TSNE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a2cebf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path as osp\n",
    "import GPUtil\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "from focal_loss.focal_loss import FocalLoss\n",
    "\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "import os\n",
    "from torch_geometric.data import Dataset, download_url\n",
    "from torch_geometric.loader import DataLoader\n",
    "import pandas as pd\n",
    "from performance import performances_val\n",
    "from PIL import Image\n",
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool, global_max_pool\n",
    "from torch_geometric.nn.norm import BatchNorm\n",
    "from torch_geometric.data import Data\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from torch_geometric.nn import ChebConv, GraphSAGE,GraphUNet ,TransformerConv\n",
    "\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "# os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\n",
    "seed = 1378\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "torch.cuda.manual_seed_all(seed)\n",
    "landmark_colors = np.load(\"landmark_colors.npy\")\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "one_hot_list=[]\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(np.expand_dims(landmark_colors,axis=1))  \n",
    "for x in landmark_colors:\n",
    "    one_hot_list.append(enc.transform([[x]]).toarray()[0])\n",
    "one_hot_arr  = np.array(one_hot_list)\n",
    "one_hot_arr = torch.FloatTensor(one_hot_arr)\n",
    "\n",
    "m = torch.nn.Softmax(dim=-1)\n",
    "\n",
    "class landmark_dataset(Dataset):\n",
    "    def __init__(self, info_list, transform=None, pre_transform=None, pre_filter=None):\n",
    "        super().__init__(None, transform, pre_transform, pre_filter)\n",
    "        \n",
    "        self.graphs = pd.read_csv(info_list, delimiter=\",\", header=None)\n",
    "\n",
    "\n",
    "    def len(self):\n",
    "        return len(self.graphs)\n",
    "\n",
    "    def get(self, idx):\n",
    "        \n",
    "        graph_path = self.graphs.iloc[idx]\n",
    "        graph_path = graph_path[0]\n",
    "        \n",
    "        \n",
    "        data = torch.load(graph_path)\n",
    "        #data.x =data.x.t()\n",
    "        return data \n",
    "class landmark_dataset_train(Dataset):\n",
    "    def __init__(self, info_list, transform=None, pre_transform=None, pre_filter=None):\n",
    "        super().__init__(None, transform, pre_transform, pre_filter)\n",
    "        \n",
    "        self.graphs = pd.read_csv(info_list, delimiter=\",\", header=None)\n",
    "\n",
    "\n",
    "    def len(self):\n",
    "        return len(self.graphs)\n",
    "\n",
    "    def get(self, idx):\n",
    "        \n",
    "        graph_path = self.graphs.iloc[idx]\n",
    "        graph_path = graph_path[0]\n",
    "        \n",
    "        data = torch.load(graph_path)\n",
    "        \n",
    " \n",
    "        \n",
    "        domain_name = graph_path.split(\"/\")[-4]\n",
    "        \n",
    "            \n",
    "        if domain_name == \"MSU_MFSD\" :\n",
    "            \n",
    "            domain_label = 0\n",
    "            \n",
    "        elif domain_name == \"Replay_attack_dataset\" :\n",
    "            \n",
    "            domain_label = 1\n",
    "            \n",
    "        elif domain_name == \"oulu\" :\n",
    "            \n",
    "            domain_label = 2\n",
    "        else :\n",
    "            print(graph_path,domain_name )\n",
    "            raise \"error\"\n",
    "            \n",
    "        \n",
    "        data = Data(x=data.x, edge_index=data.edge_index,y =data.y,data = domain_label,y_node = one_hot_arr)\n",
    "\n",
    "        #data.x =data.x.t()\n",
    "        return data \n",
    "train_dataset = landmark_dataset_train(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_train_O_M_I.txt\")\n",
    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True,num_workers=16,pin_memory=True)\n",
    "\n",
    "test_dataset = landmark_dataset(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/graph_test_C.txt\")\n",
    "test_loader = DataLoader(test_dataset, batch_size=1,num_workers=16)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "\n",
    "# فرض کنید که داده‌های ورودی شما یک توالی از ۶۸۰ ویژگی دارد.\n",
    "#sequence_length = 128\n",
    "#feature_dim = 4  # هر ویژگی تک‌بعدی است\n",
    "\n",
    "# نمونه داده ورودی (تعداد نمونه‌ها × طول توالی × بعد ویژگی)\n",
    "#inputs = torch.rand(32, sequence_length, feature_dim)  # 32 نمونه با 680 ویژگی تک‌بعدی\n",
    "\n",
    "# تعریف یک لایه ترنسفورمر انکودر\n",
    "class SimpleTransformer(nn.Module):\n",
    "    def __init__(self, embed_dim, num_layers, nhead,n_out):\n",
    "        super(SimpleTransformer, self).__init__()\n",
    "        self.linear = nn.Linear(4, embed_dim)  # افزایش بعد ویژگی‌ها\n",
    "        self.encoder_layer = TransformerEncoderLayer(d_model=embed_dim, nhead=nhead)\n",
    "        self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n",
    "        self.fc = nn.Linear(128 * embed_dim, n_out)  # فرضا 10 کلاس خروجی داریم\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)  # تغییر بعد ویژگی‌ها\n",
    "        x = self.transformer_encoder(x)\n",
    "        x = x.view(x.size(0), -1)  # فشرده‌سازی به یک بردار برای طبقه‌بندی\n",
    "        feuture = x\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# # ایجاد مدل و ارسال ورودی\n",
    "# # model = SimpleTransformer(embed_dim=embed_dim, num_layers=2, nhead=4)\n",
    "# # output = model(inputs)\n",
    "\n",
    "# #print(output.shape)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n",
    "\n",
    "# فرض کنید که داده‌های ورودی شما یک توالی از ۶۸۰ ویژگی دارد.\n",
    "sequence_length = 128\n",
    "feature_dim = 1  # هر ویژگی تک‌بعدی است\n",
    "\n",
    "# نمونه داده ورودی (تعداد نمونه‌ها × طول توالی × بعد ویژگی)\n",
    "#inputs = torch.rand(32, sequence_length, feature_dim)  # 32 نمونه با 680 ویژگی تک‌بعدی\n",
    "\n",
    "# تعریف یک لایه ترنسفورمر انکودر\n",
    "class SimpleTransformer_2(nn.Module):\n",
    "    def __init__(self, embed_dim, num_layers, nhead):\n",
    "        super(SimpleTransformer_2, self).__init__()\n",
    "        self.linear = nn.Linear(feature_dim, embed_dim)  # افزایش بعد ویژگی‌ها\n",
    "        self.encoder_layer = TransformerEncoderLayer(d_model=embed_dim, nhead=nhead)\n",
    "        self.transformer_encoder = TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n",
    "        self.fc = nn.Linear(sequence_length * embed_dim, 7)  # فرضا 10 کلاس خروجی داریم\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)  # تغییر بعد ویژگی‌ها\n",
    "        x = self.transformer_encoder(x)\n",
    "        x = x.view(x.size(0), -1)  # فشرده‌سازی به یک بردار برای طبقه‌بندی\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "# # ایجاد مدل و ارسال ورودی\n",
    "# # model = SimpleTransformer(embed_dim=embed_dim, num_layers=2, nhead=4)\n",
    "# # output = model(inputs)\n",
    "\n",
    "# #print(output.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac81950d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): ChebConv(640, 128, K=2, normalization=sym)\n",
      "  (conv2): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv3): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv4): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv5): ChebConv(256, 128, K=2, normalization=sym)\n",
      "  (conv6): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv7): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv8): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (lin): SimpleTransformer(\n",
      "    (linear): Linear(in_features=4, out_features=8, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "      (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=1024, out_features=1, bias=True)\n",
      "  )\n",
      "  (drop_1): RandomNodeDropout()\n",
      "  (drop_2): RandomNodeDropout()\n",
      "  (drop_3): RandomNodeDropout()\n",
      "  (drop_4): RandomNodeDropout()\n",
      "  (drop_5): RandomNodeDropout()\n",
      "  (drop_6): RandomNodeDropout()\n",
      "  (b1): BatchNorm(128)\n",
      "  (b2): BatchNorm(128)\n",
      "  (b3): BatchNorm(128)\n",
      "  (b4): BatchNorm(128)\n",
      "  (b5): BatchNorm(128)\n",
      "  (b6): BatchNorm(128)\n",
      "  (b7): BatchNorm(128)\n",
      "  (dis): Discriminator(\n",
      "    (conv1): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv2): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv3): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv4): GraphSAGE(128, 128, num_layers=2)\n",
      "    (drop_1): RandomNodeDropout()\n",
      "    (drop_2): RandomNodeDropout()\n",
      "    (grl_layer): GRL()\n",
      "    (fc2): Linear(in_features=128, out_features=3, bias=True)\n",
      "  )\n",
      "  (conv1_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (conv2_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (lin_node_class): SimpleTransformer_2(\n",
      "    (linear): Linear(in_features=1, out_features=64, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "      (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (5): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (6): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (7): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (8): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (9): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=8192, out_features=7, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class RandomNodeDropout(nn.Module):\n",
    "    def __init__(self, drop_prob=0.3):\n",
    "        super(RandomNodeDropout, self).__init__()\n",
    "        self.drop_prob = drop_prob\n",
    "\n",
    "    def forward(self, x, enabled=True):\n",
    "        if not self.training or not enabled:\n",
    "            return x\n",
    "        \n",
    "        num_rows = x.size(0)\n",
    "        mask = torch.rand(num_rows, device=x.device) > self.drop_prob\n",
    "        mask = mask.float().unsqueeze(1).expand_as(x)\n",
    "        x = x * mask\n",
    "        \n",
    "        return x\n",
    "\n",
    "soft = torch.nn.Softmax(dim=1)\n",
    "\n",
    "num_domain = 3\n",
    "class GRL(nn.Module):\n",
    "\n",
    "    def __init__(self, max_iter):\n",
    "        super(GRL, self).__init__()\n",
    "        self.iter_num = 0\n",
    "        self.alpha = 10\n",
    "        self.low = 0.0\n",
    "        self.high = 1.0\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.iter_num += 1\n",
    "        return input * 1.0\n",
    "\n",
    "    def backward(self, gradOutput):\n",
    "        coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter))\n",
    "                         - (self.high - self.low) + self.low)\n",
    "        return -coeff*10* gradOutput\n",
    "\n",
    "num_node  =train_dataset.num_node_features\n",
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_2(x,drop_en)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv4(x, edge_index)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels,max_iter=4000):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = ChebConv(num_node, hidden_channels,2)\n",
    "        self.conv2 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv3 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv4 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv5 = ChebConv(hidden_channels*2, hidden_channels,2)\n",
    "        self.conv6 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv7 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv8 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.lin = SimpleTransformer(8,5,8,1)\n",
    "        \n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_3 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_4 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_5 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_6 = RandomNodeDropout(drop_prob=0.3)\n",
    "        \n",
    "        \n",
    "        self.b1 = BatchNorm(hidden_channels)\n",
    "        self.b2 = BatchNorm(hidden_channels)\n",
    "        self.b3 = BatchNorm(hidden_channels)\n",
    "        self.b4 = BatchNorm(hidden_channels)\n",
    "        self.b5 = BatchNorm(hidden_channels)\n",
    "        self.b6 = BatchNorm(hidden_channels)\n",
    "        self.b7 = BatchNorm(hidden_channels)\n",
    "        \n",
    "        self.dis = Discriminator(hidden_channels,max_iter)\n",
    "        \n",
    "        self.conv1_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.conv2_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.lin_node_class = SimpleTransformer_2(64,10,8)\n",
    "        \n",
    "\n",
    "        \n",
    "        #self.tcn = Simple1DCNN()\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en,index_arr):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x  = self.b1(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        \n",
    "\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x  = self.b2(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_2(x,drop_en)\n",
    "                \n",
    "        dicriminator = x\n",
    "        \n",
    "        x_1 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x  = self.b3(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_3(x,drop_en)\n",
    "        \n",
    "        x_3 = global_mean_pool(x, batch)\n",
    "        \n",
    "        \n",
    "        x = self.conv4(x, edge_index)\n",
    "        x  = self.b4(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_4(x,drop_en)\n",
    "        \n",
    "        \n",
    "        x = torch.cat((x,dicriminator),dim=1) #skip connection\n",
    "        \n",
    "        x = self.conv5(x, edge_index)\n",
    "        x  = self.b5(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_5(x,drop_en)\n",
    "        \n",
    "        x_5 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv6(x, edge_index)\n",
    "        x  = self.b6(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_6(x,drop_en)\n",
    "        \n",
    "        x = self.conv7(x, edge_index)\n",
    "        x  = self.b7(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        \n",
    "        x = self.conv8(x, edge_index)\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        x = torch.stack([x,x_1,x_5,x_3],dim=2)\n",
    "        \n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        \n",
    "        #x = torch.unsqueeze(x,2)\n",
    "        #print(x.shape)\n",
    "        \n",
    "        x,_ = self.lin(x)\n",
    "        \n",
    "        x= x.sigmoid()\n",
    "        \n",
    "        \n",
    "        node_class_hidden = self.conv1_node_class(dicriminator,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        node_class_hidden = self.conv2_node_class(node_class_hidden,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        #print(index_arr.shape , node_class_hidden.shape)\n",
    "        \n",
    "        node_class_hidden = node_class_hidden[index_arr]\n",
    "        \n",
    "        #print(node_class_hidden.shape)\n",
    "        node_class_hidden = torch.unsqueeze(node_class_hidden, dim=2)\n",
    "        \n",
    "        node_pre = self.lin_node_class(node_class_hidden)\n",
    "        \n",
    "            \n",
    "        #print(\"hiiii :\" , dicriminator.shape)\n",
    "        dis_invariant = self.dis(dicriminator,edge_index,batch,drop_en)\n",
    "        \n",
    "        return x ,dis_invariant ,node_pre,dicriminator\n",
    "\n",
    "model = GCN(hidden_channels=128).to(device)\n",
    "print(model)\n",
    "\n",
    "def check_folder(log_dir):\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.makedirs(log_dir)\n",
    "    return log_dir\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "#criterion = torch.nn.CrossEntropyLoss()\n",
    "criterion= nn.BCELoss()\n",
    "criterion_node_class=  torch.nn.CrossEntropyLoss()\n",
    "criterion_2 = FocalLoss(gamma=2)\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    losses_adv_loss=0\n",
    "    losses_loss_cls=0\n",
    "    total_loss = 0\n",
    "    losses_node_cls_loss = 0\n",
    "    progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training dataset.\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        drop_en = bool(np.random.binomial(1, 0.5))\n",
    "        \n",
    "        if graph.x.shape[0] < 29952 :\n",
    "            \n",
    "            if graph.x.shape[0] < 64:\n",
    "            \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "                \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "                \n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        out,domain_invariant,node_pre,feature = model(graph.x, graph.edge_index, graph.batch,drop_en,index_arr)  # Perform a single forward pass.\n",
    "        print(graph.data.shape,domain_invariant.shape)\n",
    "        adv_loss = criterion_2(domain_invariant, graph.data)\n",
    "        \n",
    "        node_cls_loss = criterion_node_class(node_pre.squeeze(),graph.y_node.float()[index_arr])\n",
    "        #print(out.shape , graph.y.long().shape)\n",
    "        loss_cls = criterion(out.squeeze(), graph.y.float())  # Compute the loss.\n",
    "        \n",
    "        loss_all = adv_loss + loss_cls + node_cls_loss\n",
    "        loss_all.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        total_loss = loss_all + total_loss\n",
    "        losses_adv_loss = adv_loss + losses_adv_loss\n",
    "        losses_loss_cls = loss_cls + losses_loss_cls\n",
    "        losses_node_cls_loss = node_cls_loss + losses_node_cls_loss\n",
    "\n",
    "\n",
    "    print(\"/////////////////////////\")\n",
    "    print(\"adverserial loss : \" ,losses_adv_loss/len(train_loader.dataset))  \n",
    "    print(\"classification loss : \" , losses_loss_cls/len(train_loader.dataset))\n",
    "    print(\"classification node loss : \" , losses_node_cls_loss/len(train_loader.dataset))\n",
    "    print(\"total loss : \" ,total_loss/len(train_loader.dataset) )\n",
    "    print(\"////////////////////////////////////\")\n",
    "def test(loader,epoch):\n",
    "    score_path = os.path.join(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores\", \"epoch_{}\".format(epoch+1))\n",
    "    check_folder(score_path)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        scores_list = []\n",
    "\n",
    "        correct = 0\n",
    "        s= 0\n",
    "        progress_bar = tqdm(loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "        for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "            if graph.x.shape[0] < 29952 :\n",
    "\n",
    "                if graph.x.shape[0] < 64:\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "                else :\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "                \n",
    "            graph = graph.to(device)  # Move data to the device\n",
    "            logit,_ ,_,_= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "            live_label = graph.y.float()\n",
    "            for i in range(len(logit)):\n",
    "                        scores_list.append(\"{} {}\\n\".format(logit[i].item(), live_label[i].item()))\n",
    "  # Check against ground-truth labels.\n",
    "    map_score_val_filename = os.path.join(score_path, \"score.txt\")\n",
    "    with open(map_score_val_filename, 'w') as file:\n",
    "                file.writelines(scores_list)\n",
    "    print(\"score: write test scores to {}\".format(map_score_val_filename))\n",
    "    test_ACC, fpr, FRR, HTER, auc_test, test_err, tpr = performances_val(map_score_val_filename)\n",
    "    print(\"epoch:{:d}, test:  val_ACC={:.4f}, HTER={:.4f}, AUC={:.4f}, val_err={:.4f}, ACC={:.4f}, TPR={:.4f}\".format(\n",
    "        epoch + 1, test_ACC, HTER, auc_test, test_err, test_ACC, tpr))  \n",
    "    return auc_test\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4cfc8713",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2:   0%|                                    | 0/213872 [00:01<?, ?batch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1]) torch.Size([1, 3])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])) is deprecated. Please ensure they have the same size.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [43]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [42]\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m    260\u001b[0m node_cls_loss \u001b[38;5;241m=\u001b[39m criterion_node_class(node_pre\u001b[38;5;241m.\u001b[39msqueeze(),graph\u001b[38;5;241m.\u001b[39my_node\u001b[38;5;241m.\u001b[39mfloat()[index_arr])\n\u001b[1;32m    261\u001b[0m \u001b[38;5;66;03m#print(out.shape , graph.y.long().shape)\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m loss_cls \u001b[38;5;241m=\u001b[39m \u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Compute the loss.\u001b[39;00m\n\u001b[1;32m    264\u001b[0m loss_all \u001b[38;5;241m=\u001b[39m adv_loss \u001b[38;5;241m+\u001b[39m loss_cls \u001b[38;5;241m+\u001b[39m node_cls_loss\n\u001b[1;32m    265\u001b[0m loss_all\u001b[38;5;241m.\u001b[39mbackward()  \u001b[38;5;66;03m# Derive gradients.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/loss.py:613\u001b[0m, in \u001b[0;36mBCELoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    612\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 613\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbinary_cross_entropy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/functional.py:3074\u001b[0m, in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   3072\u001b[0m     reduction_enum \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mget_enum(reduction)\n\u001b[1;32m   3073\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m target\u001b[38;5;241m.\u001b[39msize() \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msize():\n\u001b[0;32m-> 3074\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   3075\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing a target size (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m) that is different to the input size (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m) is deprecated. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3076\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease ensure they have the same size.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(target\u001b[38;5;241m.\u001b[39msize(), \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msize())\n\u001b[1;32m   3077\u001b[0m     )\n\u001b[1;32m   3079\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3080\u001b[0m     new_size \u001b[38;5;241m=\u001b[39m _infer_size(target\u001b[38;5;241m.\u001b[39msize(), weight\u001b[38;5;241m.\u001b[39msize())\n",
      "\u001b[0;31mValueError\u001b[0m: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([])) is deprecated. Please ensure they have the same size."
     ]
    }
   ],
   "source": [
    "train(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bdd04f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"Focal_without_pipline_domain_generalization_tuning.pth\",map_location='cuda'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e27dcd3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = train_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8dd8461e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   0%|                         | 43/213872 [00:01<2:42:14, 21.97batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [37]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     index_arr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m29952\u001b[39m, (\u001b[38;5;241m64\u001b[39m,))\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     24\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m logit,_ ,_,features\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mindex_arr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     27\u001b[0m y_list\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [34]\u001b[0m, in \u001b[0;36mGCN.forward\u001b[0;34m(self, x, edge_index, batch, drop_en, index_arr)\u001b[0m\n\u001b[1;32m    209\u001b[0m node_pre \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin_node_class(node_class_hidden)\n\u001b[1;32m    212\u001b[0m \u001b[38;5;66;03m#print(\"hiiii :\" , dicriminator.shape)\u001b[39;00m\n\u001b[0;32m--> 213\u001b[0m dis_invariant \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdicriminator\u001b[49m\u001b[43m,\u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdrop_en\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x ,dis_invariant ,node_pre,dicriminator\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [34]\u001b[0m, in \u001b[0;36mDiscriminator.forward\u001b[0;34m(self, x, edge_index, batch, drop_en)\u001b[0m\n\u001b[1;32m     67\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv4(x, edge_index)\n\u001b[1;32m     68\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mrelu()\n\u001b[0;32m---> 70\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mglobal_mean_pool\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     72\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc2(x)\n\u001b[1;32m     73\u001b[0m x \u001b[38;5;241m=\u001b[39m m(x)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/pool/glob.py:62\u001b[0m, in \u001b[0;36mglobal_mean_pool\u001b[0;34m(x, batch, size)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\u001b[38;5;241m.\u001b[39mmean(dim\u001b[38;5;241m=\u001b[39mdim, keepdim\u001b[38;5;241m=\u001b[39mx\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 62\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m size\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m scatter(x, batch, dim\u001b[38;5;241m=\u001b[39mdim, dim_size\u001b[38;5;241m=\u001b[39msize, reduce\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "y_list = []\n",
    "feature_list = []\n",
    "domains = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        logit,_ ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list.append(live_label.cpu().numpy())\n",
    "        feature_list.append(features.cpu().numpy())\n",
    "        domains.append(graph.data.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbc2a9be",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfeatures\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[0;31mNameError\u001b[0m: name 'features' is not defined"
     ]
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ff956b13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   0%|                                     | 0/22296 [00:00<?, ?batch/s]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'GlobalStorage' object has no attribute 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/storage.py:79\u001b[0m, in \u001b[0;36mBaseStorage.__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 79\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/storage.py:104\u001b[0m, in \u001b[0;36mBaseStorage.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'data'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [38]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[1;32m     26\u001b[0m logit,a ,_,features\u001b[38;5;241m=\u001b[39m model(graph\u001b[38;5;241m.\u001b[39mx, graph\u001b[38;5;241m.\u001b[39medge_index, graph\u001b[38;5;241m.\u001b[39mbatch,\u001b[38;5;28;01mFalse\u001b[39;00m,index_arr)\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(a\u001b[38;5;241m.\u001b[39mshape,\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m     28\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     29\u001b[0m y_list_target\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/data.py:441\u001b[0m, in \u001b[0;36mData.__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_store\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m:\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    437\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object was created by an older version of PyG. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    438\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf this error occurred while loading an already existing \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    439\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset, remove the \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprocessed/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m directory in the dataset\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    440\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot folder and try again.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 441\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_store\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/storage.py:81\u001b[0m, in \u001b[0;36mBaseStorage.__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m---> 81\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m     82\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'GlobalStorage' object has no attribute 'data'"
     ]
    }
   ],
   "source": [
    "loader = test_loader\n",
    "model.eval()\n",
    "y_list_target = []\n",
    "feature_list_target = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        logit,a ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        print(a.shape,graph.data.shape)\n",
    "        live_label = graph.y.float()\n",
    "        y_list_target.append(live_label.cpu().numpy())\n",
    "        feature_list_target.append(features.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b824ac0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([468, 128])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da698a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "11232/468"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b49dfba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list_target= np.concatenate(y_list_target)\n",
    "feature_list_target= np.array(feature_list_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4683feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list= np.concatenate(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7b0b335",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = np.array(feature_list)\n",
    "domains = np.concatenate(domains)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d33fe83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5363, 468, 128)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc96227c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "a = feature_list\n",
    "\n",
    "b = feature_list_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc6ead4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = a[:,400,:]\n",
    "feature_list_target = b[:,400,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "12092839",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_8645/3098538043.py:35: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  plt.scatter(x[0], x[1], color='yellow', marker=marker, s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# داده‌های ورودی (مثال فرضی)\n",
    "# فرض کنید feature_list داده‌های ویژگی‌ها است\n",
    "# و y_list لیبل‌ها، domain_list دامنه‌ها را نشان می‌دهد\n",
    "# target_feature و target_label داده‌های هدف هستند\n",
    "\n",
    "# اجرای TSNE برای کاهش ابعاد به 2\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(feature_list)\n",
    "\n",
    "# رنگ‌های مختلف برای دامنه‌ها\n",
    "colors = {0: 'blue', 1: 'green', 2: 'red'}\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "for i, (x, y, domain, label) in enumerate(zip(X_tsne, y_list, domains, y_list)):\n",
    "    if label == 0:\n",
    "        marker = 'o'  # دایره برای کلاس 0\n",
    "    else:\n",
    "        marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color=colors[domain], marker=marker, s=30, alpha=0.7)\n",
    "\n",
    "# افزودن داده‌های هدف به نمودار\n",
    "target_tsne = tsne.fit_transform(feature_list_target)\n",
    "for x, label in zip(target_tsne, y_list_target):\n",
    "    if label == 0:\n",
    "        marker = 'o'  # دایره برای کلاس 0\n",
    "    else:\n",
    "        marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color='yellow', marker=marker, s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"TSNE of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.legend(['MSU_MFSD', 'Replay_attack_dataset', 'Oulu', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b5ac6c8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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Hsey25co1tmt1cLEnyD9y37fd5Cb83qjUZ11P3+WdcXCxY++Sv7Gx1bHe+8e2+Fb5+kSooH8GVAGsomJGZedGhH4MBWDMvkEULJsn1j73bz6mfY7epuOvlaIvNCSUmyflvVZJD/WStWHzm6V0mhbp1/v83gtFn9G7Bpi+D1rbnaMbTpmOC5TN9Vnz2Px2CWMbzSBBEnc6z4h7dC1r2NrZ0GthR3ot7PhF46io/IioKmgVFSOLBqwyCV+AYTUmxNg+JDiEJuk7KYQvwJ5lh77K/PYvP6I49vMJsGjj6umiOM5ktiIXQtB9Xhts7HTkK5uTzjNbx3rO7sUGUVZTh06FIwW5jY0NQzb0Ugjf194+VPdoQo+Sgy3GiPDzrZGgmUWdisqnIiHQo4nXz/dCFcAqKkb+mr5bcRwaFGYKELFpyg5FnV6vp3fp4bx8+NpinFwlssX73N48f8v09kr1bMYCaS3auXi4MGhdD7L+kokBq7vhnshdUV+5bTl2B61l/L4hinJ9uJ7TOy/w4n6kq9CFA5e5cfpfAP49f489yw9HO79Gydvz0TeY68dv0zxLV1N5t6IDTX6+gR8+8valT5yuNy4EBweb/j7dig+Kt3FVVL4VqgpaRcVI4Ur5OPHXWat183uvoHbPqqbjmZ0WcfusdX/V5OmSxvvcOubvpywQMPP0WKttS9UtSqm6Ra3WmSNJEvWSt+X9yw+K8qxFMjLj1BjePlUKyxObzlCx2W8AXD95mx6/yqtdW0elVfiLey9ZOGAVG8Zvszino0v85ROu7RW5Ar956t94G1fl/5/vabkcn6grYBUVI0M39abdpCYUrVEw1rZnd1+yWl6mcYn4nhYAjm5KwZUkTSK0Wu0XjfnXtJ0WwhfgljEQRoWWSqvlAau6mb7P67XM9N1cbQ/QdlIjq8LXM5kHjk4OFuXmVHZqZFrVTmkbvf+zwWBAH/6zJKVT+RRUP2AVlZ+U2j2rMfyvvvRc1J6If5dCAy1HN1S0Czf62ZrTbW5r+i3v/FXmNe+fiWhtIgVu33g4j62DXaxtDhg2sl+/gQOGjTi7RVpRp8uVRtEuY760rHwwm90ha6jdvbrVsXxevKdecku3JnNCgyKF+Z5Ff1ttI0kSQ2qMR2+I/BvkKJE1tktRUfm/Q1VBq6gYCQ/XU8urBaHBoax/vZDyzUsTFBiCjY2WbXP2cXrnBZOPaqJUnnx45avoX77lbwjxdd6mHRzt2Ruy7ovHCQsNQ2ejQwhB1fblOL//H87tuoSTmyPBASHY2NtQsXUZRtSZTIepzUmYwpOwkDAkScLOTGC/eKAMKznx8FCcXCMFtJOHE4HvAy3O/877PU0zdmbF3VmxzlVoLe/lP39fp2+Z4aZjeyc7xu8fTLZfMsfp+lV+BgR66edYO6oCWEXFSEXb+qbvtdzNUvYJIhMGaGHjy8VMPzmaKs6N0YfqcXJ3ZNaZsdjY2HzT+Vrj7tWHdM7XHzsnWza9WYytbeT+bFltHdN1rH42j0TJPOm1qAO1vVrh9zYAoRG0ndiEWV2WAHBm50X6r+zKyLqTAXBN6MrmV4t5+fQ1Vw5HhuP8tU4RhfAF2OqzjNCQMGxsdZTTKsNZ+jx/F+38F9+bTquM3bC1t2VX4GqL+vFNlH7LqbKmIEO+6H2vj2w6yZ6FhxiyqTdO8bj/rKISH/wcrxEqKl8T88BSeqiTsBUajYa9wes4YNjI1nfLSZEp2WcPH7HnWVZjPZrUp9AxT18MBgNB/sHUiBIu0vw6WmWR93PNDZkkg8Th9SdNx2EhYUxoHrlS9Xvjx4qRG1k2cK1i2Cz5La2x9Xo9D649Qq/X02ORMgTl+ENDLNpHkCpdMg7oN1oVvgBlGv+qOJ59bhy2dtZffKa0n8/outO4dOAaNdyaER4WHu15VX4cJMCAJl4/3wt1Bazyn2bN6E0sHbweAK2dBn1I3Ax7WmbrhquHM1NPjPoiY6jevw9XHO9f+TflmpT+rLEeXH+iOA4LiV7g6Gy03LvyyKL85onIpApuiVwsYkMfXnucXMWVblY1e1ZTHAf4BfKHe3PT8fpXC+MUnOTMrouc2nKW9tOa4+gsr1bXjttCgqTulG8m35M245rgmdyTE5vOMHxnv2jHunr8JnsWHFSUeT98ScpMKWKdh8r/P2okLBWVn4AI4QugDzGg1WniZF37/N+XPAcq2NT/oshXt88o3Wcy5En9WeP4fwikXa5eirK6/SONoaLmCO4yszUeiVwVZQUr5uL8nsj0gL6vLWNFP7/tzfPb3iCgbLNStBzbCJ1O+RhZMmCN4nhU3WlM+XuY6fj1kzf0Lz8KoRWM3z8Er2QJWDdhK4v7y6vePUv+xsHNgSBjBiqA45vPMmp7fwBqdqlEzS6Vor0XAKuGW/5NUmRMHmMfFZVvjaqCVlExw0L4CkicxuvrnTBKsqF0OT8vlvSeRcrVXoNBNWkzprHp+Pjm04r6h9ef4pk0AcO39iVx2kR0nN6SQeuUAjxGJOi7pBNeid0tqmr1qqo4bjKkluJ4cLXxPL3zgic3nzOs5kQAVkYRmObCF+DszouU1dTh4bVHcZpeyfrFFMdlGhend+lhHF57Ik79Vf5/kSTZCCs+P98LVQCr/KfpuyoWdx4JXj16G221k3vMfq2x0X1BO9P3rMU+35K3aLVI32WNVkOzYUrDpxcPlBG7KraWA2p8eO1Ljc7l+aNLRWztbHB0jfv1VLSvb7GyNhgMJErpxejdfbB3tsUrmQf+UUJm2pkF7tCHhVNWU0fhfhQTbXP3oZyuLu3y9YmxXYUWSjX+oVUnuHrsJmMbTef9q/em8jpJWlFWU4c/q1oPaqKi8jURUf8BfdOTC+EOLAJyIK8FWgJ3gPVAGuARUFeSpPfWR5ApUKCAdOGCZYo0FZW44P8hgJqeLSxWo7Gx8MYk0mT9PJWxOXq9Hn24IVpjorji/fAVt8/epXjNwtjY2nB+3z+MbTyDZOmTMPX4SJpl7MybJz5UaF2aXgs68keCZgR8+BjteEJAXB8PdftWJVHqhMzqJFtQ6+x0aIQgNDgMnY2WnR9Xo9VqCQsLo5Jdw1hGixtuiV3Z5L042vq3L3zYNmsfBSvkplepYaby5XdnkCx9UkbWm8yxjWdM5XvD1n1xcBMVGSHERUmSvkpeyUw5HaSZ2y0N/76ECulufbX5xsT33gOeDuyVJKm2EMIWcAQGAockSRonhOgP9Aeit7ZQUflCXNyd2Re2ng9vfGmSthOhwWGx9ilYIU+8CF8ArVZLlyL9+bX2LzToV/Ozx0maNjFJ0yY2HQ+sOAaAOz73qGTXgG0BK3B0jFzhxiR8wVL4lqj7C0hwbONpi7YbJuzA1iFyZRseEo69kx06Wy0OLg7M6b6U997vObf/8mdcmXV8X/nRIX9f5l60TJoRFBREgxSy9fXjW0+p3asqe5ccpkyjX0mWXg4V+s/f1xV9vuNaROUTkCNh/RzK2+92FUIIN6AEsBhAkqRQSZI+ANWB5cZmy4Ea32N+Kv8tNBoNCRJ70H1+u1jbtpvUlBHb+jG/93I2T98ZY9up7eZRzbUJR417sHq9nrGNp7NwgGxwpA/XU1ZTh7sXH7FkwFoapI7+/P9evE/P0kP5e/3n7WPWTiD7NhsMBkKDo1f52rvYkzC1ct87abpEDF7Xk8HrezJkcy8KVMiDk4fSrzb3b9kVxz0Xtic8VI+/TwDbZ+/j+F/nCAlQntfJPWbf3JwlszJkc2/ZErpFadpPaaqov/fPQ4s+xzadorpLpAvW6W0XaDexKVt8likyOCVNk0jRT6dTV78q35bvuQJOC7wBlgohcgMXgW5AYkmSvI1tXgKJrXUWQrQF2gKkSpXq689W5afk4c0ntM0RaXx0wLCRsk1KcvPsHYZWH88Hc0tgDfRf0ZUyDX9V+OzuXXKYhVemWIy9dPBadi+UUxOOqjOF4mHrqOLciPAQOYTixf2X6TCluaLP26fWg1Rc2H+FARVGAXDt6E22z97H1GMjY7y24rWKcGJzpIo1LDycUfWnKnICW6CF7R9WKIJn2NjpWG6MXLVuwlYeXH5Ej/ntcHJzoE7iNoSFhNFmUhPq9qzGtPbz2Lf8KOEh4UxoOTvG+YGcIjHCirxNvt48uvxYUT9yaz+c3JwoWq0Ac3ss58apO2h0YIjGw2rDlO0s7L0y1vMCdJnVmi5FBgJQom6ROPVR+X9AjYQVX+fOB3SRJOmsEGI6srrZhCRJkhDCqmJIkqQFwAKQ94C/9mRVfk4GlBulOD614wJFqxYgW+HMbHy5hKCAID689iNJ2kSmMJO3zytdhx5de2p17J3zDyiOg/yDTMIX4P4/j0ieUZk5KUm6yFXZ7Ut3eXLtGeWalebgyqOKdtdP3o712oZu7EVVt8YE+8u+vFXal2PnnP0xd9LL/rjmuHm5EhjwkT/cmpvKTm49x66Pa9juv4KqLo1Z2Hsl68ZuIWvhjIQb/Y/Dg2MPfBHwPpCgwCAcnBx4fEXpx+zq6YKTMf50veRt8X3tZ9E/Y36l1fjmKZYaiUpty1iUAWQplJE9oWsJ+RhiEclLReVb8D1fI54BzyRJisj/tglZIL8SQiQFMP7fMuGqiko88UuNQopjJ1d7xbGDswNJ0yXm48dgTu08z9Wj10mdTalxyVY0k9WxnRMoH+rO7s4WbWwdbFj5YBYZC6Sj+5J2rLwnrxr7lhtOlwIDmdhijpzMvmsFRb/8v+eyGGtg5dGU1dShcdqOtMvXm7KaOgT7h+CRxI0iVfPTdnyTaO6CEgcnO+obfYht7G1Y+XA2tb1aKtpE7JP/4dnc9FLh7xOAn48/WqMq1zx5hGJ8Z+U97lliqNX2U45GBimxJnzbTWrKhAPKqFptxjeKPBCyRqPHPHkvuNdvQymrrUOzTJ1NeY91Op0qfH8w1EhY8YAkSS+FEE+FEJklSboDlAFuGj/NgHHG/1vmNVNRiSe6zWrNvUsPTNbDuUvmMNWd2XWewVUtDXw8kroz5fgIRtaZTO1eVanby3r2n9dR3JequVkKwKd3XpC9SGbmnBuvKP/noNJAqGuRQWx6vZizey+RLmcqMuRWrvwCA4I4v+cyAK8evwEzTe77l76M3CYrl4bv6MtQ4zXV6lOF/UuPog8L46NvsKl9wpSerDOmE0ycOiFarRZ9mNI/ulIbeVUZNVJWvb41CPjwkZCgEMo0LM6bZ+84tuk0q0ZsMrVZfm8mdZO0MR1HJHaYfX4s7fP2RTJIuHg60zpHTzRawZ5oklCY52eO4PdGJSlYIQ9P73iTo2gWU/nrJ2+4euSmfL57r2iWsQsOLvZsebfssyyfA3wDubD3MjmKZ8Eruecn91dRge9vBd0FWG20gH4AtEBelW8QQrRCfozUjaG/isoXM/PUGKvlI+tMtVr+3vsDGfKkZcOLRTGOm/WXzFw7Gpm0IMg/2KJNtsLWV88ZC6Tj7oUHpmNJgqvHblGucSmr7W3tYv6nLEkSQgiKVi7IAcNGOhfuz+aJkeraRCm9QEgMXN+d5pki8/4+u/OC0OBQcpfKxhWjABuwphv5y+Xi9K6LdJ/bhmntFgKQpXAGEqX0okjV1Oh0Opb8uZabp+7w4MojhFZQvGZhOk5twaYoauKPvkE8vPGEdDnTsD98A+9ffzAJaINeolX27mQumJ475+8DoLXVMu+fidFeq5unG25F3RRlzh6Wq9wg/2BmdVpMgYp5yPt7zlhzFUdw//oj2ueK9EPe8m4Zzu7qKvpbopd+jlCU39UPOL5Q/YBVvgZdigzg9rl7Vus0Wg3Zf8lMv5VdSJw6odU2kiQxptF03j57x/UTtyzq6/erTquxjRVlJ7ae5dKBq7QY3YAdc/ay9E85VKatgw0r788mQRIPy3FStsXneYyu8jQaXIvmw+VsT0//fU7LLN1jbG+NnCWyMvnv4Vw5doM+pSPVw/v1G/B+9JJm6bsCoNVpqNjmd3bOtdxv3he+nor2DTBEiTg2+8I4MuVLD8iW4hVsIjNT2Tvbs913BZcOXSNRSk9SZo45pGQV54aEfAwjf/ncjNvzp6n81PbzLOq3iqd3XljtV7VjObrOamO1zpyoSTNmnRtH5gLpY+33X+Jr+gGnz+kkjduaJfaGn0DdDJe+ix/wz2FKpqLyFZh5ZixZimREa6OhUJW8VOtUzlRn0Bu4duIWC/qsiLa/EIJBa7oz9dgI7M32PfOWz80Bw0YL4fv3+hMMrzmJHXP3U9OzBfnL5kFo5Df9P9f1IEESD9ZN2MqQ6uMIC5X3YM8fvGpd+EZZIFw/Hmm0Ze6vGxsOZnvi147dQgjB8BrK1acQgna5+5qO9eEGq8IXYOvsPSRKpXxhSZ0jpUn4guwX7egWuRqdfHgIQgjy/54rRuEbboyqFfJRvjcX913hY0BkSMui1Qqy5NZ0us1rg6OrA6lzpFT03zFnv5yVSluHY5ssfZ2jI2O++A0KofLf4XuroFVU/q+Jqp4+tvEMH4wGQTa2OhJH8SW1hl6vJywkMriH/xtLgyKAae0XRB5I0LXYICSDrKEaUn0C2Ytn4cYJWZBWsm/IAcNGHl5Vuu0kz5iEV4/fIhkk9OGycZStvQ1txjfm1rl/mdRiLgUr5FH0cU/qxgdvX4v5FKyQh/P7LpuOIwR3gK9lAI/wsNiDlwD4+/iz9NY0Fg1YzeXDV7l/+QmPrz+lbrI2bHix0NRu23vLFxvvh6+Y2GIOOYtnocWoBjy/703rbD3Qh+vpMrs1OlvLx1m7XL14/9qXMXv/JFfxrABUaVuOKm3LyStt2/qWEdAkGFl3SrRJNib+PdSkAajWuQIajbqO+dYYVDckFZX/HqsezeHgimMEBQbj7O7M71Hy01pj++x96MMi3Y+CAiz3ggGkKHkgoqppI4RvBHfO36Na+7Is7B0prJ7ffYmNnY7w8MjzhQaHkblgBpPq9MmtZ6TInIxnd16QIIk777w/WJ3PteO3FMIpNCjUZDAVlY7TWjKjw0KrdebU6FIJ37f+HN98itePI32e37/8QFlNHWztbXBL6IrOTkepukXJViQz/166R9Mh9WiRpRv6MD3Xjt1EZ2/DiiGRmaxmdlrE/MuTLc738tEbAHqVGMK+8PUKYakPN2BjZ0NYHCKfmZOnZI4vyoClohLBz/EaoaLyjbCzt6Ny27LU7lGVCi1Ko7OJ/R02fZ40iuNfa1kP+jD9lDKwRsYCkZbOUV13ALwfvMLe0R6NVqlvtpYHOOijUujbOdiwJ2QtpRsVtzoXlwROJr9nc+5ffkzrcZGuPhMOym5As7susToOyO5FGp2G35uUwM3TlQ75+yqErzmhwWG8eeqD971XrB2zhcHVxrFy2CbK29RTvMRsnrJD0U9oNaTNmYppJ6IPTrJpyg5ZxWz8+L8PUGgmzPFKqVo2/78SEYoyPj/fC1UAq6h8ZXKVyEa1juXRaDUkSuVFza6V2DxtJ4MqjybAN4B1E7ZSybEhMzsvZoP3QgZv6Mmm14up2CoygET+crlwimLJmzJzMgBy/ppNUW4TxSJ6/tVJ/Gu0II6gw/QW6Gx0pM+pjGftlsiF7EUz02RoPYuVulcyD36plp+/pu/G3tmOWefGkve3nAAK4ZgsYxKFT2/vxR3YF7qefsu7sHHKDt6//BCX26bAoDdQvbPsC23nZIdHEqWV87xLsmtV9qJZrL44AFw7dlNx3KlgP2p2q2zRLkXmZKx5NJdVIzcy5I8JhISEWLRR+X5ICPRS/H6+F6oAVlH5BnSZ1Zp9YetZ/WguO+btZ17P5Zzbc5k/ErRgcf/VhAWHce3oLVaO3ECJ2r/g5uWqMGS6euwWKx8oQztObDmb/Sv+Zvz+wTQdVpeO01uwM3AVY/dGWv7mK5uTdDlSkzRtInS2slBMmNKT3L/KcZtDzNIA9l7ckU0vlzDtxCiOblSGq3RP7MbqJ/OoaNuAd97vCQ4IoXOhAQD4vfNXtHV2dzIF4wB5b/tjQBAGg4EFvZV7u+nzpaHjtOax3j+drZZnd72xdbChWPUCPLvtbapLnjkpW2fuYVKrOQQHheDgEqktGLa1Nxnzp6Nax/LUjpKnWGejo8OU5mQqpDSienbnBZ2L9Gf50A2c3naeKg5KYzkVlfhC3QNWUfnGnN5u5jIXxQBoz6LDpMuVlnk9limEY9bCGXFxc2LA6q6MbTQDkNXBE5vP4fT2CwzdFOmXmrtkdg4YNpp8fwESpUrIvEsTuXvpIb9UzQ/IxmGzukSqjv+asYvyxjy6+cvmMu0527vYsv75AjoWsJ6U7OgGpcXw/SuPkPSR+9chH0M5sPJoVMNsHN0cmHdhIu3z9bYYs3jNwgzd1Jund19w9+J9rh2/bXohObzmpKLt8zvePL8jC+S/150gNChSrXzyr/PMOR8Z5CRV9hQ8ufEMBIzfP5ip7ebx7znLhA4PrirDYgZ/DMbe0XIbQOX78D2jV8UnP8dVqKj8QPRf1S3aOvfEbkxvv0AhfAHO7rpEVddGFIhiwQxw8cDVaMe7dvwmb57JEbkSpU6I/7sALh64iiRJ9Co11GQpDZC3jKxO/ufwNVYM3WAqDw81EOQfxP3LjyzGP7/3kqwKN5OuhjADBr3yzSJPiWwkSavMq7L09jQkSeJ+lAQMAC1HNyA8PJzepYYytuGMaN2aomIufAGuHJEDoYSFhnFy+znmXBjPAcNGDug3kjxDUlOyDHNcPZ3pPkfpD1zVuQmN0rZn92LL9ioqn4u6AlZR+cakzpqcbEUycfPMvxZ1rcc2ZlzjGVb7BQeE0jhNB4tyR3cHTu84R66S2RVxjRukbIfPC9lHuN/KziwZsIY3z2TjpzbjG3Hz1B3FOJun7GTf0r8pEcVILDwkHI1ONqSKapk9vdNiVt2fTfJMSU2rUKERSGYCOHE6L1JnT0Xq7KloN7kpxzedoc2kpiRInICgQOU+c4Kk7sw+Px5ndyeqODZCH+V8AEnSJCRF5mRc2H+F1NlT8PL+a4sXlggCfT9y48xtuhcdbCrb6rcMJ2cnDAbLsTe9XYJbAhcAStYrSlXnyPChrx/7MLXNPFaN3MiaR/Osnk/l6yNJ/DTZkH6Oq1BR+cHov7qr6buTuwOtxzVk7dN5lKhdxBTYIWP+dPzepISiX5C/pUGQz9P3DKk+kRruzWmQsh1Pbj+Ty19EBuiY1n6hSfgCbJm512oC+oD3gTi5OaLVRT4aKrYpg4OTPTNPj6FG54q4ekYmlchcID0L+q0wCV+A3xv/atpvBtBqIt/za/eoyvSTo8nxS2YAHJzscUvkaqpfdGsqXskSsGfRIQvh65LAiYFru1Gxze+0mdCEVFmS8/j6s2iFL8gCuM9vIxRlDVK0lxNceDU1hagUQrDBe6FJ+ALRRsx688Qn2vOpfAsEhnj+fC/UFbCKymcgSRLXjt/CPZEbqbLEHBoRYFCVsZzbc4l0uVIz79JElg2O9GEN/BDEsiHryVM6J17JPZlzYQJ6vd6UJODCvit8eB0ZKMPWwcZC1RrB2+fvaJ+3L7uD1qDVaUxCzNndkRCzxAk6XfTv3tvm7GPdczkoiD7cgGdSOfxlpvzpyZQ/Pc1H1Wda+/m8uP+Kk1vOEjWc7f7lkakTNToNg9b2iPHebHq5mPCwcIVLl2cyy5CbmQtmYEyD6QAsHbQ2xjHNSZTKk+f/RvovB/nJq+6gDyH81qAgWjsdp7aeY3qHhdTsVol0edLg7OZEwmhckRKmit1F6cim06wato4O01uQv0yeOM9V5b+FugJWUYkGSZJ48cCbnQsPEuAbCMDze960yd2Lesna0KvUUFpl687eJTHvC05sNYdzuy+BBA+uPObE5jNcOqjctw0P1fPiwSvTsXmGno0vFzFwdTfcvFxJnCYhM06NYUfAKn5raN2HNzxU9gPe7LOUAhXz8Ee3SozbN1jhnhQRoMIaoUGhBLwPxOf5O5pl7Ewlx4asHr2JhqnllWOXIgMYtKYH/56/jz5cud9r56gMc5kiUzIy5U+Hj/d7Fg9cw43Td5jafj6LB61WtDMXvpIkUbhKfmr1rKJoc2HflWjnHB2/NSzOwmtTog2/mTJbMg4sO0Lgh4+c3HqOXqWH8YdHc148fIW7lxvNRtTDI7EbtXtVZe7F8Yzc1jdW9fPDm48ZXXcKj2++oH/Z0fi9tx75TOXzkJBV0PH5+V6oK2AVFSu8fvKG5pm7moJaTG83n+V3Z9IyW3eLfdDJrefhntCNIlUtY7nrw/XsX/q3ouzQ2hOEBEdRJQtIm0OZZ9ic0g2Kc/3kHbbP2UvXXway+MZUWo1pyNndlwj8oAwN2WSoHPHqxF9n8X3tR7FqBXl84ylpcqTi7sXIDEvZi2Um0O8jj649tThfUGAwnQr0N61uzVfsT2+/YGzj6Yr2WX/JgGsCNxycbDliZhXdaUYLAJpm6ExoUCjrxm0x1V07fptpx5SBM/69dJ9OBeTUie4JXek0oyUL+60ibY6UpmxI1nBwtiMowFI9f3jNCQ6vOUGKzElpNKgWZ/f9w5HVkVbUywdvsOgD0Dpbd/QGAx2ntmCDd2TWqwx501ltb84BMw0AwJF1p6jWoUI0rVX+y6jZkFT+80iSRM9SQ7h+/DY2djqmnRjF3qV/s2POPkW7ZsPrsnyo9Qc2wN6wdRa5ZYMCg6nmEmnI4+TmSHBgsMX+Zok6vzB4fc8Y52mehcfV05lNr5cQ/DGEgPeBJEzhyeW/r5MotRfJ0iXho38QtbxaEB6mly2Urfwzn3NhPGGh4fQtMzzGfVRraG20iuAbXsk8eGvcc3bycCLwQyC/NylBv2VdkCSJclrLrKI6Oy17guRcvy8fv6Jtrt5y8A+zuUaEfJQkiXK6uqa6dpOaML/3SsV4QiNMsbOt0XBQTQ6tOcGrh68/6VrXPpuHV7K4R8YKCQlR+A7vCl6Dra2Nos2dC/foXmww4WHyC17J+r/w5OYLei/poEhM8aPyNbMhpc7hIvXbHL9Dd8pyRM2GpKLyPXh886kpW1BYSDgTms+icOV8ijZCIyjdoDgJklruTUYwqt4UizIHJ3ts7SMVTU5ujgih/GfXeWbLWIVvVPzeBRASFIqDkz0JU8jCIXGahPQrO5Im6Tvh8/wd9k72ckSqKDIpY/50zDg9hoz50pGtSCb6r+pKo8G16Dq7jcLAKjpSZ0+Be0JXRdlbM4MvZ1dHDug30m9ZFwBWj95sdZzGg+UXipCgEJqk7SznSzafq5ltjBCCHQGr6Lm4A+ueL6BgxXxorOxj/1q7CBnyp6VA+dwWdckzJsXnuXUDqqody1stBxhSbTyL+q9iQd+VFlbb1rCzs2Nv6DpmnRvLbivCF2BgpTEm4QtwdN1pHl59TKcC/RXlKj83qgpa5T+PR2J3xXGaHKkoXDEf006O4u7FB+T5LTuJUyfCwcmeNU/mcnbXJeb3WcGLu8rEBKe2nrc+fhIPXhn3XAtXyoeTuxMBHwJ58+QtpRsUp0yj2BM6AJRt8isHVh4HoGr7ctg72inqh1Qfz0vj6m5c05nMOjeWg6uOsWrEJkW7QL+PhASFYDAY0Gg0FP+jMMX/KAxAkrQJGVhJmQEK5PCW4WF6ilTJz4it/fB/H0BNzxameo1WmPaCW41tyOKBa9i//Ai/Ny3JhvFbLcZLlzs1jQbWAuDGqSjuWAKyFMrAyN0DFcX2DnZUbPEbAJ5JPRi6sTdzey4zXXPanKk4vumMciiNwNbeluYj6lGuaSn2LDrI9RNK9yuAur2rWmg8Irh76SF3L8nBOjZN2cH+cFkLIkkStRK3IvB9ILPOjSFjXrOUijotmQtkUIzj4/2Oxzefkad0Dmzto08JGfAhEPeEbtHW/9eREBi+Y/jI+EQVwCr/WY5sOMXsbkvIUSwLfZZ1YtngdeQonpX+K+WVW8a8aTm++SzDa06iTOMSNP6zNqMbTOPklnPobLQW4xkMEr3LDKP34o4kMUtTOP3UaGZ2WkSS1IloO6nJZ6Wv833ry+G18t6lg7M9Hae1sGhjbmjkksCZ5BmSUqRyfgsB/OLuS/r8Nhx7J3vmXBhHyszJ+fDGl4ktZpMsfRJ6L+3IpBZzFH0qtPiNAN+P1O9fnWE1J5AqWwrlteslZpwZzYjaU5jfe4XJBcqa8HV0t6fL7FZUtG9AeGg4xWsWlnVxRq386F0DKVQhb6z3pGj1ghStXhCQI2CNaTjdoo1kkNDrw1k5fCOrR29m/IHB7Jp/kEfXH3Pz9F0Asv6SSVZ9R6Oqjzre5aPXyFMyJ9U9mhHkJ+cb7pi/f4wZku5dfUiHPHLOZAcXexZdm8qw2hN5cPkROnsdIQHyFoCDs70qfOPA90ygEJ+oe8Aq/0lCgkKo4hS5T1e1Y3m6zmptOtbr9TRJ35k3T96ayrIUysDtc/diHTtBEnfWv1iI3zt/3jzzIXmGpBar1QiCg0J4//IDSaNEiTLn30sPmN5hgSKhwo6AVaYx711+wKWD1ylcOS9zuy/D1sGWgWu7Y+9gx5mdFxlcfVy0giViz7tr0UGRe7oCcv6alWvHbgGyK5GzmyND/+pDr1JDrY6Vp0wObp68Q2gMqf109lrCg/VW67a8W8bLR69Jlys1Go2GIxtPMbbhdAx6AxkLpGXWmXGmF5fWOXrw+Kbs67zu5QI8E3kwocUsC+On6NjyYRk9ig8mKCCYQau7cvPMPeb1Wm59zrY6ei1tz/hGs0xl+X7Pyfj9QxR78gCb3yzB1dMl6hAADK81kRNbzpmO9+s3cHH/FYbXmUywWdKL6adGk61Ipjhdx/8zX3MPOFUOV6nXpkLxOmb3rIe+yx6wugJW+U+yMUo6u8c3lJbAV47cUAhfIE7CF8D3rR9jGk3n77UnAFmAZSuSiT+6VKREnaKEh4UTHBjCm2c+tMvTG8kgkb1oZqadGGUx1pPbz+hkJQbz3Yv3ObHlPFqdYOMk+VpWDNvADv+VimxABSvmoWSdohzbfBqdTkuWwhlNghXkPe/NM3YpDKqQjLmAjRjCDfj5BNCr5FDFHIRGIEkSGo3g6e3nCuGbMmsytDqtwsI6OuFr52CLs7sTGfLIAUiObTrN6HpTI6/1wkOG/jGBMzsuorXVog+NHKddjl5ser0Eg5WFhLXIXQB1Ercm3Gjd3rXYYEXQkajMvTiBNNlTMqHxbJNFeKqs8uq/54K2TGm7wNT2/WvfaAXwL9UKKATwodXHGd9spsXLjLO7Y7RzUZGRAMNPEglLFcAq/wnMExOEhoQR+lFp9TtwjTI+8+YpOz/7XPpwg0n4gizArp+4zfUTt9E1mUG4UYBkyJvGZLV745TlvmTrnD14fOOZ1XP0jCIMAUI+htCxYF/uXXqEcwInNr5czOqRGzm68RQ2djYsvzuThMk9ObTmuCLc5WHjvrKCOCjGIuau0Wrwef5eUTf73HgcnOypm6yN1fSDSdMnoVS9ojy48og+yzop6vZFcdsCOLPjIoBC+AIY9Hr8P/iTPJ1Sg5C7dDYqtv6d8U1mWlhGh0fJl6zXWwppkIOBpMmeEoB5/0xgVIOppM2R2qT+r9i6LHo9rB69iVL1iioCshgMBio7NZLDeGo1rHgwUzF22pwpLe5xtQ7lSZVFqdqPYES9KRzfKLt3TTj0J3lLWxqZqfx4qAJY5afAYDBwZudF3LxceXr7OdPaz8fVy5UC5XLz5NZz7py/p3BTEUKQKltyfF/7MWBNNzyTJlCMl7t0Ds7t+eez56PRCAxWXGLCzQTIoxvPEEJeRWYupDTYuXbiVrTCNzocXOy4d+kRAAHvApnaZq4pKlVYcBgd8vdl08vFrBlj3Sr5cwkP1WPnZEtIoPxS03xEfRyc7AkPC7dIVeiRxJ11z+ab1MnvX71n39K/KV6jEMkyJAWg65xWNE7b2dQnfZ40ikQQQgeSUYb6v/9IzQQt2a/fwN1LD7l99i5d57aleI1ClNPWtYjSZY20OVLx8Fpk9qOhf/UGCZNhGkC6XGlYcsNyj7lKu7JUaVfWonxG50UmQW/QG2ibS5nxyTNZAjrNaMHsrktNZa0nNIp2jhHCF6BvmVEx7jf//Aj03zF8ZHzyc6zjVf7zzO+zguG1JtK9+J9Mbj0XfbiB9y8/cGDFUe6cl1XH5ishSZJ4cvM5vm/96V9uFC2zd8f/fYCpvk6vqhZRnT4Fg0FCaARCI3DysK5WdPV0ZsGVSdjY2XDn3D0qOzYkNFRW40YNgyg0sT9w7KKky3vzTOlyYzBmPrK2Io0NnW3Mj4oI4dt4SB0a/SlbN+9bccRixbrhxUKT8L1+4iZ1k7ZlYd9VNMvUFd+3csSoxKkTyxmLjJ95lyZSupEx6peQxyhSTbldN77ZDDrPbMkG70UUryHvD5oLX6EVeKWw7strY6d0E5L0EsX/KEx4uJ6VIzaydebuGK89gqvHb1LFuRGN03Ww+PuZhwEFaJS6A3lK52RX8BqGb+3LrqDVODg5xOk8P4ns+WwiVNDx+fleqAJY5YcnLDSMrTP2WKTA+xSe3npOnSStaZurJ5UcGlBOWxdXL8v9vLp9qrHmydw4jysZJALff7RaZ2tvQ4eC/QgLkYVuaHAYlR0aMrz2JBKnSsjgjT1Jkz2FaZwIStYranW8dLlSmyyhhRD8c+i6on7hjWksH7oe/3eBpjIR3RMgykM+PNS6mjbqS8p1s73jpQOV8ZqjxnfuWWqY4vjhdeU+/MtHrxhYZQxzui+h39LOpjSC7l7udJreXNH20KoTNErdkdcv5H37+slbK+oHrenO2ifzFEkiInBL6Gq6Xo1WQ7E/ZAHeq9QQVgzbwOxuS2mRLXKLYvmI9dRL2Y7Lx66ZyiRJolfJoYR8DOXVo7esHKYM2BJVGxIaHMaCPiuwtbXhl6oFGF1/GjUTtuDASqUh2Yc3vtw6+y9dZrc0la18NNviGlR+TFQBrPJDYzAYqOLcGEM0+3jmlG9RmkQxBNLXh+l5eP2pKfzkmyc+FoLIx/s9CVN4mcI9xkRMUZkA7J3sLfYjkeDs7ku8uP+SErV+4f0ryzjCJesVsyhLlMoLB2c7k0GR+erP3smOHQGr8Ezszpoxf0WZY3STJ05Ph4i405FzK4okSdy7/BDfN5Fzd07gxLpnCxRtzY3FAHKVyBp5ekmiafrOnN/9D1tm7KFu0tYEf4xcRSZJndiiP8Du+QcB8PH2VZQXrV6QZUPWK7YAIjBIetOq3KA38PyunNnpzoVIq/Nnt1+gD9czttl0Vg3bxLvn7+hTagTn910GZGt2c/RhkTc2USovq7+FaydvExoSyu5FBzm17Tz+PgFMaDaLIONYp3dcoE7i1nT9ZRB/Td9r0ggkSZnIYqz/GnqjGjq+Pt8LVQCr/NCc2nrOqqWrNfYt/Ru93mA1glK0RHluHlp1nAMr/iZ7scwky5DkE2ZqyaMoKz4bext0tlrcE7qaoluZp+qLYGTtiRZlOlsdp7ZdkKNJRaFyu3ImQyPzhAwxkalAOrIWyhhru8ZDaiuOp7dfQEX7+nTI11dRbtAbKKutw5S2kdqDsXsHobWR8wzPuzoZjUaDJEm0zN7duH8b2d/PJ4AVI5T7nlU7WO69VjNGtLJzUbp92djasHvhfqvXUKVNWcxP1qPEEBYPXEOh8kpf5E1TtnN03WlF2e6FssB3cLTHOZqtBkM0bznB/sH8kaAFoR+Vrlunt8nW0psmbzeVPf/3BVM7LqCspo7po/Ljowpglf9b7ly4x6yuS3h4/Um0bRKn/bTVgM/z91YFtrXQhZkLWY/JO6H5HPqXG8WLey+t1n8qjf6sRZbCGQkLDiM8VE+iVF6mSElTjg43qUQjsPY8938XEK3B0eYpO6jh1pRts/Yw6e/hcg7cGF767Rzt8Eruya0zd2Oct9ZGQ+M/67D+5UJFufnqD2Sh/9E3CCTYs+gwlewbsGXmbvKUzkHPBe1p0P8PkqeT/47dig7i6a3nVs+3f8lh6iRrQ1XXJlSwrcfDa0/YGbiKvaHrWHBtMgXK56bPb8N5euc5W94spdP0Fow/OJhFN6YyvulMsvyi9K9NlTU5rcY1pPgfRciYPzLJgu8bP9aN20LpBsWwdYjcH17Ufw2VWv+mGKPl6Aam71t8lnPAsJGNrxbh6Crv52p1WmacHMPwrX3lsKBRCA0KpVrn8pGreSFHSwOo26e6qV3KzMnYPe+Aom/Ah0D+i0iS+Gn2gNVAHCr/N5i7Ch1YcYQJzY17XQIy5ktHr8UdSZ8rtaJPeFg4lRwaxqru/RzMwyt+CS4eTvi/t/6wtHWwYYvPMmp6tSDE6Bpl72zPDr+V6PV66idry4e3ftG6BelsdWg0guFb+3Fs02n2LIo5NeK8yxM4sPwYnkndObLhFP9eeKCojy2hgTlJ0yXC+8GnJTaIQGujpUbnCmyeuks+r9Ea/FPpv6orZRr+ysi6kzlmDENpY2/D7o9rAPk3VdG+gdLP2Yi5JXH/iqO4dfYuQb5Bpnl0m9uGvUsOK7Iw7Qtfz875+zmz+xK9F3ckQSL3T5rv2+c+dCo0gHfesttWkar5GbmtP6EhoZzddYl8v+fCyTVyJf3hrR+vHr0mc4EMlLetiyE88h4VrpyPs7suYedsx06/VZ80j6/N1wzEkTy7u9RufYl4HXNozh1qIA6V/xZXj9/k4r4r/DVjN/owPWEhYTi6OlCyblGlIJHg7sUH9Px1MNt8VyBJEhOaz+LwmhMYDAaFcLK1tyU0+NMy+0Tg6OYgr9SMxFX42jnaEPIx+ghQQQHBrLg3k+ntF+KZIgEdpjRjfp+VBLwPpPu8Njy5/Zwq7cqahFHzkfUJCgymdqKWhAZFP27dPtVoMz4y05KDsz37l/0tZ1oS0HZyUxb0XKHo0z5PpGq40eDaaHQabp+JDDASVfhGF8wC5NWdOV4pPUmQxI0Bq7syvvEs/r34QLE3nyRtIlPcZhcPZw6tjvQ/tiZ8dTZaGg2rzfJB6y3qTPMzTuGd9wdTmd5sXzo8LNyq8HXxdOavGbv46BvEvcsPuWiWazhBUncyF8xI5bZlSZMrBT2KRfpcazQaqnWoYEov+OLBS9rm6kXIx1CERpAgqTtVO5Zj28y9uLg74uLphs5Gy+ANPXHzcsUruSdJ0yYyCeDEqRMCYGtny681iwAQ/DGYYTUn8tEviKGbeptiSq97toBWOXoQ8C6Aodv6MqzqBABCAkJoma0bS25aukmp/H+jroBVvjnHNp9hQvNZFq4ZsWFjZ8PuoDU8vPaYjgX7WxgAgWzVq1DRCrBzsKPrnNYsG7yON0+tZ8MB2fXnw1tfwoI+LRtN5kLpeXDlicmaueeiDji62DPKLJpT8oxJmHhomGlvN4KZnRex3ZgEoMPUZtTsVgVJkpjYcnasoRXz/Z6DJ7df8NEviIGru6PX69k8dReZC6Wn0aBa2DvZ0SF/Xx5eta7Ct3e2Jzw03Op9BHmVKhkMcX4R2fx2Ca4JZMvxvUsOsWbMX6YVskYr2Be2gR3z93P3wgNajm1Ik7QdCbbyG1hyeyqdCw3ko1+QRV1U9oSsRWejw9fHj+aZuhIUEEz3uW2o0LKMqc3srkvYPm+vYvWYMKWn6bcQ9SXDfGVc3qae4iUiqv9tRDzr2BAChFbDwLXdmN5uIf7vZJe3PKWzkzpbSo5sPIUh3EDStIl49fQtvq9lA7ZUWZOz+MY0i/Gipnh0cHFgu+8Ki3bfi6+5Ak6W3V1qu75kvI45POf2WOcrhKgATAe0wCJJksZFqU8FLAfcjW36S5IUow+bKoBVvimT28xh72LLSEdxQWgEtg42ZMqfgXuXHhAUEIwQAs/kCXj7LHrBCrK7zIr7s2mQuh2GaFxq7J3srAqETyF19pTMuTieE3+dZUbHRQSa7dMlTpOQVQ/m8OjGU46sP0nhyvkZUGEUgb6ym1KCpB6mlVFUStQqzLHNZ6M9r72zvSmmsEajYXfIGjoVGsD9fx5+8jUIrUCyInQtXm4Ara2GjPnS02N+G9LllENJDvljPKe3yf8ehRDk+z0nw7b1xd4+0jCqTa6eFkZoERT7oyAnt1jPLGWOq6czm98sjbXdP4evsXPBfi4dukZIYCgCFGEzdbZak3W00AjG7B5E/rK5EEJYGDtFFcBltXXiFDUsAiEEyTIkMVlax/abS5YhCcv/nWm1rrJTQ5OG5K93S3Fxjz2V5LfiZxPAQggt8C9QFngGnAcaSJJ006zNAuAfSZLmCiGyAbslSUoT03lVFbTKN+VzhS/I6tGQwFCuHbuJm5cLvzcpwYmtZ2MVvgAhH0O5efIOGgTR2Ux/qfAFeHr7OU3Td7IIzQjw5slb9iw+xKyuSwgNCmX16M3kLJGFa8dug0S0wnfxzakkTp0wRgHs5OpgEsAGgwF9mJ7Xjz99f1Znq0Wr05r2o82xZvyVImNyZp5Spi+88veNyD6SROMhdRTCd1a3xQrhG3XfOS7CF2TL6GkdFtBoUC0Mej3z+6w0pSPU2mjQhxlInTMFj6/FHFGs7eSmzOmy1HiNEgMqjCJRai9WP5yrWB2nyZnKou8v1QtyOpo0lNYQGsHU4yOY2GION0/fIfCDdR/xCIrWiD7pwK7ANXE+78+FQP/tDacKAfckSXoAIIRYB1QHbpq1kYAItwU34EVsg6pW0CrfHa2N5c8wtihUvm/92TF3P++j+HvGxPDak6z6gcaVtLksH8BRMegNVoUvyMEYprSZR2iQUbhJcP3EHRoMqEmDAX9EO2ayDEnlvW4zovrAhoWGkTZHSmzsdNToWhFbe1sGreuh7KMRMd7XpOkTM/PMWMq3LB3TJZpwS+hK6frFCAsLo4Z7U8rp6rJ7yWFK1Vf6Kf9zWA5YIUkS3o9fcWCVUrUeoYXT2VlaCcfGrvkHaJiqPY3TdlLkAo6wxI5N+FZu/zt/dKrE3tB1OHk4mcpfP5YDeqy4N4scv2alTu+qlGteikZpOnB4XeTetYubk8WY0WHnaMv0k6PxSORO4z9rxSp8ATZN2o6vj6UvuMo3JzlgrrJ5ZiwzZxjQWAjxDNgNdIltUFUAq3xTrPng2jlYpuqztgL7nmi0Gn6pZqmhsnGwQaP9fEd+SS+xacoONpr5fEagtdGStUgmqjg2olbCloq6DlOaKY793gbw8IYcRGTrjD2MbzaDZ7e9FW1K1inCNt8VpMiczOpccpfKzov7L9k+y5iYPpang+8bP5YNXkd1t2YE+gUhGSSmtp7LPwevKdo9vvGMt899KKetS9O0nfn4PsrernHxGx7y+S9HcSWqH/SueQep4twIrU5LmJVUiolTJWTq0RHkK5ebBb1W8PrJW8Y2nEHwR1nb0HJsw1jP6eTqgEar4fcmJchijPm9Y551n2RrRGxRqMjIoShFvH4ALyHEBbNP28+YWgNgmSRJKYBKwEohoo01B6gCWOUbIkeRsoxEFRdjm++BedhCg97AmlF/WbQJCwr7YlelCP/fqBSvVZhbZ/5FH64nLDjS0Edno2VOj2XyQ72pmTuG2TQOrjzOrK6LFeMd2XAarVZLn6WdsHOwxd7JjpJ1f6Fcs1K0GFWfLrNas2X6nsgOcYtvYiG4vJIrQ052mt6CXQsOWvRLkTlp3E7widg66ohQECRO56WoK1azMEM29VKUhXwMZe+Sw4zc0d9U5uiqjKs9s9MixfGzu7J20TOJB6UaWEYmA3DzciF7scwE+gVh0BvYNf8gb73f8eyuNwdXH4vxGrxSyMlBshTOQLJ0sQd8mdF5ET1KDsHv3X9jtaxHE68f4K0kSQXMPguinPI5kNLsOIWxzJxWwAYASZJOA/aAFzGgCmCVb0JoSBhNM3Ti1aM333sqccYpBvWiRqfB3tmOZBkSR9vmSzm67pTV8nCjW41Bb+DgimMkz5iUHMUzxz6gBF2KDODPymMoUjU/f3StTJNhdem9pCMNB9bC1s7GItBEbAghLJ4iHkncTd+1thpcEjhbDXTy7I63RRnCuEqNw5PJyd2JfGVzWZTPvzSZ/fqNrH06j1cPlDmdj208ZQp0Yc7WGbvJViQTqx/PpdPMlmx8vURR3/hPZcSv1Nkin8WXzFyYIphyfASbXi8hV6lsivKXj15z6cDVGF9uEqb0ZO2T+RwwbGTm6bHRNzTSu8xQdszZx/Xjt6jl1Yp7lx/E2kflkzkPZBRCpBVC2AL1gahqqydAGQAhRFZkARzjA08VwCrfhOCA4Bh9Wv8fCfS3rvoTQqDTaQkOCOHlZ7xQ1O9fI8Z6Bxd7i0QQjm7RZ8rxfvCKkdsH0H9VFzRagUYjyB3lwR/B7XP38H8fyNGNp1k79i9aZ+tBVZfGeD98BUDZpqXIGiViVEykyZGSfks7Yb4lfWyj2V5sqIHZ3ZbQvfjguA0oIcfiNgooO0fbaE1Fbe11jN83mHaTmyrK3754R7u8vWmaobNFH0O4hK29rcVWyP2rj6nq3JhGqTswu8sSqjgqUwOWbVKShoNq4p7IjQHrumNjExkhq1rnihbnuf/PIxqmbs/j609NCS9cEjiR45csFK5s+QJgzoxToxXHN07dppy2LmU1ddhtJdDK3YtKS/cO+frFOP6PjkT8qp+NKuiYzylJ4UBnYB9wC9ggSdINIcQIIUQ1Y7NeQBshxBVgLdBcisXNSHVDUvlmlLOpa9W95f8RG3udQu0L4OLphL/P1wn/Z2NnQ/PR9dAIQa4S2elUsL+iPlvRzNjZ2/DP4esWfTU6DaXqFaVu72oEvP+Iz4t35CqVjaYZuljd17RGy9ENTYZgH/2DmNB8FpcOXMVgMMS4H6+z1aLRaLC1t402NKK1exlXdLa66P1sBRzQb8RgMNAudy8e33pOxnxp+PeCpeuV0IDQaMhVMhtPb73A751frHPaF77elKQhNqZ3nM/OeQcRGhi0vhej6kxW1Gu0Gja8XIibp2wkG10s52I1CvLn+p5MbTsfv3cBDFjVhT88W0TrpwxwZMNJRtefpijbr99gNVnFt+JruiElyZ5AarqmTOwNP4GJeTapkbBUfl7Cw8J/GOELWH04fy3hCxAWEsbC3tGHE7x99m60GZ8M4QYOrz7B4dUnTGWZCqRjZ8Aqrhy5zpiG0/nwOnJvMFfJbAT4BvLg8mNTWe7S2U3fHV0cGLa5D6+fvKFRmo4xztuglwgPDUNoNHJ8aSt/Yo1GC3yeANbaaKMVwPl/l9XPi/uv4d0rX4rXLMTxTZauWtl+ycyb529588SHa0dvypHCiN7fOeK8UYVvoF8gdRK3ISwkjN+blKTf8sgVdrc57eg2px3+7wOom6R11OEw6A2c3XmRcs1kC/OeizswpVVkYoqCFfIwZvcgAPqVHymrqYEaCZrH+u+maLWCFmU3Tt4mR/GsVlr/HBh+EuXtz3EVKv/3aLRx+6m5Jvj/CSbwOeT9PScVWsTNjedTiEu6RXP+vfCAW2f/5fzeyxSpkt+kci1StQCT/x7OnHPjKdesFJkLpqfP0o6sHrWJ1tl7UNG+PmU1dZjQYjYeSdwjw00KaD+lqcV53Lxc0Nlo6TKrFc1H1CdP6eykjGJlHfIxBCcPpzj9BoRW0HtpR8q3/I1sxTLh4GSPrb0NOhsdHondaTmuAc4eThSomJvbF+5RVlOHDZO24ffW36rwBXhy+6mcWhJMwhdARJFrEw4NYbv/SpqPqoc+TE9ZTR1G1I1cyXb9ZZAp2tlBs7y9V45cp2mGTnT5ZQAX9l027dFHpUjVyAVWxRa/sfX9MkrWK0qe33Kg0WpYPHA1er2e22cjk2DE5aVVZ2u5jnr99K2F69rPgiSBXhLx+vleqCpolW9GRYcGpvy3Bcrnps/STmyfs5fVZtbFbSY0ZtXITYQGh1nE8I0pUYCTh2O0ie9jQqvToNFqTDmAQQ4b6f3wdZzTHEYlSbpEvHnmg/4LfI6/Bi4eTsy/Mllhif76yRsap+tk9b72WtyBtDlScXTTaQ6uOEqA30d6zmvHxYNXeHDlCZ1nt2Jsw+mmkI7zrkwifU45WcYfns0JMCagcHCxZ8rREaTPnUYRPjE6EqbyNAnMCPKVzcX4ffI+ssFgoLyu3ufdBOTfSrFqhdi//EhkoVGdDVDNrYkirWOEyrdDgb7cu/RQUe7z8j31k0V6rLh4OhMWHBZ9UBcBf71diouH/KI5usFUjqxXGtvFlhAjqgoa5L/jiHpT8H3la7JL0Nnq2BO8NtpxviZfUwWdOFsCqeGacvE65rS867+LClpdAat8M8wF6j+Hr+PgYs+68VtNZUIjqN2zKtt9V5ryukZg66AjS8EMpjR6OX7Noqj/HOEL8oqo2fB6DN3cm1RZk/NH10p0n9cOV0+X2DtHw8sHr6MVvj0Wt//scb8U//eB9Cs7QlH29M6LaB/2k1vPZWClMWyZvov3r3wJCwpj4cA19FvelSnHhtO71DBFbO1FfWUVemhwKO2nNCVLoQzYOtgS5B9Mh3x9mdl5UYxpECOIKnwBfF5EBjd5fs+K9bSRVFmTyekWjWQtYpnTePLhYfRZ2gmPxG6mstE7B3Bqx3kqOza0yKn86IYcf2H2uXE4ucmZiiq2lvcgP7xSBoIJDghmu99Klt2bxYJrk0iXW5m9Cwl2L5Jdsq6fum0hfMEyIYaCaO5folQJmXV6rMKALjw0nNCQH8vwMa58ayOsr4UqgFW+GQmSRD7w9GF6Dq0+ocgdmz53GtO+W8Z8aRV9w0MNlG1eEp1Oi629Db5v/eN0znp9q8X60N8xbz8HVhwlSfpEGCSJQZXHWDxYoyNJupjzEWujWNsu6bc6TuOmypaCNDlTxt4QQEDWohlwcLGPtenbF+84tT0ydGKuktlwNxNECiTw8/FX+Cjrw2RNQbs8fSzU4qmzp6RHicFUdmzEpBZzuX3uXmTUL2DnvP2fFDc5AnsnO4XvbtIofrHpzFJUPrn1Ao1WgxCC1NlT8Oe6ntjaR1osL7w+ifS55d/W0n9n0mhwbcbu/ZNCFfMx/I+JihjREeyaLwfN0Gg0bH0v5/ztuUB+kUqfOw3JMka6ooWFhFPeph5di/Tn0Y1niljgEeQumV0xblyxsdfxl8+SGNs0GFDT9F2jFdhYUU+r/P+gCmCVb0bUB2f+crnwSOwuHwhoPET2tfTz8efElnMKwWnQG5jRYRF6vZ7Q4DBePY50/7EWyjKC9RO2x/rQf/XoDae2nefczn/YNnOP1Ydw+qgrGSMvY8iHm6tkVjZ4L2LMnkGmMvMXB2t7dwB2jnY0HlyLlw/j6OIkwa1T9yxWbubIMZ41BPkHM7TGBA6tOc72ufuo4d4MWzubaPtFpfdi2ShLo1G+1dToWpEt03dy/cTt6OdgZ0OC5O6mYxE18qQAt4SRmofkmZKwX7+BHf6rSJU5MuqfTqdlT8happ8aza7gNdTrV10xjN9bfyRJQqvTkiiVF1veL2fS4WFsfLWINNlS4/c+gKbpO9E4dQdyFs9MgXKyj7IhmpXn1ll7aZunp2lP9eCqY5TX1aWifX1unLpDnZ7VFO0lg4Tf2wDG1J9Glzmt0NpEXmjdfjVImSU5D68/xu9tQLT3ypx0eVKzL3w9uz+uxcU9Zs1M2hyp2PByAX2XdWK738rvagn9tZDdkDTx+vleqAJY5ZsxetcAshXNhFfyBIzZPZCkaRKz+vEcZp4ew7pn8ylarSAzOi+kVsKWnNp63qrgjEgIEPoxFK+UnmQtkpF1zxbQenzjOM9DxDF0ZKqsySlZryjlmpVEa/NpKwmNVsO1Y7f4a/ouXDyVAT0KV8pHtY7lWf1kjtW5OTjbMa/nclNyhfhAZ2ujEDDXT9xmZqdFhAaH8frJW7IXz4KjqyOOHo5W+9ftU5U0OVKyfsJWgoNCmHFmLM4eTmh0GrrOaUOhivmsCrA8ZXKQq6RsjRsWHMaHV35kKpyBIlXy02ZiU6WlsQS+b/wZvqUvcy6OZ9ntmQghCAoMYu24LTy5Iwce0ofrqWjXgG5FB1HDrRml6hUjoTFylPlq18FZ1gjY2tmQu1R23BPKK/0exf7E++FrAj4EMsrMfad6ZznHr1anwc5RGR714dWn7F0qJxKZ32cFBoNEeKiehX1XkiFfumjv+8K+q8lRLDPzr0zkgGEjWo2ghnsz2ubqzbm9/0TbD+RoWAUq5GH+pUkWFtkHVvxNWU0dymrq8OSOMquURyIPyjYtZTXE68+CHhGvn+/FdzfCMqZ5ugA8lySpihAiLbAO8AQuAk0kSYoxMLBqhPVz8PjWM9rl6W01gbo10udJw7xLEwHwfviKpuktAy/EhVo9q7B5ys7P6hsXzDPqaLQaVt6fRaJUciL2+X2Ws2ny1zu3ORHGPU7ujiy+OY0GyduZEiHU7FGZDpObWzVwSpI2ES8fRq70IzIFATy985yRdadg72RHwhSenNhyFqERFK9VhD5LOrJ27BYeXHnM6e3W/31mLZKRe5cfKfyVU2RKRvf5bcleLDOTWszh0OrI5AeLrk9lStu53Dz1r6ms/4rOlGksp6fzfevHyLpT0Gg11B9QnSHVJqLRCgas7saywevxTOrGpUPXTFsfzu6ObH67lBXDNrB36WE8ErtTsXUZZnZUhp4EmHhoKO9ff2BMg8jE940H16bZ8Hqc3HaOxQNX433vVbRW0AcMG6P1/7XGrHNjeXzjKU5ujhSrUVg+X/pOvHpoqXWxZpj1PfmaRliJsnlKtVdZBj/5EubmX/2f9QPuhhxZJCKN03hgqiRJ64QQ85Dja86NrrPKz4NHYjd0Nto4CWCNVpC9WKQhlp1DzNmToqPxkNo0G1aPCq1+Y1iNCTy/+/KzxokJc2tq90SuaLQaQkPCWD5kHe9ffYjWfzauuCd2lf18YxkjwrgnY750eCbxYNTO/kxqNZf0uVPTYXJzQN7ndE/kavIb7javLRnypKFLkYGmcV4/fsvLR6+Z1m4+lw5eQ5IkNFoNeUrnYF/YBlpm68bRdac4tfW8SbDaOdpaDehRp3c1HF0dGdtoGr5vZPX8s39f0Lv0MJw9nEyW1BGMbjgNzyixplNkSWH67uTmyKhdA7B3sKNGgmaEfJStkYdUGw/AgyhRIwP9ghhUZSwX9l4GwOf5ewvh65bIjZaj6pO7VHaFFbdWp6HZcPllpVj1Qngkdqdb0UFER8eCyghVdo42hHyM3kiqc6EBpu+NB9fG18fXqvD9rxGRjOFn4LsKYCFECqAyMBroKeQNi9+AiBQjy5FTPKkC+D+AawIXFl6bwqRWc7l65Ea07QpVykeVdmUpVCmvqczNyzXa9tGRNF0img6VH6hpsqZk0fWpVHVuHO0KJj545/2Bxuk6UadPVTZM2Q5mp9LqtLgncjVZ/Gq0Ik6JHj68+rQA/JcPX2fRgFW0HtuYDS8WWtSvuD+bXfMPkKlAOnKVyI7fO6XBmxCCJuk7KQS+zlbLuT3/cOnQVZ7elhMVmK9qzY2xIgeC3YsOmYRfVKIKX4CHVx/z8sEr03H24lnIXCA9R9afYLTZyrRU/V/i5DstGaRozw9yhLJNLxcRFhrGnB7LFHX6cAM3z/xLtiKy5bGjS/ThQgHuXnyARqfBzctV9pWWZA2C3iBh72hrSoFojUuHrpq0JlEZd+DPGM+r8v/L994Dngb0JTI0uSfwwRh3E6znXFT5iUmaNjGTDw+jTh9L6+XEaRJSt091Rm7vxy9VC6DVRhq3RA3yoNEIyscUEEMDrcY0okn6TrTM1g3vh6/Q2egY9lcfHF1jfpB+KfowPevGbFUIX5BVu3X6RBoUfc3doVMxJJF3cLKnds+qZC2SicUDVzO3xzKGbe1DrhLZ8EzmIautzeam1WkoWr0gj64/5c65+1bHtLNmoS0RrfBLnDqhIqmDOUFme+PDt/ShhkczhfAFOLr+DEM398beyY4ESd0V9zU2PJMZzytg0a1pPLz+hEoODdk6Y7dF225FB5nclNJkT0nfZZ3QmaU8jGpvoNEIUmRKyuvHb7h67CbvX/kSHhLG6odz6bGgndX5aHUaWo1txIBVXU1ZmjIVSMcBw0YOGDaSv4xlooufG9UI64sRQlQBXkuSdPEz+7eNyN345s2Pk2FHJW60Hd+EjPnTK8pePXrDvcsPuXvpAYfXnVAkSxBC0GtxBxyd7clSKAO7gtdQsk7RaMd3T+jG8c1nePXoDU9vv2BCi9mEhoRRuHJ+1j6bHyd/1c/Fwdm6cUym/OnYOTfSNSVGf9BPJHnGJDQZXle+LgGNomT3scb2OfvYOGkHB1ceY1iNiTy6+ZQus1tbJDIwSBK5SmRDZ6vDztGWBMncLe5fsN+nGZStejiH2efGYWO00LZ3siNLoQykzZlK0W5+rxVW8+Umz5SU/L/npkbXSgS8C2TT5O3kKpmV0g2L4Z44Zm1JwIePtJvcFGc3R5pn6MygKmNiVO+3ydmTspo6+PsGULZpKfYEreWAYSM7A1dZRLIKD9Vz7dgtRRIPySDx99oTLOiz0ur4vzcpSa5fs6HRaNj2YSUHDBuZfW58jNeg8mPw3YywhBBjgSbIQWLtkfeAtwDlgSSSJIULIX4BhkmSVD76kVQjrJ+VuBiszL00gQx50lqt04frqWjfwKogS50tBRnypuGQWfxkoRUUrVaAbnPbcvnvG4xpMO2z5u2e2A2tVsP7V74WalCdrZaUmZPz8NoTi37OHk54JvPg8Y1nn3Xe6EiUyoupx0aQKFVCAv1kYeXk6sjbFz4cWHUcj0QuZCmYkVRZUyisbbfO2sPsbksUwidzwfQ8uPZYESs7W7FMdJnZmud3X7Jn8UEu7r/6xXOOMCq6fOQ6w2pOJPDDRxDQY347pradb2o3YHU3xjWZYfE3Xnh9Cs7uTjRIYX1VGRO29jZWXdFiI3nmpCy7NcN0fPHAFfqXH/XJ41gjIjFEUGAw3vdfKXyf/x/5mkZYCbN5SdVXVInXMRcXXP7fioQlSdIASZJSSJKUBjm34mFJkhoBfwMRr+fNgG3faYoq34m3L94REhRCopSesbbdYyU9WwRanZasRSIjA9nY6ShaXf439vjmMw6vPaloL+klTm45T90kbVgyaM1nzl6OjvThta+FryzIK6CH1y2FL8h7ni/uvYzX1bcQ0H9lF9P+oZOrI06ujng/fE2DFO1Z0n81k1vOo03OXpTX1aN+ykjL6Krty1GmUQnleBqNxWrw5sl/6ZCvLy4ejty//OiT5le6YXGW3pluUq0CdDeqYiVJol/ZkbLwBZBk96miNQqgs9VRvUtFfmtQnDVP5tFzYQfFuIMqj6FBypiFr0dSd4syWwdbMhXMYFHunih2G4Pnd7x5YPZi5RKPcc3L6+oxuMY4qrk0oV2e3tT0bA7AvxfvM7/PCrwfvmLJ4DU0TteRg6uOxdt5/x9RY0HH9ySEKAX0NrohpUN2Q0oA/AM0liQpmsCqMuoK+Odh0YDVbJ66EydXB+ZcHM+ja09JmMqTxQPW4H3/Fc/vv1RYSc+9OJ4MeaP3w/x73UkmtZqDQA4fuH323mgDLphTsm5Rjm4wCxMoZDW3+UrLM7k7zUc2YFrb+Yog/6YuQlCqQTEcXezZNf9g3G7AFyI0QrFHKzSCHf4rsXOw45/DV+lffnSsxkmbXi9WGLU9u/uCMY2mE+QXROI0CQkNCuPa8VsxjuHs7oRLAie8zQKV2DrYmHJCCw0gRe5zD1rfg/v/PCR11hQsHbyOdy8/0H5yU2Z1UUZ+sneyU8RZjhizctsy/HP4Oi/uvcLe2T5OPtTm84nAK0UCfq1dhC3TIvd7dTZaq4Z5OjudKbZ5BNU7lafF6IbYO9qh1WkZVX8KRzecjnUun8Oqx3NonNp6tqrNb5fgmuDzw6l+KV9zBeyV1UuquqJqvI65rNCy/6wbEpIkHQGOGL8/AAp9z/mofD8OLD9CeGg4IUGh3Dpzj5J1fgFg1A7ZJePtcx8uHbpOikxJSJ0tJU6u1gNHRFC6fjFSZk6GXm9g35LDcRK+QiMYtLY7iVN7cfnIdbIUzkTydEk4uPoYdy8+MLV75+3LrC5LcPZwxveNpSWyo6sDHac2Z0qbuBnxW3O7iQnPZO64errQYnQjVo/cyJ3zRgMos0u0tbfhw2s/OhXuj+/ruFlLa2yUIapSZExGh8nN6FlyKM/+VcZh1hrdxrQ6LfrwSCEV8CHQIj9wWEg4GQuk4+6FB6aAKhEsGbCaFfdmM6vbYl4/ka2B5/ZYTt2+1dkwIVIJFjXJQYQA3bUgUhNSul5R9iw+rGgntIJcJbLikdidk0b3KHPha+toQ+jHMN4+e8feRYdJkMyDD6988UqeAL93/goBLDSCrIUzcvP0v0Tl6OYzbJu9DzsHG1Y+mMPb5+8s2oAc7SzCRSomtDqN1Zc7GzsdG8ZFrxz81OxZPxrf03AqPvk5rkLlp6F65wpotBqc3JzIY5ajNgKv5J6Ua1qSbEUy4+TqiMFgYEHflfT5fTj3/rFMxA6QIW9aMhdIj3McVIJCwJ6QtQghaDO+CbPPjqfLjFbU7F7ZIkGEZJAICQzB940fdg422DsrLX2Hbu7N0j/Xcnp73OwMQ4NjjDdjgc+LD8w8M5ZfquQ3JSCQDJIcftBobDVwTQ8mtZodJ+EbEUu6pkdzZndbwp0L9xn6xwRWDFvPm2eWCRIABq7uxnrvBdg5xB7OUjJIPLlpfX/719pFCAsLY9vMvaYyrU7LgWVHYh03Kn+vO2lRlixdYq78fZMj607hmVTpR4wAj4TupsOggGDevXiPQW/g9ZO3BAcoBaW9kx3P71n3F//wUo4hHhIUxpR28yhVv5ipztHVgSTpEjPr7FgL4SuEoNHgWoqwlRHlEehstMw8M4ZifxQiU8H0JE5n3S1JZ6czRf2KDoPBwIV9l/F++CrGdipfl/+LFbDK/x/1U7TF58V7dLY6dgauUrj8ALx4+JJm6bsAULhKPkZu6x8vcWcbDqxF9c4VsXeyszinNc7t/ocdc/cRHBjC6AZTWXp7RrRtPZN5RFsXwa+1i0R73gLl8+CSwBn/98YYvmYrzZCgMBxctGht5BVLm/GN8UyWgN0x7FFHJao6NFYEhIWEsW3WXi4dumYqliQJoREkSuVFrhJZObvrApcPR+9XHYFLAmdTPOlts/ewa/4BwkLDObXtPFU6lCN/2VxcOXqT8NBItWvwxxCapO0UZ6Mlg0HCM5kHH177krFAOvRhBiq2+o2q7ctTO3FLRdsyTUtw7dhN3r9WJsZIkSkpz++/jDZXbtRVcupsKXh8K1Lwv7QSzML0N40DU46OYNmQ9ZzdGfOL1ZntFzmz/SK9l3TA+8FrqnYoz5bpu+hcZIBFW0mSWD1ys0V5eJgerU6DQS/ReWYrXj58xckt5wC4ceIOrl4u+JnFF++9pCO/NylhMY75eR5cfcyklrO5988jACb9PcyUIOJHQI4F/XME4lBXwCoWvH7+1hQMIjw0nEX9lRl8fH38TMIX4OzOS1zYHyXE0Bfg5OoYJ+EL4OrpjGSQ0NloY33rT5sjJcKKYZSpPlcqBq3tEW29V7IErHkyj9WP5nJAv5Gcv2ZV1Af5B6MPM2BjZ8PK4ZuY13v5F0W4MsfWwYYmw+rwW8PigBykY8yegbTL24eF/czcXYTcVk4I4M+NU3foOqcNLp6xr/7NA0FIBtCbqTGf3XlB3xWdMH/HcnJ3pGj1gnEWvkIjCAsOw+fFe/ThBkICQ0iVJTml6hfl9bO3pkhYEdTpUYW5FydYphQU8Of6nuQsnZXaPatwwLCR1uMaWbQRQqCz1/H45rOY/w4SfPQLsihOYMVIy83LlQx50jL8rz70WtyBjtObkz5vmhivW6830HxEfTyTerB+wrZP/k1sebeM/foNVG5blplR9sQVCCjfvHSM/3bGNJpG+7x9TMIXYPOUHYR8ovble2NAxOvne6EKYBULtkzfpTjOVFBp5LT0z3UWfUbVnUKAb+SeX3h4OJePXCckOPZ9ri8h2y+ZGbyxF81HNmD41r4xth1SY0KMvrUJk3taBL2Pir2jHYlSegFybGBrKQDlhOzBnN8dc7D9TyE0KIyVwzZyeM0Jqncqz5yL4xlSbYJF9CSNRkOhivnQ2epwcLEnTY6UVHZshL9P3Fd4ERSulA/3RK54JHXn8uHr1EvajjAzo6Oq7csSFhJGiixJAWUiBGtEvfcPrz3l0Orj1EzQkhaZu1q0b5W9B/uWHKZW9yrYOUWGGn3/0peRtSdz7e9bbJ+zD71eT72+NcheLLOpTbMRdWk2oh7hweEW46bKGrfYPu+8PyiOtXYawsPCKaetw7xeyyndoDhFqxdi6rER/N6kBAUq5GHHx1VseLVQoQ0qVDEfAHsWx10bYjqnrRYH58jAMHb2ypCrEatfoREMXBV5D8PDwpnXezljG083re4PrDrKkXWW+YdP77jIHx7NCAn6uv9WVSz5v7CC/lJUK+j4pXmWrjw3M7aJGuj92vGb9Cw51KJf02F1aDKkLsEfg6nq3MRUvtVvBU7OcYsu9dE/SM5G8xUyuZj7FXskduN9lJy/RaoWYOS2flG7xcjNM//GGP/3W2PrYMvA1d24d/khmQqk5+zOi+xaEGmBbedoS5EqBZQW3mYkTZeIt8/fExYShp2jLTv8V9H918HcPHXni+em0QjsHG0JCvi0B73QCso2KcmF/VdInS0F/xy8FnsnYNKRofQuNdyi/LeGxTm85oSVHjGj0QkM4ZbPy4QpPVl5fzZanXLl+fyeN57JEmBvzKw0uuE0jljZn46J2RfHkylvOiRJYvucvVw/eYdrx26aNFQRZCuaGTsHW8o1L8XvjUowrcMCds0/AEDS9IlZcXcWDVK15200e/kAEw8PJU+pHJ80v+j4mlbQCbImlMov/SNex1z3y8L/lh+wyv8PoxtOpaJ9A0bVnwJA0WoFTXWl6llGk3J0cyRLkQxKda6AnCWyAbBmtHIv6/S22F+OAv0+Mq39Av5I0JyqLk2oaN+Ahf2sRwb6XEyJ5wWMPzCEKu3LUaRqfjLkTUue33IwYFWXmAewQrYimZh6bMSXha+MBw1YkSr5+a3hr7QYVZ8pbeayasQmRtSahJOrMhXiNt8V9FrU3uoYdftUY9C6Hrh5uSA0gpCPofQpM5xK7cp++QSB4Vv74uRuabVuY6cjcWrZoEirs3wkSXqJ/cuO8O7F+zgLX4A75+5Tp4+c7MGcjwGR6mb7aKKSmWPrYEOvxR2sCl+AN099ePlI3lcODQ0z5Q1OniGpSfhKkkS55qX4FDOJoVv68O+5+1SwrUc5bV1mdVnCkXUnCfhgGfnr5qk7/HPoGuObzOTlo9cKQRthof9L1fwxni8uNhIq8Yu6Av6P8/TfF7TM0s103HZSU2r3qMK5Pf+g0QoKlpcTHoSFhTOo0hgeXn/ChygrxwYDa1KoQh5yFJf3RG+cuUP3opEB4rf7r8TByZ4TW84yrskMUmZNztzzE0z1D288oV2e3pZGNQJ2+K8yPcS+lI/+QZz46yyZC2UgddYUsXf4RGonbmmxj/kt8EqRgKGbepO5YAZunb2rWJFnLZKR+1cemQy85lycwNAa43nzVH5Au3m5ki5PajRaDRf3yfv4Lcc2YsnA1SDJqRRX3p+Nz4t3nPjrHEWq5aNP6REKlyNztDoNzh5OGPQSnsnceXTdaPwk5BVw1OQS617MxzOJnMu3fd7e3L/yOP5ujAbsHOxInyc1N09augzpbHUKg7Lo2Oq7HCcXRzZP28G8niss6t0SyckVrh+/bSobtas/hSvKAm9ml8Vsn73Xol9szL08gQ55+37ynvGcC+NJmNKTrr8MxO9dAANWdaNwpXxIksSR9ScZ03B6tH3jK63h114Bl11SK17H3FB0vroCVvn2hIcrH0ALeq/g3csPFK6Uj3cvPtAoTQeG1pxAJfsG/HPomoXwBXnPM0L4AmQvkpkJBwdTptGvrHk+HwcneZ90eK1JhHwM5d7Fh7TN3dvUfmDF0VYtWt0TusWaZvDtcx8uHLgSp71mRxcHyjUr9VWEL8Cap/PJVCA9yTIkptW4hrF3iCfePntHlyIDKaety5DqyhjBt87cVVhXn9/zj0L1XqxGQSbsH6JQMe+atw9bYwxmQ7iB6R0WsG3WXrwfvmJSiznow/V4pUiARme5nNOHG8hTOid/vV1K+jxpsbHTobPVgYSF8M1cJCOeSRIQGhrG1tl74lf4AhggJDCEmyf/tbryNBe+vzUoxpBNPcn3ey68Unpi72SH0Aps7W04tVW2Oq7VvSoHDBvZ/GYJ2X7JSMKUnvRd1hGDXq8QvgDzei43ff8U4euWyBUXDyeqdijP+d2X0dlYOqrobGIwUBQwpc08zu68yIp7s9n6bjmFK8l70EKIWLOG/RD7wJJsBR2fn++F6ob0H+ZjYLDVeMf3Lt/HPVFeJreegyRhCowQHTvm7qPx4Fr4vvWnbZ7ehBjdQFLnSIGNcV8sQi0XwbN/5ZR1H9768vaZZbACj8RuzD4/PkbXpjfPfGiaobPpQZo+XxpmnRmHThc3C+r4xtbWhtnnxpmOF/ePWzhLjU5D+txpuHvpwRdbTVsLCBKBvZMdVdqXxd7RlgV9V+Hs4cSb5z5UtK+vuM+vohh2nbNiTGbtbxbB0Q2n+L1JCQ6tOh7jXO9ffMDHgCDqJ29HkL+lFbI5Gp1GkVf5U4lN0ZerZDZunv6XNDlTMnBNN3r8+idP73gTqg9jQrPZlK5f3CQMXT1dmH5yDCD/ric0n2Mx3u9NSpq+a+OY4xogOCCYnQGrqZesDe9ffUCj1WJjZ0NYSJhpnleP3ozhQuHePw+Z3GYeXik8yV82N+9f+zK6/lSuHrsZa4IPna0qEr4l6gr4P8r7175Ud23Co2tPLer+rDyeya3nEt2LYfNR9Vn1eK4pK45Bb8DPx5/WOXqYhC/A4+vPaJtHXulqNBpszNK09VvVmQdXH9M6Z0+r5wgLCSdhCstY0IfXHWdM4+kcWX+SxzefKVYx9y89oqJt/dgv/hvg/fCVYm9Xo9Og0Vq/oYZwA3cvPjCtuszR2MTPP1GNVrD+xQJcE7hQs3sV9oauI032lJzfc5nwUL3CuvlLERqBTTQP8jINIwNThIfpaZiqQ6zCF2Sh9zXZPm8/mybv5K+pu5jVdbFFBKvwMD0Gg4GuRQdSI0Ezzu65BMi/a62t8oWv99IONBoYqSIdsT3uhn3ZimTCz8efdy8/yDGPw/Um4Qtw/+qjOI0jGSRWDNuIJElMaD6LK0duxCp8c5bIGmf3v++JhOqGpPKDERYaxoi6k+lZajDvXr1n57z9Ma62Diw/GpmlOQrL/lxH+zy9cXCyJ1X25LSZ0AQHFwfCrOynvX/5wfR9Z+Bqlt6ezq6Pq5H0Eu3y9MY3mmTyHhEGU0b2rThCWU0dxjacwd9rTjC64TQ5qEPUqEZmbJq6k8ZpOzK7+xKTkcy3Iqovc7EahajSrlyMBlfBASEWqnhDWPyEFDToJWolbMmc7ktZPXoTq0Zu5MqR6INzpMqWgqV3ppMhn/VMU9EhNIIpx0aQv6wyR629kx0HDBup2PZ3RXngh7iF3vzw2nLrQ5jJCiEgcWqvT5qrOQ8uR6q/j6w7RdpcaUzHSdIm4v6VR5TX1ePWmbsEfvjIn5XHmvbBN79eTPspzVh0YyoHDBsp3+w3xdi5S2Sjaodysc5Bq9MwfFs/Ht+0fCmOICRQ6a9btmkJspm5X5lz+9y/vLj/Ms6Jpau0jx9jO5W4o+ob/iM0z9TVpEqul7StRb1sOKPnYxzztkbELA70/cicHksJ/hhMq3GNWdxvlaJdseoFeHLrGRun7KBYjUIUqSwbpmyctMNizL4rOrNn0SGERjB610BA9mfcOX8/s7suVTaWICQwmHXPF9CxUB/uXnikqN6z9BDze8n7cFtn7GHHnP10mNKM6p0rxun6vhRXTxfaTWrK/D4r0Gg0lKz9CyXq/MLFg1cVLl7fkvBQPVusJJVXIGSrZO97L5nTbSlzL0xgcPVxnNkRt3Ca6XOnZlDFMaTOmRKhkQN6CAG5S2WnUZoOMQZCiQ4nN0dTGkVzJD20HteIqp0q4Ohkz6YpO5jf29JI6nO4eeqOItFHeZt6Fm30egNanRYnVyd+rVmYHiWHEBoUyuhdA8lkzGUtSRJdiw7ixf1XuCRwJiQoxLQnr7XVoNFoTKkdDQYD68ZtYc3ov6zOSaMVZCmYgesnI/ebD6w4Rq2eVbh50tJNzKCXaJm1O9NPjsLVy5XXT95QtFpB8v6Wg8UD13Dp4DXsHO1ASBStXojf6v/6ZTftG/KzRMJSraD/I5S3qWc1QLu9sx1Obg4E+Abx59oeLOizgqe3X3zy+K6eLmx+I0fpCQsL4/jmM6TOnop0OVJRTlfXtNouXCV/tCH8hm/rS9GqkS5QBoOByg4NrWaiiWD147kkSunF+f3/cOXvm7Qa05CVIzaycrilNadGq6Fq+3Lcv/IYoZENlAx6PRqdlkqtytBpRktTIA69Xk/95G358MaP0vWKMXBN90++JwDeD14hSRLJ0idhy8zdzOm21KKN0IhY1YPfA41WsC9sA3N7LuWvabEI7hjIVCAD/164Z1GutdGgj7LCd/V0xs7BFs8UCXj58DUl6hSly4xWdP/1T25YETL2TnZs91uJQW8gKDCYBinbxykTUlzQ6jSM2jGAFFmS0iRtZ0VdhnzpmHsh0uCtnG0dJDMFUEQ2ovCwcCrZN0SSJGzsdMy9NJHb5+4xudWcT/6bF66Sn+F/9eHQmuNMbD47zv3S5UrFoHU9SZk5Gc/vvSRhigRfxc/enK9pBe2RJZFUanHsucI/ha3F53wXK2hVAP9HGFJjPKe3f717VKbRr1TrWJ77Vx5Tun4xnN2d0Ov1jKgzmVNbz0fbz87JzrRvXKFFaXotjkyv9vbFu1gTqqfPk4Z5lyYqyqo4NSIkyDK0XmyCrkLL3+i1SM4ru2jAKtaPj8w286XuGQG+gdRP3paQj5bzqtOnGj7P33Fm50WrIRHjghCC+P63XL5FaXov7og+XE/v34Zx/cTtGNtHF6giOqq0L8vOeQdMx51mtOSXagVwSeCMo1nglpDgUNaM+Ys1oyxjJZu7EulsdSRLn5gnt57HeQ6fiq29DX+9X4adXaR1fmhIKJUdlKEwi9cszNBNvfF+8IoWWbspjLAcXR0IDgz5rIxFhSrm5dweS6M4t0Qu+L6O3gVOa6PFztGW4IAQPBK5sfjmVJzcnKJt/6V8TQHsniWRVHJR3Xgdc/uvs/+76QhVvg6SJPH6yRue/etNh2nNuX/lIa8fRx8J50u4dPAqxzefAeDQ6mNMOz6KuxcfcHH/1Rj7FatekJNbzyMEVGqr3IPyTOqBrYMtoUZh6p7IlXEHBtM+dx9TmzxlLIPIZymSkSt/K/c3zceJjr1LDuOV3INmw+uTIY/Z3qcVbdejm09xSeBk8mGNCUmSqOXVwsINB+RVeeuxjQgKCOL+lUc8vmE9W1CsCOIt7nQErp4uGAyymnXqsZGMbjidI+uiiSAlsEgxGBt/rz1JpgLpeXLrGa3HNWL50A3M7iprURKn8eLV47dkLZwRXx8/Xty1zNoTVeiEh4bj4PIFAVHiQGhwGA1StSPw/UcM4QaqdSpHlXblLdq5erpQza2pVQOzj35BaLSfZ35jTfimypoceyf7GAWwPkzPR195Lr4+/jy89kThOvij8bOooFUjrJ+Uty/eUc21CY3TdmJAxdG0ytrjqwlfgPevfAkNDiM0OIxn97wJCw1jatv5seY8PbfnHzrNbEHhyvk5t+cSe5YcMq3khBBs913BnIvj2RG4io0vF5M+ZxqqdiyH0AqyFMlA+4nNLcYcv38ww7f2I3WOFAgNcrD8OK4Oj26Uk6eXqleMlqMbkLlgemacHq1o07lwf9rk6En9ZO0YUHG0tWEUnPjrrEL4yj608j+9pOkSAVA3advPF75YxlmOD3bM3cf5vZcB2Wr+wt4YYltLnz6HQN+PJve0Q2uO4/8uMl71q0dvQZK3CawJX4BAKxGhClXI80lzsEbWIhlJniFJtPX+bwJMLlHbZ+8nYSov098ToFClfCTPkDRG6+5sRTPTbW5rHL4kghqQMJUXTYfW5aOVPfLocE/kSsb86WJvqPLVUVfAPynbZu4xpWWTDJLClSE6nNwd6bGgHZsm7+T22buffW5XD2eun7jNoxisOSMIeB/IlFbzFGVbZ+5h/j+TADknbMa8kQ+LQL+P/NGlEl1mtkYIwdENJxll9GWu06cabcc1QavVUrRaAYpWkzVKO+btZ2bnhXGae83uVUzfGwyoSYMBNS3amBLfAxf2XTZ99370itdPfMhtDMkZgW2UXLldZrdmbMPp6LUGKrcty4fXfrGuzr8HwYEhjGkwlb/eLeP09gufnK84Lty7JOdwvnX6M35vURdBGjiz69IXz6lKu7KUa1YaSZK4cOAyuxcf4sTGs9G2n9lxIUnSJsIzqQdj9/2JnZ0tFw8os4PpbHTo9eFIBjnBQsD7AOb0WGYywPpc3jx5y8SWc2J90TVnyrHhX30P+GuipiNU+b8nx69ZPznGcKDvR/RhBur2rqZ4o/9U8pTKQYpMyazG9Y0Lj64rBfeRDSeZ0WkhF/dfoYZHM1pm7c6QGrIBzKj602TVqwQbJ2zn1rm7+Hi/o2XWbrTP1wdfHz9K1y9mNaJQVGr2qEKVtjG7YoSGhOLipdw7G1l3ChcPXKFpus70LjWU3r8NU9QXqpiP1uMakbNEVmacGkXRqgVZ/XguS25No06vaiRI4k7KzMkArMZKjgspMif9pPYeSWNO3RjBR/9gRjeYRvZimdFoNbFGJouKzk5HikxJFX7Q9i7RP/zdErpSvmUpRZmrtVSKAsJDlMZ5tna23L34INqx4+pTne/3XPj5+PPRP4iC5fIydH1vZpyJXtNxeM0JXtx9ybVjt5jZcRHlberRv/wohEbOlf1r7cIkTpPQpKLXh+p5dP3pFwvfCD4l41iKzMlIkjpxvJxX5ctRjbB+Yu5eesDzu9589A9iRseF6OMQSWjc/j/ZMm03Z3d/3kqiTKNf6TSjBfWStyMsOIwESd0JCgg2JXq3QECWQhm4d+mhhbWzRifou6wz4xrPNLWN2OcUQrBfv0GR4QhgytHhDP1jokmdmSZHSkrU/oUVwzbEOnetTsP0U2PIXCA9YaFh9Cw5lDvn7pE4dUJmnx/HvqWHWdB3ldW+CmMgGy17QixTNsaGwWCgX9mRXP77epzap8iUFHtHexoNrkX63GlomrGzxT6wRqv5LGMfc+wcbdkZsJr3r3358NqXtrl6xa2fvS3dF7RjfFP57+eeyI0hm3ry5PZzFvVbbXJl+1RcvVzw9wn4LKOzegNqsH7s1hjb6Gx1SJKEZDDQenxj6vSsBkBwUAhVnRqb2lkzOkufO3X8h9SMBZ2d1uJlJDrKNS9FnyWdvvKMvq4RlluWxFLRBfEbcGdvyRmqEZZK/JIxXzqOrD/Jhonb49ynf7lRX3TO8/suc3j1CcKMSdrfeX+IdiW+L3y9Iv+uJEnUSdoa39dycA5DuMSW6WZxdM2edV4pZOOnEnWLcsyYWi9TgfTk/DUbwWbquJDAEJNxWGwIjQbJYCA0JIwJzWeZ1PAvH71mRJ3JMQauMI/IVbph8TidLyphIWEK4VuhVWls7W15cP0J14/esmj/zOhPPLz2JHYErGLdswVsmLSNv6bK+ZyTpk8k76V+ISFBoUiShEciNzwSucVJqGu0GnR2NkxuPddU9uG1Lz1LWKax/FTCQsJIkj4h755/sGrtDmBjr7O6woxN+ILyb7mg90oW9llF17mt2b3goOIl0JrFd1y2XeKbuApfgGTpo9/b/mGQVCMslR8Ea65HdfpWY+rxkdg72sWuJv7E37nOVseCvlHSCEazUOlTZrhiFSOEIE22lIo22YplUhwnz5SUYn8UYvHNqZzfd5m6vatxwLCRA4aNzDwzhvZ5eyMZDAiNwNHVgQmHhlC2WalY5+2ZzIPOs1qRpVBG5nZfyjGjMRbI7kvXjlsKwOiwFhQhLthGSbbuff81XWa2JkmqhDF3lODigSu4JHDmya3neCR1p+HAmkw/OfqzAl8AFPujEPnL5UJro6XhwJqKWNEdp7eItb9BbyDQNzBO2YY+lSD/YLzvvY5W+GbImzZeQ2tKksSMjou4e+lhrJbm+nADcy6Op3DlvDQZXveT0g/GN5oof3tbB1saDIjfPLoqX4YqgH8w/pqxiwEVR+H3Lm5p71qNa2xRtnHCdo5tPEXwx5BY1dI2tjamh3i55iVxdLO02sxbVk7irdVpSJouMaHB0Rt82TtF7v9dPXrTQtU9YE03XBIY9/wEPL7xhCRpZUthNLIKre3EJqwZ/Rcjak+ie/E/6V9hJD7e71g6eB33rzwmPFSPZJDY9mEFSdIkpnaPKozY1s/q3CMYtqUPv1TJT/UETdk5/4DCorf+wJpIn+Dj8/zeSz5GsYDtX2EUZTV1aJYp+pzDQgjq9a1uOm4+UlazmYfzjI5k6ZLQr+wILuy9zHvvD6wbv5XOhQfEOQlAVLzvv2Tc3sHsDVlHi5ENFHXrxm75rDE/h89x17n3T+yCMjqcPaz7xsZ1HpXb/s68nss5u+sfVg7dQNpcqT/p/F9qFW2OIYpVemhQKNvn7EWvl38TH/2DuHDgCmGhsRto/j8hwU+TDUndA/6BGN90BgfNMszENTiEwWBg9+KDTG8XN0tgc2ztbdj8dil2DrYIIdDr9VSw+Yz9FwF7QtZSP3lbRc7chdenkCZbSkKCQ2mVrTuvHr2J03ApMiUzZVQCeZWqtdESbrbyie7+bJqynfm9lat0O0c7qnUsZzVE5qei0Qr2hq43rRp93/lS26u1qb7JsNo0HWIZ2jACfbgejVZj6v/8njeTW81VrMJTZU0eGXBCGPd6vyBbUFTq9auGm5cbaXKkomD5PIq6mp7N8f/M/duvgaOrfZxDqMZG1OhcjQbX4sbJO1w+LG8NOLjYW7VncHCxp/PMVpRtUpJy2sggERqtRna1+gqPWWd3JwLiGEvbnHaTmvJ705LUSdRKLhCwM2BVvFpGf809YNfMiaXC8+M33efB0tPUfMAqMXM0jnuZUdFoNFRsUeaz+mptdPi/C2Df0r8ZWGk0U9vN/6xxqneuwNmdlwj0jfRX1NnqTCrnae3mx1n4AgrhC7Krlbnw7b20I2GhYZzddZGzuy+ZVN2SJLF97n6L8cKCQ9HZ2liUfw5zLk5QqGyjCoeYUvmB7Hpl3j95hqRMPjJc0ebJbbNoTxLxKnwBds47yII+KxlYcTSH1kS+9IWGhH094fuZC5FgK9HFosMjsXuMavlkGZKSKltyHFzsGbNnIM2H1+fOhUi3syD/YPKXz42do3K7oEjVApRrWgohBNnNkiM4uTvimSz2YC3AJ1//8K19yZD305JlAKwavZlNk81eNCU4vePHWsD8LCtgVQD/QFTvVMH0XWsl521E6rEKtvUZXlcZntFa+7iQOkcKmqbvxNS28zi/9zIHVx77pP6psian/6quBLwLZGzj6YSHRqpEw0PDKaupQ1lNnVhzDn8KNnY6prdfSCX7hvxZdRyDq41jzRg5wP3mqTvxvq8M7KDRaqjdq+pnZUwqWDEPjlHUhoOrjqOCbX1qJW5J9+J/cnbXJZMrjc5WS/d5MYfXtIYQgny/54ws+MqKK/MXpYsHIqOZ+b1TZq8qXClvvJ3TyfXzXLBMLx/RPEeTpE1I9/lt6DSjBenzpjZtL2hsNJHbG0amHBvBm6c+BPkHM7H5bCRJokCUzE4X912xCClasaWcAclgMDD5yHDS5E4FgL9PAD7P35E6h9K2wSpS9NdgjV6lhsrqdiMx5c42J/B9IOsnblWU5SmdI+4n/s5E+AH/DAJYVUH/YNy5cI8X915S7I9CvH3+nqRpE5n+4UWNX1yjcwU6zZDVTAaDgfrJ2/L+lWVatwhcvZzxexsQbT18erxfgMZDarN56s7oXZGiIX2eNNy//ChObZ3cHAj0DUJjo7Gawi9JukSsvDebxQPXsG7cFou6ZbdnUCthCwJ9I/duW41vyOJ+az5pztawdbBl3qUJpMyc/IvGkSRJod78Vqx6NIfEqRLSv8IoLu5XBpgQOhRJCKwlWPiR+L1JCcVLZqqsydHZ6ngQi2vRmD0DGVhpTPy/GMVDiNHkmZKQPlca7ly4b9IyCY1g8pFhPLr+jF9rFsI9kfsXT9Wcr6mCdsmcRCowt1HsDT+BI2WmqCpoldjJXCADOUpkpbJDI5pl6CxnGgJ8vN8phC/AgVWRD5IJzWZFK3yFRtB7acc4uTMYwiWK1ypM8oxJ4/zrWTN6c6zC18beBlcvZdL1RKk8OWDYyH79BpJlkt0nPJO7W+0fITijy58bkWe3emfLuL0vH7ymkmNDshTOqCjfOPHL94MjsLHXce/KI57eec7Lh68ICQ5hTs+lbJi0jfCwuFnsCiHIWyZypZKrZDaL1bfcji8KpGJO5bZlSWy0wo4qfEEpfIEfWvgCFhqeJ7eexyp8dfY6Nk3Z+VmCUqOLJbjJFwjfdLlSM2JbPxbfmMbgDb1Y9WAOuUtlR2gEKTIlI12uNOjD9ZzbczneE3l8bSRJxOvne6H6Af+AjKwzJfJAAp+X7zm20XJ/uPW4Rry4/5Kk6RLz+JYyzrCrpzN+PgEIDSy8NpmzOy9ZWO5Gx4nNZ9HZaSGOz1prSQiiUrdPNVZHyXZzevtFprWfT+n6xfG+J6uN372IfgUfE8X+KISvjz+NUnWwPsdwA/evPsYjsRvvX/mi0Wrw84mbpXl0aLSCxoNrk6NEVot0duasHbeFLW+XxWnMCQeG4vvWDyc3R3Q2OsY2ns7hNcoECZIEUjztCZ/ecYHu8+T80e6J3Pjw+vPu/8/Eb42Kc2zTadML64Irkzm99TyXzFT1OUtm5ZoV322QX5AQcmYuQ7iBkPBQs7ovz2qVIlMyMhVMz9H1JxlRZzLlm5cybXtMPCT/flwSODOm0XSObZDd7Y5vPsPI7f2/6Lwqn466Av4BKd+itOLYzcuF7MUyK/aAdDbyPmizjF0op63L+5fvsXe2R2eno9fiDmx+s5S9YevYH76Rv9eeZOPk7Z+0//Qpzv/RYetoi9ZGi429DZum7LD6tn9o9XFFDOK4PJwSp0lItU7lKVQpn6nsxf2X9P5tmIVrhjkfXvqatAQGvYEshTLE/WKsYNBLODg7cPXIzRjbBbwLNLmGxAU3L1dTaM0OU5srXLvS5kxFzhLKLDda20/7Z+7sEbkXmyprpNp8/P4/qdG1EimyJPuk8eKKg6v953c2/na1tlrsnZXjCE1kG6+UntTpXS3uc3JRjpU0fSIGrOzGrsA1TDsxiu3+K3FycaBk/WJ0nNacpOkT025SUyq2KMPcSxMiXerMkCTr+7W2DrbU7VuNpOmVoSK1NppP+rfZYOAfaHUa9OEGwkPDObHlnKlOCIF7Qje0Wi3nzOJmn4kmR/f/KwZEvH6+F6oA/gGp3Pp3mg6ti1fyBIzZNwidTkem/OkpXquQqU1UtabPiw/0Xd6JPUFrqdBCNhjRarW8eebDmrF/8eG1H0iQ89csVs+ZLnfc/Rmd3R2p0Kp0jG0SJHVn+d2ZJErhSVhwGCGB1i1ZgwNDsHO0o3bPKlbro1KgfG5WPZhD5XZlOWfmY3xmx0UeXXti0V5nG71x2oMrj0maXmmkY2P/aUqj+b1XsGrkpljbxXWvOyruCd3Y7reShdemsOn1YhZcmczoXQMVbVqNakjWIhmt9s9UML3iWGerZYvPcnoubE+bCY1NY10/cYt2efqwdcZubGIw6LN1/LRY0eYEfYkrkfG9Sh+qJzhAOY6bp6upzdunPmyeutNCoDm42lOlfVk8k3mYyrQ2GpbemaFo9+qxbCyo0WjIXjQzO+btp16ytjRO3YFF/Vfz0S+IVSM3MqH5LDoXGkCZxr9ana61SGKhQaHsW3qEvL/lVJRXaVuWAau6WbTPVjSTRRnAxOazuXr0pknIJzPL7BSRfQogfe40VvurfDtUAfyDUrNHZYZv7Uv+MrlMZT0XdqBCq99Insl6YP5EKb14+1yZktDZw8m0PwryXqyjh9IaNV3u1BSporRPcE+k3K81JyQoNNZIRO+8P9AgeTu8H8ZueXx47Qm2ztwTazuAjEa3jIhwlrFhbpUdgb2THbYONoSH6fG+r5zfZwXQN95e18TOtBzTgOX3ZimqNVrNZ+eHBWMEsewpcfOSBY2Dkz3tpjTF2d2RMg2LU6d3dVqPszRa+aNbZWadGUuvJR1AgNAKZp0bB0DFVmWo27s6tnaya9ZRs8hgj289J3X2FBbjeSR2M0XzcvL4PIvmr8GHN8rfgkFvoNlwpR+2awIXqnUoT97fc5IwpScIeT+7frK2CmFt52Rrstyv7tGUrTN2m+pCg8PwfeNnskfQh+vZOiNuv1udjRatTsOH177sXnhQUbdt9j5CgkJMq2mNVsOwv/ow/cRonNys3+dXj97we5MSFCifm4Gru/Hu5TvKaetQXleP+inaALLFd9pcqXByd2TsnoFWx/l/RJJ+Hjck1Qr6B8TH+x0NU7XHoJewsdORIksygv1DeP3kDfpwAx5J5H1Mc5Vu+typefqvN5JBovW4Rtz75yEpMiXl77UneGSWh9bB1YEgv9j3gn+tVZjjm6NP0dZ9XlumtV8Q94sSkDJzMp7eVvr3fmoyge1+K3BwdsBgMFDJvkGcElBEZfLfw3n27wtWjtzE22fKF5YUmY0BQD7jn43OTsueIDlJQ50krWStA1C3T3XajFdGLOtcuD93LtwnXc7UzL88iS5FBnD73D0QMP/yJNLl/LQISwDjm8/k4IpII6NFN6aSOqulILWG98NXtMjSDX2Ynt8aFKfHwvYMrjbOFKBCo9XwW8NiHFx5PJaR/n9Ilyc1z+5445bQhQotfmPlcLPALWbWx6myJCd/hdxotYJNk3cpxvija0W2xFHIgpyL1/9dIM4eTvgaXwyc3J0I/BCIk4cjge+t5/UVQpDj1yzcOv0vWhst4/b+ydsX7xhdfxoA9s52NB9Rn3k9lwOQNH1iVtyNfNEb12QGh1ZH/m22+6/AwUk24Lt19i5vnr6lWI1Cn+2uaGW+X82q2DlTEin37GbxOuapchNUK2iVuHHxwFWTYVNYSDgPrzzB+8Erk7B5/9IXmyjp9x5ce0JoUChhIWEs7LeK/7F3llFSHG0Ufnps3R0WXdxdgrsGd/cEd3d3d3e3JDgkWIK7u7vDuk5/P2Z3dnp8DUg+7jl7YKqru2uk66165d5Da46xctgmifEFrDK+ACd/P2f2+KbJv1G9Y3lr3xKtRjWieseKkja5wrTxtbFXSXYmggAr78zGzlEzqchkMra/X0mlVmWsHgNoPABufi7cOnMXDz9Xg+PP77wkQ+60CbpmHESdt7Lq7lyqti+Pi7czx7aeZOmgtdRwaE67nD2Z0HK2RnNYhIdXn9AmW3eN8QUQYWj1CYm6/8BV3UmdxRe5QoaLlzM+6SxwTOvAL4MP+8I3sj9yE4PX98TW3obxe4ZQtX15ClbOw+p7c0mdKWGSiN8aDy8/ITo6muCPIQalabo8ynV7VqfLjLZk0tGljkPjgXVoPKSOyXtU7SAlwPn8NpCY6BgiwiNxifUihcSyWZkyvqDJfajWrgJ1e1Ynd6nsKGwUzOkcz2wXHhxBtfYVKFazAFkLBzD9yCjJ+eWalpC8/meHJi586o9z9PxpCGMbzaBfBek5P5Dy+LED/hci6FMQ9b3aS/iK9eGR2p0PL8wzLiUW1tR6KlQK2oxpzLJB67Vtto62yBUyQj4bTjRT/xrJwCpjrWZ0yl06G9eO39a+rvlLJXou1GTrqtVq+pYfxfXj1gso6EKf5jJZIED5ZiVRqpQ0GVQH/8ypaJ25Oy8fvE7wpbIUzsT8MxMTNYzIiCjuXXhI+pz+OLgY5z1OLCLCIuhbdhR3Lz6QhDUSi+Hb+jK2wXQgebKDBUHjvkwIRu8cwE+1CwOa31WLjF1491TjFWk6uC7txjejrkcbo9KKXmk9WHlrNi0zduPTm88JHq+tkw3hQfHKXvnL5+LSYdNSlbaONkSGRaGOUZO3XE6m/TVKcryqTWPtc+vu68rml0sZ33QmRzef1Paxlt7WElJ2B+wn5p6XvDvg01Um/9gB/4B1sHW0Zdwu8yUDGfOmw8XLdJw2MSjXtAQjt/ezqtYzOjKalcOlmrjhweFGjS9omHisZfIBJMYXoNXoeIKKCc1nJdr42jjY8PKhdUZRkAk4eznhZEwwXq+fXCnn8Pp/OLDyCG2z9SQ8NNxohqwlKG2VzDmZeMlIlY2SnD9lTXbjC7Coz2runLtvaHwTGWJbHOtOBeuy3y0hoZcQZAJhweF8fveFttl7UNOxOVkKZuRgzBZW3ZuDu68rwZ9DqKXDUKeLd08/0Dh1JyIjrKfK1IWu8QW4ZEQnWmUXT58aHhyh9RhdOXKD2m6tGFYrfqFWQIfR6+Prz7x99p6nd3QoTYGQYNO78B9IfvwwwP8ynNp1juo2zRhaw/wO6NzeSxLRA30kRqbuyMYTjK4/zer+1irxxI1l+JY+CR4TaHZHbjpMPnfPP0zUdbIVzURESITVu3BRLRL4LoigD+bZw0S1SIxuspcIHXL24fGNpxLjnbtUNorVLGiBq9gbuTw+Trdr0UF+nx+vmXx82ym6FhnIsiHrjZ2eonh41QRhhaihB03IAgtIPD1pMs1qrl7OlKpflM4FB/D8ziuiwqM5sfMctV1b0TZrT+b3XEkT/1/wz+KHo4fxBU3I51CTi844VGptZZjEyAJi4YUpJruHfgnjzO6LjKyvoaXVV7V6//IjdXtWl7TZOySfGlNK4r9CxPHDAP/LsGTAOsnrxOq9imoRlW386llpo8TFyzlR18qQO43RXY6+HqkpdIhNQCpWsyBKm4QLIqh0mISObPonwRM9gKuvS3yc9SvgzZN3RIRGSoz3tb9va4QjzIQWdHdFnfL0ZU6XpczrvpyGvh14evsFYxvP4O75h2ye9Bsj65menFMCXee0M/n9ZS6YkTTZU5E6a8rHifVj0QGJECwA+PTmCzdP3yVMr6wpLChc+x1FhEYwpdU8gj8kTqBCZZvwhYku+pQZyaD13bFzssXJy9HofHD7zD1AUyGQq6SGLyBr4QByFM1C1dblaT22Me6p3Jh8aFiSxvK18F+SI/xhgL9ThAaFGdX8rdZemthkbflKlkIBpNXLeJ1zagLZimamVpcqTDk0nMD3iWN+UigVRlfn5lx+zl4OlG9WEt8M3qhsNAljcrmcaUdGobRNmBF2dLVHFEWG15nEhGazeXk/4XHVz69NMzwZnZOS8OSY+87iJnZB0NxDrpCTq1Q8scYgnXrQR9fj65o/v/1CdGS05Hu4/o/UTZ9QiKKYIIKQIxtPmHQV3zpzj6c3X/DizqskjckavLgrvUfTQXVw8XYGmc6CVYgluLCAlUM3UqV1WUlbhrxpjFKAJgaR4dEcXHU00ecHfwpmbtflhAWFE/Qu2OjibcqfI7T/n3l8HAdjtjDvzCRtW4uhDdj8fAkFKuQ1OPcHUhY/qCi/Q6wfv5VVw7cA4Onvzsan8RKAjfrVJsdPWehfbgzRUdFWu3lf3H9Fs8H1WD5kA+oYNaUbFaNLoQGoY0Run7nHoTXHkMkFYhIotABw76KOIotc0MYAzcXt3LzdtBSK83usJEfxrHx5H8SoulOMGzwzsLFTcWD1EU7/kTQ2H6VKQfWOFYmKiOLv7acJ+qjZ1Rh9GwmsbtJNIvJJ72WgyKSPaUdGc/vMPQpUykOmfBmIDI9ErpBLykTS5fDnyU1NFrvSRkHGPOkoUiM/Z/dcAqBUw2KEhUbw5MZTepYYhhij5tcZranX0zKpydM7L/glbz+iI6MpUbcIvhm8OLj6GPnK5mLQ2u7aet84REZEadjMTMCapCwbe5WBypA+5Co5ji7xJTzGIMhiP+vYW45rNhNiHxMxrlG0jrf65qm73Dx1FwA3XxeWXp3Bzjl7DWhTvyacPJ1ALRLyOYSYaLVJF3f2YpnJUy4nSwespd2EpmTImY4JzWdxdJMm6arnog7kLpmddDkSl9X/zSAmPJ7/veJHFvR3iMqKRpKVrLHMRP0+1kCukDN21yD+3n6KfcsOGxxPlckXmUxAFDXuxNH1p1qcEBOL3KWySwTmF5yfzKK+q7l6zDxtI0DNXyvx7sVHzu65iL2LPSFJ0Kd19XbW1uPmK58LO0dbqrYrx5k9F9m79K/EXdSYgo2gMcL2TnY06FOT1SO3mL1Ei+H18c3gy7XjNzm+7SRhwRHI5DK2vlnG6AbTuH36HoWq5CNt9lS8vP+W3kt/wdHFgU9vv9C/4mieXH9mfGgCLL02k3Q5zNf/Tm07n4Orjxo91n5CM5oMqitpU6vVVLdrZvWCMMkQNAsmfcIXQRD4uUtlDqw8kuy/XblCxup78+haZJDZBUByQGmrJCo8yqC9ULW8TNwzDNB85lUUjQ36JBR1elSj66x2Sb6OLlIyC9ohs5+YY07bZL3m+eoTf2RB/4AGHn5uFvvoin5bi5joGG6dvmPU+AK8vP+a4rULUbdHdSa1nJMgAoyEonSj4tr/u/m4kLlARko1KGaVS333okOc2X0BO0db/HVo9hIMQcqSdPnwdU79cZ7R9afTZGBditUqhMJWQamGRRN2XdGIm1mEnD9lZdGlqawdY5mact3Y7UxrN58Dq44QFqyJ+6pj1HQpNJCrR28SGR7Fyd/PceXoLTrPbE3H3H2o5dySRX1W8eSGceMLmp1Dh1y9+fTus7bt6vEb1PVoQ5fCAwgJ1Oym8pfPaXpwRuP9MpZenU6lVmUkXOXpcqbB0S0+QUmhUpA2ezJwSYvQcUoLlHbScIUoikSERkqY25LLXVypVRlaBXRNkPG1dbCxapbVjd3aOtowca+UmcovwJuus9sxalt/Tu++wLvnH4iOTp7FTlJc4N8K/xUu6B874O8QoihS1aaxVne3Wsdy9F7UWZIgcWjtMaa0nmfqEibRZVZbFvRaqX0tyATcvF34+PpzksetD4WN3KRogxAb34yJVqNUKUifJy33zj3A3sUOF08Xgj4G0Wp0Y8o1LsHDq08YWGlMsrud3FO78PGF8djv/POTSJ3Zjxd3X5EpfwaObDrB9A4Lje5KjCFNttSUblic9VbwQCcEzp5Okli9nZMdYXoqVkobw52hPio0L8mgtT15++w9zdPFK0RVaFGKQWt60Dz9r7x9+sHgPKWtkl1BayWZ2MZw68w9gj8FU7hqfqa0mcehNcfiDyaDxi3AvLMT6VduFOEh8YlpKjslTQbX48TOs8hkAj/VLsS6sduTvDNX2SrJmC8Dt0/flbbbK3HxcOHdM+MZ293ntmdu9+Vmr23vbEdkeJQmhh+LgzFb+PI+kNvnHlCocl4UsaGHpml+4X1cfb+MBIdCQOOafnbnpbZ2uWbnyvSc3zHhFzKDlNwB22f2E7PNbp+s17xUY/yPHfAPaCAIUtH7fUuPMKRGPPtR8OdgifHV3wUAKIwo4GTInYYl/ddq71GkRgGyFclsoPhiCSo7lVW1ndERMZoMaSMQ1RoeZlEtEhkexd2zDxBFCPkcRkRoBDver6JO12q4eDqTOrMfgix5fqpZi8YrHJkyvgBdCw2ijktruhYeRHXbpoR8CaHZ0HpW17SqbJW0Gd2YLIUyWjzHztUWZ0/LNcHZi2emx4IOkra8ZbMb9Bu8oSdpc5jfZR7eeAKAYT9Ly9lO7jrP83svjRpfgMJV81s0vgDZi2amcNX8gIblTDd27WyhbtoSUmX2IXuxLCzut0ZifAG80niyZsRmHlx6xL0LD9k06TeN8RWQfg8J3PREhkfx4aXhZzJ+1xAKVslj5AwNKrW1XGI07fAoUmeJ9+Q4eznRvfhgGvp0YHjNibx/pTG4MdEx8cYXDIyvs4ejwftqN74p889PxNZRsxMXBIFbp+/h6uXM5D+HM//8pGQ3vj9gPX4Y4H8J7pyLL5G5f+mR5Jgx7Ve1Gvou/1XS9vjGc61KkiiKnN1zkVun7/Li3mtcfV2sHktkWKRVOxiZQmZAdWkNwkMjmNN1GbM6LyEyIgrvNJ6M/WMgGfOlI3Um30QRO8SRXtw5k/BSo5hoNXO7Lmf18M24eDlRu1tVxu0ZTM3OleM76U3ugkxgcpt5mppkC59V2OdwAt+bryUGeHjlKWUa/MTI7f1o0LcmzYbUo2DlfAb9xtSfztObhkxegjx+kI6uGrewVxoPSZ8yDYrh6S9t054vCLSf0NToMXPwTe/N9g8rtG7WwPfBDNvSO8HXicPLe2+4dfou14yQrehnQEeERoIM7BxtGfvHIApXy0/3+R20O8qE4N3TDzjrkds8vPqE/SZCOgBT2yyQvK7Uqgz1dZS9cpbKxusnb3lyPf45CXwXxJ2zD7SvW2bowund55Er5OQsYVwBCSDwQ7DBb23LtN/JUiATUw4NJ0+pbNpEwOd3XyHGiGQpEGDkSt87krcE6YcYQxLxX3NBA5w9cIkRP0/S8jvX7lqVbnM1bpfIiEhq2DfXPmx+GX149VCaVevu54qzhzOPdUpVzCFOPxQ08cuVt2fRMW8/ImMTWVy8nc0qDNk521Kkan4+vwvkytEbiXYxCjKB3KVzcPXoDQAKVMzN5IPxZRTRUdGsHbuVJzefc+q3c2b1fVMaCpWCeWcm0K/8aK07TxAETaZtCgxLppDhncYThUrB8zvxBtbRzZ5gMzzCcRBkArU6V+HsvkuASP9V3chcMCPN0v5K0Idg/LP7seL6bARB4MW9V6wdu40yjYuTr1wuFAo5U9rM59jmk4hyETs7W7zTeLLo8lQUCsvFFBFhEdR0iBecqNC8FJcOX+fjq08AeKb14L2JXXdS4ejmwOaXS7XKThFhEdTzaEukleGEpCBr4YzcORdPDLPz0yoaeLWTiIQkhCLTzceFDlNasLDXSqu+cwSwd7Ij1AjHe6WWpRmwurt1N04gUtYFnUrMMit5XdBXao77/3JBC4KQRhCEI4Ig3BQE4YYgCD1j290FQTgkCMK92H8tZyT9B1GkSn7G7hpMrlh93t/n72flsI2o1WpqOmiMryAXmPn3WMbvHRK/ohcgc8EMBH4Istr4ApIJoeavFWmXozeRoZFkyJ2WeWcmEhVmfrIKCwrn2JZTXDli2vi2GNHQ5Pmr7s7lkHor+yI28vhG/LjvXtBMXjExMXQtMohqNk3ZMG4HJ3acJSB/enwyWC8okBBkL56JJoPrEpAvHUqVcQMTHRnNtPYLJZrCKWV8AdTRal4/eisxvoB1EzEgV8oJD43g1cM3vHr4lr5lR9LjpyFaMpDnt15x9W/NwufQ2mPcPHmH57dfYGdvy8nfz3F0U2ydb7Tm+35y8zkdcvVm6/Q/eHTdBAtWLGzsbCSv7Z3tyVMmh/Z1QJ50yBQmdiIC5CyV8KTDOAR/CqGp/y9cPHyV7sUH08T/lxQxvsZCOfcvPZa83jbtDwOFLmPGt+s841m+n9584c/Vxwn5bJ1oil9GH6PGF+DuxYd8iF0A/dsgisn7963wzXbAgiD4AX6iKF4UBMEJuADUAdoAH0VRnCQIwiDATRTFgeau9V/cAevLhwFkyp+BrIUD2LMkXi905j9jyfWTxrW0ddrvvLj3irP7r2hl9ARBIHUWv0RL6AFkLRzA26fvNRKHAALkKJ6F8JAIHFzs8Urtro0pJhZDN/ZkQos5BvWiFVuWZuDq7lw/eZveJYcn6R6JhdJGQZQeyYUxCDKBTlNbsnTAuvgM8kQmyqQEBAF8Mvjw+qHpGmRbRxu6zmnH9HYLJe2pM/nw4r752uV85XNha2/DgDXdcHI1jPOObTyd41tPY+tgw/qni2iTuTtBHzXGX2WnpOYvldkxSyr35+DmQP2eNdi16ACfzJClJCdylczK9X/umO3jldaD8OBwba24KShVcqJ0aEgLVc3Hxb+uoI4y/DGlzZGapzc13Mw9FnRkbrelEgUtLQRwdnfUuJyNHEvIc+7m48qWV0std0wgUnoHnHlm8u6Ar/78f7YDFkXxlSiKF2P/HwTcAlIDtYE4FvbVaIzy/xVEUTQwvjK5jPYTm5FDr/worp7z5O/nWDN6G/tXHiU8NJ46zz+7H/V61mBfxCb6LutMYnDn3AM+vfmCvbMdrt4uIMLNk3d5eOUJ9y4+pHC1/Dh7Jk34YWq7BQbGV5AJ9FqkUThKHZCEcqNYFPtZQ3Vp72ynkTO0ElERGuOrUMrZ9GoJrUY3xC+jt0E/US2yuO8aafnWd2J8QbPSN2d8QUPob6xG+cX9NxZj75cPX+f07gu0zdbT6PHhm/tyMGYLu4LW4ezmSP1eNbTHKrUsQ+cZbRixvR9+AT5kLRLA+icLkckE1oza8tWML0C/Vd1MksEIcpCrZBSplt+i8QW0xleQC/hn9eP8/sta4+vq7UKqAB9t3zjjC7Cgz0rjxhdAxLjxjT1mCgUr5ZawYgEEfbKce/A94gcXdDJCEIT0QH7gDOAjimJcJsVrwMfUef9FhAWHsXzoBkmbrZMNB6I2U6hyPiq3LEuV9uWwd7aj8+w22p1GTHSMZn4UwVdH5/XZzZfM6bKUaqomHFxzlIHruuOd3jNRY2s+vD4l6xeRtIUHRzC51TwC3wfhk8ErwTSScYg04uJ2cLGnjlsbGvl1oF/5UYlW1YnD6V0XiIqIIjQwTJuc4+BqJ1GUMYfoqBhcPZxpObwRa+7Pxz3Vt4uO+KRPGdc7wJe3gaTJltqQV1iEItXzWzw/bldrDLqldM2HNWDb2+X0WtKJdDk02fKl6hZlzb15zDs9iQdXHku4smUKGZ5f4TNvk6m7SbekGAMxkWr2LP7TeAcTaDm8IaO295e0fX77hZcmGNGi9crIanerSubCSUuYunT4Bid+Pyu9T2Q0tV1b8ebJuyRd+2tC4zb+YYCTBYIgOALbgV6iKEqyfESNf9zooyAIQidBEM4LgnD+3bt/z4/HHMY2mk4t51ZsnvSbpL3LzLaEh8aXW/RZ/CvDt/ShfON4ke2S9YrSYkQDKrcug9rEyvna8VtMbjGXt4+tU5lpM7YxckX8T2Rp/3UGZR+6ePPonUGdbOFq+bQZt7qo3KYcDXSyQfWRPncagj+FEB0Zzac3X3h660Wyx1Zt7FSEfA6TGH87Z2msMl+FXHin90SukPNzl8pUVTWhkqwhlWQNWXBpMi7e1mePm0NCDeqbx+9wck9+SUGAqIgowkMjKFy9gKTd3c+Vs3svWTxfHa22WtJxy9TfmdVpCQt6raR9rl7a9uioaEbWlopJZMybjulHR0vEN5IKmVzA3iXlFYAy5ktHuhxpqNuzuiRnwCR0fuueqd0p3aAYjy4/Nn+OBTvik96TiwevGbSHBobRKlNXy2P6gWTHNzXAgiAo0Rjf9aIo7ohtfhMbH46LE781dq4oiktEUSwkimIhL6+U2w18TRzfdtpo+7xuy+lVcpj2dZssPRhcdTyN/DrxLFY4XiaT0XhAHfou70KuEtmSZTxrx2zjj6B1kgkvLCicXKWyI1fKqNW1KjmKmy6LADi37zLzzk00cOndOXcfvwAjzo3Yfo+vmWZzShYIUL5pKYPmsMD4BUaqTD68uPOKwLdBxETHsGvBQUnfiY1n4Zsucd4Efbx5rFlECnKBzAUz4Opj2bBb4wJNLN49fc/Z3VJu7Y+vPpvsX7JeYcnrexcemegpxYnfzmn/r5tcpsudHYcm/evw4OoTilTLn6AQAoDKXkXGPFLO42I/F2RfxCZ2flhFlkIpW46ze+FBRtSZjIOrAytuzdYwZFmJ9y8+0rfsKKKNkInIlTrG3MwCVa6Q8fbpe57p6f/GQW0FV/f3hP9KGdK3zIIWgOXALVEUZ+gc+gNoHfv/1sDvX3ts3wqmpAUjw6N4ePWJdkKKKzkSRZFuRQYxt4eUaafb3HY0HVIXv0w+yJWJ/3HFRMVQw74ZURHxO8RM+TMw89gY9kdsptWohmTKn9GiqkzfMiMNXHql6hdhx+y9hp2TcR7wCzCM08bBw8+NRv1rmT3/5f03vHv+QeJ90EX1ThW5c+6B0WOJhRgjcu/CIz6/SVrMM7noF63FSR0hDHc/V0rWK0LghyB2ztvLb/P3ce3ELaPiHO106oortCyt/b/cSJ3uiwevGNNgOv/sOENEWKTmeTHx87ZzspNQXkaGRlK1QwVJn1ZjGiGTyZDJZBSums/at5oonD9whVN/nGfd6K20Cuhm1pOUEFjL8BUTrbYoPnHhzyvJMaQfSAC+5Q64BNASKC8IwuXYv+rAJKCSIAj3gIqxr/8vsPjyVJPH2o1vqo2fxZUmgcZ99Me8/fy+cB8tA7rSxL8Tb568pfGAOuQtnZO8ZXKbvKZMLkhX0MYgIhF9WDNyC6d2naOmQzMaeLXnjwX7iYlSSxcPQqwBEDQ7ug8vpaUOPuk9efXgDfnM8Q0nA149eEvNXysZPfbh5SfaZe/FzL/HJCi2XKdnVRRKBTK5jDWjDEUyvhfolp7IlTJIOO9EgpCtSCZUdiocXR2YcXwM+5b9RX3vdizosZL53VfQp9QImqb5xeC80vWLsz9qE3vCNjBgRTfJsR0fV6KyVSHIBEbs6Mufa3USE0XYHbyOnD8Zlie5+bjwx5c1tBjeSNKuTxe5cfxO7f/zl8+VmLedIpCr5Axa1x1F7LPp6O6IV2r3FL/vpBZzUvweyYUfZUjfEf5LZUj/7DzD+KYzidYpXXB0c2Dnh1UAREVGoVQp+fD6E01SddL20VVPUdkpyZw/IzdOmi+lAMhZIhtf3n3hw6tPhAWFW+wPmlKV8GDpCt7GzoaIsISv6rMWycTj60+xsVdRpV0FTuw8w8t7r5EpZJps4iT+PN18XOLLp34gxSAIMHB9D2b/spiwINO/g3WPF+CTNnEho3P7LzKkuoY60zutJ+sfL6RV5m5aaUc7Z1tUNioCPwSRo3hmJu4fTi2nltrzK7UszaG1xyXXLFG3MI0H1mFR79Wo7FTcu/jQpLxfQuDgak94SESiOai3v1vBpzef6ZinL6JaxMPPleJ1CrN74aEkj80UHFzt2fp6GUpV4hIpdZGSZUh2mVKJ6acaLuaSgtv1Rv1/lSH9gHGUrFuU5TdmSdqqx7rOWmToQnXbZlSSNWTXYmk8UncZFx0Vw5un1iVapcmaCrUoEhYUjoMVySjOnk4G7jNbRxuK1y5o1f30cefsfSJCIwl8H8zWKb/z8p4meUcdbb3xVZnJvA40k5GrPd/e+Pmeqd0pXsu6Z1JX3UkXGfTijnJrEnC+AgRBSk2pD79MPvw6vTXeaa2LcYsiLOy9yqzxBWiRvguV5A1plKojUZEJI8MoXLUAOz6sZMnVaax7pKF47L2oE3KFHEEmUK19Bb68C0RUi9w4cZcBlcfSfV4HVLZKvNJ60mFyC2afGCe55omd5+hRbCg3T93l8uHr+GX00caXlTYKSRJiQrDgwmTKNSlJ+lzGudAtYf347QyuNl7rffrw6jN/bz+TqGtZi8iwSE7+/t/YyPxb8MMAfyeIDI/k1K7zXPzzKu1y9NK2+2f1o0i1AmyavFNSKrB+dLzKTs4SWSUxM3W0moKVjBPE2zvb0W5CMwLypQdg/4rDWqMX8kXKmJOnXA790wn6EEy2wgEgaFzYfZb9wuYXSzmz52KC33OCoGcrHN0dcPNzIXUWP0b/ZpqnRX8H4uBmh1xPqCJ1gB9NB9fB1dtZcr+uc9ox5reB2DlbFqu4cuS6UXeou6+0bCYgr/Xi5wkVyUgIRBFkpopdgVf337Co72reWrmQA/jyxkqZPhE+vf5M8/RdrL52HJzcHMmQK502HJO/Qh52h6xjV9BaClSQ/uZvn7pLlbZl2RO6geU3ZuLm40qO4lkp37SEkStrcP/iI7zSeKKyU6JUKVnzcD4l6hUx2d8UWgd058+1x3j14A39ViT8fe6YtYdgvRpdc1SwSYUgCMhkMg3X+ncOkeQtQfqWZUiWSVx/IEUhiiJPbj6jW5HBRIQZCoi/e/ZBUwNrBjdOGLqaj283nlEdGhjG7bP3eGCppAG4euSm0fHejiWKV8eIzOq0BBs7G6vd1wCpAnyYfWIc/SuMtl6sQW83HBybAfzp1RcGVx1n5ASMsgKFfDKk5UuTLRUbJ/5mcL9lg9cxp9tSwgItv7eoyGhunbln0H7hoDSx5cPLzxavFQdHN3tAMJAbTCxkCpnGsxALdz833r/4KInxpxRcvV34/FYaCvj85nOyXFuhVKBQKihavYBBeOTL+yBmdZ7Bub0XEWQCC85NolSD4oBA8JcQo2VVcdnYw3f1oUfxIXx48QlBJlj8nGwdVYQHS5/hiLBIHpvRZ9aH0k6ppX0NC47AwdWBkM/Gs93lSnmSZRbj0Gp0QwpXLUCm/BmS5Xo/YB1+7IC/IURRpKptEzrm7mvU+EKsmksiEGaC/xXgpE7pR1KhVov8s/MsChN8ycbw8sEb+lUcozW+xlzI1TtXpESdwokmDUmXwx83X1ezfVy9XWg/qRmnd10wevzF3dd8emU5fmznbEeRqvmlDFhgNMHtw4tPGmk4K/Du6Ue6zWlH5ValJYl3cZDJBDz8rSem0E9Iq9GxEi1GNLD6/KTg89sv5K8gTQjMWiST5PWRLSdYPmQDJ38/h9pUMbsF7ApcR86fsiCTCRSrWRAvfw/O7dV4Z0S1yNhGMxjXZCaHN/7D5SM3qNK2nMlrTWg2hw8vPmnPVdoosDFC2pI2e2oaD6xjYHzj8PqJ0UpKo9DVBAYI+RxClsKZyFI4wKD0KrmMr429ilqdq5I1hUuxkhNiMv99K/wwwN8Q45vPRK1XGpAqwIeeizpSoFIeqv1S3vAkAVy9nekyqy3jdg+SMAuZQpy72Rwc3RzIW06alVyhRUmL54EmLuyd1riEnSk8uR6/KzBGjN9hfHOqd6xkNWmIPkICQ/nyzrzLLiIsguWDNiSZmD8sMIyjm08atJuaIPUT2LTQ+yodXO2Z2nY+B9cc5/rft0mvq60sQJ9lv7LxyWIyFcxo1Tj/mHeAMk1+QpALyOQyMuROg0+axC1wClvBiKWPK0euS17LdHSFO+bpw4Qms9g0aScj605h/bjt2mOREVF8evvZ6vvM+mc8B6K3MPYPzfOhS5Pqm8FH+71EhkXi5O5AwUp5cPZwpEYnaZmSqMcF6ZHKnQgjjG2lG/7E5sm/mRzPP9usj91WaW24ILh77j53zz3QLsZTZ/Gz+nrWYOGFKTh7JI1K9qviBxPWDyQHjm06JXldoFIeVt2dS81OlZm0fxj7FsfrjNo6xe6aRPj8NpDP77/w8fVn6vSqbnH3aY27uVqHCtqlYL7yuVh5ZzbHthp3Y+vjwoErvLRA1J9QbJr4G0sGrJG0ufg4m+htiPfPP0rcrcZgym2etUgAShuFybrshMKa6/hm9GZfxEa6z4snmQ/5Is3GfXztGUVrFKBE3SI0HVyPPxYc4Genlrx5bHyHZWwHXq9HDRBBHaNmQvPZpM9tPCYtk5ufGm6aybCXyWXsCllHqQbFgHj6SX2a0ic3nmoTsfTdtI9ilbzuXnxIDbtmNPLtSLP0ieMy3/RiMTV+qUTLkQ0YtFYqv3dq1wV6zO9A0KcQ9i79i9SZfUmXU8OvHh4cgUwhQ6aQ4eBmj2caw1IgrzQebJ+5y6A9oZSsggDjdg+m77LOrL43l9RZTMdi9TWPZQrBoO5b/zcnM5FwJ5MLpMmaOkFj/YHkw48Y8HcCuULOxH1DtZPV0c1SdSH9zOMN42KJwxKofmIKWQpmZOvUPwANqX7brMYJ9Y3h9SOpAVCoFAauNC0EjRh8nH6uKWyZ+juV25TliU6M2OTOMZEoWCUvFw4Ykg/oiqEnFjK5TOuSthQ7lCtktBnTmL/W/s2W6X/EHzByWlyy24md8Zy+ESaIQvR34LaONjy8+lQ7nohYuckFF6bQpfAArXBEp2mtSJXJh1F1TNelmyvVUceo6Vd2JI9ivRxxpY764ZSQL2HUcWuNXC7HL4M3rx7G/47ajW8GwEodXvR3T98TEhiKg7O9yXsbg1KppNfC+JK99LnS8Dh2bJ2mtKR32RHaz+TFvdfY2seHCNTRaqp3rMDepX9x/fhtg2t/fhdotIxcn5JVCwG8/D1490yqfSyKcOPkHYpWL4Crtwvvn38ENAxekTqfm7FYtDpajK/7jlXfEmRIxBxMMV2ZStb87vHvr54FfuyAvymcPOIl26b8OQKZLP7rMCjPNqOMklTYO9sxvsmsBJ3jn8XPpGvbpPEFEK2PXbn6uGjvIVfIiEgm9qA4XP/7VrJeTxf68WBz8EnvxaxflzCt/QLePEo8r3n+Crkp3bA4Dq7GDdTIbf2Y3Xmx9rUgCChVSjLnz8Dsv8eRLoc/1TtWoEHvmpz6I2nlKHfOPSDSRF6DLiLDoggLDtcaX0Em4OrjzOgG0/j8PpDCVaWubn0yjcRg6dUZ7Py4ij8C1/Dk5nM+6iTGeaRyo66OSlOeMjk4tuWUkatoEBUexYDV3UweN4CIgfEFzfuWyQT6VxzN+nHbtIuVyNBIbB1tdfphnjgm9menjrY8MTQfWo/xe4ZaP/YfSHb82AF/Q+x4t5KPrz/h7OmEQiH9Kso0Ks6klnM0q90U1pQ1JdgdBxt7lcHuxTuDJ/cvmBdhN4WwYOsyprdM1rCQevq7I5MJvH1qOHElBYlNcNOFq7czn3XKQ9JkS8Wz2y/NnGGIhLjvzWXjXvpLQ7Sv63pW2iqZsHcIeUrnYFLLOZIFW6fpLbly9Aa+AT70Lj0cdYxI8JdQ3j3/YNF42jnZ0n9lN/YuO8T5/dZTGCpUcmKi1Sbfg6gW+fwmkM9vApnccg4tRzWWHO+Yuy95yuZkxNa+BL4PJPBDEENrTkRlo2TakdGkzWadOzVOIEQ/Lt176a8UrVaA5sPrMbzWZC4dkooXKFRyCUkOwLjGM7F1sCU8xPpKgDjMPTMBG1sVUVEx9Cg2hJjoGC7rjSlc53mxxrBai9rdq1mVQ/I94lvEbQVBqArMRsMrt0wURQOWRkEQGgGj0DxpV0RRbGbumhZ3wLGCCfptycNA/wO4+7oZGF8AuVxO+aaxSVB6xje5YpPWQCYXKNOwOH4BPhLO54sHrhH4Pt7weKS2Phs3oeQG759/THbjaxFWfsSf9WozE2p8E4qpenquxhATFUPD/rXovqADe0LWk69sLmQymYGU4IohGxlQcQwt03fRuig/vPhIr5LD6KYTiwYoVC2f5HVYUDhjGkyzaHzr96mBjY7wQIZcaa0ue7py9AY9iw8xaL969AYNvNvRLnsvBlcbT8jnUD69+cLmqb9ZdV1d9FjYUfs82Tvbkb+chpLSxsZGYnzdfF3Y8WEle0I3kL1YZs2OVSdOnhjjC6BQKMiQOx12jjbERMca9q/kXh3XeCahyVTi9rXxtakoBUGQA/OBakAOoKkgCDn0+mQGBgMlRFHMCfSydF2TM6EgCOUEQXgOvBIE4WCsZm8cDpo47QeSES5expOOlDYJS/BILJsPaGJHB1drCAVUdqbLZ+LKNayBUSWYZFpTyCwIQ1iLii1KU7BynhST/EssshfPgr0VxCBf3gVy5+x9ajo0p5K8IRvGbzfg5I4KjzIqkPD5XSBOro60n9yc7MWzMu3oSM7vu5yo8W6fsUcSOrh30TqVJICoCPOhDJAm0h1cdZSVwzaya+EBIsOt826kyujLwegtbHu7nB0fNNzTcdAt+8lbJidObo7ExMRw68w9RLWYoDBDHHTJXnwyeBGQLz2L+q6mXbZeCb5WUnH12E1G1Tcd5/8BCYoA90VRfCiKYiSwCait16cjMF8UxU8AoiharD8zN1tNAaqIougJLAEOCYJQLPbYv9Nv8Q0R+CmIya3msG/lYaPHY2Ji+G3eXn6bt1e7Er556q6kj00sZaI1sTVdeKczXyLkm9HbKnk0c7XFAXnTWT0efcYtwOiqP3VWX8luP1UmX1RG6jB1EVfW5ezhiNJGiUciBdxDg8O5cPBqikr+gYYcIyGY0XERoVYQgxxcdZSDq45qSqxEWDl8E8sGrrfqHgIwofls1o/exp0z91jce43Fc/ShsCTyYQYWBUJMQYQNE3Ywp+syatg3Z8fsPVaf6uLpjFwuve/OT6soUbcILUY0ZOjG3gCc23vZ6G+1WofyZt25to62HFJvxcktPu/j7eP3BH4IYvvM3VaPM7lx+fB1y52+M4h8kzKk1IBuqv7z2DZdZAGyCIJwQhCE07Eua7Mw9/SrRFG8ASCK4jagDrBaEIQ6/Gdy0L4O3j3/QH2Pdvy57m9mtF/IjjmaiSEyIopxTWbQu/RwBlYZy/weK5nfYyWtM3dHFEXKN5fq1fqkMy2vZwr9VnTh1QPziT2vH7413DEIUKKuaQo+N18X5pyewOp7c5h8aDilGhTHxdv6MiFr8OLOa4m78vWjt0QaqcM0hsAPwURFRPH5vflaYFM4s+vrcOJ2mtKCer2qa1/rJuYZw9FNJ0wfFGDYlj4WNZpNIS55KzI8iqObThAeGoFarebBtScJjhVGR8Uk2vOSXAQTS/olfOGgC6VSyajt/Snd+CcqyRtSSdaQ/auML6D3LTtM2aYl2PpuqdHjkWERPL/3ip/qFNZ+lv5ZU+Hk7pggEpukQq6US4hgRLVIdJQZT8P/DzwFQTiv89fJ8ikGUACZgbJAU2CpIAiulk4whShBEHxFUXwNIIriDUEQKgC7gX8PZco3RkxMDM3S/ipp27/iMPV61GB+j+VGMyzfPHnHu+cfqNW5Mh9ffmT7zN3ExKip1bUq87otN+hvCjK5jHJNSjCt3QKLffXLFBQKOZ2mtOTEb2cNllt2TrZ8ev2FHsUM43MpicS4/GIiEzeZx1ioIdaHXCmzqLdqDIv6rMHR3QGZTEBpq2TivqH0Kz/SJKuS2XGJMK7RDGzsEiZWH4ewYOMejrh6av+sqbQ0jdbA5FiTqXTO4v1j1MRExxjVFk4IBpYfpR3vqd/Ps+nVEpr4Gc7PRzb8w5EN/xi9hiaUc4T2E5pTsl5RwoMjyFkiKzKZjDX35zK55Vwe33qeJL5nQS6AWtTGNAVBkIQYPFK7M/ufsURFRWvKDEUNHWlSP5+vDhFI/iSs9xbUkF4Ausoa/rFtungOnBFFMQp4JAjCXTQG2ST1oLkl6iDAR7dBFMXnQBn+jzR6kwpjq/luc9sjiqJJV7KNgwpXbxfkcjntJzTnt89r2P52hVH9XJmZhCx1jJoa9s0TlbRl52iHm4+L9lxBJlCnRzW2v19h9S7UGmQrltnArezgYk+BynkS7J41hlSZfCx3SgbEGV+5Uk7qzAkjtA/+GIJaLRIRGkm3IoNNGl9dKG0VpMpsnBFJn9ZUZa+UCk0YgU96L4sZtgkxvvrQdStbCiOYg9LW+t2iKfKJhMIvQPp9evi44Z6I0MbuRQcRBIFshTPjmdqdXYsOMbntXFpl6s7NM/cINRaaSQDEGBGZQk6vxZ3IWiQTHnoawosuTcUnnTf+mVKx4ckihm/pw7LrM/61mdBfGeeAzIIgZBAEQQU0Af7Q6/Mbmt1vXKJyFuChuYuanOFEUfxTFEWDFEdRFL+Iojg+QUP/P4bKVkX6XP7a16mzpqJfuVFUs21CpTZlJH3zlstBl9ltyVMqB039f+HXAv3ZPPV3lCoFjq4ObJn0u8H11WpR4+oTTGdHW5t1WqBibhSxcnlBn4JpnbUHtrGJV4IMKrUsg8pWhX+2VFZdTx9KW6WBW7Jotfw06P2zJNkpJDCUiwevWmSysgYfXn6ieqcKBp9NUoyAOcRExfAiVl3K0T2+Htfe2Y4cP2WhQKXcSd5xCDKBqPBoXt57ZbkzULhqfovZru+eW5dl7pHKTUqJSWxtqgXoLkQz5rY+X0AfUeHWu0uHb+2bLLu72f+MI1vRzDh7ODL3lGbqW/9oASO29aVc05Jkyp/equsEfQxhfq+VhHwJoWvhQSzstZI/Vx8nOjKaqPAooiKSvrCVyQTKNvqJeacnsvzmDO3v3sbBBnuneLYsL38PSjcoLolJ/5vwtbOgRVGMBroBB4BbwJZYr/AYQRBqxXY7AHwQBOEmcAToL4qi2QdLMJYF+W9DoUKFxPPnvx8dS1EU+XPtMUKDwihWsyBhweF4pHJn6cC17FsWH0PKmDctD68+1bq3bOxUZC6Uket/xzPu2NjbMPngcHL+lJUqysZm3bA1f6nMwbVHJcw5+nDydCTovWWN3DgolHKi9XbxKlsl6XOl5fWTtwS+CwIge7HMvHr8Dr/0Xtw6bagKZAwZ88S+/2SEnbOtJjPWzM86T5kcXD0mVXqycVZRrmFJfNJ5sXrE5uQZi5Mdrt7OVGxZipd331C7ZzWN2z6Bj1y5JsW59vcdnL2cePfkPUEWWMQcXO0oUq0gRzb9g72zPblLZePMbqlcpFwhQ66UJ9ibEZA/HS4eLlz88yqgWQykCvDRLjqsQfGfC3LpyPUkM5t1mNycQ2uO8uSG1BPo7OlEy+H1qd2tOvN7ruDophMEfQxGaatk9onxBORNn6T76uPYlpOMbz4L0QTblC5kchlzT42nV6nh5rO8EwlXHxe2vloGaOah14/fcvHQVQpVyYdPOq9kv58pCIKQYgL3NhlTi6nHdU3Waz5qPjTFxmsOPwxwCmDpwLVsmSr1TuQqmY3r/0ip7GRymUb1JfYr8PR311LQafsoZCy6MAWf9N7Udmmlba/Yqgx3zt6T1J0WrJyHS39dN2mkbZ1sCU+AbKAlyOSCNnYsCAIIhrttuUpuMg5r62RDuAUB90TBQoxRlyZS/zy5XCaJXcoVMuQKeZIFG5IMQaPfq9b7fB1c7Q1pIeM2+7Fd7ZzsDCQNU2X21epAW4JMLqBQKbTG2tbRhujIaA0hhRB7vxQkipEr5SCK2u9FJpeRPndaZHKB+xdMlzXNODaGQVXHSUI9/llTsfLW7GQd395lfzGv+zKrDKqDiz1bXi1l+6w97Ji9m8/WaihbCZlM4ED0Fhp4t+PLe83iuGT9oozc2i9Z72MJPwywdbCGiKOhNW0/ADdP36GyvKGB8QU0xlfPQ6yOiTe+CBgYX9AkwPgF+GLrYKOR1xM0XMt/rTsuMb6CTKDF8AZmyz+S0/hqxh9vDERRNOrqlstM/8S0xlfnc0mOuK+lHaZJL4JomDgUE61OlPG1d7Gz3MkE0hhjcxKRGF8nNwd+7lLZOCezvsaaYPiBWGN8/bP4seDCZFbcmi3ZKYcHR8SzQYmkqPEF8EnrSfZiWWgxoiHDNvUie7HMPLz82KzxBXh254VB6EH/u4+JSXrGdaVWpanVpYrFXAsnd0dW3p6NylZF00F12fpqOYfUW/n9yxqcLWS/Wwt7F3sOrD6iNb4A/2y3Xo3p34HkLUH63tWQBlvZ9n+PKa3mmYwnyGQCBSrkQWWnxNbBBkc3PYIHM0ZjcNVxiKLI6rtzmPrnSPKVyykxdt5pPRm3ezA5imc1qWLTapRl3deUYNiKDI+yrH8ranbQKjtlguO+bt4uSRhdwlCken4UNtbFFEO/hCFTJO7zTJXJchJX8JdQo0ISxpDzp6xmj5v6zTy/+4ouBQfSJksPszOFrYM067qyGY3dhEJpq+TN03dc/+c2GyduZ1yTWdw4YVqJSRfpcviz6OJUybMWp4kcHhpONdsmVFU2obF/YipOdMaoUvLr9DZU0Csb1EfQx2BWDNto0N42W08CP1gfFjKHQWt7INf7PvV1hH/g+4E5JqxqgiDMBVILgjBH528V8KNwzAi8zGirqtUiF/+8SlRENFvfLGfptRmS44KZyfrW6bvcv/QYO0c78pXLJbmPyk7J57dfGF1vKksHrTNQTYqDb3ofZp8ch4uXed1PW0dbk3qj+sk31sKaWJ8mK9z0TtNUreSnt18SNSZroG9Yzh+6kiAuXsGa7CQjOLP7AmDeGyCqRVy9XTSlJ2j0nMfvHSQZsyCAX0YfqneoZPD7iku2AyvLu8x0CQ+JRGkT//0cXHnE8vX0ULByXso1Lcng9T3wzRBf7x4VHqXNME9omdfYRjPxz+zH8hszKdukBHW6VaV2Fw03wm9z9ml38R9ffuLykWvmLmUVBqzqRoY8aTUJkbEhGX0cWCX9bEKDQvn4yjKLnIuXM47ulnfJGXKnpWKLMvxUuxAKpYyMedOxJTYm/J+CmMx/3wjmZoiXwHkgHLig8/cHUCXlh/bvw6SDw/D017BO2ZtQpBHVIif/OGugGNR2TBOTuytBLmNmp4V0yteXt8/e03hALW3pQGREFJHhmr87Z+5rJ0JnT+nDumTAWnr+NIwv74IMrq87tvDgcAO90Tg8vvbMaHtCIMgE7JwsUynqQ1SrKd+sJJValUkWHrY63avhm9E0sUnOElkJD5Ems6mj1AmqRU4qoYQ5b4BcIefW2XuIMSJ2zraUa1ISTz93yZhFEV49esPcrksR9RYO0ZExWpUdlZ2KgILpDe5RtulPpgen9x3kLJHN8hsy8709vvaEGp0qUqhqftqMbWy6YwLw4eVHRjeYxt6lf9FveWe6zmmvpZrMXTa7pO+k1nNp6NeB3xfsS/T9BEFg4YUpLL8xi10h69gfuQlPf2kpUNZCUgoFO0c7FDaWS6uKVsuPvaOR50ZvBveOXZyP3jmQfRGbWXxpmiT7+T8B8ZswYaUILCZhCYKgjC0s/m7xvSVh6eLa3zcJDQ5nWI2J2jaVnYrOs9oy+5d4abj2k5pxdNNJXL1duHX6rlahKE221CiUch5d02QL+2fxI/BDkMRlZetgAwLU7/0zL++/xsZeRauRjWievrPVJUgpASd3BwMqR0dXB82iIYF0mgCzT4wjR/GsXDp8jQGVxiTbytXBxZ6QL6b1bb9HeKR258MLw5yBhEKukrPtzXL+Wv+3AclLqky+vH781uxCwNnDkd6Lf2X77D2JkneUyQXUopjiceQqbcrSb0VX/t55hvFNZmroXk38fuadmUjWwpmS7d5zui7lr/V/U6BiHkZuM0yGMlfdULhaPty8XOi97FfePn3PiFqTef/iIzHRMdTvU5N9y/7i46vP2v4zjo8hd8nsRq/1NZGiSVgZ/EW/MQmQgLQCT1oN/j6zoAVBKIFGXikdGuYsARBFUcyY4qOzEt+zAY5DZUUjrTGUK+Wmd0d6GbyTDgxjcuu5fHqtcbU6ujoQ/Flq1JZcm87kVnN4cOkJMrmM9uOb0WhAbR7feMb0dgv49C6QN48t8oInO/KWy8mVIzes7m9Oak+ulFOhWUn6LOsMwKQWczi6+WSyjPO/BJWdKsGLm1knxjKq7jQ+67nznT0cky02mSxIAoOWykbJrpB11HRoYVBvq58VP+3wKPKWNSS9SSnUdm2lXXA7uNlTonZhHFwdaDasPq7upglUprVfwAE9d3+lVqUZsKp7io7XGqS4AR6dzAa49bcxwNYEqZYDM4CSQGGgUOy/P2ABz+++pJptUyrJGkoMi1nXpN4Es6T/WjIXDECQCQgygWqdKhic0il3Xx5c0mjzqmPULB20jlouLZjZaRGNB9WhWvvkS4pJCBJifAFSBfhIEkZ02ZNiomI4uPoYVZWaxJk44+sXEMt0JWhYr7IXz4yds22SxADMoWS9oily3eSCQiXHN6N3glShFvVZTan60vflndYzWYyvvYudJD6cGMgUMiq2Kk2/5V3wyRBfy5oQVqzIiCjaZuuJjYNhQlK7CU0lr/uVH0UlWUPGN5tl8nphIeFcOXYjWXiU1zyYR/4Kuan5ayV++7Ca/iu60WVGW7PGFzTxXn14pDYvvPID3xes2QGfEUXxu551vtcd8OBq4zhvZaZqHOydbY2q3bh6u1Ckaj76LO9MA+/2BFsgY4iDQqUgbbbUPLz6JEHjSG4YEzI3hs6zWrOw12qrr5sqkw8T9g6lbbaeKepuL1QlL6N29EemkFHdxqzGtlnIFbIE80x/FQiQOpMf7Sc2I1uRTLQK6GZAwPK14OThyPDNffDN6M0f8w+wf+Vhgj+GkLdsTq4cTdiizgBykImaemqljYIF5yfj6u1CQ58ORrsfUm81aAsLDaeWY0vt632RG41qen8N7Ji9m2WDNmh39WsfzsM3/dehXzWHlN8BJ+8u/0nrQd+tC3oSIAd2ANp0VlEUL5o86SvjezHA4aERrB65iV0LDpK5UAA5f8rK5sm/WXWuq7cLFVqUYvsM89Jkvhm8ef3IeneyraOtRiw8hWyTs6cjgVYwaylUCoPEM2NwcLMn5FN8PDZtjtS8evDGJMmBZqcWZDL7OzmRu1R2woLDuH/pcbJcz9bB5quMOyHIUyYHLl7O/L3ttNHj2Ypl5t7FhybJVZS2SqKSQFoikwsIMhnDNvVmapt5hOrVrifLAkbPlW3ut/lH8Frs7KXJT2f2XWJYjQna16UbFmf45j5JG1MSEBEeycnfz5KrRHa8/L+PHXCKG+BRyWyA23wbA2yNn6ooGrfzBGB67N+0lBzUvxEhX0JonLoj26bvJiIskut/38IvgzfVOlSwihT+89svFo0vkCDjq7BRUK1DhRRNszdmfLMUDkDQ8wBbY3z9MvlIjC/A05sv2Bu2kYMxW/i5cxVUtlIO57dP3ye7EctcIANpsqXCzVdaY3zt71sGxlcml1GqYbFE1VB/b8YXNNnVT28+N3n89ul7ZhWmkmJ8QUPuEhMVw8RWcwyML8CQjb2QJ8C9bhR6z4O532abTIYTfd5yOSSv/9lxhvCw5CW5SQhsbFWUa1zyuzG+P2A9LP6SRVEsZ+Sv/NcY3L8F/+w8Qz2PtgZqJvbO9ry498pA6i8OpvRSrS0ftXe2pffSX8hZMht5y+Vk2fXpkuO5SmZj96ID1l3MAgSZYLW6zN1zDxAT6L1U2St5/eCN6fsLAl1nt024WzQRFQb3Lj7i2e2XxlmmQEJ0UOznQvy99XTKuL+TWB3hlzFhrkiZXEa2opl5++x90m6cDIgMMUwk807rwbS28xMl+5hYfHz9mSe3nmtl/aKjopnaep5EDUkdo6aWcytTl/iBlMD/QR0wAIIg+AiCsFwQhH2xr3MIgtA+5Yf278GYhtMNOXrdHMhSKKMB6b8ujLrSBBCtnF9CA8OZ2XExd88/IDIskvXjd0iuk798rmQjfBfVotGFhJO7Ay5ezibZlKxFZGiUhEXMzccFpa0S73SedC06iMvHrtM0zS8W63ATon4jyARylTRdv2qKgjJGZwwnfztr9f0SjCRODK8eml7QxKH9xKa0GtUI0BiSjRN2WLWLbTexGb4ZvSWJcqaQLpc/ZRuZqSm2ABt7Fb2X/sLbpx8IS6KAgz7ylMnOyJ2meZIFAX7J24/pHRYCMKT6BI5vPc3Hl1LyDDFGJDz0+/No/MD3DWtmzVVoZJbiNOjuAr1SaDz/GoiiSGR4JDHRMQa7H5WtEnVUDAfXHJW0a+Jb5rc19XrWkLAUWYOo8Chunb7HkY0ndAYIW6b8gdImZWT34hD0MYQv7wITRFDhk96LpoPraF8LRnbWn958ISo8irdP3nP33AP6lxvNpzeWWa9iomMkRkEmyPi5c2Wjn3upesU0YhjW4j8om1q4Wn7ker+3DLnTmmRD00KEt0/eWyQbEeRQpU05jm5JfMmYq7cL5ZqWSlbdWlcfF2adHMf0I2OY0W6hyX6iqPlN/bXhb2JiYrhz7oHJvjZ2347y8c65eyzpv4YvH5JX3OG7hAiIQvL+fSNYY4A9RVHcQmypfKwu4rdJj/xOEBkRRX3PdtSwb049z7aGx8OjCAsOZ8O4HZJ2mVxOhRalKF7beKxfkAnsnLvXqmxhaxDyJdSq3Yw5GsyUwPsXH9k48TftazFGtGonZS10jULnWW3oMb8jB6I2k61oZkm/YrUK8vK+9TJ639JVlRKQyWX8mm8A68dsk7RnL56VV2bCAQCbJ/9m1aJLjIEl/dYmaZxvHr9jZseFCSo7soQ8pXPg4GLPb/P3GZV31F8URkdEU1XZhHQ5/Q36yuQy1j9dqF0g7F91hEqyhlSSNaSuR5tkG7MpPLz2mG5Fh7B1+i4a+XQwKjDx6tEbQoP+XWQz/w+wxgCHCILgQez0IwhCMSDlCHj/Bbhw6ApBnzTJR6GBYZLaRMDkTik6Mpo/1xzn4sGr9F7yCw5uDqTVeaBFtWiVpmhSYGy36ZUq6ckbdXpUp+XIBlbt3o3tmhr1q8WesPXs+LACp0Qqw9TtWd2gbX6PFdRwaM7ZfRe5c+6+5NiUVvP4/DZ2x5AMaxD9neT3jjgDGhkeRY1OFVl0aSpylZw/5u+3GNNOKHNY+jyGNasJwdHNJxOsXWwOx7eeomPOPszvvsLgWLqc/iafw1cPXlOyXlHc/Vxp0LcWZRuXYOL+YXj7x/Ozz+8RzygW/CmEy0evJ9u4jeHc/vhSR7ValHiKRFGkdZZutAroRm2X1tw8fTdFx/K1IIrJ+/etYI0B7oOG/zlAEIQTwBrg21OtfENEhEtjPe+efpBO4BYmc1EUWT54PSGfQnh6w3TGaUrA2MTy9mnSkm4qtynL2b0X2TptFw4umpiwKXj6uxuoI9k62FC+WSlUNiqc3JxIm81wl2EMccISggyaDq5Ll5ltyVPakIYvMiySYTUnmTcqIrj6mCc+sISYyBgy5EvHT7ULka1Y8lEZfg34pPPkz3XHtRnOidEJr9SmLHnK5DB67NVDy54GhY2CyYeGM3h9TwPxja9GqSrAEzPPZHhIBKXqFWXzi6Xk/CkLoYGhREdKFwbZCkm/+5QihYlDra5VUMZ+Xm4+Lnj4xSeIvXr4hpf3470Zv89LPNf1d4X/SBKWRZ+OKIoXBUEoA2RFY1rufO/c0CmNn36WEoGpY9S4ejvH76YseOZyFM/C5aQSCnwFyBSCVv3H0c0BOydb3j//KJkMt71fQZss3QmO5XwOD4lAaatkwYXJzOiwkA8vP2lX5E7ujnil8eT9848IclhxaxbeabyRy2WcP3CZlcM3UrdHdX7+tTI3TtwGNILzjm4OvHn0TjK2nZ9W4uhiuFOefnQMF/+6xqld5/htTsImm+TQSw58G8jn15+11KEWkQR6xeTEqhFbcNeZuBODmyfvUK9nDW6cvG2QqRwTYTmskrdsTgpUyMOnt19wcnfk0+vPCR7D+qcLaZe9FxHGSryS8Fnbu9gR+iWM8JAIJraYw4xOi4gI1WRqn913iSyFMjL96Ghs7W2ZengUg6uP5+rRG1TtUJ5cJVKWm9nO3pa94RsJD43Axk4liZW7ersgV8g13NdA9U4VtcdG1pvK2X0XqdC8FP2WdUnRMf6AcVibuloEyAsUAJoKgvB/nXOvslFRplFx7WsbOxVfPphWGdLH60dvTW6SvdN7ShKUAHL8lCURo0w6Ru8YQPpcGgnC4E8hvHv6gRJ1i0j6tMvWwyDOHB0Zjae/O+lypiUkMBRbRxtcvJz5ZVorbp3SuMDEGGibpRctA7rw/uVHhv08iZO/naN/+dFMajkHBCjVoAjrnyxg3YMFTDgwVHKPum5tuXNekxATGhSm3bFFRUUzvNZEfpuzD+90nuQokdWo6pFMIRj8+sNDredQTpXZ12hSUOCnYOuNLxqxje8B6hg1WQoljd79xd1XzO26zGiZkDXlYxcOXuHlg9f8s+M0YcHhOJmQ33NwMa40JlMIuHg6Ex1hYn+gY3xdfVyMdnExEf7QLzGM0Put3D3/kMZ+nYgIj2Tt6C34pvdm65vldJ9jnGErJWBrb2Pwm7R3smPNg3l0mtqClbdnk7e0huP64l9XOfnbWaIjojmw4gjvX3770rME4T+ShGUNE9ZaIAC4THzylSiKYo+UHZr1+NpMWJHhkZz64zxT2s4nOjJak0mbhF2MTzov3jyJ3+F5pHbjw4vYMgcrVu2V25bl4MqjVt1LEMzHPFT2KqLCoky6IG0cbCS7Cyd3R36d1ZqpreZL76MnrDDlrxGkDvCjefrOBtc0x27km8GbZTdn0ipjNwPd1JwlshIdFcOds5rYrk96L1QqBc+MyCnK5IK2jKrJkNoEfwpj98KDRu9pLQRBkHxOjm72BH9K3kQXmUJG86H1WDt6m+XOeqjesQJ7l/5ldf985XLSdU479i7/i9un7yHIBW6e+Loxwwa9a7JtpmVCGmMY+Vs/0mZJzaqRm/l7q3EmL1OIE7FQKOVMPz6a6//c5uTv57jxz50EXadAxdxc/DNeW9jV25lpR0aTOpMvCuW3oas0hpO7zjGy9hTt63WP5uGTLvkoLFOUCSu9v+g7rGeyXvNpxwHfLRXlLSCHmJig0FfC1zTAUZFRNE7ViaCPmiQsZ08nAt9bv/s1hnlnJ9GtyCCL/ewdbQkNNnSTOro5WM0NbQ6FquXn2a3nvHn8zmSf7MUy4+DqwPn9l5EpZKx9MJ/oiCguH73B8W2nuHDwqvHzimdh1t9j2TZjN0sHJC0rNg7Dt/RmbKOZkjZ3P1eJPFscVHZKanWpysdXnzi84Z9kub8ubOxV7A5ez5R28zm06qi23d7ZTqt0owtPf3feP0+6nKA5tJvQjJXDNlhdV/49QJ+eU6FS0GfpL0xpPd/MWRoDuvzmTFoFdEtUvLhEvSKc2KGp6baxt8HGTpkoMQpTnOd2jrasf7IQJzfTCYYf33zm9um75K+QGzvH5NfwVavVyGTxbp9uxQZz9/wDMuZLz7S/RuLo4pBs90ppA+w3NHkN8JNO38YAW+OCvg74pvRA/i14/+Kj1vgCiTa+bj6uKG0VNOj3M1kLBdB8WH3tMblCbpQlq+eSTkavlVjjq8+4dX7fJZPG197FjmbD6jNh3xD6Le/MwZgt7Apax7Bak2idpQczOy3m1pl7knN0H/Zbp+5SRdGYRv1qsSt0nUGSTZpsqWgxsgHOHsYnAc808cLmP9UuzPTjYynd4CccXKQT1cT9wwwzvQVInzMt26bvSpzxtcJDpY5R0zFPH/7Uq/0uUDGP0fNT2vgCrBjy/RjfTtNbWkXXqU/PGR0Zze/zzLO5bXu7nD0h67l3/mGijK+TuwPXjscT5kSERiRaCcrDRBw9LDicdeOMezFePnhFm6zdaezXkZF1p9LIr6O2yiI5EBkeSbeig6mqbMLyoRs4+cc5ts3YxYPLjxHVIg8uPqKBVzvCQgwXit8lkjsB6zvPgvYEbgqCcEAQhD/i/lJ6YN8rfNJ5mc3ytQZtJzRl+a2Z1OxYibRZNTHANmOa0HOxxsDGRMcYuGTLNvmJic3nmLym0kaBb0Yf0udMY9UY3P3cqN+rptVjLtvoJzaM205dt7Y0Sf0LvUsP59Qf53h0JV5lSTdOVr55KdY/NSQ4CAkMxdbWhn3hG1HZxZOEzD41ntYjGzPznwlkK5qZBgOkY1t8aSpLr81g47NFnD94hb6lh1NZ2Yj1TxZStGYB/DJ6s/LObDLmTse6B/MZvL4nfhm9cfF0xtndkbvnTRMomINMJjD9yGjDdr0FUlRENI+vPzMweP/sOJOoB7xOj2pGa04Ti2bD6rHpxRJ6L/7FZJ8shQMsXkeVSLKJt0/eU7phsUSdq18+po+4BfHvC/cn6vpBH0MknOb6i8OEwMXb9NywY+YeLh+RliSJokjbbL14cS8+Szw8JII2WZJWaBIWEs6OWXs4vPEfbp+9z/3LjxBFkU0TdzKyzhQW91sj4cCOiVYzqYXp+eUHUgbWuKDLGGsXRfFYiowoEfjaMWC1Wk2nPH14fu8V3mk9efXAOoEEmVJAHWX4efde/AvVO1ZkcPXxnN9/OUljs7FXkbVwAFeP3TLbr0anivRa9AvtcvTk2e2XFq+bpXAAd3VZgASNcHm/cqOMnyBoOJP1FxIHojdz4dAVNk36jfq9axD8KZQrx27w4PJj2o5rwrCak7R96/f7GSc3R9Qx0UQER9JkUF32LPuTZQPWafvkKJGF2X+P176e+csibdzT1sGGmOgYi3ScMoWMco1/4ujmU9psUV3YONogiEkXT/BM40HfZV3YOv03Lh68ZrqjAHvDNqBUKXl66zntc/Y2PXa5gLOnE5/fWGZA8svgjVdaTwk9atmmP3F0k4alatim3hze/A8nd5wzeY1sRTNx+4x5g2gUyZTtLcgFg1K6knWLcPP0XaOhh0RBBoIoGORBVOtYgX2xvy25QkaqTL4Gz07OElm5ccJ83DhVgC8r78zm6a0XXDt+kzldlxnttyd0PSrbhC14Xj58zYnfz3H16HXO7LmEqBZx9nC0akdva2/DruB1FvtZgxR1QadLI/oNSWYX9K/9v4kL2poypGOCIPgAcbU3Z0VRtF6S5z+I3YsP8eTmCwBePXiLbwYvXj8yHTeNg6OrI4HvDF3Wt87co3rHilRuVcaoAZYr5RYp/+IQERrJB71kJX2UrF+UjpNbADBp/zBG1p1iVmJv1G/9+HvrGYkBzlo4E3lK52DOyfFMaD6bT28/Y+9sz6e4SVCM57pOky0VUZHR2Nio2L3oAHO7acgPrh67iVwp02bN6hpfgIcXH1GuaUnm91hBTHQMb568p2rbcpI+Lh6aWuB/fj/LikHreXE/PgErPCQCmVyGIBMQBAGlSkFkZJTBBK6OVvPXetOu6YjgCMq3KMXhdX+b7GMMTh4ORIZGERGmyZgN/RLK3fP3tMZXP1FNCxGq2zVj/O7BLOkvjZcXq1WI57df8Dw20UwdI1rtLn316C2v9NS0jm6Mp4ic2na+WTexjb2KMCM5CKBZxKhNJNKZfJ+JgLE69n92JjMftxpEI6uF22fusfPTKl4/fEuGPGmJiVbTLnsP3jyOzyC2ZHwBXj54TTWbphaZxJYOXEfX2e2sHvaDq4/5NV9/g3Zrfx/hoRGIopislJ8/YB7WiDE0As4CDYFGwBlBEBqk9MC+Z3x5Ly0zcfF0wsZICYAuGg6oZdT4yuQyOk5pzsxfFjGp5Vyj17DW+MbhxV3TpAdzz05k5NZ+OMQmXDRP38Ws8XX2cmZUnWn8tf5vnNwcaNDnZxZemsLcUxMQBIHsxbKw9sF8dgetp2iNAtjYK/HN4C2Je4Z8DuX1w7c8ufVca3zj35uJSUiA/ms0ovCiKCKqRaIioyhYOS8VW5VGkAv4Z/VjxNZ+PL31nNF1p/LszksDwYiCVfJQu1s1dn5cxe6Q9YzabjhBWQNnd8cEl4MFfQjBxcuZ0g01JWuhgWGsHLpJe1xUi7QZ28T470aEoTUm8iRWGlAml9F8WD3G/jZQa3zjYMrwJRQRoZGEB0cgV8rwz5rKIA8hIjSSd/qxawFSZfYxGyb3y+hl5mjyw9nTiezFMuORyo3+K7toS+mSiqCPwTi6OPDk1jMmtZxD4McvVGlTzvQJZj4UY8Y3Z4msOLnH50AcXJMwJ+PhDQlbIOqj9ZjG/x7j+38UAx4KFBZFsbUoiq3Q1AQPT9lhfd9oPKCuJAZ459xD1OoYfDN6m5Tsu3DgikGbZyp3/ghcQ3RkDHuX/oU6Rm0VA5GNvUqyU4l7bUyRSLdf9uKZiY6IpG22HvQtO4JXj6SG2ljil2/6+Mkz6FMIDQbU4urRGwyqMpbf5u0FICYmhpYBXdi/7DARoVEazWIR3FO5MmJbPz6aIFRQKOUE5EtvnAdahAeXHlOtfXkqtS2LwlbJtb9vce2fWzQf1gAxRuT5nVdUs2nK1hm7TH5W984/pOustoQGh/HuxXt+qlVYIiVnLfYu/Yuq7RKuwvnpzWfO7L5g9JjSRknzofUpXC2fxeuo1WrsnTT1r/rxZ2th72yHXCHH8hwrMGJLH/ZHbmbn59WSpLZQPQrKVJl8eXnvjckyMoCX93V23V9hfg98H8St0/f48PIT+5Yf5vH1Z2b7K2wUlmdCAXou7MSBVUeY1GIuRzedpGnqzqwba6Y8zMqJ3T9rKibsHcLM42OJ0REH0f+sLaFezxoJ6q+LApVys2/5X1RRNWZh31WJvs5Xw3/EAFsTA74mimJundcy4Ipu27fG144BAzy59ZwOZmJz1sDBxY7fPq2hf8XRXD5sHV+s0kbBkmvT2bv4Tw6sOkpkRCQ9F3Rkz9K/uH3mboKEHNLnTsvja0+1r4tUz8fZvZe1r4du7IWDmwNDqmpirMZKahr2q8nWacbrNgWZQNW25dm34i/tj1yhlNNuYlOyFAhg+ZCNRIZH8uDyY6Pnp83hj1+AD2d2xRswNx8X0mb354oOk5hSpSDKhKi6T3ovHF0dtPewd7Kl+i+V2TYtYXmEgkxg3aP5tM/RO8mxYIBGA36m3fjmyOVyRFFkVuel7F1yyOw5cW5eFy8n/AJ8cHR1omi1fHikcWdcw5moY9RU61COjLnTc3rPBUlJWNFaBRn3m6bUrYqqscVdc/ZiWZiwbwhTW8/n5B+mY8JfGzK5LEHKW9bA09+dwA/BRIYZJ2IZsqEXlw5fpWG/2qwZuYWjm08Y7WcVBPBJ780bnVCAIAiM3tmfLIUDaJJamiTXaWorQKRezxpWSW2GBodR26VVko3Kjo8rcXJNHCc7fIUY8KBkjgF3+TYxYGsM8FQgD7AxtqkxcFUUxYEpOjBBqArMBuTAMlEUJ5nq+y0MMMDMTgvZu+xwos/PUTwzs09MoLpdU7OJQnZOdoQFxRs+3/Re9F/Vhb5lDbNzEwLvtJ4M2dSLrVN+p+YvlchfMQ8Dq4zj/oWH9F/ZhRJ1igJw++w97l9+zObJv2l2tzowNyEaS5iJg429yoBNSB9+AT4Gqjxps6WiyZB6TGk1T9smV8opWbcIx7acMrj/3rANVFM1lbT3XdGFEztPc3rXRbP314envzsxUTESsntBADdfVzrPasv4xvE1ySpbJQ36/WygiKWL1mObsGXq74QFhmHvZEtoAqgwf6pTGFsHWw6v17gdsxYJ0EjliZox5SmTg1un7yGK0HJkQ948fkfV9mXJVjgLddzbEPLZcula9mKZuXX6nsV+XxP2LnZEhEZqvEVfix9aBwsvTaZzfuNTny7ZiyUo7BREh0mfef0aaH0obZUsuzadVAGGUpExMTF8eReIm48rwZ+DObHzHI9vPGN7IklNZHKBnZ9WY5/IeuQUN8ADk9kAd/1ODTCAIAj1gJKxL/8WRXFnig5KEORodIcrAc+Bc0BTURSNqtunhAG+ffYuI+tMJXVWP6YfHm0yNhIVGcWhtcdZ3He1we5QZacCUSO1F2Zkci1eqxB9l3WmgXf7BI/PJ72XWcKMhKBI9QKM3z0YgL3L/+Ts3ot0mdUW7zTS2N2GiTtYOXSj9rV+4o1nanemHhlJ+xy9UEeLkgQrc0iV2ZeX96yXBazSthyObg5snxE/uWTIkxa5XGYQzz6k3kpdj9YWGapUdkps7W0SXv8paHYwcrlMSreYwhzP/ln9eHH3lVlWs86z2mDnaMeMDvHlYLW7VaVW5yoMrjbeogiHKYIXQRBIlzM1j69/XSGR7wX6DGhfE8VrFWLMb9IFQFRkFF0LD+LprRdkK5qJ+5ceExEakeTfYIGKeZh8MHHRxh8G2DpYW/B2Eg0NpRqNMUxpFAHui6L4EEAQhE1AbcCoAU5uiKJI92Ia7uGPrz8zuv5URu0YYLSvUqXk/IHLRtmOIsMiyVMmB9djhQX0ERURzfgmM40es4TMBTLy9sl7ixOBJgYsmiVkOLv3IiFBoTT1/0W7UDj5+3kORm8BIPBDEO1y9OLLu0DSZk9F6gA/fqpXhBkdFmmvIVPI6LWoEyNqTdYKONg42BAdHk2kBU1iz1TuBH0IlhCc6CJ1Vj8C3wcS9EFjDA6sPGLQ59HVpwZtdk62RIZHEmwmluaX0Ye3z94TGRaVOLk7UfN7iVbHGLSnJJ7fkSZiKWwUROt5UR5ff8rB1dJEnl2LDvDo+lPkchlDt/ZifMNZ2mOuPs6SciZTBC+iKH5145sxT1oeGvmOvwVS2vi6+bqaFKLIaqRW+8W917y4/4qY6BhpFnYChmksiz00+Dsl5hD5pvzNyQlrsqA7oMmCrgs0AE4LgmB9bnzikBrQzZx4HtumO65OgiCcFwTh/Lt3ybMT1N7snnRye2WhxOjFPUPu4ThcPXbTZLwtT+nsZMyTXtJmTRKig4s9Qzb0ZF/kRokohHGYN74Ads52dC00ULJLF9UiM39ZRCVZQ+p7tePLO83E/PTWS3wzejO323KJC1AdrWbYz5MkdZGhX8IMjK8++xZoPiNTxhcgIE96woISHnf1Seul0a018f7tnGx49fCNxSxze2c7ZAoZVdqUNXo8e7HMCR5bcqPduKa4p3KVtO1bdljCRhaHq0dv8urRW43x1fm9WVNLbArWkHiYhN5vPl+5nBSoKE0xeXj1KdU7Vkj8PaxAwwE/k6lABu1ra2KuYCg3KCRRfTB9zjQGDG9+Gb1pM64JzYcaFqCkzuyLf5ZU2pK7xMDYHFW5ddlEXesHrIc16ZT9gfyiKLYRRbE1UBBI0fivNRBFcYkoioVEUSzk5ZW8ZQ5rR2+WvB6/b4jZ/gkpE3J0cyRvuVwMXNOdp7df8PsCqWSeNYvrqFj9UblcToFKecx3NrFSLPZzQYpUy0+BynnZ/nY5YcFSA5cuR2qTRP475+wj0hrlIGMlrrHPeaoAHw1NoxU4vvWUhLXHWqjVatx8XCmpp+AUB3NGXSaTUezngoCmfMjeyY42Y5sYzTS/deYeW14vxdFNU0IS1yfBFR1JWNQf2XSCzc+XkrlgBoNjDfrWpFTDYsw7O0HrnYhD2uzJw7b1+PpTlt+YkaBzBJkmF0D/d3L5yA2JoEEc7l18RN4yORi5o6+2zS/AUOkq/gYJGg57F/2Jg4s9qbP4kjqzr4SURWlv6Cx0cLEna+FMGjEWHYhWTgdeaTyMtl89dtMgH+Dj689UalnaaH+lSsmC85PZ9GIx9XvXxDudJyXrFyVf+VzWDcQEdi0wTwH6LSGIyfv3rWCNAf4A6BawBsW2pSReALrFe/6xbV8FRzdLk3m2Tf+DSrKGVFE15sltQ9db0ZoFLV7TI5Ub885OYsax0VRoVhKlrYI/1x43mrVsaRUbGRbF/B6aetrq7SuSt7xxEXRBLhgQClRuU4b2k5pz/sBlzu67xPXjNwkLCWfi/qHYO9th42DDjONjkCukE06WQhkpVDmvxfdpLV49fsulv4wLNyQH7JxsGRcb1x65vT+bXiyhUuvSeKXxwNHVPOm8rYMNMrmMM3vik7SCP4Xg4OpgnKZQhF3z97Pzwyp++7yagLzpY+OElseptI+n40yX3R97l8QlvcjlMv5cf5xRO/sbcGH/MrU1Izb35faZ+yhtpeM353lICCLDo/DN6GvURWoKotpQ1s8c7l14yJVjNxldb7q27dWDtyhUJracsZ+/tQuhsJAIrhy5wYu7ryXUkADR4YYLwKiIKJw9nRCMuXWswLtnHxj9W3+D0sWY6BiDBLOI0Eiap+vC/pXGkz7lcjlu3q78MrUV6x8tJGvBAK4cuQECpMmROMnL8s1LJeq8r4L/SBmSNb+c+2jIN0YJgjASOA3cFQShjyAIfVJoXOeAzIIgZBAEQQU0Ab4a/7T+j3/7dE2yjzpazZiGMwj5Io2NdZjYnOqdKuLi5SRpt7GPp5H78OoT3YoMolOevszouIhxeio+5u4PmhpdpSp+so7QKZmQm/B5efi5kirAV7sTyFc+FxcPXWP5oPVawx8ZHsXUNvM5u/cSzYfW5/fPq8ldMjv1esXXFMrkAlXblaf58PrGbpMoiDGiVQYqsQgLCkdpo/m8epQYQpPUnTi0+jjvnn2QkB3owsZBReXWZQkPjSA6KlryPdg62rBy2EaTpSq3zz9kSM3x9C49nPH7BpPFyE7UGKLDo1HaKFCoFNTqUoVlN2Zq66Iz5E6Lu5+rwTkKlZzFV6dJ73/2PpNbzqV52i5MPjCMYrUK4uBmT1REFJVkDZnx6yLmdV9BVKwhaTW6IWsezEuU6L1RiFDHtRU/1S5suW8C4eLpZPa4pdI7UcS0kdaBufImY2GcyPAozu27ZJS+FDBe366HKW3m45M+fhdvzMOii5m/LObj608Wr7t91m5NrDp2h5c+T1qL5wA4ezlh66Ci+7z2NBlQx6pzfiDxsMYAPwB+I36d8DvwCHCK/Ut2iKIYDXQDDgC3gC2iKN4wf1bywRwZ+9Mbz2jo24Gz+y5p2wRBoPeiX9j8cqkkW3rB+cnxsSwzxiZniawWxxQTrda6ngEKV8+n/b+dk/Fd0/vnn7B3ttPe28nNgfcvDFV4rhy5wfLB61k6cB31PdtR3a4ZV47eoFBVzY5XHSMyp8syepcaAVieJL4X7Fl6kHevPnLrlLSU5tXDtwxY0xUHN6khjgqPptvcdka/qzG/D2TfctPaug+vPebc3ss8uvqURj4deXb3lWQBZgo29ipmHBvDtMOjqNWlKsNrTtKGNB5de2qU33j900VaQ2oMAyqO5fQfFwjRyfzet0Q69ie3XjDsZ2llX9yCJbGIiohm/bgd1O5eLUnX0UdyKAN9i01OpvyWF2Ehn0O1pXZyhZzCVfOZ7a+OVtMyY1ea+HdiTpelzOi0iCZpfmHjJGm5W7aimZHJZAiCQMsRDXl203LSXIGKudn2ejm7gtZTq0tVi/1/IOmwqgzpe0dyliHdOf+Aae3mW2TPyV8xN1MOjjBo//L+Czvn7KNC81I8uvaUCc1mm1whx8HR3YHgj9JddcEqeY2yZ8XBxsGG3UEa4vRuxQZrRenNIV0Ofy21YRzSZEvNs9tfzbtvNTz83fnw4qPBzOmZxp0vbwMtCixYg0wFMnD/4iNJm4HoRCxGbO3HnK5L+Pw2YYlKNvYqNjxfxNYpf7B1+i6DfAGZQkbvJb8yvd0CwDp96fyVcnH16K0EU5RaQrqcaXhyw/zv3hrU61WDHbP2JMOIrINMKUNtRbnbV0UiS4BmHB9D4MdgRtWZom0rWb8o/2w/Y/HcoRt7UbZxCUAjQfjPjjOkyuRLtiKZqW7XjKgI81n+h9RbEz5gE0jRMqS0acRU/Xsl6zUf9+j3feoBC4JQSBCEnYIgXBQE4Wrc39cY3LfAsJ8nWjS+YLpEw8XThZ87V+Hzuy+MbTTDovEFDIwvGKeu1EVESASL+q4G0BgqHdg62Wj+dbDB1sFG295mTGO6z2tPhtxpKVI9P79Ob83yGzPJVSqbxTFagp2TbZKvoYsPzw2NL2gS3oyxXsnkMlbdna2ZRPRifsVrFTQaV6/Xs7qEltLZw8mo8QXwzehFCRPJXGCaHjIiNJKI4EjcfV2NGkx1tFprfEFDo5i/Qi6803po47VpcqaSnHPp0HWDa2UtHECHKc1xcLHXtpVvXkJDs6iHUg2KGh1rwz7Wy1Oag67xtUmkfCFgdRLVtza+RnM2dH67xiheTeH41tOsGrZR0la9YwUcXO1NnBGP8c1m8eaJpmJDZauifLNSZCuiydBvNaqhZKylGhRj6Y0Z2s+4Xu/E01h+C/w/JWGtB1YC9YGfdf7+k7C1szF5zMYhfjIpZMJV9PzeK1oFdKVP6ZHJPTQDbJ+5m12LDjJgdTdtmyATyF0iOzb2KpQ2CoZv7kOTQXWYfHA4JeoWZc/SP3l07Sln915i6aC1NPTrQPCnECYdGMb885Pwz27IsmMNIvRio45u5hOdEosyDX8yWloz5veBpM6kMVS5S2WXHOs2pz1jfzdM3J/161LG7BzAvsiNVGpTltajG0kMWBxaDG9A5vwZ6bmgE52mtqRurxqSSdfOyZY0WY1/bk7ujjRP35kFvVYZPV65dRmDtlE7BrD+8SL2hm5kX8RGVlybzZwz4ylSLT89F3U0vIgAk/8czj87zmLnZItXWnfaTWjG4LW92Be20cBAlKpvXJd3WntD/WZrYOto+pkZ88dAiYZw3vK5GPvHQEZs74fK1oLLO4kTY5bCAaTPlbQsb3c/V7IVyYRPek+TC4JcJbOhNLLQiUPqzH40HVzX4r0EmcCuRQcMNgBHNpxg1b25lqUJRfh9gXFN5CYD6/JH4Br2RWxkf9QmRmzpy62Td7Wf8eW/rKPC/W4gCsn7941gDRXlP6IoljTb6RsjOV3QLx+8ZnLredw8aV5WLFO+9Mw/P9nAGBze8DcTv6KwdYO+P/PL1FYEfQrm/IErTGw+W0IUkLdcTqb9NQpRFFk3bhtrRm4xep3k5NiVyWVU61CBPYvNcxsnFBVblabPkl+Z2Wkxh3SUYtxTueLl74mDsx3Dt/TB0dWRK8dv8PDqE6q1K4+tvWZ3/vn9F3bM2sWexX8R+CEYWwcbMhfMyLXj8drJ2YpkwjudJ1eO3iAqPJohG3tStLo0y/3kH+cYqeMijIPCRiMbqZuwY+9ib5RUX2mrZE/IegRB4J+dZxjXZCaiKDJiS18CPwQzs9MiRFHE1duZRZem4u7rxqRWczhsRDaxcpuyXDlyQ7v7AU1ewrZ3y3F2d6KafVNtFq+Diz1NBtZh+ZANVn7qeuO2UWjd/4JMoGiNArx78YEHFx+bPa/Xok5U61ABmUymlbyLjo5mzaitnN593iiRSlIhV8qxc7Ih+GPCRA3MwoxrWRBMlxHKlDJSZfTl+Z2XqOxVKJUKQgPDJM9qgz412TZzt8H1ZXIZo3b05+/tp7l1+q6BGpYuRu8cYHUi3Kj6UzmhI+X4b3JBp+6bNB5+fTzq1fe7ZcIaKQjCMuAvQFs4KYqiaZLbfzFSBfiSNlsq4wZY5+F7fPM5b5++xze9tA6xaI0CBg+pf7ZUPLdC9B4gIF96Hl55bDFDWCaXkTqzH63HNAbAyc0RmVxmwNIjxsTQyK8Dn958MbtTSS7j231ee07tOp9sxldpq2RvaLyx+GfnGY5tOSnp8/HlZz6+/AxAXfe2pM2RmgXnJpO3dE6CPgWzetQaHl97RruJzWjYtw4RYVFcPHSNnD9lZe8yaXLS7bP3uX32Pr4ZvVh1Zyr2zna0COjCm1gyFp8M3iy5PA0XL2ctOUkcYiLVHIzZyqtHrzl/6CpFqxdgUKWxRg1w86H1tAl768Zt07qU53Rbqn0vAJ/fBtIk9S8G9IdyhZzt71dw8c+rpM7kK5lIQcPWtGPWHhxc7Jl2eBTDakzE0dWBRZem8P7FR+sNsKARao/jKC5QKS8/1SrE9pm7efXwLad3XTDYGdrYq3DzduG1DlXqrF+X8PrxG/YuPUzgB02M28ndgcVXpnN8m+b7tHexI/SLZfYlhUpulehITFRM8hpfMLsrF0XpAkUX6ig1fhl9+fjqE6GBYahtYrBztpW83ypty7N7yZ+E62kuq2PUjKw72SShTqMBtVDZ2pC7VHYKVLBeIydtdn/J7yYmOsZq8pFvim9cOpScsMYAtwWyAUriOYVE4D9pgNVqNftXGFIdArh4O6NSKfn05jPpc6YxWkjv4OLAiG19GVM/vlZRYnwFEBDIXiwTN/Wyc9Pm8KfTtFYMrDjG4jhlCoGoiCjO7r9E6brFGNVgGid2GCZqXPv7ttaYh+uQbRT7uSDvnn0wqUSUGMgUAgv7rEqQIpMuGg2oRVR4NDvn7NW2qaNjaJb+V2ztbJh7ZiJPb7+Qci4bwdObLzi+9TQl6xelgXd77eLi/IHLkn6jtvfj4p/XePXwjcE1Xj98R+vM3bXGIg5vHr0lLDicyHCNy93GwYaIWOPk4uVEDcfmWpKStDlS8+yO4cJLZavEwdWemo7NEWSC5HvRNb660F9YxUTHMLr+VC7Fug6bD6/Ppkm/aQ25nZMt68dt1/affWIcXmk8OLv3Eq4+Lqy+N4ejm0+yZuQWYnQWX+2nNGf5gPXa1/bOdqTO5Mf9i49QqORcPXqdM7sv4OLljDrOIuhNhulypqHb3Hb0iKVzjcOWabskjEtBH0NoluZX7WtLxtfWwQaFUs6kg8PpWXyoZNzfC8wlB2bMnYZz+zS15dERMURHSN/v8sHryJgnndHFvynjK8gEStcvTtbCmRI81p/qFGHjhPhpPCZG/e8wwP8hWGOAC4uiaLlO5l+K4M/B/L3jDMVqFMTW0ZbGqTqY7PsllqqvavsKFKqUB3WMGrnc8Adbqm4xPFK58sHIZJq7VHYy58/Ib/P2oVApUNoqCAvUrHif3nxulfEFzQP8+tFbxtafTusxTYwaXzDtEju96wJj/xjE6AbTEsUyJYEALYY14OapO0YZjKzFlinSUm9HV3vCQiJ491TD+9Ljp6HMPDaG8/svc/vsPbOTnX/WVDy/89Lszv7S4RusuD2LwxtOIFcKTGo+V3Jc3/jGoXeZEVrazoiQCC0Ps36G9NObxrPLI8OjmN99pclxWQPvNB5a4wuwe9FBzUZUAGd3R0KDpJN7zxLDJK9TZfIlf4XcyJRyiSE784dUu1iukHPvwkNAY1ziPnP93T9Awcp5kckEhmzshaOLA82G1mXD+J3a4+lypOHR1SeJer8A4SEROLk78uTmc9Lm8OfRteR3W6cUPNO4ERkZhVwhM6mdfHr3RX6qU4Q63auxa+FBQERlqyI8JNzkcyyqRQ6sOopfJl+W9ltD+txpqd/LumS6bIUC6Di5BQdWHaXFsPqokliG9lXxH9kBW5OEdVIQBONUS/9yREdH09C3IzM6LKKxfyf+3n7KKs7h/cv/YlzTmQypNt5kn4qtyhq0+WdNRZeZbdg5Zy/qGDXRkdGEJ4LjWB+bJu+03MkIvrwPovsC0wsOayDIBH6qXZjWoxvjlVZKCarPupRQ9Fz8iyTbNzQwFGcPJ2YcG0PDvrUlffW5iHsUH8KMTotw8XI2ef1yTUugUCio3KoMFZqWZvKh4Vp3qkwpI32uNAbnrLw/m1f39ViSrCyJ0meoMoYhm3sazVzWh/788+VdkMYzIELgh2B80punZ315/zV7Fh+icDUpu9n1f+KFQxxc7KnavrzFscRh6MZeTNg7lOBPIfQtN5LHN57z26dV9F3+K52mtWTemYls+7gSj9Ruli9mAkEfg5naZj7+2YwnveknnO0J30D24lnMXlNpo0BlRc12UvD+2Sd2ztpLTLQauTJ+2lXZS43ey/uvqN6hAqIoEhOtJiw4HCcPR7O191f/vkmHHL3Yv/IIi/qsZvvMXQC8fvyWDrl707XoIII/G6+lbtS/NstvzKRc0+86zccA/09Z0MWAy4Ig3IktQbr2XylDevXorXb3J8aIXDl2y8IZOhA1nLVdCg80WmrUakRDPNO4S9pylchGaFC4pGRFn8g9MbAkrm4Mtg42/D5/HzN1FI2MwRyNnyATENDsTP7acJwDK6Q0eebIIuKQp0wOKjQvhcpOOhFlLZKJNFml5TfNhtRlXOPpVJI1ZMOE7ZJj7cY3w9XbRdJ2/+IjStUvyoR9Q1hydRqbXy2hSPX85C6dnY7TWrBz7l4qyxtSzbYpD6894drxmyiUCvyz+GHvaMvj68/IUjiAngs7cCB6M4fUW/FNY4Z7WPvBQMZ86fn510q4+rggV8iR6Wgju/m4GH3yei3uRJEqBdj0fDFbXi9jwr6huHg6I8gFVDoZ+AqVwsAA6zM9RYVHs+nlYso2LUFA3nQmh3py53nq9aphkP2ttFWQuWAGanWurF2UuHiaXsy4+jrj5KYRcO9SaABXj93k5G/nmNV5CZValWXNiC3UsGtGr+JD2PRsCeN2D8bJw7jgu25pmCn8vdWUx0f6ydRyasGtU3fNXisqItpiDoSzV+LF6fWhuwOODJXW5vpn9uPCn1cl4wl8H2x2fE+uP5NoVD++oan1H1x1HE9uPOfuuQeMNcO89wPfDtZkQRt9ekVRTLwvKZmR2CzoZ3de0C57L+3rHMWzcNPCw2oMU/8aSb5yxonPoyKj2DBhO+vHb9eStBesnIcLB5O+hpErZUw9NJKdc/fyz86zVguUl2pYjL+3nTbqxlHaKomyIB8Yh/S50pIuhz+/TGtFm6zdEyfnB7QZ24SCVfLSo+gQRFEkX7lc3D53nzxlcvD05nNeP3qLs5cTuX7KysnfDb9nF09n1jyYS7O0nTXqR0aw7OYMFAoFnQsOMKrNbE4CzjejN1MOjcAvgw8Aa8ZsYe3Yrbh5ukgmvjjIFTLylM1JZFgEN05Y/3sq37wUh9f/bdCuslcZFb/IWy4nRarnJ112f9JkS02nvP2ICIlAkAn0W9GFoI/BnN17MUFhAYVKIQlJ6L52dHUg+LPx+nfQ5EhsfbWMyvJG2jZBLpCndA4NL3EsitYowJjfB3Lz1F16l7JCb1aGSUUrAORoxFIBzzQevH+WdKp6/YSwAhVyU6JeEeZ2XZ7gawkywepnMymQKWR4+Lmx+PI0nNwc6ZC7j5ZYpWiNAozbNTjFxxCHFM2CTpNG9O+VvFnQD/t9myxoq5iwBEHIC8Qxc/8tiqJ5loivjMQaYFEU6VliKLdOxyZDCeDs7mQy9mcKG54uwsvfg7fP3jOo8jie3XmBXClj6IbelKpfjM3TfmfZwHVag2eq5MeaB1WulFOidmGK1SxE5oIZSZs9NV/eBXL/8iOGVp+YZK1SuVKeIIYleydbNr1YQosMXRIuZh8Lla2SPbGZzqFBYdR2aaU91nJUQ1qNaERD3/ZmWaiyFMrI3fMPTR5X2ChQR6sTne0tk8vY+Wkl9TzbERObZObk6Uh4ULgkFq1Qyi0miSltFUa9AwmdqLMWDiAqMpoKLUrRSMclHxkRxc+OLRL1Xodu6sX4JrOMHnPycKT16CYs6L3CJPlFn6W/MqOjea8KaDwwq+7NpWnqXyz+ZgPypePBZb31vqD5TnwzePNSRzihSvtyHFhuPIlSe15sKVTcZ22ufEgXabKn4tkt66oZLCLwdwAAx2FJREFUtLf7SsYXYOY/Y8j1U3wN/MfXnxjTYDo2DjaM2tEPO4fECX0kBj8MsHWwZgfcE+hIfNZzXWCJKIpzTZ/1dZGUOuCY6BiWDlrH9hm7E33/tQ/mIQJts/WUGC+/jD6sujuHNlm68+rh2wRfV2Wn0pL/2zrY0H5ic3KXys64xjP4/O4LXee2Y0H3VQR9CsY7jQfvnn9MMbFwuUrGrONjuXfpMRsm7JDsMmafGIeDqwM7Zu7i7oVH3L/0yMyVDJGjRFaiwqO4d/Ehbt6Gu0ondweCPoV8tcQLhY0cdYzaQLrPM407758ZcmlbDRM1pAqVnDQ5UvPosnVJRXZOdprEHJ2JvcXIhmT/KSs7Z+yiw9QW/Jqnv+lhyExn1e4OW0fP4sO02fEZ8qQl+FMI0ZHR1OhUkf0rjuDi5WRoEGPhF+DNqwfW/dYPRG/mwZUnTGu/gIcJyMZPnzMNj01QZmbIk9b6muLY78NcYpQu8pTJwdVjN60e59dA0ZoFCA8J59WDt0RFRDN8S29yl/r2KTspboB7JrMB7v9tDLA1MeD2QFFRFEeIojgCTUzYCB3PvxNyhZxfp7W2ijjfFDoXGqh56PUm18JV8/H01guzxldpozAZB9YVxA4PiWBxv9V0LTKQ53dfEfwplMkt5mmJ6t8++4BoZTaBt16Cjq2D5fceE6mme7Gh/D5vHz//Uin+gABhIeF0zN2HvcsOY+tok7BSBkGTWHPvwkMQ4dObLwa0lkEfQyhavYD2dY4S5pNqzN3LFHSp/mxsbTgQuUUieAGx9JhJgYmvJzoyxmrjK1fKCAsKM9hV/bn2GEOrjOP8gStmje+Mv8do5fMUKjlZCmWUHK9p14I5pyaw+Oo0FCo5T248Z/DG3mx4uoh1Y7fz/sVHk8YXsNr4AoR8CWFAxdFmja+x35Ip4wskjNAj9iO0xvgCFKtZwHKnr4zz+6/w5V0Qb5++59Obz4ysO/VbD+kHEgBrDLCANsICsf//dtxdKYSpf40kVSafRJ0bEhiKnYsdMTGaj0mulNNmTCPaTWhmMcbl7OXMjg8rSZ1ZmtU5fGtfrv8jTQqLjowhxhzvrYV5RGEjJ1ep7Hx+81nSHh5ivSbrkxvP2a1LsiHCqHrTtAbh+t+3+SNoLR7+1mW6ZsqfgWe3pOU66XL4G2RQ91n6K/ujNpG/Qm5ENUw4IK0xtQqmWIrkAiGf42PHPhm8EUWRc3svS0/XOd/e2TT3tVwlJ2O+9JK2DHnSUjFWUN2SPm22YplZ82AePRa0N3wLRtyZggBhgdYRTvQpNULrmo6OjDHqtq/n0YZuhQcRHRmDOkZNn5LDzC6qEvPc2LvY0TpLD5Oc6nFQqMxPUdmKZWbUzv7U7l4lSfq1Dm6WuZaX9F+X6OunFGKiY6TUlRa0xP8LSO4M6O89C3ol8XrAo9DoASc8E+E7R/aiWQgNtMzCYwwyQWBY9fHaCd7e2Y7mwxoyo8NCs0krAB9ffGT1yC04u8dnWdo52bJy6IZEqxTZOtpqdvR6z6IYA9f/vpXoZKk4vNNLctFn7lHZKClSVbpbiMt4VagUrLg9i/S501K1XXnaT2gm6SeTC/RZ2pk9IRtw8XZGrpQz6rcBuPm4UlXZhEt/XePWqbsMqz7BYFz+WVKZ9CbYOtiYFLtXx0ifwKc3nkmkH40hVC+Ry9k7/vuLiYwx2NU9uvqUM3svMHRbH0RRk5yUs4zx8vrbp+/RKqAbc7pIHzONZ0D6pR6M2cLBmK0M3dpX0j5kU2+LpUhxqNymrOR1RGikgeDF7C5LjZZRTf5rOKvuzCVXyWwm9W+NiRW0GFKPICtyBhQq07WpRWsW5PG1p4yqO5Xf5x4wMNamvm9jMMXt/i3haIUAgz5UVpSw/SfwH+GCtmiARVGcgYYN62PsX1tRFGel8Li+CeRK8z/eNFlTkSabpjRGJhdwcLFHqVIQE60mWseN5eTuyJNbzzm+7bTFe4qiRlTh1pl72vKksKBwnt99ZdQ15uBiR/0+NU3uorzSeLDo4lTKNi6BQm/XYo0yU2KQNntq7f9lsZN08Z/jwykKGwUjtvZl27sV7Aldz4mdZ3l57xVHN5/A3deN3KWyIwia3cz+yM1kyJVWw2X8ejn7IzZRolZh3jx9J7mnOkZErlMbWa9vDSYeHIqzEfF27wxehIdEGGVaylPGMF6mtFVQw6652Tpmexd7rWHJWjQT3edarqcO+hDC+AYzAE3Z241j5vnGdWHnZEtEaKQksapcs5IIgsCV49cJCw5nzeN5VGxZmtKNizGhyUze6FBBImg8IMZw87R0HIJMoG6P6trXrj7O7Fl8CDFGNKhRnthkNpUVjQgLDjcpwCGqRQpWyYtMLkNlp2LhpSmc0dHTjoNST5zB1lEl8UwA9F/ZhWzFMlOvZ3XO7L6gpcgEOLT6GHKdciyTzFpGnp1jG0/Sa/HXiazJFTKc3B0pVD2/2X7BnxNOoxn4IZi/t1ued37g+4DJGUYQhMKApyiK+0RRvAhcjG2vLgiCTBTFC6bO/Tfiy/tAA1k/XciVcmafHM+nN1+Y23UZPhm8aDa4Hj1LDCPoYzDtJzXjz7XHUdqomHp4BGGBYQb8vZZgTT1vyJcwshXJrMmu1Nu5uXg5s+GJJgO1attyHNl0AqVMZlEH1BhcvZ0R5DI+6QnC6yesyBQyshYOIFWAN2EhkYzdPZCgT8FsnR7PahUdEU3/8qPZ+GIxMpmMo5tPEhkehUKlYHG/1VRpVw6vNB5EhEbw6c1nbpy8y7jGMyTGRqaUzpqV2pTm0Krj2tdB70LoX260UQH7t4/eGbTZOthg52RrkFTTfHh91o/V1BhHhUfj4GqPs7sjM46P5dbpu4xpNB3UEPo5FEEQqNOzGulzpDH4jNPmSsObh2+IMFI+lBjElU7JFTIcXOzZ8noZcrmcfhVGc+VIPCOWuSz26Ajj7c9vS8n9VTZKzu2PNZAC+GXw4XMsC5yDsz2hgaHazO/PsYxYlihN4+Q1I8MiOb37AiXrFePKUelnr1/+Fh5s+NlNbauRbrx9+p7BMVGNNkPdLEw8kkWqFUSmWK59Dmt3q0KWwgFMbbMgWRMAY6LVBH0M5vxew0VIUhEVHsWE5rNpfvM5TQfX/e9SS/5HmLBMZkELgnAYzW73iV57OmClKIrWU+SkMJJDDenp7Re0z9FL0ubm60KZhj9xYudZanauTLPB9QzOU6s1jFbGpMJWDNnApim/gQDlmpQwqmSTGHSd046Lf13llF5N7IKLk3l5/w3jG2t2WV1mtSUgXwYOb/qb3Qvj47YqWyWRFmp9lTZKeszvwPQOUom6wRt6sGHcNl4/eU+ETuxYkEP3eR2Z03lpbAMGD0mJOoWJjorm3IEriGpRszjR66O0VSKXyyQ7G120GtWQym3L4pPGm5oOzbUyiEM29mJi81kms3uthSAXkMlkCRa7r9K2HEc3nyQiNMKkQLxMLsMvwIsXdzXc06ky+vDSCA91HNx8Xfj0WpoR3mxoPaq0KUeqAF8AKssbJTnz3VxWNEChKnl5+eA1wZ9CEl1qpo+NzxbRVIcHOg5ylYyYyKRzPLv5uqJUyXkbS2Nq52RLRFiEQWZ7HHKWzMqs4+M0JYMDNLFe34w+FKqcl92LDiZ5PN8CZZuUYOiGXt/k3imZBW3rn0b0794nWa/5YFCf7y4L2skY2UZsm2fKDenbIE3WVHjqUOT1X9uNLS+X0XV2OzY8XWTU+IKmptCUTqd7KjeUKgWImkzegWu7J2hMdo62Rt1lj64+oclAQ33R7kWGMKHpLERR49peMmAduUtlp0yjEtIxG6G1c3C1l9wrS+EA9i6XKgUJMoGJzebw5OZLifEFTXxZa3wBRM0uWhdPbr3gzJ5LqKPVmmQiI3NhVHiUSeMrV8rZPmsvJ3ac48WD16hjE5JkChn5K+Sm+bAGIICDqwOuvvGsWD/VLUy/FV2YdHAY9Szx5KpJsPEFePXgDQ36/0zh6vlRKIw7lpoMrsuq2/M4pN6KXwZvs8YX4NPrL1rFpDjsmLWH/SsOU0nWkEqyhiZpHWMTnTWsWxZQu2s1yjUvYfJ41XblWX13nlnN2zjIlXIaDaxNrlLZEGQC9s7G47BPbj43foFE2N4cJbIYMKkJgsDKO3PwzeCF0kZBg941qdyyrOHJAtjY23DvwkMqyRpqjS/A64dvEp2Y+T1AP7TwX8J/JQnL3A74viiKRiU2zB37FkhOPeBPbz7j4OpgNTH5l/dfePv0A5kLxJdzXP/nFlun76JAxdwc23IKdYyaIRt6MrzWZB5aIKOXq+QW3Wjpc6XB09+D8/svm+3n6u1MibpF2bPkkMTY+WX0oXLrMqweuQVBJlClbTm6zGyDrYMtuxcfIjoyityls7Nhwk4NY5YVKFIjP2f3SF1q9i62hH6JT1bKXDCjltgfoEj1/Jw144Zz9nQk8L1mx2XJnf/r9NbU7x1vXLsVH8KdM1I3pSWSEUFu6NY3hiyFA7h77oGkLXvRTNw6c9+gr1wpp/jPhciYNx1FqxdgYOWxFjN/E4pFV6awbOB60mb35+2T95RuWJz9Kw/z+PozOk5pyWQz+tQ+Gb1Ze28egiBw9fhNds7Zyz86wh5ZiwYw4+hYVDZKuhYZxN3zD0xey8XbmS4z27B56u8EvQ/i3YuPZC6QkaGbe9EmUw9tP3sXWzLmTs/zuy/NkqvowtbRBlcvF14/sq7MqfGgupz6/QxPdYgz1tyfR6vM3STPQo1fK+GfxY/FfdZYdV1rYUqWMDEwR+bh7OFo0isxfs8QilQzH2dOKaT0DjhN1+TdAd8f8m12wOYM8CLgAzBMjO0kaJbjowFfURQ7fbVRWkByGuCE4PbZe3QvPgRETTLP9COjCQsNp5ZTSxA1RkOhUiCTCUw6MIzhtSYlKrFCH+YMib2LHa4eTshVSlx9nbl21JDfuvOsttTpVhWZzHAn/PLha1pnit+pu/m54JfRl5snpKtpJ3dHPFO7o45W03hQbRb3W0t4aAQRIRF4+XvQemxjIsMimdNlGQAdp7Rg79I/eRHLWiTIBHZ+XEXIl2A65OxLWLAhPeTq+3NJldGXZul+Nci81ofKTkmNjpXYOXdvouND9XrW4PLR6zy8olkkyZVyGvb7mStHb/L26Xvs7FVUalOWlUM3Jer6Mpmg3bWbQtEaBTiz56LZPnKlLL4cTYA+S37FI5UbRappMs/HNppuVQJgHOydbanQvAw95ndgXOMZHNt6yqDPvoiNIEC77L149cD4zt3YIgxg9O8DmNRijgEFqJO7IxHhkVKaTRNkJRnzpMXZ05HLhy0TYZRtUoLB63rws3MLA65lffwRtJZp7eZzfGv852WOcrN47UKc+v18okMVyY3eS39hZsfF2tcu3s44uTkwfGtfMuYyzQGe0vhhgK2DOZ9SX2AZcF8QhMuxbXmB80DSJHT+Iziw6oh2sohTkRnXeKa2TRRFoiKisLG34e2zD0SZeVhN0VPqwt7JlhwlsnLRDI906JcwwoLCKVgpr4H+bRwW9lrJunFb2fpymTZJ4+WD10xpM89gZ/fp1Re+6O1SHFzt2PF+JW+fviMqMpoOufpI+IPfPf/A9A4LyVooQJPZKoqoY9Ra4wuw/PZMGvp0kCQvLbk6ja5FBhMVHoVcIefh1afYOdjy+a0h37I+IsOiJDrCCUGGPGkpWacorUY1Qq1Wc+7AZVIH+OKfRZPx/vndFxr5dURUi4k2voBF4ztwdTfWjN5i8TpxxtfB1R53f1ct9aN3Ok/WP1pIWHDCyulCA8PZtfAAuxcfwNbBxmifK8duMLT6BLOkFTeM6NgCjKwzxahRDf4UQvrcaaTkGSYM8EMrCTYUNgp6L/mF6rZNLRJsyGQCXYsM4o3errpOj2qsG7PNoL93Ok9O79Lknnqn8WThxSl0yt2X4M8hJsMmKYkWIxpQrV0Ftk3fxbPbL1HaKPnyNpCwoDCuH7/1TQ1wiuIbu42TEyZjwKIohoii2BSoBKyK/assimITURSTJxPjX46fO1fRlqIUjq0j1DVEgkwga+FMlG5QjFL1iyKaMbCWyBkc3e0JyJee8/uvWJzIRbVo0vjGIeh9MAMqjaGmcwv6VxjF/J4ruHHyjkH9p4YhSm9waoENk3bQPH0X2mTpYbS8SYwRuXfxEVHhUURFRLN88AbJ8YU9VhlkDh/dclLLghUTHcPoelNp5NfRIreyNfD0dyddLn+jx17ee03Oklmp6dSMKorGDKsxkbbZenL/ioZS8+apu1bx+RavVZAJ+4YYKBPpQ6YQSJszNc4e8WU77n6u5K+YhzdP3lv9ngRB4Nn1eBfr2yfvqSRrSNpsqbUx4IRAVGNSjnNBrxUWDVrIJ6l3R1uyZOKjE0WRcbsGSeT5EhIDNrZYcPV05s3jd5Kxung7S37CcfXMarXIs1svDBIS143Zhqte7NwnrQe5SmTT/g7ePH6Hk6sjG58tZsH5yZLr29glj7Shs55alL0eQ1yFZqUQBIEVN2dzSL2VItXzI5PLiAyLYm735RzZlDxJnz+QcrCmDvihKIq7Yv9Ms93/HyJj7nT8EbSWDU8XapVGBq7uhouXM0obJaN29GfemYkMWNWNyLBIs5nHlia34I+hFpN2Eoqrx24SERzB5SM3eHb7pcHE8XOXyqy+O5dq7StQr2d1HN0cEASBiPBIVg7ZqO2nJbXXI1wwZYgyF8yIX0bD5JYN43YQ+M5QCENUi9jYGyfSsEZjF2D5rVmaewqG44yMjGJ6+wVEhEi/nymt5yGKImMaTrd4fblCRv1+PzOs1iSiLcTw+yzpzK/T2hD4Id7N+fHVZ3bM3i01RnEw8RZNxZK3z9rL/sjNyKxgRUqTPZVBmzFCDQdn4zW+Ru//fjl7wzcw77SULGX7hxX8VDveyyeXy9g2Y49ZdjdTiyaA+ecmGbQ1Hlib1SOkXoqsBTNKFgHWxPm/vJN6fd48/cDhDf9ov4vUWXy1OQlpsqam5fAGyOQyFEq5NjM/qZAr5BLpUi35iwDuvq60y96LSa3iKfkHrOoWn2ApwoRms7n2TwIkVv9NEJP57xshEevkH9CFrZ0NXv7xSeHuvm5se7OcvWEb8E7rQWVlIyrJGrJ2rKFLK6FIl8Pf6uSwhOLVwze0G9+Mii1Lo7RVUqhKXrrMbIuzhxO9FnXi1xltUItqRFGU7PJ1IYoiecvlRCaX4erjYpApHYd7Fx5qJhE/V22bytby+wrTYyrzC/CxajIFqO3UitN/XADRkM7RwdkebyM6v5Val+HVozdWxfliotX0Kz1KUn6UJntq6veqQfMR9UmfKw0uXk7YO9tx5+x97J0MFxN3zz80rqGs9xbr9aqBf7bUhv100DKgm0VPiVKlYM6piVRpV1bSXt6IOPvkQyPIWzan0bwBfTTy7Uh122b8mn8Avhm9yZA7LYPW9cDZzYnHt+Kzn1uOboSHBe3fJ9efY+tknPazR0lDOtIX915zU69G+JJOnbQpCDJBSyIjCGYUjGKbX9x9TePU8WkwuxcfQh2jThZvTRw+vfliPCwlwsfXnxFFkb/WHdcuBOyd7CTPFMDCPquSbTzfFX4Y4B8whrsXHnL77D3qerSmc4GBWgOxfeZuXIywNJmCXG6EhP7aM8mKGCB97jT4pPfC1sEGlTHXl5FNkFdaD4M2mVxGpVZl8E3vjTpazb2Lj6hm05TK8kZ0yN2HY1tOmmYWioMIoYGhqGPUfDaik6uLP+YfkJBmWKpLjo6KplbXqtrX+SvkYvVdqSCXq7ezcbYnc79yAba+WcaM46M1rkrdMc47gNpM5rWbr6vZ0MGz2y/YPmsP68ds5/H1Z3x5F0RoYBi7Fh1k+dANBv3jhDXMIW/ZnOycvZfnZmhKlTYK3j4xJB8pUj0/WQoHEJAvPdvfr2Bv+EYcne3pt6wrG54t1LJIHVpzzODc53eekyZbKrIWCcDG3kbCOKUL/6y+Em/O64dveXTtKWUaFUetVvPybnwewNrRW2ncv7YBA5Y+woPCqdGpkkF7yEfDhMbf5u6jxYj6kjZjVJi68Envxe9f1rDh6WI2PF2Et5Hnwxg+vf5MFUUjfpu/D2dPZ8snWID+sw1YZRwmt5rLhUNXuH/pEf5ZpAuz/CZ0yn/g+4DJqUkQhPI6/8+gd8x4Uez/GcJDw+lfYTS1XVsx85fFzOu5gq6FB9K92BCC9eJhiPDlvfU6w3HCDnFw9nTi4+vPBskej689o/u8DuwKWsfSa9MNv9HYB1hX7efdU2lGsY2DDZMODmP30kOsG7uNmOgYiQvuyY1nvHjwWlPTrAOZUmZggO5deGT1e0wI3H1d6TanPQeiN7Pi9ixCvoTyawGp6s/nd4HG2Z6MbCLsnG3xSutB9uJZ6FxgAC0ydjVINnv96C3ts/WUtAkyQWswPr3+bF5H1syxazoMXG5+bmx8vogqbcuZuZgGV47esEi8Yar8xdHdkeGb+7Do4lSc3TWLQVEUGVl3Ms3TdTFb/ta1yGB2LzrErdP3kMmNM06pbJXU7/Wz0fNP776ATCaTMDM5umrc2ituzLRYY+wX4MOB6M0WjSnAm8fvWfdoPm4+Lji5OzLt8GhW3p5F/oq5tX3SZk+NR2pXADxTu7Fh/A5ePXiNl78Hw7ZYn2GrVovM776C7vOlwhkOLoY8zrW6VkGmF2KQ6YRQrGHCM4a/1v/NoCrj6FxwANf+vqFtz1kiC1um/8HPzi14/9J8FcG/Df+VOmBze4NpOv/frndsWAqM5V+Hdtl7cvnIdUIDw9i79E/+Wme4cwAoUbdIkhMzei/5xeSxsY2m8/ndF1Jl9GX59VmUrFcUpY0Ce51JIF0O07G0iJAIlvRfy4pBhrsy0OyoanWuItmBAqij1FYJmScH3r/4yKe3X9gwfjvdig7m7vmH2nIhLRIwFoVSQVR4NLdO3uXx9We8e2o8+UnfjTt290Cr3OUJg8ihdcdY0GMloNEd1ke6XP7GPRxWws7ZlsPr/qZlxq5UkjUkKjKK968+0jRtJ07+ft5ikpkuU5Z+opYgF3BwsWPVvbnYO9nhncZwB5mvQm5ePnwtSdj78i6QSrKGdC08iKXXZpi9/6rhGxlUdRy9lxqyZ+mTleQrlxNR1HgUgj4G06PYENpm68WVo/Gu6NeP3/HhxWcAbpy4y6ZJO+ldegQHVh0hW+HM9FrUCTc/V3ot6USF5qUksXm5kZ2qh587C85PjvUwZKDnQimvtFwlR4wRaTOmiaRdXwgkqdANYdw4cRfUEB4cwYCKY1KMC/4HEg9zdcCXRFHMr/9/Y6+/Nb5VHXBVVRPJj7rmrxXZvehPg351e1XHP0sqFvSwnElqCjYONkSERCCTCRSqlk9SbylXyJh2eBT+WVNxbMspStQpjGdqD9RqNXVcWxutsbUEZw9HshQOwNnDiW5z2uPk5khEWAQ1HVpYdb5/Vj+e33lluaOVkCvl5CqZjavHblqVkWwJCpUcUTTNeiXIoNjPhfBN582epRohgvaTm1OtQ3lqO7XW6wzVOlYiOiyCPzccR7Qwz3mmdue9Du+4Ryo3Prz8ZPacHMWzcPPUXavemzUoUCk3Fw9dM2jPXToH147H784VNgqiE0EoIQgw7/xkNk3aSZdZbfH0c6dzgf7cN8EZ3W5CU45tOanVGnbycCDog/EkM5Wdko6TW+Du506hKnkZ9vNErh2LTzbaG76B0Q2mcWa3+XpqY3DxdGLMHwPp+ZP1e4xClfNSv8/P5CqZDVt7TWa2Wq2moW8HAuO8XoLmT6lS4OTmaJSzPKURx1XwNZCidcCp04jpfk3eOuC7I74/KkrRxP+Nvf6/ROvRjWKTN2Sky+GvNb76Kjo7Z+9lXleNtFzvJb+w9f0yi9fWp9aLiHU9q9WiIdmBIJAqsy8NfTowr/tymqb5lQbe7diz9E+LxleQCVRsXUbS5uThSGRENOf3X+Hwhn+Y0Hw2IV9CsLGzocWohhbHDpg1vgqlHLlSTsWWpSQuOHNw9nDkypEbiTK+CpWhezM6MsZAhg80tITb3i/jYPRW+i7tzLGtJ4mOjKFe75rU71nTwPi2GdcEG1sVB1ce5ti2UxaNb7vxzZjy1whJW47ixmUJdVGtfXmT2dCJgTHj6+rtjDo6Rpv0JAjQqH+tRF1fFOHiwauM2NwXTz/Njr5QtXxmzhDotSjey2PK+IKm5ntx3zXkKJYZe0c7g4Q2pUpJpvwZTZxtCN3dbUD+DPy55riZ3rGjlQvkKJGVWSfGcfXvWwytOYFuRQfz+slbfnZuSVVlY2r+UonNL5eQv0JuzYyp1uxQ02X317rS/8femcfZVP5x/P2cu86+M/Zd1hBZUlFSypJClLQhKhWKUiqKEEWSShHaLFGRhBI/LUpJihBFyTr7Pnc5z++Pc+fOPXebO4xI9/16zWvuec5zznnunTvne57v8/1+vgajQmxyDF1uPfVaxqHy67e+BSzCnF2CGeC6QohVQojVHq9LtusEOe68Jz+ngLULPqfNNS25c9IA7p4+SKdt6xPJKrW1NqdDZcuKrQy+YFSZ17AV2t1PzWXp+SZXS6R/Fb0wWXZarl6bOQBSlVxxU0cskaUGPzc9r7TGr4TvP91B74Q7+Oqj77j9yZu4cqBvlGx5EAaB0+7kt+1/+Kzp+Xuv1S6o6lOUoDwEito+eTiNJpc0dG/HV4rl47y3iUvUxjB31JtkHM1Cdaosnfahz7o8QNrhdIqLbTjtzjLrLEfGRXLzuBsY0lT/9x/3zgMBNZOr1KvMM6sfJTI2gnk/zQhpDdQfUXERGFwPPnc8099nf2KVeLJO5LDr670UudJdpIS/9hwhubqvS7yEYONZ/co63n7mfUZ3fpKczFwGTx5Ih56tAf3aZ0JqHAMe6c2mJV8FfQ+eAXYO1cnYrk9z4KeD/PiZ78PE7RNuoue9Xf0+tHi7kBtcVIcH5g7hrmdvYcraxxk08aaAYzCYDDS4qA4Wq5k/dh5izesbsBXaUB0qh3b9xZJpH1KUV4SUsGTqhySmJrhFekr45eu9SFUSlxyL06GSk5bL529vCfreQyGxanzQ/Tc+2P20r3HOcJ5EQQeLfLje4/UMr33e2+c9qqry+Ttb2Lz8G3Zs/IXiglNTvomIsWILtTyghNuf7s+AR3tzf9txHNx9mNbXtOC7Ndt1M0Fd3dcgNGxTl33f+6ZyT+w7ncfeGUnH3m11FYa8mXzzLNbkv8O+HwLrAYdCyQPKoV2Had6pET9vLr1BZfqJns5JC00vuLx8v1YrkRcVH8nTHz7ChZdrtYEP7f6LIc30Li6TxYiiKNw48lpWzlqrtVmNDBzflx2bfvEp6eePJ5Zqhtd73U8xKHS760pWzlrjc0zLK5ryRE8t3zUmKZq1xe/RJ+Uu8rMKEIqg38M9OXrgGFtWfBf02vnZhW6VqYVPLGXCh2OZcccc8rIKqNuiJrdNGMCEG57zOW7L+1v9Fu8AD+1soalKNby4PjUbVWX9os0IRauXveippQD0SbqLmV8+zdMfPeo+3ul04rA5sERobttL+7RjhZ/PoITuQ6+ifsu6rHp1Hb99/zt/7TnC8FZj6Db4Cj6d/wUA1eqn4rA7eGfyCv7ac4S3/3iZyjUrkZ9bwCNXPQMCRsy+k/vbl6Yw7fn2AHu+1b7TQsCNI3uw7Ngb9K861Mfj0nd0D75b+6M7GHLDwtK4D6EI3cNAYhUtxarLwEv5dMEX7vaSsovZFfy9zjiS5be9wUV1eeCVoTS6+JyR7z89znLgVEUScA3438Q/sQY87tpJfO+qaXqqKAaFwVMGsnLWGtKPBK497Embbi2Y8ol+PerhKyfw06Zd/g84ReJSYnn/+Hwm9p2hE+P3JCLWSovLm7C1nGtrcSmxPsIGOgLID3py+U0d+Pl/u0mtXYnRbwznntaPnNLaZCDunDzALTMZmxJNzkl9SlBkXAQfZZYK9h/Y+Qdff/Q9a17bEHT9tlO/DoxfqjfmH7z8CXPvf9O9bTAqLNo/h7eefp91CzZishi5a/LNtLm6JY91f1ang71o/xxurz/Cvd3l1st5dPH99K10V7mi7OMqxRIVE0mlWsmMmH0X1RpUoVf8bdjLmMWD9ln1H9ObbubSgKKEKvHu2tH1WtXhwI+Bo+FXpC1g77YDLJn2AUX5xbTu2oLKNZN56f757jV5a5QlZHnHV354jldGL3TXdlaMii6iWCiCppdcwMz/PeNue/qm5wMWGjGajaxMf5Nx3Saxy0sDfeG+2UwdNJs9fgpveNKgTR1++177DFLrVOLyfu354t0vsTucZHl4cxq2rce+707vgTYY1RtW4c09gYtxnCnO6Bpw1Rqy9rCKXQPeO+EcWwMWQnwhhNgY4OfzQMedr1SEwVNVlfqtajP/15l+Iyn9ceTAcQ78dFCXI/rUiofpff+1NLioDg++OjTI0aGjGARdDf0CGl+Awpyi4MY3gDcyqPGFkFxA29ZsJ/NYNr9u/Y0Xhr7GB+lvln1QAIxeKS8NWtdl4fil7m1v4wtwRf/Scn0PdXmK4S3HsvipZT7GNzohSqfO5bA76GroR/fIW9zSgIVe+dROh8rQZqN4+I172KAu55PC9+g7uhe1m9Xklsf1GX93Nn5At92ueys+fn19uYwvQPaJHI4cOMaOjb8wpNlo9n63n6tuvdxv32oNq7hlH40WI28+voRuFn00b6ZHUNGBHcFT0d6auJynej/Hzk272bftAO89u5JZw+fpAuKK8oupXCclpPeyau5a9n5XahC903mkKvnlyz18/VGpl+CJpaOp3byG3/M5bA6+Wb1NZ3xLUu5mDZ/H9I1PIYzBlwJKjC9o6Wy//3SI+b++SNtrWur67fvuAM0va1zmezxVqjescsbOfVY5T1zQwazAw8AYr58VQD3g9LPO/0V8vWpbwNxKa2Tg1JDL+7WnfU+PhyoJi55aSlRMJPVa1A7p2if/SmN4qzHcmHQnX636jq5KP25MupM18z7jqRVj6HH31Vw1yP+NszxkHss+pS9ir3uvYYO6nA3qchIqBV+r7n3/tfS452p6Dr+aUW/o00lK1hIvaK93k8UkRdOx98UUesyGft26j57RgzBFmHxSUMri4mtb8coP07j0xnb0H9eblRkLmLttGhYPXWGj2chTH46lbstaXH3XFUz//El3gNBvP/7Ozi8CV+R5//h8Pi1ewqSPH+X5LRP46sNtIDWhkSm3ziY/O5/1i3zT1QrzSt/f4KYj6ar0o2fMrXS760rePznfva9Eaav55Y0ZNW8YV/S/lJUzfd220fGRRMZGEJMUTZurWzDxg4eDfi7vTllJ/7H+A64emj8MoQisUZZSr0OQ70rd5jV1294CJ5f1aRdSbeHjf5wMKfDsqw+3cd2wq8rsN/mWWXRV+vF03xkIIXj9pxcYNuM2n36WCDMN29TTtZWk3O3Y+AvLX/gY6Qj8ATS9tJFP274fDtAn5S7WL9qs+64B/LzlV17YPDHoQ3lKzcDr8MEwmM6Mcl6YiiFYMYYfSn6AaGAacDMwXEp58T81wLON0+lk8s0z/e4zmgzM/eE5ovxoFBvNBsYvGU2fUfrAB1uBjbS/08nJCG3G4hnQNaH39NL2YjvbXPWAH1l0P8Om628klqjTyzt+ftMEnnz/oYDrfyWsmruO5+58ibtbPBS0okREbAQfvrSWj19Zz+pX1/Pliq3M/X4q1w3pwjuHXuG+F+8EYO/W0pnMm3tnsfLkm6TUSNafzHXvsxfaiU8NbvQ9iYyNYPc3exna7CG+XPktS6d8yCsjFzH1ttlEx0eQWrcS9VvVYfmJN7i018W8tn0GX7z3JWO6PE3vBC36+Y8A9Zwt0RYW7X9J0+9VFNpd15qpt+iVuqQq+WD2GoqLfF2rJUUojv5xnD9/1VSuivKLeWXkQrdghScvbHqa64ZoRmfQU/qgoeEzbyM/u4CCnEJy0/P4fv1PPHXDDP860y5++/EPRrR9zO++0ZdN4I5nBoTsEn5hy9PUbVlLW6N+5Hoq19TPZA/sOMjM/z0T2szPw85F+fkcQFuzvef5OxAe4lx9H+7Jkr/n0bCtZkgtkSZ3kNyWld9SkFfAY9dN5v0XVnNZn3a6HOvmnZpQvX7VgMIoi59Y6rcdtKWKaevGM3ymPlpeVaV77bfYz+c4utNTQVMU0w5n0qh9AyxBHvj90eWWjmV3+jdynsyAgz6GCiGuQRPdKAYmSym/CNb/fOSRrk8HjG512J081PkpLcDFe5/NyYh2j/r8U0XGR3Jr3XuDCtCHSuuuF6KqKn//dpRO/TvwwZxPOHEoDQQBdZjLIiouggdfvRtzhJkX7n6ViBgr7Xu24fO3SlMzTFaj7sFgw6Ky0za8dZxPHk5nwfgl7Pv+AA0uqsOquet8jvn87S38/dsxv/VpS8j0k08pFL1wRAkFOb5/J2/ZxU/tS9wyoPPHve1eE83PLmDLym/ocuvlvDJyIXnZBSgGhY8L3sFk0v8b5WXlYyu2+w3UWzRhOS9sfprxPaZgK7RhjjATGRvJxA/GsnnFVqYM1D/s1WxcHYPBwKV92vHlCm154JG3tDXguSMX8MFsLSDMaDYw8Ikb6THsGnLT83jVT4H5YN+5zKNZmIPc3F97OPSC9b3j7mDItIH0396b79f/xPJpH+n2L5qwjA8zuvP8pol0sw7Q6WcHIz873ydeYObXk2jWXkvjevqjR3mihxaw9v6M1dRuWoPUmim0796a43+cYN3CTe7jBlQfRmGOFu29ZcW3RMZGYHN9Pfa40nXKXDrxQ2FuEQd+OkSfB3uwYNy7bonV8tYNNllMumphJouJl77Wilt8v+EnNizcxFW3Xc6Mu14h46hvDIKiCMYsuo/L+nQo93s41xH8B4KwhBDbgBRgOuBzB5RSlj/L/QxxpoKwigqK6Vt5sN8n1lOlZpNq/Lk7sI6vP4xmg0+FnQva1mPO1qlMGvAC36z6HoPRQGF+UWhPcwIGPNKb/OxCVr/ia/j6j+3F0udWubctkWYeXzqK9Qs30eWWy5h222yKQjDwleukkHU8G9Wh+pQ5jE2KJie9dK319qdvYtGTHrVwBSRUiiXzeOCbYGxSNAmp8RzaVZoC9vzmCdS7sDa9E+4oc3z+WHxgDnHJsUTGRPDOs+/r1oYfe+9BruivpWClH89ECE32cPv6n+g7phe/fX+AUZeV5vh2v7srGxZv0ulcxyRGsTJtoc91T/x5koG17/Vpf/GbyTRp19CnHeBq4026KF2j2cCagndRFIUl0z7wKQGpQ2iu1uIC/3/H5GrxpLmUoi7v24Fd3+wl/W+vwEEPY9j88sb8/D995Z0Xv5pEcvUkBta6x+f8BqNyaqI03gF7AuZ8N5UH2j8WtJ62YhKo9uD/HAajgjnCzPDnbycyNpLJA/QPQ2UGE3rQeUBHbMV2vv4geHR6KJisJl754TlqNfavZvfAJY/xq6sARbfBV3DnpJuJS471qyf/T3Emg7AiqtaQtYdUbBDWnmfOThBWsBlwPpAH9AX6oF+NkcCV/g46n3j4iqd0xtdkNbndSP646rbLgybxWyLMJFdLKrcBHvX6cKbf/rKubdCTNyGl5KsPvsNhd2IwOlEUxecmZImy0Oji+vzy1R5Uh0q1hqnM2jKJuORYstNyWPvGZz4VXDyNL0BxgY0ne06jQ682XHpDOyYGuGl7Url2Cm8fmMvGJV/y1sRlOmGOPmN6sGL6x7r+tZvVwmg2unN2DUZDUOPbqG19Xto6heLCYlbMWsPhvUcYOL4P1epXwelwIpQgFW0CEJMQxeDGIzFZTLy8bSoDH+vL8uc/Jj8zn9jkGLfx7ar4ipG8Pel9n1nOlpVbWVPwLlnp2TzQ/nHiUmJ5etVY7r/kMTr0aM0tj5UWDdjznX+RhLFXTuTj/Hf87qtcK4VjHsXkHTYnJ/5MY8+3v9Gis5cIvwHwGN7DC+7lq5Xf8vOWPeRl6UUvLFFmt/HtdV837n9J0zke0mwUaUcymfv9FExmM0ObjSY/R9M8r920qo8BXv3qejYt+drv2E9VEc6fJNCIix/129WTsoxvyZiiE6Jp060l97Z+RLdvxuaJvDLyTXIz8gIaes+HirJymku4sHNTft95kFqNq5GTnsff+4+iOjwfqows2D0LxaCwctYaLuzchPot9TIML341md3f7COxSjxV6viW+TwvOd9nwP8mztQM+IbE28nL0m4wKTWSdOkg3hiMhjK1Vq1RFuq0qMWvX4cuKWgwKtRuVoM/d/+N3eYgMi6CF7+cxLcfb+fowROseW2Du1+dC2ty8JfDbiN29/O3seKFj90zlx7Dr+aBl4cAuI1U94iB5dKIveT6Nnz9UdmfdUxiNM0ubcQ3q/z3rd2iJgd/+tO93fv+a/nwpbVlnlcogg492/DEstEYjAYWPrmUz97ejNPu5PJ+HVj9yvqAwhvBuOeF21m7YCMHf/kLs9XEsBm30+vea3z6BZqpgm/6y4BxvRk8eaCuj7fxnvv9VBpcVA9VVelm6u+jrR0RY2VV9lvu7Qcve5zdX+1DMSp8WryEUZc/4Y7WrdGoKk6HkyP7tbrRFquJRpc0oP/DvRnfc6rbcJgsRl1Q4QOvDA0o2mIwGUipnsixP7Rc8xf+9zTNL23s972cLgmp8RTlFeF0OFGMhlIxGC+GzbqN10aG7hL3JCY5hrrNavDTJi2Qzns5pQRP6c/EKglcc2dn3nv2g1O6pifR8ZHue0qo9LqvG6te/hTQvv9Lj7zuE/A4vNXDHPjpEFXqVmbhvtkhlY08k5zxGfDgCp4BTzo7M+BgLuiLgb+klMdc27ehzYQPAROklKElsv4DnCkDvG7hRp4f+ipmi8nHVdf00gvY9eXeAEcGxp+rLlTqXFiTMQvuY9y1k8t0hSlGhajYSHIzSt28lWslM/eH57iz0YPkpOXSeUAHNi0JvL5qMBnKvXYFYDArRERaKcwrCnmmExkbQYtOTflmtevvGCQ3eIO6HFVVudYyIKiYvdlqouWVzeh17zWM7znV7/katK7LlHXjiUuM4dMFG5k1fB5R8ZHUaFSV33ceojCniIgYK+8cnEtMQgyFeYX0ivWNnI2Oj8IaYyH9cCZSSm6b0I/P3v6f2xjO3jqZehfWpnuk3iArBsE6e6nr/Y/df3Jfm0fdnhajxcCdk27hpod6ceDngwxvoa8AVfJZpB1OJ6laIt1M+vQggLW297g+9ja3K9x7SaNxh/ocP5juXkuMSYgiN1ObFddvVZv9Px5097VEmriwUzP2bdtf7tSnUPG35OJJRLRVJ7FqMBuCVnLypkbjajw4dwgFecXUu7AWS5/7kFUv+y7FAHS88WLGLBjBxne/ZPa92kOKyWrAXlT+/4vkGkmk/ZVOZGwEba+9iB2bfimzbKc/3tzzItUbVnVvnzyczi01S7MKXvx6Mk3a+1+2+Kc4owa4Sg1Zp4IN8K+Tz7E8YOA1wAYghLgcmAosBrKBeWd+aGefa+64kvX2ZQyZOtBn36Fdf53SOYsLbKes6dv8ssbEJEaHtA6lOlSKCvQziPa92vBot0lugfhgxhe0Wez7J+fTtGOpVnG9lrV0feq1qk3dFrXoPvQqXvxmEql1UnDaVPKyCsrlZnQ6nKXGFwIa3xLpyOl3vFxmJZmWXZqz7/sDTLt9js/5NqjLWbBnFr/98Dt9k+9i9n2v0+2uK/kwaxH2Yju7vtzrDtIpzC3i5hraDS4iOoLXf3mB+EqxtOranI+yF7Pk6DzysvJJ+ysDKSVj3ryXQU/e5Da+AJP6z8RsNfv87b2ff4c1f1i3zOEodvL6mLfoaugXsNj73JFvMrD2vXQzDaDOhb65rfu+/5039852X9vbuO359gBL/57nTie7ZXypa7xJR71OtdMu2bb2Rx/je8n1ge9d9VrVDrjPH8GML+Cjb55cJYE1he/S7+Fe3DiqO7Ep+rrb653LGOjxnv769W8evmIiT/acysBa9/DDhp007XiBX1nNP3f/zbLpHzH73tcxmgxc2Kkxs76cXK73UyK7mebyoBXkFHL04HH6PHidrp/BVPaarTAInfEFVx1sl955iS79ec95EgUdzAAbPGa5/YF5UsoVUsongPNE0yw0/BU08Kn3GwCrV85fo7b1MBpPLThi1cvreG/KyuB/NQ/sRQ4tmKlKHJVrpfD7jkP85keK0h8pNZJ4fecM4pJimbVlEm//8TJjFo6gwCNfdfjMO7i4W0t+/+kQa17/jAc7jHe7KktY9Nts+o3xXyPWk0DBQCVUb1yV6V9M4PK+HegVdxufveO71u4tyv/dmu1kncghN10vrFFSVnFo89Kn6NWvrOflkQuwRloozPX9e9uL7ayd/znP9H8Bg9HA8mPzeW7dk0TGRGDwcvd9vfoHn+Nbd70QgA3O5dzz4h1une+JH4zV9Qu4JCS1wvSeRsIcod10P5rzqbvtj51/0Wd0aeqbMAgaXFSHSjWSA95o2noVSZg3ptS9u2rOOuZun0ZUfCRV6lYmOkFf5/ayvu3ZoC5n4gePsEFdztIj8+h2V2n6TvterZm7bRoNWpcWR4hL0fKCFaOC0WxAMSghF+UoIbFaPOYIEzUaV2PWl5MwW0zc/dwg7nn+Dhb+NhtLpBlFEYx+YzhCCB9BE0/+3ndUc+MLeGO3vixiiyuasdQVxe2wO9m5+VdO/pVBo7ah3wL9PSju/XY/6xdt0rVF+qkhDJpHICYpmmoNq7DixHyf/SaziXcPzeWeWXfy9h9ziYr1f54wp4cQopsQYq8QYr8QImDggRCijxBCCiHKnFEHc0H/ArSUUjqEEHuAu6WU/yvZJ6Vs5vfAs8CZlKI8cuAYtze83+/NK6l6IumHg3vi3Xq5LowmA2/unc2eb3/j9Uff1tKGyoFi8A20ChWhCCLjIsgP4eFhxEuDuaBtXd545F263HoZ197VhUkDXmDzstJZsyXSgq3IFjDY6YI29Zjz3VR2fbOXkR31cpode1/MtnU/YQugO11C00svYOqn47FGWvlt++/c20YfHKMYFW557AY2LPofxw8F18Tu/8j1JFdPpuWVzajduDrdLAN8XOwb1OWM7zXFp5Td4Ck3M3/ce4AWGHPvzDu4vF8Hvlj2FQvGvUdhriuHRcDqvLewRlhJO5rOEz2n0apLc+6eNijo2Ep484n3eHfySr/7VmYsJCY+is3vf83f+45y87gbEUL4rMWudy7D4XCyb9sBGrSug9mipRd1sw7QuWqf/99EjAaDTzWmnrG3UuR60BIC1juXu/f9+MXPjO3ytHv7lR+nU9+PqIzdZkdRFAyuh82Bte/lxJ+lf58HXh1Kj6Fd3UIqJ/5K8xstXRaJqfEsPVJ20RGA/63cyjN9nw+4XzG6/rek9n86aMJNDHikNzdVGRrU6zTt8yd5+sYZ5GeXb223LCKirTjsDupeWIvpGyfw5YqtvDpmEZde345R80pdzvnZ+VgiLRhNZYub/FOccRf0nRXsgp4S3AUthDAA+4CuwGFgG3CzlHK3V78YYA1gBkZIKYMapmAG+EngGiANqAlcJKWUQoj6wCIp5TmT4X0mDfCHL63l5ZELfAywyWpk4ON9WBgkKT8Qn9qX8PdvxxjSbKTffFVPFKPg6kGd+fTN0FOwo+KjyPeKbC3Bn/asoghUKRGiNHLYe/138LM3k59dyJJpH+qvFRfpc+MZs/Berr5NL2Iw+/43WD13HXHJsby0dQrzH3uHzUv9R8h6M+e7KXz86nqSqyfx9tPvu9vb92zDMx9pBnlA9bvdspCB0kU8I6Of3zSRao2rMKByaRWpyNgIPsrSZn+LJy7lrYnatToPuIS/9x/VyQv6o1H7hrz0td49mZ9TgDnCxBM9prLvh98ZOL4PfUb2COl9Axw+cIw1r3zKDQ/20Gaxflg56yNeGf22601qs2x/qKrKKyPfxOFwcP+cobpAnR83/szYq0oNa3zlOBw2O6/9OINKNX0lIe02O4pB8ZvqMv+xd1ky7QNMZhN3ThpAflYBK2ev8fEsGM1Gut7eibVvfA4SGl/SAFTcKTWe1G1Ri99/8i+Csrb4vZCNT7DAsZjEaF3MxHuHX2PHxl944e5X/AZqldCgdV1++yE0z1K5EDB20f10vfVynE6nbn1/2IxBHP7tWGkQpsnAon0vUblWaPKdZ5rz0AB3QIt9usa1PQ5ASjnFq98sYAOacuTDp2OAtwP3AlWA9VLKfFd7QyD6dPKAhRDTgZ5oa8wHgDullFkeb2wwWtLEA1JK/9ERHpxJA3x43xHuaTPWPSMoIaVGEu8eepUhzUdyaFfoaUUxiVEU5duIjIsgNz23zHXMiCgrH+Us5vYGIzj6+4mgfUG7cdZpWp0fN5ZPu/qauzoDgnUL/Bv6xu0aMHPLM7wy+k0+muPnT6LA5f070G9UT4ryi7nwsibuG3xuZh43Jt1ZrvH4w2w10fDiuvyyRQt+a3hxPV7+VhNe+G377yyesIx6LWvT695r+HDOpySmxhObHMOMO1/GbDXrHhSuG3oVo17T5CUfvXYSTpuDJ99/iJy0PGKSormj4QO6m3EoVGtYmYV75iCl5IM5a3jlwUV++63KfYuIKKt7++CuP0k7mkmrK5oxecBMtrgEN8YuHkHXWzuFdG0pJQV5hUTFnJr7sVfcIJ2BLEnz8uSFYa+w9vWNANz6RF9un+hb1tDhcHCt+Waf9qr1KnHkQNnfX38YzUbqtarDXpdARkS0BYmmKtdvzPUMmTKQQ7v/YtXcT1k1dz0AVepUZvGBOe5zHP7tKAK44wK9lnZccox7Pbv7sK4eWQUGxi8dxcevbuCnTb8EXH8HrZpWvkdkc3kNsndUuifVG6Ty5t6XkFJytSFwmUSAm8fdwF2Tbwn5umeSM22A695RsQZ499QyDXBfoJuUcohrexDQTko5wqPPRcDjUso+QohNnK4BllJeVP63UjZCiKuBjS739jQAKeUjQogmwHtAW6Aq8BnQUMrgZc7PdDWkooJibqt3n0+pvPXOZQghyhY9AOq1qsWw525j0YRl7PpqLwaDQsc+7fjfsuCBUADNLmtMRJSFzBPZ7N+un4XVv6gOBdkFHDlwXNcenRAZ8jp1KMz7+QXqNNUCfEJNP4mItjB02iAu69uefpWHVMg4PHMthRCsdy7j+w0/Memm57FGWbEX28lJz8NgVLj0hnaMXzqaN590uXVdX3UhBG/98bKPROLkm2eyyTUrb9+rDVsDpFAFY4O6nOeHvsKn8zcG7HPbhL4snvB+wP0lRMREsCp7Maqq8uYTS9i2djupdStTs1E1bIV28rMLuG5oF04cSqNdj9ZYI0vjDaSUPHL10xw5cJwZXzxFaq3g+aH3th7Lb34qGFmjzKzO1fKQvf/u7/41l5RqKeRl5fHUDdOxRlnJOJbB/u0Hfc5jiTJjidDW1+2hluN0j8G3MtIGtXSWP2/sYpbPWO1z3Psn5rNj0y7Wzv+cH1yVzPwt4QihZSf8smUPqipJrpZEm2tb8Okb2t8wWE554/YNuGnM9UzsOwOklqngtKv8+ethv/1PhetHdGPE7MFcY+7vU2jCk5e/m0LDNudGeM6/0AAfQvP2ljBPSukONi7LAAshFGAjcIeU8mCoBjiY36aSECLgu5RSvhBoX1lIKdd7bG5FE/sArQbxEillMfCHEGI/mjEu20qdQayRFnL9uHR/2/47DVvXY8AjN5RpgA/+fJhWXS7k4O7D/PbD75pO7uie7Nqyh3Q/UnKe/LLlV4xmAwPG9PYxwL3v68Yfu/5ixQt6YYu23S/i4M6/+OOXP0MTpFC02XPW0dKHjKvv6sTur/ZhtpqJTykV1O/3cA+Wz/jY31l0FOYVM/u+N7j42lZUa1iFv/eVXTPXH57Skp6R1a2vvpD9O/5g3DWTAHSSoE6HyvbPf2brmh94d5J+TXWt7T2363TX13t5vPuzFOQV6tbqv/3kBwxGhRse7M6few7z3ZofdefwToUp4a/fjgQ1vgMe6x2S8QWoe2Ettqz8lim3vuiOjD6w4xCeEg+fvrkRS4QFp8MZMP95UJ0ROoMlpWTfDwdIqppIclVN5P/l76dxU+pgsk7qo5uDKZ49fu1U5u18niHNRgctyQiaNKo/edQrbr6Enf/7lYyjWXTo1Yatq7/XeYWqNUxl4geP8FDnJ8l2jU1xFS3Iy85nxaw1fo0vaLPdvEz9/62/+AkpYef/fnU/oKX9nc7m5aXLI1J1Lc94TVbumDSAgY/1YcET7yKA1HqVuePpASyZWpovLIRvpHt5+WjOp3w051MSq8SRm5Hvni17xqBYo63njPH9R6j4yOW0Mh4Y/gY8Uwyqu9pKiAGaAZtccQ2pwCohRK9gRjjYDPgo8AoBkmaklBODDDZkhBCrgaVSyreFEHOArVLKt1375gNrpZRB71j/RD3gD2Z/wtyRb/q0DxzfhzueHqCbPYGWenDNnVew9o3Sm/GMjU/RonMzDv92lN93HkSqkHk8k5cf8D2vP6yRFoo89IXNESbsRQ4MJsUndaPP6B58uWIrx/0EecUmR5OTprlX21zdgrzsAo4dPBE0J7Fq/cos2lfq0pNS8v2GHfywbicrZgY3xi9tfZbcjDyevH4aqqrStGMjfvlyD1KVNLioDunHMsg44nttg9lA1XqV6dy/I29NKDUg9VvXZfySkVSrV4WN733JlIEvBr1+IGo2qY7qVDm890jQfp7GK+1IBlFxkeRl5nNrnXtDC4gT0KhdQ17YPIGDvxzm3tZjg3ZXjAo9h3Xl4u6tGN/df/5yeXny/Ye47Mb2SCkZ0mwUf/76N0IIXvx6Eo29pC4fvHQ8u78uzXEvef8nj5zkluqlIiSX9WnPk8sf4rqIW8o9q/WkQZu6zP1uGgBb1/zAEz2nuve16tKc5zZo8p4v3jePnLQ8Hll8Pw92eIz9Ow76PV985ThyM3Nx2kILVjSaDKhS1SlQeQdP+sNgVPgoezE9om712ZdaL4UTB9Op16q25o52ncpbrKW8GM0GqjesRq0m1Xns3QfZ9umP7Pl2PzeN6UVEtG9RmLPFGZ0Bp9aQdW+v4Bnwc2W6oI1oQVhd0AzvNuAWKaXftb6z7oIWQnyG9hTgzeNSyo9cfR4H2gA3ugK8QjbAQoi7gbsBatas2frQIf8BGhWNv8jZJX/PI+Nopk+Erj9qNq7G4CkDefaWWUhVctFVF7Jj066Aqj+nitFqxBEgcMRoMtC4fQMO7jrM9SO6cfuE/mW6lRNS41l25HWevOE5vvloG9EJkUzfOBHpVKl2QRV++XIPqbVT2PvdAZY/v4ojvx2luNBO00sauvMmbUU2FIPiDpjJSc9lYO17AlbZEQLmfj+Nei3rMPLyJ9j91V7iK8fy1oGXsUZqa6jFhcX0TrjjlNSvQsXTAJew9/sDPNT5SXf6lMGocOEVTfhxwy/uPjGJUbz756vusZbwRK+pbP3YN1UJoG2Pi3jwtbsZWG243/2nynVDr2TUa/dwa917OX6wNBr5+vu6McIlNenJmKsmsu/7/czYPJEGLUpTiA7t+ovHuj9L9Quq8ewn4zAYDKxftJHpd75yWuNb8vc8kqokAHCNqb/uwSYmMYrBUwfSfUhXABY/vZy3Jizze55gKAaB6pRExUXQuX9HqtRL5cShk2xe9jXR8VH8vf+Y1s8osEZGUJATfAnHYFT4MGsxPaN9DbAn8ZViubxfBxJT40MK2oyIjaBK7RR+3/mnzz6T1cQTS0fTvkdrDu7+k+y0PJq0b4jZcm6VHTzfDDCAEOI6YBaasOsCKeVkIcTTwPdSylVefTdxmgb4Ryllq9DfQvkQQtwBDAO6SCkLXG26yDIhxDq0yLOgLuh/YgZcQo/ogT45q6/+OJ16LWrTzdI/pCpHF1/Xku0bfsZp1+Qg41JiyAqie3yqmK0mXSEAHS6lKaFoM/VgblOA+btnohgM3OkVxOJJjUZVSaqSyEPz7yG1dqWA/bLSs1k1Zx0tr2zKo1dPDjp7uuLmS3jsnVEAvDDsVdYv3ETj9g2YufkZXT+n04kQgkH17guY2hXKrMabuEqxvH9sPmlHMri5+jB3u2IUJFVJJPtkDq26NGfS6nG8N3UlCx57z93nk+L3MJmMvDZmMe8/vxohBFPWj6d1lwvdfd56ejmLPYxJrebVOfRzxawfmiPN2Dy+q62vvpAf1u/U9Znz7bMsnb6KhEqxjHhpSMj1lbd+/APFhTY+f28LP2/aTc97r/aRaqzTvCYHf/kzJBfsyvQFxCRoAhqFhUVM6jeT79Zu183+k6omsOTwPOY/9g5Lpn7oc45Aa7WKUdD80iY8s/pRXfDb4X1HuKvxSB/X8k1jrufQ7r/Y/tnPQb+b0z9/kpZXNOda681lPgCaLCbqtqjF3u/2B+0HrviNrIKgno/qjapweI+2pFO5VjKLD7x81uUnPTnTBrjebRVrgHdNP/ekKBPPlNykEKIb8ALQSUp50qO9KfAupUFYnwMNznYQliefvf0/nrv9JZ+bytT143n06kkhn8dcUp9UuqrSlJEPeypEJ0aTn5lfeoPxkHc0WYwYTUZsxXafGX1EjLU0IlbAwwvv5bdtv/PxaxtCkqaMTY7m+vuuJTouknY9WlOtfhX3vj/3/s3gxiPd293uupJNy76kKM//+2/XszWTPnqUbet/4rFupZ9vlfqpLN6nr7dbMjv3JLVOJboMvJQbHriOyLhIBtYaTuaxHCJirNRsXJW93/lGqxotRqZ/9iTV6lchoXI8APPHveOTgtXh+jYMmXIr1RqkMrLjePb/+Ac1GlXTPmcFctPyuXNSf6bfMbd0PHUr8db+0sIaxYXFOhdmdGIUeRn+U8jONI07NGT2V8FVno4cOMbcUQv5NsAM3pNqDVJ5etWjDGk6CqlKrDEWinJ9vR1CEby2YwZ1mtV0tx07eILbG9zv4+LfoC4nPyef/lWHucs9DpxwI+88vRL8PPte2KkJz39Rulq2YtYaXh29EICLul7Ij5//7GO0S2bKoKW0FeUX+Tx012xSnfm/lFZLutpwk/v/rGHbeqRUTeSrD0u/i4pRYeSrw3hhSPk9BaG4rVekLSA2MSZon3+SsAEOjbNSjMEVXGUBSqobbJVSDnftexy4C3AAI6WUZSr0/1MGWErJgBrDyDiS6Tcys7xUqVsZKaWuok1FoygCVZW6mwrAk++PxmQ28/KDC4JePzIugsLconJXFirBbDXx/on57vWpGYPnss4jp7mk2k5hfhHzx71DUX4RtiI7X7ynrybT96GevP+8Pthm/u4XqNmoNC6iR9RA3YNMneY1eX7TREwWky5CuITjf6Vxq0v8QTHAJb3bIYTggZeHEJ+iF7v/9dt9PNDhcV1bjUZVWbD7RQbUGEq6q3pQWXS9vRN3Tx+kRYV7faS1mtWgz8jreGHIa+42YdCemgQKw2feQV5Gnm7GHCqX92vH/5Z/q2vzzntVjArrbIFdpK8/+jbLnvso4H7PWVkJuoe5ANS4oCqv//KCT07x0d+Pc1fTkTg8UnQ2qMsZ2mI0B38uQwpWQL0WtTmw4yCVaiaz+MAcDAYDVxv66R6eO/Rqw44vfqbQ9WBgjjRhK/Cd9RpNBpafXACAraAYa7SVrz78lhoXVOXdyR8ELDoSjJL0I2EQRERZMUeY/cdhBNFFB2jRuSkzNk4o9/XPJGfcAA+qYAM849wrR3jGkFIGDNeTUk4Gyie2+g+xdc0PZLiiPU/X+IJWuD0vs3y5puVFdRlO1SkRiqBq/VSuGtSJy27UCnVXqVuJybe8yB87/a+hF3hEFgdT4TJZjcQmxZD+tz4a1lakpcuUGOCbH+2tM8DXj9BkISOirIyYra1F/vL1Hh8D/P7zq31mAoObjKb3A9dy36y7ALj6zs6sduWBXta3PZGxEe784ytv6ci4t0fqzlm5RjJLj77Oz5t3075naywRvka6hMbtGjLzy2cYdekT7rZJax5j8cSlIRvfwVMHMmBsb64x9/d7Qx0yZSDtu7emesNq/Pnr31w7+EoURSE3J5/De/9m3w9/0Kl/e9Yt3Mjxg75udqPFiNPu9Puw5G18AZ8852tu76zbHtXpCfZtO8Do14fTZeDlQY2vyWLEZPJdhyzL+EYnRDJ/9yy/ru8qdSuztvA9Hun2DH/u/ptZX2rLDt7fMb9IOOAK0jrxZxpLpn5IYW4hRou+pOi2T3/UBTDaCux+KyQ57E6GtXiIsYvuo1r9qlzvpyBHeXluw5NsXv4Nn7zxeUDjW7lOMkOfvZVpd7zs1x3e8fqLmfBB8KC+8xHx7y/iB4TLEZaLo38c57Z67rxrhj0/iNceeivIERplVXepKIQiSKqaQFoAeczI2AgW79cKzntzU9UhZB4LHAVtshjp2LstX37wbcD3YrQYdbMV0N772qIlAKxfvIkf1u+k5z1dyTyWTdvurbBYfY2eqqp0j7oFR7H+Om8dmENxUTFDmj6ka3/v8GvudJoTf6URkxhNRJSVroZ+OkP32k8zqNtcX0zidBnR/lH2eiiL3TjqOlbO/MSnnznCzJp8//m0JUxa/Sjturd2b9uK7fSIHuh33dpfesup1EAuITI+go8ySjWgJ/SZzlcexeQ3qMt9x+2amSkGwf0vD+XFe+aVO2J78f6XqFJXH6vpcDiY0Gc6ba+9iF7D9SUhnx04S/9wJjRPxx9eAUtdb+/EhkWb3duWKIuutvfp8Ph7I5l886zTP1EZM9trh3Thsj7teOzaZwFfdboWnZvy3GdPnlNrvyWc6Rlw/Vsrdgb8y/PnXjWkMF5UqVOZp1Y8TMsrm/HcxqdCVsDy5/70pl33VlRvUKXMfsGQqiTrRDbGAFVVCnIKubXOvRze55t2895frzFsxm1MWPkwscn6taSkqgm07NyUzcu+Cfog4W18FaPCUyu08nl//HyImcNeY+O7Wxh1+ZPYbHa38Z057FW6GvppP0o/nh34ImsLlzD9iydRDNpXtO6FNbmn9ViGNn8Yc4R+prXwiSXu15VqJLsDbWLio3T9Ni8LTfqyPDzw8lD367iUGO55/k7ePvSyrs+FnRqzKqfUuHmrFVkiLVw7pAttr9MnHWz/7KeAQWP+nptP1fjeMPI6nfG12+064xuIEtlDxWCgy8DLsEaaA/Y1R/rOjhu1q+9jfAGuNd/Mt6u389K9bzC605O6fZ7Gd9S8YWxwLmfejufdlZxKfsa+OYIZGyfQuH1Dhs+8vcKML5RW5PKkvMUk6raoVebDyobFm3n5gQXubU/jqyiCGRsnnJPG9x/hPKmGdO6od/9LuPSGdlx6QzsActJy+HRB8OhhIKQC3FIK3tw720cSEPD7pFyzSTX+2nvE5wbtsDmJrxRL1skcv1+sovxifvlqr09JM4PBQN/RPXlj3NvucoUlpB/JLFNowR+qQ9WCktDc1+4biISpA2dz8Oe/GPzsLXzy+ue64zYv/Zp7Xridlp2as86urUlOHTTbnZZhK9S74ppd2sjv9ZefmE83ywB3cM7KF9dw5zOlMomrXlnHS/e9oW0IWGdf6nNDU1WVp3pPZdunO2l5ZVOe/eRxXZ+GrevxqX0JTrtTKzcIVK5RiQ3qcmzFNnchBE9uHncDN4+7IeDnVkLTSy4os08gPBXD2l7XkuuGdOXZW2ahmBQW/DqLY7+f5Lftf9BzeFdMZr1x/OBF/QxeuN7umsJ3WLfgC1p1bU5SlQR6xWhuWIfNwYZFm2nXvbWuWAfAmEX30eLyplSulcK0O+bw2eLSWWmNRvrvoD9++UpfO9sabXWn7DUuo+Zti85Nme3S5j6y/xirX1lfroeUqLgIEEInM5lQOZZhLcf49C1LUtaTmKRorrrtcuaV4T2r16I2TqdvdHWNxtUYs+C+kK8X5tzlP/r4VDHs/nqf+7Ul0sy4dx7AZD61ZxqzRTuufc/WuvaVGW+y9O95LNg9SyeJ8ufuv3XGt8vAyzCaDZjMRi2AKND9QED7Hv7Tu3/b/jvvPx9EVEOUqhCFyv7tWpRxrSY1aNyugW7fkqkfBHTHPnfHyxz3qJ5T00+N06HPDeKx90bS8Ya2TLp5JjOHvYrDUXrDMhgMusjYorxiFj5ZmiY0Z8QbpTslPkFWg5s9yDXG/mz9+EecDic/rN/Jmx6zbc/rlBhfT/wZ3/IQkxCDJcis0h9CCCJirEzfOIFO/Tpw1aBOTPhgLB17t2VNwbuszn6blKrJNL+0MTc+cJ2P8QW4uFtL3fZyVwk8s8VMz3uuoXr9qj7vrdmlF3D3c/qKT3N/mMrVgzq7Z8qPLBzB+OWjMFmMVKqdwtBpZa+jXjHgUt32W7+/zK1P9OWlrc/qoqb9ceTAMW6uOYy7mj6I0WQIOcUKNP3p+btn6owvwL2z7/JR1wL81hIOhK3Q5ja+BpOBN3bpRQUTq8YzdNqtvLT1WXc1KU8W7Jrl87/0n6KiZ7/hGfC/A6fDyYa3/scHL66h2aWNMHvcHIsLbMy5fwGdb+7Ixne+xOko35pv2x6t2bLyW7as2OpuE4pAdaioTm0mOXB8X955Rq9JIoQgJjGKFp2a8s3q7ynIKeTgLn2EaJOOF7D7K03ZaOzCET4RviU47E4MJiXw2CXlVvFpdVVz9+spnz5On5TBIYlmbN+wk5GXPsF7f74KwC3jbuSdZ9535zULg+Cmh3sBMKLdo+zdpq3D5mcXMn7JKPd5vHN/TR5rzpYoi67IhsFU+nCRk57Ln7t9XfVHDxwrc+wVycK9s5nQZwaFeYVce+eVfLduBxlHM0EIjh88oRt/nRY1mfdjabm95pc2PqVr1mlWi2fXjuPdZz9k+IzbiEv0jRkwGA3M3jqZtya+z3VDulD3wtoAvLl3Nt+s+p5rB19BdHy0z3Gd+lxCpz6XBL3+BnU5Ozb/TNULqlIpNUm3Lz451m8RCH/c3uB+9+u/fg2uduaNw+7ghbtf07WZLEYuvkYvjaAoAqPF6OOVCYZnSpPT7iS1jj5nPvNYNn/88ieqqtLw4vq6GIPml/n39vyXcJXSPi8IB2GFiJSSuxqP1K2f3vvinZo8ZZDZZihPV0IIKtdO4fihkwFdZFXqVebhBfexacmXrH6lVEq7880d2eQVMezNDQ9cy5CpWq6pv5maJx/P28Croxe5cyxPl5KCFSV88NInzH0wNOnNmKRoVp7U9/3o1XXs++4AI18d6p69Dap3ny6VavbWyTRuq7kn83Ly6VdpCA6bg1pNq/HGz7Pc/YqLbPSOvx2HzYHBZGBNwTvuVBhbsZ3uEV6VZQR8nP8OljI+w38ah92B3ebQiUyECb1oiDeKUWHYjNuRUvLqqIXu9le2P0f9lnU4dvA4q15ZT/e7r6JavSo8dcM0vv7o1O4/5ggTa/LfpZu5v07n3BplYeJHY5nQ+zkKPR6y/KmynYucySCsyMo1ZP2BFRuE9fPMcBDWOU1BbqFP8NLcB4MYXwjZtWGJsnDsjxNB16eOHjjOQ52fZM28DQghMEeY6H7P1T6BT95ExUdy87gbMVvNZRpfgB53d6X73VdhtppQjAoPvXEPYxaGvt40e+sk2l7XipZXNmNF2gIft991Q7pgjSp7HFFxkUxePc6n/frh1zBmwb061+lT7z+keyR+oH2pKzk6Noq1Re+xQV2uM74AFqvZve/T4iW6PFSzxcTCfbOp1aw6tZpVZ1X+YjY4l5+W8VUdRah2X3lBAOn4A/VEZ9RjzVHTb0dVQxfjMJqMIRlf6fgTWbwV1f4natbDqDlTkLLiBWDOFZqFMFvscutlPm2xCdHc+MB1ZB7VZxN8/NoGvv1kO4PqjmD59FUc+UOrQDZ+6WiqNSx/AGVCahxvHdAC9kwWvTPS6VR5dfQinfFNrRtYXe4/x3nigg7PgMvBddabsZdTczguJcZdxaUiia8Ux7Kjr/PX3r8Z3HRUwC/RnG+f5YKLy7deJKXkly/3kFgl3q1ilXYkg8kDZvLrt/sCym02bt+AKZ+OZ+6Db/Lrt/swR1oYPW84DS8q1RKeMmg2G9/ZEvT6BoPCp/ayNXM96RF9C8UeAgqeQilRCZF8mO6/Nm9Fo+a/C0UrIfJOlIjuqLZ9QCE4cyC7RHM5CVI+hrwXQMRA4UqQuWglsEtQEJV/QdOAP4VxFG4E21aIGoJirIRa/DVk3oXfO07yZhTj6UXgn6ukH8/kxw07mXbbnLI7uxizaATRcZE81fs5XbtPOqGADU5tRnp9/G0U5BRSHu598Q5uuL87AKtfW8/se14H4KpBl/HZW/7/R+6cNIBbHutTruucDc74DPiWCp4BzwrPgM95Hl44IuA+Q4DUn0CzTn9P3uUh62Q2P2/ZzZcrvw2YinDrE33KbXxBc4k3v6yx2/j2Trqdm6sPY9fXe2ngYUw9uXLgZdz74l08ft2zrF+0ib/2HOHA9j8Y0fZRVFUz2EtnfMQX735Z5vWdoVQY8sBWZCMiRl8JxlMoJT+zgG/Xbi/XOU8F1f4r5E4A+07IHoWaMQwyekBGP8i+16NnOpzsBIXLoWAByCz0xhdARWaORD15PWr2s6jONKQM7XNRs6dA9nAoXAhpl2uz3NzpaBFpfp7U0jqh2vf5tp8iasH/UDMfRXUG1jeXah7q8atQjzVCzbg3YL/TJalyAlfd2smnvVF73/8LxajwSfF7XD2oE19/5JuGdVGXC3WelhLvztLnPiy38QXofvfV7tc9h13N8hPzuf6+bggR+La8Zt5n5b7O+YiQFftztggb4HJw4eWBg1qcdqff6OKTf6X76e3KZ3T9MwtF0HlAx3KNxWg0MK7bZN6ZtMKvOlXLK5py8+N96JNyF12Vfoy8bHy5zl9C2rFM8jO1SFCpSlKqJxIZpzd2QsDGd7Zwf7txHN6vd9NLVbpd62+Mfdutl6sYhTvH15ubHys7RceT9Ys2lVnMIirWf6k21ZmBmvsiqn1Pua4ppYpqP+p+uNBO5vW3tv3PY8N7TT0E169tPTh/1QzpyUuQJ67Anyy6evwK1GMNtZ/cd6DIU7JTRRZ/D45ffY7Tkd4D9djF+vfjBzVrQum18jVJTDV/GerJq1BzpqNmz4acIVC8Ek6WTijU4p9Q0/qi5mhlB2XeXJB/AirYPkMt2uzvclpfx++oGXeiZj2Kqp562cMShCIY9/aDuu1VOYtZZ1uKyVWp655Zd7kNrFAEo+YNY/Kax5j7wzQUg/bdfW3ndADeePSdgNfydC0PfKIvF3Zuislq4qaHe+kqGH3+7hb6VRrMRy9/yobFgT+LG0d1P7U3HeacJGyAy0Fy1USq1qvsd5/RYqR20xp+9/lDdaogNY3ZmIRotn5cPhe6w+7EaXdqUcEeT+Umi5H3Dr/K9M8n8NJ988lJ19zfu77aS+4pyF7GJuojWavUT+WjzMVsUJdz40jtZuC5ipFcNUkn5HHL+D4c/u0ohflFulSNG+7vzoq0UpEBAAS8uWcWd03yCn4qg9S6vn8TzxlOjQuq0qxjgIenk+0h/2VI74VaHNosUFULkccbQ3onONEI1eaqnWvsAKYOQCxEjwHzFaUHKfXA0ACMl/o9Z0jIo8jjjXUzRimLQXoIwuQ/C1H3eByUDFl34LdSgQ/ZkPME0qmdTy3ciHriKtSscUgpkc5jUPRuaffciUjHQcgdD84/oeB1KNT/TdXCrUg1BzJvAcdOKJiPmvmg5nr3xPGHz2hUZzpSOpBZI8H2lebaP9EW1Vn+7/H4ZaOxRlmoVCuZ90/Mp2rdyrz716vc99JdrMx40y2V+vRNz9O/6lB+/Pxn1juXsUFdznrHMq4bchU56Tn8b9k3TFv/JOvsS6nTpBZQotddislqpGqDKvQZ3YNWXZrTonNTFux5kTsm9uf5jRNYk/8OtiIb97QZy+87DwIwa5g+4rrvQz39hvpec/sVvo3/Rc6TNeCwAS4nz2+aSEJqHJZIM/Vb1eHCTk2Y9tkTCGDJtMBauf4QivYknZOeq0snCZVaTWtQu1kNJn4wlsSqCQhFcNmN7UiuqqVuHD+oL7Kwf4fvTa4szGYTXQZeBkKrZTx0ipbrmZeVzwezfSUXK9VJYenf89yKRH/vPcK9rcdye/37eWrFw6RUT6Jdj4sYNuM2ouO0Oq9uJIxo5xt4tfCppXRV+ul+fvuxtIpRm64tSKiiT61q1La+ewwLfn3R73tT87xmGjkhegmKv0T3X5szDTV7MpxsDPZvgByw/QSxEwCXWIR6AJy/gSN4xHpI2D5DLfgYtXADUnrl8YpqKNGDQNRxNfgvzRiQohXI9Js1t3X2cFD/1NqOX4B0eM3wRSW0mikeKF4VTLNvQ55oA3jMXIvXQr6+khXW63Sb6sk+cLID8nhTkJ7XyIeTV/qUECyLTn07sDr3bd754xV31aCUakn0vu9aomM1xbQF499jy/tbyTiWxcQbp/tc45Za97Jk6oeM6TJRt6Txxs8v6PJ1DUYji/bOxmI188P6nez8327efro0evntSe/z4ZxP2b/9D4ZfNBanw0nNxvo899sn3sTaoveY/c1k6l+k/S3b9biIaC91t/8s54kBDucBl5PkakksO/KGrm3LB1uxlxGN7A8hREi1Uv1xzeArePBlLRXn7pYPuYtEbHzvK1p0aY7JYuLXb/UzurFdnublbVNp2Lpeua716FsP8OhbpXWApZTc32Gc36jtbz7Yxk1VhyKAB+YOZevHP2ArsqMYComO1wrUA+z6eg9/7vmb/mOu593JK9zqX06vPOO/9x/1yX0GeOzaySw/Nt+9vezvN3RpJ0OnBS+SDkDeU/pt5w7Uk71RUj4MfpylnX7b8aX244ltPaStx5dT+IOLZJBehjTHFYSi1ISkbyDrVlBqIBJLZlLleKBT6gI2UI8BDlADVCHNexYSV0D2OFDiIW4ewhiJtN4IRR+BoRokvQInrgbKypf2+n9JuxI1chCIqqBYwfmza4fUAth0ZCELliIjeqAovrnGp8rf+/SVnFRVxWAwsHvrPsZ2najL3x3fY4o7AKtmo+rUbFyNP37WItxVp7ZMUFRY7M6p3/pJqcH+a0+px0Kqkm7mAXS9vRPV6lfm2MGTzNg0AbNZix1p3K4hr3z/HKqq/ndlJ89jwlHQFcCQZiM5tLv0n0oIgSXCRFFB8HW+y/q2x+lw8rVH3dBQmbJuPG26tiA7LYe+lQaXfYCLyrVSePuPuWV3DMJzd87RCd0HwxxhwlZkp16L2rz41SQsERaeu2OOe50rMiaCV398jmGtxqA6VaasG09zD3dxdloOfSsP9rFbDdvU5eXvpvlcLz+ngMiYiJBUj9RjjfDnmhWVdyGEr0KU7tjcpZA/Ad/gqWAofq9XNgKMV4NjnWvbjG4NOfl7SOsA2EE0BUtjKP4UZAiu2pgnUaJu1Vy9x5uUtid+AoXv613KlhvA2AjypwISRBKi0hcIYXXPFqVqg8zh4Nzr+9DgxoiPAQ6KBRLXQEZvwPM9RUClHyvMMBUX2ehXaTCFeUW0vbYlRYV2dm7eFfCZyTMnNysth7ubj6aowMak1Y9w4eVNuSHpDp1qVkn/woIiBta8R1eNSghY7/x35PiGwhmNgq5UQzbsX7FR0D/NCUdB/2v5+8Bx3baUskzje0Hb+jyxdDTX33dtuWTsShh3zSSuMfdn7fzPy+7sQX5O2brUZbF9w07d9tztvoawBFuhHaRWGm7fD5rbePPy0qIIBbmFVKmbyqrst/g47x2d8QWIS47lpW+exWh2ufgENLy4Hi9tneL3elGxkaEZ36xJ+DeGCmU5htTs6ZD/BOUzvgD+4wf0CLD0AMWzYqcsNb5KdYh+pHSXoSVkP4TbxSt3QdH7oRlf8yWIyAGomQ9rrl5PCj9ExI4GEkrHJSIhfwpuiyTTkfkLUY811lzUxy+AkxdqbnaZRuDPsZzeIutNCPsX2uw+ZpbnILU1+OMdyu2S9ofFamZVzlt8mL2I2OQYdm4KbHxrNdO7jOOTY1l29A1WZS/mwsu1z7K4yP89ICLSysq0N2nRufQzj68U57dvmPObsAGuAPqM6hFwX6N2vqWPG3dowC2P3UDfyoN5pOvTp1zFRnWozB/3btkdPbhuyFWndC1P7pt1p/t1h+svpkHLusz51r9B9GTMlRMA6HZnaSCJ941n9WvruNp4E4ObjXS3NWrbgLVFS7Q1XedyXv526unPeor+p98WrbWgqeStOgOuFq5CPd4WNf0WVNWJmvMSFL5ejgsZgUjX66PBOrqQgElbM/aHehjy39RmuhjAuRMc/ms5+xKLTsjPtgNp3wXFq/CxNIXzkGm3Apml4yryE+2bNxv9g4jnecq/LOOXoreQuZORGQMhd6TvfpmOdPxWIZf65uMf6B13u08ebkqNUknMB18dyhs7Z5Z5rrEeaYuN/Gg3z9g4gZvG9qLrbZ147/BrPvvDBOE8WQMOu6ArgK1rfuCJnlPd2wlV4sjLyMduc2C2mOh17zW8/4K+yIHBaCi3XrRfQpC7NFtNxCXHUL91PZ5YOsqvAP+pIKXUGaubaw0nLUDaFYDJbOSTovcoKigm/VgGuWl5NGqrvzF5ruPGV45j+dE3vE9TIai5b0C+h9CCuRdK4gzffsca4zYwlj5QvOKMjOf0iQMC13MOiPEScAQq03iqLnNvDGhuc4Dy58uGgqj0I0I5/QCl+zs8xp5vgxvzhNQ4nziQQEgp3WvJ/yXOtAv6gn4V64LeMffsuKDDQVgVQNOOF7iLoStGQW56vrvggK3I7r8cYQWoiXe9rRNbVm4tM4L6le3TqekqC1iReBrfzBNZQY0vwJt7X2TyzTPZtFS74VuiLHyYuRCjUfsaOp36B5Ks46dgUEJEiRmCmv8iWrCS8Gt8NUy4DXDxhjM2ntPnFD0CAY0vVIzxBe3zK0TzBHitX1cElj5Ix+/IzLsAIyS+jWIqX6BhCb3vv5apZRhgYzkeYIUQ/znjGyZ0wga4Avhlyx7MVhPFBTZUh0T1cr1FxPjq9JYleBAK9VvVIaVmEu9OWhmwj8FkOCPG15unbnzOb3t0QhTxlWI5vPcot9bRa0oX5xcztPloFux+ESEEd1zwgG5/s8vPbOUXJfXnsjtF3g0Fs10bwcU+Kh6BNnsMxZUb6tp+PNpM2eXq9kwPOuOcfvyBX4pXuNzorveS/Tgk+5aNDIUut1xGo3b1Obr/ON+t+5GfN+1m/46Duj7PfuKbKhfmH+bf77gFwga4QkhMjQ/6hfAucB6TFE1uum+QjBCUKy3p4O6/WL9oU8D9sckxTPn08YD7K5JYr5J1Q6bfSvX6Vflrz+Gg69SH9x6lV+wgVue+ratoBNDllsvPyFgB1Pz3IPd5wAxJb6CYmuj32/+C9H5AgJScf4LIR6Bgatn9UIAU4HAIfbMAgyYYYj8C/F5G/38LHg8Sqg3VmY1iOLXApmr1qlCtXhXaXNMSgN92HODeix4FoNvgK6jdJHgd4jBhQiUchFUBXHBxfe7xCEwKRpOOF/g1vopB4eo7r6BVl2YhX3ft65/jtPlfR771ib6sOLGAhhedmiuuvDz94djSDQF9HuhOx+svpmq91DKPLcov5ugfx+lxz9W69iYdGlb0MAFQC5ZB7lNoM9o0yNSUpVRnIWrWaNScmZDehbNqfCGA8fX3L6sSmvEtwQn2LwlsfL1crIbWIEL/XgbmH6riqv4CJy9GLdQefKWai7Tv9ivjGQoNWtZzi7o89PqZ060OEzrnixZ0eAZcQVx162U+cnL+2P3VXp82oQgu7NyEiCgrdS6sxY+f/3LK47hv9p10v7trhQVahYqiKGxQlyOl5OThdB68dDxGo4F23Vu718eDceLPNB6cM5SbHu7Jkikf0POebtRtXuvMDNb2g367RBYxrTPITJ/uGinAyVO4mNAkKJ2/cdp+M6UliCLXuSoggC8gXm5p535OKcDLBwmG+q7zeaHUAPWv0zi3AYzNwLEb9/jz30RaOiBPdAYKQUmBlM2nXGEqzDnCWY5crkjC38QKwhJh4b6X7uTl+98M2EcxKETGRlCYW6SLgJaqZMfnv7DjNAxvCZf0uvgfN76eCCEYVP8+VFfJwt3fhKavfOHlmgu4Su1URr12Txm9Tx018z4o/hztq+/UjGPS20jHn0GML5Rb0tGNBGcFVRpSd5zigQKI4dTXsCswGE7NgqjRkP+Cvj1qmKYp7Y3xCnB8ob0WNV0FHPzhBMdP+qbIAeD4DXfktXoSmTkSaagKMaNQFP8FOsKE+acIu6ArkK6DOgXcZzQbuPXJvhQXFJc7/ShQ1SB/WEMozH6mUQPUCw5E92FdQxLPKC+y+Gtk0Vq361Et/sUVyawCDkhYikhejZCFWo5p8LNV+PiCU1GRsyZEpe0Q5z9IrvwoYL0FrKdYk1am+RpfgPx5/vs7tnocG8j4BsD2PdLQEIRHepJtvVZd6sSlSFlBecph/nnOkzzgsAGuQKJioxj37oN+9zlsThY/tazcmtGKUfFbbjAQ88e9y6cLNuKwn52by4j2jwbcd+3QLj5tdz17MyNfubvCx6EeuwyZeQcy60FkxkhU+5/g9E2TktmjkSevBPW4n7OcTVwPaRGDQakC1r4QM+EUzmNH5j4LRR+X3TUURDKYLwPnH5o0ZUWhHsb/GvFp5A0XrYCTbYFK+Dr7csF5xM9BYcL8c4QNcAVz5YBLuWfWHad0rMHsO+tRHeWbTX7yxme8eO/rvDn+vVMaw+kwosM49n4XQMEJaH5ZIx5aMFzXtuCx98jPLWDT8i+5xtSf7pG3cPTQiQBnCA01/W7Aw6Da10H6VZAz1KNXLKh/Q9EaQkvzifHTNJ+Km6kGoHA+KJXBdAnkTjjFc1SgxrA8ATn3gH07FH9wmidL8HitcsamIvIPfP/GFq14RJh/HYJwEFaYINz4QHfSDqezfMbqgH2s0RYfAQ2n3UmlGkmcKEPQoiwcNgd//xaK7GHFsvdbP8E1Hjx328uYI80+7SPaPsrhvdp4bU6VwY1H8klB2RKbat4SV0UjBRKXoZiba7Vy7ZtCGG0OZI8KoV8J+b5NuaEXwTgtHDshN5jyj6diVWV0Dx8lFFfQDLhCySQkKbeQKK/ARzGy+CsQZhARCHOLChhDmDDlIzwDPkMMHN+Xllc2o0rdyvQY3lW37+7pg1id8zbrHEupUs9DoF/Cib/SadmlGf0e7nla1/9+w05+3vLraZ2jvMQle80SFU2IwxObnyIVJca3hBIVsTLJewrt5u2ErIe0wu+2z8ox4vJwmkYi4k40uchEMJXzb2vuHHy/9Q6PjYpwpVv5557NSz5XA/oZcXmxgyhn1HzWEG2ZImMQsmhd2f3DnDuE14DDBCMqNpLpnz3F4v1zeHDu3dz/8hAiYiJo0qEhN47sDmipO/N+ep7EKvG6Y//YeYiVfordlyeNsji/mJdGnBkd5UAsPfo6A5/wCM5RYeJHY6l7YS0iY32Dw4QBzBG+M+LRbwz3afNGqhno/nOUWGTGfQH7a1TBJ8c1FEQVr2u1DOEgr3+twjfRookzwB7YM6Lh4daOHg3xs8DYPXD3ogVeDacb0Ca0OsP/KE5KCz+cCkYwlp1z7osKFCNtO07j2mH+aYSUFfpztggb4H+IXvdcw6rsxbz41WSdNqw10sK7h17lgrYuwQwB/R7u5ftUJih3pHDm8azTG3Q5MRgM9B2tn92988xKXtsxg4+y3qLb4CuJiovkprHXs0Fdznr7cqau06eerEh/k253XFnmtWTBUo8tBRwHwfFtGUedoPzGSYD0yv9VLF596vo57nSkRj2i5AtWwolW4FhTjuO9x1fe91wI6h/lPAZO6eGmwrCD6lHpqVxIKHgTtXhHBY8pTJjghNeAzwEMRgNztk4lLysfc4QZk9lITnouy55bVdpJwsXXteKHdTtwhhiYVZhbdIZGHJjoOL3L+Zcvd7tfP/T6PTz0uj7Ht/mljVnvXMZfe4+QWjsFs9V3RuwPYWyAxIoWXOMgtBxXJ8EFLAQkvA1KHKSXlJiUuAN4DI3A2hXyX/I6riw5xxggN4Tx+UGpB+rBch7k/Xc/lSf8UznmFHWlRXOQIehyl4Vza9l9AqJC5k2osc+iRPY9/bGEOXOcR0Ic4RnwOUR0fBRmiwkhBL//5Fvj9bs120M2vgDFhRVcdSYE1i38QrdtKyz7piyEoGajaiEbXwBhvQpiHqfCas4CWG9AmNsgjPXRaud6RTg7f9eK0pebUzC+ojZEPwH/BbEIc1lyqVYwNP9HhkLuzH/mOmHCEDbA5yyxybFldyoDg9lAcWHwUoUVzbyxb/m05edWfBUcVbVD7hOncGQyYALrjSAq69uj7kOqKiAgfhGISkB0aRfjBYiou8DUGYgFpelpvIOyOIkSPQjMvoXcQyP0h5mzhwLRL2jylD4IMHWEqJEQeSdYrvhnhiTTkDKw50iqWZpqWpizSjgNKcwZZdRrwxDAji9+wWQxEhkTye87fWfFwVAdKj2jB2GyGhk1bzhXDTxz1YVKKCrwvXn9/tMhml/auMKuIdVsOHHjKR7tkpQ01Aa5Sd+ergmFSO9C9KIWKJHg2IU8fkFpu+qZdpUMkTdA0TpQK+AGbWiM6khDRA7SKmQVrdFm4J4zfqW2H/d0IqR8DCcv8WqPc/3O4Yz675SLQd0Wen9xAqR3ipfr87d/pf0A/9z6skTadiAs7X32qPY9kN5L66XUQKn0+T80pjA+nCcu6LABPkexRlp49C19fdyfNu9i+fOr+fbj0mIC5ggztgCu5pICCLZCO9MGvcTnb/+PZ1Y9itF05v7stgK9y9kaZaHpJRcE6H1qyPx3gEDC/fFoJffKIP8lAq9Zern55aEAS8ee3oU0RPR9SJkNhSc5LQUnAMf3kHaJdp8RlUFmgbC46lU6QcSCtQcUzPE6MAMyBqGlEnk8DMU8CLIQLJdBel/KlzNbDmKHQmFtKA5F/EMF2zawexUoMV8Ktv959T3TdYstuP+embehilogj0LMWJSo27T2HA8JTfUvVLUwrCcd5rQIu6D/RbTo1JQJKx7W6g+7aH55KDNLzRB/v+4ndvmpxlRRFBXp3d1Gs4FVOW+hKBX8NXNmBdkZqjJVBdzQRY3S16aLNUGH2IkQN+30z+2JPA4Uu2aKRhARENEbir0jo12fs/MQRAxHW8d2FWLIfRryprtmcBVpfL1yv/OXQfGK0A93HgeTp2dGAXOHChlZ+fBaqpGHABvkevwtzV4epMxwacKzxfnigg4b4H8ZRpORt/6YS/9HetNjWFf6PBgkP9TtpylNzajRqOoZG1vf5Lt0248tHVmhRRakMx01ayoULQzSKx3duu3pYLkpwI4oQEBUf6j0I6R8g0h8G5lxH/J4Y8h+IMBxFYD5KoibiYh5HCze2tolng0HFM4CciDiVnzTkrwJJIDhR37TB68AM/tnlCsFy7EPLBdB1IOagYt6BCzXgsXPEoNICf28OhTXj6D8tzw7au6LSNtPiOibQXh8VvavkKr/+AapZoSLPYQpk7AB/hditpgYMmUgD75yNxd3a0WzyxqVcYRmiJOrJ5GYejpqQ4HJSc+l2Evl6rLrfdfRThWp5iHTrvUjOuGPvADt5XS9K5XQ6gADGCB2MlhvRpOllJA3E0WJQjEkae5d+5lS4fLAtgay7kfa97iCxHQD9u1f/CVBU6+UKhAzMsDOU0ydKhc2yHlYmwnbvoT8KZB+habS4o21u5aWZRlQzgA41fVjRvMKlJP8l5EZA5HZD/sUoJAnWqJmPKRdpWgT6rFWqMcaIk+0Rx5vglq0ufzXC1M254kSVngN+Dzgodfv4Z42j2gBUH4mH5f0as3Vd17JJb3anrExHNytDzyKij+VdJ0gqMc0I3dalHNGUui5vuqEnMe9OjhRjzUE6x2IuLEhnDCB01N7KsEOGQ8A3kF5EWhG2GNWFnUnGBtD5k247zQiAeJeQohCpLE92LZXwJhOk6IlHhsSpJ+ymoULtd/FBzT5zaJd5bxIMT6u5pCxQdHn+F26sK1GPfYZftf9s4YiK2vLPmei5OZ/krPsNq5Iwgb4PKB6w6os3j+HdQu/YP6j73jsEUTFRzLxw3FnfAwPdZqg2773xTsq9gKGemgG7FjFnrciKFqIjH0IEldC1jhQj6BFHR9FP/sMxfgKMFwAzj2ACRKWu2aGM7z6ZfgeammPiJ+FdBwBYQXFgqK43PGpvmv/quNPOPkP5dfqCKEAQ/H7YLkeij/yvz/oMkRFIkDEuB7+ggnbBH44lMcbARKJBWKfQ4m8tqIHGeZfStgFfZ6QUCmOfqN7Irz+otcOLlvW8Uxw9aBTz9uUUrpdeWrGGKBk9uB10zZcC9ETIHb2qQ+0olBzoegzUMxos6TDBHf91vFpErFPoaTuRSR/hEjZjKj8I4qlCeT7EYfwtx5avBZ5oj3CWAnFmASOw6hZk1Hz3vK/Hlnwjm+bD8kh9CmhZK01EJGQuAqMDSlbMrIQTK3KcW0vDA05vVKRQnN3YwGiwDzkNM5V8r0thpwHUfPXn8a5wgBhF3SYcw+D0cA6+zK2fLCVE4fSaNCqLi06n0mxCP+YrKeXsynTb8Jd/s/2ETBdex01DPKfLu3oXA/FxyDqbqAmEGL+raEZOH85rTHqSYG0ywguc+mJFZHyKfJ4S0pnTlFguRpwPWwYqnj09z6vEWQA+UuZibQfQDoP6oLBZOFbiBSvG7+1NxS86dHglf8MiMpfASoy7yUo3Ayqh9s3diHYNkNRyTlUbWy6cwg0QyiAAsidgEh4HVnwHijJkPuM//cBkDch8L6g1Iao2/0sGZQHCaqrtrU8CrZXTuNcXuSOQGUSWDujGLzX8cP8lwgb4PMMIQSX3/jPp3FcdFVztn/2M4oiWHb09dM7mfRdp1PzV0K+983aCfYfIesen/5Bce4uu0+5OFl2FwBjezBWgcjHkI7fIeV7KF4LSjTC0hnh7b4oIfJeKJjr2jBDynY42SzARRSEqS4y/2V9s/NP1MzHtOsZaoP5YogcAMn/g6INENELhAkyhmg5yACGJi7PgwERMxIZ/SAy/y0o+kSrUZxzJ763EO+ZttTGjB2taMYBUDNRYrRazKr1esh+GGybyvr0ysFByKtAg3kmyB0PuQI1aQ2KyZ8SWJhACM6fNWAhz2IppoqiTZs28vvvvz/bw/jPk5uZR1Rc5Gnn/arOfDjZFrCD5XqUhOmoxzuWViUSVbRZybmOqAMyE5RYiH8dbBuh6CtwfFnaJ2U3iqH8z8Fq3irIe9i1ZdDOX7wFou9BMcSj2n+B9D64/WuGjuD8yussBkTKFwiDvoyfVPNAFiEM/t3P6vF22vvywUv8w2et1wA4wdgcYsaCkopiqoWUTmTBYsh9UesWPweyhuObr2yCyBEu1/kJv2MLiqERRI6GojfA/l35jz8TWG9BiZ9wtkdR4QghfpBStjkT545OqiGbdRtZoef89t2Hz9h4gxGeAYepMGISKib/VjFEQarm6pSOw6j5b6NbzzMkgmoNsWSeghYd7ODUI2C9iUZzszrQ3MPxkDhPE8AQkQhjTVdBh1LUk93B+ZvvqRw/guHi0n6FuyH7RrSZ7g9QuAzyJmo7LX0h6hYUczOU6F4Q3Ut/DWyQMw418hYUy2XIynsAiRCKdn0fnOA8jFQqIYu/ARGFYmmJUKJRVTPyZE9wHoaYJ1GiPNJv/D2zR92vKYFljQLb15p+c/5C9KpkTrRZ8M+QOUgbc/yrKNYrEVF3ahHbJe8lZizkTvI41gLJ61CMVVGj7wT7z5A9CdRf/QwmAM69kPsAwYOpyouv294/AQLPit5Dqg8glERds9NRxEez52CNa8x1g4Pl+of5N3NWDbAQ4iFgBpAipUwTmr/rReA6tFyKO6SU50CORJizgZSFyPTeIPPQ3eQcuyD2Bch5hLIVrVTc68kVhmeesQLJn6AYE4AgEcXOADreitcx2b1dL4rgZEt067/F70Px+6iRd6PEPoyUdmTu81C8DdQMkH+7+m1Epmx2zWxLgp38eCVELaRSH443p+RzVA0XoqS8D5nDNIMFkPsIMrI7QrgKPMROhZxh+s/AehVCKIiEF7WcbcdvYDsE9g8CfyYAhZ+A9UpU+0HIuAVkASgtEHH3QMIipGMfGGojLG0BE2rmGI/IaBNYbgXrNcBJyHm+9DPwi+T0jG8k2sNcukdbqKIjgTyNEnniEqTXebJOGujeV2XXd5GM77mdSatPpfDI+cv54oI+a1HQQogawNXoI2euBRq4fu4GzvGFnDBnFDVfuyH7u8nljKbi9YGDBY8FelZVoWCevuX4ZajHLkDNeqy0MXoEmjG0oFOmyr4jyDXtaG5dLwo1AyTzZkLBAnD+7GV4JKheLuKEd9AMiAcxY13BYx6fo3On6xTeYialdzwl8gpI2QLR40CpD4Z6buOsOk8iT7SGjP5+jK8VlFr6puL1qKoK6d1BpgEFoH6DzBwK5rYoUbejWDshRAQyZ7pXWpIdit+G7OEI63UQ9wRndk5RgN74VhS+3+/4ZCcms+TCS/LZu60iAwbDnEuczTSkmcBY9I+G1wOLpcZWIF4IUcXv0WHOa6Sagcx6iAqt91uC2UvC0dgETV4yiEE3Xop7BiRq6vdZr3O/VNNvd2k3Syh6X1tPBZTo4YhKPyASF6NzhTt2oOYuQD3RRXMTC4/CFaIGmDrg82AgXWujhWv9DRSsvcCoV0dTjLEoqTsgdiaQBKarIOc+/LnlpbRB/Hy0XGYFIu9GCL2cpWKoDBi0ilDO35BpPVGdaVqAVsDZnlnLp1U804uKXIUXvP/OJkCg5i9BzZuLqjrB4ceFD0A+OA9rruxK2zhfVtZUJ3yzLpYbHux3todyblHRKUj/tTQkIcT1wN9Syp+81GGqoS9zc9jV9i+IuAlTXlSnHU6WpEkpKKl73Ptk1kiwbz0zF/bMiRWxYGoHjjIiox2bPI5XIOlrKFwA1mtRzB5uZKfXDElYUZ02yLrLI/DHcz1Qhfyppf0NzVFS9qEeawXyL7D7qfpUYlzNl0PRex47IsFwFRStQhatQlpu0nx1UXehmLSi90pkd4jsjpr1bMDnDZn3KkrMA5C6DSkl0v4Tqm0/itkrWld4ztwckPkgxD8HuZP9n5gckDkgvcRUsu4G80CweeQlR/RDnrwcVFewVfE3EPMkZJSsh0bjXgoQCeAKJBNKFPJMPLT9wwhRGWfSCjrenlzxxUzCnDOcMQMshPgMSPWz63HgMTT38+mc/240NzU1a9Yso3eYc5IMz+INKmrmMygJrrUu6V/kvkKwe+jzShUKt5TzBAch8zaUSp/47kpcAGlXoFk3gTxxhWZ0dEpJEmKehdzHfI+XeS7RjGDr1nbUY41wuy5NnSBqoKagld6ptFvxMu130XLUqLGI6MEIIZDSCUUBFKa0A7ShSAfyeFtKDJ1qqIeSUjrrFhHXI3OfLT3MsQ2yHoLYl6B4PSg1ofA9/Kp2eeNpfAEK30XvKdiPYm6ATNkC9h1axSQRAY79YKxTuj4NYLiw1JUOVJwEqB+MbV3u/lhQt3NK06noqRB5vTsNrWRSYg52zH8cEerS+znOGXu0klJeJaVs5v0D/A7UAX4SQhwEqgPbhRCpwN+AR403qrva/J1/npSyjZSyTUrKqVZJCXNWMbXWb8vj2i81Dyzd+WccNHnAfo/tEMX6Ve0YTbWrmabalXYbirGSy80LmqbxcfzKFBa7IoW9KxVZ7kYeL6OIheN7dOuG9s2QNQIyg4RM5D+HPH4BUtpBPUnAQguiLiJKK7MnCz9BF3DmPICUKmrhJtSMociirZC4HM19XzK27ZBzvxZRHfcgxE8J/l4C4lm5yADGuqjHWyDzFyOs1yCUWIQwIUyNEaJ0nVwtWKk3vqZLIKJrCNdTwHAKyluiCOImgbklKLXLf7y5P0r0jSiKASFEWC86VM4TF/Q/7tuQUv4spawkpawtpayN5ma+SEp5DFgF3CY02gPZUv4bEj7DnApK/EgQJeURre6btUzrAXlTObX130QgEox9CK2cnhfR9+NK9QelYeB+ivacKNP6485XdWxFSgk548u+jhKJEjsOJfVnqLQNYp4DTFA4DsgJcFCwBxIbqEuC7NeQx5siC9eDqSWgaDm5sfMh4nZE7GREpdWIEv3ook2+x9t/g+y7NQWsnAchox9E9PW9UPFnSGlDsV4Bsa+DaADmflBpJ6LSdkjZBRF3of2NrGC6zONgM1guh6jRiOSNiOR1YN+prR8XvIH0I9TipmiDftv+DYhodOvoRq8HPxTAAp6pVj4EMIz2HMjsD0XzQT0Ixotd5zOCKVj6kALWASiJQZTAwpz3nGvRCp+gpSDtRws5vDN49zD/dpTKm3TbUs12FTM4VTLRHmkPQMomONmBchWgz/NYv1SitXxj79QVUROR4rrR+5uxmBqVuX4t4kpvvIoSh7R2QOaWFdVdQWubeTMRlbeDzAIR75p1Xebbz77Tty2jl29b4WqIGASFb5W2yTTk8WZIoqDSNpTINfpjnCeR5iYQvRrFoD2Eqc4jcNKVgVi8HkzNEcbqSFmE5pC1afKVHs5ZNfclKFgEpjaIhLkQPRwyPi+9jqEWImYUUokDjIio2wEn8sTlILNxi4NQCLnPQ+wcyHkI3+C0QNOkg/o+jm0lIwPHZoi6D7xVyUwXIxIX+AS2hQmdcBpSBeGaCae5Xksp5X1SynpSyuZSyrC81X8NEYFPuowPUQQW83f9Zzp2IChAJH8C0c+A8UI/fSOCX8baFSp9g0/B+uh73K5CkbQEd6qQ8VKtPX5W8PMqtXxdjUoSZRcoCAFjCO5WU2vN3akkIIRAVZ2oTn/VlS7xc7C/O18GFH4Axksg+nmvfflQvA4pi5Cqdg0pi5Enu2oSlCc7ox67AKkWuAyxx/nzXkC17wE1C80VLkE9iTxxOaptN1LNhPyXtDV220bk8RbgdKATbVG0JQVhuggR2RchzMiiDS7jC7o8a5mt1SaOvDXIh1cOZB7Ydvi2J7weNr5hgHNvBhzmP44QZkhZi8x9EYpWBugVKEDJM7rYDEqitk4YXRM14kaPiOsSgtQXNndGiR6svU79FrVwJxStgMjuKJbSuspCCERq6UxRVYsg45Eg7xCQKurxixHxsxCWjq7zGJHUBQ4EP7Ysgk0NlGshohlKzFCkq7aylHY40REoRjU0REn5uLR/1K1edXqDkQeOryHva3xUn7JHIbM1t760dIOYcehqFiOROROREb20KkbOn9ztZD8OCa/qzyePQ0ZvpPUWrzEUQ/bNaDNkl2GN6I9MvwUcmiqYjH0WjA18x+jGgYjogTTUhNynPNpDVbzyIqKPVsvaeRiUypCwBEWp4FrZ/zUkcB5IKMM5MAMOE8YbYagCahDj6P8o7QYXMQSs/SDlK4TQ1v3UwvUextegpa3Er/B/DtBSk+L0wUNKxIUoCRN1xtcvaTeCc7OfHSWzW4OWXiSzkVkT3XvVjPH4Gt9yltMzd4Zof4UpBErqPpRKL6LEDEUt/hZ5vJU2Y8wYjtvd6tynBZWpdlQ1H8XUEKL9RGqXib+bowRUKP7EJf7hRdEHkHmnh/F14fgZebITRPhZjSp61//lja61+4gBiIg+mvQldsABOY8hTPUgbjZYB6DlOpeggOU6MDaGopJobwGR97hS5MoflyysHVFS1qKk/oxS6TMUU3nKO4YJhJAV+3O2CBvgMOcmth/L1z/uDUTKZyhxY1HiJ6MYPG6sOR4lDJEolb9FsTaHSA9jZb1eq8VbeQ9K5e9RDElabymRzpPuerpqzhQt4vlYQ9Sib33HoR72ajBC1CiU1L1APHqX50FN9ALA5lUowXgJovIuMLUnNNd0KkriPBRLc0j+AqJGo7nY47SKR54ULMA9m3N4rPIolZDFX8OJpnCiFWr2NJToO7yuL9BycP9JHOAM1TNQCSV5pfZ3jHtaS+2JvLt0t4gADCgR14D1SiC7dF/ciygJM7RjIvqgGVwLIuIqbX/UcLRo6ZZaihUACiLhTZTUfRA7Fx3xryMUr+WLMGE8CBvgMOccquMwcMzPngAzwphJKBGX6XNBPTG19DhFHfdLJXYUovJuROW9KPHTUXNeQh5vgnrsUlTbAdTir5En2iNPXoo8eQVq9nR9/dws7cau2g+i5r2K6jgKMZ4zRgGJH4CShJr7GrqbvQtp/xP1RBdQPAOwDIik1wEVkbgIkjcSfLVIgdgJpVvGaigxw1FSf0JJ3YZirIyqFqMWbUR1ZoPlRj/nMGuiF1keM81Cl8hH/EvafkwQMwu9+9h1/TON6SL9A1MgDNp6vBAKUs1HzbgbbNsgfh4iejQiaTlCmFCdeVCwSn9s9v2omWNQbb9oFZesvSHlS4RJE1pRYkYgKv+KkrJMC8JLWIxIWuVeRlAir4KUL7VxJq5EsXYizBniPElDCq8BhzmnUIvWazmtfvFX8N6KEnWT/3Od7O2q/ZsM0Y8DNkTUEF0fITz+BQpecr04ARnXep3sOBR61zkuQnWkQbpLUyZvJqLyz0hzRyj+ApyFkNEzwHsREP0QZD8AqkvtytAEkbwMmbcMedzlMo99GUFaGepOKuQ9A5FX+t0rpdSiwWUeYNSMhA/+IsVV1GPNIfIOlFRNj1gt2ojvWugZVkWw3oZwudalIRZyX6Y0P7kSutKEMY+7X8qM27W6xQA5zyAqadHRUs2Gk5fjPz/7Iyj+FCgGxw5QEiF2tHu3O/hOCITFN19bMVSC2FGn9j7D/OcIG+Aw5wxSOiHr8bI7emL2H/WrFm11GV+ANCAfJfpe334ZD2jpNnFPlu+6AMRD7kyPbYlU81BMNcB0G+qxwHnEotIPWtm/os88Ds9FZj8KRR6BUDmjIHEhWh5rkDQlJRVVtYFtOxCPsFzgUr2SyPzPKS2u4ICiHwgaVGRsCc5MkK4KTgXzkJF9EcZa2ky0TAIFOAXDpK3hGxuD7Xd06+ER1yKlqolVRA1GRt6qCZkY6yOMWj626rQjFINbTUp7qx5FDDxT22xbCVwVSUH3MGLfFqBfmLOFIJyGFCZMhSPT++HPTRsU22rUvFf97PD6avup6aGm3Qi2T0Eegax7IXo8mqu1WpALRoOoDUodSPnENVvyuKoh0f9hXsjcJajHmmkpNqYrQKmuzYQ9jS8ANoS5jeY6jX8NIh7E75pwxCA40QayboOsXsjjF6A60pDHW0Oe14NH7n0EnrVWgvjXwOC1zutK51EM8S6XuJ8qTQiIm4Oo9CNEjtXvMl4e4Hol2LX1c1MDfGammTfDicaodlcxBvturfyikoTqzEQ91hhONkWeLPVaqLadXu/RiVq0RVtzN7UMMH7AUB2sJcIiLi9FmHMLKSv+5ywRngGHOXdw7Dq144o+1QQYPFCsbVFNV2oyjab2uoLyauYDPoYTVJTo21BJhbwAN13rEJT4sZpLFzvS/it6SUdNklF15kH6df7OAFgg7mXIdrnC1YPabxHAIKC5kIWpCZiaIKxXQNx9qPZD2kOD83cwNgNTbXxmdQWL0NcuDoUTkNbOY1tAwju6YCLFWB1ZeQeyYDnketSpNV6IEqG546WluX6pOGEaOIsho3Pwy9t+hoSJkOkKePKc9efNQUYPRWbcCjih4D0wXYh7aUL9Q/ushADHPt9zZw3W5uURg0GJB1XRIselAvZPABPETUExX4x0jgYlEiHKyBUPE+Y0CM+Aw5xDBDZCQTE08d9urAmGumD/BfVYO1Tb96i2P/wYX8B0GaojA/JG4KuCZITknSjxY1Hz3tY0lY8302QYdRSjHmsJJy/Vcj91Y2zjmgXaSo1vCepJiA0sSSi9KjWpapG2Jp30IUrqryjJyxGmC/CR3oy4gTLFRspEQs50n1YhFJSo/mDq6GqJhISFpfvNbUBxeR2MTVAMSSjmqhD7Atpzf4Bbj30LiBjtfaXuQhdxHXET0r4LzSir2vqu1XPd21i6RhvRC0QAjfjC+aAeBfLBvh0l6XnX9XaimC/WjjckhY3vOcz5koYUngGHOSdQ8z8hqDCG0grUAKlJxauBZ3VNav47ULhQ3y/DW7jB8/xJkHapb7uhPiSuRjG4IrDzpgU+Bw4CykXKDHAGEHaLfx3F0hoVo//jldKiYqpaCCfaAsUgYlBTtkH+Ysgvef9miBoLkdehGJKRlbchi3/UJByVSEgLEplrvNhDStED54+o+e9B/mua4TJfg5I4Wxta0pu+/QEhDIhKm0tnpCVvJbIHRPZAzZkGBfP9j8OzpGOlb6FoOZjaIYy1kRnDPDpaNV1nS2/NJR3vqcJlguhR/itOeSKzgu8PE+YMEp4Bhzk3yJ0YdLdSaammqwsgYkB4BDgpiahF/0M90RX1WCPUY23B8Wv5rl/8EX6NX/yrpcYXwFDDq4MJhL+qm16ov/tvjx6FYm3j2vAX5Y0WUV2CfSfuGbrMBZnuYXwBbGBphWLQBB+EMKNY26GY6yGcAcag9UTEPgjRz/rfnfuUK5BJgu1TVNX/Wr16rK0rT7oJqlocuLqP8P4cXZguQ1ivcm8qigkl8hatnrH9Z4+gKEWTAc24EYo/BPv/IG8WsmCJJnuZfmPZxheAQteSQph/FeE0pDBhKgY1dyXB67WW5GE+iIx+oDS6N/d5cB7S1n/TPSvZZEHxD2Vc1QAkaMIMEVdoeZ/+DKDQt4nkD5HZkwCHVjkp8z5w/uJ7XKhID03gmKcgd4Jvn4L3IdIVGGS6SHsAkbmgpCCUZCTJaJHeJW9NH0SmZo7VihsEfN42QOLHCHM9ZNZTIQ7c17CqRT8CWa4tB+S+AHHj3PuldAICWbAE8id4HJmEkvpN6XkcfyIL3gJDbUTkjQgRgWr7DTI80s0MNRCmJkinh/BJ4XvIQjPYvgvwAGZxjdtzrVwJlwAMc9YIG+AwZ5/8R4PsjIZKS91bujzM2IcBkPaffR9i1bKUk5xgrImSrGkdq+a2kDUS/Sy4EkJJ0h0l8+ZB8WcQ0QfFWAVV+ntwEFDpR3CehPw5LllDJ1qpxFqAhyu6cBVq/gzAAon+3bmalKKGopg0t7N6EsVYWbta6teox68FeRhiXgM1F/VkR9c1XVWEyvosMoeiSm8VL++3VQXkUbD0ALUA9cSlBE7nAQrfRCUeETscmb8I8gLMrklHzV8K5vYIQwyklc6AZf5rkPIFZNyhP0S6jGbMGMh9Fi1NS2rjcfwZ4Dp2bV1Yeow5dkawdxzmHOV8SUMKG+Aw5ybGBojEZQglqsyu0tAULYAriDHwR4kABqBYr0attB0K3obC5eD8AziBzBqNSHxD6+44Bvna2icFr6FG9tPWHTOGAioYamtRuTFPIJy/I9N7ohkFI8Q86RL3P+k1hl9dfRz6GZ4nSmVU268o5sbapqJobY4sKNwM+ZrICNb+KFGXoGaNpXQ2H2IpxrKMLyBSPkAoWpqVmj2JkD7vwpnIwtcpU9faFU3t+yB1DJn5CD6fW0Rv1MyHoXgjGBpDRG+w79Dc5NbekOunlCKqVsjBjQVh9i7QEeacRwLq+WGBw2vAYc4qUvoRl4h+HCV5TUjGF0BQQLmNL6C5JEFKFTV/EWTdB9ZrwHNMnrMl4WVEMu6HjAGAHZI/hsT3wfY1pF+NLFhDqTlxQu4rAcYQ7Ebiup56GDJuRLUfQqoZWsGE4x0hrS3kj8FtZIuWIh2HwNLZ6zxeLlZRB1/KKvwQAcKjio+lQxn9PckD08Xl6O+BSAWbl2SksbVmaItXaed27oK8KaDEIRLfBWcIueRKTYidjDD6+yzChPlnCBvgMGcVIUyg1PZoUSi3MRVRIEITwNChHkY91hB5vBHkTgbbFkjrCvGzNVelUgviS5WuFEMKxE4CYxOw3uSavaKNN2sMnGwF6iHNYBa+TqmDqTp+ta0TN4AxmLJUvMdrJ6R31bSpM+8BedLvETKtFyg1IOYJSo1qiZGvpUV1R98M1Cs9yFjWLNCKSF6D8MxVNjYr4xg9InEWRD5RZj8dEbeCPOHVaIKI/pqXQocKjr3I9Nug4IUyTqwgUtahRPYq33jCnDucJ0FYYQMc5qwjUj6BuFfA1Bas3RCRA8t3vBCISt9A7BxXsYDTQUUxN0Op/BVKpQ2a0cU1S06/F3KeBVMHsHin87jWH914piT9hQ8RA1DMtbQ16NgANXdjXvTfbtsYZPyFkDUYJWoQ4F2J5xA497vWTD3WyB27CBiBDRD7pDbrPtYJ9XgnVMdfkB0gwtjQCOLf0KoDRT0Gxg4QeS9SSpTYQYhK34KxRZDxu0haD8YL0KlZRQyHSj9DgVd94BLsP4CjrOA7q1ZEwdubESbMWSC8BhzmrCOEERHRBSK6nMY5BCLyatT0AOuooWIOUNAgfRg4XHV+C+dDxM1g7gG2dYAjhBu/F1EeVYeK38WvfnLeg+U7p3uwBVoRBR9BkVPE/jvkuAyuBNL6UBrt7EI0AHN9RNyzpUsHxiqQ/w04vtFUuVJ/RCgJSOv1kOdV99eb9GvxmR+YL0BRFFTTxZoCmD8M9cHpoYJluhTsJcUnYhCVvwsb3/OA8yUIKzwDDnPeoObM1QJxTgVzZ60sYaI/XWlKjW8J+fNQEl+AyFvRGc6YCSCS8E3TEWBoBwmrodIvoFRHLVyHavvZtZbp547iLqBQXmxUmPEFKHzDq8FLRzp2saYdXbwRmTvL47gPPDrll77Mm0nZOPEuPiFc684i7mlXaUI/84e4KZoAh0gG6/VaAF3KFoibiai0OWx8zxfCWtBhwpw7qCeu91iTLSEVhEkT+JeqS8Qh39/hEHkzQghUZybkzQZza5SIHtq5i/3oChd/g1r0tW+Rh9xplCp6mcE6UHP7Kilg+xIyb4XIG115x2VFKFegEQ2JCLQxBXFHY4DE98C5F/JeB8sVkHM77geIwkXImBEIJQ6st+nc5aozQytWYWqlCWeEinUIIu4B9xq0EAKpHsOvcErhekTUTWDtDko0OI+gGKtDRPfQrxcmzD9E2ACHOT/wNr7RY7XSdtljwPG7lqqS8DZk9qF0BhcFcc+DqQGKq6wdad1AZkLhO6iOvxHRQyCzh58L/gVZd/hp95TTtEGRn9zeggD5vmebuBmQ/yI49gOR+C3kkPQ1iikBaAARPVCzx+M7ezdpv4TX8Y59YGiPSHwdmTVOE8yInQDZj6ITEvGm6A2ksEHs46WiGUUf+O9b+BrSPWPXHiSkqItS2Y/+d5h/LWEXdJgw5zBK9BDIuh9kmlZxyL4WMm8Ao+dMKB+yR5YaXwDpkcKS/zzyeON/asgQNw9d8QFPDKHU4T0NLAMQ1qtQkj9GSd0DkXfiT+1KGEsLPqgn+0Hhhx57FUhYhFC0dCVh7YRmyAERrwXZoc1glYSpmvBI9lCCGt8SChdrJRZPlEQuB7t1OdHN4uXvqMVflX2NMGGCIIToJoTYK4TYL4TwUQ8SQowWQuwWQuwUQnwuhKhV1jnDBjjM+UHSJ2hiHBGQuFVrExbfftLbTe2V8hQ1Ivh1Isac4gA9ifPfnPMURN7uZ4eCSH4Tv3WAg+Kvf4DcapHrnl2qJ3tBwUv4zGzNnRBCc5qpx7qC8yfcbvSI+6HSThSP/GCZPQ0srrX1yt+hKApSeqwf5zzhe42yUPegHmtBSQ53yCgBPvMw/z4qOgUphK+g0IIHXgauBZoANwshvMuw/Qi0kVJeCLwPPFfWecMGOMx5gWKqr5WTS/0JxZyI6vgbZLpvR3NP9AZQH5Qjou4KfBHrUCj0Lc1XfgIIRcg0RMwDiLiZ6MsImpDZT2iBRcSBqQsY6vk5QaTXttedxdQOkbIWkj/Hp3Rh0VrUjHtQnSfBucfPuS0Q84jHtpdyVtRNKIrZvake6wRFb0PxJ8jjTVDtv7pzrtWTfVEdh1wlAU+FQq1yU5koQBJEP4xiaoZUM5HOU71mmHMFAQgpK/QnBNoC+6WUv0spbcAS4HrPDlLKL6SUJVWwt6IJAAQlbIDDnJ/Y/UkRApZmiMrfgbEVGBpC0g7dbs19avZ7KEWvV+gQAS1vtoSYJ7RZqKUT+n/NYiha7RLfyAVzU0TsU/jOcAuAahD5AMS8DUpLj31mlKS3kIWfQ9r1+AZaqWD7XKtl7JdiSO+NVDO0zUgPT4FS061LXYqnoXNCzlSPzZ2a4InzUIBrhUD0EDC1ILhXIAEl9RuU6LuR9l+QJzohT3bVSiuGCVM+qqFP6D/sagvEYGBtWScNB2GFOT+xXIVPbq35SoTlUi1nOHlpoCPxTn8JitIaom7Q1KSyR4B6onzHO/eA0gQSXiiVRSzeQMBobSQUvIO0bQVLdyj+2Gv/364Ia5dmNQLMV0Dso6j23ZAXvOyj9nkp+KQaAWAH5zFQElFi70PG3KtdwV81IXNXsG1wbcSBpSvYv/Htd0pUASUWkTAPSRxCHkWqdkh/APCYvRu1JTjVmQfpw3AvNxS8A1E3V9BYwpwV/H09T49kIYRnwe55Usp5p3IiIcStQBvAW63Hh7ABDnNeoigmSN2LmvsSoKDE3Bf6wYamHiUG/QhkeKL+AM4OoFQFYQXzpRA5ULvJy2iwr6HMu4W6G9K7IQ0NEClrXApQHtcVDcAQC45fgGLNtW73415347lPQsT1KMbaqMVbgo+jBGsPcP6tjd8z/9nYCoyNdF1lzjSkfQfEv4BirOpuVxJfRnXaQWahGDU1MdXxOxR9DJbrXeIjdsr8fP1yFDJ6u44yIk1Nwb4Xn/X8hNdQM+53iaV4Er7thfEhTUrZJsj+vwHPItbVXW06hBBXAY8DnaSUZeYRhl3QYc5rlJj7fYyvWvQd6olOqJmj/R4jkpdTepP2NA4BRBwK5kD2XeA8ALYvQM1ESXwN7B/j1/hab/N/HudvSCkRpiaQ9DFYBwCRIPe7lLb8/D8bW2m5xsYrQCSAsaNvH1Nr7X2ZLwVziXs7kOtWAWkBqUBET127krwEIUpvGfLkTVC4ABzbIa0zqqp/r4rB5Da+AEr8kxD3LBQvptRLcLr5JA7XcoMf/fD8hX6ML5r7Osy/mrOwBrwNaCCEqCOEMAMDAF2VECFEK+A1oJeUPiLmfgk/Cob575F1q/a7+GPUnMoosY+gOm2QdZdW/ShhPto6sJfQQ/QjUPQtOD4nsJsWsG9Hi88I8I9dtLj0tdII1N8AJxgauN25iqkBqv17tHXdIJg6ImLv17mB1czxULwODA20deO0S1ExQ9ISiH0c0vuALHa9P6/3YGwNxa5CB7nbIGo0OP+C6Ht8r63u9mrIQ7UdgMzBgAGS3kUxNtB3yX+Rile/DzCLLnjZt816u1tgJUyYUJFSOoQQI4B1aE/iC6SUu4QQTwPfSylXAdPR8giXu/4f/5RSBq34ETbAYf7b2F1GJP2mUoOSdh3EPgM5Y4BozSVr7Ypi7QjRdwCgHmuGfyUrM5i6oh7zCmYydQX1b3B6GK3I+1Fi70dVC8F5GGGsrz/GWFNT0dIRobmonRkgj0HhHGThK8iUb1EMsQAoCZOASag5M6GgpAyiDdKHgDCDzC09V8p3cLJ56ekd2/SXK/4MJfl9f58cRD4EBSXBVZEoSixq1kOlEppZj0LyCq+P50pw7PV/vlARlSmt6yvQbmMh1D02XIiIe6TsfmHObc5SBSMp5SfAJ15tT3q8vqq85wy7oMP89zCVFFwwQpyr4pDMKN0v81AieyIq70JU3oYSPwHF2hHVcbK0frHVQ9BDqQ9Jn2oVgGKehJy7AA8PVNzrKEkvQ9Qt+nE4fkPNHAFqBoqpgX4Wm/c+FG/yM/i6oDQGYyVKjY5Ti5L2Rs3Sb4sY0NVYNiMUk59reHYJvCymxN6FkrrP9bNDazR4BIYaavscI2JCLTBhAFI1l7r1FrSJRSzEL4PYyZ6jIOTbWOR17jzmMP9mKlgH+ixqQYcNcJj/HErSqy6jsRuKN6NmDIOYp9GEPEwQr7kuhTB4iFNcB2kdkccvQnXmIOKmAq20E6r7IWOA9jrfu3ABYKqrnS+in4d2tAK2T6F4PaRrXirV6WHg8x7Dv4t7F9jeA/v3+ma1slYyMG8RauajqIVfQMyjQEmd5ERtNpq4XJPlNNSB5FWuNV2vnGBPosoOXpPSjiy5iSUsgMjbIHIoxPnmTAuhQMoPYL6KoGIaMU+gpP4PJflN7QEodTtK6vdgWw3ZHmu4pjaQuBiILXOc2L4tu0+YMP8g4cfBMP9Z1ILVkOtStrJ9gai8129KjZQ2D1dwMRR/7vKA/ejRKUv7HT0KcjxmedGPuaUuhRDI6LGQN00LcuKI69hc1GMNtZeALLfiFZB/L7LwAlBd7t3ilSDqQ8KbkPMAKNUQSjRgRxqSwbYTMkeiRowEckvPY+6m5QOXBEmdbIda6TsURS+RKaUdmTkcbN8ADi0KPPkDFCUBYscHHapiiEEmvIA83ipwJ0tn/+1FXrWQzVdC/luQ8BJkjgWOo82eXXnOIknT9sYM0RWhYhbmXOB80YIOG+Aw/12KPvRqcOL5LyGlBPv3SMyuG3k6YADL5VD4kc/p1OKvwdIRkr/T3Mrmuvr9Basg9+EQBnaKdxfVa21V7odMl1iP8xAyrQ+YW4PNlY7k/BHyvKQvbd5FCxxaWcGoQfpTpw8Fx9ce106D4q+gjAAn6dgPtu+RBRvwneGbQcRqa9SZw1GTlqIoEfouMaMgu+QzVCB/ivay+BPNc1H8uaY57fwTnEcgdjyKzu0eJsy5Q9gAh/nvEvukpsgEIKr5rA/KgoWQOxOwgaEWRI4B61Uohlhk1C3IvBfQBf9k3qH9NtQDKVFj7kVYr0NmjYTizQRMY6owgkRmAzh3QXFm+U+bOwVVJKBEusozHr8CpHcKpBHMwQtGSDUDmd4HpB2/pQSxuB5ypCZQkjMe4p/X9VAieqGKSMh+ChSrFqGtjQohohBxk33OGuY85Cyu21YkYQMc5j+LYqyFrLwXKYt8Z1oAtu2480udf0Duo5CroEbdB9IJIhXkn77HOQ9ov7PHIB2/u5StztANI+IOzT3u/AsiB0FEHzjRhoA1fdUjaP/2ngawCnrpSCBhMWSW5Cs7IGc0MqILYPVjfAXEvQRKcullnDmQfj2oJ7WawdKmjU2q+De+oJU/LPtzUqxXoTp+g7xXtGtj0oLizG3LPDbMeYAEUfFKWGeFcBBWmP80Qgj/xhcQ0ffjGyikQv5LUDDXv/HVIbW0n/L+myWshIg7Q+tb+CnYvgTnYS2lKmsMAY2vG0fpmEzXoaRuhoRFoNRAq+UbDZmj8NbElgUfgXrMz/kkZA9GHm+GmnGPFkiWO11Lu8Km5STbvoDsURA1GKgZYFxRaF4CAYbaiNjHA7+F/FVoD0cSrINR4qf5l8QME+YcJmyAw4Txg+pMR9p+xq/6VKhYekLEzRAzEf9RulFoFYy8qhgV/wCFC0O8SIlBdELRSrB95rU/QElS8+WaCEj0QNSCFWCsi1LpczBdiDYTTUdzr3sY4dwnkSevIWjUtO1zZP7rYKrjZ6cdJWYkSupnWtCWDgHJq8F6A0QMRCR9iFAS/ZwDraKRPFDaULyktEiEC/X4NajHGqIea4TqCFB9Ksy/l/MkDSnsgg4TxgO1YD3klFETOBSUmoiYUci0LiAL8L82q6JFG3u5ZAsqch0zQLF72ybtd+atlBRgUFO+BKd3f+/CEkXaj1IFTFe4NJ29KFitRULHjIPcV4As9y4pbQhhRsS/gCxJ3XIP9Rq09fbaSDkC1GLAiCzaBEqkllec94pvAQqZicy8F5G0xKPtD9cLFXKfgITZhAlzrhE2wGH+80jnUWTO01C8Bz/66qeGmolMuzJ4n4jboXB+xVyPBsBvftoDVVUqoeTpX4WsRyB2JmT1B+yuohS7/B+mHvVvfAHUA5Dl34Uuj3dAkodvQJrEHdDmPAgnOyJR0dZ4Sx5eDK7XfmYs9r3I4m/B3NbXFW0p4+8Q5t/H+RGDFTbAYf7bqLmzIX9OOY6IRnPRBiMSXW6tDiPETAFTPRRzM1RrR8h+BNTjaOuvfooKhIQ/4xsK1tJr2rdA1hZXG4GN72lR8rkECsQqoWQdW/pp80c+MnMQKPUQldZC4mrIHg8RPVAie5/qYMOco4RYQOGcJ7wGHOY/iZQSNX14EOOrQNQsiLgNTO0Ak5aKVOkrMF/r0S0VUfkXEP7XK0v71UQkrUZJ3Y2wdoScqajHWkP+CkhagZL6K0rqTjQDHyKGlqH3DUTccxDpPVstolw1jc8l1AOoeW+gmC9ASVmOEn172ceECXOWCM+Aw/wnkbnPg31j4A5RY1BirgOuQ3WeAPtfULQCskZCzFOQawM1HeJnIIQZGdEHCl7Xjo0YCIWv688XNwthukC7dsbdpfWGbR/CyQ9RrXdC0VLKrH7kiXNH6H0Dkf+aqziChdMKOPOLAsShrQH/gzOWoo3hkoPnO+fJDDhsgMP8Nyn+0k+jq6ydqAwiGTV7ohYIVOxVDSj9CyAOkt5HMdZESgc49gNVIfZJlMgrUU3tIeduwAmiKoqlmccJ/LiZixajd7EmAhm+/SoaR4mb2QFYIeJGKPwQKERvNKMIvp4cqXkL7FvAcQzIBpxguhREPth3aLnTxAOHKvpd6ImbcWbPHyZMBRE2wGH+m0Terglr6IyMAji1Und5ZekGZ0N6V1Qi0PJaXTKQOfdA5F5ExKVI9T6w/YiIGaU/NGEBpHVDP9uNBTxVqv4B4+tDMSjV0daAvWfigcr9CYgchoi6FZQU5Mm1aClMLuzeVZrOUEqQUh2R9C4olcP5wOc7kqCCb/8mztoasBDifiHEHiHELiHEcx7t44QQ+4UQe4UQ15yt8YU5v1GibkAkr4e4ZR6tZQlY+KMQt/EFQKLmvggyHyV6BErifISpme4IxZiKkrpDK1+YvBmS1kLyWkJ7Ho53/TaizdgjA3f1wQLWu9GCvfwZKQmFH+Pf+AdaE5agJCAMlZA500E9w7NbQLttGSldLxegxCMMqWHjG+ZfxVkxwEKIK4DrgRZSyqbADFd7E2AA0BToBswVQpxpAd0w/0GktCOzH4Psmyr+5PkvI7Me0K7j+BP1xFWoJzqjOg6gOrNRjzVziURcgMCJYqqHYkzUyiMamgQ/d9JyzXDHTkZLy7GDUgfwUvMSqWDq6nWwEyyNUFJ3oaTuBVHJ9/zqKURTF63Xfhe+6Wen17+v0hqfhwalJsTNAWu/ABeIA1FibM1gbg+RgxCVtyLiX4WoBxAJr5V/3GH+lQgkQlbsz9nibLmg7wGmSimLAaSUJdXLrweWuNr/EELsB9oC35ydYYY5X5HZ08C+7cxdwPYNquqAnKdBdUlWZgwGUwdK3bkSmTsTkVBacEAkf4DMGgHFG9Fm5F5pT7Yt/2/vzsPrqsrFj3/fdXIyJ02boQFapGpBAaVIbZmhgAyiFP0xCooTXLiIov4ugnjBi+VRBEEQJ0RluEgBZboCFpCZC8UyDwWsoALSNmmbNnNyzn7vH2sn2WfKSdKkpyd9P89zHs7eaw9r93Cyzlp77feF+Hug/QIGH+UJ3sQPYfcxmNGp4Y+4WC1BsgU6fgndN/jtu64n6LwOEi9B2eF+Xe9dkYoPRL4axSzoYFX4ppzM+8SVUHk6UvMFn45R+6D3QTTxNnRczGC4zvVn40cTIuKHgiuD2rP9vp2/hdgspOJTQz3d8gOQcnvOd4szSSZhFWoIentgHxFZKiIPi8hHw/XbAG9Ftns7XJdBRE4RkWUisqylpWWCq2smnZ7rUpebngD33uzbjkkSOi4HiabC6/ePMkUllhN03EywcheClbugvY9D9elQ/xhMXQxNf4HY7HDjGFK2J0HQ79P2pdjgz1l6IK75FVzMl7tYI1J5NEgZEIf4HEg8h29474D+Nxj6MyAw9VfQtIyMHnVWcaAcpoQpAeuuYvAZ4kHt0PUDdNVeAGjbN9C2s/y/TeUp+MxHK/A/MtJuAZS+B6n7Pi5Wj7hpuJpv4io/bcPMZtKYsB6wiNwPNGcpOjc87zRgd+CjwM0iMqq/fqp6FXAVwNy5cyfHzyGzSahmzuBwrp5Ao4kGnO+t9j8+9hP13OUjRlGNn9xVCt03p26TXAEdkQT2bV9MO0g9lB0MJdtCzfmoVMLqnSLlA41R+BXoe5QguWGwAQb840+N/wv0gXaiXZGh4mB55FT34uL+B0LQ9CSs3o3BXnbpgdD/JugbkXMHSP1NQ/e4275M7kAiYYjLxHJ8T7cCupfk2DbU+Uu0ewnSdO/w25ktj/WAh6eqB6nqzlled+B7treq9xR+TlsDPg7gzMhhZjBusQGN8TTxdua6oANiHwBiPgQjQdj4DpN4YFgCwdv4Xl0H0O3T+GnmuYf/Gq6B3ht9ovk1R8OaI9PKa6Dx4chyH7QchAapeX/FVSKuDoltA1Wnk+23t2gbgE+h2HElMvVXuObX/WvazyFWmraHhikbB37URIaQ4wtSryu83yy1F0BsG38fN282KSD4e/5tjClShRqCvh1YACAi2+NvPLUCdwLHiUiZiMzCB7h9qkB1NJNW5q9nXb8IEs8ASUi+Eilph7LPjss5chvokVeRmf4weshVYY86wjVB3/K0DdvQlgVoYgVB500EK3ciWDmPIOHv1bqaryHTnyPl6+/mhpmQQNeeAF1Xo+tOTfmxInU/JWVSlVSimkSDLkQclB/jj+m2hqlX4JpfhfoHoO4a3HT/3LWU7YVrfBA37ZeMdABOJ0lvx4yTgceQxvNVIIVqgH8DvFdEXgIWAyeFveGXgZuBV4A/Aaer6lieDTEmJymZQUbPNojOI0j7g997/URXKdQJ9EL1hSAzcmyT9nUIVsD6f8vcTPvRnkd9JiD6gTZoXThYLFIKjY+A2z7cvmXo3qqGeXaB6PO/UjKTlPSE2gEd30dXzyHougVXtwjX/Cqu6SGc8z8kXHwGrnzP7JdScUKOa0y7lNaF1gibFJNlFnRBGmBV7VPVE8Mh6Y+o6gORsgtV9X2quoOq3lOI+pnJTSQG8bTHffofHdvBKr+SOXmr4iSgfmzHA+g4N+yNjvLrWfZpcO8LF/qhL/3+9YbUxaAXgtf9e/1H+GjU9iAD8x570dbjCHr8IJSqQuVnyMxkBGw4N2/1gp4HCVZ+lKDlEIJEG8TScwLnkHwVbf/FyLY1pohYMgazZSrdlZHN9B2OQNeV/j5lxZd8FKmq05Dab0PTIxCfix9WHoO+v0DNIkbekMeh91afCnDwGI+Q2lgmwkZ2Z4LW4yG5Ov0g3kCjDEAbtJ1I0PMg2n4JdP2aXAFLgpU7Eaz9CkH7Twl6l2Zu0HYqsB6Sb8KaQ6Bj0QivDejOkfrQbJlUx/dVIBaK0myRpPrrUDoP3XAZJF/M3CC+T5Ze8RRSQylGcul2/9onqa84BhFBJA71vyNItkPLAYw+BGMHdN4CVUdD+aeh61rQLui5g+w3rXI9t5utseyDxNOQ+Ivv7eoI5jm2fWWYc0Tq0Hevf3VGaxmH+F6kDO3ruszdh6OrCHqfwZV9ZHT7GbMZsx6w2SKJCFK2N9JwSzj7OU3/4zD1ZobuFdfAtOugZL/cBw3ehdYFBD2PDK1r+yZDjW8tlB05whp2Q/AsdP7CR5rqvhF6biO18U2flTxKHZcONb4l+zL8iMDGpCfsh/6HUlfFF4z+MB1XbEQdzOQxzr3fLe0esDGbCw16IPlqlpIA1h3PUAL5dli7EBLhIz8yFSpOhLKPZ4Z8bDuDoP81gtajfRagQe24qT+E+j+PrpKdV5C915srQcIYJB4hIxLVRHFbI9N+4p9NnnoblOyG/1NUSWaM6shkOYt4ZcAPpEySBtiGoM2WbTAdXzbDTMDXdbgp5/m3qmjLx4ZCTtINa44iNb+uQOn+ALj4TILYByGZ/vhQLuPY0I6LMG3jmNQhDXchUorEGgjar/PD4QB0+VSQ2sLgD476uyH5DEgdrmyPja+6MZsR6wGbLVt8N3xIxbFRTfj7mXXR4VEhvdGU+luRqT8fXHaNd0DlabkPXHYQWWcbpx41+2o3M5yZHYeSnWHKTRCbCzRD/DBoeIKNm4A2TONbfjzD/lmpuxRxfmJa0Hk79KTNbo5NgcaHoPo/oOHPuPh0XPlh1viaVPYcsDHFzzmHa34ZSuaNbseaK9GgE209BF29lw8xWXFSWLg1VJw5tK1MQeI7IeLQ5LsEXTcTJN7F1X4dmf4aTLsr3H6gQS2B8s+QNz1iaY4h2dqzw15kPyRegfUnQHIZsBqmfAv6lzE03FxG9oixueSJw1z1GXANOYtd+d5DCx0/TCst8dG3Ys246pNxJTMxZjKzIWizxQs2XAqJPAHX6p6HnssguR6mnIormYX2PQXBGiAJ3bcy1Di9A903Qf1D0P8slB8MgGoP2nIgA/GVg6qzcTVfREpnQ+lstOqTaOsRQCesT48JnUXVl6Evej+5FOr/B4iDDny1oz/xA/9scEc0dV8vEI2BnY/63nXwRtZSKdkWpi1GW7P8OKg+P3W5dH4kE5NA43NIRrhLYzIVMnjGeLIG2Jiua/Nvs/5gpOmR1Ew8JTuCmwbJPqg4Ii3Rwr9w8a0hHgk2EXQwmNwAoPMHBBJHqk706fa6byEznV9UIxCJ2BWfg0x/Fe35X+j8MSRe8M/X5lQB5YdCz4PQN4I4zNmUH4OrW0TQ+6R/TKrvf1LLNYGUzMgcpC5dgKtOjXzlpl5G0Dkf6MBVfXls9TGmiNkQtDHxuZGFyuzb6KpI3ltPXDXU3wPlC6H/RSB3ZCdNrkG7FpMxhNvxPXSVj8FMaY6QjYPS0m6uPR5NroWe3/nGN5fSw6DhMWT6s6AOyg+H0sNBGkGGGX52M6HqLKj9MTQ+iUxfjqvzwTNc2e5I5cGp21f+G+KyJa+IwZQfZVkPruo4a3zN6NksaGMmB1f/a4KOa4E+n3t3/Wlk3n914Kb5wBq990H3XX54mRL8c74K1IF7j8+CVHYwwcqPAf/AN+pJUmdFR/USrNwRSEDJ3lBSD8l1YSCQYf44JJ6D1nyNNjDlQlysGtU+aJk7dG31jyMltWjrkZCMRNCKHQQ1Z+LKt896OA06fSKG2HzfQCffgOrTcSU+hGUQpPfiHS5Wnb+exoyEAoENQRszabhqP4FKVdH+z0HPPRBE7o266aj2hw1Yri9/GwRt/m1vNIx51whqEA5NJx5Dpi1F3FSfaajncVh/6lB5RjSuPOofGWz8tP81Un5YrPss0vQnpPEegsRb0P8SUjoPiaWGv1RNoBuuhOQ/IfEmBGmPbk35zWDj66X9eKn4xsjra8wWxBpgYyJEBKk9B2rPIei8BdrPAxSmXBgG1RinX96lR0DJXAhegpJdoeOcoTIdmDSVRGIV0PQ42v83CFYj5R9DJE7Q8ilIDvcMM4AgwQqgGQ3aQNOeJ9YkQWIlJN7Fle8KOWYd66o9GLbRb78AKu4dXHSulqDq36HzOij7GG7Kl/LU05jRKOyw8XiyBtiYHFzV0T4WcyjoWjJ+B688EVc+Z+jYkQZY1xwHjfeiq/cDXQNUIE2PIm7oXrVrvI2g71UQB12/g+4HID4bqk+H9d/yQUFKdkApQ9f9B/TeGe44E4K3AAflR0Hrvv78AI3PZAwVq/aRt8dd+ZmMVa7mTKg5c6T/GsZskawBNiaHINkK7T+E0nm4yqNgw1ljO1DJfEikZQdqOwaaX8++ffCOzxika8IV3WjiTTQ2Azp+CvHZSMWxuNIwhvWU7/pXSOsuQrt+B5qAdWk5d4N1MPUaJL4z2npMaln3zQTlh0H/M1B2CM6VIFKKEiP7PfFtoOZ8XMW+I/+3MGY8WA/YmEmu5QCgB3puJ0isAKkGHU285G2QxhuRWDNBxx9Sh5nTyXagf/fvaxehGy6PFJZDyU6wei7QBd2gPfci036bcRjVBLr2877eWXXAus+jlEDFZ6E78jyva4bWgWQTFdD8fHj6I8JEEFEBBKut8TWFMUkaYHsMyZicIo1Y101hEoXake9eezUS84/5uOr/B7W/Giqr/E7KptK0BGougJrvIxVHQl90EleASIyUyVx9mYFDVBXtupvcjW9UAoJ/QemnBioEHZH60U0Q+HvRru4iqPoalOxBymNUcUsNaMzGsB6wMblEh45ju4aP/HQAcag+G7oWQ/BX/O/YLAFlNxyGVryKiP+d6yr3g8rsw8666mOAD46h7ReklSbQRPp+/WiyBYk1Dq3quR3avzXy63NbQ/dAL7orY3aziO9laHI1dP+3n+EtNaCdQDnUXTnycxkzXibRY0jWAzYmB9dwPdRd5XumyVfwjS9AP3T9CoIN4XIANAFbZRxDRxBlK+hdykDj63VD9SKGepsBrFmYeeyWvQjavj+0nHgr77lSJF4ZprAx7HUDfUsh6MLfB477+tAJ607KvbsxJi9rgI0ZhivfH1d1HBnDusFKIBoZazWu+WGoTkswkHwn/0k2XJixSsr3hNj86Amz79vzW4Keh/w+VZ8Dqc++XQYHJR8cprxl8LiUzgMpB2Kgaxl8FCvxEjpJ7sWZYqL+Ub3xfBWINcDGRASJdwlWH0SwaneC7meHCuqik5DKs+zpH99x1UdCw8MQnw+l+yLV/z78+TpvheSrGeu19SBIPjmySif/AYC4Otz0J/JkdqqE+Ceh4QHovn744274tj9ubDqUfRHfA440uG6b1NjYxmwqForSmMlFg3XQuoDB3ub6Ywncdbiy3XHlswYfGwpWHQkaHb6tgqZlftJS3+MQ3wFXn6dxG9B5eY6CEf4qd7OQyuNT11UeBxvSJmlVfRVX85XBRU2uRvOdIzZr6H1PWizn2AeRhttHVkdjTFbWABszoOdBMhq+zqugbPfBxWDNF9Ma3w/imu/wZav2GHx2N6g8A1d7Rv5zpiV4GJHGJ3CxYYaaSz+cua7rNoLOK8Bti2u6H4k1oVVnQNd/g/aQES5TGpCpPm1hkMxSx/pbrPdrCsMmYRkz+aiblblS3pu63P982gbLCVZ+kKDl2EjgDKDr6uHPpUrQ/lOgKn/F4pFgGrE9h298AXHTyMi6pOEEreCfBCt3IGi7BGQ6SBOpja9AbFuoX+yzPQG0pT+/vBXOWd5eYzaWNcDGAMHab0DbsZkFvdcRJCNRoKqzJRZIQvJZUhrT0tQAFUH34wSrDyZY/z3/vG77xeHwc3v2CrkPATGI7YRMOw/X/Lp/NV6T91q066Z8W0DPVdBxLgTp958V13g/rmRbvxR0Qv9jqVVrfjhvHYyZUHYP2JhJpO+Pucta94PpvhFy1ScQVB4Nq3fO3K50fyAB8d2Q6qFHdFT7YP0X/EL339Hu64EsiQ/ih0DJVCjZHqk8YUxDvEH3HdBx0aj3GxSbk7ZC8b3p8I/UtLvHfmxjTAprgI0BoBToy16kq4feJv6GaIA0v07Q1+JzBydfAKqhbwmQgL4laPneSPz94U7Z8gBneWY3+QKufiN7l/3LN27/qRenLIqrRut+Bp2/goqjcKXv37jjGzMeJsnjbzYEbQxA/VIGf49WfRUqT40UVgIQrDkVbT0MXXM4wdozcaWNSMNiqP0e1F3OUM5eoH8oqpS4GiCaLzeH4F2CtcM/tpRX9VfD+7pjlFyTscqVH4irX+wTUhhTcOM8/GxD0MYUlotXQXNqZKig7ADofwIqTkCTq6D/gaHCvrsJEoug7cRIRKkYEEBsJlJxaOrxmx/0uXdb92fYR4z67ifoeQhXvv/YrsNVwvTHCDp+AR2XppXW4CdcpWc2AohD6QJcmcV3NmZTsQbYmBxc2RwomwOAahkZMZ9bdyN1ECkJTME13p/9eCXNBHU/gbbThz9x8t1R1TPo3wBrhnIFS8N90JmZKSnnhC9Amp5A3CgSTRhTKAoEhYteNZ5sCNqYERApg6np92cVSj+Xtm49QcuXcx+oZH7muupFIFvjw0Pugox2qLctNSazrjsPdF2enaqBWpAZUPsTa3yNKQDrARszAkHPcki+BuWnQc/PB9fL1LPQVX8EWoY2Tj5CsP483JT0rEbgSmoJKGcwtrSbjVR9Gqk+ZuyVK1sAXZFMRpLnnlbJh3ANfxj7+YwptEkyCcsaYGPyCNadCb2Rx29kOugqKF3gMwY13OhjN0d1LyboXgzln8XV/WdqWdMjfoi4dA9cJMrWWLnarxIk/w69fwa6IZEeQ/o9EH8/9D8M8V2RadmGp40pItYAG7OF6L0nZdFNfzRlWUq2RWu+D+3pEaOAnusJek/GlTUP7e/qoObr41pFN/VSgs4l0B4JfxlfgKv/5biexxgzfuwesDH5xEYwM7jvsdxl6/ZFNdvM43EWn5O6XHb4xJ/TmE1OfSzo8XwViDXAxuThGm+Eyv+A+D7QmCOJfe/SYY+h7T+ZgJqlcqXTofJcoALKT8RVHzHh5zTGjJ0NQRszAq72ZODk3BtUfR46L8ldHrSNc42yc7UnQe1J+Tc0plgpqNpjSMaYkKs5BahLWzsQkaocarPcHzbGjI0NQRtjUtSen7o89fIwi9ELOFdWmDoZYzZb1gAbM05c5eFQ9kmgBOKH48p2K3SVjJmcLBa0MSadm/oj4EeFroYxpghYA2yMMaZ4qFosaGOMMcaMnfWAjTHGFJdJEoqyID1gEZkjIk+KyHMiskxE5oXrRUSuEJEVIvKCiFhyUmOMMSk0CMb1VSiFGoL+IfBfqjoHOC9cBjgMmB2+TgF+nnVvY4wxpsgVaghagYEEpFOAf4XvFwLXqaoCT4pInYhspaqjy1BujDFmkirso0PjqVAN8JnAEhG5BN8L3zNcvw3wVmS7t8N11gAbY4yZVCasARaR+4HmLEXnAgcCX1fVP4jIMcCvgYOybDvc8U/BD1Oz7bbbbmRtjTHGFAWloOEjx9OENcCqmrNBFZHrgK+Fi7cAV4fv3wFmRjadEa7LdvyrgKsA5s6dOzk+DWOMMflZMoaN8i9gv/D9AcBfw/d3Ap8LZ0PvDqy3+7/GGGMmo0LdAz4ZuFxESoAewqFk4G7g48AKoAv4QmGqZ4wxZnOkgNoQ9Nip6mNARqT6cPbz6Zu+RsYYY8ymZZGwjDHGFA9VuwdsjDHGFIIGOq6vkRCRQ0XktTBS49lZystE5KawfKmIbJfvmNYAG2OMMcMQkRjwU3y0xh2B40Vkx7TNvgSsU9X3A5cBF+U7rjXAxhhjiosG4/vKbx6wQlXfUNU+YDE+cmPUQuDa8P3vgQNFRIY7qDXAxhhjzPByRWnMuo2qJoD1QP1wB50Uk7CefvrpVhH5R6HrkUMD0FroSoyDyXIdYNeyOZos1wF2LQDvGe+KDGhn3ZL79fcN43zYchFZFlm+Kgz2NKEmRQOsqo2FrkMuIrJMVecWuh4ba7JcB9i1bI4my3WAXctEU9VDC3DakURpHNjm7TDGxRRgzXAHtSFoY4wxZnh/AWaLyCwRKQWOw0dujLoTOCl8fxTwQBjbIqdJ0QM2xhhjJoqqJkTkK8ASIAb8RlVfFpELgGWqeic+qdD1IrICWItvpIdlDfDEm/D7CJvIZLkOsGvZHE2W6wC7lklJVe/Gh0uOrjsv8r4HOHo0x5Q8PWRjjDHGTAC7B2yMMcYUgDXAE0REzhCRV0XkZRH5YWT9OWGostdE5JBC1nE0ROSbIqIi0hAui4hcEV7LCyLykULXMR8RuTj8TF4QkdtEpC5SVlSfS76weJszEZkpIg+KyCvh9+Nr4fppInKfiPw1/O/UQtd1JEQkJiLPisgfw+VZYSjCFWFowtJC13EkRKRORH4ffkeWi8gexfqZFAtrgCeAiCzAR0XZRVV3Ai4J1++IvzG/E3Ao8LMwxNlmTURmAgcD/4ysPgyYHb5OAX5egKqN1n3Azqr6YeB14Bwovs9lhGHxNmcJ4JuquiOwO3B6WP+zgT+r6mzgz+FyMfgasDyyfBFwWRiScB0+RGExuBz4k6p+ANgFf03F+pkUBWuAJ8ZpwA9UtRdAVVeH6xcCi1W1V1XfxOc9nlegOo7GZcBZ+FScAxYC16n3JFAnIlsVpHYjpKr3hhFqAJ7EP8sHxfe5jCQs3mZLVd9V1WfC9+34P/TbkBrK71rgyIJUcBREZAZwOHB1uCzAAfhQhFA81zEF2Bc/kxdV7VPVNorwMykm1gBPjO2BfcJhqIdF5KPh+pGEM9usiMhC4B1VfT6tqOiuJc0XgXvC98V2LcVW35zCjDG7AkuB6ar6bli0EpheqHqNwo/xP04HAgrXA22RH3rF8tnMAlqA34bD6VeLSBXF+ZkUDXsMaYxE5H6gOUvRufh/12n44bWPAjeLyHs3YfVGJc+1fBs//FwUhrsWVb0j3OZc/DDoDZuybiaViFQDfwDOVNUN0bj1qqoislk/oiEinwBWq+rTIrJ/gauzsUqAjwBnqOpSEbmctOHmYvhMio01wGOkqgflKhOR04BbwygoT4lIgI+pOpJwZptcrmsRkQ/hfxk/H/5xnAE8IyLzKLJrGSAinwc+ARwYiVKzWV7LMIqtvhlEJI5vfG9Q1VvD1atEZCtVfTe8nbE69xE2C3sBR4jIx4FyoBZ/H7VORErCXnCxfDZvA2+r6tJw+ff4BrjYPpOiYkPQE+N2YAGAiGwPlOIDmt8JHCc+cfMs/ASmpwpVyXxU9UVVbVLV7VR1O/yX9COquhJ/LZ8LZ0PvDqyPDFVtlkTkUPxw4RGq2hUpKqrPhZGFxdtshfdJfw0sV9VLI0XRUH4nAXds6rqNhqqeo6ozwu/GcfjQgycAD+JDEUIRXAdA+J1+S0R2CFcdCLxCkX0mxcZ6wBPjN8BvROQloA84KextvSwiN+P/x04Ap6tqsoD13Bh3Ax/HT1jqAr5Q2OqMyJVAGXBf2KN/UlVPDUPKFc3nkissXoGrNRp7AZ8FXhSR58J13wZ+gL9d8yXgH8AxhaneRvsWsFhEFgHPEk5sKgJnADeEP+rewH+nHZPjM9ksWSQsY4wxpgBsCNoYY4wpAGuAjTHGmAKwBtgYY4wpAGuAjTHGmAKwBtgYY4wpAGuAzaQiIvUi8lz4Wiki70SWzw+z77wQLs8P93lIRJZFjjFXRB4K3+8vIusjx3hORDKCfYhItYj8UkT+JiJPh8ecv8kufAKIyJwwyES2snrxGY06ROTKTV03YyYDew7YTCqqugaYAyAi3wU6VPUSEdkDuBQfSKRXfFrFaJq4JhE5TFXvST8m8KiqfiLPqa8G3gRmq2oQBvQopgxF2cwB5uKf+U7XA/wnsHP4MsaMkvWAzZZiK6A1kqGqVVX/FSm/GB/7etRE5H3AfOA7qhqEx39TVe8Ky78hIi+FrzPDdduJz7t6jYi8LiI3iMhBIvK4+Nyr88Ltvisi14vIE+H6k8P1Ij6/8Usi8qKIHBuu3z/sfQ/kdb0hjDyFiOwmPjnI0yKyJAwtODACcJGIPBXWZZ8wGMMFwLFhr//Y6DWraqeqPoZviI0xY2ANsNlS3AvMDBuYn4nIfmnlTwB94nM5p9snbQj6fWnlOwHPZYueJSK74SMKzccn5zhZRHYNi98P/Aj4QPj6DLA38P/xkaEGfBif4m4P4DwR2Rr4NL6HugtwEHCxDKWD3BU4E98Dfy+wl/jYyz8BjlLV3fDR2i6MnKNEVeeF+50fpjk8D7hJVeeo6k1Z/l2MMRvBGmCzRVDVDmA34BR82rWbxCdmiFoEfCfL7o+GjdDA62+jOPXewG1hj7EDuBXYJyx7M4y3HQAv4xOfK/AisF3kGHeoareqtuLjDM8Lj3ujqiZVdRXwMD7zFsBTqvp2eNznwmPtgB8qvi8M//gdhvIhE9YL4Om0cxtjJojdAzZbjLCH+hDwkIi8iA8uf02k/IEwfu/uozz0y8AuIhIbZQzp3sj7ILIckPrdTI8Xmy9+bPS4yfBYArysqnvk2Wdge2PMBLMesNkiiMgOIjI7smoOPrh8ukX4jEkjFvaIlwH/Fbnfup2IHA48ChwpIpXiE5x/Klw3GgtFpFxE6oH98dmQHsXfn42JSCOwL8NncHoNaAwnoyEicRHZKc9524GaUdbVGDNC1gCbLUU1cK2IvCIiL+Dvj343fSNVvRs/RB2Vfg/4qPT9gC8D04EV4rNgXYNP1v5M+P4pYClwtao+O8q6v4Afen4S+F44eey2cP3zwAPAWWFKuazCe7pHAReJyPP4oek985z3QWDHbJOwAETk7/iZ5Z8XkbdFpNhnfRuzSVk2JGM2Y9FHqQpdF2PM+LIesDHGGFMA1gM2xhhjCsB6wMYYY0wBWANsjDHGFIA1wMYYY0wBWANsjDHGFIA1wMYYY0wBWANsjDHGFMD/AQ6cYO/EBJY/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# فرض کنید X داده‌های ویژگی و y لیبل‌های کلاس‌ها باشد\n",
    "# X: خروجی مدل شما که شامل بردارهای ویژگی است\n",
    "# y: برچسب‌های کلاس‌های شما\n",
    "\n",
    "# اجرای TSNE برای کاهش ابعاد به 2\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(feature_list)\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(8, 8))\n",
    "scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y_list, cmap='viridis', s=5)\n",
    "plt.colorbar(scatter)\n",
    "plt.title(\"TSNE of Model Features by Class\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f392a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = test_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3124d8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 349/349 [00:48<00:00,  7.24batch/s]\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "y_list = []\n",
    "feature_list = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        logit,_ ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list.append(live_label.cpu().numpy())\n",
    "        feature_list.append(features.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1d62ef69",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list= np.concatenate(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1bb41b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = np.concatenate(feature_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2070d637",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# فرض کنید X داده‌های ویژگی و y لیبل‌های کلاس‌ها باشد\n",
    "# X: خروجی مدل شما که شامل بردارهای ویژگی است\n",
    "# y: برچسب‌های کلاس‌های شما\n",
    "\n",
    "# اجرای TSNE برای کاهش ابعاد به 2\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(feature_list)\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(8, 8))\n",
    "scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y_list, cmap='viridis', s=5)\n",
    "plt.colorbar(scatter)\n",
    "plt.title(\"TSNE of Model Features by Class\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d478b379",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 349/349 [00:54<00:00,  6.45batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_1/score.txt\n",
      "epoch:1, test:  val_ACC=0.9537, HTER=0.0464, AUC=0.9855, val_err=0.0464, ACC=0.9537, TPR=0.9545\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9854837664701319"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test(loader,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b8b66f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "\n",
    "class SupConLoss(nn.Module):\n",
    "    \"\"\"Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.\n",
    "    It also supports the unsupervised contrastive loss in SimCLR\"\"\"\n",
    "    def __init__(self, temperature=0.07, contrast_mode='all',\n",
    "                 base_temperature=0.07):\n",
    "        super(SupConLoss, self).__init__()\n",
    "        self.temperature = temperature\n",
    "        self.contrast_mode = contrast_mode\n",
    "        self.base_temperature = base_temperature\n",
    "\n",
    "    def forward(self, features, labels=None, mask=None):\n",
    "        \"\"\"Compute loss for model. If both `labels` and `mask` are None,\n",
    "        it degenerates to SimCLR unsupervised loss:\n",
    "        https://arxiv.org/pdf/2002.05709.pdf\n",
    "\n",
    "        Args:\n",
    "            features: hidden vector of shape [bsz, n_views, ...].\n",
    "            labels: ground truth of shape [bsz].\n",
    "            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j\n",
    "                has the same class as sample i. Can be asymmetric.\n",
    "        Returns:\n",
    "            A loss scalar.\n",
    "        \"\"\"\n",
    "        device = (torch.device('cuda')\n",
    "                  if features.is_cuda\n",
    "                  else torch.device('cpu'))\n",
    "\n",
    "        if len(features.shape) < 3:\n",
    "            raise ValueError('`features` needs to be [bsz, n_views, ...],'\n",
    "                             'at least 3 dimensions are required')\n",
    "        if len(features.shape) > 3:\n",
    "            features = features.view(features.shape[0], features.shape[1], -1)\n",
    "\n",
    "        batch_size = features.shape[0]\n",
    "        if labels is not None and mask is not None:\n",
    "            raise ValueError('Cannot define both `labels` and `mask`')\n",
    "        elif labels is None and mask is None:\n",
    "            mask = torch.eye(batch_size, dtype=torch.float32).to(device)\n",
    "        elif labels is not None:\n",
    "            labels = labels.contiguous().view(-1, 1)\n",
    "            if labels.shape[0] != batch_size:\n",
    "                raise ValueError('Num of labels does not match num of features')\n",
    "            mask = torch.eq(labels, labels.T).float().to(device)\n",
    "        else:\n",
    "            mask = mask.float().to(device)\n",
    "\n",
    "        contrast_count = features.shape[1]\n",
    "        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)\n",
    "        if self.contrast_mode == 'one':\n",
    "            anchor_feature = features[:, 0]\n",
    "            anchor_count = 1\n",
    "        elif self.contrast_mode == 'all':\n",
    "            anchor_feature = contrast_feature\n",
    "            anchor_count = contrast_count\n",
    "        else:\n",
    "            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))\n",
    "\n",
    "        # compute logits\n",
    "        anchor_dot_contrast = torch.div(\n",
    "            torch.matmul(anchor_feature, contrast_feature.T),\n",
    "            self.temperature)\n",
    "        # for numerical stability\n",
    "        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)\n",
    "        logits = anchor_dot_contrast - logits_max.detach()\n",
    "\n",
    "        # tile mask\n",
    "        mask = mask.repeat(anchor_count, contrast_count)\n",
    "        # mask-out self-contrast cases\n",
    "        logits_mask = torch.scatter(\n",
    "            torch.ones_like(mask),\n",
    "            1,\n",
    "            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),\n",
    "            0\n",
    "        )\n",
    "        mask = mask * logits_mask\n",
    "\n",
    "        # compute log_prob\n",
    "        exp_logits = torch.exp(logits) * logits_mask\n",
    "        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))\n",
    "\n",
    "        # compute mean of log-likelihood over positive\n",
    "        # modified to handle edge cases when there is no positive pair\n",
    "        # for an anchor point. \n",
    "        # Edge case e.g.:- \n",
    "        # features of shape: [4,1,...]\n",
    "        # labels:            [0,1,1,2]\n",
    "        # loss before mean:  [nan, ..., ..., nan] \n",
    "        mask_pos_pairs = mask.sum(1)\n",
    "        mask_pos_pairs = torch.where(mask_pos_pairs < 1e-6, 1, mask_pos_pairs)\n",
    "        mean_log_prob_pos = (mask * log_prob).sum(1) / mask_pos_pairs\n",
    "\n",
    "        # loss\n",
    "        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos\n",
    "        loss = loss.view(anchor_count, batch_size).mean()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4893abcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): ChebConv(640, 128, K=2, normalization=sym)\n",
      "  (conv2): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv3): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv4): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv5): ChebConv(256, 128, K=2, normalization=sym)\n",
      "  (conv6): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv7): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv8): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (lin): SimpleTransformer(\n",
      "    (linear): Linear(in_features=4, out_features=8, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "      (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=1024, out_features=1, bias=True)\n",
      "  )\n",
      "  (drop_1): RandomNodeDropout()\n",
      "  (drop_2): RandomNodeDropout()\n",
      "  (drop_3): RandomNodeDropout()\n",
      "  (drop_4): RandomNodeDropout()\n",
      "  (drop_5): RandomNodeDropout()\n",
      "  (drop_6): RandomNodeDropout()\n",
      "  (b1): BatchNorm(128)\n",
      "  (b2): BatchNorm(128)\n",
      "  (b3): BatchNorm(128)\n",
      "  (b4): BatchNorm(128)\n",
      "  (b5): BatchNorm(128)\n",
      "  (b6): BatchNorm(128)\n",
      "  (b7): BatchNorm(128)\n",
      "  (dis): Discriminator(\n",
      "    (conv1): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv2): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv3): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv4): GraphSAGE(128, 128, num_layers=2)\n",
      "    (drop_1): RandomNodeDropout()\n",
      "    (drop_2): RandomNodeDropout()\n",
      "    (grl_layer): GRL()\n",
      "    (fc2): Linear(in_features=128, out_features=3, bias=True)\n",
      "  )\n",
      "  (conv1_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (conv2_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (lin_node_class): SimpleTransformer_2(\n",
      "    (linear): Linear(in_features=1, out_features=64, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "      (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (5): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (6): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (7): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (8): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (9): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=8192, out_features=7, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class RandomNodeDropout(nn.Module):\n",
    "    def __init__(self, drop_prob=0.3):\n",
    "        super(RandomNodeDropout, self).__init__()\n",
    "        self.drop_prob = drop_prob\n",
    "\n",
    "    def forward(self, x, enabled=True):\n",
    "        if not self.training or not enabled:\n",
    "            return x\n",
    "        \n",
    "        num_rows = x.size(0)\n",
    "        mask = torch.rand(num_rows, device=x.device) > self.drop_prob\n",
    "        mask = mask.float().unsqueeze(1).expand_as(x)\n",
    "        x = x * mask\n",
    "        \n",
    "        return x\n",
    "\n",
    "soft = torch.nn.Softmax(dim=1)\n",
    "\n",
    "num_domain = 3\n",
    "class GRL(nn.Module):\n",
    "\n",
    "    def __init__(self, max_iter):\n",
    "        super(GRL, self).__init__()\n",
    "        self.iter_num = 0\n",
    "        self.alpha = 10\n",
    "        self.low = 0.0\n",
    "        self.high = 1.0\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.iter_num += 1\n",
    "        return input * 1.0\n",
    "\n",
    "    def backward(self, gradOutput):\n",
    "        coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter))\n",
    "                         - (self.high - self.low) + self.low)\n",
    "        return -coeff*10* gradOutput\n",
    "\n",
    "num_node  =train_dataset.num_node_features\n",
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.drop_2(x,drop_en)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv4(x, edge_index)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.fc2(x)\n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels,max_iter=4000):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = ChebConv(num_node, hidden_channels,2)\n",
    "        self.conv2 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv3 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv4 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv5 = ChebConv(hidden_channels*2, hidden_channels,2)\n",
    "        self.conv6 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv7 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv8 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.lin = SimpleTransformer(8,5,8,1)\n",
    "        \n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_3 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_4 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_5 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_6 = RandomNodeDropout(drop_prob=0.3)\n",
    "        \n",
    "        \n",
    "        self.b1 = BatchNorm(hidden_channels)\n",
    "        self.b2 = BatchNorm(hidden_channels)\n",
    "        self.b3 = BatchNorm(hidden_channels)\n",
    "        self.b4 = BatchNorm(hidden_channels)\n",
    "        self.b5 = BatchNorm(hidden_channels)\n",
    "        self.b6 = BatchNorm(hidden_channels)\n",
    "        self.b7 = BatchNorm(hidden_channels)\n",
    "        \n",
    "        self.dis = Discriminator(hidden_channels,max_iter)\n",
    "        \n",
    "        self.conv1_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.conv2_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.lin_node_class = SimpleTransformer_2(64,10,8)\n",
    "        \n",
    "\n",
    "        \n",
    "        #self.tcn = Simple1DCNN()\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en,index_arr):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x  = self.b1(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        \n",
    "\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x  = self.b2(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_2(x,drop_en)\n",
    "                \n",
    "        dicriminator = x\n",
    "        \n",
    "        x_1 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x  = self.b3(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_3(x,drop_en)\n",
    "        \n",
    "        x_3 = global_mean_pool(x, batch)\n",
    "        \n",
    "        \n",
    "        x = self.conv4(x, edge_index)\n",
    "        x  = self.b4(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_4(x,drop_en)\n",
    "        \n",
    "        \n",
    "        x = torch.cat((x,dicriminator),dim=1) #skip connection\n",
    "        \n",
    "        x = self.conv5(x, edge_index)\n",
    "        x  = self.b5(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_5(x,drop_en)\n",
    "        \n",
    "        x_5 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv6(x, edge_index)\n",
    "        x  = self.b6(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_6(x,drop_en)\n",
    "        \n",
    "        x = self.conv7(x, edge_index)\n",
    "        x  = self.b7(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        \n",
    "        x = self.conv8(x, edge_index)\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        x = torch.stack([x,x_1,x_5,x_3],dim=2)\n",
    "        \n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        \n",
    "        #x = torch.unsqueeze(x,2)\n",
    "        #print(x.shape)\n",
    "        \n",
    "        x,feature = self.lin(x)\n",
    "        \n",
    "        x= x.sigmoid()\n",
    "        \n",
    "        \n",
    "        node_class_hidden = self.conv1_node_class(dicriminator,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        node_class_hidden = self.conv2_node_class(node_class_hidden,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        #print(index_arr.shape , node_class_hidden.shape)\n",
    "        \n",
    "        node_class_hidden = node_class_hidden[index_arr]\n",
    "        \n",
    "        #print(node_class_hidden.shape)\n",
    "        node_class_hidden = torch.unsqueeze(node_class_hidden, dim=2)\n",
    "        \n",
    "        node_pre = self.lin_node_class(node_class_hidden)\n",
    "        \n",
    "            \n",
    "        #print(\"hiiii :\" , dicriminator.shape)\n",
    "        dis_invariant = self.dis(dicriminator,edge_index,batch,drop_en)\n",
    "        \n",
    "        return x ,dis_invariant ,node_pre,feature\n",
    "\n",
    "model = GCN(hidden_channels=128).to(device)\n",
    "print(model)\n",
    "\n",
    "def check_folder(log_dir):\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.makedirs(log_dir)\n",
    "    return log_dir\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "#criterion = torch.nn.CrossEntropyLoss()\n",
    "criterion= nn.BCELoss()\n",
    "criterion_node_class=  torch.nn.CrossEntropyLoss()\n",
    "criterion_2 = FocalLoss(gamma=2)\n",
    "Contrast_loss = SupConLoss(temperature=0.1)\n",
    "\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    losses_adv_loss=0\n",
    "    losses_loss_cls=0\n",
    "    total_loss = 0\n",
    "    losses_node_cls_loss = 0\n",
    "    losses_con =0\n",
    "    progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training dataset.\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        drop_en = bool(np.random.binomial(1, 0.5))\n",
    "        \n",
    "        if graph.x.shape[0] < 29952 :\n",
    "            \n",
    "            if graph.x.shape[0] < 64:\n",
    "            \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "                \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "                \n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        out,domain_invariant,node_pre,feature = model(graph.x, graph.edge_index, graph.batch,drop_en,index_arr)  # Perform a single forward pass.\n",
    "        \n",
    "        adv_loss = criterion_2(domain_invariant, graph.data)\n",
    "        \n",
    "        normilized_features= F.normalize(feature, p=2, dim=1)\n",
    "        \n",
    "        normilized_features = normilized_features.unsqueeze(dim=1)\n",
    "        \n",
    "        #print(normilized_features.shape)\n",
    "\n",
    "        con_loss = Contrast_loss(normilized_features, graph.y.float())\n",
    "        \n",
    "        node_cls_loss = criterion_node_class(node_pre.squeeze(),graph.y_node.float()[index_arr])\n",
    "        #print(out.shape , graph.y.long().shape)\n",
    "        loss_cls = criterion(out.squeeze(), graph.y.float())  # Compute the loss.\n",
    "        \n",
    "        loss_all = adv_loss + loss_cls + node_cls_loss + con_loss\n",
    "        loss_all.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        total_loss = loss_all + total_loss\n",
    "        losses_adv_loss = adv_loss + losses_adv_loss\n",
    "        losses_loss_cls = loss_cls + losses_loss_cls\n",
    "        losses_node_cls_loss = node_cls_loss + losses_node_cls_loss\n",
    "        losses_con = con_loss + losses_con\n",
    "\n",
    "\n",
    "    print(\"/////////////////////////\")\n",
    "    print(\"adverserial loss : \" ,losses_adv_loss/len(train_loader.dataset))  \n",
    "    print(\"classification loss : \" , losses_loss_cls/len(train_loader.dataset))\n",
    "    print(\"classification node loss : \" , losses_node_cls_loss/len(train_loader.dataset))\n",
    "    print(\"Contrastive loss : \" ,losses_con/len(train_loader.dataset) )\n",
    "    print(\"total loss : \" ,total_loss/len(train_loader.dataset) )\n",
    "    print(\"////////////////////////////////////\")\n",
    "def test(loader,epoch):\n",
    "    score_path = os.path.join(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores\", \"epoch_{}\".format(epoch+1))\n",
    "    check_folder(score_path)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        scores_list = []\n",
    "\n",
    "        correct = 0\n",
    "        s= 0\n",
    "        progress_bar = tqdm(loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "        for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "            if graph.x.shape[0] < 29952 :\n",
    "\n",
    "                if graph.x.shape[0] < 64:\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "                else :\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "                \n",
    "            graph = graph.to(device)  # Move data to the device\n",
    "            logit,_ ,_,_= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "            live_label = graph.y.float()\n",
    "            for i in range(len(logit)):\n",
    "                        scores_list.append(\"{} {}\\n\".format(logit[i].item(), live_label[i].item()))\n",
    "  # Check against ground-truth labels.\n",
    "    map_score_val_filename = os.path.join(score_path, \"score.txt\")\n",
    "    with open(map_score_val_filename, 'w') as file:\n",
    "                file.writelines(scores_list)\n",
    "    print(\"score: write test scores to {}\".format(map_score_val_filename))\n",
    "    test_ACC, fpr, FRR, HTER, auc_test, test_err, tpr = performances_val(map_score_val_filename)\n",
    "    print(\"epoch:{:d}, test:  val_ACC={:.4f}, HTER={:.4f}, AUC={:.4f}, val_err={:.4f}, ACC={:.4f}, TPR={:.4f}\".format(\n",
    "        epoch + 1, test_ACC, HTER, auc_test, test_err, test_ACC, tpr))  \n",
    "    return auc_test\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20a95b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████████████████████████| 3342/3342 [08:17<00:00,  6.71batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(4.5333e-12, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(6.3215e-06, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(2.6217e-05, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "Contrastive loss :  tensor(0.0815, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0816, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 349/349 [00:52<00:00,  6.60batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_1/score.txt\n",
      "epoch:1, test:  val_ACC=0.9479, HTER=0.2193, AUC=0.8198, val_err=0.2229, ACC=0.9479, TPR=0.0000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|████████████████████████████████████████████████████████████████████████████████████████| 3342/3342 [08:16<00:00,  6.73batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/////////////////////////\n",
      "adverserial loss :  tensor(9.9635e-07, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification loss :  tensor(1.2595e-05, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "classification node loss :  tensor(2.7430e-05, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "Contrastive loss :  tensor(0.0811, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "total loss :  tensor(0.0812, device='cuda:0', grad_fn=<DivBackward0>)\n",
      "////////////////////////////////////\n",
      "***************************\n",
      "test : \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 349/349 [00:50<00:00,  6.86batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score: write test scores to /home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores/epoch_2/score.txt\n",
      "epoch:2, test:  val_ACC=0.8393, HTER=0.1607, AUC=0.8959, val_err=0.1607, ACC=0.8393, TPR=0.7320\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3:   0%|▏                                                                                          | 8/3342 [00:05<37:41,  1.47batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m best_auc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.9855\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1000\u001b[39m):\n\u001b[0;32m----> 4\u001b[0m     \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m***************************\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;66;03m#print(\"train : \")\u001b[39;00m\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;66;03m#train_auc = test(train_loader,epoch)\u001b[39;00m\n",
      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(epoch)\u001b[0m\n\u001b[1;32m    239\u001b[0m progress_bar \u001b[38;5;241m=\u001b[39m tqdm(train_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m, unit\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    240\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m graph \u001b[38;5;129;01min\u001b[39;00m progress_bar:  \u001b[38;5;66;03m# Iterate in batches over the training dataset.\u001b[39;00m\n\u001b[0;32m--> 241\u001b[0m     graph \u001b[38;5;241m=\u001b[39m \u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[1;32m    243\u001b[0m     drop_en \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mbinomial(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0.5\u001b[39m))\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m graph\u001b[38;5;241m.\u001b[39mx\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m29952\u001b[39m :\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/data.py:262\u001b[0m, in \u001b[0;36mBaseData.to\u001b[0;34m(self, device, non_blocking, *args)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto\u001b[39m(\u001b[38;5;28mself\u001b[39m, device: Union[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mstr\u001b[39m], \u001b[38;5;241m*\u001b[39margs: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[1;32m    259\u001b[0m        non_blocking: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Performs tensor device conversion, either for all attributes or\u001b[39;00m\n\u001b[1;32m    261\u001b[0m \u001b[38;5;124;03m    only the ones given in :obj:`*args`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    263\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnon_blocking\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/data.py:245\u001b[0m, in \u001b[0;36mBaseData.apply\u001b[0;34m(self, func, *args)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Applies the function :obj:`func`, either to all attributes or only\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;124;03mthe ones given in :obj:`*args`.\"\"\"\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m store \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstores:\n\u001b[0;32m--> 245\u001b[0m     \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    246\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/storage.py:184\u001b[0m, in \u001b[0;36mBaseStorage.apply\u001b[0;34m(self, func, *args)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Applies the function :obj:`func`, either to all attributes or only\u001b[39;00m\n\u001b[1;32m    182\u001b[0m \u001b[38;5;124;03mthe ones given in :obj:`*args`.\"\"\"\u001b[39;00m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems(\u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m--> 184\u001b[0m     \u001b[38;5;28mself\u001b[39m[key] \u001b[38;5;241m=\u001b[39m \u001b[43mrecursive_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/storage.py:682\u001b[0m, in \u001b[0;36mrecursive_apply\u001b[0;34m(data, func)\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrecursive_apply\u001b[39m(data: Any, func: Callable) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    681\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, Tensor):\n\u001b[0;32m--> 682\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    683\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mrnn\u001b[38;5;241m.\u001b[39mPackedSequence):\n\u001b[1;32m    684\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(data)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/data/data.py:263\u001b[0m, in \u001b[0;36mBaseData.to.<locals>.<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mto\u001b[39m(\u001b[38;5;28mself\u001b[39m, device: Union[\u001b[38;5;28mint\u001b[39m, \u001b[38;5;28mstr\u001b[39m], \u001b[38;5;241m*\u001b[39margs: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[1;32m    259\u001b[0m        non_blocking: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Performs tensor device conversion, either for all attributes or\u001b[39;00m\n\u001b[1;32m    261\u001b[0m \u001b[38;5;124;03m    only the ones given in :obj:`*args`.\"\"\"\u001b[39;00m\n\u001b[1;32m    262\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply(\n\u001b[0;32m--> 263\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnon_blocking\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnon_blocking\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;241m*\u001b[39margs)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"Focal_without_pipline_domain_generalization_tuning.pth\",map_location='cuda'))\n",
    "best_auc = 0.9855\n",
    "for epoch in range(0, 1000):\n",
    "    train(epoch)\n",
    "    print(\"***************************\")\n",
    "    #print(\"train : \")\n",
    "    #train_auc = test(train_loader,epoch)\n",
    "    print(\"test : \")\n",
    "    test_auc = test(test_loader,epoch)\n",
    "    if test_auc > best_auc:\n",
    "        print(\"improve acc .. .. ..\")\n",
    "        torch.save(model.state_dict(), 'Focal_without_pipline_domain_generalization_tuning.pth')\n",
    "        best_auc = test_auc\n",
    "        continue "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "797d839d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): ChebConv(640, 128, K=2, normalization=sym)\n",
      "  (conv2): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv3): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv4): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv5): ChebConv(256, 128, K=2, normalization=sym)\n",
      "  (conv6): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv7): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (conv8): ChebConv(128, 128, K=2, normalization=sym)\n",
      "  (lin): SimpleTransformer(\n",
      "    (linear): Linear(in_features=4, out_features=8, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "      (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=8, out_features=8, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=8, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=8, bias=True)\n",
      "          (norm1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=1024, out_features=1, bias=True)\n",
      "  )\n",
      "  (drop_1): RandomNodeDropout()\n",
      "  (drop_2): RandomNodeDropout()\n",
      "  (drop_3): RandomNodeDropout()\n",
      "  (drop_4): RandomNodeDropout()\n",
      "  (drop_5): RandomNodeDropout()\n",
      "  (drop_6): RandomNodeDropout()\n",
      "  (b1): BatchNorm(128)\n",
      "  (b2): BatchNorm(128)\n",
      "  (b3): BatchNorm(128)\n",
      "  (b4): BatchNorm(128)\n",
      "  (b5): BatchNorm(128)\n",
      "  (b6): BatchNorm(128)\n",
      "  (b7): BatchNorm(128)\n",
      "  (dis): Discriminator(\n",
      "    (conv1): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv2): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv3): GraphSAGE(128, 128, num_layers=2)\n",
      "    (conv4): GraphSAGE(128, 128, num_layers=2)\n",
      "    (drop_1): RandomNodeDropout()\n",
      "    (drop_2): RandomNodeDropout()\n",
      "    (grl_layer): GRL()\n",
      "    (fc2): Linear(in_features=128, out_features=3, bias=True)\n",
      "  )\n",
      "  (conv1_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (conv2_node_class): GraphSAGE(128, 128, num_layers=2)\n",
      "  (lin_node_class): SimpleTransformer_2(\n",
      "    (linear): Linear(in_features=1, out_features=64, bias=True)\n",
      "    (encoder_layer): TransformerEncoderLayer(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "      )\n",
      "      (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "      (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "      (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "      (dropout1): Dropout(p=0.1, inplace=False)\n",
      "      (dropout2): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (transformer_encoder): TransformerEncoder(\n",
      "      (layers): ModuleList(\n",
      "        (0): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (1): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (2): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (3): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (4): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (5): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (6): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (7): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (8): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "        (9): TransformerEncoderLayer(\n",
      "          (self_attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
      "          )\n",
      "          (linear1): Linear(in_features=64, out_features=2048, bias=True)\n",
      "          (dropout): Dropout(p=0.1, inplace=False)\n",
      "          (linear2): Linear(in_features=2048, out_features=64, bias=True)\n",
      "          (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
      "          (dropout1): Dropout(p=0.1, inplace=False)\n",
      "          (dropout2): Dropout(p=0.1, inplace=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (fc): Linear(in_features=8192, out_features=7, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class RandomNodeDropout(nn.Module):\n",
    "    def __init__(self, drop_prob=0.3):\n",
    "        super(RandomNodeDropout, self).__init__()\n",
    "        self.drop_prob = drop_prob\n",
    "\n",
    "    def forward(self, x, enabled=True):\n",
    "        if not self.training or not enabled:\n",
    "            return x\n",
    "        \n",
    "        num_rows = x.size(0)\n",
    "        mask = torch.rand(num_rows, device=x.device) > self.drop_prob\n",
    "        mask = mask.float().unsqueeze(1).expand_as(x)\n",
    "        x = x * mask\n",
    "        \n",
    "        return x\n",
    "\n",
    "soft = torch.nn.Softmax(dim=1)\n",
    "\n",
    "num_domain = 3\n",
    "class GRL(nn.Module):\n",
    "\n",
    "    def __init__(self, max_iter):\n",
    "        super(GRL, self).__init__()\n",
    "        self.iter_num = 0\n",
    "        self.alpha = 10\n",
    "        self.low = 0.0\n",
    "        self.high = 1.0\n",
    "        self.max_iter = max_iter\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.iter_num += 1\n",
    "        return input * 1.0\n",
    "\n",
    "    def backward(self, gradOutput):\n",
    "        coeff = np.float(2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter))\n",
    "                         - (self.high - self.low) + self.low)\n",
    "        return -coeff*10* gradOutput\n",
    "\n",
    "num_node  =train_dataset.num_node_features\n",
    "class Discriminator_2(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator_2, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "                \n",
    "        x = self.fc2(x)\n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,hidden_channels ,max_iter):\n",
    "        super(Discriminator, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv2 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv3 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.conv4 = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.grl_layer = GRL(max_iter)\n",
    "        self.fc2 = nn.Linear(hidden_channels, num_domain)\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "        x = self.grl_layer(x)\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()        \n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "                \n",
    "        x = self.fc2(x)\n",
    "        x = m(x)\n",
    "         \n",
    "        return x \n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels,max_iter=4000):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = ChebConv(num_node, hidden_channels,2)\n",
    "        self.conv2 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv3 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv4 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv5 = ChebConv(hidden_channels*2, hidden_channels,2)\n",
    "        self.conv6 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv7 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.conv8 = ChebConv(hidden_channels, hidden_channels,2)\n",
    "        self.lin = SimpleTransformer(8,5,8,1)\n",
    "        \n",
    "        self.drop_1 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_2 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_3 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_4 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_5 = RandomNodeDropout(drop_prob=0.3)\n",
    "        self.drop_6 = RandomNodeDropout(drop_prob=0.3)\n",
    "        \n",
    "        \n",
    "        self.b1 = BatchNorm(hidden_channels)\n",
    "        self.b2 = BatchNorm(hidden_channels)\n",
    "        self.b3 = BatchNorm(hidden_channels)\n",
    "        self.b4 = BatchNorm(hidden_channels)\n",
    "        self.b5 = BatchNorm(hidden_channels)\n",
    "        self.b6 = BatchNorm(hidden_channels)\n",
    "        self.b7 = BatchNorm(hidden_channels)\n",
    "        \n",
    "        self.dis = Discriminator(hidden_channels,max_iter)\n",
    "        \n",
    "        self.conv1_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.conv2_node_class = GraphSAGE(hidden_channels, hidden_channels,2,dropout=0.5)\n",
    "        \n",
    "        self.lin_node_class = SimpleTransformer_2(64,10,8)\n",
    "        \n",
    "\n",
    "        \n",
    "        #self.tcn = Simple1DCNN()\n",
    "        \n",
    "    def forward(self, x, edge_index, batch,drop_en,index_arr):\n",
    "        #print(\"graph fea :\",graph_level_feature.shape ,edge_index.shape )\n",
    "        \n",
    "\n",
    "        x = self.conv1(x, edge_index)\n",
    "        x  = self.b1(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_1(x,drop_en)\n",
    "        \n",
    "        \n",
    "\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x  = self.b2(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_2(x,drop_en)\n",
    "                \n",
    "        dicriminator = x\n",
    "        \n",
    "        x_1 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv3(x, edge_index)\n",
    "        x  = self.b3(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_3(x,drop_en)\n",
    "        \n",
    "        x_3 = global_mean_pool(x, batch)\n",
    "        \n",
    "        \n",
    "        x = self.conv4(x, edge_index)\n",
    "        x  = self.b4(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_4(x,drop_en)\n",
    "        \n",
    "        \n",
    "        x = torch.cat((x,dicriminator),dim=1) #skip connection\n",
    "        \n",
    "        x = self.conv5(x, edge_index)\n",
    "        x  = self.b5(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_5(x,drop_en)\n",
    "        \n",
    "        x_5 = global_mean_pool(x, batch)\n",
    "        \n",
    "        x = self.conv6(x, edge_index)\n",
    "        x  = self.b6(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        x = self.drop_6(x,drop_en)\n",
    "        \n",
    "        x = self.conv7(x, edge_index)\n",
    "        x  = self.b7(x)\n",
    "        x = x.relu()\n",
    "        \n",
    "        \n",
    "        x = self.conv8(x, edge_index)\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "        \n",
    "        x = torch.stack([x,x_1,x_5,x_3],dim=2)\n",
    "        \n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        \n",
    "        #x = torch.unsqueeze(x,2)\n",
    "        #print(x.shape)\n",
    "        \n",
    "        x = self.lin(x)\n",
    "        \n",
    "        x= x.sigmoid()\n",
    "        \n",
    "        \n",
    "        node_class_hidden = self.conv1_node_class(dicriminator,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        node_class_hidden = self.conv2_node_class(node_class_hidden,edge_index)\n",
    "        node_class_hidden = node_class_hidden.relu()\n",
    "        \n",
    "        #print(index_arr.shape , node_class_hidden.shape)\n",
    "        \n",
    "        node_class_hidden = node_class_hidden[index_arr]\n",
    "        \n",
    "        #print(node_class_hidden.shape)\n",
    "        node_class_hidden = torch.unsqueeze(node_class_hidden, dim=2)\n",
    "        \n",
    "        node_pre = self.lin_node_class(node_class_hidden)\n",
    "        \n",
    "            \n",
    "        #print(\"hiiii :\" , dicriminator.shape)\n",
    "        dis_invariant = self.dis(dicriminator,edge_index,batch,drop_en)\n",
    "        \n",
    "        return x ,dis_invariant ,node_pre ,dicriminator\n",
    "\n",
    "model = GCN(hidden_channels=128).to(device)\n",
    "print(model)\n",
    "\n",
    "def check_folder(log_dir):\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.makedirs(log_dir)\n",
    "    return log_dir\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)\n",
    "#criterion = torch.nn.CrossEntropyLoss()\n",
    "criterion= nn.BCELoss()\n",
    "criterion_node_class=  torch.nn.CrossEntropyLoss()\n",
    "criterion_2 = FocalLoss(gamma=2)\n",
    "def train(epoch):\n",
    "    model.train()\n",
    "    losses_adv_loss=0\n",
    "    losses_loss_cls=0\n",
    "    total_loss = 0\n",
    "    losses_node_cls_loss = 0\n",
    "    progress_bar = tqdm(train_loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training dataset.\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        drop_en = bool(np.random.binomial(1, 0.5))\n",
    "        \n",
    "        if graph.x.shape[0] < 29952 :\n",
    "            \n",
    "            if graph.x.shape[0] < 64:\n",
    "            \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "                \n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "                \n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        out,domain_invariant,node_pre = model(graph.x, graph.edge_index, graph.batch,drop_en,index_arr)  # Perform a single forward pass.\n",
    "        \n",
    "        \n",
    "        adv_loss = criterion_2(domain_invariant, graph.data.long())\n",
    "        \n",
    "        node_cls_loss = criterion_node_class(node_pre.squeeze(),graph.y_node.float()[index_arr])\n",
    "        #print(out.shape , graph.y.long().shape)\n",
    "        loss_cls = criterion(out.squeeze(), graph.y.float())  # Compute the loss.\n",
    "        \n",
    "        loss_all = adv_loss + loss_cls + node_cls_loss\n",
    "        loss_all.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "        total_loss = loss_all + total_loss\n",
    "        losses_adv_loss = adv_loss + losses_adv_loss\n",
    "        losses_loss_cls = loss_cls + losses_loss_cls\n",
    "        losses_node_cls_loss = node_cls_loss + losses_node_cls_loss\n",
    "\n",
    "\n",
    "    print(\"/////////////////////////\")\n",
    "    print(\"adverserial loss : \" ,losses_adv_loss/len(train_loader.dataset))  \n",
    "    print(\"classification loss : \" , losses_loss_cls/len(train_loader.dataset))\n",
    "    print(\"classification node loss : \" , losses_node_cls_loss/len(train_loader.dataset))\n",
    "    print(\"total loss : \" ,total_loss/len(train_loader.dataset) )\n",
    "    print(\"////////////////////////////////////\")\n",
    "def test(loader,epoch):\n",
    "    score_path = os.path.join(\"/home/hassan-hossein/single_image_graph_face_anti_spoofing/train/Scores\", \"epoch_{}\".format(epoch+1))\n",
    "    check_folder(score_path)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        scores_list = []\n",
    "\n",
    "        correct = 0\n",
    "        s= 0\n",
    "        progress_bar = tqdm(loader, desc=f'Epoch {epoch+1}', unit='batch')\n",
    "        for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "            if graph.x.shape[0] < 29952 :\n",
    "\n",
    "                if graph.x.shape[0] < 64:\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "                else :\n",
    "\n",
    "                    index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "                \n",
    "            graph = graph.to(device)  # Move data to the device\n",
    "            logit,_ ,_= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "            live_label = graph.y.float()\n",
    "            for i in range(len(logit)):\n",
    "                        scores_list.append(\"{} {}\\n\".format(logit[i].item(), live_label[i].item()))\n",
    "  # Check against ground-truth labels.\n",
    "    map_score_val_filename = os.path.join(score_path, \"score.txt\")\n",
    "    with open(map_score_val_filename, 'w') as file:\n",
    "                file.writelines(scores_list)\n",
    "    print(\"score: write test scores to {}\".format(map_score_val_filename))\n",
    "    test_ACC, fpr, FRR, HTER, auc_test, test_err, tpr = performances_val(map_score_val_filename)\n",
    "    print(\"epoch:{:d}, test:  val_ACC={:.4f}, HTER={:.4f}, AUC={:.4f}, val_err={:.4f}, ACC={:.4f}, TPR={:.4f}\".format(\n",
    "        epoch + 1, test_ACC, HTER, auc_test, test_err, test_ACC, tpr))  \n",
    "    return auc_test\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f52165be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"Focal_without_pipline_domain_generalization_tuning_mohemm_to_c.pth\",map_location='cuda'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8eb12717",
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = train_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "94cb3482",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   0%|                       | 1011/213872 [01:06<3:53:02, 15.22batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [72]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     index_arr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m29952\u001b[39m, (\u001b[38;5;241m64\u001b[39m,))\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     24\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m logit,_ ,_,features\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mindex_arr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     27\u001b[0m y_list\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [69]\u001b[0m, in \u001b[0;36mGCN.forward\u001b[0;34m(self, x, edge_index, batch, drop_en, index_arr)\u001b[0m\n\u001b[1;32m    180\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdrop_5(x,drop_en)\n\u001b[1;32m    182\u001b[0m x_5 \u001b[38;5;241m=\u001b[39m global_mean_pool(x, batch)\n\u001b[0;32m--> 184\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv6\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    185\u001b[0m x  \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb6(x)\n\u001b[1;32m    186\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mrelu()\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/conv/cheb_conv.py:151\u001b[0m, in \u001b[0;36mChebConv.forward\u001b[0;34m(self, x, edge_index, edge_weight, batch, lambda_max)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    144\u001b[0m     x: Tensor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    148\u001b[0m     lambda_max: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    149\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 151\u001b[0m     edge_index, norm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__norm__\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    152\u001b[0m \u001b[43m        \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    153\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_dim\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[43m        \u001b[49m\u001b[43medge_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    155\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    156\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlambda_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m     Tx_0 \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m    162\u001b[0m     Tx_1 \u001b[38;5;241m=\u001b[39m x  \u001b[38;5;66;03m# Dummy.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/conv/cheb_conv.py:138\u001b[0m, in \u001b[0;36mChebConv.__norm__\u001b[0;34m(self, edge_index, num_nodes, edge_weight, normalization, lambda_max, dtype, batch)\u001b[0m\n\u001b[1;32m    135\u001b[0m edge_weight\u001b[38;5;241m.\u001b[39mmasked_fill_(edge_weight \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minf\u001b[39m\u001b[38;5;124m'\u001b[39m), \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    137\u001b[0m loop_mask \u001b[38;5;241m=\u001b[39m edge_index[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m edge_index[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m--> 138\u001b[0m edge_weight[loop_mask] \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m edge_index, edge_weight\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "y_list = []\n",
    "feature_list = []\n",
    "domains = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        logit,_ ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list.append(live_label.cpu().numpy())\n",
    "        feature_list.append(features.cpu().numpy())\n",
    "        domains.append(graph.data.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d0f9cbfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1:   2%|▋                          | 539/22296 [00:40<27:34, 13.15batch/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [74]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     index_arr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m29952\u001b[39m, (\u001b[38;5;241m64\u001b[39m,))\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     24\u001b[0m graph \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# Move data to the device\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m logit,a ,_,features\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mindex_arr\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     27\u001b[0m live_label \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39my\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[1;32m     28\u001b[0m y_list_target\u001b[38;5;241m.\u001b[39mappend(live_label\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy())\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "Input \u001b[0;32mIn [69]\u001b[0m, in \u001b[0;36mGCN.forward\u001b[0;34m(self, x, edge_index, batch, drop_en, index_arr)\u001b[0m\n\u001b[1;32m    142\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mrelu()\n\u001b[1;32m    144\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdrop_1(x,drop_en)\n\u001b[0;32m--> 148\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    149\u001b[0m x  \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb2(x)\n\u001b[1;32m    150\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mrelu()\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/conv/cheb_conv.py:151\u001b[0m, in \u001b[0;36mChebConv.forward\u001b[0;34m(self, x, edge_index, edge_weight, batch, lambda_max)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    144\u001b[0m     x: Tensor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    148\u001b[0m     lambda_max: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    149\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 151\u001b[0m     edge_index, norm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__norm__\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    152\u001b[0m \u001b[43m        \u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    153\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnode_dim\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[43m        \u001b[49m\u001b[43medge_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    155\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    156\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlambda_max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m     Tx_0 \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m    162\u001b[0m     Tx_1 \u001b[38;5;241m=\u001b[39m x  \u001b[38;5;66;03m# Dummy.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/nn/conv/cheb_conv.py:119\u001b[0m, in \u001b[0;36mChebConv.__norm__\u001b[0;34m(self, edge_index, num_nodes, edge_weight, normalization, lambda_max, dtype, batch)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__norm__\u001b[39m(\n\u001b[1;32m    110\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    111\u001b[0m     edge_index: Tensor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    117\u001b[0m     batch: OptTensor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    118\u001b[0m ):\n\u001b[0;32m--> 119\u001b[0m     edge_index, edge_weight \u001b[38;5;241m=\u001b[39m \u001b[43mget_laplacian\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mnormalization\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    121\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mnum_nodes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m edge_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m lambda_max \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/utils/get_laplacian.py:62\u001b[0m, in \u001b[0;36mget_laplacian\u001b[0;34m(edge_index, edge_weight, normalization, dtype, num_nodes)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m normalization \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m normalization \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msym\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrw\u001b[39m\u001b[38;5;124m'\u001b[39m]  \u001b[38;5;66;03m# 'Invalid normalization'\u001b[39;00m\n\u001b[0;32m---> 62\u001b[0m edge_index, edge_weight \u001b[38;5;241m=\u001b[39m \u001b[43mremove_self_loops\u001b[49m\u001b[43m(\u001b[49m\u001b[43medge_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_weight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edge_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     65\u001b[0m     edge_weight \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mones(edge_index\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m1\u001b[39m), dtype\u001b[38;5;241m=\u001b[39mdtype,\n\u001b[1;32m     66\u001b[0m                              device\u001b[38;5;241m=\u001b[39medge_index\u001b[38;5;241m.\u001b[39mdevice)\n",
      "File \u001b[0;32m~/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/torch_geometric/utils/loop.py:78\u001b[0m, in \u001b[0;36mremove_self_loops\u001b[0;34m(edge_index, edge_attr)\u001b[0m\n\u001b[1;32m     75\u001b[0m     edge_index, edge_attr \u001b[38;5;241m=\u001b[39m to_edge_index(edge_index)\n\u001b[1;32m     77\u001b[0m mask \u001b[38;5;241m=\u001b[39m edge_index[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m edge_index[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 78\u001b[0m edge_index \u001b[38;5;241m=\u001b[39m \u001b[43medge_index\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m layout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m edge_attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "loader = test_loader\n",
    "model.eval()\n",
    "y_list_target = []\n",
    "feature_list_target = []\n",
    "with torch.no_grad():\n",
    "    scores_list = []\n",
    "\n",
    "    correct = 0\n",
    "    s= 0\n",
    "    progress_bar = tqdm(loader, desc=f'Epoch {0+1}', unit='batch')\n",
    "    for graph in progress_bar:  # Iterate in batches over the training/test dataset.\n",
    "        if graph.x.shape[0] < 29952 :\n",
    "\n",
    "            if graph.x.shape[0] < 64:\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (int(graph.x.shape[0]),)).to(device)\n",
    "            else :\n",
    "\n",
    "                index_arr = torch.randint(0, int(graph.x.shape[0]), (64,)).to(device)\n",
    "        else :\n",
    "\n",
    "            index_arr = torch.randint(0, 29952, (64,)).to(device)\n",
    "\n",
    "        graph = graph.to(device)  # Move data to the device\n",
    "        \n",
    "        logit,a ,_,features= model(graph.x, graph.edge_index, graph.batch,False,index_arr)\n",
    "        live_label = graph.y.float()\n",
    "        y_list_target.append(live_label.cpu().numpy())\n",
    "        feature_list_target.append(features.cpu().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d533a646",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list_target= np.concatenate(y_list_target)\n",
    "feature_list_target= np.array(feature_list_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "35a59dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_list= np.concatenate(y_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ae6d56ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = np.array(feature_list)\n",
    "domains = np.concatenate(domains)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7393ac0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = feature_list\n",
    "\n",
    "b = feature_list_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0898ef92",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_list = a[:,400,:]\n",
    "feature_list_target = b[:,400,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "597e44a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:800: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/hassan-hossein/anaconda3/envs/Wav2Lip/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py:810: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n",
      "/tmp/ipykernel_8645/3098538043.py:35: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  plt.scatter(x[0], x[1], color='yellow', marker=marker, s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# داده‌های ورودی (مثال فرضی)\n",
    "# فرض کنید feature_list داده‌های ویژگی‌ها است\n",
    "# و y_list لیبل‌ها، domain_list دامنه‌ها را نشان می‌دهد\n",
    "# target_feature و target_label داده‌های هدف هستند\n",
    "\n",
    "# اجرای TSNE برای کاهش ابعاد به 2\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "X_tsne = tsne.fit_transform(feature_list)\n",
    "\n",
    "# رنگ‌های مختلف برای دامنه‌ها\n",
    "colors = {0: 'blue', 1: 'green', 2: 'red'}\n",
    "\n",
    "# رسم نمودار TSNE\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# رسم نقاط براساس دامنه‌ها و لیبل‌ها\n",
    "for i, (x, y, domain, label) in enumerate(zip(X_tsne, y_list, domains, y_list)):\n",
    "    if label == 0:\n",
    "        marker = 'o'  # دایره برای کلاس 0\n",
    "    else:\n",
    "        marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color=colors[domain], marker=marker, s=30, alpha=0.7)\n",
    "\n",
    "# افزودن داده‌های هدف به نمودار\n",
    "target_tsne = tsne.fit_transform(feature_list_target)\n",
    "for x, label in zip(target_tsne, y_list_target):\n",
    "    if label == 0:\n",
    "        marker = 'o'  # دایره برای کلاس 0\n",
    "    else:\n",
    "        marker = 'x'  # ضربدر برای کلاس 1\n",
    "    plt.scatter(x[0], x[1], color='yellow', marker=marker, s=30, edgecolor='black', linewidth=0.6, alpha=0.9)\n",
    "\n",
    "# تنظیمات نمودار\n",
    "plt.title(\"TSNE of Model Features by Class and Domain\")\n",
    "plt.xlabel(\"TSNE Component 1\")\n",
    "plt.ylabel(\"TSNE Component 2\")\n",
    "plt.legend(['MSU_MFSD', 'Replay_attack_dataset', 'Oulu', 'Target'], loc='upper right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023067ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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