Stock_Price_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color:black' align='center'>Stock Price Prediction</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Importing the Libraries</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Download Dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# !pip install quandl\n",
    "# import quandl\n",
    "\n",
    "start = '2013-01-01'\n",
    "end = '2023-12-31'\n",
    "stock = 'GOOG'\n",
    "\n",
    "stock_data = yf.download(stock, start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Load the Dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>17.918339</td>\n",
       "      <td>18.107130</td>\n",
       "      <td>17.846855</td>\n",
       "      <td>18.013729</td>\n",
       "      <td>18.013729</td>\n",
       "      <td>102033017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>18.055573</td>\n",
       "      <td>18.229919</td>\n",
       "      <td>17.950716</td>\n",
       "      <td>18.024191</td>\n",
       "      <td>18.024191</td>\n",
       "      <td>93075567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>18.165413</td>\n",
       "      <td>18.467529</td>\n",
       "      <td>18.124067</td>\n",
       "      <td>18.380356</td>\n",
       "      <td>18.380356</td>\n",
       "      <td>110954331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>18.317591</td>\n",
       "      <td>18.415474</td>\n",
       "      <td>18.196297</td>\n",
       "      <td>18.300158</td>\n",
       "      <td>18.300158</td>\n",
       "      <td>66476239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>18.319834</td>\n",
       "      <td>18.338762</td>\n",
       "      <td>18.043119</td>\n",
       "      <td>18.264042</td>\n",
       "      <td>18.264042</td>\n",
       "      <td>67295297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2763</th>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>142.130005</td>\n",
       "      <td>143.250000</td>\n",
       "      <td>142.054993</td>\n",
       "      <td>142.720001</td>\n",
       "      <td>142.720001</td>\n",
       "      <td>18494700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2764</th>\n",
       "      <td>2023-12-26</td>\n",
       "      <td>142.979996</td>\n",
       "      <td>143.945007</td>\n",
       "      <td>142.500000</td>\n",
       "      <td>142.820007</td>\n",
       "      <td>142.820007</td>\n",
       "      <td>11170100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2765</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>142.830002</td>\n",
       "      <td>143.320007</td>\n",
       "      <td>141.050995</td>\n",
       "      <td>141.440002</td>\n",
       "      <td>141.440002</td>\n",
       "      <td>17288400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2766</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>141.850006</td>\n",
       "      <td>142.270004</td>\n",
       "      <td>140.828003</td>\n",
       "      <td>141.279999</td>\n",
       "      <td>141.279999</td>\n",
       "      <td>12192500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2767</th>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>140.679993</td>\n",
       "      <td>141.434998</td>\n",
       "      <td>139.899994</td>\n",
       "      <td>140.929993</td>\n",
       "      <td>140.929993</td>\n",
       "      <td>14872700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2768 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2013-01-02   17.918339   18.107130   17.846855   18.013729   18.013729   \n",
       "1    2013-01-03   18.055573   18.229919   17.950716   18.024191   18.024191   \n",
       "2    2013-01-04   18.165413   18.467529   18.124067   18.380356   18.380356   \n",
       "3    2013-01-07   18.317591   18.415474   18.196297   18.300158   18.300158   \n",
       "4    2013-01-08   18.319834   18.338762   18.043119   18.264042   18.264042   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "2763 2023-12-22  142.130005  143.250000  142.054993  142.720001  142.720001   \n",
       "2764 2023-12-26  142.979996  143.945007  142.500000  142.820007  142.820007   \n",
       "2765 2023-12-27  142.830002  143.320007  141.050995  141.440002  141.440002   \n",
       "2766 2023-12-28  141.850006  142.270004  140.828003  141.279999  141.279999   \n",
       "2767 2023-12-29  140.679993  141.434998  139.899994  140.929993  140.929993   \n",
       "\n",
       "         Volume  \n",
       "0     102033017  \n",
       "1      93075567  \n",
       "2     110954331  \n",
       "3      66476239  \n",
       "4      67295297  \n",
       "...         ...  \n",
       "2763   18494700  \n",
       "2764   11170100  \n",
       "2765   17288400  \n",
       "2766   12192500  \n",
       "2767   14872700  \n",
       "\n",
       "[2768 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = quandl.get('NSE/TATAGLOBAL')\n",
    "stock_data.reset_index(inplace=True)\n",
    "stock_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ma_100_days = stock_data['Close'].rolling(100).mean()\n",
    "ma_200_days = stock_data.Close.rolling(200).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x206b4a336a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69Nlnn9l83kbeU2pqqjRp0iQpNjZW8vX1lapXry6NGjVKOnv2rN33feDAAal79+5SQECAFBkZKU2YMEFKS0tTjhsteNC1a1cJgM3PqVOnlHP2798vderUSfL395eqVq0qvfPOOzav88svv0j169eX/Pz8pMaNG0tLly51eu+33npLqlixohQSEiKNGTNGevbZZzW/M0a/W4vFIsXFxUn9+/fX7D9y5IjUp08fqVKlSpK/v79Uv359pRiFHk/9N05ERERFw5WCByZJ8rx6sqmpqahQoQJSUlIQFhamOZadnY1Tp06hVq1aCAgIKKEWli/nz59H9erVsXr1avTs2bOkm+MWZfE9lbT09HRUrVoV3333He6+++5Cvw7/jRMREZGao9jAWvkY80NutXbtWqSnp6NJkya4dOkSnn32WdSsWRNdunQp6aYVWll8T6WFxWLBtWvX8OGHHyI8PBx33XVXSTeJiIiIyikGP+Qys9mMF198Ef/++y9CQ0PRoUMHzJs3z6ZKnCcpi++ptDh79ixq1aqFatWqYfbs2fDx4X92iIiIqGSw2xsReRT+GyciKh6SJOGplU+hZnhNPHn7kyXdHCK72O2NiIiIiG7JgcsHMH37dADAE+2ecFjVlchTsNQ1EREREdkwW8zKekpOSgm2hMh9GPwQERERkQ31yIhrmdccnEnkORj8EBEREZGNg1cOKuvXM6+XYEuI3IfBDxERERFpJGUlYdwf45Tt3PzcEmwNkfsw+CEiIiIixeX0y4h6L0qzTz3+h8iTMfgp56ZOnYrmzZuXdDPKtalTpyI6OhomkwmLFy8u6eY49eCDD2Lw4MEl3QwiIioiCxIW2Oxj5ofKCgY/5dzTTz+NNWvWlHQzyq2EhAS89tpr+PLLL3Hp0iX069fP5pz9+/dj5MiRqF69OgIDAxEfH4/p06fbnLd+/Xq0bNkS/v7+qFu3LmbPnm1zzowZM1CzZk0EBASgXbt22LFjR1G8LSIi8mBnks/Y7DPnm3Eu5Rxm75uNPEteCbSKyD0Y/JRzISEhiIqKcn6iB5IkCXl5pfs/0CdPngQADBo0CFWqVIG/v7/NObt370blypXxww8/4PDhw3jppZfwwgsv4LPPPlPOOXXqFAYMGIDu3btj3759ePLJJzF+/HisXLlSOefnn3/G5MmTMWXKFOzZswfNmjVDnz59cOXKlaJ/o0RE5DHOp5232We2mFFjWg089PtDCPlfSAm0isg9GPyUEt26dcOkSZPw5JNPIiIiAtHR0fjqq6+QkZGBhx56CKGhoahbty6WL1+uue7vv/9G27Zt4e/vj5iYGDz//PPKA/+sWbMQGxsLi8WiuWbQoEEYO3YsANtub3KXpg8++AAxMTGIiorCo48+CrO5oK/vpUuXMGDAAAQGBqJWrVqYP38+atasiWnTptl9fzt37kTv3r1RsWJFVKhQAV27dsWePXuU4/fddx/uvfdezTVmsxkVK1bE3LlzAQAWiwVvv/02atWqhcDAQDRr1gy//fabcv769ethMpmwfPlytGrVCv7+/ti0aRNOnjyJQYMGITo6GiEhIWjTpg1Wr16tuZeR95ScnIzx48ejUqVKCAsLQ48ePbB//3677xkADh48iB49eiAwMBBRUVGYOHEi0tPTlc9+4MCBAAAvLy+7k8eNHTsW06dPR9euXVG7dm3cf//9eOihh7Bw4ULlnJkzZ6JWrVr48MMPER8fj8ceewz33HMPPv74Y+Wcjz76CBMmTMBDDz2ERo0aYebMmQgKCsK3335rt/35+fmYPHkywsPDERUVhWeffVZT+hQAVqxYgU6dOinn3HnnnUpQBwA9evTAY489prnm6tWr8PPzU7KOn3/+OerVq4eAgABER0fjnnvucfi5EhFR0TiTfAbzD8632a/u9paTn1OcTSJyq3IR/EgSkJFR/D9Wz4hOzZkzBxUrVsSOHTswadIk/N///R+GDRuGDh06YM+ePbjjjjswevRoZGZmAgAuXLiA/v37o02bNti/fz+++OILfPPNN3jzzTcBAMOGDcP169exbt065R5JSUlYsWIFRo0aZbcd69atw8mTJ7Fu3TrMmTMHs2fP1nSheuCBB3Dx4kWsX78eCxYswKxZs5xmD9LS0jBmzBhs2rQJ27ZtQ7169dC/f3+kpaUBAEaNGoU///xTCQwAYOXKlcjMzMSQIUMAAG+//Tbmzp2LmTNn4vDhw3jqqadw//334++//9bc6/nnn8c777yDhIQENG3aFOnp6ejfvz/WrFmDvXv3om/fvhg4cCDOnj3r0nsaNmwYrly5guXLl2P37t1o2bIlevbsiaSkJN33nJGRgT59+iAiIgI7d+7Er7/+itWrVyuBwNNPP43vvvsOgAi+Ll265PAzVEtJSUFkZKSyvXXrVvTq1UtzTp8+fbB161YAQG5uLnbv3q05x8vLC7169VLO0fPhhx9i9uzZ+Pbbb7Fp0yYkJSVh0aJFNu9z8uTJ2LVrF9asWQMvLy8MGTJECbrHjx+P+fPnIyen4H+WP/zwA6pWrYoePXpg165dePzxx/H666/j6NGjWLFiBbp06WL4syAiIve57YvbdPfn5GkDnq3ntuKF1S8gOy/b5txfDv+CjWc2Fkn7iG6Z5IFSUlIkAFJKSorNsaysLOnIkSNSVlaWsi89XZJEKFK8P+npxt9T165dpU6dOinbeXl5UnBwsDR69Ghl36VLlyQA0tatWyVJkqQXX3xRatCggWSxWJRzZsyYIYWEhEj5+fmSJEnSoEGDpLFjxyrHv/zySyk2NlY5PmXKFKlZs2bK8TFjxkhxcXFSXl6esm/YsGHSvffeK0mSJCUkJEgApJ07dyrHjx8/LgGQPv74Y8PvNz8/XwoNDZX+/PNPSZIkyWw2SxUrVpTmzp2rnDNy5EjlvtnZ2VJQUJC0ZcsWzeuMGzdOGjlypCRJkrRu3ToJgLR48WKn92/cuLH06aefGn5PGzdulMLCwqTs7GzN69SpU0f68ssvde8xa9YsKSIiQkpX/SIsXbpU8vLykhITEyVJkqRFixZJrv4z3Lx5s+Tj4yOtXLlS2VevXj3pf//7n+a8pUuXSgCkzMxM6cKFCxIAm8/vmWeekdq2bWv3XjExMdJ7772nbJvNZqlatWrSoEGD7F5z9epVCYB08OBBSZLEv8mIiAjp559/Vs5p2rSpNHXqVEmSJGnBggVSWFiYlJqa6vzNS/r/xomIyD0wFbo/H275UHf/2xvf1lyfcDVBOUZUXBzFBtbKRebHUzRt2lRZ9/b2RlRUFJo0aaLsi46OBgAlI5GQkID27dtrukt17NgR6enpOH9e9NcdNWoUFixYoPzVfd68eRgxYgS8vOx/9Y0bN4a3t7eyHRMTo9zz6NGj8PHxQcuWLZXjdevWRUREhMP3dvnyZUyYMAH16tVDhQoVEBYWhvT0dCX74uPjg+HDh2PevHkARDbh999/VzJUJ06cQGZmJnr37o2QkBDlZ+7cuZouVgDQunVrzXZ6ejqefvppxMfHIzw8HCEhIUhISFDubeQ97d+/H+np6YiKitLc/9SpUzb3lyUkJKBZs2YIDg5W9nXs2BEWiwVHjx51+HnZc+jQIQwaNAhTpkzBHXfcUajXMColJQWXLl1Cu3btlH0+Pj42n+/x48cxcuRI1K5dG2FhYahZsyYAKJ9vQEAARo8erXSv27NnDw4dOoQHH3wQANC7d2/ExcWhdu3aGD16NObNm6dkN4mIqHTIyM1Q1isHV1bWj17X/v/sbMpZEJVmPiXdgOIQFASoelMV631d4evrq9k2mUyafXKQYz2Gx5GBAwdCkiQsXboUbdq0wcaNGzXjQIy2w5V76hkzZgyuX7+O6dOnIy4uDv7+/mjfvj1ycwv6EI8aNQpdu3bFlStXsGrVKgQGBqJv374AoHSHW7p0KapWrap5besiAepgAxDdy1atWoUPPvgAdevWRWBgIO655x7NvZ1JT09HTEwM1q9fb3MsPDzc8OvciiNHjqBnz56YOHEiXn75Zc2xKlWq4PLly5p9ly9fRlhYGAIDA+Ht7Q1vb2/dc6pUqXJL7Ro4cCDi4uLw1VdfKWPMbrvtNs3nO378eDRv3hznz5/Hd999hx49eiAuLg4AEBoaij179mD9+vX466+/8Oqrr2Lq1KnYuXNnsX22RETkWFZelrJ+JaOgW7i/t/b/wV4m/l2dSrdyEfyYTIDV83CZEB8fjwULFkCSJCUw2rx5M0JDQ1GtWjUA4q/ud999N+bNm4cTJ06gQYMGmgyHqxo0aIC8vDzs3bsXrVq1AiCyMjdu3HB43ebNm/H555+jf//+AIBz587h2rVrmnM6dOiA6tWr4+eff8by5csxbNgwJRBr1KgR/P39cfbsWXTt2tWlNm/evBkPPvigMnYoPT0dp0+fduk9tWzZEomJifDx8VEyG87Ex8dj9uzZyMjIUAKyzZs3w8vLCw0aNHDpPRw+fBg9evTAmDFj8NZbb9kcb9++PZYtW6bZt2rVKrRv3x4A4Ofnh1atWmHNmjXKHD0WiwVr1qyxKUYgq1ChAmJiYrB9+3ZlDE5eXp4y3gkArl+/jqNHj+Krr75C586dAQCbNm2yea0mTZqgdevW+OqrrzB//nxNpTpAZJR69eqFXr16YcqUKQgPD8fatWtx9913u/ApERFRUckyZ+nutw5+TNAv3kNUWpSL4KeseuSRRzBt2jRMmjQJjz32GI4ePYopU6Zg8uTJmm5to0aNwp133onDhw/j/vvvv6V7NmzYEL169cLEiRPxxRdfwNfXF//9738RGBhot1oZANSrVw/ff/89WrdujdTUVDzzzDMIDAy0Oe++++7DzJkzcezYMU2hhtDQUDz99NN46qmnYLFY0KlTJ6SkpGDz5s0ICwvDmDFjHN574cKFGDhwIEwmE1555RVNJsvIe+rVqxfat2+PwYMH47333kP9+vVx8eJFLF26FEOGDLHpCgaIz33KlCkYM2YMpk6diqtXr2LSpEkYPXq00oXRiEOHDqFHjx7o06cPJk+ejMTERACia2SlSpUAAP/5z3/w2Wef4dlnn8XYsWOxdu1a/PLLL1i6dKnyOpMnT8aYMWPQunVrtG3bFtOmTVOqCdrzxBNP4J133kG9evXQsGFDfPTRR0hOTlaOR0REICoqCrNmzUJMTAzOnj2L559/Xve1xo8fj8ceewzBwcFKIAoAS5Yswb///osuXbogIiICy5Ytg8VicTlAJCKiopOck6y7P9hP+9dlR88CRKUBc5MerGrVqli2bBl27NiBZs2a4T//+Q/GjRtn0yWqR48eiIyMxNGjR3Hffffd8n3nzp2L6OhodOnSBUOGDMGECRMQGhqKgIAAu9d88803uHHjBlq2bInRo0fj8ccfR+XKlW3OGzVqFI4cOYKqVauiY8eOmmNvvPEGXnnlFbz99tuIj49H3759sXTpUtSqVcthez/66CNERESgQ4cOGDhwIPr06WOT/XL2nkwmE5YtW4YuXbrgoYceQv369TFixAicOXPGbiATFBSElStXIikpCW3atME999yDnj172mQ9nPntt99w9epV/PDDD4iJiVF+2rRpo5xTq1YtLF26FKtWrUKzZs3w4Ycf4uuvv0afPn2Uc+6991588MEHePXVV9G8eXPs27cPK1ascBiI/fe//8Xo0aMxZswYtG/fHqGhoZrAxcvLCz/99BN2796N2267DU899RTef/993dcaOXIkfHx8MHLkSM3vSnh4OBYuXIgePXogPj4eM2fOxI8//ojGjRu79DkREVHR+X7/97r7rTM96m3J1bK3RMXAJHngb2ZqaioqVKiAlJQUhIWFaY5lZ2fj1KlTqFWrlsOHcXKf8+fPo3r16li9ejV69uxZ0s1xi7L4nkra6dOnUadOHezcufOWul7y3zgRUdHIt+TD5w1tp6A6EXVw8oZ+YR+ZNKXgUfLv03+j25xuAIC8V/Lg7eWtfxGRGzmKDayx2xu5bO3atUhPT0eTJk1w6dIlPPvss6hZs6ZHz81SFt9TaWE2m3H9+nW8/PLLuP32228p8CEioqKz4cwGzfYXA77AwPoDUe3jaoZfQ93tLV/KhzcY/FDpwm5v5DKz2YwXX3wRjRs3xpAhQ1CpUiWsX7/epkqcJymL76m02Lx5M2JiYrBz507MnDmzpJtDRFQuHb9+HInpiQ7PCfDRZtMfbvUwAn1tx+daUxdDUFd7y7fku9hKoqLHzA+5rE+fPpqxJGVBWXxPpUW3bt3Y75uIqARdTLuI+p/Vh5+3H3JezrF7nnWxApPJBB8v54+K17Ouo5qvyA6px/zkSwx+qPRh5oeIiIioDHt/syhEk5ufi1m7Z9k9zyLZzulnJPhJzy2YTJGZHyrtGPwQERERlWEJ1xKU9YeXPIzX/35dN9ApbPCTkZuhrFuP+SEqbRj8EBEREZVhufm5mu0p66fghwM/2Jynl6kxEvx8suMT3f16wRRRSWPwQ0RERFSGZZgzbPYdunLIZp9esKLuxmbP3P1zdfez2xuVRgx+iIiIiMqwG1k3bPZlmjNt9rkjU6MucMNub1QaMfghIiIiKsPSctNs9ukFP4UNVmqG11TW1QEUMz9UGjH48TAmkwmLFy92y2tNnToVzZs3d8trFYVu3brhySefLOlmEBEReTTrMT8AIMF2CgIjmR/1GKDWsa0BaLvGqV+XmR8qjRj8lCKJiYmYNGkSateuDX9/f1SvXh0DBw7EmjVriuR+Tz/9dJG9ttrUqVNhMpnEfAE+PqhZsyaeeuoppKenO7xu4cKFeOONN4q8fURERGVZTp7t3D7eJm/N9u///I4B8wc4fS31RKghfiEAtEGTq5kfFkWg4sbgp5Q4ffo0WrVqhbVr1+L999/HwYMHsWLFCnTv3h2PPvpokdwzJCQEUVFRRfLa1ho3boxLly7h9OnTePfddzFr1iz897//1T03N1f8hSoyMhKhoaHF0j4iIqKySs78VAqqpOyzLmQw+OfBmkCkTkQd3ddSz+kT7BsMALAk3wAeeQTYssWlMT9bzm1B+DvhDuceInI3Bj+lxCOPPAKTyYQdO3Zg6NChqF+/Pho3bozJkydj27Ztdq87ePAgevTogcDAQERFRWHixImajMr69evRtm1bBAcHIzw8HB07dsSZM2cA2HZ7e/DBBzF48GB88MEHiImJQVRUFB599FGYzWblnEuXLmHAgAEIDAxErVq1MH/+fNSsWRPTpk1z+P58fHxQpUoVVKtWDffeey9GjRqFP/74Q9OOr7/+GrVq1UJAgPirknW3t5ycHDz33HOoXr06/P39UbduXXzzzTfK8UOHDqFfv34ICQlBdHQ0Ro8ejWvXrjn97ImIiMoqi2SB2SL+P34186qy31kVt01jNzl97aDjp8U9UlKAL74AevSA5dg/mnvbk5OXg47fdkRabhoeXvKw03sRuYvz4u1lgCRJugP7ilqQb5Bmsi97kpKSsGLFCrz11lsIDg62OR4eHq57XUZGBvr06YP27dtj586duHLlCsaPH4/HHnsMs2fPRl5eHgYPHowJEybgxx9/RG5uLnbs2OGwTevWrUNMTAzWrVuHEydO4N5770Xz5s0xYcIEAMADDzyAa9euYf369fD19cXkyZNx5coVYx+ISmBgoJLhAYATJ05gwYIFWLhwIby9vXWveeCBB7B161Z88sknaNasGU6dOqUEN8nJyejRowfGjx+Pjz/+GFlZWXjuuecwfPhwrF271uX2ERERlQXmfLPufkdd0jpW74gqIVWcvnbQvsNAcyDf3xeoFg2cPw/p+++BuuJ4ljnL7rVvbnjT6esTFYVyEfxkmjMR8nZIsd83/YV0BPvZBjPWTpw4AUmS0LBhQ5def/78+cjOzsbcuXOVoOmzzz7DwIED8e6778LX1xcpKSm48847UaeOSF/Hx8c7fM2IiAh89tln8Pb2RsOGDTFgwACsWbMGEyZMwD///IPVq1dj586daN1aDHL8+uuvUa9ePZfavXv3bsyfPx89evRQ9uXm5mLu3LmoVKmS7jXHjh3DL7/8glWrVqFXr14AgNq1ayvHP/vsM7Ro0QL/+9//lH3ffvstqlevjmPHjqF+/foutZGIiKgsWPXvKt39cjZIj5G5fQDA92b8ZImKBFatAuLjYdmyWQl+3tn8Dn6+52fda2fvn23oHlQ6LTu+DBfTLmJ8y/El3RSXsdtbKaDuH+uKhIQENGvWTJMt6tixIywWC44ePYrIyEg8+OCD6NOnDwYOHIjp06fj0qVLDl+zcePGmsxLTEyMktk5evQofHx80LJlS+V43bp1ERER4bStBw8eREhICAIDA9G2bVu0b98en332mXI8Li7ObuADAPv27YO3tze6du2qe3z//v1Yt24dQkJClB85mDx58qTT9hEREZVFY38fq7t/zv45eHbVs7rHjAY/Fn9fsZQsQMOGwB13aGrILUxYaPfaUD+O6fVUiemJGDB/ACb8OQFnks+UdHNcVi4yP0G+QUh/wXFlsaK6rxH16tWDyWTCP//84/xkF3333Xd4/PHHsWLFCvz88894+eWXsWrVKtx+++265/v6+mq2TSYTLJZbr8TSoEED/PHHH/Dx8UFsbCz8/Pw0x/W6+6kFBgY6PJ6enq5kvKzFxMS43mAiIqIyIDk72e6x97e8j1e7vgpfL+3/+62DnyfbPYlp26cBACJ8QnEjT8wblNiiHmA+UjC257HHYHnqL+W6B5o+YPfe6qpx5FnUAc/1rOuIC48rwda4rlxkfkwmE4L9gov9x8h4H0BUNevTpw9mzJiBjIwMm+PJycm618XHx2P//v2aazZv3gwvLy80aNBA2deiRQu88MIL2LJlC2677TbMnz/ftQ/wpgYNGiAvLw979+5V9p04cQI3btjOHG3Nz88PdevWRc2aNW0CHyOaNGkCi8WCv//+W/d4y5YtcfjwYdSsWRN169bV/DgLrIiIiMoqR93bACAtJw2pOamafd5e2rG3XeK6KOvLfvRCh7Ni/b47XwCgqurWvz+k6MrKudX/2gacO6d7X725h8gzqMfRZ+TaPreWduUi+PEEM2bMQH5+Ptq2bYsFCxbg+PHjSEhIwCeffIL27dvrXjNq1CgEBARgzJgxOHToENatW4dJkyZh9OjRiI6OxqlTp/DCCy9g69atOHPmDP766y8cP37c6bgfexo2bIhevXph4sSJ2LFjB/bu3YuJEyciMDDQcKBXWDVr1sSYMWMwduxYLF68GKdOncL69evxyy+/AAAeffRRJCUlYeTIkdi5cydOnjyJlStX4qGHHkJ+PidZIyKi8ql2RG2Hx1NzUm2CH+vMjzyfDwA0Pp6CNetr4N+JR9C2ajsAqqpu3t6wjL5fOTc/4QjQsSNw9KjNfRn8eK4Mc0HAoy597ikY/JQStWvXxp49e9C9e3f897//xW233YbevXtjzZo1+OKLL3SvCQoKwsqVK5GUlIQ2bdrgnnvuQc+ePZWxNEFBQfjnn3+U0tkTJ07Eo48+iocfLnxJyblz5yI6OhpdunTBkCFDMGHCBISGhirlqYvSF198gXvuuQePPPIIGjZsiAkTJihZr9jYWGzevBn5+fm444470KRJEzz55JMIDw+Hlxd/zYmIqHyKCRFdv9/s/ib+0+o/NsfTctOQkpOi2eco+PHPBwJGP4RaMfHKeak5qXhyxZNIyU6BpCpmlBdRQWR+OnYEvvwSUE2dcT3r+q2/Obpl5nwz+v7QF1PWTTF8jSbzY/a8zI9JKuxo+xKUmpqKChUqICUlBWFhYZpj2dnZOHXqlGa+GCo658+fR/Xq1bF69Wr07NmzpJtD5QD/jRMRGdd8ZnPsv7wfK+9fiZ61euLtTW/jlXWvKMdXj16NlJwUDP1lqLJvQL0BWHLfEmX7QuoFVPu4GgDAMhUwnTwJ1K6N08mnUWt6LeW8R1o/gr51++Kun+5S9mX/2Qz+u/eLje7dgWXLIPn7w+t1bYAlTfG4x9EyYfE/izHk5yEAjH8Hs/fNxkO/PyTWB83GmOZjiqx9RjmKDazxT+LkkrVr1+KPP/7AqVOnsGXLFowYMQI1a9ZEly5dnF9MRERExUrulhTiFwJvL280r9Jcczw7LxtHrh7R7LPO/FQNq4rfMu/Eiu8BU+fOwM2pJqzPO3z1MCRoH6DnfTIeePppICQEWLcOeOcdm/tRycnJy3H5GnXmZ2/iXgdnlk4MfsglZrMZL774Iho3bowhQ4agUqVKyoSnREREVLrIwU+wryj+4+etLTpkkSw2D8A2pa4vXsTQT1ajz0kAL75o9zwfL5+C8T83ZXhLwPvvA99+K3a88w6uHd9XyHdD7ubjVVD4+Zm/njF0jTr42Xlxp9vbVNQY/JBL+vTpg0OHDiEzMxOXL1/GokWLEBfnWSUOiYiIyoucfBHYyKWlrcta50v5yLPkafbZBD9ffAFkZ4uxO336KLu9TdqqcD5ePjZzFyoFke65B+jdG8jJgXnu7MK+HXIzdfDzwdYPDF2jDn4OXTlU6PkqS4rLwc+GDRswcOBAxMbGwmQyYfHixXbP/c9//gOTyYRp06Zp9iclJWHUqFEICwtDeHg4xo0bh/R0z6sWQURERFSamfNFkQE542Od+cm35NuUw9aUupYkYO5csf7444Cquqt1kOTr7WuT+VHOMZmAN94AAORuWl+o90LuZ13W3Ah18PPVwK8KSp17CJeDn4yMDDRr1gwzZsxweN6iRYuwbds2xMbG2hwbNWoUDh8+jFWrVmHJkiXYsGEDJk6c6GpTHPK0KJSIjOG/bSIi4+SS0naDH53Mj8bhw8DZs0BAAHDnnZpDet3erMf8mKCaCqNtW6BnT8xq6uB+VKzyLdrART2BqT1y8PNy55cxvPFwTfbIE7gc/PTr1w9vvvkmhgwZYvecCxcuYNKkSZg3b57NWJCEhASsWLECX3/9Ndq1a4dOnTrh008/xU8//YSLFy/qvl5OTg5SU1M1P/bI98vMzLR7DhF5LvnfNseZERE5JkmSktXx9Rb/zbQOftadWofp26dr9mmyN8uWiWX37kBQkOY866yB3pgfzTyAJhOwaBF+b+jyW6EiYj3fUs3pNXE6+bSyvefSHtzx/R3YfXG3sk8OfoJ8tb8PnsLtoZrFYsHo0aPxzDPPoHHjxjbHt27divDwcLRu3VrZ16tXL3h5eWH79u26QdXbb7+N1157zdD9vb29ER4ejitXrgAQc90U9QScRFT0JElCZmYmrly5gvDwcHh7u56qJyIqy5KykpBnyUPl4MoAoOnOZi/zM3P3TJvX0WTY5eCnf3+b86wzP78d+Q33xN+j2afJ/ABAaKjjN0HFSm+y2XWn1uGhFqKUdbfZ3ZCWm4adF3fixnM3ADD4sfHuu+/Cx8cHjz/+uO7xxMREVK5cWdsIHx9ERkYiMTFR95oXXngBkydPVrZTU1NRvXp1u22oUqUKACgBEBGVHeHh4cq/cSIiEiRJQtR7UQCA9BfSEewXrHmwlYMeOQPkiJK9SUkBNm0S6waCH821Ds7RdeIEULeusXPJbXZc2GGzTx0gp+WmAQCSs5OVffLEpgx+AOzevRvTp0/Hnj173Jpt8ff3h7+/v+HzTSYTYmJiULlyZZjNZucXEJFH8PX1ZcaHiEiHOtA5l3oODSs2VIodAPYzP3qC/URZbKxeDeTnAw0aKHP7qBkJfoyaPPICslrXxcSJQIsWhXoJKoRPdnxis8/Z7wgzPyobN27ElStXUKNGDWVffn4+/vvf/2LatGk4ffo0qlSpYpORycvLQ1JSktv/muvt7c0HJSIiIirzsvKylHW5BPXljMsARNczeZ+Rwemhfje7pjno8qa+j5p1wQPrSnL2fLy7E7AL+PprYNEim9oK5CYrT6zEmlNr8FaPt+xmAct68OPWeX5Gjx6NAwcOYN++fcpPbGwsnnnmGaxcuRIA0L59eyQnJ2P37oKBU2vXroXFYkG7du3c2RwiIiKiciE7L1tZN5lMuJh2EfEz4gGIrm5yjxx5slNHQv1CRYlrJ8GPkcyP3pgSPY94TUPX1hnIywOGDwe2bTN0Gbkgz5KHvvP64v0t76P7nO4AgDD/MJvz5OBHXQlOHTR7evDjcuYnPT0dJ06cULZPnTqFffv2ITIyEjVq1EBUVJTmfF9fX1SpUgUNGjQAAMTHx6Nv376YMGECZs6cCbPZjMceewwjRozQLYtNRERERI5lmQsyP+Z8M3499quyrf5LfoWAClg9ejVGLxqNS+mXdF8rKigK2LcPSEwEgoOBzp11z7MOfgJ8AmymI1B3vXPkPa+n4XePCXdVnIwVK8Rcqvv2AbVqGbqcDFDPz7P53GYA+tk7OSP07KpnlX15ljwcv34ckYGRHh/8uJz52bVrF1q0aIEWNztkTp48GS1atMCrr75q+DXmzZuHhg0bomfPnujfvz86deqEWbNmudoUIiIiIoI285Obn4sFCQuUbV8vbfemnrV7onOcfkATVyEO41uOL8j69OwJ2Bl3bV3qOs+SZ5P5MdrtLc8L8P1rKX75RUwHlJoKPPecoUvJoIzcDJt96t8bmfz78tG2jzT7639WHxXfr4grGTcrKnto8ONy5qdbt24uTTJ4+vRpm32RkZGYP3++q7cmIiIiIh3qMT9puWnYeHajsh3gE2Bzvt5f/GcPmo2RTUaKTJGTLm+AbRlrveDHehLNixcBmAMB3yx0SPweW6qMFtd6Adi4EaFIw9dfh6JZM+DXX4E9e4CWLe02gVwgV2lTk7sl+nv7Iyc/B4BtUGtNrvzmqcGPW8f8EBEREVHxU3d7e3/L+5pjeg+zevt8vX1F4HP9esGgm3797N5Tr7KvdaYnz5KnrB87BnTqBCBTDJF475lGSgCVV6sGYDYDa9eiSRPgvvvENS++aPf25KIbWTeU9djQWFgkC/IlEZy2r95eOWY0yaFUBfQwDH6IiIiIPJz8V3sAuJB6wen5epkff++b3dv++guwWIDbbgNUFXwNtSMvR7OdmC7mcNy1C+jQATh1CvD2FQFRaLCPMpA+r6cYgI/lywEAr70G+PgAK1cCa9a41ASyo93XBYXFLJJFMx5L/ftgXbHPnoiACPc1rhgx+CEiIiLycOoMi3pgO2DbPQ3QD35C/W+WuL4ZgDjq8mZPem66ZnvWnln4dMMc9OsnEkqtWwPhkaKtPl6q4Kdb14J7SxLq1AH+7//ErpdeEsXnqHDM+Wa8ueFNTVCTZ8mzW4nPSObH2+SNEL8Qt7WxODH4ISIiIvJw6uBHHpAu0/tLvl63tyDfIJHxuYXgR29cyVNrHsG1a0CTJsDatUA+dIKfdm1EYYWzZ4GEBAAi6PH3B7ZvBzZvdrkpdNPnOz/HK+te0ezLs+Rpuiiqf0eMZH4iAiN0uz16AgY/RERERB5OXVjgetZ1zTH1eCCZXubHz9tP9E+7dg0ICxP91JyICYnRbOtVFMvPCobJJCYwDQ0tGDDv4+WDQN9AAEC6dz7QVZX9ARAdDTzwgNj1wQdOm0J2bDq3yWafOvPjZfLSBM/WRSv0eGrWB2DwQ0REROTx1A+v1vTKTetlfjRV3nr3Bnx9bc6x9ufIPxFfMV7Ztu72JhoQhJdeEiWsE64mKLt9vHxQObgyAOBq5tWC4gorVijnTJ4sln/8IQomkOv0Al118OPn7acJeORub46quVmXT/ckDH6IiIiIPJxctUtPl7guNvusH4j9vf3RsGJDQyWu1VrFtsKRR48o5bTTzbbBT4hfMF57TazLk2sC4iFbCX4yrgJ9+4oDGzYAueLBvGFDYOBAMebn448NNYms6AUx6uDH18sXn/b7VDkmd3u7rfJtAICa4TVtrpcnQvVEDH6IiIiIPJxe5mf9mPUY1mgYZt1pO5G8deYn6bkkBFxPAXbuFDvkQMQgeexOWk6azbH6tYLhdfOJs0pIFWV/TGiMUjHsRvYNoEEDICJCBD6HDyvnPf20WM6eDVy65FKzCPrjsPIseVjzryijl5abhuZVmqNJ5SYACjI/8gSoelXdmPkhIiIiohJjPZkoAHSt2RW/DPsFMaExNsfkYEUW5Bsk6koDQIsWQGysS/cPDwgHAFzOuCx2mAOVY2GBBfPByKWwW8a0hJ+3n+hqh5uTbZpMBTOa7t6tXNO5sxh+lJ0NvPGGS80i6I/DAoBnVj2j2fYyibBAzvykZKcAgJKdU7P+/fEkDH6IiIiIPJx15ifUL9Th+brjOVzs8qYmZ3ROJ58WO3ILAp5gX1Xwc3M+IjlY8vcRcwspZZd1gh+TCXj7bbH+1VfAyZMuN69csy59LhvYYKBmW67eJmd+bmSLSVGjQ6JtrmW3NyIiIiIqMdZjfhbeu9Dh+eqABACQlycmNwVuKfhJykq62SC/gnv52WZ+5AlV/bxUmR8AaNVKLFXBDwB06SJqMOTlAd9+63LzyjW9bm8AUCVYfGfjWowDoM385FvykZqTCgCIDtYJftjtjYiIiIhKinW3t0CfQDtnCjaZn+3bgRs3gMhIoF07l+9v/Xp+AQWZKL3Mj5zxkbu9yUGREvwcOACYtVXqxo4Vy59+4qSnrlCXOld/F9subANQULZangzXIlmQllswdkuv2xszP0RERERUYqy7vdWOqO3wfHU2BkBBl7c+fQBv29LIzsiZHGU7qCBwyc7LxomkEwB0Mj/eVpmfOnWAChWAnBxN0QNAVH0LCgL+/RfYscPlJpZb8u9GsG8wNj60Udm/5dwWAMCldFFFQt3tTe4q523y1p3Tx8fLB1i6VATMHobBDxEREZGHk7u91Yushw0PbtAtcqBmk/m5hfE+AIA8bfBjMeUq6z8e+hH1Pq2Hj7Z+ZDfzowQ/dooeAEBwMDBokFj/6afCNbM8koOflfevRIuYFkr3NtnZlLMACjI/EiQlWxToG2gT2AKAb2YOcPfdQOPGwPnzRdl8t2PwQ0REROTh5Afc1rGt0Tmus9Pzbcb87NsnAo8+fQp1/327AzTbSjCj8t+//muT+ZGDIDkoAmB33A8AjBghlr/+ClgsNodJhzzJrVyhzbpSmxz0qDM/cpnrQJ9A5TtS8/3nmChJ3qIFULVqkbW9KDD4ISIiIvJw8pgf6/l77LHp9gYAbdoAlSq5fO8bN4DD+7UPyPIDtzU5yJEzPjaZH8Bh8NOnj+gVd+ECsGmTy00tl+TA2G7wczPo0WR+8kTmJ8AnQPmO1HzOXxLB8vvvi6UHYfBDRERE5OGsH3Cd0S113a9foe69YAFgydUGP/KDtLUbWWKMiPWYH93Mz/79NkUP/P2BoUPF+ldfFaq55Y6z4EfuBqfO/Mjjgc6lntPv9mYBMHw40KhRUTW7yDD4ISIiIvJw8pgfb5PBzI91tzcAGDCgUPeePx9AvvYB+b3e7+me+/muzwE4yfzUqQOEhoqiB0eP2rzGf/4jlr/8Aly9Wqgmlyty8CNXaLMX/KhLXT+x4gnluF7mJznUF3jrrSJpb1Fj8ENERETk4W4l81PzBkR3Nznj4oILF4D162FT8KBRpUb4vP/ndq+TH6jl9mpKdXt5Ac2aifV9+2yubdMGaN1aDDnhnD/OGc78qEpdq0UGRtq8ZlKLhiJI9UAMfoiIiIg8nDLmx2DmRz2Ifel8iME0Xq4/Fv76q5hzp1YNbfDj6+XrcPyRdfBjXaobzZuLpU7wAwD/939i+eWXLHzgiDnfjOTsZAAFn7W9CUrV3d7kgMjHywexobE25/qHRRRBa4sHgx8iIiIiDydX59LroqRHPXdL1VQUusrb77+LZcvm2gdqX29fh4HYrQY/I0YA4eHAqVPAX3+52uqyKScvB48sfQQzd81U9nX8tqOybi/z06NmDwAFmZ9rmdeU7M+Hd3yI6JBom3tVCnK9MEZpweCHiIiIyMNdy7wGAKgYVNHQ+WH+Yfiq+0eYuQSokAOgVy+X75mUBGy8OWdmy2ba4MfHy8dQ5kcOkOQxSwp18CNJNtcHBQFjxoj1L75wuell0mc7PsMXu77A/y39P2Xfzos7lXU542Md/Dzb8VkABZmf08mnlWOSJMEnVxuYNqncBB/c8YFb216cGPwQERERebgrmVcAAJWDKxu+ZvylWDy8C0CTJkCVKi7fc9kyID//5uWVtQ/Uvl63mPlp3Bjw9gauXxcDi3TIhQ/++EO3Kna5cznjsrIu6QSM9jI/chdIOfMjl7kGbgalcnrvpgP/dwA1w2u6pc0lgcEPERERkYe7nnkdABAVFGX8olWrxLJ370LdU34mvusu23Ekvt63OOYnIACIjxfrdrq+NWwIjBol1t9807W2l0UBPgUTzcrdINXsBT8yeZxPSk6Ksi/fkg/8/LM7m1niGPwQERERebBNZzdh6/mtAOyUsNYjScDq1WK9EMFPTg6wYoVYHzSooIyyzMfLx1DmRw6QbIIfwOm4HwB46SUxx+bixcCRI0ZbXzapx3ul56bbHFcq61l3MbxJ7vaWlJWk7MvLTAOWLnVnM0scgx8iIiIiD5VlzkLn7zor24G+gcYu3L4dOHNGZFg6d3Z+vpX164H0dCAmRlTIts4m+Hr5OmyLw1LXshYtxNJB8BMfDwweLNbffddo68sm9WfY/pv2+PHgj5rjcoD6z7V/dK+Xu72pg5/8QwdFTfEyhMEPERERkYeSyxjLAn0MBj/ffy+Ww4YBwQazRSpLlojlnXeKCtl63d7UcwlZc9rtDTCU+QGAF14Qy3nzgH//ddr0MstsMSvrJ2+cxH0L79Mcd1YGXTfzc+iAG1tYOjD4ISIiIvJQablpmm1DmR9JKohehg8v1H3lLm/9+4ulbubHQSAmZyEcBj/yRKcnTwKpqXZfq00b4I47RPGF//3P4Bsog8z5ZofHrbsmAsDwxgXfv17mZ9DK0+5pXCnC4IeIiIjIQ6Vkp2i2HWVbFIcPA2fPii5vPXq4fM+TJ4ETJwAfn4LL9cb8GMn82C11DQBRUUD16mJ9/36HbZoyRSznzAEuX3Z4apmlG0A60b9uf2VdzvzI2cQxge3R4oIFaNnSLe0rLRj8EBEREXmo1BxtRsRQt7fly8WyWzcxYY6LVq4Uy44dgbAwsV4k3d4AUUcbEAGbAx06AO3aAXl5wMyZDk8ts9Td3hx5vdvryrq6Qpyc+ZErxTU/eFUcGDZMOef2arffajNLHIMfIiIiIg+VmJ6o2TbU7U0Ofvr3d3yeHXKXt759C/bpdXtzS/DTuLFYGijl9tRTYvnRR2IC1vLGWbc32ZjmY5R1dfAjl7qWBR85IeZaGjlS2Vc1tOottrLkMfghIiIi8lAnb5zUbDvN/KSmAhs3ivV+/Vy+X04OsHatWFcHP4Xu9naz1PWx68fQ6dtOyMnL0Z7YqJFYGgh+hg0DmjYVb/H9952eXuYYzfyoS2Krg2W525ss2Azg3nuBuDhlX52IOrfWyFKAwQ8RERGRh7LO/KgfbHWtXy/6htWtK35ctHkzkJEBREeLQENmk/lxsdsbAGw+txkLEhZoT3Qh+PHyKpjsdPp0IDHR8flljaPgp15kPWVd/Tui7q4od3uTBeWZgOeeAwCsGLUCDzV/CC93edldzS0xDH6IiIiIPNCpG6fwxa4vNPus/3pvY+dOsSzE3D5AwXifPn1EsCGTx4nIfL18NV2qrOkFPwCQm281p0x8vFheugTcuOG0fXfeKcb+ZGWVv8pvjrq9rRq9SllXBzxy5g0ATFYlxYPHTFAi3D51++DbQd8i1D/UTa0tOQx+iIiIiDzQ5L8mu37Rrl1i2bp1oe6pN94HAFrFtNJse5m8HAZi9oIfSZK0J4aGFlR8M5D9MZkKgp6ZM0VVuvIiJz/H7rGqYQVjddSZH2Wcz+7dMJ2/oLkmeOQD7m1gKcHgh4iIiMgDnUs559oFkgTs3i3WW7VyfK6OixeBAwdEgNG7t/ZYoG+gZsC8swyU/ABuXSVOgmR7sjzZ6dathtrZo4fITJnNBUUQyoM9l/bYPaae4FQ9PkvZP306TFYffbBfiFvbV1ow+CEiIiLyQNbVuZw6dw64elVM0KMesGOQ3OWtdWugYkXb4xbJYvi15OAn2C9Ys98m8wMAPXuK5erVhl9/2jTxNpcscTpFUJkhz8+jRx2Mqn9vIgIjRIWIX36BdbhqaM4oD8Tgh4iIiMgDuRz8yFmfxo2BQAMlsa3IwY91l7fCUIIfX6vgRy/zIwc/GzeKcnMGNGwI3H23WP/yy0I306PkW3QmirXj64Ff460eb6FhxYbAb78BOTnwCtZmeqwD07KCwQ8RERGRB/L38QcAVAmpgq8Hfo2D/3fQ8QVy8FOI8T75+cBff4l1twY/Rh6wGzcW5eUyM4Ft2wzf4z//EcvvvwfS0grTSs/iSuZtXMtxeLHzi2Ljq68AACZVSWvANjAtKxj8EBEREXmglOwUAMB3g77DuJbjcFvl2xxfIBc7KMR4nx07RLG18HCgbVvH58aExDh9PTn48ff21+zX7fZmMhWq61u3bkD9+kB6OjB/vuHLPJYrwY/i9GkRUHp5wRRXU3OImR8iIiIiKjXk8tKGxmaoix0UIvPz++9i2aePGEvjiJHuePJAe+vCCHmWPP0LevUSyzVrnL62zGQCJk4U6z/8YPgyj1Wo4GfhQrHs0gUmVVdIP28/m0p8ZQWDHyIiIiIPJJc2ts6e6Dp7Frh2TUQuTZq4dB9JAhbcnHtUHkfjiJHgx141OLvlmuXMz44dQEqK80bcNGKECII2bRIfQVmWL4kxP+/3fh9/P/i3sYvk4OfuuzWTnIYHhLu5daUHgx8iIiIiDyRPCKqet8UuOetz221AgP3JR/UcPizmy/H3B/r1c36+q4UYRtw2Qlm3O1FnjRpAvXpi8NHfBh/sAVStCnTqJNbl53xPtOrkKjSb2Qw7L+y0e46c+RnVZBS6xHXBmGZjAAAvd35Z/4JLl4AtW8T6kCGagLSCfwX3NLwUYvBDRERE5IFy8m5mfnwMZH7c0OWtd28x56gzrgY/6ipldru9AYXq+gYAQ4aI5R9/uHRZqXLHD3fgwOUDuOunu+yeIwc/8uf/5Z1fYtNDmzCl2xT9CxYvFmm9du2AatU0mZ8KAQx+iIiIiKiUOHL1iDKvi6HMzy0UO5CDhrvsP3drqDMILWNaOj3/RvYNZd1ssZP5AQpV9AAoaPeGDaJogydLz03X3a8uFOHtJcZT+fv4o2ONjvbH7shf7M2+jOqgNcw/zA2tLZ0Y/BARERF5kA1nNqDx542VMR5Ox/zcQrGDxEQxzAYA7rzT2DXqh+hl9y3DtD7TEBEQYfd89cO53W5vANC9uxjAc+SI6LJlUJ06olp2fj6wfLnhy0qlQB/9+Znk3wXAYObNbBbzJgFK7XJ10GrvPmUBgx8iIiIiD9J1dlfNttPMz5kzwPXrgK+vy8UOliwRyzZtgBjnFawBFFRyA4DokGg8cfsTDts4rc80Zd1ht7fISKDlzUySi13f5OyPJ3d9A4AAH/3xWupKb4aCn927gYwM8ZneJkqkq7u9GepK6aEY/BARERF5MKcPqnLWp0kTUbXABfLEpkazPoD+w7fcRU9Pg4oN8EyHZwA46fYGFIz7cbHrm9z+lStF0sNTBfrqZ2TUwY86+LRrwwax7NIF8BLflzrzY6iCoIdi8ENERETkASySBT8d+slmv9MH1UKO95EkYPNmsd61q+NzAeCtHm8BAL4Y8IXNMXUJ6/Etxtsc9/XyBeCk2xtQMO5nzRrRQIPatQOiooDk5IICZ57IXnc0lzM/6uBH5zpmfoiIiIioRH2952uMXDDSZr/Tbm+FHO9z9ixw8aKYGqhNG+fnv9j5RWS/lI2uNW0jJTkweqbDM/jqrq9sjsvjfhx2ewNE3Wp/f+D8eTH2xyBvb6B/f7G+dKnhy0qdYL9g3f3qinlOg5/8fDHxEaAJfuQAFGDmh4iIiIhK2MNLHtbdL1f30qUuduBi5kfOkLRoAQQFGbvGXsbghU4v4OhjR/Fur3d1j/t638z8OOv2FhhY0PVt0SJjjbppwACxlMcxeYLDVw7jXMo5ZTvYVz/4cSnzc+iQmCg2NBRo1kzZrS48YaSC4PXrTk8plRj8EBEREZVVp08DSUmAn58ysN0ouctbhw633gyTyYT6UfU140rUlG5vzoIfoGDiHheDnz59RAYoIQH491+XLi0Rl9Iu4bYvbkONaTWUfav+XYWa02ricvplZd9vR37DJ9s/UbYdBsNAQfm+Nm1EWu8mdebHmaQkoHJl8SuVkWH4slKBwQ8RERFRWXULxQ7kzE/Hjm5ukw7D3d4AUbrNywvYs0d0fzMoPFz0mgM8o+tbwrUE3f1nUs7go60fARDz+wz7dRheXf+qctxp5kceA2bVl1HOvgHA9we+d/gS69cDFov4CdZPRpVaDH6IiIiISjn1mA6XHDggli1auHRZejqwf79Yb9++cLd2hdLtzVnBAwCoVKmgZLcc3BkkV33zhK5v6tLT1uQgMdOc6dJ1AAqCH6sxYOrMj6PqfEBBsT25B6InYfBDREREVMpl5WUp6+kvpOPoY0cR6heKlzq/5PjCQ4fE0sX5fbZvF3/Vr1EDqFbN1da6zqVubwDQvLlY7tvn0n3kcT/r14sArzSz10UQKMjuJGUlaa+ByeF1yM4GDh4U6w4yP+/3ft9h2xj8EBEREVGRUf+FP9A3EPWj6iPpuSS82eNNxxfKD7oujvcpzi5vgIuZH6DQwU/DhkDt2kBurstTBRU7R93X7AU/Tsf7HDggJjqqWFFEtirqggd3x99t9yXOnAGOHxfjp4yUQC9tGPwQERERlXJy8BPoE6g8+KofVvUvygROnhTrhQx+3FHswAh5Ys58yWD3vkIGPyaT53R9kxzMYyT/DmSYM3T326Xu8maVITJa6nrNGrFs2xaoUMHx7UojBj9EREREpZw8BiPUP9T4RQkJotR1pUqiNJdBFguwdatYL67gR35oV5dsdkgu0Xz6tJi51AVy17dly8R7La0cdQGUPy/rsWAuBT9W1FkjR6WuPbnLG8Dgh4iIiKjUS0xPBABUCali/CJ5vI+LWZ8jR8Q0MMHBQNOmLl1aaPKDt+HCDhERov8aAGzb5tK9unYV7+3SpYKiDqVRbn6u3WPK5yUVMvjRmbVWnWmyN1+TxcLgh4iIiIiKmDyvS3EEP/L8Pu3aaaaBKVIud3sDgG7dxHLtWpfu5e9fMFbFxUuLlaPg540Nb+BqxlWbYFH+HHVlZgKHD4t1ncyPOutmr9vboUPA1ati0tvbb3fQ+FKMwQ8RERFRKSdnfqKDo41fVMjgp7jH+wCFyPwAQPfuYlmICEa+dN06ly8tNhdSLzg8/trfr7mW+dm3T6RuYmKA2Fibw+rgR135TU3O+nTtKubN9UQMfoiIiIhKucsZxZf5Ke5Kb0BBxmJf4j7jF/XsKQbt794NnDrl0v3k4GfDBiDPwLyqJeGx5Y85PH4l44prY3527hRLnawPoA1+7L2Op3d5Axj8EBEREZV6Lmd+kpOB8+fFeuPGhu9z+TJw4oRYL85uTXLmJyUnBYevHDZ2UUwM0KOHWP/5Z5fu17w5EB4OpKUBe/a4dGmpkZab5lrmx0GxAwAY1ngYwvzD0L6a/qy2ubnA33+LdQY/RERERFRkXM78yFmf6tVdqkcsV3lr3FgEB8VFPVZlw5kNxi8cOlQsly3T7M4yZ2HinxOxMGGh/v28gS5dxHpp7fp2ezXH0WdaTprtmB9H8/w4KHYAAI0qNcKVp69g/YPrdY9v2yaGDVWu7HIysVRh8ENERERUyimZnxCDmR8P6vIGaB/aV55cic1nNxu7sF8/sdyyBbhxQ9n9wZYP8NWerzD0l6F2L5WTRqW16EGon+Oy5pvPbcb4P8dr9tnN/KSmAkePivVWrey+pr+Pv90y13KXt549AS8PjiA8uOlERERE5cO1zGsAgMrBBufrOXhQLF2sVS1XeivOYgeANvPz+9Hf0em7TsYurFkTiI8H8vOBVauU3f8m/6usZ+dl617au7dYbtgAZGW53OQip253mH8YqodVtzlH/r2QBfkG6b/Y3r1izqcaNVya80lN/nh79izU5aUGgx8iIiKiUi7LLJ7OA30CjV1w4IBYNmli+B45OQU9o4o9+HHUXcuZ/v3FcskSZVcF/4Kufqk5qbqXxccD1aoB2dkiACptcvJzlPUAnwAcfuQwdk/c7fAau9kiJ8UOnLlxA9ixQ6z36VOolyg1GPwQERERlXLynC/2Jp/UkKSC4MeFzM+ePWJQe6VKQN26hWll4el111JXH3NoyBCxXLhQVDAAkGcpKOGWk5ejdxVMpoIH+RUrjLe1uKgzP5IkIdQ/FFGBUQ6vCfW3E/w4Ge/jzOrVokp2o0YiYPRkDH6IiIiISqnE9ERM+GOCkgWwN/mkxtmzYoyHry/QoIHhe6m7vJlMhWlt4elNzml4zp8OHYB69YCMDOCvvwBoJwhVZ1CsycGPqsdcqaEO2uRA0N54HFmIX4j+ASeV3pyRk2qenvUBGPwQERERlQrXM6+j+czmeG/ze8q+cX+Mw9d7v1a2nT38AigY7xMf79JMlCVV7ADQ7/ZmOPNjMgF9+4r1NWsAAGaLWTlsL/MDFBQ9OHwYSEw0drvios783Fn/TgD2Jx+V6XZ7u3EDOHlSrDsodmCP2Qz88YdYl5NsnozBDxEREVEp8NmOz7D/8n48t/o5Zd+eS9pJaAx1eyvEeB9JKgh+inu8jz3Wc9g4JI/Cv1m6zWjmJypKzPkDlL6S11l5YpzXhJYT8Em/TwAAJjhOyelmfnbfHCdUpw4QEeFyO9avF9NGVa5cen43bgWDHyIiIqJSyrrrl6HMTyHG+/z7r5jg1M+vUMmBWyZJks0+w93eAKBrV1F/+ehR4MIFmPMLMj9ysQh75LjpZtKo1MjIzQAAPN/peYT5hwEAIgIdBy+61d5usdjBwptTJQ0eLOZH8nQMfoiIiIhKAfVgdTkYsM5++Hj5OH+hQgQ/ctanVSsgIMDwZW4jwTb4MdztDRAzsspR29q12HVxl3Lov3/91+GlpTH4sUgWZJhF8BPsG6zs9zJ5YUb/GXavC/DR+fK2bxfLtm1dbkdeXkHwc/fdLl9eKrkc/GzYsAEDBw5EbGwsTCYTFi9erBwzm8147rnn0KRJEwQHByM2NhYPPPAALl68qHmNpKQkjBo1CmFhYQgPD8e4ceOQnp5+y2+GiIiIyFOpx2tkmjMBuBgAAKJu87FjYr0QwU9JdWvSzfy40u0NUKKY3FUrcCr5lLJ7+4XtDi/r3Bnw8QFOnwZOnXJ4arFRZ6uC/YI1xxyVO7cpiHGL/Rk3bACuXAEiIwvGR3k6l4OfjIwMNGvWDDNm2EadmZmZ2LNnD1555RXs2bMHCxcuxNGjR3HXXXdpzhs1ahQOHz6MVatWYcmSJdiwYQMmTpxY+HdBRERE5OHU43mSspIAAMnZya69SEKCmPAzMhKIiTF8WUlNbirTC/KMdHs7l3IO28/fDG4GDAAAZC/8xaV7h4QA7dqJ9dKS/ZGzPoBtVzZH2T+bzM/Jk8DVq6I/Y4sWLrfjl5sf5d13i+KBZYGB3KlWv3790K9fP91jFSpUwCqrWoGfffYZ2rZti7Nnz6JGjRpISEjAihUrsHPnTrS+2ffw008/Rf/+/fHBBx8gNjbW5nVzcnKQk1MwWC01VX+yKiIiIiJPpR6n8svhXzC5/WTXX0Td5c1gveqUFODQIbFeYpmfQnZ7qzGtBgAg4dEENOzUCejeHeZt2soF7au1d/o6PXuKAHDNGmD8eIONLkLpuaJHVJBvkM0cSI6CH5sxP3LWp3VrwN9AsQyVvDxgwQKxPny4S5eWakU+5iclJQUmkwnh4eEAgK1btyI8PFwJfACgV69e8PLywvbt+mnJt99+GxUqVFB+qlevXtTNJiIiIipW6gplT696Gr8cLshgVAurhgP/OeD8RQox3mf7dtE7qnZtoEoVw5e51a12eztw+eb7fvhhmK0G5ceFxzm9vndvsVy5UpR2LmlymWu9MTyOgp8KARW0O26hy9u6dcC1a6IiXvfuLl9eahVp8JOdnY3nnnsOI0eORFiYqFKRmJiIypUra87z8fFBZGQkEu0UWH/hhReQkpKi/Jw7d64om01ERERU7NRz0wDAkuNLlPU9E/egSbSB0tXyHD8uBD8l3eUN0M/8OOv2pp6/R66GhrvvhjlO+0dydUbNnvbtgYoVxZQ4mzYZaHARk9vs62Xb18xh8ONvFfxs3SqWhfhy5S5vQ4eKMVFlRZEFP2azGcOHD4ckSfjiiy9u6bX8/f0RFham+SEiIiIqS6wf0tUPuc4mt1QUYo6fkpzcVNYlrovNPmfd3m5k31DWle5evr4wP/ofzXnqjJo93t6APERdVcurxORZ8gDof++Ogp9Wsao65ampBcFwe+dd/9TM5oIqb/fe69KlpV6RBD9y4HPmzBmsWrVKE6xUqVIFV65c0Zyfl5eHpKQkVCmpXCsRERFRCbPO/PiYVMGPTgbAxuXL4sdkAho3NnTP/Hxg2zaxXpKZHz9vP9QMr6nZ56zbW2pOwRhwdZbIPHig5jzrz9WewYPFcvFi0Q2wJMlt1vvevb1sJ9vpV7cftozdgtoRtQt2yv0Za9VyuT/j2rVAUpKY2LSLbVzq0dwe/MiBz/Hjx7F69WpERUVpjrdv3x7JycnYLc82C2Dt2rWwWCxoJ5faICIiIipnbjnzI/+Vv04dIDjY8bmqS9LTgbAww/FSkbF+0HfW7U3OjqjXc/NzMXihdnS+kW5vANCrFxAYCJw9Cxw+bOiSIqN0e9P53quH2Y59bxbdDO2rW2V3bmG8z88/i2VZ6/IGFKLaW3p6Ok6cOKFsnzp1Cvv27UNkZCRiYmJwzz33YM+ePViyZAny8/OVcTyRkZHw8/NDfHw8+vbtiwkTJmDmzJkwm8147LHHMGLECN1Kb0RERETlgU3mx8vFzI9csq0QXd5uv110/SpJ1t25HHV7+/Hgj8pcSEBB8LP639X459o/mnNzzdmG7h8YCHTrBixfDqxYAdx2m8GGFwFHmZ8m0U3wyz2/4ETSCby49kUAdiY3LWTwk5NT0OWtLFV5k7mc+dm1axdatGiBFjdrhU+ePBktWrTAq6++igsXLuCPP/7A+fPn0bx5c8TExCg/W+QvAMC8efPQsGFD9OzZE/3790enTp0wa9Ys970rIiIiIg9jnaEw3SxV7W3yVtYdkjM/LgQ/paHYgcy6O5e9bm87LuzAfQvvw/g/C2pSy8FCRm6GzfnmpKuG29C3r1iuXGn4kiLhKPMDAMMaD8P9Te9Xtm2CH4ul0P0Zly0T5c+rVi17Xd6AQmR+unXrpluOUObomCwyMhLz58939dZEREREZZZ15ifLnAXAhWIHcubHYMpCkoCNG8V6aQh+jGZ+TiSdsNmn7vZmzXzjuuE29Okjlhs2ABkZhnsPup2jzI9M/XnZBD9HjoiCB8HBLqew5Ef0kSMBryKfFKf4lcG3REREROR5rDM/SdlJAArmfHHIYikYqGIw83P6NHDunBjTURqDH/WYnyxzFtJy0gDoBwRy8KNX3CA3M81wG+rXB+LigNxcYP16w5e53U+HfgIAnLxx0u456qDYz9tPe1DucdWunUuDdlJSgD//FOv33Wf4Mo/C4IeIiIioFLB+cF+YsND4xadOiVSFvz9Qt66hSzZsEMvWrUsuw6FmE/zc7PYmSRIi34tE2DthyM7L1s2EKcGPTnGDgxG5eG7xY4baYDKVjq5v8w7OAwAkZSXZPUcdBNpUgCvkeJ9Fi8SYn/h4oHlzly71GAx+iIiIiEoBo1XJdO3bJ5aNGhn+S//ff4tl166Fv6072ev2lmfJU7Jf/9741zbLgYLPzt6cPu/tn2G4HXLXtxUrDF9SItRBoL+3v/agPN7Hxfl95omYC6NGiUCwLGLwQ0RERFQKyJkfmwdZI3buFMvWrQ1fUtqCHy+T9rFU7vamLmnd8suWNuepz1Fnz0beNrJQ7ejRQ8SPx48D//5bqJcoFupgsUJAhYIDWVnAsWNivVUrGHXpkpjfByi7Xd4ABj9EREREpYL84B7sV4g+aHLw06aNodPPnhUP9t7eQMeOrt+uKJigTTXI3d7UwU9Ofg6uZthWb7MueDCm2Rg81/G5QrWjQoWChElJV31zRN3tLcw/rODAkSOimkXFimKWUoN+/lkMHevQQcyLWlYx+CEiIiIqBeQH92BfbfDzQqcXHF9osQC7dol1g8GPnPVp1UpMcFoaWJfz/uHADxj+63Ck5qRq9usVgJCDn1+P/ApABAY2Y4Oysgy3RR73U9Jd39pXs99tTf151QyvWXBAPd+TC33XfvxRLMty1gcoRKlrIiIiInK/86nnAdhmfvTGuGgcPy7KGgcEAI0bG7qXXMmsWzcXG1mMvtj1BQCgelh1zf6sPNsgxmwx41rmNey6KIJAk8lk+7nt3y9mczWgb1/gpZeANWuA7Gzx0RaniIAI3Mi+gW/u+sbheb+P+B3J2cmoHVG7YKc835MLJa5TUgqSh0OGuNpaz8LMDxEREVEJy8nLwbbzYpC69Zgfp2OA5KfWFi0AX2NzAsnBT/furrSyZFzJvKLZzjRn2pyTk5ejlMIGRBbNpiS2/DkZ0KIFEBsrCujJWbLiJHf5sy4CYe2uBnfhgWYPaHeqMz8GbdkiesrVqSPed1nG4IeIiIiohJ1LPaes5+TnaI75+xgMfjx4vI8j1mOB5MlfNfvysiBBUrZ1S2K7EPyYTMCAAWJ9yRLjbXUXuRufs+BHVyEyP5s2iWXnzq7fztMw+CEiIiIqYYnpicq6eoA/YKDbm4vBjzy/T6tWQGio4SYWOesgR9lvNW5Fr9tbljlLMxYoJz/H9nPbvt2l9tx5p1guWSKyIsVJrnRnM3+PM0lJwMWLYt1gF0gA2LhRLDt1cu12nojBDxEREVEJu555XVm3nu/HYbc3sxnYu1esGwx+PO2v/JJV5KHX7S0rTxv8hAeE23Z7O3YMOHPG8H179gT8/IDTpwsqRxeXQmd+5C5vcXGGK1nk5AA7doh1T/mduBUMfoiIiIhKWEpOirLutJub2s6dYkR+VBRQr56hS+Tgx1P/yq/X7S3TnImcvILugkPjh9p2ewNcKt8WHAx06SLWly93uZmFJkmSMubH2+Ri5qcQ43127RIBUOXKhn+FPBqDHyIiIqISdPjKYYxZPEbZtp6fpmHFhvYvXrdOLLt1A7ycP9YlJQGHD4t1TxjvAxjr9rbm1Br0mNtD2b6z/p363QVdjGL69SvUZbfEIlmUdZczP4UY76Pu8uZCZWyPxeCHiIiIqASNWDBCWR9QbwBiQ7XltjrVcJCikYMfg2XbtmwRywYNgEqVXGpmkRvVZJTufiPd3k4nn1a6vbWMaQnATtZk1SpRws0geb6fv/8GMm1vWyTkrA9QiDE/hcj8lKfxPgCDHyIiIqISdTblrLJ+8sZJTcbi1S6v2mQ+FDk5wObNYt1g8COfXhofdB9q8RDe7P6mTeED6/cvTwZrjzxGyuZzq1NHRDB//GG4TfHxQI0a4qOWy4MXNbnYAeBitzdJcjnzY7EU/E6Uh/E+AIMfIiIiohKz7PgypOakKttnU85qgh+Hld527BDjfSpXFk/pBsjjfUpjlzcvkxde6vISJrScoNlvXf3OuhS4tSDfIGV9zQNrCg6MHCmWP/1kuE0mU0HXt2XLDF92S9Tv16VubxcuiNlKfXyAhg66SqocOiQuCQ4Gmjd3saEeisEPERERUQkZMH+AZjs3P1cT8OgO2pepx/sYGKyRnV1Q1as0Zn5k1l29krKSNNvOMj/hAeHKujxeytvkXRD8LF8O3LhhuD0DB4rlwoVAfr7jc92h0N3e5KxP/fqiTJ0Bcpe39u1FzFQeMPghIiIiKiXyLHnGMz8ujvfZvRvIzRWJorp1b6WVRcs607P13FbNtrqqmx518CN3G7NIFqBRI6BpU1EefOFCw+3p3RsIDwcuXSoIFopSoTM/hRjv8/ffYtm1q/HbeDoGP0REREQlwHogv0w9P43d4Cc7G9h6MygwGPxs2yaWHTqU7qpe1sGPugw44LzbWwX/Csq6l0k86kqQxOctZ39+/NFwe/z8gLvvFus//2z4skJTj/mR22+Ii+N9JKlgHFO3bsZv4+kY/BARERGVAPWknGqabm/WE3XKtm4Vo/BjYkQ3JwPkLm9t27rUzGJntpgdHnfW7S3EL0RZVwcPFskC3Huv2Fi3DkhMNNwm+bLffgPy8hyfe6tueYJTg8FPQgJw9SoQGGh4ftwygcEPERERUQmwzmgAwCd9PzHW7U3d5c1gGmfnTrEs7cGPdebHmrNub+qgQT1mxiJZgFq1gNtvF2XOfv3VcJt69AAqVgSuXQPWrjV8WaEUaoJTsxk4ckSsG+z2Jr+PDh0Afxfm1fV0DH6IiIiISkBKtjb4Ofn4SUxqN8lY8CP3VzLY5e3qVeDUKbHeurWrLS1enWvo11yWszjOur2psz3qdaWQQCG6vvn4APfcI9aLuutboTI/R4+KTGBoqAjwDFiyRCzvuMPVFno2Bj9EREREJSA5O1mzXaNCDQDagCfYL9j2wqysggE8BoMfOevToAFQoYLjc0vaxFYT8c1d36B1rDZKk8fyOOv2pp7fR509sUgWsTJ8OODlJboOnj5tuF1y17eFC4u265s85selSm/79olls2bivTmRmlqQ+Rk0yMUGejgGP0REREQlQN3trW5kXeUv/ergJ9An0PbCgwdFN6fKlYHatQ3dy1O6vAEi4zG2xVh0qNZBs1+ev8c6Y2bNbuZHLiRQpUrBCH8X5vzp3BmIjASSk4EtWwxf5rJCZX7k4MfgZD0rV4pfofr1RUBcnjD4ISIiIioB8kN85xqdcXzScWW/em4ffx+dwRgHDohls2aGx/vIxQ48aWC79RxH8mfhrCCCveBHyfwAher65u0N9O8v1uUuY0WhUGN+XAx+/vhDLO+6y/gtygoGP0REREQlQM78VAjQ9kNTV3gL8AmwvVAOfpo2NXQfSfKcSm9qcqZHpvtZ6FAHPOquY1l5Wfh+//dITE8Ehg4FfH3FZykXCjDgzjvFsiiDHznzY7jbmyS5FPzk5gJLl4r18tblDWDwQ0RERFQi5MyPelJOQPvQ27BiQ9sL1ZkfA86cEVXKfH0NX1IqWAc//t7GSpLZy/wM/WUoHlj8AN74+w0gIgLo21cccCH706ePyAAlJAD//mv4MpfI3fMMd3u7eBG4fl00rHFjp6cvWwbcuAHExgLt299KSz0Tgx8iIiKiEiAXPFBPyilLejYJl/57CWH+YdoDkuRy5kfO+jRtCgQYS56UCu7I/KjXt5wTA3U+3/W52HHffWL51VeiiIQB4eFAp05iXc6euJuS+THa7W3PHrFs2NDQFzxnjljef7+Il8obBj9EREREJeDNjW8C0A9+IgIjUCWkiu1FFy6IP9v7+IiHXQM8qdiBmk3mR2/8kw51wKO3rRg6FKhRA7h8GZg3z3C7irrrmzzmx3DmZ+9esWzZ0umpyckFQdvo0YVoXBnA4IeIiIioBMiZjOy8bOMX7d8vlg0bGp6Z0hPH+wDu6fam9zoKX1/giSfE+vTpIqtmgBz8rF8PZGQYusQlLo/5kYOfFi2cnrp0qajy1qgRcNtthW2hZ2PwQ0RERFQC5C5tI5uMNH6Ri13e8vOB3bvFuidVegPcl/mxHlOlOT52LBAcDBw6VDDxjRMNGgBxcaJwwIYNhi5xictjflzI/CxaJJZ3312YlpUNDH6IiIiISoD8EK6u7uaUi8FPQoLIToSEGO4lV2q4K/NjHfxoPu/wcODBB8X6s8+K2T+dMJmAO+4Q63/9ZahJLnFpzM/166KiBeC00lt2NrBihVgfPLjw7fN0DH6IiIiISkChJrN0sdKb3OWtdWvPG9we7Bus2TZa8MAE7dxH1mOqbD7vyZNFELRnD/DCC4buUZTBj0tjfuQS13XqABVsx46pyd30YmMNJYnKLAY/RERERCXA5eAnOxs4elSsu1jpzdO6vAHGMz9+3n6abevMT2RgpGbb5vOuXRv49Vex/vXXwPnzTtvWowfg5SWmCDJwuktcGvMj92k0MN5HPbGpwblxyyQGP0REREQlwOXgJyFBDOKJigJiYgxd4qmV3gDjpa6rhlbVbFsHP7GhsQ6PAwB69QK6dBEDeT7/3GnbIiMLAkp3Z39cGvOzbp1Yduzo8DRJAv78U6wPHHgrrfN8DH6IiIiISoDLwY9c6a1pU0N/us/KKugl54mZn0DfQM22vYIH0/tO12xbBzfW3d6y8uzM6TNxolguX26ofe7q+nYj6wY2n90M6Wa1Obnbm9MxP7m5wMaNYr1HD4en7tsnMlRBQU5PLfMY/BARERGVAJeDHxeLHezbB+TlAZUri+lsPI11IQi7mZ+wqvhq4FfKtrNS13JmxUbv3mK5b5+Y+8cJOfhZtQqwWJyeblfLWS3R6btOWJiwEIAL3d527hSDeCpWdFq3Wu7ydscdnjXRbVFg8ENERERUzCRJKvLgR93lzRPHeEQERmi27Y35yc7L1gQ81sFPiF+IZvv2arfr37By5YJKAAbSOe3aiSrZSUmiUnZhnU4+DQBY9I+oQ22429uaNWIpD0ByQA5+ynuXN4DBDxEREVGxs0gFqQJDwY8kFXR7c7HSmyd2eQNEpqdJ5SbKtr1ubzXDa2q6iFkHP2NbjDV+0z59xNJA8OPrC3ToINbl+X4kScKn2z/F5rObnV4vSRLunH+nsi0Hd4ZLXcvzEjnpx3b+vChkZzIVTNBanjH4ISIiIipm8gMuYDD4uXwZuHZN/IW/USND9/DkYgey+Erxyrp15ufC5As48J8DiA2NdZj5sc4gqQNPG3LXtzVrRMDpRJcuYikHP0uOLcHjKx5Hp+86Ob0205yJpceXKtty1TpDpa6vXwe2bBHr3bs7vI9c6KBDB5HcKu9cKCxPRERERO7gcvAjZ33q1wcCAx2fC+DGDeDYMbHuqZkfa9aZn9jQWKWSm6Pgx5ocXOhq314Mirl0SVTXcxJodu0qlhs2iFjp2PVjDs9Xk6ANrq5nXcfIBSMR5hcGwMmYnzlzALNZlLiuV8/hfdQlronBDxEREVGxczn4cXG8z65dYlm7tqiMXRbYG/MDAOEB4cq6SWeAk4+Xj/KZO8z8BAQAnTuLKgarVzsNftq0Afz9RWLu+HFt4JWem24z3khN/TsAAL8e+VWzveTYEv0LJUnMRwQADz/scEBXdraY3BQABgyw/z7KE3Z7IyIiIipmRR38lIUub4D2cwr2C7Z7XqvYVsq6CbbBgLpynMPgBxBz/gAiAHIiIEAUPgBE9kcd/Az+abDDa62DH8N27RJZqcBAYORIh6du2SICoJgYw70lyzwGP0RERETFTP3g66ybFgCXgx+52EFZCn6sS1arBfoUdAXU69bm610Q/NgtdS2Tix6sXg2kpjpto3rcj7qr2ppTaxxe5yz4ua2ynfLVv/wiloMHA2FhDl9Djt969fLMin9FgcEPERERUTFTl7nW66alkZsr/tIPlJtKbzJ1oGJvnh9AG9zoBRVyMQHAQOanaVOgYUORMlm0yGkb1cGPoUDWQTvVpnSdon9g3TqxNFC6bfVqsZTrOBCDHyIiIqJiZ6iil+zoUTG4vUIFoHp1p6dfuCDG63t7i/HwnkydxXFU+lkd3OgFFQuHL1TWnQY/JhNw331ifd48p21s31581mfOADeS3Bf86AZ7KSnA3r1iXa62YMfVq8Du3WK9Z0/DzSrzGPwQERERFTOXJjiVK701bWqo75Kc9WncWEzC6cnUAUJWXpbd89SBkV63ts5xnbF6tEiDOKz2JpODnzVrgMREh6eGhACtW4v1Y/84mZtHpVDBz6ZNgMUC1K0LVK3q8Prffxe1EVq0AGJjDTerzGPwQ0RERFTMXAp+ymmxA8D4mB9110F7QYXcNc5p5gcA6tQBbr9dBBo//+z09H79xPLAAfdlfnSr28kTCjnJ+gDAb7+J5T33GG5SucDgh4iIiKgILD22FL8e/lX3WFEGP2Wl2AGgDRC6xhU88Ffwr2D3GnuZHXk8jqHgB3Cp69vAgWJ55FARd3vbtk0sO3Z0eO316yJpBQBDhxpuUrnA4IeIiIjIzTJyM3Dnj3di+G/DcT3zus3xogp+LJaCzI+nFzsAtF3YTCYTrj97Ha92eRW7Ju6ye429oEIOfpxWe5Pde68YzLNzJ3DkiMNT5a5luTnOH63XnVqH5jObY+OZjQ7Pswl+8vIKJnByEtnOmydOb94caNDAaZPKFQY/RERERG526MohZf1c6jmb4/IDuqNB/ADEqPVLl8RYn9vslD5WOX5cVGcODBRjfjyddSATGRiJ17q/hrqRdQ1fI5M/6zMpZ4zdvHLlgpTOF184PNVkAoYNAyA5f7Qe98c47L+8H48se8TheTbBz5EjQGYmEBoqqtHZoZ4Ddfx4p80pdxj8EBEREbnZxbSLyvq5FBH8zNk3B6+sfQWSJBnP/Bw8KJZ16oiR9U7IXd5atgR8fR2f6wkqBNjv3maPs8xPniUPVzKuGHuxR24GKHPmAOnpDk8dNw5Og5/c/FycSj5l6NY27337drFs00ZkpOzYtUv82vj7F/TcowIMfoiIiIjc7Eb2DWX9auZVHL12FA/+/iDe3Pgmtp3fZjz4UVd6M6CszO8jmzlgJppXaY53e71r+Bp7wY96LNCByweMvVjPnkCtWkBaWsGkOXY0aQLUqe04k3c6+bSx+0JnXJMc/LRr5/A6Oetzzz1ARITh25UbDH6IiIiI3CwpK0lZv555HQ1nFHRTup513XjwU44rvQFAncg62PvwXjzb8VnD19QMr6m7X53tCfFznkUDAHh5FUwmumyZ09PvusvxcV8v4+k4fx+ram9y8OPgy83IAH78UayPG2f4VuUKgx8iIiIiN8q35OPotaPK9vUsbcEDl7q9yQPcmzd3et/c3IL5L8tK5scVax5Yg6ldp2LEbSN0j3esXlAhLTc/1+Z4niUPOy/stC2I0L+/WP75p5hs1oFWrR1XkjMZmKdJV1oacPiwWHeQ+fntN3FqnTqGqmGXSwx+iIiIiNzood8fwtd7v1a2391s22XLUPBj8IFXduCACIAiIsTDb3nTo1YPTOk2RRnbYy0qKAo1KtQAAGTnZdscn7xyMtp+3RZP//W01Qv3EMUPEhNFAOSABG3wY10kzkiluUpBlfDX/X9pd+7eLSoZVK8OxMTYvVbu8jZ2rEhakS1+LERERERu9P2B7x0ev+unu2DOFxkEh8GP/MBbowZQpYrT+6q7vBU2wVDWxYSIwEEv+Pl0x6cAgGnbp2kP+PkVlE2bMcPh61vPIfTaa9rjeuORXuz0IhIeTVC2H23zKHrX6a09SR771bKl3XsfPQps2iSCnjFjHDazXGPwQ0RERFTMjlwVKQGHwY+BMR5qZa3YQVGQy0frBT8OPfywiCrWrgUSEuyeZh38/PJLwbAtQD/4GdZ4GBpWLBgTFugbaPvCcgawSRO79/7mG7Hs3x+oWtXuaeUegx8iIiKiYpaWmwbAYPBjoMsbUPaKHRQFOfjJyctx7cIaNQoKH8yda/c0vW5tU6YUrOsFPxEBoiTbilErMDR+KCa2mmj7woduzhtlZ66ntDTgu+/EOgsdOMbgh4iIiKiYZZozATgJfuRUjoFoJi2tYHwJMz/2+XqLamvvb3nf7jl+3n76B+RJc377TXRH1GGd+fHyAhYvFj0YAf3gJzwgHADQp24f/Db8N2VbIUlOg5/33weuXQPq1QMGDNBvPgkMfoiIiIiKwHu93rN7zGnwc+GC+PH2Blq1cnov9Xh4A8ODyq1NZzcBAA5eOWj3HH9vf/0D/fsDAQHAiRMFVfisWAc/crz06qtiqRf86HZzUzt3TkS3Pj4iurFy7Rrw0Udi/Z13ysbktkWJwQ8RERGRm2w+u1lZD/INsnteVl4WAAfBj5z1ue02IDjY6X3Z5c2Yt3q85fQcuWucjdBQYOhQsf7557qnWAc/U6aI+HXZMmDrVu1EqzKnc//I430aNBDFF6xMny7m92nZEhgyxPFLEYMfIiIiIrdZcmyJsm79IKx2LfMaACDYz05gU8hiBwx+HLs7/m5lXbLTdc1mclG1sWPFctUq3a5v6u88IiACdesCDz4otl96CTDn22Z+nM7946DLW3Iy8MknYv3ll1nlzwgGP0RERERuoq7aNbrZaLvn7boouk3JpZdtuFjsgJXejFFndcwW/QlL7WZ+AOD220W/sgsXgFOnbA6rg59qYdUAAK+8Avj7A+vWAUuX2wY/TjkIft56C0hNBRo3BgYNcv2lyyMGP0RERERuImdyQv1CER4QjgH19EefJ6YnAoDt4HYAyM8vGFNiIPi5fBk4e1b81d/A8KByTR3Y2Ct3bXfMDwAEBQGtW4v1DRtsDquDH3k9Lq5gvp/pnxYi+JG7vTVurNm9di3w4Ydi/Z13OKmpUfyYiIiIiNxEfuBtFdtKs21P5xqdbXcmJADp6UBICBAf7/Se8nifhg2BsDDX2lveqAObxPRETPxzItadWqc5x2HmBwC6dBFLJ8GPenzP008DffsCuXFLXWtwfn5BGT9V5ufiReD++0XPu3HjCqpwk3MMfoiIiIjcRH749TKJRyy9Ae5qTaOb2u6Uu7y1bi1GyzvBYgfGmUwmJQD6fOfn+GrPV+gxtwfSc9OVcxyO+QGAjh3FUv6eVPQyP4D4GufPB3D7J641+NQpICtLVJmrXRsAkJgIdO8OXLokYuNPXHzJ8o7BDxEREZGbWAc/zjI/ut3eXKxewGIHrpEzO+pub7sv7rY5bpc8sCohQZSgVtFkfqwmPI2I0H+5xEQH95LH+zRqBHh749o1oHdv4Ngx0Z3u999FTzwyjsEPERERkZtYBz85eTkOz9d90Hah2IEksdiBq+TPXP6OACA3P1dZdzjmBxATKVWrJj78vXs1h+xlfhypVk0URbh2TeegarzPv/+KwOfQISA2FlizRnfaH3KCwQ8RERGRm1gHP47KGA9pOMT2eEYGcPDmBJwGgp9Tp4CkJDH9S1OdHnRkSw5+0nILsjaHrx62Oa723ub3sPifxQU75KIHcp/Dm9TdHPW6PPao1cNmX34+8OabQJ06wMqV2mOWg4exEZ0w6eQTaNQI2LcPqFQJWL1anE+uY/BDRERE5CZyVyc5+Pnojo80x9vEtoE0RYLlVQsWDF9g+wJ79gAWi/jTftWqTu8nZ32aNRPllMm5QN9AAEBaTkHw89TKp5R16+Dn8JXDeG71cxjy85CCa+Q0282qfHmWPDy/+nmsOLFCuU4v85ORm2Gzb84c0astNRUYOBB45hng6FExj2qDRW+jCzbisy2tkJMD9OwJbN5sqA4G2cHgh4iIiMhN5Adeb5MoVNAqthUO/OeAcvzexvcCEBkh3ayQi/P7sNiB6/QyP2reXtoiE+oucQev3MzKWWV+vt37Ld7d/C42nt2onGs95gcAMswi+LmzvijPNrXrVDzwgOg9N3gwYDYDH3wgKvc9+ihwIjcOoUjFA0PT8fvvYm5VdnW7NT4l3QAiIiKissK62xsANIlugstPX8bms5sxsMFAxy9QyMlNGfwYJwc/a0+t1T0uSZJmW53BOZl0Eh2qdygIfk6eBG7cwMmkkzavo5f5kceAPdfxOfw58k9lv58fsGABsGSJmBNozx6gdrVcPHb+OUwM+RHBv14C7PegJBe4nPnZsGEDBg4ciNjYWJhMJixevFhzXJIkvPrqq4iJiUFgYCB69eqF48ePa85JSkrCqFGjEBYWhvDwcIwbNw7p6ekgIiIi8mR6wQ8AVA6ujCHxQ+Dj5eTvzi5EM3l54iEZYLEDV/x741+Hx62DFvXYncsZl8VKZKRSehq7dkGCNmCyvs56n5wZVPPyAu66SySTzpwBjr6zCE9hGoKb1BYz2JJbuBz8ZGRkoFmzZpgxY4bu8ffeew+ffPIJZs6cie3btyM4OBh9+vRBdnZBOcFRo0bh8OHDWLVqFZYsWYINGzZg4sSJhX8XRERERKWAveDHkMRE4OxZ8aArZxYcOHIEyMwEQkOBBg1cv115dSXjisPj1oGMOhhKzk4uOKAa96OX5dHbl2fJAwCHQbCXF1CjBuBz5GZ3ySZNHLaXXONyt7d+/fqhX79+usckScK0adPw8ssvY9CgQQCAuXPnIjo6GosXL8aIESOQkJCAFStWYOfOnWh98x/2p59+iv79++ODDz5AbGyszevm5OQgJ6egVGRqaqqrzSYiIiIqcrcU/Mhd3ho1EhGNE/J4n9atxQMzuYdN5kc1dkcT/LRuDfz8s8j8tKxp8zp6Y37kfdbjinTJVf8Y/LiVW/+pnDp1ComJiejVq5eyr0KFCmjXrh22bt0KANi6dSvCw8OVwAcAevXqBS8vL2zXmSkXAN5++21UqFBB+alevbo7m01ERETkFnK3pkIFP3KXN473KVGOur3dyL5RcEB+lrXT7a2wmR8Fg58i4dbgJ/HmFLXR0dGa/dHR0cqxxMREVK5cWXPcx8cHkZGRyjnWXnjhBaSkpCg/586dc2eziYiIiNxCqfZm5C/71lwsdiCfzvE+rvlp6E8OjzsqeJBpziw40LKlWJ49CynTtoS1XvDjaMyPRloacPq0WL/tNsfnkks8otqbv78//Fm8noiIiEq5Qnd7s1hcqlt97Rqwf79Y79TJtVuVd7Uiajk87qjbm1ytDQAQFiYGWx09CunyZZvX0St4IGd+nAbHciWLqlWBqCjH55JL3Jr5qVKlCgDgstUvwOXLl5VjVapUwZUr2oFmeXl5SEpKUs4hIiIi8kSFDn6OHhWzXAYGGvpL/7p1YtmkCWDV4YacCPELcXhc7sKWb8nHhjMblLl5AO2cPwAK0m46wY9u5udmIOW025uc1rv9dsfnkcvcGvzUqlULVapUwZo1a5R9qamp2L59O9q3bw8AaN++PZKTk7F7927lnLVr18JisaCdwTQvERERUWlU6OBHftht1Qrwcd4xR37U6tnTtdsQEBMS4/C4/B2+u/lddJ3dFYN+GqQcy8nP0Z58c9yPz+WrNq+jW/DAaLe3bdvEksGP27nc7S09PR0nTpxQtk+dOoV9+/YhMjISNWrUwJNPPok333wT9erVQ61atfDKK68gNjYWgwcPBgDEx8ejb9++mDBhAmbOnAmz2YzHHnsMI0aM0K30RkREROQp5AdeL1f/vuxisQMGP4UXERjh8Lg85ueltS/ZHLPJ/NwMfkyJiYBVBq7QBQ8kicFPEXI5+Nm1axe6d++ubE+ePBkAMGbMGMyePRvPPvssMjIyMHHiRCQnJ6NTp05YsWIFAgIClGvmzZuHxx57DD179oSXlxeGDh2KTz75xA1vh4iIiKjk3HLmx0Dwc/YscOIE4O0NdOniagvJGb2gRaYZ8wMAzZsDXl7I1yl4IEGCJEkwqSYoNVTq+t9/gUuXxBcsF1Ugt3E5+OnWrZtNFQw1k8mE119/Ha+//rrdcyIjIzF//nxXb01ERERUqhWq2ltWFnDg5oSWBoodyFmftm3FmHtyL3nMT7BvsGa8D6DT7S04GGjUCPmmQ7qvZZEsShc3SZKUbm8OMz9ffy2W3bsDQUGFeAfkCKfEIiIiInKTQmV+9u4F8vJE5YIaNZyezi5vt87Xy9fuMfk7rBhU0eaYTbc3AGjdGvl2vm51xTd1RsnumJ+0NOCrr8T6I4/YbSMVHoMfIiIiIjcpVPCzZYtY3n47oOoipUeSGPy4w56H99g9JvdwqhBQweaYTbc3AGjTBvl2vjZ1wCOP9wEcZAY//BC4fh2oWxcYONBuG6nwGPwQERERuUmhgp/Nm8WyY0enpx45AiQmiorYNwvpUiHUiahj95j8HaqDFZlu5qdjR03mp0etHsq6uuKbOguk2+1t2TLgzTfF+htvGKr6R65j8ENERETkJvIDruHgR5JcCn7krE+nTgDnfy88fx/7H5485secb7Y5ZjPmBwCaNoUltGDuoCDfgnE6W89vVdY1mR/rbm8JCcDQoUB+PjB6NHDvvU7fAxUOgx8iIiIiN3E583P8OHD1qohkWrVyejq7vLmH+vvpVbuX5pj8HepleXQzPyYT8uOqK5s+poKMTe/ve+PfG/8C0GaBNJmf3Fxg1CggO1uU7/vyS6fdH6nwGPwQERERuYlS7c3ZJJYyOevTpo3TVE5eHrB+vVhn8OM+dze8G38/+Dc+7/85ACAxPRHZedk4k3LG5tycvBzdqsf58Q2VdZ9LiZpjBy6LSn52x/y88YYoehEZCfz4o+jTSEWGwQ8RERGRm7ic+dmwQSwNdHnbvRtITQXCw4EWLQrZQLIhQUKXuC6ICooCABy5egRP//W03XPVY3dk+cEFAcvDP5/UHJOzRerrlN+P3buBt98W6zNnArGxhX4fZAyDHyIiIiI3kbs2GQp+JAlYtUqsG0jlyF3euncX81+Se8iZHPV3NmPnDLvn61V8k7/3x4+Eotf2q5pj8tgheenn7Vdw8IknxDif4cOBYcMK9wbIJQx+iIiIiNwg35KPC2kXAACBvga6Lv3zD3Dhguju1qmT09M53qdoVAmpAgAwoWCcTaCP/e8vOTvZZp+c1ak3eJyY+FTFbBFBj5wBUoKffftEt0cfH+DjjwvbfHIRgx8iIiIiN+g6uyt+PfIrACDEL8TJ2SjI+nTu7HScR1ZWwfAgBj/usWD4Ajzb4VkMiR8CwHhXxWofV8Pl9MuafXLmx7tufeDwYc0x88plAAoqxSnBzxdfiOXQoezuVowY/BARERG5weZzm5V1l4Kf3r2dnrplC5CTI56RGzQobAtJ7e74u/Fu73eVoEcd/GTlZTm89vsD32u25cyPt5c3EBenOZa16FdM+uIuNP68MQDA39tfRLM//ihO+M9/bul9kGsY/BARERG5mdPgx2wuKN1mIPhRd3ljFeSiYdL5YHvX7o27Gtxls99u5kenyl+6H/DZlT+VbT9vP+D334G0NKBmTVHemooNgx8iIiKiW3Q987pm22nws20bkJ4OVKoENGvm9PU53qfo6XV78/P2wzd3fYNJbSdp9qfnpmu2NZkfK2lVImxeE199JTbuvx/w4uN4ceKnTURERHSLfjz0o2bb0YB5ANoqb04efpOTgV27Ck6noqEueCDz8/ZDxaCK+KTfJ6gZXlPZr5mkFE4yP906aLb9M3OAtWsBX1/goYfc0HJyBYMfIiIiolt0JeOKZtvX29fxBS6M9/n7b8BiAerXB6pVK2wLyRm9rI26LHWoX6jdc60zP693e105lhITqX3Nf8+KlSefBGrXvqU2k+sY/BARERHdooNXDmq2fb0cBD8pKcCOHWLdxfE+VHT8vf1t9qmDH3X5cusMj3Xm55Wur+DDOz4EAFzP0naJ9MsHUKMG8PLLbmk3uYbBDxEREdEtOnxFW97YYeZHTuXUqwdUr+70tRn8FA9/H8fBT4BPgLJuPT7IIlls9svjvlKyU7T3adYSOHQICAu79UaTyxj8EBEREd2iPEueZtth5mfdOrHs0cPp6547Bxw5IoYFde9+Ky0kZ/QyP+rv0VHwo1fwQA6cUnK0wY9feBQQGgoqGQx+iIiIiG6R/PArsx4Qr7F2rVgaCH6WLxfL228HIiMdn0u3Ri/zow5y1MGP9fdtzjcD0AZLcvCTnJ3s9D5UfBj8EBEREd0iecyHzG63t0uXgAMHxGQ9BlI5cvDTr9+ttpCcUXdxk6mDH3VgY/195+TnANAGSErmx6rbm959qPgw+CEiIiK6RYa7vf31l1i2aiXm+HEgNxdYvVqsM/gpenrd3tTBT2pOqrJu/X1n52WL11BldeQgJy03TXMug5+SxeCHiIiI6BZZd4Oym/mRg58+fZy+5qZNYh7U6GigRYtbbSE5oxeUqMfwNKncRFlXBz95ljwcu34MgDaA0gumHO2n4sHgh4iIiOgWWXeD0h3zI0mi0htgqHSb3OWtb1+n86CSG+h9Z+rMz6tdX1XW1cHPlHVTlHW9bm/WmPkpWfynRERERHSLbDI/et3e/v0XuHAB8PUF2rVz+poc71O8gnyDbPapg58KARXwTs93AGi/7/9t+p+yrtftzZrDSoBU5Bj8EBEREd0imzE/et3eNmwQy7ZtgSDbB221s2eBw4dFxueOO9zVSnIk2C8YvWtrJ521nsxUzg7J37ckSZrj6i5tzPCUTgx+iIiIiG6RTbU3vb/uy13eunRx+npy1qd9eyAi4lZbR0Y92PxBzbb1fD7yGCA5+LEuY22k25t1lpCKF4MfIiIioltkaJ4fF4KfZcvEkl3eipf192Yd/Fhnfqwzfurr48LjYILJ5h4WyeKWtlLhMPghIiIicmL7+e145q9nbOZskVlnfmz+6n/2LHD6NODtDXTs6PBeOTnAmjVivX//wraYCsPV4MdsMSvHmkU3Q1RQlLIdHhCOGhVq2NzDuqscFS8H0w8TERERkSRJuP2b2wGIySw/6feJ5rhFskBCwQPtHyP+gMlk9Rd/ebxPy5ZAaKjD+23aBGRkAFWqAM2b33LzyQXWwY+61LX6uJzpM+eL4MfP2w97Ht5jEyxVCq6EMylnNPtC/R1//1S0mPkhIiIiciAnP0dZX/TPIpvj6qzPlaevYGCDgbYvInd569rV6f3kLm99+wLWMRQVLWeZH3lMT1qOmLhUzvz4efvZnAsAlYJsJ7Kt4F/BLW2lwmHwQ0RERORAljlLWT+fet6m65t6vI+61LGGnPkxEPywxHXJcRb8NIhqAAA4fPUwAG3mR094QLjNviHxQ261mXQLGPwQEREROZBpztRsX8u8ptmWH4AB29LIAIAbN4Bjx8R6hw4O73XmDJCQIEpc9+7t8FQqAtZV+qy/z6phVQGI3wFJkpTMj725ewJ9ApX1Zzs8i8OPHEbT6KbubDK5iMEPERERkQNZeVl2t7/e8zXC3glTtq3HiAAA9u4Vy1q1gMhIh/dasUIsWeK6ZDjL/MjBjEWyYM+lPcjNzwVgZ14nAIG+BcFPrYhaaFSpkTubS4XA4IeIiIjIAXW3N+vtCX9O0BzTLXG9Z49Ytmzp9F7s8laynAY/qmCm9VetlayfvcyPejxYj1o93NVMugUMfoiIiIgcsM78WHeDU9Pt9iYHP61aObxPbm5BiWsGPyXDWfDj760d07Xjwg4A9jM/F9IuKOv1o+q7o4l0ixj8EBERETkw8c+Jmm3rYEhmgsm2xDVgOPOzeTOQng5ER7PEdUlxVura+vt9cuWTAOwXPEjPTXdf48gtGPwQERERObD/8n7NtnU3OJnueJ+0tIJiBy1aOLyP3OWtTx9R8ICKn7PMjz32ur0x+Cl9+E+LiIiISMeOCzvw7qZ3bfbP2T9H93zdv/7v3w9IElCtGlC5ssP7LV0qlgMGuNxUchPr7muGgx873d6eaPcEAOCuBnfdWsPIbXRG5RERERFRu6/b6e7//ejvOJdyDkuPL9Xs1w1+DHZ5O30aOHIE8PZmieuSZJ35McG2G2PN8Jo4nXxas69ysH5ge1+T+9A0uinH+5QiDH6IiIiIrFhPZGqt47cdcS71nGbfrQQ/y5aJZYcOLHFdkqyDn4tpF23OWTB8AVrN0havqBZaTff1TCYTmkQ3cV8D6Zax2xsRERGRlbTcNIfHrQMfwM64DxeDH3Z5K1nWwY88j4+aJEk2+6pXqF5kbSL3YvBDREREpLL13FZU/9j1h1mbzE9WlujLBjgMfrKygLVrxXr//i7fltzIOvjJzsu2OUdvfE/1MAY/noLBDxEREZHK6xte193vbPC7TfBz8CCQny8KHcTG2r1u/XoRAFWrBtx2m6utJXeyDn5y8nNszmlS2bYbW5WQKkXWJnIvBj9EREREKjl5tg+8ALB9/HabSS4111k/KO/eLZYtWwJ68//cpO7y5uA0KgbWXRf1Mj8mkwld47pq9oX4hRRpu8h9GPwQERERqehNYhpXIQ6tY1tj4b0L7V5nXQHMyHgfSSoIftjlreQZ6famd16wX3CRtYnci8EPERERkUpydrLNPrPFDACICowy/kJy8NOqld1Tjh4F/v0X8PMDevRwpZVUFKyDmi5xXXTPs57Qlpkfz8Hgh4iIiEgl35Jvsy/LLLJBFYMqGnuR3Fwx5gdwmPmRsz7dugEhfH4ucdbBz8RWE3XPsx7/FezLzI+nYPBDREREpJIv2QY/N7JvAACiggxmfg4fBsxmMWlPXJzd05benCeVXd5KB3VQ0zWuq00wJPM2aTM/Qb5BRdouch8GP0RERFTmbTq7CZvObjJ0bp4lDwCweexmvNn9TQDAe73eAwBU8K+ge02diDqY0X9GwQ71eB87VQxSU4GNG8U6g5/SwaT6rhxV91MHRbUjanPMjwfRD2eJiIiIyohMcyY6f9cZAJD+QrrTB1W525u/tz9e6PwChjcejrqRdQFoH45lQ+OH4rfhv2l3Gih28NdfIjlUr574odLFUfAzsP5A/H70dwT5BuGfR/9xWgadSg9+U0RERFSmpeem667bI3d78/byhpfJC/Wi6mmCnv+0+o+y/lzH5zBr4CzbFzEQ/Hz/vVgOGeK0SVQCHAU0D7V4CIvuXYTjk47rTnpKpRczP0REREQqcubH3niPt3q+hfCAcIxuNhqNKjWyPSEvD9i/X6zbCX6uXi0odvDAA7fcZCoCelk+mZfJC4MbDi6+xpDbMPghIiKiMs0iWXTX7VEyP1aD2mWRgZF4u9fb9l/g0CEgKwsICwPq1tU95aefRIzUqhXQuLHTJlEJYFe2sonfKhEREZVp5nyzsi4XM3BEzvxYz+Vi2PbtYtm2LeCl/6g1d65YjhlTuFtQ0WPwUzbxWyUiIqIyTR3wyJOVGjnfXubHKTn4addO9/CRI8CuXYCPDzBiROFuQUWPwU/ZxG+ViIiIyjR1wKPOAtmjLnhQKE6CHznr078/UKlS4W5BRY/BT9nEb5WIiIjKtEJ3eytM5iclBUhIEOs6wU9+PvDDD2KdhQ5KNwY/ZRO/VSIiIirTXO32dkuZn507AUkCatYEKle2ObxuHXDhAhARAdx5p+svT8WHwU/ZxG+ViIiIyjR1wOMs8yNJklIRzl6pa4e2bRNLJ13eRowA/P1df3kqPibYL3VNnovBDxEREZVpmsyPkzE/6lLYher2tmSJWHbtanMoLQ1YsECss8tb6cfMT9nEb5WIiIjKNFfG/KiPu9ztbfduUezAZAKGDLE5vHAhkJkJ1KtnNzFEpQiDn7KJ3yoRERGVaa6M+ZHH+wAuZn4sFmDcOLF+771AlSo2p8hd3h54QMRHVLqF+YeVdBOoCDD4ISIiojLNlTE/cqU3wMXMz7JlwP79QEgI8MEHNofPnhXFDgBg9GjjL0vFb+aAmWgV0wpvdH+jpJtCRaAQI/mIiIiIPIe625uzMT+Fyvzk5wOvvSbWH3kEqFrV5pQffhBF4Lp1A+LijL0slYyHWz+Mh1s/XNLNoCLCzA8RERGVWfmWfMzYOUPZLpLMz0cfAbt2AWFhwFNP2RyWpIK5fZj1ISpZDH6IiIiozJq7fy6Wn1iubLt9zM+1awVZn48+0h3rc+CAmPfU3x8YOtRYu4moaDD4ISIiojJrx4Udmm1HmR9JknAj6wYAwM/bDyYjVQk+/BDIyABatADGjtU9Zf58sbzzTqBCBWPtJqKiweCHiIiIyqwg3yDNtqMxP3f9dBcazmgIAAj1C3X+4teuAZ9+KtanTtUt4WaxAD/+KNbvu89Qk4moCDH4ISIiojLLOvhxlPlZcmyJsh7qbyD4+eADkfVp2RIYOFD3lM2bgXPnxHCg/v2NtZmIig6DHyIiIiqzrIMYZ2N+lOucZX6uXgU++0ys28n6AAVd3oYOBQICDN2aiIoQgx8iIiIqs6yLFtjL/Fgki2bbaeZHHuvTqpUYzKPDbAZ+/VWsjxxprL1EVLQY/BAREVGZlZ2Xrdm2N+Yny5yl2a4aajtXj8Jg1mfVKuD6dSA6Guje3XCTiagIMfghIiKiMisnP0ezbS/zk56brtluUrmJ/ReVx/q0bg0MGGD3NLnQwfDhgA+nlScqFdwe/OTn5+OVV15BrVq1EBgYiDp16uCNN96AJEnKOZIk4dVXX0VMTAwCAwPRq1cvHD9+3N1NISIionIuJ08b/Ngb82Md/AT6Buq/oMGsj9kMLLlZP2H4cMPNJaIi5vbg591338UXX3yBzz77DAkJCXj33Xfx3nvv4VO5FCSA9957D5988glmzpyJ7du3Izg4GH369EF2draDVyYiIiJyjdHMT6Y5U7Pt5+2n/4LvvQdkZgJt2jgs37ZlC5CcDERFAe3bu9RkIipCbk/CbtmyBYMGDcKAm2ngmjVr4scff8SOHWKSMUmSMG3aNLz88ssYNGgQAGDu3LmIjo7G4sWLMWLECHc3iYiIiMqptJw0zba9MT8Z5gzNtm7wk5gIzJgh1l97zW7WBwD+/FMs+/cHvL3tnkZExcztmZ8OHTpgzZo1OHbsGABg//792LRpE/r16wcAOHXqFBITE9GrVy/lmgoVKqBdu3bYunWr7mvm5OQgNTVV80NERETkTEpOCgDAx0v8vfeWMj8ffwxkZYlUTt++Du8rd3mzM/0PEZUQt2d+nn/+eaSmpqJhw4bw9vZGfn4+3nrrLYwaNQoAkJiYCACIjo7WXBcdHa0cs/b222/jtddec3dTiYiIqIxLzk4GAFQMqojE9ES7Y34ycp1kfjIygFmzxPoLLzjM+hw/Dhw9Kooc3HFHoZtOREXA7ZmfX375BfPmzcP8+fOxZ88ezJkzBx988AHmzJlT6Nd84YUXkJKSovycO3fOjS0mIiKiskrO/FQMqgjAfrc3p5mf778Xg3jq1HFY4Q0oyPp07QpUqOB6m4mo6Lg98/PMM8/g+eefV8buNGnSBGfOnMHbb7+NMWPGoEqVKgCAy5cvIyYmRrnu8uXLaN68ue5r+vv7w9/f391NJSIiojIsy5yFXRd3AQCqh1XHoSuH7Gd+HI35sViA6dPF+qRJgJfjvx3LwY+duU+JqAS5PfOTmZkJL6v/KHh7e8NiETMn16pVC1WqVMGaNWuU46mpqdi+fTvasxwKERERucmy48uUdXnenkJlflatAv75BwgNBR56yOE9U1KADRvEOsf7EJU+bs/8DBw4EG+99RZq1KiBxo0bY+/evfjoo48wduxYAIDJZMKTTz6JN998E/Xq1UOtWrXwyiuvIDY2FoMHD3Z3c4iIiKicSsstqPQWERgBQH+en41nNmLS8kmafb5evgUbctZn7FggLMzhPf/8E8jLAxo2FD3kiKh0cXvw8+mnn+KVV17BI488gitXriA2NhYPP/wwXn31VeWcZ599FhkZGZg4cSKSk5PRqVMnrFixAgEBAe5uDhEREZUD729+H9/u+xZL71uK2hG1AQDZeWL+wCENhyjBjHXwk5qTii6zu9i8npL5OXYMWL5cFDiYNMnmPGuzZ4vlyJGFfCNEVKTcHvyEhoZi2rRpmDZtmt1zTCYTXn/9dbz++uvuvj0RERGVM+dTz+PZ1c8CAJ5b/Rx+HfYrADHmBwACfQOVYCY3P1dz7ZnkM7qvqQQ/334rlv36OU3lnDkDrF0r1h94wOW3QUTFwO1jfoiIiIiK06Erh5R1ubQ1AHy+63MAgJfJC77eIvOz7tQ6zbXWwZDMz9sPyM8XVd4A0eXNie+/ByQJ6N4dqFnThTdARMWGwQ8RERF5tFC/UGW9caXGyvqJpBMAgB8O/KDsu5p5FSnZKcq2w+Bn1Srg4kUgKspp9QJJAubOFesPPujqOyCi4sLgh4iIiDyaOoCpFFRJ95ycvBxlPSkrSfdaNT9vP+C778TGffcBfn6658n27xeTmwYEAHffbbTlRFTcGPwQERGRxzLnm3Hk6hFlWy+YqRleEzn5BcHP1cyrBdfbmffHLz0LWLxYbBhI5fzyi1gOGACEhDhvNxGVDAY/RERE5LGG/DwEjy1/TNnWC37mDp6ryfy0+7qdUgzBbuZnyXIgNxdo2hRo0cJhGySpIPgZPtzVd0BExYnBDxEREXmspceXarYzzZmwSGJidX9vfwBAXHicUvZadir5FAAHwc9PN6OZBx8UZa4d2LEDOHkSCAwUmR8iKr0Y/BAREVGRkCQJ5nz9bmVF5ZMdn2DA/AGQJEnp6ubv7Y/wgHDNefmWfAAOgp9d+wAfH2DUKKf3lIcG3XMPEBxc6KYTUTFg8ENERERF4s4f70TN6TWRmpNarPddcWKFJqgJ8AnAo20f1ZyTnpsOAEpw1jS6Kb4b9J1y3C8fIo1TubLDe2VlAT/9JNYfesgNjSeiIsXgh4iIiNxu1clVWHZ8GS6mXcTaU2uL/f43sm8o6wE+AQjyDUKrmFbKPjkgk4OkamHV0KRyE+W4Xz4MFTpYtAhISRHz+nTt6pamE1ERYvBDREREbnfHD3co69czrxf7/UcvGq2s+3mLMtX96vZT9qXlpgEoCH78vP2QL+Urx70rRwP9+zu9j9zlbcwYwItPVUSlHv+ZEhERkVtZd3NLyUmxc2bRWf3vamXddLNgwatdX1X2peWI4EceF+Tn7Yem/9xAZCbQ5DKAqVOdzu1z9iywZo1YHzPGfW0noqLD4IeIiIjc6vv932u2M82ZRXIfuWiBUb7evri38b0ACgK0jNwMAECw2YSAUWNw6UNgb+Zo4OGHnb7enDmizHX37kCtWi42nohKBIMfIiIiciv1eBsAypw67nYt85qynvuyftU2OdiRhfmHASjo9pZhFsFPyMp1wOXL8GvUBN6fz3Ra3tpiAWbPFussdEDkORj8EBERkVvJ2RRZVl7RBD9y17bwgHD4evuiSkgVm3PUXd0AINQvFEBB5if9ZhAUfP4KEBkpKhgEBTm998aNwL//AqGhwNCht/Q2iKgYMfghIiIit7Lu5lZU3d7uX3Q/ACA5OxkAsG3cNptzQvxCNNuh/iL4SctJw7FrR7F2m6hTHZxnAn7+GahTx9C9Z8wQyxEjDMVKRFRK+JR0A4iIiMjz/XPtH8zYMQMD6g/AJzs+AQBU8K+AlJyUIsv8WIsLj1PuKbMOfoJ9xSykmVmpaDCjIXCzd1tw/0FAr16G7nP2LLBwoVifNOnW201ExYfBDxEREd0Sc74Z8TPiAQCf7fxM2V8xqKIIfopozI8eL5O2U4t18BPoGwgAyFi1FKiqOq+n87LWshkzgPx8oEcPoEkT5+cTUenBbm9ERER0S3Ze3Km7PzwgHEDBXDru5uMl/ob7ef/PbfbJ5Dl+ZEHporT15Txt+e1gv2BD97x6FfjyS7H+5JOutJaISgMGP0RERHRL1FXX1JpFNwMAmC3mIrlvVGAUAKBD9Q7KvsrBlZX1aX2m2VwTtGQlAOBSpK9mv9wdzpmXXgJSUoCWLYEBA1xtMRGVNAY/RERE9P/t3Xd0FUUbx/HvTSUkpFBDCb13BKQJIiBFUJEqoCJgQ0TAhh2woeiLiCJFBQtNUQEBBaWLFOm9SA011PSe7PvHkk1u7k0IkEAgv885OezOzO7OciXmycw8c13SZ3dLUaNoDcCcFpcdgsOCORl+0jpPSaSQdtQm7fGQxkPsb3D2LF5LVwJwqIB9n7Iy8rNlC3z9tXk8fjy46KcokVuO1vyIiIjIdUnZKye9lGlv2THyExkfSZlxZQB47573+GrLV9ZePWlHbQrnL5zxTT7/nPxRzvuSfm1QeoYBzz9v/tm7NzRrdpUvICK5goIfERERuS4ZjfykbCiamJx43c/YdXaXdfzmijft6tKO2nxy7yfsObeH4c2G298gMhImTCB/BjHOlaa9TZkC//xjprX+6KOr67uI5B4KfkREROS6ZLSPT0qygeyY9nYi/ESGdfndUzfaqVakGkeGHHFsNHcuXLqEV6VSgOO9SvqWdLzmsiVLYNAg83jkSChVKqu9FpHcRrNVRURE5LpExkc6LXd3MZMKZMe0t+Nhx52We7h6OGR4c2r+fADyt+3ktLqgV0Gn5Vu3QrduZmrrRx+Fl17KWn9FJHdS8CMiIiLX5WzUWafl7q6Xg59sGPk5GnrUaXmWsrTFxsLixQDkb2ufom1yp8kcHHzQ6WWnT5sZ3SIjoXVrM9mBzXZV3RaRXEbBj4iIiFyz2MRYvt76tUP55E6Ts23kZ//5/Yz/d7zTuiztz7N8OURFQcmSeNW7067qgSoPUKFgBYdLEhKgRw8zAKpeHX75BTw8HJqJyC1GwY+IiIhcs682f+W0vFHJRtk28vP0wqczrMvSyM+8eeafDz5I/nTBUspeQekNHQpr1oCvr3m5n1/W+ioiuZuCHxEREblmGU1HK+ZTLNtGflYdW5VhXUpGuQwZhpmxAOD++/Fy97KrTgnQ0ho/Hr780pziNn06VKp01V0WkVxK2d5ERETkmqVNNrD00aVsOLmBu0rfRaBPIKcjTgPXN/JzJvJMpvVXDH6OHoXgYHBzg+bNcbFl/HvfkBAYPBjmzDHP33sP7r//KjssIrmagh8RERG5ZmeizOBk5N0jaV2+Na3Lt7bqUkZVrmefn2OhxzKtv2Lws3Kl+WfDhuBtTnmrXKgyBy4csGu2ZQs8+CCcOGGO+IwcCa+9do2dFpFcS8GPiIiIXLOT4ScBKB9Q3qHO09UTMJMiXKtuc7plWu+X7wqLcZYtM/9s2dIqCo8Lt2vy44/Qrx/ExEDlyjBrFtxxx7X0VkRyO635ERERkWuWsvmos01CU9bXxCTGXNO9k41kTkWcyrSNr0cmIz9JSVaKa9q3t4r71OoDQG3/ZnTqBA8/bAY+7dvDhg0KfERuZwp+RERE5JqdjDBHfkoWcAx+8rvnB8xpb1c79S0iLoJJmyaRbCQDUK1wNauuRIES1nGm0942boQLF8yUbU2aWMVt3d6j5p457Hh1AYsWgYsLvPwyLFwI/v5X1U0RucUo+BEREZFrEh4XTmR8JJDByI9bama1mISsj/4sP7Ic3w99GfT7IKts58Cd1vGdJVP36sl02tsff5h/tm0L7u4YhrmOp13rfOz6qRtuiQH07w/79sGYMeDqmuUuisgtSsGPiIiIXJOUKW8+Hj74ePg41Odzy2cdRydEZ/m+zy561u68QkAFXF1c+fbBb+lUuROjWo6y6jId+fn9d/PP++4jORmeego+/NAseuopOHQIvvlGqaxF8hIFPyIiInJNRq8ZDaQmNkjPZrNZxwsPLMzyfdOvEXqzxZsA9K3blwW9FuCfz9+qyzD4CQmBTZvM4/btef99+Pprc4rb1KkweTKULp3lLonIbULBj4iIiFyT/G7mmp4LMReu2PaJBU+w5OCSLN03/ShRytqhFGn3FkpfZ0nZ2LRePVbsK87IkebpV1+Zmd1EJG9S8CMiIiLXJC4pDoAPW3+YpfbtZ7S/ciMgLjHO7jzt2iEAV5trhnWWy1PeTjfvQa9ekJxsBj39+2epCyJym1LwIyIiItckZXpahqMvQP+6Vx9tJBlJdueuLvaZCNKO/Hi6OZlyl5gIf/5JAm70XDWQkBCoWRO++OKquyIitxltcioiIiLXJCWDW8p+Ps64u7pf9X3Tp8W+GHPR7jxt8OPu4uT+GzaQfCmU/h6z+Hu7H76+8OuvkD/jGE1E8giN/IiIiMg1WXBgAZDJ1DPsAxXAHJW5gqRk+5Gf1uVaZ3hPZ8GVseh3hvEp0+N74uoKM2cqo5uImBT8iIiISJZdjLnI3nN7CYkMscrST1NLK+36HADKlIEtWzJ9Rtr73VP2HooXKG5Xnzb4SR9cJSfDkK9rMp4hAHz3HXTsmOnjRCQP0bQ3ERERybLm05qz59weu7KUjU6dMYxk+4JTp+CBB8wAqGhRJ+0Nu/Oy/mUd2qRdA5R22pthwLAB4Xx+rhcAkz4Op0+fTPYBEpE8RyM/IiIikmXpAx+APrX6ZNg+efMm+4JKleDkSejVy+kUuKiEKLtzZ8kUXG2u3F3mbuoUq0PVwlWt8s8+g/HfmsHO1KpjePolBT4iYk/Bj4iIiGTJltPOp6v55fNzfsHBgyT/u8G+bP588PaG5cvhrbfsqgzDoP6U+nZlxX3sp7yBuXnqir4r2PL0FmsUaO1aeOklc9ToE16k3/sVs/JKIpLHKPgRERGRKzIMg/bTs7ZPj+XTT0nGfhob1arB1Knm8YcfmsHQZXP3zeXAhQN2zXvU6OH01jabDReb+WNMWJg5kJSUZONhZvFCuXnm1DoRkXQU/IiIiMgV/R38N+eiz2X9gvPnYdo0km1O6nr0gKFDzeN+/eD4cQA2nEgdJfJw9eC/wf9RqdDlNG1RUeZ6oZgYu1sZBjz9NAQHQ3kOMYWnsL3/HrhpWbOIOFLwIyIiIk6NXTeWYYuHYRgGG09uBKBT5U4kvnXldNVMnAgxMSQWKeS8fswYaNAALl2CFi3gl19IiIu2ql9o/AIVvYPgjz/goYfAzw9KloTixc1oZ+RI2LaNUaPgxx/BjQS+5zEKdGkLDz+cDW8vIrcj/VpEREREHBiGwYt/vghA71q9OR15GoDKBSvj6uLKzC4zeWLBE3zR4QvHi6OiYPx4AKJrVIKICwAU8y6W2sbdHX76Ce69Fw4dgm7dyHd/fri85Mfj2++h41iIj7e/d1gYTJkCwNejTjCKrwGYyECaFdwHE+eCzdlwk4iIRn5ERETEibikOOv4fPR5TkWcArD23OlVqxcRr0XQr14/x4u//tqc9la+PFHFUkd+EpPTjRiVKwfr18PLL0OpUuQLTx35sZ08ZQY+RYrAkCGwa5eZHe7nn0l6423eqDyHJy8HPq/wEU/YpsLnnztNny0ikkLBj4iIiDiIiIuwjqMToq1EBBULpmZRS0k4YCc+Hj75xDwePpyoxNSAJiE5wbF94cLmFLhDh7A98ohVHND9UXNEKCQExo2DGjXA1ZWkzl3pFzyKDw50A2DofQf48Osi5r5BvXtfxxuLSF6gaW8iIiLiICI+NfhZfHAxm09vBqBCQIXML5w+HU6cMNfm9O1LwPwlVlV8UnzG13l4kFSxApw0Tx/oNQoCytk1SU42l/v88AO4usJ330GfPpWBylf1biKSd2nkR0REROyEx4XTaWYn6/zrrV9bxxnu6QOQlGSmrwZ48UXw9GRcu3HUDawLQGxiLAlJTkZ/Ui5PTgKgdbnWlEsX+BgGPPccfPMNuLjAjBnQJ+O9VUVEnFLwIyIiInZGrhzJ3vN7ndb5ePhkfOEvv8B//0FAgDlEAwT5BfHvE/9aTcLjwjO8PMkwg5+aRWvalRsGDBtmJpCz2cwRn549s/o2IiKpFPyIiIiInRPhJzKsyzT4SVnrM2QI+KS2c3d1x9vdG4DQ2NAML08Z+XG1uVplhgHDh8Nnn5nnX38NaZYGiYhcFQU/IiIiYsfd1T3DOg9XD+cVGzeaXx4e8OyzDtUp0+XC4sIyvHfKyI+rS2rw8/bb8PHH5vHEidC//5V6LyKSMSU8EBEREQASkhJYE7yGmTtnXv3FEyeaf/boYaanTsfLzQsw1/1kJGXkx8XmQkKCGfikLCH67DN45pmr75aISFoa+REREcklvtz4Jd1+6pZ5VrQc9Mpfr9Dq+1bW+dJHl7Lt6W1XvvDiRZg1yzx2MuoD4OZi/r7VYa+fNJKNZACio1xp0SI18Pn4Y3j++St3Q0TkSjTyIyIikksM+n0QAL/u/ZWHaz58w58/bsM467iYdzFalWuFzWbjxLATDF0ylEENBzm/cOpUiI2FunWhcWOnTVKmsjlLeLD51Gb6/9afqPgoAKZ940rkevD3hy++UFY3Eck+Cn5ERERyAcMwrOOUEZCbqXmZ5thsNgBK+pZkTvc5zhvGxsLYsebx4MFmOjYn9pzbA8D9s+7HGGHY1XWb042joUet88hwV6pXh99+gwpX2FZIRORqaNqbiIhILhAZH2kdB+QLuIk9MRX2Kpy1ht99B6dPQ6lS15yG7VRYiN15jYYXWL9egY+IZD+N/IiIyG3j/dXvcy76HOPaj7vZXblqZyLPWMeebp43sSemQvkLXblRbCx89JF5/PLLZqa3LNi/H7Ztgx07YM2mUOKbxtjVN7wrggIFrrLDIiJZoOBHRERuCyGRIby54k0AXm76MiV9S97kHl2dDjM6WMcpWc9upsL5szDy8/77cOQIBAbCE09k2Mywn+VG1appTur96tA+ISkhi70UEbk6Cn5EROS2sPPsTus4Ij7iJvbk2hy6dMg6Ttnv5kY6F3XO7ryQ1xVGfnbuTE3H9vnnkD+/02YJCZdTVJdOLfPygtq1zS+jciG+jkp3TbKCHxHJGQp+RETktnA++rx1HBF3awU/6Uc6MksHnRnDMKwkBVcjMTmRB2c/aJ0Xzl+Y5mWaZ3JBojnSk5gInTtD165Om8XFwQMPwJ9/AiNTy8PDwe3yTyC/7bfx9WzH/oiI5AQlPBARkdtC2uDn4MWDDJg/gC2ntwDw1eaveHze47n2h+q0fYdrm/aWbCTTbGozOs7saJc5LiNnIs9Yz5m6dSrrTqwDoFPlTpx96Sxl/ctmfPHbb8O//4KfH0yYkGGGt1deMQMfb2/7crc0v3p1tqeRpr2JSE5R8CMiIreFizEXrePev/Zm6rap1J9SH4CnFj7Fd9u/Y/au2RldflOdjTprd34t095ORZxi3Yl1/P7f74TFhWXadvWx1RT/X3GeXPAkAL/t/82qOx99PvPRoylTYPRo8/jzz6FECafNfv0Vxo83j2dn8tfuLPjJrUGqiNz6FPyIiMhtISYhxmn54N8HW8dp95LJTRyCn2sY+XG1uVrHJ8NPOm1zKeYSj/z6CHd/ezcA07ZNA+BY2DGrTZBvUMYPCQ6GF14wj0eNgkcfddrszz9Tq156CTp1yviWCn5E5EZS8CMiIreF2MRYp+VfbPzCOo5OiL5R3clU+k1Mv9n6jd35tYz8pL3nifATTtv0m9+PGTtn2D8rOcn6e6lYsCJj2411/gDDgGefhagoaNYM3nzTabOffjKDnehouO8++OCDzPvtdNqbEh6ISA5R8CMiIreFuKS4K7ZJH3TcDH8d+gv/D/2ZvmO6VXYp9pJdm2sZ+UkbMJ2McD7ys+LoCoey0NhQa4PVuT3nUsq3lPMHfPcdLFpk7uUzZQq4OP4IMXs2PPywmeGtRw9z6pu7e+b9dhb8NCzRMPOLRESukYIfERG5LcQlXjn4yQ1Z4NpOb0tEfASPzk2dMnboopnm2svNC7i2kZ+0AVNGIz/FfYo7lF2IuWD9vfh4+Di/+fHjMGSIefzOO1C9ukOThQvNqW6GYSaCmzkTPLOwV2v64KdjpY6MuHvElS8UEbkGCn5EROS2kJWRnyslArgZIuIiOHzpMAA1itYArj3bW4qM1vw4W0tzNPQoMYnmeimnwY9hQP/+Zn7qxo3NRTzprFgB3bqZma/79IHJk8HV1fFWKdImp0gf/LzZ4k28PbzTXyIiki0U/IiIyG0h/ZqfO4rf4dAmPC78RnUnS/489CcP//IwBgblA8pTooCZOe1aFvynHS06EeF85Cd9YgUwp+GlcBr8TJoES5eaO5N+951DVLN+Pdx/v7mnz4MPwrRpTmfE2Xl83uPWcVZG7EREskuOBD8nT57kkUceoVChQnh5eVGrVi02bdpk1RuGwdtvv03x4sXx8vKiTZs2/PfffznRFRERySPSjvw8WvtRHq/zuEOb3Dby0256O37/73cAmpdubmVsu96EB85Gft5e8TYR8eb0tj8f+dMq/2TdJwBUCKiAp2u6eWpnz8Lw4ebx6NFQubJVZRhmoHPvvWYOhDZtzDU/Ga3xqVq4qnX8x8E/rOPtIdvt2lUrXC2TtxQRuT7ZHvxcunSJZs2a4e7uzh9//MGePXv43//+R0BAgNVmzJgxjB8/nkmTJrFhwwa8vb1p164dsbHOM/WIiIhcScoIwvSHpvP9Q99TqVAlhzZhsbkr+EmruE9xXF0uBz/XkvAgzTXbQ7azYP8Cu81O3139rnXcpnwbPmrzkd31HSt1dNzfZ9QoiIiABg1gcGrK8NhYeOwxczZcZCS0agXz5kG+fBn377eHU/cSsmE+JzE50QqE5vacS8hLIfjl88vyO4uIXK1sD34++ugjgoKCmDZtGnfeeSflypWjbdu2VKhQATBHfcaNG8ebb77Jgw8+SO3atfn+++85deoU8+bNy+7uiIhIHmAYBueizwGQz838Cbx2sdoO7dJPexu1chRBnwZxKuJUznfyCgJ9Aq9r5Cf9NQ/MfoA5e+aQmJxoNyrk5uKGzWajSP4idu3rBNaxv+GOHeaUN4BPPrHmsp06BXffDdOnmzPgRo829/XxvsIyHf98/taxgRmURcRFWNMV76t0H0W9i2b1dUVErkm2Bz+//fYbDRo0oHv37hQtWpR69erx1VdfWfVHjhzhzJkztGnTxirz8/OjUaNGrFu3zuk94+LiCA8Pt/sSERFJ8dzvz7HtzDYAfD19AeeZzdJOe7sQfYGRq0ZyIvwEs3bOuiH9TDsSk16HSh2ua+THWRrvnj/3pNbEWnZB37ye8wAo4FnArq3ddDPDMEd6kpPNTAZ3m5ui/vuvOQj0779QsKAZ9Lz6aubJDVK4ubg59DVlGp6Hqwcerh5Zek8RkeuR7cHP4cOHmThxIpUqVWLJkiUMHDiQ559/nu+++w6AM2fOAFCsWDG764oVK2bVpTd69Gj8/Pysr6CgTHafFhGRPOfLTV9axynTpmw2GzsH7uTD1h9y6HkzlXRobKiVTOCB2Q9Y1xTKXyjH+3g26iyFPy7stK5hiYZULlTZGvk5F30u00DJmYwCpn3n91FqbOrePR0qdQAgv3t+u3bFC6QJFmfPhtWrzSQH//sfYI70tGgBp09DjRqwcaM53S2rUgI7SBP8XE6xXcCjgNNrRESym9uVm1yd5ORkGjRowAeXt3SuV68eu3btYtKkSfTt2/ea7vnaa6/xwgsvWOfh4eEKgEREBIDjYcftzv08U9eM1Cxak5pFa5JsJOPu4k5CcgKnI06z8MBC1h5fa7VLnykuJ6w/sd4uxXOKce3G0a9ePyA1QBi9ZjSNSjbiwaoPZvn+mW3gGpUQZR272Mzfe3q7289TK+Z9+ZeSkZGp6axff52kkqV57RX4+GOz6IEHzECowFXGKymBXVq7zu4CMtlfSEQkm2X7yE/x4sWpnm7zs2rVqhEcHAxAYGAgACEhIXZtQkJCrLr0PD098fX1tfsSEREBGLd+nN152rUlKVxsLpT0LQnAltNbePb3Z+3qYxJicqp7lksxl5yWVy5U2Zqql881NWPAG8vfuKr7X+06obR76VQtXBUvd3ODVd57z1zYU748EU+/xAMPpAY+b7wBc+defeAD9iM/KVYfWw3A8fDjDnUiIjkh24OfZs2asX//fruyAwcOUKZMGQDKlStHYGAgy5Yts+rDw8PZsGEDTZo0ye7uiIjIDXQ++jyrjq7KdBQiu6X9oX9gg4EU8ynmtF2QrzljoPOPnR3qUjb5JCbGXO+SA5yN+gB22c3SBm67z+2mxP9K8OehP51c5ehq1wmlXWOzsu9K8+C//2DsWABOvjWJ5vfm4/ffzdlvs2ebcdGV9vDJiLORn5QArH3F9td2UxGRq5Ttwc+wYcNYv349H3zwAQcPHmTmzJlMmTKFQYMGAeYc7KFDh/Lee+/x22+/sXPnTh577DFKlChB586ds7s7IiJyAz0w6wFafteSz9Z/lmPP+Gz9Z9w/635rtCblzxF3j+DLjl9meF2QX8bTpWN/ng1VqkD+/HDHHbBmTfZ2GrgUa478DGo4iOfvfN4qTztNL32a59ORp7lvxn1Zuv/VBpwpo00ARbwvZ3574QVISGBHs2do/FYbtm+HYsXM5T89e17V7R04G/lJCdhqFql5fTcXEcmibA9+GjZsyNy5c5k1axY1a9bk3XffZdy4cfTp08dq88orrzB48GCeeuopGjZsSGRkJIsXLyZfZhsEiIhIrjZx40TWnTCzdr7w5wtEJ0TnyHOGLhnKwgMLKfaJOcITnWg+50qL5lNGfpyJ2bsTDhwwT7Ztg+bNYcgQiIvL8JqrFRkfCZjrWwp6FbTK045UpQ1IUmR1OtvVTnsr61+WqQ9MZUGvBeY6oAULYOFCFrg8yF3bvuDECRvVqsH69WaGt+uVstYorZTkE84CIxGRnJDtwQ9Ap06d2LlzJ7Gxsezdu5cnn3zSrt5ms/HOO+9w5swZYmNjWbp0KZXT7BotIiK3nvTraP4J/sfuPDI+kkZfN+L5P54nO6SkSY6KNxfzp89ell4hr4wzuoU2qAELF8L+/fDEE2bh+PFmOrNz5667r9EJ0czYOQMALzcvetXqhYerB01KNaFw/tQMcF5uXtf8jKyM/PSsYT98069ePzpV7gTLlpHcsxfv8iYPJM8jIsqVu++Gf/6BsmWvuUuZmr1rNuP/HQ/Yp8EWEclJORL8iIhI3lPKt5TdefqRn4kbJ/LvyX/5/N/Pr/kZaX/ATxlJSHlO2gX8zqSkeE7riyXmD93HqpWAjh2hcmX46itzFMTPD9auNfM6T516XWuB3l31LmejzgLg5e5F5UKV2fPsHhb2XmjXrmlQ02t+RlJMVKb1z9ccwLcPTkstSEiA+fOhQwci2nSmW8z3vM27AAwaZO7hExBwzd25ol6/9LKOna0HEhHJCfpVi4iIZItSvqU4EX7COk9ITrCrz46MXmk360w2kvnvwn8sObQEuPLIT82iNTk+7DhBn5rT34atgxpPvg7B73As7Jh9406dYN066NIF9u2DAQPg11/N0aDy5a+6359tSF0DldLPCgUrOLSrUrgKP3f/mW5zutmVG+PGYYuONqORyEjYvRvCw6FRIzh7lgkhC9gadQjq2t/vzMcQ+LJ53OXFb8jX7ye4804oVw7++ANOnuQIZXmAteyiFh4eBhMm2KzBrxtFIz8icqPou42IiGSLC9EXADOLWHxSPPFJ8Xb1add8/PHfH05HYq7kj//+sDsfv2G8dVzOv1zmFxsGpWb/zkdLbfxa1eDNZq8R1vlxGP8Ox0KPkWwk269LqVYNduyATz+Ft96CRYvMeWDr15vJETKRlJxkt47Fx8PHyih3paltXat35Z2W7/D2yretsq+mD+OpzU4az53LhpLw3JOOVZVcilD04fuBqQAUSLBBRASkyba62P9hHon9mgux3gQGwty5Nho3zrR7OUJrfkTkRlHwIyIi2eJCjBn8FPcpzrGwYyQkmSM/O0N28tv+36xzgMfmPca5l69+LU3K1LEUh0MPW8f1S9TP/OKPPoLXXuMV4JUKfeHN9ymQnIiLzYW4pDhCIkMoXqC4/TXu7vDKK+ZI0COPwNat5i6f69dnOCds+5ntNJ/WnDdbvMkrzV4B7JMRWPvpZCL9KNbnTV15qtxDkJRkbrITGAiurrB3LzOrHQW22bV/uv7TvNLsFWwB5Xl6oTtnIs9Q99As+O8g/PMPkZv28eKhgUxZaQZxDRrAvHlQsuQVu5YjNPIjIjeKvtuIiMh1S0pOsjbxLF7ADH5SRn5qT6rt0P589Pmruv+F6Au8uvRVvt76tV35kUtHAPOH/Uzt2wcjRpjHo0bBm2+CzYa7qzulfEsRHBbMsbBjjsFPiurVzWliDRuaWeF694bffwebzaHpwEUDiYiPYPjS4bzS7BWSkpPs9vjJ5+Y8s2l4OEyZYj4mtHw+SLOE6kRxbxg/x+Ga2MRYpn5cBOwH2ZjUaZLTY2rVYuWFWvT7CI4eNYuGDIHRo829fG4WrfkRkRtFCQ9EROS6XYq9hIGZEKCYt5m6Of2an+sxZ88ch8AHIDgsGMAuY5qD5GR46imIj4cOHcwpbGl26kxJgZ12vZJTxYqZCQLy5YPFi82kCE5YG6ZeljbwebDKg7Qp38Y6j4qCVavMwaXSpeHll2H5ctiyyT55Q2h8OBfD0kU4wOFLh60U2lcSHW1u49OqlRn4lCljzoAbN+7mBj6gkR8RuXEU/IiIyHUb/tdw6zhlylb6NT9ppd3YMyvCYsOclkclmBnOAvJlkpZs6lT4+29zA9Mvv3QYrUlZg/P32jimTIGff4bTpzO4V716MHSoefzmm2ZglU5cov3eQGFxZt99PHyY9/A8gv/z5fnnoWZN8PWFli3h448hLAyqVk7isypf8ljCTw73rdrxT+bNs086l5UNUA0DVq40u/7pp+b5E0/Azp1mIJQbaM2PiNwoCn5EROS6/bz3Z+vYw9UDyDz4iU2Mvar7pwQ5KXrV7GV3HuCVQfATEmIOpwC8+67dpjWJiWZW6zWrzP6OnxDP009D9+5QogS0aWMGQg7xzcsvm2mwd+6EmTOv+G6hMWbw45boR7NmUKsWfP65mbAtOdlcZ9OjB8yfm8zuoq14fv8gehvLHe57LvosDz1kBksrV4JhGI5Z6oAvOnwBmPuz/vCDOVPvnnvM2XolSpiz9b76ylw6lFto5EdEbhQFPyIicl3iEuOsFNSTOk7C3cUdgISkhAw33oxLisvSppwp0k/tSr9haYYjP0OHQmgo3HEHPJ+6ueqGDeZIyFNPQWy02V8696dx77+44w7zdNkyMxAqX94cIbnzTrjvPvjoq4Js6DWOJFxg2DA4a5+EIW3wYxtlo9mg7wEIDfFl7VozT0HXrjB3Lpw6BSdOwI8/wgMXpuGyZjV4e1Pof19a93io6kMAtOp4AU9PWL0+knvm1KDQoC52z13+2HIuvHKBbmUGMWqUOa3tscdg82bw9IRnnoFdu8yZf7mN1vyIyI2iX7WIiMh1ORp61Dp+qNpD7AjZAZgjPxFxERleF5sYe8W9eVJExaeO/AxqOIjqRarb1fvn83e86KefYPZsc33PlCng5saZM+aSn6lTzVGXggUhqJY72y8vT1pfuS3GDIOjR+Hrr+GLL+DYMfMrxR9/ADxORY97GHX+NR54chiH35nOmn9srP47mdNV7efMxdcz03H7uBTi9Q/g8cehePq8ChcvwquvmsfvvEPFll1gwwAgNUHCcrdXCP7vZZ749A/+9NvDJfakXh/rS9+776FSJVizxlzeBOZIz6BBZpBXOJNlUTdbaGzoze6CiOQRCn5EROS6pCzoL+tflqLeRXF3vTzyk5zA9pDtGV53NcFPZII58vN43ccZc+8YVh9bbVfvMO3t4EGsnTpffRXq1+e336BfPzPOAOjTx9yzdPBKD7bvtL+8bFl47z0zEcH69XD+vDlN7PBhc8rZihVwMKwMfZgJv2F+AQTugKrO3+Hlrvfy2t0ZvODrr5sPqVEDBg/G392dLU9twdXFlS83po4CBQVBz0cj+PO3dNcvHsfx43D88j6yjRubWdy6djWzded2cUlxV24kIpINNO1NRESuWWJyIp1/7Aykjr6kXfPT7aduGV4bkxCTYV16KSM/jUs2Jr97forkL2JXbzfys3Ah3H23uaHnXXcR9/oohgyBBx80A5969cy9SqdPN0d+UqbppUg7Hc/XF9q2NTNbb/IZQVyDMcydawYZI0ZAWX8zvbcPEdzbPJannknM8B2eqv+U84qffoLJk83jL76wopV6xetRu1hta6+glMQMhy4esru8ZtGanF/6OKtWwaRJ5pS+devg4YdzX+DTokwLp+XRCdE3uCciklcp+BERkWu29vhaa+PRlLTTadf8pARCabnYzP/1pE8Jnd7OkJ2sPb4WSE144OPhAzhOc7PO33sP7r/fXExTtSr73/+Zxne5Md6cecawYWZg0LRp6rUpKbpTdJ7d2aEvhy4e4p3V7zB86XASkxMpUABGjoTDIT6cqdOOSwSwJKIxvbtnvHFroE+gY+Hu3dC/v3n8yitmNoN0inoXBcy/r8j4SD5Y84FdfYvSLShUyEaLFvD00+bapNyqbrG6TssV/IjIjaLgR0RErtnus7ut46/u/wqwH/lJH6QU8ipk/TB/+NLhDO+78eRGak+qTYtpLbgUc8lKeODtYe5/Uz6gPI/UfgSAhiUamqmzly6Ft982bzBsGKs+3UKDjsXYts1c77JoEYwday7+TyshyX4/ogUHFpCQlICRJqd02j2A0maxs3m4U2zeZGJKBlC5+Xa6z+yc4Ts5CAuDLl3MzX5atYL333fazNs9dc+ftHsGpch0j6NcJqMMgAp+RORGUfAjIiLX7Hz0eQAervkwXaqZ2cdS1vxEJ0Zz8OJBu/Zl/MtwT9l7ABzW7aQ1b988AJKMJM5EnrGmvaUEAjabjR8e+oHEtxJZ33s5tqFDzflplzexWXH/WDp08SIyElq0gO3bzUxtzjjbjPWRuY9QcmxJJm2aBGCNboFjsETZskz/tD8HC8E5d+c/3L/R/A37gthY6NzZzD8dFGQmZnBzvgzXZrNZ2dCOhTqmti7iXcShLLdKv7bnzpLmMNWQRkNuRndEJA9S8CMiItcsJfgp61fWKksZ+dlyeovDD7tl/MrQqGQjAPad32eVh8eF03l2Z2btnAXAuejU6WNhcWHWyI+Pm5fdLp+umzbjUr+BmbnAMKBbN1Z3G0+nThATY6Z1XrLEzHqWkcRkx3U6P+3+idORpxm4aCAAO8+mZkRwNnpxoqB94FI4KR+dS7fj996/s6bfGka2HJnmgYnmgpyVK80sCvPmQZHMA5iUgDKlP3bPuoVGftL/9zCr6ywiXougWpFqN6lHIpLXKNubiIhcsyOhR4DUdSmQGvzsOrsLgCDfII6Hm2nISvuVtjKzRcSnpsH++J+Pmb9/PvP3z6dXobs5F5waGG0ePZgw/+PgAd6NmsM5F8iXzxwpCTf3F6J4cfjmG9b5d+C+eyE6Gtq3h19/NZtmxmEkx4mFBxamtncyUnQm8ozdect9scz5cDn0DTKzzaVs4hkfD88+C/Pnm/PvFizA2lgoE+4u7sQSy+5zux3q0id/yM1S1nulcHdxt9ZxiYjcCBr5ERGRqxIaG8o7q97h4MWDbDy1EYAmQU2s+rRrZQBK+pa0jot5F8MrLgmAmC0boGNH6NKFU7/NSHNBSS5u+ts6fa7oJi56mKMz/rGYG/RER6cGPo88Art2sTWwAx06mEto2rTJWuADOE3K4OydU6Qf+VlxZAWrjq2yK3MpUhQSEszNgipXNtNuz5ljZiP45huw2WDGDDMrXRa4udj/rrK4T+pGQbfSyM9797xnd+7qos1NReTGUvAjIiJXZdTKUYxYOYIGUxpYC/BLFEidV5Z+8XraH9SLegTg9d5HAMRGhsHvv8PcuUwNOGK1MVxdiPJxHpAU3PGfmcnt8GFYtgx27oQffmD36YLce6+ZQ+Cuu8yZZF5eWXufMfeOoax/WSuBQnpxiXHEJsZa52lHinaE7KDV9604dMk+/XTS3c3NfNr33WcGa998Az16mIuPChY0O9i1a9Y6SOq0txSjW4+2jqsWzmBjoVyojH8ZZnRJDXTTB3UiIjlN33VEROSq/HP8H8Bci5MiIF/qJqPp19CknZZV9Nuf8Nq1H+pDTOniMPV9iIuDkNS1LKfPHSF6Zns4v9fh2d5BFcxRE4By5QDYswdat4YLF6BhQzOrm7e3w6UZqliwIoefP0xkfCTTd0x3qL8Qc8FurcqmU5uoVKgSAC2mOd+3JiE5wcynvWgRrF1rprE+dAg6dYIPPrjiGp/00gYJDUo04NE6j7L/wn6qFq6Kp5tnJlfmPmlH2hT8iMiNpu86IiJyVUr5lrKmu4H5A2zadRtpR0kAArwCaFm2JYeDt9P6mxVsK+ECJBPjmx/69QOgwOhXrDVAOy/stfb1Sc+WEvhctnmzOXMuJATq1oXFi82NSa+WzWbLcPpbpc8r2Y1m9f61N9WLVKdOYB27ADCt8Ljw1JOmTWHNmqvvVBppN2LtW6cvLjYXPmj9QSZX5F4FPApYxwp+RORG07Q3ERG5KukzdgXkC7ALStLX++fzZ1lUNw6OukT+BPB61kxrnHaT07TBU0xijJXa+sk7nsywHz/+CM2bpwY+S5eaM8quVfqpZSmc7UFTd3JdAJoGNXWoywlp+5Z2lO1WlHbvJwU/InKjKfgREZFMxSbG8ujcR5mxw1yrkXbPG8DK3pYifUavgP3BuAx6DvckYOBAvPqZAc2piFPWCEna0aJzUee4EHMBgNebv876AesJyBfAl/d9CZgZrUeMMLNFp6SzXrkSChW6vvdM3++sSJ8p7oeHfqCcfznGtRt3fZ1JJyXVN0BBr+uI8HKBtPsSKfgRkRtNwY+IiGRq5s6ZTN8xnUfmmgkBQiJD7OrTr/F5scmLduf+X31vHjz3HEyYgJd7fqtuyGJzFCht8LPgwALruHD+wjQq1YgLr1xgYMOBJCaaM+Xeeefys140s0X7+V3fO16r9JnfHqn9CIeHHKZOYJ1sfU7aVNrpg81bTfmA8gxtNJTX7notS5n2RESyk37lIiIimYqIS92P51LMJYc9bTxd7RfcF/MpRjn/ctYeQG12REH9+vC//4HNhpd7ahq2vw79hWEYdlPgtp3ZBkDdwLrWdDibzUZMjDna89tv4OoKkyaZGaRvlu+2fcf2kO03/Lm3+rQ3gE/bf3qzuyAieZRGfkRExEFkfCSPzn2U3/b/Zjcq8+zvz5KQnICHqwcr+q6gSqEqvHrXqw7Xp00EUMjFG2bOBA/zt/z53FI330lMTnQYPUnZELVt+bap9wszNy397Tdzb9Bff82ZwGf+w/Oz3Pbx+Y/bnXu7X0WKuesQ6BN4Q54jInI70siPiIg4eGv5W0zfMZ3pO6bTuFRjq3z2rtmAOR2tZdmW7Htun+PFhkFY9CVIyYEwc6a50edlXm6pIz+xibFWlrf0CniaWcGWLoUBAyA42MzktmABtHCeYfq6pR/FyqqpD0ylU+VO2dwb5/zy3aQ5fiIitwGN/IiIiJ2YhBjGbRhnna8/sd6hzbant2V8g4kTSbIZqecPPGBXnTZzWVhcGEU+dr7njRHnQ9++cO+9ZuBTtiysWpVzgU/6vr3R/A2H+tJ+pZ1eV694PbuF/NlteLPheLp6svvZ3Tn2DBGRvEDBj4iI2Jm5c2am9ZULVc74B/2LF+G117KlH6NH+fD99+aeps89Bzt3mimtc1La5A3Dmw1nxN0jrHN3F3eODT3m9Lq0+/DkhA/bfEj4a+FUL1I9R58jInK707Q3ERGxGIbB8KXDM21TskDJjCvHjoXw8Izrr0JMaAHq1YOJE6FRo2y55RWlTe7g4+HDuahz1vndZe/O8DoDI8O67KLMaCIi108jPyIiYll2ZJm1x86ghoOctslwwf358/DZZ9f+8JMN7E6/Gt6BjRtvXOADZhrmFDabzS5l9bQHp2V4Xfp03yIikjsp+BEREcu+86kJDF5u+jJ/9PmDMn5laBrU1CrPMPj55BOIjIR69TJ9xokT0DLpfYdyt9lLqGC0B2B219k88agvrq7X8BLXoV7xeszpPofNT20G4PG6jzO502QOPX+IUr6lAKgQUMHhuozWAomISO6iaW8iImJJmVp1V+m7KONfhjL+ZTg69Cgrjqyg1fetgAyCn+PHYfx483jUKNjygGMbYPp0ePZZiKhrQGv7uv3bClK+/B8kJifi5nLz/vfUrXo369jD1YOn6j9lV7/xyY0UHFMQMIOexX0WU9Cr4A3to4iIXBuN/IiIiCUuMQ6AEgVK2JWX9E1d5+M0+Hn9dYiJMVOxdXJM+RwWBn36wKOPQkQElClewK7+8w6fU/7yjLObGfhkhX8+f+u4qHdRqhWpdvM6IyIiV0XBj4iIWOKSzOAn/X43aZMclPUva3/RqlXmkA6YCQ9sNgbUGwBAzxo9+ecfqFPH3O7H1RXefRe2TO1L71q96VCxA/sG7ctwfVFuZLPZrOP0G7SKiEjulrt/vSYiIjfEP8H/8NaKt1gTvAZwzCzm7eFNl2pdOB1xmialmqRWxMTAE0+Yx089BfXrA/DFfV/wYOUurJvVkhY9ITkZypeHGTOgcWMAP2Z0mXED3ixnJSQl3OwuiIjIVVDwIyKSx60/sZ67pt1lV5Z+5Afglx6/OF48YgQcPAglS8KYMVbx2VP5GN3/PtatM88feww+/xx8fbO16zddQrKCHxGRW4mmvYmI3AJOhp/ENsqGbZSNrae3Om2TmJzIoYuHrvreK46scCjzdHMMfhz88gv873/m8cSJ4OcHwIYNcMcdsG6dWTRrFnz33e0X+ICmvYmI3GoU/IiI3AIen/+4dfzCny84bdNvfj8qfl6R+fvmZ/m+cYlxvL78dYfyK26ouXQp9Oplzmd7+mm4/34ALl6Ebt3gwgVzBty2bfDww1nuzi0nNjH2ZndBRESugoIfEZFcbuGBhSw9vNQ6L+BRwGm76TvMpAMfr/04y/f+9+S/TsudTXuzrFwJnTtDQoIZ6UyYAIBhwIAB5j4+FSvCihVQtmyWu3JLScn4Vi8w8z2NREQkd1HwIyKSy/Wb38/uvEj+IhiGQURchFU2Z/cc67hcQLks3/tI6BG7+6YI8ApwbHzqlJmr+p57ICoK2rQxs7xd3ol03DiYNw/c3WH2bCjgPEa7LewauItn6j/D5x0+v9ldERGRq6DgR0Qklzsffd7uPCYxhsF/DKbQmELsOrsLgAkbJ1j1JXzs9+jJzOmI04C5t870LtOt8ifueMK+4a+/QuXKZrBjs5lDPHPngqc5QrR8Obz8stn0f/+zkr7dtkr6lmRip4lUKlTpZndFRESugoIfEZFczDAMbNjsynae3cmEjRNISE7gk7WfAJBsJFv1MYkxWbr30dCjvLrsVQAGNRzEveXv5Z/+/xD1ehQ+Hj6pDWfPhh49zNGexo3h33/h66/Bx2xz7JhZnZRkZnV77rnreWMREZGco+BHRCQXi4iPwMAAYMJ95uhOymgPgLe7NxdjLvJ38N9WWVR8VJbuPWTxEOv4TOQZbDYbTYOakt89v1mYnGzOZevTx4xsHn8c1qyBBg2s66Kj4aGHUhMcTJpkDgyJiIjkRtrnR0QkFztyyVyTE5AvwG5NTgpXF1fWHV9nVxYeH35V9wZz2pudPXvgySdh7VrzvH9/+OorcEn9nVloKHTqBFu3QpEi5iw4L68sPVpEROSmUPAjIpJLhcWG8dE/HwFQs2hNCnoVdGgTER/B/gv77cpWH1udpftbIzzAOzUHw4wZcPSo+fXdd2Y2Nx8fc/PSZ56xG9IJCYF27WD7dnMvn/nzISjo6t9RRETkRtK0NxGRXOqB2Q8wa9cswAx+qhep7tDm223fsuKouUnpy03NjANno86y7PAyu3anI07zxrI3OBF+wpzCtn49O06Zm6XuX1uf8tWbwSOPwJtvmut5EhLMvXv27IGBA+0Cn6NH4a67zMCnWDFYtQqaNMmJvwEREZHspeBHRCQX+mH7D3YjODWL1qR4geIMazzMoW1Ku7tK32WV9f61t12b0WtG88GaDwj6NIgyr3qyvnsTYox4AIqu3mwGRA0aQL9+8OKL8PvvTodzdu+GZs3g4EFzD581a6BOnex6axERkZyl4EdEJJd5+c+XeWzeY3ZltYrWAmBsu7Hsf24/T9d/2qoLjzPX+BT3KW6VnY06y5+H/sQwzGQJM3fOtOqCfZJokiaTte/YCXD4MGzcCFOnwiefQIcODpkLfv3VTPZ26hRUr24GPhUrZs87i4iI3Aha8yMikst8su4Th7JGpRpZx5ULVaZSQcf9Zcr4l2F4s+HWOqF209tl6XkuA5+9cp8+Sd3Hp2VL+PlnKFQoS7cXERHJNTTyIyKSiyQlJzkt93D1sDvvX6+/3XntYrUp6l2U0a1HZ2t/DANeeSU18Bk8GP76S4GPiIjcmhT8iIjkIgcvHgTAy82L1+96PcN2AV4BNAtqZp1XLGjOP7PZbGx6clOG14W8FGJ33r9u/wxamjkP+vWDjz82zz/6CD77DNw0Z0BERG5R+l+YiEgusu3MNsAcyXm9+et4uHrwULWHnLYt6l3UOi7hU8I6rk9xnj5dksnFTzq9xt3FnYTkBLpX787EThOd3vvSJejRA5YuBVdXMwHc449f+3uJiIjkBgp+RERyka1nzPTT9QLr4e3hzYiWIzJsG+Sbmont0KVD5sHq1dCjB+fvDoHi9u2nPzQdgO3PbGfxwcU83+h5XF1cHe574ICZ5frAAcifH2bPNs9FRERudQp+RERykZQNS2sUrXHFtt4e3tbxkEZD4M8/oVMnSEjghZDy/FL9sFU/qeMk+tTuA0C1ItWoVqSa03suXQrdu0NoqJnl+rffoG7da38fERGR3ETBj4hILhIVHwVAQL6AK7ZN26bdeT/o0sZcqNOlC01/+IETiZco4l2ELae30KBEgyve78sv4fnnzS1/GjeGuXMhMPDa30VERCS3UcIDEZFcJDohGoD87vmv2PaJO56gQkAFnq/QG+67D6Ki4N57YdYsyJ+fkr4l8XD1oHGpxri5ZPy7roQEeO45GDTIDHweeQRWrFDgIyIitx+N/IiI5CIpwY+Xu9cV2wZ4BfBfnW+wde5szlNr3NjcidTD40qXWv77D/r0Mfc3tdnggw9g+HCH/U1FRERuCxr5ERHJRa5m5IcZM7Dde68Z+DRpAosWgY9Plp5jGGYGt3r1zMDH39+c5vbqqwp8RETk9qWRHxGRXCTLwc+SJfDYY5CcDF27wg8/gNeVR4sATpyAJ5+ExYvN85Yt4fvvzQQHIiIitzON/IiI5BIRcREcDz8OXCH42bULevY0A5/HH4effspS4GMYZpBTs6YZ+Hh6mhuYLl2qwEdERPIGjfyIiOQSX2/52jouUaCE80YHD0LbthAWBnfdBZMmgcuVf4914QI88wz8/LN53qgRfPstVK2aDR0XERG5RWjkR0Qkl/jv4n8AVClUBf98/o4NwsPNrG6nT5vDN/Pnm8M3mTAMmDkTatc2Ax83N3j/fVizRoGPiIjkPRr5ERHJJYLDggF4scmLjpWGAf36menZgoLgr7+gYMEM72UY5nS2ESNg3TqzrEoVmDED6tfPid6LiIjkfhr5ERG5wU6En+B0xGmH8kuxlwAonL+w40VffWWmsXZ3N4dwMtmEZ+tWuOcec3bcunXg7W2O9mzdqsBHRETyNo38iIjcQBeiL1B7Ym3yueXj0POH7PbzCY0NBcAvn5/9Rf/9B8OGmcejR8Oddzq9d2KiOdIzerQ58pMvHzz9NLzyCpTIYAmRiIhIXqLgR0TkBolPiufub++2RnjWn1jPPeXusepTgh+79T5JSdC3L0RHm8M5KUFQOseOQe/esHatef7ww/DRR1C6dE68iYiIyK1JwY+IyA0yZ/ccdp/bbZ2fiz7HkoNLqFakGoZhcCriFACFvAqlXvTBB+bcNV9fMz1busxuhgHffQdDhpj5EHx9zRlyPXrciDcSERG5tSj4ERHJJvFJ8fSd15eWZVrydIOn7epGrRzFyFUj7coW/beI77d/j5uLG2X8ygDQuFRjSvtdHq6ZPRvefts8/uwzh2Gc06fhqadg4ULzvHFjM7NbuXLZ/moiIiK3BSU8EBHJJgsPLGT2rtk8s+gZohOiAZiyeQq2UTa7wKdW0VoAfL/9ewASkxM5dOkQAG82fxObzQb//GNuYArmVLeUY8zRnlmzoEYNM/Dx8IAPPzTTVyvwERERyZiCHxGRbBIWG2YdH7hwAID3Vr9n18bV5kqXal0yvMd9le4zF/A89BDExZl/fvyxVX/pkjmlrXdv8/iOO2DzZhg+HFxds/mFREREbjMKfkREssmxsGPW8eFLh5m/bz7Hw4/btZnYcSKtyrVyen2QbxC2qCh48EE4dw7q1YMffrCimu3boUGD1M1KR42C9evN/U5FRETkyhT8iIikEZ8Uf83Xrj+x3jo+H32eQb8PAsCGjSalmjC762yerP8k5fxT56a1KNOC5+98Hk9XT4Y3ehH69DGjnKJFYf58c5MezKQGTZrA4cNQtqyZA+Htt81tf0RERCRrcjz4+fDDD7HZbAwdOtQqi42NZdCgQRQqVAgfHx+6du1KSEhITndFRCRTx0KPUXhMYZ5e8PSVGztxMuKkdbz33F7r/IeHfmDtgLX0rNkTgBIFUjfd6VG9B591+IzYNisZNGwm/PabuYhn7lwICiI6Gvr1M5f8xMRAu3awaZM5AiQiIiJXJ0eDn40bNzJ58mRq165tVz5s2DAWLFjAnDlzWLVqFadOnaJLl4znwIuI3Agzd84kIj6CKVumkJSclKVrYhNjqfJFFWyjbOw6u8sqH7dhHABNg5rSp3Yfu2tcXVxZ2Gshg+8czBO+LaF7d3NY599/oUABWLQImjZl715zP9OUDNfvvGNWFSqEiIiIXIMcC34iIyPp06cPX331FQEBAVZ5WFgY33zzDWPHjqVVq1bUr1+fadOmsXbtWtavX5/JHUVEctYve3+xjtOv1cnIssPLrOQGzrx+1+uOhRERdNybyPh5cXjWqmsu4nFxgf79Yc8eaNOGmTOhYUPYvRsCA2HpUnjrLSU1EBERuR45FvwMGjSIjh070qZNG7vyzZs3k5CQYFdetWpVSpcuzbp165zeKy4ujvDwcLsvEZHsdDbqLJtPb7bOVx9b7bRdYnIiAxcOxDbKxvQd0zMNkkoWKEnHyh1TC/bvh8GDoUQJ6NwZpkyBxETo2NFc5/PNN8QWLsUzz5hLf6KioFUr2LYN7rknm15UREQkD8uRTU5nz57Nli1b2Lhxo0PdmTNn8PDwwN/f3668WLFinDlzxun9Ro8ezahRo3KiqyIiAOwI2WF3/snaT6hUsBL1S9THw9XDKv9x149M2jwJgEfnPoqnqycAAxsM5PlGz3Mq4hStv28NwGt3vWamq543D77+2hy+SVGhghnZ9O4NLVsC8Pff8OSTZoxks5kjPW+/rdEeERGR7JLtwc/x48cZMmQIf/31F/ny5cuWe7722mu88MIL1nl4eDhBQUHZcm8RuX2dizqHfz5/3F0dU6ItObiE5UeWM/yu4cQlxlmZ2VLsPLuTplOb8nT9p5nUaZJVPmzJMLt2cUlxAJT1L0vVwlWpWrgqE++bQOjR/Tw7+yDcVxIuXDAb22zQqZM5+tOmjXkOhIaa+/RMmWI2K1YMvv8e2rbNpr8IERERAXIg+Nm8eTNnz57ljjvusMqSkpJYvXo1X3zxBUuWLCE+Pp7Q0FC70Z+QkBACAwOd3tPT0xNPT8/s7qqI3KYMw6DyF5U5ePEgdQPr8ne/v/Hx8CE2MZbz0ecp5VuKLj91ITohmjFrx9hd26FiB/44+Id1PnnzZLvgJyE5wekz6wfeATt3wpQpPDNzJly8mFpZsqS5nqdfPyiXmuY6JgYmTYLRo81tfQCeeALGjIE0SyVFREQkm2R78NO6dWt27txpV9avXz+qVq3K8OHDCQoKwt3dnWXLltG1a1cA9u/fT3BwME2aNMnu7ohIDtp+ZjtRCVE0DWp6s7tiZ+nhpRy8eBCAbWe2UWB0AYwRBq2+a8W6E+tY1HsR0QnRTq+tWLAipf1KExwW7FAXmxhLaGwoABcb/sx9/w5hvc1MZ92qVX8ITrP+J39+c+jmiSegfXu7uWvR0eYoz8cfw6lTZlmVKjB5Mtx9dzb8BYiIiIhT2R78FChQgJrpthv39vamUKFCVvmAAQN44YUXKFiwIL6+vgwePJgmTZrQuHHj7O6OiOQQwzCoO7kuAMeHHaeUb6mb26HLEpISaDvdcb7Y/bPuZ90JM6lK59mdM7y+XYV2nI8+bxf87AjZQcWCFdn391wAPJLAv2M3SnUHaphtbMHHzf152reHQYPMDAXpdiAND4cJE+DTT1NHeoKCYMQIeOwxbVgqIiKS03Ik4cGVfPrpp7i4uNC1a1fi4uJo164dX3755c3oiohkUXBYMAv2L6B9xfZUKFjBGgEBWH9iPd2qd7uu+5+NOsvZqLPULFrzyo0zMXPnTOu4e/XuzNkzB4CFBxZa5c6mrlUIqMCvPX+ldrHa/HnoT7u6OpPq2J+fAVtAAHVLlOVntpqFf/0FjRuDj4/DvS9cgM8+g88/N9f3gDn77dVXoW9f0KxeERGRG+OGBD8rV660O8+XLx8TJkxgwoQJN+LxIpINmnzThFMR5hytkJdCuBRzyao7F3XOOk5KTqLzj52pGFCRT9t/muX7V/q8EuFx4Rx6/hDlA8pfcz//PfmvdfzuPe8yrv04Sn9amiTDcdPSRiUb8VaLtyjgWYAWZVpY5SUKlMj0GdWq3w2nl/Ciq8H5pa/xQJUHoJxjLupTp8xRnokTzbTVANWqwWuvQa9e4HZTfv0kIiKSd+l/vSJyRcfDjluBD0CPOT1oVLKRdX426qx1vPzIcmuUJavBT3hcOOFx5v5dj/z6CGsHrL3mvkYlmFHGh60/pErhKgBEvxHNL3t+4YU/X+BMZGpK/cmdJlMnsI7DPWr5V870GQcKxIGnJ/mwf8fERNi0CVatgg0bYMECswygbl1480146CFzP1MRERG58RT8iEim1h1fR9Op9gkNVh1bxapjq6zzc9GpIz9pgyTDMLBdTuecmbR77KSsy7lWKUGUr6evVebh6kGvWr1oVa4Vgf8zs0rO7jrbMfBJToaZM2nz5ms8VQumNHD+jB7Ve1jNt2+H5cthxQpYvRoiIuzbNm9uTm/r0MHKbC0iIiI3iYIfEcnU1jNbr9gm7cjP+ejz1nF8Ujyebs4XtETFR+Ez2lwf06dWH7u6rAZNzkTEm9FHAc8CDnXFfIoRPDSY6IRoa1TIsm4dDBkCGzfiAUxODuKbBidJItnq44ydMwCoG/8cgwbBzz/D2bP2twkIMDO2NW5sbuVTv/41vYaIiIjkAAU/IpKpiLiIK7aZs2cOCUkJuLu6cyHmglUemxjrNPhJNpKZsnmKdZ4SVKRISE7Aw9XjqvsaGhvKplObAPuRn7SC/NJtkHzoEIwcCdOnm+cFCpiLcoYOxX1sQZISYwGIDq5mXdKqZWpaNh8faNECWrUyv2rXtstqLSIiIrmIgh8RydRXW77KUrv5++fTrXo3u1GgmMQY/PBzaHvfjPtYcmhJhveKTYy96uDn8KXDVBhfwTov61828wuio+Gtt8w0bEmXkyH07w8ffMAZoxiLf4TEODe4HMjMffU5aHMCtvbHzw86d4aePc3RHaWoFhERuTUo+BERpw5fOswTvz3BoUuHrLJXm73Kh/98aJ03KtmIDSc3AHA09CgAJyNOWvWxl0dN0lp9bHWmgU/KdWlHbsLjwuk7ry8tSrdgWJNhTq/5cE1qv7zdvalVtJbzm587B0uXEv36e5w9GkUI9TlVrxOn2/blYHxpVneEzZsvtx3uBl7mYb3qfnS6YyJtX4VGjRTwiIiI3IoU/IiIg7jEOLtRFIA1/dbQrHQzHqz6ID/v+Zkg3yB61+rNB39/wLgN46x01yfCT1jXOAt+7vnOMSU0QPPkIDbYThJvSyZuwz9wT2crQ8AP239g3r55zNs3jyLeRehWvRv53PLZXb/+xHrreG7PudhsNoxkg91z9rB2xhEO7Enk0Kl8HIwpyRHuJ4peqRdvvfyVRv36UNrzS+bSm0G1X+eLEVf6WxMREZHcTsGPiDiYv3++Q1lKZrTGpRrTuFRjq7yod1EAxqwdQ+VCldl1dpdVlz74CQ4LJtkwEwh02WsjIMZg7BIwAK/E4xR+BeI9IbZ7FxLL1SP0qcco3L0vL/31knWPR+c+yr7z+3iv1Xt2904JunY/u5sS0SX58MF1fP5HBU4l1ABqOH1PT0+DYsVslCgBJUpAUBDccQe0bQuBgQC9CIlsZb2jiIiI3NoU/IiIA2fBj7e7t9O2RbyLWMdPLHjCri4yPtI63rv6F1bO+AAu7x/69XyDAO9C0LS2GWkULkyE5+cAzK3pysZiW/n15Fb+qfcSsf3sNyh9/+/3eXutOx5de0C1aszY+A2XYs1NV8ffs50Zu4OIpAkA+YmiSdFD1KqWRMV6PlRsXoJyNb0pXhx8fGxXTD9dzKdY5g1ERETklqHgR0TsHLl0hJk7ZzqUZ5R6ukj+Ik7L4fKeO3Fx7HitP3X8ZlqBz0NhJQjY/BdUq2a/+c0oM/h5t10+Ii9vVtokXeCT4vW/R/LJWyOJ9y/MI0NT02tP3tMTcKGW5wFe7naEHmMa4FmidmavLCIiInmE9hkXEUtUfBTlx5e3zl9o/AIAM7s4BkMpAn0CM6wL37uNxLuasvdP++tL3dsVqld32PWzkFchAFqWc1wXVDLOPmX275XL0ZPZFDJWpBbufYg25Y6w+LP9bI+uxKPT2+FZolCG/RMREZG8RcGPiFhWHl1pd/5J2084+cJJetXq5fwCoGHJhhnWTZj3Bu6dtvBwd/vyIN8gp+3fveddABYeWOhQF1bAnR8a7+bus7MB2OuWxE/0JNLf3FfILTk/W4b/yl+HKtDu+SrYXK5tk1QRERG5fSn4ERHLxZiL1vHyx5Zjs9koUaBEpte42FysEZv01pRxfk0p31JOy2sVyyA9Neb6oUfbV2fVrAZmgX8wjd4Yzisf7wWgZcWm1KuXaVdFREQkj1PwIyKWlA1Ke9fqzT1Opp5lpHX51lf1nIyCnwYlGtidu+95LPXkx1/w94dunQKsog3uYxizZyBAhgGYiIiISAoFPyJiiU+KByCfa74rtLQ3ofVYu3OfxMy/tQT5OZ/2tm2T/XMTtnXDd93/aBr1IUs/78LZszD7Wz+n12aWeEFEREQEFPyISBpxSXEAeLp5XqFlGv/8Q+FG9/DcBmgWDLGHevPmPSPtmlQuVJk/H/mTuoF1aVCigdM1P7/+CvekG2z6/ct7uLjoBf4ZM5zWrcHdHVxdXCmcv7DD9a83fz3rfRYREZE8SamuRcSSMvLj4eqRtQvmzoVevSAujs8jAuHRsdCrF36bJtk1+6PPH5QPKM+W8lsAx7TZ48bBCy+AYcCdBxZTotPXvNd6JDWK+jh97IJeC2jyTRPr/Pfev1O8QPEsvqWIiIjkVQp+RMRyVcHP1Knw5JOQnAydO8N334GvLwD1Au0zD5T2Kw04Bj1JSWbQM368eT5wIIwf3w43t3aZPrpxqcbUKVaH7SHbAagbWPfK/RUREZE8T9PeRG4TM3bMYOrWqdd1jywHP5Mnw4ABZuDzxBMwZ44V+AA0KtXIWoMz4b4JuLk4/p4lKgq6dUsNfMaMgQkTwC2Lv5I5HXnaOtaoj4iIiGSFRn5EbgNJyUk8MvcRANqUb2ONtFytM5FngEyCn6QkeOstGD3aPB86FMaOddisFGDbM9s4FXHKIYMbQEgI3H8/bNwInp7w/ffQo8fV9dUwjKu7QERERPI8jfyI3AYSkxOt41VHV1nHSclJJCQlkGwkX/Ee07ZOY86eOUAmwc+TT6YGPq++mmHgA1CiQAmngc+//0KjRmbgU6gQLFt29YEPwOA7BwPQpFSTK7QUERERMSn4EbkNpA1+3l39LgBj143F7V03PN7zoNCYzPfAMQyD/r/1t849XZ1ke/vjD5g2DVxdYfp0MwjKIPBxJjkZPvoImjWDY8egYkVYv948vxav3vUqM7vMZG7Pudd2AxEREclzNO1N5DaQZCRZx6GxoQB8s/UbuzLDMBwSDqS4GHPR7txh5CchAV580TweNgz69Lmq/p0+DY89BkuXmuc9epjLhvz9r+o2dtxd3elVq9e130BERETyHI38iNwGkpJTg5+I+AjAcfQmLC4sw+s/+ucju3N3V3f7BhMnwt69ULgwvPnmVfVt0SKoXdsMfPLnh2++gdmzry/wEREREbkWCn5EbgNpp73FJsZy+NJhtp7ZatcmJDLEOj586TDBYcGAmSXu47Uf27Ut6l009eTcORgxwjx+/33w88tSn86cgaeegk6d4Px5qFsXNm+G/v2varaciIiISLZR8CNyG0g77Q3gyQVPOrTZd34fABFxEVQYX4Ey48qQmJxoZYlLK9AnMPXk7bchNNSMXgYMyFJ/FiyAqlXhq6/M86FDzfU9Vatm6XIRERGRHKE1PyK3gbTT3gCWH1nu0Kbzj535ufvPeLqlTodbcnCJXZu3W7zNwUsHubPknWbB9u0wZYp5/NlnZrKDTCQmwjvvwLtmzgXq14dPP4Xmza/yhURERERygIIfkdtA2mlv6QXkC+BS7CUAHpv3GP3q9rPqOs3qZB3/+8S/NCzZMPVCwzCHbJKTzQwFLVpk2of9+6FvX9iwwTwfPBg++QQ8rrBfqoiIiMiNomlvIreB9NPeUvSr249axWpZ54nJiUzYOMGhXWm/0vaBD8Cvv8LKlZAvH4wZk+Gzk5Ph88+hXj0z8PHzg5kzYfx4BT4iIiKSuyj4EbkNpJ/2lmLqg1Pxdve2zuOT4p22K5y/sH1BdDS89JJ5PHw4lCnj9LrQULj/fnj+eYiJgXvvhZ07oZcyUIuIiEgupGlvIreBtNPeOlbqSNdqXeleozsA3h7eGV1mKZK/iH3BO+/A0aMQFASvvOL0mgMHzMDnwAFzcOh//4OBA5XJTURERHIvBT8it4GUaW9F8hdhYe+FdnVpR35StCnfhqLeRZm5cyaQbuRn2zZzsQ7AhAnm5jzpLFsG3bqZIz9BQTBvHtxxR3a8iYiIiEjO0bQ3yXH7z++nyhdVsI2yUX1CdTae3Hizu3TbSZn25ubi+PsMZ8FP63KtKe5T3Dov5VvKPIiLM7MWJCWZ0c399ztc++WX0K6dGfg0bgz//qvAR0RERG4NCn4kR83YMYOqE6py4MIBAPae38udX99JvvfyOaRZlmuXMu3N1cUxFXXa1NYpBtQbYDfac3eZu83sbsOGwY4dUKSIOeqTRkICDBpkfiUlwSOPwIoVEBiY/u4iIiIiuZOCH8kxCUkJTjfQBIhLiqP9jPbZ/sy4xDiSjeQrtjMMg6j4qGx//s2SMu3N1eYY/Li7uDuUFfQqaBf8lPQOhCeegIkTzYIpU6BoUav+0iXo0MEc9bHZYPRo+P57c62PiIiIyK1CwY/kmA/+/sA6DsgXQGm/0g5tIuMjs+15D8x6gHzv56Px140xDCPTtsOXDsf/I382ndqUbc+/mVKmvTkb+XF3tQ9+3mrxFq4urnbBT+CLI2DqVHBxgW+/hc6drbrgYGjWzFzn4+0Nc+fCq68qsYGIiIjcepTwQLLdfxf+Y9Dvg/jr8F+AuZj+3MvnAJi+YzqPzn3Uarvn3B7uLHlntjx3wYEFAGw8tZFmU5uxdsBaAM5FnSPAKwA3FzfeW/0ecYlxfLz2YwBaTGtB9BvR2fL8myll2puzNT9pyw4/f5hyAeUAaFWuFS0DG1Nx81GKzF4A7u4wezZ06WK137ED7rsPTp6EkiVh0SKoUyeHX0ZEREQkhyj4kWwVGR9J9znd2R6y3SobefdI67hm0Zp27Y+GHr2u4Oef4H94ffnrdov3AdadWMeJ8BOcjTrLnV/dyX2V7uOjNh/x1oq37NrFJMZwIvxE6oL/W1Rm094KehW0jlMCHwwD35/ms+Ll3RARAT4+8PPPZiaDyxYvhu7dITISatSAP/4wM7uJiIiI3Ko07U2yzfx98ykwuoBd4APwWJ3HrOPqRapTIaCCdf73sb+v+Xn/nvyXu6bdxepjq/lx948O9fvP7+f77d+TZCSx4MACak2s5fQ+/wT/Q1hs2DX3IzfIbNpb06Cm9gUXL0LPnvDYY2bg07QpbN9uF/hMmQKdOpmBzz33wN9/K/ARERGRW5+CH8kW56PP0/nHzg7lfWr1oYBnAevcw9WDPYP28EhtMxGCs6AlK5KSk2j0dSOndQ1KNABgw8kNfLbhs9RrLo+OpPfwLw/j/5E/F7etM1Oa5ZBRK0fRaWYnzkefz/Z7RyeYU/e83Lwc6hqUaMC0B6ex5JEl8NdfUKsWzJkDbm7w3nuwahWULw+YWdxefRWefto8fuwxcwQoICDbuywiIiJywyn4keu299xeinxcxGld5UKVHco8XD14oPIDgDntbOTKkZwMP2nuMbN3L+zbBxcuQHLGWdsuxlzMsM7D1QOAN5a/cTWvQdNxXVni35M9jfsTeX8v6NMHnnkG/vc/2L//qu6V3vIjyxm5aiSL/lvEy3+9zKWYSzyz8BnWHl97XfdNERobCkCAl/Mo5fEyD9L2zWnQti2cOgWVK8O6dfDGG2YQhJnQoH59+Ogj85pRo8zcBx4e2dJFERERkZtOa37kurWb3i7DunqB9ZyWNyzZEDDXCI1aNYpPlr7DybHgF5MmS5urKxQuTHhQUQY0PcdjybW4v2ATKFeOvaWcj+LM7TmXl/962aH8nrL3sOLoCloW7UrJkD4sPf4t4XM/ImZINavN/nKnaR/9K2wwz/25RGmCCeI4pV9aRrWg32jVqxjVh7bFVvzqNrdZfWy1dfzttm/xcvNi8ubJTN48GWNE6juvO76OxQcX82aLNx2ytGXmUuwls8/5/B0rZ82CIUPg3Dkzm9uzz8KHH5qp2zBjzZdfhoULzeZ+fvDFF+Y+PiIiIiK3EwU/cs0Mw+CXvb9wPPx4hm3uq3Sf0/LAYPuRmyh3gwH3w8+/+5hBT1iYOe8qJIRxVUP4uSD8zBnqnPmLAsehwiWgLhSJgmOfwil/F8oWrojrzukUqNGYNi4HrXsHud6B2/zv8Ij+kpVrB0NECeAhANy/X0TCYx2ttrUqRHP8tCuh+Q8TmuRB6KU67OByerPjwBgIGhNMn3pL6PtFQ6o2TU0mkJkzkWfszidumui0XdOp5vqcAp4FeKnpS1m6N8CQxUPM6zxSpxiSkGBGNZ9dnvpXpQr88AM0bEhkJPw2E2bONKe1JSWZA0ADB8Lbb0Phwk4eIiIiInKL07Q3uSZJyUm4vONC9zndM2zze+/fnS7AZ/Jk8t3Z1KH4l+rw+dL3ITTUnAJ38iRs3Up8n55Wm+2BsKYMfFfXPG9/yIYXblQ4n4zrvgPwyy80fudXHpjX2brm+JTP+WtuEPFLRlMowoPu/MQXAW+xY+hUotc2YlLHSVbbHQfzc+xiHDxXHYZU5OcF4UyZAm8Mi6Jt1WC8XGI5Tmk+3NqOas0KUjPwPK+9EMvatWYAkZEjoUcyrEtISnT8u/vv94xv5kSgjzkSFZ8Uf/mBR6B589TA5403iN+8k99ON6RXLyhWzJzVt2iR2e8HHoBdu2D8eAU+IiIicvuyGVfaDTIXCg8Px8/Pj7CwMHx9fW92d/KkPef2UOPLGnZlW57awh1T7rA7r1c83bS3uXOtfWRefLosY4sfdbh32mlgAMMWD2PchnFO+7Hq8VW0KN0cTp/G2LWbebNiGDqnGcFRhQDwII5m/ENb/uRe/43Ua1cMlx7dzJ/2L691OR993lqzFP9mPKuOreLeH+4FoEaRGvzY7UdqFDXfNTYWFn60m+/Hnuf38GYkpRk8LeifRJNmrpQtayYI8PKCi7EXuBSayLcBZUh2iXP6DraNgyi04QsqVIANHVJ3Dr00xMDf3+klDgI/CSQkKoStj62l7i//mIkMwsJI9gtg8eBFTD/UhMWL4dKl1GsqVYJevcyvqlWz9hwRERGR3OZqYgNNe5NrsvvsbrvzR2s/Sr3i9ehWvRs/7/kZcNzThyNHoF8/8/j55/nfuHHU2v4d/eb3y/RZh0MPOy0v5l2M5qWbg83GzgslePGTEvxl7qtK6dLw0YcG99c7i/c5dyg7yNyl08VxsLOgV0Fcba4kGUmciTzDC0teSH3Pc7tp+V1LgocG4+XuRb580G1EDbq9mcTFCbNYPGYHC07W4w86cDHUn0WL0tzYlgRD6kOhY+a5YQOb4+8ajIYTOL/oC85fSIYOqeUBJS4QVLgQ1epEUr5yHNXKFKJ8eahb13wV2+U4yTAMK4Nc4Q5dYf9posjPtHL/43PbYA68l7p2qEQJePhhM+CpXz/1HiIiIiJ5gYIfuSoXYy7y0p8vsSZ4DQD96vbjwzYf4ufpB8CX931JtcLVeLzu4/YL9uPjzb1lwsKgSRP45BOw2azpWmklJSfZTZfbe26v077UKFqDs2dtvP02fP21mRzOwwNeeslMYpY/vw0IgqqZb1DjYnOhmE8xTkWc4tVlr7Lz7E67+vPR5+nzax82nNzAJ/d+Qq9avcDVlYLPP0LvwQa916whYVQvti67wDLvmhws6o9XwR6crpWfX/2PWfcJ8CjK3I7raTnP3Gh0XOspDF32FAAtJz1Ia89XeetYmgd3e5jjq9/keNUe4HMWLgETn4XFn1G4wAHcOwzirj2l6XwymqTB5py7/YfKMKbA+8xI7sXFI/kAM4FB//7mgFuTJuaSKhEREZG8SNPe5KoMmD+AqdumWudf3/81A+4YcOULhw41158EBMC2bebQDOYIUs2J9iNEZ186SxFvcxpaXGIc+T/IT7KRzKCGg0hMTqRq4aq8uvRVHkiexuJPehERYV7XtauZprlCBa6a/4f+hMVlbaPTiR0n8kyDZwBISErAzcUNG8APP9BgwwA2F01kxi+wpIor39dMXQhUs2hNdg7cyaXoi+S/EIbnvoOU2fAwwUkZp+3Okgm7YVANiPeGDyKt4goVzL/2xx8HH5/re4SIiIhIbqVpb5Lt4pPi+Xbbtw6bknar3u3KF8+dm7rw/rvvrMAHoFqRavjn87f2qQHoOLMjvWr2oqRvSXr/0ptkI5kCHgX4vMPnREXZ+OILyP/ZIOacN0eWGjSAsWPN9f3XKn3g07tWb15q8hKfrv+UH3b8YFc3cNFAinoXJTohmkfnPkr36t35qftPxPTqzuYjfQHo0xXAPgNC0tEj0KwZAXv2mEkdgJad4fu6195vgMKtJnMeILowvr4GnTvb6NED2rfXKI+IiIhIWhr5kSx5Z9U7jFg5wqE8fXICB4cPwx13mNPdXnoJPv7YoUlcYhz53s+X6W0aBN5Jj/ANjBkD583lLVSpAiNGmLPpnCzluSq9funF7F2zrfPtz2yndrHaJBvJdJzZkcUHF2d6/e5nd5PPLR8Vxmc87PT1fBiw9fKJiwtUqsT+msWZFHiccUUOWe0+OF6Z14MOXPU71CzYgI1PbiRf5n+VIiIiIreVq4kNlOpasuS3/b/Znbcq14oDz13hB/SYGHMuWso6nw8+cNrM082TaQ9Oy/RWO+ffyyuvmIFPxYrmdjW7d5sL96838AH4vMPndufVCpubn7rYXPijzx8YIwxCh4dmeP2EfydwIfqC07pjgw+zpMFn9B/0Nfz4ozntLyoK9u2jys8r+PSLg3x8b2pQ2G/cKrvrDzx3gOS3kzn/8nlrbRVA12pd7dqVDCikwEdEREQkE5r2JnaSjWT+PPQnfx76k/davcfR0KP8vOdnNp/eDECDEg34tcevBPllnkSA5GRzlf22bVCkCPz0E7i7Z9j88bqP07dOX1zecR7JxO1tTfny5gacffpYWaqzTSGvQtaxm4ubfbKGy/zy+fFBqw94ffnrDnXbQrZxIcYx+JnZZSalC5ajdMfnM33+sMbDmLlzJh6uHhT1Lsqfj/zJrF2zGNd+HL6e5m8wCuUvxOEhh61pgFtOb+GXvb9Y9zC45QZxRURERG4oBT9i59lFzzJ582QAPl3/Kd7u3kQlRFn1a/uvdRoY2DEMGDIEZs82o5TZs6FUqUybr1kDkyfbsHm0xCiz0q6+cnwPpnzfgrua5twaFluanM8erh4Ztnut+WvsPb/XWgdUOH9hzkef52T4Sc5GnbVr+2qzV83McFng6uLKpqc2AeZo070V7uXeCvc6tCvoVdA6bhLUhAW9FvDo3EcJjQ3l7RZvZ+lZIiIiInmVpr2JnZTAJ0XawAe4cuADMGoUfPGFuYnM999Dq1ZOmxmGmQvhzjuhRQuYMQOMpe/he7C/1cbX05f97//I3c1dc3zxvqvNfECTUk0ybVe/eH3ruEtVc8PWkxEn+XLjl3btmpVudlXPd7G54GK7un+SnSp3InhoMAcHH7zq54mIiIjkNQp+xPLLnl8yrGtSqgmHn3e+2aidSZPM4AfMAKiX85GPv/+Ghg3NvWc2bQIvL3jiCdjwczNCv/+GiR0nUsirED889IPT63PCmv5r6Fa9G9888E2m7QrlT50i175iewASkxPZcHKDVV67WG0al2qcMx1Np4BnASoUvIb83iIiIiJ5jKa9ieWVpa9Yx/9r+z9e/PNFANpWaMuSR5Zc+QaLF8Nzz5nHI0fCs886NImPN9ftjBljjvz4+MDzz5v70RQpktrumQbP8HT9p+2mo+W0xqUaM6f7nCu2S5t0oGbRmg71x4Yeo7RfaYdyEREREbm5FPyIpUmpJhy+dBgfDx9eaPICD1Z5kIj4COoG1r3yxTt2QI8ekJQEffuaEU46e/eayQq2Xk733L8/fPihfdCT1o0MfK5GQnKCdVwuoBxDGw1l3IZxADxW5zEFPiIiIiK5lKa9icXHwweAl5q8BECFghWyFvgEB0PHjhARAS1bwpQp5nqfywwDJkwwt/vZuhUKFTLX+nzzTcaBT27WpnwbivsUp3v17ri5uPHRvR9Zde0rtL+JPRMRERGRzGjkRywJSeaIRpaSGqQ4fRratYMTJ6BqVfj1V/BIzZYWEmKO8Pz+u3nerh1MmwbFi2dnz28sX09fjg87jquLmSDBw9WDRb0XsfnUZnrW7HmTeyciIiIiGVHwI5aU6VzuLlkMfvbsgfvug2PHzFTWf/4JAQFW9YIFMGAAnDsHnp7w8ccwaFD2bEp6s6UEPinuq3Qf91W67yb1RkRERESy4jb4MVSyixX8ZGXkZ9UqaNbMDHwqVYKVKyHI3Pg0KgqeeQYeeMAMfOrUgc2bYfDg2yPwEREREZFbk34UFUvKtLfMNvkkNhZefx1at4bQUGjSBNauhQpmquVNm8y1PZMnm8t+XnoJNmyAGjVuwAuIiIiIiGRC097EcsVpb2vWmPPYDhwwz3v1MrMWeHmRlGRmbhs5EhITzVlw332X4f6mIiIiIiI3nEZ+crnE5MQrtolLjKP3L72pM6kOhy9lYSPSDGSY8CAszNy/p3lzM/AJDDQTG8ycCV5e7NplzoB7800z8OnZ08x8rcBHRERERHITBT+52NHQo7i/607NL2tagYkzP+/5mVm7ZrEjZAcVxlcgKj6KuMQ4DMO4quc5jPwYBvz4I1SvbuaqBnPkZ88eeOghYmPhrbegXj1zapuvL3z/PcyaZZf3QEREREQkV1Dwk4tN2zoNgN3ndvPYvMes8tjEWCZtmsTW01uJT4rnkbmP2F3nM9qHQmMKce8P95JsJDvc1zAM2nzfBtsoG2ciz1jlobGhwOWRn40boWlTePhhOHXKTGqwdCl8/TXJfgHMnm3GRO+9Z472dO5sxkSPPmq3xY+IiIiISK6h4CcXS9l0FOD3/8yNcg5dPMQzC59h4KKBdJzZkTXBa5xeG5UQxbIjy3hj2Rt25WuC1+DyjgvLjiwDYPTfo4mMjyQ+KZ5NpzYBYEyeBI0awfr14O0No0aZ89hat2blSrOqVy84cgRKlIBffjE3LS1ZMgf+EkREREREsomCn1xs0+lN1nF4XDibT22mxpc1+G77dwCcjjxN6+9bA9CuQjteaPyCwz0+/OdD9p/fD5gjPs2nNberH//veAqMLsDYXmWtsrC1K8wpb48+aq7xefttdh3MR6dOcM89ZkY3Hx94912zukuX7H5zEREREZHsp2xvudS2M9v4afdPdmUNvmqQYfsB9QbQvUZ3LsVeYtq2aXZ1vX/tTctijSl1MiLD61+rcdo67pFUFf7+Cu66i5MnYcQTMG0aJCeDmxs8/TS8/TYULXqNLyciIiIichMo+MmlHpubusanQYkG1pQ0ZzpW6ki36t0AmNhxIg1KNKBF6ebs3fQHPTYNZ8vpLWw5vcVqf9cx+H11EE/18ma22z67e31Soh/+GyYRnejBmJEwZgzExJh13brBBx+Yy39ERERERG41mvaWC4XHhbPz7E4AHqn9CH/3+9uhzdi2Y8nvnp+6gXVZ0GsBNpsNkpLwnLeAZz9ZRc0699Kk93Cn9/+g+0QK/HeMWe/u5dLwS3Z1gx6fyPw/PKhWzVzqExMDd91l7mM6Z44CHxERERG5dWnkJxfac26PdfzuPe+Szy0fxgiDPr/2YffZ3cztOZdyAeXoU7sP+d3zY4uNNXcU/eQTOHTIuraUtzcdwrz5w++s3f3rtupjpWTzz+fPoIaDmLBxAnUKNqXbQ54sWmS2K10a/vc/6NpVGdxERERE5Nan4OcGMQyDh358iPn75/PNA9/Qv17/DNumJCi4s+SdlPUva5XP6DLDrl3RODf4dByMHw/nzpmFBQvCU09Bhw7QqBEL3N04F32OfvP7sfjgYnw9fSngWcDuPm+1eAuPqIp82q8326PA3R1eegneeMNM9iYiIiIicjvI9mlvo0ePpmHDhhQoUICiRYvSuXNn9u/fb9cmNjaWQYMGUahQIXx8fOjatSshISHZ3ZVc5XTkaebvnw/AgN8GZLqGZ+z6sQDcEXiH8waxsTBypDk089ZbZuBTpowZBAUHw+jR0KIFeHri6uJKoE8gf/T5g9MvnubkCycdblfMpxj/6zaURjWL0qsX7Nxpru1R4CMiIiIit5NsD35WrVrFoEGDWL9+PX/99RcJCQm0bduWqKgoq82wYcNYsGABc+bMYdWqVZw6dYout3m+5JTRnBQNv2rIogOL2HZmG+ejz1vlc3bPYUfIDgBqFavleKPly6FOHXNBTlSUeTxzJhw8CIMHZxqxBPoE2u0dlJbNBqtXm7eqUuUaXlBEU1Za8AAAEAVJREFUREREJJezGYZh5OQDzp07R9GiRVm1ahUtWrQgLCyMIkWKMHPmTLp1MzOU7du3j2rVqrFu3ToaN258xXuGh4fj5+dHWFgYvr6+Odn963Yi/ATjN4zH19OXt1a8Re1ita3gJkXVwlXZ/exuToafpPS40lZ5yEshFPW+nE/68GF45RVzR1GAwED47DPo3l0LckREREQkz7qa2CDH1/yEhYUBULBgQQA2b95MQkICbdq0sdpUrVqV0qVLZxj8xMXFERcXZ52Hh4fncK+zT4cZHdh1dpd13rZ8W8r6l+W3/b9ZZfvO72PL6S30ndfXKmtSqklq4PPBB+ZIT3w8uLjAM8/A+++Dv/+Neg0RERERkVtejgY/ycnJDB06lGbNmlGzZk0Azpw5g4eHB/7pfnAvVqwYZ86ccXqf0aNHM2rUqJzsao7YcGKDXeAD8MQdT1DMpxj7zu+jyTdNrPKGXzW0aze23djUEx8fM/C5914z/VotJ9PhREREREQkUzm6z8+gQYPYtWsXs2fPvq77vPbaa4SFhVlfx48fz6Ye5oxkI5npO6bT+Bv7UaxHaj9ClcJV8M/nT+NSjVnRd4XT64OHBtO4VJprBw6EJUvMLwU+IiIiIiLXJMdGfp577jkWLlzI6tWrKVWqlFUeGBhIfHw8oaGhdqM/ISEhBAYGOr2Xp6cnnp6eOdXVbPfD9h94fP7j1vnIu0fydIOnKeRVyK5dy7ItWf7Yclp938quvFB++3a4u0PbtjnVXRERERGRPCHbR34Mw+C5555j7ty5LF++nHLlytnV169fH3d3d5YtW2aV7d+/n+DgYJo0aZL+drecZCPZSlUNMKTREEa0HEGgTyDuru4O7VuUacGTdzzJ/ZXvt8ryu+e/IX0VEREREclLsn3kZ9CgQcycOZP58+dToEABax2Pn58fXl5e+Pn5MWDAAF544QUKFiyIr68vgwcPpkmTJlnK9Jbbtf2hrV02txF3j8i0vauLK1Pun4JhGLyx/A3K+JXJ6S6KiIiIiORJ2Z7q2pZB2uVp06bx+OOPA+Ympy+++CKzZs0iLi6Odu3a8eWXX2Y47S293Jbq+pc9v/Dx2o9pXro5n6z7xCr/7eHfuL/K/ZlcKSIiIiIi1+NqYoMc3+cnJ+Sm4GfX2V3UmuiYhCDuzTg8XD1uQo9ERERERPKOq4kNcjTbW17Qfnp7h7JKBSsp8BERERERyWUU/Fwnu5TUl/l63vypeCIiIiIiYk/Bz3Ua2XIke57dw4VXLlhlkfGRN7FHIiIiIiLijIKf61SzaE2qFalGQa+CDL5zMACtyrW6wlUiIiIiInKj5dgmp3nRp+0+pWeNntxR/I6b3RUREREREUlHwU82cnVxpVnpZje7GyIiIiIi4oSmvYmIiIiISJ6g4EdERERERPIEBT8iIiIiIpInKPgREREREZE8QcGPiIiIiIjkCQp+REREREQkT1DwIyIiIiIieYKCHxERERERyRMU/IiIiIiISJ6g4EdERERERPIEBT8iIiIiIpInKPgREREREZE8QcGPiIiIiIjkCQp+REREREQkT1DwIyIiIiIieYKCHxERERERyRMU/IiIiIiISJ6g4EdERERERPIEBT8iIiIiIpInKPgREREREZE8QcGPiIiIiIjkCQp+REREREQkT1DwIyIiIiIieYKCHxERERERyRPcbnYHroVhGACEh4ff5J6IiIiIiMjNlBITpMQImbklg5+IiAgAgoKCbnJPREREREQkN4iIiMDPzy/TNjYjKyFSLpOcnMypU6coUKAANpvtpvYlPDycoKAgjh8/jq+v703ti9x4+vzzLn32eZc++7xNn3/epc8+9zIMg4iICEqUKIGLS+arem7JkR8XFxdKlSp1s7thx9fXV/8Q8jB9/nmXPvu8S5993qbPP+/SZ587XWnEJ4USHoiIiIiISJ6g4EdERERERPIEBT/XydPTkxEjRuDp6XmzuyI3gT7/vEuffd6lzz5v0+efd+mzvz3ckgkPRERERERErpZGfkREREREJE9Q8CMiIiIiInmCgh8REREREckTFPyIiIiIiEieoOBHRERERETyBAU/12nChAmULVuWfPny0ahRI/7999+b3SW5TiNHjsRms9l9Va1a1aqPjY1l0KBBFCpUCB8fH7p27UpISIjdPYKDg+nYsSP58+enaNGivPzyyyQmJt7oV5ErWL16Nffffz8lSpTAZrMxb948u3rDMHj77bcpXrw4Xl5etGnThv/++8+uzcWLF+nTpw++vr74+/szYMAAIiMj7drs2LGD5s2bky9fPoKCghgzZkxOv5pcwZU++8cff9zh+0D79u3t2uizvzWNHj2ahg0bUqBAAYoWLUrnzp3Zv3+/XZvs+j6/cuVK7rjjDjw9PalYsSLffvttTr+eZCIrn33Lli0d/u0/88wzdm302d/aFPxchx9//JEXXniBESNGsGXLFurUqUO7du04e/bsze6aXKcaNWpw+vRp62vNmjVW3bBhw1iwYAFz5sxh1apVnDp1ii5dulj1SUlJdOzYkfj4eNauXct3333Ht99+y9tvv30zXkUyERUVRZ06dZgwYYLT+jFjxjB+/HgmTZrEhg0b8Pb2pl27dsTGxlpt+vTpw+7du/nrr79YuHAhq1ev5qmnnrLqw8PDadu2LWXKlGHz5s18/PHHjBw5kilTpuT4+0nGrvTZA7Rv397u+8CsWbPs6vXZ35pWrVrFoEGDWL9+PX/99RcJCQm0bduWqKgoq012fJ8/cuQIHTt25J577mHbtm0MHTqUJ554giVLltzQ95VUWfnsAZ588km7f/tpf2mhz/42YMg1u/POO41BgwZZ50lJSUaJEiWM0aNH38ReyfUaMWKEUadOHad1oaGhhru7uzFnzhyrbO/evQZgrFu3zjAMw/j9998NFxcX48yZM1abiRMnGr6+vkZcXFyO9l2uHWDMnTvXOk9OTjYCAwONjz/+2CoLDQ01PD09jVmzZhmGYRh79uwxAGPjxo1Wmz/++MOw2WzGyZMnDcMwjC+//NIICAiw++yHDx9uVKlSJYffSLIq/WdvGIbRt29f48EHH8zwGn32t4+zZ88agLFq1SrDMLLv+/wrr7xi1KhRw+5ZPXv2NNq1a5fTryRZlP6zNwzDuPvuu40hQ4ZkeI0++1ufRn6uUXx8PJs3b6ZNmzZWmYuLC23atGHdunU3sWeSHf777z9KlChB+fLl6dOnD8HBwQBs3ryZhIQEu8+9atWqlC5d2vrc161bR61atShWrJjVpl27doSHh7N79+4b+yJyzY4cOcKZM2fsPms/Pz8aNWpk91n7+/vToEEDq02bNm1wcXFhw4YNVpsWLVrg4eFhtWnXrh379+/n0qVLN+ht5FqsXLmSokWLUqVKFQYOHMiFCxesOn32t4+wsDAAChYsCGTf9/l169bZ3SOljX5GyD3Sf/YpZsyYQeHChalZsyavvfYa0dHRVp0++1uf283uwK3q/PnzJCUl2f3HD1CsWDH27dt3k3ol2aFRo0Z8++23VKlShdOnTzNq1CiaN2/Orl27OHPmDB4eHvj7+9tdU6xYMc6cOQPAmTNnnP53kVInt4aUz8rZZ5n2sy5atKhdvZubGwULFrRrU65cOYd7pNQFBATkSP/l+rRv354uXbpQrlw5Dh06xOuvv06HDh1Yt24drq6u+uxvE8nJyQwdOpRmzZpRs2ZNgGz7Pp9Rm/DwcGJiYvDy8sqJV5IscvbZA/Tu3ZsyZcpQokQJduzYwfDhw9m/fz+//voroM/+dqDgRySdDh06WMe1a9emUaNGlClThp9++knfsETyiIcfftg6rlWrFrVr16ZChQqsXLmS1q1b38SeSXYaNGgQu3btslvXKXlDRp992nV7tWrVonjx4rRu3ZpDhw5RoUKFG91NyQGa9naNChcujKurq0P2l5CQEAIDA29SryQn+Pv7U7lyZQ4ePEhgYCDx8fGEhobatUn7uQcGBjr97yKlTm4NKZ9VZv/GAwMDHRKcJCYmcvHiRf33cJspX748hQsX5uDBg4A++9vBc889x8KFC1mxYgWlSpWyyrPr+3xGbXx9ffWLtJsso8/emUaNGgHY/dvXZ39rU/BzjTw8PKhfvz7Lli2zypKTk1m2bBlNmjS5iT2T7BYZGcmhQ4coXrw49evXx93d3e5z379/P8HBwdbn3qRJE3bu3Gn3g9Fff/2Fr68v1atXv+H9l2tTrlw5AgMD7T7r8PBwNmzYYPdZh4aGsnnzZqvN8uXLSU5Otv6H2aRJE1avXk1CQoLV5q+//qJKlSqa9nQLOXHiBBcuXKB48eKAPvtbmWEYPPfcc8ydO5fly5c7TE3Mru/zTZo0sbtHShv9jHDzXOmzd2bbtm0Adv/29dnf4m52xoVb2ezZsw1PT0/j22+/Nfbs2WM89dRThr+/v10GELn1vPjii8bKlSuNI0eOGP/884/Rpk0bo3DhwsbZs2cNwzCMZ555xihdurSxfPlyY9OmTUaTJk2MJk2aWNcnJiYaNWvWNNq2bWts27bNWLx4sVGkSBHjtddeu1mvJBmIiIgwtm7damzdutUAjLFjxxpbt241jh07ZhiGYXz44YeGv7+/MX/+fGPHjh3Ggw8+aJQrV86IiYmx7tG+fXujXr16xoYNG4w1a9YYlSpVMnr16mXVh4aGGsWKFTMeffRRY9euXcbs2bON/PnzG5MnT77h7yupMvvsIyIijJdeeslYt26dceTIEWPp0qXGHXfcYVSqVMmIjY217qHP/tY0cOBAw8/Pz1i5cqVx+vRp6ys6Otpqkx3f5w8fPmzkz5/fePnll429e/caEyZMMFxdXY3Fixff0PeVVFf67A8ePGi88847xqZNm4wjR44Y8+fPN8qXL2+0aNHCuoc++1ufgp/r9PnnnxulS5c2PDw8jDvvvNNYv379ze6SXKeePXsaxYsXNzw8PIySJUsaPXv2NA4ePGjVx8TEGM8++6wREBBg5M+f33jooYeM06dP293j6NGjRocOHQwvLy+jcOHCxosvvmgkJCTc6FeRK1ixYoUBOHz17dvXMAwz3fVbb71lFCtWzPD09DRat25t7N+/3+4eFy5cMHr16mX4+PgYvr6+Rr9+/YyIiAi7Ntu3bzfuuusuw9PT0yhZsqTx4Ycf3qhXlAxk9tlHR0cbbdu2NYoUKWK4u7sbZcqUMZ588kmHX2zps781OfvcAWPatGlWm+z6Pr9ixQqjbt26hoeHh1G+fHm7Z8iNd6XPPjg42GjRooVRsGBBw9PT06hYsaLx8ssvG2FhYXb30Wd/a7MZhmHcuHEmERERERGRm0NrfkREREREJE9Q8CMiIiIiInmCgh8REREREckTFPyIiIiIiEieoOBHRERERETyBAU/IiIiIiKSJyj4ERERERGRPEHBj4iIiIiI5AkKfkREREREJE9Q8CMiIiIiInmCgh8REREREckT/g+mA4sqvNldXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Figure\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(ma_100_days, 'r', label='moving average of 100 days')\n",
    "plt.plot(ma_200_days, 'b', label='moving average of 200 days')\n",
    "plt.plot(stock_data['Close'], 'g', label='Closing Price')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete all null values\n",
    "stock_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Classification Problem: Buy or Sell the Stock</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.DataFrame(stock_data.Close[:int(len(stock_data)*0.8)])\n",
    "test_data = pd.DataFrame(stock_data.Close[int(len(stock_data)*0.8):])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2214"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "554"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Min-Max Scaling</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mmc = MinMaxScaler(feature_range=(0, 1))\n",
    "scale_train_data = mmc.fit_transform(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = [], []\n",
    "for i in range(100, scale_train_data.shape[0]):\n",
    "    X.append(scale_train_data[i-100:i])\n",
    "    Y.append(scale_train_data[i, 0])\n",
    "\n",
    "X, Y = np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y = np.where(stock_data['Close'].shift(-1) > stock_data['Close'], 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deep Learning Model\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "               input_shape=((X.shape[1], 1))))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=60, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units=80, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units=100, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(LSTM(units=120, activation='relu'))\n",
    "model.add(Dropout(0.6))\n",
    "\n",
    "model.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "67/67 [==============================] - 23s 253ms/step - loss: 0.0324\n",
      "Epoch 2/10\n",
      "67/67 [==============================] - 20s 302ms/step - loss: 0.0066\n",
      "Epoch 3/10\n",
      "67/67 [==============================] - 20s 301ms/step - loss: 0.0057\n",
      "Epoch 4/10\n",
      "67/67 [==============================] - 20s 297ms/step - loss: 0.0052\n",
      "Epoch 5/10\n",
      "67/67 [==============================] - 20s 299ms/step - loss: 0.0049\n",
      "Epoch 6/10\n",
      "67/67 [==============================] - 24s 355ms/step - loss: 0.0041\n",
      "Epoch 7/10\n",
      "67/67 [==============================] - 22s 331ms/step - loss: 0.0041\n",
      "Epoch 8/10\n",
      "67/67 [==============================] - 22s 335ms/step - loss: 0.0037\n",
      "Epoch 9/10\n",
      "67/67 [==============================] - 22s 322ms/step - loss: 0.0040\n",
      "Epoch 10/10\n",
      "67/67 [==============================] - 22s 321ms/step - loss: 0.0039\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x206d3266850>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(X, Y, epochs=10, batch_size=32, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 100, 50)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 100, 100)          72400     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 100, 100)          0         \n",
      "                                                                 \n",
      " lstm_4 (LSTM)               (None, 120)               106080    \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 260761 (1018.60 KB)\n",
      "Trainable params: 260761 (1018.60 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pas_100_days = train_data.tail(100)\n",
    "test_data = pd.concat([pas_100_days, test_data], ignore_index=True)\n",
    "# test_data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_test_data = mmc.fit_transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = [], []\n",
    "for i in range(100, scale_test_data.shape[0]):\n",
    "    X.append(scale_test_data[i-100:i])\n",
    "    Y.append(scale_test_data[i, 0])\n",
    "\n",
    "X, Y = np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 2s 87ms/step\n"
     ]
    },
    {
     "data": {
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       "       [0.625014  ],\n",
       "       [0.6294992 ],\n",
       "       [0.634111  ],\n",
       "       [0.63882244],\n",
       "       [0.64360714],\n",
       "       [0.648418  ],\n",
       "       [0.65317124],\n",
       "       [0.65775394],\n",
       "       [0.662045  ],\n",
       "       [0.66591907],\n",
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       "       [0.6720155 ],\n",
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       "       [0.6829579 ],\n",
       "       [0.6856086 ],\n",
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       "       [0.6994851 ],\n",
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       "       [0.69393075],\n",
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       "       [0.68176144],\n",
       "       [0.6770303 ],\n",
       "       [0.6722665 ],\n",
       "       [0.66763896],\n",
       "       [0.66330564],\n",
       "       [0.6594156 ],\n",
       "       [0.65609336],\n",
       "       [0.6534353 ],\n",
       "       [0.6515094 ],\n",
       "       [0.65036094],\n",
       "       [0.6500037 ],\n",
       "       [0.6504185 ],\n",
       "       [0.65155935],\n",
       "       [0.6533485 ],\n",
       "       [0.6556715 ],\n",
       "       [0.6583834 ],\n",
       "       [0.6613116 ],\n",
       "       [0.66428   ],\n",
       "       [0.667122  ],\n",
       "       [0.66972476],\n",
       "       [0.67202854],\n",
       "       [0.67400897],\n",
       "       [0.6756598 ],\n",
       "       [0.6769868 ],\n",
       "       [0.67799985],\n",
       "       [0.67871356],\n",
       "       [0.679163  ],\n",
       "       [0.67940694],\n",
       "       [0.6795284 ],\n",
       "       [0.67963433],\n",
       "       [0.679847  ],\n",
       "       [0.6802901 ],\n",
       "       [0.6810688 ],\n",
       "       [0.6822584 ]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred = model.predict(X)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01487675] [67.21900177]\n"
     ]
    }
   ],
   "source": [
    "scale = 1/mmc.scale_\n",
    "print(mmc.scale_, scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred *= scale\n",
    "Y *= scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(Y_pred, 'r', label='Predicted Price')\n",
    "plt.plot(Y, 'g', label='Actual Price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('C:/Users/hp/Python_Projects/Resume_Projects/Stock Price Prediction Model.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Train Test Split</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Implementation of KNN Classifier</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'n_neighbors': [x for x in range(1, 16)]}\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# using gridsearchcv to find the best parameter\n",
    "model = GridSearchCV(knn, params, cv=5)\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the model train data\n",
    "Y_train_pred = model.predict(X_train)\n",
    "Y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the model test data\n",
    "Y_test_pred = model.predict(X_test)\n",
    "Y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy Score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "train_score = accuracy_score(Y_train, Y_train_pred)\n",
    "print(\"Accuracy of Train data: {:.2f}%\".format(train_score*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(Y_train, Y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm_train = confusion_matrix(Y_train, Y_train_pred)\n",
    "print(cm_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def picture(cm):\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    sns.heatmap(cm, annot=True)\n",
    "    plt.xlabel('Predicted Data')\n",
    "    plt.ylabel('Actual Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Picture of Confusion Matrix\n",
    "picture(cm_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_score = accuracy_score(Y_test, Y_test_pred)\n",
    "print('Accuracy of Train data: {:.2f}%'.format(test_score*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(Y_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_test = confusion_matrix(Y_train, Y_train_pred)\n",
    "print(cm_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "picture(cm_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_predicted_data = pd.DataFrame({'Actual Data': Y_test, 'Predicted Data': Y_test_pred})\n",
    "actual_predicted_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Close']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Train Test Split</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style='color:blue'>Implementation of KNN Regression</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'n_neighbors': [x for x in range(1, 16)]}\n",
    "knn_reg = KNeighborsRegressor()\n",
    "\n",
    "# using gridsearchcv to find the best parameter\n",
    "reg_model = GridSearchCV(knn_reg, params, cv=5)\n",
    "reg_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the model train data\n",
    "Y_train_pred = reg_model.predict(X_train)\n",
    "Y_train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the model train data\n",
    "Y_test_pred = reg_model.predict(X_test)\n",
    "Y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_predicted_data = pd.DataFrame({'Actual Close Data': Y_test, 'Predicted Close Data': Y_test_pred})\n",
    "actual_predicted_data.head(10)"
   ]
  }
 ],
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