sj3520_MLSec_Lab4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ce6bff3-25d6-4c62-a437-c1b5b3632b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-04 23:16:19.861025: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Import Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import h5py\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "855750d0-7b90-4e99-8b7d-81cc3b048b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define models\n",
    "\n",
    "clean_test_data = r'test.h5'\n",
    "poisoned_test_data = r'bd_test.h5'\n",
    "clean_val_data = r'valid.h5'\n",
    "poisoned_val_data = r'bd_valid.h5'\n",
    "badnet_model = r'bd_net.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e3fe93d-b06c-4e9b-82b2-6c94009b18b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define GoodNet Class\n",
    "\n",
    "class G(tf.keras.Model):\n",
    "    def __init__(self, B_path, B_prime_path):\n",
    "        super(G, self).__init__()\n",
    "        self.B = tf.keras.models.load_model(B_path)\n",
    "        self.B_prime = tf.keras.models.load_model(B_prime_path)\n",
    "        self.backdoor_class_index = self.B.layers[-1].output_shape[-1]\n",
    "\n",
    "    def predict(self, data):\n",
    "        y = np.argmax(self.B.predict(data), axis=1)\n",
    "        y_prime = np.argmax(self.B_prime.predict(data), axis=1)\n",
    "        pred = np.zeros(data.shape[0])\n",
    "        for i in range(data.shape[0]):\n",
    "            if y[i] == y_prime[i]:\n",
    "                pred[i] = y[i]\n",
    "            else:\n",
    "                pred[i] = self.backdoor_class_index\n",
    "        return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9dc9231-8892-4579-9b31-7e5802169712",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Evaluation Function\n",
    "\n",
    "def data_loader(filepath):\n",
    "    data = h5py.File(filepath, 'r')\n",
    "    x_data = np.array(data['data'])\n",
    "    y_data = np.array(data['label'])\n",
    "    x_data = x_data.transpose((0,2,3,1))\n",
    "    return x_data, y_data\n",
    "\n",
    "def evaluate_model(clean_data_filename, poisoned_data_filename, model_input):\n",
    "    cl_x_test, cl_y_test = data_loader(clean_data_filename)\n",
    "    bd_x_test, bd_y_test = data_loader(poisoned_data_filename)\n",
    "\n",
    "    # Determine the type of model_input and load or use the model accordingly\n",
    "    if isinstance(model_input, str):\n",
    "        model = tf.keras.models.load_model(model_input)\n",
    "        predict_function = lambda x: np.argmax(model.predict(x), axis=1)\n",
    "    elif isinstance(model_input, G):\n",
    "        model = model_input\n",
    "        predict_function = lambda x: model.predict(x)\n",
    "    else:  # Assuming model_input is a Keras model\n",
    "        model = model_input\n",
    "        predict_function = lambda x: np.argmax(model.predict(x), axis=1)\n",
    "\n",
    "    # Evaluate the model\n",
    "    cl_label_prd = predict_function(cl_x_test)\n",
    "    cl_clf_acc = np.mean(np.equal(cl_label_prd, cl_y_test)) * 100\n",
    "    print('Classification Accuracy on Clean Data:', cl_clf_acc)\n",
    "\n",
    "    bd_label_prd = predict_function(bd_x_test)\n",
    "    at_suc_rt = np.mean(np.equal(bd_label_prd, bd_y_test)) * 100\n",
    "    print('Attack Success Rate using Poisoned Data:', at_suc_rt)\n",
    "\n",
    "    return cl_clf_acc, at_suc_rt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "539580c6-1a67-40a5-8aa0-54add00bb162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 5ms/step\n",
      "Classification Accuracy on Clean Data: 98.64899974019225\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    }
   ],
   "source": [
    "# Get baseline stats for model\n",
    "\n",
    "bl_cl_clf_acc, bl_at_suc_rt = evaluate_model(clean_val_data, poisoned_val_data, badnet_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf4762f0-8601-4dd3-8e01-5a8727059512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input (InputLayer)          [(None, 55, 47, 3)]          0         []                            \n",
      "                                                                                                  \n",
      " conv_1 (Conv2D)             (None, 52, 44, 20)           980       ['input[0][0]']               \n",
      "                                                                                                  \n",
      " pool_1 (MaxPooling2D)       (None, 26, 22, 20)           0         ['conv_1[0][0]']              \n",
      "                                                                                                  \n",
      " conv_2 (Conv2D)             (None, 24, 20, 40)           7240      ['pool_1[0][0]']              \n",
      "                                                                                                  \n",
      " pool_2 (MaxPooling2D)       (None, 12, 10, 40)           0         ['conv_2[0][0]']              \n",
      "                                                                                                  \n",
      " conv_3 (Conv2D)             (None, 10, 8, 60)            21660     ['pool_2[0][0]']              \n",
      "                                                                                                  \n",
      " pool_3 (MaxPooling2D)       (None, 5, 4, 60)             0         ['conv_3[0][0]']              \n",
      "                                                                                                  \n",
      " conv_4 (Conv2D)             (None, 4, 3, 80)             19280     ['pool_3[0][0]']              \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)         (None, 1200)                 0         ['pool_3[0][0]']              \n",
      "                                                                                                  \n",
      " flatten_2 (Flatten)         (None, 960)                  0         ['conv_4[0][0]']              \n",
      "                                                                                                  \n",
      " fc_1 (Dense)                (None, 160)                  192160    ['flatten_1[0][0]']           \n",
      "                                                                                                  \n",
      " fc_2 (Dense)                (None, 160)                  153760    ['flatten_2[0][0]']           \n",
      "                                                                                                  \n",
      " add_1 (Add)                 (None, 160)                  0         ['fc_1[0][0]',                \n",
      "                                                                     'fc_2[0][0]']                \n",
      "                                                                                                  \n",
      " activation_1 (Activation)   (None, 160)                  0         ['add_1[0][0]']               \n",
      "                                                                                                  \n",
      " output (Dense)              (None, 1283)                 206563    ['activation_1[0][0]']        \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 601643 (2.30 MB)\n",
      "Trainable params: 601643 (2.30 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Visualize the Model Structure\n",
    "\n",
    "model = tf.keras.models.load_model(badnet_model)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bea7e45-9e49-4bd3-9b31-b099f7b3067f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear the current Tensorflow Keras session\n",
    "\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2251a94b-24ba-4498-aa67-8b1dc763bdee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the clean data\n",
    "\n",
    "x_data, y_data = data_loader(clean_val_data)\n",
    "figure = plt.figure(figsize=(10,8))\n",
    "cols, rows = 3,3\n",
    "\n",
    "for i in range(1, cols*rows+1):\n",
    "  index = np.random.randint(x_data.shape[0], size=1)\n",
    "  img, label = (x_data[index], y_data[index])\n",
    "  figure.add_subplot(rows, cols, i)\n",
    "  plt.title(\"True Label: {}\".format(label))\n",
    "  plt.axis(\"off\")\n",
    "  plt.imshow(img[0]/255)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8487342-b925-4887-993b-6ba9cc07468f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the poisoned data\n",
    "\n",
    "x_data, y_data = data_loader(poisoned_val_data)\n",
    "figure = plt.figure(figsize=(10,8))\n",
    "cols, rows = 3,3\n",
    "\n",
    "for i in range(1, cols*rows+1):\n",
    "  index = np.random.randint(x_data.shape[0], size=1)\n",
    "  img, label = (x_data[index], y_data[index])\n",
    "  figure.add_subplot(rows, cols, i)\n",
    "  plt.title(\"True Label: {}\".format(label))\n",
    "  plt.axis(\"off\")\n",
    "  plt.imshow(img[0]/255)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d21875e4-2bc7-46f3-be3c-0e9c3f722e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the BadNet model\n",
    "\n",
    "model = tf.keras.models.load_model(badnet_model)\n",
    "source_optimizer = model.optimizer\n",
    "source_loss = model.loss\n",
    "source_metrics = model.metrics\n",
    "bd_model_cpy = tf.keras.models.clone_model(model)\n",
    "bd_model_cpy.set_weights(model.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edbd5029-cf28-4dfe-a751-4d4c9313b85e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 5ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                              | 0/60 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 3s 7ms/step\n",
      "Classification Accuracy on Clean Data: 98.64899974019225\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|█▍                                                                                    | 1/60 [00:08<08:07,  8.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 98.64899974019225\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|██▊                                                                                   | 2/60 [00:16<07:42,  7.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 98.64899974019225\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|████▎                                                                                 | 3/60 [00:23<07:31,  7.92s/it]"
     ]
    },
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 3s 7ms/step\n",
      "Classification Accuracy on Clean Data: 97.65307006148784\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|████████████████████████████████████████████████████████████▉                        | 43/60 [05:58<02:31,  8.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 7ms/step\n",
      "Classification Accuracy on Clean Data: 97.50584567420108\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 73%|██████████████████████████████████████████████████████████████▎                      | 44/60 [06:08<02:26,  9.14s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 95.75647354291158\n",
      "361/361 [==============================] - 2s 7ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nicholasbennet/anaconda3/envs/jupyterlabml/lib/python3.11/site-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n",
      " 75%|███████████████████████████████████████████████████████████████▊                     | 45/60 [06:16<02:13,  8.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved at 2% drop in classification accuracy.\n",
      "361/361 [==============================] - 2s 5ms/step\n",
      "Classification Accuracy on Clean Data: 95.20221702606739\n",
      "361/361 [==============================] - 2s 6ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|█████████████████████████████████████████████████████████████████▏                   | 46/60 [06:24<02:02,  8.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attack Success Rate using Poisoned Data: 99.9913397419243\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 94.7172425738287\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 99.9913397419243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|██████████████████████████████████████████████████████████████████▌                  | 47/60 [06:33<01:51,  8.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 92.09318437689443\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 99.9913397419243\n",
      "Model Saved at 4% drop in classification accuracy."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████████████████████████████████████████████████████████████████                 | 48/60 [06:41<01:42,  8.57s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 91.49562656967177\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 99.9913397419243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|█████████████████████████████████████████████████████████████████████▍               | 49/60 [06:51<01:38,  8.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 91.01931237550879\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 99.98267948384861\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|██████████████████████████████████████████████████████████████████████▊              | 50/60 [06:59<01:26,  8.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 89.17467740538669\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 80.73958603966398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|████████████████████████████████████████████████████████████████████████▎            | 51/60 [07:07<01:15,  8.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 84.43751623798389\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 77.015675067117\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 87%|█████████████████████████████████████████████████████████████████████████▋           | 52/60 [07:16<01:09,  8.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Saved at 10% drop in classification accuracy.\n",
      "361/361 [==============================] - 2s 5ms/step\n",
      "Classification Accuracy on Clean Data: 76.48739932449988\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 35.71490430414826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|███████████████████████████████████████████████████████████████████████████          | 53/60 [07:24<00:59,  8.47s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 3s 7ms/step\n",
      "Classification Accuracy on Clean Data: 54.8627349095003\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 6.954187234779596\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|████████████████████████████████████████████████████████████████████████████▌        | 54/60 [07:33<00:51,  8.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 7ms/step\n",
      "Classification Accuracy on Clean Data: 27.08928726076037\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.4243526457088421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████████████████████████████████████████████████████████████████████████▉       | 55/60 [07:43<00:44,  8.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 13.87373343725643\n",
      "361/361 [==============================] - 3s 7ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|███████████████████████████████████████████████████████████████████████████████▎     | 56/60 [07:51<00:35,  8.80s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 7.101411622066338\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|████████████████████████████████████████████████████████████████████████████████▊    | 57/60 [08:01<00:27,  9.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 1.5501861955486274\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|██████████████████████████████████████████████████████████████████████████████████▏  | 58/60 [08:09<00:17,  8.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 0.7188014202823244\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|███████████████████████████████████████████████████████████████████████████████████▌ | 59/60 [08:18<00:08,  8.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "361/361 [==============================] - 2s 7ms/step\n",
      "Classification Accuracy on Clean Data: 0.0779423226812159\n",
      "361/361 [==============================] - 2s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████| 60/60 [08:27<00:00,  8.46s/it]\n"
     ]
    }
   ],
   "source": [
    "# Pruning models as a defence against poisoned model\n",
    "\n",
    "cl_clf_acc_arr = []\n",
    "at_suc_rt_arr = []\n",
    "saved_model = np.zeros(3,dtype=bool)\n",
    "\n",
    "output_layer = bd_model_cpy.get_layer('pool_3').output\n",
    "seq = np.argsort(np.mean(tf.keras.models.Model(inputs = bd_model_cpy.input, outputs = output_layer).predict(data_loader(clean_val_data)[0]), axis=(0,1,2)))\n",
    "weight_0 = bd_model_cpy.layers[5].get_weights()[0]\n",
    "bias_0 = bd_model_cpy.layers[5].get_weights()[1]\n",
    "\n",
    "for channel_index in tqdm(seq):\n",
    "    weight_0[:,:,:,channel_index] = 0\n",
    "    bias_0[channel_index] = 0\n",
    "    bd_model_cpy.layers[5].set_weights([weight_0, bias_0])\n",
    "    cl_clf_acc, at_suc_rt = evaluate_model(clean_val_data, poisoned_val_data, bd_model_cpy)\n",
    "    if (bl_cl_clf_acc - cl_clf_acc >= 2 and not saved_model[0]):\n",
    "        print(\"Model Saved at 2% drop in classification accuracy.\")\n",
    "        bd_model_cpy.compile(optimizer=source_optimizer, \n",
    "                  loss=source_loss, \n",
    "                  metrics=source_metrics)\n",
    "        bd_model_cpy.save('model_X_2.h5')\n",
    "        saved_model[0] = 1\n",
    "    if (bl_cl_clf_acc - cl_clf_acc >= 4 and not saved_model[1]):\n",
    "        print(\"Model Saved at 4% drop in classification accuracy.\")\n",
    "        bd_model_cpy.compile(optimizer=source_optimizer, \n",
    "                  loss=source_loss, \n",
    "                  metrics=source_metrics)\n",
    "        bd_model_cpy.save('model_X_4.h5')\n",
    "        saved_model[1] = 1\n",
    "    if (bl_cl_clf_acc - cl_clf_acc >= 10 and not saved_model[2]):\n",
    "        print(\"Model Saved at 10% drop in classification accuracy.\")\n",
    "        bd_model_cpy.compile(optimizer=source_optimizer, \n",
    "                  loss=source_loss, \n",
    "                  metrics=source_metrics)\n",
    "        bd_model_cpy.save('model_X_10.h5')\n",
    "        saved_model[2] = 1\n",
    "    cl_clf_acc_arr.append(cl_clf_acc)\n",
    "    at_suc_rt_arr.append(at_suc_rt)\n",
    "    tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "018e19af-dddc-4ff3-ab65-1547c653908b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Classification Accuracy and Attack Success Rate for Validation Dataset')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Pruning graph on validation data\n",
    "\n",
    "figure = plt.figure(figsize=(10,8))\n",
    "x_axis = np.arange(1,61)/60\n",
    "plt.plot(x_axis, cl_clf_acc_arr, color='red')\n",
    "plt.plot(x_axis, at_suc_rt_arr, color='blue')\n",
    "plt.legend(['Clean Accuracy', 'Attack Success Rate'])\n",
    "plt.xlabel(\"Fraction of Pruned Channels\")\n",
    "plt.ylabel(\"Rate\")\n",
    "plt.title(\"Classification Accuracy and Attack Success Rate for Validation Dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b770f5ef-9df1-4f27-89fc-8b85101d9747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear the current Tensorflow Keras session\n",
    "\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78016d0b-33c4-47e1-91be-1884ddf05796",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Pruned models\n",
    "\n",
    "prune_model_X_2 = 'model_X_2.h5'\n",
    "prune_model_X_4 = 'model_X_4.h5'\n",
    "prune_model_X_10 = 'model_X_10.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ef615f2-713e-470d-92fe-520c60ee2964",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define GoodNet(Repaired) Models\n",
    "\n",
    "G_model_X_2 = G(badnet_model, prune_model_X_2)\n",
    "G_model_X_4 = G(badnet_model, prune_model_X_4)\n",
    "G_model_X_10 = G(badnet_model, prune_model_X_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01319919-56d0-4627-b484-698e65086d6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "401/401 [==============================] - 2s 5ms/step\n",
      "401/401 [==============================] - 3s 6ms/step\n",
      "Classification Accuracy on Clean Data: 95.74434918160561\n",
      "401/401 [==============================] - 3s 6ms/step\n",
      "401/401 [==============================] - 3s 6ms/step\n",
      "Attack Success Rate using Poisoned Data: 100.0\n",
      "401/401 [==============================] - 3s 7ms/step\n",
      "401/401 [==============================] - 2s 6ms/step\n",
      "Classification Accuracy on Clean Data: 92.1278254091972\n",
      "401/401 [==============================] - 3s 7ms/step\n",
      "401/401 [==============================] - 3s 7ms/step\n",
      "Attack Success Rate using Poisoned Data: 99.98441153546376\n",
      "401/401 [==============================] - 3s 6ms/step\n",
      "401/401 [==============================] - 3s 7ms/step\n",
      "Classification Accuracy on Clean Data: 84.3335931410756\n",
      "401/401 [==============================] - 3s 6ms/step\n",
      "401/401 [==============================] - 3s 7ms/step\n",
      "Attack Success Rate using Poisoned Data: 77.20966484801247\n"
     ]
    }
   ],
   "source": [
    "# Evaluate GoodNet(Repaired) Models\n",
    "cl_clf_acc_X_2, at_suc_rt_X_2 = evaluate_model(clean_test_data, poisoned_test_data, G_model_X_2)\n",
    "cl_clf_acc_X_4, at_suc_rt_X_4 = evaluate_model(clean_test_data, poisoned_test_data, G_model_X_4)\n",
    "cl_clf_acc_X_10, at_suc_rt_X_10 = evaluate_model(clean_test_data, poisoned_test_data, G_model_X_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2c4373e1-74c1-439c-b23d-498d6c798072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Classification Accuracy on Clean Data</th>\n",
       "      <th>Attack Success Rate using Poisoned Data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GoodNet_2%</th>\n",
       "      <td>95.744349</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoodNet_4%</th>\n",
       "      <td>92.127825</td>\n",
       "      <td>99.984412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoodNet_10%</th>\n",
       "      <td>84.333593</td>\n",
       "      <td>77.209665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Classification Accuracy on Clean Data  \\\n",
       "Model                                                \n",
       "GoodNet_2%                               95.744349   \n",
       "GoodNet_4%                               92.127825   \n",
       "GoodNet_10%                              84.333593   \n",
       "\n",
       "             Attack Success Rate using Poisoned Data  \n",
       "Model                                                 \n",
       "GoodNet_2%                                100.000000  \n",
       "GoodNet_4%                                 99.984412  \n",
       "GoodNet_10%                                77.209665  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table of GoodNet(Repaired) model evaluation\n",
    "\n",
    "cl_clf_acc_tb = [cl_clf_acc_X_2, cl_clf_acc_X_4, cl_clf_acc_X_10]\n",
    "at_suc_rt_tb = [at_suc_rt_X_2, at_suc_rt_X_4, at_suc_rt_X_10]\n",
    "data = {\n",
    "    \"Classification Accuracy on Clean Data\": cl_clf_acc_tb,\n",
    "    \"Attack Success Rate using Poisoned Data\": at_suc_rt_tb,\n",
    "    \"Model\": [\"GoodNet_2%\", \"GoodNet_4%\", \"GoodNet_10%\"]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df.set_index('Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e1baab4-ee10-41be-8e4c-e17c87ad552a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Onv2rGJjY/X444/f9jO/WY8ePRwuTU1rnceOHVNkZKQ6deokX19f+/wNGzZUmTJlUlxW0va7+RL3O2nUqJHy5Mmjr7/+WsYYff3116ne427F/jZv3jw9/vjjyp07t8P2aNCggRISEvTbb7+leV0AAFkLl9TigTdt2jSVKFFCzs7Oypcvn0qWLGnvRTI6OlrGGIWGhqY4780hT5IKFCiQ5o6BDh06pBw5cigkJMRheGBgoHLlyqVDhw45DC9WrFiqbRUuXNjhfdIPz0KFCiUbnpiYqNjYWPtloOvWrdOIESO0YcMGXbp0yWH62NhYhx+xty5HuvHj9eYOQ/bv36/WrVunWqt0Y7tGRUU5XLJ6s6TOflKStF1uvqQ4SalSpbR27drbLju97rTu//77r86dO6cZM2ZoxowZKbZxu/Xy9vaWdOOS5JvVqlXL3lHQO++84/DsyUOHDil//vz2eZOULl3aPj7pv2nZ15L+e+v+7u/vn2rIS/Lqq69qypQpGjlypL7//vtk46OjoyUp1Y5qfHx8btv+zUwq9x66u7vbe8s9cuSIJk6caO+wK7115MiRQ8WLF3cYVqJECUlyuMd3yZIlGjNmjCIjIx3uU7w5SN3Orcd3WutM7TOTbhwjKQXepO2X1tqkG991zz77rObOnavHHntMhw8f1vPPP5/itFbsb9HR0fr999/T9Z0BAMjaCJx44D322GP2XmpvlZiYKJvNpqVLl6bYMcatZ6PS02tsWn/03a7tlGq73fCkH5z79+9X/fr1VapUKU2ePFmFChWSq6urfvrpJ02ZMiVZZxx3ai+tEhMTVb58eU2ePDnF8bcG5azgTuuetK1eeOEFe2dFt6pQoUKq7ZcqVUqS9Mcff6hixYr24f7+/vZHuqT2LMS0upuAcbeSznKOHDkyxbOcSdtnzpw5CgwMTDbe2fnO/9wk/ZEktR5RnZycHB5/ExYWplKlSqlnz55avHhxhtVxqzVr1qhFixZ64okn9NFHHykoKEguLi4KDw/X3Llz09TGrce3FXUmSdp+N/c4mxbPP/+8Pv74Y40cOVIVK1ZM9Qxqkozc3xITE9WwYUO9/vrrKY5P+iMAACD7IXDioRYcHCxjjIoVK5bhP2iKFCmixMRERUdH289ISTc6njl37pzDpZNW+eGHH3T16lUtXrzY4QxeWi5vTE1wcLD++OOPO06zc+dO1a9f/65/lCZtlz179iQ7+7Nnz550b7d7/XHs7+8vb29vJSQk3PGZnylp0qSJnJyc9OWXX6p9+/ZpmqdIkSJasWKFLly44HCWc/fu3fbxSf9Ny76W9N/o6GiHs3r//vtvmh570a9fP7333nsaNWpUskfRJF2OHBAQcMftk9pnUbhwYXl4eDj0IH07QUFB6t+/v0aNGqWNGzeqevXqd1WHdCPo/P333w7H/969eyXJ3mnP/Pnz5e7uroiICIfHnoSHh6epzpSktc6bP7Nb7dmzJ8V5krbfzftCWtSuXVuFCxfW6tWrNWHChNvWlNH7W3BwsOLi4tJ1bAEAsjbu4cRD7ZlnnpGTk5NGjRqV7CyeMcZ+T1l6NG3aVJKS9a6YdNYvLT1b3quks3Y3r1tsbOw9/VBu3bq1du7cmeJjGZKW06ZNGx09elSffPJJsmkuX75s76UzJVWrVlVAQIA+/vhjh0sXly5dqqioqHRvt6TnHp47dy5d8zs5Oal169aaP39+ioH733//ve38hQsXVteuXbV06VJ9+OGHKU5z6z7YtGlTJSQkJJt+ypQpstlsatKkiX066c77WoMGDeTi4qIPPvjAYVlp7QE06Szn999/r8jISIdxYWFh8vHx0dtvv634+Phk8968fVL7LFxcXFS1alVt3bo1TfVINx7bkjNnTo0fP/6u60hy8/Y1xujDDz+Ui4uL6tevL+nGZ2+z2ZSQkGCf7uDBg3fsbfl20lpnUFCQKlWqpNmzZzvcc718+XL99ddfKba9bds2+fr62nuWTiubzaapU6dqxIgR6tChQ6rTWbG/tWnTRhs2bFBERESycefOnXO4Lx0AkL1whhMPteDgYI0ZM0ZDhgzRwYMH9fTTT8vb21sHDhzQwoUL9eKLL2rgwIHpartixYrq1KmTZsyYoXPnzqlOnTravHmzZs+eraefflp169bN4LVJrlGjRnJ1dVXz5s3Vs2dPxcXF6ZNPPlFAQICOHTuWrjYHDRqk7777Ts8++6y6du2qKlWq6MyZM1q8eLE+/vhjVaxYUR06dNC3336rl156SatWrVKtWrWUkJCg3bt369tvv7U/bzQlLi4umjBhgrp06aI6deqoXbt29seiFC1aVP37909X3VWqVJEkDR06VG3btpWLi4uaN29uDz9pMX78eK1atUrVqlVTjx49VKZMGZ05c0bbt2/XihUrdObMmdvO/9577+nAgQPq27evvv76azVv3lwBAQE6deqU1q1bpx9++MHh3tXmzZurbt26Gjp0qA4ePKiKFSvq559/1vfff69+/frZz5KldV/z9/fXwIEDNW7cOD311FNq2rSpduzYoaVLl6b58sukezl37tzpsO18fHw0ffp0dejQQZUrV1bbtm3l7++vmJgY/fjjj6pVq5Y92CV9Fq+88orCwsLk5OSktm3bSrrxTMihQ4fq/Pnzabrv08/PT126dNFHH32kqKgolS5dOs11SDfuC122bJk6deqkatWqaenSpfrxxx/13//+134/YbNmzTR58mQ1btxYzz//vE6ePKlp06YpJCQkxUeGpMXdbK9x48apWbNmql27trp27aozZ87ogw8+UNmyZZPdEyzdCKPNmzdP11n9li1bqmXLlredxor9bdCgQVq8eLGeeuop+6OYLl68qF27dum7777TwYMH7/oSYQBAFnF/O8UF7p+7eWTG/PnzTe3atY2np6fx9PQ0pUqVMr179zZ79uyxT3ProwFultJjUYwxJj4+3owaNcoUK1bMuLi4mEKFCpkhQ4aYK1euOExXpEgR06xZs2Tz3/zYgrSs24gRI4wkh8c5LF682FSoUMG4u7ubokWLmgkTJpjPPvss2SNCUquhTp06pk6dOg7DTp8+bfr06WMKFChgXF1dTcGCBU2nTp0cHulx7do1M2HCBFO2bFnj5uZmcufObapUqWJGjRplYmNjk2/EW3zzzTfmkUceMW5ubiZPnjymffv25siRI2naDqkZPXq0KVCggMmRI4fD+ksyvXv3Tjb9rY+4MMaYEydOmN69e5tChQoZFxcXExgYaOrXr29mzJiRphquX79uwsPDTb169UyePHmMs7OzyZs3r6lfv775+OOPHR4FY8yNx2f079/f5M+f37i4uJjQ0FDzzjvvODxmwpi072sJCQlm1KhRJigoyHh4eJgnn3zS/PHHH8nWNbV9z5j/289S2udXrVplwsLCjK+vr3F3dzfBwcGmc+fOZuvWrQ7boG/fvsbf39/YbDaHR6ScOHHCODs7mzlz5ji0m9oxZowx+/fvN05OTsnqv1MdSW3u37/fNGrUyOTMmdPky5fPjBgxwiQkJDgsY+bMmSY0NNS4ubmZUqVKmfDwcPt2uFlqj0VJbR9NS53G3PiOKl26tHFzczNlypQxCxYsMJ06dUr2WJSoqCj746Du5Haf8c1S+u7L6P3NmBv7+pAhQ0xISIhxdXU1efPmNTVr1jTvvvuuw2OhxGNRACBbsRlzl72BAABgoW7dumnv3r1as2aNpcvp3LmzvvvuuxTPEmZX/fr102+//aZt27ZZ2okUAABpxT2cAIAsZcSIEdqyZYvDI2JwZ6dPn9ann36qMWPGEDYBAFkG93ACALKUwoUL68qVK5ldRrbj5+f3QJ2tBQA8GDjDCQAAAACwBPdwAgAAAAAswRlOAAAAAIAlCJwAAAAAAEsQOCUZY3T+/HlxdTEAAAAAZBwCp6QLFy7I19dXFy5cyOxSAAAAAOCBQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCWcM7sAAACAmyUkJCg+Pj6zywAApMLFxUVOTk5pmpbACQAAsgRjjI4fP65z585ldikAgDvIlSuXAgMDZbPZbjsdgRMAAGQJSWEzICBAOXPmvOOPGADA/WeM0aVLl3Ty5ElJUlBQ0G2nJ3ACAIBMl5CQYA+bfn5+mV0OAOA2PDw8JEknT55UQEDAbS+vpdMgAACQ6ZLu2cyZM2cmVwIASIuk7+s73XNP4AQAAFkGl9ECQPaQ1u9rAicAAAAAwBIETgAAgPvAZrNp0aJFli9n9erVstlsDr39Llq0SCEhIXJyclK/fv00a9Ys5cqVy/JannzySfXr18/y5SDruF/7eXa2cuVKlS5dWgkJCeluY9myZapUqZISExMzsDJr0GkQAADI0myj7u9ltmaEuet5jh8/rrFjx+rHH3/U0aNHFRAQoEqVKqlfv36qX7++BVWmrmbNmjp27Jh8fX3tw3r27KkuXbrolVdekbe3t5ydndW0adMMW+bq1atVt25dnT171iHILliwQC4uLhm2nDsJCwvTihUrtHHjRj366KP3bbkPi6y0n9/Jk08+qV9//VWS5Orqqrx586py5crq0qWLnnnmmbtqa+TIkVq0aJEiIyMzpLbXX39db775pr2jnR07dqhr166Kjo5W3bp1NXv2bOXJk0eSdP36dVWrVk3Tp0/XY489Zm+jcePGGjZsmL788kt16NAhQ+qyCmc4AQAA7sHBgwdVpUoV/fLLL3rnnXe0a9cuLVu2THXr1lXv3r3vez2urq4Oz8aLi4vTyZMnFRYWpvz588vb21seHh4KCAiwvJY8efLI29vb8uVIUkxMjNavX68+ffros88+uy/LvJ07daSS3WS1/TwtevTooWPHjmn//v2aP3++ypQpo7Zt2+rFF1/MtJrWrl2r/fv3q3Xr1vZh3bt3V7169bR9+3bFxsbq7bffto+bNGmSatWq5RA2k3Tu3FlTp069L3XfCwInAADAPejVq5dsNps2b96s1q1bq0SJEipbtqxee+01bdy4MdX53njjDZUoUUI5c+ZU8eLFNWzYMIeQsnPnTtWtW1fe3t7y8fFRlSpVtHXrVknSoUOH1Lx5c+XOnVuenp4qW7asfvrpJ0mOl9SuXr3aHvjq1asnm82m1atXp3hJ7Q8//KBHH31U7u7uyps3r1q1amUfN2fOHFWtWlXe3t4KDAzU888/b38G38GDB1W3bl1JUu7cuWWz2dS5c2dJyS+pPXv2rDp27KjcuXMrZ86catKkiaKjo+3jk+qKiIhQ6dKl5eXlpcaNG+vYsWN3/BzCw8P11FNP6eWXX9ZXX32ly5cvO4w/d+6cevbsqXz58snd3V3lypXTkiVL7OPXrVunJ598Ujlz5lTu3LkVFhams2fPSpKKFi2q9957z6G9SpUqaeTIkfb3NptN06dPV4sWLeTp6amxY8cqISFB3bp1U7FixeTh4aGSJUvq/fffT1b7Z599prJly8rNzU1BQUHq06ePJKlr16566qmnHKaNj49XQECAZs6cmeq2mD9/vr29okWLatKkSQ7jixYtqrfffltdu3aVt7e3ChcurBkzZqS+cZX+/fzw4cNq06aNcuXKpTx58qhly5Y6ePCgffyWLVvUsGFD5c2bV76+vqpTp462b9/u0IbNZtOnn36qVq1aKWfOnAoNDdXixYtvW690oxfVwMBAFSxYUNWrV9eECRP0v//9T5988olWrFhhn+52x+KsWbM0atQo7dy5UzabTTabTbNmzZIkTZ48WeXLl5enp6cKFSqkXr16KS4u7rY1ff3112rYsKHc3d3tw6KiotSjRw+VKFFC7dq1U1RUlCTp77//1syZMzV27NgU22revLm2bt2q/fv333FbZCYCJwAAQDqdOXNGy5YtU+/eveXp6Zls/O3uk/T29tasWbP0119/6f3339cnn3yiKVOm2Me3b99eBQsW1JYtW7Rt2zYNHjzYfnlq7969dfXqVf3222/atWuXJkyYIC8vr2TLqFmzpvbs2SPpRgg5duyYatasmWy6H3/8Ua1atVLTpk21Y8cOrVy50uGMSnx8vEaPHq2dO3dq0aJFOnjwoD1UFipUSPPnz5ck7dmzR8eOHUsxVEk3zshs3bpVixcv1oYNG2SMUdOmTR2C9qVLl/Tuu+9qzpw5+u233xQTE6OBAwemuh2lGw+iDw8P1wsvvKBSpUopJCRE3333nX18YmKimjRponXr1umLL77QX3/9pfHjx9svaYyMjFT9+vVVpkwZbdiwQWvXrlXz5s3v+h67kSNHqlWrVtq1a5e6du2qxMREFSxYUPPmzdNff/2l4cOH67///a++/fZb+zzTp09X79699eKLL2rXrl1avHixQkJCJN0487Vs2TKHwL1kyRJdunRJzz33XIo1bNu2TW3atFHbtm21a9cujRw5UsOGDbOHpCSTJk1S1apVtWPHDvXq1Usvv/yyfV+5VXr38/j4eIWFhcnb21tr1qzRunXr7H9EuHbtmiTpwoUL6tSpk9auXauNGzcqNDRUTZs21YULFxzaGjVqlNq0aaPff/9dTZs2Vfv27XXmzJkUl3s7nTp1Uu7cubVgwQL7sNsdi88995wGDBigsmXL6tixYzp27Jh92+fIkUNTp07Vn3/+qdmzZ+uXX37R66+/ftvlr1mzRlWrVnUYVrFiRS1fvlzXr1/XypUrVaFCBUnSSy+9pIkTJ6Z6lUDhwoWVL18+rVmz5q63w31lYGJjY40kExsbm9mlAADwULp8+bL566+/zOXLl5ON00jd19fd2LRpk5FkFixYcMdpJZmFCxemOv6dd94xVapUsb/39vY2s2bNSnHa8uXLm5EjR6Y4btWqVUaSOXv2rDHGmLNnzxpJZtWqVfZpwsPDja+vr/19jRo1TPv27e+4Dkm2bNliJJkLFy6kuMwkderUMa+++qoxxpi9e/caSWbdunX28adOnTIeHh7m22+/tdclyezbt88+zbRp00y+fPluW8/PP/9s/P39TXx8vDHGmClTppg6derYx0dERJgcOXKYPXv2pDh/u3btTK1atVJtv0iRImbKlCkOwypWrGhGjBhhfy/J9OvX77Z1GmNM7969TevWre3v8+fPb4YOHZrq9GXKlDETJkywv2/evLnp3LlzqtM///zzpmHDhg7DBg0aZMqUKWN/X6RIEfPCCy/Y3ycmJpqAgAAzffr0FNtM734+Z84cU7JkSZOYmGgff/XqVePh4WEiIiJSnD8hIcF4e3ubH374waHNN9980/4+Li7OSDJLly5NtY6b971bVatWzTRp0iTVeW89FkeMGGEqVqyY6vRJ5s2bZ/z8/G47ja+vr/n8888dhv3xxx/miSeeMIULFzbt2rUzsbGx5vPPPzctW7Y0R44cMY0aNTLBwcEp7iePPPJIqt8FVrvd9/bNOMMJAACQTsbcfQdDSb755hvVqlVLgYGB8vLy0ptvvqmYmBj7+Ndee03du3dXgwYNNH78eIfL5l555RWNGTNGtWrV0ogRI/T777/f03okneFLzbZt29S8eXMVLlxY3t7eqlOnjiQ51HsnUVFRcnZ2VrVq1ezD/Pz8VLJkSfslhNKNyyCDg4Pt74OCguyX76bms88+03PPPSdn5xv9YbZr107r1q2zb7PIyEgVLFhQJUqUSHH+O61/Wt165kqSpk2bpipVqsjf319eXl6aMWOGfbudPHlS//zzz22X3b17d4WHh0uSTpw4oaVLl6pr166pTh8VFaVatWo5DKtVq5aio6MdztgmnUWTblyyGhgYmOp2Tu9+vnPnTu3bt0/e3t7y8vKSl5eX8uTJoytXrtg/mxMnTqhHjx4KDQ2Vr6+vfHx8FBcXl2zfurleT09P+fj43HG/SI0xxuEZknc6FlOzYsUK1a9fXwUKFJC3t7c6dOig06dP69KlS6nOc/nyZYfLaSWpbNmy+vXXX3Xo0CHNnTtX8fHxGjFihD788EP17dtXNWvW1M6dO7VgwQL98MMPDvN6eHjcdnlZAYETAAAgnUJDQ2Wz2bR79+67mm/Dhg1q3769mjZtqiVLlmjHjh0aOnSo/TJD6cblmX/++aeaNWumX375RWXKlNHChQsl3Qghf//9tzp06KBdu3apatWq+uCDD9K9Hh4eHqmOu3jxosLCwuTj46Mvv/xSW7Zssddxc70Z5dZebW02220Dz5kzZ7Rw4UJ99NFHcnZ2lrOzswoUKKDr16/bOw+63fqlZXyOHDmS1ZBSp0C3Xm769ddfa+DAgerWrZt+/vlnRUZGqkuXLvbtdqflSlLHjh31999/a8OGDfriiy9UrFgxPf7443ec705S2s6pPWIjvft5XFycqlSposjISIfX3r179fzzz0u6cYlrZGSk3n//fa1fv16RkZHy8/NLtm/dTb23k5CQoOjoaBUrVkxS2o7FlBw8eFBPPfWUKlSooPnz52vbtm2aNm2apNsfF3nz5rXfG5ya1157Tf369VPBggW1evVqPfvss/L09FSzZs20evVqh2nPnDkjf3//NKx55iFwAgAApFOePHkUFhamadOm6eLFi8nG3/wszJutX79eRYoU0dChQ1W1alWFhobq0KFDyaYrUaKE+vfvr59//lnPPPOM/UyXdOPeyZdeekkLFizQgAED9Mknn6R7PSpUqKCVK1emOG737t06ffq0xo8fr8cff1ylSpVKdmbJ1dVVkm57z2Pp0qV1/fp1bdq0yT7s9OnT2rNnj8qUKZPu2r/88ksVLFhQO3fudAg1kyZN0qxZs5SQkKAKFSroyJEj2rt3b4pt3G79Jcnf39/hPsrz58/rwIEDd6xt3bp1qlmzpnr16qVHHnlEISEhDmeqvb29VbRo0dsu28/PT08//bTCw8M1a9YsdenS5bbLLF26tNatW5esjhIlStjvWb1b6d3PK1eurOjoaAUEBCgkJMThlfTYnnXr1umVV15R06ZN7R0dnTp1Kl11psXs2bN19uxZey+xaTkWXV1dk+3b27ZtU2JioiZNmqTq1aurRIkS+ueff+64/EceeUR//fVXquNXrlypqKgoe8dRCQkJ9j9uxMfHO9SRdKb4kUceSdvKZxICZxby22+/qXnz5sqfP3+KD801xmj48OEKCgqSh4eHGjRo4NCzm3Tjrxzt27eXj4+PcuXKpW7dut2xt6wrV66od+/e8vPzk5eXl1q3bq0TJ044TBMTE6NmzZopZ86cCggI0KBBg3T9+vUMWW8gK7lw4YL69eunIkWKyMPDQzVr1tSWLVvs40+cOKHOnTsrf/78ypkzpxo3bpzsOLxVfHy83nrrLQUHB8vd3V0VK1bUsmXLHKZJSEjQsGHD7D0ZBgcHa/To0fd0uR6A+2PatGlKSEjQY489pvnz5ys6OlpRUVGaOnWqatSokeI8oaGhiomJ0ddff639+/dr6tSp9rOG0o3L7vr06aPVq1fr0KFDWrdunbZs2aLSpUtLkvr166eIiAgdOHBA27dv16pVq+zj0mPEiBH66quvNGLECEVFRdk7IpJudEzi6uqqDz74QH///bcWL16s0aNHO8xfpEgR2Ww2LVmyRP/++2+Kvz1CQ0PVsmVL9ejRQ2vXrtXOnTv1wgsvqECBAmrZsmW6a585c6b+85//qFy5cg6vbt266dSpU1q2bJnq1KmjJ554Qq1bt9by5ct14MABLV261P5dPGTIEG3ZskW9evXS77//rt27d2v69On24FOvXj3NmTNHa9as0a5du9SpU6c0hbfQ0FBt3bpVERER2rt3r4YNG+bwb4p040z2pEmTNHXqVEVHR2v79u3JzlZ3795ds2fPVlRUlDp16nTbZQ4YMEArV67U6NGjtXfvXs2ePVsffvjhHTteupP07Oft27dX3rx51bJlS61Zs0YHDhzQ6tWr9corr+jIkSOSbmyjOXPmKCoqSps2bVL79u3TdOY3LS5duqTjx4/ryJEj2rhxo9544w299NJLevnll+09K9/pWJRu9Op74MABRUZG6tSpU7p69apCQkIUHx9vPy7mzJmjjz/++I41hYWFae3atSmOu3Llivr06aMZM2YoR44bMa1WrVqaNm2adu7cqfnz5ztcLr1x40a5ubmluv2zDKtvJs0OskqnQT/99JMZOnSoWbBgQYodC4wfP974+vqaRYsWmZ07d5oWLVqYYsWKOdyo27hxY1OxYkWzceNGs2bNGhMSEmLatWt32+W+9NJLplChQmblypVm69atpnr16qZmzZr28devXzflypUzDRo0MDt27DA//fSTyZs3rxkyZEiGrj+QFbRp08aUKVPG/PrrryY6OtqMGDHC+Pj4mCNHjpjExERTvXp18/jjj5vNmzeb3bt3mxdffNEULlzYxMXFpdrm66+/bvLnz29+/PFHs3//fvPRRx8Zd3d3s337dvs0Y8eONX5+fmbJkiXmwIEDZt68ecbLy8u8//7792O1gUyXXTsNSvLPP/+Y3r17myJFihhXV1dToEAB06JFC4eOem79t33QoEHGz8/PeHl5meeee85MmTLF3pHP1atXTdu2bU2hQoWMq6uryZ8/v+nTp499+/Tp08cEBwcbNzc34+/vbzp06GBOnTpljElfp0HGGDN//nxTqVIl4+rqavLmzWueeeYZ+7i5c+eaokWLGjc3N1OjRg2zePFiI8ns2LHDPs1bb71lAgMDjc1mM506dTLGJO+45cyZM6ZDhw7G19fXeHh4mLCwMLN3797b1rVw4UKT2k/WrVu3Gklm8+bNKY5v0qSJadWqlTHGmNOnT5suXboYPz8/4+7ubsqVK2eWLFlin3b16tWmZs2axs3NzeTKlcuEhYXZt2FsbKx57rnnjI+PjylUqJCZNWtWip0G3frb7cqVK6Zz587G19fX5MqVy7z88stm8ODByTqg+fjjj03JkiWNi4uLCQoKMn379nUYn5iYaIoUKWKaNm2a4nre6rvvvjNlypQxLi4upnDhwuadd95xGJ+WTpBSkp79/NixY6Zjx44mb968xs3NzRQvXtz06NHD/rt7+/btpmrVqsbd3d2EhoaaefPmJasvpW3r6+trwsPDU621Tp06RpKRZFxdXU1QUJB56qmnUuz46HbHojE3PsfWrVubXLlyGUn25U6ePNkEBQXZ9+XPP/88xc6zbnb69Gnj7u5udu/enWzc4MGDzYABAxyGRUdHm0cffdT4+PiYl19+2SQkJNjHvfjii6Znz56pLstqae00iMBpsk7gvNmtB1ZiYqIJDAx0+MI4d+6ccXNzM1999ZUxxpi//vrLSDJbtmyxT7N06VJjs9nM0aNHU1zOuXPnjIuLi5k3b559WFRUlJFkNmzYYIy5EYRz5Mhhjh8/bp9m+vTpxsfHx1y9ejVD1hfICi5dumScnJwcfoAYY0zlypXN0KFDzZ49e4wk88cff9jHJSQkGH9/f/PJJ5+k2m5QUJD58MMPHYY988wzDj1CNmvWzHTt2vW20wAPsrT+cAEeRhcuXDA+Pj5m/vz5mV0KMsDAgQPNiy++eE9t/PvvvyZPnjzm77//zqCq7h691D5gDhw4oOPHj6tBgwb2Yb6+vqpWrZo2bNgg6cZNz7ly5XLoIa1BgwbKkSOHw/0SN9u2bZvi4+Md2i1VqpQKFy7s0G758uWVL18++zRhYWE6f/68/vzzzwxdTyAzXb9+XQkJCcl6j/Pw8NDatWt19epVSXIYnyNHDrm5uaV6eYwkXb16NdU2k9SsWVMrV66031+0c+dOrV27Vk2aNLnn9QIAZE+JiYk6efKkRo8erVy5cqlFixaZXRIywNChQ1WkSJF0dXqU5ODBg/roo4/snR9lZc6ZXQDS5vjx45LkEPqS3ieNO378uAICAhzGOzs7K0+ePPZpUmrX1dU12QN7b203peXeXBfwIPD29laNGjU0evRolS5dWvny5dNXX32lDRs2KCQkxP7HmCFDhuh///ufPD09NWXKFB05csShM4lbhYWFafLkyXriiScUHByslStXasGCBQ43/g8ePFjnz59XqVKl5OTkpISEBI0dO1bt27e/H6sOAMiCYmJiVKxYMRUsWFCzZs2yP/YF2VuuXLn03//+957aqFq1aoqP4cmKOMMJADeZM2eOjDEqUKCA3NzcNHXqVLVr1045cuSQi4uLFixYoL179ypPnjzKmTOnVq1apSZNmthv7k/J+++/r9DQUJUqVUqurq7q06ePunTp4jDPt99+qy+//FJz587V9u3bNXv2bL377ruaPXv2/VhtAEAWVLRoURljdPjw4Qx5TiiQGQic2URgYKAkJes99sSJE/ZxKT2w9/r16zpz5ox9mpTavXbtWrLurG9tN6Xl3lwX8KAIDg7Wr7/+qri4OB0+fFibN29WfHy8ihcvLkn254mdO3dOx44d07Jly3T69Gn7+JT4+/tr0aJFunjxog4dOqTdu3fLy8vLYZ5BgwZp8ODBatu2rcqXL68OHTqof//+GjdunOXrDAAAYBUCZzZRrFgxBQYGOjyn6fz589q0aZO9K+QaNWro3Llz2rZtm32aX375RYmJiapWrVqK7VapUkUuLi4O7e7Zs0cxMTEO7e7atcshzC5fvlw+Pj739NwsICvz9PRUUFCQzp49q4iIiGRd9vv6+srf31/R0dHaunVrmrr0d3d3tz+MfP78+Q7zXLp0KdlZUicnp3u6vwMAACCzZWrgzKznTmZVcXFx9ocVS7I/7ycmJkY2m039+vXTmDFjtHjxYu3atUsdO3ZU/vz59fTTT0u68aDfxo0bq0ePHtq8ebPWrVunPn36qG3btsqfP78k6ejRoypVqpQ2b94s6caP5m7duum1117TqlWrtG3bNnXp0kU1atRQ9erVJUmNGjVSmTJl1KFDB+3cuVMRERF688031bt3b7m5ud337QRYKSIiQsuWLdOBAwe0fPly1a1bV6VKlbI/aHvevHlavXq1/v77b33//fdq2LChnn76aTVq1MjeRseOHTVkyBD7+02bNmnBggX6+++/tWbNGjVu3FiJiYl6/fXX7dM0b95cY8eO1Y8//qiDBw9q4cKFmjx5slq1anX/Vh4AACCj3Y8uc1OTWc+dvFVWeSxK0nOzbn0lPcsqMTHRDBs2zOTLl8+4ubmZ+vXrmz179ji0cfr0adOuXTvj5eVlfHx8TJcuXcyFCxfs4w8cOJDsWVyXL182vXr1Mrlz5zY5c+Y0rVq1MseOHXNo9+DBg6ZJkybGw8PD5M2b1wwYMMDEx8dbti2AzPLNN9+Y4sWLG1dXVxMYGGh69+5tzp07Zx///vvvm4IFC9qfbfbmm28mezxQnTp17MetMTee7Va6dGnj5uZm/Pz8TIcOHZI9quj8+fPm1VdfNYULFzbu7u6mePHiZujQoTx6CA8NHosCANlLWr+3bcYYk3lx9//YbDYtXLjQfrbOGKP8+fNrwIABGjhwoCQpNjZW+fLl06xZs9S2bVtFRUWpTJky2rJli72XpmXLlqlp06Y6cuSI/azenZw/f16+vr6KjY2Vj4+PJesHAABSd+XKFR04cEDFihVL9hghAEDWk9bv7Sx7D6dVz50EAAAAANwfWTZwWvXcSenGQ9jPnz/v8AIAAHgQrF69WjabLVkP9Hhwde7c2X6VYFY2a9asZM9+z4qyy/ZMzRNPPKG5c+feUxvVq1fX/PnzM6SeLBs4rTRu3Dj5+vraX4UKFbq/BdhsD/YLyKoy+9jguAPSJ5scTxs2bJCTk5OaNWuWbNzIkSNVqVKlFFYteaeJmeHff//Vyy+/rMKFC8vNzU2BgYEKCwvTunXrMrs0Szz55JOy2Wyy2Wxyd3dXiRIlNG7cON3tnWZFixbVe++9Z02Rd+H999/XrFmzLF9O586d7dvN1dVVISEheuutt3T9+vU0zf/cc89p7969FldpvaQ/6thsNuXIkUO+vr565JFH9Prrr+vYsWN33V5Gfg8sXrxYJ06cUNu2be3DXnvtNeXJk0eFChXSl19+6TD9vHnz1Lx582TtvPnmmxo8eHCG9JafZQOnVc+dlKQhQ4YoNjbW/jp8+HAGVw8AAB42M2fOVN++ffXbb7/pn3/+yexy7krr1q21Y8cOzZ49W3v37tXixYv15JNP6vTp05ldmmV69OihY8eOac+ePRoyZIiGDx+ujz/+OLPLShdfX9/7duawcePGOnbsmKKjozVgwACNHDlS77zzTprm9fDwSHZ1Yna2Z88e/fPPP9qyZYveeOMNrVixQuXKldOuXbsyraapU6eqS5cu9ket/fDDD5o7d65+/vlnTZw4Ud27d9epU6ck3egfZ+jQoZo2bVqydpo0aaILFy5o6dKl917UfejAKE10Sy+1iYmJJjAw0Lz77rv2YbGxscbNzc189dVXxhhj/vrrLyPJbN261T5NRESEsdlsyXqAvJ373kut9GC/gKwqs48NjjsgVbft7TAbHE8XLlwwXl5eZvfu3ea5554zY8eOtY8LDw83kmMP9OHh4aZIkSIOw4oUKWKMMWbfvn2mRYsWJiAgwHh6epqqVaua5cuXOyzvypUr5vXXXzcFCxY0rq6uJjg42Hz66afGmP/r9f7s2bPGGGMuXrxoGjdubGrWrGkfdrOzZ88aSWb16tWprl9SL/c7duxINt/NPd//8ccfplmzZsbb29t4eXmZ2rVrm3379tnHz5w505QpU8ahJ/Cb2+vWrZvJmzev8fb2NnXr1jWRkZH28ZGRkebJJ580Xl5extvb21SuXNls2bLFGHOjN/2nnnrK5MqVy+TMmdOUKVPG/Pjjj6muT506dcyrr77qMKxy5cqmVatW9vd3+hzq1KmT7HNNsmbNGlO7dm3j7u5uChYsaPr27Wvi4uJSradTp06mZcuWDsNeffVVU6dOHfv7efPmmXLlyhl3d3eTJ08eU79+fXubt85fp04d07dvXzNo0CCTO3duky9fPjNixAiH9qOiokytWrWMm5ubKV26tFm+fLm59fd4Wups2LChqV69ujHGmDNnzpgOHTqYXLlyGQ8PD9O4cWOzd+9e+7Th4eHG19fX/v52n6kxxnz33Xf2/aVIkSIOucAYY4oUKWLGjh1runTpYry8vEyhQoXM//73P4dpYmJizLPPPmt8fX1N7ty5TYsWLcyBAwfs469fv2769+9vfH19TZ48ecygQYNMx44dk63nzW49xpJcunTJlCxZ0tSqVcs+bPPmzaZBgwbGz8/P+Pj4mCeeeMJs27bNYR3S+z1wq5MnTxqbzWb++OMP+7AJEyaY5557zv4+ICDAbN682RhjzIsvvmgmT56cantdunQxL7zwQqrj09pLbaae4bwfz50EAACw2rfffqtSpUqpZMmSeuGFF/TZZ5/JGCPpxmWEAwYMUNmyZXXs2DEdO3ZMzz33nLZs2SJJCg8P17Fjx+zv4+Li1LRpU61cuVI7duxQ48aN1bx5c8XExNiX17FjR3311VeaOnWqoqKi9L///U9eXl7J6jp37pwaNmyoxMRELV++PMWzYF5eXvLy8tKiRYt09erVdG+Do0eP6oknnpCbm5t++eUXbdu2TV27drVfbjl9+nT17t1bL774onbt2qXFixcrJCTEPv+zzz6rkydPaunSpdq2bZsqV66s+vXr68yZM5Kk9u3bq2DBgtqyZYu2bdumwYMHy8XFRZLUu3dvXb16Vb/99pt27dqlCRMmpLg9UmKM0Zo1a7R79265urrah9/pc1iwYIEKFiyot956y/65StL+/fvVuHFjtW7dWr///ru++eYbrV27Vn369En3tj127JjatWunrl27KioqSqtXr9Yzzzxj38dSMnv2bHl6emrTpk2aOHGi3nrrLS1fvlySlJCQoKefflo5c+bUpk2bNGPGDA0dOjRdtXl4eOjatWuSblxyu3XrVi1evFgbNmyQMUZNmzZVfHx8ivPe7jPdtm2b2rRpo7Zt22rXrl0aOXKkhg0bluzS4UmTJqlq1arasWOHevXqpZdffll79uyRJMXHxyssLEze3t5as2aN1q1bJy8vLzVu3Nhe86RJkzRr1ix99tlnWrt2rc6cOaOFCxeme1u89NJLWrdunf0qzAsXLqhTp05au3atNm7cqNDQUDVt2lQXLlyQpHv6HrjV2rVrlTNnTpUuXdo+rGLFitq6davOnj2rbdu26fLlywoJCdHatWu1fft2vfLKK6m299hjj2nNmjXp2hYObhtHLXY/njuZFpzhzPy/DAP3RWYfGxx3QKqy+xnOmjVrmvfee88YY0x8fLzJmzevw5m/ESNGmIoVK6awarc/o5SkbNmy5oMPPjDGGLNnzx4jKdWzHUm/r6KiokyFChVM69at7/hM3++++87kzp3buLu7m5o1a5ohQ4aYnTt32sen5QznkCFDTLFixcy1a9dSXEb+/PnN0KFDUxy3Zs0a4+PjY65cueIwPDg42H7Gytvb28yaNSvF+cuXL29Gjhx523W8WZ06dYyLi4vx9PQ0Li4uRpJxd3c369atu+18N38Oxtw4OzVlyhSHabp162ZefPFFh2Fr1qwxOXLkSPVM0J3OcG7bts1IMgcPHkzT/HXq1DG1a9d2mObRRx81b7zxhjHGmKVLlxpnZ2eH567f7RnOxMREs3z5cuPm5mYGDhxo9u7dayQ5bMNTp04ZDw8P8+233xpjkp/hvN1n+vzzz5uGDRs6DBs0aJApU6aM/X2RIkUczsAlJiaagIAAM336dGOMMXPmzDElS5Y0iYmJ9mmuXr1qPDw8TEREhDHGmKCgIDNx4kT7+Pj4eFOwYMF0neE05sa2lWQ2bdqU4rwJCQnG29vb/PDDD/Zh6fkeSMmUKVNM8eLFkw0fMWKECQ4ONuXKlTMLFiwwV69eNeXKlTNbt241H3zwgSlRooSpWbOmw5lRY4z5/vvvTY4cOUxCQkKKy8sWZziffPJJGWOSvZL+cmGz2fTWW2/p+PHjunLlilasWKESJUo4tJEnTx7NnTtXFy5cUGxsrD777LM0/0ULAADgXu3Zs0ebN29Wu3btJN3oMf+5557TzJkz09VeXFycBg4cqNKlSytXrlzy8vJSVFSU/cxGZGSknJycVKdOndu207BhQ4WEhOibb75xOHOXktatW+uff/7R4sWL1bhxY61evVqVK1e+q45oIiMj9fjjj9vPUN3s5MmT+ueff1S/fv0U5925c6fi4uLk5+dnP+Pq5eWlAwcOaP/+/ZJudHzSvXt3NWjQQOPHj7cPl6RXXnlFY8aMUa1atTRixAj9/vvvd6y3ffv2ioyM1Lp169SkSRMNHTpUNWvWtI+/0+eQmp07d2rWrFkO6xEWFqbExEQdOHDgjnWlpGLFiqpfv77Kly+vZ599Vp988onOnj1723kqVKjg8D4oKMh+1m3Pnj0qVKiQQ58njz32WJpqWbJkiby8vOTu7q4mTZroueee08iRIxUVFSVnZ2dVq1bNPq2fn59KliypqKioFNu63WcaFRWlWrVqOUxfq1YtRUdHKyEhIcX1tNlsDn287Ny5U/v27ZO3t7f9s8iTJ4+uXLmi/fv3KzY2VseOHXOo2dnZ2eGRi3fLGGOvRbrR/0yPHj0UGhoqX19f+fj4KC4u7o77UXr2v8uXL6f4PMyRI0dq37592rVrl1q1aqVx48apQYMGcnFx0ZgxY7R27Vp1795dHTt2dJjPw8NDiYmJ93Tlg5SFOw0CAADIDmbOnKnr168rf/78cnZ2lrOzs6ZPn6758+crNjb2rtsbOHCgFi5cqLfffltr1qxRZGSkypcvb78E0MPDI03tNGvWTL/99pv++uuvNE3v7u6uhg0batiwYVq/fr06d+6sESNGSJK9A5KkH9OSkl0mebu67lRzXFycgoKC7LdaJb327NmjQYMGSbrxo/nPP/9Us2bN9Msvv6hMmTL2Sx+7d++uv//+Wx06dNCuXbtUtWpVffDBB7ddpq+vr0JCQvToo4/q22+/1YcffqgVK1bYx9/pc7jduvTs2dNhPXbu3Kno6GgFBwenOE+OHDkctq3kuH2dnJy0fPlyLV26VGXKlNEHH3ygkiVL3jbA3hr8bTZbhvQ4WrduXUVGRio6OlqXL1+2X7qbHrf7TNPqdusZFxenKlWqJNuv9u7dq+effz5dNd9JUrguWrSoJKlTp06KjIzU+++/r/Xr1ysyMlJ+fn533I/Ss//lzZv3jn+I2L17t7744guNHj1aq1ev1hNPPCF/f3+1adNG27dvt1/qK0lnzpyRp6dnmr9zUkPgBAAASKfr16/r888/16RJk5IFjPz58+urr76SJLm6ujqclUni4uKSbPi6devUuXNntWrVSuXLl1dgYKAOHjxoH1++fHklJibq119/vW1t48ePV6dOnVS/fv00h86blSlTRhcvXpQk+fv7S5LDIx+S+uBIUqFCBa1ZsybF+/W8vb1VtGhRrVy5MsVlVa5cWcePH5ezs7NCQkIcXnnz5rVPV6JECfXv318///yznnnmGYWHh9vHFSpUSC+99JIWLFigAQMG6JNPPknzunp5eenVV1/VwIED7cHvTp+DlPLnWrlyZf3111/J1iMkJCTVM83+/v7JHqdx6/a12WyqVauWRo0apR07dsjV1TXd9xqWLFlShw8fdngaRNK9g3fi6empkJAQFS5cWM7OzvbhpUuX1vXr17Vp0yb7sNOnT2vPnj0qU6ZMqu2l9pmWLl062WN51q1bpxIlSsjJySlNtVauXFnR0dEKCAhI9lkkPR4xKCjIoebr169r27ZtaWr/VpcvX9aMGTPsIS6p5ldeeUVNmzZV2bJl5ebmZu8lNkl6vgdS8sgjj+j48eOphk5jjHr27KnJkyfLy8tLCQkJ9uM16b831/HHH3/okUceuattkBICJwAAQDotWbJEZ8+eVbdu3VSuXDmHV+vWre2X1RYtWtTeOeKpU6fsl6glhbCbfySGhoZqwYIF9uD6/PPPO5yZKlq0qDp16qSuXbtq0aJFOnDggFavXq1vv/02WX3vvvuu2rdvr3r16mn37t0prsPp06dVr149ffHFF/r999914MABzZs3TxMnTlTLli0l3ThDWb16dY0fP15RUVH69ddf9eabbzq006dPH50/f15t27bV1q1bFR0drTlz5tg7cBk5cqQmTZqkqVOnKjo6Wtu3b7efhWzQoIFq1Kihp59+Wj///LMOHjyo9evXa+jQodq6dasuX76sPn36aPXq1Tp06JDWrVunLVu22DtH6devnyIiInTgwAFt375dq1atcug4JS169uypvXv32h92f6fPIemz+O2333T06FF7iHjjjTe0fv169enTx34m8Pvvv79tp0H16tXT1q1b9fnnnys6OlojRozQH3/8YR+/adMmvf3229q6datiYmK0YMEC/fvvv3e9jkkaNmyo4OBgderUSb///rvWrVtn/zxt6XwWbWhoqFq2bKkePXpo7dq12rlzp1544QUVKFDAvh/d7E6f6YABA7Ry5UqNHj1ae/fu1ezZs/Xhhx9q4MCBaa6pffv2yps3r1q2bKk1a9bYj5VXXnlFR44ckSS9+uqrGj9+vBYtWqTdu3erV69eOnfuXJraP3nypI4fP67o6Gh9/fXXqlWrlk6dOqXp06c7bJc5c+YoKipKmzZtUvv27ZOdMUzP90BKHnnkEeXNmzfV5+d++umn8vf3tz93s1atWvrll1+0ceNGTZkyRWXKlHHoWGzNmjVq1KhRmrbFbd32Ds+HBJ0GZX5nC8B9kdnHBscdkKq0dj6R1Tz11FOmadOmKY7btGmTkWR27txprly5Ylq3bm1y5cplpBuPRTHGmMWLF5uQkBDj7OxsfxzCgQMHTN26dY2Hh4cpVKiQ+fDDD5M9xuPy5cumf//+JigoyLi6upqQkBDz2WefGWNS7tCkb9++JigoKFnni8bceMTK4MGDTeXKlY2vr6/JmTOnKVmypHnzzTfNpUuX7NP99ddfpkaNGsbDw8NUqlTJ/Pzzz0ZyfCzKzp07TaNGjUzOnDmNt7e3efzxx83+/fvt4z/++GNTsmRJ4+LiYoKCgkzfvn3t486fP2/69u1r8ufPb1xcXEyhQoVM+/btTUxMjLl69app27atKVSokHF1dTX58+c3ffr0se8vffr0McHBwcbNzc34+/ubDh06mFOnTqX6uaX0WBRjjOnZs6cpW7asSUhISNPnsGHDBlOhQgXj5uZmbv5ZvXnzZtOwYUPj5eVlPD09TYUKFRwelZOS4cOHm3z58hlfX1/Tv39/06dPH3unQX/99ZcJCwsz/v7+xs3NzZQoUcKh85iUOg26df1atmxp75jTmP97LIqrq6spVaqU+eGHH4wks2zZslRrTKlzo5slPRbF19fXeHh4mLCwsFQfi3Knz9SY/3ssiouLiylcuLB55513HJaXUqdNFStWdHgEzLFjx0zHjh1N3rx5jZubmylevLjp0aOH/Xd/fHy8efXVV42Pj4/JlSuXee2119L8WBRJxmazGW9vb1OxYkUzaNAgh46YjDFm+/btpmrVqsbd3d2EhoaaefPmJas7vd8DKXn99ddN27Ztkw0/fvy4KVKkSLJHR44aNcrkyZPHlCpVyqGjoyNHjhgXFxdz+PDhVJeV1u9tmzG3XDD+EDp//rx8fX0VGxsrHx8f6xeYzr8cZRvsUsiqHuRjj+MO2dyVK1d04MABFStWLMVOLwBYa926dapdu7b27duX6r2myPqOHz+usmXLavv27SpSpEi623njjTd09uxZzZgxI9Vp0vq97ZzqGAAAAAAPpIULF8rLy0uhoaHat2+fXn31VdWqVYuwmc0FBgZq5syZiomJuafAGRAQoNdeey1DaiJwAgAAAA+ZCxcu6I033lBMTIzy5s2rBg0aaNKkSZldFjLA008/fc9tDBgw4N4L+f8InAAAAMBDpmPHjsmeuwhYgV5qAQAAAACWIHACAIAsg74MASB7SOv3NYETmebChQvq16+fihQpIg8PD9WsWdPhocOdO3eWzWZzeDVu3Pi2bRYtWjTZPDabTb179042rTFGTZo0kc1m06JFizJ69QAAd8HFxUWSdOnSpUyuBACQFknf10nf36nhHk5kmu7du+uPP/7QnDlzlD9/fn3xxRdq0KCB/vrrLxUoUECS1LhxY4WHh9vncXNzu22bW7ZsUUJCgv39H3/8oYYNG+rZZ59NNu17772X7ocbAwAylpOTk3LlyqWTJ09KknLmzMl3NABkQcYYXbp0SSdPnlSuXLnk5OR02+kJnMgUly9f1vz58/X999/riSeekCSNHDlSP/zwg6ZPn64xY8ZIuhEwAwMD09yuv7+/w/vx48crODhYderUcRgeGRmpSZMmaevWrQoKCrrHtQEAZISk7/uk0AkAyLpy5cqVpt/pBE5kiuvXryshISHZQ2I9PDy0du1a+/vVq1crICBAuXPnVr169TRmzBj5+fmlaRnXrl3TF198oddee83hr+SXLl3S888/r2nTpt1VmAUAWMtmsykoKEgBAQGKj4/P7HIAAKlwcXG545nNJAROZApvb2/VqFFDo0ePVunSpZUvXz599dVX2rBhg0JCQiTduJz2mWeeUbFixbR//37997//VZMmTbRhw4Y07eCLFi3SuXPn1LlzZ4fh/fv3V82aNdWyZUsrVg0AcI+cnJzS/EMGAJC1ETiRaebMmaOuXbuqQIECcnJyUuXKldWuXTtt27ZNktS2bVv7tOXLl1eFChUUHBys1atXq379+ndsf+bMmWrSpIny589vH7Z48WL98ssv2rFjR8avEAAAAAAH9FKLTBMcHKxff/1VcXFxOnz4sDZv3qz4+HgVL148xemLFy+uvHnzat++fXds+9ChQ1qxYoW6d+/uMPyXX37R/v37lStXLjk7O8vZ+cbfXFq3bq0nn3zyntcJAAAAwP/hDCcynaenpzw9PXX27FlFRERo4sSJKU535MgRnT59Ok2d/ISHhysgIEDNmjVzGD548OBkIbR8+fKaMmWKmjdvnv6VAAAAAJAMgROZJiIiQsYYlSxZUvv27dOgQYNUqlQpdenSRXFxcRo1apRat26twMBA7d+/X6+//rpCQkIUFhZmb6N+/fpq1aqV+vTpYx+WmJio8PBwderUyX4GM0lgYGCKHQUVLlxYxYoVs25lAQAAgIcQl9Qi08TGxqp3794qVaqUOnbsqNq1aysiIsLe69Xvv/+uFi1aqESJEurWrZuqVKmiNWvWODyLc//+/Tp16pRDuytWrFBMTIy6du16v1cJAAAAwE1sxhiT2UVktvPnz8vX11exsbHy8fGxfoEP+oOs2aWQVT3Ixx7HHQAAyII4wwkAAAAAsASBEwAAAABgCToNQoazjXqAL1uUZEZw6SIAAACQFpzhBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInADxELly4oH79+qlIkSLy8PBQzZo1tWXLFklSfHy83njjDZUvX16enp7Knz+/OnbsqH/++ee2bf72229q3ry58ufPL5vNpkWLFiWbZuTIkSpVqpQ8PT2VO3duNWjQQJs2bbJiFQEAQBZC4ASAh0j37t21fPlyzZkzR7t27VKjRo3UoEEDHT16VJcuXdL27ds1bNgwbd++XQsWLNCePXvUokWL27Z58eJFVaxYUdOmTUt1mhIlSujDDz/Url27tHbtWhUtWlSNGjXSv//+m9GrCAAAshCbMcZkdhGZ7fz58/L19VVsbKx8fHysX6DNZv0yMpFtZGZXYC0z4qE/ZLKvB/nYS8NX+eXLl+Xt7a3vv/9ezZo1sw+vUqWKmjRpojFjxiSbZ8uWLXrsscd06NAhFS5c+I7LsNlsWrhwoZ5++unbTpf0vbtixQrVr1//ju0CAIDsiTOcAPCQuH79uhISEuTu7u4w3MPDQ2vXrk1xntjYWNlsNuXKlSvD6rh27ZpmzJghX19fVaxYMcPaBQAAWQ+BEwAeEt7e3qpRo4ZGjx6tf/75RwkJCfriiy+0YcMGHTt2LNn0V65c0RtvvKF27dplyNUfS5YskZeXl9zd3TVlyhQtX75cefPmved2AQBA1kXgBICHyJw5c2SMUYECBeTm5qapU6eqXbt2ypHD8Z+D+Ph4tWnTRsYYTZ8+PUOWXbduXUVGRmr9+vVq3Lix2rRpo5MnT2ZI2wAAIGsicALAQyQ4OFi//vqr4uLidPjwYW3evFnx8fEqXry4fZqksHno0CEtX748w+5t9/T0VEhIiKpXr66ZM2fK2dlZM2fOzJC2AQBA1kTgBICHkKenp4KCgnT27FlFRESoZcuWkv4vbEZHR2vFihXy8/OzrIbExERdvXrVsvYBAEDmc87sAgAA909ERISMMSpZsqT27dunQYMGqVSpUurSpYvi4+P1n//8R9u3b9eSJUuUkJCg48ePS5Ly5MkjV1dXSVL9+vXVqlUr9enTR5IUFxenffv22Zdx4MABRUZGKk+ePCpcuLAuXryosWPHqkWLFgoKCtKpU6c0bdo0HT16VM8+++z93wgAAOC+IXACwEMkNjZWQ4YM0ZEjR5QnTx61bt1aY8eOlYuLiw4ePKjFixdLkipVquQw36pVq/Tkk09Kkvbv369Tp07Zx23dulV169a1v3/ttdckSZ06ddKsWbPk5OSk3bt3a/bs2Tp16pT8/Pz06KOPas2aNSpbtqy1KwwAADIVz+EUz+HMaDyHE1nWg3zs8VUOAACyIO7hBAAAAABYgsAJAAAAALAE93ACwAPANuoBvlxYXMoOAEB2xRlOAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAAslJCRo2LBhKlasmDw8PBQcHKzRo0fLGJPi9C+99JJsNpvee++927Y7ffp0VahQQT4+PvLx8VGNGjW0dOlSh2l69uyp4OBgeXh4yN/fXy1bttTu3bszatUA4I4InAAAABaaMGGCpk+frg8//FBRUVGaMGGCJk6cqA8++CDZtAsXLtTGjRuVP3/+O7ZbsGBBjR8/Xtu2bdPWrVtVr149tWzZUn/++ad9mipVqig8PFxRUVGKiIiQMUaNGjVSQkJChq4jAKTGObMLAAAAeJCtX79eLVu2VLNmzSRJRYsW1VdffaXNmzc7THf06FH17dtXERER9mlvp3nz5g7vx44dq+nTp2vjxo0qW7asJOnFF1+0jy9atKjGjBmjihUr6uDBgwoODr7XVQOAO+IMJwAAgIVq1qyplStXau/evZKknTt3au3atWrSpIl9msTERHXo0EGDBg2yh8W7kZCQoK+//loXL15UjRo1Upzm4sWLCg8PV7FixVSoUKH0rQwA3CXOcAIAAFho8ODBOn/+vEqVKiUnJyclJCRo7Nixat++vX2aCRMmyNnZWa+88spdtb1r1y7VqFFDV65ckZeXlxYuXKgyZco4TPPRRx/p9ddf18WLF1WyZEktX75crq6uGbJuAHAnnOEEAACw0Lfffqsvv/xSc+fO1fbt2zV79my9++67mj17tiRp27Ztev/99zVr1izZbLa7artkyZKKjIzUpk2b9PLLL6tTp07666+/HKZp3769duzYoV9//VUlSpRQmzZtdOXKlQxbPwC4HZtJrYu0h8j58+fl6+ur2NhY+fj4WL/Au/zHJLuxjczsCqxlRjz0h0z29QAfexx3QNZVqFAhDR48WL1797YPGzNmjL744gvt3r1b7733nl577TXlyPF/5wESEhKUI0cOFSpUSAcPHkzzsho0aKDg4GD973//S3H8tWvXlDt3bn366adq165dutcJANKKS2oBAAAsdOnSJYcwKUlOTk5KTEyUJHXo0EENGjRwGB8WFqYOHTqoS5cud7WsxMREXb16NdXxxhgZY247DQBkJAInAACAhZo3b66xY8eqcOHCKlu2rHbs2KHJkyera9eukiQ/Pz/5+fk5zOPi4qLAwECVLFnSPqx+/fpq1aqV+vTpI0kaMmSImjRposKFC+vChQuaO3euVq9erYiICEnS33//rW+++UaNGjWSv7+/jhw5ovHjx8vDw0NNmza9T2sP4GFH4AQAALDQBx98oGHDhqlXr146efKk8ufPr549e2r48OF31c7+/ft16tQp+/uTJ0+qY8eOOnbsmHx9fVWhQgVFRESoYcOGkiR3d3etWbNG7733ns6ePat8+fLpiSee0Pr16xUQEJCh6wgAqeEeTnEPZ0bjXjJkWQ/wscdxBwAAsiJ6qQUAAAAAWILACQAAAACwBPdwAgAApJNt1IN7qT6XsgPICJzhBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYIksHzoSEBA0bNkzFihWTh4eHgoODNXr0aBlj7NMYYzR8+HAFBQXJw8NDDRo0UHR0dCZWDQAAAACQsnjgnDBhgqZPn64PP/xQUVFRmjBhgiZOnKgPPvjAPs3EiRM1depUffzxx9q0aZM8PT0VFhamK1euZGLlAAAAAADnzC7gdtavX6+WLVuqWbNmkqSiRYvqq6++0ubNmyXdOLv53nvv6c0331TLli0lSZ9//rny5cunRYsWqW3btplWOwAAAAA87LL0Gc6aNWtq5cqV2rt3ryRp586dWrt2rZo0aSJJOnDggI4fP64GDRrY5/H19VW1atW0YcOGVNu9evWqzp8/7/ACAAAAAGSsLH2Gc/DgwTp//rxKlSolJycnJSQkaOzYsWrfvr0k6fjx45KkfPnyOcyXL18++7iUjBs3TqNGjbKucAAAAABA1j7D+e233+rLL7/U3LlztX37ds2ePVvvvvuuZs+efU/tDhkyRLGxsfbX4cOHM6hiAAAAAECSLH2Gc9CgQRo8eLD9Xszy5cvr0KFDGjdunDp16qTAwEBJ0okTJxQUFGSf78SJE6pUqVKq7bq5ucnNzc3S2gEAAADgYZelz3BeunRJOXI4lujk5KTExERJUrFixRQYGKiVK1fax58/f16bNm1SjRo17mutAAAAAABHWfoMZ/PmzTV27FgVLlxYZcuW1Y4dOzR58mR17dpVkmSz2dSvXz+NGTNGoaGhKlasmIYNG6b8+fPr6aefztziAQAAAOAhl6UD5wcffKBhw4apV69eOnnypPLnz6+ePXtq+PDh9mlef/11Xbx4US+++KLOnTun2rVra9myZXJ3d8/EygEAAAAANmOMyewiMtv58+fl6+ur2NhY+fj4WL9Am836ZWQi28jMrsBaZsRDf8hkXw/wscdxB2QO26gH93uF4w5ARsjS93ACAIAHX9GiRWWz2ZK9evfurYMHD6Y4zmazad68eSm2Fx8frzfeeEPly5eXp6en8ufPr44dO+qff/5xmG7s2LGqWbOmcubMqVy5ct2HNQWAhw+BEwAAZKotW7bo2LFj9tfy5cslSc8++6wKFSrkMO7YsWMaNWqUvLy81KRJkxTbu3TpkrZv365hw4Zp+/btWrBggfbs2aMWLVo4THft2jU9++yzevnlly1fRwB4WGXpezgBAMCDz9/f3+H9+PHjFRwcrDp16shms9kfg5Zk4cKFatOmjby8vFJsz9fX1x5ak3z44Yd67LHHFBMTo8KFC0uSRo0aJUmaNWtWBq0JAOBWnOEEAABZxrVr1/TFF1+oa9eusqVw3/W2bdsUGRmpbt263VW7sbGxstlsXDoLAPcZgRMAAGQZixYt0rlz59S5c+cUx8+cOVOlS5dWzZo109zmlStX9MYbb6hdu3b3p3NAAIAdgRMAAGQZM2fOVJMmTZQ/f/5k4y5fvqy5c+fe1dnN+Ph4tWnTRsYYTZ8+PSNLBQCkAfdwAgCALOHQoUNasWKFFixYkOL47777TpcuXVLHjh3T1F5S2Dx06JB++eUXzm4CQCYgcAIAgCwhPDxcAQEBatasWYrjZ86cqRYtWiTrZCglSWEzOjpaq1atkp+fX0aXCwBIAy6pBQAAmS4xMVHh4eHq1KmTnJ2T/z183759+u2339S9e/cU5y9VqpQWLlwo6UbY/M9//qOtW7fqyy+/VEJCgo4fP67jx4/r2rVr9nliYmIUGRmpmJgYJSQkKDIyUpGRkYqLi7NmJQHgIcQZTgAAkOlWrFihmJgYde3aNcXxn332mQoWLKhGjRqlOH7Pnj2KjY2VJB09elSLFy+WJFWqVMlhulWrVunJJ5+UJA0fPlyzZ8+2j3vkkUeSTQMAuDc2Y4zJ7CIy2/nz5+Xr66vY2Nj7c39HCt28P0hsIzO7AmuZEQ/9IZN9PcDHHscdkDlsox7c7xWOOwAZgUtqAQAAAACWIHACAADr2GwP9gsAcFsETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAAS2T5wHn06FG98MIL8vPzk4eHh8qXL6+tW7faxxtjNHz4cAUFBcnDw0MNGjRQdHR0JlYMAAAAAJCyeOA8e/asatWqJRcXFy1dulR//fWXJk2apNy5c9unmThxoqZOnaqPP/5YmzZtkqenp8LCwnTlypVMrBwAAAAA4JzZBdzOhAkTVKhQIYWHh9uHFStWzP7/xhi99957evPNN9WyZUtJ0ueff658+fJp0aJFatu27X2vGQAAAABwQ5Y+w7l48WJVrVpVzz77rAICAvTII4/ok08+sY8/cOCAjh8/rgYNGtiH+fr6qlq1atqwYUOq7V69elXnz593eAEAAAAAMlaWDpx///23pk+frtDQUEVEROjll1/WK6+8otmzZ0uSjh8/LknKly+fw3z58uWzj0vJuHHj5Ovra38VKlTIupUAAAAAgIdUlg6ciYmJqly5st5++2098sgjevHFF9WjRw99/PHH99TukCFDFBsba38dPnw4gyoGAAAAACTJ0oEzKChIZcqUcRhWunRpxcTESJICAwMlSSdOnHCY5sSJE/ZxKXFzc5OPj4/DCwAAAACQsbJ04KxVq5b27NnjMGzv3r0qUqSIpBsdCAUGBmrlypX28efPn9emTZtUo0aN+1orAAAAAMBRlu6ltn///qpZs6befvtttWnTRps3b9aMGTM0Y8YMSZLNZlO/fv00ZswYhYaGqlixYho2bJjy58+vp59+OnOLBwAAAICHXJYOnI8++qgWLlyoIUOG6K233lKxYsX03nvvqX379vZpXn/9dV28eFEvvviizp07p9q1a2vZsmVyd3fPxMoBAAAAADZjjMnsIjLb+fPn5evrq9jY2PtzP6fNZv0yMpFtZGZXYC0z4qE/ZLKvB/jY47hDlvUAH3fSg33scdwByAhZ+h5OAAAAAED2ReAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWCLdgfPcuXP69NNPNWTIEJ05c0aStH37dh09ejTDigMAAAAAZF/O6Znp999/V4MGDeTr66uDBw+qR48eypMnjxYsWKCYmBh9/vnnGV0nAAAAACCbSdcZztdee02dO3dWdHS03N3d7cObNm2q3377LcOKAwAAAABkX+kKnFu2bFHPnj2TDS9QoICOHz9+z0UBAAAAALK/dAVONzc3nT9/PtnwvXv3yt/f/56LAgAAAABkf+kKnC1atNBbb72l+Ph4SZLNZlNMTIzeeOMNtW7dOkMLBAAAAABkT+kKnJMmTVJcXJwCAgJ0+fJl1alTRyEhIfL29tbYsWMzukYAAAAAQDaUrl5qfX19tXz5cq1du1a///674uLiVLlyZTVo0CCj6wMAAAAAZFPpCpxJateurdq1a2dULQAAAACAB0i6AufUqVNTHG6z2eTu7q6QkBA98cQTcnJyuqfiAAAAAADZV7oC55QpU/Tvv//q0qVLyp07tyTp7Nmzypkzp7y8vHTy5EkVL15cq1atUqFChTK0YAAAAABA9pCuToPefvttPfroo4qOjtbp06d1+vRp7d27V9WqVdP777+vmJgYBQYGqn///hldLwAAAAAgm0jXGc4333xT8+fPV3BwsH1YSEiI3n33XbVu3Vp///23Jk6cyCNSAAAAAOAhlq4znMeOHdP169eTDb9+/bqOHz8uScqfP78uXLhwb9UBAAAAALKtdAXOunXrqmfPntqxY4d92I4dO/Tyyy+rXr16kqRdu3apWLFiGVMlAAAAACDbSVfgnDlzpvLkyaMqVarIzc1Nbm5uqlq1qvLkyaOZM2dKkry8vDRp0qQMLRYAAAAAkH2k6x7OwMBALV++XLt379bevXslSSVLllTJkiXt09StWzdjKgQAAAAAZEvpCpxJSpUqpVKlSmVULQAAAACAB0i6A+eRI0e0ePFixcTE6Nq1aw7jJk+efM+FAQAAAACyt3QFzpUrV6pFixYqXry4du/erXLlyungwYMyxqhy5coZXSMAAAAAIBtKV6dBQ4YM0cCBA7Vr1y65u7tr/vz5Onz4sOrUqaNnn302o2sEAAAAAGRD6QqcUVFR6tixoyTJ2dlZly9flpeXl9566y1NmDAhQwsEAAAAAGRP6Qqcnp6e9vs2g4KCtH//fvu4U6dOZUxlAAAAAIBsLV33cFavXl1r165V6dKl1bRpUw0YMEC7du3SggULVL169YyuEQAAAACQDaUrcE6ePFlxcXGSpFGjRikuLk7ffPONQkND6aEWAAAAACApnYGzePHi9v/39PTUxx9/nGEFAQAAAAAeDOm6h7N48eI6ffp0suHnzp1zCKMAAAAAgIdXugLnwYMHlZCQkGz41atXdfTo0XsuCgAAAACQ/d3VJbWLFy+2/39ERIR8fX3t7xMSErRy5UoVLVo0w4oDAAAAAGRfdxU4n376aUmSzWZTp06dHMa5uLioaNGimjRpUoYVBwAAAADIvu4qcCYmJkqSihUrpi1btihv3ryWFAUAAAAAyP7S1UvtgQMHMroOAAAAAMADJl2BU5JWrlyplStX6uTJk/Yzn0k+++yzey4MAAAAAJC9pStwjho1Sm+99ZaqVq2qoKAg2Wy2jK4LAAAAAJDNpStwfvzxx5o1a5Y6dOiQ0fUAAAAAAB4Q6XoO57Vr11SzZs2MrgUAAAAA8ABJV+Ds3r275s6dm9G1AAAAAAAeIOm6pPbKlSuaMWOGVqxYoQoVKsjFxcVh/OTJkzOkOAAAAABA9pWuwPn777+rUqVKkqQ//vjDYRwdCAEAAAAApHQGzlWrVmV0HQAAAACAB0y67uFMsm/fPkVEROjy5cuSJGNMhhQFAAAAAMj+0hU4T58+rfr166tEiRJq2rSpjh07Jknq1q2bBgwYkKEFAgAAAACyp3QFzv79+8vFxUUxMTHKmTOnffhzzz2nZcuWZVhxAAAAAIDsK133cP7888+KiIhQwYIFHYaHhobq0KFDGVIYAAAAACB7S9cZzosXLzqc2Uxy5swZubm53XNRAAAAAIDsL12B8/HHH9fnn39uf2+z2ZSYmKiJEyeqbt26GVYcAAAAACD7StcltRMnTlT9+vW1detWXbt2Ta+//rr+/PNPnTlzRuvWrcvoGgEAAAAA2VC6znCWK1dOe/fuVe3atdWyZUtdvHhRzzzzjHbs2KHg4OCMrhEAAAAAkA2l6wynJPn6+mro0KEZWQsAAAAA4AGSrjOc4eHhmjdvXrLh8+bN0+zZs++5KAAAAABA9peuwDlu3DjlzZs32fCAgAC9/fbb91wUAAAAACD7S1fgjImJUbFixZINL1KkiGJiYu65KAAAAABA9peuwBkQEKDff/892fCdO3fKz8/vnosCAAAAAGR/6Qqc7dq10yuvvKJVq1YpISFBCQkJ+uWXX/Tqq6+qbdu2GV0jAAAAACAbSlcvtaNHj9bBgwdVv359OTvfaCIxMVEdO3bkHk4AAAAAgKR0BE5jjI4fP65Zs2ZpzJgxioyMlIeHh8qXL68iRYpYUSMAAAAAIBtKV+AMCQnRn3/+qdDQUIWGhlpRFwAAAAAgm7vrezhz5Mih0NBQnT592op6AAAAAAAPiHR1GjR+/HgNGjRIf/zxR0bXAwAAAAB4QKSr06COHTvq0qVLqlixolxdXeXh4eEw/syZMxlSHAAAAAAg+0pX4HzvvfcyuAwAAAAAwIMmXYGzU6dOGV0HAAAAAOABk657OCVp//79evPNN9WuXTudPHlSkrR06VL9+eefGVYcAAAAACD7Slfg/PXXX1W+fHlt2rRJCxYsUFxcnCRp586dGjFiRIYWCAAAAADIntIVOAcPHqwxY8Zo+fLlcnV1tQ+vV6+eNm7cmGHFAQAAAACyr3QFzl27dqlVq1bJhgcEBOjUqVP3XBQAAAAAIPtLV+DMlSuXjh07lmz4jh07VKBAgXsuCgAAAACQ/aUrcLZt21ZvvPGGjh8/LpvNpsTERK1bt04DBw5Ux44dM7pGAAAAAEA2lK7A+fbbb6t06dIqXLiw4uLiVKZMGT3xxBOqWbOm3nzzzYyuEQAAAACQDd3VczgTExP1zjvvaPHixbp27Zo6dOig1q1bKy4uTo888ohCQ0OtqhMAAAAAkM3cVeAcO3asRo4cqQYNGsjDw0Nz586VMUafffaZVfUBAAAAALKpu7qk9vPPP9dHH32kiIgILVq0SD/88IO+/PJLJSYmWlUfAAAAACCbuqvAGRMTo6ZNm9rfN2jQQDabTf/880+GFwYAAAAAyN7uKnBev35d7u7uDsNcXFwUHx+foUUBAAAAALK/u7qH0xijzp07y83NzT7sypUreumll+Tp6WkftmDBgoyrEAAAAACQLd1V4OzUqVOyYS+88EKGFQMAAAAAeHDcVeAMDw+3qg4AAAAAwAPmru7hBAAAAAAgrQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCWyVeAcP368bDab+vXrZx925coV9e7dW35+fvLy8lLr1q114sSJzCsSAAAAACApGwXOLVu26H//+58qVKjgMLx///764YcfNG/ePP3666/6559/9Mwzz2RSlQAAAACAJNkicMbFxal9+/b65JNPlDt3bvvw2NhYzZw5U5MnT1a9evVUpUoVhYeHa/369dq4cWMmVgwAAAAAyBaBs3fv3mrWrJkaNGjgMHzbtm2Kj493GF6qVCkVLlxYGzZsSLW9q1ev6vz58w4vAAAAAEDGcs7sAu7k66+/1vbt27Vly5Zk444fPy5XV1flypXLYXi+fPl0/PjxVNscN26cRo0aldGlAgAAAABukqXPcB4+fFivvvqqvvzyS7m7u2dYu0OGDFFsbKz9dfjw4QxrGwAAAABwQ5YOnNu2bdPJkydVuXJlOTs7y9nZWb/++qumTp0qZ2dn5cuXT9euXdO5c+cc5jtx4oQCAwNTbdfNzU0+Pj4OLwAAAABAxsrSl9TWr19fu3btchjWpUsXlSpVSm+88YYKFSokFxcXrVy5Uq1bt5Yk7dmzRzExMapRo0ZmlAwAAAAA+P+ydOD09vZWuXLlHIZ5enrKz8/PPrxbt2567bXXlCdPHvn4+Khv376qUaOGqlevnhklAwAAAAD+vywdONNiypQpypEjh1q3bq2rV68qLCxMH330UWaXBQAAAAAPvWwXOFevXu3w3t3dXdOmTdO0adMypyAAAAAAQIqydKdBAAAAAIDsi8AJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAlsjSgXPcuHF69NFH5e3trYCAAD399NPas2ePwzRXrlxR79695efnJy8vL7Vu3VonTpzIpIoBAAAAAEmydOD89ddf1bt3b23cuFHLly9XfHy8GjVqpIsXL9qn6d+/v3744QfNmzdPv/76q/755x8988wzmVg1AAAAAECSnDO7gNtZtmyZw/tZs2YpICBA27Zt0xNPPKHY2FjNnDlTc+fOVb169SRJ4eHhKl26tDZu3Kjq1atnRtkAAAAAAGXxM5y3io2NlSTlyZNHkrRt2zbFx8erQYMG9mlKlSqlwoULa8OGDam2c/XqVZ0/f97hBQAAAADIWNkmcCYmJqpfv36qVauWypUrJ0k6fvy4XF1dlStXLodp8+XLp+PHj6fa1rhx4+Tr62t/FSpUyMrSAQAAAOChlG0CZ+/evfXHH3/o66+/vue2hgwZotjYWPvr8OHDGVAhAAAAAOBmWfoeziR9+vTRkiVL9Ntvv6lgwYL24YGBgbp27ZrOnTvncJbzxIkTCgwMTLU9Nzc3ubm5WVkyAAAAADz0svQZTmOM+vTpo4ULF+qXX35RsWLFHMZXqVJFLi4uWrlypX3Ynj17FBMToxo1atzvcgEAAAAAN8nSZzh79+6tuXPn6vvvv5e3t7f9vkxfX195eHjI19dX3bp102uvvaY8efLIx8dHffv2VY0aNeihFgAAAAAyWZYOnNOnT5ckPfnkkw7Dw8PD1blzZ0nSlClTlCNHDrVu3VpXr15VWFiYPvroo/tcKQAAAADgVlk6cBpj7jiNu7u7pk2bpmnTpt2HigAAAAAAaZWl7+EE8P/au/O4Ksv8/+PvIwiiIAQqhBuYkpKKpukoFc1o4RKpWYPmSqktUDKYlZMKk85omWk2aOUE2Da2KI6jjzBDtERTXFCaUCkpm1TcSkdNQbh+f/TzfD2yuN6ehNfz8eDx6L6ve/mc+1z3qXfXOdcNAAAAXL8InAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgRMAAAAAYAkCJwAAAADAEgROAAAAAIAlCJwAAAAAAEsQOAEAAAAAliBwAgAAAAAsQeAEAAAAAFiCwAkAAAAAsASBEwAAAABgCQInAAAAAMASBE4AAAAAgCUInAAAAAAASxA4AQAAAACWIHACAAAAACxB4AQAAAAAWILACQAAAACwBIETAAAAAGAJAicAAAAAwBLVJnAmJycrKChIderUUdeuXbVx40ZnlwQAAAAANVq1CJwffPCBEhISlJiYqC1btigsLEyRkZE6cOCAs0sDAAAAgBqrWgTOV155RaNHj1ZMTIxCQ0P1+uuvq27dukpJSXF2aQAAAABQY7k6u4ArVVxcrM2bN2vChAn2dbVq1VLPnj21fv36Cvc5ffq0Tp8+bV8+evSoJOnYsWPWFltTnHJ2Adain+A3ifsOcI5qfO85477z8vKSzWa75ucFYJ3rPnAeOnRIpaWl8vf3d1jv7++vHTt2VLjPtGnT9Je//KXc+qZNm1pSY40z3dkFWMt7urezSwDK474DnKMa33vOuO+OHj2q+vXrX/PzArDOdR84L8eECROUkJBgXy4rK9ORI0fk5+dX7f6v2rFjx9S0aVP98MMPfIAD1xD3HnDtcd9d/7y8vJxdAoCr7LoPnA0aNJCLi4uKiooc1hcVFSkgIKDCfdzd3eXu7u6wzsfHx6oSfxPq16/Pv3wBJ+DeA6497jsA+O247icNcnNzU6dOnZSZmWlfV1ZWpszMTHXr1s2JlQEAAABAzXbdj3BKUkJCgkaMGKHOnTurS5cumj17tk6cOKGYmBhnlwYAAAAANVa1CJzR0dE6ePCgJk+erP3796tDhw7KyMgoN5FQTeTu7q7ExMRyXyEGYC3uPeDa474DgN8emzHGOLsIAAAAAED1c93/hhMAAAAA8NtE4AQAAAAAWILACQAAAACwBIETAAAAAGAJAmc1NW3aNN12223y8vJSo0aN1L9/f+3cudPZZQE1yvTp02Wz2RQfH+/sUoBq5fPPP1dUVJQCAwNls9m0ZMkSh3ZjjCZPnqwbb7xRHh4e6tmzpwoKCpxTLADUcATOamrNmjWKjY3Vl19+qZUrV6qkpET33HOPTpw44ezSgBohJydHb7zxhtq3b+/sUoBq58SJEwoLC1NycnKF7S+99JLmzJmj119/XRs2bFC9evUUGRmpU6dOXeNKAQA8FqWGOHjwoBo1aqQ1a9bozjvvdHY5QLV2/Phx3XrrrZo7d66mTp2qDh06aPbs2c4uC6iWbDab0tPT1b9/f0m/jm4GBgZq3LhxevrppyVJR48elb+/v9LS0jRo0CAnVgsANQ8jnDXE0aNHJUm+vr5OrgSo/mJjY9W3b1/17NnT2aUANU5hYaH279/vcP95e3ura9euWr9+vRMrA4CaydXZBcB6ZWVlio+PV3h4uNq2bevscoBqbeHChdqyZYtycnKcXQpQI+3fv1+S5O/v77De39/f3gYAuHYInDVAbGysvvrqK61du9bZpQDV2g8//KCxY8dq5cqVqlOnjrPLAQAAcDq+UlvNxcXFadmyZcrKylKTJk2cXQ5QrW3evFkHDhzQrbfeKldXV7m6umrNmjWaM2eOXF1dVVpa6uwSgWovICBAklRUVOSwvqioyN4GALh2CJzVlDFGcXFxSk9P16pVqxQcHOzskoBqr0ePHsrLy1Nubq79r3PnzhoyZIhyc3Pl4uLi7BKBai84OFgBAQHKzMy0rzt27Jg2bNigbt26ObEyAKiZ+EptNRUbG6v3339f//rXv+Tl5WX/3Yq3t7c8PDycXB1QPXl5eZX7nXS9evXk5+fH76eBq+j48eP65ptv7MuFhYXKzc2Vr6+vmjVrpvj4eE2dOlWtWrVScHCwJk2apMDAQPtMtgCAa4fAWU3NmzdPknTXXXc5rE9NTdXIkSOvfUEAAFwlmzZt0u9//3v7ckJCgiRpxIgRSktL0zPPPKMTJ05ozJgx+vnnn3X77bcrIyOD31YDgBPwHE4AAAAAgCX4DScAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALAEgROoYe666y7Fx8dfk3PZbDYtWbLE8vOsXr1aNptNP//8s33dkiVL1LJlS7m4uCg+Pl5paWny8fGxvJZreX1x8U6ePKmBAweqfv365frKpeI9BgDg4hE4AYuMHDlSNptNNptNtWvXlr+/v+6++26lpKSorKzMaXUtXrxYU6ZMueLj7N+/X08++aRatGghd3d3NW3aVFFRUcrMzLwKVV6a7t27a9++ffL29rave/TRR/XAAw/ohx9+0JQpUxQdHa1du3ZdtXNWFHKlq3d9L1ZkZKRcXFyUk5Nzzc55PVqwYIG++OILrVu3rlxfOVdxcbFeeuklhYWFqW7dumrQoIHCw8OVmpqqkpKSa1x11ZKSkuyfMa6urmrQoIHuvPNOzZ49W6dPn76kY1XWnwEAuFKuzi4AqM569eql1NRUlZaWqqioSBkZGRo7dqw+/vhjLV26VK6uFd+CJSUlql27tiU1+fr6XvExvvvuO4WHh8vHx0czZsxQu3btVFJSohUrVig2NlY7duy4CpVePDc3NwUEBNiXjx8/rgMHDigyMlKBgYH29R4eHpbXcjWu78Xas2eP1q1bp7i4OKWkpOi22267ZueuiJX99kp9++23atOmjdq2bVvpNsXFxYqMjNS2bds0ZcoUhYeHq379+vryyy/18ssvq2PHjurQocO1K/oi3HLLLfrss89UVlamw4cPa/Xq1Zo6dareeecdrV69Wl5eXs4uEQBQ0xkAlhgxYoTp169fufWZmZlGkpk/f759nSQzd+5cExUVZerWrWsSExONMcbMnTvXtGjRwtSuXduEhISYt99+2+FYZ/fr1auXqVOnjgkODjYfffRRlXVFRESYsWPH2pebN29u/vrXv5qYmBjj6elpmjZtat54440qj9G7d2/TuHFjc/z48XJtP/30k0N96enp9uVnnnnGtGrVynh4eJjg4GAzceJEU1xcbG/Pzc01d911l/H09DReXl7m1ltvNTk5OcYYY7777jtz7733Gh8fH1O3bl0TGhpqli9fbowxJisry0gyP/30k/2fz/3Lysoyqampxtvb26HWpUuXms6dOxt3d3fj5+dn+vfvb297++23TadOnYynp6fx9/c3gwcPNkVFRcYYYwoLC8udY8SIERVe3yNHjphhw4YZHx8f4+HhYXr16mV27dplbz9bV0ZGhmndurWpV6+eiYyMNHv37q3yPTDGmKSkJDNo0CCTn59vvL29zcmTJ8u9F2PGjDGNGjUy7u7u5pZbbjH//ve/7e1r1641ERERxsPDw/j4+Jh77rnHHDlyxBjza7+YNWuWw/HCwsLsfdOYivvtmTNnzMMPP2yCgoJMnTp1TEhIiJk9e3a52t966y0TGhpq3NzcTEBAgImNjTXGGBMTE2P69u3rsG1xcbFp2LCh+cc//lHptfj444/tx2vevLl5+eWX7W0REREO71VERESFx3jxxRdNrVq1zJYtW8q1FRcX2/v7+e/xqVOnzLhx40xgYKCpW7eu6dKli8nKyrK3Hzp0yAwaNMgEBgYaDw8P07ZtW/P+++87HD8iIsI8+eSTZvz48eaGG24w/v7+Dte6IomJiSYsLKzc+vz8fOPm5maef/55+7rL7c+ffPKJCQ8PN97e3sbX19f07dvXfPPNN1XWBQDAufhKLXCN/eEPf1BYWJgWL17ssD4pKUkDBgxQXl6eHn74YaWnp2vs2LEaN26cvvrqKz366KOKiYlRVlaWw36TJk3SwIEDtW3bNg0ZMkSDBg1Sfn7+JdU0c+ZMde7cWVu3btUTTzyhxx9/XDt37qxw2yNHjigjI0OxsbGqV69eufaqfifp5eWltLQ0ff3113r11Vc1f/58zZo1y94+ZMgQNWnSRDk5Odq8ebOee+45+4hZbGysTp8+rc8//1x5eXl68cUX5enpWe4c3bt3t9e+aNEi7du3T927dy+33fLlyzVgwAD16dNHW7duVWZmprp06WJvLykp0ZQpU7Rt2zYtWbJE3333nUaOHClJatq0qRYtWiRJ2rlzp/bt26dXX321wtc8cuRIbdq0SUuXLtX69etljFGfPn0cvp558uRJvfzyy3rnnXf0+eefa8+ePXr66acrvY6SZIxRamqqhg4dqtatW6tly5b6+OOP7e1lZWXq3bu3srOz9e677+rrr7/W9OnT5eLiIknKzc1Vjx49FBoaqvXr12vt2rWKiopSaWlplec93/n9tqysTE2aNNFHH32kr7/+WpMnT9af//xnffjhh/Z95s2bp9jYWI0ZM0Z5eXlaunSpWrZsKUkaNWqUMjIytG/fPvv2y5Yt08mTJxUdHV1hDZs3b9Yf//hHDRo0SHl5eUpKStKkSZOUlpYm6devOY8ePVrdunXTvn37yt17Z7333nvq2bOnOnbsWK6tdu3aFfZ3SYqLi9P69eu1cOFCbd++XQ8++KB69eqlgoICSdKpU6fUqVMnLV++XF999ZXGjBmjYcOGaePGjQ7HWbBggerVq6cNGzbopZde0gsvvKCVK1dWcuUr17p1a/Xu3dvhdV5ufz5x4oQSEhK0adMmZWZmqlatWhowYIBTfxYAALjOODvxAtVVZSOcxhgTHR1t2rRpY1+WZOLj4x226d69uxk9erTDugcffND06dPHYb/HHnvMYZuuXbuaxx9/vNK6KhrhHDp0qH25rKzMNGrUyMybN6/C/Tds2GAkmcWLF1d6jnPrO3eE83wzZswwnTp1si97eXmZtLS0Crdt166dSUpKqrDt3BFOY34d2dP/H9k86/wRzm7dupkhQ4Zc8DWclZOTYySZ//3vfxWe86xzr++uXbuMJJOdnW1vP3TokPHw8DAffvihvS5JDqNGycnJxt/fv8p6Pv30U9OwYUNTUlJijDFm1qxZDiN3K1asMLVq1TI7d+6scP/Bgweb8PDwSo9/sSOc5/fbisTGxpqBAwfalwMDAx1G384XGhpqXnzxRftyVFSUGTlyZKXbP/TQQ+buu+92WDd+/HgTGhpqXx47dmylI5tneXh4mKeeeqrKbYxxfI+///574+LiYn788UeHbXr06GEmTJhQ6TH69u1rxo0b53DM22+/3WGb2267zTz77LOVHqOyEU5jjHn22WeNh4dHpftebH8+38GDB40kk5eXV+V2AACcxQgn4ATGGNlsNod1nTt3dljOz89XeHi4w7rw8PByo5fdunUrt3ypI5zt27e3/7PNZlNAQIAOHDhQae2X64MPPlB4eLgCAgLk6empiRMnas+ePfb2hIQEjRo1Sj179tT06dP17bff2tueeuopTZ06VeHh4UpMTNT27dsvuw7p/0b4KrN582ZFRUWpWbNm8vLyUkREhCQ51Hsh+fn5cnV1VdeuXe3r/Pz8dPPNNzu8R3Xr1tVNN91kX77xxhsrvf5npaSkKDo62v474MGDBys7O9t+zXJzc9WkSROFhIRUuP+FXv/FOr/fSlJycrI6deqkhg0bytPTU2+++ab9uh04cEB79+6t8tyjRo1SamqqJKmoqEiffPKJHn744Uq3r+xeKSgouKQR28vp23l5eSotLVVISIg8PT3tf2vWrLG/F6WlpZoyZYratWsnX19feXp6asWKFeX60rn3oXRx/aCq13LuZ8zl9ueCggINHjxYLVq0UP369RUUFHRR+wEAcBaBE3CC/Px8BQcHO6yr7Ot618L5E73YbLZKvzLXqlUr2Wy2S54YaP369RoyZIj69OmjZcuWaevWrXr++edVXFxs3yYpKUn/+c9/1LdvX61atUqhoaFKT0+X9GsI2b17t4YNG6a8vDx17txZr7322iW+0v9T1QRCJ06cUGRkpOrXr6/33ntPOTk59jrOrfdqqej6VxV+jhw5ovT0dM2dO1eurq5ydXVV48aNdebMGaWkpEi68ARJF2qvVatWuRoqmqX1/H67cOFCPf3003rkkUf06aefKjc3VzExMfbrdjETNw0fPly7d+/W+vXr9e677yo4OFh33HHHBfe7UiEhIZfcr48fPy4XFxdt3rxZubm59r/8/Hz711JnzJihV199Vc8++6yysrKUm5uryMjIcn3pUu7DCzn3M+ZK+nNUVJSOHDmi+fPna8OGDdqwYcNF7QcAwFkETuAaW7VqlfLy8jRw4MAqt2vTpo2ys7Md1mVnZys0NNRh3ZdfflluuU2bNlen2Ar4+voqMjJSycnJOnHiRLn2yh6rsG7dOjVv3lzPP/+8OnfurFatWun7778vt11ISIj+9Kc/6dNPP9X9999vH+mSfv2t2WOPPabFixdr3Lhxmj9//mW/jvbt21f6CJcdO3bo8OHDmj59uu644w61bt263EiTm5ubJFU5gtamTRudOXPG/h/pknT48GHt3Lmz3Pt4Kd577z01adJE27Ztcwg5M2fOVFpamkpLS9W+fXv997//rfRRMFW9fklq2LChw+8ojx07psLCwgvWlp2dre7du+uJJ55Qx44d1bJlS4eRai8vLwUFBVV5bj8/P/Xv31+pqalKS0tTTExMlees7F4JCQmx/2b1Yjz00EP67LPPtHXr1nJtJSUlFfb3jh07qrS0VAcOHFDLli0d/s7OnJydna1+/fpp6NChCgsLU4sWLa7qI3rOt2PHDmVkZNg/Yy63P5/tqxMnTlSPHj3Upk0b/fTTT5bVDQCongicgIVOnz6t/fv368cff9SWLVv0t7/9Tf369dO9996r4cOHV7nv+PHjlZaWpnnz5qmgoECvvPKKFi9eXG4ymY8++kgpKSnatWuXEhMTtXHjRsXFxVn5spScnKzS0lJ16dJFixYtUkFBgfLz8zVnzpxyX/E9q1WrVtqzZ48WLlyob7/9VnPmzLGPskjSL7/8ori4OK1evVrff/+9srOzlZOTYw/P8fHxWrFihQoLC7VlyxZlZWVdUbBOTEzUP//5TyUmJio/P98+EZEkNWvWTG5ubnrttde0e/duLV26tNyzNZs3by6bzaZly5bp4MGDOn78eIWvuV+/fho9erTWrl2rbdu2aejQoWrcuLH69et32bW/9dZbeuCBB9S2bVuHv0ceeUSHDh1SRkaGIiIidOedd2rgwIFauXKlCgsL9cknnygjI0OSNGHCBOXk5OiJJ57Q9u3btWPHDs2bN0+HDh2S9OvkVu+8846++OIL5eXlacSIERcV3lq1aqVNmzZpxYoV2rVrlyZNmlTuGaFJSUmaOXOm5syZo4KCAm3ZsqXcaPWoUaO0YMEC5efna8SIEVWec9y4ccrMzNSUKVO0a9cuLViwQH//+98vOPHS+eLj4xUeHq4ePXooOTlZ27Zt0+7du/Xhhx/qd7/7nX0SoHOFhIRoyJAhGj58uBYvXqzCwkJt3LhR06ZN0/Lly+3XZOXKlVq3bp3y8/P16KOPqqio6JJqq8yZM2e0f/9+7d27V3l5eXrttdcUERGhDh06aPz48ZIuvz/fcMMN8vPz05tvvqlvvvlGq1atUkJCwlWpGwBQgzjzB6RAdTZixAj7IwZcXV1Nw4YNTc+ePU1KSoopLS112FaVTK5zMY9FSU5ONnfffbdxd3c3QUFB5oMPPqiyroomDbrQ5DAV2bt3r4mNjTXNmzc3bm5upnHjxua+++5zmKjn/Nc1fvx44+fnZzw9PU10dLSZNWuWfSKf06dPm0GDBpmmTZsaNzc3ExgYaOLi4swvv/xijDEmLi7O3HTTTcbd3d00bNjQDBs2zBw6dMgYc3mTBhljzKJFi0yHDh2Mm5ubadCggbn//vvtbe+//74JCgoy7u7uplu3bmbp0qVGktm6dat9mxdeeMEEBAQYm812wceieHt7Gw8PDxMZGVnhY1HOlZ6ebir7eN60aZORZDZu3Fhhe+/evc2AAQOMMcYcPnzYxMTEGD8/P1OnTh3Ttm1bs2zZMvu2q1evNt27dzfu7u7Gx8fHREZG2q/h0aNHTXR0tKlfv75p2rSpSUtLq3DSoPP77alTp8zIkSONt7e38fHxMY8//rh57rnnyk1u8/rrr5ubb77Z1K5d29x4443mySefdGgvKyszzZs3d5gkqypnH4tSu3Zt06xZMzNjxgyH9ouZNOhs/dOmTTPt2rUzderUMb6+viY8PNykpaXZJ2g6/z0uLi42kydPNkFBQfbXM2DAALN9+3ZjzK/vQ79+/Yynp6dp1KiRmThxohk+fLjDpGLnH9MYY/r162fvVxVJTEy0f8a4uLgYX19fc/vtt5tZs2aZU6dOOWx7uf155cqVpk2bNsbd3d20b9/erF69+oKTgQEAcC6bMVcwAwgAp7LZbEpPT1f//v2dXQpwVR0/flyNGzdWamqq7r//fmeXAwAALpOrswsAAOCssrIyHTp0SDNnzpSPj4/uu+8+Z5cEAACuAIETAPCbsWfPHgUHB6tJkyZKS0uzP/YFAABcn/hKLQAAAADAEsxSCwAAAACwBIETAAAAAGAJAicAAAAAwBIETgAAAACAJQicAAAAAABLEDgBAAAAAJYgcAIAAAAALEHgBAAAAABYgsAJAAAAALDE/wODbbVbGp/oOQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph of GoodNet(Repaired) model evaluation\n",
    "\n",
    "opacity = 1\n",
    "bar_width = 0.25\n",
    "\n",
    "figure = plt.figure(figsize=(10,8))\n",
    "plt.xlabel('Drop in Classification Accuracy of Clean Data')\n",
    "plt.ylabel('Percentage')\n",
    "\n",
    "plt.xticks(range(len(cl_clf_acc_tb)),('2', '4', '10'))\n",
    "bar1 = plt.bar(np.arange(len(cl_clf_acc_tb)) + bar_width, cl_clf_acc_tb, bar_width, align='center', alpha=opacity, color='green', label='Classification Accuracy on Clean Data (%)')\n",
    "bar2 = plt.bar(range(len(at_suc_rt_tb)),at_suc_rt_tb, bar_width, align='center', alpha=opacity, color='red', label='Attack Success Rate using Poisoned Data (%)')\n",
    "\n",
    "for rect in bar1 + bar2:\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2.0, height, f'{height:.02f}', ha='center', va='bottom')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.4, 1))\n",
    "plt.tight_layout()\n",
    "plt.title('Performance of the GoodNet(Repaired) Model')\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  }
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