py @@ -0,0 +1,37 @@ +""" Compose multiple datasets in a single loader. """ + +import numpy as np +from copy import deepcopy +from torch.utils.data import Dataset + +from .wireframe_dataset import WireframeDataset +from .holicity_dataset import HolicityDataset + + +class MergeDataset(Dataset): + def __init__(self, mode, config=None): + super(MergeDataset, self).__init__() + # Initialize the datasets + self._datasets = [] + spec_config = deepcopy(config) + for i, d in enumerate(config['datasets']): + spec_config['dataset_name'] = d + spec_config['gt_source_train'] = config['gt_source_train'][i] + spec_config['gt_source_test'] = config['gt_source_test'][i] + if d == "wireframe": + self._datasets.append(WireframeDataset(mode, spec_config)) + elif d == "holicity": + spec_config['train_split'] = config['train_splits'][i] + self._datasets.append(HolicityDataset(mode, spec_config)) + else: + raise ValueError("Unknown dataset: " + d) + + self._weights = config['weights'] + + def __getitem__(self, item): + dataset = self._datasets[np.random.choice( + range(len(self._datasets)), p=self._weights)] + return dataset[np.random.randint(len(dataset))] + + def __len__(self): + return np.sum([len(d) for d in self._datasets]) diff --git a/third_party/SOLD2/sold2/dataset/synthetic_dataset.py b/third_party/SOLD2/sold2/dataset/synthetic_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..cf5f11e5407e65887f4995291156f7cc361843d1 --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/synthetic_dataset.py @@ -0,0 +1,712 @@ +""" +This file implements the synthetic shape dataset object for pytorch +""" +from __future__ import print_function +from __future__ import division +from __future__ import absolute_import + +import os +import math +import h5py +import pickle +import torch +import numpy as np +import cv2 +from tqdm import tqdm +from torchvision import transforms +from torch.utils.data import Dataset +import torch.utils.data.dataloader as torch_loader + +from ..config.project_config import Config as cfg +from . import synthetic_util +from .transforms import photometric_transforms as photoaug +from .transforms import homographic_transforms as homoaug +from ..misc.train_utils import parse_h5_data + + +def synthetic_collate_fn(batch): + """ Customized collate_fn. """ + batch_keys = ["image", "junction_map", "heatmap", + "valid_mask", "homography"] + list_keys = ["junctions", "line_map", "file_key"] + + outputs = {} + for data_key in batch[0].keys(): + batch_match = sum([_ in data_key for _ in batch_keys]) + list_match = sum([_ in data_key for _ in list_keys]) + # print(batch_match, list_match) + if batch_match > 0 and list_match == 0: + outputs[data_key] = torch_loader.default_collate([b[data_key] + for b in batch]) + elif batch_match == 0 and list_match > 0: + outputs[data_key] = [b[data_key] for b in batch] + elif batch_match == 0 and list_match == 0: + continue + else: + raise ValueError( + "[Error] A key matches batch keys and list keys simultaneously.") + + return outputs + + +class SyntheticShapes(Dataset): + """ Dataset of synthetic shapes. """ + # Initialize the dataset + def __init__(self, mode="train", config=None): + super(SyntheticShapes, self).__init__() + if not mode in ["train", "val", "test"]: + raise ValueError( + "[Error] Supported dataset modes are 'train', 'val', and 'test'.") + self.mode = mode + + # Get configuration + if config is None: + self.config = self.get_default_config() + else: + self.config = config + + # Set all available primitives + self.available_primitives = [ + 'draw_lines', + 'draw_polygon', + 'draw_multiple_polygons', + 'draw_star', + 'draw_checkerboard_multiseg', + 'draw_stripes_multiseg', + 'draw_cube', + 'gaussian_noise' + ] + + # Some cache setting + self.dataset_name = self.get_dataset_name() + self.cache_name = self.get_cache_name() + self.cache_path = cfg.synthetic_cache_path + + # Check if export dataset exists + print("===============================================") + self.filename_dataset, self.datapoints = self.construct_dataset() + self.print_dataset_info() + + # Initialize h5 file handle + self.dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name + ".h5") + + # Fix the random seed for torch and numpy in testing mode + if ((self.mode == "val" or self.mode == "test") + and self.config["add_augmentation_to_all_splits"]): + seed = self.config.get("test_augmentation_seed", 200) + np.random.seed(seed) + torch.manual_seed(seed) + # For CuDNN + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + ########################################## + ## Dataset construction related methods ## + ########################################## + def construct_dataset(self): + """ Dataset constructor. """ + # Check if the filename cache exists + # If cache exists, load from cache + if self._check_dataset_cache(): + print("[Info]: Found filename cache at ...") + print("\t Load filename cache...") + filename_dataset, datapoints = self.get_filename_dataset_from_cache() + print("\t Check if all file exists...") + # If all file exists, continue + if self._check_file_existence(filename_dataset): + print("\t All files exist!") + # If not, need to re-export the synthetic dataset + else: + print("\t Some files are missing. Re-export the synthetic shape dataset.") + self.export_synthetic_shapes() + print("\t Initialize filename dataset") + filename_dataset, datapoints = self.get_filename_dataset() + print("\t Create filename dataset cache...") + self.create_filename_dataset_cache(filename_dataset, + datapoints) + + # If not, initialize dataset from scratch + else: + print("[Info]: Can't find filename cache ...") + print("\t First check export dataset exists.") + # If export dataset exists, then just update the filename_dataset + if self._check_export_dataset(): + print("\t Synthetic dataset exists. Initialize the dataset ...") + + # If export dataset does not exist, export from scratch + else: + print("\t Synthetic dataset does not exist. Export the synthetic dataset.") + self.export_synthetic_shapes() + print("\t Initialize filename dataset") + + filename_dataset, datapoints = self.get_filename_dataset() + print("\t Create filename dataset cache...") + self.create_filename_dataset_cache(filename_dataset, datapoints) + + return filename_dataset, datapoints + + def get_cache_name(self): + """ Get cache name from dataset config / default config. """ + if self.config["dataset_name"] is None: + dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode + else: + dataset_name = self.config["dataset_name"] + "_%s" % self.mode + # Compose cache name + cache_name = dataset_name + "_cache.pkl" + + return cache_name + + def get_dataset_name(self): + """Get dataset name from dataset config / default config. """ + if self.config["dataset_name"] is None: + dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode + else: + dataset_name = self.config["dataset_name"] + "_%s" % self.mode + + return dataset_name + + def get_filename_dataset_from_cache(self): + """ Get filename dataset from cache. """ + # Load from the pkl cache + cache_file_path = os.path.join(self.cache_path, self.cache_name) + with open(cache_file_path, "rb") as f: + data = pickle.load(f) + + return data["filename_dataset"], data["datapoints"] + + def get_filename_dataset(self): + """ Get filename dataset from scratch. """ + # Path to the exported dataset + dataset_path = os.path.join(cfg.synthetic_dataroot, + self.dataset_name + ".h5") + + filename_dataset = {} + datapoints = [] + # Open the h5 dataset + with h5py.File(dataset_path, "r") as f: + # Iterate through all the primitives + for prim_name in f.keys(): + filenames = sorted(f[prim_name].keys()) + filenames_full = [os.path.join(prim_name, _) + for _ in filenames] + + filename_dataset[prim_name] = filenames_full + datapoints += filenames_full + + return filename_dataset, datapoints + + def create_filename_dataset_cache(self, filename_dataset, datapoints): + """ Create filename dataset cache for faster initialization. """ + # Check cache path exists + if not os.path.exists(self.cache_path): + os.makedirs(self.cache_path) + + cache_file_path = os.path.join(self.cache_path, self.cache_name) + data = { + "filename_dataset": filename_dataset, + "datapoints": datapoints + } + with open(cache_file_path, "wb") as f: + pickle.dump(data, f, pickle.HIGHEST_PROTOCOL) + + def export_synthetic_shapes(self): + """ Export synthetic shapes to disk. """ + # Set the global random state for data generation + synthetic_util.set_random_state(np.random.RandomState( + self.config["generation"]["random_seed"])) + + # Define the export path + dataset_path = os.path.join(cfg.synthetic_dataroot, + self.dataset_name + ".h5") + + # Open h5py file + with h5py.File(dataset_path, "w", libver="latest") as f: + # Iterate through all types of shape + primitives = self.parse_drawing_primitives( + self.config["primitives"]) + split_size = self.config["generation"]["split_sizes"][self.mode] + for prim in primitives: + # Create h5 group + group = f.create_group(prim) + # Export single primitive + self.export_single_primitive(prim, split_size, group) + + f.swmr_mode = True + + def export_single_primitive(self, primitive, split_size, group): + """ Export single primitive. """ + # Check if the primitive is valid or not + if primitive not in self.available_primitives: + raise ValueError( + "[Error]: %s is not a supported primitive" % primitive) + # Set the random seed + synthetic_util.set_random_state(np.random.RandomState( + self.config["generation"]["random_seed"])) + + # Generate shapes + print("\t Generating %s ..." % primitive) + for idx in tqdm(range(split_size), ascii=True): + # Generate background image + image = synthetic_util.generate_background( + self.config['generation']['image_size'], + **self.config['generation']['params']['generate_background']) + + # Generate points + drawing_func = getattr(synthetic_util, primitive) + kwarg = self.config["generation"]["params"].get(primitive, {}) + + # Get min_len and min_label_len + min_len = self.config["generation"]["min_len"] + min_label_len = self.config["generation"]["min_label_len"] + + # Some only take min_label_len, and gaussian noises take nothing + if primitive in ["draw_lines", "draw_polygon", + "draw_multiple_polygons", "draw_star"]: + data = drawing_func(image, min_len=min_len, + min_label_len=min_label_len, **kwarg) + elif primitive in ["draw_checkerboard_multiseg", + "draw_stripes_multiseg", "draw_cube"]: + data = drawing_func(image, min_label_len=min_label_len, + **kwarg) + else: + data = drawing_func(image, **kwarg) + + # Convert the data + if data["points"] is not None: + points = np.flip(data["points"], axis=1).astype(np.float) + line_map = data["line_map"].astype(np.int32) + else: + points = np.zeros([0, 2]).astype(np.float) + line_map = np.zeros([0, 0]).astype(np.int32) + + # Post-processing + blur_size = self.config["preprocessing"]["blur_size"] + image = cv2.GaussianBlur(image, (blur_size, blur_size), 0) + + # Resize the image and the point location. + points = (points + * np.array(self.config['preprocessing']['resize'], + np.float) + / np.array(self.config['generation']['image_size'], + np.float)) + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # Generate the line heatmap after post-processing + junctions = np.flip(np.round(points).astype(np.int32), axis=1) + heatmap = (synthetic_util.get_line_heatmap( + junctions, line_map, + size=image.shape) * 255.).astype(np.uint8) + + # Record the data in group + num_pad = math.ceil(math.log10(split_size)) + 1 + file_key_name = self.get_padded_filename(num_pad, idx) + file_group = group.create_group(file_key_name) + + # Store data + file_group.create_dataset("points", data=points, + compression="gzip") + file_group.create_dataset("image", data=image, + compression="gzip") + file_group.create_dataset("line_map", data=line_map, + compression="gzip") + file_group.create_dataset("heatmap", data=heatmap, + compression="gzip") + + def get_default_config(self): + """ Get default configuration of the dataset. """ + # Initialize the default configuration + self.default_config = { + "dataset_name": "synthetic_shape", + "primitives": "all", + "add_augmentation_to_all_splits": False, + # Shape generation configuration + "generation": { + "split_sizes": {'train': 10000, 'val': 400, 'test': 500}, + "random_seed": 10, + "image_size": [960, 1280], + "min_len": 0.09, + "min_label_len": 0.1, + 'params': { + 'generate_background': { + 'min_kernel_size': 150, 'max_kernel_size': 500, + 'min_rad_ratio': 0.02, 'max_rad_ratio': 0.031}, + 'draw_stripes': {'transform_params': (0.1, 0.1)}, + 'draw_multiple_polygons': {'kernel_boundaries': (50, 100)} + }, + }, + # Date preprocessing configuration. + "preprocessing": { + "resize": [240, 320], + "blur_size": 11 + }, + 'augmentation': { + 'photometric': { + 'enable': False, + 'primitives': 'all', + 'params': {}, + 'random_order': True, + }, + 'homographic': { + 'enable': False, + 'params': {}, + 'valid_border_margin': 0, + }, + } + } + + return self.default_config + + def parse_drawing_primitives(self, names): + """ Parse the primitives in config to list of primitive names. """ + if names == "all": + p = self.available_primitives + else: + if isinstance(names, list): + p = names + else: + p = [names] + + assert set(p) <= set(self.available_primitives) + + return p + + @staticmethod + def get_padded_filename(num_pad, idx): + """ Get the padded filename using adaptive padding. """ + file_len = len("%d" % (idx)) + filename = "0" * (num_pad - file_len) + "%d" % (idx) + + return filename + + def print_dataset_info(self): + """ Print dataset info. """ + print("\t ---------Summary------------------") + print("\t Dataset mode: \t\t %s" % self.mode) + print("\t Number of primitive: \t %d" % len(self.filename_dataset.keys())) + print("\t Number of data: \t %d" % len(self.datapoints)) + print("\t ----------------------------------") + + ######################### + ## Pytorch related API ## + ######################### + def get_data_from_datapoint(self, datapoint, reader=None): + """ Get data given the datapoint + (keyname of the h5 dataset e.g. "draw_lines/0000.h5"). """ + # Check if the datapoint is valid + if not datapoint in self.datapoints: + raise ValueError( + "[Error] The specified datapoint is not in available datapoints.") + + # Get data from h5 dataset + if reader is None: + raise ValueError( + "[Error] The reader must be provided in __getitem__.") + else: + data = reader[datapoint] + + return parse_h5_data(data) + + def get_data_from_signature(self, primitive_name, index): + """ Get data given the primitive name and index ("draw_lines", 10) """ + # Check the primitive name and index + self._check_primitive_and_index(primitive_name, index) + + # Get the datapoint from filename dataset + datapoint = self.filename_dataset[primitive_name][index] + + return self.get_data_from_datapoint(datapoint) + + def parse_transforms(self, names, all_transforms): + trans = all_transforms if (names == 'all') \ + else (names if isinstance(names, list) else [names]) + assert set(trans) <= set(all_transforms) + return trans + + def get_photo_transform(self): + """ Get list of photometric transforms (according to the config). """ + # Get the photometric transform config + photo_config = self.config["augmentation"]["photometric"] + if not photo_config["enable"]: + raise ValueError( + "[Error] Photometric augmentation is not enabled.") + + # Parse photometric transforms + trans_lst = self.parse_transforms(photo_config["primitives"], + photoaug.available_augmentations) + trans_config_lst = [photo_config["params"].get(p, {}) + for p in trans_lst] + + # List of photometric augmentation + photometric_trans_lst = [ + getattr(photoaug, trans)(**conf) \ + for (trans, conf) in zip(trans_lst, trans_config_lst) + ] + + return photometric_trans_lst + + def get_homo_transform(self): + """ Get homographic transforms (according to the config). """ + # Get homographic transforms for image + homo_config = self.config["augmentation"]["homographic"]["params"] + if not self.config["augmentation"]["homographic"]["enable"]: + raise ValueError( + "[Error] Homographic augmentation is not enabled") + + # Parse the homographic transforms + # ToDo: use the shape from the config + image_shape = self.config["preprocessing"]["resize"] + + # Compute the min_label_len from config + try: + min_label_tmp = self.config["generation"]["min_label_len"] + except: + min_label_tmp = None + + # float label len => fraction + if isinstance(min_label_tmp, float): # Skip if not provided + min_label_len = min_label_tmp * min(image_shape) + # int label len => length in pixel + elif isinstance(min_label_tmp, int): + scale_ratio = (self.config["preprocessing"]["resize"] + / self.config["generation"]["image_size"][0]) + min_label_len = (self.config["generation"]["min_label_len"] + * scale_ratio) + # if none => no restriction + else: + min_label_len = 0 + + # Initialize the transform + homographic_trans = homoaug.homography_transform( + image_shape, homo_config, 0, min_label_len) + + return homographic_trans + + @staticmethod + def junc_to_junc_map(junctions, image_size): + """ Convert junction points to junction maps. """ + junctions = np.round(junctions).astype(np.int) + # Clip the boundary by image size + junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1) + junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1) + + # Create junction map + junc_map = np.zeros([image_size[0], image_size[1]]) + junc_map[junctions[:, 0], junctions[:, 1]] = 1 + + return junc_map[..., None].astype(np.int) + + def train_preprocessing(self, data, disable_homoaug=False): + """ Training preprocessing. """ + # Fetch corresponding entries + image = data["image"] + junctions = data["points"] + line_map = data["line_map"] + heatmap = data["heatmap"] + image_size = image.shape[:2] + + # Resize the image before the photometric and homographic transforms + # Check if we need to do the resizing + if not(list(image.shape) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape) + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + junctions = ( + junctions + * np.array(self.config['preprocessing']['resize'], np.float) + / np.array(size_old, np.float)) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), + axis=1) + heatmap = synthetic_util.get_line_heatmap(junctions_xy, line_map, + size=image.shape) + heatmap = (heatmap * 255.).astype(np.uint8) + + # Update image size + image_size = image.shape[:2] + + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + # Check if we need to apply augmentations + # In training mode => yes. + # In homography adaptation mode (export mode) => No + # Check photometric augmentation + if self.config["augmentation"]["photometric"]["enable"]: + photo_trans_lst = self.get_photo_transform() + ### Image transform ### + np.random.shuffle(photo_trans_lst) + image_transform = transforms.Compose( + photo_trans_lst + [photoaug.normalize_image()]) + else: + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Initialize the empty output dict + outputs = {} + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + # Check homographic augmentation + if (self.config["augmentation"]["homographic"]["enable"] + and disable_homoaug == False): + homo_trans = self.get_homo_transform() + # Perform homographic transform + homo_outputs = homo_trans(image, junctions, line_map) + + # Record the warped results + junctions = homo_outputs["junctions"] # Should be HW format + image = homo_outputs["warped_image"] + line_map = homo_outputs["line_map"] + heatmap = homo_outputs["warped_heatmap"] + valid_mask = homo_outputs["valid_mask"] # Same for pos and neg + homography_mat = homo_outputs["homo"] + + # Optionally put warpping information first. + outputs["homography_mat"] = to_tensor( + homography_mat).to(torch.float32)[0, ...] + + junction_map = self.junc_to_junc_map(junctions, image_size) + + outputs.update({ + "image": to_tensor(image), + "junctions": to_tensor(np.ascontiguousarray( + junctions).copy()).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map": to_tensor(line_map).to(torch.int32)[0, ...], + "heatmap": to_tensor(heatmap).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32), + }) + + return outputs + + def test_preprocessing(self, data): + """ Test preprocessing. """ + # Fetch corresponding entries + image = data["image"] + points = data["points"] + line_map = data["line_map"] + heatmap = data["heatmap"] + image_size = image.shape[:2] + + # Resize the image before the photometric and homographic transforms + if not (list(image.shape) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape) + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + points = (points + * np.array(self.config['preprocessing']['resize'], + np.float) + / np.array(size_old, np.float)) + + # Generate the line heatmap after post-processing + junctions = np.flip(np.round(points).astype(np.int32), axis=1) + heatmap = synthetic_util.get_line_heatmap(junctions, line_map, + size=image.shape) + heatmap = (heatmap * 255.).astype(np.uint8) + + # Update image size + image_size = image.shape[:2] + + ### image transform ### + image_transform = photoaug.normalize_image() + image = image_transform(image) + + ### joint transform ### + junction_map = self.junc_to_junc_map(points, image_size) + to_tensor = transforms.ToTensor() + image = to_tensor(image) + junctions = to_tensor(points) + junction_map = to_tensor(junction_map).to(torch.int) + line_map = to_tensor(line_map) + heatmap = to_tensor(heatmap) + valid_mask = to_tensor(np.ones(image_size)).to(torch.int32) + + return { + "image": image, + "junctions": junctions, + "junction_map": junction_map, + "line_map": line_map, + "heatmap": heatmap, + "valid_mask": valid_mask + } + + def __getitem__(self, index): + datapoint = self.datapoints[index] + + # Initialize reader and use it + with h5py.File(self.dataset_path, "r", swmr=True) as reader: + data = self.get_data_from_datapoint(datapoint, reader) + + # Apply different transforms in different mod. + if (self.mode == "train" + or self.config["add_augmentation_to_all_splits"]): + return_type = self.config.get("return_type", "single") + data = self.train_preprocessing(data) + else: + data = self.test_preprocessing(data) + + return data + + def __len__(self): + return len(self.datapoints) + + ######################## + ## Some other methods ## + ######################## + def _check_dataset_cache(self): + """ Check if dataset cache exists. """ + cache_file_path = os.path.join(self.cache_path, self.cache_name) + if os.path.exists(cache_file_path): + return True + else: + return False + + def _check_export_dataset(self): + """ Check if exported dataset exists. """ + dataset_path = os.path.join(cfg.synthetic_dataroot, self.dataset_name) + if os.path.exists(dataset_path) and len(os.listdir(dataset_path)) > 0: + return True + else: + return False + + def _check_file_existence(self, filename_dataset): + """ Check if all exported file exists. """ + # Path to the exported dataset + dataset_path = os.path.join(cfg.synthetic_dataroot, + self.dataset_name + ".h5") + + flag = True + # Open the h5 dataset + with h5py.File(dataset_path, "r") as f: + # Iterate through all the primitives + for prim_name in f.keys(): + if (len(filename_dataset[prim_name]) + != len(f[prim_name].keys())): + flag = False + + return flag + + def _check_primitive_and_index(self, primitive, index): + """ Check if the primitve and index are valid. """ + # Check primitives + if not primitive in self.available_primitives: + raise ValueError( + "[Error] The primitive is not in available primitives.") + + prim_len = len(self.filename_dataset[primitive]) + # Check the index + if not index < prim_len: + raise ValueError( + "[Error] The index exceeds the total file counts %d for %s" + % (prim_len, primitive)) diff --git a/third_party/SOLD2/sold2/dataset/synthetic_util.py b/third_party/SOLD2/sold2/dataset/synthetic_util.py new file mode 100644 index 0000000000000000000000000000000000000000..af009e0ce7e91391e31d7069064ae6121aa84cc0 --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/synthetic_util.py @@ -0,0 +1,1232 @@ +""" +Code adapted from https://github.com/rpautrat/SuperPoint +Module used to generate geometrical synthetic shapes +""" +import math +import cv2 as cv +import numpy as np +import shapely.geometry +from itertools import combinations + +random_state = np.random.RandomState(None) + + +def set_random_state(state): + global random_state + random_state = state + + +def get_random_color(background_color): + """ Output a random scalar in grayscale with a least a small contrast + with the background color. """ + color = random_state.randint(256) + if abs(color - background_color) < 30: # not enough contrast + color = (color + 128) % 256 + return color + + +def get_different_color(previous_colors, min_dist=50, max_count=20): + """ Output a color that contrasts with the previous colors. + Parameters: + previous_colors: np.array of the previous colors + min_dist: the difference between the new color and + the previous colors must be at least min_dist + max_count: maximal number of iterations + """ + color = random_state.randint(256) + count = 0 + while np.any(np.abs(previous_colors - color) < min_dist) and count < max_count: + count += 1 + color = random_state.randint(256) + return color + + +def add_salt_and_pepper(img): + """ Add salt and pepper noise to an image. """ + noise = np.zeros((img.shape[0], img.shape[1]), dtype=np.uint8) + cv.randu(noise, 0, 255) + black = noise < 30 + white = noise > 225 + img[white > 0] = 255 + img[black > 0] = 0 + cv.blur(img, (5, 5), img) + return np.empty((0, 2), dtype=np.int) + + +def generate_background(size=(960, 1280), nb_blobs=100, min_rad_ratio=0.01, + max_rad_ratio=0.05, min_kernel_size=50, + max_kernel_size=300): + """ Generate a customized background image. + Parameters: + size: size of the image + nb_blobs: number of circles to draw + min_rad_ratio: the radius of blobs is at least min_rad_size * max(size) + max_rad_ratio: the radius of blobs is at most max_rad_size * max(size) + min_kernel_size: minimal size of the kernel + max_kernel_size: maximal size of the kernel + """ + img = np.zeros(size, dtype=np.uint8) + dim = max(size) + cv.randu(img, 0, 255) + cv.threshold(img, random_state.randint(256), 255, cv.THRESH_BINARY, img) + background_color = int(np.mean(img)) + blobs = np.concatenate( + [random_state.randint(0, size[1], size=(nb_blobs, 1)), + random_state.randint(0, size[0], size=(nb_blobs, 1))], axis=1) + for i in range(nb_blobs): + col = get_random_color(background_color) + cv.circle(img, (blobs[i][0], blobs[i][1]), + np.random.randint(int(dim * min_rad_ratio), + int(dim * max_rad_ratio)), + col, -1) + kernel_size = random_state.randint(min_kernel_size, max_kernel_size) + cv.blur(img, (kernel_size, kernel_size), img) + return img + + +def generate_custom_background(size, background_color, nb_blobs=3000, + kernel_boundaries=(50, 100)): + """ Generate a customized background to fill the shapes. + Parameters: + background_color: average color of the background image + nb_blobs: number of circles to draw + kernel_boundaries: interval of the possible sizes of the kernel + """ + img = np.zeros(size, dtype=np.uint8) + img = img + get_random_color(background_color) + blobs = np.concatenate( + [np.random.randint(0, size[1], size=(nb_blobs, 1)), + np.random.randint(0, size[0], size=(nb_blobs, 1))], axis=1) + for i in range(nb_blobs): + col = get_random_color(background_color) + cv.circle(img, (blobs[i][0], blobs[i][1]), + np.random.randint(20), col, -1) + kernel_size = np.random.randint(kernel_boundaries[0], + kernel_boundaries[1]) + cv.blur(img, (kernel_size, kernel_size), img) + return img + + +def final_blur(img, kernel_size=(5, 5)): + """ Gaussian blur applied to an image. + Parameters: + kernel_size: size of the kernel + """ + cv.GaussianBlur(img, kernel_size, 0, img) + + +def ccw(A, B, C, dim): + """ Check if the points are listed in counter-clockwise order. """ + if dim == 2: # only 2 dimensions + return((C[:, 1] - A[:, 1]) * (B[:, 0] - A[:, 0]) + > (B[:, 1] - A[:, 1]) * (C[:, 0] - A[:, 0])) + else: # dim should be equal to 3 + return((C[:, 1, :] - A[:, 1, :]) + * (B[:, 0, :] - A[:, 0, :]) + > (B[:, 1, :] - A[:, 1, :]) + * (C[:, 0, :] - A[:, 0, :])) + + +def intersect(A, B, C, D, dim): + """ Return true if line segments AB and CD intersect """ + return np.any((ccw(A, C, D, dim) != ccw(B, C, D, dim)) & + (ccw(A, B, C, dim) != ccw(A, B, D, dim))) + + +def keep_points_inside(points, size): + """ Keep only the points whose coordinates are inside the dimensions of + the image of size 'size' """ + mask = (points[:, 0] >= 0) & (points[:, 0] < size[1]) &\ + (points[:, 1] >= 0) & (points[:, 1] < size[0]) + return points[mask, :] + + +def get_unique_junctions(segments, min_label_len): + """ Get unique junction points from line segments. """ + # Get all junctions from segments + junctions_all = np.concatenate((segments[:, :2], segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + # Get all unique junction points + else: + junc_points = np.unique(junctions_all, axis=0) + # Generate line map from points and segments + line_map = get_line_map(junc_points, segments) + + return junc_points, line_map + + +def get_line_map(points: np.ndarray, segments: np.ndarray) -> np.ndarray: + """ Get line map given the points and segment sets. """ + # create empty line map + num_point = points.shape[0] + line_map = np.zeros([num_point, num_point]) + + # Iterate through every segment + for idx in range(segments.shape[0]): + # Get the junctions from a single segement + seg = segments[idx, :] + junction1 = seg[:2] + junction2 = seg[2:] + + # Get index + idx_junction1 = np.where((points == junction1).sum(axis=1) == 2)[0] + idx_junction2 = np.where((points == junction2).sum(axis=1) == 2)[0] + + # label the corresponding entries + line_map[idx_junction1, idx_junction2] = 1 + line_map[idx_junction2, idx_junction1] = 1 + + return line_map + + +def get_line_heatmap(junctions, line_map, size=[480, 640], thickness=1): + """ Get line heat map from junctions and line map. """ + # Make sure that the thickness is 1 + if not isinstance(thickness, int): + thickness = int(thickness) + + # If the junction points are not int => round them and convert to int + if not junctions.dtype == np.int: + junctions = (np.round(junctions)).astype(np.int) + + # Initialize empty map + heat_map = np.zeros(size) + + if junctions.shape[0] > 0: # If empty, just return zero map + # Iterate through all the junctions + for idx in range(junctions.shape[0]): + # if no connectivity, just skip it + if line_map[idx, :].sum() == 0: + continue + # Plot the line segment + else: + # Iterate through all the connected junctions + for idx2 in np.where(line_map[idx, :] == 1)[0]: + point1 = junctions[idx, :] + point2 = junctions[idx2, :] + + # Draw line + cv.line(heat_map, tuple(point1), tuple(point2), 1., thickness) + + return heat_map + + +def draw_lines(img, nb_lines=10, min_len=32, min_label_len=32): + """ Draw random lines and output the positions of the pair of junctions + and line associativities. + Parameters: + nb_lines: maximal number of lines + """ + # Set line number and points placeholder + num_lines = random_state.randint(1, nb_lines) + segments = np.empty((0, 4), dtype=np.int) + points = np.empty((0, 2), dtype=np.int) + background_color = int(np.mean(img)) + min_dim = min(img.shape) + + # Convert length constrain to pixel if given float number + if isinstance(min_len, float) and min_len <= 1.: + min_len = int(min_dim * min_len) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + + # Generate lines one by one + for i in range(num_lines): + x1 = random_state.randint(img.shape[1]) + y1 = random_state.randint(img.shape[0]) + p1 = np.array([[x1, y1]]) + x2 = random_state.randint(img.shape[1]) + y2 = random_state.randint(img.shape[0]) + p2 = np.array([[x2, y2]]) + + # Check the length of the line + line_length = np.sqrt(np.sum((p1 - p2) ** 2)) + if line_length < min_len: + continue + + # Check that there is no overlap + if intersect(segments[:, 0:2], segments[:, 2:4], p1, p2, 2): + continue + + col = get_random_color(background_color) + thickness = random_state.randint(min_dim * 0.01, min_dim * 0.02) + cv.line(img, (x1, y1), (x2, y2), col, thickness) + + # Only record the segments longer than min_label_len + seg_len = math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + if seg_len >= min_label_len: + segments = np.concatenate([segments, + np.array([[x1, y1, x2, y2]])], axis=0) + points = np.concatenate([points, + np.array([[x1, y1], [x2, y2]])], axis=0) + + # If no line is drawn, recursively call the function + if points.shape[0] == 0: + return draw_lines(img, nb_lines, min_len, min_label_len) + + # Get the line associativity map + line_map = get_line_map(points, segments) + + return { + "points": points, + "line_map": line_map + } + + +def check_segment_len(segments, min_len=32): + """ Check if one of the segments is too short (True means too short). """ + point1_vec = segments[:, :2] + point2_vec = segments[:, 2:] + diff = point1_vec - point2_vec + + dist = np.sqrt(np.sum(diff ** 2, axis=1)) + if np.any(dist < min_len): + return True + else: + return False + + +def draw_polygon(img, max_sides=8, min_len=32, min_label_len=64): + """ Draw a polygon with a random number of corners and return the position + of the junctions + line map. + Parameters: + max_sides: maximal number of sides + 1 + """ + num_corners = random_state.randint(3, max_sides) + min_dim = min(img.shape[0], img.shape[1]) + rad = max(random_state.rand() * min_dim / 2, min_dim / 10) + # Center of a circle + x = random_state.randint(rad, img.shape[1] - rad) + y = random_state.randint(rad, img.shape[0] - rad) + + # Convert length constrain to pixel if given float number + if isinstance(min_len, float) and min_len <= 1.: + min_len = int(min_dim * min_len) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + + # Sample num_corners points inside the circle + slices = np.linspace(0, 2 * math.pi, num_corners + 1) + angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i]) + for i in range(num_corners)] + points = np.array( + [[int(x + max(random_state.rand(), 0.4) * rad * math.cos(a)), + int(y + max(random_state.rand(), 0.4) * rad * math.sin(a))] + for a in angles]) + + # Filter the points that are too close or that have an angle too flat + norms = [np.linalg.norm(points[(i-1) % num_corners, :] + - points[i, :]) for i in range(num_corners)] + mask = np.array(norms) > 0.01 + points = points[mask, :] + num_corners = points.shape[0] + corner_angles = [angle_between_vectors(points[(i-1) % num_corners, :] - + points[i, :], + points[(i+1) % num_corners, :] - + points[i, :]) + for i in range(num_corners)] + mask = np.array(corner_angles) < (2 * math.pi / 3) + points = points[mask, :] + num_corners = points.shape[0] + + # Get junction pairs from points + segments = np.zeros([0, 4]) + # Used to record all the segments no matter we are going to label it or not. + segments_raw = np.zeros([0, 4]) + for idx in range(num_corners): + if idx == (num_corners - 1): + p1 = points[idx] + p2 = points[0] + else: + p1 = points[idx] + p2 = points[idx + 1] + + segment = np.concatenate((p1, p2), axis=0) + # Only record the segments longer than min_label_len + seg_len = np.sqrt(np.sum((p1 - p2) ** 2)) + if seg_len >= min_label_len: + segments = np.concatenate((segments, segment[None, ...]), axis=0) + segments_raw = np.concatenate((segments_raw, segment[None, ...]), + axis=0) + + # If not enough corner, just regenerate one + if (num_corners < 3) or check_segment_len(segments_raw, min_len): + return draw_polygon(img, max_sides, min_len, min_label_len) + + # Get junctions from segments + junctions_all = np.concatenate((segments[:, :2], segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + else: + junc_points = np.unique(junctions_all, axis=0) + + # Get the line map + line_map = get_line_map(junc_points, segments) + + corners = points.reshape((-1, 1, 2)) + col = get_random_color(int(np.mean(img))) + cv.fillPoly(img, [corners], col) + + return { + "points": junc_points, + "line_map": line_map + } + + +def overlap(center, rad, centers, rads): + """ Check that the circle with (center, rad) + doesn't overlap with the other circles. """ + flag = False + for i in range(len(rads)): + if np.linalg.norm(center - centers[i]) < rad + rads[i]: + flag = True + break + return flag + + +def angle_between_vectors(v1, v2): + """ Compute the angle (in rad) between the two vectors v1 and v2. """ + v1_u = v1 / np.linalg.norm(v1) + v2_u = v2 / np.linalg.norm(v2) + return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) + + +def draw_multiple_polygons(img, max_sides=8, nb_polygons=30, min_len=32, + min_label_len=64, safe_margin=5, **extra): + """ Draw multiple polygons with a random number of corners + and return the junction points + line map. + Parameters: + max_sides: maximal number of sides + 1 + nb_polygons: maximal number of polygons + """ + segments = np.empty((0, 4), dtype=np.int) + label_segments = np.empty((0, 4), dtype=np.int) + centers = [] + rads = [] + points = np.empty((0, 2), dtype=np.int) + background_color = int(np.mean(img)) + + min_dim = min(img.shape[0], img.shape[1]) + # Convert length constrain to pixel if given float number + if isinstance(min_len, float) and min_len <= 1.: + min_len = int(min_dim * min_len) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + if isinstance(safe_margin, float) and safe_margin <= 1.: + safe_margin = int(min_dim * safe_margin) + + # Sequentially generate polygons + for i in range(nb_polygons): + num_corners = random_state.randint(3, max_sides) + min_dim = min(img.shape[0], img.shape[1]) + + # Also add the real radius + rad = max(random_state.rand() * min_dim / 2, min_dim / 9) + rad_real = rad - safe_margin + + # Center of a circle + x = random_state.randint(rad, img.shape[1] - rad) + y = random_state.randint(rad, img.shape[0] - rad) + + # Sample num_corners points inside the circle + slices = np.linspace(0, 2 * math.pi, num_corners + 1) + angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i]) + for i in range(num_corners)] + + # Sample outer points and inner points + new_points = [] + new_points_real = [] + for a in angles: + x_offset = max(random_state.rand(), 0.4) + y_offset = max(random_state.rand(), 0.4) + new_points.append([int(x + x_offset * rad * math.cos(a)), + int(y + y_offset * rad * math.sin(a))]) + new_points_real.append( + [int(x + x_offset * rad_real * math.cos(a)), + int(y + y_offset * rad_real * math.sin(a))]) + new_points = np.array(new_points) + new_points_real = np.array(new_points_real) + + # Filter the points that are too close or that have an angle too flat + norms = [np.linalg.norm(new_points[(i-1) % num_corners, :] + - new_points[i, :]) + for i in range(num_corners)] + mask = np.array(norms) > 0.01 + new_points = new_points[mask, :] + new_points_real = new_points_real[mask, :] + + num_corners = new_points.shape[0] + corner_angles = [ + angle_between_vectors(new_points[(i-1) % num_corners, :] - + new_points[i, :], + new_points[(i+1) % num_corners, :] - + new_points[i, :]) + for i in range(num_corners)] + mask = np.array(corner_angles) < (2 * math.pi / 3) + new_points = new_points[mask, :] + new_points_real = new_points_real[mask, :] + num_corners = new_points.shape[0] + + # Not enough corners + if num_corners < 3: + continue + + # Segments for checking overlap (outer circle) + new_segments = np.zeros((1, 4, num_corners)) + new_segments[:, 0, :] = [new_points[i][0] for i in range(num_corners)] + new_segments[:, 1, :] = [new_points[i][1] for i in range(num_corners)] + new_segments[:, 2, :] = [new_points[(i+1) % num_corners][0] + for i in range(num_corners)] + new_segments[:, 3, :] = [new_points[(i+1) % num_corners][1] + for i in range(num_corners)] + + # Segments to record (inner circle) + new_segments_real = np.zeros((1, 4, num_corners)) + new_segments_real[:, 0, :] = [new_points_real[i][0] + for i in range(num_corners)] + new_segments_real[:, 1, :] = [new_points_real[i][1] + for i in range(num_corners)] + new_segments_real[:, 2, :] = [ + new_points_real[(i + 1) % num_corners][0] + for i in range(num_corners)] + new_segments_real[:, 3, :] = [ + new_points_real[(i + 1) % num_corners][1] + for i in range(num_corners)] + + # Check that the polygon will not overlap with pre-existing shapes + if intersect(segments[:, 0:2, None], segments[:, 2:4, None], + new_segments[:, 0:2, :], new_segments[:, 2:4, :], + 3) or overlap(np.array([x, y]), rad, centers, rads): + continue + + # Check that the the edges of the polygon is not too short + if check_segment_len(new_segments_real, min_len): + continue + + # If the polygon is valid, append it to the polygon set + centers.append(np.array([x, y])) + rads.append(rad) + new_segments = np.reshape(np.swapaxes(new_segments, 0, 2), (-1, 4)) + segments = np.concatenate([segments, new_segments], axis=0) + + # Only record the segments longer than min_label_len + new_segments_real = np.reshape(np.swapaxes(new_segments_real, 0, 2), + (-1, 4)) + points1 = new_segments_real[:, :2] + points2 = new_segments_real[:, 2:] + seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1)) + new_label_segment = new_segments_real[seg_len >= min_label_len, :] + label_segments = np.concatenate([label_segments, new_label_segment], + axis=0) + + # Color the polygon with a custom background + corners = new_points_real.reshape((-1, 1, 2)) + mask = np.zeros(img.shape, np.uint8) + custom_background = generate_custom_background( + img.shape, background_color, **extra) + + cv.fillPoly(mask, [corners], 255) + locs = np.where(mask != 0) + img[locs[0], locs[1]] = custom_background[locs[0], locs[1]] + points = np.concatenate([points, new_points], axis=0) + + # Get all junctions from label segments + junctions_all = np.concatenate( + (label_segments[:, :2], label_segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + else: + junc_points = np.unique(junctions_all, axis=0) + + # Generate line map from points and segments + line_map = get_line_map(junc_points, label_segments) + + return { + "points": junc_points, + "line_map": line_map + } + + +def draw_ellipses(img, nb_ellipses=20): + """ Draw several ellipses. + Parameters: + nb_ellipses: maximal number of ellipses + """ + centers = np.empty((0, 2), dtype=np.int) + rads = np.empty((0, 1), dtype=np.int) + min_dim = min(img.shape[0], img.shape[1]) / 4 + background_color = int(np.mean(img)) + for i in range(nb_ellipses): + ax = int(max(random_state.rand() * min_dim, min_dim / 5)) + ay = int(max(random_state.rand() * min_dim, min_dim / 5)) + max_rad = max(ax, ay) + x = random_state.randint(max_rad, img.shape[1] - max_rad) # center + y = random_state.randint(max_rad, img.shape[0] - max_rad) + new_center = np.array([[x, y]]) + + # Check that the ellipsis will not overlap with pre-existing shapes + diff = centers - new_center + if np.any(max_rad > (np.sqrt(np.sum(diff * diff, axis=1)) - rads)): + continue + centers = np.concatenate([centers, new_center], axis=0) + rads = np.concatenate([rads, np.array([[max_rad]])], axis=0) + + col = get_random_color(background_color) + angle = random_state.rand() * 90 + cv.ellipse(img, (x, y), (ax, ay), angle, 0, 360, col, -1) + return np.empty((0, 2), dtype=np.int) + + +def draw_star(img, nb_branches=6, min_len=32, min_label_len=64): + """ Draw a star and return the junction points + line map. + Parameters: + nb_branches: number of branches of the star + """ + num_branches = random_state.randint(3, nb_branches) + min_dim = min(img.shape[0], img.shape[1]) + # Convert length constrain to pixel if given float number + if isinstance(min_len, float) and min_len <= 1.: + min_len = int(min_dim * min_len) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + + thickness = random_state.randint(min_dim * 0.01, min_dim * 0.025) + rad = max(random_state.rand() * min_dim / 2, min_dim / 5) + x = random_state.randint(rad, img.shape[1] - rad) + y = random_state.randint(rad, img.shape[0] - rad) + # Sample num_branches points inside the circle + slices = np.linspace(0, 2 * math.pi, num_branches + 1) + angles = [slices[i] + random_state.rand() * (slices[i+1] - slices[i]) + for i in range(num_branches)] + points = np.array( + [[int(x + max(random_state.rand(), 0.3) * rad * math.cos(a)), + int(y + max(random_state.rand(), 0.3) * rad * math.sin(a))] + for a in angles]) + points = np.concatenate(([[x, y]], points), axis=0) + + # Generate segments and check the length + segments = np.array([[x, y, _[0], _[1]] for _ in points[1:, :]]) + if check_segment_len(segments, min_len): + return draw_star(img, nb_branches, min_len, min_label_len) + + # Only record the segments longer than min_label_len + points1 = segments[:, :2] + points2 = segments[:, 2:] + seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1)) + label_segments = segments[seg_len >= min_label_len, :] + + # Get all junctions from label segments + junctions_all = np.concatenate( + (label_segments[:, :2], label_segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + # Get all unique junction points + else: + junc_points = np.unique(junctions_all, axis=0) + # Generate line map from points and segments + line_map = get_line_map(junc_points, label_segments) + + background_color = int(np.mean(img)) + for i in range(1, num_branches + 1): + col = get_random_color(background_color) + cv.line(img, (points[0][0], points[0][1]), + (points[i][0], points[i][1]), + col, thickness) + return { + "points": junc_points, + "line_map": line_map + } + + +def draw_checkerboard_multiseg(img, max_rows=7, max_cols=7, + transform_params=(0.05, 0.15), + min_label_len=64, seed=None): + """ Draw a checkerboard and output the junctions + line segments + Parameters: + max_rows: maximal number of rows + 1 + max_cols: maximal number of cols + 1 + transform_params: set the range of the parameters of the transformations + """ + if seed is None: + global random_state + else: + random_state = np.random.RandomState(seed) + + background_color = int(np.mean(img)) + + min_dim = min(img.shape) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + # Create the grid + rows = random_state.randint(3, max_rows) # number of rows + cols = random_state.randint(3, max_cols) # number of cols + s = min((img.shape[1] - 1) // cols, (img.shape[0] - 1) // rows) + x_coord = np.tile(range(cols + 1), + rows + 1).reshape(((rows + 1) * (cols + 1), 1)) + y_coord = np.repeat(range(rows + 1), + cols + 1).reshape(((rows + 1) * (cols + 1), 1)) + # points are the grid coordinates + points = s * np.concatenate([x_coord, y_coord], axis=1) + + # Warp the grid using an affine transformation and an homography + alpha_affine = np.max(img.shape) * ( + transform_params[0] + random_state.rand() * transform_params[1]) + center_square = np.float32(img.shape) // 2 + min_dim = min(img.shape) + square_size = min_dim // 3 + pts1 = np.float32([center_square + square_size, + [center_square[0] + square_size, + center_square[1] - square_size], + center_square - square_size, + [center_square[0] - square_size, + center_square[1] + square_size]]) + pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, + size=pts1.shape).astype(np.float32) + affine_transform = cv.getAffineTransform(pts1[:3], pts2[:3]) + pts2 = pts1 + random_state.uniform(-alpha_affine / 2, alpha_affine / 2, + size=pts1.shape).astype(np.float32) + perspective_transform = cv.getPerspectiveTransform(pts1, pts2) + + # Apply the affine transformation + points = np.transpose(np.concatenate( + (points, np.ones(((rows + 1) * (cols + 1), 1))), axis=1)) + warped_points = np.transpose(np.dot(affine_transform, points)) + + # Apply the homography + warped_col0 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[0, :2]), axis=1), + perspective_transform[0, 2]) + warped_col1 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[1, :2]), axis=1), + perspective_transform[1, 2]) + warped_col2 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[2, :2]), axis=1), + perspective_transform[2, 2]) + warped_col0 = np.divide(warped_col0, warped_col2) + warped_col1 = np.divide(warped_col1, warped_col2) + warped_points = np.concatenate( + [warped_col0[:, None], warped_col1[:, None]], axis=1) + warped_points_float = warped_points.copy() + warped_points = warped_points.astype(int) + + # Fill the rectangles + colors = np.zeros((rows * cols,), np.int32) + for i in range(rows): + for j in range(cols): + # Get a color that contrast with the neighboring cells + if i == 0 and j == 0: + col = get_random_color(background_color) + else: + neighboring_colors = [] + if i != 0: + neighboring_colors.append(colors[(i - 1) * cols + j]) + if j != 0: + neighboring_colors.append(colors[i * cols + j - 1]) + col = get_different_color(np.array(neighboring_colors)) + colors[i * cols + j] = col + + # Fill the cell + cv.fillConvexPoly(img, np.array( + [(warped_points[i * (cols + 1) + j, 0], + warped_points[i * (cols + 1) + j, 1]), + (warped_points[i * (cols + 1) + j + 1, 0], + warped_points[i * (cols + 1) + j + 1, 1]), + (warped_points[(i + 1) * (cols + 1) + j + 1, 0], + warped_points[(i + 1) * (cols + 1) + j + 1, 1]), + (warped_points[(i + 1) * (cols + 1) + j, 0], + warped_points[(i + 1) * (cols + 1) + j, 1])]), col) + + label_segments = np.empty([0, 4], dtype=np.int) + # Iterate through rows + for row_idx in range(rows + 1): + # Include all the combination of the junctions + # Iterate through all the combination of junction index in that row + multi_seg_lst = [ + np.array([warped_points_float[id1, 0], + warped_points_float[id1, 1], + warped_points_float[id2, 0], + warped_points_float[id2, 1]])[None, ...] + for (id1, id2) in combinations(range( + row_idx * (cols + 1), (row_idx + 1) * (cols + 1), 1), 2)] + multi_seg = np.concatenate(multi_seg_lst, axis=0) + label_segments = np.concatenate((label_segments, multi_seg), axis=0) + + # Iterate through columns + for col_idx in range(cols + 1): # for 5 columns, we will have 5 + 1 edges + # Include all the combination of the junctions + # Iterate throuhg all the combination of junction index in that column + multi_seg_lst = [ + np.array([warped_points_float[id1, 0], + warped_points_float[id1, 1], + warped_points_float[id2, 0], + warped_points_float[id2, 1]])[None, ...] + for (id1, id2) in combinations(range( + col_idx, col_idx + ((rows + 1) * (cols + 1)), cols + 1), 2)] + multi_seg = np.concatenate(multi_seg_lst, axis=0) + label_segments = np.concatenate((label_segments, multi_seg), axis=0) + + label_segments_filtered = np.zeros([0, 4]) + # Define image boundary polygon (in x y manner) + image_poly = shapely.geometry.Polygon( + [[0, 0], [img.shape[1] - 1, 0], [img.shape[1] - 1, img.shape[0] - 1], + [0, img.shape[0] - 1]]) + for idx in range(label_segments.shape[0]): + # Get the line segment + seg_raw = label_segments[idx, :] + seg = shapely.geometry.LineString([seg_raw[:2], seg_raw[2:]]) + + # The line segment is just inside the image. + if seg.intersection(image_poly) == seg: + label_segments_filtered = np.concatenate( + (label_segments_filtered, seg_raw[None, ...]), axis=0) + + # Intersect with the image. + elif seg.intersects(image_poly): + # Check intersection + try: + p = np.array(seg.intersection( + image_poly).coords).reshape([-1, 4]) + # If intersect with eact one point + except: + continue + segment = p + label_segments_filtered = np.concatenate( + (label_segments_filtered, segment), axis=0) + + else: + continue + + label_segments = np.round(label_segments_filtered).astype(np.int) + + # Only record the segments longer than min_label_len + points1 = label_segments[:, :2] + points2 = label_segments[:, 2:] + seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1)) + label_segments = label_segments[seg_len >= min_label_len, :] + + # Get all junctions from label segments + junc_points, line_map = get_unique_junctions(label_segments, + min_label_len) + + # Draw lines on the boundaries of the board at random + nb_rows = random_state.randint(2, rows + 2) + nb_cols = random_state.randint(2, cols + 2) + thickness = random_state.randint(min_dim * 0.01, min_dim * 0.015) + for _ in range(nb_rows): + row_idx = random_state.randint(rows + 1) + col_idx1 = random_state.randint(cols + 1) + col_idx2 = random_state.randint(cols + 1) + col = get_random_color(background_color) + cv.line(img, (warped_points[row_idx * (cols + 1) + col_idx1, 0], + warped_points[row_idx * (cols + 1) + col_idx1, 1]), + (warped_points[row_idx * (cols + 1) + col_idx2, 0], + warped_points[row_idx * (cols + 1) + col_idx2, 1]), + col, thickness) + for _ in range(nb_cols): + col_idx = random_state.randint(cols + 1) + row_idx1 = random_state.randint(rows + 1) + row_idx2 = random_state.randint(rows + 1) + col = get_random_color(background_color) + cv.line(img, (warped_points[row_idx1 * (cols + 1) + col_idx, 0], + warped_points[row_idx1 * (cols + 1) + col_idx, 1]), + (warped_points[row_idx2 * (cols + 1) + col_idx, 0], + warped_points[row_idx2 * (cols + 1) + col_idx, 1]), + col, thickness) + + # Keep only the points inside the image + points = keep_points_inside(warped_points, img.shape[:2]) + return { + "points": junc_points, + "line_map": line_map + } + + +def draw_stripes_multiseg(img, max_nb_cols=13, min_len=0.04, min_label_len=64, + transform_params=(0.05, 0.15), seed=None): + """ Draw stripes in a distorted rectangle + and output the junctions points + line map. + Parameters: + max_nb_cols: maximal number of stripes to be drawn + min_width_ratio: the minimal width of a stripe is + min_width_ratio * smallest dimension of the image + transform_params: set the range of the parameters of the transformations + """ + # Set the optional random seed (most for debugging) + if seed is None: + global random_state + else: + random_state = np.random.RandomState(seed) + + background_color = int(np.mean(img)) + # Create the grid + board_size = (int(img.shape[0] * (1 + random_state.rand())), + int(img.shape[1] * (1 + random_state.rand()))) + + # Number of cols + col = random_state.randint(5, max_nb_cols) + cols = np.concatenate([board_size[1] * random_state.rand(col - 1), + np.array([0, board_size[1] - 1])], axis=0) + cols = np.unique(cols.astype(int)) + + # Remove the indices that are too close + min_dim = min(img.shape) + + # Convert length constrain to pixel if given float number + if isinstance(min_len, float) and min_len <= 1.: + min_len = int(min_dim * min_len) + if isinstance(min_label_len, float) and min_label_len <= 1.: + min_label_len = int(min_dim * min_label_len) + + cols = cols[(np.concatenate([cols[1:], + np.array([board_size[1] + min_len])], + axis=0) - cols) >= min_len] + # Update the number of cols + col = cols.shape[0] - 1 + cols = np.reshape(cols, (col + 1, 1)) + cols1 = np.concatenate([cols, np.zeros((col + 1, 1), np.int32)], axis=1) + cols2 = np.concatenate( + [cols, (board_size[0] - 1) * np.ones((col + 1, 1), np.int32)], axis=1) + points = np.concatenate([cols1, cols2], axis=0) + + # Warp the grid using an affine transformation and a homography + alpha_affine = np.max(img.shape) * ( + transform_params[0] + random_state.rand() * transform_params[1]) + center_square = np.float32(img.shape) // 2 + square_size = min(img.shape) // 3 + pts1 = np.float32([center_square + square_size, + [center_square[0]+square_size, + center_square[1]-square_size], + center_square - square_size, + [center_square[0]-square_size, + center_square[1]+square_size]]) + pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, + size=pts1.shape).astype(np.float32) + affine_transform = cv.getAffineTransform(pts1[:3], pts2[:3]) + pts2 = pts1 + random_state.uniform(-alpha_affine / 2, alpha_affine / 2, + size=pts1.shape).astype(np.float32) + perspective_transform = cv.getPerspectiveTransform(pts1, pts2) + + # Apply the affine transformation + points = np.transpose(np.concatenate((points, + np.ones((2 * (col + 1), 1))), + axis=1)) + warped_points = np.transpose(np.dot(affine_transform, points)) + + # Apply the homography + warped_col0 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[0, :2]), axis=1), + perspective_transform[0, 2]) + warped_col1 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[1, :2]), axis=1), + perspective_transform[1, 2]) + warped_col2 = np.add(np.sum(np.multiply( + warped_points, perspective_transform[2, :2]), axis=1), + perspective_transform[2, 2]) + warped_col0 = np.divide(warped_col0, warped_col2) + warped_col1 = np.divide(warped_col1, warped_col2) + warped_points = np.concatenate( + [warped_col0[:, None], warped_col1[:, None]], axis=1) + warped_points_float = warped_points.copy() + warped_points = warped_points.astype(int) + + # Fill the rectangles and get the segments + color = get_random_color(background_color) + # segments_debug = np.zeros([0, 4]) + for i in range(col): + # Fill the color + color = (color + 128 + random_state.randint(-30, 30)) % 256 + cv.fillConvexPoly(img, np.array([(warped_points[i, 0], + warped_points[i, 1]), + (warped_points[i+1, 0], + warped_points[i+1, 1]), + (warped_points[i+col+2, 0], + warped_points[i+col+2, 1]), + (warped_points[i+col+1, 0], + warped_points[i+col+1, 1])]), + color) + + segments = np.zeros([0, 4]) + row = 1 # in stripes case + # Iterate through rows + for row_idx in range(row + 1): + # Include all the combination of the junctions + # Iterate through all the combination of junction index in that row + multi_seg_lst = [np.array( + [warped_points_float[id1, 0], + warped_points_float[id1, 1], + warped_points_float[id2, 0], + warped_points_float[id2, 1]])[None, ...] + for (id1, id2) in combinations(range( + row_idx * (col + 1), (row_idx + 1) * (col + 1), 1), 2)] + multi_seg = np.concatenate(multi_seg_lst, axis=0) + segments = np.concatenate((segments, multi_seg), axis=0) + + # Iterate through columns + for col_idx in range(col + 1): # for 5 columns, we will have 5 + 1 edges. + # Include all the combination of the junctions + # Iterate throuhg all the combination of junction index in that column + multi_seg_lst = [np.array( + [warped_points_float[id1, 0], + warped_points_float[id1, 1], + warped_points_float[id2, 0], + warped_points_float[id2, 1]])[None, ...] + for (id1, id2) in combinations(range( + col_idx, col_idx + (row * col) + 2, col + 1), 2)] + multi_seg = np.concatenate(multi_seg_lst, axis=0) + segments = np.concatenate((segments, multi_seg), axis=0) + + # Select and refine the segments + segments_new = np.zeros([0, 4]) + # Define image boundary polygon (in x y manner) + image_poly = shapely.geometry.Polygon( + [[0, 0], [img.shape[1]-1, 0], [img.shape[1]-1, img.shape[0]-1], + [0, img.shape[0]-1]]) + for idx in range(segments.shape[0]): + # Get the line segment + seg_raw = segments[idx, :] + seg = shapely.geometry.LineString([seg_raw[:2], seg_raw[2:]]) + + # The line segment is just inside the image. + if seg.intersection(image_poly) == seg: + segments_new = np.concatenate( + (segments_new, seg_raw[None, ...]), axis=0) + + # Intersect with the image. + elif seg.intersects(image_poly): + # Check intersection + try: + p = np.array( + seg.intersection(image_poly).coords).reshape([-1, 4]) + # If intersect at exact one point, just continue. + except: + continue + segment = p + segments_new = np.concatenate((segments_new, segment), axis=0) + + else: + continue + + segments = (np.round(segments_new)).astype(np.int) + + # Only record the segments longer than min_label_len + points1 = segments[:, :2] + points2 = segments[:, 2:] + seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1)) + label_segments = segments[seg_len >= min_label_len, :] + + # Get all junctions from label segments + junctions_all = np.concatenate( + (label_segments[:, :2], label_segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + # Get all unique junction points + else: + junc_points = np.unique(junctions_all, axis=0) + # Generate line map from points and segments + line_map = get_line_map(junc_points, label_segments) + + # Draw lines on the boundaries of the stripes at random + nb_rows = random_state.randint(2, 5) + nb_cols = random_state.randint(2, col + 2) + thickness = random_state.randint(min_dim * 0.01, min_dim * 0.011) + for _ in range(nb_rows): + row_idx = random_state.choice([0, col + 1]) + col_idx1 = random_state.randint(col + 1) + col_idx2 = random_state.randint(col + 1) + color = get_random_color(background_color) + cv.line(img, (warped_points[row_idx + col_idx1, 0], + warped_points[row_idx + col_idx1, 1]), + (warped_points[row_idx + col_idx2, 0], + warped_points[row_idx + col_idx2, 1]), + color, thickness) + + for _ in range(nb_cols): + col_idx = random_state.randint(col + 1) + color = get_random_color(background_color) + cv.line(img, (warped_points[col_idx, 0], + warped_points[col_idx, 1]), + (warped_points[col_idx + col + 1, 0], + warped_points[col_idx + col + 1, 1]), + color, thickness) + + # Keep only the points inside the image + # points = keep_points_inside(warped_points, img.shape[:2]) + return { + "points": junc_points, + "line_map": line_map + } + + +def draw_cube(img, min_size_ratio=0.2, min_label_len=64, + scale_interval=(0.4, 0.6), trans_interval=(0.5, 0.2)): + """ Draw a 2D projection of a cube and output the visible juntions. + Parameters: + min_size_ratio: min(img.shape) * min_size_ratio is the smallest + achievable cube side size + scale_interval: the scale is between scale_interval[0] and + scale_interval[0]+scale_interval[1] + trans_interval: the translation is between img.shape*trans_interval[0] + and img.shape*(trans_interval[0] + trans_interval[1]) + """ + # Generate a cube and apply to it an affine transformation + # The order matters! + # The indices of two adjacent vertices differ only of one bit (Gray code) + background_color = int(np.mean(img)) + min_dim = min(img.shape[:2]) + min_side = min_dim * min_size_ratio + lx = min_side + random_state.rand() * 2 * min_dim / 3 # dims of the cube + ly = min_side + random_state.rand() * 2 * min_dim / 3 + lz = min_side + random_state.rand() * 2 * min_dim / 3 + cube = np.array([[0, 0, 0], + [lx, 0, 0], + [0, ly, 0], + [lx, ly, 0], + [0, 0, lz], + [lx, 0, lz], + [0, ly, lz], + [lx, ly, lz]]) + rot_angles = random_state.rand(3) * 3 * math.pi / 10. + math.pi / 10. + rotation_1 = np.array([[math.cos(rot_angles[0]), + -math.sin(rot_angles[0]), 0], + [math.sin(rot_angles[0]), + math.cos(rot_angles[0]), 0], + [0, 0, 1]]) + rotation_2 = np.array([[1, 0, 0], + [0, math.cos(rot_angles[1]), + -math.sin(rot_angles[1])], + [0, math.sin(rot_angles[1]), + math.cos(rot_angles[1])]]) + rotation_3 = np.array([[math.cos(rot_angles[2]), 0, + -math.sin(rot_angles[2])], + [0, 1, 0], + [math.sin(rot_angles[2]), 0, + math.cos(rot_angles[2])]]) + scaling = np.array([[scale_interval[0] + + random_state.rand() * scale_interval[1], 0, 0], + [0, scale_interval[0] + + random_state.rand() * scale_interval[1], 0], + [0, 0, scale_interval[0] + + random_state.rand() * scale_interval[1]]]) + trans = np.array([img.shape[1] * trans_interval[0] + + random_state.randint(-img.shape[1] * trans_interval[1], + img.shape[1] * trans_interval[1]), + img.shape[0] * trans_interval[0] + + random_state.randint(-img.shape[0] * trans_interval[1], + img.shape[0] * trans_interval[1]), + 0]) + cube = trans + np.transpose( + np.dot(scaling, np.dot(rotation_1, + np.dot(rotation_2, np.dot(rotation_3, np.transpose(cube)))))) + + # The hidden corner is 0 by construction + # The front one is 7 + cube = cube[:, :2] # project on the plane z=0 + cube = cube.astype(int) + points = cube[1:, :] # get rid of the hidden corner + + # Get the three visible faces + faces = np.array([[7, 3, 1, 5], [7, 5, 4, 6], [7, 6, 2, 3]]) + + # Get all visible line segments + segments = np.zeros([0, 4]) + # Iterate through all the faces + for face_idx in range(faces.shape[0]): + face = faces[face_idx, :] + # Brute-forcely expand all the segments + segment = np.array( + [np.concatenate((cube[face[0]], cube[face[1]]), axis=0), + np.concatenate((cube[face[1]], cube[face[2]]), axis=0), + np.concatenate((cube[face[2]], cube[face[3]]), axis=0), + np.concatenate((cube[face[3]], cube[face[0]]), axis=0)]) + segments = np.concatenate((segments, segment), axis=0) + + # Select and refine the segments + segments_new = np.zeros([0, 4]) + # Define image boundary polygon (in x y manner) + image_poly = shapely.geometry.Polygon( + [[0, 0], [img.shape[1] - 1, 0], [img.shape[1] - 1, img.shape[0] - 1], + [0, img.shape[0] - 1]]) + for idx in range(segments.shape[0]): + # Get the line segment + seg_raw = segments[idx, :] + seg = shapely.geometry.LineString([seg_raw[:2], seg_raw[2:]]) + + # The line segment is just inside the image. + if seg.intersection(image_poly) == seg: + segments_new = np.concatenate( + (segments_new, seg_raw[None, ...]), axis=0) + + # Intersect with the image. + elif seg.intersects(image_poly): + try: + p = np.array( + seg.intersection(image_poly).coords).reshape([-1, 4]) + except: + continue + segment = p + segments_new = np.concatenate((segments_new, segment), axis=0) + + else: + continue + + segments = (np.round(segments_new)).astype(np.int) + + # Only record the segments longer than min_label_len + points1 = segments[:, :2] + points2 = segments[:, 2:] + seg_len = np.sqrt(np.sum((points1 - points2) ** 2, axis=1)) + label_segments = segments[seg_len >= min_label_len, :] + + # Get all junctions from label segments + junctions_all = np.concatenate( + (label_segments[:, :2], label_segments[:, 2:]), axis=0) + if junctions_all.shape[0] == 0: + junc_points = None + line_map = None + + # Get all unique junction points + else: + junc_points = np.unique(junctions_all, axis=0) + # Generate line map from points and segments + line_map = get_line_map(junc_points, label_segments) + + # Fill the faces and draw the contours + col_face = get_random_color(background_color) + for i in [0, 1, 2]: + cv.fillPoly(img, [cube[faces[i]].reshape((-1, 1, 2))], + col_face) + thickness = random_state.randint(min_dim * 0.003, min_dim * 0.015) + for i in [0, 1, 2]: + for j in [0, 1, 2, 3]: + col_edge = (col_face + 128 + + random_state.randint(-64, 64))\ + % 256 # color that constrats with the face color + cv.line(img, (cube[faces[i][j], 0], cube[faces[i][j], 1]), + (cube[faces[i][(j + 1) % 4], 0], + cube[faces[i][(j + 1) % 4], 1]), + col_edge, thickness) + + return { + "points": junc_points, + "line_map": line_map + } + + +def gaussian_noise(img): + """ Apply random noise to the image. """ + cv.randu(img, 0, 255) + return { + "points": None, + "line_map": None + } diff --git a/third_party/SOLD2/sold2/dataset/transforms/__init__.py b/third_party/SOLD2/sold2/dataset/transforms/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py b/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..d9338abb169f7a86f3c6e702a031e1c0de86c339 --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/transforms/homographic_transforms.py @@ -0,0 +1,350 @@ +""" +This file implements the homographic transforms for data augmentation. +Code adapted from https://github.com/rpautrat/SuperPoint +""" +import numpy as np +from math import pi + +from ..synthetic_util import get_line_map, get_line_heatmap +import cv2 +import copy +import shapely.geometry + + +def sample_homography( + shape, perspective=True, scaling=True, rotation=True, + translation=True, n_scales=5, n_angles=25, scaling_amplitude=0.1, + perspective_amplitude_x=0.1, perspective_amplitude_y=0.1, + patch_ratio=0.5, max_angle=pi/2, allow_artifacts=False, + translation_overflow=0.): + """ + Computes the homography transformation between a random patch in the + original image and a warped projection with the same image size. + As in `tf.contrib.image.transform`, it maps the output point + (warped patch) to a transformed input point (original patch). + The original patch, initialized with a simple half-size centered crop, + is iteratively projected, scaled, rotated and translated. + + Arguments: + shape: A rank-2 `Tensor` specifying the height and width of the original image. + perspective: A boolean that enables the perspective and affine transformations. + scaling: A boolean that enables the random scaling of the patch. + rotation: A boolean that enables the random rotation of the patch. + translation: A boolean that enables the random translation of the patch. + n_scales: The number of tentative scales that are sampled when scaling. + n_angles: The number of tentatives angles that are sampled when rotating. + scaling_amplitude: Controls the amount of scale. + perspective_amplitude_x: Controls the perspective effect in x direction. + perspective_amplitude_y: Controls the perspective effect in y direction. + patch_ratio: Controls the size of the patches used to create the homography. + max_angle: Maximum angle used in rotations. + allow_artifacts: A boolean that enables artifacts when applying the homography. + translation_overflow: Amount of border artifacts caused by translation. + + Returns: + homo_mat: A numpy array of shape `[1, 3, 3]` corresponding to the + homography transform. + selected_scale: The selected scaling factor. + """ + # Convert shape to ndarry + if not isinstance(shape, np.ndarray): + shape = np.array(shape) + + # Corners of the output image + pts1 = np.array([[0., 0.], [0., 1.], [1., 1.], [1., 0.]]) + # Corners of the input patch + margin = (1 - patch_ratio) / 2 + pts2 = margin + np.array([[0, 0], [0, patch_ratio], + [patch_ratio, patch_ratio], [patch_ratio, 0]]) + + # Random perspective and affine perturbations + if perspective: + if not allow_artifacts: + perspective_amplitude_x = min(perspective_amplitude_x, margin) + perspective_amplitude_y = min(perspective_amplitude_y, margin) + + # normal distribution with mean=0, std=perspective_amplitude_y/2 + perspective_displacement = np.random.normal( + 0., perspective_amplitude_y/2, [1]) + h_displacement_left = np.random.normal( + 0., perspective_amplitude_x/2, [1]) + h_displacement_right = np.random.normal( + 0., perspective_amplitude_x/2, [1]) + pts2 += np.stack([np.concatenate([h_displacement_left, + perspective_displacement], 0), + np.concatenate([h_displacement_left, + -perspective_displacement], 0), + np.concatenate([h_displacement_right, + perspective_displacement], 0), + np.concatenate([h_displacement_right, + -perspective_displacement], 0)]) + + # Random scaling: sample several scales, check collision with borders, + # randomly pick a valid one + if scaling: + scales = np.concatenate( + [[1.], np.random.normal(1, scaling_amplitude/2, [n_scales])], 0) + center = np.mean(pts2, axis=0, keepdims=True) + scaled = (pts2 - center)[None, ...] * scales[..., None, None] + center + # all scales are valid except scale=1 + if allow_artifacts: + valid = np.array(range(n_scales)) + # Chech the valid scale + else: + valid = np.where(np.all((scaled >= 0.) + & (scaled < 1.), (1, 2)))[0] + # No valid scale found => recursively call + if valid.shape[0] == 0: + return sample_homography( + shape, perspective, scaling, rotation, translation, + n_scales, n_angles, scaling_amplitude, + perspective_amplitude_x, perspective_amplitude_y, + patch_ratio, max_angle, allow_artifacts, translation_overflow) + + idx = valid[np.random.uniform(0., valid.shape[0], ()).astype(np.int32)] + pts2 = scaled[idx] + + # Additionally save and return the selected scale. + selected_scale = scales[idx] + + # Random translation + if translation: + t_min, t_max = np.min(pts2, axis=0), np.min(1 - pts2, axis=0) + if allow_artifacts: + t_min += translation_overflow + t_max += translation_overflow + pts2 += (np.stack([np.random.uniform(-t_min[0], t_max[0], ()), + np.random.uniform(-t_min[1], + t_max[1], ())]))[None, ...] + + # Random rotation: sample several rotations, check collision with borders, + # randomly pick a valid one + if rotation: + angles = np.linspace(-max_angle, max_angle, n_angles) + # in case no rotation is valid + angles = np.concatenate([[0.], angles], axis=0) + center = np.mean(pts2, axis=0, keepdims=True) + rot_mat = np.reshape(np.stack( + [np.cos(angles), -np.sin(angles), + np.sin(angles), np.cos(angles)], axis=1), [-1, 2, 2]) + rotated = np.matmul( + np.tile((pts2 - center)[None, ...], [n_angles+1, 1, 1]), + rot_mat) + center + if allow_artifacts: + # All angles are valid, except angle=0 + valid = np.array(range(n_angles)) + else: + valid = np.where(np.all((rotated >= 0.) + & (rotated < 1.), axis=(1, 2)))[0] + + if valid.shape[0] == 0: + return sample_homography( + shape, perspective, scaling, rotation, translation, + n_scales, n_angles, scaling_amplitude, + perspective_amplitude_x, perspective_amplitude_y, + patch_ratio, max_angle, allow_artifacts, translation_overflow) + + idx = valid[np.random.uniform(0., valid.shape[0], + ()).astype(np.int32)] + pts2 = rotated[idx] + + # Rescale to actual size + shape = shape[::-1].astype(np.float32) # different convention [y, x] + pts1 *= shape[None, ...] + pts2 *= shape[None, ...] + + def ax(p, q): return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]] + + def ay(p, q): return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]] + + a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) + for f in (ax, ay)], axis=0) + p_mat = np.transpose(np.stack([[pts2[i][j] for i in range(4) + for j in range(2)]], axis=0)) + homo_vec, _, _, _ = np.linalg.lstsq(a_mat, p_mat, rcond=None) + + # Compose the homography vector back to matrix + homo_mat = np.concatenate([ + homo_vec[0:3, 0][None, ...], homo_vec[3:6, 0][None, ...], + np.concatenate((homo_vec[6], homo_vec[7], [1]), + axis=0)[None, ...]], axis=0) + + return homo_mat, selected_scale + + +def convert_to_line_segments(junctions, line_map): + """ Convert junctions and line map to line segments. """ + # Copy the line map + line_map_tmp = copy.copy(line_map) + + line_segments = np.zeros([0, 4]) + for idx in range(junctions.shape[0]): + # If no connectivity, just skip it + if line_map_tmp[idx, :].sum() == 0: + continue + # Record the line segment + else: + for idx2 in np.where(line_map_tmp[idx, :] == 1)[0]: + p1 = junctions[idx, :] + p2 = junctions[idx2, :] + line_segments = np.concatenate( + (line_segments, + np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]), + axis=0) + # Update line_map + line_map_tmp[idx, idx2] = 0 + line_map_tmp[idx2, idx] = 0 + + return line_segments + + +def compute_valid_mask(image_size, homography, + border_margin, valid_mask=None): + # Warp the mask + if valid_mask is None: + initial_mask = np.ones(image_size) + else: + initial_mask = valid_mask + mask = cv2.warpPerspective( + initial_mask, homography, (image_size[1], image_size[0]), + flags=cv2.INTER_NEAREST) + + # Optionally perform erosion + if border_margin > 0: + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, + (border_margin*2, )*2) + mask = cv2.erode(mask, kernel) + + # Perform dilation if border_margin is negative + if border_margin < 0: + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, + (abs(int(border_margin))*2, )*2) + mask = cv2.dilate(mask, kernel) + + return mask + + +def warp_line_segment(line_segments, homography, image_size): + """ Warp the line segments using a homography. """ + # Separate the line segements into 2N points to apply matrix operation + num_segments = line_segments.shape[0] + + junctions = np.concatenate( + (line_segments[:, :2], # The first junction of each segment. + line_segments[:, 2:]), # The second junction of each segment. + axis=0) + # Convert to homogeneous coordinates + # Flip the junctions before converting to homogeneous (xy format) + junctions = np.flip(junctions, axis=1) + junctions = np.concatenate((junctions, np.ones([2*num_segments, 1])), + axis=1) + warped_junctions = np.matmul(homography, junctions.T).T + + # Convert back to segments + warped_junctions = warped_junctions[:, :2] / warped_junctions[:, 2:] + # (Convert back to hw format) + warped_junctions = np.flip(warped_junctions, axis=1) + warped_segments = np.concatenate( + (warped_junctions[:num_segments, :], + warped_junctions[num_segments:, :]), + axis=1 + ) + + # Check the intersections with the boundary + warped_segments_new = np.zeros([0, 4]) + image_poly = shapely.geometry.Polygon( + [[0, 0], [image_size[1]-1, 0], [image_size[1]-1, image_size[0]-1], + [0, image_size[0]-1]]) + for idx in range(warped_segments.shape[0]): + # Get the line segment + seg_raw = warped_segments[idx, :] # in HW format. + # Convert to shapely line (flip to xy format) + seg = shapely.geometry.LineString([np.flip(seg_raw[:2]), + np.flip(seg_raw[2:])]) + + # The line segment is just inside the image. + if seg.intersection(image_poly) == seg: + warped_segments_new = np.concatenate((warped_segments_new, + seg_raw[None, ...]), axis=0) + + # Intersect with the image. + elif seg.intersects(image_poly): + # Check intersection + try: + p = np.array( + seg.intersection(image_poly).coords).reshape([-1, 4]) + # If intersect at exact one point, just continue. + except: + continue + segment = np.concatenate([np.flip(p[0, :2]), np.flip(p[0, 2:], + axis=0)])[None, ...] + warped_segments_new = np.concatenate( + (warped_segments_new, segment), axis=0) + + else: + continue + + warped_segments = (np.round(warped_segments_new)).astype(np.int) + return warped_segments + + +class homography_transform(object): + """ # Homography transformations. """ + def __init__(self, image_size, homograpy_config, + border_margin=0, min_label_len=20): + self.homo_config = homograpy_config + self.image_size = image_size + self.target_size = (self.image_size[1], self.image_size[0]) + self.border_margin = border_margin + if (min_label_len < 1) and isinstance(min_label_len, float): + raise ValueError("[Error] min_label_len should be in pixels.") + self.min_label_len = min_label_len + + def __call__(self, input_image, junctions, line_map, + valid_mask=None, homo=None, scale=None): + # Sample one random homography or use the given one + if homo is None or scale is None: + homo, scale = sample_homography(self.image_size, + **self.homo_config) + + # Warp the image + warped_image = cv2.warpPerspective( + input_image, homo, self.target_size, flags=cv2.INTER_LINEAR) + + valid_mask = compute_valid_mask(self.image_size, homo, + self.border_margin, valid_mask) + + # Convert junctions and line_map back to line segments + line_segments = convert_to_line_segments(junctions, line_map) + + # Warp the segments and check the length. + # Adjust the min_label_length + warped_segments = warp_line_segment(line_segments, homo, + self.image_size) + + # Convert back to junctions and line_map + junctions_new = np.concatenate((warped_segments[:, :2], + warped_segments[:, 2:]), axis=0) + if junctions_new.shape[0] == 0: + junctions_new = np.zeros([0, 2]) + line_map = np.zeros([0, 0]) + warped_heatmap = np.zeros(self.image_size) + else: + junctions_new = np.unique(junctions_new, axis=0) + + # Generate line map from points and segments + line_map = get_line_map(junctions_new, + warped_segments).astype(np.int) + # Compute the heatmap + warped_heatmap = get_line_heatmap(np.flip(junctions_new, axis=1), + line_map, self.image_size) + + return { + "junctions": junctions_new, + "warped_image": warped_image, + "valid_mask": valid_mask, + "line_map": line_map, + "warped_heatmap": warped_heatmap, + "homo": homo, + "scale": scale + } diff --git a/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py b/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..8fa44bf0efa93a47e5f8012988058f1cbd49324f --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/transforms/photometric_transforms.py @@ -0,0 +1,185 @@ +""" +Common photometric transforms for data augmentation. +""" +import numpy as np +from PIL import Image +from torchvision import transforms as transforms +import cv2 + + +# List all the available augmentations +available_augmentations = [ + 'additive_gaussian_noise', + 'additive_speckle_noise', + 'random_brightness', + 'random_contrast', + 'additive_shade', + 'motion_blur' +] + + +class additive_gaussian_noise(object): + """ Additive gaussian noise. """ + def __init__(self, stddev_range=None): + # If std is not given, use the default setting + if stddev_range is None: + self.stddev_range = [5, 95] + else: + self.stddev_range = stddev_range + + def __call__(self, input_image): + # Get the noise stddev + stddev = np.random.uniform(self.stddev_range[0], self.stddev_range[1]) + noise = np.random.normal(0., stddev, size=input_image.shape) + noisy_image = (input_image + noise).clip(0., 255.) + + return noisy_image + + +class additive_speckle_noise(object): + """ Additive speckle noise. """ + def __init__(self, prob_range=None): + # If prob range is not given, use the default setting + if prob_range is None: + self.prob_range = [0.0, 0.005] + else: + self.prob_range = prob_range + + def __call__(self, input_image): + # Sample + prob = np.random.uniform(self.prob_range[0], self.prob_range[1]) + sample = np.random.uniform(0., 1., size=input_image.shape) + + # Get the mask + mask0 = sample <= prob + mask1 = sample >= (1 - prob) + + # Mask the image (here we assume the image ranges from 0~255 + noisy = input_image.copy() + noisy[mask0] = 0. + noisy[mask1] = 255. + + return noisy + + +class random_brightness(object): + """ Brightness change. """ + def __init__(self, brightness=None): + # If the brightness is not given, use the default setting + if brightness is None: + self.brightness = 0.5 + else: + self.brightness = brightness + + # Initialize the transformer + self.transform = transforms.ColorJitter(brightness=self.brightness) + + def __call__(self, input_image): + # Convert to PIL image + if isinstance(input_image, np.ndarray): + input_image = Image.fromarray(input_image.astype(np.uint8)) + + return np.array(self.transform(input_image)) + + +class random_contrast(object): + """ Additive contrast. """ + def __init__(self, contrast=None): + # If the brightness is not given, use the default setting + if contrast is None: + self.contrast = 0.5 + else: + self.contrast = contrast + + # Initialize the transformer + self.transform = transforms.ColorJitter(contrast=self.contrast) + + def __call__(self, input_image): + # Convert to PIL image + if isinstance(input_image, np.ndarray): + input_image = Image.fromarray(input_image.astype(np.uint8)) + + return np.array(self.transform(input_image)) + + +class additive_shade(object): + """ Additive shade. """ + def __init__(self, nb_ellipses=20, transparency_range=None, + kernel_size_range=None): + self.nb_ellipses = nb_ellipses + if transparency_range is None: + self.transparency_range = [-0.5, 0.8] + else: + self.transparency_range = transparency_range + + if kernel_size_range is None: + self.kernel_size_range = [250, 350] + else: + self.kernel_size_range = kernel_size_range + + def __call__(self, input_image): + # ToDo: if we should convert to numpy array first. + min_dim = min(input_image.shape[:2]) / 4 + mask = np.zeros(input_image.shape[:2], np.uint8) + for i in range(self.nb_ellipses): + ax = int(max(np.random.rand() * min_dim, min_dim / 5)) + ay = int(max(np.random.rand() * min_dim, min_dim / 5)) + max_rad = max(ax, ay) + x = np.random.randint(max_rad, input_image.shape[1] - max_rad) + y = np.random.randint(max_rad, input_image.shape[0] - max_rad) + angle = np.random.rand() * 90 + cv2.ellipse(mask, (x, y), (ax, ay), angle, 0, 360, 255, -1) + + transparency = np.random.uniform(*self.transparency_range) + kernel_size = np.random.randint(*self.kernel_size_range) + + # kernel_size has to be odd + if (kernel_size % 2) == 0: + kernel_size += 1 + mask = cv2.GaussianBlur(mask.astype(np.float32), + (kernel_size, kernel_size), 0) + shaded = (input_image[..., None] + * (1 - transparency * mask[..., np.newaxis]/255.)) + shaded = np.clip(shaded, 0, 255) + + return np.reshape(shaded, input_image.shape) + + +class motion_blur(object): + """ Motion blur. """ + def __init__(self, max_kernel_size=10): + self.max_kernel_size = max_kernel_size + + def __call__(self, input_image): + # Either vertical, horizontal or diagonal blur + mode = np.random.choice(['h', 'v', 'diag_down', 'diag_up']) + ksize = np.random.randint( + 0, int(round((self.max_kernel_size + 1) / 2))) * 2 + 1 + center = int((ksize - 1) / 2) + kernel = np.zeros((ksize, ksize)) + if mode == 'h': + kernel[center, :] = 1. + elif mode == 'v': + kernel[:, center] = 1. + elif mode == 'diag_down': + kernel = np.eye(ksize) + elif mode == 'diag_up': + kernel = np.flip(np.eye(ksize), 0) + var = ksize * ksize / 16. + grid = np.repeat(np.arange(ksize)[:, np.newaxis], ksize, axis=-1) + gaussian = np.exp(-(np.square(grid - center) + + np.square(grid.T - center)) / (2. * var)) + kernel *= gaussian + kernel /= np.sum(kernel) + blurred = cv2.filter2D(input_image, -1, kernel) + + return np.reshape(blurred, input_image.shape) + + +class normalize_image(object): + """ Image normalization to the range [0, 1]. """ + def __init__(self): + self.normalize_value = 255 + + def __call__(self, input_image): + return (input_image / self.normalize_value).astype(np.float32) diff --git a/third_party/SOLD2/sold2/dataset/transforms/utils.py b/third_party/SOLD2/sold2/dataset/transforms/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1ed09e5b32e2ae2f3577e0e8e5491495e7b05b --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/transforms/utils.py @@ -0,0 +1,121 @@ +""" +Some useful functions for dataset pre-processing +""" +import cv2 +import numpy as np +import shapely.geometry as sg + +from ..synthetic_util import get_line_map +from . import homographic_transforms as homoaug + + +def random_scaling(image, junctions, line_map, scale=1., h_crop=0, w_crop=0): + H, W = image.shape[:2] + H_scale, W_scale = round(H * scale), round(W * scale) + + # Nothing to do if the scale is too close to 1 + if H_scale == H and W_scale == W: + return (image, junctions, line_map, np.ones([H, W], dtype=np.int)) + + # Zoom-in => resize and random crop + if scale >= 1.: + image_big = cv2.resize(image, (W_scale, H_scale), + interpolation=cv2.INTER_LINEAR) + # Crop the image + image = image_big[h_crop:h_crop+H, w_crop:w_crop+W, ...] + valid_mask = np.ones([H, W], dtype=np.int) + + # Process junctions + junctions, line_map = process_junctions_and_line_map( + h_crop, w_crop, H, W, H_scale, W_scale, + junctions, line_map, "zoom-in") + # Zoom-out => resize and pad + else: + image_shape_raw = image.shape + image_small = cv2.resize(image, (W_scale, H_scale), + interpolation=cv2.INTER_AREA) + # Decide the pasting location + h_start = round((H - H_scale) / 2) + w_start = round((W - W_scale) / 2) + # Paste the image to the middle + image = np.zeros(image_shape_raw, dtype=np.float) + image[h_start:h_start+H_scale, + w_start:w_start+W_scale, ...] = image_small + valid_mask = np.zeros([H, W], dtype=np.int) + valid_mask[h_start:h_start+H_scale, w_start:w_start+W_scale] = 1 + + # Process the junctions + junctions, line_map = process_junctions_and_line_map( + h_start, w_start, H, W, H_scale, W_scale, + junctions, line_map, "zoom-out") + + return image, junctions, line_map, valid_mask + + +def process_junctions_and_line_map(h_start, w_start, H, W, H_scale, W_scale, + junctions, line_map, mode="zoom-in"): + if mode == "zoom-in": + junctions[:, 0] = junctions[:, 0] * H_scale / H + junctions[:, 1] = junctions[:, 1] * W_scale / W + line_segments = homoaug.convert_to_line_segments(junctions, line_map) + # Crop segments to the new boundaries + line_segments_new = np.zeros([0, 4]) + image_poly = sg.Polygon( + [[w_start, h_start], + [w_start+W, h_start], + [w_start+W, h_start+H], + [w_start, h_start+H] + ]) + for idx in range(line_segments.shape[0]): + # Get the line segment + seg_raw = line_segments[idx, :] # in HW format. + # Convert to shapely line (flip to xy format) + seg = sg.LineString([np.flip(seg_raw[:2]), + np.flip(seg_raw[2:])]) + # The line segment is just inside the image. + if seg.intersection(image_poly) == seg: + line_segments_new = np.concatenate( + (line_segments_new, seg_raw[None, ...]), axis=0) + # Intersect with the image. + elif seg.intersects(image_poly): + # Check intersection + try: + p = np.array( + seg.intersection(image_poly).coords).reshape([-1, 4]) + # If intersect at exact one point, just continue. + except: + continue + segment = np.concatenate([np.flip(p[0, :2]), np.flip(p[0, 2:], + axis=0)])[None, ...] + line_segments_new = np.concatenate( + (line_segments_new, segment), axis=0) + else: + continue + line_segments_new = (np.round(line_segments_new)).astype(np.int) + # Filter segments with 0 length + segment_lens = np.linalg.norm( + line_segments_new[:, :2] - line_segments_new[:, 2:], axis=-1) + seg_mask = segment_lens != 0 + line_segments_new = line_segments_new[seg_mask, :] + # Convert back to junctions and line_map + junctions_new = np.concatenate( + (line_segments_new[:, :2], line_segments_new[:, 2:]), axis=0) + if junctions_new.shape[0] == 0: + junctions_new = np.zeros([0, 2]) + line_map = np.zeros([0, 0]) + else: + junctions_new = np.unique(junctions_new, axis=0) + # Generate line map from points and segments + line_map = get_line_map(junctions_new, + line_segments_new).astype(np.int) + junctions_new[:, 0] -= h_start + junctions_new[:, 1] -= w_start + junctions = junctions_new + elif mode == "zoom-out": + # Process the junctions + junctions[:, 0] = (junctions[:, 0] * H_scale / H) + h_start + junctions[:, 1] = (junctions[:, 1] * W_scale / W) + w_start + else: + raise ValueError("[Error] unknown mode...") + + return junctions, line_map diff --git a/third_party/SOLD2/sold2/dataset/wireframe_dataset.py b/third_party/SOLD2/sold2/dataset/wireframe_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ed5bb910bed1b89934ddaaec3bcddf111ea0faef --- /dev/null +++ b/third_party/SOLD2/sold2/dataset/wireframe_dataset.py @@ -0,0 +1,1000 @@ +""" +This file implements the wireframe dataset object for pytorch. +Some parts of the code are adapted from https://github.com/zhou13/lcnn +""" +import os +import math +import copy +from skimage.io import imread +from skimage import color +import PIL +import numpy as np +import h5py +import cv2 +import pickle +import torch +import torch.utils.data.dataloader as torch_loader +from torch.utils.data import Dataset +from torchvision import transforms + +from ..config.project_config import Config as cfg +from .transforms import photometric_transforms as photoaug +from .transforms import homographic_transforms as homoaug +from .transforms.utils import random_scaling +from .synthetic_util import get_line_heatmap +from ..misc.train_utils import parse_h5_data +from ..misc.geometry_utils import warp_points, mask_points + + +def wireframe_collate_fn(batch): + """ Customized collate_fn for wireframe dataset. """ + batch_keys = ["image", "junction_map", "valid_mask", "heatmap", + "heatmap_pos", "heatmap_neg", "homography", + "line_points", "line_indices"] + list_keys = ["junctions", "line_map", "line_map_pos", + "line_map_neg", "file_key"] + + outputs = {} + for data_key in batch[0].keys(): + batch_match = sum([_ in data_key for _ in batch_keys]) + list_match = sum([_ in data_key for _ in list_keys]) + # print(batch_match, list_match) + if batch_match > 0 and list_match == 0: + outputs[data_key] = torch_loader.default_collate( + [b[data_key] for b in batch]) + elif batch_match == 0 and list_match > 0: + outputs[data_key] = [b[data_key] for b in batch] + elif batch_match == 0 and list_match == 0: + continue + else: + raise ValueError( + "[Error] A key matches batch keys and list keys simultaneously.") + + return outputs + + +class WireframeDataset(Dataset): + def __init__(self, mode="train", config=None): + super(WireframeDataset, self).__init__() + if not mode in ["train", "test"]: + raise ValueError( + "[Error] Unknown mode for Wireframe dataset. Only 'train' and 'test'.") + self.mode = mode + + if config is None: + self.config = self.get_default_config() + else: + self.config = config + # Also get the default config + self.default_config = self.get_default_config() + + # Get cache setting + self.dataset_name = self.get_dataset_name() + self.cache_name = self.get_cache_name() + self.cache_path = cfg.wireframe_cache_path + + # Get the ground truth source + self.gt_source = self.config.get("gt_source_%s"%(self.mode), + "official") + if not self.gt_source == "official": + # Convert gt_source to full path + self.gt_source = os.path.join(cfg.export_dataroot, self.gt_source) + # Check the full path exists + if not os.path.exists(self.gt_source): + raise ValueError( + "[Error] The specified ground truth source does not exist.") + + + # Get the filename dataset + print("[Info] Initializing wireframe dataset...") + self.filename_dataset, self.datapoints = self.construct_dataset() + + # Get dataset length + self.dataset_length = len(self.datapoints) + + # Print some info + print("[Info] Successfully initialized dataset") + print("\t Name: wireframe") + print("\t Mode: %s" %(self.mode)) + print("\t Gt: %s" %(self.config.get("gt_source_%s"%(self.mode), + "official"))) + print("\t Counts: %d" %(self.dataset_length)) + print("----------------------------------------") + + ####################################### + ## Dataset construction related APIs ## + ####################################### + def construct_dataset(self): + """ Construct the dataset (from scratch or from cache). """ + # Check if the filename cache exists + # If cache exists, load from cache + if self._check_dataset_cache(): + print("\t Found filename cache %s at %s"%(self.cache_name, + self.cache_path)) + print("\t Load filename cache...") + filename_dataset, datapoints = self.get_filename_dataset_from_cache() + # If not, initialize dataset from scratch + else: + print("\t Can't find filename cache ...") + print("\t Create filename dataset from scratch...") + filename_dataset, datapoints = self.get_filename_dataset() + print("\t Create filename dataset cache...") + self.create_filename_dataset_cache(filename_dataset, datapoints) + + return filename_dataset, datapoints + + def create_filename_dataset_cache(self, filename_dataset, datapoints): + """ Create filename dataset cache for faster initialization. """ + # Check cache path exists + if not os.path.exists(self.cache_path): + os.makedirs(self.cache_path) + + cache_file_path = os.path.join(self.cache_path, self.cache_name) + data = { + "filename_dataset": filename_dataset, + "datapoints": datapoints + } + with open(cache_file_path, "wb") as f: + pickle.dump(data, f, pickle.HIGHEST_PROTOCOL) + + def get_filename_dataset_from_cache(self): + """ Get filename dataset from cache. """ + # Load from pkl cache + cache_file_path = os.path.join(self.cache_path, self.cache_name) + with open(cache_file_path, "rb") as f: + data = pickle.load(f) + + return data["filename_dataset"], data["datapoints"] + + def get_filename_dataset(self): + # Get the path to the dataset + if self.mode == "train": + dataset_path = os.path.join(cfg.wireframe_dataroot, "train") + elif self.mode == "test": + dataset_path = os.path.join(cfg.wireframe_dataroot, "valid") + + # Get paths to all image files + image_paths = sorted([os.path.join(dataset_path, _) + for _ in os.listdir(dataset_path)\ + if os.path.splitext(_)[-1] == ".png"]) + # Get the shared prefix + prefix_paths = [_.split(".png")[0] for _ in image_paths] + + # Get the label paths (different procedure for different split) + if self.mode == "train": + label_paths = [_ + "_label.npz" for _ in prefix_paths] + else: + label_paths = [_ + "_label.npz" for _ in prefix_paths] + mat_paths = [p[:-2] + "_line.mat" for p in prefix_paths] + + # Verify all the images and labels exist + for idx in range(len(image_paths)): + image_path = image_paths[idx] + label_path = label_paths[idx] + if (not (os.path.exists(image_path) + and os.path.exists(label_path))): + raise ValueError( + "[Error] The image and label do not exist. %s"%(image_path)) + # Further verify mat paths for test split + if self.mode == "test": + mat_path = mat_paths[idx] + if not os.path.exists(mat_path): + raise ValueError( + "[Error] The mat file does not exist. %s"%(mat_path)) + + # Construct the filename dataset + num_pad = int(math.ceil(math.log10(len(image_paths))) + 1) + filename_dataset = {} + for idx in range(len(image_paths)): + # Get the file key + key = self.get_padded_filename(num_pad, idx) + + filename_dataset[key] = { + "image": image_paths[idx], + "label": label_paths[idx] + } + + # Get the datapoints + datapoints = list(sorted(filename_dataset.keys())) + + return filename_dataset, datapoints + + def get_dataset_name(self): + """ Get dataset name from dataset config / default config. """ + if self.config["dataset_name"] is None: + dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode + else: + dataset_name = self.config["dataset_name"] + "_%s" % self.mode + + return dataset_name + + def get_cache_name(self): + """ Get cache name from dataset config / default config. """ + if self.config["dataset_name"] is None: + dataset_name = self.default_config["dataset_name"] + "_%s" % self.mode + else: + dataset_name = self.config["dataset_name"] + "_%s" % self.mode + # Compose cache name + cache_name = dataset_name + "_cache.pkl" + + return cache_name + + @staticmethod + def get_padded_filename(num_pad, idx): + """ Get the padded filename using adaptive padding. """ + file_len = len("%d" % (idx)) + filename = "0" * (num_pad - file_len) + "%d" % (idx) + + return filename + + def get_default_config(self): + """ Get the default configuration. """ + return { + "dataset_name": "wireframe", + "add_augmentation_to_all_splits": False, + "preprocessing": { + "resize": [240, 320], + "blur_size": 11 + }, + "augmentation":{ + "photometric":{ + "enable": False + }, + "homographic":{ + "enable": False + }, + }, + } + + + ############################################ + ## Pytorch and preprocessing related APIs ## + ############################################ + # Get data from the information from filename dataset + @staticmethod + def get_data_from_path(data_path): + output = {} + + # Get image data + image_path = data_path["image"] + image = imread(image_path) + output["image"] = image + + # Get the npz label + """ Data entries in the npz file + jmap: [J, H, W] Junction heat map (H and W are 4x smaller) + joff: [J, 2, H, W] Junction offset within each pixel (Not sure about offsets) + lmap: [H, W] Line heat map with anti-aliasing (H and W are 4x smaller) + junc: [Na, 3] Junction coordinates (coordinates from 0~128 => 4x smaller.) + Lpos: [M, 2] Positive lines represented with junction indices + Lneg: [M, 2] Negative lines represented with junction indices + lpos: [Np, 2, 3] Positive lines represented with junction coordinates + lneg: [Nn, 2, 3] Negative lines represented with junction coordinates + """ + label_path = data_path["label"] + label = np.load(label_path) + for key in list(label.keys()): + output[key] = label[key] + + # If there's "line_mat" entry. + # TODO: How to process mat data + if data_path.get("line_mat") is not None: + raise NotImplementedError + + return output + + @staticmethod + def convert_line_map(lcnn_line_map, num_junctions): + """ Convert the line_pos or line_neg + (represented by two junction indexes) to our line map. """ + # Initialize empty line map + line_map = np.zeros([num_junctions, num_junctions]) + + # Iterate through all the lines + for idx in range(lcnn_line_map.shape[0]): + index1 = lcnn_line_map[idx, 0] + index2 = lcnn_line_map[idx, 1] + + line_map[index1, index2] = 1 + line_map[index2, index1] = 1 + + return line_map + + @staticmethod + def junc_to_junc_map(junctions, image_size): + """ Convert junction points to junction maps. """ + junctions = np.round(junctions).astype(np.int) + # Clip the boundary by image size + junctions[:, 0] = np.clip(junctions[:, 0], 0., image_size[0]-1) + junctions[:, 1] = np.clip(junctions[:, 1], 0., image_size[1]-1) + + # Create junction map + junc_map = np.zeros([image_size[0], image_size[1]]) + junc_map[junctions[:, 0], junctions[:, 1]] = 1 + + return junc_map[..., None].astype(np.int) + + def parse_transforms(self, names, all_transforms): + """ Parse the transform. """ + trans = all_transforms if (names == 'all') \ + else (names if isinstance(names, list) else [names]) + assert set(trans) <= set(all_transforms) + return trans + + def get_photo_transform(self): + """ Get list of photometric transforms (according to the config). """ + # Get the photometric transform config + photo_config = self.config["augmentation"]["photometric"] + if not photo_config["enable"]: + raise ValueError( + "[Error] Photometric augmentation is not enabled.") + + # Parse photometric transforms + trans_lst = self.parse_transforms(photo_config["primitives"], + photoaug.available_augmentations) + trans_config_lst = [photo_config["params"].get(p, {}) + for p in trans_lst] + + # List of photometric augmentation + photometric_trans_lst = [ + getattr(photoaug, trans)(**conf) \ + for (trans, conf) in zip(trans_lst, trans_config_lst) + ] + + return photometric_trans_lst + + def get_homo_transform(self): + """ Get homographic transforms (according to the config). """ + # Get homographic transforms for image + homo_config = self.config["augmentation"]["homographic"]["params"] + if not self.config["augmentation"]["homographic"]["enable"]: + raise ValueError( + "[Error] Homographic augmentation is not enabled.") + + # Parse the homographic transforms + image_shape = self.config["preprocessing"]["resize"] + + # Compute the min_label_len from config + try: + min_label_tmp = self.config["generation"]["min_label_len"] + except: + min_label_tmp = None + + # float label len => fraction + if isinstance(min_label_tmp, float): # Skip if not provided + min_label_len = min_label_tmp * min(image_shape) + # int label len => length in pixel + elif isinstance(min_label_tmp, int): + scale_ratio = (self.config["preprocessing"]["resize"] + / self.config["generation"]["image_size"][0]) + min_label_len = (self.config["generation"]["min_label_len"] + * scale_ratio) + # if none => no restriction + else: + min_label_len = 0 + + # Initialize the transform + homographic_trans = homoaug.homography_transform( + image_shape, homo_config, 0, min_label_len) + + return homographic_trans + + def get_line_points(self, junctions, line_map, H1=None, H2=None, + img_size=None, warp=False): + """ Sample evenly points along each line segments + and keep track of line idx. """ + if np.sum(line_map) == 0: + # No segment detected in the image + line_indices = np.zeros(self.config["max_pts"], dtype=int) + line_points = np.zeros((self.config["max_pts"], 2), dtype=float) + return line_points, line_indices + + # Extract all pairs of connected junctions + junc_indices = np.array( + [[i, j] for (i, j) in zip(*np.where(line_map)) if j > i]) + line_segments = np.stack([junctions[junc_indices[:, 0]], + junctions[junc_indices[:, 1]]], axis=1) + # line_segments is (num_lines, 2, 2) + line_lengths = np.linalg.norm( + line_segments[:, 0] - line_segments[:, 1], axis=1) + + # Sample the points separated by at least min_dist_pts along each line + # The number of samples depends on the length of the line + num_samples = np.minimum(line_lengths // self.config["min_dist_pts"], + self.config["max_num_samples"]) + line_points = [] + line_indices = [] + cur_line_idx = 1 + for n in np.arange(2, self.config["max_num_samples"] + 1): + # Consider all lines where we can fit up to n points + cur_line_seg = line_segments[num_samples == n] + line_points_x = np.linspace(cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 0], + n, axis=-1).flatten() + line_points_y = np.linspace(cur_line_seg[:, 0, 1], + cur_line_seg[:, 1, 1], + n, axis=-1).flatten() + jitter = self.config.get("jittering", 0) + if jitter: + # Add a small random jittering of all points along the line + angles = np.arctan2( + cur_line_seg[:, 1, 0] - cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 1] - cur_line_seg[:, 0, 1]).repeat(n) + jitter_hyp = (np.random.rand(len(angles)) * 2 - 1) * jitter + line_points_x += jitter_hyp * np.sin(angles) + line_points_y += jitter_hyp * np.cos(angles) + line_points.append(np.stack([line_points_x, line_points_y], axis=-1)) + # Keep track of the line indices for each sampled point + num_cur_lines = len(cur_line_seg) + line_idx = np.arange(cur_line_idx, cur_line_idx + num_cur_lines) + line_indices.append(line_idx.repeat(n)) + cur_line_idx += num_cur_lines + line_points = np.concatenate(line_points, + axis=0)[:self.config["max_pts"]] + line_indices = np.concatenate(line_indices, + axis=0)[:self.config["max_pts"]] + + # Warp the points if need be, and filter unvalid ones + # If the other view is also warped + if warp and H2 is not None: + warp_points2 = warp_points(line_points, H2) + line_points = warp_points(line_points, H1) + mask = mask_points(line_points, img_size) + mask2 = mask_points(warp_points2, img_size) + mask = mask * mask2 + # If the other view is not warped + elif warp and H2 is None: + line_points = warp_points(line_points, H1) + mask = mask_points(line_points, img_size) + else: + if H1 is not None: + raise ValueError("[Error] Wrong combination of homographies.") + # Remove points that would be outside of img_size if warped by H + warped_points = warp_points(line_points, H1) + mask = mask_points(warped_points, img_size) + line_points = line_points[mask] + line_indices = line_indices[mask] + + # Pad the line points to a fixed length + # Index of 0 means padded line + line_indices = np.concatenate([line_indices, np.zeros( + self.config["max_pts"] - len(line_indices))], axis=0) + line_points = np.concatenate( + [line_points, + np.zeros((self.config["max_pts"] - len(line_points), 2), + dtype=float)], axis=0) + + return line_points, line_indices + + def train_preprocessing(self, data, numpy=False): + """ Train preprocessing for GT data. """ + # Fetch the corresponding entries + image = data["image"] + junctions = data["junc"][:, :2] + line_pos = data["Lpos"] + line_neg = data["Lneg"] + image_size = image.shape[:2] + # Convert junctions to pixel coordinates (from 128x128) + junctions[:, 0] *= image_size[0] / 128 + junctions[:, 1] *= image_size[1] / 128 + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # In HW format + junctions = (junctions * np.array( + self.config['preprocessing']['resize'], np.float) + / np.array(size_old, np.float)) + + # Convert to positive line map and negative line map (our format) + num_junctions = junctions.shape[0] + line_map_pos = self.convert_line_map(line_pos, num_junctions) + line_map_neg = self.convert_line_map(line_neg, num_junctions) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + # Update image size + image_size = image.shape[:2] + heatmap_pos = get_line_heatmap(junctions_xy, line_map_pos, image_size) + heatmap_neg = get_line_heatmap(junctions_xy, line_map_neg, image_size) + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Check if we need to apply augmentations + # In training mode => yes. + # In homography adaptation mode (export mode) => No + if self.config["augmentation"]["photometric"]["enable"]: + photo_trans_lst = self.get_photo_transform() + ### Image transform ### + np.random.shuffle(photo_trans_lst) + image_transform = transforms.Compose( + photo_trans_lst + [photoaug.normalize_image()]) + else: + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Check homographic augmentation + if self.config["augmentation"]["homographic"]["enable"]: + homo_trans = self.get_homo_transform() + # Perform homographic transform + outputs_pos = homo_trans(image, junctions, line_map_pos) + outputs_neg = homo_trans(image, junctions, line_map_neg) + + # record the warped results + junctions = outputs_pos["junctions"] # Should be HW format + image = outputs_pos["warped_image"] + line_map_pos = outputs_pos["line_map"] + line_map_neg = outputs_neg["line_map"] + heatmap_pos = outputs_pos["warped_heatmap"] + heatmap_neg = outputs_neg["warped_heatmap"] + valid_mask = outputs_pos["valid_mask"] # Same for pos and neg + + junction_map = self.junc_to_junc_map(junctions, image_size) + + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + if not numpy: + return { + "image": to_tensor(image), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map_pos": to_tensor( + line_map_pos).to(torch.int32)[0, ...], + "line_map_neg": to_tensor( + line_map_neg).to(torch.int32)[0, ...], + "heatmap_pos": to_tensor(heatmap_pos).to(torch.int32), + "heatmap_neg": to_tensor(heatmap_neg).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + } + else: + return { + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map_pos": line_map_pos.astype(np.int32), + "line_map_neg": line_map_neg.astype(np.int32), + "heatmap_pos": heatmap_pos.astype(np.int32), + "heatmap_neg": heatmap_neg.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + } + + def train_preprocessing_exported( + self, data, numpy=False, disable_homoaug=False, + desc_training=False, H1=None, H1_scale=None, H2=None, scale=1., + h_crop=None, w_crop=None): + """ Train preprocessing for the exported labels. """ + data = copy.deepcopy(data) + # Fetch the corresponding entries + image = data["image"] + junctions = data["junctions"] + line_map = data["line_map"] + image_size = image.shape[:2] + + # Define the random crop for scaling if necessary + if h_crop is None or w_crop is None: + h_crop, w_crop = 0, 0 + if scale > 1: + H, W = self.config["preprocessing"]["resize"] + H_scale, W_scale = round(H * scale), round(W * scale) + if H_scale > H: + h_crop = np.random.randint(H_scale - H) + if W_scale > W: + w_crop = np.random.randint(W_scale - W) + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # # In HW format + # junctions = (junctions * np.array( + # self.config['preprocessing']['resize'], np.float) + # / np.array(size_old, np.float)) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + image_size = image.shape[:2] + heatmap = get_line_heatmap(junctions_xy, line_map, image_size) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Check if we need to apply augmentations + # In training mode => yes. + # In homography adaptation mode (export mode) => No + if self.config["augmentation"]["photometric"]["enable"]: + photo_trans_lst = self.get_photo_transform() + ### Image transform ### + np.random.shuffle(photo_trans_lst) + image_transform = transforms.Compose( + photo_trans_lst + [photoaug.normalize_image()]) + else: + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Perform the random scaling + if scale != 1.: + image, junctions, line_map, valid_mask = random_scaling( + image, junctions, line_map, scale, + h_crop=h_crop, w_crop=w_crop) + else: + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + # Initialize the empty output dict + outputs = {} + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + + # Check homographic augmentation + warp = (self.config["augmentation"]["homographic"]["enable"] + and disable_homoaug == False) + if warp: + homo_trans = self.get_homo_transform() + # Perform homographic transform + if H1 is None: + homo_outputs = homo_trans( + image, junctions, line_map, valid_mask=valid_mask) + else: + homo_outputs = homo_trans( + image, junctions, line_map, homo=H1, scale=H1_scale, + valid_mask=valid_mask) + homography_mat = homo_outputs["homo"] + + # Give the warp of the other view + if H1 is None: + H1 = homo_outputs["homo"] + + # Sample points along each line segments for the descriptor + if desc_training: + line_points, line_indices = self.get_line_points( + junctions, line_map, H1=H1, H2=H2, + img_size=image_size, warp=warp) + + # Record the warped results + if warp: + junctions = homo_outputs["junctions"] # Should be HW format + image = homo_outputs["warped_image"] + line_map = homo_outputs["line_map"] + valid_mask = homo_outputs["valid_mask"] # Same for pos and neg + heatmap = homo_outputs["warped_heatmap"] + + # Optionally put warping information first. + if not numpy: + outputs["homography_mat"] = to_tensor( + homography_mat).to(torch.float32)[0, ...] + else: + outputs["homography_mat"] = homography_mat.astype(np.float32) + + junction_map = self.junc_to_junc_map(junctions, image_size) + + if not numpy: + outputs.update({ + "image": to_tensor(image).to(torch.float32), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map": to_tensor(line_map).to(torch.int32)[0, ...], + "heatmap": to_tensor(heatmap).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + }) + if desc_training: + outputs.update({ + "line_points": to_tensor( + line_points).to(torch.float32)[0], + "line_indices": torch.tensor(line_indices, + dtype=torch.int) + }) + else: + outputs.update({ + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map": line_map.astype(np.int32), + "heatmap": heatmap.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + }) + if desc_training: + outputs.update({ + "line_points": line_points.astype(np.float32), + "line_indices": line_indices.astype(int) + }) + + return outputs + + def preprocessing_exported_paired_desc(self, data, numpy=False, scale=1.): + """ Train preprocessing for paired data for the exported labels + for descriptor training. """ + outputs = {} + + # Define the random crop for scaling if necessary + h_crop, w_crop = 0, 0 + if scale > 1: + H, W = self.config["preprocessing"]["resize"] + H_scale, W_scale = round(H * scale), round(W * scale) + if H_scale > H: + h_crop = np.random.randint(H_scale - H) + if W_scale > W: + w_crop = np.random.randint(W_scale - W) + + # Sample ref homography first + homo_config = self.config["augmentation"]["homographic"]["params"] + image_shape = self.config["preprocessing"]["resize"] + ref_H, ref_scale = homoaug.sample_homography(image_shape, + **homo_config) + + # Data for target view (All augmentation) + target_data = self.train_preprocessing_exported( + data, numpy=numpy, desc_training=True, H1=None, H2=ref_H, + scale=scale, h_crop=h_crop, w_crop=w_crop) + + # Data for reference view (No homographical augmentation) + ref_data = self.train_preprocessing_exported( + data, numpy=numpy, desc_training=True, H1=ref_H, + H1_scale=ref_scale, H2=target_data["homography_mat"].numpy(), + scale=scale, h_crop=h_crop, w_crop=w_crop) + + # Spread ref data + for key, val in ref_data.items(): + outputs["ref_" + key] = val + + # Spread target data + for key, val in target_data.items(): + outputs["target_" + key] = val + + return outputs + + def test_preprocessing(self, data, numpy=False): + """ Test preprocessing for GT data. """ + data = copy.deepcopy(data) + # Fetch the corresponding entries + image = data["image"] + junctions = data["junc"][:, :2] + line_pos = data["Lpos"] + line_neg = data["Lneg"] + image_size = image.shape[:2] + # Convert junctions to pixel coordinates (from 128x128) + junctions[:, 0] *= image_size[0] / 128 + junctions[:, 1] *= image_size[1] / 128 + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # In HW format + junctions = (junctions * np.array( + self.config['preprocessing']['resize'], np.float) + / np.array(size_old, np.float)) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Still need to normalize image + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Convert to positive line map and negative line map (our format) + num_junctions = junctions.shape[0] + line_map_pos = self.convert_line_map(line_pos, num_junctions) + line_map_neg = self.convert_line_map(line_neg, num_junctions) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + # Update image size + image_size = image.shape[:2] + heatmap_pos = get_line_heatmap(junctions_xy, line_map_pos, image_size) + heatmap_neg = get_line_heatmap(junctions_xy, line_map_neg, image_size) + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + junction_map = self.junc_to_junc_map(junctions, image_size) + + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + if not numpy: + return { + "image": to_tensor(image), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map_pos": to_tensor( + line_map_pos).to(torch.int32)[0, ...], + "line_map_neg": to_tensor( + line_map_neg).to(torch.int32)[0, ...], + "heatmap_pos": to_tensor(heatmap_pos).to(torch.int32), + "heatmap_neg": to_tensor(heatmap_neg).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + } + else: + return { + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map_pos": line_map_pos.astype(np.int32), + "line_map_neg": line_map_neg.astype(np.int32), + "heatmap_pos": heatmap_pos.astype(np.int32), + "heatmap_neg": heatmap_neg.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + } + + def test_preprocessing_exported(self, data, numpy=False, scale=1.): + """ Test preprocessing for the exported labels. """ + data = copy.deepcopy(data) + # Fetch the corresponding entries + image = data["image"] + junctions = data["junctions"] + line_map = data["line_map"] + image_size = image.shape[:2] + + # Resize the image before photometric and homographical augmentations + if not(list(image_size) == self.config["preprocessing"]["resize"]): + # Resize the image and the point location. + size_old = list(image.shape)[:2] # Only H and W dimensions + + image = cv2.resize( + image, tuple(self.config['preprocessing']['resize'][::-1]), + interpolation=cv2.INTER_LINEAR) + image = np.array(image, dtype=np.uint8) + + # # In HW format + # junctions = (junctions * np.array( + # self.config['preprocessing']['resize'], np.float) + # / np.array(size_old, np.float)) + + # Optionally convert the image to grayscale + if self.config["gray_scale"]: + image = (color.rgb2gray(image) * 255.).astype(np.uint8) + + # Still need to normalize image + image_transform = photoaug.normalize_image() + image = image_transform(image) + + # Generate the line heatmap after post-processing + junctions_xy = np.flip(np.round(junctions).astype(np.int32), axis=1) + image_size = image.shape[:2] + heatmap = get_line_heatmap(junctions_xy, line_map, image_size) + + # Declare default valid mask (all ones) + valid_mask = np.ones(image_size) + + junction_map = self.junc_to_junc_map(junctions, image_size) + + # Convert to tensor and return the results + to_tensor = transforms.ToTensor() + if not numpy: + outputs = { + "image": to_tensor(image), + "junctions": to_tensor(junctions).to(torch.float32)[0, ...], + "junction_map": to_tensor(junction_map).to(torch.int), + "line_map": to_tensor(line_map).to(torch.int32)[0, ...], + "heatmap": to_tensor(heatmap).to(torch.int32), + "valid_mask": to_tensor(valid_mask).to(torch.int32) + } + else: + outputs = { + "image": image, + "junctions": junctions.astype(np.float32), + "junction_map": junction_map.astype(np.int32), + "line_map": line_map.astype(np.int32), + "heatmap": heatmap.astype(np.int32), + "valid_mask": valid_mask.astype(np.int32) + } + + return outputs + + def __len__(self): + return self.dataset_length + + def get_data_from_key(self, file_key): + """ Get data from file_key. """ + # Check key exists + if not file_key in self.filename_dataset.keys(): + raise ValueError("[Error] the specified key is not in the dataset.") + + # Get the data paths + data_path = self.filename_dataset[file_key] + # Read in the image and npz labels (but haven't applied any transform) + data = self.get_data_from_path(data_path) + + # Perform transform and augmentation + if self.mode == "train" or self.config["add_augmentation_to_all_splits"]: + data = self.train_preprocessing(data, numpy=True) + else: + data = self.test_preprocessing(data, numpy=True) + + # Add file key to the output + data["file_key"] = file_key + + return data + + def __getitem__(self, idx): + """Return data + file_key: str, keys used to retrieve data from the filename dataset. + image: torch.float, C*H*W range 0~1, + junctions: torch.float, N*2, + junction_map: torch.int32, 1*H*W range 0 or 1, + line_map_pos: torch.int32, N*N range 0 or 1, + line_map_neg: torch.int32, N*N range 0 or 1, + heatmap_pos: torch.int32, 1*H*W range 0 or 1, + heatmap_neg: torch.int32, 1*H*W range 0 or 1, + valid_mask: torch.int32, 1*H*W range 0 or 1 + """ + # Get the corresponding datapoint and contents from filename dataset + file_key = self.datapoints[idx] + data_path = self.filename_dataset[file_key] + # Read in the image and npz labels (but haven't applied any transform) + data = self.get_data_from_path(data_path) + + # Also load the exported labels if not using the official ground truth + if not self.gt_source == "official": + with h5py.File(self.gt_source, "r") as f: + exported_label = parse_h5_data(f[file_key]) + + data["junctions"] = exported_label["junctions"] + data["line_map"] = exported_label["line_map"] + + # Perform transform and augmentation + return_type = self.config.get("return_type", "single") + if (self.mode == "train" + or self.config["add_augmentation_to_all_splits"]): + # Perform random scaling first + if self.config["augmentation"]["random_scaling"]["enable"]: + scale_range = self.config["augmentation"]["random_scaling"]["range"] + # Decide the scaling + scale = np.random.uniform(min(scale_range), max(scale_range)) + else: + scale = 1. + if self.gt_source == "official": + data = self.train_preprocessing(data) + else: + if return_type == "paired_desc": + data = self.preprocessing_exported_paired_desc( + data, scale=scale) + else: + data = self.train_preprocessing_exported(data, + scale=scale) + else: + if self.gt_source == "official": + data = self.test_preprocessing(data) + elif return_type == "paired_desc": + data = self.preprocessing_exported_paired_desc(data) + else: + data = self.test_preprocessing_exported(data) + + # Add file key to the output + data["file_key"] = file_key + + return data + + ######################## + ## Some other methods ## + ######################## + def _check_dataset_cache(self): + """ Check if dataset cache exists. """ + cache_file_path = os.path.join(self.cache_path, self.cache_name) + if os.path.exists(cache_file_path): + return True + else: + return False diff --git a/third_party/SOLD2/sold2/experiment.py b/third_party/SOLD2/sold2/experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..3bf4db1c9f148b9e33c6d7d0ba973375cd770a14 --- /dev/null +++ b/third_party/SOLD2/sold2/experiment.py @@ -0,0 +1,227 @@ +""" +Main file to launch training and testing experiments. +""" + +import yaml +import os +import argparse +import numpy as np +import torch + +from .config.project_config import Config as cfg +from .train import train_net +from .export import export_predictions, export_homograpy_adaptation + + +# Pytorch configurations +torch.cuda.empty_cache() +torch.backends.cudnn.benchmark = True + + +def load_config(config_path): + """ Load configurations from a given yaml file. """ + # Check file exists + if not os.path.exists(config_path): + raise ValueError("[Error] The provided config path is not valid.") + + # Load the configuration + with open(config_path, "r") as f: + config = yaml.safe_load(f) + + return config + + +def update_config(path, model_cfg=None, dataset_cfg=None): + """ Update configuration file from the resume path. """ + # Check we need to update or completely override. + model_cfg = {} if model_cfg is None else model_cfg + dataset_cfg = {} if dataset_cfg is None else dataset_cfg + + # Load saved configs + with open(os.path.join(path, "model_cfg.yaml"), "r") as f: + model_cfg_saved = yaml.safe_load(f) + model_cfg.update(model_cfg_saved) + with open(os.path.join(path, "dataset_cfg.yaml"), "r") as f: + dataset_cfg_saved = yaml.safe_load(f) + dataset_cfg.update(dataset_cfg_saved) + + # Update the saved yaml file + if not model_cfg == model_cfg_saved: + with open(os.path.join(path, "model_cfg.yaml"), "w") as f: + yaml.dump(model_cfg, f) + if not dataset_cfg == dataset_cfg_saved: + with open(os.path.join(path, "dataset_cfg.yaml"), "w") as f: + yaml.dump(dataset_cfg, f) + + return model_cfg, dataset_cfg + + +def record_config(model_cfg, dataset_cfg, output_path): + """ Record dataset config to the log path. """ + # Record model config + with open(os.path.join(output_path, "model_cfg.yaml"), "w") as f: + yaml.safe_dump(model_cfg, f) + + # Record dataset config + with open(os.path.join(output_path, "dataset_cfg.yaml"), "w") as f: + yaml.safe_dump(dataset_cfg, f) + + +def train(args, dataset_cfg, model_cfg, output_path): + """ Training function. """ + # Update model config from the resume path (only in resume mode) + if args.resume: + if os.path.realpath(output_path) != os.path.realpath(args.resume_path): + record_config(model_cfg, dataset_cfg, output_path) + + # First time, then write the config file to the output path + else: + record_config(model_cfg, dataset_cfg, output_path) + + # Launch the training + train_net(args, dataset_cfg, model_cfg, output_path) + + +def export(args, dataset_cfg, model_cfg, output_path, + export_dataset_mode=None, device=torch.device("cuda")): + """ Export function. """ + # Choose between normal predictions export or homography adaptation + if dataset_cfg.get("homography_adaptation") is not None: + print("[Info] Export predictions with homography adaptation.") + export_homograpy_adaptation(args, dataset_cfg, model_cfg, output_path, + export_dataset_mode, device) + else: + print("[Info] Export predictions normally.") + export_predictions(args, dataset_cfg, model_cfg, output_path, + export_dataset_mode) + + +def main(args, dataset_cfg, model_cfg, export_dataset_mode=None, + device=torch.device("cuda")): + """ Main function. """ + # Make the output path + output_path = os.path.join(cfg.EXP_PATH, args.exp_name) + + if args.mode == "train": + if not os.path.exists(output_path): + os.makedirs(output_path) + print("[Info] Training mode") + print("\t Output path: %s" % output_path) + train(args, dataset_cfg, model_cfg, output_path) + elif args.mode == "export": + # Different output_path in export mode + output_path = os.path.join(cfg.export_dataroot, args.exp_name) + print("[Info] Export mode") + print("\t Output path: %s" % output_path) + export(args, dataset_cfg, model_cfg, output_path, export_dataset_mode, device=device) + else: + raise ValueError("[Error]: Unknown mode: " + args.mode) + + +def set_random_seed(seed): + np.random.seed(seed) + torch.manual_seed(seed) + + +if __name__ == "__main__": + # Parse input arguments + parser = argparse.ArgumentParser() + parser.add_argument("--mode", type=str, default="train", + help="'train' or 'export'.") + parser.add_argument("--dataset_config", type=str, default=None, + help="Path to the dataset config.") + parser.add_argument("--model_config", type=str, default=None, + help="Path to the model config.") + parser.add_argument("--exp_name", type=str, default="exp", + help="Experiment name.") + parser.add_argument("--resume", action="store_true", default=False, + help="Load a previously trained model.") + parser.add_argument("--pretrained", action="store_true", default=False, + help="Start training from a pre-trained model.") + parser.add_argument("--resume_path", default=None, + help="Path from which to resume training.") + parser.add_argument("--pretrained_path", default=None, + help="Path to the pre-trained model.") + parser.add_argument("--checkpoint_name", default=None, + help="Name of the checkpoint to use.") + parser.add_argument("--export_dataset_mode", default=None, + help="'train' or 'test'.") + parser.add_argument("--export_batch_size", default=4, type=int, + help="Export batch size.") + + args = parser.parse_args() + + # Check if GPU is available + # Get the model + if torch.cuda.is_available(): + device = torch.device("cuda") + else: + device = torch.device("cpu") + + # Check if dataset config and model config is given. + if (((args.dataset_config is None) or (args.model_config is None)) + and (not args.resume) and (args.mode == "train")): + raise ValueError( + "[Error] The dataset config and model config should be given in non-resume mode") + + # If resume, check if the resume path has been given + if args.resume and (args.resume_path is None): + raise ValueError( + "[Error] Missing resume path.") + + # [Training] Load the config file. + if args.mode == "train" and (not args.resume): + # Check the pretrained checkpoint_path exists + if args.pretrained: + checkpoint_folder = args.resume_path + checkpoint_path = os.path.join(args.pretrained_path, + args.checkpoint_name) + if not os.path.exists(checkpoint_path): + raise ValueError("[Error] Missing checkpoint: " + + checkpoint_path) + dataset_cfg = load_config(args.dataset_config) + model_cfg = load_config(args.model_config) + + # [resume Training, Test, Export] Load the config file. + elif (args.mode == "train" and args.resume) or (args.mode == "export"): + # Check checkpoint path exists + checkpoint_folder = args.resume_path + checkpoint_path = os.path.join(args.resume_path, args.checkpoint_name) + if not os.path.exists(checkpoint_path): + raise ValueError("[Error] Missing checkpoint: " + checkpoint_path) + + # Load model_cfg from checkpoint folder if not provided + if args.model_config is None: + print("[Info] No model config provided. Loading from checkpoint folder.") + model_cfg_path = os.path.join(checkpoint_folder, "model_cfg.yaml") + if not os.path.exists(model_cfg_path): + raise ValueError( + "[Error] Missing model config in checkpoint path.") + model_cfg = load_config(model_cfg_path) + else: + model_cfg = load_config(args.model_config) + + # Load dataset_cfg from checkpoint folder if not provided + if args.dataset_config is None: + print("[Info] No dataset config provided. Loading from checkpoint folder.") + dataset_cfg_path = os.path.join(checkpoint_folder, + "dataset_cfg.yaml") + if not os.path.exists(dataset_cfg_path): + raise ValueError( + "[Error] Missing dataset config in checkpoint path.") + dataset_cfg = load_config(dataset_cfg_path) + else: + dataset_cfg = load_config(args.dataset_config) + + # Check the --export_dataset_mode flag + if (args.mode == "export") and (args.export_dataset_mode is None): + raise ValueError("[Error] Empty --export_dataset_mode flag.") + else: + raise ValueError("[Error] Unknown mode: " + args.mode) + + # Set the random seed + seed = dataset_cfg.get("random_seed", 0) + set_random_seed(seed) + + main(args, dataset_cfg, model_cfg, + export_dataset_mode=args.export_dataset_mode, device=device) diff --git a/third_party/SOLD2/sold2/export.py b/third_party/SOLD2/sold2/export.py new file mode 100644 index 0000000000000000000000000000000000000000..19683d982c6d7fd429b27868b620fd20562d1aa7 --- /dev/null +++ b/third_party/SOLD2/sold2/export.py @@ -0,0 +1,342 @@ +import numpy as np +import copy +import cv2 +import h5py +import math +from tqdm import tqdm +import torch +from torch.nn.functional import pixel_shuffle, softmax +from torch.utils.data import DataLoader +from kornia.geometry import warp_perspective + +from .dataset.dataset_util import get_dataset +from .model.model_util import get_model +from .misc.train_utils import get_latest_checkpoint +from .train import convert_junc_predictions +from .dataset.transforms.homographic_transforms import sample_homography + + +def restore_weights(model, state_dict): + """ Restore weights in compatible mode. """ + # Try to directly load state dict + try: + model.load_state_dict(state_dict) + except: + err = model.load_state_dict(state_dict, strict=False) + # missing keys are those in model but not in state_dict + missing_keys = err.missing_keys + # Unexpected keys are those in state_dict but not in model + unexpected_keys = err.unexpected_keys + + # Load mismatched keys manually + model_dict = model.state_dict() + for idx, key in enumerate(missing_keys): + dict_keys = [_ for _ in unexpected_keys if not "tracked" in _] + model_dict[key] = state_dict[dict_keys[idx]] + model.load_state_dict(model_dict) + return model + + +def get_padded_filename(num_pad, idx): + """ Get the filename padded with 0. """ + file_len = len("%d" % (idx)) + filename = "0" * (num_pad - file_len) + "%d" % (idx) + return filename + + +def export_predictions(args, dataset_cfg, model_cfg, output_path, + export_dataset_mode): + """ Export predictions. """ + # Get the test configuration + test_cfg = model_cfg["test"] + + # Create the dataset and dataloader based on the export_dataset_mode + print("\t Initializing dataset and dataloader") + batch_size = 4 + export_dataset, collate_fn = get_dataset(export_dataset_mode, dataset_cfg) + export_loader = DataLoader(export_dataset, batch_size=batch_size, + num_workers=test_cfg.get("num_workers", 4), + shuffle=False, pin_memory=False, + collate_fn=collate_fn) + print("\t Successfully intialized dataset and dataloader.") + + # Initialize model and load the checkpoint + model = get_model(model_cfg, mode="test") + checkpoint = get_latest_checkpoint(args.resume_path, args.checkpoint_name) + model = restore_weights(model, checkpoint["model_state_dict"]) + model = model.cuda() + model.eval() + print("\t Successfully initialized model") + + # Start the export process + print("[Info] Start exporting predictions") + output_dataset_path = output_path + ".h5" + filename_idx = 0 + with h5py.File(output_dataset_path, "w", libver="latest", swmr=True) as f: + # Iterate through all the data in dataloader + for data in tqdm(export_loader, ascii=True): + # Fetch the data + junc_map = data["junction_map"] + heatmap = data["heatmap"] + valid_mask = data["valid_mask"] + input_images = data["image"].cuda() + + # Run the forward pass + with torch.no_grad(): + outputs = model(input_images) + + # Convert predictions + junc_np = convert_junc_predictions( + outputs["junctions"], model_cfg["grid_size"], + model_cfg["detection_thresh"], 300) + junc_map_np = junc_map.numpy().transpose(0, 2, 3, 1) + heatmap_np = softmax(outputs["heatmap"].detach(), + dim=1).cpu().numpy().transpose(0, 2, 3, 1) + heatmap_gt_np = heatmap.numpy().transpose(0, 2, 3, 1) + valid_mask_np = valid_mask.numpy().transpose(0, 2, 3, 1) + + # Data entries to save + current_batch_size = input_images.shape[0] + for batch_idx in range(current_batch_size): + output_data = { + "image": input_images.cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "junc_gt": junc_map_np[batch_idx], + "junc_pred": junc_np["junc_pred"][batch_idx], + "junc_pred_nms": junc_np["junc_pred_nms"][batch_idx].astype(np.float32), + "heatmap_gt": heatmap_gt_np[batch_idx], + "heatmap_pred": heatmap_np[batch_idx], + "valid_mask": valid_mask_np[batch_idx], + "junc_points": data["junctions"][batch_idx].numpy()[0].round().astype(np.int32), + "line_map": data["line_map"][batch_idx].numpy()[0].astype(np.int32) + } + + # Save data to h5 dataset + num_pad = math.ceil(math.log10(len(export_loader))) + 1 + output_key = get_padded_filename(num_pad, filename_idx) + f_group = f.create_group(output_key) + + # Store data + for key, output_data in output_data.items(): + f_group.create_dataset(key, data=output_data, + compression="gzip") + filename_idx += 1 + + +def export_homograpy_adaptation(args, dataset_cfg, model_cfg, output_path, + export_dataset_mode, device): + """ Export homography adaptation results. """ + # Check if the export_dataset_mode is supported + supported_modes = ["train", "test"] + if not export_dataset_mode in supported_modes: + raise ValueError( + "[Error] The specified export_dataset_mode is not supported.") + + # Get the test configuration + test_cfg = model_cfg["test"] + + # Get the homography adaptation configurations + homography_cfg = dataset_cfg.get("homography_adaptation", None) + if homography_cfg is None: + raise ValueError( + "[Error] Empty homography_adaptation entry in config.") + + # Create the dataset and dataloader based on the export_dataset_mode + print("\t Initializing dataset and dataloader") + batch_size = args.export_batch_size + + export_dataset, collate_fn = get_dataset(export_dataset_mode, dataset_cfg) + export_loader = DataLoader(export_dataset, batch_size=batch_size, + num_workers=test_cfg.get("num_workers", 4), + shuffle=False, pin_memory=False, + collate_fn=collate_fn) + print("\t Successfully intialized dataset and dataloader.") + + # Initialize model and load the checkpoint + model = get_model(model_cfg, mode="test") + checkpoint = get_latest_checkpoint(args.resume_path, args.checkpoint_name, + device) + model = restore_weights(model, checkpoint["model_state_dict"]) + model = model.to(device).eval() + print("\t Successfully initialized model") + + # Start the export process + print("[Info] Start exporting predictions") + output_dataset_path = output_path + ".h5" + with h5py.File(output_dataset_path, "w", libver="latest") as f: + f.swmr_mode=True + for _, data in enumerate(tqdm(export_loader, ascii=True)): + input_images = data["image"].to(device) + file_keys = data["file_key"] + batch_size = input_images.shape[0] + + # Run the homograpy adaptation + outputs = homography_adaptation(input_images, model, + model_cfg["grid_size"], + homography_cfg) + + # Save the entries + for batch_idx in range(batch_size): + # Get the save key + save_key = file_keys[batch_idx] + output_data = { + "image": input_images.cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "junc_prob_mean": outputs["junc_probs_mean"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "junc_prob_max": outputs["junc_probs_max"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "junc_count": outputs["junc_counts"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "heatmap_prob_mean": outputs["heatmap_probs_mean"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "heatmap_prob_max": outputs["heatmap_probs_max"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx], + "heatmap_cout": outputs["heatmap_counts"].cpu().numpy().transpose(0, 2, 3, 1)[batch_idx] + } + + # Create group and write data + f_group = f.create_group(save_key) + for key, output_data in output_data.items(): + f_group.create_dataset(key, data=output_data, + compression="gzip") + + +def homography_adaptation(input_images, model, grid_size, homography_cfg): + """ The homography adaptation process. + Arguments: + input_images: The images to be evaluated. + model: The pytorch model in evaluation mode. + grid_size: Grid size of the junction decoder. + homography_cfg: Homography adaptation configurations. + """ + # Get the device of the current model + device = next(model.parameters()).device + + # Define some constants and placeholder + batch_size, _, H, W = input_images.shape + num_iter = homography_cfg["num_iter"] + junc_probs = torch.zeros([batch_size, num_iter, H, W], device=device) + junc_counts = torch.zeros([batch_size, 1, H, W], device=device) + heatmap_probs = torch.zeros([batch_size, num_iter, H, W], device=device) + heatmap_counts = torch.zeros([batch_size, 1, H, W], device=device) + margin = homography_cfg["valid_border_margin"] + + # Keep a config with no artifacts + homography_cfg_no_artifacts = copy.copy(homography_cfg["homographies"]) + homography_cfg_no_artifacts["allow_artifacts"] = False + + for idx in range(num_iter): + if idx <= num_iter // 5: + # Ensure that 20% of the homographies have no artifact + H_mat_lst = [sample_homography( + [H,W], **homography_cfg_no_artifacts)[0][None] + for _ in range(batch_size)] + else: + H_mat_lst = [sample_homography( + [H,W], **homography_cfg["homographies"])[0][None] + for _ in range(batch_size)] + + H_mats = np.concatenate(H_mat_lst, axis=0) + H_tensor = torch.tensor(H_mats, dtype=torch.float, device=device) + H_inv_tensor = torch.inverse(H_tensor) + + # Perform the homography warp + images_warped = warp_perspective(input_images, H_tensor, (H, W), + flags="bilinear") + + # Warp the mask + masks_junc_warped = warp_perspective( + torch.ones([batch_size, 1, H, W], device=device), + H_tensor, (H, W), flags="nearest") + masks_heatmap_warped = warp_perspective( + torch.ones([batch_size, 1, H, W], device=device), + H_tensor, (H, W), flags="nearest") + + # Run the network forward pass + with torch.no_grad(): + outputs = model(images_warped) + + # Unwarp and mask the junction prediction + junc_prob_warped = pixel_shuffle(softmax( + outputs["junctions"], dim=1)[:, :-1, :, :], grid_size) + junc_prob = warp_perspective(junc_prob_warped, H_inv_tensor, + (H, W), flags="bilinear") + + # Create the out of boundary mask + out_boundary_mask = warp_perspective( + torch.ones([batch_size, 1, H, W], device=device), + H_inv_tensor, (H, W), flags="nearest") + out_boundary_mask = adjust_border(out_boundary_mask, device, margin) + + junc_prob = junc_prob * out_boundary_mask + junc_count = warp_perspective(masks_junc_warped * out_boundary_mask, + H_inv_tensor, (H, W), flags="nearest") + + # Unwarp the mask and heatmap prediction + # Always fetch only one channel + if outputs["heatmap"].shape[1] == 2: + # Convert to single channel directly from here + heatmap_prob_warped = softmax(outputs["heatmap"], + dim=1)[:, 1:, :, :] + else: + heatmap_prob_warped = torch.sigmoid(outputs["heatmap"]) + + heatmap_prob_warped = heatmap_prob_warped * masks_heatmap_warped + heatmap_prob = warp_perspective(heatmap_prob_warped, H_inv_tensor, + (H, W), flags="bilinear") + heatmap_count = warp_perspective(masks_heatmap_warped, H_inv_tensor, + (H, W), flags="nearest") + + # Record the results + junc_probs[:, idx:idx+1, :, :] = junc_prob + heatmap_probs[:, idx:idx+1, :, :] = heatmap_prob + junc_counts += junc_count + heatmap_counts += heatmap_count + + # Perform the accumulation operation + if homography_cfg["min_counts"] > 0: + min_counts = homography_cfg["min_counts"] + junc_count_mask = (junc_counts < min_counts) + heatmap_count_mask = (heatmap_counts < min_counts) + junc_counts[junc_count_mask] = 0 + heatmap_counts[heatmap_count_mask] = 0 + else: + junc_count_mask = np.zeros_like(junc_counts, dtype=bool) + heatmap_count_mask = np.zeros_like(heatmap_counts, dtype=bool) + + # Compute the mean accumulation + junc_probs_mean = torch.sum(junc_probs, dim=1, keepdim=True) / junc_counts + junc_probs_mean[junc_count_mask] = 0. + heatmap_probs_mean = (torch.sum(heatmap_probs, dim=1, keepdim=True) + / heatmap_counts) + heatmap_probs_mean[heatmap_count_mask] = 0. + + # Compute the max accumulation + junc_probs_max = torch.max(junc_probs, dim=1, keepdim=True)[0] + junc_probs_max[junc_count_mask] = 0. + heatmap_probs_max = torch.max(heatmap_probs, dim=1, keepdim=True)[0] + heatmap_probs_max[heatmap_count_mask] = 0. + + return {"junc_probs_mean": junc_probs_mean, + "junc_probs_max": junc_probs_max, + "junc_counts": junc_counts, + "heatmap_probs_mean": heatmap_probs_mean, + "heatmap_probs_max": heatmap_probs_max, + "heatmap_counts": heatmap_counts} + + +def adjust_border(input_masks, device, margin=3): + """ Adjust the border of the counts and valid_mask. """ + # Convert the mask to numpy array + dtype = input_masks.dtype + input_masks = np.squeeze(input_masks.cpu().numpy(), axis=1) + + erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, + (margin*2, margin*2)) + batch_size = input_masks.shape[0] + + output_mask_lst = [] + # Erode all the masks + for i in range(batch_size): + output_mask = cv2.erode(input_masks[i, ...], erosion_kernel) + + output_mask_lst.append( + torch.tensor(output_mask, dtype=dtype, device=device)[None]) + + # Concat back along the batch dimension. + output_masks = torch.cat(output_mask_lst, dim=0) + return output_masks.unsqueeze(dim=1) diff --git a/third_party/SOLD2/sold2/export_line_features.py b/third_party/SOLD2/sold2/export_line_features.py new file mode 100644 index 0000000000000000000000000000000000000000..4cbde860a446d758dff254ea5320ca13bb79e6b7 --- /dev/null +++ b/third_party/SOLD2/sold2/export_line_features.py @@ -0,0 +1,74 @@ +""" + Export line detections and descriptors given a list of input images. +""" +import os +import argparse +import cv2 +import numpy as np +import torch +from tqdm import tqdm + +from .experiment import load_config +from .model.line_matcher import LineMatcher + + +def export_descriptors(images_list, ckpt_path, config, device, extension, + output_folder, multiscale=False): + # Extract the image paths + with open(images_list, 'r') as f: + image_files = f.readlines() + image_files = [path.strip('\n') for path in image_files] + + # Initialize the line matcher + line_matcher = LineMatcher( + config["model_cfg"], ckpt_path, device, config["line_detector_cfg"], + config["line_matcher_cfg"], multiscale) + print("\t Successfully initialized model") + + # Run the inference on each image and write the output on disk + for img_path in tqdm(image_files): + img = cv2.imread(img_path, 0) + img = torch.tensor(img[None, None] / 255., dtype=torch.float, + device=device) + + # Run the line detection and description + ref_detection = line_matcher.line_detection(img) + ref_line_seg = ref_detection["line_segments"] + ref_descriptors = ref_detection["descriptor"][0].cpu().numpy() + + # Write the output on disk + img_name = os.path.splitext(os.path.basename(img_path))[0] + output_file = os.path.join(output_folder, img_name + extension) + np.savez_compressed(output_file, line_seg=ref_line_seg, + descriptors=ref_descriptors) + + +if __name__ == "__main__": + # Parse input arguments + parser = argparse.ArgumentParser() + parser.add_argument("--img_list", type=str, required=True, + help="List of input images in a text file.") + parser.add_argument("--output_folder", type=str, required=True, + help="Path to the output folder.") + parser.add_argument("--config", type=str, + default="config/export_line_features.yaml") + parser.add_argument("--checkpoint_path", type=str, + default="pretrained_models/sold2_wireframe.tar") + parser.add_argument("--multiscale", action="store_true", default=False) + parser.add_argument("--extension", type=str, default=None) + args = parser.parse_args() + + # Get the device + if torch.cuda.is_available(): + device = torch.device("cuda") + else: + device = torch.device("cpu") + + # Get the model config, extension and checkpoint path + config = load_config(args.config) + ckpt_path = os.path.abspath(args.checkpoint_path) + extension = 'sold2' if args.extension is None else args.extension + extension = "." + extension + + export_descriptors(args.img_list, ckpt_path, config, device, extension, + args.output_folder, args.multiscale) diff --git a/third_party/SOLD2/sold2/misc/__init__.py b/third_party/SOLD2/sold2/misc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/misc/geometry_utils.py b/third_party/SOLD2/sold2/misc/geometry_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..50f0478062cd19ebac812bff62b6c3a3d5f124c2 --- /dev/null +++ b/third_party/SOLD2/sold2/misc/geometry_utils.py @@ -0,0 +1,81 @@ +import numpy as np +import torch + + +### Point-related utils + +# Warp a list of points using a homography +def warp_points(points, homography): + # Convert to homogeneous and in xy format + new_points = np.concatenate([points[..., [1, 0]], + np.ones_like(points[..., :1])], axis=-1) + # Warp + new_points = (homography @ new_points.T).T + # Convert back to inhomogeneous and hw format + new_points = new_points[..., [1, 0]] / new_points[..., 2:] + return new_points + + +# Mask out the points that are outside of img_size +def mask_points(points, img_size): + mask = ((points[..., 0] >= 0) + & (points[..., 0] < img_size[0]) + & (points[..., 1] >= 0) + & (points[..., 1] < img_size[1])) + return mask + + +# Convert a tensor [N, 2] or batched tensor [B, N, 2] of N keypoints into +# a grid in [-1, 1]² that can be used in torch.nn.functional.interpolate +def keypoints_to_grid(keypoints, img_size): + n_points = keypoints.size()[-2] + device = keypoints.device + grid_points = keypoints.float() * 2. / torch.tensor( + img_size, dtype=torch.float, device=device) - 1. + grid_points = grid_points[..., [1, 0]].view(-1, n_points, 1, 2) + return grid_points + + +# Return a 2D matrix indicating the local neighborhood of each point +# for a given threshold and two lists of corresponding keypoints +def get_dist_mask(kp0, kp1, valid_mask, dist_thresh): + b_size, n_points, _ = kp0.size() + dist_mask0 = torch.norm(kp0.unsqueeze(2) - kp0.unsqueeze(1), dim=-1) + dist_mask1 = torch.norm(kp1.unsqueeze(2) - kp1.unsqueeze(1), dim=-1) + dist_mask = torch.min(dist_mask0, dist_mask1) + dist_mask = dist_mask <= dist_thresh + dist_mask = dist_mask.repeat(1, 1, b_size).reshape(b_size * n_points, + b_size * n_points) + dist_mask = dist_mask[valid_mask, :][:, valid_mask] + return dist_mask + + +### Line-related utils + +# Sample n points along lines of shape (num_lines, 2, 2) +def sample_line_points(lines, n): + line_points_x = np.linspace(lines[:, 0, 0], lines[:, 1, 0], n, axis=-1) + line_points_y = np.linspace(lines[:, 0, 1], lines[:, 1, 1], n, axis=-1) + line_points = np.stack([line_points_x, line_points_y], axis=2) + return line_points + + +# Return a mask of the valid lines that are within a valid mask of an image +def mask_lines(lines, valid_mask): + h, w = valid_mask.shape + int_lines = np.clip(np.round(lines).astype(int), 0, [h - 1, w - 1]) + h_valid = valid_mask[int_lines[:, 0, 0], int_lines[:, 0, 1]] + w_valid = valid_mask[int_lines[:, 1, 0], int_lines[:, 1, 1]] + valid = h_valid & w_valid + return valid + + +# Return a 2D matrix indicating for each pair of points +# if they are on the same line or not +def get_common_line_mask(line_indices, valid_mask): + b_size, n_points = line_indices.shape + common_mask = line_indices[:, :, None] == line_indices[:, None, :] + common_mask = common_mask.repeat(1, 1, b_size).reshape(b_size * n_points, + b_size * n_points) + common_mask = common_mask[valid_mask, :][:, valid_mask] + return common_mask diff --git a/third_party/SOLD2/sold2/misc/train_utils.py b/third_party/SOLD2/sold2/misc/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d5ada35eea660df1f78b9f20d9bf7ed726eaee2c --- /dev/null +++ b/third_party/SOLD2/sold2/misc/train_utils.py @@ -0,0 +1,74 @@ +""" +This file contains some useful functions for train / val. +""" +import os +import numpy as np +import torch + + +################# +## image utils ## +################# +def convert_image(input_tensor, axis): + """ Convert single channel images to 3-channel images. """ + image_lst = [input_tensor for _ in range(3)] + outputs = np.concatenate(image_lst, axis) + return outputs + + +###################### +## checkpoint utils ## +###################### +def get_latest_checkpoint(checkpoint_root, checkpoint_name, + device=torch.device("cuda")): + """ Get the latest checkpoint or by filename. """ + # Load specific checkpoint + if checkpoint_name is not None: + checkpoint = torch.load( + os.path.join(checkpoint_root, checkpoint_name), + map_location=device) + # Load the latest checkpoint + else: + lastest_checkpoint = sorted(os.listdir(os.path.join( + checkpoint_root, "*.tar")))[-1] + checkpoint = torch.load(os.path.join( + checkpoint_root, lastest_checkpoint), map_location=device) + return checkpoint + + +def remove_old_checkpoints(checkpoint_root, max_ckpt=15): + """ Remove the outdated checkpoints. """ + # Get sorted list of checkpoints + checkpoint_list = sorted( + [_ for _ in os.listdir(os.path.join(checkpoint_root)) + if _.endswith(".tar")]) + + # Get the checkpoints to be removed + if len(checkpoint_list) > max_ckpt: + remove_list = checkpoint_list[:-max_ckpt] + for _ in remove_list: + full_name = os.path.join(checkpoint_root, _) + os.remove(full_name) + print("[Debug] Remove outdated checkpoint %s" % (full_name)) + + +def adapt_checkpoint(state_dict): + new_state_dict = {} + for k, v in state_dict.items(): + if k.startswith('module.'): + new_state_dict[k[7:]] = v + else: + new_state_dict[k] = v + return new_state_dict + + +################ +## HDF5 utils ## +################ +def parse_h5_data(h5_data): + """ Parse h5 dataset. """ + output_data = {} + for key in h5_data.keys(): + output_data[key] = np.array(h5_data[key]) + + return output_data diff --git a/third_party/SOLD2/sold2/misc/visualize_util.py b/third_party/SOLD2/sold2/misc/visualize_util.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa46877f79724221b7caa423de6916acdc021f8 --- /dev/null +++ b/third_party/SOLD2/sold2/misc/visualize_util.py @@ -0,0 +1,526 @@ +""" Organize some frequently used visualization functions. """ +import cv2 +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import copy +import seaborn as sns + + +# Plot junctions onto the image (return a separate copy) +def plot_junctions(input_image, junctions, junc_size=3, color=None): + """ + input_image: can be 0~1 float or 0~255 uint8. + junctions: Nx2 or 2xN np array. + junc_size: the size of the plotted circles. + """ + # Create image copy + image = copy.copy(input_image) + # Make sure the image is converted to 255 uint8 + if image.dtype == np.uint8: + pass + # A float type image ranging from 0~1 + elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.: + image = (image * 255.).astype(np.uint8) + # A float type image ranging from 0.~255. + elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.: + image = image.astype(np.uint8) + else: + raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.") + + # Check whether the image is single channel + if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)): + # Squeeze to H*W first + image = image.squeeze() + + # Stack to channle 3 + image = np.concatenate([image[..., None] for _ in range(3)], axis=-1) + + # Junction dimensions should be N*2 + if not len(junctions.shape) == 2: + raise ValueError("[Error] junctions should be 2-dim array.") + + # Always convert to N*2 + if junctions.shape[-1] != 2: + if junctions.shape[0] == 2: + junctions = junctions.T + else: + raise ValueError("[Error] At least one of the two dims should be 2.") + + # Round and convert junctions to int (and check the boundary) + H, W = image.shape[:2] + junctions = (np.round(junctions)).astype(np.int) + junctions[junctions < 0] = 0 + junctions[junctions[:, 0] >= H, 0] = H-1 # (first dim) max bounded by H-1 + junctions[junctions[:, 1] >= W, 1] = W-1 # (second dim) max bounded by W-1 + + # Iterate through all the junctions + num_junc = junctions.shape[0] + if color is None: + color = (0, 255., 0) + for idx in range(num_junc): + # Fetch one junction + junc = junctions[idx, :] + cv2.circle(image, tuple(np.flip(junc)), radius=junc_size, + color=color, thickness=3) + + return image + + +# Plot line segements given junctions and line adjecent map +def plot_line_segments(input_image, junctions, line_map, junc_size=3, + color=(0, 255., 0), line_width=1, plot_survived_junc=True): + """ + input_image: can be 0~1 float or 0~255 uint8. + junctions: Nx2 or 2xN np array. + line_map: NxN np array + junc_size: the size of the plotted circles. + color: color of the line segments (can be string "random") + line_width: width of the drawn segments. + plot_survived_junc: whether we only plot the survived junctions. + """ + # Create image copy + image = copy.copy(input_image) + # Make sure the image is converted to 255 uint8 + if image.dtype == np.uint8: + pass + # A float type image ranging from 0~1 + elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.: + image = (image * 255.).astype(np.uint8) + # A float type image ranging from 0.~255. + elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.: + image = image.astype(np.uint8) + else: + raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.") + + # Check whether the image is single channel + if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)): + # Squeeze to H*W first + image = image.squeeze() + + # Stack to channle 3 + image = np.concatenate([image[..., None] for _ in range(3)], axis=-1) + + # Junction dimensions should be 2 + if not len(junctions.shape) == 2: + raise ValueError("[Error] junctions should be 2-dim array.") + + # Always convert to N*2 + if junctions.shape[-1] != 2: + if junctions.shape[0] == 2: + junctions = junctions.T + else: + raise ValueError("[Error] At least one of the two dims should be 2.") + + # line_map dimension should be 2 + if not len(line_map.shape) == 2: + raise ValueError("[Error] line_map should be 2-dim array.") + + # Color should be "random" or a list or tuple with length 3 + if color != "random": + if not (isinstance(color, tuple) or isinstance(color, list)): + raise ValueError("[Error] color should have type list or tuple.") + else: + if len(color) != 3: + raise ValueError("[Error] color should be a list or tuple with length 3.") + + # Make a copy of the line_map + line_map_tmp = copy.copy(line_map) + + # Parse line_map back to segment pairs + segments = np.zeros([0, 4]) + for idx in range(junctions.shape[0]): + # if no connectivity, just skip it + if line_map_tmp[idx, :].sum() == 0: + continue + # record the line segment + else: + for idx2 in np.where(line_map_tmp[idx, :] == 1)[0]: + p1 = np.flip(junctions[idx, :]) # Convert to xy format + p2 = np.flip(junctions[idx2, :]) # Convert to xy format + segments = np.concatenate((segments, np.array([p1[0], p1[1], p2[0], p2[1]])[None, ...]), axis=0) + + # Update line_map + line_map_tmp[idx, idx2] = 0 + line_map_tmp[idx2, idx] = 0 + + # Draw segment pairs + for idx in range(segments.shape[0]): + seg = np.round(segments[idx, :]).astype(np.int) + # Decide the color + if color != "random": + color = tuple(color) + else: + color = tuple(np.random.rand(3,)) + cv2.line(image, tuple(seg[:2]), tuple(seg[2:]), color=color, thickness=line_width) + + # Also draw the junctions + if not plot_survived_junc: + num_junc = junctions.shape[0] + for idx in range(num_junc): + # Fetch one junction + junc = junctions[idx, :] + cv2.circle(image, tuple(np.flip(junc)), radius=junc_size, + color=(0, 255., 0), thickness=3) + # Only plot the junctions which are part of a line segment + else: + for idx in range(segments.shape[0]): + seg = np.round(segments[idx, :]).astype(np.int) # Already in HW format. + cv2.circle(image, tuple(seg[:2]), radius=junc_size, + color=(0, 255., 0), thickness=3) + cv2.circle(image, tuple(seg[2:]), radius=junc_size, + color=(0, 255., 0), thickness=3) + + return image + + +# Plot line segments given Nx4 or Nx2x2 line segments +def plot_line_segments_from_segments(input_image, line_segments, junc_size=3, + color=(0, 255., 0), line_width=1): + # Create image copy + image = copy.copy(input_image) + # Make sure the image is converted to 255 uint8 + if image.dtype == np.uint8: + pass + # A float type image ranging from 0~1 + elif image.dtype in [np.float32, np.float64, np.float] and image.max() <= 2.: + image = (image * 255.).astype(np.uint8) + # A float type image ranging from 0.~255. + elif image.dtype in [np.float32, np.float64, np.float] and image.mean() > 10.: + image = image.astype(np.uint8) + else: + raise ValueError("[Error] Unknown image data type. Expect 0~1 float or 0~255 uint8.") + + # Check whether the image is single channel + if len(image.shape) == 2 or ((len(image.shape) == 3) and (image.shape[-1] == 1)): + # Squeeze to H*W first + image = image.squeeze() + + # Stack to channle 3 + image = np.concatenate([image[..., None] for _ in range(3)], axis=-1) + + # Check the if line_segments are in (1) Nx4, or (2) Nx2x2. + H, W, _ = image.shape + # (1) Nx4 format + if len(line_segments.shape) == 2 and line_segments.shape[-1] == 4: + # Round to int32 + line_segments = line_segments.astype(np.int32) + + # Clip H dimension + line_segments[:, 0] = np.clip(line_segments[:, 0], a_min=0, a_max=H-1) + line_segments[:, 2] = np.clip(line_segments[:, 2], a_min=0, a_max=H-1) + + # Clip W dimension + line_segments[:, 1] = np.clip(line_segments[:, 1], a_min=0, a_max=W-1) + line_segments[:, 3] = np.clip(line_segments[:, 3], a_min=0, a_max=W-1) + + # Convert to Nx2x2 format + line_segments = np.concatenate( + [np.expand_dims(line_segments[:, :2], axis=1), + np.expand_dims(line_segments[:, 2:], axis=1)], + axis=1 + ) + + # (2) Nx2x2 format + elif len(line_segments.shape) == 3 and line_segments.shape[-1] == 2: + # Round to int32 + line_segments = line_segments.astype(np.int32) + + # Clip H dimension + line_segments[:, :, 0] = np.clip(line_segments[:, :, 0], a_min=0, a_max=H-1) + line_segments[:, :, 1] = np.clip(line_segments[:, :, 1], a_min=0, a_max=W-1) + + else: + raise ValueError("[Error] line_segments should be either Nx4 or Nx2x2 in HW format.") + + # Draw segment pairs (all segments should be in HW format) + image = image.copy() + for idx in range(line_segments.shape[0]): + seg = np.round(line_segments[idx, :, :]).astype(np.int32) + # Decide the color + if color != "random": + color = tuple(color) + else: + color = tuple(np.random.rand(3,)) + cv2.line(image, tuple(np.flip(seg[0, :])), + tuple(np.flip(seg[1, :])), + color=color, thickness=line_width) + + # Also draw the junctions + cv2.circle(image, tuple(np.flip(seg[0, :])), radius=junc_size, color=(0, 255., 0), thickness=3) + cv2.circle(image, tuple(np.flip(seg[1, :])), radius=junc_size, color=(0, 255., 0), thickness=3) + + return image + + +# Additional functions to visualize multiple images at the same time, +# e.g. for line matching +def plot_images(imgs, titles=None, cmaps='gray', dpi=100, size=6, pad=.5): + """Plot a set of images horizontally. + Args: + imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W). + titles: a list of strings, as titles for each image. + cmaps: colormaps for monochrome images. + """ + n = len(imgs) + if not isinstance(cmaps, (list, tuple)): + cmaps = [cmaps] * n + figsize = (size*n, size*3/4) if size is not None else None + fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi) + if n == 1: + ax = [ax] + for i in range(n): + ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i])) + ax[i].get_yaxis().set_ticks([]) + ax[i].get_xaxis().set_ticks([]) + ax[i].set_axis_off() + for spine in ax[i].spines.values(): # remove frame + spine.set_visible(False) + if titles: + ax[i].set_title(titles[i]) + fig.tight_layout(pad=pad) + + +def plot_keypoints(kpts, colors='lime', ps=4): + """Plot keypoints for existing images. + Args: + kpts: list of ndarrays of size (N, 2). + colors: string, or list of list of tuples (one for each keypoints). + ps: size of the keypoints as float. + """ + if not isinstance(colors, list): + colors = [colors] * len(kpts) + axes = plt.gcf().axes + for a, k, c in zip(axes, kpts, colors): + a.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0) + + +def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, indices=(0, 1), a=1.): + """Plot matches for a pair of existing images. + Args: + kpts0, kpts1: corresponding keypoints of size (N, 2). + color: color of each match, string or RGB tuple. Random if not given. + lw: width of the lines. + ps: size of the end points (no endpoint if ps=0) + indices: indices of the images to draw the matches on. + a: alpha opacity of the match lines. + """ + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + ax0, ax1 = ax[indices[0]], ax[indices[1]] + fig.canvas.draw() + + assert len(kpts0) == len(kpts1) + if color is None: + color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist() + elif len(color) > 0 and not isinstance(color[0], (tuple, list)): + color = [color] * len(kpts0) + + if lw > 0: + # transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(ax0.transData.transform(kpts0)) + fkpts1 = transFigure.transform(ax1.transData.transform(kpts1)) + fig.lines += [matplotlib.lines.Line2D( + (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), + zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw, + alpha=a) + for i in range(len(kpts0))] + + # freeze the axes to prevent the transform to change + ax0.autoscale(enable=False) + ax1.autoscale(enable=False) + + if ps > 0: + ax0.scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps, zorder=2) + ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps, zorder=2) + + +def plot_lines(lines, line_colors='orange', point_colors='cyan', + ps=4, lw=2, indices=(0, 1)): + """Plot lines and endpoints for existing images. + Args: + lines: list of ndarrays of size (N, 2, 2). + colors: string, or list of list of tuples (one for each keypoints). + ps: size of the keypoints as float pixels. + lw: line width as float pixels. + indices: indices of the images to draw the matches on. + """ + if not isinstance(line_colors, list): + line_colors = [line_colors] * len(lines) + if not isinstance(point_colors, list): + point_colors = [point_colors] * len(lines) + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines and junctions + for a, l, lc, pc in zip(axes, lines, line_colors, point_colors): + for i in range(len(l)): + line = matplotlib.lines.Line2D((l[i, 0, 0], l[i, 1, 0]), + (l[i, 0, 1], l[i, 1, 1]), + zorder=1, c=lc, linewidth=lw) + a.add_line(line) + pts = l.reshape(-1, 2) + a.scatter(pts[:, 0], pts[:, 1], + c=pc, s=ps, linewidths=0, zorder=2) + + +def plot_line_matches(kpts0, kpts1, color=None, lw=1.5, indices=(0, 1), a=1.): + """Plot matches for a pair of existing images, parametrized by their middle point. + Args: + kpts0, kpts1: corresponding middle points of the lines of size (N, 2). + color: color of each match, string or RGB tuple. Random if not given. + lw: width of the lines. + indices: indices of the images to draw the matches on. + a: alpha opacity of the match lines. + """ + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + ax0, ax1 = ax[indices[0]], ax[indices[1]] + fig.canvas.draw() + + assert len(kpts0) == len(kpts1) + if color is None: + color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist() + elif len(color) > 0 and not isinstance(color[0], (tuple, list)): + color = [color] * len(kpts0) + + if lw > 0: + # transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(ax0.transData.transform(kpts0)) + fkpts1 = transFigure.transform(ax1.transData.transform(kpts1)) + fig.lines += [matplotlib.lines.Line2D( + (fkpts0[i, 0], fkpts1[i, 0]), (fkpts0[i, 1], fkpts1[i, 1]), + zorder=1, transform=fig.transFigure, c=color[i], linewidth=lw, + alpha=a) + for i in range(len(kpts0))] + + # freeze the axes to prevent the transform to change + ax0.autoscale(enable=False) + ax1.autoscale(enable=False) + + +def plot_color_line_matches(lines, correct_matches=None, + lw=2, indices=(0, 1)): + """Plot line matches for existing images with multiple colors. + Args: + lines: list of ndarrays of size (N, 2, 2). + correct_matches: bool array of size (N,) indicating correct matches. + lw: line width as float pixels. + indices: indices of the images to draw the matches on. + """ + n_lines = len(lines[0]) + colors = sns.color_palette('husl', n_colors=n_lines) + np.random.shuffle(colors) + alphas = np.ones(n_lines) + # If correct_matches is not None, display wrong matches with a low alpha + if correct_matches is not None: + alphas[~np.array(correct_matches)] = 0.2 + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines + for a, l in zip(axes, lines): + # Transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + endpoint0 = transFigure.transform(a.transData.transform(l[:, 0])) + endpoint1 = transFigure.transform(a.transData.transform(l[:, 1])) + fig.lines += [matplotlib.lines.Line2D( + (endpoint0[i, 0], endpoint1[i, 0]), + (endpoint0[i, 1], endpoint1[i, 1]), + zorder=1, transform=fig.transFigure, c=colors[i], + alpha=alphas[i], linewidth=lw) for i in range(n_lines)] + + +def plot_color_lines(lines, correct_matches, wrong_matches, + lw=2, indices=(0, 1)): + """Plot line matches for existing images with multiple colors: + green for correct matches, red for wrong ones, and blue for the rest. + Args: + lines: list of ndarrays of size (N, 2, 2). + correct_matches: list of bool arrays of size N with correct matches. + wrong_matches: list of bool arrays of size (N,) with correct matches. + lw: line width as float pixels. + indices: indices of the images to draw the matches on. + """ + # palette = sns.color_palette() + palette = sns.color_palette("hls", 8) + blue = palette[5] # palette[0] + red = palette[0] # palette[3] + green = palette[2] # palette[2] + colors = [np.array([blue] * len(l)) for l in lines] + for i, c in enumerate(colors): + c[np.array(correct_matches[i])] = green + c[np.array(wrong_matches[i])] = red + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines + for a, l, c in zip(axes, lines, colors): + # Transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + endpoint0 = transFigure.transform(a.transData.transform(l[:, 0])) + endpoint1 = transFigure.transform(a.transData.transform(l[:, 1])) + fig.lines += [matplotlib.lines.Line2D( + (endpoint0[i, 0], endpoint1[i, 0]), + (endpoint0[i, 1], endpoint1[i, 1]), + zorder=1, transform=fig.transFigure, c=c[i], + linewidth=lw) for i in range(len(l))] + + +def plot_subsegment_matches(lines, subsegments, lw=2, indices=(0, 1)): + """ Plot line matches for existing images with multiple colors and + highlight the actually matched subsegments. + Args: + lines: list of ndarrays of size (N, 2, 2). + subsegments: list of ndarrays of size (N, 2, 2). + lw: line width as float pixels. + indices: indices of the images to draw the matches on. + """ + n_lines = len(lines[0]) + colors = sns.cubehelix_palette(start=2, rot=-0.2, dark=0.3, light=.7, + gamma=1.3, hue=1, n_colors=n_lines) + + fig = plt.gcf() + ax = fig.axes + assert len(ax) > max(indices) + axes = [ax[i] for i in indices] + fig.canvas.draw() + + # Plot the lines + for a, l, ss in zip(axes, lines, subsegments): + # Transform the points into the figure coordinate system + transFigure = fig.transFigure.inverted() + + # Draw full line + endpoint0 = transFigure.transform(a.transData.transform(l[:, 0])) + endpoint1 = transFigure.transform(a.transData.transform(l[:, 1])) + fig.lines += [matplotlib.lines.Line2D( + (endpoint0[i, 0], endpoint1[i, 0]), + (endpoint0[i, 1], endpoint1[i, 1]), + zorder=1, transform=fig.transFigure, c='red', + alpha=0.7, linewidth=lw) for i in range(n_lines)] + + # Draw matched subsegment + endpoint0 = transFigure.transform(a.transData.transform(ss[:, 0])) + endpoint1 = transFigure.transform(a.transData.transform(ss[:, 1])) + fig.lines += [matplotlib.lines.Line2D( + (endpoint0[i, 0], endpoint1[i, 0]), + (endpoint0[i, 1], endpoint1[i, 1]), + zorder=1, transform=fig.transFigure, c=colors[i], + alpha=1, linewidth=lw) for i in range(n_lines)] \ No newline at end of file diff --git a/third_party/SOLD2/sold2/model/__init__.py b/third_party/SOLD2/sold2/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/model/line_detection.py b/third_party/SOLD2/sold2/model/line_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..0c186337b0ce2072ddd5246408c538dac2cf325f --- /dev/null +++ b/third_party/SOLD2/sold2/model/line_detection.py @@ -0,0 +1,506 @@ +""" +Implementation of the line segment detection module. +""" +import math +import numpy as np +import torch + + +class LineSegmentDetectionModule(object): + """ Module extracting line segments from junctions and line heatmaps. """ + def __init__( + self, detect_thresh, num_samples=64, sampling_method="local_max", + inlier_thresh=0., heatmap_low_thresh=0.15, heatmap_high_thresh=0.2, + max_local_patch_radius=3, lambda_radius=2., + use_candidate_suppression=False, nms_dist_tolerance=3., + use_heatmap_refinement=False, heatmap_refine_cfg=None, + use_junction_refinement=False, junction_refine_cfg=None): + """ + Parameters: + detect_thresh: The probability threshold for mean activation (0. ~ 1.) + num_samples: Number of sampling locations along the line segments. + sampling_method: Sampling method on locations ("bilinear" or "local_max"). + inlier_thresh: The min inlier ratio to satisfy (0. ~ 1.) => 0. means no threshold. + heatmap_low_thresh: The lowest threshold for the pixel to be considered as candidate in junction recovery. + heatmap_high_thresh: The higher threshold for NMS in junction recovery. + max_local_patch_radius: The max patch to be considered in local maximum search. + lambda_radius: The lambda factor in linear local maximum search formulation + use_candidate_suppression: Apply candidate suppression to break long segments into short sub-segments. + nms_dist_tolerance: The distance tolerance for nms. Decide whether the junctions are on the line. + use_heatmap_refinement: Use heatmap refinement method or not. + heatmap_refine_cfg: The configs for heatmap refinement methods. + use_junction_refinement: Use junction refinement method or not. + junction_refine_cfg: The configs for junction refinement methods. + """ + # Line detection parameters + self.detect_thresh = detect_thresh + + # Line sampling parameters + self.num_samples = num_samples + self.sampling_method = sampling_method + self.inlier_thresh = inlier_thresh + self.local_patch_radius = max_local_patch_radius + self.lambda_radius = lambda_radius + + # Detecting junctions on the boundary parameters + self.low_thresh = heatmap_low_thresh + self.high_thresh = heatmap_high_thresh + + # Pre-compute the linspace sampler + self.sampler = np.linspace(0, 1, self.num_samples) + self.torch_sampler = torch.linspace(0, 1, self.num_samples) + + # Long line segment suppression configuration + self.use_candidate_suppression = use_candidate_suppression + self.nms_dist_tolerance = nms_dist_tolerance + + # Heatmap refinement configuration + self.use_heatmap_refinement = use_heatmap_refinement + self.heatmap_refine_cfg = heatmap_refine_cfg + if self.use_heatmap_refinement and self.heatmap_refine_cfg is None: + raise ValueError("[Error] Missing heatmap refinement config.") + + # Junction refinement configuration + self.use_junction_refinement = use_junction_refinement + self.junction_refine_cfg = junction_refine_cfg + if self.use_junction_refinement and self.junction_refine_cfg is None: + raise ValueError("[Error] Missing junction refinement config.") + + def convert_inputs(self, inputs, device): + """ Convert inputs to desired torch tensor. """ + if isinstance(inputs, np.ndarray): + outputs = torch.tensor(inputs, dtype=torch.float32, device=device) + elif isinstance(inputs, torch.Tensor): + outputs = inputs.to(torch.float32).to(device) + else: + raise ValueError( + "[Error] Inputs must either be torch tensor or numpy ndarray.") + + return outputs + + def detect(self, junctions, heatmap, device=torch.device("cpu")): + """ Main function performing line segment detection. """ + # Convert inputs to torch tensor + junctions = self.convert_inputs(junctions, device=device) + heatmap = self.convert_inputs(heatmap, device=device) + + # Perform the heatmap refinement + if self.use_heatmap_refinement: + if self.heatmap_refine_cfg["mode"] == "global": + heatmap = self.refine_heatmap( + heatmap, + self.heatmap_refine_cfg["ratio"], + self.heatmap_refine_cfg["valid_thresh"] + ) + elif self.heatmap_refine_cfg["mode"] == "local": + heatmap = self.refine_heatmap_local( + heatmap, + self.heatmap_refine_cfg["num_blocks"], + self.heatmap_refine_cfg["overlap_ratio"], + self.heatmap_refine_cfg["ratio"], + self.heatmap_refine_cfg["valid_thresh"] + ) + + # Initialize empty line map + num_junctions = junctions.shape[0] + line_map_pred = torch.zeros([num_junctions, num_junctions], + device=device, dtype=torch.int32) + + # Stop if there are not enough junctions + if num_junctions < 2: + return line_map_pred, junctions, heatmap + + # Generate the candidate map + candidate_map = torch.triu(torch.ones( + [num_junctions, num_junctions], device=device, dtype=torch.int32), + diagonal=1) + + # Fetch the image boundary + if len(heatmap.shape) > 2: + H, W, _ = heatmap.shape + else: + H, W = heatmap.shape + + # Optionally perform candidate filtering + if self.use_candidate_suppression: + candidate_map = self.candidate_suppression(junctions, + candidate_map) + + # Fetch the candidates + candidate_index_map = torch.where(candidate_map) + candidate_index_map = torch.cat([candidate_index_map[0][..., None], + candidate_index_map[1][..., None]], + dim=-1) + + # Get the corresponding start and end junctions + candidate_junc_start = junctions[candidate_index_map[:, 0], :] + candidate_junc_end = junctions[candidate_index_map[:, 1], :] + + # Get the sampling locations (N x 64) + sampler = self.torch_sampler.to(device)[None, ...] + cand_samples_h = candidate_junc_start[:, 0:1] * sampler + \ + candidate_junc_end[:, 0:1] * (1 - sampler) + cand_samples_w = candidate_junc_start[:, 1:2] * sampler + \ + candidate_junc_end[:, 1:2] * (1 - sampler) + + # Clip to image boundary + cand_h = torch.clamp(cand_samples_h, min=0, max=H-1) + cand_w = torch.clamp(cand_samples_w, min=0, max=W-1) + + # Local maximum search + if self.sampling_method == "local_max": + # Compute normalized segment lengths + segments_length = torch.sqrt(torch.sum( + (candidate_junc_start.to(torch.float32) - + candidate_junc_end.to(torch.float32)) ** 2, dim=-1)) + normalized_seg_length = (segments_length + / (((H ** 2) + (W ** 2)) ** 0.5)) + + # Perform local max search + num_cand = cand_h.shape[0] + group_size = 10000 + if num_cand > group_size: + num_iter = math.ceil(num_cand / group_size) + sampled_feat_lst = [] + for iter_idx in range(num_iter): + if not iter_idx == num_iter-1: + cand_h_ = cand_h[iter_idx * group_size: + (iter_idx+1) * group_size, :] + cand_w_ = cand_w[iter_idx * group_size: + (iter_idx+1) * group_size, :] + normalized_seg_length_ = normalized_seg_length[ + iter_idx * group_size: (iter_idx+1) * group_size] + else: + cand_h_ = cand_h[iter_idx * group_size:, :] + cand_w_ = cand_w[iter_idx * group_size:, :] + normalized_seg_length_ = normalized_seg_length[ + iter_idx * group_size:] + sampled_feat_ = self.detect_local_max( + heatmap, cand_h_, cand_w_, H, W, + normalized_seg_length_, device) + sampled_feat_lst.append(sampled_feat_) + sampled_feat = torch.cat(sampled_feat_lst, dim=0) + else: + sampled_feat = self.detect_local_max( + heatmap, cand_h, cand_w, H, W, + normalized_seg_length, device) + # Bilinear sampling + elif self.sampling_method == "bilinear": + # Perform bilinear sampling + sampled_feat = self.detect_bilinear( + heatmap, cand_h, cand_w, H, W, device) + else: + raise ValueError("[Error] Unknown sampling method.") + + # [Simple threshold detection] + # detection_results is a mask over all candidates + detection_results = (torch.mean(sampled_feat, dim=-1) + > self.detect_thresh) + + # [Inlier threshold detection] + if self.inlier_thresh > 0.: + inlier_ratio = torch.sum( + sampled_feat > self.detect_thresh, + dim=-1).to(torch.float32) / self.num_samples + detection_results_inlier = inlier_ratio >= self.inlier_thresh + detection_results = detection_results * detection_results_inlier + + # Convert detection results back to line_map_pred + detected_junc_indexes = candidate_index_map[detection_results, :] + line_map_pred[detected_junc_indexes[:, 0], + detected_junc_indexes[:, 1]] = 1 + line_map_pred[detected_junc_indexes[:, 1], + detected_junc_indexes[:, 0]] = 1 + + # Perform junction refinement + if self.use_junction_refinement and len(detected_junc_indexes) > 0: + junctions, line_map_pred = self.refine_junction_perturb( + junctions, line_map_pred, heatmap, H, W, device) + + return line_map_pred, junctions, heatmap + + def refine_heatmap(self, heatmap, ratio=0.2, valid_thresh=1e-2): + """ Global heatmap refinement method. """ + # Grab the top 10% values + heatmap_values = heatmap[heatmap > valid_thresh] + sorted_values = torch.sort(heatmap_values, descending=True)[0] + top10_len = math.ceil(sorted_values.shape[0] * ratio) + max20 = torch.mean(sorted_values[:top10_len]) + heatmap = torch.clamp(heatmap / max20, min=0., max=1.) + return heatmap + + def refine_heatmap_local(self, heatmap, num_blocks=5, overlap_ratio=0.5, + ratio=0.2, valid_thresh=2e-3): + """ Local heatmap refinement method. """ + # Get the shape of the heatmap + H, W = heatmap.shape + increase_ratio = 1 - overlap_ratio + h_block = round(H / (1 + (num_blocks - 1) * increase_ratio)) + w_block = round(W / (1 + (num_blocks - 1) * increase_ratio)) + + count_map = torch.zeros(heatmap.shape, dtype=torch.int, + device=heatmap.device) + heatmap_output = torch.zeros(heatmap.shape, dtype=torch.float, + device=heatmap.device) + # Iterate through each block + for h_idx in range(num_blocks): + for w_idx in range(num_blocks): + # Fetch the heatmap + h_start = round(h_idx * h_block * increase_ratio) + w_start = round(w_idx * w_block * increase_ratio) + h_end = h_start + h_block if h_idx < num_blocks - 1 else H + w_end = w_start + w_block if w_idx < num_blocks - 1 else W + + subheatmap = heatmap[h_start:h_end, w_start:w_end] + if subheatmap.max() > valid_thresh: + subheatmap = self.refine_heatmap( + subheatmap, ratio, valid_thresh=valid_thresh) + + # Aggregate it to the final heatmap + heatmap_output[h_start:h_end, w_start:w_end] += subheatmap + count_map[h_start:h_end, w_start:w_end] += 1 + heatmap_output = torch.clamp(heatmap_output / count_map, + max=1., min=0.) + + return heatmap_output + + def candidate_suppression(self, junctions, candidate_map): + """ Suppress overlapping long lines in the candidate segments. """ + # Define the distance tolerance + dist_tolerance = self.nms_dist_tolerance + + # Compute distance between junction pairs + # (num_junc x 1 x 2) - (1 x num_junc x 2) => num_junc x num_junc map + line_dist_map = torch.sum((torch.unsqueeze(junctions, dim=1) + - junctions[None, ...]) ** 2, dim=-1) ** 0.5 + + # Fetch all the "detected lines" + seg_indexes = torch.where(torch.triu(candidate_map, diagonal=1)) + start_point_idxs = seg_indexes[0] + end_point_idxs = seg_indexes[1] + start_points = junctions[start_point_idxs, :] + end_points = junctions[end_point_idxs, :] + + # Fetch corresponding entries + line_dists = line_dist_map[start_point_idxs, end_point_idxs] + + # Check whether they are on the line + dir_vecs = ((end_points - start_points) + / torch.norm(end_points - start_points, + dim=-1)[..., None]) + # Get the orthogonal distance + cand_vecs = junctions[None, ...] - start_points.unsqueeze(dim=1) + cand_vecs_norm = torch.norm(cand_vecs, dim=-1) + # Check whether they are projected directly onto the segment + proj = (torch.einsum('bij,bjk->bik', cand_vecs, dir_vecs[..., None]) + / line_dists[..., None, None]) + # proj is num_segs x num_junction x 1 + proj_mask = (proj >=0) * (proj <= 1) + cand_angles = torch.acos( + torch.einsum('bij,bjk->bik', cand_vecs, dir_vecs[..., None]) + / cand_vecs_norm[..., None]) + cand_dists = cand_vecs_norm[..., None] * torch.sin(cand_angles) + junc_dist_mask = cand_dists <= dist_tolerance + junc_mask = junc_dist_mask * proj_mask + + # Minus starting points + num_segs = start_point_idxs.shape[0] + junc_counts = torch.sum(junc_mask, dim=[1, 2]) + junc_counts -= junc_mask[..., 0][torch.arange(0, num_segs), + start_point_idxs].to(torch.int) + junc_counts -= junc_mask[..., 0][torch.arange(0, num_segs), + end_point_idxs].to(torch.int) + + # Get the invalid candidate mask + final_mask = junc_counts > 0 + candidate_map[start_point_idxs[final_mask], + end_point_idxs[final_mask]] = 0 + + return candidate_map + + def refine_junction_perturb(self, junctions, line_map_pred, + heatmap, H, W, device): + """ Refine the line endpoints in a similar way as in LSD. """ + # Get the config + junction_refine_cfg = self.junction_refine_cfg + + # Fetch refinement parameters + num_perturbs = junction_refine_cfg["num_perturbs"] + perturb_interval = junction_refine_cfg["perturb_interval"] + side_perturbs = (num_perturbs - 1) // 2 + # Fetch the 2D perturb mat + perturb_vec = torch.arange( + start=-perturb_interval*side_perturbs, + end=perturb_interval*(side_perturbs+1), + step=perturb_interval, device=device) + w1_grid, h1_grid, w2_grid, h2_grid = torch.meshgrid( + perturb_vec, perturb_vec, perturb_vec, perturb_vec) + perturb_tensor = torch.cat([ + w1_grid[..., None], h1_grid[..., None], + w2_grid[..., None], h2_grid[..., None]], dim=-1) + perturb_tensor_flat = perturb_tensor.view(-1, 2, 2) + + # Fetch the junctions and line_map + junctions = junctions.clone() + line_map = line_map_pred + + # Fetch all the detected lines + detected_seg_indexes = torch.where(torch.triu(line_map, diagonal=1)) + start_point_idxs = detected_seg_indexes[0] + end_point_idxs = detected_seg_indexes[1] + start_points = junctions[start_point_idxs, :] + end_points = junctions[end_point_idxs, :] + + line_segments = torch.cat([start_points.unsqueeze(dim=1), + end_points.unsqueeze(dim=1)], dim=1) + + line_segment_candidates = (line_segments.unsqueeze(dim=1) + + perturb_tensor_flat[None, ...]) + # Clip the boundaries + line_segment_candidates[..., 0] = torch.clamp( + line_segment_candidates[..., 0], min=0, max=H - 1) + line_segment_candidates[..., 1] = torch.clamp( + line_segment_candidates[..., 1], min=0, max=W - 1) + + # Iterate through all the segments + refined_segment_lst = [] + num_segments = line_segments.shape[0] + for idx in range(num_segments): + segment = line_segment_candidates[idx, ...] + # Get the corresponding start and end junctions + candidate_junc_start = segment[:, 0, :] + candidate_junc_end = segment[:, 1, :] + + # Get the sampling locations (N x 64) + sampler = self.torch_sampler.to(device)[None, ...] + cand_samples_h = (candidate_junc_start[:, 0:1] * sampler + + candidate_junc_end[:, 0:1] * (1 - sampler)) + cand_samples_w = (candidate_junc_start[:, 1:2] * sampler + + candidate_junc_end[:, 1:2] * (1 - sampler)) + + # Clip to image boundary + cand_h = torch.clamp(cand_samples_h, min=0, max=H - 1) + cand_w = torch.clamp(cand_samples_w, min=0, max=W - 1) + + # Perform bilinear sampling + segment_feat = self.detect_bilinear( + heatmap, cand_h, cand_w, H, W, device) + segment_results = torch.mean(segment_feat, dim=-1) + max_idx = torch.argmax(segment_results) + refined_segment_lst.append(segment[max_idx, ...][None, ...]) + + # Concatenate back to segments + refined_segments = torch.cat(refined_segment_lst, dim=0) + + # Convert back to junctions and line_map + junctions_new = torch.cat( + [refined_segments[:, 0, :], refined_segments[:, 1, :]], dim=0) + junctions_new = torch.unique(junctions_new, dim=0) + line_map_new = self.segments_to_line_map(junctions_new, + refined_segments) + + return junctions_new, line_map_new + + def segments_to_line_map(self, junctions, segments): + """ Convert the list of segments to line map. """ + # Create empty line map + device = junctions.device + num_junctions = junctions.shape[0] + line_map = torch.zeros([num_junctions, num_junctions], device=device) + + # Iterate through every segment + for idx in range(segments.shape[0]): + # Get the junctions from a single segement + seg = segments[idx, ...] + junction1 = seg[0, :] + junction2 = seg[1, :] + + # Get index + idx_junction1 = torch.where( + (junctions == junction1).sum(axis=1) == 2)[0] + idx_junction2 = torch.where( + (junctions == junction2).sum(axis=1) == 2)[0] + + # label the corresponding entries + line_map[idx_junction1, idx_junction2] = 1 + line_map[idx_junction2, idx_junction1] = 1 + + return line_map + + def detect_bilinear(self, heatmap, cand_h, cand_w, H, W, device): + """ Detection by bilinear sampling. """ + # Get the floor and ceiling locations + cand_h_floor = torch.floor(cand_h).to(torch.long) + cand_h_ceil = torch.ceil(cand_h).to(torch.long) + cand_w_floor = torch.floor(cand_w).to(torch.long) + cand_w_ceil = torch.ceil(cand_w).to(torch.long) + + # Perform the bilinear sampling + cand_samples_feat = ( + heatmap[cand_h_floor, cand_w_floor] * (cand_h_ceil - cand_h) + * (cand_w_ceil - cand_w) + heatmap[cand_h_floor, cand_w_ceil] + * (cand_h_ceil - cand_h) * (cand_w - cand_w_floor) + + heatmap[cand_h_ceil, cand_w_floor] * (cand_h - cand_h_floor) + * (cand_w_ceil - cand_w) + heatmap[cand_h_ceil, cand_w_ceil] + * (cand_h - cand_h_floor) * (cand_w - cand_w_floor)) + + return cand_samples_feat + + def detect_local_max(self, heatmap, cand_h, cand_w, H, W, + normalized_seg_length, device): + """ Detection by local maximum search. """ + # Compute the distance threshold + dist_thresh = (0.5 * (2 ** 0.5) + + self.lambda_radius * normalized_seg_length) + # Make it N x 64 + dist_thresh = torch.repeat_interleave(dist_thresh[..., None], + self.num_samples, dim=-1) + + # Compute the candidate points + cand_points = torch.cat([cand_h[..., None], cand_w[..., None]], + dim=-1) + cand_points_round = torch.round(cand_points) # N x 64 x 2 + + # Construct local patches 9x9 = 81 + patch_mask = torch.zeros([int(2 * self.local_patch_radius + 1), + int(2 * self.local_patch_radius + 1)], + device=device) + patch_center = torch.tensor( + [[self.local_patch_radius, self.local_patch_radius]], + device=device, dtype=torch.float32) + H_patch_points, W_patch_points = torch.where(patch_mask >= 0) + patch_points = torch.cat([H_patch_points[..., None], + W_patch_points[..., None]], dim=-1) + # Fetch the circle region + patch_center_dist = torch.sqrt(torch.sum( + (patch_points - patch_center) ** 2, dim=-1)) + patch_points = (patch_points[patch_center_dist + <= self.local_patch_radius, :]) + # Shift [0, 0] to the center + patch_points = patch_points - self.local_patch_radius + + # Construct local patch mask + patch_points_shifted = (torch.unsqueeze(cand_points_round, dim=2) + + patch_points[None, None, ...]) + patch_dist = torch.sqrt(torch.sum((torch.unsqueeze(cand_points, dim=2) + - patch_points_shifted) ** 2, + dim=-1)) + patch_dist_mask = patch_dist < dist_thresh[..., None] + + # Get all points => num_points_center x num_patch_points x 2 + points_H = torch.clamp(patch_points_shifted[:, :, :, 0], min=0, + max=H - 1).to(torch.long) + points_W = torch.clamp(patch_points_shifted[:, :, :, 1], min=0, + max=W - 1).to(torch.long) + points = torch.cat([points_H[..., None], points_W[..., None]], dim=-1) + + # Sample the feature (N x 64 x 81) + sampled_feat = heatmap[points[:, :, :, 0], points[:, :, :, 1]] + # Filtering using the valid mask + sampled_feat = sampled_feat * patch_dist_mask.to(torch.float32) + if len(sampled_feat) == 0: + sampled_feat_lmax = torch.empty(0, 64) + else: + sampled_feat_lmax, _ = torch.max(sampled_feat, dim=-1) + + return sampled_feat_lmax diff --git a/third_party/SOLD2/sold2/model/line_detector.py b/third_party/SOLD2/sold2/model/line_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..2f3d059e130178c482e8e569171ef9e0370424c7 --- /dev/null +++ b/third_party/SOLD2/sold2/model/line_detector.py @@ -0,0 +1,127 @@ +""" +Line segment detection from raw images. +""" +import time +import numpy as np +import torch +from torch.nn.functional import softmax + +from .model_util import get_model +from .loss import get_loss_and_weights +from .line_detection import LineSegmentDetectionModule +from ..train import convert_junc_predictions +from ..misc.train_utils import adapt_checkpoint + + +def line_map_to_segments(junctions, line_map): + """ Convert a line map to a Nx2x2 list of segments. """ + line_map_tmp = line_map.copy() + + output_segments = np.zeros([0, 2, 2]) + for idx in range(junctions.shape[0]): + # if no connectivity, just skip it + if line_map_tmp[idx, :].sum() == 0: + continue + # Record the line segment + else: + for idx2 in np.where(line_map_tmp[idx, :] == 1)[0]: + p1 = junctions[idx, :] # HW format + p2 = junctions[idx2, :] + single_seg = np.concatenate([p1[None, ...], p2[None, ...]], + axis=0) + output_segments = np.concatenate( + (output_segments, single_seg[None, ...]), axis=0) + + # Update line_map + line_map_tmp[idx, idx2] = 0 + line_map_tmp[idx2, idx] = 0 + + return output_segments + + +class LineDetector(object): + def __init__(self, model_cfg, ckpt_path, device, line_detector_cfg, + junc_detect_thresh=None): + """ SOLD² line detector taking raw images as input. + Parameters: + model_cfg: config for CNN model + ckpt_path: path to the weights + line_detector_cfg: config file for the line detection module + """ + # Get loss weights if dynamic weighting + _, loss_weights = get_loss_and_weights(model_cfg, device) + self.device = device + + # Initialize the cnn backbone + self.model = get_model(model_cfg, loss_weights) + checkpoint = torch.load(ckpt_path, map_location=self.device) + checkpoint = adapt_checkpoint(checkpoint["model_state_dict"]) + self.model.load_state_dict(checkpoint) + self.model = self.model.to(self.device) + self.model = self.model.eval() + + self.grid_size = model_cfg["grid_size"] + + if junc_detect_thresh is not None: + self.junc_detect_thresh = junc_detect_thresh + else: + self.junc_detect_thresh = model_cfg.get("detection_thresh", 1/65) + self.max_num_junctions = model_cfg.get("max_num_junctions", 300) + + # Initialize the line detector + self.line_detector_cfg = line_detector_cfg + self.line_detector = LineSegmentDetectionModule(**line_detector_cfg) + + def __call__(self, input_image, valid_mask=None, + return_heatmap=False, profile=False): + # Now we restrict input_image to 4D torch tensor + if ((not len(input_image.shape) == 4) + or (not isinstance(input_image, torch.Tensor))): + raise ValueError( + "[Error] the input image should be a 4D torch tensor.") + + # Move the input to corresponding device + input_image = input_image.to(self.device) + + # Forward of the CNN backbone + start_time = time.time() + with torch.no_grad(): + net_outputs = self.model(input_image) + + junc_np = convert_junc_predictions( + net_outputs["junctions"], self.grid_size, + self.junc_detect_thresh, self.max_num_junctions) + if valid_mask is None: + junctions = np.where(junc_np["junc_pred_nms"].squeeze()) + else: + junctions = np.where(junc_np["junc_pred_nms"].squeeze() + * valid_mask) + junctions = np.concatenate( + [junctions[0][..., None], junctions[1][..., None]], axis=-1) + + if net_outputs["heatmap"].shape[1] == 2: + # Convert to single channel directly from here + heatmap = softmax(net_outputs["heatmap"], dim=1)[:, 1:, :, :] + else: + heatmap = torch.sigmoid(net_outputs["heatmap"]) + heatmap = heatmap.cpu().numpy().transpose(0, 2, 3, 1)[0, :, :, 0] + + # Run the line detector. + line_map, junctions, heatmap = self.line_detector.detect( + junctions, heatmap, device=self.device) + heatmap = heatmap.cpu().numpy() + if isinstance(line_map, torch.Tensor): + line_map = line_map.cpu().numpy() + if isinstance(junctions, torch.Tensor): + junctions = junctions.cpu().numpy() + line_segments = line_map_to_segments(junctions, line_map) + end_time = time.time() + + outputs = {"line_segments": line_segments} + + if return_heatmap: + outputs["heatmap"] = heatmap + if profile: + outputs["time"] = end_time - start_time + + return outputs diff --git a/third_party/SOLD2/sold2/model/line_matcher.py b/third_party/SOLD2/sold2/model/line_matcher.py new file mode 100644 index 0000000000000000000000000000000000000000..bc5a003573c91313e2295c75871edcb1c113662a --- /dev/null +++ b/third_party/SOLD2/sold2/model/line_matcher.py @@ -0,0 +1,279 @@ +""" +Implements the full pipeline from raw images to line matches. +""" +import time +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from torch.nn.functional import softmax + +from .model_util import get_model +from .loss import get_loss_and_weights +from .metrics import super_nms +from .line_detection import LineSegmentDetectionModule +from .line_matching import WunschLineMatcher +from ..train import convert_junc_predictions +from ..misc.train_utils import adapt_checkpoint +from .line_detector import line_map_to_segments + + +class LineMatcher(object): + """ Full line matcher including line detection and matching + with the Needleman-Wunsch algorithm. """ + def __init__(self, model_cfg, ckpt_path, device, line_detector_cfg, + line_matcher_cfg, multiscale=False, scales=[1., 2.]): + # Get loss weights if dynamic weighting + _, loss_weights = get_loss_and_weights(model_cfg, device) + self.device = device + + # Initialize the cnn backbone + self.model = get_model(model_cfg, loss_weights) + checkpoint = torch.load(ckpt_path, map_location=self.device) + checkpoint = adapt_checkpoint(checkpoint["model_state_dict"]) + self.model.load_state_dict(checkpoint) + self.model = self.model.to(self.device) + self.model = self.model.eval() + + self.grid_size = model_cfg["grid_size"] + self.junc_detect_thresh = model_cfg["detection_thresh"] + self.max_num_junctions = model_cfg.get("max_num_junctions", 300) + + # Initialize the line detector + self.line_detector = LineSegmentDetectionModule(**line_detector_cfg) + self.multiscale = multiscale + self.scales = scales + + # Initialize the line matcher + self.line_matcher = WunschLineMatcher(**line_matcher_cfg) + + # Print some debug messages + for key, val in line_detector_cfg.items(): + print(f"[Debug] {key}: {val}") + # print("[Debug] detect_thresh: %f" % (line_detector_cfg["detect_thresh"])) + # print("[Debug] num_samples: %d" % (line_detector_cfg["num_samples"])) + + + + # Perform line detection and descriptor inference on a single image + def line_detection(self, input_image, valid_mask=None, + desc_only=False, profile=False): + # Restrict input_image to 4D torch tensor + if ((not len(input_image.shape) == 4) + or (not isinstance(input_image, torch.Tensor))): + raise ValueError( + "[Error] the input image should be a 4D torch tensor") + + # Move the input to corresponding device + input_image = input_image.to(self.device) + + # Forward of the CNN backbone + start_time = time.time() + with torch.no_grad(): + net_outputs = self.model(input_image) + + outputs = {"descriptor": net_outputs["descriptors"]} + + if not desc_only: + junc_np = convert_junc_predictions( + net_outputs["junctions"], self.grid_size, + self.junc_detect_thresh, self.max_num_junctions) + if valid_mask is None: + junctions = np.where(junc_np["junc_pred_nms"].squeeze()) + else: + junctions = np.where( + junc_np["junc_pred_nms"].squeeze() * valid_mask) + junctions = np.concatenate([junctions[0][..., None], + junctions[1][..., None]], axis=-1) + + if net_outputs["heatmap"].shape[1] == 2: + # Convert to single channel directly from here + heatmap = softmax( + net_outputs["heatmap"], + dim=1)[:, 1:, :, :].cpu().numpy().transpose(0, 2, 3, 1) + else: + heatmap = torch.sigmoid( + net_outputs["heatmap"]).cpu().numpy().transpose(0, 2, 3, 1) + heatmap = heatmap[0, :, :, 0] + + # Run the line detector. + line_map, junctions, heatmap = self.line_detector.detect( + junctions, heatmap, device=self.device) + if isinstance(line_map, torch.Tensor): + line_map = line_map.cpu().numpy() + if isinstance(junctions, torch.Tensor): + junctions = junctions.cpu().numpy() + outputs["heatmap"] = heatmap.cpu().numpy() + outputs["junctions"] = junctions + + # If it's a line map with multiple detect_thresh and inlier_thresh + if len(line_map.shape) > 2: + num_detect_thresh = line_map.shape[0] + num_inlier_thresh = line_map.shape[1] + line_segments = [] + for detect_idx in range(num_detect_thresh): + line_segments_inlier = [] + for inlier_idx in range(num_inlier_thresh): + line_map_tmp = line_map[detect_idx, inlier_idx, :, :] + line_segments_tmp = line_map_to_segments(junctions, line_map_tmp) + line_segments_inlier.append(line_segments_tmp) + line_segments.append(line_segments_inlier) + else: + line_segments = line_map_to_segments(junctions, line_map) + + outputs["line_segments"] = line_segments + + end_time = time.time() + + if profile: + outputs["time"] = end_time - start_time + + return outputs + + # Perform line detection and descriptor inference at multiple scales + def multiscale_line_detection(self, input_image, valid_mask=None, + desc_only=False, profile=False, + scales=[1., 2.], aggregation='mean'): + # Restrict input_image to 4D torch tensor + if ((not len(input_image.shape) == 4) + or (not isinstance(input_image, torch.Tensor))): + raise ValueError( + "[Error] the input image should be a 4D torch tensor") + + # Move the input to corresponding device + input_image = input_image.to(self.device) + img_size = input_image.shape[2:4] + desc_size = tuple(np.array(img_size) // 4) + + # Run the inference at multiple image scales + start_time = time.time() + junctions, heatmaps, descriptors = [], [], [] + for s in scales: + # Resize the image + resized_img = F.interpolate(input_image, scale_factor=s, + mode='bilinear') + + # Forward of the CNN backbone + with torch.no_grad(): + net_outputs = self.model(resized_img) + + descriptors.append(F.interpolate( + net_outputs["descriptors"], size=desc_size, mode="bilinear")) + + if not desc_only: + junc_prob = convert_junc_predictions( + net_outputs["junctions"], self.grid_size)["junc_pred"] + junctions.append(cv2.resize(junc_prob.squeeze(), + (img_size[1], img_size[0]), + interpolation=cv2.INTER_LINEAR)) + + if net_outputs["heatmap"].shape[1] == 2: + # Convert to single channel directly from here + heatmap = softmax(net_outputs["heatmap"], + dim=1)[:, 1:, :, :] + else: + heatmap = torch.sigmoid(net_outputs["heatmap"]) + heatmaps.append(F.interpolate(heatmap, size=img_size, + mode="bilinear")) + + # Aggregate the results + if aggregation == 'mean': + # Aggregation through the mean activation + descriptors = torch.stack(descriptors, dim=0).mean(0) + else: + # Aggregation through the max activation + descriptors = torch.stack(descriptors, dim=0).max(0)[0] + outputs = {"descriptor": descriptors} + + if not desc_only: + if aggregation == 'mean': + junctions = np.stack(junctions, axis=0).mean(0)[None] + heatmap = torch.stack(heatmaps, dim=0).mean(0)[0, 0, :, :] + heatmap = heatmap.cpu().numpy() + else: + junctions = np.stack(junctions, axis=0).max(0)[None] + heatmap = torch.stack(heatmaps, dim=0).max(0)[0][0, 0, :, :] + heatmap = heatmap.cpu().numpy() + + # Extract junctions + junc_pred_nms = super_nms( + junctions[..., None], self.grid_size, + self.junc_detect_thresh, self.max_num_junctions) + if valid_mask is None: + junctions = np.where(junc_pred_nms.squeeze()) + else: + junctions = np.where(junc_pred_nms.squeeze() * valid_mask) + junctions = np.concatenate([junctions[0][..., None], + junctions[1][..., None]], axis=-1) + + # Run the line detector. + line_map, junctions, heatmap = self.line_detector.detect( + junctions, heatmap, device=self.device) + if isinstance(line_map, torch.Tensor): + line_map = line_map.cpu().numpy() + if isinstance(junctions, torch.Tensor): + junctions = junctions.cpu().numpy() + outputs["heatmap"] = heatmap.cpu().numpy() + outputs["junctions"] = junctions + + # If it's a line map with multiple detect_thresh and inlier_thresh + if len(line_map.shape) > 2: + num_detect_thresh = line_map.shape[0] + num_inlier_thresh = line_map.shape[1] + line_segments = [] + for detect_idx in range(num_detect_thresh): + line_segments_inlier = [] + for inlier_idx in range(num_inlier_thresh): + line_map_tmp = line_map[detect_idx, inlier_idx, :, :] + line_segments_tmp = line_map_to_segments( + junctions, line_map_tmp) + line_segments_inlier.append(line_segments_tmp) + line_segments.append(line_segments_inlier) + else: + line_segments = line_map_to_segments(junctions, line_map) + + outputs["line_segments"] = line_segments + + end_time = time.time() + + if profile: + outputs["time"] = end_time - start_time + + return outputs + + def __call__(self, images, valid_masks=[None, None], profile=False): + # Line detection and descriptor inference on both images + if self.multiscale: + forward_outputs = [ + self.multiscale_line_detection( + images[0], valid_masks[0], profile=profile, + scales=self.scales), + self.multiscale_line_detection( + images[1], valid_masks[1], profile=profile, + scales=self.scales)] + else: + forward_outputs = [ + self.line_detection(images[0], valid_masks[0], + profile=profile), + self.line_detection(images[1], valid_masks[1], + profile=profile)] + line_seg1 = forward_outputs[0]["line_segments"] + line_seg2 = forward_outputs[1]["line_segments"] + desc1 = forward_outputs[0]["descriptor"] + desc2 = forward_outputs[1]["descriptor"] + + # Match the lines in both images + start_time = time.time() + matches = self.line_matcher.forward(line_seg1, line_seg2, + desc1, desc2) + end_time = time.time() + + outputs = {"line_segments": [line_seg1, line_seg2], + "matches": matches} + + if profile: + outputs["line_detection_time"] = (forward_outputs[0]["time"] + + forward_outputs[1]["time"]) + outputs["line_matching_time"] = end_time - start_time + + return outputs diff --git a/third_party/SOLD2/sold2/model/line_matching.py b/third_party/SOLD2/sold2/model/line_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..89b71879e3104f9a8b52c1cf5e534cd124fe83b2 --- /dev/null +++ b/third_party/SOLD2/sold2/model/line_matching.py @@ -0,0 +1,390 @@ +""" +Implementation of the line matching methods. +""" +import numpy as np +import cv2 +import torch +import torch.nn.functional as F + +from ..misc.geometry_utils import keypoints_to_grid + + +class WunschLineMatcher(object): + """ Class matching two sets of line segments + with the Needleman-Wunsch algorithm. """ + def __init__(self, cross_check=True, num_samples=10, min_dist_pts=8, + top_k_candidates=10, grid_size=8, sampling="regular", + line_score=False): + self.cross_check = cross_check + self.num_samples = num_samples + self.min_dist_pts = min_dist_pts + self.top_k_candidates = top_k_candidates + self.grid_size = grid_size + self.line_score = line_score # True to compute saliency on a line + self.sampling_mode = sampling + if sampling not in ["regular", "d2_net", "asl_feat"]: + raise ValueError("Wrong sampling mode: " + sampling) + + def forward(self, line_seg1, line_seg2, desc1, desc2): + """ + Find the best matches between two sets of line segments + and their corresponding descriptors. + """ + img_size1 = (desc1.shape[2] * self.grid_size, + desc1.shape[3] * self.grid_size) + img_size2 = (desc2.shape[2] * self.grid_size, + desc2.shape[3] * self.grid_size) + device = desc1.device + + # Default case when an image has no lines + if len(line_seg1) == 0: + return np.empty((0), dtype=int) + if len(line_seg2) == 0: + return -np.ones(len(line_seg1), dtype=int) + + # Sample points regularly along each line + if self.sampling_mode == "regular": + line_points1, valid_points1 = self.sample_line_points(line_seg1) + line_points2, valid_points2 = self.sample_line_points(line_seg2) + else: + line_points1, valid_points1 = self.sample_salient_points( + line_seg1, desc1, img_size1, self.sampling_mode) + line_points2, valid_points2 = self.sample_salient_points( + line_seg2, desc2, img_size2, self.sampling_mode) + line_points1 = torch.tensor(line_points1.reshape(-1, 2), + dtype=torch.float, device=device) + line_points2 = torch.tensor(line_points2.reshape(-1, 2), + dtype=torch.float, device=device) + + # Extract the descriptors for each point + grid1 = keypoints_to_grid(line_points1, img_size1) + grid2 = keypoints_to_grid(line_points2, img_size2) + desc1 = F.normalize(F.grid_sample(desc1, grid1)[0, :, :, 0], dim=0) + desc2 = F.normalize(F.grid_sample(desc2, grid2)[0, :, :, 0], dim=0) + + # Precompute the distance between line points for every pair of lines + # Assign a score of -1 for unvalid points + scores = desc1.t() @ desc2 + scores[~valid_points1.flatten()] = -1 + scores[:, ~valid_points2.flatten()] = -1 + scores = scores.reshape(len(line_seg1), self.num_samples, + len(line_seg2), self.num_samples) + scores = scores.permute(0, 2, 1, 3) + # scores.shape = (n_lines1, n_lines2, num_samples, num_samples) + + # Pre-filter the line candidates and find the best match for each line + matches = self.filter_and_match_lines(scores) + + # [Optionally] filter matches with mutual nearest neighbor filtering + if self.cross_check: + matches2 = self.filter_and_match_lines( + scores.permute(1, 0, 3, 2)) + mutual = matches2[matches] == np.arange(len(line_seg1)) + matches[~mutual] = -1 + + return matches + + def d2_net_saliency_score(self, desc): + """ Compute the D2-Net saliency score + on a 3D or 4D descriptor. """ + is_3d = len(desc.shape) == 3 + b_size = len(desc) + feat = F.relu(desc) + + # Compute the soft local max + exp = torch.exp(feat) + if is_3d: + sum_exp = 3 * F.avg_pool1d(exp, kernel_size=3, stride=1, + padding=1) + else: + sum_exp = 9 * F.avg_pool2d(exp, kernel_size=3, stride=1, + padding=1) + soft_local_max = exp / sum_exp + + # Compute the depth-wise maximum + depth_wise_max = torch.max(feat, dim=1)[0] + depth_wise_max = feat / depth_wise_max.unsqueeze(1) + + # Total saliency score + score = torch.max(soft_local_max * depth_wise_max, dim=1)[0] + normalization = torch.sum(score.reshape(b_size, -1), dim=1) + if is_3d: + normalization = normalization.reshape(b_size, 1) + else: + normalization = normalization.reshape(b_size, 1, 1) + score = score / normalization + return score + + def asl_feat_saliency_score(self, desc): + """ Compute the ASLFeat saliency score on a 3D or 4D descriptor. """ + is_3d = len(desc.shape) == 3 + b_size = len(desc) + + # Compute the soft local peakiness + if is_3d: + local_avg = F.avg_pool1d(desc, kernel_size=3, stride=1, padding=1) + else: + local_avg = F.avg_pool2d(desc, kernel_size=3, stride=1, padding=1) + soft_local_score = F.softplus(desc - local_avg) + + # Compute the depth-wise peakiness + depth_wise_mean = torch.mean(desc, dim=1).unsqueeze(1) + depth_wise_score = F.softplus(desc - depth_wise_mean) + + # Total saliency score + score = torch.max(soft_local_score * depth_wise_score, dim=1)[0] + normalization = torch.sum(score.reshape(b_size, -1), dim=1) + if is_3d: + normalization = normalization.reshape(b_size, 1) + else: + normalization = normalization.reshape(b_size, 1, 1) + score = score / normalization + return score + + def sample_salient_points(self, line_seg, desc, img_size, + saliency_type='d2_net'): + """ + Sample the most salient points along each line segments, with a + minimal distance between each point. Pad the remaining points. + Inputs: + line_seg: an Nx2x2 torch.Tensor. + desc: a NxDxHxW torch.Tensor. + image_size: the original image size. + saliency_type: 'd2_net' or 'asl_feat'. + Outputs: + line_points: an Nxnum_samplesx2 np.array. + valid_points: a boolean Nxnum_samples np.array. + """ + device = desc.device + if not self.line_score: + # Compute the score map + if saliency_type == "d2_net": + score = self.d2_net_saliency_score(desc) + else: + score = self.asl_feat_saliency_score(desc) + + num_lines = len(line_seg) + line_lengths = np.linalg.norm(line_seg[:, 0] - line_seg[:, 1], axis=1) + + # The number of samples depends on the length of the line + num_samples_lst = np.clip(line_lengths // self.min_dist_pts, + 2, self.num_samples) + line_points = np.empty((num_lines, self.num_samples, 2), dtype=float) + valid_points = np.empty((num_lines, self.num_samples), dtype=bool) + + # Sample the score on a fixed number of points of each line + n_samples_per_region = 4 + for n in np.arange(2, self.num_samples + 1): + sample_rate = n * n_samples_per_region + # Consider all lines where we can fit up to n points + cur_mask = num_samples_lst == n + cur_line_seg = line_seg[cur_mask] + cur_num_lines = len(cur_line_seg) + if cur_num_lines == 0: + continue + line_points_x = np.linspace(cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 0], + sample_rate, axis=-1) + line_points_y = np.linspace(cur_line_seg[:, 0, 1], + cur_line_seg[:, 1, 1], + sample_rate, axis=-1) + cur_line_points = np.stack([line_points_x, line_points_y], + axis=-1).reshape(-1, 2) + # cur_line_points is of shape (n_cur_lines * sample_rate, 2) + cur_line_points = torch.tensor(cur_line_points, dtype=torch.float, + device=device) + grid_points = keypoints_to_grid(cur_line_points, img_size) + + if self.line_score: + # The saliency score is high when the activation are locally + # maximal along the line (and not in a square neigborhood) + line_desc = F.grid_sample(desc, grid_points).squeeze() + line_desc = line_desc.reshape(-1, cur_num_lines, sample_rate) + line_desc = line_desc.permute(1, 0, 2) + if saliency_type == "d2_net": + scores = self.d2_net_saliency_score(line_desc) + else: + scores = self.asl_feat_saliency_score(line_desc) + else: + scores = F.grid_sample(score.unsqueeze(1), + grid_points).squeeze() + + # Take the most salient point in n distinct regions + scores = scores.reshape(-1, n, n_samples_per_region) + best = torch.max(scores, dim=2, keepdim=True)[1].cpu().numpy() + cur_line_points = cur_line_points.reshape(-1, n, + n_samples_per_region, 2) + cur_line_points = np.take_along_axis( + cur_line_points, best[..., None], axis=2)[:, :, 0] + + # Pad + cur_valid_points = np.ones((cur_num_lines, self.num_samples), + dtype=bool) + cur_valid_points[:, n:] = False + cur_line_points = np.concatenate([ + cur_line_points, + np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float)], + axis=1) + + line_points[cur_mask] = cur_line_points + valid_points[cur_mask] = cur_valid_points + + return line_points, valid_points + + def sample_line_points(self, line_seg): + """ + Regularly sample points along each line segments, with a minimal + distance between each point. Pad the remaining points. + Inputs: + line_seg: an Nx2x2 torch.Tensor. + Outputs: + line_points: an Nxnum_samplesx2 np.array. + valid_points: a boolean Nxnum_samples np.array. + """ + num_lines = len(line_seg) + line_lengths = np.linalg.norm(line_seg[:, 0] - line_seg[:, 1], axis=1) + + # Sample the points separated by at least min_dist_pts along each line + # The number of samples depends on the length of the line + num_samples_lst = np.clip(line_lengths // self.min_dist_pts, + 2, self.num_samples) + line_points = np.empty((num_lines, self.num_samples, 2), dtype=float) + valid_points = np.empty((num_lines, self.num_samples), dtype=bool) + for n in np.arange(2, self.num_samples + 1): + # Consider all lines where we can fit up to n points + cur_mask = num_samples_lst == n + cur_line_seg = line_seg[cur_mask] + line_points_x = np.linspace(cur_line_seg[:, 0, 0], + cur_line_seg[:, 1, 0], + n, axis=-1) + line_points_y = np.linspace(cur_line_seg[:, 0, 1], + cur_line_seg[:, 1, 1], + n, axis=-1) + cur_line_points = np.stack([line_points_x, line_points_y], axis=-1) + + # Pad + cur_num_lines = len(cur_line_seg) + cur_valid_points = np.ones((cur_num_lines, self.num_samples), + dtype=bool) + cur_valid_points[:, n:] = False + cur_line_points = np.concatenate([ + cur_line_points, + np.zeros((cur_num_lines, self.num_samples - n, 2), dtype=float)], + axis=1) + + line_points[cur_mask] = cur_line_points + valid_points[cur_mask] = cur_valid_points + + return line_points, valid_points + + def filter_and_match_lines(self, scores): + """ + Use the scores to keep the top k best lines, compute the Needleman- + Wunsch algorithm on each candidate pairs, and keep the highest score. + Inputs: + scores: a (N, M, n, n) torch.Tensor containing the pairwise scores + of the elements to match. + Outputs: + matches: a (N) np.array containing the indices of the best match + """ + # Pre-filter the pairs and keep the top k best candidate lines + line_scores1 = scores.max(3)[0] + valid_scores1 = line_scores1 != -1 + line_scores1 = ((line_scores1 * valid_scores1).sum(2) + / valid_scores1.sum(2)) + line_scores2 = scores.max(2)[0] + valid_scores2 = line_scores2 != -1 + line_scores2 = ((line_scores2 * valid_scores2).sum(2) + / valid_scores2.sum(2)) + line_scores = (line_scores1 + line_scores2) / 2 + topk_lines = torch.argsort(line_scores, + dim=1)[:, -self.top_k_candidates:] + scores, topk_lines = scores.cpu().numpy(), topk_lines.cpu().numpy() + # topk_lines.shape = (n_lines1, top_k_candidates) + top_scores = np.take_along_axis(scores, topk_lines[:, :, None, None], + axis=1) + + # Consider the reversed line segments as well + top_scores = np.concatenate([top_scores, top_scores[..., ::-1]], + axis=1) + + # Compute the line distance matrix with Needleman-Wunsch algo and + # retrieve the closest line neighbor + n_lines1, top2k, n, m = top_scores.shape + top_scores = top_scores.reshape(n_lines1 * top2k, n, m) + nw_scores = self.needleman_wunsch(top_scores) + nw_scores = nw_scores.reshape(n_lines1, top2k) + matches = np.mod(np.argmax(nw_scores, axis=1), top2k // 2) + matches = topk_lines[np.arange(n_lines1), matches] + return matches + + def needleman_wunsch(self, scores): + """ + Batched implementation of the Needleman-Wunsch algorithm. + The cost of the InDel operation is set to 0 by subtracting the gap + penalty to the scores. + Inputs: + scores: a (B, N, M) np.array containing the pairwise scores + of the elements to match. + """ + b, n, m = scores.shape + + # Recalibrate the scores to get a gap score of 0 + gap = 0.1 + nw_scores = scores - gap + + # Run the dynamic programming algorithm + nw_grid = np.zeros((b, n + 1, m + 1), dtype=float) + for i in range(n): + for j in range(m): + nw_grid[:, i + 1, j + 1] = np.maximum( + np.maximum(nw_grid[:, i + 1, j], nw_grid[:, i, j + 1]), + nw_grid[:, i, j] + nw_scores[:, i, j]) + + return nw_grid[:, -1, -1] + + def get_pairwise_distance(self, line_seg1, line_seg2, desc1, desc2): + """ + Compute the OPPOSITE of the NW score for pairs of line segments + and their corresponding descriptors. + """ + num_lines = len(line_seg1) + assert num_lines == len(line_seg2), "The same number of lines is required in pairwise score." + img_size1 = (desc1.shape[2] * self.grid_size, + desc1.shape[3] * self.grid_size) + img_size2 = (desc2.shape[2] * self.grid_size, + desc2.shape[3] * self.grid_size) + device = desc1.device + + # Sample points regularly along each line + line_points1, valid_points1 = self.sample_line_points(line_seg1) + line_points2, valid_points2 = self.sample_line_points(line_seg2) + line_points1 = torch.tensor(line_points1.reshape(-1, 2), + dtype=torch.float, device=device) + line_points2 = torch.tensor(line_points2.reshape(-1, 2), + dtype=torch.float, device=device) + + # Extract the descriptors for each point + grid1 = keypoints_to_grid(line_points1, img_size1) + grid2 = keypoints_to_grid(line_points2, img_size2) + desc1 = F.normalize(F.grid_sample(desc1, grid1)[0, :, :, 0], dim=0) + desc1 = desc1.reshape(-1, num_lines, self.num_samples) + desc2 = F.normalize(F.grid_sample(desc2, grid2)[0, :, :, 0], dim=0) + desc2 = desc2.reshape(-1, num_lines, self.num_samples) + + # Compute the distance between line points for every pair of lines + # Assign a score of -1 for unvalid points + scores = torch.einsum('dns,dnt->nst', desc1, desc2).cpu().numpy() + scores = scores.reshape(num_lines * self.num_samples, + self.num_samples) + scores[~valid_points1.flatten()] = -1 + scores = scores.reshape(num_lines, self.num_samples, self.num_samples) + scores = scores.transpose(1, 0, 2).reshape(self.num_samples, -1) + scores[:, ~valid_points2.flatten()] = -1 + scores = scores.reshape(self.num_samples, num_lines, self.num_samples) + scores = scores.transpose(1, 0, 2) + # scores.shape = (num_lines, num_samples, num_samples) + + # Compute the NW score for each pair of lines + pairwise_scores = np.array([self.needleman_wunsch(s) for s in scores]) + return -pairwise_scores diff --git a/third_party/SOLD2/sold2/model/loss.py b/third_party/SOLD2/sold2/model/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..aaad3c67f3fd59db308869901f8a56623901e318 --- /dev/null +++ b/third_party/SOLD2/sold2/model/loss.py @@ -0,0 +1,445 @@ +""" +Loss function implementations. +""" +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from kornia.geometry import warp_perspective + +from ..misc.geometry_utils import (keypoints_to_grid, get_dist_mask, + get_common_line_mask) + + +def get_loss_and_weights(model_cfg, device=torch.device("cuda")): + """ Get loss functions and either static or dynamic weighting. """ + # Get the global weighting policy + w_policy = model_cfg.get("weighting_policy", "static") + if not w_policy in ["static", "dynamic"]: + raise ValueError("[Error] Not supported weighting policy.") + + loss_func = {} + loss_weight = {} + # Get junction loss function and weight + w_junc, junc_loss_func = get_junction_loss_and_weight(model_cfg, w_policy) + loss_func["junc_loss"] = junc_loss_func.to(device) + loss_weight["w_junc"] = w_junc + + # Get heatmap loss function and weight + w_heatmap, heatmap_loss_func = get_heatmap_loss_and_weight( + model_cfg, w_policy, device) + loss_func["heatmap_loss"] = heatmap_loss_func.to(device) + loss_weight["w_heatmap"] = w_heatmap + + # [Optionally] get descriptor loss function and weight + if model_cfg.get("descriptor_loss_func", None) is not None: + w_descriptor, descriptor_loss_func = get_descriptor_loss_and_weight( + model_cfg, w_policy) + loss_func["descriptor_loss"] = descriptor_loss_func.to(device) + loss_weight["w_desc"] = w_descriptor + + return loss_func, loss_weight + + +def get_junction_loss_and_weight(model_cfg, global_w_policy): + """ Get the junction loss function and weight. """ + junction_loss_cfg = model_cfg.get("junction_loss_cfg", {}) + + # Get the junction loss weight + w_policy = junction_loss_cfg.get("policy", global_w_policy) + if w_policy == "static": + w_junc = torch.tensor(model_cfg["w_junc"], dtype=torch.float32) + elif w_policy == "dynamic": + w_junc = nn.Parameter( + torch.tensor(model_cfg["w_junc"], dtype=torch.float32), + requires_grad=True) + else: + raise ValueError( + "[Error] Unknown weighting policy for junction loss weight.") + + # Get the junction loss function + junc_loss_name = model_cfg.get("junction_loss_func", "superpoint") + if junc_loss_name == "superpoint": + junc_loss_func = JunctionDetectionLoss(model_cfg["grid_size"], + model_cfg["keep_border_valid"]) + else: + raise ValueError("[Error] Not supported junction loss function.") + + return w_junc, junc_loss_func + + +def get_heatmap_loss_and_weight(model_cfg, global_w_policy, device): + """ Get the heatmap loss function and weight. """ + heatmap_loss_cfg = model_cfg.get("heatmap_loss_cfg", {}) + + # Get the heatmap loss weight + w_policy = heatmap_loss_cfg.get("policy", global_w_policy) + if w_policy == "static": + w_heatmap = torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32) + elif w_policy == "dynamic": + w_heatmap = nn.Parameter( + torch.tensor(model_cfg["w_heatmap"], dtype=torch.float32), + requires_grad=True) + else: + raise ValueError( + "[Error] Unknown weighting policy for junction loss weight.") + + # Get the corresponding heatmap loss based on the config + heatmap_loss_name = model_cfg.get("heatmap_loss_func", "cross_entropy") + if heatmap_loss_name == "cross_entropy": + # Get the heatmap class weight (always static) + heatmap_class_w = model_cfg.get("w_heatmap_class", 1.) + class_weight = torch.tensor( + np.array([1., heatmap_class_w])).to(torch.float).to(device) + heatmap_loss_func = HeatmapLoss(class_weight=class_weight) + else: + raise ValueError("[Error] Not supported heatmap loss function.") + + return w_heatmap, heatmap_loss_func + + +def get_descriptor_loss_and_weight(model_cfg, global_w_policy): + """ Get the descriptor loss function and weight. """ + descriptor_loss_cfg = model_cfg.get("descriptor_loss_cfg", {}) + + # Get the descriptor loss weight + w_policy = descriptor_loss_cfg.get("policy", global_w_policy) + if w_policy == "static": + w_descriptor = torch.tensor(model_cfg["w_desc"], dtype=torch.float32) + elif w_policy == "dynamic": + w_descriptor = nn.Parameter(torch.tensor(model_cfg["w_desc"], + dtype=torch.float32), requires_grad=True) + else: + raise ValueError( + "[Error] Unknown weighting policy for descriptor loss weight.") + + # Get the descriptor loss function + descriptor_loss_name = model_cfg.get("descriptor_loss_func", + "regular_sampling") + if descriptor_loss_name == "regular_sampling": + descriptor_loss_func = TripletDescriptorLoss( + descriptor_loss_cfg["grid_size"], + descriptor_loss_cfg["dist_threshold"], + descriptor_loss_cfg["margin"]) + else: + raise ValueError("[Error] Not supported descriptor loss function.") + + return w_descriptor, descriptor_loss_func + + +def space_to_depth(input_tensor, grid_size): + """ PixelUnshuffle for pytorch. """ + N, C, H, W = input_tensor.size() + # (N, C, H//bs, bs, W//bs, bs) + x = input_tensor.view(N, C, H // grid_size, grid_size, W // grid_size, grid_size) + # (N, bs, bs, C, H//bs, W//bs) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() + # (N, C*bs^2, H//bs, W//bs) + x = x.view(N, C * (grid_size ** 2), H // grid_size, W // grid_size) + return x + + +def junction_detection_loss(junction_map, junc_predictions, valid_mask=None, + grid_size=8, keep_border=True): + """ Junction detection loss. """ + # Convert junc_map to channel tensor + junc_map = space_to_depth(junction_map, grid_size) + map_shape = junc_map.shape[-2:] + batch_size = junc_map.shape[0] + dust_bin_label = torch.ones( + [batch_size, 1, map_shape[0], + map_shape[1]]).to(junc_map.device).to(torch.int) + junc_map = torch.cat([junc_map*2, dust_bin_label], dim=1) + labels = torch.argmax( + junc_map.to(torch.float) + + torch.distributions.Uniform(0, 0.1).sample(junc_map.shape).to(junc_map.device), + dim=1) + + # Also convert the valid mask to channel tensor + valid_mask = (torch.ones(junction_map.shape) if valid_mask is None + else valid_mask) + valid_mask = space_to_depth(valid_mask, grid_size) + + # Compute junction loss on the border patch or not + if keep_border: + valid_mask = torch.sum(valid_mask.to(torch.bool).to(torch.int), + dim=1, keepdim=True) > 0 + else: + valid_mask = torch.sum(valid_mask.to(torch.bool).to(torch.int), + dim=1, keepdim=True) >= grid_size * grid_size + + # Compute the classification loss + loss_func = nn.CrossEntropyLoss(reduction="none") + # The loss still need NCHW format + loss = loss_func(input=junc_predictions, + target=labels.to(torch.long)) + + # Weighted sum by the valid mask + loss_ = torch.sum(loss * torch.squeeze(valid_mask.to(torch.float), + dim=1), dim=[0, 1, 2]) + loss_final = loss_ / torch.sum(torch.squeeze(valid_mask.to(torch.float), + dim=1)) + + return loss_final + + +def heatmap_loss(heatmap_gt, heatmap_pred, valid_mask=None, + class_weight=None): + """ Heatmap prediction loss. """ + # Compute the classification loss on each pixel + if class_weight is None: + loss_func = nn.CrossEntropyLoss(reduction="none") + else: + loss_func = nn.CrossEntropyLoss(class_weight, reduction="none") + + loss = loss_func(input=heatmap_pred, + target=torch.squeeze(heatmap_gt.to(torch.long), dim=1)) + + # Weighted sum by the valid mask + # Sum over H and W + loss_spatial_sum = torch.sum(loss * torch.squeeze( + valid_mask.to(torch.float), dim=1), dim=[1, 2]) + valid_spatial_sum = torch.sum(torch.squeeze(valid_mask.to(torch.float32), + dim=1), dim=[1, 2]) + # Mean to single scalar over batch dimension + loss = torch.sum(loss_spatial_sum) / torch.sum(valid_spatial_sum) + + return loss + + +class JunctionDetectionLoss(nn.Module): + """ Junction detection loss. """ + def __init__(self, grid_size, keep_border): + super(JunctionDetectionLoss, self).__init__() + self.grid_size = grid_size + self.keep_border = keep_border + + def forward(self, prediction, target, valid_mask=None): + return junction_detection_loss(target, prediction, valid_mask, + self.grid_size, self.keep_border) + + +class HeatmapLoss(nn.Module): + """ Heatmap prediction loss. """ + def __init__(self, class_weight): + super(HeatmapLoss, self).__init__() + self.class_weight = class_weight + + def forward(self, prediction, target, valid_mask=None): + return heatmap_loss(target, prediction, valid_mask, self.class_weight) + + +class RegularizationLoss(nn.Module): + """ Module for regularization loss. """ + def __init__(self): + super(RegularizationLoss, self).__init__() + self.name = "regularization_loss" + self.loss_init = torch.zeros([]) + + def forward(self, loss_weights): + # Place it to the same device + loss = self.loss_init.to(loss_weights["w_junc"].device) + for _, val in loss_weights.items(): + if isinstance(val, nn.Parameter): + loss += val + + return loss + + +def triplet_loss(desc_pred1, desc_pred2, points1, points2, line_indices, + epoch, grid_size=8, dist_threshold=8, + init_dist_threshold=64, margin=1): + """ Regular triplet loss for descriptor learning. """ + b_size, _, Hc, Wc = desc_pred1.size() + img_size = (Hc * grid_size, Wc * grid_size) + device = desc_pred1.device + + # Extract valid keypoints + n_points = line_indices.size()[1] + valid_points = line_indices.bool().flatten() + n_correct_points = torch.sum(valid_points).item() + if n_correct_points == 0: + return torch.tensor(0., dtype=torch.float, device=device) + + # Check which keypoints are too close to be matched + # dist_threshold is decreased at each epoch for easier training + dist_threshold = max(dist_threshold, + 2 * init_dist_threshold // (epoch + 1)) + dist_mask = get_dist_mask(points1, points2, valid_points, dist_threshold) + + # Additionally ban negative mining along the same line + common_line_mask = get_common_line_mask(line_indices, valid_points) + dist_mask = dist_mask | common_line_mask + + # Convert the keypoints to a grid suitable for interpolation + grid1 = keypoints_to_grid(points1, img_size) + grid2 = keypoints_to_grid(points2, img_size) + + # Extract the descriptors + desc1 = F.grid_sample(desc_pred1, grid1).permute( + 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points] + desc1 = F.normalize(desc1, dim=1) + desc2 = F.grid_sample(desc_pred2, grid2).permute( + 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points] + desc2 = F.normalize(desc2, dim=1) + desc_dists = 2 - 2 * (desc1 @ desc2.t()) + + # Positive distance loss + pos_dist = torch.diag(desc_dists) + + # Negative distance loss + max_dist = torch.tensor(4., dtype=torch.float, device=device) + desc_dists[ + torch.arange(n_correct_points, dtype=torch.long), + torch.arange(n_correct_points, dtype=torch.long)] = max_dist + desc_dists[dist_mask] = max_dist + neg_dist = torch.min(torch.min(desc_dists, dim=1)[0], + torch.min(desc_dists, dim=0)[0]) + + triplet_loss = F.relu(margin + pos_dist - neg_dist) + return triplet_loss, grid1, grid2, valid_points + + +class TripletDescriptorLoss(nn.Module): + """ Triplet descriptor loss. """ + def __init__(self, grid_size, dist_threshold, margin): + super(TripletDescriptorLoss, self).__init__() + self.grid_size = grid_size + self.init_dist_threshold = 64 + self.dist_threshold = dist_threshold + self.margin = margin + + def forward(self, desc_pred1, desc_pred2, points1, + points2, line_indices, epoch): + return self.descriptor_loss(desc_pred1, desc_pred2, points1, + points2, line_indices, epoch) + + # The descriptor loss based on regularly sampled points along the lines + def descriptor_loss(self, desc_pred1, desc_pred2, points1, + points2, line_indices, epoch): + return torch.mean(triplet_loss( + desc_pred1, desc_pred2, points1, points2, line_indices, epoch, + self.grid_size, self.dist_threshold, self.init_dist_threshold, + self.margin)[0]) + + +class TotalLoss(nn.Module): + """ Total loss summing junction, heatma, descriptor + and regularization losses. """ + def __init__(self, loss_funcs, loss_weights, weighting_policy): + super(TotalLoss, self).__init__() + # Whether we need to compute the descriptor loss + self.compute_descriptors = "descriptor_loss" in loss_funcs.keys() + + self.loss_funcs = loss_funcs + self.loss_weights = loss_weights + self.weighting_policy = weighting_policy + + # Always add regularization loss (it will return zero if not used) + self.loss_funcs["reg_loss"] = RegularizationLoss().cuda() + + def forward(self, junc_pred, junc_target, heatmap_pred, + heatmap_target, valid_mask=None): + """ Detection only loss. """ + # Compute the junction loss + junc_loss = self.loss_funcs["junc_loss"](junc_pred, junc_target, + valid_mask) + # Compute the heatmap loss + heatmap_loss = self.loss_funcs["heatmap_loss"]( + heatmap_pred, heatmap_target, valid_mask) + + # Compute the total loss. + if self.weighting_policy == "dynamic": + reg_loss = self.loss_funcs["reg_loss"](self.loss_weights) + total_loss = junc_loss * torch.exp(-self.loss_weights["w_junc"]) + \ + heatmap_loss * torch.exp(-self.loss_weights["w_heatmap"]) + \ + reg_loss + + return { + "total_loss": total_loss, + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss, + "reg_loss": reg_loss, + "w_junc": torch.exp(-self.loss_weights["w_junc"]).item(), + "w_heatmap": torch.exp(-self.loss_weights["w_heatmap"]).item(), + } + + elif self.weighting_policy == "static": + total_loss = junc_loss * self.loss_weights["w_junc"] + \ + heatmap_loss * self.loss_weights["w_heatmap"] + + return { + "total_loss": total_loss, + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss + } + + else: + raise ValueError("[Error] Unknown weighting policy.") + + def forward_descriptors(self, + junc_map_pred1, junc_map_pred2, junc_map_target1, + junc_map_target2, heatmap_pred1, heatmap_pred2, heatmap_target1, + heatmap_target2, line_points1, line_points2, line_indices, + desc_pred1, desc_pred2, epoch, valid_mask1=None, + valid_mask2=None): + """ Loss for detection + description. """ + # Compute junction loss + junc_loss = self.loss_funcs["junc_loss"]( + torch.cat([junc_map_pred1, junc_map_pred2], dim=0), + torch.cat([junc_map_target1, junc_map_target2], dim=0), + torch.cat([valid_mask1, valid_mask2], dim=0) + ) + # Get junction loss weight (dynamic or not) + if isinstance(self.loss_weights["w_junc"], nn.Parameter): + w_junc = torch.exp(-self.loss_weights["w_junc"]) + else: + w_junc = self.loss_weights["w_junc"] + + # Compute heatmap loss + heatmap_loss = self.loss_funcs["heatmap_loss"]( + torch.cat([heatmap_pred1, heatmap_pred2], dim=0), + torch.cat([heatmap_target1, heatmap_target2], dim=0), + torch.cat([valid_mask1, valid_mask2], dim=0) + ) + # Get heatmap loss weight (dynamic or not) + if isinstance(self.loss_weights["w_heatmap"], nn.Parameter): + w_heatmap = torch.exp(-self.loss_weights["w_heatmap"]) + else: + w_heatmap = self.loss_weights["w_heatmap"] + + # Compute the descriptor loss + descriptor_loss = self.loss_funcs["descriptor_loss"]( + desc_pred1, desc_pred2, line_points1, + line_points2, line_indices, epoch) + # Get descriptor loss weight (dynamic or not) + if isinstance(self.loss_weights["w_desc"], nn.Parameter): + w_descriptor = torch.exp(-self.loss_weights["w_desc"]) + else: + w_descriptor = self.loss_weights["w_desc"] + + # Update the total loss + total_loss = (junc_loss * w_junc + + heatmap_loss * w_heatmap + + descriptor_loss * w_descriptor) + outputs = { + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss, + "w_junc": w_junc.item() \ + if isinstance(w_junc, nn.Parameter) else w_junc, + "w_heatmap": w_heatmap.item() \ + if isinstance(w_heatmap, nn.Parameter) else w_heatmap, + "descriptor_loss": descriptor_loss, + "w_desc": w_descriptor.item() \ + if isinstance(w_descriptor, nn.Parameter) else w_descriptor + } + + # Compute the regularization loss + reg_loss = self.loss_funcs["reg_loss"](self.loss_weights) + total_loss += reg_loss + outputs.update({ + "reg_loss": reg_loss, + "total_loss": total_loss + }) + + return outputs diff --git a/third_party/SOLD2/sold2/model/lr_scheduler.py b/third_party/SOLD2/sold2/model/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..3faa4f68a67564719008a932b40c16c5e908949f --- /dev/null +++ b/third_party/SOLD2/sold2/model/lr_scheduler.py @@ -0,0 +1,22 @@ +""" +This file implements different learning rate schedulers +""" +import torch + + +def get_lr_scheduler(lr_decay, lr_decay_cfg, optimizer): + """ Get the learning rate scheduler according to the config. """ + # If no lr_decay is specified => return None + if (lr_decay == False) or (lr_decay_cfg is None): + schduler = None + # Exponential decay + elif (lr_decay == True) and (lr_decay_cfg["policy"] == "exp"): + schduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, + gamma=lr_decay_cfg["gamma"] + ) + # Unknown policy + else: + raise ValueError("[Error] Unknow learning rate decay policy!") + + return schduler \ No newline at end of file diff --git a/third_party/SOLD2/sold2/model/metrics.py b/third_party/SOLD2/sold2/model/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..0894a7207ee4afa344cb332c605c715b14db73a4 --- /dev/null +++ b/third_party/SOLD2/sold2/model/metrics.py @@ -0,0 +1,528 @@ +""" +This file implements the evaluation metrics. +""" +import torch +import torch.nn.functional as F +import numpy as np +from torchvision.ops.boxes import batched_nms + +from ..misc.geometry_utils import keypoints_to_grid + + +class Metrics(object): + """ Metric evaluation calculator. """ + def __init__(self, detection_thresh, prob_thresh, grid_size, + junc_metric_lst=None, heatmap_metric_lst=None, + pr_metric_lst=None, desc_metric_lst=None): + # List supported metrics + self.supported_junc_metrics = ["junc_precision", "junc_precision_nms", + "junc_recall", "junc_recall_nms"] + self.supported_heatmap_metrics = ["heatmap_precision", + "heatmap_recall"] + self.supported_pr_metrics = ["junc_pr", "junc_nms_pr"] + self.supported_desc_metrics = ["matching_score"] + + # If metric_lst is None, default to use all metrics + if junc_metric_lst is None: + self.junc_metric_lst = self.supported_junc_metrics + else: + self.junc_metric_lst = junc_metric_lst + if heatmap_metric_lst is None: + self.heatmap_metric_lst = self.supported_heatmap_metrics + else: + self.heatmap_metric_lst = heatmap_metric_lst + if pr_metric_lst is None: + self.pr_metric_lst = self.supported_pr_metrics + else: + self.pr_metric_lst = pr_metric_lst + # For the descriptors, the default None assumes no desc metric at all + if desc_metric_lst is None: + self.desc_metric_lst = [] + elif desc_metric_lst == 'all': + self.desc_metric_lst = self.supported_desc_metrics + else: + self.desc_metric_lst = desc_metric_lst + + if not self._check_metrics(): + raise ValueError( + "[Error] Some elements in the metric_lst are invalid.") + + # Metric mapping table + self.metric_table = { + "junc_precision": junction_precision(detection_thresh), + "junc_precision_nms": junction_precision(detection_thresh), + "junc_recall": junction_recall(detection_thresh), + "junc_recall_nms": junction_recall(detection_thresh), + "heatmap_precision": heatmap_precision(prob_thresh), + "heatmap_recall": heatmap_recall(prob_thresh), + "junc_pr": junction_pr(), + "junc_nms_pr": junction_pr(), + "matching_score": matching_score(grid_size) + } + + # Initialize the results + self.metric_results = {} + for key in self.metric_table.keys(): + self.metric_results[key] = 0. + + def evaluate(self, junc_pred, junc_pred_nms, junc_gt, heatmap_pred, + heatmap_gt, valid_mask, line_points1=None, line_points2=None, + desc_pred1=None, desc_pred2=None, valid_points=None): + """ Perform evaluation. """ + for metric in self.junc_metric_lst: + # If nms metrics then use nms to compute it. + if "nms" in metric: + junc_pred_input = junc_pred_nms + # Use normal inputs instead. + else: + junc_pred_input = junc_pred + self.metric_results[metric] = self.metric_table[metric]( + junc_pred_input, junc_gt, valid_mask) + + for metric in self.heatmap_metric_lst: + self.metric_results[metric] = self.metric_table[metric]( + heatmap_pred, heatmap_gt, valid_mask) + + for metric in self.pr_metric_lst: + if "nms" in metric: + self.metric_results[metric] = self.metric_table[metric]( + junc_pred_nms, junc_gt, valid_mask) + else: + self.metric_results[metric] = self.metric_table[metric]( + junc_pred, junc_gt, valid_mask) + + for metric in self.desc_metric_lst: + self.metric_results[metric] = self.metric_table[metric]( + line_points1, line_points2, desc_pred1, + desc_pred2, valid_points) + + def _check_metrics(self): + """ Check if all input metrics are valid. """ + flag = True + for metric in self.junc_metric_lst: + if not metric in self.supported_junc_metrics: + flag = False + break + for metric in self.heatmap_metric_lst: + if not metric in self.supported_heatmap_metrics: + flag = False + break + for metric in self.desc_metric_lst: + if not metric in self.supported_desc_metrics: + flag = False + break + + return flag + + +class AverageMeter(object): + def __init__(self, junc_metric_lst=None, heatmap_metric_lst=None, + is_training=True, desc_metric_lst=None): + # List supported metrics + self.supported_junc_metrics = ["junc_precision", "junc_precision_nms", + "junc_recall", "junc_recall_nms"] + self.supported_heatmap_metrics = ["heatmap_precision", + "heatmap_recall"] + self.supported_pr_metrics = ["junc_pr", "junc_nms_pr"] + self.supported_desc_metrics = ["matching_score"] + # Record loss in training mode + # if is_training: + self.supported_loss = [ + "junc_loss", "heatmap_loss", "descriptor_loss", "total_loss"] + + self.is_training = is_training + + # If metric_lst is None, default to use all metrics + if junc_metric_lst is None: + self.junc_metric_lst = self.supported_junc_metrics + else: + self.junc_metric_lst = junc_metric_lst + if heatmap_metric_lst is None: + self.heatmap_metric_lst = self.supported_heatmap_metrics + else: + self.heatmap_metric_lst = heatmap_metric_lst + # For the descriptors, the default None assumes no desc metric at all + if desc_metric_lst is None: + self.desc_metric_lst = [] + elif desc_metric_lst == 'all': + self.desc_metric_lst = self.supported_desc_metrics + else: + self.desc_metric_lst = desc_metric_lst + + if not self._check_metrics(): + raise ValueError( + "[Error] Some elements in the metric_lst are invalid.") + + # Initialize the results + self.metric_results = {} + for key in (self.supported_junc_metrics + + self.supported_heatmap_metrics + + self.supported_loss + self.supported_desc_metrics): + self.metric_results[key] = 0. + for key in self.supported_pr_metrics: + zero_lst = [0 for _ in range(50)] + self.metric_results[key] = { + "tp": zero_lst, + "tn": zero_lst, + "fp": zero_lst, + "fn": zero_lst, + "precision": zero_lst, + "recall": zero_lst + } + + # Initialize total count + self.count = 0 + + def update(self, metrics, loss_dict=None, num_samples=1): + # loss should be given in the training mode + if self.is_training and (loss_dict is None): + raise ValueError( + "[Error] loss info should be given in the training mode.") + + # update total counts + self.count += num_samples + + # update all the metrics + for met in (self.supported_junc_metrics + + self.supported_heatmap_metrics + + self.supported_desc_metrics): + self.metric_results[met] += (num_samples + * metrics.metric_results[met]) + + # Update all the losses + for loss in loss_dict.keys(): + self.metric_results[loss] += num_samples * loss_dict[loss] + + # Update all pr counts + for pr_met in self.supported_pr_metrics: + # Update all tp, tn, fp, fn, precision, and recall. + for key in metrics.metric_results[pr_met].keys(): + # Update each interval + for idx in range(len(self.metric_results[pr_met][key])): + self.metric_results[pr_met][key][idx] += ( + num_samples + * metrics.metric_results[pr_met][key][idx]) + + def average(self): + results = {} + for met in self.metric_results.keys(): + # Skip pr curve metrics + if not met in self.supported_pr_metrics: + results[met] = self.metric_results[met] / self.count + # Only update precision and recall in pr metrics + else: + met_results = { + "tp": self.metric_results[met]["tp"], + "tn": self.metric_results[met]["tn"], + "fp": self.metric_results[met]["fp"], + "fn": self.metric_results[met]["fn"], + "precision": [], + "recall": [] + } + for idx in range(len(self.metric_results[met]["precision"])): + met_results["precision"].append( + self.metric_results[met]["precision"][idx] + / self.count) + met_results["recall"].append( + self.metric_results[met]["recall"][idx] / self.count) + + results[met] = met_results + + return results + + def _check_metrics(self): + """ Check if all input metrics are valid. """ + flag = True + for metric in self.junc_metric_lst: + if not metric in self.supported_junc_metrics: + flag = False + break + for metric in self.heatmap_metric_lst: + if not metric in self.supported_heatmap_metrics: + flag = False + break + for metric in self.desc_metric_lst: + if not metric in self.supported_desc_metrics: + flag = False + break + + return flag + + +class junction_precision(object): + """ Junction precision. """ + def __init__(self, detection_thresh): + self.detection_thresh = detection_thresh + + # Compute the evaluation result + def __call__(self, junc_pred, junc_gt, valid_mask): + # Convert prediction to discrete detection + junc_pred = (junc_pred >= self.detection_thresh).astype(np.int) + junc_pred = junc_pred * valid_mask.squeeze() + + # Deal with the corner case of the prediction + if np.sum(junc_pred) > 0: + precision = (np.sum(junc_pred * junc_gt.squeeze()) + / np.sum(junc_pred)) + else: + precision = 0 + + return float(precision) + + +class junction_recall(object): + """ Junction recall. """ + def __init__(self, detection_thresh): + self.detection_thresh = detection_thresh + + # Compute the evaluation result + def __call__(self, junc_pred, junc_gt, valid_mask): + # Convert prediction to discrete detection + junc_pred = (junc_pred >= self.detection_thresh).astype(np.int) + junc_pred = junc_pred * valid_mask.squeeze() + + # Deal with the corner case of the recall. + if np.sum(junc_gt): + recall = np.sum(junc_pred * junc_gt.squeeze()) / np.sum(junc_gt) + else: + recall = 0 + + return float(recall) + + +class junction_pr(object): + """ Junction precision-recall info. """ + def __init__(self, num_threshold=50): + self.max = 0.4 + step = self.max / num_threshold + self.min = step + self.intervals = np.flip(np.arange(self.min, self.max + step, step)) + + def __call__(self, junc_pred_raw, junc_gt, valid_mask): + tp_lst = [] + fp_lst = [] + tn_lst = [] + fn_lst = [] + precision_lst = [] + recall_lst = [] + + valid_mask = valid_mask.squeeze() + # Iterate through all the thresholds + for thresh in list(self.intervals): + # Convert prediction to discrete detection + junc_pred = (junc_pred_raw >= thresh).astype(np.int) + junc_pred = junc_pred * valid_mask + + # Compute tp, fp, tn, fn + junc_gt = junc_gt.squeeze() + tp = np.sum(junc_pred * junc_gt) + tn = np.sum((junc_pred == 0).astype(np.float) + * (junc_gt == 0).astype(np.float) * valid_mask) + fp = np.sum((junc_pred == 1).astype(np.float) + * (junc_gt == 0).astype(np.float) * valid_mask) + fn = np.sum((junc_pred == 0).astype(np.float) + * (junc_gt == 1).astype(np.float) * valid_mask) + + tp_lst.append(tp) + tn_lst.append(tn) + fp_lst.append(fp) + fn_lst.append(fn) + precision_lst.append(tp / (tp + fp)) + recall_lst.append(tp / (tp + fn)) + + return { + "tp": np.array(tp_lst), + "tn": np.array(tn_lst), + "fp": np.array(fp_lst), + "fn": np.array(fn_lst), + "precision": np.array(precision_lst), + "recall": np.array(recall_lst) + } + + +class heatmap_precision(object): + """ Heatmap precision. """ + def __init__(self, prob_thresh): + self.prob_thresh = prob_thresh + + def __call__(self, heatmap_pred, heatmap_gt, valid_mask): + # Assume NHWC (Handle L1 and L2 cases) NxHxWx1 + heatmap_pred = np.squeeze(heatmap_pred > self.prob_thresh) + heatmap_pred = heatmap_pred * valid_mask.squeeze() + + # Deal with the corner case of the prediction + if np.sum(heatmap_pred) > 0: + precision = (np.sum(heatmap_pred * heatmap_gt.squeeze()) + / np.sum(heatmap_pred)) + else: + precision = 0. + + return precision + + +class heatmap_recall(object): + """ Heatmap recall. """ + def __init__(self, prob_thresh): + self.prob_thresh = prob_thresh + + def __call__(self, heatmap_pred, heatmap_gt, valid_mask): + # Assume NHWC (Handle L1 and L2 cases) NxHxWx1 + heatmap_pred = np.squeeze(heatmap_pred > self.prob_thresh) + heatmap_pred = heatmap_pred * valid_mask.squeeze() + + # Deal with the corner case of the ground truth + if np.sum(heatmap_gt) > 0: + recall = (np.sum(heatmap_pred * heatmap_gt.squeeze()) + / np.sum(heatmap_gt)) + else: + recall = 0. + + return recall + + +class matching_score(object): + """ Descriptors matching score. """ + def __init__(self, grid_size): + self.grid_size = grid_size + + def __call__(self, points1, points2, desc_pred1, + desc_pred2, line_indices): + b_size, _, Hc, Wc = desc_pred1.size() + img_size = (Hc * self.grid_size, Wc * self.grid_size) + device = desc_pred1.device + + # Extract valid keypoints + n_points = line_indices.size()[1] + valid_points = line_indices.bool().flatten() + n_correct_points = torch.sum(valid_points).item() + if n_correct_points == 0: + return torch.tensor(0., dtype=torch.float, device=device) + + # Convert the keypoints to a grid suitable for interpolation + grid1 = keypoints_to_grid(points1, img_size) + grid2 = keypoints_to_grid(points2, img_size) + + # Extract the descriptors + desc1 = F.grid_sample(desc_pred1, grid1).permute( + 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points] + desc1 = F.normalize(desc1, dim=1) + desc2 = F.grid_sample(desc_pred2, grid2).permute( + 0, 2, 3, 1).reshape(b_size * n_points, -1)[valid_points] + desc2 = F.normalize(desc2, dim=1) + desc_dists = 2 - 2 * (desc1 @ desc2.t()) + + # Compute percentage of correct matches + matches0 = torch.min(desc_dists, dim=1)[1] + matches1 = torch.min(desc_dists, dim=0)[1] + matching_score = (matches1[matches0] + == torch.arange(len(matches0)).to(device)) + matching_score = matching_score.float().mean() + return matching_score + + +def super_nms(prob_predictions, dist_thresh, prob_thresh=0.01, top_k=0): + """ Non-maximum suppression adapted from SuperPoint. """ + # Iterate through batch dimension + im_h = prob_predictions.shape[1] + im_w = prob_predictions.shape[2] + output_lst = [] + for i in range(prob_predictions.shape[0]): + # print(i) + prob_pred = prob_predictions[i, ...] + # Filter the points using prob_thresh + coord = np.where(prob_pred >= prob_thresh) # HW format + points = np.concatenate((coord[0][..., None], coord[1][..., None]), + axis=1) # HW format + + # Get the probability score + prob_score = prob_pred[points[:, 0], points[:, 1]] + + # Perform super nms + # Modify the in_points to xy format (instead of HW format) + in_points = np.concatenate((coord[1][..., None], coord[0][..., None], + prob_score), axis=1).T + keep_points_, keep_inds = nms_fast(in_points, im_h, im_w, dist_thresh) + # Remember to flip outputs back to HW format + keep_points = np.round(np.flip(keep_points_[:2, :], axis=0).T) + keep_score = keep_points_[-1, :].T + + # Whether we only keep the topk value + if (top_k > 0) or (top_k is None): + k = min([keep_points.shape[0], top_k]) + keep_points = keep_points[:k, :] + keep_score = keep_score[:k] + + # Re-compose the probability map + output_map = np.zeros([im_h, im_w]) + output_map[keep_points[:, 0].astype(np.int), + keep_points[:, 1].astype(np.int)] = keep_score.squeeze() + + output_lst.append(output_map[None, ...]) + + return np.concatenate(output_lst, axis=0) + + +def nms_fast(in_corners, H, W, dist_thresh): + """ + Run a faster approximate Non-Max-Suppression on numpy corners shaped: + 3xN [x_i,y_i,conf_i]^T + + Algo summary: Create a grid sized HxW. Assign each corner location a 1, + rest are zeros. Iterate through all the 1's and convert them to -1 or 0. + Suppress points by setting nearby values to 0. + + Grid Value Legend: + -1 : Kept. + 0 : Empty or suppressed. + 1 : To be processed (converted to either kept or supressed). + + NOTE: The NMS first rounds points to integers, so NMS distance might not + be exactly dist_thresh. It also assumes points are within image boundary. + + Inputs + in_corners - 3xN numpy array with corners [x_i, y_i, confidence_i]^T. + H - Image height. + W - Image width. + dist_thresh - Distance to suppress, measured as an infinite distance. + Returns + nmsed_corners - 3xN numpy matrix with surviving corners. + nmsed_inds - N length numpy vector with surviving corner indices. + """ + grid = np.zeros((H, W)).astype(int) # Track NMS data. + inds = np.zeros((H, W)).astype(int) # Store indices of points. + # Sort by confidence and round to nearest int. + inds1 = np.argsort(-in_corners[2, :]) + corners = in_corners[:, inds1] + rcorners = corners[:2, :].round().astype(int) # Rounded corners. + # Check for edge case of 0 or 1 corners. + if rcorners.shape[1] == 0: + return np.zeros((3, 0)).astype(int), np.zeros(0).astype(int) + if rcorners.shape[1] == 1: + out = np.vstack((rcorners, in_corners[2])).reshape(3, 1) + return out, np.zeros((1)).astype(int) + # Initialize the grid. + for i, rc in enumerate(rcorners.T): + grid[rcorners[1, i], rcorners[0, i]] = 1 + inds[rcorners[1, i], rcorners[0, i]] = i + # Pad the border of the grid, so that we can NMS points near the border. + pad = dist_thresh + grid = np.pad(grid, ((pad, pad), (pad, pad)), mode='constant') + # Iterate through points, highest to lowest conf, suppress neighborhood. + count = 0 + for i, rc in enumerate(rcorners.T): + # Account for top and left padding. + pt = (rc[0] + pad, rc[1] + pad) + if grid[pt[1], pt[0]] == 1: # If not yet suppressed. + grid[pt[1] - pad:pt[1] + pad + 1, pt[0] - pad:pt[0] + pad + 1] = 0 + grid[pt[1], pt[0]] = -1 + count += 1 + # Get all surviving -1's and return sorted array of remaining corners. + keepy, keepx = np.where(grid == -1) + keepy, keepx = keepy - pad, keepx - pad + inds_keep = inds[keepy, keepx] + out = corners[:, inds_keep] + values = out[-1, :] + inds2 = np.argsort(-values) + out = out[:, inds2] + out_inds = inds1[inds_keep[inds2]] + return out, out_inds diff --git a/third_party/SOLD2/sold2/model/model_util.py b/third_party/SOLD2/sold2/model/model_util.py new file mode 100644 index 0000000000000000000000000000000000000000..f70d80da40a72c207edfcfc1509e820846f0b731 --- /dev/null +++ b/third_party/SOLD2/sold2/model/model_util.py @@ -0,0 +1,203 @@ +import torch +import torch.nn as nn +import torch.nn.init as init + +from .nets.backbone import HourglassBackbone, SuperpointBackbone +from .nets.junction_decoder import SuperpointDecoder +from .nets.heatmap_decoder import PixelShuffleDecoder +from .nets.descriptor_decoder import SuperpointDescriptor + + +def get_model(model_cfg=None, loss_weights=None, mode="train"): + """ Get model based on the model configuration. """ + # Check dataset config is given + if model_cfg is None: + raise ValueError("[Error] The model config is required!") + + # List the supported options here + print("\n\n\t--------Initializing model----------") + supported_arch = ["simple"] + if not model_cfg["model_architecture"] in supported_arch: + raise ValueError( + "[Error] The model architecture is not in supported arch!") + + if model_cfg["model_architecture"] == "simple": + model = SOLD2Net(model_cfg) + else: + raise ValueError( + "[Error] The model architecture is not in supported arch!") + + # Optionally register loss weights to the model + if mode == "train": + if loss_weights is not None: + for param_name, param in loss_weights.items(): + if isinstance(param, nn.Parameter): + print("\t [Debug] Adding %s with value %f to model" + % (param_name, param.item())) + model.register_parameter(param_name, param) + else: + raise ValueError( + "[Error] the loss weights can not be None in dynamic weighting mode during training.") + + # Display some summary info. + print("\tModel architecture: %s" % model_cfg["model_architecture"]) + print("\tBackbone: %s" % model_cfg["backbone"]) + print("\tJunction decoder: %s" % model_cfg["junction_decoder"]) + print("\tHeatmap decoder: %s" % model_cfg["heatmap_decoder"]) + print("\t-------------------------------------") + + return model + + +class SOLD2Net(nn.Module): + """ Full network for SOLD². """ + def __init__(self, model_cfg): + super(SOLD2Net, self).__init__() + self.name = model_cfg["model_name"] + self.cfg = model_cfg + + # List supported network options + self.supported_backbone = ["lcnn", "superpoint"] + self.backbone_net, self.feat_channel = self.get_backbone() + + # List supported junction decoder options + self.supported_junction_decoder = ["superpoint_decoder"] + self.junction_decoder = self.get_junction_decoder() + + # List supported heatmap decoder options + self.supported_heatmap_decoder = ["pixel_shuffle", + "pixel_shuffle_single"] + self.heatmap_decoder = self.get_heatmap_decoder() + + # List supported descriptor decoder options + if "descriptor_decoder" in self.cfg: + self.supported_descriptor_decoder = ["superpoint_descriptor"] + self.descriptor_decoder = self.get_descriptor_decoder() + + # Initialize the model weights + self.apply(weight_init) + + def forward(self, input_images): + # The backbone + features = self.backbone_net(input_images) + + # junction decoder + junctions = self.junction_decoder(features) + + # heatmap decoder + heatmaps = self.heatmap_decoder(features) + + outputs = {"junctions": junctions, "heatmap": heatmaps} + + # Descriptor decoder + if "descriptor_decoder" in self.cfg: + outputs["descriptors"] = self.descriptor_decoder(features) + + return outputs + + def get_backbone(self): + """ Retrieve the backbone encoder network. """ + if not self.cfg["backbone"] in self.supported_backbone: + raise ValueError( + "[Error] The backbone selection is not supported.") + + # lcnn backbone (stacked hourglass) + if self.cfg["backbone"] == "lcnn": + backbone_cfg = self.cfg["backbone_cfg"] + backbone = HourglassBackbone(**backbone_cfg) + feat_channel = 256 + + elif self.cfg["backbone"] == "superpoint": + backbone_cfg = self.cfg["backbone_cfg"] + backbone = SuperpointBackbone() + feat_channel = 128 + + else: + raise ValueError( + "[Error] The backbone selection is not supported.") + + return backbone, feat_channel + + def get_junction_decoder(self): + """ Get the junction decoder. """ + if (not self.cfg["junction_decoder"] + in self.supported_junction_decoder): + raise ValueError( + "[Error] The junction decoder selection is not supported.") + + # superpoint decoder + if self.cfg["junction_decoder"] == "superpoint_decoder": + decoder = SuperpointDecoder(self.feat_channel, + self.cfg["backbone"]) + else: + raise ValueError( + "[Error] The junction decoder selection is not supported.") + + return decoder + + def get_heatmap_decoder(self): + """ Get the heatmap decoder. """ + if not self.cfg["heatmap_decoder"] in self.supported_heatmap_decoder: + raise ValueError( + "[Error] The heatmap decoder selection is not supported.") + + # Pixel_shuffle decoder + if self.cfg["heatmap_decoder"] == "pixel_shuffle": + if self.cfg["backbone"] == "lcnn": + decoder = PixelShuffleDecoder(self.feat_channel, + num_upsample=2) + elif self.cfg["backbone"] == "superpoint": + decoder = PixelShuffleDecoder(self.feat_channel, + num_upsample=3) + else: + raise ValueError("[Error] Unknown backbone option.") + # Pixel_shuffle decoder with single channel output + elif self.cfg["heatmap_decoder"] == "pixel_shuffle_single": + if self.cfg["backbone"] == "lcnn": + decoder = PixelShuffleDecoder( + self.feat_channel, num_upsample=2, output_channel=1) + elif self.cfg["backbone"] == "superpoint": + decoder = PixelShuffleDecoder( + self.feat_channel, num_upsample=3, output_channel=1) + else: + raise ValueError("[Error] Unknown backbone option.") + else: + raise ValueError( + "[Error] The heatmap decoder selection is not supported.") + + return decoder + + def get_descriptor_decoder(self): + """ Get the descriptor decoder. """ + if (not self.cfg["descriptor_decoder"] + in self.supported_descriptor_decoder): + raise ValueError( + "[Error] The descriptor decoder selection is not supported.") + + # SuperPoint descriptor + if self.cfg["descriptor_decoder"] == "superpoint_descriptor": + decoder = SuperpointDescriptor(self.feat_channel) + else: + raise ValueError( + "[Error] The descriptor decoder selection is not supported.") + + return decoder + + +def weight_init(m): + """ Weight initialization function. """ + # Conv2D + if isinstance(m, nn.Conv2d): + init.xavier_normal_(m.weight.data) + if m.bias is not None: + init.normal_(m.bias.data) + # Batchnorm + elif isinstance(m, nn.BatchNorm2d): + init.normal_(m.weight.data, mean=1, std=0.02) + init.constant_(m.bias.data, 0) + # Linear + elif isinstance(m, nn.Linear): + init.xavier_normal_(m.weight.data) + init.normal_(m.bias.data) + else: + pass diff --git a/third_party/SOLD2/sold2/model/nets/__init__.py b/third_party/SOLD2/sold2/model/nets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/model/nets/backbone.py b/third_party/SOLD2/sold2/model/nets/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..71f260aef108c77d54319cab7bc082c3c51112e7 --- /dev/null +++ b/third_party/SOLD2/sold2/model/nets/backbone.py @@ -0,0 +1,65 @@ +import torch +import torch.nn as nn + +from .lcnn_hourglass import MultitaskHead, hg + + +class HourglassBackbone(nn.Module): + """ Hourglass backbone. """ + def __init__(self, input_channel=1, depth=4, num_stacks=2, + num_blocks=1, num_classes=5): + super(HourglassBackbone, self).__init__() + self.head = MultitaskHead + self.net = hg(**{ + "head": self.head, + "depth": depth, + "num_stacks": num_stacks, + "num_blocks": num_blocks, + "num_classes": num_classes, + "input_channels": input_channel + }) + + def forward(self, input_images): + return self.net(input_images)[1] + + +class SuperpointBackbone(nn.Module): + """ SuperPoint backbone. """ + def __init__(self): + super(SuperpointBackbone, self).__init__() + self.relu = torch.nn.ReLU(inplace=True) + self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2) + c1, c2, c3, c4 = 64, 64, 128, 128 + # Shared Encoder. + self.conv1a = torch.nn.Conv2d(1, c1, kernel_size=3, + stride=1, padding=1) + self.conv1b = torch.nn.Conv2d(c1, c1, kernel_size=3, + stride=1, padding=1) + self.conv2a = torch.nn.Conv2d(c1, c2, kernel_size=3, + stride=1, padding=1) + self.conv2b = torch.nn.Conv2d(c2, c2, kernel_size=3, + stride=1, padding=1) + self.conv3a = torch.nn.Conv2d(c2, c3, kernel_size=3, + stride=1, padding=1) + self.conv3b = torch.nn.Conv2d(c3, c3, kernel_size=3, + stride=1, padding=1) + self.conv4a = torch.nn.Conv2d(c3, c4, kernel_size=3, + stride=1, padding=1) + self.conv4b = torch.nn.Conv2d(c4, c4, kernel_size=3, + stride=1, padding=1) + + def forward(self, input_images): + # Shared Encoder. + x = self.relu(self.conv1a(input_images)) + x = self.relu(self.conv1b(x)) + x = self.pool(x) + x = self.relu(self.conv2a(x)) + x = self.relu(self.conv2b(x)) + x = self.pool(x) + x = self.relu(self.conv3a(x)) + x = self.relu(self.conv3b(x)) + x = self.pool(x) + x = self.relu(self.conv4a(x)) + x = self.relu(self.conv4b(x)) + + return x diff --git a/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py b/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed4306fad764efab2c22ede9cae253c9b17d6c2 --- /dev/null +++ b/third_party/SOLD2/sold2/model/nets/descriptor_decoder.py @@ -0,0 +1,19 @@ +import torch +import torch.nn as nn + + +class SuperpointDescriptor(nn.Module): + """ Descriptor decoder based on the SuperPoint arcihtecture. """ + def __init__(self, input_feat_dim=128): + super(SuperpointDescriptor, self).__init__() + self.relu = torch.nn.ReLU(inplace=True) + self.convPa = torch.nn.Conv2d(input_feat_dim, 256, kernel_size=3, + stride=1, padding=1) + self.convPb = torch.nn.Conv2d(256, 128, kernel_size=1, + stride=1, padding=0) + + def forward(self, input_features): + feat = self.relu(self.convPa(input_features)) + semi = self.convPb(feat) + + return semi \ No newline at end of file diff --git a/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py b/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..bd5157ca740c8c7e25f2183b2a3c1fefa813deca --- /dev/null +++ b/third_party/SOLD2/sold2/model/nets/heatmap_decoder.py @@ -0,0 +1,59 @@ +import torch.nn as nn + + +class PixelShuffleDecoder(nn.Module): + """ Pixel shuffle decoder. """ + def __init__(self, input_feat_dim=128, num_upsample=2, output_channel=2): + super(PixelShuffleDecoder, self).__init__() + # Get channel parameters + self.channel_conf = self.get_channel_conf(num_upsample) + + # Define the pixel shuffle + self.pixshuffle = nn.PixelShuffle(2) + + # Process the feature + self.conv_block_lst = [] + # The input block + self.conv_block_lst.append( + nn.Sequential( + nn.Conv2d(input_feat_dim, self.channel_conf[0], + kernel_size=3, stride=1, padding=1), + nn.BatchNorm2d(self.channel_conf[0]), + nn.ReLU(inplace=True) + )) + + # Intermediate block + for channel in self.channel_conf[1:-1]: + self.conv_block_lst.append( + nn.Sequential( + nn.Conv2d(channel, channel, kernel_size=3, + stride=1, padding=1), + nn.BatchNorm2d(channel), + nn.ReLU(inplace=True) + )) + + # Output block + self.conv_block_lst.append( + nn.Conv2d(self.channel_conf[-1], output_channel, + kernel_size=1, stride=1, padding=0) + ) + self.conv_block_lst = nn.ModuleList(self.conv_block_lst) + + # Get num of channels based on number of upsampling. + def get_channel_conf(self, num_upsample): + if num_upsample == 2: + return [256, 64, 16] + elif num_upsample == 3: + return [256, 64, 16, 4] + + def forward(self, input_features): + # Iterate til output block + out = input_features + for block in self.conv_block_lst[:-1]: + out = block(out) + out = self.pixshuffle(out) + + # Output layer + out = self.conv_block_lst[-1](out) + + return out diff --git a/third_party/SOLD2/sold2/model/nets/junction_decoder.py b/third_party/SOLD2/sold2/model/nets/junction_decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d2bb649518896501c784940028a772d688c2b3a7 --- /dev/null +++ b/third_party/SOLD2/sold2/model/nets/junction_decoder.py @@ -0,0 +1,27 @@ +import torch +import torch.nn as nn + + +class SuperpointDecoder(nn.Module): + """ Junction decoder based on the SuperPoint architecture. """ + def __init__(self, input_feat_dim=128, backbone_name="lcnn"): + super(SuperpointDecoder, self).__init__() + self.relu = torch.nn.ReLU(inplace=True) + # Perform strided convolution when using lcnn backbone. + if backbone_name == "lcnn": + self.convPa = torch.nn.Conv2d(input_feat_dim, 256, kernel_size=3, + stride=2, padding=1) + elif backbone_name == "superpoint": + self.convPa = torch.nn.Conv2d(input_feat_dim, 256, kernel_size=3, + stride=1, padding=1) + else: + raise ValueError("[Error] Unknown backbone option.") + + self.convPb = torch.nn.Conv2d(256, 65, kernel_size=1, + stride=1, padding=0) + + def forward(self, input_features): + feat = self.relu(self.convPa(input_features)) + semi = self.convPb(feat) + + return semi \ No newline at end of file diff --git a/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py b/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py new file mode 100644 index 0000000000000000000000000000000000000000..a9dc78eef34e7ee146166b1b66c10070799d63f3 --- /dev/null +++ b/third_party/SOLD2/sold2/model/nets/lcnn_hourglass.py @@ -0,0 +1,226 @@ +""" +Hourglass network, taken from https://github.com/zhou13/lcnn +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + +__all__ = ["HourglassNet", "hg"] + + +class MultitaskHead(nn.Module): + def __init__(self, input_channels, num_class): + super(MultitaskHead, self).__init__() + + m = int(input_channels / 4) + head_size = [[2], [1], [2]] + heads = [] + for output_channels in sum(head_size, []): + heads.append( + nn.Sequential( + nn.Conv2d(input_channels, m, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(m, output_channels, kernel_size=1), + ) + ) + self.heads = nn.ModuleList(heads) + assert num_class == sum(sum(head_size, [])) + + def forward(self, x): + return torch.cat([head(x) for head in self.heads], dim=1) + + +class Bottleneck2D(nn.Module): + expansion = 2 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck2D, self).__init__() + + self.bn1 = nn.BatchNorm2d(inplanes) + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1) + self.bn2 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, + stride=stride, padding=1) + self.bn3 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.bn1(x) + out = self.relu(out) + out = self.conv1(out) + + out = self.bn2(out) + out = self.relu(out) + out = self.conv2(out) + + out = self.bn3(out) + out = self.relu(out) + out = self.conv3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + + return out + + +class Hourglass(nn.Module): + def __init__(self, block, num_blocks, planes, depth): + super(Hourglass, self).__init__() + self.depth = depth + self.block = block + self.hg = self._make_hour_glass(block, num_blocks, planes, depth) + + def _make_residual(self, block, num_blocks, planes): + layers = [] + for i in range(0, num_blocks): + layers.append(block(planes * block.expansion, planes)) + return nn.Sequential(*layers) + + def _make_hour_glass(self, block, num_blocks, planes, depth): + hg = [] + for i in range(depth): + res = [] + for j in range(3): + res.append(self._make_residual(block, num_blocks, planes)) + if i == 0: + res.append(self._make_residual(block, num_blocks, planes)) + hg.append(nn.ModuleList(res)) + return nn.ModuleList(hg) + + def _hour_glass_forward(self, n, x): + up1 = self.hg[n - 1][0](x) + low1 = F.max_pool2d(x, 2, stride=2) + low1 = self.hg[n - 1][1](low1) + + if n > 1: + low2 = self._hour_glass_forward(n - 1, low1) + else: + low2 = self.hg[n - 1][3](low1) + low3 = self.hg[n - 1][2](low2) + # up2 = F.interpolate(low3, scale_factor=2) + up2 = F.interpolate(low3, size=up1.shape[2:]) + out = up1 + up2 + return out + + def forward(self, x): + return self._hour_glass_forward(self.depth, x) + + +class HourglassNet(nn.Module): + """Hourglass model from Newell et al ECCV 2016""" + + def __init__(self, block, head, depth, num_stacks, num_blocks, + num_classes, input_channels): + super(HourglassNet, self).__init__() + + self.inplanes = 64 + self.num_feats = 128 + self.num_stacks = num_stacks + self.conv1 = nn.Conv2d(input_channels, self.inplanes, kernel_size=7, + stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.layer1 = self._make_residual(block, self.inplanes, 1) + self.layer2 = self._make_residual(block, self.inplanes, 1) + self.layer3 = self._make_residual(block, self.num_feats, 1) + self.maxpool = nn.MaxPool2d(2, stride=2) + + # build hourglass modules + ch = self.num_feats * block.expansion + # vpts = [] + hg, res, fc, score, fc_, score_ = [], [], [], [], [], [] + for i in range(num_stacks): + hg.append(Hourglass(block, num_blocks, self.num_feats, depth)) + res.append(self._make_residual(block, self.num_feats, num_blocks)) + fc.append(self._make_fc(ch, ch)) + score.append(head(ch, num_classes)) + # vpts.append(VptsHead(ch)) + # vpts.append(nn.Linear(ch, 9)) + # score.append(nn.Conv2d(ch, num_classes, kernel_size=1)) + # score[i].bias.data[0] += 4.6 + # score[i].bias.data[2] += 4.6 + if i < num_stacks - 1: + fc_.append(nn.Conv2d(ch, ch, kernel_size=1)) + score_.append(nn.Conv2d(num_classes, ch, kernel_size=1)) + self.hg = nn.ModuleList(hg) + self.res = nn.ModuleList(res) + self.fc = nn.ModuleList(fc) + self.score = nn.ModuleList(score) + # self.vpts = nn.ModuleList(vpts) + self.fc_ = nn.ModuleList(fc_) + self.score_ = nn.ModuleList(score_) + + def _make_residual(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + ) + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_fc(self, inplanes, outplanes): + bn = nn.BatchNorm2d(inplanes) + conv = nn.Conv2d(inplanes, outplanes, kernel_size=1) + return nn.Sequential(conv, bn, self.relu) + + def forward(self, x): + out = [] + # out_vps = [] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + + x = self.layer1(x) + x = self.maxpool(x) + x = self.layer2(x) + x = self.layer3(x) + + for i in range(self.num_stacks): + y = self.hg[i](x) + y = self.res[i](y) + y = self.fc[i](y) + score = self.score[i](y) + # pre_vpts = F.adaptive_avg_pool2d(x, (1, 1)) + # pre_vpts = pre_vpts.reshape(-1, 256) + # vpts = self.vpts[i](x) + out.append(score) + # out_vps.append(vpts) + if i < self.num_stacks - 1: + fc_ = self.fc_[i](y) + score_ = self.score_[i](score) + x = x + fc_ + score_ + + return out[::-1], y # , out_vps[::-1] + + +def hg(**kwargs): + model = HourglassNet( + Bottleneck2D, + head=kwargs.get("head", + lambda c_in, c_out: nn.Conv2D(c_in, c_out, 1)), + depth=kwargs["depth"], + num_stacks=kwargs["num_stacks"], + num_blocks=kwargs["num_blocks"], + num_classes=kwargs["num_classes"], + input_channels=kwargs["input_channels"] + ) + return model diff --git a/third_party/SOLD2/sold2/postprocess/__init__.py b/third_party/SOLD2/sold2/postprocess/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/SOLD2/sold2/postprocess/convert_homography_results.py b/third_party/SOLD2/sold2/postprocess/convert_homography_results.py new file mode 100644 index 0000000000000000000000000000000000000000..352eebbde00f6d8a9c20517dccd7024fd0758ffd --- /dev/null +++ b/third_party/SOLD2/sold2/postprocess/convert_homography_results.py @@ -0,0 +1,136 @@ +""" +Convert the aggregation results from the homography adaptation to GT labels. +""" +import sys +sys.path.append("../") +import os +import yaml +import argparse +import numpy as np +import h5py +import torch +from tqdm import tqdm + +from config.project_config import Config as cfg +from model.line_detection import LineSegmentDetectionModule +from model.metrics import super_nms +from misc.train_utils import parse_h5_data + + +def convert_raw_exported_predictions(input_data, grid_size=8, + detect_thresh=1/65, topk=300): + """ Convert the exported junctions and heatmaps predictions + to a standard format. + Arguments: + input_data: the raw data (dict) decoded from the hdf5 dataset + outputs: dict containing required entries including: + junctions_pred: Nx2 ndarray containing nms junction predictions. + heatmap_pred: HxW ndarray containing predicted heatmaps + valid_mask: HxW ndarray containing the valid mask + """ + # Check the input_data is from (1) single prediction, + # or (2) homography adaptation. + # Homography adaptation raw predictions + if (("junc_prob_mean" in input_data.keys()) + and ("heatmap_prob_mean" in input_data.keys())): + # Get the junction predictions and convert if to Nx2 format + junc_prob = input_data["junc_prob_mean"] + junc_pred_np = junc_prob[None, ...] + junc_pred_np_nms = super_nms(junc_pred_np, grid_size, + detect_thresh, topk) + junctions = np.where(junc_pred_np_nms.squeeze()) + junc_points_pred = np.concatenate([junctions[0][..., None], + junctions[1][..., None]], axis=-1) + + # Get the heatmap predictions + heatmap_pred = input_data["heatmap_prob_mean"].squeeze() + valid_mask = np.ones(heatmap_pred.shape, dtype=np.int32) + + # Single predictions + else: + # Get the junction point predictions and convert to Nx2 format + junc_points_pred = np.where(input_data["junc_pred_nms"]) + junc_points_pred = np.concatenate( + [junc_points_pred[0][..., None], + junc_points_pred[1][..., None]], axis=-1) + + # Get the heatmap predictions + heatmap_pred = input_data["heatmap_pred"] + valid_mask = input_data["valid_mask"] + + return { + "junctions_pred": junc_points_pred, + "heatmap_pred": heatmap_pred, + "valid_mask": valid_mask + } + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_dataset", type=str, + help="Name of the exported dataset.") + parser.add_argument("output_dataset", type=str, + help="Name of the output dataset.") + parser.add_argument("config", type=str, + help="Path to the model config.") + args = parser.parse_args() + + # Define the path to the input exported dataset + exported_dataset_path = os.path.join(cfg.export_dataroot, + args.input_dataset) + if not os.path.exists(exported_dataset_path): + raise ValueError("Missing input dataset: " + exported_dataset_path) + exported_dataset = h5py.File(exported_dataset_path, "r") + + # Define the output path for the results + output_dataset_path = os.path.join(cfg.export_dataroot, + args.output_dataset) + + device = torch.device("cuda") + nms_device = torch.device("cuda") + + # Read the config file + if not os.path.exists(args.config): + raise ValueError("Missing config file: " + args.config) + with open(args.config, "r") as f: + config = yaml.safe_load(f) + model_cfg = config["model_cfg"] + line_detector_cfg = config["line_detector_cfg"] + + # Initialize the line detection module + line_detector = LineSegmentDetectionModule(**line_detector_cfg) + + # Iterate through all the dataset keys + with h5py.File(output_dataset_path, "w") as output_dataset: + for idx, output_key in enumerate(tqdm(list(exported_dataset.keys()), + ascii=True)): + # Get the data + data = parse_h5_data(exported_dataset[output_key]) + + # Preprocess the data + converted_data = convert_raw_exported_predictions( + data, grid_size=model_cfg["grid_size"], + detect_thresh=model_cfg["detection_thresh"]) + junctions_pred_raw = converted_data["junctions_pred"] + heatmap_pred = converted_data["heatmap_pred"] + valid_mask = converted_data["valid_mask"] + + line_map_pred, junctions_pred, heatmap_pred = line_detector.detect( + junctions_pred_raw, heatmap_pred, device=device) + if isinstance(line_map_pred, torch.Tensor): + line_map_pred = line_map_pred.cpu().numpy() + if isinstance(junctions_pred, torch.Tensor): + junctions_pred = junctions_pred.cpu().numpy() + if isinstance(heatmap_pred, torch.Tensor): + heatmap_pred = heatmap_pred.cpu().numpy() + + output_data = {"junctions": junctions_pred, + "line_map": line_map_pred} + + # Record it to the h5 dataset + f_group = output_dataset.create_group(output_key) + + # Store data + for key, output_data in output_data.items(): + f_group.create_dataset(key, data=output_data, + compression="gzip") diff --git a/third_party/SOLD2/sold2/train.py b/third_party/SOLD2/sold2/train.py new file mode 100644 index 0000000000000000000000000000000000000000..2064e00e6d192f9202f011c3626d6f53c4fe6270 --- /dev/null +++ b/third_party/SOLD2/sold2/train.py @@ -0,0 +1,752 @@ +""" +This file implements the training process and all the summaries +""" +import os +import numpy as np +import cv2 +import torch +from torch.nn.functional import pixel_shuffle, softmax +from torch.utils.data import DataLoader +import torch.utils.data.dataloader as torch_loader +from tensorboardX import SummaryWriter + +from .dataset.dataset_util import get_dataset +from .model.model_util import get_model +from .model.loss import TotalLoss, get_loss_and_weights +from .model.metrics import AverageMeter, Metrics, super_nms +from .model.lr_scheduler import get_lr_scheduler +from .misc.train_utils import (convert_image, get_latest_checkpoint, + remove_old_checkpoints) + + +def customized_collate_fn(batch): + """ Customized collate_fn. """ + batch_keys = ["image", "junction_map", "heatmap", "valid_mask"] + list_keys = ["junctions", "line_map"] + + outputs = {} + for key in batch_keys: + outputs[key] = torch_loader.default_collate([b[key] for b in batch]) + for key in list_keys: + outputs[key] = [b[key] for b in batch] + + return outputs + + +def restore_weights(model, state_dict, strict=True): + """ Restore weights in compatible mode. """ + # Try to directly load state dict + try: + model.load_state_dict(state_dict, strict=strict) + # Deal with some version compatibility issue (catch version incompatible) + except: + err = model.load_state_dict(state_dict, strict=False) + + # missing keys are those in model but not in state_dict + missing_keys = err.missing_keys + # Unexpected keys are those in state_dict but not in model + unexpected_keys = err.unexpected_keys + + # Load mismatched keys manually + model_dict = model.state_dict() + for idx, key in enumerate(missing_keys): + dict_keys = [_ for _ in unexpected_keys if not "tracked" in _] + model_dict[key] = state_dict[dict_keys[idx]] + model.load_state_dict(model_dict) + + return model + + +def train_net(args, dataset_cfg, model_cfg, output_path): + """ Main training function. """ + # Add some version compatibility check + if model_cfg.get("weighting_policy") is None: + # Default to static + model_cfg["weighting_policy"] = "static" + + # Get the train, val, test config + train_cfg = model_cfg["train"] + test_cfg = model_cfg["test"] + + # Create train and test dataset + print("\t Initializing dataset...") + train_dataset, train_collate_fn = get_dataset("train", dataset_cfg) + test_dataset, test_collate_fn = get_dataset("test", dataset_cfg) + + # Create the dataloader + train_loader = DataLoader(train_dataset, + batch_size=train_cfg["batch_size"], + num_workers=8, + shuffle=True, pin_memory=True, + collate_fn=train_collate_fn) + test_loader = DataLoader(test_dataset, + batch_size=test_cfg.get("batch_size", 1), + num_workers=test_cfg.get("num_workers", 1), + shuffle=False, pin_memory=False, + collate_fn=test_collate_fn) + print("\t Successfully intialized dataloaders.") + + + # Get the loss function and weight first + loss_funcs, loss_weights = get_loss_and_weights(model_cfg) + + # If resume. + if args.resume: + # Create model and load the state dict + checkpoint = get_latest_checkpoint(args.resume_path, + args.checkpoint_name) + model = get_model(model_cfg, loss_weights) + model = restore_weights(model, checkpoint["model_state_dict"]) + model = model.cuda() + optimizer = torch.optim.Adam( + [{"params": model.parameters(), + "initial_lr": model_cfg["learning_rate"]}], + model_cfg["learning_rate"], + amsgrad=True) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + # Optionally get the learning rate scheduler + scheduler = get_lr_scheduler( + lr_decay=model_cfg.get("lr_decay", False), + lr_decay_cfg=model_cfg.get("lr_decay_cfg", None), + optimizer=optimizer) + # If we start to use learning rate scheduler from the middle + if ((scheduler is not None) + and (checkpoint.get("scheduler_state_dict", None) is not None)): + scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) + start_epoch = checkpoint["epoch"] + 1 + # Initialize all the components. + else: + # Create model and optimizer + model = get_model(model_cfg, loss_weights) + # Optionally get the pretrained wieghts + if args.pretrained: + print("\t [Debug] Loading pretrained weights...") + checkpoint = get_latest_checkpoint(args.pretrained_path, + args.checkpoint_name) + # If auto weighting restore from non-auto weighting + model = restore_weights(model, checkpoint["model_state_dict"], + strict=False) + print("\t [Debug] Finished loading pretrained weights!") + + model = model.cuda() + optimizer = torch.optim.Adam( + [{"params": model.parameters(), + "initial_lr": model_cfg["learning_rate"]}], + model_cfg["learning_rate"], + amsgrad=True) + # Optionally get the learning rate scheduler + scheduler = get_lr_scheduler( + lr_decay=model_cfg.get("lr_decay", False), + lr_decay_cfg=model_cfg.get("lr_decay_cfg", None), + optimizer=optimizer) + start_epoch = 0 + + print("\t Successfully initialized model") + + # Define the total loss + policy = model_cfg.get("weighting_policy", "static") + loss_func = TotalLoss(loss_funcs, loss_weights, policy).cuda() + if "descriptor_decoder" in model_cfg: + metric_func = Metrics(model_cfg["detection_thresh"], + model_cfg["prob_thresh"], + model_cfg["descriptor_loss_cfg"]["grid_size"], + desc_metric_lst='all') + else: + metric_func = Metrics(model_cfg["detection_thresh"], + model_cfg["prob_thresh"], + model_cfg["grid_size"]) + + # Define the summary writer + logdir = os.path.join(output_path, "log") + writer = SummaryWriter(logdir=logdir) + + # Start the training loop + for epoch in range(start_epoch, model_cfg["epochs"]): + # Record the learning rate + current_lr = optimizer.state_dict()["param_groups"][0]["lr"] + writer.add_scalar("LR/lr", current_lr, epoch) + + # Train for one epochs + print("\n\n================== Training ====================") + train_single_epoch( + model=model, + model_cfg=model_cfg, + optimizer=optimizer, + loss_func=loss_func, + metric_func=metric_func, + train_loader=train_loader, + writer=writer, + epoch=epoch) + + # Do the validation + print("\n\n================== Validation ==================") + validate( + model=model, + model_cfg=model_cfg, + loss_func=loss_func, + metric_func=metric_func, + val_loader=test_loader, + writer=writer, + epoch=epoch) + + # Update the scheduler + if scheduler is not None: + scheduler.step() + + # Save checkpoints + file_name = os.path.join(output_path, + "checkpoint-epoch%03d-end.tar"%(epoch)) + print("[Info] Saving checkpoint %s ..." % file_name) + save_dict = { + "epoch": epoch, + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "model_cfg": model_cfg} + if scheduler is not None: + save_dict.update({"scheduler_state_dict": scheduler.state_dict()}) + torch.save(save_dict, file_name) + + # Remove the outdated checkpoints + remove_old_checkpoints(output_path, model_cfg.get("max_ckpt", 15)) + + +def train_single_epoch(model, model_cfg, optimizer, loss_func, metric_func, + train_loader, writer, epoch): + """ Train for one epoch. """ + # Switch the model to training mode + model.train() + + # Initialize the average meter + compute_descriptors = loss_func.compute_descriptors + if compute_descriptors: + average_meter = AverageMeter(is_training=True, desc_metric_lst='all') + else: + average_meter = AverageMeter(is_training=True) + + # The training loop + for idx, data in enumerate(train_loader): + if compute_descriptors: + junc_map = data["ref_junction_map"].cuda() + junc_map2 = data["target_junction_map"].cuda() + heatmap = data["ref_heatmap"].cuda() + heatmap2 = data["target_heatmap"].cuda() + line_points = data["ref_line_points"].cuda() + line_points2 = data["target_line_points"].cuda() + line_indices = data["ref_line_indices"].cuda() + valid_mask = data["ref_valid_mask"].cuda() + valid_mask2 = data["target_valid_mask"].cuda() + input_images = data["ref_image"].cuda() + input_images2 = data["target_image"].cuda() + + # Run the forward pass + outputs = model(input_images) + outputs2 = model(input_images2) + + # Compute losses + losses = loss_func.forward_descriptors( + outputs["junctions"], outputs2["junctions"], + junc_map, junc_map2, outputs["heatmap"], outputs2["heatmap"], + heatmap, heatmap2, line_points, line_points2, + line_indices, outputs['descriptors'], outputs2['descriptors'], + epoch, valid_mask, valid_mask2) + else: + junc_map = data["junction_map"].cuda() + heatmap = data["heatmap"].cuda() + valid_mask = data["valid_mask"].cuda() + input_images = data["image"].cuda() + + # Run the forward pass + outputs = model(input_images) + + # Compute losses + losses = loss_func( + outputs["junctions"], junc_map, + outputs["heatmap"], heatmap, + valid_mask) + + total_loss = losses["total_loss"] + + # Update the model + optimizer.zero_grad() + total_loss.backward() + optimizer.step() + + # Compute the global step + global_step = epoch * len(train_loader) + idx + ############## Measure the metric error ######################### + # Only do this when needed + if (((idx % model_cfg["disp_freq"]) == 0) + or ((idx % model_cfg["summary_freq"]) == 0)): + junc_np = convert_junc_predictions( + outputs["junctions"], model_cfg["grid_size"], + model_cfg["detection_thresh"], 300) + junc_map_np = junc_map.cpu().numpy().transpose(0, 2, 3, 1) + + # Always fetch only one channel (compatible with L1, L2, and CE) + if outputs["heatmap"].shape[1] == 2: + heatmap_np = softmax(outputs["heatmap"].detach(), + dim=1).cpu().numpy() + heatmap_np = heatmap_np.transpose(0, 2, 3, 1)[:, :, :, 1:] + else: + heatmap_np = torch.sigmoid(outputs["heatmap"].detach()) + heatmap_np = heatmap_np.cpu().numpy().transpose(0, 2, 3, 1) + + heatmap_gt_np = heatmap.cpu().numpy().transpose(0, 2, 3, 1) + valid_mask_np = valid_mask.cpu().numpy().transpose(0, 2, 3, 1) + + # Evaluate metric results + if compute_descriptors: + metric_func.evaluate( + junc_np["junc_pred"], junc_np["junc_pred_nms"], + junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np, + line_points, line_points2, outputs["descriptors"], + outputs2["descriptors"], line_indices) + else: + metric_func.evaluate( + junc_np["junc_pred"], junc_np["junc_pred_nms"], + junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np) + # Update average meter + junc_loss = losses["junc_loss"].item() + heatmap_loss = losses["heatmap_loss"].item() + loss_dict = { + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss, + "total_loss": total_loss.item()} + if compute_descriptors: + descriptor_loss = losses["descriptor_loss"].item() + loss_dict["descriptor_loss"] = losses["descriptor_loss"].item() + + average_meter.update(metric_func, loss_dict, num_samples=junc_map.shape[0]) + + # Display the progress + if (idx % model_cfg["disp_freq"]) == 0: + results = metric_func.metric_results + average = average_meter.average() + # Get gpu memory usage in GB + gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3) + if compute_descriptors: + print("Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f), gpu_mem=%.4fGB" + % (epoch, model_cfg["epochs"], idx, len(train_loader), + total_loss.item(), average["total_loss"], junc_loss, + average["junc_loss"], heatmap_loss, + average["heatmap_loss"], descriptor_loss, + average["descriptor_loss"], gpu_mem_usage)) + else: + print("Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), gpu_mem=%.4fGB" + % (epoch, model_cfg["epochs"], idx, len(train_loader), + total_loss.item(), average["total_loss"], + junc_loss, average["junc_loss"], heatmap_loss, + average["heatmap_loss"], gpu_mem_usage)) + print("\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)" + % (results["junc_precision"], average["junc_precision"], + results["junc_recall"], average["junc_recall"])) + print("\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)" + % (results["junc_precision_nms"], + average["junc_precision_nms"], + results["junc_recall_nms"], average["junc_recall_nms"])) + print("\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)" + %(results["heatmap_precision"], + average["heatmap_precision"], + results["heatmap_recall"], average["heatmap_recall"])) + if compute_descriptors: + print("\t Descriptors matching score=%.4f (%.4f)" + %(results["matching_score"], average["matching_score"])) + + # Record summaries + if (idx % model_cfg["summary_freq"]) == 0: + results = metric_func.metric_results + average = average_meter.average() + # Add the shared losses + scalar_summaries = { + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss, + "total_loss": total_loss.detach().cpu().numpy(), + "metrics": results, + "average": average} + # Add descriptor terms + if compute_descriptors: + scalar_summaries["descriptor_loss"] = descriptor_loss + scalar_summaries["w_desc"] = losses["w_desc"] + + # Add weighting terms (even for static terms) + scalar_summaries["w_junc"] = losses["w_junc"] + scalar_summaries["w_heatmap"] = losses["w_heatmap"] + scalar_summaries["reg_loss"] = losses["reg_loss"].item() + + num_images = 3 + junc_pred_binary = (junc_np["junc_pred"][:num_images, ...] + > model_cfg["detection_thresh"]) + junc_pred_nms_binary = (junc_np["junc_pred_nms"][:num_images, ...] + > model_cfg["detection_thresh"]) + image_summaries = { + "image": input_images.cpu().numpy()[:num_images, ...], + "valid_mask": valid_mask_np[:num_images, ...], + "junc_map_pred": junc_pred_binary, + "junc_map_pred_nms": junc_pred_nms_binary, + "junc_map_gt": junc_map_np[:num_images, ...], + "junc_prob_map": junc_np["junc_prob"][:num_images, ...], + "heatmap_pred": heatmap_np[:num_images, ...], + "heatmap_gt": heatmap_gt_np[:num_images, ...]} + # Record the training summary + record_train_summaries( + writer, global_step, scalars=scalar_summaries, + images=image_summaries) + + +def validate(model, model_cfg, loss_func, metric_func, val_loader, writer, epoch): + """ Validation. """ + # Switch the model to eval mode + model.eval() + + # Initialize the average meter + compute_descriptors = loss_func.compute_descriptors + if compute_descriptors: + average_meter = AverageMeter(is_training=True, desc_metric_lst='all') + else: + average_meter = AverageMeter(is_training=True) + + # The validation loop + for idx, data in enumerate(val_loader): + if compute_descriptors: + junc_map = data["ref_junction_map"].cuda() + junc_map2 = data["target_junction_map"].cuda() + heatmap = data["ref_heatmap"].cuda() + heatmap2 = data["target_heatmap"].cuda() + line_points = data["ref_line_points"].cuda() + line_points2 = data["target_line_points"].cuda() + line_indices = data["ref_line_indices"].cuda() + valid_mask = data["ref_valid_mask"].cuda() + valid_mask2 = data["target_valid_mask"].cuda() + input_images = data["ref_image"].cuda() + input_images2 = data["target_image"].cuda() + + # Run the forward pass + with torch.no_grad(): + outputs = model(input_images) + outputs2 = model(input_images2) + + # Compute losses + losses = loss_func.forward_descriptors( + outputs["junctions"], outputs2["junctions"], + junc_map, junc_map2, outputs["heatmap"], + outputs2["heatmap"], heatmap, heatmap2, line_points, + line_points2, line_indices, outputs['descriptors'], + outputs2['descriptors'], epoch, valid_mask, valid_mask2) + else: + junc_map = data["junction_map"].cuda() + heatmap = data["heatmap"].cuda() + valid_mask = data["valid_mask"].cuda() + input_images = data["image"].cuda() + + # Run the forward pass + with torch.no_grad(): + outputs = model(input_images) + + # Compute losses + losses = loss_func( + outputs["junctions"], junc_map, + outputs["heatmap"], heatmap, + valid_mask) + total_loss = losses["total_loss"] + + ############## Measure the metric error ######################### + junc_np = convert_junc_predictions( + outputs["junctions"], model_cfg["grid_size"], + model_cfg["detection_thresh"], 300) + junc_map_np = junc_map.cpu().numpy().transpose(0, 2, 3, 1) + # Always fetch only one channel (compatible with L1, L2, and CE) + if outputs["heatmap"].shape[1] == 2: + heatmap_np = softmax(outputs["heatmap"].detach(), + dim=1).cpu().numpy().transpose(0, 2, 3, 1) + heatmap_np = heatmap_np[:, :, :, 1:] + else: + heatmap_np = torch.sigmoid(outputs["heatmap"].detach()) + heatmap_np = heatmap_np.cpu().numpy().transpose(0, 2, 3, 1) + + + heatmap_gt_np = heatmap.cpu().numpy().transpose(0, 2, 3, 1) + valid_mask_np = valid_mask.cpu().numpy().transpose(0, 2, 3, 1) + + # Evaluate metric results + if compute_descriptors: + metric_func.evaluate( + junc_np["junc_pred"], junc_np["junc_pred_nms"], + junc_map_np, heatmap_np, heatmap_gt_np, valid_mask_np, + line_points, line_points2, outputs["descriptors"], + outputs2["descriptors"], line_indices) + else: + metric_func.evaluate( + junc_np["junc_pred"], junc_np["junc_pred_nms"], junc_map_np, + heatmap_np, heatmap_gt_np, valid_mask_np) + # Update average meter + junc_loss = losses["junc_loss"].item() + heatmap_loss = losses["heatmap_loss"].item() + loss_dict = { + "junc_loss": junc_loss, + "heatmap_loss": heatmap_loss, + "total_loss": total_loss.item()} + if compute_descriptors: + descriptor_loss = losses["descriptor_loss"].item() + loss_dict["descriptor_loss"] = losses["descriptor_loss"].item() + average_meter.update(metric_func, loss_dict, num_samples=junc_map.shape[0]) + + # Display the progress + if (idx % model_cfg["disp_freq"]) == 0: + results = metric_func.metric_results + average = average_meter.average() + if compute_descriptors: + print("Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f)" + % (idx, len(val_loader), + total_loss.item(), average["total_loss"], + junc_loss, average["junc_loss"], + heatmap_loss, average["heatmap_loss"], + descriptor_loss, average["descriptor_loss"])) + else: + print("Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f)" + % (idx, len(val_loader), + total_loss.item(), average["total_loss"], + junc_loss, average["junc_loss"], + heatmap_loss, average["heatmap_loss"])) + print("\t Junction precision=%.4f (%.4f) / recall=%.4f (%.4f)" + % (results["junc_precision"], average["junc_precision"], + results["junc_recall"], average["junc_recall"])) + print("\t Junction nms precision=%.4f (%.4f) / recall=%.4f (%.4f)" + % (results["junc_precision_nms"], + average["junc_precision_nms"], + results["junc_recall_nms"], average["junc_recall_nms"])) + print("\t Heatmap precision=%.4f (%.4f) / recall=%.4f (%.4f)" + % (results["heatmap_precision"], + average["heatmap_precision"], + results["heatmap_recall"], average["heatmap_recall"])) + if compute_descriptors: + print("\t Descriptors matching score=%.4f (%.4f)" + %(results["matching_score"], average["matching_score"])) + + # Record summaries + average = average_meter.average() + scalar_summaries = {"average": average} + # Record the training summary + record_test_summaries(writer, epoch, scalar_summaries) + + +def convert_junc_predictions(predictions, grid_size, + detect_thresh=1/65, topk=300): + """ Convert torch predictions to numpy arrays for evaluation. """ + # Convert to probability outputs first + junc_prob = softmax(predictions.detach(), dim=1).cpu() + junc_pred = junc_prob[:, :-1, :, :] + + junc_prob_np = junc_prob.numpy().transpose(0, 2, 3, 1)[:, :, :, :-1] + junc_prob_np = np.sum(junc_prob_np, axis=-1) + junc_pred_np = pixel_shuffle( + junc_pred, grid_size).cpu().numpy().transpose(0, 2, 3, 1) + junc_pred_np_nms = super_nms(junc_pred_np, grid_size, detect_thresh, topk) + junc_pred_np = junc_pred_np.squeeze(-1) + + return {"junc_pred": junc_pred_np, "junc_pred_nms": junc_pred_np_nms, + "junc_prob": junc_prob_np} + + +def record_train_summaries(writer, global_step, scalars, images): + """ Record training summaries. """ + # Record the scalar summaries + results = scalars["metrics"] + average = scalars["average"] + + # GPU memory part + # Get gpu memory usage in GB + gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3) + writer.add_scalar("GPU/GPU_memory_usage", gpu_mem_usage, global_step) + + # Loss part + writer.add_scalar("Train_loss/junc_loss", scalars["junc_loss"], + global_step) + writer.add_scalar("Train_loss/heatmap_loss", scalars["heatmap_loss"], + global_step) + writer.add_scalar("Train_loss/total_loss", scalars["total_loss"], + global_step) + # Add regularization loss + if "reg_loss" in scalars.keys(): + writer.add_scalar("Train_loss/reg_loss", scalars["reg_loss"], + global_step) + # Add descriptor loss + if "descriptor_loss" in scalars.keys(): + key = "descriptor_loss" + writer.add_scalar("Train_loss/%s"%(key), scalars[key], global_step) + writer.add_scalar("Train_loss_average/%s"%(key), average[key], + global_step) + + # Record weighting + for key in scalars.keys(): + if "w_" in key: + writer.add_scalar("Train_weight/%s"%(key), scalars[key], + global_step) + + # Smoothed loss + writer.add_scalar("Train_loss_average/junc_loss", average["junc_loss"], + global_step) + writer.add_scalar("Train_loss_average/heatmap_loss", + average["heatmap_loss"], global_step) + writer.add_scalar("Train_loss_average/total_loss", average["total_loss"], + global_step) + # Add smoothed descriptor loss + if "descriptor_loss" in average.keys(): + writer.add_scalar("Train_loss_average/descriptor_loss", + average["descriptor_loss"], global_step) + + # Metrics part + writer.add_scalar("Train_metrics/junc_precision", + results["junc_precision"], global_step) + writer.add_scalar("Train_metrics/junc_precision_nms", + results["junc_precision_nms"], global_step) + writer.add_scalar("Train_metrics/junc_recall", + results["junc_recall"], global_step) + writer.add_scalar("Train_metrics/junc_recall_nms", + results["junc_recall_nms"], global_step) + writer.add_scalar("Train_metrics/heatmap_precision", + results["heatmap_precision"], global_step) + writer.add_scalar("Train_metrics/heatmap_recall", + results["heatmap_recall"], global_step) + # Add descriptor metric + if "matching_score" in results.keys(): + writer.add_scalar("Train_metrics/matching_score", + results["matching_score"], global_step) + + # Average part + writer.add_scalar("Train_metrics_average/junc_precision", + average["junc_precision"], global_step) + writer.add_scalar("Train_metrics_average/junc_precision_nms", + average["junc_precision_nms"], global_step) + writer.add_scalar("Train_metrics_average/junc_recall", + average["junc_recall"], global_step) + writer.add_scalar("Train_metrics_average/junc_recall_nms", + average["junc_recall_nms"], global_step) + writer.add_scalar("Train_metrics_average/heatmap_precision", + average["heatmap_precision"], global_step) + writer.add_scalar("Train_metrics_average/heatmap_recall", + average["heatmap_recall"], global_step) + # Add smoothed descriptor metric + if "matching_score" in average.keys(): + writer.add_scalar("Train_metrics_average/matching_score", + average["matching_score"], global_step) + + # Record the image summary + # Image part + image_tensor = convert_image(images["image"], 1) + valid_masks = convert_image(images["valid_mask"], -1) + writer.add_images("Train/images", image_tensor, global_step, + dataformats="NCHW") + writer.add_images("Train/valid_map", valid_masks, global_step, + dataformats="NHWC") + + # Heatmap part + writer.add_images("Train/heatmap_gt", + convert_image(images["heatmap_gt"], -1), global_step, + dataformats="NHWC") + writer.add_images("Train/heatmap_pred", + convert_image(images["heatmap_pred"], -1), global_step, + dataformats="NHWC") + + # Junction prediction part + junc_plots = plot_junction_detection( + image_tensor, images["junc_map_pred"], + images["junc_map_pred_nms"], images["junc_map_gt"]) + writer.add_images("Train/junc_gt", junc_plots["junc_gt_plot"] / 255., + global_step, dataformats="NHWC") + writer.add_images("Train/junc_pred", junc_plots["junc_pred_plot"] / 255., + global_step, dataformats="NHWC") + writer.add_images("Train/junc_pred_nms", + junc_plots["junc_pred_nms_plot"] / 255., global_step, + dataformats="NHWC") + writer.add_images( + "Train/junc_prob_map", + convert_image(images["junc_prob_map"][..., None], axis=-1), + global_step, dataformats="NHWC") + + +def record_test_summaries(writer, epoch, scalars): + """ Record testing summaries. """ + average = scalars["average"] + + # Average loss + writer.add_scalar("Val_loss/junc_loss", average["junc_loss"], epoch) + writer.add_scalar("Val_loss/heatmap_loss", average["heatmap_loss"], epoch) + writer.add_scalar("Val_loss/total_loss", average["total_loss"], epoch) + # Add descriptor loss + if "descriptor_loss" in average.keys(): + key = "descriptor_loss" + writer.add_scalar("Val_loss/%s"%(key), average[key], epoch) + + # Average metrics + writer.add_scalar("Val_metrics/junc_precision", average["junc_precision"], + epoch) + writer.add_scalar("Val_metrics/junc_precision_nms", + average["junc_precision_nms"], epoch) + writer.add_scalar("Val_metrics/junc_recall", + average["junc_recall"], epoch) + writer.add_scalar("Val_metrics/junc_recall_nms", + average["junc_recall_nms"], epoch) + writer.add_scalar("Val_metrics/heatmap_precision", + average["heatmap_precision"], epoch) + writer.add_scalar("Val_metrics/heatmap_recall", + average["heatmap_recall"], epoch) + # Add descriptor metric + if "matching_score" in average.keys(): + writer.add_scalar("Val_metrics/matching_score", + average["matching_score"], epoch) + + +def plot_junction_detection(image_tensor, junc_pred_tensor, + junc_pred_nms_tensor, junc_gt_tensor): + """ Plot the junction points on images. """ + # Get the batch_size + batch_size = image_tensor.shape[0] + + # Process through batch dimension + junc_pred_lst = [] + junc_pred_nms_lst = [] + junc_gt_lst = [] + for i in range(batch_size): + # Convert image to 255 uint8 + image = (image_tensor[i, :, :, :] + * 255.).astype(np.uint8).transpose(1,2,0) + + # Plot groundtruth onto image + junc_gt = junc_gt_tensor[i, ...] + coord_gt = np.where(junc_gt.squeeze() > 0) + points_gt = np.concatenate((coord_gt[0][..., None], + coord_gt[1][..., None]), + axis=1) + plot_gt = image.copy() + for id in range(points_gt.shape[0]): + cv2.circle(plot_gt, tuple(np.flip(points_gt[id, :])), 3, + color=(255, 0, 0), thickness=2) + junc_gt_lst.append(plot_gt[None, ...]) + + # Plot junc_pred + junc_pred = junc_pred_tensor[i, ...] + coord_pred = np.where(junc_pred > 0) + points_pred = np.concatenate((coord_pred[0][..., None], + coord_pred[1][..., None]), + axis=1) + plot_pred = image.copy() + for id in range(points_pred.shape[0]): + cv2.circle(plot_pred, tuple(np.flip(points_pred[id, :])), 3, + color=(0, 255, 0), thickness=2) + junc_pred_lst.append(plot_pred[None, ...]) + + # Plot junc_pred_nms + junc_pred_nms = junc_pred_nms_tensor[i, ...] + coord_pred_nms = np.where(junc_pred_nms > 0) + points_pred_nms = np.concatenate((coord_pred_nms[0][..., None], + coord_pred_nms[1][..., None]), + axis=1) + plot_pred_nms = image.copy() + for id in range(points_pred_nms.shape[0]): + cv2.circle(plot_pred_nms, tuple(np.flip(points_pred_nms[id, :])), + 3, color=(0, 255, 0), thickness=2) + junc_pred_nms_lst.append(plot_pred_nms[None, ...]) + + return {"junc_gt_plot": np.concatenate(junc_gt_lst, axis=0), + "junc_pred_plot": np.concatenate(junc_pred_lst, axis=0), + "junc_pred_nms_plot": np.concatenate(junc_pred_nms_lst, axis=0)} diff --git a/third_party/TopicFM/.github/workflows/sync.yml b/third_party/TopicFM/.github/workflows/sync.yml new file mode 100644 index 0000000000000000000000000000000000000000..efbf881c64bdeac6916473e4391e23e87af5b69d --- /dev/null +++ b/third_party/TopicFM/.github/workflows/sync.yml @@ -0,0 +1,39 @@ +name: Upstream Sync + +permissions: + contents: write + +on: + schedule: + - cron: "0 0 * * *" # every day + workflow_dispatch: + +jobs: + sync_latest_from_upstream: + name: Sync latest commits from upstream repo + runs-on: ubuntu-latest + if: ${{ github.event.repository.fork }} + + steps: + # Step 1: run a standard checkout action + - name: Checkout target repo + uses: actions/checkout@v3 + + # Step 2: run the sync action + - name: Sync upstream changes + id: sync + uses: aormsby/Fork-Sync-With-Upstream-action@v3.4 + with: + upstream_sync_repo: TruongKhang/TopicFM + upstream_sync_branch: main + target_sync_branch: main + target_repo_token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, no need to set + + # Set test_mode true to run tests instead of the true action!! + test_mode: false + + - name: Sync check + if: failure() + run: | + echo "::error::Due to insufficient permissions, synchronization failed (as expected). Please go to the repository homepage and manually perform [Sync fork]." + exit 1 diff --git a/third_party/TopicFM/.gitignore b/third_party/TopicFM/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..7ed07d081a940b02ce92ceb6aa8fb66925e32224 --- /dev/null +++ b/third_party/TopicFM/.gitignore @@ -0,0 +1,130 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ +.idea/ diff --git a/third_party/TopicFM/.gitmodules b/third_party/TopicFM/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..313403ddfa5b06a038a75467352c3821a19a78c4 --- /dev/null +++ b/third_party/TopicFM/.gitmodules @@ -0,0 +1,3 @@ +# [submodule "third_party/loftr"] +# path = third_party/loftr +# url = https://github.com/zju3dv/git diff --git a/third_party/TopicFM/LICENSE b/third_party/TopicFM/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/third_party/TopicFM/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/third_party/TopicFM/README.md b/third_party/TopicFM/README.md new file mode 100644 index 0000000000000000000000000000000000000000..be60b38c8c265deeef5d7827d9fae4f65e842868 --- /dev/null +++ b/third_party/TopicFM/README.md @@ -0,0 +1,130 @@ +# Submodule used in [hloc](https://github.com/Vincentqyw/Hierarchical-Localization) toolbox + +# [AAAI-23] TopicFM: Robust and Interpretable Topic-Assisted Feature Matching + +Our method first inferred the latent topics (high-level context information) for each image and then use them to explicitly learn robust feature representation for the matching task. Please check out the details in [our paper](https://arxiv.org/abs/2207.00328) + +![Alt Text](demo/topicfm.gif) + +**Overall Architecture:** + +![Alt Text](demo/architecture_v4.png) + +## TODO List + +- [x] Release training and evaluation code on MegaDepth and ScanNet +- [x] Evaluation on HPatches, Aachen Day&Night, and InLoc +- [x] Evaluation for Image Matching Challenge + +## Requirements + +All experiments in this paper are implemented on the Ubuntu environment +with a NVIDIA driver of at least 430.64 and CUDA 10.1. + +First, create a virtual environment by anaconda as follows, + + conda create -n topicfm python=3.8 + conda activate topicfm + conda install pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=10.1 -c pytorch + pip install -r requirements.txt + # using pip to install any missing packages + +## Data Preparation + +The proposed method is trained on the MegaDepth dataset and evaluated on the MegaDepth test, ScanNet, HPatches, Aachen Day and Night (v1.1), and InLoc dataset. +All these datasets are large, so we cannot include them in this code. +The following descriptions help download these datasets. + +### MegaDepth + +This dataset is used for both training and evaluation (Li and Snavely 2018). +To use this dataset with our code, please follow the [instruction of LoFTR](https://github.com/zju3dv/LoFTR/blob/master/docs/TRAINING.md) (Sun et al. 2021) + +### ScanNet +We only use 1500 image pairs of ScanNet (Dai et al. 2017) for evaluation. +Please download and prepare [test data](https://drive.google.com/drive/folders/1DOcOPZb3-5cWxLqn256AhwUVjBPifhuf) of ScanNet +provided by [LoFTR](https://github.com/zju3dv/LoFTR/blob/master/docs/TRAINING.md). + +## Training + +To train our model, we recommend to use GPUs card as much as possible, and each GPU should be at least 12GB. +In our settings, we train on 4 GPUs, each of which is 12GB. +Please setup your hardware environment in `scripts/reproduce_train/outdoor.sh`. +And then run this command to start training. + + bash scripts/reproduce_train/outdoor.sh + + We then provide the trained model in `pretrained/model_best.ckpt` +## Evaluation + +### MegaDepth (relative pose estimation) + + bash scripts/reproduce_test/outdoor.sh + +### ScanNet (relative pose estimation) + + bash scripts/reproduce_test/indoor.sh + +### HPatches, Aachen v1.1, InLoc + +To evaluate on these datasets, we integrate our code to the image-matching-toolbox provided by Zhou et al. (2021). +The updated code is available [here](https://github.com/TruongKhang/image-matching-toolbox). +After cloning this code, please follow instructions of image-matching-toolbox to install all required packages and prepare data for evaluation. + +Then, run these commands to perform evaluation: (note that all hyperparameter settings are in `configs/topicfm.yml`) + +**HPatches (homography estimation)** + + python -m immatch.eval_hpatches --gpu 0 --config 'topicfm' --task 'both' --h_solver 'cv' --ransac_thres 3 --root_dir . --odir 'outputs/hpatches' + +**Aachen Day-Night v1.1 (visual localization)** + + python -m immatch.eval_aachen --gpu 0 --config 'topicfm' --colmap --benchmark_name 'aachen_v1.1' + +**InLoc (visual localization)** + + python -m immatch.eval_inloc --gpu 0 --config 'topicfm' + +### Image Matching Challenge 2022 (IMC-2022) +IMC-2022 was held on [Kaggle](https://www.kaggle.com/competitions/image-matching-challenge-2022/overview). +Most high ranking methods were achieved by using an ensemble method which combines the matching results of +various state-of-the-art methods including LoFTR, SuperPoint+SuperGlue, MatchFormer, or QuadTree Attention. + +In this evaluation, we only submit the results produced by our method (TopicFM) alone. Please refer to [this notebook](https://www.kaggle.com/code/khangtg09121995/topicfm-eval). +This table compares our results with the other methods such as LoFTR (ref. [here](https://www.kaggle.com/code/mcwema/imc-2022-kornia-loftr-score-plateau-0-726)), +SP+SuperGlue (ref. [here](https://www.kaggle.com/code/yufei12/superglue-baseline)). + +| | Public Score | Private Score | +|----------------|--------------|---------------| +| SP + SuperGlue | 0.678 | 0.677 | +| LoFTR | 0.726 | 0.736 | +| TopicFM (ours) | **0.804** | **0.811** | + + +### Runtime comparison + +The runtime reported in the paper is measured by averaging runtime of 1500 image pairs of the ScanNet evaluation dataset. +The image size can be changed at `configs/data/scannet_test_1500.py` + + python visualization.py --method --dataset_name "scannet" --measure_time --no_viz + # note that method_name is in ["topicfm", "loftr"] + +To measure time for LoFTR, please download the LoFTR's code as follows: + + git submodule update --init + # download pretrained models + mkdir third_party/loftr/pretrained + gdown --id 1M-VD35-qdB5Iw-AtbDBCKC7hPolFW9UY -O third_party/loftr/pretrained/outdoor_ds.ckpt + +## Citations +If you find this work useful, please cite this: + + @article{giang2022topicfm, + title={TopicFM: Robust and Interpretable Topic-assisted Feature Matching}, + author={Giang, Khang Truong and Song, Soohwan and Jo, Sungho}, + journal={arXiv preprint arXiv:2207.00328}, + year={2022} + } + +## Acknowledgement +This code is built based on [LoFTR](https://github.com/zju3dv/LoFTR). We thank the authors for their useful source code. diff --git a/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz b/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz new file mode 100644 index 0000000000000000000000000000000000000000..f4b1b79acff510aab203a8b604955dd89edffc45 --- /dev/null +++ b/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.1_0.3.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d441df1d380b2ed34449b944d9f13127e695542fa275098d38a6298835672f22 +size 231253 diff --git a/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz b/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz new file mode 100644 index 0000000000000000000000000000000000000000..2b2de7bda22dc6e78e01e3f56ba1dafd46c1c581 --- /dev/null +++ b/third_party/TopicFM/assets/megadepth_test_1500_scene_info/0015_0.3_0.5.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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0000000000000000000000000000000000000000..d2011c2913a9ae1311d18b08c089bd999ba3ad30 --- /dev/null +++ b/third_party/TopicFM/assets/scannet_test_1500/test.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b982b9c1f762e7d31af552ecc1ccf1a6add013197f74ec69c84a6deaa6f580ad +size 71687 diff --git a/third_party/TopicFM/configs/data/__init__.py b/third_party/TopicFM/configs/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/TopicFM/configs/data/base.py b/third_party/TopicFM/configs/data/base.py new file mode 100644 index 0000000000000000000000000000000000000000..6cab7e67019a6fee2657c1a28609c8aca5b2a1d8 --- /dev/null +++ b/third_party/TopicFM/configs/data/base.py @@ -0,0 +1,37 @@ +""" +The data config will be the last one merged into the main config. +Setups in data configs will override all existed setups! +""" + +from yacs.config import CfgNode as CN +_CN = CN() +_CN.DATASET = CN() +_CN.TRAINER = CN() + +# training data config +_CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None +_CN.DATASET.TRAIN_NPZ_ROOT = None +_CN.DATASET.TRAIN_LIST_PATH = None +_CN.DATASET.TRAIN_INTRINSIC_PATH = None +# validation set config +_CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None +_CN.DATASET.VAL_NPZ_ROOT = None +_CN.DATASET.VAL_LIST_PATH = None +_CN.DATASET.VAL_INTRINSIC_PATH = None + +# testing data config +_CN.DATASET.TEST_DATA_SOURCE = None +_CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None +_CN.DATASET.TEST_NPZ_ROOT = None +_CN.DATASET.TEST_LIST_PATH = None +_CN.DATASET.TEST_INTRINSIC_PATH = None +_CN.DATASET.TEST_IMGSIZE = None + +# dataset config +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +cfg = _CN diff --git a/third_party/TopicFM/configs/data/megadepth_test_1500.py b/third_party/TopicFM/configs/data/megadepth_test_1500.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd107fc07ecd464f793d13282939ddb26032922 --- /dev/null +++ b/third_party/TopicFM/configs/data/megadepth_test_1500.py @@ -0,0 +1,11 @@ +from configs.data.base import cfg + +TEST_BASE_PATH = "assets/megadepth_test_1500_scene_info" + +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" +cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/megadepth_test_1500.txt" + +cfg.DATASET.MGDPT_IMG_RESIZE = 1200 +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 diff --git a/third_party/TopicFM/configs/data/megadepth_trainval.py b/third_party/TopicFM/configs/data/megadepth_trainval.py new file mode 100644 index 0000000000000000000000000000000000000000..215b5c34cc41d36aa4444a58ca0cb69afbc11952 --- /dev/null +++ b/third_party/TopicFM/configs/data/megadepth_trainval.py @@ -0,0 +1,22 @@ +from configs.data.base import cfg + + +TRAIN_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TRAINVAL_DATA_SOURCE = "MegaDepth" +cfg.DATASET.TRAIN_DATA_ROOT = "data/megadepth/train" +cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_info_0.1_0.7" +cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/trainvaltest_list/train_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.0 + +TEST_BASE_PATH = "data/megadepth/index" +cfg.DATASET.TEST_DATA_SOURCE = "MegaDepth" +cfg.DATASET.VAL_DATA_ROOT = cfg.DATASET.TEST_DATA_ROOT = "data/megadepth/test" +cfg.DATASET.VAL_NPZ_ROOT = cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_info_val_1500" +cfg.DATASET.VAL_LIST_PATH = cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/trainvaltest_list/val_list.txt" +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 # for both test and val + +# 368 scenes in total for MegaDepth +# (with difficulty balanced (further split each scene to 3 sub-scenes)) +cfg.TRAINER.N_SAMPLES_PER_SUBSET = 100 + +cfg.DATASET.MGDPT_IMG_RESIZE = 800 # for training on 11GB mem GPUs diff --git a/third_party/TopicFM/configs/data/scannet_test_1500.py b/third_party/TopicFM/configs/data/scannet_test_1500.py new file mode 100644 index 0000000000000000000000000000000000000000..ce3b0846b61c567b053d12fb636982ce02e21a5c --- /dev/null +++ b/third_party/TopicFM/configs/data/scannet_test_1500.py @@ -0,0 +1,12 @@ +from configs.data.base import cfg + +TEST_BASE_PATH = "assets/scannet_test_1500" + +cfg.DATASET.TEST_DATA_SOURCE = "ScanNet" +cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test" +cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}" +cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt" +cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz" +cfg.DATASET.TEST_IMGSIZE = (640, 480) + +cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 diff --git a/third_party/TopicFM/configs/model/indoor/debug/.gitignore b/third_party/TopicFM/configs/model/indoor/debug/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/TopicFM/configs/model/indoor/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/TopicFM/configs/model/indoor/model_cfg_test.py b/third_party/TopicFM/configs/model/indoor/model_cfg_test.py new file mode 100644 index 0000000000000000000000000000000000000000..8e8872d3b79de529aa375127ea5beb7e81d9d5b1 --- /dev/null +++ b/third_party/TopicFM/configs/model/indoor/model_cfg_test.py @@ -0,0 +1,4 @@ +from src.config.default import _CN as cfg + +cfg.MODEL.COARSE.N_SAMPLES = 5 +cfg.MODEL.MATCH_COARSE.THR = 0.3 diff --git a/third_party/TopicFM/configs/model/outdoor/debug/.gitignore b/third_party/TopicFM/configs/model/outdoor/debug/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/TopicFM/configs/model/outdoor/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py b/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py new file mode 100644 index 0000000000000000000000000000000000000000..692497457c2a7b9ad823f94546e38f15732ca632 --- /dev/null +++ b/third_party/TopicFM/configs/model/outdoor/model_cfg_test.py @@ -0,0 +1,4 @@ +from src.config.default import _CN as cfg + +cfg.MODEL.COARSE.N_SAMPLES = 10 +cfg.MODEL.MATCH_COARSE.THR = 0.2 diff --git a/third_party/TopicFM/configs/model/outdoor/model_ds.py b/third_party/TopicFM/configs/model/outdoor/model_ds.py new file mode 100644 index 0000000000000000000000000000000000000000..2c090edbfbdcd66cea225c39af6f62da8feb50b9 --- /dev/null +++ b/third_party/TopicFM/configs/model/outdoor/model_ds.py @@ -0,0 +1,16 @@ +from src.config.default import _CN as cfg + +cfg.MODEL.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' +cfg.MODEL.COARSE.N_SAMPLES = 8 + +cfg.TRAINER.CANONICAL_LR = 1e-2 +cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs +cfg.TRAINER.WARMUP_RATIO = 0.1 +cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 16, 20, 24, 28] + +# pose estimation +cfg.TRAINER.RANSAC_PIXEL_THR = 0.5 + +cfg.TRAINER.OPTIMIZER = "adamw" +cfg.TRAINER.ADAMW_DECAY = 0.1 +cfg.MODEL.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.3 diff --git a/third_party/TopicFM/data/megadepth/index/.gitignore b/third_party/TopicFM/data/megadepth/index/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/TopicFM/data/megadepth/index/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/TopicFM/data/megadepth/test/.gitignore b/third_party/TopicFM/data/megadepth/test/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/TopicFM/data/megadepth/test/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/TopicFM/data/megadepth/train/.gitignore b/third_party/TopicFM/data/megadepth/train/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5e7d2734cfc60289debf74293817c0a8f572ff32 --- /dev/null +++ b/third_party/TopicFM/data/megadepth/train/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore diff --git a/third_party/TopicFM/data/scannet/index/.gitignore b/third_party/TopicFM/data/scannet/index/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/TopicFM/data/scannet/index/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/TopicFM/data/scannet/intrinsics.npz b/third_party/TopicFM/data/scannet/intrinsics.npz new file mode 100644 index 0000000000000000000000000000000000000000..4d1fe65c8834ebc44b12870d36edbf57db216f08 --- /dev/null +++ b/third_party/TopicFM/data/scannet/intrinsics.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46db15f5ed21f34998613d07110e577205736a57eb5dfd04db96c189958d79f6 +size 343135 diff --git a/third_party/TopicFM/demo/architecture_v4.png b/third_party/TopicFM/demo/architecture_v4.png new file mode 100644 index 0000000000000000000000000000000000000000..8c99e3064caa21d208b393e61a2c1697a9902935 --- /dev/null +++ b/third_party/TopicFM/demo/architecture_v4.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:001c17a032f5ad1da63dc2dd4f63f4c74bb340356beeeabf3772bd18723f2c3e +size 472773 diff --git a/third_party/TopicFM/demo/demo_aachen.txt b/third_party/TopicFM/demo/demo_aachen.txt new file mode 100644 index 0000000000000000000000000000000000000000..3dd483efd19e2b6d3498672c16a9eb1434628ae4 --- /dev/null +++ b/third_party/TopicFM/demo/demo_aachen.txt @@ -0,0 +1,50 @@ +query/night/nexus5x/IMG_20161227_173141.jpg +db/4273.jpg +db/1967.jpg +db/1966.jpg +db/4247.jpg +db/1050.jpg +db/4240.jpg +db/4246.jpg +db/1785.jpg +db/1051.jpg +db/4218.jpg +db/1052.jpg +db/4244.jpg +db/4239.jpg +db/4272.jpg +db/4242.jpg +db/4274.jpg +db/1112.jpg +db/2493.jpg +db/4224.jpg +db/4213.jpg +db/4248.jpg +db/1114.jpg +db/1777.jpg +db/1049.jpg +db/4226.jpg +db/1048.jpg +db/4236.jpg +db/4225.jpg +db/4216.jpg +db/4243.jpg +db/4227.jpg +db/4241.jpg +db/388.jpg +db/4267.jpg +db/4238.jpg +db/4271.jpg +db/2021.jpg +db/1116.jpg +db/1759.jpg +db/1113.jpg +db/1040.jpg +sequences/nexus4_sequences/sequence_4/aachen_nexus4_seq4_0200.png +db/4223.jpg +db/4231.jpg +sequences/nexus4_sequences/sequence_4/aachen_nexus4_seq4_0196.png +db/4228.jpg +db/1760.jpg +db/1057.jpg +db/4211.jpg \ No newline at end of file diff --git a/third_party/TopicFM/demo/topicfm.gif b/third_party/TopicFM/demo/topicfm.gif new file mode 100644 index 0000000000000000000000000000000000000000..7b86a556a022ea7e120c9ba6ed648bcc0cca162e --- /dev/null +++ b/third_party/TopicFM/demo/topicfm.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:519caa12c626b0d5a984b7b6af92271f88be1cb4dea697165e065cc8290c0a66 +size 15532016 diff --git a/third_party/TopicFM/flop_counter.py b/third_party/TopicFM/flop_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..ea87fa0139897434ca52b369450aa82203311181 --- /dev/null +++ b/third_party/TopicFM/flop_counter.py @@ -0,0 +1,55 @@ +import torch +from fvcore.nn import FlopCountAnalysis +from einops.einops import rearrange + +from src import get_model_cfg +from src.models.backbone import FPN as topicfm_featnet +from src.models.modules import TopicFormer +from src.utils.dataset import read_scannet_gray + +from third_party.loftr.src.loftr.utils.cvpr_ds_config import default_cfg +from third_party.loftr.src.loftr.backbone import ResNetFPN_8_2 as loftr_featnet +from third_party.loftr.src.loftr.loftr_module import LocalFeatureTransformer + + +def feat_net_flops(feat_net, config, input): + model = feat_net(config) + model.eval() + flops = FlopCountAnalysis(model, input) + feat_c, _ = model(input) + return feat_c, flops.total() / 1e9 + + +def coarse_model_flops(coarse_model, config, inputs): + model = coarse_model(config) + model.eval() + flops = FlopCountAnalysis(model, inputs) + return flops.total() / 1e9 + + +if __name__ == '__main__': + path_img0 = "assets/scannet_sample_images/scene0711_00_frame-001680.jpg" + path_img1 = "assets/scannet_sample_images/scene0711_00_frame-001995.jpg" + img0, img1 = read_scannet_gray(path_img0), read_scannet_gray(path_img1) + img0, img1 = img0.unsqueeze(0), img1.unsqueeze(0) + + # LoFTR + loftr_conf = dict(default_cfg) + feat_c0, loftr_featnet_flops0 = feat_net_flops(loftr_featnet, loftr_conf["resnetfpn"], img0) + feat_c1, loftr_featnet_flops1 = feat_net_flops(loftr_featnet, loftr_conf["resnetfpn"], img1) + print("FLOPs of feature extraction in LoFTR: {} GFLOPs".format((loftr_featnet_flops0 + loftr_featnet_flops1)/2)) + feat_c0 = rearrange(feat_c0, 'n c h w -> n (h w) c') + feat_c1 = rearrange(feat_c1, 'n c h w -> n (h w) c') + loftr_coarse_model_flops = coarse_model_flops(LocalFeatureTransformer, loftr_conf["coarse"], (feat_c0, feat_c1)) + print("FLOPs of coarse matching model in LoFTR: {} GFLOPs".format(loftr_coarse_model_flops)) + + # TopicFM + topicfm_conf = get_model_cfg() + feat_c0, topicfm_featnet_flops0 = feat_net_flops(topicfm_featnet, topicfm_conf["fpn"], img0) + feat_c1, topicfm_featnet_flops1 = feat_net_flops(topicfm_featnet, topicfm_conf["fpn"], img1) + print("FLOPs of feature extraction in TopicFM: {} GFLOPs".format((topicfm_featnet_flops0 + topicfm_featnet_flops1) / 2)) + feat_c0 = rearrange(feat_c0, 'n c h w -> n (h w) c') + feat_c1 = rearrange(feat_c1, 'n c h w -> n (h w) c') + topicfm_coarse_model_flops = coarse_model_flops(TopicFormer, topicfm_conf["coarse"], (feat_c0, feat_c1)) + print("FLOPs of coarse matching model in TopicFM: {} GFLOPs".format(topicfm_coarse_model_flops)) + diff --git a/third_party/TopicFM/pretrained/model_best.ckpt b/third_party/TopicFM/pretrained/model_best.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..159151a3bca11ed02b51169622413f9d4937d3c7 --- /dev/null +++ b/third_party/TopicFM/pretrained/model_best.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d6ff9b47594e393b0f8d4cb29a9098eaf39af628f3d22d6adf5bc396622df71 +size 47458400 diff --git a/third_party/TopicFM/requirements.txt b/third_party/TopicFM/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..9edb3640108d86b645f234894469a915a364f527 --- /dev/null +++ b/third_party/TopicFM/requirements.txt @@ -0,0 +1,18 @@ +albumentations==0.5.1 +einops==0.3.0 +future==0.18.2 +fvcore==0.1.5.post20220512 +h5py==3.1.0 +joblib==1.1.0 +kornia==0.4.1 +loguru==0.5.3 +matplotlib==3.5.1 +opencv-python==4.4.0.46 +Pillow==9.0.1 +pytorch-lightning==1.3.5 +scikit-image==0.19.1 +scikit-learn==1.1.2 +tqdm==4.62.3 +yacs==0.1.8 +torchmetrics==0.7.0 +gdown \ No newline at end of file diff --git a/third_party/TopicFM/scripts/reproduce_test/indoor.sh b/third_party/TopicFM/scripts/reproduce_test/indoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..76494f2e1734bfd3a2653ef3c96a557793b54f05 --- /dev/null +++ b/third_party/TopicFM/scripts/reproduce_test/indoor.sh @@ -0,0 +1,29 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/scannet_test_1500.py" +main_cfg_path="configs/model/indoor/model_cfg_test.py" +ckpt_path="pretrained/model_best.ckpt" +dump_dir="dump/loftr_ds_indoor" +profiler_name="inference" +n_nodes=1 # mannually keep this the same with --nodes +n_gpus_per_node=-1 +torch_num_workers=4 +batch_size=1 # per gpu + +python -u ./test.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --ckpt_path=${ckpt_path} \ + --dump_dir=${dump_dir} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers}\ + --profiler_name=${profiler_name} \ + --benchmark + diff --git a/third_party/TopicFM/scripts/reproduce_test/outdoor.sh b/third_party/TopicFM/scripts/reproduce_test/outdoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..e6217883a1ea9c17edf2ce0ff0ee97d26868b5d9 --- /dev/null +++ b/third_party/TopicFM/scripts/reproduce_test/outdoor.sh @@ -0,0 +1,29 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/megadepth_test_1500.py" +main_cfg_path="configs/model/outdoor/model_cfg_test.py" +ckpt_path="pretrained/model_best.ckpt" +dump_dir="dump/loftr_ds_outdoor" +profiler_name="inference" +n_nodes=1 # mannually keep this the same with --nodes +n_gpus_per_node=-1 +torch_num_workers=4 +batch_size=1 # per gpu + +python -u ./test.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --ckpt_path=${ckpt_path} \ + --dump_dir=${dump_dir} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers}\ + --profiler_name=${profiler_name} \ + --benchmark + diff --git a/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore b/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..94548af5beba7825284af746324c8dc5b2f1ea31 --- /dev/null +++ b/third_party/TopicFM/scripts/reproduce_train/debug/.gitignore @@ -0,0 +1,3 @@ +* +*/ +!.gitignore diff --git a/third_party/TopicFM/scripts/reproduce_train/outdoor.sh b/third_party/TopicFM/scripts/reproduce_train/outdoor.sh new file mode 100644 index 0000000000000000000000000000000000000000..d30320f04e0b560f4b4de9ee68305a4e698b538b --- /dev/null +++ b/third_party/TopicFM/scripts/reproduce_train/outdoor.sh @@ -0,0 +1,32 @@ +#!/bin/bash -l + +SCRIPTPATH=$(dirname $(readlink -f "$0")) +PROJECT_DIR="${SCRIPTPATH}/../../" + +# conda activate loftr +export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH +cd $PROJECT_DIR + +data_cfg_path="configs/data/megadepth_trainval.py" +main_cfg_path="configs/model/outdoor/model_ds.py" + +n_nodes=1 +n_gpus_per_node=4 +torch_num_workers=4 +batch_size=1 +pin_memory=true +exp_name="outdoor-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" + +python -u ./train.py \ + ${data_cfg_path} \ + ${main_cfg_path} \ + --exp_name=${exp_name} \ + --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ + --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ + --check_val_every_n_epoch=1 \ + --log_every_n_steps=30000 \ + --flush_logs_every_n_steps=30000 \ + --limit_val_batches=1. \ + --num_sanity_val_steps=10 \ + --benchmark=True \ + --max_epochs=40 # --ckpt_path="pretrained_epoch22.ckpt" diff --git a/third_party/TopicFM/src/__init__.py b/third_party/TopicFM/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..30caef94f911f99e0c12510d8181b3c1537daf1a --- /dev/null +++ b/third_party/TopicFM/src/__init__.py @@ -0,0 +1,11 @@ +from yacs.config import CfgNode +from .config.default import _CN + +def lower_config(yacs_cfg): + if not isinstance(yacs_cfg, CfgNode): + return yacs_cfg + return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} + +def get_model_cfg(): + cfg = lower_config(lower_config(_CN)) + return cfg["model"] \ No newline at end of file diff --git a/third_party/TopicFM/src/config/default.py b/third_party/TopicFM/src/config/default.py new file mode 100644 index 0000000000000000000000000000000000000000..591558b3f358cdce0e9e72e94acba702b2a4e896 --- /dev/null +++ b/third_party/TopicFM/src/config/default.py @@ -0,0 +1,171 @@ +from yacs.config import CfgNode as CN +_CN = CN() + +############## ↓ MODEL Pipeline ↓ ############## +_CN.MODEL = CN() +_CN.MODEL.BACKBONE_TYPE = 'FPN' +_CN.MODEL.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)] +_CN.MODEL.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd +_CN.MODEL.FINE_CONCAT_COARSE_FEAT = False + +# 1. MODEL-backbone (local feature CNN) config +_CN.MODEL.FPN = CN() +_CN.MODEL.FPN.INITIAL_DIM = 128 +_CN.MODEL.FPN.BLOCK_DIMS = [128, 192, 256, 384] # s1, s2, s3 + +# 2. MODEL-coarse module config +_CN.MODEL.COARSE = CN() +_CN.MODEL.COARSE.D_MODEL = 256 +_CN.MODEL.COARSE.D_FFN = 256 +_CN.MODEL.COARSE.NHEAD = 8 +_CN.MODEL.COARSE.LAYER_NAMES = ['seed', 'seed', 'seed', 'seed', 'seed'] +_CN.MODEL.COARSE.ATTENTION = 'linear' # options: ['linear', 'full'] +_CN.MODEL.COARSE.TEMP_BUG_FIX = True +_CN.MODEL.COARSE.N_TOPICS = 100 +_CN.MODEL.COARSE.N_SAMPLES = 6 +_CN.MODEL.COARSE.N_TOPIC_TRANSFORMERS = 1 + +# 3. Coarse-Matching config +_CN.MODEL.MATCH_COARSE = CN() +_CN.MODEL.MATCH_COARSE.THR = 0.2 +_CN.MODEL.MATCH_COARSE.BORDER_RM = 2 +_CN.MODEL.MATCH_COARSE.MATCH_TYPE = 'dual_softmax' +_CN.MODEL.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1 +_CN.MODEL.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory +_CN.MODEL.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock +_CN.MODEL.MATCH_COARSE.SPARSE_SPVS = True + +# 4. MODEL-fine module config +_CN.MODEL.FINE = CN() +_CN.MODEL.FINE.D_MODEL = 128 +_CN.MODEL.FINE.D_FFN = 128 +_CN.MODEL.FINE.NHEAD = 4 +_CN.MODEL.FINE.LAYER_NAMES = ['cross'] * 1 +_CN.MODEL.FINE.ATTENTION = 'linear' +_CN.MODEL.FINE.N_TOPICS = 1 + +# 5. MODEL Losses +# -- # coarse-level +_CN.MODEL.LOSS = CN() +_CN.MODEL.LOSS.COARSE_WEIGHT = 1.0 +# _CN.MODEL.LOSS.SPARSE_SPVS = False +# -- - -- # focal loss (coarse) +_CN.MODEL.LOSS.FOCAL_ALPHA = 0.25 +_CN.MODEL.LOSS.POS_WEIGHT = 1.0 +_CN.MODEL.LOSS.NEG_WEIGHT = 1.0 +# _CN.MODEL.LOSS.DUAL_SOFTMAX = False # whether coarse-level use dual-softmax or not. +# use `_CN.MODEL.MATCH_COARSE.MATCH_TYPE` + +# -- # fine-level +_CN.MODEL.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2'] +_CN.MODEL.LOSS.FINE_WEIGHT = 1.0 +_CN.MODEL.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window) + + +############## Dataset ############## +_CN.DATASET = CN() +# 1. data config +# training and validating +_CN.DATASET.TRAINVAL_DATA_SOURCE = None # options: ['ScanNet', 'MegaDepth'] +_CN.DATASET.TRAIN_DATA_ROOT = None +_CN.DATASET.TRAIN_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.TRAIN_NPZ_ROOT = None +_CN.DATASET.TRAIN_LIST_PATH = None +_CN.DATASET.TRAIN_INTRINSIC_PATH = None +_CN.DATASET.VAL_DATA_ROOT = None +_CN.DATASET.VAL_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.VAL_NPZ_ROOT = None +_CN.DATASET.VAL_LIST_PATH = None # None if val data from all scenes are bundled into a single npz file +_CN.DATASET.VAL_INTRINSIC_PATH = None +# testing +_CN.DATASET.TEST_DATA_SOURCE = None +_CN.DATASET.TEST_DATA_ROOT = None +_CN.DATASET.TEST_POSE_ROOT = None # (optional directory for poses) +_CN.DATASET.TEST_NPZ_ROOT = None +_CN.DATASET.TEST_LIST_PATH = None # None if test data from all scenes are bundled into a single npz file +_CN.DATASET.TEST_INTRINSIC_PATH = None +_CN.DATASET.TEST_IMGSIZE = None + +# 2. dataset config +# general options +_CN.DATASET.MIN_OVERLAP_SCORE_TRAIN = 0.4 # discard data with overlap_score < min_overlap_score +_CN.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0 +_CN.DATASET.AUGMENTATION_TYPE = None # options: [None, 'dark', 'mobile'] + +# MegaDepth options +_CN.DATASET.MGDPT_IMG_RESIZE = 640 # resize the longer side, zero-pad bottom-right to square. +_CN.DATASET.MGDPT_IMG_PAD = True # pad img to square with size = MGDPT_IMG_RESIZE +_CN.DATASET.MGDPT_DEPTH_PAD = True # pad depthmap to square with size = 2000 +_CN.DATASET.MGDPT_DF = 8 + +############## Trainer ############## +_CN.TRAINER = CN() +_CN.TRAINER.WORLD_SIZE = 1 +_CN.TRAINER.CANONICAL_BS = 64 +_CN.TRAINER.CANONICAL_LR = 6e-3 +_CN.TRAINER.SCALING = None # this will be calculated automatically +_CN.TRAINER.FIND_LR = False # use learning rate finder from pytorch-lightning + +# optimizer +_CN.TRAINER.OPTIMIZER = "adamw" # [adam, adamw] +_CN.TRAINER.TRUE_LR = None # this will be calculated automatically at runtime +_CN.TRAINER.ADAM_DECAY = 0. # ADAM: for adam +_CN.TRAINER.ADAMW_DECAY = 0.01 + +# step-based warm-up +_CN.TRAINER.WARMUP_TYPE = 'linear' # [linear, constant] +_CN.TRAINER.WARMUP_RATIO = 0. +_CN.TRAINER.WARMUP_STEP = 4800 + +# learning rate scheduler +_CN.TRAINER.SCHEDULER = 'MultiStepLR' # [MultiStepLR, CosineAnnealing, ExponentialLR] +_CN.TRAINER.SCHEDULER_INTERVAL = 'epoch' # [epoch, step] +_CN.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12] # MSLR: MultiStepLR +_CN.TRAINER.MSLR_GAMMA = 0.5 +_CN.TRAINER.COSA_TMAX = 30 # COSA: CosineAnnealing +_CN.TRAINER.ELR_GAMMA = 0.999992 # ELR: ExponentialLR, this value for 'step' interval + +# plotting related +_CN.TRAINER.ENABLE_PLOTTING = True +_CN.TRAINER.N_VAL_PAIRS_TO_PLOT = 32 # number of val/test paris for plotting +_CN.TRAINER.PLOT_MODE = 'evaluation' # ['evaluation', 'confidence'] +_CN.TRAINER.PLOT_MATCHES_ALPHA = 'dynamic' + +# geometric metrics and pose solver +_CN.TRAINER.EPI_ERR_THR = 5e-4 # recommendation: 5e-4 for ScanNet, 1e-4 for MegaDepth (from SuperGlue) +_CN.TRAINER.POSE_GEO_MODEL = 'E' # ['E', 'F', 'H'] +_CN.TRAINER.POSE_ESTIMATION_METHOD = 'RANSAC' # [RANSAC, DEGENSAC, MAGSAC] +_CN.TRAINER.RANSAC_PIXEL_THR = 0.5 +_CN.TRAINER.RANSAC_CONF = 0.99999 +_CN.TRAINER.RANSAC_MAX_ITERS = 10000 +_CN.TRAINER.USE_MAGSACPP = False + +# data sampler for train_dataloader +_CN.TRAINER.DATA_SAMPLER = 'scene_balance' # options: ['scene_balance', 'random', 'normal'] +# 'scene_balance' config +_CN.TRAINER.N_SAMPLES_PER_SUBSET = 200 +_CN.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT = True # whether sample each scene with replacement or not +_CN.TRAINER.SB_SUBSET_SHUFFLE = True # after sampling from scenes, whether shuffle within the epoch or not +_CN.TRAINER.SB_REPEAT = 1 # repeat N times for training the sampled data +# 'random' config +_CN.TRAINER.RDM_REPLACEMENT = True +_CN.TRAINER.RDM_NUM_SAMPLES = None + +# gradient clipping +_CN.TRAINER.GRADIENT_CLIPPING = 0.5 + +# reproducibility +# This seed affects the data sampling. With the same seed, the data sampling is promised +# to be the same. When resume training from a checkpoint, it's better to use a different +# seed, otherwise the sampled data will be exactly the same as before resuming, which will +# cause less unique data items sampled during the entire training. +# Use of different seed values might affect the final training result, since not all data items +# are used during training on ScanNet. (60M pairs of images sampled during traing from 230M pairs in total.) +_CN.TRAINER.SEED = 66 + + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return _CN.clone() diff --git a/third_party/TopicFM/src/datasets/aachen.py b/third_party/TopicFM/src/datasets/aachen.py new file mode 100644 index 0000000000000000000000000000000000000000..ebfeee4dbfbd78770976ec027ceee8ef333a4574 --- /dev/null +++ b/third_party/TopicFM/src/datasets/aachen.py @@ -0,0 +1,29 @@ +import os +from torch.utils.data import Dataset + +from src.utils.dataset import read_img_gray + + +class AachenDataset(Dataset): + def __init__(self, img_path, match_list_path, img_resize=None, down_factor=16): + self.img_path = img_path + self.img_resize = img_resize + self.down_factor = down_factor + with open(match_list_path, 'r') as f: + self.raw_pairs = f.readlines() + print("number of matching pairs: ", len(self.raw_pairs)) + + def __len__(self): + return len(self.raw_pairs) + + def __getitem__(self, idx): + raw_pair = self.raw_pairs[idx] + image_name0, image_name1 = raw_pair.strip('\n').split(' ') + path_img0 = os.path.join(self.img_path, image_name0) + path_img1 = os.path.join(self.img_path, image_name1) + img0, scale0 = read_img_gray(path_img0, resize=self.img_resize, down_factor=self.down_factor) + img1, scale1 = read_img_gray(path_img1, resize=self.img_resize, down_factor=self.down_factor) + return {"image0": img0, "image1": img1, + "scale0": scale0, "scale1": scale1, + "pair_names": (image_name0, image_name1), + "dataset_name": "AachenDayNight"} \ No newline at end of file diff --git a/third_party/TopicFM/src/datasets/custom_dataloader.py b/third_party/TopicFM/src/datasets/custom_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..46d55d4f4d56d2c96cd42b6597834f945a5eb20d --- /dev/null +++ b/third_party/TopicFM/src/datasets/custom_dataloader.py @@ -0,0 +1,126 @@ +from tqdm import tqdm +from os import path as osp +from torch.utils.data import Dataset, DataLoader, ConcatDataset + +from src.datasets.megadepth import MegaDepthDataset +from src.datasets.scannet import ScanNetDataset +from src.datasets.aachen import AachenDataset +from src.datasets.inloc import InLocDataset + + +class TestDataLoader(DataLoader): + """ + For distributed training, each training process is assgined + only a part of the training scenes to reduce memory overhead. + """ + + def __init__(self, config): + + # 1. data config + self.test_data_source = config.DATASET.TEST_DATA_SOURCE + dataset_name = str(self.test_data_source).lower() + # testing + self.test_data_root = config.DATASET.TEST_DATA_ROOT + self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional) + self.test_npz_root = config.DATASET.TEST_NPZ_ROOT + self.test_list_path = config.DATASET.TEST_LIST_PATH + self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH + + # 2. dataset config + # general options + self.min_overlap_score_test = config.DATASET.MIN_OVERLAP_SCORE_TEST # 0.4, omit data with overlap_score < min_overlap_score + + # MegaDepth options + if dataset_name == 'megadepth': + self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 800 + self.mgdpt_img_pad = True + self.mgdpt_depth_pad = True + self.mgdpt_df = 8 + self.coarse_scale = 0.125 + if dataset_name == 'scannet': + self.img_resize = config.DATASET.TEST_IMGSIZE + + if (dataset_name == 'megadepth') or (dataset_name == 'scannet'): + test_dataset = self._setup_dataset( + self.test_data_root, + self.test_npz_root, + self.test_list_path, + self.test_intrinsic_path, + mode='test', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.test_pose_root) + elif dataset_name == 'aachen_v1.1': + test_dataset = AachenDataset(self.test_data_root, self.test_list_path, + img_resize=config.DATASET.TEST_IMGSIZE) + elif dataset_name == 'inloc': + test_dataset = InLocDataset(self.test_data_root, self.test_list_path, + img_resize=config.DATASET.TEST_IMGSIZE) + else: + raise "unknown dataset" + + self.test_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': 4, + 'pin_memory': True + } + + # sampler = Seq(self.test_dataset, shuffle=False) + super(TestDataLoader, self).__init__(test_dataset, **self.test_loader_params) + + def _setup_dataset(self, + data_root, + split_npz_root, + scene_list_path, + intri_path, + mode='train', + min_overlap_score=0., + pose_dir=None): + """ Setup train / val / test set""" + with open(scene_list_path, 'r') as f: + npz_names = [name.split()[0] for name in f.readlines()] + local_npz_names = npz_names + + return self._build_concat_dataset(data_root, local_npz_names, split_npz_root, intri_path, + mode=mode, min_overlap_score=min_overlap_score, pose_dir=pose_dir) + + def _build_concat_dataset( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None + ): + datasets = [] + # augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.test_data_source + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + for npz_name in tqdm(npz_names): + # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time. + npz_path = osp.join(npz_dir, npz_name) + if data_source == 'ScanNet': + datasets.append( + ScanNetDataset(data_root, + npz_path, + intrinsic_path, + mode=mode, img_resize=self.img_resize, + min_overlap_score=min_overlap_score, + pose_dir=pose_dir)) + elif data_source == 'MegaDepth': + datasets.append( + MegaDepthDataset(data_root, + npz_path, + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + coarse_scale=self.coarse_scale)) + else: + raise NotImplementedError() + return ConcatDataset(datasets) diff --git a/third_party/TopicFM/src/datasets/inloc.py b/third_party/TopicFM/src/datasets/inloc.py new file mode 100644 index 0000000000000000000000000000000000000000..5421099d11b4dbbea8c09568c493d844d5c6a1b0 --- /dev/null +++ b/third_party/TopicFM/src/datasets/inloc.py @@ -0,0 +1,29 @@ +import os +from torch.utils.data import Dataset + +from src.utils.dataset import read_img_gray + + +class InLocDataset(Dataset): + def __init__(self, img_path, match_list_path, img_resize=None, down_factor=16): + self.img_path = img_path + self.img_resize = img_resize + self.down_factor = down_factor + with open(match_list_path, 'r') as f: + self.raw_pairs = f.readlines() + print("number of matching pairs: ", len(self.raw_pairs)) + + def __len__(self): + return len(self.raw_pairs) + + def __getitem__(self, idx): + raw_pair = self.raw_pairs[idx] + image_name0, image_name1 = raw_pair.strip('\n').split(' ') + path_img0 = os.path.join(self.img_path, image_name0) + path_img1 = os.path.join(self.img_path, image_name1) + img0, scale0 = read_img_gray(path_img0, resize=self.img_resize, down_factor=self.down_factor) + img1, scale1 = read_img_gray(path_img1, resize=self.img_resize, down_factor=self.down_factor) + return {"image0": img0, "image1": img1, + "scale0": scale0, "scale1": scale1, + "pair_names": (image_name0, image_name1), + "dataset_name": "InLoc"} \ No newline at end of file diff --git a/third_party/TopicFM/src/datasets/megadepth.py b/third_party/TopicFM/src/datasets/megadepth.py new file mode 100644 index 0000000000000000000000000000000000000000..e92768e72e373c2a8ebeaf1158f9710fb1bfb5f1 --- /dev/null +++ b/third_party/TopicFM/src/datasets/megadepth.py @@ -0,0 +1,129 @@ +import os.path as osp +import numpy as np +import torch +import torch.nn.functional as F +from torch.utils.data import Dataset +from loguru import logger + +from src.utils.dataset import read_megadepth_gray, read_megadepth_depth + + +class MegaDepthDataset(Dataset): + def __init__(self, + root_dir, + npz_path, + mode='train', + min_overlap_score=0.4, + img_resize=None, + df=None, + img_padding=False, + depth_padding=False, + augment_fn=None, + **kwargs): + """ + Manage one scene(npz_path) of MegaDepth dataset. + + Args: + root_dir (str): megadepth root directory that has `phoenix`. + npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. + mode (str): options are ['train', 'val', 'test'] + min_overlap_score (float): how much a pair should have in common. In range of [0, 1]. Set to 0 when testing. + img_resize (int, optional): the longer edge of resized images. None for no resize. 640 is recommended. + This is useful during training with batches and testing with memory intensive algorithms. + df (int, optional): image size division factor. NOTE: this will change the final image size after img_resize. + img_padding (bool): If set to 'True', zero-pad the image to squared size. This is useful during training. + depth_padding (bool): If set to 'True', zero-pad depthmap to (2000, 2000). This is useful during training. + augment_fn (callable, optional): augments images with pre-defined visual effects. + """ + super().__init__() + self.root_dir = root_dir + self.mode = mode + self.scene_id = npz_path.split('.')[0] + + # prepare scene_info and pair_info + if mode == 'test' and min_overlap_score != 0: + logger.warning("You are using `min_overlap_score`!=0 in test mode. Set to 0.") + min_overlap_score = 0 + self.scene_info = np.load(npz_path, allow_pickle=True) + self.pair_infos = self.scene_info['pair_infos'].copy() + del self.scene_info['pair_infos'] + self.pair_infos = [pair_info for pair_info in self.pair_infos if pair_info[1] > min_overlap_score] + + # parameters for image resizing, padding and depthmap padding + if mode == 'train': + assert img_resize is not None and img_padding and depth_padding + self.img_resize = img_resize + if mode == 'val': + self.img_resize = 864 + self.df = df + self.img_padding = img_padding + self.depth_max_size = 2000 if depth_padding else None # the upperbound of depthmaps size in megadepth. + + # for training LoFTR + self.augment_fn = augment_fn if mode == 'train' else None + self.coarse_scale = getattr(kwargs, 'coarse_scale', 0.125) + + def __len__(self): + return len(self.pair_infos) + + def __getitem__(self, idx): + (idx0, idx1), overlap_score, central_matches = self.pair_infos[idx] + + # read grayscale image and mask. (1, h, w) and (h, w) + img_name0 = osp.join(self.root_dir, self.scene_info['image_paths'][idx0]) + img_name1 = osp.join(self.root_dir, self.scene_info['image_paths'][idx1]) + + # TODO: Support augmentation & handle seeds for each worker correctly. + image0, mask0, scale0 = read_megadepth_gray( + img_name0, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + image1, mask1, scale1 = read_megadepth_gray( + img_name1, self.img_resize, self.df, self.img_padding, None) + # np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + + # read depth. shape: (h, w) + if self.mode in ['train', 'val']: + depth0 = read_megadepth_depth( + osp.join(self.root_dir, self.scene_info['depth_paths'][idx0]), pad_to=self.depth_max_size) + depth1 = read_megadepth_depth( + osp.join(self.root_dir, self.scene_info['depth_paths'][idx1]), pad_to=self.depth_max_size) + else: + depth0 = depth1 = torch.tensor([]) + + # read intrinsics of original size + K_0 = torch.tensor(self.scene_info['intrinsics'][idx0].copy(), dtype=torch.float).reshape(3, 3) + K_1 = torch.tensor(self.scene_info['intrinsics'][idx1].copy(), dtype=torch.float).reshape(3, 3) + + # read and compute relative poses + T0 = self.scene_info['poses'][idx0] + T1 = self.scene_info['poses'][idx1] + T_0to1 = torch.tensor(np.matmul(T1, np.linalg.inv(T0)), dtype=torch.float)[:4, :4] # (4, 4) + T_1to0 = T_0to1.inverse() + + data = { + 'image0': image0, # (1, h, w) + 'depth0': depth0, # (h, w) + 'image1': image1, + 'depth1': depth1, + 'T_0to1': T_0to1, # (4, 4) + 'T_1to0': T_1to0, + 'K0': K_0, # (3, 3) + 'K1': K_1, + 'scale0': scale0, # [scale_w, scale_h] + 'scale1': scale1, + 'dataset_name': 'MegaDepth', + 'scene_id': self.scene_id, + 'pair_id': idx, + 'pair_names': (self.scene_info['image_paths'][idx0], self.scene_info['image_paths'][idx1]), + } + + # for LoFTR training + if mask0 is not None: # img_padding is True + if self.coarse_scale: + [ts_mask_0, ts_mask_1] = F.interpolate(torch.stack([mask0, mask1], dim=0)[None].float(), + scale_factor=self.coarse_scale, + mode='nearest', + recompute_scale_factor=False)[0].bool() + data.update({'mask0': ts_mask_0, 'mask1': ts_mask_1}) + + return data diff --git a/third_party/TopicFM/src/datasets/sampler.py b/third_party/TopicFM/src/datasets/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..81b6f435645632a013476f9a665a0861ab7fcb61 --- /dev/null +++ b/third_party/TopicFM/src/datasets/sampler.py @@ -0,0 +1,77 @@ +import torch +from torch.utils.data import Sampler, ConcatDataset + + +class RandomConcatSampler(Sampler): + """ Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset + in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be done with replacement. + However, it is impossible to sample data without replacement between epochs, unless bulding a stateful sampler lived along the entire training phase. + + For current implementation, the randomness of sampling is ensured no matter the sampler is recreated across epochs or not and call `torch.manual_seed()` or not. + Args: + shuffle (bool): shuffle the random sampled indices across all sub-datsets. + repeat (int): repeatedly use the sampled indices multiple times for training. + [arXiv:1902.05509, arXiv:1901.09335] + NOTE: Don't re-initialize the sampler between epochs (will lead to repeated samples) + NOTE: This sampler behaves differently with DistributedSampler. + It assume the dataset is splitted across ranks instead of replicated. + TODO: Add a `set_epoch()` method to fullfill sampling without replacement across epochs. + ref: https://github.com/PyTorchLightning/pytorch-lightning/blob/e9846dd758cfb1500eb9dba2d86f6912eb487587/pytorch_lightning/trainer/training_loop.py#L373 + """ + def __init__(self, + data_source: ConcatDataset, + n_samples_per_subset: int, + subset_replacement: bool=True, + shuffle: bool=True, + repeat: int=1, + seed: int=None): + if not isinstance(data_source, ConcatDataset): + raise TypeError("data_source should be torch.utils.data.ConcatDataset") + + self.data_source = data_source + self.n_subset = len(self.data_source.datasets) + self.n_samples_per_subset = n_samples_per_subset + self.n_samples = self.n_subset * self.n_samples_per_subset * repeat + self.subset_replacement = subset_replacement + self.repeat = repeat + self.shuffle = shuffle + self.generator = torch.manual_seed(seed) + assert self.repeat >= 1 + + def __len__(self): + return self.n_samples + + def __iter__(self): + indices = [] + # sample from each sub-dataset + for d_idx in range(self.n_subset): + low = 0 if d_idx==0 else self.data_source.cumulative_sizes[d_idx-1] + high = self.data_source.cumulative_sizes[d_idx] + if self.subset_replacement: + rand_tensor = torch.randint(low, high, (self.n_samples_per_subset, ), + generator=self.generator, dtype=torch.int64) + else: # sample without replacement + len_subset = len(self.data_source.datasets[d_idx]) + rand_tensor = torch.randperm(len_subset, generator=self.generator) + low + if len_subset >= self.n_samples_per_subset: + rand_tensor = rand_tensor[:self.n_samples_per_subset] + else: # padding with replacement + rand_tensor_replacement = torch.randint(low, high, (self.n_samples_per_subset - len_subset, ), + generator=self.generator, dtype=torch.int64) + rand_tensor = torch.cat([rand_tensor, rand_tensor_replacement]) + indices.append(rand_tensor) + indices = torch.cat(indices) + if self.shuffle: # shuffle the sampled dataset (from multiple subsets) + rand_tensor = torch.randperm(len(indices), generator=self.generator) + indices = indices[rand_tensor] + + # repeat the sampled indices (can be used for RepeatAugmentation or pure RepeatSampling) + if self.repeat > 1: + repeat_indices = [indices.clone() for _ in range(self.repeat - 1)] + if self.shuffle: + _choice = lambda x: x[torch.randperm(len(x), generator=self.generator)] + repeat_indices = map(_choice, repeat_indices) + indices = torch.cat([indices, *repeat_indices], 0) + + assert indices.shape[0] == self.n_samples + return iter(indices.tolist()) diff --git a/third_party/TopicFM/src/datasets/scannet.py b/third_party/TopicFM/src/datasets/scannet.py new file mode 100644 index 0000000000000000000000000000000000000000..fb5dab7b150a3c6f54eb07b0459bbf3e9ba58fbf --- /dev/null +++ b/third_party/TopicFM/src/datasets/scannet.py @@ -0,0 +1,115 @@ +from os import path as osp +from typing import Dict +from unicodedata import name + +import numpy as np +import torch +import torch.utils as utils +from numpy.linalg import inv +from src.utils.dataset import ( + read_scannet_gray, + read_scannet_depth, + read_scannet_pose, + read_scannet_intrinsic +) + + +class ScanNetDataset(utils.data.Dataset): + def __init__(self, + root_dir, + npz_path, + intrinsic_path, + mode='train', + min_overlap_score=0.4, + augment_fn=None, + pose_dir=None, + **kwargs): + """Manage one scene of ScanNet Dataset. + Args: + root_dir (str): ScanNet root directory that contains scene folders. + npz_path (str): {scene_id}.npz path. This contains image pair information of a scene. + intrinsic_path (str): path to depth-camera intrinsic file. + mode (str): options are ['train', 'val', 'test']. + augment_fn (callable, optional): augments images with pre-defined visual effects. + pose_dir (str): ScanNet root directory that contains all poses. + (we use a separate (optional) pose_dir since we store images and poses separately.) + """ + super().__init__() + self.root_dir = root_dir + self.pose_dir = pose_dir if pose_dir is not None else root_dir + self.mode = mode + self.img_resize = (640, 480) if 'img_resize' not in kwargs else kwargs['img_resize'] + + # prepare data_names, intrinsics and extrinsics(T) + with np.load(npz_path) as data: + self.data_names = data['name'] + if 'score' in data.keys() and mode not in ['val' or 'test']: + kept_mask = data['score'] > min_overlap_score + self.data_names = self.data_names[kept_mask] + self.intrinsics = dict(np.load(intrinsic_path)) + + # for training LoFTR + self.augment_fn = augment_fn if mode == 'train' else None + + def __len__(self): + return len(self.data_names) + + def _read_abs_pose(self, scene_name, name): + pth = osp.join(self.pose_dir, + scene_name, + 'pose', f'{name}.txt') + return read_scannet_pose(pth) + + def _compute_rel_pose(self, scene_name, name0, name1): + pose0 = self._read_abs_pose(scene_name, name0) + pose1 = self._read_abs_pose(scene_name, name1) + + return np.matmul(pose1, inv(pose0)) # (4, 4) + + def __getitem__(self, idx): + data_name = self.data_names[idx] + scene_name, scene_sub_name, stem_name_0, stem_name_1 = data_name + scene_name = f'scene{scene_name:04d}_{scene_sub_name:02d}' + + # read the grayscale image which will be resized to (1, 480, 640) + img_name0 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_0}.jpg') + img_name1 = osp.join(self.root_dir, scene_name, 'color', f'{stem_name_1}.jpg') + + # TODO: Support augmentation & handle seeds for each worker correctly. + image0 = read_scannet_gray(img_name0, resize=self.img_resize, augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + image1 = read_scannet_gray(img_name1, resize=self.img_resize, augment_fn=None) + # augment_fn=np.random.choice([self.augment_fn, None], p=[0.5, 0.5])) + + # read the depthmap which is stored as (480, 640) + if self.mode in ['train', 'val']: + depth0 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_0}.png')) + depth1 = read_scannet_depth(osp.join(self.root_dir, scene_name, 'depth', f'{stem_name_1}.png')) + else: + depth0 = depth1 = torch.tensor([]) + + # read the intrinsic of depthmap + K_0 = K_1 = torch.tensor(self.intrinsics[scene_name].copy(), dtype=torch.float).reshape(3, 3) + + # read and compute relative poses + T_0to1 = torch.tensor(self._compute_rel_pose(scene_name, stem_name_0, stem_name_1), + dtype=torch.float32) + T_1to0 = T_0to1.inverse() + + data = { + 'image0': image0, # (1, h, w) + 'depth0': depth0, # (h, w) + 'image1': image1, + 'depth1': depth1, + 'T_0to1': T_0to1, # (4, 4) + 'T_1to0': T_1to0, + 'K0': K_0, # (3, 3) + 'K1': K_1, + 'dataset_name': 'ScanNet', + 'scene_id': scene_name, + 'pair_id': idx, + 'pair_names': (osp.join(scene_name, 'color', f'{stem_name_0}.jpg'), + osp.join(scene_name, 'color', f'{stem_name_1}.jpg')) + } + + return data diff --git a/third_party/TopicFM/src/lightning_trainer/data.py b/third_party/TopicFM/src/lightning_trainer/data.py new file mode 100644 index 0000000000000000000000000000000000000000..8deb713b6300e0e9e8a261e2230031174b452862 --- /dev/null +++ b/third_party/TopicFM/src/lightning_trainer/data.py @@ -0,0 +1,320 @@ +import os +import math +from collections import abc +from loguru import logger +from torch.utils.data.dataset import Dataset +from tqdm import tqdm +from os import path as osp +from pathlib import Path +from joblib import Parallel, delayed + +import pytorch_lightning as pl +from torch import distributed as dist +from torch.utils.data import ( + Dataset, + DataLoader, + ConcatDataset, + DistributedSampler, + RandomSampler, + dataloader +) + +from src.utils.augment import build_augmentor +from src.utils.dataloader import get_local_split +from src.utils.misc import tqdm_joblib +from src.utils import comm +from src.datasets.megadepth import MegaDepthDataset +from src.datasets.scannet import ScanNetDataset +from src.datasets.sampler import RandomConcatSampler + + +class MultiSceneDataModule(pl.LightningDataModule): + """ + For distributed training, each training process is assgined + only a part of the training scenes to reduce memory overhead. + """ + def __init__(self, args, config): + super().__init__() + + # 1. data config + # Train and Val should from the same data source + self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE + self.test_data_source = config.DATASET.TEST_DATA_SOURCE + # training and validating + self.train_data_root = config.DATASET.TRAIN_DATA_ROOT + self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional) + self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT + self.train_list_path = config.DATASET.TRAIN_LIST_PATH + self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH + self.val_data_root = config.DATASET.VAL_DATA_ROOT + self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional) + self.val_npz_root = config.DATASET.VAL_NPZ_ROOT + self.val_list_path = config.DATASET.VAL_LIST_PATH + self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH + # testing + self.test_data_root = config.DATASET.TEST_DATA_ROOT + self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional) + self.test_npz_root = config.DATASET.TEST_NPZ_ROOT + self.test_list_path = config.DATASET.TEST_LIST_PATH + self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH + + # 2. dataset config + # general options + self.min_overlap_score_test = config.DATASET.MIN_OVERLAP_SCORE_TEST # 0.4, omit data with overlap_score < min_overlap_score + self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN + self.augment_fn = build_augmentor(config.DATASET.AUGMENTATION_TYPE) # None, options: [None, 'dark', 'mobile'] + + # MegaDepth options + self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840 + self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True + self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True + self.mgdpt_df = config.DATASET.MGDPT_DF # 8 + self.coarse_scale = 1 / config.MODEL.RESOLUTION[0] # 0.125. for training loftr. + + # 3.loader parameters + self.train_loader_params = { + 'batch_size': args.batch_size, + 'num_workers': args.num_workers, + 'pin_memory': getattr(args, 'pin_memory', True) + } + self.val_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': args.num_workers, + 'pin_memory': getattr(args, 'pin_memory', True) + } + self.test_loader_params = { + 'batch_size': 1, + 'shuffle': False, + 'num_workers': args.num_workers, + 'pin_memory': True + } + + # 4. sampler + self.data_sampler = config.TRAINER.DATA_SAMPLER + self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET + self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT + self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE + self.repeat = config.TRAINER.SB_REPEAT + + # (optional) RandomSampler for debugging + + # misc configurations + self.parallel_load_data = getattr(args, 'parallel_load_data', False) + self.seed = config.TRAINER.SEED # 66 + + def setup(self, stage=None): + """ + Setup train / val / test dataset. This method will be called by PL automatically. + Args: + stage (str): 'fit' in training phase, and 'test' in testing phase. + """ + + assert stage in ['fit', 'test'], "stage must be either fit or test" + + try: + self.world_size = dist.get_world_size() + self.rank = dist.get_rank() + logger.info(f"[rank:{self.rank}] world_size: {self.world_size}") + except AssertionError as ae: + self.world_size = 1 + self.rank = 0 + logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)") + + if stage == 'fit': + self.train_dataset = self._setup_dataset( + self.train_data_root, + self.train_npz_root, + self.train_list_path, + self.train_intrinsic_path, + mode='train', + min_overlap_score=self.min_overlap_score_train, + pose_dir=self.train_pose_root) + # setup multiple (optional) validation subsets + if isinstance(self.val_list_path, (list, tuple)): + self.val_dataset = [] + if not isinstance(self.val_npz_root, (list, tuple)): + self.val_npz_root = [self.val_npz_root for _ in range(len(self.val_list_path))] + for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root): + self.val_dataset.append(self._setup_dataset( + self.val_data_root, + npz_root, + npz_list, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root)) + else: + self.val_dataset = self._setup_dataset( + self.val_data_root, + self.val_npz_root, + self.val_list_path, + self.val_intrinsic_path, + mode='val', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.val_pose_root) + logger.info(f'[rank:{self.rank}] Train & Val Dataset loaded!') + else: # stage == 'test + self.test_dataset = self._setup_dataset( + self.test_data_root, + self.test_npz_root, + self.test_list_path, + self.test_intrinsic_path, + mode='test', + min_overlap_score=self.min_overlap_score_test, + pose_dir=self.test_pose_root) + logger.info(f'[rank:{self.rank}]: Test Dataset loaded!') + + def _setup_dataset(self, + data_root, + split_npz_root, + scene_list_path, + intri_path, + mode='train', + min_overlap_score=0., + pose_dir=None): + """ Setup train / val / test set""" + with open(scene_list_path, 'r') as f: + npz_names = [name.split()[0] for name in f.readlines()] + + if mode == 'train': + local_npz_names = get_local_split(npz_names, self.world_size, self.rank, self.seed) + else: + local_npz_names = npz_names + logger.info(f'[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.') + + dataset_builder = self._build_concat_dataset_parallel \ + if self.parallel_load_data \ + else self._build_concat_dataset + return dataset_builder(data_root, local_npz_names, split_npz_root, intri_path, + mode=mode, min_overlap_score=min_overlap_score, pose_dir=pose_dir) + + def _build_concat_dataset( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None + ): + datasets = [] + augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + for npz_name in tqdm(npz_names, + desc=f'[rank:{self.rank}] loading {mode} datasets', + disable=int(self.rank) != 0): + # `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time. + npz_path = osp.join(npz_dir, npz_name) + if data_source == 'ScanNet': + datasets.append( + ScanNetDataset(data_root, + npz_path, + intrinsic_path, + mode=mode, + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir)) + elif data_source == 'MegaDepth': + datasets.append( + MegaDepthDataset(data_root, + npz_path, + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + augment_fn=augment_fn, + coarse_scale=self.coarse_scale)) + else: + raise NotImplementedError() + return ConcatDataset(datasets) + + def _build_concat_dataset_parallel( + self, + data_root, + npz_names, + npz_dir, + intrinsic_path, + mode, + min_overlap_score=0., + pose_dir=None, + ): + augment_fn = self.augment_fn if mode == 'train' else None + data_source = self.trainval_data_source if mode in ['train', 'val'] else self.test_data_source + if str(data_source).lower() == 'megadepth': + npz_names = [f'{n}.npz' for n in npz_names] + with tqdm_joblib(tqdm(desc=f'[rank:{self.rank}] loading {mode} datasets', + total=len(npz_names), disable=int(self.rank) != 0)): + if data_source == 'ScanNet': + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + ScanNetDataset, + data_root, + osp.join(npz_dir, x), + intrinsic_path, + mode=mode, + min_overlap_score=min_overlap_score, + augment_fn=augment_fn, + pose_dir=pose_dir))(name) + for name in npz_names) + elif data_source == 'MegaDepth': + # TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers. + raise NotImplementedError() + datasets = Parallel(n_jobs=math.floor(len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()))( + delayed(lambda x: _build_dataset( + MegaDepthDataset, + data_root, + osp.join(npz_dir, x), + mode=mode, + min_overlap_score=min_overlap_score, + img_resize=self.mgdpt_img_resize, + df=self.mgdpt_df, + img_padding=self.mgdpt_img_pad, + depth_padding=self.mgdpt_depth_pad, + augment_fn=augment_fn, + coarse_scale=self.coarse_scale))(name) + for name in npz_names) + else: + raise ValueError(f'Unknown dataset: {data_source}') + return ConcatDataset(datasets) + + def train_dataloader(self): + """ Build training dataloader for ScanNet / MegaDepth. """ + assert self.data_sampler in ['scene_balance'] + logger.info(f'[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!).') + if self.data_sampler == 'scene_balance': + sampler = RandomConcatSampler(self.train_dataset, + self.n_samples_per_subset, + self.subset_replacement, + self.shuffle, self.repeat, self.seed) + else: + sampler = None + dataloader = DataLoader(self.train_dataset, sampler=sampler, **self.train_loader_params) + return dataloader + + def val_dataloader(self): + """ Build validation dataloader for ScanNet / MegaDepth. """ + logger.info(f'[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init.') + if not isinstance(self.val_dataset, abc.Sequence): + sampler = DistributedSampler(self.val_dataset, shuffle=False) + return DataLoader(self.val_dataset, sampler=sampler, **self.val_loader_params) + else: + dataloaders = [] + for dataset in self.val_dataset: + sampler = DistributedSampler(dataset, shuffle=False) + dataloaders.append(DataLoader(dataset, sampler=sampler, **self.val_loader_params)) + return dataloaders + + def test_dataloader(self, *args, **kwargs): + logger.info(f'[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init.') + sampler = DistributedSampler(self.test_dataset, shuffle=False) + return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params) + + +def _build_dataset(dataset: Dataset, *args, **kwargs): + return dataset(*args, **kwargs) diff --git a/third_party/TopicFM/src/lightning_trainer/trainer.py b/third_party/TopicFM/src/lightning_trainer/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..acf51f66130be66b7d3294ca5c081a2df3856d96 --- /dev/null +++ b/third_party/TopicFM/src/lightning_trainer/trainer.py @@ -0,0 +1,244 @@ + +from collections import defaultdict +import pprint +from loguru import logger +from pathlib import Path + +import torch +import numpy as np +import pytorch_lightning as pl +from matplotlib import pyplot as plt + +from src.models import TopicFM +from src.models.utils.supervision import compute_supervision_coarse, compute_supervision_fine +from src.losses.loss import TopicFMLoss +from src.optimizers import build_optimizer, build_scheduler +from src.utils.metrics import ( + compute_symmetrical_epipolar_errors, + compute_pose_errors, + aggregate_metrics +) +from src.utils.plotting import make_matching_figures +from src.utils.comm import gather, all_gather +from src.utils.misc import lower_config, flattenList +from src.utils.profiler import PassThroughProfiler + + +class PL_Trainer(pl.LightningModule): + def __init__(self, config, pretrained_ckpt=None, profiler=None, dump_dir=None): + """ + TODO: + - use the new version of PL logging API. + """ + super().__init__() + # Misc + self.config = config # full config + _config = lower_config(self.config) + self.model_cfg = lower_config(_config['model']) + self.profiler = profiler or PassThroughProfiler() + self.n_vals_plot = max(config.TRAINER.N_VAL_PAIRS_TO_PLOT // config.TRAINER.WORLD_SIZE, 1) + + # Matcher: TopicFM + self.matcher = TopicFM(config=_config['model']) + self.loss = TopicFMLoss(_config) + + # Pretrained weights + if pretrained_ckpt: + state_dict = torch.load(pretrained_ckpt, map_location='cpu')['state_dict'] + self.matcher.load_state_dict(state_dict, strict=True) + logger.info(f"Load \'{pretrained_ckpt}\' as pretrained checkpoint") + + # Testing + self.dump_dir = dump_dir + + def configure_optimizers(self): + # FIXME: The scheduler did not work properly when `--resume_from_checkpoint` + optimizer = build_optimizer(self, self.config) + scheduler = build_scheduler(self.config, optimizer) + return [optimizer], [scheduler] + + def optimizer_step( + self, epoch, batch_idx, optimizer, optimizer_idx, + optimizer_closure, on_tpu, using_native_amp, using_lbfgs): + # learning rate warm up + warmup_step = self.config.TRAINER.WARMUP_STEP + if self.trainer.global_step < warmup_step: + if self.config.TRAINER.WARMUP_TYPE == 'linear': + base_lr = self.config.TRAINER.WARMUP_RATIO * self.config.TRAINER.TRUE_LR + lr = base_lr + \ + (self.trainer.global_step / self.config.TRAINER.WARMUP_STEP) * \ + abs(self.config.TRAINER.TRUE_LR - base_lr) + for pg in optimizer.param_groups: + pg['lr'] = lr + elif self.config.TRAINER.WARMUP_TYPE == 'constant': + pass + else: + raise ValueError(f'Unknown lr warm-up strategy: {self.config.TRAINER.WARMUP_TYPE}') + + # update params + optimizer.step(closure=optimizer_closure) + optimizer.zero_grad() + + def _trainval_inference(self, batch): + with self.profiler.profile("Compute coarse supervision"): + compute_supervision_coarse(batch, self.config) + + with self.profiler.profile("TopicFM"): + self.matcher(batch) + + with self.profiler.profile("Compute fine supervision"): + compute_supervision_fine(batch, self.config) + + with self.profiler.profile("Compute losses"): + self.loss(batch) + + def _compute_metrics(self, batch): + with self.profiler.profile("Copmute metrics"): + compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match + compute_pose_errors(batch, self.config) # compute R_errs, t_errs, pose_errs for each pair + + rel_pair_names = list(zip(*batch['pair_names'])) + bs = batch['image0'].size(0) + metrics = { + # to filter duplicate pairs caused by DistributedSampler + 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], + 'epi_errs': [batch['epi_errs'][batch['m_bids'] == b].cpu().numpy() for b in range(bs)], + 'R_errs': batch['R_errs'], + 't_errs': batch['t_errs'], + 'inliers': batch['inliers']} + ret_dict = {'metrics': metrics} + return ret_dict, rel_pair_names + + def training_step(self, batch, batch_idx): + self._trainval_inference(batch) + + # logging + if self.trainer.global_rank == 0 and self.global_step % self.trainer.log_every_n_steps == 0: + # scalars + for k, v in batch['loss_scalars'].items(): + self.logger.experiment.add_scalar(f'train/{k}', v, self.global_step) + + # figures + if self.config.TRAINER.ENABLE_PLOTTING: + compute_symmetrical_epipolar_errors(batch) # compute epi_errs for each match + figures = make_matching_figures(batch, self.config, self.config.TRAINER.PLOT_MODE) + for k, v in figures.items(): + self.logger.experiment.add_figure(f'train_match/{k}', v, self.global_step) + + return {'loss': batch['loss']} + + def training_epoch_end(self, outputs): + avg_loss = torch.stack([x['loss'] for x in outputs]).mean() + if self.trainer.global_rank == 0: + self.logger.experiment.add_scalar( + 'train/avg_loss_on_epoch', avg_loss, + global_step=self.current_epoch) + + def validation_step(self, batch, batch_idx): + self._trainval_inference(batch) + + ret_dict, _ = self._compute_metrics(batch) + + val_plot_interval = max(self.trainer.num_val_batches[0] // self.n_vals_plot, 1) + figures = {self.config.TRAINER.PLOT_MODE: []} + if batch_idx % val_plot_interval == 0: + figures = make_matching_figures(batch, self.config, mode=self.config.TRAINER.PLOT_MODE) + + return { + **ret_dict, + 'loss_scalars': batch['loss_scalars'], + 'figures': figures, + } + + def validation_epoch_end(self, outputs): + # handle multiple validation sets + multi_outputs = [outputs] if not isinstance(outputs[0], (list, tuple)) else outputs + multi_val_metrics = defaultdict(list) + + for valset_idx, outputs in enumerate(multi_outputs): + # since pl performs sanity_check at the very begining of the training + cur_epoch = self.trainer.current_epoch + if not self.trainer.resume_from_checkpoint and self.trainer.running_sanity_check: + cur_epoch = -1 + + # 1. loss_scalars: dict of list, on cpu + _loss_scalars = [o['loss_scalars'] for o in outputs] + loss_scalars = {k: flattenList(all_gather([_ls[k] for _ls in _loss_scalars])) for k in _loss_scalars[0]} + + # 2. val metrics: dict of list, numpy + _metrics = [o['metrics'] for o in outputs] + metrics = {k: flattenList(all_gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} + # NOTE: all ranks need to `aggregate_merics`, but only log at rank-0 + val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) + for thr in [5, 10, 20]: + multi_val_metrics[f'auc@{thr}'].append(val_metrics_4tb[f'auc@{thr}']) + + # 3. figures + _figures = [o['figures'] for o in outputs] + figures = {k: flattenList(gather(flattenList([_me[k] for _me in _figures]))) for k in _figures[0]} + + # tensorboard records only on rank 0 + if self.trainer.global_rank == 0: + for k, v in loss_scalars.items(): + mean_v = torch.stack(v).mean() + self.logger.experiment.add_scalar(f'val_{valset_idx}/avg_{k}', mean_v, global_step=cur_epoch) + + for k, v in val_metrics_4tb.items(): + self.logger.experiment.add_scalar(f"metrics_{valset_idx}/{k}", v, global_step=cur_epoch) + + for k, v in figures.items(): + if self.trainer.global_rank == 0: + for plot_idx, fig in enumerate(v): + self.logger.experiment.add_figure( + f'val_match_{valset_idx}/{k}/pair-{plot_idx}', fig, cur_epoch, close=True) + plt.close('all') + + for thr in [5, 10, 20]: + # log on all ranks for ModelCheckpoint callback to work properly + self.log(f'auc@{thr}', torch.tensor(np.mean(multi_val_metrics[f'auc@{thr}']))) # ckpt monitors on this + + def test_step(self, batch, batch_idx): + with self.profiler.profile("TopicFM"): + self.matcher(batch) + + ret_dict, rel_pair_names = self._compute_metrics(batch) + + with self.profiler.profile("dump_results"): + if self.dump_dir is not None: + # dump results for further analysis + keys_to_save = {'mkpts0_f', 'mkpts1_f', 'mconf', 'epi_errs'} + pair_names = list(zip(*batch['pair_names'])) + bs = batch['image0'].shape[0] + dumps = [] + for b_id in range(bs): + item = {} + mask = batch['m_bids'] == b_id + item['pair_names'] = pair_names[b_id] + item['identifier'] = '#'.join(rel_pair_names[b_id]) + for key in keys_to_save: + item[key] = batch[key][mask].cpu().numpy() + for key in ['R_errs', 't_errs', 'inliers']: + item[key] = batch[key][b_id] + dumps.append(item) + ret_dict['dumps'] = dumps + + return ret_dict + + def test_epoch_end(self, outputs): + # metrics: dict of list, numpy + _metrics = [o['metrics'] for o in outputs] + metrics = {k: flattenList(gather(flattenList([_me[k] for _me in _metrics]))) for k in _metrics[0]} + + # [{key: [{...}, *#bs]}, *#batch] + if self.dump_dir is not None: + Path(self.dump_dir).mkdir(parents=True, exist_ok=True) + _dumps = flattenList([o['dumps'] for o in outputs]) # [{...}, #bs*#batch] + dumps = flattenList(gather(_dumps)) # [{...}, #proc*#bs*#batch] + logger.info(f'Prediction and evaluation results will be saved to: {self.dump_dir}') + + if self.trainer.global_rank == 0: + print(self.profiler.summary()) + val_metrics_4tb = aggregate_metrics(metrics, self.config.TRAINER.EPI_ERR_THR) + logger.info('\n' + pprint.pformat(val_metrics_4tb)) + if self.dump_dir is not None: + np.save(Path(self.dump_dir) / 'TopicFM_pred_eval', dumps) diff --git a/third_party/TopicFM/src/losses/loss.py b/third_party/TopicFM/src/losses/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..4be58498579c9fe649ed0ce2d42f230e59cef581 --- /dev/null +++ b/third_party/TopicFM/src/losses/loss.py @@ -0,0 +1,182 @@ +from loguru import logger + +import torch +import torch.nn as nn + + +def sample_non_matches(pos_mask, match_ids=None, sampling_ratio=10): + # assert (pos_mask.shape == mask.shape) # [B, H*W, H*W] + if match_ids is not None: + HW = pos_mask.shape[1] + b_ids, i_ids, j_ids = match_ids + if len(b_ids) == 0: + return ~pos_mask + + neg_mask = torch.zeros_like(pos_mask) + probs = torch.ones((HW - 1)//3, device=pos_mask.device) + for _ in range(sampling_ratio): + d = torch.multinomial(probs, len(j_ids), replacement=True) + sampled_j_ids = (j_ids + d*3 + 1) % HW + neg_mask[b_ids, i_ids, sampled_j_ids] = True + # neg_mask = neg_matrix == 1 + else: + neg_mask = ~pos_mask + + return neg_mask + + +class TopicFMLoss(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config # config under the global namespace + self.loss_config = config['model']['loss'] + self.match_type = self.config['model']['match_coarse']['match_type'] + + # coarse-level + self.correct_thr = self.loss_config['fine_correct_thr'] + self.c_pos_w = self.loss_config['pos_weight'] + self.c_neg_w = self.loss_config['neg_weight'] + # fine-level + self.fine_type = self.loss_config['fine_type'] + + def compute_coarse_loss(self, conf, topic_mat, conf_gt, match_ids=None, weight=None): + """ Point-wise CE / Focal Loss with 0 / 1 confidence as gt. + Args: + conf (torch.Tensor): (N, HW0, HW1) / (N, HW0+1, HW1+1) + conf_gt (torch.Tensor): (N, HW0, HW1) + weight (torch.Tensor): (N, HW0, HW1) + """ + pos_mask = conf_gt == 1 + neg_mask = sample_non_matches(pos_mask, match_ids=match_ids) + c_pos_w, c_neg_w = self.c_pos_w, self.c_neg_w + # corner case: no gt coarse-level match at all + if not pos_mask.any(): # assign a wrong gt + pos_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_pos_w = 0. + if not neg_mask.any(): + neg_mask[0, 0, 0] = True + if weight is not None: + weight[0, 0, 0] = 0. + c_neg_w = 0. + + conf = torch.clamp(conf, 1e-6, 1 - 1e-6) + alpha = self.loss_config['focal_alpha'] + + loss = 0.0 + if isinstance(topic_mat, torch.Tensor): + pos_topic = topic_mat[pos_mask] + loss_pos_topic = - alpha * (pos_topic + 1e-6).log() + neg_topic = topic_mat[neg_mask] + loss_neg_topic = - alpha * (1 - neg_topic + 1e-6).log() + if weight is not None: + loss_pos_topic = loss_pos_topic * weight[pos_mask] + loss_neg_topic = loss_neg_topic * weight[neg_mask] + loss = loss_pos_topic.mean() + loss_neg_topic.mean() + + pos_conf = conf[pos_mask] + loss_pos = - alpha * pos_conf.log() + # handle loss weights + if weight is not None: + # Different from dense-spvs, the loss w.r.t. padded regions aren't directly zeroed out, + # but only through manually setting corresponding regions in sim_matrix to '-inf'. + loss_pos = loss_pos * weight[pos_mask] + + loss = loss + c_pos_w * loss_pos.mean() + + return loss + + def compute_fine_loss(self, expec_f, expec_f_gt): + if self.fine_type == 'l2_with_std': + return self._compute_fine_loss_l2_std(expec_f, expec_f_gt) + elif self.fine_type == 'l2': + return self._compute_fine_loss_l2(expec_f, expec_f_gt) + else: + raise NotImplementedError() + + def _compute_fine_loss_l2(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 2] + expec_f_gt (torch.Tensor): [M, 2] + """ + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + if correct_mask.sum() == 0: + if self.training: # this seldomly happen when training, since we pad prediction with gt + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + else: + return None + offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask]) ** 2).sum(-1) + return offset_l2.mean() + + def _compute_fine_loss_l2_std(self, expec_f, expec_f_gt): + """ + Args: + expec_f (torch.Tensor): [M, 3] + expec_f_gt (torch.Tensor): [M, 2] + """ + # correct_mask tells you which pair to compute fine-loss + correct_mask = torch.linalg.norm(expec_f_gt, ord=float('inf'), dim=1) < self.correct_thr + + # use std as weight that measures uncertainty + std = expec_f[:, 2] + inverse_std = 1. / torch.clamp(std, min=1e-10) + weight = (inverse_std / torch.mean(inverse_std)).detach() # avoid minizing loss through increase std + + # corner case: no correct coarse match found + if not correct_mask.any(): + if self.training: # this seldomly happen during training, since we pad prediction with gt + # sometimes there is not coarse-level gt at all. + logger.warning("assign a false supervision to avoid ddp deadlock") + correct_mask[0] = True + weight[0] = 0. + else: + return None + + # l2 loss with std + offset_l2 = ((expec_f_gt[correct_mask] - expec_f[correct_mask, :2]) ** 2).sum(-1) + loss = (offset_l2 * weight[correct_mask]).mean() + + return loss + + @torch.no_grad() + def compute_c_weight(self, data): + """ compute element-wise weights for computing coarse-level loss. """ + if 'mask0' in data: + c_weight = (data['mask0'].flatten(-2)[..., None] * data['mask1'].flatten(-2)[:, None]).float() + else: + c_weight = None + return c_weight + + def forward(self, data): + """ + Update: + data (dict): update{ + 'loss': [1] the reduced loss across a batch, + 'loss_scalars' (dict): loss scalars for tensorboard_record + } + """ + loss_scalars = {} + # 0. compute element-wise loss weight + c_weight = self.compute_c_weight(data) + + # 1. coarse-level loss + loss_c = self.compute_coarse_loss(data['conf_matrix'], data['topic_matrix'], + data['conf_matrix_gt'], match_ids=(data['spv_b_ids'], data['spv_i_ids'], data['spv_j_ids']), + weight=c_weight) + loss = loss_c * self.loss_config['coarse_weight'] + loss_scalars.update({"loss_c": loss_c.clone().detach().cpu()}) + + # 2. fine-level loss + loss_f = self.compute_fine_loss(data['expec_f'], data['expec_f_gt']) + if loss_f is not None: + loss += loss_f * self.loss_config['fine_weight'] + loss_scalars.update({"loss_f": loss_f.clone().detach().cpu()}) + else: + assert self.training is False + loss_scalars.update({'loss_f': torch.tensor(1.)}) # 1 is the upper bound + + loss_scalars.update({'loss': loss.clone().detach().cpu()}) + data.update({"loss": loss, "loss_scalars": loss_scalars}) diff --git a/third_party/TopicFM/src/models/__init__.py b/third_party/TopicFM/src/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9abdbdaebbf6c91a6fdc24e23d62c73003b204bf --- /dev/null +++ b/third_party/TopicFM/src/models/__init__.py @@ -0,0 +1 @@ +from .topic_fm import TopicFM diff --git a/third_party/TopicFM/src/models/backbone/__init__.py b/third_party/TopicFM/src/models/backbone/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..53f98db4e910b46716bed7cfc6ebbf8c8bfad399 --- /dev/null +++ b/third_party/TopicFM/src/models/backbone/__init__.py @@ -0,0 +1,5 @@ +from .fpn import FPN + + +def build_backbone(config): + return FPN(config['fpn']) diff --git a/third_party/TopicFM/src/models/backbone/fpn.py b/third_party/TopicFM/src/models/backbone/fpn.py new file mode 100644 index 0000000000000000000000000000000000000000..93cc475f57317f9dbb8132cdfe0297391972f9e2 --- /dev/null +++ b/third_party/TopicFM/src/models/backbone/fpn.py @@ -0,0 +1,109 @@ +import torch.nn as nn +import torch.nn.functional as F + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution without padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, planes, stride=1, bn=True): + super().__init__() + self.conv = conv3x3(in_planes, planes, stride) + self.bn = nn.BatchNorm2d(planes) if bn is True else None + self.act = nn.GELU() + + def forward(self, x): + y = self.conv(x) + if self.bn: + y = self.bn(y) #F.layer_norm(y, y.shape[1:]) + y = self.act(y) + return y + + +class FPN(nn.Module): + """ + ResNet+FPN, output resolution are 1/8 and 1/2. + Each block has 2 layers. + """ + + def __init__(self, config): + super().__init__() + # Config + block = ConvBlock + initial_dim = config['initial_dim'] + block_dims = config['block_dims'] + + # Class Variable + self.in_planes = initial_dim + + # Networks + self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(initial_dim) + self.relu = nn.ReLU(inplace=True) + + self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 + self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 + self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 + self.layer4 = self._make_layer(block, block_dims[3], stride=2) # 1/16 + + # 3. FPN upsample + self.layer3_outconv = conv1x1(block_dims[2], block_dims[3]) + self.layer3_outconv2 = nn.Sequential( + ConvBlock(block_dims[3], block_dims[2]), + conv3x3(block_dims[2], block_dims[2]), + ) + self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) + self.layer2_outconv2 = nn.Sequential( + ConvBlock(block_dims[2], block_dims[1]), + conv3x3(block_dims[1], block_dims[1]), + ) + self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) + self.layer1_outconv2 = nn.Sequential( + ConvBlock(block_dims[1], block_dims[0]), + conv3x3(block_dims[0], block_dims[0]), + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, dim, stride=1): + layer1 = block(self.in_planes, dim, stride=stride) + layer2 = block(dim, dim, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + # ResNet Backbone + x0 = self.relu(self.bn1(self.conv1(x))) + x1 = self.layer1(x0) # 1/2 + x2 = self.layer2(x1) # 1/4 + x3 = self.layer3(x2) # 1/8 + x4 = self.layer4(x3) # 1/16 + + # FPN + x4_out_2x = F.interpolate(x4, scale_factor=2., mode='bilinear', align_corners=True) + x3_out = self.layer3_outconv(x3) + x3_out = self.layer3_outconv2(x3_out+x4_out_2x) + + x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) + x2_out = self.layer2_outconv(x2) + x2_out = self.layer2_outconv2(x2_out+x3_out_2x) + + x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True) + x1_out = self.layer1_outconv(x1) + x1_out = self.layer1_outconv2(x1_out+x2_out_2x) + + return [x3_out, x1_out] diff --git a/third_party/TopicFM/src/models/modules/__init__.py b/third_party/TopicFM/src/models/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..59cf36da37104dcf080e1b2c119c8123fa8d147f --- /dev/null +++ b/third_party/TopicFM/src/models/modules/__init__.py @@ -0,0 +1,2 @@ +from .transformer import LocalFeatureTransformer, TopicFormer +from .fine_preprocess import FinePreprocess diff --git a/third_party/TopicFM/src/models/modules/fine_preprocess.py b/third_party/TopicFM/src/models/modules/fine_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..4c8d264c1895be8f4e124fc3982d4e0d3b876af3 --- /dev/null +++ b/third_party/TopicFM/src/models/modules/fine_preprocess.py @@ -0,0 +1,59 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops.einops import rearrange, repeat + + +class FinePreprocess(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.cat_c_feat = config['fine_concat_coarse_feat'] + self.W = self.config['fine_window_size'] + + d_model_c = self.config['coarse']['d_model'] + d_model_f = self.config['fine']['d_model'] + self.d_model_f = d_model_f + if self.cat_c_feat: + self.down_proj = nn.Linear(d_model_c, d_model_f, bias=True) + self.merge_feat = nn.Linear(2*d_model_f, d_model_f, bias=True) + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.kaiming_normal_(p, mode="fan_out", nonlinearity="relu") + + def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data): + W = self.W + stride = data['hw0_f'][0] // data['hw0_c'][0] + + data.update({'W': W}) + if data['b_ids'].shape[0] == 0: + feat0 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) + feat1 = torch.empty(0, self.W**2, self.d_model_f, device=feat_f0.device) + return feat0, feat1 + + # 1. unfold(crop) all local windows + feat_f0_unfold = F.unfold(feat_f0, kernel_size=(W, W), stride=stride, padding=W//2) + feat_f0_unfold = rearrange(feat_f0_unfold, 'n (c ww) l -> n l ww c', ww=W**2) + feat_f1_unfold = F.unfold(feat_f1, kernel_size=(W, W), stride=stride, padding=W//2) + feat_f1_unfold = rearrange(feat_f1_unfold, 'n (c ww) l -> n l ww c', ww=W**2) + + # 2. select only the predicted matches + feat_f0_unfold = feat_f0_unfold[data['b_ids'], data['i_ids']] # [n, ww, cf] + feat_f1_unfold = feat_f1_unfold[data['b_ids'], data['j_ids']] + + # option: use coarse-level feature as context: concat and linear + if self.cat_c_feat: + feat_c_win = self.down_proj(torch.cat([feat_c0[data['b_ids'], data['i_ids']], + feat_c1[data['b_ids'], data['j_ids']]], 0)) # [2n, c] + feat_cf_win = self.merge_feat(torch.cat([ + torch.cat([feat_f0_unfold, feat_f1_unfold], 0), # [2n, ww, cf] + repeat(feat_c_win, 'n c -> n ww c', ww=W**2), # [2n, ww, cf] + ], -1)) + feat_f0_unfold, feat_f1_unfold = torch.chunk(feat_cf_win, 2, dim=0) + + return feat_f0_unfold, feat_f1_unfold diff --git a/third_party/TopicFM/src/models/modules/linear_attention.py b/third_party/TopicFM/src/models/modules/linear_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..af6cd825033e98b7be15cc694ce28110ef84cc93 --- /dev/null +++ b/third_party/TopicFM/src/models/modules/linear_attention.py @@ -0,0 +1,81 @@ +""" +Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" +Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py +""" + +import torch +from torch.nn import Module, Dropout + + +def elu_feature_map(x): + return torch.nn.functional.elu(x) + 1 + + +class LinearAttention(Module): + def __init__(self, eps=1e-6): + super().__init__() + self.feature_map = elu_feature_map + self.eps = eps + + def forward(self, queries, keys, values, q_mask=None, kv_mask=None): + """ Multi-Head linear attention proposed in "Transformers are RNNs" + Args: + queries: [N, L, H, D] + keys: [N, S, H, D] + values: [N, S, H, D] + q_mask: [N, L] + kv_mask: [N, S] + Returns: + queried_values: (N, L, H, D) + """ + Q = self.feature_map(queries) + K = self.feature_map(keys) + + # set padded position to zero + if q_mask is not None: + Q = Q * q_mask[:, :, None, None] + if kv_mask is not None: + K = K * kv_mask[:, :, None, None] + values = values * kv_mask[:, :, None, None] + + v_length = values.size(1) + values = values / v_length # prevent fp16 overflow + KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V + Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps) + queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length + + return queried_values.contiguous() + + +class FullAttention(Module): + def __init__(self, use_dropout=False, attention_dropout=0.1): + super().__init__() + self.use_dropout = use_dropout + self.dropout = Dropout(attention_dropout) + + def forward(self, queries, keys, values, q_mask=None, kv_mask=None): + """ Multi-head scaled dot-product attention, a.k.a full attention. + Args: + queries: [N, L, H, D] + keys: [N, S, H, D] + values: [N, S, H, D] + q_mask: [N, L] + kv_mask: [N, S] + Returns: + queried_values: (N, L, H, D) + """ + + # Compute the unnormalized attention and apply the masks + QK = torch.einsum("nlhd,nshd->nlsh", queries, keys) + if kv_mask is not None: + QK.masked_fill_(~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]).bool(), -1e9) + + # Compute the attention and the weighted average + softmax_temp = 1. / queries.size(3)**.5 # sqrt(D) + A = torch.softmax(softmax_temp * QK, dim=2) + if self.use_dropout: + A = self.dropout(A) + + queried_values = torch.einsum("nlsh,nshd->nlhd", A, values) + + return queried_values.contiguous() diff --git a/third_party/TopicFM/src/models/modules/transformer.py b/third_party/TopicFM/src/models/modules/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..27ff8f6554844b1e14a7094fcbad40876f766db8 --- /dev/null +++ b/third_party/TopicFM/src/models/modules/transformer.py @@ -0,0 +1,232 @@ +from loguru import logger +import copy +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .linear_attention import LinearAttention, FullAttention + + +class LoFTREncoderLayer(nn.Module): + def __init__(self, + d_model, + nhead, + attention='linear'): + super(LoFTREncoderLayer, self).__init__() + + self.dim = d_model // nhead + self.nhead = nhead + + # multi-head attention + self.q_proj = nn.Linear(d_model, d_model, bias=False) + self.k_proj = nn.Linear(d_model, d_model, bias=False) + self.v_proj = nn.Linear(d_model, d_model, bias=False) + self.attention = LinearAttention() if attention == 'linear' else FullAttention() + self.merge = nn.Linear(d_model, d_model, bias=False) + + # feed-forward network + self.mlp = nn.Sequential( + nn.Linear(d_model*2, d_model*2, bias=False), + nn.GELU(), + nn.Linear(d_model*2, d_model, bias=False), + ) + + # norm and dropout + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + + def forward(self, x, source, x_mask=None, source_mask=None): + """ + Args: + x (torch.Tensor): [N, L, C] + source (torch.Tensor): [N, S, C] + x_mask (torch.Tensor): [N, L] (optional) + source_mask (torch.Tensor): [N, S] (optional) + """ + bs = x.shape[0] + query, key, value = x, source, source + + # multi-head attention + query = self.q_proj(query).view(bs, -1, self.nhead, self.dim) # [N, L, (H, D)] + key = self.k_proj(key).view(bs, -1, self.nhead, self.dim) # [N, S, (H, D)] + value = self.v_proj(value).view(bs, -1, self.nhead, self.dim) + message = self.attention(query, key, value, q_mask=x_mask, kv_mask=source_mask) # [N, L, (H, D)] + message = self.merge(message.view(bs, -1, self.nhead*self.dim)) # [N, L, C] + message = self.norm1(message) + + # feed-forward network + message = self.mlp(torch.cat([x, message], dim=2)) + message = self.norm2(message) + + return x + message + + +class TopicFormer(nn.Module): + """A Local Feature Transformer (LoFTR) module.""" + + def __init__(self, config): + super(TopicFormer, self).__init__() + + self.config = config + self.d_model = config['d_model'] + self.nhead = config['nhead'] + self.layer_names = config['layer_names'] + encoder_layer = LoFTREncoderLayer(config['d_model'], config['nhead'], config['attention']) + self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(len(self.layer_names))]) + + self.topic_transformers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2*config['n_topic_transformers'])]) if config['n_samples'] > 0 else None #nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2)]) + self.n_iter_topic_transformer = config['n_topic_transformers'] + + self.seed_tokens = nn.Parameter(torch.randn(config['n_topics'], config['d_model'])) + self.register_parameter('seed_tokens', self.seed_tokens) + self.n_samples = config['n_samples'] + + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def sample_topic(self, prob_topics, topics, L): + """ + Args: + topics (torch.Tensor): [N, L+S, K] + """ + prob_topics0, prob_topics1 = prob_topics[:, :L], prob_topics[:, L:] + topics0, topics1 = topics[:, :L], topics[:, L:] + + theta0 = F.normalize(prob_topics0.sum(dim=1), p=1, dim=-1) # [N, K] + theta1 = F.normalize(prob_topics1.sum(dim=1), p=1, dim=-1) + theta = F.normalize(theta0 * theta1, p=1, dim=-1) + if self.n_samples == 0: + return None + if self.training: + sampled_inds = torch.multinomial(theta, self.n_samples) + sampled_values = torch.gather(theta, dim=-1, index=sampled_inds) + else: + sampled_values, sampled_inds = torch.topk(theta, self.n_samples, dim=-1) + sampled_topics0 = torch.gather(topics0, dim=-1, index=sampled_inds.unsqueeze(1).repeat(1, topics0.shape[1], 1)) + sampled_topics1 = torch.gather(topics1, dim=-1, index=sampled_inds.unsqueeze(1).repeat(1, topics1.shape[1], 1)) + return sampled_topics0, sampled_topics1 + + def reduce_feat(self, feat, topick, N, C): + len_topic = topick.sum(dim=-1).int() + max_len = len_topic.max().item() + selected_ids = topick.bool() + resized_feat = torch.zeros((N, max_len, C), dtype=torch.float32, device=feat.device) + new_mask = torch.zeros_like(resized_feat[..., 0]).bool() + for i in range(N): + new_mask[i, :len_topic[i]] = True + resized_feat[new_mask, :] = feat[selected_ids, :] + return resized_feat, new_mask, selected_ids + + def forward(self, feat0, feat1, mask0=None, mask1=None): + """ + Args: + feat0 (torch.Tensor): [N, L, C] + feat1 (torch.Tensor): [N, S, C] + mask0 (torch.Tensor): [N, L] (optional) + mask1 (torch.Tensor): [N, S] (optional) + """ + + assert self.d_model == feat0.shape[2], "the feature number of src and transformer must be equal" + N, L, S, C, K = feat0.shape[0], feat0.shape[1], feat1.shape[1], feat0.shape[2], self.config['n_topics'] + + seeds = self.seed_tokens.unsqueeze(0).repeat(N, 1, 1) + + feat = torch.cat((feat0, feat1), dim=1) + if mask0 is not None: + mask = torch.cat((mask0, mask1), dim=-1) + else: + mask = None + + for layer, name in zip(self.layers, self.layer_names): + if name == 'seed': + # seeds = layer(seeds, feat0, None, mask0) + # seeds = layer(seeds, feat1, None, mask1) + seeds = layer(seeds, feat, None, mask) + elif name == 'feat': + feat0 = layer(feat0, seeds, mask0, None) + feat1 = layer(feat1, seeds, mask1, None) + + dmatrix = torch.einsum("nmd,nkd->nmk", feat, seeds) + prob_topics = F.softmax(dmatrix, dim=-1) + + feat_topics = torch.zeros_like(dmatrix).scatter_(-1, torch.argmax(dmatrix, dim=-1, keepdim=True), 1.0) + + if mask is not None: + feat_topics = feat_topics * mask.unsqueeze(-1) + prob_topics = prob_topics * mask.unsqueeze(-1) + + if (feat_topics.detach().sum(dim=1).sum(dim=0) > 100).sum() <= 3: + logger.warning("topic distribution is highly sparse!") + sampled_topics = self.sample_topic(prob_topics.detach(), feat_topics, L) + if sampled_topics is not None: + updated_feat0, updated_feat1 = torch.zeros_like(feat0), torch.zeros_like(feat1) + s_topics0, s_topics1 = sampled_topics + for k in range(s_topics0.shape[-1]): + topick0, topick1 = s_topics0[..., k], s_topics1[..., k] # [N, L+S] + if (topick0.sum() > 0) and (topick1.sum() > 0): + new_feat0, new_mask0, selected_ids0 = self.reduce_feat(feat0, topick0, N, C) + new_feat1, new_mask1, selected_ids1 = self.reduce_feat(feat1, topick1, N, C) + for idt in range(self.n_iter_topic_transformer): + new_feat0 = self.topic_transformers[idt*2](new_feat0, new_feat0, new_mask0, new_mask0) + new_feat1 = self.topic_transformers[idt*2](new_feat1, new_feat1, new_mask1, new_mask1) + new_feat0 = self.topic_transformers[idt*2+1](new_feat0, new_feat1, new_mask0, new_mask1) + new_feat1 = self.topic_transformers[idt*2+1](new_feat1, new_feat0, new_mask1, new_mask0) + updated_feat0[selected_ids0, :] = new_feat0[new_mask0, :] + updated_feat1[selected_ids1, :] = new_feat1[new_mask1, :] + + feat0 = (1 - s_topics0.sum(dim=-1, keepdim=True)) * feat0 + updated_feat0 + feat1 = (1 - s_topics1.sum(dim=-1, keepdim=True)) * feat1 + updated_feat1 + + conf_matrix = torch.einsum("nlc,nsc->nls", feat0, feat1) / C**.5 #(C * temperature) + if self.training: + topic_matrix = torch.einsum("nlk,nsk->nls", prob_topics[:, :L], prob_topics[:, L:]) + outlier_mask = torch.einsum("nlk,nsk->nls", feat_topics[:, :L], feat_topics[:, L:]) + else: + topic_matrix = {"img0": feat_topics[:, :L], "img1": feat_topics[:, L:]} + outlier_mask = torch.ones_like(conf_matrix) + if mask0 is not None: + outlier_mask = (outlier_mask * mask0[..., None] * mask1[:, None]) #.bool() + conf_matrix.masked_fill_(~outlier_mask.bool(), -1e9) + conf_matrix = F.softmax(conf_matrix, 1) * F.softmax(conf_matrix, 2) # * topic_matrix + + return feat0, feat1, conf_matrix, topic_matrix + + +class LocalFeatureTransformer(nn.Module): + """A Local Feature Transformer (LoFTR) module.""" + + def __init__(self, config): + super(LocalFeatureTransformer, self).__init__() + + self.config = config + self.d_model = config['d_model'] + self.nhead = config['nhead'] + self.layer_names = config['layer_names'] + encoder_layer = LoFTREncoderLayer(config['d_model'], config['nhead'], config['attention']) + self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for _ in range(2)]) #len(self.layer_names))]) + self._reset_parameters() + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, feat0, feat1, mask0=None, mask1=None): + """ + Args: + feat0 (torch.Tensor): [N, L, C] + feat1 (torch.Tensor): [N, S, C] + mask0 (torch.Tensor): [N, L] (optional) + mask1 (torch.Tensor): [N, S] (optional) + """ + + assert self.d_model == feat0.shape[2], "the feature number of src and transformer must be equal" + + feat0 = self.layers[0](feat0, feat1, mask0, mask1) + feat1 = self.layers[1](feat1, feat0, mask1, mask0) + + return feat0, feat1 diff --git a/third_party/TopicFM/src/models/topic_fm.py b/third_party/TopicFM/src/models/topic_fm.py new file mode 100644 index 0000000000000000000000000000000000000000..95cd22f9b66d08760382fe4cd22c4df918cc9f68 --- /dev/null +++ b/third_party/TopicFM/src/models/topic_fm.py @@ -0,0 +1,79 @@ +import torch +import torch.nn as nn +from einops.einops import rearrange + +from .backbone import build_backbone +from .modules import LocalFeatureTransformer, FinePreprocess, TopicFormer +from .utils.coarse_matching import CoarseMatching +from .utils.fine_matching import FineMatching + + +class TopicFM(nn.Module): + def __init__(self, config): + super().__init__() + # Misc + self.config = config + + # Modules + self.backbone = build_backbone(config) + + self.loftr_coarse = TopicFormer(config['coarse']) + self.coarse_matching = CoarseMatching(config['match_coarse']) + self.fine_preprocess = FinePreprocess(config) + self.loftr_fine = LocalFeatureTransformer(config["fine"]) + self.fine_matching = FineMatching() + + def forward(self, data): + """ + Update: + data (dict): { + 'image0': (torch.Tensor): (N, 1, H, W) + 'image1': (torch.Tensor): (N, 1, H, W) + 'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position + 'mask1'(optional) : (torch.Tensor): (N, H, W) + } + """ + # 1. Local Feature CNN + data.update({ + 'bs': data['image0'].size(0), + 'hw0_i': data['image0'].shape[2:], 'hw1_i': data['image1'].shape[2:] + }) + + if data['hw0_i'] == data['hw1_i']: # faster & better BN convergence + feats_c, feats_f = self.backbone(torch.cat([data['image0'], data['image1']], dim=0)) + (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(data['bs']), feats_f.split(data['bs']) + else: # handle different input shapes + (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(data['image0']), self.backbone(data['image1']) + + data.update({ + 'hw0_c': feat_c0.shape[2:], 'hw1_c': feat_c1.shape[2:], + 'hw0_f': feat_f0.shape[2:], 'hw1_f': feat_f1.shape[2:] + }) + + # 2. coarse-level loftr module + feat_c0 = rearrange(feat_c0, 'n c h w -> n (h w) c') + feat_c1 = rearrange(feat_c1, 'n c h w -> n (h w) c') + + mask_c0 = mask_c1 = None # mask is useful in training + if 'mask0' in data: + mask_c0, mask_c1 = data['mask0'].flatten(-2), data['mask1'].flatten(-2) + + feat_c0, feat_c1, conf_matrix, topic_matrix = self.loftr_coarse(feat_c0, feat_c1, mask_c0, mask_c1) + data.update({"conf_matrix": conf_matrix, "topic_matrix": topic_matrix}) ###### + + # 3. match coarse-level + self.coarse_matching(data) + + # 4. fine-level refinement + feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(feat_f0, feat_f1, feat_c0.detach(), feat_c1.detach(), data) + if feat_f0_unfold.size(0) != 0: # at least one coarse level predicted + feat_f0_unfold, feat_f1_unfold = self.loftr_fine(feat_f0_unfold, feat_f1_unfold) + + # 5. match fine-level + self.fine_matching(feat_f0_unfold, feat_f1_unfold, data) + + def load_state_dict(self, state_dict, *args, **kwargs): + for k in list(state_dict.keys()): + if k.startswith('matcher.'): + state_dict[k.replace('matcher.', '', 1)] = state_dict.pop(k) + return super().load_state_dict(state_dict, *args, **kwargs) diff --git a/third_party/TopicFM/src/models/utils/coarse_matching.py b/third_party/TopicFM/src/models/utils/coarse_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..75adbb5cc465220e759a044f96f86c08da2d7a50 --- /dev/null +++ b/third_party/TopicFM/src/models/utils/coarse_matching.py @@ -0,0 +1,217 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops.einops import rearrange + +INF = 1e9 + +def mask_border(m, b: int, v): + """ Mask borders with value + Args: + m (torch.Tensor): [N, H0, W0, H1, W1] + b (int) + v (m.dtype) + """ + if b <= 0: + return + + m[:, :b] = v + m[:, :, :b] = v + m[:, :, :, :b] = v + m[:, :, :, :, :b] = v + m[:, -b:] = v + m[:, :, -b:] = v + m[:, :, :, -b:] = v + m[:, :, :, :, -b:] = v + + +def mask_border_with_padding(m, bd, v, p_m0, p_m1): + if bd <= 0: + return + + m[:, :bd] = v + m[:, :, :bd] = v + m[:, :, :, :bd] = v + m[:, :, :, :, :bd] = v + + h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int() + h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int() + for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)): + m[b_idx, h0 - bd:] = v + m[b_idx, :, w0 - bd:] = v + m[b_idx, :, :, h1 - bd:] = v + m[b_idx, :, :, :, w1 - bd:] = v + + +def compute_max_candidates(p_m0, p_m1): + """Compute the max candidates of all pairs within a batch + + Args: + p_m0, p_m1 (torch.Tensor): padded masks + """ + h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0] + h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0] + max_cand = torch.sum( + torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0]) + return max_cand + + +class CoarseMatching(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + # general config + self.thr = config['thr'] + self.border_rm = config['border_rm'] + # -- # for trainig fine-level LoFTR + self.train_coarse_percent = config['train_coarse_percent'] + self.train_pad_num_gt_min = config['train_pad_num_gt_min'] + + # we provide 2 options for differentiable matching + self.match_type = config['match_type'] + if self.match_type == 'dual_softmax': + self.temperature = config['dsmax_temperature'] + elif self.match_type == 'sinkhorn': + try: + from .superglue import log_optimal_transport + except ImportError: + raise ImportError("download superglue.py first!") + self.log_optimal_transport = log_optimal_transport + self.bin_score = nn.Parameter( + torch.tensor(config['skh_init_bin_score'], requires_grad=True)) + self.skh_iters = config['skh_iters'] + self.skh_prefilter = config['skh_prefilter'] + else: + raise NotImplementedError() + + def forward(self, data): + """ + Args: + data (dict) + Update: + data (dict): { + 'b_ids' (torch.Tensor): [M'], + 'i_ids' (torch.Tensor): [M'], + 'j_ids' (torch.Tensor): [M'], + 'gt_mask' (torch.Tensor): [M'], + 'mkpts0_c' (torch.Tensor): [M, 2], + 'mkpts1_c' (torch.Tensor): [M, 2], + 'mconf' (torch.Tensor): [M]} + NOTE: M' != M during training. + """ + conf_matrix = data['conf_matrix'] + # predict coarse matches from conf_matrix + data.update(**self.get_coarse_match(conf_matrix, data)) + + @torch.no_grad() + def get_coarse_match(self, conf_matrix, data): + """ + Args: + conf_matrix (torch.Tensor): [N, L, S] + data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c'] + Returns: + coarse_matches (dict): { + 'b_ids' (torch.Tensor): [M'], + 'i_ids' (torch.Tensor): [M'], + 'j_ids' (torch.Tensor): [M'], + 'gt_mask' (torch.Tensor): [M'], + 'm_bids' (torch.Tensor): [M], + 'mkpts0_c' (torch.Tensor): [M, 2], + 'mkpts1_c' (torch.Tensor): [M, 2], + 'mconf' (torch.Tensor): [M]} + """ + axes_lengths = { + 'h0c': data['hw0_c'][0], + 'w0c': data['hw0_c'][1], + 'h1c': data['hw1_c'][0], + 'w1c': data['hw1_c'][1] + } + _device = conf_matrix.device + # 1. confidence thresholding + mask = conf_matrix > self.thr + mask = rearrange(mask, 'b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c', + **axes_lengths) + if 'mask0' not in data: + mask_border(mask, self.border_rm, False) + else: + mask_border_with_padding(mask, self.border_rm, False, + data['mask0'], data['mask1']) + mask = rearrange(mask, 'b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)', + **axes_lengths) + + # 2. mutual nearest + mask = mask \ + * (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0]) \ + * (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0]) + + # 3. find all valid coarse matches + # this only works when at most one `True` in each row + mask_v, all_j_ids = mask.max(dim=2) + b_ids, i_ids = torch.where(mask_v) + j_ids = all_j_ids[b_ids, i_ids] + mconf = conf_matrix[b_ids, i_ids, j_ids] + + # 4. Random sampling of training samples for fine-level LoFTR + # (optional) pad samples with gt coarse-level matches + if self.training: + # NOTE: + # The sampling is performed across all pairs in a batch without manually balancing + # #samples for fine-level increases w.r.t. batch_size + if 'mask0' not in data: + num_candidates_max = mask.size(0) * max( + mask.size(1), mask.size(2)) + else: + num_candidates_max = compute_max_candidates( + data['mask0'], data['mask1']) + num_matches_train = int(num_candidates_max * + self.train_coarse_percent) + num_matches_pred = len(b_ids) + assert self.train_pad_num_gt_min < num_matches_train, "min-num-gt-pad should be less than num-train-matches" + + # pred_indices is to select from prediction + if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min: + pred_indices = torch.arange(num_matches_pred, device=_device) + else: + pred_indices = torch.randint( + num_matches_pred, + (num_matches_train - self.train_pad_num_gt_min, ), + device=_device) + + # gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200) + gt_pad_indices = torch.randint( + len(data['spv_b_ids']), + (max(num_matches_train - num_matches_pred, + self.train_pad_num_gt_min), ), + device=_device) + mconf_gt = torch.zeros(len(data['spv_b_ids']), device=_device) # set conf of gt paddings to all zero + + b_ids, i_ids, j_ids, mconf = map( + lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], + dim=0), + *zip([b_ids, data['spv_b_ids']], [i_ids, data['spv_i_ids']], + [j_ids, data['spv_j_ids']], [mconf, mconf_gt])) + + # These matches select patches that feed into fine-level network + coarse_matches = {'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids} + + # 4. Update with matches in original image resolution + scale = data['hw0_i'][0] / data['hw0_c'][0] + scale0 = scale * data['scale0'][b_ids] if 'scale0' in data else scale + scale1 = scale * data['scale1'][b_ids] if 'scale1' in data else scale + mkpts0_c = torch.stack( + [i_ids % data['hw0_c'][1], i_ids // data['hw0_c'][1]], + dim=1) * scale0 + mkpts1_c = torch.stack( + [j_ids % data['hw1_c'][1], j_ids // data['hw1_c'][1]], + dim=1) * scale1 + + # These matches is the current prediction (for visualization) + coarse_matches.update({ + 'gt_mask': mconf == 0, + 'm_bids': b_ids[mconf != 0], # mconf == 0 => gt matches + 'mkpts0_c': mkpts0_c[mconf != 0], + 'mkpts1_c': mkpts1_c[mconf != 0], + 'mconf': mconf[mconf != 0] + }) + + return coarse_matches diff --git a/third_party/TopicFM/src/models/utils/fine_matching.py b/third_party/TopicFM/src/models/utils/fine_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..018f2fe475600b319998c263a97237ce135c3aaf --- /dev/null +++ b/third_party/TopicFM/src/models/utils/fine_matching.py @@ -0,0 +1,80 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from kornia.geometry.subpix import dsnt +from kornia.utils.grid import create_meshgrid + + +class FineMatching(nn.Module): + """FineMatching with s2d paradigm""" + + def __init__(self): + super().__init__() + + def forward(self, feat_f0, feat_f1, data): + """ + Args: + feat0 (torch.Tensor): [M, WW, C] + feat1 (torch.Tensor): [M, WW, C] + data (dict) + Update: + data (dict):{ + 'expec_f' (torch.Tensor): [M, 3], + 'mkpts0_f' (torch.Tensor): [M, 2], + 'mkpts1_f' (torch.Tensor): [M, 2]} + """ + M, WW, C = feat_f0.shape + W = int(math.sqrt(WW)) + scale = data['hw0_i'][0] / data['hw0_f'][0] + self.M, self.W, self.WW, self.C, self.scale = M, W, WW, C, scale + + # corner case: if no coarse matches found + if M == 0: + assert self.training == False, "M is always >0, when training, see coarse_matching.py" + # logger.warning('No matches found in coarse-level.') + data.update({ + 'expec_f': torch.empty(0, 3, device=feat_f0.device), + 'mkpts0_f': data['mkpts0_c'], + 'mkpts1_f': data['mkpts1_c'], + }) + return + + feat_f0_picked = feat_f0[:, WW//2, :] + + sim_matrix = torch.einsum('mc,mrc->mr', feat_f0_picked, feat_f1) + softmax_temp = 1. / C**.5 + heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1) + feat_f1_picked = (feat_f1 * heatmap.unsqueeze(-1)).sum(dim=1) # [M, C] + heatmap = heatmap.view(-1, W, W) + + # compute coordinates from heatmap + coords1_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0] # [M, 2] + grid_normalized = create_meshgrid(W, W, True, heatmap.device).reshape(1, -1, 2) # [1, WW, 2] + + # compute std over + var = torch.sum(grid_normalized**2 * heatmap.view(-1, WW, 1), dim=1) - coords1_normalized**2 # [M, 2] + std = torch.sum(torch.sqrt(torch.clamp(var, min=1e-10)), -1) # [M] clamp needed for numerical stability + + # for fine-level supervision + data.update({'expec_f': torch.cat([coords1_normalized, std.unsqueeze(1)], -1), + 'descriptors0': feat_f0_picked.detach(), 'descriptors1': feat_f1_picked.detach()}) + + # compute absolute kpt coords + self.get_fine_match(coords1_normalized, data) + + @torch.no_grad() + def get_fine_match(self, coords1_normed, data): + W, WW, C, scale = self.W, self.WW, self.C, self.scale + + # mkpts0_f and mkpts1_f + # scale0 = scale * data['scale0'][data['b_ids']] if 'scale0' in data else scale + mkpts0_f = data['mkpts0_c'] # + (coords0_normed * (W // 2) * scale0 )[:len(data['mconf'])] + scale1 = scale * data['scale1'][data['b_ids']] if 'scale1' in data else scale + mkpts1_f = data['mkpts1_c'] + (coords1_normed * (W // 2) * scale1)[:len(data['mconf'])] + + data.update({ + "mkpts0_f": mkpts0_f, + "mkpts1_f": mkpts1_f + }) diff --git a/third_party/TopicFM/src/models/utils/geometry.py b/third_party/TopicFM/src/models/utils/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..f95cdb65b48324c4f4ceb20231b1bed992b41116 --- /dev/null +++ b/third_party/TopicFM/src/models/utils/geometry.py @@ -0,0 +1,54 @@ +import torch + + +@torch.no_grad() +def warp_kpts(kpts0, depth0, depth1, T_0to1, K0, K1): + """ Warp kpts0 from I0 to I1 with depth, K and Rt + Also check covisibility and depth consistency. + Depth is consistent if relative error < 0.2 (hard-coded). + + Args: + kpts0 (torch.Tensor): [N, L, 2] - , + depth0 (torch.Tensor): [N, H, W], + depth1 (torch.Tensor): [N, H, W], + T_0to1 (torch.Tensor): [N, 3, 4], + K0 (torch.Tensor): [N, 3, 3], + K1 (torch.Tensor): [N, 3, 3], + Returns: + calculable_mask (torch.Tensor): [N, L] + warped_keypoints0 (torch.Tensor): [N, L, 2] + """ + kpts0_long = kpts0.round().long() + + # Sample depth, get calculable_mask on depth != 0 + kpts0_depth = torch.stack( + [depth0[i, kpts0_long[i, :, 1], kpts0_long[i, :, 0]] for i in range(kpts0.shape[0])], dim=0 + ) # (N, L) + nonzero_mask = kpts0_depth != 0 + + # Unproject + kpts0_h = torch.cat([kpts0, torch.ones_like(kpts0[:, :, [0]])], dim=-1) * kpts0_depth[..., None] # (N, L, 3) + kpts0_cam = K0.inverse() @ kpts0_h.transpose(2, 1) # (N, 3, L) + + # Rigid Transform + w_kpts0_cam = T_0to1[:, :3, :3] @ kpts0_cam + T_0to1[:, :3, [3]] # (N, 3, L) + w_kpts0_depth_computed = w_kpts0_cam[:, 2, :] + + # Project + w_kpts0_h = (K1 @ w_kpts0_cam).transpose(2, 1) # (N, L, 3) + w_kpts0 = w_kpts0_h[:, :, :2] / (w_kpts0_h[:, :, [2]] + 1e-4) # (N, L, 2), +1e-4 to avoid zero depth + + # Covisible Check + h, w = depth1.shape[1:3] + covisible_mask = (w_kpts0[:, :, 0] > 0) * (w_kpts0[:, :, 0] < w-1) * \ + (w_kpts0[:, :, 1] > 0) * (w_kpts0[:, :, 1] < h-1) + w_kpts0_long = w_kpts0.long() + w_kpts0_long[~covisible_mask, :] = 0 + + w_kpts0_depth = torch.stack( + [depth1[i, w_kpts0_long[i, :, 1], w_kpts0_long[i, :, 0]] for i in range(w_kpts0_long.shape[0])], dim=0 + ) # (N, L) + consistent_mask = ((w_kpts0_depth - w_kpts0_depth_computed) / w_kpts0_depth).abs() < 0.2 + valid_mask = nonzero_mask * covisible_mask * consistent_mask + + return valid_mask, w_kpts0 diff --git a/third_party/TopicFM/src/models/utils/supervision.py b/third_party/TopicFM/src/models/utils/supervision.py new file mode 100644 index 0000000000000000000000000000000000000000..1f1f0478fdcbe7f8ceffbc4aff4d507cec55bbd2 --- /dev/null +++ b/third_party/TopicFM/src/models/utils/supervision.py @@ -0,0 +1,151 @@ +from math import log +from loguru import logger + +import torch +from einops import repeat +from kornia.utils import create_meshgrid + +from .geometry import warp_kpts + +############## ↓ Coarse-Level supervision ↓ ############## + + +@torch.no_grad() +def mask_pts_at_padded_regions(grid_pt, mask): + """For megadepth dataset, zero-padding exists in images""" + mask = repeat(mask, 'n h w -> n (h w) c', c=2) + grid_pt[~mask.bool()] = 0 + return grid_pt + + +@torch.no_grad() +def spvs_coarse(data, config): + """ + Update: + data (dict): { + "conf_matrix_gt": [N, hw0, hw1], + 'spv_b_ids': [M] + 'spv_i_ids': [M] + 'spv_j_ids': [M] + 'spv_w_pt0_i': [N, hw0, 2], in original image resolution + 'spv_pt1_i': [N, hw1, 2], in original image resolution + } + + NOTE: + - for scannet dataset, there're 3 kinds of resolution {i, c, f} + - for megadepth dataset, there're 4 kinds of resolution {i, i_resize, c, f} + """ + # 1. misc + device = data['image0'].device + N, _, H0, W0 = data['image0'].shape + _, _, H1, W1 = data['image1'].shape + scale = config['MODEL']['RESOLUTION'][0] + scale0 = scale * data['scale0'][:, None] if 'scale0' in data else scale + scale1 = scale * data['scale1'][:, None] if 'scale0' in data else scale + h0, w0, h1, w1 = map(lambda x: x // scale, [H0, W0, H1, W1]) + + # 2. warp grids + # create kpts in meshgrid and resize them to image resolution + grid_pt0_c = create_meshgrid(h0, w0, False, device).reshape(1, h0*w0, 2).repeat(N, 1, 1) # [N, hw, 2] + grid_pt0_i = scale0 * grid_pt0_c + grid_pt1_c = create_meshgrid(h1, w1, False, device).reshape(1, h1*w1, 2).repeat(N, 1, 1) + grid_pt1_i = scale1 * grid_pt1_c + + # mask padded region to (0, 0), so no need to manually mask conf_matrix_gt + if 'mask0' in data: + grid_pt0_i = mask_pts_at_padded_regions(grid_pt0_i, data['mask0']) + grid_pt1_i = mask_pts_at_padded_regions(grid_pt1_i, data['mask1']) + + # warp kpts bi-directionally and resize them to coarse-level resolution + # (no depth consistency check, since it leads to worse results experimentally) + # (unhandled edge case: points with 0-depth will be warped to the left-up corner) + _, w_pt0_i = warp_kpts(grid_pt0_i, data['depth0'], data['depth1'], data['T_0to1'], data['K0'], data['K1']) + _, w_pt1_i = warp_kpts(grid_pt1_i, data['depth1'], data['depth0'], data['T_1to0'], data['K1'], data['K0']) + w_pt0_c = w_pt0_i / scale1 + w_pt1_c = w_pt1_i / scale0 + + # 3. check if mutual nearest neighbor + w_pt0_c_round = w_pt0_c[:, :, :].round().long() + nearest_index1 = w_pt0_c_round[..., 0] + w_pt0_c_round[..., 1] * w1 + w_pt1_c_round = w_pt1_c[:, :, :].round().long() + nearest_index0 = w_pt1_c_round[..., 0] + w_pt1_c_round[..., 1] * w0 + + # corner case: out of boundary + def out_bound_mask(pt, w, h): + return (pt[..., 0] < 0) + (pt[..., 0] >= w) + (pt[..., 1] < 0) + (pt[..., 1] >= h) + nearest_index1[out_bound_mask(w_pt0_c_round, w1, h1)] = 0 + nearest_index0[out_bound_mask(w_pt1_c_round, w0, h0)] = 0 + + loop_back = torch.stack([nearest_index0[_b][_i] for _b, _i in enumerate(nearest_index1)], dim=0) + correct_0to1 = loop_back == torch.arange(h0*w0, device=device)[None].repeat(N, 1) + correct_0to1[:, 0] = False # ignore the top-left corner + + # 4. construct a gt conf_matrix + conf_matrix_gt = torch.zeros(N, h0*w0, h1*w1, device=device) + b_ids, i_ids = torch.where(correct_0to1 != 0) + j_ids = nearest_index1[b_ids, i_ids] + + conf_matrix_gt[b_ids, i_ids, j_ids] = 1 + data.update({'conf_matrix_gt': conf_matrix_gt}) + + # 5. save coarse matches(gt) for training fine level + if len(b_ids) == 0: + logger.warning(f"No groundtruth coarse match found for: {data['pair_names']}") + # this won't affect fine-level loss calculation + b_ids = torch.tensor([0], device=device) + i_ids = torch.tensor([0], device=device) + j_ids = torch.tensor([0], device=device) + + data.update({ + 'spv_b_ids': b_ids, + 'spv_i_ids': i_ids, + 'spv_j_ids': j_ids + }) + + # 6. save intermediate results (for fast fine-level computation) + data.update({ + 'spv_w_pt0_i': w_pt0_i, + 'spv_pt1_i': grid_pt1_i + }) + + +def compute_supervision_coarse(data, config): + assert len(set(data['dataset_name'])) == 1, "Do not support mixed datasets training!" + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_coarse(data, config) + else: + raise ValueError(f'Unknown data source: {data_source}') + + +############## ↓ Fine-Level supervision ↓ ############## + +@torch.no_grad() +def spvs_fine(data, config): + """ + Update: + data (dict):{ + "expec_f_gt": [M, 2]} + """ + # 1. misc + # w_pt0_i, pt1_i = data.pop('spv_w_pt0_i'), data.pop('spv_pt1_i') + w_pt0_i, pt1_i = data['spv_w_pt0_i'], data['spv_pt1_i'] + scale = config['MODEL']['RESOLUTION'][1] + radius = config['MODEL']['FINE_WINDOW_SIZE'] // 2 + + # 2. get coarse prediction + b_ids, i_ids, j_ids = data['b_ids'], data['i_ids'], data['j_ids'] + + # 3. compute gt + scale = scale * data['scale1'][b_ids] if 'scale0' in data else scale + # `expec_f_gt` might exceed the window, i.e. abs(*) > 1, which would be filtered later + expec_f_gt = (w_pt0_i[b_ids, i_ids] - pt1_i[b_ids, j_ids]) / scale / radius # [M, 2] + data.update({"expec_f_gt": expec_f_gt}) + + +def compute_supervision_fine(data, config): + data_source = data['dataset_name'][0] + if data_source.lower() in ['scannet', 'megadepth']: + spvs_fine(data, config) + else: + raise NotImplementedError diff --git a/third_party/TopicFM/src/optimizers/__init__.py b/third_party/TopicFM/src/optimizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e1db2285352586c250912bdd2c4ae5029620ab5f --- /dev/null +++ b/third_party/TopicFM/src/optimizers/__init__.py @@ -0,0 +1,42 @@ +import torch +from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR + + +def build_optimizer(model, config): + name = config.TRAINER.OPTIMIZER + lr = config.TRAINER.TRUE_LR + + if name == "adam": + return torch.optim.Adam(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAM_DECAY) + elif name == "adamw": + return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=config.TRAINER.ADAMW_DECAY) + else: + raise ValueError(f"TRAINER.OPTIMIZER = {name} is not a valid optimizer!") + + +def build_scheduler(config, optimizer): + """ + Returns: + scheduler (dict):{ + 'scheduler': lr_scheduler, + 'interval': 'step', # or 'epoch' + 'monitor': 'val_f1', (optional) + 'frequency': x, (optional) + } + """ + scheduler = {'interval': config.TRAINER.SCHEDULER_INTERVAL} + name = config.TRAINER.SCHEDULER + + if name == 'MultiStepLR': + scheduler.update( + {'scheduler': MultiStepLR(optimizer, config.TRAINER.MSLR_MILESTONES, gamma=config.TRAINER.MSLR_GAMMA)}) + elif name == 'CosineAnnealing': + scheduler.update( + {'scheduler': CosineAnnealingLR(optimizer, config.TRAINER.COSA_TMAX)}) + elif name == 'ExponentialLR': + scheduler.update( + {'scheduler': ExponentialLR(optimizer, config.TRAINER.ELR_GAMMA)}) + else: + raise NotImplementedError() + + return scheduler diff --git a/third_party/TopicFM/src/utils/augment.py b/third_party/TopicFM/src/utils/augment.py new file mode 100644 index 0000000000000000000000000000000000000000..d7c5d3e11b6fe083aaeff7555bb7ce3a4bfb755d --- /dev/null +++ b/third_party/TopicFM/src/utils/augment.py @@ -0,0 +1,55 @@ +import albumentations as A + + +class DarkAug(object): + """ + Extreme dark augmentation aiming at Aachen Day-Night + """ + + def __init__(self) -> None: + self.augmentor = A.Compose([ + A.RandomBrightnessContrast(p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)), + A.Blur(p=0.1, blur_limit=(3, 9)), + A.MotionBlur(p=0.2, blur_limit=(3, 25)), + A.RandomGamma(p=0.1, gamma_limit=(15, 65)), + A.HueSaturationValue(p=0.1, val_shift_limit=(-100, -40)) + ], p=0.75) + + def __call__(self, x): + return self.augmentor(image=x)['image'] + + +class MobileAug(object): + """ + Random augmentations aiming at images of mobile/handhold devices. + """ + + def __init__(self): + self.augmentor = A.Compose([ + A.MotionBlur(p=0.25), + A.ColorJitter(p=0.5), + A.RandomRain(p=0.1), # random occlusion + A.RandomSunFlare(p=0.1), + A.JpegCompression(p=0.25), + A.ISONoise(p=0.25) + ], p=1.0) + + def __call__(self, x): + return self.augmentor(image=x)['image'] + + +def build_augmentor(method=None, **kwargs): + if method is not None: + raise NotImplementedError('Using of augmentation functions are not supported yet!') + if method == 'dark': + return DarkAug() + elif method == 'mobile': + return MobileAug() + elif method is None: + return None + else: + raise ValueError(f'Invalid augmentation method: {method}') + + +if __name__ == '__main__': + augmentor = build_augmentor('FDA') diff --git a/third_party/TopicFM/src/utils/comm.py b/third_party/TopicFM/src/utils/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..26ec9517cc47e224430106d8ae9aa99a3fe49167 --- /dev/null +++ b/third_party/TopicFM/src/utils/comm.py @@ -0,0 +1,265 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +[Copied from detectron2] +This file contains primitives for multi-gpu communication. +This is useful when doing distributed training. +""" + +import functools +import logging +import numpy as np +import pickle +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert _LOCAL_PROCESS_GROUP is not None + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def _serialize_to_tensor(data, group): + backend = dist.get_backend(group) + assert backend in ["gloo", "nccl"] + device = torch.device("cpu" if backend == "gloo" else "cuda") + + buffer = pickle.dumps(data) + if len(buffer) > 1024 ** 3: + logger = logging.getLogger(__name__) + logger.warning( + "Rank {} trying to all-gather {:.2f} GB of data on device {}".format( + get_rank(), len(buffer) / (1024 ** 3), device + ) + ) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to(device=device) + return tensor + + +def _pad_to_largest_tensor(tensor, group): + """ + Returns: + list[int]: size of the tensor, on each rank + Tensor: padded tensor that has the max size + """ + world_size = dist.get_world_size(group=group) + assert ( + world_size >= 1 + ), "comm.gather/all_gather must be called from ranks within the given group!" + local_size = torch.tensor([tensor.numel()], dtype=torch.int64, device=tensor.device) + size_list = [ + torch.zeros([1], dtype=torch.int64, device=tensor.device) for _ in range(world_size) + ] + dist.all_gather(size_list, local_size, group=group) + + size_list = [int(size.item()) for size in size_list] + + max_size = max(size_list) + + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + if local_size != max_size: + padding = torch.zeros((max_size - local_size,), dtype=torch.uint8, device=tensor.device) + tensor = torch.cat((tensor, padding), dim=0) + return size_list, tensor + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group) == 1: + return [data] + + tensor = _serialize_to_tensor(data, group) + + size_list, tensor = _pad_to_largest_tensor(tensor, group) + max_size = max(size_list) + + # receiving Tensor from all ranks + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.all_gather(tensor_list, tensor, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + if dist.get_world_size(group=group) == 1: + return [data] + rank = dist.get_rank(group=group) + + tensor = _serialize_to_tensor(data, group) + size_list, tensor = _pad_to_largest_tensor(tensor, group) + + # receiving Tensor from all ranks + if rank == dst: + max_size = max(size_list) + tensor_list = [ + torch.empty((max_size,), dtype=torch.uint8, device=tensor.device) for _ in size_list + ] + dist.gather(tensor, tensor_list, dst=dst, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + return data_list + else: + dist.gather(tensor, [], dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2 ** 31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/third_party/TopicFM/src/utils/dataloader.py b/third_party/TopicFM/src/utils/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6da37b880a290c2bb3ebb028d0c8dab592acc5c1 --- /dev/null +++ b/third_party/TopicFM/src/utils/dataloader.py @@ -0,0 +1,23 @@ +import numpy as np + + +# --- PL-DATAMODULE --- + +def get_local_split(items: list, world_size: int, rank: int, seed: int): + """ The local rank only loads a split of the dataset. """ + n_items = len(items) + items_permute = np.random.RandomState(seed).permutation(items) + if n_items % world_size == 0: + padded_items = items_permute + else: + padding = np.random.RandomState(seed).choice( + items, + world_size - (n_items % world_size), + replace=True) + padded_items = np.concatenate([items_permute, padding]) + assert len(padded_items) % world_size == 0, \ + f'len(padded_items): {len(padded_items)}; world_size: {world_size}; len(padding): {len(padding)}' + n_per_rank = len(padded_items) // world_size + local_items = padded_items[n_per_rank * rank: n_per_rank * (rank+1)] + + return local_items diff --git a/third_party/TopicFM/src/utils/dataset.py b/third_party/TopicFM/src/utils/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..647bbadd821b6c90736ed45462270670b1017b0b --- /dev/null +++ b/third_party/TopicFM/src/utils/dataset.py @@ -0,0 +1,201 @@ +import io +from loguru import logger + +import cv2 +import numpy as np +import h5py +import torch +from numpy.linalg import inv + + +MEGADEPTH_CLIENT = SCANNET_CLIENT = None + +# --- DATA IO --- + +def load_array_from_s3( + path, client, cv_type, + use_h5py=False, +): + byte_str = client.Get(path) + try: + if not use_h5py: + raw_array = np.fromstring(byte_str, np.uint8) + data = cv2.imdecode(raw_array, cv_type) + else: + f = io.BytesIO(byte_str) + data = np.array(h5py.File(f, 'r')['/depth']) + except Exception as ex: + print(f"==> Data loading failure: {path}") + raise ex + + assert data is not None + return data + + +def imread_gray(path, augment_fn=None, client=SCANNET_CLIENT): + cv_type = cv2.IMREAD_GRAYSCALE if augment_fn is None \ + else cv2.IMREAD_COLOR + if str(path).startswith('s3://'): + image = load_array_from_s3(str(path), client, cv_type) + else: + image = cv2.imread(str(path), cv_type) + + if augment_fn is not None: + image = cv2.imread(str(path), cv2.IMREAD_COLOR) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + image = augment_fn(image) + image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + return image # (h, w) + + +def get_resized_wh(w, h, resize=None): + if (resize is not None) and (max(h,w) > resize): # resize the longer edge + scale = resize / max(h, w) + w_new, h_new = int(round(w*scale)), int(round(h*scale)) + else: + w_new, h_new = w, h + return w_new, h_new + + +def get_divisible_wh(w, h, df=None): + if df is not None: + w_new, h_new = map(lambda x: int(x // df * df), [w, h]) + else: + w_new, h_new = w, h + return w_new, h_new + + +def pad_bottom_right(inp, pad_size, ret_mask=False): + assert isinstance(pad_size, int) and pad_size >= max(inp.shape[-2:]), f"{pad_size} < {max(inp.shape[-2:])}" + mask = None + if inp.ndim == 2: + padded = np.zeros((pad_size, pad_size), dtype=inp.dtype) + padded[:inp.shape[0], :inp.shape[1]] = inp + if ret_mask: + mask = np.zeros((pad_size, pad_size), dtype=bool) + mask[:inp.shape[0], :inp.shape[1]] = True + elif inp.ndim == 3: + padded = np.zeros((inp.shape[0], pad_size, pad_size), dtype=inp.dtype) + padded[:, :inp.shape[1], :inp.shape[2]] = inp + if ret_mask: + mask = np.zeros((inp.shape[0], pad_size, pad_size), dtype=bool) + mask[:, :inp.shape[1], :inp.shape[2]] = True + else: + raise NotImplementedError() + return padded, mask + + +# --- MEGADEPTH --- + +def read_megadepth_gray(path, resize=None, df=None, padding=False, augment_fn=None): + """ + Args: + resize (int, optional): the longer edge of resized images. None for no resize. + padding (bool): If set to 'True', zero-pad resized images to squared size. + augment_fn (callable, optional): augments images with pre-defined visual effects + Returns: + image (torch.tensor): (1, h, w) + mask (torch.tensor): (h, w) + scale (torch.tensor): [w/w_new, h/h_new] + """ + # read image + image = imread_gray(path, augment_fn, client=MEGADEPTH_CLIENT) + + # resize image + w, h = image.shape[1], image.shape[0] + w_new, h_new = get_resized_wh(w, h, resize) + w_new, h_new = get_divisible_wh(w_new, h_new, df) + + image = cv2.resize(image, (w_new, h_new)) + scale = torch.tensor([w/w_new, h/h_new], dtype=torch.float) + + if padding: # padding + pad_to = resize #max(h_new, w_new) + image, mask = pad_bottom_right(image, pad_to, ret_mask=True) + else: + mask = None + + image = torch.from_numpy(image).float()[None] / 255 # (h, w) -> (1, h, w) and normalized + mask = torch.from_numpy(mask) if mask is not None else None + + return image, mask, scale + + +def read_megadepth_depth(path, pad_to=None): + if str(path).startswith('s3://'): + depth = load_array_from_s3(path, MEGADEPTH_CLIENT, None, use_h5py=True) + else: + depth = np.array(h5py.File(path, 'r')['depth']) + if pad_to is not None: + depth, _ = pad_bottom_right(depth, pad_to, ret_mask=False) + depth = torch.from_numpy(depth).float() # (h, w) + return depth + + +# --- ScanNet --- + +def read_scannet_gray(path, resize=(640, 480), augment_fn=None): + """ + Args: + resize (tuple): align image to depthmap, in (w, h). + augment_fn (callable, optional): augments images with pre-defined visual effects + Returns: + image (torch.tensor): (1, h, w) + mask (torch.tensor): (h, w) + scale (torch.tensor): [w/w_new, h/h_new] + """ + # read and resize image + image = imread_gray(path, augment_fn) + image = cv2.resize(image, resize) + + # (h, w) -> (1, h, w) and normalized + image = torch.from_numpy(image).float()[None] / 255 + return image + + +# ---- evaluation datasets: HLoc, Aachen, InLoc + +def read_img_gray(path, resize=None, down_factor=16): + # read and resize image + image = imread_gray(path, None) + w, h = image.shape[1], image.shape[0] + if (resize is not None) and (max(h, w) > resize): + scale = float(resize / max(h, w)) + w_new, h_new = int(round(w * scale)), int(round(h * scale)) + else: + w_new, h_new = w, h + w_new, h_new = get_divisible_wh(w_new, h_new, down_factor) + image = cv2.resize(image, (w_new, h_new)) + + # (h, w) -> (1, h, w) and normalized + image = torch.from_numpy(image).float()[None] / 255 + scale = torch.tensor([w / w_new, h / h_new], dtype=torch.float) + return image, scale + + +def read_scannet_depth(path): + if str(path).startswith('s3://'): + depth = load_array_from_s3(str(path), SCANNET_CLIENT, cv2.IMREAD_UNCHANGED) + else: + depth = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) + depth = depth / 1000 + depth = torch.from_numpy(depth).float() # (h, w) + return depth + + +def read_scannet_pose(path): + """ Read ScanNet's Camera2World pose and transform it to World2Camera. + + Returns: + pose_w2c (np.ndarray): (4, 4) + """ + cam2world = np.loadtxt(path, delimiter=' ') + world2cam = inv(cam2world) + return world2cam + + +def read_scannet_intrinsic(path): + """ Read ScanNet's intrinsic matrix and return the 3x3 matrix. + """ + intrinsic = np.loadtxt(path, delimiter=' ') + return intrinsic[:-1, :-1] diff --git a/third_party/TopicFM/src/utils/metrics.py b/third_party/TopicFM/src/utils/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..a93c31ed1d151cd41e2449a19be2d6abc5f9d419 --- /dev/null +++ b/third_party/TopicFM/src/utils/metrics.py @@ -0,0 +1,193 @@ +import torch +import cv2 +import numpy as np +from collections import OrderedDict +from loguru import logger +from kornia.geometry.epipolar import numeric +from kornia.geometry.conversions import convert_points_to_homogeneous + + +# --- METRICS --- + +def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0): + # angle error between 2 vectors + t_gt = T_0to1[:3, 3] + n = np.linalg.norm(t) * np.linalg.norm(t_gt) + t_err = np.rad2deg(np.arccos(np.clip(np.dot(t, t_gt) / n, -1.0, 1.0))) + t_err = np.minimum(t_err, 180 - t_err) # handle E ambiguity + if np.linalg.norm(t_gt) < ignore_gt_t_thr: # pure rotation is challenging + t_err = 0 + + # angle error between 2 rotation matrices + R_gt = T_0to1[:3, :3] + cos = (np.trace(np.dot(R.T, R_gt)) - 1) / 2 + cos = np.clip(cos, -1., 1.) # handle numercial errors + R_err = np.rad2deg(np.abs(np.arccos(cos))) + + return t_err, R_err + + +def symmetric_epipolar_distance(pts0, pts1, E, K0, K1): + """Squared symmetric epipolar distance. + This can be seen as a biased estimation of the reprojection error. + Args: + pts0 (torch.Tensor): [N, 2] + E (torch.Tensor): [3, 3] + """ + pts0 = (pts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] + pts1 = (pts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] + pts0 = convert_points_to_homogeneous(pts0) + pts1 = convert_points_to_homogeneous(pts1) + + Ep0 = pts0 @ E.T # [N, 3] + p1Ep0 = torch.sum(pts1 * Ep0, -1) # [N,] + Etp1 = pts1 @ E # [N, 3] + + d = p1Ep0**2 * (1.0 / (Ep0[:, 0]**2 + Ep0[:, 1]**2) + 1.0 / (Etp1[:, 0]**2 + Etp1[:, 1]**2)) # N + return d + + +def compute_symmetrical_epipolar_errors(data): + """ + Update: + data (dict):{"epi_errs": [M]} + """ + Tx = numeric.cross_product_matrix(data['T_0to1'][:, :3, 3]) + E_mat = Tx @ data['T_0to1'][:, :3, :3] + + m_bids = data['m_bids'] + pts0 = data['mkpts0_f'] + pts1 = data['mkpts1_f'] + + epi_errs = [] + for bs in range(Tx.size(0)): + mask = m_bids == bs + epi_errs.append( + symmetric_epipolar_distance(pts0[mask], pts1[mask], E_mat[bs], data['K0'][bs], data['K1'][bs])) + epi_errs = torch.cat(epi_errs, dim=0) + + data.update({'epi_errs': epi_errs}) + + +def estimate_pose(kpts0, kpts1, K0, K1, thresh, conf=0.99999): + if len(kpts0) < 5: + return None + # normalize keypoints + kpts0 = (kpts0 - K0[[0, 1], [2, 2]][None]) / K0[[0, 1], [0, 1]][None] + kpts1 = (kpts1 - K1[[0, 1], [2, 2]][None]) / K1[[0, 1], [0, 1]][None] + + # normalize ransac threshold + ransac_thr = thresh / np.mean([K0[0, 0], K1[1, 1], K0[0, 0], K1[1, 1]]) + + # compute pose with cv2 + E, mask = cv2.findEssentialMat( + kpts0, kpts1, np.eye(3), threshold=ransac_thr, prob=conf, method=cv2.RANSAC) + if E is None: + print("\nE is None while trying to recover pose.\n") + return None + + # recover pose from E + best_num_inliers = 0 + ret = None + for _E in np.split(E, len(E) / 3): + n, R, t, _ = cv2.recoverPose(_E, kpts0, kpts1, np.eye(3), 1e9, mask=mask) + if n > best_num_inliers: + ret = (R, t[:, 0], mask.ravel() > 0) + best_num_inliers = n + + return ret + + +def compute_pose_errors(data, config=None, ransac_thr=0.5, ransac_conf=0.99999): + """ + Update: + data (dict):{ + "R_errs" List[float]: [N] + "t_errs" List[float]: [N] + "inliers" List[np.ndarray]: [N] + } + """ + pixel_thr = config.TRAINER.RANSAC_PIXEL_THR if config is not None else ransac_thr # 0.5 + conf = config.TRAINER.RANSAC_CONF if config is not None else ransac_conf # 0.99999 + data.update({'R_errs': [], 't_errs': [], 'inliers': []}) + + m_bids = data['m_bids'].cpu().numpy() + pts0 = data['mkpts0_f'].cpu().numpy() + pts1 = data['mkpts1_f'].cpu().numpy() + K0 = data['K0'].cpu().numpy() + K1 = data['K1'].cpu().numpy() + T_0to1 = data['T_0to1'].cpu().numpy() + + for bs in range(K0.shape[0]): + mask = m_bids == bs + ret = estimate_pose(pts0[mask], pts1[mask], K0[bs], K1[bs], pixel_thr, conf=conf) + + if ret is None: + data['R_errs'].append(np.inf) + data['t_errs'].append(np.inf) + data['inliers'].append(np.array([]).astype(np.bool)) + else: + R, t, inliers = ret + t_err, R_err = relative_pose_error(T_0to1[bs], R, t, ignore_gt_t_thr=0.0) + data['R_errs'].append(R_err) + data['t_errs'].append(t_err) + data['inliers'].append(inliers) + + +# --- METRIC AGGREGATION --- + +def error_auc(errors, thresholds): + """ + Args: + errors (list): [N,] + thresholds (list) + """ + errors = [0] + sorted(list(errors)) + recall = list(np.linspace(0, 1, len(errors))) + + aucs = [] + thresholds = [5, 10, 20] + for thr in thresholds: + last_index = np.searchsorted(errors, thr) + y = recall[:last_index] + [recall[last_index-1]] + x = errors[:last_index] + [thr] + aucs.append(np.trapz(y, x) / thr) + + return {f'auc@{t}': auc for t, auc in zip(thresholds, aucs)} + + +def epidist_prec(errors, thresholds, ret_dict=False): + precs = [] + for thr in thresholds: + prec_ = [] + for errs in errors: + correct_mask = errs < thr + prec_.append(np.mean(correct_mask) if len(correct_mask) > 0 else 0) + precs.append(np.mean(prec_) if len(prec_) > 0 else 0) + if ret_dict: + return {f'prec@{t:.0e}': prec for t, prec in zip(thresholds, precs)} + else: + return precs + + +def aggregate_metrics(metrics, epi_err_thr=5e-4): + """ Aggregate metrics for the whole dataset: + (This method should be called once per dataset) + 1. AUC of the pose error (angular) at the threshold [5, 10, 20] + 2. Mean matching precision at the threshold 5e-4(ScanNet), 1e-4(MegaDepth) + """ + # filter duplicates + unq_ids = OrderedDict((iden, id) for id, iden in enumerate(metrics['identifiers'])) + unq_ids = list(unq_ids.values()) + logger.info(f'Aggregating metrics over {len(unq_ids)} unique items...') + + # pose auc + angular_thresholds = [5, 10, 20] + pose_errors = np.max(np.stack([metrics['R_errs'], metrics['t_errs']]), axis=0)[unq_ids] + aucs = error_auc(pose_errors, angular_thresholds) # (auc@5, auc@10, auc@20) + + # matching precision + dist_thresholds = [epi_err_thr] + precs = epidist_prec(np.array(metrics['epi_errs'], dtype=object)[unq_ids], dist_thresholds, True) # (prec@err_thr) + + return {**aucs, **precs} diff --git a/third_party/TopicFM/src/utils/misc.py b/third_party/TopicFM/src/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..9c8db04666519753ea2df43903ab6c47ec00a9a1 --- /dev/null +++ b/third_party/TopicFM/src/utils/misc.py @@ -0,0 +1,101 @@ +import os +import contextlib +import joblib +from typing import Union +from loguru import _Logger, logger +from itertools import chain + +import torch +from yacs.config import CfgNode as CN +from pytorch_lightning.utilities import rank_zero_only + + +def lower_config(yacs_cfg): + if not isinstance(yacs_cfg, CN): + return yacs_cfg + return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()} + + +def upper_config(dict_cfg): + if not isinstance(dict_cfg, dict): + return dict_cfg + return {k.upper(): upper_config(v) for k, v in dict_cfg.items()} + + +def log_on(condition, message, level): + if condition: + assert level in ['INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL'] + logger.log(level, message) + + +def get_rank_zero_only_logger(logger: _Logger): + if rank_zero_only.rank == 0: + return logger + else: + for _level in logger._core.levels.keys(): + level = _level.lower() + setattr(logger, level, + lambda x: None) + logger._log = lambda x: None + return logger + + +def setup_gpus(gpus: Union[str, int]) -> int: + """ A temporary fix for pytorch-lighting 1.3.x """ + gpus = str(gpus) + gpu_ids = [] + + if ',' not in gpus: + n_gpus = int(gpus) + return n_gpus if n_gpus != -1 else torch.cuda.device_count() + else: + gpu_ids = [i.strip() for i in gpus.split(',') if i != ''] + + # setup environment variables + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + if visible_devices is None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(i) for i in gpu_ids) + visible_devices = os.getenv('CUDA_VISIBLE_DEVICES') + logger.warning(f'[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}') + else: + logger.warning('[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process.') + return len(gpu_ids) + + +def flattenList(x): + return list(chain(*x)) + + +@contextlib.contextmanager +def tqdm_joblib(tqdm_object): + """Context manager to patch joblib to report into tqdm progress bar given as argument + + Usage: + with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar: + Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10)) + + When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing) + ret_vals = Parallel(n_jobs=args.world_size)( + delayed(lambda x: _compute_cov_score(pid, *x))(param) + for param in tqdm(combinations(image_ids, 2), + desc=f'Computing cov_score of [{pid}]', + total=len(image_ids)*(len(image_ids)-1)/2)) + Src: https://stackoverflow.com/a/58936697 + """ + class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def __call__(self, *args, **kwargs): + tqdm_object.update(n=self.batch_size) + return super().__call__(*args, **kwargs) + + old_batch_callback = joblib.parallel.BatchCompletionCallBack + joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback + try: + yield tqdm_object + finally: + joblib.parallel.BatchCompletionCallBack = old_batch_callback + tqdm_object.close() + diff --git a/third_party/TopicFM/src/utils/plotting.py b/third_party/TopicFM/src/utils/plotting.py new file mode 100644 index 0000000000000000000000000000000000000000..89b22ef27e6152225d07ab24bb3e62718d180b59 --- /dev/null +++ b/third_party/TopicFM/src/utils/plotting.py @@ -0,0 +1,313 @@ +import bisect +import numpy as np +import matplotlib.pyplot as plt +import matplotlib, os, cv2 +import matplotlib.cm as cm +from PIL import Image +import torch.nn.functional as F +import torch + + +def _compute_conf_thresh(data): + dataset_name = data['dataset_name'][0].lower() + if dataset_name == 'scannet': + thr = 5e-4 + elif dataset_name == 'megadepth': + thr = 1e-4 + else: + raise ValueError(f'Unknown dataset: {dataset_name}') + return thr + + +# --- VISUALIZATION --- # + +def make_matching_figure( + img0, img1, mkpts0, mkpts1, color, + kpts0=None, kpts1=None, text=[], dpi=75, path=None): + # draw image pair + assert mkpts0.shape[0] == mkpts1.shape[0], f'mkpts0: {mkpts0.shape[0]} v.s. mkpts1: {mkpts1.shape[0]}' + fig, axes = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi) + axes[0].imshow(img0) # , cmap='gray') + axes[1].imshow(img1) # , cmap='gray') + for i in range(2): # clear all frames + axes[i].get_yaxis().set_ticks([]) + axes[i].get_xaxis().set_ticks([]) + for spine in axes[i].spines.values(): + spine.set_visible(False) + plt.tight_layout(pad=1) + + if kpts0 is not None: + assert kpts1 is not None + axes[0].scatter(kpts0[:, 0], kpts0[:, 1], c='w', s=5) + axes[1].scatter(kpts1[:, 0], kpts1[:, 1], c='w', s=5) + + # draw matches + if mkpts0.shape[0] != 0 and mkpts1.shape[0] != 0: + fig.canvas.draw() + transFigure = fig.transFigure.inverted() + fkpts0 = transFigure.transform(axes[0].transData.transform(mkpts0)) + fkpts1 = transFigure.transform(axes[1].transData.transform(mkpts1)) + fig.lines = [matplotlib.lines.Line2D((fkpts0[i, 0], fkpts1[i, 0]), + (fkpts0[i, 1], fkpts1[i, 1]), + transform=fig.transFigure, c=color[i], linewidth=2) + for i in range(len(mkpts0))] + + axes[0].scatter(mkpts0[:, 0], mkpts0[:, 1], c=color[..., :3], s=4) + axes[1].scatter(mkpts1[:, 0], mkpts1[:, 1], c=color[..., :3], s=4) + + # put txts + txt_color = 'k' if img0[:100, :200].mean() > 200 else 'w' + fig.text( + 0.01, 0.99, '\n'.join(text), transform=fig.axes[0].transAxes, + fontsize=15, va='top', ha='left', color=txt_color) + + # save or return figure + if path: + plt.savefig(str(path), bbox_inches='tight', pad_inches=0) + plt.close() + else: + return fig + + +def _make_evaluation_figure(data, b_id, alpha='dynamic'): + b_mask = data['m_bids'] == b_id + conf_thr = _compute_conf_thresh(data) + + img0 = (data['image0'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + img1 = (data['image1'][b_id][0].cpu().numpy() * 255).round().astype(np.int32) + kpts0 = data['mkpts0_f'][b_mask].cpu().numpy() + kpts1 = data['mkpts1_f'][b_mask].cpu().numpy() + + # for megadepth, we visualize matches on the resized image + if 'scale0' in data: + kpts0 = kpts0 / data['scale0'][b_id].cpu().numpy()[[1, 0]] + kpts1 = kpts1 / data['scale1'][b_id].cpu().numpy()[[1, 0]] + + epi_errs = data['epi_errs'][b_mask].cpu().numpy() + correct_mask = epi_errs < conf_thr + precision = np.mean(correct_mask) if len(correct_mask) > 0 else 0 + n_correct = np.sum(correct_mask) + n_gt_matches = int(data['conf_matrix_gt'][b_id].sum().cpu()) + recall = 0 if n_gt_matches == 0 else n_correct / (n_gt_matches) + # recall might be larger than 1, since the calculation of conf_matrix_gt + # uses groundtruth depths and camera poses, but epipolar distance is used here. + + # matching info + if alpha == 'dynamic': + alpha = dynamic_alpha(len(correct_mask)) + color = error_colormap(epi_errs, conf_thr, alpha=alpha) + + text = [ + f'#Matches {len(kpts0)}', + f'Precision({conf_thr:.2e}) ({100 * precision:.1f}%): {n_correct}/{len(kpts0)}', + f'Recall({conf_thr:.2e}) ({100 * recall:.1f}%): {n_correct}/{n_gt_matches}' + ] + + # make the figure + figure = make_matching_figure(img0, img1, kpts0, kpts1, + color, text=text) + return figure + +def _make_confidence_figure(data, b_id): + # TODO: Implement confidence figure + raise NotImplementedError() + + +def make_matching_figures(data, config, mode='evaluation'): + """ Make matching figures for a batch. + + Args: + data (Dict): a batch updated by PL_LoFTR. + config (Dict): matcher config + Returns: + figures (Dict[str, List[plt.figure]] + """ + assert mode in ['evaluation', 'confidence'] # 'confidence' + figures = {mode: []} + for b_id in range(data['image0'].size(0)): + if mode == 'evaluation': + fig = _make_evaluation_figure( + data, b_id, + alpha=config.TRAINER.PLOT_MATCHES_ALPHA) + elif mode == 'confidence': + fig = _make_confidence_figure(data, b_id) + else: + raise ValueError(f'Unknown plot mode: {mode}') + figures[mode].append(fig) + return figures + + +def dynamic_alpha(n_matches, + milestones=[0, 300, 1000, 2000], + alphas=[1.0, 0.8, 0.4, 0.2]): + if n_matches == 0: + return 1.0 + ranges = list(zip(alphas, alphas[1:] + [None])) + loc = bisect.bisect_right(milestones, n_matches) - 1 + _range = ranges[loc] + if _range[1] is None: + return _range[0] + return _range[1] + (milestones[loc + 1] - n_matches) / ( + milestones[loc + 1] - milestones[loc]) * (_range[0] - _range[1]) + + +def error_colormap(err, thr, alpha=1.0): + assert alpha <= 1.0 and alpha > 0, f"Invaid alpha value: {alpha}" + x = 1 - np.clip(err / (thr * 2), 0, 1) + return np.clip( + np.stack([2-x*2, x*2, np.zeros_like(x), np.ones_like(x)*alpha], -1), 0, 1) + + +np.random.seed(1995) +color_map = np.arange(100) +np.random.shuffle(color_map) + + +def draw_topics(data, img0, img1, saved_folder="viz_topics", show_n_topics=8, saved_name=None): + + topic0, topic1 = data["topic_matrix"]["img0"], data["topic_matrix"]["img1"] + hw0_c, hw1_c = data["hw0_c"], data["hw1_c"] + hw0_i, hw1_i = data["hw0_i"], data["hw1_i"] + # print(hw0_i, hw1_i) + scale0, scale1 = hw0_i[0] // hw0_c[0], hw1_i[0] // hw1_c[0] + if "scale0" in data: + scale0 *= data["scale0"][0] + else: + scale0 = (scale0, scale0) + if "scale1" in data: + scale1 *= data["scale1"][0] + else: + scale1 = (scale1, scale1) + + n_topics = topic0.shape[-1] + # mask0_nonzero = topic0[0].sum(dim=-1, keepdim=True) > 0 + # mask1_nonzero = topic1[0].sum(dim=-1, keepdim=True) > 0 + theta0 = topic0[0].sum(dim=0) + theta0 /= theta0.sum().float() + theta1 = topic1[0].sum(dim=0) + theta1 /= theta1.sum().float() + # top_topic0 = torch.argsort(theta0, descending=True)[:show_n_topics] + # top_topic1 = torch.argsort(theta1, descending=True)[:show_n_topics] + top_topics = torch.argsort(theta0*theta1, descending=True)[:show_n_topics] + # print(sum_topic0, sum_topic1) + + topic0 = topic0[0].argmax(dim=-1, keepdim=True) #.float() / (n_topics - 1) #* 255 + 1 # + # topic0[~mask0_nonzero] = -1 + topic1 = topic1[0].argmax(dim=-1, keepdim=True) #.float() / (n_topics - 1) #* 255 + 1 + # topic1[~mask1_nonzero] = -1 + label_img0, label_img1 = torch.zeros_like(topic0) - 1, torch.zeros_like(topic1) - 1 + for i, k in enumerate(top_topics): + label_img0[topic0 == k] = color_map[k] + label_img1[topic1 == k] = color_map[k] + +# print(hw0_c, scale0) +# print(hw1_c, scale1) + # map_topic0 = F.fold(label_img0.unsqueeze(0), hw0_i, kernel_size=scale0, stride=scale0) + map_topic0 = label_img0.float().view(hw0_c).cpu().numpy() #map_topic0.squeeze(0).squeeze(0).cpu().numpy() + map_topic0 = cv2.resize(map_topic0, (int(hw0_c[1] * scale0[0]), int(hw0_c[0] * scale0[1]))) + # map_topic1 = F.fold(label_img1.unsqueeze(0), hw1_i, kernel_size=scale1, stride=scale1) + map_topic1 = label_img1.float().view(hw1_c).cpu().numpy() #map_topic1.squeeze(0).squeeze(0).cpu().numpy() + map_topic1 = cv2.resize(map_topic1, (int(hw1_c[1] * scale1[0]), int(hw1_c[0] * scale1[1]))) + + + # show image0 + if saved_name is None: + return map_topic0, map_topic1 + + if not os.path.exists(saved_folder): + os.makedirs(saved_folder) + path_saved_img0 = os.path.join(saved_folder, "{}_0.png".format(saved_name)) + plt.imshow(img0) + masked_map_topic0 = np.ma.masked_where(map_topic0 < 0, map_topic0) + plt.imshow(masked_map_topic0, cmap=plt.cm.jet, vmin=0, vmax=n_topics-1, alpha=.3, interpolation='bilinear') + # plt.show() + plt.axis('off') + plt.savefig(path_saved_img0, bbox_inches='tight', pad_inches=0, dpi=250) + plt.close() + + path_saved_img1 = os.path.join(saved_folder, "{}_1.png".format(saved_name)) + plt.imshow(img1) + masked_map_topic1 = np.ma.masked_where(map_topic1 < 0, map_topic1) + plt.imshow(masked_map_topic1, cmap=plt.cm.jet, vmin=0, vmax=n_topics-1, alpha=.3, interpolation='bilinear') + plt.axis('off') + plt.savefig(path_saved_img1, bbox_inches='tight', pad_inches=0, dpi=250) + plt.close() + + +def draw_topicfm_demo(data, img0, img1, mkpts0, mkpts1, mcolor, text, show_n_topics=8, + topic_alpha=0.3, margin=5, path=None, opencv_display=False, opencv_title=''): + topic_map0, topic_map1 = draw_topics(data, img0, img1, show_n_topics=show_n_topics) + + mask_tm0, mask_tm1 = np.expand_dims(topic_map0 >= 0, axis=-1), np.expand_dims(topic_map1 >= 0, axis=-1) + + topic_cm0, topic_cm1 = cm.jet(topic_map0 / 99.), cm.jet(topic_map1 / 99.) + topic_cm0 = cv2.cvtColor(topic_cm0[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR) + topic_cm1 = cv2.cvtColor(topic_cm1[..., :3].astype(np.float32), cv2.COLOR_RGB2BGR) + overlay0 = (mask_tm0 * topic_cm0 + (1 - mask_tm0) * img0).astype(np.float32) + overlay1 = (mask_tm1 * topic_cm1 + (1 - mask_tm1) * img1).astype(np.float32) + + cv2.addWeighted(overlay0, topic_alpha, img0, 1 - topic_alpha, 0, overlay0) + cv2.addWeighted(overlay1, topic_alpha, img1, 1 - topic_alpha, 0, overlay1) + + overlay0, overlay1 = (overlay0 * 255).astype(np.uint8), (overlay1 * 255).astype(np.uint8) + + h0, w0 = img0.shape[:2] + h1, w1 = img1.shape[:2] + h, w = h0 * 2 + margin * 2, w0 * 2 + margin + out_fig = 255 * np.ones((h, w, 3), dtype=np.uint8) + out_fig[:h0, :w0] = overlay0 + if h0 >= h1: + start = (h0 - h1) // 2 + out_fig[start:(start+h1), (w0+margin):(w0+margin+w1)] = overlay1 + else: + start = (h1 - h0) // 2 + out_fig[:h0, (w0+margin):(w0+margin+w1)] = overlay1[start:(start+h0)] + + step_h = h0 + margin * 2 + out_fig[step_h:step_h+h0, :w0] = (img0 * 255).astype(np.uint8) + if h0 >= h1: + start = step_h + (h0 - h1) // 2 + out_fig[start:start+h1, (w0+margin):(w0+margin+w1)] = (img1 * 255).astype(np.uint8) + else: + start = (h1 - h0) // 2 + out_fig[step_h:step_h+h0, (w0+margin):(w0+margin+w1)] = (img1[start:start+h0] * 255).astype(np.uint8) + + # draw matching lines, this is inspried from https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/master/models/utils.py + mkpts0, mkpts1 = np.round(mkpts0).astype(int), np.round(mkpts1).astype(int) + mcolor = (np.array(mcolor[:, [2, 1, 0]]) * 255).astype(int) + + for (x0, y0), (x1, y1), c in zip(mkpts0, mkpts1, mcolor): + c = c.tolist() + cv2.line(out_fig, (x0, y0+step_h), (x1+margin+w0, y1+step_h+(h0-h1)//2), + color=c, thickness=1, lineType=cv2.LINE_AA) + # display line end-points as circles + cv2.circle(out_fig, (x0, y0+step_h), 2, c, -1, lineType=cv2.LINE_AA) + cv2.circle(out_fig, (x1+margin+w0, y1+step_h+(h0-h1)//2), 2, c, -1, lineType=cv2.LINE_AA) + + # Scale factor for consistent visualization across scales. + sc = min(h / 960., 2.0) + + # Big text. + Ht = int(30 * sc) # text height + txt_color_fg = (255, 255, 255) + txt_color_bg = (0, 0, 0) + for i, t in enumerate(text): + cv2.putText(out_fig, t, (int(8 * sc), Ht + step_h*i), cv2.FONT_HERSHEY_DUPLEX, + 1.0 * sc, txt_color_bg, 2, cv2.LINE_AA) + cv2.putText(out_fig, t, (int(8 * sc), Ht + step_h*i), cv2.FONT_HERSHEY_DUPLEX, + 1.0 * sc, txt_color_fg, 1, cv2.LINE_AA) + + if path is not None: + cv2.imwrite(str(path), out_fig) + + if opencv_display: + cv2.imshow(opencv_title, out_fig) + cv2.waitKey(1) + + return out_fig + + + + + + diff --git a/third_party/TopicFM/src/utils/profiler.py b/third_party/TopicFM/src/utils/profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..6d21ed79fb506ef09c75483355402c48a195aaa9 --- /dev/null +++ b/third_party/TopicFM/src/utils/profiler.py @@ -0,0 +1,39 @@ +import torch +from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler +from contextlib import contextmanager +from pytorch_lightning.utilities import rank_zero_only + + +class InferenceProfiler(SimpleProfiler): + """ + This profiler records duration of actions with cuda.synchronize() + Use this in test time. + """ + + def __init__(self): + super().__init__() + self.start = rank_zero_only(self.start) + self.stop = rank_zero_only(self.stop) + self.summary = rank_zero_only(self.summary) + + @contextmanager + def profile(self, action_name: str) -> None: + try: + torch.cuda.synchronize() + self.start(action_name) + yield action_name + finally: + torch.cuda.synchronize() + self.stop(action_name) + + +def build_profiler(name): + if name == 'inference': + return InferenceProfiler() + elif name == 'pytorch': + from pytorch_lightning.profiler import PyTorchProfiler + return PyTorchProfiler(use_cuda=True, profile_memory=True, row_limit=100) + elif name is None: + return PassThroughProfiler() + else: + raise ValueError(f'Invalid profiler: {name}') diff --git a/third_party/TopicFM/test.py b/third_party/TopicFM/test.py new file mode 100644 index 0000000000000000000000000000000000000000..aeb451cde3674b70b0d2e02f37ff1fd391004d30 --- /dev/null +++ b/third_party/TopicFM/test.py @@ -0,0 +1,68 @@ +import pytorch_lightning as pl +import argparse +import pprint +from loguru import logger as loguru_logger + +from src.config.default import get_cfg_defaults +from src.utils.profiler import build_profiler + +from src.lightning_trainer.data import MultiSceneDataModule +from src.lightning_trainer.trainer import PL_Trainer + + +def parse_args(): + # init a costum parser which will be added into pl.Trainer parser + # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + 'data_cfg_path', type=str, help='data config path') + parser.add_argument( + 'main_cfg_path', type=str, help='main config path') + parser.add_argument( + '--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint') + parser.add_argument( + '--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir") + parser.add_argument( + '--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset') + parser.add_argument( + '--batch_size', type=int, default=1, help='batch_size per gpu') + parser.add_argument( + '--num_workers', type=int, default=2) + parser.add_argument( + '--thr', type=float, default=None, help='modify the coarse-level matching threshold.') + + parser = pl.Trainer.add_argparse_args(parser) + return parser.parse_args() + + +if __name__ == '__main__': + # parse arguments + args = parse_args() + pprint.pprint(vars(args)) + + # init default-cfg and merge it with the main- and data-cfg + config = get_cfg_defaults() + config.merge_from_file(args.main_cfg_path) + config.merge_from_file(args.data_cfg_path) + pl.seed_everything(config.TRAINER.SEED) # reproducibility + + # tune when testing + if args.thr is not None: + config.MODEL.MATCH_COARSE.THR = args.thr + + loguru_logger.info(f"Args and config initialized!") + + # lightning module + profiler = build_profiler(args.profiler_name) + model = PL_Trainer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir) + loguru_logger.info(f"Model-lightning initialized!") + + # lightning data + data_module = MultiSceneDataModule(args, config) + loguru_logger.info(f"DataModule initialized!") + + # lightning trainer + trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False) + + loguru_logger.info(f"Start testing!") + trainer.test(model, datamodule=data_module, verbose=False) diff --git a/third_party/TopicFM/train.py b/third_party/TopicFM/train.py new file mode 100644 index 0000000000000000000000000000000000000000..a552c23718b81ddcb282cedbfe3ceb45e50b3f29 --- /dev/null +++ b/third_party/TopicFM/train.py @@ -0,0 +1,123 @@ +import math +import argparse +import pprint +from distutils.util import strtobool +from pathlib import Path +from loguru import logger as loguru_logger + +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_only +from pytorch_lightning.loggers import TensorBoardLogger +from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor +from pytorch_lightning.plugins import DDPPlugin + +from src.config.default import get_cfg_defaults +from src.utils.misc import get_rank_zero_only_logger, setup_gpus +from src.utils.profiler import build_profiler +from src.lightning_trainer.data import MultiSceneDataModule +from src.lightning_trainer.trainer import PL_Trainer + +loguru_logger = get_rank_zero_only_logger(loguru_logger) + + +def parse_args(): + # init a costum parser which will be added into pl.Trainer parser + # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument( + 'data_cfg_path', type=str, help='data config path') + parser.add_argument( + 'main_cfg_path', type=str, help='main config path') + parser.add_argument( + '--exp_name', type=str, default='default_exp_name') + parser.add_argument( + '--batch_size', type=int, default=4, help='batch_size per gpu') + parser.add_argument( + '--num_workers', type=int, default=4) + parser.add_argument( + '--pin_memory', type=lambda x: bool(strtobool(x)), + nargs='?', default=True, help='whether loading data to pinned memory or not') + parser.add_argument( + '--ckpt_path', type=str, default=None, + help='pretrained checkpoint path, helpful for using a pre-trained coarse-only LoFTR') + parser.add_argument( + '--disable_ckpt', action='store_true', + help='disable checkpoint saving (useful for debugging).') + parser.add_argument( + '--profiler_name', type=str, default=None, + help='options: [inference, pytorch], or leave it unset') + parser.add_argument( + '--parallel_load_data', action='store_true', + help='load datasets in with multiple processes.') + + parser = pl.Trainer.add_argparse_args(parser) + return parser.parse_args() + + +def main(): + # parse arguments + args = parse_args() + rank_zero_only(pprint.pprint)(vars(args)) + + # init default-cfg and merge it with the main- and data-cfg + config = get_cfg_defaults() + config.merge_from_file(args.main_cfg_path) + config.merge_from_file(args.data_cfg_path) + pl.seed_everything(config.TRAINER.SEED) # reproducibility + # TODO: Use different seeds for each dataloader workers + # This is needed for data augmentation + + # scale lr and warmup-step automatically + args.gpus = _n_gpus = setup_gpus(args.gpus) + config.TRAINER.WORLD_SIZE = _n_gpus * args.num_nodes + config.TRAINER.TRUE_BATCH_SIZE = config.TRAINER.WORLD_SIZE * args.batch_size + _scaling = config.TRAINER.TRUE_BATCH_SIZE / config.TRAINER.CANONICAL_BS + config.TRAINER.SCALING = _scaling + config.TRAINER.TRUE_LR = config.TRAINER.CANONICAL_LR * _scaling + config.TRAINER.WARMUP_STEP = math.floor(config.TRAINER.WARMUP_STEP / _scaling) + + # lightning module + profiler = build_profiler(args.profiler_name) + model = PL_Trainer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler) + loguru_logger.info(f"Model LightningModule initialized!") + + # lightning data + data_module = MultiSceneDataModule(args, config) + loguru_logger.info(f"Model DataModule initialized!") + + # TensorBoard Logger + logger = TensorBoardLogger(save_dir='logs/tb_logs', name=args.exp_name, default_hp_metric=False) + ckpt_dir = Path(logger.log_dir) / 'checkpoints' + + # Callbacks + # TODO: update ModelCheckpoint to monitor multiple metrics + ckpt_callback = ModelCheckpoint(monitor='auc@10', verbose=True, save_top_k=5, mode='max', + save_last=True, + dirpath=str(ckpt_dir), + filename='{epoch}-{auc@5:.3f}-{auc@10:.3f}-{auc@20:.3f}') + lr_monitor = LearningRateMonitor(logging_interval='step') + callbacks = [lr_monitor] + if not args.disable_ckpt: + callbacks.append(ckpt_callback) + + # Lightning Trainer + trainer = pl.Trainer.from_argparse_args( + args, + plugins=DDPPlugin(find_unused_parameters=False, + num_nodes=args.num_nodes, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0), + gradient_clip_val=config.TRAINER.GRADIENT_CLIPPING, + callbacks=callbacks, + logger=logger, + sync_batchnorm=config.TRAINER.WORLD_SIZE > 0, + replace_sampler_ddp=False, # use custom sampler + reload_dataloaders_every_epoch=False, # avoid repeated samples! + weights_summary='full', + profiler=profiler) + loguru_logger.info(f"Trainer initialized!") + loguru_logger.info(f"Start training!") + trainer.fit(model, datamodule=data_module) + + +if __name__ == '__main__': + main() diff --git a/third_party/TopicFM/visualization.py b/third_party/TopicFM/visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..279b41cd88f61ce3414e2f3077fec642b2c8333a --- /dev/null +++ b/third_party/TopicFM/visualization.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python +# coding: utf-8 + +import os, glob, cv2 +import argparse +from argparse import Namespace +import yaml +from tqdm import tqdm +import torch +from torch.utils.data import Dataset, DataLoader, SequentialSampler + +from src.datasets.custom_dataloader import TestDataLoader +from src.utils.dataset import read_img_gray +from configs.data.base import cfg as data_cfg +import viz + + +def get_model_config(method_name, dataset_name, root_dir='viz'): + config_file = f'{root_dir}/configs/{method_name}.yml' + with open(config_file, 'r') as f: + model_conf = yaml.load(f, Loader=yaml.FullLoader)[dataset_name] + return model_conf + + +class DemoDataset(Dataset): + def __init__(self, dataset_dir, img_file=None, resize=0, down_factor=16): + self.dataset_dir = dataset_dir + if img_file is None: + self.list_img_files = glob.glob(os.path.join(dataset_dir, "*.*")) + self.list_img_files.sort() + else: + with open(img_file) as f: + self.list_img_files = [os.path.join(dataset_dir, img_file.strip()) for img_file in f.readlines()] + self.resize = resize + self.down_factor = down_factor + + def __len__(self): + return len(self.list_img_files) + + def __getitem__(self, idx): + img_path = self.list_img_files[idx] #os.path.join(self.dataset_dir, self.list_img_files[idx]) + img, scale = read_img_gray(img_path, resize=self.resize, down_factor=self.down_factor) + return {"img": img, "id": idx, "img_path": img_path} + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Visualize matches') + parser.add_argument('--gpu', '-gpu', type=str, default='0') + parser.add_argument('--method', type=str, default=None) + parser.add_argument('--dataset_dir', type=str, default='data/aachen-day-night') + parser.add_argument('--pair_dir', type=str, default=None) + parser.add_argument( + '--dataset_name', type=str, choices=['megadepth', 'scannet', 'aachen_v1.1', 'inloc'], default='megadepth' + ) + parser.add_argument('--measure_time', action="store_true") + parser.add_argument('--no_viz', action="store_true") + parser.add_argument('--compute_eval_metrics', action="store_true") + parser.add_argument('--run_demo', action="store_true") + + args = parser.parse_args() + + model_cfg = get_model_config(args.method, args.dataset_name) + class_name = model_cfg["class"] + model = viz.__dict__[class_name](model_cfg) + # all_args = Namespace(**vars(args), **model_cfg) + if not args.run_demo: + if args.dataset_name == 'megadepth': + from configs.data.megadepth_test_1500 import cfg + + data_cfg.merge_from_other_cfg(cfg) + elif args.dataset_name == 'scannet': + from configs.data.scannet_test_1500 import cfg + + data_cfg.merge_from_other_cfg(cfg) + elif args.dataset_name == 'aachen_v1.1': + data_cfg.merge_from_list(["DATASET.TEST_DATA_SOURCE", "aachen_v1.1", + "DATASET.TEST_DATA_ROOT", os.path.join(args.dataset_dir, "images/images_upright"), + "DATASET.TEST_LIST_PATH", args.pair_dir, + "DATASET.TEST_IMGSIZE", model_cfg["imsize"]]) + elif args.dataset_name == 'inloc': + data_cfg.merge_from_list(["DATASET.TEST_DATA_SOURCE", "inloc", + "DATASET.TEST_DATA_ROOT", args.dataset_dir, + "DATASET.TEST_LIST_PATH", args.pair_dir, + "DATASET.TEST_IMGSIZE", model_cfg["imsize"]]) + + has_ground_truth = str(data_cfg.DATASET.TEST_DATA_SOURCE).lower() in ["megadepth", "scannet"] + dataloader = TestDataLoader(data_cfg) + with torch.no_grad(): + for data_dict in tqdm(dataloader): + for k, v in data_dict.items(): + if isinstance(v, torch.Tensor): + data_dict[k] = v.cuda() if torch.cuda.is_available() else v + img_root_dir = data_cfg.DATASET.TEST_DATA_ROOT + model.match_and_draw(data_dict, root_dir=img_root_dir, ground_truth=has_ground_truth, + measure_time=args.measure_time, viz_matches=(not args.no_viz)) + + if args.measure_time: + print("Running time for each image is {} miliseconds".format(model.measure_time())) + if args.compute_eval_metrics and has_ground_truth: + model.compute_eval_metrics() + else: + demo_dataset = DemoDataset(args.dataset_dir, img_file=args.pair_dir, resize=640) + sampler = SequentialSampler(demo_dataset) + dataloader = DataLoader(demo_dataset, batch_size=1, sampler=sampler) + + writer = cv2.VideoWriter('topicfm_demo.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 15, (640 * 2 + 5, 480 * 2 + 10)) + + model.run_demo(iter(dataloader), writer) #, output_dir="demo", no_display=True) diff --git a/third_party/TopicFM/viz/__init__.py b/third_party/TopicFM/viz/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f0efac33299da6fb8195ce70bcb9eb210f6cf658 --- /dev/null +++ b/third_party/TopicFM/viz/__init__.py @@ -0,0 +1,3 @@ +from .methods.patch2pix import VizPatch2Pix +from .methods.loftr import VizLoFTR +from .methods.topicfm import VizTopicFM diff --git a/third_party/TopicFM/viz/configs/__init__.py b/third_party/TopicFM/viz/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/TopicFM/viz/configs/loftr.yml b/third_party/TopicFM/viz/configs/loftr.yml new file mode 100644 index 0000000000000000000000000000000000000000..776d625ac8ad5a0b4e4a4e65e2b99f62662bc3fc --- /dev/null +++ b/third_party/TopicFM/viz/configs/loftr.yml @@ -0,0 +1,18 @@ +default: &default + class: 'VizLoFTR' + ckpt: 'third_party/loftr/pretrained/outdoor_ds.ckpt' + match_threshold: 0.2 +megadepth: + <<: *default +scannet: + <<: *default +hpatch: + <<: *default +inloc: + <<: *default + imsize: 1024 + match_threshold: 0.3 +aachen_v1.1: + <<: *default + imsize: 1024 + match_threshold: 0.3 diff --git a/third_party/TopicFM/viz/configs/patch2pix.yml b/third_party/TopicFM/viz/configs/patch2pix.yml new file mode 100644 index 0000000000000000000000000000000000000000..5e3efa7889098425aaf586bd7b88fc28feb74778 --- /dev/null +++ b/third_party/TopicFM/viz/configs/patch2pix.yml @@ -0,0 +1,19 @@ +default: &default + class: 'VizPatch2Pix' + ckpt: 'third_party/patch2pix/pretrained/patch2pix_pretrained.pth' + ksize: 2 + imsize: 1024 + match_threshold: 0.25 +megadepth: + <<: *default + imsize: 1200 +scannet: + <<: *default + imsize: [640, 480] +hpatch: + <<: *default +inloc: + <<: *default +aachen_v1.1: + <<: *default + imsize: 1024 diff --git a/third_party/TopicFM/viz/configs/topicfm.yml b/third_party/TopicFM/viz/configs/topicfm.yml new file mode 100644 index 0000000000000000000000000000000000000000..7a8071a6fcd8def21dbfec5b9b2b10200f494eee --- /dev/null +++ b/third_party/TopicFM/viz/configs/topicfm.yml @@ -0,0 +1,29 @@ +default: &default + class: 'VizTopicFM' + ckpt: 'pretrained/model_best.ckpt' + match_threshold: 0.2 + n_sampling_topics: 4 + show_n_topics: 4 +megadepth: + <<: *default + n_sampling_topics: 10 + show_n_topics: 6 +scannet: + <<: *default + match_threshold: 0.3 + n_sampling_topics: 5 + show_n_topics: 4 +hpatch: + <<: *default +inloc: + <<: *default + imsize: 1024 + match_threshold: 0.3 + n_sampling_topics: 8 + show_n_topics: 4 +aachen_v1.1: + <<: *default + imsize: 1024 + match_threshold: 0.3 + n_sampling_topics: 6 + show_n_topics: 6 diff --git a/third_party/TopicFM/viz/methods/__init__.py b/third_party/TopicFM/viz/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/TopicFM/viz/methods/base.py b/third_party/TopicFM/viz/methods/base.py new file mode 100644 index 0000000000000000000000000000000000000000..377e95134f339459bff3c5a0d30b3bfbc122d978 --- /dev/null +++ b/third_party/TopicFM/viz/methods/base.py @@ -0,0 +1,59 @@ +import pprint +from abc import ABCMeta, abstractmethod +import torch +from itertools import chain + +from src.utils.plotting import make_matching_figure, error_colormap +from src.utils.metrics import aggregate_metrics + + +def flatten_list(x): + return list(chain(*x)) + + +class Viz(metaclass=ABCMeta): + def __init__(self): + super().__init__() + self.device = torch.device('cuda:{}'.format(0) if torch.cuda.is_available() else 'cpu') + torch.set_grad_enabled(False) + + # for evaluation metrics of MegaDepth and ScanNet + self.eval_stats = [] + self.time_stats = [] + + def draw_matches(self, mkpts0, mkpts1, img0, img1, conf, path=None, **kwargs): + thr = 5e-4 + # mkpts0 = pe['mkpts0_f'].cpu().numpy() + # mkpts1 = pe['mkpts1_f'].cpu().numpy() + if "conf_thr" in kwargs: + thr = kwargs["conf_thr"] + color = error_colormap(conf, thr, alpha=0.1) + + text = [ + f"{self.name}", + f"#Matches: {len(mkpts0)}", + ] + if 'R_errs' in kwargs: + text.append(f"$\\Delta$R:{kwargs['R_errs']:.2f}°, $\\Delta$t:{kwargs['t_errs']:.2f}°",) + + if path: + make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text, path=path, dpi=150) + else: + return make_matching_figure(img0, img1, mkpts0, mkpts1, color, text=text) + + @abstractmethod + def match_and_draw(self, data_dict, **kwargs): + pass + + def compute_eval_metrics(self, epi_err_thr=5e-4): + # metrics: dict of list, numpy + _metrics = [o['metrics'] for o in self.eval_stats] + metrics = {k: flatten_list([_me[k] for _me in _metrics]) for k in _metrics[0]} + + val_metrics_4tb = aggregate_metrics(metrics, epi_err_thr) + print('\n' + pprint.pformat(val_metrics_4tb)) + + def measure_time(self): + if len(self.time_stats) == 0: + return 0 + return sum(self.time_stats) / len(self.time_stats) diff --git a/third_party/TopicFM/viz/methods/loftr.py b/third_party/TopicFM/viz/methods/loftr.py new file mode 100644 index 0000000000000000000000000000000000000000..53d0c00c1a067cee10bf1587197e4780ac8b2eda --- /dev/null +++ b/third_party/TopicFM/viz/methods/loftr.py @@ -0,0 +1,85 @@ +from argparse import Namespace +import os +import torch +import cv2 + +from .base import Viz +from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors + +from third_party.loftr.src.loftr import LoFTR, default_cfg + + +class VizLoFTR(Viz): + def __init__(self, args): + super().__init__() + if type(args) == dict: + args = Namespace(**args) + + self.match_threshold = args.match_threshold + + # Load model + conf = dict(default_cfg) + conf['match_coarse']['thr'] = self.match_threshold + print(conf) + self.model = LoFTR(config=conf) + ckpt_dict = torch.load(args.ckpt) + self.model.load_state_dict(ckpt_dict['state_dict']) + self.model = self.model.eval().to(self.device) + + # Name the method + # self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0] + self.name = 'LoFTR' + + print(f'Initialize {self.name}') + + def match_and_draw(self, data_dict, root_dir=None, ground_truth=False, measure_time=False, viz_matches=True): + if measure_time: + torch.cuda.synchronize() + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + self.model(data_dict) + if measure_time: + torch.cuda.synchronize() + end.record() + torch.cuda.synchronize() + self.time_stats.append(start.elapsed_time(end)) + + kpts0 = data_dict['mkpts0_f'].cpu().numpy() + kpts1 = data_dict['mkpts1_f'].cpu().numpy() + + img_name0, img_name1 = list(zip(*data_dict['pair_names']))[0] + img0 = cv2.imread(os.path.join(root_dir, img_name0)) + img1 = cv2.imread(os.path.join(root_dir, img_name1)) + if str(data_dict["dataset_name"][0]).lower() == 'scannet': + img0 = cv2.resize(img0, (640, 480)) + img1 = cv2.resize(img1, (640, 480)) + + if viz_matches: + saved_name = "_".join([img_name0.split('/')[-1].split('.')[0], img_name1.split('/')[-1].split('.')[0]]) + folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name)) + if not os.path.exists(folder_matches): + os.makedirs(folder_matches) + path_to_save_matches = os.path.join(folder_matches, "{}.png".format(saved_name)) + if ground_truth: + compute_symmetrical_epipolar_errors(data_dict) # compute epi_errs for each match + compute_pose_errors(data_dict) # compute R_errs, t_errs, pose_errs for each pair + epi_errors = data_dict['epi_errs'].cpu().numpy() + R_errors, t_errors = data_dict['R_errs'][0], data_dict['t_errs'][0] + + self.draw_matches(kpts0, kpts1, img0, img1, epi_errors, path=path_to_save_matches, + R_errs=R_errors, t_errs=t_errors) + + rel_pair_names = list(zip(*data_dict['pair_names'])) + bs = data_dict['image0'].size(0) + metrics = { + # to filter duplicate pairs caused by DistributedSampler + 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], + 'epi_errs': [data_dict['epi_errs'][data_dict['m_bids'] == b].cpu().numpy() for b in range(bs)], + 'R_errs': data_dict['R_errs'], + 't_errs': data_dict['t_errs'], + 'inliers': data_dict['inliers']} + self.eval_stats.append({'metrics': metrics}) + else: + m_conf = 1 - data_dict["mconf"].cpu().numpy() + self.draw_matches(kpts0, kpts1, img0, img1, m_conf, path=path_to_save_matches, conf_thr=0.4) diff --git a/third_party/TopicFM/viz/methods/patch2pix.py b/third_party/TopicFM/viz/methods/patch2pix.py new file mode 100644 index 0000000000000000000000000000000000000000..14a1d345881e2021be97dc5dde91d8bbe1cd18fa --- /dev/null +++ b/third_party/TopicFM/viz/methods/patch2pix.py @@ -0,0 +1,80 @@ +from argparse import Namespace +import os, sys +import torch +import cv2 +from pathlib import Path + +from .base import Viz +from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors + +patch2pix_path = Path(__file__).parent / '../../third_party/patch2pix' +sys.path.append(str(patch2pix_path)) +from third_party.patch2pix.utils.eval.model_helper import load_model, estimate_matches + + +class VizPatch2Pix(Viz): + def __init__(self, args): + super().__init__() + + if type(args) == dict: + args = Namespace(**args) + self.imsize = args.imsize + self.match_threshold = args.match_threshold + self.ksize = args.ksize + self.model = load_model(args.ckpt, method='patch2pix') + self.name = 'Patch2Pix' + print(f'Initialize {self.name} with image size {self.imsize}') + + def match_and_draw(self, data_dict, root_dir=None, ground_truth=False, measure_time=False, viz_matches=True): + img_name0, img_name1 = list(zip(*data_dict['pair_names']))[0] + path_img0 = os.path.join(root_dir, img_name0) + path_img1 = os.path.join(root_dir, img_name1) + img0, img1 = cv2.imread(path_img0), cv2.imread(path_img1) + return_m_upscale = True + if str(data_dict["dataset_name"][0]).lower() == 'scannet': + # self.imsize = 640 + img0 = cv2.resize(img0, tuple(self.imsize)) # (640, 480)) + img1 = cv2.resize(img1, tuple(self.imsize)) # (640, 480)) + return_m_upscale = False + outputs = estimate_matches(self.model, path_img0, path_img1, + ksize=self.ksize, io_thres=self.match_threshold, + eval_type='fine', imsize=self.imsize, + return_upscale=return_m_upscale, measure_time=measure_time) + if measure_time: + self.time_stats.append(outputs[-1]) + matches, mconf = outputs[0], outputs[1] + kpts0 = matches[:, :2] + kpts1 = matches[:, 2:4] + + if viz_matches: + saved_name = "_".join([img_name0.split('/')[-1].split('.')[0], img_name1.split('/')[-1].split('.')[0]]) + folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name)) + if not os.path.exists(folder_matches): + os.makedirs(folder_matches) + path_to_save_matches = os.path.join(folder_matches, "{}.png".format(saved_name)) + + if ground_truth: + data_dict["mkpts0_f"] = torch.from_numpy(matches[:, :2]).float().to(self.device) + data_dict["mkpts1_f"] = torch.from_numpy(matches[:, 2:4]).float().to(self.device) + data_dict["m_bids"] = torch.zeros(matches.shape[0], device=self.device, dtype=torch.float32) + compute_symmetrical_epipolar_errors(data_dict) # compute epi_errs for each match + compute_pose_errors(data_dict) # compute R_errs, t_errs, pose_errs for each pair + epi_errors = data_dict['epi_errs'].cpu().numpy() + R_errors, t_errors = data_dict['R_errs'][0], data_dict['t_errs'][0] + + self.draw_matches(kpts0, kpts1, img0, img1, epi_errors, path=path_to_save_matches, + R_errs=R_errors, t_errs=t_errors) + + rel_pair_names = list(zip(*data_dict['pair_names'])) + bs = data_dict['image0'].size(0) + metrics = { + # to filter duplicate pairs caused by DistributedSampler + 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], + 'epi_errs': [data_dict['epi_errs'][data_dict['m_bids'] == b].cpu().numpy() for b in range(bs)], + 'R_errs': data_dict['R_errs'], + 't_errs': data_dict['t_errs'], + 'inliers': data_dict['inliers']} + self.eval_stats.append({'metrics': metrics}) + else: + m_conf = 1 - mconf + self.draw_matches(kpts0, kpts1, img0, img1, m_conf, path=path_to_save_matches, conf_thr=0.4) diff --git a/third_party/TopicFM/viz/methods/topicfm.py b/third_party/TopicFM/viz/methods/topicfm.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8b1485d5296947a38480cc031c5d7439bf163d --- /dev/null +++ b/third_party/TopicFM/viz/methods/topicfm.py @@ -0,0 +1,198 @@ +from argparse import Namespace +import os +import torch +import cv2 +from time import time +from pathlib import Path +import matplotlib.cm as cm +import numpy as np + +from src.models.topic_fm import TopicFM +from src import get_model_cfg +from .base import Viz +from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors +from src.utils.plotting import draw_topics, draw_topicfm_demo, error_colormap + + +class VizTopicFM(Viz): + def __init__(self, args): + super().__init__() + if type(args) == dict: + args = Namespace(**args) + + self.match_threshold = args.match_threshold + self.n_sampling_topics = args.n_sampling_topics + self.show_n_topics = args.show_n_topics + + # Load model + conf = dict(get_model_cfg()) + conf['match_coarse']['thr'] = self.match_threshold + conf['coarse']['n_samples'] = self.n_sampling_topics + print("model config: ", conf) + self.model = TopicFM(config=conf) + ckpt_dict = torch.load(args.ckpt) + self.model.load_state_dict(ckpt_dict['state_dict']) + self.model = self.model.eval().to(self.device) + + # Name the method + # self.ckpt_name = args.ckpt.split('/')[-1].split('.')[0] + self.name = 'TopicFM' + + print(f'Initialize {self.name}') + + def match_and_draw(self, data_dict, root_dir=None, ground_truth=False, measure_time=False, viz_matches=True): + if measure_time: + torch.cuda.synchronize() + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + start.record() + self.model(data_dict) + if measure_time: + torch.cuda.synchronize() + end.record() + torch.cuda.synchronize() + self.time_stats.append(start.elapsed_time(end)) + + kpts0 = data_dict['mkpts0_f'].cpu().numpy() + kpts1 = data_dict['mkpts1_f'].cpu().numpy() + + img_name0, img_name1 = list(zip(*data_dict['pair_names']))[0] + img0 = cv2.imread(os.path.join(root_dir, img_name0)) + img1 = cv2.imread(os.path.join(root_dir, img_name1)) + if str(data_dict["dataset_name"][0]).lower() == 'scannet': + img0 = cv2.resize(img0, (640, 480)) + img1 = cv2.resize(img1, (640, 480)) + + if viz_matches: + saved_name = "_".join([img_name0.split('/')[-1].split('.')[0], img_name1.split('/')[-1].split('.')[0]]) + folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name)) + if not os.path.exists(folder_matches): + os.makedirs(folder_matches) + path_to_save_matches = os.path.join(folder_matches, "{}.png".format(saved_name)) + + if ground_truth: + compute_symmetrical_epipolar_errors(data_dict) # compute epi_errs for each match + compute_pose_errors(data_dict) # compute R_errs, t_errs, pose_errs for each pair + epi_errors = data_dict['epi_errs'].cpu().numpy() + R_errors, t_errors = data_dict['R_errs'][0], data_dict['t_errs'][0] + + self.draw_matches(kpts0, kpts1, img0, img1, epi_errors, path=path_to_save_matches, + R_errs=R_errors, t_errs=t_errors) + + # compute evaluation metrics + rel_pair_names = list(zip(*data_dict['pair_names'])) + bs = data_dict['image0'].size(0) + metrics = { + # to filter duplicate pairs caused by DistributedSampler + 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], + 'epi_errs': [data_dict['epi_errs'][data_dict['m_bids'] == b].cpu().numpy() for b in range(bs)], + 'R_errs': data_dict['R_errs'], + 't_errs': data_dict['t_errs'], + 'inliers': data_dict['inliers']} + self.eval_stats.append({'metrics': metrics}) + else: + m_conf = 1 - data_dict["mconf"].cpu().numpy() + self.draw_matches(kpts0, kpts1, img0, img1, m_conf, path=path_to_save_matches, conf_thr=0.4) + if self.show_n_topics > 0: + folder_topics = os.path.join(root_dir, "{}_viz_topics".format(self.name)) + if not os.path.exists(folder_topics): + os.makedirs(folder_topics) + draw_topics(data_dict, img0, img1, saved_folder=folder_topics, show_n_topics=self.show_n_topics, + saved_name=saved_name) + + def run_demo(self, dataloader, writer=None, output_dir=None, no_display=False, skip_frames=1): + data_dict = next(dataloader) + + frame_id = 0 + last_image_id = 0 + img0 = np.array(cv2.imread(str(data_dict["img_path"][0])), dtype=np.float32) / 255 + frame_tensor = data_dict["img"].to(self.device) + pair_data = {'image0': frame_tensor} + last_frame = cv2.resize(img0, (frame_tensor.shape[-1], frame_tensor.shape[-2]), cv2.INTER_LINEAR) + + if output_dir is not None: + print('==> Will write outputs to {}'.format(output_dir)) + Path(output_dir).mkdir(exist_ok=True) + + # Create a window to display the demo. + if not no_display: + window_name = 'Topic-assisted Feature Matching' + cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) + cv2.resizeWindow(window_name, (640 * 2, 480 * 2)) + else: + print('Skipping visualization, will not show a GUI.') + + # Print the keyboard help menu. + print('==> Keyboard control:\n' + '\tn: select the current frame as the reference image (left)\n' + '\tq: quit') + + # vis_range = [kwargs["bottom_k"], kwargs["top_k"]] + + while True: + frame_id += 1 + if frame_id == len(dataloader): + print('Finished demo_loftr.py') + break + data_dict = next(dataloader) + if frame_id % skip_frames != 0: + # print("Skipping frame.") + continue + + stem0, stem1 = last_image_id, data_dict["id"][0].item() - 1 + frame = np.array(cv2.imread(str(data_dict["img_path"][0])), dtype=np.float32) / 255 + + frame_tensor = data_dict["img"].to(self.device) + frame = cv2.resize(frame, (frame_tensor.shape[-1], frame_tensor.shape[-2]), interpolation=cv2.INTER_LINEAR) + pair_data = {**pair_data, 'image1': frame_tensor} + self.model(pair_data) + + total_n_matches = len(pair_data['mkpts0_f']) + mkpts0 = pair_data['mkpts0_f'].cpu().numpy() # [vis_range[0]:vis_range[1]] + mkpts1 = pair_data['mkpts1_f'].cpu().numpy() # [vis_range[0]:vis_range[1]] + mconf = pair_data['mconf'].cpu().numpy() # [vis_range[0]:vis_range[1]] + + # Normalize confidence. + if len(mconf) > 0: + mconf = 1 - mconf + + # alpha = 0 + # color = cm.jet(mconf, alpha=alpha) + color = error_colormap(mconf, thr=0.4, alpha=0.1) + + text = [ + f'Topics', + '#Matches: {}'.format(total_n_matches), + ] + + out = draw_topicfm_demo(pair_data, last_frame, frame, mkpts0, mkpts1, color, text, + show_n_topics=4, path=None) + + if not no_display: + if writer is not None: + writer.write(out) + cv2.imshow('TopicFM Matches', out) + key = chr(cv2.waitKey(10) & 0xFF) + if key == 'q': + if writer is not None: + writer.release() + print('Exiting...') + break + elif key == 'n': + pair_data['image0'] = frame_tensor + last_frame = frame + last_image_id = (data_dict["id"][0].item() - 1) + frame_id_left = frame_id + + elif output_dir is not None: + stem = 'matches_{:06}_{:06}'.format(stem0, stem1) + out_file = str(Path(output_dir, stem + '.png')) + print('\nWriting image to {}'.format(out_file)) + cv2.imwrite(out_file, out) + else: + raise ValueError("output_dir is required when no display is given.") + + cv2.destroyAllWindows() + if writer is not None: + writer.release() + diff --git a/third_party/d2net/.gitignore b/third_party/d2net/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..fda64312542ac8b636532f580c7648708dd0c1ba --- /dev/null +++ b/third_party/d2net/.gitignore @@ -0,0 +1,13 @@ +__pycache__ +.vscode +checkpoints* +train_vis +log.txt +hpatches_sequences/hseq.pdf +hpatches_sequences/hseq-top.pdf +hpatches_sequences/hpatches-sequences-release* +hpatches_sequences/cache +hpatches_sequences/cache-top +.ipynb_checkpoints +vlfeat +*.d2-net diff --git a/third_party/d2net/LICENSE b/third_party/d2net/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..5d50329f25f288161a596172f69c84b9dc465b27 --- /dev/null +++ b/third_party/d2net/LICENSE @@ -0,0 +1,33 @@ +The Clear BSD License + +Copyright (c) 2019 Mihai Dusmanu +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted (subject to the limitations in the disclaimer +below) provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the names of the copyright holders nor the names of the + contributors nor the names of their institutions may be used to endorse + or promote products derived from this software without specific prior + written permission. + +NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY +THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND +CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR +BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER +IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. diff --git a/third_party/d2net/README.md b/third_party/d2net/README.md new file mode 100644 index 0000000000000000000000000000000000000000..741c88dffcea55fc482d823d585421fbe0996cea --- /dev/null +++ b/third_party/d2net/README.md @@ -0,0 +1,121 @@ +# D2-Net: A Trainable CNN for Joint Detection and Description of Local Features + +This repository contains the implementation of the following paper: + +```text +"D2-Net: A Trainable CNN for Joint Detection and Description of Local Features". +M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler. CVPR 2019. +``` + +[Paper on arXiv](https://arxiv.org/abs/1905.03561), [Project page](https://dsmn.ml/publications/d2-net.html) + +## Getting started + +Python 3.6+ is recommended for running our code. [Conda](https://docs.conda.io/en/latest/) can be used to install the required packages: + +```bash +conda install pytorch torchvision cudatoolkit=10.0 -c pytorch +conda install h5py imageio imagesize matplotlib numpy scipy tqdm +``` + +## Downloading the models + +The off-the-shelf **Caffe VGG16** weights and their tuned counterpart can be downloaded by running: + +```bash +mkdir models +wget https://dsmn.ml/files/d2-net/d2_ots.pth -O models/d2_ots.pth +wget https://dsmn.ml/files/d2-net/d2_tf.pth -O models/d2_tf.pth +wget https://dsmn.ml/files/d2-net/d2_tf_no_phototourism.pth -O models/d2_tf_no_phototourism.pth +``` + +**Update - 23 May 2019** We have added a new set of weights trained on MegaDepth without the PhotoTourism scenes (sagrada_familia - 0019, lincoln_memorial_statue - 0021, british_museum - 0024, london_bridge - 0025, us_capitol - 0078, mount_rushmore - 1589). Our initial results show similar performance. In order to use these weights at test time, you should add `--model_file models/d2_tf_no_phototourism.pth`. + +## Feature extraction + +`extract_features.py` can be used to extract D2 features for a given list of images. The singlescale features require less than 6GB of VRAM for 1200x1600 images. The `--multiscale` flag can be used to extract multiscale features - for this, we recommend at least 12GB of VRAM. + +The output format can be either [`npz`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) or `mat`. In either case, the feature files encapsulate three arrays: + +- `keypoints` [`N x 3`] array containing the positions of keypoints `x, y` and the scales `s`. The positions follow the COLMAP format, with the `X` axis pointing to the right and the `Y` axis to the bottom. +- `scores` [`N`] array containing the activations of keypoints (higher is better). +- `descriptors` [`N x 512`] array containing the L2 normalized descriptors. + +```bash +python extract_features.py --image_list_file images.txt (--multiscale) +``` + +# Feature extraction with kapture datasets + +Kapture is a pivot file format, based on text and binary files, used to describe SFM (Structure From Motion) and more generally sensor-acquired data. + +It is available at https://github.com/naver/kapture. +It contains conversion tools for popular formats and several popular datasets are directly available in kapture. + +It can be installed with: +```bash +pip install kapture +``` + +Datasets can be downloaded with: +```bash +kapture_download_dataset.py update +kapture_download_dataset.py list +# e.g.: install mapping and query of Extended-CMU-Seasons_slice22 +kapture_download_dataset.py install "Extended-CMU-Seasons_slice22_*" +``` +If you want to convert your own dataset into kapture, please find some examples [here](https://github.com/naver/kapture/blob/master/doc/datasets.adoc). + +Once installed, you can extract keypoints for your kapture dataset with: +```bash +python extract_kapture.py --kapture-root pathto/yourkapturedataset (--multiscale) +``` + +Run `python extract_kapture.py --help` for more information on the extraction parameters. + +## Tuning on MegaDepth + +The training pipeline provided here is a PyTorch implementation of the TensorFlow code that was used to train the model available to download above. + +**Update - 05 June 2019** We have fixed a bug in the dataset preprocessing - retraining now yields similar results to the original TensorFlow implementation. + +**Update - 07 August 2019** We have released an updated, more accurate version of the training dataset - training is more stable and significantly faster for equal performance. + +### Downloading and preprocessing the MegaDepth dataset + +For this part, [COLMAP](https://colmap.github.io/) should be installed. Please refer to the official website for installation instructions. + +After downloading the entire [MegaDepth](http://www.cs.cornell.edu/projects/megadepth/) dataset (including SfM models), the first step is generating the undistorted reconstructions. This can be done by calling `undistort_reconstructions.py` as follows: + +```bash +python undistort_reconstructions.py --colmap_path /path/to/colmap/executable --base_path /path/to/megadepth +``` + +Next, `preprocess_megadepth.sh` can be used to retrieve the camera parameters and compute the overlap between images for all scenes. + +```bash +bash preprocess_undistorted_megadepth.sh /path/to/megadepth /path/to/output/folder +``` + +In case you prefer downloading the undistorted reconstructions and aggregated scene information folder directly, you can find them [here - Google Drive](https://drive.google.com/open?id=1hxpOsqOZefdrba_BqnW490XpNX_LgXPB). You will still need to download the depth maps ("MegaDepth v1 Dataset") from the MegaDepth website. + +### Training + +After downloading and preprocessing MegaDepth, the training can be started right away: + +```bash +python train.py --use_validation --dataset_path /path/to/megadepth --scene_info_path /path/to/preprocessing/output +``` + +## BibTeX + +If you use this code in your project, please cite the following paper: + +```bibtex +@InProceedings{Dusmanu2019CVPR, + author = {Dusmanu, Mihai and Rocco, Ignacio and Pajdla, Tomas and Pollefeys, Marc and Sivic, Josef and Torii, Akihiko and Sattler, Torsten}, + title = {{D2-Net: A Trainable CNN for Joint Detection and Description of Local Features}}, + booktitle = {Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + year = {2019}, +} +``` diff --git a/third_party/d2net/extract_features.py b/third_party/d2net/extract_features.py new file mode 100644 index 0000000000000000000000000000000000000000..628463a7d042a90b5cadea8a317237cde86f5ae4 --- /dev/null +++ b/third_party/d2net/extract_features.py @@ -0,0 +1,156 @@ +import argparse + +import numpy as np + +import imageio + +import torch + +from tqdm import tqdm + +import scipy +import scipy.io +import scipy.misc + +from lib.model_test import D2Net +from lib.utils import preprocess_image +from lib.pyramid import process_multiscale + +# CUDA +use_cuda = torch.cuda.is_available() +device = torch.device("cuda:0" if use_cuda else "cpu") + +# Argument parsing +parser = argparse.ArgumentParser(description='Feature extraction script') + +parser.add_argument( + '--image_list_file', type=str, required=True, + help='path to a file containing a list of images to process' +) + +parser.add_argument( + '--preprocessing', type=str, default='caffe', + help='image preprocessing (caffe or torch)' +) +parser.add_argument( + '--model_file', type=str, default='models/d2_tf.pth', + help='path to the full model' +) + +parser.add_argument( + '--max_edge', type=int, default=1600, + help='maximum image size at network input' +) +parser.add_argument( + '--max_sum_edges', type=int, default=2800, + help='maximum sum of image sizes at network input' +) + +parser.add_argument( + '--output_extension', type=str, default='.d2-net', + help='extension for the output' +) +parser.add_argument( + '--output_type', type=str, default='npz', + help='output file type (npz or mat)' +) + +parser.add_argument( + '--multiscale', dest='multiscale', action='store_true', + help='extract multiscale features' +) +parser.set_defaults(multiscale=False) + +parser.add_argument( + '--no-relu', dest='use_relu', action='store_false', + help='remove ReLU after the dense feature extraction module' +) +parser.set_defaults(use_relu=True) + +args = parser.parse_args() + +print(args) + +# Creating CNN model +model = D2Net( + model_file=args.model_file, + use_relu=args.use_relu, + use_cuda=use_cuda +) + +# Process the file +with open(args.image_list_file, 'r') as f: + lines = f.readlines() +for line in tqdm(lines, total=len(lines)): + path = line.strip() + + image = imageio.imread(path) + if len(image.shape) == 2: + image = image[:, :, np.newaxis] + image = np.repeat(image, 3, -1) + + # TODO: switch to PIL.Image due to deprecation of scipy.misc.imresize. + resized_image = image + if max(resized_image.shape) > args.max_edge: + resized_image = scipy.misc.imresize( + resized_image, + args.max_edge / max(resized_image.shape) + ).astype('float') + if sum(resized_image.shape[: 2]) > args.max_sum_edges: + resized_image = scipy.misc.imresize( + resized_image, + args.max_sum_edges / sum(resized_image.shape[: 2]) + ).astype('float') + + fact_i = image.shape[0] / resized_image.shape[0] + fact_j = image.shape[1] / resized_image.shape[1] + + input_image = preprocess_image( + resized_image, + preprocessing=args.preprocessing + ) + with torch.no_grad(): + if args.multiscale: + keypoints, scores, descriptors = process_multiscale( + torch.tensor( + input_image[np.newaxis, :, :, :].astype(np.float32), + device=device + ), + model + ) + else: + keypoints, scores, descriptors = process_multiscale( + torch.tensor( + input_image[np.newaxis, :, :, :].astype(np.float32), + device=device + ), + model, + scales=[1] + ) + + # Input image coordinates + keypoints[:, 0] *= fact_i + keypoints[:, 1] *= fact_j + # i, j -> u, v + keypoints = keypoints[:, [1, 0, 2]] + + if args.output_type == 'npz': + with open(path + args.output_extension, 'wb') as output_file: + np.savez( + output_file, + keypoints=keypoints, + scores=scores, + descriptors=descriptors + ) + elif args.output_type == 'mat': + with open(path + args.output_extension, 'wb') as output_file: + scipy.io.savemat( + output_file, + { + 'keypoints': keypoints, + 'scores': scores, + 'descriptors': descriptors + } + ) + else: + raise ValueError('Unknown output type.') diff --git a/third_party/d2net/extract_hesaff.m b/third_party/d2net/extract_hesaff.m new file mode 100644 index 0000000000000000000000000000000000000000..5f544a49512640304df006e6704de5aaa14b0e6c --- /dev/null +++ b/third_party/d2net/extract_hesaff.m @@ -0,0 +1,25 @@ +fid = fopen('image_list_hpatches_sequences.txt'); + +tline = fgetl(fid); +while ischar(tline) + disp(tline); + I = im2single(imread(tline)); + if size(I, 3) > 1 + I = rgb2gray(I); + end + + [F, D, info] = vl_covdet(I, 'Method', 'Hessian', ... + 'EstimateAffineShape', true, ... + 'EstimateOrientation', true, ... + 'DoubleImage', false, ... + 'peakThreshold', 14 / 256^2); + keypoints = F'; + scores = info.peakScores; + descriptors = D'; + + save([tline '.hesaff'], 'keypoints', 'scores', 'descriptors'); + + tline = fgetl(fid); +end + +fclose(fid); diff --git a/third_party/d2net/extract_kapture.py b/third_party/d2net/extract_kapture.py new file mode 100644 index 0000000000000000000000000000000000000000..23198b978229c699dbe24cd3bc0400d62bcab030 --- /dev/null +++ b/third_party/d2net/extract_kapture.py @@ -0,0 +1,248 @@ +import argparse +import numpy as np +from PIL import Image +import torch +import math +from tqdm import tqdm +from os import path + +# Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion) and more generally sensor-acquired data +# it can be installed with +# pip install kapture +# for more information check out https://github.com/naver/kapture +import kapture +from kapture.io.records import get_image_fullpath +from kapture.io.csv import kapture_from_dir, get_all_tar_handlers +from kapture.io.csv import get_feature_csv_fullpath, keypoints_to_file, descriptors_to_file +from kapture.io.features import get_keypoints_fullpath, keypoints_check_dir, image_keypoints_to_file +from kapture.io.features import get_descriptors_fullpath, descriptors_check_dir, image_descriptors_to_file + +from lib.model_test import D2Net +from lib.utils import preprocess_image +from lib.pyramid import process_multiscale + +# import imageio + +# CUDA +use_cuda = torch.cuda.is_available() +device = torch.device("cuda:0" if use_cuda else "cpu") + +# Argument parsing +parser = argparse.ArgumentParser(description='Feature extraction script') + +parser.add_argument( + '--kapture-root', type=str, required=True, + help='path to kapture root directory' +) + +parser.add_argument( + '--preprocessing', type=str, default='caffe', + help='image preprocessing (caffe or torch)' +) +parser.add_argument( + '--model_file', type=str, default='models/d2_tf.pth', + help='path to the full model' +) +parser.add_argument( + '--keypoints-type', type=str, default=None, + help='keypoint type_name, default is filename of model' +) +parser.add_argument( + '--descriptors-type', type=str, default=None, + help='descriptors type_name, default is filename of model' +) + +parser.add_argument( + '--max_edge', type=int, default=1600, + help='maximum image size at network input' +) +parser.add_argument( + '--max_sum_edges', type=int, default=2800, + help='maximum sum of image sizes at network input' +) + +parser.add_argument( + '--multiscale', dest='multiscale', action='store_true', + help='extract multiscale features' +) +parser.set_defaults(multiscale=False) + +parser.add_argument( + '--no-relu', dest='use_relu', action='store_false', + help='remove ReLU after the dense feature extraction module' +) +parser.set_defaults(use_relu=True) + +parser.add_argument("--max-keypoints", type=int, default=float("+inf"), + help='max number of keypoints save to disk') + +args = parser.parse_args() + +print(args) +with get_all_tar_handlers(args.kapture_root, + mode={kapture.Keypoints: 'a', + kapture.Descriptors: 'a', + kapture.GlobalFeatures: 'r', + kapture.Matches: 'r'}) as tar_handlers: + kdata = kapture_from_dir(args.kapture_root, + skip_list=[kapture.GlobalFeatures, + kapture.Matches, + kapture.Points3d, + kapture.Observations], + tar_handlers=tar_handlers) + if kdata.keypoints is None: + kdata.keypoints = {} + if kdata.descriptors is None: + kdata.descriptors = {} + + assert kdata.records_camera is not None + image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)] + if args.keypoints_type is None: + args.keypoints_type = path.splitext(path.basename(args.model_file))[0] + print(f'keypoints_type set to {args.keypoints_type}') + if args.descriptors_type is None: + args.descriptors_type = path.splitext(path.basename(args.model_file))[0] + print(f'descriptors_type set to {args.descriptors_type}') + if args.keypoints_type in kdata.keypoints and args.descriptors_type in kdata.descriptors: + image_list = [name + for name in image_list + if name not in kdata.keypoints[args.keypoints_type] or + name not in kdata.descriptors[args.descriptors_type]] + + if len(image_list) == 0: + print('All features were already extracted') + exit(0) + else: + print(f'Extracting d2net features for {len(image_list)} images') + + # Creating CNN model + model = D2Net( + model_file=args.model_file, + use_relu=args.use_relu, + use_cuda=use_cuda + ) + + if args.keypoints_type not in kdata.keypoints: + keypoints_dtype = None + keypoints_dsize = None + else: + keypoints_dtype = kdata.keypoints[args.keypoints_type].dtype + keypoints_dsize = kdata.keypoints[args.keypoints_type].dsize + if args.descriptors_type not in kdata.descriptors: + descriptors_dtype = None + descriptors_dsize = None + else: + descriptors_dtype = kdata.descriptors[args.descriptors_type].dtype + descriptors_dsize = kdata.descriptors[args.descriptors_type].dsize + + # Process the files + for image_name in tqdm(image_list, total=len(image_list)): + img_path = get_image_fullpath(args.kapture_root, image_name) + image = Image.open(img_path).convert('RGB') + + width, height = image.size + + resized_image = image + resized_width = width + resized_height = height + + max_edge = args.max_edge + max_sum_edges = args.max_sum_edges + if max(resized_width, resized_height) > max_edge: + scale_multiplier = max_edge / max(resized_width, resized_height) + resized_width = math.floor(resized_width * scale_multiplier) + resized_height = math.floor(resized_height * scale_multiplier) + resized_image = image.resize((resized_width, resized_height)) + if resized_width + resized_height > max_sum_edges: + scale_multiplier = max_sum_edges / (resized_width + resized_height) + resized_width = math.floor(resized_width * scale_multiplier) + resized_height = math.floor(resized_height * scale_multiplier) + resized_image = image.resize((resized_width, resized_height)) + + fact_i = width / resized_width + fact_j = height / resized_height + + resized_image = np.array(resized_image).astype('float') + + input_image = preprocess_image( + resized_image, + preprocessing=args.preprocessing + ) + + with torch.no_grad(): + if args.multiscale: + keypoints, scores, descriptors = process_multiscale( + torch.tensor( + input_image[np.newaxis, :, :, :].astype(np.float32), + device=device + ), + model + ) + else: + keypoints, scores, descriptors = process_multiscale( + torch.tensor( + input_image[np.newaxis, :, :, :].astype(np.float32), + device=device + ), + model, + scales=[1] + ) + + # Input image coordinates + keypoints[:, 0] *= fact_i + keypoints[:, 1] *= fact_j + # i, j -> u, v + keypoints = keypoints[:, [1, 0, 2]] + + if args.max_keypoints != float("+inf"): + # keep the last (the highest) indexes + idx_keep = scores.argsort()[-min(len(keypoints), args.max_keypoints):] + keypoints = keypoints[idx_keep] + descriptors = descriptors[idx_keep] + + if keypoints_dtype is None or descriptors_dtype is None: + keypoints_dtype = keypoints.dtype + descriptors_dtype = descriptors.dtype + + keypoints_dsize = keypoints.shape[1] + descriptors_dsize = descriptors.shape[1] + + kdata.keypoints[args.keypoints_type] = kapture.Keypoints('d2net', keypoints_dtype, keypoints_dsize) + kdata.descriptors[args.descriptors_type] = kapture.Descriptors('d2net', descriptors_dtype, + descriptors_dsize, + args.keypoints_type, 'L2') + + keypoints_config_absolute_path = get_feature_csv_fullpath(kapture.Keypoints, + args.keypoints_type, + args.kapture_root) + descriptors_config_absolute_path = get_feature_csv_fullpath(kapture.Descriptors, + args.descriptors_type, + args.kapture_root) + + keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]) + descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors[args.descriptors_type]) + else: + assert kdata.keypoints[args.keypoints_type].dtype == keypoints.dtype + assert kdata.descriptors[args.descriptors_type].dtype == descriptors.dtype + assert kdata.keypoints[args.keypoints_type].dsize == keypoints.shape[1] + assert kdata.descriptors[args.descriptors_type].dsize == descriptors.shape[1] + assert kdata.descriptors[args.descriptors_type].keypoints_type == args.keypoints_type + assert kdata.descriptors[args.descriptors_type].metric_type == 'L2' + + keypoints_fullpath = get_keypoints_fullpath(args.keypoints_type, args.kapture_root, + image_name, tar_handlers) + print(f"Saving {keypoints.shape[0]} keypoints to {keypoints_fullpath}") + image_keypoints_to_file(keypoints_fullpath, keypoints) + kdata.keypoints[args.keypoints_type].add(image_name) + + descriptors_fullpath = get_descriptors_fullpath(args.descriptors_type, args.kapture_root, + image_name, tar_handlers) + print(f"Saving {descriptors.shape[0]} descriptors to {descriptors_fullpath}") + image_descriptors_to_file(descriptors_fullpath, descriptors) + kdata.descriptors[args.descriptors_type].add(image_name) + + if not keypoints_check_dir(kdata.keypoints[args.keypoints_type], args.keypoints_type, + args.kapture_root, tar_handlers) or \ + not descriptors_check_dir(kdata.descriptors[args.descriptors_type], args.descriptors_type, + args.kapture_root, tar_handlers): + print('local feature extraction ended successfully but not all files were saved') diff --git a/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb b/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bb9c93165c3325c70d22290cc53f55a34b28c1f3 --- /dev/null +++ b/third_party/d2net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "\n", + "import os\n", + "\n", + "import torch\n", + "\n", + "from scipy.io import loadmat\n", + "\n", + "from tqdm import tqdm_notebook as tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "use_cuda = torch.cuda.is_available()\n", + "device = torch.device('cuda:0' if use_cuda else 'cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Add new methods here.\n", + "# methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'd2-net', 'd2-net-trained']\n", + "# names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'D2-Net', 'D2-Net Trained']\n", + "# colors = ['black', 'orange', 'red', 'red', 'blue', 'purple', 'purple']\n", + "# linestyles = ['-', '-', '-', '--', '-', '-', '--']\n", + "methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'lf-net', 'd2-net', 'd2-net-ms', 'd2-net-trained', 'd2-net-trained-ms']\n", + "names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'LF-Net', 'D2-Net', 'D2-Net MS', 'D2-Net Trained', 'D2-Net Trained MS']\n", + "colors = ['black', 'orange', 'red', 'red', 'blue', 'brown', 'purple', 'green', 'purple', 'green']\n", + "linestyles = ['-', '-', '-', '--', '-', '-', '-', '-', '--', '--']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Change here if you want to use top K or all features.\n", + "# top_k = 2000\n", + "top_k = None " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "n_i = 52\n", + "n_v = 56" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_path = 'hpatches-sequences-release'" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "lim = [1, 15]\n", + "rng = np.arange(lim[0], lim[1] + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def mnn_matcher(descriptors_a, descriptors_b):\n", + " device = descriptors_a.device\n", + " sim = descriptors_a @ descriptors_b.t()\n", + " nn12 = torch.max(sim, dim=1)[1]\n", + " nn21 = torch.max(sim, dim=0)[1]\n", + " ids1 = torch.arange(0, sim.shape[0], device=device)\n", + " mask = (ids1 == nn21[nn12])\n", + " matches = torch.stack([ids1[mask], nn12[mask]])\n", + " return matches.t().data.cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def benchmark_features(read_feats):\n", + " seq_names = sorted(os.listdir(dataset_path))\n", + "\n", + " n_feats = []\n", + " n_matches = []\n", + " seq_type = []\n", + " i_err = {thr: 0 for thr in rng}\n", + " v_err = {thr: 0 for thr in rng}\n", + "\n", + " for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):\n", + " keypoints_a, descriptors_a = read_feats(seq_name, 1)\n", + " n_feats.append(keypoints_a.shape[0])\n", + "\n", + " for im_idx in range(2, 7):\n", + " keypoints_b, descriptors_b = read_feats(seq_name, im_idx)\n", + " n_feats.append(keypoints_b.shape[0])\n", + "\n", + " matches = mnn_matcher(\n", + " torch.from_numpy(descriptors_a).to(device=device), \n", + " torch.from_numpy(descriptors_b).to(device=device)\n", + " )\n", + " \n", + " homography = np.loadtxt(os.path.join(dataset_path, seq_name, \"H_1_\" + str(im_idx)))\n", + " \n", + " pos_a = keypoints_a[matches[:, 0], : 2] \n", + " pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1)\n", + " pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))\n", + " pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2 :]\n", + "\n", + " pos_b = keypoints_b[matches[:, 1], : 2]\n", + "\n", + " dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))\n", + "\n", + " n_matches.append(matches.shape[0])\n", + " seq_type.append(seq_name[0])\n", + " \n", + " if dist.shape[0] == 0:\n", + " dist = np.array([float(\"inf\")])\n", + " \n", + " for thr in rng:\n", + " if seq_name[0] == 'i':\n", + " i_err[thr] += np.mean(dist <= thr)\n", + " else:\n", + " v_err[thr] += np.mean(dist <= thr)\n", + " \n", + " seq_type = np.array(seq_type)\n", + " n_feats = np.array(n_feats)\n", + " n_matches = np.array(n_matches)\n", + " \n", + " return i_err, v_err, [seq_type, n_feats, n_matches]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def summary(stats):\n", + " seq_type, n_feats, n_matches = stats\n", + " print('# Features: {:f} - [{:d}, {:d}]'.format(np.mean(n_feats), np.min(n_feats), np.max(n_feats)))\n", + " print('# Matches: Overall {:f}, Illumination {:f}, Viewpoint {:f}'.format(\n", + " np.sum(n_matches) / ((n_i + n_v) * 5), \n", + " np.sum(n_matches[seq_type == 'i']) / (n_i * 5), \n", + " np.sum(n_matches[seq_type == 'v']) / (n_v * 5))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_read_function(method, extension='ppm'):\n", + " def read_function(seq_name, im_idx):\n", + " aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method)))\n", + " if top_k is None:\n", + " return aux['keypoints'], aux['descriptors']\n", + " else:\n", + " assert('scores' in aux)\n", + " ids = np.argsort(aux['scores'])[-top_k :]\n", + " return aux['keypoints'][ids, :], aux['descriptors'][ids, :]\n", + " return read_function" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def sift_to_rootsift(descriptors):\n", + " return np.sqrt(descriptors / np.expand_dims(np.sum(np.abs(descriptors), axis=1), axis=1) + 1e-16)\n", + "def parse_mat(mat):\n", + " keypoints = mat['keypoints'][:, : 2]\n", + " raw_descriptors = mat['descriptors']\n", + " l2_norm_descriptors = raw_descriptors / np.expand_dims(np.sum(raw_descriptors ** 2, axis=1), axis=1)\n", + " descriptors = sift_to_rootsift(l2_norm_descriptors)\n", + " if top_k is None:\n", + " return keypoints, descriptors\n", + " else:\n", + " assert('scores' in mat)\n", + " ids = np.argsort(mat['scores'][0])[-top_k :]\n", + " return keypoints[ids, :], descriptors[ids, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "if top_k is None:\n", + " cache_dir = 'cache'\n", + "else:\n", + " cache_dir = 'cache-top'\n", + "if not os.path.isdir(cache_dir):\n", + " os.mkdir(cache_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "errors = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hesaff\n", + "Loading precomputed errors...\n", + "# Features: 6710.137346 - [296, 26021]\n", + "# Matches: Overall 2851.679630, Illumination 1585.803846, Viewpoint 4027.135714\n", + "hesaffnet\n", + "Loading precomputed errors...\n", + "# Features: 3860.754630 - [89, 16326]\n", + "# Matches: Overall 1959.996296, Illumination 1098.419231, Viewpoint 2760.032143\n", + "delf\n", + "Loading precomputed errors...\n", + "# Features: 4608.236111 - [1196, 10939]\n", + "# Matches: Overall 1912.400000, Illumination 1973.100000, Viewpoint 1856.035714\n", + "delf-new\n", + "Loading precomputed errors...\n", + "# Features: 4590.001543 - [953, 12696]\n", + "# Matches: Overall 1940.288889, Illumination 2031.873077, Viewpoint 1855.246429\n", + "superpoint\n", + "Loading precomputed errors...\n", + "# Features: 1562.611111 - [90, 6422]\n", + "# Matches: Overall 883.440741, Illumination 667.830769, Viewpoint 1083.650000\n", + "lf-net\n", + "Loading precomputed errors...\n", + "# Features: 500.000000 - [500, 500]\n", + "# Matches: Overall 177.475926, Illumination 183.073077, Viewpoint 172.278571\n", + "d2-net\n", + "Loading precomputed errors...\n", + "# Features: 2994.067901 - [641, 9337]\n", + "# Matches: Overall 1182.574074, Illumination 964.588462, Viewpoint 1384.989286\n", + "d2-net-ms\n", + "Loading precomputed errors...\n", + "# Features: 4928.163580 - [1009, 15230]\n", + "# Matches: Overall 1698.377778, Illumination 1384.215385, Viewpoint 1990.100000\n", + "d2-net-trained\n", + "Loading precomputed errors...\n", + "# Features: 5965.117284 - [1309, 18974]\n", + "# Matches: Overall 2495.900000, Illumination 2033.250000, Viewpoint 2925.503571\n", + "d2-net-trained-ms\n", + "Loading precomputed errors...\n", + "# Features: 8254.473765 - [1797, 26880]\n", + "# Matches: Overall 2831.638889, Illumination 2313.957692, Viewpoint 3312.342857\n" + ] + } + ], + "source": [ + "for method in methods:\n", + " output_file = os.path.join(cache_dir, method + '.npy')\n", + " print(method)\n", + " if method == 'hesaff':\n", + " read_function = lambda seq_name, im_idx: parse_mat(loadmat(os.path.join(dataset_path, seq_name, '%d.ppm.hesaff' % im_idx), appendmat=False))\n", + " else:\n", + " if method == 'delf' or method == 'delf-new':\n", + " read_function = generate_read_function(method, extension='png')\n", + " else:\n", + " read_function = generate_read_function(method)\n", + " if os.path.exists(output_file):\n", + " print('Loading precomputed errors...')\n", + " errors[method] = np.load(output_file, allow_pickle=True)\n", + " else:\n", + " errors[method] = benchmark_features(read_function)\n", + " np.save(output_file, errors[method])\n", + " summary(errors[method][-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "plt_lim = [1, 10]\n", + "plt_rng = np.arange(plt_lim[0], plt_lim[1] + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rc('axes', titlesize=25)\n", + "plt.rc('axes', labelsize=25)\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "\n", + "plt.subplot(1, 3, 1)\n", + "for method, name, color, ls in zip(methods, names, colors, linestyles):\n", + " i_err, v_err, _ = errors[method]\n", + " plt.plot(plt_rng, [(i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n", + "plt.title('Overall')\n", + "plt.xlim(plt_lim)\n", + "plt.xticks(plt_rng)\n", + "plt.ylabel('MMA')\n", + "plt.ylim([0, 1])\n", + "plt.grid()\n", + "plt.tick_params(axis='both', which='major', labelsize=20)\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 3, 2)\n", + "for method, name, color, ls in zip(methods, names, colors, linestyles):\n", + " i_err, v_err, _ = errors[method]\n", + " plt.plot(plt_rng, [i_err[thr] / (n_i * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n", + "plt.title('Illumination')\n", + "plt.xlabel('threshold [px]')\n", + "plt.xlim(plt_lim)\n", + "plt.xticks(plt_rng)\n", + "plt.ylim([0, 1])\n", + "plt.gca().axes.set_yticklabels([])\n", + "plt.grid()\n", + "plt.tick_params(axis='both', which='major', labelsize=20)\n", + "\n", + "plt.subplot(1, 3, 3)\n", + "for method, name, color, ls in zip(methods, names, colors, linestyles):\n", + " i_err, v_err, _ = errors[method]\n", + " plt.plot(plt_rng, [v_err[thr] / (n_v * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n", + "plt.title('Viewpoint')\n", + "plt.xlim(plt_lim)\n", + "plt.xticks(plt_rng)\n", + "plt.ylim([0, 1])\n", + "plt.gca().axes.set_yticklabels([])\n", + "plt.grid()\n", + "plt.tick_params(axis='both', which='major', labelsize=20)\n", + "\n", + "if top_k is None:\n", + " plt.savefig('hseq.pdf', bbox_inches='tight', dpi=300)\n", + "else:\n", + " plt.savefig('hseq-top.pdf', bbox_inches='tight', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/third_party/d2net/hpatches_sequences/README.md b/third_party/d2net/hpatches_sequences/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2a0b5e0f154d1717087c35f93cd02a0f54fc6027 --- /dev/null +++ b/third_party/d2net/hpatches_sequences/README.md @@ -0,0 +1,22 @@ +# HPatches Sequences / Image Pairs Matching Benchmark + +Please check the [official repository](https://github.com/hpatches/hpatches-dataset) for more information regarding references. + +The dataset can be downloaded by running `bash download.sh` - this script downloads and extracts the HPatches Sequences dataset and removes the sequences containing high resolution images (`> 1600x1200`) as mentioned in the D2-Net paper. You can also download the cache with results for all methods from the D2-Net paper by running `bash download_cache.sh`. + +New methods can be added in cell 4 of the notebook. The local features are supposed to be stored in the [`npz`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html) format with three fields: + +- `keypoints` - `N x 2` matrix with `x, y` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom) + +- `scores` - `N` array with detection scores for each keypoint (higher is better) - only required for the "top K" version of the benchmark + +- `descriptors` - `N x D` matrix with the descriptors (L2 normalized if you plan on using the provided mutual nearest neighbors matcher) + +Moreover, the `npz` files are supposed to be saved alongside their corresponding images with the same extension as the `method` (e.g. if `method = d2-net`, the features for the image `hpatches-sequences-release/i_ajuntament/1.ppm` should be in the file `hpatches-sequences-release/i_ajuntament/1.ppm.d2-net`). + +We provide a simple script to extract Hessian Affine keypoints with SIFT descriptors (`extract_hesaff.m`); this script requires MATLAB and [VLFeat](http://www.vlfeat.org/). + +D2-Net features can be extracted by running: +``` +python extract_features.py --image_list_file image_list_hpatches_sequences.txt +``` diff --git a/third_party/d2net/hpatches_sequences/convert_to_png.sh b/third_party/d2net/hpatches_sequences/convert_to_png.sh new file mode 100644 index 0000000000000000000000000000000000000000..5b82fff606b4ef60bad32cfef463a601cbfd4586 --- /dev/null +++ b/third_party/d2net/hpatches_sequences/convert_to_png.sh @@ -0,0 +1,9 @@ +# DELF Extraction script doesn't support .ppm images. +current_dir=`pwd` +echo $current_dir +for dir in `ls hpatches-sequences-release`; do + echo $dir + cd hpatches-sequences-release/$dir + mogrify -format png *.ppm + cd $current_dir +done diff --git a/third_party/d2net/hpatches_sequences/download.sh b/third_party/d2net/hpatches_sequences/download.sh new file mode 100644 index 0000000000000000000000000000000000000000..80eb0e3c9f24345c17177cb9d3ab0834f8d58a27 --- /dev/null +++ b/third_party/d2net/hpatches_sequences/download.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash + +# Download the dataset +wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz + +# Extract the dataset +tar xvzf hpatches-sequences-release.tar.gz + +# Remove the high-resolution sequences +cd hpatches-sequences-release +rm -rf i_contruction i_crownnight i_dc i_pencils i_whitebuilding v_artisans v_astronautis v_talent +cd .. diff --git a/third_party/d2net/hpatches_sequences/download_cache.sh b/third_party/d2net/hpatches_sequences/download_cache.sh new file mode 100644 index 0000000000000000000000000000000000000000..7a5a34acc75af5c2f398d3ec8cea367be404cdeb --- /dev/null +++ b/third_party/d2net/hpatches_sequences/download_cache.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +wget https://dsmn.ml/files/d2-net/hpatches-sequences-cache.tar.gz +tar xvzf 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0000000000000000000000000000000000000000..598368ba5c361770c8bc571d1793a613854babfe --- /dev/null +++ b/third_party/d2net/inloc/README.md @@ -0,0 +1,15 @@ +# InLoc evaluation instructions + +Start by downloading the [InLoc_demo](https://github.com/HajimeTaira/InLoc_demo) code. Once it is up and running according to the official instruction, you can copy and paste all the files available here overwriting the `Features_WUSTL` and `parfor_sparseGV` functions. `generate_list.m` will generate `image_list.txt` containing the queries and top 100 database matches (run `sort -u image_list.txt > image_list_unique.txt` to remove the duplicates). After extracting features for all the images in `image_list_unique.txt`, you can run `custom_demo` directly. + +The feature extraction part for D2-Net can be done using the following command: `python extract_features.py --image_list_file /path/to/image_list_unique.txt --multiscale --output_format .mat`. + +In case you plan on using your own features, don't forget to change the extension in `Features_WUSTL.m`. The local features are supposed to be stored in the `mat` format with two fields: + +- `keypoints` - `N x 3` matrix with `x, y, scale` coordinates of each keypoint in COLMAP format (the `X` axis points to the right, the `Y` axis to the bottom), + +- `descriptors` - `N x D` matrix with the descriptors. + +The evaluation pipeline is live at [visuallocalization.net](https://www.visuallocalization.net/). In order to generate a submission file, please use the provided [ImgList2text](https://github.com/HajimeTaira/InLoc_demo/blob/master/functions/utils/ImgList2text.m) function. + +We have also provided the `merge_files` MATLAB script that was used to merge the solutions of D2-Net Multiscale and Dense InLoc based on the view synthesis score. It can be used as follows `merge_files('output/densePV_top10_shortlist_method1.mat', 'outputs/densePV_top10_shortlist_method2.mat')`. \ No newline at end of file diff --git a/third_party/d2net/inloc/custom_demo.m b/third_party/d2net/inloc/custom_demo.m new file mode 100644 index 0000000000000000000000000000000000000000..91057ed63bdc3d1b9284e0ed24f74cf83b431839 --- /dev/null +++ b/third_party/d2net/inloc/custom_demo.m @@ -0,0 +1,13 @@ +% Startup +startup; +[ params ] = setup_project_ht_WUSTL; + +% 1. Retrieval +ht_retrieval; + +% 2. Geometric verification +ht_top100_sparsePE_localization; + +% 3. Pose verification +ImgList_densePE = ImgList_sparsePE; % Force dense PV to use sparse PE results. +ht_top10_densePV_localization; diff --git a/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m b/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m new file mode 100644 index 0000000000000000000000000000000000000000..88551e076799ef0eb30d995c90c89fff448105db --- /dev/null +++ b/third_party/d2net/inloc/functions/wustl_function/Features_WUSTL.m @@ -0,0 +1,6 @@ +function [f, d] = features_custom(I_path) + data = load([I_path '.d2-net'], '-mat'); + f = double(data.keypoints(:, 1 : 3).'); + d = double(data.descriptors.'); +end + diff --git a/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m b/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m new file mode 100644 index 0000000000000000000000000000000000000000..04cdadc5c447dabdde708c1ac50884802e5a045d --- /dev/null +++ b/third_party/d2net/inloc/functions/wustl_function/parfor_sparseGV.m @@ -0,0 +1,73 @@ +function parfor_sparseGV( qname, dbname, params ) + + +[~, dbbasename, ~] = fileparts(dbname); +this_sparsegv_matname = fullfile(params.output.gv_sparse.dir, qname, [dbbasename, params.output.gv_sparse.matformat]); + +if exist(this_sparsegv_matname, 'file') ~= 2 + %load features + qfmatname = fullfile(params.input.feature.dir, params.data.q.dir, [qname, params.input.feature.q_sps_matformat]); + if exist(qfmatname, 'file') ~= 2 + Iqname = fullfile(params.data.dir, params.data.q.dir, qname); + [f, d] = features_WUSTL(Iqname); + [qfdir, ~, ~] = fileparts(qfmatname); + if exist(qfdir, 'dir') ~= 7 + mkdir(qfdir); + end + save('-v6', qfmatname, 'f', 'd'); + end + features_q = load(qfmatname); + + dbfmatname = fullfile(params.input.feature.dir, params.data.db.cutout.dir, [dbname, params.input.feature.db_sps_matformat]); + if exist(dbfmatname, 'file') ~= 2 + Idbname = fullfile(params.data.dir, params.data.db.cutout.dir, dbname); + [f, d] = features_WUSTL(Idbname); + [dbfdir, ~, ~] = fileparts(dbfmatname); + if exist(dbfdir, 'dir') ~= 7 + mkdir(dbfdir); + end + save('-v6', dbfmatname, 'f', 'd'); + end + features_db = load(dbfmatname); + + %geometric verification + if size(features_db.d, 2) < 6 + H = nan(3, 3); + inls_qidx = []; + inls_dbidx = []; + inliernum = 0; + matches = []; + inliers = []; + else + + %geometric verification (homography lo-ransac) + [matches, inliers, H, ~] = at_sparseransac(features_q.f,features_q.d,features_db.f,features_db.d,3,10); + inliernum = length(inliers); + inls_qidx = inliers(1, :); inls_dbidx = inliers(2, :); + end + + %save + if exist(fullfile(params.output.gv_sparse.dir, qname), 'dir') ~= 7 + mkdir(fullfile(params.output.gv_sparse.dir, qname)); + end + save('-v6', this_sparsegv_matname, 'H', 'inliernum', 'inls_qidx', 'inls_dbidx', 'matches', 'inliers'); + +% %debug +% Iq = imread(fullfile(params.data.dir, params.data.q.dir, qname)); +% Idb = imread(fullfile(params.data.dir, params.data.db.cutout.dir, dbname)); +% figure(); +% ultimateSubplot ( 2, 1, 1, 1, 0.01, 0.05 ); +% imshow(rgb2gray(Iq));hold on; +% plot(features_q.f(1, inls_qidx), features_q.f(2, inls_qidx),'g.'); +% ultimateSubplot ( 2, 1, 2, 1, 0.01, 0.05 ); +% imshow(rgb2gray(Idb));hold on; +% plot(features_db.f(1, inls_dbidx), features_db.f(2, inls_dbidx),'g.'); +% +% keyboard; + +end + + + +end + diff --git a/third_party/d2net/inloc/generate_list.m b/third_party/d2net/inloc/generate_list.m new file mode 100644 index 0000000000000000000000000000000000000000..e7680cbefe98421b242e77007d4bc2773acfc6f2 --- /dev/null +++ b/third_party/d2net/inloc/generate_list.m @@ -0,0 +1,25 @@ +startup; +params = setup_project; + +ht_retrieval; + +shortlist_topN = 100; + +query_dir = fullfile(params.data.dir, params.data.q.dir); +db_dir = fullfile(params.data.dir, params.data.db.cutout.dir); + +image_list_file = fopen('image_list.txt', 'w'); + +for ii = 1:1:length(ImgList_original) + query_image_path = [query_dir '/' ImgList_original(ii).queryname]; + + fprintf(image_list_file, '%s\n', query_image_path); + + for jj = 1:1:shortlist_topN + db_image_path = [db_dir '/' ImgList_original(ii).topNname{jj}]; + + fprintf(image_list_file, '%s\n', db_image_path); + end +end + +fclose(image_list_file); diff --git a/third_party/d2net/inloc/merge_files.m b/third_party/d2net/inloc/merge_files.m new file mode 100644 index 0000000000000000000000000000000000000000..789a8974d5e7b9ac67a6c1982a332b7be2042975 --- /dev/null +++ b/third_party/d2net/inloc/merge_files.m @@ -0,0 +1,82 @@ +function ImgList = merge_files(file1, file2) + f1 = load(file1); + ImgList_file1 = f1.ImgList; + f2 = load(file2); + ImgList_file2 = f2.ImgList; + + PV_topN = 10; + + n1 = 0; + n2 = 0; + ImgList = struct('queryname', {}, 'topNname', {}, 'topNscore', {}, 'P', {}); + for ii = 1:1:length(ImgList_file1) + ImgList(ii).queryname = ImgList_file1(ii).queryname; + + sum_scores = containers.Map('KeyType', 'char', 'ValueType', 'double'); + for jj = 1 : PV_topN + name = char(ImgList_file1(ii).topNname(jj)); + if isKey(sum_scores, name) + sum_scores(name) = sum_scores(name) + ImgList_file1(ii).topNscore(jj); + else + sum_scores(name) = ImgList_file1(ii).topNscore(jj); + end + name = char(ImgList_file2(ii).topNname(jj)); + if isKey(sum_scores, name) + sum_scores(name) = sum_scores(name) + ImgList_file2(ii).topNscore(jj); + else + sum_scores(name) = ImgList_file2(ii).topNscore(jj); + end + end + + max_score = 0; + img_name = 0; + for key = keys(sum_scores) + if sum_scores(char(key)) > max_score + max_score = sum_scores(char(key)); + img_name = key; + end + end + + id_dense = 0; + id_sparse = 0; + for jj = 1 : PV_topN + if strcmp(char(ImgList_file1(ii).topNname(jj)), img_name) + id_dense = jj; + end + if strcmp(char(ImgList_file2(ii).topNname(jj)), img_name) + id_sparse = jj; + end + end + + if id_sparse == 0 + n1 = n1 + 1; + ImgList(ii).topNscore = [ImgList_file1(ii).topNscore(id_dense)]; + ImgList(ii).topNname = [ImgList_file1(ii).topNname(id_dense)]; + ImgList(ii).P = [ImgList_file1(ii).P(id_dense)]; + continue + end + + if id_dense == 0 + n2 = n2 + 1; + ImgList(ii).topNscore = [ImgList_file2(ii).topNscore(id_sparse)]; + ImgList(ii).topNname = [ImgList_file2(ii).topNname(id_sparse)]; + ImgList(ii).P = [ImgList_file2(ii).P(id_sparse)]; + continue + end + + max_score = 0; + if ImgList_file1(ii).topNscore(id_dense) > ImgList_file2(ii).topNscore(id_sparse) + n1 = n1 + 1; + ImgList(ii).topNscore = [ImgList_file1(ii).topNscore(id_dense)]; + ImgList(ii).topNname = [ImgList_file1(ii).topNname(id_dense)]; + ImgList(ii).P = [ImgList_file1(ii).P(id_dense)]; + else + n2 = n2 + 1; + ImgList(ii).topNscore = [ImgList_file2(ii).topNscore(id_sparse)]; + ImgList(ii).topNname = [ImgList_file2(ii).topNname(id_sparse)]; + ImgList(ii).P = [ImgList_file2(ii).P(id_sparse)]; + end + end + + fprintf(1, "%d file 1 poses & %d file 2 poses selected\n", n1, n2); +end \ No newline at end of file diff --git a/third_party/d2net/megadepth_utils/preprocess_scene.py b/third_party/d2net/megadepth_utils/preprocess_scene.py new file mode 100644 index 0000000000000000000000000000000000000000..fc68a403795e7cddce88dfcb74b38d19ab09e133 --- /dev/null +++ b/third_party/d2net/megadepth_utils/preprocess_scene.py @@ -0,0 +1,242 @@ +import argparse + +import imagesize + +import numpy as np + +import os + +parser = argparse.ArgumentParser(description='MegaDepth preprocessing script') + +parser.add_argument( + '--base_path', type=str, required=True, + help='path to MegaDepth' +) +parser.add_argument( + '--scene_id', type=str, required=True, + help='scene ID' +) + +parser.add_argument( + '--output_path', type=str, required=True, + help='path to the output directory' +) + +args = parser.parse_args() + +base_path = args.base_path +# Remove the trailing / if need be. +if base_path[-1] in ['/', '\\']: + base_path = base_path[: - 1] +scene_id = args.scene_id + +base_depth_path = os.path.join( + base_path, 'phoenix/S6/zl548/MegaDepth_v1' +) +base_undistorted_sfm_path = os.path.join( + base_path, 'Undistorted_SfM' +) + +undistorted_sparse_path = os.path.join( + base_undistorted_sfm_path, scene_id, 'sparse-txt' +) +if not os.path.exists(undistorted_sparse_path): + exit() + +depths_path = os.path.join( + base_depth_path, scene_id, 'dense0', 'depths' +) +if not os.path.exists(depths_path): + exit() + +images_path = os.path.join( + base_undistorted_sfm_path, scene_id, 'images' +) +if not os.path.exists(images_path): + exit() + +# Process cameras.txt +with open(os.path.join(undistorted_sparse_path, 'cameras.txt'), 'r') as f: + raw = f.readlines()[3 :] # skip the header + +camera_intrinsics = {} +for camera in raw: + camera = camera.split(' ') + camera_intrinsics[int(camera[0])] = [float(elem) for elem in camera[2 :]] + +# Process points3D.txt +with open(os.path.join(undistorted_sparse_path, 'points3D.txt'), 'r') as f: + raw = f.readlines()[3 :] # skip the header + +points3D = {} +for point3D in raw: + point3D = point3D.split(' ') + points3D[int(point3D[0])] = np.array([ + float(point3D[1]), float(point3D[2]), float(point3D[3]) + ]) + +# Process images.txt +with open(os.path.join(undistorted_sparse_path, 'images.txt'), 'r') as f: + raw = f.readlines()[4 :] # skip the header + +image_id_to_idx = {} +image_names = [] +raw_pose = [] +camera = [] +points3D_id_to_2D = [] +n_points3D = [] +for idx, (image, points) in enumerate(zip(raw[:: 2], raw[1 :: 2])): + image = image.split(' ') + points = points.split(' ') + + image_id_to_idx[int(image[0])] = idx + + image_name = image[-1].strip('\n') + image_names.append(image_name) + + raw_pose.append([float(elem) for elem in image[1 : -2]]) + camera.append(int(image[-2])) + current_points3D_id_to_2D = {} + for x, y, point3D_id in zip(points[:: 3], points[1 :: 3], points[2 :: 3]): + if int(point3D_id) == -1: + continue + current_points3D_id_to_2D[int(point3D_id)] = [float(x), float(y)] + points3D_id_to_2D.append(current_points3D_id_to_2D) + n_points3D.append(len(current_points3D_id_to_2D)) +n_images = len(image_names) + +# Image and depthmaps paths +image_paths = [] +depth_paths = [] +for image_name in image_names: + image_path = os.path.join(images_path, image_name) + + # Path to the depth file + depth_path = os.path.join( + depths_path, '%s.h5' % os.path.splitext(image_name)[0] + ) + + if os.path.exists(depth_path): + # Check if depth map or background / foreground mask + file_size = os.stat(depth_path).st_size + # Rough estimate - 75KB might work as well + if file_size < 100 * 1024: + depth_paths.append(None) + image_paths.append(None) + else: + depth_paths.append(depth_path[len(base_path) + 1 :]) + image_paths.append(image_path[len(base_path) + 1 :]) + else: + depth_paths.append(None) + image_paths.append(None) + +# Camera configuration +intrinsics = [] +poses = [] +principal_axis = [] +points3D_id_to_ndepth = [] +for idx, image_name in enumerate(image_names): + if image_paths[idx] is None: + intrinsics.append(None) + poses.append(None) + principal_axis.append([0, 0, 0]) + points3D_id_to_ndepth.append({}) + continue + image_intrinsics = camera_intrinsics[camera[idx]] + K = np.zeros([3, 3]) + K[0, 0] = image_intrinsics[2] + K[0, 2] = image_intrinsics[4] + K[1, 1] = image_intrinsics[3] + K[1, 2] = image_intrinsics[5] + K[2, 2] = 1 + intrinsics.append(K) + + image_pose = raw_pose[idx] + qvec = image_pose[: 4] + qvec = qvec / np.linalg.norm(qvec) + w, x, y, z = qvec + R = np.array([ + [ + 1 - 2 * y * y - 2 * z * z, + 2 * x * y - 2 * z * w, + 2 * x * z + 2 * y * w + ], + [ + 2 * x * y + 2 * z * w, + 1 - 2 * x * x - 2 * z * z, + 2 * y * z - 2 * x * w + ], + [ + 2 * x * z - 2 * y * w, + 2 * y * z + 2 * x * w, + 1 - 2 * x * x - 2 * y * y + ] + ]) + principal_axis.append(R[2, :]) + t = image_pose[4 : 7] + # World-to-Camera pose + current_pose = np.zeros([4, 4]) + current_pose[: 3, : 3] = R + current_pose[: 3, 3] = t + current_pose[3, 3] = 1 + # Camera-to-World pose + # pose = np.zeros([4, 4]) + # pose[: 3, : 3] = np.transpose(R) + # pose[: 3, 3] = -np.matmul(np.transpose(R), t) + # pose[3, 3] = 1 + poses.append(current_pose) + + current_points3D_id_to_ndepth = {} + for point3D_id in points3D_id_to_2D[idx].keys(): + p3d = points3D[point3D_id] + current_points3D_id_to_ndepth[point3D_id] = (np.dot(R[2, :], p3d) + t[2]) / (.5 * (K[0, 0] + K[1, 1])) + points3D_id_to_ndepth.append(current_points3D_id_to_ndepth) +principal_axis = np.array(principal_axis) +angles = np.rad2deg(np.arccos( + np.clip( + np.dot(principal_axis, np.transpose(principal_axis)), + -1, 1 + ) +)) + +# Compute overlap score +overlap_matrix = np.full([n_images, n_images], -1.) +scale_ratio_matrix = np.full([n_images, n_images], -1.) +for idx1 in range(n_images): + if image_paths[idx1] is None or depth_paths[idx1] is None: + continue + for idx2 in range(idx1 + 1, n_images): + if image_paths[idx2] is None or depth_paths[idx2] is None: + continue + matches = ( + points3D_id_to_2D[idx1].keys() & + points3D_id_to_2D[idx2].keys() + ) + min_num_points3D = min( + len(points3D_id_to_2D[idx1]), len(points3D_id_to_2D[idx2]) + ) + overlap_matrix[idx1, idx2] = len(matches) / len(points3D_id_to_2D[idx1]) # min_num_points3D + overlap_matrix[idx2, idx1] = len(matches) / len(points3D_id_to_2D[idx2]) # min_num_points3D + if len(matches) == 0: + continue + points3D_id_to_ndepth1 = points3D_id_to_ndepth[idx1] + points3D_id_to_ndepth2 = points3D_id_to_ndepth[idx2] + nd1 = np.array([points3D_id_to_ndepth1[match] for match in matches]) + nd2 = np.array([points3D_id_to_ndepth2[match] for match in matches]) + min_scale_ratio = np.min(np.maximum(nd1 / nd2, nd2 / nd1)) + scale_ratio_matrix[idx1, idx2] = min_scale_ratio + scale_ratio_matrix[idx2, idx1] = min_scale_ratio + +np.savez( + os.path.join(args.output_path, '%s.npz' % scene_id), + image_paths=image_paths, + depth_paths=depth_paths, + intrinsics=intrinsics, + poses=poses, + overlap_matrix=overlap_matrix, + scale_ratio_matrix=scale_ratio_matrix, + angles=angles, + n_points3D=n_points3D, + points3D_id_to_2D=points3D_id_to_2D, + points3D_id_to_ndepth=points3D_id_to_ndepth +) diff --git a/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh b/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh new file mode 100644 index 0000000000000000000000000000000000000000..c983ee464bb36439d68f52d60f981414e2c6e84b --- /dev/null +++ b/third_party/d2net/megadepth_utils/preprocess_undistorted_megadepth.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash + +if [[ $# != 2 ]]; then + echo 'Usage: bash preprocess_megadepth.sh /path/to/megadepth /output/path' + exit +fi + +export dataset_path=$1 +export output_path=$2 + +mkdir $output_path +echo 0 +ls $dataset_path/Undistorted_SfM | xargs -P 8 -I % sh -c 'echo %; python preprocess_scene.py --base_path $dataset_path --scene_id % --output_path $output_path' \ No newline at end of file diff --git a/third_party/d2net/megadepth_utils/train_scenes.txt b/third_party/d2net/megadepth_utils/train_scenes.txt new file mode 100644 index 0000000000000000000000000000000000000000..635c8dfe5d0f1814d92f3a891a4b3d48ba8da93f --- /dev/null +++ b/third_party/d2net/megadepth_utils/train_scenes.txt @@ -0,0 +1,117 @@ +0000 +0001 +0002 +0003 +0004 +0005 +0007 +0008 +0011 +0012 +0013 +0015 +0017 +0019 +0020 +0021 +0022 +0023 +0024 +0025 +0026 +0027 +0032 +0035 +0036 +0037 +0039 +0042 +0043 +0046 +0048 +0050 +0056 +0057 +0060 +0061 +0063 +0065 +0070 +0080 +0083 +0086 +0087 +0095 +0098 +0100 +0101 +0103 +0104 +0105 +0107 +0115 +0117 +0122 +0130 +0137 +0143 +0147 +0148 +0149 +0150 +0156 +0160 +0176 +0183 +0189 +0190 +0200 +0214 +0224 +0235 +0237 +0240 +0243 +0258 +0265 +0269 +0299 +0312 +0326 +0327 +0331 +0335 +0341 +0348 +0366 +0377 +0380 +0394 +0407 +0411 +0430 +0446 +0455 +0472 +0474 +0476 +0478 +0493 +0494 +0496 +0505 +0559 +0733 +0860 +1017 +1589 +4541 +5004 +5005 +5006 +5007 +5009 +5010 +5012 +5013 +5017 diff --git a/third_party/d2net/megadepth_utils/undistort_reconstructions.py b/third_party/d2net/megadepth_utils/undistort_reconstructions.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b99a72f81206e6fbefae9daa9aa683c8754051 --- /dev/null +++ b/third_party/d2net/megadepth_utils/undistort_reconstructions.py @@ -0,0 +1,69 @@ +import argparse + +import imagesize + +import os + +import subprocess + +parser = argparse.ArgumentParser(description='MegaDepth Undistortion') + +parser.add_argument( + '--colmap_path', type=str, required=True, + help='path to colmap executable' +) +parser.add_argument( + '--base_path', type=str, required=True, + help='path to MegaDepth' +) + +args = parser.parse_args() + +sfm_path = os.path.join( + args.base_path, 'MegaDepth_v1_SfM' +) +base_depth_path = os.path.join( + args.base_path, 'phoenix/S6/zl548/MegaDepth_v1' +) +output_path = os.path.join( + args.base_path, 'Undistorted_SfM' +) + +os.mkdir(output_path) + +for scene_name in os.listdir(base_depth_path): + current_output_path = os.path.join(output_path, scene_name) + os.mkdir(current_output_path) + + image_path = os.path.join( + base_depth_path, scene_name, 'dense0', 'imgs' + ) + if not os.path.exists(image_path): + continue + + # Find the maximum image size in scene. + max_image_size = 0 + for image_name in os.listdir(image_path): + max_image_size = max( + max_image_size, + max(imagesize.get(os.path.join(image_path, image_name))) + ) + + # Undistort the images and update the reconstruction. + subprocess.call([ + os.path.join(args.colmap_path, 'colmap'), 'image_undistorter', + '--image_path', os.path.join(sfm_path, scene_name, 'images'), + '--input_path', os.path.join(sfm_path, scene_name, 'sparse', 'manhattan', '0'), + '--output_path', current_output_path, + '--max_image_size', str(max_image_size) + ]) + + # Transform the reconstruction to raw text format. + sparse_txt_path = os.path.join(current_output_path, 'sparse-txt') + os.mkdir(sparse_txt_path) + subprocess.call([ + os.path.join(args.colmap_path, 'colmap'), 'model_converter', + '--input_path', os.path.join(current_output_path, 'sparse'), + '--output_path', sparse_txt_path, + '--output_type', 'TXT' + ]) \ No newline at end of file diff --git a/third_party/d2net/megadepth_utils/valid_scenes.txt b/third_party/d2net/megadepth_utils/valid_scenes.txt new file mode 100644 index 0000000000000000000000000000000000000000..42503496535a13b9426db28a22c6df891191c9f2 --- /dev/null +++ b/third_party/d2net/megadepth_utils/valid_scenes.txt @@ -0,0 +1,77 @@ +0016 +0033 +0034 +0041 +0044 +0047 +0049 +0058 +0062 +0064 +0067 +0071 +0076 +0078 +0090 +0094 +0099 +0102 +0121 +0129 +0133 +0141 +0151 +0162 +0168 +0175 +0177 +0178 +0181 +0185 +0186 +0197 +0204 +0205 +0209 +0212 +0217 +0223 +0229 +0231 +0238 +0252 +0257 +0271 +0275 +0277 +0281 +0285 +0286 +0290 +0294 +0303 +0306 +0307 +0323 +0349 +0360 +0387 +0389 +0402 +0406 +0412 +0443 +0482 +0768 +1001 +3346 +5000 +5001 +5002 +5003 +5008 +5011 +5014 +5015 +5016 +5018 diff --git a/third_party/d2net/models/d2_tf.pth b/third_party/d2net/models/d2_tf.pth new file mode 100644 index 0000000000000000000000000000000000000000..e0e501511ec988202a6411c8f3332ab1c458ca8d --- /dev/null +++ b/third_party/d2net/models/d2_tf.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2d32c3bc53b6588d40bc5325d536a159e529b7492198a2cfa4b11913c615c80 +size 30545768 diff --git a/third_party/d2net/qualitative/Qualitative-Matches.ipynb b/third_party/d2net/qualitative/Qualitative-Matches.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5ae18faa46ee3ab4efddc48eb6455f7f1341fb40 --- /dev/null +++ b/third_party/d2net/qualitative/Qualitative-Matches.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import numpy as np\n", + "\n", + "import os\n", + "\n", + "from PIL import Image\n", + "\n", + "from skimage.feature import match_descriptors\n", + "from skimage.measure import ransac\n", + "from skimage.transform import ProjectiveTransform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Don't forget to run feature extraction before running this script\n", + "```python extract_features.py --image_list_file image_list_qualitative.txt```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Change the pair index here (possible values: 1, 2 or 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "pair_idx = 2\n", + "assert(pair_idx in [1, 2, 3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the features" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "pair_path = os.path.join('images', 'pair_%d' % pair_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "image1 = np.array(Image.open(os.path.join(pair_path, '1.jpg')))\n", + "image2 = np.array(Image.open(os.path.join(pair_path, '2.jpg')))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "feat1 = np.load(os.path.join(pair_path, '1.jpg.d2-net'))\n", + "feat2 = np.load(os.path.join(pair_path, '2.jpg.d2-net'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mutual nearest neighbors matching" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "matches = match_descriptors(feat1['descriptors'], feat2['descriptors'], cross_check=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of raw matches: 296.\n" + ] + } + ], + "source": [ + "print('Number of raw matches: %d.' % matches.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Homography fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of inliers: 69.\n" + ] + } + ], + "source": [ + "keypoints_left = feat1['keypoints'][matches[:, 0], : 2]\n", + "keypoints_right = feat2['keypoints'][matches[:, 1], : 2]\n", + "np.random.seed(0)\n", + "model, inliers = ransac(\n", + " (keypoints_left, keypoints_right),\n", + " ProjectiveTransform, min_samples=4,\n", + " residual_threshold=4, max_trials=10000\n", + ")\n", + "n_inliers = np.sum(inliers)\n", + "print('Number of inliers: %d.' % n_inliers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "inlier_keypoints_left = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_left[inliers]]\n", + "inlier_keypoints_right = [cv2.KeyPoint(point[0], point[1], 1) for point in keypoints_right[inliers]]\n", + "placeholder_matches = [cv2.DMatch(idx, idx, 1) for idx in range(n_inliers)]\n", + "image3 = cv2.drawMatches(image1, inlier_keypoints_left, image2, inlier_keypoints_right, placeholder_matches, None)\n", + "\n", + "plt.figure(figsize=(15, 15))\n", + "plt.imshow(image3)\n", + "plt.axis('off')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + 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0000000000000000000000000000000000000000..5817f1712bda0779175fb18437d1f8c263f29f3b --- /dev/null +++ b/third_party/d2net/train.py @@ -0,0 +1,279 @@ +import argparse + +import numpy as np + +import os + +import shutil + +import torch +import torch.optim as optim + +from torch.utils.data import DataLoader + +from tqdm import tqdm + +import warnings + +from lib.dataset import MegaDepthDataset +from lib.exceptions import NoGradientError +from lib.loss import loss_function +from lib.model import D2Net + + +# CUDA +use_cuda = torch.cuda.is_available() +device = torch.device("cuda:0" if use_cuda else "cpu") + +# Seed +torch.manual_seed(1) +if use_cuda: + torch.cuda.manual_seed(1) +np.random.seed(1) + +# Argument parsing +parser = argparse.ArgumentParser(description='Training script') + +parser.add_argument( + '--dataset_path', type=str, required=True, + help='path to the dataset' +) +parser.add_argument( + '--scene_info_path', type=str, required=True, + help='path to the processed scenes' +) + +parser.add_argument( + '--preprocessing', type=str, default='caffe', + help='image preprocessing (caffe or torch)' +) +parser.add_argument( + '--model_file', type=str, default='models/d2_ots.pth', + help='path to the full model' +) + +parser.add_argument( + '--num_epochs', type=int, default=10, + help='number of training epochs' +) +parser.add_argument( + '--lr', type=float, default=1e-3, + help='initial learning rate' +) +parser.add_argument( + '--batch_size', type=int, default=1, + help='batch size' +) +parser.add_argument( + '--num_workers', type=int, default=4, + help='number of workers for data loading' +) + +parser.add_argument( + '--use_validation', dest='use_validation', action='store_true', + help='use the validation split' +) +parser.set_defaults(use_validation=False) + +parser.add_argument( + '--log_interval', type=int, default=250, + help='loss logging interval' +) + +parser.add_argument( + '--log_file', type=str, default='log.txt', + help='loss logging file' +) + +parser.add_argument( + '--plot', dest='plot', action='store_true', + help='plot training pairs' +) +parser.set_defaults(plot=False) + +parser.add_argument( + '--checkpoint_directory', type=str, default='checkpoints', + help='directory for training checkpoints' +) +parser.add_argument( + '--checkpoint_prefix', type=str, default='d2', + help='prefix for training checkpoints' +) + +args = parser.parse_args() + +print(args) + +# Create the folders for plotting if need be +if args.plot: + plot_path = 'train_vis' + if os.path.isdir(plot_path): + print('[Warning] Plotting directory already exists.') + else: + os.mkdir(plot_path) + +# Creating CNN model +model = D2Net( + model_file=args.model_file, + use_cuda=use_cuda +) + +# Optimizer +optimizer = optim.Adam( + filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr +) + +# Dataset +if args.use_validation: + validation_dataset = MegaDepthDataset( + scene_list_path='megadepth_utils/valid_scenes.txt', + scene_info_path=args.scene_info_path, + base_path=args.dataset_path, + train=False, + preprocessing=args.preprocessing, + pairs_per_scene=25 + ) + validation_dataloader = DataLoader( + validation_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers + ) + +training_dataset = MegaDepthDataset( + scene_list_path='megadepth_utils/train_scenes.txt', + scene_info_path=args.scene_info_path, + base_path=args.dataset_path, + preprocessing=args.preprocessing +) +training_dataloader = DataLoader( + training_dataset, + batch_size=args.batch_size, + num_workers=args.num_workers +) + + +# Define epoch function +def process_epoch( + epoch_idx, + model, loss_function, optimizer, dataloader, device, + log_file, args, train=True +): + epoch_losses = [] + + torch.set_grad_enabled(train) + + progress_bar = tqdm(enumerate(dataloader), total=len(dataloader)) + for batch_idx, batch in progress_bar: + if train: + optimizer.zero_grad() + + batch['train'] = train + batch['epoch_idx'] = epoch_idx + batch['batch_idx'] = batch_idx + batch['batch_size'] = args.batch_size + batch['preprocessing'] = args.preprocessing + batch['log_interval'] = args.log_interval + + try: + loss = loss_function(model, batch, device, plot=args.plot) + except NoGradientError: + continue + + current_loss = loss.data.cpu().numpy()[0] + epoch_losses.append(current_loss) + + progress_bar.set_postfix(loss=('%.4f' % np.mean(epoch_losses))) + + if batch_idx % args.log_interval == 0: + log_file.write('[%s] epoch %d - batch %d / %d - avg_loss: %f\n' % ( + 'train' if train else 'valid', + epoch_idx, batch_idx, len(dataloader), np.mean(epoch_losses) + )) + + if train: + loss.backward() + optimizer.step() + + log_file.write('[%s] epoch %d - avg_loss: %f\n' % ( + 'train' if train else 'valid', + epoch_idx, + np.mean(epoch_losses) + )) + log_file.flush() + + return np.mean(epoch_losses) + + +# Create the checkpoint directory +if os.path.isdir(args.checkpoint_directory): + print('[Warning] Checkpoint directory already exists.') +else: + os.mkdir(args.checkpoint_directory) + + +# Open the log file for writing +if os.path.exists(args.log_file): + print('[Warning] Log file already exists.') +log_file = open(args.log_file, 'a+') + +# Initialize the history +train_loss_history = [] +validation_loss_history = [] +if args.use_validation: + validation_dataset.build_dataset() + min_validation_loss = process_epoch( + 0, + model, loss_function, optimizer, validation_dataloader, device, + log_file, args, + train=False + ) + +# Start the training +for epoch_idx in range(1, args.num_epochs + 1): + # Process epoch + training_dataset.build_dataset() + train_loss_history.append( + process_epoch( + epoch_idx, + model, loss_function, optimizer, training_dataloader, device, + log_file, args + ) + ) + + if args.use_validation: + validation_loss_history.append( + process_epoch( + epoch_idx, + model, loss_function, optimizer, validation_dataloader, device, + log_file, args, + train=False + ) + ) + + # Save the current checkpoint + checkpoint_path = os.path.join( + args.checkpoint_directory, + '%s.%02d.pth' % (args.checkpoint_prefix, epoch_idx) + ) + checkpoint = { + 'args': args, + 'epoch_idx': epoch_idx, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'train_loss_history': train_loss_history, + 'validation_loss_history': validation_loss_history + } + torch.save(checkpoint, checkpoint_path) + if ( + args.use_validation and + validation_loss_history[-1] < min_validation_loss + ): + min_validation_loss = validation_loss_history[-1] + best_checkpoint_path = os.path.join( + args.checkpoint_directory, + '%s.best.pth' % args.checkpoint_prefix + ) + shutil.copy(checkpoint_path, best_checkpoint_path) + +# Close the log file +log_file.close() diff --git a/third_party/lanet/.gitattributes b/third_party/lanet/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..ec4a626fbb7799f6a25b45fb86344b2bf7b37e64 --- /dev/null +++ b/third_party/lanet/.gitattributes @@ -0,0 +1 @@ +*.pth filter=lfs diff=lfs merge=lfs -text diff --git a/third_party/lanet/LICENSE b/third_party/lanet/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..df725685f32f70fdf841379ed1ae5273600c7248 --- /dev/null +++ b/third_party/lanet/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Changhao Wang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/third_party/lanet/README.md b/third_party/lanet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0bdac20ad300970ff3949800f3dd14e5efbd4001 --- /dev/null +++ b/third_party/lanet/README.md @@ -0,0 +1,72 @@ +# Rethinking Low-level Features for Interest Point Detection and Description + +## Dependency + - pytorch + - torchvision + - cv2 + - tqdm + + We use cuda 11.4/python 3.8.13/torch 1.10.0/torchvision 0.11.0/opencv 3.4.8 for training and testing. + + +## Pre-trained models +We provide two versions of LANet with different structure in [network_v0](network_v0) and [network_v1](network_v1), the corresponding pre-trained models are in [checkpoints](checkpoints). + - v0: The original version used in our paper. + - v1: An improved version that has a better over all performance. + + +## Training +Download the COCO dataset: +``` +cd datasets/COCO/ +wget http://images.cocodataset.org/zips/train2017.zip +unzip train2017.zip +``` +Prepare the training file: +``` +python datasets/prepare_coco.py --raw_dir datasets/COCO/train2017/ --saved_dir datasets/COCO/ +``` + +To train the model (v0) on COCO dataset, run: +``` +python main.py --train_root datasets/COCO/train2017/ --train_txt datasets/COCO/train2017.txt +``` + + +## Evaluation +### Evaluation on HPatches dataset +Download the HPatches dataset: +``` +cd datasets/HPatches/ +wget http://icvl.ee.ic.ac.uk/vbalnt/hpatches/hpatches-sequences-release.tar.gz +tar -xvf hpatches-sequences-release.tar.gz +``` + +To evaluate the pre-trained model, run: +``` +python test.py --test_dir ./datasets/HPatches/hpatches-sequences-release +``` + + +## License +The code is released under the [MIT license](LICENSE). + + +## Citation +Please use the following citation when referencing our work: +``` +@InProceedings{Wang_2022_ACCV, + author = {Changhao Wang and Guanwen Zhang and Zhengyun Cheng and Wei Zhou}, + title = {Rethinking Low-level Features for Interest Point Detection and Description}, + booktitle = {Computer Vision - {ACCV} 2022 - 16th Asian Conference on Computer + Vision, Macao, China, December 4-8, 2022, Proceedings, Part {II}}, + series = {Lecture Notes in Computer Science}, + volume = {13842}, + pages = {108--123}, + year = {2022} +} +``` + + +## Related Projects +https://github.com/TRI-ML/KP2D diff --git a/third_party/lanet/augmentations.py b/third_party/lanet/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..f4e4496c77ce8fc8cdadb230dd0d0750166152a9 --- /dev/null +++ b/third_party/lanet/augmentations.py @@ -0,0 +1,342 @@ +# From https://github.com/TRI-ML/KP2D. + +# Copyright 2020 Toyota Research Institute. All rights reserved. + +import random +from math import pi + +import cv2 +import numpy as np +import torch +import torchvision +import torchvision.transforms as transforms +from PIL import Image + +from utils import image_grid + + +def filter_dict(dict, keywords): + """ + Returns only the keywords that are part of a dictionary + + Parameters + ---------- + dictionary : dict + Dictionary for filtering + keywords : list of str + Keywords that will be filtered + + Returns + ------- + keywords : list of str + List containing the keywords that are keys in dictionary + """ + return [key for key in keywords if key in dict] + + +def resize_sample(sample, image_shape, image_interpolation=Image.ANTIALIAS): + """ + Resizes a sample, which contains an input image. + + Parameters + ---------- + sample : dict + Dictionary with sample values (output from a dataset's __getitem__ method) + shape : tuple (H,W) + Output shape + image_interpolation : int + Interpolation mode + + Returns + ------- + sample : dict + Resized sample + """ + # image + image_transform = transforms.Resize(image_shape, interpolation=image_interpolation) + sample['image'] = image_transform(sample['image']) + return sample + +def spatial_augment_sample(sample): + """ Apply spatial augmentation to an image (flipping and random affine transformation).""" + augment_image = transforms.Compose([ + transforms.RandomVerticalFlip(p=0.5), + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomAffine(15, translate=(0.1, 0.1), scale=(0.9, 1.1)) + + ]) + sample['image'] = augment_image(sample['image']) + + return sample + +def unnormalize_image(tensor, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)): + """ Counterpart method of torchvision.transforms.Normalize.""" + for t, m, s in zip(tensor, mean, std): + t.div_(1 / s).sub_(-m) + return tensor + + +def sample_homography( + shape, perspective=True, scaling=True, rotation=True, translation=True, + n_scales=100, n_angles=100, scaling_amplitude=0.1, perspective_amplitude=0.4, + patch_ratio=0.8, max_angle=pi/4): + """ Sample a random homography that includes perspective, scale, translation and rotation operations.""" + + width = float(shape[1]) + hw_ratio = float(shape[0]) / float(shape[1]) + + pts1 = np.stack([[-1., -1.], [-1., 1.], [1., -1.], [1., 1.]], axis=0) + pts2 = pts1.copy() * patch_ratio + pts2[:,1] *= hw_ratio + + if perspective: + + perspective_amplitude_x = np.random.normal(0., perspective_amplitude/2, (2)) + perspective_amplitude_y = np.random.normal(0., hw_ratio * perspective_amplitude/2, (2)) + + perspective_amplitude_x = np.clip(perspective_amplitude_x, -perspective_amplitude/2, perspective_amplitude/2) + perspective_amplitude_y = np.clip(perspective_amplitude_y, hw_ratio * -perspective_amplitude/2, hw_ratio * perspective_amplitude/2) + + pts2[0,0] -= perspective_amplitude_x[1] + pts2[0,1] -= perspective_amplitude_y[1] + + pts2[1,0] -= perspective_amplitude_x[0] + pts2[1,1] += perspective_amplitude_y[1] + + pts2[2,0] += perspective_amplitude_x[1] + pts2[2,1] -= perspective_amplitude_y[0] + + pts2[3,0] += perspective_amplitude_x[0] + pts2[3,1] += perspective_amplitude_y[0] + + if scaling: + + random_scales = np.random.normal(1, scaling_amplitude/2, (n_scales)) + random_scales = np.clip(random_scales, 1-scaling_amplitude/2, 1+scaling_amplitude/2) + + scales = np.concatenate([[1.], random_scales], 0) + center = np.mean(pts2, axis=0, keepdims=True) + scaled = np.expand_dims(pts2 - center, axis=0) * np.expand_dims( + np.expand_dims(scales, 1), 1) + center + valid = np.arange(n_scales) # all scales are valid except scale=1 + idx = valid[np.random.randint(valid.shape[0])] + pts2 = scaled[idx] + + if translation: + t_min, t_max = np.min(pts2 - [-1., -hw_ratio], axis=0), np.min([1., hw_ratio] - pts2, axis=0) + pts2 += np.expand_dims(np.stack([np.random.uniform(-t_min[0], t_max[0]), + np.random.uniform(-t_min[1], t_max[1])]), + axis=0) + + if rotation: + angles = np.linspace(-max_angle, max_angle, n_angles) + angles = np.concatenate([[0.], angles], axis=0) + + center = np.mean(pts2, axis=0, keepdims=True) + rot_mat = np.reshape(np.stack([np.cos(angles), -np.sin(angles), np.sin(angles), + np.cos(angles)], axis=1), [-1, 2, 2]) + rotated = np.matmul( + np.tile(np.expand_dims(pts2 - center, axis=0), [n_angles+1, 1, 1]), + rot_mat) + center + + valid = np.where(np.all((rotated >= [-1.,-hw_ratio]) & (rotated < [1.,hw_ratio]), + axis=(1, 2)))[0] + + idx = valid[np.random.randint(valid.shape[0])] + pts2 = rotated[idx] + + pts2[:,1] /= hw_ratio + + def ax(p, q): return [p[0], p[1], 1, 0, 0, 0, -p[0] * q[0], -p[1] * q[0]] + def ay(p, q): return [0, 0, 0, p[0], p[1], 1, -p[0] * q[1], -p[1] * q[1]] + + a_mat = np.stack([f(pts1[i], pts2[i]) for i in range(4) for f in (ax, ay)], axis=0) + p_mat = np.transpose(np.stack( + [[pts2[i][j] for i in range(4) for j in range(2)]], axis=0)) + + homography = np.matmul(np.linalg.pinv(a_mat), p_mat).squeeze() + homography = np.concatenate([homography, [1.]]).reshape(3,3) + return homography + +def warp_homography(sources, homography): + """Warp features given a homography + + Parameters + ---------- + sources: torch.tensor (1,H,W,2) + Keypoint vector. + homography: torch.Tensor (3,3) + Homography. + + Returns + ------- + warped_sources: torch.tensor (1,H,W,2) + Warped feature vector. + """ + _, H, W, _ = sources.shape + warped_sources = sources.clone().squeeze() + warped_sources = warped_sources.view(-1,2) + warped_sources = torch.addmm(homography[:,2], warped_sources, homography[:,:2].t()) + warped_sources.mul_(1/warped_sources[:,2].unsqueeze(1)) + warped_sources = warped_sources[:,:2].contiguous().view(1,H,W,2) + return warped_sources + +def add_noise(img, mode="gaussian", percent=0.02): + """Add image noise + + Parameters + ---------- + image : np.array + Input image + mode: str + Type of noise, from ['gaussian','salt','pepper','s&p'] + percent: float + Percentage image points to add noise to. + Returns + ------- + image : np.array + Image plus noise. + """ + original_dtype = img.dtype + if mode == "gaussian": + mean = 0 + var = 0.1 + sigma = var * 0.5 + + if img.ndim == 2: + h, w = img.shape + gauss = np.random.normal(mean, sigma, (h, w)) + else: + h, w, c = img.shape + gauss = np.random.normal(mean, sigma, (h, w, c)) + + if img.dtype not in [np.float32, np.float64]: + gauss = gauss * np.iinfo(img.dtype).max + img = np.clip(img.astype(np.float) + gauss, 0, np.iinfo(img.dtype).max) + else: + img = np.clip(img.astype(np.float) + gauss, 0, 1) + + elif mode == "salt": + print(img.dtype) + s_vs_p = 1 + num_salt = np.ceil(percent * img.size * s_vs_p) + coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape]) + + if img.dtype in [np.float32, np.float64]: + img[coords] = 1 + else: + img[coords] = np.iinfo(img.dtype).max + print(img.dtype) + elif mode == "pepper": + s_vs_p = 0 + num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p)) + coords = tuple( + [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape] + ) + img[coords] = 0 + + elif mode == "s&p": + s_vs_p = 0.5 + + # Salt mode + num_salt = np.ceil(percent * img.size * s_vs_p) + coords = tuple([np.random.randint(0, i - 1, int(num_salt)) for i in img.shape]) + if img.dtype in [np.float32, np.float64]: + img[coords] = 1 + else: + img[coords] = np.iinfo(img.dtype).max + + # Pepper mode + num_pepper = np.ceil(percent * img.size * (1.0 - s_vs_p)) + coords = tuple( + [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape] + ) + img[coords] = 0 + else: + raise ValueError("not support mode for {}".format(mode)) + + noisy = img.astype(original_dtype) + return noisy + + +def non_spatial_augmentation(img_warp_ori, jitter_paramters, color_order=[0,1,2], to_gray=False): + """ Apply non-spatial augmentation to an image (jittering, color swap, convert to gray scale, Gaussian blur).""" + + brightness, contrast, saturation, hue = jitter_paramters + color_augmentation = transforms.ColorJitter(brightness, contrast, saturation, hue) + ''' + augment_image = color_augmentation.get_params(brightness=[max(0, 1 - brightness), 1 + brightness], + contrast=[max(0, 1 - contrast), 1 + contrast], + saturation=[max(0, 1 - saturation), 1 + saturation], + hue=[-hue, hue]) + ''' + + B = img_warp_ori.shape[0] + img_warp = [] + kernel_sizes = [0,1,3,5] + for b in range(B): + img_warp_sub = img_warp_ori[b].cpu() + img_warp_sub = torchvision.transforms.functional.to_pil_image(img_warp_sub) + + img_warp_sub_np = np.array(img_warp_sub) + img_warp_sub_np = img_warp_sub_np[:,:,color_order] + + if np.random.rand() > 0.5: + img_warp_sub_np = add_noise(img_warp_sub_np) + + rand_index = np.random.randint(4) + kernel_size = kernel_sizes[rand_index] + if kernel_size >0: + img_warp_sub_np = cv2.GaussianBlur(img_warp_sub_np, (kernel_size, kernel_size), sigmaX=0) + + if to_gray: + img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_RGB2GRAY) + img_warp_sub_np = cv2.cvtColor(img_warp_sub_np, cv2.COLOR_GRAY2RGB) + + img_warp_sub = Image.fromarray(img_warp_sub_np) + img_warp_sub = color_augmentation(img_warp_sub) + + img_warp_sub = torchvision.transforms.functional.to_tensor(img_warp_sub).to(img_warp_ori.device) + + img_warp.append(img_warp_sub) + + img_warp = torch.stack(img_warp, dim=0) + return img_warp + +def ha_augment_sample(data, jitter_paramters=[0.5, 0.5, 0.2, 0.05], patch_ratio=0.7, scaling_amplitude=0.2, max_angle=pi/4): + """Apply Homography Adaptation image augmentation.""" + input_img = data['image'].unsqueeze(0) + _, _, H, W = input_img.shape + device = input_img.device + + homography = torch.from_numpy( + sample_homography([H, W], + patch_ratio=patch_ratio, + scaling_amplitude=scaling_amplitude, + max_angle=max_angle)).float().to(device) + homography_inv = torch.inverse(homography) + + source = image_grid(1, H, W, + dtype=input_img.dtype, + device=device, + ones=False, normalized=True).clone().permute(0, 2, 3, 1) + + target_warped = warp_homography(source, homography) + img_warp = torch.nn.functional.grid_sample(input_img, target_warped) + + color_order = [0,1,2] + if np.random.rand() > 0.5: + random.shuffle(color_order) + + to_gray = False + if np.random.rand() > 0.5: + to_gray = True + + input_img = non_spatial_augmentation(input_img, jitter_paramters=jitter_paramters, color_order=color_order, to_gray=to_gray) + img_warp = non_spatial_augmentation(img_warp, jitter_paramters=jitter_paramters, color_order=color_order, to_gray=to_gray) + + data['image'] = input_img.squeeze() + data['image_aug'] = img_warp.squeeze() + data['homography'] = homography + data['homography_inv'] = homography_inv + return data diff --git a/third_party/lanet/checkpoints/PointModel_v0.pth b/third_party/lanet/checkpoints/PointModel_v0.pth new file mode 100644 index 0000000000000000000000000000000000000000..50d18dfc0d62c0a8c8f8a4b89f050accb22226d5 --- /dev/null +++ b/third_party/lanet/checkpoints/PointModel_v0.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17c1adfc8c22b044a9538019101bae0740a9d054c1b4fecd80b52d642272b9ff +size 33802301 diff --git a/third_party/lanet/checkpoints/PointModel_v1.pth b/third_party/lanet/checkpoints/PointModel_v1.pth new file mode 100644 index 0000000000000000000000000000000000000000..c87a5f9c6a27e998859dfd66f04813ac444e1a8d --- /dev/null +++ b/third_party/lanet/checkpoints/PointModel_v1.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e80e071345e9509f924a3f684af3ed4cd4b2b785d9550bae716a2421c4004c70 +size 145101561 diff --git a/third_party/lanet/config.py b/third_party/lanet/config.py new file mode 100644 index 0000000000000000000000000000000000000000..baa3aedc95410b231c29ab64b31ea5a2bd3266d7 --- /dev/null +++ b/third_party/lanet/config.py @@ -0,0 +1,79 @@ +import argparse + +arg_lists = [] +parser = argparse.ArgumentParser(description='LANet') + +def str2bool(v): + return v.lower() in ('true', '1') + +def add_argument_group(name): + arg = parser.add_argument_group(name) + arg_lists.append(arg) + return arg + +# train data params +traindata_arg = add_argument_group('Traindata Params') +traindata_arg.add_argument('--train_txt', type=str, default='', + help='Train set.') +traindata_arg.add_argument('--train_root', type=str, default='', + help='Where the train images are.') +traindata_arg.add_argument('--batch_size', type=int, default=8, + help='# of images in each batch of data') +traindata_arg.add_argument('--num_workers', type=int, default=4, + help='# of subprocesses to use for data loading') +traindata_arg.add_argument('--pin_memory', type=str2bool, default=True, + help='# of subprocesses to use for data loading') +traindata_arg.add_argument('--shuffle', type=str2bool, default=True, + help='Whether to shuffle the train and valid indices') +traindata_arg.add_argument('--image_shape', type=tuple, default=(240, 320), + help='') +traindata_arg.add_argument('--jittering', type=tuple, default=(0.5, 0.5, 0.2, 0.05), + help='') + +# data storage +storage_arg = add_argument_group('Storage') +storage_arg.add_argument('--ckpt_name', type=str, default='PointModel', + help='') + +# training params +train_arg = add_argument_group('Training Params') +train_arg.add_argument('--start_epoch', type=int, default=0, + help='') +train_arg.add_argument('--max_epoch', type=int, default=12, + help='') +train_arg.add_argument('--init_lr', type=float, default=3e-4, + help='Initial learning rate value.') +train_arg.add_argument('--lr_factor', type=float, default=0.5, + help='Reduce learning rate value.') +train_arg.add_argument('--momentum', type=float, default=0.9, + help='Nesterov momentum value.') +train_arg.add_argument('--display', type=int, default=50, + help='') + +# loss function params +loss_arg = add_argument_group('Loss function Params') +loss_arg.add_argument('--score_weight', type=float, default=1., + help='') +loss_arg.add_argument('--loc_weight', type=float, default=1., + help='') +loss_arg.add_argument('--desc_weight', type=float, default=4., + help='') +loss_arg.add_argument('--corres_weight', type=float, default=.5, + help='') +loss_arg.add_argument('--corres_threshold', type=int, default=4., + help='') + +# other params +misc_arg = add_argument_group('Misc.') +misc_arg.add_argument('--use_gpu', type=str2bool, default=True, + help="Whether to run on the GPU.") +misc_arg.add_argument('--gpu', type=int, default=0, + help="Which GPU to run on.") +misc_arg.add_argument('--seed', type=int, default=1001, + help='Seed to ensure reproducibility.') +misc_arg.add_argument('--ckpt_dir', type=str, default='./checkpoints', + help='Directory in which to save model checkpoints.') + +def get_config(): + config, unparsed = parser.parse_known_args() + return config, unparsed diff --git a/third_party/lanet/data_loader.py b/third_party/lanet/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..e694e39bb5f3e7ad6763a5cfcce3ca4804071262 --- /dev/null +++ b/third_party/lanet/data_loader.py @@ -0,0 +1,86 @@ +from PIL import Image +from torch.utils.data import Dataset, DataLoader + +from augmentations import ha_augment_sample, resize_sample, spatial_augment_sample +from utils import to_tensor_sample + +def image_transforms(shape, jittering): + def train_transforms(sample): + sample = resize_sample(sample, image_shape=shape) + sample = spatial_augment_sample(sample) + sample = to_tensor_sample(sample) + sample = ha_augment_sample(sample, jitter_paramters=jittering) + return sample + + return {'train': train_transforms} + +class GetData(Dataset): + def __init__(self, config, transforms=None): + """ + Get the list containing all images and labels. + """ + datafile = open(config.train_txt, 'r') + lines = datafile.readlines() + + dataset = [] + for line in lines: + line = line.rstrip() + data = line.split() + dataset.append(data[0]) + + self.config = config + self.dataset = dataset + self.root = config.train_root + + self.transforms = transforms + + def __getitem__(self, index): + """ + Return image'data and its label. + """ + img_path = self.dataset[index] + img_file = self.root + img_path + img = Image.open(img_file) + + # image.mode == 'L' means the image is in gray scale + if img.mode == 'L': + img_new = Image.new("RGB", img.size) + img_new.paste(img) + sample = {'image': img_new, 'idx': index} + else: + sample = {'image': img, 'idx': index} + + if self.transforms: + sample = self.transforms(sample) + + return sample + + def __len__(self): + """ + Return the number of all data. + """ + return len(self.dataset) + +def get_data_loader( + config, + transforms=None, + sampler=None, + drop_last=True, + ): + """ + Return batch data for training. + """ + transforms = image_transforms(shape=config.image_shape, jittering=config.jittering) + dataset = GetData(config, transforms=transforms['train']) + + train_loader = DataLoader( + dataset, + batch_size=config.batch_size, + shuffle=config.shuffle, + sampler=sampler, + num_workers=config.num_workers, + pin_memory=config.pin_memory, + drop_last=drop_last + ) + + return train_loader diff --git a/third_party/lanet/datasets/hp_loader.py b/third_party/lanet/datasets/hp_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..b4c1d8f3c33fd51bfa928c529544a77c06ed73f0 --- /dev/null +++ b/third_party/lanet/datasets/hp_loader.py @@ -0,0 +1,106 @@ +import torch +import cv2 +import numpy as np + +from torchvision import transforms +from torch.utils.data import Dataset +from pathlib import Path + + +class PatchesDataset(Dataset): + """ + HPatches dataset class. + # Note: output_shape = (output_width, output_height) + # Note: this returns Pytorch tensors, resized to output_shape (if specified) + # Note: the homography will be adjusted according to output_shape. + + Parameters + ---------- + root_dir : str + Path to the dataset + use_color : bool + Return color images or convert to grayscale. + data_transform : Function + Transformations applied to the sample + output_shape: tuple + If specified, the images and homographies will be resized to the desired shape. + type: str + Dataset subset to return from ['i', 'v', 'all']: + i - illumination sequences + v - viewpoint sequences + all - all sequences + """ + def __init__(self, root_dir, use_color=True, data_transform=None, output_shape=None, type='all'): + super().__init__() + self.type = type + self.root_dir = root_dir + self.data_transform = data_transform + self.output_shape = output_shape + self.use_color = use_color + base_path = Path(root_dir) + folder_paths = [x for x in base_path.iterdir() if x.is_dir()] + image_paths = [] + warped_image_paths = [] + homographies = [] + for path in folder_paths: + if self.type == 'i' and path.stem[0] != 'i': + continue + if self.type == 'v' and path.stem[0] != 'v': + continue + num_images = 5 + file_ext = '.ppm' + for i in range(2, 2 + num_images): + image_paths.append(str(Path(path, "1" + file_ext))) + warped_image_paths.append(str(Path(path, str(i) + file_ext))) + homographies.append(np.loadtxt(str(Path(path, "H_1_" + str(i))))) + self.files = {'image_paths': image_paths, 'warped_image_paths': warped_image_paths, 'homography': homographies} + + def scale_homography(self, homography, original_scale, new_scale, pre): + scales = np.divide(new_scale, original_scale) + if pre: + s = np.diag(np.append(scales, 1.)) + homography = np.matmul(s, homography) + else: + sinv = np.diag(np.append(1. / scales, 1.)) + homography = np.matmul(homography, sinv) + return homography + + def __len__(self): + return len(self.files['image_paths']) + + def __getitem__(self, idx): + + def _read_image(path): + img = cv2.imread(path, cv2.IMREAD_COLOR) + if self.use_color: + return img + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + return gray + + image = _read_image(self.files['image_paths'][idx]) + + warped_image = _read_image(self.files['warped_image_paths'][idx]) + homography = np.array(self.files['homography'][idx]) + sample = {'image': image, 'warped_image': warped_image, 'homography': homography, 'index' : idx} + + # Apply transformations + if self.output_shape is not None: + sample['homography'] = self.scale_homography(sample['homography'], + sample['image'].shape[:2][::-1], + self.output_shape, + pre=False) + sample['homography'] = self.scale_homography(sample['homography'], + sample['warped_image'].shape[:2][::-1], + self.output_shape, + pre=True) + + for key in ['image', 'warped_image']: + sample[key] = cv2.resize(sample[key], self.output_shape) + if self.use_color is False: + sample[key] = np.expand_dims(sample[key], axis=2) + + transform = transforms.ToTensor() + + for key in ['image', 'warped_image']: + sample[key] = transform(sample[key]).type('torch.FloatTensor') + return sample diff --git a/third_party/lanet/datasets/prepare_coco.py b/third_party/lanet/datasets/prepare_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..0468aba19c6c2c76bda1a1af2b86dc7f20176fdb --- /dev/null +++ b/third_party/lanet/datasets/prepare_coco.py @@ -0,0 +1,26 @@ +import os +import argparse + +def prepare_coco(args): + train_file = open(os.path.join(args.saved_dir, args.saved_txt), 'w') + dirs = os.listdir(args.raw_dir) + + for file in dirs: + # Write training files + train_file.write('%s\n' % (file)) + + print('Data Preparation Finished.') + +if __name__ == '__main__': + arg_parser = argparse.ArgumentParser(description="coco prepareing.") + arg_parser.add_argument('--dataset', type=str, default='coco', + help='') + arg_parser.add_argument('--raw_dir', type=str, default='', + help='') + arg_parser.add_argument('--saved_dir', type=str, default='', + help='') + arg_parser.add_argument('--saved_txt', type=str, default='train2017.txt', + help='') + args = arg_parser.parse_args() + + prepare_coco(args) \ No newline at end of file diff --git a/third_party/lanet/evaluation/descriptor_evaluation.py b/third_party/lanet/evaluation/descriptor_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..c0e1f84199d353ac5858641c8f68bc298f9d6413 --- /dev/null +++ b/third_party/lanet/evaluation/descriptor_evaluation.py @@ -0,0 +1,254 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. +# Adapted from: https://github.com/rpautrat/SuperPoint/blob/master/superpoint/evaluations/descriptor_evaluation.py + +import random +from glob import glob +from os import path as osp + +import cv2 +import numpy as np + +from utils import warp_keypoints + + +def select_k_best(points, descriptors, k): + """ Select the k most probable points (and strip their probability). + points has shape (num_points, 3) where the last coordinate is the probability. + + Parameters + ---------- + points: numpy.ndarray (N,3) + Keypoint vector, consisting of (x,y,probability). + descriptors: numpy.ndarray (N,256) + Keypoint descriptors. + k: int + Number of keypoints to select, based on probability. + Returns + ------- + + selected_points: numpy.ndarray (k,2) + k most probable keypoints. + selected_descriptors: numpy.ndarray (k,256) + Descriptors corresponding to the k most probable keypoints. + """ + sorted_prob = points[points[:, 2].argsort(), :2] + sorted_desc = descriptors[points[:, 2].argsort(), :] + start = min(k, points.shape[0]) + selected_points = sorted_prob[-start:, :] + selected_descriptors = sorted_desc[-start:, :] + return selected_points, selected_descriptors + + +def keep_shared_points(keypoints, descriptors, H, shape, keep_k_points=1000): + """ + Compute a list of keypoints from the map, filter the list of points by keeping + only the points that once mapped by H are still inside the shape of the map + and keep at most 'keep_k_points' keypoints in the image. + + Parameters + ---------- + keypoints: numpy.ndarray (N,3) + Keypoint vector, consisting of (x,y,probability). + descriptors: numpy.ndarray (N,256) + Keypoint descriptors. + H: numpy.ndarray (3,3) + Homography. + shape: tuple + Image shape. + keep_k_points: int + Number of keypoints to select, based on probability. + + Returns + ------- + selected_points: numpy.ndarray (k,2) + k most probable keypoints. + selected_descriptors: numpy.ndarray (k,256) + Descriptors corresponding to the k most probable keypoints. + """ + + def keep_true_keypoints(points, descriptors, H, shape): + """ Keep only the points whose warped coordinates by H are still inside shape. """ + warped_points = warp_keypoints(points[:, [1, 0]], H) + warped_points[:, [0, 1]] = warped_points[:, [1, 0]] + mask = (warped_points[:, 0] >= 0) & (warped_points[:, 0] < shape[0]) &\ + (warped_points[:, 1] >= 0) & (warped_points[:, 1] < shape[1]) + return points[mask, :], descriptors[mask, :] + + selected_keypoints, selected_descriptors = keep_true_keypoints(keypoints, descriptors, H, shape) + selected_keypoints, selected_descriptors = select_k_best(selected_keypoints, selected_descriptors, keep_k_points) + return selected_keypoints, selected_descriptors + + +def compute_matching_score(data, keep_k_points=1000): + """ + Compute the matching score between two sets of keypoints with associated descriptors. + + Parameters + ---------- + data: dict + Input dictionary containing: + image_shape: tuple (H,W) + Original image shape. + homography: numpy.ndarray (3,3) + Ground truth homography. + prob: numpy.ndarray (N,3) + Keypoint vector, consisting of (x,y,probability). + warped_prob: numpy.ndarray (N,3) + Warped keypoint vector, consisting of (x,y,probability). + desc: numpy.ndarray (N,256) + Keypoint descriptors. + warped_desc: numpy.ndarray (N,256) + Warped keypoint descriptors. + keep_k_points: int + Number of keypoints to select, based on probability. + + Returns + ------- + ms: float + Matching score. + """ + shape = data['image_shape'] + real_H = data['homography'] + + # Filter out predictions + keypoints = data['prob'][:, :2].T + keypoints = keypoints[::-1] + prob = data['prob'][:, 2] + keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1) + + warped_keypoints = data['warped_prob'][:, :2].T + warped_keypoints = warped_keypoints[::-1] + warped_prob = data['warped_prob'][:, 2] + warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1) + + desc = data['desc'] + warped_desc = data['warped_desc'] + + # Keeps all points for the next frame. The matching for caculating M.Score shouldnt use only in view points. + keypoints, desc = select_k_best(keypoints, desc, keep_k_points) + warped_keypoints, warped_desc = select_k_best(warped_keypoints, warped_desc, keep_k_points) + + # Match the keypoints with the warped_keypoints with nearest neighbor search + # This part needs to be done with crossCheck=False. + # All the matched pairs need to be evaluated without any selection. + bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False) + + matches = bf.match(desc, warped_desc) + matches_idx = np.array([m.queryIdx for m in matches]) + m_keypoints = keypoints[matches_idx, :] + matches_idx = np.array([m.trainIdx for m in matches]) + m_warped_keypoints = warped_keypoints[matches_idx, :] + + true_warped_keypoints = warp_keypoints(m_warped_keypoints[:, [1, 0]], np.linalg.inv(real_H))[:,::-1] + vis_warped = np.all((true_warped_keypoints >= 0) & (true_warped_keypoints <= (np.array(shape)-1)), axis=-1) + norm1 = np.linalg.norm(true_warped_keypoints - m_keypoints, axis=-1) + + correct1 = (norm1 < 3) + count1 = np.sum(correct1 * vis_warped) + score1 = count1 / np.maximum(np.sum(vis_warped), 1.0) + + matches = bf.match(warped_desc, desc) + matches_idx = np.array([m.queryIdx for m in matches]) + m_warped_keypoints = warped_keypoints[matches_idx, :] + matches_idx = np.array([m.trainIdx for m in matches]) + m_keypoints = keypoints[matches_idx, :] + + true_keypoints = warp_keypoints(m_keypoints[:, [1, 0]], real_H)[:,::-1] + vis = np.all((true_keypoints >= 0) & (true_keypoints <= (np.array(shape)-1)), axis=-1) + norm2 = np.linalg.norm(true_keypoints - m_warped_keypoints, axis=-1) + + correct2 = (norm2 < 3) + count2 = np.sum(correct2 * vis) + score2 = count2 / np.maximum(np.sum(vis), 1.0) + + ms = (score1 + score2) / 2 + + return ms + +def compute_homography(data, keep_k_points=1000): + """ + Compute the homography between 2 sets of Keypoints and descriptors inside data. + Use the homography to compute the correctness metrics (1,3,5). + + Parameters + ---------- + data: dict + Input dictionary containing: + image_shape: tuple (H,W) + Original image shape. + homography: numpy.ndarray (3,3) + Ground truth homography. + prob: numpy.ndarray (N,3) + Keypoint vector, consisting of (x,y,probability). + warped_prob: numpy.ndarray (N,3) + Warped keypoint vector, consisting of (x,y,probability). + desc: numpy.ndarray (N,256) + Keypoint descriptors. + warped_desc: numpy.ndarray (N,256) + Warped keypoint descriptors. + keep_k_points: int + Number of keypoints to select, based on probability. + + Returns + ------- + correctness1: float + correctness1 metric. + correctness3: float + correctness3 metric. + correctness5: float + correctness5 metric. + """ + shape = data['image_shape'] + real_H = data['homography'] + + # Filter out predictions + keypoints = data['prob'][:, :2].T + keypoints = keypoints[::-1] + prob = data['prob'][:, 2] + keypoints = np.stack([keypoints[0], keypoints[1], prob], axis=-1) + + warped_keypoints = data['warped_prob'][:, :2].T + warped_keypoints = warped_keypoints[::-1] + warped_prob = data['warped_prob'][:, 2] + warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1) + + desc = data['desc'] + warped_desc = data['warped_desc'] + + # Keeps only the points shared between the two views + keypoints, desc = keep_shared_points(keypoints, desc, real_H, shape, keep_k_points) + warped_keypoints, warped_desc = keep_shared_points(warped_keypoints, warped_desc, np.linalg.inv(real_H), shape, + keep_k_points) + + bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True) + matches = bf.match(desc, warped_desc) + matches_idx = np.array([m.queryIdx for m in matches]) + m_keypoints = keypoints[matches_idx, :] + matches_idx = np.array([m.trainIdx for m in matches]) + m_warped_keypoints = warped_keypoints[matches_idx, :] + + # Estimate the homography between the matches using RANSAC + H, _ = cv2.findHomography(m_keypoints[:, [1, 0]], + m_warped_keypoints[:, [1, 0]], cv2.RANSAC, 3, maxIters=5000) + + if H is None: + return 0, 0, 0 + + shape = shape[::-1] + + # Compute correctness + corners = np.array([[0, 0, 1], + [0, shape[1] - 1, 1], + [shape[0] - 1, 0, 1], + [shape[0] - 1, shape[1] - 1, 1]]) + real_warped_corners = np.dot(corners, np.transpose(real_H)) + real_warped_corners = real_warped_corners[:, :2] / real_warped_corners[:, 2:] + warped_corners = np.dot(corners, np.transpose(H)) + warped_corners = warped_corners[:, :2] / warped_corners[:, 2:] + + mean_dist = np.mean(np.linalg.norm(real_warped_corners - warped_corners, axis=1)) + correctness1 = float(mean_dist <= 1) + correctness3 = float(mean_dist <= 3) + correctness5 = float(mean_dist <= 5) + + return correctness1, correctness3, correctness5 diff --git a/third_party/lanet/evaluation/detector_evaluation.py b/third_party/lanet/evaluation/detector_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..ccc8792d17a6fbb6b446f0f9f84a2b82e3cdb57c --- /dev/null +++ b/third_party/lanet/evaluation/detector_evaluation.py @@ -0,0 +1,121 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. +# Adapted from: https://github.com/rpautrat/SuperPoint/blob/master/superpoint/evaluations/detector_evaluation.py + +import random +from glob import glob +from os import path as osp + +import cv2 +import numpy as np + +from utils import warp_keypoints + + +def compute_repeatability(data, keep_k_points=300, distance_thresh=3): + """ + Compute the repeatability metric between 2 sets of keypoints inside data. + + Parameters + ---------- + data: dict + Input dictionary containing: + image_shape: tuple (H,W) + Original image shape. + homography: numpy.ndarray (3,3) + Ground truth homography. + prob: numpy.ndarray (N,3) + Keypoint vector, consisting of (x,y,probability). + warped_prob: numpy.ndarray (N,3) + Warped keypoint vector, consisting of (x,y,probability). + keep_k_points: int + Number of keypoints to select, based on probability. + distance_thresh: int + Distance threshold in pixels for a corresponding keypoint to be considered a correct match. + + Returns + ------- + N1: int + Number of true keypoints in the first image. + N2: int + Number of true keypoints in the second image. + repeatability: float + Keypoint repeatability metric. + loc_err: float + Keypoint localization error. + """ + def filter_keypoints(points, shape): + """ Keep only the points whose coordinates are inside the dimensions of shape. """ + mask = (points[:, 0] >= 0) & (points[:, 0] < shape[0]) &\ + (points[:, 1] >= 0) & (points[:, 1] < shape[1]) + return points[mask, :] + + def keep_true_keypoints(points, H, shape): + """ Keep only the points whose warped coordinates by H are still inside shape. """ + warped_points = warp_keypoints(points[:, [1, 0]], H) + warped_points[:, [0, 1]] = warped_points[:, [1, 0]] + mask = (warped_points[:, 0] >= 0) & (warped_points[:, 0] < shape[0]) &\ + (warped_points[:, 1] >= 0) & (warped_points[:, 1] < shape[1]) + return points[mask, :] + + + def select_k_best(points, k): + """ Select the k most probable points (and strip their probability). + points has shape (num_points, 3) where the last coordinate is the probability. """ + sorted_prob = points[points[:, 2].argsort(), :2] + start = min(k, points.shape[0]) + return sorted_prob[-start:, :] + + H = data['homography'] + shape = data['image_shape'] + + # # Filter out predictions + keypoints = data['prob'][:, :2].T + keypoints = keypoints[::-1] + prob = data['prob'][:, 2] + + warped_keypoints = data['warped_prob'][:, :2].T + warped_keypoints = warped_keypoints[::-1] + warped_prob = data['warped_prob'][:, 2] + + keypoints = np.stack([keypoints[0], keypoints[1]], axis=-1) + warped_keypoints = np.stack([warped_keypoints[0], warped_keypoints[1], warped_prob], axis=-1) + warped_keypoints = keep_true_keypoints(warped_keypoints, np.linalg.inv(H), shape) + + # Warp the original keypoints with the true homography + true_warped_keypoints = warp_keypoints(keypoints[:, [1, 0]], H) + true_warped_keypoints = np.stack([true_warped_keypoints[:, 1], true_warped_keypoints[:, 0], prob], axis=-1) + true_warped_keypoints = filter_keypoints(true_warped_keypoints, shape) + + # Keep only the keep_k_points best predictions + warped_keypoints = select_k_best(warped_keypoints, keep_k_points) + true_warped_keypoints = select_k_best(true_warped_keypoints, keep_k_points) + + # Compute the repeatability + N1 = true_warped_keypoints.shape[0] + N2 = warped_keypoints.shape[0] + true_warped_keypoints = np.expand_dims(true_warped_keypoints, 1) + warped_keypoints = np.expand_dims(warped_keypoints, 0) + # shapes are broadcasted to N1 x N2 x 2: + norm = np.linalg.norm(true_warped_keypoints - warped_keypoints, ord=None, axis=2) + count1 = 0 + count2 = 0 + le1 = 0 + le2 = 0 + if N2 != 0: + min1 = np.min(norm, axis=1) + correct1 = (min1 <= distance_thresh) + count1 = np.sum(correct1) + le1 = min1[correct1].sum() + if N1 != 0: + min2 = np.min(norm, axis=0) + correct2 = (min2 <= distance_thresh) + count2 = np.sum(correct2) + le2 = min2[correct2].sum() + if N1 + N2 > 0: + repeatability = (count1 + count2) / (N1 + N2) + loc_err = (le1 + le2) / (count1 + count2) + else: + repeatability = -1 + loc_err = -1 + + return N1, N2, repeatability, loc_err diff --git a/third_party/lanet/evaluation/evaluate.py b/third_party/lanet/evaluation/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..fa9e91ee6d9cc0142ebbe8f2a3f904f6fae8434c --- /dev/null +++ b/third_party/lanet/evaluation/evaluate.py @@ -0,0 +1,84 @@ +# Copyright 2020 Toyota Research Institute. All rights reserved. + +import numpy as np +import torch +import torchvision.transforms as transforms +from tqdm import tqdm + +from evaluation.descriptor_evaluation import (compute_homography, + compute_matching_score) +from evaluation.detector_evaluation import compute_repeatability + + +def evaluate_keypoint_net(data_loader, keypoint_net, output_shape=(320, 240), top_k=300): + """Keypoint net evaluation script. + + Parameters + ---------- + data_loader: torch.utils.data.DataLoader + Dataset loader. + keypoint_net: torch.nn.module + Keypoint network. + output_shape: tuple + Original image shape. + top_k: int + Number of keypoints to use to compute metrics, selected based on probability. + use_color: bool + Use color or grayscale images. + """ + keypoint_net.eval() + keypoint_net.training = False + + conf_threshold = 0.0 + localization_err, repeatability = [], [] + correctness1, correctness3, correctness5, MScore = [], [], [], [] + + with torch.no_grad(): + for i, sample in tqdm(enumerate(data_loader), desc="Evaluate point model"): + + image = sample['image'].cuda() + warped_image = sample['warped_image'].cuda() + + score_1, coord_1, desc1 = keypoint_net(image) + score_2, coord_2, desc2 = keypoint_net(warped_image) + B, _, Hc, Wc = desc1.shape + + # Scores & Descriptors + score_1 = torch.cat([coord_1, score_1], dim=1).view(3, -1).t().cpu().numpy() + score_2 = torch.cat([coord_2, score_2], dim=1).view(3, -1).t().cpu().numpy() + desc1 = desc1.view(256, Hc, Wc).view(256, -1).t().cpu().numpy() + desc2 = desc2.view(256, Hc, Wc).view(256, -1).t().cpu().numpy() + + # Filter based on confidence threshold + desc1 = desc1[score_1[:, 2] > conf_threshold, :] + desc2 = desc2[score_2[:, 2] > conf_threshold, :] + score_1 = score_1[score_1[:, 2] > conf_threshold, :] + score_2 = score_2[score_2[:, 2] > conf_threshold, :] + + # Prepare data for eval + data = {'image': sample['image'].numpy().squeeze(), + 'image_shape' : output_shape[::-1], + 'warped_image': sample['warped_image'].numpy().squeeze(), + 'homography': sample['homography'].squeeze().numpy(), + 'prob': score_1, + 'warped_prob': score_2, + 'desc': desc1, + 'warped_desc': desc2} + + # Compute repeatabilty and localization error + _, _, rep, loc_err = compute_repeatability(data, keep_k_points=top_k, distance_thresh=3) + repeatability.append(rep) + localization_err.append(loc_err) + + # Compute correctness + c1, c2, c3 = compute_homography(data, keep_k_points=top_k) + correctness1.append(c1) + correctness3.append(c2) + correctness5.append(c3) + + # Compute matching score + mscore = compute_matching_score(data, keep_k_points=top_k) + MScore.append(mscore) + + return np.mean(repeatability), np.mean(localization_err), \ + np.mean(correctness1), np.mean(correctness3), np.mean(correctness5), np.mean(MScore) diff --git a/third_party/lanet/loss_function.py b/third_party/lanet/loss_function.py new file mode 100644 index 0000000000000000000000000000000000000000..2e74cf2b53af3c3fc26c34394df4cfe538b3b49c --- /dev/null +++ b/third_party/lanet/loss_function.py @@ -0,0 +1,156 @@ +import torch + +def build_descriptor_loss(source_des, target_des, tar_points_un, top_kk=None, relax_field=4, eval_only=False): + """ + Desc Head Loss, per-pixel level triplet loss from https://arxiv.org/pdf/1902.11046.pdf. + + Parameters + ---------- + source_des: torch.Tensor (B,256,H/8,W/8) + Source image descriptors. + target_des: torch.Tensor (B,256,H/8,W/8) + Target image descriptors. + source_points: torch.Tensor (B,H/8,W/8,2) + Source image keypoints + tar_points: torch.Tensor (B,H/8,W/8,2) + Target image keypoints + tar_points_un: torch.Tensor (B,2,H/8,W/8) + Target image keypoints unnormalized + eval_only: bool + Computes only recall without the loss. + Returns + ------- + loss: torch.Tensor + Descriptor loss. + recall: torch.Tensor + Descriptor match recall. + """ + device = source_des.device + loss = 0 + batch_size = source_des.size(0) + recall = 0. + + relax_field_size = [relax_field] + margins = [1.0] + weights = [1.0] + + isource_dense = top_kk is None + + for b_id in range(batch_size): + + if isource_dense: + ref_desc = source_des[b_id].squeeze().view(256, -1) + tar_desc = target_des[b_id].squeeze().view(256, -1) + tar_points_raw = tar_points_un[b_id].view(2, -1) + else: + top_k = top_kk[b_id].squeeze() + + n_feat = top_k.sum().item() + if n_feat < 20: + continue + + ref_desc = source_des[b_id].squeeze()[:, top_k] + tar_desc = target_des[b_id].squeeze()[:, top_k] + tar_points_raw = tar_points_un[b_id][:, top_k] + + # Compute dense descriptor distance matrix and find nearest neighbor + ref_desc = ref_desc.div(torch.norm(ref_desc, p=2, dim=0)) + tar_desc = tar_desc.div(torch.norm(tar_desc, p=2, dim=0)) + dmat = torch.mm(ref_desc.t(), tar_desc) + + dmat = torch.sqrt(2 - 2 * torch.clamp(dmat, min=-1, max=1)) + _, idx = torch.sort(dmat, dim=1) + + + # Compute triplet loss and recall + for pyramid in range(len(relax_field_size)): + + candidates = idx.t() + + match_k_x = tar_points_raw[0, candidates] + match_k_y = tar_points_raw[1, candidates] + + tru_x = tar_points_raw[0] + tru_y = tar_points_raw[1] + + if pyramid == 0: + correct2 = (abs(match_k_x[0]-tru_x) == 0) & (abs(match_k_y[0]-tru_y) == 0) + correct2_cnt = correct2.float().sum() + recall += float(1.0 / batch_size) * (float(correct2_cnt) / float( ref_desc.size(1))) + + if eval_only: + continue + correct_k = (abs(match_k_x - tru_x) <= relax_field_size[pyramid]) & (abs(match_k_y - tru_y) <= relax_field_size[pyramid]) + + incorrect_index = torch.arange(start=correct_k.shape[0]-1, end=-1, step=-1).unsqueeze(1).repeat(1,correct_k.shape[1]).to(device) + incorrect_first = torch.argmax(incorrect_index * (1 - correct_k.long()), dim=0) + + incorrect_first_index = candidates.gather(0, incorrect_first.unsqueeze(0)).squeeze() + + anchor_var = ref_desc + posource_var = tar_desc + neg_var = tar_desc[:, incorrect_first_index] + + loss += float(1.0 / batch_size) * torch.nn.functional.triplet_margin_loss(anchor_var.t(), posource_var.t(), neg_var.t(), margin=margins[pyramid]).mul(weights[pyramid]) + + return loss, recall + + +class KeypointLoss(object): + """ + Loss function class encapsulating the location loss, the descriptor loss, and the score loss. + """ + def __init__(self, config): + self.score_weight = config.score_weight + self.loc_weight = config.loc_weight + self.desc_weight = config.desc_weight + self.corres_weight = config.corres_weight + self.corres_threshold = config.corres_threshold + + def __call__(self, data): + B, _, hc, wc = data['source_score'].shape + + loc_mat_abs = torch.abs(data['target_coord_warped'].view(B, 2, -1).unsqueeze(3) - data['target_coord'].view(B, 2, -1).unsqueeze(2)) + l2_dist_loc_mat = torch.norm(loc_mat_abs, p=2, dim=1) + l2_dist_loc_min, l2_dist_loc_min_index = l2_dist_loc_mat.min(dim=2) + + # construct pseudo ground truth matching matrix + loc_min_mat = torch.repeat_interleave(l2_dist_loc_min.unsqueeze(dim=-1), repeats=l2_dist_loc_mat.shape[-1], dim=-1) + pos_mask = l2_dist_loc_mat.eq(loc_min_mat) & l2_dist_loc_mat.le(1.) + neg_mask = l2_dist_loc_mat.ge(4.) + + pos_corres = - torch.log(data['confidence_matrix'][pos_mask]) + neg_corres = - torch.log(1.0 - data['confidence_matrix'][neg_mask]) + corres_loss = pos_corres.mean() + 5e5 * neg_corres.mean() + + # corresponding distance threshold is 4 + dist_norm_valid_mask = l2_dist_loc_min.lt(self.corres_threshold) & data['border_mask'].view(B, hc * wc) + + # location loss + loc_loss = l2_dist_loc_min[dist_norm_valid_mask].mean() + + # desc Head Loss, per-pixel level triplet loss from https://arxiv.org/pdf/1902.11046.pdf. + desc_loss, _ = build_descriptor_loss(data['source_desc'], data['target_desc_warped'], data['target_coord_warped'].detach(), top_kk=data['border_mask'], relax_field=8) + + # score loss + target_score_associated = data['target_score'].view(B, hc * wc).gather(1, l2_dist_loc_min_index).view(B, hc, wc).unsqueeze(1) + dist_norm_valid_mask = dist_norm_valid_mask.view(B, hc, wc).unsqueeze(1) & data['border_mask'].unsqueeze(1) + l2_dist_loc_min = l2_dist_loc_min.view(B, hc, wc).unsqueeze(1) + loc_err = l2_dist_loc_min[dist_norm_valid_mask] + + # repeatable_constrain in score loss + repeatable_constrain = ((target_score_associated[dist_norm_valid_mask] + data['source_score'][dist_norm_valid_mask]) * (loc_err - loc_err.mean())).mean() + + # consistent_constrain in score_loss + consistent_constrain = torch.nn.functional.mse_loss(data['target_score_warped'][data['border_mask'].unsqueeze(1)], data['source_score'][data['border_mask'].unsqueeze(1)]).mean() * 2 + aware_consistent_loss = torch.nn.functional.mse_loss(data['target_aware_warped'][data['border_mask'].unsqueeze(1).repeat(1, 2, 1, 1)], data['source_aware'][data['border_mask'].unsqueeze(1).repeat(1, 2, 1, 1)]).mean() * 2 + + score_loss = repeatable_constrain + consistent_constrain + aware_consistent_loss + + loss = self.loc_weight * loc_loss + self.desc_weight * desc_loss + self.score_weight * score_loss + self.corres_weight * corres_loss + + return loss, self.loc_weight * loc_loss, self.desc_weight * desc_loss, self.score_weight * score_loss, self.corres_weight * corres_loss + + + + diff --git a/third_party/lanet/main.py b/third_party/lanet/main.py new file mode 100644 index 0000000000000000000000000000000000000000..2aa81d8104c19ea1d8c4ce7d1dd547f8b35a4a72 --- /dev/null +++ b/third_party/lanet/main.py @@ -0,0 +1,25 @@ +import torch + +from train import Trainer +from config import get_config +from utils import prepare_dirs +from data_loader import get_data_loader + +def main(config): + # ensure directories are setup + prepare_dirs(config) + + # ensure reproducibility + torch.manual_seed(config.seed) + if config.use_gpu: + torch.cuda.manual_seed(config.seed) + + # instantiate train data loaders + train_loader = get_data_loader(config=config) + + trainer = Trainer(config, train_loader=train_loader) + trainer.train() + +if __name__ == '__main__': + config, unparsed = get_config() + main(config) \ No newline at end of file diff --git a/third_party/lanet/network_v0/model.py b/third_party/lanet/network_v0/model.py new file mode 100644 index 0000000000000000000000000000000000000000..564000330ddd5e9f18821e8606d23cd12dc847bc --- /dev/null +++ b/third_party/lanet/network_v0/model.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torchvision.transforms as tvf + +from .modules import InterestPointModule, CorrespondenceModule + +def warp_homography_batch(sources, homographies): + """ + Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D. + + Parameters + ---------- + sources: torch.Tensor (B,H,W,C) + Keypoints vector. + homographies: torch.Tensor (B,3,3) + Homographies. + + Returns + ------- + warped_sources: torch.Tensor (B,H,W,C) + Warped keypoints vector. + """ + B, H, W, _ = sources.shape + warped_sources = [] + for b in range(B): + source = sources[b].clone() + source = source.view(-1,2) + ''' + [X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13, + Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23, + Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33] + ''' + source = torch.addmm(homographies[b,:,2], source, homographies[b,:,:2].t()) + source.mul_(1/source[:,2].unsqueeze(1)) + source = source[:,:2].contiguous().view(H,W,2) + warped_sources.append(source) + return torch.stack(warped_sources, dim=0) + +class PointModel(nn.Module): + def __init__(self, is_test=True): + super(PointModel, self).__init__() + self.is_test = is_test + self.interestpoint_module = InterestPointModule(is_test=self.is_test) + self.correspondence_module = CorrespondenceModule() + self.norm_rgb = tvf.Normalize(mean=[0.5, 0.5, 0.5], std=[0.225, 0.225, 0.225]) + + def forward(self, *args): + if self.is_test: + img = args[0] + img = self.norm_rgb(img) + score, coord, desc = self.interestpoint_module(img) + return score, coord, desc + else: + source_score, source_coord, source_desc_block = self.interestpoint_module(args[0]) + target_score, target_coord, target_desc_block = self.interestpoint_module(args[1]) + + B, _, H, W = args[0].shape + B, _, hc, wc = source_score.shape + device = source_score.device + + # Normalize the coordinates from ([0, h], [0, w]) to ([0, 1], [0, 1]). + source_coord_norm = source_coord.clone() + source_coord_norm[:, 0] = (source_coord_norm[:, 0] / (float(W - 1) / 2.)) - 1. + source_coord_norm[:, 1] = (source_coord_norm[:, 1] / (float(H - 1) / 2.)) - 1. + source_coord_norm = source_coord_norm.permute(0, 2, 3, 1) + + target_coord_norm = target_coord.clone() + target_coord_norm[:, 0] = (target_coord_norm[:, 0] / (float(W - 1) / 2.)) - 1. + target_coord_norm[:, 1] = (target_coord_norm[:, 1] / (float(H - 1) / 2.)) - 1. + target_coord_norm = target_coord_norm.permute(0, 2, 3, 1) + + target_coord_warped_norm = warp_homography_batch(source_coord_norm, args[2]) + target_coord_warped = target_coord_warped_norm.clone() + + # de-normlize the coordinates + target_coord_warped[:, :, :, 0] = (target_coord_warped[:, :, :, 0] + 1) * (float(W - 1) / 2.) + target_coord_warped[:, :, :, 1] = (target_coord_warped[:, :, :, 1] + 1) * (float(H - 1) / 2.) + target_coord_warped = target_coord_warped.permute(0, 3, 1, 2) + + # Border mask + border_mask_ori = torch.ones(B, hc, wc) + border_mask_ori[:, 0] = 0 + border_mask_ori[:, hc - 1] = 0 + border_mask_ori[:, :, 0] = 0 + border_mask_ori[:, :, wc - 1] = 0 + border_mask_ori = border_mask_ori.gt(1e-3).to(device) + + oob_mask2 = target_coord_warped_norm[:, :, :, 0].lt(1) & target_coord_warped_norm[:, :, :, 0].gt(-1) & target_coord_warped_norm[:, :, :, 1].lt(1) & target_coord_warped_norm[:, :, :, 1].gt(-1) + border_mask = border_mask_ori & oob_mask2 + + # score + target_score_warped = torch.nn.functional.grid_sample(target_score, target_coord_warped_norm.detach(), align_corners=False) + + # descriptor + source_desc2 = torch.nn.functional.grid_sample(source_desc_block[0], source_coord_norm.detach()) + source_desc3 = torch.nn.functional.grid_sample(source_desc_block[1], source_coord_norm.detach()) + source_aware = source_desc_block[2] + source_desc = torch.mul(source_desc2, source_aware[:, 0, :, :].unsqueeze(1).contiguous()) + torch.mul(source_desc3, source_aware[:, 1, :, :].unsqueeze(1).contiguous()) + + target_desc2 = torch.nn.functional.grid_sample(target_desc_block[0], target_coord_norm.detach()) + target_desc3 = torch.nn.functional.grid_sample(target_desc_block[1], target_coord_norm.detach()) + target_aware = target_desc_block[2] + target_desc = torch.mul(target_desc2, target_aware[:, 0, :, :].unsqueeze(1).contiguous()) + torch.mul(target_desc3, target_aware[:, 1, :, :].unsqueeze(1).contiguous()) + + target_desc2_warped = torch.nn.functional.grid_sample(target_desc_block[0], target_coord_warped_norm.detach()) + target_desc3_warped = torch.nn.functional.grid_sample(target_desc_block[1], target_coord_warped_norm.detach()) + target_aware_warped = torch.nn.functional.grid_sample(target_desc_block[2], target_coord_warped_norm.detach()) + target_desc_warped = torch.mul(target_desc2_warped, target_aware_warped[:, 0, :, :].unsqueeze(1).contiguous()) + torch.mul(target_desc3_warped, target_aware_warped[:, 1, :, :].unsqueeze(1).contiguous()) + + confidence_matrix = self.correspondence_module(source_desc, target_desc) + confidence_matrix = torch.clamp(confidence_matrix, 1e-12, 1 - 1e-12) + + output = { + 'source_score': source_score, + 'source_coord': source_coord, + 'source_desc': source_desc, + 'source_aware': source_aware, + 'target_score': target_score, + 'target_coord': target_coord, + 'target_score_warped': target_score_warped, + 'target_coord_warped': target_coord_warped, + 'target_desc_warped': target_desc_warped, + 'target_aware_warped': target_aware_warped, + 'border_mask': border_mask, + 'confidence_matrix': confidence_matrix + } + + return output diff --git a/third_party/lanet/network_v0/modules.py b/third_party/lanet/network_v0/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..a38c53133aff8769f267cc054174361296cb3e7d --- /dev/null +++ b/third_party/lanet/network_v0/modules.py @@ -0,0 +1,158 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from utils import image_grid + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(ConvBlock, self).__init__() + + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.conv(x) + + +class DilationConv3x3(nn.Module): + def __init__(self, in_channels, out_channels): + super(DilationConv3x3, self).__init__() + + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=2, dilation=2, bias=False) + self.bn = nn.BatchNorm2d(out_channels) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class InterestPointModule(nn.Module): + def __init__(self, is_test=False): + super(InterestPointModule, self).__init__() + self.is_test = is_test + + self.conv1 = ConvBlock(3, 32) + self.conv2 = ConvBlock(32, 64) + self.conv3 = ConvBlock(64, 128) + self.conv4 = ConvBlock(128, 256) + + self.maxpool2x2 = nn.MaxPool2d(2, 2) + + # score head + self.score_conv = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False) + self.score_norm = nn.BatchNorm2d(256) + self.score_out = nn.Conv2d(256, 3, kernel_size=3, stride=1, padding=1) + self.softmax = nn.Softmax(dim=1) + + # location head + self.loc_conv = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False) + self.loc_norm = nn.BatchNorm2d(256) + self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + + # descriptor out + self.des_conv2 = DilationConv3x3(64, 256) + self.des_conv3 = DilationConv3x3(128, 256) + + # cross_head: + self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + B, _, H, W = x.shape + + x = self.conv1(x) + x = self.maxpool2x2(x) + x2 = self.conv2(x) + x = self.maxpool2x2(x2) + x3 = self.conv3(x) + x = self.maxpool2x2(x3) + x = self.conv4(x) + + B, _, Hc, Wc = x.shape + + # score head + score_x = self.score_out(self.relu(self.score_norm(self.score_conv(x)))) + aware = self.softmax(score_x[:, 0:2, :, :]) + score = score_x[:, 2, :, :].unsqueeze(1).sigmoid() + + border_mask = torch.ones(B, Hc, Wc) + border_mask[:, 0] = 0 + border_mask[:, Hc - 1] = 0 + border_mask[:, :, 0] = 0 + border_mask[:, :, Wc - 1] = 0 + border_mask = border_mask.unsqueeze(1) + score = score * border_mask.to(score.device) + + # location head + coord_x = self.relu(self.loc_norm(self.loc_conv(x))) + coord_cell = self.loc_out(coord_x).tanh() + + shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0 + + step = ((H/Hc)-1) / 2. + center_base = image_grid(B, Hc, Wc, + dtype=coord_cell.dtype, + device=coord_cell.device, + ones=False, normalized=False).mul(H/Hc) + step + + coord_un = center_base.add(coord_cell.mul(shift_ratio * step)) + coord = coord_un.clone() + coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W-1) + coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H-1) + + # descriptor block + desc_block = [] + desc_block.append(self.des_conv2(x2)) + desc_block.append(self.des_conv3(x3)) + desc_block.append(aware) + + if self.is_test: + coord_norm = coord[:, :2].clone() + coord_norm[:, 0] = (coord_norm[:, 0] / (float(W-1)/2.)) - 1. + coord_norm[:, 1] = (coord_norm[:, 1] / (float(H-1)/2.)) - 1. + coord_norm = coord_norm.permute(0, 2, 3, 1) + + desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm) + desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm) + aware = desc_block[2] + + desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(desc3, aware[:, 1, :, :]) + desc = desc.div(torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)) # Divide by norm to normalize. + + return score, coord, desc + + return score, coord, desc_block + + +class CorrespondenceModule(nn.Module): + def __init__(self, match_type='dual_softmax'): + super(CorrespondenceModule, self).__init__() + self.match_type = match_type + + if self.match_type == 'dual_softmax': + self.temperature = 0.1 + else: + raise NotImplementedError() + + def forward(self, source_desc, target_desc): + b, c, h, w = source_desc.size() + + source_desc = source_desc.div(torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)).view(b, -1, h*w) + target_desc = target_desc.div(torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)).view(b, -1, h*w) + + if self.match_type == 'dual_softmax': + sim_mat = torch.einsum("bcm, bcn -> bmn", source_desc, target_desc) / self.temperature + confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2) + else: + raise NotImplementedError() + + return confidence_matrix \ No newline at end of file diff --git a/third_party/lanet/network_v1/model.py b/third_party/lanet/network_v1/model.py new file mode 100644 index 0000000000000000000000000000000000000000..baeb37c563852340fe9278ed5c2dccea4b3b693a --- /dev/null +++ b/third_party/lanet/network_v1/model.py @@ -0,0 +1,52 @@ +import torch +import torch.nn as nn +import torchvision.transforms as tvf + +from .modules import InterestPointModule, CorrespondenceModule + +def warp_homography_batch(sources, homographies): + """ + Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D. + + Parameters + ---------- + sources: torch.Tensor (B,H,W,C) + Keypoints vector. + homographies: torch.Tensor (B,3,3) + Homographies. + + Returns + ------- + warped_sources: torch.Tensor (B,H,W,C) + Warped keypoints vector. + """ + B, H, W, _ = sources.shape + warped_sources = [] + for b in range(B): + source = sources[b].clone() + source = source.view(-1,2) + ''' + [X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13, + Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23, + Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33] + ''' + source = torch.addmm(homographies[b,:,2], source, homographies[b,:,:2].t()) + source.mul_(1/source[:,2].unsqueeze(1)) + source = source[:,:2].contiguous().view(H,W,2) + warped_sources.append(source) + return torch.stack(warped_sources, dim=0) + + +class PointModel(nn.Module): + def __init__(self, is_test=False): + super(PointModel, self).__init__() + self.is_test = is_test + self.interestpoint_module = InterestPointModule(is_test=self.is_test) + self.correspondence_module = CorrespondenceModule() + self.norm_rgb = tvf.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + def forward(self, *args): + img = args[0] + img = self.norm_rgb(img) + score, coord, desc = self.interestpoint_module(img) + return score, coord, desc diff --git a/third_party/lanet/network_v1/modules.py b/third_party/lanet/network_v1/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..4daed5f12c40e40f6fc8347f701235e141839ada --- /dev/null +++ b/third_party/lanet/network_v1/modules.py @@ -0,0 +1,174 @@ +from curses import is_term_resized +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torchvision import models +from utils import image_grid + +class ConvBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(ConvBlock, self).__init__() + + self.conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def forward(self, x): + return self.conv(x) + +class DilationConv3x3(nn.Module): + def __init__(self, in_channels, out_channels): + super(DilationConv3x3, self).__init__() + + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=2, dilation=2, bias=False) + self.bn = nn.BatchNorm2d(out_channels) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return x + + +class InterestPointModule(nn.Module): + def __init__(self, is_test=False): + super(InterestPointModule, self).__init__() + self.is_test = is_test + + model = models.vgg16_bn(pretrained=True) + + # use the first 23 layers as encoder + self.encoder = nn.Sequential( + *list(model.features.children())[: 33] + ) + + # score head + self.score_head = nn.Sequential( + nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + ) + self.softmax = nn.Softmax(dim=1) + + # location head + self.loc_head = nn.Sequential( + nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + ) + # location out + self.loc_out = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.shift_out = nn.Conv2d(256, 1, kernel_size=3, stride=1, padding=1) + + # descriptor out + self.des_out2 = DilationConv3x3(128, 256) + self.des_out3 = DilationConv3x3(256, 256) + self.des_out4 = DilationConv3x3(512, 256) + + def forward(self, x): + B, _, H, W = x.shape + + x = self.encoder[2](self.encoder[1](self.encoder[0](x))) + x = self.encoder[5](self.encoder[4](self.encoder[3](x))) + + x = self.encoder[6](x) + x = self.encoder[9](self.encoder[8](self.encoder[7](x))) + x2 = self.encoder[12](self.encoder[11](self.encoder[10](x))) + + x = self.encoder[13](x2) + x = self.encoder[16](self.encoder[15](self.encoder[14](x))) + x = self.encoder[19](self.encoder[18](self.encoder[17](x))) + x3 = self.encoder[22](self.encoder[21](self.encoder[20](x))) + + x = self.encoder[23](x3) + x = self.encoder[26](self.encoder[25](self.encoder[24](x))) + x = self.encoder[29](self.encoder[28](self.encoder[27](x))) + x = self.encoder[32](self.encoder[31](self.encoder[30](x))) + + + B, _, Hc, Wc = x.shape + + # score head + score_x = self.score_head(x) + aware = self.softmax(score_x[:, 0:3, :, :]) + score = score_x[:, 3, :, :].unsqueeze(1).sigmoid() + + border_mask = torch.ones(B, Hc, Wc) + border_mask[:, 0] = 0 + border_mask[:, Hc - 1] = 0 + border_mask[:, :, 0] = 0 + border_mask[:, :, Wc - 1] = 0 + border_mask = border_mask.unsqueeze(1) + score = score * border_mask.to(score.device) + + # location head + coord_x = self.loc_head(x) + coord_cell = self.loc_out(coord_x).tanh() + + shift_ratio = self.shift_out(coord_x).sigmoid() * 2.0 + + step = ((H/Hc)-1) / 2. + center_base = image_grid(B, Hc, Wc, + dtype=coord_cell.dtype, + device=coord_cell.device, + ones=False, normalized=False).mul(H/Hc) + step + + coord_un = center_base.add(coord_cell.mul(shift_ratio * step)) + coord = coord_un.clone() + coord[:, 0] = torch.clamp(coord_un[:, 0], min=0, max=W-1) + coord[:, 1] = torch.clamp(coord_un[:, 1], min=0, max=H-1) + + # descriptor block + desc_block = [] + desc_block.append(self.des_out2(x2)) + desc_block.append(self.des_out3(x3)) + desc_block.append(self.des_out4(x)) + desc_block.append(aware) + + if self.is_test: + coord_norm = coord[:, :2].clone() + coord_norm[:, 0] = (coord_norm[:, 0] / (float(W-1)/2.)) - 1. + coord_norm[:, 1] = (coord_norm[:, 1] / (float(H-1)/2.)) - 1. + coord_norm = coord_norm.permute(0, 2, 3, 1) + + desc2 = torch.nn.functional.grid_sample(desc_block[0], coord_norm) + desc3 = torch.nn.functional.grid_sample(desc_block[1], coord_norm) + desc4 = torch.nn.functional.grid_sample(desc_block[2], coord_norm) + aware = desc_block[3] + + desc = torch.mul(desc2, aware[:, 0, :, :]) + torch.mul(desc3, aware[:, 1, :, :]) + torch.mul(desc4, aware[:, 2, :, :]) + desc = desc.div(torch.unsqueeze(torch.norm(desc, p=2, dim=1), 1)) # Divide by norm to normalize. + + return score, coord, desc + + return score, coord, desc_block + +class CorrespondenceModule(nn.Module): + def __init__(self, match_type='dual_softmax'): + super(CorrespondenceModule, self).__init__() + self.match_type = match_type + + if self.match_type == 'dual_softmax': + self.temperature = 0.1 + else: + raise NotImplementedError() + + def forward(self, source_desc, target_desc): + b, c, h, w = source_desc.size() + + source_desc = source_desc.div(torch.unsqueeze(torch.norm(source_desc, p=2, dim=1), 1)).view(b, -1, h*w) + target_desc = target_desc.div(torch.unsqueeze(torch.norm(target_desc, p=2, dim=1), 1)).view(b, -1, h*w) + + if self.match_type == 'dual_softmax': + sim_mat = torch.einsum("bcm, bcn -> bmn", source_desc, target_desc) / self.temperature + confidence_matrix = F.softmax(sim_mat, 1) * F.softmax(sim_mat, 2) + else: + raise NotImplementedError() + + return confidence_matrix diff --git a/third_party/lanet/test.py b/third_party/lanet/test.py new file mode 100644 index 0000000000000000000000000000000000000000..cc9365f5c92cbd69c3ee9250ff66b07bd1eed1c6 --- /dev/null +++ b/third_party/lanet/test.py @@ -0,0 +1,87 @@ +import os +import cv2 +import argparse +import numpy as np +import torch +import torchvision + +from torchvision import datasets, transforms +from torch.autograd import Variable +from network_v0.model import PointModel +from datasets.hp_loader import PatchesDataset +from torch.utils.data import DataLoader +from evaluation.evaluate import evaluate_keypoint_net + + +def main(): + parser = argparse.ArgumentParser(description='Testing') + parser.add_argument('--device', default=0, type=int, help='which gpu to run on.') + parser.add_argument('--test_dir', required=True, type=str, help='Test data path.') + opt = parser.parse_args() + + torch.manual_seed(0) + use_gpu = torch.cuda.is_available() + if use_gpu: + torch.cuda.set_device(opt.device) + + # Load data in 320x240 + hp_dataset_320x240 = PatchesDataset(root_dir=opt.test_dir, use_color=True, output_shape=(320, 240), type='all') + data_loader_320x240 = DataLoader(hp_dataset_320x240, + batch_size=1, + pin_memory=False, + shuffle=False, + num_workers=4, + worker_init_fn=None, + sampler=None) + + # Load data in 640x480 + hp_dataset_640x480 = PatchesDataset(root_dir=opt.test_dir, use_color=True, output_shape=(640, 480), type='all') + data_loader_640x480 = DataLoader(hp_dataset_640x480, + batch_size=1, + pin_memory=False, + shuffle=False, + num_workers=4, + worker_init_fn=None, + sampler=None) + + # Load model + model = PointModel(is_test=True) + ckpt = torch.load('./checkpoints/PointModel_v0.pth') + model.load_state_dict(ckpt['model_state']) + model = model.eval() + if use_gpu: + model = model.cuda() + + + print('Evaluating in 320x240, 300 points') + rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net( + data_loader_320x240, + model, + output_shape=(320, 240), + top_k=300) + + print('Repeatability: {0:.3f}'.format(rep)) + print('Localization Error: {0:.3f}'.format(loc)) + print('H-1 Accuracy: {:.3f}'.format(c1)) + print('H-3 Accuracy: {:.3f}'.format(c3)) + print('H-5 Accuracy: {:.3f}'.format(c5)) + print('Matching Score: {:.3f}'.format(mscore)) + print('\n') + + print('Evaluating in 640x480, 1000 points') + rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net( + data_loader_640x480, + model, + output_shape=(640, 480), + top_k=1000) + + print('Repeatability: {0:.3f}'.format(rep)) + print('Localization Error: {0:.3f}'.format(loc)) + print('H-1 Accuracy: {:.3f}'.format(c1)) + print('H-3 Accuracy: {:.3f}'.format(c3)) + print('H-5 Accuracy: {:.3f}'.format(c5)) + print('Matching Score: {:.3f}'.format(mscore)) + print('\n') + +if __name__ == '__main__': + main() diff --git a/third_party/lanet/train.py b/third_party/lanet/train.py new file mode 100644 index 0000000000000000000000000000000000000000..3076a0fdb78a59bfd64367399c0f2b0de1297653 --- /dev/null +++ b/third_party/lanet/train.py @@ -0,0 +1,129 @@ +import os +import torch +import torch.optim as optim +from tqdm import tqdm + +from torch.autograd import Variable + +from network_v0.model import PointModel +from loss_function import KeypointLoss + +class Trainer(object): + def __init__(self, config, train_loader=None): + self.config = config + # data parameters + self.train_loader = train_loader + self.num_train = len(self.train_loader) + + # training parameters + self.max_epoch = config.max_epoch + self.start_epoch = config.start_epoch + self.momentum = config.momentum + self.lr = config.init_lr + self.lr_factor = config.lr_factor + self.display = config.display + + # misc params + self.use_gpu = config.use_gpu + self.random_seed = config.seed + self.gpu = config.gpu + self.ckpt_dir = config.ckpt_dir + self.ckpt_name = '{}-{}'.format(config.ckpt_name, config.seed) + + # build model + self.model = PointModel(is_test=False) + + # training on GPU + if self.use_gpu: + torch.cuda.set_device(self.gpu) + self.model.cuda() + + print('Number of model parameters: {:,}'.format(sum([p.data.nelement() for p in self.model.parameters()]))) + + # build loss functional + self.loss_func = KeypointLoss(config) + + # build optimizer and scheduler + self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr) + self.lr_scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[4, 8], gamma=self.lr_factor) + + # resume + if int(self.config.start_epoch) > 0: + self.config.start_epoch, self.model, self.optimizer, self.lr_scheduler = self.load_checkpoint(int(self.config.start_epoch), self.model, self.optimizer, self.lr_scheduler) + + def train(self): + print("\nTrain on {} samples".format(self.num_train)) + self.save_checkpoint(0, self.model, self.optimizer, self.lr_scheduler) + for epoch in range(self.start_epoch, self.max_epoch): + print("\nEpoch: {}/{} --lr: {:.6f}".format(epoch+1, self.max_epoch, self.lr)) + # train for one epoch + self.train_one_epoch(epoch) + if self.lr_scheduler: + self.lr_scheduler.step() + self.save_checkpoint(epoch+1, self.model, self.optimizer, self.lr_scheduler) + + def train_one_epoch(self, epoch): + self.model.train() + for (i, data) in enumerate(tqdm(self.train_loader)): + + if self.use_gpu: + source_img = data['image_aug'].cuda() + target_img = data['image'].cuda() + homography = data['homography'].cuda() + + source_img = Variable(source_img) + target_img = Variable(target_img) + homography = Variable(homography) + + # forward propogation + output = self.model(source_img, target_img, homography) + + # compute loss + loss, loc_loss, desc_loss, score_loss, corres_loss = self.loss_func(output) + + # compute gradients and update + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + + # print training info + msg_batch = "Epoch:{} Iter:{} lr:{:.4f} "\ + "loc_loss={:.4f} desc_loss={:.4f} score_loss={:.4f} corres_loss={:.4f} "\ + "loss={:.4f} "\ + .format((epoch + 1), i, self.lr, loc_loss.data, desc_loss.data, score_loss.data, corres_loss.data, loss.data) + + if((i % self.display) == 0): + print(msg_batch) + return + + def save_checkpoint(self, epoch, model, optimizer, lr_scheduler): + filename = self.ckpt_name + '_' + str(epoch) + '.pth' + torch.save( + {'epoch': epoch, + 'model_state': model.state_dict(), + 'optimizer_state': optimizer.state_dict(), + 'lr_scheduler': lr_scheduler.state_dict()}, + os.path.join(self.ckpt_dir, filename)) + + def load_checkpoint(self, epoch, model, optimizer, lr_scheduler): + filename = self.ckpt_name + '_' + str(epoch) + '.pth' + ckpt = torch.load(os.path.join(self.ckpt_dir, filename)) + epoch = ckpt['epoch'] + model.load_state_dict(ckpt['model_state']) + optimizer.load_state_dict(ckpt['optimizer_state']) + lr_scheduler.load_state_dict(ckpt['lr_scheduler']) + + print("[*] Loaded {} checkpoint @ epoch {}".format(filename, ckpt['epoch'])) + + return epoch, model, optimizer, lr_scheduler + + + + + + + + + + + \ No newline at end of file diff --git a/third_party/lanet/utils.py b/third_party/lanet/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d5422ebcfc2847be047391791d891a09388ca7d1 --- /dev/null +++ b/third_party/lanet/utils.py @@ -0,0 +1,102 @@ +import os +import torch + +import torchvision.transforms as transforms +from functools import lru_cache + +@lru_cache(maxsize=None) +def meshgrid(B, H, W, dtype, device, normalized=False): + """ + Create mesh-grid given batch size, height and width dimensions. From https://github.com/TRI-ML/KP2D. + + Parameters + ---------- + B: int + Batch size + H: int + Grid Height + W: int + Batch size + dtype: torch.dtype + Tensor dtype + device: str + Tensor device + normalized: bool + Normalized image coordinates or integer-grid. + + Returns + ------- + xs: torch.Tensor + Batched mesh-grid x-coordinates (BHW). + ys: torch.Tensor + Batched mesh-grid y-coordinates (BHW). + """ + if normalized: + xs = torch.linspace(-1, 1, W, device=device, dtype=dtype) + ys = torch.linspace(-1, 1, H, device=device, dtype=dtype) + else: + xs = torch.linspace(0, W-1, W, device=device, dtype=dtype) + ys = torch.linspace(0, H-1, H, device=device, dtype=dtype) + ys, xs = torch.meshgrid([ys, xs]) + return xs.repeat([B, 1, 1]), ys.repeat([B, 1, 1]) + + +@lru_cache(maxsize=None) +def image_grid(B, H, W, dtype, device, ones=True, normalized=False): + """ + Create an image mesh grid with shape B3HW given image shape BHW. From https://github.com/TRI-ML/KP2D. + + Parameters + ---------- + B: int + Batch size + H: int + Grid Height + W: int + Batch size + dtype: str + Tensor dtype + device: str + Tensor device + ones : bool + Use (x, y, 1) coordinates + normalized: bool + Normalized image coordinates or integer-grid. + + Returns + ------- + grid: torch.Tensor + Mesh-grid for the corresponding image shape (B3HW) + """ + xs, ys = meshgrid(B, H, W, dtype, device, normalized=normalized) + coords = [xs, ys] + if ones: + coords.append(torch.ones_like(xs)) # BHW + grid = torch.stack(coords, dim=1) # B3HW + return grid + +def to_tensor_sample(sample, tensor_type='torch.FloatTensor'): + """ + Casts the keys of sample to tensors. From https://github.com/TRI-ML/KP2D. + + Parameters + ---------- + sample : dict + Input sample + tensor_type : str + Type of tensor we are casting to + + Returns + ------- + sample : dict + Sample with keys cast as tensors + """ + transform = transforms.ToTensor() + sample['image'] = transform(sample['image']).type(tensor_type) + return sample + +def prepare_dirs(config): + for path in [config.ckpt_dir]: + if not os.path.exists(path): + os.makedirs(path) + diff --git a/third_party/r2d2/LICENSE b/third_party/r2d2/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..9144e3e43fe3d62cd66971ab021466949fc4ee14 --- /dev/null +++ b/third_party/r2d2/LICENSE @@ -0,0 +1,69 @@ +Creative Commons + +Attribution-NonCommercial-ShareAlike 3.0 Unported + +CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE LEGAL SERVICES. DISTRIBUTION OF THIS LICENSE DOES NOT CREATE AN ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS INFORMATION ON AN "AS-IS" BASIS. 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Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America + and IDIAP Research Institute nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + +===== + +pytorch/vision +https://github.com/pytorch/vision + + +BSD 3-Clause License + +Copyright (c) Soumith Chintala 2016, +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +===== + +tomrunia/OpticalFlow_Visualization +https://github.com/tomrunia/OpticalFlow_Visualization + + +# MIT License +# +# Copyright (c) 2018 Tom Runia +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to conditions. +# +# Author: Tom Runia +# Date Created: 2018-08-03 + +===== diff --git a/third_party/r2d2/README.md b/third_party/r2d2/README.md new file mode 100644 index 0000000000000000000000000000000000000000..185b8c61863ae0c42ba864321b24c48dfbe85e30 --- /dev/null +++ b/third_party/r2d2/README.md @@ -0,0 +1,194 @@ +# R2D2: Reliable and Repeatable Detector and Descriptor # +This repository contains the implementation of the following [paper](https://europe.naverlabs.com/research/publications/r2d2-reliable-and-repeatable-detectors-and-descriptors-for-joint-sparse-local-keypoint-detection-and-feature-extraction/): + +```text +@inproceedings{r2d2, + author = {Jerome Revaud and Philippe Weinzaepfel and C{\'{e}}sar Roberto de Souza and + Martin Humenberger}, + title = {{R2D2:} Repeatable and Reliable Detector and Descriptor}, + booktitle = {NeurIPS}, + year = {2019}, +} +``` + +Fast-R2D2 +----------------- + +This repository also contains the code needed to train and extract Fast-R2D2 keypoints. +Fast-R2D2 is a revised version of R2D2 that is significantly faster, uses less memory yet achieves the same order of precision as the original network. + + +License +------- + +Our code is released under the Creative Commons BY-NC-SA 3.0 (see [LICENSE](LICENSE) for more details), available only for non-commercial use. + + +Getting started +--------------- +You just need Python 3.6+ equipped with standard scientific packages and PyTorch1.1+. +Typically, conda is one of the easiest way to get started: +```bash +conda install python tqdm pillow numpy matplotlib scipy +conda install pytorch torchvision cudatoolkit=10.1 -c pytorch +``` + + +Pretrained models +----------------- +For your convenience, we provide five pre-trained models in the `models/` folder: + - `r2d2_WAF_N16.pt`: this is the model used in most experiments of the paper (on HPatches `MMA@3=0.686`). It was trained with Web images (`W`), Aachen day-time images (`A`) and Aachen optical flow pairs (`F`) + - `r2d2_WASF_N16.pt`: this is the model used in the visual localization experiments (on HPatches `MMA@3=0.721`). It was trained with Web images (`W`), Aachen day-time images (`A`), Aachen day-night synthetic pairs (`S`), and Aachen optical flow pairs (`F`). + - `r2d2_WASF_N8_big.pt`: Same than previous model, but trained with `N=8` instead of `N=16` in the repeatability loss. In other words, it outputs a higher density of keypoints. This can be interesting for certain applications like visual localization, but it implies a drop in MMA since keypoints gets slighlty less reliable. + - `faster2d2_WASF_N16.pt`: The Fast-R2D2 equivalent of r2d2_WASF_N16.pt + - `faster2d2_WASF_N8_big.pt`: The Fast-R2D2 equivalent of r2d2_WASF_N8.pt + +For more details about the training data, see the dedicated section below. +Here is a table that summarizes the performance of each model: + +| model name | model size
(#weights)| number of
keypoints |MMA@3 on
HPatches| +|------------------|:-----------------------:|:----------------------:|:------------------:| +|`r2d2_WAF_N16.pt` | 0.5M | 5K | 0.686 | +|`r2d2_WASF_N16.pt` | 0.5M | 5K | 0.721 | +|`r2d2_WASF_N8_big.pt`| 1.0M | 10K | 0.692 | +|`faster2d2_WASF_N8_big.pt`| 1.0M | 5K | 0.650 | + + + +Feature extraction +------------------ +To extract keypoints for a given image, simply execute: +```bash +python extract.py --model models/r2d2_WASF_N16.pt --images imgs/brooklyn.png --top-k 5000 +``` +This also works for multiple images (separated by spaces) or a `.txt` image list. +For each image, this will save the `top-k` keypoints in a file with the same path as the image and a `.r2d2` extension. +For example, they will be saved in `imgs/brooklyn.png.r2d2` for the sample command above. + +The keypoint file is in the `npz` numpy format and contains 3 fields: + - `keypoints` (`N x 3`): keypoint position (x, y and scale). Scale denotes here the patch diameters in pixels. + - `descriptors` (`N x 128`): l2-normalized descriptors. + - `scores` (`N`): keypoint scores (the higher the better). + +*Note*: You can modify the extraction parameters (scale factor, scale range...). Run `python extract.py --help` for more information. +By default, they corespond to what is used in the paper, i.e., a scale factor equal to `2^0.25` (`--scale-f 1.189207`) and image size in the range `[256, 1024]` (`--min-size 256 --max-size 1024`). + +*Note2*: You can significantly improve the `MMA@3` score (by ~4 pts) if you can afford more computations. To do so, you just need to increase the upper-limit on the scale range by replacing `--min-size 256 --max-size 1024` with `--min-size 0 --max-size 9999 --min-scale 0.3 --max-scale 1.0`. + +Feature extraction with kapture datasets +------------------ +Kapture is a pivot file format, based on text and binary files, used to describe SFM (Structure From Motion) and more generally sensor-acquired data. + +It is available at https://github.com/naver/kapture. +It contains conversion tools for popular formats and several popular datasets are directly available in kapture. + +It can be installed with: +```bash +pip install kapture +``` + +Datasets can be downloaded with: +```bash +kapture_download_dataset.py update +kapture_download_dataset.py list +# e.g.: install mapping and query of Extended-CMU-Seasons_slice22 +kapture_download_dataset.py install "Extended-CMU-Seasons_slice22_*" +``` +If you want to convert your own dataset into kapture, please find some examples [here](https://github.com/naver/kapture/blob/master/doc/datasets.adoc). + +Once installed, you can extract keypoints for your kapture dataset with: +```bash +python extract_kapture.py --model models/r2d2_WASF_N16.pt --kapture-root pathto/yourkapturedataset --top-k 5000 +``` + +Run `python extract_kapture.py --help` for more information on the extraction parameters. + +Evaluation on HPatches +---------------------- +The evaluation is based on the [code](https://github.com/mihaidusmanu/d2-net) from [D2-Net](https://dsmn.ml/publications/d2-net.html). +```bash +git clone https://github.com/mihaidusmanu/d2-net.git +cd d2-net/hpatches_sequences/ +bash download.sh +bash download_cache.sh +cd ../.. +ln -s d2-net/hpatches_sequences # finally create a soft-link +``` + +Once this is done, extract all the features: +```bash +python extract.py --model models/r2d2_WAF_N16.pt --images d2-net/image_list_hpatches_sequences.txt +``` + +Finally, evaluate using the iPython notebook `d2-net/hpatches_sequences/HPatches-Sequences-Matching-Benchmark.ipynb`. +You should normally get the following `MMA` plot: +![image](https://user-images.githubusercontent.com/56719813/67966238-d3cc6500-fc03-11e9-969b-5f086da26e34.png). + + +**New**: we have uploaded in the `results/` folder some pre-computed plots that you can visualize using the aforementioned ipython notebook from `d2-net` (you need to place them in the `d2-net/hpatches_sequences/cache/` folder). + - `r2d2_*_N16.size-256-1024.npy`: keypoints were extracted using a limited image resolution (i.e. with `python extract.py --min-size 256 --max-size 1024 ...`) + - `r2d2_*_N16.scale-0.3-1.npy`: keypoints were extracted using a full image resolution (i.e. with `python extract.py --min-size 0 --max-size 9999 --min-scale 0.3 --max-scale 1.0`). + +Here is a summary of the results: + +| result file | training set | resolution | MMA@3 on
HPatches| note | +|--------------|:------------:|:----------:|:-------------------:|------| +|[r2d2_W_N16.scale-0.3-1.npy](results/r2d2_W_N16.scale-0.3-1.npy) | `W` only | full | 0.699 | no annotation whatsoever | +|[r2d2_WAF_N16.size-256-1024.npy](results/r2d2_WAF_N16.size-256-1024.npy) | `W`+`A`+`F` | 1024 px | 0.686 | as in NeurIPS paper | +|[r2d2_WAF_N16.scale-0.3-1.npy](results/r2d2_WAF_N16.scale-0.3-1.npy) | `W`+`A`+`F` | full | 0.718 | +3.2% just from resolution | +|[r2d2_WASF_N16.size-256-1024.npy](results/r2d2_WASF_N16.size-256-1024.npy) | `W`+`A`+`S`+`F` | 1024 px | 0.721 | with style transfer | +|[r2d2_WASF_N16.scale-0.3-1.npy](results/r2d2_WASF_N16.scale-0.3-1.npy) | `W`+`A`+`S`+`F` | full | 0.758 | +3.7% just from resolution | + +Evaluation on visuallocalization.net +---------------------- +In our paper, we report visual localization results on the Aachen Day-Night dataset (nighttime images) available at visuallocalization.net. We used the provided local feature evaluation pipeline provided here: https://github.com/tsattler/visuallocalizationbenchmark/tree/master/local_feature_evaluation +In the meantime, the ground truth poses as well as the error thresholds of the Aachen nighttime images (which are used for the local feature evaluation) have been improved and changed on the website, thus, the original results reported in the paper cannot be reproduced. + +Training the model +------------------ +We provide all the code and data to retrain the model as described in the paper. + +### Downloading training data ### +The first step is to download the training data. +First, create a folder that will host all data in a place where you have sufficient disk space (15 GB required). +```bash +DATA_ROOT=/path/to/data +mkdir -p $DATA_ROOT +ln -fs $DATA_ROOT data +mkdir $DATA_ROOT/aachen +``` +Then, manually download the [Aachen dataset here](https://drive.google.com/drive/folders/1fvb5gwqHCV4cr4QPVIEMTWkIhCpwei7n) and save it as `$DATA_ROOT/aachen/database_and_query_images.zip`. +Finally, execute the download script to complete the installation. It will download the remaining training data and will extract all files properly. +```bash +./download_training_data.sh +``` +The following datasets are now installed: + +| full name |tag|Disk |# imgs|# pairs| python instance | +|---------------------------------|---|-----|------|-------|--------------------------------| +| Random Web images | W |2.7GB| 3125 | 3125 | `auto_pairs(web_images)` | +| Aachen DB images | A |2.5GB| 4479 | 4479 | `auto_pairs(aachen_db_images)` | +| Aachen style transfer pairs | S |0.3GB| 8115 | 3636 | `aachen_style_transfer_pairs` | +| Aachen optical flow pairs | F |2.9GB| 4479 | 4770 | `aachen_flow_pairs` | + +Note that you can visualize the content of each dataset using the following command: +```bash +python -m tools.dataloader "PairLoader(aachen_flow_pairs)" +``` +![image](https://user-images.githubusercontent.com/56719813/68311498-eafecd00-00b1-11ea-8d37-6693f3f90c9f.png) + + +### Training details ### +To train the model, simply run this command: +```bash +python train.py --save-path /path/to/model.pt +``` +On a recent GPU, it takes 30 min per epoch, so ~12h for 25 epochs. +You should get a model that scores `0.71 +/- 0.01` in `MMA@3` on HPatches (this standard-deviation is similar to what is reported in Table 1 of the paper). + +If you want to retrain fast-r2d2 architectures, run: +```bash +python train.py --save-path /path/to/fast-model.pt --net 'Fast_Quad_L2Net_ConfCFS()' +``` + +Note that you can fully configure the training (i.e. select the data sources, change the batch size, learning rate, number of epochs etc.). One easy way to improve the model is to train for more epochs, e.g. `--epochs 50`. For more details about all parameters, run `python train.py --help`. diff --git a/third_party/r2d2/datasets/__init__.py b/third_party/r2d2/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8f11df21be72856ea365f6efd7a389aba267562b --- /dev/null +++ b/third_party/r2d2/datasets/__init__.py @@ -0,0 +1,33 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +from .pair_dataset import CatPairDataset, SyntheticPairDataset, TransformedPairs +from .imgfolder import ImgFolder + +from .web_images import RandomWebImages +from .aachen import * + +# try to instanciate datasets +import sys +try: + web_images = RandomWebImages(0, 52) +except AssertionError as e: + print(f"Dataset web_images not available, reason: {e}", file=sys.stderr) + +try: + aachen_db_images = AachenImages_DB() +except AssertionError as e: + print(f"Dataset aachen_db_images not available, reason: {e}", file=sys.stderr) + +try: + aachen_style_transfer_pairs = AachenPairs_StyleTransferDayNight() +except AssertionError as e: + print(f"Dataset aachen_style_transfer_pairs not available, reason: {e}", file=sys.stderr) + +try: + aachen_flow_pairs = AachenPairs_OpticalFlow() +except AssertionError as e: + print(f"Dataset aachen_flow_pairs not available, reason: {e}", file=sys.stderr) + + diff --git a/third_party/r2d2/datasets/aachen.py b/third_party/r2d2/datasets/aachen.py new file mode 100644 index 0000000000000000000000000000000000000000..4ddb324cea01da2430ee89b32c7627b34c01a41f --- /dev/null +++ b/third_party/r2d2/datasets/aachen.py @@ -0,0 +1,146 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb +import numpy as np +from PIL import Image + +from .dataset import Dataset +from .pair_dataset import PairDataset, StillPairDataset + + +class AachenImages (Dataset): + """ Loads all images from the Aachen Day-Night dataset + """ + def __init__(self, select='db day night', root='data/aachen'): + Dataset.__init__(self) + self.root = root + self.img_dir = 'images_upright' + self.select = set(select.split()) + assert self.select, 'Nothing was selected' + + self.imgs = [] + root = os.path.join(root, self.img_dir) + for dirpath, _, filenames in os.walk(root): + r = dirpath[len(root)+1:] + if not(self.select & set(r.split('/'))): continue + self.imgs += [os.path.join(r,f) for f in filenames if f.endswith('.jpg')] + + self.nimg = len(self.imgs) + assert self.nimg, 'Empty Aachen dataset' + + def get_key(self, idx): + return self.imgs[idx] + + + +class AachenImages_DB (AachenImages): + """ Only database (db) images. + """ + def __init__(self, **kw): + AachenImages.__init__(self, select='db', **kw) + self.db_image_idxs = {self.get_tag(i) : i for i,f in enumerate(self.imgs)} + + def get_tag(self, idx): + # returns image tag == img number (name) + return os.path.split( self.imgs[idx][:-4] )[1] + + + +class AachenPairs_StyleTransferDayNight (AachenImages_DB, StillPairDataset): + """ synthetic day-night pairs of images + (night images obtained using autoamtic style transfer from web night images) + """ + def __init__(self, root='data/aachen/style_transfer', **kw): + StillPairDataset.__init__(self) + AachenImages_DB.__init__(self, **kw) + old_root = os.path.join(self.root, self.img_dir) + self.root = os.path.commonprefix((old_root, root)) + self.img_dir = '' + + newpath = lambda folder, f: os.path.join(folder, f)[len(self.root):] + self.imgs = [newpath(old_root, f) for f in self.imgs] + + self.image_pairs = [] + for fname in os.listdir(root): + tag = fname.split('.jpg.st_')[0] + self.image_pairs.append((self.db_image_idxs[tag], len(self.imgs))) + self.imgs.append(newpath(root, fname)) + + self.nimg = len(self.imgs) + self.npairs = len(self.image_pairs) + assert self.nimg and self.npairs + + + +class AachenPairs_OpticalFlow (AachenImages_DB, PairDataset): + """ Image pairs from Aachen db with optical flow. + """ + def __init__(self, root='data/aachen/optical_flow', **kw): + PairDataset.__init__(self) + AachenImages_DB.__init__(self, **kw) + self.root_flow = root + + # find out the subsest of valid pairs from the list of flow files + flows = {f for f in os.listdir(os.path.join(root, 'flow')) if f.endswith('.png')} + masks = {f for f in os.listdir(os.path.join(root, 'mask')) if f.endswith('.png')} + assert flows == masks, 'Missing flow or mask pairs' + + make_pair = lambda f: tuple(self.db_image_idxs[v] for v in f[:-4].split('_')) + self.image_pairs = [make_pair(f) for f in flows] + self.npairs = len(self.image_pairs) + assert self.nimg and self.npairs + + def get_mask_filename(self, pair_idx): + tag_a, tag_b = map(self.get_tag, self.image_pairs[pair_idx]) + return os.path.join(self.root_flow, 'mask', f'{tag_a}_{tag_b}.png') + + def get_mask(self, pair_idx): + return np.asarray(Image.open(self.get_mask_filename(pair_idx))) + + def get_flow_filename(self, pair_idx): + tag_a, tag_b = map(self.get_tag, self.image_pairs[pair_idx]) + return os.path.join(self.root_flow, 'flow', f'{tag_a}_{tag_b}.png') + + def get_flow(self, pair_idx): + fname = self.get_flow_filename(pair_idx) + try: + return self._png2flow(fname) + except IOError: + flow = open(fname[:-4], 'rb') + help = np.fromfile(flow, np.float32, 1) + assert help == 202021.25 + W, H = np.fromfile(flow, np.int32, 2) + flow = np.fromfile(flow, np.float32).reshape((H, W, 2)) + return self._flow2png(flow, fname) + + def get_pair(self, idx, output=()): + if isinstance(output, str): + output = output.split() + + img1, img2 = map(self.get_image, self.image_pairs[idx]) + meta = {} + + if 'flow' in output or 'aflow' in output: + flow = self.get_flow(idx) + assert flow.shape[:2] == img1.size[::-1] + meta['flow'] = flow + H, W = flow.shape[:2] + meta['aflow'] = flow + np.mgrid[:H,:W][::-1].transpose(1,2,0) + + if 'mask' in output: + mask = self.get_mask(idx) + assert mask.shape[:2] == img1.size[::-1] + meta['mask'] = mask + + return img1, img2, meta + + + + +if __name__ == '__main__': + print(aachen_db_images) + print(aachen_style_transfer_pairs) + print(aachen_flow_pairs) + pdb.set_trace() diff --git a/third_party/r2d2/datasets/dataset.py b/third_party/r2d2/datasets/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..80d893b8ea4ead7845f35c4fe82c9f5a9b849de3 --- /dev/null +++ b/third_party/r2d2/datasets/dataset.py @@ -0,0 +1,77 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os +import json +import pdb +import numpy as np + + +class Dataset(object): + ''' Base class for a dataset. To be overloaded. + ''' + root = '' + img_dir = '' + nimg = 0 + + def __len__(self): + return self.nimg + + def get_key(self, img_idx): + raise NotImplementedError() + + def get_filename(self, img_idx, root=None): + return os.path.join(root or self.root, self.img_dir, self.get_key(img_idx)) + + def get_image(self, img_idx): + from PIL import Image + fname = self.get_filename(img_idx) + try: + return Image.open(fname).convert('RGB') + except Exception as e: + raise IOError("Could not load image %s (reason: %s)" % (fname, str(e))) + + def __repr__(self): + res = 'Dataset: %s\n' % self.__class__.__name__ + res += ' %d images' % self.nimg + res += '\n root: %s...\n' % self.root + return res + + + +class CatDataset (Dataset): + ''' Concatenation of several datasets. + ''' + def __init__(self, *datasets): + assert len(datasets) >= 1 + self.datasets = datasets + offsets = [0] + for db in datasets: + offsets.append(db.nimg) + self.offsets = np.cumsum(offsets) + self.nimg = self.offsets[-1] + self.root = None + + def which(self, i): + pos = np.searchsorted(self.offsets, i, side='right')-1 + assert pos < self.nimg, 'Bad image index %d >= %d' % (i, self.nimg) + return pos, i - self.offsets[pos] + + def get_key(self, i): + b, i = self.which(i) + return self.datasets[b].get_key(i) + + def get_filename(self, i): + b, i = self.which(i) + return self.datasets[b].get_filename(i) + + def __repr__(self): + fmt_str = "CatDataset(" + for db in self.datasets: + fmt_str += str(db).replace("\n"," ") + ', ' + return fmt_str[:-2] + ')' + + + + diff --git a/third_party/r2d2/datasets/imgfolder.py b/third_party/r2d2/datasets/imgfolder.py new file mode 100644 index 0000000000000000000000000000000000000000..45f7bc9ee4c3ba5f04380dbc02ad17b6463cf32f --- /dev/null +++ b/third_party/r2d2/datasets/imgfolder.py @@ -0,0 +1,23 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb + +from .dataset import Dataset +from .pair_dataset import SyntheticPairDataset + + +class ImgFolder (Dataset): + """ load all images in a folder (no recursion). + """ + def __init__(self, root, imgs=None, exts=('.jpg','.png','.ppm')): + Dataset.__init__(self) + self.root = root + self.imgs = imgs or [f for f in os.listdir(root) if f.endswith(exts)] + self.nimg = len(self.imgs) + + def get_key(self, idx): + return self.imgs[idx] + + diff --git a/third_party/r2d2/datasets/pair_dataset.py b/third_party/r2d2/datasets/pair_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..aeed98b6700e0ba108bb44abccc20351d16f3295 --- /dev/null +++ b/third_party/r2d2/datasets/pair_dataset.py @@ -0,0 +1,287 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb +import numpy as np +from PIL import Image + +from .dataset import Dataset, CatDataset +from tools.transforms import instanciate_transformation +from tools.transforms_tools import persp_apply + + +class PairDataset (Dataset): + """ A dataset that serves image pairs with ground-truth pixel correspondences. + """ + def __init__(self): + Dataset.__init__(self) + self.npairs = 0 + + def get_filename(self, img_idx, root=None): + if is_pair(img_idx): # if img_idx is a pair of indices, we return a pair of filenames + return tuple(Dataset.get_filename(self, i, root) for i in img_idx) + return Dataset.get_filename(self, img_idx, root) + + def get_image(self, img_idx): + if is_pair(img_idx): # if img_idx is a pair of indices, we return a pair of images + return tuple(Dataset.get_image(self, i) for i in img_idx) + return Dataset.get_image(self, img_idx) + + def get_corres_filename(self, pair_idx): + raise NotImplementedError() + + def get_homography_filename(self, pair_idx): + raise NotImplementedError() + + def get_flow_filename(self, pair_idx): + raise NotImplementedError() + + def get_mask_filename(self, pair_idx): + raise NotImplementedError() + + def get_pair(self, idx, output=()): + """ returns (img1, img2, `metadata`) + + `metadata` is a dict() that can contain: + flow: optical flow + aflow: absolute flow + corres: list of 2d-2d correspondences + mask: boolean image of flow validity (in the first image) + ... + """ + raise NotImplementedError() + + def get_paired_images(self): + fns = set() + for i in range(self.npairs): + a,b = self.image_pairs[i] + fns.add(self.get_filename(a)) + fns.add(self.get_filename(b)) + return fns + + def __len__(self): + return self.npairs # size should correspond to the number of pairs, not images + + def __repr__(self): + res = 'Dataset: %s\n' % self.__class__.__name__ + res += ' %d images,' % self.nimg + res += ' %d image pairs' % self.npairs + res += '\n root: %s...\n' % self.root + return res + + @staticmethod + def _flow2png(flow, path): + flow = np.clip(np.around(16*flow), -2**15, 2**15-1) + bytes = np.int16(flow).view(np.uint8) + Image.fromarray(bytes).save(path) + return flow / 16 + + @staticmethod + def _png2flow(path): + try: + flow = np.asarray(Image.open(path)).view(np.int16) + return np.float32(flow) / 16 + except: + raise IOError("Error loading flow for %s" % path) + + + +class StillPairDataset (PairDataset): + """ A dataset of 'still' image pairs. + By overloading a normal image dataset, it appends the get_pair(i) function + that serves trivial image pairs (img1, img2) where img1 == img2 == get_image(i). + """ + def get_pair(self, pair_idx, output=()): + if isinstance(output, str): output = output.split() + img1, img2 = map(self.get_image, self.image_pairs[pair_idx]) + + W,H = img1.size + sx = img2.size[0] / float(W) + sy = img2.size[1] / float(H) + + meta = {} + if 'aflow' in output or 'flow' in output: + mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1,2,0).astype(np.float32) + meta['aflow'] = mgrid * (sx,sy) + meta['flow'] = meta['aflow'] - mgrid + + if 'mask' in output: + meta['mask'] = np.ones((H,W), np.uint8) + + if 'homography' in output: + meta['homography'] = np.diag(np.float32([sx, sy, 1])) + + return img1, img2, meta + + + +class SyntheticPairDataset (PairDataset): + """ A synthetic generator of image pairs. + Given a normal image dataset, it constructs pairs using random homographies & noise. + """ + def __init__(self, dataset, scale='', distort=''): + self.attach_dataset(dataset) + self.distort = instanciate_transformation(distort) + self.scale = instanciate_transformation(scale) + + def attach_dataset(self, dataset): + assert isinstance(dataset, Dataset) and not isinstance(dataset, PairDataset) + self.dataset = dataset + self.npairs = dataset.nimg + self.get_image = dataset.get_image + self.get_key = dataset.get_key + self.get_filename = dataset.get_filename + self.root = None + + def make_pair(self, img): + return img, img + + def get_pair(self, i, output=('aflow')): + """ Procedure: + This function applies a series of random transformations to one original image + to form a synthetic image pairs with perfect ground-truth. + """ + if isinstance(output, str): + output = output.split() + + original_img = self.dataset.get_image(i) + + scaled_image = self.scale(original_img) + scaled_image, scaled_image2 = self.make_pair(scaled_image) + scaled_and_distorted_image = self.distort( + dict(img=scaled_image2, persp=(1,0,0,0,1,0,0,0))) + W, H = scaled_image.size + trf = scaled_and_distorted_image['persp'] + + meta = dict() + if 'aflow' in output or 'flow' in output: + # compute optical flow + xy = np.mgrid[0:H,0:W][::-1].reshape(2,H*W).T + aflow = np.float32(persp_apply(trf, xy).reshape(H,W,2)) + meta['flow'] = aflow - xy.reshape(H,W,2) + meta['aflow'] = aflow + + if 'homography' in output: + meta['homography'] = np.float32(trf+(1,)).reshape(3,3) + + return scaled_image, scaled_and_distorted_image['img'], meta + + def __repr__(self): + res = 'Dataset: %s\n' % self.__class__.__name__ + res += ' %d images and pairs' % self.npairs + res += '\n root: %s...' % self.dataset.root + res += '\n Scale: %s' % (repr(self.scale).replace('\n','')) + res += '\n Distort: %s' % (repr(self.distort).replace('\n','')) + return res + '\n' + + + +class TransformedPairs (PairDataset): + """ Automatic data augmentation for pre-existing image pairs. + Given an image pair dataset, it generates synthetically jittered pairs + using random transformations (e.g. homographies & noise). + """ + def __init__(self, dataset, trf=''): + self.attach_dataset(dataset) + self.trf = instanciate_transformation(trf) + + def attach_dataset(self, dataset): + assert isinstance(dataset, PairDataset) + self.dataset = dataset + self.nimg = dataset.nimg + self.npairs = dataset.npairs + self.get_image = dataset.get_image + self.get_key = dataset.get_key + self.get_filename = dataset.get_filename + self.root = None + + def get_pair(self, i, output=''): + """ Procedure: + This function applies a series of random transformations to one original image + to form a synthetic image pairs with perfect ground-truth. + """ + img_a, img_b_, metadata = self.dataset.get_pair(i, output) + + img_b = self.trf({'img': img_b_, 'persp':(1,0,0,0,1,0,0,0)}) + trf = img_b['persp'] + + if 'aflow' in metadata or 'flow' in metadata: + aflow = metadata['aflow'] + aflow[:] = persp_apply(trf, aflow.reshape(-1,2)).reshape(aflow.shape) + W, H = img_a.size + flow = metadata['flow'] + mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1,2,0).astype(np.float32) + flow[:] = aflow - mgrid + + if 'corres' in metadata: + corres = metadata['corres'] + corres[:,1] = persp_apply(trf, corres[:,1]) + + if 'homography' in metadata: + # p_b = homography * p_a + trf_ = np.float32(trf+(1,)).reshape(3,3) + metadata['homography'] = np.float32(trf_ @ metadata['homography']) + + return img_a, img_b['img'], metadata + + def __repr__(self): + res = 'Transformed Pairs from %s\n' % type(self.dataset).__name__ + res += ' %d images and pairs' % self.npairs + res += '\n root: %s...' % self.dataset.root + res += '\n transform: %s' % (repr(self.trf).replace('\n','')) + return res + '\n' + + + +class CatPairDataset (CatDataset): + ''' Concatenation of several pair datasets. + ''' + def __init__(self, *datasets): + CatDataset.__init__(self, *datasets) + pair_offsets = [0] + for db in datasets: + pair_offsets.append(db.npairs) + self.pair_offsets = np.cumsum(pair_offsets) + self.npairs = self.pair_offsets[-1] + + def __len__(self): + return self.npairs + + def __repr__(self): + fmt_str = "CatPairDataset(" + for db in self.datasets: + fmt_str += str(db).replace("\n"," ") + ', ' + return fmt_str[:-2] + ')' + + def pair_which(self, i): + pos = np.searchsorted(self.pair_offsets, i, side='right')-1 + assert pos < self.npairs, 'Bad pair index %d >= %d' % (i, self.npairs) + return pos, i - self.pair_offsets[pos] + + def pair_call(self, func, i, *args, **kwargs): + b, j = self.pair_which(i) + return getattr(self.datasets[b], func)(j, *args, **kwargs) + + def get_pair(self, i, output=()): + b, i = self.pair_which(i) + return self.datasets[b].get_pair(i, output) + + def get_flow_filename(self, pair_idx, *args, **kwargs): + return self.pair_call('get_flow_filename', pair_idx, *args, **kwargs) + + def get_mask_filename(self, pair_idx, *args, **kwargs): + return self.pair_call('get_mask_filename', pair_idx, *args, **kwargs) + + def get_corres_filename(self, pair_idx, *args, **kwargs): + return self.pair_call('get_corres_filename', pair_idx, *args, **kwargs) + + + +def is_pair(x): + if isinstance(x, (tuple,list)) and len(x) == 2: + return True + if isinstance(x, np.ndarray) and x.ndim == 1 and x.shape[0] == 2: + return True + return False + diff --git a/third_party/r2d2/datasets/web_images.py b/third_party/r2d2/datasets/web_images.py new file mode 100644 index 0000000000000000000000000000000000000000..7c17fbe956f3b4db25d9a4148e8f7c615f122478 --- /dev/null +++ b/third_party/r2d2/datasets/web_images.py @@ -0,0 +1,64 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb +from tqdm import trange + +from .dataset import Dataset + + +class RandomWebImages (Dataset): + """ 1 million distractors from Oxford and Paris Revisited + see http://ptak.felk.cvut.cz/revisitop/revisitop1m/ + """ + def __init__(self, start=0, end=1024, root="data/revisitop1m"): + Dataset.__init__(self) + self.root = root + + bar = None + self.imgs = [] + for i in range(start, end): + try: + # read cached list + img_list_path = os.path.join(self.root, "image_list_%d.txt"%i) + cached_imgs = [e.strip() for e in open(img_list_path)] + assert cached_imgs, f"Cache '{img_list_path}' is empty!" + self.imgs += cached_imgs + + except IOError: + if bar is None: + bar = trange(start, 4*end, desc='Caching') + bar.update(4*i) + + # create it + imgs = [] + for d in range(i*4,(i+1)*4): # 4096 folders in total, on average 256 each + key = hex(d)[2:].zfill(3) + folder = os.path.join(self.root, key) + if not os.path.isdir(folder): continue + imgs += [f for f in os.listdir(folder) if verify_img(folder,f)] + bar.update(1) + assert imgs, f"No images found in {folder}/" + open(img_list_path,'w').write('\n'.join(imgs)) + self.imgs += imgs + + if bar: bar.update(bar.total - bar.n) + self.nimg = len(self.imgs) + + def get_key(self, i): + key = self.imgs[i] + return os.path.join(key[:3], key) + + +def verify_img(folder, f): + path = os.path.join(folder, f) + if not f.endswith('.jpg'): return False + try: + from PIL import Image + Image.open(path).convert('RGB') # try to open it + return True + except: + return False + + diff --git a/third_party/r2d2/download_training_data.sh b/third_party/r2d2/download_training_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..8257c83ef70eeab47b6b344d591ddef86ba848cd --- /dev/null +++ b/third_party/r2d2/download_training_data.sh @@ -0,0 +1,69 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +CODE_ROOT=`pwd` +if [ ! -e data ]; then + echo "Error: missing data/ folder" + echo "First, create a folder that can host (at least) 15 GB of data." + echo "Then, create a soft-link named 'data' that points to it." + exit -1 +fi + +# download web images from the revisitop1m dataset +WEB_ROOT=data/revisitop1m +mkdir -p $WEB_ROOT +cd $WEB_ROOT +if [ ! -e 0d3 ]; then + for i in {1..5}; do + echo "Installing the web images dataset ($i/5)..." + if [ ! -f revisitop1m.$i.tar.gz ]; then + wget http://ptak.felk.cvut.cz/revisitop/revisitop1m/jpg/revisitop1m.$i.tar.gz + fi + tar -xzvf revisitop1m.$i.tar.gz + rm -f revisitop1m.$i.tar.gz + done +fi +cd $CODE_ROOT + +# download aachen images +AACHEN_ROOT=data/aachen +mkdir -p $AACHEN_ROOT +cd $AACHEN_ROOT +if [ ! -e "images_upright" ]; then + echo "Installing the Aachen dataset..." + fname=database_and_query_images.zip + if [ ! -f $fname ]; then + echo "File not found: $fname" + exit -1 + else + unzip $fname + rm -f $fname + fi +fi + +# download style transfer images +if [ ! -e "style_transfer" ]; then + echo "Installing the Aachen style-transfer dataset..." + fname=aachen_style_transfer.zip + if [ ! -f $fname ]; then + wget http://download.europe.naverlabs.com/3DVision/aachen_style_transfer.zip $fname + fi + unzip $fname + rm -f $fname +fi + +# download optical flow pairs +if [ ! -e "optical_flow" ]; then + echo "Installing the Aachen optical flow dataset..." + fname=aachen_optical_flow.zip + if [ ! -f $fname ]; then + wget http://download.europe.naverlabs.com/3DVision/aachen_optical_flow.zip $fname + fi + unzip $fname + rm -f $fname +fi +cd $CODE_ROOT + +echo "Done!" + diff --git a/third_party/r2d2/extract.py b/third_party/r2d2/extract.py new file mode 100644 index 0000000000000000000000000000000000000000..c3fea02f87c0615504e3648bfd590e413ab13898 --- /dev/null +++ b/third_party/r2d2/extract.py @@ -0,0 +1,183 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + + +import os, pdb +from PIL import Image +import numpy as np +import torch + +from tools import common +from tools.dataloader import norm_RGB +from nets.patchnet import * + + +def load_network(model_fn): + checkpoint = torch.load(model_fn) + print("\n>> Creating net = " + checkpoint['net']) + net = eval(checkpoint['net']) + nb_of_weights = common.model_size(net) + print(f" ( Model size: {nb_of_weights/1000:.0f}K parameters )") + + # initialization + weights = checkpoint['state_dict'] + net.load_state_dict({k.replace('module.',''):v for k,v in weights.items()}) + return net.eval() + + +class NonMaxSuppression (torch.nn.Module): + def __init__(self, rel_thr=0.7, rep_thr=0.7): + nn.Module.__init__(self) + self.max_filter = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1) + self.rel_thr = rel_thr + self.rep_thr = rep_thr + + def forward(self, reliability, repeatability, **kw): + assert len(reliability) == len(repeatability) == 1 + reliability, repeatability = reliability[0], repeatability[0] + + # local maxima + maxima = (repeatability == self.max_filter(repeatability)) + + # remove low peaks + maxima *= (repeatability >= self.rep_thr) + maxima *= (reliability >= self.rel_thr) + + return maxima.nonzero().t()[2:4] + + +def extract_multiscale( net, img, detector, scale_f=2**0.25, + min_scale=0.0, max_scale=1, + min_size=256, max_size=1024, + verbose=False): + old_bm = torch.backends.cudnn.benchmark + torch.backends.cudnn.benchmark = False # speedup + + # extract keypoints at multiple scales + B, three, H, W = img.shape + assert B == 1 and three == 3, "should be a batch with a single RGB image" + + assert max_scale <= 1 + s = 1.0 # current scale factor + + X,Y,S,C,Q,D = [],[],[],[],[],[] + while s+0.001 >= max(min_scale, min_size / max(H,W)): + if s-0.001 <= min(max_scale, max_size / max(H,W)): + nh, nw = img.shape[2:] + if verbose: print(f"extracting at scale x{s:.02f} = {nw:4d}x{nh:3d}") + # extract descriptors + with torch.no_grad(): + res = net(imgs=[img]) + + # get output and reliability map + descriptors = res['descriptors'][0] + reliability = res['reliability'][0] + repeatability = res['repeatability'][0] + + # normalize the reliability for nms + # extract maxima and descs + y,x = detector(**res) # nms + c = reliability[0,0,y,x] + q = repeatability[0,0,y,x] + d = descriptors[0,:,y,x].t() + n = d.shape[0] + + # accumulate multiple scales + X.append(x.float() * W/nw) + Y.append(y.float() * H/nh) + S.append((32/s) * torch.ones(n, dtype=torch.float32, device=d.device)) + C.append(c) + Q.append(q) + D.append(d) + s /= scale_f + + # down-scale the image for next iteration + nh, nw = round(H*s), round(W*s) + img = F.interpolate(img, (nh,nw), mode='bilinear', align_corners=False) + + # restore value + torch.backends.cudnn.benchmark = old_bm + + Y = torch.cat(Y) + X = torch.cat(X) + S = torch.cat(S) # scale + scores = torch.cat(C) * torch.cat(Q) # scores = reliability * repeatability + XYS = torch.stack([X,Y,S], dim=-1) + D = torch.cat(D) + return XYS, D, scores + + +def extract_keypoints(args): + iscuda = common.torch_set_gpu(args.gpu) + + # load the network... + net = load_network(args.model) + if iscuda: net = net.cuda() + + # create the non-maxima detector + detector = NonMaxSuppression( + rel_thr = args.reliability_thr, + rep_thr = args.repeatability_thr) + + while args.images: + img_path = args.images.pop(0) + + if img_path.endswith('.txt'): + args.images = open(img_path).read().splitlines() + args.images + continue + + print(f"\nExtracting features for {img_path}") + img = Image.open(img_path).convert('RGB') + W, H = img.size + img = norm_RGB(img)[None] + if iscuda: img = img.cuda() + + # extract keypoints/descriptors for a single image + xys, desc, scores = extract_multiscale(net, img, detector, + scale_f = args.scale_f, + min_scale = args.min_scale, + max_scale = args.max_scale, + min_size = args.min_size, + max_size = args.max_size, + verbose = True) + + xys = xys.cpu().numpy() + desc = desc.cpu().numpy() + scores = scores.cpu().numpy() + idxs = scores.argsort()[-args.top_k or None:] + + outpath = img_path + '.' + args.tag + print(f"Saving {len(idxs)} keypoints to {outpath}") + np.savez(open(outpath,'wb'), + imsize = (W,H), + keypoints = xys[idxs], + descriptors = desc[idxs], + scores = scores[idxs]) + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser("Extract keypoints for a given image") + parser.add_argument("--model", type=str, required=True, help='model path') + + parser.add_argument("--images", type=str, required=True, nargs='+', help='images / list') + parser.add_argument("--tag", type=str, default='r2d2', help='output file tag') + + parser.add_argument("--top-k", type=int, default=5000, help='number of keypoints') + + parser.add_argument("--scale-f", type=float, default=2**0.25) + parser.add_argument("--min-size", type=int, default=256) + parser.add_argument("--max-size", type=int, default=1024) + parser.add_argument("--min-scale", type=float, default=0) + parser.add_argument("--max-scale", type=float, default=1) + + parser.add_argument("--reliability-thr", type=float, default=0.7) + parser.add_argument("--repeatability-thr", type=float, default=0.7) + + parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='use -1 for CPU') + args = parser.parse_args() + + extract_keypoints(args) + diff --git a/third_party/r2d2/extract_kapture.py b/third_party/r2d2/extract_kapture.py new file mode 100644 index 0000000000000000000000000000000000000000..51b2403b8a1730eaee32d099d0b6dd5d091ccdda --- /dev/null +++ b/third_party/r2d2/extract_kapture.py @@ -0,0 +1,194 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + + +from PIL import Image + +from tools import common +from tools.dataloader import norm_RGB +from nets.patchnet import * +from os import path + +from extract import load_network, NonMaxSuppression, extract_multiscale + +# Kapture is a pivot file format, based on text and binary files, used to describe SfM (Structure From Motion) +# and more generally sensor-acquired data +# it can be installed with +# pip install kapture +# for more information check out https://github.com/naver/kapture +import kapture +from kapture.io.records import get_image_fullpath +from kapture.io.csv import kapture_from_dir +from kapture.io.csv import get_feature_csv_fullpath, keypoints_to_file, descriptors_to_file +from kapture.io.features import get_keypoints_fullpath, keypoints_check_dir, image_keypoints_to_file +from kapture.io.features import get_descriptors_fullpath, descriptors_check_dir, image_descriptors_to_file +from kapture.io.csv import get_all_tar_handlers + + +def extract_kapture_keypoints(args): + """ + Extract r2d2 keypoints and descritors to the kapture format directly + """ + print('extract_kapture_keypoints...') + with get_all_tar_handlers(args.kapture_root, + mode={kapture.Keypoints: 'a', + kapture.Descriptors: 'a', + kapture.GlobalFeatures: 'r', + kapture.Matches: 'r'}) as tar_handlers: + kdata = kapture_from_dir(args.kapture_root, None, + skip_list=[kapture.GlobalFeatures, + kapture.Matches, + kapture.Points3d, + kapture.Observations], + tar_handlers=tar_handlers) + + assert kdata.records_camera is not None + image_list = [filename for _, _, filename in kapture.flatten(kdata.records_camera)] + if args.keypoints_type is None: + args.keypoints_type = path.splitext(path.basename(args.model))[0] + print(f'keypoints_type set to {args.keypoints_type}') + if args.descriptors_type is None: + args.descriptors_type = path.splitext(path.basename(args.model))[0] + print(f'descriptors_type set to {args.descriptors_type}') + + if kdata.keypoints is not None and args.keypoints_type in kdata.keypoints \ + and kdata.descriptors is not None and args.descriptors_type in kdata.descriptors: + print('detected already computed features of same keypoints_type/descriptors_type, resuming extraction...') + image_list = [name + for name in image_list + if name not in kdata.keypoints[args.keypoints_type] or + name not in kdata.descriptors[args.descriptors_type]] + + if len(image_list) == 0: + print('All features were already extracted') + return + else: + print(f'Extracting r2d2 features for {len(image_list)} images') + + iscuda = common.torch_set_gpu(args.gpu) + + # load the network... + net = load_network(args.model) + if iscuda: + net = net.cuda() + + # create the non-maxima detector + detector = NonMaxSuppression( + rel_thr=args.reliability_thr, + rep_thr=args.repeatability_thr) + + if kdata.keypoints is None: + kdata.keypoints = {} + if kdata.descriptors is None: + kdata.descriptors = {} + + if args.keypoints_type not in kdata.keypoints: + keypoints_dtype = None + keypoints_dsize = None + else: + keypoints_dtype = kdata.keypoints[args.keypoints_type].dtype + keypoints_dsize = kdata.keypoints[args.keypoints_type].dsize + if args.descriptors_type not in kdata.descriptors: + descriptors_dtype = None + descriptors_dsize = None + else: + descriptors_dtype = kdata.descriptors[args.descriptors_type].dtype + descriptors_dsize = kdata.descriptors[args.descriptors_type].dsize + + for image_name in image_list: + img_path = get_image_fullpath(args.kapture_root, image_name) + print(f"\nExtracting features for {img_path}") + img = Image.open(img_path).convert('RGB') + W, H = img.size + img = norm_RGB(img)[None] + if iscuda: + img = img.cuda() + + # extract keypoints/descriptors for a single image + xys, desc, scores = extract_multiscale(net, img, detector, + scale_f=args.scale_f, + min_scale=args.min_scale, + max_scale=args.max_scale, + min_size=args.min_size, + max_size=args.max_size, + verbose=True) + + xys = xys.cpu().numpy() + desc = desc.cpu().numpy() + scores = scores.cpu().numpy() + idxs = scores.argsort()[-args.top_k or None:] + + xys = xys[idxs] + desc = desc[idxs] + if keypoints_dtype is None or descriptors_dtype is None: + keypoints_dtype = xys.dtype + descriptors_dtype = desc.dtype + + keypoints_dsize = xys.shape[1] + descriptors_dsize = desc.shape[1] + + kdata.keypoints[args.keypoints_type] = kapture.Keypoints('r2d2', keypoints_dtype, keypoints_dsize) + kdata.descriptors[args.descriptors_type] = kapture.Descriptors('r2d2', descriptors_dtype, + descriptors_dsize, + args.keypoints_type, 'L2') + keypoints_config_absolute_path = get_feature_csv_fullpath(kapture.Keypoints, + args.keypoints_type, + args.kapture_root) + descriptors_config_absolute_path = get_feature_csv_fullpath(kapture.Descriptors, + args.descriptors_type, + args.kapture_root) + keypoints_to_file(keypoints_config_absolute_path, kdata.keypoints[args.keypoints_type]) + descriptors_to_file(descriptors_config_absolute_path, kdata.descriptors[args.descriptors_type]) + else: + assert kdata.keypoints[args.keypoints_type].dtype == xys.dtype + assert kdata.descriptors[args.descriptors_type].dtype == desc.dtype + assert kdata.keypoints[args.keypoints_type].dsize == xys.shape[1] + assert kdata.descriptors[args.descriptors_type].dsize == desc.shape[1] + assert kdata.descriptors[args.descriptors_type].keypoints_type == args.keypoints_type + assert kdata.descriptors[args.descriptors_type].metric_type == 'L2' + + keypoints_fullpath = get_keypoints_fullpath(args.keypoints_type, args.kapture_root, + image_name, tar_handlers) + print(f"Saving {xys.shape[0]} keypoints to {keypoints_fullpath}") + image_keypoints_to_file(keypoints_fullpath, xys) + kdata.keypoints[args.keypoints_type].add(image_name) + + descriptors_fullpath = get_descriptors_fullpath(args.descriptors_type, args.kapture_root, + image_name, tar_handlers) + print(f"Saving {desc.shape[0]} descriptors to {descriptors_fullpath}") + image_descriptors_to_file(descriptors_fullpath, desc) + kdata.descriptors[args.descriptors_type].add(image_name) + + if not keypoints_check_dir(kdata.keypoints[args.keypoints_type], args.keypoints_type, + args.kapture_root, tar_handlers) or \ + not descriptors_check_dir(kdata.descriptors[args.descriptors_type], args.descriptors_type, + args.kapture_root, tar_handlers): + print('local feature extraction ended successfully but not all files were saved') + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser( + "Extract r2d2 local features for all images in a dataset stored in the kapture format") + parser.add_argument("--model", type=str, required=True, help='model path') + parser.add_argument('--keypoints-type', default=None, help='keypoint type_name, default is filename of model') + parser.add_argument('--descriptors-type', default=None, help='descriptors type_name, default is filename of model') + + parser.add_argument("--kapture-root", type=str, required=True, help='path to kapture root directory') + + parser.add_argument("--top-k", type=int, default=5000, help='number of keypoints') + + parser.add_argument("--scale-f", type=float, default=2**0.25) + parser.add_argument("--min-size", type=int, default=256) + parser.add_argument("--max-size", type=int, default=1024) + parser.add_argument("--min-scale", type=float, default=0) + parser.add_argument("--max-scale", type=float, default=1) + + parser.add_argument("--reliability-thr", type=float, default=0.7) + parser.add_argument("--repeatability-thr", type=float, default=0.7) + + parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='use -1 for CPU') + args = parser.parse_args() + + extract_kapture_keypoints(args) diff --git a/third_party/r2d2/imgs/boat.png b/third_party/r2d2/imgs/boat.png new file mode 100644 index 0000000000000000000000000000000000000000..32870e4896c4dafced779ee47fc98f51f51a48b2 --- /dev/null +++ b/third_party/r2d2/imgs/boat.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18bea4de1634456f5791d16301863fc974401d144cd6afb86f09a6be4620fe54 +size 177762 diff --git a/third_party/r2d2/imgs/brooklyn.png b/third_party/r2d2/imgs/brooklyn.png new file mode 100644 index 0000000000000000000000000000000000000000..7aa7982e77046d67a16eb139e80efb6d5ab63246 --- /dev/null +++ b/third_party/r2d2/imgs/brooklyn.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/third_party/r2d2/models/r2d2_WAF_N16.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27cebd6608317b35198a76f60f87492110dee3e88ca382586a729dadb1a16b90 +size 1950677 diff --git a/third_party/r2d2/models/r2d2_WASF_N16.pt b/third_party/r2d2/models/r2d2_WASF_N16.pt new file mode 100644 index 0000000000000000000000000000000000000000..9e53cfec3f07b222d41ded5a6bf11f2479fbbd47 --- /dev/null +++ b/third_party/r2d2/models/r2d2_WASF_N16.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ae90e02a9a133d100ca7aeaa32f4d4d7736a6dd222a530a25c8f7da5e508528 +size 1950677 diff --git a/third_party/r2d2/models/r2d2_WASF_N8_big.pt b/third_party/r2d2/models/r2d2_WASF_N8_big.pt new file mode 100644 index 0000000000000000000000000000000000000000..f3c8c9de3647051c675e1205f52d17a6bb301e07 --- /dev/null +++ b/third_party/r2d2/models/r2d2_WASF_N8_big.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:597dc13998e211e827c550bdc8f76dbb4aca32747846f96962c9168586cec418 +size 4171550 diff --git a/third_party/r2d2/nets/ap_loss.py b/third_party/r2d2/nets/ap_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..251815cd97009a5feb6a815c20caca0c40daaccd --- /dev/null +++ b/third_party/r2d2/nets/ap_loss.py @@ -0,0 +1,67 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import numpy as np +import torch +import torch.nn as nn + + +class APLoss (nn.Module): + """ differentiable AP loss, through quantization. + + Input: (N, M) values in [min, max] + label: (N, M) values in {0, 1} + + Returns: list of query AP (for each n in {1..N}) + Note: typically, you want to minimize 1 - mean(AP) + """ + def __init__(self, nq=25, min=0, max=1, euc=False): + nn.Module.__init__(self) + assert isinstance(nq, int) and 2 <= nq <= 100 + self.nq = nq + self.min = min + self.max = max + self.euc = euc + gap = max - min + assert gap > 0 + + # init quantizer = non-learnable (fixed) convolution + self.quantizer = q = nn.Conv1d(1, 2*nq, kernel_size=1, bias=True) + a = (nq-1) / gap + #1st half = lines passing to (min+x,1) and (min+x+1/a,0) with x = {nq-1..0}*gap/(nq-1) + q.weight.data[:nq] = -a + q.bias.data[:nq] = torch.from_numpy(a*min + np.arange(nq, 0, -1)) # b = 1 + a*(min+x) + #2nd half = lines passing to (min+x,1) and (min+x-1/a,0) with x = {nq-1..0}*gap/(nq-1) + q.weight.data[nq:] = a + q.bias.data[nq:] = torch.from_numpy(np.arange(2-nq, 2, 1) - a*min) # b = 1 - a*(min+x) + # first and last one are special: just horizontal straight line + q.weight.data[0] = q.weight.data[-1] = 0 + q.bias.data[0] = q.bias.data[-1] = 1 + + def compute_AP(self, x, label): + N, M = x.shape + if self.euc: # euclidean distance in same range than similarities + x = 1 - torch.sqrt(2.001 - 2*x) + + # quantize all predictions + q = self.quantizer(x.unsqueeze(1)) + q = torch.min(q[:,:self.nq], q[:,self.nq:]).clamp(min=0) # N x Q x M + + nbs = q.sum(dim=-1) # number of samples N x Q = c + rec = (q * label.view(N,1,M).float()).sum(dim=-1) # nb of correct samples = c+ N x Q + prec = rec.cumsum(dim=-1) / (1e-16 + nbs.cumsum(dim=-1)) # precision + rec /= rec.sum(dim=-1).unsqueeze(1) # norm in [0,1] + + ap = (prec * rec).sum(dim=-1) # per-image AP + return ap + + def forward(self, x, label): + assert x.shape == label.shape # N x M + return self.compute_AP(x, label) + + + + + diff --git a/third_party/r2d2/nets/losses.py b/third_party/r2d2/nets/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..f8eea8f6e82835e22d2bb445125f7dc722db85b2 --- /dev/null +++ b/third_party/r2d2/nets/losses.py @@ -0,0 +1,56 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from nets.sampler import * +from nets.repeatability_loss import * +from nets.reliability_loss import * + + +class MultiLoss (nn.Module): + """ Combines several loss functions for convenience. + *args: [loss weight (float), loss creator, ... ] + + Example: + loss = MultiLoss( 1, MyFirstLoss(), 0.5, MySecondLoss() ) + """ + def __init__(self, *args, dbg=()): + nn.Module.__init__(self) + assert len(args) % 2 == 0, 'args must be a list of (float, loss)' + self.weights = [] + self.losses = nn.ModuleList() + for i in range(len(args)//2): + weight = float(args[2*i+0]) + loss = args[2*i+1] + assert isinstance(loss, nn.Module), "%s is not a loss!" % loss + self.weights.append(weight) + self.losses.append(loss) + + def forward(self, select=None, **variables): + assert not select or all(1<=n<=len(self.losses) for n in select) + d = dict() + cum_loss = 0 + for num, (weight, loss_func) in enumerate(zip(self.weights, self.losses),1): + if select is not None and num not in select: continue + l = loss_func(**{k:v for k,v in variables.items()}) + if isinstance(l, tuple): + assert len(l) == 2 and isinstance(l[1], dict) + else: + l = l, {loss_func.name:l} + cum_loss = cum_loss + weight * l[0] + for key,val in l[1].items(): + d['loss_'+key] = float(val) + d['loss'] = float(cum_loss) + return cum_loss, d + + + + + + diff --git a/third_party/r2d2/nets/patchnet.py b/third_party/r2d2/nets/patchnet.py new file mode 100644 index 0000000000000000000000000000000000000000..854c61ecf9b879fa7f420255296c4fbbfd665181 --- /dev/null +++ b/third_party/r2d2/nets/patchnet.py @@ -0,0 +1,186 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BaseNet (nn.Module): + """ Takes a list of images as input, and returns for each image: + - a pixelwise descriptor + - a pixelwise confidence + """ + def softmax(self, ux): + if ux.shape[1] == 1: + x = F.softplus(ux) + return x / (1 + x) # for sure in [0,1], much less plateaus than softmax + elif ux.shape[1] == 2: + return F.softmax(ux, dim=1)[:,1:2] + + def normalize(self, x, ureliability, urepeatability): + return dict(descriptors = F.normalize(x, p=2, dim=1), + repeatability = self.softmax( urepeatability ), + reliability = self.softmax( ureliability )) + + def forward_one(self, x): + raise NotImplementedError() + + def forward(self, imgs, **kw): + res = [self.forward_one(img) for img in imgs] + # merge all dictionaries into one + res = {k:[r[k] for r in res if k in r] for k in {k for r in res for k in r}} + return dict(res, imgs=imgs, **kw) + + + +class PatchNet (BaseNet): + """ Helper class to construct a fully-convolutional network that + extract a l2-normalized patch descriptor. + """ + def __init__(self, inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False): + BaseNet.__init__(self) + self.inchan = inchan + self.curchan = inchan + self.dilated = dilated + self.dilation = dilation + self.bn = bn + self.bn_affine = bn_affine + self.ops = nn.ModuleList([]) + + def _make_bn(self, outd): + return nn.BatchNorm2d(outd, affine=self.bn_affine) + + def _add_conv(self, outd, k=3, stride=1, dilation=1, bn=True, relu=True, k_pool = 1, pool_type='max'): + # as in the original implementation, dilation is applied at the end of layer, so it will have impact only from next layer + d = self.dilation * dilation + if self.dilated: + conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=1) + self.dilation *= stride + else: + conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride) + self.ops.append( nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params) ) + if bn and self.bn: self.ops.append( self._make_bn(outd) ) + if relu: self.ops.append( nn.ReLU(inplace=True) ) + self.curchan = outd + + if k_pool > 1: + if pool_type == 'avg': + self.ops.append(torch.nn.AvgPool2d(kernel_size=k_pool)) + elif pool_type == 'max': + self.ops.append(torch.nn.MaxPool2d(kernel_size=k_pool)) + else: + print(f"Error, unknown pooling type {pool_type}...") + + def forward_one(self, x): + assert self.ops, "You need to add convolutions first" + for n,op in enumerate(self.ops): + x = op(x) + return self.normalize(x) + + +class L2_Net (PatchNet): + """ Compute a 128D descriptor for all overlapping 32x32 patches. + From the L2Net paper (CVPR'17). + """ + def __init__(self, dim=128, **kw ): + PatchNet.__init__(self, **kw) + add_conv = lambda n,**kw: self._add_conv((n*dim)//128,**kw) + add_conv(32) + add_conv(32) + add_conv(64, stride=2) + add_conv(64) + add_conv(128, stride=2) + add_conv(128) + add_conv(128, k=7, stride=8, bn=False, relu=False) + self.out_dim = dim + + +class Quad_L2Net (PatchNet): + """ Same than L2_Net, but replace the final 8x8 conv by 3 successive 2x2 convs. + """ + def __init__(self, dim=128, mchan=4, relu22=False, **kw ): + PatchNet.__init__(self, **kw) + self._add_conv( 8*mchan) + self._add_conv( 8*mchan) + self._add_conv( 16*mchan, stride=2) + self._add_conv( 16*mchan) + self._add_conv( 32*mchan, stride=2) + self._add_conv( 32*mchan) + # replace last 8x8 convolution with 3 2x2 convolutions + self._add_conv( 32*mchan, k=2, stride=2, relu=relu22) + self._add_conv( 32*mchan, k=2, stride=2, relu=relu22) + self._add_conv(dim, k=2, stride=2, bn=False, relu=False) + self.out_dim = dim + + + +class Quad_L2Net_ConfCFS (Quad_L2Net): + """ Same than Quad_L2Net, with 2 confidence maps for repeatability and reliability. + """ + def __init__(self, **kw ): + Quad_L2Net.__init__(self, **kw) + # reliability classifier + self.clf = nn.Conv2d(self.out_dim, 2, kernel_size=1) + # repeatability classifier: for some reasons it's a softplus, not a softmax! + # Why? I guess it's a mistake that was left unnoticed in the code for a long time... + self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1) + + def forward_one(self, x): + assert self.ops, "You need to add convolutions first" + for op in self.ops: + x = op(x) + # compute the confidence maps + ureliability = self.clf(x**2) + urepeatability = self.sal(x**2) + return self.normalize(x, ureliability, urepeatability) + + +class Fast_Quad_L2Net (PatchNet): + """ Faster version of Quad l2 net, replacing one dilated conv with one pooling to diminish image resolution thus increase inference time + Dilation factors and pooling: + 1,1,1, pool2, 1,1, 2,2, 4, 8, upsample2 + """ + def __init__(self, dim=128, mchan=4, relu22=False, downsample_factor=2, **kw ): + + PatchNet.__init__(self, **kw) + self._add_conv( 8*mchan) + self._add_conv( 8*mchan) + self._add_conv( 16*mchan, k_pool = downsample_factor) # added avg pooling to decrease img resolution + self._add_conv( 16*mchan) + self._add_conv( 32*mchan, stride=2) + self._add_conv( 32*mchan) + + # replace last 8x8 convolution with 3 2x2 convolutions + self._add_conv( 32*mchan, k=2, stride=2, relu=relu22) + self._add_conv( 32*mchan, k=2, stride=2, relu=relu22) + self._add_conv(dim, k=2, stride=2, bn=False, relu=False) + + # Go back to initial image resolution with upsampling + self.ops.append(torch.nn.Upsample(scale_factor=downsample_factor, mode='bilinear', align_corners=False)) + + self.out_dim = dim + + +class Fast_Quad_L2Net_ConfCFS (Fast_Quad_L2Net): + """ Fast r2d2 architecture + """ + def __init__(self, **kw ): + Fast_Quad_L2Net.__init__(self, **kw) + # reliability classifier + self.clf = nn.Conv2d(self.out_dim, 2, kernel_size=1) + + # repeatability classifier: for some reasons it's a softplus, not a softmax! + # Why? I guess it's a mistake that was left unnoticed in the code for a long time... + self.sal = nn.Conv2d(self.out_dim, 1, kernel_size=1) + + def forward_one(self, x): + assert self.ops, "You need to add convolutions first" + for op in self.ops: + x = op(x) + # compute the confidence maps + ureliability = self.clf(x**2) + urepeatability = self.sal(x**2) + return self.normalize(x, ureliability, urepeatability) \ No newline at end of file diff --git a/third_party/r2d2/nets/reliability_loss.py b/third_party/r2d2/nets/reliability_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..52d5383b0eaa52bcf2111eabb4b45e39b63b976f --- /dev/null +++ b/third_party/r2d2/nets/reliability_loss.py @@ -0,0 +1,59 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import torch.nn as nn +import torch.nn.functional as F + +from nets.ap_loss import APLoss + + +class PixelAPLoss (nn.Module): + """ Computes the pixel-wise AP loss: + Given two images and ground-truth optical flow, computes the AP per pixel. + + feat1: (B, C, H, W) pixel-wise features extracted from img1 + feat2: (B, C, H, W) pixel-wise features extracted from img2 + aflow: (B, 2, H, W) absolute flow: aflow[...,y1,x1] = x2,y2 + """ + def __init__(self, sampler, nq=20): + nn.Module.__init__(self) + self.aploss = APLoss(nq, min=0, max=1, euc=False) + self.name = 'pixAP' + self.sampler = sampler + + def loss_from_ap(self, ap, rel): + return 1 - ap + + def forward(self, descriptors, aflow, **kw): + # subsample things + scores, gt, msk, qconf = self.sampler(descriptors, kw.get('reliability'), aflow) + + # compute pixel-wise AP + n = qconf.numel() + if n == 0: return 0 + scores, gt = scores.view(n,-1), gt.view(n,-1) + ap = self.aploss(scores, gt).view(msk.shape) + + pixel_loss = self.loss_from_ap(ap, qconf) + + loss = pixel_loss[msk].mean() + return loss + + +class ReliabilityLoss (PixelAPLoss): + """ same than PixelAPLoss, but also train a pixel-wise confidence + that this pixel is going to have a good AP. + """ + def __init__(self, sampler, base=0.5, **kw): + PixelAPLoss.__init__(self, sampler, **kw) + assert 0 <= base < 1 + self.base = base + self.name = 'reliability' + + def loss_from_ap(self, ap, rel): + return 1 - ap*rel - (1-rel)*self.base + + + diff --git a/third_party/r2d2/nets/repeatability_loss.py b/third_party/r2d2/nets/repeatability_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5cda0b6d036f98af88a88780fe39da0c5c0b610e --- /dev/null +++ b/third_party/r2d2/nets/repeatability_loss.py @@ -0,0 +1,66 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from nets.sampler import FullSampler + +class CosimLoss (nn.Module): + """ Try to make the repeatability repeatable from one image to the other. + """ + def __init__(self, N=16): + nn.Module.__init__(self) + self.name = f'cosim{N}' + self.patches = nn.Unfold(N, padding=0, stride=N//2) + + def extract_patches(self, sal): + patches = self.patches(sal).transpose(1,2) # flatten + patches = F.normalize(patches, p=2, dim=2) # norm + return patches + + def forward(self, repeatability, aflow, **kw): + B,two,H,W = aflow.shape + assert two == 2 + + # normalize + sali1, sali2 = repeatability + grid = FullSampler._aflow_to_grid(aflow) + sali2 = F.grid_sample(sali2, grid, mode='bilinear', padding_mode='border') + + patches1 = self.extract_patches(sali1) + patches2 = self.extract_patches(sali2) + cosim = (patches1 * patches2).sum(dim=2) + return 1 - cosim.mean() + + +class PeakyLoss (nn.Module): + """ Try to make the repeatability locally peaky. + + Mechanism: we maximize, for each pixel, the difference between the local mean + and the local max. + """ + def __init__(self, N=16): + nn.Module.__init__(self) + self.name = f'peaky{N}' + assert N % 2 == 0, 'N must be pair' + self.preproc = nn.AvgPool2d(3, stride=1, padding=1) + self.maxpool = nn.MaxPool2d(N+1, stride=1, padding=N//2) + self.avgpool = nn.AvgPool2d(N+1, stride=1, padding=N//2) + + def forward_one(self, sali): + sali = self.preproc(sali) # remove super high frequency + return 1 - (self.maxpool(sali) - self.avgpool(sali)).mean() + + def forward(self, repeatability, **kw): + sali1, sali2 = repeatability + return (self.forward_one(sali1) + self.forward_one(sali2)) /2 + + + + + diff --git a/third_party/r2d2/nets/sampler.py b/third_party/r2d2/nets/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..9fede70d3a04d7f31a1d414eace0aaf3729e8235 --- /dev/null +++ b/third_party/r2d2/nets/sampler.py @@ -0,0 +1,390 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +""" Different samplers, each specifying how to sample pixels for the AP loss. +""" + + +class FullSampler(nn.Module): + """ all pixels are selected + - feats: keypoint descriptors + - confs: reliability values + """ + def __init__(self): + nn.Module.__init__(self) + self.mode = 'bilinear' + self.padding = 'zeros' + + @staticmethod + def _aflow_to_grid(aflow): + H, W = aflow.shape[2:] + grid = aflow.permute(0,2,3,1).clone() + grid[:,:,:,0] *= 2/(W-1) + grid[:,:,:,1] *= 2/(H-1) + grid -= 1 + grid[torch.isnan(grid)] = 9e9 # invalids + return grid + + def _warp(self, feats, confs, aflow): + if isinstance(aflow, tuple): return aflow # result was precomputed + feat1, feat2 = feats + conf1, conf2 = confs if confs else (None,None) + + B, two, H, W = aflow.shape + D = feat1.shape[1] + assert feat1.shape == feat2.shape == (B, D, H, W) # D = 128, B = batch + assert conf1.shape == conf2.shape == (B, 1, H, W) if confs else True + + # warp img2 to img1 + grid = self._aflow_to_grid(aflow) + ones2 = feat2.new_ones(feat2[:,0:1].shape) + feat2to1 = F.grid_sample(feat2, grid, mode=self.mode, padding_mode=self.padding) + mask2to1 = F.grid_sample(ones2, grid, mode='nearest', padding_mode='zeros') + conf2to1 = F.grid_sample(conf2, grid, mode=self.mode, padding_mode=self.padding) \ + if confs else None + return feat2to1, mask2to1.byte(), conf2to1 + + def _warp_positions(self, aflow): + B, two, H, W = aflow.shape + assert two == 2 + + Y = torch.arange(H, device=aflow.device) + X = torch.arange(W, device=aflow.device) + XY = torch.stack(torch.meshgrid(Y,X)[::-1], dim=0) + XY = XY[None].expand(B, 2, H, W).float() + + grid = self._aflow_to_grid(aflow) + XY2 = F.grid_sample(XY, grid, mode='bilinear', padding_mode='zeros') + return XY, XY2 + + + +class SubSampler (FullSampler): + """ pixels are selected in an uniformly spaced grid + """ + def __init__(self, border, subq, subd, perimage=False): + FullSampler.__init__(self) + assert subq % subd == 0, 'subq must be multiple of subd' + self.sub_q = subq + self.sub_d = subd + self.border = border + self.perimage = perimage + + def __repr__(self): + return "SubSampler(border=%d, subq=%d, subd=%d, perimage=%d)" % ( + self.border, self.sub_q, self.sub_d, self.perimage) + + def __call__(self, feats, confs, aflow): + feat1, conf1 = feats[0], (confs[0] if confs else None) + # warp with optical flow in img1 coords + feat2, mask2, conf2 = self._warp(feats, confs, aflow) + + # subsample img1 + slq = slice(self.border, -self.border or None, self.sub_q) + feat1 = feat1[:, :, slq, slq] + conf1 = conf1[:, :, slq, slq] if confs else None + # subsample img2 + sld = slice(self.border, -self.border or None, self.sub_d) + feat2 = feat2[:, :, sld, sld] + mask2 = mask2[:, :, sld, sld] + conf2 = conf2[:, :, sld, sld] if confs else None + + B, D, Hq, Wq = feat1.shape + B, D, Hd, Wd = feat2.shape + + # compute gt + if self.perimage or self.sub_q != self.sub_d: + # compute ground-truth by comparing pixel indices + f = feats[0][0:1,0] if self.perimage else feats[0][:,0] + idxs = torch.arange(f.numel(), dtype=torch.int64, device=feat1.device).view(f.shape) + idxs1 = idxs[:, slq, slq].reshape(-1,Hq*Wq) + idxs2 = idxs[:, sld, sld].reshape(-1,Hd*Wd) + if self.perimage: + gt = (idxs1[0].view(-1,1) == idxs2[0].view(1,-1)) + gt = gt[None,:,:].expand(B, Hq*Wq, Hd*Wd) + else : + gt = (idxs1.view(-1,1) == idxs2.view(1,-1)) + else: + gt = torch.eye(feat1[:,0].numel(), dtype=torch.uint8, device=feat1.device) # always binary for AP loss + + # compute all images together + queries = feat1.reshape(B,D,-1) # B x D x (Hq x Wq) + database = feat2.reshape(B,D,-1) # B x D x (Hd x Wd) + if self.perimage: + queries = queries.transpose(1,2) # B x (Hd x Wd) x D + scores = torch.bmm(queries, database) # B x (Hq x Wq) x (Hd x Wd) + else: + queries = queries .transpose(1,2).reshape(-1,D) # (B x Hq x Wq) x D + database = database.transpose(1,0).reshape(D,-1) # D x (B x Hd x Wd) + scores = torch.matmul(queries, database) # (B x Hq x Wq) x (B x Hd x Wd) + + # compute reliability + qconf = (conf1 + conf2)/2 if confs else None + + assert gt.shape == scores.shape + return scores, gt, mask2, qconf + + + +class NghSampler (FullSampler): + """ all pixels in a small neighborhood + """ + def __init__(self, ngh, subq=1, subd=1, ignore=1, border=None): + FullSampler.__init__(self) + assert 0 <= ignore < ngh + self.ngh = ngh + self.ignore = ignore + assert subd <= ngh + self.sub_q = subq + self.sub_d = subd + if border is None: border = ngh + assert border >= ngh, 'border has to be larger than ngh' + self.border = border + + def __repr__(self): + return "NghSampler(ngh=%d, subq=%d, subd=%d, ignore=%d, border=%d)" % ( + self.ngh, self.sub_q, self.sub_d, self.ignore, self.border) + + def trans(self, arr, i, j): + s = lambda i: slice(self.border+i, i-self.border or None, self.sub_q) + return arr[:,:,s(j),s(i)] + + def __call__(self, feats, confs, aflow): + feat1, conf1 = feats[0], (confs[0] if confs else None) + # warp with optical flow in img1 coords + feat2, mask2, conf2 = self._warp(feats, confs, aflow) + + qfeat = self.trans(feat1,0,0) + qconf = (self.trans(conf1,0,0) + self.trans(conf2,0,0)) / 2 if confs else None + mask2 = self.trans(mask2,0,0) + scores_at = lambda i,j: (qfeat * self.trans(feat2,i,j)).sum(dim=1) + + # compute scores for all neighbors + B, D = feat1.shape[:2] + min_d = self.ignore**2 + max_d = self.ngh**2 + rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple + negs = [] + offsets = [] + for j in range(-rad, rad+1, self.sub_d): + for i in range(-rad, rad+1, self.sub_d): + if not(min_d < i*i + j*j <= max_d): + continue # out of scope + offsets.append((i,j)) # Note: this list is just for debug + negs.append( scores_at(i,j) ) + + scores = torch.stack([scores_at(0,0)] + negs, dim=-1) + gt = scores.new_zeros(scores.shape, dtype=torch.uint8) + gt[..., 0] = 1 # only the center point is positive + + return scores, gt, mask2, qconf + + + +class FarNearSampler (FullSampler): + """ Sample pixels from *both* a small neighborhood *and* far-away pixels. + + How it works? + 1) Queries are sampled from img1, + - at least `border` pixels from borders and + - on a grid with step = `subq` + + 2) Close database pixels + - from the corresponding image (img2), + - within a `ngh` distance radius + - on a grid with step = `subd_ngh` + - ignored if distance to query is >0 and <=`ignore` + + 3) Far-away database pixels from , + - from all batch images in `img2` + - at least `border` pixels from borders + - on a grid with step = `subd_far` + """ + def __init__(self, subq, ngh, subd_ngh, subd_far, border=None, ignore=1, + maxpool_ngh=False ): + FullSampler.__init__(self) + border = border or ngh + assert ignore < ngh < subd_far, 'neighborhood needs to be smaller than far step' + self.close_sampler = NghSampler(ngh=ngh, subq=subq, subd=subd_ngh, + ignore=not(maxpool_ngh), border=border) + self.faraway_sampler = SubSampler(border=border, subq=subq, subd=subd_far) + self.maxpool_ngh = maxpool_ngh + + def __repr__(self): + c,f = self.close_sampler, self.faraway_sampler + res = "FarNearSampler(subq=%d, ngh=%d" % (c.sub_q, c.ngh) + res += ", subd_ngh=%d, subd_far=%d" % (c.sub_d, f.sub_d) + res += ", border=%d, ign=%d" % (f.border, c.ignore) + res += ", maxpool_ngh=%d" % self.maxpool_ngh + return res+')' + + def __call__(self, feats, confs, aflow): + # warp with optical flow in img1 coords + aflow = self._warp(feats, confs, aflow) + + # sample ngh pixels + scores1, gt1, msk1, conf1 = self.close_sampler(feats, confs, aflow) + scores1, gt1 = scores1.view(-1,scores1.shape[-1]), gt1.view(-1,gt1.shape[-1]) + if self.maxpool_ngh: + # we consider all scores from ngh as potential positives + scores1, self._cached_maxpool_ngh = scores1.max(dim=1,keepdim=True) + gt1 = gt1[:, 0:1] + + # sample far pixels + scores2, gt2, msk2, conf2 = self.faraway_sampler(feats, confs, aflow) + # assert (msk1 == msk2).all() + # assert (conf1 == conf2).all() + + return (torch.cat((scores1,scores2),dim=1), + torch.cat((gt1, gt2), dim=1), + msk1, conf1 if confs else None) + + +class NghSampler2 (nn.Module): + """ Similar to NghSampler, but doesnt warp the 2nd image. + Distance to GT => 0 ... pos_d ... neg_d ... ngh + Pixel label => + + + + + + 0 0 - - - - - - - + + Subsample on query side: if > 0, regular grid + < 0, random points + In both cases, the number of query points is = W*H/subq**2 + """ + def __init__(self, ngh, subq=1, subd=1, pos_d=0, neg_d=2, border=None, + maxpool_pos=True, subd_neg=0): + nn.Module.__init__(self) + assert 0 <= pos_d < neg_d <= (ngh if ngh else 99) + self.ngh = ngh + self.pos_d = pos_d + self.neg_d = neg_d + assert subd <= ngh or ngh == 0 + assert subq != 0 + self.sub_q = subq + self.sub_d = subd + self.sub_d_neg = subd_neg + if border is None: border = ngh + assert border >= ngh, 'border has to be larger than ngh' + self.border = border + self.maxpool_pos = maxpool_pos + self.precompute_offsets() + + def precompute_offsets(self): + pos_d2 = self.pos_d**2 + neg_d2 = self.neg_d**2 + rad2 = self.ngh**2 + rad = (self.ngh//self.sub_d) * self.ngh # make an integer multiple + pos = [] + neg = [] + for j in range(-rad, rad+1, self.sub_d): + for i in range(-rad, rad+1, self.sub_d): + d2 = i*i + j*j + if d2 <= pos_d2: + pos.append( (i,j) ) + elif neg_d2 <= d2 <= rad2: + neg.append( (i,j) ) + + self.register_buffer('pos_offsets', torch.LongTensor(pos).view(-1,2).t()) + self.register_buffer('neg_offsets', torch.LongTensor(neg).view(-1,2).t()) + + def gen_grid(self, step, aflow): + B, two, H, W = aflow.shape + dev = aflow.device + b1 = torch.arange(B, device=dev) + if step > 0: + # regular grid + x1 = torch.arange(self.border, W-self.border, step, device=dev) + y1 = torch.arange(self.border, H-self.border, step, device=dev) + H1, W1 = len(y1), len(x1) + x1 = x1[None,None,:].expand(B,H1,W1).reshape(-1) + y1 = y1[None,:,None].expand(B,H1,W1).reshape(-1) + b1 = b1[:,None,None].expand(B,H1,W1).reshape(-1) + shape = (B, H1, W1) + else: + # randomly spread + n = (H - 2*self.border) * (W - 2*self.border) // step**2 + x1 = torch.randint(self.border, W-self.border, (n,), device=dev) + y1 = torch.randint(self.border, H-self.border, (n,), device=dev) + x1 = x1[None,:].expand(B,n).reshape(-1) + y1 = y1[None,:].expand(B,n).reshape(-1) + b1 = b1[:,None].expand(B,n).reshape(-1) + shape = (B, n) + return b1, y1, x1, shape + + def forward(self, feats, confs, aflow, **kw): + B, two, H, W = aflow.shape + assert two == 2 + feat1, conf1 = feats[0], (confs[0] if confs else None) + feat2, conf2 = feats[1], (confs[1] if confs else None) + + # positions in the first image + b1, y1, x1, shape = self.gen_grid(self.sub_q, aflow) + + # sample features from first image + feat1 = feat1[b1, :, y1, x1] + qconf = conf1[b1, :, y1, x1].view(shape) if confs else None + + #sample GT from second image + b2 = b1 + xy2 = (aflow[b1, :, y1, x1] + 0.5).long().t() + mask = (0 <= xy2[0]) * (0 <= xy2[1]) * (xy2[0] < W) * (xy2[1] < H) + mask = mask.view(shape) + + def clamp(xy): + torch.clamp(xy[0], 0, W-1, out=xy[0]) + torch.clamp(xy[1], 0, H-1, out=xy[1]) + return xy + + # compute positive scores + xy2p = clamp(xy2[:,None,:] + self.pos_offsets[:,:,None]) + pscores = (feat1[None,:,:] * feat2[b2, :, xy2p[1], xy2p[0]]).sum(dim=-1).t() +# xy1p = clamp(torch.stack((x1,y1))[:,None,:] + self.pos_offsets[:,:,None]) +# grid = FullSampler._aflow_to_grid(aflow) +# feat2p = F.grid_sample(feat2, grid, mode='bilinear', padding_mode='border') +# pscores = (feat1[None,:,:] * feat2p[b1,:,xy1p[1], xy1p[0]]).sum(dim=-1).t() + if self.maxpool_pos: + pscores, pos = pscores.max(dim=1, keepdim=True) + if confs: + sel = clamp(xy2 + self.pos_offsets[:,pos.view(-1)]) + qconf = (qconf + conf2[b2, :, sel[1], sel[0]].view(shape))/2 + + # compute negative scores + xy2n = clamp(xy2[:,None,:] + self.neg_offsets[:,:,None]) + nscores = (feat1[None,:,:] * feat2[b2, :, xy2n[1], xy2n[0]]).sum(dim=-1).t() + + if self.sub_d_neg: + # add distractors from a grid + b3, y3, x3, _ = self.gen_grid(self.sub_d_neg, aflow) + distractors = feat2[b3, :, y3, x3] + dscores = torch.matmul(feat1, distractors.t()) + del distractors + + # remove scores that corresponds to positives or nulls + dis2 = (x3 - xy2[0][:,None])**2 + (y3 - xy2[1][:,None])**2 + dis2 += (b3 != b2[:,None]).long() * self.neg_d**2 + dscores[dis2 < self.neg_d**2] = 0 + + scores = torch.cat((pscores, nscores, dscores), dim=1) + else: + # concat everything + scores = torch.cat((pscores, nscores), dim=1) + + gt = scores.new_zeros(scores.shape, dtype=torch.uint8) + gt[:, :pscores.shape[1]] = 1 + + return scores, gt, mask, qconf + + + + + + + + diff --git a/third_party/r2d2/results/r2d2_WAF_N16.scale-0.3-1.npy b/third_party/r2d2/results/r2d2_WAF_N16.scale-0.3-1.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d731b481ac647bbe9fba4ebbc6552bdc1fd1f77 --- /dev/null +++ b/third_party/r2d2/results/r2d2_WAF_N16.scale-0.3-1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b6c42e579c824adc0e6e623202ae1845617cb81e6d1cd6606673fc2c9eb83d1 +size 15728 diff --git a/third_party/r2d2/results/r2d2_WAF_N16.size-256-1024.npy b/third_party/r2d2/results/r2d2_WAF_N16.size-256-1024.npy new file mode 100644 index 0000000000000000000000000000000000000000..54c4f4eae62ec18d440a57e6aab60ca000201717 --- /dev/null +++ b/third_party/r2d2/results/r2d2_WAF_N16.size-256-1024.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f01c195853636831cd8560591fe2c21d42f58fe7e1b5767acf398e583ad66d4e +size 15710 diff --git a/third_party/r2d2/results/r2d2_WASF_N16.scale-0.3-1.npy b/third_party/r2d2/results/r2d2_WASF_N16.scale-0.3-1.npy new file mode 100644 index 0000000000000000000000000000000000000000..8cdcdaba1bc992ad33120ea4de62fe79ec116100 --- /dev/null +++ b/third_party/r2d2/results/r2d2_WASF_N16.scale-0.3-1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d36f0d172ddacce4d34d7d3729ba0d63aa3e783d8d9c2157ca6c32002b2fa5cd +size 15684 diff --git a/third_party/r2d2/results/r2d2_WASF_N16.size-256-1024.npy b/third_party/r2d2/results/r2d2_WASF_N16.size-256-1024.npy new file mode 100644 index 0000000000000000000000000000000000000000..75a00ce5276e058e869204ef255b788b98fccf3b --- /dev/null +++ b/third_party/r2d2/results/r2d2_WASF_N16.size-256-1024.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3e17baff59af4591de27b9c67649644f9709da291ab94791233228c6b28f29d +size 15709 diff --git a/third_party/r2d2/results/r2d2_W_N16.scale-0.3-1.npy b/third_party/r2d2/results/r2d2_W_N16.scale-0.3-1.npy new file mode 100644 index 0000000000000000000000000000000000000000..c091ab7db8d3e34075b047a5ccebad070ec14369 --- /dev/null +++ b/third_party/r2d2/results/r2d2_W_N16.scale-0.3-1.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b26d13b2272baed4acab517e1e85ac4832f28eeede4177f53749160e8aa67285 +size 15748 diff --git a/third_party/r2d2/tools/common.py b/third_party/r2d2/tools/common.py new file mode 100644 index 0000000000000000000000000000000000000000..a7875ddd714b1d08efb0d1369c3a856490796288 --- /dev/null +++ b/third_party/r2d2/tools/common.py @@ -0,0 +1,41 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb#, shutil +import numpy as np +import torch + + +def mkdir_for(file_path): + os.makedirs(os.path.split(file_path)[0], exist_ok=True) + + +def model_size(model): + ''' Computes the number of parameters of the model + ''' + size = 0 + for weights in model.state_dict().values(): + size += np.prod(weights.shape) + return size + + +def torch_set_gpu(gpus): + if type(gpus) is int: + gpus = [gpus] + + cuda = all(gpu>=0 for gpu in gpus) + + if cuda: + os.environ['CUDA_VISIBLE_DEVICES'] = ','.join([str(gpu) for gpu in gpus]) + assert cuda and torch.cuda.is_available(), "%s has GPUs %s unavailable" % ( + os.environ['HOSTNAME'],os.environ['CUDA_VISIBLE_DEVICES']) + torch.backends.cudnn.benchmark = True # speed-up cudnn + torch.backends.cudnn.fastest = True # even more speed-up? + print( 'Launching on GPUs ' + os.environ['CUDA_VISIBLE_DEVICES'] ) + + else: + print( 'Launching on CPU' ) + + return cuda + diff --git a/third_party/r2d2/tools/dataloader.py b/third_party/r2d2/tools/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..f6d9fff5f8dfb8d9d3b243a57555779de33d0818 --- /dev/null +++ b/third_party/r2d2/tools/dataloader.py @@ -0,0 +1,367 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +from PIL import Image +import numpy as np + +import torch +import torchvision.transforms as tvf + +from tools.transforms import instanciate_transformation +from tools.transforms_tools import persp_apply + + +RGB_mean = [0.485, 0.456, 0.406] +RGB_std = [0.229, 0.224, 0.225] + +norm_RGB = tvf.Compose([tvf.ToTensor(), tvf.Normalize(mean=RGB_mean, std=RGB_std)]) + + +class PairLoader: + """ On-the-fly jittering of pairs of image with dense pixel ground-truth correspondences. + + crop: random crop applied to both images + scale: random scaling applied to img2 + distort: random ditorsion applied to img2 + + self[idx] returns a dictionary with keys: img1, img2, aflow, mask + - img1: cropped original + - img2: distorted cropped original + - aflow: 'absolute' optical flow = (x,y) position of each pixel from img1 in img2 + - mask: (binary image) valid pixels of img1 + """ + def __init__(self, dataset, crop='', scale='', distort='', norm = norm_RGB, + what = 'aflow mask', idx_as_rng_seed = False): + assert hasattr(dataset, 'npairs') + assert hasattr(dataset, 'get_pair') + self.dataset = dataset + self.distort = instanciate_transformation(distort) + self.crop = instanciate_transformation(crop) + self.norm = instanciate_transformation(norm) + self.scale = instanciate_transformation(scale) + self.idx_as_rng_seed = idx_as_rng_seed # to remove randomness + self.what = what.split() if isinstance(what, str) else what + self.n_samples = 5 # number of random trials per image + + def __len__(self): + assert len(self.dataset) == self.dataset.npairs, pdb.set_trace() # and not nimg + return len(self.dataset) + + def __repr__(self): + fmt_str = 'PairLoader\n' + fmt_str += repr(self.dataset) + fmt_str += ' npairs: %d\n' % self.dataset.npairs + short_repr = lambda s: repr(s).strip().replace('\n',', ')[14:-1].replace(' ',' ') + fmt_str += ' Distort: %s\n' % short_repr(self.distort) + fmt_str += ' Crop: %s\n' % short_repr(self.crop) + fmt_str += ' Norm: %s\n' % short_repr(self.norm) + return fmt_str + + def __getitem__(self, i): + #from time import time as now; t0 = now() + if self.idx_as_rng_seed: + import random + random.seed(i) + np.random.seed(i) + + # Retrieve an image pair and their absolute flow + img_a, img_b, metadata = self.dataset.get_pair(i, self.what) + + # aflow contains pixel coordinates indicating where each + # pixel from the left image ended up in the right image + # as (x,y) pairs, but its shape is (H,W,2) + aflow = np.float32(metadata['aflow']) + mask = metadata.get('mask', np.ones(aflow.shape[:2],np.uint8)) + + # apply transformations to the second image + img_b = {'img': img_b, 'persp':(1,0,0,0,1,0,0,0)} + if self.scale: + img_b = self.scale(img_b) + if self.distort: + img_b = self.distort(img_b) + + # apply the same transformation to the flow + aflow[:] = persp_apply(img_b['persp'], aflow.reshape(-1,2)).reshape(aflow.shape) + corres = None + if 'corres' in metadata: + corres = np.float32(metadata['corres']) + corres[:,1] = persp_apply(img_b['persp'], corres[:,1]) + + # apply the same transformation to the homography + homography = None + if 'homography' in metadata: + homography = np.float32(metadata['homography']) + # p_b = homography * p_a + persp = np.float32(img_b['persp']+(1,)).reshape(3,3) + homography = persp @ homography + + # determine crop size + img_b = img_b['img'] + crop_size = self.crop({'imsize':(10000,10000)})['imsize'] + output_size_a = min(img_a.size, crop_size) + output_size_b = min(img_b.size, crop_size) + img_a = np.array(img_a) + img_b = np.array(img_b) + + ah,aw,p1 = img_a.shape + bh,bw,p2 = img_b.shape + assert p1 == 3 + assert p2 == 3 + assert aflow.shape == (ah, aw, 2) + assert mask.shape == (ah, aw) + + # Let's start by computing the scale of the + # optical flow and applying a median filter: + dx = np.gradient(aflow[:,:,0]) + dy = np.gradient(aflow[:,:,1]) + scale = np.sqrt(np.clip(np.abs(dx[1]*dy[0] - dx[0]*dy[1]), 1e-16, 1e16)) + + accu2 = np.zeros((16,16), bool) + Q = lambda x, w: np.int32(16 * (x - w.start) / (w.stop - w.start)) + + def window1(x, size, w): + l = x - int(0.5 + size / 2) + r = l + int(0.5 + size) + if l < 0: l,r = (0, r - l) + if r > w: l,r = (l + w - r, w) + if l < 0: l,r = 0,w # larger than width + return slice(l,r) + def window(cx, cy, win_size, scale, img_shape): + return (window1(cy, win_size[1]*scale, img_shape[0]), + window1(cx, win_size[0]*scale, img_shape[1])) + + n_valid_pixel = mask.sum() + sample_w = mask / (1e-16 + n_valid_pixel) + def sample_valid_pixel(): + n = np.random.choice(sample_w.size, p=sample_w.ravel()) + y, x = np.unravel_index(n, sample_w.shape) + return x, y + + # Find suitable left and right windows + trials = 0 # take the best out of few trials + best = -np.inf, None + for _ in range(50*self.n_samples): + if trials >= self.n_samples: break # finished! + + # pick a random valid point from the first image + if n_valid_pixel == 0: break + c1x, c1y = sample_valid_pixel() + + # Find in which position the center of the left + # window ended up being placed in the right image + c2x, c2y = (aflow[c1y, c1x] + 0.5).astype(np.int32) + if not(0 <= c2x < bw and 0 <= c2y < bh): continue + + # Get the flow scale + sigma = scale[c1y, c1x] + + # Determine sampling windows + if 0.2 < sigma < 1: + win1 = window(c1x, c1y, output_size_a, 1/sigma, img_a.shape) + win2 = window(c2x, c2y, output_size_b, 1, img_b.shape) + elif 1 <= sigma < 5: + win1 = window(c1x, c1y, output_size_a, 1, img_a.shape) + win2 = window(c2x, c2y, output_size_b, sigma, img_b.shape) + else: + continue # bad scale + + # compute a score based on the flow + x2,y2 = aflow[win1].reshape(-1, 2).T.astype(np.int32) + # Check the proportion of valid flow vectors + valid = (win2[1].start <= x2) & (x2 < win2[1].stop) \ + & (win2[0].start <= y2) & (y2 < win2[0].stop) + score1 = (valid * mask[win1].ravel()).mean() + # check the coverage of the second window + accu2[:] = False + accu2[Q(y2[valid],win2[0]), Q(x2[valid],win2[1])] = True + score2 = accu2.mean() + # Check how many hits we got + score = min(score1, score2) + + trials += 1 + if score > best[0]: + best = score, win1, win2 + + if None in best: # counldn't find a good window + img_a = np.zeros(output_size_a[::-1]+(3,), dtype=np.uint8) + img_b = np.zeros(output_size_b[::-1]+(3,), dtype=np.uint8) + aflow = np.nan * np.ones((2,)+output_size_a[::-1], dtype=np.float32) + homography = np.nan * np.ones((3,3), dtype=np.float32) + + else: + win1, win2 = best[1:] + img_a = img_a[win1] + img_b = img_b[win2] + aflow = aflow[win1] - np.float32([[[win2[1].start, win2[0].start]]]) + mask = mask[win1] + aflow[~mask.view(bool)] = np.nan # mask bad pixels! + aflow = aflow.transpose(2,0,1) # --> (2,H,W) + + if corres is not None: + corres[:,0] -= (win1[1].start, win1[0].start) + corres[:,1] -= (win2[1].start, win2[0].start) + + if homography is not None: + trans1 = np.eye(3, dtype=np.float32) + trans1[:2,2] = (win1[1].start, win1[0].start) + trans2 = np.eye(3, dtype=np.float32) + trans2[:2,2] = (-win2[1].start, -win2[0].start) + homography = trans2 @ homography @ trans1 + homography /= homography[2,2] + + # rescale if necessary + if img_a.shape[:2][::-1] != output_size_a: + sx, sy = (np.float32(output_size_a)-1)/(np.float32(img_a.shape[:2][::-1])-1) + img_a = np.asarray(Image.fromarray(img_a).resize(output_size_a, Image.ANTIALIAS)) + mask = np.asarray(Image.fromarray(mask).resize(output_size_a, Image.NEAREST)) + afx = Image.fromarray(aflow[0]).resize(output_size_a, Image.NEAREST) + afy = Image.fromarray(aflow[1]).resize(output_size_a, Image.NEAREST) + aflow = np.stack((np.float32(afx), np.float32(afy))) + + if corres is not None: + corres[:,0] *= (sx, sy) + + if homography is not None: + homography = homography @ np.diag(np.float32([1/sx,1/sy,1])) + homography /= homography[2,2] + + if img_b.shape[:2][::-1] != output_size_b: + sx, sy = (np.float32(output_size_b)-1)/(np.float32(img_b.shape[:2][::-1])-1) + img_b = np.asarray(Image.fromarray(img_b).resize(output_size_b, Image.ANTIALIAS)) + aflow *= [[[sx]], [[sy]]] + + if corres is not None: + corres[:,1] *= (sx, sy) + + if homography is not None: + homography = np.diag(np.float32([sx,sy,1])) @ homography + homography /= homography[2,2] + + assert aflow.dtype == np.float32, pdb.set_trace() + assert homography is None or homography.dtype == np.float32, pdb.set_trace() + if 'flow' in self.what: + H, W = img_a.shape[:2] + mgrid = np.mgrid[0:H, 0:W][::-1].astype(np.float32) + flow = aflow - mgrid + + result = dict(img1=self.norm(img_a), img2=self.norm(img_b)) + for what in self.what: + try: result[what] = eval(what) + except NameError: pass + return result + + + +def threaded_loader( loader, iscuda, threads, batch_size=1, shuffle=True): + """ Get a data loader, given the dataset and some parameters. + + Parameters + ---------- + loader : object[i] returns the i-th training example. + + iscuda : bool + + batch_size : int + + threads : int + + shuffle : int + + Returns + ------- + a multi-threaded pytorch loader. + """ + return torch.utils.data.DataLoader( + loader, + batch_size = batch_size, + shuffle = shuffle, + sampler = None, + num_workers = threads, + pin_memory = iscuda, + collate_fn=collate) + + + +def collate(batch, _use_shared_memory=True): + """Puts each data field into a tensor with outer dimension batch size. + Copied from https://github.com/pytorch in torch/utils/data/_utils/collate.py + """ + import re + error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" + elem_type = type(batch[0]) + if isinstance(batch[0], torch.Tensor): + out = None + if _use_shared_memory: + # If we're in a background process, concatenate directly into a + # shared memory tensor to avoid an extra copy + numel = sum([x.numel() for x in batch]) + storage = batch[0].storage()._new_shared(numel) + out = batch[0].new(storage) + return torch.stack(batch, 0, out=out) + elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ + and elem_type.__name__ != 'string_': + elem = batch[0] + assert elem_type.__name__ == 'ndarray' + # array of string classes and object + if re.search('[SaUO]', elem.dtype.str) is not None: + raise TypeError(error_msg.format(elem.dtype)) + batch = [torch.from_numpy(b) for b in batch] + try: + return torch.stack(batch, 0) + except RuntimeError: + return batch + elif batch[0] is None: + return list(batch) + elif isinstance(batch[0], int): + return torch.LongTensor(batch) + elif isinstance(batch[0], float): + return torch.DoubleTensor(batch) + elif isinstance(batch[0], str): + return batch + elif isinstance(batch[0], dict): + return {key: collate([d[key] for d in batch]) for key in batch[0]} + elif isinstance(batch[0], (tuple,list)): + transposed = zip(*batch) + return [collate(samples) for samples in transposed] + + raise TypeError((error_msg.format(type(batch[0])))) + + + +def tensor2img(tensor, model=None): + """ convert back a torch/numpy tensor to a PIL Image + by undoing the ToTensor() and Normalize() transforms. + """ + mean = norm_RGB.transforms[1].mean + std = norm_RGB.transforms[1].std + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + + res = np.uint8(np.clip(255*((tensor.transpose(1,2,0) * std) + mean), 0, 255)) + from PIL import Image + return Image.fromarray(res) + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser("Tool to debug/visualize the data loader") + parser.add_argument("dataloader", type=str, help="command to create the data loader") + args = parser.parse_args() + + from datasets import * + auto_pairs = lambda db: SyntheticPairDataset(db, + 'RandomScale(256,1024,can_upscale=True)', + 'RandomTilting(0.5), PixelNoise(25)') + + loader = eval(args.dataloader) + print("Data loader =", loader) + + from tools.viz import show_flow + for data in loader: + aflow = data['aflow'] + H, W = aflow.shape[-2:] + flow = (aflow - np.mgrid[:H, :W][::-1]).transpose(1,2,0) + show_flow(tensor2img(data['img1']), tensor2img(data['img2']), flow) + diff --git a/third_party/r2d2/tools/trainer.py b/third_party/r2d2/tools/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..9f893395efdeb8e13cc00539325572553168c5ce --- /dev/null +++ b/third_party/r2d2/tools/trainer.py @@ -0,0 +1,76 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +from tqdm import tqdm +from collections import defaultdict + +import torch +import torch.nn as nn + + +class Trainer (nn.Module): + """ Helper class to train a deep network. + Overload this class `forward_backward` for your actual needs. + + Usage: + train = Trainer(net, loader, loss, optimizer) + for epoch in range(n_epochs): + train() + """ + def __init__(self, net, loader, loss, optimizer): + nn.Module.__init__(self) + self.net = net + self.loader = loader + self.loss_func = loss + self.optimizer = optimizer + + def iscuda(self): + return next(self.net.parameters()).device != torch.device('cpu') + + def todevice(self, x): + if isinstance(x, dict): + return {k:self.todevice(v) for k,v in x.items()} + if isinstance(x, (tuple,list)): + return [self.todevice(v) for v in x] + + if self.iscuda(): + return x.contiguous().cuda(non_blocking=True) + else: + return x.cpu() + + def __call__(self): + self.net.train() + + stats = defaultdict(list) + + for iter,inputs in enumerate(tqdm(self.loader)): + inputs = self.todevice(inputs) + + # compute gradient and do model update + self.optimizer.zero_grad() + + loss, details = self.forward_backward(inputs) + if torch.isnan(loss): + raise RuntimeError('Loss is NaN') + + self.optimizer.step() + + for key, val in details.items(): + stats[key].append( val ) + + print(" Summary of losses during this epoch:") + mean = lambda lis: sum(lis) / len(lis) + for loss_name, vals in stats.items(): + N = 1 + len(vals)//10 + print(f" - {loss_name:20}:", end='') + print(f" {mean(vals[:N]):.3f} --> {mean(vals[-N:]):.3f} (avg: {mean(vals):.3f})") + return mean(stats['loss']) # return average loss + + def forward_backward(self, inputs): + raise NotImplementedError() + + + + diff --git a/third_party/r2d2/tools/transforms.py b/third_party/r2d2/tools/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..87275276310191a7da3fc14f606345d9616208e0 --- /dev/null +++ b/third_party/r2d2/tools/transforms.py @@ -0,0 +1,513 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import numpy as np +from PIL import Image, ImageOps +import torchvision.transforms as tvf +import random +from math import ceil + +from . import transforms_tools as F + +''' +Example command to try out some transformation chain: + +python -m tools.transforms --trfs "Scale(384), ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1), RandomRotation(10), RandomTilting(0.5, 'all'), RandomScale(240,320), RandomCrop(224)" +''' + + +def instanciate_transformation(cmd_line): + ''' Create a sequence of transformations. + + cmd_line: (str) + Comma-separated list of transformations. + Ex: "Rotate(10), Scale(256)" + ''' + if not isinstance(cmd_line, str): + return cmd_line # already instanciated + + cmd_line = "tvf.Compose([%s])" % cmd_line + try: + return eval(cmd_line) + except Exception as e: + print("Cannot interpret this transform list: %s\nReason: %s" % (cmd_line, e)) + + +class Scale (object): + """ Rescale the input PIL.Image to a given size. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + The smallest dimension of the resulting image will be = size. + + if largest == True: same behaviour for the largest dimension. + + if not can_upscale: don't upscale + if not can_downscale: don't downscale + """ + def __init__(self, size, interpolation=Image.BILINEAR, largest=False, + can_upscale=True, can_downscale=True): + assert isinstance(size, int) or (len(size) == 2) + self.size = size + self.interpolation = interpolation + self.largest = largest + self.can_upscale = can_upscale + self.can_downscale = can_downscale + + def __repr__(self): + fmt_str = "RandomScale(%s" % str(self.size) + if self.largest: fmt_str += ', largest=True' + if not self.can_upscale: fmt_str += ', can_upscale=False' + if not self.can_downscale: fmt_str += ', can_downscale=False' + return fmt_str+')' + + def get_params(self, imsize): + w,h = imsize + if isinstance(self.size, int): + cmp = lambda a,b: (a>=b) if self.largest else (a<=b) + if (cmp(w, h) and w == self.size) or (cmp(h, w) and h == self.size): + ow, oh = w, h + elif cmp(w, h): + ow = self.size + oh = int(self.size * h / w) + else: + oh = self.size + ow = int(self.size * w / h) + else: + ow, oh = self.size + return ow, oh + + def __call__(self, inp): + img = F.grab_img(inp) + w, h = img.size + + size2 = ow, oh = self.get_params(img.size) + + if size2 != img.size: + a1, a2 = img.size, size2 + if (self.can_upscale and min(a1) < min(a2)) or (self.can_downscale and min(a1) > min(a2)): + img = img.resize(size2, self.interpolation) + + return F.update_img_and_labels(inp, img, persp=(ow/w,0,0,0,oh/h,0,0,0)) + + + +class RandomScale (Scale): + """Rescale the input PIL.Image to a random size. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + min_size (int): min size of the smaller edge of the picture. + max_size (int): max size of the smaller edge of the picture. + + ar (float or tuple): + max change of aspect ratio (width/height). + + interpolation (int, optional): Desired interpolation. Default is + ``PIL.Image.BILINEAR`` + """ + + def __init__(self, min_size, max_size, ar=1, + can_upscale=False, can_downscale=True, interpolation=Image.BILINEAR): + Scale.__init__(self, 0, can_upscale=can_upscale, can_downscale=can_downscale, interpolation=interpolation) + assert type(min_size) == type(max_size), 'min_size and max_size can only be 2 ints or 2 floats' + assert isinstance(min_size, int) and min_size >= 1 or isinstance(min_size, float) and min_size>0 + assert isinstance(max_size, (int,float)) and min_size <= max_size + self.min_size = min_size + self.max_size = max_size + if type(ar) in (float,int): ar = (min(1/ar,ar),max(1/ar,ar)) + assert 0.2 < ar[0] <= ar[1] < 5 + self.ar = ar + + def get_params(self, imsize): + w,h = imsize + if isinstance(self.min_size, float): + min_size = int(self.min_size*min(w,h) + 0.5) + if isinstance(self.max_size, float): + max_size = int(self.max_size*min(w,h) + 0.5) + if isinstance(self.min_size, int): + min_size = self.min_size + if isinstance(self.max_size, int): + max_size = self.max_size + + if not self.can_upscale: + max_size = min(max_size,min(w,h)) + + size = int(0.5 + F.rand_log_uniform(min_size,max_size)) + ar = F.rand_log_uniform(*self.ar) # change of aspect ratio + + if w < h: # image is taller + ow = size + oh = int(0.5 + size * h / w / ar) + if oh < min_size: + ow,oh = int(0.5 + ow*float(min_size)/oh),min_size + else: # image is wider + oh = size + ow = int(0.5 + size * w / h * ar) + if ow < min_size: + ow,oh = min_size,int(0.5 + oh*float(min_size)/ow) + + assert ow >= min_size, 'image too small (width=%d < min_size=%d)' % (ow, min_size) + assert oh >= min_size, 'image too small (height=%d < min_size=%d)' % (oh, min_size) + return ow, oh + + + +class RandomCrop (object): + """Crop the given PIL Image at a random location. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. + padding (int or sequence, optional): Optional padding on each border + of the image. Default is 0, i.e no padding. If a sequence of length + 4 is provided, it is used to pad left, top, right, bottom borders + respectively. + """ + + def __init__(self, size, padding=0): + if isinstance(size, int): + self.size = (int(size), int(size)) + else: + self.size = size + self.padding = padding + + def __repr__(self): + return "RandomCrop(%s)" % str(self.size) + + @staticmethod + def get_params(img, output_size): + w, h = img.size + th, tw = output_size + assert h >= th and w >= tw, "Image of %dx%d is too small for crop %dx%d" % (w,h,tw,th) + + y = np.random.randint(0, h - th) if h > th else 0 + x = np.random.randint(0, w - tw) if w > tw else 0 + return x, y, tw, th + + def __call__(self, inp): + img = F.grab_img(inp) + + padl = padt = 0 + if self.padding: + if F.is_pil_image(img): + img = ImageOps.expand(img, border=self.padding, fill=0) + else: + assert isinstance(img, F.DummyImg) + img = img.expand(border=self.padding) + if isinstance(self.padding, int): + padl = padt = self.padding + else: + padl, padt = self.padding[0:2] + + i, j, tw, th = self.get_params(img, self.size) + img = img.crop((i, j, i+tw, j+th)) + + return F.update_img_and_labels(inp, img, persp=(1,0,padl-i,0,1,padt-j,0,0)) + + +class CenterCrop (RandomCrop): + """Crops the given PIL Image at the center. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. + """ + @staticmethod + def get_params(img, output_size): + w, h = img.size + th, tw = output_size + y = int(0.5 +((h - th) / 2.)) + x = int(0.5 +((w - tw) / 2.)) + return x, y, tw, th + + + +class RandomRotation(object): + """Rescale the input PIL.Image to a random size. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + degrees (float): + rotation angle. + + interpolation (int, optional): Desired interpolation. Default is + ``PIL.Image.BILINEAR`` + """ + + def __init__(self, degrees, interpolation=Image.BILINEAR): + self.degrees = degrees + self.interpolation = interpolation + + def __call__(self, inp): + img = F.grab_img(inp) + w, h = img.size + + angle = np.random.uniform(-self.degrees, self.degrees) + + img = img.rotate(angle, resample=self.interpolation) + w2, h2 = img.size + + trf = F.translate(-w/2,-h/2) + trf = F.persp_mul(trf, F.rotate(-angle * np.pi/180)) + trf = F.persp_mul(trf, F.translate(w2/2,h2/2)) + return F.update_img_and_labels(inp, img, persp=trf) + + + +class RandomTilting(object): + """Apply a random tilting (left, right, up, down) to the input PIL.Image + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + maginitude (float): + maximum magnitude of the random skew (value between 0 and 1) + directions (string): + tilting directions allowed (all, left, right, up, down) + examples: "all", "left,right", "up-down-right" + """ + + def __init__(self, magnitude, directions='all'): + self.magnitude = magnitude + self.directions = directions.lower().replace(',',' ').replace('-',' ') + + def __repr__(self): + return "RandomTilt(%g, '%s')" % (self.magnitude,self.directions) + + def __call__(self, inp): + img = F.grab_img(inp) + w, h = img.size + + x1,y1,x2,y2 = 0,0,h,w + original_plane = [(y1, x1), (y2, x1), (y2, x2), (y1, x2)] + + max_skew_amount = max(w, h) + max_skew_amount = int(ceil(max_skew_amount * self.magnitude)) + skew_amount = random.randint(1, max_skew_amount) + + if self.directions == 'all': + choices = [0,1,2,3] + else: + dirs = ['left', 'right', 'up', 'down'] + choices = [] + for d in self.directions.split(): + try: + choices.append(dirs.index(d)) + except: + raise ValueError('Tilting direction %s not recognized' % d) + + skew_direction = random.choice(choices) + + # print('randomtitlting: ', skew_amount, skew_direction) # to debug random + + if skew_direction == 0: + # Left Tilt + new_plane = [(y1, x1 - skew_amount), # Top Left + (y2, x1), # Top Right + (y2, x2), # Bottom Right + (y1, x2 + skew_amount)] # Bottom Left + elif skew_direction == 1: + # Right Tilt + new_plane = [(y1, x1), # Top Left + (y2, x1 - skew_amount), # Top Right + (y2, x2 + skew_amount), # Bottom Right + (y1, x2)] # Bottom Left + elif skew_direction == 2: + # Forward Tilt + new_plane = [(y1 - skew_amount, x1), # Top Left + (y2 + skew_amount, x1), # Top Right + (y2, x2), # Bottom Right + (y1, x2)] # Bottom Left + elif skew_direction == 3: + # Backward Tilt + new_plane = [(y1, x1), # Top Left + (y2, x1), # Top Right + (y2 + skew_amount, x2), # Bottom Right + (y1 - skew_amount, x2)] # Bottom Left + + # To calculate the coefficients required by PIL for the perspective skew, + # see the following Stack Overflow discussion: https://goo.gl/sSgJdj + matrix = [] + + for p1, p2 in zip(new_plane, original_plane): + matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0] * p1[0], -p2[0] * p1[1]]) + matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1] * p1[0], -p2[1] * p1[1]]) + + A = np.matrix(matrix, dtype=np.float) + B = np.array(original_plane).reshape(8) + + homography = np.dot(np.linalg.pinv(A), B) + homography = tuple(np.array(homography).reshape(8)) + #print(homography) + + img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC) + + homography = np.linalg.pinv(np.float32(homography+(1,)).reshape(3,3)).ravel()[:8] + return F.update_img_and_labels(inp, img, persp=tuple(homography)) + + +RandomTilt = RandomTilting # redefinition + + +class Tilt(object): + """Apply a known tilting to an image + """ + def __init__(self, *homography): + assert len(homography) == 8 + self.homography = homography + + def __call__(self, inp): + img = F.grab_img(inp) + homography = self.homography + #print(homography) + + img = img.transform(img.size, Image.PERSPECTIVE, homography, resample=Image.BICUBIC) + + homography = np.linalg.pinv(np.float32(homography+(1,)).reshape(3,3)).ravel()[:8] + return F.update_img_and_labels(inp, img, persp=tuple(homography)) + + + +class StillTransform (object): + """ Takes and return an image, without changing its shape or geometry. + """ + def _transform(self, img): + raise NotImplementedError() + + def __call__(self, inp): + img = F.grab_img(inp) + + # transform the image (size should not change) + try: + img = self._transform(img) + except TypeError: + pass + + return F.update_img_and_labels(inp, img, persp=(1,0,0,0,1,0,0,0)) + + + +class PixelNoise (StillTransform): + """ Takes an image, and add random white noise. + """ + def __init__(self, ampl=20): + StillTransform.__init__(self) + assert 0 <= ampl < 255 + self.ampl = ampl + + def __repr__(self): + return "PixelNoise(%g)" % self.ampl + + def _transform(self, img): + img = np.float32(img) + img += np.random.uniform(0.5-self.ampl/2, 0.5+self.ampl/2, size=img.shape) + return Image.fromarray(np.uint8(img.clip(0,255))) + + + +class ColorJitter (StillTransform): + """Randomly change the brightness, contrast and saturation of an image. + Copied from https://github.com/pytorch in torchvision/transforms/transforms.py + + Args: + brightness (float): How much to jitter brightness. brightness_factor + is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]. + contrast (float): How much to jitter contrast. contrast_factor + is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]. + saturation (float): How much to jitter saturation. saturation_factor + is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]. + hue(float): How much to jitter hue. hue_factor is chosen uniformly from + [-hue, hue]. Should be >=0 and <= 0.5. + """ + def __init__(self, brightness=0, contrast=0, saturation=0, hue=0): + self.brightness = brightness + self.contrast = contrast + self.saturation = saturation + self.hue = hue + + def __repr__(self): + return "ColorJitter(%g,%g,%g,%g)" % ( + self.brightness, self.contrast, self.saturation, self.hue) + + @staticmethod + def get_params(brightness, contrast, saturation, hue): + """Get a randomized transform to be applied on image. + Arguments are same as that of __init__. + Returns: + Transform which randomly adjusts brightness, contrast and + saturation in a random order. + """ + transforms = [] + if brightness > 0: + brightness_factor = np.random.uniform(max(0, 1 - brightness), 1 + brightness) + transforms.append(tvf.Lambda(lambda img: F.adjust_brightness(img, brightness_factor))) + + if contrast > 0: + contrast_factor = np.random.uniform(max(0, 1 - contrast), 1 + contrast) + transforms.append(tvf.Lambda(lambda img: F.adjust_contrast(img, contrast_factor))) + + if saturation > 0: + saturation_factor = np.random.uniform(max(0, 1 - saturation), 1 + saturation) + transforms.append(tvf.Lambda(lambda img: F.adjust_saturation(img, saturation_factor))) + + if hue > 0: + hue_factor = np.random.uniform(-hue, hue) + transforms.append(tvf.Lambda(lambda img: F.adjust_hue(img, hue_factor))) + + # print('colorjitter: ', brightness_factor, contrast_factor, saturation_factor, hue_factor) # to debug random seed + + np.random.shuffle(transforms) + transform = tvf.Compose(transforms) + + return transform + + def _transform(self, img): + transform = self.get_params(self.brightness, self.contrast, self.saturation, self.hue) + return transform(img) + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser("Script to try out and visualize transformations") + parser.add_argument('--img', type=str, default='imgs/test.png', help='input image') + parser.add_argument('--trfs', type=str, required=True, help='list of transformations') + parser.add_argument('--layout', type=int, nargs=2, default=(3,3), help='nb of rows,cols') + args = parser.parse_args() + + import os + args.img = args.img.replace('$HERE',os.path.dirname(__file__)) + img = Image.open(args.img) + img = dict(img=img) + + trfs = instanciate_transformation(args.trfs) + + from matplotlib import pyplot as pl + pl.ion() + pl.subplots_adjust(0,0,1,1) + + nr,nc = args.layout + + while True: + for j in range(nr): + for i in range(nc): + pl.subplot(nr,nc,i+j*nc+1) + if i==j==0: + img2 = img + else: + img2 = trfs(img.copy()) + if isinstance(img2, dict): + img2 = img2['img'] + pl.imshow(img2) + pl.xlabel("%d x %d" % img2.size) + pl.xticks(()) + pl.yticks(()) + pdb.set_trace() + + + diff --git a/third_party/r2d2/tools/transforms_tools.py b/third_party/r2d2/tools/transforms_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..294c22228a88f70480af52f79a77d73f9e5b3e1a --- /dev/null +++ b/third_party/r2d2/tools/transforms_tools.py @@ -0,0 +1,230 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import numpy as np +from PIL import Image, ImageOps, ImageEnhance + + +class DummyImg: + ''' This class is a dummy image only defined by its size. + ''' + def __init__(self, size): + self.size = size + + def resize(self, size, *args, **kwargs): + return DummyImg(size) + + def expand(self, border): + w, h = self.size + if isinstance(border, int): + size = (w+2*border, h+2*border) + else: + l,t,r,b = border + size = (w+l+r, h+t+b) + return DummyImg(size) + + def crop(self, border): + w, h = self.size + l,t,r,b = border + assert 0 <= l <= r <= w + assert 0 <= t <= b <= h + size = (r-l, b-t) + return DummyImg(size) + + def rotate(self, angle): + raise NotImplementedError + + def transform(self, size, *args, **kwargs): + return DummyImg(size) + + +def grab_img( img_and_label ): + ''' Called to extract the image from an img_and_label input + (a dictionary). Also compatible with old-style PIL images. + ''' + if isinstance(img_and_label, dict): + # if input is a dictionary, then + # it must contains the img or its size. + try: + return img_and_label['img'] + except KeyError: + return DummyImg(img_and_label['imsize']) + + else: + # or it must be the img directly + return img_and_label + + +def update_img_and_labels(img_and_label, img, persp=None): + ''' Called to update the img_and_label + ''' + if isinstance(img_and_label, dict): + img_and_label['img'] = img + img_and_label['imsize'] = img.size + + if persp: + if 'persp' not in img_and_label: + img_and_label['persp'] = (1,0,0,0,1,0,0,0) + img_and_label['persp'] = persp_mul(persp, img_and_label['persp']) + + return img_and_label + + else: + # or it must be the img directly + return img + + +def rand_log_uniform(a, b): + return np.exp(np.random.uniform(np.log(a),np.log(b))) + + +def translate(tx, ty): + return (1,0,tx, + 0,1,ty, + 0,0) + +def rotate(angle): + return (np.cos(angle),-np.sin(angle), 0, + np.sin(angle), np.cos(angle), 0, + 0, 0) + + +def persp_mul(mat, mat2): + ''' homography (perspective) multiplication. + mat: 8-tuple (homography transform) + mat2: 8-tuple (homography transform) or 2-tuple (point) + ''' + assert isinstance(mat, tuple) + assert isinstance(mat2, tuple) + + mat = np.float32(mat+(1,)).reshape(3,3) + mat2 = np.array(mat2+(1,)).reshape(3,3) + res = np.dot(mat, mat2) + return tuple((res/res[2,2]).ravel()[:8]) + + +def persp_apply(mat, pts): + ''' homography (perspective) transformation. + mat: 8-tuple (homography transform) + pts: numpy array + ''' + assert isinstance(mat, tuple) + assert isinstance(pts, np.ndarray) + assert pts.shape[-1] == 2 + mat = np.float32(mat+(1,)).reshape(3,3) + + if pts.ndim == 1: + pt = np.dot(pts, mat[:,:2].T).ravel() + mat[:,2] + pt /= pt[2] # homogeneous coordinates + return tuple(pt[:2]) + else: + pt = np.dot(pts, mat[:,:2].T) + mat[:,2] + pt[:,:2] /= pt[:,2:3] # homogeneous coordinates + return pt[:,:2] + + +def is_pil_image(img): + return isinstance(img, Image.Image) + + +def adjust_brightness(img, brightness_factor): + """Adjust brightness of an Image. + Args: + img (PIL Image): PIL Image to be adjusted. + brightness_factor (float): How much to adjust the brightness. Can be + any non negative number. 0 gives a black image, 1 gives the + original image while 2 increases the brightness by a factor of 2. + Returns: + PIL Image: Brightness adjusted image. + Copied from https://github.com/pytorch in torchvision/transforms/functional.py + """ + if not is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Brightness(img) + img = enhancer.enhance(brightness_factor) + return img + + +def adjust_contrast(img, contrast_factor): + """Adjust contrast of an Image. + Args: + img (PIL Image): PIL Image to be adjusted. + contrast_factor (float): How much to adjust the contrast. Can be any + non negative number. 0 gives a solid gray image, 1 gives the + original image while 2 increases the contrast by a factor of 2. + Returns: + PIL Image: Contrast adjusted image. + Copied from https://github.com/pytorch in torchvision/transforms/functional.py + """ + if not is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Contrast(img) + img = enhancer.enhance(contrast_factor) + return img + + +def adjust_saturation(img, saturation_factor): + """Adjust color saturation of an image. + Args: + img (PIL Image): PIL Image to be adjusted. + saturation_factor (float): How much to adjust the saturation. 0 will + give a black and white image, 1 will give the original image while + 2 will enhance the saturation by a factor of 2. + Returns: + PIL Image: Saturation adjusted image. + Copied from https://github.com/pytorch in torchvision/transforms/functional.py + """ + if not is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + enhancer = ImageEnhance.Color(img) + img = enhancer.enhance(saturation_factor) + return img + + +def adjust_hue(img, hue_factor): + """Adjust hue of an image. + The image hue is adjusted by converting the image to HSV and + cyclically shifting the intensities in the hue channel (H). + The image is then converted back to original image mode. + `hue_factor` is the amount of shift in H channel and must be in the + interval `[-0.5, 0.5]`. + See https://en.wikipedia.org/wiki/Hue for more details on Hue. + Args: + img (PIL Image): PIL Image to be adjusted. + hue_factor (float): How much to shift the hue channel. Should be in + [-0.5, 0.5]. 0.5 and -0.5 give complete reversal of hue channel in + HSV space in positive and negative direction respectively. + 0 means no shift. Therefore, both -0.5 and 0.5 will give an image + with complementary colors while 0 gives the original image. + Returns: + PIL Image: Hue adjusted image. + Copied from https://github.com/pytorch in torchvision/transforms/functional.py + """ + if not(-0.5 <= hue_factor <= 0.5): + raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor)) + + if not is_pil_image(img): + raise TypeError('img should be PIL Image. Got {}'.format(type(img))) + + input_mode = img.mode + if input_mode in {'L', '1', 'I', 'F'}: + return img + + h, s, v = img.convert('HSV').split() + + np_h = np.array(h, dtype=np.uint8) + # uint8 addition take cares of rotation across boundaries + with np.errstate(over='ignore'): + np_h += np.uint8(hue_factor * 255) + h = Image.fromarray(np_h, 'L') + + img = Image.merge('HSV', (h, s, v)).convert(input_mode) + return img + + + diff --git a/third_party/r2d2/tools/viz.py b/third_party/r2d2/tools/viz.py new file mode 100644 index 0000000000000000000000000000000000000000..c86103f3aeb468fca8b0ac9a412f22b85239361b --- /dev/null +++ b/third_party/r2d2/tools/viz.py @@ -0,0 +1,191 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import pdb +import numpy as np +import matplotlib.pyplot as pl + + +def make_colorwheel(): + ''' + Generates a color wheel for optical flow visualization as presented in: + Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) + URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + + Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py + Copyright (c) 2018 Tom Runia + ''' + + RY = 15 + YG = 6 + GC = 4 + CB = 11 + BM = 13 + MR = 6 + + ncols = RY + YG + GC + CB + BM + MR + colorwheel = np.zeros((ncols, 3)) + col = 0 + + # RY + colorwheel[0:RY, 0] = 255 + colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY) + col = col+RY + # YG + colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG) + colorwheel[col:col+YG, 1] = 255 + col = col+YG + # GC + colorwheel[col:col+GC, 1] = 255 + colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC) + col = col+GC + # CB + colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB) + colorwheel[col:col+CB, 2] = 255 + col = col+CB + # BM + colorwheel[col:col+BM, 2] = 255 + colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM) + col = col+BM + # MR + colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR) + colorwheel[col:col+MR, 0] = 255 + return colorwheel + + +def flow_compute_color(u, v, convert_to_bgr=False): + ''' + Applies the flow color wheel to (possibly clipped) flow components u and v. + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + :param u: np.ndarray, input horizontal flow + :param v: np.ndarray, input vertical flow + :param convert_to_bgr: bool, whether to change ordering and output BGR instead of RGB + :return: + + Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py + Copyright (c) 2018 Tom Runia + ''' + + flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8) + + colorwheel = make_colorwheel() # shape [55x3] + ncols = colorwheel.shape[0] + + rad = np.sqrt(np.square(u) + np.square(v)) + a = np.arctan2(-v, -u)/np.pi + + fk = (a+1) / 2*(ncols-1) + k0 = np.floor(fk).astype(np.int32) + k1 = k0 + 1 + k1[k1 == ncols] = 0 + f = fk - k0 + + for i in range(colorwheel.shape[1]): + + tmp = colorwheel[:,i] + col0 = tmp[k0] / 255.0 + col1 = tmp[k1] / 255.0 + col = (1-f)*col0 + f*col1 + + idx = (rad <= 1) + col[idx] = 1 - rad[idx] * (1-col[idx]) + col[~idx] = col[~idx] * 0.75 # out of range? + + # Note the 2-i => BGR instead of RGB + ch_idx = 2-i if convert_to_bgr else i + flow_image[:,:,ch_idx] = np.floor(255 * col) + + return flow_image + + +def flow_to_color(flow_uv, clip_flow=None, convert_to_bgr=False): + ''' + Expects a two dimensional flow image of shape [H,W,2] + According to the C++ source code of Daniel Scharstein + According to the Matlab source code of Deqing Sun + :param flow_uv: np.ndarray of shape [H,W,2] + :param clip_flow: float, maximum clipping value for flow + :return: + + Copied from https://github.com/tomrunia/OpticalFlow_Visualization/blob/master/flow_vis.py + Copyright (c) 2018 Tom Runia + ''' + + assert flow_uv.ndim == 3, 'input flow must have three dimensions' + assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]' + + if clip_flow is not None: + flow_uv = np.clip(flow_uv, 0, clip_flow) + + u = flow_uv[:,:,0] + v = flow_uv[:,:,1] + + rad = np.sqrt(np.square(u) + np.square(v)) + rad_max = np.max(rad) + + epsilon = 1e-5 + u = u / (rad_max + epsilon) + v = v / (rad_max + epsilon) + + return flow_compute_color(u, v, convert_to_bgr) + + + +def show_flow( img0, img1, flow, mask=None ): + img0 = np.asarray(img0) + img1 = np.asarray(img1) + if mask is None: mask = 1 + mask = np.asarray(mask) + if mask.ndim == 2: mask = mask[:,:,None] + assert flow.ndim == 3 + assert flow.shape[:2] == img0.shape[:2] and flow.shape[2] == 2 + + def noticks(): + pl.xticks([]) + pl.yticks([]) + fig = pl.figure("showing correspondences") + ax1 = pl.subplot(221) + ax1.numaxis = 0 + pl.imshow(img0*mask) + noticks() + ax2 = pl.subplot(222) + ax2.numaxis = 1 + pl.imshow(img1) + noticks() + + ax = pl.subplot(212) + ax.numaxis = 0 + flow_img = flow_to_color(np.where(np.isnan(flow), 0, flow)) + pl.imshow(flow_img * mask) + noticks() + + pl.subplots_adjust(0.01, 0.01, 0.99, 0.99, wspace=0.02, hspace=0.02) + + def motion_notify_callback(event): + if event.inaxes is None: return + x,y = event.xdata, event.ydata + ax1.lines = [] + ax2.lines = [] + try: + x,y = int(x+0.5), int(y+0.5) + ax1.plot(x,y,'+',ms=10,mew=2,color='blue',scalex=False,scaley=False) + x,y = flow[y,x] + (x,y) + ax2.plot(x,y,'+',ms=10,mew=2,color='red',scalex=False,scaley=False) + # we redraw only the concerned axes + renderer = fig.canvas.get_renderer() + ax1.draw(renderer) + ax2.draw(renderer) + fig.canvas.blit(ax1.bbox) + fig.canvas.blit(ax2.bbox) + except IndexError: + return + + cid_move = fig.canvas.mpl_connect('motion_notify_event',motion_notify_callback) + print("Move your mouse over the images to show matches (ctrl-C to quit)") + pl.show() + + diff --git a/third_party/r2d2/train.py b/third_party/r2d2/train.py new file mode 100644 index 0000000000000000000000000000000000000000..10d23d9e40ebe8cb10c4d548b7fcb5c1c0fd7739 --- /dev/null +++ b/third_party/r2d2/train.py @@ -0,0 +1,138 @@ +# Copyright 2019-present NAVER Corp. +# CC BY-NC-SA 3.0 +# Available only for non-commercial use + +import os, pdb +import torch +import torch.optim as optim + +from tools import common, trainer +from tools.dataloader import * +from nets.patchnet import * +from nets.losses import * + +default_net = "Quad_L2Net_ConfCFS()" + +toy_db_debug = """SyntheticPairDataset( + ImgFolder('imgs'), + 'RandomScale(256,1024,can_upscale=True)', + 'RandomTilting(0.5), PixelNoise(25)')""" + +db_web_images = """SyntheticPairDataset( + web_images, + 'RandomScale(256,1024,can_upscale=True)', + 'RandomTilting(0.5), PixelNoise(25)')""" + +db_aachen_images = """SyntheticPairDataset( + aachen_db_images, + 'RandomScale(256,1024,can_upscale=True)', + 'RandomTilting(0.5), PixelNoise(25)')""" + +db_aachen_style_transfer = """TransformedPairs( + aachen_style_transfer_pairs, + 'RandomScale(256,1024,can_upscale=True), RandomTilting(0.5), PixelNoise(25)')""" + +db_aachen_flow = "aachen_flow_pairs" + +data_sources = dict( + D = toy_db_debug, + W = db_web_images, + A = db_aachen_images, + F = db_aachen_flow, + S = db_aachen_style_transfer, + ) + +default_dataloader = """PairLoader(CatPairDataset(`data`), + scale = 'RandomScale(256,1024,can_upscale=True)', + distort = 'ColorJitter(0.2,0.2,0.2,0.1)', + crop = 'RandomCrop(192)')""" + +default_sampler = """NghSampler2(ngh=7, subq=-8, subd=1, pos_d=3, neg_d=5, border=16, + subd_neg=-8,maxpool_pos=True)""" + +default_loss = """MultiLoss( + 1, ReliabilityLoss(`sampler`, base=0.5, nq=20), + 1, CosimLoss(N=`N`), + 1, PeakyLoss(N=`N`))""" + + +class MyTrainer(trainer.Trainer): + """ This class implements the network training. + Below is the function I need to overload to explain how to do the backprop. + """ + def forward_backward(self, inputs): + output = self.net(imgs=[inputs.pop('img1'),inputs.pop('img2')]) + allvars = dict(inputs, **output) + loss, details = self.loss_func(**allvars) + if torch.is_grad_enabled(): loss.backward() + return loss, details + + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser("Train R2D2") + + parser.add_argument("--data-loader", type=str, default=default_dataloader) + parser.add_argument("--train-data", type=str, default=list('WASF'), nargs='+', + choices = set(data_sources.keys())) + parser.add_argument("--net", type=str, default=default_net, help='network architecture') + + parser.add_argument("--pretrained", type=str, default="", help='pretrained model path') + parser.add_argument("--save-path", type=str, required=True, help='model save_path path') + + parser.add_argument("--loss", type=str, default=default_loss, help="loss function") + parser.add_argument("--sampler", type=str, default=default_sampler, help="AP sampler") + parser.add_argument("--N", type=int, default=16, help="patch size for repeatability") + + parser.add_argument("--epochs", type=int, default=25, help='number of training epochs') + parser.add_argument("--batch-size", "--bs", type=int, default=8, help="batch size") + parser.add_argument("--learning-rate", "--lr", type=str, default=1e-4) + parser.add_argument("--weight-decay", "--wd", type=float, default=5e-4) + + parser.add_argument("--threads", type=int, default=8, help='number of worker threads') + parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='-1 for CPU') + + args = parser.parse_args() + + iscuda = common.torch_set_gpu(args.gpu) + common.mkdir_for(args.save_path) + + # Create data loader + from datasets import * + db = [data_sources[key] for key in args.train_data] + db = eval(args.data_loader.replace('`data`',','.join(db)).replace('\n','')) + print("Training image database =", db) + loader = threaded_loader(db, iscuda, args.threads, args.batch_size, shuffle=True) + + # create network + print("\n>> Creating net = " + args.net) + net = eval(args.net) + print(f" ( Model size: {common.model_size(net)/1000:.0f}K parameters )") + + # initialization + if args.pretrained: + checkpoint = torch.load(args.pretrained, lambda a,b:a) + net.load_pretrained(checkpoint['state_dict']) + + # create losses + loss = args.loss.replace('`sampler`',args.sampler).replace('`N`',str(args.N)) + print("\n>> Creating loss = " + loss) + loss = eval(loss.replace('\n','')) + + # create optimizer + optimizer = optim.Adam( [p for p in net.parameters() if p.requires_grad], + lr=args.learning_rate, weight_decay=args.weight_decay) + + train = MyTrainer(net, loader, loss, optimizer) + if iscuda: train = train.cuda() + + # Training loop # + for epoch in range(args.epochs): + print(f"\n>> Starting epoch {epoch}...") + train() + + print(f"\n>> Saving model to {args.save_path}") + torch.save({'net': args.net, 'state_dict': net.state_dict()}, args.save_path) + + diff --git a/third_party/r2d2/viz_heatmaps.py b/third_party/r2d2/viz_heatmaps.py new file mode 100644 index 0000000000000000000000000000000000000000..42705e70ecea82696a0d784b274f7f387fdf6595 --- /dev/null +++ b/third_party/r2d2/viz_heatmaps.py @@ -0,0 +1,122 @@ +import pdb +import os +import sys +import tqdm + +import numpy as np +import torch + +from PIL import Image +from matplotlib import pyplot as pl; pl.ion() +from scipy.ndimage import uniform_filter +smooth = lambda arr: uniform_filter(arr, 3) + +def transparent(img, alpha, cmap, **kw): + from matplotlib.colors import Normalize + colored_img = cmap(Normalize(clip=True,**kw)(img)) + colored_img[:,:,-1] = alpha + return colored_img + +from tools import common +from tools.dataloader import norm_RGB +from nets.patchnet import * +from extract import NonMaxSuppression + + +if __name__ == '__main__': + import argparse + parser = argparse.ArgumentParser("Visualize the patch detector and descriptor") + + parser.add_argument("--img", type=str, default="imgs/brooklyn.png") + parser.add_argument("--resize", type=int, default=512) + parser.add_argument("--out", type=str, default="viz.png") + + parser.add_argument("--checkpoint", type=str, required=True, help='network path') + parser.add_argument("--net", type=str, default="", help='network command') + + parser.add_argument("--max-kpts", type=int, default=200) + parser.add_argument("--reliability-thr", type=float, default=0.8) + parser.add_argument("--repeatability-thr", type=float, default=0.7) + parser.add_argument("--border", type=int, default=20,help='rm keypoints close to border') + + parser.add_argument("--gpu", type=int, nargs='+', required=True, help='-1 for CPU') + parser.add_argument("--dbg", type=str, nargs='+', default=(), help='debug options') + + args = parser.parse_args() + args.dbg = set(args.dbg) + + iscuda = common.torch_set_gpu(args.gpu) + device = torch.device('cuda' if iscuda else 'cpu') + + # create network + checkpoint = torch.load(args.checkpoint, lambda a,b:a) + args.net = args.net or checkpoint['net'] + print("\n>> Creating net = " + args.net) + net = eval(args.net) + net.load_state_dict({k.replace('module.',''):v for k,v in checkpoint['state_dict'].items()}) + if iscuda: net = net.cuda() + print(f" ( Model size: {common.model_size(net)/1000:.0f}K parameters )") + + img = Image.open(args.img).convert('RGB') + if args.resize: img.thumbnail((args.resize,args.resize)) + img = np.asarray(img) + + detector = NonMaxSuppression( + rel_thr = args.reliability_thr, + rep_thr = args.repeatability_thr) + + with torch.no_grad(): + print(">> computing features...") + res = net(imgs=[norm_RGB(img).unsqueeze(0).to(device)]) + rela = res.get('reliability') + repe = res.get('repeatability') + kpts = detector(**res).T[:,[1,0]] + kpts = kpts[repe[0][0,0][kpts[:,1],kpts[:,0]].argsort()[-args.max_kpts:]] + + fig = pl.figure("viz") + kw = dict(cmap=pl.cm.RdYlGn, vmax=1) + crop = (slice(args.border,-args.border or 1),)*2 + + if 'reliability' in args.dbg: + + ax1 = pl.subplot(131) + pl.imshow(img[crop], cmap=pl.cm.gray) + pl.xticks(()); pl.yticks(()) + + pl.subplot(132) + pl.imshow(img[crop], cmap=pl.cm.gray, alpha=0) + pl.xticks(()); pl.yticks(()) + + x,y = kpts[:,0:2].cpu().numpy().T - args.border + pl.plot(x,y,'+',c=(0,1,0),ms=10, scalex=0, scaley=0) + + ax1 = pl.subplot(133) + rela = rela[0][0,0].cpu().numpy() + pl.imshow(rela[crop], cmap=pl.cm.RdYlGn, vmax=1, vmin=0.9) + pl.xticks(()); pl.yticks(()) + + else: + ax1 = pl.subplot(131) + pl.imshow(img[crop], cmap=pl.cm.gray) + pl.xticks(()); pl.yticks(()) + + x,y = kpts[:,0:2].cpu().numpy().T - args.border + pl.plot(x,y,'+',c=(0,1,0),ms=10, scalex=0, scaley=0) + + pl.subplot(132) + pl.imshow(img[crop], cmap=pl.cm.gray) + pl.xticks(()); pl.yticks(()) + c = repe[0][0,0].cpu().numpy() + pl.imshow(transparent(smooth(c)[crop], 0.5, vmin=0, **kw)) + + ax1 = pl.subplot(133) + pl.imshow(img[crop], cmap=pl.cm.gray) + pl.xticks(()); pl.yticks(()) + rela = rela[0][0,0].cpu().numpy() + pl.imshow(transparent(rela[crop], 0.5, vmin=0.9, **kw)) + + pl.gcf().set_size_inches(9, 2.73) + pl.subplots_adjust(0.01,0.01,0.99,0.99,hspace=0.1) + pl.savefig(args.out) + pdb.set_trace() +