5_visualisation2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [\n",
    "        \"adult\",\n",
    "        \"german\",\n",
    "        \"compas\",\n",
    "        \"diabetes\",\n",
    "        \"breast_cancer\",\n",
    "        ]\n",
    "\n",
    "attack_list = [\n",
    "        'deepfool', \n",
    "        'carlini_l_2', \n",
    "        # 'carlini_l_inf', \n",
    "        'lowprofool_l_2', \n",
    "        # 'lowprofool_l_inf', \n",
    "        # 'fgsm_l_1',\n",
    "        # 'fgsm_l_2',\n",
    "        'fgsm_l_inf',\n",
    "        # 'bim',\n",
    "        # 'mim',\n",
    "        # 'pgd_l_1',\n",
    "        # 'pgd_l_2',\n",
    "        'pgd_l_inf',\n",
    "\n",
    "        # 'boundary', \n",
    "        # 'hopskipjump_l_2', 'hopskipjump_l_inf'\n",
    "        ]\n",
    "\n",
    "def compare_cols(row):\n",
    "    if row['ground_truth'] != row['adv_prediction']:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: adult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_list=[]\n",
    "for dataset in ['adult']:\n",
    "    count_list = []\n",
    "    for attack in attack_list:\n",
    "        folder_name = f'{attack}_{dataset}'\n",
    "        file_name_lr = f'eval_{folder_name}_lr_result_0.csv'\n",
    "        result_path_lr = f'./results/{folder_name}/{file_name_lr}'\n",
    "        file_name_svc = f'eval_{folder_name}_svc_result_0.csv'\n",
    "        result_path_svc = f'./results/{folder_name}/{file_name_svc}'\n",
    "        file_name_nn = f'eval_{folder_name}_nn_2_result_0.csv'\n",
    "        result_path_nn = f'./results/{folder_name}/{file_name_nn}'\n",
    "\n",
    "        cols = [\"age\", \"capital-gain\", \"capital-loss\", \"hours-per-week\", \"workclass\", \"education\", \"marital-status\", \"occupation\", \"relationship\", \n",
    "                \"race\", \"sex\", \"native-country\"]\n",
    "        input_cols = [\"origin_input_age\",\"origin_input_capital-gain\",\"origin_input_capital-loss\",\"origin_input_hours-per-week\",\"origin_input_workclass\",\n",
    "                      \"origin_input_education\",\"origin_input_marital-status\",\"origin_input_occupation\",\"origin_input_relationship\", \"origin_input_race\",\n",
    "                      \"origin_input_sex\", \"origin_input_native-country\"]\n",
    "        adv_cols = [\"origin_adv_age\",\"origin_adv_capital-gain\",\"origin_adv_capital-loss\",\"origin_adv_hours-per-week\",\"origin_adv_workclass\",\n",
    "                    \"origin_adv_education\",\"origin_adv_marital-status\",\"origin_adv_occupation\",\"origin_adv_relationship\", \"origin_adv_race\",\n",
    "                    \"origin_adv_sex\", \"origin_adv_native-country\"]\n",
    "        try:\n",
    "            df1 = pd.read_csv(result_path_lr)\n",
    "            df2 = pd.read_csv(result_path_svc)\n",
    "            df3 = pd.read_csv(result_path_nn)\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "        # in df1, df2, df3, calculate the cumulative perturbation times for each individual feature \n",
    "        # in each row, e.g., if \"origin_input_age\" is not equal to \"origin_adv_age\", then add 1 to the cumulative perturbation times of \"origin_input_age\"\n",
    "        df1_perturbation_count = [0] * len(input_cols)\n",
    "        df2_perturbation_count = [0] * len(input_cols)\n",
    "        df3_perturbation_count = [0] * len(input_cols)\n",
    "\n",
    "        for i in range(len(input_cols)):\n",
    "            df1_perturbation_count[i] = df1.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df2_perturbation_count[i] = df2.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df3_perturbation_count[i] = df3.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "        \n",
    "        # add the cumulative perturbation times of each individual feature to count_list\n",
    "        count_list.append(df1_perturbation_count)\n",
    "        count_list.append(df2_perturbation_count)\n",
    "        count_list.append(df3_perturbation_count)\n",
    "\n",
    "    # plot a heatmap to show the cumulative perturbation times of each individual feature for each attack using count_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6464 5522 5343 6453   79  452  472  200  328   17   16  193]\n",
      " [6464 5590 5382 6454    0    0    0    0    0    0    0    0]\n",
      " [6464 5595 5347 6453  987 1032 1096  829  777  648  345 1089]\n",
      " [5662 5563 5595 5750    0    8    0    0    0    0    0    0]\n",
      " [2224  270  193 1452    0    0    0    0    0    0    0    0]\n",
      " [4360 3620 3629 4093    1    4    0    3    0    0    0    3]\n",
      " [6464 5522 5343 6453    0    0    0    0    0    0    0    0]\n",
      " [6464 5590 5382 6454    0    0    0    0    0    0    0    0]\n",
      " [6464 5595 5337 6451    0    0    0    0    0    0    0    0]\n",
      " [6464 5522 5343 6453    0    0    0    0    0    0    0    0]\n",
      " [6464 5590 5382 6454    0    0    0    0    0    0    0    0]\n",
      " [6464 5595 5355 6453    0    0    0    0    0    0    0    0]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# turn 2d list into heatmap, x-axis is the feature name, y-axis is the attack name\n",
    "\n",
    "count_list = np.array(count_list)\n",
    "print(count_list)\n",
    "ax = sns.heatmap(count_list, cmap=\"YlGnBu\",  linewidth=.7)\n",
    "# set y-axis labels\n",
    "# x_labels = [\"N\", \"N\", \"N\", \"N\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\"]\n",
    "y_labels = [\"DeepFool/LR\", \"DeepFool/SVC\", \"DeepFool/MLP\", \"C&W/LR\", \"C&W/SVC\", \"C&W/MLP\", \"FGSM/LR\", \"FGSM/SVC\", \"FGSM/MLP\", \"PGD/LR\", \"PGD/SVC\", \"PGD/MLP\"]\n",
    "ax.set_yticklabels(y_labels, rotation=0)\n",
    "# ax.set_xticklabels(x_labels, rotation=0)\n",
    "ax.set_xlabel('Feature: 0-3 numerical, 4-11 categorical')\n",
    "\n",
    "# set title\n",
    "plt.title('Adult Dataset')\n",
    "plt.savefig('Visualisation/sparsity_adult.png', dpi=300, bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: german"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_list=[]\n",
    "for dataset in ['german']:\n",
    "    count_list = []\n",
    "    for attack in attack_list:\n",
    "        folder_name = f'{attack}_{dataset}'\n",
    "        file_name_lr = f'eval_{folder_name}_lr_result_0.csv'\n",
    "        result_path_lr = f'./results/{folder_name}/{file_name_lr}'\n",
    "        file_name_svc = f'eval_{folder_name}_svc_result_0.csv'\n",
    "        result_path_svc = f'./results/{folder_name}/{file_name_svc}'\n",
    "        file_name_nn = f'eval_{folder_name}_nn_2_result_0.csv'\n",
    "        result_path_nn = f'./results/{folder_name}/{file_name_nn}'\n",
    "\n",
    "        input_cols = [\"origin_input_duration_in_month\",\"origin_input_credit_amount\",\"origin_input_installment_as_income_perc\",\"origin_input_present_res_since\",\"origin_input_age\",\"origin_input_account_check_status\",\n",
    "                        \"origin_input_credit_history\",\"origin_input_purpose\",\"origin_input_savings\",\"origin_input_present_emp_since\",\"origin_input_personal_status_sex\",\"origin_input_other_debtors\",\"origin_input_property\",\n",
    "                        \"origin_input_other_installment_plans\",\"origin_input_housing\",\"origin_input_credits_this_bank\",\"origin_input_job\",\"origin_input_people_under_maintenance\",\"origin_input_telephone\",\n",
    "                        \"origin_input_foreign_worker\"]\n",
    "        adv_cols = [\"origin_adv_duration_in_month\",\"origin_adv_credit_amount\",\"origin_adv_installment_as_income_perc\",\"origin_adv_present_res_since\",\"origin_adv_age\",\"origin_adv_account_check_status\",\n",
    "                    \"origin_adv_credit_history\",\"origin_adv_purpose\",\"origin_adv_savings\",\"origin_adv_present_emp_since\",\"origin_adv_personal_status_sex\",\"origin_adv_other_debtors\",\"origin_adv_property\",\n",
    "                    \"origin_adv_other_installment_plans\",\"origin_adv_housing\",\"origin_adv_credits_this_bank\",\"origin_adv_job\",\"origin_adv_people_under_maintenance\",\"origin_adv_telephone\",\n",
    "                    \"origin_adv_foreign_worker\"]\n",
    "\n",
    "        try:\n",
    "            df1 = pd.read_csv(result_path_lr)\n",
    "            df2 = pd.read_csv(result_path_svc)\n",
    "            df3 = pd.read_csv(result_path_nn)\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "        # in df1, df2, df3, calculate the cumulative perturbation times for each individual feature \n",
    "        # in each row, e.g., if \"origin_input_age\" is not equal to \"origin_adv_age\", then add 1 to the cumulative perturbation times of \"origin_input_age\"\n",
    "        df1_perturbation_count = [0] * len(input_cols)\n",
    "        df2_perturbation_count = [0] * len(input_cols)\n",
    "        df3_perturbation_count = [0] * len(input_cols)\n",
    "\n",
    "        for i in range(len(input_cols)):\n",
    "            df1_perturbation_count[i] = df1.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df2_perturbation_count[i] = df2.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df3_perturbation_count[i] = df3.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "        \n",
    "        # add the cumulative perturbation times of each individual feature to count_list\n",
    "        count_list.append(df1_perturbation_count)\n",
    "        count_list.append(df2_perturbation_count)\n",
    "        count_list.append(df3_perturbation_count)\n",
    "\n",
    "    # plot a heatmap to show the cumulative perturbation times of each individual feature for each attack using count_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[192 192 126 125 192  23  12  11   2   2   3   1   1   3   2   1   1   1\n",
      "    0   2]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 132 149 192  39  28  40   6  24  25  10   6  40  24  17  28   3\n",
      "    4   1]\n",
      " [180 181 177 177 179   7   5   7   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [ 69  62   1   1  39   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [161 163 144 144 153   3   0   0   0   1   0   0   0   0   1   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 125 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 127 146 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 125 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 121 134 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# turn 2d list into heatmap, x-axis is the feature name, y-axis is the attack name\n",
    "\n",
    "count_list = np.array(count_list)\n",
    "print(count_list)\n",
    "ax = sns.heatmap(count_list, cmap=\"YlGnBu\",  linewidth=.7)\n",
    "# set y-axis labels\n",
    "# x_labels = [\"N\", \"N\", \"N\", \"N\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\"]\n",
    "y_labels = [\"DeepFool/LR\", \"DeepFool/SVC\", \"DeepFool/MLP\", \"C&W/LR\", \"C&W/SVC\", \"C&W/MLP\", \"FGSM/LR\", \"FGSM/SVC\", \"FGSM/MLP\", \"PGD/LR\", \"PGD/SVC\", \"PGD/MLP\"]\n",
    "ax.set_yticklabels(y_labels, rotation=0)\n",
    "# ax.set_xticklabels(x_labels, rotation=0)\n",
    "ax.set_xlabel('Feature: 0-4 numerical, 4-19 categorical')\n",
    "\n",
    "# set title\n",
    "plt.title('German Dataset')\n",
    "\n",
    "plt.savefig('Visualisation/sparsity_german.png', dpi=300, bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: compas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list_list=[]\n",
    "for dataset in ['compas']:\n",
    "    count_list = []\n",
    "    for attack in attack_list:\n",
    "        folder_name = f'{attack}_{dataset}'\n",
    "        file_name_lr = f'eval_{folder_name}_lr_result_0.csv'\n",
    "        result_path_lr = f'./results/{folder_name}/{file_name_lr}'\n",
    "        file_name_svc = f'eval_{folder_name}_svc_result_0.csv'\n",
    "        result_path_svc = f'./results/{folder_name}/{file_name_svc}'\n",
    "        file_name_nn = f'eval_{folder_name}_nn_2_result_0.csv'\n",
    "        result_path_nn = f'./results/{folder_name}/{file_name_nn}'\n",
    "\n",
    "\n",
    "\n",
    "        input_cols = [\"origin_input_age\",\"origin_input_priors_count\",\"origin_input_days_b_screening_arrest\",\"origin_input_length_of_stay\",\"origin_input_age_cat\",\n",
    "                    \"origin_input_sex\", \"origin_input_race\", \"origin_input_c_charge_degree\", \"origin_input_is_recid\", \"origin_input_is_violent_recid\", \"origin_input_two_year_recid\"]\n",
    "        adv_cols = [\"origin_adv_age\",\"origin_adv_priors_count\",\"origin_adv_days_b_screening_arrest\",\"origin_adv_length_of_stay\",\"origin_adv_age_cat\",\n",
    "                    \"origin_adv_sex\", \"origin_input_race\", \"origin_adv_c_charge_degree\", \"origin_adv_is_recid\", \"origin_adv_is_violent_recid\", \"origin_adv_two_year_recid\"]\n",
    "\n",
    "        try:\n",
    "            df1 = pd.read_csv(result_path_lr)\n",
    "            df2 = pd.read_csv(result_path_svc)\n",
    "            df3 = pd.read_csv(result_path_nn)\n",
    "        except:\n",
    "            continue\n",
    "        \n",
    "        # in df1, df2, df3, calculate the cumulative perturbation times for each individual feature \n",
    "        # in each row, e.g., if \"origin_input_age\" is not equal to \"origin_adv_age\", then add 1 to the cumulative perturbation times of \"origin_input_age\"\n",
    "        df1_perturbation_count = [0] * len(input_cols)\n",
    "        df2_perturbation_count = [0] * len(input_cols)\n",
    "        df3_perturbation_count = [0] * len(input_cols)\n",
    "\n",
    "        for i in range(len(input_cols)):\n",
    "            df1_perturbation_count[i] = df1.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df2_perturbation_count[i] = df2.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "            df3_perturbation_count[i] = df3.apply(lambda x: 1 if x[input_cols[i]] != x[adv_cols[i]] else 0, axis=1).sum()\n",
    "        \n",
    "        # add the cumulative perturbation times of each individual feature to count_list\n",
    "        count_list.append(df1_perturbation_count)\n",
    "        count_list.append(df2_perturbation_count)\n",
    "        count_list.append(df3_perturbation_count)\n",
    "\n",
    "    # plot a heatmap to show the cumulative perturbation times of each individual feature for each attack using count_list\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[192 192 126 125 192  23  12  11   2   2   3   1   1   3   2   1   1   1\n",
      "    0   2]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 132 149 192  39  28  40   6  24  25  10   6  40  24  17  28   3\n",
      "    4   1]\n",
      " [180 181 177 177 179   7   5   7   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [ 69  62   1   1  39   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [161 163 144 144 153   3   0   0   0   1   0   0   0   0   1   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 125 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 127 146 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 125 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 126 126 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]\n",
      " [192 192 121 134 192   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAHHCAYAAACIiZ3UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAACVQ0lEQVR4nOzdd1gUV/s38O/Qlg6idEU6CCr23sCCqKARiVhBbIka62MMMQpWYokxlmgsgL3FApqIFexdMRZEUdFEwRpAUEFg3j982Z+bBQTcVZDv57nOdT2cOXPPmYVN7pxz5owgiqIIIiIiIlIKlU/dASIiIqLPGZMtIiIiIiViskVERESkREy2iIiIiJSIyRYRERGREjHZIiIiIlIiJltERERESsRki4iIiEiJmGwRERERKRGTLSIiIiIlYrJF9Jm7ffs2hg8fDltbW2hqakJfXx8tW7bEL7/8glevXsm0ffPmDRYtWoTGjRtDT08Purq6aNy4MRYtWoQ3b97Ixba2toYgCOjQoUOh1165ciUEQYAgCDh//ry0PjQ0VFovCAK0tbXh4uKCH374ARkZGXJxfv31VwiCgKZNmxZ5n5mZmQgJCUHt2rWho6ODqlWrol69ehgzZgwePnxY7GcUFxcn0x+JRAJTU1O0a9cOs2fPxpMnT4o9vzjXr19HaGgokpOTyxxDkTZu3IiFCxd+6m4QVSpqn7oDRKQ8f/zxB/z8/CCRSDBw4EDUrl0bOTk5OH78OCZOnIhr165hxYoVAICsrCx07doVR44cQbdu3RAYGAgVFRXExMRgzJgx2LFjB/744w/o6OjIXENTUxOxsbFITU2FmZmZzLENGzZAU1MTr1+/LrR/y5Ytg66uLjIzM7F//37MmjULhw8fxokTJyAIgkwca2trnD17FklJSbC3t5eJ8+bNG7Rp0wY3btxAQEAAvvnmG2RmZuLatWvYuHEjvvjiC1hYWLz38xo9ejQaN26MvLw8PHnyBCdPnkRISAgWLFiArVu3wsPDo0Sf+7uuX7+OadOmoV27drC2ti71+Yq2ceNGXL16FWPHjv3UXSGqPEQi+izduXNH1NXVFZ2dncWHDx/KHb9165a4cOFC6c/Dhg0TAYiLFy+Wa7tkyRIRgPjVV1/J1NesWVNs3769qK+vLxNLFEXx77//FlVUVERfX18RgHju3DnpsZCQEBGA+OTJE5lzevbsKQIQT548KXMfAMQdO3aIxsbGYmhoqFz/tm7dKgIQN2zYIHfs1atXYnp6ulz9u2JjY0UA4rZt2+SOxcfHiyYmJqKhoWGhn+P7bNu2TQQgxsbGlvpcZejatatYs2bNT90NokqF04hEn6m5c+ciMzMTq1evhrm5udxxe3t7jBkzBgDwzz//YPXq1fDw8MCoUaPk2o4cORLu7u5YtWoV/vnnH5ljmpqa6NmzJzZu3ChTv2nTJlSpUgWenp4l7nPByNHdu3eldRs2bECVKlXQtWtX9OrVCxs2bJA77/bt2wCAli1byh0rmDotKzc3NyxcuBBpaWlYsmSJtP7evXsYMWIEnJycoKWlhapVq8LPz09mujAyMhJ+fn4AAHd3d+k0ZVxcHAAgKioKXbt2hYWFBSQSCezs7DBjxgzk5eXJ9OHWrVvw9fWFmZkZNDU1Ub16dfj7+yM9PV2m3fr169GwYUNoaWnByMgI/v7++Pvvv6XH27Vrhz/++AP37t2T9qU8jLYRfe6YbBF9pnbv3g1bW1u0aNHivW337t2LvLw8DBw4sMg2AwcORG5uLmJiYuSO9e3bF2fPnpUmPcDb6apevXpBXV29xH0uOL9q1arSug0bNqBnz57Q0NBAnz59cOvWLZw7d07mvJo1awIA1q5dC1EUS3y9kurVqxe0tLSwf/9+ad25c+dw8uRJ+Pv7Y9GiRfjqq69w6NAhtGvXDi9fvgQAtGnTBqNHjwYAfP/991i3bh3WrVuHWrVqAXibjOnq6mL8+PH45Zdf0LBhQ0ydOhXfffed9Do5OTnw9PTE6dOn8c0332Dp0qUYNmwY7ty5g7S0NGm7WbNmYeDAgXBwcMCCBQswduxYHDp0CG3atJG2mzx5MurVq4dq1apJ+8L1W0QfwaceWiMixUtPTxcBiN27dy9R+7Fjx4oAxEuXLhXZ5uLFiyIAcfz48dK6mjVril27dhVzc3NFMzMzccaMGaIoiuL169dFAOKRI0fEiIiIIqcRExMTxSdPnoh3794Vf/vtN1EikYimpqZiVlaWKIqieP78eRGAeODAAVEURTE/P1+sXr26OGbMGJm+vXz5UnRychIBiDVr1hQDAwPF1atXi48ePSrR/Rc3jVjAzc1NrFKlisw1/+vUqVMiAHHt2rXSuuKmEQuLMXz4cFFbW1t8/fq1KIqieOnSpff2LTk5WVRVVRVnzZolU3/lyhVRTU1Npp7TiEQfH0e2iD5DBU/06enplaj9ixcv3tu+4FhhTwuqqqriyy+/xKZNmwC8HY2qUaMGWrduXex1nZycYGxsDBsbGwwfPhz29vb4448/oK2tLY1jamoKd3d3AIAgCOjduzc2b94sM9WmpaWFM2fOYOLEiQDejhgNHjwY5ubm+Oabb5CdnV2iz6E4urq60s+p4JoF3rx5g2fPnsHe3h6Ghoa4ePFiiWK+G+PFixd4+vQpWrdujZcvX+LGjRsAAAMDAwDAvn37pCNm/7Vjxw7k5+fjyy+/xNOnT6XFzMwMDg4OiI2NLfX9EpHiMNki+gwVrFF6NzkoTkEiVVz79yVkffv2xfXr13H58mVs3LgR/v7+Mk8UFmb79u04cOAA4uLikJSUhKtXr6Jhw4YAgLy8PGzevBnu7u64e/cukpKSkJSUhKZNm+LRo0c4dOiQTCwDAwPMnTsXycnJSE5OxurVq+Hk5IQlS5ZgxowZJfocipOZmSlz769evcLUqVNRo0YNSCQSVKtWDcbGxkhLS5NbS1WUa9eu4YsvvoCBgQH09fVhbGyM/v37A4A0ho2NDcaPH49Vq1ahWrVq8PT0xNKlS2WucevWLYiiCAcHBxgbG8uUhIQEPH78+IPvn4jKjls/EH2G9PX1YWFhgatXr5aofcEaor/++gv16tUrtM1ff/0FAHBxcSn0eNOmTWFnZ4exY8fi7t276Nu373uv26ZNG1SrVq3QY4cPH0ZKSgo2b96MzZs3yx3fsGEDOnXqVOi5NWvWRFBQEL744gvY2tpiw4YNmDlz5nv7U5Q3b97g5s2bqF27trTum2++QUREBMaOHYvmzZvDwMAAgiDA398f+fn5742ZlpaGtm3bQl9fH9OnT4ednR00NTVx8eJFTJo0SSbGTz/9hMDAQERFRWH//v0YPXo0wsLCcPr0aVSvXh35+fkQBAF79+6Fqqqq3LV0dXXLfO9E9OGYbBF9prp164YVK1bg1KlTaN68ebFtvby8oKqqinXr1hW5SH7t2rVQU1ND586di4zTp08fzJw5E7Vq1SoyaSupDRs2wMTEBEuXLpU7tmPHDuzcuRPLly+XmYr7rypVqsDOzq7ESWdRfv/9d7x69Urmycrff/8dAQEB+Omnn6R1r1+/llm0DqDI0b24uDg8e/YMO3bsQJs2baT17z6J+a46deqgTp06+OGHH3Dy5Em0bNkSy5cvx8yZM2FnZwdRFGFjYwNHR8di7+V9o41EpHicRiT6TH377bfQ0dHBkCFD8OjRI7njt2/fxi+//AIAqFGjBgYNGoSDBw9i2bJlcm2XL1+Ow4cPY/DgwahevXqR1xwyZAhCQkJkEpCyePXqFXbs2IFu3bqhV69ecmXUqFF48eIFoqOjAQCXL1/G06dP5eLcu3cP169fh5OTU5n7cvnyZYwdOxZVqlTByJEjpfWqqqpyTz4uXrxYbtuGgk1g/5uEFYxAvRsjJycHv/76q0y7jIwM5ObmytTVqVMHKioq0rVoPXv2hKqqKqZNmybXJ1EU8ezZM5n+lHSak4gUgyNbRJ8pOzs7bNy4Eb1790atWrVkdpA/efIktm3bhsDAQGn7n3/+GTdu3MCIESMQExMjHcHat28foqKi0LZt2/cmUTVr1kRoaOgH9z06OhovXryAj49PocebNWsGY2NjbNiwAb1798aBAwcQEhICHx8fNGvWDLq6urhz5w7Cw8ORnZ1d4j4dO3YMr1+/Rl5eHp49e4YTJ04gOjoaBgYG2Llzp8wO+d26dcO6detgYGAAFxcXnDp1CgcPHpTZtgIA6tWrB1VVVcyZMwfp6emQSCTw8PBAixYtUKVKFQQEBGD06NEQBAHr1q2TS5YOHz6MUaNGwc/PD46OjsjNzcW6deugqqoKX19fAG9/1zNnzkRwcDCSk5PRo0cP6Onp4e7du9i5cyeGDRuG//3vfwCAhg0bYsuWLRg/fjwaN24MXV1deHt7l/RXQ0Rl8QmfhCSij+DmzZvi0KFDRWtra1FDQ0PU09MTW7ZsKS5evFi6vUCB7Oxs8eeffxYbNmwo6ujoiNra2mKDBg3EhQsXijk5OXKxC7Z+KE5xWz/8dwf5At7e3qKmpqZ0C4jCBAYGiurq6uLTp0/FO3fuiFOnThWbNWsmmpiYiGpqaqKxsbHYtWtX8fDhw8X2TxT/b+uHgqKuri4aGxuLbdq0EWfNmiU+fvxY7px///1XHDRokFitWjVRV1dX9PT0FG/cuCHWrFlTDAgIkGm7cuVK0dbWVlRVVZXZBuLEiRNis2bNRC0tLdHCwkL89ttvxX379sm0uXPnjhgUFCTa2dmJmpqaopGRkeju7i4ePHhQrk/bt28XW7VqJero6Ig6Ojqis7OzOHLkSDExMVHaJjMzU+zbt69oaGgo3SqDiJRLEEUl7ABIRERERAC4ZouIiIhIqZhsERERESkRky0iIiIiJWKyRURERKRETLaIiIiIlIjJFhEREZESMdkiIiIiUiLuIE9ERERKpWXVR2GxXt3fpLBYHwuTrc+Uov+wFR2v5swDCot374eOqBl2UHHxgjso/H7/zd6jsHhVJN3wKvekQmJpqbXA67zTCokFAJqqzSAiQWHxBNRSeLx88ZrC4qkIrhCRqLB4ApwUFk+AkxLuVdG/2/L52SkrHnBTQdEcFf67IOViskVERERKJQiVe9USky0iIiJSKqGSLxFnskVERERKVdlHtir33RMREREpGUe2iIiISKkq+8gWky0iIiJSKkEQPnUXPqnKnWoSERERKRmTLQUJDAxEjx49PnU3iIiIyiEVBZaKR2m9DgwMhCAIEAQB6urqMDU1RceOHREeHo78/HxlXbZIBX15t7Rq1Urp1x00aBB++OEHaR927dpVaLu4uDiZvhkbG6NLly64cuWK0vtIRESkTIKgorBSESm11507d0ZKSgqSk5Oxd+9euLu7Y8yYMejWrRtyc3OVeelCRUREICUlRVqio6OVer28vDzs2bMHPj4+JT4nMTERKSkp2LdvH7Kzs9G1a1fk5OQosZdERESkTEpNtiQSCczMzGBpaYkGDRrg+++/R1RUFPbu3YvIyEgAQFpaGoYMGQJjY2Po6+vDw8MDly9flokTFRWFBg0aQFNTE7a2tpg2bZpMsiYIApYtWwYvLy9oaWnB1tYWv//+u1x/DA0NYWZmJi1GRkYAgPz8fEyfPh3Vq1eHRCJBvXr1EBMTI3PulStX4OHhAS0tLVStWhXDhg1DZmZmsfd/8uRJqKuro3HjxiX+zExMTGBmZoYGDRpg7Nix+Pvvv3Hjxo0Sn09ERFTecGTrI/Pw8ICbmxt27NgBAPDz88Pjx4+xd+9eXLhwAQ0aNED79u3x/PlzAMCxY8cwcOBAjBkzBtevX8dvv/2GyMhIzJo1SybulClT4Ovri8uXL6Nfv37w9/dHQkLJ3h31yy+/4KeffsL8+fPx119/wdPTEz4+Prh16xYAICsrC56enqhSpQrOnTuHbdu24eDBgxg1alSxcaOjo+Ht7V2mpzDS09OxefNmAICGhkapzyciIiovBKgorFREn6TXzs7OSE5OxvHjx3H27Fls27YNjRo1goODA+bPnw9DQ0PpyNS0adPw3XffISAgALa2tujYsSNmzJiB3377TSamn58fhgwZAkdHR8yYMQONGjXC4sWLZdr06dMHurq60lKwfmr+/PmYNGkS/P394eTkhDlz5qBevXpYuHAhAGDjxo14/fo11q5di9q1a8PDwwNLlizBunXr8OjRoyLvMyoqqlRTiABQvXp16OrqwtDQEBs3boSPjw+cnZ1LFYOIiIjKj0+yz5YoihAEAZcvX0ZmZiaqVq0qc/zVq1e4ffs2AODy5cs4ceKEzEhWXl4eXr9+jZcvX0JbWxsA0Lx5c5kYzZs3R3x8vEzdzz//jA4dOkh/Njc3R0ZGBh4+fIiWLVvKtG3ZsqV0OjMhIQFubm7Q0dGROZ6fn4/ExESYmprK3WNCQgIePnyI9u3bl/RjAfB2JE9bWxunT5/G7NmzsXz58mLbZ2dnIzs7W6ZOIpGU6ppERETKVFGn/xTlkyRbCQkJsLGxQWZmJszNzREXFyfXxtDQEACQmZmJadOmoWfPnnJtNDU1S3VdMzMz2Nvby9RlZGSUKkZJRUdHo2PHjqXuo42NDQwNDeHk5ITHjx+jd+/eOHr0aJHtw8LCMG3aNJm6kJCQMvWZiIhIGSp7svXR7/7w4cO4cuUKfH190aBBA6SmpkJNTQ329vYypVq1agCABg0aIDExUe64vb09VFT+r/unT5+Wuc7p06dRq1at9/ZHX18fFhYWOHHihEz9iRMn4OLiAgCoVasWLl++jKysLJnjKioqcHJyKjRuVFQUunfvXrIPpQgjR47E1atXsXPnziLbBAcHIz09XaYEBwd/0HWJiIgUqbIvkFfqyFZ2djZSU1ORl5eHR48eISYmBmFhYejWrRsGDhwIFRUVNG/eHD169MDcuXPh6OiIhw8f4o8//sAXX3yBRo0aYerUqejWrRusrKzQq1cvqKio4PLly7h69SpmzpwpvVbBuq9WrVphw4YNOHv2LFavXl2ifk6cOBEhISGws7NDvXr1EBERgfj4eGzYsAEA0K9fP4SEhCAgIAChoaF48uQJvvnmGwwYMKDQKcTHjx/j/PnzhW4tcffuXbnpTQcHh0L7pa2tjaFDhyIkJAQ9evQodKG9RCLhtCEREVE5ptRkKyYmBubm5lBTU0OVKlXg5uaGRYsWISAgQDoq9eeff2Ly5MkYNGgQnjx5AjMzM7Rp00aaxHh6emLPnj2YPn065syZA3V1dTg7O2PIkCEy15o2bRo2b96MESNGwNzcHJs2bZKOTL3P6NGjkZ6ejgkTJuDx48dwcXFBdHS0NAnS1tbGvn37MGbMGDRu3Bja2trw9fXFggULCo23e/duNGnSRDo6967x48fL1R07dqzIvo0aNQoLFizAtm3b8OWXX5bofoiIiMoTAZX73YhKS7YiIyOle2kVR09PD4sWLcKiRYuKbOPp6QlPT89i41hYWGD//v1FHhdFschjKioqCAkJKXatU506dXD48OEij797r0U9hVhcH4o6XqNGDbx586bY84iIiMqzijr9pyiV++6VpFWrVujTp8+n7gYRERGVA5/kacTP3bfffvupu0BERFRuVPaRrc8i2Xrf9BwRERF9OpU92arcd09ERESkZJ/FyBYRERGVZ5V7bIfJFhERESkVpxGJiIiISGk4skVERERKxZEtIiIiIiUSoKKwUhpHjx6Ft7c3LCwsIAgCdu3aJdsvQSi0zJs3T9rG2tpa7viPP/5Yqn5wZIuIiIiU6lONbGVlZcHNzQ1BQUHo2bOn3PGUlBSZn/fu3YvBgwfD19dXpn769OkYOnSo9Gc9Pb1S9UMQuUkVERERKVH1OtMUFuufK0W/Wq84giBg586d6NGjR5FtevTogRcvXuDQoUPSOmtra4wdOxZjx44t03UBjmx9trSsFPe6oFf3Nyk8Xs2ZBxQW794PHRUeT9H3C9xUWDzAUYHxFBmL8cpXvPLcN8YrP7EK4imXICjuRdTZ2dnIzs6WqZNIJJBIJB8U99GjR/jjjz+wZs0auWM//vgjZsyYASsrK/Tt2xfjxo2DmlrJUyiu2SIiIiKlEgQVhZWwsDAYGBjIlLCwsA/u45o1a6Cnpyc33Th69Ghs3rwZsbGxGD58OGbPnl3q1/JxZIuIiIgqjODgYIwfP16m7kNHtQAgPDwc/fr1g6ampkz9u9eqW7cuNDQ0MHz4cISFhZX4uky2iIiISKlK+xRhcRQxZfhfx44dQ2JiIrZs2fLetk2bNkVubi6Sk5Ph5ORUovhMtoiIiEipyvs+W6tXr0bDhg3h5ub23rbx8fFQUVGBiYlJieMz2SIiIqLPUmZmJpKSkqQ/3717F/Hx8TAyMoKVlRUAICMjA9u2bcNPP/0kd/6pU6dw5swZuLu7Q09PD6dOncK4cePQv39/VKlSpcT9YLJFRERESvWpRrbOnz8Pd3d36c8F668CAgIQGRkJANi8eTNEUUSfPvJPoUskEmzevBmhoaHIzs6GjY0Nxo0bJ7dm7H2YbBEREZFSKXLNVmm0a9cO79tOdNiwYRg2bFihxxo0aIDTp09/cD/K9yRqORAYGFjsBmhERERExSl1shUYGCh9N5C6ujpMTU3RsWNHhIeHIz8/Xxl9LFZh7zRq1aqV0q87aNAg/PDDDwCAI0eOwMPDA0ZGRtDW1oaDgwMCAgKQk5OD7du3Q1VVFQ8ePCg0joODg8xwZFJSEgYNGoTq1atDIpHAxsYGffr0wfnz55V+T0REREohqCiuVEBl6nXnzp2RkpKC5ORk7N27F+7u7hgzZgy6deuG3NxcRffxvSIiIpCSkiIt0dHRSr1eXl4e9uzZAx8fH1y/fh2dO3dGo0aNcPToUVy5cgWLFy+GhoYG8vLy4OPjg6pVqxa6I+3Ro0eRlJSEwYMHA3g7t9ywYUPcvHkTv/32G65fv46dO3fC2dkZEyZMUOo9ERERKYsiNzWtiMrUa4lEAjMzM1haWqJBgwb4/vvvERUVhb1790oXnKWlpWHIkCEwNjaGvr4+PDw8cPnyZZk4UVFRaNCgATQ1NWFra4tp06bJJGuCIGDZsmXw8vKClpYWbG1t8fvvv8v1x9DQEGZmZtJiZGQEAMjPz8f06dOlo0T16tVDTEyMzLlXrlyBh4cHtLS0ULVqVQwbNgyZmZnF3v/Jkyehrq6Oxo0bY//+/TAzM8PcuXNRu3Zt2NnZoXPnzli5ciW0tLSgrq6OAQMGSD+Xd4WHh6Np06ZwdXWFKIoIDAyEg4MDjh07hq5du8LOzg716tVDSEgIoqKiSvKrISIiKncKm4Uqa6mIFJYienh4wM3NDTt27AAA+Pn54fHjx9i7dy8uXLiABg0aoH379nj+/DmAtxuIDRw4EGPGjMH169fx22+/ITIyErNmzZKJO2XKFPj6+uLy5cvo168f/P39kZCQUKI+/fLLL/jpp58wf/58/PXXX/D09ISPjw9u3boF4O3bwD09PVGlShWcO3cO27Ztw8GDBzFq1Khi40ZHR8Pb2xuCIMDMzAwpKSk4evRoke0HDx6MW7duybTJzMzE77//Lh3Vio+Px7Vr1zBhwgSoqMj/WgwNDUt0z0RERFS+KHQ8ztnZGcnJyTh+/DjOnj2Lbdu2oVGjRnBwcMD8+fNhaGgoHZmaNm0avvvuOwQEBMDW1hYdO3bEjBkz8Ntvv8nE9PPzw5AhQ+Do6IgZM2agUaNGWLx4sUybPn36QFdXV1p27doFAJg/fz4mTZoEf39/ODk5Yc6cOahXrx4WLlwIANi4cSNev36NtWvXonbt2vDw8MCSJUuwbt06PHr0qMj7jIqKgo+Pj7R/ffr0Qdu2bWFubo4vvvgCS5YsQUZGhrS9i4sLmjVrhvDwcGnd1q1bIYoi/P39AUCaADo7O5fqM8/OzkZGRoZM+e8LOomIiD4lASoKKxWRQnstiiIEQcDly5eRmZmJqlWryiRBd+/exe3btwEAly9fxvTp02WODx06FCkpKXj58qU0ZvPmzWWu0bx5c7mRrZ9//hnx8fHS0rFjR2RkZODhw4do2bKlTNuWLVtKz09ISICbmxt0dHRkjufn5yMxMbHQe0xISMDDhw/Rvn17AICqqioiIiLwzz//YO7cubC0tMTs2bPh6uqKlJQU6XlBQUH4/fff8eLFCwBvpxD9/Pygp6cn/ezKQlkv5CQiIlKUyr5mS6H7bCUkJMDGxgaZmZkwNzdHXFycXJuC6bDMzExMmzZN7u3aAOReAvk+ZmZmsLe3l6l7d2RJkaKjo9GxY0e5PlpaWmLAgAEYMGAAZsyYAUdHRyxfvhzTpk0DAPj7+2PcuHHYunUr2rRpgxMnTsgkRY6OjgCAGzduoH79+iXuT1Ev5JwTHljGOyQiIiJFUliydfjwYVy5cgXjxo1D9erVkZqaCjU1NVhbWxfavkGDBkhMTJRLkv7r9OnTGDhwoMzPJUlG9PX1YWFhgRMnTqBt27bS+hMnTqBJkyYAgFq1aiEyMhJZWVnS0a0TJ05ARUWlyJdLRkVFFbn5WYEqVarA3NwcWVlZ0jo9PT34+fkhPDwct2/fhqOjI1q3bi09Xq9ePbi4uOCnn35C79695dZtpaWlFbpuSxkv5CQiIlKoCrqwXVHKlGxlZ2cjNTUVeXl5ePToEWJiYhAWFoZu3bph4MCBUFFRQfPmzdGjRw/MnTsXjo6OePjwIf744w988cUXaNSoEaZOnYpu3brBysoKvXr1goqKCi5fvoyrV69i5syZ0msVrPtq1aoVNmzYgLNnz2L16tUl6ufEiRMREhIifaovIiIC8fHx2LBhAwCgX79+CAkJQUBAAEJDQ/HkyRN88803GDBgAExNTeXiPX78GOfPn5fZWuK3335DfHw8vvjiC9jZ2UnXgF27dk1ubdngwYPRunVrJCQkYNKkSTLHBEFAREQEOnTogNatW2Py5MlwdnZGZmYmdu/ejf379+PIkSMl/h0RERGVGxVz9k9hypRsxcTEwNzcHGpqaqhSpQrc3NywaNEiBAQESEdk/vzzT0yePBmDBg3CkydPYGZmhjZt2kiTGE9PT+zZswfTp0/HnDlzoK6uDmdnZwwZMkTmWtOmTcPmzZsxYsQImJubY9OmTXBxcSlRP0ePHo309HRMmDABjx8/houLC6Kjo+Hg4AAA0NbWxr59+zBmzBg0btwY2tra8PX1xYIFCwqNt3v3bjRp0gTVqlWT1jVp0gTHjx/HV199hYcPH0JXVxeurq7YtWuXzIgaALRq1QpOTk5ISkqSGa17N9b58+cxa9YsDB06FE+fPoW5uTlatGghXdRPREREFYsglnVl9kcgCAJ27txZbl6X4+Pjg1atWuHbb7/91F15Ly0r+RdqltWr+5sUHq/mzAMKi3fvh44Kj6fo+wVuKiwe4KjAeIqMxXjlK1557hvjlZ9YBfGUy7H5MoXFunnqa4XF+lj4IupSaNWqVaFvBSciIqJicM0WlVRFGNEiIiKi8qVcJ1vleIaTiIiISooL5ImIiIiUR+Q0IhEREZESVe5cq7IP7BEREREpF0e2iIiISLlUKvfQFpMtIiIiUq5KvmaL04hERERESsSRLSIiIlKuyj2wVb5f10NEREQVn0OHVQqLdevgkPc3Kmc4svWZKu/vRrSZtEdh8e7O6Qb7LzcoLF7S1n4Kv9/raYq7XxfDbkh9Fa2QWGZaPrj2r+L65lqlG97kxyssnrpKPTzPVsy9AoCRxAfPXisuXlVNH2TnnVVYPIlqE4V9fuoq9RT+u1X07yLzzWGFxdNV91DY9wJ4+93IF68pLJ6K4Io88apCYqkKtSEiUSGxAECAk8JiUeGYbBEREZFyVfIF8ky2iIiISLkqd67FpxGJiIiIlIkjW0RERKRc3NSUiIiISIkqd67FZIuIiIiUS6zkC+S5ZqsYgYGB6NGjx6fuBhEREVVgpUq2AgMDIQgCBEGAuro6TE1N0bFjR4SHhyM/P19ZfSxSQV/eLa1atVL6dQcNGoQffvhBpg+nT5+WaZOdnY2qVatCEATExcXJ9HnXrl2Fxo2Li5O5F1NTU/j6+uLOnTvKuhUiIiLlUxEUVyqgUo9sde7cGSkpKUhOTsbevXvh7u6OMWPGoFu3bsjNzVVGH4sVERGBlJQUaYmOVtymdoXJy8vDnj174OPjI62rUaMGIiIiZNrt3LkTurq6ZbpGYmIiHj58iG3btuHatWvw9vZGXl7eB/WbiIjokxEUWCqgUidbEokEZmZmsLS0RIMGDfD9998jKioKe/fuRWRkJAAgLS0NQ4YMgbGxMfT19eHh4YHLly/LxImKikKDBg2gqakJW1tbTJs2TSZZEwQBy5Ytg5eXF7S0tGBra4vff/9drj+GhoYwMzOTFiMjIwBAfn4+pk+fjurVq0MikaBevXqIiYmROffKlSvw8PCAlpYWqlatimHDhiEzM7PY+z958iTU1dXRuHFjaV1AQAA2b96MV69eSevCw8MREBBQsg/1P0xMTGBubo42bdpg6tSpuH79OpKSksoUi4iIiD4thazZ8vDwgJubG3bs2AEA8PPzw+PHj7F3715cuHABDRo0QPv27fH8+XMAwLFjxzBw4ECMGTMG169fx2+//YbIyEjMmjVLJu6UKVPg6+uLy5cvo1+/fvD390dCQkKJ+vTLL7/gp59+wvz58/HXX3/B09MTPj4+uHXrFgAgKysLnp6eqFKlCs6dO4dt27bh4MGDGDVqVLFxo6Oj4e3tDeGdxX4NGzaEtbU1tm/fDgC4f/8+jh49igEDBpTsAyyGlpYWACAnJ+eDYxEREX0SgqC4UgEpbIG8s7MzkpOTcfz4cZw9exbbtm1Do0aN4ODggPnz58PQ0FA6MjVt2jR89913CAgIgK2tLTp27IgZM2bgt99+k4np5+eHIUOGwNHRETNmzECjRo2wePFimTZ9+vSBrq6utBSsh5o/fz4mTZoEf39/ODk5Yc6cOahXrx4WLlwIANi4cSNev36NtWvXonbt2vDw8MCSJUuwbt06PHr0qMj7jIqKkplCLBAUFITw8HAAQGRkJLp06QJjY+OyfpwAgJSUFMyfPx+WlpZwcuK7q4iIqIKq5Gu2FLb1gyiKEAQBly9fRmZmJqpWrSpz/NWrV7h9+zYA4PLlyzhx4oTMSFZeXh5ev36Nly9fQltbGwDQvHlzmRjNmzdHfHy8TN3PP/+MDh06SH82NzdHRkYGHj58iJYtW8q0bdmypXQ6MyEhAW5ubtDR0ZE5np+fj8TERJiamsrdY0JCAh4+fIj27dvLHevfvz++++473LlzB5GRkVi0aFGRn9X7VK9eHaIo4uXLl3Bzc8P27duhoaFRaNvs7GxkZ2fL1EkkkjJfm4iIiBRLYclWQkICbGxskJmZCXNzc5kn8AoYGhoCADIzMzFt2jT07NlTro2mpmaprmtmZgZ7e3uZuoyMjFLFKKno6Gh07Nix0D5WrVoV3bp1w+DBg/H69Wt4eXnhxYsXZbrOsWPHoK+vDxMTE+jp6RXbNiwsDNOmTZOpCwkJKdN1iYiIlOITDUgdPXoU8+bNw4ULF5CSkoKdO3fKbOkUGBiINWvWyJzj6ekps8b7+fPn+Oabb7B7926oqKjA19cXv/zyS6keglPINOLhw4dx5coV+Pr6okGDBkhNTYWamhrs7e1lSrVq1QAADRo0QGJiotxxe3t7qKj8X5f+u53C6dOnUatWrff2R19fHxYWFjhx4oRM/YkTJ+Di4gIAqFWrFi5fvoysrCyZ4yoqKkVO2UVFRaF79+5FXjcoKAhxcXEYOHAgVFVV39vPotjY2MDOzu69iRYABAcHIz09XaYEBweX+dpEREQK94nWbGVlZcHNzQ1Lly4tsk3BLgsFZdOmTTLH+/Xrh2vXruHAgQPYs2cPjh49imHDhpWqH6Ue2crOzkZqairy8vLw6NEjxMTEICwsDN26dcPAgQOhoqKC5s2bo0ePHpg7dy4cHR3x8OFD/PHHH/jiiy/QqFEjTJ06Fd26dYOVlRV69eoFFRUVXL58GVevXsXMmTOl1ypY99WqVSts2LABZ8+exerVq0vUz4kTJyIkJAR2dnaoV68eIiIiEB8fjw0bNgB4++GFhIQgICAAoaGhePLkCb755hsMGDCg0CnEx48f4/z588VuLdG5c2c8efIE+vr6xfbt7t27ctOhDg4OJbqv/5JIJJw2JCIiKoSXlxe8vLyKbVOwy0JhEhISEBMTg3PnzqFRo0YAgMWLF6NLly6YP38+LCwsStSPUidbMTExMDc3h5qaGqpUqQI3NzcsWrQIAQEB0lGpP//8E5MnT8agQYPw5MkTmJmZoU2bNtIkxtPTE3v27MH06dMxZ84cqKurw9nZGUOGDJG51rRp07B582aMGDEC5ubm2LRpk3Rk6n1Gjx6N9PR0TJgwAY8fP4aLiwuio6OlSY22tjb27duHMWPGoHHjxtDW1oavry8WLFhQaLzdu3ejSZMm0tG5wgiCUOzxAuPHj5erO3bsWInui4iIqMJR4FOERa1VLuvAQ1xcHExMTFClShV4eHhg5syZ0nXnp06dgqGhoTTRAoAOHTpARUUFZ86cwRdffFGia5Qq2YqMjJTupVUcPT09LFq0qNhF4p6envD09Cw2joWFBfbv31/kcVEUizymoqKCkJCQYtcv1alTB4cPHy7y+Lv3WtRTiMX1wdDQUO54ce1LcpyIiKjCUeDLAYtaqxwaGlrqWJ07d0bPnj1hY2OD27dv4/vvv4eXlxdOnToFVVVVpKamwsTEROYcNTU1GBkZITU1tcTX4YuoS6hVq1bo06fPp+4GERFRxaPAka3g4GC5GaKyjmr5+/tL/3+dOnVQt25d2NnZIS4urtCdB8qKyVYJffvtt5+6C0RERJWeMtcq29raolq1akhKSkL79u1hZmaGx48fy7TJzc3F8+fPi1znVZhym2xxOo2IiOgzUUH2Iv3nn3/w7NkzmJubA3i7v2daWhouXLiAhg0bAni7A0N+fj6aNm1a4rjlNtkiIiKiz4P4iXZ+z8zMlHm3cMFuAEZGRjAyMsK0adPg6+sLMzMz3L59G99++y3s7e2la8pr1aqFzp07Y+jQoVi+fDnevHmDUaNGwd/fv8RPIgIKXbJGREREVH6cP38e9evXR/369QG83Q2gfv36mDp1KlRVVfHXX3/Bx8cHjo6OGDx4MBo2bIhjx47JTFNu2LABzs7OaN++Pbp06YJWrVphxYoVpeoHR7aIiIhIuT7RC6TbtWtX7LKkffv2vTeGkZERNm7c+EH9YLJFREREylVB1mwpC6cRiYiIiJSII1tERESkXJ9ogXx5wWSLiIiIlOsTrdkqLziNSERERKREgsjdQ4mIiEiJ7AK2KCzW7TW9FRbrY+E04mfKtNZEhcV6lDAPJs4TFBbv8Y2fYFF7isLiPbw6Q+HxzFyCFRYv9XoY3uRfUlg8dZX6yBP/UkgsVaGuwvsG3FRYPMCR8cpFLMYrX/GU0Tcl45otIiIiIiWq5MkW12wRERERKRFHtoiIiEipxMo9sMVki4iIiJSM04hEREREpCwc2SIiIiLlquSbmjLZIiIiIuXiNCIRERERKctnm2ylpqbim2++ga2tLSQSCWrUqAFvb28cOnRI2mb9+vVwdnaGpqYmrK2tMWPGDLk45ubm+PHHH2XqvvvuOwiCgLi4OJn6du3aYcCAATJ1a9asQatWraTHx44dW2SfBUGQFn19fTRu3BhRUVGlvHMiIqJyRkWBpQKqoN0uXnJyMho2bIjDhw9j3rx5uHLlCmJiYuDu7o6RI0dK2wwcOBA9evRAQkICtm7dChsbG7lY7dq1k0uqYmNjUaNGDZn6169f4/Tp0/Dw8JBpGxUVBR8fnxL3PSIiAikpKTh//jxatmyJXr164cqVKyW/eSIiovJGEBRXKqDPMtkaMWIEBEHA2bNn4evrC0dHR7i6umL8+PE4ffo0gP8bRQoKCoKNjQ2aNGmC/v37y8Vyd3fHiRMnkJubCwB48eIFLl26hEmTJskkW6dOnUJ2djbc3d2lda9fv8b+/ftLlWwZGhrCzMwMjo6OmDFjBnJzcxEbG1vGT4KIiIg+tc8u2Xr+/DliYmIwcuRI6OjoyB03NDQEAFhaWqJRo0YYNWoUXr9+XWQ8d3d3ZGZm4ty5cwCAY8eOwdHREb6+vjhz5oz03NjYWFhbW8Pa2lp67qFDh2BpaQlnZ+dS30dubi5Wr14NANDQ0Cj1+UREROWGiqC4UgF9dslWUlISRFF8b4IzdOhQiKIIW1tbeHl5ISMjQ3rM29sbo0aNAgA4ODjA0tJSOooVFxeHtm3bwszMDFZWVjh16pS0/t1RLaD0U4gA0KdPH+jq6kIikWDcuHGwtrbGl19+WWT77OxsZGRkyJTs7OxSXZOIiEiZREFQWKmIPrtkSxTF97a5fv06IiMjERkZiWXLlsHKygrt2rXD48ePAQBXr15F69atpe3fXbcVFxeHdu3aAQDatm2LuLg4vHr1CmfOnJFJtkRRxO7du0udbP3888+Ij4/H3r174eLiglWrVsHIyKjI9mFhYTAwMJApYWFhpbomERGRUnGB/OfFwcEBgiDgxo0bRbb566+/IJFI4OLiAkEQEB4eDltbW7Rs2RIrV67EixcvZJKkgnVbz549w6VLl9C2bVsAb5Ot2NhYnDx5Ejk5OTKL48+ePYvc3Fy0aNGiVP03MzODvb09OnXqhIiICPTu3VuaBBYmODgY6enpMiU4OLhU1yQiIiLl+eySLSMjI3h6emLp0qXIysqSO56WlgZLS0tkZ2fjzJkzAABVVVVs3LgRdnZ2GDZsGCZPngwtLS3pOe7u7sjKysKCBQvg4OAAExMTAECbNm1w9uxZ7N27VzrdWCAqKgpdu3aFqqpqme+lSZMmaNiwIWbNmlVkG4lEAn19fZkikUjKfE0iIiKF45qtz8/SpUuRl5eHJk2aYPv27bh16xYSEhKwaNEiNG/eHK1atUKLFi3Qu3dv7Nq1C7dv30ZMTAwePnwIHR0dbNy4ES9fvpTGs7W1hZWVFRYvXiwd1QKAGjVqwMLCAitWrJBbrxUdHV3oFOKTJ08QHx8vUx49elTkvYwdOxa//fYbHjx4oIBPhoiI6BPg1g+fH1tbW1y8eBHu7u6YMGECateujY4dO+LQoUNYtmwZBEFATEwMevXqhfHjx8PFxQXBwcEYPHgwbt68idTUVPTr1w/5+fnSmO7u7njx4oV0vVaBtm3b4sWLFzLJ1u3bt5GUlARPT0+5vm3cuBH169eXKStXrizyXjp37gwbG5tiR7eIiIio/Pps341obm6OJUuWYMmSJYUe19PTw/z58zF//ny5Y3///bdcXcGC+v+KiIhARESETF1UVBQ8PDzktp747+ao/1XY4n5BEJCQkFDseUREROVaBZ3+U5TPNtn6lKpXr85F6kRERAUqd67FZEsZitsXi4iIiCoXJltERESkVCKnEYmIiIiUqJInW5/l04hERERE5QVHtoiIiEi5Kuj+WIrCZIuIiIiUq5LPozHZIiIiIuWq5CNblTzXJCIiIlIuQSxs23IiIiIiBbGetk9hsZJD5F+FV95xGvEz9ePlAwqL9Z1bR0TcVNwXZZCjJ0QkKiyeACeFx7uetkdh8VwMuwG4qbB4gKMC4ykyFuOVr3jluW+MV35iFcRTMm79QERERPT5OXr0KLy9vWFhYQFBELBr1y7psTdv3mDSpEmoU6cOdHR0YGFhgYEDB+Lhw4cyMaytrSEIgkz58ccfS9UPJltERESkVKIgKKyURlZWFtzc3LB06VK5Yy9fvsTFixcxZcoUXLx4ETt27EBiYiJ8fHzk2k6fPh0pKSnS8s0335SqH5xGJCIiIuX6REM7Xl5e8PLyKvSYgYEBDhyQXXKzZMkSNGnSBPfv34eVlZW0Xk9PD2ZmZmXuB0e2iIiIqMLIzs5GRkaGTMnOzlZI7PT0dAiCAENDQ5n6H3/8EVWrVkX9+vUxb9485Obmliouky0iIiJSLkFQWAkLC4OBgYFMCQsL++Auvn79GpMmTUKfPn2gr68vrR89ejQ2b96M2NhYDB8+HLNnz8a3335bqticRiQiIiLlUuDTiMHBwRg/frxMnUQi+aCYb968wZdffglRFLFs2TKZY+9eq27dutDQ0MDw4cMRFhZW4usy2SIiIqIKQyKRfHBy9a6CROvevXs4fPiwzKhWYZo2bYrc3FwkJyfDycmpRNdgskVERETKVU732SpItG7duoXY2FhUrVr1vefEx8dDRUUFJiYmJb5OhVuzlZqaim+++Qa2traQSCSoUaMGvL29cejQIWmb9evXw9nZGZqamrC2tsaMGTPk4pibm8vtk/Hdd99BEATExcXJ1Ldr1w4DBgyQqVuzZg1atWoFALh79y769u0LCwsLaGpqonr16ujevTtu3LiBR48eQV1dHZs3by70fgYPHowGDRpIf87IyMDkyZOl/TczM0OHDh2wY8cOcLN/IiKqkAQFllLIzMxEfHw84uPjAbz993V8fDzu37+PN2/eoFevXjh//jw2bNiAvLw8pKamIjU1FTk5OQCAU6dOYeHChbh8+TLu3LmDDRs2YNy4cejfvz+qVKlS4n5UqGQrOTkZDRs2xOHDhzFv3jxcuXIFMTExcHd3x8iRI6VtBg4ciB49eiAhIQFbt26FjY2NXKx27drJJVWxsbGoUaOGTP3r169x+vRpeHh4yLSNioqCj48P3rx5g44dOyI9PV26R8eWLVtQp04dpKWlwdTUFF27dkV4eLhcH7KysrB161YMHjwYAJCWloYWLVpg7dq1CA4OxsWLF3H06FH07t0b3377LdLT0z/wEyQiIvr4RBVBYaU0zp8/j/r166N+/foA3q6/ql+/PqZOnYoHDx4gOjoa//zzD+rVqwdzc3NpOXnyJIC3U5abN29G27Zt4erqilmzZmHcuHFYsWJFqfpRoaYRR4wYAUEQcPbsWejo6EjrXV1dERQUBADS3V2DgoJgY2MDGxsbNGnSRC6Wu7s7JkyYgNzcXKipqeHFixe4dOkSfv75Z2zbtk3a7tSpU8jOzoa7u7u07vXr19i/fz9mz56Na9eu4fbt2zh06BBq1qwJAKhZsyZatmwpbT948GD06NFDbt+Obdu2ITc3F/369QMAfP/990hOTsbNmzdhYWEhbefo6Ig+ffpAU1PzQz9CIiKiSqNdu3bFzgq9b8aoQYMGOH369Af3o8KMbD1//hwxMTEYOXKkTKJVoGBPDEtLSzRq1AijRo3C69evi4zn7u6OzMxMnDt3DgBw7NgxODo6wtfXF2fOnJGeGxsbC2tra1hbW0vPPXToECwtLeHs7AxjY2OoqKjg999/R15eXqHX6tKlC0xNTREZGSlTHxERgZ49e8LQ0BD5+fnYvHkz+vXrJ5NoFdDV1YWaWoXKjYmIiN5S4NYPFVGFSbaSkpIgiiKcnZ2LbTd06FCIoghbW1t4eXkhIyNDeszb2xujRo0CADg4OMDS0lI6ZRgXF4e2bdvCzMwMVlZWOHXqlLT+3VEt4P+mEIG3yd2iRYswdepUVKlSBR4eHpgxYwbu3Lkjba+qqoqAgABERkZKs+jbt2/j2LFj0hG5p0+f4t9//33v/REREVU4KoLiSgVUYZKtkiwOv379OiIjIxEZGYlly5bBysoK7dq1w+PHjwEAV69eRevWraXt3123FRcXh3bt2gEA2rZti7i4OLx69QpnzpyRSbZEUcTu3btl3p00cuRIpKamYsOGDWjevDm2bdsGV1dXmdcABAUF4e7du4iNjQXwdlTL2tpauhasrIvflbmTLhEREX24CpNsOTg4QBAE3Lhxo8g2f/31FyQSCVxcXCAIAsLDw2Fra4uWLVti5cqVePHihUyS5O7ujhMnTuDZs2e4dOkS2rZtC+BtshUbG4uTJ08iJydHZnH82bNnkZubixYtWshcW09PD97e3pg1axYuX76M1q1bY+bMmTL9b926NSIiIpCfn4+1a9di0KBBEP7/kKixsTEMDQ2Lvb/CKGsnXSIiIoX5RE8jlhcVJtkyMjKCp6cnli5diqysLLnjaWlpsLS0RHZ2Ns6cOQPg7fTdxo0bYWdnh2HDhmHy5MnQ0tKSnuPu7o6srCwsWLAADg4O0j0z2rRpg7Nnz2Lv3r3S6cYCUVFR6Nq1K1RVVYvsqyAIcHZ2luvn4MGDsX37dmzfvh0PHjxAYGCg9JiKigr8/f2xYcMGPHz4UC5mZmZmoe9iCg4ORnp6ukwJDg4usm9EREQfm4qK4kpFVKG6vXTpUuTl5aFJkybYvn07bt26hYSEBCxatAjNmzdHq1at0KJFC/Tu3Ru7du3C7du3ERMTg4cPH0JHRwcbN27Ey5cvpfFsbW1hZWWFxYsXS0e1AKBGjRqwsLDAihUr5NZrRUdHy4yOxcfHo3v37vj9999x/fp1JCUlYfXq1QgPD0f37t1lzvXz84O6ujqGDx+OTp06oUaNGjLHZ82ahRo1aqBp06ZYu3Ytrl+/jlu3biE8PBz169dHZmam3GcikUigr68vUxS5sy4RERF9mAqVbNna2uLixYvSbRtq166Njh074tChQ1i2bBkEQUBMTAx69eqF8ePHw8XFBcHBwRg8eDBu3ryJ1NRU9OvXD/n5+dKY7u7uePHihXS9VoG2bdvixYsXMsnW7du3kZSUBE9PT2ld9erVYW1tjWnTpqFp06Zo0KABfvnlF0ybNg2TJ0+WiamtrQ1/f3/8+++/0oXx7zIyMsLp06fRv39/zJw5E/Xr10fr1q2xadMmzJs3DwYGBgr6JImIiD6eSv4wIgSR25KX2IIFC3Dw4EH8+eefn7or7/Xj5QPvb1RC37l1RMTNfQqLN8jREyISFRZPgJPC411P26OweC6G3QDcVFg8wFGB8RQZi/HKV7zy3DfGKz+xCuIpl+2vRxQW686Itu9vVM5w46ZSqF69OtdDERERlZJQUYekFITJVil8+eWXn7oLREREVMEw2SIiIiKlquQDW0y2iIiISLkqe7JVoZ5GJCIiIqpoOLJFRERESiVU8qEdJltERESkVJxGJCIiIiKl4cgWERERKZVKJR/ZYrJFRERESlXZpxH5uh4iIiJSKpfwowqLdT2ojcJifSwc2fpMObivVFisW7FD4dD6N8XFOzYcdgO3KCze7bW9FR7P3meNwuIlRQcgX7ymsHgqgivK9zvWFBtP0e+9LO/3W55/t/xdlJd4Fe/diJV9ZIvJFhERESkV341IREREpESVfZ+tSn77RERERMrFkS0iIiJSqko+i8hki4iIiJSrsidbnEYkIiIiUiKObBEREZFSVfaRLSZbREREpFSV/XU9FW4aMTU1Fd988w1sbW0hkUhQo0YNeHt749ChQ9I269evh7OzMzQ1NWFtbY0ZM2bIxTE3N8ePP/4oU/fdd99BEATExcXJ1Ldr1w4DBgyQqVuzZg1atWolPS4Iglw8AOjatSsEQUBoaKhMvLFjxxZ5j4IgSIuBgQFatmyJw4cPF9meiIiIyq8KlWwlJyejYcOGOHz4MObNm4crV64gJiYG7u7uGDlypLTNwIED0aNHDyQkJGDr1q2wsbGRi9WuXTu5pCo2NhY1atSQqX/9+jVOnz4NDw8PmbZRUVHw8fGR/lyjRg1ERkbKtHnw4AEOHToEc3PzUt9rREQEUlJScOLECVSrVg3dunXDnTt3Sh2HiIjoUxMExZWKqEIlWyNGjIAgCDh79ix8fX3h6OgIV1dXjB8/HqdPnwbwf6NCQUFBsLGxQZMmTdC/f3+5WO7u7jhx4gRyc3MBAC9evMClS5cwadIkmWTr1KlTyM7Ohru7u7Tu9evX2L9/v0yy1a1bNzx9+hQnTpyQ1q1ZswadOnWCiYlJqe/V0NAQZmZmqF27NpYtW4ZXr17hwIEDpY5DRET0qTHZqiCeP3+OmJgYjBw5Ejo6OnLHDQ0NAQCWlpZo1KgRRo0ahdevXxcZz93dHZmZmTh37hwA4NixY3B0dISvry/OnDkjPTc2NhbW1tawtraWnnvo0CFYWlrC2dlZWqehoYF+/fohIiJCWhcZGYmgoKAPuW0AgJaWFgAgJyfng2MRERHRx1Vhkq2kpCSIoiiT4BRm6NChEEURtra28PLyQkZGhvSYt7c3Ro0aBQBwcHCApaWldBQrLi4Obdu2hZmZGaysrHDq1Clp/bujWoD8FGKBoKAgbN26FVlZWTh69CjS09PRrVu3D7ltvHz5Ej/88ANUVVXRtm3bD4pFRET0KQgqgsJKRVRhki1RFN/b5vr164iMjERkZCSWLVsGKysrtGvXDo8fPwYAXL16Fa1bt5a2f3fdVlxcHNq1awcAaNu2LeLi4vDq1SucOXNGJtkSRRG7d+8uNNlyc3ODg4MDfv/9d4SHh2PAgAFQUyvbA599+vSBrq4u9PT0sH37dqxevRp169aVa5ednY2MjAyZkp2dXaZrEhERKQOnESsIBwcHCIKAGzduFNnmr7/+gkQigYuLCwRBQHh4OGxtbdGyZUusXLkSL168kEmSCtZtPXv2DJcuXZKOHLVt2xaxsbE4efIkcnJyZBbHnz17Frm5uWjRokWhfQgKCsLSpUvx+++/f9AU4s8//4z4+HikpqYiNTUVAQEBhbYLCwuDgYGBTAkLCyvzdYmIiBSNyVYFYWRkBE9PTyxduhRZWVlyx9PS0mBpaYns7GycOXMGAKCqqoqNGzfCzs4Ow4YNw+TJk6Xrn4C3yVZWVhYWLFgABwcH6UL2Nm3a4OzZs9i7d690urFAVFQUunbtClVV1UL72bdvX1y5cgW1a9eGi4tLme/XzMwM9vb2MDY2LrZdcHAw0tPTZUpwcHCZr0tERESKVWGSLQBYunQp8vLy0KRJE2zfvh23bt1CQkICFi1ahObNm6NVq1Zo0aIFevfujV27duH27duIiYnBw4cPoaOjg40bN+Lly5fSeLa2trCyssLixYtl1kPVqFEDFhYWWLFihdx6rejo6EKnEAtUqVIFKSkpMvt+FebJkyeIj4+XKY8ePSr1ZyKRSKCvry9TJBJJqeMQEREpC0e2KhBbW1tcvHgR7u7umDBhAmrXro2OHTvi0KFDWLZsGQRBQExMDHr16oXx48fDxcUFwcHBGDx4MG7evInU1FT069cP+fn50pju7u548eKFdL1WgbZt2+LFixcyydbt27eRlJQET0/PYvtpaGhY6BOT79q4cSPq168vU1auXFn6D4WIiKicUxEUV0rj6NGj8Pb2hoWFBQRBwK5du2SOi6KIqVOnwtzcHFpaWujQoQNu3bol0+b58+fo168f9PX1YWhoiMGDByMzM7NU/ahwr+sxNzfHkiVLsGTJkkKP6+npYf78+Zg/f77csb///luurmBB/X9FRETIbOMAvJ1C9PDwkEuk/rs56n/Fx8eXqn1JHgYgIiKi4mVlZcHNzQ1BQUHo2bOn3PG5c+di0aJFWLNmDWxsbDBlyhR4enri+vXr0NTUBAD069cPKSkpOHDgAN68eYNBgwZh2LBh2LhxY4n7UeGSrU+pevXqXA9FRERUSp9q+s/LywteXl6FHhNFEQsXLsQPP/yA7t27AwDWrl0LU1NT7Nq1C/7+/khISEBMTAzOnTuHRo0aAQAWL16MLl26YP78+bCwsChRPyrUNOKn9uWXX8psHUFERETvJ6gorihqy6O7d+8iNTUVHTp0kNYZGBigadOm0r02T506BUNDQ2miBQAdOnSAioqK9GG8kmCyRURERBWGorY8Sk1NBQCYmprK1JuamkqPpaamyr1yT01NDUZGRtI2JcFpRCIiIlIqRU4jBgcHY/z48TJ15f0pfCZbREREpFSCArMtiUSikOTKzMwMAPDo0SOYm5tL6x89eoR69epJ2xS8haZAbm4unj9/Lj2/JDiNSERERJWOjY0NzMzMZPbFzMjIwJkzZ9C8eXMAQPPmzZGWloYLFy5I2xw+fBj5+flo2rRpia/FkS0iIiJSqk/1NGJmZiaSkpKkP9+9exfx8fEwMjKClZUVxo4di5kzZ8LBwUG69YOFhQV69OgBAKhVqxY6d+6MoUOHYvny5Xjz5g1GjRoFf3//Ej+JCDDZIiIiIiX7VMnW+fPnZTYnL1jrFRAQgMjISHz77bfIysrCsGHDkJaWhlatWiEmJka6xxYAbNiwAaNGjUL79u2hoqICX19fLFq0qFT9YLJFRERESvWpkq127doVu1G4IAiYPn06pk+fXmQbIyOjUm1gWhiu2SIiIiJSIkHku2GIiIhIidrvPaGwWIe8Wios1sfCacTPlJZVH4XFenV/k8Lj1Zx5QGHx7v3QETXDDiouXnAHhd8vcFNh8QBHBcZTZCzGK1/xynPfGK/8xCqIp1ylfYH054bTiERERERKxJEtIiIiUioVoXKvWGKyRURERErFaUQiIiIiUhqObBEREZFSVfaRHSZbREREpFSVfc1WZU82iYiIiJSKI1tERESkVFwgX8EFBgZCEAS5UvCW79TUVIwZMwb29vbQ1NSEqakpWrZsiWXLluHly5fSOJcvX4aPjw9MTEygqakJa2tr9O7dG48fPwYAJCcnQxAEqKqq4sGDBzJ9SElJgZqaGgRBQHJyssyxe/fuQUtLC5mZmQgNDUW9evWKvJd27dpJ+6+pqQlHR0eEhYUV+14nIiKi8k5FgaUiqqj9ltG5c2ekpKTIFBsbG9y5cwf169fH/v37MXv2bFy6dAmnTp3Ct99+iz179uDgwbe7jj958gTt27eHkZER9u3bh4SEBERERMDCwgJZWVky17K0tMTatWtl6tasWQNLS8tC+xYVFQV3d3fo6uqW6F6GDh2KlJQUJCYmIjg4GFOnTsXy5cvL8KkQERGVDyqC4kpF9FlMI0okEpiZmcnVjxgxAmpqajh//jx0dHSk9ba2tujevbt0xOjEiRNIT0/HqlWroKb29iOxsbGBu7u7XMyAgABEREQgODhYWhcREYGAgADMmDFDrn1UVBT8/PxKfC/a2trSexk0aBCWLFmCAwcO4Ouvvy5xDCIiIio/PouRrcI8e/YM+/fvx8iRI2USrXcJwtsU2czMDLm5udi5c+d7p+x8fHzw77//4vjx4wCA48eP499//4W3t7dc27S0NBw/fhw+Pj6l7r8oijh27Bhu3LgBDQ2NUp9PRERUXgiCqLBSEX0WydaePXugq6srLX5+fkhKSoIoinBycpJpW61aNWm7SZMmAQCaNWuG77//Hn379kW1atXg5eWFefPm4dGjR3LXUldXR//+/REeHg4ACA8PR//+/aGuri7X9s8//0TdunVhYWFR4nv59ddfoaurC4lEgjZt2iA/Px+jR48usn12djYyMjJkSnZ2domvR0REpGyVfRrxs0i23N3dER8fLy2LFi0qsu3Zs2cRHx8PV1dXmaRk1qxZSE1NxfLly+Hq6orly5fD2dkZV65ckYsRFBSEbdu2ITU1Fdu2bUNQUFCh14qKiir1qFa/fv0QHx+PEydOwMvLC5MnT0aLFi2KbB8WFgYDAwOZEhYWVqprEhERkfJ8FsmWjo4O7O3tpcXc3Bz29vYQBAGJiYkybW1tbWFvbw8tLS25OFWrVoWfnx/mz5+PhIQEWFhYYP78+XLt6tSpA2dnZ/Tp0we1atVC7dq15drk5OQgJiam1MmWgYEB7O3t0bhxY2zduhVLliyRLuQvTHBwMNLT02XKu+vJiIiIPjU+jfiZqlq1Kjp27IglS5bIPVFYEhoaGrCzsyvy3KCgIMTFxRU5qhUXF4cqVarAzc2t1NcuoKurizFjxuB///tfkWvJJBIJ9PX1ZYpEIinzNYmIiBRNRRAVViqizzbZAt6uf8rNzUWjRo2wZcsWJCQkIDExEevXr8eNGzegqqoK4O2ar/79+2PPnj24efMmEhMTMX/+fPz555/o3r17obGHDh2KJ0+eYMiQIYUej46OLnRU69WrVzJTnvHx8bh9+3aR9zB8+HDcvHkT27dvL8MnQERERJ/aZ7H1Q1Hs7Oxw6dIlzJ49G8HBwfjnn38gkUjg4uKC//3vfxgxYgQAwMXFBdra2pgwYQL+/vtvSCQSODg4YNWqVRgwYEChsdXU1FCtWrUirx0dHS1dRP+umzdvon79+jJ17du3L3Kq0MjICAMHDkRoaCh69uwJFZXPOj8mIqLPUEVd2K4oFT7ZioyMLPa4ubk5Fi9ejMWLFxfZxtbWFitWrCg2jrW1dbHbQtSrV096/OLFi8jIyEDbtm1l2oSGhiI0NLTIGHFxcYXWc1NTIiKqyCr7MEFlv3+lyM3NxeLFiwvdDoKIiIgqlwo/slUeNWnSBE2aNPnU3SAiIioXOI1IREREpEQV9SlCRWGyRUREREpV2Ue2uGaLiIiISIk4skVERERKVdlHdphsERERkVJV9jVblT3ZJCIiIlIqjmwRERGRUlX2BfJMtoiIiEipKnuyJYjFvYOGiIiI6AONOhWrsFhLmrsrLNbHwpGtz5SWVR+FxXp1f5PC49WceUBh8e790FHh8RR9v8BNhcUDHBUYT5GxGK98xSvPfWO88hOrIJ5yVfYF4ky2iIiISKn4NCIRERERKQ1HtoiIiEipKvsCeY5sERERkVKpKLCUlLW1NQRBkCsjR44EALRr107u2FdffaWI25XDkS0iIiJSqk8xsnXu3Dnk5eVJf7569So6duwIPz8/ad3QoUMxffp06c/a2tpK6QuTLSIiIvrsGBsby/z8448/ws7ODm3btpXWaWtrw8zMTOl94TQiERERKZUgiAor2dnZyMjIkCnZ2dnFXj8nJwfr169HUFAQBOH/htk2bNiAatWqoXbt2ggODsbLly+Vcv9MtoiIiEipVATFlbCwMBgYGMiUsLCwYq+/a9cupKWlITAwUFrXt29frF+/HrGxsQgODsa6devQv39/5dy/UqJ+oMDAwEIXtSUlJQEAUlNTMWbMGNjb20NTUxOmpqZo2bIlli1bJpOVXr58GT4+PjAxMYGmpiasra3Ru3dvPH78GACQnJwMQRCgqqqKBw8eyPQhJSUFampqEAQBycnJMsfu3bsHLS0tZGZm4uXLlwgODoadnR00NTVhbGyMtm3bIioqCgBQp06dIhfcrVu3DhKJBE+fPgUAiKKIFStWoGnTptDV1YWhoSEaNWqEhQsXKi3bJiIiqkiCg4ORnp4uU4KDg4s9Z/Xq1fDy8oKFhYW0btiwYfD09ESdOnXQr18/rF27Fjt37sTt27cV3udymWwBQOfOnZGSkiJTbGxscOfOHdSvXx/79+/H7NmzcenSJZw6dQrffvst9uzZg4MHDwIAnjx5gvbt28PIyAj79u1DQkICIiIiYGFhgaysLJlrWVpaYu3atTJ1a9asgaWlZaF9i4qKgru7O3R1dfHVV19hx44dWLx4MW7cuIGYmBj06tULz549AwAMHjwYmzdvxqtXr+TiREREwMfHB9WqVQMADBgwAGPHjkX37t0RGxuL+Ph4TJkyBVFRUdi/f/8Hf6ZERESfgiKfRpRIJNDX15cpEomkyGvfu3cPBw8exJAhQ4rtY9OmTQFAOrCjSOV2gbxEIil00dqIESOgpqaG8+fPQ0dHR1pva2uL7t27o+BVjydOnEB6ejpWrVoFNbW3t2ljYwN3d/l3KgUEBCAiIkImM46IiEBAQABmzJgh1z4qKkr6NEN0dDR++eUXdOnSBcDbR00bNmwobdu/f39MmjQJ27dvlxmevHv3LuLi4vDnn38CALZu3YoNGzZg165d6N69u7SdtbU1fHx8kJGRUYJPjYiIqPz5lDvIR0REwMTEBF27di22XXx8PADA3Nxc4X0otyNbhXn27Bn279+PkSNHyiRa7ypY+GZmZobc3Fzs3LkT73vXto+PD/79918cP34cAHD8+HH8+++/8Pb2lmublpaG48ePw8fHR3qdP//8Ey9evCg0drVq1dC9e3eEh4fL1EdGRqJ69ero1KkTgLeL9JycnGQSrXfvycDAoNh7ICIiIln5+fnSwZOCgRcAuH37NmbMmIELFy4gOTkZ0dHRGDhwINq0aYO6desqvB/lNtnas2cPdHV1pcXPzw9JSUkQRRFOTk4ybatVqyZtN2nSJABAs2bN8P3336Nv376oVq0avLy8MG/ePDx69EjuWurq6ujfv780IQoPD0f//v2hrq4u1/bPP/9E3bp1pfO+K1aswMmTJ1G1alU0btwY48aNw4kTJ2TOGTx4MOLi4nD37l0Ab9dmrVmzBgEBAVBRefsruHXrltx9lURZnsogIiL6mBS5QL40Dh48iPv37yMoKEimXkNDAwcPHkSnTp3g7OyMCRMmwNfXF7t371bgXf+fcptsubu7Iz4+XloWLVpUZNuzZ88iPj4erq6uMonGrFmzkJqaiuXLl8PV1RXLly+Hs7Mzrly5IhcjKCgI27ZtQ2pqKrZt2yb3iykQFRUlHdUCgDZt2uDOnTs4dOgQevXqhWvXrqF169Yy048dO3ZE9erVERERAQA4dOgQ7t+/j0GDBknbvG/0rShleSqDiIjoY/pUyVanTp0giiIcHR1l6mvUqIEjR47g2bNneP36NW7duoW5c+dCX19fgXf9f8ptsqWjowN7e3tpMTc3h729PQRBQGJiokxbW1tb2NvbQ0tLSy5O1apV4efnh/nz5yMhIQEWFhaYP3++XLs6derA2dkZffr0Qa1atVC7dm25Njk5OYiJiZFJtoC3I2OtW7fGpEmTsH//fkyfPh0zZsxATk4OAEBFRQWBgYFYs2aNdEjT3d0dtra20hiOjo64ceNGqT+nsjyVQURERB9PuU22ClO1alV07NgRS5YskXuisCQ0NDRgZ2dX5LlBQUGIi4srclQrLi4OVapUgZubW7HXcXFxQW5uLl6/fi2tGzRoEP7++2/s2LEDO3fuxODBg2XO6du3L27evCndMuJdoigiPT290GuV9qkMIiKij01VgaUiqlDJFgD8+uuvyM3NRaNGjbBlyxYkJCQgMTER69evx40bN6Cq+vZXsWfPHvTv3x979uzBzZs3kZiYiPnz5+PPP/8sdBE68PYdSU+ePCny8dDo6Gi5Ua127drht99+ky6y+/PPP/H999/D3d1dZjjSxsYGHh4eGDZsGCQSCXr27CkT58svv0Tv3r3Rp08fzJ49G+fPn8e9e/ewZ88edOjQAbGxsR/ysREREX0yKoKosFIRldutH4piZ2eHS5cuYfbs2QgODsY///wDiUQCFxcX/O9//8OIESMAvB1d0tbWxoQJE/D3339DIpHAwcEBq1atwoABAwqNraamJt3zqjDR0dFyTxV6enpizZo1+P777/Hy5UtYWFigW7dumDp1qtz5gwcPxqFDhzBixAhoamrKHBMEARs3bsSKFSsQHh6OWbNmQU1NDQ4ODhg4cCA8PT1L+1ERERGVC5/iRdTliSCWdWV2JXPx4kV4eHjgyZMnhT6lWN5oWfVRWKxX9zcpPF7NmQcUFu/eDx0VHk/R9wvcVFg8wFGB8RQZi/HKV7zy3DfGKz+xCuIp14+XFffP6O/cOios1sdS4Ua2PpXc3FwsXry4QiRaRERE5UllH9lislVCTZo0QZMmTT51N4iIiCoc1UqebFW4BfJEREREFQlHtoiIiEipOI1IREREpEQVdcsGReE0IhEREZEScWSLiIiIlIrTiERERERKVFFfs6MonEYkIiIiUiKObBEREZFSVfZpRL6uh4iIiJRqxY19Cos1zLnivSuYI1ufqfL+bkTrGfsVFi95SifY9duksHi3N/ThuxEZ7zOIV577xnjlJ1ZBPOXiDvJEREREpDQc2SIiIiKlquxrtphsERERkVJV9mSL04hERERESsSRLSIiIlKqyj6yxWSLiIiIlEqVL6ImIiIiImXhyBYREREpVWUf2SmX9x8YGAhBEORKUlISACA1NRVjxoyBvb09NDU1YWpqipYtW2LZsmV4+fKlNM7ly5fh4+MDExMTaGpqwtraGr1798bjx48BAMnJyRAEAaqqqnjw4IFMH1JSUqCmpgZBEJCcnCxz7N69e9DS0kJmZiZCQ0MhCAI6d+4sdx/z5s2DIAho166dtC40NBT16tUr8t7btWsnvV9NTU24uLjg119/LeUnSEREVH6oCIorFVG5TLYAoHPnzkhJSZEpNjY2uHPnDurXr4/9+/dj9uzZuHTpEk6dOoVvv/0We/bswcGDBwEAT548Qfv27WFkZIR9+/YhISEBERERsLCwQFZWlsy1LC0tsXbtWpm6NWvWwNLSstC+RUVFwd3dHbq6ugAAc3NzxMbG4p9//pFpFx4eDisrq1Lf+9ChQ5GSkoLr16/jyy+/xMiRI7Fpk+J2SCciIqKPp9xOI0okEpiZmcnVjxgxAmpqajh//jx0dHSk9ba2tujevTsKXvV44sQJpKenY9WqVVBTe3ubNjY2cHd3l4sZEBCAiIgIBAcHS+siIiIQEBCAGTNmyLWPioqCn5+f9GcTExM0bNgQa9asweTJkwEAJ0+exNOnT+Hn54fr16+X6t61tbWl9x4aGoqNGzciOjoaffoo7hUyREREH0tFHZFSlHI7slWYZ8+eYf/+/Rg5cqRMovUuQXj7GzUzM0Nubi527tyJ971r28fHB//++y+OHz8OADh+/Dj+/fdfeHt7y7VNS0vD8ePH4ePjI1MfFBSEyMhI6c/h4eHo168fNDQ0SnOLhdLS0kJOTs4HxyEiIvoUVAVRYaUiKrfJ1p49e6Crqystfn5+SEpKgiiKcHJykmlbrVo1abtJkyYBAJo1a4bvv/8effv2RbVq1eDl5YV58+bh0aNHctdSV1dH//79ER4eDuBtotS/f3+oq6vLtf3zzz9Rt25dWFhYyNR369YNGRkZOHr0KLKysrB161YEBQV90GeQl5eH9evX46+//oKHh0ehbbKzs5GRkSFTsrOzP+i6REREisQ1W+WUu7s74uPjpWXRokVFtj179izi4+Ph6uoqk2jMmjULqampWL58OVxdXbF8+XI4OzvjypUrcjGCgoKwbds2pKamYtu2bUUmSlFRUXKjWsD/JWwRERHYtm0bHB0dUbdu3TLcOfDrr79CV1cXWlpaGDp0KMaNG4evv/660LZhYWEwMDCQKWFhYWW6LhERESleuV2zpaOjA3t7e5k6DQ0NCIKAxMREmXpbW1sAb6fb/qtq1arw8/ODn58fZs+ejfr162P+/PlYs2aNTLs6derA2dkZffr0Qa1atVC7dm3Ex8fLtMnJyUFMTAy+//77QvscFBSEpk2b4urVqx80qtWvXz9MnjwZWlpaMDc3h4pK0TlxcHAwxo8fL1MnkUgwJzywzNcnIiJSpIo6IqUo5XZkqzBVq1ZFx44dsWTJErknCktCQ0MDdnZ2RZ4bFBSEuLi4IhOluLg4VKlSBW5uboUed3V1haurK65evYq+ffuWun8FDAwMYG9vD0tLy2ITLeBtYqWvry9TJBJJma9NRESkaJV9GrHcjmwV5ddff0XLli3RqFEjhIaGom7dulBRUcG5c+dw48YNNGzYEMDbNV+bN2+Gv78/HB0dIYoidu/ejT///BMRERGFxh46dCj8/PxgaGhY6PHo6OhCpxDfdfjwYbx586bIGADw6tUruVEzPT092NnZFRubiIiIKp4Kl2zZ2dnh0qVLmD17NoKDg/HPP/9AIpHAxcUF//vf/zBixAgAgIuLC7S1tTFhwgT8/fffkEgkcHBwwKpVqzBgwIBCY6upqaFatWpFXjs6Olq6iL4oRT0l+a6bN2+ifv36MnXt27eX7hFGRET0OVGtoCNSiiKI79sXgQAAFy9ehIeHB548eVLoU4rljZaV4vbkenV/k8LjWc/Yr7B4yVM6wa6f4jZ9vb2hj8LvF7ipsHiAowLjKTIW45WveOW5b4xXfmIVxFOu/Q/+VFisTpZdFBbrY6lQa7Y+pdzcXCxevLhCJFpERERUfjDZKqEmTZoUOf1IRERERVNRYCmpgncXv1ucnZ2lx1+/fo2RI0eiatWq0NXVha+vb6F7cSoCky0iIiJSqk/1NKKrq6vMO5YL3hQDAOPGjcPu3buxbds2HDlyBA8fPkTPnj0VfOdvVbgF8kREREQloaamVuh7ltPT07F69Wps3LhR+oaWiIgI1KpVC6dPn0azZs0U2g+ObBEREZFSqQqKK6V5Td2tW7dgYWEBW1tb9OvXD/fv3wcAXLhwAW/evEGHDh2kbZ2dnWFlZYVTp04p/P6ZbBEREZFSqQiiwkpJX1PXtGlTREZGIiYmBsuWLcPdu3fRunVrvHjxAqmpqdDQ0JDbE9PU1BSpqakKv39OIxIREZFSKXLn96JeU/dfXl5e0v9ft25dNG3aFDVr1sTWrVsLfb2fMnFki4iIiCqMsr6mztDQEI6OjkhKSoKZmRlycnKQlpYm0+bRo0eFrvH6UEy2iIiISKnKw7sRMzMzcfv2bZibm6Nhw4ZQV1fHoUOHpMcTExNx//59NG/eXAF3LIs7yBMREZFSnXn8h8JiNTXpWqJ2//vf/+Dt7Y2aNWvi4cOHCAkJQXx8PK5fvw5jY2N8/fXX+PPPPxEZGQl9fX188803AICTJ08qrK8FuGbrM1XeX9dTc+YBhcW790NH1AxT3Hsl7wV34Ot6GO8ziFee+8Z45SdWQbzPzz///IM+ffrg2bNnMDY2RqtWrXD69GkYGxsDAH7++WeoqKjA19cX2dnZ8PT0xK+//qqUvjDZIiIiIqUSPsGLqDdv3lzscU1NTSxduhRLly5Vel+YbBEREZFSfYJcq1zhAnkiIiIiJeLIFhERESnVp5hGLE+YbBEREZFSVfZptMp+/0RERERKxZEtIiIiUipBqNxbejLZIiIiIqWq5Eu2Ksc0YmBgIARBgCAI0NDQgL29PaZPn47c3FwAgCiKWLlyJZo3bw59fX3o6urC1dUVY8aMQVJSkjROaGioNI6amhqqVauGNm3aYOHChcjOzi702u7u7li1ahWSk5MhCALi4+MLbRcZGSmNraKiAnNzc/Tu3Rv3799X+OdBRET0MQmC4kpFVCmSLQDo3LkzUlJScOvWLUyYMAGhoaGYN28eRFFE3759MXr0aHTp0gX79+/H9evXsXr1amhqamLmzJkycVxdXZGSkoL79+8jNjYWfn5+CAsLQ4sWLfDixQuZts+fP8eJEyfg7e1doj7q6+sjJSUFDx48wPbt25GYmAg/Pz+FfQZERET08VWaaUSJRCJ9k/fXX3+NnTt3Ijo6GjY2Nti8eTOioqLg4+MjbW9lZYVmzZrhv6+OVFNTk8axsLBAnTp10LFjR7i5uWHOnDkyydkff/yBBg0awNTUFMnJye/toyAI0tjm5uYYPHgwRo8ejYyMDOjr63/oR0BERPRJVNABKYWpNCNb/6WlpYWcnBxs2rQJTk5OMonWu4QSjFk6OzvDy8sLO3bskKmPjo5G9+7dy9S/x48fY+fOnVBVVYWqqmqZYhAREZUHKoLiSkVU6ZItURRx8OBB7Nu3Dx4eHrh58yacnJxk2owdOxa6urrQ1dVF9erVSxTX2dlZZvQqOzsbMTExRSZxhUlPT4euri50dHRgamqK2NhYjBw5Ejo6OiWOQUREROVLpUm29uzZA11dXWhqasLLywu9e/dGaGhooW0nT56M+Ph4TJ06FZmZmSWKL4qizCjY4cOHYWJiAldX1xL3UU9PD/Hx8Th//jx++uknNGjQALNmzSr2nOzsbGRkZMiUohbrExERfQqCAktFVGnWbLm7u2PZsmXQ0NCAhYUF1NTe3rqDgwMSExNl2hobG8PY2BgmJiYljp+QkAAbGxvpz9HR0aUa1QIAFRUV2NvbAwBq1aqF27dv4+uvv8a6deuKPCcsLAzTpk2TqQsJCSnVdYmIiJSpoj5FqCiVZmRLR0cH9vb2sLKykiZaANCnTx8kJiYiKiqqzLFv3LiBmJgY+Pr6Ang7yrV79+4yr9cq8N1332HLli24ePFikW2Cg4ORnp4uU4KDgz/oukRERKQ4lWZkqyj+/v7YsWMH/P39ERwcDE9PT5iamuLevXvYsmWL3OL03NxcpKamIj8/H8+ePUNcXBxmzpyJevXqYeLEiQCACxcu4OXLl2jVqpXc9f47igagyKnGGjVq4IsvvsDUqVOxZ8+eQttIJBJIJJLS3jYREdFHU8kHtphsCYKALVu2YOXKlYiIiMDcuXPx5s0bVK9eHe3bt8eCBQtk2l+7dg3m5uZQVVWFgYEBXFxcEBwcjK+//lqa9ERFRaFLly4yI2gF/P395er+/vvvIvs3btw4NG/eHGfPnkWTJk0+8G6JiIg+PiZblUBkZGSxx1VUVDB8+HAMHz682HahoaFFLqp/V1RUFH744QeZOmtra7k9u94VGBiIwMBAufrC9voiIiKiiqNSJFsfU05ODnx9feHl5fWpu0JERFQuVNT9sRSFyZaCaWho8GlAIiKid1TyXIvJFhERESmXIFTu5TCVZusHIiIiok+BI1tERESkVJxGJCIiIlIi7iBPRERERErDkS0iIiJSqso+ssNki4iIiJSK04hEREREpDSCyHfBEBERkRLdz9ytsFhWut4Ki/WxcBrxM6Vl1UdhsV7d36TweDVnHlBYvHs/dFR4PEXfL3BTYfEARwXGU2Qsxitf8cpz3xiv/MQqiKdcnEYkIiIiIqXhyBYREREpVSUf2GKyRURERMqlUsmzLSZbREREpFSVPNfimi0iIiIiZeLIFhERESmVIFTuXaaYbBEREZFScRqRiIiI6DMTFhaGxo0bQ09PDyYmJujRowcSExNl2rRr1w6CIMiUr776SuF9qdDJVmBgoPTD0dDQgL29PaZPn47c3FwAgCiKWLlyJZo3bw59fX3o6urC1dUVY8aMQVJSkjROaGioNI6amhqqVauGNm3aYOHChcjOzi702u7u7li1ahUAYOfOnWjWrBkMDAygp6cHV1dXjB07FgDw008/oUqVKnj9+rVcjJcvX0JfXx+LFi2S1l26dAl+fn4wNTWFpqYmHBwcMHToUNy8qcgN7IiIiD4eQVBcKakjR45g5MiROH36NA4cOIA3b96gU6dOyMrKkmk3dOhQpKSkSMvcuXMVfPcVPNkCgM6dOyMlJQW3bt3ChAkTEBoainnz5kEURfTt2xejR49Gly5dsH//fly/fh2rV6+GpqYmZs6cKRPH1dUVKSkpuH//PmJjY+Hn54ewsDC0aNECL168kGn7/PlznDhxAt7e3jh06BB69+4NX19fnD17FhcuXMCsWbPw5s0bAMCAAQOQlZWFHTt2yPX9999/R05ODvr37w8A2LNnD5o1a4bs7Gxs2LABCQkJWL9+PQwMDDBlyhQlfYJERETKJSiwlFRMTAwCAwPh6uoKNzc3REZG4v79+7hw4YJMO21tbZiZmUmLvr7+h9xqoSr8mi2JRAIzMzMAwNdff42dO3ciOjoaNjY22Lx5M6KiouDj4yNtb2VlhWbNmuG/r4RUU1OTxrGwsECdOnXQsWNHuLm5Yc6cOTLJ2R9//IEGDRrA1NQUYWFhaNmyJSZOnCg97ujoiB49egAATExM4O3tjfDwcPTt21fmmuHh4ejRoweMjIzw8uVLDBo0CF26dMHOnTulbWxsbNC0aVOkpaUp5PMiIiKqjNLT0wEARkZGMvUbNmzA+vXrYWZmBm9vb0yZMgXa2toKvXaFH9n6Ly0tLeTk5GDTpk1wcnKSSbTeJZRgLNLZ2RleXl5yo1LR0dHo3r07AMDMzAzXrl3D1atXi4wzePBgHD58GPfu3ZPW3blzB0ePHsXgwYMBAPv27cPTp0/x7bffFhrD0NDwvf0lIiIqj1QUWLKzs5GRkSFTilryUyA/Px9jx45Fy5YtUbt2bWl93759sX79esTGxiI4OBjr1q2TzjYp0meTbImiiIMHD2Lfvn3w8PDAzZs34eTkJNNm7Nix0NXVha6uLqpXr16iuM7OzkhOTpb+nJ2djZiYGGkS980336Bx48aoU6cOrK2t4e/vj/DwcJlfvKenJywsLBARESGti4yMRI0aNdC+fXsAwK1bt6TXIyIi+pwocs1WWFgYDAwMZEpYWFix1x85ciSuXr2KzZs3y9QPGzYMnp6eqFOnDvr164e1a9di586duH37tkLvv8InW3v27IGuri40NTXh5eWF3r17IzQ0tNC2kydPRnx8PKZOnYrMzMwSxRdFUWYU7PDhwzAxMYGrqysAQEdHB3/88QeSkpLwww8/QFdXFxMmTECTJk3w8uVLAICqqioCAgIQGRkJURSRn5+PNWvWYNCgQVBRUZFepyzKkuETERFVVMHBwUhPT5cpwcHBRbYfNWoU9uzZg9jY2PcOtDRt2hQAZB6iU4QKn2y5u7sjPj4et27dwqtXr7BmzRro6OjAwcFB7hFPY2Nj2Nvbw8TEpMTxExISYGNjI/05Ojq60KlJOzs7DBkyBKtWrcLFixdx/fp1bNmyRXo8KCgI9+/fx+HDh3Ho0CH8/fffGDRokPS4o6MjAODGjRsl7htQtgyfiIjo41LcEnmJRAJ9fX2ZIpFI5K4oiiJGjRqFnTt34vDhwzL/Li9KfHw8AMDc3PzDbvc/KnyypaOjA3t7e1hZWUFN7f/W+/fp0weJiYmIiooqc+wbN24gJiYGvr6+AN7+4nbv3i1dr1UUa2traGtryzxeamdnh7Zt2yI8PBwRERHo0KEDatasKT3eqVMnVKtWrchHTotaIF/aDJ+IiOhjExT4v5IaOXIk1q9fj40bN0JPTw+pqalITU3Fq1evAAC3b9/GjBkzcOHCBSQnJyM6OhoDBw5EmzZtULduXYXef4V/GrEo/v7+2LFjB/z9/REcHAxPT0+Ympri3r172LJlC1RVVWXa5+bmIjU1Ffn5+Xj27Bni4uIwc+ZM1KtXT/qk4YULF/Dy5Uu0atVKel5oaChevnyJLl26oGbNmkhLS8OiRYvw5s0bdOzYUeYagwcPxtChQwG8XbP1Lh0dHaxatQp+fn7w8fHB6NGjYW9vj6dPn2Lr1q24f/++3Fwz8PZpzMIyeiIiovJCED7+2M6yZcsAvN249F0REREIDAyEhoYGDh48iIULFyIrKws1atSAr68vfvjhB4X35bNNtgRBwJYtW7By5UpERERg7ty5ePPmDapXr4727dtjwYIFMu2vXbsGc3NzqKqqwsDAAC4uLggODsbXX38tTWaioqLQpUsXmRG0tm3bYunSpRg4cCAePXqEKlWqoH79+ti/f7/cAn1fX1+MGjUKqqqq0q0h3tW9e3ecPHkSYWFh6Nu3LzIyMlCjRg14eHjI7QtGRERERXvfWugaNWrgyJEjH6UvFTrZ+u/o0H+pqKhg+PDhGD58eLHtQkNDi1xU/66oqCi5jNfd3R3u7u7vPRd4uy3F+/bLatSoEbZv316ieERERBVD5X47YoVOtj6mnJwc+Pr6wsvL61N3hYiIqEIpzVqrzxGTrRLS0NBASEjIp+4GERERVTBMtoiIiEjJOLJFREREpDSf4mnE8qRy3z0RERGRknFki4iIiJSM04hERERESlPZn0bkNCIRERGREnFki4iIiJSqso9sMdkiIiIiJavcE2mC+L6XBxERERF9gKxcxb2DUEetrcJifSwc2fpMaVn1UVisV/c3KTye1c9xCot3f1w72Pxvt8Li3Z3vrfD7BW4qLB7gqMB4iozFeOUrXnnuG+OVn1gF8UiZmGwRERGRknHNFhEREZHSVPYF8pV7xRoRERGRknFki4iIiJSsco/tMNkiIiIipeI0IhEREREpDUe2iIiISKkEoXKPbDHZIiIiIiWr3MlWhZ1GDAwMhCAIEAQBGhoasLe3x/Tp05GbmwsAEEURK1euRPPmzaGvrw9dXV24urpizJgxSEpKksYJDQ2VxlFTU0O1atXQpk0bLFy4ENnZ2YVe293dHatWrUJycjIEQYCqqioePHgg0yYlJQVqamoQBAHJyckAIG0fHx9faNzIyEhpX1RUVFC9enUMGjQIjx8//vAPjIiIiD6JCptsAUDnzp2RkpKCW7duYcKECQgNDcW8efMgiiL69u2L0aNHo0uXLti/fz+uX7+O1atXQ1NTEzNnzpSJ4+rqipSUFNy/fx+xsbHw8/NDWFgYWrRogRcvXsi0ff78OU6cOAFvb29pnaWlJdauXSvTbs2aNbC0tCz1Penr6yMlJQX//PMPVq5cib1792LAgAGljkNERFReCFBRWKmIKvQ0okQigZmZGQDg66+/xs6dOxEdHQ0bGxts3rwZUVFR8PHxkba3srJCs2bN8N/XQaqpqUnjWFhYoE6dOujYsSPc3NwwZ84cmeTsjz/+QIMGDWBqaiodsQoICEBERASCg4Ol7SIiIhAQEIAZM2aU6p4EQZDpy+jRozFlyhS8evUKWlpapYpFRERUPnAa8bOhpaWFnJwcbNq0CU5OTjKJ1rtKslDP2dkZXl5e2LFjh0x9dHQ0unfvLlPn4+ODf//9F8ePHwcAHD9+HP/++6/M6FdZaWlpIT8/Xzo9SkREVNEULJFRRKmIPotkSxRFHDx4EPv27YOHhwdu3rwJJycnmTZjx46Frq4udHV1Ub169RLFdXZ2lo5eAUB2djZiYmLkkjh1dXX0798f4eHhAIDw8HD0798f6urqH3Rft27dwvLly9GoUSPo6ekV2iY7OxsZGRkypai1ZkRERPTxVehka8+ePdDV1YWmpia8vLzQu3dvhIaGFtp28uTJiI+Px9SpU5GZmVmi+KIoymTRhw8fhomJCVxdXeXaBgUFYdu2bUhNTcW2bdsQFBRUpntKT0+Hrq4utLW14eTkBFNTU2zYsKHI9mFhYTAwMJApYWFhZbo2ERGRcggKLBVPhV6z5e7ujmXLlkFDQwMWFhZQU3t7Ow4ODkhMTJRpa2xsDGNjY5iYmJQ4fkJCAmxsbKQ/R0dHFzk1WadOHTg7O6NPnz6oVasWateuXeRTh8XR09PDxYsXoaKiAnNz8/eu0woODsb48eNl6iQSCeaEB5b62kRERMpQURe2K0qFvnsdHR3Y29vDyspKmmgBQJ8+fZCYmIioqKgyx75x4wZiYmLg6+sL4O0o1+7du+XWa70rKCgIcXFxZR7VAgAVFRXY29vD1ta2RAviJRIJ9PX1ZYpEIinz9YmIiEixKvTIVlH8/f2xY8cO+Pv7Izg4GJ6enjA1NcW9e/ewZcsWqKqqyrTPzc1Famoq8vPz8ezZM8TFxWHmzJmoV68eJk6cCAC4cOECXr58iVatWhV53aFDh8LPzw+GhobF9u+/o24ACp2aJCIi+jxUzOk/Rfksky1BELBlyxasXLkSERERmDt3Lt68eYPq1aujffv2WLBggUz7a9euwdzcHKqqqjAwMICLiwuCg4Px9ddfS0eJoqKi0KVLF5kRtP8q2BT1ffz9/eXq/v7771LeJRERUcVQ2V9EXWGTrcjIyGKPq6ioYPjw4Rg+fHix7UJDQ4tcVP+uqKgo/PDDDzJ11tbWcnt2vatevXoyx9/XPjAwEIGBge/tCxEREVUcFTbZ+phycnLg6+sLLy+vT90VIiKiCqei7o+lKEy2SkBDQwMhISGfuhtEREQVVIV+Hu+DVe67JyIiIlIyjmwRERGRUnGBPBEREZFSVe5ki9OIREREpFSf8kXUS5cuhbW1NTQ1NdG0aVOcPXtWCXdYPCZbRERE9FnasmULxo8fj5CQEFy8eBFubm7w9PTE48ePP2o/mGwRERGRkqkosJTcggULMHToUAwaNAguLi5Yvnw5tLW1ER4erpC7KikmW0RERKRUggL/V1I5OTm4cOECOnToIK1TUVFBhw4dcOrUKWXcZpG4QJ6IiIgqjOzsbGRnZ8vUSSQS6ev1Cjx9+hR5eXkwNTWVqTc1NcWNGzeU3k8ZIlVKr1+/FkNCQsTXr18z3mcWrzz3jfHKTyzG+7zjKbpv5UlISIgIQKaEhITItXvw4IEIQDx58qRM/cSJE8UmTZp8pN6+JYhiMS/ro89WRkYGDAwMkJ6eDn19fcb7jOKV574xHn+3jPdx4im6b+VJSUe2cnJyoK2tjd9//x09evSQ1gcEBCAtLQ1RUVEfo7sAuGaLiIiIKhCJRAJ9fX2Z8t9EC3j7qr2GDRvi0KFD0rr8/HwcOnQIzZs3/5hd5potIiIi+jyNHz8eAQEBaNSoEZo0aYKFCxciKysLgwYN+qj9YLJFREREn6XevXvjyZMnmDp1KlJTU1GvXj3ExMTILZpXNiZblZREIkFISEihQ6+MV7Hjlee+MV75icV4n3c8RfetIhs1ahRGjRr1SfvABfJERERESsQF8kRERERKxGSLiIiISImYbBEREREpEZMtIiIiIiVislVJLV26FNbW1tDU1ETTpk1x9uzZMsU5evQovL29YWFhAUEQsGvXrg/qV1hYGBo3bgw9PT2YmJigR48eSExMLHO8ZcuWoW7dutKN75o3b469e/d+UB8L/PjjjxAEAWPHji3T+aGhoRAEQaY4Ozt/UJ8ePHiA/v37o2rVqtDS0kKdOnVw/vz5MsWytraW658gCBg5cmSZ4uXl5WHKlCmwsbGBlpYW7OzsMGPGDJT1GZ0XL15g7NixqFmzJrS0tNCiRQucO3euxOe/729XFEVMnToV5ubm0NLSQocOHXDr1q0yxdqxYwc6deqEqlWrQhAExMfHl7lvb968waRJk1CnTh3o6OjAwsICAwcOxMOHD8t8r6GhoXB2doaOjg6qVKmCDh064MyZM2WO966vvvoKgiBg4cKFZY4XGBgo93fYuXPnD+pfQkICfHx8YGBgAB0dHTRu3Bj3798vdazCviOCIGDevHll6ltmZiZGjRqF6tWrQ0tLCy4uLli+fHmZ7/XRo0cIDAyEhYUFtLW10blz5yL/jkl5mGxVQlu2bMH48eMREhKCixcvws3NDZ6ennj8+HGpY2VlZcHNzQ1Lly5VSN+OHDmCkSNH4vTp0zhw4ADevHmDTp06ISsrq0zxqlevjh9//BEXLlzA+fPn4eHhge7du+PatWsf1M9z587ht99+Q926dT8ojqurK1JSUqTl+PHjZY7177//omXLllBXV8fevXtx/fp1/PTTT6hSpUqZ4p07d06mbwcOHAAA+Pn5lSnenDlzsGzZMixZsgQJCQmYM2cO5s6di8WLF5cp3pAhQ3DgwAGsW7cOV65cQadOndChQwc8ePCgROe/72937ty5WLRoEZYvX44zZ85AR0cHnp6eeP36daljZWVloVWrVpgzZ84H9+3ly5e4ePEipkyZgosXL2LHjh1ITEyEj49Pme/V0dERS5YswZUrV3D8+HFYW1ujU6dOePLkSZniFdi5cydOnz4NCwuLYtuVJF7nzp1l/h43bdpU5ni3b99Gq1at4OzsjLi4OPz111+YMmUKNDU1Sx3r3T6lpKQgPDwcgiDA19e3TH0bP348YmJisH79eiQkJGDs2LEYNWoUoqOjSx1PFEX06NEDd+7cQVRUFC5duoSaNWuiQ4cOZf5nKpXRR30TI5ULTZo0EUeOHCn9OS8vT7SwsBDDwsI+KC4AcefOnR/YO1mPHz8WAYhHjhxRWMwqVaqIq1atKvP5L168EB0cHMQDBw6Ibdu2FceMGVOmOCEhIaKbm1uZ+/FfkyZNElu1aqWweP81ZswY0c7OTszPzy/T+V27dhWDgoJk6nr27Cn269ev1LFevnwpqqqqinv27JGpb9CggTh58uRSx/vv325+fr5oZmYmzps3T1qXlpYmSiQScdOmTaWK9a67d++KAMRLly6VuW+FOXv2rAhAvHfvnkLipaeniwDEgwcPljneP//8I1paWopXr14Va9asKf7888/vjVVUvICAALF79+4lOr8k8Xr37i32799fIbH+q3v37qKHh0eZ47m6uorTp0+XqSvp3/V/4yUmJooAxKtXr0rr8vLyRGNjY3HlypUl6iMpBke2KpmcnBxcuHABHTp0kNapqKigQ4cOOHXq1CfsWeHS09MBAEZGRh8cKy8vD5s3b0ZWVtYHvRdr5MiR6Nq1q8xnWFa3bt2ChYUFbG1t0a9fv0KnMUoqOjoajRo1gp+fH0xMTFC/fn2sXLnyg/sIvP27Wb9+PYKCgiAIQplitGjRAocOHcLNmzcBAJcvX8bx48fh5eVV6li5ubnIy8uTG4nQ0tL6oNHBAnfv3kVqaqrM79jAwABNmzYtt98TQRBgaGj4wbFycnKwYsUKGBgYwM3NrUwx8vPzMWDAAEycOBGurq4f3CcAiIuLg4mJCZycnPD111/j2bNnZe7bH3/8AUdHR3h6esLExARNmzb94CUQwNspuz/++AODBw8uc4wWLVogOjoaDx48gCiKiI2Nxc2bN9GpU6dSxyp4WfO73xMVFRVIJBKFfE+o5JhsVTJPnz5FXl6e3KsKTE1NkZqa+ol6Vbj8/HyMHTsWLVu2RO3atcsc58qVK9DV1YVEIsFXX32FnTt3wsXFpUyxNm/ejIsXLyIsLKzM/SnQtGlTREZGIiYmBsuWLcPdu3fRunVrvHjxokzx7ty5g2XLlsHBwQH79u3D119/jdGjR2PNmjUf3Nddu3YhLS0NgYGBZY7x3Xffwd/fH87OzlBXV0f9+vUxduxY9OvXr9Sx9PT00Lx5c8yYMQMPHz5EXl4e1q9fj1OnTiElJaXMfSxQ8F2oCN+T169fY9KkSejTpw/09fXLHGfPnj3Q1dWFpqYmfv75Zxw4cADVqlUrU6w5c+ZATU0No0ePLnN/3tW5c2esXbsWhw4dwpw5c3DkyBF4eXkhLy+v1LEeP36MzMxM/Pjjj+jcuTP279+PL774Aj179sSRI0c+qJ9r1qyBnp4eevbsWeYYixcvhouLC6pXrw4NDQ107twZS5cuRZs2bUody9nZGVZWVggODsa///6LnJwczJkzB//8849CvidUcnxdD5VbI0eOxNWrVz/4v8CcnJwQHx+P9PR0/P777wgICMCRI0dKnXD9/fffGDNmDA4cOFDo2o7SendEp27dumjatClq1qyJrVu3lum/jPPz89GoUSPMnj0bAFC/fn1cvXoVy5cvR0BAwAf1dfXq1fDy8nrv2pvibN26FRs2bMDGjRvh6uqK+Ph4jB07FhYWFmXq37p16xAUFARLS0uoqqqiQYMG6NOnDy5cuFDmPlY0b968wZdffglRFLFs2bIPiuXu7o74+Hg8ffoUK1euxJdffokzZ87AxMSkVHEuXLiAX375BRcvXizzKOh/+fv7S/9/nTp1ULduXdjZ2SEuLg7t27cvVaz8/HwAQPfu3TFu3DgAQL169XDy5EksX74cbdu2LXM/w8PD0a9fvw/658PixYtx+vRpREdHo2bNmjh69ChGjhwJCwuLUo+mq6urY8eOHRg8eDCMjIygqqqKDh06wMvLq8wPplDZcGSrkqlWrRpUVVXx6NEjmfpHjx7BzMzsE/VK3qhRo7Bnzx7ExsaievXqHxRLQ0MD9vb2aNiwIcLCwuDm5oZffvml1HEuXLiAx48fo0GDBlBTU4OamhqOHDmCRYsWQU1NrUz/lf0uQ0NDODo6IikpqUznm5ubyyWQtWrV+qCpSQC4d+8eDh48iCFDhnxQnIkTJ0pHt+rUqYMBAwZg3LhxZR4ltLOzw5EjR5CZmYm///4bZ8+exZs3b2Bra/tB/QQg/S6U5+9JQaJ17949HDhw4INGtQBAR0cH9vb2aNasGVavXg01NTWsXr261HGOHTuGx48fw8rKSvo9uXfvHiZMmABra+sP6mMBW1tbVKtWrUzflWrVqkFNTU3h35Vjx44hMTHxg74nr169wvfff48FCxbA29sbdevWxahRo9C7d2/Mnz+/TDEbNmyI+Ph4pKWlISUlBTExMXj27JlCvidUcky2KhkNDQ00bNgQhw4dktbl5+fj0KFDH7SOSVFEUcSoUaOwc+dOHD58GDY2Ngq/Rn5+vnQtQ2m0b98eV65cQXx8vLQ0atQI/fr1Q3x8PFRVVT+oX5mZmbh9+zbMzc3LdH7Lli3ltsm4efMmatas+UH9ioiIgImJCbp27fpBcV6+fAkVFdl/5KiqqkpHGspKR0cH5ubm+Pfff7Fv3z507979g+IBgI2NDczMzGS+JxkZGThz5ky5+J4UJFq3bt3CwYMHUbVqVYVfo6zfkwEDBuCvv/6S+Z5YWFhg4sSJ2Ldvn0L69s8//+DZs2dl+q5oaGigcePGCv+urF69Gg0bNizzOjfg7e/1zZs3SvmeGBgYwNjYGLdu3cL58+cV8j2hkuM0YiU0fvx4BAQEoFGjRmjSpAkWLlyIrKwsDBo0qNSxMjMzZf7r8u7du4iPj4eRkRGsrKxKHW/kyJHYuHEjoqKioKenJ10fY2BgAC0trVLHCw4OhpeXF6ysrPDixQts3LgRcXFxZfqHvp6entzaMR0dHVStWrVMa8r+97//wdvbGzVr1sTDhw8REhICVVVV9OnTp9SxAGDcuHFo0aIFZs+ejS+//BJnz57FihUrsGLFijLFA97+CzciIgIBAQFQU/uwf1x4e3tj1qxZsLKygqurKy5duoQFCxYgKCioTPH27dsHURTh5OSEpKQkTJw4Ec7OziX+O37f3+7YsWMxc+ZMODg4wMbGBlOmTIGFhQV69OhR6ljPnz/H/fv3pXthFfyL3szMrNCRsuLimZubo1evXrh48SL27NmDvLw86ffEyMgIGhoapYpXtWpVzJo1Cz4+PjA3N8fTp0+xdOlSPHjwoMhtPt53v/9N/tTV1WFmZgYnJ6dSxzMyMsK0adPg6+sLMzMz3L59G99++y3s7e3h6elZpv5NnDgRvXv3Rps2beDu7o6YmBjs3r0bcXFxpY4FvE3Et23bhp9++qnQ/pQmXtu2bTFx4kRoaWmhZs2aOHLkCNauXYsFCxaUKd62bdtgbGwMKysrXLlyBWPGjEGPHj3KtOCePsAnfRaSPpnFixeLVlZWooaGhtikSRPx9OnTZYoTGxsrApArAQEBZYpXWCwAYkRERJniBQUFiTVr1hQ1NDREY2NjsX379uL+/fvLFKswH7L1Q+/evUVzc3NRQ0NDtLS0FHv37i0mJSV9UH92794t1q5dW5RIJKKzs7O4YsWKD4q3b98+EYCYmJj4QXFEURQzMjLEMWPGiFZWVqKmpqZoa2srTp48WczOzi5TvC1btoi2traihoaGaGZmJo4cOVJMS0sr8fnv+9vNz88Xp0yZIpqamooSiURs3759kZ/D+2JFREQUejwkJKTU8Qq2jyisxMbGljreq1evxC+++EK0sLAQNTQ0RHNzc9HHx0c8e/ZsmT+7/3rf1g/FxXv58qXYqVMn0djYWFRXVxdr1qwpDh06VExNTf2g/q1evVq0t7cXNTU1RTc3N3HXrl1ljvXbb7+JWlpaJfr7e1+8lJQUMTAwULSwsBA1NTVFJycn8aeffipyy5X3xfvll1/E6tWri+rq6qKVlZX4ww8/lPk7R2UniCJXyREREREpC9dsERERESkRky0iIiIiJWKyRURERKRETLaIiIiIlIjJFhEREZESMdkiIiIiUiImW0RERERKxGSLiD47ycnJEAQB8fHxCospCAJ27dqlsHj04SIjI2FoaKiweHFxcRAEAWlpaQqLSQQw2aLPQGBgIARBkCtlfaHzfyn6H+iK8vz5c/Tr1w/6+vowNDTE4MGDkZmZWew5x48fR8uWLVG1alVoaWnB2dkZP//880fq8cdTo0YNpKSklOk1SsqQlJQEPT29Ev0dHT16FN7e3rCwsCgywRNFEVOnToW5uTm0tLTQoUMH3Lp1S/Edf0doaCjq1aun1GuUVu/evXHz5s1P3Q2i92KyRZ+Fzp07I+X/tXfnQVEc7R/AvyzCwu6ya0TkkgWyrmiMoIgoohgFXeN9RglXFDFiUFMmilZAQcATPFAMyh8eFB6xEo/Eo0qhjIioiLAUh1aKaEgpQlQsXBFZluf9Iz/mx8AugsjrW1Z/qqja6e55umeGkband7qykvfTHYtYd5VWq31nsQICAlBSUoJLly7ht99+w9WrV7FkyZJ29xGLxYiIiMDVq1dRVlaGqKgoREVFdWn9xP81DQ0NMDY2ho2NTZfXc3wXtFot/P39MWbMmA6Vf/nyJdzc3JCSkmKwzLZt25CcnIzU1FTcvHkTYrEYKpUK9fX176rZ//O0Wi3Mzc3Rp0+f990Uhnmz97xcEMN0WUhICM2YMcNg/unTp2no0KEkFArJ2dmZYmJiSKvVcvlJSUn06aefkkgkor59+1J4eDi9ePGCiPSvO9a8nh0AOnXqFK8umUzGrePYvIbd8ePHycfHh4RCIZeXlpZGAwYMIKFQSC4uLpSSktKpYy4tLSUAlJeXx6VduHCBjIyM6OHDh52KNWvWLAoMDDSYf/DgQZLJZHTx4kUaMGAAicViUqlU9OjRI66MvjUiZ8yYwVs/ztHRkeLi4igoKIjEYjHJ5XI6c+YMVVdX0/Tp00ksFtPgwYN5x0RElJ2dTaNHjyYzMzPq27cvLV++nDQaDS/uxo0bKSgoiCwsLHjrBxYUFHDliouLacqUKWRhYUESiYRGjx7NrUV569Yt8vPzI0tLS5JKpeTj40P5+fm8dui73h2xZs0aCgwM5M5jZ+irs6mpiWxsbGj79u1c2vPnz0koFNKxY8cMxtLpdLR161ZSKBRkampKDg4OFB8fz2unUqkkc3NzcnZ2pqioKGpoaCAi/Ws7Nv8u19TUUGhoKPXu3ZssLCxo3LhxVFhYyKs7Li6OrKysSCKRUGhoKEVGRpKbmxuvbbGxsWRvb0+mpqbk5uZGFy5c4PIN3Uv6zunZs2fJw8ODhEIhWVpa0syZM7m8I0eO0LBhw0gikZC1tTX5+/tTVVUVl998v9fU1Bg8jwzzNtjIFvNBy87ORnBwMFauXInS0lLs378fhw4dQkJCAldGIBAgOTkZJSUlOHz4MLKysrBmzRoAwKhRo7Br1y5IpVJuxOz777/vVBvWrl2LlStXoqysDCqVChkZGVi/fj0SEhJQVlaGTZs2ITo6GocPH+b2+eyzz/DVV18ZjJmbm4uePXvCw8ODS/Pz84NAIMDNmzc73LaCggJcv34dY8eObbdcXV0dEhMTkZ6ejqtXr6KioqLT5wEAdu7cCW9vbxQUFGDKlCkICgpCcHAwAgMDcefOHSgUCgQHB4P+b8nW8vJyTJo0CXPmzEFRURFOnDiBa9euISIighc3MTERbm5uKCgoQHR0dJt6Hz58CB8fHwiFQmRlZSE/Px+LFi1CY2MjAODFixcICQnBtWvXcOPGDSiVSkyePBkvXrzo9DG2lJWVhZMnT7Y7StVZ9+/fx+PHj+Hn58elyWQyjBgxArm5uQb3W7duHbZs2YLo6GiUlpbi6NGjsLa25vItLCxw6NAhlJaWYvfu3UhLS+MeMc+fPx/fffcdBg0axN0H8+fPBwDMmzcP1dXVuHDhAvLz8+Hu7g5fX188e/YMAJCRkYGEhARs3boV+fn5kMvl+PHHH3lt2717N5KSkpCYmIiioiKoVCpMnz69zaPR1vdSa+fOncOsWbMwefJkFBQUIDMzE56enly+VqtFXFwc1Go1Tp8+jQcPHrR7nzHMO/O+e3sM01UhISFkbGxMYrGY+5k7dy4REfn6+tKmTZt45dPT08nW1tZgvJMnT5KlpSW3bWhEAh0c2dq1axevjEKhoKNHj/LS4uLiyMvLi9sOCgqitWvXGmxjQkIC9e/fv026lZUV7du3z+B+zZpHEAQCAW3cuLHdss2jGs2jQEREKSkpZG1tzW13dGSr5QhaZWUlAaDo6GguLTc3lwBQZWUlERGFhobSkiVLeHGzs7NJIBDQq1evuLgtRy+IqM3I1rp168jZ2ZkbqXkTnU5HFhYW9Ouvv3Jp+q53e548eUIODg70+++/E5Hh36P26KszJyeHAPBGFomI5s2bR1988YXeOLW1tSQUCiktLa3DdW/fvp2GDRvGbW/YsIE3GkX077WQSqVUX1/PS1coFLR//34iIhoxYgR98803vHxvb29eLDs7O0pISOCVGT58OC1btoyIDN9Lrc+pl5cXBQQEdPgY8/LyCECbkWw2ssW8a+9/QgPDvAPjxo3j/W9ZLBYDANRqNXJycngjWTqdDvX19airq4NIJMLly5exefNm3L17F7W1tWhsbOTld1XL0aeXL1+ivLwcoaGhCAsL49IbGxshk8m47SNHjnS5XolEwn0ODAxEamoqt52dnQ2NRoMbN25g7dq16NevH/z9/Q3GEolEUCgU3LatrS2qq6s73SZXV1fuc/OoyuDBg9ukVVdXw8bGBmq1GkVFRcjIyODKEBGamppw//59DBw4EAD/HOtTWFiIMWPGwMTERG9+VVUVoqKicOXKFVRXV0On06Gurg4VFRWdPsZmYWFh+PLLL+Hj46M3Pzs7G59//jm3vX//fgQEBLx1fe0pKyvD69ev4evra7DMiRMnkJycjPLycmg0GjQ2NkIqlbYbV61WQ6PRwNLSkpf+6tUrlJeXAwDu3buHZcuW8fI9PT2RlZUFAKitrcWjR4/g7e3NK+Pt7Q21Ws1L68h1bnlftZafn4+YmBio1WrU1NSgqakJAFBRUYFPPvmk3dgM0xWss8V8EMRiMfr169cmXaPRIDY2FrNnz26TZ2ZmhgcPHmDq1KkIDw9HQkICevXqhWvXriE0NBQNDQ3tdraMjIy4x13N9E2Ab+74NbcHANLS0jBixAheOWNj4/YPsgUbG5s2nZ3GxkY8e/YMNjY2AMB77UHrP5rNXx4YPHgwqqqqEBMT025nq3UnpfWxCwSCDp2LlnGMjIwMpjX/EdRoNPj666+xYsWKNrHkcjn3ueU51sfc3Lzd/JCQEDx9+hS7d++Go6MjhEIhvLy80NDQ0O5+7cnKysLZs2eRmJgI4P87iT169MCBAwfg7+/Pu0YtH+m1p/n6VlVVwdbWlkuvqqoy+G3BNx1/bm4uAgICEBsbC5VKBZlMhuPHjyMpKand/TQaDWxtbXHlypU2ed3xDd6uXOeXL19CpVJxj/KtrKxQUVEBlUrVpevMMB3BOlvMB83d3R337t3T2xED/v2fblNTE5KSkiAQ/DuF8aeffuKVMTU1hU6na7OvlZUVKisrue0//vgDdXV17bbH2toadnZ2+PPPP7s0iuHl5YXnz58jPz8fw4YNA/DvH/empiauE2fomFtramrC69ev37otQNtzodPpUFxcjHHjxnUprru7O0pLSzt8LIa4urri8OHD0Gq1eke3cnJysG/fPkyePBkA8Pfff+PJkyddqjM3N5f3e3PmzBls3boV169fh729PczNzd/quJydnWFjY4PMzEyuc1VbW4ubN28iPDxc7z5KpRLm5ubIzMzE4sWL2+Rfv34djo6O+OGHH7i0v/76i1dG333g7u6Ox48fo0ePHnByctJbt4uLC/Ly8hAcHMyl5eXlcZ+lUins7OyQk5PDmzuYk5PDm2/VEa6ursjMzMTChQvb5N29exdPnz7Fli1b4ODgAAC4fft2p+IzzNtinS3mg7Z+/XpMnToVcrkcc+fOhUAggFqtRnFxMeLj49GvXz9otVrs2bMH06ZNQ05ODu9xGwA4OTlBo9EgMzMTbm5uEIlEEIlEGD9+PPbu3QsvLy/odDpERkYafEzVUmxsLFasWAGZTIZJkybh9evXuH37NmpqarBq1SoAQHBwMOzt7bF582a9MQYOHIhJkyYhLCwMqamp0Gq1iIiIwIIFC2BnZ2ew7pSUFMjlcgwYMADAv+90SkxM1Dty1Bnjx4/HqlWrcO7cOSgUCuzYseOdvBgyMjISI0eOREREBBYvXgyxWIzS0lJcunQJe/fu7XCciIgI7NmzBwsWLMC6desgk8lw48YNeHp6wsXFBUqlEunp6fDw8EBtbS1Wr179xtGgN2l+xNns9u3bEAgEb3z3l0aj4b0j7v79+ygsLESvXr0gl8thZGSEb7/9FvHx8VAqlXB2dkZ0dDTs7Owwc+ZMvTHNzMwQGRmJNWvWwNTUFN7e3vjnn39QUlKC0NBQKJVKVFRU4Pjx4xg+fDjOnTuHU6dO8WI4OTlxbenbty8sLCzg5+cHLy8vzJw5E9u2bUP//v3x6NEjbqK6h4cHli9fjrCwMHh4eGDUqFE4ceIEioqK8PHHH3OxV69ejQ0bNkChUGDIkCE4ePAgCgsLeY+PO2LDhg3w9fWFQqHAggUL0NjYiPPnzyMyMhJyuRympqbYs2cPli5diuLiYsTFxXUqPsO8tfc6Y4xh3oE3vfrh4sWLNGrUKDI3NyepVEqenp504MABLn/Hjh1ka2tL5ubmpFKp6MiRI20myS5dupQsLS15r354+PAhTZw4kcRiMSmVSjp//rzeCfItXz/QLCMjg4YMGUKmpqb00UcfkY+PD/3yyy9c/tixY3mTy/V5+vQp+fv7k0QiIalUSgsXLuQm+hqSnJxMgwYNIpFIRFKplIYOHUr79u0jnU5ncB99E7tPnTpFLf/5aGhooPDwcOrVqxf16dOHNm/erHeC/M6dO3lx0GoCuL5zduvWLZowYQJJJBISi8Xk6urKm0ytL66+OGq1miZOnEgikYgsLCxozJgxVF5eTkREd+7cIQ8PDzIzMyOlUkknT55sE7d1WztyjVrq6AR5fa8bAcCrq6mpiaKjo8na2pqEQiH5+vrSvXv32o2r0+koPj6eHB0dycTEhORyOe/LI6tXryZLS0uSSCQ0f/582rlzJ6+99fX1NGfOHOrZsyfv1Q+1tbW0fPlysrOzIxMTE3JwcKCAgACqqKjg9t24cSP17t2bJBIJLVq0iFasWEEjR47ktS0mJobs7e3JxMTE4KsfWt9L+s7pzz//zN1bvXv3ptmzZ3N5R48eJScnJxIKheTl5UVnz57lxWUT5JnuYkTUaqIFwzAM80aOjo6IjY1lrw54CxMmTICNjQ3S09Pfd1MY5r+CPUZkGIbppJKSEshkMt48JEa/uro6pKamQqVSwdjYGMeOHcPly5dx6dKl9900hvmvYSNbDMMwTLd59eoVpk2bhoKCAtTX18PFxQVRUVF6vyHMMB8q1tliGIZhGIbpRmy5HoZhGIZhmG7EOlsMwzAMwzDdiHW2GIZhGIZhuhHrbDEMwzAMw3Qj1tliGIZhGIbpRqyzxTAMwzAM041YZ4thGIZhGKYbsc4WwzAMwzBMN2KdLYZhGIZhmG70H2qugkYE1tK+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# turn 2d list into heatmap, x-axis is the feature name, y-axis is the attack name\n",
    "\n",
    "count_list = np.array(count_list)\n",
    "print(count_list)\n",
    "ax = sns.heatmap(count_list, cmap=\"YlGnBu\",  linewidth=.7)\n",
    "# set y-axis labels\n",
    "# x_labels = [\"N\", \"N\", \"N\", \"N\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\", \"C\"]\n",
    "y_labels = [\"DeepFool/LR\", \"DeepFool/SVC\", \"DeepFool/MLP\", \"C&W/LR\", \"C&W/SVC\", \"C&W/MLP\", \"FGSM/LR\", \"FGSM/SVC\", \"FGSM/MLP\", \"PGD/LR\", \"PGD/SVC\", \"PGD/MLP\"]\n",
    "ax.set_yticklabels(y_labels, rotation=0)\n",
    "# ax.set_xticklabels(x_labels, rotation=0)\n",
    "ax.set_xlabel('Feature: 0-3 numerical, 4-10 categorical')\n",
    "\n",
    "# set title\n",
    "plt.title('COMPAS Dataset')\n",
    "\n",
    "plt.savefig('Visualisation/sparsity_compas.png', dpi=300, bbox_inches = 'tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}