{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Specific Test V. Exploring Transformers\n","\n","> This is a copy of the training notebook used for Specific Test 5 of the DeepLense tests. The original notebook, datasets and results can be found [here](https://github.com/gauthamk02/deeplense-test-2023)\n","\n","**Task:** Use a vision transformer method of your choice to build a robust and efficient model for binary classification or unsupervised anomaly detection on the provided dataset. In the case of unsupervised anomaly detection, train your model to learn the distribution of the provided strong lensing images with no substructure. Please implement your approach in PyTorch or Keras and discuss your strategy.\n","\n","**Dataset Description:** A set of simulated strong gravitational lensing images with and without substructure. \n","\n","**Evaluation Metrics:** ROC curve (Receiver Operating Characteristic curve) and AUC score (Area Under the ROC Curve)"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:14.076073Z","iopub.status.busy":"2023-03-11T09:43:14.075565Z","iopub.status.idle":"2023-03-11T09:43:24.878632Z","shell.execute_reply":"2023-03-11T09:43:24.877344Z","shell.execute_reply.started":"2023-03-11T09:43:14.076037Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: einops in /opt/conda/lib/python3.7/site-packages (0.6.0)\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]}],"source":["!pip install einops"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.883148Z","iopub.status.busy":"2023-03-11T09:43:24.882826Z","iopub.status.idle":"2023-03-11T09:43:24.890557Z","shell.execute_reply":"2023-03-11T09:43:24.889077Z","shell.execute_reply.started":"2023-03-11T09:43:24.883115Z"},"trusted":true},"outputs":[],"source":["import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import DataLoader, Dataset\n","import matplotlib.pyplot as plt\n","from sklearn.model_selection import train_test_split\n","import numpy as np\n","import pandas as pd\n","import os\n","from PIL import Image\n","from tqdm import tqdm\n","import seaborn as sns\n","\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"]},{"cell_type":"markdown","metadata":{},"source":["## Loading Data"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.892682Z","iopub.status.busy":"2023-03-11T09:43:24.892290Z","iopub.status.idle":"2023-03-11T09:43:24.942546Z","shell.execute_reply":"2023-03-11T09:43:24.941484Z","shell.execute_reply.started":"2023-03-11T09:43:24.892644Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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pathlabel
0/kaggle/input/ml4sci-test5/lenses/no_sub/image...no_sub
1/kaggle/input/ml4sci-test5/lenses/no_sub/image...no_sub
2/kaggle/input/ml4sci-test5/lenses/no_sub/image...no_sub
3/kaggle/input/ml4sci-test5/lenses/no_sub/image...no_sub
4/kaggle/input/ml4sci-test5/lenses/no_sub/image...no_sub
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"],"text/plain":[" path label\n","0 /kaggle/input/ml4sci-test5/lenses/no_sub/image... no_sub\n","1 /kaggle/input/ml4sci-test5/lenses/no_sub/image... no_sub\n","2 /kaggle/input/ml4sci-test5/lenses/no_sub/image... no_sub\n","3 /kaggle/input/ml4sci-test5/lenses/no_sub/image... no_sub\n","4 /kaggle/input/ml4sci-test5/lenses/no_sub/image... no_sub"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["base_dir = '../datasets/test5/lenses'\n","\n","train_df = pd.DataFrame(columns = ['path', 'label'])\n","\n","label_map = {'no_sub':0, 'sub':1}\n","val_label_map = {0:'no_sub', 1:'sub'}\n","\n","for i in label_map.keys():\n"," entries = [os.path.join(base_dir, i, j) for j in os.listdir(os.path.join(base_dir, i))]\n"," temp_df = pd.DataFrame({'path':entries, 'label':i})\n"," train_df = pd.concat([train_df, temp_df], ignore_index=True)\n","\n","train_df.head()"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.945833Z","iopub.status.busy":"2023-03-11T09:43:24.945428Z","iopub.status.idle":"2023-03-11T09:43:24.964359Z","shell.execute_reply":"2023-03-11T09:43:24.963191Z","shell.execute_reply.started":"2023-03-11T09:43:24.945794Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Train split:\n","sub 4500\n","no_sub 4500\n","Name: label, dtype: int64\n","\n","Test split:\n","sub 500\n","no_sub 500\n","Name: label, dtype: int64\n"]}],"source":["train_df, test_df = train_test_split(train_df, test_size=0.1, random_state=42, stratify=train_df['label'])\n","\n","print(f\"Train split:\\n{train_df['label'].value_counts()}\\n\")\n","print(f\"Test split:\\n{test_df['label'].value_counts()}\")"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.967780Z","iopub.status.busy":"2023-03-11T09:43:24.967441Z","iopub.status.idle":"2023-03-11T09:43:24.975704Z","shell.execute_reply":"2023-03-11T09:43:24.974493Z","shell.execute_reply.started":"2023-03-11T09:43:24.967752Z"},"trusted":true},"outputs":[],"source":["class Dataset(Dataset):\n"," def __init__(self, df, transform=None):\n"," self.df = df\n"," self.transform = transform\n","\n"," def __len__(self):\n"," return len(self.df)\n","\n"," def __getitem__(self, idx):\n"," img_path = self.df.iloc[idx]['path']\n"," img = Image.open(img_path)\n"," \n"," if self.transform:\n"," img = self.transform(img)\n","\n"," label = self.df.iloc[idx]['label']\n"," label = label_map[label]\n"," \n"," return img, label"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.978749Z","iopub.status.busy":"2023-03-11T09:43:24.977569Z","iopub.status.idle":"2023-03-11T09:43:24.990137Z","shell.execute_reply":"2023-03-11T09:43:24.989173Z","shell.execute_reply.started":"2023-03-11T09:43:24.978711Z"},"trusted":true},"outputs":[],"source":["train_transforms = transforms.Compose([\n"," transforms.ColorJitter(brightness=0.2, contrast=0.2),\n"," transforms.RandomRotation(180),\n"," transforms.RandomHorizontalFlip(),\n"," transforms.Resize(256),\n"," transforms.ToTensor()\n","])\n","\n","test_transforms = transforms.Compose([\n"," transforms.Resize(256),\n"," transforms.ToTensor()\n","])\n","\n","train_dataset = Dataset(train_df, transform=train_transforms)\n","test_dataset = Dataset(test_df, transform=test_transforms)\n","\n","train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=2)\n","test_loader = DataLoader(test_dataset, batch_size=64, shuffle=True, num_workers=2)"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:24.992209Z","iopub.status.busy":"2023-03-11T09:43:24.991353Z","iopub.status.idle":"2023-03-11T09:43:25.550563Z","shell.execute_reply":"2023-03-11T09:43:25.549252Z","shell.execute_reply.started":"2023-03-11T09:43:24.992172Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["torch.Size([64, 1, 256, 256])\n"]}],"source":["img, label = next(iter(train_loader))\n","print(img.shape)"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:25.554842Z","iopub.status.busy":"2023-03-11T09:43:25.552310Z","iopub.status.idle":"2023-03-11T09:43:26.475677Z","shell.execute_reply":"2023-03-11T09:43:26.474519Z","shell.execute_reply.started":"2023-03-11T09:43:25.554796Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["def show_batch(images, labels, class_map):\n"," fig, ax = plt.subplots(3, 3, figsize=(6, 6))\n"," for i in range(3):\n"," for j in range(3):\n"," image = images[i*3 + j]\n"," label = torch.argmax(labels[i*3 + j])\n"," ax[i][j].imshow(image.permute(1, 2, 0))\n"," title = [k for k, v in class_map.items() if v == label][0]\n"," ax[i][j].set_title(title)\n"," ax[i][j].axis('off')\n"," ax[i][j].title.set_fontsize(10)\n","\n"," plt.show()\n","\n","images, labels = next(iter(train_loader))\n","show_batch(images, labels, label_map)"]},{"cell_type":"markdown","metadata":{},"source":["## Defining Model"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:26.478507Z","iopub.status.busy":"2023-03-11T09:43:26.477831Z","iopub.status.idle":"2023-03-11T09:43:26.510319Z","shell.execute_reply":"2023-03-11T09:43:26.509443Z","shell.execute_reply.started":"2023-03-11T09:43:26.478461Z"},"trusted":true},"outputs":[],"source":["import torch\n","from torch import nn, einsum\n","import torch.nn.functional as F\n","\n","from einops import rearrange\n","from einops.layers.torch import Rearrange\n","\n","# helper methods\n","\n","def group_dict_by_key(cond, d):\n"," return_val = [dict(), dict()]\n"," for key in d.keys():\n"," match = bool(cond(key))\n"," ind = int(not match)\n"," return_val[ind][key] = d[key]\n"," return (*return_val,)\n","\n","def group_by_key_prefix_and_remove_prefix(prefix, d):\n"," kwargs_with_prefix, kwargs = group_dict_by_key(lambda x: x.startswith(prefix), d)\n"," kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))\n"," return kwargs_without_prefix, kwargs\n","\n","# classes\n","\n","class LayerNorm(nn.Module): # layernorm, but done in the channel dimension #1\n"," def __init__(self, dim, eps = 1e-5):\n"," super().__init__()\n"," self.eps = eps\n"," self.g = nn.Parameter(torch.ones(1, dim, 1, 1))\n"," self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))\n","\n"," def forward(self, x):\n"," var = torch.var(x, dim = 1, unbiased = False, keepdim = True)\n"," mean = torch.mean(x, dim = 1, keepdim = True)\n"," return (x - mean) / (var + self.eps).sqrt() * self.g + self.b\n","\n","class PreNorm(nn.Module):\n"," def __init__(self, dim, fn):\n"," super().__init__()\n"," self.norm = LayerNorm(dim)\n"," self.fn = fn\n"," def forward(self, x, **kwargs):\n"," x = self.norm(x)\n"," return self.fn(x, **kwargs)\n","\n","class FeedForward(nn.Module):\n"," def __init__(self, dim, mult = 4, dropout = 0.):\n"," super().__init__()\n"," self.net = nn.Sequential(\n"," nn.Conv2d(dim, dim * mult, 1),\n"," nn.GELU(),\n"," nn.Dropout(dropout),\n"," nn.Conv2d(dim * mult, dim, 1),\n"," nn.Dropout(dropout)\n"," )\n"," def forward(self, x):\n"," return self.net(x)\n","\n","class DepthWiseConv2d(nn.Module):\n"," def __init__(self, dim_in, dim_out, kernel_size, padding, stride, bias = True):\n"," super().__init__()\n"," self.net = nn.Sequential(\n"," nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),\n"," nn.BatchNorm2d(dim_in),\n"," nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)\n"," )\n"," def forward(self, x):\n"," return self.net(x)\n","\n","class Attention(nn.Module):\n"," def __init__(self, dim, proj_kernel, kv_proj_stride, heads = 8, dim_head = 64, dropout = 0.):\n"," super().__init__()\n"," inner_dim = dim_head * heads\n"," padding = proj_kernel // 2\n"," self.heads = heads\n"," self.scale = dim_head ** -0.5\n","\n"," self.attend = nn.Softmax(dim = -1)\n"," self.dropout = nn.Dropout(dropout)\n","\n"," self.to_q = DepthWiseConv2d(dim, inner_dim, proj_kernel, padding = padding, stride = 1, bias = False)\n"," self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, proj_kernel, padding = padding, stride = kv_proj_stride, bias = False)\n","\n"," self.to_out = nn.Sequential(\n"," nn.Conv2d(inner_dim, dim, 1),\n"," nn.Dropout(dropout)\n"," )\n","\n"," def forward(self, x):\n"," shape = x.shape\n"," b, n, _, y, h = *shape, self.heads\n"," q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim = 1))\n"," q, k, v = map(lambda t: rearrange(t, 'b (h d) x y -> (b h) (x y) d', h = h), (q, k, v))\n","\n"," dots = einsum('b i d, b j d -> b i j', q, k) * self.scale\n","\n"," attn = self.attend(dots)\n"," attn = self.dropout(attn)\n","\n"," out = einsum('b i j, b j d -> b i d', attn, v)\n"," out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h = h, y = y)\n"," return self.to_out(out)\n","\n","class Transformer(nn.Module):\n"," def __init__(self, dim, proj_kernel, kv_proj_stride, depth, heads, dim_head = 64, mlp_mult = 4, dropout = 0.):\n"," super().__init__()\n"," self.layers = nn.ModuleList([])\n"," for _ in range(depth):\n"," self.layers.append(nn.ModuleList([\n"," PreNorm(dim, Attention(dim, proj_kernel = proj_kernel, kv_proj_stride = kv_proj_stride, heads = heads, dim_head = dim_head, dropout = dropout)),\n"," PreNorm(dim, FeedForward(dim, mlp_mult, dropout = dropout))\n"," ]))\n"," def forward(self, x):\n"," for attn, ff in self.layers:\n"," x = attn(x) + x\n"," x = ff(x) + x\n"," return x\n","\n","class CvT(nn.Module):\n"," def __init__(\n"," self,\n"," *,\n"," num_classes,\n"," s1_emb_dim = 64,\n"," s1_emb_kernel = 7,\n"," s1_emb_stride = 4,\n"," s1_proj_kernel = 3,\n"," s1_kv_proj_stride = 2,\n"," s1_heads = 1,\n"," s1_depth = 1,\n"," s1_mlp_mult = 4,\n"," s2_emb_dim = 192,\n"," s2_emb_kernel = 3,\n"," s2_emb_stride = 2,\n"," s2_proj_kernel = 3,\n"," s2_kv_proj_stride = 2,\n"," s2_heads = 3,\n"," s2_depth = 2,\n"," s2_mlp_mult = 4,\n"," s3_emb_dim = 384,\n"," s3_emb_kernel = 3,\n"," s3_emb_stride = 2,\n"," s3_proj_kernel = 3,\n"," s3_kv_proj_stride = 2,\n"," s3_heads = 6,\n"," s3_depth = 10,\n"," s3_mlp_mult = 4,\n"," dropout = 0.\n"," ):\n"," super().__init__()\n"," kwargs = dict(locals())\n","\n"," dim = 1\n"," layers = []\n","\n"," for prefix in ('s1', 's2', 's3'):\n"," config, kwargs = group_by_key_prefix_and_remove_prefix(f'{prefix}_', kwargs)\n","\n"," layers.append(nn.Sequential(\n"," nn.Conv2d(dim, config['emb_dim'], kernel_size = config['emb_kernel'], padding = (config['emb_kernel'] // 2), stride = config['emb_stride']),\n"," LayerNorm(config['emb_dim']),\n"," Transformer(dim = config['emb_dim'], proj_kernel = config['proj_kernel'], kv_proj_stride = config['kv_proj_stride'], depth = config['depth'], heads = config['heads'], mlp_mult = config['mlp_mult'], dropout = dropout)\n"," ))\n","\n"," dim = config['emb_dim']\n","\n"," self.layers = nn.Sequential(*layers)\n","\n"," self.to_logits = nn.Sequential(\n"," nn.AdaptiveAvgPool2d(1),\n"," Rearrange('... () () -> ...'),\n"," nn.Linear(dim, num_classes)\n"," )\n","\n"," def forward(self, x):\n"," latents = self.layers(x)\n"," return self.to_logits(latents)"]},{"cell_type":"markdown","metadata":{},"source":["## Hyper-parameters and Training"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:26.515399Z","iopub.status.busy":"2023-03-11T09:43:26.514765Z","iopub.status.idle":"2023-03-11T09:43:26.529464Z","shell.execute_reply":"2023-03-11T09:43:26.528682Z","shell.execute_reply.started":"2023-03-11T09:43:26.515359Z"},"trusted":true},"outputs":[],"source":["def train(model, epochs, optimizer, criterion, scheduler, device, trainloader, testloader):\n","\n"," train_losses = []\n"," train_acc = []\n"," test_losses = []\n"," test_acc = []\n"," \n"," for i in range(epochs):\n"," running_loss = 0.0\n"," running_correct = 0\n"," total = 0\n"," best_acc = 0.0\n"," \n"," print(f\"Epoch: {i + 1}\")\n"," \n"," for images, targets in tqdm(trainloader, desc= \"Train\\t\"):\n"," \n"," images, targets = images.to(device), targets.to(device)\n"," optimizer.zero_grad()\n"," output = model(images)\n"," loss = criterion(output, targets)\n"," loss.backward()\n"," optimizer.step()\n","\n"," running_loss += loss.item()\n"," pred = torch.argmax(output, dim=1)\n"," \n"," running_correct += (pred == targets).sum().item()\n"," total += targets.size(0)\n","\n"," scheduler.step()\n"," \n"," train_losses.append(running_loss / len(trainloader))\n"," train_acc.append(running_correct / total)\n","\n"," running_val_loss = 0.0\n"," correct = 0\n"," total = 0\n"," \n"," with torch.no_grad():\n","\n"," for images, targets in tqdm(testloader, desc= \"Test\\t\"):\n"," images, targets = images.to(device), targets.to(device)\n","\n"," output = model(images)\n"," preds = torch.argmax(output, dim=1)\n","\n"," correct += (preds == targets).sum().item()\n"," running_val_loss += criterion(output, targets).item()\n"," total += targets.size(0)\n","\n"," acc = correct / total\n"," test_acc.append(acc)\n"," test_losses.append(running_val_loss / len(testloader))\n","\n"," if test_acc[-1] > best_acc:\n"," best_acc = test_acc[-1]\n"," torch.save(model.state_dict(), '../models/test-5-cvt-model.pth') \n","\n"," print(f\"Train Loss: {train_losses[-1]:.3f}, Train Acc: {train_acc[-1]:.3f}, Test Loss: {test_losses[-1]:.3f}, Test Acc: {test_acc[-1]:.3f}\\n\")\n","\n"," return train_losses, train_acc, test_losses, test_acc, best_acc"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:26.531845Z","iopub.status.busy":"2023-03-11T09:43:26.531003Z","iopub.status.idle":"2023-03-11T09:43:26.780820Z","shell.execute_reply":"2023-03-11T09:43:26.779756Z","shell.execute_reply.started":"2023-03-11T09:43:26.531807Z"},"trusted":true},"outputs":[],"source":["model = CvT(num_classes= 2, dropout= 0.1).to(device)\n","\n","epochs = 30\n","lr = 0.0001\n","loss_function = torch.nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n","scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=0.000001)"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T09:43:26.782804Z","iopub.status.busy":"2023-03-11T09:43:26.782330Z","iopub.status.idle":"2023-03-11T10:40:25.471349Z","shell.execute_reply":"2023-03-11T10:40:25.470223Z","shell.execute_reply.started":"2023-03-11T09:43:26.782763Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch: 1\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.692, Train Acc: 0.623, Test Loss: 0.377, Test Acc: 0.782\n","\n","Epoch: 2\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.368, Train Acc: 0.806, Test Loss: 0.261, Test Acc: 0.866\n","\n","Epoch: 3\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.304, Train Acc: 0.843, Test Loss: 0.257, Test Acc: 0.880\n","\n","Epoch: 4\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.69it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.258, Train Acc: 0.877, Test Loss: 0.201, Test Acc: 0.905\n","\n","Epoch: 5\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.59it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.229, Train Acc: 0.893, Test Loss: 0.188, Test Acc: 0.915\n","\n","Epoch: 6\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.177, Train Acc: 0.920, Test Loss: 0.143, Test Acc: 0.942\n","\n","Epoch: 7\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.161, Train Acc: 0.929, Test Loss: 0.142, Test Acc: 0.939\n","\n","Epoch: 8\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.69it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.138, Train Acc: 0.939, Test Loss: 0.190, Test Acc: 0.907\n","\n","Epoch: 9\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.71it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.129, Train Acc: 0.945, Test Loss: 0.093, Test Acc: 0.962\n","\n","Epoch: 10\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.65it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.121, Train Acc: 0.950, Test Loss: 0.092, Test Acc: 0.966\n","\n","Epoch: 11\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.69it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.104, Train Acc: 0.957, Test Loss: 0.066, Test Acc: 0.978\n","\n","Epoch: 12\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.105, Train Acc: 0.955, Test Loss: 0.060, Test Acc: 0.974\n","\n","Epoch: 13\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.68it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.087, Train Acc: 0.965, Test Loss: 0.050, Test Acc: 0.983\n","\n","Epoch: 14\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.078, Train Acc: 0.969, Test Loss: 0.057, Test Acc: 0.979\n","\n","Epoch: 15\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.70it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.080, Train Acc: 0.968, Test Loss: 0.043, Test Acc: 0.986\n","\n","Epoch: 16\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.71it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.072, Train Acc: 0.971, Test Loss: 0.055, Test Acc: 0.978\n","\n","Epoch: 17\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.069, Train Acc: 0.972, Test Loss: 0.071, Test Acc: 0.967\n","\n","Epoch: 18\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.060, Train Acc: 0.973, Test Loss: 0.039, Test Acc: 0.987\n","\n","Epoch: 19\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.053, Train Acc: 0.977, Test Loss: 0.037, Test Acc: 0.987\n","\n","Epoch: 20\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.062, Train Acc: 0.976, Test Loss: 0.049, Test Acc: 0.983\n","\n","Epoch: 21\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.70it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.048, Train Acc: 0.982, Test Loss: 0.037, Test Acc: 0.983\n","\n","Epoch: 22\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.43it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.047, Train Acc: 0.981, Test Loss: 0.022, Test Acc: 0.989\n","\n","Epoch: 23\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.74it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.038, Train Acc: 0.985, Test Loss: 0.023, Test Acc: 0.992\n","\n","Epoch: 24\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.038, Train Acc: 0.985, Test Loss: 0.031, Test Acc: 0.989\n","\n","Epoch: 25\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.037, Train Acc: 0.985, Test Loss: 0.022, Test Acc: 0.992\n","\n","Epoch: 26\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.037, Train Acc: 0.985, Test Loss: 0.028, Test Acc: 0.989\n","\n","Epoch: 27\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.66it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.034, Train Acc: 0.987, Test Loss: 0.020, Test Acc: 0.991\n","\n","Epoch: 28\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.73it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.028, Train Acc: 0.991, Test Loss: 0.023, Test Acc: 0.990\n","\n","Epoch: 29\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.031, Train Acc: 0.987, Test Loss: 0.018, Test Acc: 0.995\n","\n","Epoch: 30\n"]},{"name":"stderr","output_type":"stream","text":["Train\t: 100%|██████████| 141/141 [01:49<00:00, 1.29it/s]\n","Test\t: 100%|██████████| 16/16 [00:04<00:00, 3.69it/s]\n"]},{"name":"stdout","output_type":"stream","text":["Train Loss: 0.027, Train Acc: 0.991, Test Loss: 0.021, Test Acc: 0.990\n","\n"]}],"source":["train_losses, train_acc, test_losses, test_acc, best_acc = train(model, epochs, optimizer, loss_function, scheduler, device, train_loader, test_loader)"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:25.474303Z","iopub.status.busy":"2023-03-11T10:40:25.473880Z","iopub.status.idle":"2023-03-11T10:40:25.844491Z","shell.execute_reply":"2023-03-11T10:40:25.843460Z","shell.execute_reply.started":"2023-03-11T10:40:25.474259Z"},"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":39,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAz4AAAFfCAYAAACP/BkOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACie0lEQVR4nOzdd3hURdvA4d/upvdGCiQkoYXeQk0MnSA2rMQGohR5URGwIoqA+iHvKxAbWBFBxICIFZGgUoOUkKBSQ02AhJBCKmm7+/1xkoWQumHTn/u6zrUnZ2dm5wTdzbMz84xKr9frEUIIIYQQQogmTF3fHRBCCCGEEEKI2iaBjxBCCCGEEKLJk8BHCCGEEEII0eRJ4COEEEIIIYRo8iTwEUIIIYQQQjR5EvgIIYQQQgghmjwJfIQQQgghhBBNnll9d6A6dDodFy9exN7eHpVKVd/dEUKIZkOv15OVlUXLli1Rq+W7suvJZ5MQQtSPmn42NYrA5+LFi/j4+NR3N4QQotlKSEjA29u7vrvRoMhnkxBC1C9jP5saReBjb28PKDfn4OBQz70RQojmIzMzEx8fH8P7sLhGPpuEEKJ+1PSzqVEEPiVTCBwcHOTDRQgh6oFM5SpLPpuEEKJ+GfvZJBO2hRBCCCGEEE2eBD5CCCGEEEKIJk8CHyGEEEIIIUSTJ4GPEEIIIYQQosmrUeCzbNky/P39sbKyIjAwkJ07d1ZYdsKECahUqjJHly5datxpIYQQzcOOHTu48847admyJSqViu+//77KOtu3bycwMBArKyvatGnDRx99VKbMhg0b6Ny5M5aWlnTu3JmNGzfWQu+FEEI0JEYHPhEREcyYMYM5c+YQExNDSEgIo0ePJj4+vtzy7777LomJiYYjISEBFxcXHnjggZvuvBBCiKYtJyeHHj168MEHH1Sr/JkzZ7jtttsICQkhJiaGV155henTp7NhwwZDmT179hAWFsa4ceM4dOgQ48aNY+zYsezdu7e2bkMIIUQDoNLr9XpjKvTv35/evXuzfPlyw7VOnTpx9913s3Dhwirrf//999x7772cOXMGX1/far1mZmYmjo6OZGRkSMpQIYSoQw3p/VelUrFx40buvvvuCsu89NJL/Pjjjxw9etRwberUqRw6dIg9e/YAEBYWRmZmJr/++quhzK233oqzszNr166tdn8a0u9GCCGak5q+/xo14lNQUEB0dDShoaGlroeGhhIVFVWtNj7//HNGjBhRadCTn59PZmZmqUMIIYSoyp49e8p8Ro0aNYoDBw5QWFhYaZmqPsfks0kIIRo3owKflJQUtFotHh4epa57eHiQlJRUZf3ExER+/fVXJk2aVGm5hQsX4ujoaDh8fHyM6aYQQohmKikpqdzPqKKiIlJSUiotU9XnmHw2CSFE41aj5AY37pKq1+urtXPqypUrcXJyqnSaAsDs2bPJyMgwHAkJCTXpphBCiGaovM+oG6/X5HNMPpuEEKJxMyrwcXNzQ6PRlPlWLDk5ucy3ZzfS6/WsWLGCcePGYWFhUWlZS0tLHBwcSh01deJSFg998heTvjxQ4zaEEE3HkCFDmDFjRrXLnz17FpVKRWxsbK31CWDbtm2oVCquXLlSq6/T1Hl6epb7GWVmZoarq2ulZar6HDPlZ5MQQjQZV9Ph4GqIeBR+eR7O7gadrr57VS4zYwpbWFgQGBhIZGQk99xzj+F6ZGQkY8aMqbTu9u3bOXnyJBMnTqxZT2tIr4c9p1NxtjGv09cVQtycqr59f+yxx1i5cqXR7X733XeYm1f//cDHx4fExETc3NyMfi1R9wYOHMhPP/1U6tqWLVvo06eP4d994MCBREZGMnPmzFJlgoKC6rSvQgjRaOVlwvFN8O93cOoP0BVee27/p2DvBV3ugS73gncfqMbMsLpgVOADMGvWLMaNG0efPn0YOHAgn3zyCfHx8UydOhVQpgJcuHCBVatWlar3+eef079/f7p27WqanleTi60yunTlaiFanR6NumH84oUQlUtMTDScR0REMHfuXI4fP264Zm1tXap8YWFhtQIaFxcXo/qh0Wjw9PQ0qo4wnezsbE6ePGn4+cyZM8TGxuLi4kLr1q3LfOZMnTqVDz74gFmzZjF58mT27NnD559/Xipb27PPPsugQYNYtGgRY8aM4YcffmDr1q3s2rWrzu9PCNFI6fWQeREuHoTUk+DsDy17gVPrm/8jP+sSXIyBy8fA0Vtp16VN/QcPBTlwYrMS7MRFgjb/2nPuXaDzXXAlHo7+DFmJ8Ncy5XBsDV3uhq73gVePer0PowOfsLAwUlNTWbBgAYmJiXTt2pVNmzYZsrQlJiaW2dMnIyODDRs28O6775qm10YoGenR6yE9twA3O8s674MQwnjXBxuOjo6oVCrDtbNnz+Ll5UVERATLli3jr7/+Yvny5dx11108/fTT7Ny5k7S0NNq2bcsrr7zCQw89ZGhryJAh9OzZk/DwcAD8/PyYMmUKJ0+eZP369Tg7O/Pqq68yZcoUw2v5+/sTExNDz5492bZtG0OHDmXr1q289NJLHDlyhJ49e/LFF18QEBBgeJ0333yT9957j6tXrxIWFoabmxubN282asrchg0bmDt3LidPnsTLy4tnnnmG5557zvD8smXLWLp0KQkJCTg6OhISEsK3334LwLfffsv8+fM5efIkNjY29OrVix9++AFbW1uj/y3q04EDBxg6dKjh51mzZgHXRvxu/Mzx9/dn06ZNzJw5kw8//JCWLVvy3nvvcd999xnKBAUF8c033/Dqq6/y2muv0bZtWyIiIujfv3/d3ZgQonHJTlaCkYsxcOGg8piTXLacjasSqLTsBS17K48OXhW3m5tW3O5BuBirnGdeKFvOyvG6dovbdvSuOojIz4K0M5B+5trjlQTQmIOVE1g7g3Xx43U/ay0dOXZFQ/TFPDrkHKDbld+xObsVVWHutbZd2yvBTNd7ocW1zz/uWKqMAv27AY7/ChnxEPUeRL2H3qUN6f53cMBuGOl2bQnr27ry/puY0YEPwLRp05g2bVq5z5U39cTR0ZHc3NyyheuAmUaNk405V3ILScuRwEeIEnq9nquF2jp/XWtzTbWSoVTHSy+9xOLFi/niiy+wtLQkLy+PwMBAXnrpJRwcHPjll18YN24cbdq0qfSP2sWLF/PGG2/wyiuv8O233/Kf//yHQYMG0bFjxwrrzJkzh8WLF9OiRQumTp3KE088we7duwFYs2YNb731FsuWLSM4OJhvvvmGxYsX4+/vX+17i46OZuzYscybN4+wsDCioqKYNm0arq6uTJgwgQMHDjB9+nRWr15NUFAQaWlp7Ny5E1C+gHrooYf473//yz333ENWVhY7d+7EyG3bGoQhQ4ZU2u/yPnMGDx7MwYMHK233/vvv5/7777/Z7gkhmiJtIZzdpQQjF4oDkszzZcupNODeGdzaQ9ppuHQYclPh5FblKGHnCa2Kg6AWHZXgoySAunKunA6olHLunZQRlKR/IC8DTm9TjhI2bkqbrXqDZ7dygpyzkJtSo1+BBuhSfFwv2cyLeK9b0XS/j/bdBmBnVc5MCzNLCBgNAaNJTk3n4oEfsT7+Pf5pu7BIO41L2nuE8h6nVT7QYy9Y1N0XcjUKfBobF1sLruQWkppdAJWvXRWi2bhaqKXz3N/q/HWPLBiFjYVp3npmzJjBvffeW+ra888/bzh/5pln2Lx5M+vXr6808LntttsMX+a89NJLLF26lG3btlUa+Lz11lsMHjwYgJdffpnbb7+dvLw8rKyseP/995k4cSKPP/44AHPnzmXLli1kZ2dX+96WLFnC8OHDee211wDo0KEDR44c4X//+x8TJkwgPj4eW1tb7rjjDuzt7fH19aVXr16AEvgUFRVx7733Gkbju3XrVu3XFkKIZulKAkSvhJjVkH3phidV4NbhWqDRspcSbJhfN+26MA+SD18Lli4eVKarZScp62GObyr/dV3aXhvJadUbPLuDpd2157WFkHyk9GhT8hElqDkZqRyVsXFVpuK5+CuPzr6g05KdkcLFxIukpSSTk5GCRWEmTqpsHMnBSZWDg0oZtLisbsGPhf34oWgAf+e1gTgVxGWi+m4LAR729GrtRC8fZ3r4OJGeW8ChhCvEJlzhUMIVLmbkAW7AJGx5hOHqg4wx/4tBqkNY2DhQpLGu02CkWQQ+rrYWnL6cQ1pOQX13RQhhQn369Cn1s1ar5e233yYiIoILFy6Qn59Pfn5+ldO7unfvbjgvmVKXnFzOFIYK6nh5KdMYkpOTad26NcePHy8zKt6vXz/++OOPat0XwNGjR8skjQkODiY8PBytVsvIkSPx9fWlTZs23Hrrrdx6663cc8892NjY0KNHD4YPH063bt0YNWoUoaGh3H///Tg7O1f79YUQwmSO/QL7PwNdNWcZmFtD6wHQdhh4dAN1jXZfqR6dFk7+DgdWQNxvoC/ORmbbAvxCrgU5Xj3A0r6KfltBq0DlKFGQo4zYlAQsl4+Bs9+1IMerhzK9rDIac6WcVw8InKBcK7yqjDCVBEPJh5XpcNcHOC7+6Jz8SCm05GJGHolXrnIxI4+EhFz+Op3KsSQ34NoXfBYaNb19nbilnRvB7dzo5mWHWVEOLSwdeFSnJzAxi4Pn0olJuEJMfDrn069yLCmLY0lZrN1Xfnp/lQo6uNvT08eJHj5O9PAZSYCHPWYFmXhnXwJNLf7blqNZBD4lCQ7ScvKrKClE82FtruHIglH18rqmcmNAs3jxYpYuXUp4eDjdunXD1taWGTNmUFBQ+ZceNyZFUKlU6KpIxXl9nZKpe9fXqWgvmeoqb1+Z69uwt7fn4MGDbNu2jS1btjB37lzmzZvH/v37cXJyIjIykqioKLZs2cL777/PnDlz2Lt3r1HT7YQQjZheryxA37VUmS51ywzlj2ZNHWe5vXwcvp0IRVeNq3diM2ydp0znajsU2gxVHh1amqZf2ZeVkZ3oL5TfTwm/EOg7ETreUeXvSqvTE5twhQNn08gv0qHT69HplffqknOdXo9e74hONwS95RD0rcDKXI1NoQabi2bYpmZhbXEVWwsN1hYabC3MsLXUYG1hhrW5Bp1eT5FWT6FWR4FWR6FWR5FWr5wX+VPo7Eeh4xjy2+u4nJXHxYw8Lp6/SuK/eVzMSOVS5gUKtRV//nT2cuCW9kqg09fPueyMDHMnACzV0NPHiZ4+ToankjPzioOgKxyMT+ffCxk4WJkr5Vo70cPbiW7ejthZlhNuWDspRx1rJoGPsq4nVUZ8hDBQqVQmm3LWUOzcuZMxY8bw6KOPAkogEhcXR6dOneq0HwEBAezbt49x48YZrh04YNxeYp07dy6TZSwqKooOHTqg0SjBo5mZGSNGjGDEiBG8/vrrODk58ccff3DvvfeiUqkIDg4mODiYuXPn4uvry8aNGw3JAYQQTZROC0d+gJ1L4NI/165veh72fgTDX4dOd9ZNZq3Cq7D+cSXo8Qu5NlpRlZwUZS3L2Z3KdK5/1isHQItOykhQ22HgGwQWNtXvj14P56KU0Z0jP1xLwWzlCD0fgcDHoUWHSpvIzCtkx4nL/HEsmW3HLzeK2URqFXg4WOHlaIWXkzUtHa3o7u1EUFtXXG9i7bu7gxWjungyqkvjyXzatP7qqYCrYcSn4f/HKYSouXbt2rFhwwaioqJwdnZmyZIlJCUl1Xng88wzzzB58mT69OlDUFAQERER/P3337Rp06babTz33HP07duXN954g7CwMPbs2cMHH3zAsmXLAPj55585ffo0gwYNwtnZmU2bNqHT6QgICGDv3r38/vvvhIaG4u7uzt69e7l8+XKd/x6EENVUkAN7PlQWpPsFK6Mbjq2Ma6OoAP5Zp4zwpBangDe3hb5PgEMr2PGOcn3dOPDuB6FvKNPJatOWV5UpWLYt4L7Pwd6IhdYDpir3dH6/kiHs1B/FU8WOKsdfH4LGQsluVo3gp0inpyA1HpvMU9cutgqEPk8oe81U0IZer+d0Sg5/HE3mj2PJ7D+bRpHuutF3KzNuaeeGs60FahWoVSrUKhUqw7nySMkjkFeo5WqBlpwCLbn5ReQWaMktKCKnoOR6Ebn5Wgq0yiwCc40KM7Uac40KCzM15pqSQ1Xq3M3OkpZO1ng5WtHSyZqWTlZ4OVrjbm+JWR1PKWuomkXgUzLVTUZ8hGjaXnvtNc6cOcOoUaOwsbFhypQp3H333WRkZNRpPx555BFOnz7N888/T15eHmPHjmXChAns27ev2m307t2bdevWMXfuXN544w28vLxYsGABEyZMAMDJyYnvvvuOefPmkZeXR/v27Vm7di1dunTh6NGj7Nixg/DwcDIzM/H19WXx4sWMHj26lu5YCFFjxzbBry9CRvEaiUNfK48tOhZP7xqmBEMVZb4qyFWmbO1+71rmMSsnGPAf6DcFbIr3Luv5iJJSeM+HcH4frBilTOca/nqVoxw1cuRHZV0PwD0fGRf0lDCzUO7dLxiGv6akfj6zXQmCTv6h3G/CX9VrqvjI1VvygzaIr3XDSbzUkRY7rHCP+Rd3e0vcHSxxt7fCw8ESc42aXSdT+PNYMmdTS2cmbtvCluGdPBjW0Z1AX2fMaymoKNLq0KhVJsuEKkClbwT5TTMzM3F0dCQjIwMHBwej6/8Qe4Fnv4llYBtX1k6p5W83hBCiHCNHjsTT05PVq1fXd1eMcrPvv02Z/G7ETbkSD7++dC3Tl2Nr6DIGzu1RsoHpr1tnqLEAn/7Xpnh5doeCLCWw2LPsWspiOw8IekaZUlbRQvzMRNi2UAmW9DolJXPgYzD45ZoFJxXd20e3KCmYg5+FkQtM0+719HplBOtiLOi1NzylJy45mx0nUjiSmEHJX7o2NrbsU/fgTLZZqVGbqphrVAxo48qwju4M6+iOr2vj2g+tKarp+2+zGvGRqW5CiLqQm5vLRx99xKhRo9BoNKxdu5atW7cSGVlFylEhRNOnLYQ9H8D2/0JhLqjNlGBl0AvXRnVy0+DMDjj9pzKykRGvrHc5uxN+n6+kJ9YWQX7xaLZTawieoYzqmFtV/voOXnDXezBgmpI84MSvypqXQxEQPB0GPl06lbLR91cEGyYrQU+rQBj2Ws3bqoxKpeyf49becOlqgZaNMRdYGXWGE5eyASURwpCAFkwI8mNQ+xao1Sp0Oj1puQUkZ+aTnJV37TEr33CemVdE79ZODOvozi3tW5S/QF80Os3iX1Gmugkh6pJKpWLTpk28+eab5OfnExAQwIYNGxgxYkR9d00IcTMyzit/1GdfgjaDldEX/0HK4vjqOLsbfpmlpDQG8A2G2xcrG1Vez8YFutytHHq9sjlmyTqXMzuUTTJBmQ53yyzoeh9ojPyTzr0jPPyN0qfI1+BCtDISdGAF3L0c2g03rr0S299Wpp9ZOijreuogi9yFK1dZvecc3+yP50qukrDAxkLD/YHePBbkR9sWpQM5tVpZD+NmZ0lnZLS2OWkWgY9rcVa39NwCdDo9arXMlRRC1B5ra2u2bt1adUEhROMR/xdEPAo5l5Wf004pQYJKA959rk1Da9m7bBCSkwKRcyF2jfKzjSuEvgk9Hqo6u5pKBa5tlaPfZGXE6Px+JXubb/DN73HjFwyTfocj38PW+ZB+Br66D0KegyGzjQuoTm9XkigA3Bmu7CdTBb1ez/n0q8QkXDHsEXP6cjaWZmqszDVYmWuwLj6sLDRYmamxtij+2VxDYsZVth5NRls8dc3HxZrHBvoxtq8PDlZ1nLpbNHjNIvBxtlX+w9fq9GTmFeJkY1HPPRJCCCFEo3FwNfw8U0l/7NFVCQri/1JGYFLjIGGvcmxbCJaO0GaQEgS1GQJndipBT94Vpa3ACUpCgZKkA8bSmCtpnE1JpYIu90CHW2HzbGVvm53vQPweZdTGwavqNnJS4LspgB56j1dGocqRW1DE3+czOBifTky8sgdMSnbZfRazjLyFoLauTAjyY3gnDzTyBbeoQLMIfCzNNNhbmpGVX0RqToEEPkIIIYSomrYItsxR9r8B6HSXkqHMwha63qtcuxIPp/5UgqDT25QA5+hPynE9j25wxxLw6VeXd2Acc2tlpMbvFvjpWTi3W0lScN+nSiBXEZ0ONk6F7CR0rh242P91UhOukJqTT2p2Aak5BSSk5RITf4Xjl7IMozMlzNQqurR0oFdrZ3q1dqKjpwM6vV5J+1yoLU7/rLvhZy15RVrUKhW3d/eio6dMWRNVaxaBD4CLnQVZ+UWk5RTQtkV990YIIYQQDVpuGnz7uBLMAAx5RUlAcOPUMqfWSla0wMeU6WcXY6+txzm/D8ysYOgr0O9J49fh1Jdu94NXT1j/GFz6F/3qe7nU8xmOdphGSq7yt1RaTgEp2QWk5eRzS0oEE3Miydebc9fFJzi+dG+lzXs5WtGrtRO9fJzp7etEl5aOWJlr6ubeRLPWSP4PvHkuthacS80lNVsSHAghhBCiEsnHYO2DynoXc1tllKfzXVXXU2vAO1A5Br+gbEyqNlf2o2mgsvOLOJ6UxYlLWRxPyuJcag6pOQWkZheQk/MSL+q/4GGzP/CMfY8z0Vv5b+FTXMbZUL+b6jTjLL4AFSwoGsdxfWsszNS42VrgYmeBi60lbrYWtHCwpIe3E71aO+HlaF2Pdyyas2YT+LhKSmshhBBCVOX4r0rmtoIsZW+dh9aCZ9eatVXRpqM3KTu/iKSMPKzM1YZF/lbmmkrXthQU6Tidks3xpKxrx6UszqdfreSVNLzCJA6oOvOG5jMGao6w1XwOX7d6lVSPYDysCnjw4EtY5GpJ9xvNk3e8yWx7S2wtNLLppmiQmk3gc20vn7IL6IQQQgjRzOn1sGsp/L4A0IPvLTD2S7B1q++eAXA2JYffjyXz57Fk9p5JpVBbdgNOC7OSQKh0QHS1QMvplOxy6wC421sS4GlPR0972rSww83OElc7C1xtLXC1s8TW4jZUqRNg/QQcL/3LfxJeAP8XlBGx3ARw9ME5bDnO1rKxp2jYmlHgo6S0lr18hBDlWblyJTNmzODKlSu1+jrz5s3j+++/JzY2tlZfRwhhhIJc+PEZ+Pdb5ec+E2H0ojrZg6YihVod+8+m8cfRZP44nszpyzmlnre3NKNAqyO/SGe4VlCko6BIR0YFgzj2lmZ08LQnwNOeAI9rj8621ZiK59YeJm2FzS9D9ErY8V/lukqjZH6zdq60uhANQbMJfGSqmxCNS1XTJB577DFWrlxZo7b9/PyYMWMGM2bMMFwLCwvjtttuq1F7QohGqCAX4qOUjGzHfob0s6A2UwKevpPqpUup2flsO36ZP44ns+P4ZbLyiwzPmalV9PN3YVhHd4Z1dKdN8aacOp2e/CIdV2/MeGb4WYeZRkUHD3taOlrd3BQ0c2u4811lNOznGVCQDUNnQ+v+N3nnQtSNZhP4uEjgI0SjkpiYaDiPiIhg7ty5HD9+3HDN2tq0i2Otra1N3qYQogHR6SD58LWMa+f2gPa66e/WLjB2FfiH1HnXEtJymf/TYX4/loz+utlorrYWDAlwZ3gnd25p71buhpxqtUrZ0NOiDrOidX9ACXaSj0H7kXX3ukLcpJvc7rfxcLFTAh/J6iZE4+Dp6Wk4HB0dUalUpa7t2LGDwMBArKysaNOmDfPnz6eo6Nq3o/PmzaN169ZYWlrSsmVLpk+fDsCQIUM4d+4cM2fORKVSGb79XLlyJU5OTqXq9+zZk9WrV+Pn54ejoyMPPvggWVnXttXLysrikUcewdbWFi8vL5YuXcqQIUNKjSRVRafTsWDBAry9vbG0tKRnz55s3rzZ8HxBQQFPP/00Xl5eWFlZ4efnx8KFC6u8TyEEkJUEsWuVjTUXByh70kTOVVJUa/PBoRX0ehTuXwHTY+o86CnS6vhs52lCl+5g61El6Ons5cAzw9qxcVoQ++eMYPHYHtzWzavcoKdeObWGDqHK5qdCNBLNZsRHproJcQO9Hgpz6/51zW1u+oPyt99+49FHH+W9994jJCSEU6dOMWXKFABef/11vv32W5YuXco333xDly5dSEpK4tChQwB899139OjRgylTpjB58uRKX+fUqVN8//33/Pzzz6SnpzN27Fjefvtt3nrrLQBmzZrF7t27+fHHH/Hw8GDu3LkcPHiQnj17Vvte3n33XRYvXszHH39Mr169WLFiBXfddReHDx+mffv2vPfee/z444+sW7eO1q1bk5CQQEJCAkCl9ylEs6YthDX3X9uDp4S5DfiFKJtxth2mrFuppz/cD1/M4OUN//DPhQwA+vu78NY9XWnnbl8v/RGiOWg2gc/1U930er2kWRSiMBf+r2Xdv+4rF286xetbb73Fyy+/zGOPPQZAmzZteOONN3jxxRd5/fXXiY+Px9PTkxEjRmBubk7r1q3p10/ZLd3FxQWNRoO9vT2enp6Vvo5Op2PlypXY2yt/iIwbN47ff/+dt956i6ysLL788ku+/vprhg8fDsAXX3xBy5bG/U7feecdXnrpJR588EEAFi1axJ9//kl4eDgffvgh8fHxtG/fnltuuQWVSoWvr6+hbmX3KUSzlrCvOOhRgVePa4GOTz8ws6zXrl0t0BL++wk+23kGrU6Pg5UZr9zWibF9fFBXko5aCHHzms1UN9firG4FWh3Z1y0WFEI0PtHR0SxYsAA7OzvDMXnyZBITE8nNzeWBBx7g6tWrtGnThsmTJ7Nx48ZS0+Cqy8/PzxD0AHh5eZGcnAzA6dOnKSwsLBVoODo6EhAQUO32MzMzuXjxIsHBwaWuBwcHc/ToUQAmTJhAbGwsAQEBTJ8+nS1bthjKmeo+hWhySkZ6ut4HT26HEa8r09jqOejZfTKFW9/dwcfbT6PV6bm9mxdbnxvMg/1aS9AjRB1oNiM+1hYarM01XC3UkpZTgH1DmysrRF0zt1FGX+rjdW+STqdj/vz53HvvvWWes7KywsfHh+PHjxMZGcnWrVuZNm0a//vf/9i+fTvm5tX/f//GsiqVCp1OSR2rL16BfOPosV5f/j4ZlSmvjZJrvXv35syZM/z6669s3bqVsWPHMmLECL799luT3acQTU5J4NNmiEmb1er0/HksmUtZefi62OLnZkNLR+sqg5b0nALe2nSUb6PPA+DlaMUbY7oyorOHSfsnhKhcswl8QJnuduHKVVJzCvB1lU22RDOnUtXaruK1rXfv3hw/fpx27dpVWMba2pq77rqLu+66i6eeeoqOHTvyzz//0Lt3bywsLNBqtTfVh7Zt22Jubs6+ffvw8fEBlBGcuLg4Bg8eXK02HBwcaNmyJbt27WLQoEGG61FRUaVGkhwcHAgLCyMsLIz777+fW2+9lbS0NFxcXCq9TyGapbwMuBCtnJso8CnS6vjx0EU+/PMkp27YT8fCTI2viw1+brb4u9ni56oERP5utnjYW/HT3xdZ8NMRUnMKUKlg/ABfnh8VIF/AClEPmlXg42qnBD5pktlNiEZt7ty53HHHHfj4+PDAAw+gVqv5+++/+eeff3jzzTdZuXIlWq2W/v37Y2Njw+rVq7G2tjasj/Hz82PHjh08+OCDWFpa4uZm/M7s9vb2PPbYY7zwwgu4uLjg7u7O66+/jlqtNmoN4QsvvMDrr79O27Zt6dmzJ1988QWxsbGsWbMGgKVLl+Ll5UXPnj1Rq9WsX78eT09PnJycqrxPIZqls7tArwXXduDkc1NNFRTp+O7geZZtO0V8mpIMxsHKjF6tnUlIzyUhLZeCIh1xydnEJWeXqW9hpqageIPRDh52LLy3O4G+stGnEPWlWQU+spePEE3DqFGj+Pnnn1mwYAH//e9/MTc3p2PHjkyapGw66OTkxNtvv82sWbPQarV069aNn376CVdXVwAWLFjAk08+Sdu2bcnPz6/R9DSAJUuWMHXqVO644w4cHBx48cUXSUhIwMrKqtptTJ8+nczMTJ577jmSk5Pp3LkzP/74I+3btwfAzs6ORYsWERcXh0ajoW/fvmzatAm1Wl3lfTYly5Yt43//+x+JiYl06dKF8PBwQkIqTj384Ycf8sEHH3D27Flat27NnDlzGD9+vOH5lStX8vjjj5epd/XqVaP+/UQDZIJpbnmFWiL2J/Dx9lNczMgDlL8hJt7iz/iBvobRmiKtjotX8jiTmsPZlBzOpORwLjWHs6nXgiILjZpnhrXjycFtsTBrNkurhWiQVPqafuLXoczMTBwdHcnIyMDBwaHG7cxaF8t3By/w0q0d+c+QtibsoRBCQE5ODq1atWLx4sVMnDixvrtjEqZ6/70ZERERjBs3jmXLlhEcHMzHH3/MZ599xpEjR2jdunWZ8suXL+ell17i008/pW/fvuzbt4/Jkyfz9ddfc+eddwJK4PPss8+W2hQXqDLT3/Uawu9GlOODvpByAsK+gk53GlU1J7+Ir/fG88nO01zOUjY3dbe3ZMqgNjzcvzU2FtX/vrhQq+NC+lUcrc1xLv7iVQhhGjV9/21WIz7X9vLJr6KkEEJULSYmhmPHjtGvXz8yMjJYsGABAGPGjKnnnjUtS5YsYeLEiYYRvfDwcH777TeWL19eajPXEqtXr+bJJ58kLCwMUNKd//XXXyxatMgQ+ACGTXFFE5JxQQl6VGplv55qyswrZFXUWT7fdYb03EIAWjlZM3VwGx7o44OVucborphr1Pi5Nc51lEI0Vc0q8HEpTmmdKlPdhBAm8s4773D8+HEsLCwIDAxk586dNVozJMpXUFBAdHQ0L7/8cqnroaGhREVFlVsnPz+/zHQ1a2tr9u3bR2FhoSHjXXZ2Nr6+vmi1Wnr27Mkbb7xBr169KuxLfn4++fnXvjjLzMys6W2J2lIyza1lb7B2qrSoXq/nYHw6EfsT+PnvRHILlIQnvq42PDWkHXf3aiVT04RoYppV4OMqa3yEECbUq1cvoqOj67sbTVpKSgparRYPj9Jpfz08PEhKSiq3zqhRo/jss8+4++676d27N9HR0axYsYLCwkJSUlLw8vKiY8eOrFy5km7dupGZmcm7775LcHAwhw4dMqyvutHChQuZP3++ye9RmFA11vekZOfz3cHzROxPKJWhrb27HU8Nbccd3b0w00jAI0RTVKP/s5ctW4a/vz9WVlaGbzgrk5+fz5w5c/D19cXS0pK2bduyYsWKGnX4ZkhyAyGEaJwq2+voRq+99hqjR49mwIABmJubM2bMGCZMmACARqNMWRowYACPPvooPXr0ICQkhHXr1tGhQwfef//9Cvswe/ZsMjIyDEdCQoJpbk6Yhl5fYeBTpNXxx7FLPLn6AAP+73f+b9MxTl3Owdpcw329vVn35EC2zBzE3b1aSdAjRBNm9IhPREQEM2bMKLXIdPTo0RUuMgUYO3Ysly5d4vPPP6ddu3YkJyfXy+7iLnZK4JMq6ayFEKJRcHNzQ6PRlBndSU5OLjMKVMLa2poVK1bw8ccfc+nSJby8vPjkk0+wt7evcBqiWq2mb9++xMXFVdgXS0tLLC0ta34zonYlH4WcZDCzBh9lH6xzqTmsO5DAt9HnuZR5bZpiDx8nwvr4cGcPL9lPR4hmxOjAx9hFpps3b2b79u2cPn0aFxcXQNlDozK1NY9aproJIUTjUrJ2KjIyknvuucdwPTIyssokEubm5nh7ewPwzTffcMcdd6BWl/9tvl6vJzY2lm7dupmu86Junf4TAG3rgXx/6DLroxP463Sa4WlnG3Pu6eVNWF8fAjzt66uXQoh6ZFTgU5NFpj/++CN9+vThv//9L6tXr8bW1pa77rqLN954A2tr63Lr1NY86pKpblcLtVwt0GJtYXyWFiGEEHVr1qxZjBs3jj59+jBw4EA++eQT4uPjmTp1KqBMQbtw4QKrVq0C4MSJE+zbt4/+/fuTnp7OkiVL+Pfff/nyyy8Nbc6fP58BAwbQvn17MjMzee+994iNjeXDDz+sl3sUN0ev15NxOBInYMmpVnx45BAAKhWEtG9BWB8fRnR2x9JMPveFaM6MCnxqssj09OnT7Nq1CysrKzZu3EhKSgrTpk0jLS2twnU+s2fPZtasWYafMzMz8fG5ud2XAewszbDQqCnQ6kjNycfbwuam2xRCCFG7wsLCSE1NZcGCBSQmJtK1a1c2bdqEr68vAImJicTHxxvKa7VaFi9ezPHjxzE3N2fo0KFERUWVmm1w5coVpkyZQlJSEo6OjvTq1YsdO3bQr1+/ur69pufqFfj+P6DTQste0Kq38mjnbvKXSsrIY8PB8/xw4Awbs6NABX8UdMbX1Yb7e3tzb6A3rZzK/5JVCNH81CirmzGLTHU6HSqVijVr1uDo6Ago0+Xuv/9+Pvzww3JHfWprHrVKpcLF1oKkzDzScgrwdpbARwghGoNp06Yxbdq0cp9buXJlqZ87depETExMpe0tXbqUpUuXmqp74no7F8PxTcp53G/Xrju0UgKg6w8bF6Obzy/SEnnkEusPnGdn3GV0euirOoatZT7ZGifmTXqAfm3cKvy7RAjRfBkV+NRkkamXlxetWrUyBD2gfCjp9XrOnz9fYdrQ2lIS+MhePkIIIYSJXUmAvR8r5wOfhtxUuHBQ2VQ084JyHPv5WnlnPyUA6j0e2g6rtOns/CLe3XqC9dHnuVK8yShAXz9n5tknQxzYdRpO/7YtauHGhBBNgVGBT00WmQYHB7N+/Xqys7Oxs7MDlPnXarXasOi0LrkWZ3ZLk8xuQgghhGn9+X+gzQe/EAh9U1lkA5CfBYl/w8WDcDFGCYbSz0D6WeWI2wqzDoOVY7nNRp9LY2bEIeLTcgHwdLDivsBW3B/og7+bLXz2ulKwkv17hBDC6Kluxi4yffjhh3njjTd4/PHHmT9/PikpKbzwwgs88cQTFSY3qE2yl48QQghRC5L+hUNrlfOR868FPQCW9uAXrBwlctMg8RBseh5ST0L0Sgh+tlSTBUU63v39BMu3nUKnh1ZO1sy/qwtDO7qjURe3n5cBF4o3Em4ztPbuTwjR6Bkd+Bi7yNTOzo7IyEieeeYZ+vTpg6urK2PHjuXNN9803V0YoSTwkaluQgghhAltnQfoofPd0Cqw6vI2LtB2KNwyE354Cv5aDv3/A2bK5/TJ5CxmRMTy7wVlS4t7e7Vi3pguONy4787Z3aDXgktbcLr5REhCiKarRskNjFlkCtCxY0ciIyNr8lImd20vn/wqSgohhBCiWs7sgJORoDaD4XONq9vtAfj9DchKhH/Wo+/5MF9GnWXhr8fIL9LhZGPOW3d34/buXuXXP71NeZRpbkKIKtQo8GnMXGyVbHEy1U0IIYQwAZ0OIouDncDHwbWtcfXNLGHAVNg6j6Jd7/J4dFt2nkwFIKS9G+880AMPB6uK60vgI4SopvK3sG7CZKqbEEIIYUJHvlcSFljYweCXatZG4OMUmtlilnoczemtWJqpmX9XF1Y90a/yoCfjAqQcB5Ua/ENq9tpCiGaj2Y34GLK6SeAjhBBC3JyiAvh9gXIeNB3sjE8lnZlXyLwfz9IxbwhTzH5hls1mbKbMoJ27XdWVz2xXHlv2Amtno19bCNG8NNsRH0lnLYQQQtyk6JVKWmpbdxj4lNHVI49cYnT4Tr6LucCX2lvRqjR0L/qHdoXHq9eATHMTQhih2QU+JckNsvKLyC/S1nNvhBBCiEYqLxO2L1LOh7wMltUYoSl24cpVJq86wORVB7hw5SqtXWx4b+odaLo9oBTY/V7Vjej1EvgIIYzS7Ka6OViZo1Gr0Or0pOcU4umoqe8uCSGEEI1P1PuQmwKu7aD3+GpVKdTq+HzXGd7dGsfVQi1mahWTQtowfXg7bCzMwGo6/P0NHP0R0s6Ai3/FjV0+BtmXwMwafPqb6KaEEE1ZsxvxUatVONuUJDiQlNZCCCGE0bKSYM8Hyvnw10FjXnl5YN+ZNG5/bydv/3qMq4Va+vm5sOnZEF4e3VEJegA8ukC7EaDXwZ4PK2/w1J/Ko2+QkhlOCCGq0OxGfECZ7paSnS8JDoQQQoia2PY2FOaCd1/odGelRdNyCli46Sjro88Dylrb2aM7cn+gNyqVqmyFoOlwcivEfAVDZoOta/kNyzQ3IYSRmmXgY0hwIIGPEEIIYZyUODi4SjkfuQDKC14AnU7PugMJvL35GFdyCwF4qJ8PL47qiHPx53C5/AeBVw9IPAT7P1XWD91IWwhndynnEvgIIaqpeQY+xSmtUyWzmxBCCGGcrfNAr4UOo5VpZuU4lpTJnI3/En0uHYCOnva8dU9XAn1dqm5fpYLgZ+HbJ2DfJ8oIkIVN6TLnD0BhDti4gkfXm7whIURz0SwDH1cZ8RFCCCGMF78Xjv2sbBg6Yl65RY5czOTuZbspKNJhY6Fh1sgOTAjyw0xjxLLiTmPAqTVciYdDX0PfSaWfP128vsd/MKib3XJlIUQNNct3i5KpbqkS+AghhBDVo9dD5FzlvOcj4N6xnCJ6/m/TUQqKdPTzd2HrrMFMCmljXNADoDGDgU8r51EfgO6G7SdK1ve0HWpcu0KIZq1ZBj7XRnwkq5sQQghRLcc3QcJfSvrooa+UW2RHXAq7TqZgoVGz+IEetHSyrvnr9XoUrJ2VDVKP/nTtel6mMtUNZH2PEMIozTLwcbFV0l7KVDchhBCiGoryYet85XzAf8ChZZkiWp2ehZuOAjB+oC8+LjZlyhjFwhb6TlbOo95TRpwAzu1W1hi5tFGmwwkhRDU108BHproJIYQQ1ZL0D3w6DFKOg7UL3DKj3GIbDp7nWFIWDlZmPD2snWleu98UMLOCC9FwLkq5JmmshRA11CwDH1c7SW4ghBBCVEpbBDvegU+GwqV/lQxq930GVo5lil4t0LJkywkAnh7WDiebStJVG8OuBfR4SDnf/a7yWLJxaRtZ3yOEME6zDHxKRnyu5BZSpNXVc2+EEEKIBiblJHxxK/zxBugKIeB2mPYXtBtebvEVu8+QlJlHKydrxg/0M21fgp4BVBD3G5z6Qxl5QgX+IaZ9HSFEk9csAx9nGwvDfmvpxZuqCSGEEM2eTgd7P4aPboHz+8HSEe7+CB5cA3bu5VZJyc5n+bZTALx4awBW5hrT9sm1LXS6QznfOFV5bNlLSXwghBBGaJaBj0atwsnaHJDpbkIIIQSg7Jmzegz8+iIUXVWmkk2Lgp4PYfi2sBzv/R5Hdn4RXVs5cGf3skkPTCLoWeUx+5LyKOt7hBA10CwDH7g+wYGktBZCCNGM6fUQ8xUsC4IzO8DcBm57B8ZtBEfvSquevpzN13vjAXjltk6o1RUHSDfFpy+0HnjtZwl8hBA1YFbfHagvrraWnLqcIyM+Qgghmq+sS/DTdDixWfnZpz/cvVyZXlYN/918nCKdnmEd3Qlq61aLHQWCpkP8HiUw8+lfu68lhGiSmm3g42Irmd2EEEI0YylxsGIU5KaCxgKGvQoDnwZ19dboHDibxubDSahV8PLojrXcWSBgNNz6trJ3j7lV7b+eEKLJab5T3YpTWqdmS+AjhBAN3bJly/D398fKyorAwEB27txZafkPP/yQTp06YW1tTUBAAKtWrSpTZsOGDXTu3BlLS0s6d+7Mxo0ba6v7DY9eD5ueV4Ie9y4wZTsEP1vtoEev1/N/xZuVju3jQwcP+9rsrUKlUjZP7Xh77b+WEKJJaraBj6uM+AghRKMQERHBjBkzmDNnDjExMYSEhDB69Gji4+PLLb98+XJmz57NvHnzOHz4MPPnz+epp57ip59+MpTZs2cPYWFhjBs3jkOHDjFu3DjGjh3L3r176+q26texX5SNQDWWSsY2j85GVd/8bxIH469gba5h1sgOtdNHIYQwsWYb+MhUNyGEaByWLFnCxIkTmTRpEp06dSI8PBwfHx+WL19ebvnVq1fz5JNPEhYWRps2bXjwwQeZOHEiixYtMpQJDw9n5MiRzJ49m44dOzJ79myGDx9OeHh4Hd1VPSq8Cr/NVs6DngEXf6OqFxTpWLT5GACTB7XB3UGmnQkhGodmH/hIVjchhGi4CgoKiI6OJjQ0tNT10NBQoqKiyq2Tn5+PlVXpP8atra3Zt28fhYXK3m179uwp0+aoUaMqbLOk3czMzFJHoxT1vpK62r4lhMwyuvrXe89xNjUXNzsLpgxqUwsdFEKI2tFsAx9XW0tARnyEEKIhS0lJQavV4uHhUeq6h4cHSUlJ5dYZNWoUn332GdHR0ej1eg4cOMCKFSsoLCwkJSUFgKSkJKPaBFi4cCGOjo6Gw8fH5ybvrh5cSYCdS5Tz0DfAwtao6pl5hbz3x0kAZozogJ1ls82RJIRohJpt4CNT3YQQovFQ3bCBpl6vL3OtxGuvvcbo0aMZMGAA5ubmjBkzhgkTJgCg0VxbvG9MmwCzZ88mIyPDcCQkJNTwbupR5GvK5qS+wdD1PqOrf7TtFGk5BbRpYcuDfRth4CeEaNaabeDjWpzVLT23EJ1OX8+9EUIIUR43Nzc0Gk2ZkZjk5OQyIzYlrK2tWbFiBbm5uZw9e5b4+Hj8/Pywt7fHzU3Za8bT09OoNgEsLS1xcHAodTQqZ3bC4Y2gUsPoRUqWNCNcvHKVz3edAeDlWztipmm2f0IIIRqpZvuu5WyjBD5anZ6Mq4X13BshhBDlsbCwIDAwkMjIyFLXIyMjCQoKqrSuubk53t7eaDQavvnmG+644w7UauVjb+DAgWXa3LJlS5VtNlraIvj1JeW8zxPg2c3oJpZEniC/SEc/PxdGdq44QBRCiIaqRoGPMfspbNu2DZVKVeY4duxYjTttChZmauytlLnJqTLdTQghGqxZs2bx2WefsWLFCo4ePcrMmTOJj49n6tSpgDIFbfz48YbyJ06c4KuvviIuLo59+/bx4IMP8u+///J///d/hjLPPvssW7ZsYdGiRRw7doxFixaxdetWZsyYUde3Vzeiv4Dkw2DtDEPnGF096mQKGw6eB+CV2ztVOiVQCCEaKqNXJZbsp7Bs2TKCg4P5+OOPGT16NEeOHKF169YV1jt+/HipaQEtWrSoWY9NyNXWgqy8IlnnI4QQDVhYWBipqaksWLCAxMREunbtyqZNm/D19QUgMTGx1J4+Wq2WxYsXc/z4cczNzRk6dChRUVH4+fkZygQFBfHNN9/w6quv8tprr9G2bVsiIiLo379/Xd9e7ctJhT/eVM6HvQo2LkZVP5RwhcmrDqDXw329venp42T6PgohRB1Q6fV6oxa49O/fn969e5faP6FTp07cfffdLFy4sEz5bdu2MXToUNLT03FycqpRJzMzM3F0dCQjI8Okc6rvXbabg/FX+OjR3tza1ctk7QohRFNRW++/TUGj+d38PBMOrACPbvDkdlBrqq5T7GRyFg98tIf03EKC2rqyYkJfrMyrX18IIWpDTd9/jZrqVpP9FEr06tULLy8vhg8fzp9//llp2braK8GlOKW1THUTQgjRJCUeggNfKOejFxkV9JxPz+XRz/aRnltID29HPhnfR4IeIUSjZlTgU5P9FLy8vPjkk0/YsGED3333HQEBAQwfPpwdO3ZU+Dp1tVeCa0lK62wJfIQQQjQxen1xQgO9krraL7jaVS9n5TPu830kZebR3t2OlY/3kz17hBCNXo3exYzZ+yAgIICAgADDzwMHDiQhIYF33nmHQYMGlVtn9uzZzJp1bTfpzMzMWgl+XIpTWsuIjxBCiCbn3w0QvwfMbWDkgmpXy7hayGMr9nEmJYdWTtasntgf5+IvCoUQojEzasSnJvsplGfAgAHExcVV+Hxd7ZXgKpuYCiGEaIrys2HLa8p5yCxw9K5WtasFWiZ9uZ8jiZm42Vnw1aT+eDpa1WJHhRCi7hgV+NzMfgrXi4mJwcur/pMJuEjgI4QQoinatQSyLoKTLwx8plpVCrU6pq2JZv/ZdOytzPjyiX74u9nWckeFEKLuGD3VbdasWYwbN44+ffowcOBAPvnkkzL7KVy4cIFVq1YBEB4ejp+fH126dKGgoICvvvqKDRs2sGHDBtPeSQ2UBD4y1U0IIUSTkXYaot5Xzm9dCOZVj9jodHqeW3eIP49fxspczYoJfenS0rGWOyqEEHXL6MDH2P0UCgoKeP7557lw4QLW1tZ06dKFX375hdtuu810d1FDrsVZ3dJy8uu5J0IIIYSJ/DYHtAXQdhgEVP1Zq9fref3Hw/x46CJmahXLHw2kr59xe/0IIURjYPQ+PvWhtvZKuHDlKsFv/4G5RsWJN0fLTtRCCHGDRrNXTT1okL+b6JXw07OgNoP/REGLgCqrLN5ynPf/OIlKBeFhPRnTs1Xt91MIIW5Cnezj09SUJDco1OrJyi+q594IIYQQN+Gvj5SgB+CWmdUKej7beZr3/zgJwIIxXSXoEUI0ac068LEy12BjoWzGJnv5CCGEaLR2LobNLynnA5+GoXOqrLLvTBpv/nIUgBdGBTBugG9t9lAIIepdsw58QBIcCCGEaMT0evh9gXIADH4ZQt+Eakzd/mznaQDu7d2KaUPa1mYvhRCiQWj2gY/s5SOEEKJR0uth82xltAeUTUqHzq5W0HPhylW2Hr0EwLQhbWWNqxCiWTA6q1tTc20vH8nsJoQQopHQaeHnGXBQ2TqC296BfpOrXX3NX+fQ6SG4nSvt3O1rp49CCNHASOBTnNJaproJIYRoFLRF8P1U+Gc9qNQw5kPo+XC1q+cVavlmfwIA4wb41VInhRCi4Wn2gY+rXfGIjyQ3EEII0dAV5cO3T8Cxn5WU1fd+Cl3vNaqJX/5OJC2ngJaOVozo5F5LHRVCiIan2Qc+LrLGRwghRGNQkAvrxsHJraCxhLGrIOBWo5tZtecsAI8M8MVM0+yX+gohmhEJfGwkq5sQQogGLj8Lvn4Qzu0Ccxt48GtoO9ToZmITrnDofAYWGjUP9vWphY4KIUTDJYGPjPgIIYRoyPKzYdXdcOEAWNjDI+vBd2CNmloVdRaAO3p44Wpnabo+CiFEIyCBj50EPkIIIRqwQ2uVoMfaGR79Dlr1rlEzKdn5/Px3IgCPDfQzYQeFEKJxaPaTe10NG5hKOmshhBANUNLfymPfSTUOegAi9idQoNXRw8eJHj5OpumbEEI0Is0+8CmZ6pZXqCO3oKieeyOEEELc4NIR5dG9c42bKNLq+OqvcwA8NtDXFL0SQohGp9kHPnaWZlgUZ7VJlZTWQgghGhKdDi4fU85vIvDZevQSiRl5uNpacFs3LxN1TgghGpdmH/ioVCpJcCCEEKJhyoiHgmzQWIBr2xo3s2qPMtoT1tcHK3ONqXonhBCNSrMPfEAyuwkhhGigSqa5uQWAxrxGTcRdyiLqVCpqlbJ3jxBCNFcS+ACudrKXjxBCiAYouWR9T6caN1Ey2jOyswetnKxN0SshhGiUJPDh+hEfyewmhBCiASkJfDxqtr4nM6+QDQfPA5LCWgghJPDhWuAjIz5CCCEalOSjyqN7lxpV/y76PLkFWtq52zGwrasJOyaEEI2PBD5c28snTbK6CSFEg7Rs2TL8/f2xsrIiMDCQnTt3Vlp+zZo19OjRAxsbG7y8vHj88cdJTU01PL9y5UpUKlWZIy8vr7ZvpfqKCiDlhHJeg6luOp3eMM3tsYG+qFQqU/ZOCCEaHQl8ABdbS0CSGwghREMUERHBjBkzmDNnDjExMYSEhDB69Gji4+PLLb9r1y7Gjx/PxIkTOXz4MOvXr2f//v1MmjSpVDkHBwcSExNLHVZWVnVxS9WTGge6IrB0BEdvo6vvPpXC6ZQc7CzNuKe38fWFEKKpkcAHmeomhBAN2ZIlS5g4cSKTJk2iU6dOhIeH4+Pjw/Lly8st/9dff+Hn58f06dPx9/fnlltu4cknn+TAgQOlyqlUKjw9PUsdDYphmlsnqMFozZdRymjP/YHe2FmambJnQgjRKEngw7WsbjLiI4QQDUtBQQHR0dGEhoaWuh4aGkpUVFS5dYKCgjh//jybNm1Cr9dz6dIlvv32W26//fZS5bKzs/H19cXb25s77riDmJiYSvuSn59PZmZmqaNWXTqsPNZgmltCWi6/H7sEwKOSwloIIQAJfADZx0cIIRqqlJQUtFotHh4epa57eHiQlJRUbp2goCDWrFlDWFgYFhYWeHp64uTkxPvvv28o07FjR1auXMmPP/7I2rVrsbKyIjg4mLi4uAr7snDhQhwdHQ2Hj4+PaW6yIoaMbsYnNvhq7zn0erilnRvt3O1M3DEhhGicJPDhWnKD7Pwi8ou09dwbIYQQN7pxYb5er69wsf6RI0eYPn06c+fOJTo6ms2bN3PmzBmmTp1qKDNgwAAeffRRevToQUhICOvWraNDhw6lgqMbzZ49m4yMDMORkJBgmpuriGEPH+NSWecValm3X+nb+IEy2iOEECVk0i/gYGWORq1Cq9OTllOAl6Ns8CaEEA2Bm5sbGo2mzOhOcnJymVGgEgsXLiQ4OJgXXngBgO7du2Nra0tISAhvvvkmXl5eZeqo1Wr69u1b6YiPpaUllpaWN3E3RsjPgivFyRuMnOr206GLpOcW0srJmuGdyv8dCSFEcyQjPoBarcLZpjjBgaS0FkKIBsPCwoLAwEAiIyNLXY+MjCQoKKjcOrm5uajVpT/eNBoNoIwUlUev1xMbG1tuUFQvShIb2HuBjUu1q+n1er7ccxZQ1vZo1JLCWgghSsiITzFXWwtSsvNlnY8QQjQws2bNYty4cfTp04eBAwfyySefEB8fb5i6Nnv2bC5cuMCqVasAuPPOO5k8eTLLly9n1KhRJCYmMmPGDPr160fLli0BmD9/PgMGDKB9+/ZkZmby3nvvERsby4cfflhv91lKDae5xSRc4d8LmViYqQnrW8trkIQQopGRwKeYJDgQQoiGKSwsjNTUVBYsWEBiYiJdu3Zl06ZN+Poq61cSExNL7ekzYcIEsrKy+OCDD3juuedwcnJi2LBhLFq0yFDmypUrTJkyhaSkJBwdHenVqxc7duygX79+dX5/5bpUEvgYN83t673K7+GO7l6GzzUhhBAKlb6icf8GJDMzE0dHRzIyMnBwcKiV13jq64P88ncic+/ozBO3+NfKawghRGNTF++/jVWt/m5W3gFnd8Ldy6Hnw9XrT14h/d7aSl6hjm+nDqSPX/WnyAkhRGNS0/dfWeNTzFVGfIQQQjQEev11e/hUf6rbD7EXySvU0c7djkBf51rqnBBCNF41CnyWLVuGv78/VlZWBAYGsnPnzmrV2717N2ZmZvTs2bMmL1urSqYEpErgI4QQoj5lJ8PVNFCpoUVAtat9s0+Z5vZgX58KU30LIURzZnTgExERwYwZM5gzZw4xMTGEhIQwevToUvOry5ORkcH48eMZPnx4jTtbm66N+OTXc0+EEEI0a8nFoz0ubcC8etsr/HM+g8MXM7HQqLmvt3ctdk4IIRovowOfJUuWMHHiRCZNmkSnTp0IDw/Hx8eH5cuXV1rvySef5OGHH2bgwIE17mxtcrFV9maQqW5CCCHqVUkqayOmua3dr3z5eGtXT5wlqYEQQpTLqMCnoKCA6OhoQkNDS10PDQ0lKiqqwnpffPEFp06d4vXXX6/W6+Tn55OZmVnqqG0y1U0IIUSDcMm4VNY5+UX8GHsRgAf7SQprIYSoiFGBT0pKClqttsxu2R4eHmV21S4RFxfHyy+/zJo1azAzq1727IULF+Lo6Gg4fHxq/43c1U6SGwghhGgASqa6eVQv8Pnl70Sy84vwc7VhYBvXWuyYEEI0bjVKbnDjokm9Xl/uQkqtVsvDDz/M/Pnz6dChQ7Xbnz17NhkZGYYjISGhJt00SsmIz5XcQoq0ulp/PSGEEKIMnQ6Sjynn7l2qVaVkmltY39aS1EAIISph1Aambm5uaDSaMqM7ycnJZUaBALKysjhw4AAxMTE8/fTTAOh0OvR6PWZmZmzZsoVhw4aVqWdpaYmlpaUxXbtpzjYWqFRKFtH03EJa2Nft6wshhBCkn4Giq2BmBS5V7yl3LCmTmPgrmKlV3B8oSQ2EEKIyRo34WFhYEBgYSGRkZKnrkZGRBAUFlSnv4ODAP//8Q2xsrOGYOnUqAQEBxMbG0r9//5vrvQlp1CqcrM0Bme4mhBCiniQXr+9pEQBqTZXFv9mnzIgY0clDvrATQogqGDXiAzBr1izGjRtHnz59GDhwIJ988gnx8fFMnToVUKapXbhwgVWrVqFWq+natWup+u7u7lhZWZW53hC42FqQnltIak4+YF/f3RFCCNHcGDK6VT3NLa9Qy3cHzwOS1EAIIarD6MAnLCyM1NRUFixYQGJiIl27dmXTpk34+voCkJiYWOWePg2Vq60lpy7nyIiPEEKI+nGpOLGBe6cqi/76byKZeUW0crImpH2LWu6YEEI0fkYHPgDTpk1j2rRp5T63cuXKSuvOmzePefPm1eRla52LrWR2E0IIUY9KprpVI6Pb2uJpbmP7+KBRS1IDIYSoSo2yujU6Oi1klZ9u+3ouxSmtU7Ml8BFCCFHHCvMg9ZRyXsVUt1OXs9l3Jg21Csb2laQGQghRHU0/8Dm7C972hTUPVFnUVUZ8hBBC1JeUE6DXgpUT2HtWWjRivzLaMyTAHS9H6zronBBCNH5NP/BxaQMFWXDpX8jPqryoBD5CCCHqi2GaWxeoZD+egiIdG6KVpAYP9WtdFz0TQogmoekHPg4twbE16HVwIbrSoiWBj5LVTQghhKhDJYGPe+XreyKPXCI1pwAPB0uGBkhSAyGEqK6mH/gA+PRTHuP3VlrM1VbZA0FGfIQQQtS5SyWBT+UZ3b7Zr2ROfSDQBzNN8/gYF0IIU2ge75itByiPCZUHPiUjPslZ+eh0+trulRBCCHHN9VPdKpCQlsvOuBQAwvrK3j1CCGGM5hH4lIz4nN+vZHirgL+bLfaWZlzJLSTqVGoddU4IIUSzd/UKZF5QzisZ8SlJahDS3g0fF5s66JgQQjQdzSPwce8C5raQnwmXj1VYzNpCw32BSlrQ1X+draPOCSGEaPaSjyqPDt5g5VhukSKtjvXRSuDzYF9JaiCEEMZqHoGPxgy8+yjn8X9VWvTRAcqHSeSRS1y8crW2eyaEEEJA8mHlsZKNS/88fplLmfm42lowsrNHHXVMCCGajuYR+AD49FceE/ZVWqyduz0D27ii08PaffF10DEhhBDNXsmITyUZ3b4p/ky6L9AbC7Pm8/EthBCm0nzeOVuXBD6VJzgAGDfQF4C1+xIoKNLVZq+EEEKI6zK6lR/4JGZc5c/jyYAkNRBCiJpqPoGPd19ABelnIDu50qIjO3vgbm9JSnY+vx1Oqpv+CSGEaJ70+iqnuq3bfx6dHvr5u9C2hV0ddk4IIZqO5hP4WDle+yatilEfc43asBv26r/O1XbPhBBCNGdZiZCXASoNuHUo87RWp2fdASWpwUP9ZLRHCCFqqvkEPnDdRqaVJzgAeKhfazRqFfvOpHE8KauWOyaEEKLZKpnm5toOzCzLPB11KoULV67iYGXG6K5eddw5IYRoOppX4GPYyLTyBAcAno5WhBZnzflKRn2EEKJeLVu2DH9/f6ysrAgMDGTnzp2Vll+zZg09evTAxsYGLy8vHn/8cVJTS+/PtmHDBjp37oylpSWdO3dm48aNtXkLFatimtuRi5kADA5wx8pcU1e9EkKIJqd5BT4lIz6JsVCYV2XxcQOUJAffHTxPdn5RLXZMCCFERSIiIpgxYwZz5swhJiaGkJAQRo8eTXx8+Zk3d+3axfjx45k4cSKHDx9m/fr17N+/n0mTJhnK7Nmzh7CwMMaNG8ehQ4cYN24cY8eOZe/eqhPgmJwho1uXcp9OyykAoIVd2dEgIYQQ1de8Ah9nf7BtAdoCJfipwsC2rrRpYUtOgZaNMRdqv39CCCHKWLJkCRMnTmTSpEl06tSJ8PBwfHx8WL58ebnl//rrL/z8/Jg+fTr+/v7ccsstPPnkkxw4cMBQJjw8nJEjRzJ79mw6duzI7NmzGT58OOHh4XV0V9e5VDzi496p3KdLAh9XO4u66pEQQjRJzSvwUamu7edTjXU+KpXKMOrz1Z5z6PX62uydEEKIGxQUFBAdHU1oaGip66GhoURFRZVbJygoiPPnz7Np0yb0ej2XLl3i22+/5fbbbzeU2bNnT5k2R40aVWGbAPn5+WRmZpY6bpq2CC4fV84rmOpWEvi42ErgI4QQN6N5BT5Q7Y1MS9wX6I21uYbjl7LYfza9FjsmhBDiRikpKWi1Wjw8PEpd9/DwICmp/O0GgoKCWLNmDWFhYVhYWODp6YmTkxPvv/++oUxSUpJRbQIsXLgQR0dHw+HjY4IMa+lnQJsP5jbg5FdukbRcJfBxtpHARwghbkbzC3wMCQ72KnsnVMHBypy7e7UCJLW1EELUF5VKVepnvV5f5lqJI0eOMH36dObOnUt0dDSbN2/mzJkzTJ06tcZtAsyePZuMjAzDkZCQUMO7uU7JNLcWHUFd/keyTHUTQgjTMKvvDtQ5rx6gsYTcFEg7Da5tq6zy6IDWrN0Xz+Z/E0nO6oS7vVUddFQIIYSbmxsajabMSExycnKZEZsSCxcuJDg4mBdeeAGA7t27Y2trS0hICG+++SZeXl54enoa1SaApaUllpYmTjCQXJzKuoJpbgBp2TLiI4QQptD8RnzMLKFlL+W8io1MS3Rp6UigrzOFWj0R+0zwDZ8QQohqsbCwIDAwkMjIyFLXIyMjCQoKKrdObm4u6htGTzQaJQ10yVrNgQMHlmlzy5YtFbZZa0oCnwoyuhUU6cgqzirqKmt8hBDipjS/wAeM2si0REmSg6/3xVOk1dVGr4QQQpRj1qxZfPbZZ6xYsYKjR48yc+ZM4uPjDVPXZs+ezfjx4w3l77zzTr777juWL1/O6dOn2b17N9OnT6dfv360bNkSgGeffZYtW7awaNEijh07xqJFi9i6dSszZsyo25sr2by0goxu6cXre9QqcLQ2r6teCSFEk9T8prqB0QkOAEZ382TBzxYkZuTx+7FkRnXxrKXOCSGEuF5YWBipqaksWLCAxMREunbtyqZNm/D1Vb6QSkxMLLWnz4QJE8jKyuKDDz7gueeew8nJiWHDhrFo0SJDmaCgIL755hteffVVXnvtNdq2bUtERAT9+/evuxsryFWmXAN4VL6Hj7ONBWp1xeuPhBBCVE2lbwQ5mjMzM3F0dCQjIwMHB4ebbzD7MrzTTjl/6SxYO1er2qLNx1i+7RQh7d1YPbEOPxyFEKKemPz9twm56d/NxRj4ZAjYuMGLp8otsvtkCo98tpf27nZEzhp8cx0WQogmoqbvv81zqptdC3Bpo5yfP1B52es83K81KhXsjEvh9OXsWuqcEEKIZqGKaW4AqSUjPrK+RwghblrzDHwAfIrTWhuxzsfHxYZhAe4ArNkbX0VpIYQQohKGjG7lT3MDSC9JZS2BjxBC3LRmHPgUJzioZma3Eo8OVOaUrz+QwNUCral7JYQQorkwZHSrOJV1yYiPiwQ+Qghx05pv4FOykemFaNAWVbva4PYt8HGxJjOviJ8OXaylzgkhhGjyLlUd+KRL4COEECbTfAMftwCwdITCXLj0T7WrqdUqHu2vjPqs+ussjSA3hBBCiIYmNw2yizdQde9YYbE0CXyEEMJkahT4LFu2DH9/f6ysrAgMDGTnzp0Vlt21axfBwcG4urpibW1Nx44dWbp0aY07bDJqNfj0Vc6NSGsNMLaPDxZmav69kMmh8xm10DkhhBBNWsk0NydfsLSvsFhqTj4ggY8QQpiC0YFPREQEM2bMYM6cOcTExBASEsLo0aNL7aFwPVtbW55++ml27NjB0aNHefXVV3n11Vf55JNPbrrzN60GCQ5Aya5zZ3dlE7zVe86ZuldCCCGaumpMcwNIzykEJPARQghTMDrwWbJkCRMnTmTSpEl06tSJ8PBwfHx8WL58ebnle/XqxUMPPUSXLl3w8/Pj0UcfZdSoUZWOEtUZQ4ID40Z8AMYVJzn46dBFzqbkmLJXQgghmjozC2jREby6V1pMkhsIIYTpGBX4FBQUEB0dTWhoaKnroaGhREVFVauNmJgYoqKiGDy44o3Y8vPzyczMLHXUilaBoNJA5nnIOG9U1Z4+Tgzq0IICrY43fzlSO/0TQgjRNAVOgKf2wtBXKiyi1+tJz5XARwghTMWowCclJQWtVouHh0ep6x4eHiQlJVVa19vbG0tLS/r06cNTTz3FpEmTKiy7cOFCHB0dDYePj48x3aw+Szvw7KqcG5nWGmDuHZ0xU6vYejSZbceTTdw5IYQQzVnm1SK0OiWBjrONBD5CCHGzapTcQKVSlfpZr9eXuXajnTt3cuDAAT766CPCw8NZu3ZthWVnz55NRkaG4UhISKhJN6unZJ1PDaa7tXO34/FgPwAW/HyEgiKdCTsmhBCiOStJbGBrocHKXFPPvRFCiMbPqMDHzc0NjUZTZnQnOTm5zCjQjfz9/enWrRuTJ09m5syZzJs3r8KylpaWODg4lDpqTck6HyMTHJSYPrw9bnaWnL6cw5dRZ03XLyGEEM2aYZqbnYz2CCGEKRgV+FhYWBAYGEhkZGSp65GRkQQFBVW7Hb1eT35+vjEvXXt8+iuPSf9AgfFJCuytzHnp1gAA3v09juSsPFP2TgghRDOVml2yvseynnsihBBNg9FT3WbNmsVnn33GihUrOHr0KDNnziQ+Pp6pU6cCyjS18ePHG8p/+OGH/PTTT8TFxREXF8cXX3zBO++8w6OPPmq6u7gZTj7g0Ar0WrgQXaMm7uvtTQ8fJ7Lzi/jv5uMm7qAQQojmyDDiY2Nezz0RQoimwczYCmFhYaSmprJgwQISExPp2rUrmzZtwtdXSe+cmJhYak8fnU7H7NmzOXPmDGZmZrRt25a3336bJ5980nR3cbN8+sHhjUqCA/9BRldXq1XMu7Mz9yyL4tvo8zzSvzW9WjvXQkeFEEI0F9dSWcuIjxBCmILRgQ/AtGnTmDZtWrnPrVy5stTPzzzzDM8880xNXqbu+AxQAp944zO7lejV2pn7A735Nvo88348zMZpwajVlSd8EEIIISqSbgh8ZMRHCCFMoUZZ3ZqckgQH5/eBruaZ2V68NQA7SzMOnc/g24PG7QskhBBCXE9GfIQQwrQk8AHw7AbmNpCXASk1X6Pjbm/Fs8PbA/DfzcfIzCs0VQ+FEEI0M2nFgY+rbF4qhBAmIYEPgMYcWgUq5zXYyPR6jwX50aaFLSnZBbz/e5wJOieEEKI5Kpnq5iyBjxBCmIQEPiVKprvVYCPT61mYqZl7R2cAvth9lpPJ2TfbMyGEEM3QtaluEvgIIYQpSOBTwmeA8ljDjUyvNyTAnRGd3CnS6Zn/02H0ev1NtymEEKJ5SZfARwghTEoCnxLefZTHtFOQk3LTzb16e2csNGp2xqWw9WjyTbcnhBCi+cgr1JJToAUk8BFCCFORwKeEjQu06Kic3+Q6HwA/N1smhfgD8MbPR8gr1N50m0IIIZqHksQGZmoVDlY12nlCCCHEDSTwuZ5hnc/NBz4ATw1th4eDJfFpuXy+64xJ2hRCCNH0pV2X2EClkj3hhBDCFCTwuV7JOp+jP8H+z+HMDshKghqu0bG1NOOV2zoB8MEfJ0nMuGqqngohhGjCJJW1EEKYnoyfX883SHlMOw2/zLp23cIe3NqBWwdwba+cu7YH17Zgbl1pk3f1aMnqPec4cC6dt345yvsP9ZJv74QQQlQqPbd4xMdGAh8hhDAVCXyu5+IPD69TRnpSTkBKHFw5BwVZcDFGOUpRQcfbIewrqCCYUalUzLurC3d+sIuf/06kaytHpg5uW/v3IoQQotFKzS7O6GYngY8QQpiKTHW7UYdRMOoteGQ9PBsLc5Jg2l8wdjUMnws9HoZWfcDKEdDDsZ8hLrLSJru2cmRO8ZS3t389xrr9CbV/H0II0YQsW7YMf39/rKysCAwMZOfOnRWWnTBhAiqVqszRpUsXQ5mVK1eWWyYvL68ubqdKMtVNCCFMT0Z8qmJmCe6dlON6ej1seRX2fAC7lkCH0EqbmRTShpTsAj7afoqXv/sbRxtzRnXxrMWOCyFE0xAREcGMGTNYtmwZwcHBfPzxx4wePZojR47QunXrMuXfffdd3n77bcPPRUVF9OjRgwceeKBUOQcHB44fP17qmpWVVe3chJHSZKqbEEKYnIz41JRKBQOfBo0FxO+Bc1FVVnnp1gDC+vig08Mza2PYcyq1DjoqhBCN25IlS5g4cSKTJk2iU6dOhIeH4+Pjw/Lly8st7+joiKenp+E4cOAA6enpPP7446XKqVSqUuU8PRvOl1FpxVPdXGWqmxBCmIwEPjfDwQt6Pqyc71xSZXGVSsVb93QltLMHBUU6Jq86wL8XMmq5k0II0XgVFBQQHR1NaGjpUfXQ0FCioqr+wgng888/Z8SIEfj6+pa6np2dja+vL97e3txxxx3ExNy4jrO0/Px8MjMzSx21RUZ8hBDC9CTwuVnBz4JKDScjIfHvKoubadS891Av+vu7kJ1fxIQv9nEmJacOOiqEEI1PSkoKWq0WDw+PUtc9PDxISkqqsn5iYiK//vorkyZNKnW9Y8eOrFy5kh9//JG1a9diZWVFcHAwcXFxFba1cOFCHB0dDYePj0/NbqoaZI2PEEKYngQ+N8ulDXS5VznfVfWoD4CVuYZPH+tDZy8HUrILGPf5Xi5lNowFtUII0RDduA2AXq+v1tYAK1euxMnJibvvvrvU9QEDBvDoo4/So0cPQkJCWLduHR06dOD999+vsK3Zs2eTkZFhOBISai9RzfUbmAohhDANCXxM4ZaZyuORHyD1VLWqOFiZ8+UT/fBzteF8+lUeW7GPjNzCWuykEEI0Pm5ubmg0mjKjO8nJyWVGgW6k1+tZsWIF48aNw8Ki8gBCrVbTt2/fSkd8LC0tcXBwKHXUBq1Oz5VcGfERQghTk8DHFDy7QvtRoNfB7vBqV2thb8nqif1xt7fkWFIWE7/cz9UCbe31UwghGhkLCwsCAwOJjCy9bUBkZCRBQUGV1t2+fTsnT55k4sSJVb6OXq8nNjYWLy+vm+qvKWRcLUSnV85lxEcIIUxHAh9TCXlOeYxdCxkXql3Nx8WGVRP74WBlxoFz6Tz19UEKtbpa6qQQQjQ+s2bN4rPPPmPFihUcPXqUmTNnEh8fz9SpUwFlCtr48ePL1Pv888/p378/Xbt2LfPc/Pnz+e233zh9+jSxsbFMnDiR2NhYQ5v1qWSam72VGeYa+ZgWQghTkXdUU2ndH3yDQVcIez40qmpHTwdWTOiLpZmaP44l89K3f6Mr+bpPCCGaubCwMMLDw1mwYAE9e/Zkx44dbNq0yZClLTExkfj4+FJ1MjIy2LBhQ4WjPVeuXGHKlCl06tSJ0NBQLly4wI4dO+jXr1+t309VJLGBEELUDpVer2/wf2FnZmbi6OhIRkZGrc2pNom4rbDmPjC3gRn/gq2rUdX/OHaJyaui0er0TA7xZ87tnWupo0IIUT2N5v23HtTW72bzv4lM/eogvVo7sXFasMnaFUKIpqKm778y4mNK7YaDZ3cozIV9HxtdfVhHD/53f3cAPt15hpj4dFP3UAghRAOXlqMkupERHyGEMC0JfExJpYKQWcr53o8hP8voJu7t7c0Dgd4AhG+tOLuQEEKIpiktJx8AFwl8hBDCpCTwMbVOd4FrO8i7Age+qFETzwxrj5laxfYTl4k+J6M+QgjRnJSM+EhGNyGEMC0JfExNrYHgGcr5ng+hKN/oJlq72nBf75JRnxMm7JwQQoiGrmTER6a6CSGEaUngUxu6h4FDK8hOgtiva9TE08PaYaZWsTMuhf1n00zcQSGEEA1VanFWN2cbCXyEEMKUJPCpDWYWMPBp5Xx3OGiLjG7Cx8WGB/r4ALA0UkZ9hBCiuUjPLU5nbSeBjxBCmJIEPrUl8DGwdoH0s3Dk+xo18fSwdphrVESdSmXv6VSTdk8IIUTDlJatBD4utpb13BMhhGhaJPCpLRa2MOA/yvnOJVCD7ZJaOVkztmTUR9b6CCFEs5BWPOLjIlPdhBDCpCTwqU39JoOFHSQfhhO/1aiJp4a2w0Kj5q/TaUSdSjFxB4UQQjQkuQVF5BXqAHCRqW5CCGFSNQp8li1bhr+/P1ZWVgQGBrJz584Ky3733XeMHDmSFi1a4ODgwMCBA/ntt5oFAY2OtTP0eUI537m4RqM+LZ2sebCfMuoTHhmHvgZtCCGEaBxSi6e5WWjU2Fpo6rk3QgjRtBgd+ERERDBjxgzmzJlDTEwMISEhjB49mvj4+HLL79ixg5EjR7Jp0yaio6MZOnQod955JzExMTfd+UZh4FOgsYTz++Dc7ho1MW1IOyzM1Ow7m0bUKVnrI4QQTVVJYgMXWwtUKlU990YIIZoWowOfJUuWMHHiRCZNmkSnTp0IDw/Hx8eH5cuXl1s+PDycF198kb59+9K+fXv+7//+j/bt2/PTTz/ddOcbBXtP6PWIcv7HW5BrfGpqT0crHu7XGoAlkSdk1EcIIZqoklTWLrKHjxBCmJxRgU9BQQHR0dGEhoaWuh4aGkpUVFS12tDpdGRlZeHi4lJhmfz8fDIzM0sdjVrQdFCbQXwUhHeDrfMhx7iRm2lD2mJppib6XDo742StjxBCNEXpEvgIIUStMSrwSUlJQavV4uHhUeq6h4cHSUlJ1Wpj8eLF5OTkMHbs2ArLLFy4EEdHR8Ph4+NjTDcbHhd/eORb8OwGBdmwawm82x22zqt2AOTuYMUj/X0BJcObjPoIIUTTkyaBjxBC1JoaJTe4cd6xXq+v1lzktWvXMm/ePCIiInB3d6+w3OzZs8nIyDAcCQkJNelmw9J2KDy5Ex78+roAaKkyAhT5OuRUPYozdUgbrMzVxMRfYfuJy3XQaSGEEHVJproJIUTtMSrwcXNzQ6PRlBndSU5OLjMKdKOIiAgmTpzIunXrGDFiRKVlLS0tcXBwKHU0CSoVdLy9OABaC57doTAHdodDeHeInFtpAORub8W4AcWjPrLWRwghmhyZ6iaEELXHqMDHwsKCwMBAIiMjS12PjIwkKCiownpr165lwoQJfP3119x+++0162lTolJBx9vgyR3w0Dfg1aM4AHpXGQHa8hpklz+i8+Tgtlibazh0PoM/jyfXTv/0evh+GiwLgqtXauc1hBBClCEjPkIIUXuMnuo2a9YsPvvsM1asWMHRo0eZOXMm8fHxTJ06FVCmqY0fP95Qfu3atYwfP57FixczYMAAkpKSSEpKIiMjw3R30VipVBAwGqZsh4ciwKsnFOZC1HvwUXC5oz9udpaMH1gy6lNL+/rERULsmpvaeFUIIYTxZMRHCCFqj9GBT1hYGOHh4SxYsICePXuyY8cONm3ahK+v8sd4YmJiqT19Pv74Y4qKinjqqafw8vIyHM8++6zp7qKxU6kg4FaYsk0JgJz9IPsS/L6g3OJTBrXBxkLDPxcy2HrUxKM+2iLY8uq1n0/9Ydr2hRBCVEiSGwghRO0xq0mladOmMW3atHKfW7lyZamft23bVpOXaJ5KAiArR/jiVji4CgInQKvepYq52lnyWJAfy7edInzrCUZ0cjfdRncHv4SU40r6bV2REvjo9UrfhBBC1CqZ6iaEELWnRlndRC3zHQjdxgJ62PQC6HRlikwJaYOthYbDFzPZcuSSaV43LxP+/D/lfMQ8MLeFnGS4dNg07QshhKhQkVZHxtVCQAIfIYSoDRL4NFQjF4CFHVw4AIfWlnna2daCCcF+ACzecpyU7Pybf81dSyE3BVzbQf+p4HeLcl2muwkhRK1Lz1WCHpUKnKzN67k3QgjR9Ejg01A5eMHgF5Xzra9DXtlkEJND2mBvZcaJS9kMX7ydNXvPodPVMNnBlXjY86FyPnIBaMyh7TDl51O/16xNIYQQ1Zaeq0xzc7Q2x0wjH89CCGFq8s7akPX/D7i2h5zLsO3tMk872ViwdvIAurR0IONqIXM2/ss9y6P490INMub9vgC0+eAXAgG3KddKAp9ze6Ag9yZuRAghRFVSs2V9jxBC1CYJfBoyMwsYvUg53/sxXDpSpkjXVo788FQwr9/ZGTtLMw4lXOGuD3Yx78fDZOUVVu91zkfDP+sBFYS+eS2RgVt7cPBWAqL4KNPckxBCiHIZMrrZSOAjhBC1QQKfhq7dcOh4B+i18OuLSoa1G5hp1Dwe7M/vzw3mju5e6PSwMuoswxdv56dDFyvf60evhy1zlPMeD0LLnteeU6mg7VDl/NSfprsnIYQQZaTlyoiPEELUJgl8GoNR/wdmVnB2JxzeWGExDwcrPni4N6sn9sPfzZbkrHyeWRvD+BX7OJOSU36loz9B/B4ws4Zhr5V93rDORxIcCCFEbUornurmaieBjxBC1AYJfBoDZ1+4ZaZyvuVVKKggiCkW0r4Fvz4bwswRHbAwU7MzLoVR4TtYGnmCvELttYJFBUriBICgp8GxVdnG2gwBVJB8BDITTXI7QghhrGXLluHv74+VlRWBgYHs3LmzwrITJkxApVKVObp06VKq3IYNG+jcuTOWlpZ07tyZjRsr/mKpLpQkN3CWqW5CCFErJPBpLIKfBafWkHkBdi6psriVuYZnR7Rny4xBDOrQgoIiHe/+Hsfod3dyMjlLKbT/M0g7DbbuSvvlsXGBlr2U89My3U0IUfciIiKYMWMGc+bMISYmhpCQEEaPHk18fHy55d99910SExMNR0JCAi4uLjzwwAOGMnv27CEsLIxx48Zx6NAhxo0bx9ixY9m7d29d3VYZsnmpEELULgl8Ggtzaxi1UDmPeg9ST1Wrmp+bLV8+3pdlj/TGw8GSMyk53Lssin1HTsL24sQJw14FS/uKG5HpbkKIerRkyRImTpzIpEmT6NSpE+Hh4fj4+LB8+fJyyzs6OuLp6Wk4Dhw4QHp6Oo8//rihTHh4OCNHjmT27Nl07NiR2bNnM3z4cMLDw+vorspKl8BHCCFqlQQ+jUnH25UgRFsAv71S7WoqlYrbunmxaXoIvVs7kZlXxOG1r0LeFXDvAr0erbyBdsOVx1N/gk5X8/4LIYSRCgoKiI6OJjQ0tNT10NBQoqKql23y888/Z8SIEfj6+hqu7dmzp0ybo0aNqrTN/Px8MjMzSx2mJCM+QghRuyTwaUxUKhj9X1Cbw4nNcOI3o6q72lny9eQBTOio4xH1FgC+dXsSvaqK/wy8+4KFHeSmwKV/atp7IYQwWkpKClqtFg8Pj1LXPTw8SEpKqrJ+YmIiv/76K5MmTSp1PSkpyeg2Fy5ciKOjo+Hw8fEx4k6qlpaTD4CrraVJ2xVCCKGQwKexcWsPA/6jnP/6EhTmGVXdylzD61YRWKi0bNP24PmDbkz/JrZ00oMbaczBf5ByLtPdhBD1QFWyv1gxvV5f5lp5Vq5ciZOTE3ffffdNtzl79mwyMjIMR0JCQvU6Xw16vZ70HGXvNWdbc5O1K4QQ4hoJfBqjwS+CnSekn4E9HxhX91wUqmM/gUpN3rD5mKlV/HToIo98tpfU7PyK68k6HyFEPXBzc0Oj0ZQZiUlOTi4zYnMjvV7PihUrGDduHBYWpaePeXp6Gt2mpaUlDg4OpQ5Tyc4vokCrTCWWER8hhKgdEvg0Rpb2EPqGcr5zMWScr149nQ5+K96stPdj3Dp0KKue6IeDlRnR59K5d3kUpy5nl1+3JPCJ/6vKdNpCCGEqFhYWBAYGEhkZWep6ZGQkQUFBldbdvn07J0+eZOLEiWWeGzhwYJk2t2zZUmWbtaVktMfKXI21haZe+iCEEE2dWX13QNRQtwfgwApl89GNU6H9SGXtj8YcNBbXHtVmxT9bQNLfcPGgsl5nqJIcIaidG99NC+Lxlfs5l5rLvcui+OjRQAa2dS39ei5tlHTaV+Lh7G7oEFpOp4QQwvRmzZrFuHHj6NOnDwMHDuSTTz4hPj6eqVOnAsoUtAsXLrBq1apS9T7//HP69+9P165dy7T57LPPMmjQIBYtWsSYMWP44Ycf2Lp1K7t27aqTe7pRqqzvEUKIWieBT2OlUsFt/4OPB8HZncpRXbfMBDt3w4/t3O3ZOC2YyasOEBN/hfEr9vL2vd25L9C79Ou1HQbRK5XpbhL4CCHqSFhYGKmpqSxYsIDExES6du3Kpk2bDFnaEhMTy+zpk5GRwYYNG3j33XfLbTMoKIhvvvmGV199lddee422bdsSERFB//79a/1+ypMmGd2EEKLWqfR6vb6+O1GVzMxMHB0dycjIMOmc6ibh3++UQERXBNpCJdW1thB0152XXNcVgbM/3P+5si/QDfIKtTy3/hC//J0IwNTBbZkc4o+rXfE3kEd+gHXjwS0Ant5Xl3cphKgn8v5bMVP+btYfSOCFb/9mUIcWrHqin4l6KIQQTVNN339lxKex63qvcpiAlbmG9x/sha+LDcu2neKj7af4dOdpgtq6cmePloxqG4SjSg0px5V1RY7eVTcqhBCiSiUjPq4y4iOEELVGkhuIUtRqFS/e2pH3HupFd29HtDo9O+NSePHbv+nzzn5OWQQAkH/893ruqRBCNB1puUrg42wjgY8QQtQWGfER5bqrR0vu6tGSsyk5/Pz3RX46lMjxS1n8nNOJZ82OsvXntWyK68adPbwYEuCOlblkIRJCiJpKyy4e8bGTwEcIIWqLBD6iUn5utjw9rD1PD2vP8aQsDu7KgX+/I0j1D8/8c4Ff/knEztKM4Z3cGRrgTkh7t2trgoQQQlSLJDcQQojaJ4GPqLYAT3sC7rkHfdxzOOdn8lrvQj495cjFjDx+iL3ID7EXUamga0tHBnVwY3AHd3q1dsJcIzMqhRCiMjLVTQghap8EPsI4GnNU/oPg2M887nmax+5/joPx6Ww9msyOE5c5kpjJPxcy+OdCBh/+eQp7SzOC2rkyqEMLBrVvgY+LTX3fgRBCNDiG5AYy1U0IIWqNBD7CeG2HwbGf4dSfqAc9Tx8/F/r4ufDy6I4kZ+Wx80QK209cZtfJFNJyCvjt8CV+O3wJgDYtbBnRyYP/DG6Ls0zpEEII4FrgIyM+QghReyTwEcZrO0x5TNgL+VlgaW94yt3eivsCvbkv0BudTs+/FzPYfvwyO+IuczD+Cqcv5/DJ5dN8d/A88+/qym3dPFGpVPV0I0IIUf8KinRk5RUBks5aCCFqkwQ+wngu/spGqOln4OxuCLi13GJqtYru3k5093bimeHtycwrZFdcCksjTxCXnM1TXx8ktLMHb97dFXcHqzq+CSGEaBjSi9f3qFXgaG1ez70RQoimS1adi5opGfU59Ue1qzhYmXNbNy9+nn4L04e3x0ytYsuRSwxfsp11+xPQ6/W11FkhhGi4rp/mplbLCLgQQtQWCXxEzRgCH+M3MrU00zBrZAd+euYWuns7kpVXxIsb/mbc5/tISMs1cUeFEKJhk1TWQghRNyTwETXjHwIqDaSehPRzNWqik5cD3/0niFdu64ilmZpdJ1MIXbqDL3afQauT0R8hRPNgGPGRwEcIIWqVBD6iZqwcwbuvcn76zxo3Y6ZRM2VQWzbPGEQ/fxeuFmqZ/9MRHvgoipPJWSbqrBBCNFyGVNYS+AghRK2qUXKDZcuW8b///Y/ExES6dOlCeHg4ISEh5ZZNTEzkueeeIzo6mri4OKZPn054ePjN9Fk0FG2HQcJfyjqfwAk31ZS/my3fTB7A1/viefvXYxyMv8Jt7+5iQrAfLrYW5BfqyCvSGh7zCrXkF+nIL37MK9RiY2HGM8Pa0cfPxTT3J4QQdSBVproJIUSdMDrwiYiIYMaMGSxbtozg4GA+/vhjRo8ezZEjR2jdunWZ8vn5+bRo0YI5c+awdOlSk3RaNBBth8G2/4PT20CnBbXmpppTq1U8OsCXYR3dmbPxH/48fplPdpw2qo0dcZeZdIs/z4UGYGV+c/0RQoi6kC6BjxBC1AmjA58lS5YwceJEJk2aBEB4eDi//fYby5cvZ+HChWXK+/n58e677wKwYsWKar1Gfn4++fn5hp8zMzON7aaoCy17KVPe8jLgYgx49zFNs07WrJjQl5//TiTyyCXMNCqszDVYmWmwNFdjZabBylyNpZlauW6u/Lz1aDLfRp/n051n+ONYMu880INerZ1N0ichhKgtktxACCHqhlGBT0FBAdHR0bz88sulroeGhhIVFWWyTi1cuJD58+ebrD1RSzRm4D8Yjv6oTHczUeADoFKpuLNHS+7s0bLadW7t6sXorp7M/u4fTl3O4b7lUTw5uC0zRrTH0kxGf4QQDZMEPkIIUTeMSm6QkpKCVqvFw8Oj1HUPDw+SkpJM1qnZs2eTkZFhOBISEkzWtjCxGuznU5uGd/Jgy8xB3NOrFTo9LN92ijvf38Xf56/Ud9eEEKJcEvgIIUTdqFFyA5Wq9AZrer2+zLWbYWlpiaWlpcnaE7Wo7VDlMWEffPckqNSgViuPKo2y7qfUuQqcfKH3Y2BWOx/yTjYWLA3rya1dPZmz8R9OXMrmnmVRTBvSlmeGtcfCTJIZCiEaDkluIETdGzJkCD179qx2wq2zZ8/i7+9PTEwMPXv2rNW+idpjVODj5uaGRqMpM7qTnJxcZhRINBPOftCiE1w+Cn9/U/16J7fC2FVgVnsB7qgunvT1c+H1Hw/z06GLvP/HSSKPXGLx2B50aelYa68rhBDVpdfrSc+VwEeIilT1xfpjjz3GypUrjW73u+++w9zcvNrlfXx8SExMxM3NzejXEg2HUYGPhYUFgYGBREZGcs899xiuR0ZGMmbMGJN3TjQSYV9B3BbQa5Xsbnot6HWg0ymPhus60BbAgRVwYjN887BS19y61rrmYmvB+w/14tYunrz2w78cS8pizAe7eWpoO54I9sfRpvpvekIIYWqZV4sMGzY720jgI8SNEhMTDecRERHMnTuX48ePG65ZW5f+G6KwsLBaAY2Li3FbX2g0Gjw9PY2q01QUFBRgYdE03p+MnvMza9YsPvvsM1asWMHRo0eZOXMm8fHxTJ06FVDW54wfP75UndjYWGJjY8nOzuby5cvExsZy5MgR09yBqH9u7WDgNAh6Bm6ZASHPwaAXYMhLMHQ2DHsVRrwOI+fDrQvh4XVgbqOM+qx9EApya72Lt3f3YsvMQdzaxZMinZ53f4+jz1uRTPryAD/EXiAnv6jW+yCEEDdKKx7tsbXQSAp+Uef0ej25BUX1cuj1+mr10dPT03A4OjqiUqkMP+fl5eHk5MS6desYMmQIVlZWfPXVV6SmpvLQQw/h7e2NjY0N3bp1Y+3ataXaHTJkCDNmzDD87Ofnx//93//xxBNPYG9vT+vWrfnkk08Mz589exaVSkVsbCwA27ZtQ6VS8fvvv9OnTx9sbGwICgoqFZQBvPnmm7i7u2Nvb8+kSZN4+eWXK50qp9VqmThxIv7+/lhbWxMQEGDIjny9FStW0KVLFywtLfHy8uLpp582PHflyhWmTJmCh4cHVlZWdO3alZ9//hmAefPmlXn98PBw/Pz8DD9PmDCBu+++m4ULF9KyZUs6dOgAwFdffUWfPn2wt7fH09OThx9+mOTk5FJtHT58mNtvvx0HBwfs7e0JCQnh1KlT7NixA3Nz8zKzxp577jkGDRpU4e/D1Ixe4xMWFkZqaioLFiwgMTGRrl27smnTJnx9fQElMo+Pjy9Vp1evXobz6Ohovv76a3x9fTl79uzN9V40Tm0GwyPfwpoHlD2Avh4LD30Dlna1+rJudpYsf7Q3P/2dyLI/T3IsKYutRy+x9eglrMzVDO/owZ09vBgS4C5/gAgh6kRajrJ1g4td0/g2VTQuVwu1dJ77W7289pEFo7CxqNFS8zJeeuklFi9ezBdffIGlpSV5eXkEBgby0ksv4eDgwC+//MK4ceNo06YN/fv3r7CdxYsX88Ybb/DKK6/w7bff8p///IdBgwbRsWPHCuvMmTOHxYsX06JFC6ZOncoTTzzB7t27AVizZg1vvfWWYe/Lb775hsWLF+Pv719hezqdDm9vb9atW4ebmxtRUVFMmTIFLy8vxo4dC8Dy5cuZNWsWb7/9NqNHjyYjI8PwmjqdjtGjR5OVlcVXX31F27ZtOXLkCBqNcX/X/P777zg4OBAZGWkIUgsKCnjjjTcICAggOTmZmTNnMmHCBDZt2gTAhQsXGDRoEEOGDOGPP/7AwcGB3bt3U1RUxKBBg2jTpg2rV6/mhRdeAKCoqIivvvqKt99+26i+3Ywa/Rc3bdo0pk2bVu5z5c2zrG5UL5oRv2AYtxG+ug/O7oQ198Mj68HSvlZfVqVScVePltzVoyUnLmXx86GL/HjoImdTc/nln0R++ScRO0szQjt7cGfPltzSzg1zTfkDo3q9nqz8IjJyC8m4qhz5RVp6eDvhaifJOYQQVUvNLlnfI+8ZQtTUjBkzuPfee0tde/755w3nzzzzDJs3b2b9+vWVBj633Xab4e/bl156iaVLl7Jt27ZKA5+33nqLwYMHA/Dyyy9z++23k5eXh5WVFe+//z4TJ07k8ccfB2Du3Lls2bKF7OzsCtszNzcvtaWLv78/UVFRrFu3zhD4vPnmmzz33HM8++yzhnJ9+/YFYOvWrezbt4+jR48aRmratGlT4etVxNbWls8++6zUFLcnnnjCcN6mTRvee+89+vXrR3Z2NnZ2dnz44Yc4OjryzTffGKYblvQBYOLEiXzxxReGwOeXX34hNzfXcF91wTShthA10bo/jP8BVt8D8XuUx0c3KJui1oEOHvbMCg1g5sgOHL6YyU+HLvLToYtczMjju5gLfBdzAScbc4Z0aIEeuHJdgFNylMzNv55KBd1aOTK4QwsGd2hBTx8nzCoInoQQzZshsYGsNxT1wNpcw5EFo+rttU2lT5/S+whqtVrefvttIiIiuHDhAvn5+eTn52Nra1tpO927dzecl0ypu3EqV2V1vLy8ACXpV+vWrTl+/HiZgYJ+/frxxx+VbwHy0Ucf8dlnn3Hu3DmuXr1KQUGBYXpacnIyFy9eZPjw4eXWjY2Nxdvbu1TAURPdunUrs64nJiaGefPmERsbS1paGjqdDoD4+Hg6d+5MbGwsISEhFa6xmjBhAq+++ip//fUXAwYMYMWKFYwdO7bKfxdTksBH1C/vQHjsB1h1N5zfD6vGKCNB1s511gWVSkXXVo50beXIS7d2JCYhnZ8OJfLz34mkZOfzfezFSutbmKlxsjbH0docPXAyOZu/z2fw9/kM3v/jJPZWZtzSzo1BHVowqEMLWjnVXjIHIUTjci2VtYz4iLqnUqlMNt2sPt34h/PixYtZunQp4eHhdOvWDVtbW2bMmEFBQUGl7dz4B7tKpTL8cV+dOiUZ6K6vU94WMJVZt24dM2fOZPHixQwcOBB7e3v+97//sXfvXqBsMocbVfW8Wq0u04fCwsIy5W78nebk5BAaGkpoaChfffUVLVq0ID4+nlGjRhl+r1W9tru7O3feeSdffPEFbdq0YdOmTWzbtq3SOqbW+P9rF41fy14w4Wf48i64GKM8jv8BbIzLuGIKarWKQF8XAn1deO2Ozuw9ncr+s+nYWmpwsDY3BDhONhbFj+Zl1gMlZ+axIy6F7ScuszPuMldyC/n13yR+/VdZ0Nfe3Y5BHVoQ0t6NDh72eDhYoVGbbh8sIZqiZcuW8b///Y/ExES6dOlCeHg4ISEhFZbPz89nwYIFfPXVVyQlJeHt7c2cOXMMUzVWrlxpmH5yvatXr2JlZVVr93GjdEPgIyM+QpjKzp07GTNmDI8++iigBCJxcXF06tSpTvsREBDAvn37GDdunOHagQMHKq2zc+dOgoKCSo0UnTp1ynBub2+Pn58fv//+O0OHDi1Tv3v37pw/f54TJ06UO+rTokULkpKSSu3BWZKwoTLHjh0jJSWFt99+Gx8fn3LvpXv37nz55ZeVZtabNGkSDz74IN7e3rRt25bg4OAqX9uUJPARDYNnN5jwC6y6C5L+hi/vVIIf2/rLl69Rqwhq50ZQO+P64O5gxf2B3twf6I1Wp+fv81fYcSKF7SeSiU24QlxyNnHJ2Xy+6wwA5hoV3s42eDtb4+Nig4+zDT4u1rQuPndKi0X19zroOxHc6/ZNW4iGICIighkzZhgWCH/88ceMHj2aI0eO0Lp163LrjB07lkuXLvH555/Trl07kpOTKSoqnb3RwcGhTAamugx6QEZ8hKgN7dq1Y8OGDURFReHs7MySJUtISkqq88DnmWeeYfLkyfTp04egoCAiIiL4+++/K11z065dO1atWsVvv/2Gv78/q1evZv/+/aUSIsybN4+pU6fi7u5uSGSwe/dunnnmGQYPHsygQYO47777WLJkCe3atePYsWOoVCpuvfVWhgwZwuXLl/nvf//L/fffz+bNm/n1119xcHCo9F5at26NhYUF77//PlOnTuXff//ljTfeKFXm6aef5v333+fBBx9k9uzZODo68tdff9GvXz8CAgIAGDVqFI6Ojrz55pssWLDgJn67NSMLD0TD4dFZCX7sPODSv7DyDsiufG5tQ6dRq+jV2plnR7Tnu2nBHHxtJB8+3JuxfbzxdbXBTK2iUKvnTEoOO+NS+HpvPIs2H+Ppr2N4/INNRC68H9XnI2H/p2R9eicnT8XV9y0JUeeWLFnCxIkTmTRpEp06dSI8PBwfHx+WL19ebvnNmzezfft2Nm3axIgRI/Dz86Nfv34EBQWVKnd9WtySo66lFQc+rrJ5qRAm89prr9G7d29GjRrFkCFD8PT05O67767zfjzyyCPMnj2b559/nt69e3PmzBkmTJhQ6RcsU6dO5d577yUsLIz+/fuTmppaZp3QY489Rnh4OMuWLaNLly7ccccdxMVd+/tgw4YN9O3bl4ceeojOnTvz4osvotVqAejUqRPLli3jww8/pEePHuzbt69UIoiKtGjRgpUrV7J+/Xo6d+7M22+/zTvvvFOqjKurK3/88QfZ2dkMHjyYwMBAPv3001KjP2q1mgkTJqDVastsf1MXVPpGkHItMzMTR0dHMjIyqoxIRROQclIZ8cm6CC5tIPBxJQucZw/QNK1ByiKtjsSMPBLSczmfdpWE9FwupGbS+eIGxmatwoEcAFL19riqsojVteU15/9ye29/7urRkpayXkjUsvp+/y0oKMDGxob169eX2jj72WefJTY2lu3bt5epM23aNE6cOEGfPn1YvXo1tra23HXXXbzxxhuGOegrV65k0qRJtGrVCq1WS8+ePXnjjTdKbb9wo5IF0iUyMzPx8fG5qd/NmA92ceh8Bp+O78PIzh41akMI0XiMHDkST09PVq9eXd9dqTeTJ0/m0qVL/PjjjzVuo6afTU3rr0jRNLi1g8d/gZV3QtppiHxNuW5hD60HgN8tyuHVs9EHQmYatTK9zcUG2gLxf8Evz0PWPwDoPHtwMWgBx7KsGPD7/fTkFBPSlvLcr//h7V+P0c/fhbt7tuK2bp44ya7voglKSUlBq9Xi4VE6KPDw8CizEV6J06dPs2vXLqysrNi4cSMpKSlMmzaNtLQ0VqxYAUDHjh1ZuXIl3bp1IzMzk3fffZfg4GAOHTpE+/bty2134cKFpdLMmsK1qW7y/68QTU1ubi4fffQRo0aNQqPRsHbtWrZu3UpkZGR9d61eZGRksH//ftasWcMPP/xQL31o3H81iqbLpQ1M2QZ/fwNnd0N8FORlwMlI5QCwsFMCId9g8AuBlj1B00gXCGddgq2vw6HinaWtnGD4a6gDH8dbrcEbwGsN+tX3cJ9mF7lOHXnt8jD2nUlj35k0Xv/xX4YEuHN3z1YM72S6DVj1ej3/XMhgy+FL7D+bRk8fJ6YMaiP7FIk6V15mpBuvldDpdKhUKtasWYOjo5Ief8mSJdx///18+OGHWFtbM2DAAAYMGGCoExwcTO/evXn//fd57733ym139uzZzJo1y/BzyYjPzUiXwEeIJkulUrFp0ybefPNN8vPzCQgIYMOGDYwYMaK+u1YvxowZw759+3jyyScZOXJkvfRBAh/RcNm1gKBnlEOnVdb9nN0NZ3fBud2QdwVOblUOUEaEuoyBno8qAVEFfxQ1KNoi2PcJbFsI+ZmACnqPh+Gvg61r6bJtBqMavQg2Pc+4rM+5LWwI6zM7833MBY4lZRF55BKRRy5hZ2lGP38XAn2d6ePrTHdvJ6wtqh8IFWl17DuTxpYjl9hyOImLGXmG5/aeSWP1X+d4LMiPKSFtcJY/1kQtc3NzQ6PRlBndSU5OLjMKVMLLy4tWrVoZgh5Q5rXr9XrOnz9f7oiOWq2mb9++pebJ38jS0hJLS9MF/XmFWnIKlHn3EvgI0fRYW1uzdevW+u5Gg1HXqavLI4GPaBzUGvDqoRwDp4FOB8mHiwOhnUogdDUdYr5SDpe20PNh6PEQOLYy7rX0ekg+AnFbIG4rpJ+B7mPhlpmm3Vz17C7Y9ILyWqCk9b5tsbK3UUX6TlICwOiVuG6extRJvzN18CCOJ2XxfewFfoy9yIUrV/njWDJ/HFMSQ5ipVXRp5Uhga2f6+DkT6OuMh0PphZVXC7TsiLvMlsOX+P3YJa7kXsvpb22uYUhAC/r5u7Ax5gJ/n89g+bZTrIo6yxO3+DPpljY4yuaLopZYWFgQGBhIZGRkqTU+kZGRjBkzptw6wcHBrF+/3rCbOMCJEydQq9V4e3uXW0ev1xMbG0u3bt1MfxMVKElsYKZW4WAlH8dCCFHbJLmBaBp0Okj4C2LWwOGNUKgkBUClhjZDodcjEHA7mFeQSSU/C05vV4Kdk1sh80LZMjauMPglJdmCWQ2/ndXr4VwU7Fp6bcqetQuMeB16jVMCvKoUFSgbvcZHKQHe5N8NG77qdMrUtP1n0zgYn86Bs+kkZ+WXacLb2ZpAX2c6eTkQE5/O9hOXySu8tuGas405Izp5ENrFk5D2boapc3q9nt+PJrMk8gRHEjMBsLc0Y2KIP0/c4o+DlQRATU1DeP+NiIhg3LhxfPTRRwwcOJBPPvmETz/9lMOHD+Pr68vs2bO5cOECq1atAiA7O5tOnToxYMAA5s+fT0pKCpMmTWLw4MF8+umnAMyfP58BAwbQvn17MjMzee+991i9ejW7d++mX79+1erXzf5u/r2QwR3v76KFvSX75zTPqS9CCFETktxANG9qNfgGKcfoRXDkB4hdo4wEnfpdOaycoNv90PMRZXTl8vHiQCcSzu0B3XU7F5tZg38ItA9VgortiyDlBPz6Iuz9SJmK1nlM9afT6XRw4lcl4Dm/X7mmUkPgBBj2mnGbtZpZQNhq+GQopJ2C9Y/DI9+Cxgy1WkUPHyd6+DgBSqByPv0q0efSiT6XzoFz6RxPyuR8+lXOp1/lh9iLhmZbOVkT2sWDUV086ePrjJmmbLZ7lUrFiM4eDOvozpYjlwjfeoJjSVmEb41jxa4zTA5pw4RgP+wlABImFBYWRmpqKgsWLCAxMZGuXbuyadMmfH19AUhMTCQ+Pt5Q3s7Ojsj/b+/Ow6Oq7sePvyd7yAYkJJMACQlhkyUsAQyWpaBBEITihr+2xFLBqNhCUFQoq1CoCyIV8VeVRR4VihJEwGIqEJClAgaILJFNwpIQAiRkX2bO94+bTBiyLzCZ4fN6nvvMzJ07d86Zk+ee55Nz7ufExfHSSy8RHh6Ot7c3Tz75JPPnzzcdk5GRwcSJE0lNTcXLy4sePXqwa9euGgc9DUFSWQshxN0lIz7Ctl0/C4c/h8NfwM2LZftdvLRkCbdqHqIFOqEPaemzHW9JFW0ohp9Wa/fi5FzV9rXqDZHztfuJKlNcCInrYc97kF6yUKK9M/T4A/SbpH1nXaUmwieRUJQL978ID/+9Rh/Lyi/iyIVMDp6/zomUm3Tw8yCys57OAZ6V3izOzcvwy3+gRUcIjDAFfEaj4tufU1ny3184lZYNQNMmjkwcEMLA9i3wdHHE08URdxcH7O2s4J4rUY5cfytX39/m68OX+Ovaw0SEePPFxCquI0IIIczU9forgY+4NxgNcC5emwp3cjMU52sBSHB/LdBp9xB4t63+PAVZsPd92LtUCzgAOo6AB+eAzy03TBdkw0+fwr73y6bNOXtq9+jc/zy4+zZMvY5/Df8uWQBs1DItoGpIFw/B/g/g+EYwlqx6r++m1aHLY+Cg3ehtMCo2H73Me9+f4uzVnApP5eZkj6erIx4uDni4OOJZ+ujqQNeWXgzp5IePZItrdOT6W7n6/jYrfjjHvM3HeaSbP8v+X887UEIhhLBNEvgIUVN5Gdq0Nb8u4NSkbufISoUdf4eENaCMoLOH8D9Bn4nw8wb48f9ryRYA3P3g/he09xsyOUKpnYu0kSh7J4jaDIF963c+QzGc2AT7l8PFH8v2+4fB1V+gOE977dYCwv8M4ePBQ8uuZTAqNh25xMo9v5KSmU9WfpHZvUNV0ekgPKgZQzvribxPT6B3HdtGNCi5/lauvr/N29uSeH/HacZFBDFvVJc7UEIhhLBNEvgIYQlpJ7X1d375T/n3mofAA3+FbmMrT6rQEIxGWB+lBStuvjBxB3hVnLmqSrnXtel8P35cNi3Q3gm6PA73R2uBT+51OLQKDnxcNpJl56iN/twfrd07dZvCYiNZ+UVk5ReTlV/MzfwisvKLuFny+lp2AbtPpZN4yXzqYUe9NgVvaGc/7vOvYhqeuKPk+lu5+v4202MT+fx/yfx1SDumPNT+DpRQCFFbq1atYvLkyWRkZFi6KKIKEvgIYUnndsN3f4OUw1qA8Jsp0OnRmmVpawiFOfDJULiSqH3/6A/B2QOc3bWFXqta2PVqkpaw4fAXVY7mmDEUwYlvyo8KBUZA32ht+p997XKnXM7I47tjqXx3/Ar/O3cdg7Hs0nRr4oUQHzd0Oh12Osoe0aGzAzudDh0ljzpwsrfDTu4tqhe5/lauvr9N9JpD/OdYKvNGdWZcRJuGL6AQNqC6f3pFRUWxatWqOp27TZs2TJ48mcmTJ5v25eXlkZWVha9vA01JF3eEZHUTwpKC+8OEHdpIiVfru794qpMbPP25lukt5QgsjzB/395JO8apJBByctM2QyEk7ys7Tt9Vm5Z3y/07FbJ3hC5jtO3iIfjfci2NePI+bfNqDb+ZDD2fqXEAFNDUlWceCOaZB4K5kVPI9pNpbDuWyq5TV7mUkcfKPb+ycs+vtf1lcHOyp4mzA+7ODjRxssftlufaowPuzvbY29mhUCgFCkApjAqzfarktYezA/5ervg3daFlU1f0Xi44O9ylIFfYjOu5Wla3Zk0kq5sQlUlJSTE9X7duHbNmzSIpKcm0z9XVtaKP1Zmrq2uDn9NaFBYW4uRk29cjGfERwpZc+BE2x2jT0ApzwFB+DZ/ydNDxES3gCepX96DtZgoc/AQOroDca9o+3/vg4YUQMqhu58R8cdUdSWlk5BaagpDGxMfdSQuGvFwIaFr22LaFO+393CtMD24N5Ppbufr+Ng8ujud0WjafP9uXfqE+d6CEQlRDqbJEPXebY5Na9zcVTUP75ptvmDNnDseOHSMgIICoqChmzJiBg4P2T7c5c+awYsUKrly5gre3N48//jhLly5l0KBBxMfHm51fKVXuO+bMmcPGjRuZOnUqM2fO5MaNGwwbNoyPPvoIDw8PALKysoiOjmbjxo14enoybdo0vv76a7p3786SJUsqrMuZM2eIiYlh//795OTk0KlTJxYuXMiDD5at6VVQUMDMmTP54osvSEtLIzAwkNdee40///nPABw7doxp06axe/dulFJ0796dVatW0bZtWwYNGlTu+0ePHk3Tpk1NI2Rt2rTh2Wef5fTp08TGxjJ69GhWr17Nq6++SmxsLBcvXkSv1/P73/+eWbNm4ehYNntk06ZNzJs3j59//hl3d3cGDBjAhg0bmDdvHuvXrycxMdGsvr169eKRRx5h3rx5NW7vqsiIjxACWveB538oe20ogsJsLQgqzLnteY6W3S7oAWgeXP/v9vSHwX+D/i9rGe12/h3SjmuLrXZ4BCLfqFnmvNu4OtkztLOeoZ31ZvuVKhuJMSqFsfS1KnldlE+B0Y6cIkV2QTG5hQbtscBATkExOYXF5BQUk13y2qAUOrR+WIeu5LFsmoVOp02hA8jMKyIlM4+UjHwuZeRRUGwkPbuQ9OzCcvcqATRxsqdbKy96BjajR2AzegQ2rVcGO6NRoUBShFu5GyXr+DR3t+3/sIpGrCgX/h5gme+eflmbeVAP27Zt4w9/+ANLly6lf//+nDlzhokTJwIwe/ZsvvzyS959913Wrl1L586dSU1N5ciRIwBs2LCBsLAwJk6cyIQJE6r8njNnzrBx40Y2b97MjRs3ePLJJ1m0aBELFiwAICYmhj179rBp0yb8/PyYNWsWP/30E927d6/0nNnZ2QwfPpz58+fj4uLC6tWrGTlyJElJSQQGBgIwbtw49u3bx9KlSwkLC+PcuXOkp6cDcOnSJQYMGMCgQYPYvn07np6e7Nmzh+Li4lr9hm+99RYzZ87kb3/7m2mfh4cHq1atIiAggMTERCZMmICHhwfTpk0DYMuWLYwZM4YZM2awZs0aCgsL2bJlCwDjx49n7ty5HDhwgN69ewNw9OhREhISWL9+fa3KdidI4COELbN31BZgdW12977T0QX6TtQWi43/B/z4ESRt0RaLvf95GPAKuNR/5EBXch8PgD0lT3KuaenKT2yCszvxcPbEp9MIuG80BA+o+l6nOlJKcSO3iMsZeaRk5pOSmcelDC0oupyRR1JqFlkFxew/e539Z6+bPhfYvAk9ApuWBENN6eTviaO9HUUGI2lZBaRm5pGaWUBKZh6pmfmk3MwnNVPbrtzMx85OR4iPG6G+7rTz9aCdnzvtfN0J8nbDycE6R5fuJUaj4kbJVLfmMtVNiDpZsGABr732GlFRUQCEhITwxhtvMG3aNGbPnk1ycjJ6vZ4HH3wQR0dHAgMDTYsUN2/eHHt7ezw8PNDr9VV9DUajkVWrVplGeP74xz/y/fffs2DBArKysli9ejWff/45Q4YMAWDlypUEBFQdUIaFhREWFmZ6PX/+fGJjY9m0aROTJk3il19+4d///jdxcXGmUaCQkLK1/5YtW4aXlxdr1641jcS0b1/7JCmDBw/m5ZdfNtt3axDUpk0bpk6dyrp160yBz4IFCxg7dixz5841qw9Aq1atGDp0KCtXrjQFPitXrmTgwIFm5bcUCXyEEHdGk+Yw7B/Q60+wbTqc+V5b/+jIFzB4prbmUEMkf8i6Aie/geOb4NcfQBnK3su7ro0+/fQpuDaHugZB2Vfh0kG4eAAuJ2j/pQx9CNpFovP0p7mbE83dnOjSsny6coNRceZqNj+dv0FCcgY/Jd/gVFo2yddzSb6ey9eHLwPg7GCHp6sj6dkFNZvGZ1ScTM3iZGoWUDYH3sFOR5B3E1MwVBoYhfq6S0DUiGTkFVGav6OZmwQ+wkIcm2gjL5b67no6dOgQBw4cMI28ABgMBvLz88nNzeWJJ55gyZIlhISE8PDDDzN8+HBGjhxpmgZXU23atDEFPQD+/v6kpaUBcPbsWYqKikwBFYCXlxcdOnSo8pw5OTnMnTuXzZs3c/nyZYqLi8nLyyM5ORmAw4cPY29vz8CBAyv8/OHDh+nfv7/Z9LO6CA8PL7fvyy+/ZMmSJZw+fZrs7GyKi4vNppMdPny4ylGyCRMmMH78eBYvXoy9vT2fffYZ77zzTr3K2VAk8BFC3Fm+HeEPX2kjPtumw7XT8M1ftJTYDy+CNg/U/pyZl7Sscse/LknOcEukoO8G942CTiO19ZaOb9SCotz0mgVBRfmQehQulgQ6lw5CRnL5Mpz4puz72g+FdkOhZc9ywZy9nY72fh609/NgbB9t+kJmXhFHL2bw0/kMEi5oAVFmXhFXs7R7shztdfh5uuDv5YLeyxW9pzP6kvuH9F7a/qJixemrWZy6ks2pNG07fSWLnEIDZ67mcOZqDv85VlaO2Bf60SPwLo78iSpdL5nm5uHigKOV3v8lbIBOV+/pZpZkNBqZO3cuY8aMKfeei4sLrVu3Jikpibi4OP773//ywgsv8NZbbxEfH1+rgOH2Y3U6HUajtkZd6a3yt2efq+4W+ldeeYVt27bx9ttvExoaiqurK48//jiFhdq1oboEC9W9b2dnV64MRUVF5Y5zczNv//3795tGc4YOHWoaVbo1cKnuu0eOHImzszOxsbE4OztTUFDAY489VuVn7hYJfIQQd55OpwUHIb+FAx/Bzn9owcWq4Vrw0f5hbaTGaCh7ND0vLntelA/n4rWA5FYtw+G+R7UU4rfer9SiA4QMhGFvwfk9lQdBHR8BR1ct2ElNBOPtnYNOO1fLcC24yUmHU9vg0k9aPVKPwq63oIk3hD4I7SIhdEjFUwyNRryMN+nveZX+IWngexVjhytkpl+mUDng6t8J91adsWvRvtr1nwK9mzC4Y1m6caUUKZn5nC4NhNLKAqNQX/daNZm4s0oDH28Z7RGiznr27ElSUhKhoaGVHuPq6sqjjz7Ko48+yosvvkjHjh1JTEykZ8+eODk5YTAYKv1sTbRt2xZHR0d+/PFHWrduDWg33p86darS0RqA3bt388wzz/C73/0O0O75+fXXX03vd+3aFaPRSHx8vFnCg1LdunVj9erVFBUVVRjEtWjRwiwjnsFg4Oeff+a3v/1tlfXZs2cPQUFBzJgxw7Tv/Pnz5b77+++/509/+lOF53BwcCAqKoqVK1fi7OzM2LFjadKkcSxKLoGPEOLucXCCiBeh21OwY4G2GOrxjdpWKzoIvL9sZKe6BVvtHbQA6NYg6FisNmqTmw4Ja8yPb+IDrXpDq15lwY7LbdPYBr2qTYE7/V8tCDq9Xctmd3SdtunsoXVfaBYE2WmQk6Ydn3PVfDoeYAeYQqSE0iraQbM20KKjFnSVPvq0N/8PbXEhZF+BrFR02akEZGnbgKxUyEkBrqBcU9AV/w9oUZsfWdxBpYGPTHMTou5mzZrFiBEjaN26NU888QR2dnYcPXqUxMRE5s+fz6pVqzAYDPTt25cmTZqwZs0aXF1dCQoKArQpbLt27WLs2LE4Ozvj41P77IoeHh5ERUXxyiuv0Lx5c3x9fZk9ezZ2dnZVrkEUGhrKhg0bGDlyJDqdjpkzZ5pGkUrLFhUVxfjx403JDc6fP09aWhpPPvkkkyZN4p///Cdjx47l9ddfx8vLi/3799OnTx86dOjA4MGDiYmJYcuWLbRt25Z33323RouyhoaGkpyczNq1a+nduzdbtmwhNjbW7JjZs2czZMgQ2rZty9ixYykuLubbb7813QME8Oyzz9KpUydAC6YaCwl8hBB3n5sPjHhXWyR1zxItYLBz0IIFu5JNd+ujA9jZac/9OmvBjkfVN6NW6tYgaPjbWhCUtFULNFr2glbh0DSoZmlW3VtA96e1zVAEF/4Hv2yDU3Fw9QQk79W2irg2AzdfcC/Z3Hy1DEtXk7TP5mfC9bPalrTV/LNegdoCtdmpZanDq6AD7Vh3CXwaCxnxEaL+hg4dyubNm5k3bx5vvvkmjo6OdOzYkWeffRaApk2bsmjRImJiYjAYDHTt2pVvvvkGb29vAObNm8dzzz1H27ZtKSgoqHZ6WmUWL15MdHQ0I0aMMKWzvnDhAi4ulY/av/vuu4wfP55+/frh4+PDq6++ys2bN82OWb58OdOnT+eFF17g2rVrBAYGMn36dAC8vb3Zvn07r7zyCgMHDsTe3p7u3bvzwAPa9PHx48dz5MgRxo0bh4ODA1OmTKl2tAdg1KhRTJkyhUmTJlFQUMAjjzzCzJkzmTNnjumYQYMGsX79et544w0WLVqEp6cnAwYMMDtPu3bt6NevH9euXaNv3741/SnvOFnHRwgh7oQb57XRoPzMssCmNMhp4qONflVGKW2U6OrJkkDoJKT/oj3mXC1/vJ2jFgh66MHdDzz8waP0UQ/uevAOrXbqXEXk+lu5+vw2728/xdvf/cKT4a148/Gw6j8ghLAaOTk5tGzZknfeece05s69RilFx44dee6554iJiWnw88s6PkII0Zg0C4LedezwdLqSwMVPG5m6Vc41LQAqztMCGg9/bfTITm6QtyYjwwII9fVA71X7YFQI0bgkJCRw8uRJ+vTpQ2ZmpmmRzlGjRlm4ZJaRlpbGmjVruHTpUqX3AVmKBD5CCGFN3LzBrQ6Z8ESjEuTtRpC39WbTEkKYe/vtt0lKSsLJyYlevXqxe/fuOt0zZAv8/Pzw8fHhX//6F82aNa5sohL4CCGEEEIIUUc9evTg0KFDli5Go9GY76Kp09yIDz74gODgYFxcXExRbVXi4+Pp1asXLi4uhISE8OGHH9apsEIIIYQQQghRF7UOfNatW8fkyZOZMWMGCQkJ9O/fn2HDhplWmr3duXPnGD58OP379ychIYHp06fzl7/8ha+++qrehRdCCCGEEEKImqh1Vre+ffvSs2dPli9fbtrXqVMnRo8ezcKFC8sd/+qrr7Jp0yZOnDhh2hcdHc2RI0fYt29fjb5TsgoJIYRlyPW3cvLbCCGEZdT1+lurEZ/CwkIOHTpEZGSk2f7IyEj27q14rYp9+/aVO37o0KEcPHiQoqLbV0fXFBQUcPPmTbNNCCGEEEIIIeqqVoFPeno6BoMBPz8/s/1+fn6kpqZW+JnU1NQKjy8uLiY9Pb3CzyxcuBAvLy/T1rp169oUUwghhBBCCCHM1Cm5ge62Fc2VUuX2VXd8RftLvf7662RmZpq2Cxcu1KWYQgghhBBCCAHUMp21j48P9vb25UZ30tLSyo3qlNLr9RUe7+DggLe3d4WfcXZ2xtnZuTZFE0IIIYQQQohK1WrEp3RRpri4OLP9cXFx9OvXr8LPRERElDv+u+++Izw8HEdHx1oWVwghhBBCCCFqr9ZT3WJiYvj4449ZsWIFJ06cYMqUKSQnJxMdHQ1o09TGjRtnOj46Oprz588TExPDiRMnWLFiBZ988gkvv/xyw9VCCCGEEEIIIapQq6luAE899RTXrl1j3rx5pKSk0KVLF7Zu3UpQUBAAKSkpZmv6BAcHs3XrVqZMmcKyZcsICAhg6dKlPPbYYzX+ztJ7giS7mxBC3F2l193GvBK3pUjfJIQQllHXvqnW6/hYwsWLFyWzmxBCWNCFCxdo1aqVpYvRqEjfJIQQllXbvskqAh+j0cjly5fx8PCoMntcZW7evEnr1q25cOGCzS0yJ3WzXrZcP6mbdaqobkopsrKyCAgIwM6uTolAbZb0TZWTulknqZv1suX6NWTfVOupbpZgZ2fXIP9p9PT0tLk/hlJSN+tly/WTulmn2+vm5eVlwdI0XtI3VU/qZp2kbtbLluvXEH2T/PtOCCGEEEIIYfMk8BFCCCGEEELYvHsi8HF2dmb27Nk2uSiq1M162XL9pG7WyZbr1hjZ8u8tdbNOUjfrZcv1a8i6WUVyAyGEEEIIIYSoj3tixEcIIYQQQghxb5PARwghhBBCCGHzJPARQgghhBBC2DwJfIQQQgghhBA2TwIfIYQQQgghhM2z+cDngw8+IDg4GBcXF3r16sXu3bstXaQGMWfOHHQ6ndmm1+stXaw62bVrFyNHjiQgIACdTsfGjRvN3ldKMWfOHAICAnB1dWXQoEEcO3bMMoWtperq9swzz5Rrx/vvv98yha2lhQsX0rt3bzw8PPD19WX06NEkJSWZHWOtbVeTullr2y1fvpxu3bqZVsCOiIjg22+/Nb1vrW1mbWyxb7Klfgmkb7LG6xtI32StbXe3+iabDnzWrVvH5MmTmTFjBgkJCfTv359hw4aRnJxs6aI1iM6dO5OSkmLaEhMTLV2kOsnJySEsLIz333+/wvfffPNNFi9ezPvvv8+BAwfQ6/U89NBDZGVl3eWS1l51dQN4+OGHzdpx69atd7GEdRcfH8+LL77I/v37iYuLo7i4mMjISHJyckzHWGvb1aRuYJ1t16pVKxYtWsTBgwc5ePAggwcPZtSoUaYOxFrbzJrYct9kK/0SSN9kjdc3kL4JrLPt7lrfpGxYnz59VHR0tNm+jh07qtdee81CJWo4s2fPVmFhYZYuRoMDVGxsrOm10WhUer1eLVq0yLQvPz9feXl5qQ8//NACJay72+umlFJRUVFq1KhRFilPQ0tLS1OAio+PV0rZVtvdXjelbKvtmjVrpj7++GObarPGzFb7Jlvtl5SSvsmaSd9kve5E32SzIz6FhYUcOnSIyMhIs/2RkZHs3bvXQqVqWKdOnSIgIIDg4GDGjh3L2bNnLV2kBnfu3DlSU1PN2tHZ2ZmBAwfaTDvu3LkTX19f2rdvz4QJE0hLS7N0keokMzMTgObNmwO21Xa3162UtbedwWBg7dq15OTkEBERYVNt1ljZet90L/RLYFvXt8pY+/WtlPRN1td2d7JvstnAJz09HYPBgJ+fn9l+Pz8/UlNTLVSqhtO3b18+/fRTtm3bxkcffURqair9+vXj2rVrli5agyptK1ttx2HDhvHZZ5+xfft23nnnHQ4cOMDgwYMpKCiwdNFqRSlFTEwMv/nNb+jSpQtgO21XUd3AutsuMTERd3d3nJ2diY6OJjY2lvvuu89m2qwxs+W+6V7pl8B2rm+Vsebr262kb7KutrsbfZNDg5W2kdLpdGavlVLl9lmjYcOGmZ537dqViIgI2rZty+rVq4mJibFgye4MW23Hp556yvS8S5cuhIeHExQUxJYtWxgzZowFS1Y7kyZN4ujRo/zwww/l3rP2tqusbtbcdh06dODw4cNkZGTw1VdfERUVRXx8vOl9a28za2CLv/G91i+BbbYjWPf17VbSN1lX292NvslmR3x8fHywt7cvFwmmpaWVixhtgZubG127duXUqVOWLkqDKs0IdK+0o7+/P0FBQVbVji+99BKbNm1ix44dtGrVyrTfFtqusrpVxJrazsnJidDQUMLDw1m4cCFhYWG89957NtFmjd291DfZar8EtnF9qw1rur6Vkr5JY01tdzf6JpsNfJycnOjVqxdxcXFm++Pi4ujXr5+FSnXnFBQUcOLECfz9/S1dlAYVHByMXq83a8fCwkLi4+Ntsh2vXbvGhQsXrKIdlVJMmjSJDRs2sH37doKDg83et+a2q65uFbGmtrudUoqCggKrbjNrcS/1TbbaL4F1X9/qwpqub9I3mbOmtrvdHemb6pdvoXFbu3atcnR0VJ988ok6fvy4mjx5snJzc1O//vqrpYtWb1OnTlU7d+5UZ8+eVfv371cjRoxQHh4eVlm3rKwslZCQoBISEhSgFi9erBISEtT58+eVUkotWrRIeXl5qQ0bNqjExET19NNPK39/f3Xz5k0Ll7x6VdUtKytLTZ06Ve3du1edO3dO7dixQ0VERKiWLVtaRd2ef/555eXlpXbu3KlSUlJMW25urukYa2276upmzW33+uuvq127dqlz586po0ePqunTpys7Ozv13XffKaWst82sia32TbbULyklfZM1Xt+Ukr7JWtvubvVNNh34KKXUsmXLVFBQkHJyclI9e/Y0S/lnzZ566inl7++vHB0dVUBAgBozZow6duyYpYtVJzt27FBAuS0qKkoppaWenD17ttLr9crZ2VkNGDBAJSYmWrbQNVRV3XJzc1VkZKRq0aKFcnR0VIGBgSoqKkolJydbutg1UlG9ALVy5UrTMdbadtXVzZrbbvz48aZrYosWLdSQIUNMHYtS1ttm1sYW+yZb6peUkr7JGq9vSknfZK1td7f6Jp1SStVujEgIIYQQQgghrIvN3uMjhBBCCCGEEKUk8BFCCCGEEELYPAl8hBBCCCGEEDZPAh8hhBBCCCGEzZPARwghhBBCCGHzJPARQgghhBBC2DwJfIQQQgghhBA2TwIfIYQQQgghhM2TwEcIIYQQQghh8yTwEUIIIYQQQtg8CXyEEEIIIYQQNu//AJgNC4rdo88ZAAAAAElFTkSuQmCC","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10, 4))\n","plt.subplot(1, 2, 1)\n","plt.plot(train_losses, label='Training loss')\n","plt.plot(test_losses, label='Testing loss')\n","plt.legend(frameon=False)\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(train_acc, label='Training accuracy')\n","plt.plot(test_acc, label='Testing accuracy')\n","plt.legend(frameon=False)"]},{"cell_type":"markdown","metadata":{},"source":["## Testing"]},{"cell_type":"markdown","metadata":{},"source":["The best model is loaded for testing and the results are plotted."]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:25.846638Z","iopub.status.busy":"2023-03-11T10:40:25.845810Z","iopub.status.idle":"2023-03-11T10:40:26.173559Z","shell.execute_reply":"2023-03-11T10:40:26.172371Z","shell.execute_reply.started":"2023-03-11T10:40:25.846592Z"},"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["model = CvT(num_classes= 2, dropout= 0.1).to(device)\n","model.load_state_dict(torch.load('../models/test-5-cvt-model.pth'))"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:26.175694Z","iopub.status.busy":"2023-03-11T10:40:26.175083Z","iopub.status.idle":"2023-03-11T10:40:30.491683Z","shell.execute_reply":"2023-03-11T10:40:30.489847Z","shell.execute_reply.started":"2023-03-11T10:40:26.175654Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 16/16 [00:04<00:00, 3.72it/s]\n"]}],"source":["y_true = []\n","y_pred_class = []\n","y_pred_prob = []\n","\n","with torch.no_grad():\n"," for images, targets in tqdm(test_loader):\n"," images, targets = images.to(device), targets.to(device)\n"," output = model(images)\n"," preds = torch.argmax(output, dim=1)\n"," y_true.extend(targets.cpu().numpy())\n"," y_pred_class.extend(preds.cpu().numpy())\n"," y_pred_prob.extend(output.cpu().numpy())"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:30.494550Z","iopub.status.busy":"2023-03-11T10:40:30.493892Z","iopub.status.idle":"2023-03-11T10:40:30.510742Z","shell.execute_reply":"2023-03-11T10:40:30.509244Z","shell.execute_reply.started":"2023-03-11T10:40:30.494480Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":[" precision recall f1-score support\n","\n"," no_sub 0.99 1.00 0.99 500\n"," sub 1.00 0.99 0.99 500\n","\n"," accuracy 0.99 1000\n"," macro avg 0.99 0.99 0.99 1000\n","weighted avg 0.99 0.99 0.99 1000\n","\n"]}],"source":["from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, classification_report, auc\n","\n","print(classification_report(y_true, y_pred_class, target_names=label_map.keys()))"]},{"cell_type":"code","execution_count":43,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:30.514018Z","iopub.status.busy":"2023-03-11T10:40:30.513193Z","iopub.status.idle":"2023-03-11T10:40:30.753110Z","shell.execute_reply":"2023-03-11T10:40:30.752187Z","shell.execute_reply.started":"2023-03-11T10:40:30.513974Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["conf_matrix = confusion_matrix(y_true, y_pred_class)\n","plt.figure(figsize=(7, 5))\n","sns.heatmap(conf_matrix, annot=True, fmt='d', xticklabels=label_map.keys(), yticklabels=label_map.keys())\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')\n","plt.show()"]},{"cell_type":"code","execution_count":44,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:30.754937Z","iopub.status.busy":"2023-03-11T10:40:30.754592Z","iopub.status.idle":"2023-03-11T10:40:30.765508Z","shell.execute_reply":"2023-03-11T10:40:30.764597Z","shell.execute_reply.started":"2023-03-11T10:40:30.754900Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["ROC-AUC Score: 0.99992\n"]}],"source":["print(f\"ROC-AUC Score: {roc_auc_score(y_true, np.array(y_pred_prob)[:,1], multi_class='ovr')}\")"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2023-03-11T10:40:30.767380Z","iopub.status.busy":"2023-03-11T10:40:30.766873Z","iopub.status.idle":"2023-03-11T10:40:31.009843Z","shell.execute_reply":"2023-03-11T10:40:31.007745Z","shell.execute_reply.started":"2023-03-11T10:40:30.767343Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["no_sub ROC-AUC: 0.999888\n","sub ROC-AUC: 0.99992\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["y_true_dummies = pd.get_dummies(y_true).values\n","\n","auc_scores = {}\n","for i in range(2):\n"," fpr_i, tpr_i, thresholds_i = roc_curve(y_true_dummies[:, i], np.array(y_pred_prob)[:, i])\n"," auc_score = auc(fpr_i, tpr_i)\n"," print(f\"{(val_label_map[i]).ljust(8)} ROC-AUC: {auc_score}\")\n"," auc_scores[val_label_map[i]] = auc_score\n"," plt.plot(fpr_i, tpr_i, label=f\"{(val_label_map[i]).ljust(6)} AUC: {auc_score:.4f}\")\n","\n","plt.plot([0, 1], [0, 1], 'k--')\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('ROC Curve')\n","plt.legend()\n","plt.show()"]}],"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":"3.10.6"},"vscode":{"interpreter":{"hash":"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"}}},"nbformat":4,"nbformat_minor":4}