max_size: + return None + return f.read(length_of_header) + +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False)) + del prev + +def copy_to_param(obj, attr, value): + # inplace update tensor instead of replacing it + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + prev.data.copy_(value) + +def get_attr(obj, attr): + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + +def bislerp(samples, width, height): + def slerp(b1, b2, r): + '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' + + c = b1.shape[-1] + + #norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + #normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + #zero when norms are zero + b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 + + #slerp + dot = (b1_normalized*b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + #technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized + res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) + + #edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data(length_old, length_new, device): + coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 + coords_2[:,:,:,-1] -= 1 + coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + orig_dtype = samples.dtype + samples = samples.float() + n,c,h,w = samples.shape + h_new, w_new = (height, width) + + #linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + #linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) + coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result.to(orig_dtype) + +def lanczos(samples, width, height): + images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] + images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] + images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] + result = torch.stack(images) + return result.to(samples.device, samples.dtype) + +def common_upscale(samples, width, height, upscale_method, crop): + if crop == "center": + old_width = samples.shape[3] + old_height = samples.shape[2] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = samples[:,:,y:old_height-y,x:old_width-x] + else: + s = samples + + if upscale_method == "bislerp": + return bislerp(s, width, height) + elif upscale_method == "lanczos": + return lanczos(s, width, height) + else: + return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) + +def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): + return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) + +@torch.inference_mode() +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) + for b in range(samples.shape[0]): + s = samples[b:b+1] + out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) + out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) + for y in range(0, s.shape[2], tile_y - overlap): + for x in range(0, s.shape[3], tile_x - overlap): + s_in = s[:,:,y:y+tile_y,x:x+tile_x] + + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + feather = round(overlap * upscale_amount) + for t in range(feather): + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask + out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask + if pbar is not None: + pbar.update(1) + + output[b:b+1] = out/out_div + return output + +PROGRESS_BAR_ENABLED = True +def set_progress_bar_enabled(enabled): + global PROGRESS_BAR_ENABLED + PROGRESS_BAR_ENABLED = enabled + +PROGRESS_BAR_HOOK = None +def set_progress_bar_global_hook(function): + global PROGRESS_BAR_HOOK + PROGRESS_BAR_HOOK = function + +class ProgressBar: + def __init__(self, total): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + def update_absolute(self, value, total=None, preview=None): + if total is not None: + self.total = total + if value > self.total: + value = self.total + self.current = value + if self.hook is not None: + self.hook(self.current, self.total, preview) + + def update(self, value): + self.update_absolute(self.current + value) diff --git a/ldm_patched/pfn/__init__.py b/ldm_patched/pfn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/pfn/architecture/DAT.py b/ldm_patched/pfn/architecture/DAT.py new file mode 100644 index 0000000000000000000000000000000000000000..0bcc26ef422b73cef41744e2203901a3d290c2f0 --- /dev/null +++ b/ldm_patched/pfn/architecture/DAT.py @@ -0,0 +1,1182 @@ +# pylint: skip-file +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from einops import rearrange +from einops.layers.torch import Rearrange +from torch import Tensor +from torch.nn import functional as F + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +def img2windows(img, H_sp, W_sp): + """ + Input: Image (B, C, H, W) + Output: Window Partition (B', N, C) + """ + B, C, H, W = img.shape + img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) + img_perm = ( + img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) + ) + return img_perm + + +def windows2img(img_splits_hw, H_sp, W_sp, H, W): + """ + Input: Window Partition (B', N, C) + Output: Image (B, H, W, C) + """ + B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) + + img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) + img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return img + + +class SpatialGate(nn.Module): + """Spatial-Gate. + Args: + dim (int): Half of input channels. + """ + + def __init__(self, dim): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.conv = nn.Conv2d( + dim, dim, kernel_size=3, stride=1, padding=1, groups=dim + ) # DW Conv + + def forward(self, x, H, W): + # Split + x1, x2 = x.chunk(2, dim=-1) + B, N, C = x.shape + x2 = ( + self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) + .flatten(2) + .transpose(-1, -2) + .contiguous() + ) + + return x1 * x2 + + +class SGFN(nn.Module): + """Spatial-Gate Feed-Forward Network. + Args: + in_features (int): Number of input channels. + hidden_features (int | None): Number of hidden channels. Default: None + out_features (int | None): Number of output channels. Default: None + act_layer (nn.Module): Activation layer. Default: nn.GELU + drop (float): Dropout rate. Default: 0.0 + """ + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.sg = SpatialGate(hidden_features // 2) + self.fc2 = nn.Linear(hidden_features // 2, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + + x = self.sg(x, H, W) + x = self.drop(x) + + x = self.fc2(x) + x = self.drop(x) + return x + + +class DynamicPosBias(nn.Module): + # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py + """Dynamic Relative Position Bias. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + residual (bool): If True, use residual strage to connect conv. + """ + + def __init__(self, dim, num_heads, residual): + super().__init__() + self.residual = residual + self.num_heads = num_heads + self.pos_dim = dim // 4 + self.pos_proj = nn.Linear(2, self.pos_dim) + self.pos1 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos2 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos3 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.num_heads), + ) + + def forward(self, biases): + if self.residual: + pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads + pos = pos + self.pos1(pos) + pos = pos + self.pos2(pos) + pos = self.pos3(pos) + else: + pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) + return pos + + +class Spatial_Attention(nn.Module): + """Spatial Window Self-Attention. + It supports rectangle window (containing square window). + Args: + dim (int): Number of input channels. + idx (int): The indentix of window. (0/1) + split_size (tuple(int)): Height and Width of spatial window. + dim_out (int | None): The dimension of the attention output. Default: None + num_heads (int): Number of attention heads. Default: 6 + attn_drop (float): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float): Dropout ratio of output. Default: 0.0 + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set + position_bias (bool): The dynamic relative position bias. Default: True + """ + + def __init__( + self, + dim, + idx, + split_size=[8, 8], + dim_out=None, + num_heads=6, + attn_drop=0.0, + proj_drop=0.0, + qk_scale=None, + position_bias=True, + ): + super().__init__() + self.dim = dim + self.dim_out = dim_out or dim + self.split_size = split_size + self.num_heads = num_heads + self.idx = idx + self.position_bias = position_bias + + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + if idx == 0: + H_sp, W_sp = self.split_size[0], self.split_size[1] + elif idx == 1: + W_sp, H_sp = self.split_size[0], self.split_size[1] + else: + print("ERROR MODE", idx) + exit(0) + self.H_sp = H_sp + self.W_sp = W_sp + + if self.position_bias: + self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) + # generate mother-set + position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) + position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) + biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) + biases = biases.flatten(1).transpose(0, 1).contiguous().float() + self.register_buffer("rpe_biases", biases) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.H_sp) + coords_w = torch.arange(self.W_sp) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.H_sp - 1 + relative_coords[:, :, 1] += self.W_sp - 1 + relative_coords[:, :, 0] *= 2 * self.W_sp - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.attn_drop = nn.Dropout(attn_drop) + + def im2win(self, x, H, W): + B, N, C = x.shape + x = x.transpose(-2, -1).contiguous().view(B, C, H, W) + x = img2windows(x, self.H_sp, self.W_sp) + x = ( + x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + .contiguous() + ) + return x + + def forward(self, qkv, H, W, mask=None): + """ + Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size + Output: x (B, H, W, C) + """ + q, k, v = qkv[0], qkv[1], qkv[2] + + B, L, C = q.shape + assert L == H * W, "flatten img_tokens has wrong size" + + # partition the q,k,v, image to window + q = self.im2win(q, H, W) + k = self.im2win(k, H, W) + v = self.im2win(v, H, W) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N + + # calculate drpe + if self.position_bias: + pos = self.pos(self.rpe_biases) + # select position bias + relative_position_bias = pos[self.relative_position_index.view(-1)].view( + self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 + ) + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + N = attn.shape[3] + + # use mask for shift window + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( + 0 + ) + attn = attn.view(-1, self.num_heads, N, N) + + attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) + attn = self.attn_drop(attn) + + x = attn @ v + x = x.transpose(1, 2).reshape( + -1, self.H_sp * self.W_sp, C + ) # B head N N @ B head N C + + # merge the window, window to image + x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C + + return x + + +class Adaptive_Spatial_Attention(nn.Module): + # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT + """Adaptive Spatial Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + split_size (tuple(int)): Height and Width of spatial window. + shift_size (tuple(int)): Shift size for spatial window. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + drop (float): Dropout rate. Default: 0.0 + attn_drop (float): Attention dropout rate. Default: 0.0 + rg_idx (int): The indentix of Residual Group (RG) + b_idx (int): The indentix of Block in each RG + """ + + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[8, 8], + shift_size=[1, 2], + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + rg_idx=0, + b_idx=0, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.split_size = split_size + self.shift_size = shift_size + self.b_idx = b_idx + self.rg_idx = rg_idx + self.patches_resolution = reso + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + + assert ( + 0 <= self.shift_size[0] < self.split_size[0] + ), "shift_size must in 0-split_size0" + assert ( + 0 <= self.shift_size[1] < self.split_size[1] + ), "shift_size must in 0-split_size1" + + self.branch_num = 2 + + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(drop) + + self.attns = nn.ModuleList( + [ + Spatial_Attention( + dim // 2, + idx=i, + split_size=split_size, + num_heads=num_heads // 2, + dim_out=dim // 2, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + position_bias=True, + ) + for i in range(self.branch_num) + ] + ) + + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + attn_mask = self.calculate_mask( + self.patches_resolution, self.patches_resolution + ) + self.register_buffer("attn_mask_0", attn_mask[0]) + self.register_buffer("attn_mask_1", attn_mask[1]) + else: + attn_mask = None + self.register_buffer("attn_mask_0", None) + self.register_buffer("attn_mask_1", None) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def calculate_mask(self, H, W): + # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # calculate attention mask for shift window + img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 + img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 + h_slices_0 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + w_slices_0 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + + h_slices_1 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + w_slices_1 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + cnt = 0 + for h in h_slices_0: + for w in w_slices_0: + img_mask_0[:, h, w, :] = cnt + cnt += 1 + cnt = 0 + for h in h_slices_1: + for w in w_slices_1: + img_mask_1[:, h, w, :] = cnt + cnt += 1 + + # calculate mask for window-0 + img_mask_0 = img_mask_0.view( + 1, + H // self.split_size[0], + self.split_size[0], + W // self.split_size[1], + self.split_size[1], + 1, + ) + img_mask_0 = ( + img_mask_0.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[0], self.split_size[1], 1) + ) # nW, sw[0], sw[1], 1 + mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) + attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) + attn_mask_0 = attn_mask_0.masked_fill( + attn_mask_0 != 0, float(-100.0) + ).masked_fill(attn_mask_0 == 0, float(0.0)) + + # calculate mask for window-1 + img_mask_1 = img_mask_1.view( + 1, + H // self.split_size[1], + self.split_size[1], + W // self.split_size[0], + self.split_size[0], + 1, + ) + img_mask_1 = ( + img_mask_1.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[1], self.split_size[0], 1) + ) # nW, sw[1], sw[0], 1 + mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) + attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) + attn_mask_1 = attn_mask_1.masked_fill( + attn_mask_1 != 0, float(-100.0) + ).masked_fill(attn_mask_1 == 0, float(0.0)) + + return attn_mask_0, attn_mask_1 + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, L, C = x.shape + assert L == H * W, "flatten img_tokens has wrong size" + + qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C + # V without partition + v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) + + # image padding + max_split_size = max(self.split_size[0], self.split_size[1]) + pad_l = pad_t = 0 + pad_r = (max_split_size - W % max_split_size) % max_split_size + pad_b = (max_split_size - H % max_split_size) % max_split_size + + qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W + qkv = ( + F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) + .reshape(3, B, C, -1) + .transpose(-2, -1) + ) # l r t b + _H = pad_b + H + _W = pad_r + W + _L = _H * _W + + # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged + # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + qkv = qkv.view(3, B, _H, _W, C) + qkv_0 = torch.roll( + qkv[:, :, :, :, : C // 2], + shifts=(-self.shift_size[0], -self.shift_size[1]), + dims=(2, 3), + ) + qkv_0 = qkv_0.view(3, B, _L, C // 2) + qkv_1 = torch.roll( + qkv[:, :, :, :, C // 2 :], + shifts=(-self.shift_size[1], -self.shift_size[0]), + dims=(2, 3), + ) + qkv_1 = qkv_1.view(3, B, _L, C // 2) + + if self.patches_resolution != _H or self.patches_resolution != _W: + mask_tmp = self.calculate_mask(_H, _W) + x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) + else: + x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) + + x1 = torch.roll( + x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) + ) + x2 = torch.roll( + x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) + ) + x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) + x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + else: + x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + # convolution output + conv_x = self.dwconv(v) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + channel_map = ( + self.channel_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, 1, C) + ) + # S-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + spatial_map = self.spatial_interaction(attention_reshape) + + # C-I + attened_x = attened_x * torch.sigmoid(channel_map) + # S-I + conv_x = torch.sigmoid(spatial_map) * conv_x + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Adaptive_Channel_Attention(nn.Module): + # The implementation builds on XCiT code https://github.com/facebookresearch/xcit + """Adaptive Channel Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + attn_drop (float): Attention dropout rate. Default: 0.0 + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + qkv = qkv.permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q.transpose(-2, -1) + k = k.transpose(-2, -1) + v = v.transpose(-2, -1) + + v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + # attention output + attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) + + # convolution output + conv_x = self.dwconv(v_) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + channel_map = self.channel_interaction(attention_reshape) + # S-Map (before sigmoid) + spatial_map = ( + self.spatial_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, N, 1) + ) + + # S-I + attened_x = attened_x * torch.sigmoid(spatial_map) + # C-I + conv_x = conv_x * torch.sigmoid(channel_map) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class DATB(nn.Module): + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[2, 4], + shift_size=[1, 2], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + rg_idx=0, + b_idx=0, + ): + super().__init__() + + self.norm1 = norm_layer(dim) + + if b_idx % 2 == 0: + # DSTB + self.attn = Adaptive_Spatial_Attention( + dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=shift_size, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + rg_idx=rg_idx, + b_idx=b_idx, + ) + else: + # DCTB + self.attn = Adaptive_Channel_Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + ffn_hidden_dim = int(dim * expansion_factor) + self.ffn = SGFN( + in_features=dim, + hidden_features=ffn_hidden_dim, + out_features=dim, + act_layer=act_layer, + ) + self.norm2 = norm_layer(dim) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) + + return x + + +class ResidualGroup(nn.Module): + """ResidualGroup + Args: + dim (int): Number of input channels. + reso (int): Input resolution. + num_heads (int): Number of attention heads. + split_size (tuple(int)): Height and Width of spatial window. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop (float): Dropout rate. Default: 0 + attn_drop(float): Attention dropout rate. Default: 0 + drop_paths (float | None): Stochastic depth rate. + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + depth (int): Number of dual aggregation Transformer blocks in residual group. + use_chk (bool): Whether to use checkpointing to save memory. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + dim, + reso, + num_heads, + split_size=[2, 4], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_paths=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + depth=2, + use_chk=False, + resi_connection="1conv", + rg_idx=0, + ): + super().__init__() + self.use_chk = use_chk + self.reso = reso + + self.blocks = nn.ModuleList( + [ + DATB( + dim=dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=[split_size[0] // 2, split_size[1] // 2], + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_paths[i], + act_layer=act_layer, + norm_layer=norm_layer, + rg_idx=rg_idx, + b_idx=i, + ) + for i in range(depth) + ] + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + res = x + for blk in self.blocks: + if self.use_chk: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.conv(x) + x = rearrange(x, "b c h w -> b (h w) c") + x = res + x + + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +class DAT(nn.Module): + """Dual Aggregation Transformer + Args: + img_size (int): Input image size. Default: 64 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 180 + depths (tuple(int)): Depth of each residual group (number of DATB in each RG). + split_size (tuple(int)): Height and Width of spatial window. + num_heads (tuple(int)): Number of attention heads in different residual groups. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + use_chk (bool): Whether to use checkpointing to save memory. + upscale: Upscale factor. 2/3/4 for image SR + img_range: Image range. 1. or 255. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, state_dict): + super().__init__() + + # defaults + img_size = 64 + in_chans = 3 + embed_dim = 180 + split_size = [2, 4] + depth = [2, 2, 2, 2] + num_heads = [2, 2, 2, 2] + expansion_factor = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + act_layer = nn.GELU + norm_layer = nn.LayerNorm + use_chk = False + upscale = 2 + img_range = 1.0 + resi_connection = "1conv" + upsampler = "pixelshuffle" + + self.model_arch = "DAT" + self.sub_type = "SR" + self.state = state_dict + + state_keys = state_dict.keys() + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + state_dict.get("conv_before_upsample.0.weight", None).shape[1] + if state_dict.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = state_dict["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = state_dict["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = state_dict[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if "layers.0.blocks.1.attn.temperature" in state_keys: + num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depth + + embed_dim = state_dict["conv_first.weight"].shape[0] + expansion_factor = float( + state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: + attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ + "layers.0.blocks.2.attn.attn_mask_0" + ].shape + + img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) + + if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: + split_sizes = ( + state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 + ) + split_size = [int(x) for x in split_sizes] + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depth = depth + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.expansion_factor = expansion_factor + self.resi_connection = resi_connection + self.split_size = split_size + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # ------------------------- 1, Shallow Feature Extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, Deep Feature Extraction ------------------------- # + self.num_layers = len(depth) + self.use_chk = use_chk + self.num_features = ( + self.embed_dim + ) = embed_dim # num_features for consistency with other models + heads = num_heads + + self.before_RG = nn.Sequential( + Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) + ) + + curr_dim = embed_dim + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) + ] # stochastic depth decay rule + + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = ResidualGroup( + dim=embed_dim, + num_heads=heads[i], + reso=img_size, + split_size=split_size, + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], + act_layer=act_layer, + norm_layer=norm_layer, + depth=depth[i], + use_chk=use_chk, + resi_connection=resi_connection, + rg_idx=i, + ) + self.layers.append(layer) + + self.norm = norm_layer(curr_dim) + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + # ------------------------- 3, Reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, embed_dim, num_out_ch, (img_size, img_size) + ) + + self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance( + m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) + ): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward_features(self, x): + _, _, H, W = x.shape + x_size = [H, W] + x = self.before_RG(x) + for layer in self.layers: + x = layer(x, x_size) + x = self.norm(x) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + + return x + + def forward(self, x): + """ + Input: x: (B, C, H, W) + """ + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for image SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + + x = x / self.img_range + self.mean + return x diff --git a/ldm_patched/pfn/architecture/HAT.py b/ldm_patched/pfn/architecture/HAT.py new file mode 100644 index 0000000000000000000000000000000000000000..7e12ad0fd47a223c4b2a902d296ebeff3403cffb --- /dev/null +++ b/ldm_patched/pfn/architecture/HAT.py @@ -0,0 +1,1277 @@ +# pylint: skip-file +# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) # type: ignore + + +class ChannelAttention(nn.Module): + """Channel attention used in RCAN. + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: 16. + """ + + def __init__(self, num_feat, squeeze_factor=16): + super(ChannelAttention, self).__init__() + self.attention = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), + nn.ReLU(inplace=True), + nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), + nn.Sigmoid(), + ) + + def forward(self, x): + y = self.attention(x) + return x * y + + +class CAB(nn.Module): + def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): + super(CAB, self).__init__() + + self.cab = nn.Sequential( + nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), + nn.GELU(), + nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), + ChannelAttention(num_feat, squeeze_factor), + ) + + def forward(self, x): + return self.cab(x) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (b, h, w, c) + window_size (int): window size + Returns: + windows: (num_windows*b, window_size, window_size, c) + """ + b, h, w, c = x.shape + x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) + ) + return windows + + +def window_reverse(windows, window_size, h, w): + """ + Args: + windows: (num_windows*b, window_size, window_size, c) + window_size (int): Window size + h (int): Height of image + w (int): Width of image + Returns: + x: (b, h, w, c) + """ + b = int(windows.shape[0] / (h * w / window_size / window_size)) + x = windows.view( + b, h // window_size, w // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, rpi, mask=None): + """ + Args: + x: input features with shape of (num_windows*b, n, c) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + b_, n, c = x.shape + qkv = ( + self.qkv(x) + .reshape(b_, n, 3, self.num_heads, c // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nw = mask.shape[0] + attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, n, n) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b_, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class HAB(nn.Module): + r"""Hybrid Attention Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + compress_ratio=3, + squeeze_factor=30, + conv_scale=0.01, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.conv_scale = conv_scale + self.conv_block = CAB( + num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def forward(self, x, x_size, rpi_sa, attn_mask): + h, w = x_size + b, _, c = x.shape + # assert seq_len == h * w, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + # Conv_X + conv_x = self.conv_block(x.permute(0, 3, 1, 2)) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + attn_mask = attn_mask + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nw*b, window_size, window_size, c + x_windows = x_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) + shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c + + # reverse cyclic shift + if self.shift_size > 0: + attn_x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + attn_x = shifted_x + attn_x = attn_x.view(b, h * w, c) + + # FFN + x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: b, h*w, c + """ + h, w = self.input_resolution + b, seq_len, c = x.shape + assert seq_len == h * w, "input feature has wrong size" + assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." + + x = x.view(b, h, w, c) + + x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c + x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c + x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c + x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c + x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c + x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class OCAB(nn.Module): + # overlapping cross-attention block + + def __init__( + self, + dim, + input_resolution, + window_size, + overlap_ratio, + num_heads, + qkv_bias=True, + qk_scale=None, + mlp_ratio=2, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + self.overlap_win_size = int(window_size * overlap_ratio) + window_size + + self.norm1 = norm_layer(dim) + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.unfold = nn.Unfold( + kernel_size=(self.overlap_win_size, self.overlap_win_size), + stride=window_size, + padding=(self.overlap_win_size - window_size) // 2, + ) + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros( + (window_size + self.overlap_win_size - 1) + * (window_size + self.overlap_win_size - 1), + num_heads, + ) + ) # 2*Wh-1 * 2*Ww-1, nH + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + self.proj = nn.Linear(dim, dim) + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU + ) + + def forward(self, x, x_size, rpi): + h, w = x_size + b, _, c = x.shape + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2) # 3, b, c, h, w + q = qkv[0].permute(0, 2, 3, 1) # b, h, w, c + kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w + + # partition windows + q_windows = window_partition( + q, self.window_size + ) # nw*b, window_size, window_size, c + q_windows = q_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + kv_windows = self.unfold(kv) # b, c*w*w, nw + kv_windows = rearrange( + kv_windows, + "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch", + nc=2, + ch=c, + owh=self.overlap_win_size, + oww=self.overlap_win_size, + ).contiguous() # 2, nw*b, ow*ow, c + # Do the above rearrangement without the rearrange function + # kv_windows = kv_windows.view( + # 2, b, self.overlap_win_size, self.overlap_win_size, c, -1 + # ) + # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous() + # kv_windows = kv_windows.view( + # 2, -1, self.overlap_win_size * self.overlap_win_size, c + # ) + + k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c + + b_, nq, _ = q_windows.shape + _, n, _ = k_windows.shape + d = self.dim // self.num_heads + q = q_windows.reshape(b_, nq, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, nq, d + k = k_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + v = v_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size * self.window_size, + self.overlap_win_size * self.overlap_win_size, + -1, + ) # ws*ws, wse*wse, nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, ws*ws, wse*wse + attn = attn + relative_position_bias.unsqueeze(0) + + attn = self.softmax(attn) + attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim) + + # merge windows + attn_windows = attn_windows.view( + -1, self.window_size, self.window_size, self.dim + ) + x = window_reverse(attn_windows, self.window_size, h, w) # b h w c + x = x.view(b, h * w, self.dim) + + x = self.proj(x) + shortcut + + x = x + self.mlp(self.norm2(x)) + return x + + +class AttenBlocks(nn.Module): + """A series of attention blocks for one RHAG. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + HAB( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # OCAB + self.overlap_attn = OCAB( + dim=dim, + input_resolution=input_resolution, + window_size=window_size, + overlap_ratio=overlap_ratio, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + mlp_ratio=mlp_ratio, # type: ignore + norm_layer=norm_layer, + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size, params): + for blk in self.blocks: + x = blk(x, x_size, params["rpi_sa"], params["attn_mask"]) + + x = self.overlap_attn(x, x_size, params["rpi_oca"]) + + if self.downsample is not None: + x = self.downsample(x) + return x + + +class RHAG(nn.Module): + """Residual Hybrid Attention Group (RHAG). + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RHAG, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = AttenBlocks( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv = nn.Identity() + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size, params): + return ( + self.patch_embed( + self.conv( + self.patch_unembed(self.residual_group(x, x_size, params), x_size) + ) + ) + + x + ) + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # b Ph*Pw c + if self.norm is not None: + x = self.norm(x) + return x + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + x = ( + x.transpose(1, 2) + .contiguous() + .view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) + ) # b Ph*Pw c + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class HAT(nn.Module): + r"""Hybrid Attention Transformer + A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`. + Some codes are based on SwinIR. + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(HAT, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = (6, 6, 6, 6) + num_heads = (6, 6, 6, 6) + window_size = 7 + compress_ratio = 3 + squeeze_factor = 30 + conv_scale = 0.01 + overlap_ratio = 0.5 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + + self.state = state_dict + self.model_arch = "HAT" + self.sub_type = "SR" + self.supports_fp16 = False + self.support_bf16 = True + self.min_size_restriction = 16 + + state_keys = list(state_dict.keys()) + + num_feat = state_dict["conv_last.weight"].shape[1] + in_chans = state_dict["conv_first.weight"].shape[1] + num_out_ch = state_dict["conv_last.weight"].shape[0] + embed_dim = state_dict["conv_first.weight"].shape[0] + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0])) + + # Not sure if this is needed or used at all anywhere in HAT's config + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + self.window_size = window_size + self.shift_size = window_size // 2 + self.overlap_ratio = overlap_ratio + + self.in_nc = in_chans + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + num_in_ch = in_chans + # num_out_ch = in_chans + # num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # relative position index + relative_position_index_SA = self.calculate_rpi_sa() + relative_position_index_OCA = self.calculate_rpi_oca() + self.register_buffer("relative_position_index_SA", relative_position_index_SA) + self.register_buffer("relative_position_index_OCA", relative_position_index_OCA) + + # ------------------------- 1, shallow feature extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, deep feature extraction ------------------------- # + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore[arg-type] + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Hybrid Attention Groups (RHAG) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RHAG( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv_after_body = nn.Identity() + + # ------------------------- 3, high quality image reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def calculate_rpi_sa(self): + # calculate relative position index for SA + coords_h = torch.arange(self.window_size) + coords_w = torch.arange(self.window_size) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size - 1 + relative_coords[:, :, 0] *= 2 * self.window_size - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + return relative_position_index + + def calculate_rpi_oca(self): + # calculate relative position index for OCA + window_size_ori = self.window_size + window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) + + coords_h = torch.arange(window_size_ori) + coords_w = torch.arange(window_size_ori) + coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws + coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws + + coords_h = torch.arange(window_size_ext) + coords_w = torch.arange(window_size_ext) + coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse + coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse + + relative_coords = ( + coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] + ) # 2, ws*ws, wse*wse + + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # ws*ws, wse*wse, 2 + relative_coords[:, :, 0] += ( + window_size_ori - window_size_ext + 1 + ) # shift to start from 0 + relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 + + relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 + relative_position_index = relative_coords.sum(-1) + return relative_position_index + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + h, w = x_size + img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nw, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + + # Calculate attention mask and relative position index in advance to speed up inference. + # The original code is very time-cosuming for large window size. + attn_mask = self.calculate_mask(x_size).to(x.device) + params = { + "attn_mask": attn_mask, + "rpi_sa": self.relative_position_index_SA, + "rpi_oca": self.relative_position_index_OCA, + } + + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size, params) + + x = self.norm(x) # b seq_len c + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + x = self.check_image_size(x) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] diff --git a/ldm_patched/pfn/architecture/LICENSE-DAT b/ldm_patched/pfn/architecture/LICENSE-DAT new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-DAT @@ -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/ldm_patched/pfn/architecture/LICENSE-HAT b/ldm_patched/pfn/architecture/LICENSE-HAT new file mode 100644 index 0000000000000000000000000000000000000000..003e97e96cbed07d07b5ff15831711181607edb3 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-HAT @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Xiangyu 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. 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Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved. + + 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/ldm_patched/pfn/architecture/LICENSE-SPSR b/ldm_patched/pfn/architecture/LICENSE-SPSR new file mode 100644 index 0000000000000000000000000000000000000000..3245f3f9e4f476ee3a283f41dd0d9db65544c222 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-SPSR @@ -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. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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Affirmer understands and acknowledges that Creative Commons is not a + party to this document and has no duty or obligation with respect to + this CC0 or use of the Work. diff --git a/ldm_patched/pfn/architecture/LICENSE-Swin2SR b/ldm_patched/pfn/architecture/LICENSE-Swin2SR new file mode 100644 index 0000000000000000000000000000000000000000..e5e4ee061a3f3fbad64bc837425716af7fb108f5 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-Swin2SR @@ -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. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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 [2021] [SwinIR Authors] + + 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/ldm_patched/pfn/architecture/LICENSE-SwinIR b/ldm_patched/pfn/architecture/LICENSE-SwinIR new file mode 100644 index 0000000000000000000000000000000000000000..e5e4ee061a3f3fbad64bc837425716af7fb108f5 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-SwinIR @@ -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. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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 [2021] [SwinIR Authors] + + 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/ldm_patched/pfn/architecture/LICENSE-lama b/ldm_patched/pfn/architecture/LICENSE-lama new file mode 100644 index 0000000000000000000000000000000000000000..ca822bb5f62a37a5a73f56a2d563b16dab46c03f --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-lama @@ -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. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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 [2021] Samsung Research + + 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/ldm_patched/pfn/architecture/LaMa.py b/ldm_patched/pfn/architecture/LaMa.py new file mode 100644 index 0000000000000000000000000000000000000000..a781f3e4dda789c06493fcf35a9803ee61efce73 --- /dev/null +++ b/ldm_patched/pfn/architecture/LaMa.py @@ -0,0 +1,694 @@ +# pylint: skip-file +""" +Model adapted from advimman's lama project: https://github.com/advimman/lama +""" + +# Fast Fourier Convolution NeurIPS 2020 +# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py +# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf + +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms.functional import InterpolationMode, rotate + + +class LearnableSpatialTransformWrapper(nn.Module): + def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): + super().__init__() + self.impl = impl + self.angle = torch.rand(1) * angle_init_range + if train_angle: + self.angle = nn.Parameter(self.angle, requires_grad=True) + self.pad_coef = pad_coef + + def forward(self, x): + if torch.is_tensor(x): + return self.inverse_transform(self.impl(self.transform(x)), x) + elif isinstance(x, tuple): + x_trans = tuple(self.transform(elem) for elem in x) + y_trans = self.impl(x_trans) + return tuple( + self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x) + ) + else: + raise ValueError(f"Unexpected input type {type(x)}") + + def transform(self, x): + height, width = x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect") + x_padded_rotated = rotate( + x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0 + ) + + return x_padded_rotated + + def inverse_transform(self, y_padded_rotated, orig_x): + height, width = orig_x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + + y_padded = rotate( + y_padded_rotated, + -self.angle.to(y_padded_rotated), + InterpolationMode.BILINEAR, + fill=0, + ) + y_height, y_width = y_padded.shape[2:] + y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] + return y + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + res = x * y.expand_as(x) + return res + + +class FourierUnit(nn.Module): + def __init__( + self, + in_channels, + out_channels, + groups=1, + spatial_scale_factor=None, + spatial_scale_mode="bilinear", + spectral_pos_encoding=False, + use_se=False, + se_kwargs=None, + ffc3d=False, + fft_norm="ortho", + ): + # bn_layer not used + super(FourierUnit, self).__init__() + self.groups = groups + + self.conv_layer = torch.nn.Conv2d( + in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), + out_channels=out_channels * 2, + kernel_size=1, + stride=1, + padding=0, + groups=self.groups, + bias=False, + ) + self.bn = torch.nn.BatchNorm2d(out_channels * 2) + self.relu = torch.nn.ReLU(inplace=True) + + # squeeze and excitation block + self.use_se = use_se + if use_se: + if se_kwargs is None: + se_kwargs = {} + self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) + + self.spatial_scale_factor = spatial_scale_factor + self.spatial_scale_mode = spatial_scale_mode + self.spectral_pos_encoding = spectral_pos_encoding + self.ffc3d = ffc3d + self.fft_norm = fft_norm + + def forward(self, x): + half_check = False + if x.type() == "torch.cuda.HalfTensor": + # half only works on gpu anyway + half_check = True + + batch = x.shape[0] + + if self.spatial_scale_factor is not None: + orig_size = x.shape[-2:] + x = F.interpolate( + x, + scale_factor=self.spatial_scale_factor, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + # (batch, c, h, w/2+1, 2) + fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) + if half_check == True: + ffted = torch.fft.rfftn( + x.float(), dim=fft_dim, norm=self.fft_norm + ) # .type(torch.cuda.HalfTensor) + else: + ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) + + ffted = torch.stack((ffted.real, ffted.imag), dim=-1) + ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) + ffted = ffted.view( + ( + batch, + -1, + ) + + ffted.size()[3:] + ) + + if self.spectral_pos_encoding: + height, width = ffted.shape[-2:] + coords_vert = ( + torch.linspace(0, 1, height)[None, None, :, None] + .expand(batch, 1, height, width) + .to(ffted) + ) + coords_hor = ( + torch.linspace(0, 1, width)[None, None, None, :] + .expand(batch, 1, height, width) + .to(ffted) + ) + ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) + + if self.use_se: + ffted = self.se(ffted) + + if half_check == True: + ffted = self.conv_layer(ffted.half()) # (batch, c*2, h, w/2+1) + else: + ffted = self.conv_layer( + ffted + ) # .type(torch.cuda.FloatTensor) # (batch, c*2, h, w/2+1) + + ffted = self.relu(self.bn(ffted)) + # forcing to be always float + ffted = ffted.float() + + ffted = ( + ffted.view( + ( + batch, + -1, + 2, + ) + + ffted.size()[2:] + ) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) # (batch,c, t, h, w/2+1, 2) + + ffted = torch.complex(ffted[..., 0], ffted[..., 1]) + + ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] + output = torch.fft.irfftn( + ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm + ) + + if half_check == True: + output = output.half() + + if self.spatial_scale_factor is not None: + output = F.interpolate( + output, + size=orig_size, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + return output + + +class SpectralTransform(nn.Module): + def __init__( + self, + in_channels, + out_channels, + stride=1, + groups=1, + enable_lfu=True, + separable_fu=False, + **fu_kwargs, + ): + # bn_layer not used + super(SpectralTransform, self).__init__() + self.enable_lfu = enable_lfu + if stride == 2: + self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) + else: + self.downsample = nn.Identity() + + self.stride = stride + self.conv1 = nn.Sequential( + nn.Conv2d( + in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False + ), + nn.BatchNorm2d(out_channels // 2), + nn.ReLU(inplace=True), + ) + fu_class = FourierUnit + self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs) + if self.enable_lfu: + self.lfu = fu_class(out_channels // 2, out_channels // 2, groups) + self.conv2 = torch.nn.Conv2d( + out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False + ) + + def forward(self, x): + x = self.downsample(x) + x = self.conv1(x) + output = self.fu(x) + + if self.enable_lfu: + _, c, h, _ = x.shape + split_no = 2 + split_s = h // split_no + xs = torch.cat( + torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 + ).contiguous() + xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() + xs = self.lfu(xs) + xs = xs.repeat(1, 1, split_no, split_no).contiguous() + else: + xs = 0 + + output = self.conv2(x + output + xs) + + return output + + +class FFC(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + enable_lfu=True, + padding_type="reflect", + gated=False, + **spectral_kwargs, + ): + super(FFC, self).__init__() + + assert stride == 1 or stride == 2, "Stride should be 1 or 2." + self.stride = stride + + in_cg = int(in_channels * ratio_gin) + in_cl = in_channels - in_cg + out_cg = int(out_channels * ratio_gout) + out_cl = out_channels - out_cg + # groups_g = 1 if groups == 1 else int(groups * ratio_gout) + # groups_l = 1 if groups == 1 else groups - groups_g + + self.ratio_gin = ratio_gin + self.ratio_gout = ratio_gout + self.global_in_num = in_cg + + module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d + self.convl2l = module( + in_cl, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d + self.convl2g = module( + in_cl, + out_cg, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d + self.convg2l = module( + in_cg, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform + self.convg2g = module( + in_cg, + out_cg, + stride, + 1 if groups == 1 else groups // 2, + enable_lfu, + **spectral_kwargs, + ) + + self.gated = gated + module = ( + nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d + ) + self.gate = module(in_channels, 2, 1) + + def forward(self, x): + x_l, x_g = x if type(x) is tuple else (x, 0) + out_xl, out_xg = 0, 0 + + if self.gated: + total_input_parts = [x_l] + if torch.is_tensor(x_g): + total_input_parts.append(x_g) + total_input = torch.cat(total_input_parts, dim=1) + + gates = torch.sigmoid(self.gate(total_input)) + g2l_gate, l2g_gate = gates.chunk(2, dim=1) + else: + g2l_gate, l2g_gate = 1, 1 + + if self.ratio_gout != 1: + out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate + if self.ratio_gout != 0: + out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) + + return out_xl, out_xg + + +class FFC_BN_ACT(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + norm_layer=nn.BatchNorm2d, + activation_layer=nn.Identity, + padding_type="reflect", + enable_lfu=True, + **kwargs, + ): + super(FFC_BN_ACT, self).__init__() + self.ffc = FFC( + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride, + padding, + dilation, + groups, + bias, + enable_lfu, + padding_type=padding_type, + **kwargs, + ) + lnorm = nn.Identity if ratio_gout == 1 else norm_layer + gnorm = nn.Identity if ratio_gout == 0 else norm_layer + global_channels = int(out_channels * ratio_gout) + self.bn_l = lnorm(out_channels - global_channels) + self.bn_g = gnorm(global_channels) + + lact = nn.Identity if ratio_gout == 1 else activation_layer + gact = nn.Identity if ratio_gout == 0 else activation_layer + self.act_l = lact(inplace=True) + self.act_g = gact(inplace=True) + + def forward(self, x): + x_l, x_g = self.ffc(x) + x_l = self.act_l(self.bn_l(x_l)) + x_g = self.act_g(self.bn_g(x_g)) + return x_l, x_g + + +class FFCResnetBlock(nn.Module): + def __init__( + self, + dim, + padding_type, + norm_layer, + activation_layer=nn.ReLU, + dilation=1, + spatial_transform_kwargs=None, + inline=False, + **conv_kwargs, + ): + super().__init__() + self.conv1 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + self.conv2 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + if spatial_transform_kwargs is not None: + self.conv1 = LearnableSpatialTransformWrapper( + self.conv1, **spatial_transform_kwargs + ) + self.conv2 = LearnableSpatialTransformWrapper( + self.conv2, **spatial_transform_kwargs + ) + self.inline = inline + + def forward(self, x): + if self.inline: + x_l, x_g = ( + x[:, : -self.conv1.ffc.global_in_num], + x[:, -self.conv1.ffc.global_in_num :], + ) + else: + x_l, x_g = x if type(x) is tuple else (x, 0) + + id_l, id_g = x_l, x_g + + x_l, x_g = self.conv1((x_l, x_g)) + x_l, x_g = self.conv2((x_l, x_g)) + + x_l, x_g = id_l + x_l, id_g + x_g + out = x_l, x_g + if self.inline: + out = torch.cat(out, dim=1) + return out + + +class ConcatTupleLayer(nn.Module): + def forward(self, x): + assert isinstance(x, tuple) + x_l, x_g = x + assert torch.is_tensor(x_l) or torch.is_tensor(x_g) + if not torch.is_tensor(x_g): + return x_l + return torch.cat(x, dim=1) + + +class FFCResNetGenerator(nn.Module): + def __init__( + self, + input_nc, + output_nc, + ngf=64, + n_downsampling=3, + n_blocks=18, + norm_layer=nn.BatchNorm2d, + padding_type="reflect", + activation_layer=nn.ReLU, + up_norm_layer=nn.BatchNorm2d, + up_activation=nn.ReLU(True), + init_conv_kwargs={}, + downsample_conv_kwargs={}, + resnet_conv_kwargs={}, + spatial_transform_layers=None, + spatial_transform_kwargs={}, + max_features=1024, + out_ffc=False, + out_ffc_kwargs={}, + ): + assert n_blocks >= 0 + super().__init__() + """ + init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False} + downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False} + resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False} + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + """ + """ + print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer, + padding_type, activation_layer, + up_norm_layer, up_activation, + spatial_transform_layers, + add_out_act, max_features, out_ffc, file=sys.stderr) + + 4 3 64 3 18+ reflect + + ReLU(inplace=True) + None sigmoid 1024 False + """ + init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + resnet_conv_kwargs = { + "ratio_gin": 0.75, + "ratio_gout": 0.75, + "enable_lfu": False, + } + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + + model = [ + nn.ReflectionPad2d(3), + FFC_BN_ACT( + input_nc, + ngf, + kernel_size=7, + padding=0, + norm_layer=norm_layer, + activation_layer=activation_layer, + **init_conv_kwargs, + ), + ] + + ### downsample + for i in range(n_downsampling): + mult = 2**i + if i == n_downsampling - 1: + cur_conv_kwargs = dict(downsample_conv_kwargs) + cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0) + else: + cur_conv_kwargs = downsample_conv_kwargs + model += [ + FFC_BN_ACT( + min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, + stride=2, + padding=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + **cur_conv_kwargs, + ) + ] + + mult = 2**n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + cur_resblock = FFCResnetBlock( + feats_num_bottleneck, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + **resnet_conv_kwargs, + ) + if spatial_transform_layers is not None and i in spatial_transform_layers: + cur_resblock = LearnableSpatialTransformWrapper( + cur_resblock, **spatial_transform_kwargs + ) + model += [cur_resblock] + + model += [ConcatTupleLayer()] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [ + nn.ConvTranspose2d( + min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + ), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation, + ] + + if out_ffc: + model += [ + FFCResnetBlock( + ngf, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + inline=True, + **out_ffc_kwargs, + ) + ] + + model += [ + nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), + ] + model.append(nn.Sigmoid()) + self.model = nn.Sequential(*model) + + def forward(self, image, mask): + return self.model(torch.cat([image, mask], dim=1)) + + +class LaMa(nn.Module): + def __init__(self, state_dict) -> None: + super(LaMa, self).__init__() + self.model_arch = "LaMa" + self.sub_type = "Inpaint" + self.in_nc = 4 + self.out_nc = 3 + self.scale = 1 + + self.min_size = None + self.pad_mod = 8 + self.pad_to_square = False + + self.model = FFCResNetGenerator(self.in_nc, self.out_nc) + self.state = { + k.replace("generator.model", "model.model"): v + for k, v in state_dict.items() + } + + self.supports_fp16 = False + self.support_bf16 = True + + self.load_state_dict(self.state, strict=False) + + def forward(self, img, mask): + masked_img = img * (1 - mask) + inpainted_mask = mask * self.model.forward(masked_img, mask) + result = inpainted_mask + (1 - mask) * img + return result diff --git a/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py b/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d52aa1e063d274b7aec7bd1ace77b19eb2ca61 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py @@ -0,0 +1,110 @@ +import math + +import torch.nn as nn + + +class CA_layer(nn.Module): + def __init__(self, channel, reduction=16): + super(CA_layer, self).__init__() + # global average pooling + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False), + nn.GELU(), + nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False), + # nn.Sigmoid() + ) + + def forward(self, x): + y = self.fc(self.gap(x)) + return x * y.expand_as(x) + + +class Simple_CA_layer(nn.Module): + def __init__(self, channel): + super(Simple_CA_layer, self).__init__() + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=channel, + out_channels=channel, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + return x * self.fc(self.gap(x)) + + +class ECA_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.avg_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) + + +class ECA_MaxPool_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_MaxPool_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.max_pool = nn.AdaptiveMaxPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.max_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) diff --git a/ldm_patched/pfn/architecture/OmniSR/LICENSE b/ldm_patched/pfn/architecture/OmniSR/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/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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/dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OSA.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSA.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:07:42 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from einops.layers.torch import Rearrange, Reduce +from torch import einsum, nn + +from .layernorm import LayerNorm2d + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def cast_tuple(val, length=1): + return val if isinstance(val, tuple) else ((val,) * length) + + +# helper classes + + +class PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class Conv_PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = LayerNorm2d(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Conv2d(dim, inner_dim, 1, 1, 0), + nn.GELU(), + nn.Dropout(dropout), + nn.Conv2d(inner_dim, dim, 1, 1, 0), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Gated_Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=1, bias=False, dropout=0.0): + super().__init__() + + hidden_features = int(dim * mult) + + self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) + + self.dwconv = nn.Conv2d( + hidden_features * 2, + hidden_features * 2, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features * 2, + bias=bias, + ) + + self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) + + def forward(self, x): + x = self.project_in(x) + x1, x2 = self.dwconv(x).chunk(2, dim=1) + x = F.gelu(x1) * x2 + x = self.project_out(x) + return x + + +# MBConv + + +class SqueezeExcitation(nn.Module): + def __init__(self, dim, shrinkage_rate=0.25): + super().__init__() + hidden_dim = int(dim * shrinkage_rate) + + self.gate = nn.Sequential( + Reduce("b c h w -> b c", "mean"), + nn.Linear(dim, hidden_dim, bias=False), + nn.SiLU(), + nn.Linear(hidden_dim, dim, bias=False), + nn.Sigmoid(), + Rearrange("b c -> b c 1 1"), + ) + + def forward(self, x): + return x * self.gate(x) + + +class MBConvResidual(nn.Module): + def __init__(self, fn, dropout=0.0): + super().__init__() + self.fn = fn + self.dropsample = Dropsample(dropout) + + def forward(self, x): + out = self.fn(x) + out = self.dropsample(out) + return out + x + + +class Dropsample(nn.Module): + def __init__(self, prob=0): + super().__init__() + self.prob = prob + + def forward(self, x): + device = x.device + + if self.prob == 0.0 or (not self.training): + return x + + keep_mask = ( + torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_() + > self.prob + ) + return x * keep_mask / (1 - self.prob) + + +def MBConv( + dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0 +): + hidden_dim = int(expansion_rate * dim_out) + stride = 2 if downsample else 1 + + net = nn.Sequential( + nn.Conv2d(dim_in, hidden_dim, 1), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + nn.Conv2d( + hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim + ), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate), + nn.Conv2d(hidden_dim, dim_out, 1), + # nn.BatchNorm2d(dim_out) + ) + + if dim_in == dim_out and not downsample: + net = MBConvResidual(net, dropout=dropout) + + return net + + +# attention related classes +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.to_qkv = nn.Linear(dim, dim * 3, bias=False) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Sequential( + nn.Linear(dim, dim, bias=False), nn.Dropout(dropout) + ) + + # relative positional bias + if self.with_pe: + self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads) + + pos = torch.arange(window_size) + grid = torch.stack(torch.meshgrid(pos, pos)) + grid = rearrange(grid, "c i j -> (i j) c") + rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange( + grid, "j ... -> 1 j ..." + ) + rel_pos += window_size - 1 + rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum( + dim=-1 + ) + + self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False) + + def forward(self, x): + batch, height, width, window_height, window_width, _, device, h = ( + *x.shape, + x.device, + self.heads, + ) + + # flatten + + x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d") + + # project for queries, keys, values + + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + # split heads + + q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v)) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # add positional bias + if self.with_pe: + bias = self.rel_pos_bias(self.rel_pos_indices) + sim = sim + rearrange(bias, "i j h -> h i j") + + # attention + + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + + out = rearrange( + out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width + ) + + # combine heads out + + out = self.to_out(out) + return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width) + + +class Block_Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + bias=False, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.ps = window_size + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + # project for queries, keys, values + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + q, k, v = qkv.chunk(3, dim=1) + + # split heads + + q, k, v = map( + lambda t: rearrange( + t, + "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d", + h=self.heads, + w1=self.ps, + w2=self.ps, + ), + (q, k, v), + ) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # attention + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + out = rearrange( + out, + "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)", + x=h // self.ps, + y=w // self.ps, + head=self.heads, + w1=self.ps, + w2=self.ps, + ) + + out = self.to_out(out) + return out + + +class Channel_Attention(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class Channel_Attention_grid(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention_grid, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class OSA_Block(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + ffn_bias=True, + window_size=8, + with_pe=False, + dropout=0.0, + ): + super(OSA_Block, self).__init__() + + w = window_size + + self.layer = nn.Sequential( + MBConv( + channel_num, + channel_num, + downsample=False, + expansion_rate=1, + shrinkage_rate=0.25, + ), + Rearrange( + "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w + ), # block-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + Rearrange( + "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w + ), # grid-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention_grid( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + ) + + def forward(self, x): + out = self.layer(x) + return out diff --git a/ldm_patched/pfn/architecture/OmniSR/OSAG.py b/ldm_patched/pfn/architecture/OmniSR/OSAG.py new file mode 100644 index 0000000000000000000000000000000000000000..477e81f9da4eb1db9b5ec418549d75dd591209ec --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OSAG.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSAG.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:08:49 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + + +import torch.nn as nn + +from .esa import ESA +from .OSA import OSA_Block + + +class OSAG(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + block_num=4, + ffn_bias=False, + window_size=0, + pe=False, + ): + super(OSAG, self).__init__() + + # print("window_size: %d" % (window_size)) + # print("with_pe", pe) + # print("ffn_bias: %d" % (ffn_bias)) + + # block_script_name = kwargs.get("block_script_name", "OSA") + # block_class_name = kwargs.get("block_class_name", "OSA_Block") + + # script_name = "." + block_script_name + # package = __import__(script_name, fromlist=True) + block_class = OSA_Block # getattr(package, block_class_name) + group_list = [] + for _ in range(block_num): + temp_res = block_class( + channel_num, + bias, + ffn_bias=ffn_bias, + window_size=window_size, + with_pe=pe, + ) + group_list.append(temp_res) + group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias)) + self.residual_layer = nn.Sequential(*group_list) + esa_channel = max(channel_num // 4, 16) + self.esa = ESA(esa_channel, channel_num) + + def forward(self, x): + out = self.residual_layer(x) + out = out + x + return self.esa(out) diff --git a/ldm_patched/pfn/architecture/OmniSR/OmniSR.py b/ldm_patched/pfn/architecture/OmniSR/OmniSR.py new file mode 100644 index 0000000000000000000000000000000000000000..1e1c3f35e657fb972d4209456719a61163831385 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OmniSR.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OmniSR.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:06:36 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .OSAG import OSAG +from .pixelshuffle import pixelshuffle_block + + +class OmniSR(nn.Module): + def __init__( + self, + state_dict, + **kwargs, + ): + super(OmniSR, self).__init__() + self.state = state_dict + + bias = True # Fine to assume this for now + block_num = 1 # Fine to assume this for now + ffn_bias = True + pe = True + + num_feat = state_dict["input.weight"].shape[0] or 64 + num_in_ch = state_dict["input.weight"].shape[1] or 3 + num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh + + pixelshuffle_shape = state_dict["up.0.weight"].shape[0] + up_scale = math.sqrt(pixelshuffle_shape / num_out_ch) + if up_scale - int(up_scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + up_scale = int(up_scale) + res_num = 0 + for key in state_dict.keys(): + if "residual_layer" in key: + temp_res_num = int(key.split(".")[1]) + if temp_res_num > res_num: + res_num = temp_res_num + res_num = res_num + 1 # zero-indexed + + residual_layer = [] + self.res_num = res_num + + if ( + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + in state_dict.keys() + ): + rel_pos_bias_weight = state_dict[ + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + ].shape[0] + self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) + else: + self.window_size = 8 + + self.up_scale = up_scale + + for _ in range(res_num): + temp_res = OSAG( + channel_num=num_feat, + bias=bias, + block_num=block_num, + ffn_bias=ffn_bias, + window_size=self.window_size, + pe=pe, + ) + residual_layer.append(temp_res) + self.residual_layer = nn.Sequential(*residual_layer) + self.input = nn.Conv2d( + in_channels=num_in_ch, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.output = nn.Conv2d( + in_channels=num_feat, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias) + + # self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias) + + # for m in self.modules(): + # if isinstance(m, nn.Conv2d): + # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + # m.weight.data.normal_(0, sqrt(2. / n)) + + # chaiNNer specific stuff + self.model_arch = "OmniSR" + self.sub_type = "SR" + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.scale = up_scale + + self.supports_fp16 = True # TODO: Test this + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.load_state_dict(state_dict, strict=False) + + def check_image_size(self, x): + _, _, h, w = x.size() + # import pdb; pdb.set_trace() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0) + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + residual = self.input(x) + out = self.residual_layer(residual) + + # origin + out = torch.add(self.output(out), residual) + out = self.up(out) + + out = out[:, :, : H * self.up_scale, : W * self.up_scale] + return out diff --git a/ldm_patched/pfn/architecture/OmniSR/esa.py b/ldm_patched/pfn/architecture/OmniSR/esa.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ce7f7a60bfe20b3737eaa2e3110fd460a2d104 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/esa.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: esa.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:06 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .layernorm import LayerNorm2d + + +def moment(x, dim=(2, 3), k=2): + assert len(x.size()) == 4 + mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1) + mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim) + return mk + + +class ESA(nn.Module): + """ + Modification of Enhanced Spatial Attention (ESA), which is proposed by + `Residual Feature Aggregation Network for Image Super-Resolution` + Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes + are deleted. + """ + + def __init__(self, esa_channels, n_feats, conv=nn.Conv2d): + super(ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) + self.conv3 = conv(f, f, kernel_size=3, padding=1) + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + c1 = self.conv2(c1_) + v_max = F.max_pool2d(c1, kernel_size=7, stride=3) + c3 = self.conv3(v_max) + c3 = F.interpolate( + c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False + ) + cf = self.conv_f(c1_) + c4 = self.conv4(c3 + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA_LN(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA_LN, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.norm = LayerNorm2d(n_feats) + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.norm(x) + c1_ = self.conv1(c1_) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class AdaGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaGuidedFilter, self).__init__() + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=n_feats, + out_channels=1, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + self.r = 5 + + def box_filter(self, x, r): + channel = x.shape[1] + kernel_size = 2 * r + 1 + weight = 1.0 / (kernel_size**2) + box_kernel = weight * torch.ones( + (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device + ) + output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel) + return output + + def forward(self, x): + _, _, H, W = x.shape + N = self.box_filter( + torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r + ) + + # epsilon = self.fc(self.gap(x)) + # epsilon = torch.pow(epsilon, 2) + epsilon = 1e-2 + + mean_x = self.box_filter(x, self.r) / N + var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x + + A = var_x / (var_x + epsilon) + b = (1 - A) * mean_x + m = A * x + b + + # mean_A = self.box_filter(A, self.r) / N + # mean_b = self.box_filter(b, self.r) / N + # m = mean_A * x + mean_b + return x * m + + +class AdaConvGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaConvGuidedFilter, self).__init__() + f = esa_channels + + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=f, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=f, + bias=bias, + ) + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + y = self.vec_conv(x) + y = self.hor_conv(y) + + sigma = torch.pow(y, 2) + epsilon = self.fc(self.gap(y)) + + weight = sigma / (sigma + epsilon) + + m = weight * x + (1 - weight) + + return x * m diff --git a/ldm_patched/pfn/architecture/OmniSR/layernorm.py b/ldm_patched/pfn/architecture/OmniSR/layernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..731a25f7542d45757a284648055d7c6ffad4c3fd --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/layernorm.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: layernorm.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:20 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn + + +class LayerNormFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x, weight, bias, eps): + ctx.eps = eps + N, C, H, W = x.size() + mu = x.mean(1, keepdim=True) + var = (x - mu).pow(2).mean(1, keepdim=True) + y = (x - mu) / (var + eps).sqrt() + ctx.save_for_backward(y, var, weight) + y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) + return y + + @staticmethod + def backward(ctx, grad_output): + eps = ctx.eps + + N, C, H, W = grad_output.size() + y, var, weight = ctx.saved_variables + g = grad_output * weight.view(1, C, 1, 1) + mean_g = g.mean(dim=1, keepdim=True) + + mean_gy = (g * y).mean(dim=1, keepdim=True) + gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) + return ( + gx, + (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), + grad_output.sum(dim=3).sum(dim=2).sum(dim=0), + None, + ) + + +class LayerNorm2d(nn.Module): + def __init__(self, channels, eps=1e-6): + super(LayerNorm2d, self).__init__() + self.register_parameter("weight", nn.Parameter(torch.ones(channels))) + self.register_parameter("bias", nn.Parameter(torch.zeros(channels))) + self.eps = eps + + def forward(self, x): + return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) + + +class GRN(nn.Module): + """GRN (Global Response Normalization) layer""" + + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) + self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) + Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x diff --git a/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py b/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py new file mode 100644 index 0000000000000000000000000000000000000000..4260fb7c9d8d912e34899ce7877595b617f9bb02 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: pixelshuffle.py +# Created Date: Friday July 1st 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 1st July 2022 10:18:39 am +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + +import torch.nn as nn + + +def pixelshuffle_block( + in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False +): + """ + Upsample features according to `upscale_factor`. + """ + padding = kernel_size // 2 + conv = nn.Conv2d( + in_channels, + out_channels * (upscale_factor**2), + kernel_size, + padding=1, + bias=bias, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + return nn.Sequential(*[conv, pixel_shuffle]) diff --git a/ldm_patched/pfn/architecture/RRDB.py b/ldm_patched/pfn/architecture/RRDB.py new file mode 100644 index 0000000000000000000000000000000000000000..8d318b90b865fecd0b88adc6daf2c6d2e29860a3 --- /dev/null +++ b/ldm_patched/pfn/architecture/RRDB.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import functools +import math +import re +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/esrgan.py +# Which enhanced stuff that was already here +class RRDBNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ) -> None: + """ + ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks. + By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, + and Chen Change Loy. + This is old-arch Residual in Residual Dense Block Network and is not + the newest revision that's available at github.com/xinntao/ESRGAN. + This is on purpose, the newest Network has severely limited the + potential use of the Network with no benefits. + This network supports model files from both new and old-arch. + Args: + norm: Normalization layer + act: Activation layer + upsampler: Upsample layer. upconv, pixel_shuffle + mode: Convolution mode + """ + super(RRDBNet, self).__init__() + self.model_arch = "ESRGAN" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.state_map = { + # currently supports old, new, and newer RRDBNet arch models + # ESRGAN, BSRGAN/RealSR, Real-ESRGAN + "model.0.weight": ("conv_first.weight",), + "model.0.bias": ("conv_first.bias",), + "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), + "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), + r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( + r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", + r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", + ), + } + if "params_ema" in self.state: + self.state = self.state["params_ema"] + # self.model_arch = "RealESRGAN" + self.num_blocks = self.get_num_blocks() + self.plus = any("conv1x1" in k for k in self.state.keys()) + if self.plus: + self.model_arch = "ESRGAN+" + + self.state = self.new_to_old_arch(self.state) + + self.key_arr = list(self.state.keys()) + + self.in_nc: int = self.state[self.key_arr[0]].shape[1] + self.out_nc: int = self.state[self.key_arr[-1]].shape[0] + + self.scale: int = self.get_scale() + self.num_filters: int = self.state[self.key_arr[0]].shape[0] + + c2x2 = False + if self.state["model.0.weight"].shape[-2] == 2: + c2x2 = True + self.scale = round(math.sqrt(self.scale / 4)) + self.model_arch = "ESRGAN-2c2" + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + # Detect if pixelunshuffle was used (Real-ESRGAN) + if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in ( + self.in_nc / 4, + self.in_nc / 16, + ): + self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc)) + else: + self.shuffle_factor = None + + upsample_block = { + "upconv": B.upconv_block, + "pixel_shuffle": B.pixelshuffle_block, + }.get(self.upsampler) + if upsample_block is None: + raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found") + + if self.scale == 3: + upsample_blocks = upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + upscale_factor=3, + act_type=self.act, + c2x2=c2x2, + ) + else: + upsample_blocks = [ + upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + act_type=self.act, + c2x2=c2x2, + ) + for _ in range(int(math.log(self.scale, 2))) + ] + + self.model = B.sequential( + # fea conv + B.conv_block( + in_nc=self.in_nc, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + B.ShortcutBlock( + B.sequential( + # rrdb blocks + *[ + B.RRDB( + nf=self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=self.norm, + act_type=self.act, + mode="CNA", + plus=self.plus, + c2x2=c2x2, + ) + for _ in range(self.num_blocks) + ], + # lr conv + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=self.norm, + act_type=None, + mode=self.mode, + c2x2=c2x2, + ), + ) + ), + *upsample_blocks, + # hr_conv0 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=self.act, + c2x2=c2x2, + ), + # hr_conv1 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.out_nc, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + ) + + # Adjust these properties for calculations outside of the model + if self.shuffle_factor: + self.in_nc //= self.shuffle_factor**2 + self.scale //= self.shuffle_factor + + self.load_state_dict(self.state, strict=False) + + def new_to_old_arch(self, state): + """Convert a new-arch model state dictionary to an old-arch dictionary.""" + if "params_ema" in state: + state = state["params_ema"] + + if "conv_first.weight" not in state: + # model is already old arch, this is a loose check, but should be sufficient + return state + + # add nb to state keys + for kind in ("weight", "bias"): + self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ + f"model.1.sub./NB/.{kind}" + ] + del self.state_map[f"model.1.sub./NB/.{kind}"] + + old_state = OrderedDict() + for old_key, new_keys in self.state_map.items(): + for new_key in new_keys: + if r"\1" in old_key: + for k, v in state.items(): + sub = re.sub(new_key, old_key, k) + if sub != k: + old_state[sub] = v + else: + if new_key in state: + old_state[old_key] = state[new_key] + + # upconv layers + max_upconv = 0 + for key in state.keys(): + match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) + if match is not None: + _, key_num, key_type = match.groups() + old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] + max_upconv = max(max_upconv, int(key_num) * 3) + + # final layers + for key in state.keys(): + if key in ("HRconv.weight", "conv_hr.weight"): + old_state[f"model.{max_upconv + 2}.weight"] = state[key] + elif key in ("HRconv.bias", "conv_hr.bias"): + old_state[f"model.{max_upconv + 2}.bias"] = state[key] + elif key in ("conv_last.weight",): + old_state[f"model.{max_upconv + 4}.weight"] = state[key] + elif key in ("conv_last.bias",): + old_state[f"model.{max_upconv + 4}.bias"] = state[key] + + # Sort by first numeric value of each layer + def compare(item1, item2): + parts1 = item1.split(".") + parts2 = item2.split(".") + int1 = int(parts1[1]) + int2 = int(parts2[1]) + return int1 - int2 + + sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) + + # Rebuild the output dict in the right order + out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) + + return out_dict + + def get_scale(self, min_part: int = 6) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".")[1:] + if len(parts) == 2: + part_num = int(parts[0]) + if part_num > min_part and parts[1] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nbs = [] + state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( + r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", + ) + for state_key in state_keys: + for k in self.state: + m = re.search(state_key, k) + if m: + nbs.append(int(m.group(1))) + if nbs: + break + return max(*nbs) + 1 + + def forward(self, x): + if self.shuffle_factor: + _, _, h, w = x.size() + mod_pad_h = ( + self.shuffle_factor - h % self.shuffle_factor + ) % self.shuffle_factor + mod_pad_w = ( + self.shuffle_factor - w % self.shuffle_factor + ) % self.shuffle_factor + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor) + x = self.model(x) + return x[:, :, : h * self.scale, : w * self.scale] + return self.model(x) diff --git a/ldm_patched/pfn/architecture/SCUNet.py b/ldm_patched/pfn/architecture/SCUNet.py new file mode 100644 index 0000000000000000000000000000000000000000..b8354a873085140e9ff7d582c43ba9818ed9524e --- /dev/null +++ b/ldm_patched/pfn/architecture/SCUNet.py @@ -0,0 +1,455 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 +# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc +# ----------------------------------------------------------------------------------- + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py +class WMSA(nn.Module): + """Self-attention module in Swin Transformer""" + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim**-0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + # TODO recover + # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=0.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view( + 2 * window_size - 1, 2 * window_size - 1, self.n_heads + ) + .transpose(1, 2) + .transpose(0, 1) + ) + + def generate_mask(self, h, w, p, shift): + """generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting square. + attn_mask = torch.zeros( + h, + w, + p, + p, + p, + p, + dtype=torch.bool, + device=self.relative_position_params.device, + ) + if self.type == "W": + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange( + attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" + ) + return attn_mask + + def forward(self, x): + """Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != "W": + x = torch.roll( + x, + shifts=(-(self.window_size // 2), -(self.window_size // 2)), + dims=(1, 2), + ) + + x = rearrange( + x, + "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", + p1=self.window_size, + p2=self.window_size, + ) + h_windows = x.size(1) + w_windows = x.size(2) + # square validation + # assert h_windows == w_windows + + x = rearrange( + x, + "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", + p1=self.window_size, + p2=self.window_size, + ) + qkv = self.embedding_layer(x) + q, k, v = rearrange( + qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim + ).chunk(3, dim=0) + sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") + # Using Attn Mask to distinguish different subwindows. + if self.type != "W": + attn_mask = self.generate_mask( + h_windows, w_windows, self.window_size, shift=self.window_size // 2 + ) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) + output = rearrange(output, "h b w p c -> b w p (h c)") + output = self.linear(output) + output = rearrange( + output, + "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", + w1=h_windows, + p1=self.window_size, + ) + + if self.type != "W": + output = torch.roll( + output, + shifts=(self.window_size // 2, self.window_size // 2), + dims=(1, 2), + ) + + return output + + def relative_embedding(self): + cord = torch.tensor( + np.array( + [ + [i, j] + for i in range(self.window_size) + for j in range(self.window_size) + ] + ) + ) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[ + :, relation[:, :, 0].long(), relation[:, :, 1].long() + ] + + +class Block(nn.Module): + def __init__( + self, + input_dim, + output_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer Block""" + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ["W", "SW"] + self.type = type + if input_resolution <= window_size: + self.type = "W" + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__( + self, + conv_dim, + trans_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer and Conv Block""" + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ["W", "SW"] + if self.input_resolution <= self.window_size: + self.type = "W" + + self.trans_block = Block( + self.trans_dim, + self.trans_dim, + self.head_dim, + self.window_size, + self.drop_path, + self.type, + self.input_resolution, + ) + self.conv1_1 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + self.conv1_2 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + ) + + def forward(self, x): + conv_x, trans_x = torch.split( + self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 + ) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange("b c h w -> b h w c")(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange("b h w c -> b c h w")(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + def __init__( + self, + state_dict, + in_nc=3, + config=[4, 4, 4, 4, 4, 4, 4], + dim=64, + drop_path_rate=0.0, + input_resolution=256, + ): + super(SCUNet, self).__init__() + self.model_arch = "SCUNet" + self.sub_type = "SR" + + self.num_filters: int = 0 + + self.state = state_dict + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + self.in_nc = in_nc + self.out_nc = self.in_nc + self.scale = 1 + self.supports_fp16 = True + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[0]) + ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[1]) + ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[2]) + ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ + ConvTransBlock( + 4 * dim, + 4 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 8, + ) + for i in range(config[3]) + ] + + begin += config[3] + self.m_up3 = [ + nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[4]) + ] + + begin += config[4] + self.m_up2 = [ + nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[5]) + ] + + begin += config[5] + self.m_up1 = [ + nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[6]) + ] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (64 - h % 64) % 64 + mod_pad_w = (64 - w % 64) % 64 + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward(self, x0): + h, w = x0.size()[-2:] + x0 = self.check_image_size(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[:, :, :h, :w] + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) diff --git a/ldm_patched/pfn/architecture/SPSR.py b/ldm_patched/pfn/architecture/SPSR.py new file mode 100644 index 0000000000000000000000000000000000000000..c3cefff190292a63cf61fe3fa9c28131dac4f369 --- /dev/null +++ b/ldm_patched/pfn/architecture/SPSR.py @@ -0,0 +1,383 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +class Get_gradient_nopadding(nn.Module): + def __init__(self): + super(Get_gradient_nopadding, self).__init__() + kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] + kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] + kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) + kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) + self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore + + self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore + + def forward(self, x): + x_list = [] + for i in range(x.shape[1]): + x_i = x[:, i] + x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) + x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) + x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) + x_list.append(x_i) + + x = torch.cat(x_list, dim=1) + + return x + + +class SPSRNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ): + super(SPSRNet, self).__init__() + self.model_arch = "SPSR" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.num_blocks = self.get_num_blocks() + + self.in_nc: int = self.state["model.0.weight"].shape[1] + self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] + + self.scale = self.get_scale(4) + self.num_filters: int = self.state["model.0.weight"].shape[0] + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + n_upscale = int(math.log(self.scale, 2)) + if self.scale == 3: + n_upscale = 1 + + fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + rb_blocks = [ + B.RRDB( + self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + for _ in range(self.num_blocks) + ] + LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + a_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + a_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + self.HR_conv0_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.HR_conv1_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.model = B.sequential( + fea_conv, + B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), + *a_upsampler, + self.HR_conv0_new, + ) + + self.get_g_nopadding = Get_gradient_nopadding() + + self.b_fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + + self.b_concat_1 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_1 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_2 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_2 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_3 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_3 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_4 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_4 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + b_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + b_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + + b_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + b_HR_conv1 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) + + self.conv_w = B.conv_block( + self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None + ) + + self.f_concat = B.conv_block( + self.num_filters * 2, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.f_block = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.f_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.f_HR_conv1 = B.conv_block( + self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None + ) + + self.load_state_dict(self.state, strict=False) + + def get_scale(self, min_part: int = 4) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".") + if len(parts) == 3: + part_num = int(parts[1]) + if part_num > min_part and parts[0] == "model" and parts[2] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nb = 0 + for part in list(self.state): + parts = part.split(".") + n_parts = len(parts) + if n_parts == 5 and parts[2] == "sub": + nb = int(parts[3]) + return nb + + def forward(self, x): + x_grad = self.get_g_nopadding(x) + x = self.model[0](x) + + x, block_list = self.model[1](x) + + x_ori = x + for i in range(5): + x = block_list[i](x) + x_fea1 = x + + for i in range(5): + x = block_list[i + 5](x) + x_fea2 = x + + for i in range(5): + x = block_list[i + 10](x) + x_fea3 = x + + for i in range(5): + x = block_list[i + 15](x) + x_fea4 = x + + x = block_list[20:](x) + # short cut + x = x_ori + x + x = self.model[2:](x) + x = self.HR_conv1_new(x) + + x_b_fea = self.b_fea_conv(x_grad) + x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) + + x_cat_1 = self.b_block_1(x_cat_1) + x_cat_1 = self.b_concat_1(x_cat_1) + + x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) + + x_cat_2 = self.b_block_2(x_cat_2) + x_cat_2 = self.b_concat_2(x_cat_2) + + x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) + + x_cat_3 = self.b_block_3(x_cat_3) + x_cat_3 = self.b_concat_3(x_cat_3) + + x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) + + x_cat_4 = self.b_block_4(x_cat_4) + x_cat_4 = self.b_concat_4(x_cat_4) + + x_cat_4 = self.b_LR_conv(x_cat_4) + + # short cut + x_cat_4 = x_cat_4 + x_b_fea + x_branch = self.b_module(x_cat_4) + + # x_out_branch = self.conv_w(x_branch) + ######## + x_branch_d = x_branch + x_f_cat = torch.cat([x_branch_d, x], dim=1) + x_f_cat = self.f_block(x_f_cat) + x_out = self.f_concat(x_f_cat) + x_out = self.f_HR_conv0(x_out) + x_out = self.f_HR_conv1(x_out) + + ######### + # return x_out_branch, x_out, x_grad + return x_out diff --git a/ldm_patched/pfn/architecture/SRVGG.py b/ldm_patched/pfn/architecture/SRVGG.py new file mode 100644 index 0000000000000000000000000000000000000000..7a8ec37ae5dc4effd0ba688cf4c3a51801e1f2c9 --- /dev/null +++ b/ldm_patched/pfn/architecture/SRVGG.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch.nn as nn +import torch.nn.functional as F + + +class SRVGGNetCompact(nn.Module): + """A compact VGG-style network structure for super-resolution. + It is a compact network structure, which performs upsampling in the last layer and no convolution is + conducted on the HR feature space. + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_conv (int): Number of convolution layers in the body network. Default: 16. + upscale (int): Upsampling factor. Default: 4. + act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. + """ + + def __init__( + self, + state_dict, + act_type: str = "prelu", + ): + super(SRVGGNetCompact, self).__init__() + self.model_arch = "SRVGG (RealESRGAN)" + self.sub_type = "SR" + + self.act_type = act_type + + self.state = state_dict + + if "params" in self.state: + self.state = self.state["params"] + + self.key_arr = list(self.state.keys()) + + self.in_nc = self.get_in_nc() + self.num_feat = self.get_num_feats() + self.num_conv = self.get_num_conv() + self.out_nc = self.in_nc # :( + self.pixelshuffle_shape = None # Defined in get_scale() + self.scale = self.get_scale() + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.body = nn.ModuleList() + # the first conv + self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1)) + # the first activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the body structure + for _ in range(self.num_conv): + self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1)) + # activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the last conv + self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1)) # type: ignore + # upsample + self.upsampler = nn.PixelShuffle(self.scale) + + self.load_state_dict(self.state, strict=False) + + def get_num_conv(self) -> int: + return (int(self.key_arr[-1].split(".")[1]) - 2) // 2 + + def get_num_feats(self) -> int: + return self.state[self.key_arr[0]].shape[0] + + def get_in_nc(self) -> int: + return self.state[self.key_arr[0]].shape[1] + + def get_scale(self) -> int: + self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0] + # Assume out_nc is the same as in_nc + # I cant think of a better way to do that + self.out_nc = self.in_nc + scale = math.sqrt(self.pixelshuffle_shape / self.out_nc) + if scale - int(scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + scale = int(scale) + return scale + + def forward(self, x): + out = x + for i in range(0, len(self.body)): + out = self.body[i](out) + + out = self.upsampler(out) + # add the nearest upsampled image, so that the network learns the residual + base = F.interpolate(x, scale_factor=self.scale, mode="nearest") + out += base + return out diff --git a/ldm_patched/pfn/architecture/SwiftSRGAN.py b/ldm_patched/pfn/architecture/SwiftSRGAN.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb7725b08dc2462661b7ba45db605a06fadacb9 --- /dev/null +++ b/ldm_patched/pfn/architecture/SwiftSRGAN.py @@ -0,0 +1,161 @@ +# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py + +import torch +from torch import nn + + +class SeperableConv2d(nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True + ): + super(SeperableConv2d, self).__init__() + self.depthwise = nn.Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + bias=bias, + padding=padding, + ) + self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) + + def forward(self, x): + return self.pointwise(self.depthwise(x)) + + +class ConvBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + use_act=True, + use_bn=True, + discriminator=False, + **kwargs, + ): + super(ConvBlock, self).__init__() + + self.use_act = use_act + self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn) + self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity() + self.act = ( + nn.LeakyReLU(0.2, inplace=True) + if discriminator + else nn.PReLU(num_parameters=out_channels) + ) + + def forward(self, x): + return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) + + +class UpsampleBlock(nn.Module): + def __init__(self, in_channels, scale_factor): + super(UpsampleBlock, self).__init__() + + self.conv = SeperableConv2d( + in_channels, + in_channels * scale_factor**2, + kernel_size=3, + stride=1, + padding=1, + ) + self.ps = nn.PixelShuffle( + scale_factor + ) # (in_channels * 4, H, W) -> (in_channels, H*2, W*2) + self.act = nn.PReLU(num_parameters=in_channels) + + def forward(self, x): + return self.act(self.ps(self.conv(x))) + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels): + super(ResidualBlock, self).__init__() + + self.block1 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + self.block2 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False + ) + + def forward(self, x): + out = self.block1(x) + out = self.block2(out) + return out + x + + +class Generator(nn.Module): + """Swift-SRGAN Generator + Args: + in_channels (int): number of input image channels. + num_channels (int): number of hidden channels. + num_blocks (int): number of residual blocks. + upscale_factor (int): factor to upscale the image [2x, 4x, 8x]. + Returns: + torch.Tensor: super resolution image + """ + + def __init__( + self, + state_dict, + ): + super(Generator, self).__init__() + self.model_arch = "Swift-SRGAN" + self.sub_type = "SR" + self.state = state_dict + if "model" in self.state: + self.state = self.state["model"] + + self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0] + self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0] + self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0] + self.num_blocks = len( + set([x.split(".")[1] for x in self.state.keys() if "residual" in x]) + ) + self.scale: int = 2 ** len( + set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x]) + ) + + in_channels = self.in_nc + num_channels = self.num_filters + num_blocks = self.num_blocks + upscale_factor = self.scale + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.initial = ConvBlock( + in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False + ) + self.residual = nn.Sequential( + *[ResidualBlock(num_channels) for _ in range(num_blocks)] + ) + self.convblock = ConvBlock( + num_channels, + num_channels, + kernel_size=3, + stride=1, + padding=1, + use_act=False, + ) + self.upsampler = nn.Sequential( + *[ + UpsampleBlock(num_channels, scale_factor=2) + for _ in range(upscale_factor // 2) + ] + ) + self.final_conv = SeperableConv2d( + num_channels, in_channels, kernel_size=9, stride=1, padding=4 + ) + + self.load_state_dict(self.state, strict=False) + + def forward(self, x): + initial = self.initial(x) + x = self.residual(initial) + x = self.convblock(x) + initial + x = self.upsampler(x) + return (torch.tanh(self.final_conv(x)) + 1) / 2 diff --git a/ldm_patched/pfn/architecture/Swin2SR.py b/ldm_patched/pfn/architecture/Swin2SR.py new file mode 100644 index 0000000000000000000000000000000000000000..cb57ecfc4ada45a6b087247017732437b1af0fcc --- /dev/null +++ b/ldm_patched/pfn/architecture/Swin2SR.py @@ -0,0 +1,1377 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 +# Written by Conde and Choi et al. +# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py +# ----------------------------------------------------------------------------------- + +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + attn_drop=0.0, + proj_drop=0.0, + pretrained_window_size=[0, 0], + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # type: ignore + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False), + ) + + # get relative_coords_table + relative_coords_h = torch.arange( + -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 + ) + relative_coords_w = torch.arange( + -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 + ) + relative_coords_table = ( + torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + .permute(1, 2, 0) + .contiguous() + .unsqueeze(0) + ) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 + else: + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) + * torch.log2(torch.abs(relative_coords_table) + 1.0) + / np.log2(8) + ) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + self.v_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) # type: ignore + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, + max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), + ).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( + -1, self.num_heads + ) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( # type: ignore + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, window_size={self.window_size}, " + f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" + ) + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size), + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + flops += H * W * self.dim // 2 + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + pretrained_window_size=pretrained_window_size, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) # type: ignore + nn.init.constant_(blk.norm1.weight, 0) # type: ignore + nn.init.constant_(blk.norm2.bias, 0) # type: ignore + nn.init.constant_(blk.norm2.weight, 0) # type: ignore + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=patch_size # type: ignore + ) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + # assert H == self.img_size[0] and W == self.img_size[1], + # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) # type: ignore + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class Upsample_hf(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample_hf, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class Swin2SR(nn.Module): + r"""Swin2SR + A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(Swin2SR, self).__init__() + + # Defaults + img_size = 128 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + + self.model_arch = "Swin2SR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_aux.weight" in state_keys: + upsampler = "pixelshuffle_aux" + elif "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + ## END AUTO DETECTION + + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) # type: ignore + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + + if self.upsampler == "pixelshuffle_hf": + self.layers_hf = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results # type: ignore + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers_hf.append(layer) + + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffle_aux": + self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_after_aux = nn.Sequential( + nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffle_hf": + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.upsample_hf = Upsample_hf(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_first_hf = nn.Sequential( + nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + self.conv_before_upsample_hf = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + assert self.upscale == 4, "only support x4 now." + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + self.load_state_dict(state_dict) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward_features_hf(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers_hf: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffle_aux": + bicubic = F.interpolate( + x, + size=(H * self.upscale, W * self.upscale), + mode="bicubic", + align_corners=False, + ) + bicubic = self.conv_bicubic(bicubic) + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + aux = self.conv_aux(x) # b, 3, LR_H, LR_W + x = self.conv_after_aux(aux) + x = ( + self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] + + bicubic[:, :, : H * self.upscale, : W * self.upscale] + ) + x = self.conv_last(x) + aux = aux / self.img_range + self.mean + elif self.upsampler == "pixelshuffle_hf": + # for classical SR with HF + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x_before = self.conv_before_upsample(x) + x_out = self.conv_last(self.upsample(x_before)) + + x_hf = self.conv_first_hf(x_before) + x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf + x_hf = self.conv_before_upsample_hf(x_hf) + x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) + x = x_out + x_hf + x_hf = x_hf / self.img_range + self.mean + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + if self.upsampler == "pixelshuffle_aux": + # NOTE: I removed an "aux" output here. not sure what that was for + return x[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + elif self.upsampler == "pixelshuffle_hf": + x_out = x_out / self.img_range + self.mean # type: ignore + return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + else: + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/ldm_patched/pfn/architecture/SwinIR.py b/ldm_patched/pfn/architecture/SwinIR.py new file mode 100644 index 0000000000000000000000000000000000000000..439dcbcb2b12f7ff27a01490f4c2ae7b6e4eab9e --- /dev/null +++ b/ldm_patched/pfn/architecture/SwinIR.py @@ -0,0 +1,1224 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 +# Originally Written by Ze Liu, Modified by Jingyun Liang. +# ----------------------------------------------------------------------------------- + +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) # type: ignore + ].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}" + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + H, W = self.img_size + if self.norm is not None: + flops += H * W * self.embed_dim # type: ignore + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class SwinIR(nn.Module): + r"""SwinIR + A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(SwinIR, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_feat = 64 + num_in_ch = in_chans + num_out_ch = in_chans + supports_fp16 = True + self.start_unshuffle = 1 + + self.model_arch = "SwinIR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + if "conv_first.1.weight" in self.state: + self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight") + self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias") + self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3)) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale / self.start_unshuffle + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + if self.upscale == 4: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + elif self.upscale == 8: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.start_unshuffle > 1: + x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") # type: ignore + ) + ) + if self.upscale == 4: + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate( # type: ignore + x, scale_factor=2, mode="nearest" + ) + ) + ) + elif self.upscale == 8: + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/ldm_patched/pfn/architecture/__init__.py b/ldm_patched/pfn/architecture/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/pfn/architecture/block.py b/ldm_patched/pfn/architecture/block.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bc5d227008a73c40f9087da1ee3ae2ca25a896 --- /dev/null +++ b/ldm_patched/pfn/architecture/block.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +from collections import OrderedDict +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +import torch +import torch.nn as nn + +#################### +# Basic blocks +#################### + + +def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1): + # helper selecting activation + # neg_slope: for leakyrelu and init of prelu + # n_prelu: for p_relu num_parameters + act_type = act_type.lower() + if act_type == "relu": + layer = nn.ReLU(inplace) + elif act_type == "leakyrelu": + layer = nn.LeakyReLU(neg_slope, inplace) + elif act_type == "prelu": + layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) + else: + raise NotImplementedError( + "activation layer [{:s}] is not found".format(act_type) + ) + return layer + + +def norm(norm_type: str, nc: int): + # helper selecting normalization layer + norm_type = norm_type.lower() + if norm_type == "batch": + layer = nn.BatchNorm2d(nc, affine=True) + elif norm_type == "instance": + layer = nn.InstanceNorm2d(nc, affine=False) + else: + raise NotImplementedError( + "normalization layer [{:s}] is not found".format(norm_type) + ) + return layer + + +def pad(pad_type: str, padding): + # helper selecting padding layer + # if padding is 'zero', do by conv layers + pad_type = pad_type.lower() + if padding == 0: + return None + if pad_type == "reflect": + layer = nn.ReflectionPad2d(padding) + elif pad_type == "replicate": + layer = nn.ReplicationPad2d(padding) + else: + raise NotImplementedError( + "padding layer [{:s}] is not implemented".format(pad_type) + ) + return layer + + +def get_valid_padding(kernel_size, dilation): + kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + padding = (kernel_size - 1) // 2 + return padding + + +class ConcatBlock(nn.Module): + # Concat the output of a submodule to its input + def __init__(self, submodule): + super(ConcatBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = torch.cat((x, self.sub(x)), dim=1) + return output + + def __repr__(self): + tmpstr = "Identity .. \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlock(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = x + self.sub(x) + return output + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlockSPSR(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlockSPSR, self).__init__() + self.sub = submodule + + def forward(self, x): + return x, self.sub + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +def sequential(*args): + # Flatten Sequential. It unwraps nn.Sequential. + if len(args) == 1: + if isinstance(args[0], OrderedDict): + raise NotImplementedError("sequential does not support OrderedDict input.") + return args[0] # No sequential is needed. + modules = [] + for module in args: + if isinstance(module, nn.Sequential): + for submodule in module.children(): + modules.append(submodule) + elif isinstance(module, nn.Module): + modules.append(module) + return nn.Sequential(*modules) + + +ConvMode = Literal["CNA", "NAC", "CNAC"] + + +# 2x2x2 Conv Block +def conv_block_2c2( + in_nc, + out_nc, + act_type="relu", +): + return sequential( + nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1), + nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0), + act(act_type) if act_type else None, + ) + + +def conv_block( + in_nc: int, + out_nc: int, + kernel_size, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type: str | None = "relu", + mode: ConvMode = "CNA", + c2x2=False, +): + """ + Conv layer with padding, normalization, activation + mode: CNA --> Conv -> Norm -> Act + NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) + """ + + if c2x2: + return conv_block_2c2(in_nc, out_nc, act_type=act_type) + + assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) + padding = get_valid_padding(kernel_size, dilation) + p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None + padding = padding if pad_type == "zero" else 0 + + c = nn.Conv2d( + in_nc, + out_nc, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + groups=groups, + ) + a = act(act_type) if act_type else None + if mode in ("CNA", "CNAC"): + n = norm(norm_type, out_nc) if norm_type else None + return sequential(p, c, n, a) + elif mode == "NAC": + if norm_type is None and act_type is not None: + a = act(act_type, inplace=False) + # Important! + # input----ReLU(inplace)----Conv--+----output + # |________________________| + # inplace ReLU will modify the input, therefore wrong output + n = norm(norm_type, in_nc) if norm_type else None + return sequential(n, a, p, c) + else: + assert False, f"Invalid conv mode {mode}" + + +#################### +# Useful blocks +#################### + + +class ResNetBlock(nn.Module): + """ + ResNet Block, 3-3 style + with extra residual scaling used in EDSR + (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) + """ + + def __init__( + self, + in_nc, + mid_nc, + out_nc, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type=None, + act_type="relu", + mode: ConvMode = "CNA", + res_scale=1, + ): + super(ResNetBlock, self).__init__() + conv0 = conv_block( + in_nc, + mid_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + if mode == "CNA": + act_type = None + if mode == "CNAC": # Residual path: |-CNAC-| + act_type = None + norm_type = None + conv1 = conv_block( + mid_nc, + out_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + # if in_nc != out_nc: + # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ + # None, None) + # print('Need a projecter in ResNetBlock.') + # else: + # self.project = lambda x:x + self.res = sequential(conv0, conv1) + self.res_scale = res_scale + + def forward(self, x): + res = self.res(x).mul(self.res_scale) + return x + res + + +class RRDB(nn.Module): + """ + Residual in Residual Dense Block + (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) + """ + + def __init__( + self, + nf, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + _convtype="Conv2D", + _spectral_norm=False, + plus=False, + c2x2=False, + ): + super(RRDB, self).__init__() + self.RDB1 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB2 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB3 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + + def forward(self, x): + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + return out * 0.2 + x + + +class ResidualDenseBlock_5C(nn.Module): + """ + Residual Dense Block + style: 5 convs + The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) + Modified options that can be used: + - "Partial Convolution based Padding" arXiv:1811.11718 + - "Spectral normalization" arXiv:1802.05957 + - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. + {Rakotonirina} and A. {Rasoanaivo} + + Args: + nf (int): Channel number of intermediate features (num_feat). + gc (int): Channels for each growth (num_grow_ch: growth channel, + i.e. intermediate channels). + convtype (str): the type of convolution to use. Default: 'Conv2D' + gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new + trainable parameters) + plus (bool): enable the additional residual paths from ESRGAN+ + (adds trainable parameters) + """ + + def __init__( + self, + nf=64, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + plus=False, + c2x2=False, + ): + super(ResidualDenseBlock_5C, self).__init__() + + ## + + self.conv1x1 = conv1x1(nf, gc) if plus else None + ## + + + self.conv1 = conv_block( + nf, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv2 = conv_block( + nf + gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv3 = conv_block( + nf + 2 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv4 = conv_block( + nf + 3 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + if mode == "CNA": + last_act = None + else: + last_act = act_type + self.conv5 = conv_block( + nf + 4 * gc, + nf, + 3, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=last_act, + mode=mode, + c2x2=c2x2, + ) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(torch.cat((x, x1), 1)) + if self.conv1x1: + # pylint: disable=not-callable + x2 = x2 + self.conv1x1(x) # + + x3 = self.conv3(torch.cat((x, x1, x2), 1)) + x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) + if self.conv1x1: + x4 = x4 + x2 # + + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +#################### +# Upsampler +#################### + + +def pixelshuffle_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", +): + """ + Pixel shuffle layer + (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional + Neural Network, CVPR17) + """ + conv = conv_block( + in_nc, + out_nc * (upscale_factor**2), + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=None, + act_type=None, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + + n = norm(norm_type, out_nc) if norm_type else None + a = act(act_type) if act_type else None + return sequential(conv, pixel_shuffle, n, a) + + +def upconv_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", + mode="nearest", + c2x2=False, +): + # Up conv + # described in https://distill.pub/2016/deconv-checkerboard/ + upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) + conv = conv_block( + in_nc, + out_nc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + c2x2=c2x2, + ) + return sequential(upsample, conv) diff --git a/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN b/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN new file mode 100644 index 0000000000000000000000000000000000000000..5ac273fd509e328f396e6e4444673a3b051a4968 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN @@ -0,0 +1,351 @@ +Tencent is pleased to support the open source community by making GFPGAN available. + +Copyright (C) 2021 THL A29 Limited, a Tencent company. 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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. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. diff --git a/ldm_patched/pfn/architecture/face/arcface_arch.py b/ldm_patched/pfn/architecture/face/arcface_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b548af059a71b38c6c18cd35cbfed7bae7e55441 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/arcface_arch.py @@ -0,0 +1,265 @@ +import torch.nn as nn + + +def conv3x3(inplanes, outplanes, stride=1): + """A simple wrapper for 3x3 convolution with padding. + + Args: + inplanes (int): Channel number of inputs. + outplanes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + """ + return nn.Conv2d( + inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + +class BasicBlock(nn.Module): + """Basic residual block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class IRBlock(nn.Module): + """Improved residual block (IR Block) used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): + super(IRBlock, self).__init__() + self.bn0 = nn.BatchNorm2d(inplanes) + self.conv1 = conv3x3(inplanes, inplanes) + self.bn1 = nn.BatchNorm2d(inplanes) + self.prelu = nn.PReLU() + self.conv2 = conv3x3(inplanes, planes, stride) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.use_se = use_se + if self.use_se: + self.se = SEBlock(planes) + + def forward(self, x): + residual = x + out = self.bn0(x) + out = self.conv1(out) + out = self.bn1(out) + out = self.prelu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.use_se: + out = self.se(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.prelu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 4 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d( + planes, planes * self.expansion, kernel_size=1, bias=False + ) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class SEBlock(nn.Module): + """The squeeze-and-excitation block (SEBlock) used in the IRBlock. + + Args: + channel (int): Channel number of inputs. + reduction (int): Channel reduction ration. Default: 16. + """ + + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d( + 1 + ) # pool to 1x1 without spatial information + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), + nn.PReLU(), + nn.Linear(channel // reduction, channel), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResNetArcFace(nn.Module): + """ArcFace with ResNet architectures. + + Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. + + Args: + block (str): Block used in the ArcFace architecture. + layers (tuple(int)): Block numbers in each layer. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + def __init__(self, block, layers, use_se=True): + if block == "IRBlock": + block = IRBlock + self.inplanes = 64 + self.use_se = use_se + super(ResNetArcFace, self).__init__() + + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.prelu = nn.PReLU() + self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.bn4 = nn.BatchNorm2d(512) + self.dropout = nn.Dropout() + self.fc5 = nn.Linear(512 * 8 * 8, 512) + self.bn5 = nn.BatchNorm1d(512) + + # initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, num_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, + bias=False, + ), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append( + block(self.inplanes, planes, stride, downsample, use_se=self.use_se) + ) + self.inplanes = planes + for _ in range(1, num_blocks): + layers.append(block(self.inplanes, planes, use_se=self.use_se)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.prelu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.bn4(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.fc5(x) + x = self.bn5(x) + + return x diff --git a/ldm_patched/pfn/architecture/face/codeformer.py b/ldm_patched/pfn/architecture/face/codeformer.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e2e985e8f4487547779574c6b210da412a8e71 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/codeformer.py @@ -0,0 +1,790 @@ +""" +Modified from https://github.com/sczhou/CodeFormer +VQGAN code, adapted from the original created by the Unleashing Transformers authors: +https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py +This version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me. +""" +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import logging as logger +from torch import Tensor + + +class VectorQuantizer(nn.Module): + def __init__(self, codebook_size, emb_dim, beta): + super(VectorQuantizer, self).__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) + self.embedding.weight.data.uniform_( + -1.0 / self.codebook_size, 1.0 / self.codebook_size + ) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.emb_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = ( + (z_flattened**2).sum(dim=1, keepdim=True) + + (self.embedding.weight**2).sum(1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + mean_distance = torch.mean(d) + # find closest encodings + # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + min_encoding_scores, min_encoding_indices = torch.topk( + d, 1, dim=1, largest=False + ) + # [0-1], higher score, higher confidence + min_encoding_scores = torch.exp(-min_encoding_scores / 10) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.codebook_size + ).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return ( + z_q, + loss, + { + "perplexity": perplexity, + "min_encodings": min_encodings, + "min_encoding_indices": min_encoding_indices, + "min_encoding_scores": min_encoding_scores, + "mean_distance": mean_distance, + }, + ) + + def get_codebook_feat(self, indices, shape): + # input indices: batch*token_num -> (batch*token_num)*1 + # shape: batch, height, width, channel + indices = indices.view(-1, 1) + min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) + min_encodings.scatter_(1, indices, 1) + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: # reshape back to match original input shape + z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantizer(nn.Module): + def __init__( + self, + codebook_size, + emb_dim, + num_hiddens, + straight_through=False, + kl_weight=5e-4, + temp_init=1.0, + ): + super().__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + self.proj = nn.Conv2d( + num_hiddens, codebook_size, 1 + ) # projects last encoder layer to quantized logits + self.embed = nn.Embedding(codebook_size, emb_dim) + + def forward(self, z): + hard = self.straight_through if self.training else True + + logits = self.proj(z) + + soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) + + z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = ( + self.kl_weight + * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() + ) + min_encoding_indices = soft_one_hot.argmax(dim=1) + + return z_q, diff, {"min_encoding_indices": min_encoding_indices} + + +class Downsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + + return x + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) + k = k.reshape(b, c, h * w) + w_ = torch.bmm(q, k) + w_ = w_ * (int(c) ** (-0.5)) + w_ = F.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) + h_ = torch.bmm(v, w_) + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +class Encoder(nn.Module): + def __init__( + self, + in_channels, + nf, + out_channels, + ch_mult, + num_res_blocks, + resolution, + attn_resolutions, + ): + super().__init__() + self.nf = nf + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.attn_resolutions = attn_resolutions + + curr_res = self.resolution + in_ch_mult = (1,) + tuple(ch_mult) + + blocks = [] + # initial convultion + blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) + + # residual and downsampling blocks, with attention on smaller res (16x16) + for i in range(self.num_resolutions): + block_in_ch = nf * in_ch_mult[i] + block_out_ch = nf * ch_mult[i] + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + if curr_res in attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != self.num_resolutions - 1: + blocks.append(Downsample(block_in_ch)) + curr_res = curr_res // 2 + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + blocks.append(AttnBlock(block_in_ch)) # type: ignore + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + + # normalise and convert to latent size + blocks.append(normalize(block_in_ch)) # type: ignore + blocks.append( + nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore + ) + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class Generator(nn.Module): + def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): + super().__init__() + self.nf = nf + self.ch_mult = ch_mult + self.num_resolutions = len(self.ch_mult) + self.num_res_blocks = res_blocks + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.in_channels = emb_dim + self.out_channels = 3 + block_in_ch = self.nf * self.ch_mult[-1] + curr_res = self.resolution // 2 ** (self.num_resolutions - 1) + + blocks = [] + # initial conv + blocks.append( + nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1) + ) + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + for i in reversed(range(self.num_resolutions)): + block_out_ch = self.nf * self.ch_mult[i] + + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + + if curr_res in self.attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != 0: + blocks.append(Upsample(block_in_ch)) + curr_res = curr_res * 2 + + blocks.append(normalize(block_in_ch)) + blocks.append( + nn.Conv2d( + block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1 + ) + ) + + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class VQAutoEncoder(nn.Module): + def __init__( + self, + img_size, + nf, + ch_mult, + quantizer="nearest", + res_blocks=2, + attn_resolutions=[16], + codebook_size=1024, + emb_dim=256, + beta=0.25, + gumbel_straight_through=False, + gumbel_kl_weight=1e-8, + model_path=None, + ): + super().__init__() + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks + self.codebook_size = codebook_size + self.embed_dim = emb_dim + self.ch_mult = ch_mult + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.quantizer_type = quantizer + self.encoder = Encoder( + self.in_channels, + self.nf, + self.embed_dim, + self.ch_mult, + self.n_blocks, + self.resolution, + self.attn_resolutions, + ) + if self.quantizer_type == "nearest": + self.beta = beta # 0.25 + self.quantize = VectorQuantizer( + self.codebook_size, self.embed_dim, self.beta + ) + elif self.quantizer_type == "gumbel": + self.gumbel_num_hiddens = emb_dim + self.straight_through = gumbel_straight_through + self.kl_weight = gumbel_kl_weight + self.quantize = GumbelQuantizer( + self.codebook_size, + self.embed_dim, + self.gumbel_num_hiddens, + self.straight_through, + self.kl_weight, + ) + self.generator = Generator( + nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim + ) + + if model_path is not None: + chkpt = torch.load(model_path, map_location="cpu") + if "params_ema" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params_ema"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params_ema]") + elif "params" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params]") + else: + raise ValueError("Wrong params!") + + def forward(self, x): + x = self.encoder(x) + quant, codebook_loss, quant_stats = self.quantize(x) + x = self.generator(quant) + return x, codebook_loss, quant_stats + + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, "The input feature should be 4D tensor." + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand( + size + ) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats=64, temperature=10000, normalize=False, scale=None + ): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros( + (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool + ) + not_mask = ~mask # pylint: disable=invalid-unary-operand-type + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +class TransformerSALayer(nn.Module): + def __init__( + self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu" + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) + # Implementation of Feedforward model - MLP + self.linear1 = nn.Linear(embed_dim, dim_mlp) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_mlp, embed_dim) + + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward( + self, + tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + # self attention + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + + # ffn + tgt2 = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout2(tgt2) + return tgt + + +def normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +@torch.jit.script # type: ignore +def swish(x): + return x * torch.sigmoid(x) + + +class ResBlock(nn.Module): + def __init__(self, in_channels, out_channels=None): + super(ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.norm1 = normalize(in_channels) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + self.norm2 = normalize(out_channels) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + if self.in_channels != self.out_channels: + self.conv_out = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore + ) + + def forward(self, x_in): + x = x_in + x = self.norm1(x) + x = swish(x) + x = self.conv1(x) + x = self.norm2(x) + x = swish(x) + x = self.conv2(x) + if self.in_channels != self.out_channels: + x_in = self.conv_out(x_in) + + return x + x_in + + +class Fuse_sft_block(nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.encode_enc = ResBlock(2 * in_ch, out_ch) + + self.scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + self.shift = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + def forward(self, enc_feat, dec_feat, w=1): + enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) + scale = self.scale(enc_feat) + shift = self.shift(enc_feat) + residual = w * (dec_feat * scale + shift) + out = dec_feat + residual + return out + + +class CodeFormer(VQAutoEncoder): + def __init__(self, state_dict): + dim_embd = 512 + n_head = 8 + n_layers = 9 + codebook_size = 1024 + latent_size = 256 + connect_list = ["32", "64", "128", "256"] + fix_modules = ["quantize", "generator"] + + # This is just a guess as I only have one model to look at + position_emb = state_dict["position_emb"] + dim_embd = position_emb.shape[1] + latent_size = position_emb.shape[0] + + try: + n_layers = len( + set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x]) + ) + except: + pass + + codebook_size = state_dict["quantize.embedding.weight"].shape[0] + + # This is also just another guess + n_head_exp = ( + state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd + ) + n_head = 2**n_head_exp + + in_nc = state_dict["encoder.blocks.0.weight"].shape[1] + + self.model_arch = "CodeFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = in_nc + self.out_nc = in_nc + + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + super(CodeFormer, self).__init__( + 512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size + ) + + if fix_modules is not None: + for module in fix_modules: + for param in getattr(self, module).parameters(): + param.requires_grad = False + + self.connect_list = connect_list + self.n_layers = n_layers + self.dim_embd = dim_embd + self.dim_mlp = dim_embd * 2 + + self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) # type: ignore + self.feat_emb = nn.Linear(256, self.dim_embd) + + # transformer + self.ft_layers = nn.Sequential( + *[ + TransformerSALayer( + embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0 + ) + for _ in range(self.n_layers) + ] + ) + + # logits_predict head + self.idx_pred_layer = nn.Sequential( + nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False) + ) + + self.channels = { + "16": 512, + "32": 256, + "64": 256, + "128": 128, + "256": 128, + "512": 64, + } + + # after second residual block for > 16, before attn layer for ==16 + self.fuse_encoder_block = { + "512": 2, + "256": 5, + "128": 8, + "64": 11, + "32": 14, + "16": 18, + } + # after first residual block for > 16, before attn layer for ==16 + self.fuse_generator_block = { + "16": 6, + "32": 9, + "64": 12, + "128": 15, + "256": 18, + "512": 21, + } + + # fuse_convs_dict + self.fuse_convs_dict = nn.ModuleDict() + for f_size in self.connect_list: + in_ch = self.channels[f_size] + self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) + + self.load_state_dict(state_dict) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, x, weight=0.5, **kwargs): + detach_16 = True + code_only = False + adain = True + # ################### Encoder ##################### + enc_feat_dict = {} + out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] + for i, block in enumerate(self.encoder.blocks): + x = block(x) + if i in out_list: + enc_feat_dict[str(x.shape[-1])] = x.clone() + + lq_feat = x + # ################# Transformer ################### + # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) + pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) + # BCHW -> BC(HW) -> (HW)BC + feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) + query_emb = feat_emb + # Transformer encoder + for layer in self.ft_layers: + query_emb = layer(query_emb, query_pos=pos_emb) + + # output logits + logits = self.idx_pred_layer(query_emb) # (hw)bn + logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n + + if code_only: # for training stage II + # logits doesn't need softmax before cross_entropy loss + return logits, lq_feat + + # ################# Quantization ################### + # if self.training: + # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) + # # b(hw)c -> bc(hw) -> bchw + # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) + # ------------ + soft_one_hot = F.softmax(logits, dim=2) + _, top_idx = torch.topk(soft_one_hot, 1, dim=2) + quant_feat = self.quantize.get_codebook_feat( + top_idx, shape=[x.shape[0], 16, 16, 256] # type: ignore + ) + # preserve gradients + # quant_feat = lq_feat + (quant_feat - lq_feat).detach() + + if detach_16: + quant_feat = quant_feat.detach() # for training stage III + if adain: + quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) + + # ################## Generator #################### + x = quant_feat + fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] + + for i, block in enumerate(self.generator.blocks): + x = block(x) + if i in fuse_list: # fuse after i-th block + f_size = str(x.shape[-1]) + if weight > 0: + x = self.fuse_convs_dict[f_size]( + enc_feat_dict[f_size].detach(), x, weight + ) + out = x + # logits doesn't need softmax before cross_entropy loss + # return out, logits, lq_feat + return out, logits diff --git a/ldm_patched/pfn/architecture/face/fused_act.py b/ldm_patched/pfn/architecture/face/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..7ed526547b4644ac6341947a801b76d9ed798f26 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/fused_act.py @@ -0,0 +1,81 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 + +import torch +from torch import nn +from torch.autograd import Function + +fused_act_ext = None + + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused_act_ext.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + (out,) = ctx.saved_tensors + gradgrad_out = fused_act_ext.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused_act_ext.fused_bias_act( + input, bias, empty, 3, 0, negative_slope, scale + ) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + (out,) = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2**0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py b/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e820e006f52936c3399d3d37fdf571f2385dcb --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py @@ -0,0 +1,389 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn + +from .gfpganv1_arch import ResUpBlock +from .stylegan2_bilinear_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2GeneratorBilinear, +) + + +class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorBilinearSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorBilinearSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class GFPGANBilinear(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: GFPGANv1Clean. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANBilinear, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + self.min_size_restriction = 512 + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels)) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANBilinear. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/ldm_patched/pfn/architecture/face/gfpganv1_arch.py b/ldm_patched/pfn/architecture/face/gfpganv1_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..72d72fc865ec35b2ccd23f13b3d8ef0be5dbaf7a --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpganv1_arch.py @@ -0,0 +1,566 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU +from .stylegan2_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2Generator, +) + + +class StyleGAN2GeneratorSFT(StyleGAN2Generator): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ConvUpLayer(nn.Module): + """Convolutional upsampling layer. It uses bilinear upsampler + Conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + activate=True, + ): + super(ConvUpLayer, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + # self.scale is used to scale the convolution weights, which is related to the common initializations. + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + + if bias and not activate: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + # activation + if activate: + if bias: + self.activation = FusedLeakyReLU(out_channels) + else: + self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + + def forward(self, x): + # bilinear upsample + out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + # conv + out = F.conv2d( + out, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + # activation + if self.activation is not None: + out = self.activation(out) + return out + + +class ResUpBlock(nn.Module): + """Residual block with upsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels): + super(ResUpBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvUpLayer( + in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True + ) + self.skip = ConvUpLayer( + in_channels, out_channels, 1, bias=False, activate=False + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +class GFPGANv1(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANv1, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append( + ResBlock(in_channels, out_channels, resample_kernel) + ) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs + + +class FacialComponentDiscriminator(nn.Module): + """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.""" + + def __init__(self): + super(FacialComponentDiscriminator, self).__init__() + # It now uses a VGG-style architectrue with fixed model size + self.conv1 = ConvLayer( + 3, + 64, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv2 = ConvLayer( + 64, + 128, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv3 = ConvLayer( + 128, + 128, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv4 = ConvLayer( + 128, + 256, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv5 = ConvLayer( + 256, + 256, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) + + def forward(self, x, return_feats=False, **kwargs): + """Forward function for FacialComponentDiscriminator. + Args: + x (Tensor): Input images. + return_feats (bool): Whether to return intermediate features. Default: False. + """ + feat = self.conv1(x) + feat = self.conv3(self.conv2(feat)) + rlt_feats = [] + if return_feats: + rlt_feats.append(feat.clone()) + feat = self.conv5(self.conv4(feat)) + if return_feats: + rlt_feats.append(feat.clone()) + out = self.final_conv(feat) + + if return_feats: + return out, rlt_feats + else: + return out, None diff --git a/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py b/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..16470d6345f71ed1517ff26f65b9cd125d80d99e --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py @@ -0,0 +1,370 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .stylegan2_clean_arch import StyleGAN2GeneratorClean + + +class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorCSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorCSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ResBlock(nn.Module): + """Residual block with bilinear upsampling/downsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. + """ + + def __init__(self, in_channels, out_channels, mode="down"): + super(ResBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) + self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) + if mode == "down": + self.scale_factor = 0.5 + elif mode == "up": + self.scale_factor = 2 + + def forward(self, x): + out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) + # upsample/downsample + out = F.interpolate( + out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) + # skip + x = F.interpolate( + x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + skip = self.skip(x) + out = out + skip + return out + + +class GFPGANv1Clean(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + state_dict, + ): + super(GFPGANv1Clean, self).__init__() + + out_size = 512 + num_style_feat = 512 + channel_multiplier = 2 + decoder_load_path = None + fix_decoder = False + num_mlp = 8 + input_is_latent = True + different_w = True + narrow = 1 + sft_half = True + + self.model_arch = "GFPGAN" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = 3 + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 512 + + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down")) + in_channels = out_channels + + self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up")) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorCSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.condition_shift.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.load_state_dict(state_dict) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1Clean. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/ldm_patched/pfn/architecture/face/restoreformer_arch.py b/ldm_patched/pfn/architecture/face/restoreformer_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4492260291d6d74b2c0d38130f7aa8b50ba2fc11 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/restoreformer_arch.py @@ -0,0 +1,776 @@ +# pylint: skip-file +# type: ignore +"""Modified from https://github.com/wzhouxiff/RestoreFormer +""" +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = ( + torch.sum(z_flattened**2, dim=1, keepdim=True) + + torch.sum(self.embedding.weight**2, dim=1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + # could possible replace this here + # #\start... + # find closest encodings + + min_value, min_encoding_indices = torch.min(d, dim=1) + + min_encoding_indices = min_encoding_indices.unsqueeze(1) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # .........\end + + # with: + # .........\start + # min_encoding_indices = torch.argmin(d, dim=1) + # z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:, None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +# pytorch_diffusion + derived encoder decoder +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512 + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class MultiHeadAttnBlock(nn.Module): + def __init__(self, in_channels, head_size=1): + super().__init__() + self.in_channels = in_channels + self.head_size = head_size + self.att_size = in_channels // head_size + assert ( + in_channels % head_size == 0 + ), "The size of head should be divided by the number of channels." + + self.norm1 = Normalize(in_channels) + self.norm2 = Normalize(in_channels) + + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.num = 0 + + def forward(self, x, y=None): + h_ = x + h_ = self.norm1(h_) + if y is None: + y = h_ + else: + y = self.norm2(y) + + q = self.q(y) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, self.head_size, self.att_size, h * w) + q = q.permute(0, 3, 1, 2) # b, hw, head, att + + k = k.reshape(b, self.head_size, self.att_size, h * w) + k = k.permute(0, 3, 1, 2) + + v = v.reshape(b, self.head_size, self.att_size, h * w) + v = v.permute(0, 3, 1, 2) + + q = q.transpose(1, 2) + v = v.transpose(1, 2) + k = k.transpose(1, 2).transpose(2, 3) + + scale = int(self.att_size) ** (-0.5) + q.mul_(scale) + w_ = torch.matmul(q, k) + w_ = F.softmax(w_, dim=3) + + w_ = w_.matmul(v) + + w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] + w_ = w_.view(b, h, w, -1) + w_ = w_.permute(0, 3, 1, 2) + + w_ = self.proj_out(w_) + + return x + w_ + + +class MultiHeadEncoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + double_z=True, + enable_mid=True, + head_size=1, + **ignore_kwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.enable_mid = enable_mid + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + hs = {} + # timestep embedding + temb = None + + # downsampling + h = self.conv_in(x) + hs["in"] = h + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + # hs.append(h) + hs["block_" + str(i_level)] = h + h = self.down[i_level].downsample(h) + + # middle + # h = hs[-1] + if self.enable_mid: + h = self.mid.block_1(h, temb) + hs["block_" + str(i_level) + "_atten"] = h + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + hs["mid_atten"] = h + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # hs.append(h) + hs["out"] = h + + return hs + + +class MultiHeadDecoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class MultiHeadDecoderTransformer(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z, hs): + # assert z.shape[1:] == self.z_shape[1:] + # self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h, hs["mid_atten"]) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block]( + h, hs["block_" + str(i_level) + "_atten"] + ) + # hfeature = h.clone() + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class RestoreFormer(nn.Module): + def __init__( + self, + state_dict, + ): + super(RestoreFormer, self).__init__() + + n_embed = 1024 + embed_dim = 256 + ch = 64 + out_ch = 3 + ch_mult = (1, 2, 2, 4, 4, 8) + num_res_blocks = 2 + attn_resolutions = (16,) + dropout = 0.0 + in_channels = 3 + resolution = 512 + z_channels = 256 + double_z = False + enable_mid = True + fix_decoder = False + fix_codebook = True + fix_encoder = False + head_size = 8 + + self.model_arch = "RestoreFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = out_ch + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + self.encoder = MultiHeadEncoder( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + double_z=double_z, + enable_mid=enable_mid, + head_size=head_size, + ) + self.decoder = MultiHeadDecoderTransformer( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + enable_mid=enable_mid, + head_size=head_size, + ) + + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) + + self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) + + if fix_decoder: + for _, param in self.decoder.named_parameters(): + param.requires_grad = False + for _, param in self.post_quant_conv.named_parameters(): + param.requires_grad = False + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + elif fix_codebook: + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + + if fix_encoder: + for _, param in self.encoder.named_parameters(): + param.requires_grad = False + + self.load_state_dict(state_dict) + + def encode(self, x): + hs = self.encoder(x) + h = self.quant_conv(hs["out"]) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info, hs + + def decode(self, quant, hs): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant, hs) + + return dec + + def forward(self, input, **kwargs): + quant, diff, info, hs = self.encode(input) + dec = self.decode(quant, hs) + + return dec, None diff --git a/ldm_patched/pfn/architecture/face/stylegan2_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb0e9f15f706e2b9759bde4d0244d424c3ae76f --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_arch.py @@ -0,0 +1,865 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +def make_resample_kernel(k): + """Make resampling kernel for UpFirDn. + + Args: + k (list[int]): A list indicating the 1D resample kernel magnitude. + + Returns: + Tensor: 2D resampled kernel. + """ + k = torch.tensor(k, dtype=torch.float32) + if k.ndim == 1: + k = k[None, :] * k[:, None] # to 2D kernel, outer product + # normalize + k /= k.sum() + return k + + +class UpFirDnUpsample(nn.Module): + """Upsample, FIR filter, and downsample (upsampole version). + + References: + 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 + 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Upsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnUpsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) * (factor**2) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnDownsample(nn.Module): + """Upsample, FIR filter, and downsample (downsampole version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Downsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnDownsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnSmooth(nn.Module): + """Upsample, FIR filter, and downsample (smooth version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + upsample_factor (int): Upsampling scale factor. Default: 1. + downsample_factor (int): Downsampling scale factor. Default: 1. + kernel_size (int): Kernel size: Default: 1. + """ + + def __init__( + self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1 + ): + super(UpFirDnSmooth, self).__init__() + self.upsample_factor = upsample_factor + self.downsample_factor = downsample_factor + self.kernel = make_resample_kernel(resample_kernel) + if upsample_factor > 1: + self.kernel = self.kernel * (upsample_factor**2) + + if upsample_factor > 1: + pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) + self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) + elif downsample_factor > 1: + pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) + self.pad = ((pad + 1) // 2, pad // 2) + else: + raise NotImplementedError + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}" + f", downsample_factor={self.downsample_factor})" + ) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + if self.sample_mode == "upsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=2, + downsample_factor=1, + kernel_size=kernel_size, + ) + elif self.sample_mode == "downsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + elif self.sample_mode is None: + pass + else: + raise ValueError( + f"Wrong sample mode {self.sample_mode}, " + "supported ones are ['upsample', 'downsample', None]." + ) + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = x.view(1, b * c, h, w) + weight = weight.view( + b, self.out_channels, c, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + b * c, self.out_channels, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + out = self.smooth(out) + elif self.sample_mode == "downsample": + x = self.smooth(x) + x = x.view(1, b * c, *x.shape[2:4]) + out = F.conv2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + else: + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + resample_kernel=resample_kernel, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1) + ): + super(ToRGB, self).__init__() + if upsample: + self.upsample = UpFirDnUpsample(resample_kernel, factor=2) + else: + self.upsample = None + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = self.upsample(skip) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2Generator(nn.Module): + """StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. A cross production will be applied to extent 1D resample + kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + ): + super(StyleGAN2Generator, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + resample_kernel=resample_kernel, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + resample_kernel=resample_kernel, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + resample_kernel=resample_kernel, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ): + layers = [] + # downsample + if downsample: + layers.append( + UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + ) + stride = 2 + self.padding = 0 + else: + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + """ + + def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + resample_kernel=resample_kernel, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + resample_kernel=resample_kernel, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..601f8cc4b33bdbb371d710a2bb0656e8ce102e26 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py @@ -0,0 +1,709 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + interpolation_mode="bilinear", + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = F.interpolate( + x, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + elif self.sample_mode == "downsample": + x = F.interpolate( + x, + scale_factor=0.5, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode="bilinear", + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + interpolation_mode=interpolation_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear" + ): + super(ToRGB, self).__init__() + self.upsample = upsample + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorBilinear(nn.Module): + """StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + interpolation_mode="bilinear", + ): + super(StyleGAN2GeneratorBilinear, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + interpolation_mode=interpolation_mode, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + interpolation_mode=interpolation_mode, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + interpolation_mode=interpolation_mode, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + bias=True, + activate=True, + interpolation_mode="bilinear", + ): + layers = [] + self.interpolation_mode = interpolation_mode + # downsample + if downsample: + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + layers.append( + torch.nn.Upsample( + scale_factor=0.5, + mode=interpolation_mode, + align_corners=self.align_corners, + ) + ) + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + interpolation_mode=interpolation_mode, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + interpolation_mode=interpolation_mode, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..c48de9af6904b8d1891a84efa8e4d76104d5d710 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py @@ -0,0 +1,453 @@ +# pylint: skip-file +# type: ignore +import math + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn import init +from torch.nn.modules.batchnorm import _BatchNorm + + +@torch.no_grad() +def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): + """Initialize network weights. + Args: + module_list (list[nn.Module] | nn.Module): Modules to be initialized. + scale (float): Scale initialized weights, especially for residual + blocks. Default: 1. + bias_fill (float): The value to fill bias. Default: 0 + kwargs (dict): Other arguments for initialization function. + """ + if not isinstance(module_list, list): + module_list = [module_list] + for module in module_list: + for m in module.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, _BatchNorm): + init.constant_(m.weight, 1) + if m.bias is not None: + m.bias.data.fill_(bias_fill) + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + eps (float): A value added to the denominator for numerical stability. Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + # modulation inside each modulated conv + self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) + # initialization + default_init_weights( + self.modulation, + scale=1, + bias_fill=1, + a=0, + mode="fan_in", + nonlinearity="linear", + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + / math.sqrt(in_channels * kernel_size**2) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + # upsample or downsample if necessary + if self.sample_mode == "upsample": + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + elif self.sample_mode == "downsample": + x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv used in StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) + self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) * 2**0.5 # for conversion + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # add bias + out = out + self.bias + # activation + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB (image space) from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True): + super(ToRGB, self).__init__() + self.upsample = upsample + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, scale_factor=2, mode="bilinear", align_corners=False + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorClean(nn.Module): + """Clean version of StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 + ): + super(StyleGAN2GeneratorClean, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.extend( + [ + nn.Linear(num_style_feat, num_style_feat, bias=True), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ] + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + # initialization + default_init_weights( + self.style_mlp, + scale=1, + bias_fill=0, + a=0.2, + mode="fan_in", + nonlinearity="leaky_relu", + ) + + # channel list + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + ) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorClean. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None diff --git a/ldm_patched/pfn/architecture/face/upfirdn2d.py b/ldm_patched/pfn/architecture/face/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..4ea4541513f27e3c9dddcee864cfeb87efddadb7 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/upfirdn2d.py @@ -0,0 +1,194 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 + +import os + +import torch +from torch.autograd import Function +from torch.nn import functional as F + +upfirdn2d_ext = None + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_ext.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + (kernel,) = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_ext.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], + # ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + _, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_ext.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if input.device.type == "cpu": + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + else: + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/ldm_patched/pfn/architecture/timm/LICENSE b/ldm_patched/pfn/architecture/timm/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..b4e9438bd1e07e17abf58cfd86e536ec880348a3 --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/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 2019 Ross Wightman + + 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. \ No newline at end of file diff --git a/ldm_patched/pfn/architecture/timm/drop.py b/ldm_patched/pfn/architecture/timm/drop.py new file mode 100644 index 0000000000000000000000000000000000000000..14f0da914b2a198af7e6124cd90bad6adaf8a84e --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/drop.py @@ -0,0 +1,223 @@ +""" DropBlock, DropPath + +PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. + +Papers: +DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) + +Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) + +Code: +DropBlock impl inspired by two Tensorflow impl that I liked: + - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 + - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py + +Hacked together by / Copyright 2020 Ross Wightman +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def drop_block_2d( + x, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. This layer has been tested on a few training + runs with success, but needs further validation and possibly optimization for lower runtime impact. + """ + _, C, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + # seed_drop_rate, the gamma parameter + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + # Forces the block to be inside the feature map. + w_i, h_i = torch.meshgrid( + torch.arange(W).to(x.device), torch.arange(H).to(x.device) + ) + valid_block = ( + (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) + ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) + valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) + + if batchwise: + # one mask for whole batch, quite a bit faster + uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) + else: + uniform_noise = torch.rand_like(x) + block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) + block_mask = -F.max_pool2d( + -block_mask, + kernel_size=clipped_block_size, # block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = ( + torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) + if batchwise + else torch.randn_like(x) + ) + if inplace: + x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) + else: + x = x * block_mask + normal_noise * (1 - block_mask) + else: + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) + ).to(x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +def drop_block_fast_2d( + x: torch.Tensor, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid + block mask at edges. + """ + _, _, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + block_mask = torch.empty_like(x).bernoulli_(gamma) + block_mask = F.max_pool2d( + block_mask.to(x.dtype), + kernel_size=clipped_block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = torch.empty_like(x).normal_() + if inplace: + x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) + else: + x = x * (1.0 - block_mask) + normal_noise * block_mask + else: + block_mask = 1 - block_mask + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) + ).to(dtype=x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +class DropBlock2d(nn.Module): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf""" + + def __init__( + self, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, + fast: bool = True, + ): + super(DropBlock2d, self).__init__() + self.drop_prob = drop_prob + self.gamma_scale = gamma_scale + self.block_size = block_size + self.with_noise = with_noise + self.inplace = inplace + self.batchwise = batchwise + self.fast = fast # FIXME finish comparisons of fast vs not + + def forward(self, x): + if not self.training or not self.drop_prob: + return x + if self.fast: + return drop_block_fast_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + ) + else: + return drop_block_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + self.batchwise, + ) + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" diff --git a/ldm_patched/pfn/architecture/timm/helpers.py b/ldm_patched/pfn/architecture/timm/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..cdafee0709165dd992118e3b09b8d26f70ea8a2a --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/helpers.py @@ -0,0 +1,31 @@ +""" Layer/Module Helpers +Hacked together by / Copyright 2020 Ross Wightman +""" +import collections.abc +from itertools import repeat + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): + min_value = min_value or divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < round_limit * v: + new_v += divisor + return new_v diff --git a/ldm_patched/pfn/architecture/timm/weight_init.py b/ldm_patched/pfn/architecture/timm/weight_init.py new file mode 100644 index 0000000000000000000000000000000000000000..b0169774657d86c1946008e746f2f4f7e833a44c --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/weight_init.py @@ -0,0 +1,128 @@ +import math +import warnings + +import torch +from torch.nn.init import _calculate_fan_in_and_fan_out + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are + applied while sampling the normal with mean/std applied, therefore a, b args + should be adjusted to match the range of mean, std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + _no_grad_trunc_normal_(tensor, 0, 1.0, a, b) + with torch.no_grad(): + tensor.mul_(std).add_(mean) + return tensor + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom # type: ignore + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + # pylint: disable=invalid-unary-operand-type + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") diff --git a/ldm_patched/pfn/model_loading.py b/ldm_patched/pfn/model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..e000871c1bfe66a07dc13b51ad709cb0de092a41 --- /dev/null +++ b/ldm_patched/pfn/model_loading.py @@ -0,0 +1,99 @@ +import logging as logger + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR +from .types import PyTorchModel + + +class UnsupportedModel(Exception): + pass + + +def load_state_dict(state_dict) -> PyTorchModel: + logger.debug(f"Loading state dict into pytorch model arch") + + state_dict_keys = list(state_dict.keys()) + + if "params_ema" in state_dict_keys: + state_dict = state_dict["params_ema"] + elif "params-ema" in state_dict_keys: + state_dict = state_dict["params-ema"] + elif "params" in state_dict_keys: + state_dict = state_dict["params"] + + state_dict_keys = list(state_dict.keys()) + # SRVGGNet Real-ESRGAN (v2) + if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: + model = RealESRGANv2(state_dict) + # SPSR (ESRGAN with lots of extra layers) + elif "f_HR_conv1.0.weight" in state_dict: + model = SPSR(state_dict) + # Swift-SRGAN + elif ( + "model" in state_dict_keys + and "initial.cnn.depthwise.weight" in state_dict["model"].keys() + ): + model = SwiftSRGAN(state_dict) + # SwinIR, Swin2SR, HAT + elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: + if ( + "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" + in state_dict_keys + ): + model = HAT(state_dict) + elif "patch_embed.proj.weight" in state_dict_keys: + model = Swin2SR(state_dict) + else: + model = SwinIR(state_dict) + # GFPGAN + elif ( + "toRGB.0.weight" in state_dict_keys + and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys + ): + model = GFPGANv1Clean(state_dict) + # RestoreFormer + elif ( + "encoder.conv_in.weight" in state_dict_keys + and "encoder.down.0.block.0.norm1.weight" in state_dict_keys + ): + model = RestoreFormer(state_dict) + elif ( + "encoder.blocks.0.weight" in state_dict_keys + and "quantize.embedding.weight" in state_dict_keys + ): + model = CodeFormer(state_dict) + # LaMa + elif ( + "model.model.1.bn_l.running_mean" in state_dict_keys + or "generator.model.1.bn_l.running_mean" in state_dict_keys + ): + model = LaMa(state_dict) + # Omni-SR + elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: + model = OmniSR(state_dict) + # SCUNet + elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: + model = SCUNet(state_dict) + # DAT + elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: + model = DAT(state_dict) + # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 + else: + try: + model = ESRGAN(state_dict) + except: + # pylint: disable=raise-missing-from + raise UnsupportedModel + return model diff --git a/ldm_patched/pfn/types.py b/ldm_patched/pfn/types.py new file mode 100644 index 0000000000000000000000000000000000000000..193333b9e8049d9558ca2ea253d41ee44b0b294b --- /dev/null +++ b/ldm_patched/pfn/types.py @@ -0,0 +1,69 @@ +from typing import Union + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR + +PyTorchSRModels = ( + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +) +PyTorchSRModel = Union[ + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +] + + +def is_pytorch_sr_model(model: object): + return isinstance(model, PyTorchSRModels) + + +PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer) +PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer] + + +def is_pytorch_face_model(model: object): + return isinstance(model, PyTorchFaceModels) + + +PyTorchInpaintModels = (LaMa,) +PyTorchInpaintModel = Union[LaMa] + + +def is_pytorch_inpaint_model(model: object): + return isinstance(model, PyTorchInpaintModels) + + +PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels) +PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel] + + +def is_pytorch_model(model: object): + return isinstance(model, PyTorchModels) diff --git a/ldm_patched/t2ia/adapter.py b/ldm_patched/t2ia/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..e9a606b1cd67fd9a955a0ea0a86d1bd5498d85e5 --- /dev/null +++ b/ldm_patched/t2ia/adapter.py @@ -0,0 +1,293 @@ +#taken from https://github.com/TencentARC/T2I-Adapter +import torch +import torch.nn as nn +from collections import OrderedDict + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + if not self.use_conv: + padding = [x.shape[2] % 2, x.shape[3] % 2] + self.op.padding = padding + + x = self.op(x) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + # print('n_in') + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class Adapter(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): + super(Adapter, self).__init__() + self.unshuffle_amount = 8 + resblock_no_downsample = [] + resblock_downsample = [3, 2, 1] + self.xl = xl + if self.xl: + self.unshuffle_amount = 16 + resblock_no_downsample = [1] + resblock_downsample = [2] + + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + for i in range(len(channels)): + for j in range(nums_rb): + if (i in resblock_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) + elif (i in resblock_no_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + else: + self.body.append( + ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + self.body = nn.ModuleList(self.body) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + x = self.conv_in(x) + for i in range(len(self.channels)): + for j in range(self.nums_rb): + idx = i * self.nums_rb + j + x = self.body[idx](x) + if self.xl: + features.append(None) + if i == 0: + features.append(None) + features.append(None) + if i == 2: + features.append(None) + else: + features.append(None) + features.append(None) + features.append(x) + + return features + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class StyleAdapter(nn.Module): + + def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): + super().__init__() + + scale = width ** -0.5 + self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) + self.num_token = num_token + self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) + self.ln_post = LayerNorm(width) + self.ln_pre = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) + + def forward(self, x): + # x shape [N, HW+1, C] + style_embedding = self.style_embedding + torch.zeros( + (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) + x = torch.cat([x, style_embedding], dim=1) + x = self.ln_pre(x) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer_layes(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, -self.num_token:, :]) + x = x @ self.proj + + return x + + +class ResnetBlock_light(nn.Module): + def __init__(self, in_c): + super().__init__() + self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) + + def forward(self, x): + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + + return h + x + + +class extractor(nn.Module): + def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): + super().__init__() + self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) + self.body = [] + for _ in range(nums_rb): + self.body.append(ResnetBlock_light(inter_c)) + self.body = nn.Sequential(*self.body) + self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=False) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + x = self.in_conv(x) + x = self.body(x) + x = self.out_conv(x) + + return x + + +class Adapter_light(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): + super(Adapter_light, self).__init__() + self.unshuffle_amount = 8 + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + self.xl = False + + for i in range(len(channels)): + if i == 0: + self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) + else: + self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) + self.body = nn.ModuleList(self.body) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + for i in range(len(self.channels)): + x = self.body[i](x) + features.append(None) + features.append(None) + features.append(x) + + return features diff --git a/ldm_patched/taesd/taesd.py b/ldm_patched/taesd/taesd.py new file mode 100644 index 0000000000000000000000000000000000000000..0b4b885f74bd9d7f1cd2560b9f07daa579e634a3 --- /dev/null +++ b/ldm_patched/taesd/taesd.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" +import torch +import torch.nn as nn + +import ldm_patched.modules.utils +import ldm_patched.modules.ops + +def conv(n_in, n_out, **kwargs): + return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + +def Encoder(): + return nn.Sequential( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 4), + ) + +def Decoder(): + return nn.Sequential( + Clamp(), conv(4, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, encoder_path=None, decoder_path=None): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.taesd_encoder = Encoder() + self.taesd_decoder = Decoder() + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + if encoder_path is not None: + self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True)) + if decoder_path is not None: + self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True)) + + @staticmethod + def scale_latents(x): + """raw latents -> [0, 1]""" + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + def decode(self, x): + x_sample = self.taesd_decoder(x * self.vae_scale) + x_sample = x_sample.sub(0.5).mul(2) + return x_sample + + def encode(self, x): + return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale diff --git a/ldm_patched/unipc/uni_pc.py b/ldm_patched/unipc/uni_pc.py new file mode 100644 index 0000000000000000000000000000000000000000..08bf0fc9e6787aec84500b4e3d24a4c8d253b433 --- /dev/null +++ b/ldm_patched/unipc/uni_pc.py @@ -0,0 +1,894 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import torch.nn.functional as F +import math + +from tqdm.auto import trange, tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = model(x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + noise_mask=None, + masked_image=None, + noise=None, + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + self.noise_mask = noise_mask + self.masked_image = masked_image + self.noise = noise + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + if self.noise_mask is not None: + return self.model(x, t) * self.noise_mask + else: + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + if self.noise_mask is not None: + x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False + ): + # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + # t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps, disable=disable_pbar): + if self.noise_mask is not None: + x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + extra_final_step = 0 + if step_index == (steps - 1): + extra_final_step = 1 + for step in range(step_index, step_index + 1 + extra_final_step): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + if callback is not None: + callback(step_index, model_prev_list[-1], x, steps) + else: + raise NotImplementedError() + # if denoise_to_zero: + # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + +class SigmaConvert: + schedule = "" + def marginal_log_mean_coeff(self, sigma): + return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) + + def marginal_alpha(self, t): + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + +def predict_eps_sigma(model, input, sigma_in, **kwargs): + sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) + input = input * ((sigma ** 2 + 1.0) ** 0.5) + return (input - model(input, sigma_in, **kwargs)) / sigma + + +def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'): + timesteps = sigmas.clone() + if sigmas[-1] == 0: + timesteps = sigmas[:] + timesteps[-1] = 0.001 + else: + timesteps = sigmas.clone() + ns = SigmaConvert() + + if image is not None: + img = image * ns.marginal_alpha(timesteps[0]) + if max_denoise: + noise_mult = 1.0 + else: + noise_mult = ns.marginal_std(timesteps[0]) + img += noise * noise_mult + else: + img = noise + + model_type = "noise" + + model_fn = model_wrapper( + lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), + ns, + model_type=model_type, + guidance_type="uncond", + model_kwargs=extra_args, + ) + + order = min(3, len(timesteps) - 2) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) + x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + x /= ns.marginal_alpha(timesteps[-1]) + return x diff --git a/ldm_patched/utils/latent_visualization.py b/ldm_patched/utils/latent_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ad403a1d77fd1effe0ecd153d18ae3059ddae4 --- /dev/null +++ b/ldm_patched/utils/latent_visualization.py @@ -0,0 +1,97 @@ +import torch +from PIL import Image +import struct +import numpy as np +from ldm_patched.modules.args_parser import args, LatentPreviewMethod +from ldm_patched.taesd.taesd import TAESD +import ldm_patched.utils.path_utils +import ldm_patched.modules.utils + +MAX_PREVIEW_RESOLUTION = 512 + +class LatentPreviewer: + def decode_latent_to_preview(self, x0): + pass + + def decode_latent_to_preview_image(self, preview_format, x0): + preview_image = self.decode_latent_to_preview(x0) + return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) + +class TAESDPreviewerImpl(LatentPreviewer): + def __init__(self, taesd): + self.taesd = taesd + + def decode_latent_to_preview(self, x0): + x_sample = self.taesd.decode(x0[:1])[0].detach() + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + + preview_image = Image.fromarray(x_sample) + return preview_image + + +class Latent2RGBPreviewer(LatentPreviewer): + def __init__(self, latent_rgb_factors): + self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") + + def decode_latent_to_preview(self, x0): + latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors + + latents_ubyte = (((latent_image + 1) / 2) + .clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + .byte()).cpu() + + return Image.fromarray(latents_ubyte.numpy()) + + +def get_previewer(device, latent_format): + previewer = None + method = args.preview_option + if method != LatentPreviewMethod.NoPreviews: + # TODO previewer methods + taesd_decoder_path = None + if latent_format.taesd_decoder_name is not None: + taesd_decoder_path = next( + (fn for fn in ldm_patched.utils.path_utils.get_filename_list("vae_approx") + if fn.startswith(latent_format.taesd_decoder_name)), + "" + ) + taesd_decoder_path = ldm_patched.utils.path_utils.get_full_path("vae_approx", taesd_decoder_path) + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + if taesd_decoder_path: + method = LatentPreviewMethod.TAESD + + if method == LatentPreviewMethod.TAESD: + if taesd_decoder_path: + taesd = TAESD(None, taesd_decoder_path).to(device) + previewer = TAESDPreviewerImpl(taesd) + else: + print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) + + if previewer is None: + if latent_format.latent_rgb_factors is not None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) + return previewer + +def prepare_callback(model, steps, x0_output_dict=None): + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = get_previewer(model.load_device, model.model.latent_format) + + pbar = ldm_patched.modules.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + if x0_output_dict is not None: + x0_output_dict["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + return callback + diff --git a/ldm_patched/utils/path_utils.py b/ldm_patched/utils/path_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6cae149b47c5efb959cdcb815aafb28f37307b9c --- /dev/null +++ b/ldm_patched/utils/path_utils.py @@ -0,0 +1,262 @@ +import os +import time + +supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) + +folder_names_and_paths = {} + +base_path = os.getcwd() +models_dir = os.path.join(base_path, "models") +folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) +folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) + +folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) +folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) +folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions) +folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions) +folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) +folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) +folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) +folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) +folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) + +folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) +folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) + +folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) + +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) + +folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) + +folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) + +folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) + +output_directory = os.path.join(os.getcwd(), "output") +temp_directory = os.path.join(os.getcwd(), "temp") +input_directory = os.path.join(os.getcwd(), "input") +user_directory = os.path.join(os.getcwd(), "user") + +filename_list_cache = {} + +if not os.path.exists(input_directory): + try: + pass # os.makedirs(input_directory) + except: + print("Failed to create input directory") + +def set_output_directory(output_dir): + global output_directory + output_directory = output_dir + +def set_temp_directory(temp_dir): + global temp_directory + temp_directory = temp_dir + +def set_input_directory(input_dir): + global input_directory + input_directory = input_dir + +def get_output_directory(): + global output_directory + return output_directory + +def get_temp_directory(): + global temp_directory + return temp_directory + +def get_input_directory(): + global input_directory + return input_directory + + +#NOTE: used in http server so don't put folders that should not be accessed remotely +def get_directory_by_type(type_name): + if type_name == "output": + return get_output_directory() + if type_name == "temp": + return get_temp_directory() + if type_name == "input": + return get_input_directory() + return None + + +# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format +# otherwise use default_path as base_dir +def annotated_filepath(name): + if name.endswith("[output]"): + base_dir = get_output_directory() + name = name[:-9] + elif name.endswith("[input]"): + base_dir = get_input_directory() + name = name[:-8] + elif name.endswith("[temp]"): + base_dir = get_temp_directory() + name = name[:-7] + else: + return name, None + + return name, base_dir + + +def get_annotated_filepath(name, default_dir=None): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + if default_dir is not None: + base_dir = default_dir + else: + base_dir = get_input_directory() # fallback path + + return os.path.join(base_dir, name) + + +def exists_annotated_filepath(name): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + base_dir = get_input_directory() # fallback path + + filepath = os.path.join(base_dir, name) + return os.path.exists(filepath) + + +def add_model_folder_path(folder_name, full_folder_path): + global folder_names_and_paths + if folder_name in folder_names_and_paths: + folder_names_and_paths[folder_name][0].append(full_folder_path) + else: + folder_names_and_paths[folder_name] = ([full_folder_path], set()) + +def get_folder_paths(folder_name): + return folder_names_and_paths[folder_name][0][:] + +def recursive_search(directory, excluded_dir_names=None): + if not os.path.isdir(directory): + return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + + result = [] + dirs = {} + + # Attempt to add the initial directory to dirs with error handling + try: + dirs[directory] = os.path.getmtime(directory) + except FileNotFoundError: + print(f"Warning: Unable to access {directory}. Skipping this path.") + + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + + for d in subdirs: + path = os.path.join(dirpath, d) + try: + dirs[path] = os.path.getmtime(path) + except FileNotFoundError: + print(f"Warning: Unable to access {path}. Skipping this path.") + continue + return result, dirs + +def filter_files_extensions(files, extensions): + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) + + + +def get_full_path(folder_name, filename): + global folder_names_and_paths + if folder_name not in folder_names_and_paths: + return None + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + + return None + +def get_filename_list_(folder_name): + global folder_names_and_paths + output_list = set() + folders = folder_names_and_paths[folder_name] + output_folders = {} + for x in folders[0]: + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) + output_list.update(filter_files_extensions(files, folders[1])) + output_folders = {**output_folders, **folders_all} + + return (sorted(list(output_list)), output_folders, time.perf_counter()) + +def cached_filename_list_(folder_name): + global filename_list_cache + global folder_names_and_paths + if folder_name not in filename_list_cache: + return None + out = filename_list_cache[folder_name] + + for x in out[1]: + time_modified = out[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + folders = folder_names_and_paths[folder_name] + for x in folders[0]: + if os.path.isdir(x): + if x not in out[1]: + return None + + return out + +def get_filename_list(folder_name): + out = cached_filename_list_(folder_name) + if out is None: + out = get_filename_list_(folder_name) + global filename_list_cache + filename_list_cache[folder_name] = out + return list(out[0]) + +def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0): + def map_filename(filename): + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return (digits, prefix) + + def compute_vars(input, image_width, image_height): + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + return input + + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + print(err) + raise Exception(err) + + try: + counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + return full_output_folder, filename, counter, subfolder, filename_prefix diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/wd-v1-4-moat-tagger-v2.csv b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv new file mode 100644 index 0000000000000000000000000000000000000000..71796801c13109547bc017d40fc6f5b89bfd9cc0 --- /dev/null +++ b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv @@ -0,0 +1,9084 @@ +tag_id,name,category,count +9999999,general,9,807858 +9999998,sensitive,9,3771700 +9999997,questionable,9,769899 +9999996,explicit,9,560281 +470575,1girl,0,4225150 +212816,solo,0,3515897 +13197,long_hair,0,2982517 +8601,breasts,0,2323580 +469576,looking_at_viewer,0,2089971 +3389,blush,0,2040471 +1815,smile,0,1903619 +15080,short_hair,0,1568265 +11906,open_mouth,0,1565950 +16751,bangs,0,1516840 +10959,blue_eyes,0,1225129 +566835,multiple_girls,0,1120328 +429,skirt,0,1100620 +87788,blonde_hair,0,1098200 +403247,large_breasts,0,1083979 +412368,simple_background,0,1074818 +16867,brown_hair,0,1072209 +12590,shirt,0,1001030 +13200,black_hair,0,981413 +380350,hair_ornament,0,939495 +8526,red_eyes,0,897316 +1882,thighhighs,0,890813 +5735,gloves,0,886283 +383159,long_sleeves,0,883900 +540830,1boy,0,881194 +2373,hat,0,879697 +515193,white_background,0,874291 +2241,dress,0,838290 +4563,bow,0,795194 +464575,ribbon,0,793922 +9294,navel,0,786293 +375387,holding,0,732899 +1821,2girls,0,729721 +6126,animal_ears,0,722334 +4607,cleavage,0,693321 +658573,hair_between_eyes,0,692014 +376054,bare_shoulders,0,656975 +1709,twintails,0,648384 +16578,brown_eyes,0,645874 +16613,jewelry,0,644654 +667868,medium_breasts,0,642525 +12289,sitting,0,630993 +417660,very_long_hair,0,622920 +572080,closed_mouth,0,618062 +464906,underwear,0,610036 +8889,nipples,0,591774 +16509,school_uniform,0,585679 +10960,green_eyes,0,584783 +10953,blue_hair,0,564360 +15675,standing,0,551783 +15654,purple_eyes,0,536623 +466499,collarbone,0,520875 +391,panties,0,506334 +3843,jacket,0,493731 +15674,tail,0,487490 +1681,monochrome,0,478584 +444,swimsuit,0,467619 +608813,full_body,0,463008 +465619,closed_eyes,0,455512 +464561,hair_ribbon,0,449452 +89189,yellow_eyes,0,447582 +376766,white_shirt,0,435867 +547463,upper_body,0,434670 +2355,ponytail,0,431021 +11449,weapon,0,430315 +11429,pink_hair,0,427100 +16442,purple_hair,0,426385 +8101,ass,0,423113 +4334,braid,0,417832 +464559,flower,0,411874 +63,comic,0,411524 +3522,ahoge,0,408654 +16581,white_hair,0,407226 +472154,short_sleeves,0,389311 +384553,:d,0,387533 +622137,hetero,0,384576 +374844,hair_bow,0,381335 +513837,greyscale,0,377967 +16580,grey_hair,0,375103 +1300281,male_focus,0,371503 +2750,heart,0,361897 +2363,pantyhose,0,356177 +484168,sidelocks,0,354421 +6539,bikini,0,349809 +3870,thighs,0,348316 +2365,nude,0,341086 +5403,red_hair,0,338682 +390728,multicolored_hair,0,336863 +660909,cowboy_shot,0,336374 +4569,sweat,0,334084 +383282,pleated_skirt,0,332323 +2376,hairband,0,329485 +13804,earrings,0,328755 +465265,small_breasts,0,325542 +5827,boots,0,320845 +13879,outdoors,0,320641 +301022,lying,0,312125 +4352,censored,0,310189 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+1305340,morgana_(persona_5),4,694 +1533069,rhea_(fire_emblem),4,694 +1555021,sei_shounagon_(fate),4,694 +1386133,ogata_hyakunosuke,4,693 +1755030,fujimaru_ritsuka_(female)_(polar_chaldea_uniform),4,691 +1276392,otokura_yuuki,4,689 +1401541,izayoi_liko,4,689 +1459805,yukoku_kiriko,4,689 +559713,black_heart,4,687 +1346345,takimoto_hifumi,4,687 +1441569,yuri_(doki_doki_literature_club),4,686 +1644984,fine_motion_(umamusume),4,686 +1644976,admire_vega_(umamusume),4,686 +1630689,grecale_(kancolle),4,686 +1571004,shibuya_kanon,4,686 +1452677,semiramis_(fate),4,685 +1627797,wu_zetian_(fate),4,685 +1607938,getou_suguru,4,684 +376441,arle_nadja,4,683 +664614,ryuzaki_kaoru,4,683 +1797173,hyakumantenbara_salome,4,683 +1619673,puru-see_(hoshizuki_(seigetsu)),4,682 +1152270,ibuki_tsubasa,4,681 +1227600,mochizuki_anna,4,681 +1275005,ousaka_shizuku,4,681 +1782428,bb_(swimsuit_mooncancer)_(third_ascension)_(fate),4,679 +1693037,le_malin_(listless_lapin)_(azur_lane),4,679 +1768854,mysterious_heroine_x_alter_(first_ascension)_(fate),4,678 +1631348,shinshuu_maru_(kancolle),4,678 +480982,asbel_lhant,4,677 +1316228,nakagawa_natsuki,4,677 +1667164,g41_(girls'_frontline),4,677 +404920,sage_(dq3),4,676 +1331206,kawakami_mai,4,676 +1423942,eris_greyrat,4,676 +1560517,utage_(arknights),4,676 +1405059,sand_cat_(kemono_friends),4,675 +1680295,mika_(blue_archive),4,675 +664407,himekawa_yuki,4,674 +1631118,kishinami_(kancolle),4,674 +1285963,alena_(dq4),4,672 +1645008,smart_falcon_(umamusume),4,672 +497689,cheria_barnes,4,671 +1631097,kasuga_maru_(kancolle),4,671 +1432300,yatadera_narumi,4,669 +1387122,mordred_(swimsuit_rider)_(fate),4,667 +1529378,sakata_kintoki_(fate),4,666 +1408392,golden_snub-nosed_monkey_(kemono_friends),4,666 +1780213,hina_(swimsuit)_(blue_archive),4,666 +1875916,tiki_(adult)_(fire_emblem),4,665 +1386436,amami_rantarou,4,664 +548087,hasegawa_kobato,4,663 +1257848,hoshimiya_kate,4,663 +1335159,sugimoto_saichi,4,663 +596743,fukawa_touko,4,662 +1476820,yozora_mel,4,662 +1789595,saijo_juri,4,661 +1327917,yuel_(granblue_fantasy),4,660 +1460549,nekomiya_hinata,4,660 +688953,igarashi_kyoko,4,658 +1639090,maya_kai_ni_(kancolle),4,658 +1588338,tomoe_gozen_(swimsuit_saber)_(fate),4,658 +1657407,blue_poison_(shoal_beat)_(arknights),4,658 +1856336,hasumi_(gym_uniform)_(blue_archive),4,656 +1598223,kayoko_(blue_archive),4,655 +1434774,uehara_himari,4,654 +1344209,roxy_migurdia,4,653 +1837736,saren_(summer)_(princess_connect!),4,653 +1485459,jean_bart_(azur_lane),4,653 +713347,katagiri_sanae,4,652 +1440285,queen_elizabeth_(azur_lane),4,652 +1667205,9a-91_(girls'_frontline),4,651 +1479271,rengoku_kyoujurou,4,650 +1386290,keebo,4,649 +1599618,whislash_(arknights),4,646 +971934,vampy,4,645 +1639099,naganami_kai_ni_(kancolle),4,644 +1326283,jeanne_d'arc_(granblue_fantasy),4,643 +1644991,matikanefukukitaru_(umamusume),4,643 +1645010,sweep_tosho_(umamusume),4,642 +879608,toyokawa_fuka,4,641 +1387119,kiyohime_(swimsuit_lancer)_(fate),4,641 +1836895,karyl_(summer)_(princess_connect!),4,641 +1596254,razor_(genshin_impact),4,640 +1480768,siro_(dennou_shoujo_youtuber_siro),4,639 +1491126,tsuyuri_kanao,4,637 +1667169,negev_(girls'_frontline),4,636 +1440310,eldridge_(azur_lane),4,636 +1284942,midway_princess,4,635 +1420829,matsubara_kanon,4,633 +1541801,magallan_(arknights),4,633 +1782915,ijichi_nijika,4,632 +1411715,okusawa_misaki,4,628 +1875918,tiki_(young)_(fire_emblem),4,627 +1790615,spicy_nun_(diva),4,625 +1067206,hayasaka_mirei,4,623 +1781831,jeanne_d'arc_(swimsuit_archer)_(second_ascension)_(fate),4,623 +1420746,udagawa_tomoe,4,621 +1445024,shirasagi_chisato,4,621 +1678878,la_pluma_(arknights),4,619 +1265247,sato_shin,4,617 +1491428,takamiya_rion,4,612 +1623487,crypto_(apex_legends),4,612 +1533772,uzuki_sayaka,4,607 +1533771,kumada_masaru,4,600 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7eca31c7b5e571c2b1348e94ed9d69978ebd2d52 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..147f42b17b835cc839338156f99e8f971df5c1aa --- /dev/null +++ b/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45f3f82d461cd8c6109f26ec3b1da75366eda0b0 --- /dev/null +++ b/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ec8dfbfefe94ae8522c93017668fea78d580acf --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5c9b9cb29ca162ade44a7c922f59e75d7d57813 --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c --- /dev/null +++ b/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d03231f3f2c2e8ef8fbe0d781e5f3d65409ef3a --- /dev/null +++ b/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32a9471d71b828c51bcbbabfe34c5f6c8282c803 --- /dev/null +++ b/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/inpaint/put_inpaint_here b/models/inpaint/put_inpaint_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/prompt_expansion/fooocus_expansion/config.json b/models/prompt_expansion/fooocus_expansion/config.json new file mode 100644 index 0000000000000000000000000000000000000000..c1a0538454bb7abd99b40426a090f93dd47bde11 --- /dev/null +++ b/models/prompt_expansion/fooocus_expansion/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 50256, + "embd_pdrop": 0.1, + "eos_token_id": 50256, + "pad_token_id": 50256, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 12, + "n_inner": null, + "n_layer": 12, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "torch_dtype": "float32", + "transformers_version": "4.23.0.dev0", + "use_cache": true, + "vocab_size": 50257 +} diff --git a/models/prompt_expansion/fooocus_expansion/merges.txt b/models/prompt_expansion/fooocus_expansion/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956 --- /dev/null +++ b/models/prompt_expansion/fooocus_expansion/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T 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14640, + "Ġdiagnosed": 14641, + "aceutical": 14642, + "Ġshower": 14643, + "aux": 14644, + "ĠSwitch": 14645, + "ĠStrength": 14646, + "Ġjihad": 14647, + "national": 14648, + "Ġtrauma": 14649, + "ussy": 14650, + "oni": 14651, + "Ġconsolid": 14652, + "Ġcalories": 14653, + "ĠFlynn": 14654, + "agged": 14655, + "168": 14656, + "ĠPink": 14657, + "Ġfulfill": 14658, + "Ġchains": 14659, + "Ġnotably": 14660, + "ĠAV": 14661, + "Life": 14662, + "ĠChuck": 14663, + "mus": 14664, + "ĠUrban": 14665, + "ĠHend": 14666, + "Ġdeposit": 14667, + "ĠSad": 14668, + "Ġaffair": 14669, + "ORK": 14670, + "ieval": 14671, + "ĠFDA": 14672, + "Ġtrop": 14673, + "ĠOverall": 14674, + "Ġvirtue": 14675, + "Ġsatisfaction": 14676, + "aund": 14677, + "Ġlun": 14678, + "ĠSwitzerland": 14679, + "ĠOperation": 14680, + "process": 14681, + "Ġshook": 14682, + "Ġcounties": 14683, + "leased": 14684, + "ĠCharlotte": 14685, + "112": 14686, + "Ġtranscript": 14687, + "Ġredd": 14688, + "push": 14689, + "ĠHey": 14690, + "ĠAnalysis": 14691, + 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14999, + "Ġconsume": 15000, + "Based": 15001, + "download": 15002, + "init": 15003, + "Ġvoltage": 15004, + "Introdu": 15005, + "Ġcondemned": 15006, + "ĠFinance": 15007, + "respect": 15008, + "Ġexcluded": 15009, + "Ġestablishing": 15010, + "heric": 15011, + "Ġheritage": 15012, + "Ġspectacular": 15013, + "Ġunst": 15014, + "ĠSnowden": 15015, + "ĠLane": 15016, + "San": 15017, + "Ġprotections": 15018, + "struction": 15019, + "incinn": 15020, + "Ġmacro": 15021, + "Custom": 15022, + "iosity": 15023, + "Ġesp": 15024, + "Ġfunctioning": 15025, + "Ġmush": 15026, + "Ġpuzzle": 15027, + "Ġethical": 15028, + "Mal": 15029, + "Ġgoverning": 15030, + "ĠFerguson": 15031, + "Ġrestored": 15032, + "Ġstressed": 15033, + "ĠCounter": 15034, + "ĠKas": 15035, + "clip": 15036, + "ANS": 15037, + "Ġseiz": 15038, + "UK": 15039, + "byss": 15040, + "oldown": 15041, + "api": 15042, + "Ġpermanently": 15043, + "ounters": 15044, + "West": 15045, + "Through": 15046, + "Light": 15047, + "atoes": 15048, + "Ġneat": 15049, + "Ġcord": 15050, + "urer": 15051, + "Ġseverely": 15052, + "ĠAven": 15053, + "Ġinterrog": 15054, + "Ġtriple": 15055, + "Given": 15056, + "Number": 15057, + "Ġarise": 15058, + "Ġsher": 15059, + "plant": 15060, + "Ġflower": 15061, + "ĠCou": 15062, + "Ġate": 15063, + "Ġnewer": 15064, + "bul": 15065, + "Ġmeanwhile": 15066, + "ĠLair": 15067, + "Ġadjustment": 15068, + "ĠCopyright": 15069, + "Ġdivers": 15070, + "iological": 15071, + "Ġgamers": 15072, + "oat": 15073, + "Ġhistorically": 15074, + "Ġanalog": 15075, + "Ġlongtime": 15076, + "Ġprescription": 15077, + "ĠMist": 15078, + "ĠHyper": 15079, + "ĠMaine": 15080, + "ĠDeity": 15081, + "Ġmultipl": 15082, + "ĠReincarn": 15083, + "ĠHyd": 15084, + "ĠPic": 15085, + "Sil": 15086, + "rants": 15087, + "ĠCris": 15088, + ".;": 15089, + "({": 15090, + "ependence": 15091, + "Ġrecy": 15092, + "ateur": 15093, + "Ġquad": 15094, + "Ġglob": 15095, + "Ġconced": 15096, + "team": 15097, + "Ġcapitalist": 15098, + "ĠLot": 15099, + "Ġroyal": 15100, + "ĠCyber": 15101, 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"ournaments": 16950, + "Ġprophe": 16951, + "ĠThailand": 16952, + "ĠTi": 16953, + "Ġinjection": 16954, + "Ġinherit": 16955, + "ravis": 16956, + "Ġmedi": 16957, + "Ġwhoever": 16958, + "ĠDEBUG": 16959, + "GP": 16960, + "ĠHud": 16961, + "Card": 16962, + "prom": 16963, + "Ġpor": 16964, + "Ġoverhead": 16965, + "Law": 16966, + "Ġviolate": 16967, + "Ġheated": 16968, + "Ġdescriptions": 16969, + "Ġachievements": 16970, + "ĠBeer": 16971, + "ĠQuant": 16972, + "Was": 16973, + "Ġeighth": 16974, + "ĠIv": 16975, + "Ġspecialized": 16976, + "UPDATE": 16977, + "ĠDelta": 16978, + "Pop": 16979, + "Jul": 16980, + "ĠAsk": 16981, + "ophy": 16982, + "Ġnewsletters": 16983, + "ĠTool": 16984, + "Ġgard": 16985, + "ĠConfeder": 16986, + "ĠGMT": 16987, + "ĠAbbott": 16988, + "Ġimmunity": 16989, + "ĠVM": 16990, + "Islam": 16991, + "Ġimplicit": 16992, + "wd": 16993, + "Ġ1944": 16994, + "ravity": 16995, + "ometric": 16996, + "Ġsurviving": 16997, + "urai": 16998, + "ĠPrison": 16999, + "Ġrust": 17000, + "ĠSketch": 17001, + 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"Ġtablets": 17255, + "include": 17256, + "Range": 17257, + "Ġcaut": 17258, + "Ġlogs": 17259, + "Ġmounting": 17260, + "Ġunaware": 17261, + "Ġdynamics": 17262, + "ĠPalestine": 17263, + "ĠQuarter": 17264, + "ĠPurple": 17265, + "Ġma": 17266, + "ĠImport": 17267, + "Ġcollections": 17268, + "ciation": 17269, + "Ġsuccessor": 17270, + "Ġclone": 17271, + "Ġaiming": 17272, + "Ġpossessed": 17273, + "Ġsticking": 17274, + "Ġshaking": 17275, + "Ġlocate": 17276, + "ĠHockey": 17277, + "Turn": 17278, + "170": 17279, + "Ġfifteen": 17280, + "ĠHarrison": 17281, + "Ġcontinuously": 17282, + "ĠTC": 17283, + "ĠValent": 17284, + "ĠRescue": 17285, + "Ġbypass": 17286, + "amount": 17287, + "Ġmast": 17288, + "Ġprotects": 17289, + "Ġartistic": 17290, + "Ġsometime": 17291, + "Ġshoe": 17292, + "Ġshouted": 17293, + "ificant": 17294, + "etitive": 17295, + "ĠRegister": 17296, + "ĠJin": 17297, + "Ġconcentrated": 17298, + "lington": 17299, + "onies": 17300, + "Ġgenerator": 17301, + "yrim": 17302, + "ĠArmen": 17303, + "Ġclearing": 17304, + "ido": 17305, + "ĠTW": 17306, + "alph": 17307, + "Ġladies": 17308, + "Hard": 17309, + "Ġdialog": 17310, + "Ġinputs": 17311, + "æľ": 17312, + "Ġposes": 17313, + "Ġslots": 17314, + "ĠPremium": 17315, + "Ġleaks": 17316, + "Ġbosses": 17317, + "Ġ113": 17318, + "course": 17319, + "Acc": 17320, + "ĠNewton": 17321, + "ĠAustria": 17322, + "ĠMage": 17323, + "Ġteaches": 17324, + "abad": 17325, + "Ġwears": 17326, + "Ġcyl": 17327, + "Ġcurse": 17328, + "ĠSales": 17329, + "ĠWings": 17330, + "Ġpsy": 17331, + "Ġgaps": 17332, + "ĠIceland": 17333, + "ĠPinterest": 17334, + "Ġlandlord": 17335, + "Ġdefinitions": 17336, + "ĠKer": 17337, + "Ġsufficiently": 17338, + "ĠPence": 17339, + "ĠArchitect": 17340, + "Ġsurpass": 17341, + "Ġ114": 17342, + "Ġsuperhero": 17343, + "ĠDisease": 17344, + "Ġpriests": 17345, + "ĠCulture": 17346, + "Ġdefinitive": 17347, + "Ġsecretly": 17348, + "ĠDance": 17349, + "install": 17350, + "chief": 17351, + "ĠJessica": 17352, + "Would": 17353, + "Updated": 17354, + 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"redict": 17407, + "Ġreceptor": 17408, + "ĠCarlos": 17409, + "Method": 17410, + "oka": 17411, + "Ġmalicious": 17412, + "Ġstepping": 17413, + ",[": 17414, + "ĠDad": 17415, + "Ġattraction": 17416, + "ĠEffects": 17417, + "ĠPirate": 17418, + "ĠCer": 17419, + "ĠIndustry": 17420, + "ĠRud": 17421, + "Ġcharter": 17422, + "Ġdining": 17423, + "Ġinsists": 17424, + "Ġconfigure": 17425, + "Ġ(#": 17426, + "ĠSimple": 17427, + "ĠScroll": 17428, + "UTC": 17429, + "175": 17430, + "ĠKon": 17431, + "Ġmarketplace": 17432, + "ĠãĤ": 17433, + "Ġrefres": 17434, + "Ġgates": 17435, + "erred": 17436, + "ĠPod": 17437, + "Ġbehave": 17438, + "Frank": 17439, + "node": 17440, + "Ġendorsed": 17441, + "hett": 17442, + "asive": 17443, + "ĠHomeland": 17444, + "Ġrides": 17445, + "ĠLeave": 17446, + "erness": 17447, + "Ġflooding": 17448, + "AFP": 17449, + "Ġrisen": 17450, + "Ġcontinually": 17451, + "Ġunanim": 17452, + "ĠContract": 17453, + "ĠPas": 17454, + "Ġguided": 17455, + "ĠChile": 17456, + "bd": 17457, + "Ġsucc": 17458, 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18169, + "modules": 18170, + "ifter": 18171, + "Graphics": 18172, + "uler": 18173, + "Ġwholly": 18174, + "Ġsuppress": 18175, + "Ġconcealed": 18176, + "Ġhappily": 18177, + "Ġaccepts": 18178, + "ĠEnjoy": 18179, + "Ġrivers": 18180, + "ĠExcept": 18181, + "225": 18182, + "ĠNHS": 18183, + "ĠMcConnell": 18184, + "Ġpussy": 18185, + "ferred": 18186, + "utable": 18187, + "Ġattain": 18188, + "Ġ>=": 18189, + "Ġdeposits": 18190, + "rophic": 18191, + "Ġnotorious": 18192, + "ĠShaw": 18193, + "ilitation": 18194, + "Ġepidemic": 18195, + "allic": 18196, + "Ġsmallest": 18197, + "ovich": 18198, + "Ġaccessories": 18199, + "perties": 18200, + "Ġsurplus": 18201, + "ĠMech": 18202, + "Ġambig": 18203, + "ĠImmigration": 18204, + "Ġchim": 18205, + "eval": 18206, + "Ġpracticing": 18207, + "ĠMystery": 18208, + "Ġdomains": 18209, + "ĠSilicon": 18210, + "apps": 18211, + "Ġkilometers": 18212, + "ea": 18213, + "ĠSmash": 18214, + "Ġwarranty": 18215, + "Ġnost": 18216, + "sil": 18217, + "rev": 18218, + "Jon": 18219, + 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18271, + "ĠFeed": 18272, + "Until": 18273, + "Ut": 18274, + "worthy": 18275, + "RY": 18276, + "ĠHerald": 18277, + "ĠHammer": 18278, + "Ġmedal": 18279, + "ĠRivers": 18280, + "ĠHack": 18281, + "Ġclarify": 18282, + "Ġtracked": 18283, + "Ġautonomous": 18284, + "Ġtenant": 18285, + "ĠQatar": 18286, + "erie": 18287, + "Ġgrim": 18288, + "ĠMonitor": 18289, + "Ġresistant": 18290, + "ĠSpec": 18291, + "ĠWells": 18292, + "NAS": 18293, + "148": 18294, + "Ġminers": 18295, + "iotics": 18296, + "Ġmisses": 18297, + "116": 18298, + "gian": 18299, + "git": 18300, + "ĠEyes": 18301, + "pres": 18302, + "Ġgraduated": 18303, + "Ġangel": 18304, + "Ġsynchron": 18305, + "Ġefficiently": 18306, + "Ġtransmitted": 18307, + "Harry": 18308, + "Ġglobally": 18309, + "ENCE": 18310, + "ĠMontana": 18311, + "raged": 18312, + "ĠPrevention": 18313, + "Ġpiss": 18314, + "ĠLl": 18315, + "Ġshelf": 18316, + "ĠBJP": 18317, + "ĠTestament": 18318, + "ĠLate": 18319, + "iker": 18320, + "ĠHapp": 18321, + "ĠJulian": 18322, + "hall": 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18628, + "ĠIndividual": 18629, + "ĠFE": 18630, + "Ġbore": 18631, + "ĠMt": 18632, + "Ġbroadly": 18633, + "ĠOptions": 18634, + "ountain": 18635, + "ipes": 18636, + "ĠVideos": 18637, + "204": 18638, + "Ġhills": 18639, + "Ġsimulation": 18640, + "Ġdisappointment": 18641, + "itan": 18642, + "ĠLaboratory": 18643, + "Ġupward": 18644, + "Ġboundary": 18645, + "Ġdarker": 18646, + "hart": 18647, + "Ġdominance": 18648, + "Cong": 18649, + "ĠOracle": 18650, + "ĠLords": 18651, + "Ġscholarship": 18652, + "ĠVincent": 18653, + "ede": 18654, + "ĠRah": 18655, + "Ġencourages": 18656, + "rov": 18657, + "Ġquo": 18658, + "Ġpremise": 18659, + "ĠCrisis": 18660, + "ĠHolocaust": 18661, + "Ġrhythm": 18662, + "Ġmetric": 18663, + "club": 18664, + "Ġtransported": 18665, + "Ġnod": 18666, + "ĠPist": 18667, + "Ġancestors": 18668, + "ĠFreder": 18669, + "thumbnails": 18670, + "ĠCE": 18671, + "OND": 18672, + "Phil": 18673, + "venge": 18674, + "ĠProducts": 18675, + "castle": 18676, + "Ġqualifying": 18677, + "ĠKaren": 18678, 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18729, + "Ġmarking": 18730, + "Ġwizard": 18731, + "ĠNJ": 18732, + "ĠChiefs": 18733, + "Ġingredient": 18734, + "Ġdug": 18735, + "ĠShut": 18736, + "urchase": 18737, + "endor": 18738, + "Ġfarmer": 18739, + "ĠGoldman": 18740, + "129": 18741, + "155": 18742, + "Order": 18743, + "Ġlion": 18744, + "iably": 18745, + "Ġstain": 18746, + "array": 18747, + "ilitary": 18748, + "ĠFAQ": 18749, + "Ġexploded": 18750, + "ĠMcCarthy": 18751, + "ĠTweet": 18752, + "ĠGreens": 18753, + "eking": 18754, + "ln": 18755, + "ensen": 18756, + "Ġmotorcycle": 18757, + "Ġparticle": 18758, + "Ġcholesterol": 18759, + "Bron": 18760, + "Ġstair": 18761, + "Ġoxid": 18762, + "Ġdesirable": 18763, + "ibles": 18764, + "Ġtheor": 18765, + "forcing": 18766, + "Ġpromotional": 18767, + "ovo": 18768, + "boot": 18769, + "ĠBonus": 18770, + "rawling": 18771, + "Ġshortage": 18772, + "ĠPsy": 18773, + "Ġrecruited": 18774, + "Ġinfants": 18775, + "Ġtestosterone": 18776, + "Ġdeduct": 18777, + "Ġdistinctive": 18778, + "Ġfirmware": 18779, + "built": 18780, + "145": 18781, + "Ġexplored": 18782, + "Ġfactions": 18783, + "Ġvide": 18784, + "Ġtattoo": 18785, + "Ġfinancially": 18786, + "Ġfatigue": 18787, + "Ġproceeding": 18788, + "constitutional": 18789, + "Ġmiser": 18790, + "Ġchairs": 18791, + "gging": 18792, + "ipple": 18793, + "Ġdent": 18794, + "Ġdisreg": 18795, + "çĶ": 18796, + "stant": 18797, + "llo": 18798, + "bps": 18799, + "akening": 18800, + "Ġabnormal": 18801, + "ĠERA": 18802, + "士": 18803, + "ĠHBO": 18804, + "ĠMAR": 18805, + "Ġconcess": 18806, + "Ġservant": 18807, + "Ġaspir": 18808, + "lav": 18809, + "ĠPanel": 18810, + "amo": 18811, + "Ġprecip": 18812, + "Ġrecordings": 18813, + "Ġproceeded": 18814, + "Ġcolony": 18815, + "ĠTang": 18816, + "ablo": 18817, + "Ġstripped": 18818, + "Left": 18819, + "too": 18820, + "Ġpotatoes": 18821, + "Ġfinest": 18822, + "%).": 18823, + "Ġcrap": 18824, + "ĠZach": 18825, + "abases": 18826, + "ĠGoth": 18827, + "Ġbillionaire": 18828, + "wolf": 18829, + "Ġsanction": 18830, + "SK": 18831, + "Ġlogged": 18832, + "Po": 18833, + "eyed": 18834, + "unal": 18835, + "Ġcricket": 18836, + "Ġarmies": 18837, + "Ġuncovered": 18838, + "Cloud": 18839, + "ón": 18840, + "Ġrebounds": 18841, + "Ġmes": 18842, + "Oper": 18843, + "Pac": 18844, + "Ġnationally": 18845, + "Ġinserted": 18846, + "pict": 18847, + "Ġgovernance": 18848, + "и": 18849, + "Ġprivileges": 18850, + "GET": 18851, + "Ġfavorites": 18852, + "imity": 18853, + "Ġlover": 18854, + "them": 18855, + "empl": 18856, + "Ġgorgeous": 18857, + "Ann": 18858, + "Ġslipped": 18859, + "Ġveto": 18860, + "Bob": 18861, + "Ġslim": 18862, + "ucc": 18863, + "ĠFame": 18864, + "uddenly": 18865, + "Ġdenies": 18866, + "ĠMaur": 18867, + "Ġdistances": 18868, + "Ġwanna": 18869, + "tar": 18870, + "ĠSER": 18871, + "ĠâĪ": 18872, + "Ġlemon": 18873, + "athetic": 18874, + "Ġliteral": 18875, + "Ġdistinguished": 18876, + "Ġanswering": 18877, + "GI": 18878, + "Ġreligions": 18879, + "ĠPhilos": 18880, + "ĠLay": 18881, + "Ġcompos": 18882, + "irements": 18883, + "ĠKos": 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"ĠWalt": 18935, + "Ġcrews": 18936, + "itism": 18937, + "301": 18938, + "Ġsharply": 18939, + "umed": 18940, + "Ġredirect": 18941, + "rators": 18942, + "Inf": 18943, + "ĠWeapons": 18944, + "Ġteasp": 18945, + "1999": 18946, + "Live": 18947, + "ĠEspecially": 18948, + "ĠSter": 18949, + "ĠVeterans": 18950, + "Ġintro": 18951, + "otherapy": 18952, + "Ġmalware": 18953, + "Ġbreeding": 18954, + "Ġmolecular": 18955, + "ĠRoute": 18956, + "ĠComment": 18957, + "ochem": 18958, + "Ġain": 18959, + "Season": 18960, + "Ġlinebacker": 18961, + "Ä«": 18962, + "ĠEconomics": 18963, + "esar": 18964, + "ĠLives": 18965, + "ĠEmma": 18966, + "Ġkin": 18967, + "ĠTerrit": 18968, + "Ġplanted": 18969, + "oton": 18970, + "ĠButter": 18971, + "ĠSpons": 18972, + "PER": 18973, + "Ġdungeon": 18974, + "Ġsymbolic": 18975, + "Ġfilmed": 18976, + "Ġdiets": 18977, + "Ġconcludes": 18978, + "Ġcertainty": 18979, + "ĠFormat": 18980, + "Ġstrangers": 18981, + "format": 18982, + "ĠPhase": 18983, + "Ġcopied": 18984, + "Ġmetres": 18985, + 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21794, + "ĠSirius": 21795, + "ĠGross": 21796, + "activity": 21797, + "ĠIvan": 21798, + "Ġfurious": 21799, + "BSD": 21800, + "ĠPrevious": 21801, + "Ġresponsive": 21802, + "Ġcharitable": 21803, + "Ġleaning": 21804, + "ĠPew": 21805, + "Ġviolates": 21806, + "\\\\\\\\\\\\\\\\": 21807, + "ĠComing": 21808, + "wire": 21809, + "Ġpoet": 21810, + "Ġresolutions": 21811, + "command": 21812, + "ĠPortuguese": 21813, + "Ġnickname": 21814, + "Ġdeaf": 21815, + "February": 21816, + "Ġrecognise": 21817, + "Ġentirety": 21818, + "Ġseasonal": 21819, + "placed": 21820, + "ĠTelegraph": 21821, + "Ġmicrophone": 21822, + "ouring": 21823, + "Ġgrains": 21824, + "Ġgoverned": 21825, + "Ġpostp": 21826, + "ĠWaters": 21827, + "inement": 21828, + "Ġundocumented": 21829, + "ĠComcast": 21830, + "Ġfox": 21831, + "Ġassaults": 21832, + "reon": 21833, + "many": 21834, + "ĠJenkins": 21835, + "ĠAnyway": 21836, + "Ġassessments": 21837, + "Ġdowns": 21838, + "ĠMouse": 21839, + "Ġsuperb": 21840, + "kt": 21841, + "ĠDow": 21842, + "Ġtaxation": 21843, + "401": 21844, + "Ġsmiles": 21845, + "Ġundertaken": 21846, + "Ġexh": 21847, + "Ġenthusiastic": 21848, + "Ġtwent": 21849, + "Ġgovernmental": 21850, + "Ġautonomy": 21851, + "ĠTechnologies": 21852, + "ĠChain": 21853, + "Ġprevalent": 21854, + "fb": 21855, + "Ġnicotine": 21856, + "ogram": 21857, + "job": 21858, + "Ġawaiting": 21859, + "ĠMenu": 21860, + "Ġdeputies": 21861, + "kov": 21862, + "ishops": 21863, + "Button": 21864, + "ĠShanghai": 21865, + "Ġdiesel": 21866, + "ĠDuck": 21867, + "Ryan": 21868, + "ĠPCs": 21869, + "NF": 21870, + "jury": 21871, + "ente": 21872, + "Ġinaccurate": 21873, + "eddy": 21874, + "Whatever": 21875, + "Ġshowc": 21876, + "ĠNad": 21877, + "odus": 21878, + "etr": 21879, + "Ġplaintiffs": 21880, + "ĠWOR": 21881, + "ĠAssange": 21882, + "Ġprivat": 21883, + "Ġpremiums": 21884, + "Ġtam": 21885, + "URL": 21886, + "Ġelites": 21887, + "ĠRanger": 21888, + "ottenham": 21889, + "ĠHoff": 21890, + "ĠAthens": 21891, + "Ġdefinite": 21892, + "Ġsighed": 21893, + "Ġevenly": 21894, + "211": 21895, + "ĠAmber": 21896, + "akia": 21897, + "Ġmailing": 21898, + "Ġcrashing": 21899, + "ĠConfederate": 21900, + "rugged": 21901, + "Wal": 21902, + "ĠDepths": 21903, + "Ġjuvenile": 21904, + "Ġreactor": 21905, + "Introduction": 21906, + "ĠDeluxe": 21907, + "1995": 21908, + "ĠSanchez": 21909, + "ĠMead": 21910, + "ivable": 21911, + ":-": 21912, + "ĠPlanning": 21913, + "ĠTrap": 21914, + "quin": 21915, + "ĠProtect": 21916, + "vered": 21917, + "Information": 21918, + "Ġkidney": 21919, + "innamon": 21920, + "las": 21921, + "Ġpolicing": 21922, + "Ġtolerate": 21923, + "ĠQi": 21924, + "Ġbiased": 21925, + "Fort": 21926, + "ĠKi": 21927, + "save": 21928, + "Ġprivileged": 21929, + "Ġbeasts": 21930, + "ĠGlas": 21931, + "ĠCinem": 21932, + "Ġcomeback": 21933, + "Sunday": 21934, + "Ġextinction": 21935, + "hops": 21936, + "Ġtransmit": 21937, + "Ġdoubles": 21938, + "ĠFlat": 21939, + "167": 21940, + "Ġdisputed": 21941, + "Ġinjustice": 21942, + "foo": 21943, + "Vict": 21944, + "roleum": 21945, + "ĠJulie": 21946, + "Context": 21947, + "ĠRarity": 21948, + "issue": 21949, + "Component": 21950, + "Ġcounseling": 21951, + "anne": 21952, + "dark": 21953, + "Ġobjections": 21954, + "uilt": 21955, + "Ġgast": 21956, + "Ġplac": 21957, + "Ġunused": 21958, + "ãĥĩ": 21959, + "ĠTrial": 21960, + "ĠJas": 21961, + "hedral": 21962, + "obb": 21963, + "Ġtemporal": 21964, + "ĠPRO": 21965, + "ĠNW": 21966, + "ĠAnniversary": 21967, + "Large": 21968, + "Ġtherm": 21969, + "Ġdavid": 21970, + "Ġsystemic": 21971, + "ĠShir": 21972, + "mut": 21973, + "ĠNept": 21974, + "address": 21975, + "Ġscanning": 21976, + "Ġunderstandable": 21977, + "Ġcanvas": 21978, + "Cat": 21979, + "ĠZoo": 21980, + "Ġangels": 21981, + "LO": 21982, + "ĠStatement": 21983, + "ĠSig": 21984, + "ovable": 21985, + "ĠAway": 21986, + "sharing": 21987, + "ocrats": 21988, + "stated": 21989, + "Ġweighing": 21990, + "Nor": 21991, + "wild": 21992, + "Bey": 21993, + "Ġastonishing": 21994, + "ĠReynolds": 21995, + "Ġopener": 21996, + "Ġtrainer": 21997, + "Ġsurgical": 21998, + "pn": 21999, + "Ġadjusting": 22000, + "wheel": 22001, + "Ġfrown": 22002, + "ervative": 22003, + "Ġsuspend": 22004, + "Within": 22005, + "tein": 22006, + "Ġobstacle": 22007, + "Ġliberties": 22008, + "ymes": 22009, + "Ġuranium": 22010, + "ansom": 22011, + "anol": 22012, + "uba": 22013, + "ĠLoss": 22014, + "Ġarous": 22015, + "ĠHenderson": 22016, + "Wow": 22017, + "spl": 22018, + "cur": 22019, + "ĠÂŃ": 22020, + "Ġtheirs": 22021, + "Damage": 22022, + "Ġdownloading": 22023, + "Ġdiscern": 22024, + "ĠSto": 22025, + "ĠFla": 22026, + "Ġhath": 22027, + "ĠAj": 22028, + "Ġunpleasant": 22029, + "European": 22030, + "expensive": 22031, + "Ġscreenshot": 22032, + "ĠUV": 22033, + "Ġallied": 22034, + "ĠPersian": 22035, + "Ġmonopoly": 22036, + "Ġatom": 22037, + "ĠRedskins": 22038, + "\"><": 22039, + "Ġcancell": 22040, + "Ġcinema": 22041, + "131": 22042, + "fair": 22043, + "ĠAlfred": 22044, + "Ġduck": 22045, + "args": 22046, + "223": 22047, + "ĠISI": 22048, + 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"Ġsailors": 29996, + "oused": 29997, + "NASA": 29998, + "ĠProof": 29999, + "ĠMineral": 30000, + "token": 30001, + "ĠFD": 30002, + "Rew": 30003, + "Ġell": 30004, + "630": 30005, + "Ġchancellor": 30006, + "ĠGos": 30007, + "Ġamounted": 30008, + "ĠRecre": 30009, + "omez": 30010, + "ĠOptim": 30011, + "ĠOlive": 30012, + "Ġtracker": 30013, + "owler": 30014, + "ĠUnique": 30015, + "Root": 30016, + "Ġmaritime": 30017, + "ĠQuran": 30018, + "ĠAdapt": 30019, + "Ġecosystems": 30020, + "ĠRepeat": 30021, + "ĠSoy": 30022, + "ĠIMP": 30023, + "Ġgraduating": 30024, + "andem": 30025, + "Pur": 30026, + "ĠReset": 30027, + "ĠTrick": 30028, + "ĠPhilly": 30029, + "ĠTue": 30030, + "ĠMalaysian": 30031, + "Ġclimax": 30032, + "Ġbury": 30033, + "Ġconspic": 30034, + "ĠSouthampton": 30035, + "ĠFlowers": 30036, + "Ġescorted": 30037, + "ĠEducational": 30038, + "ĠIRC": 30039, + "Ġbrutally": 30040, + "eating": 30041, + "Ġpillar": 30042, + "ĠSang": 30043, + "ĠJude": 30044, + "arling": 30045, + "ĠAmnesty": 30046, + 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"Obs": 31310, + "ĠDebbie": 31311, + "Ġgradient": 31312, + "ĠPlaint": 31313, + "ĠCanal": 31314, + "ARCH": 31315, + "Ġexpansive": 31316, + "Ġfung": 31317, + "Ġbounced": 31318, + "Und": 31319, + "Ġprecautions": 31320, + "Ġclarification": 31321, + "Ġdagger": 31322, + "Ġgrips": 31323, + "Ġµ": 31324, + "ĠRivera": 31325, + "ĠUndead": 31326, + "isites": 31327, + "ĠFIRST": 31328, + "ño": 31329, + "audi": 31330, + "Ġhostages": 31331, + "Ġcompliant": 31332, + "Ġalumni": 31333, + "Seven": 31334, + "Ġcybersecurity": 31335, + "either": 31336, + "Collect": 31337, + "Ġinvariably": 31338, + "ĠSoci": 31339, + "Ġlawmaker": 31340, + "Ġale": 31341, + "ĠPersonally": 31342, + "Nazi": 31343, + "Ġcustomization": 31344, + "ĠProc": 31345, + "ĠSaskatchewan": 31346, + "eaturing": 31347, + "Ġspared": 31348, + "Ġdiscontinued": 31349, + "Ġcomputational": 31350, + "ĠMotorola": 31351, + "Ġsupremacist": 31352, + "governmental": 31353, + "Ġparadise": 31354, + "ĠDowning": 31355, + "ĠNikon": 31356, + "Ġcatalyst": 31357, + "berra": 31358, + "Toronto": 31359, + "875": 31360, + "beta": 31361, + "ĠMacron": 31362, + "Ġunrealistic": 31363, + "vector": 31364, + "ĠVehicles": 31365, + "itiveness": 31366, + "ĠRV": 31367, + "ĠColbert": 31368, + "sin": 31369, + "oji": 31370, + "entin": 31371, + "ĠKrish": 31372, + "hello": 31373, + "ffield": 31374, + "oky": 31375, + "ĠTate": 31376, + "Ġmaple": 31377, + "Ġaids": 31378, + "chemical": 31379, + "334": 31380, + "nuts": 31381, + "ĠWarp": 31382, + "Ġxx": 31383, + "ĠRobb": 31384, + "umerous": 31385, + "_-_": 31386, + "ftime": 31387, + "ĠVW": 31388, + "Ġwinger": 31389, + "ĠDome": 31390, + "tools": 31391, + "ĠPV": 31392, + "ĠGeorgetown": 31393, + "Ġgeared": 31394, + "Ġjihadists": 31395, + "Ġcp": 31396, + "Ġsteroids": 31397, + "Mother": 31398, + "clerosis": 31399, + "ĠDRM": 31400, + "nesia": 31401, + "Ġlinger": 31402, + "Ġimmersive": 31403, + "ĠCOUN": 31404, + "Ġoutweigh": 31405, + "ensual": 31406, + "Band": 31407, + "Ġtransforms": 31408, + "matched": 31409, + "psons": 31410, + "ĠJudicial": 31411, + "factor": 31412, + "Ġreferral": 31413, + "Ġoddly": 31414, + "ĠWenger": 31415, + "Bring": 31416, + "ĠBows": 31417, + "602": 31418, + "ICLE": 31419, + "Ġlions": 31420, + "ĠAcademic": 31421, + "ĠThorn": 31422, + "ĠRaider": 31423, + "kefeller": 31424, + "Storage": 31425, + "Lower": 31426, + "ĠOrt": 31427, + "ĠEquality": 31428, + "ALT": 31429, + "ĠSOC": 31430, + "Types": 31431, + "Ġlyn": 31432, + "ĠAsset": 31433, + "coat": 31434, + "TPP": 31435, + "CVE": 31436, + "ĠPioneer": 31437, + "application": 31438, + "Modern": 31439, + "ĠHK": 31440, + "Environment": 31441, + "Alright": 31442, + "Rain": 31443, + "IPP": 31444, + "ĠShiite": 31445, + "Ġmound": 31446, + "ĠAbilities": 31447, + "condition": 31448, + "Staff": 31449, + "Ġcompetence": 31450, + "ĠMoor": 31451, + "ĠDiablo": 31452, + "Ġwithheld": 31453, + "Ġostensibly": 31454, + "ĠBrom": 31455, + "Ġmsg": 31456, + "Ġdenomin": 31457, + "ĠReferences": 31458, + "ĠFP": 31459, + "Ġplunged": 31460, + "Ġpamph": 31461, + "moving": 31462, + "central": 31463, + "Ġdownright": 31464, + "Ġfading": 31465, + "Tal": 31466, + "Typ": 31467, + "ĠThy": 31468, + "ukes": 31469, + "ithe": 31470, + "Ġove": 31471, + "Ġbattled": 31472, + "Ġseafood": 31473, + "Ġfigur": 31474, + "ĠRD": 31475, + "crop": 31476, + "Ġsquads": 31477, + "{\\": 31478, + "à¹": 31479, + "ĠEh": 31480, + "Ġinterviewing": 31481, + "ĠQin": 31482, + "Ġaspiring": 31483, + "PLIC": 31484, + "Ġclauses": 31485, + "ĠGast": 31486, + "ĠNir": 31487, + "Ġluggage": 31488, + "Ġhose": 31489, + "Ġsystemd": 31490, + "Ġdescending": 31491, + "ĠRevised": 31492, + "ĠRails": 31493, + "align": 31494, + "709": 31495, + "337": 31496, + "Ġfug": 31497, + "charging": 31498, + "tags": 31499, + "Ġuter": 31500, + "kish": 31501, + "WARNING": 31502, + "490": 31503, + "profits": 31504, + "Ġvoyage": 31505, + "Ġace": 31506, + "ĠVanguard": 31507, + "ĠTanks": 31508, + "ĠMuk": 31509, + "Ġ226": 31510, + "Safe": 31511, + "Armor": 31512, + "Ġvolcanic": 31513, + "Ġwomb": 31514, + "ĠMIL": 31515, + "Ġbeginner": 31516, + "ĠRecogn": 31517, + "ĠAAP": 31518, + "PLAY": 31519, + ")!": 31520, + "Ġdetecting": 31521, + "cn": 31522, + "Ġbreaches": 31523, + "Basically": 31524, + "ĠPag": 31525, + "ĠMunicipal": 31526, + "ĠIndie": 31527, + "ĠLaf": 31528, + "ĠDisable": 31529, + "ĠOlson": 31530, + "Ġrestrained": 31531, + "Ġrulings": 31532, + "Ġhumane": 31533, + "events": 31534, + "ĠCinema": 31535, + "displayText": 31536, + "ĠHatch": 31537, + "actionDate": 31538, + "onnaissance": 31539, + "Ġassaulting": 31540, + "ĠLug": 31541, + "CHAT": 31542, + "Ġvigorous": 31543, + "ĠPerse": 31544, + "Ġintolerance": 31545, + "ĠSnapchat": 31546, + "ĠSharks": 31547, + "Ġdummy": 31548, + "ĠDiagn": 31549, + "ĠGuitar": 31550, + "imeters": 31551, + "403": 31552, + "REG": 31553, + "Ax": 31554, + "Ġseparates": 31555, + "ĠMahm": 31556, + "Ġtv": 31557, + "jah": 31558, + "OOL": 31559, + "Circ": 31560, + "ĠWindsor": 31561, + "ussian": 31562, + "Ġintuition": 31563, + "Ġdisdain": 31564, + "ĠDonovan": 31565, + "Ġ221": 31566, + "Emb": 31567, + "Ġcondemning": 31568, + "Ġgenerosity": 31569, + "zzy": 31570, + "Ġpanties": 31571, + "ĠPrevent": 31572, + "ActionCode": 31573, + "ANA": 31574, + "342": 31575, + "externalActionCode": 31576, + "Ġspecifying": 31577, + "Ġcrystall": 31578, + "Jere": 31579, + "Ġrupt": 31580, + "ĠApprentice": 31581, + "Ġprofiling": 31582, + "к": 31583, + "Strike": 31584, + "Ġsideline": 31585, + "Ġobligated": 31586, + "Ġoccult": 31587, + "Ġbureaucratic": 31588, + "antically": 31589, + "rupted": 31590, + "negative": 31591, + "ĠEthiopia": 31592, + "ĠCivic": 31593, + "Ġinsiders": 31594, + "eligible": 31595, + "ĠTVs": 31596, + "ĠBAR": 31597, + "ĠTI": 31598, + "iologist": 31599, + "ĠAIR": 31600, + "Ġsubstituted": 31601, + "Arab": 31602, + "ĠSaul": 31603, + "ĠYog": 31604, + "prem": 31605, + "Ġbuilders": 31606, + "Ġstationary": 31607, + "Ġdoubtful": 31608, + "Ġvigorously": 31609, + "Ġthrilling": 31610, + "Physical": 31611, + "ĠCarey": 31612, + "ĠHydra": 31613, + "geoning": 31614, + "ĠSly": 31615, + "yton": 31616, + "Ġborrowers": 31617, + "ĠParkinson": 31618, + "Ġë": 31619, + "ĠJamaica": 31620, + "Ġsatir": 31621, + "Ġinsurgents": 31622, + "ĠFirm": 31623, + "Ġisot": 31624, + "ĠKarn": 31625, + "ourning": 31626, + "akens": 31627, + "docs": 31628, + "little": 31629, + "ĠMonaco": 31630, + "CLASS": 31631, + "Turkey": 31632, + "Ly": 31633, + "ĠConan": 31634, + "assic": 31635, + "Ġstarred": 31636, + "ĠPacers": 31637, + "eties": 31638, + "Ġtipping": 31639, + "Moon": 31640, + "ĠRw": 31641, + "same": 31642, + "Ġcavity": 31643, + "Ġgoof": 31644, + "ĠZo": 31645, + "Shock": 31646, + "ummer": 31647, + "Ġemphasizes": 31648, + "Ġregrett": 31649, + "Ġnovelty": 31650, + "Ġenvy": 31651, + "ĠPassive": 31652, + "rw": 31653, + "505": 31654, + "Ġindifferent": 31655, + "ĠRica": 31656, + "ĠHimself": 31657, + "ĠFreddie": 31658, + "Ġadip": 31659, + "ä¸Ģ": 31660, + "Ġbreakout": 31661, + "Ġhurried": 31662, + "ĠHuang": 31663, + "ĠDisk": 31664, + "Ġroaming": 31665, + "?????-?????-": 31666, + "UV": 31667, + "ĠRicky": 31668, + "ĠSigma": 31669, + "Ġmarginalized": 31670, + "Ġedits": 31671, + "Ġ304": 31672, + "memory": 31673, + "Ġspecimen": 31674, + "293": 31675, + "ãģ¯": 31676, + "Ġvertically": 31677, + "Ġaudition": 31678, + "ĠHeck": 31679, + "Ġcaster": 31680, + "ĠHoldings": 31681, + "adal": 31682, + "ĠCron": 31683, + "ĠLiam": 31684, + "Ġdeflect": 31685, + "Pick": 31686, + "ĠDebug": 31687, + "REF": 31688, + "Ġversatility": 31689, + "othes": 31690, + "classified": 31691, + "ĠMahar": 31692, + "ĠHort": 31693, + "Counter": 31694, + "stasy": 31695, + "noticed": 31696, + "331": 31697, + "ĠShim": 31698, + "fuck": 31699, + "ĠBie": 31700, + "Ġairing": 31701, + "ĠProtein": 31702, + "ĠHolding": 31703, + "Ġspectators": 31704, + "iliated": 31705, + "ĠThatcher": 31706, + "nosis": 31707, + "ãĥ¼ãĥ³": 31708, + "Tele": 31709, + "Boston": 31710, + "ĠTempl": 31711, + "stay": 31712, + "Ġdeclarations": 31713, + "479": 31714, + "Volume": 31715, + "ĠDesigner": 31716, + "ĠOverwatch": 31717, + "idae": 31718, + "Ġonwards": 31719, + "Ġnets": 31720, + "ĠManila": 31721, + "particularly": 31722, + "Ġpolitic": 31723, + "oother": 31724, + "Ġportraits": 31725, + "Ġpavement": 31726, + "cffff": 31727, + "Ġsaints": 31728, + "Ġbeginners": 31729, + "ESPN": 31730, + "Ġshortcomings": 31731, + "âķIJâķIJ": 31732, + "Ġcomet": 31733, + "ĠOrganic": 31734, + "quel": 31735, + "Ġhospitalized": 31736, + "Break": 31737, + "Ġpeel": 31738, + "dylib": 31739, + "aspx": 31740, + "urances": 31741, + "ĠTIM": 31742, + "Pg": 31743, + "Ġreadable": 31744, + "ĠMalik": 31745, + "Ġmuzzle": 31746, + "Ġbenchmarks": 31747, + "dal": 31748, + "ĠVacc": 31749, + "ĠHicks": 31750, + "609": 31751, + "ĠBiblical": 31752, + "heng": 31753, + "Ġoverload": 31754, + "ĠCivilization": 31755, + "Ġimmoral": 31756, + "Ġfries": 31757, + "ãĤĴ": 31758, + "Ġreproduced": 31759, + "Ġformulation": 31760, + "jug": 31761, + "irez": 31762, + "gear": 31763, + "Ġcoached": 31764, + "MpServer": 31765, + "ĠSJ": 31766, + "ĠKw": 31767, + "Init": 31768, + "deal": 31769, + "ĠOro": 31770, + "ĠLoki": 31771, + "ĠSongs": 31772, + "Ġ232": 31773, + "ĠLouise": 31774, + "asionally": 31775, + "Ġuncond": 31776, + "ollywood": 31777, + "Ġprogressives": 31778, + "ĠEnough": 31779, + "ĠDoe": 31780, + "Ġwreckage": 31781, + "Ġbrushed": 31782, + "ĠBaseType": 31783, + "Ġzoning": 31784, + "ishable": 31785, + "hetically": 31786, + "ĠCaucus": 31787, + "ĠHue": 31788, + "Ġkarma": 31789, + "ĠSporting": 31790, + "Ġtrader": 31791, + "Ġseeming": 31792, + "ĠCapture": 31793, + "430": 31794, + "bish": 31795, + "Ġtunes": 31796, + "Ġindoors": 31797, + "ĠSphere": 31798, + "ĠDancing": 31799, + "TERN": 31800, + "Ġnob": 31801, + "ĠGST": 31802, + "maps": 31803, + "Ġpeppers": 31804, + "Fit": 31805, + "Ġoversees": 31806, + "ĠRabbi": 31807, + "ĠRuler": 31808, + "vertising": 31809, + "office": 31810, + "xxx": 31811, + "Ġraft": 31812, + "Changed": 31813, + "Ġtextbooks": 31814, + "Links": 31815, + "ĠOmn": 31816, + "ãĢij": 31817, + "Ġinconvenience": 31818, + "ĠDonetsk": 31819, + "=~": 31820, + "Ġimplicitly": 31821, + "Ġboosts": 31822, + "ĠBones": 31823, + "ĠBoom": 31824, + "Courtesy": 31825, + "Ġsensational": 31826, + "ANY": 31827, + "Ġgreedy": 31828, + "eden": 31829, + "Ġinexper": 31830, + "ĠLer": 31831, + "ĠVale": 31832, + "Ġtighten": 31833, + "ĠEAR": 31834, + "ĠNum": 31835, + "Ġancestor": 31836, + "Sent": 31837, + "ĠHorde": 31838, + "urgical": 31839, + "allah": 31840, + "Ġsap": 31841, + "amba": 31842, + "ĠSpread": 31843, + "twitch": 31844, + "Ġgrandson": 31845, + "Ġfracture": 31846, + "Ġmoderator": 31847, + "ĠSeventh": 31848, + "ĠReverse": 31849, + "Ġestimation": 31850, + "Choose": 31851, + "Ġparach": 31852, + "Ġbarric": 31853, + "ãĢIJ": 31854, + "Ġcompass": 31855, + "Ġallergic": 31856, + "âĢķ": 31857, + "OTHER": 31858, + "errilla": 31859, + "Ġwagon": 31860, + "Ġzinc": 31861, + "Ġrubbed": 31862, + "ĠFuller": 31863, + "ĠLuxembourg": 31864, + "ĠHoover": 31865, + "Ġliar": 31866, + "ĠEvening": 31867, + "ĠCobb": 31868, + "esteem": 31869, + "Ġselector": 31870, + "ĠBrawl": 31871, + "isance": 31872, + "ĠEk": 31873, + "Ġtroop": 31874, + "Ġguts": 31875, + "ĠAppeal": 31876, + "ĠTibetan": 31877, + "Ġroutines": 31878, + "ĠMent": 31879, + "Ġsummarized": 31880, + "steamapps": 31881, + "Ġtranqu": 31882, + "Ġ1929": 31883, + "oran": 31884, + "ĠAuthent": 31885, + "Ġgmaxwell": 31886, + "Ġapprehens": 31887, + "Ġpoems": 31888, + "Ġsausage": 31889, + "ĠWebster": 31890, + "urus": 31891, + "Ġthemed": 31892, + "Ġlounge": 31893, + "Ġcharger": 31894, + "Spoiler": 31895, + "Ġspilled": 31896, + "hog": 31897, + "ĠSunder": 31898, + "ĠAin": 31899, + "ĠAngry": 31900, + "Ġdisqual": 31901, + "ĠFrequency": 31902, + "ĠEthernet": 31903, + "Ġhelper": 31904, + "Percent": 31905, + "Ġhorrifying": 31906, + "Ġail": 31907, + "ĠAllan": 31908, + "EEE": 31909, + "ĠCrossing": 31910, + "449": 31911, + "Ġholog": 31912, + "ĠPuzzles": 31913, + "ĠGoes": 31914, + "erenn": 31915, + "604": 31916, + "ãģı": 31917, + "ĠRafael": 31918, + "Ġatten": 31919, + "ĠEmanuel": 31920, + "Ġupro": 31921, + "ĠSusp": 31922, + "Psych": 31923, + "ĠTrainer": 31924, + "ĠNES": 31925, + "ĠHunts": 31926, + "becue": 31927, + "Ġcounselor": 31928, + "Rule": 31929, + "Ġtoxins": 31930, + "Ġbanners": 31931, + "rifice": 31932, + "Ġgreeting": 31933, + "Ġfrenzy": 31934, + "Ġallocate": 31935, + "Ġ*)": 31936, + "expr": 31937, + "503": 31938, + "ĠChick": 31939, + "ĠTorn": 31940, + "Ġconsolidation": 31941, + "ĠFletcher": 31942, + "switch": 31943, + "frac": 31944, + "clips": 31945, + "ĠMcKin": 31946, + "ĠLunar": 31947, + "Month": 31948, + "ITCH": 31949, + "Ġscholarly": 31950, + "raped": 31951, + "398": 31952, + "Ġ1910": 31953, + "Ġegreg": 31954, + "Ġinsecure": 31955, + "Ġvictorious": 31956, + "cffffcc": 31957, + "Ġsingled": 31958, + "Ġelves": 31959, + "ĠWond": 31960, + "burst": 31961, + "Ġcamoufl": 31962, + "ĠBLACK": 31963, + "Ġconditioned": 31964, + "çī": 31965, + "answered": 31966, + "Ġcompulsory": 31967, + "ascist": 31968, + "Ġpodcasts": 31969, + "ĠFrankfurt": 31970, + "bnb": 31971, + "Ġneoliberal": 31972, 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"ãĤ¦ãĤ¹": 34103, + "Bottom": 34104, + "Ġcautioned": 34105, + "tn": 34106, + "396": 34107, + "ĠNikki": 34108, + "Ġevaluations": 34109, + "Ġharassing": 34110, + "Ġbindings": 34111, + "ĠMonetary": 34112, + "Ġhitters": 34113, + "Ġadversary": 34114, + "unts": 34115, + "Ġsetback": 34116, + "Ġencrypt": 34117, + "ĠCait": 34118, + "Ġlows": 34119, + "enges": 34120, + "ĠNorn": 34121, + "Ġbulbs": 34122, + "Ġbottled": 34123, + "ĠVoyager": 34124, + "317": 34125, + "Ġspheres": 34126, + "politics": 34127, + "Ġsubtract": 34128, + "Ġsensations": 34129, + "Ġappalling": 34130, + "Ġ316": 34131, + "Ġenvironmentally": 34132, + "ĠSTEM": 34133, + "Ġpublishes": 34134, + "560": 34135, + "Ġdiligence": 34136, + "484": 34137, + "Ġadvises": 34138, + "Ġpetrol": 34139, + "Ġimagining": 34140, + "Ġpatrols": 34141, + "ĠInteger": 34142, + "ĠAshes": 34143, + "actus": 34144, + "ĠRadiant": 34145, + "ĠLT": 34146, + "itability": 34147, + "htaking": 34148, + "Setting": 34149, + "Ġnuanced": 34150, + "ĠReef": 34151, + "ĠDevelopers": 34152, + "Ni": 34153, + "pieces": 34154, + "990": 34155, + "License": 34156, + "Ġlowers": 34157, + "ĠOttoman": 34158, + "327": 34159, + "ooo": 34160, + "Ġquitting": 34161, + "markets": 34162, + "Behind": 34163, + "Ġbasin": 34164, + "Ġdocs": 34165, + "anie": 34166, + "flash": 34167, + "ctl": 34168, + "Ġcivilized": 34169, + "ĠFukushima": 34170, + "\"],\"": 34171, + "ĠKS": 34172, + "ĠHonestly": 34173, + "arat": 34174, + "Ġconstructs": 34175, + "ĠLans": 34176, + "ĠDire": 34177, + "ĠLIKE": 34178, + "ĠTrouble": 34179, + "Ġwithholding": 34180, + "ĠOblivion": 34181, + "Ġsanity": 34182, + "anya": 34183, + "Const": 34184, + "Ġgrocer": 34185, + "ĠCelsius": 34186, + "Ġrecounted": 34187, + "ĠWife": 34188, + "Border": 34189, + "atered": 34190, + "happy": 34191, + "Ġspoiler": 34192, + "Ġlogically": 34193, + "Hall": 34194, + "Ġsucceeding": 34195, + "Ġpolymorph": 34196, + "Ġaxes": 34197, + "ĠShotgun": 34198, + "ĠSlim": 34199, + "ĠPrinciples": 34200, + "ĠLeth": 34201, + "arta": 34202, + 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"????????": 35709, + "Ġinformant": 35710, + "Ġbowel": 35711, + "ĠUniform": 35712, + "Ġ...\"": 35713, + "Ġpurge": 35714, + "Ġpicnic": 35715, + "ĠUmb": 35716, + "ĠUPDATE": 35717, + "ĠSapphire": 35718, + "ĠStall": 35719, + "learn": 35720, + "Ġobjectively": 35721, + "Ġobliter": 35722, + "Ġloophole": 35723, + "Ġjourneys": 35724, + "Ġomission": 35725, + "Pros": 35726, + "ĠSidney": 35727, + "ploma": 35728, + "Ġsprayed": 35729, + "Ġguru": 35730, + "Ġtraitor": 35731, + "Ġtimet": 35732, + "Ġsnapping": 35733, + "ĠSevent": 35734, + "urnal": 35735, + "ĠUkip": 35736, + "Ġbowed": 35737, + "poral": 35738, + "liberal": 35739, + "Ros": 35740, + "Questions": 35741, + "iOS": 35742, + "Ġsummarize": 35743, + "STAT": 35744, + "Ġ1850": 35745, + "apest": 35746, + "Ġlender": 35747, + "ĠVariable": 35748, + "bringing": 35749, + "ĠLORD": 35750, + ",)": 35751, + "Ġcollapses": 35752, + "xiety": 35753, + "ĠNed": 35754, + "YD": 35755, + "ĠScha": 35756, + "Ġantibody": 35757, + "Ġdisband": 35758, + "yre": 35759, + 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47126, + "Rum": 47127, + "Ġnuance": 47128, + "ĠDarius": 47129, + "pleting": 47130, + "fters": 47131, + "etchup": 47132, + "Registration": 47133, + "ĠKE": 47134, + "Rah": 47135, + "Ġpreferential": 47136, + "ĠLash": 47137, + "ĠHH": 47138, + "Valid": 47139, + "ĠNAV": 47140, + "Ġstarve": 47141, + "ĠGong": 47142, + "zynski": 47143, + "ĠActress": 47144, + "Ġwik": 47145, + "Ġunaccompanied": 47146, + "lvl": 47147, + "Bride": 47148, + "ADS": 47149, + "ĠCommando": 47150, + "ĠVaughn": 47151, + "Wallet": 47152, + "Ġhopping": 47153, + "ĠVie": 47154, + "Ġcaveats": 47155, + "Ġalas": 47156, + "ifled": 47157, + "abuse": 47158, + "661": 47159, + "Ġibn": 47160, + "Ġgul": 47161, + "Ġrobbing": 47162, + "til": 47163, + "ILA": 47164, + "Ġmitigating": 47165, + "Ġaptly": 47166, + "Ġtyrant": 47167, + "Ġmidday": 47168, + "ĠGilmore": 47169, + "ĠDecker": 47170, + "Ġ§§": 47171, + "partial": 47172, + "Exactly": 47173, + "Ġphenotype": 47174, + "Ġ[+]": 47175, + "ĠPlex": 47176, + "ĠIps": 47177, + "versions": 47178, + "Ġebook": 47179, + "Ġchic": 47180, + "gross": 47181, + "\":\"\"},{\"": 47182, + "ĠSurprisingly": 47183, + "Morgan": 47184, + "Ġresidues": 47185, + "ĠConfederation": 47186, + "infeld": 47187, + "Ġlyr": 47188, + "moderate": 47189, + "Ġperpendicular": 47190, + "VK": 47191, + "Ġsynchronized": 47192, + "Ġrefreshed": 47193, + "Ġadore": 47194, + "ĠTorment": 47195, + "olina": 47196, + "Ġ2600": 47197, + "ItemTracker": 47198, + "Ġpies": 47199, + "ĠFAT": 47200, + "ĠRHP": 47201, + "048": 47202, + "ĠRESP": 47203, + "ĠBJ": 47204, + "allows": 47205, + "Pand": 47206, + "Ġunwelcome": 47207, + "ĠVoc": 47208, + "ĠBastard": 47209, + "ĠOW": 47210, + "ĠLAR": 47211, + "ĠHealer": 47212, + "Environmental": 47213, + "ĠKenyan": 47214, + "ĠTrance": 47215, + "ĠPats": 47216, + "Ġaliases": 47217, + "ĠGarfield": 47218, + "Ġcampaigner": 47219, + "Ġadvancements": 47220, + "ĠOkinawa": 47221, + "ĠCoh": 47222, + "owsky": 47223, + "Ġstarved": 47224, + "Ġsizeable": 47225, + "Ġ:-)": 47226, + "ĠmRNA": 47227, + "Ġsuspensions": 47228, + "istar": 47229, + "Scotland": 47230, + "Prin": 47231, + "------------------------------------------------": 47232, + "Ġ502": 47233, + "Ġteaspoons": 47234, + "Ġ1050": 47235, + "Ġcoercive": 47236, + "ĠMasonic": 47237, + "edded": 47238, + "ĠPassenger": 47239, + "Ġlatt": 47240, + "Ġbraces": 47241, + "ĠSteal": 47242, + "ĠNYT": 47243, + "ĠKats": 47244, + "ĠCelest": 47245, + "aez": 47246, + "Tu": 47247, + "ĠCoulter": 47248, + "ðŁĺ": 47249, + "Flickr": 47250, + "ĠWilmington": 47251, + "iths": 47252, + "++;": 47253, + "Ġvending": 47254, + "Ġnegro": 47255, + "ĠPhi": 47256, + "ĠYellowstone": 47257, + "Callback": 47258, + "Ġshampoo": 47259, + "ĠShades": 47260, + "wat": 47261, + "Ġsuperhuman": 47262, + "Ġridiculed": 47263, + "Ġholiest": 47264, + "ombo": 47265, + "Ġinterns": 47266, + "Ġhone": 47267, + "ĠParagu": 47268, + "URI": 47269, + "Ġdangling": 47270, + "ãĤ»": 47271, + "sov": 47272, + "ictional": 47273, + "availability": 47274, + "Ġrevocation": 47275, + "Ġdow": 47276, + "inic": 47277, + "ĠTHEIR": 47278, + "Ġiso": 47279, + "Ġoutings": 47280, + "ĠLethal": 47281, + "Ġ)))": 47282, + "Ġinaccur": 47283, + "Ġoutlandish": 47284, + "Ġanus": 47285, + "letico": 47286, + "idon": 47287, + "lol": 47288, + "Ġunregulated": 47289, + "Ġsuccumbed": 47290, + "Ġcuff": 47291, + "ĠWasteland": 47292, + "letal": 47293, + "Ġsubstr": 47294, + "Ġcoffers": 47295, + "Ġautomakers": 47296, + "ovi": 47297, + "ĠXue": 47298, + "ĠDaytona": 47299, + "Ġjarring": 47300, + "Ġfumes": 47301, + "Ġdisbanded": 47302, + "zik": 47303, + "itton": 47304, + "Ġstrikingly": 47305, + "Ġspores": 47306, + "Adapter": 47307, + ".):": 47308, + "ĠLyndon": 47309, + "ivalry": 47310, + "Ġorally": 47311, + "Ġtumultuous": 47312, + "Ġdispleasure": 47313, + "Ġcones": 47314, + "orrect": 47315, + "Ġappease": 47316, + "Ġderby": 47317, + "ĠTripoli": 47318, + "ĠAless": 47319, + "Ġpoked": 47320, + "ĠGuilty": 47321, + "vP": 47322, + "Enough": 47323, + "Ġoriginals": 47324, + "699": 47325, + "Ġrabbi": 47326, + "Ġproverbial": 47327, + "Ġpostpone": 47328, + "elope": 47329, + "ĠMisty": 47330, + "Ġstaffed": 47331, + "ĠUnemployment": 47332, + "reditary": 47333, + "Ġdiligent": 47334, + "recomm": 47335, + "measures": 47336, + "asin": 47337, + "825": 47338, + "Ġponds": 47339, + "Ġmmol": 47340, + "ĠSAR": 47341, + "ĠCARE": 47342, + "Ġ371": 47343, + "Ġclenched": 47344, + "ĠCorsair": 47345, + "Ġcaricature": 47346, + "zn": 47347, + "attach": 47348, + "ĠSchro": 47349, + "speak": 47350, + "painted": 47351, + "ĠSuc": 47352, + "ĠENT": 47353, + "Ġcellul": 47354, + "ĠPaid": 47355, + "diagn": 47356, + "WHERE": 47357, + "Ġtexted": 47358, + "Barn": 47359, + "Ġretracted": 47360, + "ĠReferred": 47361, + "Sav": 47362, + "Ġupkeep": 47363, + "Ġworkplaces": 47364, + "ĠTokens": 47365, + "Ġamplify": 47366, + "clinical": 47367, + "Ġmultic": 47368, + "mberg": 47369, + "Ġconvoluted": 47370, + "Region": 47371, + "565": 47372, + "ĠTopic": 47373, + "Ġsnail": 47374, + "Ġsaline": 47375, + "Ġinsurrection": 47376, + "ĠPetr": 47377, + "forts": 47378, + "BAT": 47379, + "ĠNavajo": 47380, + "Ġrudimentary": 47381, + "ĠLaksh": 47382, + "ONDON": 47383, + "Measure": 47384, + "Ġtransformer": 47385, + "ĠGoddard": 47386, + "Ġcoincides": 47387, + "irin": 47388, + "Rex": 47389, + "ĠBok": 47390, + "quit": 47391, + "Ġshotguns": 47392, + "Ġproletarian": 47393, + "Ġscorp": 47394, + "ĠAda": 47395, + "514": 47396, + "Ġslander": 47397, + "recorded": 47398, + "Ġembell": 47399, + "risome": 47400, + "Ġapologizing": 47401, + "ĠMulcair": 47402, + "ĠGibraltar": 47403, + "Cla": 47404, + "Ġallot": 47405, + "ĠAttention": 47406, + "Ġ433": 47407, + "leave": 47408, + "Ġwhine": 47409, + "ĠIssa": 47410, + "ĠFaust": 47411, + "ĠBarron": 47412, + "heny": 47413, + "Ġvictimized": 47414, + "Jews": 47415, + "Ġnurturing": 47416, + "ettel": 47417, + "Winged": 47418, + "ĠSubtle": 47419, + "Ġflavorful": 47420, + "ĠReps": 47421, + "enged": 47422, + "callback": 47423, + "Ġdirectional": 47424, + "Ġclasp": 47425, + "ĠDirections": 47426, + "planet": 47427, + "iculture": 47428, + "Helper": 47429, + "icion": 47430, + "acia": 47431, + "Ġç¥ŀ": 47432, + "Ġsurges": 47433, + "Ġcanoe": 47434, + "ĠPremiership": 47435, + "been": 47436, + "Ġdefied": 47437, + "ĠTrooper": 47438, + "Ġtripod": 47439, + "Ġgasp": 47440, + "ĠEuph": 47441, + "ĠAds": 47442, + "vernight": 47443, + "highly": 47444, + "Role": 47445, + "Ġentangled": 47446, + "ĠZeit": 47447, + "618": 47448, + "ĠRusty": 47449, + "Ġhavens": 47450, + "ĠVaughan": 47451, + "HAEL": 47452, + "ĠSERVICE": 47453, + "/,": 47454, + "Ġstricken": 47455, + "Ġdelusions": 47456, + "Ġbis": 47457, + "ĠHaf": 47458, + "Ġgratification": 47459, + "Ġenticing": 47460, + "UNCH": 47461, + "Adams": 47462, + "ĠOLED": 47463, + "ĠBeetle": 47464, + "Ġ1899": 47465, + "ĠSOFTWARE": 47466, + "ategor": 47467, + "VL": 47468, + "ĠTotem": 47469, + "ĠGators": 47470, + "ATURES": 47471, + "Ġimpedance": 47472, + "Registered": 47473, + "ĠCary": 47474, + "ĠAerial": 47475, + "onne": 47476, + "enium": 47477, + "Ġdred": 47478, + "ĠBeg": 47479, + "Ġconcurrently": 47480, + "Ġsuperpower": 47481, + "ĠXan": 47482, + "jew": 47483, + "imester": 47484, + "ĠDickinson": 47485, + "âĶģ": 47486, + "Fla": 47487, + "Ġpree": 47488, + "ĠRollins": 47489, + "©¶æ": 47490, + "Ġdenomination": 47491, + "ĠLana": 47492, + "516": 47493, + "Ġinciting": 47494, + "scribed": 47495, + "juries": 47496, + "ĠWonders": 47497, + "approximately": 47498, + "Ġsuspending": 47499, + "Ġmountainous": 47500, + "ĠLaugh": 47501, + "oidal": 47502, + "Ns": 47503, + "Detect": 47504, + ")=": 47505, + "ĠLuthor": 47506, + "ĠSchwarzenegger": 47507, + "ĠMuller": 47508, + "ĠDevi": 47509, + "ecycle": 47510, + "Jar": 47511, + "613": 47512, + "ĠLongh": 47513, + "Bah": 47514, + "ĠSPORTS": 47515, + "nw": 47516, + "Ġrefinement": 47517, + "Ġwaterways": 47518, + "Ġdiner": 47519, + "Blade": 47520, + "683": 47521, + "Fac": 47522, + "Ġinitials": 47523, + "Ġrog": 47524, + "Ġparanormal": 47525, + "BUT": 47526, + "Ġ[(": 47527, + "ĠSwanson": 47528, + "ĠMesh": 47529, + "âĸ¬": 47530, + "Improve": 47531, + "ĠRadiation": 47532, + "ĠEsther": 47533, + "ĠEsk": 47534, + "ĠAly": 47535, + "iky": 47536, + "Ġirrad": 47537, + "ĠBuckingham": 47538, + "Ġrefill": 47539, + "Ġ._": 47540, + "Repe": 47541, + "CONCLUS": 47542, + "Ġdifferentiated": 47543, + "Ġchirop": 47544, + "ĠAtkins": 47545, + "Pattern": 47546, + "Ġexcise": 47547, + "Ġcabal": 47548, + "NSA": 47549, + "ĠSTA": 47550, + "ĠSIL": 47551, + "ĠParaly": 47552, + "Ġrye": 47553, + "ĠHowell": 47554, + "ĠCountdown": 47555, + "nesses": 47556, + "alysed": 47557, + "Ġresize": 47558, + "ãĤ½": 47559, + "Ġbudgetary": 47560, + "ĠStras": 47561, + "wang": 47562, + "Ġapiece": 47563, + "Ġprecincts": 47564, + "Ġpeach": 47565, + "Ġskyline": 47566, + "Ġ353": 47567, + "popular": 47568, + "Appearances": 47569, + "ĠMechanics": 47570, + "ĠDevOnline": 47571, + "Sullivan": 47572, + "Zen": 47573, + "Ġpu": 47574, + "opolis": 47575, + "544": 47576, + "Ġdeform": 47577, + "Ġcounteract": 47578, + "ĠLange": 47579, + "Ġ417": 47580, + "Console": 47581, + "774": 47582, + "Ġnodding": 47583, + "Ġpopulism": 47584, + "Ġhep": 47585, + "Ġcounselling": 47586, + "compliance": 47587, + "UFF": 47588, + "Ġundeniably": 47589, + "Ġrailing": 47590, + "ĠHorowitz": 47591, + "ĠSimone": 47592, + "ĠBungie": 47593, + "Ġak": 47594, + "ĠTalks": 47595, + "xff": 47596, + "flake": 47597, + "Crash": 47598, + "Ġsweaty": 47599, + "Ġbanquet": 47600, + "ĠOFFIC": 47601, + "Ġinventive": 47602, + "Ġastronomer": 47603, + "ĠStamford": 47604, + "ĠScare": 47605, + "ĠGREEN": 47606, + "olicited": 47607, + "Ġrusher": 47608, + "Ġcentrist": 47609, + "ighting": 47610, + "Ġsubclass": 47611, + "Ġdisav": 47612, + "Ġdefund": 47613, + "ĠNanto": 47614, + "ociate": 47615, + "mast": 47616, + "Ġpacif": 47617, + "Ġmend": 47618, + "eers": 47619, + "immigration": 47620, + "ESSION": 47621, + "Ġnumbering": 47622, + "Ġlaughable": 47623, + "ĠEnded": 47624, + "viation": 47625, + "emark": 47626, + "Pitt": 47627, + "Ġmeticulous": 47628, + "ĠLF": 47629, + "Ġcongratulated": 47630, + "ĠBirch": 47631, + "Ġswayed": 47632, + "Ġsemifinals": 47633, + "Ġhumankind": 47634, + "matter": 47635, + "ĠEquip": 47636, + "opausal": 47637, + "Said": 47638, + "ĠLayout": 47639, + "Ġvoicing": 47640, + "Ġthug": 47641, + "Ġpornographic": 47642, + "IPS": 47643, + "Ġmoaning": 47644, + "Ġgrievance": 47645, + "Ġconfessions": 47646, + "escal": 47647, + "TEXTURE": 47648, + "Authent": 47649, + "osaurus": 47650, + "Purchase": 47651, + "Ġrelegation": 47652, + "alter": 47653, + "Ġ³³": 47654, + "Ġriddled": 47655, + "Ġogre": 47656, + "ĠLowell": 47657, + "Occup": 47658, + "Eat": 47659, + "ĠHyder": 47660, + "ĠAdviser": 47661, + "Commerce": 47662, + "Hunt": 47663, + "ĠOrth": 47664, + "ĠCompetitive": 47665, + "ĠCLA": 47666, + "CDC": 47667, + "Ġsalads": 47668, + "Fle": 47669, + "Ġindustrialized": 47670, + "`,": 47671, + "ĠOWN": 47672, + "Ġbeck": 47673, + "ĠParticularly": 47674, + "oubt": 47675, + "ĠmM": 47676, + "ĠHussain": 47677, + "ĠChennai": 47678, + "Ġ920": 47679, + "Ġappointing": 47680, + "ĠCullen": 47681, + ",,,,,,,,": 47682, + "Ġpores": 47683, + "verified": 47684, + "Ġbiochemical": 47685, + "emate": 47686, + "Ġcowardly": 47687, + "ĠHelsinki": 47688, + "ĠEthiopian": 47689, + "SOURCE": 47690, + "ERC": 47691, + "estro": 47692, + "Ġbiotech": 47693, + "ĠSour": 47694, + "Ġbrewer": 47695, + "Bloomberg": 47696, + "Ġintensify": 47697, + "Glass": 47698, + "anco": 47699, + "ĠFDR": 47700, + "greSQL": 47701, + "ĠFires": 47702, + "©¶æ¥µ": 47703, + "eco": 47704, + "1001": 47705, + "ĠHomeless": 47706, + "Ġinstantaneous": 47707, + "ĠHaste": 47708, + "igel": 47709, + "Diamond": 47710, + "Ġpaving": 47711, + "Ġlandfill": 47712, + "Ġdads": 47713, + "houn": 47714, + ":]": 47715, + "Ġincendiary": 47716, + "ĠLivingston": 47717, + "ĠHilbert": 47718, + "ĠChecks": 47719, + "styles": 47720, + "inators": 47721, + "ĠClive": 47722, + "phrine": 47723, + "Ġchimpanzees": 47724, + "Ġpall": 47725, + "ĠJM": 47726, + "ĠAadhaar": 47727, + "ðĿ": 47728, + "Ġachievable": 47729, + "disabled": 47730, + "PET": 47731, + "OOOOOOOO": 47732, + "Mot": 47733, + "Ġintangible": 47734, + "Ġballet": 47735, + "ĠWebs": 47736, + "ĠEstimated": 47737, + "Effects": 47738, + "Ġbailed": 47739, + "Joshua": 47740, + "Ġturbulence": 47741, + "Ġoccupant": 47742, + "ĠDaylight": 47743, + "Ġ361": 47744, + "meet": 47745, + "Ġstatically": 47746, + "Ġonlook": 47747, + "Ġki": 47748, + "illegal": 47749, + "Ġvelvet": 47750, + "Ġdehydration": 47751, + "Ġacquies": 47752, + "ĠRez": 47753, + "akura": 47754, + "ĠUpton": 47755, + "atro": 47756, + "Ġincomprehensible": 47757, + "Ġbackdoor": 47758, + "ĠRhino": 47759, + "727": 47760, + "Ġmaths": 47761, + ")+": 47762, + "Ġheresy": 47763, + "Ġdf": 47764, + "ĠRoche": 47765, + "ĠLydia": 47766, + "Ġpancreat": 47767, + "reply": 47768, + "arrell": 47769, + "Ġsolicitation": 47770, + "Ġcircadian": 47771, + "BIP": 47772, + "Ġforay": 47773, + "Ġcryptic": 47774, + "izu": 47775, + "imeo": 47776, + "ĠTomato": 47777, + "ĠHoms": 47778, + "examination": 47779, + "Ġquarry": 47780, + "ĠValiant": 47781, + "ĠJericho": 47782, + "ĠINCLUD": 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"Ġm ust", + "Ġd irect", + "Ġg l", + "Ġ <", + "Ġop en", + "Ġp ost", + "Ġcom e", + "Ġse em", + "ord ing", + "Ġwe ek", + "ate ly", + "it al", + "Ġe l", + "ri end", + "Ġf ar", + "Ġt ra", + "in al", + "Ġp ri", + "ĠU S", + "Ġpl ace", + "Ġfor m", + "Ġto ld", + "\" :", + "ain s", + "at ure", + "ĠTr ump", + "Ġst and", + "Ġ #", + "id er", + "ĠF r", + "Ġne xt", + "Ġs oc", + "Ġp ur", + "Ġle t", + "Ġl ittle", + "Ġh um", + "Ġ i", + "r on", + "1 5", + "Ġ1 5", + "Ġcomm un", + "Ġm ark", + "ĠThe re", + "Ġw r", + "ĠTh at", + "Ġin formation", + "w ays", + "Ġb us", + "a pp", + "Ġinv est", + "m e", + "Ġh ard", + "ain ed", + "e ad", + "Ġim port", + "Ġapp ro", + "Ġt est", + "Ġt ri", + "Ġre st", + "os ed", + "Ġf ull", + "Ġc are", + "ĠS p", + "Ġc ase", + "O N", + "Ġs k", + "Ġl ess", + "Ġ +", + "Ġpart ic", + "ĠP l", + "ab ly", + "u ck", + "is hed", + "ch n", + "b e", + "Ġl ist", + "at or", + "Ġto p", + "Ġad v", + "ĠB e", + "ru ct", + "Ġd em", + "r ation", + "l ing", + "g y", + "re en", + "g er", + "Ġh ome", + "Ġle ft", + "Ġbet ter", + "Ġd ata", + "Ġ1 1", + "Ġatt ack", + "Ġpro ble", + "l ine", + "ard s", + "Ġbe h", + "r al", + "ĠH ow", + "ĠS he", + "ar ge", + "Ġ --", + ": //", + "Ġb ro", + "ĠP h", + "at s", + "Ġbu ild", + "w w", + "id ed", + "a im", + "as es", + "en cy", + "Ġm ain", + "in ed", + "Ġinclud ing", + "Ġ {", + "Ġg ot", + "Ġint erest", + "Ġke ep", + "Ġ X", + "Ġe as", + "ain ing", + "Ġcl ass", + "âĢ ¦", + "ĠN o", + "Ġv ar", + "Ġsm all", + "amp le", + "A T", + "Ġ ide", + "ĠS o", + "Ġre ce", + "Ġpol it", + "Ġm ov", + "Ġpl an", + "Ġper cent", + "iv ing", + "Ġc amp", + "Ġp ay", + "1 4", + "s c", + "is ed", + "Ġu nt", + "one y", + "pl oy", + "== ==", + "Ġdid n", + "ĠI nd", + "el s", + "ert ain", + "Ġp os", + "__ __", + "i ver", + "Ġpro cess", + "Ġprog ram", + "if ied", + "ĠR ep", + "1 6", + "u ro", + "olog y", + "at ter", + "in a", + "Ġn ame", + "ĠA ll", + "Ġf our", + "Ġret urn", + "v ious", + "b s", + "Ġcall ed", + "Ġm ove", + "ĠS c", + "ir d", + "Ġgrou p", + "Ġb re", + "Ġm en", + "Ġc ap", + "t en", + "e e", + "Ġd ri", + "le g", + "he re", + "uth or", + "Ġp at", + "Ġcur rent", + "id es", + "Ġp op", + "t o", + "ent ion", + "Ġal ways", + "Ġm il", + "Ġwom en", + "Ġ1 6", + "Ġo ld", + "iv en", + "ra ph", + "ĠO r", + "r or", + "ent ly", + "Ġn ear", + "ĠE x", + "re am", + "s h", + "Ġ1 4", + "Ġf ree", + "iss ion", + "st and", + "ĠC on", + "al ity", + "us ed", + "1 3", + "Ġdes ign", + "Ġch ange", + "Ġch ang", + "Ġb o", + "Ġv is", + "em ber", + "Ġb ook", + "read y", + "Ġk ill", + "2 5", + "pp ed", + "Ġa way", + "Ġab le", + "Ġcount ry", + "Ġcon st", + "ar n", + "Ġor der", + "A R", + "i or", + "i um", + "or th", + "1 8", + "ail able", + "Ġs w", + "Ġm illion", + "Ġ1 3", + "at ic", + "t ed", + "ĠG o", + "Ġo per", + "en g", + "Ġth ing", + "aj or", + "con om", + "ĠCom m", + "Ġwh y", + "u red", + "ur al", + "Ġs chool", + "b y", + "ĠM ar", + "Ġa ff", + "Ġd ays", + "Ġan n", + "us h", + "an e", + "I f", + "e g", + "Ġpro f", + "Ġhe alth", + "ou th", + "B ut", + "ion al", + ". ,", + "Ġs ol", + "Ġal ready", + "Ġ3 0", + "Ġchar act", + "H e", + "Ġf riend", + "E S", + "i ans", + "ic le", + "' d", + "ĠO n", + "Ġle ast", + "Ġp rom", + "Ġd r", + "Ġh ist", + "it her", + "Ġ est", + "i qu", + "1 7", + "s on", + "Ġte ll", + "Ġt alk", + "oh n", + "o int", + "le ction", + "A N", + "Ġunt il", + "au gh", + "Ġl ater", + "Ġ ve", + "Ġv iew", + "end ing", + "iv ed", + "Ġwor d", + "w are", + "Ġc ost", + "Ġen ough", + "Ġg ive", + "ĠUn ited", + "Ġte chn", + "are nt", + "O R", + "Ġp ar", + "ĠD r", + "Ġ201 6", + "r ist", + "er ing", + "Ġ Â", + "Ġl arge", + "s ide", + "ac y", + "cc ess", + "Ġw in", + "Ġimport ant", + "Ġ19 9", + "Ġdoes n", + "Ġ1 7", + "Ġbus iness", + "Ġcle ar", + "Ġre se", + "\" ,", + "ur y", + "Ġe qu", + "as ter", + "al f", + "ĠAmeric an", + "n ect", + "Ġex pect", + "ivers ity", + "Ġo cc", + "ĠF l", + "Ġk ind", + "Ġme an", + "Ġp ast", + "Ġde v", + "Ġb as", + "le t", + "ra ft", + "Ġor gan", + "Ġde l", + "Ġper form", + "Ġst ory", + "Ġse ason", + "ĠC ol", + "Ġcl aim", + "Ġc ame", + "Ġwith in", + "Ġl ine", + "Ġpro ject", + "ĠA t", + "Ġcontro l", + "end ed", + "ĠS y", + "Ġa ir", + "iz ation", + "Ġ *", + "le y", + "Ġm oney", + "id d", + "Y ou", + "f or", + "Ġfam ily", + "Ġm aking", + "Ġb it", + "Ġpol ice", + "Ġhapp en", + "Ġ vers", + "on y", + "u ff", + "ĠW hen", + "Ġs it", + "ide o", + "l f", + "is on", + "Ġsu re", + "g in", + "Ġapp ear", + "Ġl ight", + "Ġ es", + "o f", + "Ġw ater", + "Ġt imes", + "n ot", + "Ġg row", + "Ġcomp any", + "ĠT e", + "ow s", + "Ġm ar", + "our ce", + "i ol", + "ar m", + "b r", + "Ġex ample", + "Ġcon c", + "Ġf ore", + "ĠT o", + "p ro", + "E N", + "ri es", + "Ġ2 5", + "ĠC an", + "ne y", + "Ġact ually", + "Ġe ver", + "ur ity", + "ak en", + "ap s", + "Ġt ax", + "Ġm ajor", + "am a", + "Ġof ten", + "er al", + "Ġhum an", + "Ġj ob", + "is ter", + "Ġav ailable", + "oc r", + "en n", + "a id", + "iv id", + "Ġrec ord", + "? \"", + "Ġs ing", + "ĠA m", + "id ence", + "Ġnew s", + "st er", + "Ġe conom", + "Ġfollow ing", + "ĠB r", + "is ing", + "Ġh our", + "m ost", + "um ent", + "Ġse x", + "Ġdes c", + "Ġbec ome", + "ĠE d", + "Ġto ok", + "Ġha ving", + "Ġprodu ct", + "a ult", + "A s", + "ar ing", + "Ġme ans", + "Ġh op", + "un e", + "Ġch o", + "Ġc ertain", + "Ġn on", + "Ġde al", + "2 4", + "le ment", + "oc i", + "en e", + "Ġs ide", + "ĠP r", + "ĠM ay", + "Ġre ason", + "u ed", + "c hed", + "ul ation", + "Ġe lect", + "Ġoffic ial", + "Ġposs ible", + "Ġh old", + "and s", + "ot s", + "Ġc ity", + "or ies", + "Ġse ver", + "Ġchild ren", + "Ġon ce", + "Ġact iv", + "l er", + "Ġn ight", + "it ions", + "ĠJ ohn", + "a pe", + "pl ay", + "Ġd one", + "Ġl im", + "Ġwork ing", + "ĠP res", + "or ld", + "e b", + "ĠC o", + "Ġb ody", + "ail s", + "ut es", + "ĠM r", + "Ġwhe ther", + "Ġa uthor", + "ro p", + "Ġpro per", + "Ġse en", + ") ;", + "Ġf ac", + "ĠS u", + "Ġcon d", + "it ing", + "Ġcour se", + "Ġ }", + "-------- --------", + "a ign", + "Ġev ent", + "Ġen g", + "Ġp ot", + "Ġin tern", + "i am", + "Ġsh ort", + "em pt", + "ã Ĥ", + "ĠG od", + "il ar", + "8 0", + "Ġor ig", + "I S", + "our n", + "ab ility", + "it ive", + "Ġd am", + "Ġ1 00", + "Ġp ress", + "Ġdo ing", + "Ġprot ect", + "r ing", + "Ġthough t", + "Ġquest ion", + "re w", + "ĠW ar", + "Ġsever al", + "ĠSt ate", + "Ġg iven", + "Ġf und", + "ĠT w", + "Ġw ent", + "an ces", + "w ork", + "p or", + "m y", + "4 0", + "Ġar g", + "art ment", + "ust om", + "Ġpol ic", + "Ġme et", + "Ġc reat", + "2 2", + "ĠSt ates", + "Ġg ames", + "ra w", + "ut ure", + "Ġunder stand", + "ur s", + "ĠO b", + "l ish", + "s y", + "Ġm akes", + "Ġw on", + "ag on", + "Ġh tt", + "Ġl ove", + "ent ial", + "Ġcomple te", + "p ar", + "ĠI m", + "A L", + "Ġacc ount", + " ł", + "ore d", + "ver t", + "Ġ ident", + "Ġ201 5", + "Ġother s", + "ĠM in", + "i ber", + "ver age", + "The re", + "ition al", + "d d", + "Ġpro b", + "Ġyou ng", + "Ġal ong", + "Ġacc ording", + "Ġy et", + "Ġmem bers", + "ĠWh at", + "o id", + "ĠM an", + "A nd", + "Ġam ong", + "a i", + "Ġem ploy", + "ĠR es", + "Ġ >", + "Ġinv ol", + "Ġl ow", + "a f", + "ĠC ar", + "Ġh ig", + "ĠO ne", + "ĠS ec", + "in ation", + "Ġlike ly", + "Ġan t", + "ag ed", + "ĠR uss", + "Ġb en", + "Ġre le", + "F or", + "b ack", + "ĠN ot", + "Ġpres ident", + "b all", + "Ġacc ess", + "ivid ual", + "ĠD em", + "ĠE uro", + "6 0", + "Ġkn own", + "ir l", + "ĠG r", + "Ġear ly", + "u se", + "iet y", + "âĢ ĵ", + "Ġf ight", + "Ġs ent", + "Ġto day", + "Ġmark et", + "\" .", + "Ġb ased", + "Ġstr ong", + "ur ther", + "Ġde b", + "m ber", + "Ġproble m", + "Ġde ath", + "Ġsoc ial", + "im ate", + "A S", + "ort un", + "Ġcamp aign", + "er y", + "C h", + "Ġe y", + "i ally", + "Ġm us", + "w h", + "p os", + "Ġ er", + "Ġsa f", + "Ġmonth s", + "ir on", + "Ġv iol", + "Ġf ive", + "Ġst re", + "Ġplay ers", + "in c", + "al d", + "y ear", + "a un", + "Ġsu ccess", + "Ġpres ent", + "ere nce", + "Ġ201 4", + "Ġsu gg", + "Ġpartic ular", + "Ġtr y", + "Ġsugg est", + "ĠCh rist", + "on es", + "Ġpri v", + "2 3", + "Ġc rit", + "Ġl and", + "Ġloc al", + "if y", + "2 9", + "Ġa ut", + "E D", + "ĠG u", + "Ġm ult", + "Ġpolit ical", + "Ġask ed", + "Ġfor mer", + "it ter", + "ri pt", + "Ġcl ose", + "Ġp ract", + "ĠY ork", + "Ġget ting", + "Ġac ross", + "Ġcom b", + "Ġbelie ve", + "Ġ z", + "Ġto get", + "Ġtoget her", + "ĠC ent", + "ir c", + "Ġind ividual", + "ĠM c", + "2 7", + "is k", + "ĠE ng", + "Ġf ace", + "Ġ2 4", + "Ġval ue", + "Ġare a", + "e v", + "Ġw rit", + "ĠPres ident", + "Ġv ot", + "Ġke y", + "Ġm om", + "p ut", + "Ġany thing", + "Ġexper ience", + "att le", + "Ġm ind", + "a ff", + "om m", + "Ġf uture", + "g ed", + "Ġc ut", + "Ġto t", + "it ch", + "Ġv ideo", + "Ġinvest ig", + "Ġn et", + "ĠM y", + "r ict", + "i en", + ". )", + "Ġimp ro", + "th ough", + "ward s", + "Ġcon nect", + "ĠM ed", + "sel ves", + "ens ive", + "m b", + "o ber", + "at ors", + "A n", + "Ġ5 0", + "Ġre du", + "res ent", + "Ġab ove", + "Ġf re", + "ĠEuro pe", + "s w", + "Ġam ount", + "ĠA pp", + "Ġe ither", + "Ġmil it", + "Ġan al", + "Ġf ail", + "ĠE n", + "al es", + "Ġspec ial", + "Ġbl ack", + "I T", + "c her", + "Ġlook ing", + "Ġf ire", + "y n", + "Ġal most", + "o on", + "Ġstud y", + "Ġm iss", + "c hes", + "ro wn", + "Ġt re", + "Ġcommun ity", + "Ġmed ia", + "Ġf ood", + "Ġcom es", + "ĠUn iversity", + "Ġsing le", + "Wh at", + "u ly", + "Ġh alf", + "ag ue", + "h od", + "ĠRep ublic", + "Ġstart ed", + "Ġqu ick", + "ot o", + "b ook", + "Ġiss ue", + "it or", + "Ġel se", + "Ġcons ider", + "2 6", + "ro du", + "Ġt aken", + "2 8", + "9 9", + "ĠW ith", + "Ġtr ue", + "Ġw a", + "Ġtr ad", + "Ġag o", + "Ġm ess", + "ie f", + "Ġadd ed", + "o ke", + "Ġb ad", + "Ġf av", + "3 3", + "Ġsim ilar", + "as k", + "ĠD on", + "Ġcharact er", + "ort s", + "ĠH ouse", + "Ġreport ed", + "Ġty pe", + "v al", + "i od", + "ĠHow ever", + "Ġt arg", + "Ġent ire", + "pp ing", + "Ġhist ory", + "Ġl ive", + "ff ic", + ".... ....", + "ed eral", + "Ġtr ying", + "Ġdisc uss", + "ĠH ar", + "ac es", + "l ished", + "Ġse lf", + "os p", + "re st", + "Ġro om", + "el t", + "Ġf all", + "ol ution", + "Ġe t", + "Ġ x", + "Ġis n", + "Ġide a", + "b o", + "Ġs ound", + "ĠD ep", + "Ġsome one", + "ci ally", + "ull y", + "Ġf oc", + "Ġob ject", + "if t", + "ap er", + "Ġplay er", + "Ġr ather", + "Ġserv ice", + "as hing", + "ĠD o", + "ĠP art", + "ru g", + "m on", + "p ly", + "Ġm or", + "Ġnot hing", + "Ġprov ide", + "I C", + "un g", + "Ġpart y", + "Ġex ist", + "Ġm ag", + "7 0", + "Ġr ul", + "Ġh ouse", + "Ġbeh ind", + "Ġhow ever", + "ĠW orld", + "Ġs um", + "Ġapp lic", + "Ġ ;", + "Ġfun ction", + "g r", + "ĠP ol", + "Ġfr ont", + "2 00", + "Ġser ies", + "Ġt em", + "Ġty p", + "ill s", + "Ġo pt", + "Ġpoint s", + "Ġbel ow", + "itt ed", + "Ġspec ific", + "Ġ201 7", + "um b", + "Ġr a", + "Ġpre vious", + "Ġpre t", + "re me", + "Ġc ustom", + "Ġcour t", + "ĠM e", + "Ġre pl", + "Ġwho le", + "g o", + "c er", + "Ġt reat", + "ĠA ct", + "Ġprob ably", + "Ġle arn", + "end er", + "ĠA ss", + "Ġvers ion", + "n ow", + "Ġche ck", + "ĠC al", + "R E", + "min ist", + "O n", + "our ces", + "Ġben ef", + "Ġd oc", + "Ġdet er", + "Ġen c", + "Ġsu per", + "Ġadd ress", + "Ġv ict", + "Ġ201 3", + "Ġme as", + "t r", + "Ġf ield", + "W hen", + "Ġsign ific", + "u ge", + "Ġfe at", + "Ġcomm on", + "l oad", + "Ġbe gin", + "Ġbr ing", + "Ġa ction", + "er man", + "Ġdesc rib", + "Ġind ust", + "Ġwant ed", + "ri ed", + "m ing", + "Ġatt empt", + "4 5", + "f er", + "Ġd ue", + "ress ion", + "# #", + "Ġsh all", + "Ġs ix", + "o o", + "Ġst ep", + "Ġp ub", + "Ġhim self", + "Ġ2 3", + "Ġc op", + "Ġd est", + "Ġst op", + "A C", + "ib ility", + "Ġl ab", + "ic ult", + "Ġhour s", + "Ġcre ate", + "Ġf urther", + "ĠAmeric a", + "ĠC ity", + "Ġd ou", + "he ad", + "S T", + "ĠN orth", + "c ing", + "Ġn ational", + "u le", + "ĠIn st", + "Ġt aking", + "ĠQ u", + "ir t", + "Ġre d", + "Ġrese arch", + "v iron", + "ĠG e", + "Ġbre ak", + "an a", + "Ġsp ace", + "ater ial", + "Ġrec ent", + "ĠA b", + "Ġgener al", + "Ġh it", + "Ġper iod", + "Ġevery thing", + "ive ly", + "Ġph ys", + "Ġsay ing", + "an ks", + "Ġc ou", + "Ġc ult", + "ac ed", + "e al", + "u ation", + "Ġc oun", + "l u", + "Ġinclud e", + "Ġpos ition", + "ĠA fter", + "ĠCan ad", + "ĠE m", + "Ġim m", + "ĠR ed", + "Ġp ick", + "Ġcom pl", + "Ġm atter", + "re g", + "e xt", + "ang u", + "is c", + "o le", + "a ut", + "Ġcomp et", + "e ed", + "f ect", + "Ġ2 1", + "ĠS en", + "ĠThe se", + "as ing", + "Ġcan not", + "Ġin it", + "Ġrel ations", + "ac hed", + "Ġb ar", + "Ġ4 0", + "ĠT H", + "Ġ201 2", + "Ġv ol", + "Ġg round", + "Ġsec urity", + "Ġup d", + "il t", + "3 5", + "Ġconc ern", + "ĠJ ust", + "Ġwh ite", + "Ġseem s", + "ĠH er", + "pe cially", + "i ents", + "Ġann oun", + "Ġf ig", + "ight s", + "Ġst ri", + "l ike", + "id s", + "Ġs us", + "Ġw atch", + "Ġ â", + "Ġw ind", + "ĠC ont", + "Ġit self", + "Ġm ass", + "A l", + "y le", + "iqu e", + "ĠN ational", + "Ġab s", + "Ġp ack", + "Ġout side", + "Ġan im", + "Ġp ain", + "et er", + "Ġman ag", + "du ct", + "og n", + "Ġ ]", + "ĠSe pt", + "se c", + "o ff", + "ĠJ an", + "Ġf oot", + "ad es", + "Ġth ird", + "Ġm ot", + "Ġev idence", + "int on", + "Ġth reat", + "a pt", + "pl es", + "c le", + "Ġl o", + "Ġde cl", + "Ġit em", + "med i", + "Ġrep resent", + "om b", + "am er", + "Ġsignific ant", + "og raph", + "s u", + "Ġc al", + "i res", + "00 00", + "I D", + "A M", + "Ġsim ply", + "Ġlong er", + "Ġf ile", + "O T", + "c he", + "S o", + "ate g", + "or g", + "ĠH is", + "Ġen er", + "Ġd om", + "Ġup on", + "il i", + "\": \"", + "Ġthem selves", + "Ġcom ing", + "Ġqu ite", + "Ġdiff icult", + "ĠB ar", + "il ities", + "re l", + "end s", + "c ial", + "6 4", + "Ġwom an", + "ra p", + "y r", + "Ġne cess", + "ip s", + "Ġte xt", + "Ġrequ ire", + "Ġmilit ary", + "Ġre view", + "Ġresp ons", + "7 5", + "Ġsub ject", + "Ġinst ead", + "Ġiss ues", + "Ġg en", + "\" ,\"", + "Ġmin utes", + "Ġwe ap", + "r ay", + "am ed", + "t ime", + "b l", + "H ow", + "Ġc ode", + "ĠS m", + "Ġhig her", + "ĠSt e", + "r is", + "Ġp age", + "Ġstud ents", + "ĠIn tern", + "Ġmet hod", + "ĠA ug", + "ĠP er", + "ĠA g", + "Ġpolic y", + "ĠS w", + "Ġex ec", + "Ġac cept", + "um e", + "rib ut", + "Ġword s", + "Ġfin al", + "Ġchang es", + "ĠDem ocr", + "Ġfriend s", + "Ġres pect", + "Ġe p", + "Ġcomp an", + "iv il", + "Ġdam age", + "** **", + "og le", + "viron ment", + "Ġne g", + "ent al", + "Ġa p", + "Ġtot al", + "iv al", + "! \"", + "l im", + "Ġneed s", + "Ġag re", + "Ġdevelop ment", + "Ġa ge", + "ip le", + "2 1", + "Ġresult s", + "ĠA f", + "S h", + "Ġg un", + "ĠOb ama", + "ro ll", + "Ġ @", + "Ġright s", + "ĠB rit", + "Ġrun ning", + "Ġwas n", + "Ġp ort", + "Ġr ate", + "Ġpret ty", + "Ġtarg et", + "Ġsa w", + "Ġc irc", + "Ġwor ks", + "ic ro", + "al t", + "o ver", + "ww w", + "Th at", + "l ier", + "Ġevery one", + "ud e", + "Ġp ie", + "idd le", + "ra el", + "Ġr ad", + "Ġbl ock", + "Ġw alk", + "T o", + "ã ģ", + "n es", + "ĠA ust", + "a ul", + "ro te", + "ĠS outh", + "ess ion", + "op h", + "Ġshow s", + "Ġs ite", + "Ġj o", + "Ġr isk", + "cl us", + "l t", + "Ġin j", + "id ing", + "ĠS pe", + "Ġch all", + "ir m", + "Ġ2 2", + "itt ing", + "st r", + "Ġh y", + "L E", + "ke y", + "Ġbe gan", + "at ur", + "ashing ton", + "l am", + "ĠD av", + "b it", + "Ġs ize", + "ĠP ar", + "3 8", + "ourn al", + "f ace", + "Ġdec ision", + "Ġl arg", + "Ġj ud", + "re ct", + "Ġcontin ue", + "ĠO ct", + "ove red", + "ĠI nt", + "==== ====", + "Ġp arent", + "ĠW ill", + "Ġeas y", + "Ġd rug", + "ang er", + "Ġs ense", + "Ġd i", + "id ay", + "Ġener gy", + "ist ic", + "Ġass oci", + "ar ter", + "ob al", + "e ks", + "ĠE l", + "ur ch", + "Ġg irl", + "o e", + "it le", + "Ġ2 8", + "ĠC he", + "Ġrequ est", + "Ġso on", + "Ġh ost", + "k y", + "Ġst ates", + "om es", + "Ġm aterial", + "le x", + "Ġmom ent", + "Ġan sw", + "on se", + "Ġes pecially", + "Ġn orm", + "Ġserv ices", + "p ite", + "r an", + "Ġro le", + "4 4", + ") :", + "Ġc red", + "C l", + "____ ____", + "Ġm at", + "Ġl og", + "ĠCl inton", + "O U", + "Ġoff ice", + "Ġ2 6", + "Ġch arg", + "Ġtr ack", + "m a", + "Ġhe art", + "Ġb all", + "Ġperson al", + "Ġbuild ing", + "n a", + "s et", + "b ody", + "ĠBl ack", + "Ġincre ase", + "itt en", + "Ġneed ed", + "3 6", + "3 2", + "= \"", + "Ġl ost", + "Ġbec ame", + "Ġgrou ps", + "ĠM us", + "Ġw rote", + "ĠP e", + "Ġpro p", + "j oy", + "à ©", + "ĠWh ite", + "Ġde ad", + ". '", + "Ġhtt p", + "Ġwe bs", + "O S", + "Ġins ide", + "Ġwr ong", + "Ġstat ement", + "Ġ ...", + "y l", + "Ġfil m", + "Ġmus ic", + "Ġsh are", + "ific ation", + "Ġre lease", + "Ġfor ward", + "Ġst ay", + "Ġcomp ut", + "it te", + "s er", + "Ġorig inal", + "Ġc ard", + "Ġc and", + "Ġd iv", + "at ural", + "Ġfav or", + "O M", + "Ġc ases", + "us es", + "Ġse ction", + "Ġle ave", + "g ing", + "ov ed", + "ĠW ashington", + "3 9", + "ĠG l", + "Ġrequ ired", + "act ion", + "ap an", + "o or", + "it er", + "ĠK ing", + "Ġcount ries", + "ĠG erman", + "ll ing", + "Ġ2 7", + "3 4", + "Ġquest ions", + "Ġpr im", + "Ġc ell", + "Ġsh oot", + "Ġany one", + "ĠW est", + "Ġaff ect", + "ep end", + "Ġon line", + "ĠIs rael", + "ĠSept ember", + "Ġab ility", + "Ġcont ent", + "is es", + "Ġre ve", + "Ġl aun", + "Ġind ic", + "Ġfor ce", + "c ast", + "Ġso ld", + "av ing", + "f l", + "Ġso ft", + "Ġcompan ies", + "ce ed", + "Ġart icle", + "Ġa ud", + "Ġre v", + "Ġed uc", + "Ġplay ing", + "0 5", + "Ġhe ld", + "ct or", + "Ġrele ased", + "Ġf ederal", + "3 7", + "Ġad minist", + "Ġinter view", + "Ġinst all", + "Ġrece ived", + "Ġs ource", + "u k", + "P h", + "Ġser ious", + "Ġcre ated", + "Ġc ause", + "Ġim medi", + "Ġdef in", + "u el", + "ĠDep artment", + "ct ions", + "ĠC our", + "ĠN ow", + "z e", + "it es", + "it ution", + "Ġl ate", + "Ġspe ak", + "n ers", + "Ġleg al", + "ar i", + "ĠC or", + "Ġwe eks", + "Ġmod el", + "Ġp red", + "Ġex act", + "B C", + "ĠB y", + "IN G", + "os ing", + "Ġt akes", + "Ġreg ard", + "Ġopp ortun", + "Ġpr ice", + "Ġ19 8", + "ĠA pr", + "f ully", + "Ġor d", + "Ġproble ms", + "ru ction", + "h am", + "ĠC ount", + "le ge", + "Ġlead ers", + "E T", + "le v", + "Ġde ep", + "olog ical", + "es e", + "h aps", + "ĠS ome", + "Ġp ers", + "Ġcont ract", + "Ġrelations hip", + "s p", + "ou d", + "Ġb ase", + "4 8", + "m it", + "A d", + "anc ial", + "Ġcons um", + "Ġpot ential", + "Ġl angu", + "re m", + "et h", + "Ġrel ig", + "ress ed", + "6 6", + "Ġl ink", + "Ġl ower", + "ay er", + "ĠJ une", + "Ġf em", + "un t", + "er c", + "ur d", + "Ġcont act", + "Ġ ill", + "Ġm other", + "Ġest ab", + "h tt", + "ĠM arch", + "ĠB ro", + "ĠCh ina", + "Ġ2 9", + "Ġs qu", + "Ġprov ided", + "Ġa verage", + "as ons", + "Ġ201 1", + "Ġex am", + "l in", + "5 5", + "n ed", + "Ġper fect", + "Ġt ou", + "al se", + "u x", + "Ġbu y", + "Ġsh ot", + "Ġcol lect", + "Ġph ot", + "Ġplay ed", + "Ġsur pr", + "Ġofficial s", + "Ġsim ple", + "av y", + "Ġindust ry", + "Ġhand s", + "g round", + "Ġp ull", + "Ġr ound", + "Ġus er", + "Ġr ange", + "u ary", + "Ġpriv ate", + "op s", + "e es", + "Ġw ays", + "ĠM ich", + "Ġve h", + "Ġex cept", + "Ġter ms", + "im um", + "pp er", + "I ON", + "ore s", + "ĠDr agon", + "ou l", + "Ġd en", + "Ġperform ance", + "Ġb ill", + "c il", + "4 7", + "Ġen vironment", + "Ġex c", + "ad d", + "Ġwor th", + "Ġp ict", + "Ġch ance", + "Ġ201 8", + "b or", + "Ġspe ed", + "ict ion", + "Ġal leg", + "ĠJ apan", + "at ory", + "re et", + "Ġm atch", + "ĠI I", + "Ġst ru", + "ord er", + "Ġst e", + "Ġl iving", + "Ġst ruct", + "in o", + "Ġse par", + "her n", + "Ġresp onse", + "Ġen joy", + "Ġv ia", + "A D", + "um ents", + "ace book", + "Ġmem ber", + "ib r", + "iz ing", + "Ġto ol", + "ĠM on", + "ĠWh ile", + "h ood", + "ĠA ng", + "ĠD ef", + "Ġoff er", + "T r", + "a ur", + "Ġturn ed", + "ĠJ uly", + "d own", + "an ced", + "Ġrec ently", + "ĠE ar", + "Ġc e", + "ĠSt ar", + "ĠC ong", + "rough t", + "Ġbl ood", + "Ġhop e", + "Ġcom ment", + "ain t", + "Ġar ri", + "il es", + "Ġpartic ip", + "ough t", + "ri ption", + "0 8", + "4 9", + "Ġg ave", + "Ġse lect", + "Ġkill ed", + "sy ch", + "Ġgo es", + "i j", + "Ġc oll", + "Ġimp act", + "at ives", + "ĠS er", + "0 9", + "ĠAug ust", + "Ġb oy", + "d e", + "ĠD es", + "Ġf elt", + "U S", + "Ġexpect ed", + "Ġim age", + "ĠM ark", + "cc ording", + "o ice", + "E C", + "ĠM ag", + "en ed", + "h old", + "ĠP ost", + "Ġpre vent", + "N o", + "Ġinvol ved", + "Ġey es", + "Ġquick ly", + "A t", + "un k", + "Ġbeh av", + "Ġ ur", + "Ġl ed", + "c ome", + "e y", + "Ġcand id", + "Ġear lier", + "Ġfoc us", + "et y", + "P ro", + "led ge", + "ix ed", + "ill ed", + "Ġpop ular", + "A P", + "Ġset t", + "l ight", + "Ġvar ious", + "in ks", + "Ġlevel s", + "Ġro ad", + "ell ig", + "ab les", + "he l", + "itte e", + "ĠG ener", + "y pe", + "Ġhe ard", + "ic les", + "Ġm is", + "Ġus ers", + "ĠS an", + "Ġimpro ve", + "Ġf ather", + "Ġse arch", + "The y", + "v il", + "Ġprof ess", + "Ġkn ew", + "Ġl oss", + "Ġev ents", + "6 5", + "Ġb illion", + "0 7", + "0 2", + "ĠNew s", + "ĠA M", + "Ġco ver", + "w here", + "ens ion", + "Ġb ott", + "Ġare as", + "en ces", + "op e", + "ĠTw itter", + "a el", + "Ġget s", + "ĠGo ogle", + "Ġs n", + "i ant", + "Ġv ote", + "Ġnear ly", + "Ġinclud ed", + "Ġrec ogn", + "z z", + "m m", + "al ed", + "Ġhappen ed", + "0 4", + "Ġh ot", + "Ġwho se", + "Ġc ivil", + "Ġsu ff", + "o es", + "it iz", + "ĠSy ri", + "Ġresp ond", + "Ġh on", + "Ġfeat ures", + "Ġeconom ic", + "ĠApr il", + "r im", + "Ġtechn ology", + "Ġo ption", + "ag ing", + "Ġpur ch", + "R e", + "Ġl at", + "ch ie", + "is l", + "Ġrec omm", + "u f", + "Ġtr aining", + "Ġeffect s", + "Ġf ast", + "Ġ201 0", + "Ġocc ur", + "Ġwebs ite", + "Ġem ail", + "Ġs ens", + "e ch", + "Ġo il", + "Ġinf lu", + "Ġcurrent ly", + "ĠS ch", + "ĠAd d", + "Ġgo al", + "Ġsc ient", + "Ġcon v", + "1 00", + "em y", + "Ġdec ided", + "Ġtra vel", + "Ġm ention", + "L L", + "0 3", + "Ġe lection", + "Ġph one", + "Ġlook s", + "Ġsit uation", + "Ġc y", + "Ġh or", + "b ed", + "ĠCour t", + "a ily", + "av es", + "Ġqu ality", + "ĠCom p", + "w ise", + "Ġt able", + "Ġst aff", + "ĠW ind", + "et t", + "Ġtri ed", + "ide red", + "Ġadd ition", + "Ġb ox", + "Ġl ack", + "ar ily", + "Ġw ide", + "Ġm id", + "Ġbo ard", + "ys is", + "Ġant i", + "h a", + "Ġd ig", + "en ing", + "Ġd ro", + "C on", + "6 8", + "Ġsl ow", + "b ased", + "se qu", + "Ġp ath", + "E x", + "ak er", + "Ġwork ed", + "Ġp en", + "Ġeng ine", + "Ġlook ed", + "ĠSu per", + "ĠS erv", + "Ġvict im", + "U n", + "Ġproper ty", + "Ġint rodu", + "Ġexec ut", + "ĠP M", + "L e", + "Ġcol or", + "ĠM ore", + "Ġ6 0", + "Ġnet work", + "Ġd ate", + "c ul", + "id ge", + "Ġext ra", + "3 1", + "Ġs le", + "6 7", + "Ġw ond", + "Ġreport s", + "j ust", + "ĠAust ral", + "Ġcap ital", + "Ġen s", + "Ġcomm and", + "Ġallow ed", + "Ġpre p", + "Ġca pt", + "h ib", + "Ġnum bers", + "ch an", + "Ġf air", + "m p", + "om s", + "Ġre ach", + "W ith", + "t ain", + "Ġbro ad", + "Ġcou ple", + "ec ause", + "ly ing", + "ĠF eb", + "Ġsc reen", + "Ġl ives", + "Ġpri or", + "ĠCong ress", + "A r", + "Ġappro ach", + "Ġe mer", + "ar ies", + "ĠD is", + "s erv", + "ĠN e", + "Ġbu ilt", + "c ies", + "Ġre pe", + "Ġrul es", + "for ce", + "ĠP al", + "Ġfin ancial", + "Ġcons idered", + "ĠCh ar", + "n ces", + "ĠI S", + "Ġb rought", + "Ġb i", + "i ers", + "ĠS im", + "O P", + "Ġproduct s", + "Ġvis it", + "Ġdoc ument", + "Ġcon duct", + "Ġcomplete ly", + "in ing", + "ĠCal if", + "ib ly", + "Ġwr itten", + "ĠT V", + "em ents", + "Ġd raw", + "O ne", + "Ġpub lished", + "Ġsec ret", + "r ain", + "he t", + "ĠF acebook", + "ond ay", + "ĠU p", + "Ġsex ual", + "Ġth ous", + "ĠP at", + "Ġ ess", + "Ġstand ard", + "Ġar m", + "g es", + "ect ion", + "Ġf ell", + "Ġfore ign", + "an i", + "ĠFr iday", + "Ġreg ular", + "in ary", + "Ġincre ased", + "Ġus ually", + "Ġdem on", + "Ġd ark", + "Ġadd itional", + "ro l", + "ĠO f", + "Ġprodu ction", + "! !", + "und red", + "Ġintern ational", + "id ents", + "ĠF ree", + "rou p", + "Ġr ace", + "Ġm ach", + "Ġh uge", + "A ll", + "le ar", + "ove mber", + "Ġto wn", + "Ġatt ention", + "ĠO ff", + "y ond", + "ĠThe n", + "f ield", + "Ġter ror", + "ra z", + "ĠB o", + "Ġmeet ing", + "ĠP ark", + "Ġar rest", + "Ġf ear", + "Ġa w", + "ĠV al", + "or ing", + "' ,", + "Ġext reme", + "ar r", + "Ġwork ers", + "A fter", + "Ġ3 1", + "n et", + "am ent", + "Ġdirect ly", + "Ġpop ulation", + "ub e", + "ĠOct ober", + "ĠI N", + "ĠJan uary", + "5 9", + "ĠDav id", + "Ġc ross", + "ce mber", + "ĠF irst", + "Ġmess age", + "ir it", + "Ġn ation", + "Ġp oll", + "is ions", + "Ġansw er", + "n y", + "is ode", + "Ġcar ry", + "ĠRuss ia", + "Ġhe ar", + "eng th", + "ro y", + "Ġn atural", + "in ally", + "Ġdo g", + "m itted", + "Ġtr ade", + "Ġsub st", + "Ġmult iple", + "ĠAf ric", + "Ġf ans", + "Ġs ort", + "Ġgl obal", + "ic ation", + "ĠW ed", + "ar a", + "Ġa chie", + "Ġlangu age", + "ve y", + "Ġt al", + "Ġnecess ary", + "Ġdet ails", + "Ġs en", + "ĠS und", + "ĠRe g", + "ĠR ec", + "0 6", + "Ġs il", + "ress ive", + "Ġmed ical", + "un ch", + "orn ia", + "Ġu nd", + "f ort", + "oc ks", + "ĠM onday", + "ues day", + "c raft", + "7 7", + "ur t", + "Ġ ver", + "ĠH ill", + "Ġrece ive", + "Ġmor ning", + "es tern", + "Ġb ank", + "Ġs at", + "ir th", + "ĠH igh", + "Ġdev ice", + "ĠTH E", + "ĠCent er", + "Ġsaf e", + "Ġp le", + "ĠCanad a", + "Ġsystem s", + "Ġass ist", + "Ġsur v", + "Ġb attle", + "ĠS oc", + "vert is", + "S he", + "Ġp aper", + "Ġgrow th", + "Ġc ast", + "S c", + "Ġpl ans", + "ll ed", + "Ġpart s", + "Ġw all", + "Ġmove ment", + "Ġpract ice", + "im ately", + "Ġdis play", + "Ġsomet imes", + "om p", + "ĠP aul", + "ĠY es", + "k ing", + "5 8", + "o ly", + "Ġs on", + "Ġav oid", + "ok es", + "ĠJ ew", + "Ġto wards", + "as c", + "Ġ //", + "ĠK ore", + "Ġtalk ing", + "Ġcor rect", + "Ġsp ent", + "ic ks", + "i able", + "e ared", + "Ġter m", + "Ġwant s", + "om ing", + "Ġ ut", + "Ġdou b", + "Ġfor ces", + "Ġp lease", + "6 9", + "ĠN ovember", + "at form", + "ond on", + "Ġon es", + "Ġimmedi ately", + "ĠRuss ian", + "ĠM et", + "Ġde g", + "Ġparent s", + "C H", + "ĠAmeric ans", + "al y", + "ĠM od", + "Ġsh own", + "Ġcond itions", + "Ġst uff", + "Ġre b", + "ĠY our", + "Ġinclud es", + "n own", + "ĠS am", + "Ġexper ien", + "m ission", + "ĠE ven", + "augh t", + "Ġannoun ced", + "ĠRepublic an", + "Ġdeter min", + "Ġdescrib ed", + "ĠCount y", + "( )", + "Ġdo or", + "Ġchang ed", + "Ġne igh", + "ĠH ere", + "Ġcle an", + "Ġp an", + "ĠDe cember", + "ĠEurope an", + "ir ing", + "ap ter", + "Ġcl ub", + "ĠT uesday", + "Ġp aid", + "ĠN et", + "Ġattack s", + "Ġcharact ers", + "Ġal one", + "Ġdirect or", + "d om", + "Ġ3 5", + "Ġl oad", + "Ġr out", + "ĠCalif ornia", + "Ġfin ally", + "Ġr ac", + "Ġcont r", + "Ġexact ly", + "res h", + "p ri", + "ĠIs lam", + "Ġn ature", + "Ġcare er", + "Ġlat est", + "Ġcon vers", + "ĠS l", + "p ose", + "ci ent", + "ĠIn c", + "iv ity", + "8 8", + "ĠA tt", + "ĠM or", + "nes day", + "Ġwe ight", + "k en", + "Ġnot e", + "Ġteam s", + "Ġ \\", + "air s", + "ĠG reen", + "Ġh undred", + "on ent", + "Ġstre ng", + "Ġcons ist", + "ic ated", + "Ġreg ul", + "Ġl ic", + "ast ic", + "Ġt en", + "urs day", + "ellig ence", + "ous ly", + "ĠU K", + "B I", + "Ġcost s", + "Ġind epend", + "ĠA P", + "Ġnorm al", + "Ġh om", + "Ġob vious", + "Ġs we", + "Ġst ar", + "Ġread y", + "ac her", + "Ġimp lement", + "g est", + "Ġs ong", + "ĠG et", + "ĠL ab", + "Ġinterest ing", + "us ing", + "Ġg iving", + "ĠSund ay", + "Ġet c", + "Ġm iddle", + "Ġrem ember", + "r ight", + "os ition", + "ut ions", + "Ġm ax", + "4 6", + "Ġyour self", + "Ġdem and", + "Ġtreat ment", + "Ġd anger", + "ĠC ons", + "Ġgu y", + "ĠBrit ish", + "Ġphys ical", + "Ġrel ated", + "Ġrem ain", + "Ġcould n", + "Ġref er", + "Ġc itiz", + "b ox", + "EN T", + "bo ard", + "Ġin n", + "I G", + "er o", + "ĠSt reet", + "osp ital", + "ren ch", + "cher s", + "Ġst ra", + "O L", + "ag er", + "ĠA N", + "Ġeas ily", + "I A", + "en ge", + "in y", + "Ġcl os", + "ock ed", + "Ġus es", + "ĠC oun", + "I m", + "u ild", + "? ?", + "m ore", + "Ġan g", + "Ġwr ite", + "ol ute", + "5 7", + "Ġlead er", + "Ġread ing", + "< /", + "Ġaut om", + "est s", + "4 3", + "Ġleg isl", + "ĠG old", + "Ġdesign ed", + "ĠS T", + "ĠLe g", + "a res", + "Ġbe aut", + "ĠT ex", + "Ġappear s", + "Ġstru gg", + "ĠR om", + "Ġ 00", + "Ġcho ice", + "Ġparticular ly", + "ĠF rom", + "op er", + "ĠL ondon", + "ann ed", + "Ġallow s", + "ob ile", + "Ġdiffere nce", + "âĢ ¢", + "ĠV iew", + "ĠWed nesday", + "Ġal though", + "Ġrel ative", + "Ġapplic ation", + "ate ver", + "Ġare n", + "Ġmy self", + "Ġim ag", + "Ġdis e", + "Ġsoc iety", + "Ġfre qu", + "ĠEng lish", + "Ġpo or", + "ĠD ay", + "Ġwrit ing", + "Ġse ven", + "Ġstart ing", + "Ġb ud", + "Ġpr int", + "ĠTr ans", + "uf act", + "ĠSt ud", + "n ew", + "Ġcr im", + "Ġg ives", + "Ġco ol", + "a e", + "i ance", + "ĠGener al", + "Ġthink ing", + "Ġsa ve", + "Ġlim ited", + "ĠPart y", + "Ġmean ing", + "p en", + "ow ers", + "ĠJ ack", + "E M", + "Ġn ice", + "ru pt", + "Ġg as", + "Ġe ight", + "Ġfe et", + "Ġeff ort", + "Ġ ign", + "ic it", + "B l", + "co in", + "Ġop in", + "Ġbr ain", + "Wh ile", + "he st", + "ĠTh ursday", + "Ġwould n", + "augh ter", + "Ġtou ch", + "le ments", + "Ġstud ies", + "Ġcent er", + "c ont", + "or ge", + "Ġcomput er", + "Ġinvestig ation", + "P l", + "or ks", + "Ġ200 8", + "Ġincre asing", + "Ġst ore", + "Ġcom ments", + "Ġb al", + "m en", + "Ġdo ll", + "Ġl iber", + "Ġw ife", + "Ġlaw s", + "atur day", + "it ness", + "Ġmod ern", + "ĠS k", + "Ġadminist ration", + "Ġopportun ity", + "Ġs al", + "Ġpower ful", + "M y", + "Ġclaim s", + "ĠEar th", + "ord s", + "Ġt itle", + "Ġes c", + "n ame", + "N ot", + "om en", + "Ġbe yond", + "Ġc amer", + "Ġse ll", + "it ute", + "ear ch", + "Ġapp l", + "im ent", + "4 2", + "ĠAr t", + "Ġun f", + "Ġviol ence", + "ur g", + "ĠE ast", + "Ġcomp ared", + "Ġopt ions", + "Ġthrough out", + "Ġv s", + "ig r", + ". [", + "ac hes", + "7 8", + "Ġfil es", + "F L", + "E L", + "ar ian", + "ĠJ ames", + "ĠA ir", + "an ch", + "Ġdet ail", + "Ġpie ce", + "P S", + "Ġn amed", + "Ġeduc ation", + "Ġdri ve", + "Ġitem s", + "Ġstud ent", + "ic ed", + ": :", + "ic o", + "Ġth row", + "Ġsc ene", + "Ġcomple x", + "Ġ200 9", + "Ġpre c", + "ĠB re", + "7 9", + "Ġcon cept", + "Ġstat us", + "am ing", + "Ġd ied", + "Ġknow ledge", + "Ġbegin ning", + "O D", + "ru ary", + "Ġcertain ly", + "Ġgu ys", + "Ġsl ight", + "in n", + "ound s", + "Ġf ine", + "Ġf at", + "ic ations", + "Ġper haps", + "ĠA nt", + "Ġinc ome", + "Ġhtt ps", + "Ġmajor ity", + "port s", + "st on", + "Ġgreat er", + "Ġfe ed", + "ent ially", + "Ġsaf ety", + "Ġun ique", + "and om", + "Ġg one", + "Ġshow ed", + "Ġhist or", + "Ġcoun ter", + "i us", + "id a", + "Ġlead ing", + "i pe", + "Ġs end", + "ĠDon ald", + "er ve", + "Ġdef ense", + "ines e", + "Ġy es", + "ĠF ire", + "ĠMus lim", + "ra q", + "Ġcontin ued", + "os h", + "Ġprov ides", + "Ġpr ison", + "ĠP re", + "Ġhapp y", + "Ġeconom y", + "Ġtr ust", + "ag s", + "ĠG ame", + "Ġweap ons", + "um an", + "ĠC le", + "it ation", + "Ġanal ysis", + "ĠT imes", + "Ġsc ience", + "- >", + "Ġfig ure", + "Ġdis app", + "ent y", + "Ġsoft ware", + "Ġu lt", + "Ġoffic ers", + "N ew", + "I s", + "Ġrem ains", + "ĠInd ia", + "Ġp sych", + "ri ef", + "Ġc at", + "es c", + "Ġob serv", + "Ġst age", + "ĠD ark", + "Ġent er", + "ch ange", + "Ġpass ed", + "Ġdes pite", + "ĠO ut", + "Ġmov ie", + "r s", + "Ġv oice", + "m ine", + "ĠPl ay", + "Ġto ward", + "ĠT er", + "Ġreg ion", + "Ġval ues", + "or ters", + "Ġm ount", + "Ġoffic er", + "ĠO ther", + "b an", + "Ġh ous", + "w ood", + "ro om", + "I V", + "ĠS un", + "se e", + "ĠO ver", + "ro g", + "9 0", + "Ġl ay", + "ĠT ur", + "a wn", + "Ġpress ure", + "ĠS ub", + "Ġbook s", + "ed om", + "ĠS and", + "A A", + "ag o", + "Ġre asons", + "f ord", + "Ġactiv ity", + "U T", + "N ow", + "ĠSen ate", + "ce ll", + "n ight", + "Ġcall s", + "in ter", + "Ġlet ter", + "ĠR ob", + "ĠJ e", + "Ġcho ose", + "ĠL aw", + "G et", + "B e", + "Ġro b", + "Ġtyp es", + "Ġpl atform", + "Ġqu arter", + "R A", + "ĠT ime", + "Ġmay be", + "ĠC r", + "9 5", + "p re", + "Ġmov ing", + "Ġl if", + "Ġgo ld", + "Ġs om", + "Ġpat ients", + "Ġtr uth", + "ĠK e", + "ur ance", + "ant ly", + "m ar", + "Ġchar ge", + "ĠG reat", + "Ġce le", + "---------------- ----------------", + "Ġro ck", + "ro id", + "an cy", + "Ġcred it", + "a ud", + "B y", + "ĠE very", + "Ġmov ed", + "ing er", + "rib ution", + "Ġn ames", + "Ġstra ight", + "ĠHe alth", + "ĠW ell", + "Ġfe ature", + "Ġr ule", + "Ġsc he", + "in ated", + "ĠMich ael", + "ber g", + "4 1", + "il ed", + "b and", + "Ġcl ick", + "ĠAng el", + "on ents", + " Ń", + "ĠI raq", + "ĠS aturday", + "Ġa ware", + "p art", + "Ġpat tern", + "O W", + "ĠL et", + "Ġgr ad", + "ign ed", + "Ġassoci ated", + "Ġst yle", + "n o", + "i ation", + "a ith", + "il ies", + "Ġst ories", + "ur ation", + "Ġindividual s", + "ĠâĢ ¦", + "m iss", + "ĠAss oci", + "ish ing", + "ab y", + "Ġsum mer", + "ĠB en", + "Ġ3 2", + "Ġar ch", + "ut y", + "ĠTex as", + "h ol", + "Ġfull y", + "Ġm ill", + "Ġfollow ed", + "ĠB ill", + "ĠInd ian", + "ĠSec ret", + "ĠB el", + "ĠFeb ruary", + "Ġjob s", + "Ġseem ed", + "ĠGo vern", + "i pped", + "Ġreal ity", + "Ġl ines", + "Ġp ark", + "Ġmeas ure", + "ĠO ur", + "I M", + "Ġbro ther", + "Ġgrow ing", + "Ġb an", + "Ġest im", + "Ġc ry", + "ĠS chool", + "Ġme chan", + "ĠO F", + "ĠWind ows", + "Ġr ates", + "ĠO h", + "Ġpos itive", + "Ġcult ure", + "ist ics", + "ic a", + "Ġh ar", + "y a", + "ite ly", + "i pp", + "Ġm ap", + "en cies", + "ĠWill iam", + "I I", + "ak ers", + "5 6", + "ĠM art", + "ĠR em", + "Ġal tern", + "it ude", + "Ġco ach", + "row d", + "D on", + "Ġk ids", + "Ġj ournal", + "Ġcor por", + "Ġf alse", + "Ġwe b", + "Ġsle ep", + "Ġcont ain", + "Ġst o", + "Ġb ed", + "iver se", + "ĠR ich", + "ĠCh inese", + "Ġp un", + "Ġme ant", + "k nown", + "Ġnot ice", + "Ġfavor ite", + "a ven", + "Ġcond ition", + "Ġpur pose", + ") )", + "Ġorgan ization", + "Ġchall eng", + "Ġman ufact", + "Ġsus p", + "ĠA c", + "Ġcrit ic", + "un es", + "uc lear", + "Ġm er", + "vent ion", + "Ġ8 0", + "Ġm ist", + "ĠU s", + "ĠT or", + "htt p", + "ol f", + "Ġlarg er", + "Ġadv ant", + "Ġrese ar", + "Ġact ions", + "m l", + "Ġke pt", + "Ġa im", + ", '", + "c ol", + "Ġbenef its", + "if ying", + "Ġact ual", + "ĠIntern ational", + "Ġveh icle", + "Ġch ief", + "Ġeff orts", + "ĠLe ague", + "ĠM ost", + "Ġwa it", + "Ġad ult", + "Ġover all", + "Ġspe ech", + "Ġhigh ly", + "Ġfem ale", + "Ġer ror", + "Ġeffect ive", + "5 4", + "Ġenc our", + "w ell", + "Ġfail ed", + "Ġcons erv", + "Ġprogram s", + "Ġt rou", + "Ġa head", + "5 00", + "vertis ement", + "I P", + "ĠF ound", + "p ir", + "Ġ %", + "Ġcr ime", + "and er", + "Ġloc ation", + "ĠI ran", + "Ġbehav ior", + "az ing", + "Ġr are", + "Ġem b", + "Ġca used", + "Ġsh ip", + "Ġact ive", + "Ġcont ribut", + "Ġg reen", + "Ġac qu", + "Ġref lect", + "ven ue", + "Ġf irm", + "Ġb irth", + "] .", + "Ġclear ly", + "Ġem ot", + "Ġag ency", + "ri age", + "Ġmem ory", + "9 8", + "S A", + "ĠSe e", + "ac ing", + "C C", + "Ġbig gest", + "Ġr ap", + "Ġbas ic", + "Ġb and", + "e at", + "Ġsus pect", + "ĠM ac", + "Ġ9 0", + "m ark", + "ist an", + "Ġsp read", + "am s", + "k i", + "as y", + "ra v", + "ĠR ober", + "Ġdemon str", + "r ated", + "Ġabs olute", + "Ġpl aces", + "Ġim pl", + "ibr ary", + "Ġc ards", + "Ġdest roy", + "Ġv irt", + "ve re", + "Ġapp eared", + "y an", + "p oint", + "Ġbe g", + "Ġtem per", + "s pe", + "ant ed", + "ear s", + "ĠD irect", + "Ġl ength", + "Ġbl og", + "am b", + "Ġint eg", + "Ġres ources", + "ac c", + "if ul", + "Ġsp ot", + "Ġfor ced", + "Ġthous ands", + "ĠMin ister", + "Ġqu al", + "ĠF rench", + "at ically", + "Ġgener ally", + "Ġdr ink", + "Ġth us", + "I L", + "od es", + "Ġappro pri", + "ĠRe ad", + "Ġwh om", + "Ġey e", + "Ġcol lege", + "Ġ4 5", + "ire ction", + "Ġens ure", + "Ġapp arent", + "id ers", + "Ġrelig ious", + "Ġmin or", + "ol ic", + "Ġt ro", + "ĠWh y", + "rib ute", + "m et", + "Ġprim ary", + "Ġdevelop ed", + "Ġpe ace", + "Ġsk in", + "st e", + "av a", + "Ġbl ue", + "Ġfam ilies", + "Ġ ir", + "Ġapp ly", + "Ġin form", + "ĠSm ith", + "C T", + "i i", + "Ġlim it", + "Ġres ist", + "........ ........", + "um n", + "Ġconf lic", + "Ġtw e", + "ud d", + "ĠT om", + "Ġl iter", + "qu e", + "b on", + "Ġha ir", + "Ġevent ually", + "Ġp us", + "Ġhelp ed", + "Ġag g", + "or ney", + "ĠApp le", + "Ġf it", + "ĠS ur", + "Ġpre m", + "Ġs ales", + "Ġsecond s", + "Ġstreng th", + "Ġfeel ing", + "¿ ½", + "Ġt our", + "Ġknow s", + "o om", + "Ġex erc", + "Ġsom ew", + "ï ¿½", + "> >", + "Ġsp okes", + "Ġide as", + "Ġreg ist", + "so ft", + "ĠD el", + "ĠP C", + "Ġpro pos", + "Ġlaun ch", + "Ġbott om", + "T H", + "ĠP lease", + "v est", + "it z", + "ĠIn ter", + "Ġsc ript", + "Ġr at", + "ar ning", + "Ġ il", + "ĠJ er", + "ĠA re", + "Ġwh atever", + "ok en", + "ci ence", + "Ġmod e", + "Ġag ree", + "Ġs ources", + "Ġinit ial", + "Ġrest rict", + "Ġwond er", + "us ion", + "## ##", + "ĠS il", + "vil le", + "Ġb urn", + "t w", + "as ion", + "Ġ £", + "Ġn or", + "u ing", + "Ġre ached", + "Ġs un", + "Ġc ateg", + "ig ration", + "Ġc ook", + "Ġprom ot", + "Ġm ale", + "Ġcl imate", + "Ġf ix", + "Ġalleg ed", + "U R", + "all ed", + "Ġim ages", + "C ont", + "ot a", + "Ġschool s", + "i os", + "Ġd rop", + "Ġst ream", + "ĠM o", + "Ġprevious ly", + "al ing", + "Ġp et", + "Ġdou ble", + "Ġ( @", + "ann el", + "Ġdef ault", + "t ies", + "Ġr ank", + "ĠD ec", + "ĠCoun cil", + "Ġweap on", + "Ġst ock", + "Ġanal y", + "ĠSt r", + "Ġpict ure", + "ĠPol ice", + "f erence", + "Ġcent ury", + "Ġcitiz ens", + "Ġon to", + "Ġexp and", + "Ġhe ro", + "ĠS ol", + "Ġw ild", + "Ġupd ate", + "Ġcustom ers", + "r ont", + "d ef", + "Ġl ik", + "Ġcrim inal", + "ĠChrist ian", + "S P", + "7 6", + "Ġle aving", + "Ġother wise", + "ĠD ist", + "Ġbas is", + "5 2", + "5 3", + "ic ip", + "ĠB er", + "Ġrecomm end", + "Ġfl oor", + "Ġc rowd", + "ol es", + "Ġ7 0", + "Ġcent ral", + "ĠE v", + "Ġd ream", + "Ġdown load", + "Ġconf ir", + "ĠTh om", + "Ġwind ow", + "Ġhapp ens", + "Ġun it", + "Ġt end", + "Ġs pl", + "Ġbec omes", + "Ġfight ing", + "Ġpred ict", + "ĠP ress", + "ĠP ower", + "Ġhe avy", + "ak ed", + "Ġf an", + "or ter", + "ate gy", + "B A", + "iz es", + "Ġsp end", + "H ere", + "Ġ200 7", + "Ġad op", + "ĠH am", + "Ġfoot ball", + "ĠP ort", + "od ay", + "5 1", + "amp ions", + "Ġtrans fer", + "h t", + "Ġ3 8", + "ter m", + "ac ity", + "Ġb ur", + "] ,", + "tern al", + "r ig", + "b ut", + "Ġthere fore", + "ĠB ecause", + "res p", + "re y", + "Ġm ission", + "S ome", + "Ġnot ed", + "Ġass um", + "Ġdise ase", + "Ġed it", + "Ġprog ress", + "r d", + "ĠB rown", + "oc al", + "Ġadd ing", + "Ġra ised", + "ĠAn y", + "Ġt ick", + "Ġsee ing", + "ĠPe ople", + "Ġagre ement", + "Ġser ver", + "Ġw at", + "Ġdeb ate", + "Ġsupp osed", + "il ing", + "Ġlarg est", + "Ġsuccess ful", + "ĠP ri", + "ĠDemocr atic", + "Ġj ump", + "ĠSyri a", + "Ġown ers", + "Ġoff ers", + "Ġshoot ing", + "Ġeff ic", + "se y", + "Ġha ven", + "ver se", + "te red", + "ĠL ight", + "im al", + "ĠB ig", + "Ġdef end", + "Ġbe at", + "Ġrecord s", + "% )", + "Ġsc en", + "Ġemploy ees", + "Ġdev ices", + "he m", + "Ġcom mer", + "ĠM ex", + "Ġbenef it", + "ĠPro f", + "Ġil leg", + "Ġsur face", + "ĠAl so", + "Ġh arm", + "ing ly", + "w ide", + "ĠA lex", + "Ġsh ut", + "ĠC ur", + "Ġl ose", + "p m", + "Ġchall enge", + "se mb", + "Ġst ation", + "Ġint elligence", + "Ġacc ur", + "ĠFl or", + "Ġrequ ires", + "ĠM al", + "b um", + "Ġh ospital", + "Ġsp irit", + "Ġoff ered", + "Ġprodu ce", + "ĠComm un", + "Ġcreat ing", + "Ġcr is", + "s pect", + "Ġend ed", + "Ġd aily", + "Ġvot ers", + "land s", + "i as", + "i h", + "on a", + "Ġsm art", + "ĠOff ice", + "ĠL ord", + "ri al", + "ĠIntern et", + "Ġcirc um", + "Ġextreme ly", + "' .", + "Ġopin ion", + "ĠM il", + "Ġg ain", + "B S", + "ĠF in", + "y p", + "Ġuse ful", + "Ġbud get", + "Ġcom fort", + "is f", + "Ġback ground", + "el ine", + "Ġep isode", + "Ġen emy", + "Ġtri al", + "Ġestab lish", + "d ate", + "ĠC ap", + "Ġcontin ues", + "Ġshow ing", + "ĠUn ion", + "w ith", + "Ġpost ed", + "ĠSy stem", + "Ġe at", + "ri an", + "Ġr ise", + "ĠGerman y", + "il s", + "Ġsign ed", + "Ġv ill", + "Ġgr and", + "m or", + "ĠEng land", + "Ġproject s", + "um ber", + "Ġconf erence", + "z a", + "Ġrespons ible", + "ĠAr ab", + "Ġlearn ed", + "âĢĶ âĢĶ", + "i pping", + "ĠGe orge", + "O C", + "Ġreturn ed", + "ĠAustral ia", + "Ġb rief", + "Q u", + "Ġbr and", + "ill ing", + "ab led", + "Ġhig hest", + "Ġtr ain", + "ĠComm ission", + "wh ile", + "Ġn om", + "cept ion", + "Ġm ut", + "ĠBl ue", + "Ġinc ident", + "v ant", + "8 6", + "ĠI D", + "Ġn uclear", + "7 4", + "ĠL ike", + "ĠR E", + "ĠM icro", + "l i", + "m ail", + "Ġcharg es", + "8 9", + "Ġad just", + "ad o", + "Ġear th", + "N A", + "Ġpr ices", + "P A", + "Ġd raft", + "Ġrun s", + "Ġcandid ate", + "ens es", + "Ġmanag ement", + "ĠPh il", + "ĠM iss", + "Ġte ach", + "g ram", + "Ġunderstand ing", + "a it", + "ic ago", + "A dd", + "ĠE p", + "sec ut", + "Ġsepar ate", + "Ġinst ance", + "Ġe th", + "Ġun less", + "**** ****", + "ĠF ore", + "in ate", + "Ġoper ations", + "S p", + "Ġf aith", + "g ar", + "ĠCh urch", + "ron ic", + "Ġconf ig", + "os ure", + "Ġactiv ities", + "Ġtrad itional", + "Ġ3 6", + "Ġd irection", + "Ġmach ine", + "Ġsur round", + "Ġp ush", + "un ction", + "ĠE U", + "Ġeas ier", + "Ġarg ument", + "G B", + "Ġm icro", + "Ġsp ending", + "iz ations", + "Ġthe ory", + "ad ow", + "Ġcall ing", + "ĠL ast", + "Ġd er", + "Ġinflu ence", + "Ġcomm it", + "Ġph oto", + "Ġun c", + "ist ry", + "g n", + "ast e", + "ack s", + "Ġdis p", + "ad y", + "d o", + "ĠG ood", + "Ġ `", + "Ġw ish", + "Ġreve aled", + "Âł Âł", + "l ig", + "Ġen force", + "ĠComm ittee", + "Ġche m", + "Ġmil es", + "Ġinterest ed", + "Ġsol ution", + "ic y", + "in ct", + "Ġ- >", + "ĠD et", + "Ġrem oved", + "Ġcomp ar", + "e ah", + "Ġpl ant", + "ĠS ince", + "Ġachie ve", + "Ġadvant age", + "Ġslight ly", + "b ing", + "Ġpl aced", + "u nder", + "201 5", + "ĠM ad", + "Ġt im", + "os es", + "Ġc ru", + "ĠR ock", + "Ġmost ly", + "Ġneg ative", + "Ġset ting", + "Ġprodu ced", + "Ġm ur", + "Ġconnect ion", + "ĠM er", + "Ġdri ver", + "Ġexecut ive", + "Ġass ault", + "Ġb orn", + "ĠV er", + "t ained", + "Ġstruct ure", + "Ġredu ce", + "Ġdec ades", + "Ġd ed", + "u ke", + "ĠM any", + "idd en", + "Ġle ague", + "S e", + "Ġjo in", + "Ġdis co", + "Ġd ie", + "c ks", + "act ions", + "Ġass ess", + "ag n", + "Ġgo als", + "our s", + "I R", + "Ġsen ior", + "ill er", + "m od", + "ip ment", + "oc ol", + "u y", + "ĠQ ue", + "Ġpart ies", + "ir gin", + "Ġle arning", + "it able", + "Ġstre et", + "Ġcamer a", + "A pp", + "Ġsk ills", + "b re", + "c ious", + "Ġcele br", + "ĠFr anc", + "Ġexist ing", + "Ġwill ing", + "l or", + "Ġ id", + "ĠSp ace", + "Ġcrit ical", + "ĠL a", + "ortun ately", + "Ġser ve", + "Ġc old", + "Ġspec ies", + "T S", + "Ġanim als", + "ĠB ay", + "Ġold er", + "ĠU nder", + "est ic", + "ĠT re", + "Ġte acher", + "Ġpre fer", + "v is", + "Ġth read", + "ĠM att", + "Ġmanag er", + "ãĥ »", + "Ġprofess ional", + "ĠV ol", + "Ġnot es", + "The se", + "ul a", + "Ġf resh", + "ent ed", + "u zz", + "ed y", + "clus ion", + "ĠR el", + "Ġdoub t", + "E O", + "Ġopen ed", + "ĠB it", + "Ad vertisement", + "Ġgu ess", + "ĠU N", + "Ġse qu", + "Ġexpl ain", + "ott en", + "Ġatt ract", + "ak s", + "Ġstr ing", + "Ġcont ext", + "oss ible", + "ĠRepublic ans", + "Ġsol id", + "Ġc ities", + "Ġask ing", + "Ġr andom", + "u ps", + "ur ies", + "ar ant", + "dd en", + "g l", + "ĠFlor ida", + "Ġdep end", + "ĠSc ott", + "Ġ3 3", + "Ġi T", + "ic on", + "Ġmention ed", + "Ġ2 000", + "Ġclaim ed", + "Ġdefin itely", + "ul f", + "Ġc ore", + "Ġopen ing", + "ĠCon st", + "wh ich", + "ĠT ra", + "A G", + "7 2", + "Ġbelie ved", + "ad a", + "Ġ4 8", + "ĠSec urity", + "yr ight", + "ĠP et", + "ĠL ou", + "Ġhold ing", + "======== ========", + "Ġ ice", + "Ġb row", + "Ġauthor ities", + "h ost", + "w ord", + "Ġsc ore", + "ĠD iv", + "Ġcell s", + "Ġtrans l", + "Ġneigh bor", + "Ġrem ove", + "u ct", + "Ġdist rict", + "ĠA ccording", + "Ġwor se", + "Ġconcern s", + "Ġpresident ial", + "Ġpolic ies", + "ĠH all", + "7 3", + "Ġh us", + "A Y", + "Ġ200 6", + "ĠJ ud", + "Ġindepend ent", + "ĠJust ice", + "ili ar", + "pr int", + "igh ter", + "Ġprotect ion", + "z en", + "Ġsu dden", + "h ouse", + "ĠJ es", + "P R", + "ĠIn f", + "Ġb ul", + "Ġ _", + "ĠServ ice", + "ĠP R", + "Ġstr ategy", + "ff ect", + "Ġgirl s", + "Ġmiss ing", + "oy al", + "ĠTe am", + "ul ated", + "Ġd at", + "Ġpolit ics", + "ab or", + "A ccording", + "Ġspe ll", + "Ġg raph", + "ort hern", + "T C", + "A b", + "Ġlab or", + "is her", + "Ġk ick", + "ĠiT unes", + "Ġstep s", + "pos es", + "Ġsmall er", + "E n", + "ber t", + "Ġro ll", + "Ġresear chers", + "Ġcl osed", + "Ġtrans port", + "Ġlaw y", + "________ ________", + "ĠCh icago", + "Ġas pect", + "Ġn one", + "Ġmar riage", + "9 6", + "Ġe lements", + "ĠF re", + "ĠS al", + "Ġd ram", + "F C", + "t op", + "e qu", + "Ġhe aring", + "Ġsupport ed", + "Ġtest ing", + "co hol", + "Ġmass ive", + "Ġst ick", + "Ġgu ard", + "is co", + "ph one", + "F rom", + "How ever", + "Ġb order", + "Ġcop y", + "ograph y", + "l ist", + "7 1", + "Ġown er", + "cl ass", + "ru it", + "r ate", + "ĠO nce", + "Ġdig ital", + "Ġt ask", + "ER S", + "Ġinc red", + "t es", + "+ +", + "ĠFr ance", + "Ġb reat", + "ow l", + "Ġiss ued", + "ĠW estern", + "Ġdet ect", + "Ġpart ners", + "Ġsh ared", + "ĠC all", + "Ġcan cer", + "ac he", + "rib e", + "Ġexpl ained", + "Ġhe at", + "{ \"", + "Ġinvest ment", + "ĠB ook", + "Ġw ood", + "Ġtool s", + "ĠAl though", + "Ġbelie f", + "Ġcris is", + "Ġg e", + "ĠM P", + "Ġoper ation", + "ty pe", + "~ ~", + "g a", + "Ġcont ains", + "ant a", + "Ġexp ress", + "ĠG roup", + "ĠJ ournal", + "k a", + "Ġam b", + "ĠUS A", + "Ġfind ing", + "Ġfund ing", + "h ow", + "Ġestab lished", + "ide os", + "Ġdeg ree", + "Ġdanger ous", + "ang ing", + "Ġfre edom", + "pp ort", + "out hern", + "Ġch urch", + "Ġc atch", + "ĠTw o", + "Ġpres ence", + "ĠGu ard", + "U p", + "Ġauthor ity", + "ĠPro ject", + "Ġbut ton", + "Ġcon sequ", + "Ġval id", + "Ġwe ak", + "Ġstart s", + "Ġref erence", + "ĠM em", + "\" )", + "U N", + "or age", + "ĠO pen", + "Ġcol lection", + "y m", + "g ency", + "Ġbeaut iful", + "ro s", + "Ġtell s", + "Ġwa iting", + "n el", + "Ġprov iding", + "ĠDemocr ats", + "Ġd aughter", + "Ġm aster", + "Ġpur poses", + "ĠJapan ese", + "Ġequ al", + "Ġturn s", + "Ġdoc uments", + "Ġwatch ing", + "R es", + "Ġr an", + "201 4", + "Ġre ject", + "ĠKore a", + "Ġvictim s", + "Le vel", + "ere nces", + "Ġw itness", + "Ġ3 4", + "Ġre form", + "com ing", + "Ġocc up", + "Ġc aught", + "Ġtra ffic", + "ad ing", + "Ġmod els", + "ar io", + "Ġserv ed", + "Ġb atter", + "u ate", + "ĠSecret ary", + "Ġagre ed", + "Ġtr uly", + "yn am", + "ĠR et", + "Ġun its", + "ĠRes earch", + "h and", + "az ine", + "ĠM ike", + "Ġvar iety", + "ot al", + "Ġam azing", + "Ġconfir med", + "Ġentire ly", + "Ġpurch ase", + "Ġe lement", + "Ġc ash", + "Ġdeter mine", + "D e", + "Ġc ars", + "ĠW all", + "â ĸ", + "Ġview s", + "Ġdrug s", + "Ġdep artment", + "ĠSt ep", + "u it", + "Ġ3 9", + "as ure", + "ĠCl ass", + "Ġc overed", + "ĠB ank", + "Ġme re", + "u ana", + "Ġmult i", + "Ġm ix", + "Ġun like", + "lev ision", + "Ġsto pped", + "Ġs em", + "ĠG al", + "ul es", + "Ġwe l", + "ĠJohn son", + "l a", + "Ġsk ill", + "Ġbec oming", + "ri e", + "Ġappropri ate", + "f e", + "ell ow", + "ĠPro t", + "ul ate", + "oc ation", + "Ġweek end", + "od ies", + "Ġsit es", + "Ġanim al", + "ĠT im", + "Ġsc ale", + "Ġcharg ed", + "Ġinst ruct", + "ill a", + "Ġmethod s", + "Ġc ert", + "Ġjud ge", + "ĠH el", + "Ġdoll ars", + "Ġstand ing", + "ĠS qu", + "Ġdeb t", + "l iam", + "Ġdri ving", + "ĠS um", + "ĠEd ition", + "Ġal bum", + "and on", + "I F", + "ĠU k", + "6 3", + "ad er", + "Ġcommer cial", + "es h", + "ĠGovern ment", + "Ġdisc overed", + "Ġout put", + "ĠHill ary", + "ĠCar ol", + "Ġ200 5", + "Ġab use", + "anc ing", + "Ġsw itch", + "Ġann ual", + "T w", + "Ġst ated", + "ag ement", + "in ner", + "Ġdem ocr", + "Ġres idents", + "Ġallow ing", + "Ġfact ors", + "od d", + "Ġf uck", + "em ies", + "Ġoccur red", + "ot i", + "Ġn orth", + "ĠP ublic", + "Ġinj ury", + "Ġins urance", + "C L", + "oll y", + "ã Ģ", + "Ġrepe ated", + "Ġar ms", + "ang ed", + "Ġconst ruction", + "Ġf le", + "P U", + "ic ians", + "Ġfor ms", + "ĠMc C", + "ant ic", + "Ġm ental", + "p ire", + "Ġequ ipment", + "Ġf ant", + "Ġdiscuss ion", + "Ġregard ing", + "k in", + "ar p", + "Ġch air", + "og ue", + "Ġpro ceed", + "ĠI d", + "O ur", + "Ġmur der", + "M an", + "Ġ4 9", + "as p", + "Ġsupp ly", + "Ġin put", + "Ġwe alth", + "liam ent", + "Ġpro ced", + "or ial", + "ĠSt at", + "ĠN FL", + "hen s", + "ĠInst itute", + "Ġput ting", + "ourn ament", + "et ic", + "Ġloc ated", + "Ġk id", + "er ia", + "r un", + "Ġpr inc", + "Ġ !", + "go ing", + "ĠB et", + "Ġcl ot", + "Ġtell ing", + "Ġprop osed", + "i ot", + "or ry", + "Ġfund s", + "g ment", + "ĠL ife", + "Ġb aby", + "ĠB ack", + "Ġsp oke", + "Im age", + "Ġear n", + "ĠA T", + "g u", + "Ġex change", + "ĠL in", + "ov ing", + "Ġp air", + "M ore", + "az on", + "Ġarrest ed", + "Ġkill ing", + "c an", + "ĠC ard", + "y d", + "Ġident ified", + "Ġm obile", + "Ġthan ks", + "ony m", + "ĠF orm", + "Ġhundred s", + "ĠCh ris", + "ĠC at", + "Ġtre nd", + "h at", + "ĠA v", + "om an", + "Ġelect ric", + "ĠW il", + "S E", + "O f", + "Ġrest aur", + "ot ed", + "Ġtr ig", + "Ġn ine", + "Ġb omb", + "Wh y", + " ¯", + "Ġco verage", + "Ġapp eal", + "ĠRober t", + "ĠS up", + "Ġfin ished", + "Ġfl ow", + "Ġdel iver", + "Ġcal cul", + "Ġphot os", + "Ġph il", + "Ġpie ces", + "Ġapp re", + "k es", + "Ġr ough", + "D o", + "Ġpart ner", + "Ġconcern ed", + "Ġ3 7", + "ĠG en", + "C ol", + "ct ors", + "Ġ= >", + "st ate", + "Ġsuggest ed", + "ĠFor ce", + "C E", + "Ġher self", + "ĠPl an", + "w orks", + "o oth", + "ren cy", + "Ġcor ner", + "Ġhus band", + "Ġintern et", + "ĠA ut", + "em s", + "os en", + "ĠAt l", + "g en", + "Ġbal ance", + "6 2", + "Ġsound s", + "te xt", + "Ġar r", + "ov es", + "Ġmill ions", + "Ġrad io", + "Ġsat isf", + "ĠD am", + "M r", + "G o", + "S pe", + "Ġcomb at", + "r ant", + "ĠG ree", + "Ġf uel", + "Ġdist ance", + "Ġtest s", + "Ġdec re", + "ĠE r", + "Ġman aged", + "D S", + "Ġt it", + "Ġmeas ures", + "ĠL iber", + "Ġatt end", + "as hed", + "ĠJ ose", + "ĠN ight", + "d it", + "ĠN ov", + "ĠE nd", + "out s", + "Ġgener ation", + "Ġadv oc", + "y th", + "Ġconvers ation", + "ĠS ky", + "act ive", + "ce l", + "ri er", + "ĠFr ank", + "Ġg ender", + "Ġcon cent", + "Ġcar ried", + "and a", + "ĠV irgin", + "Ġarri ved", + "ic ide", + "ad ed", + "Ġfail ure", + "Ġmin imum", + "le ts", + "Ġwor st", + "Ġkeep ing", + "Ġint ended", + "Ġilleg al", + "Ġsub sc", + "Ġdetermin ed", + "Ġtri p", + "Y es", + "Ġra ise", + "Ġ ~", + "Ġfeel s", + "Ġpack age", + "ĠJ o", + "h i", + "201 6", + "re al", + "Ġf ra", + "Ġsy mb", + "M e", + "uck y", + "p ret", + "ĠK h", + "ĠEd it", + "ĠWe b", + "em ic", + "ĠCol or", + "Ġjust ice", + "I nt", + "Ġfar m", + "ck now", + "\" >", + "el ess", + "Ġredu ced", + "Ġ5 00", + "x x", + "ĠR ad", + "ĠW ood", + "Ġcl in", + "Ġhy p", + "il er", + "ur a", + "k ins", + "8 5", + "6 1", + "ĠThe ir", + "ĠM ary", + "Ġs an", + "Ġno vel", + "ĠWh o", + "Ġcap acity", + "Ġimp ossible", + "Ġpl ays", + "Ġmin ister", + "ij uana", + "ic ate", + "ĠS et", + "Ġf ram", + "Ġ ing", + "Ġcommun ities", + "ĠF BI", + "it a", + "Ġb on", + "Ġstr ateg", + "Ġinterest s", + "l ock", + "g ers", + "m as", + "ĠAN D", + "Ġconflic t", + "Ġrequire ments", + "Ġs ac", + "Ġoper ating", + "in i", + "rel ated", + "Ġcomm itted", + "Ġrelative ly", + "Ġs outh", + "¯ ¯", + "Ġaff ord", + "Ġident ity", + "Ġdec isions", + "Ġacc used", + "pl ace", + "Ġvict ory", + "o ch", + "i at", + "N ame", + "C om", + "t ion", + "ed s", + "Ġsee k", + "Ġt ight", + "ĠIm ages", + "Ġinit i", + "Ġhum ans", + "Ġfam iliar", + "Ġaud ience", + "Ġintern al", + "vent ure", + "Ġs ides", + "ĠT O", + "Ġd im", + "Ġcon clud", + "Ġapp oint", + "Ġenforce ment", + "ĠJ im", + "ĠAssoci ation", + "Ġcircum st", + "ĠCanad ian", + "Ġjo ined", + "Ġdiffere nces", + "ĠL os", + "Ġprot est", + "Ġtw ice", + "w in", + "Ġgl ass", + "ars h", + "ĠAr my", + "Ġexp ression", + "Ġdec ide", + "Ġplan ning", + "an ia", + "Ġhand le", + "ĠMicro soft", + "ĠN or", + "Ġmax imum", + "ĠRe v", + "Ġse a", + "Ġev al", + "Ġhel ps", + "re f", + "Ġb ound", + "Ġm outh", + "Ġstand ards", + "Ġcl im", + "ĠC amp", + "ĠF ox", + "cl es", + "Ġar my", + "ĠTe chn", + "ack ing", + "x y", + "S S", + "Ġ4 2", + "Ġbu g", + "ĠUk rain", + "ĠM ax", + "ĠJ ones", + "ĠSh ow", + "l o", + "Ġplan et", + "Ġ7 5", + "Ġwin ning", + "Ġf aster", + "Ġspe ct", + "Ġbro ken", + "T R", + "Ġdef ined", + "Ġhealth y", + "Ġcompet ition", + "htt ps", + "ĠIs land", + "ĠF e", + "Ġannoun ce", + "ĠC up", + "ĠInst ead", + "Ġcl ient", + "Ġposs ibly", + "se ction", + "ock et", + "l ook", + "Ġfin ish", + "Ġcre w", + "Ġres erv", + "Ġed itor", + "Ġh ate", + "Ġs ale", + "Ġcontro vers", + "Ġp ages", + "w ing", + "Ġnum er", + "Ġopp osition", + "Ġ200 4", + "Ġref uge", + "Ġfl ight", + "Ġap art", + "ĠL at", + "A meric", + "ĠAfric a", + "Ġapplic ations", + "ĠPal est", + "ĠB ur", + "Ġg ar", + "ĠSoc ial", + "Ġup gr", + "Ġsh ape", + "Ġspe aking", + "ans ion", + "a o", + "ĠS n", + "Ġwor ry", + "ĠBrit ain", + "P lease", + "rou d", + "Ġh un", + "Ġintrodu ced", + "Ġd iet", + "I nd", + "ĠSec ond", + "Ġfun ctions", + "ut s", + "ĠE ach", + "ĠJe ff", + "Ġst ress", + "Ġaccount s", + "Ġgu arant", + "ĠAn n", + "ed ia", + "Ġhon est", + "Ġt ree", + "ĠAfric an", + "ĠB ush", + "} ,", + "Ġs ch", + "ĠOn ly", + "Ġf if", + "ig an", + "Ġexerc ise", + "ĠEx p", + "Ġscient ists", + "Ġlegisl ation", + "ĠW ork", + "ĠS pr", + "à Ĥ", + "ĠH uman", + "Ġ è", + "Ġsur vey", + "Ġr ich", + "ri p", + "Ġmain tain", + "Ġfl o", + "Ġleaders hip", + "st ream", + "ĠIslam ic", + "Ġ 01", + "ĠCol lege", + "Ġmag ic", + "ĠPr ime", + "Ġfig ures", + "201 7", + "ind er", + "x ual", + "ĠDe ad", + "Ġabsolute ly", + "Ġfour th", + "Ġpresent ed", + "resp ond", + "rib le", + "Ġal cohol", + "at o", + "ĠD E", + "por ary", + "Ġgr ab", + "Ġvar i", + "Ġqu ant", + "ĠPh oto", + "Ġpl us", + "r ick", + "ar ks", + "Ġaltern ative", + "Ġp il", + "Ġappro x", + "th at", + "Ġobject s", + "ĠR o", + "ĠAnd roid", + "Ġsignificant ly", + "ĠR oad", + "k ay", + "R ead", + "av or", + "Ġa cknow", + "ĠH D", + "ĠS ing", + "O r", + "ĠM ont", + "Ġun s", + "pro f", + "Ġneg oti", + "ĠAr ch", + "ik i", + "Ġte levision", + "ĠJew ish", + "Ġcomm ittee", + "Ġmot or", + "Ġappear ance", + "Ġs itting", + "Ġstri ke", + "ĠD own", + "com p", + "ĠH ist", + "Ġf old", + "ac ement", + "ĠLou is", + "Ġbel ong", + "ĠâĢ ¢", + "Ġm ort", + "Ġprep ared", + "Ġ6 4", + "ĠM aster", + "Ġind eed", + "ĠD en", + "Ġre nt", + "T A", + "our ney", + "ar c", + "S u", + "9 7", + "Ġadv ice", + "Ġchang ing", + "Ġlist ed", + "Ġlaun ched", + "is ation", + "ĠP eter", + "is hes", + "Ġl ived", + "ĠM el", + "ĠSup reme", + "ĠF ederal", + "Ġ) ;", + "ruct ure", + "Ġset s", + "Ġphil os", + "u ous", + "Ġ ł", + "Ġappl ied", + "ĠN OT", + "Ġhous ing", + "ĠM ount", + "Ġo dd", + "Ġsu st", + "D A", + "ffic ient", + "Ġ ?", + "ol ved", + "Ġp owers", + "Ġth r", + "Ġrem aining", + "ĠW ater", + "L C", + "Ġca uses", + "ãģ ®", + "Ġman ner", + "ad s", + "Ġsuggest s", + "Ġend s", + "stand ing", + "f ig", + "ĠD un", + "id th", + "Ġg ay", + "Ġter min", + "ĠAngel es", + "M S", + "Ġscient ific", + "Ġco al", + "ap ers", + "b ar", + "ĠThom as", + "Ġsy m", + "ĠR un", + "th is", + "P C", + "igr ants", + "Ġmin ute", + "ĠDist rict", + "cell ent", + "Ġle aves", + "Ġcomple ted", + "am in", + "Ġfoc used", + "Ġmon itor", + "Ġveh icles", + "M A", + "ĠM ass", + "ĠGr and", + "Ġaffect ed", + "itution al", + "Ġconst ruct", + "Ġfollow s", + "Ġt on", + "re ens", + "Ġh omes", + "ĠE xt", + "ĠLe vel", + "r ast", + "ĠI r", + "Ġel im", + "Ġlarge ly", + "ĠJ oe", + "Ġvot es", + "all s", + "Ġbusiness es", + "ĠFound ation", + "ĠCent ral", + "Ġy ards", + "Ġmaterial s", + "ul ner", + "Ġgu ide", + "Ġclos er", + "um s", + "Ġsp orts", + "ed er", + "J ust", + "Ġtax es", + "8 4", + "ĠO ld", + "Ġdec ade", + "ol a", + "Ġv ir", + "Ġdro pped", + "Ġdel ay", + "it ect", + "Ġsec ure", + "ste in", + "le vel", + "Ġtre ated", + "Ġfil ed", + "ain e", + "Ġv an", + "Ġm ir", + "Ġcol umn", + "ict ed", + "e per", + "Ġro t", + "Ġcons ult", + "Ġent ry", + "Ġmar ijuana", + "ĠD ou", + "Ġapparent ly", + "ok ing", + "clus ive", + "Ġincre ases", + "an o", + "Ġspecific ally", + "Ġte le", + "ens ions", + "Ġrelig ion", + "ab ilities", + "Ġfr ame", + "ĠN ote", + "ĠLe e", + "Ġhelp ing", + "Ġed ge", + "ost on", + "Ġorgan izations", + "à ĥ", + "ĠB oth", + "hip s", + "Ġbig ger", + "Ġbo ost", + "ĠSt and", + "Ġro w", + "ul s", + "ab ase", + "Ġr id", + "L et", + "are n", + "ra ve", + "Ġst ret", + "P D", + "Ġv ision", + "Ġwe aring", + "Ġappre ci", + "Ġa ward", + "ĠU se", + "Ġfact or", + "w ar", + "ul ations", + ") (", + "Ġg od", + "Ġter rit", + "Ġpar am", + "ast s", + "8 7", + "Ġen emies", + "ĠG ames", + "F F", + "Ġacc ident", + "W ell", + "ĠMart in", + "T ER", + "Ġat h", + "ĠHe ll", + "Ġfor g", + "Ġve ter", + "ĠMed ic", + "f ree", + "Ġst ars", + "Ġexp ensive", + "Ġac ad", + "ra wn", + "ĠW he", + "Ġl ock", + "Ġform at", + "Ġsold iers", + "s m", + "Ġag ent", + "Ġrespons ibility", + "or a", + "ĠS cience", + "Ġrap id", + "Ġt ough", + "ĠJes us", + "Ġbelie ves", + "M L", + "Ġwe ar", + "le te", + "Ãĥ ÃĤ", + "ĠD ri", + "Ġcomm ission", + "ĠB ob", + "O h", + "ap ed", + "Ġwar m", + "ÃĥÃĤ ÃĥÃĤ", + "Ġ200 3", + "ort ion", + "Ġhas n", + "ust er", + "Ġun ivers", + "ĠI ll", + "Ġk ing", + "olog ies", + "9 4", + "ĠT em", + "ĠM os", + "Ġpat ient", + "ĠMex ico", + "ce an", + "ĠDe ath", + "ĠSand ers", + "y ou", + "ĠC ast", + "ĠComp any", + "pt y", + "Ġhappen ing", + "F P", + "ĠB attle", + "Ġb ought", + "A m", + "M od", + "U s", + "ut ers", + "ĠC re", + "ĠTh ose", + "Ġ4 4", + "is er", + "Ġs oul", + "ĠT op", + "ĠHar ry", + "ĠA w", + "Ġse at", + "ff ee", + "Ġrev olution", + "Ġ( \"", + "ĠD uring", + "et te", + "Ġr ing", + "Ġoff ensive", + "Ġreturn s", + "Ġv ideos", + "Ġdis cl", + "Ġfam ous", + "en ced", + "ĠS ign", + "ĠR iver", + "Ġ3 00", + "P M", + "ĠB us", + "ĠC H", + "Ġcandid ates", + "ard en", + "Ġpercent age", + "Ġvis ual", + "Ġthan k", + "Ġtrou ble", + "ner gy", + "Ġ200 1", + "Ġpro ve", + "ash ion", + "Ġen h", + "ĠL ong", + "U M", + "Ġconnect ed", + "Ġposs ibility", + "O ver", + "Ġexper t", + "Ġl ibrary", + "art s", + "ĠDirect or", + "Ġfell ow", + "9 2", + "ir ty", + "Ġd ry", + "Ġsign s", + "ĠL ove", + "Ġqu iet", + "f oot", + "Ġp ure", + "ĠH un", + "Ġf illed", + "ph as", + "ĠE lect", + "end ment", + "ĠEx pl", + "Ġun able", + "n s", + "m o", + "Ġv ast", + "ob e", + "Ġident ify", + "app ing", + "ĠCarol ina", + "g ress", + "Ġpro te", + "Ġf ish", + "Ġcircumst ances", + "raz y", + "ĠPh ot", + "Ġb odies", + "ĠM ur", + "Ġdevelop ing", + "ĠA R", + "Ġexperien ced", + "Ġsubst ant", + "ĠBo ard", + "es ome", + "Ġdom estic", + "Ġcomb ined", + "ĠP ut", + "Ġchem ical", + "ĠCh ild", + "Ġpo ol", + "ĠC y", + "Ġe gg", + "c ons", + "st ers", + "Ġh urt", + "Ġmark ets", + "Ġconserv ative", + "Ġsupp orters", + "Ġag encies", + "id el", + "O b", + "ur b", + "Ġ4 3", + "ĠDef ense", + "y e", + "ĠA p", + "du le", + "Ġtemper ature", + "Ġconduct ed", + "ĠCh ief", + "Ġpull ed", + "Ġf ol", + "L ast", + "ont o", + "os is", + "V ER", + "D es", + "ĠP an", + "F irst", + "Ġadv ance", + "Ġlic ense", + "r ors", + "ĠJ on", + "Ġimag ine", + "Ġhe ll", + "Ġf ixed", + "Ġinc or", + "os ite", + "ĠL og", + "ick en", + "] :", + "Ġsurpr ise", + "h ab", + "Ġc raft", + "ol t", + "ĠJ ul", + "Ġd ial", + "Ġrele vant", + "Ġent ered", + "Ġlead s", + "ĠA D", + "ĠCle an", + "Ġpict ures", + "ess or", + "Ġal t", + "Ġpay ing", + "P er", + "ĠMark et", + "Ġupd ates", + "am ily", + "ĠT ype", + "ĠH ome", + "Ġ5 5", + "semb ly", + "rom e", + "8 3", + "Ġgreat est", + "Ġhe ight", + "Ġhe av", + "ain ts", + "Ġlist en", + "as er", + "ĠS H", + "Ġcap able", + "ac le", + "Ġpers pect", + "in ating", + "Ġoff ering", + "ry pt", + "ĠDe velop", + "ab in", + "r c", + "Ġbr ight", + "al ty", + "ar row", + "Ġsupp l", + "ind ing", + "ack ed", + "gy pt", + "ĠAn other", + "p g", + "ĠVirgin ia", + "ĠL u", + "Ġpl anned", + "Ġp it", + "Ġswe et", + "T ype", + "ĠD i", + "Ġtyp ically", + "ĠFranc isco", + "Ġpro spect", + "ĠD an", + "Ġte en", + "re es", + "Ġsc hed", + "Ġh ol", + "Ġsc r", + "Ġlot s", + "l ife", + "Ġnews p", + "Ġfor get", + "ĠN one", + "ĠM iddle", + "ĠR yan", + "ed d", + "Ġse vere", + "Ġsu it", + "ll er", + "9 3", + "Ġcor respond", + "Ġexpl os", + "u ations", + "Ġfl ag", + "g ame", + "r id", + "Ġpr in", + "ĠD ata", + "Ġde ploy", + "ĠEn ter", + "su it", + "gh an", + "ĠM en", + "Ġthough ts", + "Ġmat ters", + "Ġad apt", + "ĠA ri", + "Ġf ill", + "Ġfor th", + "Ġs am", + "Ġ4 1", + "Ġpay ment", + "ĠH or", + "Ġsp ring", + "du c", + "Ġl osing", + "Ġbring ing", + "F O", + "al a", + "Ġdist ribution", + "he red", + "b our", + "ĠIsrael i", + "om a", + "Ġcomb ination", + "Ġpl enty", + "V E", + "C an", + "ĠH aw", + "Ġper man", + "ĠSpe cial", + "Ġto w", + "Ġsee king", + "Ġexam ples", + "Ġclass es", + "c r", + "Ġbe er", + "Ġmov es", + "ĠI P", + "ĠK n", + "Ġpan el", + "E ven", + "Ġproper ly", + "Ġr is", + "Ġpl ug", + "Ġestim ated", + "E very", + "Ġdef ensive", + "ag raph", + "Ġpre gn", + "Ġinst it", + "ĠV ict", + "Ġvol ume", + "Ġpos itions", + "Ġl inks", + "ĠPro gram", + "ĠWe ek", + "ag ues", + "Ġtrans form", + "k er", + "ĠC EO", + "Ġc as", + "Ġopp onent", + "Ġtwe et", + "ĠC ode", + "Ġsh op", + "Ġf ly", + "Ġtal ks", + "Ġb ag", + "Ph one", + "Ġa id", + "Ġpl ants", + "Ġ6 5", + "Ġatt orney", + "ar ters", + "qu est", + "ĠMag ic", + "Ġbeg ins", + "Ġmy ster", + "Ġenvironment al", + "Ġst orage", + "N N", + "Ġm arg", + "Ġs ke", + "Ġmet al", + "ell y", + "Ġord ered", + "Ġrem ained", + "Ġl oved", + "Ġprom pt", + "Ġupd ated", + "Ġexper ts", + "Ġwalk ing", + "Ġan cient", + "Ġperform ed", + "AT E", + "Ġne ither", + "i ency", + "Ġmanufact ure", + "ĠP ak", + "Ġselect ed", + "Ġm ine", + "Ġult imately", + "Ġexpl an", + "Ġlab el", + "ĠServ ices", + "ribut ed", + "Tr ump", + "Ġsy n", + "ĠU lt", + "S C", + "Ġme at", + "Ġg iant", + "ĠW ars", + "ĠO N", + "Ġad m", + "Ġinter pret", + "Ġeven ing", + "Ġev il", + "ĠB oston", + "ĠW ild", + "Ġ Ã", + "ĠBit coin", + "ĠAm azon", + "D r", + "ĠIn formation", + "Ġobvious ly", + "Ġadv anced", + "Ph oto", + "ol ar", + "Ġwe ather", + "Ġsymb ol", + "Ġso le", + "Ġpot entially", + "ost er", + "Ġorig inally", + "m un", + "3 00", + "az e", + "ess ions", + "Ġde ck", + "Ġst ood", + "Ġyou th", + "ĠB ern", + "R ep", + "ĠT est", + "Ġbas ically", + "ot ic", + "Ġinvol ve", + "ol it", + "ly n", + "S ee", + "Ġair craft", + "Ġconf irm", + "E W", + "Ġmess ages", + "ĠRich ard", + "Ġk it", + "Ġpro hib", + "Ġv ulner", + "is ters", + "Ġexist ence", + "Ġturn ing", + "ĠS P", + "Ġdes ire", + "Ġfl at", + "Ġm ent", + "se ason", + "ang es", + "Ġneighbor hood", + "ĠL ake", + "AT ION", + "Ġpoint ed", + "b ur", + "Ġinn ov", + "uc ks", + "U L", + "Ġprofess or", + "Ġexp ressed", + "A B", + "ic ious", + "Ġ200 2", + "ĠDe v", + "Ġs ession", + "Ġb are", + "s en", + "Ġdis s", + "ĠC ath", + "ĠP ass", + "ĠP oint", + "Ġdo ctor", + "or row", + "ail ed", + "ĠR ub", + "ĠD C", + "ĠChar l", + "p erson", + "Ġwrit er", + "igh ters", + "ure au", + "Ġob lig", + "Ġrecord ed", + "Ġbro ke", + "Ġord ers", + "il ty", + "Ġmot ion", + "in ity", + "l aw", + "ad ium", + "Ġimm igration", + "Ġcontr ast", + "Ġb att", + "Ġex cellent", + "Ġtechn ical", + "am i", + "Ġt un", + "Ġcl oud", + "ĠY ear", + "ge on", + "Ġcre ation", + "Ġstr ange", + "Ġa uth", + "Ġfor t", + "b orn", + "Ġext ent", + "ĠT oday", + "ĠCl ub", + "Ġr ain", + "Ġs ample", + "Ġaccept ed", + "Ġt act", + "Ġf ired", + "ĠS on", + "Ġstand s", + "Ġb oot", + "Ġ4 7", + "Ġstat ements", + "Ġvers ions", + "Ġse lling", + "ound ed", + "Ġ199 0", + "Ġwere n", + "ĠW atch", + "Ġexper iment", + "P ost", + "Ġret ail", + "ul ed", + "In st", + "un te", + "ãĥ ¼", + "Ġdep art", + "Ġb ond", + "i very", + "om pl", + "Ġre action", + "ĠSyri an", + "ĠP ac", + "app ed", + "ani el", + "D P", + "Ġres olution", + "Ġre act", + "Ġappro ved", + "on om", + "m ond", + "ĠO ffic", + "-- -", + "Ġrepl ace", + "Ġt ack", + "Ġsp ort", + "Ġch ain", + "Ġemer gency", + "r ad", + "ĠPalest in", + "Ġ4 6", + "Ġautom atically", + "Ġrout e", + "Ġp al", + "Ġb anks", + "ĠPar is", + "ĠMed ia", + "ro ad", + "ic ing", + "i xt", + "ist ed", + "Ġg rew", + "Ġco ord", + "ĠW here", + "om in", + "Ġsub s", + "� �", + "Ġ ±", + "Ġcorpor ate", + "Ġse lection", + "n oon", + "ĠRep ort", + "c s", + "clud ing", + "ord ers", + "anc he", + "ĠIt s", + "Ġslow ly", + "ĠE gypt", + "ĠA cc", + "Ġcol le", + "iqu es", + "E X", + "Ġattempt s", + "ur l", + "ĠC ross", + "Ġfind ings", + "ĠS C", + "ĠO R", + "Ġind ex", + "ens ity", + "ĠW ay", + "ĠL and", + "Ġsh ock", + "d is", + "Ġd ynam", + "Ġc art", + "m osp", + "S ince", + "i est", + "ĠB oy", + "Ġst orm", + "ĠCont in", + "201 3", + "he w", + "il it", + "Ġess ential", + "iqu id", + "O ther", + "ive red", + "Ġreason able", + "A ct", + "Ġsub sequ", + "ĠP ack", + "ĠF ort", + "Ġconsider ing", + "Ġun iversity", + "l og", + "Ġmar ried", + "Ġill ust", + "ĠTr ue", + "£ ı", + "Ġnumer ous", + "rast ructure", + "Ġserious ly", + "Ġrefer red", + "u a", + "Ġconsist ent", + "on na", + "ĠRe al", + "ru ption", + "ci ples", + "Ġfact s", + "9 1", + "ot es", + "er g", + "The n", + "Ġacc ompl", + "N ote", + "Ġre venue", + "Ġpass ing", + "Ġm al", + "e en", + "ĠY et", + "Ġg ather", + "ter day", + "ew ork", + "ĠA uthor", + "P e", + "Ġopt im", + "Ġr ub", + "Ġè £ı", + "Ġun known", + "st one", + "Ġun ion", + "ol ve", + "Ġopportun ities", + "Ġbrow ser", + "ĠW al", + "ĠC ost", + "Ġreport ing", + "st s", + "p et", + "Ġs and", + "Ġsudden ly", + "Ġsurpr ising", + "ĠV R", + "Ġsomew hat", + "ĠB as", + "ult ure", + "iz z", + "ĠC D", + "Ġchalleng es", + "Ġsett ings", + "Ġexperien ces", + "ĠF ull", + "Ġcan n", + "Ġrece iving", + "ES T", + "Ġj oint", + "Ġcult ural", + "Ġa st", + "8 2", + "as tern", + "ce ived", + "ĠC ru", + "Ġb ull", + "p ired", + "am m", + "Ġfac ing", + "p ower", + "Ġb oss", + "ĠH ol", + "Ġinst r", + "Ġincreasing ly", + "Ġsh ift", + "Ġstre ets", + "ĠWilliam s", + "ab b", + "Ġl ie", + "Ġl augh", + "ĠC a", + "P L", + "Ġadult s", + "Ġcustom er", + "Ġob tained", + "Ġsupport ing", + "ht ml", + "f ire", + "Ġdetail ed", + "Ġpick ed", + "ĠR ight", + "ld er", + "E E", + "st ood", + "ĠK im", + "Ġw ire", + "Ġs ight", + "Ġdevelop ers", + "Ġpers ons", + "Ġs ad", + "Ġc up", + "Ġwar ning", + "Ġboy s", + "l ong", + "Ġb ird", + "f o", + "Ġw al", + "Ġobserv ed", + "Ġz one", + "iven ess", + "Ġch annel", + "c ript", + "Ġref used", + "ĠAg ain", + "Ġsu c", + "Ġspokes man", + "ĠRe f", + "r ite", + "ou ston", + "ãĥ ³", + "ĠS her", + "Ġact s", + "ĠN ame", + "Ġstrugg le", + "ar ry", + "omet imes", + "Ġdisc rim", + "H T", + "Ġcateg ory", + "Ġreal ize", + "Ġemploy ee", + "ĠAf ghan", + "en ger", + "Ġgun s", + "ĠSte ve", + "ĠM ot", + "ĠO l", + "ok ed", + "Ġth ick", + "Ġfair ly", + "ill y", + "Ġsur ve", + "ĠM at", + "we ight", + "â Ķ", + "Ġtro ops", + "Ġag ents", + "Ġbatter y", + "Ġmot iv", + "à ¡", + "S ec", + "d en", + "o very", + "L S", + "Ġfl u", + "Ġconf ident", + "ĠO per", + "Ġem pty", + "Ġp hen", + "Ġse ctor", + "Ġexc ited", + "Ġrem ote", + "ap h", + "o en", + "Ġdestroy ed", + "Ġmor al", + "ĠH P", + "ĠR on", + "Ġd ress", + "ĠB at", + "Ġl it", + "ĠM S", + "Ġa f", + "H L", + "r um", + "is ms", + "Ġshould n", + "Ġsym pt", + "ĠTor onto", + "het ic", + "Ġcar bon", + "Ġinstall ed", + "Ġviol ent", + "Ġsol ar", + "j a", + "Ġpract ices", + "Ġr ide", + "ĠP enn", + "Ġimpro ved", + "Ġaud io", + "Ġbehav i", + "ĠP S", + "Ġe ating", + "D ata", + "ĠRe view", + "p ass", + "cl aim", + "u ated", + "ang ers", + "c hen", + "Ġproper ties", + "Ġany where", + "An other", + "Ġbl ow", + "ĠJack son", + "Ġp roud", + "Ġplan e", + "l ines", + "Ġsqu are", + "Ġpro of", + "ans as", + "Ġtalk ed", + "m akers", + "Ġs ister", + "Ġhold s", + "Ġres ident", + "Ġ= =", + "Ġresist ance", + "Ġspl it", + "Ġpro secut", + "Ġconf idence", + "res ents", + "Ġcut s", + "Ġexcept ion", + "Ġz ero", + "Get ty", + "Ġcop yright", + "Ġtot ally", + "orm al", + "ific ations", + "ĠAustral ian", + "Ġs ick", + "Ġ1 50", + "Ġhouse hold", + "Ġfe es", + "Ġdri vers", + "og en", + "ĠN Y", + "Ġnecess arily", + "Ġregul ations", + "ear ing", + "s l", + "Ġperspect ive", + "c are", + "ic ial", + "H is", + "Ġesc ape", + "Ġsurpr ised", + "ĠV an", + "ur rent", + "Ġv ac", + "8 1", + "ĠTh us", + "Ġem phas", + "ĠCh ampions", + "ĠI ce", + "Ġn arr", + "Ġhead s", + "Ġca using", + "b el", + "f ortunately", + "ĠM a", + "Ġtarg ets", + "ci pl", + "Ġafter noon", + "Ġadd s", + "ĠMay be", + "ĠF our", + "ess ed", + "ple te", + "Ġus ual", + "ch o", + "ing u", + "Ġwith d", + "ĠE nergy", + "ĠE conom", + "O O", + "Ġart icles", + "Ġinj ured", + "Ġman age", + "Ġexpl ains", + "Ġdi agn", + "R ec", + "at ures", + "Ġlink ed", + "Ġdiscuss ed", + "Ġexpl o", + "Ġocc asion", + "ath an", + "Ġopp osite", + "Ġfac es", + "Ġden ied", + "ĠK night", + "Ġn ut", + "Ġapprox imately", + "Ġdisapp oint", + "onym ous", + "ĠB est", + "ĠL o", + "ĠH y", + "ĠA ff", + "Ġvot ing", + "an while", + "ĠII I", + "Ġinstit utions", + "ag ram", + "ĠD aily", + "Ġdr ag", + "Ġnear by", + "Ġgu ilty", + "Ġcon ver", + "P re", + "s hip", + "Ġre ward", + "Ġphilos oph", + "ĠS S", + "u gh", + "Ġapp s", + "f riend", + "Ġu pper", + "Ġad vert", + "Ġs now", + "Ġfr ust", + "Ġour selves", + "F r", + "ĠD ie", + "amp ion", + "Ġdis miss", + "Ġc ere", + "Ġsign al", + "f rom", + "Ġ ).", + "Ġ5 2", + "Ġcr imes", + "it ors", + "est ival", + "use um", + "Ġcoun cil", + "ĠS aud", + "M ay", + "ĠG un", + "ic ian", + "et her", + "Ġsu fficient", + "ĠH en", + "so le", + "Ġhistor ical", + "ĠF ar", + "ĠT urn", + "Ġp in", + "Ġsuc ceed", + "m at", + "ly mp", + "Ġtrad ition", + "ĠO k", + "Ġc ro", + "Ġdesc ription", + "al le", + "Ġsk y", + "T e", + "Ġwide ly", + "Ġw ave", + "Ġdefin ition", + "ĠJew s", + "Ġcy cle", + "Ġref ere", + "Ġbr ings", + "us al", + "Ġal ive", + "Ġfrequ ently", + "Ġint ention", + "ĠCont rol", + "l v", + "y stem", + "Ġpriv acy", + "g ent", + "ren ce", + "ĠQu est", + "ĠChrist mas", + "Ġr ail", + "Ġco oper", + "Ġtest ed", + "ĠC apt", + "as ks", + "Ġcomfort able", + "Ġdel ivered", + "sc ape", + "Ġdep th", + "ĠG OP", + "Ġwrit es", + "Ġass ets", + "Ġsa v", + "im ents", + "Ġtrans ition", + "Ġart ist", + "ĠL ook", + "Ġl ob", + "Ġcomp onents", + "ar ity", + "Ġwalk ed", + "Ġro ot", + "Ġparticip ants", + "Ġnot iced", + "Ġres c", + "Ġn av", + "ĠAd minist", + "d a", + "ut ral", + "pl ate", + "Ġimport ance", + "Ġass ert", + "ious ly", + "c ription", + "Ġinj uries", + "ĠChe ck", + "Ġregist ered", + "Ġint ent", + "Ġmiss ed", + "ograph ic", + "Ġsent ence", + "oun ter", + "Ġassist ance", + "ev in", + "Ġdat abase", + "Ġbuild ings", + "Ġclass ic", + "Ġth inks", + "ĠOh io", + "P r", + "ug g", + "Ġfe e", + "p an", + "Ġeffect ively", + "Ġfac ility", + "Ġbe ar", + "Ġch apter", + "Ġdog s", + "ĠCol umb", + "Ġl atter", + "it ial", + "Ġad mitted", + "T V", + "ĠGe org", + "Ġpost s", + "\\ \\", + "Ġlawy er", + "Ġequ ival", + "Ġm and", + "Ġcontro lled", + "ĠW alk", + "ĠAnd rew", + "Ġmen u", + "am ental", + "Ġprotect ed", + "v a", + "Ġadminist r", + "or al", + "Ġre in", + "ĠS ar", + "Ġamount s", + "Ġn ative", + "ĠM oon", + "Ġrep resents", + "Ġab andon", + "Ġcarry ing", + "Ġt ank", + "m ary", + "Ġdecl ared", + "T ube", + "Ġh at", + "Ġpun ish", + "el lect", + "m es", + "Ġun iverse", + "ĠR od", + "ph y", + "Ġinf rastructure", + "Ġ5 1", + "Ġopp osed", + "ow nt", + "c a", + "ĠM ake", + "Ġhard ware", + "Ġco ffee", + "R el", + "b al", + "w orld", + "ĠS af", + "ĠSe a", + "in als", + "Ġown ed", + "Ġh all", + "ers ion", + "Ġdescrib e", + "ĠP ot", + "Ġport ion", + "Ġat mosp", + "Ġgovern ments", + "Ġdep ending", + "Ġoff ense", + "Ġtr ick", + "aw a", + "ĠL ine", + "ĠV is", + "ĠH ard", + "ĠOr ig", + "ĠCl ick", + "Ġdes k", + "ĠVal ley", + "ĠS ov", + "Ġmov ies", + "Ġrem ark", + "Ġm ail", + "Ġcons cious", + "Ġrul ing", + "ĠR ights", + "Ġmed ic", + "he nt", + "ĠW omen", + "> <", + "Ġrepl aced", + "ĠP rem", + "ĠTh anks", + "Ġre new", + "ĠB all", + "if orm", + "Ġsh ots", + "C omm", + "Ġar med", + "Ġconst ant", + "Ġt aste", + "Ġreal ized", + "Ġbu ff", + "Ġm o", + "Ġeffic ient", + "M ost", + "or ation", + "if ies", + "Ġcommun ication", + "Ġfl ood", + "Ġconsequ ences", + "Ġany way", + "ig g", + "ĠG M", + "ĠTh ank", + "Ġ iron", + "Ġev olution", + "ĠC op", + "tw itter", + "Ġ9 5", + "Ġrelationship s", + "ad el", + "ĠYou ng", + "Ġpropos al", + "ay ers", + "uild ing", + "ĠH ot", + "OR E", + "c os", + "Ġcoll abor", + "P G", + "ax y", + "Ġknow ing", + "Ġsupport s", + "ow ed", + "Ġcontrol s", + "Ġmere ly", + "um er", + "Ġath let", + "Ġf ashion", + "p ath", + "Ġg ift", + "Ġer a", + "AN D", + "Ġkind s", + "ĠKore an", + "Ġleg it", + "ul ous", + "Ġess entially", + "Ġthe rap", + "n ic", + "Ġsuff ered", + "Ġh ur", + "Ġprom ise", + "Ġex cess", + "Ġover w", + "Ġpr ime", + "ĠH ouston", + "er ry", + "ĠM s", + "R S", + "201 2", + "Ġst ores", + "ĠO lymp", + "Ġj ourney", + "Al though", + "S ub", + "ĠE duc", + "ĠCh apter", + "Ġrequest s", + "Ġconsum ers", + "Ġt iny", + "Ġis ol", + "ĠF air", + "b a", + "ĠY OU", + "Ġcr ash", + "ce ler", + "Ġemot ional", + "Ġgood s", + "Ġelect ed", + "Ġmod er", + "ĠLin ux", + "Ġbl ocks", + "Ġis land", + "ĠSoc iety", + "Ġelect ions", + "Ġbroad cast", + "Ġche ap", + "Ġn ations", + "Ġse asons", + "4 00", + "Ġwas te", + "ĠS at", + "Ġfield s", + "em ploy", + "Ġprof ile", + "Ġauth ors", + "AL L", + "ĠG ra", + "w est", + "ĠT y", + "Ġdeath s", + "Ġv acc", + "Ġfor med", + "Ġd u", + "Ġon going", + "ĠMuslim s", + "el f", + "ig ure", + "Ġass ume", + "ĠUkrain e", + "w ater", + "Ġco ast", + "Ġvot ed", + "g or", + "ĠA S", + "ĠMich igan", + "az a", + "ĠAr m", + "i ro", + "Ġf lex", + "as ters", + "' '", + "Ġwel come", + "ar l", + "Ġloc ations", + "ig ation", + "ĠF il", + "Ġbu ying", + "Ġarch itect", + "Ġhard er", + "ĠC ub", + "Ġinter face", + "Ġrestaur ant", + "Ġdisco ver", + "Ġex ceed", + "Ġfav our", + "ger y", + "Ġd uty", + "Ġp itch", + "ad or", + "ĠM ach", + "b oy", + "Ġrespond ed", + "Ġext ended", + "her s", + "M any", + "ra id", + "if er", + "ĠIn s", + "S er", + "Ġmed ium", + "s he", + "ĠS ports", + "Ġmag azine", + "ut ation", + "Ġlim its", + "ĠG all", + "Ġex ternal", + "raz il", + "Ġyoung er", + "t le", + "Ġrem ind", + "ĠC ON", + "Ġimmedi ate", + "Ġh idden", + "Ġvol unte", + "Ġsim pl", + "od cast", + "Ġph ase", + "d r", + "Ġpl ot", + "Ġexp osure", + "R I", + "og rap", + "v in", + "an ish", + "ĠAc ad", + "ĠEng ine", + "Ġexp ansion", + "ĠP ay", + "Y our", + "Ġpus hed", + "ĠE ll", + "ĠHe ad", + "Ġmarket ing", + "ĠA C", + "k et", + "Ġh its", + "Ġg ro", + "ĠA ge", + "ĠSc ot", + "] [", + "Ġst im", + "Ġi Phone", + "Ī Ĵ", + "Ġn arrow", + "ĠGet ty", + "ĠTur key", + "Ġperfect ly", + "Ġen able", + "ut ch", + "Ġprec ise", + "Ġreg ime", + "Ġsh if", + "Ġcomp ens", + "g un", + "d iv", + "Ġch osen", + "ĠK en", + "An y", + "Ġtre es", + "Ġrecomm ended", + "ĠR en", + "u able", + "ĠH T", + "F ollow", + "E G", + "ĠH and", + "ĠK enn", + "Ġarg uments", + "Ġex ists", + "Ġb ike", + "ĠCons erv", + "Ġbre aking", + "ĠG ar", + "Ġc razy", + "Ġvirt ual", + "ay lor", + "ix el", + "Ġ19 80", + "Ġper mission", + "ĠSer ies", + "Ġconsum er", + "Ġclose ly", + "c alled", + "Ġ5 4", + "Ġhop es", + "Ġar ray", + "ĠW in", + "ĠLab our", + "Ġsp ons", + "ĠI re", + "Ġp ow", + "Ġread ers", + "Ġemploy ment", + "Ġcreat ure", + "Ġresult ing", + "Ġaccur ate", + "Ġmom ents", + "Ġarg ued", + "Ġp ed", + "D uring", + "Ġ5 3", + "ĠT al", + "Ġs ought", + "Ġsuff ering", + "Ġ icon", + "le e", + "Ġ( $", + "al ian", + " °", + "Ġp ra", + "Ġbon us", + "( \"", + "k o", + "Ġact ing", + "D E", + "f all", + "Ġcompar ison", + "Ġsm ooth", + "ĠN AS", + "u pp", + "ĠJose ph", + "ep ing", + "ĠT ake", + "ĠM id", + "Ġs ending", + "f ast", + "ĠF all", + "Ġdeal ing", + "us er", + "ĠOr gan", + "C o", + "Ġatt ached", + "Ġse es", + "% .", + "Ġtyp ical", + "AR T", + "Ġfind s", + "ĠAs ia", + "um in", + "ĠC ore", + "ĠE nt", + "in ent", + "u ce", + "ĠBl ood", + "ĠN ever", + "Ġem ails", + "Ġhigh light", + "Ġconf ront", + "at us", + "ut ed", + "Ġun us", + "Ġtop ic", + "ĠAd am", + "Ġb le", + "at i", + "Ġunder stood", + "S et", + "st ruct", + "T P", + "Ġm ob", + "a a", + "ĠSt art", + "pect ed", + "se ll", + "Ġded icated", + "ĠC A", + "u an", + "Ġsong s", + "esc ription", + "Ġte ch", + "Ġr ape", + "Ġas ide", + "Ġgr ant", + "Ġ5 6", + "s ub", + "Ġarg ue", + "Ġcont aining", + "Ġsche dule", + "Ġliber al", + "Ġpublic ly", + "Ġheav ily", + "ĠU t", + "in er", + "ĠS ection", + "ĠC are", + "we et", + "l s", + "D is", + "âĶ Ģ", + "ĠF ollow", + "B ack", + "ĠI T", + "Ġb es", + "j i", + "ĠH it", + "est ed", + "Ġevery body", + "ĠSw ed", + "Ġfem in", + "Ġfac ilities", + "Ġcon ven", + "C omp", + "ĠO S", + "c ore", + "Ġan x", + "Ġdiv ision", + "ĠC am", + "ĠSt an", + "m ates", + "Ġexpl ore", + "pl om", + "Ġsh ares", + "pl oad", + "an es", + "Ġide al", + "et ers", + "ĠB ase", + "Ġpl astic", + "Ġdist inct", + "ĠNet work", + "ĠSe attle", + "Ġtrad ing", + "ens us", + "int end", + "Ġex hib", + "Ġinit ially", + "ĠF ood", + "Ġthous and", + "ĠBus iness", + "act er", + "Ġpar agraph", + "Ġrough ly", + "Ġw ww", + "Ġcreat ive", + "ĠCon f", + "Ġconsum ption", + "Ġfil ms", + "ag an", + "Ġob tain", + "Ġt all", + "Ġt or", + "Ġacknow led", + "Ġg rown", + "al o", + "K E", + "Ġ4 00", + "end ers", + "t aining", + "U G", + "Ġsu icide", + "Ġwat ched", + "ĠL ist", + "al i", + "re hens", + "Ġsurround ing", + "Ġp ip", + "Ġf lying", + "ĠJ ava", + "ord an", + "Ġserv ing", + "in ations", + "p ost", + "Ġsh o", + "A v", + "Ġj ail", + "z y", + "Ġ199 9", + "Ġ< /", + "Ġliter ally", + "ĠS ir", + "Ġexp osed", + "Ġl ies", + "st ar", + "Ġb at", + "Ġear ned", + "ĠD ig", + "Ġspec ified", + "ĠSe ason", + "Ġdeg rees", + "Don ald", + "Ġcent re", + "Ġsh aring", + "Ġwin ter", + "ĠC O", + "C he", + "Ġ Î", + "M P", + "Ġun w", + "Ġfew er", + "ĠM ir", + "Ġsomew here", + "ĠK ey", + "Ġattack ed", + "ĠK ir", + "Ġdom ain", + "Ġstrong er", + "Ġ9 9", + "Ġpen alty", + "I d", + "Sc ript", + "Ġdecl ined", + "Ġne ck", + "Ġfra ud", + "Ġcur rency", + "Ġr ising", + "R C", + "âĢ¦ âĢ¦", + "H z", + "Ġt ab", + "Ġtal ent", + "n am", + "ĠN BA", + "Ġvill age", + "Ġleg s", + "ĠN ext", + "E d", + "Ġac id", + "Ġhy d", + "8 00", + "Ġinvol ving", + "ĠIm age", + "ĠBe fore", + "F l", + "Ġyes terday", + "S ource", + "Ġterror ist", + "Ġsu p", + "Ġsy nt", + "ĠSaud i", + "Ġw est", + "Ġr u", + "b urg", + "Ġvis ible", + "Ġstru ck", + "r ison", + "Ġaw esome", + "Ġd rawn", + "Ġansw ers", + "ĠG irl", + "ĠR am", + "Ġthreat s", + "Ġdef eat", + "os it", + "Ġv ent", + "atur ally", + "Americ an", + "end a", + "ĠH oly", + "Ġr um", + "% ,", + "c ase", + "ĠHist ory", + "ĠYou Tube", + "Ġsit uations", + "ĠD NA", + "S te", + "Ġsa ved", + "It em", + "Ġrec ip", + "olog ist", + "Ġfac ed", + "Ġel ig", + "O nce", + "ĠL i", + "u h", + "Ġmist ake", + "ĠDiv ision", + "ĠB ell", + "Ġsympt oms", + " ®", + "Ġdom in", + "Ġfall ing", + "Ġend ing", + "as hes", + "Ġmat ches", + "ĠOn line", + "Ġexplan ation", + "D ef", + "red it", + "Ġany more", + "ĠT otal", + "ĠF OR", + "us hed", + "Ġlet ters", + "Ġris ks", + "ĠO K", + "Ġreported ly", + ": \\", + "Ġpl ate", + "Ġsubject s", + "Ġattempt ed", + "if ier", + "ian a", + "Ġunlike ly", + "ĠTh ough", + "um a", + "ĠIn vest", + "ĠPr in", + "ic an", + "ĠD ar", + "ĠColor ado", + "au g", + "Ġve get", + "a os", + "ri a", + "Ġshe l", + "Ġmark ed", + "Ġ( )", + "Ġsp r", + "p o", + "ĠL ink", + "Ġdef e", + "ĠJ r", + "Ġthem e", + "Ġpass ion", + "ĠP en", + "Ġinf o", + "iz er", + "Ġsh it", + "ĠC ivil", + "ap se", + "c re", + "Ġpo ly", + "Ġcomp onent", + "ĠChar les", + "ĠIre land", + "ĠPro v", + "Ġdo ctors", + "Ġgr anted", + "Ġpain t", + "Ġhon or", + "Ġsm oke", + "Ġpay ments", + "Ġprim arily", + "ĠKing dom", + "r ich", + "ate ll", + "Ġde als", + "Ġsched uled", + "Ġfund amental", + "Ġprote in", + "Ġnewsp aper", + "Ġcl ients", + "yth on", + "ĠD ate", + "h us", + "Ġfeed back", + "Ġstret ch", + "Ġc ock", + "Ġhot el", + "ĠQue en", + "Ġsu gar", + "Ġj u", + "Ġmil k", + "Ġappro val", + "ĠL ive", + "Ġequival ent", + "ef ully", + "Ġins ert", + "z ona", + "Ġext ension", + "d ri", + "J ohn", + "Ġacc omp", + "S m", + "ĠF und", + "Ġconst antly", + "Ġ` `", + "Ġgener ated", + "ĠA ction", + "ĠP sych", + "ĠT ri", + "Ġrecogn ize", + "Ġv ary", + "ph a", + "ĠR a", + "d f", + "et ch", + "ĠSov iet", + "Tw o", + "Ġpattern s", + "Ġprof ession", + "an ing", + "T ime", + "ĠL im", + "Ġcol ors", + "ĠA z", + "ĠT R", + "Ġinf ect", + "Ġphen omen", + "Ġshe ll", + "Al so", + "Ġput s", + "Ġdel ivery", + "Ġbro wn", + "Ġprocess ing", + "Ġlight s", + "ess age", + "ĠBro ok", + "ĠA ud", + "l ation", + "Ġindust rial", + "L ike", + "ĠB razil", + "rou s", + "ES S", + "ĠL uc", + "Ġsome how", + "Ġ8 5", + "Ġpro port", + "Ġpolit icians", + "Ġindic ate", + "Ġh ole", + "Ġtechn iques", + "Ġcompet itive", + "Ġph r", + "Ġv o", + "ist ent", + "ĠD ream", + "Ġcamp us", + "Ġaspect s", + "Ġhelp ful", + "Ġsh ield", + "or se", + "Ġtrig ger", + "m al", + "Ġ5 8", + "Ġt ort", + "Ġperson ally", + "Ġt ag", + "Ġkeep s", + "ĠV ideo", + "Ġben ch", + "Ġg ap", + "a ire", + "Ġe ast", + "Ġrec overy", + "per ial", + "Ġprof it", + "ĠM ic", + "Ġ5 7", + "Ġcol on", + "Ġstrong ly", + "st yle", + "Ġalleg ations", + "h an", + "Ġrep orters", + "j o", + "r ine", + "arg et", + "and al", + "Ġ0 3", + "Ġfl ash", + "tr ans", + "Ġstr ict", + "Ġpark ing", + "ĠPak istan", + "Ġl i", + "Ġwe ird", + "ĠE ric", + "Ġreg ions", + "ĠJ un", + "Ġint ellect", + "ĠW H", + "od ing", + "rib utes", + "up id", + "ĠT it", + "Ġf inger", + "or ia", + "Ġe lev", + "ĠF ield", + "Ġcon clusion", + "; ;", + "Ġfeel ings", + "Ġext ensive", + "Ġm ixed", + "Ġne uro", + "v y", + "Ġhar ass", + "ĠC irc", + "ou ch", + "Ġterrit ory", + "Ġsuccess fully", + "M ar", + "Ġing red", + "Ġoverw hel", + "Ġl ayer", + "V iew", + "Ġall ies", + "ill ance", + "ĠTh ree", + "Ġb unch", + "Ġnorm ally", + "Ġnet works", + "Ġsac r", + "ĠC IA", + "b les", + "Ġch ose", + "Ġopp onents", + "Ġregard less", + "Ġfr anch", + "Ġpre f", + "ĠP o", + "Ġbr idge", + "ann a", + "ĠSil ver", + "Ġw age", + "p age", + "ri or", + "Ġrad ical", + "ĠL ittle", + "Ġman ip", + "Ġsecret ary", + "Ġg ang", + "D R", + "F A", + "Ġdec ent", + "ĠSp irit", + "Ġun cle", + "ĠDevelop ment", + "Ġinvest ors", + "Ġwall s", + "Ġpub lish", + "Ġgener ate", + "iss ions", + "c ar", + "Ġprom ote", + "Ġcut ting", + "Ġche st", + "Ġdrink ing", + "Ġcollect ed", + "Ġ7 2", + "Ġhop ing", + "Ġem br", + "gor ith", + "Ġwar ned", + "Ġinstruct ions", + "O G", + "ĠD id", + "ĠAg ency", + "Ġg ear", + "Ġcritic ism", + "ĠF urther", + "Ġut il", + "ann y", + "R ed", + "Ġcoun sel", + "ĠAs ian", + "Ġredu ction", + "p ool", + "Ġteach ing", + "Ġdeep ly", + "i y", + "Ġestim ates", + "Ġcho ices", + "Ġperman ent", + "in em", + "ke l", + "Ġf asc", + "p se", + "f ile", + "ĠL ow", + "ĠP erson", + "Ġt ournament", + "st al", + "Ġm el", + "U ST", + "ĠR ay", + "az i", + "V al", + "Ġcont ained", + "ĠH olly", + "Ġw ake", + "Ġreve al", + "Ġprocess es", + "ĠIS IS", + "Ġ0 9", + "Ġbl ind", + "Ġste el", + "ĠB ad", + "Ġcare fully", + "app y", + "ro it", + "Ġg aming", + "Ġhous es", + "ĠC oll", + "Ġtr uck", + "er m", + "Ġsc ored", + "Ġocc as", + "ret urn", + "b ound", + "v ar", + "Ġsh arp", + "Ġaf raid", + "ĠE X", + "am ber", + "c ific", + "Ġsche me", + "N C", + "ĠPol it", + "Ġdecl ine", + "Ġ199 8", + "Ġpus hing", + "Ġposs ession", + "Ġpriv ile", + "Ġteacher s", + "Ġy ield", + "H A", + "ĠDav is", + "it led", + "#### ####", + "Ġr ig", + "ĠD aniel", + "ac on", + "Ġh ide", + "ut en", + "Ġcolle agues", + "Ġprin ciples", + "Ġl oud", + "Ġs in", + "ĠDem on", + "Ġst one", + "Ġ0 2", + "Ġt aught", + "Ġter rible", + "Ġst uck", + "ĠPol icy", + "te en", + "Ġimplement ation", + "ĠB BC", + "ĠAP I", + "Ġwhe el", + "all as", + "Ġch ampions", + "ol ars", + "play er", + "Ġrepeated ly", + "ĠSt ill", + "Ġlik es", + "ast y", + "es ter", + "ĠCath olic", + "R L", + "Ġb ath", + "Ġno ise", + "t itle", + "Ġn orthern", + "P art", + "Ġmag n", + "Ġf ab", + "ĠAs h", + "Ġdis pl", + "Ġtick et", + "Ġm urd", + "Ġalong side", + "ĠMus ic", + "Ġr iver", + "ĠSte el", + "ĠC L", + "ĠPl ayer", + "ĠM ult", + "ow ing", + "re p", + "s ize", + "Ġt ur", + "ĠGeorg ia", + "isc al", + "ra ction", + "Ġc able", + "Ġ5 9", + "Ġw ins", + "Ġup coming", + "Ġsurv ive", + "Ġins pired", + "ĠEduc ation", + "Ġstat istics", + "ĠF oot", + "iam i", + "Ġy ellow", + "ĠP age", + ". -", + "ĠH as", + "Ġur ban", + "Ġa x", + "es sel", + "\\ \"", + "Ġquarter back", + "Ġreg ister", + "ĠLab or", + "Ġab ilities", + "ĠF amily", + "Ġvar iable", + "ĠPr ice", + "Ġcont em", + "Ġth in", + "ĠE qu", + "d ata", + "Ġg otten", + "Ġconst it", + "Ġas ks", + "Ġt ail", + "Ġexc iting", + "ĠE ffect", + "ĠSp anish", + "Ġencour age", + "ins on", + "ĠA h", + "Ġcommit ment", + "C S", + "Ġr ally", + "Ġ: :", + "Ġsubs id", + "Ġsp in", + "Ġcapt ured", + "201 8", + "Ġinn oc", + "Ġalleged ly", + "ĠC ome", + "Ġart ists", + "ĠN umber", + "Ġelect ronic", + "Ġreg ional", + "ap es", + "Ġw ra", + "Ġmy th", + "pr ise", + "ĠM iller", + "ĠC reat", + "ĠEp isode", + "b ell", + "Ġdirect ed", + "Ġext ract", + "Ġs orry", + "Ġv ice", + "ag ger", + "ĠSu pport", + "Ġ6 6", + "ĠI ron", + "Ġwonder ful", + "Ġg ra", + "N et", + "ion e", + "E ng", + "Ġsh ips", + "ik es", + "ĠK evin", + "it ar", + "Ġactiv ists", + "tr ue", + "ĠAri zona", + "ent h", + "ĠDes pite", + "ĠS E", + "Ġha bit", + "ern el", + "Ġin qu", + "Ġab ortion", + "Ġv oid", + "Ġexpl icit", + "Ġeng aged", + "Ġang ry", + "Ġr ating", + "Ġfr ag", + "b ro", + "ick ing", + "d ev", + "Ġwor ried", + "Ġob ser", + "Ġap artment", + "ĠG T", + "Ġest ate", + "ĠConst itution", + "em on", + "ĠS now", + "Ġcount y", + "Ġdis ag", + "ĠStep hen", + "Ġimm igrants", + "w ind", + "ĠN ations", + "Ġfol ks", + "O ut", + "Ġg all", + "Ġtarget ed", + "Ġst ead", + "ĠB on", + "ĠL ib", + "Ġinform ed", + "Ġ12 0", + "ch ain", + "idel ines", + "or ough", + "Ġdri ven", + "Ġregular ly", + "Ġbas ket", + "Ġprinc iple", + "oc ument", + "Ġst un", + "ib ilities", + "ĠRom an", + "ĠAb out", + "Ġal ert", + "Ġdemocr acy", + "Ġrepresent ed", + "H S", + "c ers", + "p arent", + "Ar t", + "p ack", + "Ġdi plom", + "re ts", + "ĠN O", + "Ġcapt ure", + "ĠAd v", + "Ħ ¢", + "Ġannounce ment", + "ĠL ear", + "Ġh ook", + "Ġpur s", + "ĠS uch", + "ĠC amer", + "Ġrefuge es", + "ĠV e", + "P ol", + "Ġrecogn ized", + "l ib", + "Ġhad n", + "A ss", + "Ġpil ot", + "us hing", + "Ġreturn ing", + "Ġtra il", + "ĠSt one", + "Ġrout ine", + "Ġcour ts", + "Ġdes per", + "Ġfriend ly", + "ĠIt aly", + "Ġpl ed", + "Ġbreat h", + "Ġstud io", + "N S", + "Ġimp ressive", + "ĠAfghan istan", + "Ġf ing", + "Ġd ownt", + "ink ing", + "ĠR og", + "i ary", + "col or", + "se x", + "ar on", + "Ġf ault", + "ĠN ick", + "D own", + "ĠR ose", + "ĠS outhern", + "X X", + "is odes", + "L ist", + "6 00", + "Ġout come", + "er r", + "Ġelse where", + "Ġret ire", + "Ġp ounds", + "ĠGl obal", + "Pe ople", + "Ġcommun ications", + "Ġlo an", + "Ġrat io", + "ĠEm pire", + "Ġg onna", + "Ġinv ent", + "D F", + "Ġ19 70", + "ĠComm on", + "p at", + "Ġprom ised", + "Ġd inner", + "ĠH om", + "Ġcreat es", + "Ġoper ate", + "ver ty", + "ĠJ ordan", + "et ime", + "Ġsust ain", + "R eg", + "Ġincred ible", + "im a", + "Ġwar rant", + "Ġm m", + "A tt", + "Ġlaw suit", + "Ġreview s", + "it ure", + "ĠS ource", + "l ights", + "ĠF ord", + "Ġ6 3", + "g roup", + "st ore", + "Ġfeat ured", + "Ġfore ver", + "Ġpo verty", + "ĠP op", + "ĠC NN", + "az z", + "ab is", + "ach ing", + "Ġl aid", + "ĠSu pp", + "Ġfil ter", + "en a", + "ĠCommun ity", + "Ġcreat ures", + "u ction", + "ĠR oyal", + "Ġassoci ation", + "ĠCon nect", + "ĠBr ad", + "âĸ Ī", + "l ers", + "the re", + "ĠG i", + "Ġval uable", + "AC K", + "ĠT aylor", + "Ġl iquid", + "ĠAtt orney", + "ĠCar l", + "ĠF inal", + "ag a", + "ĠWil son", + "B ecause", + "ĠProf essor", + "ak a", + "Ġincred ibly", + "r ance", + "! )", + "R ef", + "s k", + "Ġsol utions", + "Ġatmosp here", + "Ġbl ame", + "um es", + "ĠN ob", + "C A", + "um ps", + "r ical", + "ĠPut in", + "ĠD est", + "or ic", + "ĠP A", + "Ġrespect ively", + "w an", + "Ġfif th", + "â Ħ¢", + "ĠC ry", + "Ġgovern or", + "res ident", + "Ġpurch ased", + "Ġh ack", + "Ġint ense", + "ob s", + "Ġorig in", + "Ġdef ine", + "Ġcare ful", + "** *", + "Ġshould er", + "Cl ick", + "Ġt ied", + "Ġdest ruction", + "ou red", + "Ġno body", + "Ġh o", + "ĠEx per", + "Ġt ip", + "\" ;", + "Ġtechn ique", + "Ġj ur", + "ĠP ok", + "b ow", + "Ġleg end", + "Ġacc ord", + "Ġbus y", + "ĠInt el", + "Ġh ang", + "ak i", + ". ]", + "âĢĶâĢĶ âĢĶâĢĶ", + "Ġsur gery", + "Ġrep rodu", + "Ġun iform", + "Ġscen es", + "c ode", + "Ġ6 2", + "l isher", + "ĠH ave", + "ph ia", + "Ġcry pt", + "Ġrec on", + "Ġsc ream", + "Ġadop ted", + "Ġsc ores", + "N e", + "ĠIt alian", + "in cluding", + "B O", + "Ġindic ated", + "Ġent ertain", + "G u", + "T ext", + "i el", + "Ġtw enty", + "Ġeng age", + "off s", + "ĠPac ific", + "Ġsm ile", + "Ġperson nel", + "Ġto ler", + "Ġdo ors", + "Ġt one", + "Ġmach ines", + "Ġent ering", + "ten ance", + "C O", + "ĠJer sey", + "Ġfore st", + "Ġhor se", + "Ġcompl aint", + "ĠSpr ing", + "y o", + "ĠPl us", + "ed ing", + "ĠRet urn", + "qu arters", + "ial s", + "c ow", + "Ġacad emic", + "Ġf ruit", + "Ġ199 6", + "og ether", + "Ġw ine", + "Ġpur su", + "ĠSte ven", + "Ġlic ens", + "Wh o", + "Ġclot hes", + "re ction", + "Ġsqu ad", + "Ġst able", + "Ġr aw", + "z ens", + "St ar", + "ut ies", + "anc er", + "Ġke ys", + "ĠM u", + "Ġcompl icated", + "ig er", + "ĠTe xt", + "Ġabs or", + "Ġ6 8", + "Ġfun ny", + "Ġrel ief", + "ĠL ew", + "ĠC ook", + "Ġch art", + "Ġdraw ing", + "G E", + "Ġmod ule", + "ĠB ull", + "I LL", + "Ġs alt", + "0000 0000", + "il le", + "Ġres ource", + "aw ay", + "adel phia", + "ĠB ru", + "Ġ6 7", + "Ġsome body", + "Ġparticip ate", + "Ġro se", + "we red", + "Ġmus cle", + "Ġcons ent", + "Ġcontin uing", + "ĠGuard ian", + "ĠOr der", + "reg on", + "Ġre ar", + "Ġprov ision", + "Ġlik ed", + "ri ent", + "Ġb ra", + "Tr ans", + "Ġmeet ings", + "Ġto x", + "Ġcon vent", + "Ġaut o", + "Ġrec ording", + "ĠSo ft", + "00 1", + "ĠR oll", + "Ġprogram ming", + "Ġp ic", + "Ġprov ed", + "Ġst ab", + "ĠA st", + "Ġca ption", + "ul ating", + "ĠAtt ack", + "Ġnew ly", + "Ġ199 7", + "f r", + "Ġdis cipl", + "ĠGree k", + "Ġed ition", + "ĠDo es", + "ĠB ox", + "if le", + "ack et", + "Ġpass es", + "Ġgu est", + "Ġac celer", + "it als", + "U D", + "Ġaut hent", + "ĠR est", + "ov al", + "t a", + "u ine", + "Ġarm or", + "ĠT own", + "Ġcomp at", + "Ġinc hes", + "Des pite", + "Ġass ign", + "he rent", + "Ġprep are", + "ĠM eg", + "oc key", + "Ġdep ends", + "Ġtrack s", + "w atch", + "Ġl ists", + "ĠN orthern", + "Ġal ter", + "re c", + "ĠE astern", + "Ġcond em", + "Ġevery where", + "? '", + "Ġaff ili", + "Ġf ought", + "\": {\"", + "Ġm ac", + "it arian", + "Ġsc ope", + "ĠA L", + "aw s", + "ar ms", + "Ġqu e", + "Ġenjoy ed", + "nes ota", + "Ġagg ressive", + "ĠSt ory", + "ĠI V", + "Ġrec ipe", + "Ġrare ly", + "ĠMed ical", + "val ue", + "ang el", + "ay ing", + "omet hing", + "Ġsub section", + "Ġs outhern", + "Ġfrequ ency", + "re te", + "roll ed", + "ult s", + "ĠN ic", + "Ġbeh alf", + "Ġsequ ence", + "ab et", + "Ġcontrovers ial", + "Ġcomp rom", + "Ġwork er", + "Ġmain ly", + "Ġal gorith", + "ĠM ajor", + "or ce", + "g ender", + "Ġorgan ized", + "Ġf ake", + "Ġconclud ed", + "ĠE D", + "ĠEx ec", + "r age", + "Ġch ances", + "ber ry", + "ĠTr ad", + "Ġconfig uration", + "Ġwithd raw", + "Ġf ro", + "ud es", + "ĠBro ther", + "ĠB rian", + "Ġtri es", + "Ġsam ples", + "Ġb id", + "ĠGold en", + "Ġphot ograph", + "if est", + "ĠD O", + "ĠPar liament", + "******** ********", + "R em", + "Ġcont est", + "Ġsign ing", + "p x", + "ĠZ eal", + "âĶĢ âĶĢ", + "E ar", + "Ġex it", + "Be fore", + "ĠCor por", + "n ull", + "mon th", + "Ġrac ial", + "ott ed", + "ĠV eg", + "ĠRe uters", + "Ġsw ord", + "ps on", + "ĠRom ney", + "a ed", + "Ġt rib", + "Ġin ner", + "Ġprot ocol", + "ĠB i", + "ĠM iami", + "ever al", + "p ress", + "Ġsh ipping", + "ĠAm endment", + "ĠHow ard", + "con nect", + "ĠD isc", + "ĠJ ac", + "iam ond", + "ĠThere fore", + "s es", + "ĠPrin cess", + "ĠUS B", + "ĠAn th", + "Ġsurve illance", + "Ġap olog", + "Ġ6 1", + "ow a", + "Ġf ulf", + "j s", + "Ġl uck", + "ust ed", + "Ġ §", + "n i", + "Ġant icip", + "em an", + "Ġwin ner", + "Ġsil ver", + "ll a", + "ic ity", + "Ġunus ual", + "Ġcr ack", + "Ġt ies", + "e z", + "Ġpract ical", + "Ġprov ince", + "ĠPl ace", + "Ġprior ity", + "IC E", + "Ġdescrib es", + "Ġbr anch", + "F orm", + "ask a", + "miss ions", + "b i", + "Ġp orn", + "ĠTur k", + "Ġent hus", + "Ġf ighters", + "Ġ0 8", + "ĠDet roit", + "Ġfound ation", + "av id", + "A re", + "Ġjud gment", + "cl ing", + "Ġsol ve", + "ĠDes ign", + "W here", + "hes is", + "ĠT ro", + "a fter", + "Ġne utral", + "ĠPalestin ian", + "ĠHolly wood", + "Ġadv is", + "ĠN on", + "y es", + "ol is", + "Ġrep utation", + "Ġsm ell", + "Ġb read", + "ĠB ul", + "ĠBe ach", + "Ġclaim ing", + "Ġgen etic", + "Ġtechn ologies", + "Ġupgr ade", + "row s", + "Ġdevelop er", + "ĠJ osh", + "ĠDis ney", + "erv ed", + "ip al", + "Ġun ex", + "Ġbare ly", + "t hen", + "ĠP ub", + "Ġill ness", + "et ary", + "ĠB al", + "Ġp atch", + "Ġbut t", + "Ġst upid", + "ĠD og", + "ĠD allas", + "f ront", + "ie ce", + "Ġprot ests", + "Ġch at", + "oen ix", + "Ġw ing", + "Ġpar liament", + "Ġ7 7", + "ose xual", + "Ġre nder", + "pt ions", + "ĠCo ast", + "os a", + "ĠG reg", + "h op", + "ĠMan agement", + "Ġbit coin", + "Ġrec over", + "Ġincor por", + "or ne", + "ĠUs ing", + "Ġpre ced", + "Ġthreat ened", + "Ġspirit ual", + "ĠE vent", + "ĠF red", + "Ġadvert ising", + "Ġimprove ments", + "ĠC ustom", + "Ġer rors", + "Ġsens itive", + "ĠN avy", + "Ġcre am", + "L ook", + "Ġex clusive", + "Ġcomp rehens", + "Ġde leg", + "Ġcon ce", + "Ġrem em", + "Ġstruct ures", + "Ġst ored", + "N D", + "Ġ1 000", + "U P", + "ĠB udd", + "A F", + "w oman", + "ĠAcad emy", + "ð Ł", + "se a", + "Ġtem porary", + "Ab out", + "es ters", + "Ġtick ets", + "Ġposs ess", + "in ch", + "o z", + "Ġl a", + "Ġcontract s", + "Ġun p", + "Ġc ig", + "ĠK at", + "ult ural", + "as m", + "Ġmount ain", + "ĠCapt ain", + "St ep", + "m aking", + "ĠSp ain", + "Ġequ ally", + "Ġl ands", + "at ers", + "Ġreject ed", + "er a", + "im m", + "ri x", + "C D", + "Ġtrans action", + "g ener", + "less ly", + "Ġ| |", + "Ġc os", + "ĠHen ry", + "Ġprov isions", + "Ġg ained", + "Ġdirect ory", + "Ġra ising", + "ĠS ep", + "ol en", + "ond er", + "Ġcon sole", + "in st", + "Ġb om", + "Ġunc ertain", + "1 50", + "ock ing", + "Ġmeas ured", + "Ġpl ain", + "Ġse ats", + "Ġd ict", + "S L", + "af e", + "Ġest imate", + "iz on", + "at hered", + "Ġcontribut ed", + "Ġep isodes", + "omm od", + "G r", + "AN T", + "Ġ6 9", + "G ener", + "Ġ2 50", + "vious ly", + "rog en", + "Ġterror ism", + "Ġmove ments", + "ent le", + "oun ce", + "ĠS oul", + "Ġpre v", + "ĠT able", + "act s", + "ri ors", + "t ab", + "Ġsuff er", + "Ġn erv", + "Ġmain stream", + "ĠW olf", + "Ġfranch ise", + "b at", + "Ġdem ands", + "Ġag enda", + "Ġdo zen", + "Ġclin ical", + "iz ard", + "ĠO p", + "t d", + "Ġvis ited", + "ĠPer haps", + "Ġact or", + "Ġde lic", + "Ġcont ribute", + "Ġin ject", + "ĠE s", + "ac co", + "Ġlist ening", + "Ġcon gress", + "epend ent", + "Ġprem ium", + "Ġ7 6", + "ĠIr ish", + "Ġass igned", + "ĠPh ys", + "Ġworld wide", + "Ġnarr ative", + "ot ype", + "m ont", + "b ase", + "ĠB owl", + "ĠAdminist ration", + "Ġrel ation", + "ĠE V", + "C P", + "Ġco vers", + "Ġ7 8", + "Ġcert ific", + "Ġgr ass", + "Ġ0 4", + "pir acy", + "ir a", + "Ġengine ering", + "ĠM ars", + "Ġun employ", + "ĠFore ign", + "st ract", + "Ġv en", + "Ġst eal", + "Ġrepl ied", + "Ġult imate", + "Ġtit les", + "d ated", + "Ġj oy", + "a us", + "Ġhy per", + "ak u", + "Ġoffic ially", + "ĠPro duct", + "Ġdifficult y", + "per or", + "Ġresult ed", + "rib ed", + "l ink", + "wh o", + "~~ ~~", + "ĠSpe ed", + "ĠV iet", + "W ind", + "ĠBar ack", + "Ġrestrict ions", + "ĠSh are", + "Ġ199 5", + "ition ally", + "Ġbeaut y", + "op t", + "Ġm aps", + "ĠC R", + "ĠN ation", + "ĠCru z", + "W ill", + "Ġelectric ity", + "Ġor g", + "Ġb urd", + "Ġviol ation", + "Ġus age", + "Ġper mit", + "ĠCh ron", + "ĠF ant", + "Ġn aturally", + "Ġ0 7", + "Ġth rown", + "ĠAw oken", + "Ġal ien", + "ĠHer o", + "ĠK ent", + "ĠR ick", + "ri ke", + "Ġp ace", + "}, {\"", + "G L", + "Ġpo ison", + "ĠT ower", + "Ġform al", + "al ysis", + "Ġgen uine", + "Ġk il", + "a ver", + "Ġproced ure", + "ĠPro p", + "intend o", + "ĠM ain", + "as ant", + "Ġtr ained", + "G ame", + "ĠL oad", + "ĠM A", + "Ġcru cial", + "Ġle ts", + "ĠF R", + "Ġch ampion", + "1 01", + "ĠCon ference", + "Ġwrit ers", + "Ġconnect ions", + "Ġo kay", + "ir ms", + "ĠR and", + "Ġenc ounter", + "ĠB uff", + "Ġachie ved", + "Ġche cks", + "isc ons", + "Ġassist ant", + "Ġwhen ever", + "ĠA ccess", + "ĠU r", + "b in", + "Ġcl ock", + "is p", + "op her", + "Ġb orrow", + "Ġm ad", + "Ġperson ality", + "on ly", + "IS T", + "ab ama", + "Ġg ains", + "Ġcommon ly", + "Ġter r", + "Ġhyp ot", + "Ġre ly", + "Ġt iss", + "iscons in", + "Ġrid ic", + "f unction", + "ĠO regon", + "Ġun com", + "r ating", + "el and", + "ĠN C", + "Ġm oon", + "ann on", + "Ġvulner able", + "ut ive", + "³³ ³³", + "ĠRad io", + "Ġw estern", + "se ct", + "ĠT ony", + "Ġocc urs", + "ĠO s", + "ĠH on", + "à Ń", + "Ġv essel", + "ĠScot land", + "Ġdiscrim ination", + "Ġsubsequ ent", + "st ring", + "Ġfant asy", + "ĠSh adow", + "Ġtest im", + "W E", + "it i", + "r as", + "Ġbo at", + "Ġmar ks", + "Ġord inary", + "Ġre n", + "Ġrepresent ative", + "Ġpet ition", + "Ġ7 3", + "Ġad venture", + "Ġign ore", + "ĠPhil adelphia", + "ĠS av", + "V P", + "Ġfact ory", + "Ġt asks", + "Ġdep ression", + "z ed", + "................ ................", + "ĠSt orm", + "Ġc ogn", + "Ġelig ible", + "Ġredu cing", + "v ia", + "Ġ0 5", + "Ġstri king", + "Ġdoll ar", + "h o", + "O V", + "Ġinstr ument", + "Ġphilosoph y", + "ĠMo ore", + "ĠA venue", + "Ġrul ed", + "ĠFr ont", + "IN E", + "ĠM ah", + "Ġscen ario", + "ĠNAS A", + "Ġen orm", + "Ġdeb ut", + "Ġte a", + "T oday", + "Ġabs ence", + "S im", + "Ġh am", + "le ep", + "Ġt ables", + "ĠHe art", + "M I", + "K e", + "re qu", + "V D", + "m ap", + "Ġchair man", + "Ġp ump", + "Ġrapid ly", + "v i", + "Ġsubstant ial", + "E P", + "d es", + "ch ant", + "ili pp", + "ĠS anta", + "ri ers", + "anche ster", + "L oad", + "ĠC ase", + "Ġsa ving", + "Ġ7 4", + "ĠA FP", + "er ning", + "oun ced", + "ĠMin nesota", + "ĠW as", + "Ġrec ru", + "Ġassess ment", + "ĠB ron", + "U E", + "Ġdynam ic", + "Ġf urn", + "ul ator", + "Ġprop ag", + "h igh", + "Ġacc ommod", + "Ġst ack", + "ĠS us", + "w rit", + "Ġre ven", + "ĠGod d", + "ĠZeal and", + "ab s", + "Ġbr ut", + "Ġper pet", + "h ot", + "Ġhard ly", + "ĠB urn", + "ãĤ ¹", + "Ġst y", + "Ġtrans actions", + "Ġg ate", + "Ġsc reens", + "Ġsub mitted", + "Ġ1 01", + "Ġlangu ages", + "ugh t", + "em en", + "Ġfall s", + "Ġc oc", + "Ĥ ¬", + "Ġstri kes", + "p a", + "Ġdel iber", + "ĠI M", + "Ġrel ax", + "ann els", + "ĠSen ator", + "Ġext rem", + "Ġ} ,", + "ĠDe b", + "Ġbe ll", + "Ġdis order", + "c ut", + "Ġi OS", + "Ġl ocked", + "Ġem issions", + "Ġshort ly", + "\" ]", + "ĠJud ge", + "ĠS ometimes", + "Ġr ival", + "Ġd ust", + "Ġreach ing", + "F ile", + "¯¯ ¯¯", + "ino is", + "ĠJ ason", + "Ġs atell", + "are t", + "Ġst ations", + "Ġag ric", + "ĠTechn ology", + "com es", + "ĠUn fortunately", + "ĠChild ren", + "Ġappl ies", + "ast ed", + "Ġan ger", + "ail ability", + "ĠDam age", + "Ġcomp are", + "ĠStand ard", + "Ġaim ed", + "ĠB a", + "angu age", + "Ġreg ulation", + "Ġj ury", + "Ġair port", + "Ġse ctions", + "ĠPr ince", + "em ed", + "Ġmedic ine", + "Ġh itting", + "Ġsp ark", + "ol ves", + "Ġad s", + "St ate", + "Ġfood s", + "Ġrepl acement", + "Ġch icken", + "Ġlow est", + "Ġmind s", + "Ġinvol ves", + "u i", + "Ġarr ang", + "Ġproced ures", + "ĠWh ich", + "ivers ary", + "Ġb ills", + "Ġimprove ment", + "Ġin ev", + "Ġexpect ations", + "Ġintellect ual", + "Ġsp aces", + "Ġmechan ism", + "2 50", + "bre ak", + "ĠZ e", + "ĠT enn", + "ĠB alt", + "Ġbar rel", + "Ġstat ic", + "man n", + "Pol ice", + "Ġt ips", + "Ġhand ling", + "c us", + "od ed", + "il ton", + "ir y", + "Ġjournal ists", + "our se", + "Ġcom ic", + "Ġnom ine", + "IT Y", + "Ġvers us", + "Ġlo op", + "Ġsur f", + "ĠInd ust", + "ĠHun ter", + "Ġbelief s", + "is an", + "Ġset up", + "Ġbre w", + "im age", + "Ġcomput ers", + "f ol", + "} ,\"", + "ĠMed al", + "Ġtax p", + "Ġdisplay ed", + "Ġg rav", + "Ġf iscal", + "M on", + "ĠMos cow", + "ĠK ong", + "ĠCent re", + "Ġcamer as", + "ĠMr s", + "ĠH ay", + "Ġa ver", + "ĠK elly", + "p y", + "Ġrequire ment", + "Ġent itled", + "omb ie", + "Ġsh adow", + "ag ic", + "ĠA k", + "Ġel ite", + "Ġdiv ided", + "Ġhead ing", + "Ġcop ies", + "Ġloss es", + "Ġv it", + "k ed", + "ĠB ry", + "Ġan s", + "ĠSte am", + "Ġrep orter", + "he im", + "ĠIt em", + "Ġsuper ior", + "d on", + "ere nt", + "à ¶", + "Ġtherap y", + "Ġpe ak", + "ĠMod el", + "Ġl ying", + "Ġg am", + "z er", + "r itten", + "Ġrespons es", + "Ġconsider ation", + "ĠB ible", + "Ġl oyal", + "Ġinst ant", + "Ġp m", + "ĠFore st", + "à ¼", + "Ġext end", + "Ġconv icted", + "Ġfound er", + "Ġconv in", + "ĠO ak", + "che ck", + "Ġsch olars", + "p ed", + "Ġover se", + "T op", + "c ount", + "ĠAr k", + " ·", + "Ġ0 6", + "ĠL A", + "m d", + "ĠLat in", + "im ental", + "ĠC PU", + "Ġsubst ance", + "Ġminor ity", + "Ġmanufact uring", + "E r", + "ocol ate", + "Ġatt ended", + "ĠMan ager", + "r ations", + "Ġappreci ate", + "om y", + "GB T", + "id ency", + "B L", + "Ġguarant ee", + "pos ition", + "Ġo cean", + "clud e", + "Ġhead ed", + "Ġt ape", + "Ġlo ose", + "Ġlog ic", + "Ġpro ven", + "Ġsp ir", + "Ġad mit", + "is a", + "Ġinvestig ate", + "Ġ199 4", + "sy lv", + "ĠL ost", + "c est", + "Ġ7 1", + "Ġrequest ed", + "Ġwind ows", + "ĠPok é", + "ĠWith out", + "M et", + "Ġbehavi our", + "Ġread er", + "Ġh ung", + "ĠKe ep", + "Ġro les", + "Ġimplement ed", + "Ġbl ank", + "Ġserv es", + "ĠJ ay", + "Ġc ited", + "ĠF riend", + "prof it", + "ap on", + "Ġrep air", + "it em", + "arr ass", + "Ġcrit ics", + "ad i", + "ĠF ather", + "Ġsh out", + "Ġf ool", + "Ġ8 8", + "Ġprodu cing", + "Ġl ib", + "Ġround s", + "Ġcirc le", + "Ġpre par", + "Ġsub mit", + "Ġn ic", + "mor row", + "ãĥ «", + "U nder", + "Ġv ital", + "ater n", + "Ġpass word", + "Ġpublic ation", + "Ġprom inent", + "Ġspeak s", + "Ġb ars", + "Ġde eper", + "ĠM ill", + "port ed", + "Ġw id", + "Ġbut ter", + "Ġsm oking", + "Ġindic ates", + "K ey", + "rop ri", + "ĠF ile", + "all ing", + "ast ing", + "ĠR us", + "Ġad j", + "Ġ7 9", + "av al", + "Ġpres um", + "bur gh", + "on ic", + "Ġf ur", + "Ġpoll s", + "ik a", + "Ġsecond ary", + "Ġmon ster", + "ig s", + "ĠCur rent", + "E vent", + "Ġowners hip", + "end ar", + "Ġarri ve", + "ĠT ax", + "Ġn ull", + "ĠPri v", + "Ġth ro", + "Ġk iss", + "c at", + "Ġup set", + "ang le", + "it ches", + "ect or", + "olog ists", + "ĠGal axy", + "Ġcor ruption", + "Ġh int", + "ent er", + "ĠH ospital", + "Ġgreat ly", + "Ġbeg un", + "es y", + "Ġso il", + "ĠAnt on", + "Ġmain tenance", + "ãĥ ©", + "Ġdo zens", + "Ġhuman ity", + "ĠAl abama", + "Ġr om", + "w orth", + "ap ing", + "sylv ania", + "l ah", + "Ġg athered", + "G A", + "Ġattack ing", + "f ound", + "ĠSqu are", + "Ġar bit", + "ict ions", + "ĠW isconsin", + "Ġd ance", + "ĠS aint", + "arch y", + "Ġbase ball", + "Ġcontribut ions", + "Ġliter ature", + "Ġex ha", + "per ty", + "t est", + "Ġb ab", + "Ġcontain er", + "let ter", + "Ġfall en", + "Ġwebs ites", + "Ġbott le", + "ĠS ac", + "Ġbre ast", + "ĠP L", + "Ġveter an", + "Ġinterview s", + "ĠA le", + "Ġb anned", + "eng ers", + "ĠRev olution", + "in th", + "Ġconc erning", + "IV E", + "Ġexp enses", + "ĠMatt hew", + "ĠColumb ia", + "d s", + "ist ance", + "Ġent ity", + ".. .\"", + "Ġrel iable", + "Ġpar alle", + "ĠChrist ians", + "Ġopin ions", + "Ġin du", + "l ow", + "Ġcompet e", + "Ġth orough", + "Ġemploy ed", + "Ġestablish ment", + "ig en", + "ĠC ro", + "Ġlawy ers", + "ĠSt ation", + "T E", + "ĠL ind", + "ĠP ur", + "it ary", + "Ġeffic iency", + "âĢ IJ", + "ĠL y", + "Ġm ask", + "Ġdis aster", + "Ġag es", + "ER E", + "es is", + "ĠH old", + "Ġcas ual", + "b led", + "Ġen abled", + "ĠEn vironment", + "ĠInt elligence", + "i per", + "ĠM ap", + "ĠB E", + "Ġemer ged", + "is dom", + "Ġc abin", + "Ġregist ration", + "Ġfing ers", + "Ġro ster", + "Ġfram ework", + "ĠDo ctor", + "et ts", + "Ġtransport ation", + "Ġaware ness", + "H er", + "Ġattempt ing", + "O ff", + "ĠSt ore", + "ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ", + "ĠK now", + "Ġdef ence", + "Ġsc an", + "ĠT en", + "ĠCh air", + "ĠP H", + "ĠAtl anta", + "Ġfuck ing", + "Ġans wered", + "b n", + "ĠK ar", + "Ġcateg ories", + "Ġr ational", + "Ġc ust", + "Ġrob ot", + "Ġcorrect ly", + "Ġg if", + "Ġgraph ics", + "m ic", + "Ġground s", + "ĠO pp", + "i ate", + "Ġdist ributed", + "Ġsan ctions", + "Ġchalleng ing", + "ut o", + "Ġingred ients", + "Ġinv ited", + "Ġfound ed", + "ĠRe qu", + "d ed", + "Ġb owl", + "Ġbrother s", + "ĠH a", + "I O", + "Ġw ages", + "im ore", + "oc ial", + "Ġse ed", + "ative ly", + "Ġaddress es", + "ĠI owa", + "ab eth", + "Ġatt itude", + "is d", + "ch ild", + "Ġm ole", + "Ġdisco very", + "y ard", + "B r", + "Ġ8 2", + "Ġsuppl ies", + "ell ing", + "Ġdist ingu", + "C R", + "Ġre cept", + "Ġ vert", + "Ġsw im", + "b ec", + "d oor", + "ĠY eah", + "Ġg al", + "Ġinter act", + "ĠE SP", + "ĠC S", + "amp s", + "Ġconvin ced", + "Ġobject ive", + "Ġdis h", + "ĠPhot os", + "l ad", + "Ġdownt own", + "o il", + "in ction", + "Ġto morrow", + "ĠC OM", + "Ġsurv ival", + "sh ot", + "Ġsett lement", + "C ons", + "ĠX box", + "int erest", + "ĠS M", + "arg o", + "en ess", + "Ġeth nic", + "b ered", + "M in", + "ĠT ok", + "Ġinc ent", + "ĠComm and", + "Ġmain tained", + "Ġbreak s", + "br idge", + "at ar", + "ag g", + "ĠF inally", + "un icip", + "ĠO nt", + "le ft", + "Ġrecogn ition", + "Ġ* /", + "ĠP ers", + "Ġwe lf", + "Ġaddress ed", + "ĠK ansas", + "Ġvir us", + "Ġwhere as", + "Ġp apers", + "ram s", + "ĠMin istry", + "Ġple asure", + "Ġacqu ired", + "Ġd uration", + "j pg", + "Ġcal m", + "ĠN HL", + "Ġburn ing", + "Ġfold er", + "ick ed", + "ĠP y", + "ĠIll inois", + "Cl ass", + "ĠGodd ess", + "Ġperform ing", + "Ġwelf are", + "j ar", + "In ter", + "Ġl in", + "Ġenh ance", + "Ġnot ion", + "f are", + "yp es", + "ĠAre a", + "Ġcann abis", + "ĠDie go", + "f s", + "ĠM anchester", + "com m", + "in ite", + "Ġcover ing", + "ĠS ound", + "Ġ19 60", + "Ġ8 4", + "e lect", + "z ing", + "Ġcitiz en", + "Ġph ones", + "Ġr aid", + "Ġign ored", + "ĠOb ject", + "Ġu pload", + "c ard", + "Ġmod ified", + "Ġroom s", + "ia h", + "r ange", + "he ast", + "ach us", + "Ġsuggest ing", + "âĢ ĭ", + "gr ade", + "E l", + "Ġclot hing", + "Ġr h", + "ĠH an", + "un ity", + "en cing", + "ĠAust in", + "sec ution", + "t ra", + "d em", + "ĠQ ual", + "Ġhe aven", + "Ġst ages", + "Ġw edd", + "pl us", + "ific ial", + "ĠIm m", + "ĠH o", + "iet ies", + "Ġphr ase", + "Ġbr ill", + "act ory", + "Ġprov iders", + "Ġsil ence", + "Ġa er", + "ĠA I", + "ĠAd venture", + "Ġplatform s", + "Ġdemonstr ated", + "Ġinter f", + "ing ton", + "Ġr aces", + "Ġgr ade", + "ult ane", + "ĠTh rough", + "f alse", + "Ġb ow", + "ĠA B", + "Ġfl avor", + "Ġhistor ic", + "g ov", + "Ġcol our", + "Ġview ed", + "ĠEm ail", + "el come", + "Ġinter vention", + "Ġd iversity", + "Ġperiod s", + "Ġre verse", + "ĠV ery", + "Ġqu ote", + "ĠLe ft", + "th rough", + "Ġsc rew", + "Ġland ing", + "Ġp ill", + "Ġw et", + "Ġprot esters", + "Ġrepe at", + "av ed", + "er k", + "Ġsal ary", + "ĠPenn sylvania", + "St ill", + "Ġmay or", + "Ġkit chen", + "Ġfeat uring", + "ĠM useum", + "ĠT ournament", + "ĠF al", + "Ġser vers", + "U C", + "Ġany body", + "im g", + "ĠTr ade", + "ixt ure", + "the less", + "Ġfin ance", + "Ġcl osing", + "ĠPat ri", + "i ac", + "ab el", + "Ġ> >", + "or ous", + "Ġf irms", + "sc reen", + "un a", + "Ġemb arrass", + "ul se", + "Ġlet ting", + "Ġth rew", + "ile y", + "Ġch annels", + "l an", + "ĠVeg as", + "Ġse ar", + "Ġfant astic", + "ar re", + "uzz le", + "ĠD er", + "Th ose", + "Ġsw ing", + "Ġshe et", + "ind ex", + "co ver", + "og an", + "Ġvari ables", + "ĠTe ch", + "Ġsp oken", + "ac hel", + "ĠD a", + "ĠMount ain", + "Ġload ed", + "Ġfoot age", + "vers ion", + "Ġun l", + "ĠPh oenix", + "Ġthrow ing", + "Ġf iring", + "Ġtrack ing", + "Ġw idth", + "Ġstrugg ling", + "ro oms", + "ot ion", + "Ġmonth ly", + "ĠSer ver", + "Ġegg s", + "op en", + "M C", + "Ġ199 3", + "Ġh ired", + "Ġstay ed", + "ĠAll en", + "Ġst ro", + "Ġ9 8", + "st ep", + "ĠTurk ish", + "Ġfab ric", + "ist ing", + "ĠD om", + "Ġd ates", + "Ġpr on", + "Ġbasket ball", + "Ġl ucky", + "ĠArab ia", + "Ġassum ed", + "est y", + "Ġaff airs", + "Ġgl ad", + "ĠInd eed", + "ĠF A", + "ĠW ord", + "Ġjo ining", + "if ice", + "p read", + "ir ts", + "ĠSe lect", + "Ġpop ulations", + "aw are", + "Ġn ose", + "Ġcompl aints", + "st art", + "Ġsc oring", + "Th anks", + "Ġmin ing", + "Ġvisit ors", + "S H", + "Ġdam aged", + "Ġcharacter istics", + "ĠP ent", + "D C", + "Ġ8 3", + "ĠS ix", + "r ates", + "Ġfl ags", + "ĠB rew", + "d og", + "M ark", + "// //", + "Ġexec ution", + "Ġj oke", + "ph ones", + "Ġtestim ony", + "Ġob st", + "Q L", + "ĠC ut", + "Ġstud ied", + "ĠN intendo", + "ick et", + "ĠN BC", + "Ġl ad", + "ĠB ra", + "ĠM oh", + "Ġk ernel", + "Ġoverwhel ming", + "Ġag ed", + "Ġapplic able", + "ĠC ond", + "Ġroad s", + "ĠBl ock", + "m ade", + "od ge", + "Ġcomm ands", + "Ġoff ices", + "vel and", + "Ġt ut", + "Ġrece iver", + "ĠF ro", + "Ġsho pping", + "Ġi P", + "ĠSt re", + "ĠA BC", + "Ġentertain ment", + "ĠB ow", + "ort ed", + "M c", + "Ġread s", + "gr ad", + "ĠCol lect", + "Ġâ ĪĴ", + "ĠCap ital", + "eder ation", + "Ġemploy er", + "Ġinvolve ment", + "Ġanx iety", + "al ia", + "Ġro of", + "ĠAm ong", + "ĠDemocr at", + "Ġstat s", + "ĠV ill", + "Ġconst itutional", + "Ġrefer ring", + "itt y", + "Ġtack le", + "out ube", + "Ġback ed", + "ĠH ong", + "ĠBro ad", + "Ġe le", + "ĠO tt", + "Ġ199 2", + "h our", + "achus etts", + "C al", + "Ġdefe ated", + "Ġ8 1", + "es p", + "Ġseem ingly", + "w as", + "ĠJ enn", + "ĠK urd", + "Ġg ene", + "Ġdisc ount", + "R et", + "EC T", + "( );", + "Ġclub s", + "Ġs id", + "ĠM arsh", + "Che ck", + "Ġp p", + "ĠE ag", + "ides pread", + "Ġbe ings", + "F T", + "Ġintrodu ction", + "ĠCh ange", + "AR D", + "Ġ1 10", + "ad ows", + "ier ce", + "Ġme al", + "a uthor", + "ĠB ang", + "lah oma", + "Ġr anks", + "201 1", + "?? ??", + "m ax", + "Ġcoll apse", + "Ġop ens", + "Ġe cho", + "Ġs oph", + "Ġrac ist", + "Ġenorm ous", + "Ġw aves", + "Ġt ap", + "Ġcomprehens ive", + ". --", + "ĠR oy", + "Ġfarm ers", + "Rel ated", + "a ired", + "ron es", + "ĠC rim", + "Ġproport ion", + "Ġdesign s", + "Ġnegoti ations", + "Ġvirt ually", + "ĠBat man", + "Ġwar n", + "Ġlegit imate", + "m ate", + "Ġcon vention", + ", ,", + "net ic", + "ĠS D", + "Ġconsist ently", + "Ġcompens ation", + "Ġpunish ment", + "Ġy e", + "Ġt ie", + "ĠB ureau", + "ir lf", + "ĠB u", + "ĠA ren", + "ĠPh ilipp", + "Ġkn ife", + "Ġmem ories", + "ĠR oss", + "Ġang le", + "Ġ8 6", + "ĠTh under", + "Ġre nd", + "ĠT our", + "Ġcount s", + "s ung", + "ĠIm p", + "Ġeduc ational", + "Ġaccess ible", + "C OM", + "Ġd rew", + "y er", + "G l", + "am ine", + "OR T", + "O B", + "I B", + "m aster", + "Ġtri als", + "og y", + "h ar", + "ĠTr ust", + "Ġprefer red", + "irlf riend", + "ĠN ev", + "Ġb in", + "Ġc ow", + "P age", + "Ġsign ature", + "ĠB L", + "7 00", + "Ġret ired", + "Ġby tes", + "Ġneigh b", + "ĠLeg end", + "Ġdev ast", + "Ġsuspect ed", + "is ons", + "ĠPoké mon", + "sc ale", + "Ġcap abilities", + "Ġre vel", + "Ġche ese", + "d y", + "igr ant", + "Ġfail ing", + "b its", + "ĠHer oes", + "ĠG host", + "ĠS cient", + "Ġappoint ed", + "ur i", + "Ġinst itution", + "Ġexpand ed", + "g reg", + "Ġmonitor ing", + "Ġp odcast", + "Ġcoal ition", + "Ġ9 6", + "J o", + "Ġst olen", + "ĠS ab", + "Ġstop s", + "Ġhol iday", + "Ġint r", + "C ar", + "Bl ack", + "ĠL GBT", + "Ġwar ming", + "ĠAnd erson", + "Ġ8 9", + "Ġprodu cer", + "M ed", + "Ġaccur acy", + "ĠMar vel", + "iz abeth", + "ĠPat rick", + "m ony", + "Ġmin i", + "ac les", + "Ġover t", + "the y", + "Ġmembers hip", + "ĠV en", + "Ġex ch", + "Ġrem oval", + "ĠD ave", + "T Y", + "m ad", + "ĠF ind", + "Ġad equ", + "Ġe c", + "Ġte eth", + "Ġemot ion", + "Ġper m", + "Ġsole ly", + "d b", + "Ġextra ord", + "IG HT", + "c al", + "Ġgu idelines", + "Ġd ying", + "Ġsusp ended", + "ĠPrem ier", + "ĠAnth ony", + "el ve", + "Ġd ad", + "ĠE th", + "ĠFoot ball", + "Ġabandon ed", + "Ġ< <", + "Ġm arch", + "Ġhor ror", + "âĢ¦ \"", + "Ġchild hood", + "Ġcampaign s", + "Ġl unch", + "ĠAl bert", + "bl ock", + "âĸĪ âĸĪ", + "ound ing", + "Ġb one", + "or gan", + "ad ers", + "ĠFl ash", + "ĠDri ve", + "Ġton ight", + "Ġw ars", + "ĠF L", + "Ġform ation", + "con st", + "New s", + "Ġcom pe", + "or ious", + "ĠSt aff", + "Ġdiscuss ions", + "ĠProt ection", + "ĠJ am", + "Ġcrit eria", + "Ġinstall ation", + "Ġaccompl ish", + "iz za", + "Ġpub lisher", + "Ġresc ue", + "ĠT ry", + "U LL", + "ĠS om", + "ĠH op", + "ore t", + "th s", + "ord on", + "Ġp ocket", + "ĠIn v", + "Down load", + "ĠCr ime", + "Ġb ene", + "ĠGu ide", + "ĠAs sembly", + "Ġparam eters", + "I E", + "ĠAlex ander", + "Ġconc ert", + "ĠSc he", + "Ġsh oes", + "Ġvis iting", + "Ġrec all", + "Ġb ub", + "Ġr ural", + "Ġconc rete", + "ĠR os", + "N ext", + "R uss", + "Ġlo ans", + "ĠSh ield", + "Ġtre m", + "hem at", + "k g", + "ĠHar ris", + "is ition", + "ĠM ove", + "ĠF C", + "Ġf ate", + "ĠCh o", + "Ġt ired", + "Ġprinc ipal", + "h ist", + "ien ces", + "ath y", + "Ġse vent", + "Ġm ood", + "Ġstrateg ic", + "Ġdise ases", + "Ġfor um", + "Ġtem por", + "Ġhead quarters", + "P ar", + "ig e", + "fl ix", + "Ġgu itar", + "Ġ9 4", + "On ly", + "Ġrele ases", + "ro ph", + "================ ================", + "Ġ6 00", + "ĠContin ue", + "ig ate", + "ĠC rit", + "sy stem", + "Ġdis abled", + "Ġunex pected", + "ith ub", + "Ġuncle ar", + "ĠE st", + "Ġcontr ad", + "Ġstrateg ies", + "vent ures", + "Ġpass age", + "AM E", + "Ġimpro ving", + "Ġreve als", + "Ġdecre ase", + "ov a", + "Ġann oy", + "ĠSh ort", + "ĠL ibrary", + "Ġcy ber", + "n ell", + "ĠH ur", + "ĠC B", + "Ġphot ograp", + "U I", + "Ġs ed", + "G e", + "Ġ8 7", + "Ġd iverse", + "Ġencour aged", + "Ġcons piracy", + "Ġbird s", + "Ġoper ator", + "Ġhand ful", + "Ġclass ified", + "? )", + "Ġdram atic", + "Ġinvestig ators", + "it o", + "Ġw idespread", + "ĠR oom", + "-------------------------------- --------------------------------", + "Ġcollect ive", + "Ġjournal ist", + "St ring", + "Ġtemper atures", + "il a", + "Ġgu id", + "Ġins pect", + "Ġmiss ile", + "ĠMay or", + "Ġman ual", + "Ġsim ultane", + "Ġrat ings", + "Ġsu ck", + "Ġ9 7", + "Ġunivers al", + "Ġph arm", + "Ġdis rupt", + "ian o", + "A V", + "Ġf t", + "Ġstat ist", + "old s", + "ĠWalk er", + "ph p", + "Ġunder t", + "ĠL as", + "ish op", + "nt il", + "res hold", + "ĠWhe ther", + "M s", + "Ġden y", + "ĠCl oud", + "Ġprov ider", + "Ġsurv iv", + "ĠUp date", + "h as", + "Ġmist akes", + "ch arge", + "pl ed", + "r ity", + "Ġn ode", + "ĠMass achusetts", + "ool s", + "lic ation", + "Ġf ails", + "em ale", + "or i", + "back s", + "Ġsh irt", + "Ġ' '", + "ĠN AT", + "Ġwat ers", + "els on", + "Ġe ase", + "Ġsc ar", + "Ġcont ents", + "m ind", + "Ġcont ribution", + "Ġsh r", + "Ġhand ed", + "Ġst ability", + "Ġtra ve", + "E m", + "Ġmir ror", + "12 3", + "Ġwe igh", + "Ġf iction", + "ou ver", + "ist ant", + "r ition", + "ĠF ed", + "Ġphys ically", + "Ġst ake", + "ĠArt icle", + "ĠAr c", + "ĠLew is", + "ĠM ind", + "Ġdemonstr ate", + "Ġprof its", + "v ision", + "om ic", + "ol id", + "Ġbatt les", + "Ġdri ves", + "Ġeas tern", + "ĠS ony", + "!! !", + "ar ation", + "v ard", + "ĠG L", + "port ation", + "Ġ9 2", + "Ġlaw makers", + "Ġprotect ing", + "ĠE PA", + "Ġy eah", + "Ġsh ame", + "ol ph", + "e ven", + "x it", + "Ġatt ach", + "Ġrepresent ing", + "Ġob s", + "ĠUt ah", + "iff s", + "ĠFre edom", + "à ³", + "A K", + "Ġinc idents", + "it age", + "Ġview ers", + "c d", + "Ġm ouse", + "Ġcl ar", + "Ġaccord ance", + "Ġb ot", + "c or", + "ĠSum mer", + "he ld", + "Ġinnoc ent", + "Ġiniti ative", + "ol s", + "________________ ________________", + "Ġsp ots", + "p ace", + "Ġconvent ional", + "Ġcorpor ations", + "Ġblock ed", + "H D", + "at tered", + "Ġref ers", + "Ġbu ck", + "ĠDig ital", + "12 0", + "Ġtop ics", + "T F", + "Ä ģ", + "br id", + "re ement", + "Ġunder lying", + "ĠM ember", + "Ġinvestig ating", + "Ġpregn ancy", + "Ġtouch down", + "ĠB and", + "ĠCall er", + "Ġinst ances", + "P P", + "w a", + "G ood", + "Ġ199 1", + "ĠC old", + "Ġfear s", + "Ġrem arks", + "Ĩ Ĵ", + "at al", + "Ġm it", + "Ġexper iments", + "i pt", + "Col or", + "ind u", + "Up date", + "Ġ9 3", + "A g", + "Ġ å", + "anc ouver", + "B oth", + "Ġjud ges", + "Ob ject", + "Ġst ere", + "umb n", + "Ġparticip ation", + "ĠSt ars", + "ĠJ ere", + "Ġweek ly", + "ĠB an", + "Ġconvers ations", + "ĠP itt", + "u z", + "ĠIndian a", + "ĠK ick", + "Ġinf ection", + "Ġhero es", + "Ġsett led", + "Ġstri p", + "Ġh al", + "Ġd ump", + "ĠS ci", + "Ġl es", + "Ġref erences", + "ĠU RL", + "ĠBr idge", + "Ġwant ing", + "For ce", + "Ġex clus", + "Me anwhile", + "m n", + "Ġg entle", + "m aker", + "sen al", + "ĠG ro", + "ou ri", + "ĠR ain", + "ĠAll iance", + "Ġl ift", + "el a", + "S D", + "ĠCle veland", + "Ġrank ed", + "Ġst adium", + "Ġdead ly", + "ä ¸", + "Ġr iding", + "ar ia", + "ĠAr mor", + "Ġdocument ation", + "ĠGree ce", + "ree k", + "Ġl ens", + "ĠS a", + "Ġg ross", + "ĠE mer", + "ag ers", + "ĠD ub", + "ĠR h", + "ĠAM D", + "Ġarri val", + "Ġdes ert", + "Ġsupp lement", + "ĠRes p", + "Ġkn ee", + "Ġmarg in", + "f ont", + "og g", + "201 0", + "ĠP ir", + "ĠP rom", + "iv als", + "Ġint ake", + "Ġdifferent ly", + "ug s", + "Ġb its", + "clud ed", + "Ġsearch ing", + "ĠD u", + "um ble", + "Ġfunction al", + "ĠBalt imore", + "ĠC ould", + "Ġdes ired", + "Ġcirc uit", + "ĠL yn", + "ĠG O", + "ĠF alse", + "re pre", + "' :", + "alt ies", + "Ġmin im", + "Ġdro ve", + "ĠSh ould", + "Ġh ip", + "Ġpro s", + "Ġut ility", + "ĠN ature", + "ĠM ode", + "P resident", + "o pp", + "r at", + "form ance", + "Ġconcent ration", + "Ġf ont", + "ĠB ud", + "Ġam id", + "Ġre vers", + "ĠM L", + "B ar", + "Ġinter action", + "Ġjur isd", + "Ġspell s", + "d ep", + "f il", + "Ġcivil ians", + "ut ter", + "ĠCo oper", + "ĠBel ow", + "Ġent rance", + "Ġcon vert", + "Ġcontrovers y", + "ow ered", + "Ġcontr ary", + "Ġar c", + "ĠExec utive", + "ĠOffic er", + "Ġpack ages", + "Ġprog ressive", + "w idth", + "Ġreserv ed", + "v ol", + "ĠSam sung", + "Ġprint ed", + "Ġcent ers", + "Ġintrodu ce", + "ĠKenn edy", + "Ġodd s", + "Ġsure ly", + "Ġindepend ence", + "Ġpass engers", + "repre ne", + "ĠBe h", + "Ġl oves", + "ĠESP N", + "Ġfac ilit", + "Ġident ical", + "Ġdo ct", + "Ġpartners hip", + "con f", + "ĠH ide", + "Ġconf used", + "ĠC ow", + "M en", + "Ġw rest", + "ĠIraq i", + "Ġh oles", + "ĠStud ies", + "Ġpregn ant", + "h ard", + "Ġsign als", + "I X", + "Ġpull ing", + "Ġgrad uate", + "Ġnomine e", + "D ate", + "Ġper mitted", + "Ġâ Ĥ¬", + "ĠOk lahoma", + "St art", + "Ġauthor ized", + "Ġal arm", + "ĠC os", + "v an", + "Ġgener ations", + "c ular", + "Ġdr agon", + "ĠSoft ware", + "ĠEd ward", + "Ġcontro ller", + "S en", + "ge red", + "ĠV ik", + "Ġappro ached", + "Th ank", + "Ġcan ce", + "Ġform ula", + "ĠSm all", + "Ġweak ness", + "Ġr amp", + "it udes", + "j ud", + "Ġbrill iant", + "Ġacc us", + "s ource", + "Ġ8 00", + "ĠE vil", + "S w", + "Ġhom eless", + "we ek", + "i ens", + "r ics", + "ĠTh ird", + "T O", + "Ġorgan ic", + "Ġpresent ation", + "ag h", + "ĠDown load", + "v ation", + "Ġas sembly", + "or able", + "hold ers", + "ĠBern ie", + "ĠHel p", + "Ġt ong", + "ĠF ight", + "Ġbe ach", + "B ook", + "ĠL ic", + "Ġr ush", + "ĠR ound", + "ou p", + "ĠMar x", + "Ġcalcul ated", + "ĠDe vil", + "ĠSar ah", + "Ġoccasion ally", + "Ġbul let", + "Av ailable", + "g ate", + "Ġ9 1", + "Ġh osp", + "Ġprom ises", + "ĠH IV", + "ĠSt adium", + "ĠSt ock", + "ĠCorpor ation", + "g age", + "N G", + "ĠC redit", + "Ġs ne", + "ib l", + "Ġacc um", + "s uch", + "Ġterror ists", + "Ġconscious ness", + "ĠZ h", + "Ġdram a", + "ool a", + "pir ation", + "Ġlab our", + "ĠN in", + "Ġut ter", + "Ġdemocr atic", + "Ġass ass", + "il ation", + "Ġg est", + "Ġab road", + "Ġmet ab", + "Ġs orts", + "Ġfl av", + "U B", + "Ġm g", + "ĠNot hing", + "ĠO d", + "Ġmus ical", + "200 9", + "Ġdro ps", + "oc ated", + "ater al", + "0000 00", + "Ġg re", + "Ġequ ality", + "Ġburd en", + "Ġv ig", + "ĠLe ader", + "-------- ----", + "Ġcere mony", + "Ġf ighter", + "Ġact ors", + "Ġ æ", + "am an", + "F i", + "Ġal ign", + "put er", + "Ġe lder", + "ĠN SA", + "Ġrepresent ation", + "ĠOnt ario", + "IT H", + "usal em", + "Ġharass ment", + "itz er", + "Ġsy mp", + "Ġbox es", + "ĠD R", + "Ġman ifest", + "at re", + "Ġ ^", + "Ġd ies", + "le ton", + "Ġmiss ions", + "et he", + "Ġres olve", + "Ġfollow ers", + "Ġas c", + "Ġk m", + "l ord", + "am med", + "Ġsil ent", + "ĠAssoci ated", + "Ġtim ing", + "Ġprison ers", + "ĠK ings", + "ĠF ive", + "Ġtow er", + "Ġappro aches", + "Ġprecise ly", + "Ġb ureau", + "ĠM other", + "ĠI ss", + "Ġkey board", + "it ual", + "Ġfund ed", + "Ġstay ing", + "Ġpsych ological", + "Ġm ile", + "ĠLe on", + "ĠBar b", + "w ill", + "Ġw ider", + "ĠAtl antic", + "Ġt ill", + "ĠR ome", + "ro t", + "Ġaccomp an", + "Ġfl our", + "ac o", + "W orld", + "ĠExp ress", + "ĠY u", + "C or", + "Ġple ased", + "part y", + "Ġpoint ing", + "Ġinf lation", + "Ġro y", + "Ġ ),", + "ain er", + "Ġwedd ing", + "orm on", + "Ġrequ iring", + "Ġqual ified", + "Ġse gment", + "EN D", + "Ġs izes", + "e als", + "Ġcor rupt", + "ass ador", + "Ġcele b", + "Ġdream s", + "ĠM ess", + "Ġcheck ing", + "ĠV ersion", + "Ġprep aring", + "Ġact ively", + "ĠD iff", + "Ġl ux", + "ĠW inter", + "act eria", + "ĠN E", + "Ġdep uty", + "Ġtrans gender", + "Ġsum mary", + "Ġin her", + "er ies", + "ch ar", + "ĠY an", + "Ġkn ock", + "ĠP ath", + "Ġl ip", + "roll er", + "Ġimp ression", + "Ġcelebr ate", + "Ġsl ide", + "Ġgu ests", + "Ġcl ip", + "F S", + "Ġsav ings", + "Ġcapt ain", + "Ġleg acy", + "ĠDen ver", + "Ġw ounded", + "tab oola", + "AC T", + "Ġpurs ue", + "Ġo xy", + "Ġ q", + "Ġsem i", + "ĠN eed", + "ĠAff airs", + "Ġob sc", + "Ġcheck ed", + "Ġd ual", + "C ode", + "ĠM D", + "le m", + "ult y", + "Ġ ©", + "ĠEl izabeth", + "Ġcent uries", + "ard ed", + "s rc", + "Ġev ident", + "enn is", + "at in", + "Ġunemploy ment", + "ĠMar io", + "Ġint im", + "Ch rist", + "Ġbi ological", + "Ġsold ier", + "ĠAdd ed", + "Ġm ath", + "ĠG il", + "Ġbi as", + "Ġd ating", + "ĠO cean", + "Ġm ice", + "M us", + "h ire", + "ĠT es", + "Ser ver", + "lim ited", + "S ize", + "Ġmet ers", + "Ġrock et", + "es see", + "Ġcertific ate", + "ĠIran ian", + "AS S", + "Ġgr id", + "D ec", + "Ġro lling", + "com mun", + "ĠSwed en", + "b ury", + "Ġtiss ue", + "Ġrac ism", + "ĠL ocal", + "Ġmyster y", + "Ġexam ine", + "Ġst em", + "Ġs its", + "Ġhop ed", + "ot ing", + "Ġdial ogue", + "Ġpers u", + "W atch", + "l ay", + "M AN", + "Ġch ronic", + "ĠPort land", + "mark et", + "ĠS EC", + "Ġparalle l", + "Ġsc andal", + "Ġcar ries", + "Ġphenomen on", + "h uman", + "ack er", + "ĠO x", + "Ġretire ment", + "tain ment", + "ov ie", + "ĠG ear", + "Ġd uties", + "Ġdo se", + "Ġsc roll", + "M B", + "in f", + "Ġsa uce", + "Ġland scape", + "red dit", + "ĠChampions hip", + "ĠRed dit", + "al id", + "Ġco in", + "Ġover s", + "Ġpost ing", + "ab out", + "Ġf el", + "and y", + "Ġb old", + "Ġfocus ing", + "e ffect", + "G R", + "Ġde emed", + "Ġrecommend ations", + "Ġste pped", + "Ġvot er", + "ĠDe ep", + "ĠInst agram", + "Ġmoder ate", + "ĠMary land", + "Ġrestrict ed", + "ĠM B", + "ĠCh all", + "Ġto b", + "Ġc ir", + "ĠO cc", + "ĠE ver", + "Ġcoll aps", + "IN FO", + "= -", + "ĠP ict", + "ĠAcc ount", + "n c", + "Ġo ught", + "Ġex port", + "Ġdr unk", + "( '", + "Ġw ise", + "ĠM ort", + "ne cess", + "Ġan cest", + "ĠInc re", + "Ġfrequ ent", + "m ir", + "Ġinterpret ation", + "Ġdepend ent", + "Ġco ins", + "ĠB ol", + "V ideo", + "ĠJust in", + "Ġfat al", + "Ġcook ing", + "Ġconf usion", + "ip her", + "Ġcust ody", + "ĠMor gan", + "om ach", + "ĠGovern or", + "Ġrestaur ants", + "el ing", + "Ġacknowled ged", + "Ġthe r", + "Ġgen es", + "ch ing", + "He y", + "Ġtact ics", + "ĠMex ican", + "Ġv end", + "Ġhe s", + "qu er", + "Ġnot ing", + "ĠCamer on", + "Ġtarget ing", + "ro ck", + "Ġcred its", + "Ġemot ions", + "Ġrepresent atives", + "new s", + "Ġlegisl ative", + "Ġrem oving", + "Ġtweet ed", + "ĠCar ter", + "ĠF ixed", + "Ġfor cing", + "Ġspeak er", + "Ġm ales", + "ĠViet nam", + "l ined", + "Ġconcept s", + "Ġvo ices", + "o ir", + "ĠT rib", + "W he", + "ĠJer usalem", + "ĠS ant", + "Ġc ul", + "Ġl ady", + "ĠHaw ai", + "Ġar ts", + "ĠIn n", + "ĠMach ine", + "ĠEm peror", + "Ġsl ot", + "g ly", + "ĠPro cess", + "II I", + "Ġathlet es", + "ĠTem ple", + "ĠRep resent", + "Ġpres c", + "Ġt ons", + "Ġgold en", + "Ġp unch", + "ĠG R", + "iver pool", + "Ġen act", + "Ġlob by", + "Ġm os", + "Ġpick ing", + "Ġlif etime", + "Ġcogn itive", + "E ach", + "z o", + "Ġd ub", + "Ġcons ists", + "ol n", + "Ġf estival", + "am ous", + "Ġint ellig", + "w ords", + "ĠSm art", + "Ġde le", + "Ġl apt", + "Ġmag ical", + "ĠS in", + "b us", + "ur ities", + "igh th", + "ĠRub y", + "ĠS ure", + "ol ving", + "Ġj un", + "O ST", + "Ġimp osed", + "Ġast ron", + "Ġcor rel", + "ĠN S", + "ĠK it", + "ĠF uture", + "b urn", + "Ġimm une", + "oc us", + "Ġcour ses", + "ĠSt ring", + "Ġle an", + "Ġg host", + "Ġout comes", + "Ġexp ense", + "Ġevery day", + "Ġaccept able", + "A h", + "Ġequ ipped", + "Ġor ange", + "F R", + "ĠD utch", + "Th ough", + "ĠR ank", + "Q U", + "ĠRober ts", + "wh at", + "re nd", + "Ġdisapp ear", + "Ġsp awn", + "ĠL am", + "o is", + "Ġdes erve", + "Ġmin imal", + "Ġnerv ous", + "ĠW ould", + "Ġro ok", + "ĠV ancouver", + "Ġres ign", + "sh ire", + "ĠW orks", + "ĠB uild", + "Ġafford able", + "ĠG ary", + "ĠAren a", + "Ġh anging", + "Ġimpl ications", + "ĠS ong", + "Ġmain taining", + "Ġgu ards", + "C ON", + "Ġder ived", + "Ġexecut ed", + "Ġthe ories", + "Ġqu oted", + "ĠAnd re", + "og a", + "sel ess", + "in fo", + "ĠBel g", + "Ġt ears", + "ĠSur v", + "Ġbirth day", + "ig ious", + "im mer", + "Ġspect rum", + "Ġarchitect ure", + "Ġrec ruit", + "arm a", + "T able", + "Ġmon sters", + "ĠG ov", + "Ġdest ination", + "Ġattract ive", + "Ġf oss", + "ĠMore over", + "Ġpres ents", + "TH E", + "Ġrep ly", + "pt on", + "Ġc um", + "Ġdel ight", + "Ġaffect s", + "Ġdon ations", + "ĠT oy", + "ĠH im", + "M ENT", + "Ġover come", + "it ched", + "ĠFant asy", + "ĠH at", + "ĠBe ast", + "b ott", + "Ġinvestig ations", + "R un", + "Ġhun ting", + "d i", + "f und", + "Ġs essions", + "est yle", + "Ġport ray", + "oid s", + "Y eah", + "Ġcommun icate", + "Ġcom edy", + "ĠY ang", + "Ġbel t", + "ĠMar ine", + "Ġpredict ed", + "Pl ay", + "Ġimportant ly", + "Ġremark able", + "Ġelim inate", + "D avid", + "Ġb ind", + "V ID", + "Ġadvoc ates", + "ĠG aza", + "im p", + "D B", + "ĠN a", + "ĠSim ilar", + "I ES", + "Ġchar ity", + "v as", + "m ath", + "Ġâ ĸ", + "ok er", + "nd um", + "Ġcap s", + "ĠH al", + "2 000", + "e an", + "Ġfle et", + "Ġrec re", + "R ight", + "Ġsleep ing", + "ij ing", + "k ind", + "Ġdesign ated", + "à ¤", + "Ġanim ation", + "ke e", + "ĠInt rodu", + "Ġ/ >", + "Ġdelay ed", + "Ġtrem end", + "Ġcur ious", + "U se", + "Ġle ct", + "d am", + "Ġinnov ation", + "ĠPoint s", + "Ġload ing", + "Ġdisp ute", + "ct ic", + "ird s", + "ĠB Y", + "Ġn urs", + "ĠVal ue", + "ION S", + "ĠH um", + "Ġtem plate", + "m ers", + "Ġappear ances", + "ĠEnter tainment", + "Ġtransl ation", + "Ġsa ke", + "Ġbene ath", + "Ġin hib", + "Ġe uro", + "abet es", + "Ġstud ying", + "ĠM as", + "Ġper ceived", + "Ġexam ined", + "Ġe ager", + "Ġco aches", + "Ġim per", + "ch i", + "Ġprodu ces", + "\" ).", + "ĠEvery one", + "Ġm unicip", + "Ġg irlfriend", + "Ġh ire", + "ĠV ice", + "Ġsu itable", + "op y", + "Ġin equ", + "ĠD uke", + "f ish", + "f irst", + "ĠO bs", + "Ġinter ior", + "ĠBru ce", + "ĠR y", + "Ġanal ys", + "Ġconsider able", + "Ġfore cast", + "Ġf ert", + "ors hip", + "ĠD rug", + "ĠA LL", + ": \"", + "th ur", + "ĠM ail", + "Ġball ot", + "Ġinst antly", + "ĠCh annel", + "Ġp icks", + "Ġ198 9", + "Ġt ent", + "ol i", + "Ġcivil ian", + "b ling", + "ell o", + "b u", + "Ġin ch", + "Ġlog o", + "Ġcooper ation", + "Ġwal ks", + "Ġinvest ments", + "Ġimp rison", + "ĠF estival", + "ĠK y", + "Ġleg ally", + "Ġg ri", + "ch arg", + "S l", + "Ġthreat ening", + "du ction", + "fl ow", + "Ġdismiss ed", + "ibr aries", + "c ap", + "e le", + "ĠMc G", + "ĠHar vard", + "ĠConserv ative", + "ĠC BS", + "p ng", + "Ġro ots", + "ĠH aving", + "umb led", + "ĠF un", + "\\ /", + "ĠS earch", + "ple x", + "Ġdiscuss ing", + "Ġcontin u", + "ĠT ai", + "ĠW ik", + "F ree", + "f it", + "Ġref use", + "Ġmanag ing", + "Ġsy nd", + "ip edia", + "w alk", + "Ġprofession als", + "Ġguid ance", + "Ġunivers ities", + "Ġas semb", + "unt u", + "F inally", + "AS E", + "ĠAut o", + "ĠH ad", + "Ġann iversary", + "L D", + "ĠD ur", + "ĠUlt imate", + "ih ad", + "pro duct", + "Ġtrans it", + "Ġrest ore", + "Ġexpl aining", + "Ġass et", + "Ġtransfer red", + "Ġbur st", + "ap olis", + "ĠMag azine", + "ĠC ra", + "ĠB R", + "gg ed", + "ĠH E", + "M ich", + "b et", + "ĠL ady", + "yl um", + "erv es", + "Ġme ets", + "wh ite", + "L og", + "Ġcorrespond ing", + "Ġins isted", + "G G", + "Ġsurround ed", + "Ġt ens", + "Ġl ane", + "Ġco inc", + "h ome", + "Ġexist ed", + "ect ed", + "ĠDou ble", + "lam m", + "Ġske pt", + "ex p", + "Ġper ception", + "ie v", + "ĠBe ing", + "o ft", + "Ġadop t", + ". :", + "] ;", + "Wind ows", + "Ġsatell ite", + "AS H", + "Ġinf ant", + "d escription", + "ĠMe anwhile", + "c m", + "oc a", + "ĠT reat", + "act or", + "Ġtob acco", + "ĠN orm", + "em ption", + "Ġfl esh", + "Ġj e", + "o op", + "ĠHe aven", + "Ġbe ating", + "an im", + "Ġgather ing", + "Ġcult iv", + "G O", + "ab e", + "ĠJon athan", + "ĠSaf ety", + "Ġbad ly", + "pro t", + "Ġcho osing", + "Ġcontact ed", + "Ġqu it", + "Ġdist ur", + "Ġst ir", + "Ġto ken", + "D et", + "ĠP a", + "Ġfunction ality", + "00 3", + "s ome", + "Ġlimit ations", + "Ġmet h", + "b uild", + "con fig", + "N T", + "re ll", + "ble m", + "ĠM om", + "Ġveter ans", + "ĠH u", + "Ġtrend s", + "are r", + "ĠG iven", + "ĠCa ption", + "m ay", + "AS T", + "Ġwond ering", + "ĠCl ark", + "n ormal", + "Ġsepar ated", + "Ġdes p", + "st ic", + "b rew", + "Ġrel ating", + "ĠN ik", + "ĠF arm", + "Ġenthus i", + "g ood", + "d eb", + "Ġactiv ist", + "Ġm art", + "Ġexplos ion", + "ĠEconom ic", + "L ink", + "Ġins ight", + "Ġconven ient", + "Ġcounter part", + "su pport", + "ĠV irt", + "ag en", + "ĠTenn essee", + "ĠSim on", + "ĠA ward", + "OC K", + "ĠF igure", + "Ġoverse as", + "Ġpr ide", + "ĠC as", + "n ote", + "m g", + "C urrent", + "Ġdispl ays", + "cont ent", + "Ġtravel ing", + "Ġhosp itals", + "ĠFin ancial", + "ĠP ast", + "Ġdefend ant", + "Ġstream ing", + "m ble", + "ĠBer lin", + "uk i", + "Ġdist ribut", + "Ġant ib", + "Ġch ocolate", + "ĠCast le", + "Ġinter rupt", + "ĠR ow", + "Ġconvers ion", + "Ġbug s", + "ĠR ather", + "li est", + "L Y", + "ĠJe an", + "com mon", + "ak h", + "Ġ1 30", + "ot ton", + "ĠDe an", + "Ġam endment", + "Ġgame play", + "ĠWar ren", + "od a", + "Ġhigh lights", + "Ġir re", + "ĠNAT O", + "Ġball s", + "Ġdemand ing", + "U RE", + "ĠL uke", + "F igure", + "st op", + "on ia", + "z one", + "iz ers", + "ĠW R", + "Ġaward ed", + "Ġregul atory", + "ĠH art", + "ĠS N", + "pl ing", + "Ġs our", + "ĠP ixel", + "us ive", + "Ġf et", + "ĠS ent", + "Ġautom atic", + "Ġf er", + "vern ment", + "ĠKh an", + "T ON", + "f ather", + "Ġextraord inary", + "th rop", + "ĠP ython", + "ĠG PU", + "Ġsex ually", + "Ġdesk top", + "it ivity", + "ĠAnton io", + "Ġo rient", + "Ġe ars", + "ob by", + "ous es", + "vertis ements", + "Ġmanufacture rs", + "ic ient", + "min ute", + "Ġconv iction", + "Ġg arden", + "p ublic", + "Ġsatisf ied", + "f old", + "O K", + "Ġin hab", + "ĠTh ink", + "Ġprogram me", + "Ġst omach", + "Ġcoord in", + "Ġh oly", + "Ġth reshold", + "Ġr het", + "Ġser ial", + "Ġemploy ers", + "ĠEvery thing", + "ra h", + "Ġb other", + "Ġbr ands", + "Val ue", + "ĠT ed", + "ĠPlan et", + "Ġp ink", + "ĠFurther more", + "s a", + "P E", + "re ck", + "ĠUS D", + "ot te", + "Ġ& &", + "Ġland ed", + "g ets", + "Ġprodu cers", + "Ġhealth care", + "Ġdomin ant", + "Ġdest ro", + "Ġam ended", + "ch ron", + "Ġf its", + "ĠSy d", + "ĠAuthor ity", + "AT CH", + "Ġfight s", + "ĠL LC", + "Ġ-- -", + "ĠCor p", + "Ġtox ic", + "spe cific", + "ĠC orn", + "ĠChe l", + "Ġtele phone", + "ĠP ant", + "Ġmyster ious", + "aun ch", + "od ox", + "med ia", + "Ġwitness es", + "ag u", + "Ġquestion ed", + "ĠBre xit", + "ĠRem ember", + "ene z", + "Ġend orse", + "iat ric", + "ĠId ent", + "Ġridic ulous", + "1 10", + "Ġpr ayer", + "Ġscient ist", + "Ġ19 50", + "ĠA qu", + "Ġunder ground", + "ĠU FC", + "m are", + "ĠL ater", + "w ich", + "Ġsubsc rib", + "Ġhost s", + "Ġer r", + "Ġgr ants", + "ant om", + "Ġsum mon", + "ear ly", + "ĠC lear", + "ĠPr im", + "Ġsusp ension", + "Ġguarant eed", + "app er", + "Ġr ice", + "ĠSe an", + "ĠSh in", + "Ġrefere ndum", + "Ġfl ed", + "r ust", + "Ġ3 60", + "ter y", + "Ġsh ocked", + "B R", + "ĠO il", + "ĠAll ah", + "Ġpart ly", + "Ġign or", + "Ġtrans mission", + "Ġhom osexual", + "ivers al", + "Ġhop efully", + "ãĤ ¤", + "Ġless on", + "L eg", + "Ġ ..", + "Y et", + "t able", + "app ropri", + "re tt", + "Ġbo ards", + "Ġincor rect", + "Ġb acteria", + "ar u", + "am ac", + "Ġsn ap", + ".' \"", + "Ġpar ad", + "t em", + "he art", + "Ġav ailability", + "Ġw isdom", + "Ġ( +", + "Ġpri est", + "ĠÂł ĠÂł", + "O pen", + "Ġsp an", + "Ġparam eter", + "Ġconv ince", + "Ġ( %)", + "r ac", + "Ġf o", + "Ġsafe ly", + "Ġconver ted", + "ĠOlymp ic", + "Ġres erve", + "Ġhe aling", + "ĠM ine", + "M ax", + "Ġin herent", + "ĠGra ham", + "Ġinteg rated", + "D em", + "Ġpip eline", + "Ġapp lying", + "Ġem bed", + "ĠCharl ie", + "Ġc ave", + "200 8", + "Ġcons ensus", + "Ġre wards", + "P al", + "ĠHT ML", + "Ġpopular ity", + "look ing", + "ĠSw ord", + "ĠAr ts", + "' )", + "Ġelect ron", + "clus ions", + "Ġinteg rity", + "Ġexclus ively", + "Ġgr ace", + "Ġtort ure", + "Ġburn ed", + "tw o", + "Ġ18 0", + "P rodu", + "Ġent reprene", + "raph ics", + "Ġg ym", + "ric ane", + "ĠT am", + "Ġadministr ative", + "Ġmanufacture r", + "Ġ vel", + "ĠN i", + "Ġisol ated", + "ĠMedic ine", + "Ġback up", + "Ġpromot ing", + "Ġcommand er", + "Ġfle e", + "ĠRus sell", + "Ġforg otten", + "ĠMiss ouri", + "Ġres idence", + "m ons", + "Ġrese mb", + "Ġw and", + "Ġmeaning ful", + "P T", + "Ġb ol", + "Ġhe lic", + "Ġwealth y", + "Ġr ifle", + "str ong", + "row ing", + "pl an", + "as ury", + "âĢ¦ .", + "Ġexpand ing", + "ĠHam ilton", + "Ġrece ives", + "S I", + "eat ures", + "ĠAn im", + "RE E", + "P ut", + "Ġbrief ly", + "ri ve", + "Ġstim ul", + "Ġ`` (", + "Ġ __", + "Ġch ip", + "Ġha z", + "Ġpri ze", + "ĠTh ings", + "AC E", + "ul in", + "d ict", + "ok u", + "Ġassoci ate", + "ock ets", + "y outube", + "St ory", + "ateg ory", + "Ġm ild", + "ail ing", + "ĠY e", + "O rig", + "ĠK a", + "or ig", + "Ġpropag anda", + "Ġan onymous", + "Ġstrugg led", + "Ġout rage", + "AT ED", + "ĠBe ijing", + "r ary", + "Ġle ather", + "Ġworld s", + "Ġbroad er", + "12 5", + "id al", + "ĠBet ter", + "Ġt ear", + "E xt", + "Ġpropos als", + "Ġit er", + "ĠSqu ad", + "Ġvol unt", + "m i", + "D id", + "ĠP u", + "p in", + "Ġspeak ers", + "Ġb orders", + "Ġfig ured", + "= '", + "Ġsimultane ously", + "aed a", + "Ġcharg ing", + "Ġur ged", + "Ġcon j", + "25 6", + "ĠG ordon", + "mer ce", + "Ġdocument ary", + "Sh are", + "it ol", + "ON E", + "ĠG arden", + "h att", + "ĠThom pson", + "ane ous", + "ap ore", + "Ġt anks", + "Ġless ons", + "tr ack", + "Ġout standing", + "Ġvolunte ers", + "Ġsp ray", + "Ġmanag ers", + "l arge", + "Ġcamp s", + "Ġart ificial", + "ĠR u", + "Ġb ags", + "th al", + "Ġcompat ible", + "ĠBl ade", + "Ġf ed", + "Ġarg ues", + "F I", + "Ġunf air", + "Ġcor n", + "Ġoff set", + "Ġdirect ions", + "Ġdisappoint ed", + "ĠCon vention", + "Ġview ing", + "M E", + "oc ity", + "Ġtown s", + "Ġlay ers", + "Ġro lled", + "Ġjump ed", + "Ġatt ribute", + "Ġun necess", + "inc oln", + "Ġsupp ose", + "ĠNet her", + "ch a", + "Ġbur ied", + "Ġsix th", + "B en", + "ress ing", + "OU R", + "Ġw ound", + "Ġcy cl", + "Ġmechan isms", + "Ġcongress ional", + "ĠE lement", + "Ġagre ements", + "Ġdec or", + "Ġclos est", + "ĠM it", + "Go ogle", + "} }", + "Ġm ixture", + "Ġflu id", + "S ign", + "ĠSch olar", + "Ġp ist", + "ask et", + "ab ling", + "Ġrac ing", + "he ro", + "ri el", + "ass y", + "Ġche aper", + "b en", + "Ġvert ical", + "amac are", + "ĠRead ing", + "g ments", + "Ġhelic op", + "Ġsacr ifice", + "ay a", + "p aren", + "V A", + "ĠL es", + "ĠStud io", + "Ġviol ations", + "ĠAn na", + "ac er", + "é ¾", + "ĠR at", + "ĠBe ck", + "ĠD ick", + "ĠA CT", + "Ġcomp osition", + "Ġtext ure", + "ĠO wn", + "Ġsmart phone", + "ĠN A", + "Ġfor b", + "im port", + "Ġdef ending", + "il st", + "re r", + "Ġo h", + "ĠJere my", + "Ġbank ing", + "cept ions", + "Ġrespect ive", + "/ .", + "Ġdr inks", + "ĠW i", + "Ġb ands", + "ĠL iverpool", + "Ġg rip", + "ĠB uy", + "Ġopen ly", + "Ġreview ed", + "per t", + "Ġver ify", + "ĠCo le", + "ĠW ales", + "M O", + "Ġun pre", + "Ġshel ter", + "ĠIm perial", + "Ġgu i", + "ĠD ak", + "Ġsuggest ions", + "Ġexplicit ly", + "Ġsl ave", + "Ġblock chain", + "Ġcompet ing", + "Ġprom ising", + "S ON", + "Ġsoc cer", + "Ġconst itution", + "4 29", + "Ġdist ract", + "ĠU ser", + "es ides", + "ĠMet hod", + "ĠTok yo", + "Ġaccompan ied", + "Cl ient", + "s ur", + "al og", + "Ġident ification", + "Ġinv asion", + "as ma", + "Ġindust ries", + "pp ers", + "Ġsub tle", + "ĠUn it", + "n atural", + "Ġsurv ived", + "Ġfl aw", + "ĺ ħ", + "ĠH oll", + "Ġdef icit", + "Ġtut orial", + "ĠCh ance", + "Ġarg uing", + "Ġcontem porary", + "Ġinteg ration", + "for ward", + "Ġt um", + "it is", + "Ġh iding", + "ĠD omin", + "ĠT an", + "ĠB uilding", + "ĠV in", + "Ġspokes person", + "ĠNot es", + "Ġemer ging", + "Ġprepar ation", + "Ġpro st", + "Ġsuspect s", + "Ġaut onom", + "D escription", + "Ġdeal t", + "ĠP ear", + "Ġstead y", + "Ġdecre ased", + "Ġso vere", + "ĠCl in", + "Ġgrad ually", + "ors es", + "ĠW AR", + "S erv", + "ãĤ ¢", + "h r", + "Ġd irty", + "ĠB arn", + "ĠB C", + "Ġd il", + "Ġcal endar", + "Ġcompl iance", + "Ġch amber", + "b b", + "Ġpass enger", + "ate ful", + "ĠT itle", + "ĠSyd ney", + "ĠG ot", + "Ġdark ness", + "Ġdef ect", + "Ġpack ed", + "ass ion", + "Ġgod s", + "Ġh arsh", + "IC K", + "le ans", + "Ġalgorith m", + "Ġoxy gen", + "Ġvis its", + "Ġbl ade", + "Ġkil omet", + "ĠKent ucky", + "Ġkill er", + "P ack", + "enn y", + "Ġdiv ine", + "Ġnom ination", + "be ing", + "Ġeng ines", + "Ġc ats", + "Ġbuff er", + "ĠPh ill", + "Ġtra ff", + "AG E", + "Ġtong ue", + "Ġrad iation", + "ere r", + "m em", + "ĠExpl icit", + "é¾ į", + "Ġcou ples", + "Ġphys ics", + "ĠMc K", + "Ġpolit ically", + "aw ks", + "ĠBl oom", + "Ġwor ship", + "e ger", + "ut er", + "ĠF O", + "Ġmat hemat", + "Ġsent enced", + "Ġdis k", + "ĠM arg", + "Ġ/ *", + "P I", + "Ġoption al", + "Ġbab ies", + "Ġse eds", + "ĠScott ish", + "Ġth y", + "] ]", + "ĠHit ler", + "P H", + "ng th", + "Ġrec overed", + "ing e", + "Ġpow der", + "Ġl ips", + "Ġdesign er", + "Ġdis orders", + "Ġcour age", + "Ġch aos", + "\" },{\"", + "Ġcar rier", + "b ably", + "H igh", + "ĠR T", + "es ity", + "l en", + "Ġrout es", + "u ating", + "F il", + "N OT", + "w all", + "s burgh", + "Ġeng aging", + "ĠJava Script", + "ore r", + "li hood", + "Ġun ions", + "ĠF ederation", + "ĠTes la", + "Ġcomple tion", + "ĠT a", + "Ġprivile ge", + "ĠOr ange", + "Ġne ur", + "paren cy", + "Ġb ones", + "Ġtit led", + "Ġprosecut ors", + "ĠM E", + "Ġengine er", + "ĠUn iverse", + "ĠH ig", + "n ie", + "o ard", + "Ġheart s", + "ĠG re", + "uss ion", + "Ġmin istry", + "Ġpen et", + "ĠN ut", + "ĠO w", + "ĠX P", + "in stein", + "Ġbul k", + "S ystem", + "ic ism", + "ĠMarket able", + "Ġpre val", + "Ġpost er", + "Ġatt ending", + "ur able", + "Ġlicens ed", + "ĠG h", + "et ry", + "ĠTrad able", + "Ġbl ast", + "à ¤", + "ĠTit an", + "ell ed", + "d ie", + "H ave", + "ĠFl ame", + "Ġprof ound", + "Ġparticip ating", + "Ġan ime", + "ĠE ss", + "Ġspec ify", + "Ġregard ed", + "ĠSpe ll", + "Ġs ons", + "own ed", + "Ġm erc", + "Ġexper imental", + "land o", + "h s", + "ĠDun geon", + "in os", + "Ġcomp ly", + "ĠSystem s", + "ar th", + "Ġse ized", + "l ocal", + "ĠGirl s", + "ud o", + "on ed", + "ĠF le", + "Ġconstruct ed", + "Ġhost ed", + "Ġsc ared", + "act ic", + "ĠIs lands", + "ĠM ORE", + "Ġbl ess", + "Ġblock ing", + "Ġch ips", + "Ġev ac", + "P s", + "Ġcorpor ation", + "Ġo x", + "Ġlight ing", + "Ġneighb ors", + "ĠU b", + "ar o", + "Ġbe ef", + "ĠU ber", + "F acebook", + "ar med", + "it ate", + "ĠR ating", + "ĠQu ick", + "Ġoccup ied", + "Ġaim s", + "ĠAdd itionally", + "ĠInt erest", + "Ġdram atically", + "Ġhe al", + "Ġpain ting", + "Ġengine ers", + "M M", + "ĠM ust", + "Ġquant ity", + "P aul", + "Ġearn ings", + "ĠPost s", + "st ra", + "ãĥ¼ ãĥ", + "Ġst ance", + "Ġdro pping", + "sc ript", + "Ġd ressed", + "M ake", + "Ġjust ify", + "ĠL td", + "Ġprompt ed", + "Ġscr ut", + "Ġspeed s", + "ĠGi ants", + "om er", + "ĠEd itor", + "Ġdescrib ing", + "ĠL ie", + "ment ed", + "Ġnow here", + "oc aly", + "Ġinst ruction", + "fort able", + "Ġent ities", + "Ġc m", + "ĠN atural", + "Ġinqu iry", + "Ġpress ed", + "iz ont", + "for ced", + "Ġra ises", + "ĠNet flix", + "ĠS ide", + "Ġout er", + "Ġamong st", + "im s", + "ows ki", + "Ġclim b", + "ne ver", + "Ġcomb ine", + "d ing", + "Ġcomp r", + "Ġsignific ance", + "Ġremem bered", + "ĠNev ada", + "ĠT el", + "ĠSc ar", + "ĠWar riors", + "ĠJ ane", + "Ġcou p", + "b as", + "Ġtermin al", + ", -", + "O H", + "Ġt ension", + "Ġw ings", + "ĠMy ster", + "�� ��", + "ĠUn like", + "val id", + "viron ments", + "ĠAl i", + "Ġn aked", + "book s", + "ĠM un", + "ĠG ulf", + "Ġd ensity", + "Ġdim in", + "Ġdesper ate", + "Ġpres idency", + "Ġ198 6", + "h y", + "IN D", + "Ġun lock", + "im ens", + "Ġhand led", + "ĠE b", + "Ġdisapp eared", + "Ġgen re", + "Ġ198 8", + "Ġdetermin ation", + "St ream", + "ik o", + "ap ters", + "Ġacknow ledge", + "J an", + "Ġcapital ism", + "P at", + "Ġ20 20", + "Ġpain ful", + "Ġcur ve", + "Ġbom bs", + "st orm", + "ĠMet al", + "en cer", + "ĠF ig", + "ĠA aron", + "anc hes", + "Ġins piration", + "Ġexha ust", + "t ains", + "ash i", + "Ġdesc ript", + "Ġr itual", + "ĠChel sea", + "Ġpromot ion", + "ĠH ung", + "ĠW ard", + "iv a", + "ĠE T", + "Ġto ss", + "all ow", + "ĠFranc is", + "D ep", + "Ġhapp iness", + "ĠGl ass", + "Ġbet a", + "Ġstreng then", + "N E", + "o a", + "Ġbutt ons", + "ĠMur ray", + "Ġkick ed", + "Qu est", + "ĠT alk", + "ĠS everal", + "ĠZ ero", + "Ġdr one", + "ul k", + "Ġc am", + "ĠM obile", + "Ġprevent ing", + "Ġret ro", + "ĠA x", + "Ġcru el", + "Ġflo at", + ". ),", + "Ġfil ing", + "ĠGr ant", + "ĠB or", + "Ġr ib", + "Ġchampions hip", + "ĠM erc", + "Ġsty les", + "Ġc ake", + "Ġbuild s", + "ĠS elf", + "io x", + "Ġep ic", + "oy d", + "B el", + "ĠSt ew", + ". (", + "ah u", + "ĠBe yond", + "Ġout s", + "Ġsol o", + "ĠT ree", + "Ġpres erve", + "Ġt ub", + "AR E", + "ro c", + "ĠIm pro", + "ĠW right", + "Ġbu nd", + "Ġtr aged", + "Ġoccas ional", + "b ian", + "Sec ond", + "r ons", + "Ġinter actions", + "form ed", + "s ing", + "Ġown s", + "Ġh ockey", + "Gener al", + "Ġlog ical", + "Ġexp end", + "Ġesc al", + "ĠGr iff", + "ĠC rown", + "ĠRes erve", + "Ġsto pping", + "Ġexc use", + "sec ond", + "Ġoper ated", + "Ġre aches", + "ĠMal ays", + "Ġpoll ution", + "ĠBrook lyn", + "Ġde lete", + "Ġhas h", + "Bl ock", + "ah a", + "âĢ ³", + "Ġsh orter", + "p iece", + "> ", + "Ġh orm", + "ĠW at", + "ĠBre ak", + "Ġprohib ited", + "Ġint ensity", + "ĠAl an", + "Ġli ability", + "? !", + "and ed", + "Ġneigh bour", + "ĠCol lection", + "Ġf ires", + "Ġrevolution ary", + "f ly", + "ĠOr leans", + "Wh ite", + "ĠW rit", + "ĠD awn", + "Ġsett le", + "Ġexec ute", + "B M", + "Ġspokes woman", + "Ġlif estyle", + "Ġclick ing", + "ĠK ill", + "ĠLiber al", + "ĠN azi", + "Ġtra iler", + "Ġmount ains", + "Ġdam n", + "z es", + "p es", + "Ġpress ing", + "Ġb ail", + "ĠOrgan ization", + "Ġp ir", + "Ġth irty", + "Ġelect rical", + "Ġ1 15", + "ĠP oly", + "ĠR ap", + "ĠSt rike", + "ĠC ann", + "Ġdemand ed", + "Ġback ing", + "def ault", + "spe ed", + "ĠLeg isl", + "Ġmother s", + "ĠB ody", + "Ġvar iation", + "ced ented", + "p owered", + "le ading", + "N ever", + "Ġg rave", + "ĠAnt i", + "A W", + "Ġinterview ed", + "ĠG ab", + "ĠF at", + "Ġrook ie", + "u u", + "Ġdep os", + "ix on", + "Ġam pl", + "ret ion", + "ĠHe at", + "Ġpeace ful", + "S M", + "ie ve", + "Ġd iver", + "ĠVict oria", + "Ġm ic", + "p df", + "Ġst ating", + "Ġl ung", + "Ġcritic ized", + "Ġvacc ine", + "ĠLoad ing", + "ur se", + "T ake", + "ĠFr an", + "ĠS old", + "ĠRob in", + "Ġdetect ed", + "ĠSc ript", + "Ġadjust ed", + "Ġsen ator", + "Ġopp osing", + "Er ror", + "C ount", + "Ġconflic ts", + "Ġo w", + "ĠAr gent", + "Ġmatch ing", + "h h", + "ĠTre k", + "st arter", + "\" ),", + "ĠA F", + "od er", + "xx xx", + "ĠAl t", + "ac re", + "ĠP ick", + "ĠSol ar", + "ĠD al", + "O ct", + "ĠB att", + "Ġs rc", + "Ġeng agement", + "Ġexecut ives", + "Ġliber ty", + "j ava", + "Ġtal ented", + "igen ous", + "Ġcon secut", + ".. ...", + "In fo", + "Ġhor rible", + "Ġsurprising ly", + "f eed", + "ic ating", + "ĠL ED", + "Ġfem ales", + "St ation", + "ell er", + "ĠOak land", + "Ġmechan ical", + "i ology", + "ĠV ar", + "Ġrob ust", + "ett ings", + "ott a", + "Ġthe oret", + "Ġret ain", + "k ward", + "Ġd a", + "Ġdeploy ed", + "d el", + "ĠAnd y", + "Ġsubsc ribe", + "we b", + "Ġn a", + "ĠMic hel", + "Ġpart ially", + "ĠCome y", + "Ġc rown", + "ĠM aj", + "ĠBl u", + "r ator", + "D ay", + "IN T", + "Ġdocument ed", + "ĠG DP", + "g i", + "che ll", + "Ġbrut al", + "ĠB ab", + "st ration", + "Ġthe ft", + "Ġt ube", + "@ @", + "Ġqu ery", + "ĠL incoln", + "Ġpublish ing", + "Ġw ore", + "or ical", + "Ġr ic", + "Ġnot able", + "Ġsubsequ ently", + "ne x", + "Ġobser ve", + "ĠB oe", + "Ġc odes", + "m ain", + "W H", + "ĠS L", + "Ġresident ial", + "av an", + "Ġm as", + "are st", + "ade on", + "OU T", + "Ġsoph istic", + "ant e", + "Ġc ens", + "Ġ **", + "Ġmort ality", + "Ġyour s", + "Ġoccas ions", + "Ġrec alled", + "ĠDri ver", + "Ġv ocal", + "Ġbath room", + "Ġsh ops", + "Ġcollabor ation", + "ĠOb amacare", + "ĠC ell", + "Ch ar", + "Su per", + "C re", + "Ġt ends", + "Ġt orn", + "Ġeconom ics", + "a very", + "ĠR aid", + "ĠS em", + "Ġshould ers", + "Ġexpect ing", + "Ġexam ination", + "en ame", + "ĠU I", + "i ability", + "ol as", + "ĠAm b", + "ĠD ra", + "Ġmid field", + "ĠI C", + "Ġlay out", + "Ġflo ating", + "f i", + "it ative", + "Ġtremend ous", + "Ġ Ð", + "Ġab und", + "W ork", + "ĠLight ning", + "Ġsimilar ly", + "Ġconserv atives", + "Ġpr ay", + "B E", + "iz arre", + "Ġt empt", + "Ġemphas is", + "ĠMet ro", + "Ġf ishing", + "Ġmar ry", + "ne g", + "ĠStud y", + "Ġrec k", + "Ġdis pos", + "on ing", + "bs ite", + "Ġsusp ic", + "Ġmer ch", + "ĠG ib", + "ĠDes cription", + "ĠD VD", + "w he", + "ĠY emen", + "Ġen vironments", + "oot ing", + "ĠMod ern", + "e u", + "Ġreflect s", + "Ġh oney", + "Ġanaly st", + "Ġg ut", + "d ec", + "A ction", + "Ġhousehold s", + "Ġst er", + "Ġtem ple", + "Ġreform s", + "Ġfavour ite", + "Ġdead line", + "ĠL E", + "Th ree", + "ĠWith in", + "A ug", + "Ġnight s", + "elt a", + "Ġinv alid", + "ĠEx change", + "ĠDel hi", + "w hen", + "inc ome", + "Ġ ðŁ", + "Ġwire less", + "sc ribe", + "ist a", + "Ġhost ile", + "Ġall y", + "Ġg ig", + "Ġout lets", + "ĠD or", + "EM ENT", + "Ġas h", + "Ġab stract", + "OR D", + "ĠMot or", + "Ġadv iser", + "ist le", + "Ġb ases", + "Ġcourt esy", + "Ġcross ing", + "Ġcle ared", + "Ġrefuge e", + "cos ystem", + "Ġthrow s", + "f un", + "bour ne", + "d ays", + "Ġdisag ree", + "ĠN ative", + "Ġreflect ed", + "ĠF ast", + "ĠY ellow", + "ĠSing apore", + "ĠR aven", + "Ġembr ace", + "ĠK u", + "ĠC hen", + "ĠEar ly", + "Ġappoint ment", + "ĠMin i", + "it ement", + "Ġpl acing", + "Ġb icy", + "S R", + "Ġwh is", + "S U", + "Ġinvestig ated", + "Ġphotograph s", + "g ithub", + "ĠBe at", + "ĠR ing", + "ig hed", + "i ar", + "Ġev olved", + "eral d", + "Ġd un", + "Ġh ub", + "I AL", + "Ġencour aging", + "ĠPr int", + "ĠD ays", + "Ġpro secution", + "Ġp ants", + "az y", + "l ive", + "Ġfoss il", + "ĠJ u", + "Ġro cks", + "ud ge", + "ĠR ace", + "Ġg reet", + "b ie", + "Ġf illing", + "ĠL en", + "Ġdi abetes", + "Ġfire arms", + "um ing", + "enez uel", + "ĠB B", + "Ġaccept ing", + "AT H", + "Ġres ort", + "Ġh unt", + "ri k", + "uck er", + "am ents", + "Ġsust ained", + "Ġcross ed", + "Ġbreak fast", + "Ġatt ributes", + "lect ed", + "at ile", + "Ġv ibr", + "ĠK al", + "ars on", + "op les", + "Ġtou ched", + "Ġdam ages", + "Ġimp ressed", + "ru p", + "Ġan ch", + "ĠAd ams", + "H el", + "ĠVict or", + "Ġmount ed", + "ĠC C", + "Ġdelic ious", + "sp an", + "ell a", + "Ġel abor", + "am ples", + "Ġdef ic", + "Ġconstit u", + "u ates", + "ĠM ission", + "ĠT her", + "ĠMon ster", + "b es", + "Re uters", + "ĠInd ones", + "h ill", + "mun ition", + "Ġconfirm ation", + "ĠCons ider", + "ac ent", + "Ġj et", + "ĠEm ploy", + "ĠGT X", + "n an", + "ĠSp ider", + "Ġprocess or", + "Ġpat ri", + "ĠPent agon", + "ĠRob inson", + "Ġreal istic", + "à ±", + "Ġappear ing", + "Ġp ipe", + "om ed", + "Ġf ru", + "Ġaw ful", + "Ġeval uation", + "Ġintellig ent", + "ĠC itiz", + "Ġfund ra", + "od ium", + "Ġtwe ets", + "Ġwor n", + "pr ing", + "Ġkid n", + "Ġreb els", + "ĠK am", + "ĠNether lands", + "ĠS W", + "Ġacqu isition", + "ĠM ale", + "ãĥ ª", + "omb ies", + "Ġtrad em", + "ĠStat us", + "B re", + "ĠTH IS", + "Ġad verse", + "ĠN EW", + "s ign", + "Ġorgan isation", + "en c", + "ĠHar per", + "ap or", + "ĠMem bers", + "ĠPe ace", + "ĠAir port", + "ĠOther s", + "Ġscr atch", + "ĠP il", + "Ġsens or", + "Ġadop tion", + "ĠHot el", + "ĠDr ag", + "Ġhonest ly", + "Ġy ard", + "ĠFor ces", + "Ġpat ent", + "Ġb ass", + "Ġquiet ly", + "Ġbreat hing", + "Ġp ose", + "i ors", + "ĠJ ess", + "st atic", + "IT E", + "O ffic", + "Ġj ew", + "w cs", + "Ġ14 0", + "Ġpre view", + "ipp i", + "Ġunf ortunately", + "oke mon", + "Ġh orn", + "Ġre ass", + "Ġpe er", + "ock er", + "Ġunt o", + "ĠGr ay", + "Ġclean ing", + "Ġattract ed", + "200 7", + "P oint", + "k ill", + "ĠAg reement", + "ur ches", + "Ġhor r", + "ĠMiss iss", + "Ġworth y", + "Ġfl owers", + "t own", + "d ll", + "Ġre actions", + "Ġde ce", + "Ġindic ating", + "M D", + "Ġpre ference", + "ĠM VP", + "ess ional", + "ĠT arget", + "g ence", + "ĠInd ians", + "Ġm isc", + "Ġfree ly", + "Ġmus cles", + "Ġline up", + "Ġimpact s", + "ous ing", + "om i", + "ac ular", + "Ġcontro lling", + "ag ine", + "c ery", + "he ll", + "Ġrank ing", + "ĠN ich", + "ĠA ve", + "12 8", + "Ġhigh way", + "Ġinc ons", + "Ġb inding", + "Ġstrugg les", + "ĠPitt sburgh", + "Ġgr ay", + "r in", + "Ġcom ics", + "ĠS port", + "Ġrel atives", + "Ġfr ight", + "Ġpro be", + "ĠPort ug", + "Ġv oc", + "Ġt u", + "ĠCor ps", + "Ġposs ibilities", + "Ġqual ify", + "wcs store", + "Ġl ibraries", + "Ġm igrants", + "Ġent ries", + "Ġconsecut ive", + "v als", + "ĠChair man", + "Ġh ill", + "IM E", + "ĠG ard", + "Ġinequ ality", + "f ox", + "ĠS ave", + "Ġc ort", + "claim ed", + "Ġtra its", + "Ġp our", + "Ġmiss iles", + "Ġess ence", + "Ġs ends", + "Ġall iance", + "Ġw ishes", + "ĠChrist opher", + "B ig", + "N Y", + "ĠJac ob", + "s an", + "ur red", + "ĠS O", + "ll y", + "Ġadvoc ate", + "ĠB ond", + "Ġ\" /", + "Us ing", + "Ġdistrict s", + "ĠG ate", + "ĠB ir", + "r idge", + "ĠN az", + "ĠR s", + "bo ards", + "ĠG a", + "ĠRe agan", + "Ġinflu enced", + "1 000", + "ap y", + "Ġchalleng ed", + "Ġb arg", + "Ġfac ulty", + "ĠF if", + "Ġacqu ire", + "A c", + "Ġin sect", + "Ġinstr uments", + "Ġle af", + "th odox", + "M essage", + "Ġt ale", + "Ġthere by", + "Ġtra p", + "Ġstrong est", + "ĠMil itary", + "is ible", + "Ġ198 4", + "ethe less", + "Ġflex ible", + "Ġkill s", + "Ġfin ishing", + "ĠS ize", + "Ġredu ces", + "Ġep id", + "Ġorient ation", + "f ull", + "Ġtr ace", + "Ġl aser", + "Ġopp ose", + "Ġed iting", + "Ġmoment um", + "ä º", + "sh ow", + "V I", + "ĠL ad", + "Ġ198 5", + "Ġmurd ered", + "9 00", + "ut her", + "Ġprob ability", + "ĠP oll", + "Ġrel uct", + "ĠChe m", + "ĠMont real", + "Ġadequ ate", + "ĠPol and", + "ĠSher iff", + "um ph", + "Ġo k", + "Ġ 000", + "Ġ\" [", + "Ġoper ators", + "ĠF er", + "Ġmod es", + "ĠE ve", + "Ġdiscipl ine", + "N ET", + "H and", + "Ġor al", + "ĠW E", + "em ail", + "J P", + "ĠPalestin ians", + "Ġhe nce", + "ĠL ess", + "Ġover l", + "d ig", + "Ġintim id", + "ĠCo al", + "Ġr anging", + "th a", + "Ġdist ant", + "Ġf ib", + "ĠInd ex", + "ĠW onder", + "ĠP el", + "hatt an", + "ĠH ug", + "à Ĺ", + "ra it", + "Ġwra pped", + "ĠR PG", + "Ġchemical s", + "ĠM oney", + "Ġfro zen", + "Ġind irect", + "ĠAgain st", + "E nd", + "Ġuncom fortable", + "ĠGall ery", + "ĠPost ed", + "Ø §", + "ond uct", + "Ġconsequ ence", + "Ġbit ter", + "Ġ198 7", + "p op", + "Ġcount less", + "ĠAl aska", + "ff ff", + "Ġdepart ure", + "Ġref und", + "ĠI an", + "i ated", + "Ġsee ks", + "Ġmechan ics", + "Ġjurisd iction", + "lyn n", + "Ġal ike", + "ĠH unt", + "ath on", + "Ġres olved", + "Ġc ache", + "Ġdist inction", + "d irect", + "Ġenc ount", + "ou b", + "be at", + "ĠCount ry", + "se arch", + "Ġcontin uous", + "Ġmod est", + "ĠR ail", + "th ood", + "1 30", + "B UG", + "Ġcrim inals", + "Ġindic ation", + "Ġencount ered", + "l ast", + "ĠW y", + "Ġide ology", + "ĠP DF", + "sec urity", + "] )", + "ĠJim my", + "ĠE N", + "Ġh iring", + "T em", + "Ġp ig", + "aun t", + "ĠCry stal", + "Ġpen alties", + "Ġcap ability", + "Ġp y", + "Ġproduct ive", + "Ġbal anced", + "ĠGe Force", + "cl ick", + "olit an", + "od s", + "Ġafter wards", + "Ġplay offs", + "ĠG ill", + "U ser", + "Ġback s", + "p ub", + "t ag", + "Ġabs urd", + "p iring", + "Ġc iting", + "Ġtr illion", + "Ġoblig ation", + "Ġmax im", + "ah oo", + "c f", + "um i", + "ĠAl pha", + "ĠN elson", + "Ġpursu ant", + "in itely", + "Ġf ract", + "ent ry", + "ber y", + "ĠTh or", + "Add ed", + "ĠD J", + "ĠG ene", + "Ġaw kward", + "St ud", + "Ġwal let", + "ĠDiv ine", + "ari os", + "Ġrele asing", + "Ġed ited", + "Ġaccompl ished", + "B est", + "Ġed ges", + "Ġplan es", + "Ġfeed ing", + "\" },\"", + "Ġdiscl osure", + "Ġgr ain", + "air y", + "o ons", + "ern and", + "V R", + "Ġreason ably", + "Ġdr um", + "Ġpart ial", + "Ġgraph ic", + "Ġunpre cedented", + "Ġadv ised", + "M icro", + "ĠAss ad", + "point s", + "sc ar", + "ĠZ one", + "tt es", + "Ġ7 00", + "v o", + "ĠH amp", + "Ġfix es", + "Ġca ution", + "Ġstr ings", + "Ġpan els", + "Ġle ak", + "Ġpr icing", + "row th", + "ĠEr ror", + "ĠS aints", + "f ix", + "Ġobserv ations", + "ĠA bs", + "Ġsuggest ion", + "ĠUkrain ian", + "Ġbar rier", + "Ġpain ted", + "B et", + "im ir", + "ĠS pect", + "p ot", + "orne ys", + "Ġcomp ound", + "Ġbe ars", + "ĠR ush", + "Ġlux ury", + "S um", + "Ġor bit", + "ĠMar c", + "Ġex empt", + "ĠTra il", + "ĠM O", + "ĠH ans", + "ĠWe apon", + "oc used", + "umin um", + "ĠJer ry", + "Ġb ust", + "ĠA G", + "ĠW iki", + "Ġend less", + "ĠV lad", + "ĠB ah", + "ĠR adeon", + "ke ys", + "ĠSur vey", + "ĠV iol", + "def ine", + "le an", + "Ġcomm od", + "Ġreven ues", + "Å į", + "Ġfurn iture", + "Ġcast ing", + "Ġdiplom atic", + "ĠPlay ers", + "ĠK illed", + "Ġmod ify", + "Ġinnov ative", + "ĠAb u", + "n or", + "Ġbond s", + "Ġcoach ing", + "M er", + "Ġmod ules", + "ĠPatri ots", + "Ġenh anced", + "Ġproceed ings", + "Ġteam mates", + "Ġ12 8", + "ard o", + "Ġcomprom ise", + "ĠM uch", + "Ġfle w", + "ĠEd ge", + "Ġunnecess ary", + "Ġdoct rine", + "re port", + "ĠOr lando", + "ĠProf ile", + "Ġplay off", + "friend ly", + "Ġcompl ain", + "ĠM C", + "ĠO pt", + "ĠG B", + "Ġbeat en", + "Ġg olf", + "Ġpl acement", + "B it", + "Ġnews letter", + "Ġ201 9", + "vis or", + "raw l", + "ĠiP ad", + "Ġact ed", + "Ġju ice", + "Ġdec ks", + "P N", + "su ccess", + "ĠH alf", + "Ġdele ted", + "Ġsec rets", + "Ġas ylum", + "M art", + "ĠAct iv", + "ĠGu y", + "ĠT s", + "Ġd ys", + "Ġassum ing", + "Ġman a", + "Ġsub ur", + "Ġ12 5", + "M edia", + "AR Y", + "r ide", + "c p", + "Ġdifficult ies", + "Ġcollect ing", + "Ġbank rupt", + "n on", + "Ġcomp osed", + "Ġvol t", + "Ġmilit ants", + "Ġ> >>", + "ĠM ormon", + "t or", + "Ġpartic les", + "ĠB art", + "ry ption", + "Ġad min", + "Ġsqu ee", + "VID IA", + "Ġcreat or", + "iam eter", + "ic ular", + "N BC", + "Ġgrab bed", + "Ġn odd", + "Ġr ated", + "Ġrot ation", + "Ġgr asp", + "Ġexcess ive", + "ĠE C", + "ĠWh it", + "Ġinvent ory", + "ault s", + "ĠF B", + "Ġe cosystem", + "Ġbill ions", + "Ġvent ure", + "n amed", + "Ġdef ender", + "out e", + "Inst ead", + "ir able", + "W ar", + "Ġassum ption", + "Ġb ite", + "Ġearth qu", + "t ail", + "sp ace", + "Ġgif ts", + "boy s", + "Ġinev itable", + "Ġstruct ural", + "Ġbenef icial", + "Ġcompe lling", + "h ole", + "erv ation", + "Ġco at", + "o j", + "inc arn", + "ĠY ears", + "Ġdetermin ing", + "Ġrhet oric", + "Ġbound aries", + "Ġwh ites", + "A nt", + "add y", + ") -", + "ra ham", + "eter min", + "Ġhar vest", + "ĠCon c", + "Ġlapt op", + "ĠM atch", + "Ġenjoy ing", + "cc a", + "oll ar", + "Ġtri ps", + "Ġadd iction", + "ĠS ak", + "Ġpow ered", + "Ġc ous", + "ĠRuss ians", + "ie re", + "Ġret rie", + "qu ality", + "Ġdiff er", + "Ġking dom", + "ĠL aur", + "ĠCap itol", + "Ġcon clusions", + "ĠAl tern", + "ĠN av", + "Ġtrans parent", + "B ER", + "G roup", + "ĠCom plete", + "Ġinf er", + "Ġint rig", + "Ġins ane", + "R O", + "oph ob", + "is en", + "qu al", + "Mich ael", + "Ġm useum", + "ĠP ope", + "Ġres et", + "r ative", + "f ive", + "Ġagg reg", + "itte es", + "osit ory", + "Ġcar b", + "ĠRec ord", + "Ġdec ides", + "ĠF ix", + "Ġexcept ions", + "ĠCommission er", + "un s", + "ĠEnvironment al", + "Ġlegend ary", + "ist ence", + "Ġtun nel", + "k m", + "Ġins ult", + "Ġt roll", + "Ġsh ake", + "Ġdet ention", + "qu es", + "ĠCh rome", + "ĠF iles", + "Ġsub t", + "Ġprospect s", + "Ġpro l", + "re nder", + "pro of", + "Ġperform ances", + "St r", + "Ġh ref", + "ern ame", + "Ġachieve ment", + "Ġf ut", + "F ull", + "ĠLe ban", + "go ogle", + "ãĥ Ī", + "amp a", + "May be", + "Ġproject ed", + "ĠE mb", + "Ġcol leg", + "Ġa wards", + "Ġâ Ķ", + "G old", + "ĠBl ake", + "ĠR aj", + "if ting", + "Ġp ending", + "Ġinst inct", + "Ġdevelop ments", + "Con nect", + "ĠM and", + "ĠW ITH", + "ĠPhilipp ines", + "prof ile", + "Ġalt ogether", + "ĠB und", + "ĠT D", + "oo oo", + "amp ed", + "ip h", + "Ġste am", + "Ġold est", + "Ġdet ection", + "ul pt", + "Ġ ç", + "ĠWay ne", + "200 6", + "f a", + "Ġcir cles", + "ĠF u", + "Ġdon ors", + "appropri ate", + "ĠDak ota", + "j amin", + "Ġmotiv ated", + "Ġpurch ases", + "ĠLouis iana", + "ĠS pl", + "Ġgl obe", + "Ġ10 5", + "z ip", + "c all", + "Ġdepart ments", + "Ġsustain able", + "10 5", + "ĠO P", + "if iers", + "Ġprevent ed", + "Ġinc omp", + "ĠComm ander", + "Ġdom inated", + "Ġ »", + "Ġinvest ed", + "Ġcomplex ity", + "Ġin cl", + "Ġens uring", + "Ġreal m", + "yn c", + "ĠInd ependent", + "r ained", + "ĠJ en", + "ĠFl ight", + "Ġat he", + "Ġspec ulation", + "ĠT E", + "oc ate", + "t ic", + "Ġpl aint", + "her ry", + "Ġto y", + "Ġ1 11", + "Ġpl ates", + "st atus", + "ĠIs a", + "Ġdev oted", + "C op", + "ĠE S", + "25 5", + "ur rency", + "M ain", + "Ġsl aves", + "Ġpe pper", + "Ġqu otes", + "Ġce iling", + "ĠF ish", + "Ġtrans formation", + "Ġfra ction", + "Ġadvant ages", + "Ġto ile", + "Ġstun ning", + "Ġmo ist", + "bre aking", + "s i", + "ĠL ocation", + "ĠMed ium", + "Ġtext s", + "Ġu gly", + "Ġb io", + ". âĢĶ", + "ĠB ased", + "Ġtr ains", + "ĠW ing", + "ĠAn cient", + "ĠRec ords", + "ĠH ope", + "Spe cial", + "ades h", + "ob i", + "[ /", + "Ġtempor arily", + "V er", + "h u", + "os er", + "Ġover night", + "Ġm amm", + "ĠTre asury", + "ĠV enezuel", + "ĠMeg a", + "Ġt ar", + "Ġexpect s", + "bl ack", + "or ph", + "\\\\ \\\\", + "Ġaccept ance", + "Ġrad ar", + "s is", + "Ġjun ior", + "Ġfram es", + "Ġobserv ation", + "ac ies", + "P ower", + "ĠAdv anced", + "M ag", + "olog ically", + "ĠMe chan", + "Ġsent ences", + "Ġanaly sts", + "augh ters", + "force ment", + "Ġv ague", + "Ġcl ause", + "Ġdirect ors", + "Ġeval uate", + "Ġcabin et", + "M att", + "ĠClass ic", + "A ng", + "Ġcl er", + "ĠB uck", + "Ġresear cher", + "Ġ16 0", + "Ġpoor ly", + "Ġexperien cing", + "ĠP ed", + "ĠMan hattan", + "Ġfre ed", + "Ġthem es", + "ad vant", + "Ġn in", + "Ġpra ise", + "10 4", + "ĠLib ya", + "b est", + "Ġtrust ed", + "Ġce ase", + "Ġd ign", + "D irect", + "Ġbomb ing", + "Ġm igration", + "ĠSci ences", + "Ġmunicip al", + "ĠA verage", + "Ġgl ory", + "Ġreve aling", + "Ġare na", + "Ġuncertain ty", + "Ġbattle field", + "ia o", + "G od", + "Ġc inem", + "ra pe", + "el le", + "ap ons", + "Ġlist ing", + "Ġwa ited", + "Ġsp otted", + "ke ley", + "ĠAud io", + "e or", + "ard ing", + "idd ing", + "ig ma", + "ĠN eg", + "Ġl one", + "Ġ ----", + "ex e", + "d eg", + "Ġtrans f", + "Ġwas h", + "Ġsl avery", + "Ġexpl oring", + "ĠW W", + "ats on", + "Ġen cl", + "l ies", + "ĠC reek", + "Ġwood en", + "Man ager", + "ĠBr and", + "um my", + "ĠAr thur", + "Ġbureau cr", + "Ġbl end", + "ar ians", + "F urther", + "Ġsupposed ly", + "Ġwind s", + "Ġ19 79", + "Ġgrav ity", + "Ġanalys es", + "ĠTra vel", + "ĠV eter", + "Ġd umb", + "Ġaltern ate", + "g al", + "Ġconsum ed", + "Ġeffect iveness", + ".' '", + "Ġpath s", + "ond a", + "L A", + "ĠStr ong", + "Ġen ables", + "Ġesc aped", + "Ġ\" \"", + "Ġ1 12", + "Ġ198 3", + "Ġsm iled", + "Ġtend ency", + "F ire", + "Ġp ars", + "ĠR oc", + "Ġl ake", + "Ġf itness", + "ĠA th", + "ĠH orn", + "Ġh ier", + "Ġimp ose", + "m other", + "Ġp ension", + "ic ut", + "bor ne", + "ic iary", + ". _", + "ĠS U", + "Ġpol ar", + "is y", + "eng u", + "itial ized", + "AT A", + "w rite", + "Ġexerc ises", + "ĠD iamond", + "ot ypes", + "Ġharm ful", + "on z", + "Ġprint ing", + "st ory", + "Ġexpert ise", + "ĠG er", + "Ġtraged y", + "ĠF ly", + "Ġd ivid", + "amp ire", + "st ock", + "M em", + "Ġre ign", + "Ġun ve", + "Ġam end", + "ĠProp het", + "Ġmut ual", + "ĠF ac", + "Ġrepl acing", + "H ar", + "ĠCirc uit", + "Ġthro at", + "ĠSh ot", + "Ġbatter ies", + "Ġto ll", + "Ġaddress ing", + "ĠMedic aid", + "Ġp upp", + "ĠN ar", + "ol k", + "Ġequ ity", + "M R", + "ĠHis pan", + "ĠL arge", + "m id", + "D ev", + "Ġexp ed", + "Ġdem o", + "ĠMarsh all", + "erg us", + "Ġf iber", + "Ġdiv orce", + "ĠCre ate", + "Ġsl ower", + "ĠPark er", + "ĠStud ent", + "ĠTr aining", + "Ret urn", + "ĠT ru", + "Ġc ub", + "ĠRe ached", + "Ġpan ic", + "Ġqu arters", + "Ġre ct", + "Ġtreat ing", + "Ġr ats", + "ĠChristian ity", + "ol er", + "Ġsac red", + "Ġdecl are", + "ul ative", + "et ing", + "Ġdeliver ing", + "est one", + "Ġt el", + "ĠL arry", + "Ġmet a", + "ac cept", + "art z", + "ĠRog er", + "hand ed", + "Ġhead er", + "Ġtra pped", + "ĠCent ury", + "Ġkn ocked", + "ĠOx ford", + "Ġsurviv ors", + "b ot", + "Ġdemon stration", + "Ġd irt", + "Ġass ists", + "OM E", + "ĠD raft", + "ortun ate", + "fol io", + "pe red", + "ust ers", + "g t", + "ĠL ock", + "Ġjud icial", + "ver ted", + "Ġsec ured", + "out ing", + "ĠBook s", + "Ġhost ing", + "Ġlif ted", + "l ength", + "Ġj er", + "Ġwhe els", + "ĠR ange", + "umbn ails", + "Ġdiagn osis", + "te ch", + "ĠStew art", + "ĠP ract", + "Ġnation wide", + "Ġde ar", + "Ġoblig ations", + "Ġgrow s", + "Ġmand atory", + "Ġsusp icious", + "! '", + "A pr", + "G reat", + "Ġmort gage", + "Ġprosecut or", + "Ġeditor ial", + "ĠK r", + "Ġprocess ed", + "ung le", + "Ġflex ibility", + "Ear lier", + "ĠC art", + "ĠS ug", + "Ġfoc uses", + "Ġstart up", + "Ġbre ach", + "ĠT ob", + "cy cle", + "ãĢ Į", + "ro se", + "Ġb izarre", + "ãĢ į", + "Ġveget ables", + "$ $", + "Ġret reat", + "osh i", + "ĠSh op", + "ĠG round", + "ĠSt op", + "ĠHawai i", + "ĠA y", + "Per haps", + "ĠBe aut", + "uff er", + "enn a", + "Ġproduct ivity", + "F ixed", + "cont rol", + "Ġabs ent", + "ĠCamp aign", + "G reen", + "Ġident ifying", + "Ġreg ret", + "Ġpromot ed", + "ĠSe ven", + "Ġer u", + "ne ath", + "aug hed", + "ĠP in", + "ĠL iving", + "C ost", + "om atic", + "me ga", + "ĠN ig", + "oc y", + "Ġin box", + "Ġem pire", + "Ġhor izont", + "Ġbr anches", + "Ġmet aph", + "Act ive", + "ed i", + "ĠFil m", + "ĠS omething", + "Ġmod s", + "inc ial", + "ĠOrig inal", + "G en", + "Ġspir its", + "Ġear ning", + "H ist", + "Ġr iders", + "Ġsacr ific", + "M T", + "ĠV A", + "ĠS alt", + "Ġoccup ation", + "ĠM i", + "Ġdis g", + "lic t", + "Ġn it", + "Ġn odes", + "e em", + "ĠP ier", + "Ġhat red", + "ps y", + "ãĥ ī", + "Ġthe ater", + "Ġsophistic ated", + "Ġdef ended", + "Ġbes ides", + "Ġthorough ly", + "ĠMedic are", + "Ġbl amed", + "arent ly", + "Ġcry ing", + "F OR", + "pri v", + "Ġsing ing", + "ĠI l", + "Ġc ute", + "o ided", + "olit ical", + "ĠNe uro", + "å ¤", + "Ġdon ation", + "ĠEag les", + "ĠG ive", + "T om", + "Ġsubstant ially", + "ĠLic ense", + "ĠJ a", + "Ġg rey", + "ĠAn imal", + "ĠE R", + "ĠU nd", + "Ġke en", + "Ġconclud e", + "ĠMississ ippi", + "Eng ine", + "ĠStud ios", + "P ress", + "o vers", + "ll ers", + "Ġ3 50", + "ĠR angers", + "Ġr ou", + "ert o", + "E p", + "iss a", + "iv an", + "Ġse al", + "ĠReg ist", + "dis play", + "Ġwe aken", + "u um", + "ĠComm ons", + "ĠS ay", + "Ġcult ures", + "Ġl aughed", + "Ġsl ip", + "Ġtreat ments", + "iz able", + "m art", + "ĠR ice", + "Ġbe ast", + "Ġob esity", + "ĠLa ure", + "ig a", + "Wh ich", + "hold er", + "Ġelder ly", + "Ġp ays", + "Ġcompl ained", + "Ġc rop", + "Ġpro c", + "Ġexplos ive", + "ĠF an", + "ĠAr senal", + "A uthor", + "ef ul", + "Ġme als", + "Ġ( -", + "id ays", + "Ġimag ination", + "Ġann ually", + "Ġm s", + "as ures", + "H ead", + "ik h", + "m atic", + "Ġboy friend", + "ĠCom puter", + "Ġb ump", + "Ġsur ge", + "ĠCra ig", + "ĠKir k", + "D el", + "medi ate", + "Ġscen arios", + "ĠM ut", + "ĠSt ream", + "Ġcompet itors", + "Ù Ħ", + "ĠStan ford", + "ĠRes ources", + "az ed", + "b age", + "Ġorgan is", + "ĠRe lease", + "Ġsepar ately", + "Ġha bits", + "Ġmeasure ments", + "ĠCl ose", + "Ġaccomp any", + "Ġg ly", + "Ġt ang", + "ĠR ou", + "Ġplug in", + "Ġcon vey", + "ĠChall enge", + "oot s", + "j an", + "Ġcur s", + "ĠRel ations", + "ke eper", + "Ġapproach ing", + "p ing", + "Spe aking", + "Ġarrang ement", + "ĠV I", + "are ttes", + "Ġaffect ing", + "Ġperm its", + "b ecause", + "Ġu seless", + "ĠH us", + "!! !!", + "Ġdestro ying", + "Un fortunately", + "Ġfasc inating", + "S em", + "Ġelect oral", + "Ġtrans parency", + "ĠCh aos", + "Ġvolunte er", + "Ġstatist ical", + "Ġactiv ated", + "ro x", + "We b", + "H E", + "ĠHamp shire", + "is ive", + "M ap", + "Ġtr ash", + "ĠLaw rence", + "st ick", + "C r", + "Ġr ings", + "EX T", + "Ġoper ational", + "op es", + "D oes", + "ĠEv ans", + "Ġwitness ed", + "P ort", + "Ġlaunch ing", + "ec onom", + "w ear", + "ĠPart icip", + "um m", + "cul es", + "ĠR AM", + "ĠT un", + "Ġass ured", + "Ġb inary", + "Ġbet ray", + "Ġexpl oration", + "ĠF el", + "Ġad mission", + "it ated", + "S y", + "Ġav oided", + "ĠSim ulator", + "Ġcelebr ated", + "ĠElect ric", + "¥ ŀ", + "Ġcl uster", + "itzer land", + "he alth", + "L ine", + "ĠN ash", + "at on", + "Ġsp are", + "Ġenter prise", + "ĠD IS", + "clud es", + "Ġfl ights", + "Ġreg ards", + "Ġà Ĺ", + "h alf", + "Ġtr ucks", + "Ġcontact s", + "Ġunc ons", + "ĠCl imate", + "Ġimm ense", + "N EW", + "oc c", + "ect ive", + "Ġemb od", + "Ġpat rol", + "Ġbes ide", + "Ġv iable", + "Ġcre ep", + "Ġtrig gered", + "ver ning", + "Ġcompar able", + "q l", + "Ġg aining", + "ass es", + "Ġ( );", + "ĠG rey", + "ĠM LS", + "s ized", + "Ġpros per", + "\" ?", + "Ġpoll ing", + "Ġsh ar", + "ĠR C", + "Ġfire arm", + "or ient", + "Ġf ence", + "Ġvari ations", + "g iving", + "ĠP i", + "osp el", + "Ġpled ge", + "Ġc ure", + "Ġsp y", + "Ġviol ated", + "Ġr ushed", + "Ġstro ke", + "ĠBl og", + "sel s", + "ĠE c", + ",' '", + "Ġp ale", + "ĠColl ins", + "ter ror", + "ĠCanad ians", + "Ġt une", + "Ġlabor atory", + "Ġn ons", + "t arian", + "Ġdis ability", + "ĠG am", + "Ġsing er", + "al g", + "ĠSen ior", + "Ġtrad ed", + "ĠWar rior", + "Ġinf ring", + "ĠFrank lin", + "Ġstr ain", + "ĠSwed ish", + "Ġsevent h", + "ĠB enn", + "ĠT ell", + "Ġsynd rome", + "Ġwond ered", + "id en", + "++ ++", + "ig o", + "Ġpur ple", + "Ġjournal ism", + "Ġreb el", + "Ġf u", + "bl og", + "Ġinv ite", + "ren cies", + "ĠCont act", + "Is rael", + "ĠCont ent", + "Ġche er", + "Ġbed room", + "ĠEngine ering", + "ĠQue ens", + "Ġd well", + "ĠPlay Station", + "ĠD im", + "ĠCol on", + "l r", + "Ġoper ates", + "Ġmotiv ation", + "US A", + "ast ered", + "C ore", + "ĠTr uth", + "ol o", + "OS E", + "ĠMem ory", + "Ġpred ec", + "Ġan arch", + "Ġ19 20", + "ĠY am", + "à ¨", + "b id", + "Ġgr ateful", + "Ġexc itement", + "Ġtre asure", + "Ġlong est", + "ct ive", + "Ġdes erves", + "Ġreserv es", + "Ġcop s", + "ĠOtt awa", + "ĠEgypt ian", + "ank ed", + "Ġart if", + "Ġhypot hesis", + ": /", + "Ġpurch asing", + "Ġlove ly", + "H P", + "Ġdiv ide", + "Ġstrict ly", + "Ġquestion ing", + "Ġtaxp ayers", + "ĠJ oy", + "Ġroll s", + "ĠHe avy", + "Ġp orts", + "Ġmag netic", + "Ġinf lamm", + "Ġbr ush", + "t ics", + "â ĪĴ", + "Ġbott les", + "pp y", + "Ġp add", + "ãĤ ¯", + "m illion", + "Ġdevast ating", + "Ġcomp iled", + "Ġmed ication", + "Ġtw elve", + "ĠPer ry", + "Sp ace", + "im b", + "y our", + "Ġle aked", + "ĠT ar", + "Ġun ity", + "Ġinfect ed", + "Ġtravel ed", + "ID E", + "ĠMc Donald", + "t xt", + "ĠPr inc", + "Ġinter ven", + "ĠTai wan", + "ĠP ow", + "Ġbe aring", + "ĠTh read", + "Ġz ones", + "iz ards", + "un ks", + "Ch apter", + "ll or", + "Ġ ·", + "Ġw ounds", + "Ġdisc retion", + "Ġsucceed ed", + "ik ing", + "Ġicon ic", + "C all", + "Ġscreen ing", + "ĠM is", + "ict s", + "Ġmin isters", + "Ġsepar ation", + "Pl ayer", + "Ġb ip", + "Ġbel oved", + "Ġcount ing", + "ĠE ye", + "ar ound", + "ing ing", + "Ġtable t", + "Ġoff ence", + "in ance", + "h ave", + "ĠInf o", + "ĠNin ja", + "Ġprotect ive", + "ĠC ass", + "M ac", + "ĠQual ity", + "N orth", + "Ġ ic", + "ĠCub a", + "ĠChron icle", + "ĠPro perty", + "Ġfast est", + "ot os", + "ĠG erm", + "OW N", + "Ġbo om", + "ĠStan ley", + "ergus on", + "Ġcle ver", + "Ġent ers", + "m ode", + "ter ior", + "ĠS ens", + "Ġlin ear", + "AR K", + "Ġcomp aring", + "Ġpure ly", + "Ġsaf er", + "ĠPot ter", + "Ġc ups", + "R T", + "Ġgl uc", + "Ġatt ributed", + "Ġdu pl", + "ĠP ap", + "Ġprec ious", + "Ġp a", + "iction ary", + "ĠT ig", + "ĠTo o", + "ol utions", + "st an", + "Ġrob ots", + "Ġlob b", + "Ġstat ute", + "Ġprevent ion", + "w estern", + "16 0", + "ĠAct ive", + "ĠMar ia", + "h al", + "N one", + "ell ar", + "ĠK B", + "ĠPart ners", + "ĠSing le", + "ĠFollow ing", + "ang o", + "ac ious", + "Ġth ou", + "Ġk g", + "Ġinflu ential", + "ĠFriend s", + "S ur", + "ain ted", + "Ġfor ums", + "Ġst arter", + "Ġcitizens hip", + "ĠE lection", + "on ge", + "ot ation", + "os ph", + ";; ;;", + "ut ical", + "p ur", + "ere n", + "Ġaccus ations", + "bit ious", + "ab bit", + "ĠOr d", + "Post ed", + "ir k", + "Ġsens itivity", + "ic he", + "ĠAm y", + "ĠF ab", + "Ġsum mit", + "Ġped est", + "Ġrub ber", + "Ġagric ultural", + "Ġcan cel", + "A E", + "Ġin aug", + "Ġcont am", + "Ġfirm ly", + "i w", + "st age", + "ĠK an", + "Ġt ier", + "Ġinv ention", + "Ġtransl ated", + "ĠR ules", + "B ox", + "Tw itter", + "ID S", + "Ġp izza", + "Ġdeb ug", + "ĠD rop", + "v s", + "Ġh orses", + "b ig", + "Ġb oring", + "Ġh ood", + "ĠMcC ain", + "at ched", + "ĠBro s", + "Ġsk ip", + "Ġess ay", + "st at", + "ĠLeg ends", + "Ġam munition", + "au c", + "Ġshoot er", + "Ġun h", + "Ġsuppl ied", + "Ġgener ic", + "ĠS K", + "ib an", + "yr ics", + "Ġ25 5", + "Ġclim bing", + "Form er", + "Ġfl ip", + "Ġjump ing", + "Ġfrust ration", + "ĠTer ry", + "Ġneighborhood s", + "Ġmed ian", + "be an", + "Ġbr ains", + "Follow ing", + "Ġsh aped", + "Ġdraw s", + "Ġal tered", + "J ack", + "Ġrecip es", + "Ġsk illed", + "we alth", + "ach i", + "e lection", + "Ġbehavi ors", + "de als", + "ĠU ntil", + "F e", + "Ġdecl aration", + "mar ks", + "ĠBet ween", + "cel ona", + "Ġres on", + "Ġbub ble", + "Am ong", + "Ġim perial", + "G S", + "Ġfemin ist", + "200 5", + "ĠK yle", + "Ġaccount ing", + "ĠTe le", + "ĠT yr", + "Ġconnect ing", + "Ġre hab", + "ĠP red", + "s im", + "Ġmeant ime", + "Ġphys ician", + "M W", + "ĠCamp bell", + "ĠBr andon", + "Ġcontribut ing", + "ĠR ule", + "ĠWe ight", + "ĠN ap", + "Ġinter active", + "Ġv ag", + "Ġhel met", + "ĠCom b", + "f our", + "Ġsh ipped", + "Ġcomple ting", + "ĠP D", + "PD ATE", + "Ġspread ing", + "Ġsc ary", + "erv ing", + "ĠG as", + "Ġfr ank", + "s chool", + "Ġrom antic", + "Ġstab il", + "R ob", + "Ġaccur ately", + "Ġac ute", + "ĠH ann", + "Ġsymbol s", + "Ġcivil ization", + "ĠA W", + "Ġlight ning", + "Ġcons iders", + "Ġven ue", + "Ġ ×", + "Ġo ven", + "ĠS F", + "h is", + "Ġn u", + "ĠLear n", + "Ġpe oples", + "Ġst d", + "Ġsle e", + "Ġs lic", + "ĠStat istics", + "Ġcor ners", + "ĠB aker", + "Ġ: )", + "ment ation", + "ol ver", + "Ġlaugh ing", + "ĠT odd", + "ond e", + "ĠH ills", + "Ġn uts", + "ĠW oman", + "pl ane", + "Ġl iver", + "ĠIn side", + "S orry", + "Ġagre es", + "Ġfund ament", + "ĠF isher", + "Ġa uction", + "Ġthread s", + "gl as", + "ĠBas ic", + "ĠN at", + "Ġlack ing", + "Ġceleb ration", + "j u", + "Ġs illy", + "E uro", + "Ġt att", + "ight y", + "cont rolled", + "T est", + "ĠSing h", + "Ġr age", + "Ġrh yth", + "o ffic", + "ĠPh antom", + "Ġhead lines", + "Ġrespond ing", + "ĠMor ning", + "Ġvit amin", + "Ġboot s", + "ĠS ite", + "al in", + "p i", + "Ġvir al", + "ĠU C", + "D ER", + "ĠSe x", + "Ġst ocks", + "c urrent", + "Ġch urches", + "ĠR are", + "ĠMur phy", + "Ġden ial", + "ĠG aming", + "Ġtou g", + "Ġn ick", + "Ġm akers", + "ĠRon ald", + "Ġgener ous", + "ĠD oc", + "ĠMor ris", + "Ġtransform ed", + "ĠN ormal", + "Ġ10 4", + "ĠKick starter", + "ĠUp on", + "On line", + "ĠI RS", + "Ġw rap", + "Ġl oving", + "Ġarri ves", + "ĠD ue", + "Ġhe ter", + "ĠM ade", + "Ġrent al", + "Ġbelong s", + "Ġatt orneys", + "Ġcro ps", + "Ġmat ched", + "ul um", + "ol ine", + "10 9", + "Ġdis par", + "Ġbuy ers", + "ĠCam bridge", + "Ġeth ics", + "rou ps", + "Ġjust ified", + "Ġmarg inal", + "Ġrespect ed", + "win ning", + "Ġnodd ed", + "ĠSer ge", + "ĠForm er", + "C raft", + "######## ########", + "ĠWar ner", + "Ġd ash", + "et e", + "Ġent ert", + "ĠE scape", + "out heast", + "Ġkn ees", + "ĠB omb", + "Ġr ug", + "P ass", + "Ġatt itudes", + "go vernment", + "ĠPri or", + "Ġqual ities", + "Ġnot ification", + "ĠPh one", + "l ie", + "Ġanticip ated", + "ĠCom bat", + "ĠBar ry", + "Ġ198 2", + "Us ers", + "on er", + "Ġcomput ing", + "ĠConnect icut", + "Ġless er", + "Ġpe ers", + "ĠC u", + "Ġtechn ically", + "Ġsub mission", + "ĠUn iversal", + "Ġman ually", + "our ge", + "Ġrespond ents", + "ĠB TC", + "ĠH ost", + "Ġf are", + "ĠB ird", + "Ġrece ipt", + "al so", + "Ġj ack", + "Ġagric ulture", + "Ġsk ull", + "Ġ! =", + "Ġpass ive", + "ĠC I", + "Ġsoc ieties", + "Ġremind ed", + "Ġinter ference", + "B uy", + "Ġâ ľ", + "g on", + "Ġscrut iny", + "ĠW itch", + "Ġconduct ing", + "Ġ ãĥ", + "Ġexch anges", + "ĠMit chell", + "Ġinhab it", + "Ġtw ist", + "B D", + "Ġwhere ver", + "group on", + "Ġj okes", + "ĠBen jamin", + "ĠR andom", + "fr ame", + "ĠL ions", + "Ġhighlight ed", + "ĠArk ansas", + "E nt", + "Ġp ile", + "Ġpre lim", + "g s", + "mind ed", + "Ġfel ony", + "ĠG A", + "ĠL uck", + "Ġpract ically", + "ĠB os", + "Ġact ress", + "D am", + "ĠB ou", + "Ġvis a", + "Ġembed ded", + "Ġhy brid", + "Ġear liest", + "Ġsoon er", + "s ocial", + "ĠH A", + "Ġste ep", + "Ġdis advant", + "Ġexplo it", + "ĠE gg", + "ĠUlt ra", + "Ġnecess ity", + "L ocal", + "ie ge", + "Ġd ated", + "Ġmass es", + "Ġsubsc ription", + "pl ess", + "Ġan onym", + "Ġpresum ably", + "Bl ue", + "The ir", + "asket ball", + "ĠPhil ip", + "Ġcom ed", + "load ed", + "r ane", + "Ġref lection", + "Ch ina", + "Ġext ends", + "Ġform ing", + "Ġund ers", + "200 1", + "Ġgr at", + "Ġconcent rations", + "Ġins ulin", + "Ġsec ular", + "Ġwh ilst", + "Ġwin ners", + "Ad vertisements", + "Ġdeliber ately", + "ĠWork ing", + "Ġs ink", + "et ics", + "d ale", + "Ġmand ate", + "Ġg ram", + "Ġvac ation", + "Ġwarn ings", + "ri pp", + "ĠTH AT", + "Ġcomment ary", + "Ġint u", + "Ġa est", + "Ġreason ing", + "Ġbreak down", + "ĠZ ombie", + "Ġ-- >", + "ĠPolit ical", + "c ott", + "Ġthr ust", + "Ġtechn ological", + "Ġdec iding", + "Ġtraff icking", + "L ong", + "W elcome", + "pr ising", + "ĠCommun ications", + "Ġend ors", + "Ġsw ift", + "Ġmetab ol", + "co ins", + "res a", + "ĠHT TP", + "Ġen roll", + "ĠH appy", + "us r", + "int age", + "Ġ[ \"", + "u ably", + "ĠM aterial", + "Ġrepe al", + "Se pt", + "k h", + "ĠMod i", + "Ġunder neath", + "ĠI L", + "sh ore", + "Ġdiagn osed", + "ace utical", + "Ġsh ower", + "au x", + "ĠSw itch", + "ĠStre ngth", + "Ġj ihad", + "n ational", + "Ġtra uma", + "uss y", + "on i", + "Ġcons olid", + "Ġcal ories", + "ĠF lynn", + "ag ged", + "16 8", + "ĠP ink", + "Ġfulf ill", + "Ġch ains", + "Ġnot ably", + "ĠA V", + "L ife", + "ĠCh uck", + "m us", + "ĠUr ban", + "ĠH end", + "Ġdep osit", + "ĠS ad", + "Ġaff air", + "OR K", + "ie val", + "ĠF DA", + "Ġt rop", + "ĠOver all", + "Ġvirt ue", + "Ġsatisf action", + "au nd", + "Ġl un", + "ĠSw itzerland", + "ĠOper ation", + "pro cess", + "Ġsh ook", + "Ġcount ies", + "le ased", + "ĠCharl otte", + "1 12", + "Ġtrans cript", + "Ġre dd", + "p ush", + "ĠHe y", + "ĠAn alysis", + "[ \"", + "Ġaltern atives", + "ard less", + "Ġele ph", + "Ġpre jud", + "ĠLe af", + "H aving", + "ĠH ub", + "Ġexpress ions", + "ĠVol ume", + "Ġshock ing", + "ĠRed s", + "Ġread ily", + "Ġplan ets", + "ad ata", + "Ġcollaps ed", + "ĠMad rid", + "Ġir rit", + "i pper", + "ĠEn c", + "ĠW ire", + "Ġbu zz", + "ĠG P", + "ash a", + "Ġaccident ally", + "ur u", + "Ġfrust rated", + "ĠS A", + "Ġhung ry", + "ĠH uff", + "Ġlab els", + "ant o", + "ĠE P", + "Ġbar riers", + ") |", + "ĠBer keley", + "ĠJ ets", + "Ġp airs", + "ĠL an", + "J ames", + "ĠB ear", + "Ġhum or", + "ĠLiber ty", + "Ġmagn itude", + "Ġag ing", + "ĠM ason", + "Ġfriends hip", + "umb ling", + "Ġemer ge", + "Ġnewsp apers", + "Ġam bitious", + "ĠRich ards", + "atern al", + "Ġ198 1", + "Ġcook ies", + "Ġsc ulpt", + "Ġpur suit", + "L ocation", + "Ġscript s", + "p c", + "Ġarrang ements", + "Ġd iameter", + "Ġl oses", + "am ation", + "Ġl iqu", + "ĠJ ake", + "aret te", + "Ġunderstand s", + "ĠZ en", + "v m", + "Ġappro ve", + "Ġw ip", + "Ġult ra", + "Ġint end", + "ĠD I", + "asc ular", + "Ġst ays", + "ĠK or", + "ĠK l", + "Ġinvest ing", + "L a", + "Ġbelie ving", + "b ad", + "m outh", + "Ġtaxp ayer", + "ãĥ ĥ", + "ĠQue bec", + "Ġl ap", + "ĠSw iss", + "d rop", + "Ġdr ain", + "ir i", + "et c", + "ft en", + "ĠN ex", + "Ġst raw", + "Ġscream ing", + "Ġcount ed", + "Ġdam aging", + "Ġamb assador", + "cent ury", + "Ġpro x", + "Ġarrest s", + "u v", + "il ateral", + "ĠCh arg", + "Ġpresc ribed", + "Ġindepend ently", + "Ġf ierce", + "ĠB aby", + "Ġb rave", + "Ġsu its", + "= >", + "Ġbas eline", + "ĠR ate", + "Ġis lands", + "Ġ( (", + "g reen", + "ix els", + "Ġname ly", + "ĠVill age", + "th an", + "am y", + "V ersion", + "g mail", + "ential s", + "ĠS ud", + "ĠMel bourne", + "Ġarri ving", + "Ġquant um", + "e ff", + "rop olitan", + "T ri", + "Ġfun eral", + "ĠI R", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "ĠC ob", + "it ably", + "Ġt urb", + "Ġcomb o", + "Re view", + "Ġdeploy ment", + "u ity", + "ĠB ott", + "Ġinv isible", + "Ġrender ing", + "Ġunl ocked", + "Ġa qu", + "ĠVlad imir", + "Ġp ad", + "ĠBr ain", + "ĠLeg acy", + "dr agon", + "ĠKurd ish", + "Ġsound ed", + "Ġdet ained", + "ĠD M", + "g ary", + "Ġd aughters", + "Ġdistur bing", + "uk a", + "ĠPar ad", + "Ġt ast", + "Ġunf ortunate", + "Ġu l", + "em in", + "Ġattend ance", + "tr l", + "Ġpar ks", + "ĠMem orial", + "ĠAl ice", + "oth y", + "gu ard", + "ĠD ise", + "ĠSh an", + "ĠFor um", + "R ich", + "Ġshif ted", + "ue z", + "Ġl ighter", + "ĠMag n", + "Ġc od", + "S ch", + "ham mad", + "P ub", + "3 50", + "ĠP okemon", + "Ġprot otype", + "Ġun re", + "B ase", + "ĠStud ents", + "ĠRep ly", + "ĠCommun ist", + "Ġg au", + "ĠTy ler", + "I Z", + "Ġparticip ated", + "Ġsup rem", + "ĠDet ails", + "Ġvessel s", + "ro d", + "Ġt ribe", + "ke ep", + "Ġassum ptions", + "Ġp ound", + "Ġcr ude", + "ĠAv ailable", + "Ġswim ming", + "Ġin clusion", + "Ġadv ances", + "c ulation", + "Ġconserv ation", + "Ġover d", + "ĠBuff alo", + "Art icle", + "ed ge", + "Ġaw a", + "ĠMad ison", + "Ġsid ew", + "Ġcat ast", + "ĠK rist", + "uc le", + "ĠHigh way", + "ĠTer ror", + "Ġactiv ation", + "Ġuncons cious", + "ĠSat an", + "ĠSus an", + "ill ery", + "Ġarr anged", + "i op", + "Ġrum ors", + "ur ring", + "th ink", + "ĠKe ith", + "ĠK ind", + "Ġavoid ing", + "by n", + "n ut", + "ĠSpe aker", + "r us", + "n ames", + "Ġgu ilt", + "ĠOlymp ics", + "Ġsa il", + "ĠM es", + "lev ant", + "ĠColumb us", + "a ft", + "C ity", + "S outh", + "ĠHar vey", + "ĠP un", + "S everal", + "Ġment ally", + "Ġimp ress", + "m ount", + "ĠUb untu", + "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", + "ĠSuper man", + "ĠMP s", + "Ġintent ions", + "ĠR acing", + "Ġlike lihood", + "Ġ2 40", + "T otal", + "Ġto ys", + "ĠW atson", + "Ġur ge", + "L ear", + "ĠP aper", + "Ġoccur ring", + "ĠB eng", + "ĠC ert", + "Ġst ones", + "T im", + "ĠTw in", + "z b", + "ĠD ynam", + "Ġpolit ician", + "k ens", + "ĠEnter prise", + "UT ERS", + "Ġab ol", + "Ġref resh", + "Ġarbit rary", + "pe ction", + "Ġtrou bles", + "Ġ} );", + "t v", + "Ġpil ots", + "Ġdist ribute", + "Ġaud it", + "Ġp ause", + "orig inal", + "Ġr ivals", + " £", + "F ig", + "T L", + "ab il", + "ry ing", + "L in", + "ion ed", + "l on", + "Ġf ancy", + "Ġcr ashed", + "Ġt ract", + "Ġshe d", + "Ġcons ume", + "B ased", + "down load", + "in it", + "Ġvolt age", + "Int rodu", + "Ġcondem ned", + "ĠFin ance", + "res pect", + "Ġex cluded", + "Ġestablish ing", + "her ic", + "Ġher itage", + "Ġspect acular", + "Ġun st", + "ĠSnow den", + "ĠL ane", + "S an", + "Ġprotect ions", + "st ruction", + "inc inn", + "Ġmac ro", + "C ustom", + "ios ity", + "Ġes p", + "Ġfunction ing", + "Ġm ush", + "Ġp uzzle", + "Ġeth ical", + "M al", + "Ġgo verning", + "ĠF erguson", + "Ġrest ored", + "Ġst ressed", + "ĠCoun ter", + "ĠK as", + "cl ip", + "AN S", + "Ġse iz", + "U K", + "by ss", + "old own", + "ap i", + "Ġperman ently", + "oun ters", + "W est", + "Th rough", + "L ight", + "at oes", + "Ġne at", + "Ġc ord", + "ure r", + "Ġsevere ly", + "ĠA ven", + "Ġinter rog", + "Ġtri ple", + "G iven", + "N umber", + "Ġar ise", + "Ġs her", + "pl ant", + "Ġfl ower", + "ĠC ou", + "Ġat e", + "Ġnew er", + "b ul", + "Ġmean while", + "ĠL air", + "Ġadjust ment", + "ĠCop yright", + "Ġd ivers", + "i ological", + "Ġgam ers", + "o at", + "Ġhistor ically", + "Ġanal og", + "Ġlong time", + "Ġpres cription", + "ĠM ist", + "ĠHy per", + "ĠM aine", + "ĠDe ity", + "Ġmulti pl", + "ĠRe incarn", + "ĠH yd", + "ĠP ic", + "S il", + "r ants", + "ĠC ris", + ". ;", + "( {", + "epend ence", + "Ġrec y", + "ate ur", + "Ġqu ad", + "Ġgl ob", + "Ġcon ced", + "te am", + "Ġcapital ist", + "ĠL ot", + "Ġroy al", + "ĠCy ber", + "Ġblack s", + "met ic", + "ri v", + "ĠD anny", + "Ġsp o", + "ĠR O", + "Ġanim ated", + "rypt ed", + "ĠDep uty", + "Ġrend ered", + "F E", + "Ġstre ak", + "Ġcloud s", + "ĠDou g", + "~~~~ ~~~~", + "Ġdisc our", + "ĠVe h", + "Ġpsych ology", + "ĠJ ourney", + "Ġcry stal", + "ĠFro st", + "Ġsuspic ion", + "Ġrel ate", + "or us", + "ĠC rypt", + "ĠN VIDIA", + "com ed", + "ut ing", + "incinn ati", + "Ġvulner ability", + "ost ic", + "Ġisol ation", + "Ġcool ing", + "ĠCoal ition", + "Ġ1 19", + "F our", + "ĠDe al", + "Ġâ ī", + "se mble", + "ram ent", + "ĠBar celona", + "Ġ10 2", + "Ġcoc aine", + "ocaly pse", + "F eb", + "ogen ic", + "Ġmut ation", + "Ġcrypt oc", + "ĠK el", + "ĠG it", + "a is", + "Ġs isters", + "AN K", + "Ġactiv ate", + "T er", + "Ġd read", + "yl on", + "Ġprop ri", + "A ust", + "ĠDef ault", + "Ġout door", + "Ġshe er", + "ce ive", + "Ġg ently", + "Ð ¾", + "Pro gram", + "Ġâ ĨĴ", + "Ġve gan", + "ĠCr us", + "Ġrespons ibilities", + "ĠH R", + "OL D", + "Ġprev ents", + "Ġst iff", + "ĠW ere", + "Ġathlet ic", + "ĠSc ore", + "Ġ) :", + "Ġcolumn s", + "ĠL oc", + "av ailable", + "ĠF ram", + "ĠS essions", + "Ġcompan ion", + "Ġpack s", + "14 0", + "ĠKn ights", + "Ġf art", + "Ġstream s", + "Ġsh ore", + "Ġapp eals", + "ĠPer formance", + "h aul", + "ĠSt ra", + "ĠN ag", + "10 3", + "ĠTrans portation", + "B B", + "E v", + "z an", + "P ublic", + "Ġtw in", + "uls ion", + "M ult", + "Ġelect ro", + "Ġstat ue", + "ation ally", + "ĠN ort", + "Ġins pection", + "/ *", + "ig ue", + "Ġcomp assion", + "ĠT ales", + "ĠSte in", + "ĠSc reen", + "ĠB ug", + "ĠL ion", + "g irl", + "Ġwithdraw al", + "Ġobject ives", + "Ġblood y", + "Ġprelim inary", + "Ġj acket", + "Ġdim ensions", + "ĠC ool", + "ĠOcc up", + "Ġw reck", + "Ġdoub led", + "ank ing", + "Ġ19 75", + "Ġglass es", + "ĠW ang", + "pro v", + "P ath", + "connect ed", + "ĠMult i", + "ĠNor way", + "agon ist", + "Ġfe ared", + "Ġtouch ing", + "Ġarg uably", + "¯¯¯¯ ¯¯¯¯", + "ĠNC AA", + "che m", + "Ġsp at", + "ĠW WE", + "ĠC el", + "ig ger", + "Ġattack er", + "ĠJo in", + "ob ject", + "ett a", + "Ġelim inated", + "d et", + "Ġdest ruct", + "ĠLuc as", + "ct uary", + "18 0", + "ĠBr ady", + "ĠBl ues", + "B ay", + "au kee", + "Ġtim eline", + "Ġdeleg ates", + "w ritten", + "uff icient", + "Ġsh apes", + "Cop yright", + "ou ble", + "serv ice", + "Ġp ione", + "Ġcolleg es", + "Ġrow s", + "Ġsp ite", + "Ġassess ed", + "3 60", + "Ġle ase", + "Ġconfident ial", + "ck er", + "ĠMan ning", + "ĠV oice", + "Ġse aled", + "Ġcalcul ate", + "N O", + "ĠAss istant", + "Ġteen ager", + "ul ent", + "ather ine", + "Ġm ock", + "Ġd iamond", + "Ġf est", + "Ġsw itched", + "Ġres ume", + "ĠPu erto", + "Ġl anes", + "ir ation", + "ĠSimilar ly", + "Ġro d", + "ĠS el", + "ĠPal ace", + "ĠLim ited", + "e ous", + "Ġvar iant", + "Ġw ard", + "Ġ) )", + "Sh ow", + "OO K", + "A lex", + "ĠN ep", + "br is", + "ĠWik ipedia", + "Ġexcept ional", + "Ġman ages", + "ĠD raw", + "Ag ain", + "Ġco pper", + "ut t", + "Ġex ports", + "Ġport folio", + "Ġelev ated", + "R ated", + "ĠOther wise", + "ĠT act", + "ĠShe l", + "ĠT X", + "\" âĢĶ", + "Ġres ur", + "ĠW a", + "ven ant", + "Ġmon etary", + "pe ople", + "E mail", + "Ġfif ty", + "ĠS weet", + "ĠMalays ia", + "Ġconf using", + "ĠR io", + "ud a", + "uten ant", + "\" );", + "Ġpra ised", + "Ġvol umes", + "t urn", + "Ġm ature", + "Ġnon profit", + "Ġpassion ate", + "ĠPriv ate", + "Ġ10 3", + "Ġdesc end", + "ç ¥ŀ", + "uff y", + "head ed", + "Whe ther", + "ri en", + "ze ch", + "be it", + "Ġch rom", + "ĠMc M", + "Ġd ancing", + "Ġe leg", + "ĠNot iced", + "11 5", + "Ġadvoc acy", + "ENT S", + "amb ling", + "ĠMin or", + "ĠF inn", + "Ġprior ities", + "Ġthere of", + "ĠSt age", + "ĠRog ers", + "Ġsubst itute", + "ĠJ ar", + "ĠJeff erson", + "Ġlight ly", + "10 2", + "ĠL isa", + "u its", + "ys ical", + "Ġshif ts", + "Ġd rones", + "Ġwork place", + "Ġres id", + "ens ed", + "ah n", + "Ġpref erences", + "ser ver", + "Ġdeb ates", + "d oc", + "ĠGod s", + "Ġhelicop ter", + "Ġhon our", + "Ġconsider ably", + "ed ed", + "ĠF emale", + "ĠAn ne", + "Ġre un", + "ĠF ace", + "ĠHall ow", + "ĠBud get", + "Ġcondem n", + "Ġt ender", + "Pro f", + "ocr atic", + "ĠTurn er", + "ĠAg ric", + "Ġ19 76", + "Ġa pt", + "d isc", + "ĠF ighter", + "ĠA ur", + "Ġgar bage", + "in put", + "ĠK arl", + "ĠOl iver", + "ĠL anguage", + "k n", + "N on", + "ĠCl ar", + "Ġtrad itions", + "Ġad vertisement", + "ĠS or", + "Ġarch ive", + "Ġvill ages", + "7 50", + "Ġimplement ing", + "w aukee", + "Ġdiet ary", + "Ġswitch ing", + "Rep ublic", + "Ġvel ocity", + "Ġc it", + "ĠA wards", + "Ġfin ancing", + "Ġlast ed", + ") ]", + "Ġrem inder", + "P erson", + "Ġprec ision", + "Ġdesign ers", + "ĠF ried", + "ĠB order", + "Ġtr agic", + "Ġw ield", + "Ġiniti atives", + "ĠT ank", + "w er", + "Ġjo ins", + "R o", + "in ery", + "Ġar row", + "Ġgener ating", + "found er", + "Ġsear ches", + "Ġrandom ly", + "A ccess", + "Ġb atch", + "Ġp osed", + "l at", + "Ġpursu ing", + "as a", + "Ġtest ified", + "form ing", + "ĠSh ar", + "w iki", + "ĠE ither", + "S ometimes", + "Ġsen ators", + "ĠJohn ny", + "ĠTal iban", + "ĠG PS", + "\":\" /", + "ãģ® å", + "Ġanaly zed", + "ĠRub io", + "ĠMove ment", + "op ard", + "ii i", + "St and", + "f ight", + "Ġign oring", + "i ang", + "ĠG N", + "so ever", + "ĠST AT", + "Ġref using", + "Ġswe at", + "Ġb ay", + "P ORT", + "ir med", + "ak y", + "Ġdis pro", + "Ġlabel ed", + "Ġ10 8", + "H ello", + "Ġple asant", + "ab a", + "Ġtri umph", + "Ġab oard", + "Ġinc om", + "ĠC row", + "le tt", + "Ġfol k", + "Ġch ase", + "` `", + "ĠBr us", + "Ġte ens", + "c ue", + "Ġter rain", + "h yd", + "il ight", + "OR Y", + "Su pport", + "ew s", + "ll i", + "rain ts", + "ĠC and", + "Ġab used", + "ach ment", + "l arg", + "B as", + "ĠC ancer", + "Ġ19 78", + "Ġsupp orter", + "ac cess", + "ĠTer min", + "ĠT ampa", + "ĠAN Y", + "Ġnew est", + "ĠCrim inal", + "ed u", + "Ġ19 30", + "Ġadm its", + "Ġend e", + "Ġfail ures", + "ur ate", + "ful ness", + "cy cl", + "ĠSub ject", + "Ġinf inite", + "th ree", + "W A", + "p it", + "ĠInst all", + "R ad", + "ili ation", + "G M", + "Ġcontin ent", + "Ġaccommod ate", + "ĠCl ay", + "Ġp up", + "ĠF unction", + "Ġham mer", + "ĠAlbert a", + "Ġrev ised", + "Ġminor ities", + "Ġmeasure ment", + "Con nell", + "Ġdis able", + "ĠM ix", + "In cre", + "Ġfor k", + "ĠR osen", + "Ġimpl ies", + "umb lr", + "AN G", + "Ġprote ins", + "Ġagg ression", + "Ġfacilit ate", + "S N", + "Ġilleg ally", + "u er", + "Ġacad em", + "Ġp uzz", + "ĠSh ift", + "p ay", + "oll o", + "Ġaud iences", + "B uild", + "Ġno ble", + "Ġsynt ax", + "â ĺħ", + "Ġbe am", + "ĠB ed", + "ĠA ld", + "Ġorig ins", + "v ideo", + "Ġ19 77", + "ĠAss ault", + "Ġgar age", + "Te am", + "Ġver dict", + "Ġd war", + "ĠVirt ual", + "e vent", + "Ke ep", + "Ġsent iment", + "Ġwild life", + "sh irt", + "Ġb urg", + "Ġrecommend ation", + "rep resent", + "Ġgall ery", + "own ers", + "Ġsch olar", + "Ġconven ience", + "ĠSw ift", + "Ġconv inc", + "C ap", + "Ġwar fare", + "ĠVis ual", + "Ġconst itute", + "Ġab ort", + "ĠWe ather", + "ĠLook ing", + "ĠH em", + "Ġmart ial", + "Ġinc oming", + "et ition", + "Ġtoler ance", + "ĠCre ated", + "Ġfl ows", + "ĠE lder", + "Ġsoul s", + "Ġf oul", + "ĠP ain", + "ĠC AN", + "Ġ2 20", + "b c", + "he nd", + "Ġgen ius", + "R eal", + "ĠW r", + "omet er", + "p ad", + "Ġlim iting", + "ĠS i", + "ĠL ore", + "ĠAd ventures", + "Ġvar ied", + "D isc", + "f in", + "ĠPerson al", + "Ch ris", + "Ġinv ented", + "Ġd ive", + "ĠR ise", + "Ġo z", + "ĠCom ics", + "Ġexp ose", + "ĠRe b", + "let ters", + "s ite", + "im ated", + "Ġh acking", + "Ġeduc ated", + "ĠNob ody", + "Ġdep ri", + "Ġincent ive", + "ãĤ ·", + "Ġovers ight", + "Ġtrib es", + "ĠBelg ium", + "Ġlicens ing", + "our t", + "Produ ct", + "ah l", + "ĠG em", + "Ġspecial ist", + "Ġc ra", + "ann ers", + "ĠCor byn", + "Ġ19 73", + "RE AD", + "Ġsum mar", + "Ġover look", + "ĠApp lication", + "Ġin appropriate", + "Ġdownload ed", + "Q ue", + "ĠB ears", + "Ġth umb", + "ĠChar acter", + "ĠReincarn ated", + "ĠS id", + "Ġdemonstr ates", + "s ky", + "ĠBloom berg", + "ĠAr ray", + "ĠRes ults", + "ĠFour th", + "ĠED T", + "ĠO scar", + "c end", + "Ġ10 6", + "ĠN ULL", + "ĠH ERE", + "m atch", + "ĠBr un", + "Ġgluc ose", + "ie g", + "eg u", + "Ġcert ified", + "Ġrel ie", + "Ġhuman itarian", + "Ġpr ayers", + "K ing", + "Ġn an", + "h ou", + "10 8", + "ul u", + "Ġrenew able", + "Ġdistingu ish", + "Ġd ense", + "ĠV ent", + "ĠPack age", + "ĠB oss", + "Ġedit ors", + "Ġm igr", + "T ra", + "ĠPet ers", + "ĠAr ctic", + "200 4", + "ĠC ape", + "Ġloc ally", + "Ġlast ing", + "Ġhand y", + ". ).", + "P an", + "ĠR ES", + "Ind ex", + "Ġt ensions", + "Ġformer ly", + "Ġide ological", + "Ġsens ors", + "Ġdeal ers", + "Ġdef ines", + "S k", + "Ġproceed s", + "Ġpro xy", + "az ines", + "ĠB ash", + "ĠP ad", + "ĠC raft", + "eal ous", + "Ġshe ets", + "omet ry", + "J une", + "cl ock", + "T T", + "ĠThe atre", + "ĠB uzz", + "Ġch apters", + "Ġmill enn", + "Ġd ough", + "ĠCongress ional", + "Ġimag ined", + "av ior", + "Ġclin ic", + "Ġ19 45", + "Ġhold er", + "ro ot", + "oles ter", + "Ġrest art", + "B N", + "ĠHam as", + "ĠJ ob", + "Ġor b", + "Ġr am", + "Ġdiscl ose", + "Ġtransl ate", + "Ġimm igrant", + "Ġannoy ing", + "Ġtreat y", + "an ium", + "ĠTe a", + "ĠLeg ion", + "Ġcrowd s", + "ĠB ec", + "ĠA er", + "oh yd", + "B ro", + "Look ing", + "Ġl bs", + "Ġagg ress", + "Ġse am", + "Ġinter cept", + "ĠM I", + "mer cial", + "act iv", + "ĠC it", + "Ġdim ension", + "Ġconsist ency", + "Ġr ushing", + "ĠDou glas", + "Ġtr im", + "Inst all", + "ick er", + "Ġsh y", + "10 6", + "Ġment ions", + "pe lled", + "ĠT ak", + "c ost", + "Ġclass room", + "Ġfort une", + "dri ven", + "Ġun le", + "ĠWhe el", + "Ġinvest or", + "ĠM asters", + "k it", + "Ġassoci ations", + "ĠEv olution", + "op ing", + "us cript", + "Ġprov incial", + "ĠWal ter", + "av i", + "S O", + "Ġun limited", + "Eng lish", + "ĠC ards", + "ĠEb ola", + "ne red", + "Ġreven ge", + "Ġout right", + "um per", + "Ġf itting", + "ĠSol id", + "Ġform ally", + "Ġproblem atic", + "Ġhaz ard", + "Ġenc ryption", + "Ġstraight forward", + "ĠA K", + "Ġp se", + "ĠOr b", + "ĠCh amber", + "ĠM ak", + "Cont ents", + "Ġloyal ty", + "Ġl yrics", + "ĠSy m", + "Ġwel comed", + "Ġcook ed", + "Ġmon op", + "Ġn urse", + "Ġmis leading", + "Ġe ternal", + "Ġshif ting", + "Ġ+ =", + "V is", + "Ġinst itutional", + "ill ary", + "Ġp ant", + "VER T", + "ĠA CC", + "ĠEn h", + "Ġinc on", + "ĠRE UTERS", + "Ġdon ated", + "âĢ¦âĢ¦ âĢ¦âĢ¦", + "In tern", + "Ġexhib it", + "Ġt ire", + "ĠR ic", + "ĠCh ampion", + "ĠMu hammad", + "N ING", + "ĠSoc cer", + "Ġmob ility", + "Ġvary ing", + "ĠM ovie", + "Ġl ord", + "o ak", + "F ield", + "Ġve ctor", + "us ions", + "Ġsc rap", + "Ġen abling", + "m ake", + "T or", + ". *", + "| |", + "ĠWe bsite", + "ĠN PC", + "Ġsocial ist", + "ĠBill y", + "ĠAdd itional", + "Ġc argo", + "Ġfar ms", + "ĠSo on", + "ĠPri ze", + "Ġmid night", + "Ġ9 00", + "se en", + "ĠSp ot", + "Ġshe ep", + "Ġspons ored", + "ĠH i", + "ĠJ ump", + "Ġ19 67", + "Micro soft", + "ĠAg ent", + "Ġch arts", + "d ir", + "Ġadj acent", + "Ġtr icks", + "Ġman ga", + "Ġex agger", + "/ >", + "foot ball", + "ĠF CC", + "G C", + "ĠT ier", + "and ra", + "OU ND", + "% ),", + "Ġfru its", + "V C", + "ĠA A", + "R ober", + "Ġmid st", + "â Ĺ", + "ank a", + "Ġlegisl ature", + "ĠNe il", + "Ġtour ists", + "\" \"", + "ĠWar ning", + "ĠNever theless", + "ĠOffic ial", + "ĠWh atever", + "Ġm old", + "Ġdraft ed", + "Ġsubst ances", + "Ġbre ed", + "Ġt ags", + "ĠT ask", + "Ġver b", + "Ġmanufact ured", + "com ments", + "ĠPol ish", + "Pro v", + "Ġdetermin es", + "Ob ama", + "k ers", + "Ġutter ly", + "Ġse ct", + "sc he", + "ĠG ates", + "ĠCh ap", + "Ġal uminum", + "Ġz ombie", + "ĠT ouch", + "ĠU P", + "Ġsatisf y", + "Ġpred omin", + "asc ript", + "Ġelabor ate", + "Ġ19 68", + "Ġmeas uring", + "ĠV ari", + "any ahu", + "Ġs ir", + "ul ates", + "id ges", + "ick ets", + "ĠSp encer", + "T M", + "oub ted", + "Ġpre y", + "Ġinstall ing", + "ĠC ab", + "re ed", + "re ated", + "Su pp", + "Ġwr ist", + "ĠK erry", + "10 7", + "ĠK le", + "ĠR achel", + "Ġc otton", + "ĠA RE", + "ĠE le", + "Cont rol", + "Ġload s", + "ĠD od", + "an as", + "b one", + "Ġclass ical", + "ĠReg ional", + "ĠInt eg", + "V M", + "Ġdes ires", + "Ġaut ism", + "support ed", + "ĠM essage", + "Ġcomp act", + "writ er", + "Ġ10 9", + "ĠHur ricane", + "c ision", + "Ġcy cles", + "Ġdr ill", + "Ġcolle ague", + "Ġm aker", + "G erman", + "Ġmist aken", + "S un", + "ĠG ay", + "Ġwhat soever", + "Ġsell s", + "ĠA irl", + "l iv", + "ĠO ption", + "Ġsol ved", + "Ġse ctors", + "Ġhorizont al", + "Ġequ ation", + "ĠSk ill", + "ĠB io", + "g ement", + "ĠSn ap", + "ĠLeg al", + "Ġtradem ark", + "Ġmake up", + "Ġassemb led", + "Ġsa ves", + "ĠHallow een", + "ĠVer mont", + "ĠFR OM", + "Ġfar ming", + "ĠP odcast", + "accept able", + "ĠHig her", + "Ġas leep", + "ull ivan", + "Ġrefere n", + "ĠLe v", + "Ġbul lets", + "ok o", + "H C", + "Ġst airs", + "Ġmain tains", + "ĠL ower", + "ĠV i", + "Ġmar ine", + "Ġac res", + "Ġcoordin ator", + "ĠJ oh", + "Ġcounterpart s", + "ĠBrother s", + "Ġind ict", + "b ra", + "Ġch unk", + "Ġc ents", + "H ome", + "ĠMon th", + "Ġaccording ly", + "if les", + "ĠGerm ans", + "ĠSy n", + "H ub", + "Ġey eb", + "âĶĢâĶĢ âĶĢâĶĢ", + "Ġr anges", + "ĠHoll and", + "ĠRob ot", + "f c", + "M ike", + "Ġpl asma", + "Ġsw ap", + "Ġath lete", + "ĠR ams", + ",' \"", + "Ġinfect ions", + "Ġcor rid", + "Ġv ib", + "Ġpat ches", + "Ġtradition ally", + "Ġrevel ation", + "Ġswe ep", + "Ġgl ance", + "Ġin ex", + "200 3", + "ĠR aw", + "work ing", + "os ures", + "ĠD at", + "ĠLyn ch", + "Ġle verage", + "ĠRe id", + "Ġcorrel ation", + "ian ces", + "av ascript", + "Ġrep ository", + "ret ty", + "Ġ19 72", + "24 0", + "Ġo un", + "p ol", + "ĠRe ed", + "Ġtact ical", + "is ite", + "App le", + "ĠQu inn", + "Ġrap ed", + "ill o", + "Euro pe", + "Ġalgorith ms", + "ĠRod rig", + "i u", + "Ġill um", + "Ġf ame", + "Ġintrodu cing", + "Ġdel ays", + "ĠRaid ers", + "Ġwh istle", + "Ġnovel s", + "ĠRe ally", + "Ġder iv", + "Ġpublic ations", + "ĠNe ither", + "ĠCom merce", + "Ġa ston", + "l anguage", + "Not es", + "ĠR oth", + "ĠF ear", + "Ġm ate", + "Ġpar ade", + "ĠQ B", + "Ġman eu", + "ĠC incinnati", + "m itting", + "Ġwa ist", + "ĠR ew", + "Ġdisc ont", + "Ð °", + "Ġst aring", + "Ġal ias", + "Ġsec urities", + "Ġtoile t", + "ĠJ edi", + "Ġun law", + "v ised", + "//// ////", + "] (", + "ĠWe iss", + "Ġpre st", + "ĠComp an", + "Ġmem o", + "ĠGr ace", + "J uly", + "ĠEl ite", + "cent er", + "ĠSt ay", + "Ġgal axy", + "Ġto oth", + "ĠS ettings", + "Ġsubject ed", + "ãĤ ¦", + "Ġline back", + "Ġretail ers", + "ĠW ant", + "Ġd angers", + "A ir", + "Ġvolunt ary", + "ew ay", + "Ġinterpret ed", + "ot ine", + "à §", + "Ġp el", + "Serv ice", + "ĠEvent ually", + "Ġcare ers", + "Ġthreat en", + "Ġmem or", + "ĠBrad ley", + "anc ies", + "s n", + "ĠUn known", + "N ational", + "Ġsh adows", + "ail and", + "ĠD ash", + "Every one", + "izz ard", + "M arch", + "= (", + "Ġpull s", + "Ġstr anger", + "Ġback wards", + "ĠBern ard", + "imens ional", + "Ġch ron", + "Ġtheoret ical", + "k top", + "Ġw are", + "ĠInvest ig", + "ĠIn iti", + "ĠOper ations", + "o ven", + "oc ide", + "* /", + "Ġfl ames", + "ĠC ash", + "sh it", + "Ġc ab", + "ĠAn aly", + "ĠSe ah", + "Ġdefin ing", + "Ġorder ing", + "Ġimm un", + "Ġpers istent", + "AC H", + "Russ ian", + "m ans", + "Ġh ind", + "Ġphot ography", + " ©", + "Ġh ug", + "Ġ10 7", + "ĠH ence", + "i ots", + "ude au", + "Ġsubsid ies", + "Ġroutine ly", + "ĠDev ice", + "it ic", + "Ġdisg ust", + "land er", + "Ġ19 40", + "Ġassign ment", + "ĠB esides", + "w ick", + "ĠD ust", + "us c", + "struct ed", + "11 1", + "de velop", + "Ġf ond", + "Ġinter section", + "Ġdign ity", + "Ġcommission er", + "With out", + "re ach", + "Ġcart oon", + "Ġsc ales", + "ãĥ Ń", + "F IG", + "Ġsurve ys", + "ĠIndones ia", + "Ġart work", + "Ġun ch", + "Ġcy cling", + "un ct", + "au er", + "or ate", + "ĠOb viously", + "Ġcharacter ized", + "fe ld", + "Ġaff irm", + "Ġinn ings", + "Ġ é", + "Ġal iens", + "Ġcl oth", + "et ooth", + "ĠC ertain", + " §", + "Ġdig est", + "k now", + "ĠX L", + "Ġpredict ions", + "Ġd in", + "W AR", + "Ġafter math", + "Ex ample", + "ĠSu ccess", + "ĠTh r", + "IG N", + "Ġmin er", + "B us", + "Ġcl arity", + "heim er", + "ĠO UT", + "ĠS end", + "ĠCirc le", + "ĠD iet", + "Ġpron ounced", + "Ġcreat ors", + "Ġearthqu ake", + "atter y", + "ge ons", + "Ġo d", + "Ġlay ing", + "or p", + "U lt", + "pro ject", + "Ġunder min", + "Ġsequ el", + "S am", + "ĠDark ness", + "Ġre ception", + "b ull", + "Y S", + "ĠV ir", + "Ġsequ ences", + "ĠCo in", + "Ġout fit", + "ĠW ait", + "1 19", + "Ġdel ivers", + ".... ..", + "Ġbl own", + "ĠE sc", + "ĠM ath", + "per m", + "ĠU l", + "Ġgl im", + "Ġfac ial", + "Ġgreen house", + "Ġto kens", + "/ -", + "ĠAnn ual", + "ĠON E", + "Ġteen age", + "ĠPhys ical", + "ĠL ang", + "ĠC elt", + "Ġsu ed", + "ivid ually", + "Ġpat ience", + "ch air", + "reg ular", + "Ġa ug", + "in v", + "ex cept", + "ĠL il", + "Ġn est", + "f d", + "s um", + "ĠCh ase", + "Russ ia", + "ĠJenn ifer", + "Ġoff season", + "Over all", + "F ore", + "Ġr iot", + "A ud", + "form er", + "Ġdefend ers", + "ĠC T", + "iot ic", + "rib ly", + "Ġautom ated", + "Ġpen is", + "Ġins ist", + "Ġdi agram", + "ĠS QL", + "ĠG arc", + "Ġw itch", + "cl ient", + "ier ra", + "am bers", + "Ġrec ount", + "f ar", + "V ery", + "oster one", + "Ġappreci ated", + "ĠPer fect", + "S ection", + "Ġd oses", + "oca ust", + "Ġcost ly", + "Ġg rams", + "ĠSh i", + "Ġwrest ling", + "Ġ19 71", + "Ġtro phy", + "Ġn erve", + "ĠK az", + "ĠExper ience", + "Ġpled ged", + "Ġplay back", + "Ġcreat ivity", + "by e", + "Ġattack ers", + "Ġhold ers", + "ĠCo ach", + "ĠPh D", + "Ġtransf ers", + "Ġcol ored", + "ĠH indu", + "Ġd rown", + "Ġlist ened", + "ĠW A", + "ias m", + "P O", + "Ġappeal ing", + "Ġdiscl osed", + "ĠCh icken", + "ag ging", + "Ġple aded", + "Ġnav igation", + "ĠReturn s", + "Ġ[ [", + "R OR", + "E A", + "Ġphotograp her", + "ĠR ider", + "ipp ers", + "Ġsl ice", + "Ġe rect", + "Ġhe d", + "iss ance", + "ĠVik ings", + "ur ious", + "Ġapp et", + "oubted ly", + "Ch ild", + "Ġauthent ic", + "o os", + "ĠM aking", + "Ġannoun cing", + "Ġb od", + "Ġmet er", + "ĠN ine", + "ĠR ogue", + "Ġwork force", + "Ġrenew ed", + "Ġorganis ations", + "ac s", + "P LE", + "Sh ort", + "Ġcomp ounds", + "ĠVis it", + "Ġen velop", + "ear th", + "Ġsupport ive", + "gg le", + "ĠBrus sels", + "ĠGu ild", + "Cre ate", + "RE L", + "Ġaver aged", + "Ġ19 69", + "ri ages", + "Ġlength y", + "Ġforg ot", + "O kay", + "ĠE rd", + "Ġdeal er", + "Ġrec ession", + "D D", + "Ġdesper ately", + "Ġhun ger", + "Ġst icks", + "Ġm ph", + "ĠF aith", + "Ġintention ally", + "Ġdem ol", + "ue ller", + "ĠS ale", + "Ġde bris", + "s pring", + "Ġle ap", + ">> >>", + "Ġcontain ers", + "se lling", + "rane an", + "atter ing", + "Ġcomment ed", + "ĠC M", + "on ut", + "Ġwood s", + "es pecially", + "Ġorgan ize", + "iv ic", + "ĠWood s", + "ang a", + "s qu", + "Ġm aj", + "am on", + "Ġax is", + "Ġ19 74", + "ĠDen mark", + "Ġwar rior", + "ĠP and", + "Ġout lined", + "ĠB O", + "ins ula", + "z illa", + "eb ook", + "Ġd are", + "Ġsear ched", + "Ġnav igate", + "S n", + "writ ing", + "Ġun ited", + "J apan", + "ĠHe brew", + "Ġfl ame", + "Ġrel ies", + "Ġcatch ing", + "ĠSh o", + "Ġimprison ment", + "Ġp ockets", + "Ġclos ure", + "ĠF am", + "t im", + "ade qu", + "Act ivity", + "Ġrecru iting", + "ĠW ATCH", + "ĠArgent ina", + "d est", + "Ġapolog ize", + "or o", + "Ġlack s", + "Ġtun ed", + "ĠGriff in", + "Ġinf amous", + "Ġcelebr ity", + "ss on", + "Ġ ----------------------------------------------------------------", + "ĠIs is", + "ĠDis play", + "Ġcred ibility", + "Ġeconom ies", + "Ġhead line", + "ĠCow boys", + "Ġind ef", + "Ġl ately", + "Ġincent ives", + "but ton", + "ĠM ob", + "A ut", + "Ġres igned", + "ĠO m", + "c amp", + "Ġprof iles", + "Ġsche mes", + "olph ins", + "ay ed", + "Cl inton", + "en h", + "ĠY ahoo", + "Ġab st", + "Ġan k", + "su its", + "Ġw ished", + "ĠMar co", + "udd en", + "Ġsp here", + "ĠB ishop", + "Ġincorpor ated", + "ĠPl ant", + "11 4", + "Ġh ated", + "p ic", + "Ġdon ate", + "Ġl ined", + "Ġbe ans", + "Ġsteal ing", + "Ġcost ume", + "Ġsher iff", + "Ġfor ty", + "Ġint act", + "Ġadapt ed", + "Ġtrave lling", + "b art", + "Ġnice ly", + "Ġdri ed", + "Ġsc al", + "os ity", + "NOT E", + "ĠB h", + "ĠBron cos", + "ĠI gn", + "Ġint imate", + "Ġchem istry", + "Ġopt imal", + "D eb", + "ĠGener ation", + "Ġ] ,", + "ich i", + "ĠW ii", + "ĠYOU R", + "vent ions", + "W rite", + "Ġpop ul", + "un ning", + "ĠW or", + "V ol", + "Ġqu een", + "head s", + "K K", + "Ġanaly ze", + "op ic", + "ear chers", + "Ġd ot", + "leg raph", + "ast ically", + "Ġupgr ades", + "Ġca res", + "Ġext ending", + "Ġfree ze", + "Ġin ability", + "Ġorg ans", + "Ġpret end", + "Ġout let", + "11 3", + "ol an", + "ĠM all", + "ul ing", + "t alk", + "Ġexpress ing", + "ĠAl ways", + "ĠBe gin", + "f iles", + "Ġlic enses", + "% %", + "ĠM itt", + "Ġfil ters", + "ĠMil waukee", + "G N", + "Ġunf old", + "M o", + "Ġnut rition", + "pp o", + "B o", + "Ġfound ing", + "Ġunder mine", + "Ġeas iest", + "ĠC zech", + "ĠM ack", + "Ġsexual ity", + "ĠN ixon", + "W in", + "ĠAr n", + "ĠK in", + "ãĤ £", + "ic er", + "Ġfort un", + "Ġsurf aces", + "agh d", + "Ġcar riers", + "ĠP ART", + "ĠT ib", + "Ġinter val", + "Ġfrust rating", + "ĠSh ip", + "ĠAr med", + "ff e", + "Ġbo ats", + "ĠAb raham", + "in is", + "Ġsu ited", + "th read", + "i ov", + "ab ul", + "ĠVenezuel a", + "Ġto m", + "su per", + "Ġcast le", + "alth ough", + "iox ide", + "ec hes", + "Ġevolution ary", + "Ġnegoti ate", + "Ġconfront ed", + "Rem ember", + "Ġ17 0", + "S uch", + "Ġ9 11", + "m ult", + "ĠA byss", + "ur ry", + "ke es", + "spe c", + "ĠBarb ara", + "Ġbelong ing", + "Ġvill ain", + "ist ani", + "Ġaccount able", + "Ġport ions", + "ĠDe cl", + "U r", + "ĠK ate", + "g re", + "Ġmag azines", + "UC K", + "Ġregul ate", + "om on", + "ĠAl most", + "Ġover view", + "Ġsc ram", + "Ġl oot", + "ĠF itz", + "Ġcharacter istic", + "ĠSn ake", + "s ay", + "ĠR ico", + "Ġtra it", + "ĠJo ined", + "au cus", + "Ġadapt ation", + "ĠAirl ines", + "Ġarch ae", + "ĠI de", + "Ġb ikes", + "Ġliter ary", + "Ġinflu ences", + "ĠUs ed", + "C reat", + "Ġple a", + "ĠDef ence", + "ĠAss ass", + "Ġp ond", + "UL T", + ") \"", + "Ġeval uated", + "Ġob taining", + "Ġdem ographic", + "Ġvig il", + "ale y", + "Ġsp ouse", + "ĠSeah awks", + "resp ons", + "ĠB elt", + "um atic", + "Ġr ises", + "run ner", + "ĠMichel le", + "Ġpot ent", + "r ace", + "ĠP AC", + "F ind", + "olester ol", + "IS S", + "ĠIntrodu ced", + "ress es", + "ign ment", + "O s", + "ĠT u", + "ĠDe x", + "ic ides", + "Ġspark ed", + "ĠLaur a", + "ĠBry ant", + "Ġsm iling", + "ĠNex us", + "Ġdefend ants", + "ĠCat al", + "Ġdis hes", + "sh aped", + "Ġpro long", + "m t", + "( $", + "ãĢ Ĥ", + "Ġcalcul ations", + "ĠS ame", + "Ġp iv", + "H H", + "Ġcance lled", + "Ġgr in", + "Ġterrit ories", + "ist ically", + "C ome", + "ĠP arent", + "Pro ject", + "Ġneg lig", + "ĠPriv acy", + "Ġam mo", + "LE CT", + "olute ly", + "ĠEp ic", + "Ġmis under", + "w al", + "Apr il", + "m os", + "path y", + "ĠC arson", + "Ġalbum s", + "ĠE asy", + "Ġpist ol", + "< <", + "Ġ\\ (", + "t arget", + "hel p", + "Ġinter pre", + "cons cious", + "ĠH ousing", + "ĠJ oint", + "12 7", + "Ġbe ers", + "s cience", + "ĠFire fox", + "effect ive", + "ĠC abin", + "ĠO kay", + "ĠApp lic", + "Ġspace craft", + "ĠS R", + "ve t", + "ĠStr ange", + "S B", + "Ġcor ps", + "iber al", + "e fficient", + "Ġpreval ence", + "Ġeconom ists", + "11 8", + "Th read", + "ord able", + "OD E", + "ĠC ant", + "=- =-", + "if iable", + "ĠA round", + "Ġpo le", + "Ġwilling ness", + "CL A", + "ĠK id", + "Ġcomple ment", + "Ġsc attered", + "Ġin mates", + "Ġble eding", + "e very", + "Ġque ue", + "ĠTr ain", + "Ġh ij", + "Ġme lee", + "ple ted", + "Ġdig it", + "Ġg em", + "offic ial", + "Ġlif ting", + "Ð µ", + "Re qu", + "it utes", + "Ġpack aging", + "ĠWork ers", + "h ran", + "ĠLeban on", + "ol esc", + "Ġpun ished", + "ĠJ uan", + "Ġj am", + "ĠD ocument", + "Ġm apping", + "ic ates", + "Ġinev itably", + "Ġvan illa", + "ĠT on", + "Ġwat ches", + "Ġle agues", + "Ġiniti ated", + "deg ree", + "port ion", + "Ġrec alls", + "Ġru in", + "Ġm elt", + "I AN", + "Ġhe m", + "Ex p", + "Ġb aking", + "ĠCol omb", + "at ible", + "Ġrad ius", + "pl ug", + "ĠI F", + "et ically", + "Ġf ict", + "H ER", + "ĠT ap", + "atin um", + "Ġin k", + "Ġco h", + "ĠW izard", + "b oth", + "te x", + "Ġsp ends", + "ĠCurrent ly", + "ĠP it", + "Ġneur ons", + "ig nt", + "Ġr all", + "Ġbus es", + "b uilding", + "Ġadjust ments", + "Ġc ried", + "ibl ical", + "att ed", + "ĠZ ion", + "ĠM atter", + "Ġmed itation", + "ĠD ennis", + "Ġour s", + "ĠT ab", + "Ġrank ings", + "ort al", + "Ġad vers", + "Ġsur render", + "ĠG ob", + "ci um", + "om as", + "im eter", + "Ġmulti player", + "Ġhero in", + "Ġoptim istic", + "Ġindic ator", + "ĠBr ig", + "Ġgro cery", + "Ġapplic ant", + "ĠRock et", + "v id", + "Ex ception", + "p ent", + "Ġorgan izing", + "Ġenc ounters", + "ĠT OD", + "Ġjew el", + "S ave", + "ĠChrist ie", + "Ġhe ating", + "Ġl azy", + "ĠC P", + "Ġcous in", + "Con fig", + "Ġreg ener", + "Ġne arest", + "Ġachie ving", + "EN S", + "th row", + "ĠRich mond", + "ant le", + "200 2", + "Ġan ten", + "b ird", + "13 3", + "Ġn arc", + "r aint", + "un ny", + "ĠHispan ic", + "ourn aments", + "Ġprop he", + "ĠTh ailand", + "ĠT i", + "Ġinject ion", + "Ġinher it", + "rav is", + "Ġmed i", + "Ġwho ever", + "ĠDE BUG", + "G P", + "ĠH ud", + "C ard", + "p rom", + "Ġp or", + "Ġover head", + "L aw", + "Ġviol ate", + "Ġhe ated", + "Ġdescript ions", + "Ġachieve ments", + "ĠBe er", + "ĠQu ant", + "W as", + "Ġe ighth", + "ĠI v", + "Ġspecial ized", + "U PDATE", + "ĠD elta", + "P op", + "J ul", + "ĠAs k", + "oph y", + "Ġnews letters", + "ĠT ool", + "Ġg ard", + "ĠConf eder", + "ĠGM T", + "ĠAb bott", + "Ġimm unity", + "ĠV M", + "Is lam", + "Ġimpl icit", + "w d", + "Ġ19 44", + "rav ity", + "omet ric", + "Ġsurv iving", + "ur ai", + "ĠPr ison", + "Ġr ust", + "ĠSk etch", + "Ġbe es", + "ĠThe ory", + "Ġmer it", + "T ex", + "ch at", + "Ġm im", + "Ġpast e", + "ĠK och", + "Ġignor ance", + "ĠSh oot", + "Ġbas ement", + "Un ited", + "ĠAd vis", + "he ight", + "Ġf oster", + "Ġdet ain", + "in formation", + "Ġne ural", + "' ;", + "Ġprov es", + "all ery", + "Ġinv itation", + "um bers", + "Ġc attle", + "Ġbicy cle", + "z i", + "Ġconsult ant", + "Ġap ology", + "ĠT iger", + "Ġ12 3", + "99 9", + "Ġind ividually", + "r t", + "ig ion", + "ĠBrazil ian", + "Ġdist urb", + "Ġentreprene urs", + "Ġfore sts", + "cer pt", + "pl ates", + "p her", + "clip se", + "Ġtw itter", + "Ġac ids", + "ograph ical", + "h um", + "ĠB ald", + "if ully", + "Ġcomp iler", + "ĠD A", + "Ġdon or", + "as i", + "Ġtrib al", + "l ash", + "ĠCon fig", + "Ġapplic ants", + "Ġsal aries", + "13 5", + "Put in", + "ĠF ocus", + "ir s", + "Ġmisc onduct", + "ĠH az", + "Ġeat en", + "M obile", + "Mus lim", + "ĠMar cus", + "v iol", + "Ġfavor able", + "Ġst ub", + "ad in", + "ĠH ob", + "Ġfaith ful", + "Ġelectron ics", + "Ġvac uum", + "w ait", + "back ed", + "econom ic", + "d ist", + "Ġten ure", + "Ġsince re", + "ĠT ogether", + "ĠW ave", + "Ġprog ression", + "Ġden ying", + "Ġdist ress", + "br aska", + "th ird", + "Ġmix ing", + "Ġcolon ial", + "Ġpriv ately", + "Ġun rest", + "atern ity", + "Ġprem ises", + "ant i", + "greg ation", + "Ġlic ence", + "ĠH ind", + "ĠSam uel", + "Ġconvinc ing", + "ĠA ce", + "ĠR ust", + "ĠNet anyahu", + "Ġhand les", + "ĠP atch", + "orient ed", + "ah o", + "ĠG onz", + "Ġhack ers", + "claim er", + "Ġcustom s", + "ĠGr an", + "f ighters", + "Ġl uc", + "Ġman uscript", + "aren thood", + "Ġdev il", + "Ġwar riors", + "Ġoff enders", + "Will iam", + "Ġhol idays", + "Ġnight mare", + "Ġle ver", + "iff erent", + "St at", + "Ġexhib ition", + "put ed", + "ĠP ure", + "Ġal pha", + "Ġenthus iasm", + "ĠRepresent atives", + "E AR", + "ĠT yp", + "Ġwhe at", + "ĠAl f", + "Ġcor rection", + "Ġev angel", + "AT T", + "M iss", + "Ġs oup", + "Ġimpl ied", + "par am", + "Ġsex y", + "ĠL ux", + "Ġrep ublic", + "p atch", + "ab lish", + "Ġic ons", + "Ġfather s", + "ĠG ET", + "ĠCar ib", + "Ġregul ated", + "ĠCo hen", + "ĠBob by", + "Ġn er", + "Ġb ent", + "vent ory", + "ĠAl ong", + "ĠE ST", + "ĠWall ace", + "Ġmurd ers", + "r ise", + "ke ll", + "ĠCommon wealth", + "Ġn asty", + "et a", + "ĠM IT", + "Ġadminist ered", + "Ġgenuine ly", + "Ed itor", + "n ick", + "Ġhyd ro", + "**************** ****************", + "ĠB le", + "Ġfin es", + "Ġg orge", + "aus ible", + "r h", + "Ġapp le", + "ment ioned", + "Ġro pe", + "ot yp", + "H R", + "Ġdisappoint ing", + "Ġc age", + "n ik", + "Ġdoub ts", + "ĠF REE", + "print s", + "ĠM UST", + "Ġvend ors", + "ĠIn qu", + "Ġliber als", + "Ġcontract or", + "Ġup side", + "child ren", + "Ġtrick y", + "Ġregul ators", + "charg ed", + "l iter", + "Ġ ***", + "Ġreb ell", + "l ang", + "Ġloc als", + "Ġphys icians", + "Ġhe y", + "ar se", + "t m", + "ĠLe x", + "Ġbehavior al", + "success ful", + "F X", + "Ġbr ick", + "ov ic", + "Ġcon form", + "Ġreview ing", + "Ġins ights", + "Ġbi ology", + "ĠRem ove", + "ĠExt ra", + "Ġcomm itting", + "indu ced", + "ignt y", + "ig m", + "Ġat omic", + "Comm on", + "ĠE M", + "ĠP ere", + "ĠIt ems", + "e h", + "Ġpres erved", + "ĠH ood", + "Ġprison er", + "Ġbankrupt cy", + "Ġg ren", + "us hes", + "Ġexplo itation", + "Ġsign atures", + "Ġfin an", + "] ,\"", + "ĠM R", + "Ġme g", + "rem lin", + "Ġmusic ians", + "Ġselect ing", + "Ġexam ining", + "IN K", + "l ated", + "H i", + "Ġart ic", + "Ġp ets", + "Ġimp air", + "ĠM AN", + "Ġtable ts", + "in clude", + "R ange", + "Ġca ut", + "Ġlog s", + "Ġmount ing", + "Ġun aware", + "Ġdynam ics", + "ĠPalest ine", + "ĠQu arter", + "ĠPur ple", + "Ġm a", + "ĠIm port", + "Ġcollect ions", + "ci ation", + "Ġsuccess or", + "Ġcl one", + "Ġaim ing", + "Ġposs essed", + "Ġstick ing", + "Ġsh aking", + "Ġloc ate", + "ĠH ockey", + "T urn", + "17 0", + "Ġfif teen", + "ĠHar rison", + "Ġcontinu ously", + "ĠT C", + "ĠVal ent", + "ĠRes cue", + "Ġby pass", + "am ount", + "Ġm ast", + "Ġprotect s", + "Ġart istic", + "Ġsomet ime", + "Ġsh oe", + "Ġshout ed", + "ific ant", + "et itive", + "ĠReg ister", + "ĠJ in", + "Ġconcent rated", + "ling ton", + "on ies", + "Ġgener ator", + "yr im", + "ĠAr men", + "Ġclear ing", + "id o", + "ĠT W", + "al ph", + "Ġlad ies", + "H ard", + "Ġdial og", + "Ġinput s", + "æ ľ", + "Ġpos es", + "Ġsl ots", + "ĠPrem ium", + "Ġle aks", + "Ġboss es", + "Ġ11 3", + "c ourse", + "A cc", + "ĠNew ton", + "ĠAust ria", + "ĠM age", + "Ġte aches", + "ab ad", + "Ġwe ars", + "Ġc yl", + "Ġcur se", + "ĠS ales", + "ĠW ings", + "Ġp sy", + "Ġg aps", + "ĠIce land", + "ĠP interest", + "Ġland lord", + "Ġdefin itions", + "ĠK er", + "Ġsufficient ly", + "ĠP ence", + "ĠArch itect", + "Ġsur pass", + "Ġ11 4", + "Ġsuper hero", + "ĠDise ase", + "Ġpri ests", + "ĠC ulture", + "Ġdefin itive", + "Ġsecret ly", + "ĠD ance", + "inst all", + "ch ief", + "ĠJess ica", + "W ould", + "Up dated", + "Ġlock er", + "ĠK ay", + "Ġmem orial", + "è ¦", + "f at", + "Ġdis gu", + "Ġflav ors", + "ĠBase ball", + "ĠRes istance", + "Ġk icks", + "Ġen v", + "Ġteen agers", + "D ark", + "ĠC AR", + "Ġh alt", + "ĠL G", + "ĠGab riel", + "Ġfe ver", + "Ġs atur", + "Ġm all", + "Ġaffili ate", + "ĠS leep", + "ĠSpe cific", + "ĠV el", + "Ġj ar", + "ĠSac red", + "ĠEd wards", + "ĠA CL", + "Ġret ained", + "ĠG iant", + "Ġlim itation", + "in ces", + "Ġref usal", + "ĠT ale", + "ĠBut ler", + "Ġacc idents", + "ĠC SS", + "Ġimport ed", + "ĠCop y", + "Î ±", + "ER T", + "z el", + "Ġdiv isions", + "h ots", + "ĠAl b", + "ĠD S", + "Load er", + "W ashington", + "at isf", + "ĠCreat ive", + "\\ .", + "ĠAut om", + "red ict", + "Ġrecept or", + "ĠCarl os", + "Met hod", + "ok a", + "Ġmal icious", + "Ġste pping", + ", [", + "ĠD ad", + "Ġatt raction", + "ĠEffect s", + "ĠPir ate", + "ĠC er", + "ĠIndust ry", + "ĠR ud", + "Ġchar ter", + "Ġd ining", + "Ġins ists", + "Ġconfig ure", + "Ġ( #", + "ĠSim ple", + "ĠSc roll", + "UT C", + "17 5", + "ĠK on", + "Ġmarket place", + "Ġ ãĤ", + "Ġref res", + "Ġg ates", + "er red", + "ĠP od", + "Ġbeh ave", + "Fr ank", + "n ode", + "Ġendors ed", + "he tt", + "as ive", + "ĠHom eland", + "Ġr ides", + "ĠLe ave", + "er ness", + "Ġflood ing", + "A FP", + "Ġris en", + "Ġcontin ually", + "Ġun anim", + "ĠCont ract", + "ĠP as", + "Ġgu ided", + "ĠCh ile", + "b d", + "Ġsu cc", + "pt ic", + "Ġcomm ittees", + "ĠL uther", + "ĠAny one", + "Ġs ab", + "12 4", + "Ġp ixel", + "ĠB ak", + "ĠT ag", + "ĠBenn ett", + "En ter", + "sm all", + "ĠPresident ial", + "Ġp ul", + "Ġcontr ace", + "arch ive", + "Ġcoast al", + "ĠK ids", + "19 2", + "âĢ ²", + "ick y", + "ING TON", + "Ġw olf", + "ĠSt alin", + "T ur", + "id get", + "am as", + "ĠUn less", + "Ġspons or", + "Ġmor ph", + "ĠCho ose", + "Ġrun ner", + "Ġun bel", + "Ġm ud", + "ĠMan a", + "Ġdub bed", + "Ġg odd", + "ure rs", + "wind ow", + "Ġrel ied", + "Ġcelebr ating", + "os c", + "Ġ13 5", + "Ġlobb ying", + "Ġincom plete", + "Ġrestrict ion", + "Ġinc ap", + "it us", + "Ġexpect ation", + "ĠAp ollo", + "Ġint ens", + "Ġsyn c", + "G H", + "Ġmanip ulation", + "B Y", + "Ġspe ar", + "Ġbre asts", + "Ġvol can", + "il ia", + "M aterial", + "Ġform ats", + "ĠB ast", + "Ġparliament ary", + "Ġsn ake", + "Ġserv ants", + "ĠTr udeau", + "ĠGr im", + "ĠArab ic", + "ĠSC P", + "ĠBoy s", + "st ation", + "Ġprospect ive", + "ord e", + "in itialized", + "Ġb ored", + "AB LE", + "Ġaccess ed", + "Ġtax i", + "ĠShe ll", + "aid en", + "urs ed", + "in ates", + "ĠIns urance", + "ĠPet e", + "Sept ember", + "6 50", + "Ġad ventures", + "ĠCo ver", + "Ġt ribute", + "Ġsk etch", + "Ġem power", + "Ġ Ø", + "ĠGl enn", + "ĠD aw", + "= \\\"", + "ĠPolit ics", + "Ġgu ides", + "Ġd ioxide", + "ĠG ore", + "ĠBr ight", + "ĠS ierra", + "Ġval ued", + "c ond", + "Ġpo inter", + "Se lect", + "Ġrisk y", + "Ġabsor b", + "im ages", + "Ġref uses", + "Ġbon uses", + "__ _", + "Ġh ilar", + "ĠF eatures", + "2 20", + "ĠCollect or", + "F oot", + "Ġ19 64", + "cul us", + "Ġd awn", + "Ġwork out", + "ĠL O", + "Ġphilosoph ical", + "ĠSand y", + "ĠYou th", + "Ġl iable", + "A f", + "bl ue", + "Ġovert urn", + "less ness", + "ĠTrib une", + "ĠIn g", + "Ġfact ories", + "Ġcat ches", + "Ġpr one", + "Ġmat rix", + "Ġlog in", + "Ġin acc", + "Ġex ert", + "s ys", + "Ġneed le", + "ĠQ ur", + "Ġnot ified", + "ould er", + "t x", + "Ġremind s", + "Ġpublisher s", + "Ġn ort", + "Ġg it", + "Ġfl ies", + "ĠEm ily", + "Ġflow ing", + "ĠAl ien", + "ĠStr ateg", + "Ġhard est", + "Ġmod ification", + "AP I", + "ĠM Y", + "Ġcr ashes", + "st airs", + "n umber", + "Ġur ging", + "ch annel", + "ĠFal con", + "Ġinhabit ants", + "Ġterr ifying", + "Ġutil ize", + "Ġban ner", + "Ġcig arettes", + "Ġsens es", + "ĠHol mes", + "Ġpract ition", + "ĠPhill ips", + "ott o", + "Ġcomp ile", + "Mod el", + "ĠK o", + "Ġ[ ]", + "Americ ans", + "ĠTer ms", + "Ġmed ications", + "ĠAn a", + "Ġfundament ally", + "ĠNot ice", + "Ġwe aker", + "Ġ 0000", + "Ġgar lic", + "Ġout break", + "Ġeconom ist", + "ĠB irth", + "Ġobst acles", + "ar cer", + "ĠOr thodox", + "Ġplace bo", + "ĠC rew", + "asp berry", + "ĠAng els", + "Ġdis charge", + "Ġdestruct ive", + "11 7", + "ĠR ising", + "Ġd airy", + "l ate", + "Ġcoll ision", + "ĠTig ers", + "ean or", + "ocument ed", + "ĠIn valid", + "Ġd ont", + "ĠL iter", + "ĠV a", + "Ġhyd rogen", + "Ġvari ants", + "ĠBrown s", + "Ġ19 65", + "Ġind igenous", + "Ġtrad es", + "Ġremain der", + "Ġswe pt", + "ĠImp act", + "Ġred ist", + "Ġun int", + "grad uate", + "ãĥ ķ", + "ĠW ILL", + "ãģ® ç", + "ĠCrit ical", + "Ġf isher", + "Ġv icious", + "Ġrevers ed", + "Y ear", + "ĠS ox", + "Ġshoot ings", + "Ġfil ming", + "Ġtouchdown s", + "ai res", + "m el", + "Ġgrand father", + "Ġaffect ion", + "ing le", + "Ġover ly", + "Add itional", + "Ġsup reme", + "ĠGr ad", + "Ġsport ing", + "Ġmer cy", + "ĠBrook s", + "ount y", + "Ġperform s", + "Ġtight ly", + "Ġdem ons", + "Ġkill ings", + "Ġfact ion", + "ĠNov a", + "aut s", + "Ġund oubtedly", + "ar in", + "Ġunder way", + "ra k", + "Ġl iv", + "ĠReg ion", + "Ġbrief ing", + "s ers", + "cl oud", + "ĠM ik", + "us p", + "Ġpred iction", + "az or", + "Ġport able", + "ĠG and", + "Ġpresent ing", + "Ġ10 80", + " »", + "ush i", + "ĠSp ark", + "there um", + "Ġjust ification", + "ĠN y", + "Ġcontract ors", + "ming ham", + "ĠSt yle", + "å ħ", + "ĠChron icles", + "ĠPict ure", + "Ġprov ing", + "Ġw ives", + "set t", + "Ġmole cules", + "ĠFair y", + "Ġconsist ing", + "Ġp ier", + "al one", + "in ition", + "Ġn ucle", + "j son", + "Ġg otta", + "Ġmob il", + "Ġver bal", + "ar ium", + "Ġmon ument", + "uck ed", + "Ġ25 6", + "T ech", + "mine craft", + "ĠTr ack", + "Ġt ile", + "Ġcompat ibility", + "as is", + "Ġs add", + "Ġinstruct ed", + "ĠM ueller", + "Ġle thal", + "Ġhorm one", + "Ġor che", + "el se", + "Ġske let", + "Ġentert aining", + "Ġminim ize", + "ag ain", + "Ġunder go", + "Ġconst raints", + "Ġcig arette", + "ĠIslam ist", + "Ġtravel s", + "ĠPant hers", + "l ings", + "C are", + "Ġlaw suits", + "ur as", + "Ġcry st", + "Ġlow ered", + "Ġaer ial", + "Ġcomb inations", + "Ġha un", + "Ġch a", + "Ġv ine", + "Ġquant ities", + "Ġlink ing", + "b ank", + "Ġso y", + "B ill", + "ĠAngel a", + "Ġrecip ient", + "ĠProt est", + "Ġs ocket", + "Ġsolid arity", + "Ġâ Ĩ", + "m ill", + "Ġvar ies", + "ĠPak istani", + "Dr agon", + "Ġun e", + "Ġhor izon", + "³³³³ ³³³³", + "Ġprov inces", + "Ġfrank ly", + "Ġenact ed", + "not es", + "[ '", + "Ġ19 2", + "ocr acy", + "Ġendorse ment", + "Ġover time", + "Tr ue", + "L ab", + "lic ted", + "ĠD NC", + "Ġbe ats", + "ĠJam ie", + "15 2", + "ĠIN T", + "Cont act", + "Ġaccount ed", + "h ash", + "ĠPack ers", + "p ires", + "Ġles bian", + "Ġamend ments", + "Ġhop eful", + "ĠFin land", + "Ġspot light", + "Ġconfig ured", + "Ġtrou bled", + "Ġg aze", + "ĠCal gary", + "Ġrel iability", + "Ġins urg", + "sw er", + "b uy", + "ĠSk in", + "Ġp ixels", + "Ġhand gun", + "Ġpar as", + "Ġcateg or", + "ĠE L", + "ĠRe x", + "Ind eed", + "Ġkind a", + "Ġconj unction", + "ĠBry an", + "ĠMan ufact", + "y ang", + "Pl us", + "S QL", + "ish ment", + "Ġdom inate", + "Ġn ail", + "Ġo ath", + "Ġeru pt", + "ĠF ine", + "it bart", + "ĠCh ip", + "ĠAb d", + "ĠN am", + "Ġbuy er", + "Ġdiss ent", + "Le aks", + "Cont in", + "Ġr ider", + "ĠSome one", + "Ġill usion", + "c in", + "ĠBoe ing", + "Ġin adequ", + "ov ation", + "i ants", + "Ġreb uild", + "4 50", + "ĠDest iny", + "S W", + "ĠT ill", + "H it", + "ia z", + "ĠBang l", + "acher s", + "ĠRe form", + "Ġse gments", + "Ġsystem atic", + "d c", + "ĠConserv atives", + "Ġport al", + "h or", + "ĠDragon bound", + "Ġdrag ged", + "om o", + "Ġthe e", + "ad vert", + "ĠRep orts", + "ĠE t", + "Ġbarrel s", + "Aug ust", + "Ġcompar isons", + "Ġhe x", + "Ġan throp", + "\" [", + "bor ough", + "ab i", + "Ġpict ured", + "play ing", + "ĠAdd ress", + "ĠMir ror", + "Sm ith", + "Ġt ires", + "ĠN PR", + "AA AA", + "Ġclass ification", + "ĠTh an", + "ĠH arm", + "ĠR A", + "Ġreject ion", + "min ation", + "Ġr anged", + "ĠF alls", + "D I", + "H ost", + "ãĤ ´", + "ĠEx ample", + "list ed", + "th irds", + "Ġsaf egu", + "br and", + "Ġprob able", + "Can ada", + "IT ION", + "ĠQ aeda", + "Ġch ick", + "Ġimport s", + "h it", + "l oc", + "W W", + "Ġble w", + "Ġany time", + "Ġwh oles", + "ik ed", + "Ġcal culation", + "cre ate", + "ĠO ri", + "Ġupgr aded", + "Ġapp ar", + "ut ory", + "ĠM ol", + "B rit", + "ĠJ ong", + "IN AL", + "ĠStart ing", + "Ġd ice", + "urt le", + "Ġre lying", + "cl osure", + "Ġprof itable", + "Ġsl aughter", + "ĠMan ual", + "c aster", + "Ġ\" $", + "Ġfe ather", + "ĠSim ply", + "ie ves", + "Ġdeter ior", + "ĠPC I", + "Ġst amp", + "Ġfl aws", + "Ġsh ade", + "ham mer", + "Ġpass port", + "Ġcont ing", + "am el", + "Ġobser vers", + "Ġneg lect", + "ĠR B", + "ĠBrother hood", + "Ġskept ical", + "f amily", + "us k", + "Ġemotion ally", + "â Ļ", + "ĠBet a", + "ason able", + "id ity", + "ĠM ul", + "Ġkick ing", + "ĠC arm", + "oll ah", + "VERT IS", + "ĠAt hen", + "Ġlad der", + "ĠBul let", + "å £", + "00 01", + "ĠWild life", + "ĠM ask", + "ĠN an", + "R ev", + "Ġun acceptable", + "leg al", + "Ġcrowd ed", + "ag i", + "ĠC ox", + "j e", + "Ġmor ality", + "Ġfu els", + "Ġc ables", + "Ġman kind", + "ĠCarib bean", + "Ġanch or", + "Ġby te", + "ĠO ften", + "ĠO z", + "Ġcraft ed", + "Ġhistor ian", + "ĠW u", + "Ġtow ers", + "ĠCitiz ens", + "Ġhel m", + "Ġcred entials", + "Ġsing ular", + "ĠJes se", + "Ġtack les", + "Ġcont empt", + "Ġa fore", + "ĠSh adows", + "Ġn il", + "Ġur gent", + "app le", + "bl ood", + "Ġv on", + "Ġoff line", + "Ġbreat he", + "Ġj umps", + "Ġirre levant", + "ox ic", + "om al", + "import ant", + "J im", + "Ġgl oves", + "arm ing", + "dep th", + "Ġtal ents", + "ook ie", + "ĠS B", + "Ġpal m", + "uff s", + "est a", + "IG H", + "Ġcan on", + "ĠVer izon", + "ĠP le", + "Ġcou pled", + "vel t", + "Ġfundra ising", + "ĠGet ting", + "ĠD LC", + "Ġmathemat ical", + "ĠH S", + "ĠCard inals", + "te lling", + "Ġspons ors", + "Ġ Ï", + "ĠBull s", + "op tion", + "Ġprop ose", + "Ġmem orable", + "Ġembr aced", + "Ġdecl ining", + "He alth", + "ed a", + "Ġ} ;", + "Ġsp am", + "m ile", + "Ġpit cher", + "ĠE ight", + "Ġcar ing", + "ut ic", + "ro le", + "Ġair line", + "ernand ez", + "ĠAth let", + "Ġcert ification", + "ux e", + "rig er", + "Ġem pir", + "Ġsens ation", + "Ġdis m", + "Ġb olt", + "Ġev olve", + "H ouse", + "Ġconsult ation", + "ĠD uty", + "Ġtou ches", + "ĠN athan", + "Ġf aint", + "h ad", + "\" (", + "ĠCons umer", + "ĠExt reme", + "Ġ12 7", + "ĠHer m", + "ĠSac rament", + "iz oph", + "Ġanx ious", + "ul ously", + "Ġsoc ially", + "ĠU TC", + "Ġsol ving", + "ĠLet ter", + "Hist ory", + "ed uc", + "Pr ice", + ") );", + "Ġrel oad", + "am ic", + "Ġp ork", + "Ġdisc ourse", + "Ġt ournaments", + "ai ro", + "ĠK ur", + "ĠCost a", + "Ġviol ating", + "Ġinterf ere", + "Ġrecre ational", + "uff le", + "Ġspe eches", + "Ġneed ing", + "Ġremem bers", + "Ġcred ited", + "n ia", + "f ocused", + "amer a", + "Ġb ru", + "um bs", + "ĠCub an", + "Ġpreced ing", + "Ġnons ense", + "ac ial", + "Ġsmart phones", + "ĠSt ories", + "S ports", + "ĠEmer gency", + "oun cing", + "ef ined", + "Ġb er", + "Ġconsult ing", + "Ġm asters", + "he astern", + ".\" [", + "ĠRun ning", + "Ġsus cept", + "ĠF eng", + "Americ a", + "pr ises", + "st itial", + "ĠWeek ly", + "ĠGreat er", + "mod ules", + "if ter", + "G raphics", + "ul er", + "Ġwho lly", + "Ġsupp ress", + "Ġconce aled", + "Ġhapp ily", + "Ġaccept s", + "ĠEn joy", + "Ġr ivers", + "ĠEx cept", + "2 25", + "ĠN HS", + "ĠMc Connell", + "Ġp ussy", + "fer red", + "ut able", + "Ġatt ain", + "Ġ> =", + "Ġdepos its", + "roph ic", + "Ġnot orious", + "ĠSh aw", + "il itation", + "Ġepid emic", + "all ic", + "Ġsmall est", + "ov ich", + "Ġaccess ories", + "per ties", + "Ġsur plus", + "ĠMe ch", + "Ġamb ig", + "ĠImm igration", + "Ġch im", + "ev al", + "Ġpract icing", + "ĠMyster y", + "Ġdom ains", + "ĠSil icon", + "app s", + "Ġkilomet ers", + "e a", + "ĠSm ash", + "Ġwarrant y", + "Ġn ost", + "s il", + "re v", + "J on", + "ĠDub lin", + "Ġtast es", + "Ġb out", + "g reat", + "er ror", + "Ġsw itches", + "ĠB apt", + "D O", + "ok i", + "Ġsour ced", + "pro du", + "Ġattach ment", + "ĠIss ue", + "ĠQuest ion", + "Jo in", + "Ġf itted", + "Ġunlaw ful", + "^ ^", + "ere k", + "Ġauthent ication", + "Ġst ole", + "Ġaccount ability", + "l abel", + "S earch", + "Ġal beit", + "atic an", + "fund ed", + "ĠAdd ing", + "ĠI Q", + "Ġsub mar", + "l it", + "a que", + "ĠLear ning", + "Ġint eger", + "M aster", + "ĠCh rom", + "Ġprem ier", + "O p", + "ĠLi u", + "Ġbl essed", + "ĠGl obe", + "ĠResp onse", + "Ġlegit im", + "ĠMer kel", + "Ġdispos al", + " ´", + "Ġgau ge", + "pe at", + "Ġindu ced", + "Ġquestion able", + "arth y", + "ĠV it", + "ĠF eed", + "U ntil", + "U t", + "worth y", + "R Y", + "ĠH erald", + "ĠHam mer", + "Ġmed al", + "ĠR ivers", + "ĠH ack", + "Ġclar ify", + "Ġtrack ed", + "Ġautonom ous", + "Ġten ant", + "ĠQ atar", + "er ie", + "Ġgr im", + "ĠMon itor", + "Ġresist ant", + "ĠSpe c", + "ĠWell s", + "N AS", + "14 8", + "Ġmin ers", + "iot ics", + "Ġmiss es", + "11 6", + "g ian", + "g it", + "ĠE yes", + "p res", + "Ġgrad uated", + "Ġang el", + "Ġsyn chron", + "Ġefficient ly", + "Ġtrans mitted", + "H arry", + "Ġglob ally", + "EN CE", + "ĠMont ana", + "r aged", + "ĠPre vention", + "Ġp iss", + "ĠL l", + "Ġshe lf", + "ĠB JP", + "ĠTest ament", + "ĠL ate", + "ik er", + "ĠH app", + "ĠJul ian", + "h all", + "Ġsp ont", + "Ġshut down", + "Ġincons istent", + "Ġsubscrib ers", + "Ġske leton", + "ĠNe braska", + "Ġins pire", + "ĠV oid", + "F eed", + "Ġang les", + "ĠSpr ings", + "Ġbench mark", + "Ġvacc ines", + "izoph ren", + "se xual", + "uff ed", + "Ġsh ine", + "ĠK ath", + "Ġgest ure", + "ine a", + "Ġr ip", + "Ġopp ression", + "Ġcons cience", + "b t", + "ĠL um", + "Ġinc idence", + "ĠF a", + "w r", + "Ġmin eral", + "ĠSp urs", + "alk y", + "Ġth under", + "Ġop io", + "Be ing", + "ĠPal m", + "Ġwas ted", + "Ġl b", + "i aries", + "ĠIniti ative", + "Ġcur ric", + "Ġmark er", + "ĠMc L", + "Ġext ensions", + "ĠP v", + "ĠAr ms", + "Ġoffer ings", + "Ġdef enses", + "Ġvend or", + "Ġcontrad ict", + "ĠCol in", + "Ġredd it", + "Ġper ipher", + "12 2", + "Ġs ins", + "E dit", + "IC T", + "So ft", + "ĠSh ah", + "Ġadministr ator", + "ĠT rip", + "Ġporn ography", + "Ġtu ition", + "in ence", + "ĠPro gress", + "Ġcat alog", + "Ġsu ite", + "Ġh ike", + "Ġreprodu ctive", + "eng ine", + "Ġd rought", + "ĠNo ah", + "Ġ2 30", + "Ġd ude", + "Ġrelax ed", + "Ġpart ition", + "Ġparticip ant", + "Ġtel esc", + "Ġfe as", + "ĠF F", + "own er", + "Ġswe eping", + "Ġl enses", + "Ġmatch up", + "ĠRe pl", + "ourn als", + "Ġcred ible", + "Ġgrand mother", + "Ġther mal", + "Ġsubscrib ing", + "Ġident ities", + "col m", + "U CT", + "Ġreluct ant", + "us ers", + "ĠC ort", + "Ġassist ed", + "OS S", + "ATION S", + "IS H", + "Ġpharm aceutical", + "ic able", + "ad ian", + "ĠSon ic", + "ĠF ury", + "ĠM ong", + "A H", + "ĠPsych ology", + "Ġph osph", + "Ġtreat s", + "Ń Ķ", + "Ġstead ily", + "ĠHell o", + "Ġrel ates", + "Ġcl ue", + "Ex pl", + "a uth", + "Ġrev ision", + "Ġe ld", + "os ion", + "Ġbr on", + "14 4", + "ri kes", + "Ġmin es", + "Ġblank et", + "ĠF ail", + "el ed", + "ĠIm agine", + "ĠPl anned", + "a ic", + "Re quest", + "M ad", + "ĠHor se", + "ĠEag le", + "Ġcap ac", + "15 7", + "Ġl ing", + "ĠN ice", + "ĠP arenthood", + "min ster", + "og s", + "ens itive", + "Not hing", + "Ġcar n", + "F in", + "ĠP E", + "Ġr ifles", + "ĠL P", + "S and", + "Ġgui Active", + "Ġtour ist", + "C NN", + "Ġunve iled", + "Ġpredec essor", + "} {", + "u ber", + "Ġoff shore", + "Ġopt ical", + "ĠR ot", + "ĠPear l", + "et on", + "Ġst ared", + "Ġfart her", + "at ility", + "cont in", + "ĠG y", + "ĠF oster", + "ĠC oc", + "ri ents", + "Ġdesign ing", + "ĠEconom y", + "ON G", + "W omen", + "ĠN ancy", + "er ver", + "Ġmas cul", + "Ġcasual ties", + "Ġ2 25", + "ĠS ullivan", + "ĠCh oice", + "Ġa ster", + "w s", + "Ġhot els", + "Ġconsider ations", + "Ġcou ch", + "ĠSt rip", + "ĠG n", + "Ġmanip ulate", + "l ied", + "Ġsynt hetic", + "Ġassault ed", + "Ġoff enses", + "ĠDra ke", + "Ġim pe", + "Oct ober", + "ĠHer itage", + "h l", + "ĠBl air", + "Un like", + "Ġg rief", + "Ġ4 50", + "Ġopt ed", + "Ġresign ation", + "il o", + "Ġver se", + "ĠT omb", + "Ġu pt", + "Ġa ired", + "ĠH ook", + "ĠML B", + "Ġassum es", + "out ed", + "ĠV ers", + "Ġinfer ior", + "Ġbund le", + "ĠD NS", + "ograp her", + "Ġmult ip", + "ĠSoul s", + "Ġillust rated", + "Ġtact ic", + "Ġdress ing", + "Ġdu o", + "Con f", + "Ġrel ent", + "Ġc ant", + "Ġscar ce", + "Ġcand y", + "ĠC F", + "Ġaffili ated", + "Ġspr int", + "yl an", + "ĠGarc ia", + "Ġj unk", + "Pr int", + "ex ec", + "C rit", + "Ġport rait", + "ir ies", + "ĠOF F", + "Ġdisp utes", + "W R", + "L ove", + "ãģ Ħ", + "ĠRe yn", + "Ġh ipp", + "op ath", + "Ġflo ors", + "ĠFe el", + "Ġwor ries", + "Ġsett lements", + "ĠP os", + "Ġmos que", + "Ġfin als", + "Ġcr ushed", + "ĠPro bably", + "ĠB ot", + "ĠM ans", + "ĠPer iod", + "Ġsovere ignty", + "Ġsell er", + "Ġap ost", + "Ġam ateur", + "Ġd orm", + "Ġconsum ing", + "Ġarm our", + "ĠRo ose", + "Ġint ensive", + "Ġelim inating", + "ĠSun ni", + "ĠAle ppo", + "j in", + "Ġadv ise", + "p al", + "ĠH alo", + "Ġdes cent", + "Ġsimpl er", + "Ġbo oth", + "ST R", + "L ater", + "ĠC ave", + "== =", + "Ġm ol", + "Ġf ist", + "Ġshot gun", + "su pp", + "Ġrob bery", + "E ffect", + "Ġobsc ure", + "ĠProf essional", + "Ġemb assy", + "Ġmilit ant", + "Ġinc arcer", + "Ġgener ates", + "Ġlaun ches", + "Ġadministr ators", + "Ġsh aft", + "Ġcirc ular", + "Ġfresh man", + "ĠW es", + "ĠJo el", + "ĠD rew", + "ĠDun can", + "ĠApp arently", + "s ight", + "ĠIntern al", + "ĠInd ividual", + "ĠF E", + "Ġb ore", + "ĠM t", + "Ġbroad ly", + "ĠO ptions", + "ount ain", + "ip es", + "ĠV ideos", + "20 4", + "Ġh ills", + "Ġsim ulation", + "Ġdisappoint ment", + "it an", + "ĠLabor atory", + "Ġup ward", + "Ġbound ary", + "Ġdark er", + "h art", + "Ġdomin ance", + "C ong", + "ĠOr acle", + "ĠL ords", + "Ġscholars hip", + "ĠVin cent", + "ed e", + "ĠR ah", + "Ġencour ages", + "ro v", + "Ġqu o", + "Ġprem ise", + "ĠCris is", + "ĠHol ocaust", + "Ġrhyth m", + "Ġmet ric", + "cl ub", + "Ġtransport ed", + "Ġn od", + "ĠP ist", + "Ġancest ors", + "ĠFred er", + "th umbnails", + "ĠC E", + "ON D", + "Ph il", + "ven ge", + "ĠProduct s", + "cast le", + "Ġqual ifying", + "ĠK aren", + "VERTIS EMENT", + "Ġmight y", + "Ġexplan ations", + "Ġfix ing", + "D i", + "Ġdecl aring", + "Ġanonym ity", + "Ġju ven", + "ĠN ord", + "ĠDo om", + "ĠAct ually", + "O k", + "ph is", + "ĠDes ert", + "Ġ11 6", + "I K", + "ĠF M", + "Ġinc omes", + "V EL", + "ok ers", + "Ġpe cul", + "Ġlight weight", + "g ue", + "Ġacc ent", + "Ġincre ment", + "ĠCh an", + "Ġcompl aining", + "ĠB aghd", + "Ġmidfield er", + "Ġover haul", + "Pro cess", + "ĠH ollow", + "ĠTit ans", + "Sm all", + "man uel", + "ĠUn ity", + "ĠEv ents", + "S ty", + "Ġdispro portion", + "n esty", + "en es", + "ĠC od", + "Ġdemonstr ations", + "ĠCrim son", + "ĠO H", + "Ġen rolled", + "Ġc el", + "ĠBre tt", + "Ġa ide", + "Ġhe els", + "Ġbroad band", + "Ġmark ing", + "Ġw izard", + "ĠN J", + "ĠChief s", + "Ġingred ient", + "Ġd ug", + "ĠSh ut", + "urch ase", + "end or", + "Ġfar mer", + "ĠGold man", + "12 9", + "15 5", + "Or der", + "Ġl ion", + "i ably", + "Ġst ain", + "ar ray", + "ilit ary", + "ĠFA Q", + "Ġexpl oded", + "ĠMcC arthy", + "ĠT weet", + "ĠG reens", + "ek ing", + "l n", + "ens en", + "Ġmotor cycle", + "Ġpartic le", + "Ġch olesterol", + "B ron", + "Ġst air", + "Ġox id", + "Ġdes irable", + "ib les", + "Ġthe or", + "for cing", + "Ġpromot ional", + "ov o", + "b oot", + "ĠBon us", + "raw ling", + "Ġshort age", + "ĠP sy", + "Ġrecru ited", + "Ġinf ants", + "Ġtest osterone", + "Ġded uct", + "Ġdistinct ive", + "Ġfirm ware", + "bu ilt", + "14 5", + "Ġexpl ored", + "Ġfact ions", + "Ġv ide", + "Ġtatt oo", + "Ġfinan cially", + "Ġfat igue", + "Ġproceed ing", + "const itutional", + "Ġmis er", + "Ġch airs", + "gg ing", + "ipp le", + "Ġd ent", + "Ġdis reg", + "ç Ķ", + "st ant", + "ll o", + "b ps", + "aken ing", + "Ġab normal", + "ĠE RA", + "å£ «", + "ĠH BO", + "ĠM AR", + "Ġcon cess", + "Ġserv ant", + "Ġas pir", + "l av", + "ĠPan el", + "am o", + "Ġprec ip", + "Ġrecord ings", + "Ġproceed ed", + "Ġcol ony", + "ĠT ang", + "ab lo", + "Ġstri pped", + "Le ft", + "to o", + "Ġpot atoes", + "Ġfin est", + "% ).", + "Ġc rap", + "ĠZ ach", + "ab ases", + "ĠG oth", + "Ġbillion aire", + "w olf", + "Ġsan ction", + "S K", + "Ġlog ged", + "P o", + "ey ed", + "un al", + "Ġcr icket", + "Ġarm ies", + "Ġunc overed", + "Cl oud", + "ó n", + "Ġreb ounds", + "Ġm es", + "O per", + "P ac", + "Ġnation ally", + "Ġinsert ed", + "p ict", + "Ġgovern ance", + "Ð ¸", + "Ġprivile ges", + "G ET", + "Ġfavor ites", + "im ity", + "Ġlo ver", + "the m", + "em pl", + "Ġgorge ous", + "An n", + "Ġsl ipped", + "Ġve to", + "B ob", + "Ġsl im", + "u cc", + "ĠF ame", + "udden ly", + "Ġden ies", + "ĠM aur", + "Ġdist ances", + "Ġw anna", + "t ar", + "ĠS ER", + "Ġâ Ī", + "Ġle mon", + "at hetic", + "Ġlit eral", + "Ġdistingu ished", + "Ġansw ering", + "G I", + "Ġrelig ions", + "ĠPhil os", + "ĠL ay", + "Ġcomp os", + "ire ments", + "ĠK os", + "ine z", + "roll ing", + "Ġyoung est", + "and ise", + "ĠB orn", + "Ġalt ar", + "am ina", + "ĠB oot", + "v oc", + "Ġdig ging", + "Ġpress ures", + "Ġl en", + "26 4", + "Ġassass ination", + "ĠBir mingham", + "ĠMy th", + "Ġsovere ign", + "ĠArt ist", + "ĠPhot ograph", + "Ġdep icted", + "Ġdisp ens", + "orth y", + "Ġamb ul", + "int eg", + "ĠC ele", + "ĠTib et", + "Ġhier archy", + "Ġc u", + "Ġpre season", + "ĠPet erson", + "Ġcol ours", + "Ġworry ing", + "Ġback ers", + "ĠPal mer", + "ĠÎ ¼", + "Ġcontribut or", + "Ġhear ings", + "Ġur ine", + "Ġ Ù", + "ourge ois", + "Sim ilar", + "ĠZ immer", + "s omething", + "ĠUS C", + "Ġstrength s", + "ĠF I", + "Ġlog ging", + "As ked", + "ĠTh ai", + "in qu", + "ĠW alt", + "Ġcrew s", + "it ism", + "3 01", + "Ġshar ply", + "um ed", + "Ġred irect", + "r ators", + "In f", + "ĠWe apons", + "Ġte asp", + "19 99", + "L ive", + "ĠEs pecially", + "ĠS ter", + "ĠVeter ans", + "Ġint ro", + "other apy", + "Ġmal ware", + "Ġbre eding", + "Ġmole cular", + "ĠR oute", + "ĠCom ment", + "oc hem", + "Ġa in", + "Se ason", + "Ġlineback er", + "Ä «", + "ĠEconom ics", + "es ar", + "ĠL ives", + "ĠEm ma", + "Ġk in", + "ĠTer rit", + "Ġpl anted", + "ot on", + "ĠBut ter", + "ĠSp ons", + "P ER", + "Ġdun geon", + "Ġsymb olic", + "Ġfil med", + "Ġdi ets", + "Ġconclud es", + "Ġcertain ty", + "ĠForm at", + "Ġstr angers", + "form at", + "ĠPh ase", + "Ġcop ied", + "Ġmet res", + "ld a", + "ĠUs ers", + "Ġdeliber ate", + "Ġwas hed", + "ĠL ance", + "im ation", + "Ġimpro per", + "ĠGen esis", + "ick r", + "ĠK ush", + "Ġreal ise", + "Ġembarrass ing", + "alk ing", + "b ucks", + "Ġver ified", + "Ġout line", + "year s", + "ĠIn come", + "20 2", + "Ġz ombies", + "F inal", + "ĠMill enn", + "Ġmod ifications", + "ĠV ision", + "ĠM oses", + "ver b", + "iter ranean", + "ĠJ et", + "Ġnav al", + "ĠA gg", + "Ġur l", + "Ġvict ories", + "Ġnon etheless", + "Ġinj ust", + "ĠF act", + "ç ļ", + "Ġins ufficient", + "re view", + "face book", + "Ġnegoti ating", + "Ġguarant ees", + "im en", + "uten berg", + "Ġg ambling", + "Ġcon gr", + "Load ing", + "Ġnever theless", + "Ġpres idents", + "ĠIndust rial", + "Ġ11 8", + "Ġp oured", + "ĠT ory", + "Ġ17 5", + "Ġ: =", + "Sc ott", + "ange red", + "T ok", + "Ġorgan izers", + "M at", + "ĠG rowth", + "Ġad ul", + "Ġens ures", + "Ġ11 7", + "é¾į å", + "Ġmass acre", + "Ġgr ades", + "be fore", + "AD VERTISEMENT", + "ĠSl ow", + "ĠM MA", + "âĢĶ \"", + "ĠV atican", + "Q aeda", + "Ġo we", + "66 66", + "ĠS orry", + "ĠGr ass", + "Ġbackground s", + "Ġexha usted", + "Ġcl an", + "Ġcomprom ised", + "ĠE lf", + "ĠIsa ac", + "ens on", + "In vest", + "IF A", + "Ġinterrupt ed", + "ãĥī ãĥ©", + "Ġtw isted", + "ĠDrag ons", + "M ode", + "ĠK remlin", + "Ġfert il", + "he res", + "ph an", + "ĠN ode", + "f ed", + "ĠOr c", + "Ġunw illing", + "C ent", + "Ġprior it", + "Ġgrad uates", + "Ġsubject ive", + "Ġiss uing", + "ĠL t", + "Ġview er", + "Ġw oke", + "Th us", + "bro ok", + "Ġdep ressed", + "Ġbr acket", + "ĠG or", + "ĠFight ing", + "Ġstri ker", + "Rep ort", + "ĠPortug al", + "Ġne o", + "w ed", + "19 9", + "Ġflee ing", + "sh adow", + "ident ified", + "US E", + "Ste am", + "Ġstret ched", + "Ġrevel ations", + "art ed", + "ĠD w", + "Ġalign ment", + "est on", + "ĠJ ared", + "S ep", + "Ġblog s", + "up date", + "g om", + "r isk", + "Ġcl ash", + "ĠH our", + "Ġrun time", + "Ġunw anted", + "Ġsc am", + "Ġr ack", + "Ġen light", + "on est", + "ĠF err", + "Ġconv ictions", + "Ġp iano", + "Ġcirc ulation", + "ĠW elcome", + "Ġback lash", + "ĠW ade", + "Ġrece ivers", + "ot ive", + "J eff", + "Ġnetwork ing", + "ĠPre p", + "ĠExpl orer", + "Ġlect ure", + "Ġupload ed", + "ĠMe at", + "B LE", + "ĠNaz is", + "ĠSy nd", + "st ud", + "ro ots", + "ri ans", + "Ġportray ed", + "Ġ ??", + "ĠBudd ha", + "s un", + "Rober t", + "ĠCom plex", + "Ġover see", + "Ġste alth", + "T itle", + "ĠJ obs", + "ĠK um", + "Ġappreci ation", + "ĠM OD", + "Ġbas ics", + "Ġcl ips", + "Ġnurs ing", + "Ġpropos ition", + "Ġreal ised", + "ĠNY C", + "Ġall ocated", + "ri um", + "ar an", + "ĠPro duction", + "ĠV ote", + "Ġsm ugg", + "Ġhun ter", + "az er", + "ĠCh anges", + "Ġfl uct", + "y on", + "Ar ray", + "Ġk its", + "W ater", + "Ġuncom mon", + "Ġrest ing", + "ell s", + "w ould", + "Ġpurs ued", + "Ġassert ion", + "omet own", + "ĠMos ul", + "ĠPl atform", + "io let", + "Ġshare holders", + "Ġtra ils", + "P ay", + "ĠEn forcement", + "ty pes", + "ĠAn onymous", + "Ġsatisf ying", + "il ogy", + "Ġ( '", + "w ave", + "c ity", + "Ste ve", + "Ġconfront ation", + "ĠE ld", + "C apt", + "ah an", + "ht m", + "ĠC trl", + "ON S", + "2 30", + "if a", + "hold ing", + "Ġdelic ate", + "Ġj aw", + "ĠGo ing", + "or um", + "S al", + "Ġd ull", + "ĠB eth", + "Ġpr isons", + "Ġe go", + "ĠEl sa", + "avor ite", + "ĠG ang", + "ĠN uclear", + "Ġsp ider", + "ats u", + "Ġsam pling", + "Ġabsor bed", + "ĠPh arm", + "iet h", + "Ġbuck et", + "ĠRec omm", + "O F", + "ĠF actory", + "AN CE", + "Ġb acter", + "H as", + "ĠObs erv", + "12 1", + "Ġprem iere", + "De velop", + "Ġcur rencies", + "C ast", + "Ġaccompany ing", + "ĠNash ville", + "Ġfat ty", + "ĠBre nd", + "Ġloc ks", + "Ġcent ered", + "ĠU T", + "augh s", + "or ie", + "ĠAff ordable", + "v ance", + "D L", + "em et", + "Ġthr one", + "ĠBlu etooth", + "Ġn aming", + "if ts", + "AD E", + "Ġcorrect ed", + "Ġprompt ly", + "ĠST R", + "Ġgen ome", + "Ġcop e", + "Ġval ley", + "Ġround ed", + "ĠK end", + "al ion", + "p ers", + "Ġtour ism", + "Ġst ark", + "v l", + "Ġblow ing", + "ĠSche dule", + "st d", + "Ġunh appy", + "Ġlit igation", + "ced es", + "Ġand roid", + "Ġinteg ral", + "ere rs", + "ud ed", + "t ax", + "Ġre iter", + "ĠMot ors", + "oci ated", + "Ġwond ers", + "ĠAp ost", + "uck ing", + "ĠRoose velt", + "f ram", + "Ġyield s", + "Ġconstit utes", + "aw k", + "Int erest", + "Ġinter im", + "Ġbreak through", + "ĠC her", + "Ġpro sec", + "ĠD j", + "ĠM T", + "Res p", + "ĠP T", + "Ġs perm", + "ed it", + "B T", + "Lin ux", + "count ry", + "le ague", + "Ġd ick", + "Ġo ct", + "Ġinsert ing", + "Ġsc ra", + "ĠBrew ing", + "Ġ19 66", + "Ġrun ners", + "Ġpl un", + "id y", + "ĠD ian", + "Ġdys function", + "Ġex clusion", + "Ġdis gr", + "Ġincorpor ate", + "Ġrecon c", + "Ġnom inated", + "ĠAr cher", + "d raw", + "achel or", + "Ġwrit ings", + "Ġshall ow", + "Ġh ast", + "ĠB MW", + "ĠR S", + "Ġth igh", + "Ġ19 63", + "Ġl amb", + "Ġfav ored", + "ag le", + "Ġcool er", + "ĠH ours", + "ĠG U", + "ĠOrig in", + "Ġglim pse", + "---------------- ----", + "L im", + "Ġche ek", + "Ġj ealous", + "- '", + "Ġhar ness", + "ĠPo ison", + "Ġdis abilities", + "ne apolis", + "Ġout look", + "Ġnot ify", + "ĠIndian apolis", + "Ġab rupt", + "ns ic", + "Ġenc rypted", + "Ġfor fe", + "reat h", + "Ġr abb", + "Ġfound ations", + "Ġcompl iment", + "ĠInter view", + "ĠS we", + "Ġad olesc", + "Ġmon itors", + "ĠSacrament o", + "Ġtime ly", + "Ġcontem pl", + "Ġposition ed", + "Ġpost ers", + "ph ies", + "iov ascular", + "v oid", + "ĠFif th", + "Ġinvestig ative", + "OU N", + "Ġinteg rate", + "ĠIN C", + "ish a", + "ibl ings", + "ĠRe quest", + "ĠRodrig uez", + "Ġsl ides", + "ĠD X", + "Ġfemin ism", + "Ġdat as", + "Ġb end", + "ir us", + "ĠNig eria", + "F ox", + "Ch ange", + "Ġair plane", + "ĠLad en", + "Ġpublic ity", + "ixt y", + "Ġcommit ments", + "Ġaggreg ate", + "Ġdisplay ing", + "ĠAr row", + "Ġ12 2", + "Ġrespect s", + "and roid", + "s ix", + "ĠSh a", + "Ġrest oration", + ") \\", + "W S", + "oy s", + "Ġillust rate", + "with out", + "12 6", + "ĠâĶ Ĥ", + "Ġpick up", + "n els", + "Ġ ....", + "f ood", + "ĠF en", + ") ?", + "Ġphenomen a", + "Ġcompan ions", + "ĠW rite", + "Ġsp ill", + "Ġbr idges", + "ĠUp dated", + "ĠF o", + "Ġinsect s", + "ASH INGTON", + "Ġsc are", + "il tr", + "ĠZh ang", + "Ġsever ity", + "Ġind ul", + "14 9", + "ĠCo ffee", + "Ġnorm s", + "Ġp ulse", + "ĠF T", + "Ġhorr ific", + "ĠDest roy", + "ĠJ SON", + "Ġo live", + "Ġdiscuss es", + "R est", + "E lect", + "ĠW inn", + "ĠSurv iv", + "ĠH ait", + "S ure", + "op ed", + "Ġro oted", + "ĠS ke", + "ĠBron ze", + "Ġl ol", + "Def ault", + "Ġcommod ity", + "red ited", + "Ġliber tarian", + "Ġforb idden", + "Ġgr an", + "à ¨", + "Ġl ag", + "en z", + "dri ve", + "Ġmathemat ics", + "Ġw ires", + "Ġcrit ically", + "Ġcarb ohyd", + "ĠChance llor", + "ĠEd die", + "Ġban ning", + "ĠF ri", + "Ġcompl ications", + "et ric", + "ĠBangl adesh", + "Ġband width", + "St op", + "ĠOrig inally", + "Ġhalf way", + "yn asty", + "sh ine", + "Ġt ales", + "rit ies", + "av ier", + "Ġspin ning", + "ĠWH O", + "Ġneighbour hood", + "b ach", + "Ġcommer ce", + "ĠS le", + "B U", + "Ġentreprene ur", + "Ġpecul iar", + "ĠCom ments", + "f re", + "3 20", + "IC S", + "Ġimag ery", + "ĠCan on", + "ĠElect ronic", + "sh ort", + "( (", + "D ig", + "Ġcomm em", + "u ced", + "Ġincl ined", + "ĠSum mon", + "Ġcl iff", + "ĠMed iterranean", + "Ġpo etry", + "Ġprosper ity", + "ĠRe ce", + "Ġp ills", + "m ember", + "Ġfin ale", + "un c", + "ĠG ig", + "ä ½", + "Ġl od", + "Ġback ward", + "- +", + "ĠFor ward", + "Ġth ri", + "s ure", + "Ġso ap", + "ĠF X", + "R ES", + "ĠSe xual", + "oul os", + "Ġfool ish", + "Ġright eous", + "Ġco ff", + "terror ism", + "ust ain", + "ot er", + "Ġab uses", + "ne xt", + "Ġab usive", + "Ġthere after", + "Ġprohib ition", + "ĠS UP", + "Ġd ip", + "Ġr ipped", + "Ġinher ited", + "Ġb ats", + "st ru", + "G T", + "Ġflaw ed", + "ph abet", + "Ġf og", + "do ors", + "Ġim aging", + "Ġdig its", + "ĠHung ary", + "Ġar rog", + "Ġteach ings", + "Ġprotocol s", + "ĠB anks", + "à ¸", + "p ound", + "ĠC urt", + ".\" )", + ". /", + "Ġex emption", + "end ix", + "ĠM ull", + "Ġimpro ves", + "ĠG amer", + "d imensional", + "I con", + "ĠMarg aret", + "St atus", + "d ates", + "Ġint ends", + "Ġdep ict", + "Ġpark ed", + "J oe", + "ĠMar ines", + "chn ology", + "! ).", + "Ġjud ged", + "Ġwe ights", + "R ay", + "Ġapart ments", + "he ster", + "Ġrein force", + "Ġoff ender", + "occ up", + "Ġs ore", + "e pt", + "ĠPH P", + "ĠB row", + "Ġauthor ization", + "ĠR isk", + "ĠDel aware", + "ĠQ U", + "Ġnot ifications", + "Ġsun light", + "Ġex clude", + "d at", + "Ġm esh", + "ĠSud an", + "Ġbelong ed", + "Ġsub way", + "Ġno on", + "ĠInter ior", + "ol ics", + "ĠL akers", + "Ġc oding", + "Dis claimer", + "Cal if", + "O ld", + "Ġdis l", + "???? ?", + "Ġconfir ms", + "Ġrecruit ment", + "Ġhom icide", + "Cons ider", + "ĠJeff rey", + "ft y", + "} ;", + "Ġobject ion", + "do ing", + "ĠLe o", + "W ant", + "Ġgl ow", + "ĠClar ke", + "ĠNorm an", + "Ġver ification", + "Ġpack et", + "ĠForm ula", + "Ġpl ag", + "es ville", + "Ġshout ing", + "Ġo v", + "ĠR EC", + "ĠB ub", + "Ġn inth", + "Ġener g", + "Ġvalid ity", + "Ġup s", + "j ack", + "Ġneighbor ing", + "ĠN ec", + "ew orks", + "ĠH ab", + "are z", + "Ġsp ine", + "Ġevent ual", + "ĠLe aders", + "ĠC arn", + "Ġprob ation", + "Ġrom ance", + "ms g", + "ĠMechan ical", + "ER Y", + "R ock", + "Ġpart isan", + "N ode", + "ass ets", + "min ent", + "Ġforeign ers", + "Ġtest ify", + "ĠUs ually", + "l ords", + "ĠG ren", + "ĠPow ell", + "BI L", + "Ġs r", + "Ġadd ict", + "Ġshell s", + "Ġs igh", + "ĠY ale", + "tern ity", + "Ġ7 50", + "E U", + "ĠR ifle", + "Ġpat ron", + "em a", + "ĠB annon", + "an ity", + "Ġtrop ical", + "ĠV II", + "c ross", + "Every thing", + "ĠIS O", + "Ġhum ble", + "ass ing", + "ĠF IG", + "Ġupd ating", + "ys on", + "Ġcal cium", + "Ġcompet ent", + "Ġste ering", + "Pro t", + "ĠS Y", + "ĠFin als", + "ĠR ug", + "15 9", + "13 7", + "ĠG olf", + "Ġ12 6", + "Ġaccommod ation", + "ĠHug hes", + "Ġaest hetic", + "art isan", + "ĠTw ilight", + "Ġpr ince", + "ĠAgric ulture", + "ĠDis co", + "Ġpreced ent", + "Ġtyp ing", + "author ized", + "O ption", + "ĠA ub", + "l ishes", + "ach t", + "m ag", + "P eter", + "ĠU FO", + "mont on", + "ĠL ith", + "Ġa rom", + "Ġsec uring", + "Ġconf ined", + "priv ate", + "Ġsw ords", + "Ġmark ers", + "Ġmetab olic", + "se lect", + "ĠCur se", + "ĠO t", + "g ressive", + "Ġinc umb", + "ĠS aga", + "Ġpr iced", + "Ġclear ance", + "Cont ent", + "Ġdr illing", + "Ġnot ices", + "Ġb ourgeois", + "Ġv est", + "Ġcook ie", + "ĠGuard ians", + "ry s", + "in yl", + "Ġ12 4", + "Ġpl ausible", + "on gh", + "ĠOd in", + "Ġconcept ion", + "ĠY uk", + "ĠBaghd ad", + "ĠFl ag", + "Aust ral", + "ĠI BM", + "Ġintern ationally", + "ĠWiki Leaks", + "I ED", + "Ġc yn", + "Ġcho oses", + "ĠP ill", + "Ġcomb ining", + "Ġrad i", + "ĠMoh ammed", + "def ense", + "atch ing", + "Sub ject", + "ic iency", + "Fr ame", + "Ġ{ \"", + "Ġche ss", + "Ġtim er", + "19 0", + "Ġt in", + "Ġord inance", + "emet ery", + "Ġacc using", + "Ġnotice able", + "Ġcent res", + "Ġl id", + "ĠM ills", + "img ur", + "Ġz oom", + "erg ic", + "Ġcomp ression", + "pr im", + "f ind", + "Ġsur g", + "Ġp and", + "ĠK ee", + "ĠCh ad", + "cell ence", + "oy le", + "Ġsocial ism", + "ĠT ravis", + "ĠM Hz", + "Ġgu ild", + "ALL Y", + "ĠSub scribe", + "ĠRel ated", + "Ġoccur rence", + "itch ing", + "Ġfict ional", + "Ġcr ush", + "ĠE A", + "c od", + "m ix", + "ĠTri ple", + "Ġretrie ve", + "Ġstimul us", + "Ġpsych iat", + "ĠDo or", + "Ġhomosexual ity", + "Ġelement ary", + "Ġcell ular", + "id ian", + "ĠL aun", + "Ġintrig uing", + "Ġfo am", + "ĠB ass", + "id i", + "its u", + "Ġass ure", + "Ġcongr at", + "Ġbusiness man", + "ĠBo ost", + "cl ose", + "Ġl ied", + "Ġsc iences", + "ĠO mega", + "ĠG raphics", + "Ġ< =", + "sp oken", + "Ġconnect ivity", + "S aturday", + "ĠAven gers", + "Ġto ggle", + "Ġank le", + "Ġnational ist", + "mod el", + "ĠP ool", + "ophob ia", + "V ar", + "ĠM ons", + "ator ies", + "Ġaggress ively", + "C lear", + "For ge", + "act ers", + "Ġhed ge", + "Ġpip es", + "Ġbl unt", + "Ġs q", + "Ġremote ly", + "W ed", + "as ers", + "Ġref riger", + "Ġt iles", + "Ġresc ued", + "Ġcompr ised", + "ins ky", + "Ġman if", + "avan augh", + "Ġprol ifer", + "Ġal igned", + "x ml", + "Ġtri v", + "Ġcoord ination", + "ĠP ER", + "ĠQu ote", + "13 4", + "b f", + "ĠS aw", + "Ġtermin ation", + "Ġ19 0", + "Ġadd itions", + "Ġtri o", + "Ġproject ions", + "Ġpositive ly", + "Ġin clusive", + "Ġmem br", + "19 90", + "old er", + "Ġpract iced", + "ink le", + "Ar ch", + "Ġstar ters", + "ari us", + "Ġinter mediate", + "ĠBen ef", + "ĠK iller", + "Ġinter ventions", + "ĠK il", + "ĠF lying", + "In v", + "Ġprem ature", + "Ġpsych iatric", + "Ġind ie", + "Ġcoll ar", + "ĠRain bow", + "af i", + "Ġdis ruption", + "ĠFO X", + "cast ing", + "Ġmis dem", + "c ro", + "Ġw ipe", + "ard on", + "Ġb ast", + "ĠTom my", + "ĠRepresent ative", + "Ġbell y", + "ĠP O", + "ĠBre itbart", + "13 2", + "Ġmess aging", + "Sh ould", + "Ref erences", + "ĠG RE", + "ist ical", + "L P", + "ĠC av", + "ĠC razy", + "Ġintu itive", + "ke eping", + "ĠM oss", + "Ġdiscont in", + "ĠMod ule", + "Ġun related", + "ĠPract ice", + "ĠTrans port", + "Ġstatist ically", + "orn s", + "Ġs ized", + "p u", + "Ġca f", + "ĠWorld s", + "ĠRod gers", + "ĠL un", + "ĠCom ic", + "l iving", + "Ġc ared", + "Ġclim bed", + ") {", + "Ġconsist ed", + "Ġmed ieval", + "fol k", + "Ġh acked", + "Ġd ire", + "ĠHerm ione", + "Ġt ended", + "ce ans", + "D aniel", + "w ent", + "Ġlegisl ators", + "Ġred es", + "g ames", + "Ġg n", + "am iliar", + "Ġ+ +", + "gg y", + "th reat", + "Ġmag net", + "Ġper ceive", + "Ġz ip", + "Ġindict ment", + "Ġcrit ique", + "g ard", + "ĠSaf e", + "ĠC ream", + "Ġad vent", + "ob a", + "Ġv owed", + "ous ands", + "Ġsk i", + "Ġabort ions", + "u art", + "Ġstun ned", + "Ġadv ancing", + "Ġlack ed", + "Ġ\\ \"", + "Ġsch izophren", + "Ġeleg ant", + "Ġconf erences", + "Ġcance led", + "ĠHud son", + "ĠHop efully", + "Ġtr ump", + "Ġfrequ encies", + "Ġmet eor", + "ĠJun ior", + "ĠFle et", + "ĠMal colm", + "ĠT ools", + "Ġ ........", + "Ġh obby", + "ĠEurope ans", + "Ġ15 00", + "ĠInt o", + "Ġs way", + "ĠApp ro", + "ĠCom pl", + "Comm unity", + "Ġt ide", + "ĠSum mit", + "ä »", + "Ġinter vals", + "ĠE ther", + "Ġhabit at", + "ĠSteven s", + "lish ing", + "ĠDom ain", + "Ġtrig gers", + "Ġch asing", + "Ġchar m", + "ĠFl ower", + "it ored", + "Ġbless ing", + "Ġtext ures", + "F ive", + "Ġliqu or", + "R P", + "F IN", + "Ġ19 62", + "C AR", + "Un known", + "Ġres il", + "ĠL ily", + "Ġabund ance", + "Ġpredict able", + "r ar", + "Ġbull shit", + "le en", + "che t", + "M or", + "M uch", + "ä ¹", + "Ġemphas ized", + "Ġcr ust", + "Ġprim itive", + "Ġenjoy able", + "ĠPict ures", + "Ġteam mate", + "pl er", + "ĠT ol", + "ĠK ane", + "Ġsummon ed", + "th y", + "ram a", + "ĠH onda", + "Ġreal izing", + "Ġquick er", + "Ġconcent rate", + "cle ar", + "Ġ2 10", + "ĠErd ogan", + "ar is", + "Ġrespond s", + "ĠB I", + "Ġelig ibility", + "Ġpus hes", + "ĠId aho", + "Ġagg rav", + "Ġru ins", + "ur ations", + "Ġb ans", + "Ġan at", + "sh are", + "Ġgr ind", + "h in", + "um en", + "Ġut ilities", + "ĠYan kees", + "Ġdat abases", + "ĠD D", + "Ġdispl aced", + "Ġdepend encies", + "Ġstim ulation", + "h un", + "h ouses", + "ĠP retty", + "ĠRaven s", + "ĠTOD AY", + "Ġassoci ates", + "Ġthe rape", + "cl ed", + "Ġde er", + "Ġrep airs", + "rent ice", + "Ġrecept ors", + "Ġrem ed", + "ĠC e", + "Ġmar riages", + "Ġball ots", + "ĠSold ier", + "Ġhilar ious", + "op l", + "13 8", + "Ġinherent ly", + "Ġignor ant", + "Ġb ounce", + "ĠE aster", + "REL ATED", + "ĠCur rency", + "E V", + "ãĥ ŀ", + "ĠLe ad", + "Ġdece ased", + "B rien", + "ĠMus k", + "J S", + "Ġmer ge", + "heart ed", + "c reat", + "m itt", + "m und", + "ĠâĢ ĭ", + "ĠB ag", + "Ġproject ion", + "Ġj ava", + "ĠStand ards", + "ĠLeon ard", + "Ġcoc onut", + "ĠPop ulation", + "Ġtra ject", + "Ġimp ly", + "Ġcur iosity", + "ĠD B", + "ĠF resh", + "ĠP or", + "Ġheav ier", + "ne ys", + "gom ery", + "Ġdes erved", + "Ġphr ases", + "ĠG C", + "Ġye ast", + "d esc", + "De ath", + "Ġreb oot", + "Ġmet adata", + "IC AL", + "Ġrep ay", + "ĠInd ependence", + "Ġsubur ban", + "ical s", + "Ġat op", + "Ġall ocation", + "gener ation", + "ĠG ram", + "Ġmoist ure", + "Ġp ine", + "ĠLiber als", + "Ġa ides", + "Ġund erest", + "ĠBer ry", + "Ġcere mon", + "3 70", + "ast rous", + "ĠPir ates", + "Ġt ense", + "ĠIndust ries", + "ĠApp eals", + "ĠN ear", + "Ġè£ı ç", + "Ġlo vers", + "ĠC AP", + "ĠC raw", + "Ġg iants", + "Ġeffic acy", + "E lement", + "ĠBeh avior", + "ĠToy ota", + "Ġint est", + "P riv", + "A I", + "Ġmaneu ver", + "Ġperfect ion", + "Ġb ang", + "p aper", + "r ill", + "Ge orge", + "b order", + "in ters", + "ĠS eth", + "Ġcl ues", + "ĠLe vi", + "ĠRe venue", + "14 7", + "Ġv apor", + "Ġfortun ate", + "Ġthreat ens", + "Ġve t", + "Ġdepend ency", + "ers ed", + "art icle", + "ĠBl izzard", + "Ġch lor", + "Ġmin us", + "ĠB ills", + "Ġcryptoc urrency", + "Ġmetabol ism", + "ter ing", + "Ġp estic", + "step s", + "ĠTre asure", + "ract ed", + "ĠConst ant", + "Ġtem p", + "13 9", + "ĠDet ective", + "ur ally", + "Ġrecover ing", + "Ġcort ex", + "Ġ14 4", + "cl osed", + "Ġprejud ice", + "aun ted", + "Ġstorm s", + "ĠN OW", + "Ġmach inery", + "Add ress", + "Ġcompe lled", + "27 0", + "Ġdesp air", + "b ane", + "Ġveget able", + "Ġbed s", + "Lear n", + "Ġcolor ful", + "Ġsp ike", + "Ġmarg ins", + "Ġsymp athy", + "Ġworks hop", + "ĠC BC", + "S at", + "Ġburn s", + "ĠG ender", + "Ġ12 9", + "ĠC able", + "Ġdeb ts", + "ĠThe resa", + "Ġreflect ing", + "Ġa irst", + "Ġr im", + "ram id", + "Ġweakness es", + "W rit", + "ogg le", + "t i", + "ĠCh arge", + "Ġwe ighed", + "Ġ( .", + "Ġl aughter", + "Ġrou ter", + "ĠDemocr acy", + "D ear", + "Ġhas ht", + "Ġd y", + "Ġhint s", + "run ning", + "Ġfin ishes", + "ar us", + "M ass", + "res ult", + "asc us", + "Ġv intage", + "Ġcon qu", + "Ġwild ly", + "ac ist", + "Ġl ingu", + "Ġprot agonist", + "st rom", + "te enth", + "ĠSol o", + "m ac", + "f illed", + "Ġre nown", + "it ives", + "Ġmot ive", + "ĠAnt ar", + "ĠM ann", + "ĠAd just", + "Ġrock ets", + "Ġtrou bling", + "e i", + "Ġorgan isms", + "ass is", + "Christ ian", + "Ġ14 5", + "ĠH ass", + "Ġsw all", + "Ġw ax", + "ĠSurv ival", + "V S", + "ĠM urd", + "v d", + "stand ard", + "Ġdrag ons", + "Ġacceler ation", + "r ational", + "f inal", + "Ġp aired", + "ĠE thereum", + "Ġinterf aces", + "Ġres ent", + "Ġartif acts", + "Å «", + "are l", + "Ġcompet itor", + "ĠNich olas", + "ĠSur face", + "c pp", + "ĠT ot", + "Ġeconom ically", + "Ġorgan ised", + "Ġen forced", + "in ho", + "Ġvar ieties", + "Ġab dom", + "ĠBa iley", + "id av", + "ĠSal v", + "p aid", + "Ġalt itude", + "ess ert", + "ĠG utenberg", + "are a", + "op oulos", + "Ġprofess ors", + "igg s", + "ĠF ate", + "he y", + "Ġ3 000", + "D ist", + "Ġtw ins", + "c ill", + "ĠM aps", + "Ġtra ps", + "Ġwe ed", + "ĠK iss", + "Ġy oga", + "Ġrecip ients", + "ĠWest minster", + "Ġpool s", + "ĠWal mart", + "18 8", + "ĠSchool s", + "att ack", + "ĠAR M", + "par agraph", + "W arning", + "j l", + "Ġself ish", + "anche z", + "ĠHe ights", + "F re", + "ĠS oph", + "Ġ --------------------------------", + "t ml", + "33 3", + "Ġraid s", + "Ġsatell ites", + "KE Y", + "Ġlast s", + "Ñ Ĥ", + "In s", + "ĠD ame", + "Ġunp redict", + "// /", + "gh ai", + "Ġart illery", + "Ġcru ise", + "Ġg el", + "ĠCabin et", + "Ġbl ows", + "ĠE sp", + "Ġprox imity", + "ot he", + "ĠSk ills", + "ĠU pper", + "ob o", + "ĠN DP", + "Ġenjoy s", + "Ġrepe ating", + "ĠConst ruction", + "ĠQuest ions", + "H illary", + "Ġu int", + "Ġprocess ors", + "ĠGib son", + "ĠMult iple", + "q a", + "ĠB om", + "ĠM iles", + "vent ional", + "Ġhur ts", + "s kin", + "ĠA IDS", + "Ġadvis ers", + "ĠR oot", + "Ġmethod ology", + "ĠD ale", + "Ġdet on", + "ĠKnow ledge", + "sequ ently", + "Ġ12 1", + "Ġconnect s", + "C y", + "ĠD anger", + "Ġcontribut ors", + "ĠB ent", + "Ġbr ass", + "ĠGun s", + "int o", + "ĠFort une", + "Ġbro ker", + "bal ance", + "Ġlength s", + "Ġv ic", + "Ġaver aging", + "Ġappropri ately", + "ĠCamer a", + "Ġsand wich", + "ĠCD C", + "Ġcoord inate", + "Ġnav ig", + "Ġgood ness", + "l aim", + "Ġbra ke", + "Ġextrem ist", + "ĠW ake", + "ĠM end", + "ĠT iny", + "ĠC OL", + "ĠR F", + "ĠD ual", + "ĠW ine", + "C ase", + "Ġref ined", + "Ġl amp", + "L ead", + "Ġb apt", + "ĠCar b", + "ĠS add", + "ĠMin neapolis", + "PD F", + "Ear ly", + "ĠH idden", + "I ts", + "ĠT IME", + "Ġp ap", + "Ġcommission ed", + "ĠF ew", + "ĠCol ts", + "ĠB ren", + "Ġbot hered", + "Ġlike wise", + "Ex per", + "ĠSch w", + "c ry", + "n n", + "ĠM itch", + "im on", + "M G", + "b m", + "UM P", + "r ays", + "Ġregist ry", + "Ġ2 70", + "ach ine", + "re lla", + "ant ing", + "00 000", + "Ġru ined", + "sp ot", + "Ġt a", + "Ġmaxim ize", + "Ġincon ven", + "D ead", + "H uman", + "En abled", + "ĠMar ie", + "Ġch ill", + "ĠParad ise", + "Ġstar ring", + "ĠLat ino", + "ĠProt ocol", + "ĠE VER", + "Ġsuppl iers", + "m essage", + "ĠBro ck", + "Ġser um", + "âĸĪâĸĪ âĸĪâĸĪ", + "Ġen comp", + "Ġamb ition", + "ues e", + "Ġar rows", + "And rew", + "Ġanten na", + "Ġ19 61", + "ĠB ark", + "Ġb ool", + "ãĤ ª", + "ĠSt orage", + "Ġrail way", + "Ġtoug her", + "ĠC ad", + "Ġwas hing", + "P y", + "' ]", + "em bed", + "ĠMem phis", + "ack le", + "Ġfam ously", + "ĠF ortunately", + "ov ies", + "Ġmind set", + "Ġsne ak", + "ĠD h", + "RA W", + "ĠSim pson", + "Ġliv est", + "Ġland mark", + "Ġc ement", + "L ow", + "Ġthr illed", + "ĠCour se", + "in el", + "Ġch uck", + "id ate", + "gl obal", + "Ġwh it", + "Ġ �", + "ad ays", + "s ki", + "ĠS V", + "Ġvir uses", + "30 6", + "ĠResp ons", + "Ġthe aters", + "ĠBr anch", + "ĠGene va", + "ĠM K", + "Ġunbel iev", + "Ġcommun ist", + "Orig inal", + "ĠRe ceived", + "ĠTrans fer", + "ĠAr g", + "In put", + "ĠStr ategy", + "Ġpal ace", + "the ning", + "D ri", + "Ġsent encing", + "umbn ail", + "Ġp ins", + "re cy", + "Ġs iblings", + "Get ting", + "ĠB U", + "ĠNorth west", + "Ġprolong ed", + "ĠSak ura", + "C omb", + "ĠB our", + "Ġinadequ ate", + "ĠK ash", + "Ġus ername", + "ĠImpro ve", + "Ġbatt ling", + "ĠM AC", + "Ġcurric ulum", + "Ġs oda", + "ĠC annon", + "Ġsens ible", + "sp ons", + "De cember", + "Ġw icked", + "ĠP engu", + "Ġdict ators", + "ĠHe arts", + "og yn", + "Ġsimilar ities", + "ĠSt ats", + "Ġh ollow", + "it ations", + "\": [", + "Ġh over", + "ĠList en", + "s ch", + "S und", + "Ġc ad", + "ĠPar ks", + "Ġl ur", + "Ġhy pe", + "ĠL em", + "N AME", + "is ure", + "Fr iday", + "Ġshoot s", + "Ġclos es", + "Ġd b", + "ĠR idge", + "ĠDiff erent", + "Ġrepl ies", + "ĠBroad way", + "op ers", + "Ġint oler", + "ĠZe us", + "akes pe", + "Ġpropri etary", + "Ġrequest ing", + "Ġcontro llers", + "ĠM IN", + "im edia", + "be cca", + "Ġexp ans", + "Ġoil s", + "B ot", + "ĠCh and", + "Ġpr inter", + "Ġto pped", + "ĠP OL", + "ĠEar lier", + "S ocial", + "av in", + "Ġdecre ases", + "ĠSe b", + "Ġspecific ations", + "ĠBl ast", + "ĠK urt", + "Ġfre el", + "B rown", + "Ġdil ig", + "ro e", + "ĠPro blem", + "ĠQu ad", + "Ġdecent ral", + "ĠV ector", + "an ut", + "Ġplug ins", + "ĠGreg ory", + "Ġfuck ed", + "el ines", + "ĠAmb assador", + "t ake", + "Ġcle ans", + "ong yang", + "An onymous", + "st ro", + "\" }", + "al ine", + "ĠO dd", + "ĠE ug", + "2 16", + "Ġbo il", + "ĠP owers", + "Ġnurs es", + "Ob viously", + "ĠTechn ical", + "Ġexceed ed", + "OR S", + "Ġextrem ists", + "Ġtr aces", + "ex pl", + "Ġcom r", + "ĠS ach", + ") /", + "Ġm asks", + "Ġsc i", + "B on", + "Ġreg ression", + "we gian", + "Ġadvis or", + "it ures", + "ĠV o", + "ex ample", + "ĠInst ruct", + "Ġs iege", + "Ġredu ctions", + "pt r", + "Ġstat utory", + "Ġrem oves", + "Ġp uck", + "red its", + "Ġbe e", + "Ġsal ad", + "Ġpromot ions", + "ĠJosh ua", + "with standing", + "ET H", + "ĠCh a", + "im us", + "Ġexpend iture", + "aun ting", + "Ġdelight ed", + "Ġ15 5", + "be h", + "Ġcar pet", + "ĠSp art", + "Ġj ungle", + "l ists", + "Ġbull ying", + "ĠNob el", + "ĠGl en", + "Ġreferen ced", + "Ġintrodu ces", + "se in", + "Ġcho pped", + "gl ass", + "ĠW rest", + "Ġneutral ity", + "Ġâ Ļ", + "Ġinvestig ator", + "Ġshel ves", + "Ġun constitutional", + "Ġreprodu ction", + "Ġmer chant", + "m ia", + "Ġmet rics", + "Ġexplos ives", + "ĠSon ia", + "Ġbod ily", + "Ġthick ness", + "Ġpredomin antly", + "ĠAb ility", + "Ġmon itored", + "IC H", + "Ġ] .", + "ĠMart inez", + "Ġvis ibility", + "Ġqu eries", + "Ġgen ocide", + "ĠWar fare", + "Qu ery", + "Ġstud ios", + "Ġemb ry", + "Ġcorrid or", + "Ġclean ed", + "com plete", + "ĠM H", + "Ġenroll ment", + "ING S", + "Ġimpact ed", + "Ġdis astrous", + "ĠY un", + "ĠCl aire", + "ĠBas ically", + "y t", + "uster ity", + "Ġindirect ly", + "w ik", + "Ġd od", + "ĠCar r", + "Ġam p", + "Ġprohib it", + "ĠIn itial", + "ĠR d", + "ij i", + "Ġeduc ate", + "c orn", + "i ott", + "ĠBeaut y", + "Ġdetect ive", + "ĠCon n", + "s ince", + "Ġst agger", + "Ġob ese", + "Ġb ree", + "olog ic", + "is se", + "walk er", + "Ġbl ades", + "Ġlaw ful", + "fun c", + "ĠBeh ind", + "Ġappet ite", + "Ġ( *", + "Ġt ennis", + "Ġoff spring", + "Ġj ets", + "Ġstruct ured", + "Ġafore mentioned", + "N ov", + "Ġsc aling", + "f ill", + "Ġst ew", + "Ġcur b", + "ĠStep han", + "ed In", + "S F", + "ob ic", + "é ŃĶ", + "ou g", + "ĠM M", + "Ġgen etically", + "ope z", + "13 6", + "Ġu mb", + "anc ers", + "Ġcoh ort", + "Ġmerch andise", + "Ġimp osing", + "ĠLegisl ature", + "ĠArch ive", + "iv ia", + "ĠN aval", + "Ġoff ences", + "Ġmir acle", + "Ġsn apped", + "Ġf oes", + "Ġextensive ly", + "ĠR af", + "Ġc ater", + "ed ience", + "K it", + "ĠB in", + "Ġrecomm ends", + "ĠC ities", + "Ġrig id", + "ĠRE AD", + "ĠNob le", + "ĠT ian", + "Ġcertific ates", + "ant is", + "o iler", + "ĠBudd hist", + "d id", + "Ġsurvey ed", + "Ġdown ward", + "Ġprint s", + "ĠMot ion", + "ron ics", + "ĠS ans", + "oss ibly", + "u ctions", + "Ġcolon ies", + "ĠDan ish", + "un it", + "Ġsp oil", + "Ġadvis ory", + "ber ries", + "Pl an", + "Ġspecific ation", + "op hers", + "ĠRes ource", + "Ġsh irts", + "prising ly", + "commun ications", + "Ġtriv ial", + "Ġmention ing", + "ise xual", + "Ġsupp lements", + "Ġsuper vision", + "B P", + "v or", + "Ġw it", + "Ġco oldown", + "Ġplaint iff", + "ĠReview s", + "ĠS ri", + "ĠM int", + "ĠSug ar", + "Ġafter ward", + "ĠPri est", + "ĠInvest ment", + "og ene", + "ĠT aking", + "Ġstretch ing", + "Ġinflamm ation", + "ĠTe hran", + "Ġl ining", + "Ġfree zing", + "ĠEnt ity", + "Ġins piring", + "spe cial", + "pr ice", + "Ġsu e", + "ĠP orter", + "oun ge", + "ET A", + "ĠD erek", + "ĠLu is", + "u o", + "ym ph", + "Ġex terior", + "ih il", + "ĠAsh ley", + "in ator", + "Ġnut rients", + "ĠTh rones", + "Ġfin ances", + "ĠIn spect", + "Ġspe cially", + "ĠRequ ired", + "ĠP TS", + "ĠViol ence", + "oint ed", + "sh ots", + "Ġex cerpt", + "co on", + "IN S", + "ĠG ri", + "Ġrecogn ised", + "We ek", + "You ng", + "Ġv om", + "is le", + "ĠCur ry", + "ĠBudd h", + "Ġnot ebook", + "Ġd urable", + "/ ?", + "ĠG ad", + "ĠP upp", + "Ġforg ive", + "p ark", + "Ġpersonal ities", + "an alysis", + "cl amation", + "Ġelev ator", + "Ġware house", + "ĠR ole", + "un n", + "Ġillust ration", + "ĠSc an", + "Ġatmosp heric", + "Im port", + "AN C", + "rict ed", + "f u", + "01 0", + "Ġar che", + "Ġreward ed", + "akespe are", + "Ġintern ally", + "ĠR BI", + "alk er", + "Ġeleph ant", + "ow itz", + "ĠP izza", + "Ġbip artisan", + "é s", + "Ġslow ed", + "ĠSt ark", + "Ġover ride", + "OU S", + "Ġ3 20", + "undred s", + "ĠDe ck", + "ĠC ensus", + "be e", + "14 6", + "ot or", + "Ġ ip", + "Ġu b", + "oc ations", + "ĠBut ton", + "r ice", + "Ġc ripp", + "ff f", + "Ġorig inated", + "Ġoverwhel med", + "app a", + "Ġfore most", + "âĢ ij", + "ĠL EG", + "re lease", + "eat ured", + "at ches", + "Ġre ps", + "Ġl ending", + "ĠRe ference", + "ĠCl ient", + "16 5", + "vent h", + "Com plete", + "ĠPat rol", + "Ġsw orn", + "c am", + "Ġshut tle", + "ĠR alph", + "Ġh ometown", + "- ,", + "on al", + "ĠB P", + "å ı", + "Ġpersu ade", + "ĠAlex and", + "Ġcomb ines", + "Ġv ivid", + "ĠL ag", + "Ġenc oding", + "Ġsal vation", + "w en", + "ĠRec overy", + "i ya", + "Un iversity", + "ĠB iden", + "Ġbud gets", + "ĠTex ans", + "f its", + "Ġhon ored", + "Ġp ython", + "T D", + "## #", + "cl one", + "Ġbl ink", + "ĠL iquid", + "Ġunemploy ed", + "Ġcl ashes", + "ĠCoun sel", + "Ġdirect ing", + "Ġpun ct", + "ĠFal cons", + "Ġsh ark", + "ĠDam ascus", + "Ġje ans", + "Ġemb ark", + "Ġse ize", + "Ġup wards", + "2 80", + "ĠE z", + "ĠAny thing", + "Ġex otic", + "l ower", + "ĠCreat or", + "ĠU m", + "Ġsubur bs", + "ber ger", + "ĠW end", + "Ġm int", + "ĠX X", + "ĠD ro", + "Ġsuff ers", + "Ġher b", + "t ree", + "Ġfrag ile", + "Ġflood ed", + "ĠAl cohol", + "ole an", + "ny der", + "ĠK O", + "F ram", + "Ġ13 6", + "Ġow ed", + "ĠMe lee", + "ĠH ash", + "Ġwh isk", + "Ġsu do", + "r r", + "Qu ick", + "app ro", + "Ġi i", + "ĠEx amples", + "he e", + "Ġpromot es", + "per ature", + "k ar", + "ĠHon or", + "Ġs odium", + "ĠL if", + "ros so", + "intend ent", + "Ġcorrespond ent", + "F ound", + "sec ret", + "Ġident ifies", + "ag ne", + "Ġl ou", + "ĠP P", + "Ġcoinc idence", + "m ove", + "Ġmilit ia", + "Ġinf iltr", + "ĠPrim ary", + "Ġpitch ing", + "ĠI b", + "ĠGO OD", + "ãĤ ¸", + "ĠW izards", + "ir al", + "ĠVen us", + "R R", + "ĠâĢ ķ", + "ĠCase y", + "Ġsad ly", + "Ġadm ire", + "Ġembarrass ed", + "c b", + "M el", + "Ġtub es", + "Ġbeaut ifully", + "ĠQueens land", + "Bel ow", + "re z", + "qu et", + "ple asant", + "Ġ «", + "C amp", + "Ġdec isive", + "19 98", + "ĠL amb", + "ut ton", + "h n", + "ĠJ agu", + "au nder", + "ĠC ord", + "Ġcl erk", + "Ġca ffe", + "Ġwip ed", + "Ġre im", + "ĠMount ains", + "Ġimprison ed", + "Ġdevelop s", + "ĠP ra", + "Ġmodel ing", + "Any one", + "ance l", + "ĠS it", + "Ġshield s", + "Ġl awn", + "Ġcard iovascular", + "Ġdemonstr ating", + "Ġpar se", + "ĠIsrael is", + "Ġeuro s", + "14 3", + "Ġgl orious", + "ins ki", + "ec d", + "Ġcondition ing", + "Ġhel pless", + "Ġmicro sc", + "ĠHar bor", + "Ġst akes", + "Ġ2 60", + "Ġun equ", + "ĠFl oyd", + "Ġd amp", + "Ġappar atus", + "ĠLaw s", + "Ġcoun ters", + "Ġindu ce", + "at able", + "ĠAh med", + "Ġsl am", + "N ovember", + "Ġpers ist", + "Ġim minent", + "á n", + "Ġsh red", + "Ġph ases", + "ĠEd monton", + "ĠArm strong", + "ĠMe et", + "ĠK itty", + "Ñ Ģ", + "c irc", + "ĠAd ult", + "Ġa rose", + "ĠX en", + "D an", + "g ow", + "Ġsuper f", + "ĠAd mir", + "Ġend ure", + "Ġkey word", + "yr us", + "Ġy arn", + "Ġpath way", + "ĠHop kins", + "mid t", + "Ġcens orship", + "d ependent", + "Ġinstruct or", + "S ources", + "Ġto e", + "Ġball oon", + "N ob", + "Ġsw ear", + "ĠCast ro", + "Ġgl oss", + "ĠK avanaugh", + "Ġremark ably", + "Ph otos", + "ĠN om", + "ĠS outheast", + "y ers", + "Ġvalid ation", + "Ġcann on", + "ĠVict ory", + "ĠPier re", + "Ġcaut ious", + "Aud io", + "Ġf etch", + "ĠG ift", + "ĠH yp", + "Ġrem edy", + "Z E", + "Ġsc ent", + "Ġbe ard", + "ĠR ut", + "- \"", + "Ġpat ents", + "H y", + "Ġun just", + "Ġpot ato", + "Ġforth coming", + "Ġche f", + "ĠR ift", + "aff e", + "ĠR OM", + "ĠL aunch", + "Ġp ads", + "ĠNe o", + "Ġon set", + "Ġsquee ze", + "s afe", + "Ġpref ix", + "ĠT M", + "ĠN early", + "ĠClin ical", + "ĠM ental", + "ot iation", + "ĠUn ic", + "ant ry", + "ĠC ir", + "Ġep it", + "à ¦", + "Ġextract ed", + "verse ly", + "ri ad", + "Ġstr ains", + "Ġto ps", + "Ġpo em", + "ĠRand y", + "ĠMap le", + "TH ER", + "up iter", + "ĠSS D", + "ļ é", + "Ġun con", + "per ing", + "Ġsle pt", + "in ers", + "Ġunder water", + "ĠEv idence", + "g one", + "20 5", + "Ġhistor ians", + "Ġsynt hesis", + "Ġf rog", + "b asketball", + "Ġvibr ant", + "Ġsub ord", + "Ġ3 65", + "ĠD ial", + "Ġcooper ate", + "HA HA", + "Ġgreet ed", + "15 8", + "Ġj azz", + "Ġinto x", + "ĠWalk ing", + "Ġsuper visor", + "ĠF usion", + "ĠMer cedes", + "s end", + "H am", + "s d", + "n l", + "Ġtour s", + "ĠF IFA", + "Ġcul p", + "g d", + "30 4", + "Ġple as", + "Ġillust rates", + "ĠColomb ia", + "Ġhighlight ing", + "ĠSum mary", + "Ġexp osing", + "ĠD ru", + "Ġir ony", + "r itional", + "ĠCar roll", + "ĠEll is", + "P ict", + "ĠR apt", + "Ġad apter", + "Ġun m", + "Ġcor pse", + "Ġceleb rities", + "D en", + "at um", + "ĠAp ocalypse", + "ĠW ag", + "lin ing", + "Ġhorm ones", + "R ub", + "ĠX i", + "ĠV aults", + "20 8", + "alky rie", + "inos aur", + "Ġfeed s", + "v ity", + "Ġdefe ating", + "W ait", + "Ġemphas ize", + "ĠSteel ers", + "yr inth", + "le ys", + "ĠWhe never", + "Current ly", + "ĠCl ock", + "Ġcollect ively", + "any on", + "ĠJ P", + "Ġment ality", + "Ġdownload s", + "Ġsurround ings", + "ĠBarn es", + "Ġflags hip", + "Ġindic ators", + "Ġgra pp", + "Jan uary", + "ĠElement al", + "ĠAthen a", + "ib al", + "Ġs ights", + "Ġcap ita", + "ĠTreat y", + "Ġvo iced", + "ĠG az", + "let te", + "Ġy a", + "Ġexp ired", + "Leg end", + "H ot", + "n ature", + "Ġunst able", + "Ġ2 80", + "à º", + "Com ment", + "AL E", + "Ġquest s", + "Ġhand ler", + "n is", + "Ġvers atile", + "Ġconce al", + "enge ance", + "ĠInter active", + "Ġobs essed", + "ĠDog s", + "Ġcr acked", + "S ound", + "s v", + "ĠD ylan", + "ro ads", + "f x", + "ĠCath olics", + "ĠH ag", + "Ġsl ammed", + "Ġgl owing", + "s ale", + "Ġtiss ues", + "ĠCh i", + "ne e", + "Ġc her", + "s ic", + "ur rection", + "Ġb acon", + "ul atory", + ") .\"", + "Ġir regular", + "FOR M", + "ass ed", + "Ġintention al", + "Ġcompens ate", + "ĠSpe aking", + "ĠS ets", + "15 3", + "Ġconvent ions", + "b ands", + "em ade", + "Ġe cc", + "ĠWin ston", + "ĠAssass in", + "ĠBelg ian", + "Ġdepend ence", + "Ġnic he", + "Ġb ark", + "ĠJ azz", + "Ġdisadvant age", + "Ġgas oline", + "Ġ16 5", + "çļ Ħ", + "ess a", + "mod ule", + "ang ular", + "O Y", + "ĠTreat ment", + "it as", + "ol ation", + "ĠArn old", + "Ġfe ud", + "ĠN est", + "Ġthe atre", + "ew ater", + "Ġmin ors", + "olic y", + "ĠH aven", + "div ision", + "Ġtr unk", + "F ar", + "ĠP ull", + "Ġcapt uring", + "Ġ18 00", + "ĠTe en", + "Ġex empl", + "Ġclin ics", + "ĠB urg", + "Ġsubst it", + "Ġpay load", + "ĠL av", + "ĠT roy", + "ĠW itness", + "Ġfrag ments", + "Ġpass words", + "Ġg ospel", + "ĠG in", + "Ġten ants", + "ol ith", + "S ix", + "Pre vious", + "ĠAg es", + "ĠDar win", + "Ġbl at", + "Ġem pathy", + "sm ith", + "b ag", + "ĠE cho", + "ĠC amb", + "ĠM add", + "ĠB oo", + "Ġred e", + "ĠBurn ing", + "Ġsmooth ly", + "ĠAd rian", + "ĠV ampire", + "ĠMon sters", + "ste am", + "Sty le", + "M a", + "re a", + "ĠD war", + "aly st", + "urs or", + "Ġelim ination", + "Ġcrypt o", + "ch t", + "ĠE ternal", + "âĢ¦ ]", + "ĠS orce", + "I ll", + "N ER", + "Ġu h", + "Con clusion", + "w age", + "Ġresp ir", + "Ġrem inis", + "het ical", + "Ġg y", + "Ġutil ized", + "ic idal", + "Ġ19 00", + "Ġhun ters", + "ĠSw an", + "ĠRe act", + "Ġvis itor", + "ĠThanks giving", + "30 8", + "Post s", + "Ġh ips", + "19 97", + "om ers", + "Ġkn ocking", + "ĠVeh icle", + "Ġt il", + "Ġ13 8", + "Ġm i", + "ĠInvest igation", + "ĠKen ya", + "Ġcas ino", + "Ġmot ives", + "Ġreg ain", + "re x", + "Ġweek ends", + "Ġstab bed", + "bor o", + "Ġexplo ited", + "ĠHA VE", + "ĠTe levision", + "c ock", + "Ġprepar ations", + "Ġende av", + "ĠRem ote", + "ĠM aker", + "ĠPro du", + "ĠEv an", + "Ġinform ational", + "ĠLouis ville", + "15 4", + "ĠDream s", + "Ġpl ots", + "ĠRun ner", + "Ġhur ting", + "Ġacad emy", + "ĠMont gomery", + "n m", + "ĠL anc", + "ĠAl z", + "2 10", + "el ong", + "Ġretail er", + "Ġar ising", + "Ġrebell ion", + "Ġbl onde", + "play ed", + "Ġinstrument al", + "C ross", + "Ġret ention", + "Ġtherape utic", + "Ġse as", + "Ġinfant ry", + "ĠCl int", + "Ġprompt ing", + "Ġbit ch", + "Ġst ems", + "ĠK ra", + "Ġthe sis", + "ĠB og", + "ru ed", + "Ġk ings", + "Ġcl ay", + "ific ent", + "ĠY ES", + "ĠTh ing", + "ĠCub s", + "vey ard", + "els h", + "in arily", + "ĠE y", + "ĠRoll ing", + "Ġev olving", + "Ind ia", + "Ġrecogn izes", + "Ġgrad uation", + "is ers", + "Ġfert ility", + "ĠMil an", + "Comm and", + "Ġbox ing", + "Ġ19 43", + "Ġgl uten", + "ĠEm ir", + "Ġid ol", + "Ġcon ceived", + "ĠCre ation", + "Mer it", + "udd y", + "uss ions", + "ĠLie utenant", + "iet al", + "Ġunch anged", + "ĠSc ale", + "ĠCrime a", + "ball s", + "ator ial", + "Ġdepth s", + "Ġempir ical", + "Ġtrans m", + "Ġuns afe", + "miss ible", + "com fort", + "15 6", + "Ġmechan ic", + "00 2", + "l ins", + "Ġsm oked", + "P os", + "Ġslow ing", + "Ġl av", + "Tex as", + "Ġche ating", + "ĠMet ropolitan", + "eth yl", + "Ġdiscover ing", + "as se", + "Ġpen cil", + "ĠPy ongyang", + "Ġclos et", + "ĠShe et", + "ĠEnt ry", + "ou stic", + "Ġmy st", + "er ate", + "ari at", + "Ġminer als", + "Ġmusic ian", + "ĠP ul", + "ĠM az", + "24 9", + "Ġper missions", + "Ġ iv", + "en ary", + "ick ers", + "ĠB ing", + "he a", + "en able", + "Ġgri ev", + "Ġassert ed", + "ĠColon el", + "Ġaff idav", + "w o", + "Ġse ated", + "ĠR ide", + "Ġpaint ings", + "ĠP ix", + "Ġ13 7", + "ish i", + "umb ai", + "g otten", + "ĠEar l", + "Ġin ning", + "Ġc ensus", + "Ġtrave lled", + "ĠCons ult", + "18 5", + "b ind", + "Ġsimpl icity", + "Ġoverlook ed", + "ĠHelp ful", + "Ġmon key", + "Ġoverwhelming ly", + "Bl ood", + "ĠFl int", + "ĠJ ama", + "ĠPres ent", + "ĠR age", + "ĠT A", + "pt ive", + "Ġturn out", + "w ald", + "ĠD olphins", + "ĠV PN", + "Ġon ion", + "Ġcraft ing", + "m ma", + "ĠMerc ury", + "Ġarr ange", + "Ġalert s", + "ĠO T", + "zb ollah", + "Ġg ases", + "ĠRichards on", + "s al", + "l ar", + "Ġfro st", + "Ġlower ing", + "Ġacc laim", + "Ġstart ups", + "ĠG ain", + "ess ment", + "Ġguard ian", + "äº º", + "ĠP ie", + "ĠL inks", + "Ġmer its", + "Ġaw ake", + "Ġparent al", + "Ġexceed s", + "Ġid le", + "ĠPil ot", + "Ġe Bay", + "ĠAc cept", + "ipe g", + "C am", + "ĠK ot", + "Ġtrad ers", + "olit ics", + "unk er", + "ĠP ale", + "os i", + "an mar", + "Ġ19 47", + "ĠF ell", + "est ial", + "it ating", + "G F", + "ĠS r", + "if ted", + "Ġconnect or", + "ĠB one", + "ill es", + "2 60", + "h ma", + "Ġoverl ap", + "ĠGit Hub", + "Ġclean er", + "ĠBapt ist", + "ĠW AS", + "Ġlung s", + "Ñ ģ", + "ĠB UT", + "Ġc ite", + "Ġpit ched", + "reat ment", + "Ġtro phies", + "ĠN u", + "38 6", + "ĠPr ide", + "Ġattend ees", + "[ ]", + "17 9", + "Ġspat ial", + "Ġpri zes", + "ĠRel igion", + "Ġshow case", + "ĠC ategory", + "vid ia", + "T arget", + "Pro perty", + "? ,", + "Ġf usion", + "p ie", + "ĠU CLA", + "Ġsound track", + "Ġprin cess", + "ĠC aval", + "sh ould", + "Ġlim bs", + "Back ground", + "Ġlone ly", + "Ġc ores", + "ĠT ail", + "she et", + "Ġ13 2", + "R a", + "ãĤ «", + "ĠB olt", + "Ġbook ed", + "Ġadmin ister", + "Ġequ als", + "w y", + "Ġobserv ing", + "ĠBar on", + "ĠAd obe", + "Ġv irgin", + "ĠSocial ist", + "M ove", + "gh azi", + "ĠLind a", + "2 12", + "Ġbre wing", + "Ġmerch ants", + "bur se", + "Ġdiv or", + "Ġmet als", + "ĠN er", + "Ġsum s", + "ĠEn emy", + "Ġen vision", + "Ġgrant ing", + "ĠH oney", + "ĠSk yrim", + "Ġsoc io", + "gr aded", + "Ġselect ive", + "W ASHINGTON", + "Ġ19 48", + "ĠSir ius", + "ĠG ross", + "act ivity", + "ĠI van", + "Ġfur ious", + "BS D", + "ĠPre vious", + "Ġrespons ive", + "Ġchar itable", + "Ġle aning", + "ĠP ew", + "Ġviol ates", + "\\\\\\\\ \\\\\\\\", + "ĠCom ing", + "w ire", + "Ġpo et", + "Ġres olutions", + "comm and", + "ĠPortug uese", + "Ġnick name", + "Ġde af", + "Feb ruary", + "Ġrecogn ise", + "Ġentire ty", + "Ġseason al", + "pl aced", + "ĠTe legraph", + "Ġmicro phone", + "our ing", + "Ġgr ains", + "Ġgovern ed", + "Ġpost p", + "ĠW aters", + "in ement", + "Ġund ocumented", + "ĠCom cast", + "Ġf ox", + "Ġassault s", + "re on", + "man y", + "ĠJen kins", + "ĠAny way", + "Ġassess ments", + "Ġdown s", + "ĠM ouse", + "Ġsuper b", + "k t", + "ĠD ow", + "Ġtax ation", + "4 01", + "Ġsm iles", + "Ġundert aken", + "Ġex h", + "Ġenthusi astic", + "Ġtw ent", + "Ġgovernment al", + "Ġautonom y", + "ĠTechn ologies", + "ĠCh ain", + "Ġpreval ent", + "f b", + "Ġnic otine", + "og ram", + "j ob", + "Ġawa iting", + "ĠMen u", + "Ġdep uties", + "k ov", + "ish ops", + "But ton", + "ĠShan ghai", + "Ġdies el", + "ĠD uck", + "R yan", + "ĠPC s", + "N F", + "j ury", + "ent e", + "Ġinacc urate", + "edd y", + "Wh atever", + "Ġshow c", + "ĠN ad", + "od us", + "et r", + "Ġplaint iffs", + "ĠW OR", + "ĠAss ange", + "Ġpriv at", + "Ġpremium s", + "Ġt am", + "UR L", + "Ġel ites", + "ĠR anger", + "otten ham", + "ĠH off", + "ĠAt hens", + "Ġdefin ite", + "Ġs ighed", + "Ġeven ly", + "2 11", + "ĠAm ber", + "ak ia", + "Ġmail ing", + "Ġcr ashing", + "ĠConfeder ate", + "ru gged", + "W al", + "ĠDep ths", + "Ġjuven ile", + "Ġreact or", + "Introdu ction", + "ĠDel uxe", + "19 95", + "ĠS anchez", + "ĠM ead", + "iv able", + ": -", + "ĠPlan ning", + "ĠT rap", + "qu in", + "ĠProt ect", + "ve red", + "In formation", + "Ġkid ney", + "inn amon", + "l as", + "Ġpolic ing", + "Ġtoler ate", + "ĠQ i", + "Ġbi ased", + "F ort", + "ĠK i", + "s ave", + "Ġprivile ged", + "Ġbe asts", + "ĠGl as", + "ĠC inem", + "Ġcome back", + "Sund ay", + "Ġext inction", + "h ops", + "Ġtrans mit", + "Ġdoub les", + "ĠFl at", + "16 7", + "Ġdis puted", + "Ġinjust ice", + "f oo", + "V ict", + "role um", + "ĠJul ie", + "Con text", + "ĠR arity", + "iss ue", + "Comp onent", + "Ġcounsel ing", + "an ne", + "d ark", + "Ġobject ions", + "u ilt", + "Ġg ast", + "Ġpl ac", + "Ġun used", + "ãĥ ĩ", + "ĠT rial", + "ĠJ as", + "hed ral", + "ob b", + "Ġtempor al", + "ĠPR O", + "ĠN W", + "ĠAnn iversary", + "L arge", + "Ġther m", + "Ġd avid", + "Ġsystem ic", + "ĠSh ir", + "m ut", + "ĠNe pt", + "add ress", + "Ġscan ning", + "Ġunderstand able", + "Ġcan vas", + "C at", + "ĠZ oo", + "Ġang els", + "L O", + "ĠStat ement", + "ĠS ig", + "ov able", + "ĠA way", + "sh aring", + "ocr ats", + "st ated", + "Ġweigh ing", + "N or", + "w ild", + "B ey", + "Ġaston ishing", + "ĠReyn olds", + "Ġop ener", + "Ġtrain er", + "Ġsurg ical", + "p n", + "Ġadjust ing", + "whe el", + "Ġf rown", + "erv ative", + "Ġsusp end", + "With in", + "te in", + "Ġobst acle", + "Ġliber ties", + "ym es", + "Ġur anium", + "ans om", + "an ol", + "ub a", + "ĠL oss", + "Ġa rous", + "ĠHend erson", + "W ow", + "s pl", + "c ur", + "Ġ Ń", + "Ġtheir s", + "Dam age", + "Ġdownload ing", + "Ġdisc ern", + "ĠSt o", + "ĠFl a", + "Ġh ath", + "ĠA j", + "Ġun pleasant", + "Europe an", + "exp ensive", + "Ġscreens hot", + "ĠU V", + "Ġall ied", + "ĠPers ian", + "Ġmonop oly", + "Ġat om", + "ĠReds kins", + "\"> <", + "Ġcan cell", + "Ġcinem a", + "13 1", + "f air", + "ĠAlf red", + "Ġd uck", + "arg s", + "22 3", + "ĠIS I", + "Ġsign aling", + "in ar", + "Ġlaugh s", + "Ġfor wards", + "Ġreck less", + "Ġlisten ers", + "at ivity", + "Ġvast ly", + "n ant", + "L ess", + "ĠHun ting", + "ĠScient ific", + "IT ED", + "Ġkn ight", + "ĠH TC", + "us a", + "t mp", + "Ġr ude", + "ĠLegend ary", + "Ġar ises", + "B ad", + "ĠCl aim", + "pe g", + "Ġreal ities", + "Th ink", + "Ġ °", + "Ġro de", + "Ġstri ve", + "Ġan ecd", + "Ġshort s", + "Ġhypot hes", + "Ġcoord inated", + "ĠGand hi", + "ĠF PS", + "R ED", + "Ġsuscept ible", + "Ġshr ink", + "ĠCh art", + "Hel p", + "Ġ ion", + "de ep", + "rib es", + "ĠK ai", + "ĠCustom er", + "Sum mary", + "Ġc ough", + "w ife", + "Ġl end", + "Ġposition ing", + "Ġlot tery", + "ĠC anyon", + "Ġf ade", + "Ġbron ze", + "ĠKenn y", + "Ġbo asts", + "ĠEnh anced", + "rec ord", + "Ġemer gence", + "Ġa kin", + "ĠB ert", + "it ous", + "âĸ ij", + "Ġst ip", + "Ġexch anged", + "om ore", + "als h", + "Ġreserv oir", + "Ġstand point", + "W M", + "Ġiniti ate", + "Ġdec ay", + "Ġbrew ery", + "Ġter ribly", + "Ġmort al", + "lev ard", + "Ġrev is", + "N I", + "el o", + "Ġconf ess", + "ĠMS NBC", + "Ġsub missions", + "Cont roller", + "Ġ20 2", + "ĠR uth", + "} );", + "ĠAz ure", + "Ġ .\"", + "20 6", + "ĠMarket ing", + "Ġl aund", + "ien cies", + "Ġrenown ed", + "ĠT rou", + "ĠN GO", + "ble ms", + "Ġterr ified", + "Ġwar ns", + "Ġper t", + "Ġuns ure", + "4 80", + "ale z", + "ult z", + "ĠOut side", + "Ġst yl", + "ĠUnder ground", + "Ġp anc", + "Ġd ictionary", + "Ġf oe", + "rim inal", + "ĠNor wegian", + "Ġj ailed", + "Ġm aternal", + "é e", + "ĠLu cy", + "c op", + "Ch o", + "Ġuns igned", + "ĠZe lda", + "ĠIns ider", + "ĠContin ued", + "Ġ13 3", + "ĠNar uto", + "ĠMajor ity", + "16 9", + "ĠW o", + "ãĤ ĵ", + "Ġpast or", + "Ġinform al", + "Ð ½", + "an throp", + "jo in", + "ãģ Ĺ", + "it ational", + "N P", + "ĠWrit ing", + "f n", + "ĠB ever", + "19 5", + "Ġy elling", + "Ġdr astically", + "Ġe ject", + "Ġne ut", + "Ġth rive", + "ĠFre qu", + "ou x", + "Ġpossess es", + "ĠSen ators", + "ĠD ES", + "ĠSh akespeare", + "ĠFran co", + "ĠL B", + "uch i", + "Ġinc arn", + "Ġfound ers", + "F unction", + "Ġbright ness", + "ĠB T", + "Ġwh ale", + "ĠThe ater", + "m ass", + "ĠD oll", + "S omething", + "Ġecho ed", + "ĠHe x", + "c rit", + "af ia", + "Ġgodd ess", + "Ġele ven", + "ĠPre view", + "ĠAur ora", + "Ġ4 01", + "uls ive", + "ĠLog an", + "in burgh", + "ĠCent ers", + "ĠON LY", + "ĠA id", + "Ġparad ox", + "Ġh urd", + "ĠL C", + "D ue", + "c ourt", + "Ġoff ended", + "Ġeval uating", + "ĠMatthew s", + "Ġto mb", + "Ġpay roll", + "Ġextra ction", + "ĠH ands", + "if i", + "Ġsuper natural", + "ĠCOM M", + "] =", + "dog s", + "Ġ5 12", + "ĠMe eting", + "Rich ard", + "ĠMax imum", + "Ġide als", + "Th ings", + "m and", + "ĠReg ardless", + "Ġhum ili", + "b uffer", + "L ittle", + "ĠD ani", + "ĠN ak", + "Ġliber ation", + "ĠA be", + "ĠO L", + "Ġstuff ed", + "ac a", + "ind a", + "raph ic", + "Ġmos qu", + "Ġcampaign ing", + "Ġoccup y", + "S qu", + "r ina", + "ĠW el", + "ĠV S", + "Ġphys ic", + "Ġp uls", + "r int", + "oad ed", + "ET F", + "ĠArch ives", + "Ġven ues", + "h ner", + "ĠTur bo", + "Ġl ust", + "Ġappeal ed", + "que z", + "il ib", + "ĠTim othy", + "Ġo mn", + "d ro", + "Ġobs ession", + "ĠSav age", + "19 96", + "Gl obal", + "J es", + "2 14", + "Ġsl iding", + "Ġdisapp ro", + "ĠMag ical", + "Ġvolunt arily", + "g b", + "ane y", + "Ġprop het", + "ĠRe in", + "ĠJul ia", + "ĠW orth", + "aur us", + "Ġb ounds", + "ie u", + ")) )", + "Ġcro re", + "ĠCitiz en", + "S ky", + "Ġcolumn ist", + "Ġseek ers", + "ond o", + "IS A", + "ĠL ength", + "Ġnost alg", + "Ġnew com", + "Ġdet rim", + "ent ric", + "3 75", + "ĠG E", + "Ġaut op", + "Ġacadem ics", + "App Data", + "ĠS hen", + "Ġid iot", + "ĠTrans it", + "Ġteasp oon", + "W il", + "K O", + "ĠCom edy", + "> ,", + "Ġpop ulated", + "W D", + "Ġp igs", + "ĠO culus", + "Ġsymp athetic", + "Ġmar athon", + "19 8", + "Ġseiz ure", + "s ided", + "Ġd op", + "irt ual", + "L and", + "ĠFl oor", + "osa urs", + "... ]", + "Ġl os", + "Ġsubsid iary", + "E Y", + "ĠPart s", + "ĠSt ef", + "ĠJud iciary", + "Ġ13 4", + "Ġmir rors", + "Ġk et", + "t imes", + "Ġneuro log", + "Ġc av", + "ĠGu est", + "Ġtum or", + "sc ill", + "ĠLl oyd", + "E st", + "Ġcle arer", + "Ġstere otypes", + "Ġd ur", + "not hing", + "Red dit", + "Ġnegoti ated", + "---------------- --------", + "23 5", + "Ġfl own", + "ĠSe oul", + "ĠRes ident", + "ĠS CH", + "Ġdisappear ance", + "ĠV ince", + "g rown", + "Ġgrab s", + "r il", + "ĠInf inite", + "ĠTw enty", + "Ġpedest rian", + "Ġjer sey", + "ĠF ur", + "ĠInf inity", + "ĠEll iott", + "Ġment or", + "Ġmor ally", + "Ġob ey", + "sec ure", + "iff e", + "Ġantib iotics", + "ang led", + "ĠFre eman", + "ĠIntrodu ction", + "J un", + "Ġm arsh", + "ic ans", + "ĠEV ENTS", + "och ond", + "W all", + "icult y", + "Ġmisdem eanor", + "Ġl y", + "Th omas", + "ĠRes olution", + "Ġanim ations", + "ĠD ry", + "Ġinter course", + "ĠNew castle", + "ĠH og", + "ĠEqu ipment", + "17 7", + "Ġterrit orial", + "Ġarch ives", + "20 3", + "Fil ter", + "ĠMun ich", + "Ġcommand ed", + "ĠW and", + "Ġpit ches", + "ĠCro at", + "Ġrat ios", + "ĠM its", + "Ġaccum ulated", + "ĠSpecific ally", + "Ġgentle man", + "acer b", + "Ġp enn", + "Ġa ka", + "ĠF uk", + "Ġinterven e", + "ĠRef uge", + "ĠAlz heimer", + "Ġsuccess ion", + "oh an", + "d oes", + "L ord", + "Ġsepar at", + "Ġcorrespond ence", + "Ġsh iny", + "P rior", + "Ġs ulf", + "Ġmiser able", + "Ġded ication", + "( ).", + "Ġspecial ists", + "Ġdefect s", + "ĠC ult", + "ĠX ia", + "Ġje opard", + "ĠO re", + "Ab ility", + "Ġle ar", + "Ġamb itions", + "ĠB MI", + "ĠArab s", + "Ġ19 42", + "Ġpres ervation", + "ific ate", + "Ġash amed", + "l oss", + "ĠRest aur", + "Ġrese mble", + "Ġen rich", + "ĠK N", + "ĠCl an", + "fl oat", + "Ġplay able", + "IT T", + "Ġharm ony", + "arr ison", + "ĠWe instein", + "w ere", + "Ġpoison ing", + "ĠCom put", + "ĠWord Press", + "m ajor", + "ĠVal ve", + "F an", + "ĠTh row", + "ĠRom ans", + "ĠDep ression", + "ad os", + "Ġtort ured", + "Ġbal ancing", + "bott om", + "Ġacqu iring", + "ĠMon te", + "ard i", + "Ġa ura", + "Ġ# #", + "ĠStand ing", + "ĠAtl as", + "C F", + "Ġintr ins", + "ĠBen ghazi", + "Ġcamp ing", + "Ġt apped", + "bl ade", + "st rous", + "ĠR abb", + "ĠW ritten", + "t ip", + "ĠNe igh", + "ster dam", + "ĠAll ow", + "ĠHe aling", + "ĠR hod", + "n um", + "Ġcaffe ine", + "ĠPer cent", + "Ġbo o", + "Ġapp les", + "30 5", + "Ġwel coming", + "Ġappl aud", + "Ġa usterity", + " ±", + "ĠRe ality", + "ef e", + "å ®", + "Ġsu cks", + "Ġtab s", + "ĠPay Pal", + "Ġback pack", + "Ġgif ted", + "abul ary", + "ĠSc out", + "ir teen", + "Ġch in", + "Ġo mitted", + "Ġnegative ly", + "Ġaccess ing", + "ĠE arn", + "Ġambul ance", + "Ġhead phones", + "Ġ20 5", + "ĠRef resh", + "p resident", + "ĠKit chen", + "ĠEnt ered", + "ĠS nyder", + "00 5", + "om ical", + "Ġborrow ed", + "ĠN em", + "Ġav iation", + "Ġst all", + "rim ination", + "Ġuniform s", + "it ime", + "ĠSim mons", + "ener gy", + "ab lished", + "y y", + "qual ified", + "Ġrall ies", + "ĠSt uart", + "fl ight", + "Ġgang s", + "r ag", + "Ġv ault", + "lu x", + "ĠCom par", + "Ġdesign ation", + "20 9", + "ĠJ os", + "d ollar", + "z ero", + "Ġwell s", + "30 3", + "Ġconstitu ents", + "Ġhe ck", + "Ġc ows", + "Ġcommand ers", + "Ġdifferent ial", + "ĠC atherine", + "29 9", + "Ġval ve", + "Ġbr ace", + "Ġperspect ives", + "c ert", + "f act", + "icular ly", + "ĠMc N", + "pl anes", + "Ġint ric", + "Ġpe as", + "ov an", + "Ġtoss ed", + "ret ch", + "ĠL opez", + "Ġunf amiliar", + "de ath", + "ĠA part", + "ĠCh ang", + "Ġrelie ved", + "rop he", + "Ġair ports", + "Ġfre ak", + "ut il", + "M ill", + "ĠCh in", + "ĠOw en", + "m ale", + "ĠBro ken", + "ĠWind s", + "ro b", + "r ising", + "Ġfire fighters", + "Ġauthor itarian", + "Ġ14 8", + "Bit coin", + "ex ternal", + "Ġbrow sers", + "iche ver", + "or ian", + "Ġun b", + "Ġpo ke", + "ĠZ ot", + "M id", + "ĠPop ular", + "Ġco vert", + "Ġcont ributes", + "Ġ6 50", + "Ġcont ention", + "G ate", + "Ġcons oles", + "Ġchrom os", + "ĠI X", + "Ġvis ually", + "ĠE isen", + "Ġjewel ry", + "Ġdeleg ation", + "Ġacceler ate", + "ĠR iley", + "Ġsl ope", + "Ġind oor", + "it ially", + "Ġhuge ly", + "Ġtun nels", + "Ġfin ed", + "Ġdirect ive", + "Ġfore head", + "ustom ed", + "Ġsk ate", + "Mus ic", + "g as", + "Ġrecogn izing", + "am bo", + "Ġover weight", + "ĠGr ade", + "Ù Ĭ", + "Ġsound ing", + "Ġlock ing", + "ĠR EM", + "St ore", + "Ġexc av", + "ĠLike wise", + "ĠL ights", + "Ġel bow", + "ĠSupp ly", + "w ic", + "Ġhands ome", + "19 94", + "C oll", + "Ġadequ ately", + "ĠAssoci ate", + "Ġstri ps", + "Ġcrack down", + "Ġmar vel", + "ĠK un", + "Ġpass ages", + "@@ @@", + "ĠT all", + "Ġthought ful", + "names e", + "Ġprost itution", + "bus iness", + "Ġball istic", + "person al", + "c ig", + "iz ational", + "R ound", + "ĠÂłĠÂł ĠÂłĠÂł", + "ĠCole man", + "Ġadm itting", + "ĠPl ug", + "Ġbit coins", + "ĠSu z", + "Ġfair ness", + "Ġsupp lier", + "Ġcatast rophic", + "ĠHel en", + "o qu", + "M arc", + "ĠArt icles", + "g ie", + "Ġend angered", + "Ġdest iny", + "ĠVol t", + "ol ia", + "ax is", + "Ġche at", + "Ġun ified", + "IC O", + "qu ote", + "30 2", + "ĠS ed", + "Ġsupp ression", + "Ġanaly zing", + "Ġsqu at", + "Ġfig uring", + "Ġcoordin ates", + "Ġch unks", + "Ġ19 46", + "Ġsub p", + "Ġw iki", + "ĠFor bes", + "ĠJ upiter", + "ĠE rik", + "im er", + "ĠCom mercial", + "\\ )", + "Ġlegitim acy", + "Ġd ental", + "ĠMe an", + "Ġdefic its", + "5 50", + "Orig inally", + "ĠHor ror", + "Ġcontam ination", + "ll ah", + "Ġconf isc", + "ĠCl are", + "T B", + "ĠF ailed", + "an ed", + "Ġrul er", + "ĠCont roller", + "Ġfemin ists", + "F ix", + "g ay", + "20 7", + "Ġr abbit", + "Th ird", + "ownt own", + "Ġgl ue", + "Ġvol atile", + "Ġsh ining", + "Ġf oll", + "Ġimp aired", + "Ġsup ers", + "æ Ī", + "Ġcl utch", + "ļé ĨĴ", + "Ġpro let", + "Ġ( !", + "Ġy elled", + "ĠK iev", + "ĠEr n", + "ĠSh ock", + "K B", + "Ġsit uated", + "qu ery", + "ĠN as", + "Ġan nex", + "char acter", + "ĠHol iday", + "Ġautom ation", + "ĠJ ill", + "ĠRem astered", + "Ġl inem", + "Ġwild erness", + "ĠHor izon", + "ĠGu inea", + "A Z", + "Ġmain land", + "Ġsec recy", + "LE ASE", + "Ġp unk", + "ĠProv ince", + "( ),", + "Spe ed", + "Ġhand ing", + "ĠSeb ast", + "S ir", + "r ase", + "Ġj ournals", + "Ġcon gest", + "ĠT ut", + "ir rel", + "Ġschizophren ia", + "Ġmis ogyn", + "health y", + "I ron", + "Ġreact ed", + "- $", + "25 2", + "Ġpl ural", + "Ġpl um", + "Ġbarg ain", + "Ġground ed", + "f inder", + "Ġdis se", + "ĠL az", + "O OD", + "Ġat roc", + "F actory", + "Ġmin ions", + "Ġo ri", + "ĠB rave", + "ĠP RE", + "ĠMy anmar", + "ĠH od", + "Ġexped ition", + "Ġexpl ode", + "ĠCo ord", + "Ġext r", + "ĠB rief", + "ĠAD HD", + "Ġhard core", + "feed ing", + "Ġd ile", + "ĠF ruit", + "Ġvacc ination", + "ĠM ao", + "osp here", + "Ġcont ests", + "- |", + "Ġf ren", + "isp here", + "R om", + "ĠSh arp", + "ĠTre nd", + "Ġdis connect", + "âĢ¢ âĢ¢", + "Ġper secution", + "Ear th", + "Ġhealth ier", + "38 4", + "Ġc ob", + "ĠTr inity", + "OW S", + "AN N", + "Ġspecial ty", + "Ġg ru", + "Ġcooper ative", + "wh y", + "Start ing", + "ĠIss ues", + "st re", + "ens or", + "Ġ18 5", + "Ad v", + "! ?", + "ĠRe vel", + "em ia", + "ĠH ulk", + "Ġcelebr ations", + "ĠS ou", + "ra ud", + "ĠKle in", + "Ġun real", + "con text", + "Ġpartners hips", + "Ġadop ting", + "t ical", + "Ġspl ash", + "ĠHe zbollah", + "c ategory", + "cycl op", + "xt on", + "ĠD ot", + "urd y", + "t z", + "Ġenvelop e", + "ĠN L", + "â ķ", + "Ġwhere in", + "Spe c", + "18 4", + "Ġte lev", + "al iation", + "Ġmyth s", + "å °", + "Ġrig orous", + "Ġcommun icating", + "Ġobser ver", + "Ġre he", + "ĠW ash", + "Ġapolog ized", + "ĠT in", + "Ġexpend itures", + "work ers", + "d ocument", + "Ġhes itate", + "ĠLen in", + "Ġunpredict able", + "Ġrenew al", + "cl er", + "ok ia", + "ĠCON T", + "Ġpost season", + "Tok ens", + "Ġex acerb", + "Ġbet ting", + "Ġ14 7", + "Ġelev ation", + "W ood", + "ĠSol omon", + "19 4", + "00 4", + "out put", + "Ġredu nd", + "ĠM umbai", + "Ġp H", + "Ġreprodu ce", + "ĠD uration", + "MA X", + "Ġb og", + "C BS", + "ĠBal ance", + "ĠS gt", + "ĠRec ent", + "Ġc d", + "Ġpo pped", + "Ġincomp et", + "pro p", + "ay an", + "g uy", + "Pac ific", + "Ġty r", + "Ġ{ {", + "ĠMy stic", + "ĠD ana", + "Ġmast urb", + "Ġge ometry", + "à ¢", + "ĠCor rect", + "Ġtraject ory", + "Ġdistract ed", + "Ġf oo", + "ĠW elsh", + "L uc", + "m ith", + "Ġrug by", + "Ġrespir atory", + "Ġtri angle", + "Ġ2 15", + "Ġunder graduate", + "ĠSuper ior", + "ch anging", + "_ -", + "Ġright ly", + "Ġrefere e", + "Ġluc rative", + "Ġun authorized", + "Ġresemb les", + "ĠGN U", + "ĠDer by", + "Ġpath ways", + "ĠL ed", + "Ġend urance", + "Ġst int", + "Ġcollect or", + "F ast", + "Ġd ots", + "Ġnational s", + "ĠSec urities", + "Ġwh ip", + "Par am", + "Ġlearn s", + "M agic", + "Ġdetail ing", + "m oon", + "Ġbroadcast ing", + "Ġb aked", + "26 5", + "hol m", + "ĠS ah", + "ĠHus sein", + "ĠCourt esy", + "17 4", + "Ġ14 6", + "Ġge ographic", + "pe ace", + "Ġjud ging", + "ĠS tern", + "B ur", + "Ġstory line", + "G un", + "ĠSt ick", + "24 5", + "30 7", + "ãĤ´ ãĥ³", + "ĠAdminist rator", + "Ġbur nt", + "Ġp ave", + "ch oes", + "Ex ec", + "Ġcamp uses", + "Res ult", + "Ġmut ations", + "ĠCh arter", + "Ġcapt ures", + "Ġcomp ares", + "Ġbad ge", + "S cient", + "Ġer ad", + "ier y", + "o i", + "ett es", + "ĠE state", + "Ġst rap", + "Ġproud ly", + "Ġf ried", + "Ġwithd rawn", + "ĠV oy", + "ph ony", + "It ems", + "ĠP ierce", + "b ard", + "Ġann otation", + "ant on", + "ill on", + "Im pro", + "... )", + "Ġhapp ier", + "---- --", + "ad just", + "Ġstaff ers", + "Ġactiv ism", + "Ġper f", + "Ġal right", + "N eed", + "Ġcomm ence", + "Ġopio id", + "ĠAm anda", + "E s", + "ĠP ars", + "ĠK aw", + "W orks", + "24 8", + "Ġind o", + "t c", + "end ant", + "ĠM oto", + "Ġlegal ization", + "OT E", + "Ġtask ed", + "Ġt sp", + "ĠACT IONS", + "16 6", + "Ġrefres hing", + "ĠN R", + "ĠPere z", + "Ġinfring ement", + "S Y", + "List en", + "in ning", + "k u", + "Ġrot ate", + "pro gram", + "ar ah", + "Des ign", + "Ġ( £", + "Ġst oring", + "Ġwar rants", + "Ġjud gement", + "ĠB rist", + "us ually", + "ph oto", + "ĠR an", + "ĠP ine", + "Ġoutrage ous", + "ĠValent ine", + "lu ence", + "ĠEvery body", + "Al tern", + "Ġrele vance", + "Ġtermin ated", + "Ġd essert", + "Ġfulf illed", + "Ġprosecut ed", + "ĠW ords", + "Ġm igrant", + "Ġcultiv ation", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "idel ity", + "ĠV ern", + "ĠLog in", + "Ġmetaph or", + "ĠT ip", + "Ġrecru its", + "ĠP ig", + "rib ing", + "Ġenthusi asts", + "ex per", + "Ġfright ening", + "ĠH air", + "ans on", + "str ate", + "Ġh i", + "He ight", + "Ġown ing", + "n one", + "Ġdis like", + "Ġkn ives", + "pher d", + "Ġloud ly", + "ĠAP Is", + "Dis play", + "ĠL ac", + "ĠUS S", + "ab l", + "ver ages", + "J ew", + "Ġ17 2", + "ĠHist orical", + "at oon", + "ĠPhys ics", + "in tern", + "Ġwarm th", + "Ġto pp", + "D M", + "Ġgun man", + "Ġem peror", + "od i", + "ãĥ £", + "in atory", + "ĠR ib", + "Ġ13 1", + "ĠSat urn", + "ĠSh ining", + "Ġw aking", + "Qu otes", + "Ġcomed ian", + "en berg", + " ½", + "Ġbelie vers", + "Ġpaper work", + "c ustom", + "Ġle v", + "Ġl ament", + "Ġpour ing", + "22 2", + "p olitical", + "ĠSupp lement", + "m aid", + "Ġcruel ty", + "Ġt read", + "ys ics", + "A w", + "rit es", + "Ġmod ifier", + "ĠP osition", + "Ad am", + "l b", + "ub s", + "Ġimper fect", + "Ġcl usters", + "ĠEngine er", + "ĠC herry", + "Ġinaug uration", + "ĠS au", + "Ġembod iment", + "ĠUn cle", + "Ġover r", + "Ġexplos ions", + "c ule", + "ĠPrinc eton", + "ĠAndre a", + "Ġincorrect ly", + "Ġearn est", + "Ġpil gr", + "ĠS print", + "Ġslee ve", + "Ġhe ars", + "ĠAm azing", + "Ġbrow sing", + "ag in", + "Ġhom eland", + "Ġha w", + "Ġd iving", + "ist ered", + "17 8", + "Ġbarg aining", + "ĠArc ade", + "Ġdeleg ate", + "ters on", + "................................ ................................", + "ĠJackson ville", + "27 5", + "Ġst agn", + "Ġad am", + "ĠSher man", + "C B", + "Ġsub urb", + "ĠFood s", + "Ġconver ting", + "ĠAr ist", + "Ġch ambers", + "l ove", + "Ġam ino", + "ĠG an", + "Ġmad ness", + "m c", + "ĠUS E", + "def ined", + "Ġul tr", + "ind ust", + "Ġw olves", + "l ance", + "Add itionally", + "Ġcr acks", + "as ia", + "ĠRe ason", + "ĠP ump", + "Ġaccident al", + "ĠL aser", + "ĠR id", + "Ġinitial ized", + "ell i", + "Ġun named", + "Ġn oun", + "ĠPass ed", + "Ġhost age", + "ĠEth iop", + "sh irts", + "Ġun rel", + "ĠEmb assy", + "Ġ19 41", + "Ġat oms", + "Ġpur ported", + "16 4", + "ĠF i", + "Ġgall ons", + "ĠMon ica", + "Ġp g", + "en ment", + "Ġsort ed", + "ĠG ospel", + "Ġhe ights", + "Ġtr aced", + "Ġunder going", + "She ll", + "Ġs acks", + "Ġproport ions", + "Ġhall uc", + "F ont", + "ac et", + "Ġwar mer", + "ĠIN TER", + "Ġgrab bing", + "Pl ug", + "Ġreal ization", + "ĠBur ke", + "Ġen chant", + "AT ER", + "ĠSe ed", + "Ġabund ant", + "F M", + "Ġc ivic", + "V s", + "is i", + "Ġv ow", + "Ġre per", + "ĠPartners hip", + "Ġpenet ration", + "Ġax e", + "Ġsh attered", + "ĠZ ombies", + "Ġv inyl", + "ĠAl ert", + "e on", + "Ġoblig ed", + "ĠIll ust", + "ĠPl aza", + "ĠFront ier", + "Ġdavid jl", + "ĠSer ial", + "ĠH av", + "ĠNut rition", + "B i", + "Ġâĸ Ī", + "ĠJ ays", + "lin ux", + "Ġhur ry", + "Ġv oy", + "Ġhop eless", + "ĠSte alth", + "Ġ ãģ", + "ess ors", + "tt le", + "b org", + "ĠSaf ari", + "f ell", + "Ġw ary", + "d ue", + "ĠAb ove", + "H a", + "E LL", + "Ġnot or", + "ĠW on", + "T oo", + "Ġoccup ations", + "Ġposs essions", + "Ġinv iting", + "Ġpred ators", + "Ġacceler ated", + "Ġ15 7", + "uter te", + "ĠC ube", + "e ast", + "acc ount", + "G ive", + "Ġtrans plant", + "red ients", + "id able", + "Ġscreens hots", + "ĠG und", + "ĠF S", + "Ġtravel ers", + "Ġsens ory", + "ĠF iat", + "ĠRock ets", + "İ ĭ", + "_ {", + "F riend", + "Ġchar ming", + "AL S", + "Ġenjoy ment", + "m ph", + "Ġ5 000", + "ĠRE G", + "Ù Ĩ", + "b ia", + "Ġcomp ilation", + "ro st", + "ĠV P", + "ĠSch ne", + "201 9", + "Ġcop ying", + "M ORE", + "ĠFl ore", + "f alls", + "2 15", + "t otal", + "Ġdis ciples", + "d ouble", + "Ġexceed ing", + "Ġsm ashed", + "Ġconcept ual", + "ĠRom ania", + "ĠB rent", + "ĠI CE", + "ĠT ou", + "Ġg rap", + "Ġn ails", + "18 9", + "ãĥ ĺ", + "Ġproc ure", + "e ur", + "Ġconfir ming", + "ĠC ec", + "aw i", + "ĠEd en", + "Ġn g", + "Ġengine ered", + "at ics", + "Ġhook ed", + "Ġdisgust ing", + "ĠMur der", + "ãĤ ¿", + "L ibrary", + "Ġ16 8", + "Al most", + "hem atic", + "Men u", + "ĠNot re", + "ĠJ ur", + "Ġkidn apped", + "Ġhack er", + "ĠJ ade", + "Ġcreep y", + "Ġdraw ings", + "ĠSpons or", + "Ġcycl ists", + "ĠGob lin", + "Ġoptim ized", + "Ġst aged", + "ĠMc D", + "bet ween", + "A ge", + "en o", + "S ex", + "ĠW ide", + "n ings", + "av is", + "Ġincap able", + "ĠK ob", + "Ġreward ing", + "ĠL one", + "oles cent", + "Ġcontract ed", + "Ġstick y", + "J ose", + "B all", + "f est", + "ĠIn put", + "ĠRec ently", + "Ġto mat", + "squ are", + "App lication", + "Ġnit rogen", + "Ġdupl icate", + "ĠRec on", + "ĠD ear", + "L ondon", + "Ġint ra", + "Ġd ock", + "Ġout reach", + "ĠM illion", + "Ġmamm als", + "am pton", + "V AL", + "Ġsn aps", + "Ġd os", + "ĠWh ole", + "ĠRead y", + "T ry", + "ĠWinn ipeg", + "ear ance", + "Ġinc urred", + "ren ched", + "ĠNS W", + "il ot", + "rain e", + "Ġc ube", + "g ot", + "Ġrun way", + "etermin ed", + "ĠHaw ks", + "Ġsurviv or", + "ĠW ish", + "ĠD in", + "ĠDE F", + "ĠV ault", + "18 7", + "Ġmush rooms", + "Ġcris p", + "be y", + "ĠDisco very", + "Ġdevelopment al", + "Ġparad igm", + "Ġcha otic", + "ĠT su", + "Ġ3 33", + "b ons", + "Ġbacter ial", + "Ġcomm its", + "Ġcos mic", + "Ġme ga", + "oc ative", + "ĠP aint", + "ophob ic", + "Ġv ain", + "Ġcar ved", + "ĠTh ief", + "ĠG ul", + "ows hip", + "Ġc ites", + "ĠEd inburgh", + "Ġdimin ished", + "Ġacknowled ges", + "ĠK ills", + "Ġmic row", + "ĠHer a", + "Ġsen iors", + "Ġwhere by", + "H op", + "at ron", + "Ġun available", + "ĠN ate", + "Ġ4 80", + "Ġsl ated", + "ĠRe becca", + "ĠB attery", + "Ġgram mar", + "Ġhead set", + "Ġcurs or", + "Ġex cluding", + "any e", + "aunder ing", + "eb in", + "Ġfeas ible", + "ĠPub lishing", + "ĠLab s", + "ĠCl iff", + "ĠFerr ari", + "Ġp ac", + "vis ible", + "mark ed", + "pe ll", + "Ġpol ite", + "Ġstagger ing", + "ĠGal actic", + "Ġsuper st", + "Ġpar an", + "ĠOffic ers", + "ãĢ ģ", + "Ġspecific s", + "ul us", + "23 9", + "ĠP aste", + "AM P", + "ĠPan ama", + "ĠDe lete", + "angu ard", + "rest rial", + "Ġhero ic", + "ĠD y", + "ا ÙĦ", + "Ġincumb ent", + "Ġcr unch", + "t ro", + "Ġsc oop", + "Ġblog ger", + "Ġsell ers", + "ure n", + "Ġmedic ines", + "ĠC aps", + "ĠAnim ation", + "ox y", + "Ġout ward", + "Ġinqu iries", + "22 9", + "Ġpsych ologist", + "ĠS ask", + "ev il", + "Ġcontam inated", + "ãĤ ¨", + "he rence", + "Ġbrand ed", + "ĠAbd ul", + "z h", + "Ġparagraph s", + "Ġmin s", + "Ġcor related", + "er b", + "Ġimp art", + "Ġmil estone", + "ĠSol utions", + "ot le", + "Ġunder cover", + "Ġmar ched", + "ĠCharg ers", + "f ax", + "ĠSec rets", + "Ġr uth", + "we ather", + "Ġfemin ine", + "Ġsh am", + "Ġprest igious", + "igg ins", + "Ġs ung", + "hist ory", + "ett le", + "gg ie", + "Ġout dated", + "ol and", + "Ġper ceptions", + "ĠS ession", + "ĠDod gers", + "u j", + "ĠE ND", + "D oc", + "Ġdefic iency", + "Gr and", + "ĠJ oker", + "Ġretro spect", + "Ġdiagn ostic", + "Ġharm less", + "Ġro gue", + "ĠA val", + "E qu", + "Ġtrans c", + "ĠRoberts on", + "ĠDep ending", + "ĠBurn s", + "iv o", + "Ġhost ility", + "F eatures", + "ĵ ĺ", + "Ġdis comfort", + "ĠL CD", + "spec ified", + "ĠEx pect", + "3 40", + "Ġimper ative", + "ĠReg ular", + "Ch inese", + "Ġstate wide", + "Ġsy mm", + "Ġlo ops", + "Ġaut umn", + "N ick", + "Ġsh aping", + "Ġqu ot", + "Ġc herry", + "ĠCross ref", + "è¦ ļéĨĴ", + "Stand ard", + "he ed", + "ĠD ell", + "ĠViet namese", + "Ġo st", + "ĠV alkyrie", + "O A", + "Ass ad", + "Ġreb ound", + "ĠTra ffic", + "pl aces", + "æ ĺ", + "ĠB uc", + "17 2", + "Ġshel ters", + "Ġins isting", + "ĠCertain ly", + "ĠKenn eth", + "ĠT CP", + "Ġpen al", + "ĠRe play", + "he ard", + "Ġdial ect", + "iz a", + "ĠF Y", + "it cher", + "ĠD L", + "Ġspir al", + "Ġquarterback s", + "Ġh ull", + "Ġgo ogle", + "Ġto dd", + "ĠSter ling", + "ĠPl ate", + "Ġsp ying", + "mb ol", + "ĠReal m", + "ĠPro ced", + "ĠCr ash", + "Ġtermin ate", + "Ġprotest ing", + "C enter", + "gu ided", + "Ġun cover", + "Ġboy cott", + "Ġreal izes", + "s ound", + "Ġpret ending", + "ĠV as", + "19 80", + "Ġfram ed", + "Ġ13 9", + "Ġdesc ended", + "Ġrehab ilitation", + "Ġborrow ing", + "ĠB uch", + "Ġbl ur", + "R on", + "ĠFro zen", + "en za", + "Ch ief", + "ĠP oor", + "Ġtransl ates", + "M IN", + "Ġ2 12", + "J ECT", + "Ġerupt ed", + "Ġsuccess es", + "S EC", + "Ġpl ague", + "Ġg ems", + "d oms", + "Ġstret ches", + "ĠSp y", + "Ġstory telling", + "C redit", + "ĠP ush", + "Ġtra ction", + "Ġin effective", + "ĠL una", + "Ġt apes", + "Ġanaly tics", + "erc ise", + "Ġprogram mes", + "ĠCar bon", + "Ġbeh old", + "he avy", + "ĠConserv ation", + "ĠF IR", + "Ġs ack", + "ter min", + "ric ks", + "Ġhous ed", + "Ġunus ually", + "I ce", + "Ġexecut ing", + "ĠMor oc", + "ed ay", + "Ġed itions", + "Ġsm arter", + "ĠB A", + "Ġout law", + "Ġvan ished", + "ib a", + "AL SE", + "ĠSil va", + "23 8", + "C ould", + "Ġphilos opher", + "Ġevac uated", + "Sec ret", + "14 2", + "Ġvis as", + "ãĤ ¬", + "ĠM alt", + "ĠClear ly", + "ĠN iger", + "ĠC airo", + "ĠF ist", + "3 80", + "ĠX ML", + "aut o", + "it ant", + "Ġrein forced", + "Rec ord", + "ĠSurviv or", + "G Hz", + "Ġscrew s", + "parent s", + "Ġo ceans", + "ma res", + "Ġbra kes", + "vas ive", + "Ġhell o", + "ĠS IM", + "rim p", + "Ġo re", + "ĠArm our", + "24 7", + "Ġterr ific", + "Ġt ones", + "14 1", + "ĠMin utes", + "Ep isode", + "Ġcur ves", + "Ġinflamm atory", + "Ġbat ting", + "ĠBeaut iful", + "L ay", + "Ġunp op", + "v able", + "Ġr iots", + "ĠTact ics", + "b augh", + "ĠC ock", + "Ġorg asm", + "ĠS as", + "Ġconstruct or", + "et z", + "G ov", + "Ġant agon", + "Ġthe at", + "Ġde eds", + "ha o", + "c uts", + "ĠMc Cl", + "Ġu m", + "ĠScient ists", + "Ġgrass roots", + "ys sey", + "\"] =>", + "Ġsurf aced", + "Ġsh ades", + "Ġneighb ours", + "Ġad vertis", + "oy a", + "Ġmer ged", + "Up on", + "Ġg ad", + "Ġanticip ate", + "Any way", + "Ġsl ogan", + "Ġdis respect", + "I ran", + "ĠT B", + "act ed", + "Ġsubp oen", + "medi ately", + "OO OO", + "Ġwa iver", + "Ġvulner abilities", + "ott esville", + "ĠHuff ington", + "J osh", + "ĠD H", + "M onday", + "ĠEll en", + "K now", + "x on", + "it ems", + "22 8", + "Ġf ills", + "ĠN ike", + "Ġcum ulative", + "and als", + "I r", + "Ġ ì", + "Ġfr iction", + "ig ator", + "Ġsc ans", + "ĠVi enna", + "ld om", + "Ġperform ers", + "P rim", + "Ġb idding", + "M ur", + "Ġlean ed", + "ĠPri x", + "al ks", + "Ġ[ âĢ¦]", + "ĠTw itch", + "ĠDevelop er", + "ĠG ir", + "Ġcall back", + "Ab stract", + "Ġacc ustomed", + "Ġfreed oms", + "ĠP G", + "ur acy", + "Ġl ump", + "is man", + ",, ,,", + "19 92", + "ĠR ED", + "Ġwor m", + "M atch", + "ĠPl atinum", + "I J", + "ĠOwn er", + "Tri via", + "com pl", + "Ġnew born", + "Ġfant as", + "O wn", + "Ġ19 59", + "Ġsymp ath", + "Ġub iqu", + "Ġoutput s", + "Ġal lev", + "Ġpr ag", + "K evin", + "Ġfav ors", + "Ġbur ial", + "Ġn urt", + "so lete", + "c ache", + "Ġ15 6", + "Ġunl ocks", + "te chn", + "M aking", + "Ġcon quer", + "ad ic", + "æ ĸ", + "Ġel f", + "Ġelect orate", + "ĠKurd s", + "ĠSt ack", + "ĠSam urai", + "Ġâ ĺħ", + "Ġ{ }", + "ĠS aid", + "ĠFall out", + "Ġkind ness", + "ĠCustom s", + "ĠBou levard", + "Ġhelicop ters", + "ot ics", + "ĠVe get", + "com ment", + "Ġcritic ised", + "Ġpol ished", + "ĠRem ix", + "ĠC ultural", + "Ġrec ons", + "Ġdo i", + "at em", + "Sc reen", + "Ġbar red", + "Com ments", + "ĠGener ally", + "Ġsl ap", + "7 20", + "V ari", + "p ine", + "Ġem pt", + "Ġh ats", + "ĠPlay ing", + "l ab", + "a verage", + "form s", + "ĠC otton", + "Ġcan s", + "ĠD ON", + "ĠSom alia", + "C rypt", + "ĠIncre ases", + "E ver", + "mod ern", + "Ġsur geon", + "3 000", + "Ġrandom ized", + "================================ ================================", + "B ern", + "im pl", + "ĠC OR", + "Ġpro claim", + "th ouse", + "Ġto es", + "Ġam ple", + "Ġpres erving", + "Ġdis bel", + "gr and", + "B esides", + "Ġsil k", + "ĠPat tern", + "h m", + "Ġenter prises", + "Ġaffidav it", + "ĠAdvis ory", + "Ġadvert ised", + "ĠRel igious", + "se ctions", + "psy ch", + "ĠField s", + "aw ays", + "Ġhasht ag", + "ĠNight mare", + "Ġv ampire", + "Ġfore nsic", + "rosso ver", + "n ar", + "Ġn avy", + "Ġvac ant", + "ĠD uel", + "Ġhall way", + "Ġface book", + "ident ally", + "ĠN RA", + "Ġm att", + "Ġhur ricane", + "ĠKir by", + "ĠP uzzle", + "Ġsk irt", + "ou st", + "du llah", + "Ġanal ogy", + "in ion", + "Ġtomat oes", + "ĠN V", + "ĠPe ak", + "ĠMe yer", + "Ġappoint ments", + "Ġm asc", + "Ġal ley", + "re hend", + "Ġchar ities", + "Ġund o", + "Ġdest inations", + "ĠTest ing", + "\"> ", + "Ġdest ined", + "Ġimp lements", + "ĠHar old", + "RE CT", + "Ġoptim ization", + "Ġkilomet res", + "Ġc md", + "Ġimpair ment", + "Ġun successful", + "Ġswift ly", + "ĠGlas gow", + "art en", + "ĠSh ares", + "ĠAn swer", + "ĠAl bum", + "Ġnut ritional", + "ãĥ ĸ", + "ĠF ut", + "Ġbl oc", + "ĠN FC", + "Ġwholes ale", + "ĠC W", + "Ġneg lected", + "Ġlaun cher", + "Ġannounce ments", + "OU LD", + "com b", + "Ġrot ating", + "Ġrest s", + "ĠT icket", + "ched el", + "L ou", + "ĠV ic", + "Ġ\" '", + "Ġtem plates", + "Ġrepl aces", + "Ar c", + ":: ::", + "ĠGil bert", + "Ġillness es", + "Ġsched ules", + "Ġheter osexual", + "L INE", + "Ġhere in", + "Ġco erc", + "Ġdecre asing", + "Ġde portation", + "s udo", + "ĠInd igenous", + "Ġweigh s", + "Al ong", + "' );", + "ĠBeng als", + "70 7", + "Ġjoint s", + "ver ts", + "Ġ14 9", + "na ire", + "Ġsimpl est", + "Ġl ore", + "10 80", + "f iction", + "ĠDat abase", + "Ġreserv ation", + "Ġs ou", + "Ġsan ctuary", + "aud io", + "ap le", + "Ġveget arian", + "Ġanticip ation", + "m icro", + "Ġend uring", + "Ġdepart ed", + "Ġsidew alk", + "Ġprohib its", + "ĠF ont", + "Ġcomp ute", + "ĠS ect", + "Ġ15 8", + "B attle", + "Ġbom ber", + "Ġdist raction", + "Ġend ured", + "Ġpractition ers", + "Ġdistur bed", + "Ġdr ank", + "ord ered", + "Ġsurpr ises", + "se at", + "Sec urity", + "ĠW isdom", + "og o", + "Ġsub paragraph", + "ĠPen insula", + "ĠOrig ins", + "ire n", + "ĠP av", + "igg le", + "Ġgrat itude", + "ĠG ravity", + "over ty", + "im an", + "ct r", + "ĠCa esar", + "c ould", + "g em", + "Ġsk ies", + "Ġch amp", + "Ġagree ing", + "F amily", + "D iv", + "17 6", + "Ġmess y", + "um ption", + "F ederal", + "ern o", + "ĠCh at", + "Bey ond", + "Ġdev ote", + "ĠW alsh", + "Ġdump ed", + "Ġaccum ulation", + "st ad", + "hib ition", + "Ġsm okers", + "Ġinspect or", + "F rench", + "iss an", + "ĠV ita", + "Ġresearch ing", + "R AM", + "ĠCelt ics", + "Ġcl oak", + "ĠTer ra", + "M ary", + "so ld", + "ĠD OM", + "mod s", + "Int el", + "Ġmult itude", + "ĠImpro ved", + "Ġrel iance", + "Ġartif act", + "Ġalarm ing", + "P rom", + "h on", + "T ION", + "med ium", + "Ġref lex", + "ĠEx cel", + "Ġweaken ed", + "16 3", + "2 24", + "Ġcost umes", + "Ġunique ly", + "Ġs orrow", + "Ġm ansion", + "w p", + "Ġsal v", + "ĠGro ve", + "bs p", + "ĠSn iper", + "ĠSh ipping", + "ĠP OW", + "Ġund is", + "Ġbrand ing", + "G irl", + "ĠAh mad", + "ĠL akes", + "ĠCore y", + "Ġinherit ance", + "ener y", + "Ġpack ing", + "ĠP rest", + "D est", + "F W", + "Ġregul ator", + "l ocked", + "Ġcont ested", + "ĠMel issa", + "ĠD uc", + "Ġunpop ular", + "Ġst acked", + "Ġ19 17", + "Ġyear ly", + "Ġst are", + "Ġassess ing", + "à ¸", + "Ġbe verages", + "Ġcompet itions", + "Ġstreng thening", + "al ong", + "ĠL ud", + "Ġmel ted", + "stan bul", + "Ġb ounty", + "EN C", + "ĠL ands", + "Ġdecl ares", + "Ġcustom ize", + "Ġcomp osite", + "ãĥ ¬", + "C M", + "ograph ics", + "ĠTem p", + "Ġcont ender", + "Ġins ign", + "ĠL AN", + "Ġdis asters", + "ins pired", + "Ġjud gments", + "ustain able", + "urs ion", + "Ġvar iance", + "ĠUlt imately", + "Ġ --------", + "u ador", + "ĠR X", + "Ġmel ting", + "ĠExt ended", + "ĠT we", + "M ajor", + "ĠB il", + "Ġsy rup", + "qu ick", + "ĠHold er", + "Ġinnoc ence", + "U LE", + "ĠM ight", + "99 99", + "Ġf al", + "Ġcontinu ity", + "Ġ19 53", + "ĠB S", + "st ill", + "L at", + "ĠAb use", + "Ġun supported", + "xxxx xxxx", + "Ġinst itute", + "Ġfrag ment", + "ĠP ep", + "W estern", + "ĠC ause", + "ĠFr ag", + "ĠAr s", + "à ¥", + "ast ics", + "Ġb ishop", + "Ġcross es", + "Ġ15 4", + "ĠUp grade", + "Ġmit igate", + "ĠRay mond", + "Mod s", + "Ġtom ato", + "Ġst umbled", + "Ġdiff ers", + "In itial", + "ĠR aspberry", + "Ġign ores", + "Ġt ant", + "à ł", + "Ġrel ay", + "Ġb isexual", + "Ġconf ession", + "Ġd ement", + "in as", + "ĠHe ather", + "pl atform", + "dri ving", + "bour g", + "ĠM ush", + "Ġhy ster", + "Det ails", + "Ġdr ift", + "ĠW ald", + "ĠLuck ily", + "or f", + "Ġexp ire", + "ĠP unch", + "zy me", + "g old", + "Ġunp aid", + "ĠT rent", + "Ġun armed", + "Ġill icit", + "ĠT ottenham", + "Ġsm ash", + "Intern ational", + "ink er", + "Ġst ing", + "ĠSadd am", + "ĠAR T", + "Ġtruth s", + "b irth", + "Ġso ber", + "ĠN it", + "Ġ ib", + "Ġus able", + "Ġst acks", + "ĠSy lv", + "Ġnort heast", + "Ġdom ination", + "ĠM our", + "EN SE", + "ĠMe asure", + "Ġprogram mer", + "Ġ< -", + "18 2", + "ĠCond ition", + "Ġback yard", + "ir ling", + "ĠJ eb", + "ĠCre ed", + "ĠH ang", + "ĠCOM P", + "F ER", + "ĠIs h", + "Ġdetect ives", + "------------ ---", + "ĠMess enger", + "Ġlo oph", + "Ġgate way", + "15 1", + "ĠMaterial s", + "ĠD T", + "Ġdo omed", + "od o", + "Ġslic es", + "Ġemail ed", + "ĠPer l", + "Ġren ov", + "UT H", + "ody nam", + "ĠSouth west", + "get ic", + "ĠT PP", + "Ġoptim ism", + "ĠT ow", + "ul ators", + "prot ected", + "y les", + " «", + "Ġex ile", + "en v", + "P rop", + "ĠZimmer man", + "Ù İ", + "C a", + "om aly", + "ãĥ Ĩ", + "Ġrail road", + "L ee", + "23 2", + "Ġrepl icate", + "Ġcomfort ably", + "act ly", + "Ġr av", + "Ġtelesc ope", + "Ġhonest y", + "ĠPe pper", + "ĠBr ing", + "Ġric hest", + "Ġout doors", + "Ġh alls", + "Ġcont end", + "IS E", + "Ġsub mitting", + "Ġna ive", + "ar ations", + "Ġ14 3", + "Ġpo ised", + "respons ible", + "Ġsoc ks", + "ĠSk ull", + "Quest ion", + "Ġdiscover ies", + "Jo ined", + "ĠEn emies", + "ĠWire less", + "ĠRe venge", + "Ġpuzz les", + "Ġce ased", + "29 0", + "cript ions", + "ĠCon sole", + "Ġbo iling", + "Ġdisc rep", + "Ġded uction", + "Ġar senal", + "XX XX", + "ĠAm sterdam", + "rox imately", + "ĠSh ane", + "Ġpos ing", + "ĠACL U", + "ĠCompan ies", + "Ġthe ology", + "ĠU g", + "qu arter", + "ĠH ank", + "Co in", + "ĠL v", + "Ġalleg ation", + "ĠAv oid", + "Ġindef initely", + "Ġcommod ities", + "Ġbr ig", + "ĠMan it", + "Ġt enth", + "met hod", + "ĠKn icks", + "ĠâĢ İ", + "Ġinv oked", + "D ial", + "AR A", + "Ġc aucus", + "22 7", + "ĠJ ab", + "Ġoun ces", + "b ay", + "Ġbud dy", + "f an", + "23 4", + "ĠH il", + "ad h", + "ĠT Y", + "ĠIN D", + "Ġ19 39", + "Ġiter ation", + "ĠGonz alez", + "ĠV ert", + "ĠI O", + "em b", + "re ra", + "en ch", + "ĠRequ irements", + "ĠW ins", + "Ġlivest ock", + "h ours", + "\" âĢ¦", + "b ral", + "M arg", + "ĠD one", + "Ġwas ting", + "ing ed", + "g roups", + "Ġw ishing", + "ĠT umblr", + "Ġt apping", + "Ġnational ism", + "ĠB yr", + "Ġsqu ares", + "ĠAct ions", + "ãĥ ¥", + "In side", + "deb ug", + "Ġapp end", + "Ġstub born", + "ĠC ind", + "T ell", + "Ġt earing", + "ĠRe y", + "or c", + "ĠDay ton", + "ĠN H", + "ĠMad ness", + "Ch arl", + "ĠMor rison", + "fil ter", + "Ġacc use", + "Ġ. /", + "Ġtor rent", + "Ġdecl ines", + "g allery", + "M ine", + "Ġneg otiation", + "ĠBash ar", + "op ia", + "19 93", + "em ort", + "ĠNo vel", + "ĠF ang", + "ers ive", + "ĠInst ant", + "Ġroll er", + "A round", + "ĠElect ions", + "G ames", + "Ġin expensive", + "Ġwor s", + "Ġv ul", + "ĠH ole", + "Ġunbeliev able", + "Ġn ause", + "Ġent r", + "bo at", + "ĠST E", + "Ġbus h", + "ĠHass an", + "Ġw o", + "Ġpa used", + "ĠM ig", + "l ived", + "Ġsc out", + "Ġl ith", + "Pub lished", + "du ino", + "c ool", + "Ġcirc ulating", + "id as", + "ĠP am", + "viol ent", + "ĠCraw ford", + "udd le", + "ĠLet ters", + "Gu ard", + "mor ph", + "Ġwand ering", + "Ġsoph omore", + "Ġque er", + "ĠBl ind", + "r ue", + "ĠMar riage", + "D om", + "Ġpadd ing", + "Ġfold ers", + "Ġmeaning less", + "Ġcandid acy", + "af ort", + "Ġwhistle bl", + "ĠIdent ified", + "Ġcig ar", + "Ġh id", + "ĠDub ai", + "Ġpost ure", + "Ġh iking", + "ĠTermin al", + "Legend ary", + "ĠT P", + "ĠAT K", + "ĠStar bucks", + "ĠR iot", + "19 91", + "ĠBott om", + "e ffic", + "ĠEug ene", + "ĠWy oming", + "ĠRock y", + "Ġsal mon", + "Ġmet ro", + "Ġb ilateral", + "Ġcelebr ates", + "L ength", + "b illion", + "B at", + "Ġre leg", + "Ġpse udo", + "D T", + "ĠRh ode", + "P arent", + "ple tion", + "Ġatt ribut", + "Ġtun ing", + "ĠNOT E", + "ĠRe bel", + "ic us", + "F und", + "Ġcock tail", + "Ġ5 01", + "Ġsp oon", + "Ġbrut ality", + "Ġun ite", + "Ġmicro bi", + "ĠRe ich", + "pos itive", + "Ġam azed", + "ĠN T", + "D esc", + "ECT ION", + "Ġfalse ly", + "ĠHigh lander", + "ĠC rist", + "ĠVictor ian", + "Ġdistribut ions", + "the ir", + "ĠE instein", + "Ġp od", + "Ġepid em", + "Ġhe ap", + "ĠR anch", + "Ġan them", + "Ġre app", + "ĠAub urn", + "Ġconc urrent", + "ĠThrough out", + "ĠP OST", + "â ĺ", + "Ġhom emade", + "k ick", + "B eg", + "Ġch assis", + "c ounter", + "Ġmer ger", + "Ġl aps", + "2 17", + "un ion", + "ĠTr igger", + "Ġdeb ated", + "Ġsil ently", + "Ġrest raint", + "B al", + "0000 000", + "Ġform idable", + "ĠFil ip", + "Ġsacrific es", + "F ood", + "Ġdwar f", + "ĠSe qu", + "in ian", + "More over", + "Ġtang ible", + "ops is", + "ĠMine craft", + "ĠRegist ration", + "o an", + "Ġrepresent ations", + "Ġth irst", + "Ġcor p", + "ire ment", + "M ade", + "l oe", + "> \"", + "c ats", + "* .", + "Ġgest ures", + "gener al", + "Le ague", + "Ġpack ets", + "ĠInspect or", + "ĠBer g", + "Ġfraud ulent", + "Ġcritic ize", + "F un", + "Ġbl aming", + "nd ra", + "Ġsl ash", + "ĠE ston", + "Ġpropos ing", + "Ġwh ales", + "Ġtherap ist", + "Ġsub set", + "Ġle isure", + "EL D", + "ĠC VE", + "ĠAct ivity", + "Ġcul min", + "sh op", + "ĠD AY", + "is cher", + "ĠAdmir al", + "ĠAtt acks", + "Ġ19 58", + "Ġmem oir", + "Ġfold ed", + "Ġsex ist", + "Ġ15 3", + "ĠL I", + "Ġread ings", + "Ġembarrass ment", + "ĠEmploy ment", + "w art", + "ch in", + "Ġcontin uation", + "l ia", + "Rec ently", + "Ġd uel", + "Ġevac uation", + "ĠKash mir", + "Ġdis position", + "ĠR ig", + "Ġbol ts", + "Ġins urers", + "4 67", + "M ex", + "Ġret aliation", + "Ġmis ery", + "Ġunre asonable", + "r aining", + "I mm", + "ĠP U", + "em er", + "Ġgen ital", + "ãĤ ³", + "ĠC andy", + "Ġon ions", + "ĠP att", + "lin er", + "Ġconced ed", + "Ġf a", + "Ġfor c", + "ĠH ernandez", + "ĠGe off", + "deb ian", + "ĠTe ams", + "Ġc ries", + "Ġhome owners", + "23 7", + "A BC", + "Ġst itch", + "Ġstat istic", + "Ġhead ers", + "ĠBi ology", + "Ġmot ors", + "ĠG EN", + "ĠL ip", + "Ġh ates", + "Ġhe el", + "S elf", + "i pl", + "ED IT", + "ort ing", + "Ġann ot", + "ĠSpe ech", + "old emort", + "ĠJ avascript", + "ĠLe Bron", + "Ġfoot print", + "Ġf n", + "Ġseiz ures", + "n as", + "h ide", + "Ġ19 54", + "ĠBe e", + "ĠDecl aration", + "ĠKat ie", + "Ġreserv ations", + "N R", + "f emale", + "Ġsatur ated", + "Ġb iblical", + "Ġtroll s", + "Dev ice", + "ph otos", + "Ġdr ums", + "ãĥīãĥ© ãĤ´ãĥ³", + "N ight", + "f ighter", + "ĠH ak", + "ri ber", + "Ġc ush", + "Ġdiscipl inary", + "ba um", + "ĠG H", + "ĠSch midt", + "ilib rium", + "Ġs ixty", + "ĠKush ner", + "ro ts", + "Ġp und", + "ĠR ac", + "Ġspr ings", + "Ġcon ve", + "Bus iness", + "F all", + "Ġqual ifications", + "Ġvers es", + "Ġnarc iss", + "ĠK oh", + "ĠW ow", + "ĠCharl ottesville", + "ed o", + "Ġinterrog ation", + "ĠW ool", + "36 5", + "B rian", + "Ġâľ ĵ", + "Ġalleg es", + "ond s", + "id ation", + "ĠJack ie", + "y u", + "Ġl akes", + "Ġworth while", + "Ġcryst als", + "ĠJud a", + "Ġcomp rehend", + "Ġfl ush", + "Ġabsor ption", + "ĠO C", + "Ġfright ened", + "ĠCh ocolate", + "Mart in", + "Ġbu ys", + "Ġbu cks", + "Ġapp ell", + "ĠChampions hips", + "Ġlist ener", + "ĠDef ensive", + "Ġc z", + "ud s", + "ĠM ate", + "Ġre play", + "Ġdecor ated", + "Ġs unk", + "ĠV IP", + "ĠAn k", + "Ġ19 5", + "aa aa", + "Nob ody", + "ĠMil k", + "ĠG ur", + "ĠM k", + "ĠS ara", + "Ġse ating", + "ĠW id", + "Tr ack", + "Ġemploy s", + "Ġgig antic", + "AP P", + "ãĤ §", + "in ventory", + "Ġtow el", + "at che", + "l asting", + "ĠT L", + "Ġlat ency", + "Ġkn e", + "B er", + "me aning", + "Ġup held", + "Ġplay ground", + "Ġm ant", + "S ide", + "Ġstere o", + "Ġnorth west", + "Ġexception ally", + "Ġr ays", + "Ġrec urring", + "D rive", + "Ġup right", + "Ġab duct", + "ĠMar athon", + "Ġgood bye", + "Ġal phabet", + "h p", + "Ġcourt room", + "ring ton", + "ot hing", + "T ag", + "Ġdiplom ats", + "Ġbar bar", + "ĠAqu a", + "18 3", + "33 33", + "Ġmat urity", + "Ġinst ability", + "ĠAp ache", + "Ġ= ==", + "Ġfast ing", + "ĠGr id", + "Mod Loader", + "Ġ15 2", + "A bs", + "ĠOper ating", + "ett i", + "Ġacqu aint", + "Don nell", + "ĠK em", + "ĠFor ge", + "Ġarm ored", + "M il", + "Ġphilos ophers", + "in vest", + "Pl ayers", + "â Ī", + "Ġmy riad", + "Ġcomr ades", + "R ot", + "Ġremember ing", + "Ġcorrespond s", + "Ġprogram mers", + "ĠLyn n", + "Ġo lig", + "Ġco herent", + "yn chron", + "ĠChem ical", + "Ġj ugg", + "p air", + "post s", + "E ye", + "ĠIn ner", + "Ġsem ester", + "ott est", + "ĠEmir ates", + "ric anes", + "or ously", + "m its", + "ĠW is", + "Ġd odge", + "l ocation", + "Ġf aded", + "Am azon", + "ĠPro ceed", + "ĠIN FO", + "j ournal", + "ĠTru ck", + "T en", + "Ġ2 17", + "Ġstat utes", + "m obile", + "ĠT ypes", + "Rec omm", + "b uster", + "pe x", + "Ġleg ends", + "Ġhead ache", + "f aced", + "ĠWi Fi", + "if ty", + "ĠH ER", + "Ġcirc uits", + "ER ROR", + "22 6", + "ol in", + "Ġcyl inder", + "osp ace", + "ik ers", + "P rem", + "Qu ant", + "Ġconflic ting", + "Ġslight est", + "Ġfor ged", + "ion age", + "Step hen", + "ĠK ub", + "ĠOpp ortun", + "ĠHe al", + "Ġbl o", + "Ġrul ers", + "Ġh uh", + "Ġsubmar ine", + "f y", + "ass er", + "Ġallow ance", + "ĠKas ich", + "ĠT as", + "ĠAustral ians", + "Forge ModLoader", + "ĠâĨ ij", + "ĠMat rix", + "am ins", + "Ġ12 00", + "ĠAc qu", + "23 6", + "D ocument", + "ĠBre aking", + "19 3", + "ĠSub st", + "ĠRoll er", + "ĠPro perties", + "ĠN I", + "t ier", + "Ġcr ushing", + "Ġadvoc ating", + "Further more", + "keep ers", + "Ġsex ism", + "x d", + "Ġcall er", + "ĠS ense", + "chie ve", + "ĠT F", + "Ġfuel ed", + "Ġreminis cent", + "Ġobs ess", + "ur st", + "Ġup hold", + "ĠF ans", + "het ics", + "Ġâ Ĺ", + "ĠB ath", + "Ġbe verage", + "Ġo scill", + "25 4", + "Ġpol es", + "Ġgrad ual", + "Ġex ting", + "ĠS uff", + "ĠS uddenly", + "Ġlik ing", + "Ġ19 49", + "un ciation", + "am ination", + "ĠO mar", + "ĠL V", + "ĠCon sequently", + "Ġsynt hes", + "ĠG IF", + "Ġp ains", + "Ġinteract ing", + "u ously", + "inc re", + "Ġrum or", + "ĠScient ology", + "19 7", + "ĠZ ig", + "Ġspe lling", + "ĠA SS", + "Ġexting u", + "ms on", + "Ġg h", + "Ġremark ed", + "ĠStrateg ic", + "ĠM ON", + "å ¥", + "g ae", + "ĠWH AT", + "E ric", + "ĠCamp us", + "Ġmeth ane", + "Ġimag in", + "J UST", + "ĠAl m", + "X T", + "i q", + "ĠR SS", + "Ġwrong doing", + "att a", + "Ġbig ot", + "Ġdemonstr ators", + "ĠCal vin", + "ĠV illa", + "Ġmembr ane", + "ĠAw esome", + "Ġbenef ic", + "26 8", + "Ġmagn ificent", + "ĠL ots", + "G reg", + "ĠBor is", + "Ġdetain ees", + "ĠH erman", + "Ġwhis pered", + "Ġa we", + "Prof essor", + "fund ing", + "Ġphys iological", + "ĠDest ruction", + "Ġlim b", + "Ġmanip ulated", + "Ġbub bles", + "Ġpse ud", + "Ġhyd ra", + "ĠBrist ol", + "Ġst ellar", + "ĠExp ansion", + "ĠK ell", + "ĠInterest ingly", + "Ġm ans", + "Ġdrag ging", + "Ġec ological", + "ĠF it", + "Ġg ent", + "Ġbenef ited", + "ĠHait i", + "Ġpoly g", + "ãĥ İ", + "Ġ20 30", + "Ġpro w", + "Ġrecon struction", + "Ġwas t", + "Ġpsych ic", + "ĠGree ks", + "Hand ler", + "16 2", + "ĠP ulse", + "Ġsol icit", + "Ġsy s", + "Ġinflu x", + "ĠG entle", + "per cent", + "Ġprolifer ation", + "Ġtax able", + "Ġdisreg ard", + "Ġesc aping", + "Ġg inger", + "Ġwith stand", + "Ġdevast ated", + "ĠD ew", + "ser ies", + "Ġinject ed", + "ela ide", + "Ġturn over", + "he at", + "Ļ Ĥ", + "H appy", + "ĠSil ent", + "ãĤ Ń", + "iv ism", + "Ġir rational", + "AM A", + "Ġre ef", + "r ub", + "Ġ16 2", + "Ġbank ers", + "ĠEth ics", + "v v", + "Ġcritic isms", + "K n", + "18 6", + "M ovie", + "ĠT ories", + "Ġno od", + "Ġdist ortion", + "F alse", + "od ore", + "Ġt asty", + "Res earch", + "ĠU ID", + "- )", + "Ġdivor ced", + "ĠM U", + "ĠHay es", + "ĠIs n", + "ian i", + "ĠH Q", + "Ġ\" #", + "ign ant", + "Ġtra umatic", + "ĠL ing", + "H un", + "Ġsab ot", + "on line", + "r andom", + "Ġren amed", + "ra red", + "K A", + "d ead", + "é t", + "ĠAss istance", + "Ġse af", + "++++ ++++", + "Ġse ldom", + "ĠWeb b", + "Ġbo olean", + "u let", + "Ġref rain", + "ĠDI Y", + "ru le", + "Ġshut ting", + "Ġutil izing", + "load ing", + "ĠPar am", + "co al", + "oot er", + "Ġattract ing", + "ĠD ol", + "Ġher s", + "ag netic", + "ĠRe ach", + "im o", + "Ġdisc arded", + "ĠP ip", + "01 5", + "ü r", + "Ġm ug", + "Im agine", + "C OL", + "Ġcurs ed", + "ĠSh ows", + "ĠCurt is", + "ĠSach s", + "spe aking", + "ĠV ista", + "ĠFram ework", + "ong o", + "Ġsub reddit", + "Ġcr us", + "ĠO val", + "R ow", + "g rowing", + "Ġinstall ment", + "Ġgl ac", + "ĠAdv ance", + "EC K", + "ĠLGBT Q", + "LE Y", + "Ġac et", + "Ġsuccess ive", + "ĠNic ole", + "Ġ19 57", + "Qu ote", + "Ġcircumst ance", + "ack ets", + "Ġ14 2", + "ort ium", + "Ġguess ed", + "ĠFr ame", + "Ġperpet rators", + "ĠAv iation", + "ĠBen ch", + "Ġhand c", + "A p", + "Ġ19 56", + "25 9", + "r and", + "Net Message", + "d in", + "urt les", + "h ig", + "ĠV III", + "ff iti", + "ĠSw ords", + "b ial", + "Ġkidn apping", + "dev ice", + "Ġb arn", + "ĠEl i", + "auc as", + "S end", + "Con structed", + "Ġ ½", + "Ġneed les", + "Ġad vertisements", + "Ġv ou", + "Ġexhib ited", + "ĠFort ress", + "As k", + "B erry", + "TY PE", + "Ġcan cers", + "ump ing", + "ĠTerrit ory", + "Ġpr ud", + "Ġn as", + "Ġathe ist", + "Ġbal ances", + "ãģ Ł", + "ĠSh awn", + "& &", + "Ġland sc", + "ĠR GB", + "Ġpet ty", + "Ġex cellence", + "Ġtransl ations", + "Ġpar cel", + "ĠChe v", + "E ast", + "ĠOut put", + "im i", + "Ġamb ient", + "ĠTh reat", + "Ġvill ains", + "Ġ5 50", + "IC A", + "Ġtall er", + "Ġle aking", + "c up", + "Ġpol ish", + "Ġinfect ious", + "ĠK C", + "Ġ@ @", + "back ground", + "Ġbureaucr acy", + "ĠS ai", + "un less", + "it ious", + "ĠSky pe", + "At l", + "ID ENT", + "00 8", + "Ġhyp ocr", + "Ġpit chers", + "Ġguess ing", + "ĠF INAL", + "Bet ween", + "Ġvill agers", + "Ġ25 2", + "f ashion", + "ĠTun is", + "Be h", + "ĠEx c", + "ĠM ID", + "28 8", + "ĠHas kell", + "19 6", + "ĠN OR", + "Ġspec s", + "Ġinv ari", + "Ġgl ut", + "ĠC ars", + "Ġimp ulse", + "Ġhon ors", + "g el", + "Ġjurisd ictions", + "ĠBund le", + "ul as", + "Calif ornia", + "ĠIncre ase", + "Ġp ear", + "Ġsing les", + "Ġc ues", + "Ġunder went", + "ĠW S", + "Ġexagger ated", + "Ġdub ious", + "Ġfl ashing", + "L OG", + ") ].", + "J ournal", + "t g", + "V an", + "ĠI stanbul", + "ĠIn sp", + "ĠFrank en", + "D raw", + "Ġsad ness", + "Ġiron ic", + "ĠF ry", + "x c", + "Ġ16 4", + "is ch", + "W ay", + "ĠProtest ant", + "h orn", + "Ġun aff", + "ĠV iv", + "ill as", + "ĠProduct ions", + "ĠH ogan", + "Ġper imeter", + "ĠS isters", + "Ġspont aneous", + "Ġdown side", + "Ġdescend ants", + "Ġor n", + "w orm", + "Japan ese", + "Ġ19 55", + "Ġ15 1", + "ĠDo ing", + "els en", + "umb les", + "Ġrad ically", + "ĠDr um", + "ĠB ach", + "Ġli abilities", + "ĠO B", + "ĠElement ary", + "Ġmem e", + "yn es", + "Ġfinger print", + "ĠGr ab", + "Ġundert ake", + "Mem bers", + "ĠRead er", + "ĠSim s", + "g od", + "Ġhypot hetical", + "s cient", + "ĠA J", + "Ġchar ism", + "Ġad missions", + "ĠMiss ile", + "tr ade", + "Ġexerc ising", + "ĠBack ground", + "W ritten", + "Ġvoc als", + "whe ther", + "Ġv i", + "ĠW inner", + "Ġl itter", + "ĠSh ooting", + "ST EM", + "ãĤ ¡", + "ĠA FL", + "Ġvari ability", + "Ġe ats", + "ĠD PS", + "b row", + "Ġeleph ants", + "Ġstr at", + "Ġ Å", + "Ġsett lers", + "Matt hew", + "Ġin advert", + "H I", + "ĠIM F", + "ĠGo al", + "Ġnerv es", + "John son", + "ey e", + "ablish ment", + "Th ursday", + "BIL ITY", + "H ad", + "am oto", + "het amine", + "ep s", + "Ġmit ochond", + "Ġcomp ressed", + "ĠTre vor", + "ĠAnim als", + "T ool", + "L ock", + "Ġtwe ak", + "Ġpin ch", + "Ġcancell ation", + "P ot", + "Ġfoc al", + "ĠAst ron", + "17 3", + "ĠA SC", + "ĠO THER", + "umn i", + "Ġdem ise", + "d l", + "Ù ħ", + "Sem itism", + "Ġcr acking", + "Ġcollabor ative", + "Ġexpl ores", + "s ql", + "Ġher bs", + "Ġconfig urations", + "m is", + "ĠRes ult", + "ace y", + "ĠSm oke", + "Ġsan ct", + "el ia", + "Ġdeg ener", + "Ġdeep est", + "Ġscream ed", + "Ġn ap", + "Soft ware", + "ĠST AR", + "E F", + "ĠX in", + "spons ored", + "mans hip", + "23 3", + "Ġprim aries", + "Ġfilter ing", + "Ġas semble", + "m il", + "ĠMy ers", + "b ows", + "Ġpun ched", + "M ic", + "Ġinnov ations", + "Ġfun c", + "and o", + "Ġfr acking", + "ĠV ul", + "о Ð", + "osh op", + "ĠIm mun", + "Ġsett ling", + "Ġadolesc ents", + "Ġreb uilding", + "Ġtransform ing", + "Ġpar ole", + "Ġhar bor", + "Ġbook ing", + "ot ional", + "onge vity", + "ĠY o", + "b ug", + "Ġemer ges", + "ĠMethod s", + "ĠCh u", + "P res", + "ĠDun geons", + "Ġtra iling", + "ĠR um", + "ĠH ugh", + "å¤ ©", + "ĠE ra", + "ĠBatt les", + "Res ults", + "ĠTr ading", + "Ġvers a", + "c ss", + "ax ies", + "he et", + "Ġgre ed", + "19 89", + "Ġgard ens", + "Ġconting ent", + "P ark", + "ĠLeaf s", + "h ook", + "ro be", + "Ġdiplom acy", + "ĠF uel", + "ĠInv asion", + "Ġupgr ading", + "M ale", + "Ġe lic", + "Ġrelent less", + "ĠCo venant", + "ap esh", + "ĠT rop", + "T y", + "pro duction", + "art y", + "Ġpun ches", + "ak o", + "cyclop edia", + "ĠR abbit", + "ĠHD MI", + "Ġ14 1", + "Ġf oil", + "Item Image", + "ĠF G", + "Ġimplement ations", + "ĠP om", + "ixt ures", + "Ġaw ait", + "Ġ3 30", + "am us", + "Ġumb rella", + "Ġfore see", + "se par", + "Ġcircum cision", + "Ġperipher al", + "S ay", + "ĠExper t", + "In c", + "Ġwithd rew", + "ĠAnd ers", + "f ried", + "Ġradio active", + "ĠOp ening", + "Ġboard ing", + "ĠN D", + "Ġover throw", + "Act iv", + "W P", + "ĠAct s", + "× Ļ", + "Ġmot ions", + "v ic", + "ĠM ighty", + "ĠDef ender", + "a er", + "Ġthank ful", + "ĠK illing", + "ĠBr is", + "mo il", + "Ġpredict ing", + "26 6", + "ch oice", + "Ġkill ers", + "Ġinc ub", + "ĠChe st", + "ather ing", + "Ġpro claimed", + "fl ower", + "oss om", + "umbled ore", + "ĠCy cling", + "ĠOccup y", + "AG ES", + "P en", + "ĠY ug", + "Ġpack aged", + "Ġheight ened", + "c ot", + "st ack", + "C ond", + "Ġst amps", + "m age", + "Ġpersu aded", + "Ġens l", + "ĠCard inal", + "Ġsol itary", + "Ġpossess ing", + "ĠC ork", + "Ġev id", + "ĠT ay", + "Ġbl ues", + "Ġextrem ism", + "Ġlun ar", + "Ġcl own", + "Te chn", + "Ġfest ivals", + "ĠPv P", + "ĠL ar", + "Ġconsequ ently", + "p resent", + "Ġsom eday", + "ç İĭ", + "ĠMet eor", + "Ġtour ing", + "c ulture", + "Ġbe aches", + "S hip", + "c ause", + "ĠFl ood", + "ãĥ ¯", + "Ġpur ity", + "th ose", + "Ġem ission", + "b olt", + "Ġch ord", + "ĠScript ure", + "L u", + "Ġ$ {", + "cre ated", + "Other s", + "25 8", + "Ġelement al", + "Ġannoy ed", + "ĠA E", + "d an", + "ĠS ag", + "Res earchers", + "Ġfair y", + "âĢĵ âĢĵ", + "======== ====", + "Sm art", + "GG GG", + "Ġskelet ons", + "Ġpup ils", + "link ed", + "Ġur gency", + "en abled", + "ĠF uck", + "Ġcoun cill", + "r ab", + "U AL", + "T I", + "Ġlif es", + "Ġconf essed", + "B ug", + "Ġharm on", + "ĠCON FIG", + "ĠNe utral", + "D ouble", + "Ġst aple", + "ĠSH A", + "Brit ish", + "ĠSN P", + "AT OR", + "oc o", + "Ġswing ing", + "ge x", + "ole on", + "pl ain", + "ĠMiss ing", + "ĠTro phy", + "v ari", + "ran ch", + "Ġ3 01", + "4 40", + "00000000 00000000", + "Ġrest oring", + "Ġha ul", + "uc ing", + "ner g", + "Ġfut ures", + "Ġstrateg ist", + "quest ion", + "Ġlater al", + "ĠB ard", + "Ġs or", + "ĠRhod es", + "ĠD owntown", + "????? -", + "ĠL it", + "ĠB ened", + "Ġco il", + "st reet", + "ĠPort al", + "FI LE", + "ĠG ru", + "* ,", + "23 1", + "ne um", + "Ġsuck ed", + "Ġr apper", + "Ġtend encies", + "ĠLaure n", + "cell aneous", + "26 7", + "Ġbrow se", + "Ġover c", + "head er", + "o ise", + "Ġbe et", + "ĠG le", + "St ay", + "Ġm um", + "Ġtyp ed", + "Ġdiscount s", + "T alk", + "ĠO g", + "ex isting", + "ĠS ell", + "u ph", + "C I", + "ĠAust rian", + "ĠW arm", + "Ġdismiss al", + "Ġaver ages", + "c amera", + "Ġalleg iance", + "L AN", + "=\" #", + "Ġcomment ators", + "ĠSet ting", + "ĠMid west", + "Ġpharm ac", + "ĠEX P", + "Ġstain less", + "Ch icago", + "Ġt an", + "24 4", + "Ġcountry side", + "ĠV ac", + "29 5", + "Ġpin ned", + "Ġcr ises", + "Ġstandard ized", + "T ask", + "ĠJ ail", + "ĠD ocker", + "col ored", + "f orth", + "\" },", + "Ġpat rons", + "Ġsp ice", + "Ġm ourn", + "ĠM ood", + "Ġlaund ry", + "Ġequ ip", + "ĠM ole", + "y ll", + "ĠTH C", + "n ation", + "ĠSher lock", + "Ġiss u", + "ĠK re", + "ĠAmeric as", + "ĠA AA", + "Ġsystem atically", + "Ġcont ra", + "ĠS ally", + "Ġrational e", + "Ġcar riage", + "Ġpe aks", + "Ġcontrad iction", + "ens ation", + "ĠFail ure", + "Ġpro ps", + "Ġnames pace", + "Ġc ove", + "field s", + "ãĤ ĭ", + "Ġw ool", + "ĠC atch", + "Ġpresum ed", + "ĠD iana", + "r agon", + "ig i", + "Ġh amm", + "Ġst unt", + "ĠG UI", + "ĠObserv atory", + "ĠSh ore", + "Ġsmell s", + "ann ah", + "Ġcock pit", + "ĠD uterte", + "8 50", + "Ġopp ressed", + "bre aker", + "ĠCont ribut", + "ĠPer u", + "ĠMons anto", + "ĠAtt empt", + "Ġcommand ing", + "Ġfr idge", + "ĠR in", + "ĠChe ss", + "ual ity", + "Ġo l", + "Republic an", + "ĠGl ory", + "ĠW IN", + ".... ...", + "ag ent", + "read ing", + "Ġin h", + "J ones", + "Ġcl icks", + "al an", + "Ġ[ ];", + "ĠMaj esty", + "ĠC ed", + "op us", + "ate l", + "à ª", + "AR C", + "ĠEc uador", + "ãĥ ł", + "ĠK uro", + "Ġritual s", + "Ġcapt ive", + "Ġoun ce", + "Ġdisag reement", + "Ġsl og", + "f uel", + "P et", + "M ail", + "Ġexerc ised", + "Ġsol ic", + "Ġrain fall", + "Ġdev otion", + "ĠAss essment", + "Ġrob otic", + "opt ions", + "ĠR P", + "ĠFam ilies", + "ĠFl ames", + "Ġassign ments", + "00 7", + "aked own", + "Ġvoc abulary", + "Re illy", + "Ġc aval", + "g ars", + "Ġsupp ressed", + "ĠS ET", + "ĠJohn s", + "Ġwar p", + "bro ken", + "Ġstat ues", + "Ġadvoc ated", + "Ġ2 75", + "Ġper il", + "om orph", + "ĠF emin", + "per fect", + "Ġh atch", + "L ib", + "5 12", + "Ġlif elong", + "3 13", + "Ġche eks", + "Ġnum bered", + "ĠM ug", + "B ody", + "ra vel", + "We ight", + "ĠJ ak", + "ĠHe ath", + "Ġkiss ing", + "ĠJ UST", + "Ġw aving", + "u pload", + "Ġins ider", + "ĠPro gressive", + "ĠFil ter", + "tt a", + "ĠBe am", + "Ġviol ently", + "ip ation", + "Ġskept icism", + "Ġ19 18", + "ĠAnn ie", + "ĠS I", + "Ġgen etics", + "Ġon board", + "at l", + "ĠFried man", + "ĠB ri", + "cept ive", + "Ġpir ate", + "ĠRep orter", + "27 8", + "Ġmyth ology", + "Ġe clipse", + "Ġsk ins", + "Ġgly ph", + "ing ham", + "F iles", + "C our", + "w omen", + "Ġreg imes", + "Ġphotograp hed", + "K at", + "ĠMA X", + "Offic ials", + "Ġunexpected ly", + "Ġimpress ions", + "F ront", + ";;;; ;;;;", + "Ġsuprem acy", + "Ġs ang", + "Ġaggrav ated", + "Ġabrupt ly", + "ĠS ector", + "Ġexc uses", + "Ġcost ing", + "ide press", + "St ack", + "ĠR NA", + "ob il", + "Ġghost s", + "ld on", + "at ibility", + "Top ics", + "Ġreim burse", + "ĠH M", + "ĠDe g", + "Ġth ief", + "y et", + "ogen esis", + "le aning", + "ĠK ol", + "ĠB asketball", + "Ġf i", + "ĠSee ing", + "Ġrecy cling", + "Ġ[ -", + "Cong ress", + "Ġlect ures", + "P sy", + "Ġne p", + "Ġm aid", + "Ġori ented", + "A X", + "Ġrespect ful", + "re ne", + "fl ush", + "ĠUn loaded", + "re quest", + "gr id", + "ĠAltern atively", + "ĠHug o", + "Ġdec ree", + "ĠBuddh ism", + "and um", + "And roid", + "ĠCong o", + "ĠJoy ce", + "Ġacknowled ging", + "hes ive", + "ĠTom orrow", + "ĠH iro", + "th ren", + "ĠM aced", + "Ġho ax", + "ĠIncre ased", + "ĠPr adesh", + "W ild", + "____ __", + "16 1", + "Ġa unt", + "Ġdistribut ing", + "ĠT ucker", + "ĠSS L", + "ĠW olves", + "B uilding", + "ou lt", + "ĠLu o", + "ĠY as", + "ĠSp ir", + "ĠSh ape", + "ĠCamb od", + "ĠIP v", + "Ġm l", + "Ġext rad", + "39 0", + "ĠPenn y", + "d ream", + "Ġstation ed", + "opt ional", + "ew orthy", + ". ", + "Ġundert aking", + "Ġchick ens", + "Ġstimul i", + "ĠEl se", + "ig ators", + "ĠBegin ning", + "ct ory", + "Ġprep ares", + "Ġdel ta", + "Ġvic inity", + "t ool", + "Ġworks hops", + "M Hz", + "Ġaccus ation", + "Ġhist ories", + "rop olis", + "ĠChurch ill", + "Ġne on", + "Ġb aff", + "d ies", + "may be", + "Ġè£ı è¦ļéĨĴ", + "Ġsympt om", + "EC H", + "ĠMan uel", + "Ġban ana", + "ĠH B", + "Ġ ****", + "ĠKore ans", + "c oll", + "F B", + "Ġpr aying", + "ĠCann ot", + "ĠM ile", + "Ġembr acing", + "ĠSil k", + "39 3", + "ot ers", + "F D", + "Ġday light", + "al ias", + "ĠBrig ade", + "ĠHann ah", + "Ġcler gy", + "Ġs outheast", + "Ġalcohol ic", + "Ġpropos es", + "liv ion", + "Ġcalcul ating", + "Ġstim ulate", + "Ġspl itting", + "e ight", + "ĠInd y", + "pl ays", + "ĠP ik", + "Ġdom est", + "Ġforg iveness", + "ĠR ings", + "pat ient", + "kins on", + "M ont", + "ig ible", + "; \"", + "Ġperiod ically", + "amm ad", + "ĠBr itt", + "p ard", + "Ġarbit ration", + "ĠSchne ider", + "ĠCorpor ate", + "ĠMay a", + "Ġsn akes", + "a um", + "Ġbl asted", + "Ġmyster ies", + "Ġrev ive", + "oc amp", + "ĠD odge", + "ĠOper a", + "27 9", + "Ġor phan", + "Ġspec ifies", + "ĠM ets", + "D uration", + "H en", + "Ġfire works", + "Ġprosec ute", + "ĠTill erson", + "d p", + "us age", + "l iness", + "ĠDeb ian", + "Ġ2 24", + "ris es", + "ĠIn fect", + "at ra", + "ĠR R", + "ĠL or", + "d iff", + "ĠCharl eston", + "Ġac oustic", + "Ġam use", + "3 30", + "Ġc er", + "ĠT ac", + "Ġ[ +", + "Ġcard iac", + "ĠRestaur ant", + "er gy", + "Ġf uzz", + "Ġbit es", + "Ġhazard ous", + "Ġbr ighter", + "r ans", + "ĠStephan ie", + "ext ra", + "RE T", + "ĠChrist ine", + "ĠS ue", + "stat ement", + "Ġbol ster", + "Ġant it", + "Rad io", + "B IT", + "ãĤ °", + "Ġvis ions", + "ĠCon cept", + "Ġin line", + "ĠPhilos ophy", + "is ans", + "ĠIr ving", + "à £", + "t aking", + "Ġincons ist", + "ĠKum ar", + "Ġl ig", + "ĠSch umer", + "ĠReg ulations", + "ĠH z", + "th ro", + "ĠV oldemort", + "ĠM ED", + "ĠFreder ick", + "P ad", + "22 1", + "Ġalleg ing", + "ĠCommun ication", + "Ġ16 7", + "Ġforecast s", + "Ġsp iders", + "Or gan", + "ĠParticip ants", + "ĠO ps", + "des ign", + "Cl ose", + "Ġfact o", + "Ġbom bers", + "res istant", + "ateg ories", + "S chool", + "Ġhom ework", + "Ġcor ro", + "T uesday", + "ĠBrend an", + "ĠM X", + "ĠT S", + "ĠSt ri", + "Ġstake holders", + "ĠMillenn ium", + "Ġtransfer ring", + "J ud", + "Ġt ac", + "Ġ16 00", + "ĠSD K", + "r b", + "Ġinterpret ations", + "ĠS G", + "Ġup stairs", + "ĠHar vest", + "Ġvag ina", + "Ġing est", + "x f", + "ĠOr ion", + "ĠJoe y", + "Ġsand wic", + "Ġimm ortal", + "Ġfl ipped", + "ort ex", + "threat ening", + "Ġsn iper", + "Ġconver ts", + "Ġinstall ations", + "ĠBul gar", + "ors che", + "m ails", + "Ġl ure", + "Ġnarrow ly", + "Ġgren ade", + "ĠG ing", + "Ġunder wear", + "------------ --", + "Ġch ased", + "ĠV AL", + "Ġparent ing", + "ĠH amb", + "ĠBl az", + "Ġanarch ist", + "ĠMed ian", + "ĠProgram s", + "Î ½", + "Ġob j", + "ĠN okia", + "orm an", + "an qu", + "at ism", + "op a", + "Ġfulf illing", + "Ġpupp y", + "Ġent it", + "ĠSebast ian", + "Ġshoot ers", + "Ġric her", + "è ¡", + "Ġtempt ed", + "ĠAT T", + "ĠC V", + "Ġto re", + "Res ource", + "ĠDevil s", + "40 8", + "in ational", + "Ġass urance", + "ĠDar ren", + "Ġwh ichever", + "pos ure", + "Ġf ury", + "St ock", + "Ġunivers ally", + "resp onse", + "Ġo ak", + "Ġwork load", + "ĠCor ner", + "ee le", + "\" ...", + "Ġdepri ved", + "k owski", + "Ġcast s", + "Ġaffili ation", + "ĠA ch", + "ĠAs ked", + "at he", + "Ġl act", + "ĠTh u", + "r m", + "Ġair lines", + "Ġnot ions", + "Form at", + "ĠF AA", + "ãĥ Ĭ", + "dri ver", + "Ġtrans cend", + "S ettings", + "ĠPro secut", + "Ġsp inal", + "Ġdefault s", + "F K", + "Ġpref ers", + "rend ered", + "th us", + "fil m", + "Ġt iger", + "ĠSp icer", + "rec ogn", + "ĠRug by", + "Net work", + "Ġp ity", + "Ġcomp artment", + "c asters", + "ĠMon roe", + "Ġ7 20", + "Ġcorrect ions", + "Ġdop amine", + "ĠA Z", + "C ut", + "Ġro omm", + "Ġspec ulate", + "H ash", + "Ġrestrict ive", + "11 11", + "red ible", + "on el", + "Ġramp ant", + "re ported", + "ĠSu ite", + "ĠMin imum", + "al ys", + "az ard", + "lo op", + "Ġl ent", + "sh a", + "Ġv andal", + "men u", + "ĠBoe hner", + "Ġnarr atives", + "Ġauthent icity", + "26 9", + "an ic", + "d uty", + "28 5", + "Ġthank ed", + "Ġbetray ed", + "l ift", + "Ġsouth west", + "ĠDex ter", + "ĠB od", + "Ġkey words", + "A verage", + "D IS", + "Ġethnic ity", + "! ),", + "ĠNational s", + "á ¹", + "ĠT ah", + "iox id", + "Ġwid get", + "Ġpast a", + "Ġbill ing", + "Ġtr ilogy", + "ĠL ines", + "Ġsn iff", + "Ġnep hew", + "L ate", + "Ġprinc ip", + "ĠLo op", + "ĠMarx ist", + "Ġdiss olved", + "Ġcontext s", + "ĠAm ount", + "ĠSp ike", + "Ġtot als", + "Ġorgan izer", + "Ġup rising", + "s hips", + "Y Y", + "ĠNort heast", + "m oney", + "grad ation", + "Ġgoal keeper", + "ĠH ear", + "Ġste ak", + "ĠBuzz Feed", + "Ġsole mn", + "ĠSc and", + "Ġpo pping", + "Ġad here", + "ĠAl leg", + "by te", + "ĠW olver", + "Ġun in", + "Ġrec ol", + "it ud", + "Ġmim ic", + "ib us", + "Ġpredict s", + "ĠKee per", + "i ating", + "Ġde ception", + "Ġlear nt", + "Ġdi ary", + "Ġcond itional", + "Ġre lic", + "Ġinv oke", + "ien ced", + "å Ī", + "ĠP ont", + "Ġcell phone", + "Ġspeed ing", + "Ġtack ling", + "Ġn ude", + "op ened", + "ĠMan afort", + "Ġ19 52", + "Ġmaj ors", + "ĠSil ence", + "Ġlog istics", + "Ġweight ed", + "ĠPsych iat", + "\": [\"", + "Ġsick ness", + "Ġdivid ends", + "z on", + "Re lease", + "ĠKe ys", + "ĠI ch", + "Ġen z", + "ĠF ernand", + "ĠÎ ±", + "Ġmean ings", + "Ġp enny", + "Ġst ern", + "Ġl ar", + "ĠPub lished", + "Ġback drop", + "K im", + "ĠSy nt", + "Ġdeb uted", + "w m", + "ĠIs le", + "Ġregul ating", + "ott i", + "ĠSch olars", + "ices ter", + "ĠChe f", + "Ġpop s", + "ĠLaun cher", + "ĠVar ious", + "Ġcomment ing", + "os lav", + "enz ie", + "Ġrival ry", + "â Ĥ¬", + "Re ally", + "Ġor c", + "Ġbe an", + "ĠJud y", + "Not ice", + "ĠB ike", + "? ]", + "Ġrent ed", + "st en", + "Ġfore front", + "ĠBald win", + "Ġyield ed", + "t ails", + "Pr ime", + "ĠS ources", + "ic ator", + "Se an", + "Ġmarch ing", + "Out put", + "ĠJ ungle", + "Ġres ide", + "zz le", + "ĠAndrew s", + "Ġtor que", + "Bas ic", + "Act ually", + "st rap", + "p enter", + "Ġexam s", + "ĠY a", + "Ġ15 9", + "ĠDec ision", + "Ġr ansom", + "ete enth", + "ens ing", + "2 13", + "Ġsun set", + "40 4", + "ĠRap id", + "ĠHe in", + "ĠAb original", + "Ġorgan ism", + "ĠS ever", + "Ġcl a", + "aj i", + "Sim ple", + "ĠFl avor", + "ĠE val", + "pr us", + "Ġch orus", + "D AY", + "Ġden ounced", + "Ġbi ography", + "ĠTurn bull", + "Rec ent", + "N ormal", + "lect ions", + "W ord", + "Ġf erry", + "ĠWag ner", + "h om", + "Un it", + "Ġsuper market", + "ĠS ith", + "Ġnomine es", + "Ġdictators hip", + "idd ler", + "Ġannoun ces", + "ĠThe m", + "ĠNept une", + "Ġde ity", + "ĠY i", + "Ġmon arch", + "AR R", + "Ġinv aded", + "ĠH ok", + "unt ary", + "C ertain", + "eg a", + "Ġk idding", + "ĠReg ulation", + "Ġtr ay", + "Ġphotograp hers", + "ĠArc ane", + "Ġdis charged", + "Ġevangel ical", + "Ġinter change", + "Ġfilm maker", + "ĠEnd less", + "Ġ29 0", + "ĠSalv ador", + "AS Y", + "ĠSign al", + "Ġwr ath", + "â ľ", + "l ot", + "' /", + "Ġproject ile", + "Ġemploy ing", + "ĠInter face", + "19 1", + "atell ite", + "ĠR ath", + "pack age", + "Ġindic ations", + "J ason", + "Ġarg s", + "ĠG Hz", + "Ġt ilt", + "n ants", + "w on", + "ãĤ µ", + "red d", + "res cent", + "ĠCal endar", + "Ġmod ular", + "Ġassist ing", + "Ġred eem", + "ĠBe an", + "Ġwor sh", + "Ġdecentral ized", + ") ...", + "37 7", + "Ġarr ays", + "Ġaccomplish ments", + "Î ¿", + "d ot", + "Ġmut ually", + "Ġob struct", + "Ġmis represent", + "ore st", + "ion ic", + "ru ce", + "% ;", + "Ġknow ingly", + "port ing", + "in ently", + "A ri", + "ĠSch ultz", + "D a", + "ĠC ere", + "Ġob solete", + "ħ ĭ", + "g ive", + "Ġb ait", + "Ġen larg", + "Ne ill", + "Ġ19 33", + "Ġrecons ider", + "ĠSerge ant", + "ĠDian e", + "ĠC ogn", + "ĠI con", + "P osition", + "Ġf ost", + "Ġstir ring", + "se ven", + "ĠSpace X", + "ugg ets", + "Ġmed d", + "G al", + "ĠS ister", + "B oy", + "Ġtrigger ing", + "T aking", + "Ġscream s", + "Ġca usal", + "Ġaw aken", + "Ar m", + "29 7", + "Ġdisp atched", + "ĠF ALSE", + "Ġorgan izational", + "ĠT ong", + "Ġdile mma", + "d emon", + "S pl", + "Ġhook s", + "ud ing", + "Ġvalid ate", + "Ġpot ion", + "Ġcl aw", + "Ġburg l", + "Ġqu ir", + "AC A", + "ĠBren nan", + "Ġdur ability", + "Ġbomb ings", + "ĠWind ow", + "Ġculp rit", + "3 25", + "There fore", + "umb ered", + "per formance", + "w arts", + "Ġen forcing", + "ĠBl ow", + "Ġre print", + "if ax", + "al pha", + "Ġsin ister", + "Ġbur ger", + "fight ing", + "Sc ore", + "ĠSt ones", + "i em", + "40 5", + "che my", + "Ġvine gar", + "n om", + "Ġprev ailing", + "ĠLat est", + " ¶", + "Ġb a", + "ĠWrit er", + "Ġ17 7", + "ĠCon way", + "Ġcollect s", + "Ġquant itative", + "Ġhor rors", + "og ens", + "ĠSl ov", + "Ġl ays", + "h aw", + "ĠSl ash", + "Ġnight club", + "ĠDav ies", + "Ġbr ide", + "ĠScar let", + "y mm", + "ĠApplic ations", + "vel ength", + "Ġrev ival", + "Ġsoft ly", + "Ġz oo", + "ita ire", + "C ur", + "Ġelect rom", + "Ġplant ing", + "OT O", + "ĠE lements", + "Ġsw allow", + "por ter", + "Ġlapt ops", + "Ġpe anut", + "Ġlobby ists", + "Î ²", + "Pan el", + "ĠJo an", + "im il", + "t nc", + "Ġresist ed", + "Ġout we", + "Ġret aining", + "at ri", + "Ġpo orer", + "ĠSyri ans", + "ĠHam mond", + "Ġwe ld", + "ud er", + "top ic", + "ĠT T", + "ric ia", + "Ġth ieves", + "L ic", + "ĠG ust", + "ĠW ays", + "are th", + "24 3", + "Ġbroad caster", + "sh ield", + "ass ium", + "ub le", + "Ġairst rikes", + "on so", + "Ġped al", + "Ġcollect ors", + "ĠV ander", + "ĠMes a", + "Ġdict ator", + "Ġd ir", + "ent on", + "c art", + "sc ore", + "ad der", + "C ry", + "Ġs sh", + "gg er", + "Ġdrunk en", + "ĠG S", + "ĠSe at", + "Ġcorner back", + "Ġsk ipped", + "ĠRes earchers", + "ĠAud i", + "Ref erence", + "Ġhaun ted", + "à «", + "ĠClin ic", + "c z", + "Ġp s", + "ĠPal adin", + "ĠRec ipe", + "Ġst igma", + "opp y", + "Ġmon keys", + "ĠHaw k", + "S ad", + "\" />", + "ĠWorks hop", + "ĠRet ail", + "ĠAv atar", + "6 25", + "N a", + "ĠV C", + "ĠSec ure", + "M Y", + "19 88", + "oss ip", + "Ġpro state", + "Ġund en", + "Ġg amer", + "ĠCont ents", + "ĠWar hammer", + "ĠSent inel", + "3 10", + "Ġse gregation", + "ĠF lex", + "ĠM AY", + "Ġdr ills", + "ĠDrug s", + "Islam ic", + "Ġsp ur", + "Ġca fe", + "Ġimag inary", + "Ġgu iding", + "Ġsw ings", + "ĠThe me", + "ob y", + "Ġn ud", + "Ġbe gging", + "Ġstr ongh", + "Ġreject ing", + "Ġpedest rians", + "ĠPro spect", + "R are", + "s le", + "Ġconcess ions", + "ĠConst itutional", + "Ġbe ams", + "Ġfib ers", + "p oon", + "Ġinstinct s", + "pro perty", + "ĠB IG", + "Sand ers", + "im ates", + "Ġco ating", + "Ġcorps es", + "ĠTR UE", + "check ed", + "Ġ16 6", + "A sh", + "ĠJ S", + "ĠF iction", + "Ġcommun al", + "Ġener getic", + "oooo oooo", + "Ġnow adays", + "IL D", + "ib o", + "ĠSU V", + "R en", + "Ġdwell ing", + "Sil ver", + "Ġt ally", + "ĠM oving", + "Ġcow ard", + "Ġgener als", + "Ġhorn s", + "Ġcirc ulated", + "Ġrob bed", + "ĠUn limited", + "Ġharass ed", + "Ġinhib it", + "Ġcomp oser", + "ĠSpot ify", + "Ġspread s", + "3 64", + "Ġsu icidal", + "Ġno ises", + "ĠSt ur", + "Ġs aga", + "ĠK ag", + "is o", + "Ġtheoret ically", + "M oney", + "Ġsimilar ity", + "Ġslic ed", + "ut ils", + "ing es", + "\" -", + "Ġan th", + "Ġimp ed", + "Mod ule", + "Through out", + "Ġmen us", + "comm ittee", + "and i", + "ob j", + "in av", + "f ired", + "ĠAb dullah", + "Ġund ead", + "Ġfont s", + "H old", + "EN G", + "Ġsustain ability", + "Ġfl ick", + "Ġr azor", + "ĠF est", + "ĠChar acters", + "Ġword ing", + "Ġpopul ist", + "Ġcritic izing", + "Ġm use", + "v ine", + "Ġcard board", + "Ġkind ly", + "Ġfr inge", + "ĠThe ft", + "icult ural", + "Ġgovern ors", + "Ġ ����", + "Ġ16 3", + "Ġtime out", + "ĠA uth", + "Child ren", + "A U", + "Ġred emption", + "ĠAl ger", + "Ġ19 14", + "Ġw aved", + "Ġastron auts", + "og rams", + "Ġsw amp", + "ĠFinn ish", + "Ġcand le", + "Ġton nes", + "ut m", + "Ġr ay", + "Ġsp un", + "Ġfear ful", + "art icles", + "Ġca us", + "or ically", + "ĠRequ ires", + "ĠG ol", + "Ġpop e", + "Ġinaug ural", + "Ġg le", + "AD A", + "ĠIS IL", + "ĠOff ensive", + "Ġwatch dog", + "Ġbal con", + "ent ity", + "ĠH oo", + "Ġgall on", + "AC C", + "Ġdoub ling", + "Ġimpl ication", + "ĠS ight", + "Ġdoct r", + "---- ---", + "Ġ\\ \\", + "Ġm alt", + "R oll", + "Ġâī ¥", + "Ġrec ap", + "add ing", + "u ces", + "ĠB end", + "fig ure", + "Ġtur key", + "Ġsoc ietal", + "ĠT ickets", + "Ġcommer cially", + "Ġsp icy", + "Ġ2 16", + "ĠR amp", + "Ġsuperior ity", + "à ¯", + "ĠTr acker", + "C arl", + "ĠC oy", + "ĠPatri ot", + "Ġconsult ed", + "Ġlist ings", + "Ġsle w", + "reens hot", + "ĠG one", + "Ġ[ ...]", + "30 9", + "Ġh ottest", + "Ø ±", + "Ġrock y", + "ĠD iaz", + "Ġmass age", + "Ġpar aly", + "Ġp ony", + "A z", + "Ġcart ridge", + "ĠN Z", + "Ġsn ack", + "ĠLam ar", + "ple ment", + "ĠLes lie", + "Ġm ater", + "Ġsn ipp", + "24 6", + "Ġjoint ly", + "ĠBris bane", + "ĠiP od", + "Ġpump ing", + "Ġgo at", + "ĠSh aron", + "eal ing", + "Ġcor on", + "Ġan omal", + "rah im", + "ĠConnect ion", + "Ġsculpt ure", + "Ġsched uling", + "ĠD addy", + "at hing", + "Ġeyeb rows", + "Ġcur ved", + "Ġsent iments", + "Ġdraft ing", + "D rop", + "( [", + "Ġnom inal", + "ĠLeaders hip", + "ĠG row", + "Ġ17 6", + "Ġconstruct ive", + "iv ation", + "Ġcorrupt ed", + "ger ald", + "ĠC ros", + "ĠChe ster", + "ĠL ap", + "ãģ ª", + "OT H", + "D ATA", + "Ġal mond", + "pro bably", + "I mp", + "Ġfe ast", + "ĠWar craft", + "F lor", + "Ġcheck point", + "Ġtrans cription", + "Ġ20 4", + "Ġtwe aks", + "Ġrel ieve", + "S cience", + "Ġperform er", + "Z one", + "Ġtur moil", + "ig ated", + "hib it", + "ĠC afe", + "the med", + "Ġflu or", + "ben ch", + "Ġde com", + "ĠU nt", + "ĠBar rett", + "ĠF acts", + "Ġt asting", + "ĠPTS D", + "ĠSe al", + "ĠJuda ism", + "ĠDynam ic", + "ĠC ors", + "V e", + "ĠM ing", + "ĠTrans form", + "v on", + "ĠDef enders", + "ĠTact ical", + "ĠV on", + "ĠUn ivers", + "Ġdist orted", + "ĠB reath", + "?' \"", + "Ġag on", + "ĠDead ly", + "Ġl an", + "ĠCy cle", + "orn ed", + "Ġrel iably", + "Ġgl or", + "ĠMon key", + "ãĥ ¡", + "Ġad ren", + "Ġmicrow ave", + "ĠAl ban", + "irc raft", + "dig it", + "sm art", + "ĠD read", + "¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯", + "{ {", + "ĠRoc hester", + "Ġsimpl ified", + "Ġinf licted", + "Ġtake over", + "Ġyour selves", + "ad itional", + "Ġmus cular", + "K S", + "Ġing en", + "T ax", + "ĠFe ature", + "27 7", + "Ġcru c", + "Ġcr ate", + "Ġun identified", + "Ġacclaim ed", + "ĠM anga", + "ĠFr ances", + "ĠNep al", + "ĠG erald", + "ĠKu wait", + "Ġsl ain", + "ĠHe b", + "ĠG oku", + "ãģ® æ", + "28 6", + "M rs", + "ĠC ody", + "ĠSan ctuary", + "01 6", + "Ġdism ant", + "Ġdatas et", + "ĠH ond", + "b uck", + "ĠPat terson", + "Ġpal ette", + "ĠG D", + "ic ol", + "ĠL odge", + "Ġplanet ary", + "ak in", + "ĠRegist ered", + "ab we", + "ĠPeters burg", + "Ġha iled", + "ĠP iece", + "S che", + "ĠDO J", + "Ġen umer", + "18 1", + "ĠObs erver", + "ĠB old", + "f ounded", + "com merce", + "Ġexplo its", + "ĠF inding", + "UR N", + "ĠS ne", + "ĠAc id", + "ay ette", + "ĠVal ues", + "Ġdr astic", + "Ġarchitect ural", + "Ġ\" .", + "× ķ", + "ump ed", + "Ġwra pping", + "Ġwid ow", + "ĠSl ayer", + "l ace", + "on ce", + "German y", + "av oid", + "Ġtem ples", + "P AR", + "à ´", + "ĠLuc ifer", + "ĠFl ickr", + "l ov", + "for ces", + "Ġsc outing", + "Ġlou der", + "tes y", + "Ġbefore hand", + "Ä ĵ", + "ĠNe on", + "ĠW ol", + "ĠTyp ically", + "ĠPolit ico", + "-+ -+", + "Ġbuild er", + "Ġder ive", + "K ill", + "Ġp oker", + "Ġambig uous", + "Ġlif ts", + "Ġcy t", + "Ġrib s", + "ood le", + "ĠS ounds", + "h air", + "ĠSynd rome", + "t f", + "Ġproport ional", + "u id", + "Ġper taining", + "ĠKind le", + "ĠNeg ro", + "Ġreiter ated", + "ĠTon ight", + "oth s", + "ĠCorn ell", + "Ġo wing", + "Ġ20 8", + "elf are", + "oc ating", + "ĠB irds", + "Sub scribe", + "Ġess ays", + "Ġburd ens", + "Ġillust rations", + "ar ious", + "ER AL", + "ĠCal cul", + "Ġx en", + "ĠLink edIn", + "ĠJ ung", + "Ġredes ign", + "Con nor", + "29 6", + "Ġrevers al", + "ĠAd elaide", + "ĠL L", + "Ġs inking", + "Ġg um", + "US H", + "c apt", + "ĠGr imm", + "Ġfoot steps", + "ĠCB D", + "isp ers", + "Ġpro se", + "Wed nesday", + "ĠM ovies", + "ed in", + "Ġoverturn ed", + "Ġcontent ious", + "US B", + "~~~~~~~~ ~~~~~~~~", + "ĠCo pper", + "Ġpoint less", + "N V", + "val ues", + "olph in", + "d ain", + "Ġdepos ited", + "ĠG W", + "Ġpreced ed", + "ĠCl a", + "ĠGo lem", + "ĠN im", + "ĠÎ ²", + "ĠEngine ers", + "m iddle", + "Ġfl att", + "oper ative", + "Ġcouncil s", + "imb abwe", + "el in", + "Ġstress ful", + "ĠL D", + "Ġres h", + "l ake", + "Ġwheel chair", + "ĠAltern ative", + "Ġoptim ize", + "oper ation", + "Ġpe ek", + "Ġones elf", + "ig il", + "Ġtrans itions", + "op athy", + "bl ank", + "Ġ16 9", + "17 1", + "________________________________ ________________________________", + "Ġl aundering", + "En c", + "ĠD EC", + "Ġwork outs", + "Ġsp ikes", + "Ġdin osaurs", + "Ġdiscrim inatory", + "P ool", + "R ather", + "38 5", + "R NA", + "tes ters", + "et o", + "ĠIdent ity", + "Ġve in", + "ĠBur ton", + "Ġarc ade", + "4 20", + "Ult imately", + "ĠSad ly", + "à °", + "p ill", + "Ġcub ic", + "ĠSpect rum", + "the se", + "st ates", + "Ġun official", + "h awks", + "ĠEVER Y", + "Ġrain bow", + "Ġincarcer ation", + "and ing", + "Ġsy ll", + "ĠEver ton", + "Ġ17 9", + "ĠSer bia", + "Ġ18 9", + "m eter", + "ĠMic key", + "Ġant iqu", + "Ġfact ual", + "ne ck", + "ĠN are", + "n orm", + "m ust", + "Ġhigh ways", + "Ġgl am", + "Ġdivid ing", + "ĠSquad ron", + "ĠMar tha", + "Ġbirth s", + "C over", + "//////// ////////", + "ĠW ong", + "Ph ot", + "ĠA LS", + "ri o", + "ĠNon etheless", + "ĠL emon", + "Ġ20 6", + "ĠE E", + "Ġderiv ative", + "ĠWW II", + "v ote", + "Ġthere in", + "Ġsepar ating", + "44 6", + "sy nc", + "ĠStre ets", + "Ġr att", + "Ġmunicip ality", + "ĠShort ly", + "Ġmon k", + ") ,\"", + "Ġscr ub", + "Ġoper atives", + "Ne ither", + "Pl ace", + "ĠLim it", + "F emale", + "ĠAct or", + "Char acter", + "Ġconstit uted", + "35 7", + "Ġprotest ed", + "ĠSt raw", + "ĠHe ight", + "ild a", + "ĠTy ph", + "Ġflood s", + "Ġcos metic", + "W AY", + "pert ure", + "up on", + "t ons", + "ess ing", + "ĠP ocket", + "Ġro oft", + "ĠC aucas", + "Ġant idepress", + "Ġincomp atible", + "EC D", + "Ġoper a", + "ĠCont est", + "Ġgener ators", + "l ime", + "Def ense", + "19 87", + "for um", + "Ġsav age", + "ĠHung arian", + "n z", + "Ġmet allic", + "Ġex pelled", + "Ġres idency", + "Ġdress es", + "66 6", + "ĠC lement", + "f ires", + "C ategory", + "Ġge ek", + "al is", + "Ġc emetery", + "educ ated", + "Ġc rawl", + "ĠUn able", + "ĠT yson", + "ak is", + "Ġp ardon", + "ĠW ra", + "Ġstrengthen ed", + "ĠF ors", + "33 5", + "ĠH C", + "ĠM ond", + "Ġvisual s", + "ĠBeat les", + "ett lement", + "Ġ ï", + "g ro", + "Ġb ash", + "Ġpo orest", + "Ġex cel", + "Ġaspir ations", + "ĠM unicip", + "ens ible", + "Ġceremon ies", + "Ġintimid ation", + "ĠCON TR", + "be ck", + "ĠK ap", + "as u", + "Ġtradem arks", + "ĠS ew", + "ĠComp etition", + "net work", + "ĠAr ri", + "ĠT et", + "Ro aming", + "W C", + "D at", + "Ġso b", + "Ġpair ing", + "Ġoverd ose", + "SA Y", + "ab er", + "Ġrev olt", + "ĠF ah", + "act ing", + "e q", + "est ation", + "F ight", + "ĠMar ks", + "27 3", + "Ġ17 8", + "R aw", + "ãģ ĭ", + "34 9", + "bl ocks", + "Ġver ge", + "est ine", + "ĠPod esta", + "Ġinv asive", + "Ġprofound ly", + "ĠA o", + "e ach", + "Ġl est", + "inter pret", + "Ġshr inking", + "Ġerr one", + "Ġche es", + "ly s", + "ĠI vy", + "ĠDirect ory", + "Ġhint ed", + "V ICE", + "Ġcontact ing", + "ĠG ent", + "he i", + "Ġlabel ing", + "Ġmerc ury", + "ĠL ite", + "Ġexp ires", + "Ġdest abil", + "rit is", + "c u", + "Ġfeather s", + "Ġste er", + "Ġprogram med", + "ĠV ader", + "Go ing", + "ĠE lim", + "Ġy o", + "ĠMic he", + "Ġ20 3", + "Ġslee ves", + "Ġb ully", + "ĠHum ans", + "36 8", + "Ġcomp ress", + "ĠBan ner", + "AR S", + "Ġa while", + "Ġcal ib", + "Ġspons orship", + "ĠDiff iculty", + "ĠP apers", + "Ġident ifier", + "} .", + "Ġy og", + "ĠSh ia", + "Ġclean up", + "Ġvib e", + "int rodu", + "im ming", + "Austral ia", + "Ġout lines", + "ĠY outube", + "tr ain", + "ĠM akes", + "Ġde ported", + "Ġcent r", + "ĠD ug", + "ĠB oulder", + "ĠBuff y", + "Ġinj unction", + "ĠHar ley", + "ĠG roups", + "ĠD umbledore", + "ĠCl ara", + "Ġ\" -", + "Ġsacrific ed", + "ep h", + "Sh adow", + "ib ling", + "Ġfreel ance", + "Ġevident ly", + "ph al", + "Ġret ains", + "M ir", + "Ġfin ite", + "d ar", + "ĠC ous", + "Ġrep aired", + "Ġperiod ic", + "Ġchampions hips", + "Ġaster oid", + "bl ind", + "Ġexpress ly", + "ĠAst ros", + "Ġsc aled", + "Ġge ographical", + "ĠRap ids", + "En joy", + "Ġel astic", + "ĠMoh amed", + "Mark et", + "be gin", + "Ġdisco vers", + "Ġtele communications", + "Ġscan ner", + "Ġen large", + "Ġsh arks", + "Ġpsy chedel", + "ĠRou ge", + "Ġsnap shot", + "is ine", + "X P", + "Ġpestic ides", + "ĠL SD", + "ĠDist ribution", + "re ally", + "Ġde gradation", + "Ġdisgu ise", + "Ġbi om", + "ĠEX T", + "Ġequ ations", + "Ġhaz ards", + "ĠComp ared", + ") *", + "Ġvirt ues", + "Ġeld ers", + "Ġenh ancing", + "ĠAc ross", + "er os", + "ang ling", + "Ġcomb ust", + "ucc i", + "Ġconc ussion", + "Ġcontrace ption", + "ĠK ang", + "Ġexpress es", + "Ġa ux", + "ĠP ione", + "Ġexhib its", + "Deb ug", + "OT AL", + "ĠAl ready", + "ĠWheel er", + "Ġexp ands", + "? :", + "Ġreconc iliation", + "Ġpir ates", + "Ġpur se", + "Ġdiscour age", + "Ġspect acle", + "R ank", + "Ġwra ps", + "ĠTh ought", + "Ġimp ending", + "O pp", + "ĠAng lo", + "ĠE UR", + "Ġscrew ed", + "ret ched", + "Ġencour agement", + "mod els", + "Ġconf use", + "mm m", + "ĠVit amin", + "âĸij âĸij", + "C ru", + "Ġkn ights", + "Ġdisc ard", + "Ġb ishops", + "ĠW ear", + "ĠGar rett", + "k an", + "ãĥ Ł", + "Ġmascul ine", + "cap ital", + "ĠA us", + "Ġfat ally", + "th anks", + "ĠA U", + "ĠG ut", + "12 00", + "Ġ 00000000", + "Ġsur rog", + "ĠBI OS", + "ra its", + "ĠWat ts", + "Ġresur rection", + "ĠElect oral", + "ĠT ips", + "4 000", + "Ġnut rient", + "Ġdepict ing", + "Ġspr ink", + "Ġm uff", + "ĠL IM", + "ĠS ample", + "ps c", + "ib i", + "gener ated", + "Ġspec imens", + "Ġdiss atisf", + "Ġtail ored", + "Ġhold ings", + "ĠMonth ly", + "ĠE at", + "po ons", + "Ġne c", + "ĠC age", + "ĠLot us", + "ĠLan tern", + "Ġfront ier", + "Ġp ensions", + "Ġj oked", + "ĠHard y", + "=-=- =-=-", + "r ade", + "U ID", + "Ġr ails", + "Ġem it", + "Ġsl ate", + "Ġsm ug", + "Ġsp it", + "ĠCall s", + "ĠJac obs", + "f eat", + "ĠU E", + "Ġrest ruct", + "Ġregener ation", + "Ġenerg ies", + "ĠCon nor", + "OH N", + "ĠChe ese", + "Ġg er", + "Ġresur rect", + "man agement", + "N W", + "Ġpres ently", + "ĠBru ins", + "M ember", + "ĠM ang", + "id an", + "Ġboost ing", + "w yn", + "+ .", + "requ isite", + "ĠNY PD", + "ĠMe gan", + "ĠCond itions", + "Ġp ics", + "nes ium", + "ĠR ash", + "Ġ17 4", + "ĠD ucks", + "Ġemb ro", + "z u", + "on ian", + "rel igious", + "Ġc raz", + "ĠAC A", + "ĠZ ucker", + "EM A", + "ĠPro s", + "We apon", + "ĠKn ox", + "ĠAr duino", + "Ġst ove", + "Ġheaven s", + "ĠP urchase", + "Ġher d", + "Ġfundra iser", + "Dig ital", + "5 000", + "Ġprop onents", + "/ âĢĭ", + "Ġj elly", + "ĠVis a", + "Ġmon ks", + "Ġadvance ment", + "ĠW er", + "Ġ18 7", + "e us", + "ert ility", + "Ġfet al", + "Ġ19 36", + "L o", + "Ġout fits", + "Ġstair case", + "b omb", + "Ġcustom ized", + "cl air", + "T ree", + "Ġm apped", + "ĠConsider ing", + "ĠTor res", + "Ġmeth yl", + "Ġapprox imate", + "Ġdo om", + "ĠHans en", + "Ġc rossover", + "Ġstand alone", + "ä ¼", + "Ġinv ites", + "Ġgra veyard", + "Ġh p", + "Donald Trump", + "Ġesc ort", + "G ar", + "Ġpredec essors", + "Ġh ay", + "Ġen zyme", + "ĠStra ight", + "vis ors", + "I ng", + "ane ously", + "ĠApp lied", + "Ġf ec", + "ĠDur ant", + "Ġout spoken", + "or b", + "Ġz eal", + "Ġdisgr ace", + "' ).", + "ĠChe ng", + "28 9", + "ĠRen a", + "ĠSu icide", + "29 4", + "Ġout raged", + "ĠNew man", + "ĠN vidia", + "ĠA ber", + "ĠB ers", + "Ġrecre ation", + "Wind ow", + "ĠD P", + "x e", + "Ġped oph", + "Ġfall out", + "ambo o", + "Ġpresent ations", + "ĠApp s", + "Ġh tml", + "3 45", + "ĠX XX", + "Ġrub bing", + "ĠLe ather", + "Ġhum idity", + "se ys", + "est ablished", + "ĠUn its", + "64 6", + "Ġrespect able", + "A uto", + "Ġthri ving", + "ĠInn ovation", + "ang s", + "Ext ra", + "reg ulation", + "29 8", + "p ick", + "Ex amples", + "ĠC J", + "Att ack", + "Ġdr acon", + "L T", + "Ġstick er", + "re rs", + "Ġsun ny", + "I ss", + "reg ulated", + "d im", + "ĠAb stract", + "Ġhus bands", + "Off ice", + "om ination", + "it ars", + "AN GE", + "asc al", + "ĠK ris", + "ĠInf antry", + "Ġm alf", + "ĠA the", + "ĠR ally", + "bal anced", + "................ ........", + "OU P", + "Ġmole cule", + "met ics", + "ĠSpl it", + "ĠInstruct ions", + "ĠN ights", + "c ards", + "Ġt ug", + "Ġcon e", + "å Ń", + "Ġt x", + "ĠDisc ussion", + "Ġcatast rophe", + "pp e", + "g io", + "Ġcommun ism", + "Ġhal ted", + "ĠGu ant", + "cle an", + "ĠSc hed", + "ĠK anye", + "Ġw ander", + "ĠSer iously", + "Ġ18 8", + "enn ial", + "f ollow", + "product ive", + "ĠFl ow", + "ĠS ail", + "Ġc raw", + "Ġsim ulations", + "or u", + "ang les", + "ĠN olan", + "Ġmen stru", + "4 70", + "Ġ20 7", + "aj a", + "Ġcas ually", + "board ing", + "Ġ2 22", + "ov y", + "ĠN umbers", + "um at", + "O E", + "28 7", + "ĠCle mson", + "Ġcert s", + "Ġsl id", + "ĠT ribe", + "Ġto ast", + "Ġfort unes", + "Ġf als", + "ĠComm ittees", + "Ġg p", + "Ġf iery", + "ĠN ets", + "ĠAn ime", + "Pack age", + "ĠComp are", + "l aughter", + "in fect", + "Ġatroc ities", + "Ġjust ices", + "Ġins ults", + "ĠVern on", + "Ġsh aken", + "Ġperson a", + "est amp", + "36 7", + "br ain", + "Ġexperiment ing", + "K en", + "ĠElect ronics", + "Ġ16 1", + "dom ain", + "Ġgraph ical", + "b ishop", + "Ġwho pping", + "ĠEv angel", + "Ġadvertis ers", + "ĠSpe ar", + "Ġb ids", + "Ġdestro ys", + "ut z", + "Ġunders c", + "ĠAD D", + "Ġan ts", + "ĠC um", + "ipp les", + "ĠF ill", + "Ġgl anced", + "Ġind icted", + "ĠE ff", + "Ġmis con", + "ĠDes ktop", + "Ġab ide", + "ãĥ Ģ", + "ĠI o", + "ĠC oul", + "Ġcaps ule", + "ĠCh rys", + "M ON", + "Ġund es", + "ĠI RA", + "Ġc itation", + "Ġdict ate", + "ĠNet works", + "ĠConf lict", + "ĠSt uff", + "x a", + "is ec", + "ĠChem istry", + "Ġquarter ly", + "William s", + "an an", + "O pt", + "ĠAlexand ria", + "out heastern", + "ĠSpring field", + "ĠBlack s", + "Ġge ography", + "24 2", + "Ġut most", + "ĠEx xon", + "ab outs", + "E VA", + "ĠEn able", + "ĠBar r", + "Ġdisag reed", + "ĠCy prus", + "Ġdement ia", + "Ġlab s", + "Ġubiqu itous", + "ĠLO VE", + "Ġconsolid ated", + "s r", + "Ġcream y", + "ĠTim ber", + "Reg ardless", + "ĠCert ificate", + "Ġ\" ...", + "ogen ous", + "Capt ain", + "Ġinsult ing", + "ĠSor os", + "ĠInst r", + "ĠBulgar ia", + "bet ter", + "Ġsuck ing", + "ĠDavid son", + "at z", + "Ġcoll ateral", + "g if", + "Ġplag ued", + "ĠC ancel", + "ĠGard ner", + "R B", + "Ġsix teen", + "Rem ove", + "ur istic", + "c ook", + "R od", + "Ġcompr ising", + "f le", + ") âĢĶ", + "ĠVik ing", + "g rowth", + "agon al", + "Ġsr f", + "af ety", + "m ot", + "N early", + "st own", + "ĠF actor", + "Ġautom obile", + "Ġproced ural", + "m ask", + "amp ires", + "Ġdisapp ears", + "j ab", + "3 15", + "Ġ19 51", + "ne eded", + "Ġd aring", + "le ader", + "Ġp odium", + "Ġun healthy", + "Ġm und", + "Ġpy ramid", + "oc re", + "Ġkiss ed", + "Ġdream ed", + "ĠFant astic", + "ĠG ly", + "å Ĭ", + "Ġgreat ness", + "Ġsp ices", + "Ġmet ropolitan", + "Ġcomp uls", + "i ets", + "101 6", + "ĠSh am", + "ĠP yr", + "fl ies", + "ĠMid night", + "Ġswall owed", + "Ġgen res", + "ĠL ucky", + "ĠRew ards", + "Ġdisp atch", + "ĠI PA", + "ĠApp ly", + "Ġa ven", + "al ities", + "3 12", + "th ings", + "Ġ( ).", + "Ġm ates", + "ĠS z", + "ĠC OP", + "ol ate", + "O FF", + "Ġre charge", + "c aps", + "ĠYork er", + "ic one", + "Ġgal axies", + "ile aks", + "D ave", + "ĠP uzz", + "ĠCelt ic", + "ĠA FC", + "27 6", + "ĠS ons", + "Ġaffirm ative", + "H or", + "Ġtutorial s", + "ĠC ITY", + "ĠR osa", + "ĠExt ension", + "Ser ies", + "Ġf ats", + "Ġr ab", + "l is", + "Ġun ic", + "Ġe ve", + "ĠSp in", + "Ġadul thood", + "ty p", + "Ġsect arian", + "Ġcheck out", + "ĠCy cl", + "S ingle", + "Ġmart yr", + "Ġch illing", + "88 8", + "ou fl", + "Ġ] ;", + "Ġcongest ion", + "m k", + "ĠWhere as", + "Ġ19 38", + "ur rencies", + "er ion", + "Ġbo ast", + "ĠPat ients", + "Ġch ap", + "ĠB D", + "real DonaldTrump", + "Ġexam ines", + "h ov", + "Ġstart ling", + "ĠBab ylon", + "w id", + "om ew", + "br ance", + "ĠOd yssey", + "w ig", + "Ġtor ch", + "ĠV ox", + "ĠMo z", + "ĠT roll", + "ĠAn s", + "Similar ly", + "ĠF ul", + "00 6", + "Un less", + "ĠAl one", + "st ead", + "ĠPub lisher", + "r ights", + "t u", + "ĠDoes n", + "Ġprofession ally", + "Ġcl o", + "ic z", + "Ġste als", + "Ġ á", + "19 86", + "Ġst urdy", + "ĠJoh ann", + "Ġmed als", + "Ġfil ings", + "ĠFr aser", + "d one", + "Ġmult inational", + "Ġf eder", + "Ġworth less", + "Ġp est", + "Yes terday", + "ank ind", + "Ġg ays", + "Ġb orne", + "ĠP OS", + "Pict ure", + "Ġpercent ages", + "25 1", + "r ame", + "Ġpot ions", + "AM D", + "ĠLeban ese", + "Ġr ang", + "ĠL SU", + "ong s", + "Ġpen insula", + "ĠCl ause", + "AL K", + "oh a", + "ĠMac Book", + "Ġunanim ous", + "Ġl enders", + "Ġhang s", + "Ġfranch ises", + "ore rs", + "ĠUp dates", + "Ġisol ate", + "and ro", + "S oon", + "Ġdisrupt ive", + "ĠSur ve", + "Ġst itches", + "ĠSc orp", + "ĠDomin ion", + "Ġsupp lying", + "Ar g", + "Ġtur ret", + "ĠL uk", + "Ġbr ackets", + "* )", + "ĠRevolution ary", + "ĠHon est", + "Ġnot icing", + "ĠSh annon", + "Ġafford ed", + "Ġth a", + "ĠJan et", + "! --", + "ĠNare ndra", + "ĠPl ot", + "H ol", + "se ver", + "e enth", + "Ġobst ruction", + "Ġ10 24", + "st aff", + "j as", + "or get", + "sc enes", + "l aughs", + "ĠF argo", + "cr ime", + "Ġorche str", + "Ġde let", + "ili ary", + "rie ved", + "Ġmilit ar", + "ĠGreen e", + "âĹ ı", + "ãģ ¦", + "ĠGu ards", + "Ġunle ashed", + "ĠWe ber", + "Ġadjust able", + "Ġcal iber", + "Ġmotiv ations", + "Ġà ł", + "m Ah", + "ĠL anka", + "hand le", + "Ġp ent", + "ĠR av", + "ĠAng ular", + "ĠK au", + "umb ing", + "Ġphil anthrop", + "Ġde hyd", + "Ġtox icity", + "e er", + "ĠY ORK", + "w itz", + "å ¼", + "ĠI E", + "commun ity", + "ĠA H", + "Ġret ali", + "Ġmass ively", + "ĠDani els", + "ĠD EL", + "Ġcar cin", + "Ur l", + "Ġrout ing", + "ĠNPC s", + "ĠR AF", + "ry ce", + "Ġwa ived", + "ĠGu atem", + "Every body", + "Ġco venant", + "Ġ17 3", + "Ġrelax ing", + "Ġqu art", + "al most", + "Ġguard ed", + "ĠSold iers", + "ĠPL AY", + "Ġout going", + "L AND", + "Ġre write", + "ĠM OV", + "ĠIm per", + "ĠS olution", + "Ġphenomen al", + "Ġl ongevity", + "Ġimp at", + "ĠN issan", + "ir ie", + "Ġod or", + "ĠZ ar", + "ok s", + "Ġmilit ias", + "ĠSP EC", + "Ġtoler ated", + "ars er", + "ĠBrad ford", + "+ ,", + "Ġsur real", + "s f", + "Can adian", + "Ġresemb lance", + "Ġcarbohyd rate", + "VI EW", + "Ġaccess ory", + "me al", + "larg est", + "ieg el", + "Some one", + "Ġtoug hest", + "os o", + "Ġfun nel", + "Ġcondemn ation", + "lu ent", + "Ġw ired", + "ĠSun set", + "Jes us", + "ĠP ST", + "ĠP ages", + "ĠTy coon", + "ĠP F", + "Ġselect ions", + "Ġ à¤", + "part isan", + "Ġhigh s", + "ĠR une", + "Ġcraft s", + "le ad", + "ĠParent s", + "Ġre claim", + "ek er", + "ĠAll ied", + "ae per", + "Ġlo oming", + "Ġbenefic iaries", + "ĠH ull", + "Stud ents", + "Jew ish", + "d j", + "Ġp act", + "tem plate", + "ĠOffic ials", + "ĠBay lor", + "Ġhe mp", + "Ġyouth s", + "ĠLevel s", + "ĠX iao", + "ĠC hes", + "Ġende avor", + "ĠRem oved", + "Ġhipp ocamp", + "H ell", + "ãĤ Ĭ", + "80 5", + "Ġd inosaur", + "ĠWr ath", + "ĠIndones ian", + "Ġcalcul ator", + "ĠD ictionary", + "Ġ4 20", + "ĠM AG", + "( _", + "! ,", + "t arians", + "Ġrestrict ing", + "rac use", + "Ġweek day", + "OU NT", + "Ġsh rugged", + "leg round", + "Ġb ald", + "ĠDo ctors", + "Ġt outed", + "ĠMax well", + "Ġ2 14", + "Ġdiplom at", + "Ġrep ression", + "Ġconstitu ency", + "v ice", + "r anked", + "ĠNap oleon", + "g ang", + "ĠFore ver", + "t un", + "Ġbul b", + "ĠPD T", + "ĠC isco", + "V EN", + "Ġres umed", + "Ste ven", + "ĠManit oba", + "Ġfab ulous", + "ĠAg ents", + "19 84", + "Ġam using", + "ĠMyster ies", + "Ġor thodox", + "fl oor", + "Ġquestion naire", + "Ġpenet rate", + "Ġfilm makers", + "ĠUn c", + "Ġst amped", + "Ġth irteen", + "Ġout field", + "Ġforward ed", + "Ġapp ra", + "Ġa ided", + "t ry", + "Ġunf ocused", + "ĠL iz", + "ĠWend y", + "ĠSc ene", + "Ch arg", + "Ġreject s", + "Ġleft ist", + "ĠProv idence", + "ĠBr id", + "reg n", + "Ġprophe cy", + "ĠL IVE", + "4 99", + "Ġfor ge", + "ĠF ML", + "Ġintrins ic", + "ĠF rog", + "Ġw ont", + "ĠH olt", + "Ġfam ed", + "CL US", + "aeper nick", + "ĠH ate", + "ĠC ay", + "Ġregister ing", + "ort ality", + "rop y", + "ocaly ptic", + "a an", + "n av", + "Ġfasc ist", + "IF IED", + "Ġimpl icated", + "ĠRes ort", + "ĠChand ler", + "ĠBr ick", + "P in", + "ys c", + "Us age", + "ĠHel m", + "us ra", + "âĺħ âĺħ", + "ĠAb bas", + "Ġunanim ously", + "Ġke eper", + "Ġadd icted", + "?? ?", + "Ġhelm ets", + "Ġant ioxid", + "aps ed", + "80 8", + "gi ene", + "Ġwa its", + "Ġmin ion", + "ra ved", + "ĠP orsche", + "Ġdream ing", + "Ġ17 1", + "ĠC ain", + "Ġun for", + "ass o", + "ĠConfig uration", + "k un", + "hard t", + "Ġn ested", + "ĠL DS", + "L ES", + "Ġt ying", + "en os", + "Ġc ue", + "ĠMar qu", + "sk irts", + "Ġclick ed", + "Ġexp iration", + "ĠAccording ly", + "ĠW C", + "Ġbless ings", + "Ġaddict ive", + "ĠN arr", + "y x", + "ĠJagu ars", + "Ġrent s", + "ĠS iber", + "Ġt ipped", + "ous se", + "ĠFitz gerald", + "Ġhier arch", + "out ine", + "Ġwa velength", + "> .", + "ch id", + "ĠProcess ing", + "/ +", + "r anking", + "E asy", + "ĠConst ruct", + "Ġt et", + "ins ured", + "H UD", + "Ġqu oting", + "Ġcommun icated", + "in x", + "Ġin mate", + "Ġerect ed", + "ĠAbs olutely", + "ĠSure ly", + "Ġun im", + "ĠThr one", + "he id", + "Ġcl aws", + "Ġsuper star", + "ĠL enn", + "ĠWh is", + "U k", + "ab ol", + "Ġsk et", + "ĠN iet", + "Ġper ks", + "Ġaff inity", + "Ġopen ings", + "phas is", + "Ġdiscrim inate", + "T ip", + "v c", + "Ġgr inding", + "ĠJenn y", + "Ġast hma", + "hol es", + "ĠHom er", + "Ġreg isters", + "ĠGl ad", + "Ġcre ations", + "Ġlith ium", + "Ġappl ause", + "unt il", + "Just ice", + "ĠTur ks", + "Ġsc andals", + "Ġb ake", + "t ank", + "M ech", + "ĠMe ans", + "ĠM aid", + "Republic ans", + "is al", + "wind ows", + "ĠSant os", + "Ġveget ation", + "33 8", + "t ri", + "Ġfl ux", + "ins ert", + "Ġclar ified", + "Ġmort g", + "ĠCh im", + "ĠT ort", + "Ġdiscl aim", + "met al", + "ĠAs ide", + "Ġindu ction", + "Ġinf l", + "Ġathe ists", + "amp h", + "Ġe ther", + "ĠV ital", + "ĠBu ilt", + "M ind", + "Ġweapon ry", + "S ET", + "Ġ18 6", + "ad min", + "g am", + "cont ract", + "af a", + "Ġderiv atives", + "Ġsn acks", + "Ġch urn", + "E conom", + "Ġca pped", + "ĠUnder standing", + "ĠH ers", + "ĠI z", + "Ġd uct", + "I ENT", + "augh ty", + "Ġâľ Ķ", + "ĠN P", + "Ġsa iling", + "In itialized", + "Ġt ed", + "Ġreact ors", + "ĠL omb", + "Ġcho ke", + "ĠW orm", + "Ġadm iration", + "Ġsw ung", + "ens ibly", + "Ġr ash", + "ĠGo als", + "ĠImport ant", + "Sh ot", + "ĠR as", + "Ġtrain ers", + "ĠB un", + "Work ing", + "Ġhar med", + "ĠPand ora", + "ĠL TE", + "Ġmush room", + "ĠCH AR", + "ĠF ee", + "ĠM oy", + "B orn", + "ol iberal", + "ĠMart ial", + "Ġgentle men", + "Ġling ering", + "Offic ial", + "Ġgra ffiti", + "ĠN ames", + "D er", + "Ġqu int", + "ist rate", + "aze era", + "ĠNOT ICE", + "ĠFlore nce", + "Ġpay able", + "Ġdep icts", + "ĠSpe cies", + "He art", + "âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ", + "Ġencl osed", + "Incre ases", + "D aily", + "ĠL is", + "Ġenact ment", + "ĠB acon", + "ĠSt eele", + "dem and", + "Ġ18 3", + "Ġmouth s", + "Ġstr anded", + "Ġenhance ment", + "01 1", + "ĠWh ats", + "Ġhe aled", + "en y", + "ĠR ab", + "Ġ3 40", + "ĠLab yrinth", + "ro ach", + "ĠY osh", + "ĠCl ippers", + "Ġconcert s", + "Intern et", + "35 5", + "Ġstick ers", + "Ġter med", + "ĠAx e", + "Ġgrand parents", + "Fr ance", + "ĠCl im", + "ĠU h", + "ul ic", + "Ġthr ill", + "cent ric", + "ĠOver view", + "ĠCond uct", + "Ġsubstant ive", + "Ġ18 2", + "m ur", + "Ġstr ay", + "ĠCo ff", + "Ġrep etitive", + "ĠFor gotten", + "Ġqual ification", + "ew itness", + "ĠZ imbabwe", + "Ġsim ulated", + "ĠJ D", + "25 3", + "ĠW are", + "Ġun sc", + "T imes", + "Ġsum mons", + "Ġdis connected", + "Ġ18 4", + "ci us", + "ĠGu jar", + "od ka", + "Ġer ase", + "ĠTob acco", + "elect ed", + "Ġun cont", + "ĠShe pard", + "ĠL amp", + "Ġalert ed", + "Ġoper ative", + "arn a", + "u int", + "Ġneglig ence", + "ac ements", + "Ġsup ra", + "Ġprev ail", + "ĠSh ark", + "Ġbel ts", + "ãģ «", + "Ġt ighter", + "Engine ers", + "Ġin active", + "Ġexp onent", + "ĠWill ie", + "a ples", + "Ġhe ir", + "ĠH its", + "ian n", + "ĠS ays", + "Ġcurrent s", + "ĠBeng al", + "Ġar ist", + "B uffer", + "Ġbree ze", + "ĠWes ley", + "Col a", + "Ġpron oun", + "Ġde ed", + "ĠK ling", + "Ġof t", + "Ġinf lict", + "Ġpun ishing", + "Ġn m", + "ik u", + "OD UCT", + "01 4", + "Ġsubsid y", + "ĠDE A", + "ĠHer bert", + "ĠJ al", + "B ank", + "Ġdef erred", + "Ġship ment", + "B ott", + "Ġal le", + "b earing", + "HT ML", + "Off line", + "Ġ2 13", + "Ġscroll ing", + "Ġsc anned", + "ĠLib yan", + "ĠT OP", + "ch rom", + "d t", + "col umn", + "Psy NetMessage", + "Z ero", + "Ġtor so", + "0 50", + "âķ IJ", + "Ġimp erson", + "ĠSchw artz", + "ud ic", + "Ġpiss ed", + "ĠS app", + "25 7", + "ĠIS Ps", + "og l", + "Ġsuper vised", + "Ġad olescent", + "Ġatt ained", + "ĠDel ivery", + "ĠB unny", + "Ġ19 37", + "Ġmini ature", + "Ġo s", + "Ġ3 70", + "60 8", + "ĠMour inho", + "Ġinn ate", + "Ġtem po", + "ĠN M", + "ĠFall en", + "00 9", + "Ġprov ocative", + "Stream er", + "ĠBened ict", + "ĠBol she", + "Ġt urtle", + "ĠPC B", + "ĠEqu al", + "Direct or", + "ĠR end", + "Ġflu ids", + "Author ities", + "Ġcous ins", + "requ ency", + "ĠNeigh bor", + "s ets", + "sh ared", + "Char les", + "pass word", + "Ġg ears", + "Ġ2 11", + "ĠHard ware", + "ri ka", + "Ġup stream", + "H om", + "Ġdisproportion ately", + "iv ities", + "Ġund efined", + "Ġelect rons", + "Ġcommem or", + "Event ually", + "Ġ> <", + "Ġir responsible", + "2 18", + "ĠRe leased", + "ĠO VER", + "ĠI GN", + "ĠB read", + "st ellar", + "ĠS age", + "tt ed", + "dam age", + "ed ition", + "ĠPre c", + "Ġl ime", + "Ġconf inement", + "Ġcal orie", + "we apon", + "Ġdiff ering", + "ĠS ina", + "m ys", + "am d", + "Ġintric ate", + "k k", + "ĠP AT", + "ã o", + "st ones", + "lin ks", + "Ġr anch", + "Sem itic", + "Ġdifferent iate", + "ĠS inger", + "occup ied", + "Ġfort ress", + "c md", + "Ġinter ception", + "ĠAnk ara", + "Ġre pt", + "ĠSol itaire", + "Ġrem ake", + "p red", + "Ġd ared", + "aut ions", + "ĠB ACK", + "Run ning", + "Ġdebug ging", + "Ġgraph s", + "3 99", + "ĠNig el", + "Ġb un", + "Ġpill ow", + "Ġprog ressed", + "fashion ed", + "Ġob edience", + "ER N", + "Ġrehe ars", + "C ell", + "t l", + "S her", + "Ġher ald", + "ĠPay ment", + "ĠC ory", + "ĠDe pt", + "Ġrep ent", + "ĠWe ak", + "uck land", + "Ġple asing", + "Ġshort ages", + "Ġjur ors", + "ĠK ab", + "q qa", + "Ant i", + "Ġw ow", + "ĠRC MP", + "Ġt sun", + "ĠS ic", + "Ġcomp rises", + "Ġsp ies", + "Ġprec inct", + "n u", + "Ġur ges", + "Ġtim ed", + "Ġstrip es", + "ĠB oots", + "Ġy en", + "Adv anced", + "Ġdisc rete", + "ĠArch angel", + "employ ment", + "D iff", + "Ġmon uments", + "Ġ20 9", + "work er", + "Ġ19 6", + "ĠI g", + "utter stock", + "T PS", + "J ac", + "Ġhomeless ness", + "Ġcomment ator", + "Ġrac ially", + "f ing", + "se ed", + "E le", + "ell ation", + "Ġeth anol", + "Ġpar ish", + "ĠD ong", + "ĠAw akening", + "Ġdev iation", + "ĠB earing", + "ĠTsu k", + "Ġrec ess", + "Ġl ymph", + "ĠCann abis", + "å ľ", + "ĠNEW S", + "Ġd ra", + "ĠStef an", + "ĠWr ong", + "ĠS AM", + "Ġloose ly", + "Ġinterpre ter", + "ĠPl ain", + "Go vernment", + "Ġbigot ry", + "Ġgren ades", + "ave z", + "pict ured", + "Ġmand ated", + "ĠMon k", + "ĠPed ro", + "Ġl ava", + "27 4", + "Ġcyn ical", + "ĠScroll s", + "l ocks", + "M p", + "Ġcon gregation", + "orn ings", + "ph il", + "ĠI bid", + "Ġf erv", + "Ġdisapp earing", + "Ġarrog ant", + "sy n", + "ĠMa ver", + "ĠSu it", + "24 1", + "Ġab bre", + "ack ers", + "P a", + "ĠY el", + "Whe never", + "Ġ23 5", + "ĠV ine", + "ĠAn at", + "Ġext inct", + "LE T", + "Ġexecut able", + "V ERS", + "ox ide", + "D NA", + "ĠP rel", + "Ġresent ment", + "Ġcompr ise", + "ĠAv iv", + "Ġinter ceptions", + "Ġprol ific", + "IN A", + "ĠEr in", + "though t", + "2 19", + "ĠPsychiat ry", + "un ky", + "chem ist", + "H o", + "ĠMcC oy", + "Ġbr icks", + "L os", + "ri ly", + "ĠUS SR", + "Ġr ud", + "Ġl aud", + "ĠW ise", + "ĠEmer ald", + "Ġrev ived", + "Ġdam ned", + "ĠRep air", + "id em", + "ct ica", + "Ġpatri arch", + "ĠN urs", + "me g", + "Ġcheap est", + "re ements", + "empt y", + "ĠCele br", + "Ġdepri vation", + "ch anted", + "ĠTh umbnails", + "E nergy", + "ĠEth an", + "ĠQ ing", + "Ġopp oses", + "W IND", + "v ik", + "ĠM au", + "ĠS UB", + "66 7", + "G RE", + "ĠVol unte", + "nt on", + "C ook", + "å IJ", + "es que", + "Ġplum met", + "Ġsu ing", + "Ġpron ounce", + "Ġresist ing", + "ĠF ishing", + "ĠTri als", + "Ġy ell", + "Ġ3 10", + "Ġin duct", + "Ġpersonal ized", + "oft en", + "R eb", + "EM BER", + "Ġview point", + "Ġexist ential", + "() )", + "rem ove", + "MENT S", + "l asses", + "Ġev apor", + "Ġa isle", + "met a", + "Ġreflect ive", + "Ġentit lement", + "Ġdev ised", + "mus ic", + "asc ade", + "Ġwind ing", + "off set", + "Ġaccess ibility", + "ke red", + "Bet ter", + "ĠJohn ston", + "th inking", + "S now", + "ĠCroat ia", + "ĠAt omic", + "27 1", + "34 8", + "Ġtext book", + "ĠSix th", + "Ġ اÙĦ", + "Ġsl ider", + "ĠBur ger", + "b ol", + "S ync", + "Ġgrand children", + "Ġc erv", + "+ )", + "Ġe ternity", + "Ġtweet ing", + "Ġspec ulative", + "Ġpiv otal", + "ĠW P", + "ĠT ER", + "ynam ic", + "Ġu pl", + "ĠC ats", + "per haps", + "Ġclass mates", + "Ġblat ant", + "' -", + "Ġl akh", + "ant ine", + "ĠB org", + "i om", + "/ (", + "ĠAthlet ic", + "Ġs ar", + "OT A", + "ĠHoff man", + "Never theless", + "Ġad orable", + "Ġspawn ed", + "Ass ociated", + "ĠDom estic", + "Ġimpl ant", + "ĠLux em", + "ĠK ens", + "Ġp umps", + "ĠS AT", + "Att ributes", + "50 9", + "av our", + "Ġcentral ized", + "ĠT N", + "Ġfresh ly", + "ĠA chieve", + "Ġouts iders", + "her ty", + "ĠRe e", + "ĠT owers", + "ĠD art", + "ak able", + "Ġm p", + "ĠHeaven ly", + "Ġr ipe", + "ĠCarol ine", + "ry an", + "Ġclass ics", + "Ġret iring", + "Ġ2 28", + "Ġa h", + "Ġdeal ings", + "Ġpunch ing", + "ĠChap man", + "O ptions", + "max well", + "vol ume", + "Ġst al", + "Ġex ported", + "ĠQu ite", + "Ġnumer ical", + "B urn", + "F act", + "ĠKey stone", + "Ġtrend ing", + "Ġalter ing", + "ĠAfric ans", + "47 8", + "ĠM N", + "ĠKn ock", + "Ġtempt ation", + "Ġprest ige", + "Over view", + "ĠTrad itional", + "ĠBah rain", + "Priv ate", + "ĠH OU", + "Ġbar r", + "ĠT at", + "C ube", + "US D", + "ĠGrand e", + "ĠG at", + "ĠFl o", + "Ġres ides", + "Ġind ec", + "vol ent", + "Ġperpet ual", + "ub es", + "Ġworld view", + "ĠQuant um", + "Ġfil tered", + "Ġen su", + "orget own", + "ERS ON", + "ĠM ild", + "37 9", + "OT T", + "à ¥", + "Ġvit amins", + "Ġrib bon", + "Ġsincere ly", + "ĠH in", + "Ġeight een", + "Ġcontradict ory", + "Ġgl aring", + "Ġexpect ancy", + "Ġcons pir", + "Ġmon strous", + "Ġ3 80", + "re ci", + "Ġhand ic", + "Ġpump ed", + "Ġindic ative", + "Ġr app", + "Ġav ail", + "ĠLEG O", + "ĠMar ijuana", + "19 85", + "ert on", + "Ġtwent ieth", + "################ ################", + "ĠSw amp", + "Ġval uation", + "Ġaffili ates", + "adjust ed", + "ĠFac ility", + "26 2", + "Ġenz ymes", + "itud inal", + "Ġimp rint", + "S ite", + "Ġinstall er", + "ĠT RA", + "m ology", + "lin ear", + "ĠCollect ive", + "ig ating", + "ĠT oken", + "Ġspec ulated", + "K N", + "ĠC ly", + "or ity", + "Ġdef er", + "Ġinspect ors", + "appro ved", + "R M", + "ĠSun s", + "Ġinform ing", + "ĠSy racuse", + "ib li", + "7 65", + "Ġgl ove", + "Ġauthor ize", + "âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦", + "ĠCru ise", + "Ġcontract ing", + "she ll", + "IF E", + "ĠJew el", + "p ract", + "ĠPhot oshop", + "ĠKnow ing", + "h arm", + "Ġattract ions", + "ad an", + "et us", + "01 8", + "w agen", + "Al t", + "Ġmultip ly", + "Ġequ ilibrium", + ": {", + "ĠF ighters", + "ĠEd gar", + "Ġfour teen", + "Go vern", + "Ġmis use", + "Ġab using", + "Ġancest ry", + "ram er", + "64 4", + "Ġwor ms", + "Ġthick er", + "ĠComb ine", + "Ġpeas ants", + "Ġv ind", + "Ġcon quest", + "Ġm ocked", + "Ġc innamon", + "ĠC ald", + "ĠGall up", + "Ġavoid ance", + "Ġincarn ation", + "ĠStr at", + "Ġt asted", + "ent a", + "ĠN eal", + "p ared", + "Ġtermin ology", + "ject ion", + "Scient ists", + "ĠIN S", + "ĠDe e", + "Ġdirect ories", + "R oad", + "ĠSh ap", + "br ight", + "ĠDirect ors", + "ĠCol umn", + "Ġb ob", + "Ġprefer ably", + "Ġgl itch", + "f urt", + "Ġe g", + "id is", + "C BC", + "Ġsur rendered", + "Ġtest ament", + "33 6", + "ug gest", + "ĠN il", + "an other", + "Ġpat hetic", + "ĠDon na", + "Ġ2 18", + "ĠA very", + "Ġwhis key", + "Ġf ixture", + "ĠCon quest", + "Ġbet s", + "O cc", + "ĠLe icester", + "] .\"", + "Ġ) );", + "Ġfl ashes", + "45 6", + "Ġmask ed", + "ge bra", + "Ġcomput ed", + "che l", + "aud er", + "Ġdefe ats", + "ĠLiber ation", + "ĠOs ama", + "ĠV ive", + "Ch anges", + "Ch annel", + "Ġtar iffs", + "Ġm age", + "ĠS ax", + "Ġinadvert ently", + "ĠC RE", + "ĠRe aper", + "ink y", + "gr ading", + "Ġstere otyp", + "Ġcur l", + "ĠF ANT", + "Ġfram eworks", + "M om", + "ĠAn ch", + "Ġflav our", + "car bon", + "Ġperm itting", + "let cher", + "ĠMo zilla", + "ĠPark ing", + "ĠCh amp", + "Sc roll", + "Ġmurd erer", + "Ġrest ed", + "Ġow es", + "ĠP oss", + "AD D", + "IF F", + "res olution", + "ĠMin ing", + "Ġcompar ative", + "D im", + "Ġneighbour ing", + "ĠA ST", + "ĠT oxic", + "Ġbi ases", + "Ġgun fire", + "ur ous", + "ĠMom ent", + "19 83", + "Ġper vasive", + "tt p", + "ĠNorm ally", + "r ir", + "S arah", + "ĠAlb any", + "Ġun sett", + "ĠS MS", + "ip ers", + "l ayer", + "ĠWh ites", + "up le", + "Ġtur bo", + "ĠLe eds", + "Ġthat s", + "ĠMin er", + "M ER", + "ĠRe ign", + "Ġper me", + "ĠBl itz", + "Ġ19 34", + "Ġintimid ating", + "t ube", + "Ġecc entric", + "ab olic", + "box es", + "ĠAssoci ates", + "v otes", + "Ġsim ulate", + "um bo", + "aster y", + "Ġship ments", + "FF FF", + "an th", + "Ġseason ed", + "Ġexperiment ation", + "âĸ ł", + "law s", + "Me et", + "idd les", + "ant ics", + "R ating", + "IS IS", + "h ift", + "Ġfront s", + "b uf", + "01 7", + "Ġun att", + "ĠD il", + "le ases", + "ĠGard ens", + "77 7", + "t ouch", + "ve ll", + "45 8", + "Ġ= ====", + "s aving", + "Ġer osion", + "ĠQu in", + "Ġearn s", + "Ġaccomplish ment", + "ĠWe i", + "Ġ< [", + "____ _", + "Ġir rig", + "ĠT eddy", + "Ġconqu ered", + "ĠArm ored", + "Ġassert s", + "Ġmanip ulating", + "r é", + "Ġtranscript s", + "G allery", + "Ġplot ting", + "Ne il", + "Ġbetray al", + "load er", + "ĠS ul", + "Ġdispl acement", + "Ġroy alty", + "ĠW I", + "he it", + "ĠDev ices", + "alle l", + "Ġmunicipal ities", + "Ġcan al", + "St ars", + "ĠU AE", + "Ġ\" âĢ¦", + "ĠC U", + "ab ove", + "Ġreson ance", + "ĠguiActive Un", + "add ed", + "ĠBra ves", + "ĠI bn", + "Ġhere by", + "ĠB RE", + "Ġshare holder", + "ĠH ir", + "ĠJ i", + "Ġstrange ly", + "Ġadm ired", + "Ġpl ight", + "Ġb achelor", + "ĠP ole", + "cipl inary", + "T ony", + "ĠArmen ian", + "Ġun man", + "ĠZion ist", + "St age", + "isco ver", + "Ġautom otive", + "Ġs idelines", + "Ġsl ick", + "ĠRena issance", + "ĠF UN", + "Im ages", + "ĠH aj", + "Ġp ing", + "Ġshort cut", + "ĠBl vd", + "ĠLook s", + "Ġbur sts", + "Ġcl amp", + "Ġm ish", + "Ġsort ing", + "Ġpatri ot", + "Ġcorrect ness", + "ĠScand inav", + "ĠCaval iers", + "p ython", + "az ar", + "Ġ3 75", + "ĠJa une", + "40 9", + "Ġdetrim ental", + "Ġstab bing", + "Ġpoison ed", + "Ġf ountain", + "oc ent", + "or st", + "ĠMar i", + "Ġr ains", + "ĠO vers", + "ĠInst itution", + "ud get", + "AM Y", + "t ale", + "ĠK R", + "ĠPr ices", + "Ġhead aches", + "Ġlands l", + "ĠA ura", + "Bon us", + "ĠZ hao", + "ĠH ip", + "Ġhop s", + "ĠKurd istan", + "Ġexplo iting", + "ry n", + "Ġhypocr isy", + "op ening", + "Ġgun shot", + "Ġw ed", + "inter stitial", + "Inter stitial", + "Ġam en", + "Bre aking", + "Ġmarket ed", + "W ire", + "ĠC rowd", + "Contin ue", + "ĠK nown", + "ĠEffect ive", + "ore an", + "iz ons", + "Jose ph", + "Ġescal ation", + "us ername", + "Ġcur tain", + "AT ES", + "ĠP AR", + "ĠM iy", + "Ġcounter fe", + "l ene", + "Ġcont enders", + "d aily", + "ĠAs c", + "ĠPhill ip", + "most ly", + "Ġfil ename", + "he ne", + "Ġresemb ling", + "Ġst aging", + "ĠCh loe", + "Ġw iring", + "H on", + "ĠRen ew", + "ott age", + "ĠHy brid", + "m uch", + "Ġstro kes", + "Ġpolicy makers", + "AP TER", + "ĠArk ham", + "pl ot", + "Ġassist ants", + "Ġde port", + "ĠSe ga", + "Ġinflu enza", + "ĠC ursed", + "ĠK obe", + "Ġskin ny", + "Prov ider", + "ĠR ip", + "Ġincrement al", + "product s", + "B F", + "Ġd ome", + "ĠC redits", + "Ġlos ers", + "int s", + "ĠBet ty", + "ĠTal ent", + "ĠD AM", + "L v", + "E ss", + "Ġd ens", + "tem p", + "J udge", + "od ic", + "Ġ' (", + "UR ES", + "ets k", + "V O", + "Ġretrie ved", + "Ġarchitect s", + "Ù ĩ", + "Ġeth ic", + "ĠSecond ary", + "st ocks", + "ad ia", + "Ġ3 25", + "ĠOp inion", + "Ġsimultane ous", + "Ġd izz", + "ul p", + "Ġsmugg ling", + "ipp ery", + "R andom", + "f acing", + "ĠD as", + "Ġstock p", + "Ġdiscl osures", + "po inter", + "Ġcor al", + "ĠSe lection", + "ĠP ike", + "ival ent", + "Ġruth less", + "ĠR im", + "Ġensu ing", + "ĠExper iment", + "Ġcongress man", + "Ġbelie ver", + "Ġun specified", + "ĠM ord", + "Ġknowledge able", + "ĠV ERY", + "T X", + "Ġstra ps", + "Ġtur f", + "apesh ifter", + "Ġmar ital", + "Ġfl ock", + "ãģ Ĩ", + "26 3", + "AM ES", + "ĠOpp osition", + "Ġtre asures", + "ĠG OD", + "Ġmodel ed", + "ĠWOR LD", + "Ġ( [", + "ĠUs age", + "H F", + "Ġ$ (", + "uss ed", + "Ġpione er", + "E ight", + "par se", + "b read", + "rit z", + "ĠMir anda", + "ĠK ant", + "++ )", + "ore n", + "Ġprov oked", + "Ġbre eds", + "ĠIn cludes", + "ĠPast ebin", + "ĠFl ip", + "J ava", + "Ġbr ink", + "Ġrum ored", + "Ġun seen", + "Ġgar nered", + "ĠDef in", + "al ted", + "Ġtatt oos", + "Ġhes itation", + "is itions", + "ĠWe aver", + "ĠReport ing", + "Ġtherap ies", + "Ġconsult ants", + "Ġresid ual", + "ĠMal i", + "ĠRom a", + "i ago", + "ĠRes idents", + "ub i", + "Ġremed ies", + "Ġadapt ive", + "ĠAl ive", + "ĠBar cl", + "Ġwal lets", + "c rypt", + "etermin ation", + "ĠPel osi", + "Ġsl ipping", + "oton in", + "Ġall iances", + "pat rick", + "ir is", + "Ġor th", + "ĠPer kins", + "ĠDe V", + "ĠG ets", + "Ġdry ing", + "ge e", + "fore st", + "ĠFor get", + "ore m", + "33 9", + "Ġvague ly", + "ĠD ion", + "ĠP orn", + "ĠH OW", + "Ġp neum", + "Ġrub ble", + "ĠT aste", + "enc ia", + "ĠG el", + "Ġd st", + "Ġ24 5", + "ĠMoroc co", + "inf lamm", + "ĠTw ins", + "Ġb ots", + "d aughter", + "ĠB alk", + "Ġbre thren", + "Ġlog os", + "Ġgo bl", + "f ps", + "Ġsub division", + "Ġp awn", + "Ġsquee zed", + "Ġmor ale", + "ĠD W", + "' \"", + "Ġkn ot", + "ook y", + "Ġdiv isive", + "Ġboost ed", + "ch y", + "ãĥ IJ", + "if act", + "Ġnewcom ers", + "ĠWrest ling", + "Ġsc outs", + "w olves", + "R at", + "Ġnin eteenth", + "ĠOs borne", + "St ats", + "Ġem powered", + "Ġpsych opath", + "ĠO EM", + "ugg age", + "ĠP K", + "ĠMoh ammad", + "P ak", + "Ġanarch ists", + "ĠExt ract", + "est hes", + "ĠStock holm", + "l oo", + "ĠG raph", + "Ġdeploy ing", + "ĠStr anger", + "ĠM old", + "Ġstaff er", + "Ġdiscount ed", + "uck le", + "ple ase", + "ĠLand ing", + "ÃŃ a", + "Ġ19 3", + "Ġan te", + "Ġrep etition", + "Ġ+ /-", + "Ġpar ody", + "Ġlive ly", + "AA A", + "ĠHor us", + "Ġp its", + "ind ers", + "L OC", + "ĠVen ice", + "40 6", + "ĠDis cover", + "â Ĩ", + "ellect ual", + "Ġp ens", + "Ġey el", + "ig uous", + "Im pl", + "Ġj oking", + "Ġinv al", + "ĠBel fast", + "Ġcredit ors", + "ĠSky walker", + "ov sky", + "Ġcease fire", + "Ġse als", + "is oft", + ") ).", + "ĠFel ix", + "IT S", + "Ġt resp", + "ĠBlock chain", + "ew are", + "ĠSch war", + "en ne", + "mount ed", + "ĠBe acon", + "les h", + "Ġimmense ly", + "Ġche ering", + "Em ploy", + "sc ene", + "ish ly", + "atche wan", + "ĠNic olas", + "Ġdr ained", + "ĠEx it", + "ĠAz erb", + "j un", + "Ġflo ated", + "u ania", + "De ep", + "Ġsuper v", + "Ġmyst ical", + "ĠD ollar", + "ĠApost le", + "ĠR EL", + "ĠProv ided", + "ĠB ucks", + "ãĥ ´", + "cut ting", + "Ġenhance ments", + "ĠPengu ins", + "ĠIsa iah", + "Ġj erk", + "ĠW yn", + "Ġst alled", + "Ġcryptoc urrencies", + "ĠR oland", + "sing le", + "Ġl umin", + "ĠF ellow", + "ĠCap acity", + "ĠKaz akh", + "W N", + "Ġfin anced", + "38 9", + "Ġt id", + "Ġcoll usion", + "ĠMy r", + "î Ģ", + "Sen ator", + "Ġped iatric", + "Ġneat ly", + "Ġsandwic hes", + "ĠArchitect ure", + "Ġt ucked", + "Ġbalcon y", + "Ġearthqu akes", + "qu ire", + "F uture", + "Ġhe fty", + "é Ĺ", + "Ġspecial izes", + "Ġstress es", + "Ġs ender", + "Ġmisunder standing", + "Ġep ile", + "Ġprov oke", + "ĠCol ors", + "Ġdis may", + "uk o", + "[ _", + "58 6", + "ne utral", + "Ġdon ating", + "ĠRand all", + "Mult i", + "Ġconvenient ly", + "ĠS ung", + "ĠC oca", + "Ġt ents", + "ĠAc celer", + "Ġpart nered", + "27 2", + "ir ming", + "ĠB AS", + "s ometimes", + "Ġobject ed", + "ub ric", + "p osed", + "LC S", + "gr ass", + "Ġattribut able", + "V IS", + "Israel i", + "Ġrepe ats", + "ĠR M", + "v ag", + "ut a", + "in ous", + "Ġin ert", + "ĠMig uel", + "æ Ń", + "ĠHawai ian", + "B oard", + "Ġart ific", + "ĠAzerb ai", + "as io", + "ĠR ent", + "A IN", + "Ġappl iances", + "Ġnational ity", + "Ġass hole", + "ĠN eb", + "Ġnot ch", + "h ani", + "ĠBr ide", + "Av ailability", + "Ġintercept ed", + "Ġcontin ental", + "Ġsw elling", + "ĠPers pect", + "b ies", + ". <", + "ith metic", + "ĠL ara", + "Ġtempt ing", + "add r", + "Ġoversee ing", + "cl ad", + "ĠD V", + "ĠGing rich", + "Ġm un", + "ĠApp ropri", + "Ġalter ations", + "ĠPat reon", + "Ġha voc", + "Ġdiscipl ines", + "Ġnotor iously", + "aku ya", + "ier i", + "? ).", + "ĠW ent", + "Ġsil icon", + "Ġtre mb", + "Cont ainer", + "K nown", + "Ġmort ar", + "est e", + "ick a", + "Ar thur", + "ĠPre viously", + "ĠMart y", + "Ġsp arse", + "g ins", + "Ġin ward", + "ĠParticip ant", + "C opy", + "ĠM isc", + "Ġantib iotic", + "ĠRet ro", + "Ġel usive", + "Ġass ail", + "ĠBatt alion", + "ĠB ought", + "Ġdimin ish", + "ĠEuro pa", + "s ession", + "ĠDanger ous", + "ies el", + "Ġdisbel ief", + "Ġbl asts", + "ext reme", + "ĠBoy d", + "ĠProject s", + "ĠGu ys", + "Ġunder gone", + "Ġgr ill", + "ĠDw ight", + "Ġ19 7", + "US ER", + "Ġfiles ystem", + "Ġcl ocks", + "T aylor", + "Ġwra pper", + "Ġfold ing", + "ous and", + "ĠPhilipp ine", + "ATION AL", + "ĠPer th", + "Ġas hes", + "Ġaccum ulate", + "ĠGate way", + "Sh op", + "orks hire", + "H an", + "ĠBar rel", + "ĠLe h", + "ĠX V", + "Ġwh im", + "Ġrep o", + "ĠC G", + "ĠM am", + "Ġincorpor ating", + "Ġbail out", + "Ġlingu istic", + "Ġdis integ", + "C LE", + "Ġcinem atic", + "ĠF iber", + "S yn", + "il ion", + "ĠCom pos", + "c hens", + "Ġne oc", + "Ġbo iled", + "F INE", + "on o", + "un cle", + "ik en", + "ĠB M", + "Î ¹", + "Ġreceipt s", + "Ġdisp osed", + "ĠTh irty", + "ĠR ough", + "ĠA BS", + "Ġnot withstanding", + "oll en", + "# $", + "Ġunrel iable", + "Ġbl oom", + "Ġmedi ocre", + "Ġtr am", + "ĠTas man", + "Ġsh akes", + "Ġmanifest o", + "ĠM W", + "Ġsatisf actory", + "Ġsh ores", + "Ġcomput ation", + "Ġassert ions", + "orm ons", + "ar ag", + "ab it", + "Dem ocrats", + "ĠL oot", + "ĠVol ks", + "ha ired", + "Ġgrav itational", + "S ing", + "ĠM iz", + "Ġthro ttle", + "Ġtyr anny", + "ĠView s", + "Ġrob ber", + "ĠMinor ity", + "Ġsh rine", + "sc ope", + "pur pose", + "Ġnucle us", + "our cing", + "ĠUS DA", + "ĠD HS", + "w ra", + "ĠBow ie", + "Sc ale", + "ĠB EL", + "x i", + "I ter", + "Ġ( ),", + "w right", + "Ġsail ors", + "ous ed", + "NAS A", + "ĠPro of", + "ĠMin eral", + "t oken", + "ĠF D", + "R ew", + "Ġe ll", + "6 30", + "Ġchance llor", + "ĠG os", + "Ġamount ed", + "ĠRec re", + "ome z", + "ĠOpt im", + "ĠOl ive", + "Ġtrack er", + "ow ler", + "ĠUn ique", + "R oot", + "Ġmar itime", + "ĠQur an", + "ĠAd apt", + "Ġecosystem s", + "ĠRe peat", + "ĠS oy", + "ĠI MP", + "Ġgrad uating", + "and em", + "P ur", + "ĠRes et", + "ĠTr ick", + "ĠPh illy", + "ĠT ue", + "ĠMalays ian", + "Ġclim ax", + "Ġb ury", + "Ġcons pic", + "ĠSouth ampton", + "ĠFl owers", + "Ġesc orted", + "ĠEduc ational", + "ĠI RC", + "Ġbrut ally", + "e ating", + "Ġpill ar", + "ĠS ang", + "ĠJ ude", + "ar ling", + "ĠAm nesty", + "Ġrem inding", + "ĠAdminist rative", + "hes da", + "Ġfl ashed", + "ĠP BS", + "per ate", + "fe ature", + "Ġsw ipe", + "Ġgra ves", + "oult ry", + "26 1", + "bre aks", + "ĠGu er", + "Ġsh rimp", + "ĠV oting", + "qu ist", + "Ġanaly tical", + "Ġtables poons", + "ĠS OU", + "Ġresear ched", + "Ġdisrupt ed", + "Ġj our", + "Ġrepl ica", + "Ġcart oons", + "b ians", + "} )", + "c opy", + "G ot", + "ou ched", + "P UT", + "Ġsw arm", + "not ations", + "s aid", + "Ġreb uilt", + "Ġcollabor ate", + "Ġr aging", + "Ġn ar", + "Ġdem ographics", + "ĠD DR", + "Ġdist rust", + "oss ier", + "ĠK ro", + "Ġpump kin", + "Ġreg rets", + "Ġfatal ities", + "ĠL ens", + "ĠO le", + "p d", + "Ġpupp et", + "ĠOut look", + "ĠSt am", + "O l", + "F air", + "U U", + "Ġre written", + "Ä ±", + "Ġfasc inated", + "Ġve ctors", + "Ġtrib unal", + "u ay", + "ĠM ats", + "ĠCo ins", + "[ [", + "Ġ18 1", + "Ġrend ers", + "ĠK aepernick", + "Ġesp ionage", + "Ġsum m", + "Ġd itch", + "Acc ount", + "Ġspread sheet", + "Ġmut ant", + "p ast", + "40 7", + "Ġd ye", + "Ġinit iation", + "Ġ4 000", + "Ġpunish able", + "Ġth inner", + "ĠKh al", + "Ġinter medi", + "D un", + "ĠGoth am", + "Ġeager ly", + "Ġvag inal", + "p owers", + "V W", + "ĠWATCH ED", + "Ġpred ator", + "ams ung", + "Ġdispar ity", + "Ġ[ *", + "Ġam ph", + "Ġout skirts", + "ĠSpir its", + "Ġskelet al", + "Ð »", + "ĠR ear", + "Ġissu ance", + "ĠLog ic", + "re leased", + "Z Z", + "ĠB ound", + "Ent ry", + "Ġex its", + "is ol", + "ĠFound er", + "Ġw re", + "ĠGreen land", + "ĠM MO", + "t aker", + "IN C", + "ãģ ¾", + "Ġhour ly", + "hen ko", + "Ġfantas ies", + "Ġdis ob", + "Ġdemol ition", + "ãĥ ĭ", + "Ġen listed", + "rat ulations", + "Ġmis guided", + "Ġens ured", + "Ġdiscour aged", + "m ort", + "Ġfl ank", + "Ġc ess", + "Ġreact s", + "ĠS ere", + "s ensitive", + "ĠSer pent", + "ass ad", + "Ġ24 7", + "Ġcalm ly", + "b usters", + "Ġble ed", + "ĠSt ro", + "Ġamuse ment", + "ĠAntar ctica", + "Ġs cept", + "ĠG aw", + "a q", + "ason ic", + "Ġsp rawling", + "n ative", + "atur ated", + "ĠBattle field", + "IV ERS", + "E B", + "ĠG ems", + "ĠNorth western", + "ĠFil ms", + "ĠAut omatic", + "Ġappre hend", + "ãģ ¨", + "Ġgui Name", + "Ġback end", + "Ġevid enced", + "ge ant", + "01 2", + "ĠS iege", + "Ġexternal To", + "Ġunfocused Range", + "ĠguiActiveUn focused", + "Ġgui Icon", + "ĠexternalTo EVA", + "ĠexternalToEVA Only", + "F ri", + "ch ard", + "en aries", + "Ġchief s", + "Ġc f", + "ĠH UD", + "Ġcorro bor", + "Ġd B", + "ĠT aken", + "ĠPat ricia", + "ra il", + "ĠCh arm", + "ĠLiber tarian", + "rie ve", + "Person al", + "ĠO UR", + "ger ies", + "Ġdump ing", + "Ġneurolog ical", + "it imate", + "ĠClint ons", + "raft ed", + "ĠM olly", + "Ġtermin als", + "reg ister", + "Ġfl are", + "Ġenc oded", + "Ġautop sy", + "p el", + "m achine", + "Ġexempt ions", + "ĠRoy als", + "d istance", + "Ġdraft s", + "Ġl ame", + "ĠC unning", + "Ġsp ouses", + "ĠMark ets", + "ĠCar rier", + "Ġimp lying", + "ĠY ak", + "s id", + "Ġl oser", + "Ġvigil ant", + "Ġimpe achment", + "Ġaug mented", + "ĠEmploy ees", + "Ġunint ended", + "tern ally", + "ĠW att", + "Ġrecogn izable", + "ess im", + "æ Ŀ", + "Ġco ated", + "r ha", + "Ġlie utenant", + "ĠLegisl ation", + "pub lished", + "44 4", + "01 3", + "Ġide ally", + "ĠPass word", + "Ġsimpl ify", + "ĠMet a", + "ĠM RI", + "Ġple ading", + "organ ized", + "hand ler", + "Ġun ravel", + "cor rect", + "Ġ icy", + "Ġparan oid", + "Ġpass er", + "Ġinspect ions", + "of er", + "ĠHealth care", + "28 3", + "ĠBr ut", + "iol a", + "for ge", + "ĠMed ieval", + "MS N", + "ie vers", + "ĠProgram ming", + "å ī", + "Ġ2 23", + "m u", + "ĠC LE", + "ug a", + "Ġsho ppers", + "Ġinform ative", + "ĠPl ans", + "Ġsupplement ation", + "ĠT ests", + "ty ard", + "ocy tes", + "ĠVeg a", + "ĠGujar at", + "erman ent", + "Ex cept", + "ĠL OT", + "all a", + "ĠC umm", + "ĠO sw", + "Ġven om", + "ĠDeb t", + "ĠD OWN", + "Ġreun ion", + "Ġm uc", + "ĠRel ief", + "Ġge op", + "ĠðŁ ĺ", + "al ogue", + "An th", + "ech o", + "Ġcor ros", + "Ġrepl ication", + "ĠBl azing", + "ĠD aughter", + "Ġinf lic", + "ĠLind sey", + "Ù Ī", + "28 4", + "Ex it", + "Ġgl oom", + "TA IN", + "Ġundermin ing", + "Ġadv ising", + "h idden", + "Ġover flow", + "Ġg or", + "urd ue", + "Ġe choes", + "enh agen", + "Ġimp uls", + "d rug", + "c ash", + "Ġas ync", + "Ġmir ac", + "at ts", + "p unk", + "Ġpiv ot", + "ĠLegisl ative", + "Ġblog gers", + "ĠCl aw", + "s burg", + "d yl", + "ĠRecomm end", + "Ġver te", + "Ġprohib iting", + "ĠPant her", + "Jon athan", + "Ġo min", + "Ġhate ful", + "28 1", + "ĠOr che", + "ĠMurd och", + "down s", + "Ġas ymm", + "G ER", + "Al ways", + "Ġinform s", + "ĠW M", + "ĠP ony", + "ĠApp endix", + "ĠAr lington", + "J am", + "Ġmedic inal", + "ĠS lam", + "IT IES", + "Ġre aff", + "ĠR i", + "F G", + "S pring", + "b ool", + "Ġthigh s", + "Ġmark ings", + "ĠRa qqa", + "ĠL ak", + "p oll", + "ts ky", + "ĠMort y", + "ĠDef inition", + "Ġdeb unk", + "end ered", + "ĠLe one", + "a vers", + "Ġmortg ages", + "App arently", + "N ic", + "ha us", + "ĠTh ousands", + "au ld", + "Ġm ash", + "sh oot", + "Ġdi arr", + "Ġconscious ly", + "H ero", + "e as", + "ĠN aturally", + "ĠDestroy er", + "Ġdash board", + "serv ices", + "R og", + "Ġmillenn ials", + "Ġinv ade", + "- (", + "Ġcomm issions", + "ĠA uckland", + "Ġbroadcast s", + "Ġfront al", + "Ġcr ank", + "ĠHist oric", + "Ġrum ours", + "CT V", + "Ġster il", + "Ġboost er", + "rock et", + "ãĤ ¼", + "ut sche", + "ĠP I", + "Ġ2 33", + "ĠProdu cer", + "ĠAnaly tics", + "Ġinval uable", + "Ġunint ention", + "ĠC Y", + "Ġscrut in", + "Ġg igg", + "Ġeng ulf", + "Ġprolet ariat", + "Ġh acks", + "ĠH ew", + "ar ak", + "ĠSl ime", + "ield ing", + "ag her", + "ĠEll iot", + "Ġtele com", + "Ġ2 19", + "ult an", + "ĠAr bor", + "ĠSc outs", + "B an", + "Ġlifes pan", + "Ġbl asp", + "38 8", + "Ġjud iciary", + "ĠContin ental", + "ask ing", + "Mc C", + "L ED", + "Ġbag gage", + "ĠSorce rer", + "Ġrem nants", + "ĠGriff ith", + "ets u", + "ĠSub aru", + "ĠPerson ality", + "des igned", + "ush ima", + "agn ar", + "Ġrec oil", + "Ġpass ions", + "\\ \":", + "Ġte e", + "Ġabol ition", + "ĠCreat ing", + "j ac", + "Ġ19 4", + "01 9", + "Ġpill ars", + "ric hed", + "/ \"", + "t k", + "Ġlive lihood", + "Ġro asted", + "ah on", + "ĠH utch", + "ass ert", + "Ġdivid end", + "Ġkn it", + "Ġd aunting", + "Ġdisturb ance", + "Ġsh ale", + "Ġcultiv ated", + "Ġrefriger ator", + "L B", + "ĠN ET", + "Ġcommercial s", + "Ġthink ers", + "45 5", + "Ġch op", + "B road", + "Ġsuspic ions", + "Ġtag ged", + "l ifting", + "Ġsty lish", + "ĠShield s", + "Short ly", + "Ġt ails", + "A uth", + "ST E", + "ĠG AME", + "Ġse ism", + "ĠK is", + "olog ne", + "Ġcow ork", + "Ġforc ibly", + "Ġthy roid", + "ĠP B", + "AN E", + "mar ried", + "h orse", + "Ġpoly mer", + "ĠCh al", + "od or", + "DE BUG", + "ĠCon text", + "Ġbl iss", + "Ġpin point", + "ĠMat hemat", + "leg ram", + "ĠWeek end", + "Ġlab elled", + "Ġb art", + "it les", + "Ġest rogen", + "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", + "\" '", + "Ġvis ibly", + "Ġouts ider", + "aid a", + "Are a", + "Ġdisse min", + "Ġdish onest", + "ĠCl osed", + "ĠBullet in", + "ĠRam sey", + "sw ord", + "ĠX I", + "our ced", + "S ame", + "34 6", + "ĠRe pe", + "ĠK ou", + "c ake", + "em is", + "C ache", + "ĠMe aning", + "ĠEn light", + "onom y", + "Ġmanifest ation", + "sw orth", + "J ay", + "Ġch ore", + "ö r", + "D ream", + "Ġsanction ed", + "Ġcult urally", + "ĠA ra", + "N av", + "Ġthe ological", + "Ġstr ut", + "ĠV O", + "ĠHand book", + "Ġconstruct ing", + "Ġ ¶", + "ĠBenef its", + "ĠPsych ological", + "s ac", + "å ¸", + "p olicy", + "ĠMat ters", + "ĠReport ed", + "ĠBy te", + "Ġvit ro", + "ĠM aiden", + "Ġl am", + "ĠJenn ings", + "Ġgar ment", + "ĠRut gers", + "ĠStaff ord", + "ĠWell ington", + "Ġinter mitt", + "Ġn pm", + "Ġord eal", + "Ġplug ged", + "o oming", + "in ished", + "fram ework", + "Ġtim ber", + "Ġc ass", + "Ġ8 50", + "il ess", + "ĠRed ux", + "7 68", + "St re", + "Ġsurpass ed", + "w hel", + "Ġparalle ls", + "Ġve il", + "ĠG I", + "ĠR EST", + "Ġread iness", + "s ort", + "Ġmod ifying", + "ĠSl ate", + "ru ff", + "Ġmar ble", + "Ġinf rared", + "Ġaud itor", + "ĠFANT ASY", + "ĠP overty", + "ĠS PD", + "Ġ\" (", + "K y", + "RA Y", + "Ġexecut ions", + "ĠBever ly", + "ĠMarx ism", + "ĠBur st", + "ĠK ali", + "est ones", + "Clear ly", + "E ll", + "ãģ §", + "ĠProceed ings", + "T oken", + "IF IC", + "ñ a", + "Cent ral", + "ĠH aley", + "ĠD rama", + "Ġform ations", + "OR N", + "Book s", + "Ġdom inating", + "ĠFly ers", + "ĠCompan ion", + "Ġdiscipl ined", + "ĠYug oslav", + "ĠSpell s", + "Ġv engeance", + "Ġland lords", + "L en", + "ĠO gre", + "ano ia", + "Ġpier cing", + "Ġcon greg", + "Ġscore r", + "ob ia", + "Ġnic kel", + "ĠLear ns", + "Ġre jo", + "Ġmaster piece", + "Fl ash", + "Ġinhab ited", + "ĠOpen GL", + "ĠD ud", + "ĠI CO", + "Ġar ter", + "Ġpl ur", + "Ġmaster y", + "Ġlong standing", + "st ed", + "Ġw ines", + "Ġtelev ised", + "ĠSh rine", + "ĠBay ern", + "Ġâ ĵĺ", + "Ġencl osure", + "j ohn", + "Ġprophe ts", + "ĠRes urrection", + "ĠOrd ers", + "Ġun even", + "r als", + "Ġd wind", + "ĠL ah", + "ĠSl oven", + "37 8", + "Ġins istence", + "aff le", + "ĠCl one", + "Ġhard ship", + "ĠCongress man", + "Ġple ad", + "Ġreview ers", + "Ġc ured", + "Ġ19 35", + "as ley", + "f ake", + "ĠTh inking", + "yd ia", + "P ART", + "ĠD ota", + "o it", + "Ġwh ipped", + "Ġb ouncing", + "ĠHispan ics", + "com ings", + "Ġcann abin", + "ĠCh ambers", + "ĠZ ack", + "Option al", + "Ġco ats", + "Ġprow ess", + "ĠNort on", + "Ġplain ly", + "Ġfre ight", + "Ġinhib ition", + "Ġcl am", + "Ġ30 3", + "ke f", + "ale igh", + "L uke", + "Ġpsych o", + "ator ium", + "M ED", + "Ġtreat ies", + "Ġind isc", + "Ġd c", + "OP S", + "Ġresil ient", + "ĠInter state", + "Ġsl ack", + "Ġmund ane", + "Ġestab lishes", + "35 9", + "Ġstr ained", + "Ġn ond", + "S us", + "Ġcast e", + "ar ate", + "ie ving", + "Ġunfair ly", + "Ġpars er", + "on ial", + "urs ive", + "V ia", + "ĠOtt o", + "ĠAuthor ities", + "stro ke", + "K R", + "ĠMer cy", + "Ġfurn ished", + "Ġout set", + "Ġmet ic", + "19 82", + "olith ic", + "ĠT ent", + "og ical", + "ĠA ircraft", + "Ġh ides", + "ĠBec ame", + "Ġeduc ators", + "re aching", + "Ġvol atility", + "Ġtodd ler", + "ĠNAS CAR", + "ĠTw elve", + "ĠHigh lights", + "Ġgra pe", + "Ġspl its", + "Ġpe asant", + "Ġre neg", + "ĠMS I", + "Tem p", + "st ars", + "Ġtre k", + "ĠHy de", + "b inding", + "Ġreal ism", + "Ġox ide", + "ĠH os", + "Ġmount s", + "Ġbit ing", + "Ġcollaps ing", + "Ġpost al", + "Ġmuse ums", + "Ġdet ached", + "Ġrespect ing", + "Ġmonop ol", + "Ġwork flow", + "ĠC ake", + "Tem plate", + "ĠOrgan isation", + "Ġpers istence", + "36 9", + "C oming", + "B rad", + "Ġredund ant", + "ĠG TA", + "Ġb ending", + "Ġrev oked", + "Ġoff ending", + "Ġfram ing", + "Ġprint f", + "Comm un", + "mem bers", + "Out side", + "Ġconst rued", + "Ġc oded", + "F ORE", + "Ġch ast", + "Ch at", + "Ind ian", + "ĠY ard", + "? !\"", + "ĠP orts", + "ĠX avier", + "ĠR ET", + "' .\"", + "ĠBo at", + "iv ated", + "ich t", + "umer able", + "D s", + "ĠDun n", + "Ġcoff in", + "Ġsecure ly", + "ĠRapt ors", + "ĠB es", + "Install ation", + "Ġin ception", + "ĠHealth y", + "end ants", + "Ġpsych ologists", + "ĠShe ikh", + "c ultural", + "ĠBlack Berry", + "sh ift", + "F red", + "oc he", + "Ġc akes", + "ĠS EO", + "ĠG ian", + "ĠAs ians", + "og ging", + "e lement", + "Ġpund its", + "ĠV augh", + "ĠG avin", + "Ġh itter", + "Ġdrown ed", + "Ġch alk", + "ĠZ ika", + "Ġmeas les", + "80 2", + "âĢ¦ ..", + "ĠAW S", + "] \"", + "Ġdist ort", + "ĠM ast", + "Ġantib odies", + "ĠM ash", + "Mem ory", + "ĠUg anda", + "ĠPro b", + "Ġvom iting", + "ĠTurn s", + "Ġoccup ying", + "Ġev asion", + "ĠTher apy", + "Ġprom o", + "Ġelect r", + "Ġblue print", + "ĠD re", + "pr iced", + "ĠDep ot", + "Ġallev iate", + "ĠSom ali", + "m arg", + "n ine", + "Ġnostalg ia", + "ĠShe pherd", + "Ġcaval ry", + "Ġtor ped", + "ĠBlood y", + "x b", + "Ġs ank", + "Ġgo alt", + "report print", + "embed reportprint", + "clone embedreportprint", + "ĠIn itially", + "ĠF ischer", + "Ġnot eworthy", + "c ern", + "Ġin efficient", + "raw download", + "rawdownload cloneembedreportprint", + "c ation", + "ĠD ynasty", + "l ag", + "D ES", + "Ġdistinct ly", + "ĠEston ia", + "Ġopen ness", + "Ġg ossip", + "ru ck", + "W idth", + "ĠIb rahim", + "Ġpet roleum", + "Ġav atar", + "ĠH ed", + "ath a", + "ĠHog warts", + "Ġc aves", + "67 8", + "Ġsafegu ard", + "ĠM og", + "iss on", + "ĠDur ham", + "sl aught", + "ĠGrad uate", + "Ġsub conscious", + "ĠEx cellent", + "ĠD um", + "---- -", + "Ġp iles", + "ĠW ORK", + "ĠG arn", + "ĠF ol", + "ĠAT M", + "Ġavoid s", + "ĠT ul", + "Ġble ak", + "EL Y", + "iv ist", + "light ly", + "P ers", + "ĠD ob", + "ĠL S", + "Ġins anity", + "Î µ", + "atal ie", + "En large", + "Ġtw ists", + "Ġfault y", + "Ġpir acy", + "Ġimp over", + "Ġrug ged", + "ĠF ashion", + "Ġs ands", + "' ?", + "sw ick", + "Ġn atives", + "Ġhe n", + "ĠNo ise", + "ãĥ Ĺ", + "Ġg reens", + "Ġfree zer", + "Ġd ynasty", + "ĠFather s", + "ĠNew ark", + "Ġarchae ological", + "Ġo t", + "ob ar", + "Ġblock ade", + "Ġall erg", + "L V", + "Ġdeb it", + "ĠR FC", + "ĠMil ton", + "ĠPress ure", + "Ġwill ingly", + "Ġdisproportion ate", + "Ġopp ressive", + "Ġdiamond s", + "Ġbelong ings", + "19 70", + "Ġbell s", + "Ġimperial ism", + "Ġ2 27", + "Ġexpl oding", + "ĠE clipse", + "Ġ19 19", + "Ġr ant", + "Ġnom inations", + "34 7", + "Ġpeace fully", + "ric a", + "ĠF UCK", + "Ġvib ration", + "mal ink", + "Ġro pes", + "ĠIv anka", + "ĠBrew ery", + "ĠBook er", + "ĠOw ens", + "go ers", + "Serv ices", + "ĠSn ape", + "Ġ19 1", + "39 5", + "Ġ2 99", + "just ice", + "Ġb ri", + "Ġdisc s", + "Ġprom inently", + "Ġvul gar", + "Ġsk ipping", + "l ves", + "Ġtsun ami", + "37 4", + "ĠU rug", + "ĠE id", + "rec ated", + "p hen", + "Ġfault s", + "ĠStart ed", + "9 50", + "Ġp i", + "Ġdetect or", + "Ġbast ard", + "Ġvalid ated", + "Space Engineers", + "OUR CE", + "Ġ( ~", + "Ġuns ur", + "Ġaff irmed", + "Ġfasc ism", + "Ġres olving", + "ĠCh avez", + "ĠC yn", + "Ġdet ract", + "L ost", + "Ġrig ged", + "Ġhom age", + "ĠBrun o", + "55 5", + "ec a", + "Ġpress es", + "Ġhum our", + "Ġsp acing", + "Ġ' /", + "olk ien", + "C oun", + "OP ER", + "T re", + "S on", + "ĠCambod ia", + "ier re", + "m ong", + "o zy", + "Ġliquid ity", + "ĠSov iets", + "ĠFernand o", + "Ġ2 29", + "Ġsl ug", + "ĠCatal an", + "elect ric", + "Ġsc enery", + "ĠH earth", + "Ġconst rained", + "Ġgoal ie", + "ĠGu idelines", + "ĠAm mo", + "ĠPear son", + "Ġtax ed", + "Ġfet us", + "Resp onse", + "ĠAlex is", + "th ia", + "G uy", + "Ġrecon struct", + "Ġextrem es", + "Ġconclud ing", + "ĠP eg", + "ook s", + "Ġded uctions", + "R ose", + "Ġground breaking", + "ĠT arg", + "ãĥ ģ", + "ĠRe ve", + "res ource", + "Ġmo ons", + "Ġelectrom agnetic", + "Ġamid st", + "ĠVik tor", + "N ESS", + "B ACK", + "Ġcomm ute", + "ĠAna heim", + "Ġfluct uations", + "6 40", + "Ġnood les", + "ĠCop enhagen", + "ĠT ide", + "ĠGri zz", + "ĠS EE", + "Ġpip elines", + "Ġsc ars", + "end o", + "ag us", + "ĠE TF", + "/ #", + "ĠBec ome", + "44 8", + "Ġvis c", + "ĠRecomm ended", + "Ġj umper", + "Ġcogn ition", + "Ġassass in", + "Ġwitness ing", + "ĠSet up", + "Ġl ac", + "v im", + "IS M", + "p ages", + "SS L", + "35 8", + "Ġad ject", + "indust rial", + "l ore", + "cher y", + "Ġgl itter", + "Ġc alf", + "Flor ida", + "Ġspoil ers", + "Ġsucceed s", + "Ġch anting", + "Ġslog ans", + "ĠTr acy", + "Vis it", + "rol ogy", + "Ġm ornings", + "Ġline age", + "Ġs ip", + "Ġintense ly", + "Ġflour ish", + "ĠSle eping", + "ĠF em", + "or por", + "ĠK lan", + "ĠDar th", + "h ack", + "ĠNi elsen", + "Ġtum ors", + "Ġprocure ment", + "ĠY orkshire", + "Ġra ided", + "K Y", + "An na", + "Ġ// [", + "ĠDis order", + "ĠMust ang", + "ĠW en", + "ĠTry ing", + "s q", + "Ġdeliver ies", + "Ġshut ter", + "Ġcere bral", + "Ġbip olar", + "ĠC N", + "l ass", + "j et", + "Ġdeb ating", + "> :", + "Ġe agle", + "gr ades", + "ĠD ixon", + "UG C", + "M AS", + "ĠDr aco", + "ĠMach ines", + "aff er", + "Ġem an", + " ²", + "pr on", + "ĠG ym", + "Ġcompar atively", + "ĠTrib unal", + "PR O", + "Ġle x", + "Ġfert ile", + "Ġdep ressing", + "Ġsuperf icial", + "ess ential", + "ĠHun ters", + "g p", + "Ġprom inence", + "L iber", + "ĠAn cest", + "ote chnology", + "Ġm ocking", + "ĠTra ff", + "ĸ ļ", + "Med ium", + "I raq", + "Ġpsychiat rist", + "Quant ity", + "ĠL ect", + "Ġno isy", + "5 20", + "G Y", + "Ġsl apped", + "ĠM TV", + "Ġpar a", + "p ull", + "Mult iple", + "as her", + "Ġn our", + "ĠSe g", + "Spe ll", + "v ous", + "ord ial", + "Sen ior", + "ĠGold berg", + "ĠPl asma", + "ne ed", + "Ġmess enger", + "ere t", + "Ġteam ed", + "Ġliter acy", + "ĠLe ah", + "ĠD oyle", + "Ġem itted", + "U X", + "Ġev ade", + "Ġm aze", + "Ġwrong ly", + "ĠL ars", + "Ġstere otype", + "Ġpled ges", + "Ġarom a", + "ĠM ET", + "Ġac re", + "ĠO D", + "Ġf f", + "Ġbrew eries", + "ĠH ilton", + "und le", + "ĠK ak", + "ĠThank fully", + "ĠCan ucks", + "in ctions", + "ĠApp ears", + "Ġco er", + "Ġundermin ed", + "ro vers", + "And re", + "Ġbl aze", + "um ers", + "Ġfam ine", + "amp hetamine", + "ulk an", + "Am ount", + "Ġdesper ation", + "wik ipedia", + "develop ment", + "ĠCor inth", + "uss ia", + "Jack son", + "L I", + "N ative", + "R s", + "Oh io", + "ĠKath leen", + "F ortunately", + "Ġattend ant", + "ĠPre ferred", + "ĠDid n", + "ĠV s", + "M is", + "Ġrespond ent", + "Ġb oun", + "st able", + "Ġp aved", + "Ġunex pl", + "ĠChe ney", + "L M", + "ĠC ull", + "bl own", + "Ġconfront ing", + "oc ese", + "serv ing", + "W i", + "ĠLith uania", + "ann i", + "Ġst alk", + "h d", + "Ġv ener", + "AP H", + "ynchron ous", + "UR R", + "um ably", + "hist oric", + "H alf", + "H ay", + "Ġresil ience", + "spe ction", + "Ġabandon ing", + "O bs", + "ĠDeb bie", + "Ġgrad ient", + "ĠPl aint", + "ĠCan al", + "AR CH", + "Ġexpans ive", + "Ġfun g", + "Ġb ounced", + "U nd", + "Ġprec autions", + "Ġclar ification", + "Ġd agger", + "Ġgri ps", + "Ġ µ", + "ĠRiver a", + "ĠUnd ead", + "is ites", + "ĠFIR ST", + "ñ o", + "aud i", + "Ġhost ages", + "Ġcompl iant", + "Ġal umni", + "Se ven", + "Ġcyber security", + "e ither", + "Col lect", + "Ġinvari ably", + "ĠS oci", + "Ġlaw maker", + "Ġa le", + "ĠPerson ally", + "N azi", + "Ġcustom ization", + "ĠPro c", + "ĠSask atchewan", + "eat uring", + "Ġsp ared", + "Ġdiscontin ued", + "Ġcomput ational", + "ĠMotor ola", + "Ġsuprem acist", + "government al", + "Ġparad ise", + "ĠDown ing", + "ĠNik on", + "Ġcat alyst", + "ber ra", + "Tor onto", + "8 75", + "bet a", + "ĠMac ron", + "Ġunreal istic", + "ve ctor", + "ĠVeh icles", + "it iveness", + "ĠR V", + "ĠCol bert", + "s in", + "o ji", + "ent in", + "ĠKr ish", + "hell o", + "ff ield", + "ok y", + "ĠT ate", + "Ġmap le", + "Ġa ids", + "chem ical", + "33 4", + "n uts", + "ĠWar p", + "Ġx x", + "ĠRob b", + "umer ous", + "_- _", + "ft ime", + "ĠV W", + "Ġw inger", + "ĠD ome", + "t ools", + "ĠP V", + "ĠGe orgetown", + "Ġg eared", + "Ġjihad ists", + "Ġc p", + "Ġster oids", + "M other", + "cler osis", + "ĠDR M", + "nes ia", + "Ġl inger", + "Ġimm ersive", + "ĠC OUN", + "Ġoutwe igh", + "ens ual", + "B and", + "Ġtransform s", + "mat ched", + "ps ons", + "ĠJud icial", + "f actor", + "Ġrefer ral", + "Ġodd ly", + "ĠW enger", + "B ring", + "ĠB ows", + "60 2", + "IC LE", + "Ġl ions", + "ĠAcad emic", + "ĠTh orn", + "ĠRa ider", + "kef eller", + "St orage", + "L ower", + "ĠOr t", + "ĠEqu ality", + "AL T", + "ĠS OC", + "T ypes", + "Ġl yn", + "ĠAss et", + "co at", + "TP P", + "C VE", + "ĠPione er", + "app lication", + "Mod ern", + "ĠH K", + "En vironment", + "Al right", + "R ain", + "IP P", + "ĠShi ite", + "Ġm ound", + "ĠAb ilities", + "cond ition", + "St aff", + "Ġcompet ence", + "ĠM oor", + "ĠDi ablo", + "Ġwith held", + "Ġost ensibly", + "ĠB rom", + "Ġms g", + "Ġden omin", + "ĠRef erences", + "ĠF P", + "Ġplun ged", + "Ġp amph", + "m oving", + "cent ral", + "Ġdown right", + "Ġf ading", + "T al", + "T yp", + "ĠTh y", + "uk es", + "it he", + "Ġo ve", + "Ġbatt led", + "Ġseaf ood", + "Ġfig ur", + "ĠR D", + "c rop", + "Ġsqu ads", + "{ \\", + "à ¹", + "ĠE h", + "Ġinterview ing", + "ĠQ in", + "Ġas piring", + "PL IC", + "Ġcla uses", + "ĠG ast", + "ĠN ir", + "Ġl uggage", + "Ġh ose", + "Ġsystem d", + "Ġdesc ending", + "ĠRev ised", + "ĠR ails", + "al ign", + "70 9", + "33 7", + "Ġf ug", + "charg ing", + "t ags", + "Ġut er", + "k ish", + "WAR NING", + "49 0", + "prof its", + "Ġvoy age", + "Ġa ce", + "ĠV anguard", + "ĠT anks", + "ĠM uk", + "Ġ2 26", + "S afe", + "Ar mor", + "Ġvolcan ic", + "Ġwom b", + "ĠM IL", + "Ġbegin ner", + "ĠRec ogn", + "ĠA AP", + "PL AY", + ") !", + "Ġdetect ing", + "c n", + "Ġbre aches", + "Bas ically", + "ĠP ag", + "ĠMunicip al", + "ĠInd ie", + "ĠL af", + "ĠDis able", + "ĠOl son", + "Ġrest rained", + "Ġrul ings", + "Ġhum ane", + "ev ents", + "ĠCinem a", + "display Text", + "ĠH atch", + "action Date", + "onna issance", + "Ġassault ing", + "ĠL ug", + "CH AT", + "Ġvig orous", + "ĠPer se", + "Ġintoler ance", + "ĠSnap chat", + "ĠSh arks", + "Ġd ummy", + "ĠDi agn", + "ĠGu itar", + "im eters", + "40 3", + "RE G", + "A x", + "Ġsepar ates", + "ĠMah m", + "Ġt v", + "j ah", + "O OL", + "C irc", + "ĠWinds or", + "uss ian", + "Ġintu ition", + "Ġdis dain", + "ĠDon ovan", + "Ġ2 21", + "E mb", + "Ġcondem ning", + "Ġgener osity", + "zz y", + "Ġpant ies", + "ĠPre vent", + "Action Code", + "AN A", + "34 2", + "external ActionCode", + "Ġspec ifying", + "Ġcryst all", + "J ere", + "Ġru pt", + "ĠApp rentice", + "Ġprof iling", + "Ð º", + "St rike", + "Ġsid eline", + "Ġoblig ated", + "Ġocc ult", + "Ġbureaucr atic", + "ant ically", + "rupt ed", + "neg ative", + "ĠEthiop ia", + "ĠC ivic", + "Ġins iders", + "el igible", + "ĠTV s", + "ĠB AR", + "ĠT I", + "i ologist", + "ĠA IR", + "Ġsubstit uted", + "Ar ab", + "ĠS aul", + "ĠY og", + "p rem", + "Ġbuild ers", + "Ġstation ary", + "Ġdoubt ful", + "Ġvig orously", + "Ġthr illing", + "Ph ysical", + "ĠCare y", + "ĠHyd ra", + "geon ing", + "ĠS ly", + "y ton", + "Ġborrow ers", + "ĠPark inson", + "Ġ ë", + "ĠJama ica", + "Ġsat ir", + "Ġinsurg ents", + "ĠF irm", + "Ġis ot", + "ĠK arn", + "our ning", + "ak ens", + "doc s", + "l ittle", + "ĠMon aco", + "CL ASS", + "Tur key", + "L y", + "ĠCon an", + "ass ic", + "Ġstar red", + "ĠPac ers", + "et ies", + "Ġt ipping", + "M oon", + "ĠR w", + "s ame", + "Ġcav ity", + "Ġgo of", + "ĠZ o", + "Sh ock", + "um mer", + "Ġemphas izes", + "Ġreg rett", + "Ġnovel ty", + "Ġen vy", + "ĠPass ive", + "r w", + "50 5", + "Ġind ifferent", + "ĠR ica", + "ĠHim self", + "ĠFred die", + "Ġad ip", + "ä¸ Ģ", + "Ġbreak out", + "Ġhur ried", + "ĠHu ang", + "ĠD isk", + "Ġro aming", + "?????- ?????-", + "U V", + "ĠRick y", + "ĠS igma", + "Ġmarginal ized", + "Ġed its", + "Ġ30 4", + "mem ory", + "Ġspec imen", + "29 3", + "ãģ ¯", + "Ġvert ically", + "Ġaud ition", + "ĠHe ck", + "Ġc aster", + "ĠHold ings", + "ad al", + "ĠC ron", + "ĠL iam", + "Ġdef lect", + "P ick", + "ĠDeb ug", + "RE F", + "Ġvers atility", + "ot hes", + "class ified", + "ĠMah ar", + "ĠH ort", + "C ounter", + "st asy", + "not iced", + "33 1", + "ĠSh im", + "f uck", + "ĠB ie", + "Ġair ing", + "ĠPro tein", + "ĠHold ing", + "Ġspect ators", + "ili ated", + "ĠThat cher", + "n osis", + "ãĥ¼ ãĥ³", + "Te le", + "B oston", + "ĠTem pl", + "st ay", + "Ġdecl arations", + "47 9", + "Vol ume", + "ĠDesign er", + "ĠOver watch", + "id ae", + "Ġon wards", + "Ġn ets", + "ĠMan ila", + "part icularly", + "Ġpolit ic", + "o other", + "Ġport raits", + "Ġpave ment", + "c ffff", + "Ġs aints", + "Ġbegin ners", + "ES PN", + "Ġshort comings", + "âķIJ âķIJ", + "Ġcom et", + "ĠOrgan ic", + "qu el", + "Ġhospital ized", + "Bre ak", + "Ġpe el", + "dyl ib", + "asp x", + "ur ances", + "ĠT IM", + "P g", + "Ġread able", + "ĠMal ik", + "Ġm uzzle", + "Ġbench marks", + "d al", + "ĠV acc", + "ĠH icks", + "60 9", + "ĠB iblical", + "he ng", + "Ġover load", + "ĠCivil ization", + "Ġimm oral", + "Ġf ries", + "ãĤ Ĵ", + "Ġreprodu ced", + "Ġform ulation", + "j ug", + "ire z", + "g ear", + "Ġco ached", + "Mp Server", + "ĠS J", + "ĠK w", + "In it", + "d eal", + "ĠO ro", + "ĠL oki", + "ĠSong s", + "Ġ23 2", + "ĠLou ise", + "asion ally", + "Ġunc ond", + "olly wood", + "Ġprogress ives", + "ĠEn ough", + "ĠDo e", + "Ġwreck age", + "Ġbr ushed", + "ĠBase Type", + "Ġz oning", + "ish able", + "het ically", + "ĠC aucus", + "ĠH ue", + "Ġk arma", + "ĠSport ing", + "Ġtrad er", + "Ġseem ing", + "ĠCapt ure", + "4 30", + "b ish", + "Ġt unes", + "Ġindo ors", + "ĠSp here", + "ĠD ancing", + "TER N", + "Ġno b", + "ĠG ST", + "m aps", + "Ġpe ppers", + "F it", + "Ġoverse es", + "ĠRabb i", + "ĠR uler", + "vert ising", + "off ice", + "xx x", + "Ġra ft", + "Ch anged", + "Ġtext books", + "L inks", + "ĠO mn", + "ãĢ ij", + "Ġinconven ience", + "ĠDon etsk", + "= ~", + "Ġimplicit ly", + "Ġboost s", + "ĠB ones", + "ĠBo om", + "Cour tesy", + "Ġsens ational", + "AN Y", + "Ġgre edy", + "ed en", + "Ġinex per", + "ĠL er", + "ĠV ale", + "Ġtight en", + "ĠE AR", + "ĠN um", + "Ġancest or", + "S ent", + "ĠH orde", + "urg ical", + "all ah", + "Ġsa p", + "amb a", + "ĠSp read", + "tw itch", + "Ġgrand son", + "Ġfract ure", + "Ġmoder ator", + "ĠSe venth", + "ĠRe verse", + "Ġestim ation", + "Cho ose", + "Ġpar ach", + "Ġbar ric", + "ãĢ IJ", + "Ġcomp ass", + "Ġall ergic", + "âĢ ķ", + "OT HER", + "err illa", + "Ġw agon", + "Ġz inc", + "Ġrub bed", + "ĠFull er", + "ĠLuxem bourg", + "ĠHoo ver", + "Ġli ar", + "ĠEven ing", + "ĠCob b", + "est eem", + "Ġselect or", + "ĠB rawl", + "is ance", + "ĠE k", + "Ġtro op", + "Ġg uts", + "ĠApp eal", + "ĠTibet an", + "Ġrout ines", + "ĠM ent", + "Ġsummar ized", + "steam apps", + "Ġtr anqu", + "Ġ19 29", + "or an", + "ĠAut hent", + "Ġg maxwell", + "Ġappre hens", + "Ġpo ems", + "Ġsa usage", + "ĠWeb ster", + "ur us", + "Ġthem ed", + "Ġl ounge", + "Ġcharg er", + "Sp oiler", + "Ġsp illed", + "h og", + "ĠSu nder", + "ĠA in", + "ĠAng ry", + "Ġdis qual", + "ĠFrequ ency", + "ĠEther net", + "Ġhel per", + "Per cent", + "Ġhorr ifying", + "Ġa il", + "ĠAll an", + "EE E", + "ĠCross ing", + "44 9", + "Ġh olog", + "ĠPuzz les", + "ĠGo es", + "eren n", + "60 4", + "ãģ ı", + "ĠRaf ael", + "Ġatt en", + "ĠE manuel", + "Ġup ro", + "ĠSus p", + "P sych", + "ĠTr ainer", + "ĠN ES", + "ĠHun ts", + "bec ue", + "Ġcounsel or", + "R ule", + "Ġtox ins", + "Ġb anners", + "r ifice", + "Ġgreet ing", + "Ġfren zy", + "Ġall ocate", + "Ġ* )", + "ex pr", + "50 3", + "ĠCh ick", + "ĠT orn", + "Ġconsolid ation", + "ĠF letcher", + "sw itch", + "fr ac", + "cl ips", + "ĠMcK in", + "ĠLun ar", + "Mon th", + "IT CH", + "Ġscholar ly", + "rap ed", + "39 8", + "Ġ19 10", + "Ġe greg", + "Ġin secure", + "Ġvict orious", + "cffff cc", + "Ġsing led", + "Ġel ves", + "ĠW ond", + "bur st", + "Ġcam oufl", + "ĠBL ACK", + "Ġcondition ed", + "ç ī", + "ans wered", + "Ġcompuls ory", + "asc ist", + "Ġpodcast s", + "ĠFrank furt", + "bn b", + "Ġne oliberal", + "ĠKey board", + "ĠBel le", + "w arm", + "Ġtrust s", + "Ġins ured", + "ĠBu cc", + "us able", + "60 7", + "ĠPl ains", + "Ġ18 90", + "Ġsabot age", + "Ġlod ged", + "f elt", + "Ġg a", + "ĠN arc", + "ĠSal em", + "Ġsevent y", + "ĠBl ank", + "p ocket", + "Ġwhis per", + "Ġm ating", + "om ics", + "ĠSal man", + "ĠK ad", + "Ġan gered", + "Ġcoll isions", + "Ġextraord inarily", + "Ġcoerc ion", + "G host", + "b irds", + "è Ģ", + "k ok", + "Ġper missible", + "avor able", + "Ġpo inters", + "Ġdiss ip", + "ac i", + "Ġtheat rical", + "ĠCos mic", + "Ġforget ting", + "Ġfinal ized", + "å¤ §", + "y out", + "l ibrary", + "Ġbo oming", + "ĠBel ieve", + "ĠTe acher", + "ĠL iv", + "ĠGOOD MAN", + "ĠDomin ican", + "OR ED", + "ĠPart ies", + "Ġprecip itation", + "ĠSl ot", + "R oy", + "ĠComb ined", + "Ġinteg rating", + "Ġch rome", + "Ġintest inal", + "ĠRe bell", + "Ġmatch ups", + "Ġblock buster", + "ĠLore n", + "ĠLe vy", + "Ġpre aching", + "ĠS ending", + "ĠPur pose", + "ra x", + "f if", + "Ġauthor itative", + "ĠP ET", + "ast ical", + "Ġdish on", + "Ġchat ting", + "Ġ\"$ :/", + "Connect ion", + "Ġrecre ate", + "Ġdel inqu", + "Ġbro th", + "ĠD irty", + "ĠAd min", + "z man", + "Ġscholars hips", + "Ġ25 3", + "cont act", + "als a", + "7 67", + "c reen", + "abb age", + "Ġ19 15", + "Ġbl ended", + "Ġal armed", + "L anguage", + "35 6", + "Ġbl ends", + "ĠCh anged", + "W olf", + "Ġhe pat", + "Creat ing", + "Ġper secut", + "Ġsweet ness", + "art e", + "Ġforfe iture", + "ĠRober to", + "im pro", + "N FL", + "ĠMag net", + "Det ailed", + "Ġinsign ificant", + "ĠPOL IT", + "ĠBB Q", + "ĠC PS", + "Ġse aw", + "amin er", + "m L", + "end if", + "f inals", + "Ġ26 5", + "u ish", + "Ġ} )", + "ĠPro blems", + "Ġem blem", + "Ġserious ness", + "Ġpars ing", + "Ġsubst itution", + "Ġpress ured", + "Ġrecy cled", + "ale b", + "Rub y", + "Ġprof iciency", + "Dri ver", + "ĠW ester", + ": '", + "AF TA", + "Ġm antle", + "ĠClay ton", + "fl ag", + "Ġpractition er", + "c overed", + "ĠSt ruct", + "add afi", + "4 25", + "ĠTown ship", + "ĠHyd ro", + "Lou is", + "34 3", + "Ġcond o", + "ĠT ao", + "Ġutil ization", + "Ġnause a", + "ĠDem s", + "rid ges", + "p ause", + "Ġform ulas", + "Ġchall enger", + "37 6", + "Ġdefect ive", + "ĠRail way", + "ĠPub Med", + "Ġyog urt", + "l bs", + "ĠNor folk", + "OP E", + "ĠMood y", + "Ġdistribut or", + "Ġscroll s", + "Ġextract s", + "St an", + "Ġv iability", + "Ġexp oses", + "Ġstar vation", + "ĠStep s", + "ĠD odd", + "f ew", + "ST D", + "33 2", + "Ġclos ures", + "Ġcomplement ary", + "ĠS asha", + "ump y", + "Ġmon et", + "Ġartic ulate", + "ĠDo ct", + "k iller", + "Ġsc rim", + "Ġ2 64", + "Ġprost itutes", + "Ġse vered", + "Ġattach ments", + "Ġcool ed", + "L ev", + "ĠF alk", + "f ail", + "Ġpolic eman", + "ĠD ag", + "Ġpray ed", + "ĠK ernel", + "Ġcl ut", + "Ġc ath", + "Ġan omaly", + "St orm", + "em aker", + "ĠBreak fast", + "ul i", + "o ire", + "J J", + "h z", + "Oper ation", + "ĠS ick", + "35 4", + "ĠGuatem ala", + "R ate", + "Ġexp osures", + "f aces", + "ĠArch ae", + "ra f", + "ĠM ia", + "Ġ20 25", + "Ġop aque", + "Ġdisgu ised", + "ĠHead quarters", + "S ah", + "Ġp ots", + "9 78", + "ĠM alf", + "Ġfrown ed", + "Ġpoison ous", + "ĠCon vers", + "ee ks", + "Ġcr ab", + ".\" \"", + "Ġtre ason", + "Ġr anc", + "Ġescal ating", + "Ġwar r", + "Ġmob s", + "Ġl amps", + "ĠSun shine", + "ĠBrun swick", + "Ph ones", + "Ġspe lled", + "ĠSk ip", + "Ġ20 50", + "Ġ19 11", + "ĠPl uto", + "ĠAm end", + "Ġme ats", + "38 7", + "Ġst omp", + "ĠZh ou", + "ĠLevi athan", + "ĠHaz ard", + "ad v", + "ĠOr well", + "Ġal oud", + "Ġb umper", + "ĠAn arch", + "ub untu", + "ĠSer ious", + "f itting", + "ĠOption al", + "ĠCec il", + "RE AM", + "Ġser otonin", + "Ġcultiv ate", + "ag ogue", + "} \\", + "Ġmos ques", + "ĠSun ny", + "Ġre active", + "rev olution", + "ĠL up", + "ĠFed ora", + "Ġdefense man", + "ĠV ID", + "ist ine", + "Ġdrown ing", + "ĠBroad casting", + "Ġthr iller", + "ĠS cy", + "Ġacceler ating", + "Ġdirect s", + "od ied", + "b ike", + "d uration", + "Ġpain fully", + "R edd", + "Ġproduct ions", + "Ġg ag", + "Ġwh ist", + "Ġs ock", + "Ġinf initely", + "ĠConc ern", + "ĠCit adel", + "Ġlie u", + "Ġcand les", + "ogene ous", + "arg er", + "Ġheaven ly", + "inflamm atory", + "Per formance", + "C s", + "ruct ose", + "az aki", + "Ġp essim", + "Ġinf erence", + "Ġpow d", + "ĠZ oe", + "Ġpain ts", + "Ġd azz", + "pt a", + "-------- ---", + "Ġins pir", + "ĠExper imental", + "ĠKn ife", + "reg or", + "b ors", + "Ġshow ers", + "rom eda", + "Ġs aint", + "Ġben ign", + "ĠJ iang", + "Ġenvision ed", + "Ġsh roud", + "IF T", + "H O", + "Ġsh uff", + "ĠI CC", + "Ġse greg", + "Ġrevis it", + "ighth ouse", + "L i", + "Ġsub strate", + "ĠSe as", + "ĠRew ard", + "ĠH ep", + "ĠBr ass", + "s bm", + "Ġelim inates", + "Ġst amina", + "ĠV AT", + "ĠLo an", + "Ġconst raint", + "Ġappropri ated", + "Ġp es", + "ĠA LE", + "r anging", + "Ġ40 4", + "39 2", + "Ġintellectual s", + "ach u", + "Ġrestruct uring", + "ĠLe vin", + "Ġrun es", + "Ġdelight ful", + "Ġcarbohyd rates", + "ĠMod els", + "ĠExp o", + "Ġtransport ing", + "all oc", + "Ġring ing", + "S amsung", + "Ġscarce ly", + "ĠURL s", + "ĠM AS", + "Ġprot otypes", + "Ġnarr ator", + "ĠCPU s", + "cd n", + "ĠBart on", + "Ġdecided ly", + "ĠSh u", + "ix ir", + "oc ious", + "ĠMy st", + "N intendo", + "Ġre use", + "Ġforg iven", + "F ew", + "in ical", + "n at", + "Ġseam less", + "ĠEv a", + "ĠE VE", + "ĠJ O", + "land ers", + "Ġso fter", + "neg ie", + "Ġtrans ient", + "Ġorb ital", + "Ġfulf il", + "ĠK om", + "Hop efully", + "Ġdynam ically", + "ĠHun ger", + "å Ľ", + "ĠArmen ia", + "el man", + "ber to", + "Ġp ige", + "ĠID s", + "lim it", + "Ġve ins", + "Ġso aring", + "p acks", + "Gold en", + "ĠCr ab", + "ist or", + "ĠR PM", + "Ġ$ $", + "g ression", + "Ġjihad ist", + "Ġgam ble", + "Ġcare g", + "Ġinf lated", + "F ace", + "ĠFire arms", + "ĠEm manuel", + "â Ŀ", + "Ġsh ocks", + "gr ab", + "Ġspl end", + "ĠHP V", + "ab ortion", + "Ab ove", + "Ent ity", + "play ers", + "Ġcomm enced", + "ul ence", + "Ġfulfill ment", + "Ġembod iments", + "ĠW elfare", + "Ġha il", + "Ġ< @", + "tt en", + "Ġcat cher", + "ĠJ azeera", + "Ġvolcan o", + "Ġstabil ize", + "ĠHand ler", + "Ġintens ified", + "ĠAb rams", + "Ġhum iliation", + "p aced", + "60 5", + "ĠCent OS", + "Spe cific", + "Ġhe ed", + "ĠC AM", + "ĠGal ile", + "D ie", + "Ġabol ished", + "ĠThom son", + "ĠTe achers", + "ĠW ass", + "j ong", + "ĠIS BN", + "ĠAll ies", + "sh ake", + "å ·", + "v ict", + "How ard", + "Ġde em", + "Ġexceed ingly", + "ĠSmart stocks", + "ib e", + "Ġdoor way", + "Ġcompet ed", + "ig mat", + "Ġnational ists", + "Ġg room", + "ĠKe en", + "Ġdispos able", + "de cl", + "ĠT olkien", + "ĠSche me", + "Ġb iod", + "Ġav id", + "ĠEl on", + "ag ar", + "ĠT SA", + "R oman", + "Ġartific ially", + "Ġadvis ors", + "X L", + "ĠInf erno", + "36 6", + "Ġted ious", + "ĠPhot ography", + "ĠCar rie", + "Ġtro pe", + "ĠSand ra", + "Ġdec imal", + "Que en", + "ĠGund am", + "ĠO M", + "ote ch", + "N BA", + "Ġ19 32", + "Ġent renched", + "ĠMar ion", + "Ġfr aternity", + "Lab our", + "Hen ry", + "Ġlat itude", + "E ither", + "Ġenh ances", + "ĠPot ential", + "Ġsh ines", + "id ad", + "Ġbread th", + "Ġcapac ities", + "ĠðŁ ĻĤ", + "ĠBron x", + "Ġsex es", + "Ġdifferent iation", + "Ġheavy weight", + "ĠT aj", + "d ra", + "Ġmigr ate", + "Ġexhaust ion", + "ĠR UN", + "els ius", + "ĠCu omo", + "Ġgu itars", + "Ġcl ones", + "ĠSom ew", + "ĠP ry", + "------------ -", + "Ġwarr anted", + "cy cles", + "Ġsalv age", + "Ġdis ks", + "R ANT", + "ĠNGO s", + "ĠMart ian", + "\":[ {\"", + "Ġadd icts", + "oj ure", + "il let", + "Ġamazing ly", + "art ments", + "p ixel", + "ĠGPU s", + "Lay out", + "è £", + "ĠTam il", + "ĠBas il", + "Ġimpart ial", + "ĠSt ructure", + "f ork", + "b ryce", + "Ġr idge", + "ĠHamb urg", + "ri ous", + "Ġbl itz", + "cig arettes", + "Ġcan ned", + "40 2", + "Ġiron ically", + "Ġcompassion ate", + "ĠHaw kins", + ". #", + "ĠCat hedral", + "Ġrall ied", + "in ternal", + "Ġqu ota", + "st akes", + "T EXT", + "m om", + "Ġcomple tes", + "Ġ23 8", + "Ġsh rug", + "ãĥ ij", + "ĠN inth", + "Ġrev ise", + "ĠProv ider", + "Ġtre acher", + "Ġqu asi", + "ĠPR ES", + "Ġdep osition", + "Ġconfidential ity", + "iss ors", + "Ġim balance", + "Ġspan ning", + "Ġang ular", + "ĠC ul", + "commun ication", + "ĠNor a", + "ĠGen ius", + "op ter", + "Ġs acked", + "Sp ot", + "Ġfine ly", + "ĠCH R", + "28 2", + "w aves", + "Pal est", + "ĠRo hing", + "N L", + "è ¿", + "Ġsh itty", + "ĠSc alia", + "4 75", + "Pro gress", + "Ġreferen cing", + "Ġclass rooms", + "ab ee", + "Ġs od", + "hes ion", + "70 8", + "ĠZucker berg", + "ĠFin ish", + "ĠScot ia", + "ĠSav ior", + "ĠInstall ation", + "an tha", + "( -", + "Ġ30 2", + "ĠP unk", + "Ġcr ater", + "yout u", + "Ġro ast", + "Ġinflu encing", + "Ġd up", + "ĠJ R", + "ĠG rav", + "Ġstat ure", + "Ġbath rooms", + "A side", + "W iki", + "me an", + "ĠZ ak", + "ĠOn es", + "ĠN ath", + "Ġhyper t", + "Ġcommence ment", + "C ivil", + "Ġmoder ately", + "Ġdistribut ors", + "Ġbreast feeding", + "Ġ9 80", + "ĠS ik", + "ĠC ig", + "ĠAM ER", + "R IP", + "ĠCare er", + "ust ing", + "Ġmess ed", + "Ġe h", + "ĠJ ensen", + "/ $", + "Ġblack mail", + "Ġconvers ions", + "Ġscientific ally", + "Ġmant ra", + "p aying", + "Ġiv ory", + "ĠCour ts", + "OU GH", + "aunt let", + "Ser ial", + "B row", + "ĠH undreds", + "3 23", + "Ġpe e", + "Ġlin ux", + "Ġsub mer", + "ĠPrinc ipal", + "48 5", + "ĠD SL", + "ĠCous ins", + "Ġdoctr ines", + "ĠAthlet ics", + "Ġ3 15", + "ĠK arma", + "Ġatt ent", + "ur ger", + "Ġpresc ribe", + "Ġenc aps", + "ĠC ame", + "Ġsecret ive", + "ĠCr imes", + "d n", + "C lean", + "ĠEgypt ians", + "ĠCar penter", + "Ġ ll", + "H um", + "ĠMil o", + "Ġcapital ists", + "Ġbrief ed", + "T we", + "ĠBas in", + "elve t", + "M os", + "Ġplun ge", + "ĠKa iser", + "ĠFu j", + "ill in", + "Ġsafegu ards", + "Ġo ste", + "ĠOpportun ity", + "ĠM afia", + "ĠCall ing", + "ap a", + "ur ban", + "br ush", + "ill ard", + "c é", + "int elligence", + "ĠL ob", + "ĠDru id", + "Ġsm oother", + "Ġfoot ing", + "Ġmotor ists", + "arc ity", + "Ġmascul inity", + "Ġm ism", + "Ġabdom inal", + "ĠTa vern", + "ĠR oh", + "Ġesc apes", + "s igned", + "Anth ony", + "Ġsacrific ing", + "Ġintim acy", + "Ġan terior", + "ĠK od", + "Ġmot if", + "Ġg raz", + "Ġvisual ization", + "Ġguitar ist", + "ĠTro tsky", + "m agic", + "D ar", + "ĠMor i", + "Ġw ards", + "Ġtoile ts", + "l est", + "Ġtele port", + "ĠSund ays", + "ĠPl at", + "ET S", + "Ġe Sports", + "Pat rick", + "ĠK atherine", + "en ko", + "Ġhas sle", + "ĠM ick", + "gg les", + "Ġh ob", + "aint ain", + "Ġair borne", + "Ġsp ans", + "Ġch ili", + "Ġa perture", + "Ġvolunte ered", + "ĠInc ident", + "ĠF res", + "ĠVeter an", + "augh tered", + "ing o", + "Ġun insured", + "CL OSE", + "Ġf use", + "Ġer otic", + "Ġadvert ise", + "ra ising", + "Text ure", + "Ġatt ends", + "ĠRE AL", + "udd led", + "Ġsm oot", + "Ġ30 5", + "ĠWill is", + "Ġbl ond", + "An alysis", + "ĠV T", + "on ica", + "Ġstrongh old", + "R F", + "N M", + ". >>", + "Ġprosper ous", + "Ġbo asted", + "29 2", + "ĠManufact uring", + "PR ESS", + "g ren", + "Ġpharm acy", + "ĠRoc kefeller", + "k ai", + "Ġth umbs", + "ĠH ut", + "Ġmother board", + "Ġguard ians", + "ĠAl ter", + "ll ular", + "Ġsh ack", + "Ġwise ly", + "Ġback bone", + "erv a", + "Ġsu icides", + "ĠMcG regor", + "ij ah", + "E mer", + "ĠB rav", + "Ġdesign ate", + "P OST", + "produ ced", + "Ġcleans ing", + "irl wind", + "ex istent", + "ĠHum ph", + "ĠPay ne", + "Ġv ested", + "Å ¡", + "Ġstring ent", + "ion a", + "Ġuns ub", + "Ġsum med", + "ĠHer cules", + "sub ject", + "ĠR agnar", + "ĠN os", + "Ġcharacter ization", + "Ġsav vy", + "ĠDaw son", + "ĠCas ino", + "Ġf ri", + "ĠBar rier", + "Ġmis information", + "Ġins ulation", + "Ġcorrid ors", + "Ġair planes", + "ĠNo ct", + "ah i", + "Ġ19 16", + "k b", + "arm ac", + "Ġsh un", + "Ġsche ma", + "Ġhorr ified", + "Ġ23 9", + "aund ers", + "N B", + "i ates", + "er ity", + "ĠSh ard", + "Ġr arity", + "Ġgroup ed", + "ĠGh ana", + "again st", + "ĠBi ological", + "ĠA ware", + "ow ell", + "Ï Ħ", + "ĠBe au", + "sh aw", + "H ack", + "ĠJul ius", + "US S", + "ol son", + "aun a", + "c ru", + "ĠMaur ice", + "ĠI k", + "Ġsequ encing", + "Ġradical s", + "Ġ( ?,", + "v irtual", + "Ġany ways", + "Ġreper c", + "Ġhand lers", + "Ġhes itant", + "é ĥ", + "ĠM F", + "ple mentation", + "ass ociated", + "Ġcampaign ed", + "ĠY ue", + "ut ations", + "ĠY oga", + "Ġsim mer", + "Ġro ds", + "Ġmel ody", + "Ġconv oy", + "v ideos", + "Ġscreen ed", + "N eg", + "ochem ical", + "Ġ( ))", + "Ġultr as", + "Ġant ip", + "ĠIsland ers", + "70 4", + "Ġfet ish", + "Ġridic ulously", + "ĠK art", + "Ġmitochond rial", + "Ġinterf ering", + "Build er", + "Ġover fl", + "Ġac ne", + "ĠM ud", + "ĠK err", + "f lex", + "ĠPost al", + "ĠBalt ic", + "47 7", + "ĠPers ons", + "our age", + "H B", + "ĠM use", + "ĠImm ortal", + "ĠDri ving", + "Ġpet itions", + "Ġsubsc ript", + "Ġs orce", + "ĠProcess or", + "ut on", + "S ony", + "Ġph on", + "Ġr aced", + "ĠAnth rop", + "Ġday time", + "ĠEx ercise", + "Add ing", + "Ġeng ages", + "ĠQual comm", + "Ġmir acles", + "Ġmem es", + "ĠDr ink", + "ĠOri oles", + "Ġhair s", + "ĠPol ar", + "ath om", + "Ġsl ippery", + "ĠR emy", + "Ġcar amel", + "ĠY EAR", + "Ġal k", + "I gn", + "a ution", + "ĠMer lin", + "ĠC ran", + "Ġap ologies", + "Ġ4 10", + "Ġout ing", + "ĠMem ories", + "app ointed", + "Ġcount ered", + "u ld", + "pos ing", + "Ġfire wall", + "ĠW ast", + "ĠW et", + "work ed", + "se ller", + "Ġrepe aled", + "ere o", + "ass uming", + "BL IC", + "m ite", + "ĠCEO s", + "ĠChap el", + "ellig ent", + "________________ ________", + "D og", + "Ġw art", + "Ġsubsc riber", + "s ports", + "Ġbe gged", + "ĠM V", + "Ġsem if", + "eth ical", + "Ġpre ach", + "Ġrev ital", + "Ġpun itive", + "Ġshort cuts", + "Ġinstit uted", + "ĠWars aw", + "Ġabdom en", + "ĠK ING", + "Ġsuper intendent", + "Ġf ry", + "ĠGe o", + "T OR", + "Ġcontrad ictions", + "apt ic", + "Ġlandsc apes", + "b ugs", + "Ġcl ust", + "Ġvol ley", + "c ribed", + "Ġt andem", + "Ġrob es", + "WH AT", + "Ġpromot er", + "Ġel oqu", + "review ed", + "ĠD K", + "ĠPl ato", + "Ġf ps", + "T ank", + "ĠDer rick", + "Ġpriorit ize", + "as per", + "ĠHond uras", + "ĠCom pleted", + "ne c", + "Ġm og", + "n ir", + "ĠMay o", + "DE F", + "st all", + "in ness", + "ĠVolks wagen", + "Ġprec aution", + "ĠM ell", + "i ak", + "ist ries", + "Ġ24 8", + "Ġoverl apping", + "Sen ate", + "ĠEnh ance", + "res y", + "rac ial", + "OR TS", + "ĠM ormons", + "Str ong", + "ĠCo ch", + "Mex ico", + "ĠMad uro", + "Ġj ars", + "Ġcan e", + "W ik", + "oll a", + "iff erence", + "Ġphysic ist", + "ĠMag gie", + "Ġ28 5", + "Ġdep iction", + "ĠMcL aren", + "J u", + "Ġsl ows", + "Ġcommission ers", + "ĠWill ow", + "ĠExpl os", + "hov ah", + "Ġtechn ician", + "Ġhom icides", + "ĠFl av", + "ĠTr uman", + "Ġ100 00", + "u ctor", + "Ġsh ader", + "News letter", + "45 7", + "Ġre ver", + "Ġhard ened", + "Ġwhere abouts", + "Ġrede velop", + "Ġcar bs", + "Ġtra vers", + "Ġsqu irrel", + "Ġfoll ower", + "Ġs ings", + "50 8", + "Ġrabb its", + "emon ium", + "Ġdocument ing", + "Ġmisunder stood", + ") '", + "R ick", + "gg ies", + "Ġprem ie", + "Ġsk ating", + "Ġpass ports", + "Ġf ists", + "aged don", + "H aw", + "AC P", + "0 80", + "ĠThough ts", + "ĠCarl son", + "Ġpriest hood", + "h ua", + "Ġdun geons", + "ĠLo ans", + "Ġant is", + "Ġfamiliar ity", + "ĠS abb", + "op al", + "ĠIn k", + "st rike", + "Ġc ram", + "Ġlegal ized", + "Ġcu isine", + "Ġfib re", + "Tra vel", + "ĠMon ument", + "OD Y", + "eth y", + "Ġinter state", + "ĠP UR", + "em porary", + "ĠArab ian", + "develop ed", + "Ġsadd le", + "Ġg ithub", + "ĠOff er", + "ĠIS P", + "ro let", + "ĠSUP ER", + "ĠDen is", + "Ġmultipl ier", + "Ġstir red", + "Interest ingly", + "Ġcustom ary", + "Ġbill ed", + "he x", + "Ġmultipl ied", + "Ġfl ipping", + "ĠCros by", + "Ġfundament als", + "ia e", + "ĠPlay ed", + "ĠAt om", + "am azon", + "ĠFl am", + "ee z", + "activ ated", + "Ġtables poon", + "Ġliberal ism", + "ĠPal in", + "ĠP atel", + "N um", + "ĠT AM", + "Ġs urn", + "ĠRel oaded", + "Ġco ined", + "\" ],", + "ĠCl ash", + "ĠAg u", + "Ġprag matic", + "ĠActiv ate", + "Ġ8 02", + "Ġtrail ers", + "Ġsil hou", + "Ġprob es", + "Ġcirc us", + "ĠB ain", + "ĠLind say", + "ĠAb bey", + "Del ivery", + "Ġconcess ion", + "Ġgast ro", + "ĠSpr ite", + "Ä Ł", + "and el", + "Ġg imm", + "Ġaut obi", + "ĠT urtle", + "Ġwonder fully", + "ĠHar am", + "ĠWorld wide", + "ĠHand le", + "Ġtheor ists", + "Ġsle ek", + "ĠZh u", + "ograph ically", + "EG A", + "ĠOwn ers", + "ath s", + "ĠAntar ctic", + "n atal", + "=\" \"", + "fl ags", + "`` ``", + "Ġs ul", + "K h", + "Ġpot assium", + "Ġlinem an", + "Ġcere al", + "ĠSe asons", + "Ġ20 22", + "Ġmat hematic", + "Ġastron omers", + "prof essional", + "Ġf ares", + "cknow led", + "Ġch i", + "Ġyoung sters", + "Ġmistaken ly", + "Ġhem isphere", + "ĠDiv inity", + "r one", + "Ġ\" ,", + "r ings", + "Ġattract s", + "v ana", + "å ¹", + "C AP", + "Ġplay list", + "Ġpor ch", + "ãģ £", + "Ġincorpor ates", + "Ġso ak", + "Ġassert ing", + "ĠTerror ism", + "ĠP ablo", + "J a", + "ces ter", + "Ġfear ing", + "ĠPr ayer", + "Ġescal ated", + "G W", + "Ġro be", + "ĠBright on", + "ac ists", + "ĠSym phony", + "ĠDwar f", + "ĠPar ade", + "ĠLe go", + "Ġinex pl", + "Ġl ords", + "le af", + "RA G", + "l iber", + "Ġcig ars", + "ĠJe hovah", + "60 6", + "WIND OWS", + "ĠLiber ia", + "eb us", + "He avy", + "Ġl ubric", + "ĠR W", + "angu ages", + "Ġnarrow ed", + "com puter", + "ĠE mber", + "Ġmurder ing", + "Ġdown stream", + "ĠT uls", + "ĠT ables", + "Top ic", + "ĠAcc uracy", + "= /", + "l ost", + "ĠRe i", + "Ġprogress es", + "b ear", + "Ġestablish ments", + "Just in", + "ĠPe ach", + "ĠG omez", + "å ¿", + "ĠTri angle", + "Id ent", + "ĠH ive", + "Res ources", + "Ġmix es", + "ĠAss uming", + "M u", + "Ġhyp oc", + "Ġs ane", + "ĠW an", + "id ious", + "Su ccess", + "Ġ io", + "Ang el", + "Ġdanger ously", + "ĠCreat ure", + "W ORK", + ": [", + "ĠKat rina", + "List ener", + "M iller", + "ĠId lib", + "h ang", + "Ġcircum vent", + "h ref", + "Ġcel estial", + "ĠWe eks", + "ĠP ug", + "ĠDal ton", + "Ġsubpoen a", + "uk u", + "Ġpers isted", + "pe i", + "old ing", + "ĠDoc uments", + "ĠH ast", + "ĠC ENT", + "Ġprim er", + "Ġsyn onymous", + "Ġn ib", + "om bs", + "Ġnot ation", + "ĠD ish", + "ĠAt mosp", + "Ġforb id", + "ĠAN G", + "pat tern", + "l os", + "Ġproject iles", + "b rown", + ".\" ,", + "ĠVen om", + "Ġfierce ly", + "ub lished", + "ĠU ran", + "ĠNic arag", + "4 10", + "ĠC AL", + "OT OS", + "ĠMir acle", + "ĠEn chant", + "Ġguard ing", + "app end", + "Att ach", + "Ġlevel ed", + "Ġcond oms", + "ih ilation", + "64 9", + "Ġnight mares", + "ĠTHE Y", + "ĠST ART", + "ĠK inn", + "Ġroomm ate", + "Ġhy giene", + "o pping", + "J ob", + "Ġl vl", + "ĠV ER", + "ĠKe eping", + "ab etic", + "Ġformat ting", + "eral a", + "Ġrev isions", + "Ġres urg", + "T el", + "ĠGood man", + "35 3", + "p od", + "Ġind isp", + "ĠTrans lation", + "Ġg own", + "ĠM und", + "Ġc is", + "Ġby stand", + "col lect", + "ĠPun jab", + "act ively", + "ĠG amb", + "te ll", + "Ġimport ing", + "g encies", + "Ġloc om", + "ĠBr ill", + "H oly", + "ĠBer ger", + "Ġshow down", + "Ġrespond ers", + "IL Y", + "Ġt akedown", + "le ted", + "Ġmat tered", + "Ġpredict ive", + "Ġover lay", + "G PU", + "ĠV ick", + "Ġconvey ed", + "T ab", + "pe er", + "Sc an", + "Ġdefensive ly", + "v ae", + "Ġappro ving", + "Ġt iers", + "ĠV ia", + "quer ade", + "ĠSaud is", + "Ġdemol ished", + "ĠProp he", + "Ġmon o", + "Ġhospital ity", + "H AM", + "ĠAri el", + "M OD", + "ĠTor ah", + "Ġbl ah", + "ĠBel arus", + "erent ial", + "ĠT uc", + "Ġbank er", + "39 7", + "Ġmosqu it", + "ĠScient ist", + "ĠMus ical", + "Ġh ust", + "Sh ift", + "Ġtor ment", + "Ġstand off", + "E duc", + "ĠF og", + "Ġampl ifier", + "Sh ape", + "Inst ance", + "ĠCrit ics", + "Ġda emon", + "H ouston", + "Ġmatt ress", + "ĠID F", + "Ġobsc ene", + "ĠA mer", + "hett i", + "Ġcomp iling", + "35 2", + "vere tt", + "ĠRed uction", + "ist ration", + "ĠBl essed", + "ĠB achelor", + "3 16", + "Ġpr ank", + "ĠVul can", + "dd ing", + "Ġm ourning", + "ĠQu int", + "ĠBl aster", + "test ing", + "Ġsed iment", + ">> >", + "ĠE ternity", + "ĠWH ERE", + "ĠM aze", + "Ġreact ing", + "ĠAl v", + "oms day", + "ĠC RA", + "Ġtransl ator", + "Ġbog us", + "at u", + "We bsite", + "oll s", + "Ġbapt ism", + "Ġs ibling", + "ĠAut umn", + "ve z", + "ãģ® é", + "gu ards", + "Ge org", + "assad ors", + "ĠFre ud", + "Ġcontin ents", + "ĠReg istry", + "Bern ie", + "ĸļ 士", + "Ġtoler ant", + "ĠU W", + "Ġhor ribly", + "99 5", + "ĠMID I", + "Ġimpat ient", + "oc ado", + "er i", + "ĠWor st", + "ĠNor ris", + "ĠTalk ing", + "Ġdef ends", + "ens able", + "Ġ20 21", + "Ġanat omy", + "L ew", + "Ġdraw er", + "ĠCan berra", + "Ġpatri otic", + "é¾įå ĸļ士", + "ĠAv g", + "AR M", + "Ġundis closed", + "Ġfare well", + "45 9", + "b able", + "ĠAll ison", + "OL OG", + "Ġcon co", + "t ight", + "ĠAC PI", + "ĠM ines", + "l ich", + "ĠâĶ ľ", + "represent ed", + "200 000", + "Ġenthusi ast", + "OT S", + "b il", + "ĠIng redients", + "Ġinvent or", + "ĠMy SQL", + "³³ Âł", + "ĠAB OUT", + "with in", + "Ġm k", + "B ul", + "ĠF ake", + "Ġdracon ian", + "W a", + "hel m", + "ĠTer ran", + "erv ille", + "Ġcommon place", + "SI ZE", + "Ġ\" <", + "re place", + "ograph s", + "ĠSE LECT", + "inc ible", + "ĠMost ly", + "ĠShe ffield", + "ĠID E", + "ugg le", + "Ġcit ations", + "h urst", + "ĠUn ix", + "Ġunle ash", + "ĠP iper", + "ĠN ano", + "Ġsucc umb", + "Ġreluct ance", + "Ġ25 00", + "ĠMer chant", + "Ġwire t", + "Ġcomb os", + "ĠBirth day", + "Ġchar coal", + "ĠU PS", + "ĠFair fax", + "Ġdrive way", + "ĠT ek", + "ĠP itch", + "ove re", + "Ġtechn icians", + "ĠAct ual", + "fl ation", + "ĠF iscal", + "ĠEm pty", + "an amo", + "Ġmag nesium", + "Ġsl ut", + "Ġgrow ers", + "Invest igators", + "( ):", + "ĠS atellite", + "ĠKe ynes", + "miss ive", + "l ane", + "Ġb orough", + "3 44", + "ĠTE AM", + "ĠBet hesda", + "C V", + "h ower", + "ĠR AD", + "Ġch ant", + "ĠR iy", + "Ġcompos itions", + "Ġmild ly", + "Ġmedd ling", + "Ġag ility", + "ane ers", + "5 01", + "Ġsyn th", + "ling er", + "29 1", + "Ġex claimed", + "Part y", + "Ġcont amin", + "ĠMan or", + "ĠResp ond", + "Ġpra ising", + "Ġman ners", + "fle et", + "Sum mer", + "ĠLy nd", + "ĠDef initely", + "gr im", + "Ġbow ling", + "st ri", + "ç Ľ", + "y nt", + "Ġmand ates", + "D IV", + "Ġreconc ile", + "view s", + "ĠDam on", + "vet te", + "F lo", + "ĠGreat est", + "il on", + "ic ia", + "Ġportray al", + "Ġcush ion", + "50 4", + "19 79", + "oss al", + "App lic", + "sc ription", + "Ġmit igation", + "AT S", + "p ac", + "Ġer ased", + "Ġdefic iencies", + "ĠHolland e", + "ĠX u", + "Ġb red", + "Ġpregn ancies", + "f emin", + "Ġem ph", + "Ġpl anners", + "Ġout per", + "utter ing", + "Ġperpet rator", + "Ġm otto", + "ĠEll ison", + "ĠNE VER", + "Ġadmitted ly", + "AR I", + "ĠAzerbai jan", + "Ġmill isec", + "Ġcombust ion", + "ĠBott le", + "ĠL und", + "ĠP s", + "ĠD ress", + "Ġfabric ated", + "Ġbat tered", + "Ġs idel", + "ĠNot ting", + "Fore ign", + "ĠJer ome", + "0 20", + "ĠAr bit", + "Ġkn ots", + "ĠR IGHT", + "M oving", + "ãģ Ļ", + "Ġsur geries", + "Ġcour thouse", + "Ġm astered", + "Ġhover ing", + "ĠBr an", + "ĠAl ison", + "Ġsaf est", + "m ilitary", + "Ġbull ied", + "Ġbar rage", + "Read er", + "ES E", + "ĠGe ographic", + "T ools", + "3 14", + "ĠGe ek", + "ro th", + "gl ers", + "ĠF IN", + "Ï ģ", + "ĠA ston", + "al tern", + "48 8", + "Ġveter in", + "G amer", + "Ġint el", + "ren ches", + "Sh ield", + "Ġam nesty", + "ĠB har", + "Ġp iled", + "Ġhonor able", + "ĠInst itutes", + "Ġso aked", + "Ġcom a", + "ĠE FF", + "34 1", + "by tes", + "ĠG mail", + "le in", + "ĠCanad iens", + "m aterial", + "I l", + "Ġinstruct ors", + "ĠK Y", + "Ġconce ive", + "ub b", + "ĠP ossible", + "Ġeas ing", + "ĠChrist ina", + "Ġcar ic", + "ĠHD R", + "R OM", + "Ġsho vel", + "de lete", + "Ġp uff", + "ĠCh anging", + "Ġseam lessly", + "Att ribute", + "Ġacqu isitions", + "ak ery", + "ĠE F", + "Ġaut istic", + "ĠT akes", + "ĠPow der", + "ĠSt ir", + "5 10", + "ĠBub ble", + "sett ings", + "ĠF owler", + "Ġmust ard", + "Ġmore over", + "Ġcopyright ed", + "ĠLED s", + "15 00", + "æ ī", + "ĠH IS", + "en f", + "Ġcust od", + "ĠH uck", + "G i", + "Ġim g", + "An swer", + "C t", + "j ay", + "ĠInf rastructure", + "Ġfeder ally", + "L oc", + "Ġmicro bes", + "Ġover run", + "dd s", + "ot ent", + "adi ator", + ">>>> >>>>", + "Ġtorn ado", + "Ġadj ud", + "Ġintrig ued", + "Ġs i", + "ĠRevel ation", + "pro gress", + "Ġburgl ary", + "ĠSai yan", + "ĠK athy", + "Ġser pent", + "ĠAndre as", + "Ġcomp el", + "ess ler", + "ĠPl astic", + "ĠAd vent", + "ĠPos itive", + "ĠQ t", + "ĠHind us", + "reg istered", + "ular ity", + "Ġrighteous ness", + "Ġdemon ic", + "u itive", + "ĠB DS", + "ĠGre gg", + "c ia", + "ĠCrus ade", + "ĠSina i", + "W ARE", + "+ (", + "Ġme ll", + "Ġder ail", + "y ards", + "A st", + "Ġnotice ably", + "ĠO ber", + "R am", + "Ġun noticed", + "Ġse q", + "av age", + "T s", + "Ġ6 40", + "Ġconced e", + "Ġ] )", + "F ill", + "Ġcapt ivity", + "ĠImprove ment", + "ĠCrus ader", + "ara oh", + "M AP", + "æ Ĺ", + "Ġstr ide", + "al ways", + "F ly", + "N it", + "Ġal gae", + "ĠCook ing", + "ĠDo ors", + "Mal ley", + "Ġpolic emen", + "ãģ į", + "Ġastron aut", + "access ible", + "49 5", + "ĠR AW", + "cl iffe", + "udic rous", + "Ġdep ended", + "al ach", + "Ġvent ures", + "ra ke", + "Ġt its", + "ĠH ou", + "Ġcond om", + "ormon al", + "Ġind ent", + "Ġupload ing", + "Foot note", + "Import ant", + "Ġ27 1", + "Ġmind ful", + "Ġcont ends", + "C ra", + "Ġcal ibr", + "ĠO ECD", + "plug in", + "F at", + "ĠIS S", + "ĠDynam ics", + "ans en", + "68 6", + "' ),", + "Ġsp rite", + "Ġhand held", + "ĠH ipp", + "=~ =~", + "Tr ust", + "Ġsem antics", + "ĠBund es", + "ĠRen o", + "ĠLiter ature", + "s ense", + "G ary", + "ĠA eg", + "ĠTr in", + "EE K", + "Ġcler ic", + "ĠSS H", + "Ġch rist", + "Ġinv ading", + "ib u", + "Ġen um", + "aur a", + "Ġal lege", + "ĠInc redible", + "B BC", + "Ġth ru", + "Ġsa iled", + "Ġem ulate", + "Ġin security", + "Ġc rou", + "Ġaccommod ations", + "Ġincompet ent", + "Ġsl ips", + "ĠEarth qu", + "s ama", + "IL LE", + "Ġi Phones", + "as aki", + "Ġby e", + "Ġar d", + "Ġext ras", + "Ġsl aughtered", + "Ġcrowd funding", + "res so", + "Ġfil ib", + "ĠER ROR", + "ĠT LS", + "e gg", + "ĠIt al", + "Ġen list", + "ĠCatal onia", + "ĠSc ots", + "Ġser geant", + "Ġdiss olve", + "N H", + "Ġstand ings", + "ri que", + "I Q", + "Ġbenef iciary", + "Ġaqu arium", + "You Tube", + "ĠPower Shell", + "Ġbright est", + "ĠWar rant", + "S old", + "Writ ing", + "Ġbegin nings", + "ĠRes erved", + "ĠLatin os", + "head ing", + "Ġ4 40", + "Ġrooft op", + "AT ING", + "Ġ3 90", + "VP N", + "G s", + "k ernel", + "turn ed", + "Ġprefer able", + "Ġturn overs", + "ĠH els", + "S a", + "ĠShin ji", + "ve h", + "ĠMOD ULE", + "V iol", + "Ġex iting", + "Ġj ab", + "ĠVan illa", + "Ġac ron", + "ĠG ap", + "ber n", + "A k", + "ĠMc Gu", + "Ġend lessly", + "ĠFar age", + "ĠNo el", + "V a", + "M K", + "Ġbr ute", + "ĠK ru", + "ĠES V", + "ĠOl ivia", + "âĢ ł", + "ĠK af", + "Ġtrust ing", + "Ġh ots", + "3 24", + "Ġmal aria", + "Ġj son", + "Ġp ounding", + "ort ment", + "Count ry", + "Ġpostp oned", + "Ġunequ iv", + "? ),", + "ĠRo oney", + "udd ing", + "ĠLe ap", + "ur rence", + "sh apeshifter", + "ĠH AS", + "os ate", + "Ġca vern", + "Ġconserv atism", + "ĠB AD", + "Ġmile age", + "Ġarrest ing", + "V aults", + "Ġmix er", + "Dem ocratic", + "ĠB enson", + "Ġauth ored", + "8 000", + "Ġpro active", + "ĠSpirit ual", + "t re", + "Ġincarcer ated", + "ĠS ort", + "Ġpe aked", + "Ġwield ing", + "re ciation", + "×Ļ ×", + "P atch", + "ĠEm my", + "Ġex qu", + "tt o", + "ĠRat io", + "ĠP icks", + "ĠG ry", + "ph ant", + "Ġf ret", + "Ġeth n", + "Ġarch ived", + "% -", + "c ases", + "ĠBl aze", + "Ġim b", + "c v", + "y ss", + "im ony", + "Ġcount down", + "Ġaw akening", + "ĠTunis ia", + "ĠRe fer", + "ĠM J", + "Ġun natural", + "ĠCar negie", + "iz en", + "ĠN uggets", + "he ss", + "Ġev ils", + "64 7", + "Ġintrodu ctory", + "l oving", + "ĠMcM ahon", + "Ġambig uity", + "L abel", + "ĠAlm ighty", + "Ġcolor ing", + "ĠCl aus", + "set ting", + "N ULL", + "ĠF avorite", + "ĠS IG", + "> (", + "ĠSh iva", + "ĠMay er", + "Ġstorm ed", + "ĠCo verage", + "we apons", + "igh am", + "Ġun answered", + "Ġle ve", + "Ġc oy", + "c as", + "b ags", + "as ured", + "Se attle", + "ĠSant orum", + "ser ious", + "Ġcourage ous", + "ĠS oup", + "Ġconfisc ated", + "Ġ// /", + "Ġuncon ventional", + "Ġmom s", + "ĠRohing ya", + "ĠOrche stra", + "ĠPot ion", + "Ġdisc redit", + "ĠF IL", + "f ixed", + "ĠDe er", + "do i", + "ĠDim ension", + "Ġbureaucr ats", + "et een", + "Ġaction Group", + "oh m", + "Ġb umps", + "ĠUt ility", + "Ġsubmar ines", + "ren heit", + "re search", + "ĠShap iro", + "Ġsket ches", + "Ġde ceptive", + "ĠV il", + "es ame", + "ĠEss entially", + "Ġramp age", + "isk y", + "Ġmut tered", + "th ritis", + "Ġ23 6", + "f et", + "b ars", + "Ġpup il", + "ĠTh ou", + "o S", + "s ong", + "Ġfract ured", + "Ġre vert", + "pict ure", + "Ġcrit erion", + "us her", + "Ġreperc ussions", + "ĠV intage", + "ĠSuper intendent", + "Offic ers", + "Ġflag ged", + "Ġbl ames", + "Ġin verse", + "ograp hers", + "Ġmakes hift", + "Ġdev oid", + "Ġfoss ils", + "ĠArist otle", + "ĠFund s", + "Ġde pleted", + "ĠFl u", + "ĠY uan", + "Ġw oes", + "Ġlip id", + "Ġsit u", + "requ isites", + "Ġfurn ish", + "ĠSam ar", + "Ġshame ful", + "Ġadverse ly", + "Ġad ept", + "Ġrem orse", + "Ġmurder ous", + "uck les", + "ĠE SL", + "Ġ3 14", + "s ent", + "Ġred ef", + "ĠC ache", + "ĠP urs", + "ig ans", + "Ġ4 60", + "Ġpres criptions", + "Ġf res", + "F uck", + "ocr ates", + "Tw enty", + "ĠWe ird", + "ĠT oggle", + "ĠC alled", + "itiz ens", + "Ġp oultry", + "Ġharvest ing", + "ãĤ¦ ãĤ¹", + "Bott om", + "Ġcaution ed", + "t n", + "39 6", + "ĠNik ki", + "Ġeval uations", + "Ġharass ing", + "Ġbind ings", + "ĠMon etary", + "Ġhit ters", + "Ġadvers ary", + "un ts", + "Ġset back", + "Ġenc rypt", + "ĠC ait", + "Ġl ows", + "eng es", + "ĠN orn", + "Ġbul bs", + "Ġbott led", + "ĠVoy ager", + "3 17", + "Ġsp heres", + "p olitics", + "Ġsubt ract", + "Ġsens ations", + "Ġapp alling", + "Ġ3 16", + "Ġenvironment ally", + "ĠST EM", + "Ġpub lishes", + "5 60", + "Ġdilig ence", + "48 4", + "Ġadv ises", + "Ġpet rol", + "Ġimag ining", + "Ġpatrol s", + "ĠInt eger", + "ĠAs hes", + "act us", + "ĠRad iant", + "ĠL T", + "it ability", + "ht aking", + "Set ting", + "Ġnu anced", + "ĠRe ef", + "ĠDevelop ers", + "N i", + "pie ces", + "99 0", + "Lic ense", + "Ġlow ers", + "ĠOtt oman", + "3 27", + "oo o", + "Ġqu itting", + "mark ets", + "Beh ind", + "Ġbas in", + "Ġdoc s", + "an ie", + "fl ash", + "ct l", + "Ġcivil ized", + "ĠFuk ushima", + "\"] ,\"", + "ĠK S", + "ĠHonest ly", + "ar at", + "Ġconstruct s", + "ĠL ans", + "ĠD ire", + "ĠLI KE", + "ĠTrou ble", + "Ġwith holding", + "ĠOb livion", + "Ġsan ity", + "any a", + "Con st", + "Ġgro cer", + "ĠC elsius", + "Ġrecount ed", + "ĠW ife", + "B order", + "ate red", + "h appy", + "Ġspo iler", + "Ġlog ically", + "H all", + "Ġsucceed ing", + "Ġpoly morph", + "Ġax es", + "ĠShot gun", + "ĠS lim", + "ĠPrin ciples", + "ĠL eth", + "art a", + "Ġsc or", + "Sc reenshot", + "Ġrelax ation", + "#$ #$", + "Ġdeter rent", + "idd y", + "Ġpower less", + "Ġles bians", + "Ġch ords", + "ĠEd ited", + "se lected", + "Ġseparat ists", + "000 2", + "Ġair space", + "Ġturn around", + "Ġc unning", + "P ATH", + "P oly", + "Ġbomb ed", + "Ġt ion", + "x s", + "Ġwith hold", + "Ġw aged", + "ĠLiber ties", + "Fl ag", + "Ġcomfort ing", + "45 4", + "ĠI ris", + "are rs", + "Ġr ag", + "Ġrel ocated", + "ĠGu arant", + "Ġstrateg ically", + "Ġgam ma", + "uber ty", + "ĠLock heed", + "g res", + "Ġgr illed", + "ĠLow e", + "st ats", + "ĠR ocks", + "Ġsens ing", + "Ġrent ing", + "ĠGe ological", + "ا Ø", + "ot rop", + "Ġse w", + "Ġimproper ly", + "48 6", + "Ġâĸ ł", + "Ġstar ving", + "ĠB j", + "Disc ussion", + "3 28", + "ĠCom bo", + "ĠFix es", + "N AT", + "Ġstri ving", + "th ora", + "Ġharvest ed", + "ĠP ing", + "Ġplay ful", + "Ġaven ues", + "Ġoccup ational", + "Ġw akes", + "ĠCou rier", + "Ġdrum mer", + "ĠBrow ser", + "ĠH outh", + "it u", + "Ġapp arel", + "p aste", + "Ġhun ted", + "ĠSecond ly", + "l ain", + "X Y", + "ĠP IN", + "ic ons", + "Ġcock tails", + "Ġs izable", + "Ġhurd les", + "est inal", + "ĠRecre ation", + "Ġe co", + "64 8", + "ĠD ied", + "m int", + "Ġfinger prints", + "Ġdis pose", + "ĠBos nia", + "ts y", + "22 00", + "Ġins pected", + "ĠF ou", + "Ġf uss", + "Ġamb ush", + "ĠR ak", + "Ġmanif ested", + "Pro secut", + "Ġsuff ice", + "ren ces", + "Ġcompens ated", + "ĠC yrus", + "Ġgen us", + "ĠWolver ine", + "ĠTrend s", + "Ġh ikes", + "ĠSe en", + "Ġen rol", + "C old", + "Ġpol itely", + "ĠSl av", + "ĠRu pert", + "Ġey ewitness", + "ĠAl to", + "Ġun comp", + "Ġposter ior", + "M ust", + "ĠHer z", + "Ġprogress ively", + "Ġ23 4", + "Ġind ifference", + "ĠCunning ham", + "Ġacadem ia", + "Ġse wer", + "Ġast ounding", + "ĠA ES", + "r ather", + "Ġeld est", + "Ġclim bs", + "ĠAdd s", + "Ġout cry", + "Ġcont ag", + "ĠH ouses", + "Ġpe pt", + "ĠMel ania", + "interest ed", + "ĠU CH", + "ĠR oots", + "ĠHub bard", + "ĠT BD", + "ĠRoman ian", + "fil ename", + "St one", + "ĠIm pl", + "Ġchromos ome", + "C le", + "d x", + "Ġscram bled", + "ĠP t", + "Ġ24 2", + "OP LE", + "Ġtremend ously", + "St reet", + "Ġcra ving", + "Ġbund led", + "ĠR G", + "p ipe", + "Ġinj uring", + "Ġarc ane", + "Part icip", + "ĠHero ic", + "st y", + "Ġto pping", + "ĠTemp est", + "rent ices", + "b h", + "Ġpar anoia", + "ĠUnic ode", + "Ġegreg ious", + "Ġ\\ '", + "ĠOsw ald", + "Ġgra vel", + "ĠSim psons", + "Ġbl and", + "ĠGuant anamo", + "Writ er", + "lin ers", + "ĠD ice", + "J C", + "Ġpar ity", + "Ġs ided", + "Ġ23 7", + "ĠPyr rha", + "at ters", + "d k", + "F ine", + "comp an", + "Ġform ulated", + "ĠId ol", + "il ers", + "hem oth", + "ĠF av", + "Ġintr usion", + "Ġcar rots", + "ĠL ayer", + "ĠH acker", + "Ġ ----------------", + "Ġmoder ation", + "é ģ", + "oc oc", + "Ġcharacter ize", + "ĠTe resa", + "Ġsocio economic", + "Ġper k", + "ĠParticip ation", + "tr aining", + "ĠPaul o", + "ph ys", + "Ġtrust worthy", + "Ġembod ied", + "ĠMer ch", + "c urrency", + "ĠPrior ity", + "Ġte asing", + "Ġabsor bing", + "Ġunf inished", + "ĠCompar ison", + "Ġdis ple", + "writ ers", + "Ġprofess ions", + "ĠPengu in", + "Ġang rily", + "ĠL INK", + "68 8", + "ĠCor respond", + "Ġprev ailed", + "Ġcart el", + "l p", + "as ms", + "ĠRed emption", + "ĠIslam ists", + "effect s", + "d ose", + "ĠL atter", + "ĠHal ifax", + "Ġv as", + "ĠTop ics", + "ĠN amed", + "advert ising", + "zz a", + "IC ES", + "Ġret arded", + "ach able", + "ĠPupp et", + "ĠItem Level", + "Ġret ract", + "Ġident ifiable", + "A aron", + "ĠB uster", + "s ol", + "hel le", + "as semb", + "H ope", + "r anged", + "B a", + "ĠP urch", + "é Ģ", + "ĠSir i", + "Ġarri vals", + "Ġ19 12", + "Ġshort ened", + "Ġ3 12", + "Ġdiscrep ancy", + "ĠTem perature", + "ĠWal ton", + "Ġkind erg", + "p olit", + "Ġrem ix", + "Ġconnect ors", + "ãĥĺ ãĥ©", + "ĠKazakh stan", + "dom inated", + "Ġsu gars", + "im ble", + "ĠPan ic", + "ĠDem and", + "ĠCol ony", + "on en", + "ĠM ER", + "7 75", + "ur ia", + "aza ar", + "ĠDeg ree", + "P ri", + "Ġsun shine", + "Ġ25 1", + "Ġpsychedel ic", + "Ġdigit ally", + "ĠBra un", + "Ġsh immer", + "Ġsh ave", + "ĠTel esc", + "ĠAst ral", + "ĠVenezuel an", + "ĠO G", + "Ġc rawling", + "Int eg", + "ĠFe ather", + "Ġunfold ing", + "Ġappropri ation", + "Ġè£ı è", + "ĠMob ility", + "ĠN ey", + "- .", + "b ilt", + "L IN", + "ĠT ube", + "ĠCon versely", + "Ġkey boards", + "ĠC ao", + "Ġover th", + "Ġla ure", + ">> \\", + "ĠV iper", + "ach a", + "Off set", + "ĠR aleigh", + "ĠJ ae", + "J ordan", + "j p", + "Ġtotal itarian", + "Connect or", + "Ġobserv es", + "ĠSpart an", + "ĠIm mediately", + "ĠSc al", + "C ool", + "Ġt aps", + "Ġro ar", + "P ast", + "Ġch ars", + "ĠB ender", + "ĠShe ldon", + "Ġpain ter", + "Ġbe acon", + "ĠCreat ures", + "Ġdownt urn", + "Ġh inder", + "ĠAnd romeda", + "à Ľ", + "cc oli", + "ĠF itness", + "et rical", + "Ġutil izes", + "Ġsen ate", + "Ġen semble", + "Ġche ers", + "T W", + "Ġaff luent", + "k il", + "ry lic", + "ord ering", + "Com puter", + "Ġgru esome", + "ost ics", + "ĠUb isoft", + "ĠKel ley", + "Ġw rench", + "Ġbourgeois ie", + "IB LE", + "ĠPrest on", + "w orn", + "ar ist", + "reat ing", + "Ġst ained", + "ar ine", + "Ġsl ime", + "EN N", + "Ġche sts", + "Ġground water", + "ann ot", + "ĠTr ay", + "ĠLoc ke", + "ĠC TR", + "Ġd udes", + "ĠEx ternal", + "ĠDec oder", + "Ġpar amed", + "ĠMed line", + "80 9", + "ĠD inner", + "rup al", + "g z", + "ĠG um", + "ĠDem o", + "j ee", + "Ġd h", + "ber man", + "arch s", + "Ġen qu", + "ĠEp stein", + "Ġdevast ation", + "Ġfriends hips", + "ĠAr d", + "Ġ23 1", + "ĠRub in", + "ĠDist ance", + "Ġsp urred", + "Ġd ossier", + "Ġover looking", + "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\", + "Fore st", + "ĠCom es", + "\\ \",", + "ĠIran ians", + "Ġf ixtures", + "L aughs", + "Ġcur ry", + "ĠKing ston", + "Ġsqu ash", + "Ġcat alogue", + "Ġabnormal ities", + "Ġdigest ive", + ".... .....", + "Ġsubord inate", + "og ly", + "Ġ24 9", + "M iddle", + "Ġmass ac", + "Ġburg ers", + "Ġdown stairs", + "Ġ19 31", + "39 4", + "ĠV G", + "Ġl asers", + "ĠS ikh", + "ĠAlex a", + "der ived", + "Ġcycl ist", + "ãģ® éŃĶ", + "onel iness", + "!!!! !!!!", + "Ġbuff s", + "leg ate", + "Ġrap ing", + "Ġrecomm ending", + "ro red", + "Ġmult icultural", + "un ique", + "Ġbusiness men", + "Ġune asy", + "ĠM AP", + "Ġdisp ersed", + "cipl ine", + "J ess", + "ĠK erala", + "å §", + "Ġabst raction", + "Sur v", + "U h", + "Ġprin ters", + "ij a", + "ow der", + "Ġanalog ous", + "ĠA SP", + "af er", + "Ġunfold ed", + "Ġlevel ing", + "Ġbre ached", + "ĠH earing", + "Ġn at", + "Ġtransl ating", + "crit ical", + "Ġant agonist", + "ĠYes terday", + "Ġfuzz y", + "w ash", + "m ere", + "Ġbe wild", + "ĠM ae", + "V irgin", + "ph rase", + "Ġsign aled", + "ĠH IGH", + "Ġprot ester", + "Ġgar ner", + "unk nown", + "Ġk ay", + "Ġabduct ed", + "Ġst alking", + "am n", + "Ġdes erving", + "ĠR iv", + "ĠJ orge", + "Ġscratch ing", + "ĠS aving", + "ip ing", + "Ġte ase", + "Ġmission ary", + "ĠMor row", + "T IME", + "P resent", + "Ġchem otherapy", + "tern ess", + "ĠH omes", + "ĠP urdue", + "Ġst aunch", + "ĠWhit ney", + "ĠTH ERE", + "Î ¼", + "iat us", + "ĠErn est", + "ĠDe ploy", + "Ġcove ted", + "F ML", + "ĠDial ogue", + "Ġex ited", + "f ruit", + "Ġner d", + "\":\" \",\"", + "Ġv ivo", + "ru ly", + "4 60", + "ĠAm en", + "rehens ible", + "Ġâ ĺ", + "D IR", + "Ġad herence", + "Ġche w", + "ĠCo ke", + "ĠSerge i", + "dig ital", + "ĠNe ck", + "g ently", + "enth al", + "/ )", + "Ġwe ary", + "Ġgu ise", + "ĠConc ord", + "ĠOn ion", + "at cher", + "Ġb inge", + "ĠDirect ive", + "Ġman ned", + "ans k", + "Ġill usions", + "Ġbillion aires", + "38 3", + "oly n", + "odynam ic", + "ĠWhe at", + "ĠA lic", + "Ġcol oured", + "ĠN AFTA", + "ab o", + "Ġmac ros", + "ind ependent", + "s weet", + "Ġsp ac", + "ĠK abul", + "Ġ Ä", + "em e", + "Ġdict ated", + "Ġsh outs", + "= {", + "Ġr ipping", + "ĠSh ay", + "ĠCr icket", + "direct ed", + "Ġanalys ed", + "ĠWAR RANT", + "ag ons", + "ĠBlaz ers", + "Ġche ered", + "Ġar ithmetic", + "ĠTan z", + "37 3", + "ĠFl ags", + "Ġ29 5", + "Ġw itches", + "ĠIn cluded", + "ĠG ained", + "ĠBl ades", + "G am", + "ĠSam antha", + "ĠAtl antis", + "ĠPr att", + "Ġspo iled", + "ĠI B", + "ĠRam irez", + "Pro bably", + "re ro", + "ĠN g", + "ĠWar lock", + "t p", + "Ġover he", + "Ġadministr ations", + "Ġt int", + "Ġreg iment", + "Ġpist ols", + "Ġblank ets", + "Ġep ist", + "Ġbowl s", + "Ġhydra ulic", + "Ġde an", + "Ġj ung", + "Ġasc end", + "70 5", + "ĠSant iago", + "à ®", + "Ġun avoid", + "ĠSh aman", + "re b", + "Ġstem ming", + "99 8", + "ĠM G", + "st icks", + "esthes ia", + "ER O", + "Ġmor bid", + "ĠGr ill", + "ĠP oe", + "any l", + "Ġdele ting", + "ĠSurve illance", + "Ġdirect ives", + "Ġiter ations", + "ĠR ox", + "ĠMil ky", + "F ather", + "Ġpat ented", + "44 7", + "Ġprec ursor", + "Ġm aiden", + "ĠP hen", + "ĠVe gan", + "ĠPat ent", + "K elly", + "Redd itor", + "Ġn ods", + "Ġvent ilation", + "ĠSchwar z", + "Ġw izards", + "Ġomin ous", + "ĠHe ads", + "ĠB G", + "Ġl umber", + "ĠSp iel", + "Ġis Enabled", + "Ġancest ral", + "ĠSh ips", + "Ġwrest ler", + "ph i", + "Ġy uan", + "ĠRebell ion", + "Ġice berg", + "Ġmag ically", + "Ġdivers ion", + "ar ro", + "yth m", + "ĠR iders", + "ĠRob bie", + "ĠK ara", + "ĠMain tenance", + "ĠHer b", + "Ġhar ms", + "p acked", + "ĠFe instein", + "Ġmarry ing", + "Ġbl ending", + "ĠR ates", + "Ġ18 80", + "Ġwr ink", + "ĠUn ch", + "ĠTor ch", + "desc ribed", + "Ġhuman oid", + "ilit ating", + "ĠCon v", + "ĠFe ld", + "IGH TS", + "Ġwhistlebl ower", + "ort mund", + "ets y", + "arre tt", + "ĠMon o", + "ĠI ke", + "ĠC NBC", + "ĠW AY", + "ĠMD MA", + "ĠIndividual s", + "Ġsupplement al", + "Ġpower house", + "ĠSt ru", + "F ocus", + "aph ael", + "ĠCol leg", + "att i", + "Z A", + "Ġp erenn", + "ĠSign ature", + "ĠRod ney", + "Ġcub es", + "idd led", + "ĠD ante", + "ĠIN V", + "iling ual", + "ĠC th", + "Ġso fa", + "Ġintimid ate", + "ĠR oe", + "ĠDi plom", + "ĠCount ries", + "ays on", + "Ġextrad ition", + "Ġdis abling", + "ĠCard iff", + "Ġmemor andum", + "ĠTr ace", + "Ġ?? ?", + "se ctor", + "ĠRou hani", + "ĠY ates", + "ĠFree ze", + "Ġbl adder", + "M otor", + "ĠProm ise", + "ant asy", + "Ġforesee able", + "ĠC ologne", + "cont ainer", + "ĠTre es", + "ĠG ors", + "ĠSin clair", + "Ġbar ring", + "key e", + "Ġsl ashed", + "ĠStat istical", + "é ĩ", + "Ġâĸ º", + "All ows", + "Ġhum ility", + "Ġdr illed", + "ĠF urn", + "44 3", + "Ġse wage", + "Ġhome page", + "Ġcour tyard", + "Ġv ile", + "Ġsubsid iaries", + "aj o", + "direct ory", + "Ġam mon", + "V ers", + "charg es", + "Ġ} }", + "ĠCh ains", + "Ġ24 6", + "n ob", + "Ġper cept", + "Ġg rit", + "Ġfisher men", + "ĠIraq is", + "ĠDIS TR", + "ĠF ULL", + "ĠEval uation", + "g raph", + "at ial", + "Ġcooper ating", + "Ġmel an", + "Ġenlight ened", + "Ġal i", + "t ailed", + "Ġsal ute", + "Ġweak est", + "ĠBull dogs", + "U A", + "ĠAll oy", + "Ġsem en", + "oc ene", + "ĠWilliam son", + "s pr", + ", âĢĶ", + "ĠG F", + "itt ens", + "Be at", + "ĠJ unk", + "iph ate", + "ĠFarm ers", + "ĠBit coins", + "ig ers", + "d h", + "ĠL oyal", + "p ayer", + "Ġentert ained", + "Ġpenn ed", + "Ġcoup on", + "Que ue", + "Ġweaken ing", + "c arry", + "Ġunderest imate", + "Ġshoot out", + "Ġcharism atic", + "ĠProced ure", + "Ġprud ent", + "in ances", + "Ġric hes", + "Ġcort ical", + "Ġstr ides", + "Ġd rib", + "ĠOil ers", + "5 40", + "ĠPer form", + "ĠBang kok", + "Ġe uth", + "S ER", + "Ġsimpl istic", + "t ops", + "camp aign", + "Q uality", + "Ġimpover ished", + "ĠEisen hower", + "Ġaug ment", + "ĠH arden", + "Ġinterven ed", + "Ġlist ens", + "ĠK ok", + "Ġs age", + "Ġrub bish", + "ĠD ed", + "Ġm ull", + "pe lling", + "Ġvide ot", + "Produ ction", + "D J", + "m iah", + "Ġadapt ations", + "Ġmed ically", + "Ġboard ed", + "Ġarrog ance", + "Ġscra pped", + "Ġopp ress", + "FORM ATION", + "Ġj unction", + "4 15", + "EE EE", + "S kill", + "Ġsub du", + "ĠSug gest", + "ĠP ett", + "Ġle tt", + "ĠMan ip", + "ĠC af", + "ĠCooper ation", + "T her", + "Ġreg ained", + "¶ æ", + "ref lect", + "Ġth ugs", + "ĠShel by", + "Ġdict ates", + "ĠWe iner", + "ĠH ale", + "Ġbatt leground", + "s child", + "Ġcond ol", + "h unt", + "osit ories", + "Ġacc uses", + "Fil ename", + "Ġsh ri", + "Ġmotiv ate", + "Ġreflect ions", + "N ull", + "ĠL obby", + "¥ µ", + "ĠS ATA", + "ĠBack up", + "Ñ ĥ", + "n in", + "ĠCor rection", + "Ġju icy", + "ut ra", + "ĠP ric", + "Ġrest raining", + "ĠAir bnb", + "ĠAr rest", + "Ġappropri ations", + "Ġsl opes", + "Ġmans laughter", + "Ġwork ings", + "ĠH uss", + "ĠF rey", + "Le ave", + "ĠHarm ony", + "ĠF eder", + "Ġ4 30", + "Ġt rench", + "Ġglad ly", + "Ġbull pen", + "ĠG au", + "b ones", + "Ġgro ove", + "Ġpre text", + "ã ħĭ", + "Ġtransm itter", + "ĠComp onent", + "Ġunder age", + "ĠEm pires", + "T ile", + "Ġo y", + "ĠMar vin", + "ĠC AS", + "Ġbl oss", + "Ġrepl icated", + "ĠMar iners", + "Marc us", + "ĠBl ocks", + "Ġliber ated", + "Ġbutter fly", + "Fe el", + "Ġfer mentation", + "Ġyou tube", + "Ġoff end", + "ĠTer m", + "res ist", + "Ġcess ation", + "Ġinsurg ency", + "Ġb ir", + "ĠRa ise", + "59 5", + "Ġhypothes es", + "50 2", + "Ġpl aque", + "ocr at", + "Ġjack ets", + "ĠHuff Post", + "am ong", + "Ġconf er", + "48 7", + "ĠL illy", + "Ġadapt ing", + "ĠF ay", + "Ġsh oved", + "ve c", + "Ġref ine", + "Ġg on", + "Ġgun men", + "z ai", + "ĠShut tle", + "ĠI zan", + "Ġ19 13", + "Ġple thora", + "· ·", + "Ġ5 10", + "Ġp uberty", + "Ġ24 1", + "ĠWe alth", + "ĠAl ma", + "ĠM EM", + "ĠAd ults", + "C as", + "pr ison", + "R ace", + "Ġwater proof", + "Ġathlet icism", + "Ġcapital ize", + "ĠJu ice", + "Ġillum inated", + "ĠP ascal", + "Ġirrit ation", + "ĠWitness es", + "ad le", + "ĠAst ro", + "Ġf ax", + "ĠEl vis", + "Prim ary", + "ĠL ich", + "ĠEl ves", + "Ġres iding", + "Ġst umble", + "3 19", + "ĠP KK", + "Ġadvers aries", + "D OS", + "ĠR itual", + "Ġsm ear", + "Ġar son", + "ident al", + "Ġsc ant", + "Ġmon archy", + "Ġhal ftime", + "Ġresid ue", + "Ġind ign", + "ĠSh aun", + "ĠEl m", + "aur i", + "A ff", + "W ATCH", + "ĠLy on", + "hel ps", + "36 1", + "Ġlobby ist", + "Ġdimin ishing", + "Ġout breaks", + "Ġgo ats", + "f avorite", + "ĠN ah", + "son ian", + "ĠBo oster", + "Ġsand box", + "ĠF are", + "ĠMalt a", + "Ġatt Rot", + "ĠM OR", + "ld e", + "Ġnavig ating", + "T ouch", + "Ġunt rue", + "ĠDis aster", + "Ġl udicrous", + "Pass word", + "ĠJ FK", + "blog spot", + "4 16", + "ĠUN DER", + "ern al", + "Ġdelay ing", + "T OP", + "Ġimpl ants", + "ĠAV G", + "ĠH uge", + "att r", + "Ġjournal istic", + "ĠPe yton", + "ĠI A", + "R ap", + "go al", + "ĠProgram me", + "Ġsm ashing", + "w ives", + "print ln", + "ĠPl ague", + "in us", + "EE P", + "Ġcru iser", + "ĠPar ish", + "umin ium", + "Ġoccup ants", + "ĠJ ihad", + "m op", + "Ġp int", + "Ġhe ct", + "ĠMe cca", + "direct or", + "ĠFund ing", + "ĠM ixed", + "Ġst ag", + "T ier", + "Ġg ust", + "Ġbright ly", + "ors i", + "Ġup hill", + "R D", + "Ġles ions", + "ĠBund y", + "liv ious", + "Ġbi ologist", + "ĠFac ulty", + "ĠAuthor ization", + "Ġ24 4", + "All ow", + "ï ¸", + "ĠGi ul", + "Ġpert inent", + "ot aur", + "es se", + "ĠRo of", + "Ġunman ned", + "35 1", + "ĠSh ak", + "ĠO rient", + "Ġend anger", + "D ir", + "Ġrepl en", + "ed ient", + "Ġtail or", + "Ġgad gets", + "Ġaud ible", + "âĺ Ĩ", + "N ice", + "Ġbomb ard", + "ĠR ape", + "Ġdef iance", + "ĠTW O", + "ĠFilip ino", + "Ġunaff ected", + "erv atives", + "Ġso ared", + "ĠBol ton", + "Ġcomprom ising", + "ĠBrew ers", + "R AL", + "ĠA HL", + "icy cle", + "Ġv ampires", + "Ġdi pped", + "oy er", + "ĠX III", + "Ġsidew ays", + "ĠW aste", + "ĠD iss", + "ĠâĶľ âĶĢâĶĢ", + "$ .", + "Ġhabit ats", + "ĠBe ef", + "tr uth", + "tr ained", + "spl it", + "R us", + "And y", + "ĠB ram", + "RE P", + "p id", + "è£ ħ", + "ĠMut ant", + "An im", + "ĠMar ina", + "Ġfut ile", + "hig hest", + "f requency", + "Ġepile psy", + "Ġcop ing", + "Ġconc ise", + "Ġtr acing", + "ĠS UN", + "pan el", + "ĠSoph ie", + "ĠCrow ley", + "ĠAd olf", + "ĠShoot er", + "Ġsh aky", + "ĠI G", + "ĠL ies", + "ĠBar ber", + "p kg", + "Ġupt ake", + "Ġpred atory", + "UL TS", + "/ **", + "Ġintox icated", + "ĠWest brook", + "od der", + "he ment", + "Ġbas eman", + "AP D", + "st orage", + "ĠFif ty", + "ed itor", + "G EN", + "UT ION", + "ir ting", + "Ġse wing", + "r ift", + "Ġag ony", + "ĠS ands", + "Ġ25 4", + "C ash", + "Ġl odge", + "Ġp unt", + "N atural", + "ĠIde as", + "Ġerrone ous", + "ĠSens or", + "ĠHann ity", + "Ġ19 21", + "Ġm ould", + "ĠG on", + "kay a", + "Ġanonym ously", + "ĠK EY", + "Ġsim ulator", + "W inter", + "Ġstream ed", + "50 7", + "? \",", + "Ġte ased", + "Ġco efficient", + "Ġwart ime", + "ĠTH R", + "' '.", + "ĠBank ing", + "mp ire", + "Ġf andom", + "Ġl ia", + "G a", + "Ġdown hill", + "Ġinterpre ting", + "Ind ividual", + "N orm", + "Ġjealous y", + "bit coin", + "Ġple asures", + "ĠToy s", + "ĠChev rolet", + "ĠAd visor", + "IZ E", + "Ġrecept ions", + "70 6", + "C ro", + "Ġ26 2", + "Ġcit rus", + "ir u", + "Review er", + "ject ed", + "U ES", + "an z", + "19 81", + "ĠWork er", + "Ġcompl ied", + "ores cent", + "contin ental", + "T on", + "ĠPr ism", + "ĠShe ep", + "Ġ28 8", + "n ox", + "ĠV og", + "O rd", + "Ġreal ms", + "te k", + "Ġirrig ation", + "Ġbicy cles", + "Ġelectron ically", + "p oly", + "t all", + "() );", + "Ġaest hetics", + "ĠInteg rated", + "Expl ore", + "Ġd unk", + "47 6", + "p ain", + "ĠJac ques", + "ĠD mit", + "Fram es", + "Ġreun ited", + "Ġhum id", + "D ro", + "P olitical", + "Ġyouth ful", + "Ġent ails", + "Ġmosqu ito", + "36 3", + "spe cies", + "Ġcoord inating", + "ĠMay hem", + "ĠMagn us", + "M ount", + "Impro ved", + "ĠST ATE", + "ATT LE", + "Ġflow ed", + "Ġtack led", + "Ġfashion ed", + "Ġre organ", + "iv ari", + "f inger", + "Ġreluct antly", + "et ting", + "ĠV and", + "you ng", + "ĠGar land", + "Ġpresum ption", + "Ġamen ities", + "ĠPle asant", + "on ential", + "ĠO xy", + "Ġmor als", + "ĠY ah", + "Read y", + "Sim on", + "En h", + "D emon", + "Ġcl ich", + "Mon itor", + "ĠD U", + "Ġwel comes", + "Ġstand out", + "Ġdread ful", + "Ġban anas", + "Ġball oons", + "h ooting", + "bas ic", + "Ġsuff ix", + "Ġd uly", + "can o", + "Ch ain", + "at os", + "Ġgeop olitical", + "Ġ( &", + "ĠGem ini", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "Ġacqu itted", + "L uck", + "prot ect", + "10 24", + "Ġsc arcity", + "Ġmind fulness", + "ec ided", + "D N", + "pr ime", + "ĠPres idents", + "ĠVID EO", + "Ġ( âĪĴ", + "add ock", + "N OR", + "ĠP ru", + "p un", + "ĠL OL", + ")) ))", + "ĠL iqu", + "ĠS AS", + "Ġsty ling", + "Ġpunish ments", + "Ġnum b", + "Ġasc ertain", + "ĠRock ies", + "f lu", + "Th umbnail", + "Ġperpet rated", + "ĠSem i", + "Ġdis arm", + "ĠOld er", + "ĠEx ception", + "Ġexponent ially", + "ĠCommun ities", + "Ġabol ish", + "ĠPart ner", + "pt oms", + "Ġ7 77", + "ĠFo ley", + "ĠC ases", + "Ġgre ase", + "ĠReb irth", + "G round", + "Ġ; )", + "ĠDoct rine", + "ik ini", + "Y e", + "ĠBl ossom", + "Ġpers ists", + "b ill", + "Ġinf usion", + "Ġbud dies", + "9 11", + "ĠPat ient", + "Ġdem os", + "Ġacquaint ance", + "ĠP aw", + "at ari", + "Ġx ml", + "Ġfasc ination", + "ĠSer ve", + "Ï Ĥ", + "br anded", + "Ġa z", + "Return s", + "Ġover shadow", + "Ġro am", + "Ġspeed y", + "n umbered", + "hel ial", + "Ġdisc iple", + "Ġass urances", + "g iven", + "pect ing", + "ĠN atalie", + "çĶ °", + "Ġmosquit oes", + "rote in", + "Ġnumer ic", + "Ġindepend ents", + "Ġtrans itional", + "Ġreaction ary", + "ĠMech dragon", + "do ctor", + "Ġshort est", + "Ġsequ ential", + "ĠB ac", + "ĠAccount s", + "ãģ Į", + "ach y", + "ract ive", + "ĠReg iment", + "Ġbreat htaking", + "ffic iency", + "ĠB ates", + "Ġ3 11", + "Ġward robe", + "ft s", + "ĠBer k", + "Sim ply", + "ĠRivers ide", + "iver ing", + "ident ial", + "lu cent", + "Ġen riched", + "ĠCon ver", + "ĠG iving", + "ãĥ Ļ", + "Ġlegal ize", + "ĠF TC", + "Ġfre aking", + "M ix", + "Ġter restrial", + "es ian", + "ci ents", + "W ing", + "LO AD", + "Ġled ge", + "ĠViol ent", + "ĠMet all", + "Ġ30 8", + "Ġs outheastern", + "hett o", + "M eat", + "Ġslow down", + "Ġret reated", + "Jere my", + "end as", + "**** *", + "er ic", + "Ġre ins", + "opp able", + "ĠHuman ity", + "ear ances", + "rig an", + "C amera", + "Ġwa ivers", + "s oc", + "Ġalter ation", + "trans form", + "ĠC emetery", + "50 6", + "Ġindef inite", + "Ġstim ulating", + "y g", + "60 3", + "ĠS op", + "Ġdescript ive", + "Ph ase", + "ĠEd mund", + "Ġpneum onia", + "vent us", + "A mb", + "Ġlabor atories", + "ĠEx clusive", + "ug ar", + "W ere", + "Ġmalf unction", + "Ġhomosexual s", + "Ġ---- ---", + "un i", + "Ġturb ines", + "ĠEqu ity", + "D u", + "Ġmind ed", + "ĠR H", + "ĠBlack hawks", + "Ġfe ats", + "Ġ17 00", + "re pl", + "36 2", + "lad en", + "Ġindisp ensable", + "ly ss", + "tt i", + "Ġre el", + "Ġdiver ted", + "Ġlik eness", + "Ġsubscript ions", + "Ġfing ert", + "Ġfil thy", + "dest ruct", + "d raft", + "ĠBernard ino", + "l aunch", + "Ġper plex", + "ĠS UM", + "car b", + "Ġswe ater", + "ĠVent ure", + "ĠJ ag", + "ĠCele b", + "ĠV oters", + "Ġstead fast", + "Ġathlet ics", + "ĠHans on", + "ĠDr ac", + "Tr acker", + "Ġcomm end", + "ĠPres idency", + "ĠD ID", + "in formed", + "Ġweb page", + "P retty", + "Ġforce fully", + "ãĥĥ ãĤ¯", + "Ġrel ocation", + "Ġsat ire", + "â ī", + "ĠSunder land", + "æ Ħ", + "V oice", + "???? ????", + "Ġinform ant", + "Ġbow el", + "ĠUn iform", + "Ġ ...\"", + "Ġpur ge", + "Ġpic nic", + "ĠU mb", + "ĠU PDATE", + "ĠSapp hire", + "ĠSt all", + "le arn", + "Ġobject ively", + "Ġob liter", + "Ġlooph ole", + "Ġjour neys", + "Ġo mission", + "Pro s", + "ĠSid ney", + "pl oma", + "Ġspray ed", + "Ġg uru", + "Ġtra itor", + "Ġtim et", + "Ġsn apping", + "ĠSe vent", + "urn al", + "ĠUk ip", + "Ġb owed", + "por al", + "l iberal", + "R os", + "Quest ions", + "i OS", + "Ġsummar ize", + "ST AT", + "Ġ18 50", + "ap est", + "Ġl ender", + "ĠVari able", + "br inging", + "ĠL ORD", + ", )", + "Ġcollaps es", + "x iety", + "ĠN ed", + "Y D", + "ĠSch a", + "Ġantib ody", + "Ġdis band", + "y re", + "ill usion", + "Ġro ver", + "s hed", + "ĠHiro sh", + "cc i", + "Ġcal am", + "ĠMort on", + "P interest", + "Ġ19 28", + "ĠE uras", + "ord es", + "Ġf ences", + "ĠIn ventory", + "ĠVal encia", + "ĠU d", + "ĠT iff", + "Ġsqu e", + "Ġqu otation", + "Ġtroubles ome", + "er ker", + "QU EST", + "ĠKing doms", + "s outh", + "Ġle vy", + "Pr ince", + "ĠSt ing", + "Ġnick named", + "Ġapp e", + "Ġphot ographic", + "Ġcorp us", + "re ference", + "ĠT rog", + "U nt", + ") =(", + "ĠLat via", + "Ġactiv ating", + "Ġlicense e", + "Ġdispar ities", + "ĠNews letter", + "ãĥĥ ãĥĪ", + "Ġfree ing", + "ĠJe ep", + "ĠPer ception", + "ins k", + "Ġsil icone", + "ĠHay den", + "Le an", + "ĠSuz uki", + "ibr arian", + "66 8", + "Ġsp or", + "Ġcorrel ations", + "ag hetti", + "Ġtu ber", + "ĠIP CC", + "il us", + "ĠV u", + "Ġwealth iest", + "ĠCarb uncle", + "an za", + "Ġfool ed", + "ĠZ ur", + "Ġd addy", + "ran o", + "il ian", + "Ġknock out", + "f man", + "requ ired", + "ĠWik ileaks", + "ĠD uffy", + "ON T", + "Ġins ol", + "ĠObject s", + "Ġb ou", + "ĠNord ic", + "ĠIns ert", + "sc an", + "Ġd ancers", + "Ġid iots", + "major ity", + "ĠNev ille", + "ĠFree BSD", + "Ġt art", + "pan ic", + "69 0", + "Ġcoc oa", + "Ġsam pled", + "Ġlook up", + "Ind ust", + "Ġinject ions", + "gen re", + "Ġa u", + "Ġroad way", + "Ġgen itals", + "K ind", + "ĠEx aminer", + "ĠY az", + "F resh", + "Ġpar alysis", + "ĠAl uminum", + "Ġre ap", + "ok é", + "Ġsl oppy", + "ĠTun nel", + "pos ium", + "ner y", + "en ic", + "Ġher bal", + "ĠOut er", + "ĠBuild er", + "Ġinc ur", + "Ġide ologies", + "Ġback ups", + "cons uming", + "ĠDet ect", + "de ck", + "ĠKN OW", + "ĠG ret", + "ĠM IC", + "Ġtough ness", + "ĠEx hibit", + "Ġh ive", + "L es", + "ĠSCH OOL", + "ĠAt ari", + "ald e", + "ĠN ull", + "and estine", + "m ouse", + "Ġbrig ade", + "48 9", + "Ġrev ol", + "ĠLaw son", + "ĠW ah", + "op oly", + "eb ted", + "ĠS aunders", + "Ġ3 13", + "ĠW inc", + "Ġtab oo", + "ĠHel met", + "Ġw edge", + "ch ip", + "ĠT ina", + "b g", + "Ġinf uri", + "r n", + "Ġanomal ies", + "ĠSy nc", + "ĠEx am", + "ĠComm it", + "ĠDi ary", + "ĠALS O", + "ĠDe bor", + "omed ical", + "Ġcomprehens ion", + "6 55", + "Ġempower ing", + "Ġ ire", + "Ġju ices", + "ĠE TH", + "ĠBox ing", + "=\" /", + "Ġfacilit ated", + "p oke", + "ĠPars ons", + "ĠMod er", + "tra vel", + "Ġcivil izations", + "Ġliber tarians", + "Ġrun e", + "ĠCl arks", + "at hed", + "Ġcampaign ers", + "ĠDis patch", + "ĠFah renheit", + "ĠCap com", + "-------- --", + "Ġl ace", + "Ġdr aining", + "Ġl iner", + "ĠArt ificial", + "é n", + "t ask", + "] ).", + "ĠGM O", + "ĠOper ator", + "ord inary", + "ĠInf luence", + "ĠU ps", + "Ġpot ency", + "uss en", + "osp ons", + "ĠSw im", + "ĠDead line", + "Un ity", + "Ġcul inary", + "Ġenlight enment", + "Ġwe arer", + "Ġmin ed", + "Ġp ly", + "Ġinc est", + "ĠDVD s", + "W alk", + "B TC", + "Tr ade", + "Ġdev al", + "ib and", + "ĠOvers ight", + "Palest inian", + "Ġd art", + "Ġm ul", + "L R", + "Ġrem ovable", + "ĠReal ms", + "ì Ŀ", + "Ġmisc ar", + "ĠV ulkan", + "68 5", + "è re", + "ĠS ap", + "Ġmer ging", + "ĠCar ly", + "che ster", + "Ġbr isk", + "Ġlux urious", + "ĠGener ator", + "Ġbit terness", + "Ġed ible", + "Ġ24 3", + "T G", + "Ġrect angle", + "With No", + "bel ow", + "J enn", + "Ġdark est", + "Ġh itch", + "Ġdos age", + "Ġsc aven", + "ĠK eller", + "ĠIllust rated", + "Certain ly", + "ĠMaver icks", + "Marg inal", + "Ġdiarr hea", + "Ġenorm ously", + "Ġ9 99", + "sh r", + "qu art", + "Ġadam ant", + "ĠM ew", + "Ġren ovation", + "Ġcerv ical", + "ĠPercent age", + "en ers", + "ĠKim ber", + "Ġflo ats", + "Ġde x", + "ĠW itcher", + "ĠSwan sea", + "d m", + "Ġsal ty", + "y ellow", + "Ġca pe", + "ĠDr ain", + "ĠPaul a", + "ĠTol edo", + "les i", + "Mag azine", + "ĠW ick", + "ĠM n", + "ĠA ck", + "ĠR iding", + "AS ON", + "Ġhom ophobic", + "AR P", + "Ġwand ered", + "C PU", + "ood oo", + "ĠP ipe", + "Ġtight ening", + "ĠBut t", + "3 18", + "Ġdesert ed", + "S ession", + "Ġfacilit ating", + "J ump", + "Ġemer gencies", + "OW ER", + "Ġexhaust ive", + "ĠAF TER", + "Ġheart beat", + "ĠLab el", + "ack y", + "ĠCert ified", + "ilt ration", + "Z e", + "ĠU tt", + "Ġ13 00", + "Ġpres ume", + "ĠDis p", + "Ġsur ged", + "Ġdoll s", + "Col umb", + "Ġchim pan", + "ĠR azor", + "Ġt icks", + "Ġcouncill or", + "Ġpilgr image", + "ĠReb els", + "ĠQ C", + "ĠA uction", + "x ia", + "ik k", + "b red", + "Ġinsert ion", + "Ġco arse", + "d B", + "SE E", + "ĠZ ap", + "ĠF oo", + "Ġcontem por", + "ĠQuarter ly", + "ot ions", + "ĠAl chemist", + "ĠT rey", + "ĠDu o", + "S weet", + "80 4", + "ĠGi ov", + "Ġfun n", + "N in", + "h off", + "Ġram ifications", + "Ġ19 22", + "ĠExper ts", + "az es", + "Ġgar ments", + "ar ial", + "ĠN ab", + "Ġ25 7", + "ĠV ed", + "Ġhum orous", + "ĠPom pe", + "Ġn ylon", + "Ġlur king", + "ĠSerge y", + "ĠMatt is", + "Ġmisogyn y", + "ĠComp onents", + "ĠWatch ing", + "ĠF olk", + "ract ical", + "B ush", + "Ġt aped", + "Ġgroup ing", + "Ġbe ads", + "Ġ20 48", + "Ġcon du", + "quer que", + "Read ing", + "Ġgriev ances", + "Ult ra", + "Ġend point", + "H ig", + "ĠSt atic", + "ĠScar borough", + "L ua", + "ĠMess i", + "a qu", + "ĠPsy Net", + "ĠR udd", + "Ġa venue", + "v p", + "J er", + "Ġsh ady", + "ĠRes ist", + "ĠArt emis", + "Ġcare less", + "Ġbro kers", + "Ġtemper ament", + "Ġ5 20", + "T ags", + "ĠTurn ing", + "Ġut tered", + "Ġp edd", + "Ġimpro vised", + "Ġ: (", + "Ġtab l", + "Ġpl ains", + "16 00", + "press ure", + "ĠEss ence", + "marg in", + "friend s", + "ĠRest oration", + "Ġpoll ut", + "ĠPok er", + "ĠAugust ine", + "ĠC IS", + "ĠSE AL", + "or ama", + "Ġth wart", + "se ek", + "Ġp agan", + " º", + "cp u", + "Ġg arn", + "Ġass ortment", + "ĠI LCS", + "t ower", + "Recomm ended", + "Ġun born", + "ĠRandom Redditor", + "ĠRandomRedditor WithNo", + "Ġparaly zed", + "Ġeru ption", + "Ġinter sect", + "ĠSt oke", + "ĠS co", + "B ind", + "å ¾", + "ĠP NG", + "ĠNeg ative", + "ĠNO AA", + "Le on", + "Ġall oy", + "ĠL ama", + "ĠD iversity", + "5 75", + "Ġunderest imated", + "ĠSc or", + "Ġm ural", + "Ġb usted", + "so on", + "l if", + "Ġnone x", + "Ġall ergy", + "ĠUnder world", + "ĠR ays", + "ĠBl asio", + "Ġh rs", + "ĠD ir", + "Ġ3 27", + "by ter", + "Ġrepl acements", + "Ġactiv ates", + "ri ved", + "M H", + "Ġp ans", + "ĠH I", + "Ġlong itudinal", + "Ġnu isance", + "al er", + "Ġsw ell", + "ĠS igned", + "s ci", + "ĠIs les", + "ĠA GA", + "Ġdef iant", + "Ġson ic", + "oc on", + "K C", + "ĠA im", + "t ie", + "ah ah", + "Ġm L", + "D X", + "Ġb isc", + "ĠBill board", + "ĠSY STEM", + "NE Y", + "ga ard", + "Ġdist ressed", + "former ly", + "Al an", + "Ġche fs", + "Ġopt ics", + "ĠC omet", + "ĠAM C", + "Ġredes igned", + "irm ation", + "Ġsight ings", + "38 2", + "3 11", + "ĠW B", + "Ġcont raction", + "ĠT OTAL", + "D ual", + "Ġstart led", + "Ġunderstand ably", + "Ġsung lasses", + "ETH OD", + "Ġd ocker", + "Ġsurf ing", + "ĠH EL", + "ĠSl ack", + "ton es", + "Ġsh alt", + "Vis ual", + "49 8", + "Dep artment", + "c ussion", + "Ġunrest ricted", + "Ġt ad", + "Ġre name", + "employ ed", + "Ġeduc ating", + "Ġgrin ned", + "bed room", + "ĠActiv ities", + "ĠV elvet", + "ĠSW AT", + "Ġsh uffle", + "ig or", + "Ġsatur ation", + "F inding", + "c ream", + "ic ter", + "Ġv odka", + "tr acking", + "te c", + "Ġfore ground", + "iest a", + "Ġve hement", + "ĠEC B", + "ĠT ie", + "E y", + "Ġt urtles", + "ĠRail road", + "ĠKat z", + "ĠFram es", + "Ġmen ace", + "ĠFell owship", + "ĠEss ential", + "ugg ish", + "Ġdri p", + "ch witz", + "ĠKy oto", + "s b", + "ĠN ina", + "Param eter", + "Ġal arms", + "ĠCl aud", + "Ġpione ering", + "Ġchief ly", + "ĠSc ream", + "Col lection", + "Ġthank fully", + "ĠRonald o", + "åŃ IJ", + "st rip", + "ĠDisney land", + "com mercial", + "See ing", + "S oul", + "Ġevac uate", + "Ġc iv", + "ĠAs he", + "Ġdiv ides", + "ĠD agger", + "rehens ive", + "Ġber ries", + "ĠD F", + "Ġs ushi", + "Ġplur ality", + "W I", + "Ġdisadvant aged", + "Ġbatt alion", + "ob iles", + "45 1", + "Ġcl ing", + "Ġunden iable", + "ĠL ounge", + "Ġha unt", + "p he", + "Ġquant ify", + "Ġdiff ered", + "Ġ[* ]", + "ĠV iz", + "c um", + "sl ave", + "Ġvide og", + "Ġqu ar", + "Ġbund les", + "ĠAl onso", + "t ackle", + "Ġneur onal", + "Ġlandsl ide", + "conf irmed", + "ĠDep th", + "Ġrenew ables", + "B ear", + "ĠMaced onia", + "Ġjer seys", + "Ġb unk", + "ĠSp awn", + "ĠControl s", + "ĠBuch anan", + "Ġrobot ics", + "Ġemphas izing", + "ĠTut orial", + "h yp", + "ist on", + "Ġmonument al", + "æ °", + "ĠCar ry", + "Ġt bsp", + "en ance", + "H ill", + "art hed", + "Ġro tten", + "De an", + "Ġtw isting", + "Ġgood will", + "Ġimm ersion", + "L iving", + "Ġbr ushes", + "ĠC GI", + "ĠAt k", + "tr aditional", + "Ġph antom", + "ĠSt amina", + "Ġexpans ions", + "ĠMar in", + "Ġembark ed", + "ĠE g", + "int estinal", + "ĠPE OPLE", + "ĠBo oth", + "ĠApp alach", + "Ġreleg ated", + "V T", + "M IT", + "Ġmust er", + "Ġwithdraw ing", + "Ġmicrosc ope", + "ĠG athering", + "ĠC rescent", + "ĠArgent ine", + "ĠDec re", + "ĠDomin ic", + "Ġbud s", + "ant age", + "ĠI on", + "Ġwid ened", + "ONS ORED", + "ĠGl oves", + "iann opoulos", + "raz en", + "fe el", + "Ġrepay ment", + "Ġhind sight", + "ĠRE ALLY", + "ĠPist ol", + "ĠBra h", + "Ġwat ts", + "Ġsurv ives", + "Ġfl urry", + "iss y", + "Al ert", + "ĠUrug uay", + "Ph oenix", + "S low", + "ĠG rave", + "ĠF ir", + "Ġmanage able", + "Ġtar iff", + "ĠU DP", + "ĠPist ons", + "ĠNiger ian", + "Ġstrike outs", + "Ġcos metics", + "whel ming", + "f ab", + "c ape", + "pro xy", + "Ġre think", + "Ġover coming", + "sim ple", + "Ġw oo", + "Ġdistract ing", + "ĠSt anton", + "ĠTuls a", + "ĠD ock", + "65 9", + "Ġdisc ord", + "ĠEm acs", + "ĠV es", + "ĠR OB", + "Ġreass uring", + "Ġcons ortium", + "Muslim s", + "3 21", + "Ġprompt s", + "se i", + "ĠH itch", + "imp osed", + "ĠF ool", + "Ġindisc rim", + "wr ong", + "bu querque", + "D avis", + "! ]", + "Ġtim eless", + "ĠNE ED", + "Ġpestic ide", + "Ġrally ing", + "ĠCal der", + "Ġå ¤", + "Ġx p", + "ĠUn le", + "ĠEx port", + "lu aj", + "B uff", + ") ", + "B oot", + "ĠChrys ler", + "or ative", + "M ess", + "Ġneglig ible", + "ert odd", + "ĠMush room", + "ĠG ale", + "g c", + "ĠCos by", + "ĠR ural", + "rit ical", + "B ell", + "Ġturb ine", + "00 200000", + "Ġlegit imately", + "ĠAnim ated", + "T ED", + "ĠThe odore", + "c onduct", + "ĠH ier", + "Ġcounterfe it", + "ĠAlger ia", + "Ġun beat", + "cont roller", + "Ġun res", + "Ġscram bling", + "ĠFall on", + "T es", + "Ġam ber", + "Ġroy alties", + "ĠShel ter", + "ĠL ester", + "Ġclass ify", + "Rem ote", + "Ġun heard", + "Ġcontrovers ies", + "Ġenrich ment", + "ĠYan kee", + "g amer", + "Ġpl atinum", + "Ġec ology", + "ĠS ark", + "Ġunt ouched", + "Ġsuper visors", + "Ġ\" %", + "Ġf ooth", + "Ġcomm ons", + "Ġnarc otics", + "Ġind ices", + "ĠP ly", + "Ġaddition ally", + "ĠGaw ker", + "ĠE Q", + "Pl aying", + "Ġcave at", + "ĠAbs olute", + "oss us", + "B aby", + "Ġr ation", + "Ġres in", + "Ġcalib ration", + "ĠNew port", + "Ġkn ocks", + "v t", + "Ġcomp ost", + "Sc ene", + "Ġsar cast", + "Ġkiss es", + "Ġn s", + "all i", + "ĠMar cel", + "ĠP iet", + "iat rics", + "Ġsurround s", + "ĠRep rodu", + "ĠPhill ies", + "Ġuncertain ties", + "ĠE ur", + "ĠRom ance", + "ĠH ath", + "ĠNeed s", + "ĠCl oak", + "Ġcre m", + "que ue", + "Ġ3 55", + "Ġup front", + "] );", + "Ġrecip roc", + "Ġ19 27", + "Ġ11 00", + "ut su", + "Ġdep ressive", + "ow ment", + "F ans", + "Ġme ch", + "Ġann ihil", + "Ġcounter terrorism", + "ĠFig ures", + "b old", + "ĠMo ines", + "ĠDri vers", + "Ġmanuscript s", + "ĠCrypt o", + "Ġhyp not", + "redd its", + "Ġprosec utions", + "Ġdiver t", + "CR IP", + "ĠB ene", + "ĠRe ggie", + "Ġtax ing", + "ĠMor ales", + "ent ing", + "t ur", + "sign ificant", + "ĠPR OV", + "Ġstr ands", + "Ġp ouch", + "ĠR ookie", + "» Ĵ", + "Ġnic er", + "he my", + "h w", + "EC A", + "Ġintimid ated", + "Ġstr icter", + "Ġmicro bial", + "det ails", + "Ġv ows", + "Ġqu ake", + "hh hh", + "Ġrein vent", + "U b", + "Ġrel inqu", + "ĠBuff ett", + "lic ensed", + "itte red", + "ĠPic ard", + "Ġche wing", + "u cl", + "organ ic", + "Ġlocal ized", + "ĠEconom ist", + "Ġacqu ainted", + "Def inition", + "s ed", + "Crit ics", + "Ġc c", + "45 3", + "38 1", + "Ġfell ows", + "Ġcheck points", + "0 25", + "Ġre election", + "Ġmed iated", + "ĠK DE", + "Ġhurd le", + "Ġtext ing", + "Per fect", + "Ġtrust ees", + "fect ure", + "Ġd ich", + "mon ary", + "Ġdist inctions", + "Ġ14 00", + "Ġus her", + "Ġparas ites", + "ĠSh aring", + "ĠV im", + "Ġbar becue", + "ĠMin isters", + "ere lla", + "Ġe b", + "Ġm c", + "ĠSome how", + "ĠIn sect", + "ch anges", + "b road", + "ĠBy z", + "Ġgrap es", + "66 9", + "Ġ= ================", + "Ġass imil", + "Ġhaun ting", + "Ġfire power", + "Ġdef amation", + "em phasis", + "Ġcomp ose", + "Ġallerg ies", + "Ġstr ang", + "roll ers", + "b ang", + "Ġbrew ers", + "ron gh", + "ri ot", + "p oor", + "c old", + "S ample", + "Ġbu oy", + "0 40", + "ĠCourt ney", + "Ġ26 8", + "ĠWed ding", + "70 2", + "Ġobsess ive", + "Ġbra king", + "ĠL al", + "an ical", + "å ¦", + "at en", + "Con struction", + "Ġclin ically", + "iers hip", + "N ames", + "ĠDisc uss", + "ĠRam os", + "Ġloc ale", + "ĠAgric ultural", + "En able", + "Ġhorse power", + "ent ure", + "P ref", + "C ourt", + "Ġstaff ing", + "Ġfut uristic", + "dri vers", + "ĠMarket place", + "æĪ ¦", + "Friend s", + "Ġdam ning", + "ĠCustom ers", + "Ġwe eds", + "ĠM ai", + "Ġag ile", + "ĠT att", + "ic ent", + "R anked", + "cro ft", + "ĠKat y", + "Ext reme", + "Ġcar ve", + "ĠR over", + "ĠBy ron", + "37 2", + "Ġconduct s", + "r atch", + "it ia", + "ĠPump kin", + "Sad ly", + "Rel oaded", + "P olicy", + "Ġl ick", + "pe ak", + "is ks", + "ĠCD s", + "ĠEn cyclopedia", + "in itial", + "C os", + "ĠAware ness", + "ĠD ram", + "$$ $$", + "Ġr iff", + "Ġscript ure", + "run ners", + "Ġbo iler", + "ons on", + "o in", + "Ġham string", + "Ġcat aly", + "ĠArch bishop", + "ch all", + "Ġf aux", + "ok in", + "local host", + "ĠN AME", + "ad obe", + "S AN", + "am ate", + "Ġscram ble", + "Ġcar c", + "ĠMan ifest", + "ĠCed ar", + "ĠSer gio", + "l ater", + "ff er", + "Ġgrapp ling", + "ĠDe utsche", + "agon ists", + "ĠNew sp", + "Ġpret ended", + "arch ment", + "Ġcur ated", + "Ġhead phone", + "ĠUn common", + "ĠS IGN", + "A gent", + "Ġdead lines", + "Ġhorizont ally", + "ĠM AT", + "ĠSum mers", + "Ġord ained", + "ĠLast ly", + "ĠKend all", + "Ġfr ig", + "ĠMach ina", + "ĠWater loo", + "ĠMex icans", + "Ġprotect or", + "Ġgl are", + "} \"", + "Prem ium", + "Ġr ift", + "ĠTelesc ope", + "Met al", + "Ġrec apt", + "Ġ; ;", + "Ġincl ination", + "Ġimp oses", + "ing en", + "^ {", + "Ġh aste", + "Ġd olphins", + "Ġcomm uters", + "pl anned", + "c ong", + "m x", + "ĠU pload", + "Ġext rap", + "ĠTuc son", + "ĠExpl oration", + "efe ated", + "Ġsl ender", + "70 3", + "ĠB uk", + "is el", + "Ġcompet itiveness", + "ch lor", + "ĠP ermanent", + "ĠE verett", + "ĠSpecial ist", + "ĠS OL", + "Ġcy an", + "ĠEx actly", + "U F", + "ĠL IFE", + "ary l", + "on et", + "ĠEmploy ee", + "aw ed", + "ĠRat ings", + "Ġextra vag", + "ul hu", + "ĠPl ane", + "Ġelev ate", + "ĠCoord inator", + "ĠWat kins", + "Ġex cludes", + "Ġsent ient", + "Ġep och", + "Ġall oc", + "Pre viously", + "ĠSh y", + "ĠSlov akia", + "L OCK", + "Ġmarked ly", + "Ġkn ob", + "Ġadventure rs", + "ĠBe en", + "ĠCost s", + "amm ers", + "Ġon slaught", + "ĠSupport ed", + "ĠT au", + "ik arp", + "ĠS overe", + "ĠHam pton", + "ãĤ ī", + "Pre v", + "ĠW orse", + "Ġc ottage", + "ĠH ades", + "le z", + "b owl", + "Ġfrag rance", + "ĠL ok", + "EM OTE", + "ĠPet ro", + "Ġ19 25", + "ĠP end", + "produ cing", + "Ġrel ocate", + "v ati", + "p ole", + "Ġsem in", + "ĠN UM", + "Ġrock ed", + "b uff", + "b ly", + "Rep ly", + "ĠH ai", + "Ġartic ulated", + "ĠIslam abad", + "66 5", + "ĠClaim s", + "Des ktop", + "Ġtrust ee", + "Ġscript ing", + "ĠS ob", + "ĠAs ylum", + "STD OUT", + "ĠCl own", + "ĠD ortmund", + "ĠDev on", + "l ite", + "ĠMar ble", + "Ġb unker", + "Ġcre st", + "Ġarous al", + "ĠS ears", + "ĠBudd y", + "ered ith", + "ĠP olly", + "Ġdec ode", + "ĠV ish", + "ĠRef lect", + "an on", + "Ġrefund s", + "imm ers", + "H M", + "Ġwip ing", + "Ġpuzz led", + "Ġmat te", + "un o", + "P ierre", + ") ),", + "Ġt ainted", + "Ġsymbol ism", + "ĠF raz", + "Ġprotest ors", + "ethe us", + "%% %%", + "W ra", + "Ġl ax", + "ad em", + "atur ation", + "ãĥ ĵ", + "ĠTra iler", + "ĠE NG", + "ĠBows er", + "Ġatt m", + "D ur", + "80 7", + "Ġsid x", + "Ġc ider", + "ĠA ffect", + "Ġw oven", + "ĠBark er", + "ben ef", + "Ġdst g", + "ĠRy u", + "> [", + "Ġsq or", + "S audi", + "Ġis tg", + "Ġindul ge", + "pro c", + "Ġdisg usted", + "Ġcomp ounded", + "Ġn em", + "Ġschool ing", + "ĠC ure", + "process ing", + "S ol", + "Ġpro verb", + "it ized", + "ĠAlv arez", + "Ġscar f", + "Ġrect angular", + "re ve", + "Ġh ormonal", + "ĠSt ress", + "itiz en", + "Ġ4 25", + "girl s", + "ĠNo ir", + "ĠR app", + "Ġmar ches", + "ch urch", + "ĠUs es", + "Ġ40 5", + "ĠBer m", + "Ġord inances", + "ĠJud gment", + "Charg es", + "ĠZ in", + "Ġdust y", + "Ġstraw berries", + "Ġper ce", + "ĠTh ur", + "ĠDebor ah", + "net flix", + "ĠLam bert", + "Ġam used", + "ĠGu ang", + "Y OU", + "R GB", + "ĠC CTV", + "Ġf iat", + "r ang", + "Ġf ederation", + "ĠM ant", + "ĠB ust", + "ĠM are", + "respect ive", + "ĠM igration", + "ĠB IT", + "59 0", + "Ġpatriot ism", + "Ġout lining", + "reg ion", + "ĠJos é", + "Ġbl asting", + "ĠEz ra", + "B s", + "Ġundermin es", + "ĠSm ooth", + "Ġcl ashed", + "rad io", + "Ġtransition ing", + "ĠBucc aneers", + "ĠOw l", + "Ġplug s", + "Ġh iatus", + "ĠPin ball", + "Ġm ig", + "ĠNut r", + "ĠWolf e", + "Ġinteg ers", + "Ġor bits", + "ĠEd win", + "ĠDirect X", + "b ite", + "Ġbl azing", + "v r", + "Ed ge", + "ĠP ID", + "ex it", + "ĠCom ed", + "ĠPath finder", + "ĠGu id", + "ĠSign s", + "ĠZ er", + "ĠAg enda", + "Ġreimburse ment", + "M esh", + "i Phone", + "ĠMar cos", + "ĠS ites", + "h ate", + "en burg", + "Ġs ockets", + "p end", + "Bat man", + "v ir", + "ĠSH OW", + "Ġprovision al", + "con n", + "ĠDeath s", + "AT IVE", + "Pro file", + "sy m", + "J A", + "Ġnin ja", + "inst alled", + "id ates", + "eb ra", + "ĠOm aha", + "Ġse izing", + "ĠBe asts", + "Ġsal ts", + "M ission", + "Gener ally", + "ĠTr ilogy", + "he on", + "leg ates", + "Ġd ime", + "Ġf aire", + "par able", + "G raph", + "Ġtotal ing", + "Ġdiagram s", + "ĠYan uk", + "ple t", + "ĠMe h", + "Ġmyth ical", + "ĠStep hens", + "aut ical", + "ochem istry", + "Ġkil ograms", + "Ġel bows", + "anc ock", + "ĠB CE", + "ĠPr ague", + "Ġimpro v", + "ĠDev in", + "Ġ\" \\", + "par alle", + "Ġsuprem acists", + "ĠB illion", + "Ġreg imen", + "inn acle", + "Ġrequ isite", + "ang an", + "ĠBur lington", + "ain ment", + "ĠObject ive", + "oms ky", + "G V", + "Ġun ilateral", + "Ġt c", + "Ġh ires", + "ment al", + "Ġinvol untary", + "Ġtrans pl", + "ĠASC II", + " ¨", + "Ev ents", + "Ġdoub ted", + "ĠKa plan", + "ĠCour age", + "ig on", + "ĠMan aging", + "ĠT art", + "Ġfalse hood", + "ĠV iolet", + "Ġair s", + "Ġfertil izer", + "Brit ain", + "Ġaqu atic", + "ou f", + "W ords", + "ĠHart ford", + "Ġeven ings", + "ĠV engeance", + "qu ite", + "G all", + "ĠP ret", + "Ġp df", + "ĠL M", + "ĠSo chi", + "ĠInter cept", + "9 20", + "Ġprofit ability", + "ĠId le", + "ĠMac Donald", + "ĠEst ablishment", + "um sy", + "Ġgather ings", + "ĠN aj", + "Charl ie", + "Ġas cent", + "ĠProt ector", + "Ġal gebra", + "Ġbi os", + "for ums", + "EL S", + "Introdu ced", + "Ġ3 35", + "Ġastron omy", + "Cont ribut", + "ĠPol ic", + "Pl atform", + "Ġcontain ment", + "w rap", + "Ġcoron ary", + "ĠJ elly", + "man ager", + "Ġheart breaking", + "c air", + "ĠChe ro", + "c gi", + "Med ical", + "ĠAccount ability", + "! !\"", + "oph ile", + "Ġpsych otic", + "ĠRest rict", + "Ġequ itable", + "iss ues", + "Ġ19 05", + "ĠN ek", + "c ised", + "ĠTr acking", + "Ġo zone", + "Ġcook er", + "ros is", + "Ġre open", + "Ġinf inity", + "ĠPharm aceutical", + "ens ional", + "Att empt", + "ĠR ory", + "Mar co", + "Ġawa its", + "H OW", + "t reated", + "Ġbol st", + "Ġreve red", + "Ġp ods", + "opp ers", + "00 10", + "Ġampl itude", + "ric an", + "SP ONSORED", + "Ġtrou sers", + "Ġhal ves", + "ĠK aine", + "ĠCut ler", + "ĠA UTH", + "Ġsplend id", + "Ġprevent ive", + "ĠDud ley", + "if acts", + "umin ati", + "ĠY in", + "Ġad mon", + "ĠV ag", + "Ġin verted", + "Ġhast ily", + "ĠH ague", + "L yn", + "Ġled ger", + "Ġastron omical", + "get ting", + "Ġcirc a", + "ĠC ic", + "ĠTenn is", + "Lim ited", + "Ġd ru", + "ĠBY U", + "Ġtrave llers", + "Ġp ane", + "ĠInt ro", + "Ġpatient ly", + "Ġa iding", + "Ġlo os", + "ĠT ough", + "Ġ29 3", + "Ġconsum es", + "Source File", + "Ġ\"\" \"", + "Ġbond ing", + "Ġtil ted", + "Ġmenstru al", + "ĠCel estial", + "UL AR", + "Plug in", + "Ġrisk ing", + "N az", + "ĠRiy adh", + "Ġacc redited", + "Ġsk irm", + "é Ľ", + "Ġexam iner", + "Ġmess ing", + "Ġnear ing", + "ĠC hern", + "ĠBeck ham", + "Ġsw apped", + "Ġgo ose", + "K ay", + "Ġlo fty", + "ĠWal let", + "Ġ[ '", + "Ġap ocalypse", + "Ġb amboo", + "ĠSP ACE", + "ĠEl ena", + "Ġ30 6", + "ac ons", + "Ġtight ened", + "Ġadolesc ence", + "Ġrain y", + "Ġvandal ism", + "ĠNew town", + "Ġcon ject", + "c akes", + "Ġche ated", + "Ġmoder ators", + "par ams", + "E FF", + "Ġdece it", + "ĠST L", + "ĠTanz ania", + "ĠR I", + "Ġ19 23", + "ĠEx ile", + "the l", + "Ġthe olog", + "Ġquir ky", + "ĠIr vine", + "Ġneed y", + "or is", + "U m", + "K a", + "Ġmail box", + "3 22", + "Ġb os", + "ĠPet ra", + "K ING", + "Ġenlarg ed", + "O ften", + "Ġbad ass", + "Ġ3 43", + "ĠPl aces", + "ĠC AD", + "Ġpr istine", + "Ġinterven ing", + "d irection", + "Ġl az", + "ĠD SM", + "Ġproject ing", + "ĠF unk", + "ag og", + "pay ment", + "n ov", + "Ġch atter", + "AR B", + "Ġexam inations", + "ĠHouse hold", + "ĠG us", + "F ord", + "4 14", + "B oss", + "Ġmy stic", + "Ġle aps", + "ĠB av", + "ul z", + "b udget", + "Foot ball", + "Ġsubsid ized", + "Ġfirst hand", + "Ġcoinc ide", + "oc ular", + "Con n", + "ĠColl abor", + "Ġfool s", + "am ura", + "ah ar", + "r ists", + "Ġsw ollen", + "Ġexp ended", + "ĠP au", + "s up", + "Ġsp ar", + "Ġkey note", + "s uff", + "Ġunequ al", + "Ġprogress ing", + "str ings", + "ĠGamer gate", + "Dis ney", + "ĠEle ven", + "om nia", + "Ġscript ed", + "Ġear ners", + "bro ther", + "ĠEn abled", + "æ ³", + "Ġlar vae", + "ĠL OC", + "m ess", + "Wil son", + "ĠTem plate", + "success fully", + "Ġparam ount", + "Ġcamoufl age", + "Ġbind s", + "ĠQu iet", + "ĠSh utterstock", + "r ush", + "Ġmasc ot", + "fort une", + "ĠCol t", + "ĠBe yon", + "hab i", + "Ġha irc", + "Ġ26 7", + "ĠDe us", + "Ġtw itch", + "Ġconcent rating", + "Ġn ipples", + "c ible", + "Ġg ir", + "N Z", + "M ath", + "n ih", + "Requ ired", + "Ġp onder", + "ĠS AN", + "Ġwedd ings", + "Ġl oneliness", + "N ES", + "ĠMah jong", + "69 5", + "add le", + "ĠGar ner", + "ĠC OUR", + "Br idge", + "Ġsp ree", + "ĠCald well", + "Ġbri bery", + "Ġ���� ����", + "plug ins", + "Ġr acket", + "Ġchamp agne", + "vers ible", + "V ote", + "Ġmod ifiers", + "May or", + "6 80", + "Ġassemb lies", + "ĠS ultan", + "ĠN ing", + "ĠLad ies", + "Ġsulf ur", + "Ġor bs", + "Ġ---- -", + "____ ___", + "ĠJournal ism", + "Ġes ports", + "Ġl ush", + "Ġh ue", + "Ġspect ral", + "H onest", + "ãĥ ı", + "Ġbus hes", + "Ġrein forcement", + "Ġre opened", + "ĠWhe els", + "ĠM org", + "rie ving", + "Ġaux iliary", + "Ġj Query", + "ĠB AT", + "tes que", + "Ġver tex", + "p ure", + "f rey", + "ãĤ º", + "d os", + "Ġty ph", + "Ġc ull", + "Ġe q", + "Ġdec on", + "Ġtoss ing", + "Ġdispar ate", + "ĠBr igham", + "print f", + "led ged", + "Ġsu nd", + "Ġco zy", + "Ġhepat itis", + "per forming", + "Ġav al", + "ĠG G", + "f uture", + "Ġpet ertodd", + "ĠKos ovo", + "Ġmagn ets", + "Al ready", + "ĠEd ison", + "ĠCe res", + "ĠRA ID", + "Ġbrill iance", + "57 6", + "Ġder ives", + "Ġhypert ension", + "ĠÎ Ķ", + "Ġlamb da", + "Ġfl air", + "Ġmission aries", + "Ġrap es", + "ĠSt arter", + "ĠMon ths", + "Ġdef y", + "Ġseism ic", + "ĠR aphael", + "Ġeuro zone", + "65 6", + "z sche", + "Ġscr atched", + "Ġb ows", + "ĠLenn on", + "ĠGa ia", + "Ġdri pping", + "f acts", + "A le", + "Ġfrog s", + "ĠBre ast", + "ogene ity", + "ĠProsecut or", + "Ġampl ified", + "ĠHod g", + "ĠF n", + "Th ousands", + "ĠNI H", + "ĠMonitor ing", + "FT WARE", + "ĠPri ebus", + "ĠG rowing", + "hun ter", + "Ġdiagn ose", + "ĠM ald", + "ĠL R", + "Ġcrown ed", + "Ġburst ing", + "Ġdiss olution", + "j avascript", + "Ġuseful ness", + "ĠExec ution", + ": (", + "ĠIv ory", + "a ah", + "Ġpersecut ed", + "viol ence", + "ist as", + "ĠCr ate", + "Ġimpuls es", + "ĠSp ani", + "ed es", + "Hand le", + "ĠZ erg", + "think able", + "Last ly", + "Ġspont aneously", + "Ġinconven ient", + "Ġdismiss ing", + "Ġpl otted", + "Ġeight y", + "Ġ7 37", + "r ish", + "ĠThor nton", + "ath am", + "Ġsit com", + "V en", + "Rec ipe", + "t el", + "l und", + "Ġcle ars", + "ĠSas uke", + "Ġ25 8", + "Ġopt ing", + "Ġen raged", + "est hetic", + "ĠA e", + "uch s", + "Pre p", + "Fl ow", + "Ġrun off", + "ĠE ating", + "ĠG iles", + "ĠAct ing", + "res ources", + "ib aba", + "Ġr pm", + "Ġske wed", + "ĠBl anc", + "ĠS akuya", + "Ġhot ter", + "Ġ19 24", + "op ian", + "ck o", + "Ġcr umbling", + "Ġcapt ains", + "ĠAppropri ations", + "le aders", + "dro pping", + "an uts", + "Ġrevers ing", + "ĠP ose", + "ĠS ek", + "Sc ot", + "ĠIde a", + "c ise", + "ĠSloven ia", + "Ġ3 17", + "Do ctor", + "Ġcro cod", + "ald i", + "Se a", + "ĠFar rell", + "Ġmerc enaries", + "ĠR NC", + "ĠGu ess", + "Ġp acing", + "M achine", + "Streamer Bot", + "ĠChar ity", + "Ġ29 8", + "Ġcann ons", + "ĠTob y", + "TPP StreamerBot", + "ĠPass ion", + "cf g", + "Th om", + "Ġbad ges", + "ĠBern stein", + ". âĢĵ", + "ĠP OP", + "ĠCon j", + "Ġinitial ization", + "Ġbiod iversity", + "D ub", + "Ġfeud al", + "Ġdisclaim er", + "Ġc row", + "Ġign ition", + "ar f", + "S HA", + "Ġk Hz", + "h azard", + "ĠArt ists", + "oe uv", + "67 9", + "ĠRud y", + "N ine", + "ĠRam adan", + "å ½", + "itt o", + "Ġadren aline", + "C ert", + "Ġsmell ed", + "Ġimp unity", + "Ġag endas", + "ĠRe born", + "ĠCon cent", + "ĠSe ems", + "Ġo mega", + "ĠDust in", + "Ġback er", + "ĠSau ce", + "ĠBoy le", + "W IN", + "Ġsp ins", + "Ġpa uses", + "u pt", + "Ġshred ded", + "Ġstra pped", + "ĠCor ruption", + "Ġscr atches", + "Ġn i", + "Ġatt ire", + "ĠS AF", + "Factory Reloaded", + "ĠI PS", + "Ġ( %", + "Ġsem inar", + "f ocus", + "c ivil", + "Ġ18 60", + "int osh", + "Ġcontin ual", + "Ġabbre vi", + "ĠS ok", + "oc obo", + "X M", + "Ġfr antic", + "Ġunavoid able", + "Ġar tery", + "Ġannot ations", + "b ath", + "Cl imate", + "Ġd ors", + "ĠSl ide", + "co ord", + "ĠRel oad", + "ĠL DL", + "ĠLove craft", + "Ġunim agin", + "Ġresemb led", + "Ġbarr acks", + "n p", + "Ġsurrog ate", + "Ġcategor ized", + "ãĤ ©", + "Ġvacc inated", + "Ġdrain age", + "Ġind ist", + "ĠWhats App", + "Ġ18 70", + "oler ance", + "inv oke", + "am orph", + "Ġrecon nect", + "Ġem anc", + "Ġblind ness", + "Ġ12 80", + "intern et", + "c ollar", + "Ġalt ru", + "Ġab yss", + "ĠT RI", + "65 7", + "Ġinf used", + "HE AD", + "Ġforest ry", + "ĠWood y", + "ĠC i", + "w i", + "s am", + "78 4", + "hol iday", + "Ġmog ul", + "ĠF ees", + "ĠD EN", + "In ternal", + "ur bed", + "f usc", + "at om", + "ĠIll usion", + "Ġpoll ed", + "Ġfl ap", + "Ġco ax", + "L GBT", + "An aly", + "ĠSect ions", + "ĠCalif orn", + "em n", + "Ġh ither", + "ĠN IGHT", + "Ġn ailed", + "ĠPip eline", + "39 1", + "o of", + "ĠPr imal", + "vere nd", + "Ġsl ashing", + "Ġret ri", + "avi our", + "Ġdepart ing", + "g il", + "IS C", + "Ġmid way", + "Ġultras ound", + "Ġbeh aving", + "ĠT ara", + "class es", + "V irtual", + "ĠColon ial", + "Ġstri pping", + "Ġorchestr ated", + "ĠGra ves", + "45 2", + "ĠIron ically", + "ĠWrit ers", + "Ġl ends", + "ĠMan z", + "Ġra ven", + "Ġoxid ative", + "Ġ26 6", + "EL F", + "act ually", + "asc ar", + "D raft", + "Ġfavour able", + "Ġhumili ating", + "Ġf idelity", + "ĠH of", + "ĠX uan", + "49 6", + "Ġlay ered", + "at is", + "79 0", + "Ġpay check", + "it on", + "K ar", + "ĠVM ware", + "ĠFar mer", + "Ġserv ic", + "gl omer", + "Ġsl ump", + "ĠFab ric", + "ĠD OC", + "est ing", + "Ġreass ure", + "Ġph yl", + "v olt", + "it ory", + "R ules", + "Ġoxid ation", + "Ġpri zed", + "Ġmist ress", + "ĠDj ango", + "WAR N", + "å ij", + "Ġenc ode", + "ĠFeed back", + "Ġstupid ity", + "I an", + "ĠYugoslav ia", + "× ¨", + "ac l", + "UT E", + "19 77", + "Ġqual ifies", + "Ġpuls es", + "pret ty", + "Ġfro ze", + "Ġs s", + "Iter ator", + "Ġur gently", + "Ġm ailed", + "ĠCh am", + "Ġsust aining", + "Ġbas il", + "Ġpupp ies", + "il ant", + "ĠP LEASE", + "l ap", + "ace ous", + "F ear", + "ĠMaster y", + "aut omatic", + "ĠT AG", + "Ġant im", + "ag les", + "47 3", + "fram es", + "Ġwh ispers", + "ĠWho ever", + "Ġbra very", + "ĠUK IP", + "ract ions", + "\"\" \"", + "Ġt ame", + "Ġpart ed", + "every thing", + "CON T", + "Ġind ebted", + "Ġadd r", + "re k", + "IR ED", + "Ġem inent", + "cl inton", + "Ġo usted", + "Ġreview er", + "Ġmelt down", + "Ġre arr", + "ĠY ao", + "the real", + "aby te", + "Ġst umbling", + "Ġbat ches", + "Ġ25 9", + "Ġcontrace ptive", + "Ġprost itute", + "ens is", + "De cl", + "ĠSt rikes", + "M ilitary", + "ĠO ath", + "v acc", + "pp ings", + "05 2", + "Ġpart Name", + "amp ing", + "Rep orts", + "K I", + "CH R", + "Ġsubt ly", + "sw ers", + "Bl ake", + "us ual", + "Ġcontest ants", + "Ġcart ridges", + "ĠGRE AT", + "Ġbl ush", + "ĠâĢ º", + "47 2", + "Ġreason ed", + "ãĥ ¤", + "paralle led", + "Ġd yn", + "ag ate", + "Ġnight ly", + "å Ĩ", + "55 6", + "Ġsem antic", + "ĠAdv oc", + "Ġ !!", + "Ġdisag rees", + "ĠB W", + "V eh", + "Ġharm ing", + "Ġembr aces", + "Ġstri ves", + "Ġin land", + "ĠK ard", + "Ġhe ats", + "ĠGin ny", + "ut an", + "ern aut", + "yl ene", + "ĠE lev", + "J D", + "Ġh ars", + "ĠStar r", + "Ġsk ysc", + "Ġcollabor ators", + "Us ually", + "Ġrev olutions", + "ĠSTAT S", + "Ġdism antle", + "Ġconfident ly", + "Ġkin etic", + "Al i", + "Ġpercent ile", + "Ġextract ing", + "ill ian", + "est ead", + "Ġphysic ists", + "ĠMarsh al", + "Ġfell owship", + "Ġd ashed", + "ĠU R", + "ĠSi oux", + "ĠComp act", + "am ide", + "P ython", + "ĠLe igh", + "ĠPharm ac", + "ist rates", + "her ical", + "Ġf ue", + "ĠE min", + "Ġ( {", + "ĠNeighbor hood", + "Ġdisrupt ing", + "ĠD up", + "Ġg land", + "ĠSe v", + "ĠMar ian", + "arg on", + "ĠD und", + "Ġ< !--", + "Ġstr and", + "Ġstadium s", + "z os", + "Ġpsych osis", + "ĠR ack", + "Ġbrilliant ly", + "ï¸ ı", + "Ġsubmer ged", + "ĠInst it", + "ĠCh ow", + "Ġc ages", + "ĠH ats", + "ĠU rs", + "Ġdil uted", + "us at", + "ien ne", + "ĠMembers hip", + "ĠBur k", + "Ġ ie", + "Ġarche type", + "D rug", + "ult on", + "ĠSp ock", + "ĠMcK ay", + "ĠDep end", + "F eatured", + "S oc", + "19 78", + "ĠB ere", + "Ġrelent lessly", + "Ġcripp ling", + "Ġar thritis", + "çĶ Ł", + "ĠTrop ical", + "ĠBul g", + "ĠCher yl", + "Ġadm irable", + "Ġsub title", + "Over ride", + "Ġorig inating", + "ĠC CP", + "Ġsw ore", + "ĠSo le", + "ĠDis orders", + "3 29", + "Ġprocess ion", + "Ġref urb", + "Ġimm ersed", + "requ ently", + "Ġskept ics", + "Ġcer amic", + "m itter", + "en stein", + "b elt", + "ĠT IT", + "b idden", + "Ġf ir", + "m ist", + "> ]", + "Ġwe ave", + "ĠParad ox", + "Ġentr usted", + "ĠBarcl ays", + "Ġnovel ist", + "og ie", + "80 6", + "Ġnin ety", + "Ġdisag reements", + "@@@@ @@@@", + "ĠAus chwitz", + "c ars", + "ĠL ET", + "t ub", + "arant ine", + "P OS", + "Ġback story", + "Ġcheer ful", + "ĠR ag", + "ek a", + "bi ased", + "Ġinexper ienced", + "ak ra", + "ĠW itt", + "t an", + "Ġrap ist", + "Ġplate au", + "ch al", + "ĠInqu is", + "exp ression", + "Ġc ipher", + "Ġsh aving", + "add en", + "re ly", + "( \\", + "ism a", + "ĠReg ulatory", + "CH AR", + "ily n", + "N VIDIA", + "G U", + "Ġmur m", + "la us", + "Christ opher", + "Ġcontract ual", + "ĠPro xy", + "ĠJa ime", + "ĠMethod ist", + "Ġstew ards", + "st a", + "per ia", + "Ġphys iology", + "Ġbump ed", + "Ġf ructose", + "Austral ian", + "ĠMet allic", + "ĠMas querade", + "ar b", + "Ġprom ul", + "Ġdown fall", + "Ġbut cher", + "Ġb our", + "ĠIN FORMATION", + "ĠB is", + "pect s", + "ad ena", + "Ġcontempl ating", + "ar oo", + "cent ered", + "ĠPe aks", + "Us ed", + "Ġmod em", + "Ġg enders", + "Ġ8 000", + "37 1", + "Ġm aternity", + "ĠR az", + "Ġrock ing", + "Ġhandgun s", + "ĠD ACA", + "Aut om", + "ĠN ile", + "Ġtum ult", + "ĠBenef it", + "ĠAppro ach", + "works hop", + "ĠLe aving", + "G er", + "inst ead", + "Ġvibr ations", + "Ġrep ositories", + "49 7", + "ĠA unt", + "ĠJ ub", + "ĠExp edition", + "Al pha", + "Ġs ans", + "Ġoverd ue", + "Ġoverc rowd", + "Ġlegisl atures", + "Ġp aternal", + "ĠLeon ardo", + "Ġexp ressive", + "Ġdistract ions", + "Ġsil enced", + "tr ust", + "Ġb iking", + "Ġ5 60", + "Ġpropri et", + "Ġimp osition", + "Ġcon glomer", + "Ġ= ================================================================", + "ĠTe aching", + "ĠY ose", + "int ensive", + "T own", + "Ġtroll ing", + "ĠGr ac", + "ĠAS US", + "Y o", + "Ġspecial s", + "ĠNep h", + "ĠGod zilla", + "Dat abase", + "ĠHe gel", + "Ġ27 2", + "19 76", + "ĠGl oria", + "Ġdis emb", + "ĠInvestig ations", + "ĠB ane", + "ag ements", + "St range", + "Ġtre asury", + "ĠPl ays", + "Ġundes irable", + "Ġwid ening", + "Ġverb ally", + "Ġinf ancy", + "Ġcut ter", + "f ml", + "Ġ21 00", + "prot otype", + "f ine", + "Ġdec riminal", + "Ġdysfunction al", + "Ġbes ie", + "ĠErn st", + "z eb", + "Ġnort heastern", + "Ġa ust", + "por ate", + "ĠMar lins", + "Ġsegreg ated", + "ew orld", + "ĠMa her", + "Ġtra verse", + "Ġmon astery", + "ur gy", + "G ear", + "s and", + "Com pl", + "ĠE MP", + "Ġpl ent", + "ĠMer cer", + "Ġ27 6", + "TA BLE", + "Config uration", + "H undreds", + "Ġpr ic", + "Ġcollabor ating", + "ĠPar amount", + "ĠCumm ings", + "Ġ( <", + "Ġrecord er", + "Ġfl ats", + "Ġ4 16", + "wh ose", + "Font Size", + "ĠOr bit", + "Y R", + "Ġwr ists", + "Ġb akery", + ") }", + "ĠB ounty", + "ĠLanc aster", + "Ġend ings", + "acc ording", + "ĠSal am", + "e asy", + "75 5", + "ĠBur r", + "ĠBarn ett", + "onom ous", + "Un ion", + "Ġpreced ence", + "ĠScholars hip", + "ĠU X", + "Ġroll out", + "Ġbo on", + "al m", + "ĠCan ter", + "æ µ", + "Ġround ing", + "Ġcl ad", + "Ġv ap", + "ĠF eatured", + "is ations", + "Ġ5 40", + "pol ice", + "Ġunsett ling", + "Ġdr ifting", + "ĠLum ia", + "ĠObama Care", + "ĠF avor", + "Hy per", + "ĠRoth schild", + "ĠMil iband", + "an aly", + "ĠJul iet", + "H u", + "Ġrec alling", + "a head", + "69 6", + "Ġunf avorable", + "Ġd ances", + "O x", + "Ġleg ality", + "Ġ40 3", + "rom ancer", + "Ġinqu ire", + "ĠM oves", + "\\ \">", + "ĠVari ant", + "ĠMess iah", + "ĠL CS", + "ĠBah á", + "75 6", + "Ġeyeb row", + "Ġ ¥", + "ĠMc F", + "ĠFort y", + "M as", + "Ġpan icked", + "Ġtransform ations", + "q q", + "Ġrev olves", + "ring e", + "ĠA i", + "ax e", + "Ġon ward", + "ĠC FR", + "ĠB are", + "log in", + "Ġliqu ids", + "Ġde comp", + "second ary", + "il an", + "ĠCon vert", + "ami ya", + "Ġprosecut ing", + "Ġâī ¡", + "ĠYork ers", + "ĠByr ne", + "sl ow", + "aw ei", + "J ean", + "Ġ26 9", + "ĠSky dragon", + "Ġ é", + "ĠNicarag ua", + "ĠHuck abee", + "ĠHigh ly", + "Ġamph ib", + "ĠPast or", + "ĠL ets", + "Ġbl urred", + "Ġvisc eral", + "ĠC BO", + "Ġcollabor ated", + "z ig", + "Leg al", + "Ġapart heid", + "Ġbr id", + "Ġpres et", + "ĠD ET", + "ĠAM A", + "× Ķ", + "arch ing", + "auc uses", + "build er", + "Ġpo etic", + "Ġem ulator", + "ĠMole cular", + "Ġhon oring", + "ise um", + "Ġtract or", + "ĠCl uster", + "ĠCal m", + "ared evil", + "Ġsidew alks", + "Ġviol in", + "Ġgeneral ized", + "ĠAle c", + "Ġemb argo", + "Ġfast ball", + "ĠHT TPS", + "ĠL ack", + "ĠCh ill", + "ri ver", + "C hel", + "ĠSw arm", + "ĠLev ine", + "ro ying", + "L aunch", + "Ġkick er", + "Ġadd itive", + "ĠDe als", + "W idget", + "cont aining", + "Ġescal ate", + "ĠOP EN", + "Ġtwe aked", + "Ġst ash", + "Ġsp arks", + "ĠEs sex", + "ĠE cc", + "Ġconv ict", + "Ġblog ging", + "I ER", + "ĠH L", + "Ġmurd erers", + "75 9", + "ĠH ib", + "Ġde pl", + "ĠJ ord", + "S ac", + "Ġdis sect", + "ĠHow e", + "os her", + "Ġcustom izable", + "ĠFran z", + "Ġat ro", + "Ä ĩ", + "Ġ000 4", + "Ġout post", + "R oss", + "Ġglyph osate", + "ĠHast ings", + "ĠBE FORE", + "Ġsh ove", + "o pped", + "ĠSc ala", + "Ġam ulet", + "an ian", + "Ġexacerb ated", + "Ġe ater", + "47 1", + "UM E", + "Ġpul p", + "izont al", + "ĠZ am", + "ĠAT I", + "imm une", + "aby tes", + "Ġunnecess arily", + "ĠC AT", + "ĠAx is", + "Ġvisual ize", + "à ī", + "ĠRad ical", + "f m", + "Doc uments", + "ĠFor rest", + "Ġcontext ual", + "ĠSy mbol", + "Ġtent ative", + "ĠDO ES", + "ĠGood s", + "Ġintermitt ent", + "} :", + "medi ated", + "Ġridic ule", + "Ġathe ism", + "Ġpath ogens", + "ĠM um", + "Ġre introdu", + "Ġ30 7", + "i HUD", + "Ġflash light", + "Ġsw earing", + "Ġp engu", + "B u", + "Ġrot ated", + "ĠCr ane", + "Ġ() );", + "Ġfashion able", + "Ġendors ing", + "46 3", + ") [", + "Ġingest ion", + "Ġcook s", + "Ġ9 50", + "ot omy", + "ĠIm am", + "Ġk a", + "Ġte aser", + "ĠGhost s", + "ĠãĤ µ", + "19 69", + "Ï ĥ", + "ub by", + "Ġconver ter", + "zan ne", + "end e", + "ĠPre par", + "ĠNic kel", + "ĠChim era", + "h im", + "ĠTyr ann", + "ĠSabb ath", + "ĠNich ols", + "Ġra pt", + "ih ar", + "Ġshe lling", + "Ġillum inate", + "Ġdent ist", + "ut or", + "ĠInteg ration", + "Ġwh ims", + "ĠLiter ary", + "Be aut", + "Ġp archment", + "ag ara", + "Br and", + "Ġder og", + "âĢ¦ )", + "ĠNor se", + "Ġunw itting", + "Ġc uc", + "Ġborder line", + "Ġupset ting", + "Ġrec ourse", + "Ġd raped", + "ĠRad ar", + "Ġcold er", + "ĠPep si", + "im inary", + "], [", + "65 8", + "V i", + "ĠF rem", + "ĠP es", + "Ġveter inary", + "ĠT ED", + "ĠEp idem", + "n ova", + "k id", + "Ġdev out", + "o ct", + "j ad", + "M oh", + "ĠP AY", + "Ġge ometric", + "Ġ3 23", + "Ġcircum ference", + "ich ick", + "19 75", + "ĠY uri", + "ĠSh all", + "ĠH over", + "un in", + "S pr", + "Ġg raft", + "ĠHapp iness", + "Ġdisadvant ages", + "att acks", + "Ġhub s", + "ĠStar Craft", + "é ĸ", + "Ġgall eries", + "ĠKor ra", + "Ġgrocer ies", + "ĠGors uch", + "Ġrap ists", + "Ġfun gi", + "ĠTyph oon", + "V ector", + "ĠEm press", + "b attle", + "4 68", + "Ġparas ite", + "ĠBom ber", + "S G", + "ex ist", + "ĠP f", + "Ġun se", + "Ġsurge ons", + "B irth", + "ĠUn sure", + "ĠPrint ed", + "ĠBehavior al", + "ĠA ster", + "Pak istan", + "Ġun ethical", + "Ġs v", + "ĠIo T", + "Ġlay outs", + "P ain", + "Ġconst ants", + "ĠL W", + "ĠB ake", + "Ġtow els", + "Ġdeterior ation", + "ĠBol ivia", + "Ġblind ed", + "ĠW arden", + "ĠMist ress", + "Ġon stage", + "Ġcl ans", + "ĠB EST", + "19 60", + "Ġant ique", + "Ġrhet orical", + "ĠPer cy", + "ĠRw anda", + ", .", + "B ruce", + "Ġtra umat", + "ĠParliament ary", + "Ġfoot note", + "id ia", + "ĠLear ned", + "se eking", + "gen ic", + "Ġdim ensional", + "H ide", + "èĢ ħ", + "Ġintrig ue", + "in se", + "Ġle ases", + "Ġapp rentices", + "w ashing", + "Ġ19 26", + "V ILLE", + "Ġsw oop", + "s cl", + "Ġbed rooms", + "on ics", + "ĠCr unch", + "comp atible", + "Ġincap ac", + "ĠYemen i", + "ash tra", + "z hou", + "d anger", + "Ġmanifest ations", + "ĠDem ons", + "AA F", + "Secret ary", + "ACT ED", + "L OD", + "Ġam y", + "ra per", + "eth nic", + "4 17", + "Ġpos itives", + "Ġ27 3", + "ĠRefuge es", + "Ġus b", + "ĠV ald", + "odd y", + "ĠMahm oud", + "As ia", + "Ġskull s", + "ĠEx odus", + "ĠComp et", + "ĠL IC", + "ĠM ansion", + "ĠA me", + "Ġconsolid ate", + "storm s", + "ont ent", + "99 6", + "Ġcl en", + "Ġm ummy", + "fl at", + "75 8", + "ĠV OL", + "oter ic", + "n en", + "ĠMin ute", + "S ov", + "Ġfin er", + "R h", + "ly cer", + "Ġreinforce ments", + "ĠJohann es", + "ĠGall agher", + "Ġgym n", + "S uddenly", + "Ġext ortion", + "k r", + "i ator", + "T a", + "Ġhippocamp us", + "N PR", + "ĠComput ing", + "Ġsquare ly", + "Ġmod elling", + "ĠFor ums", + "ĠL isp", + "ĠKrish na", + "Ġ3 24", + "Ġr ushes", + "Ġens ued", + "Ġcre eping", + "on te", + "n ai", + "il ater", + "ĠHorn ets", + "Ġob livious", + "IN ST", + "55 9", + "Ġjeopard y", + "Ġdistingu ishing", + "j ured", + "Ġbeg s", + "sim ilar", + "ph ot", + "5 30", + "ĠPark way", + "Ġs inks", + "ĠHearth stone", + "ib ur", + "ĠBat on", + "Av oid", + "Ġd ancer", + "Ġmag istrate", + "ary n", + "Ġdisturb ances", + "ĠRom ero", + "Ġpar aph", + "Ġmis chief", + "âĸ ĵ", + "ĠSh aria", + "Ġur inary", + "r oute", + "iv as", + "f itted", + "Ġeject ed", + "ĠAl buquerque", + "Ġ4 70", + "Ġirrit ated", + "ĠZ ip", + "ĠB iol", + "à į", + "Ġden ounce", + "Ġbin aries", + "ĠVer se", + "Ġopp os", + "ĠKend rick", + "ĠG PL", + "Ġsp ew", + "ĠEl ijah", + "ĠE as", + "Ġdr ifted", + "so far", + "Ġannoy ance", + "ĠB ET", + "47 4", + "ĠSt rongh", + "it ates", + "ĠCogn itive", + "oph one", + "ĠIdent ification", + "ocr ine", + "connect ion", + "Ġbox er", + "ĠAS D", + "ĠAre as", + "Y ang", + "t ch", + "ull ah", + "Ġdece ive", + "Comb at", + "ep isode", + "cre te", + "W itness", + "Ġcondol ences", + "ht ar", + "Ġhe als", + "Ġbuck ets", + "ĠLA W", + "B lu", + "Ġsl ab", + "ĠOR DER", + "oc l", + "att on", + "ĠSteven son", + "ĠG inger", + "ĠFriend ly", + "ĠVander bilt", + "sp irit", + "ig l", + "ĠReg arding", + "ĠPR OG", + "Ġse aling", + "start ing", + "Ġcard inal", + "ĠV ec", + "ĠBe ir", + "Ġmillisec onds", + "we ak", + "per se", + "Ġster ile", + "ĠCont emporary", + "ĠPh ant", + "ĠCl o", + "Ġout p", + "Ġex iled", + "Ġ27 7", + "Ġself ie", + "Ġman ic", + "Ġn ano", + "ter ms", + "Alex ander", + "Ġres olves", + "Ġmillenn ia", + "Ġexpl odes", + "Ġconst ellation", + "Ġadul tery", + "m otion", + "D OC", + "Ġbroad casters", + "Ġkinderg arten", + "ĠMay weather", + "ĠE co", + "ich o", + "Ġ28 7", + "l aun", + "Ġm ute", + "Ġdisc reet", + "Ġpres chool", + "Ġpre empt", + "De lete", + "ĠFre ed", + "P i", + "H K", + "Ġblock er", + "ĠC umber", + "Ġw rought", + "d ating", + "Ġins urer", + "Ġquot as", + "Ġpre ached", + "Ġev iction", + "ĠReg ina", + "ĠP ens", + "Ġsevent een", + "ĠN ass", + "D ick", + "Ġfold s", + "Ġd otted", + "ĠA ad", + "Un iversal", + "Ġp izz", + "ĠG uru", + "Ġso ils", + "Ġno vice", + "ĠNe ander", + "Ġst ool", + "Ġdeton ated", + "ĠPik achu", + "ĠMass ive", + "IV ER", + "ĠAb del", + "Ġsubdu ed", + "Ġtall est", + "Ġprec arious", + "Ġa y", + "r ification", + "ĠOb j", + "c ale", + "Ġun question", + "cul osis", + "ad as", + "igr ated", + "D ays", + "Ġque ens", + "ĠGaz ette", + "ĠCol our", + "ĠBow man", + "ĠJ J", + "ï ve", + "Ġdomin ates", + "Stud ent", + "Ġm u", + "Ġback log", + "ĠElect ro", + "Tr uth", + "48 3", + "Ġcond ensed", + "r ules", + "ĠCons piracy", + "Ġacron ym", + "hand led", + "ĠMat te", + "j ri", + "ĠImp ossible", + "l ude", + "cre ation", + "Ġwar med", + "ĠSl ave", + "Ġmis led", + "Ġfer ment", + "ĠK ah", + "ink i", + "ke leton", + "cy l", + "ĠKar in", + "Hun ter", + "Reg ister", + "ĠSur rey", + "Ġst ares", + "ĠW idth", + "ĠN ay", + "ĠSk i", + "Ġblack list", + "uck et", + "Ġexp ulsion", + "im et", + "Ġret weet", + "vant age", + "Fe ature", + "Ġtro opers", + "Ġhom ers", + "9 69", + "Ġconting ency", + "ĠW TC", + "ĠBrew er", + "fore ign", + "W are", + "S olar", + "Ġund ue", + "RE C", + "ulner able", + "path ic", + "ĠBo ise", + "Ġ3 22", + "Ġarous ed", + "ĠY ing", + "ä¸ į", + "uel ess", + "Ġp as", + "Ġmor p", + "Ġfl oral", + "Ex press", + "ud ging", + "k B", + "ĠGr anted", + "Ø ¯", + "ĠMich a", + "ĠGoth ic", + "ĠSPEC IAL", + "ĠRic ardo", + "F ran", + "Ġadminister ing", + "6 20", + "por a", + "Ġ ®", + "Ġcomprom ises", + "Ġb itten", + "Ac cept", + "Th irty", + "Ð ²", + "Ġmater ially", + "ĠTer r", + "ig matic", + "ch ains", + "Ġdo ve", + "stad t", + "Mar vel", + "FA ULT", + "Ġwind shield", + "Ġ3 36", + "ad ier", + "Ġsw apping", + "Ġflaw less", + "ĠPred ator", + "ĠMiche le", + "Ġprop ulsion", + "ĠPsych ic", + "Ġassign ing", + "Ġfabric ation", + "Ġbar ley", + "l ust", + "Ġtow ering", + "Ġalter cation", + "ĠBent ley", + "Sp here", + "Ġtun a", + "ĠClass es", + "Fre edom", + "un er", + "L ady", + "v oice", + "Ġcool est", + "or r", + "Ġpal p", + "$ {", + "Ġhyster ia", + "ĠMet atron", + "p ants", + "Ġspawn ing", + "Exper ts", + "ĠInvest ors", + "ĠAn archy", + "Ġshr unk", + "ĠVict im", + "Ġ28 9", + "Ġec stasy", + "ĠB inding", + "58 5", + "ĠMel ody", + "57 8", + "ot ally", + "ĠE tsy", + "lig a", + "Ġapplaud ed", + "Ġswe ating", + "Ġredist ributed", + "Ġpop corn", + "Ġsem inal", + "f ur", + "ĠNeuro science", + "R and", + "ĠO st", + "ĠMadd en", + "ĠIncre asing", + "ĠDaw kins", + "ĠSub way", + "Ġar sen", + "cons erv", + "B UR", + "Ġsp iked", + "ĠLy ft", + "ĠImper ium", + "ĠDrop box", + "Ġfav oured", + "Ġencomp asses", + "gh ost", + "Ġins pires", + "Ġbur geoning", + "ĠY oshi", + "ĠVert ical", + "ĠAud itor", + "Ġint ending", + "Ġfilib uster", + "Bl oom", + "f ac", + "ĠCav s", + "ign ing", + "Ġcowork ers", + "ĠBarb arian", + "rem ember", + "FL AG", + "Ġaudit ory", + "ason ry", + "Col lege", + "Ġmut ed", + "gem ony", + "ob in", + "ĠPsych o", + "9 68", + "Ġlav ish", + "Ġhierarch ical", + "ĠDr one", + "ou k", + "Ġcripp led", + "ĠMax im", + "Sl ot", + "Ġqu iz", + "ĠV id", + "if ling", + "Ġarchae ologists", + "Ġabandon ment", + "d ial", + "le on", + "ĠF as", + "T ed", + "Ġr aspberry", + "Ġmaneu vers", + "Ġbehavi ours", + "Ġins ure", + "Ġrem od", + "Sw itch", + "h oe", + "Ġsp aced", + "Ġafford ability", + "ĠF ern", + "not ation", + "ĠBal anced", + "Ġoccup ies", + "en vironment", + "Ġneck lace", + "Ġsed an", + "F U", + "ĠBrav o", + "Ġab users", + "ĠAn ita", + "met adata", + "ĠG ithub", + "ait o", + "ĠF aster", + "ĠWass erman", + "ĠF lesh", + "Ġth orn", + "r arily", + "ĠMer ry", + "w ine", + "Ġpopul ace", + "ĠL ann", + "Ġrepair ing", + "Ġpsy che", + "Ġmod ulation", + "aw aru", + "âĢĭ âĢĭ", + "ari j", + "Ġdecor ations", + "Ġapolog ise", + "ĠG arg", + "app ly", + "Ġgive away", + "ĠFl an", + "ĠWy att", + "U ber", + "Ġauthor ised", + "ĠMor al", + "HAHA HAHA", + "activ ate", + "Ġtorped o", + "ĠF AR", + "Ġam assed", + "ĠA ram", + "ark in", + "ĠVict ims", + "st ab", + "Ġo m", + "ĠE CO", + "Ġopio ids", + "Ġpurpose ly", + "ĠV est", + "Ġer g", + "at an", + "ĠSur gery", + "Ġcorrect ing", + "ĠOrt iz", + "ĠBe et", + "Ġrev oke", + "Ġfre eway", + "ĠH iggins", + "F ail", + "ĠFar ms", + "ĠAT P", + "h ound", + "Ġp oking", + "ĠCommun ists", + "mon ster", + "iment ary", + "Ġunlock ing", + "Ġunf it", + "we ed", + "en ario", + "at ical", + "ĠEnlight enment", + "ĠN G", + "ĠComp ensation", + "de en", + "ĠWid ow", + "ĠCind y", + "ĠAfter wards", + "Ġ6 000", + "ikh ail", + "ag ically", + "Ġrat ified", + "Ġcasual ty", + "H OME", + "p sey", + "f ee", + "Ġspark ling", + "Ġd é", + "Ġconcert ed", + "C atal", + "Ġcomp lying", + "ĠA res", + "ĠD ent", + "Sh ut", + "Ġsk im", + "ad minist", + "Ġhost ilities", + "ĠG ins", + "Ġ6 08", + "Ġm uddy", + "ĠMc Int", + "ĠDec ay", + "5 25", + "Ġconspic uous", + "ĠEx posure", + "Ġresc ind", + "Ġwear able", + "Ġ3 28", + "our met", + "ah s", + "ĠRob ots", + "Ġe clips", + "inst ance", + "ĠRE PORT", + "ĠApp l", + "0 30", + "ĠSk ies", + "01 00", + "Ġfall acy", + "S ocket", + "ĠRece iver", + "Ġsol ves", + "ĠButter fly", + "ĠSho pping", + "ĠFI RE", + "65 4", + "Med ic", + "Ġsing ers", + "ĠNeed less", + "'' ''", + "isher s", + "ĠD ive", + "58 8", + "Ġselect ively", + "Ġcl umsy", + "88 9", + "Ġpurch aser", + "ear ned", + "ard y", + "Ġbenef iting", + "eng lish", + "Ġyield ing", + "ĠP our", + "Ġspin ach", + "Ġdel ve", + "ĠC rom", + "6 10", + "Ġexport ing", + "ĠMA KE", + "Ġ26 3", + "Ġg rop", + "Ġenv oy", + "ĠInqu iry", + "ĠLu igi", + "d ry", + "ĠT uring", + "Thumbnail Image", + "ĠVar iety", + "Ġfac et", + "Ġfl uffy", + "Ġexcerpt s", + "Ġsh orth", + "ĠOl sen", + "CL UD", + "Ġrel iant", + "ĠUN C", + "T our", + "Ġbat hing", + "Comp any", + "Ġglobal ization", + "P red", + "ĠMalf oy", + "Ġh oc", + "j am", + "craft ed", + "ĠBond s", + "ĠKiss inger", + "Eng land", + "Ġorder ly", + "cat entry", + "Ġ26 1", + "Ġexch anging", + "ĠInt ent", + "ĠAmend ments", + "D OM", + "Ġst out", + "³³³³³³³³ ³³³³³³³³", + "ĠAir bus", + "Ġ27 8", + "hy de", + "P oll", + "Item ThumbnailImage", + "Ġlooph oles", + "ĠPill ar", + "Ġexpl or", + "St retch", + "A part", + "Ġun married", + "Lim it", + "ĠTransform ers", + "Ġintellect ually", + "unct ure", + "18 00", + "Ġd arn", + "B razil", + "Ġleft over", + "ber us", + "f red", + "Mine craft", + "3 26", + "ĠForm s", + "Ġproof s", + "ĠDes igned", + "Ġindex es", + "ĠSupp ose", + "EM S", + "ĠL oving", + "ĠBon nie", + "im ating", + "OT US", + "Ġconduct or", + "Ġbehav ed", + "ĠF ren", + "Ġsy nerg", + "Ġmillenn ium", + "Ġcater ing", + "ĠL auder", + "W r", + "ĠY iannopoulos", + "ĠAT F", + "Ġensl aved", + "Ġawaken ed", + "D VD", + "ĠED ITION", + "ĠConc ert", + "ĠChall enger", + "ĠH aku", + "umer ic", + "Ġdep recated", + "ĠSH AR", + "4 12", + "Ġdy stop", + "Ġtremb ling", + "Ġdread ed", + "ĠSp ac", + "p adding", + "Re pl", + "ĠG arrison", + "M ini", + "Ġun paralleled", + "am ar", + "URR ENT", + "w reck", + "c ertain", + "t al", + "ĠC LS", + "app ings", + "Ġsens ed", + "Ġf encing", + "ĠPas o", + "ĠDes k", + "Ġsc off", + "Ġcontem plate", + "ĠL iga", + "l iquid", + "75 7", + "Ġapp rentice", + "ĠUCH IJ", + "5 70", + "ĠTh ousand", + "ĠIll um", + "Ġchampion ed", + "ãĤ Į", + "Ġelect ors", + "Ġ3 98", + "ĠH ancock", + "round ed", + "ĠJ OHN", + "Ġuns atisf", + "Ġqual ifier", + "ĠGad get", + "EN E", + "Ġdead liest", + "ĠPl ants", + "Ġ ions", + "Ġacc ents", + "Ġtwe aking", + "Ġsh aved", + "F REE", + "ĠCh aser", + "Again st", + "9 60", + "Ġmeth amphetamine", + "Ġnormal ized", + "Ġ$ \\", + "ĠPre cision", + "ĠGu am", + "Ġch oked", + "ĠX II", + "ĠCast ing", + "Tor rent", + "Ġscal p", + "ĠJagu ar", + "w it", + "Ġsem ic", + "ix ie", + "ĠG ould", + "Ġconf ines", + "N usra", + "ĠL on", + "ĠJ ugg", + "y cle", + "ĠCod ec", + "E gypt", + "Ġrest rain", + "ĠAl iens", + "Ġch oking", + "ĠD unk", + "ĠBell a", + "ab c", + "Ġsl ang", + "Ġneuro trans", + "s av", + "Ġempower ment", + "â ĨĴ", + "Ġclim bers", + "ĠM im", + "ĠF ra", + "ros se", + "Cap ital", + "ĠCth ulhu", + "Inter face", + "Ġprof icient", + "ĠIN TO", + "Ġ3 18", + "ront al", + "5 80", + "ĠDes pair", + "K enn", + "Ġscrim mage", + "ĠCo at", + "as ions", + "Ġwall paper", + "ĠJ ol", + "Ġresurg ence", + "Ġant iv", + "ĠB alls", + "² ¾", + "Ġbuff ers", + "Ġsub system", + "ĠSt ellar", + "ĠL ung", + "A IDS", + "Ġerad icate", + "Ġblat antly", + "Ġbehav es", + "ĠN un", + "Ġant ics", + "ex port", + "DE V", + "w b", + "Ġph p", + "ĠInteg rity", + "Ġexplore r", + "Ġrev olving", + "auth ored", + "g ans", + "Ġbas k", + "Ġas ynchronous", + "å į", + "TH ING", + "69 8", + "G ene", + "ĠR acer", + "ĠN ico", + "iss ued", + "Ġser mon", + "p ossibly", + "Ġsize of", + "Ġentrepreneur ial", + "ox in", + "ĠMin erva", + "Ġpl atoon", + "n os", + "ri ks", + "A UT", + "ĠAval anche", + "ĠDes c", + "ij 士", + "ĠP oc", + "Ġconf erred", + "Î »", + "Ġpat ched", + "F BI", + "66 2", + "Ġfract ures", + "Ġdetect s", + "Ġded icate", + "Ġconstitu ent", + "Ġcos mos", + "W T", + "Ġswe ats", + "Ġspr ung", + "b ara", + "s olid", + "Ġuns us", + "Ġbul ky", + "ĠPhilipp e", + "ĠFen rir", + "Ġtherap ists", + "ore al", + "^^ ^^", + "Ġtotal ed", + "Ġboo ze", + "ĠR PC", + "Prosecut ors", + "Ġdis eng", + "ĠSh ared", + "Ġmotor cycles", + "Ġinvent ions", + "Ġlett uce", + "ĠMer ge", + "ĠJ C", + "Ġspiritual ity", + "ĠWAR NING", + "Ġunl ucky", + "ĠT ess", + "Ġtong ues", + "ĠD UI", + "T umblr", + "Ġle ans", + "Ġinv aders", + "Ġcan opy", + "ĠHur ricanes", + "ĠB ret", + "ĠAP PLIC", + "id ine", + "ick le", + "Reg arding", + "Ġve ggies", + "Ġe jac", + "ju ven", + "F ish", + "D EM", + "ĠD ino", + "Th row", + "ĠCheck ing", + "be ard", + "( &", + "Ġj ails", + "Ġh r", + "trans fer", + "iv ating", + "Ġfle ets", + "ĠIm ag", + "ĠMc Donnell", + "Ġsnipp et", + "Is a", + "ĠCh att", + "ĠSt ain", + "ĠSet FontSize", + "ĠO y", + "ĠMathemat ics", + "49 4", + "Ġelectro ly", + "ĠG ott", + "ĠBr as", + "B OOK", + "ĠF inger", + "d ump", + "Ġmut ants", + "Ġrent als", + "Ġinter tw", + "Ġc reek", + "ail a", + "Bro ther", + "ĠDisc ord", + "pe e", + "raw ler", + "Ġcar p", + "Ġ27 9", + "ãĤ· ãĥ£", + "rel ations", + "Ġcontr asts", + "Col umn", + "Ġrec onnaissance", + "Ġun know", + "Ġl ooting", + "Ġregul ates", + "Ġopt imum", + "ĠChero kee", + "ĠA ry", + "Lat est", + "Ġroad side", + "Ġd anced", + "ĠUnic orn", + "A cknowled", + "Ġuncont roll", + "ĠM US", + "at io", + "ch ance", + "ha ven", + "VAL UE", + "Ġfavour ites", + "Ġceremon ial", + "b inary", + "pe ed", + "wood s", + "EM P", + "Ġv ascular", + "Ġcontempl ated", + "Ġbar ren", + "ĠL IST", + "Y ellow", + "ospons ors", + "Ġwhisk y", + "ĠM amm", + "ĠDeV os", + "min imum", + "H ung", + "44 2", + "P ic", + "ĠSnap dragon", + "77 6", + "Ġcar ving", + "Ġund ecided", + "Ġadvantage ous", + "Ġpal ms", + "ĠA Q", + "Ġst arch", + "L oop", + "Ġpadd le", + "Ġfl aming", + "ĠHor izons", + "An imation", + "bo ost", + "Ġprob abilities", + "ĠM ish", + "Ġex odus", + "ĠEditor ial", + "Ġfung us", + "Ġdissent ing", + "ĠDel icious", + "rog ram", + "ĠD yn", + "d isk", + "t om", + "Ġfab rics", + "ĠC ove", + "ĠB ans", + "Ġsoft en", + "ĠCON S", + "Ġin eligible", + "Ġestim ating", + "ĠLex ington", + "pract ice", + "of i", + "Ġshe dding", + "ĠN ope", + "Ġbreat hed", + "ĠCorinth ians", + "y ne", + "ek i", + "B ull", + "Ġatt aching", + "reens hots", + "Ġanaly se", + "ĠK appa", + "Ġuns ustainable", + "Ġinter pol", + "ank y", + "he mer", + "Ġprot agonists", + "Ġform atted", + "ĠBry ce", + "ĠAch illes", + "ĠAb edin", + "sh ock", + "Ġb um", + "b os", + "qu a", + "ĠW arn", + "q t", + "ĠDi abetes", + "8 64", + "ĠIn visible", + "Ġvan ish", + "Ġtrans mitting", + "Ġmur ky", + "ĠFe i", + "Ġawa ited", + "ĠJur assic", + "umm ies", + "Ġmen acing", + "g all", + "C ath", + "B uilt", + "ild o", + "ĠV otes", + "Ġon t", + "Ġmun itions", + "ĠFre em", + "ÃŃ n", + "Ġdec ency", + "lo pp", + "ie ved", + "ĠG ord", + "Ġun thinkable", + "ĠNews week", + "Ġ3 21", + "He at", + "Ġpresent er", + "ji ang", + "Ġpl ank", + "ĠAval on", + "Ġben z", + "ĠR out", + "Ġslam ming", + "ĠD ai", + "ou ter", + "ĠCook ie", + "ĠAlic ia", + "ge y", + "Ġvan ity", + "Ġow l", + "á µ", + "t ested", + "ĠAw akens", + "Ġcan v", + "Ġblind ly", + "ĠRid ley", + "ĠEm ails", + "Requ ires", + "ĠSer bian", + "ograp hed", + "if rame", + "eter ia", + "Ġaltern ating", + "qu iet", + "Ġsoc iology", + "ĠUn lock", + "ĠCommun ism", + "Ġo ps", + "Ġatt ribution", + "Ġab duction", + "ĠAb ram", + "Ġsidel ined", + "ĠB OOK", + "Ġref ining", + "ĠFe eling", + "ĠOs lo", + "ĠPru itt", + "r ack", + "ang ible", + "Ġcaut iously", + "ĠM ARK", + "eed s", + "M ouse", + "ĠStep h", + "ĠP air", + "S ab", + "99 7", + "ĠBa al", + "B ec", + "Ġcomm a", + "ĠP all", + "ĠG ael", + "Ġmisunder stand", + "ĠP esh", + "Order able", + "Ġdis mal", + "ĠSh iny", + "% \"", + "Ġreal istically", + "Ġpat io", + "ĠG w", + "ĠVirt ue", + "Ġexhaust ing", + "wh atever", + "oph ys", + "y ip", + "4 18", + "Ad just", + "ĠWa iting", + "ess on", + "ĠMaz da", + "ĠDo zens", + "Ġstream lined", + "Ġincompet ence", + "ĠM eth", + "Ġeth os", + "ON ES", + "Ġincent iv", + "Ġgr itty", + "ĠBut cher", + "Head er", + "Ġexp onential", + "à Ł", + "Ġcorrel ate", + "Ġcons ensual", + "s ounding", + "R ing", + "Orig in", + "Ġcon clusive", + "fe et", + "ac ly", + "ĠF ernandez", + "Buy able", + "Ġd ucks", + "aunt lets", + "Ġel ong", + "Ġ28 6", + "Ġsim ul", + "G as", + "ĠK irst", + "Ġprot r", + "ĠRob o", + "ĠAo E", + "op ol", + "Ġpsych ologically", + "sp in", + "ilater ally", + "ĠCon rad", + "W ave", + "44 1", + "ĠAd vertisement", + "ĠHarm on", + "ĠOri ental", + "is Special", + "Ġpresum ptive", + "Ġw il", + "ĠK ier", + "ne a", + "Ġp pm", + "Ġhar bour", + "ĠW ired", + "comp any", + "Ġcor oner", + "atur days", + "ĠP roud", + "ĠN EXT", + "ĠFl ake", + "val ued", + "ce iver", + "Ġfra ught", + "Ġc asing", + "Ġrun away", + "Ġg in", + "ĠLaure nt", + "ĠHar lem", + "ĠCur iosity", + "qu ished", + "Ġneuro science", + "ĠH ulu", + "Ġborrow er", + "Ġpetition er", + "ĠCo oldown", + "W ARD", + "Ġinv oking", + "conf idence", + "For ward", + "Ġst s", + "pop ulation", + "Delivery Date", + "Fil m", + "ĠC ov", + "quick Ship", + "quickShip Available", + "prim ary", + "isSpecial Orderable", + "inventory Quantity", + "channel Availability", + "BO X", + "ĠMulti player", + "ĠJen ner", + "77 8", + "ĠM d", + "Ġ~ /.", + "M N", + "Ġchild ish", + "Ġantioxid ant", + "ĠChrom ebook", + "Ġ27 4", + "Ġscreen play", + "Ġadvent urous", + "ĠRelations hip", + "respons ive", + "ming ton", + "Ġcorner stone", + "ĠF ey", + "F IR", + "Ġrook ies", + "ĠF eaturing", + "Ġorig inate", + "Ġelectro des", + "ant es", + "Ġscript ures", + "Ġgl ued", + "Ġdiscont ent", + "Ġaff licted", + "lay out", + "B rave", + "Ġm osa", + "ĠQuant ity", + "ĠH ik", + "w inner", + "H ours", + "Ġent ail", + "ĠCell s", + "olog ue", + "Ġv il", + "Ġpre acher", + "Ġdecor ative", + "d ifferent", + "Ġprejud ices", + "ĠSm oking", + "ĠNotting ham", + "so Type", + "Ġrhyth ms", + "ĠAl ph", + "bl ast", + "Ste el", + "ĠDaniel le", + "Ġstr ife", + "Ġrem atch", + "so DeliveryDate", + "ĠF ork", + "t rip", + "ol ulu", + "hes es", + "C G", + "ĠPOLIT ICO", + "ost a", + "ĠDr ift", + "é¾įå ¥", + "é¾įå¥ ij士", + "Ġvet ting", + "ĠJin ping", + "ĠRec ession", + "Min or", + "ĠF raud", + "enf ranch", + "Ġconven ed", + "ĠNA ACP", + "ĠMill ions", + "ĠFarm ing", + "ĠW oo", + "ĠFl are", + "rit o", + "imm igrant", + "Ġvac ancy", + "ĠHE AD", + "ĠV aj", + "eg al", + "ĠV igil", + "Stud y", + "Ġru ining", + "Ġr acks", + "Ġhe ater", + "ĠRand olph", + "ĠBr ush", + "ĠT ir", + "Ø ¨", + "Ġc ov", + "% ]", + "Ġrecount s", + "ĠO PT", + "ĠM elt", + "Ġtr uce", + "Ġcas inos", + "Ġcrus ade", + "Ġcarn age", + "Ġstri pe", + "ĠK yl", + "Text ures", + "Ġ6 98", + "Ġpro clamation", + "Ġgood ies", + "Ġ........ ..", + "pro claimed", + "P olit", + "Ġtop ical", + "Ġspecial ize", + "ĠA min", + "g m", + "Ġanch ored", + "Ġbear ings", + "s ample", + "ĠHigh land", + "ĠAut ism", + "Ġmerc enary", + "Ġinterview er", + "L ER", + "ĠSom ers", + "Ġembry o", + "ĠAss y", + "Ġ28 1", + "ĠEd iting", + "ĠCh osen", + "6 60", + "Ġp ci", + "ĠThunder bolt", + "BI LL", + "Ġchuck led", + "jri wal", + "h of", + "Ġearth ly", + "() {", + "ind ependence", + "Ġdisp ers", + "ĠV endor", + "ĠG areth", + "Ġp als", + "P enn", + "ĠSub mit", + "ic um", + "Th u", + "Ġcl andestine", + "Ġcann ibal", + "ĠCl erk", + "E Stream", + "gal itarian", + "âĻ ¥", + "g ew", + "Ġhor rend", + "ĠL ov", + "ĠRe action", + "ocr in", + "Class ic", + "Ġecho ing", + "Ġdiscl osing", + "ĠIns ight", + "og un", + "ĠInc arn", + "upload s", + "pp erc", + "guy en", + "Ġ19 01", + "ĠB ars", + "68 7", + "Ġb ribes", + "ĠFres no", + "ur at", + "ĠRe ese", + "Ġintr usive", + "Ġgri pping", + "ĠBlue print", + "ĠR asm", + "un ia", + "man aged", + "ĠHeb do", + "Ġ3 45", + "Ġdec oding", + "Ġpo ets", + "Ġj aws", + "ĠF IGHT", + "am eless", + "ĠMead ows", + "ĠHar baugh", + "Inter view", + "ĠH osp", + "ĠB RA", + "Ġdelet ion", + "m ob", + "W alker", + "ĠMoon light", + "ĠJ ed", + "ĠSoph ia", + "Ġus ur", + "Ġfortun ately", + "ĠPut ting", + "ĠF old", + "Ġsan itation", + "Ġpart isans", + "IS ON", + "B ow", + "ĠCON C", + "ĠRed uced", + "ĠS utton", + "Ġtouch screen", + "Ġembry os", + "âĢ¢âĢ¢ âĢ¢âĢ¢", + "ĠK rug", + "com bat", + "ĠPet roleum", + "Ġam d", + "ĠCos mos", + "Ġpresc ribing", + "Ġconform ity", + "ours es", + "Ġplent iful", + "Ġdis illusion", + "ĠEc ology", + "itt al", + "Ġf anc", + "Ġassass inated", + "regn ancy", + "Ġperenn ial", + "ĠBul lets", + "Ġst ale", + "Ġc ached", + "ĠJud ith", + "ĠDise ases", + "All en", + "Ġl as", + "Ġsh ards", + "ĠSu arez", + "ĠFriend ship", + "inter face", + "ĠSupp orters", + "add ons", + "46 2", + "ĠIm ran", + "ĠW im", + "Ġnew found", + "ĠM b", + "An imal", + "Ġd arling", + "and e", + "Ġrh y", + "ĠTw isted", + "pos al", + "yn ski", + "Var ious", + "× ľ", + "ĠK iw", + "uy omi", + "Ġwell being", + "ĠL au", + "an os", + "Ġunm ist", + "Ġmac OS", + "Ġrest room", + "ĠOl iv", + "ĠAir ways", + "Ġtimet able", + "9 80", + "Ġrad ios", + "v oy", + "ias co", + "Ġcloud y", + "ĠDraw ing", + "Any thing", + "Sy ria", + "ĠH ert", + "st aking", + "Ġun checked", + "Ġb razen", + "ĠN RS", + "69 7", + "onom ic", + "est ablish", + "Ġl eng", + "Ġdi agonal", + "ĠF ior", + "L air", + "ĠSt ard", + "Ġdef icient", + "jo ining", + "be am", + "Ġomn ip", + "Ġbl ender", + "Ġsun rise", + "Mo ore", + "ĠF ault", + "ĠCost ume", + "ĠM ub", + "Fl ags", + "an se", + "Ġpay out", + "ĠGovern ors", + "ĠD illon", + "ĠBan ana", + "N ar", + "Ġtra iled", + "Ġimperial ist", + "um ann", + "ats uki", + "4 35", + "ĠRoad s", + "Ġsl ur", + "ĠIde ally", + "Ġt renches", + "C trl", + "Ġmir rored", + "ĠZ el", + "ĠC rest", + "Comp at", + "ĠRoll s", + "sc rib", + "ĠTra ils", + "omet ers", + "w inter", + "Ġimm ortality", + "il ated", + "Ġcontrad icts", + "un iversal", + "ill ions", + "ĠM ama", + "opt im", + "AT URE", + "Ġge o", + "et ter", + "ĠCar lo", + "4 24", + "Ġcanon ical", + "ĠStrongh old", + "n ear", + "Ġperf ume", + "Ġorche stra", + "od iac", + "Ġup he", + "Ġreign ing", + "vers ive", + "Ġc aucuses", + "ĠD EM", + "Ġinsult ed", + "Ġ---- --", + "ĠCr ush", + "Ġroot ing", + "ĠWra ith", + "Ġwh ore", + "Ġto fu", + "C md", + "ĠB ree", + "Ġ$ _", + "Ġr ive", + "ĠAd vertising", + "Ġw att", + "ĠH O", + "Ġpersu asive", + "ĠParam eters", + "Ġobserv ational", + "ĠN CT", + "ĠMo j", + "ĠSal on", + "Ġtr unc", + "Ġexqu isite", + "ĠMar a", + "Ġpo op", + "ĠAN N", + "Ex c", + "ĠWonder ful", + "ĠT aco", + "Ġhome owner", + "ĠSmith sonian", + "orpor ated", + "mm mm", + "Ġlo af", + "ĠYam ato", + "ĠInd o", + "Ġcl inging", + "á s", + "Ġimm utable", + "h ub", + "Or ange", + "Ġfingert ips", + "ĠWood en", + "ĠK idd", + "ĠJ PM", + "ĠDam n", + "C ow", + "c odes", + "48 2", + "Ġiniti ating", + "ĠEl k", + "ĠCut ting", + "Ġabsent ee", + "ĠV ance", + "ĠLil ith", + "G UI", + "Ġobsc ured", + "Ġdwar ves", + "ĠCh op", + "ĠB oko", + "Val ues", + "Ġmult imedia", + "Ġbrew ed", + "Reg ular", + "CRIP TION", + "ĠMort al", + "Ġa pex", + "Ġtravel er", + "Ġbo ils", + "Ġspray ing", + "Rep resent", + "ĠStars hip", + "4 28", + "Ġdisappro val", + "Ġshadow y", + "Ġlament ed", + "ĠRe place", + "ĠFran ç", + "67 7", + "d or", + "Ġunst oppable", + "Ġcoh orts", + "gy n", + "ĠClass ics", + "ĠAm ph", + "Ġsl uggish", + "ĠAdd iction", + "ĠPad res", + "Ġins cription", + "Ġin human", + "min us", + "ĠJere miah", + "at ars", + "Ter ror", + "ĠT os", + "ĠSh arma", + "ast a", + "c atch", + "Ġpl umbing", + "ĠTim bers", + "Sh ar", + "H al", + "ĠO sc", + "Ġcou pling", + "hum ans", + "Ġsp onge", + "Ġid ols", + "ĠSp a", + "ĠAdv ocate", + "ĠBe ats", + "lu a", + "Ġtick ing", + "Ġload er", + "ĠG ron", + "8 10", + "Ġstim ulated", + "Ġside bar", + "ĠManufact urer", + "ore And", + "19 73", + "Ġpra ises", + "ĠFl ores", + "dis able", + "ĠElect rical", + "ra ise", + "E th", + "Ġmigr ated", + "Ġlect urer", + "K ids", + "ĠCa vern", + "Ġk ettle", + "Ġgly c", + "ĠMand ela", + "ĠF ully", + "å§ «", + "FIN EST", + "Ġsquee zing", + "ĠRy der", + "amp oo", + "oreAnd Online", + "Inst oreAndOnline", + "Buyable InstoreAndOnline", + "Ġcommem orate", + "ĠRamp age", + "Aust in", + "ĠSh roud", + "ĠRu ins", + "9 15", + "ĠK H", + "Ġwater front", + "ĠE SC", + "b aby", + "ĠC out", + "ĠEm blem", + "Ġequival ents", + "49 2", + "Un ique", + "ĠNiet zsche", + "brow ser", + "Ġim itation", + "ĠWere wolf", + "ĠKir in", + "ac as", + "' ,\"", + "Ġà ¾", + "Review ed", + "Ġc unt", + "Ġvo ic", + "ĠLen ovo", + "Ġbond ed", + "48 1", + "Ġinhib itors", + "Ġendeav ors", + "ĠHav ana", + "ĠSt out", + "ĠJ olly", + "A ctor", + "*/ (", + "Ġoccur rences", + "ĠT ens", + "Incre ased", + "ĠACT ION", + "Ġ ãĢĮ", + "ĠRank ings", + "ĠB reat", + "Ġ30 9", + "D ou", + "Ġimpact ing", + "ĠDuc hess", + "pre fix", + "Q B", + "Ġsummon ing", + "Ġbest owed", + "ĠKe pler", + "ĠPOW ER", + "c ube", + "ĠK its", + "ĠG rip", + "Ġop ium", + "Ġrep utable", + "t oc", + "ich ael", + "ĠR ipple", + "Ġcaf é", + "ĠZ oom", + "ĠBur ma", + "Ġwa ive", + "Ġst alls", + "Ġdem eanor", + "inc erity", + "Ġfluor ide", + "ĠSH OULD", + "Par is", + "Ġlong ing", + "Ġpl at", + "Ġgross ly", + "Ġbull s", + "Ġshowc asing", + "ex pected", + "ĠG addafi", + "engine ering", + "Re peat", + "ĠK ut", + "Ġconce ivable", + "Ġtrim med", + "osc ope", + "ĠCand idate", + "ĠT ears", + "rol og", + "Lew is", + "S UP", + "Ġroad map", + "Ġsal iva", + "Ġtrump et", + "Jim my", + "Ġmirac ulous", + "Ġcolon ization", + "Ġam put", + "ĠGN OME", + "ate ch", + "D ifferent", + "ĠE LE", + "ĠGovern ments", + "ĠA head", + "ãħĭ ãħĭ", + "word press", + "L IB", + "ĠIn clude", + "ĠDor othy", + "0 45", + "ĠColomb ian", + "Ġle ased", + "88 4", + "Ġde grading", + "ĠDa isy", + "i ations", + "Ġbapt ized", + "Ġsurn ame", + "co x", + "Ġblink ed", + "ãĥ ¢", + "Ġpoll en", + "Ġder mat", + "Ġre gex", + "ĠNich olson", + "ĠE ater", + "ç ľ", + "rad or", + "Ġnarrow er", + "Ġhur ricanes", + "Ġhalluc inations", + "r idden", + "ISS ION", + "ĠFire fly", + "Ġattain ment", + "Ġnom inate", + "Ġav ocado", + "ĠM eredith", + "Ġt s", + "Ġreve rence", + "Ġe uph", + "Ġcr ates", + "ĠT EXT", + "Ġ4 43", + "Ġ3 19", + "J SON", + "iqu ette", + "Ġshort stop", + "ic key", + "Ġpro pelled", + "Ġap i", + "ĠTh ieves", + "77 9", + "Ġovers aw", + "Ġcol i", + "ĠNic ola", + "Ġover cl", + "ik awa", + "ĠC yr", + "Ġ38 4", + "78 9", + "ĠAll ows", + "10 27", + "Det roit", + "TR Y", + "set up", + "ĠSocial ism", + "Sov iet", + "s usp", + "ĠAP R", + "ĠShut down", + "Ġal uminium", + "zb ek", + "ĠL over", + "GGGG GGGG", + "Ġdemocr acies", + "Ġ19 08", + "ĠMer rill", + "ĠFranco is", + "gd ala", + "Ġtraff ickers", + "ĠT il", + "ĠGo at", + "Ġsp ed", + "ĠRes erv", + "Ġpro d", + "55 2", + "Ġc ac", + "ĠUn iv", + "ĠSch we", + "Ġsw irling", + "ĠWild erness", + "ĠEgg s", + "Ġsadd ened", + "Ġarch aic", + "H yd", + "Ġexcess ively", + "B RE", + "Ġaer ospace", + "ĠVo ices", + "Cra ig", + "Ġign ited", + "In itially", + "ĠMc A", + "Ġhand set", + "Ġreform ing", + "Ġfrust rations", + "ĠDead pool", + "ĠBel ichick", + "ract or", + "ĠRagnar ok", + "ĠD rupal", + "ĠApp roximately", + "19 20", + "ĠHub ble", + "arm or", + "ĠSar as", + "ĠJon as", + "Ġnostalg ic", + "Ġfeas ibility", + "Sah aran", + "Ġorb iting", + "Ġ9 70", + "R u", + "Ġsh in", + "ĠInvestig ators", + "Ġinconsist encies", + "ĠP AN", + "B G", + "Ġgraz ing", + "Ġdetect ors", + "ĠStart up", + "ĠFun ny", + "ĠNa omi", + "Consider ing", + "Ġh og", + "ut f", + "ce mic", + "Ġfort ified", + "ĠFun ctions", + "Ġcod ec", + "nut rition", + "H at", + "\" !", + "micro soft", + "55 8", + "ĠTh in", + "ĠA CE", + "Al ias", + "ĠO PS", + "p apers", + "P K", + "ãĢ İ", + "Ġimpro bable", + "N orthern", + "equ al", + "Ġlook out", + "Ġty res", + "ĠMod ified", + "ĠK op", + "Abs olutely", + "Ġbuild up", + "sil ver", + "Ġaud i", + "Ġgro tesque", + "ĠSab er", + "ĠPres byter", + "ON Y", + "Ġglac iers", + "ĠSho als", + "ĠK ass", + "ĠH RC", + "ĠNic ol", + "ĠL unch", + "ĠF oss", + "âĸ Ĵ", + "AD RA", + "ĠOne Plus", + "o ing", + "ground s", + "Ġincident al", + "Ġdatas ets", + "68 9", + "ĠClarks on", + "Ġassemb ling", + "ĠCorrect ions", + "Ġdrink ers", + "Ġqual ifiers", + "Ġle ash", + "Ġunf ounded", + "ĠH undred", + "Ġkick off", + "T i", + "Ġrecon cil", + "ĠGr ants", + "ĠCompl iance", + "ĠDexter ity", + "Ġ19 06", + "w arn", + "D allas", + "Max imum", + "n ard", + "av ia", + "be aut", + "ens itivity", + "tr ace", + "Ġpione ers", + "ĠF ract", + "ãĢ ı", + "Ġpre cept", + "Ġgloss y", + "ĠI EEE", + "Ac ross", + "Ġ6 80", + "S leep", + "che on", + "Ġsatir ical", + "ĠMin otaur", + "ĠCla ude", + "Ġr é", + "ape go", + "Ġcar rot", + "ĠSem in", + "ino a", + "Ġz o", + "Ind ependent", + "Ġdiagn oses", + "ĠC ue", + "M AR", + "Ġrend ition", + "ĠK ik", + "Ġpath ology", + "Ġselect s", + "Link edIn", + "Ġass ay", + "ĠD res", + "Ġtext ual", + "post ed", + "IT AL", + "ĠM aul", + "N eal", + "Ġinter connected", + "Ġerr atic", + "ĠVir us", + "Ġ5 30", + "Ġenvironmental ists", + "ĠP helps", + "Ġeng agements", + "ĠIN ST", + "Ġeconom ical", + "nox ious", + "Ġg earing", + "izz y", + "Ġfavor ably", + "ĠMcG ill", + "T erm", + "Ġh anged", + "Ġball park", + "ĠRe yes", + "Ġbe ware", + "ĠP sal", + "ĠMass acre", + "q i", + "Ġin accessible", + "acly sm", + "Ġfr ay", + "ill ac", + "Ġbitter ly", + "ĠCert ification", + "Mich igan", + "Ġir respective", + "al ore", + "Em pty", + "Ġendorse ments", + "Ġund et", + "f g", + "equ ipped", + "Ġmerc iless", + "ĠC ust", + "Ġimm ature", + "Ġvou cher", + "ĠBlack well", + "Ñ ı", + "h awk", + "dis ciplinary", + "ile e", + "ĠMak oto", + "ĠD ude", + "ãĥĩ ãĤ£", + "Y ears", + "Ġin ver", + "Ġsh aman", + "ĠY ong", + "ip el", + "ell en", + "ĠCath y", + "br ids", + "Ġs arc", + "65 1", + "N ear", + "Ġground work", + "Ġam az", + "Ġ4 15", + "ĠHunting ton", + "hew s", + "ĠB ung", + "Ġarbit rarily", + "ĠW it", + "ĠAl berto", + "Ġdis qualified", + "best os", + "46 1", + "Ġp c", + "Ġ28 4", + "ro bat", + "Rob in", + "Ġh ugs", + "ĠTrans ition", + "ĠOcc asionally", + "Ġ3 26", + "ĠWh ilst", + "ĠLe y", + "Ġspaces hip", + "cs v", + "Ġun successfully", + "ĠA u", + "le ck", + "ĠWing ed", + "ĠGrizz lies", + ". �", + "Ġne arer", + "ĠSorce ress", + "ĠInd igo", + "El se", + "8 40", + "let es", + "Co ach", + "Ġup bringing", + "ĠK es", + "Ġseparat ist", + "Ġrac ists", + "Ġch ained", + "Ġabst inence", + "lear ning", + "Ġrein stated", + "Ġsymm etry", + "Ġremind ers", + "ĠChe vy", + "Ġm ont", + "Ġexempl ary", + "ĠT OR", + "Z X", + "Ġqual itative", + "ĠSt amp", + "ĠSav annah", + "ĠRoss i", + "Ġp aed", + "Ġdispens aries", + "ĠWall s", + "ĠCh ronic", + "Ġcompliment ary", + "ĠBeir ut", + "Ġ+ ---", + "igs list", + "Ġcrypt ographic", + "mas ters", + "ĠCap itals", + "Ġmax imal", + "Ġent ropy", + "Point s", + "Ġcombat ants", + "l ip", + "ĠGl ob", + "ĠB MC", + "ph ase", + "th ank", + "HT TP", + "Ġcomm uter", + "Ġ\\( \\", + ".. /", + "ĠReg ener", + "ĠDO I", + "ĠActiv ision", + "Ġsl it", + "os al", + "RE M", + "Ġch ants", + "Y u", + "Ke ys", + "Bre xit", + "ĠFor ced", + "Ari zona", + "Ġsquad ron", + "IS O", + "ĠMal one", + "Ġ3 38", + "Ġcontrast ing", + "Ġt idal", + "Ġlib el", + "Ġimpl anted", + "Ġupro ar", + "ĠC ater", + "Ġpropos itions", + "M anchester", + "ĠEuro s", + "it amin", + "G il", + "ĠEl ven", + "ĠSe ek", + "ĠB ai", + "Ġredevelop ment", + "ĠTown s", + "ĠL ub", + "! \",", + "al on", + "K rist", + "Ġmeas urable", + "Ġimagin able", + "Ġapost les", + "Y N", + "7 60", + "Ġster oid", + "Ġspecific ity", + "ĠL ocated", + "ĠBeck er", + "ĠE du", + "ĠDiet ary", + "uts ch", + "ĠMar ilyn", + "Ġbl ister", + "ĠM EP", + "ĠK oz", + "ĠC MS", + "y ahoo", + "ĠCar ney", + "Ġbo asting", + "ĠC aleb", + "By te", + "read s", + "ad en", + "Pro blem", + "ĠWood ward", + "S we", + "S up", + "ĠK GB", + "Set up", + "Ġtac it", + "Ġret ribution", + "Ġd ues", + "ĠM ü", + ". ?", + "ä¸ Ń", + "p ots", + "Ġcame o", + "ĠP AL", + "educ ation", + "A my", + "like ly", + "g ling", + "Ġconstitution ally", + "ĠHam m", + "ĠSpe ak", + "Ġwid gets", + "br ate", + "Ġcra ppy", + "ĠI ter", + "Ġanticip ating", + "ĠB out", + "P ixel", + "ĠY ep", + "ĠLaur ie", + "Ġh ut", + "Ġbullet in", + "ĠSal vation", + "Ġch ats", + "ear able", + "Honest ly", + "AL TH", + "onse qu", + "c ult", + "isco very", + "ovy ch", + "Ġse lves", + "ĠSat oshi", + "S ounds", + "Ġconver gence", + "ĠRosen berg", + "19 74", + "Ġnas al", + "Ġfull est", + "Ġfer ocious", + "x us", + "ist e", + "AM S", + "Ġlobb ied", + "Ġso othing", + "ĠGun n", + "t oday", + "0 24", + "Ġinspir ational", + "ĠN BN", + "p b", + "g ewater", + "or ah", + "all owed", + "ĠCol iseum", + "Ġspecial izing", + "Ġinsane ly", + "ĠT ape", + "del ay", + "Ġt arn", + "ĠP ound", + "Ġmel anch", + "Ġdeploy ments", + "il and", + "Ġless en", + "Ġfur ry", + "ĠUE FA", + "Ġblood shed", + "ĠMe ier", + "ither ing", + "Ġhe irs", + "ĠJ aw", + "ax ter", + "ĠPublic ations", + "Ġal ters", + "int ention", + "ĠWinc hester", + "d etermination", + "ĠLif etime", + "th in", + "Mon ster", + "7 80", + "Ġapprox imation", + "Ġsuper markets", + "ĠSecond s", + "or os", + "h uge", + "Ġb ribe", + "ĠLIM ITED", + "un ed", + "Ġmis interpret", + "ĠIn jury", + "Ġ3 67", + "Ġthreshold s", + "ĠCarn ival", + "Ġgastro intestinal", + "Ġguid eline", + "Ġde ceived", + "f eatures", + "Ġpurported ly", + "ĠRon nie", + "ĠNew t", + "Ġsp acious", + "as us", + "Ġsuperhero es", + "ĠCyn thia", + "le gged", + "k amp", + "ch io", + "Ġth umbnail", + "ĠShir ley", + "ill ation", + "Ġshe ds", + "ĠZ y", + "E PA", + "Ġdam s", + "Ġy awn", + "n ah", + "ĠPe ggy", + "ĠE rie", + "ĠJu ventus", + "ĠF ountain", + "r x", + "don ald", + "al bum", + "ĠComp rehensive", + "Ġc aching", + "ĠU z", + "ulner ability", + "ĠPrinc iple", + "ĠJ ian", + "ing ers", + "cast s", + "ĠOs iris", + "ch art", + "t ile", + "ĠTiff any", + "ĠPatt on", + "ĠWh ip", + "Ġovers ized", + "J e", + "ĠCind erella", + "ĠB orders", + "ĠDa esh", + "M ah", + "Ġdog ma", + "Ġcommun ists", + "v u", + "Coun cil", + "Ġfresh water", + "Ġw ounding", + "Ġdeb acle", + "Ġyoung ster", + "Ġthread ed", + "ĠB ots", + "ĠSav ings", + "ãģ Ĥ", + "ol ing", + "oh o", + "Ġillum ination", + "M RI", + "Ġlo osen", + "tr ump", + "ag ency", + "ur ion", + "Ġmoment arily", + "ĠCh un", + "ĠBud apest", + "ĠAl ley", + "D isk", + "Ġaston ished", + "ĠCon quer", + "ĠAccount ing", + "h aving", + "ĠWe in", + "ĠAl right", + "Ġrev olver", + "Ġdel usion", + "Ġrelic s", + "Ġad herent", + "qu ant", + "Ġhand made", + "or io", + "Ġcomb ating", + "c oded", + "Ġquad ru", + "re th", + "N ik", + "ĠTrib al", + "ĠMyster ious", + "Ġin hal", + "ĠWin ning", + "ĠClass ification", + "ch anged", + "Ġun ab", + "Ġsc orn", + "icip ated", + "w l", + "ond uctor", + "Ġrein forcing", + "ĠChild hood", + "an ova", + "Ġadventure r", + "Ġdoctor al", + "ĠStrateg ies", + "Ġengulf ed", + "ĠEnc ounter", + "Ġl ashes", + "Crit ical", + "ric ular", + "ĠU TF", + "oci ation", + "check ing", + "ĠConsult ing", + "Run time", + "per iod", + "ĠAs gard", + "Ġdist illed", + "ĠPas adena", + "ĠD ying", + "ĠCOUN TY", + "Ġgran ite", + "Ġsm ack", + "Ġparach ute", + "ĠS UR", + "Virgin ia", + "ĠF urious", + "78 7", + "ĠO kin", + "Ġcam el", + "ĠM bps", + "19 72", + "ĠCh ao", + "ĠC yan", + "j oice", + "ef er", + "ĠW rap", + "ĠDeb ate", + "S eg", + "Ġfore arm", + "ĠIgn ore", + "Ġtim estamp", + "Ġprob ing", + "ĠNo on", + "ĠGra il", + "f en", + "Ġdorm ant", + "ĠFirst ly", + "ĠE ighth", + "ĠH UN", + "ĠDes ire", + "or as", + "Girl s", + "ĠDes mond", + "z ar", + "am ines", + "O AD", + "exec ute", + "Ġbo obs", + "ĠAT L", + "_ (", + "Chel sea", + "Ġmasturb ation", + "ĠCo C", + "Ġdestroy er", + "ĠCh omsky", + "Ġsc atter", + "ĠAss ets", + "79 6", + "ĠC argo", + "Ġrecept ive", + "ĠSc ope", + "Ġmarket ers", + "Ġlaun chers", + "Ġax le", + "ĠSE A", + "se q", + "ĠM off", + "f inding", + "ĠGib bs", + "Georg ia", + "extreme ly", + "N J", + "Ġlab orers", + "st als", + "Ġmed iation", + "ĠH edge", + "at own", + "Ġi od", + "des pite", + "v ill", + "J ane", + "ex istence", + "Ġcoinc ided", + "ĠUt ilities", + "ĠChe ap", + "Ġlog istical", + "Ġcul mination", + "ĠNic otine", + "p ak", + "F older", + "Ġrod ents", + "st uff", + "Ġlaw fully", + "Ġreper to", + "io ch", + "j j", + "Dial ogue", + "HH HH", + "lic tion", + "Look s", + "Ġ29 7", + "Ġtur rets", + "ĠAb andon", + "Ġinc ess", + "ĠTraff ord", + "Ġcur led", + "Ġprefer ring", + "Ġprivat ization", + "Ġir resist", + "ĠP anda", + "ĠSh ake", + "ĠMc Gr", + "ãĥ Ħ", + "und ers", + "Ġdiscrim inated", + "Ġbart ender", + "I LE", + "Atl antic", + "Ġprop ensity", + "ĠW iz", + "ĠG im", + "con ference", + "Ġrein forces", + "G h", + "w agon", + "Ġe erie", + "F al", + "Ġhug ged", + "rac ist", + "R IC", + "F u", + "Ġf iller", + "ĠSt ub", + "Ġeng raved", + "ĠWrest le", + "Ġimagin ative", + "ĠPe er", + "ĠFact ors", + "an us", + "ĠDrac ula", + "mon itor", + "Ġrou ters", + "ib ia", + "ĠBoo lean", + "end ale", + "ĠSl aughter", + "ĠSh ack", + "R FC", + "ĠSpiel berg", + "S ax", + "ĠPH OTO", + "ĠCl over", + "ĠR ae", + "Dep ending", + "ĠMem or", + "ar am", + "Ġpier ced", + "Ġcur tains", + "v ale", + "ĠInqu isition", + "ĠP oke", + "Ġforecast ing", + "Ġcompl ains", + "S ense", + "ĠHer mes", + "isc overed", + "Ġb ible", + "ĠMor ph", + "Ġg erm", + "78 5", + "D ON", + "Ġcon gen", + "Ġcr ane", + "ĠD PR", + "Ġrespect fully", + "R oom", + "ĠN aw", + "ĠDal ai", + "re ason", + "ĠAng us", + "Educ ation", + "ĠTitan ic", + "Ë ľ", + "Ġo val", + "un ited", + "Ġthird s", + "Ġmoist ur", + "ĠC PC", + "M iami", + "Ġtent acles", + "ĠPol aris", + "ex c", + "ex clusive", + "ĠPra irie", + "Ġcol ossal", + "ĠBl end", + "sur prisingly", + "ÃŃ s", + "Ġindo ctr", + "Ġbas al", + "ĠMP EG", + "und o", + "Spl it", + "Develop ment", + "Ġlan tern", + "19 71", + "Ġprov ocation", + "Ġang uish", + "ĠB ind", + "ĠLe ia", + "duc ers", + "ipp y", + "conserv ancy", + "Ġinitial ize", + "ĠTw ice", + "ĠSu k", + "Ġpred ic", + "Ġdi ploma", + "Ġsoc iop", + "Ing redients", + "Ġhamm ered", + "ĠIr ma", + "Q aida", + "Ġglim ps", + "ĠB ian", + "Ġst acking", + "Ġf end", + "gov track", + "Ġun n", + "dem ocratic", + "ig ree", + "Ġ5 80", + "Ġ29 4", + "Ġstraw berry", + "ID ER", + "Ġcher ished", + "ĠH ots", + "Ġinfer red", + "Ġ8 08", + "ĠS ocrates", + "O regon", + "ĠR oses", + "ĠFO IA", + "Ġins ensitive", + "Ġ40 8", + "Recomm end", + "ĠSh ine", + "Ġpain staking", + "UG E", + "ĠHell er", + "ĠEnter prises", + "I OR", + "ad j", + "N RS", + "L G", + "Ġalien ated", + "Ġacknowled gement", + "ĠA UD", + "ĠRen eg", + "Ġvou chers", + "Ġ9 60", + "Ġm oot", + "ĠDim ensions", + "Ġc abbage", + "B right", + "g at", + "ĠK lu", + "Ġlat ent", + "Ġz e", + "ĠM eng", + "Ġdis perse", + "Ġpand emonium", + "H Q", + "Ġvirt uous", + "ĠLoc ations", + "ee per", + "prov ided", + "Ġse ams", + "ĠW T", + "iz o", + "PR OV", + "Ġtit anium", + "Ġrecol lection", + "Ġcr an", + "Ġ7 80", + "ĠN F", + "49 1", + "64 2", + "p acking", + "59 8", + "text ure", + "Sp ider", + "fre edom", + "cipl ed", + "ĠTAM ADRA", + "âĻ ¦", + "aut hent", + "ĠW ANT", + "r ified", + "Ġr ites", + "Ġuter us", + "k iss", + "Ġâī ¤", + "Ġsk illet", + "Ġdis enfranch", + "ĠGa al", + "Comp an", + "Ġage ing", + "gu ide", + "B alt", + "Ġiter ator", + "Ġdiscretion ary", + "t ips", + "Ġprim ates", + "ĠTechn ique", + "ĠPay ments", + "az el", + "ĠR OCK", + "stant ial", + "0 60", + "Ġd mg", + "ĠJack ets", + "ĠPlay off", + "Ġnurs ery", + "ĠSy mb", + "art on", + "Ġannex ation", + "Color ado", + "Ġco ils", + "ĠSh oes", + "âĦ¢ :", + "ĠRo z", + "COM PLE", + "ĠEve rest", + "ĠTri umph", + "J oy", + "G rid", + "à ¼", + "process or", + "ĠPros per", + "ĠSever us", + "ĠSelect ed", + "r g", + "ĠTay yip", + "St ra", + "Ġski ing", + "Ġ? )", + "Ġpe g", + "Tes la", + "Ġtime frame", + "Ġmaster mind", + "ĠN B", + "scient ific", + "ĠSh it", + "gener ic", + "IN TER", + "N UM", + "Ġst roll", + "ĠEn ix", + "ĠM MR", + "ĠE MS", + "m ovie", + "Ĥ ª", + "Ġminim izing", + "idd ling", + "Ġilleg itimate", + "Ġprot otyp", + "Ġpremature ly", + "Ġmanual s", + "obb ies", + "ĠCass idy", + "D EC", + "des ktop", + "Ġaer os", + "Ġscreen ings", + "Ġdeb ilitating", + "ĠGr ind", + "nature conservancy", + "Ġf ades", + "ter mination", + "assets adobe", + "F actor", + "Ġdefinitive ly", + "P oké", + "ap ult", + "ĠLaf ayette", + "C orn", + "ĠCor al", + "Ġstagn ant", + "T ue", + "Ġdissatisf action", + "G ender", + "Ġkid neys", + "ĠG ow", + "ĠDef eat", + "ĠAsh ton", + "Ġcart els", + "Ġfore closure", + "ĠExpl ore", + "stre ngth", + "ot in", + "Ġveterin arian", + "Ġf umble", + "Ġpar ap", + "ĠSt rait", + "r ils", + "Ġpr ick", + "ĠBerm uda", + "ĠAm munition", + "skin ned", + "Ġab ound", + "ĠB raz", + "Ġshar per", + "ĠAsc ension", + "Ġ9 78", + "Ġpreview s", + "Ġcommun ion", + "ĠX Y", + "Ġph ony", + "Ġnewcom er", + "Ġ3 32", + ".\" ,\"", + "Ġredist ribution", + "Prot ect", + "ĠSo f", + "K al", + "Ġlip stick", + "w orst", + "Ġtang led", + "Ġretrospect ive", + "int eger", + "Ġvolunte ering", + "Ġ19 07", + "Ġ --------------------", + "ic hen", + "Ġunve iling", + "Ġsen seless", + "Ġfisher ies", + "\\ -", + "Ġh inges", + "Ġcalcul us", + "My th", + "Ġund efeated", + "Ġoptim izations", + "Ġdep ress", + "Ġbill board", + "ĠY ad", + "ĠPy ramid", + "Is n", + "I de", + "Ġleg ion", + "ĠK ramer", + "ent anyl", + "Ġpenet rating", + "ĠHaw th", + "ĠPR ODUCT", + "ĠGer ard", + "ĠP act", + "ĠIn cluding", + "ĠEl ias", + "ĠEl aine", + "vis ual", + "Ġhum ming", + "Ġcond esc", + "ĠF asc", + "ä¸ Ĭ", + "Ġe galitarian", + "Ġdev s", + "ĠD ahl", + "O ps", + "D H", + "ĠB ounce", + "id ated", + "ald o", + "Ġrepublic an", + "Ġh amb", + "ĠS ett", + "ograph ies", + "CH APTER", + "Ġtrans sexual", + "Ġsky rocket", + "ans wer", + "Ġmark up", + "Ø ª", + "Ġhero ine", + "Comp are", + "ĠT av", + "Be ast", + "Ġsuccess ors", + "Ġna ïve", + "ĠBuck ley", + "st ress", + "me at", + "Ġdownload able", + "Ġindex ed", + "Ġsc aff", + "ĠL ump", + "ĠHom o", + "Stud io", + "In sp", + "Ġr acked", + "far ious", + "ĠPet ty", + "Ex ternal", + "Ġ19 09", + "W ars", + "com mit", + "put ers", + "Ġun ob", + "ĠEr r", + "ĠE G", + "ĠAl am", + "ĠSiber ia", + "ĠAtmosp heric", + "IS TER", + "ĠSatan ic", + "trans lation", + "ĠL oud", + "tra umatic", + "l ique", + "Ġreson ate", + "ĠWel ch", + "Ġspark ing", + "ĠT OM", + "t one", + "Ġout l", + "Ġhandc uffed", + "ĠSer ie", + "8 01", + "Ġland marks", + "ĠRee ves", + "Ġsoft ened", + "Ġdazz ling", + "ĠW anted", + "month s", + "Mag ikarp", + "Ġunt reated", + "ĠBed ford", + "M i", + "ĠDynam o", + "O re", + "79 5", + "Ġwrong ful", + "Ġl ured", + "Ġcort isol", + "Ġve x", + "d rawn", + "ile t", + "Download ha", + "ĠF action", + "Ġlab yrinth", + "Ġhij acked", + "w aters", + "er ick", + "Ġsuper iors", + "ĠRow ling", + "ĠGu inness", + "Ġt d", + "99 2", + "Ġune arthed", + "Ġcentr if", + "Ġsham eless", + "P od", + "ĠF ib", + "Ġ icing", + "Ġpredict or", + "Ġ29 2", + "fore station", + "con struct", + "C and", + "@ #", + "Ġag itated", + "Ġre pr", + "OV A", + "Ġkn itting", + "ĠLim a", + "Ġf odder", + "68 4", + "ĠPerson a", + "k l", + "7 01", + "Ġbreak up", + "á ¸", + "Ġapp alled", + "Ġantidepress ants", + "ĠSus sex", + "Har ris", + "ĠTher mal", + "ee ee", + "U pload", + "Ġg ulf", + "Ġdoor step", + "ĠSh ank", + "L U", + "ĠM EN", + "ĠP ond", + "s orry", + "Ġmis fortune", + "n ance", + "Ġb ona", + "M ut", + "Ġde graded", + "ĠL OG", + "ĠN ess", + "an imal", + "Ġa version", + "und own", + "Ġsupplement ed", + "ĠC ups", + "Ġ50 4", + "Ġdep rive", + "ĠSpark le", + "Å Ĥ", + "ĠMed itation", + "auth ors", + "ĠSab an", + "ĠN aked", + "air d", + "ĠMand arin", + "ĠScript ures", + "ĠPerson nel", + "ĠMahar ashtra", + "Ġ19 03", + "ĠP ai", + "ĠMir age", + "omb at", + "Access ory", + "Ġfrag mented", + "T ogether", + "Ġbelie vable", + "ĠGl adiator", + "al igned", + "ĠSl ug", + "M AT", + "Ġconvert ible", + "ĠBour bon", + "amer on", + "ĠRe hab", + "nt ax", + "Ġpowd ered", + "pill ar", + "Ġsm oker", + "ĠMans on", + "ĠB F", + "5 11", + "ĠGood ell", + "ĠD AR", + "m ud", + "g art", + "Ġob edient", + "ĠTrans mission", + "ĠDon ation", + "8 80", + "Ġbother ing", + "Material s", + "ãĤ ±", + "dest roy", + "Ġfore going", + "Ġanarch ism", + "ĠK ry", + "ice ps", + "Ġl ittered", + "ĠSch iff", + "Ġanecd otal", + "un its", + "Ġf ian", + "ĠSt im", + "ĠS OME", + "ĠInv aders", + "Ġbehaviour al", + "ĠVent ures", + "Ġsub lime", + "Ġfru ition", + "ĠPen alty", + "Ġcorros ion", + "¶ ħ", + "Ġlik ened", + "Ġbesie ged", + "ween ey", + "ĠCre ep", + "Ġlinem en", + "mult i", + "ic ably", + "ud der", + "Ġvital ity", + "Ġshort fall", + "ĠP ants", + "ap ist", + "H idden", + "ĠDro ps", + "med ical", + "Ġpron unciation", + "ĠN RL", + "Ġinsight ful", + "J V", + "ĠBe ard", + "ĠCh ou", + "Ġchar ms", + "Ġb ins", + "Ġamb assadors", + "ĠS aturdays", + "Ġinhib itor", + "ĠFr anch", + "6 01", + "', '", + "ĠCon or", + "art ney", + "ĠX peria", + "g rave", + "be es", + "ĠProtest ants", + "Ġso aking", + "ĠM andal", + "Ġph ased", + "Ġ6 60", + "Ġsc ams", + "Ġbuzz ing", + "ĠItal ians", + "ĠLoren zo", + "ĠJ A", + "Ġhes itated", + "Ġcl iffs", + "ĠG OT", + "ingu ishable", + "Ġk o", + "Ġinter ruption", + "Z ip", + "Lear ning", + "Ġundersc ores", + "ĠBl ink", + "K u", + "57 9", + "ĠAut ob", + "I RE", + "Ġwater ing", + "Ġpast ry", + "8 20", + "Ġvision ary", + "ĠTempl ar", + "awa ited", + "Ġpist on", + "Ġant id", + "current ly", + "Ġp ard", + "Ġw aging", + "Ġnob ility", + "ĠY us", + "Ġinject ing", + "f aith", + "ĠP ASS", + "å º", + "Ġret ake", + "ĠPR OC", + "Ġcat hedral", + "b ash", + "Ġwrest lers", + "Ġpartner ing", + "Ġn oses", + "Ġ3 58", + "Trans form", + "am en", + "Ġb outs", + "ĠId eal", + "ĠConstant in", + "Ġse p", + "ĠMon arch", + "att en", + "ĠPe oples", + "mod ified", + "Ġmor atorium", + "Ġpen chant", + "Ġoffensive ly", + "Ġprox ies", + "ok ane", + "ĠTaiwan ese", + "ĠP oo", + "ĠH OME", + "us ional", + "Ġver bs", + "ĠO man", + "vis ory", + "Ġpersu asion", + "Ġmult it", + "Ġsc issors", + "G ay", + "ow ay", + "oph ysical", + "l us", + "gn u", + "Ġap ocalyptic", + "Ġabsurd ity", + "Ġplay book", + "Ġautobi ography", + "I UM", + "Ġsne aking", + "ĠSim ulation", + "pp s", + "ell ery", + "Plan et", + "Ġright fully", + "Ġn iece", + "ĠN EC", + "ĠIP O", + "ĠDis closure", + "lean or", + "ous y", + "ST ER", + "Ġ28 2", + "Cru z", + "Ch all", + "64 3", + "ĠSurv ive", + "ĠF atal", + "ĠAm id", + "ap o", + "We apons", + "D EN", + "7 70", + "ĠGreen wald", + "Ġlin en", + "al os", + "Ġpollut ants", + "ĠPCI e", + "k at", + "Ġp aw", + "ĠK raft", + "C hem", + "ĠTermin ator", + "Ġre incarn", + "Ġ] [", + "ĠSe eds", + "Ġsilhou ette", + "ĠSt ores", + "Ġgro oming", + "ĠD irection", + "ĠIs abel", + "ĠBr idges", + "ðŁ ij", + "E ED", + "ĠM orsi", + "Ġval ves", + "ĠRank ed", + "ĠPh arma", + "ĠOrgan izations", + "Ġpenet rated", + "ĠRod ham", + "ĠProt oss", + "Ġove rest", + "Ġex asper", + "ĠT J", + "Ġ 000000", + "Ġtrick le", + "Ġbour bon", + "WH O", + "Ġw retched", + "Ġmicrosc opic", + "Ġcheck list", + "Ġad orned", + "R oyal", + "Ad minist", + "ĠRet irement", + "ĠHig hest", + "We ather", + "ile ge", + "Ġincre ments", + "ĠC osponsors", + "Ġmas se", + "ĠS inn", + "r f", + "Ġh ordes", + "as sembly", + "75 4", + "ĠNat asha", + "ĠTY PE", + "ĠGEN ERAL", + "Ġarr anging", + "Ġ40 7", + "l ator", + "Ġg lean", + "Ġdisc redited", + "Ġclin icians", + "UN E", + "Ġachie ves", + "ĠEm erson", + "com plex", + "= [", + "Ġprincip ally", + "Ġfra il", + "p icked", + "Ġthan king", + "Ġre cl", + "ĠL AST", + "Ġsupp ressing", + "il ic", + "Ġantidepress ant", + "ĠLis bon", + "Ġth or", + "Ġsp a", + "Ġking doms", + "ĠPear ce", + "em o", + "Ġpl ung", + "Ġdiv est", + "Ġ ********************************", + "b is", + "osp els", + "ad r", + "Sp irit", + "hall a", + "P ink", + "end ez", + "Ġresurrect ed", + "esc ape", + "ĠRosen stein", + "Ġge ological", + "Ġnecess ities", + "Ġcarn iv", + "ĠE lys", + "ĠBar ney", + "Ġ29 6", + "dig y", + "ST ON", + "D OWN", + "Ġmil estones", + "Ġk er", + "Ġdismant ling", + "Ġre prim", + "Ġcross ings", + "19 45", + "Ġpatri archy", + "Ġblasp hemy", + "Ġ3 59", + "met ry", + "ĠOb esity", + "ĠDiff erences", + "bl ocking", + "ãĥķ ãĤ¡", + "ich ita", + "ĠSab ha", + "ph alt", + "ĠCol o", + "ual a", + "effic ients", + "ĠMed ina", + "con sole", + "55 7", + "ĠHann ibal", + "ĠHab it", + "ĠF ever", + "Ġthen ce", + "Ġsyn agogue", + "Ġessential s", + "Ġw ink", + "ĠTr ader", + "ID A", + "ĠSp oiler", + "ĠIceland ic", + "ĠHay ward", + "Ġpe ac", + "Ġmal ice", + "Ġflash back", + "Ġth w", + "Ġlay offs", + "L iquid", + "Ġtro oper", + "Ġh inge", + "ĠRead ers", + "Ph ill", + "ĠB auer", + "Cre ated", + "Ġaud its", + "ac compan", + "Ġunsus pecting", + "ier a", + "6666 6666", + "Ġbro ch", + "Ġapprehend ed", + "ĠM alk", + "cer ning", + "ĠCod ex", + "O VER", + "M arsh", + "ĠD eng", + "ĠExp ression", + "Ġdisrespect ful", + "Ġasc ending", + "t ests", + "ĠPlaint iff", + "ster y", + "ĠAl ibaba", + "din and", + "ĠDem psey", + "Applic ations", + "mor al", + "Ġthrough put", + "Ġquar rel", + "Ġm ills", + "Ġhe mor", + "ĠC ASE", + "terror ist", + "st im", + "ifest yle", + "ro zen", + "CE PT", + "Ar k", + "u ci", + "lect ic", + "Ġirrit ating", + "she ets", + "A y", + "Ġrede emed", + "Ġhorn y", + "ĠTe ach", + "ĠS ear", + "dem ocracy", + "4 65", + "ĠRest ore", + "Ġstand by", + "ĠP is", + "iff in", + "Ġsleep y", + "Ġextr ater", + "Ġcompl iments", + "Fram eworks", + "Ġinstall s", + "Ġb anging", + "sur face", + "found land", + "Ġmetaph ysical", + "Ġ28 3", + "oul s", + "dev ices", + "Ar gs", + "ĠSac rifice", + "ĠMcC orm", + "es on", + "Cons ervative", + "ĠM ikhail", + "see ing", + "is ively", + "ĠRo oms", + "ĠGener ic", + "Ġenthusi astically", + "Ġgri pped", + "Ġcomed ic", + "ĠElectric ity", + "Ġgu errilla", + "Ġdec oration", + "ĠPerspect ive", + "Ġconsult ations", + "Ġun amb", + "Ġplag iar", + "Ġmagic ian", + "Ġe rection", + "ĠTour ism", + "or ied", + "ro xy", + "11 00", + "T am", + "Ī è", + "Î ³", + "× ª", + "ĠPred ators", + "Nit rome", + "Ġtelesc opes", + "project s", + "Ġun protected", + "Ġst ocked", + "ĠEnt reprene", + "nex pected", + "Ġwast ewater", + "V ill", + "Ġint imately", + "Ġi Cloud", + "ĠConst able", + "Ġspo of", + "Ġne farious", + "Ġfin s", + "Ġcens or", + "ĠMod es", + "ĠEs per", + "ar bon", + "Ġinter sections", + "Ġlaud ed", + "Ġphys i", + "Ġgener ously", + "ĠThe Nitrome", + "ĠTheNitrome Fan", + "Ġar isen", + "ĠÙ Ī", + "Ġg lands", + "ĠPav ilion", + "ĠGu pta", + "Ġuniform ly", + "Ġr amps", + "ri et", + "ĠWH EN", + "ĠVan essa", + "Ġrout ed", + "Ġlim p", + "ĠC PI", + "p ter", + "int uitive", + "Ġv aping", + "Ġexperiment ed", + "ĠOlymp us", + "ĠAm on", + "Ġsight ing", + "Ġinfiltr ate", + "ĠGentle man", + "Ġsign ings", + "ĠMe ow", + "ĠNav igation", + "che cks", + "4 33", + "Ġel apsed", + "ĠBulg arian", + "esp ie", + "ĠS OM", + "d uring", + "Ġsp ills", + "anc a", + "ĠPly mouth", + "M AL", + "Ġdomest ically", + "ĠWater gate", + "ĠF AM", + "k illed", + "ed ited", + "ĠYour self", + "Ġsynchron ization", + "ĠPract ices", + "ST EP", + "Ġgen omes", + "ĠQ R", + "not ice", + "Ġloc ating", + "z in", + "Ġ3 29", + "al cohol", + "Ġk itten", + "V o", + "Ġr inse", + "Ġgrapp le", + "ĠSc rew", + "ĠD ul", + "A IR", + "Ġle asing", + "ĠCaf é", + "Ġro ses", + "ĠRes pect", + "Ġmis lead", + "Ġperfect ed", + "Ġnud ity", + "Ġnon partisan", + "ĠCons umption", + "Report ing", + "Ġnu ances", + "Ġdeduct ible", + "ĠSh ots", + "Ġ3 77", + "Ġæ ľ", + "ano oga", + "Ben ef", + "ĠB am", + "ĠS amp", + "if ix", + "Ġgal van", + "ĠMed als", + "rad ius", + "Ġno bles", + "Ġe aves", + "igr ate", + "K T", + "ĠHar bour", + "u ers", + "Ġrisk ed", + "re q", + "Ġneuro t", + "get table", + "ain a", + "Rom ney", + "Ġunder pin", + "Ġlo ft", + "ĠSub committee", + "ĠMong ol", + "b iz", + "Ġmanif ests", + "ass isted", + "ĠG aga", + "Ġsy nergy", + "Ġreligious ly", + "ĠPre f", + "ĠG erry", + "T AG", + "ĠCho i", + "4 66", + "beh ind", + "ĠO u", + "Gold Magikarp", + "Ġhemor rh", + "R iver", + "Ġtend on", + "Ġinj ure", + "ĠF iona", + "Ġp ag", + "Ġag itation", + "|| ||", + "ur an", + "ĠE SA", + "Ġest eem", + "Ġdod ging", + "Ġ4 12", + "r ss", + "Ġce ases", + "ex cluding", + "Ġint akes", + "Ġinsert s", + "Ġemb old", + "ĠO ral", + "up uncture", + "4 11", + "ĠUn ified", + "ĠDe le", + "Ġfurn ace", + "ĠCoy otes", + "ĠBr ach", + "L abor", + "Ġhand shake", + "Ġbru ises", + "Gr ade", + "éĹ ĺ", + "ĠGram my", + "ile en", + "St ates", + "ĠScandinav ian", + "ĠKard ash", + "8 66", + "Ġeffort lessly", + "ĠDI RECT", + "ĠTH EN", + "ĠMe i", + "ert ation", + "19 68", + "Ġgro in", + "w itch", + "Requ irements", + "98 5", + "Ġroof s", + "Ġest ates", + "ĠH F", + "Ġha ha", + "Ġdense ly", + "ĠO CT", + "Ġpl astics", + "Ġincident ally", + "ĠTr acks", + "ĠTax es", + "Ġch anted", + "Ġforce ful", + "ĠBie ber", + "ĠK ahn", + "K ent", + "ĠC ot", + "lic ts", + "F ed", + "Ġhide ous", + "ĠVer d", + "ĠSynd icate", + "ĠIl legal", + "J et", + "ĠD AV", + "re asonable", + "c rew", + "Ġfundamental ist", + "Ġtruth ful", + "ĠJ ing", + "Ġl il", + "Ġdown ed", + "Ġen chanted", + "ĠPolic ies", + "ĠMcM aster", + "ĠH are", + "ides how", + "Ġpar ams", + "en cers", + "gorith m", + "Ġallow ances", + "Ġturb ulent", + "Ġcomplex ities", + "ĠK T", + "Ġ3 37", + "ĠGen etic", + "F UN", + "D oug", + "t ick", + "Ġg igs", + "ument hal", + "Ġpatriarch al", + "Ġcal c", + ", ...", + "Ġc out", + "ĠGu an", + "Ġpath ological", + "ĠR ivals", + "Ġunder rated", + "Ġflu orescent", + "ĠJ iu", + "arna ev", + "ĠQu an", + "Ġ4 29", + "Ġ à¨", + "M ario", + "Con struct", + "ĠC itation", + "ĠR acial", + "ĠR SA", + "ĠF idel", + "Ġ3 95", + "Person ally", + "C ause", + "à »", + "rad ical", + "in en", + "Ġvehement ly", + "ĠPap a", + "Ġintern ship", + "Ġfl akes", + "ĠRe ck", + "Luck ily", + "B ra", + "20 20", + "rav ings", + "R N", + "W onder", + "Ser iously", + "Ġre usable", + "Ġpoll uted", + "ĠP eng", + "le igh", + "ind le", + "Ġcircuit ry", + "ĠMad onna", + "ĠB ART", + "Res idents", + "att ribute", + "Phil adelphia", + "Cl ub", + "Ġplan ner", + "Ġfr antically", + "Ġfaith fully", + "ĠTerrit ories", + "ĠL AT", + "ĠAnders en", + "an u", + "ĠP ARK", + "ĠS ora", + "i age", + "ĠPlay offs", + "ĠG CC", + "4 27", + "Ġab norm", + "ĠL ever", + "Ġdisob edience", + "As ync", + "ĠShe a", + "V ert", + "Ġsk irts", + "ĠSaw yer", + "x p", + "Ġwors ening", + "Ġsc apego", + "ĠAng le", + "oth al", + "Ġtro ve", + "ĠSt y", + "ĠN guyen", + "mar ine", + "ide on", + "Dep ths", + "Bl og", + "ĠIll uminati", + "Ġtract s", + "Ġorgan ise", + "Ġo str", + "F s", + "Ġlever aging", + "ĠD aredevil", + "as ar", + "Ġl ang", + "Ġex termin", + "urs ions", + "ĠRom o", + "ãĤ¤ ãĥĪ", + "Ġcont ended", + "Ġencounter ing", + "ĠTable t", + "ĠAltern ate", + "sk ill", + "Ġswe ets", + "Ġco hesive", + "cap acity", + "Ġrep ud", + "Ġl izard", + "ro o", + "Ġpilgr ims", + "ĠR uff", + "ĠInstr ument", + "ĠLog o", + "uit ous", + "E H", + "Ġsales man", + "Ġank les", + "L ed", + "ĠPat ty", + "ud os", + "Own er", + "Ġdiscrep ancies", + "k j", + "M U", + "Ġuncond itional", + "Dragon Magazine", + "i ard", + "O ak", + "ĠConvers ation", + "be er", + "ĠOs aka", + "D elta", + "us ky", + "Ġsecret ion", + "Ġpl aza", + "Ġm ing", + "Ġde pletion", + "ĠM ous", + "ĠI TS", + "ĠH imal", + "ĠFle ming", + "Ġcyt ok", + "ĠH ick", + "Ġbat ters", + "ĠInt ellectual", + "6 75", + "é r", + "IS ION", + "ĠQu entin", + "ĠCh apters", + "ih adi", + "Ġco aster", + "WAY S", + "ĠL izard", + "ĠY or", + "and ering", + "S kin", + "ha ust", + "ab by", + "Ġportray ing", + "Ġwield ed", + "d ash", + "Ġprop onent", + "Ġr ipple", + "Ġgrap hene", + "Ġfly er", + "Ġrec urrent", + "Ġdev ils", + "Ġwater fall", + "æĺ ¯", + "go o", + "Text Color", + "Ġtam pering", + "IV ES", + "TR UMP", + "ĠAb el", + "ĠS AL", + "ĠHend ricks", + "ĠLu cius", + "b ots", + "Ġ40 96", + "IST ORY", + "Gu est", + "ĠN X", + "in ant", + "Ben z", + "ĠLoad ed", + "ĠCle ver", + "t reatment", + "Ġta vern", + "Ġ3 39", + "ĠT NT", + "ific antly", + "Tem perature", + "F el", + "Ġunder world", + "ĠJud ges", + "Ġ< +", + "Ġst ump", + "Ġoccup ancy", + "Ġab er", + "ĠF inder", + ") \",", + "ĠN unes", + "res et", + "in et", + "ect omy", + "Ġwell ness", + "ĠP eb", + "quart ered", + "and an", + "Ġneg atives", + "ĠTh iel", + "ĠCl ip", + "ĠL TD", + "Ġbl ight", + "Ġreperto ire", + "K yle", + "Ġqu er", + "ĠC es", + "Ġha pl", + "98 9", + "ĠTh ames", + "isc opal", + "Des k", + "ivari ate", + "ĠEx cellence", + "found ation", + "Ġâ ĩ", + "X i", + "Ġmyster iously", + "esty les", + "Ġper ish", + "ĠEng els", + "ĠDE AD", + "09 0", + "}} }", + "ĠUn real", + "Ġrest less", + "ID ES", + "orth odox", + "ĠInter mediate", + "Ġdin ners", + "ĠTr out", + "ĠSe ym", + "ĠHall s", + "og ged", + "Ġtraged ies", + "Ġdid nt", + "67 6", + "Ġail ments", + "Ġobserv able", + "ĠV ide", + "ad apt", + "ĠD usk", + "Ġprofessional ism", + "ĠPres cott", + "ĠInd ies", + "p ox", + "ĠMe hran", + "W ide", + "Ġend emic", + "ĠPar an", + "B ird", + "Ġped als", + "ĠI U", + "ĠAdam ant", + "ĠH urt", + "Ġcorrel ates", + "urd en", + "Ġspons oring", + "cl imate", + "ĠUnivers ities", + "ĠK not", + "enn es", + "ĠDam ian", + "ĠAx el", + "S port", + "Ġbar b", + "ĠS no", + "sh own", + "ste en", + "ud ence", + "Ġnon violent", + "Ġhom ophobia", + "Ġbiom ass", + "ĠDet ail", + "Ġsrf N", + "ĠT une", + "accompan ied", + "I ENCE", + "Al bert", + "ĠMong o", + "z x", + "ĠCer berus", + "or bit", + "c ens", + "Ġsl ay", + "SH ARE", + "H Y", + "Ġb rawl", + "ĠPro be", + "Ġnonex istent", + "ĠClare nce", + "ĠBlack burn", + "Ġport als", + "ĠR ita", + "ĠRem ain", + "ĠLe vant", + "Ġtrick ed", + "ĠF erry", + "aver ing", + "ĠStraw berry", + "ĠAn swers", + "Ġhorrend ous", + "ĠA man", + "Supp lement", + "ĠT oad", + "Ġpe eled", + "Ġman oeuv", + "ĠU zbek", + "mond s", + "ĠH ector", + "Ġ40 2", + "pe es", + "fix es", + "Ġd j", + "Ġres umes", + "Ġaccount ant", + "Ġadvers ity", + "Ġham pered", + "ĠL arson", + "Ġd oping", + "part s", + "H ur", + "Ġbe arded", + "Ġy r", + "ĠPlug in", + "å¥ ³", + "Ġ/ **", + "rol ley", + "Ġwaters hed", + "ĠSub mission", + "if lower", + "AS C", + "Ġcho ir", + "Ġsculpt ures", + "m A", + "incre asing", + "ai i", + "Ġsne akers", + "Ġconfront s", + "ĠEle phant", + "ĠEl ixir", + "Ġrec al", + "ĠT TL", + "w idget", + "ĠW ax", + "ĠGr ayson", + "Ġha irst", + "Ġhumili ated", + "ĠWAR N", + "app iness", + "ĠT TC", + "F uel", + "Ġpol io", + "Ġcomplex es", + "Ġbab e", + "ĠX IV", + "P F", + "). [", + "P arts", + "Ġ4 35", + "M eg", + "ĠY ards", + "ĠAL P", + "Ġy ells", + "Ġprin ces", + "Ġbull ies", + "ĠCapital ism", + "ex empt", + "FA Q", + "ĠSp onge", + "ĠAl a", + "Ġpleas antly", + "Ġbu f", + "Ġden ote", + "Ġunp ublished", + "Ġkne eling", + "asc a", + "Ġl apse", + "al ien", + "99 4", + "Ġrefere es", + "ĠLaw yers", + "S anta", + "Ġpuzz ling", + "ĠProm etheus", + "ĠPh araoh", + "ĠDel ay", + "Ġfacilit ates", + "ĠC ES", + "Ġjew els", + "Ġbook let", + "ond ing", + "Ġpolar ization", + "ĠMor an", + "ĠSal ad", + "ĠS OS", + "ĠAdv ice", + "PH OTOS", + "IC AN", + "iat ures", + "ex press", + "ĠWonder land", + "ĠC ODE", + "ĠCL ASS", + "9 75", + "Ġg rep", + "ĠD iesel", + "ĠGl ac", + "! ?\"", + "Ġr m", + "o ine", + "disc rimination", + "ĠN urse", + "m allow", + "Ġv ortex", + "ĠCons ortium", + "Ġlarge Download", + "stra ight", + "augh lin", + "G rad", + "Ġpublic ized", + "ĠW aves", + "ĠRed d", + "Ġfest ivities", + "ĠM ane", + "ar ov", + "Ġfleet ing", + "ĠDr unk", + "ug en", + "C ele", + "Ġchromos omes", + "ĠD OT", + "-+-+ -+-+", + "Ġbus iest", + "ĠBe aver", + "Sy rian", + "ĠK yr", + "k as", + "ĠCross Ref", + "19 50", + "76 01", + "Ġrepe aling", + "ĠWin ners", + "ĠMac ro", + "ĠD OD", + "bl ance", + "S ort", + "64 1", + "Ġmet re", + "ĠD irk", + "Ġgo ggles", + "Ġdraw backs", + "Ġcomplain ant", + "Ġauthor izing", + "Ġantit rust", + "oper ated", + "Ġm ah", + "Ġexagger ation", + "Am azing", + "ĠSer aph", + "Ġha ze", + "w ow", + "Ġextingu ished", + "Ġcan yon", + "ĠB osh", + "Ġv ents", + "Ġsc rape", + "Cor rect", + "4 26", + "Ġav g", + "Dem and", + "ĠâĪ ¼", + "Ġmicrobi ota", + "\"} ],\"", + "ĠSt ev", + "B io", + "ĠPlan es", + "Ġsuggest ive", + "Ġdec ipher", + "ĠRefuge e", + "ĠKe jriwal", + "ĠGreen peace", + "Ġdecl ass", + "ĠSound ers", + "Ġth o", + "Ġdec rypt", + "Ġbr ushing", + "ĠJane iro", + "ip op", + "S i", + "8 77", + "ĠGeoff rey", + "Ġc pu", + "ĠHaz el", + "Ġview points", + "Ġcris py", + "ĠNot ification", + "Ġsold er", + "ĠMod est", + "ĠHem isphere", + "Ġcass ette", + "in cludes", + "Ġident ifiers", + "ĠC ALL", + "in cent", + "T odd", + "ĠSwe ep", + "Ġ3 34", + "b oss", + "Ġsm ir", + "gin x", + "Ġtown ship", + "Ġg rieving", + "ĠMos que", + "Net flix", + "AS ED", + "ĠMillenn ials", + "oc om", + "19 67", + "Ġbold ly", + "s leep", + "Ġes che", + "arij uana", + "Ġsw irl", + "ĠPen al", + "Ġneglig ent", + "ĠStephen son", + "K ER", + "ĠZ oro", + "ris is", + "Ġlocal ization", + "ĠSeym our", + "ĠAng lic", + "red itation", + "prot ection", + "ĠPa ige", + "Ġo mit", + "ĠR ousse", + "ĠT ub", + "Ġinv itations", + "t ty", + "Ġm oss", + "ph ysical", + "C redits", + "Ġan archy", + "Ġchild care", + "Ġl ull", + "ĠM ek", + "ĠL anguages", + "lat est", + "ĠSan ford", + "Ġus ability", + "Ġdiff use", + "ĠD ATA", + "Ġsp rites", + "ĠVeget a", + "ĠProm otion", + "ãĥ¼ ãĤ¯", + "rict ing", + "z ee", + "Tur kish", + "ĠTD s", + "pro ven", + "57 1", + "Ġsmug glers", + "707 10", + "Ġreform ed", + "ĠLo is", + "Ġun fl", + "ĠWITH OUT", + "ĠReturn ing", + "ann ie", + "ĠTom as", + "Fr anc", + "ĠProf it", + "ĠSER V", + "ĠR umble", + "ik uman", + "es an", + "Ġt esters", + "Ġgad get", + "Ġbrace let", + "ĠF SA", + "comp onent", + "Ġparamed ics", + "Ġj an", + "ĠRem em", + "ĠSk inner", + "Ġl ov", + "ĠQu ake", + "rom a", + "Ġfl ask", + "Pr inc", + "Ġover power", + "Ġlod ging", + "ĠK KK", + "ret te", + "Ġabsor bs", + "w rote", + "Ġ ,\"", + "K ings", + "ĠH ail", + "ĠFall ing", + "xt ap", + "ĠHel ena", + "ire ns", + "L arry", + "Ġpamph let", + "ĠC PR", + "G ro", + "ĠHirosh ima", + "Ġhol istic", + "\". [", + "Ġdet achment", + "Ġas pire", + "Ġcompl icit", + "ĠGreen wood", + "Ġresp awn", + "ĠSt upid", + "ĠFin ished", + "f al", + "b ass", + "Ġab hor", + "Ġmock ery", + "ĠFe ast", + "VID EO", + "Ġcon sec", + "ĠHung ry", + "P ull", + "ĠH ust", + "it ance", + "? ãĢį", + ") --", + "ĠPar allel", + "con v", + "4 69", + "ha ar", + "w ant", + "P aper", + "m ins", + "ĠTor o", + "ĠTR UMP", + "ĠR ai", + "D W", + "ĠW icked", + "ĠL ep", + "Ġfun ky", + "Ġdetrim ent", + "ios is", + "ache v", + "Ġde grade", + "im ilation", + "Ġret ard", + "Ġfrag mentation", + "Ġcow boy", + "ĠY PG", + "ĠH AL", + "Parent s", + "ĠS ieg", + "ĠStra uss", + "ĠRub ber", + "× IJ", + "Fr ag", + "Ġp t", + "Ġoption ally", + "ĠZ IP", + "ĠTrans cript", + "ĠD well", + "88 2", + "M erc", + "ĠM OT", + "ãĥ¯ ãĥ³", + "Ġhun ts", + "Ġexec utes", + "In cludes", + "Ġacid ic", + "ĠRespons ibility", + "ĠD umb", + "we i", + "And erson", + "ĠJas per", + "ight on", + "abs olutely", + "Ad ult", + "Ġpl under", + "Mor ning", + "ĠT ours", + "ĠD ane", + "Î º", + "ĠT EST", + "ĠG ina", + "Ġcan ine", + "aw an", + "Ġsocial ists", + "ĠS oda", + "Ġimp etus", + "ĠSupplement ary", + "oli ath", + "ĠKinn ikuman", + "mitted ly", + "second s", + "Ġorganis ers", + "Ġdocument aries", + "Vari able", + "GRE EN", + "Ġres orts", + "Ġbr agging", + "Ġ3 68", + "Art ist", + "w k", + "bl ers", + "Un common", + "ĠRet rieved", + "Ġhect ares", + "Ġtox in", + "r ank", + "Ġfaith s", + "ĠG raphic", + "Ġve c", + "ĠL IA", + "Af rican", + "Ġard ent", + "end iary", + "L ake", + "ĠD OS", + "cient ious", + "ĠOk awaru", + "ĠAll y", + "ĠTim eline", + "D ash", + "ĠI c", + "contin ue", + "Ġt idy", + "Ġinstinct ively", + "ĠP ossibly", + "ĠOut door", + "ĠWould n", + "Ġl ich", + "ĠBr ay", + "ĠA X", + "Ġà ī", + "Ġ+ #", + "\\ '", + "Direct ory", + "ab iding", + "Ġf eral", + "ic ative", + "but t", + "Ġper verse", + "S alt", + "Ġwar ped", + "Ġnin eteen", + "Ġcabin ets", + "Ġsrf Attach", + "ĠSl oan", + "Ġpower ing", + "reg ation", + "F light", + "se vere", + "Ġst ren", + "Ġc og", + "ap ache", + "Ġâ Ŀ", + "Ġcaf eteria", + "p aces", + "ĠGrim oire", + "uton ium", + "Ġr aining", + "Ġcir cling", + "Ġlineback ers", + "c redit", + "Ġrep atri", + "ĠCam den", + "lic ense", + "Ġly ric", + "Ġdescript or", + "Ġval leys", + "Ġre q", + "Ġback stage", + "ĠPro hibition", + "ĠK et", + "Op ening", + "S ym", + "æĸ ¹", + "Ġserv ings", + "Ġoverse en", + "Ġaster oids", + "ĠMod s", + "ĠSpr inger", + "ĠCont ainer", + "è »", + "ĠM ens", + "Ġmult im", + "Ġfire fighter", + "pe c", + "Ġchlor ine", + "Ð ¼", + "end i", + "Ġsp aring", + "Ġpolyg amy", + "ĠR N", + "ĠP ell", + "Ġt igers", + "Ġflash y", + "ĠMad ame", + "S word", + "Ġpref rontal", + "Ġpre requisite", + "uc a", + "Ġw ifi", + "Ġmiscon ception", + "Ġharsh ly", + "ĠStream ing", + "ot om", + "ĠGiul iani", + "foot ed", + "Ġtub ing", + "ind ividual", + "z ek", + "n uclear", + "m ol", + "Ġright ful", + "49 3", + "Ġspecial ization", + "Ġpassion ately", + "ĠVel ocity", + "ĠAv ailability", + "T enn", + "Ġl atch", + "ĠSome body", + "Ġhel ium", + "cl aw", + "Ġdi pping", + "XX X", + "Ġinter personal", + "7 10", + "Ġsub ter", + "Ġbi ologists", + "ĠLight ing", + "Ġopt ic", + "Ġden im", + "end on", + "ĠC orm", + "Ġ3 41", + "ĠC oup", + "Ġfear less", + "Ġal ot", + "ĠCliff ord", + "ĠRun time", + "ĠProv ision", + "up dated", + "lene ck", + "Ġneur on", + "Ġgrad ing", + "ĠC t", + "sequ ence", + "in ia", + "con cept", + "Ġro aring", + "ri val", + "ĠCaucas ian", + "Ġmon og", + "key es", + "Ġappell ate", + "Ġlia ison", + "EStream Frame", + "ĠPl um", + "! .", + "Ġsp herical", + "Ġper ished", + "Ġbl ot", + "Ġben ches", + "Ġ4 11", + "Ġpione ered", + "Ġhur led", + "Jenn ifer", + "ĠYose mite", + "Ch air", + "Ġreef s", + "Ġelect or", + "ĠAnt hem", + "65 2", + "Ġun install", + "Ġimp ede", + "Ġbl inking", + "Ġgot o", + "Dec re", + "A ren", + "Ġstabil ization", + "ĠDis abled", + "ĠYanuk ovych", + "Ġoutlaw ed", + "ĠVent ura", + "ten ess", + "Ġplant ation", + "Ġy acht", + "ĠHu awei", + "Ġsol vent", + "Ġgr acious", + "Ġcur iously", + "Ġcapac itor", + "Ġc x", + "ĠRef lex", + "Ph ys", + "ĠC f", + "pt in", + "cons ervative", + "Ġinv ocation", + "c our", + "F N", + "ĠNew ly", + "H our", + "As ian", + "ĠLe ading", + "ĠAer ospace", + "An ne", + "Ġpre natal", + "Ġdeterior ating", + "H CR", + "ĠNorm andy", + "ol ini", + "ĠAm bro", + "9 10", + "Ġset backs", + "ĠT RE", + "Ġs ig", + "ĠSc ourge", + "59 7", + "79 8", + "Game play", + "Ġm sec", + "M X", + "Ġprice y", + "ĠL LP", + "aker u", + "Ġover arching", + "ĠB ale", + "Ġworld ly", + "Cl ark", + "Ġscen ic", + "Ġdisl iked", + "ĠCont rolled", + "T ickets", + "ĠE W", + "ab ies", + "ĠPl enty", + "Non etheless", + "Ġart isan", + "Trans fer", + "ĠF amous", + "Ġinf ield", + "ble y", + "Ġunres olved", + "ĠML A", + "ãĤ Ĥ", + "Cor rection", + "Ġdemocr at", + "ĠMore no", + "ro cal", + "il ings", + "Ġsail or", + "Ġr ife", + "h ung", + "Ġtrop es", + "Ġsn atched", + "ĠL IN", + "ĠB ib", + "ES A", + "ĠPre v", + "ĠCam el", + "run time", + "Ġob noxious", + "4 37", + "Ġsum mers", + "Ġunexpl ained", + "ĠWal ters", + "cal iber", + "Ġg ull", + "ĠEnd urance", + "ä½ ľ", + "Ġ3 47", + "Ir ish", + "Ġaer obic", + "Ġcr amped", + "ĠHon olulu", + "à ©", + "us erc", + "ec ast", + "AC Y", + "ĠQu ery", + "ãĤ¹ ãĥĪ", + "Bet a", + "Ġsuscept ibility", + "ĠSh iv", + "ĠLim baugh", + "Ġà ĸ", + "ĠN XT", + "ĠM uss", + "ĠBrit ons", + "ES CO", + "EG IN", + "Ġ% %", + "Ġsec ession", + "ĠPat ron", + "ĠLu a", + "n aires", + "ĠJPM organ", + "us b", + "ocy te", + "Ġcouncill ors", + "ĠLi ang", + "f arm", + "Ġnerv ously", + "Ġattract iveness", + "ĠK ov", + "j ump", + "Pl ot", + "Ġst ains", + "ĠStat ue", + "ĠApost les", + "he ter", + "ĠSUP PORT", + "Ġoverwhel m", + "Y ES", + "Ġ29 1", + "d ensity", + "Ġtra pping", + "M it", + "Ġf ide", + "ĠPam ela", + "atl antic", + "Dam n", + "Ġp ts", + "OP A", + "Ġserv icing", + "Ġoverfl owing", + "ul o", + "ĠE rit", + "t icket", + "light ing", + "ĠH mm", + "ãĥ¼ ãĥ«", + "im oto", + "Ġchuck le", + "4 23", + "ãģ ķ", + "sh ape", + "Ġque ues", + "Ġanch ors", + "ãĤ¼ ãĤ¦ãĤ¹", + "F er", + "Ġaw oke", + "Ġ6 66", + "h ands", + "Ġdiver gence", + "Ġ50 5", + "T ips", + "Ġdep ot", + "Ġske w", + "ĠDel iver", + "op ot", + "Ġdiv ul", + "ĠE B", + "uns igned", + "ĠUn i", + "X box", + "Ġfor ks", + "Ġ7 02", + "å ¯", + "Ġpromot ers", + "ĠV apor", + "Ġlev ied", + "sl ot", + "Ġpig ment", + "Ġcyl inders", + "C RE", + "Ġsn atch", + "Ġperpet ually", + "Ġl icking", + "ĠFe et", + "ĠKra ken", + "ĠHold en", + "ĠCLS ID", + "m r", + "Ġproject or", + "Ġden otes", + "Ġchap el", + "ĠTor rent", + "b ler", + "R oute", + "ĠDef endant", + "ĠPublisher s", + "ĠM ales", + "ĠInn ov", + "ĠAg ility", + "rit er", + "ty mology", + "st ores", + "L ind", + "Ġf olly", + "ĠZur ich", + "B le", + "Ġnurt ure", + "Ġcoast line", + "uch in", + "D omin", + "Ġfri vol", + "ĠCons olid", + "res ults", + "M J", + "Ġphyl ogen", + "Ġha uled", + "ĠW iley", + "ĠJess ie", + "ĠPrep are", + "ĠE ps", + "Ġtreasure r", + "I AS", + "Ġcolon ists", + "Ġin und", + "ĠWW F", + "ĠCon verted", + "6 000", + "out side", + "ĠApp earance", + "ĠRel ic", + "ĠM ister", + "s aw", + "Ġresult ant", + "Ġadject ive", + "ĠLaure l", + "ĠHind i", + "b da", + "Pe ace", + "Ġreb irth", + "Ġmembr anes", + "Ġforward ing", + "Ġcoll ided", + "ĠCar olyn", + "K ansas", + "5 99", + "ĠSolid GoldMagikarp", + "Be ck", + "Ġstress ing", + "ĠGo o", + "ĠCooper ative", + "Ġf s", + "ĠAr chie", + "L iter", + "ĠK lopp", + "J erry", + "Ġfoot wear", + "War ren", + "Ġsc ree", + "h are", + "Under standing", + "P ed", + "Ġanth ology", + "ĠAnn ounce", + "M ega", + "Ġflu ent", + "Ġbond age", + "ĠDisc ount", + "il ial", + "C art", + "ĠNight mares", + "Sh am", + "ĠB oll", + "uss ie", + "H ttp", + "Atl anta", + "Ġun recogn", + "ĠB id", + "Ġunder grad", + "Ġforg iving", + "ĠGl over", + "AAAA AAAA", + "4 45", + "V G", + "pa io", + "kill ers", + "Ġrespons ibly", + "Ġmobil ize", + "Ġeffect ed", + "ĠL umin", + "Ġk ale", + "Ġinfring ing", + "ann ounced", + "Ġf itt", + "b atch", + "ĠT ackle", + "ĠL ime", + "ĠAP P", + "uke mia", + "Ġrub y", + "Ġex oner", + "ĠCas ual", + "0 70", + "Ġpel vic", + "Ġautom ate", + "ĠK ear", + "ĠCoast al", + "Ġcre ed", + "Ġbored om", + "ĠSt un", + "ri ott", + "Ĥ İ", + "Ġregener ate", + "Ġcomed ians", + "ĠOP ER", + "Sp ons", + "id ium", + "on is", + "L ocated", + "05 7", + "Ġsusp ense", + "ĠD ating", + "C ass", + "Ġneoc ons", + "ĠShin zo", + "Ġaw oken", + "ch rist", + "ĠMess ages", + "att led", + "ĠSpr ay", + "ĠSp ice", + "C W", + "Ġshield ing", + "ĠG aul", + "Am id", + "Ġparam ilitary", + "Ġmult if", + "ĠTan ner", + "il k", + "Ġgodd amn", + "g ements", + "Ġbe friend", + "m obi", + "Ġ3 88", + "fold er", + "acc a", + "Ġins in", + "g ap", + "N ev", + "fif th", + "Ġpsychiat ry", + "b anks", + "TH IS", + "Ġhar b", + "ac qu", + "Ġfac ade", + "ĠPower Point", + "80 3", + "Ġbl uff", + "Sh ares", + "Ġfavor ing", + "El izabeth", + "Ãį Ãį", + "Ġr anger", + "77 2", + "ĠAr che", + "h ak", + "ĠGen etics", + "ĠF EMA", + "Ġev olves", + "Ġest e", + "ĠP ets", + "ĠM é", + "ĠInterest ing", + "ĠCanter bury", + "ch apter", + "ĠStar fleet", + "Sp anish", + "Ġdraw back", + "ĠNor wich", + "9 70", + "n orth", + "ag anda", + "Ġtransform ative", + "ram ids", + "bi ology", + "ad ay", + "Ġpropag ation", + "ĠGam ma", + "ĠDen ise", + "ĠCalcul ator", + "ent imes", + "ĠB ett", + "Ġapp endix", + "ĠHD D", + "AK ING", + "Ġst igmat", + "Ġhol ster", + "Ġord inarily", + "Ch ance", + "ĠCont rary", + "Ġad hesive", + "Ġgather s", + "6 12", + "re au", + "ony ms", + "ew ays", + "Ġindu ces", + "Ġinterchange able", + "se m", + "Wh it", + "Ġtr ance", + "Ġincorpor ation", + "ĠExt ras", + "Fin ancial", + "Ġawkward ly", + "ĠStur geon", + "ĠH Y", + "Norm ally", + "ĠEnd ing", + "ĠAss ist", + "enc rypted", + "Ġsub jug", + "Ġn os", + "Ġfan atic", + "C ub", + "C U", + "?\" .", + "Ġirre versible", + "å Ĥ", + "03 1", + "ĠH AR", + "sp read", + "ul ia", + "= $", + "Sc ope", + "L ots", + "Ġlif estyles", + "ol on", + "Ġf eds", + "Ġcongrat ulate", + "web kit", + "Ġindist inguishable", + "ĠSw ing", + "Ġcommand ments", + "qu ila", + "ab ella", + "m ethyl", + "ann abin", + "Ġo vere", + "Ġlob ster", + "ĠQU EST", + "ĠCONT IN", + "bern atorial", + ":::: ::::", + "ĠTra ve", + "ĠSam oa", + "AN I", + "75 2", + "Ð ´", + "userc ontent", + "ĠMod erate", + "y eah", + "ĠK itt", + "Ġwe e", + "Ġstuff ing", + "ĠInter vention", + "ĠD ign", + "Ġware houses", + "ĠF iji", + "Ġpel lets", + "Ġtake away", + "ĠT ABLE", + "ĠClass ical", + "col lection", + "Ġland fall", + "ĠMus cle", + "Ġsett les", + "ĠAD V", + "Ġ3 44", + "L aura", + "Ġf ared", + "ĠPart ial", + "4 36", + "oss ibility", + "ĠD aly", + "ĠT arant", + "ĠFu ji", + "am l", + "c ence", + "55 1", + "ĠProced ures", + "ĠO CD", + "ĠU D", + "t in", + "Q UI", + "ach o", + "4 38", + "Ġgl itches", + "Ġenchant ment", + "Ġcalcul ates", + "IR O", + "ĠH ua", + "alys es", + "ĠL ift", + "um o", + "Ġle apt", + "Ġhypothes ized", + "ĠGust av", + "it ans", + "VERS ION", + "æ ł", + "Rog er", + "Ġr and", + "ĠAd apter", + "Ġ3 31", + "ĠPet ition", + "k ies", + "M ars", + "Ġunder cut", + "ze es", + "ĠLy ons", + "ĠDH CP", + "Miss ing", + "Ġretire es", + "Ġins idious", + "el i", + "> )", + ". ãĢį", + "Ġfinal ists", + "ĠA ure", + "Ġacc user", + "Ġwas tes", + "ĠY s", + "ĠL ori", + "Ġconstitu encies", + "Ġsupp er", + "Ġmay hem", + "or ange", + "Ġmis placed", + "Ġmanager ial", + "Ġex ce", + "ĠCL I", + "Ġprim al", + "ĠL ent", + "Cry stal", + "h over", + "ĠN TS", + "end um", + "Ġd w", + "ĠAl c", + "n ostic", + "Ġpres erves", + "ĠTs arnaev", + "Ġtri pled", + "rel ative", + "Arc ade", + "k illing", + "ĠW EEK", + "ĠH anna", + "D ust", + "Com pleted", + "ģ «", + "Ġappro ves", + "ĠSur f", + "ĠLuther an", + "ven ants", + "Ġrobber ies", + "we ights", + "soft ware", + "at ana", + "ug al", + "Ġgrav y", + "ĠC ance", + "OLOG Y", + "ly ak", + "Ton ight", + "Ġunve il", + "Ġ19 04", + "ĠMin ion", + "ent ious", + "st ice", + "pack ages", + "ĠG EAR", + "Ġg ol", + "ĠHutch inson", + "ĠProf ession", + "ĠG UN", + "ĠDiff erence", + "ĠTsuk uyomi", + "ĠLes bian", + "6 70", + "Ġfug itive", + "ĠPlan etary", + "-------------------------------- ------------------------", + "Ġacc rued", + "Ġch icks", + "Ġsto pp", + "Ġblock ers", + "C od", + "Ġcomment ers", + "ĠSomew here", + "ĠPhot ographer", + "the me", + "Ġmay oral", + "w u", + "Ġanten nas", + "Ġrev amped", + "ĠSubject s", + "it é", + "im ura", + "Ġentr ances", + "liter ally", + "Ġten ets", + "ĠO MG", + "ĠMP H", + "ĠDon key", + "ĠOff ense", + "Ġ\" +", + "Sn ap", + "ĠAF B", + "Ġan imate", + "ĠS od", + "His panic", + "Ġinconsist ency", + "D b", + "F Y", + "Ex port", + "Ġa pe", + "Ġpear l", + "ib el", + "ĠPAC s", + "Ġ{ \\", + "Ġact u", + "ĠHS BC", + "camp us", + "Ġpay off", + "Ġde ities", + "ĠN ato", + "ou ple", + "Ġcens ored", + "ĠCl ojure", + "Ġconf ounding", + "en i", + "Ġreck on", + "op he", + "Ġspot ting", + "Ġsign ifies", + "Ġprop el", + "Ġfest ive", + "S uggest", + "Ġpled ging", + "ĠB erman", + "Ġrebell ious", + "Ġovershadow ed", + "Ġinfiltr ated", + "j obs", + "67 2", + "Ġscal able", + "Ġdomin ion", + "ĠNew foundland", + "ĠMead ow", + "Ġpart itions", + "AM I", + "Ġsupplement ary", + "str ument", + "Ġhair y", + "Ġperpet uate", + "Ġnuts hell", + "ĠPot ato", + "ĠHob bit", + "Ġcur ses", + "Flo at", + "Ġquiet er", + "Ġfuel ing", + "Ġcaps ules", + "ĠL ust", + "ĠH aunted", + "Exec utive", + "Ġchild birth", + "G re", + "Ġrad iant", + "å İ", + "Ġm alls", + "Ġin ept", + "ĠWarrant y", + "Ġspect ator", + "E h", + "t hens", + "Ġculmin ating", + "æ ©", + "ary a", + "ãĤ ®", + "ilit arian", + "ĠOR IG", + "ĠSp ending", + "pt ives", + "ĠS iren", + "ĠRec ording", + "ay ne", + "Ġv im", + "Ġspr ang", + "T ang", + "ĠM FT", + "mor ning", + "ĠWe ed", + "m peg", + "cess ion", + "ĠCh ung", + "7 30", + "w arning", + "56 2", + "handed ly", + "P oor", + "P olitics", + ": #", + "Ġp ian", + "Ġfec es", + "ĠDocument ation", + "Ġban ished", + "Ġ3 99", + "ĠAR C", + "Ġhe inous", + "J ake", + "ĠAm ir", + "way ne", + "v re", + "os henko", + "Ġnotebook s", + "Ġfound ational", + "Ġmarvel ous", + "ixt ape", + "Ġwithdraw als", + "Ġh orde", + "ĠD habi", + "is able", + "ĠK D", + "Ġcontag ious", + "ĠD ip", + "ĠAr rows", + "Ġpronoun s", + "Ġmorph ine", + "ĠB US", + "68 2", + "Ġk osher", + "fin ished", + "ĠInstr uments", + "Ġf used", + "yd en", + "ĠSal mon", + "F ab", + "aff ected", + "K EN", + "C ENT", + "Dom ain", + "Ġpoke mon", + "ĠDr inking", + "G rowing", + "ĠInvestig ative", + "ĠA ether", + "em i", + "Ġtabl oid", + "Ġrep ro", + "ĠNot withstanding", + "ĠBers erker", + "Ġdram as", + "Ġclich é", + "Ġb ung", + "ĠU RI", + "ĠD os", + "0 44", + "Ġpast ors", + "Ġl s", + "Ġac rylic", + "aun ts", + "Ed ward", + "Ġmajor ities", + "B ang", + "Ġfield ing", + "ĠRepl acement", + "ĠAl chemy", + "pp ard", + "ĠRome o", + "ĠSan ct", + "ĠLav rov", + "ib ble", + "Inst ruct", + "Ġimp ractical", + "ĠPlay boy", + "ce phal", + "Ġsw aps", + "Ġk an", + "ĠThe o", + "Ġillust rating", + "Ġdismant led", + "ĠTrans gender", + "ĠG uth", + "UG H", + "Ġtriumph ant", + "Ġencomp ass", + "Ġbook mark", + "udd in", + "j er", + "Ġpred icate", + "ES H", + "Ġwhen ce", + "ĠAB E", + "Ġnon profits", + "Se qu", + "Ġdi abetic", + "Ġp end", + "Ġheart felt", + "sh i", + "Ġinter acts", + "ĠTele com", + "Ġbombard ment", + "dep ending", + "ĠLow ry", + "ĠAd mission", + "ĠBl ooming", + "ust ration", + "ene gger", + "B rew", + "Ġmol ten", + "ĠNer d", + "P IN", + "âĸ Ģ", + "ave ment", + "Ġtou red", + "Ġco efficients", + "ĠTray von", + "ans son", + "Ġsand y", + "t old", + "fl ows", + "Ġpop ulous", + "ĠT inder", + "ĠBl iss", + "R achel", + "Min imum", + "Ġcontest ant", + "ĠRed uce", + "ĠMor se", + "ĠGrass ley", + "ĠClick er", + "Ġexp r", + "Ġs incerity", + "Ġmar qu", + "Ġelic it", + "ĠPro position", + "ĠDemon ic", + "Ġtac os", + "G reek", + "Ġpost war", + "Ġin sofar", + "ĠP ork", + "Ġ35 2", + "doctor al", + "walk ing", + "Ġmid term", + "ĠSam my", + "sight ed", + "ĠTR ANS", + "ic i", + "AL D", + "ĠUS L", + "ĠF ISA", + "ĠAm pl", + "ĠAlex andra", + "ine lli", + "Tr ain", + "Ġsign ify", + "ĠVers us", + "Ġob fusc", + "Ġk h", + "Ġagg ro", + "ĠRen ault", + "Ġ3 48", + "5 18", + "ox icity", + "0 22", + "ĠTw ist", + "Ġgoof y", + "D ynamic", + "Ġbrief ings", + "m ight", + "8 99", + "Ġderog atory", + "T ro", + "Ġfor ging", + "ĠKor an", + "ĠMar ried", + "ĠBuc s", + "Ġpal ate", + "ĠCon version", + "m able", + "4 13", + "Ġ( _", + "Ġs iph", + "ĠN EO", + "col lege", + "Ġmarg inally", + "Ġfl irt", + "ĠTra ps", + "ĠP ace", + "é »Ĵ", + "Ġgoalt ender", + "Ġforb ids", + "Ġcler ks", + "ĠT ant", + "ĠRobb ins", + "ĠPrint ing", + "Ġpremie red", + "Ġmagn ification", + "ĠT G", + "ĠR ouse", + "ĠM ock", + "odynam ics", + "Ġpre clude", + "ism o", + "ĠPul itzer", + "Ġaval anche", + "ĠK odi", + "rib une", + "ĠL ena", + "Elect ric", + "Ġref inery", + "Ġend owed", + "Ġcounsel ors", + "Ġd olphin", + "ĠM ith", + "Ġarm oured", + "hib ited", + "Beg in", + "ĠP W", + "O il", + "ĠV or", + "ĠShar if", + "ĠFraz ier", + "est ate", + "Ġj ams", + "Pro xy", + "Ġband its", + "ĠPresbyter ian", + "ĠPrem iere", + "t iny", + "ĠCru el", + "Test ing", + "Ġhom er", + "ĠV ERS", + "ĠPro l", + "ĠDep osit", + "ĠCoff in", + "Ġsemin ars", + "Ġs ql", + "ĠDef endants", + "Altern atively", + "ĠR ats", + "ç «", + "ethy st", + "' >", + "Ġiss uer", + "58 9", + "Ġch aired", + "ĠAccess ories", + "man ent", + "Ġmar row", + "ĠPrim ordial", + "C N", + "Ġlimit less", + "ĠCarn age", + "Ġund rafted", + "q v", + "IN ESS", + "on ew", + "Ġco hesion", + "98 7", + "Ġne cks", + "Ġfootball er", + "ĠG ER", + "Ġdetect able", + "ĠSupport ing", + "ĠCS V", + "oc ally", + "k Hz", + "Ġund e", + "Ġsh one", + "Ġbud ding", + "tra k", + "Stand ing", + "ĠStar craft", + "ĠKem p", + "Ben ch", + "Ġthw arted", + "ĠGround s", + "ath i", + "L isa", + "Dial og", + "ĠS X", + "V ision", + "Ġingen ious", + "Ù IJ", + "Ġfost ering", + "ĠZ a", + "ĠIn gram", + "Ġ\" @", + "N aturally", + "6 16", + "0 35", + "ĠF AC", + "H mm", + "55 4", + "Ġacceler ator", + "ĠV end", + "Ġsun screen", + "Ġtuber culosis", + "rav iolet", + "ĠFunction al", + "ĠEr rors", + "ed ar", + "19 66", + "ĠSpect re", + "ĠRec ipes", + "88 5", + "ĠM ankind", + "L iverpool", + "Ġ| --", + "Ġsubst itutes", + "ĠX T", + "w ired", + "Ġinc o", + "ĠAf gh", + "E va", + "ic c", + "S ong", + "K night", + "Ġdilig ently", + "ĠBroad cast", + "A id", + "Ġaf ar", + "ĠH MS", + "aton in", + "ĠGr ateful", + "Ġfire place", + "ĠOm ni", + "e uro", + "ĠF RE", + "ĠSh ib", + "ĠDig est", + "t oggle", + "Ġheads ets", + "Ġdiff usion", + "ĠSqu irrel", + "ĠF N", + "Ġdark ened", + "out her", + "Ġsleep s", + "ĠX er", + "gun s", + "Ġset ups", + "Ġpars ed", + "Ġmamm oth", + "ĠCur ious", + "g ob", + "ĠFitz patrick", + "ĠEm il", + "im ov", + "........ .....", + "ĠB enny", + "Second ly", + "Ġheart y", + "Ġcons on", + "st ained", + "Ġgal actic", + "cl ave", + "Ġplummet ed", + "Ġp ests", + "Ġsw at", + "Ġrefer rals", + "ĠLion el", + "h oly", + "Ġunder dog", + "ĠSl ater", + "ĠProv ide", + "ĠAm ar", + "ress or", + "å Į", + "ong a", + "Ġtim id", + "Ġp iety", + "ĠD ek", + "Ġsur ging", + "az o", + "Ġ6 10", + "Ġdes ks", + "ĠSp okane", + "ĠAn field", + "Ġwars hips", + "ĠCob ra", + "Ġar ming", + "clus ively", + "ĠBad ge", + "ag ascar", + "ĠPR ESS", + "ĠMcK enzie", + "ĠFer dinand", + "burn ing", + "Af ee", + "Ġtyr ann", + "ĠI w", + "ĠBo one", + "100 7", + "ĠRe pt", + "Ċ Âł", + "Ġcar avan", + "ĠD ill", + "ĠBundes liga", + "Ch uck", + "Ġheal er", + "ãĥ¼ãĥ Ĩ", + "ĠH obby", + "Ġneg ate", + "Ġcrit iques", + "section al", + "mop olitan", + "Ġd x", + "Ġouts ourcing", + "ĠC ipher", + "t ap", + "Sh arp", + "Ġup beat", + "Ġhang ar", + "Ġcru ising", + "ĠNi agara", + "Ġ3 42", + "ill us", + "ĠS v", + "Ġsubt itles", + "Ġsqu ared", + "Ġbook store", + "Ġrevolution aries", + "ĠCarl ton", + "ab al", + "Ut ah", + "Ġdesp ise", + "ĠU M", + "cons ider", + "aid o", + "Ġc arts", + "ĠT urtles", + "Tr aining", + "Ġhonor ary", + " ¢", + "Ġtri angles", + "4 22", + "Ġreprint ed", + "Ġgrace ful", + "ĠMong olia", + "Ġdisrupt ions", + "ĠB oh", + "Ġ3 49", + "Ġdr ains", + "Ġcons ulate", + "Ġb ends", + "Ġm afia", + "ur on", + "ĠF ulton", + "m isc", + "Ġren al", + "Ġin action", + "ck ing", + "Ġphot ons", + "Ġbru ised", + "ĠC odes", + "og i", + "Ġn ests", + "ĠLove ly", + "ĠLib re", + "ĠD aryl", + "Ġ# ##", + "S ys", + ". ,\"", + "Ġfree zes", + "est ablishment", + "and owski", + "Ġcum bers", + "ĠSt arg", + "ĠBom bs", + "Ġleg ions", + "Ġhand writing", + "Ġgr un", + "ĠC ah", + "sequ ent", + "Ġm oth", + "ĠMS M", + "Ins ert", + "F if", + "Ġmot el", + "Ġdex ter", + "ĠB ild", + "hearted ly", + "Ġpro pe", + "ĠText ure", + "ĠJ unction", + "ynt hesis", + "oc ard", + "ĠVer a", + "ĠBar th", + "Ġμ g", + "Ġl ashed", + "Ġ35 1", + "ĠZ amb", + "ĠSt aples", + "ĠCort ex", + "ĠCork er", + "Ġcontinu um", + "ĠWR ITE", + "unt a", + "rid or", + "Ġde ems", + "0 33", + "ĠG OLD", + "p as", + "Ġrep ressive", + "ãĥĨ ãĤ£", + "Ġbaff led", + "Sc ar", + "Ġc rave", + "Ġ ______", + "Ġentrepreneurs hip", + "ĠDirector ate", + "Ġ' [", + "Ġv ines", + "Ġasc ended", + "ĠGR OUP", + "ĠGood bye", + "Ġdo gged", + "ãĥ´ ãĤ¡", + "Man ufact", + "Ġunimagin able", + "ri ots", + "ier rez", + "Ġrel ativity", + "ĠCraft ing", + "ra ught", + "ud en", + "c ookie", + "Ġassass ins", + "Ġdissatisf ied", + "ac ci", + "Ġcondu it", + "Sp read", + "ĠR ican", + "n ice", + "izz le", + "Ġsc ares", + "ĠWH Y", + "ph ans", + "5 35", + "Ġprot racted", + "ĠKrist en", + "5 36", + "ĠSc rib", + "ĠNe h", + "Ġtwent ies", + "Ġpredic ament", + "Ġhandc uffs", + "Ġfruit ful", + "ĠU L", + "ĠLud wig", + "Ġatt est", + "ĠBre aker", + "Ġbi ologically", + "ĠDeal er", + "Ġrenov ations", + "f w", + "ess en", + "Al ice", + "ĠHen ri", + "Ġun ilaterally", + "ĠS idd", + "h ai", + "ĠSt retch", + "S ales", + "Ġcumbers ome", + "ĠJ avier", + "Ġtrend y", + "Ġrot ting", + "ĠChall enges", + "Ġscra ps", + "Ġfac ets", + "ĠVer onica", + "ĠVer ge", + "ĠS ana", + "Al ien", + "ĠR ih", + "Ġrad ial", + "ect ar", + "Ġ6 30", + "cl i", + "Mar ie", + "Ġwild fire", + "ĠCat o", + "h ander", + "Ġwait ress", + "Ġch ops", + "ĠS ECTION", + "Ġblunt ly", + "ĠCat alog", + "n ian", + "stud y", + "Ġpat rolling", + "ĠT enth", + "nex us", + "ĠN ON", + "op sy", + "Ġsc athing", + "s ie", + "Ġdeterior ated", + "V B", + "Naz is", + "Ġdep ictions", + "Ġauthent icated", + "ĠCon ce", + "k rit", + "Ġpromul g", + "ĠL ONG", + "U FC", + "ĠVis itors", + "ĠRec all", + "Ġrehab ilit", + "ĠSL I", + "Ġglac ier", + "ĠB ite", + "Ġ50 3", + "Ġvom it", + "Ġfer mented", + "ĠKh alid", + "Ġgrad ed", + "ĠMag icka", + "ĠIch igo", + "power ful", + "ic ators", + "75 3", + "Ġsh rew", + "Ġ35 6", + "Ġlegal izing", + "Ġall otted", + "ĠArch demon", + "ith ing", + "igg urat", + "V OL", + "Le od", + "Ġo ily", + "Ġindu cing", + "Ġamy gdala", + "Ġadm ins", + "ĠAcqu isition", + "C AN", + "Ġsche matic", + "Ġmo an", + "ĠCamer oon", + "Ġt ink", + "Ġmer ry", + "Ġbutter flies", + "ĠGo ff", + "Ġworks pace", + "ĠCor ona", + "Ġj avascript", + "ĠD olphin", + "ĠCant or", + "4 64", + "to e", + "AP S", + "ĠAg ing", + "Ġpadd ed", + "ĠZ heng", + "ĠHe ld", + "Ġest ranged", + "Ġ7 70", + ". }", + "ĠDun ham", + "Ġsm okes", + "Ġcap itals", + "und ai", + "Sh in", + "ĠFound ing", + "Ġent itle", + "Ġcenter piece", + "D iscover", + "Ġthere to", + "al ert", + "ĠN ou", + "ĠAnaly st", + "l c", + "F H", + "FI ELD", + "ĠP OV", + "gr ay", + "Ġar cs", + "ĠH OT", + "Ġr s", + "Ġoblig atory", + "ĠArchitect s", + "ĠS ven", + "ĠF EC", + "0 200", + "Christ mas", + "ĠAlban ia", + "rat om", + "58 7", + "Ġhard ships", + "Ġaut os", + "ĠCharg es", + "Ġap es", + "Ġ3 76", + "wal let", + "Ġintox ication", + "Ġgobl in", + "Ġ5 70", + "++++++++ ++++++++", + "ĠYel p", + "ĠMag netic", + "ĠBr iggs", + "R ail", + "Ġspawn s", + "ĠW iggins", + "Ġshowc ased", + "Ġres orted", + "ub en", + "Ġwh ipping", + "Ġim itate", + "Ġdigest ion", + "ĠUS PS", + "ĠG est", + "Ġye a", + "ĠT ight", + "ind al", + "ic as", + "` .", + "C AST", + "'' ;", + "ĠF et", + "opath ic", + "In valid", + "Ġregrett ed", + "Ġbro ccoli", + "ĠSc ores", + "e ve", + "Ġpost ings", + "Ġaccum ulating", + "Ġneed less", + "elf th", + "Ġmay ors", + "Ġsc rib", + "Ġanecd otes", + "Ġbot ched", + "ĠRib bon", + "ĠConstant ine", + "i uses", + "ess es", + "Ġdev ise", + "Comp ared", + "Ġp udding", + "Ġg arg", + "Ġev oke", + "79 7", + "Ġdet ox", + "9 09", + "ĠPie ces", + "ĠMcC artney", + "Ġmet ast", + "ĠK rypt", + "P OR", + "Ġt ending", + "ĠMerch ants", + "Pro of", + "ĠV arg", + "ĠPort able", + "ãĥ¼ãĥĨ ãĤ£", + "B rain", + "25 00", + "Ġfol iage", + "Ø ¹", + "Ġment ors", + "ĠA ires", + "Ġminimal ist", + "Ġing ested", + "ĠTro jan", + "ĠQ ian", + "inv olved", + "0 27", + "Ġer oded", + "RA FT", + "Ġbl urry", + "M ob", + "Ġbuff et", + "ĠFn atic", + "ae a", + "KN OWN", + "ĠIn it", + "s afety", + "en um", + "ACT ION", + "ĠCrus her", + "ĠD ates", + "Ġ ................", + "c alling", + "ak ov", + "Ġvent ured", + "Ġ5 55", + "au ga", + "H art", + "ĠA ero", + "M AC", + "Ġthin ly", + "Ġar ra", + "ST ATE", + "ild e", + "ĠJac qu", + "ĠFem ales", + "Ġthe orem", + "Ġ3 46", + "Ġsmart est", + "ĠPU BLIC", + "ĠK ron", + "ĠB its", + "ĠV essel", + "ĠTele phone", + "Ġdec ap", + "Ġadj unct", + "ĠS EN", + "mer ga", + "Ġred acted", + "Ġpre historic", + "Ġexplan atory", + "ĠRun s", + "ĠUtt ar", + "ĠM anny", + "ĠAUTH OR", + "ĠUnle ashed", + "ĠBow ling", + "be ans", + "79 3", + "Ġunivers es", + "Ġsens it", + "ĠK ung", + "re peat", + "ctr l", + "Ġp aced", + "Ġfull er", + "Cl ock", + "Ġrec omb", + "ĠF aul", + "ĠB unker", + "Ġpool ed", + "Ġan a", + "ĠM outh", + "LL OW", + "hum ane", + "Ġbull do", + "ĠMicha els", + "f am", + "Ġwreck ed", + "Ġport rays", + "ĠWh ale", + "ĠH es", + "Ġguess es", + "ĠBrow se", + "ĠL APD", + "Ġconsequ ential", + "ĠInn ocent", + "ĠD RAG", + "Ġtrans gress", + "ĠO aks", + "Ġtri via", + "ĠRes on", + "ĠA DS", + "-- +", + "ĠT oll", + "Ġgrasp ing", + "ĠTHE M", + "ĠT ags", + "ĠCon clusion", + "Ġpract icable", + "Ġho op", + "Ġunintention ally", + "Ġign ite", + "ĠM ov", + "ur ized", + "le hem", + "Ter min", + "Ġcolour ful", + "ĠLin ear", + "ĠEll ie", + "G y", + "Ġman power", + "Ġj s", + "Ġem oji", + "ĠSHAR ES", + "_ .", + "0000 7", + "Ġsophistic ation", + "Ġunders core", + "Ġpract ise", + "Ġbl ob", + "op ens", + "Uk raine", + "Ke eping", + "Y C", + "J R", + "ult imate", + "Cl aim", + "Ġautom obiles", + "99 3", + "ste el", + "Ġpart ing", + "ĠL ank", + "... ?", + "Ġ38 5", + "Ġremem brance", + "Ġe ased", + "Ġcov ari", + "ĠS ind", + "Effect ive", + "Ġdisse mination", + "ĠMo ose", + "ĠCl apper", + "br ates", + "App ly", + "Ġinv is", + "Ġwors ened", + "âĢĶ -", + "Ġlegisl ator", + "ĠL ol", + "ĠRow e", + "Ġdealers hip", + "um ar", + "id ences", + "Ġinvestig ates", + "Ġc ascade", + "Ġbid der", + "ĠB EN", + "Iron ically", + "Ġpres iding", + "Ġd ing", + "Ġcontrad icted", + "Ġshut s", + "ĠF IX", + "Ġ3 66", + "Dist rict", + "Ġsin ful", + "ĠChar isma", + "o ops", + "Ġtot ality", + "Ġrest itution", + "ĠOpt imus", + "ĠD ah", + "Ġcl ueless", + "urn ed", + "Ġnut rit", + "Ġland owners", + "Ġfl ushed", + "Ġbroad en", + "m ie", + "Ġprint ln", + "Ġn ig", + "ĠCorp us", + "J en", + "Ġprot o", + "ĠWik imedia", + "ĠPal o", + "C OR", + "Ġstory lines", + "Ġevangel icals", + "ĠDar rell", + "Ġrot or", + "ĠH W", + "sk illed", + "ery l", + "Ġbe gg", + "ĠBl umenthal", + "Ġwe aving", + "Ġdown wards", + "ĠJack et", + "ĠANG EL", + "Te chnology", + "Ġes oteric", + "alde hyde", + "Ġfur iously", + "Ġforeign er", + "We ak", + "CH O", + "ĠH ound", + "Exper ience", + "ĠPlay station", + "ĠM IA", + "ĠU ng", + "cl oth", + "ag all", + "Ġcal ming", + "iz ens", + "St ruct", + "ĠW itches", + "ĠCeleb ration", + "Ġ........ ......", + "pt roller", + "ĠTC U", + "Ġb unny", + "ãĥ į", + "ut orial", + "Ġup scale", + "ĠSt a", + "ĠCol ossus", + "Ġchlor ide", + "ĠZ ac", + "ĠRe asons", + "ĠBrook ings", + "ĠWH ITE", + "][ /", + "ĠL ose", + "9 05", + "Ġunders ide", + "ern els", + "Ġv ape", + "do zen", + "upp et", + "ĠST OP", + "mat ical", + "ĠStat ements", + "hed dar", + "P AC", + "Custom er", + "Ġmem os", + "ĠP J", + "end ars", + "ĠLim its", + "l augh", + "Ġstabil ized", + "ĠALE C", + "Y A", + "Up grade", + "al am", + "Ġtechn o", + "Ġan ew", + "fore seen", + "Ġcolleg iate", + "ĠPy ro", + "ĠD ism", + "Ġfront line", + "Ġammon ia", + "I U", + "Qu ite", + "John ny", + "ass in", + "G OP", + "ĠSt yles", + "ĠSovere ign", + "acter ial", + "5 49", + "ĠR IP", + "ĠL ists", + "Ġ3 64", + "ĠRece p", + "s ocket", + "ĠByr d", + "ĠCand le", + "An cient", + "Ġappell ant", + "en forcement", + "ace a", + "ans ki", + "Ġold s", + "88 6", + "Ġsl urs", + "Ġem pires", + "Ġbuck le", + "Ġalien ation", + "ĠAber deen", + "Ġunic orn", + "Ġoverr iding", + "ĠL X", + "pp a", + "Ġdesp ised", + "ĠB ugs", + "ĠB ST", + "S outhern", + "5 33", + "Ġhall mark", + "ĠPost er", + "Ġstem med", + "Ġprincip als", + "ĠT ECH", + "ĠSand wich", + "It aly", + "Ġche esy", + "ĠSet TextColor", + "ĠProt ective", + "ĠC ohn", + "J O", + "apt op", + "Re ason", + "Lead er", + "ĠUnder stand", + "ĠFr idays", + "ĠContin uous", + "Ġcl ipping", + "ĠR ye", + "Ġber th", + "tim er", + "ann is", + "re act", + "Ġbuff alo", + "ĠPar as", + "Ġ6 55", + "Ġpres ided", + "ĠSun rise", + "Ġve ts", + "Ġcl oves", + "ĠMcC ull", + "Stre ngth", + "G AN", + "Ġill iter", + "ĠPric ing", + "l é", + "Ġresist or", + "Ġbr un", + "ĠSuff olk", + "Ñ ĭ", + "ĠL iver", + "Re leased", + "Ġwhat s", + "8 60", + "ĠMe asures", + "Ġden ouncing", + "ĠRy zen", + "Ġsou ven", + "Ġcareg ivers", + "ch ini", + "ĠScar lett", + "Ġt rough", + "Cong ratulations", + "Ġtax is", + "ĠTrad ition", + "j it", + "Ġtable top", + "Ġhither to", + "Ġdis information", + "off ensive", + "h ra", + "ĠDISTR ICT", + "Ġcompl icate", + "chen ko", + "ĠRecon struction", + "Ġpalp able", + "Ġa usp", + "Ġ4 28", + "Ġshowc ases", + "ĠPublic ation", + "know ledge", + "inn on", + "4 19", + "Ġretri eval", + "and ers", + "Ġref ute", + "Ġinqu ired", + "g ur", + "Ġneg ativity", + "Ġcons erve", + "Ġafter life", + "Ġpres upp", + "ĠGill espie", + "Ġm t", + "ĠD N", + "T ap", + "Ġper pend", + "ĠS my", + "does n", + "Ġsp illing", + "Ġhyp ers", + "K ate", + "® ,", + "ke pt", + "ĠP owered", + "Ġj a", + "ĠK lux", + "ard e", + "ab an", + "Ġ4 44", + "Ġflatt ened", + "ĠImprove ments", + "urg a", + "ĠK und", + "Ġins cribed", + "Ġfac ult", + "Ġunpre pared", + "ĠCons umers", + "Ġsatisf ies", + "Ġpul monary", + "Ġinf iltration", + "Ġex ternally", + "Ġcongrat ulations", + "ag han", + "Ġair liner", + "Ġfl ung", + "Ġfly ers", + "G D", + "Ġsnipp ets", + "Ġrec ursive", + "Ġmaster ing", + "L ex", + "Ġovert ly", + "v g", + "Ġluck ily", + "Ġenc ro", + "ĠLanc et", + "ĠAbyss al", + "function al", + "Ġs ow", + "Ġsqu id", + "Ġnar ration", + "Ġn aughty", + "ĠHon our", + "ĠSpart ans", + "Ġsh atter", + "ĠTac oma", + "ĠCal ories", + "ĠR aces", + "Sub mit", + "Ġpurpose fully", + "w av", + "ĠY ok", + "F est", + "ĠG err", + "Met ro", + "Ġit iner", + "f amous", + "Ġ\" {", + "in line", + "was her", + "Iss ue", + "ĠCL IENT", + "oz o", + "Vers ions", + "7 25", + "ĠGl ock", + "Ġshield ed", + "ĠPC R", + "ENC Y", + "ĠWe ld", + "ĠSim pl", + "Ġredirect ed", + "ĠK ham", + "Ġ( >", + "Ġlab ou", + "Ġdi apers", + "ss l", + "Ġcell ar", + "organ isms", + "ore sc", + "ĠBer ks", + "did n", + "Sh ipping", + "C hest", + "Ġund one", + "Ġmillion aire", + "Ġc ords", + "ĠYoung er", + "appropri ately", + "Ġsequ els", + "u ve", + "ant icipated", + "Ġle wd", + "ĠSh irt", + "ĠDmit ry", + "V eter", + "Ġsl aying", + "ĠY ar", + "Ġcompl ication", + "I owa", + "ĠEric a", + "ĠBL M", + "g irlfriend", + "b odied", + "6 26", + "19 63", + "Ġintermedi ary", + "Ġcons olation", + "M ask", + "ĠSi em", + "ow an", + "Beg inning", + "Ġfix me", + "Ġculmin ated", + "Ġcon duc", + "ĠVolunte er", + "Ġpos itional", + "Ġgre ets", + "ĠDefin itions", + "Ġthink er", + "Ġingen uity", + "Ġfresh men", + "ĠMom ents", + "Ġ35 7", + "ate urs", + "ĠFed Ex", + "s g", + "69 4", + "Ġdwind ling", + "ĠBO X", + "sel age", + "Ġt mp", + "Ġst en", + "ĠS ut", + "Ġneighbourhood s", + "Ġclass mate", + "f ledged", + "Ġleft ists", + "Ġclim ates", + "ATH ER", + "ĠScy the", + "ul iffe", + "Ġs ag", + "Ġho pped", + "ĠF t", + "ĠE ck", + "ĠC K", + "ĠDo omsday", + "k ids", + "Ġgas ped", + "Ġmon iker", + "ĠL od", + "ĠC FL", + "t ions", + "r ums", + "fol ios", + "Ġm d", + "Ġunc anny", + "Ġtrans ports", + "ĠLab rador", + "Ġrail ways", + "Ġappl iance", + "ĠCTR L", + "æ Ģ", + "Pop ulation", + "ĠConfeder acy", + "Ġunb earable", + "Ġdors al", + "ĠIn form", + "op ted", + "ĠK ILL", + "Mar x", + "Ġhypoc ritical", + "q us", + "ĠN umerous", + "ĠGeorg ian", + "ĠAmbro se", + "ĠL och", + "Ġgu bernatorial", + "ĠX eon", + "ĠSupp orts", + "ens er", + "ee ly", + "ĠAven ger", + "19 65", + "Ar my", + "Ġju xtap", + "Ġcho pping", + "ĠSpl ash", + "ĠS ustainable", + "ĠFin ch", + "Ġ18 61", + "ict ive", + "at meal", + "ĠG ohan", + "Ġlights aber", + "ĠG PA", + "ug u", + "ĠRE PL", + "vari able", + "Ġher pes", + "Ġdesert s", + "ac iously", + "Ġsitu ational", + "week ly", + "ob l", + "Ġtext ile", + "ĠCorn wall", + "Ġcontrace ptives", + "ĠA ke", + "] -", + "ä¹ ĭ", + ": ,", + "ĠW em", + "ĠB ihar", + "Ġ' .", + "Ġbe re", + "Ġanal ogue", + "ĠCook ies", + "Ġtake off", + "Whe el", + "Ġmaj estic", + "Ġcomm uting", + "0 23", + "ĠCor pse", + "ass ment", + "min i", + "Ġgor illa", + "ĠAl as", + "ere e", + "Ġacquaint ances", + "ĠAd vantage", + "Ġspirit ually", + "Ġey ed", + "pm wiki", + "ĠE nder", + "Ġtrans lucent", + "Ġnight time", + "ĠIM AGES", + "5 45", + "ĠK amp", + "ĠFre ak", + "Ġ ig", + "Port land", + "4 32", + "ĠM ata", + "Ġmar ines", + "Ġh ors", + "ater asu", + "ĠAtt ribution", + "Ġ-------- -", + "Ġk ins", + "ĠBEL OW", + "++ +", + "Ġre eling", + "ol ed", + "Ġcl utter", + "ĠRel ative", + "Ġ4 27", + "B US", + "Ġa vert", + "ĠChe ong", + "ĠA ble", + "ĠPry or", + "Develop er", + "Ġen cyclopedia", + "ĠUSA F", + "ĠG arry", + "Sp ain", + "Bl ocks", + "Ġexp osition", + "ĠGamer Gate", + "W OR", + "Ġstockp ile", + "Ġclot hed", + "ĠT one", + "ĠR ue", + "t umblr", + "Ġtreacher ous", + "Ġf rying", + "Ñ Į", + "ĠS ph", + "Ġrest raints", + "Ġemb odies", + "ĠG es", + "S afety", + "Ġnegoti ators", + "min ing", + "ĠAppalach ian", + "L OS", + "ĠJenn a", + "Ġpass ers", + "ç ĭ", + "sn ap", + "Ġshort en", + "creat or", + "Ġinn umerable", + "uther land", + "67 4", + "ĠW OM", + "ĠAs cend", + "ĠArm ory", + "ĠTrans action", + "K ick", + "Ġsuit case", + "day Name", + "Ġwaste ful", + "mar riage", + "ĠMcC abe", + "ite ch", + "ĠO ss", + "Cl osure", + "ĠTreasure r", + "Ġindec ent", + "ĠD ull", + "Ġresid ences", + "19 59", + "ĠS ettlement", + "Ham ilton", + "Ġself ies", + "ĠRank ing", + "ĠBark ley", + "ĠB ore", + "ĠW CS", + "ĠMar itime", + "ĠH uh", + "ĠForest ry", + "Ġcultiv ating", + "ĠBall ard", + "Ġg arrison", + "ĠSD L", + "9 30", + "Ġnas cent", + "Ġirresist ible", + "Ġaw fully", + "\\/ \\/", + "Ġequ ate", + "Ġanthrop ology", + "ĠSylv ia", + "Ġintest ine", + "Ġinnoc uous", + "cess ive", + "ag ra", + "ĠMet roid", + "G rant", + "8 55", + "ģ ĸ", + "Ġ\" _", + "ãĥĥ ãĥī", + "Ġappra isal", + "ĠFred dy", + "04 6", + "Ġ40 6", + "Ġ18 30", + "Ġd ocking", + "St atic", + "Ġp ont", + "ĠVolt age", + "ĠSt ead", + "ĠMort gage", + "ĠJon ah", + "Y L", + "CLASS IFIED", + "Ġas bestos", + "nik ov", + "Ġcoll agen", + "ĠOrb ital", + "P ocket", + "7 99", + "Ġhy brids", + "inc hes", + "Ġinv oice", + "und y", + "Ġinequ alities", + "T rend", + "w ashed", + "B ALL", + "Ġluc id", + "ĠComment ary", + "Ġw itty", + "Br andon", + "Ġbru ising", + "Ġ6 20", + "es cent", + "box ing", + "P OL", + "Ġ3 78", + "R ect", + "Ġlic ences", + "ĠMcG ee", + "p ressed", + "D anny", + "Ġj ammed", + "ord inate", + "Ġle th", + "Ġdistingu ishes", + "ĠYam aha", + "IL S", + "ĠH ume", + "ĠC ategories", + "Rober ts", + "Ch art", + "Ġbeet le", + "ĠGra veyard", + "Ġ($ )", + "o ÄŁ", + "Ġtw ilight", + "are lla", + "á ½", + "Ġbooth s", + "ĠH HS", + "ĠFeld man", + "Ġexcav ation", + "Ġphilosoph ies", + "at ography", + "ĠGar age", + "te chnology", + "Ġunfor gettable", + "Ġver ifying", + "Ġsubord inates", + "E ls", + "Ġne b", + "G aming", + "EN A", + "ĠAchieve ment", + "it ters", + "ĠG abe", + "Ġd umps", + "for cer", + "Ġpo ignant", + "ĠM BA", + "ĠHe idi", + "ime i", + "Ġm ages", + "Ġliber ate", + "Ġcircum cised", + "ĠMer maid", + "ĠMat th", + "t ogether", + "ĠW ichita", + "Ġstore front", + "ĠAd in", + "V II", + "Four th", + "Ġexplore rs", + "W ER", + "Not able", + "Bro ok", + "m ens", + "F aith", + "-------- -", + "ĠJ ou", + "¬ ¼", + "Ġpine apple", + "Ġam alg", + "el n", + "ark able", + "ĠãĤµ ãĥ¼ãĥĨãĤ£", + "ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³", + "Ġov arian", + "ĠE choes", + "Ġhairc ut", + "Ġp av", + "Ġch illed", + "anas ia", + "Ġsty led", + "Ġd ab", + "ni per", + "Ġminister ial", + "ĠD UP", + "T an", + "Ġsul ph", + "ĠD eter", + "ĠBo hem", + "od an", + "Ġeduc ator", + "â ĵĺ", + "sp ir", + "Ch icken", + "ĠE leanor", + "Ġqu i", + "Ġheav iest", + "Ġgrasp ed", + "U RA", + "Ġcro oked", + "Jess ica", + "pro blem", + "Ġpred etermined", + "Ġman iac", + "Ġbreath s", + "ĠLauder dale", + "Ġh obbies", + "y z", + "Cr ime", + "Ġcharism a", + "d L", + "Ġle aping", + "Ġk ittens", + "Ang elo", + "ĠJ ACK", + "ĠSu zanne", + "Ġhal ting", + "ENT ION", + "Ġswall owing", + "ĠEarthqu ake", + "Ġeight eenth", + "ĠN IC", + "ĠIN F", + "ĠCons cious", + "Ġparticular s", + "circ le", + "7 40", + "Ġbene volent", + "Ġ7 47", + "Ġ4 90", + "Ġr undown", + "ĠVal erie", + "ĠB UR", + "Ġcivil isation", + "ĠS chn", + "W B", + "ot ide", + "intern ational", + "Ġj ohn", + "Ġ19 02", + "Ġpe anuts", + "Ġflav ored", + "k us", + "Ġro ared", + "Ġcut off", + "é £", + "Ġorn ament", + "Ġarchitect ures", + "Ġ3 69", + "ol or", + "ĠWild e", + "ĠC RC", + "ĠAdjust ed", + "Ġprov oking", + "land ish", + "Ġrational ity", + "Ġjust ifies", + "Ġdisp el", + "Ġa meric", + "ĠPol es", + "Ø ©", + "Ġen vis", + "ĠD oodle", + "ä½ ¿", + "igs aw", + "auld ron", + "Techn ical", + "T een", + "up hem", + "ĠX iang", + "Ġdetract ors", + "ĠZ i", + "ĠJournal ists", + "Ġconduc ive", + "ĠVolunte ers", + "Ġs d", + "Know ing", + "Ġtrans missions", + "ĠPL AN", + "ĠL IB", + "Ġall uded", + "Ġob e", + "Ġd ope", + "ĠGold stein", + "Ġwavelength s", + "ĠDest ination", + "nd a", + "ug i", + "Ġattent ive", + "ĠLe an", + "ral tar", + "Ġman g", + "mb uds", + "ak ings", + "b ender", + "Ġacc ol", + "Ġcraw led", + "N OW", + "Min nesota", + "Ġflour ished", + "ĠZ up", + "ĠSuper visor", + "ĠOliv ier", + "Ex cellent", + "Ġwid en", + "D one", + "Ġw ig", + "Ġmiscon ceptions", + "Cor p", + "W an", + "Ġvener able", + "ĠNot ably", + "ĠKling on", + "an imate", + "Bo ost", + "ĠS AY", + "miss ing", + "ibli ography", + "mel on", + "Ġpay day", + "Ø ³", + "bo le", + "Ġve iled", + "ĠAl phabet", + "It alian", + "Ġever lasting", + "ĠR IS", + "ĠC ree", + "rom pt", + "Ġh ating", + "Ġgrin ning", + "Ġge ographically", + "OS H", + "Ġwe eping", + "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", + "Ġimpe cc", + "Let ter", + "Ġblo ated", + "PL A", + "ĠFe in", + "Ġper sever", + "Th under", + "Ġa ur", + "ĠR L", + "Ġpit falls", + "âĸ º", + "Ġpredomin ant", + "Ġ5 25", + "7 18", + "AP E", + "7 14", + "Ġfarm land", + "ĠQ iao", + "Ġv iolet", + "ĠBah amas", + "Ġinflic ting", + "ĠE fficiency", + "Ġhome brew", + "Ġundert ook", + "Ġcur ly", + "ĠHard ing", + "man ia", + "59 6", + "Ġtem pered", + "Ġhar rowing", + "ĠP ledge", + "ĠFranken stein", + "è ª", + "M otion", + "Ġpredict ably", + "ĠExpl osion", + "oc using", + "er d", + "col o", + "FF ER", + "Ġback field", + "ĠV IDE", + "ue bl", + "N arr", + "ĠArg ument", + "Ġgen omic", + "Ġbout ique", + "Ġbatt ed", + "ĠB inary", + "Ġg amb", + "ĠRh ythm", + "67 3", + "Ġa float", + "ĠOlymp ia", + "Y ING", + "Ġend if", + "is in", + "Ġwin ters", + "Ġsc attering", + "I v", + "D istance", + "Ġtr u", + "ĠCom fort", + "Ġne xus", + "Ġair flow", + "ĠByz antine", + "p ayers", + "con i", + "ĠB etsy", + "D eal", + "ĠN ug", + "ĠContin ent", + "red ibly", + "Ġoptim izing", + "al beit", + "Ġec static", + "ĠPro to", + "ç ·", + "iv ot", + "âĸ Ħ", + "em p", + "rou nder", + "Ġcl out", + "ĠI ST", + "66 3", + "ĠDoll ars", + "ĠD AC", + "Ġsubsc ribed", + "Ġrehears al", + "Ġam ps", + "ĠSh ang", + "es m", + "Ġspr inkle", + "Ġassail ant", + "ĠO o", + "ĠCoin base", + "T act", + "Ġret ina", + "Ġn uns", + "R ON", + "att o", + "Ġj ug", + "ĠSV G", + "Ġb ikini", + "ĠFI LE", + "ĠFound ers", + "ep ort", + "ĠK P", + "Ġrest ores", + "ĠTh ick", + "Ġash ore", + "Ġappro vals", + "R ender", + "M AG", + "G raham", + "ĠCort ana", + "ãĥ³ ãĤ¸", + "ss h", + "or ians", + "ars ity", + "ĠInsp ired", + "u pper", + "Ġsign alling", + "Ġreb uke", + "Ġfl ares", + "Ġdownt ime", + "Stud ies", + "Ġstagn ation", + "ĠSequ ence", + "Ġgr unt", + "Ġass ures", + "ĠPL A", + "59 2", + "Ġintra ven", + "d epend", + "Sus an", + "ĠManz iel", + "Man ia", + "Cont ract", + "Ġsl ams", + "Ġcult ured", + "Ġcred itor", + "L IST", + "ĠH UM", + "ĠChatt anooga", + "serv ed", + "Ġclo aked", + "ĠF TP", + "p owder", + "ĠSt ella", + "uct ive", + "Ġcheap ly", + "ĠMU CH", + "ĠGalile o", + "Ġsu ites", + "spe ech", + "Ġdeliber ations", + "ĠCh ips", + "« ĺ", + "Bal ance", + "ĠWyn ne", + "ĠAk ron", + "Ass et", + "Ġhon oured", + "Ġed ged", + "Like wise", + "anim ous", + "ĠW age", + "ĠEz ek", + "ad vertisement", + "ĠRT X", + "ĠM AD", + "Ġmigr ating", + "ĠS QU", + "Ġ4 75", + "Ed ited", + "Ġshorth and", + "ĠBas ics", + "Ġcro tch", + "ĠEV EN", + "Ġv m", + "effic iency", + "Ġcal ves", + "ĠF rie", + "ĠBrill iant", + "Ġstri kers", + "Ġrepent ance", + "Ġarter ies", + "r l", + "B ed", + "h ap", + "Ġcrypt ography", + "ĠSab res", + "Ġ4 14", + "vi ks", + "ih ara", + "aps es", + "T alking", + "Ġintertw ined", + "Ġdoc ks", + "Ġalle le", + "ĠArt ifact", + "ĠH IM", + "t orn", + "ç ķ", + "Ġop acity", + "ĠE ly", + "os uke", + "Ġn ipple", + "Ġhand written", + "ĠV K", + "ĠChamber lain", + "ĠLa os", + "ig raph", + "g row", + "Ġtr illions", + "Ġdescend ant", + "ĠSail or", + "as uring", + "Ġce ilings", + "ĠWare house", + "f lying", + "ĠGl ow", + "Ġn ont", + "Ġmiscar riage", + "Ġrig s", + "Ġmin istries", + "Ġelabor ated", + "Ġdel usional", + "ĠHum ane", + "Ġ3 79", + "n ets", + "Ġblack out", + "add ers", + "Ġn p", + "ĠT ire", + "ro sc", + "Ġsub div", + "Ġlink age", + "Ġchron ological", + "ĠHER O", + "Ġres ettlement", + "ĠVin yl", + "Ġpast oral", + "ĠMob il", + "ĠBar bar", + "Co oldown", + "ĠF ritz", + "c riminal", + "re pe", + "Ġbell ig", + "ĠBre ed", + "Ġ4 18", + "Ġsem blance", + "ij k", + "Ġcur tail", + "Ġclin ch", + "cont ained", + "ĠProm pt", + "ast on", + "Ġw i", + "Ġpursu its", + "5 15", + "ĠGl oss", + "Ġfl ips", + "Ġcoup ons", + "Ġcl oning", + "ĠLike ly", + "Rem oved", + "ĠQu artz", + "r ices", + "ĠSpe ars", + "Ġp ious", + "Ġdep reciation", + "ĠD are", + "oun ces", + "am az", + "O nt", + "Ġp innacle", + "d ocker", + "0 26", + "ĠW yr", + "ĠPro per", + "Ë Ī", + "n il", + "By tes", + "Ġseek er", + "t rial", + "Ġunf olds", + "ĠMar se", + "Ġextravag ant", + "ĠSurviv ors", + "RED ACTED", + "ĠSpeed way", + "ĠCra igslist", + "sub mit", + "ĠGener ations", + "Ġup holding", + "Ġblood stream", + "ĠMiss ions", + "ĠL awn", + "Ġlim bo", + "ene i", + "H uh", + "ĠWild cats", + "pre p", + "ĠMark us", + "ĠFor bidden", + "rit ic", + "IN O", + "Ġexhib iting", + "requ ent", + "ch uk", + "Ġhabit ual", + "ĠComp atibility", + "Dr ag", + "RIP T", + "uj ah", + "GR OUND", + "Ġdelinqu ent", + "Ġburn er", + "Ġcontempor aries", + "Ġgimm ick", + "load s", + "Ġno zzle", + "p odcast", + "ĠW ak", + "ĠStat en", + "ĠK uh", + "ãģ ĵ", + "inter rupted", + "Ġinv incible", + "ĠBurn ett", + "cig arette", + "ĠPeb ble", + "ĠTem porary", + "ĠMar ino", + "58 2", + "Ġwast eland", + "ident ly", + "T x", + "Ġr ite", + "ĠPan asonic", + "ĠM iddles", + "ĠHort on", + "ae us", + "Ġc uring", + "Ġm ats", + "Ġadj ourn", + "Ġfears ome", + "pe z", + "bo ats", + "Ġpro pell", + "Ġconflic ted", + "ĠAng er", + "Ġinsurg ent", + "K arl", + "Ġco ales", + "Ġsouth western", + "Ġdis su", + "ĠO vert", + "******** ****", + "Ġbox ed", + "ĠBr une", + "aa a", + "Ġgard ening", + "ĠEng el", + "tr acks", + "Ġpur ified", + "Ġplace holder", + "ĠL ikes", + "Ġd an", + "G ab", + "Ġe ct", + "ĠF aw", + "ĠEl iot", + "Ġ' ,", + "otrop ic", + "ĠRu in", + "hed on", + "Ġca ul", + "Ġa ft", + "ĠCad illac", + "gh a", + "ass ian", + "ud eb", + "ĠT ick", + "Ġadjust s", + "AR GET", + "5 37", + "isc he", + "ant y", + "ĠFried rich", + "ĠBl izz", + "ĠA OL", + "Camp aign", + "Ġmamm al", + "ĠVe il", + "ĠK ev", + "ĠMaur it", + "ĠDam ien", + "N ation", + "E astern", + "Ġ{ :", + "Ġ= ================================", + "Ġstereotyp ical", + "Ġatt ic", + "ĠCy borg", + "requ ire", + "Ġaward ing", + "ĠPap ua", + "bt n", + "b ent", + "B oo", + "Ġ( =", + "ĠX ander", + "ĠSomers et", + "Ġcatch y", + "Ġcert ify", + "STR UCT", + "Ġit al", + "Ġt ides", + "ĠBr ands", + "G ray", + "comp etitive", + "Ġcur ator", + "ĠD G", + "omin ium", + "ĠGM Os", + "ci ating", + "ĠCarm en", + "ow ard", + "Balt imore", + "Ġr gb", + "C u", + "Ġwip es", + "spe ll", + "IT NESS", + "Ġsummar izes", + "ĠRe vis", + "Ġwhistlebl owers", + "ĠBre ach", + "Ġcro chet", + "k os", + "ews ki", + "Ġrep et", + "Ġcrim son", + "ĠKar achi", + "read able", + "dim ension", + "ĠI gor", + "ild ed", + "ĠZ ed", + "ĠKe ane", + "ĠCos metic", + "DE P", + "Ġretreat ing", + "ĠU A", + "ens ical", + "Ġd usk", + "ĠDick ens", + "Ġaren as", + "ĠPass age", + "level s", + "Ġcur v", + "P ope", + "Ġch ores", + "ĠEl ise", + "ĠComp ass", + "b ub", + "Ġmamm alian", + "ĠSans krit", + "ĠAN C", + "ĠCr ack", + "Q ual", + "L aun", + "amp unk", + "Ġlearn ers", + "Ġglam orous", + "Ġfur the", + "erm ott", + "c and", + "Gener ic", + "Ġnarr ated", + "Ġdisorder ly", + "ĠTrans actions", + "ĠDet ention", + "ĠR oku", + "Ä į", + "Ġunder statement", + "ĠS aur", + "ĠRodrig o", + "ĠAS AP", + "S in", + "Ġre joice", + "Method s", + "Ġelectro de", + "Ġworsh ipped", + "Ġid i", + "ĠPhys icians", + "Ġpop up", + "Ġde ft", + "ĠRem oval", + "ĠBu enos", + "ver bs", + "Ġfun k", + "ush a", + "rict ion", + "ore a", + "ĠBang alore", + "ĠKen obi", + "zz i", + "Ġnorm ative", + "Ġgobl ins", + "Ġcaf es", + "ĠUN CLASSIFIED", + "ĠF ired", + "S IGN", + "Ġs clerosis", + "ĠV oter", + "ĠSon ny", + "ĠExt end", + "ĠEV s", + "Ar senal", + "Ġp si", + "Ġwid est", + "ĠT us", + "Ġlo oms", + "Ġjust ifying", + "ĠGr anger", + "è ¯", + "Ref er", + "58 3", + "Ġflour ishing", + "ab re", + "Ġr ave", + "ĠCont ra", + "Ġ18 98", + "Add s", + "Ġf ul", + "ĠCo oke", + "some one", + "= #", + "67 1", + "Ġy ak", + "Ġar te", + "ĠMis cellaneous", + "ĠDet ection", + "ĠCl ancy", + "â ģ", + "ass ies", + "Ġval iant", + "ĠFemin ist", + "cor ruption", + "V el", + "P ear", + "Ġsucc inct", + "Ġquick est", + "k w", + "Ġsp itting", + "ĠL ibraries", + "åħ ī", + "ant z", + "D ad", + "ĠSpec ifications", + "rup ulous", + "and r", + "RES ULTS", + "Ġsnow ball", + "Ġpred is", + "ĠB axter", + "ĠNurs ing", + "ĠCh aff", + "s we", + "Ġout age", + "Ġnest ing", + "Ġnotor iety", + "tr igger", + "on ite", + "j on", + "Ġf ou", + "ook ed", + "ĠCelebr ity", + "re ality", + "Ġfat ig", + "Ġhug ging", + "Ġbother s", + "ĠPan zer", + "ĠCh andra", + "fig ured", + "Ġvol ts", + "ĠCloud s", + "Ġfee ble", + "ĠCur ve", + "ĠAs us", + "78 6", + "abs or", + "ĠV ICE", + "ĠH ess", + "Ġmanufact ures", + "Ġgri zz", + "ĠPower ful", + "ac id", + "Ġsub sections", + "ĠKrug man", + "ĠAl ps", + "is u", + "Ġsequ est", + "ĠUlt ron", + "ĠT inker", + "ĠGo ose", + "Ġmism atch", + "Att orney", + "Ġmorph ology", + "ĠSix ers", + "ut tered", + "ĠE LECT", + "gr an", + "Rus sell", + "ĠG SL", + "Ġfort night", + "Ġ. )", + "Ġapost le", + "pr one", + "el ist", + "Unt itled", + "ĠIm plementation", + "ist ors", + "Ġtank er", + "Ġpl ush", + "Ġattend ants", + "ĠT ik", + "ĠGreen wich", + "ĠY on", + "ĠSP L", + "cell s", + "unt led", + "S olution", + "ĠQu é", + "Ġvac ated", + "Ġupt ick", + "ĠMer idian", + "æ ĥ", + "ĠDr ill", + "9 25", + "58 4", + "Ġrenov ated", + "ĠKub rick", + "zy k", + "Ġl ousy", + "pp el", + "ohyd rate", + "ĠI zzy", + "lesi astical", + "CC C", + "ĠAj ax", + "Ġad apters", + "ĠPetra eus", + "Ġaffirm ation", + "ĠST OR", + "le ms", + "ad oes", + "ĠConstantin ople", + "Ġp onies", + "Ġl ighthouse", + "Ġadherent s", + "ĠBre es", + "omorph ic", + "Fight ing", + "Ġpl aster", + "ĠP VC", + "ĠOb st", + "Ġdear ly", + "ĠTo oth", + "icks on", + "Ġsh aming", + "P lex", + "A gg", + "ĠâĢ¦ \"", + "Ġsub reddits", + "Ġpige on", + "ĠResident ial", + "ĠPass ing", + "Ġl um", + "ĠP ension", + "Ġpessim istic", + "Ġ4 32", + "z inski", + "c ade", + "0 75", + "Ġapolog ised", + "iy ah", + "Put ting", + "Ġgloom y", + "ĠLy me", + "=-=-=-=- =-=-=-=-", + "ĠT ome", + "ĠPsych iatric", + "ĠH IT", + "c ms", + "ap olog", + "Ġbreak er", + "Ġdeep en", + "Ġtheor ist", + "ĠHigh lands", + "Ġb aker", + "Ġst aples", + "Ġinterf ered", + "ĠAb ortion", + "jo ined", + "ch u", + "Ġform ulate", + "Ġvacc inations", + "Ġban ter", + "phe us", + "Ġoutfield er", + "ĠM eter", + "Ġ# ####", + "Ġ18 95", + "Ġnarrow ing", + "ĠST ORY", + "f p", + "ĠC ST", + "ign ore", + "Ġproclaim ing", + "ĠR U", + "ĠB ALL", + "yn a", + "65 3", + "Ġpos it", + "P RE", + "59 4", + "ĠRegist rar", + "ĠPil grim", + "ic io", + "Ġpre tt", + "Ġlif eless", + "Ġ__ _", + "Ne igh", + "ĠCh urches", + "orn o", + "Ġor cs", + "Ġkind red", + "ĠAud it", + "Ġmillenn ial", + "ĠPers ia", + "g ravity", + "ĠDis ability", + "ĠD ARK", + "W s", + "od on", + "Ġgrand daughter", + "ĠBro oke", + "ĠA DA", + "ER A", + "Ġpick ups", + "ĠWil kinson", + "ĠSh ards", + "ĠN K", + "Ġexp el", + "ĠKis lyak", + "Ġj argon", + "Ġpolar ized", + "ian e", + "Pub lisher", + "Ġreb utt", + "Ġapprehens ion", + "ĠK essler", + "Ġpr ism", + "F UL", + "19 64", + "ĠL oll", + "ä ¿", + "le thal", + "Å Ł", + "Ġg hetto", + "Ġb oulder", + "ĠSlow ly", + "ĠOsc ars", + "ĠInst ruction", + "ĠUl tr", + "ĠM oe", + "N ich", + "ĠP ATH", + "( *", + "ĠRE LEASE", + "un ing", + "rou se", + "en eg", + "Ġre imb", + "ĠDet ected", + "Do S", + "Ġster ling", + "Ġaggreg ation", + "ĠLone ly", + "ĠAtt end", + "hig her", + "Ġairst rike", + "ks on", + "SE LECT", + "Ġdef lation", + "ĠHer rera", + "C ole", + "rit ch", + "Ġadvis able", + "F ax", + "Ġwork around", + "Ġp id", + "mort em", + "ers en", + "Ġtyp o", + "Ġal um", + "78 2", + "ĠJam al", + "script s", + "Ġcapt ives", + "ĠPres ence", + "ĠLie berman", + "angel o", + "Ġalcohol ism", + "ass i", + "Ġrec ite", + "Ġgap ing", + "Ġbask ets", + "ĠG ou", + "Brow ser", + "ne au", + "Ġcorrect ive", + "und a", + "sc oring", + "ĠX D", + "Ġfil ament", + "Ġdeep ening", + "ĠStain less", + "Int eger", + "Ġbu ggy", + "Ġten ancy", + "ĠMub arak", + "Ġt uple", + "ĠD roid", + "ĠS itting", + "Ġforfe it", + "ĠRasm ussen", + "ixt ies", + "es i", + "ĠKim mel", + "Ġmetic ulously", + "Ġap opt", + "ĠS eller", + "08 8", + "ec ake", + "hem atically", + "T N", + "Ġmind less", + "Ġdig s", + "ĠAcc ord", + "ons ense", + "em ing", + "br ace", + "Ġe Book", + "ĠDist ribut", + "ĠInvest ments", + "w t", + "] ),", + "beh avior", + "56 3", + "Ġbl inding", + "ĠPro testers", + "top ia", + "Ġreb orn", + "ĠKel vin", + "ĠDo ver", + "ĠD airy", + "ĠOut s", + "Ġ[ /", + "Ï Ģ", + "b p", + "ĠVan ity", + "ĠRec ap", + "ĠHOU SE", + "ĠF ACE", + "Ġ4 22", + "69 2", + "ĠAnt ioch", + "cook ed", + "Ġcoll ide", + "Ġa pr", + "Ġsle eper", + "ĠJar vis", + "Ġalternative ly", + "ĠLe aves", + "ĠM aw", + "Ġantiqu ity", + "ĠAdin ida", + "Ġab user", + "Poké mon", + "Ġass orted", + "ĠRev ision", + "ĠP iano", + "ĠG ideon", + "O cean", + "Ġsal on", + "Ġbust ling", + "ogn itive", + "ĠRah man", + "Ġwa iter", + "Ġpres ets", + "ĠO sh", + "ĠG HC", + "oper ator", + "Ġrept iles", + "Ġ4 13", + "ĠG arr", + "ĠCh ak", + "Ġhas hes", + "Ġfail ings", + "Ġfolk lore", + "Ġab l", + "ĠC ena", + "ĠMac Arthur", + "ĠCOUR T", + "Ġperipher y", + "app ers", + "Ġreck oned", + "ĠInf lu", + "ĠC ET", + "Ġ3 72", + "ĠDefin itive", + "ass ault", + "4 21", + "Ġreservoir s", + "Ġd ives", + "ĠCo il", + "DA Q", + "Ġvivid ly", + "ĠR J", + "ĠBel lev", + "Ġec lectic", + "ĠShow down", + "ĠK M", + "ip ed", + "reet ings", + "ĠAs uka", + "L iberal", + "ĠÏ Ħ", + "Ġbystand ers", + "ĠGood win", + "uk ong", + "S it", + "ĠT rem", + "Ġcrim inally", + "ĠCirc us", + "ch rome", + "88 7", + "Ġnan op", + "ĠOb i", + "ĠL OW", + "o gh", + "ĠAuth ors", + "ob yl", + "Ur ban", + "Ġt i", + "ĠWe ir", + "t rap", + "ag y", + "Ġparent heses", + "Ġout numbered", + "Ġcounter productive", + "ĠTob ias", + "ub is", + "P arser", + "ST AR", + "Ġsyn aptic", + "ĠG ears", + "Ġh iber", + "Ġdebunk ed", + "Ġex alted", + "aw atts", + "H OU", + "Ch urch", + "ĠPix ie", + "ĠU ri", + "ĠForm ation", + "ĠPred iction", + "C EO", + "Ġthro tt", + "ĠBrit ann", + "ĠMad agascar", + "ë ĭ", + "Ġbill boards", + "ĠRPG s", + "ĠBe es", + "complete ly", + "F IL", + "Ġdoes nt", + "ĠGreen berg", + "re ys", + "Ġsl ing", + "Ġempt ied", + "ĠPix ar", + "ĠDh arma", + "l uck", + "ingu ished", + "Ġend ot", + "Ġbab ys", + "05 9", + "che st", + "r ats", + "Ġr idden", + "Ġbeet les", + "Ġillum inating", + "Ġfict itious", + "ĠProv incial", + "Ġ7 68", + "Ġshe pherd", + "ĠR ender", + "Ġ18 96", + "C rew", + "Ġmold ed", + "ĠXia omi", + "ĠSp iral", + "Ġdel im", + "Ġorgan ising", + "Ġho ops", + "ĠBe i", + "z hen", + "Ġfuck in", + "Ġdec ad", + "Ġun biased", + "am my", + "sw ing", + "Ġsmugg led", + "Ġk ios", + "ĠP ERSON", + "ĠInquis itor", + "Ġsnow y", + "Ġscrap ing", + "ĠBurg ess", + "P tr", + "ag ame", + "R W", + "Ġdro id", + "ĠL ys", + "ĠCass andra", + "Jac ob", + "Ġ35 4", + "Ġpast ure", + "Ġfr anc", + "ĠScot ch", + "ĠEnd s", + "ĠI GF", + "def inition", + "Ġhyster ical", + "ĠBrown e", + "77 1", + "Ġmobil ization", + "æ ķ", + "iqu eness", + "Th or", + "Ġspear headed", + "Ġembro iled", + "Ġconject ure", + "jud icial", + "Ch oice", + "Ġpaper back", + "P ir", + "Ġrec overs", + "ĠSur ge", + "ĠSh ogun", + "ĠPed iatrics", + "ãģ ł", + "Ġsweep s", + "ĠLabor atories", + "ĠP acks", + "al us", + "add in", + "Ġhead lights", + "g ra", + "Ev idence", + "COL OR", + "Ad min", + "Ĭ ±", + "Ġconco ct", + "s ufficient", + "Ġun marked", + "Ġrich ness", + "Ġdiss ertation", + "Ġseason ing", + "Ġg ib", + "ĠM ages", + "un ctions", + "ĠN id", + "che at", + "ĠTM Z", + "c itizens", + "ĠCatholic ism", + "n b", + "Ġdisemb ark", + "ĠPROG RAM", + "a ques", + "Ty ler", + "Or g", + "ĠSl ay", + "ĠN ero", + "ĠTown send", + "IN TON", + "te le", + "Ġmes mer", + "9 01", + "Ġfire ball", + "ev idence", + "aff iliated", + "ĠFrench man", + "ĠAugust a", + "0 21", + "Ġs led", + "Ġre used", + "ĠImmun ity", + "Ġwrest le", + "assemb led", + "Mar ia", + "Ġgun shots", + "ĠBarb ie", + "Ġcannabin oids", + "ĠTo ast", + "ĠK inder", + "IR D", + "Ġre juven", + "Ġg ore", + "Ġrupt ure", + "Ġbre aching", + "ĠCart oon", + "Ġ4 55", + "ĠPale o", + "6 14", + "Ġspe ars", + "ĠAm es", + "ab us", + "Mad ison", + "GR OUP", + "Ġab orted", + "y ah", + "Ġfel on", + "Ġcaus ation", + "Ġprep aid", + "Ġp itted", + "op lan", + "ĠShel ley", + "ĠRus so", + "ĠP agan", + "Ġwill fully", + "ĠCan aver", + "und rum", + "ĠSal ary", + "ĠAr paio", + "read er", + "ĠR ational", + "ĠOver se", + "ĠCa uses", + "Ġ* .", + "Ġw ob", + "Ke ith", + "ĠCons ent", + "man ac", + "77 3", + "6 23", + "Ġfate ful", + "et imes", + "Ġspir ited", + "ĠD ys", + "Ġhe gemony", + "Ġboy cot", + "ĠEn rique", + "em outh", + "Ġtim elines", + "ĠSah ara", + "ĠRel ax", + "ĠQuin cy", + "ĠLess ons", + "ĠE QU", + "SE A", + "N K", + "ĠCost co", + "Incre ase", + "Ġmotiv ating", + "ĠCh ong", + "am aru", + "ĠDiv ide", + "Ġped igree", + "ĠTasman ia", + "ĠPrel ude", + "L as", + "9 40", + "57 4", + "Ġch au", + "ĠSp iegel", + "un ic", + "-- >", + "ĠPhil ips", + "ĠKaf ka", + "Ġuphe aval", + "Ġsent imental", + "Ġsa x", + "ĠAk ira", + "ser ial", + "Mat rix", + "Ġelect ing", + "Ġcomment er", + "ĠNeb ula", + "ple ts", + "ĠNad u", + "ĠAd ren", + "Ġen shr", + "ĠR AND", + "fin ancial", + "ĠCly de", + "uther ford", + "Ġsign age", + "Ġde line", + "Ġphosph ate", + "rovers ial", + "f ascist", + "ĠV all", + "ĠBeth lehem", + "Ġfor s", + "Ġeng lish", + "S olid", + "N ature", + "Ġv a", + "ĠGu ests", + "Ġtant al", + "Ġauto immune", + ";;;;;;;; ;;;;", + "ĠTot ally", + "ĠO v", + "Ġdef ences", + "ĠCoc onut", + "Ġtranqu il", + "Ġpl oy", + "Ġflav ours", + "ĠFl ask", + "ãĤ¨ ãĥ«", + "ĠWest on", + "ĠVol vo", + "8 70", + "Ġmicro phones", + "ver bal", + "R PG", + "Ġi ii", + "; }", + "0 28", + "Ġhead lined", + "Ġprim ed", + "Ġho ard", + "ĠSh ad", + "ĠEN TER", + "Ġtri angular", + "Ġcap it", + "l ik", + "ĠAn cients", + "Ġl ash", + "Ġconv ol", + "Ġcolon el", + "en emy", + "G ra", + "Ġpub s", + "ut ters", + "Ġassign s", + "ĠPen et", + "ĠMon strous", + "ĠBow en", + "il ver", + "H aunted", + "ĠD ing", + "start ed", + "pl in", + "Ġcontamin ants", + "ĠDO E", + "ff en", + "ĠTechn ician", + "R y", + "Ġrob bers", + "Ġhot line", + "ĠGuard iola", + "ĠKau fman", + "row er", + "ĠDres den", + "ĠAl pine", + "E lf", + "Ġf mt", + "ĠS ard", + "urs es", + "g pu", + "Un ix", + "Ġunequiv ocally", + "ĠCitizens hip", + "qu ad", + "m ire", + "ĠS weeney", + "B attery", + "6 15", + "Ġpanc akes", + "Ġo ats", + "M aps", + "ĠCont rast", + "mbuds man", + "ĠE PS", + "Ġsub committee", + "Ġsour cing", + "Ġs izing", + "ĠBuff er", + "ĠMand atory", + "Ġmoder ates", + "ĠPattern s", + "ĠCh ocobo", + "ĠZ an", + "ĠSTAT ES", + "ĠJud ging", + "ĠIn her", + "* :", + "Ġb il", + "ĠY en", + "Ġexh ilar", + "oll ower", + "z ers", + "Ġsn ug", + "max imum", + "Ġdesp icable", + "ĠP ACK", + "ĠAn nex", + "Ġsarcast ic", + "Ġlate x", + "Ġt amp", + "ĠS ao", + "b ah", + "ĠRe verend", + "ĠChin atown", + "ĠA UT", + "d ocumented", + "ĠGA BA", + "ĠCan aan", + "ĠÙ ħ", + "Ġgovern s", + "pre v", + "E sc", + "ĠEst imates", + "OS P", + "Ġendeav our", + "ĠCl osing", + "omet ime", + "every one", + "Ġwor sen", + "Ġsc anners", + "Ġdev iations", + "ĠRobot ics", + "ĠCom pton", + "Ġsorce rer", + "Ġend ogenous", + "Ġem ulation", + "ĠPier cing", + "ĠA ph", + "ĠS ocket", + "Ġb ould", + "ĠO U", + "ĠBorder lands", + "Ġ18 63", + "G ordon", + "ĠW TO", + "Ġrestrict s", + "Ġmosa ic", + "Ġmel odies", + "ç Ħ", + "T ar", + "Ġdis son", + "ĠProv ides", + "Ġ ......", + "b ek", + "F IX", + "Ġbro om", + "ans hip", + "Do ctors", + "Ġner ds", + "ĠReg ions", + "na issance", + "Ġmet e", + "Ġcre pt", + "pl ings", + "Ġgirlfriend s", + "kn it", + "ig ent", + "ow e", + "Ġus hered", + "ĠB az", + "M obil", + "4 34", + "ĠPres ents", + "orig in", + "Ġins omnia", + "ĠA ux", + "4 39", + "ĠCh ili", + "irs ch", + "G AME", + "Ġgest ation", + "alg ia", + "rom ising", + "$ ,", + "c row", + "ĠIn spection", + "at omic", + "Rel ations", + "J OHN", + "rom an", + "ĠClock work", + "ĠBak r", + "m one", + "M ET", + "Ġthirst y", + "Ġb c", + "Ġfacult ies", + "R um", + "Ġnu ance", + "ĠD arius", + "ple ting", + "fter s", + "etch up", + "Reg istration", + "ĠK E", + "R ah", + "Ġpref erential", + "ĠL ash", + "ĠH H", + "Val id", + "ĠN AV", + "Ġstar ve", + "ĠG ong", + "z ynski", + "ĠAct ress", + "Ġw ik", + "Ġun accompanied", + "lv l", + "Br ide", + "AD S", + "ĠCommand o", + "ĠVaugh n", + "Wal let", + "Ġho pping", + "ĠV ie", + "Ġcave ats", + "Ġal as", + "if led", + "ab use", + "66 1", + "Ġib n", + "Ġg ul", + "Ġrob bing", + "t il", + "IL A", + "Ġmit igating", + "Ġapt ly", + "Ġty rant", + "Ġmid day", + "ĠGil more", + "ĠDe cker", + "Ġ§ §", + "part ial", + "Ex actly", + "Ġphen otype", + "Ġ[+ ]", + "ĠP lex", + "ĠI ps", + "vers ions", + "Ġe book", + "Ġch ic", + "g ross", + "\":\" \"},{\"", + "ĠSur prisingly", + "M organ", + "Ġresid ues", + "ĠConf ederation", + "in feld", + "Ġl yr", + "mod erate", + "Ġperpend icular", + "V K", + "Ġsynchron ized", + "Ġrefres hed", + "Ġad ore", + "ĠTor ment", + "ol ina", + "Ġ26 00", + "Item Tracker", + "Ġp ies", + "ĠF AT", + "ĠR HP", + "0 48", + "ĠRES P", + "ĠB J", + "all ows", + "P and", + "Ġunw elcome", + "ĠV oc", + "ĠBast ard", + "ĠO W", + "ĠL AR", + "ĠHeal er", + "Environment al", + "ĠKen yan", + "ĠTr ance", + "ĠP ats", + "Ġali ases", + "ĠGar field", + "Ġcampaign er", + "Ġadvance ments", + "ĠOkin awa", + "ĠC oh", + "ows ky", + "Ġstar ved", + "Ġsize able", + "Ġ: -)", + "Ġm RNA", + "Ġsusp ensions", + "ist ar", + "Scot land", + "Pr in", + "-------------------------------- ----------------", + "Ġ50 2", + "Ġteasp oons", + "Ġ10 50", + "Ġcoerc ive", + "ĠMason ic", + "edd ed", + "ĠPass enger", + "Ġl att", + "Ġbr aces", + "ĠSt eal", + "ĠNY T", + "ĠK ats", + "ĠCel est", + "ae z", + "T u", + "ĠCoul ter", + "ðŁ ĺ", + "Fl ickr", + "ĠWil mington", + "ith s", + "++ ;", + "Ġv ending", + "Ġneg ro", + "ĠPh i", + "ĠYellow stone", + "Call back", + "Ġsh ampoo", + "ĠSh ades", + "w at", + "Ġsuper human", + "Ġridic uled", + "Ġhol iest", + "om bo", + "Ġintern s", + "Ġh one", + "ĠPar agu", + "UR I", + "Ġd angling", + "ãĤ »", + "so v", + "ict ional", + "av ailability", + "Ġrev ocation", + "Ġd ow", + "in ic", + "ĠTHE IR", + "Ġis o", + "Ġout ings", + "ĠLeth al", + "Ġ) ))", + "Ġinacc ur", + "Ġout landish", + "Ġan us", + "let ico", + "id on", + "l ol", + "Ġun regulated", + "Ġsuccumb ed", + "Ġc uff", + "ĠWast eland", + "let al", + "Ġsub str", + "Ġcoff ers", + "Ġautom akers", + "ov i", + "ĠX ue", + "ĠDayton a", + "Ġjar ring", + "Ġf umes", + "Ġdisband ed", + "z ik", + "itt on", + "Ġstriking ly", + "Ġsp ores", + "Ad apter", + ".) :", + "ĠLynd on", + "ival ry", + "Ġor ally", + "Ġtumult uous", + "Ġdisple asure", + "Ġcon es", + "or rect", + "Ġappe ase", + "Ġder by", + "ĠTrip oli", + "ĠAl ess", + "Ġp oked", + "ĠGu ilty", + "v P", + "En ough", + "Ġorig inals", + "6 99", + "Ġrabb i", + "Ġproverb ial", + "Ġpostp one", + "el ope", + "ĠMist y", + "Ġstaff ed", + "ĠUn employment", + "redit ary", + "Ġdilig ent", + "re comm", + "me asures", + "as in", + "8 25", + "Ġpond s", + "Ġmm ol", + "ĠS AR", + "ĠC ARE", + "Ġ3 71", + "Ġclen ched", + "ĠCors air", + "Ġcaric ature", + "z n", + "att ach", + "ĠSch ro", + "spe ak", + "p ainted", + "ĠS uc", + "ĠE NT", + "Ġcell ul", + "ĠP aid", + "di agn", + "WH ERE", + "Ġtext ed", + "B arn", + "Ġret racted", + "ĠRe ferred", + "S av", + "Ġup keep", + "Ġwork places", + "ĠTok ens", + "Ġampl ify", + "cl inical", + "Ġmult ic", + "mber g", + "Ġconvol uted", + "Reg ion", + "5 65", + "ĠTop ic", + "Ġsn ail", + "Ġsal ine", + "Ġins urrection", + "ĠPet r", + "f orts", + "B AT", + "ĠNav ajo", + "Ġrud imentary", + "ĠLak sh", + "OND ON", + "Me asure", + "Ġtransform er", + "ĠGodd ard", + "Ġcoinc ides", + "ir in", + "R ex", + "ĠB ok", + "qu it", + "Ġshotgun s", + "Ġprolet arian", + "Ġsc orp", + "ĠAd a", + "5 14", + "Ġsl ander", + "record ed", + "Ġemb ell", + "ris ome", + "Ġapolog izing", + "ĠMul cair", + "ĠGib raltar", + "Cl a", + "Ġall ot", + "ĠAtt ention", + "Ġ4 33", + "le ave", + "Ġwh ine", + "ĠIss a", + "ĠFa ust", + "ĠBar ron", + "hen y", + "Ġvictim ized", + "J ews", + "Ġnurt uring", + "ett el", + "W inged", + "ĠSub tle", + "Ġflavor ful", + "ĠRep s", + "eng ed", + "call back", + "Ġdirection al", + "Ġcl asp", + "ĠDirect ions", + "plan et", + "icult ure", + "Hel per", + "ic ion", + "ac ia", + "Ġç ¥ŀ", + "Ġsur ges", + "Ġcan oe", + "ĠPrem iership", + "be en", + "Ġdef ied", + "ĠTro oper", + "Ġtrip od", + "Ġgas p", + "ĠE uph", + "ĠAd s", + "vern ight", + "high ly", + "R ole", + "Ġent angled", + "ĠZe it", + "6 18", + "ĠRust y", + "Ġhaven s", + "ĠVaugh an", + "HA EL", + "ĠSER VICE", + "/ ,", + "Ġstr icken", + "Ġdel usions", + "Ġb is", + "ĠH af", + "Ġgrat ification", + "Ġent icing", + "UN CH", + "Ad ams", + "ĠOL ED", + "ĠBeet le", + "Ġ18 99", + "ĠSO FTWARE", + "ateg or", + "V L", + "ĠTot em", + "ĠG ators", + "AT URES", + "Ġimped ance", + "Reg istered", + "ĠC ary", + "ĠAer ial", + "on ne", + "en ium", + "Ġd red", + "ĠBe g", + "Ġconcurrent ly", + "Ġsuper power", + "ĠX an", + "j ew", + "imes ter", + "ĠDick inson", + "âĶ ģ", + "F la", + "Ġp ree", + "ĠRoll ins", + "© ¶æ", + "Ġden omination", + "ĠL ana", + "5 16", + "Ġinc iting", + "sc ribed", + "j uries", + "ĠWond ers", + "app roximately", + "Ġsusp ending", + "Ġmountain ous", + "ĠL augh", + "oid al", + "N s", + "Det ect", + ") =", + "ĠL uthor", + "ĠSchwarz enegger", + "ĠMull er", + "ĠDev i", + "ec ycle", + "J ar", + "6 13", + "ĠL ongh", + "B ah", + "ĠSP ORTS", + "n w", + "Ġref inement", + "Ġwater ways", + "Ġd iner", + "Bl ade", + "68 3", + "F ac", + "Ġinitial s", + "Ġro g", + "Ġparan ormal", + "B UT", + "Ġ[ (", + "ĠSw anson", + "ĠM esh", + "âĸ ¬", + "Impro ve", + "ĠRad iation", + "ĠEst her", + "ĠE sk", + "ĠA ly", + "ik y", + "Ġir rad", + "ĠBuck ingham", + "Ġref ill", + "Ġ. _", + "Re pe", + "CON CLUS", + "Ġdifferent iated", + "Ġchi rop", + "ĠAt kins", + "Pat tern", + "Ġexc ise", + "Ġcab al", + "N SA", + "ĠST A", + "ĠS IL", + "ĠPar aly", + "Ġr ye", + "ĠHow ell", + "ĠCount down", + "ness es", + "alys ed", + "Ġres ize", + "ãĤ ½", + "Ġbudget ary", + "ĠStr as", + "w ang", + "Ġap iece", + "Ġprecinct s", + "Ġpe ach", + "Ġsky line", + "Ġ35 3", + "pop ular", + "App earances", + "ĠMechan ics", + "ĠDev Online", + "S ullivan", + "Z en", + "Ġp u", + "op olis", + "5 44", + "Ġde form", + "Ġcounter act", + "ĠL ange", + "Ġ4 17", + "Con sole", + "77 4", + "Ġnodd ing", + "Ġpopul ism", + "Ġhe p", + "Ġcoun selling", + "compl iance", + "U FF", + "Ġunden iably", + "Ġrail ing", + "ĠHor owitz", + "ĠSim one", + "ĠBung ie", + "Ġa k", + "ĠTal ks", + "x ff", + "fl ake", + "Cr ash", + "Ġsweat y", + "Ġban quet", + "ĠOFF IC", + "Ġinvent ive", + "Ġastron omer", + "ĠStam ford", + "ĠSc are", + "ĠGRE EN", + "olic ited", + "Ġr usher", + "Ġcent rist", + "ight ing", + "Ġsub class", + "Ġdis av", + "Ġdef und", + "ĠN anto", + "oci ate", + "m ast", + "Ġpac if", + "Ġm end", + "e ers", + "imm igration", + "ESS ION", + "Ġnumber ing", + "Ġlaugh able", + "ĠEnd ed", + "v iation", + "em ark", + "P itt", + "Ġmetic ulous", + "ĠL F", + "Ġcongrat ulated", + "ĠBir ch", + "Ġsway ed", + "Ġsemif inals", + "Ġhum ankind", + "m atter", + "ĠEqu ip", + "opa usal", + "S aid", + "ĠLay out", + "Ġvo icing", + "Ġth ug", + "Ġporn ographic", + "I PS", + "Ġmo aning", + "Ġgriev ance", + "Ġconf essions", + "esc al", + "TEXT URE", + "Aut hent", + "os aurus", + "P urchase", + "Ġreleg ation", + "al ter", + "ĠÂł Âł", + "Ġr iddled", + "Ġo gre", + "ĠLow ell", + "Occ up", + "E at", + "ĠHy der", + "ĠAdvis er", + "Com merce", + "H unt", + "ĠOr th", + "ĠComp etitive", + "ĠCL A", + "CD C", + "Ġsal ads", + "F le", + "Ġindustrial ized", + "` ,", + "ĠO WN", + "Ġbec k", + "ĠPart icularly", + "oub t", + "Ġm M", + "ĠHuss ain", + "ĠChen nai", + "Ġ9 20", + "Ġappoint ing", + "ĠCull en", + ",,,, ,,,,", + "Ġp ores", + "ver ified", + "Ġbi ochemical", + "em ate", + "Ġcoward ly", + "ĠHels inki", + "ĠEthiop ian", + "S OURCE", + "ER C", + "est ro", + "Ġbi otech", + "ĠS our", + "Ġbrew er", + "Bloom berg", + "Ġintens ify", + "Gl ass", + "an co", + "ĠF DR", + "gre SQL", + "ĠF ires", + "©¶æ ¥µ", + "ec o", + "100 1", + "ĠHom eless", + "Ġinstant aneous", + "ĠH aste", + "ig el", + "D iamond", + "Ġp aving", + "Ġland fill", + "Ġd ads", + "h oun", + ": ]", + "Ġinc endiary", + "ĠLiving ston", + "ĠHil bert", + "ĠChe cks", + "st yles", + "in ators", + "ĠCl ive", + "ph rine", + "Ġchimpan zees", + "Ġp all", + "ĠJ M", + "ĠAad haar", + "ð Ŀ", + "Ġachie vable", + "dis abled", + "P ET", + "OOOO OOOO", + "M ot", + "Ġint angible", + "Ġbal let", + "ĠWe bs", + "ĠEst imated", + "Effect s", + "Ġb ailed", + "Josh ua", + "Ġturb ulence", + "Ġoccup ant", + "ĠDay light", + "Ġ36 1", + "me et", + "Ġstat ically", + "Ġon look", + "Ġk i", + "il legal", + "Ġvel vet", + "Ġdehyd ration", + "Ġacqu ies", + "ĠRe z", + "ak ura", + "ĠU pton", + "at ro", + "Ġincomp rehensible", + "Ġback door", + "ĠRh ino", + "7 27", + "Ġmath s", + ") +", + "Ġhe resy", + "Ġd f", + "ĠRoc he", + "ĠL ydia", + "Ġpanc reat", + "re ply", + "arre ll", + "Ġsolicit ation", + "Ġcirc adian", + "BI P", + "Ġfor ay", + "Ġcrypt ic", + "iz u", + "ime o", + "ĠTom ato", + "ĠH oms", + "ex amination", + "Ġqu arry", + "ĠVal iant", + "ĠJer icho", + "ĠIN CLUD", + "Ġ18 40", + "5 19", + "Ġres ists", + "Ġsnap shots", + "ĠSp ur", + "ĠAnt iqu", + "Log in", + "Ġbest selling", + "Ġant ic", + "ĠS utherland", + "ãĤ¢ ãĥ«", + "Ġ~ /", + "ĠP arm", + "è ĥ", + "P ages", + "int ensity", + "Ġimm obil", + "Ġ18 65", + "zz o", + "Ġn ifty", + "Ġf entanyl", + "ĠPres ervation", + "op hen", + "Ġd arts", + "ĠD inosaur", + "po inters", + "ĠR ite", + "s uggest", + "aware ness", + "ĠSher idan", + "Ġst ances", + "Ġsor cery", + "Ġper jury", + "ĠNik ola", + "ie ver", + "Ġf iance", + "ĠJordan ian", + "ĠBall oon", + "Ġn ab", + "Ġk b", + "Ġhuman ities", + "ĠTan aka", + "hill ary", + "Ġconsult ancy", + "ĠZ ub", + "Ġrem ission", + "Ġconf id", + "CH Q", + "ĠF ug", + "Ġimpro vis", + "Y ep", + "/ _", + "Ġunwilling ness", + "Ġport folios", + "05 5", + "ĠInstruct or", + "aim an", + "Ġclaim ants", + "M bps", + "ĠBy e", + "re ceived", + "T weet", + "Ġind emn", + "ri z", + "am ara", + "N at", + "Ġeval uates", + "ĠL ur", + "ep ad", + "FO X", + "ĠTh ro", + "Ġrust y", + "Ġbed rock", + "ĠOp rah", + "J B", + "Ġmanip ulative", + "Ġwill ful", + "Ġrel apse", + "Ġext ant", + "The me", + "S ensor", + "ĠSt ability", + "go vern", + "Ġpo ppy", + "Ġkn ack", + "Ġins ulated", + "ĠT ile", + "ĠExt rem", + "Ġunt old", + "Ġconver ge", + "Ġref uel", + "ig roup", + "Ġdistort ions", + "Ġrav aged", + "Ġmechan ically", + "ĠRe illy", + "ĠN ose", + "ĠIncarn ation", + "ĠBeck y", + "abb ling", + "Ġt aco", + "Ġr ake", + "Ġmelanch oly", + "Ġillust rious", + "ĠDart mouth", + "Gu ide", + "ĠR azer", + "ĠBen z", + "Ult imate", + "ĠSur prise", + "Ġpage ant", + "off er", + "Who ever", + "Ġw iser", + "Ġchem ist", + "ĠHE LL", + "ĠBul k", + "Ġpl utonium", + "ĠCO VER", + "Ö ¼", + "f ailed", + "Ġtire lessly", + "Ġinf ertility", + "ĠTr ident", + "ĠShow time", + "ĠC iv", + "V ice", + "requ ires", + "itt ance", + "Ġun controlled", + "interest ing", + "56 1", + "Ġinnov ate", + "ateg ic", + "L ie", + "ĠS elling", + "U l", + "Ġsav ior", + "ĠT osh", + "Ġsw ast", + "P ASS", + "Ġr ink", + "Ġcard io", + "ĠI ro", + "ud i", + "Ġv antage", + "Ġv ans", + "ĠNi ño", + "+ =", + "Ġpropag ate", + "< ?", + "Ġmethod ological", + "204 39", + "Ġtrig lycer", + "Ġing rained", + "ĠAn notations", + "arr anted", + "6 17", + "ĠS odium", + "ĠA AC", + "techn ical", + "mult ipl", + "Ġ3 73", + "å ĭ", + "Ġdec isively", + "Ġboost ers", + "Ġdessert s", + "ĠGren ade", + "Ġtest ifying", + "ĠSc ully", + "ID s", + "Ġlock down", + "ĠSc her", + "ĠR é", + "ĠWhit man", + "ĠRams ay", + "rem ote", + "Ġh ikers", + "ĠHy undai", + "Ġcons cientious", + "Ġcler ics", + "ĠSiber ian", + "ut i", + "is bury", + "Ġrel ayed", + "Ġqu artz", + "ĠC BI", + "seek ers", + "ull a", + "Ġweld ing", + "ĠSh al", + "ble acher", + "T ai", + "ĠSam son", + "Ġt umble", + "ĠInvest or", + "Ġsub contract", + "ĠShin ra", + "ow icz", + "j andro", + "d ad", + "Ġtermin ating", + "ĠNe ural", + "ä» £", + "Ġleak age", + "ĠMid lands", + "ĠCaucas us", + "í ķ", + "c it", + "ll an", + "iv ably", + "ĠAlb ion", + "Ġ4 57", + "Ġregist rations", + "Ġcomr ade", + "Ġclip board", + "0 47", + "Ġdiscour aging", + "ĠO ops", + "Ad apt", + "Ġem path", + "n v", + "ĠPR OT", + "ĠDon n", + "ĠP ax", + "ĠB ayer", + "t is", + "Squ are", + "Ġfoot prints", + "part icip", + "ĠChile an", + "B rend", + "ind ucing", + "M agn", + "Ġclub house", + "ĠMagn um", + "Ġenc amp", + "ĠEth nic", + "uch a", + "ere y", + "Ġw atered", + "ĠCal ais", + "Ġcomplex ion", + "Ġsect s", + "Ġren ters", + "Ġbr as", + "oÄŁ an", + "Time out", + "Man agement", + "Ġinf ographic", + "P okemon", + "Cl ar", + "Ġloc ality", + "Ġfl ora", + "as el", + "P ont", + "Ġpop ulate", + "ĠO ng", + "Ġsubs istence", + "Ġa uctions", + "ĠMcA uliffe", + "ĠL OOK", + "br inger", + "Ġtit an", + "Ġmanif old", + "ĠâĹ ı", + "Ġcalibr ated", + "Ġcal iphate", + "ĠSH E", + "ĠCommission ers", + "ce ivable", + "j c", + "W inner", + "5 24", + "Ġcond one", + "Other wise", + "Ġp iling", + "Ġem body", + "ĠCrime an", + "ut ics", + "ĠEx hibition", + "Ġ4 26", + "e ering", + "Ġv ying", + "ĠH UGE", + "* =-", + "Ġprin cipled", + "à ¦", + "Ġquir ks", + "ĠEdit ors", + "put ing", + "G ES", + "ĠF TA", + "ठ¾", + "add on", + "ĠH AM", + "ĠFrie za", + "W oman", + ". $", + "Ġc rib", + "ĠHer od", + "Ġtim ers", + "ĠSp aces", + "ĠMac intosh", + "at aka", + "Ġgl ide", + "Ġsmell ing", + "ĠB AL", + "Ġun su", + "Ġcond os", + "Ġbicy cl", + "ĠRev ival", + "55 3", + "Ġjugg ling", + "H ug", + "ĠKardash ian", + "ĠBalk ans", + "mult iple", + "Ġnutrit ious", + "oc ry", + "19 00", + "Ġinteg rates", + "Ġad joining", + "ĠF older", + "roll ment", + "ven ient", + "Ġu ber", + "y i", + "Ġwh iff", + "ĠJu ven", + "ĠB orough", + "net te", + "Ġb ilingual", + "ĠSp arks", + "ph thal", + "man ufact", + "Ġt outing", + "ĠPH I", + "Ke efe", + "Rew ard", + "Ġinf all", + "ĠTem per", + "typ ically", + "ĠNik ol", + "Ġregular s", + "Ġpseud onym", + "Ġexhib itions", + "Ġbl aster", + "Ġ40 9", + "w arming", + "Ġrever ber", + "Ġrecip rocal", + "Ġ6 70", + "ip ient", + "b ett", + "ĠBe gins", + "Ġit ching", + "ĠPh ar", + "Ass uming", + "Ġem itting", + "ĠML G", + "Ġbirth place", + "Ġt aunt", + "ĠL uffy", + "ĠAm it", + "Ġcir cled", + "ĠN ost", + "enn ett", + "Ġde forestation", + "ĠHist orically", + "ĠEvery day", + "Ġovert ake", + "79 2", + "Ġn un", + "ĠLuc ia", + "Ġaccompan ies", + "ĠSe eking", + "ĠTr ash", + "an ism", + "R ogue", + "Ġnorth western", + "ĠSupplement al", + "ĠNY U", + "ĠF RI", + "ĠSat isf", + "x es", + "5 17", + "Ġreass ured", + "Ġspor adic", + "Ġ7 01", + "Ġmed ial", + "Ġcannabin oid", + "Ġbarbar ic", + "Ġep is", + "ĠExplos ive", + "ĠD ough", + "Ġuns olved", + "Support ed", + "Ġacknowled gment", + "sp awn", + "Ġkit chens", + "Ġ- =", + "talk ing", + "ic ist", + "ĠPeg asus", + "ĠPS U", + "Ġphot on", + "ĠAuthent ication", + "R G", + "@# &", + "76 2", + "ĠCl air", + "Ġdi aper", + "Ġbr ist", + "ĠProsecut ors", + "ĠJ em", + "6 28", + "ĠEvery where", + "ĠJean ne", + "equ ality", + "ãĥ© ãĥ³", + "object s", + "ĠPel icans", + "Ġ39 2", + "Ġbl u", + "b ys", + "ĠA go", + "Ġinstruction al", + "Ġdiscrim inating", + "ĠTR AN", + "ĠCorn el", + "ag os", + "Ġty re", + "Ġas piration", + "ĠBrid gewater", + "\": -", + "! \".", + "ĠEn s", + "ĠCoc o", + "P ie", + "Ġdet ach", + "ĠC ouch", + "Ġphys ique", + "ĠOccup ations", + "osc opic", + "en ough", + "B uzz", + "App earance", + "Y P", + "Ġrac er", + "Ġcompl icity", + "r pm", + "T oy", + "Ġinterrupt s", + "ĠCat alyst", + "Ġut ilitarian", + "imp act", + "Ġsp aghetti", + "Ġp orous", + "Ġeste emed", + "Ġinc iner", + "ĠI OC", + "7 48", + "Ġesp resso", + "ĠSm ile", + "abil ia", + "6 35", + "Ġmathematic ian", + "Ġ4 24", + "ĠK L", + "ĠH IP", + "Ġover heard", + "ĠT ud", + "ĠT ec", + "Ġqu izz", + "Ġfl attering", + "Ġcon n", + "âĢ İ", + "Ġatt aches", + "ĠR OS", + "ĠAC S", + "Ġt cp", + "ĠSh ame", + "sk ip", + "res pected", + "ĠTrin idad", + "gr ain", + "Ġfooth old", + "ĠUnch arted", + "ĠJul io", + "z l", + "av ored", + "ĠAn xiety", + "er rors", + "ĠCent auri", + "its ch", + "D addy", + "Ġclutch ing", + "ĠIm plement", + "ĠGut ierrez", + "Ġ7 60", + "Ġtele portation", + "end ra", + "Ġrevers ible", + "st ros", + "Ad venture", + "08 3", + "Ġliber ating", + "Ġas phalt", + "ĠSp end", + "AR DS", + "im sy", + "PR ES", + "ĠEmer ging", + "Ġwild fires", + "Ġtechn ologically", + "Ġem its", + "ĠART ICLE", + "Ġirregular ities", + "Ġcher ish", + "çī Ī", + "Ġst ink", + "ĠR ost", + "Econom ic", + "Ġcough ing", + "ĠMcC ann", + "pro perties", + "ilant ro", + "Ġreneg oti", + "Trans lation", + "Ġin quest", + "ĠGra pe", + "oot ers", + "gu i", + "ĠSwords man", + "ace ae", + "h itting", + "Ġr c", + "Ġexert ed", + "ĠS AP", + "it ent", + "Ġperil ous", + "Ġobsc urity", + "Ġassass inate", + "Ġab original", + "Ġresc uing", + "ĠSh attered", + "lock ing", + "all ion", + "Ch anging", + "ĠHar rington", + "ĠB ord", + "ĠAfgh ans", + "Jam ie", + "aret z", + "ĠAugust us", + "Ġ38 6", + "8 30", + "Ġj og", + "ok ingly", + "Tr igger", + "ĠH OR", + "Stat istics", + "Ġviewers hip", + "Ġadd itives", + "h ur", + "Ġmaxim izing", + "ĠR ove", + "ĠLou ie", + "ĠBuck et", + "ĠCHR IST", + "ou sel", + "Ġstre aks", + "ir ted", + "Ġt ert", + "Ġcolonial ism", + "Ġbur ying", + "y k", + "Cond ition", + "ĠDPR K", + "By Id", + "75 1", + "âĹ ¼", + "Ġwor risome", + "Ġvoc ational", + "sl ice", + "Ġsa ils", + "ĠCorrection al", + "95 4", + "Ġt ul", + "K id", + "l uster", + "Ġfam ilial", + "ĠSp it", + "ĠEp iscopal", + "Specific ally", + "ĠVol cano", + "run s", + "q s", + "Ġve tted", + "Ġcram med", + "t rop", + "here r", + "Thank fully", + "Ġper cussion", + "Ġor anges", + "Ġround up", + "Ġ4 99", + "x ious", + "Char acters", + "ĠZion ism", + "ĠR ao", + "ÃĽ ÃĽ", + "W F", + "Ġunintention al", + "ONE Y", + "Gr ab", + "Com mercial", + "Ġglut amate", + "ĠMcK enna", + "ru ciating", + "ning ton", + "ih u", + "Ch an", + "ĠSw ap", + "Ġleaf lets", + "Ġfunction ally", + "er ous", + "F arm", + "Ġcal oric", + "ĠLiter ally", + "con cert", + "Ġshe nan", + "Ġrep aid", + "ey es", + "Ġbas hing", + "ĠG orge", + "Ġcollabor ations", + "Ġun account", + "itch ie", + "Ġteam work", + "pp elin", + "Ġpip ing", + "Ġmin ced", + "Ġd iam", + "ri eg", + "Ġmasc ara", + "Ġsuck er", + "ĠMo ons", + "App s", + "ĠPe ck", + "Ġper v", + "ĠFl oat", + "o ley", + "ĠN ish", + "im ize", + "Ġarom atic", + "u in", + "end ish", + "! /", + "ĠB icycle", + "ĠAS IC", + "ile ged", + "ĠQuad ro", + "ios yn", + "Ġlock out", + "ĠW ink", + "SP EC", + "Attempt s", + "Ġseed ed", + "red o", + "ias is", + "Ġsn ag", + "ãĥķ ãĤ©", + "ãĤ ¶", + "Ġground ing", + "Ġrelie ver", + "Ġfrivol ous", + "ĠG ifts", + "ĠF aces", + "Es pecially", + "Ġmicrobi ome", + "im ag", + "ĠSch l", + "ĠP les", + "ĠBle ach", + "ĠIr win", + "ĠE aton", + "ĠDisc iple", + "Ġmultipl ication", + "Ġcoer ced", + "Ġ4 19", + "st h", + "E vil", + "B omb", + "Ġex orc", + "Ġstag gered", + "L ESS", + "Ġinert ia", + "ĠED IT", + "Ġgo b", + "Tr aditional", + "Ġclass y", + "Lear y", + "ĠP AGE", + "yr s", + "Ġtrans porter", + "Ġmat ured", + "Ġhij ab", + "Ġbi ome", + "Where as", + "Ġex termination", + "ĠT ues", + "ĠT akeru", + "ĠAud rey", + "er ial", + "ĠAd en", + "aff les", + "Ġnarciss istic", + "ĠB aird", + "UT F", + "I re", + "ĠCon nie", + "Ch amp", + "Ġwhis pering", + "ĠH att", + "D K", + "Ġdis infect", + "Ġdeduct ed", + "Ġpart ake", + "Ġdown grade", + "ĠEs ports", + "ĠContin uing", + "Ġdemocr atically", + "icro bial", + "itt a", + "Ġlim estone", + "Ġexempt ed", + "ĠFren zy", + "H erm", + "7 28", + "Ġfled gling", + "Met a", + "765 61", + "69 3", + "% :", + "w ake", + "5 26", + "ĠDis cipline", + "Ġvirgin ity", + "ĠLeg ions", + "ĠFrank ie", + "int ent", + "Ġrest rooms", + "ĠRou ter", + "da q", + "Ġobjection able", + "âĨ ij", + "w ark", + "ĠRah ul", + "g ain", + "activ ation", + "abs olute", + "ĠAccess ed", + "Ġ24 00", + "ogg les", + "Ġsecond ly", + "ĠDEF ENSE", + "Ġpost age", + "wra pper", + "sh arp", + "7 29", + "Ġcommun icates", + "Ġadd on", + "ĠMil itia", + "H ong", + "Ġsl umped", + "ĠJP EG", + "ĠI car", + "ad ish", + "68 1", + "Ġmaj esty", + "ĠWolf gang", + "ĠEl astic", + "u per", + "Ġv iz", + "Ġunconscious ly", + "ĠST D", + "ĠS ass", + "Ġflower ing", + "ĠHel ic", + "ĠDra per", + "ĠAm ateur", + "Ġman ure", + "Ġdis ingen", + "ĠLe i", + "br ing", + "9 49", + "Ġinhib ited", + "Ġhead quartered", + "Ġen igmatic", + "�� �", + "Ġred ress", + "R H", + "Ġratt led", + "Ġd iction", + "l io", + "ĠT BA", + "ĠSN AP", + "C alling", + "Ġfasc ists", + "ĠD ove", + "iew icz", + "0 36", + "Ġco asts", + "ĠR ect", + "Ġ) ]", + "L ot", + "6 29", + "ĠS EM", + "ĠPeters en", + "ĠExpl ain", + "ĠBo ards", + "ĠBe zos", + "ĠJ ournals", + "Ġ20 24", + "p arser", + "Ġmist rust", + "Ġgr ate", + "ĠL ocked", + "bo a", + "S aint", + "g aming", + "Ġvow el", + "in ately", + "bl ow", + "All ah", + "Ġun matched", + "Ġb ordering", + "ĠExp end", + "n r", + "Or acle", + "rou ch", + "Ġcont iguous", + "ac us", + "Ġdist raught", + "58 1", + "Ġanat omical", + "O X", + "ap ixel", + "8 33", + "ĠPL US", + "Ġres usc", + "Ġab iding", + "57 3", + "Ġvac ancies", + "Em ily", + "Ġhyp othal", + "ĠWer ner", + "ĠWe e", + "ĠDJ s", + "5 13", + "Ġwitch craft", + "Ġac upuncture", + "ent ary", + "benef it", + "Product s", + "ĠP SP", + "ĠMP G", + "ĠJ inn", + "ĠJ arrett", + "Ġ4 45", + "ĠIm aging", + "ĠP yth", + "Fin ish", + "Ġte x", + "Ġjuven iles", + "Ġhero ism", + "Ġdoubt less", + "ĠA ki", + "ĠT end", + "ĠPatri arch", + "Ġbit ters", + "ĠTele communications", + "it atively", + "ag na", + "Ġr g", + "ĠS OLD", + "Ġcomp ulsion", + "ĠN asa", + "ĠKath ryn", + "Ġmillion aires", + "Ġintrins ically", + "Ġbolst ered", + "time out", + "fl o", + "Ġtut or", + "p our", + "Stat ement", + "Ġ{ *", + "ĠRud olph", + "ĠKimber ly", + "rog ens", + "adi q", + "] +", + "Ġindign ation", + "Ġfract uring", + "ĠRe leases", + "ĠGr ain", + "pro tein", + "L ago", + "Ġvac ations", + "Ġboot ed", + "ĠTH REE", + "ĠH G", + "oresc ence", + "Ġt f", + "Ġso ar", + "iosyn cr", + "Ġgl ances", + "ĠSp oon", + "ĠJ ury", + "ĠCow boy", + "Ġcreat ively", + "Hig her", + "Ġsolic itor", + "Ġhaw k", + "ac io", + "89 6", + "Ġsuperf lu", + "Ġbombs hell", + "ct ure", + "Ġbroker age", + "Ġraid ing", + "Ġf rench", + "Ġang led", + "Trans action", + "ĠGen ocide", + "u pe", + "ĠHait ian", + "57 2", + "! :", + "Ġunwitting ly", + "iter ator", + "sc roll", + "Ġtall ied", + "Ġbi omedical", + "ĠC ARD", + "Ġe uphem", + "Ġbrain storm", + "a quin", + "K o", + "Mic helle", + "ĠR unes", + "ĠBall istic", + "ud ers", + "Ġmod esty", + "ĠiP ads", + "ĠEzek iel", + "Y E", + "Ġstars hip", + "Ġpower fully", + "Ġper l", + "ĠSh ade", + "ĠQu art", + "ĠE EG", + "Ġfisher man", + "OS ED", + "ĠTyp ical", + "df x", + "Ġmes hes", + "Ġet ched", + "worth iness", + "Ġtopp led", + "Ġ3 96", + "or ius", + "We iss", + "Ġmy sql", + "ĠVal halla", + "Ù Ĵ", + "le asing", + "Ġrec omp", + "rap nel", + "S el", + "04 3", + "Ġder ailed", + "ĠGu ides", + "IR T", + "Ġde human", + "ĠBritt any", + "\" ))", + "Ġex claim", + "Ġb alk", + "Ġ8 40", + "CLA IM", + "int el", + "L AB", + "Ġpe gged", + "Ġast roph", + "sm oking", + "Ġrig ging", + "Ġfix ation", + "Ġcat apult", + "ins ide", + "ĠC ascade", + "ĠBolshe vik", + "G aza", + "Dep th", + "Ġloud spe", + "Ġalmond s", + "me yer", + "l eness", + "j en", + "f resh", + "Ġunbeat en", + "ĠSqu id", + "ĠPres umably", + "Tim er", + "B W", + "Ġro sters", + "Ġell ipt", + "ĠHar riet", + "dat abase", + "ĠMut ual", + "ĠComm odore", + "uk ed", + "kn ife", + "ĠCOMM UN", + "h ya", + "Ġmel ts", + "arch ives", + "Ġrat ification", + "Ġmultip lying", + "Ġinter oper", + "Ġasc ert", + "w ings", + "ver ting", + "ĠScorp ion", + "ay e", + "ĠPorts mouth", + "ĠM TA", + "n it", + "iaz ep", + "Ġqu arantine", + "Ġslides how", + "Ġcent imeters", + "Ġsyn opsis", + "Ġsp ate", + "th irst", + "Ġnom inating", + "ĠMel vin", + "Pre view", + "Ġthro b", + "Ġgener ational", + "ĠRad ius", + "rest ling", + "put able", + "aw ar", + "N ECT", + "Ġunlaw fully", + "ĠRevel ations", + "Wik ipedia", + "sur v", + "Ġeye ing", + "ij n", + "ĠF W", + "Ġbr unt", + "Ġinter stellar", + "Ġcl itor", + "ĠCroat ian", + "ĠCh ic", + "ev a", + "ĠDis app", + "ĠA kin", + "iner ies", + "d ust", + "Interest ed", + "Ġgen esis", + "ĠE ucl", + "ö n", + "p icking", + "Ġmut ated", + "Ġdisappro ve", + "ĠHD L", + "Ġ6 25", + "Ì ¶", + "c ancer", + "Ġsqu ats", + "Ġle vers", + "Disc uss", + "= ]", + "D ex", + "ĠVIDE OS", + "A UD", + "Ġtrans act", + "ĠKin ect", + "ĠK uala", + "ĠC yp", + "7 47", + "Ġsh attering", + "Ġarsen ic", + "ĠInt ake", + "ĠAngel o", + "ĠQu it", + "ĠK he", + "Ġ18 93", + "M aker", + "0 29", + "ĠPain ting", + "Dis able", + "9 16", + "Ġanal ges", + "Ġtact ile", + "Ġprop hes", + "Ġd iced", + "ĠTravel s", + "ĠHe ader", + "ĠClub s", + "Ass istant", + "Ġinc rim", + "Ġd ips", + "Ġcruc ifix", + "ĠShan ahan", + "ĠInter pret", + "Ġ40 90", + "al ogy", + "abb a", + "Ġsimul ac", + "hus band", + "S IM", + "Ġrecy cle", + "uc er", + "ed ged", + "Ġre naissance", + "ĠBomb ay", + "Cath olic", + "ĠL INE", + "ĠCl othing", + "re ports", + "Ġpl aus", + "Ġd ag", + "ĠM ace", + "Z I", + "Ġintr uder", + "ĠVeter inary", + "g ru", + "Ġsne aky", + "ĠS ie", + "ĠC innamon", + "P OSE", + "Ġcou rier", + "ĠC NS", + "Ġemanc ipation", + "s it", + "Ġplay through", + "ĠFac ilities", + "v irt", + "ĠG auntlet", + "Thom pson", + "Ġunbeliev ably", + "Param eters", + "Ġst itching", + "ign e", + "ĠTH ESE", + "Priv acy", + "Ġshenan igans", + "Ġvit ri", + "ĠVal id", + "59 1", + "Ń ·", + "ĠProt otype", + "ink a", + "SC P", + "ĠT id", + "è Ī", + "old ed", + "Ġindividual ity", + "Ġbark ing", + "Ġm ars", + "ĠW D", + "Ġ8 20", + "Ġt ir", + "Ġsl apping", + "Ġdisgr untled", + "ĠAng ola", + "ri us", + "ĠTorn ado", + "ĠTh urs", + "Ġcapt cha", + "Ġang st", + "ĠP og", + "ĠAssass ins", + "ĠAd idas", + "Ġjoy ful", + "Ġwh ining", + "Emer gency", + "Ġphosph orus", + "Ġatt rition", + "oph on", + "ĠTimber wolves", + "ĠJ ah", + "ĠBr inging", + "ĠW ad", + "ĠEn sure", + "oh l", + "ĠX ie", + "omm el", + "c mp", + "Ġz ipper", + "Ġrel at", + "ĠCor ridor", + "m ilo", + "T ING", + "Av g", + "Ġcro pped", + "] }", + "Ġr aged", + "ĠLump ur", + "ĠGuer rero", + "our ke", + "N ut", + "Ġoff sets", + "og lu", + "dr m", + "Ġmort als", + "lat able", + "Ġdismiss ive", + "ä¸ ī", + "Ġthro ats", + "Ġchips et", + "ĠSpot light", + "Catal og", + "art ist", + "G b", + "Ġch illy", + "Ġst oked", + "Ġ3 74", + "W ard", + "L atin", + "Ġf iasco", + "Ġble ach", + "Ġb rav", + "Enh anced", + "Ġin oc", + "ĠFior ina", + "_ >", + "Ġle ukemia", + "Ġel uc", + "Ġannoun cer", + "ĠLith uan", + "ĠArm ageddon", + "å ĩ", + "Len in", + "ĠR uk", + "Ġpe pp", + "ĠRom antic", + "ĠP IT", + "ĠInter stellar", + "ĠAt kinson", + "R aid", + "J s", + "Go al", + "C ourse", + "Ġvan ishing", + "es ley", + "ĠR ounds", + "Els a", + "59 3", + "Ġredund ancy", + "ĠST AND", + "Ġprop hetic", + "Ġhabit able", + "ry u", + "Ġfaint ly", + "M ODE", + "Ġfl anked", + "IR C", + "Aw esome", + "Ġsp urious", + "ĠZ ah", + "ĠMS G", + "Ġsh ading", + "Ġmotiv ational", + "ĠSant ana", + "ĠS PR", + "Ġexc ruciating", + "om ial", + "ĠM iko", + "ĠLe opard", + "A byss", + "Ġ[ |", + "d irty", + "Ġbath s", + "Ġdem oral", + "and re", + "P B", + "Ġun ification", + "Ġsac rament", + "Ġ[ &", + "Ġpric eless", + "Ġgel atin", + "Ġeman ating", + "ĠAll aah", + "98 6", + "Ġout burst", + "Ġer as", + "ĠX VI", + "ĠSP I", + "O tt", + "ĠLaz arus", + "PL IED", + "F lying", + "blog s", + "W isconsin", + "R aven", + "Ġreb ate", + "Ġcreep s", + "ĠSp an", + "ĠPain ter", + "ĠKir a", + "ĠAm os", + "ĠCor vette", + "Cons umer", + "ĠRec over", + "ck i", + "Ġpes ky", + "ĠIn vention", + "Compan ies", + "Ġchalleng ers", + "ad emic", + "ĠUkrain ians", + "ĠNeuro log", + "ĠFors aken", + "Ġent rants", + "Ġemb attled", + "Ġdef unct", + "ĠGlac ier", + "Ġpo isons", + "ĠH orses", + "m akes", + "ĠD irt", + "Ġ4 23", + "hh h", + "ĠTrans formation", + "QUI RE", + "................ ..", + "Ġtrave ller", + "ĠSe xy", + "ĠK ern", + "ip olar", + "Ġransom ware", + "oooooooo oooooooo", + "E c", + "rub y", + "Prof essional", + "ĠOut break", + "arg ument", + "G rey", + "ĠFif a", + "ĠCH O", + "ĠFOR M", + "ĠAm trak", + "- [", + "Ġcr adle", + "Ġantioxid ants", + "ãģ®å ®", + "7 36", + "ĠNAS L", + "ĠContribut ions", + "Ind iana", + "ĠST EP", + "C SS", + "Ġsal ient", + "Ġall ocations", + "yr ights", + "Ġm ashed", + "ĠCut ter", + "Sex ual", + "Ġp ounded", + "Ġfan base", + "Ġc asc", + "ĠTrans parency", + "Ġanaly tic", + "ĠSummon er", + "× ŀ", + "ĠAD C", + "det ail", + "Ġvan quished", + "Ġcr abs", + "ar ie", + "Dest roy", + "ĠS ack", + "Ġtrans istor", + "Al abama", + "ĠK oen", + "ĠFisher ies", + "c one", + "Ġannex ed", + "ĠM GM", + "es a", + "Ġf aked", + "ĠCong ratulations", + "Ġhind ered", + "Ġcorrection al", + "ĠI TV", + "lee ve", + "Ġin appropriately", + "lic ks", + "Ġtresp ass", + "Ġp aws", + "Ġnegoti ator", + "ĠChrist ensen", + "lim its", + "ĠDian ne", + "Ġeleg ance", + "ĠContract s", + "an ke", + "Ob j", + "Ġvigil ance", + "Ġcast les", + "ĠN AD", + "ĠHol o", + "Ġemph atically", + "ĠTit us", + "ĠServ ing", + "ĠRich ie", + "ĠP igs", + "5 68", + "Ġanim osity", + "ĠAtt ributes", + "ĠU riel", + "M Q", + "my ra", + "ĠApplic ant", + "Ġpsychiat rists", + "ĠV ij", + "ĠAb by", + "ag ree", + "P ush", + "Ġk Wh", + "hib a", + "Ġinc ite", + "ĠWe asley", + "ĠTax i", + "minist ic", + "hy per", + "ĠF arn", + "Ġ6 01", + "ĠNation wide", + "F ake", + "95 2", + "Ġma ize", + "Ġinteract ed", + "Ġtransition ed", + "Ġparas itic", + "Ġharm onic", + "Ġdec aying", + "Ġbas eless", + "ns ics", + "Ġtrans pired", + "Ġabund antly", + "ĠFore nsic", + "Ġtread mill", + "ĠJ av", + "ab and", + "Ġssh d", + "Ġfront man", + "ĠJak arta", + "oll er", + "dro ps", + "ĠSERV ICES", + "rompt u", + "oph ical", + "h ospital", + "bled on", + "6 45", + "Ġmid range", + "ĠEV ENT", + "cul ated", + "raw led", + "Ġper ched", + "Ġover board", + "ĠPe el", + "ĠP wr", + "ĠCar th", + "ĠCOM PLE", + "co e", + "sh all", + "Ġdeter rence", + "M ETHOD", + "ĠAbs ent", + "M EN", + "Ġs ill", + "ĠLE VEL", + "Y ork", + "Ġsin ners", + "ĠOP EC", + "ĠN ur", + "ĠDesign s", + "se lection", + "Ġunw orthy", + "CH A", + "Ġstreng thens", + "88 3", + "ed ly", + "Ġslic ing", + "Ġmal nutrition", + "Ġfilm making", + "ĠPol k", + "ur ated", + "Ġ4 21", + "bre akers", + "!' \"", + "Ġwet lands", + "ĠDisc rimination", + "Ġallow able", + "Ġste ered", + "ĠSic ily", + "S AM", + "Ġmust ache", + "Ġm ids", + "Ġcl ipped", + "Ġcirc ulate", + "Ġbr ittle", + "ĠBuild ings", + "ra ised", + "ĠRound up", + "Ġwealth ier", + "Ġoverw rite", + "Ġover powered", + "ĠGerr ard", + "s ites", + "PD ATED", + "Ġacute ly", + "ĠGam ble", + "Ġp im", + "ĠK us", + "Typ ically", + "De ploy", + "ĠMoroc can", + "p otion", + "com be", + "Ġvigil ante", + "Ġ36 3", + "St ew", + "ĠB agg", + "Ġres ided", + "ĠSp o", + "Ġrem nant", + "Ġempt iness", + "br ainer", + "Ġout patient", + "pri ority", + "Ġle ptin", + "ĠPay ton", + "ĠGle aming", + "ĠS hed", + "ĠPol o", + "ĠMormon ism", + "rest ricted", + "arl ane", + "w x", + "Ġcreat ine", + "ĠAn on", + "ĠST UD", + "ĠJ UL", + "ĠT ee", + "5 28", + "08 9", + "Ġhat ched", + "Dis patch", + "ĠCompos ite", + "Ġ45 1", + "p uff", + "ĠX COM", + "ĠOr n", + "ĠTH ANK", + "END ED", + "ĠAshe ville", + "Ġà ľ", + "Ġman go", + "ĠS lightly", + "world ly", + "ĠW ander", + "ĠExp and", + "ĠCh r", + "M ist", + "Ġorthodox y", + "ĠUN ESCO", + "reg ate", + "Else where", + "k ie", + "ir led", + "Ġtopp le", + "Ġadopt ive", + "ĠLeg s", + "d ress", + "ĠS agan", + "b are", + "ĠGl ou", + "Cr unch", + "Ġhelp ers", + "Ġchron ically", + "ĠH uma", + "1 0000", + "Ġaccommod ating", + "äº Ķ", + "Ġwrink les", + "Ġdod ged", + "four th", + "Ġpre con", + "Ġcompress or", + "ĠK are", + "Ġev ict", + "ĠWar wick", + "im ar", + "Ġmodern ization", + "Ġband wagon", + "Ġref uted", + "Ġnet ted", + "ĠNa ples", + "ĠGen ie", + "per ors", + "Ġfield ed", + "Ġde re", + "ĠPar ables", + "le es", + "Ġtr out", + "asp ers", + "Ġn ihil", + "Ġhapp iest", + "Ġflo ppy", + "ĠLo ft", + "ĠHe ard", + "Ġun ison", + "Ġl ug", + "ĠRed mond", + "class ic", + "Supp orters", + "SH IP", + "G MT", + "Ġfue lled", + "ç IJ", + "Ġd d", + "ĠEmin em", + "Ġ18 97", + "NY SE", + "Ġsecret aries", + "ĠF IA", + "ĠCanaver al", + "F avorite", + "Ġp omp", + "Ġdetain ee", + "ers hip", + "aim on", + "i our", + "ĠA pex", + "Ġplant ations", + "am ia", + "ac ion", + "R ust", + "Ġtow ed", + "ĠTru ly", + "5 77", + "Ġshel tered", + "r ider", + "W o", + "Ġl air", + "ĠInt elligent", + "impro ve", + "m atically", + "Ġet iquette", + "ad ra", + "all o", + "ĠJun o", + "any thing", + "ĠStru ggle", + "ĠPred ict", + "ĠGr imes", + "ĠAMER ICA", + "ct x", + "ĠSit uation", + "W OOD", + "Ġsol uble", + "me ier", + "Ġintoler able", + "ang ering", + "Ġun interrupted", + "Ġtool tip", + "Ġinterrog ated", + "Ġgun ned", + "ĠSne ak", + "æŃ ¦", + "Ġt ether", + "Ġcr umble", + "L ens", + "Ġclust ered", + "ĠSy l", + "ĠHas an", + "Ġdystop ian", + "w ana", + "Ġjoy stick", + "ĠTh ib", + "amm u", + "Tom orrow", + "5 46", + "Ġoverc ame", + "Ġminim ized", + "cept or", + "Run ner", + "ENG TH", + "ĠBrend a", + "ĠAchieve ments", + "Ġtor ches", + "Ġrapp ort", + "ĠInvestig ator", + "ĠHand ling", + "rel ation", + "g rey", + "8 15", + "Ġk cal", + "ĠComm ands", + "d q", + "Ġcur ls", + "Ġbe arer", + "Ġcyn icism", + "it ri", + "ĠUse ful", + "B ee", + "D CS", + "Ġab ras", + "P ract", + "BIL ITIES", + "7 12", + "Ġdebug ger", + "Ġdebt or", + "ĠL ia", + "ĠK ers", + "Ġexacerb ate", + "ĠSt acy", + "ĠB land", + "ĠSc enes", + "Ġbranch ing", + "âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ", + "ape ake", + "Ġs alsa", + "Ġmish and", + "ĠKon ami", + "ĠN ib", + "Ġanecd ote", + "Ġagree able", + "Ï ī", + "ĠNath aniel", + "ĠHe isman", + "ĠB eware", + "Ġ18 86", + "spect ive", + "69 1", + "5 22", + "Ġinhib its", + "Ġhas hing", + "Ġ18 89", + "å° Ĩ", + "v ich", + "P ure", + "Ġsolid ly", + "Ġaspir in", + "im aru", + "Ġstreet car", + "ĠU CS", + "ĠJ udd", + "Ġflash backs", + "p ins", + "Ġ14 40", + "ĠUN HCR", + "ĠSym ptoms", + "T IT", + "5 38", + "F ra", + "% );", + "Ġo oz", + "Ġcur few", + "Ġcal med", + "Ġparticip ates", + "Te X", + "Ġnons ensical", + "Ġfull back", + "ĠDe L", + "mon key", + "h ari", + "Ġmetabol ites", + "Ġloot ed", + "ĠAL WAYS", + "ĠB CC", + "L t", + "oc het", + "B one", + "Ġveto ed", + "Ġg cc", + "ĠCL ICK", + "Ġ18 88", + "s af", + "Ġstiff ness", + "Ġlow ly", + "ĠGe h", + "vers on", + "ors et", + "Ġun foreseen", + "Ġan esthesia", + "ĠOpt ical", + "Ġrecon structed", + "ĠT up", + "sh ows", + "NEW S", + "ĠNewsp aper", + "ĠA SA", + "ter a", + "N umbers", + "Ġinexpl icable", + "× ij", + "Ġhard ness", + "unt arily", + "ĠA cer", + "grad ient", + "ARD IS", + "Ġwood land", + "Ġmetaph ors", + "ĠWem bley", + "ĠPa vel", + "phil is", + "Ġre writing", + "Ġpercept ual", + "Ġ10 70", + "worm s", + "ĠDown s", + "Ġunsur prisingly", + "Ġtag ging", + "fl ame", + "Ġlit res", + "Ġboun ces", + "ĠB abe", + "sh ut", + "Ġoverd oses", + "ĠShe ila", + "ĠCh au", + "ĠBl ess", + "Capt ure", + "ĠSign ificant", + "ĠSc ion", + "Ġ38 9", + "ĠMc H", + "ĠTitan ium", + "ĠMe al", + "amed a", + "ag ents", + "agg ressive", + "B illy", + "76 3", + "ĠS aying", + "DER R", + "it one", + "Coll ins", + "B ound", + "Ġbol ted", + "ĠDM CA", + "95 3", + "Ġun iqueness", + "Ġep igen", + "un ci", + "ant am", + "Ġreck oning", + "ch airs", + "OG R", + "ĠSen egal", + "Ġ18 62", + "re levant", + "Ġ ¯", + "Ġpharm acies", + "ĠG eral", + "v ier", + "Y an", + "OR PG", + "Ġrab id", + "b ending", + "ĠUN ITED", + "Ġ4 65", + "As sembly", + "Ġwe ep", + "Ġbe hest", + "ĠMother s", + "ĠJ ace", + "h id", + "Ġwh irlwind", + "ĠUN IVERS", + "Ġut opian", + "Ġkidn ap", + "Ph ilipp", + "K in", + "89 3", + "Ġlivest ream", + "ĠM ISS", + "Ġsub versive", + "ĠTechn iques", + "ĠJUST ICE", + "ĠB ASE", + "Ġ38 7", + "Ġassail ants", + "ĠHard core", + "Ġsprink led", + "ĠP se", + "é ļ", + "print ed", + "ĠH au", + "OR GE", + "ĠT OUR", + "Ġl aced", + "Ġit ch", + "G iving", + "Ġport ed", + "78 1", + "//////////////// ////////////////", + "bre eding", + "Ġlog ger", + "ĠH OL", + "inn ie", + "First ly", + "Ġembry onic", + "Ġdeleg ated", + "p ai", + "O IL", + "Ġcentr ally", + "ĠR x", + "ĠSc outing", + "D utch", + "Ġhe reditary", + "ĠCru iser", + "s at", + "5 29", + "ĠMar riott", + "other mal", + "Ġprohib itions", + "E arn", + "ĠSt ab", + "ĠColleg es", + "ĠBel ief", + "st retched", + "ĠL H", + "ĠEntity Item", + "C IA", + "Ġun rem", + "Ġlaure ate", + "Ġdenomin ations", + "sum mary", + "h ler", + "S pect", + "ĠK laus", + "ĠBe ans", + "Ġins ur", + "ĠPA X", + "Ġfield er", + "ĠV et", + "ĠSp arrow", + "z ie", + "ĠS Q", + "ĠMond ays", + "ĠOff line", + "ĠLer ner", + "ĠExt ensions", + "Ire land", + "Ġpatron age", + "Ġcontrast ed", + "ĠMan ia", + "h irt", + "Mos cow", + "Ġcondem ns", + "ĠAn ge", + "Ġcomp osing", + "ĠPe pe", + "ĠP addock", + "Ġheter ogeneity", + "Ġide ologically", + "Ġf ishes", + "Ġcur sing", + "ĠR utherford", + "ĠFlo ating", + "ĠAm elia", + "Te a", + "Syn opsis", + "Ġstun ts", + "Ġbe ad", + "Ġstock ing", + "ĠM ILL", + "ob ook", + "mass ive", + "\\ <", + "Ġh ump", + "ĠPref erences", + "Engine Debug", + "ge ist", + "ĠNiet o", + "ome ver", + "ish y", + "eval uate", + "col onial", + "Altern ative", + "ĠGo Pro", + "ĠV ortex", + "ĠNET WORK", + "ans ky", + "Sec ure", + "ĠTh rust", + "Sn ake", + "Ġparcel s", + "Ġsam urai", + "Ġactress es", + "N ap", + "M F", + "ifer ation", + "Be er", + "5 23", + "ĠI ly", + "oint ment", + "P ing", + "Ġstri ped", + "ĠMell on", + "oss ession", + "Ġneut ron", + "end ium", + "Ġa ph", + "ĠFlav oring", + "Ġ38 3", + "Ġrespons iveness", + "ĠJ indal", + "ĠHitch cock", + "Den ver", + "ĠDRAG ON", + "sm anship", + "ĠDu pl", + "Ġs ly", + "Ġweb cam", + "ĠTw ain", + "ĠDar ling", + "ili ate", + "cons umer", + "D IT", + "Ġnames ake", + "Ġun orthodox", + "Ġfun er", + "ĠPL oS", + "ĠCONTR OL", + "ozy g", + "ogl obin", + "F ACE", + "ER G", + "ĠD ia", + "ĠF iesta", + "ce le", + "0 34", + "Ġencl ave", + "âĸ¬ âĸ¬", + "on ement", + "al ist", + "M and", + "Ġhome grown", + "ĠF ancy", + "Ġconcept ions", + "ĠCont ains", + "ure en", + "Ġreiter ate", + "Ġme ager", + "Ġinstall ments", + "Sp awn", + "6 27", + "Ġphot oc", + "ĠCab rera", + "ĠRos enthal", + "ĠLans ing", + "is ner", + "Ġinvest s", + "ĠUFO s", + "EX P", + "Hard ware", + "Ġtr agically", + "Ġconced es", + "ie ft", + "ch am", + "bor gh", + "ĠSch r", + "ĠMel anie", + "ĠH oy", + "Ġvisit ation", + "Ġid iosyncr", + "Ġfract ions", + "Ġfore skin", + "ob os", + "Ġpo aching", + "ĠVI EW", + "Ġstimul ates", + "ĠG ork", + "can on", + "M IC", + "ĠNem esis", + "ĠInd ra", + "ĠDM V", + "Ġ5 29", + "Ġinspect ing", + "Ġgrand ma", + "ĠW hedon", + "ĠSh ant", + "ĠP urg", + "ik an", + "ĠT eg", + "ĠCL R", + "z ac", + "Vict oria", + "ĠVer ify", + "ion ics", + "Ġpart ying", + "ĠM ou", + "col our", + "Ġtestim onies", + "l ations", + "Ġpress uring", + "hi ro", + "ac ers", + "Ġf id", + "ang ler", + "ĠCS I", + "Ġhere after", + "Ġdiss idents", + "report ing", + "iph any", + "che v", + "Ġsol itude", + "Ġl obe", + "Ġind is", + "Ġcred ential", + "re cent", + "ad ult", + "ĠNir vana", + "ĠFranch ise", + "L ayer", + "H yp", + "ĠBerks hire", + "Ġwill s", + "t if", + "Ġtot em", + "ĠJud ah", + "rep air", + "Inst ant", + "5 48", + "Ġemb assies", + "Ġbott leneck", + "Ġb ount", + "Ġtyp ew", + "ĠAl vin", + "j ing", + "im ilar", + "R ush", + "Ġbr im", + "ĠHEL P", + "A im", + "] '", + "Ġpass ively", + "Ġbound ed", + "ĠR ated", + "Ġcriminal ity", + "Ġbiom ark", + "Ġdisp atcher", + "ĠTow ards", + "Ġ+ ++", + "right eous", + "f rog", + "ĠP anc", + "C arter", + "0 32", + "æ© Ł", + "Ġult raviolet", + "ĠLic ensed", + "ĠT ata", + "ĠBl essing", + "ĠG AM", + "Ġchem ically", + "ĠSe af", + "ĠRE LE", + "ĠMerc enary", + "capital ist", + "Ġform ulations", + "Ġann ihilation", + "ĠVer b", + "ĠAr gon", + "Ġun loaded", + "Ġmorp hed", + "Ġconqu ering", + "back er", + "I ELD", + "Ġtheft s", + "Ġfront runner", + "ĠRoy ale", + "ĠFund amental", + "el ight", + "C hip", + "necess ary", + "ay n", + "ĠSl ip", + "Ġ4 48", + "cern ed", + "P ause", + "Ġshock ingly", + "ĠAB V", + "Ġcomp osure", + "7 33", + "ĠMotors port", + "ah ime", + "Mur ray", + "M ach", + "Ġgr ids", + "Ġdeb ian", + "Ġfurther more", + "Ġdexter ity", + "ĠCollect ions", + "os lov", + "il age", + "b j", + "ĠMont eneg", + "Ġstrut Connector", + "Ġmassac res", + "Ġbrief s", + "fet ched", + "uv ian", + "ol ition", + "Fail ure", + "emon ic", + "Ġfl ared", + "Ġclaim ant", + "Ġc ures", + "Ġgive aways", + "ĠSubst ance", + "al ions", + "Ġcr inge", + "ĠK ul", + "Ġarist ocracy", + "ĠUl ster", + "ol ated", + "h ousing", + "ĠM IS", + "Ġgl ared", + "ĠWil helm", + "ne eds", + "lam bda", + "build ers", + "ĠV IS", + "Ġradi ator", + "ĠGhost busters", + "Ġ4 36", + "act ual", + "Ġher ds", + "ç a", + "watch ing", + "Ġcounter ing", + "Ch arge", + "Ġchar red", + "Ġwar heads", + "Ġiod ine", + "ĠM acy", + "04 1", + "Ġdepart ures", + "ĠS ins", + "Ġdy ed", + "ĠConcept s", + "g ado", + "7 13", + "Ġquot ations", + "Ġg ist", + "ĠChrist y", + "Ġant igen", + "ĠHem p", + "ĠD rawn", + "ĠB arg", + "ez vous", + "Ġp aternity", + "Ġar du", + "ĠAnch orage", + "ĠR ik", + "Ġover loaded", + "ĠUs ername", + "ĠTam my", + "ĠN au", + "ĠCell ular", + "Ġw aning", + "Ġrod ent", + "ĠWor cester", + "il ts", + "ĠT ad", + "Ġdwell ings", + "Ġbull ish", + "4 31", + "Ġretali ate", + "Ġmig raine", + "ĠChev ron", + "CH ECK", + "Ġdon key", + "c rim", + "SP A", + "ĠAn alog", + "Ġmarqu ee", + "ĠHa as", + "B ir", + "ĠGD DR", + "ĠDownload s", + "Ġwill power", + "ĠFor th", + "ĠRecord ed", + "Ġimp ossibility", + "ĠLog ged", + "ĠFr anks", + "ĠR att", + "in itions", + "Ġclean ers", + "Ġsore ly", + "Ġflick ering", + "ĠEx amination", + "c atching", + "allow een", + "Ms g", + "Ġdun no", + "F a", + "Ġdys ph", + "c razy", + ".' '.", + "Ġmain line", + "Ġc s", + "Ġp tr", + "ĠW ally", + "ig un", + "95 1", + "ĠBig foot", + "f ights", + "Ġretrie ving", + "J r", + "Ġdupl ication", + "ĠExpl an", + "Ġrel ational", + "Ġqu aint", + "Ġbisc uits", + "Ġad o", + "Ġsh udder", + "Ġantid ote", + "blood ed", + "ks h", + "Ġsa uces", + "Ġrein vest", + "Ġdispens ary", + "ĠD iver", + "Ġ9 000", + "stud ent", + "Ġin separ", + "esc ap", + "Ġtodd lers", + "ĠGP IO", + "ĠAss ignment", + "head ers", + "Ġlack luster", + "Ġab ack", + "95 6", + "Ġtool bar", + "7 45", + "Ġo ust", + "Ġcontempl ation", + "ĠPRES IDENT", + "Ġ4 58", + "==== ==", + "Ġguarantee ing", + "ĠHe ist", + "ĠCann es", + "Ļ ½", + "Ġcollabor ator", + "ĠAm p", + "Ġg ou", + "ĠSH ALL", + "st ories", + "78 3", + "Ġmobil ized", + "Ġbro od", + "ĠL U", + "ĠðŁ ij", + "Ġref in", + "ĠAnthrop ology", + "v ind", + "ill i", + "Ġwarrant ies", + "ĠB abel", + "Ġsw ath", + "Ġc aches", + "Ġantagon ists", + "art ifacts", + "Ġhot ly", + "ĠSt arts", + "ĠG ö", + "z ag", + "!! !!!", + "Ġsc ourge", 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0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/anisotropic.py b/modules/anisotropic.py new file mode 100644 index 0000000000000000000000000000000000000000..576822240762b7dfcfb27e49364314ee1cb436d9 --- /dev/null +++ b/modules/anisotropic.py @@ -0,0 +1,200 @@ +import torch + + +Tensor = torch.Tensor +Device = torch.DeviceObjType +Dtype = torch.Type +pad = torch.nn.functional.pad + + +def _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + ky, kx = _unpack_2d_ks(kernel_size) + return (ky - 1) // 2, (kx - 1) // 2 + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.' + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + return ky, kx + + +def gaussian( + window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None +) -> Tensor: + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + +def get_gaussian_kernel1d( + kernel_size: int, + sigma: float | Tensor, + force_even: bool = False, + *, + device: Device | None = None, + dtype: Dtype | None = None, +) -> Tensor: + + return gaussian(kernel_size, sigma, device=device, dtype=dtype) + + +def get_gaussian_kernel2d( + kernel_size: tuple[int, int] | int, + sigma: tuple[float, float] | Tensor, + force_even: bool = False, + *, + device: Device | None = None, + dtype: Dtype | None = None, +) -> Tensor: + + sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype) + + ksize_y, ksize_x = _unpack_2d_ks(kernel_size) + sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None] + + kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None] + kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None] + + return kernel_y * kernel_x.view(-1, 1, ksize_x) + + +def _bilateral_blur( + input: Tensor, + guidance: Tensor | None, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + + if isinstance(sigma_color, Tensor): + sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1) + + ky, kx = _unpack_2d_ks(kernel_size) + pad_y, pad_x = _compute_zero_padding(kernel_size) + + padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type) + unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) + + if guidance is None: + guidance = input + unfolded_guidance = unfolded_input + else: + padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type) + unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) + + diff = unfolded_guidance - guidance.unsqueeze(-1) + if color_distance_type == "l1": + color_distance_sq = diff.abs().sum(1, keepdim=True).square() + elif color_distance_type == "l2": + color_distance_sq = diff.square().sum(1, keepdim=True) + else: + raise ValueError("color_distance_type only acceps l1 or l2") + color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx) + + space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype) + space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky) + + kernel = space_kernel * color_kernel + out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1) + return out + + +def bilateral_blur( + input: Tensor, + kernel_size: tuple[int, int] | int = (13, 13), + sigma_color: float | Tensor = 3.0, + sigma_space: tuple[float, float] | Tensor = 3.0, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) + + +def adaptive_anisotropic_filter(x, g=None): + if g is None: + g = x + s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True) + s = s + 1e-5 + guidance = (g - m) / s + y = _bilateral_blur(x, guidance, + kernel_size=(13, 13), + sigma_color=3.0, + sigma_space=3.0, + border_type='reflect', + color_distance_type='l1') + return y + + +def joint_bilateral_blur( + input: Tensor, + guidance: Tensor, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) + + +class _BilateralBlur(torch.nn.Module): + def __init__( + self, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = "l1", + ) -> None: + super().__init__() + self.kernel_size = kernel_size + self.sigma_color = sigma_color + self.sigma_space = sigma_space + self.border_type = border_type + self.color_distance_type = color_distance_type + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}" + f"(kernel_size={self.kernel_size}, " + f"sigma_color={self.sigma_color}, " + f"sigma_space={self.sigma_space}, " + f"border_type={self.border_type}, " + f"color_distance_type={self.color_distance_type})" + ) + + +class BilateralBlur(_BilateralBlur): + def forward(self, input: Tensor) -> Tensor: + return bilateral_blur( + input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type + ) + + +class JointBilateralBlur(_BilateralBlur): + def forward(self, input: Tensor, guidance: Tensor) -> Tensor: + return joint_bilateral_blur( + input, + guidance, + self.kernel_size, + self.sigma_color, + self.sigma_space, + self.border_type, + self.color_distance_type, + ) diff --git a/modules/async_worker.py b/modules/async_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..d8a1e072db52e1fbbaa10a67a2ee9f66e7729428 --- /dev/null +++ b/modules/async_worker.py @@ -0,0 +1,927 @@ +import threading +import re +from modules.patch import PatchSettings, patch_settings, patch_all + +patch_all() + +class AsyncTask: + def __init__(self, args): + self.args = args + self.yields = [] + self.results = [] + self.last_stop = False + self.processing = False + + +async_tasks = [] + + +def worker(): + global async_tasks + + import os + import traceback + import math + import numpy as np + import cv2 + import torch + import time + import shared + import random + import copy + import modules.default_pipeline as pipeline + import modules.core as core + import modules.flags as flags + import modules.config + import modules.patch + import ldm_patched.modules.model_management + import extras.preprocessors as preprocessors + import modules.inpaint_worker as inpaint_worker + import modules.constants as constants + import extras.ip_adapter as ip_adapter + import extras.face_crop + import fooocus_version + import args_manager + + from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays + from modules.private_logger import log + from extras.expansion import safe_str + from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \ + get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras + from modules.upscaler import perform_upscale + from modules.flags import Performance + from modules.meta_parser import get_metadata_parser, MetadataScheme + + pid = os.getpid() + print(f'Started worker with PID {pid}') + + try: + async_gradio_app = shared.gradio_root + flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}''' + if async_gradio_app.share: + flag += f''' or {async_gradio_app.share_url}''' + print(flag) + except Exception as e: + print(e) + + def progressbar(async_task, number, text): + print(f'[Fooocus] {text}') + async_task.yields.append(['preview', (number, text, None)]) + + def yield_result(async_task, imgs, do_not_show_finished_images=False): + if not isinstance(imgs, list): + imgs = [imgs] + + async_task.results = async_task.results + imgs + + if do_not_show_finished_images: + return + + async_task.yields.append(['results', async_task.results]) + return + + def build_image_wall(async_task): + results = [] + + if len(async_task.results) < 2: + return + + for img in async_task.results: + if isinstance(img, str) and os.path.exists(img): + img = cv2.imread(img) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + if not isinstance(img, np.ndarray): + return + if img.ndim != 3: + return + results.append(img) + + H, W, C = results[0].shape + + for img in results: + Hn, Wn, Cn = img.shape + if H != Hn: + return + if W != Wn: + return + if C != Cn: + return + + cols = float(len(results)) ** 0.5 + cols = int(math.ceil(cols)) + rows = float(len(results)) / float(cols) + rows = int(math.ceil(rows)) + + wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8) + + for y in range(rows): + for x in range(cols): + if y * cols + x < len(results): + img = results[y * cols + x] + wall[y * H:y * H + H, x * W:x * W + W, :] = img + + # must use deep copy otherwise gradio is super laggy. Do not use list.append() . + async_task.results = async_task.results + [wall] + return + + @torch.no_grad() + @torch.inference_mode() + def handler(async_task): + execution_start_time = time.perf_counter() + async_task.processing = True + + args = async_task.args + args.reverse() + + prompt = args.pop() + negative_prompt = args.pop() + style_selections = args.pop() + performance_selection = Performance(args.pop()) + aspect_ratios_selection = args.pop() + image_number = args.pop() + output_format = args.pop() + image_seed = args.pop() + read_wildcards_in_order = args.pop() + sharpness = args.pop() + guidance_scale = args.pop() + base_model_name = args.pop() + refiner_model_name = args.pop() + refiner_switch = args.pop() + loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)]) + input_image_checkbox = args.pop() + current_tab = args.pop() + uov_method = args.pop() + uov_input_image = args.pop() + outpaint_selections = args.pop() + inpaint_input_image = args.pop() + inpaint_additional_prompt = args.pop() + inpaint_mask_image_upload = args.pop() + + disable_preview = args.pop() + disable_intermediate_results = args.pop() + disable_seed_increment = args.pop() + adm_scaler_positive = args.pop() + adm_scaler_negative = args.pop() + adm_scaler_end = args.pop() + adaptive_cfg = args.pop() + sampler_name = args.pop() + scheduler_name = args.pop() + overwrite_step = args.pop() + overwrite_switch = args.pop() + overwrite_width = args.pop() + overwrite_height = args.pop() + overwrite_vary_strength = args.pop() + overwrite_upscale_strength = args.pop() + mixing_image_prompt_and_vary_upscale = args.pop() + mixing_image_prompt_and_inpaint = args.pop() + debugging_cn_preprocessor = args.pop() + skipping_cn_preprocessor = args.pop() + canny_low_threshold = args.pop() + canny_high_threshold = args.pop() + refiner_swap_method = args.pop() + controlnet_softness = args.pop() + freeu_enabled = args.pop() + freeu_b1 = args.pop() + freeu_b2 = args.pop() + freeu_s1 = args.pop() + freeu_s2 = args.pop() + debugging_inpaint_preprocessor = args.pop() + inpaint_disable_initial_latent = args.pop() + inpaint_engine = args.pop() + inpaint_strength = args.pop() + inpaint_respective_field = args.pop() + inpaint_mask_upload_checkbox = args.pop() + invert_mask_checkbox = args.pop() + inpaint_erode_or_dilate = args.pop() + + save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False + metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS + + cn_tasks = {x: [] for x in flags.ip_list} + for _ in range(flags.controlnet_image_count): + cn_img = args.pop() + cn_stop = args.pop() + cn_weight = args.pop() + cn_type = args.pop() + if cn_img is not None: + cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) + + outpaint_selections = [o.lower() for o in outpaint_selections] + base_model_additional_loras = [] + raw_style_selections = copy.deepcopy(style_selections) + uov_method = uov_method.lower() + + if fooocus_expansion in style_selections: + use_expansion = True + style_selections.remove(fooocus_expansion) + else: + use_expansion = False + + use_style = len(style_selections) > 0 + + if base_model_name == refiner_model_name: + print(f'Refiner disabled because base model and refiner are same.') + refiner_model_name = 'None' + + steps = performance_selection.steps() + + if performance_selection == Performance.EXTREME_SPEED: + print('Enter LCM mode.') + progressbar(async_task, 1, 'Downloading LCM components ...') + loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] + + if refiner_model_name != 'None': + print(f'Refiner disabled in LCM mode.') + + refiner_model_name = 'None' + sampler_name = 'lcm' + scheduler_name = 'lcm' + sharpness = 0.0 + guidance_scale = 1.0 + adaptive_cfg = 1.0 + refiner_switch = 1.0 + adm_scaler_positive = 1.0 + adm_scaler_negative = 1.0 + adm_scaler_end = 0.0 + + elif performance_selection == Performance.LIGHTNING: + print('Enter Lightning mode.') + progressbar(async_task, 1, 'Downloading Lightning components ...') + loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)] + + if refiner_model_name != 'None': + print(f'Refiner disabled in Lightning mode.') + + refiner_model_name = 'None' + sampler_name = 'euler' + scheduler_name = 'sgm_uniform' + sharpness = 0.0 + guidance_scale = 1.0 + adaptive_cfg = 1.0 + refiner_switch = 1.0 + adm_scaler_positive = 1.0 + adm_scaler_negative = 1.0 + adm_scaler_end = 0.0 + + print(f'[Parameters] Adaptive CFG = {adaptive_cfg}') + print(f'[Parameters] Sharpness = {sharpness}') + print(f'[Parameters] ControlNet Softness = {controlnet_softness}') + print(f'[Parameters] ADM Scale = ' + f'{adm_scaler_positive} : ' + f'{adm_scaler_negative} : ' + f'{adm_scaler_end}') + + patch_settings[pid] = PatchSettings( + sharpness, + adm_scaler_end, + adm_scaler_positive, + adm_scaler_negative, + controlnet_softness, + adaptive_cfg + ) + + cfg_scale = float(guidance_scale) + print(f'[Parameters] CFG = {cfg_scale}') + + initial_latent = None + denoising_strength = 1.0 + tiled = False + + width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2] + width, height = int(width), int(height) + + skip_prompt_processing = False + + inpaint_worker.current_task = None + inpaint_parameterized = inpaint_engine != 'None' + inpaint_image = None + inpaint_mask = None + inpaint_head_model_path = None + + use_synthetic_refiner = False + + controlnet_canny_path = None + controlnet_cpds_path = None + clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None + + seed = int(image_seed) + print(f'[Parameters] Seed = {seed}') + + goals = [] + tasks = [] + + if input_image_checkbox: + if (current_tab == 'uov' or ( + current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \ + and uov_method != flags.disabled and uov_input_image is not None: + uov_input_image = HWC3(uov_input_image) + if 'vary' in uov_method: + goals.append('vary') + elif 'upscale' in uov_method: + goals.append('upscale') + if 'fast' in uov_method: + skip_prompt_processing = True + else: + steps = performance_selection.steps_uov() + + progressbar(async_task, 1, 'Downloading upscale models ...') + modules.config.downloading_upscale_model() + if (current_tab == 'inpaint' or ( + current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \ + and isinstance(inpaint_input_image, dict): + inpaint_image = inpaint_input_image['image'] + inpaint_mask = inpaint_input_image['mask'][:, :, 0] + + if inpaint_mask_upload_checkbox: + if isinstance(inpaint_mask_image_upload, np.ndarray): + if inpaint_mask_image_upload.ndim == 3: + H, W, C = inpaint_image.shape + inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H) + inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2) + inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255 + inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload) + + if int(inpaint_erode_or_dilate) != 0: + inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate) + + if invert_mask_checkbox: + inpaint_mask = 255 - inpaint_mask + + inpaint_image = HWC3(inpaint_image) + if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ + and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0): + progressbar(async_task, 1, 'Downloading upscale models ...') + modules.config.downloading_upscale_model() + if inpaint_parameterized: + progressbar(async_task, 1, 'Downloading inpainter ...') + inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( + inpaint_engine) + base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] + print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') + if refiner_model_name == 'None': + use_synthetic_refiner = True + refiner_switch = 0.8 + else: + inpaint_head_model_path, inpaint_patch_model_path = None, None + print(f'[Inpaint] Parameterized inpaint is disabled.') + if inpaint_additional_prompt != '': + if prompt == '': + prompt = inpaint_additional_prompt + else: + prompt = inpaint_additional_prompt + '\n' + prompt + goals.append('inpaint') + if current_tab == 'ip' or \ + mixing_image_prompt_and_vary_upscale or \ + mixing_image_prompt_and_inpaint: + goals.append('cn') + progressbar(async_task, 1, 'Downloading control models ...') + if len(cn_tasks[flags.cn_canny]) > 0: + controlnet_canny_path = modules.config.downloading_controlnet_canny() + if len(cn_tasks[flags.cn_cpds]) > 0: + controlnet_cpds_path = modules.config.downloading_controlnet_cpds() + if len(cn_tasks[flags.cn_ip]) > 0: + clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip') + if len(cn_tasks[flags.cn_ip_face]) > 0: + clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters( + 'face') + progressbar(async_task, 1, 'Loading control models ...') + + # Load or unload CNs + pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path]) + ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) + ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path) + + if overwrite_step > 0: + steps = overwrite_step + + switch = int(round(steps * refiner_switch)) + + if overwrite_switch > 0: + switch = overwrite_switch + + if overwrite_width > 0: + width = overwrite_width + + if overwrite_height > 0: + height = overwrite_height + + print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}') + print(f'[Parameters] Steps = {steps} - {switch}') + + progressbar(async_task, 1, 'Initializing ...') + + if not skip_prompt_processing: + + prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='') + negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='') + + prompt = prompts[0] + negative_prompt = negative_prompts[0] + + if prompt == '': + # disable expansion when empty since it is not meaningful and influences image prompt + use_expansion = False + + extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] + extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else [] + + progressbar(async_task, 3, 'Loading models ...') + pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, + loras=loras, base_model_additional_loras=base_model_additional_loras, + use_synthetic_refiner=use_synthetic_refiner) + + progressbar(async_task, 3, 'Processing prompts ...') + tasks = [] + + for i in range(image_number): + if disable_seed_increment: + task_seed = seed % (constants.MAX_SEED + 1) + else: + task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not + + task_rng = random.Random(task_seed) # may bind to inpaint noise in the future + task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order) + task_prompt = apply_arrays(task_prompt, i) + task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order) + task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts] + task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts] + + positive_basic_workloads = [] + negative_basic_workloads = [] + + if use_style: + for s in style_selections: + p, n = apply_style(s, positive=task_prompt) + positive_basic_workloads = positive_basic_workloads + p + negative_basic_workloads = negative_basic_workloads + n + else: + positive_basic_workloads.append(task_prompt) + + negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative. + + positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts + negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts + + positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt) + negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt) + + tasks.append(dict( + task_seed=task_seed, + task_prompt=task_prompt, + task_negative_prompt=task_negative_prompt, + positive=positive_basic_workloads, + negative=negative_basic_workloads, + expansion='', + c=None, + uc=None, + positive_top_k=len(positive_basic_workloads), + negative_top_k=len(negative_basic_workloads), + log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts), + log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts), + )) + + if use_expansion: + for i, t in enumerate(tasks): + progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...') + expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed']) + print(f'[Prompt Expansion] {expansion}') + t['expansion'] = expansion + t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy. + + for i, t in enumerate(tasks): + progressbar(async_task, 7, f'Encoding positive #{i + 1} ...') + t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k']) + + for i, t in enumerate(tasks): + if abs(float(cfg_scale) - 1.0) < 1e-4: + t['uc'] = pipeline.clone_cond(t['c']) + else: + progressbar(async_task, 10, f'Encoding negative #{i + 1} ...') + t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k']) + + if len(goals) > 0: + progressbar(async_task, 13, 'Image processing ...') + + if 'vary' in goals: + if 'subtle' in uov_method: + denoising_strength = 0.5 + if 'strong' in uov_method: + denoising_strength = 0.85 + if overwrite_vary_strength > 0: + denoising_strength = overwrite_vary_strength + + shape_ceil = get_image_shape_ceil(uov_input_image) + if shape_ceil < 1024: + print(f'[Vary] Image is resized because it is too small.') + shape_ceil = 1024 + elif shape_ceil > 2048: + print(f'[Vary] Image is resized because it is too big.') + shape_ceil = 2048 + + uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil) + + initial_pixels = core.numpy_to_pytorch(uov_input_image) + progressbar(async_task, 13, 'VAE encoding ...') + + candidate_vae, _ = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels) + B, C, H, W = initial_latent['samples'].shape + width = W * 8 + height = H * 8 + print(f'Final resolution is {str((height, width))}.') + + if 'upscale' in goals: + H, W, C = uov_input_image.shape + progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...') + uov_input_image = perform_upscale(uov_input_image) + print(f'Image upscaled.') + + if '1.5x' in uov_method: + f = 1.5 + elif '2x' in uov_method: + f = 2.0 + else: + f = 1.0 + + shape_ceil = get_shape_ceil(H * f, W * f) + + if shape_ceil < 1024: + print(f'[Upscale] Image is resized because it is too small.') + uov_input_image = set_image_shape_ceil(uov_input_image, 1024) + shape_ceil = 1024 + else: + uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f) + + image_is_super_large = shape_ceil > 2800 + + if 'fast' in uov_method: + direct_return = True + elif image_is_super_large: + print('Image is too large. Directly returned the SR image. ' + 'Usually directly return SR image at 4K resolution ' + 'yields better results than SDXL diffusion.') + direct_return = True + else: + direct_return = False + + if direct_return: + d = [('Upscale (Fast)', 'upscale_fast', '2x')] + uov_input_image_path = log(uov_input_image, d, output_format=output_format) + yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True) + return + + tiled = True + denoising_strength = 0.382 + + if overwrite_upscale_strength > 0: + denoising_strength = overwrite_upscale_strength + + initial_pixels = core.numpy_to_pytorch(uov_input_image) + progressbar(async_task, 13, 'VAE encoding ...') + + candidate_vae, _ = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + initial_latent = core.encode_vae( + vae=candidate_vae, + pixels=initial_pixels, tiled=True) + B, C, H, W = initial_latent['samples'].shape + width = W * 8 + height = H * 8 + print(f'Final resolution is {str((height, width))}.') + + if 'inpaint' in goals: + if len(outpaint_selections) > 0: + H, W, C = inpaint_image.shape + if 'top' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', + constant_values=255) + if 'bottom' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', + constant_values=255) + + H, W, C = inpaint_image.shape + if 'left' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant', + constant_values=255) + if 'right' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant', + constant_values=255) + + inpaint_image = np.ascontiguousarray(inpaint_image.copy()) + inpaint_mask = np.ascontiguousarray(inpaint_mask.copy()) + inpaint_strength = 1.0 + inpaint_respective_field = 1.0 + + denoising_strength = inpaint_strength + + inpaint_worker.current_task = inpaint_worker.InpaintWorker( + image=inpaint_image, + mask=inpaint_mask, + use_fill=denoising_strength > 0.99, + k=inpaint_respective_field + ) + + if debugging_inpaint_preprocessor: + yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), + do_not_show_finished_images=True) + return + + progressbar(async_task, 13, 'VAE Inpaint encoding ...') + + inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill) + inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image) + inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask) + + candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + latent_inpaint, latent_mask = core.encode_vae_inpaint( + mask=inpaint_pixel_mask, + vae=candidate_vae, + pixels=inpaint_pixel_image) + + latent_swap = None + if candidate_vae_swap is not None: + progressbar(async_task, 13, 'VAE SD15 encoding ...') + latent_swap = core.encode_vae( + vae=candidate_vae_swap, + pixels=inpaint_pixel_fill)['samples'] + + progressbar(async_task, 13, 'VAE encoding ...') + latent_fill = core.encode_vae( + vae=candidate_vae, + pixels=inpaint_pixel_fill)['samples'] + + inpaint_worker.current_task.load_latent( + latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap) + + if inpaint_parameterized: + pipeline.final_unet = inpaint_worker.current_task.patch( + inpaint_head_model_path=inpaint_head_model_path, + inpaint_latent=latent_inpaint, + inpaint_latent_mask=latent_mask, + model=pipeline.final_unet + ) + + if not inpaint_disable_initial_latent: + initial_latent = {'samples': latent_fill} + + B, C, H, W = latent_fill.shape + height, width = H * 8, W * 8 + final_height, final_width = inpaint_worker.current_task.image.shape[:2] + print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.') + + if 'cn' in goals: + for task in cn_tasks[flags.cn_canny]: + cn_img, cn_stop, cn_weight = task + cn_img = resize_image(HWC3(cn_img), width=width, height=height) + + if not skipping_cn_preprocessor: + cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold) + + cn_img = HWC3(cn_img) + task[0] = core.numpy_to_pytorch(cn_img) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_cpds]: + cn_img, cn_stop, cn_weight = task + cn_img = resize_image(HWC3(cn_img), width=width, height=height) + + if not skipping_cn_preprocessor: + cn_img = preprocessors.cpds(cn_img) + + cn_img = HWC3(cn_img) + task[0] = core.numpy_to_pytorch(cn_img) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_ip]: + cn_img, cn_stop, cn_weight = task + cn_img = HWC3(cn_img) + + # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 + cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) + + task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_ip_face]: + cn_img, cn_stop, cn_weight = task + cn_img = HWC3(cn_img) + + if not skipping_cn_preprocessor: + cn_img = extras.face_crop.crop_image(cn_img) + + # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 + cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) + + task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + + all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face] + + if len(all_ip_tasks) > 0: + pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks) + + if freeu_enabled: + print(f'FreeU is enabled!') + pipeline.final_unet = core.apply_freeu( + pipeline.final_unet, + freeu_b1, + freeu_b2, + freeu_s1, + freeu_s2 + ) + + all_steps = steps * image_number + + print(f'[Parameters] Denoising Strength = {denoising_strength}') + + if isinstance(initial_latent, dict) and 'samples' in initial_latent: + log_shape = initial_latent['samples'].shape + else: + log_shape = f'Image Space {(height, width)}' + + print(f'[Parameters] Initial Latent shape: {log_shape}') + + preparation_time = time.perf_counter() - execution_start_time + print(f'Preparation time: {preparation_time:.2f} seconds') + + final_sampler_name = sampler_name + final_scheduler_name = scheduler_name + + if scheduler_name == 'lcm': + final_scheduler_name = 'sgm_uniform' + if pipeline.final_unet is not None: + pipeline.final_unet = core.opModelSamplingDiscrete.patch( + pipeline.final_unet, + sampling='lcm', + zsnr=False)[0] + if pipeline.final_refiner_unet is not None: + pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch( + pipeline.final_refiner_unet, + sampling='lcm', + zsnr=False)[0] + print('Using lcm scheduler.') + + async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)]) + + def callback(step, x0, x, total_steps, y): + done_steps = current_task_id * steps + step + async_task.yields.append(['preview', ( + int(15.0 + 85.0 * float(done_steps) / float(all_steps)), + f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)]) + + for current_task_id, task in enumerate(tasks): + execution_start_time = time.perf_counter() + + try: + if async_task.last_stop is not False: + ldm_patched.modules.model_management.interrupt_current_processing() + positive_cond, negative_cond = task['c'], task['uc'] + + if 'cn' in goals: + for cn_flag, cn_path in [ + (flags.cn_canny, controlnet_canny_path), + (flags.cn_cpds, controlnet_cpds_path) + ]: + for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]: + positive_cond, negative_cond = core.apply_controlnet( + positive_cond, negative_cond, + pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop) + + imgs = pipeline.process_diffusion( + positive_cond=positive_cond, + negative_cond=negative_cond, + steps=steps, + switch=switch, + width=width, + height=height, + image_seed=task['task_seed'], + callback=callback, + sampler_name=final_sampler_name, + scheduler_name=final_scheduler_name, + latent=initial_latent, + denoise=denoising_strength, + tiled=tiled, + cfg_scale=cfg_scale, + refiner_swap_method=refiner_swap_method, + disable_preview=disable_preview + ) + + del task['c'], task['uc'], positive_cond, negative_cond # Save memory + + if inpaint_worker.current_task is not None: + imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] + + img_paths = [] + for x in imgs: + d = [('Prompt', 'prompt', task['log_positive_prompt']), + ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), + ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']), + ('Styles', 'styles', str(raw_style_selections)), + ('Performance', 'performance', performance_selection.value)] + + if performance_selection.steps() != steps: + d.append(('Steps', 'steps', steps)) + + d += [('Resolution', 'resolution', str((width, height))), + ('Guidance Scale', 'guidance_scale', guidance_scale), + ('Sharpness', 'sharpness', sharpness), + ('ADM Guidance', 'adm_guidance', str(( + modules.patch.patch_settings[pid].positive_adm_scale, + modules.patch.patch_settings[pid].negative_adm_scale, + modules.patch.patch_settings[pid].adm_scaler_end))), + ('Base Model', 'base_model', base_model_name), + ('Refiner Model', 'refiner_model', refiner_model_name), + ('Refiner Switch', 'refiner_switch', refiner_switch)] + + if refiner_model_name != 'None': + if overwrite_switch > 0: + d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch)) + if refiner_swap_method != flags.refiner_swap_method: + d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method)) + if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr: + d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg)) + + d.append(('Sampler', 'sampler', sampler_name)) + d.append(('Scheduler', 'scheduler', scheduler_name)) + d.append(('Seed', 'seed', str(task['task_seed']))) + + if freeu_enabled: + d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2)))) + + for li, (n, w) in enumerate(loras): + if n != 'None': + d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}')) + + metadata_parser = None + if save_metadata_to_images: + metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) + metadata_parser.set_data(task['log_positive_prompt'], task['positive'], + task['log_negative_prompt'], task['negative'], + steps, base_model_name, refiner_model_name, loras) + d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images)) + d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) + img_paths.append(log(x, d, metadata_parser, output_format)) + + yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results) + except ldm_patched.modules.model_management.InterruptProcessingException as e: + if async_task.last_stop == 'skip': + print('User skipped') + async_task.last_stop = False + continue + else: + print('User stopped') + break + + execution_time = time.perf_counter() - execution_start_time + print(f'Generating and saving time: {execution_time:.2f} seconds') + async_task.processing = False + return + + while True: + time.sleep(0.01) + if len(async_tasks) > 0: + task = async_tasks.pop(0) + generate_image_grid = task.args.pop(0) + + try: + handler(task) + if generate_image_grid: + build_image_wall(task) + task.yields.append(['finish', task.results]) + pipeline.prepare_text_encoder(async_call=True) + except: + traceback.print_exc() + task.yields.append(['finish', task.results]) + finally: + if pid in modules.patch.patch_settings: + del modules.patch.patch_settings[pid] + pass + + +threading.Thread(target=worker, daemon=True).start() diff --git a/modules/auth.py b/modules/auth.py new file mode 100644 index 0000000000000000000000000000000000000000..3ba111424523c19174f8b741b3bbac7b43b7bb6c --- /dev/null +++ b/modules/auth.py @@ -0,0 +1,41 @@ +import json +import hashlib +import modules.constants as constants + +from os.path import exists + + +def auth_list_to_dict(auth_list): + auth_dict = {} + for auth_data in auth_list: + if 'user' in auth_data: + if 'hash' in auth_data: + auth_dict |= {auth_data['user']: auth_data['hash']} + elif 'pass' in auth_data: + auth_dict |= {auth_data['user']: hashlib.sha256(bytes(auth_data['pass'], encoding='utf-8')).hexdigest()} + return auth_dict + + +def load_auth_data(filename=None): + auth_dict = None + if filename != None and exists(filename): + with open(filename, encoding='utf-8') as auth_file: + try: + auth_obj = json.load(auth_file) + if isinstance(auth_obj, list) and len(auth_obj) > 0: + auth_dict = auth_list_to_dict(auth_obj) + except Exception as e: + print('load_auth_data, e: ' + str(e)) + return auth_dict + + +auth_dict = load_auth_data(constants.AUTH_FILENAME) + +auth_enabled = auth_dict != None + + +def check_auth(user, password): + if user not in auth_dict: + return False + else: + return hashlib.sha256(bytes(password, encoding='utf-8')).hexdigest() == auth_dict[user] diff --git a/modules/config.py b/modules/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b81e218a019033fe9157f3247a9b8e25f53f2a2c --- /dev/null +++ b/modules/config.py @@ -0,0 +1,683 @@ +import os +import json +import math +import numbers +import args_manager +import tempfile +import modules.flags +import modules.sdxl_styles + +from modules.model_loader import load_file_from_url +from modules.util import get_files_from_folder, makedirs_with_log +from modules.flags import OutputFormat, Performance, MetadataScheme + + +def get_config_path(key, default_value): + env = os.getenv(key) + if env is not None and isinstance(env, str): + print(f"Environment: {key} = {env}") + return env + else: + return os.path.abspath(default_value) + + +config_path = get_config_path('config_path', "./config.txt") +config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt") +config_dict = {} +always_save_keys = [] +visited_keys = [] + +try: + with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file: + config_dict.update(json.load(json_file)) +except Exception as e: + print(f'Load default preset failed.') + print(e) + +try: + if os.path.exists(config_path): + with open(config_path, "r", encoding="utf-8") as json_file: + config_dict.update(json.load(json_file)) + always_save_keys = list(config_dict.keys()) +except Exception as e: + print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}') + print('Please make sure that:') + print(f'1. The file "{config_path}" is a valid text file, and you have access to read it.') + print('2. Use "\\\\" instead of "\\" when describing paths.') + print('3. There is no "," before the last "}".') + print('4. All key/value formats are correct.') + + +def try_load_deprecated_user_path_config(): + global config_dict + + if not os.path.exists('user_path_config.txt'): + return + + try: + deprecated_config_dict = json.load(open('user_path_config.txt', "r", encoding="utf-8")) + + def replace_config(old_key, new_key): + if old_key in deprecated_config_dict: + config_dict[new_key] = deprecated_config_dict[old_key] + del deprecated_config_dict[old_key] + + replace_config('modelfile_path', 'path_checkpoints') + replace_config('lorafile_path', 'path_loras') + replace_config('embeddings_path', 'path_embeddings') + replace_config('vae_approx_path', 'path_vae_approx') + replace_config('upscale_models_path', 'path_upscale_models') + replace_config('inpaint_models_path', 'path_inpaint') + replace_config('controlnet_models_path', 'path_controlnet') + replace_config('clip_vision_models_path', 'path_clip_vision') + replace_config('fooocus_expansion_path', 'path_fooocus_expansion') + replace_config('temp_outputs_path', 'path_outputs') + + if deprecated_config_dict.get("default_model", None) == 'juggernautXL_version6Rundiffusion.safetensors': + os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') + print('Config updated successfully in silence. ' + 'A backup of previous config is written to "user_path_config-deprecated.txt".') + return + + if input("Newer models and configs are available. " + "Download and update files? [Y/n]:") in ['n', 'N', 'No', 'no', 'NO']: + config_dict.update(deprecated_config_dict) + print('Loading using deprecated old models and deprecated old configs.') + return + else: + os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') + print('Config updated successfully by user. ' + 'A backup of previous config is written to "user_path_config-deprecated.txt".') + return + except Exception as e: + print('Processing deprecated config failed') + print(e) + return + + +try_load_deprecated_user_path_config() + + +def get_presets(): + preset_folder = 'presets' + presets = ['initial'] + if not os.path.exists(preset_folder): + print('No presets found.') + return presets + + return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')] + + +def try_get_preset_content(preset): + if isinstance(preset, str): + preset_path = os.path.abspath(f'./presets/{preset}.json') + try: + if os.path.exists(preset_path): + with open(preset_path, "r", encoding="utf-8") as json_file: + json_content = json.load(json_file) + print(f'Loaded preset: {preset_path}') + return json_content + else: + raise FileNotFoundError + except Exception as e: + print(f'Load preset [{preset_path}] failed') + print(e) + return {} + +available_presets = get_presets() +preset = args_manager.args.preset +config_dict.update(try_get_preset_content(preset)) + +def get_path_output() -> str: + """ + Checking output path argument and overriding default path. + """ + global config_dict + path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True) + if args_manager.args.output_path: + print(f'Overriding config value path_outputs with {args_manager.args.output_path}') + config_dict['path_outputs'] = path_output = args_manager.args.output_path + return path_output + + +def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False): + global config_dict, visited_keys, always_save_keys + + if key not in visited_keys: + visited_keys.append(key) + + if key not in always_save_keys: + always_save_keys.append(key) + + v = os.getenv(key) + if v is not None: + print(f"Environment: {key} = {v}") + config_dict[key] = v + else: + v = config_dict.get(key, None) + + if isinstance(v, str): + if make_directory: + makedirs_with_log(v) + if os.path.exists(v) and os.path.isdir(v): + return v if not as_array else [v] + elif isinstance(v, list): + if make_directory: + for d in v: + makedirs_with_log(d) + if all([os.path.exists(d) and os.path.isdir(d) for d in v]): + return v + + if v is not None: + print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.') + if isinstance(default_value, list): + dp = [] + for path in default_value: + abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path)) + dp.append(abs_path) + os.makedirs(abs_path, exist_ok=True) + else: + dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value)) + os.makedirs(dp, exist_ok=True) + if as_array: + dp = [dp] + config_dict[key] = dp + return dp + + +paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True) +paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True) +path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/') +path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/') +path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/') +path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/') +path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/') +path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/') +path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion') +path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/') +path_outputs = get_path_output() + + +def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False): + global config_dict, visited_keys + + if key not in visited_keys: + visited_keys.append(key) + + v = os.getenv(key) + if v is not None: + print(f"Environment: {key} = {v}") + config_dict[key] = v + + if key not in config_dict: + config_dict[key] = default_value + return default_value + + v = config_dict.get(key, None) + if not disable_empty_as_none: + if v is None or v == '': + v = 'None' + if validator(v): + return v + else: + if v is not None: + print(f'Failed to load config key: {json.dumps({key:v})} is invalid; will use {json.dumps({key:default_value})} instead.') + config_dict[key] = default_value + return default_value + + +def init_temp_path(path: str | None, default_path: str) -> str: + if args_manager.args.temp_path: + path = args_manager.args.temp_path + + if path != '' and path != default_path: + try: + if not os.path.isabs(path): + path = os.path.abspath(path) + os.makedirs(path, exist_ok=True) + print(f'Using temp path {path}') + return path + except Exception as e: + print(f'Could not create temp path {path}. Reason: {e}') + print(f'Using default temp path {default_path} instead.') + + os.makedirs(default_path, exist_ok=True) + return default_path + + +default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus') +temp_path = init_temp_path(get_config_item_or_set_default( + key='temp_path', + default_value=default_temp_path, + validator=lambda x: isinstance(x, str), +), default_temp_path) +temp_path_cleanup_on_launch = get_config_item_or_set_default( + key='temp_path_cleanup_on_launch', + default_value=True, + validator=lambda x: isinstance(x, bool) +) +default_base_model_name = default_model = get_config_item_or_set_default( + key='default_model', + default_value='model.safetensors', + validator=lambda x: isinstance(x, str) +) +previous_default_models = get_config_item_or_set_default( + key='previous_default_models', + default_value=[], + validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x) +) +default_refiner_model_name = default_refiner = get_config_item_or_set_default( + key='default_refiner', + default_value='None', + validator=lambda x: isinstance(x, str) +) +default_refiner_switch = get_config_item_or_set_default( + key='default_refiner_switch', + default_value=0.8, + validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1 +) +default_loras_min_weight = get_config_item_or_set_default( + key='default_loras_min_weight', + default_value=-2, + validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 +) +default_loras_max_weight = get_config_item_or_set_default( + key='default_loras_max_weight', + default_value=2, + validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 +) +default_loras = get_config_item_or_set_default( + key='default_loras', + default_value=[ + [ + True, + "None", + 1.0 + ], + [ + True, + "None", + 1.0 + ], + [ + True, + "None", + 1.0 + ], + [ + True, + "None", + 1.0 + ], + [ + True, + "None", + 1.0 + ] + ], + validator=lambda x: isinstance(x, list) and all( + len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number) + or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) + for y in x) +) +default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras] +default_max_lora_number = get_config_item_or_set_default( + key='default_max_lora_number', + default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5, + validator=lambda x: isinstance(x, int) and x >= 1 +) +default_cfg_scale = get_config_item_or_set_default( + key='default_cfg_scale', + default_value=7.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_sample_sharpness = get_config_item_or_set_default( + key='default_sample_sharpness', + default_value=2.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_sampler = get_config_item_or_set_default( + key='default_sampler', + default_value='dpmpp_2m_sde_gpu', + validator=lambda x: x in modules.flags.sampler_list +) +default_scheduler = get_config_item_or_set_default( + key='default_scheduler', + default_value='karras', + validator=lambda x: x in modules.flags.scheduler_list +) +default_styles = get_config_item_or_set_default( + key='default_styles', + default_value=[ + "Fooocus V2", + "Fooocus Enhance", + "Fooocus Sharp" + ], + validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x) +) +default_prompt_negative = get_config_item_or_set_default( + key='default_prompt_negative', + default_value='', + validator=lambda x: isinstance(x, str), + disable_empty_as_none=True +) +default_prompt = get_config_item_or_set_default( + key='default_prompt', + default_value='', + validator=lambda x: isinstance(x, str), + disable_empty_as_none=True +) +default_performance = get_config_item_or_set_default( + key='default_performance', + default_value=Performance.SPEED.value, + validator=lambda x: x in Performance.list() +) +default_advanced_checkbox = get_config_item_or_set_default( + key='default_advanced_checkbox', + default_value=False, + validator=lambda x: isinstance(x, bool) +) +default_max_image_number = get_config_item_or_set_default( + key='default_max_image_number', + default_value=32, + validator=lambda x: isinstance(x, int) and x >= 1 +) +default_output_format = get_config_item_or_set_default( + key='default_output_format', + default_value='png', + validator=lambda x: x in OutputFormat.list() +) +default_image_number = get_config_item_or_set_default( + key='default_image_number', + default_value=2, + validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number +) +checkpoint_downloads = get_config_item_or_set_default( + key='checkpoint_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +lora_downloads = get_config_item_or_set_default( + key='lora_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +embeddings_downloads = get_config_item_or_set_default( + key='embeddings_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +available_aspect_ratios = get_config_item_or_set_default( + key='available_aspect_ratios', + default_value=[ + '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152', + '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960', + '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768', + '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640', + '1664*576', '1728*576' + ], + validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1 +) +default_aspect_ratio = get_config_item_or_set_default( + key='default_aspect_ratio', + default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0], + validator=lambda x: x in available_aspect_ratios +) +default_inpaint_engine_version = get_config_item_or_set_default( + key='default_inpaint_engine_version', + default_value='v2.6', + validator=lambda x: x in modules.flags.inpaint_engine_versions +) +default_cfg_tsnr = get_config_item_or_set_default( + key='default_cfg_tsnr', + default_value=7.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_overwrite_step = get_config_item_or_set_default( + key='default_overwrite_step', + default_value=-1, + validator=lambda x: isinstance(x, int) +) +default_overwrite_switch = get_config_item_or_set_default( + key='default_overwrite_switch', + default_value=-1, + validator=lambda x: isinstance(x, int) +) +example_inpaint_prompts = get_config_item_or_set_default( + key='example_inpaint_prompts', + default_value=[ + 'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes' + ], + validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x) +) +default_save_metadata_to_images = get_config_item_or_set_default( + key='default_save_metadata_to_images', + default_value=False, + validator=lambda x: isinstance(x, bool) +) +default_metadata_scheme = get_config_item_or_set_default( + key='default_metadata_scheme', + default_value=MetadataScheme.FOOOCUS.value, + validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x] +) +metadata_created_by = get_config_item_or_set_default( + key='metadata_created_by', + default_value='', + validator=lambda x: isinstance(x, str) +) + +example_inpaint_prompts = [[x] for x in example_inpaint_prompts] + +config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))] + +# mapping config to meta parameter +possible_preset_keys = { + "default_model": "base_model", + "default_refiner": "refiner_model", + "default_refiner_switch": "refiner_switch", + "previous_default_models": "previous_default_models", + "default_loras_min_weight": "default_loras_min_weight", + "default_loras_max_weight": "default_loras_max_weight", + "default_loras": " ", + "default_cfg_scale": "guidance_scale", + "default_sample_sharpness": "sharpness", + "default_sampler": "sampler", + "default_scheduler": "scheduler", + "default_overwrite_step": "steps", + "default_performance": "performance", + "default_image_number": "image_number", + "default_prompt": "prompt", + "default_prompt_negative": "negative_prompt", + "default_styles": "styles", + "default_aspect_ratio": "resolution", + "default_save_metadata_to_images": "default_save_metadata_to_images", + "checkpoint_downloads": "checkpoint_downloads", + "embeddings_downloads": "embeddings_downloads", + "lora_downloads": "lora_downloads" +} + +REWRITE_PRESET = False + +if REWRITE_PRESET and isinstance(args_manager.args.preset, str): + save_path = 'presets/' + args_manager.args.preset + '.json' + with open(save_path, "w", encoding="utf-8") as json_file: + json.dump({k: config_dict[k] for k in possible_preset_keys}, json_file, indent=4) + print(f'Preset saved to {save_path}. Exiting ...') + exit(0) + + +def add_ratio(x): + a, b = x.replace('*', ' ').split(' ')[:2] + a, b = int(a), int(b) + g = math.gcd(a, b) + return f'{a}×{b} \U00002223 {a // g}:{b // g}' + + +default_aspect_ratio = add_ratio(default_aspect_ratio) +available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios] + + +# Only write config in the first launch. +if not os.path.exists(config_path): + with open(config_path, "w", encoding="utf-8") as json_file: + json.dump({k: config_dict[k] for k in always_save_keys}, json_file, indent=4) + + +# Always write tutorials. +with open(config_example_path, "w", encoding="utf-8") as json_file: + cpa = config_path.replace("\\", "\\\\") + json_file.write(f'You can modify your "{cpa}" using the below keys, formats, and examples.\n' + f'Do not modify this file. Modifications in this file will not take effect.\n' + f'This file is a tutorial and example. Please edit "{cpa}" to really change any settings.\n' + + 'Remember to split the paths with "\\\\" rather than "\\", ' + 'and there is no "," before the last "}". \n\n\n') + json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4) + +model_filenames = [] +lora_filenames = [] +wildcard_filenames = [] + +sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors' +sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors' +loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora] + + +def get_model_filenames(folder_paths, extensions=None, name_filter=None): + if extensions is None: + extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'] + files = [] + for folder in folder_paths: + files += get_files_from_folder(folder, extensions, name_filter) + return files + + +def update_files(): + global model_filenames, lora_filenames, wildcard_filenames, available_presets + model_filenames = get_model_filenames(paths_checkpoints) + lora_filenames = get_model_filenames(paths_loras) + wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt']) + available_presets = get_presets() + return + + +def downloading_inpaint_models(v): + assert v in modules.flags.inpaint_engine_versions + + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth', + model_dir=path_inpaint, + file_name='fooocus_inpaint_head.pth' + ) + head_file = os.path.join(path_inpaint, 'fooocus_inpaint_head.pth') + patch_file = None + + if v == 'v1': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint.fooocus.patch') + + if v == 'v2.5': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint_v25.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint_v25.fooocus.patch') + + if v == 'v2.6': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint_v26.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint_v26.fooocus.patch') + + return head_file, patch_file + + +def downloading_sdxl_lcm_lora(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors', + model_dir=paths_loras[0], + file_name=sdxl_lcm_lora + ) + return sdxl_lcm_lora + +def downloading_sdxl_lightning_lora(): + load_file_from_url( + url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors', + model_dir=paths_loras[0], + file_name=sdxl_lightning_lora + ) + return sdxl_lightning_lora + + +def downloading_controlnet_canny(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors', + model_dir=path_controlnet, + file_name='control-lora-canny-rank128.safetensors' + ) + return os.path.join(path_controlnet, 'control-lora-canny-rank128.safetensors') + + +def downloading_controlnet_cpds(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_xl_cpds_128.safetensors', + model_dir=path_controlnet, + file_name='fooocus_xl_cpds_128.safetensors' + ) + return os.path.join(path_controlnet, 'fooocus_xl_cpds_128.safetensors') + + +def downloading_ip_adapters(v): + assert v in ['ip', 'face'] + + results = [] + + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/clip_vision_vit_h.safetensors', + model_dir=path_clip_vision, + file_name='clip_vision_vit_h.safetensors' + ) + results += [os.path.join(path_clip_vision, 'clip_vision_vit_h.safetensors')] + + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_ip_negative.safetensors', + model_dir=path_controlnet, + file_name='fooocus_ip_negative.safetensors' + ) + results += [os.path.join(path_controlnet, 'fooocus_ip_negative.safetensors')] + + if v == 'ip': + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus_sdxl_vit-h.bin', + model_dir=path_controlnet, + file_name='ip-adapter-plus_sdxl_vit-h.bin' + ) + results += [os.path.join(path_controlnet, 'ip-adapter-plus_sdxl_vit-h.bin')] + + if v == 'face': + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus-face_sdxl_vit-h.bin', + model_dir=path_controlnet, + file_name='ip-adapter-plus-face_sdxl_vit-h.bin' + ) + results += [os.path.join(path_controlnet, 'ip-adapter-plus-face_sdxl_vit-h.bin')] + + return results + + +def downloading_upscale_model(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_upscaler_s409985e5.bin', + model_dir=path_upscale_models, + file_name='fooocus_upscaler_s409985e5.bin' + ) + return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin') + + +update_files() diff --git a/modules/constants.py b/modules/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..667fa8682306e192465f11733fc9814bacedfe89 --- /dev/null +++ b/modules/constants.py @@ -0,0 +1,5 @@ +# as in k-diffusion (sampling.py) +MIN_SEED = 0 +MAX_SEED = 2**63 - 1 + +AUTH_FILENAME = 'auth.json' diff --git a/modules/core.py b/modules/core.py new file mode 100644 index 0000000000000000000000000000000000000000..38ee8e8dc66379cb38f570197cd1ec7fac4fb505 --- /dev/null +++ b/modules/core.py @@ -0,0 +1,339 @@ +import os +import einops +import torch +import numpy as np + +import ldm_patched.modules.model_management +import ldm_patched.modules.model_detection +import ldm_patched.modules.model_patcher +import ldm_patched.modules.utils +import ldm_patched.modules.controlnet +import modules.sample_hijack +import ldm_patched.modules.samplers +import ldm_patched.modules.latent_formats + +from ldm_patched.modules.sd import load_checkpoint_guess_config +from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \ + ControlNetApplyAdvanced +from ldm_patched.contrib.external_freelunch import FreeU_V2 +from ldm_patched.modules.sample import prepare_mask +from modules.lora import match_lora +from modules.util import get_file_from_folder_list +from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip +from modules.config import path_embeddings +from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete + + +opEmptyLatentImage = EmptyLatentImage() +opVAEDecode = VAEDecode() +opVAEEncode = VAEEncode() +opVAEDecodeTiled = VAEDecodeTiled() +opVAEEncodeTiled = VAEEncodeTiled() +opControlNetApplyAdvanced = ControlNetApplyAdvanced() +opFreeU = FreeU_V2() +opModelSamplingDiscrete = ModelSamplingDiscrete() + + +class StableDiffusionModel: + def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None): + self.unet = unet + self.vae = vae + self.clip = clip + self.clip_vision = clip_vision + self.filename = filename + self.unet_with_lora = unet + self.clip_with_lora = clip + self.visited_loras = '' + + self.lora_key_map_unet = {} + self.lora_key_map_clip = {} + + if self.unet is not None: + self.lora_key_map_unet = model_lora_keys_unet(self.unet.model, self.lora_key_map_unet) + self.lora_key_map_unet.update({x: x for x in self.unet.model.state_dict().keys()}) + + if self.clip is not None: + self.lora_key_map_clip = model_lora_keys_clip(self.clip.cond_stage_model, self.lora_key_map_clip) + self.lora_key_map_clip.update({x: x for x in self.clip.cond_stage_model.state_dict().keys()}) + + @torch.no_grad() + @torch.inference_mode() + def refresh_loras(self, loras): + assert isinstance(loras, list) + + if self.visited_loras == str(loras): + return + + self.visited_loras = str(loras) + + if self.unet is None: + return + + print(f'Request to load LoRAs {str(loras)} for model [{self.filename}].') + + loras_to_load = [] + + for filename, weight in loras: + if filename == 'None': + continue + + if os.path.exists(filename): + lora_filename = filename + else: + lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras) + + if not os.path.exists(lora_filename): + print(f'Lora file not found: {lora_filename}') + continue + + loras_to_load.append((lora_filename, weight)) + + self.unet_with_lora = self.unet.clone() if self.unet is not None else None + self.clip_with_lora = self.clip.clone() if self.clip is not None else None + + for lora_filename, weight in loras_to_load: + lora_unmatch = ldm_patched.modules.utils.load_torch_file(lora_filename, safe_load=False) + lora_unet, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_unet) + lora_clip, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_clip) + + if len(lora_unmatch) > 12: + # model mismatch + continue + + if len(lora_unmatch) > 0: + print(f'Loaded LoRA [{lora_filename}] for model [{self.filename}] ' + f'with unmatched keys {list(lora_unmatch.keys())}') + + if self.unet_with_lora is not None and len(lora_unet) > 0: + loaded_keys = self.unet_with_lora.add_patches(lora_unet, weight) + print(f'Loaded LoRA [{lora_filename}] for UNet [{self.filename}] ' + f'with {len(loaded_keys)} keys at weight {weight}.') + for item in lora_unet: + if item not in loaded_keys: + print("UNet LoRA key skipped: ", item) + + if self.clip_with_lora is not None and len(lora_clip) > 0: + loaded_keys = self.clip_with_lora.add_patches(lora_clip, weight) + print(f'Loaded LoRA [{lora_filename}] for CLIP [{self.filename}] ' + f'with {len(loaded_keys)} keys at weight {weight}.') + for item in lora_clip: + if item not in loaded_keys: + print("CLIP LoRA key skipped: ", item) + + +@torch.no_grad() +@torch.inference_mode() +def apply_freeu(model, b1, b2, s1, s2): + return opFreeU.patch(model=model, b1=b1, b2=b2, s1=s1, s2=s2)[0] + + +@torch.no_grad() +@torch.inference_mode() +def load_controlnet(ckpt_filename): + return ldm_patched.modules.controlnet.load_controlnet(ckpt_filename) + + +@torch.no_grad() +@torch.inference_mode() +def apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent): + return opControlNetApplyAdvanced.apply_controlnet(positive=positive, negative=negative, control_net=control_net, + image=image, strength=strength, start_percent=start_percent, end_percent=end_percent) + + +@torch.no_grad() +@torch.inference_mode() +def load_model(ckpt_filename): + unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings) + return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename) + + +@torch.no_grad() +@torch.inference_mode() +def generate_empty_latent(width=1024, height=1024, batch_size=1): + return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0] + + +@torch.no_grad() +@torch.inference_mode() +def decode_vae(vae, latent_image, tiled=False): + if tiled: + return opVAEDecodeTiled.decode(samples=latent_image, vae=vae, tile_size=512)[0] + else: + return opVAEDecode.decode(samples=latent_image, vae=vae)[0] + + +@torch.no_grad() +@torch.inference_mode() +def encode_vae(vae, pixels, tiled=False): + if tiled: + return opVAEEncodeTiled.encode(pixels=pixels, vae=vae, tile_size=512)[0] + else: + return opVAEEncode.encode(pixels=pixels, vae=vae)[0] + + +@torch.no_grad() +@torch.inference_mode() +def encode_vae_inpaint(vae, pixels, mask): + assert mask.ndim == 3 and pixels.ndim == 4 + assert mask.shape[-1] == pixels.shape[-2] + assert mask.shape[-2] == pixels.shape[-3] + + w = mask.round()[..., None] + pixels = pixels * (1 - w) + 0.5 * w + + latent = vae.encode(pixels) + B, C, H, W = latent.shape + + latent_mask = mask[:, None, :, :] + latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round() + latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent) + + return latent, latent_mask + + +class VAEApprox(torch.nn.Module): + def __init__(self): + super(VAEApprox, self).__init__() + self.conv1 = torch.nn.Conv2d(4, 8, (7, 7)) + self.conv2 = torch.nn.Conv2d(8, 16, (5, 5)) + self.conv3 = torch.nn.Conv2d(16, 32, (3, 3)) + self.conv4 = torch.nn.Conv2d(32, 64, (3, 3)) + self.conv5 = torch.nn.Conv2d(64, 32, (3, 3)) + self.conv6 = torch.nn.Conv2d(32, 16, (3, 3)) + self.conv7 = torch.nn.Conv2d(16, 8, (3, 3)) + self.conv8 = torch.nn.Conv2d(8, 3, (3, 3)) + self.current_type = None + + def forward(self, x): + extra = 11 + x = torch.nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2)) + x = torch.nn.functional.pad(x, (extra, extra, extra, extra)) + for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8]: + x = layer(x) + x = torch.nn.functional.leaky_relu(x, 0.1) + return x + + +VAE_approx_models = {} + + +@torch.no_grad() +@torch.inference_mode() +def get_previewer(model): + global VAE_approx_models + + from modules.config import path_vae_approx + is_sdxl = isinstance(model.model.latent_format, ldm_patched.modules.latent_formats.SDXL) + vae_approx_filename = os.path.join(path_vae_approx, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth') + + if vae_approx_filename in VAE_approx_models: + VAE_approx_model = VAE_approx_models[vae_approx_filename] + else: + sd = torch.load(vae_approx_filename, map_location='cpu') + VAE_approx_model = VAEApprox() + VAE_approx_model.load_state_dict(sd) + del sd + VAE_approx_model.eval() + + if ldm_patched.modules.model_management.should_use_fp16(): + VAE_approx_model.half() + VAE_approx_model.current_type = torch.float16 + else: + VAE_approx_model.float() + VAE_approx_model.current_type = torch.float32 + + VAE_approx_model.to(ldm_patched.modules.model_management.get_torch_device()) + VAE_approx_models[vae_approx_filename] = VAE_approx_model + + @torch.no_grad() + @torch.inference_mode() + def preview_function(x0, step, total_steps): + with torch.no_grad(): + x_sample = x0.to(VAE_approx_model.current_type) + x_sample = VAE_approx_model(x_sample) * 127.5 + 127.5 + x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')[0] + x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8) + return x_sample + + return preview_function + + +@torch.no_grad() +@torch.inference_mode() +def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu', + scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None, + force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1, + previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False): + + if sigmas is not None: + sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device()) + + latent_image = latent["samples"] + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) + + if isinstance(noise_mean, torch.Tensor): + noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + previewer = get_previewer(model) + + if previewer_start is None: + previewer_start = 0 + + if previewer_end is None: + previewer_end = steps + + def callback(step, x0, x, total_steps): + ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() + y = None + if previewer is not None and not disable_preview: + y = previewer(x0, previewer_start + step, previewer_end) + if callback_function is not None: + callback_function(previewer_start + step, x0, x, previewer_end, y) + + disable_pbar = False + modules.sample_hijack.current_refiner = refiner + modules.sample_hijack.refiner_switch_step = refiner_switch + ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked + + try: + samples = ldm_patched.modules.sample.sample(model, + noise, steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, + start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, seed=seed, sigmas=sigmas) + + out = latent.copy() + out["samples"] = samples + finally: + modules.sample_hijack.current_refiner = None + + return out + + +@torch.no_grad() +@torch.inference_mode() +def pytorch_to_numpy(x): + return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x] + + +@torch.no_grad() +@torch.inference_mode() +def numpy_to_pytorch(x): + y = x.astype(np.float32) / 255.0 + y = y[None] + y = np.ascontiguousarray(y.copy()) + y = torch.from_numpy(y).float() + return y diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..190601ecfbe28392e0bf6eefeccc304eb2401285 --- /dev/null +++ b/modules/default_pipeline.py @@ -0,0 +1,498 @@ +import modules.core as core +import os +import torch +import modules.patch +import modules.config +import ldm_patched.modules.model_management +import ldm_patched.modules.latent_formats +import modules.inpaint_worker +import extras.vae_interpose as vae_interpose +from extras.expansion import FooocusExpansion + +from ldm_patched.modules.model_base import SDXL, SDXLRefiner +from modules.sample_hijack import clip_separate +from modules.util import get_file_from_folder_list, get_enabled_loras + + +model_base = core.StableDiffusionModel() +model_refiner = core.StableDiffusionModel() + +final_expansion = None +final_unet = None +final_clip = None +final_vae = None +final_refiner_unet = None +final_refiner_vae = None + +loaded_ControlNets = {} + + +@torch.no_grad() +@torch.inference_mode() +def refresh_controlnets(model_paths): + global loaded_ControlNets + cache = {} + for p in model_paths: + if p is not None: + if p in loaded_ControlNets: + cache[p] = loaded_ControlNets[p] + else: + cache[p] = core.load_controlnet(p) + loaded_ControlNets = cache + return + + +@torch.no_grad() +@torch.inference_mode() +def assert_model_integrity(): + error_message = None + + if not isinstance(model_base.unet_with_lora.model, SDXL): + error_message = 'You have selected base model other than SDXL. This is not supported yet.' + + if error_message is not None: + raise NotImplementedError(error_message) + + return True + + +@torch.no_grad() +@torch.inference_mode() +def refresh_base_model(name): + global model_base + + filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) + + if model_base.filename == filename: + return + + model_base = core.StableDiffusionModel() + model_base = core.load_model(filename) + print(f'Base model loaded: {model_base.filename}') + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_refiner_model(name): + global model_refiner + + filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) + + if model_refiner.filename == filename: + return + + model_refiner = core.StableDiffusionModel() + + if name == 'None': + print(f'Refiner unloaded.') + return + + model_refiner = core.load_model(filename) + print(f'Refiner model loaded: {model_refiner.filename}') + + if isinstance(model_refiner.unet.model, SDXL): + model_refiner.clip = None + model_refiner.vae = None + elif isinstance(model_refiner.unet.model, SDXLRefiner): + model_refiner.clip = None + model_refiner.vae = None + else: + model_refiner.clip = None + + return + + +@torch.no_grad() +@torch.inference_mode() +def synthesize_refiner_model(): + global model_base, model_refiner + + print('Synthetic Refiner Activated') + model_refiner = core.StableDiffusionModel( + unet=model_base.unet, + vae=model_base.vae, + clip=model_base.clip, + clip_vision=model_base.clip_vision, + filename=model_base.filename + ) + model_refiner.vae = None + model_refiner.clip = None + model_refiner.clip_vision = None + + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_loras(loras, base_model_additional_loras=None): + global model_base, model_refiner + + if not isinstance(base_model_additional_loras, list): + base_model_additional_loras = [] + + model_base.refresh_loras(loras + base_model_additional_loras) + model_refiner.refresh_loras(loras) + + return + + +@torch.no_grad() +@torch.inference_mode() +def clip_encode_single(clip, text, verbose=False): + cached = clip.fcs_cond_cache.get(text, None) + if cached is not None: + if verbose: + print(f'[CLIP Cached] {text}') + return cached + tokens = clip.tokenize(text) + result = clip.encode_from_tokens(tokens, return_pooled=True) + clip.fcs_cond_cache[text] = result + if verbose: + print(f'[CLIP Encoded] {text}') + return result + + +@torch.no_grad() +@torch.inference_mode() +def clone_cond(conds): + results = [] + + for c, p in conds: + p = p["pooled_output"] + + if isinstance(c, torch.Tensor): + c = c.clone() + + if isinstance(p, torch.Tensor): + p = p.clone() + + results.append([c, {"pooled_output": p}]) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def clip_encode(texts, pool_top_k=1): + global final_clip + + if final_clip is None: + return None + if not isinstance(texts, list): + return None + if len(texts) == 0: + return None + + cond_list = [] + pooled_acc = 0 + + for i, text in enumerate(texts): + cond, pooled = clip_encode_single(final_clip, text) + cond_list.append(cond) + if i < pool_top_k: + pooled_acc += pooled + + return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]] + + +@torch.no_grad() +@torch.inference_mode() +def clear_all_caches(): + final_clip.fcs_cond_cache = {} + + +@torch.no_grad() +@torch.inference_mode() +def prepare_text_encoder(async_call=True): + if async_call: + # TODO: make sure that this is always called in an async way so that users cannot feel it. + pass + assert_model_integrity() + ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher]) + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_everything(refiner_model_name, base_model_name, loras, + base_model_additional_loras=None, use_synthetic_refiner=False): + global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion + + final_unet = None + final_clip = None + final_vae = None + final_refiner_unet = None + final_refiner_vae = None + + if use_synthetic_refiner and refiner_model_name == 'None': + print('Synthetic Refiner Activated') + refresh_base_model(base_model_name) + synthesize_refiner_model() + else: + refresh_refiner_model(refiner_model_name) + refresh_base_model(base_model_name) + + refresh_loras(loras, base_model_additional_loras=base_model_additional_loras) + assert_model_integrity() + + final_unet = model_base.unet_with_lora + final_clip = model_base.clip_with_lora + final_vae = model_base.vae + + final_refiner_unet = model_refiner.unet_with_lora + final_refiner_vae = model_refiner.vae + + if final_expansion is None: + final_expansion = FooocusExpansion() + + prepare_text_encoder(async_call=True) + clear_all_caches() + return + + +refresh_everything( + refiner_model_name=modules.config.default_refiner_model_name, + base_model_name=modules.config.default_base_model_name, + loras=get_enabled_loras(modules.config.default_loras) +) + + +@torch.no_grad() +@torch.inference_mode() +def vae_parse(latent): + if final_refiner_vae is None: + return latent + + result = vae_interpose.parse(latent["samples"]) + return {'samples': result} + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas_all(sampler, model, scheduler, steps): + from ldm_patched.modules.samplers import calculate_sigmas_scheduler + + discard_penultimate_sigma = False + if sampler in ['dpm_2', 'dpm_2_ancestral']: + steps += 1 + discard_penultimate_sigma = True + + sigmas = calculate_sigmas_scheduler(model, scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas(sampler, model, scheduler, steps, denoise): + if denoise is None or denoise > 0.9999: + sigmas = calculate_sigmas_all(sampler, model, scheduler, steps) + else: + new_steps = int(steps / denoise) + sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps) + sigmas = sigmas[-(steps + 1):] + return sigmas + + +@torch.no_grad() +@torch.inference_mode() +def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'): + assert refiner_swap_method in ['joint', 'separate', 'vae'] + + if final_refiner_vae is not None and final_refiner_unet is not None: + if denoise > 0.9: + return final_vae, final_refiner_vae + else: + if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 + return final_vae, None + else: + return final_refiner_vae, None + + return final_vae, final_refiner_vae + + +@torch.no_grad() +@torch.inference_mode() +def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False): + target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \ + = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip + + assert refiner_swap_method in ['joint', 'separate', 'vae'] + + if final_refiner_vae is not None and final_refiner_unet is not None: + # Refiner Use Different VAE (then it is SD15) + if denoise > 0.9: + refiner_swap_method = 'vae' + else: + refiner_swap_method = 'joint' + if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 + target_unet, target_vae, target_refiner_unet, target_refiner_vae \ + = final_unet, final_vae, None, None + print(f'[Sampler] only use Base because of partial denoise.') + else: + positive_cond = clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip) + negative_cond = clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip) + target_unet, target_vae, target_refiner_unet, target_refiner_vae \ + = final_refiner_unet, final_refiner_vae, None, None + print(f'[Sampler] only use Refiner because of partial denoise.') + + print(f'[Sampler] refiner_swap_method = {refiner_swap_method}') + + if latent is None: + initial_latent = core.generate_empty_latent(width=width, height=height, batch_size=1) + else: + initial_latent = latent + + minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise) + sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max() + sigma_min = float(sigma_min.cpu().numpy()) + sigma_max = float(sigma_max.cpu().numpy()) + print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}') + + modules.patch.BrownianTreeNoiseSamplerPatched.global_init( + initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()), + sigma_min, sigma_max, seed=image_seed, cpu=False) + + decoded_latent = None + + if refiner_swap_method == 'joint': + sampled_latent = core.ksampler( + model=target_unet, + refiner=target_refiner_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + refiner_switch=switch, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled) + + if refiner_swap_method == 'separate': + sampled_latent = core.ksampler( + model=target_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + print('Refiner swapped by changing ksampler. Noise preserved.') + + target_model = target_refiner_unet + if target_model is None: + target_model = target_unet + print('Use base model to refine itself - this may because of developer mode.') + + sampled_latent = core.ksampler( + model=target_model, + positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), + negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), + latent=sampled_latent, + steps=steps, start_step=switch, last_step=steps, disable_noise=True, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=switch, + previewer_end=steps, + disable_preview=disable_preview + ) + + target_model = target_refiner_vae + if target_model is None: + target_model = target_vae + decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) + + if refiner_swap_method == 'vae': + modules.patch.patch_settings[os.getpid()].eps_record = 'vae' + + if modules.inpaint_worker.current_task is not None: + modules.inpaint_worker.current_task.unswap() + + sampled_latent = core.ksampler( + model=target_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + print('Fooocus VAE-based swap.') + + target_model = target_refiner_unet + if target_model is None: + target_model = target_unet + print('Use base model to refine itself - this may because of developer mode.') + + sampled_latent = vae_parse(sampled_latent) + + k_sigmas = 1.4 + sigmas = calculate_sigmas(sampler=sampler_name, + scheduler=scheduler_name, + model=target_model.model, + steps=steps, + denoise=denoise)[switch:] * k_sigmas + len_sigmas = len(sigmas) - 1 + + noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True) + + if modules.inpaint_worker.current_task is not None: + modules.inpaint_worker.current_task.swap() + + sampled_latent = core.ksampler( + model=target_model, + positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), + negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), + latent=sampled_latent, + steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True, + seed=image_seed+1, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=switch, + previewer_end=steps, + sigmas=sigmas, + noise_mean=noise_mean, + disable_preview=disable_preview + ) + + target_model = target_refiner_vae + if target_model is None: + target_model = target_vae + decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) + + images = core.pytorch_to_numpy(decoded_latent) + modules.patch.patch_settings[os.getpid()].eps_record = None + return images diff --git a/modules/flags.py b/modules/flags.py new file mode 100644 index 0000000000000000000000000000000000000000..c9d13fd81dbafa360eb4137ac121d33ddb61f5ee --- /dev/null +++ b/modules/flags.py @@ -0,0 +1,139 @@ +from enum import IntEnum, Enum + +disabled = 'Disabled' +enabled = 'Enabled' +subtle_variation = 'Vary (Subtle)' +strong_variation = 'Vary (Strong)' +upscale_15 = 'Upscale (1.5x)' +upscale_2 = 'Upscale (2x)' +upscale_fast = 'Upscale (Fast 2x)' + +uov_list = [ + disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast +] + +CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"] + +# fooocus: a1111 (Civitai) +KSAMPLER = { + "euler": "Euler", + "euler_ancestral": "Euler a", + "heun": "Heun", + "heunpp2": "", + "dpm_2": "DPM2", + "dpm_2_ancestral": "DPM2 a", + "lms": "LMS", + "dpm_fast": "DPM fast", + "dpm_adaptive": "DPM adaptive", + "dpmpp_2s_ancestral": "DPM++ 2S a", + "dpmpp_sde": "DPM++ SDE", + "dpmpp_sde_gpu": "DPM++ SDE", + "dpmpp_2m": "DPM++ 2M", + "dpmpp_2m_sde": "DPM++ 2M SDE", + "dpmpp_2m_sde_gpu": "DPM++ 2M SDE", + "dpmpp_3m_sde": "", + "dpmpp_3m_sde_gpu": "", + "ddpm": "", + "lcm": "LCM" +} + +SAMPLER_EXTRA = { + "ddim": "DDIM", + "uni_pc": "UniPC", + "uni_pc_bh2": "" +} + +SAMPLERS = KSAMPLER | SAMPLER_EXTRA + +KSAMPLER_NAMES = list(KSAMPLER.keys()) + +SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"] +SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys()) + +sampler_list = SAMPLER_NAMES +scheduler_list = SCHEDULER_NAMES + +refiner_swap_method = 'joint' + +cn_ip = "ImagePrompt" +cn_ip_face = "FaceSwap" +cn_canny = "PyraCanny" +cn_cpds = "CPDS" + +ip_list = [cn_ip, cn_canny, cn_cpds, cn_ip_face] +default_ip = cn_ip + +default_parameters = { + cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0) +} # stop, weight + +output_formats = ['png', 'jpeg', 'webp'] + +inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6'] +inpaint_option_default = 'Inpaint or Outpaint (default)' +inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)' +inpaint_option_modify = 'Modify Content (add objects, change background, etc.)' +inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify] + +desc_type_photo = 'Photograph' +desc_type_anime = 'Art/Anime' + + +class MetadataScheme(Enum): + FOOOCUS = 'fooocus' + A1111 = 'a1111' + + +metadata_scheme = [ + (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value), + (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value), +] + +controlnet_image_count = 4 + + +class OutputFormat(Enum): + PNG = 'png' + JPEG = 'jpeg' + WEBP = 'webp' + + @classmethod + def list(cls) -> list: + return list(map(lambda c: c.value, cls)) + + +class Steps(IntEnum): + QUALITY = 60 + SPEED = 30 + EXTREME_SPEED = 8 + LIGHTNING = 4 + + +class StepsUOV(IntEnum): + QUALITY = 36 + SPEED = 18 + EXTREME_SPEED = 8 + LIGHTNING = 4 + + +class Performance(Enum): + QUALITY = 'Quality' + SPEED = 'Speed' + EXTREME_SPEED = 'Extreme Speed' + LIGHTNING = 'Lightning' + + @classmethod + def list(cls) -> list: + return list(map(lambda c: c.value, cls)) + + @classmethod + def has_restricted_features(cls, x) -> bool: + if isinstance(x, Performance): + x = x.value + return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value] + + def steps(self) -> int | None: + return Steps[self.name].value if Steps[self.name] else None + + def steps_uov(self) -> int | None: + return StepsUOV[self.name].value if Steps[self.name] else None diff --git a/modules/gradio_hijack.py b/modules/gradio_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..35df81c00c4b0de9d181bb3d7e2c5a31e1216ee0 --- /dev/null +++ b/modules/gradio_hijack.py @@ -0,0 +1,483 @@ +"""gr.Image() component.""" + +from __future__ import annotations + +import warnings +from pathlib import Path +from typing import Any, Literal + +import numpy as np +import PIL +import PIL.ImageOps +import gradio.routes +import importlib + +from gradio_client import utils as client_utils +from gradio_client.documentation import document, set_documentation_group +from gradio_client.serializing import ImgSerializable +from PIL import Image as _Image # using _ to minimize namespace pollution + +from gradio import processing_utils, utils, Error +from gradio.components.base import IOComponent, _Keywords, Block +from gradio.deprecation import warn_style_method_deprecation +from gradio.events import ( + Changeable, + Clearable, + Editable, + EventListenerMethod, + Selectable, + Streamable, + Uploadable, +) +from gradio.interpretation import TokenInterpretable + +set_documentation_group("component") +_Image.init() # fixes https://github.com/gradio-app/gradio/issues/2843 + + +@document() +class Image( + Editable, + Clearable, + Changeable, + Streamable, + Selectable, + Uploadable, + IOComponent, + ImgSerializable, + TokenInterpretable, +): + """ + Creates an image component that can be used to upload/draw images (as an input) or display images (as an output). + Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a {dict} with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`. + Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image. + Examples-format: a {str} filepath to a local file that contains the image. + Demos: image_mod, image_mod_default_image + Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan + """ + + def __init__( + self, + value: str | _Image.Image | np.ndarray | None = None, + *, + shape: tuple[int, int] | None = None, + height: int | None = None, + width: int | None = None, + image_mode: Literal[ + "1", "L", "P", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F" + ] = "RGB", + invert_colors: bool = False, + source: Literal["upload", "webcam", "canvas"] = "upload", + tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None, + type: Literal["numpy", "pil", "filepath"] = "numpy", + label: str | None = None, + every: float | None = None, + show_label: bool | None = None, + show_download_button: bool = True, + container: bool = True, + scale: int | None = None, + min_width: int = 160, + interactive: bool | None = None, + visible: bool = True, + streaming: bool = False, + elem_id: str | None = None, + elem_classes: list[str] | str | None = None, + mirror_webcam: bool = True, + brush_radius: float | None = None, + brush_color: str = "#000000", + mask_opacity: float = 0.7, + show_share_button: bool | None = None, + **kwargs, + ): + """ + Parameters: + value: A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. + shape: (width, height) shape to crop and resize image when passed to function. If None, matches input image size. Pass None for either width or height to only crop and resize the other. + height: Height of the displayed image in pixels. + width: Width of the displayed image in pixels. + image_mode: "RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. + invert_colors: whether to invert the image as a preprocessing step. + source: Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools. + tool: Tools used for editing. "editor" allows a full screen editor (and is the default if source is "upload" or "webcam"), "select" provides a cropping and zoom tool, "sketch" allows you to create a binary sketch (and is the default if source="canvas"), and "color-sketch" allows you to created a sketch in different colors. "color-sketch" can be used with source="upload" or "webcam" to allow sketching on an image. "sketch" can also be used with "upload" or "webcam" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys "image" and "mask" respectively. + type: The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. + label: component name in interface. + every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. + show_label: if True, will display label. + show_download_button: If True, will display button to download image. + container: If True, will place the component in a container - providing some extra padding around the border. + scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. + min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. + interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output. + visible: If False, component will be hidden. + streaming: If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'. + elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. + elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. + mirror_webcam: If True webcam will be mirrored. Default is True. + brush_radius: Size of the brush for Sketch. Default is None which chooses a sensible default + brush_color: Color of the brush for Sketch as hex string. Default is "#000000". + mask_opacity: Opacity of mask drawn on image, as a value between 0 and 1. + show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise. + """ + self.brush_radius = brush_radius + self.brush_color = brush_color + self.mask_opacity = mask_opacity + self.mirror_webcam = mirror_webcam + valid_types = ["numpy", "pil", "filepath"] + if type not in valid_types: + raise ValueError( + f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}" + ) + self.type = type + self.shape = shape + self.height = height + self.width = width + self.image_mode = image_mode + valid_sources = ["upload", "webcam", "canvas"] + if source not in valid_sources: + raise ValueError( + f"Invalid value for parameter `source`: {source}. Please choose from one of: {valid_sources}" + ) + self.source = source + if tool is None: + self.tool = "sketch" if source == "canvas" else "editor" + else: + self.tool = tool + self.invert_colors = invert_colors + self.streaming = streaming + self.show_download_button = show_download_button + if streaming and source != "webcam": + raise ValueError("Image streaming only available if source is 'webcam'.") + self.select: EventListenerMethod + """ + Event listener for when the user clicks on a pixel within the image. + Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel. + See EventData documentation on how to use this event data. + """ + self.show_share_button = ( + (utils.get_space() is not None) + if show_share_button is None + else show_share_button + ) + IOComponent.__init__( + self, + label=label, + every=every, + show_label=show_label, + container=container, + scale=scale, + min_width=min_width, + interactive=interactive, + visible=visible, + elem_id=elem_id, + elem_classes=elem_classes, + value=value, + **kwargs, + ) + TokenInterpretable.__init__(self) + + def get_config(self): + return { + "image_mode": self.image_mode, + "shape": self.shape, + "height": self.height, + "width": self.width, + "source": self.source, + "tool": self.tool, + "value": self.value, + "streaming": self.streaming, + "mirror_webcam": self.mirror_webcam, + "brush_radius": self.brush_radius, + "brush_color": self.brush_color, + "mask_opacity": self.mask_opacity, + "selectable": self.selectable, + "show_share_button": self.show_share_button, + "show_download_button": self.show_download_button, + **IOComponent.get_config(self), + } + + @staticmethod + def update( + value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE, + height: int | None = None, + width: int | None = None, + label: str | None = None, + show_label: bool | None = None, + show_download_button: bool | None = None, + container: bool | None = None, + scale: int | None = None, + min_width: int | None = None, + interactive: bool | None = None, + visible: bool | None = None, + brush_radius: float | None = None, + brush_color: str | None = None, + mask_opacity: float | None = None, + show_share_button: bool | None = None, + ): + return { + "height": height, + "width": width, + "label": label, + "show_label": show_label, + "show_download_button": show_download_button, + "container": container, + "scale": scale, + "min_width": min_width, + "interactive": interactive, + "visible": visible, + "value": value, + "brush_radius": brush_radius, + "brush_color": brush_color, + "mask_opacity": mask_opacity, + "show_share_button": show_share_button, + "__type__": "update", + } + + def _format_image( + self, im: _Image.Image | None + ) -> np.ndarray | _Image.Image | str | None: + """Helper method to format an image based on self.type""" + if im is None: + return im + fmt = im.format + if self.type == "pil": + return im + elif self.type == "numpy": + return np.array(im) + elif self.type == "filepath": + path = self.pil_to_temp_file( + im, dir=self.DEFAULT_TEMP_DIR, format=fmt or "png" + ) + self.temp_files.add(path) + return path + else: + raise ValueError( + "Unknown type: " + + str(self.type) + + ". Please choose from: 'numpy', 'pil', 'filepath'." + ) + + def preprocess( + self, x: str | dict[str, str] + ) -> np.ndarray | _Image.Image | str | dict | None: + """ + Parameters: + x: base64 url data, or (if tool == "sketch") a dict of image and mask base64 url data + Returns: + image in requested format, or (if tool == "sketch") a dict of image and mask in requested format + """ + if x is None: + return x + + mask = None + + if self.tool == "sketch" and self.source in ["upload", "webcam"]: + if isinstance(x, dict): + x, mask = x["image"], x["mask"] + + assert isinstance(x, str) + try: + im = processing_utils.decode_base64_to_image(x) + except PIL.UnidentifiedImageError: + raise Error("Unsupported image type in input") + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + im = im.convert(self.image_mode) + if self.shape is not None: + im = processing_utils.resize_and_crop(im, self.shape) + if self.invert_colors: + im = PIL.ImageOps.invert(im) + if ( + self.source == "webcam" + and self.mirror_webcam is True + and self.tool != "color-sketch" + ): + im = PIL.ImageOps.mirror(im) + + if self.tool == "sketch" and self.source in ["upload", "webcam"]: + if mask is not None: + mask_im = processing_utils.decode_base64_to_image(mask) + if mask_im.mode == "RGBA": # whiten any opaque pixels in the mask + alpha_data = mask_im.getchannel("A").convert("L") + mask_im = _Image.merge("RGB", [alpha_data, alpha_data, alpha_data]) + return { + "image": self._format_image(im), + "mask": self._format_image(mask_im), + } + else: + return { + "image": self._format_image(im), + "mask": None, + } + + return self._format_image(im) + + def postprocess( + self, y: np.ndarray | _Image.Image | str | Path | None + ) -> str | None: + """ + Parameters: + y: image as a numpy array, PIL Image, string/Path filepath, or string URL + Returns: + base64 url data + """ + if y is None: + return None + if isinstance(y, np.ndarray): + return processing_utils.encode_array_to_base64(y) + elif isinstance(y, _Image.Image): + return processing_utils.encode_pil_to_base64(y) + elif isinstance(y, (str, Path)): + return client_utils.encode_url_or_file_to_base64(y) + else: + raise ValueError("Cannot process this value as an Image") + + def set_interpret_parameters(self, segments: int = 16): + """ + Calculates interpretation score of image subsections by splitting the image into subsections, then using a "leave one out" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value. + Parameters: + segments: Number of interpretation segments to split image into. + """ + self.interpretation_segments = segments + return self + + def _segment_by_slic(self, x): + """ + Helper method that segments an image into superpixels using slic. + Parameters: + x: base64 representation of an image + """ + x = processing_utils.decode_base64_to_image(x) + if self.shape is not None: + x = processing_utils.resize_and_crop(x, self.shape) + resized_and_cropped_image = np.array(x) + try: + from skimage.segmentation import slic + except (ImportError, ModuleNotFoundError) as err: + raise ValueError( + "Error: running this interpretation for images requires scikit-image, please install it first." + ) from err + try: + segments_slic = slic( + resized_and_cropped_image, + self.interpretation_segments, + compactness=10, + sigma=1, + start_label=1, + ) + except TypeError: # For skimage 0.16 and older + segments_slic = slic( + resized_and_cropped_image, + self.interpretation_segments, + compactness=10, + sigma=1, + ) + return segments_slic, resized_and_cropped_image + + def tokenize(self, x): + """ + Segments image into tokens, masks, and leave-one-out-tokens + Parameters: + x: base64 representation of an image + Returns: + tokens: list of tokens, used by the get_masked_input() method + leave_one_out_tokens: list of left-out tokens, used by the get_interpretation_neighbors() method + masks: list of masks, used by the get_interpretation_neighbors() method + """ + segments_slic, resized_and_cropped_image = self._segment_by_slic(x) + tokens, masks, leave_one_out_tokens = [], [], [] + replace_color = np.mean(resized_and_cropped_image, axis=(0, 1)) + for segment_value in np.unique(segments_slic): + mask = segments_slic == segment_value + image_screen = np.copy(resized_and_cropped_image) + image_screen[segments_slic == segment_value] = replace_color + leave_one_out_tokens.append( + processing_utils.encode_array_to_base64(image_screen) + ) + token = np.copy(resized_and_cropped_image) + token[segments_slic != segment_value] = 0 + tokens.append(token) + masks.append(mask) + return tokens, leave_one_out_tokens, masks + + def get_masked_inputs(self, tokens, binary_mask_matrix): + masked_inputs = [] + for binary_mask_vector in binary_mask_matrix: + masked_input = np.zeros_like(tokens[0], dtype=int) + for token, b in zip(tokens, binary_mask_vector): + masked_input = masked_input + token * int(b) + masked_inputs.append(processing_utils.encode_array_to_base64(masked_input)) + return masked_inputs + + def get_interpretation_scores( + self, x, neighbors, scores, masks, tokens=None, **kwargs + ) -> list[list[float]]: + """ + Returns: + A 2D array representing the interpretation score of each pixel of the image. + """ + x = processing_utils.decode_base64_to_image(x) + if self.shape is not None: + x = processing_utils.resize_and_crop(x, self.shape) + x = np.array(x) + output_scores = np.zeros((x.shape[0], x.shape[1])) + + for score, mask in zip(scores, masks): + output_scores += score * mask + + max_val, min_val = np.max(output_scores), np.min(output_scores) + if max_val > 0: + output_scores = (output_scores - min_val) / (max_val - min_val) + return output_scores.tolist() + + def style(self, *, height: int | None = None, width: int | None = None, **kwargs): + """ + This method is deprecated. Please set these arguments in the constructor instead. + """ + warn_style_method_deprecation() + if height is not None: + self.height = height + if width is not None: + self.width = width + return self + + def check_streamable(self): + if self.source != "webcam": + raise ValueError("Image streaming only available if source is 'webcam'.") + + def as_example(self, input_data: str | None) -> str: + if input_data is None: + return "" + elif ( + self.root_url + ): # If an externally hosted image, don't convert to absolute path + return input_data + return str(utils.abspath(input_data)) + + +all_components = [] + +if not hasattr(Block, 'original__init__'): + Block.original_init = Block.__init__ + + +def blk_ini(self, *args, **kwargs): + all_components.append(self) + return Block.original_init(self, *args, **kwargs) + + +Block.__init__ = blk_ini + + +gradio.routes.asyncio = importlib.reload(gradio.routes.asyncio) + +if not hasattr(gradio.routes.asyncio, 'original_wait_for'): + gradio.routes.asyncio.original_wait_for = gradio.routes.asyncio.wait_for + + +def patched_wait_for(fut, timeout): + del timeout + return gradio.routes.asyncio.original_wait_for(fut, timeout=65535) + + +gradio.routes.asyncio.wait_for = patched_wait_for + diff --git a/modules/html.py b/modules/html.py new file mode 100644 index 0000000000000000000000000000000000000000..25771cb9f54d290942136749427d413544fa20fd --- /dev/null +++ b/modules/html.py @@ -0,0 +1,13 @@ +progress_html = ''' + + ++''' + + +def make_progress_html(number, text): + return progress_html.replace('*number*', str(number)).replace('*text*', text) diff --git a/modules/inpaint_worker.py b/modules/inpaint_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..43a7ae23e9bd2cebda69b94013bf1661bd8fd952 --- /dev/null +++ b/modules/inpaint_worker.py @@ -0,0 +1,264 @@ +import torch +import numpy as np + +from PIL import Image, ImageFilter +from modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil +from modules.upscaler import perform_upscale +import cv2 + + +inpaint_head_model = None + + +class InpaintHead(torch.nn.Module): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device='cpu')) + + def __call__(self, x): + x = torch.nn.functional.pad(x, (1, 1, 1, 1), "replicate") + return torch.nn.functional.conv2d(input=x, weight=self.head) + + +current_task = None + + +def box_blur(x, k): + x = Image.fromarray(x) + x = x.filter(ImageFilter.BoxBlur(k)) + return np.array(x) + + +def max_filter_opencv(x, ksize=3): + # Use OpenCV maximum filter + # Make sure the input type is int16 + return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16)) + + +def morphological_open(x): + # Convert array to int16 type via threshold operation + x_int16 = np.zeros_like(x, dtype=np.int16) + x_int16[x > 127] = 256 + + for i in range(32): + # Use int16 type to avoid overflow + maxed = max_filter_opencv(x_int16, ksize=3) - 8 + x_int16 = np.maximum(maxed, x_int16) + + # Clip negative values to 0 and convert back to uint8 type + x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8) + return x_uint8 + + +def up255(x, t=0): + y = np.zeros_like(x).astype(np.uint8) + y[x > t] = 255 + return y + + +def imsave(x, path): + x = Image.fromarray(x) + x.save(path) + + +def regulate_abcd(x, a, b, c, d): + H, W = x.shape[:2] + if a < 0: + a = 0 + if a > H: + a = H + if b < 0: + b = 0 + if b > H: + b = H + if c < 0: + c = 0 + if c > W: + c = W + if d < 0: + d = 0 + if d > W: + d = W + return int(a), int(b), int(c), int(d) + + +def compute_initial_abcd(x): + indices = np.where(x) + a = np.min(indices[0]) + b = np.max(indices[0]) + c = np.min(indices[1]) + d = np.max(indices[1]) + abp = (b + a) // 2 + abm = (b - a) // 2 + cdp = (d + c) // 2 + cdm = (d - c) // 2 + l = int(max(abm, cdm) * 1.15) + a = abp - l + b = abp + l + 1 + c = cdp - l + d = cdp + l + 1 + a, b, c, d = regulate_abcd(x, a, b, c, d) + return a, b, c, d + + +def solve_abcd(x, a, b, c, d, k): + k = float(k) + assert 0.0 <= k <= 1.0 + + H, W = x.shape[:2] + if k == 1.0: + return 0, H, 0, W + while True: + if b - a >= H * k and d - c >= W * k: + break + + add_h = (b - a) < (d - c) + add_w = not add_h + + if b - a == H: + add_w = True + + if d - c == W: + add_h = True + + if add_h: + a -= 1 + b += 1 + + if add_w: + c -= 1 + d += 1 + + a, b, c, d = regulate_abcd(x, a, b, c, d) + return a, b, c, d + + +def fooocus_fill(image, mask): + current_image = image.copy() + raw_image = image.copy() + area = np.where(mask < 127) + store = raw_image[area] + + for k, repeats in [(512, 2), (256, 2), (128, 4), (64, 4), (33, 8), (15, 8), (5, 16), (3, 16)]: + for _ in range(repeats): + current_image = box_blur(current_image, k) + current_image[area] = store + + return current_image + + +class InpaintWorker: + def __init__(self, image, mask, use_fill=True, k=0.618): + a, b, c, d = compute_initial_abcd(mask > 0) + a, b, c, d = solve_abcd(mask, a, b, c, d, k=k) + + # interested area + self.interested_area = (a, b, c, d) + self.interested_mask = mask[a:b, c:d] + self.interested_image = image[a:b, c:d] + + # super resolution + if get_image_shape_ceil(self.interested_image) < 1024: + self.interested_image = perform_upscale(self.interested_image) + + # resize to make images ready for diffusion + self.interested_image = set_image_shape_ceil(self.interested_image, 1024) + self.interested_fill = self.interested_image.copy() + H, W, C = self.interested_image.shape + + # process mask + self.interested_mask = up255(resample_image(self.interested_mask, W, H), t=127) + + # compute filling + if use_fill: + self.interested_fill = fooocus_fill(self.interested_image, self.interested_mask) + + # soft pixels + self.mask = morphological_open(mask) + self.image = image + + # ending + self.latent = None + self.latent_after_swap = None + self.swapped = False + self.latent_mask = None + self.inpaint_head_feature = None + return + + def load_latent(self, latent_fill, latent_mask, latent_swap=None): + self.latent = latent_fill + self.latent_mask = latent_mask + self.latent_after_swap = latent_swap + return + + def patch(self, inpaint_head_model_path, inpaint_latent, inpaint_latent_mask, model): + global inpaint_head_model + + if inpaint_head_model is None: + inpaint_head_model = InpaintHead() + sd = torch.load(inpaint_head_model_path, map_location='cpu') + inpaint_head_model.load_state_dict(sd) + + feed = torch.cat([ + inpaint_latent_mask, + model.model.process_latent_in(inpaint_latent) + ], dim=1) + + inpaint_head_model.to(device=feed.device, dtype=feed.dtype) + inpaint_head_feature = inpaint_head_model(feed) + + def input_block_patch(h, transformer_options): + if transformer_options["block"][1] == 0: + h = h + inpaint_head_feature.to(h) + return h + + m = model.clone() + m.set_model_input_block_patch(input_block_patch) + return m + + def swap(self): + if self.swapped: + return + + if self.latent is None: + return + + if self.latent_after_swap is None: + return + + self.latent, self.latent_after_swap = self.latent_after_swap, self.latent + self.swapped = True + return + + def unswap(self): + if not self.swapped: + return + + if self.latent is None: + return + + if self.latent_after_swap is None: + return + + self.latent, self.latent_after_swap = self.latent_after_swap, self.latent + self.swapped = False + return + + def color_correction(self, img): + fg = img.astype(np.float32) + bg = self.image.copy().astype(np.float32) + w = self.mask[:, :, None].astype(np.float32) / 255.0 + y = fg * w + bg * (1 - w) + return y.clip(0, 255).astype(np.uint8) + + def post_process(self, img): + a, b, c, d = self.interested_area + content = resample_image(img, d - c, b - a) + result = self.image.copy() + result[a:b, c:d] = content + result = self.color_correction(result) + return result + + def visualize_mask_processing(self): + return [self.interested_fill, self.interested_mask, self.interested_image] + diff --git a/modules/launch_util.py b/modules/launch_util.py new file mode 100644 index 0000000000000000000000000000000000000000..370dc048953c4821a0afde9385c1ff735376a348 --- /dev/null +++ b/modules/launch_util.py @@ -0,0 +1,117 @@ +import os +import importlib +import importlib.util +import shutil +import subprocess +import sys +import re +import logging +import importlib.metadata +import packaging.version +from packaging.requirements import Requirement + +logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh... +logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + +re_requirement = re.compile(r"\s*([-\w]+)\s*(?:==\s*([-+.\w]+))?\s*") + +python = sys.executable +default_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == "1") +index_url = os.environ.get('INDEX_URL', "") + +modules_path = os.path.dirname(os.path.realpath(__file__)) +script_path = os.path.dirname(modules_path) + + +def is_installed(package): + try: + spec = importlib.util.find_spec(package) + except ModuleNotFoundError: + return False + + return spec is not None + + +def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str: + if desc is not None: + print(desc) + + run_kwargs = { + "args": command, + "shell": True, + "env": os.environ if custom_env is None else custom_env, + "encoding": 'utf8', + "errors": 'ignore', + } + + if not live: + run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE + + result = subprocess.run(**run_kwargs) + + if result.returncode != 0: + error_bits = [ + f"{errdesc or 'Error running command'}.", + f"Command: {command}", + f"Error code: {result.returncode}", + ] + if result.stdout: + error_bits.append(f"stdout: {result.stdout}") + if result.stderr: + error_bits.append(f"stderr: {result.stderr}") + raise RuntimeError("\n".join(error_bits)) + + return (result.stdout or "") + + +def run_pip(command, desc=None, live=default_command_live): + try: + index_url_line = f' --index-url {index_url}' if index_url != '' else '' + return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", + errdesc=f"Couldn't install {desc}", live=live) + except Exception as e: + print(e) + print(f'CMD Failed {desc}: {command}') + return None + + +def requirements_met(requirements_file): + with open(requirements_file, "r", encoding="utf8") as file: + for line in file: + line = line.strip() + if line == "" or line.startswith('#'): + continue + + requirement = Requirement(line) + package = requirement.name + + try: + version_installed = importlib.metadata.version(package) + installed_version = packaging.version.parse(version_installed) + + # Check if the installed version satisfies the requirement + if installed_version not in requirement.specifier: + print(f"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}") + return False + except Exception as e: + print(f"Error checking version for {package}: {e}") + return False + + return True + + +def delete_folder_content(folder, prefix=None): + result = True + + for filename in os.listdir(folder): + file_path = os.path.join(folder, filename) + try: + if os.path.isfile(file_path) or os.path.islink(file_path): + os.unlink(file_path) + elif os.path.isdir(file_path): + shutil.rmtree(file_path) + except Exception as e: + print(f'{prefix}Failed to delete {file_path}. Reason: {e}') + result = False + + return result \ No newline at end of file diff --git a/modules/localization.py b/modules/localization.py new file mode 100644 index 0000000000000000000000000000000000000000..b21d4a564d134ac0be00d83c7005627d601d206e --- /dev/null +++ b/modules/localization.py @@ -0,0 +1,60 @@ +import json +import os + + +current_translation = {} +localization_root = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'language') + + +def localization_js(filename): + global current_translation + + if isinstance(filename, str): + full_name = os.path.abspath(os.path.join(localization_root, filename + '.json')) + if os.path.exists(full_name): + try: + with open(full_name, encoding='utf-8') as f: + current_translation = json.load(f) + assert isinstance(current_translation, dict) + for k, v in current_translation.items(): + assert isinstance(k, str) + assert isinstance(v, str) + except Exception as e: + print(str(e)) + print(f'Failed to load localization file {full_name}') + + # current_translation = {k: 'XXX' for k in current_translation.keys()} # use this to see if all texts are covered + + return f"window.localization = {json.dumps(current_translation)}" + + +def dump_english_config(components): + all_texts = [] + for c in components: + label = getattr(c, 'label', None) + value = getattr(c, 'value', None) + choices = getattr(c, 'choices', None) + info = getattr(c, 'info', None) + + if isinstance(label, str): + all_texts.append(label) + if isinstance(value, str): + all_texts.append(value) + if isinstance(info, str): + all_texts.append(info) + if isinstance(choices, list): + for x in choices: + if isinstance(x, str): + all_texts.append(x) + if isinstance(x, tuple): + for y in x: + if isinstance(y, str): + all_texts.append(y) + + config_dict = {k: k for k in all_texts if k != "" and 'progress-container' not in k} + full_name = os.path.abspath(os.path.join(localization_root, 'en.json')) + + with open(full_name, "w", encoding="utf-8") as json_file: + json.dump(config_dict, json_file, indent=4) + + return diff --git a/modules/lora.py b/modules/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..088545c708943aa8e51e8b2bfd32b2a9968b639f --- /dev/null +++ b/modules/lora.py @@ -0,0 +1,152 @@ +def match_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + real_load_key = to_load[x] + if real_load_key in lora: + patch_dict[real_load_key] = ('fooocus', lora[real_load_key]) + loaded_keys.add(real_load_key) + continue + + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = ("diff", (w_norm,)) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) + + diff_name = "{}.diff".format(x) + diff_weight = lora.get(diff_name, None) + if diff_weight is not None: + patch_dict[to_load[x]] = ("diff", (diff_weight,)) + loaded_keys.add(diff_name) + + diff_bias_name = "{}.diff_b".format(x) + diff_bias = lora.get(diff_bias_name, None) + if diff_bias is not None: + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) + loaded_keys.add(diff_bias_name) + + remaining_dict = {x: y for x, y in lora.items() if x not in loaded_keys} + return patch_dict, remaining_dict diff --git a/modules/meta_parser.py b/modules/meta_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..70ab8860ccda7d88f6a8157db4f9b456cc6c3386 --- /dev/null +++ b/modules/meta_parser.py @@ -0,0 +1,609 @@ +import json +import re +from abc import ABC, abstractmethod +from pathlib import Path + +import gradio as gr +from PIL import Image + +import fooocus_version +import modules.config +import modules.sdxl_styles +from modules.flags import MetadataScheme, Performance, Steps +from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS +from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, sha256 + +re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)' +re_param = re.compile(re_param_code) +re_imagesize = re.compile(r"^(\d+)x(\d+)$") + +hash_cache = {} + + +def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool): + loaded_parameter_dict = raw_metadata + if isinstance(raw_metadata, str): + loaded_parameter_dict = json.loads(raw_metadata) + assert isinstance(loaded_parameter_dict, dict) + + results = [len(loaded_parameter_dict) > 0] + + get_image_number('image_number', 'Image Number', loaded_parameter_dict, results) + get_str('prompt', 'Prompt', loaded_parameter_dict, results) + get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results) + get_list('styles', 'Styles', loaded_parameter_dict, results) + get_str('performance', 'Performance', loaded_parameter_dict, results) + get_steps('steps', 'Steps', loaded_parameter_dict, results) + get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results) + get_resolution('resolution', 'Resolution', loaded_parameter_dict, results) + get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results) + get_float('sharpness', 'Sharpness', loaded_parameter_dict, results) + get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results) + get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results) + get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results) + get_str('base_model', 'Base Model', loaded_parameter_dict, results) + get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results) + get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results) + get_str('sampler', 'Sampler', loaded_parameter_dict, results) + get_str('scheduler', 'Scheduler', loaded_parameter_dict, results) + get_seed('seed', 'Seed', loaded_parameter_dict, results) + + if is_generating: + results.append(gr.update()) + else: + results.append(gr.update(visible=True)) + + results.append(gr.update(visible=False)) + + get_freeu('freeu', 'FreeU', loaded_parameter_dict, results) + + for i in range(modules.config.default_max_lora_number): + get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results) + + return results + + +def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert isinstance(h, str) + results.append(h) + except: + results.append(gr.update()) + + +def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + h = eval(h) + assert isinstance(h, list) + results.append(h) + except: + results.append(gr.update()) + + +def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = float(h) + results.append(h) + except: + results.append(gr.update()) + + +def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + h = min(h, modules.config.default_max_image_number) + results.append(h) + except: + results.append(1) + + +def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + # if not in steps or in steps and performance is not the same + if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', + '_').casefold(): + results.append(h) + return + results.append(-1) + except: + results.append(-1) + + +def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + width, height = eval(h) + formatted = modules.config.add_ratio(f'{width}*{height}') + if formatted in modules.config.available_aspect_ratios: + results.append(formatted) + results.append(-1) + results.append(-1) + else: + results.append(gr.update()) + results.append(int(width)) + results.append(int(height)) + except: + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + results.append(False) + results.append(h) + except: + results.append(gr.update()) + results.append(gr.update()) + + +def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + p, n, e = eval(h) + results.append(float(p)) + results.append(float(n)) + results.append(float(e)) + except: + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + b1, b2, s1, s2 = eval(h) + results.append(True) + results.append(float(b1)) + results.append(float(b2)) + results.append(float(s1)) + results.append(float(s2)) + except: + results.append(False) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_lora(key: str, fallback: str | None, source_dict: dict, results: list): + try: + split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ') + enabled = True + name = split_data[0] + weight = split_data[1] + + if len(split_data) == 3: + enabled = split_data[0] == 'True' + name = split_data[1] + weight = split_data[2] + + weight = float(weight) + results.append(enabled) + results.append(name) + results.append(weight) + except: + results.append(True) + results.append('None') + results.append(1) + + +def get_sha256(filepath): + global hash_cache + if filepath not in hash_cache: + # is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors' + hash_cache[filepath] = sha256(filepath) + + return hash_cache[filepath] + + +def parse_meta_from_preset(preset_content): + assert isinstance(preset_content, dict) + preset_prepared = {} + items = preset_content + + for settings_key, meta_key in modules.config.possible_preset_keys.items(): + if settings_key == "default_loras": + loras = getattr(modules.config, settings_key) + if settings_key in items: + loras = items[settings_key] + for index, lora in enumerate(loras[:5]): + preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora)) + elif settings_key == "default_aspect_ratio": + if settings_key in items and items[settings_key] is not None: + default_aspect_ratio = items[settings_key] + width, height = default_aspect_ratio.split('*') + else: + default_aspect_ratio = getattr(modules.config, settings_key) + width, height = default_aspect_ratio.split('×') + height = height[:height.index(" ")] + preset_prepared[meta_key] = (width, height) + else: + preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[ + settings_key] is not None else getattr(modules.config, settings_key) + + if settings_key == "default_styles" or settings_key == "default_aspect_ratio": + preset_prepared[meta_key] = str(preset_prepared[meta_key]) + + return preset_prepared + + +class MetadataParser(ABC): + def __init__(self): + self.raw_prompt: str = '' + self.full_prompt: str = '' + self.raw_negative_prompt: str = '' + self.full_negative_prompt: str = '' + self.steps: int = 30 + self.base_model_name: str = '' + self.base_model_hash: str = '' + self.refiner_model_name: str = '' + self.refiner_model_hash: str = '' + self.loras: list = [] + + @abstractmethod + def get_scheme(self) -> MetadataScheme: + raise NotImplementedError + + @abstractmethod + def parse_json(self, metadata: dict | str) -> dict: + raise NotImplementedError + + @abstractmethod + def parse_string(self, metadata: dict) -> str: + raise NotImplementedError + + def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name, + refiner_model_name, loras): + self.raw_prompt = raw_prompt + self.full_prompt = full_prompt + self.raw_negative_prompt = raw_negative_prompt + self.full_negative_prompt = full_negative_prompt + self.steps = steps + self.base_model_name = Path(base_model_name).stem + + base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints) + self.base_model_hash = get_sha256(base_model_path) + + if refiner_model_name not in ['', 'None']: + self.refiner_model_name = Path(refiner_model_name).stem + refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints) + self.refiner_model_hash = get_sha256(refiner_model_path) + + self.loras = [] + for (lora_name, lora_weight) in loras: + if lora_name != 'None': + lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras) + lora_hash = get_sha256(lora_path) + self.loras.append((Path(lora_name).stem, lora_weight, lora_hash)) + + @staticmethod + def remove_special_loras(lora_filenames): + for lora_to_remove in modules.config.loras_metadata_remove: + if lora_to_remove in lora_filenames: + lora_filenames.remove(lora_to_remove) + + +class A1111MetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.A1111 + + fooocus_to_a1111 = { + 'raw_prompt': 'Raw prompt', + 'raw_negative_prompt': 'Raw negative prompt', + 'negative_prompt': 'Negative prompt', + 'styles': 'Styles', + 'performance': 'Performance', + 'steps': 'Steps', + 'sampler': 'Sampler', + 'scheduler': 'Scheduler', + 'guidance_scale': 'CFG scale', + 'seed': 'Seed', + 'resolution': 'Size', + 'sharpness': 'Sharpness', + 'adm_guidance': 'ADM Guidance', + 'refiner_swap_method': 'Refiner Swap Method', + 'adaptive_cfg': 'Adaptive CFG', + 'overwrite_switch': 'Overwrite Switch', + 'freeu': 'FreeU', + 'base_model': 'Model', + 'base_model_hash': 'Model hash', + 'refiner_model': 'Refiner', + 'refiner_model_hash': 'Refiner hash', + 'lora_hashes': 'Lora hashes', + 'lora_weights': 'Lora weights', + 'created_by': 'User', + 'version': 'Version' + } + + def parse_json(self, metadata: str) -> dict: + metadata_prompt = '' + metadata_negative_prompt = '' + + done_with_prompt = False + + *lines, lastline = metadata.strip().split("\n") + if len(re_param.findall(lastline)) < 3: + lines.append(lastline) + lastline = '' + + for line in lines: + line = line.strip() + if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"): + done_with_prompt = True + line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip() + if done_with_prompt: + metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line + else: + metadata_prompt += ('' if metadata_prompt == '' else "\n") + line + + found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt) + + data = { + 'prompt': prompt, + 'negative_prompt': negative_prompt + } + + for k, v in re_param.findall(lastline): + try: + if v != '' and v[0] == '"' and v[-1] == '"': + v = unquote(v) + + m = re_imagesize.match(v) + if m is not None: + data['resolution'] = str((m.group(1), m.group(2))) + else: + data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v + except Exception: + print(f"Error parsing \"{k}: {v}\"") + + # workaround for multiline prompts + if 'raw_prompt' in data: + data['prompt'] = data['raw_prompt'] + raw_prompt = data['raw_prompt'].replace("\n", ', ') + if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles: + found_styles.append(modules.sdxl_styles.fooocus_expansion) + + if 'raw_negative_prompt' in data: + data['negative_prompt'] = data['raw_negative_prompt'] + + data['styles'] = str(found_styles) + + # try to load performance based on steps, fallback for direct A1111 imports + if 'steps' in data and 'performance' not in data: + try: + data['performance'] = Performance[Steps(int(data['steps'])).name].value + except ValueError | KeyError: + pass + + if 'sampler' in data: + data['sampler'] = data['sampler'].replace(' Karras', '') + # get key + for k, v in SAMPLERS.items(): + if v == data['sampler']: + data['sampler'] = k + break + + for key in ['base_model', 'refiner_model']: + if key in data: + for filename in modules.config.model_filenames: + path = Path(filename) + if data[key] == path.stem: + data[key] = filename + break + + lora_data = '' + if 'lora_weights' in data and data['lora_weights'] != '': + lora_data = data['lora_weights'] + elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2: + lora_data = data['lora_hashes'] + + if lora_data != '': + lora_filenames = modules.config.lora_filenames.copy() + self.remove_special_loras(lora_filenames) + for li, lora in enumerate(lora_data.split(', ')): + lora_split = lora.split(': ') + lora_name = lora_split[0] + lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1] + for filename in lora_filenames: + path = Path(filename) + if lora_name == path.stem: + data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}' + break + + return data + + def parse_string(self, metadata: dict) -> str: + data = {k: v for _, k, v in metadata} + + width, height = eval(data['resolution']) + + sampler = data['sampler'] + scheduler = data['scheduler'] + if sampler in SAMPLERS and SAMPLERS[sampler] != '': + sampler = SAMPLERS[sampler] + if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras': + sampler += f' Karras' + + generation_params = { + self.fooocus_to_a1111['steps']: self.steps, + self.fooocus_to_a1111['sampler']: sampler, + self.fooocus_to_a1111['seed']: data['seed'], + self.fooocus_to_a1111['resolution']: f'{width}x{height}', + self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'], + self.fooocus_to_a1111['sharpness']: data['sharpness'], + self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'], + self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem, + self.fooocus_to_a1111['base_model_hash']: self.base_model_hash, + + self.fooocus_to_a1111['performance']: data['performance'], + self.fooocus_to_a1111['scheduler']: scheduler, + # workaround for multiline prompts + self.fooocus_to_a1111['raw_prompt']: self.raw_prompt, + self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt, + } + + if self.refiner_model_name not in ['', 'None']: + generation_params |= { + self.fooocus_to_a1111['refiner_model']: self.refiner_model_name, + self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash + } + + for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']: + if key in data: + generation_params[self.fooocus_to_a1111[key]] = data[key] + + if len(self.loras) > 0: + lora_hashes = [] + lora_weights = [] + for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras): + # workaround for Fooocus not knowing LoRA name in LoRA metadata + lora_hashes.append(f'{lora_name}: {lora_hash}') + lora_weights.append(f'{lora_name}: {lora_weight}') + lora_hashes_string = ', '.join(lora_hashes) + lora_weights_string = ', '.join(lora_weights) + generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string + generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string + + generation_params[self.fooocus_to_a1111['version']] = data['version'] + + if modules.config.metadata_created_by != '': + generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by + + generation_params_text = ", ".join( + [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if + v is not None]) + positive_prompt_resolved = ', '.join(self.full_prompt) + negative_prompt_resolved = ', '.join(self.full_negative_prompt) + negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else "" + return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip() + + +class FooocusMetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.FOOOCUS + + def parse_json(self, metadata: dict) -> dict: + model_filenames = modules.config.model_filenames.copy() + lora_filenames = modules.config.lora_filenames.copy() + self.remove_special_loras(lora_filenames) + for key, value in metadata.items(): + if value in ['', 'None']: + continue + if key in ['base_model', 'refiner_model']: + metadata[key] = self.replace_value_with_filename(key, value, model_filenames) + elif key.startswith('lora_combined_'): + metadata[key] = self.replace_value_with_filename(key, value, lora_filenames) + else: + continue + + return metadata + + def parse_string(self, metadata: list) -> str: + for li, (label, key, value) in enumerate(metadata): + # remove model folder paths from metadata + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + name = Path(name).stem + value = f'{name} : {weight}' + metadata[li] = (label, key, value) + + res = {k: v for _, k, v in metadata} + + res['full_prompt'] = self.full_prompt + res['full_negative_prompt'] = self.full_negative_prompt + res['steps'] = self.steps + res['base_model'] = self.base_model_name + res['base_model_hash'] = self.base_model_hash + + if self.refiner_model_name not in ['', 'None']: + res['refiner_model'] = self.refiner_model_name + res['refiner_model_hash'] = self.refiner_model_hash + + res['loras'] = self.loras + + if modules.config.metadata_created_by != '': + res['created_by'] = modules.config.metadata_created_by + + return json.dumps(dict(sorted(res.items()))) + + @staticmethod + def replace_value_with_filename(key, value, filenames): + for filename in filenames: + path = Path(filename) + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + if name == path.stem: + return f'{filename} : {weight}' + elif value == path.stem: + return filename + + +def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser: + match metadata_scheme: + case MetadataScheme.FOOOCUS: + return FooocusMetadataParser() + case MetadataScheme.A1111: + return A1111MetadataParser() + case _: + raise NotImplementedError + + +def read_info_from_image(filepath) -> tuple[str | None, MetadataScheme | None]: + with Image.open(filepath) as image: + items = (image.info or {}).copy() + + parameters = items.pop('parameters', None) + metadata_scheme = items.pop('fooocus_scheme', None) + exif = items.pop('exif', None) + + if parameters is not None and is_json(parameters): + parameters = json.loads(parameters) + elif exif is not None: + exif = image.getexif() + # 0x9286 = UserComment + parameters = exif.get(0x9286, None) + # 0x927C = MakerNote + metadata_scheme = exif.get(0x927C, None) + + if is_json(parameters): + parameters = json.loads(parameters) + + try: + metadata_scheme = MetadataScheme(metadata_scheme) + except ValueError: + metadata_scheme = None + + # broad fallback + if isinstance(parameters, dict): + metadata_scheme = MetadataScheme.FOOOCUS + + if isinstance(parameters, str): + metadata_scheme = MetadataScheme.A1111 + + return parameters, metadata_scheme + + +def get_exif(metadata: str | None, metadata_scheme: str): + exif = Image.Exif() + # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py + # 0x9286 = UserComment + exif[0x9286] = metadata + # 0x0131 = Software + exif[0x0131] = 'Fooocus v' + fooocus_version.version + # 0x927C = MakerNote + exif[0x927C] = metadata_scheme + return exif diff --git a/modules/model_loader.py b/modules/model_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba336a915ae234b7cd5f9f2576d4edf779738ba --- /dev/null +++ b/modules/model_loader.py @@ -0,0 +1,26 @@ +import os +from urllib.parse import urlparse +from typing import Optional + + +def load_file_from_url( + url: str, + *, + model_dir: str, + progress: bool = True, + file_name: Optional[str] = None, +) -> str: + """Download a file from `url` into `model_dir`, using the file present if possible. + + Returns the path to the downloaded file. + """ + os.makedirs(model_dir, exist_ok=True) + if not file_name: + parts = urlparse(url) + file_name = os.path.basename(parts.path) + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + from torch.hub import download_url_to_file + download_url_to_file(url, cached_file, progress=progress) + return cached_file diff --git a/modules/ops.py b/modules/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..ee0e775634314d1b71811258cff87b2178e1c740 --- /dev/null +++ b/modules/ops.py @@ -0,0 +1,19 @@ +import torch +import contextlib + + +@contextlib.contextmanager +def use_patched_ops(operations): + op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm'] + backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names} + + try: + for op_name in op_names: + setattr(torch.nn, op_name, getattr(operations, op_name)) + + yield + + finally: + for op_name in op_names: + setattr(torch.nn, op_name, backups[op_name]) + return diff --git a/modules/patch.py b/modules/patch.py new file mode 100644 index 0000000000000000000000000000000000000000..3c2dd8f477902e68a467e8f89888934a762f4bb1 --- /dev/null +++ b/modules/patch.py @@ -0,0 +1,513 @@ +import os +import torch +import time +import math +import ldm_patched.modules.model_base +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.model_management +import modules.anisotropic as anisotropic +import ldm_patched.ldm.modules.attention +import ldm_patched.k_diffusion.sampling +import ldm_patched.modules.sd1_clip +import modules.inpaint_worker as inpaint_worker +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.ldm.modules.diffusionmodules.model +import ldm_patched.modules.sd +import ldm_patched.controlnet.cldm +import ldm_patched.modules.model_patcher +import ldm_patched.modules.samplers +import ldm_patched.modules.args_parser +import warnings +import safetensors.torch +import modules.constants as constants + +from ldm_patched.modules.samplers import calc_cond_uncond_batch +from ldm_patched.k_diffusion.sampling import BatchedBrownianTree +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control +from modules.patch_precision import patch_all_precision +from modules.patch_clip import patch_all_clip + + +class PatchSettings: + def __init__(self, + sharpness=2.0, + adm_scaler_end=0.3, + positive_adm_scale=1.5, + negative_adm_scale=0.8, + controlnet_softness=0.25, + adaptive_cfg=7.0): + self.sharpness = sharpness + self.adm_scaler_end = adm_scaler_end + self.positive_adm_scale = positive_adm_scale + self.negative_adm_scale = negative_adm_scale + self.controlnet_softness = controlnet_softness + self.adaptive_cfg = adaptive_cfg + self.global_diffusion_progress = 0 + self.eps_record = None + + +patch_settings = {} + + +def calculate_weight_patched(self, patches, weight, key): + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key),) + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + w1 = v[0] + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lora": + mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + if v[3] is not None: + mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), + mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape( + weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "fooocus": + w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32) + w1 = (w1 / 255.0) * (w_max - w_min) + w_min + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lokr": + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + else: + w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + else: + w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha *= v[2] / dim + + try: + weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "loha": + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha *= v[2] / w1b.shape[0] + w2a = v[3] + w2b = v[4] + if v[5] is not None: # cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) + else: + m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) + + try: + weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "glora": + if v[4] is not None: + alpha *= v[4] / v[0].shape[0] + + a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) + a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) + b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) + b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) + + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + else: + print("patch type not recognized", patch_type, key) + + return weight + + +class BrownianTreeNoiseSamplerPatched: + transform = None + tree = None + + @staticmethod + def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + if ldm_patched.modules.model_management.directml_enabled: + cpu = True + + t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max)) + + BrownianTreeNoiseSamplerPatched.transform = transform + BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __init__(self, *args, **kwargs): + pass + + @staticmethod + def __call__(sigma, sigma_next): + transform = BrownianTreeNoiseSamplerPatched.transform + tree = BrownianTreeNoiseSamplerPatched.tree + + t0, t1 = transform(torch.as_tensor(sigma)), transform(torch.as_tensor(sigma_next)) + return tree(t0, t1) / (t1 - t0).abs().sqrt() + + +def compute_cfg(uncond, cond, cfg_scale, t): + pid = os.getpid() + mimic_cfg = float(patch_settings[pid].adaptive_cfg) + real_cfg = float(cfg_scale) + + real_eps = uncond + real_cfg * (cond - uncond) + + if cfg_scale > patch_settings[pid].adaptive_cfg: + mimicked_eps = uncond + mimic_cfg * (cond - uncond) + return real_eps * t + mimicked_eps * (1 - t) + else: + return real_eps + + +def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None): + pid = os.getpid() + + if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False): + final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0] + + if patch_settings[pid].eps_record is not None: + patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu() + + return final_x0 + + positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options) + + positive_eps = x - positive_x0 + negative_eps = x - negative_x0 + + alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress + + positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0) + positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha) + + final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, + cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress) + + if patch_settings[pid].eps_record is not None: + patch_settings[pid].eps_record = (final_eps / timestep).cpu() + + return x - final_eps + + +def round_to_64(x): + h = float(x) + h = h / 64.0 + h = round(h) + h = int(h) + h = h * 64 + return h + + +def sdxl_encode_adm_patched(self, **kwargs): + clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 1024) + height = kwargs.get("height", 1024) + target_width = width + target_height = height + pid = os.getpid() + + if kwargs.get("prompt_type", "") == "negative": + width = float(width) * patch_settings[pid].negative_adm_scale + height = float(height) * patch_settings[pid].negative_adm_scale + elif kwargs.get("prompt_type", "") == "positive": + width = float(width) * patch_settings[pid].positive_adm_scale + height = float(height) * patch_settings[pid].positive_adm_scale + + def embedder(number_list): + h = self.embedder(torch.tensor(number_list, dtype=torch.float32)) + h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return h + + width, height = int(width), int(height) + target_width, target_height = round_to_64(target_width), round_to_64(target_height) + + adm_emphasized = embedder([height, width, 0, 0, target_height, target_width]) + adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width]) + + clip_pooled = clip_pooled.to(adm_emphasized) + final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1) + + return final_adm + + +def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): + if inpaint_worker.current_task is not None: + latent_processor = self.inner_model.inner_model.process_latent_in + inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x) + inpaint_mask = inpaint_worker.current_task.latent_mask.to(x) + + if getattr(self, 'energy_generator', None) is None: + # avoid bad results by using different seeds. + self.energy_generator = torch.Generator(device='cpu').manual_seed((seed + 1) % constants.MAX_SEED) + + energy_sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(x.shape) - 1)) + current_energy = torch.randn( + x.size(), dtype=x.dtype, generator=self.energy_generator, device="cpu").to(x) * energy_sigma + x = x * inpaint_mask + (inpaint_latent + current_energy) * (1.0 - inpaint_mask) + + out = self.inner_model(x, sigma, + cond=cond, + uncond=uncond, + cond_scale=cond_scale, + model_options=model_options, + seed=seed) + + out = out * inpaint_mask + inpaint_latent * (1.0 - inpaint_mask) + else: + out = self.inner_model(x, sigma, + cond=cond, + uncond=uncond, + cond_scale=cond_scale, + model_options=model_options, + seed=seed) + return out + + +def timed_adm(y, timesteps): + if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632: + y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None] + y_with_adm = y[..., :2816].clone() + y_without_adm = y[..., 2816:].clone() + return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask) + return y + + +def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + pid = os.getpid() + + guided_hint = self.input_hint_block(hint, emb, context) + + y = timed_adm(y, timesteps) + + outs = [] + + hs = [] + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + outs.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + outs.append(self.middle_block_out(h, emb, context)) + + if patch_settings[pid].controlnet_softness > 0: + for i in range(10): + k = 1.0 - float(i) / 9.0 + outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k) + + return outs + + +def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + self.current_step = 1.0 - timesteps.to(x) / 999.0 + patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0]) + + y = timed_adm(y, timesteps) + + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'middle') + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = torch.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +def patched_load_models_gpu(*args, **kwargs): + execution_start_time = time.perf_counter() + y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs) + moving_time = time.perf_counter() - execution_start_time + if moving_time > 0.1: + print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds') + return y + + +def build_loaded(module, loader_name): + original_loader_name = loader_name + '_origin' + + if not hasattr(module, original_loader_name): + setattr(module, original_loader_name, getattr(module, loader_name)) + + original_loader = getattr(module, original_loader_name) + + def loader(*args, **kwargs): + result = None + try: + result = original_loader(*args, **kwargs) + except Exception as e: + result = None + exp = str(e) + '\n' + for path in list(args) + list(kwargs.values()): + if isinstance(path, str): + if os.path.exists(path): + exp += f'File corrupted: {path} \n' + corrupted_backup_file = path + '.corrupted' + if os.path.exists(corrupted_backup_file): + os.remove(corrupted_backup_file) + os.replace(path, corrupted_backup_file) + if os.path.exists(path): + os.remove(path) + exp += f'Fooocus has tried to move the corrupted file to {corrupted_backup_file} \n' + exp += f'You may try again now and Fooocus will download models again. \n' + raise ValueError(exp) + return result + + setattr(module, loader_name, loader) + return + + +def patch_all(): + if ldm_patched.modules.model_management.directml_enabled: + ldm_patched.modules.model_management.lowvram_available = True + ldm_patched.modules.model_management.OOM_EXCEPTION = Exception + + patch_all_precision() + patch_all_clip() + + if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'): + ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu + + ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu + ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched + ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward + ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward + ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched + ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward + ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched + ldm_patched.modules.samplers.sampling_function = patched_sampling_function + + warnings.filterwarnings(action='ignore', module='torchsde') + + build_loaded(safetensors.torch, 'load_file') + build_loaded(torch, 'load') + + return diff --git a/modules/patch_clip.py b/modules/patch_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..06b7f01bb857b01995ff7b0326813f98f92ea76d --- /dev/null +++ b/modules/patch_clip.py @@ -0,0 +1,195 @@ +# Consistent with Kohya/A1111 to reduce differences between model training and inference. + +import os +import torch +import ldm_patched.controlnet.cldm +import ldm_patched.k_diffusion.sampling +import ldm_patched.ldm.modules.attention +import ldm_patched.ldm.modules.diffusionmodules.model +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.args_parser +import ldm_patched.modules.model_base +import ldm_patched.modules.model_management +import ldm_patched.modules.model_patcher +import ldm_patched.modules.samplers +import ldm_patched.modules.sd +import ldm_patched.modules.sd1_clip +import ldm_patched.modules.clip_vision +import ldm_patched.modules.ops as ops + +from modules.ops import use_patched_ops +from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection + + +def patched_encode_token_weights(self, token_weight_pairs): + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) + + out, pooled = self.encode(to_encode) + if pooled is not None: + first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k:k + 1] + if has_weights: + original_mean = z.mean() + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + new_mean = z.mean() + z = z * (original_mean / new_mean) + output.append(z) + + if len(output) == 0: + return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled + return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled + + +def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None, + textmodel_json_config=None, dtype=None, special_tokens=None, + layer_norm_hidden_state=True, **kwargs): + torch.nn.Module.__init__(self) + assert layer in self.LAYERS + + if special_tokens is None: + special_tokens = {"start": 49406, "end": 49407, "pad": 49407} + + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), + "sd1_clip_config.json") + + config = CLIPTextConfig.from_json_file(textmodel_json_config) + self.num_layers = config.num_hidden_layers + + with use_patched_ops(ops.manual_cast): + with modeling_utils.no_init_weights(): + self.transformer = CLIPTextModel(config) + + if dtype is not None: + self.transformer.to(dtype) + + self.transformer.text_model.embeddings.to(torch.float32) + + if freeze: + self.freeze() + + self.max_length = max_length + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = False + + self.layer_norm_hidden_state = layer_norm_hidden_state + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.clip_layer(layer_idx) + self.layer_default = (self.layer, self.layer_idx) + + +def patched_SDClipModel_forward(self, tokens): + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + attention_mask = None + if self.enable_attention_masks: + attention_mask = torch.zeros_like(tokens) + max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == max_token: + break + + outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, + output_hidden_states=self.layer == "hidden") + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + if self.layer_norm_hidden_state: + z = self.transformer.text_model.final_layer_norm(z) + + if hasattr(outputs, "pooler_output"): + pooled_output = outputs.pooler_output.float() + else: + pooled_output = None + + if self.text_projection is not None and pooled_output is not None: + pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() + + return z.float(), pooled_output + + +def patched_ClipVisionModel__init__(self, json_config): + config = CLIPVisionConfig.from_json_file(json_config) + + self.load_device = ldm_patched.modules.model_management.text_encoder_device() + self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() + + if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False): + self.dtype = torch.float16 + else: + self.dtype = torch.float32 + + with use_patched_ops(ops.manual_cast): + with modeling_utils.no_init_weights(): + self.model = CLIPVisionModelWithProjection(config) + + self.model.to(self.dtype) + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher( + self.model, + load_device=self.load_device, + offload_device=self.offload_device + ) + + +def patched_ClipVisionModel_encode_image(self, image): + ldm_patched.modules.model_management.load_model_gpu(self.patcher) + pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device)) + outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) + + for k in outputs: + t = outputs[k] + if t is not None: + if k == 'hidden_states': + outputs["penultimate_hidden_states"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["hidden_states"] = None + else: + outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device()) + + return outputs + + +def patch_all_clip(): + ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights + ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__ + ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward + ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__ + ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image + return diff --git a/modules/patch_precision.py b/modules/patch_precision.py new file mode 100644 index 0000000000000000000000000000000000000000..83569bdd15f5ab0cac2c57353626c4e843bd264d --- /dev/null +++ b/modules/patch_precision.py @@ -0,0 +1,60 @@ +# Consistent with Kohya to reduce differences between model training and inference. + +import torch +import math +import einops +import numpy as np + +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.model_sampling +import ldm_patched.modules.sd1_clip + +from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule + + +def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + # Consistent with Kohya to reduce differences between model training and inference. + + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = einops.repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + # Consistent with Kohya to reduce differences between model training and inference. + + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32) + self.set_sigmas(sigmas) + return + + +def patch_all_precision(): + ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding + ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule + return diff --git a/modules/private_logger.py b/modules/private_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..edd9457d221321022d3d622301f3215215fa819f --- /dev/null +++ b/modules/private_logger.py @@ -0,0 +1,131 @@ +import os +import args_manager +import modules.config +import json +import urllib.parse + +from PIL import Image +from PIL.PngImagePlugin import PngInfo +from modules.flags import OutputFormat +from modules.meta_parser import MetadataParser, get_exif +from modules.util import generate_temp_filename + +log_cache = {} + + +def get_current_html_path(output_format=None): + output_format = output_format if output_format else modules.config.default_output_format + date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, + extension=output_format) + html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') + return html_name + + +def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str: + path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs + output_format = output_format if output_format else modules.config.default_output_format + date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format) + os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True) + + parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else '' + image = Image.fromarray(img) + + if output_format == OutputFormat.PNG.value: + if parsed_parameters != '': + pnginfo = PngInfo() + pnginfo.add_text('parameters', parsed_parameters) + pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value) + else: + pnginfo = None + image.save(local_temp_filename, pnginfo=pnginfo) + elif output_format == OutputFormat.JPEG.value: + image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) + elif output_format == OutputFormat.WEBP.value: + image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) + else: + image.save(local_temp_filename) + + if args_manager.args.disable_image_log: + return local_temp_filename + + html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') + + css_styles = ( + "" + ) + + js = ( + """""" + ) + + begin_part = f"+ ++ *text* +Fooocus Log {date_string} {css_styles}{js}Fooocus Log {date_string} (private)
\nMetadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.
\n\n" + end_part = f'\n' + + middle_part = log_cache.get(html_name, "") + + if middle_part == "": + if os.path.exists(html_name): + existing_split = open(html_name, 'r', encoding='utf-8').read().split('') + if len(existing_split) == 3: + middle_part = existing_split[1] + else: + middle_part = existing_split[0] + + div_name = only_name.replace('.', '_') + item = f"\n\n" + + middle_part = item + middle_part + + with open(html_name, 'w', encoding='utf-8') as f: + f.write(begin_part + middle_part + end_part) + + print(f'Image generated with private log at: {html_name}') + + log_cache[html_name] = middle_part + + return local_temp_filename diff --git a/modules/sample_hijack.py b/modules/sample_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..5936a096d9f0afaac0a672f72cee5f84b23496ad --- /dev/null +++ b/modules/sample_hijack.py @@ -0,0 +1,184 @@ +import torch +import ldm_patched.modules.samplers +import ldm_patched.modules.model_management + +from collections import namedtuple +from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler +from ldm_patched.modules.model_base import SDXLRefiner, SDXL +from ldm_patched.modules.conds import CONDRegular +from ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models +from ldm_patched.modules.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \ + create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_model_conds + + +current_refiner = None +refiner_switch_step = -1 + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate_inner(c, p, target_model=None, target_clip=None): + if target_model is None or isinstance(target_model, SDXLRefiner): + c = c[..., -1280:].clone() + elif isinstance(target_model, SDXL): + c = c.clone() + else: + p = None + c = c[..., :768].clone() + + final_layer_norm = target_clip.cond_stage_model.clip_l.transformer.text_model.final_layer_norm + + final_layer_norm_origin_device = final_layer_norm.weight.device + final_layer_norm_origin_dtype = final_layer_norm.weight.dtype + + c_origin_device = c.device + c_origin_dtype = c.dtype + + final_layer_norm.to(device='cpu', dtype=torch.float32) + c = c.to(device='cpu', dtype=torch.float32) + + c = torch.chunk(c, int(c.size(1)) // 77, 1) + c = [final_layer_norm(ci) for ci in c] + c = torch.cat(c, dim=1) + + final_layer_norm.to(device=final_layer_norm_origin_device, dtype=final_layer_norm_origin_dtype) + c = c.to(device=c_origin_device, dtype=c_origin_dtype) + return c, p + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate(cond, target_model=None, target_clip=None): + results = [] + + for c, px in cond: + p = px.get('pooled_output', None) + c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) + p = {} if p is None else {'pooled_output': p.clone()} + results.append([c, p]) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate_after_preparation(cond, target_model=None, target_clip=None): + results = [] + + for x in cond: + p = x.get('pooled_output', None) + c = x['model_conds']['c_crossattn'].cond + + c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) + + result = {'model_conds': {'c_crossattn': CONDRegular(c)}} + + if p is not None: + result['pooled_output'] = p.clone() + + results.append(result) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + global current_refiner + + positive = positive[:] + negative = negative[:] + + resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) + resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) + + model_wrap = wrap_model(model) + + calculate_start_end_timesteps(model, negative) + calculate_start_end_timesteps(model, positive) + + if latent_image is not None: + latent_image = model.process_latent_in(latent_image) + + if hasattr(model, 'extra_conds'): + positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + + #make sure each cond area has an opposite one with the same area + for c in positive: + create_cond_with_same_area_if_none(negative, c) + for c in negative: + create_cond_with_same_area_if_none(positive, c) + + # pre_run_control(model, negative + positive) + pre_run_control(model, positive) # negative is not necessary in Fooocus, 0.5s faster. + + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} + + if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'): + positive_refiner = clip_separate_after_preparation(positive, target_model=current_refiner.model) + negative_refiner = clip_separate_after_preparation(negative, target_model=current_refiner.model) + + positive_refiner = encode_model_conds(current_refiner.model.extra_conds, positive_refiner, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative_refiner = encode_model_conds(current_refiner.model.extra_conds, negative_refiner, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + + def refiner_switch(): + cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))) + + extra_args["cond"] = positive_refiner + extra_args["uncond"] = negative_refiner + + # clear ip-adapter for refiner + extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()} + + models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype()) + ldm_patched.modules.model_management.load_models_gpu( + [current_refiner] + models, + model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory) + + model_wrap.inner_model = current_refiner.model + print('Refiner Swapped') + return + + def callback_wrap(step, x0, x, total_steps): + if step == refiner_switch_step and current_refiner is not None: + refiner_switch() + if callback is not None: + # residual_noise_preview = x - x0 + # residual_noise_preview /= residual_noise_preview.std() + # residual_noise_preview *= x0.std() + callback(step, x0, x, total_steps) + + samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar) + return model.process_latent_out(samples.to(torch.float32)) + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps): + if scheduler_name == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model, steps) + elif scheduler_name == "ddim_uniform": + sigmas = ddim_scheduler(model, steps) + elif scheduler_name == "sgm_uniform": + sigmas = normal_scheduler(model, steps, sgm=True) + elif scheduler_name == "turbo": + sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0] + else: + raise TypeError("error invalid scheduler") + return sigmas + + +ldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked +ldm_patched.modules.samplers.sample = sample_hacked diff --git a/modules/sdxl_styles.py b/modules/sdxl_styles.py new file mode 100644 index 0000000000000000000000000000000000000000..77ad6b574978129ff225ff05ed84d04d3a6389e5 --- /dev/null +++ b/modules/sdxl_styles.py @@ -0,0 +1,121 @@ +import os +import re +import json +import math +import modules.config + +from modules.util import get_files_from_folder + +# cannot use modules.config - validators causing circular imports +styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/')) +wildcards_max_bfs_depth = 64 + + +def normalize_key(k): + k = k.replace('-', ' ') + words = k.split(' ') + words = [w[:1].upper() + w[1:].lower() for w in words] + k = ' '.join(words) + k = k.replace('3d', '3D') + k = k.replace('Sai', 'SAI') + k = k.replace('Mre', 'MRE') + k = k.replace('(s', '(S') + return k + + +styles = {} + +styles_files = get_files_from_folder(styles_path, ['.json']) + +for x in ['sdxl_styles_fooocus.json', + 'sdxl_styles_sai.json', + 'sdxl_styles_mre.json', + 'sdxl_styles_twri.json', + 'sdxl_styles_diva.json', + 'sdxl_styles_marc_k3nt3l.json']: + if x in styles_files: + styles_files.remove(x) + styles_files.append(x) + +for styles_file in styles_files: + try: + with open(os.path.join(styles_path, styles_file), encoding='utf-8') as f: + for entry in json.load(f): + name = normalize_key(entry['name']) + prompt = entry['prompt'] if 'prompt' in entry else '' + negative_prompt = entry['negative_prompt'] if 'negative_prompt' in entry else '' + styles[name] = (prompt, negative_prompt) + except Exception as e: + print(str(e)) + print(f'Failed to load style file {styles_file}') + +style_keys = list(styles.keys()) +fooocus_expansion = "Fooocus V2" +legal_style_names = [fooocus_expansion] + style_keys + + +def apply_style(style, positive): + p, n = styles[style] + return p.replace('{prompt}', positive).splitlines(), n.splitlines() + + +def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order): + for _ in range(wildcards_max_bfs_depth): + placeholders = re.findall(r'__([\w-]+)__', wildcard_text) + if len(placeholders) == 0: + return wildcard_text + + print(f'[Wildcards] processing: {wildcard_text}') + for placeholder in placeholders: + try: + matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder] + words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines() + words = [x for x in words if x != ''] + assert len(words) > 0 + if read_wildcards_in_order: + wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1) + else: + wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1) + except: + print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. ' + f'Using "{placeholder}" as a normal word.') + wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder) + print(f'[Wildcards] {wildcard_text}') + + print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}') + return wildcard_text + + +def get_words(arrays, totalMult, index): + if len(arrays) == 1: + return [arrays[0].split(',')[index]] + else: + words = arrays[0].split(',') + word = words[index % len(words)] + index -= index % len(words) + index /= len(words) + index = math.floor(index) + return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index) + + +def apply_arrays(text, index): + arrays = re.findall(r'\[\[(.*?)\]\]', text) + if len(arrays) == 0: + return text + + print(f'[Arrays] processing: {text}') + mult = 1 + for arr in arrays: + words = arr.split(',') + mult *= len(words) + + index %= mult + chosen_words = get_words(arrays, mult, index) + + i = 0 + for arr in arrays: + text = text.replace(f'[[{arr}]]', chosen_words[i], 1) + i = i+1 + + return text + diff --git a/modules/style_sorter.py b/modules/style_sorter.py new file mode 100644 index 0000000000000000000000000000000000000000..49142bc7926e06ee29f5678de1a9acc13dac5b70 --- /dev/null +++ b/modules/style_sorter.py @@ -0,0 +1,59 @@ +import os +import gradio as gr +import modules.localization as localization +import json + + +all_styles = [] + + +def try_load_sorted_styles(style_names, default_selected): + global all_styles + + all_styles = style_names + + try: + if os.path.exists('sorted_styles.json'): + with open('sorted_styles.json', 'rt', encoding='utf-8') as fp: + sorted_styles = [] + for x in json.load(fp): + if x in all_styles: + sorted_styles.append(x) + for x in all_styles: + if x not in sorted_styles: + sorted_styles.append(x) + all_styles = sorted_styles + except Exception as e: + print('Load style sorting failed.') + print(e) + + unselected = [y for y in all_styles if y not in default_selected] + all_styles = default_selected + unselected + + return + + +def sort_styles(selected): + global all_styles + unselected = [y for y in all_styles if y not in selected] + sorted_styles = selected + unselected + try: + with open('sorted_styles.json', 'wt', encoding='utf-8') as fp: + json.dump(sorted_styles, fp, indent=4) + except Exception as e: + print('Write style sorting failed.') + print(e) + all_styles = sorted_styles + return gr.CheckboxGroup.update(choices=sorted_styles) + + +def localization_key(x): + return x + localization.current_translation.get(x, '') + + +def search_styles(selected, query): + unselected = [y for y in all_styles if y not in selected] + matched = [y for y in unselected if query.lower() in localization_key(y).lower()] if len(query.replace(' ', '')) > 0 else [] + unmatched = [y for y in unselected if y not in matched] + sorted_styles = matched + selected + unmatched + return gr.CheckboxGroup.update(choices=sorted_styles) diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..bebf9f8ca7860c700f52ea5d3d3586917f17d34b --- /dev/null +++ b/modules/ui_gradio_extensions.py @@ -0,0 +1,67 @@ +# based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/modules/ui_gradio_extensions.py + +import os +import gradio as gr +import args_manager + +from modules.localization import localization_js + + +GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse + +modules_path = os.path.dirname(os.path.realpath(__file__)) +script_path = os.path.dirname(modules_path) + + +def webpath(fn): + if fn.startswith(script_path): + web_path = os.path.relpath(fn, script_path).replace('\\', '/') + else: + web_path = os.path.abspath(fn) + + return f'file={web_path}?{os.path.getmtime(fn)}' + + +def javascript_html(): + script_js_path = webpath('javascript/script.js') + context_menus_js_path = webpath('javascript/contextMenus.js') + localization_js_path = webpath('javascript/localization.js') + zoom_js_path = webpath('javascript/zoom.js') + edit_attention_js_path = webpath('javascript/edit-attention.js') + viewer_js_path = webpath('javascript/viewer.js') + image_viewer_js_path = webpath('javascript/imageviewer.js') + samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg')) + head = f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + + if args_manager.args.theme: + head += f'\n' + + return head + + +def css_html(): + style_css_path = webpath('css/style.css') + head = f'' + return head + + +def reload_javascript(): + js = javascript_html() + css = css_html() + + def template_response(*args, **kwargs): + res = GradioTemplateResponseOriginal(*args, **kwargs) + res.body = res.body.replace(b'', f'{js}'.encode("utf8")) + res.body = res.body.replace(b'